[ { "path": "July Cohort 2023/1_Python Programming/.ipynb_checkpoints/01_Python Basics-checkpoint.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"id\": \"5effdff7\",\n \"metadata\": {},\n \"source\": [\n \"# Python Basics\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"id\": \"d04e5b22\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Hello User\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(\\\"Hello User\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"id\": \"b0091bf1\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Hello\\n\",\n \"User\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(\\\"Hello\\\")\\n\",\n \"print(\\\"User\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"id\": \"fb83814b\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Hello\\n\",\n \"User\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(\\\"Hello\\\\nUser\\\") # \\\\n -> new line character\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"id\": \"791c342a\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Hello\\tUser\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(\\\"Hello\\\\tUser\\\") # \\\\t -> horizontal tab - 8 spaces\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"9fad4ea5\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# binary language -> 101010111 -> bits\\n\",\n \"\\n\",\n \"0 -> low voltage, no-charge, false, off\\n\",\n \"1 -> high voltage, charge, true, on\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"a1885e98\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"bits > numbers > character > instructions > program > software\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"cbe99e7d\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"source code ---------> Byte Code --------> output\\n\",\n \" .c compile .bak run .exe\\n\",\n \" .cpp .obj\\n\",\n \" .java .javac\\n\",\n \" .py .pyc\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"8a713554\",\n \"metadata\": {},\n \"source\": [\n \"# Variables\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"id\": \"5c20abe8\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"10\\n\"\n ]\n }\n ],\n \"source\": [\n \"a = 10\\n\",\n \"\\n\",\n \"print(a)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"id\": \"995009df\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"45.13\\n\"\n ]\n }\n ],\n \"source\": [\n \"b = 45.13\\n\",\n \"\\n\",\n \"print(b)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"id\": \"17194c9e\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"India\\n\"\n ]\n }\n ],\n \"source\": [\n \"a = \\\"India\\\"\\n\",\n \"\\n\",\n \"print(a)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"9141a786\",\n \"metadata\": {},\n \"source\": [\n \"# Data Types\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"id\": \"6ba6bfd9\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"10\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"a = 10\\n\",\n \"\\n\",\n \"print(a)\\n\",\n \"\\n\",\n \"print(type(a))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"id\": \"a6e1a232\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"10.65\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"b = 10.65\\n\",\n \"\\n\",\n \"print(b)\\n\",\n \"\\n\",\n \"print(type(b))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"id\": \"1298d5d3\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"colors\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"c = \\\"colors\\\"\\n\",\n \"\\n\",\n \"print(c)\\n\",\n \"\\n\",\n \"print(type(c))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"id\": \"47c38fa9\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"colors\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"c = 'colors'\\n\",\n \"\\n\",\n \"print(c)\\n\",\n \"\\n\",\n \"print(type(c))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"id\": \"0acb14e3\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"it's an apple\\n\"\n ]\n }\n ],\n \"source\": [\n \"x = \\\"it's an apple\\\"\\n\",\n \"\\n\",\n \"print(x)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"id\": \"fb2beb48\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"it\\\"s an apple\\n\"\n ]\n }\n ],\n \"source\": [\n \"x = 'it\\\"s an apple'\\n\",\n \"\\n\",\n \"print(x)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"814feca5\",\n \"metadata\": {},\n \"source\": [\n \"# Input Function\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"id\": \"437ffd33\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"10\\n\",\n \"10\\n\"\n ]\n }\n ],\n \"source\": [\n \"a = input()\\n\",\n \"\\n\",\n \"print(a)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"id\": \"8c71996c\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Enter Your Name: Himanshu\\n\",\n \"Himanshu\\n\"\n ]\n }\n ],\n \"source\": [\n \"a = input(\\\"Enter Your Name: \\\")\\n\",\n \"\\n\",\n \"print(a)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"id\": \"792f782d\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Enter Your Name: Himanshu\\n\",\n \"Hello! How are you? Himanshu\\n\"\n ]\n }\n ],\n \"source\": [\n \"a = input(\\\"Enter Your Name: \\\")\\n\",\n \"\\n\",\n \"print(\\\"Hello! How are you?\\\", a)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"id\": \"8ab7e6a7\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Hello Him\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(\\\"Hello Him\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"84b26329\",\n \"metadata\": {},\n \"source\": [\n \"# Questions\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"376a1bc0\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"Your Name\\n\",\n \"------8-------College/Company\\n\",\n \"------------16-------------Country\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"f60668a8\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"*\\n\",\n \"**\\n\",\n \"***\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"7c2fa48a\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"--4--*\\n\",\n \"*----8----*\\n\",\n \"--4--*\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"2650f9ed\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \" *\\n\",\n \"* *\\n\",\n \" *\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.12\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n" }, { "path": "July Cohort 2023/1_Python Programming/.ipynb_checkpoints/02_03_While Loop and List-checkpoint.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"id\": \"13c6ac1c\",\n \"metadata\": {},\n \"source\": [\n \"# While Loop\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"id\": \"0290b931\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Himanshu\\n\",\n \"Himanshu\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(\\\"Himanshu\\\")\\n\",\n \"print(\\\"Himanshu\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"id\": \"aab5a9d5\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"True\\n\"\n ]\n }\n ],\n \"source\": [\n \"i = 1\\n\",\n \"\\n\",\n \"print(i <= 5)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"id\": \"be002b98\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"False\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(i >= 5)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"id\": \"903683b3\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"True\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(i >= 1)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"id\": \"76da14cf\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Himanshu\\n\",\n \"Himanshu\\n\",\n \"Himanshu\\n\",\n \"Himanshu\\n\",\n \"Himanshu\\n\"\n ]\n }\n ],\n \"source\": [\n \"i = 1\\n\",\n \"\\n\",\n \"while i <= 5:\\n\",\n \" print(\\\"Himanshu\\\")\\n\",\n \" i = i + 1 # i = 1, 2, 3, 4, 5, 6\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"id\": \"145ad944\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"1\\n\",\n \"2\\n\",\n \"3\\n\",\n \"4\\n\",\n \"5\\n\"\n ]\n }\n ],\n \"source\": [\n \"i = 1\\n\",\n \"\\n\",\n \"while i <= 5:\\n\",\n \" print(i)\\n\",\n \" i = i + 1\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"cdad3efc\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"1\\n\",\n \"2\\n\",\n \"3\\n\",\n \"4\\n\",\n \"5\\n\",\n \"6\\n\",\n \"7\\n\",\n \"8\\n\",\n \"9\\n\",\n \"10\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"id\": \"d7d2dfab\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"1\\n\",\n \"2\\n\",\n \"3\\n\",\n \"4\\n\",\n \"5\\n\",\n \"6\\n\",\n \"7\\n\",\n \"8\\n\",\n \"9\\n\",\n \"10\\n\"\n ]\n }\n ],\n \"source\": [\n \"i = 1\\n\",\n \"\\n\",\n \"while i <= 10:\\n\",\n \" print(i)\\n\",\n \" i = i + 1\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"id\": \"a103c74c\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Hello\\n\",\n \"World\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(\\\"Hello\\\")\\n\",\n \"print(\\\"World\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"id\": \"5df8d4f6\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Hello\\n\",\n \"World\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(\\\"Hello\\\", end=\\\"\\\\n\\\")\\n\",\n \"print(\\\"World\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"id\": \"c89f0b40\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Hello+World\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(\\\"Hello\\\", end=\\\"+\\\")\\n\",\n \"print(\\\"World\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"id\": \"8a0e5ca8\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Hello World\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(\\\"Hello\\\", end=\\\" \\\")\\n\",\n \"print(\\\"World\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"id\": \"bce809e5\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Hello @@@ World\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(\\\"Hello\\\", end=\\\" @@@ \\\")\\n\",\n \"print(\\\"World\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"id\": \"54435a01\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Hello@World$\"\n ]\n }\n ],\n \"source\": [\n \"print(\\\"Hello\\\", end=\\\"@\\\")\\n\",\n \"print(\\\"World\\\", end=\\\"$\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"95e933ab\",\n \"metadata\": {},\n \"source\": [\n \"### print the following series of numbers\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"7a728f28\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"1 2 3 4 5 6 7 8 9 10\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"id\": \"35896fd2\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"1 2 3 4 5 6 7 8 9 10 \"\n ]\n }\n ],\n \"source\": [\n \"i = 1\\n\",\n \"\\n\",\n \"while i <= 10:\\n\",\n \" print(i, end=\\\" \\\")\\n\",\n \" i = i + 1\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"id\": \"284eda1a\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"100\\n\",\n \"10\\n\"\n ]\n }\n ],\n \"source\": [\n \"a = 10\\n\",\n \"\\n\",\n \"print(a * a)\\n\",\n \"\\n\",\n \"print(a)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"d1cabe31\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"1 4 9 16 25 36 49 64 81 100\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"id\": \"32f564a1\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"1 4 9 16 25 36 49 64 81 100 \"\n ]\n }\n ],\n \"source\": [\n \"i = 1\\n\",\n \"\\n\",\n \"while i <= 10:\\n\",\n \" print(i*i, end=\\\" \\\")\\n\",\n \" i = i + 1\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"49b1b14e\",\n \"metadata\": {},\n \"source\": [\n \"**Write a program to enter 5 values from user and print them.**\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"id\": \"a1cd06ee\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Enter your number: 10\\n\",\n \"10\\n\",\n \"Enter your number: 20\\n\",\n \"20\\n\",\n \"Enter your number: 30\\n\",\n \"30\\n\",\n \"Enter your number: 40\\n\",\n \"40\\n\",\n \"Enter your number: 50\\n\",\n \"50\\n\"\n ]\n }\n ],\n \"source\": [\n \"i = 1\\n\",\n \"\\n\",\n \"while i <= 5:\\n\",\n \" a = input(\\\"Enter your number: \\\")\\n\",\n \" print(a)\\n\",\n \" i = i + 1\\n\",\n \" # 1 + 1\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"id\": \"a1b92062\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"3.5\"\n ]\n },\n \"execution_count\": 18,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"7 / 2\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"id\": \"b1e0fc38\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"1\"\n ]\n },\n \"execution_count\": 19,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"7 % 2 # modulo - return the reminder\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"id\": \"7279d00f\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"0\"\n ]\n },\n \"execution_count\": 20,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"10 % 2\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"id\": \"7ba7e050\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"3\"\n ]\n },\n \"execution_count\": 21,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"11 % 4\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"id\": \"3c3ea69f\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"3\"\n ]\n },\n \"execution_count\": 22,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"7 // 2 # floor division\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"id\": \"e378bbe4\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"2\"\n ]\n },\n \"execution_count\": 23,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"15 // 7\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"id\": \"5947157a\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"3\"\n ]\n },\n \"execution_count\": 24,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"153 % 10\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"07a156bd\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"15)7(\\n\",\n \" 7\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"d490f9d3\",\n \"metadata\": {},\n \"source\": [\n \"**Write a program to enter a number from user and print reverse of it.**\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"7959c4e2\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"153\\n\",\n \"\\n\",\n \"351\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"960cfd0b\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"153 % 10 = 3\\n\",\n \"153 // 10 = 15\\n\",\n \"\\n\",\n \"15 % 10 = 5\\n\",\n \"15 // 10 = 1\\n\",\n \"\\n\",\n \"1 % 10 = 1\\n\",\n \"1 // 10 = 0\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"id\": \"3597feec\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Enter your number: 10\\n\",\n \"10\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"n = input(\\\"Enter your number: \\\")\\n\",\n \"\\n\",\n \"print(n)\\n\",\n \"\\n\",\n \"print(type(n))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"id\": \"71b967e4\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Enter your number: 10\\n\",\n \"10\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"n = int(input(\\\"Enter your number: \\\"))\\n\",\n \"\\n\",\n \"print(n)\\n\",\n \"\\n\",\n \"print(type(n))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 31,\n \"id\": \"5759b2ec\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Enter a number: 153\\n\",\n \"351\"\n ]\n }\n ],\n \"source\": [\n \"n = int(input(\\\"Enter a number: \\\"))\\n\",\n \"\\n\",\n \"while n > 0:\\n\",\n \" r = n % 10 # r = 3, 5, 1\\n\",\n \" print(r, end=\\\"\\\") # 351\\n\",\n \" n = n // 10 # n = 153, 15, 1, 0\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"adf5586f\",\n \"metadata\": {},\n \"source\": [\n \"**Write a program to enter a number from user and print sum of its individual digits**\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"cbd3db08\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"153\\n\",\n \"\\n\",\n \"1 + 5 + 3\\n\",\n \"\\n\",\n \"9\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"1d76dd47\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"0 = s\\n\",\n \"3 = s + 3 = s\\n\",\n \"5 = s + 5 = s\\n\",\n \"1\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 32,\n \"id\": \"52deeb6d\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Enter a number: 153\\n\",\n \"Sum = 9\\n\"\n ]\n }\n ],\n \"source\": [\n \"n = int(input(\\\"Enter a number: \\\"))\\n\",\n \"s = 0\\n\",\n \"\\n\",\n \"while n > 0:\\n\",\n \" r = n % 10\\n\",\n \" s = s + r # s = 0, 3, 8, 9\\n\",\n \" n = n // 10\\n\",\n \"\\n\",\n \"print(\\\"Sum =\\\", s)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"c2bb6d4f\",\n \"metadata\": {},\n \"source\": [\n \"# Day 3\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"f4cb0aee\",\n \"metadata\": {},\n \"source\": [\n \"**Write a program to reverse a number**\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"85d9cb69\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"153\\n\",\n \"\\n\",\n \"351 - this should be a whole number\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"id\": \"f31986e9\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Enter a number: 153\\n\",\n \"351\\n\",\n \"702\\n\"\n ]\n }\n ],\n \"source\": [\n \"n = int(input(\\\"Enter a number: \\\"))\\n\",\n \"s = 0\\n\",\n \"\\n\",\n \"while n > 0:\\n\",\n \" r = n % 10 # r = 3, 5, 1\\n\",\n \" s = s*10 + r # s = 0, 3, 35, 351\\n\",\n \" n = n // 10 # n = 153, 15, 1, 0\\n\",\n \"\\n\",\n \"print(s)\\n\",\n \"print(s * 2)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"949c7e7d\",\n \"metadata\": {},\n \"source\": [\n \"**Write a program to enter a number from user and check if it palindrome or not**\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"4fbfc3b5\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"121\\n\",\n \"\\n\",\n \"121\\n\",\n \"\\n\",\n \"153 = 351 - not a palindrome number\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"id\": \"4855f13b\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Enter a number: 121\\n\",\n \"Not a Palindrome\\n\"\n ]\n }\n ],\n \"source\": [\n \"n = int(input(\\\"Enter a number: \\\"))\\n\",\n \"s = 0\\n\",\n \"\\n\",\n \"while n > 0:\\n\",\n \" r = n % 10 # r = 3, 5, 1\\n\",\n \" s = s*10 + r # s = 0, 3, 35, 351\\n\",\n \" n = n // 10 # n = 153, 15, 1, 0\\n\",\n \"\\n\",\n \"if s == n:\\n\",\n \" print(\\\"Palindrome\\\")\\n\",\n \"else:\\n\",\n \" print(\\\"Not a Palindrome\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"id\": \"90712f12\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Enter a number: 1234321\\n\",\n \"Palindrome\\n\"\n ]\n }\n ],\n \"source\": [\n \"n = int(input(\\\"Enter a number: \\\"))\\n\",\n \"temp = n\\n\",\n \"s = 0\\n\",\n \"\\n\",\n \"while n > 0:\\n\",\n \" r = n % 10 # r = 3, 5, 1\\n\",\n \" s = s*10 + r # s = 0, 3, 35, 351\\n\",\n \" n = n // 10 # n = 153, 15, 1, 0\\n\",\n \"\\n\",\n \"if s == temp:\\n\",\n \" print(\\\"Palindrome\\\")\\n\",\n \"else:\\n\",\n \" print(\\\"Not a Palindrome\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"e1a01ec1\",\n \"metadata\": {},\n \"source\": [\n \"**Write a program to enter a number from user and check if it is armstrong or not**\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"20722f78\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"153\\n\",\n \"\\n\",\n \"cube(1) + cube(5) + cube(3)\\n\",\n \"\\n\",\n \"370\\n\",\n \"\\n\",\n \"371\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"id\": \"50334900\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Enter a number: 371\\n\",\n \"Armstrong\\n\"\n ]\n }\n ],\n \"source\": [\n \"n = int(input(\\\"Enter a number: \\\"))\\n\",\n \"temp = n\\n\",\n \"s = 0\\n\",\n \"\\n\",\n \"while n > 0:\\n\",\n \" r = n % 10 # r = 3, 5, 1\\n\",\n \" s = s + (r*r*r) # s = 0, 27, 152, 153\\n\",\n \" n = n // 10 # n = 153, 15, 1, 0\\n\",\n \"\\n\",\n \"if s == temp:\\n\",\n \" print(\\\"Armstrong\\\")\\n\",\n \"else:\\n\",\n \" print(\\\"Not an Armstrong\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"f93f4400\",\n \"metadata\": {},\n \"source\": [\n \"**Write a program to enter a number from user and print its individual digits on seperate line**\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"5ef97f05\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"153\\n\",\n \"\\n\",\n \"1\\n\",\n \"5\\n\",\n \"3\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"id\": \"e3d62d35\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Enter a number: 153\\n\",\n \"3\\n\",\n \"5\\n\",\n \"1\\n\"\n ]\n }\n ],\n \"source\": [\n \"n = int(input(\\\"Enter a number: \\\"))\\n\",\n \"s = 0\\n\",\n \"\\n\",\n \"while n > 0:\\n\",\n \" r = n % 10 # r = 3, 5, 1\\n\",\n \" print(r) # s = 0, 27, 152, 153\\n\",\n \" n = n // 10 # n = 153, 15, 1, 0\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"id\": \"a5bbc5c8\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Enter a number: 153\\n\",\n \"1\\n\",\n \"5\\n\",\n \"3\\n\"\n ]\n }\n ],\n \"source\": [\n \"n = int(input(\\\"Enter a number: \\\"))\\n\",\n \"s = 0\\n\",\n \"\\n\",\n \"while n > 0:\\n\",\n \" r = n % 10 # r = 3, 5, 1\\n\",\n \" s = s*10 + r # s = 0, 27, 152, 153\\n\",\n \" n = n // 10 # n = 153, 15, 1, 0\\n\",\n \"\\n\",\n \"while s > 0:\\n\",\n \" r = s % 10\\n\",\n \" print(r)\\n\",\n \" s = s // 10\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"decf806e\",\n \"metadata\": {},\n \"source\": [\n \"# List\\n\",\n \"\\n\",\n \"collection of different type of elements\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"9b33c6ed\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"a = 10 # int\\n\",\n \"b = 20\\n\",\n \"c = 30\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"id\": \"366b6a7a\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[10, 'A', 'India', 10.6]\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"l = [10, \\\"A\\\", \\\"India\\\", 10.6]\\n\",\n \"\\n\",\n \"print(l)\\n\",\n \"\\n\",\n \"print(type(l))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"id\": \"1af4f02d\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"10\\n\"\n ]\n }\n ],\n \"source\": [\n \"l = [10, \\\"A\\\", \\\"India\\\", 10.6]\\n\",\n \"# 0 1 2 3\\n\",\n \"\\n\",\n \"print(l[0])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"id\": \"ced31696\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"10.6\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l[3])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"id\": \"0c0905a0\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"India\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l[2])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"id\": \"1fe65d17\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"ename\": \"IndexError\",\n \"evalue\": \"list index out of range\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[1;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[1;31mIndexError\\u001b[0m Traceback (most recent call last)\",\n \"Input \\u001b[1;32mIn [16]\\u001b[0m, in \\u001b[0;36m\\u001b[1;34m()\\u001b[0m\\n\\u001b[1;32m----> 1\\u001b[0m \\u001b[38;5;28mprint\\u001b[39m(\\u001b[43ml\\u001b[49m\\u001b[43m[\\u001b[49m\\u001b[38;5;241;43m5\\u001b[39;49m\\u001b[43m]\\u001b[49m)\\n\",\n \"\\u001b[1;31mIndexError\\u001b[0m: list index out of range\"\n ]\n }\n ],\n \"source\": [\n \"print(l[5])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"id\": \"c21b0555\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"l = [10, \\\"A\\\", \\\"India\\\", 10.6]\\n\",\n \"# 0 1 2 3\\n\",\n \"\\n\",\n \"print(type(l[0]))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"id\": \"e9319c1c\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[200, 'A', 'India', 10.6]\\n\"\n ]\n }\n ],\n \"source\": [\n \"l[0] = 200\\n\",\n \"\\n\",\n \"print(l)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"id\": \"f8fe43a4\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"ename\": \"IndexError\",\n \"evalue\": \"list assignment index out of range\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[1;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[1;31mIndexError\\u001b[0m Traceback (most recent call last)\",\n \"Input \\u001b[1;32mIn [19]\\u001b[0m, in \\u001b[0;36m\\u001b[1;34m()\\u001b[0m\\n\\u001b[1;32m----> 1\\u001b[0m l[\\u001b[38;5;241m5\\u001b[39m] \\u001b[38;5;241m=\\u001b[39m \\u001b[38;5;241m1000\\u001b[39m\\n\",\n \"\\u001b[1;31mIndexError\\u001b[0m: list assignment index out of range\"\n ]\n }\n ],\n \"source\": [\n \"l[5] = 1000\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"2aec1fac\",\n \"metadata\": {},\n \"source\": [\n \"### negative index\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"id\": \"d86147ba\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"10.6\\n\"\n ]\n }\n ],\n \"source\": [\n \"# -4 -3 -2 -1\\n\",\n \"l = [10, \\\"A\\\", \\\"India\\\", 10.6]\\n\",\n \"# 0 1 2 3\\n\",\n \"\\n\",\n \"print(l[-1])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"id\": \"c3288372\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"10.6\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l[3])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"id\": \"393bd2e7\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"10\\n\",\n \"10\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l[0])\\n\",\n \"\\n\",\n \"print(l[-4])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"id\": \"07f8d2b4\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"India\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l[-2])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"18ef7bed\",\n \"metadata\": {},\n \"source\": [\n \"# List Slicing\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"id\": \"f556294d\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[11, 22, 33, 44, 10, 24, 36, 68, 99, 81, 100]\\n\"\n ]\n }\n ],\n \"source\": [\n \"# -11-10 -9 -8 -7 -6 -5 -4 -3 -2 -1\\n\",\n \"l = [11,22,33,44,10,24,36,68,99,81,100]\\n\",\n \"# 0 1 2 3 4 5 6 7 8 9 10\\n\",\n \"\\n\",\n \"print(l)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"id\": \"61499c7e\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[22, 33, 44, 10, 24, 36]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l[1:7]) # l[start : end]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"id\": \"696ceede\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[11, 22, 33, 44, 10, 24, 36, 68, 99, 81, 100]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"id\": \"9c790873\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[10, 24, 36, 68, 99, 81]\\n\"\n ]\n }\n ],\n \"source\": [\n \"# -11-10 -9 -8 -7 -6 -5 -4 -3 -2 -1\\n\",\n \"l = [11,22,33,44,10,24,36,68,99,81,100]\\n\",\n \"# 0 1 2 3 4 5 6 7 8 9 10\\n\",\n \"\\n\",\n \"print(l[4:10])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 28,\n \"id\": \"88f74cab\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[99, 81]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l[8:10])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 29,\n \"id\": \"59a8810d\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[11]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l[0:1])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 30,\n \"id\": \"da404e2d\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[22, 33, 44, 10, 24, 36, 68, 99, 81, 100]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l[1:])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 31,\n \"id\": \"411185c6\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[99, 81, 100]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l[8:])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 32,\n \"id\": \"f3427d62\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[11, 22, 33, 44, 10, 24, 36, 68, 99, 81, 100]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l[:])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 33,\n \"id\": \"8cb04435\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[11, 22, 33, 44, 10]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l[:5])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 34,\n \"id\": \"07b3d8c8\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[11, 22, 33, 44, 10, 24, 36]\\n\"\n ]\n }\n ],\n \"source\": [\n \"# -11-10 -9 -8 -7 -6 -5 -4 -3 -2 -1\\n\",\n \"l = [11,22,33,44,10,24,36,68,99,81,100]\\n\",\n \"# 0 1 2 3 4 5 6 7 8 9 10\\n\",\n \"\\n\",\n \"print(l[0:-4])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 35,\n \"id\": \"6b72269e\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[33, 44, 10, 24, 36, 68, 99]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l[2:-2])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 36,\n \"id\": \"34eb2422\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l[2:0])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 37,\n \"id\": \"ea9e9d22\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l[0:0])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 38,\n \"id\": \"d4d6dc2c\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[24, 36, 68, 99, 81, 100]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l[5:15])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 39,\n \"id\": \"327f5a53\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"ename\": \"IndexError\",\n \"evalue\": \"list index out of range\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[1;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[1;31mIndexError\\u001b[0m Traceback (most recent call last)\",\n \"Input \\u001b[1;32mIn [39]\\u001b[0m, in \\u001b[0;36m\\u001b[1;34m()\\u001b[0m\\n\\u001b[1;32m----> 1\\u001b[0m \\u001b[38;5;28mprint\\u001b[39m(\\u001b[43ml\\u001b[49m\\u001b[43m[\\u001b[49m\\u001b[38;5;241;43m15\\u001b[39;49m\\u001b[43m]\\u001b[49m)\\n\",\n \"\\u001b[1;31mIndexError\\u001b[0m: list index out of range\"\n ]\n }\n ],\n \"source\": [\n \"print(l[15])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 40,\n \"id\": \"1654f679\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[22, 33, 44]\\n\"\n ]\n }\n ],\n \"source\": [\n \"# -11-10 -9 -8 -7 -6 -5 -4 -3 -2 -1\\n\",\n \"l = [11,22,33,44,10,24,36,68,99,81,100]\\n\",\n \"# 0 1 2 3 4 5 6 7 8 9 10\\n\",\n \"\\n\",\n \"print(l[1:4])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 41,\n \"id\": \"ef550430\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l[4:1])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 42,\n \"id\": \"64a79df5\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[68, 99, 81]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l[-4:-1])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 43,\n \"id\": \"70fecd4e\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l[-1:-4])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"a2b17316\",\n \"metadata\": {},\n \"source\": [\n \"## steps\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 44,\n \"id\": \"e9af568f\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[11, 33, 10, 36, 99, 100]\\n\"\n ]\n }\n ],\n \"source\": [\n \"# -11-10 -9 -8 -7 -6 -5 -4 -3 -2 -1\\n\",\n \"l = [11,22,33,44,10,24,36,68,99,81,100]\\n\",\n \"# 0 1 2 3 4 5 6 7 8 9 10\\n\",\n \"\\n\",\n \"print(l[0:11:2]) # l[start : end : steps]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 45,\n \"id\": \"d0127e99\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[11, 44, 36, 81]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l[0:11:3])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 46,\n \"id\": 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\"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[]\\n\"\n ]\n }\n ],\n \"source\": [\n \"# -11-10 -9 -8 -7 -6 -5 -4 -3 -2 -1\\n\",\n \"l = [11,22,33,44,10,24,36,68,99,81,100]\\n\",\n \"# 0 1 2 3 4 5 6 7 8 9 10\\n\",\n \"\\n\",\n \"print(l[4:8:-1])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 50,\n \"id\": \"1f16a88d\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[99, 68, 36, 24]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l[8:4:-1])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 51,\n \"id\": \"11575dee\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[100, 99, 36, 10, 33, 11]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l[::-2])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 52,\n \"id\": \"1a639620\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"ename\": \"ValueError\",\n \"evalue\": \"slice step cannot be zero\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[1;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[1;31mValueError\\u001b[0m Traceback (most recent call last)\",\n \"Input \\u001b[1;32mIn [52]\\u001b[0m, in \\u001b[0;36m\\u001b[1;34m()\\u001b[0m\\n\\u001b[1;32m----> 1\\u001b[0m \\u001b[38;5;28mprint\\u001b[39m(\\u001b[43ml\\u001b[49m\\u001b[43m[\\u001b[49m\\u001b[43m:\\u001b[49m\\u001b[43m:\\u001b[49m\\u001b[38;5;241;43m0\\u001b[39;49m\\u001b[43m]\\u001b[49m)\\n\",\n \"\\u001b[1;31mValueError\\u001b[0m: slice step cannot be zero\"\n ]\n }\n ],\n \"source\": [\n \"print(l[::0])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"20862888\",\n \"metadata\": {},\n \"source\": [\n \"## list functions\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 53,\n \"id\": \"8aa952c9\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"5\\n\"\n ]\n }\n ],\n \"source\": [\n \"l = [10,20,30,40,50]\\n\",\n \"\\n\",\n \"print(len(l))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 54,\n \"id\": \"74856061\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"4\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(len(l) - 1)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 55,\n \"id\": \"250b458e\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"150\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(sum(l))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 56,\n \"id\": \"7ea78614\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"50\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(max(l))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 57,\n \"id\": \"bad8aadc\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"10\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(min(l))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 58,\n \"id\": \"27a21618\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"ename\": \"TypeError\",\n \"evalue\": \"unsupported operand type(s) for +: 'int' and 'str'\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[1;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[1;31mTypeError\\u001b[0m Traceback (most recent call last)\",\n \"Input \\u001b[1;32mIn [58]\\u001b[0m, in \\u001b[0;36m\\u001b[1;34m()\\u001b[0m\\n\\u001b[0;32m 1\\u001b[0m l \\u001b[38;5;241m=\\u001b[39m [\\u001b[38;5;124m\\\"\\u001b[39m\\u001b[38;5;124mA\\u001b[39m\\u001b[38;5;124m\\\"\\u001b[39m, \\u001b[38;5;241m10\\u001b[39m,\\u001b[38;5;241m20\\u001b[39m,\\u001b[38;5;241m30\\u001b[39m]\\n\\u001b[1;32m----> 3\\u001b[0m \\u001b[38;5;28mprint\\u001b[39m(\\u001b[38;5;28;43msum\\u001b[39;49m\\u001b[43m(\\u001b[49m\\u001b[43ml\\u001b[49m\\u001b[43m)\\u001b[49m)\\n\",\n \"\\u001b[1;31mTypeError\\u001b[0m: unsupported operand type(s) for +: 'int' and 'str'\"\n ]\n }\n ],\n \"source\": [\n \"l = [\\\"A\\\", 10,20,30]\\n\",\n \"\\n\",\n \"print(sum(l))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 59,\n \"id\": \"a176b651\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"4\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(len(l))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"1ee80d7b\",\n \"metadata\": {},\n \"source\": [\n \"# List methods\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 61,\n \"id\": \"e802bef5\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# help(print)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 63,\n \"id\": \"5a500f31\",\n \"metadata\": {\n \"scrolled\": false\n },\n \"outputs\": [],\n \"source\": [\n \"# help(list)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 64,\n \"id\": \"0538c1e8\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[10, 20, 30, 40]\\n\"\n ]\n }\n ],\n \"source\": [\n \"l = [10,20,30]\\n\",\n \"\\n\",\n \"l.append(40)\\n\",\n \"\\n\",\n \"print(l)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 65,\n \"id\": \"ed712c2b\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"l.append(\\\"India\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 66,\n \"id\": \"265b8be2\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[10, 20, 30, 40, 'India']\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 67,\n \"id\": \"ea15af1f\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"4\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l.index('India'))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 68,\n \"id\": \"c422ce83\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"0\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l.index(10))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 69,\n \"id\": \"0e88a9f3\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[10, 20, 30, 40, 'India']\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 72,\n \"id\": \"e3042d8a\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"l.reverse()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 73,\n \"id\": \"02a28ba4\",\n \"metadata\": {},\n \"outputs\": 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\"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"s = ''\\n\",\n \"\\n\",\n \"print(type(s))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"id\": \"2d3570c7\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"noitacin\\n\"\n ]\n }\n ],\n \"source\": [\n \"s = 'communication'\\n\",\n \"# 0123456789\\n\",\n \"\\n\",\n \"print(s[-1:-9:-1])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"ef70cbf4\",\n \"metadata\": {},\n \"source\": [\n \"# String Functions\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"id\": \"f12266cf\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"13\\n\"\n ]\n }\n ],\n \"source\": [\n \"s = 'communication'\\n\",\n \"\\n\",\n \"print(len(s))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"id\": \"50320c72\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"ename\": \"TypeError\",\n \"evalue\": \"unsupported operand type(s) for +: 'int' and 'str'\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[1;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[1;31mTypeError\\u001b[0m Traceback (most recent call last)\",\n \"Input \\u001b[1;32mIn [15]\\u001b[0m, in \\u001b[0;36m\\u001b[1;34m()\\u001b[0m\\n\\u001b[1;32m----> 1\\u001b[0m \\u001b[38;5;28mprint\\u001b[39m(\\u001b[38;5;28;43msum\\u001b[39;49m\\u001b[43m(\\u001b[49m\\u001b[43ms\\u001b[49m\\u001b[43m)\\u001b[49m)\\n\",\n \"\\u001b[1;31mTypeError\\u001b[0m: unsupported operand type(s) for +: 'int' and 'str'\"\n ]\n }\n ],\n \"source\": [\n \"print(sum(s)) # s = 0\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"id\": \"55b002c3\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"u\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(max(s))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"id\": \"7ccced34\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"a\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(min(s))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"8b30e14f\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# ASCII\\n\",\n \"\\n\",\n \"'A' -> 65\\n\",\n \"'B' -> 66\\n\",\n \"'Z' -> 90\\n\",\n \"\\n\",\n \"'a' -> 97\\n\",\n \"'z' -> 122\\n\",\n \"\\n\",\n \"' ' -> 32 -> space\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"afa17626\",\n \"metadata\": {},\n \"source\": [\n \"## string methods\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"id\": \"e3ef73fb\",\n \"metadata\": {\n \"scrolled\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# help(str)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"id\": \"53f436a8\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"False\"\n ]\n },\n \"execution_count\": 20,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"'raj123' == 'RAJ123'\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"id\": \"9a9f840d\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"raj123\\n\"\n ]\n }\n ],\n \"source\": [\n \"s = 'RAJ123'\\n\",\n \"\\n\",\n \"print(s.casefold())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"id\": \"4e6cd479\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"INDIA\\n\"\n ]\n }\n ],\n \"source\": [\n \"s = 'India'\\n\",\n \"\\n\",\n \"print(s.upper())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"id\": \"76b04479\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"india\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(s.lower())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"id\": \"2e8f43e5\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"['This', 'is', 'an', 'apple']\\n\"\n ]\n }\n ],\n \"source\": [\n \"# split\\n\",\n \"\\n\",\n \"s = 'This is an apple'\\n\",\n \"\\n\",\n \"print(s.split(\\\" \\\"))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"id\": \"94237ec7\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"['H', 'llo Us', 'r']\\n\"\n ]\n }\n ],\n \"source\": [\n \"s = \\\"Hello User\\\"\\n\",\n \"\\n\",\n \"print(s.split(\\\"e\\\"))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"id\": \"42a71402\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"This+is+an+apple\\n\"\n ]\n }\n ],\n \"source\": [\n \"# join\\n\",\n \"\\n\",\n \"l = ['This', 'is', 'an', 'apple']\\n\",\n \"\\n\",\n \"a = \\\"+\\\"\\n\",\n \"\\n\",\n \"print(a.join(l))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"id\": \"6ef43d06\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"This is an apple\\n\"\n ]\n }\n ],\n \"source\": [\n \"# join\\n\",\n \"\\n\",\n \"l = ['This', 'is', 'an', 'apple']\\n\",\n \"\\n\",\n \"a = \\\" \\\"\\n\",\n \"\\n\",\n \"print(a.join(l))\\n\",\n \"# \\\" \\\".join(l)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 28,\n \"id\": \"dae0d39a\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"ThisisanApple\\n\"\n ]\n }\n ],\n \"source\": [\n \"# remove all the spaces from the string\\n\",\n \"\\n\",\n \"s = \\\"This is an Apple\\\"\\n\",\n \"\\n\",\n \"l = s.split(\\\" \\\")\\n\",\n \"\\n\",\n \"x = \\\"\\\".join(l)\\n\",\n \"\\n\",\n \"print(x)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"5e18c7bc\",\n \"metadata\": {},\n \"source\": [\n \"**Write a program to enter a string from user and print the biggest word from the para**\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"fc9ffad3\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"08b8681d\",\n \"metadata\": {},\n \"source\": [\n \"# range function\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 29,\n \"id\": \"cd95ff5d\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"range(0, 5)\\n\"\n ]\n }\n ],\n \"source\": [\n \"n = range(5) # this will create a series of numbers from 0 to 4\\n\",\n \"\\n\",\n \"print(n)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 30,\n \"id\": \"5faa24f2\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(type(n))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 31,\n \"id\": \"b9b1b6fa\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[0, 1, 2, 3, 4]\\n\"\n ]\n }\n ],\n \"source\": [\n \"n = list(range(5))\\n\",\n \"\\n\",\n \"print(n)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 32,\n \"id\": \"00bf5fc9\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[3, 4, 5, 6, 7]\\n\"\n ]\n }\n ],\n \"source\": [\n \"n = list(range(3,8))\\n\",\n \"\\n\",\n \"print(n)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 33,\n \"id\": \"85f51f76\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"True\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(range(20) == range(0,20))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 34,\n \"id\": \"cf56e7cc\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[5, 7, 9, 11, 13, 15, 17, 19]\\n\"\n ]\n }\n ],\n \"source\": [\n \"n = list(range(5,20,2)) # range(start, end, steps)\\n\",\n \"\\n\",\n \"print(n)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"9a9e11b2\",\n \"metadata\": {},\n \"source\": [\n \"## for loop\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 35,\n \"id\": \"eb461d52\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"False\\n\"\n ]\n }\n ],\n \"source\": [\n \"l = [\\\"A\\\", \\\"B\\\", \\\"C\\\", \\\"D\\\"]\\n\",\n \"\\n\",\n \"print(\\\"Z\\\" in l)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 36,\n \"id\": \"f3d37b0a\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"A\\n\",\n \"B\\n\",\n \"C\\n\",\n \"D\\n\"\n ]\n }\n ],\n \"source\": [\n \"l = [\\\"A\\\", \\\"B\\\", \\\"C\\\", \\\"D\\\"]\\n\",\n \"# i\\n\",\n \"\\n\",\n \"# \\\"B\\\" in l\\n\",\n \"for i in l:\\n\",\n \" print(i)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 37,\n \"id\": \"15a3f0ba\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"A\\n\",\n \"Hi\\n\",\n \"B\\n\",\n \"Hi\\n\",\n \"C\\n\",\n \"Hi\\n\",\n \"D\\n\",\n \"Hi\\n\"\n ]\n }\n ],\n \"source\": [\n \"l = [\\\"A\\\", \\\"B\\\", \\\"C\\\", \\\"D\\\"]\\n\",\n \"# i\\n\",\n \"\\n\",\n \"# \\\"B\\\" in l\\n\",\n \"for i in l:\\n\",\n \" print(i)\\n\",\n \" print(\\\"Hi\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 40,\n \"id\": \"59f4f57f\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"a\\n\",\n \"b\\n\",\n \"c\\n\",\n \"d\\n\"\n ]\n }\n ],\n \"source\": [\n \"s = 'abcd'\\n\",\n \"\\n\",\n \"for i in s:\\n\",\n \" print(i)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 42,\n \"id\": \"be302d25\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"AB\\n\",\n \"ab\\n\"\n ]\n }\n ],\n \"source\": [\n \"x = 'ab'\\n\",\n \"\\n\",\n \"print(x.upper())\\n\",\n \"\\n\",\n \"print(x)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 43,\n \"id\": \"b3a22f15\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"['ab', 'cd']\\n\"\n ]\n }\n ],\n \"source\": [\n \"x = ['ab', 'cd']\\n\",\n \"\\n\",\n \"for i in x:\\n\",\n \" i.upper() # 'ab'.upper() -> 'AB'\\n\",\n \" \\n\",\n \"print(x)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 44,\n \"id\": \"4e0873d9\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"AB\\n\",\n \"CD\\n\",\n \"['ab', 'cd']\\n\"\n ]\n }\n ],\n \"source\": [\n \"x = ['ab', 'cd']\\n\",\n \"\\n\",\n \"for i in x:\\n\",\n \" print(i.upper()) # 'ab'.upper() -> 'AB'\\n\",\n \" \\n\",\n \"print(x)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 45,\n \"id\": \"77e96be5\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"a\\n\",\n \"b\\n\",\n \"c\\n\",\n \"d\\n\"\n ]\n }\n ],\n \"source\": [\n \"x = 'abcd'\\n\",\n \"\\n\",\n \"for i in x:\\n\",\n \" print(i)\\n\",\n \" x.upper()\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 46,\n \"id\": \"d6c07e4d\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"0\\n\",\n \"1\\n\",\n \"2\\n\",\n \"3\\n\",\n \"4\\n\"\n ]\n }\n ],\n \"source\": [\n \"# i\\n\",\n \"# 0,1,2,3,4\\n\",\n \"for i in range(5):\\n\",\n \" print(i)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 47,\n \"id\": \"bba46637\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"5\\n\",\n \"6\\n\",\n \"7\\n\",\n \"8\\n\",\n \"9\\n\"\n ]\n }\n ],\n \"source\": [\n \"for i in range(5,10):\\n\",\n \" print(i)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 48,\n \"id\": \"fdb6483c\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"ename\": \"TypeError\",\n \"evalue\": \"'str' object cannot be interpreted as an integer\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[1;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[1;31mTypeError\\u001b[0m Traceback (most recent call last)\",\n \"Input \\u001b[1;32mIn [48]\\u001b[0m, in \\u001b[0;36m\\u001b[1;34m()\\u001b[0m\\n\\u001b[0;32m 1\\u001b[0m x \\u001b[38;5;241m=\\u001b[39m \\u001b[38;5;124m'\\u001b[39m\\u001b[38;5;124mabcd\\u001b[39m\\u001b[38;5;124m'\\u001b[39m\\n\\u001b[1;32m----> 3\\u001b[0m \\u001b[38;5;28;01mfor\\u001b[39;00m i \\u001b[38;5;129;01min\\u001b[39;00m \\u001b[38;5;28;43mrange\\u001b[39;49m\\u001b[43m(\\u001b[49m\\u001b[43mx\\u001b[49m\\u001b[43m)\\u001b[49m:\\n\\u001b[0;32m 4\\u001b[0m \\u001b[38;5;28mprint\\u001b[39m(i)\\n\",\n \"\\u001b[1;31mTypeError\\u001b[0m: 'str' object cannot be interpreted as an integer\"\n ]\n }\n ],\n \"source\": [\n \"x = 'abcd'\\n\",\n \"\\n\",\n \"for i in range(x):\\n\",\n \" print(i)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 49,\n \"id\": \"dac1b7e1\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"0\\n\",\n \"1\\n\",\n \"2\\n\",\n \"3\\n\"\n ]\n }\n ],\n \"source\": [\n \"x = 'abcd'\\n\",\n \"\\n\",\n \"# 0,1,2,3\\n\",\n \"for i in range(len(x)):\\n\",\n \" print(i)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 50,\n \"id\": \"ce020a17\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"ename\": \"AttributeError\",\n \"evalue\": \"'int' object has no attribute 'upper'\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[1;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[1;31mAttributeError\\u001b[0m Traceback (most recent call last)\",\n \"Input \\u001b[1;32mIn [50]\\u001b[0m, in \\u001b[0;36m\\u001b[1;34m()\\u001b[0m\\n\\u001b[0;32m 3\\u001b[0m \\u001b[38;5;66;03m# 0,1,2,3\\u001b[39;00m\\n\\u001b[0;32m 4\\u001b[0m \\u001b[38;5;28;01mfor\\u001b[39;00m i \\u001b[38;5;129;01min\\u001b[39;00m \\u001b[38;5;28mrange\\u001b[39m(\\u001b[38;5;28mlen\\u001b[39m(x)):\\n\\u001b[1;32m----> 5\\u001b[0m \\u001b[38;5;28mprint\\u001b[39m(\\u001b[43mi\\u001b[49m\\u001b[38;5;241;43m.\\u001b[39;49m\\u001b[43mupper\\u001b[49m())\\n\",\n \"\\u001b[1;31mAttributeError\\u001b[0m: 'int' object has no attribute 'upper'\"\n ]\n }\n ],\n \"source\": [\n \"x = 'abcd'\\n\",\n \"\\n\",\n \"# 0,1,2,3\\n\",\n \"for i in range(len(x)):\\n\",\n \" print(i.upper())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 51,\n \"id\": \"e4b348f0\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"abcd\\n\"\n ]\n }\n ],\n \"source\": [\n \"x = 'abcd'\\n\",\n \"\\n\",\n \"# 0,1,2,3\\n\",\n \"for i in range(len(x)):\\n\",\n \" x[i].upper()\\n\",\n \"\\n\",\n \"print(x)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 52,\n \"id\": \"e4afa49b\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"A\\n\",\n \"B\\n\",\n \"C\\n\",\n \"D\\n\",\n \"abcd\\n\"\n ]\n }\n ],\n \"source\": [\n \"x = 'abcd'\\n\",\n \"\\n\",\n \"# 0,1,2,3\\n\",\n \"for i in range(len(x)):\\n\",\n \" print(x[i].upper())\\n\",\n \"\\n\",\n \"print(x)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 53,\n \"id\": \"9d691550\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"a\\n\",\n \"a\\n\",\n \"a\\n\",\n \"a\\n\"\n ]\n }\n ],\n \"source\": [\n \"x = 'abcd'\\n\",\n \"\\n\",\n \"# 0,1,2,3\\n\",\n \"for i in range(len(x)):\\n\",\n \" x = 'a' # x = 'abcd', 'a', 'a'\\n\",\n \" print(x)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"9fe9971b\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.12\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n" }, { "path": "July Cohort 2023/1_Python Programming/.ipynb_checkpoints/05_Dictionary Tuples and Set-checkpoint.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"id\": \"34a4e5e6\",\n \"metadata\": {},\n \"source\": [\n \"# Dictionary\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"cb73a69f\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# { key : value }\\n\",\n \"\\n\",\n \"unordered data structures\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"id\": \"9c5ff7b6\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"{'ravi': 24, 'shyam': 21, 'roy': 41}\\n\"\n ]\n }\n ],\n \"source\": [\n \"ages = {\\\"ravi\\\":24, \\\"shyam\\\": 21, \\\"roy\\\": 41}\\n\",\n \"\\n\",\n \"print(ages)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"id\": \"fc51983d\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(type(ages))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"id\": \"45957197\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"24\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(ages['ravi'])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"id\": \"8587855f\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"41\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(ages['roy'])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"id\": \"bcc42db7\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[255, 0, 0]\\n\"\n ]\n }\n ],\n \"source\": [\n \"color = {\\n\",\n \" \\\"red\\\": [255,0,0],\\n\",\n \" \\\"green\\\": [0,255,0],\\n\",\n \" \\\"blue\\\": [0,0,255]\\n\",\n \"}\\n\",\n \"print(color['red'])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"id\": \"f28a6d43\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"ename\": \"KeyError\",\n \"evalue\": \"'yellow'\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[1;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[1;31mKeyError\\u001b[0m Traceback (most recent call last)\",\n \"Input \\u001b[1;32mIn [7]\\u001b[0m, in \\u001b[0;36m\\u001b[1;34m()\\u001b[0m\\n\\u001b[1;32m----> 1\\u001b[0m \\u001b[38;5;28mprint\\u001b[39m(\\u001b[43mcolor\\u001b[49m\\u001b[43m[\\u001b[49m\\u001b[38;5;124;43m'\\u001b[39;49m\\u001b[38;5;124;43myellow\\u001b[39;49m\\u001b[38;5;124;43m'\\u001b[39;49m\\u001b[43m]\\u001b[49m)\\n\",\n \"\\u001b[1;31mKeyError\\u001b[0m: 'yellow'\"\n ]\n }\n ],\n \"source\": [\n \"print(color['yellow'])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"id\": \"98fd771e\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"{}\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"d = {}\\n\",\n \"print(d)\\n\",\n \"print(type(d))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"id\": \"02b6f0ff\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"ename\": \"TypeError\",\n \"evalue\": \"unhashable type: 'list'\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[1;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[1;31mTypeError\\u001b[0m Traceback (most recent call last)\",\n \"Input \\u001b[1;32mIn [9]\\u001b[0m, in \\u001b[0;36m\\u001b[1;34m()\\u001b[0m\\n\\u001b[1;32m----> 1\\u001b[0m d \\u001b[38;5;241m=\\u001b[39m {[\\u001b[38;5;241m1\\u001b[39m,\\u001b[38;5;241m2\\u001b[39m]:\\u001b[38;5;241m10\\u001b[39m, \\u001b[38;5;241m2\\u001b[39m:\\u001b[38;5;241m2\\u001b[39m, \\u001b[38;5;241m3\\u001b[39m:\\u001b[38;5;241m3\\u001b[39m}\\n\\u001b[0;32m 2\\u001b[0m \\u001b[38;5;28mprint\\u001b[39m(d)\\n\",\n \"\\u001b[1;31mTypeError\\u001b[0m: unhashable type: 'list'\"\n ]\n }\n ],\n \"source\": [\n \"d = {[1,2]:10, 2:2, 3:3}\\n\",\n \"print(d)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"id\": \"080bd938\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[100, 20]\\n\"\n ]\n }\n ],\n \"source\": [\n \"# list are mutable objects\\n\",\n \"\\n\",\n \"l = [10,20]\\n\",\n \"l[0] = 100\\n\",\n \"print(l)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"id\": \"0efe853a\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"ename\": \"TypeError\",\n \"evalue\": \"'str' object does not support item assignment\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[1;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[1;31mTypeError\\u001b[0m Traceback (most recent call last)\",\n \"Input \\u001b[1;32mIn [11]\\u001b[0m, in \\u001b[0;36m\\u001b[1;34m()\\u001b[0m\\n\\u001b[0;32m 1\\u001b[0m \\u001b[38;5;66;03m# strings are immutable objects\\u001b[39;00m\\n\\u001b[0;32m 3\\u001b[0m s \\u001b[38;5;241m=\\u001b[39m \\u001b[38;5;124m'\\u001b[39m\\u001b[38;5;124mindore\\u001b[39m\\u001b[38;5;124m'\\u001b[39m\\n\\u001b[1;32m----> 4\\u001b[0m s[\\u001b[38;5;241m0\\u001b[39m] \\u001b[38;5;241m=\\u001b[39m \\u001b[38;5;124m\\\"\\u001b[39m\\u001b[38;5;124mA\\u001b[39m\\u001b[38;5;124m\\\"\\u001b[39m\\n\\u001b[0;32m 5\\u001b[0m \\u001b[38;5;28mprint\\u001b[39m(s)\\n\",\n \"\\u001b[1;31mTypeError\\u001b[0m: 'str' object does not support item assignment\"\n ]\n }\n ],\n \"source\": [\n \"# strings are immutable objects\\n\",\n \"\\n\",\n \"s = 'indore'\\n\",\n \"s[0] = \\\"A\\\"\\n\",\n \"print(s)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"6de43fed\",\n \"metadata\": {},\n \"source\": [\n \"# Dictionary Functions\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"id\": \"da7184d8\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"4\\n\"\n ]\n }\n ],\n \"source\": [\n \"d = {1:11, 2:41, 13:9, 14:16}\\n\",\n \"\\n\",\n \"print(len(d))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"id\": \"2439c0d6\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"30\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(sum(d))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"id\": \"3842f621\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"ename\": \"TypeError\",\n \"evalue\": \"unsupported operand type(s) for +: 'int' and 'str'\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[1;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[1;31mTypeError\\u001b[0m Traceback (most recent call last)\",\n \"Input \\u001b[1;32mIn [14]\\u001b[0m, in \\u001b[0;36m\\u001b[1;34m()\\u001b[0m\\n\\u001b[0;32m 1\\u001b[0m a \\u001b[38;5;241m=\\u001b[39m {\\u001b[38;5;241m1\\u001b[39m:\\u001b[38;5;241m2\\u001b[39m, \\u001b[38;5;241m3\\u001b[39m:\\u001b[38;5;241m4\\u001b[39m, \\u001b[38;5;124m\\\"\\u001b[39m\\u001b[38;5;124mhi\\u001b[39m\\u001b[38;5;124m\\\"\\u001b[39m:\\u001b[38;5;241m16\\u001b[39m}\\n\\u001b[1;32m----> 3\\u001b[0m \\u001b[38;5;28mprint\\u001b[39m(\\u001b[38;5;28;43msum\\u001b[39;49m\\u001b[43m(\\u001b[49m\\u001b[43ma\\u001b[49m\\u001b[43m)\\u001b[49m)\\n\",\n \"\\u001b[1;31mTypeError\\u001b[0m: unsupported operand type(s) for +: 'int' and 'str'\"\n ]\n }\n ],\n \"source\": [\n \"a = {1:2, 3:4, \\\"hi\\\":16}\\n\",\n \"\\n\",\n \"print(sum(a))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"72a7d21d\",\n \"metadata\": {},\n \"source\": [\n \"# Dictionary Methods\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"id\": \"dda87168\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"{1: 'Apple', 'orange': [2, 3, 4], '2': '23'}\\n\"\n ]\n }\n ],\n \"source\": [\n \"pair = {1:\\\"Apple\\\", \\\"orange\\\": [2,3,4], \\\"2\\\":\\\"23\\\"}\\n\",\n \"\\n\",\n \"print(pair)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"id\": \"031cb949\",\n \"metadata\": {\n \"scrolled\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[2, 3, 4]\\n\"\n ]\n }\n ],\n \"source\": [\n \"# get\\n\",\n \"# help(dict)\\n\",\n \"\\n\",\n \"print(pair.get(\\\"orange\\\"))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"id\": \"cba8f060\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"None\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(pair.get(7))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"32604209\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"None ==> NULL ==> nothing\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"id\": \"d7d265af\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Apple\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(pair.get(1,\\\"Element not found\\\"))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"id\": \"ff1c3065\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Element not found\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(pair.get(10,\\\"Element not found\\\"))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"id\": \"a75186b5\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"{1: 'Apple', 'orange': [2, 3, 4], '2': '23'}\\n\",\n \"{}\\n\"\n ]\n }\n ],\n \"source\": [\n \"# clear\\n\",\n \"\\n\",\n \"p = {1:\\\"Apple\\\", \\\"orange\\\": [2,3,4], \\\"2\\\":\\\"23\\\"}\\n\",\n \"\\n\",\n \"print(p)\\n\",\n \"p.clear()\\n\",\n \"print(p)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"id\": \"d153a547\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"dict_keys([1, 2, 3])\\n\"\n ]\n }\n ],\n \"source\": [\n \"# keys\\n\",\n \"\\n\",\n \"r = {1:\\\"A\\\", 2:'Zara', 3:\\\"Maza\\\"}\\n\",\n \"\\n\",\n \"print(r.keys())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"id\": \"b0c31ff7\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[1, 2, 3]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(list(r.keys()))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"id\": \"0afa95ad\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"dict_values(['A', 'Zara', 'Maza'])\\n\"\n ]\n }\n ],\n \"source\": [\n \"# values\\n\",\n \"\\n\",\n \"r = {1:\\\"A\\\", 2:'Zara', 3:\\\"Maza\\\"}\\n\",\n \"\\n\",\n \"print(r.values())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"id\": \"9c024e2a\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"['A', 'Zara', 'Maza']\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(list(r.values()))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 28,\n \"id\": \"4d0c19d7\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"{3: 'Maza', 2: 'Zara'}\\n\",\n \"2\\n\"\n ]\n }\n ],\n \"source\": [\n \"r = {3:\\\"A\\\", 2:'Zara', 3:\\\"Maza\\\"}\\n\",\n \"\\n\",\n \"print(r)\\n\",\n \"\\n\",\n \"print(len(r))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"104a3ab9\",\n \"metadata\": {},\n \"source\": [\n \"# Question\\n\",\n \"\\n\",\n \"print the following series\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"7f5a1e63\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"{1:1, 2:4, 3:9, 4:16, .........10:100}\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 29,\n \"id\": \"7c6a9d9a\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"{1: 2, 3: 'Hi'}\\n\"\n ]\n }\n ],\n \"source\": [\n \"a = {1:2}\\n\",\n \"a[3] = \\\"Hi\\\"\\n\",\n \"print(a)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 30,\n \"id\": \"e1ec63ea\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"{1: 1, 2: 4, 3: 9, 4: 16, 5: 25, 6: 36, 7: 49, 8: 64, 9: 81, 10: 100}\\n\"\n ]\n }\n ],\n \"source\": [\n \"d = {}\\n\",\n \"\\n\",\n \"for i in range(1,11):\\n\",\n \" d[i] = i * i\\n\",\n \"\\n\",\n \"print(d)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"ae9ad8ce\",\n \"metadata\": {},\n \"source\": [\n \"# Tuples\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 31,\n \"id\": \"926d2c80\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"(4, 3, 5, 6)\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"t = (4,3,5,6)\\n\",\n \"# 0 1 2 3\\n\",\n \"\\n\",\n \"print(t)\\n\",\n \"\\n\",\n \"print(type(t))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 32,\n \"id\": \"0184a528\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"4\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(t[0])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 33,\n \"id\": \"67632ef3\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"ename\": \"TypeError\",\n \"evalue\": \"'tuple' object does not support item assignment\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[1;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[1;31mTypeError\\u001b[0m Traceback (most recent call last)\",\n \"Input \\u001b[1;32mIn [33]\\u001b[0m, in \\u001b[0;36m\\u001b[1;34m()\\u001b[0m\\n\\u001b[1;32m----> 1\\u001b[0m t[\\u001b[38;5;241m0\\u001b[39m] \\u001b[38;5;241m=\\u001b[39m \\u001b[38;5;241m11\\u001b[39m\\n\",\n \"\\u001b[1;31mTypeError\\u001b[0m: 'tuple' object does not support item assignment\"\n ]\n }\n ],\n \"source\": [\n \"t[0] = 11 # tuple are immutable objects\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 34,\n \"id\": \"b6045c0f\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"('one', 'two', 'three')\\n\"\n ]\n }\n ],\n \"source\": [\n \"t = \\\"one\\\", \\\"two\\\", \\\"three\\\"\\n\",\n \"\\n\",\n \"print(t)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 35,\n \"id\": \"ffa2af8b\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(type(t))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 36,\n \"id\": \"2efc8510\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"10\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"t = (10)\\n\",\n \"print(t)\\n\",\n \"print(type(t))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 37,\n \"id\": \"da1bb799\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"(10,)\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"t = (10,)\\n\",\n \"print(t)\\n\",\n \"print(type(t))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"4cae5690\",\n \"metadata\": {},\n \"source\": [\n \"# Tuple unpacking\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 38,\n \"id\": \"58a607f3\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"1\\n\",\n \"2\\n\",\n \"3\\n\"\n ]\n }\n ],\n \"source\": [\n \"n = (1,2,3)\\n\",\n \"\\n\",\n \"a,b,c = n\\n\",\n \"\\n\",\n \"print(a)\\n\",\n \"print(b)\\n\",\n \"print(c)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"b276c6bf\",\n \"metadata\": {},\n \"source\": [\n \"# Tuple Slicing\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 39,\n \"id\": \"92e79ee4\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"(11, 22, 33, 44, 55, 66, 77, 88, 99, 100)\\n\"\n ]\n }\n ],\n \"source\": [\n \"# -1\\n\",\n \"t = (11,22,33,44,55,66,77,88,99,100)\\n\",\n \"# 0 1 \\n\",\n \"\\n\",\n \"print(t)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 40,\n \"id\": \"57630079\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"(44, 55)\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(t[3:5])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 41,\n \"id\": \"fec889a9\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"(11, 22, 33, 44, 55, 66)\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(t[:6])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 42,\n \"id\": \"503f8dba\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"(33, 44, 55, 66)\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(t[-8:-4])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 43,\n \"id\": \"a0ba748f\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"()\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(t[-4:-8])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 44,\n \"id\": \"fa4cc944\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"(100, 99, 88, 77, 66, 55, 44, 33, 22, 11)\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(t[::-1])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"9b4af08a\",\n \"metadata\": {},\n \"source\": [\n \"# Tuple Methods\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 45,\n \"id\": \"7bbdf0aa\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"3\\n\"\n ]\n }\n ],\n \"source\": [\n \"# count\\n\",\n \"\\n\",\n \"t = (2,3,4,5,5,6,6,4,4,)\\n\",\n \"\\n\",\n \"print(t.count(4))\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 46,\n \"id\": \"ad80d733\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"2\\n\"\n ]\n }\n ],\n \"source\": [\n \"# index\\n\",\n \"\\n\",\n \"t = (2,3,4,5,5,6,6,4,4,)\\n\",\n \"\\n\",\n \"print(t.index(4))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 47,\n \"id\": \"df41de26\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Help on built-in function index:\\n\",\n \"\\n\",\n \"index(value, start=0, stop=9223372036854775807, /) method of builtins.tuple instance\\n\",\n \" Return first index of value.\\n\",\n \" \\n\",\n \" Raises ValueError if the value is not present.\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"help(t.index)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 48,\n \"id\": \"86d6e3a9\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"7\\n\"\n ]\n }\n ],\n \"source\": [\n \"# index\\n\",\n \"\\n\",\n \"t = (2,3,4,5,5,6,6,4,4,)\\n\",\n \"\\n\",\n \"print(t.index(4, 5))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"ff55ace0\",\n \"metadata\": {},\n \"source\": [\n \"# Tuple functions\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 49,\n \"id\": \"d2e05c64\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"5\\n\"\n ]\n }\n ],\n \"source\": [\n \"t = (2,3,4,5,6)\\n\",\n \"\\n\",\n \"print(len(t))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 50,\n \"id\": \"2f71a91b\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"20\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(sum(t))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 51,\n \"id\": \"f9c6d5a3\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"6\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(max(t))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"23dad4a2\",\n \"metadata\": {},\n \"source\": [\n \"# Question\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"c1ee0321\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# remove i from this tuple\\n\",\n \"\\n\",\n \"t = ('a', 'e','i', 'o', 'u')\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.12\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n" }, { "path": "July Cohort 2023/1_Python Programming/01_Python Basics.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"id\": \"5effdff7\",\n \"metadata\": {},\n \"source\": [\n \"# Python Basics\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"id\": \"d04e5b22\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Hello User\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(\\\"Hello User\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"id\": \"b0091bf1\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Hello\\n\",\n \"User\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(\\\"Hello\\\")\\n\",\n \"print(\\\"User\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"id\": \"fb83814b\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Hello\\n\",\n \"User\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(\\\"Hello\\\\nUser\\\") # \\\\n -> new line character\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"id\": \"791c342a\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Hello\\tUser\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(\\\"Hello\\\\tUser\\\") # \\\\t -> horizontal tab - 8 spaces\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"9fad4ea5\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# binary language -> 101010111 -> bits\\n\",\n \"\\n\",\n \"0 -> low voltage, no-charge, false, off\\n\",\n \"1 -> high voltage, charge, true, on\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"a1885e98\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"bits > numbers > character > instructions > program > software\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"cbe99e7d\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"source code ---------> Byte Code --------> output\\n\",\n \" .c compile .bak run .exe\\n\",\n \" .cpp .obj\\n\",\n \" .java .javac\\n\",\n \" .py .pyc\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"8a713554\",\n \"metadata\": {},\n \"source\": [\n \"# Variables\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"id\": \"5c20abe8\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"10\\n\"\n ]\n }\n ],\n \"source\": [\n \"a = 10\\n\",\n \"\\n\",\n \"print(a)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"id\": \"995009df\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"45.13\\n\"\n ]\n }\n ],\n \"source\": [\n \"b = 45.13\\n\",\n \"\\n\",\n \"print(b)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"id\": \"17194c9e\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"India\\n\"\n ]\n }\n ],\n \"source\": [\n \"a = \\\"India\\\"\\n\",\n \"\\n\",\n \"print(a)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"9141a786\",\n \"metadata\": {},\n \"source\": [\n \"# Data Types\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"id\": \"6ba6bfd9\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"10\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"a = 10\\n\",\n \"\\n\",\n \"print(a)\\n\",\n \"\\n\",\n \"print(type(a))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"id\": \"a6e1a232\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"10.65\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"b = 10.65\\n\",\n \"\\n\",\n \"print(b)\\n\",\n \"\\n\",\n \"print(type(b))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"id\": \"1298d5d3\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"colors\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"c = \\\"colors\\\"\\n\",\n \"\\n\",\n \"print(c)\\n\",\n \"\\n\",\n \"print(type(c))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"id\": \"47c38fa9\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"colors\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"c = 'colors'\\n\",\n \"\\n\",\n \"print(c)\\n\",\n \"\\n\",\n \"print(type(c))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"id\": \"0acb14e3\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"it's an apple\\n\"\n ]\n }\n ],\n \"source\": [\n \"x = \\\"it's an apple\\\"\\n\",\n \"\\n\",\n \"print(x)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"id\": \"fb2beb48\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"it\\\"s an apple\\n\"\n ]\n }\n ],\n \"source\": [\n \"x = 'it\\\"s an apple'\\n\",\n \"\\n\",\n \"print(x)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"814feca5\",\n \"metadata\": {},\n \"source\": [\n \"# Input Function\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"id\": \"437ffd33\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"10\\n\",\n \"10\\n\"\n ]\n }\n ],\n \"source\": [\n \"a = input()\\n\",\n \"\\n\",\n \"print(a)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"id\": \"8c71996c\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Enter Your Name: Himanshu\\n\",\n \"Himanshu\\n\"\n ]\n }\n ],\n \"source\": [\n \"a = input(\\\"Enter Your Name: \\\")\\n\",\n \"\\n\",\n \"print(a)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"id\": \"792f782d\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Enter Your Name: Himanshu\\n\",\n \"Hello! How are you? Himanshu\\n\"\n ]\n }\n ],\n \"source\": [\n \"a = input(\\\"Enter Your Name: \\\")\\n\",\n \"\\n\",\n \"print(\\\"Hello! How are you?\\\", a)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"id\": \"8ab7e6a7\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Hello Him\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(\\\"Hello Him\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"84b26329\",\n \"metadata\": {},\n \"source\": [\n \"# Questions\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"376a1bc0\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"Your Name\\n\",\n \"------8-------College/Company\\n\",\n \"------------16-------------Country\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"f60668a8\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"*\\n\",\n \"**\\n\",\n \"***\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"7c2fa48a\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"--4--*\\n\",\n \"*----8----*\\n\",\n \"--4--*\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"2650f9ed\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \" *\\n\",\n \"* *\\n\",\n \" *\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.12\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n" }, { "path": "July Cohort 2023/1_Python Programming/02_03_While Loop and List.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"id\": \"13c6ac1c\",\n \"metadata\": {},\n \"source\": [\n \"# While Loop\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"id\": \"0290b931\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Himanshu\\n\",\n \"Himanshu\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(\\\"Himanshu\\\")\\n\",\n \"print(\\\"Himanshu\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"id\": \"aab5a9d5\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"True\\n\"\n ]\n }\n ],\n \"source\": [\n \"i = 1\\n\",\n \"\\n\",\n \"print(i <= 5)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"id\": \"be002b98\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"False\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(i >= 5)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"id\": \"903683b3\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"True\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(i >= 1)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"id\": \"76da14cf\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Himanshu\\n\",\n \"Himanshu\\n\",\n \"Himanshu\\n\",\n \"Himanshu\\n\",\n \"Himanshu\\n\"\n ]\n }\n ],\n \"source\": [\n \"i = 1\\n\",\n \"\\n\",\n \"while i <= 5:\\n\",\n \" print(\\\"Himanshu\\\")\\n\",\n \" i = i + 1 # i = 1, 2, 3, 4, 5, 6\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"id\": \"145ad944\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"1\\n\",\n \"2\\n\",\n \"3\\n\",\n \"4\\n\",\n \"5\\n\"\n ]\n }\n ],\n \"source\": [\n \"i = 1\\n\",\n \"\\n\",\n \"while i <= 5:\\n\",\n \" print(i)\\n\",\n \" i = i + 1\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"cdad3efc\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"1\\n\",\n \"2\\n\",\n \"3\\n\",\n \"4\\n\",\n \"5\\n\",\n \"6\\n\",\n \"7\\n\",\n \"8\\n\",\n \"9\\n\",\n \"10\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"id\": \"d7d2dfab\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"1\\n\",\n \"2\\n\",\n \"3\\n\",\n \"4\\n\",\n \"5\\n\",\n \"6\\n\",\n \"7\\n\",\n \"8\\n\",\n \"9\\n\",\n \"10\\n\"\n ]\n }\n ],\n \"source\": [\n \"i = 1\\n\",\n \"\\n\",\n \"while i <= 10:\\n\",\n \" print(i)\\n\",\n \" i = i + 1\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"id\": \"a103c74c\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Hello\\n\",\n \"World\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(\\\"Hello\\\")\\n\",\n \"print(\\\"World\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"id\": \"5df8d4f6\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Hello\\n\",\n \"World\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(\\\"Hello\\\", end=\\\"\\\\n\\\")\\n\",\n \"print(\\\"World\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"id\": \"c89f0b40\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Hello+World\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(\\\"Hello\\\", end=\\\"+\\\")\\n\",\n \"print(\\\"World\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"id\": \"8a0e5ca8\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Hello World\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(\\\"Hello\\\", end=\\\" \\\")\\n\",\n \"print(\\\"World\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"id\": \"bce809e5\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Hello @@@ World\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(\\\"Hello\\\", end=\\\" @@@ \\\")\\n\",\n \"print(\\\"World\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"id\": \"54435a01\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Hello@World$\"\n ]\n }\n ],\n \"source\": [\n \"print(\\\"Hello\\\", end=\\\"@\\\")\\n\",\n \"print(\\\"World\\\", end=\\\"$\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"95e933ab\",\n \"metadata\": {},\n \"source\": [\n \"### print the following series of numbers\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"7a728f28\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"1 2 3 4 5 6 7 8 9 10\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"id\": \"35896fd2\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"1 2 3 4 5 6 7 8 9 10 \"\n ]\n }\n ],\n \"source\": [\n \"i = 1\\n\",\n \"\\n\",\n \"while i <= 10:\\n\",\n \" print(i, end=\\\" \\\")\\n\",\n \" i = i + 1\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"id\": \"284eda1a\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"100\\n\",\n \"10\\n\"\n ]\n }\n ],\n \"source\": [\n \"a = 10\\n\",\n \"\\n\",\n \"print(a * a)\\n\",\n \"\\n\",\n \"print(a)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"d1cabe31\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"1 4 9 16 25 36 49 64 81 100\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"id\": \"32f564a1\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"1 4 9 16 25 36 49 64 81 100 \"\n ]\n }\n ],\n \"source\": [\n \"i = 1\\n\",\n \"\\n\",\n \"while i <= 10:\\n\",\n \" print(i*i, end=\\\" \\\")\\n\",\n \" i = i + 1\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"49b1b14e\",\n \"metadata\": {},\n \"source\": [\n \"**Write a program to enter 5 values from user and print them.**\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"id\": \"a1cd06ee\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Enter your number: 10\\n\",\n \"10\\n\",\n \"Enter your number: 20\\n\",\n \"20\\n\",\n \"Enter your number: 30\\n\",\n \"30\\n\",\n \"Enter your number: 40\\n\",\n \"40\\n\",\n \"Enter your number: 50\\n\",\n \"50\\n\"\n ]\n }\n ],\n \"source\": [\n \"i = 1\\n\",\n \"\\n\",\n \"while i <= 5:\\n\",\n \" a = input(\\\"Enter your number: \\\")\\n\",\n \" print(a)\\n\",\n \" i = i + 1\\n\",\n \" # 1 + 1\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"id\": \"a1b92062\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"3.5\"\n ]\n },\n \"execution_count\": 18,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"7 / 2\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"id\": \"b1e0fc38\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"1\"\n ]\n },\n \"execution_count\": 19,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"7 % 2 # modulo - return the reminder\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"id\": \"7279d00f\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"0\"\n ]\n },\n \"execution_count\": 20,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"10 % 2\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"id\": \"7ba7e050\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"3\"\n ]\n },\n \"execution_count\": 21,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"11 % 4\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"id\": \"3c3ea69f\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"3\"\n ]\n },\n \"execution_count\": 22,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"7 // 2 # floor division\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"id\": \"e378bbe4\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"2\"\n ]\n },\n \"execution_count\": 23,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"15 // 7\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"id\": \"5947157a\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"3\"\n ]\n },\n \"execution_count\": 24,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"153 % 10\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"07a156bd\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"15)7(\\n\",\n \" 7\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"d490f9d3\",\n \"metadata\": {},\n \"source\": [\n \"**Write a program to enter a number from user and print reverse of it.**\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"7959c4e2\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"153\\n\",\n \"\\n\",\n \"351\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"960cfd0b\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"153 % 10 = 3\\n\",\n \"153 // 10 = 15\\n\",\n \"\\n\",\n \"15 % 10 = 5\\n\",\n \"15 // 10 = 1\\n\",\n \"\\n\",\n \"1 % 10 = 1\\n\",\n \"1 // 10 = 0\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"id\": \"3597feec\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Enter your number: 10\\n\",\n \"10\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"n = input(\\\"Enter your number: \\\")\\n\",\n \"\\n\",\n \"print(n)\\n\",\n \"\\n\",\n \"print(type(n))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"id\": \"71b967e4\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Enter your number: 10\\n\",\n \"10\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"n = int(input(\\\"Enter your number: \\\"))\\n\",\n \"\\n\",\n \"print(n)\\n\",\n \"\\n\",\n \"print(type(n))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 31,\n \"id\": \"5759b2ec\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Enter a number: 153\\n\",\n \"351\"\n ]\n }\n ],\n \"source\": [\n \"n = int(input(\\\"Enter a number: \\\"))\\n\",\n \"\\n\",\n \"while n > 0:\\n\",\n \" r = n % 10 # r = 3, 5, 1\\n\",\n \" print(r, end=\\\"\\\") # 351\\n\",\n \" n = n // 10 # n = 153, 15, 1, 0\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"adf5586f\",\n \"metadata\": {},\n \"source\": [\n \"**Write a program to enter a number from user and print sum of its individual digits**\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"cbd3db08\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"153\\n\",\n \"\\n\",\n \"1 + 5 + 3\\n\",\n \"\\n\",\n \"9\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"1d76dd47\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"0 = s\\n\",\n \"3 = s + 3 = s\\n\",\n \"5 = s + 5 = s\\n\",\n \"1\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 32,\n \"id\": \"52deeb6d\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Enter a number: 153\\n\",\n \"Sum = 9\\n\"\n ]\n }\n ],\n \"source\": [\n \"n = int(input(\\\"Enter a number: \\\"))\\n\",\n \"s = 0\\n\",\n \"\\n\",\n \"while n > 0:\\n\",\n \" r = n % 10\\n\",\n \" s = s + r # s = 0, 3, 8, 9\\n\",\n \" n = n // 10\\n\",\n \"\\n\",\n \"print(\\\"Sum =\\\", s)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"c2bb6d4f\",\n \"metadata\": {},\n \"source\": [\n \"# Day 3\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"f4cb0aee\",\n \"metadata\": {},\n \"source\": [\n \"**Write a program to reverse a number**\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"85d9cb69\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"153\\n\",\n \"\\n\",\n \"351 - this should be a whole number\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"id\": \"f31986e9\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Enter a number: 153\\n\",\n \"351\\n\",\n \"702\\n\"\n ]\n }\n ],\n \"source\": [\n \"n = int(input(\\\"Enter a number: \\\"))\\n\",\n \"s = 0\\n\",\n \"\\n\",\n \"while n > 0:\\n\",\n \" r = n % 10 # r = 3, 5, 1\\n\",\n \" s = s*10 + r # s = 0, 3, 35, 351\\n\",\n \" n = n // 10 # n = 153, 15, 1, 0\\n\",\n \"\\n\",\n \"print(s)\\n\",\n \"print(s * 2)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"949c7e7d\",\n \"metadata\": {},\n \"source\": [\n \"**Write a program to enter a number from user and check if it palindrome or not**\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"4fbfc3b5\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"121\\n\",\n \"\\n\",\n \"121\\n\",\n \"\\n\",\n \"153 = 351 - not a palindrome number\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"id\": \"4855f13b\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Enter a number: 121\\n\",\n \"Not a Palindrome\\n\"\n ]\n }\n ],\n \"source\": [\n \"n = int(input(\\\"Enter a number: \\\"))\\n\",\n \"s = 0\\n\",\n \"\\n\",\n \"while n > 0:\\n\",\n \" r = n % 10 # r = 3, 5, 1\\n\",\n \" s = s*10 + r # s = 0, 3, 35, 351\\n\",\n \" n = n // 10 # n = 153, 15, 1, 0\\n\",\n \"\\n\",\n \"if s == n:\\n\",\n \" print(\\\"Palindrome\\\")\\n\",\n \"else:\\n\",\n \" print(\\\"Not a Palindrome\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"id\": \"90712f12\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Enter a number: 1234321\\n\",\n \"Palindrome\\n\"\n ]\n }\n ],\n \"source\": [\n \"n = int(input(\\\"Enter a number: \\\"))\\n\",\n \"temp = n\\n\",\n \"s = 0\\n\",\n \"\\n\",\n \"while n > 0:\\n\",\n \" r = n % 10 # r = 3, 5, 1\\n\",\n \" s = s*10 + r # s = 0, 3, 35, 351\\n\",\n \" n = n // 10 # n = 153, 15, 1, 0\\n\",\n \"\\n\",\n \"if s == temp:\\n\",\n \" print(\\\"Palindrome\\\")\\n\",\n \"else:\\n\",\n \" print(\\\"Not a Palindrome\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"e1a01ec1\",\n \"metadata\": {},\n \"source\": [\n \"**Write a program to enter a number from user and check if it is armstrong or not**\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"20722f78\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"153\\n\",\n \"\\n\",\n \"cube(1) + cube(5) + cube(3)\\n\",\n \"\\n\",\n \"370\\n\",\n \"\\n\",\n \"371\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"id\": \"50334900\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Enter a number: 371\\n\",\n \"Armstrong\\n\"\n ]\n }\n ],\n \"source\": [\n \"n = int(input(\\\"Enter a number: \\\"))\\n\",\n \"temp = n\\n\",\n \"s = 0\\n\",\n \"\\n\",\n \"while n > 0:\\n\",\n \" r = n % 10 # r = 3, 5, 1\\n\",\n \" s = s + (r*r*r) # s = 0, 27, 152, 153\\n\",\n \" n = n // 10 # n = 153, 15, 1, 0\\n\",\n \"\\n\",\n \"if s == temp:\\n\",\n \" print(\\\"Armstrong\\\")\\n\",\n \"else:\\n\",\n \" print(\\\"Not an Armstrong\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"f93f4400\",\n \"metadata\": {},\n \"source\": [\n \"**Write a program to enter a number from user and print its individual digits on seperate line**\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"5ef97f05\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"153\\n\",\n \"\\n\",\n \"1\\n\",\n \"5\\n\",\n \"3\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"id\": \"e3d62d35\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Enter a number: 153\\n\",\n \"3\\n\",\n \"5\\n\",\n \"1\\n\"\n ]\n }\n ],\n \"source\": [\n \"n = int(input(\\\"Enter a number: \\\"))\\n\",\n \"s = 0\\n\",\n \"\\n\",\n \"while n > 0:\\n\",\n \" r = n % 10 # r = 3, 5, 1\\n\",\n \" print(r) # s = 0, 27, 152, 153\\n\",\n \" n = n // 10 # n = 153, 15, 1, 0\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"id\": \"a5bbc5c8\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Enter a number: 153\\n\",\n \"1\\n\",\n \"5\\n\",\n \"3\\n\"\n ]\n }\n ],\n \"source\": [\n \"n = int(input(\\\"Enter a number: \\\"))\\n\",\n \"s = 0\\n\",\n \"\\n\",\n \"while n > 0:\\n\",\n \" r = n % 10 # r = 3, 5, 1\\n\",\n \" s = s*10 + r # s = 0, 27, 152, 153\\n\",\n \" n = n // 10 # n = 153, 15, 1, 0\\n\",\n \"\\n\",\n \"while s > 0:\\n\",\n \" r = s % 10\\n\",\n \" print(r)\\n\",\n \" s = s // 10\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"decf806e\",\n \"metadata\": {},\n \"source\": [\n \"# List\\n\",\n \"\\n\",\n \"collection of different type of elements\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"9b33c6ed\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"a = 10 # int\\n\",\n \"b = 20\\n\",\n \"c = 30\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"id\": \"366b6a7a\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[10, 'A', 'India', 10.6]\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"l = [10, \\\"A\\\", \\\"India\\\", 10.6]\\n\",\n \"\\n\",\n \"print(l)\\n\",\n \"\\n\",\n \"print(type(l))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"id\": \"1af4f02d\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"10\\n\"\n ]\n }\n ],\n \"source\": [\n \"l = [10, \\\"A\\\", \\\"India\\\", 10.6]\\n\",\n \"# 0 1 2 3\\n\",\n \"\\n\",\n \"print(l[0])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"id\": \"ced31696\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"10.6\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l[3])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"id\": \"0c0905a0\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"India\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l[2])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"id\": \"1fe65d17\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"ename\": \"IndexError\",\n \"evalue\": \"list index out of range\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[1;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[1;31mIndexError\\u001b[0m Traceback (most recent call last)\",\n \"Input \\u001b[1;32mIn [16]\\u001b[0m, in \\u001b[0;36m\\u001b[1;34m()\\u001b[0m\\n\\u001b[1;32m----> 1\\u001b[0m \\u001b[38;5;28mprint\\u001b[39m(\\u001b[43ml\\u001b[49m\\u001b[43m[\\u001b[49m\\u001b[38;5;241;43m5\\u001b[39;49m\\u001b[43m]\\u001b[49m)\\n\",\n \"\\u001b[1;31mIndexError\\u001b[0m: list index out of range\"\n ]\n }\n ],\n \"source\": [\n \"print(l[5])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"id\": \"c21b0555\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"l = [10, \\\"A\\\", \\\"India\\\", 10.6]\\n\",\n \"# 0 1 2 3\\n\",\n \"\\n\",\n \"print(type(l[0]))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"id\": \"e9319c1c\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[200, 'A', 'India', 10.6]\\n\"\n ]\n }\n ],\n \"source\": [\n \"l[0] = 200\\n\",\n \"\\n\",\n \"print(l)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"id\": \"f8fe43a4\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"ename\": \"IndexError\",\n \"evalue\": \"list assignment index out of range\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[1;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[1;31mIndexError\\u001b[0m Traceback (most recent call last)\",\n \"Input \\u001b[1;32mIn [19]\\u001b[0m, in \\u001b[0;36m\\u001b[1;34m()\\u001b[0m\\n\\u001b[1;32m----> 1\\u001b[0m l[\\u001b[38;5;241m5\\u001b[39m] \\u001b[38;5;241m=\\u001b[39m \\u001b[38;5;241m1000\\u001b[39m\\n\",\n \"\\u001b[1;31mIndexError\\u001b[0m: list assignment index out of range\"\n ]\n }\n ],\n \"source\": [\n \"l[5] = 1000\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"2aec1fac\",\n \"metadata\": {},\n \"source\": [\n \"### negative index\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"id\": \"d86147ba\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"10.6\\n\"\n ]\n }\n ],\n \"source\": [\n \"# -4 -3 -2 -1\\n\",\n \"l = [10, \\\"A\\\", \\\"India\\\", 10.6]\\n\",\n \"# 0 1 2 3\\n\",\n \"\\n\",\n \"print(l[-1])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"id\": \"c3288372\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"10.6\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l[3])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"id\": \"393bd2e7\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"10\\n\",\n \"10\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l[0])\\n\",\n \"\\n\",\n \"print(l[-4])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"id\": \"07f8d2b4\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"India\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l[-2])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"18ef7bed\",\n \"metadata\": {},\n \"source\": [\n \"# List Slicing\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"id\": \"f556294d\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[11, 22, 33, 44, 10, 24, 36, 68, 99, 81, 100]\\n\"\n ]\n }\n ],\n \"source\": [\n \"# -11-10 -9 -8 -7 -6 -5 -4 -3 -2 -1\\n\",\n \"l = [11,22,33,44,10,24,36,68,99,81,100]\\n\",\n \"# 0 1 2 3 4 5 6 7 8 9 10\\n\",\n \"\\n\",\n \"print(l)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"id\": \"61499c7e\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[22, 33, 44, 10, 24, 36]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l[1:7]) # l[start : end]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"id\": \"696ceede\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[11, 22, 33, 44, 10, 24, 36, 68, 99, 81, 100]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"id\": \"9c790873\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[10, 24, 36, 68, 99, 81]\\n\"\n ]\n }\n ],\n \"source\": [\n \"# -11-10 -9 -8 -7 -6 -5 -4 -3 -2 -1\\n\",\n \"l = [11,22,33,44,10,24,36,68,99,81,100]\\n\",\n \"# 0 1 2 3 4 5 6 7 8 9 10\\n\",\n \"\\n\",\n \"print(l[4:10])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 28,\n \"id\": \"88f74cab\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[99, 81]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l[8:10])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 29,\n \"id\": \"59a8810d\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[11]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l[0:1])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 30,\n \"id\": \"da404e2d\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[22, 33, 44, 10, 24, 36, 68, 99, 81, 100]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l[1:])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 31,\n \"id\": \"411185c6\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[99, 81, 100]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l[8:])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 32,\n \"id\": \"f3427d62\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[11, 22, 33, 44, 10, 24, 36, 68, 99, 81, 100]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l[:])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 33,\n \"id\": \"8cb04435\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[11, 22, 33, 44, 10]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l[:5])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 34,\n \"id\": \"07b3d8c8\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[11, 22, 33, 44, 10, 24, 36]\\n\"\n ]\n }\n ],\n \"source\": [\n \"# -11-10 -9 -8 -7 -6 -5 -4 -3 -2 -1\\n\",\n \"l = [11,22,33,44,10,24,36,68,99,81,100]\\n\",\n \"# 0 1 2 3 4 5 6 7 8 9 10\\n\",\n \"\\n\",\n \"print(l[0:-4])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 35,\n \"id\": \"6b72269e\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[33, 44, 10, 24, 36, 68, 99]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l[2:-2])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 36,\n \"id\": \"34eb2422\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l[2:0])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 37,\n \"id\": \"ea9e9d22\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l[0:0])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 38,\n \"id\": \"d4d6dc2c\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[24, 36, 68, 99, 81, 100]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l[5:15])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 39,\n \"id\": \"327f5a53\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"ename\": \"IndexError\",\n \"evalue\": \"list index out of range\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[1;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[1;31mIndexError\\u001b[0m Traceback (most recent call last)\",\n \"Input \\u001b[1;32mIn [39]\\u001b[0m, in \\u001b[0;36m\\u001b[1;34m()\\u001b[0m\\n\\u001b[1;32m----> 1\\u001b[0m \\u001b[38;5;28mprint\\u001b[39m(\\u001b[43ml\\u001b[49m\\u001b[43m[\\u001b[49m\\u001b[38;5;241;43m15\\u001b[39;49m\\u001b[43m]\\u001b[49m)\\n\",\n \"\\u001b[1;31mIndexError\\u001b[0m: list index out of range\"\n ]\n }\n ],\n \"source\": [\n \"print(l[15])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 40,\n \"id\": \"1654f679\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[22, 33, 44]\\n\"\n ]\n }\n ],\n \"source\": [\n \"# -11-10 -9 -8 -7 -6 -5 -4 -3 -2 -1\\n\",\n \"l = [11,22,33,44,10,24,36,68,99,81,100]\\n\",\n \"# 0 1 2 3 4 5 6 7 8 9 10\\n\",\n \"\\n\",\n \"print(l[1:4])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 41,\n \"id\": \"ef550430\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l[4:1])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 42,\n \"id\": \"64a79df5\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[68, 99, 81]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l[-4:-1])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 43,\n \"id\": \"70fecd4e\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l[-1:-4])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"a2b17316\",\n \"metadata\": {},\n \"source\": [\n \"## steps\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 44,\n \"id\": \"e9af568f\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[11, 33, 10, 36, 99, 100]\\n\"\n ]\n }\n ],\n \"source\": [\n \"# -11-10 -9 -8 -7 -6 -5 -4 -3 -2 -1\\n\",\n \"l = [11,22,33,44,10,24,36,68,99,81,100]\\n\",\n \"# 0 1 2 3 4 5 6 7 8 9 10\\n\",\n \"\\n\",\n \"print(l[0:11:2]) # l[start : end : steps]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 45,\n \"id\": \"d0127e99\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[11, 44, 36, 81]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l[0:11:3])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 46,\n \"id\": \"75e4fefa\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[11, 22, 33, 44, 10, 24, 36, 68, 99, 81, 100]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l[::])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 47,\n \"id\": \"3449b6af\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[11, 22, 33, 44, 10, 24, 36, 68, 99, 81, 100]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l[::1])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 48,\n \"id\": \"2e6345c0\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[100, 81, 99, 68, 36, 24, 10, 44, 33, 22, 11]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l[::-1]) # reverse\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 49,\n \"id\": \"f284f488\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[]\\n\"\n ]\n }\n ],\n \"source\": [\n \"# -11-10 -9 -8 -7 -6 -5 -4 -3 -2 -1\\n\",\n \"l = [11,22,33,44,10,24,36,68,99,81,100]\\n\",\n \"# 0 1 2 3 4 5 6 7 8 9 10\\n\",\n \"\\n\",\n \"print(l[4:8:-1])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 50,\n \"id\": \"1f16a88d\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[99, 68, 36, 24]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l[8:4:-1])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 51,\n \"id\": \"11575dee\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[100, 99, 36, 10, 33, 11]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l[::-2])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 52,\n \"id\": \"1a639620\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"ename\": \"ValueError\",\n \"evalue\": \"slice step cannot be zero\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[1;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[1;31mValueError\\u001b[0m Traceback (most recent call last)\",\n \"Input \\u001b[1;32mIn [52]\\u001b[0m, in \\u001b[0;36m\\u001b[1;34m()\\u001b[0m\\n\\u001b[1;32m----> 1\\u001b[0m \\u001b[38;5;28mprint\\u001b[39m(\\u001b[43ml\\u001b[49m\\u001b[43m[\\u001b[49m\\u001b[43m:\\u001b[49m\\u001b[43m:\\u001b[49m\\u001b[38;5;241;43m0\\u001b[39;49m\\u001b[43m]\\u001b[49m)\\n\",\n \"\\u001b[1;31mValueError\\u001b[0m: slice step cannot be zero\"\n ]\n }\n ],\n \"source\": [\n \"print(l[::0])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"20862888\",\n \"metadata\": {},\n \"source\": [\n \"## list functions\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 53,\n \"id\": \"8aa952c9\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"5\\n\"\n ]\n }\n ],\n \"source\": [\n \"l = [10,20,30,40,50]\\n\",\n \"\\n\",\n \"print(len(l))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 54,\n \"id\": \"74856061\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"4\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(len(l) - 1)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 55,\n \"id\": \"250b458e\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"150\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(sum(l))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 56,\n \"id\": \"7ea78614\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"50\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(max(l))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 57,\n \"id\": \"bad8aadc\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"10\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(min(l))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 58,\n \"id\": \"27a21618\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"ename\": \"TypeError\",\n \"evalue\": \"unsupported operand type(s) for +: 'int' and 'str'\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[1;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[1;31mTypeError\\u001b[0m Traceback (most recent call last)\",\n \"Input \\u001b[1;32mIn [58]\\u001b[0m, in \\u001b[0;36m\\u001b[1;34m()\\u001b[0m\\n\\u001b[0;32m 1\\u001b[0m l \\u001b[38;5;241m=\\u001b[39m [\\u001b[38;5;124m\\\"\\u001b[39m\\u001b[38;5;124mA\\u001b[39m\\u001b[38;5;124m\\\"\\u001b[39m, \\u001b[38;5;241m10\\u001b[39m,\\u001b[38;5;241m20\\u001b[39m,\\u001b[38;5;241m30\\u001b[39m]\\n\\u001b[1;32m----> 3\\u001b[0m \\u001b[38;5;28mprint\\u001b[39m(\\u001b[38;5;28;43msum\\u001b[39;49m\\u001b[43m(\\u001b[49m\\u001b[43ml\\u001b[49m\\u001b[43m)\\u001b[49m)\\n\",\n \"\\u001b[1;31mTypeError\\u001b[0m: unsupported operand type(s) for +: 'int' and 'str'\"\n ]\n }\n ],\n \"source\": [\n \"l = [\\\"A\\\", 10,20,30]\\n\",\n \"\\n\",\n \"print(sum(l))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 59,\n \"id\": \"a176b651\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"4\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(len(l))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"1ee80d7b\",\n \"metadata\": {},\n \"source\": [\n \"# List methods\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 61,\n \"id\": \"e802bef5\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# help(print)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 63,\n \"id\": \"5a500f31\",\n \"metadata\": {\n \"scrolled\": false\n },\n \"outputs\": [],\n \"source\": [\n \"# help(list)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 64,\n \"id\": \"0538c1e8\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[10, 20, 30, 40]\\n\"\n ]\n }\n ],\n \"source\": [\n \"l = [10,20,30]\\n\",\n \"\\n\",\n \"l.append(40)\\n\",\n \"\\n\",\n \"print(l)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 65,\n \"id\": \"ed712c2b\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"l.append(\\\"India\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 66,\n \"id\": \"265b8be2\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[10, 20, 30, 40, 'India']\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 67,\n \"id\": \"ea15af1f\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"4\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l.index('India'))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 68,\n \"id\": \"c422ce83\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"0\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l.index(10))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 69,\n \"id\": \"0e88a9f3\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[10, 20, 30, 40, 'India']\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 72,\n \"id\": \"e3042d8a\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"l.reverse()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 73,\n \"id\": \"02a28ba4\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[10, 20, 30, 40, 'India']\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 74,\n \"id\": \"7c861f47\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"['India', 40, 30, 20, 10]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l[::-1])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 75,\n \"id\": \"e1210f8f\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[10, 20, 30, 40, 'India']\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"ef9c13af\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.12\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n" }, { "path": "July Cohort 2023/1_Python Programming/04_Strings and For loop.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"id\": \"89086545\",\n \"metadata\": {},\n \"source\": [\n \"# Strings\\n\",\n \"\\n\",\n \"set of characters\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"id\": \"663dd2fc\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"hello\\n\"\n ]\n }\n ],\n \"source\": [\n \"# -1\\n\",\n \"s = 'hello'\\n\",\n \"# 01234\\n\",\n \"\\n\",\n \"print(s)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"id\": \"d1f891e2\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"h\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(s[0])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"id\": \"549cabfa\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"o\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(s[4])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"id\": \"e9b26f8e\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"o\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(s[-1])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"id\": \"bef31594\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"l\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(s[-2])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"id\": \"00f3823f\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(type(s))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"id\": \"7326e7a9\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(type(s[0]))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"4899cd30\",\n \"metadata\": {},\n \"source\": [\n \"# string slicing\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"id\": \"12c56ed2\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"commu\\n\"\n ]\n }\n ],\n \"source\": [\n \"s = 'communication'\\n\",\n \"# 0123456789\\n\",\n \"\\n\",\n \"print(s[0:5])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"id\": \"781ea5cd\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"noitacinummoc\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(s[::-1])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"id\": \"90aa93d3\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"omm\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(s[1:4])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"id\": \"5c24d8b3\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"s = 'communication'\\n\",\n \"# 0123456789\\n\",\n \"\\n\",\n \"print(s[4:1])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"id\": \"a8f6af2e\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"s = ''\\n\",\n \"\\n\",\n \"print(type(s))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"id\": \"2d3570c7\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"noitacin\\n\"\n ]\n }\n ],\n \"source\": [\n \"s = 'communication'\\n\",\n \"# 0123456789\\n\",\n \"\\n\",\n \"print(s[-1:-9:-1])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"ef70cbf4\",\n \"metadata\": {},\n \"source\": [\n \"# String Functions\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"id\": \"f12266cf\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"13\\n\"\n ]\n }\n ],\n \"source\": [\n \"s = 'communication'\\n\",\n \"\\n\",\n \"print(len(s))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"id\": \"50320c72\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"ename\": \"TypeError\",\n \"evalue\": \"unsupported operand type(s) for +: 'int' and 'str'\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[1;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[1;31mTypeError\\u001b[0m Traceback (most recent call last)\",\n \"Input \\u001b[1;32mIn [15]\\u001b[0m, in \\u001b[0;36m\\u001b[1;34m()\\u001b[0m\\n\\u001b[1;32m----> 1\\u001b[0m \\u001b[38;5;28mprint\\u001b[39m(\\u001b[38;5;28;43msum\\u001b[39;49m\\u001b[43m(\\u001b[49m\\u001b[43ms\\u001b[49m\\u001b[43m)\\u001b[49m)\\n\",\n \"\\u001b[1;31mTypeError\\u001b[0m: unsupported operand type(s) for +: 'int' and 'str'\"\n ]\n }\n ],\n \"source\": [\n \"print(sum(s)) # s = 0\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"id\": \"55b002c3\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"u\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(max(s))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"id\": \"7ccced34\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"a\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(min(s))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"8b30e14f\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# ASCII\\n\",\n \"\\n\",\n \"'A' -> 65\\n\",\n \"'B' -> 66\\n\",\n \"'Z' -> 90\\n\",\n \"\\n\",\n \"'a' -> 97\\n\",\n \"'z' -> 122\\n\",\n \"\\n\",\n \"' ' -> 32 -> space\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"afa17626\",\n \"metadata\": {},\n \"source\": [\n \"## string methods\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"id\": \"e3ef73fb\",\n \"metadata\": {\n \"scrolled\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# help(str)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"id\": \"53f436a8\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"False\"\n ]\n },\n \"execution_count\": 20,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"'raj123' == 'RAJ123'\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"id\": \"9a9f840d\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"raj123\\n\"\n ]\n }\n ],\n \"source\": [\n \"s = 'RAJ123'\\n\",\n \"\\n\",\n \"print(s.casefold())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"id\": \"4e6cd479\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"INDIA\\n\"\n ]\n }\n ],\n \"source\": [\n \"s = 'India'\\n\",\n \"\\n\",\n \"print(s.upper())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"id\": \"76b04479\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"india\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(s.lower())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"id\": \"2e8f43e5\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"['This', 'is', 'an', 'apple']\\n\"\n ]\n }\n ],\n \"source\": [\n \"# split\\n\",\n \"\\n\",\n \"s = 'This is an apple'\\n\",\n \"\\n\",\n \"print(s.split(\\\" \\\"))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"id\": \"94237ec7\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"['H', 'llo Us', 'r']\\n\"\n ]\n }\n ],\n \"source\": [\n \"s = \\\"Hello User\\\"\\n\",\n \"\\n\",\n \"print(s.split(\\\"e\\\"))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"id\": \"42a71402\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"This+is+an+apple\\n\"\n ]\n }\n ],\n \"source\": [\n \"# join\\n\",\n \"\\n\",\n \"l = ['This', 'is', 'an', 'apple']\\n\",\n \"\\n\",\n \"a = \\\"+\\\"\\n\",\n \"\\n\",\n \"print(a.join(l))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"id\": \"6ef43d06\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"This is an apple\\n\"\n ]\n }\n ],\n \"source\": [\n \"# join\\n\",\n \"\\n\",\n \"l = ['This', 'is', 'an', 'apple']\\n\",\n \"\\n\",\n \"a = \\\" \\\"\\n\",\n \"\\n\",\n \"print(a.join(l))\\n\",\n \"# \\\" \\\".join(l)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 28,\n \"id\": \"dae0d39a\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"ThisisanApple\\n\"\n ]\n }\n ],\n \"source\": [\n \"# remove all the spaces from the string\\n\",\n \"\\n\",\n \"s = \\\"This is an Apple\\\"\\n\",\n \"\\n\",\n \"l = s.split(\\\" \\\")\\n\",\n \"\\n\",\n \"x = \\\"\\\".join(l)\\n\",\n \"\\n\",\n \"print(x)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"5e18c7bc\",\n \"metadata\": {},\n \"source\": [\n \"**Write a program to enter a string from user and print the biggest word from the para**\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"fc9ffad3\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"08b8681d\",\n \"metadata\": {},\n \"source\": [\n \"# range function\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 29,\n \"id\": \"cd95ff5d\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"range(0, 5)\\n\"\n ]\n }\n ],\n \"source\": [\n \"n = range(5) # this will create a series of numbers from 0 to 4\\n\",\n \"\\n\",\n \"print(n)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 30,\n \"id\": \"5faa24f2\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(type(n))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 31,\n \"id\": \"b9b1b6fa\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[0, 1, 2, 3, 4]\\n\"\n ]\n }\n ],\n \"source\": [\n \"n = list(range(5))\\n\",\n \"\\n\",\n \"print(n)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 32,\n \"id\": \"00bf5fc9\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[3, 4, 5, 6, 7]\\n\"\n ]\n }\n ],\n \"source\": [\n \"n = list(range(3,8))\\n\",\n \"\\n\",\n \"print(n)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 33,\n \"id\": \"85f51f76\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"True\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(range(20) == range(0,20))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 34,\n \"id\": \"cf56e7cc\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[5, 7, 9, 11, 13, 15, 17, 19]\\n\"\n ]\n }\n ],\n \"source\": [\n \"n = list(range(5,20,2)) # range(start, end, steps)\\n\",\n \"\\n\",\n \"print(n)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"9a9e11b2\",\n \"metadata\": {},\n \"source\": [\n \"## for loop\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 35,\n \"id\": \"eb461d52\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"False\\n\"\n ]\n }\n ],\n \"source\": [\n \"l = [\\\"A\\\", \\\"B\\\", \\\"C\\\", \\\"D\\\"]\\n\",\n \"\\n\",\n \"print(\\\"Z\\\" in l)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 36,\n \"id\": \"f3d37b0a\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"A\\n\",\n \"B\\n\",\n \"C\\n\",\n \"D\\n\"\n ]\n }\n ],\n \"source\": [\n \"l = [\\\"A\\\", \\\"B\\\", \\\"C\\\", \\\"D\\\"]\\n\",\n \"# i\\n\",\n \"\\n\",\n \"# \\\"B\\\" in l\\n\",\n \"for i in l:\\n\",\n \" print(i)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 37,\n \"id\": \"15a3f0ba\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"A\\n\",\n \"Hi\\n\",\n \"B\\n\",\n \"Hi\\n\",\n \"C\\n\",\n \"Hi\\n\",\n \"D\\n\",\n \"Hi\\n\"\n ]\n }\n ],\n \"source\": [\n \"l = [\\\"A\\\", \\\"B\\\", \\\"C\\\", \\\"D\\\"]\\n\",\n \"# i\\n\",\n \"\\n\",\n \"# \\\"B\\\" in l\\n\",\n \"for i in l:\\n\",\n \" print(i)\\n\",\n \" print(\\\"Hi\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 40,\n \"id\": \"59f4f57f\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"a\\n\",\n \"b\\n\",\n \"c\\n\",\n \"d\\n\"\n ]\n }\n ],\n \"source\": [\n \"s = 'abcd'\\n\",\n \"\\n\",\n \"for i in s:\\n\",\n \" print(i)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 42,\n \"id\": \"be302d25\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"AB\\n\",\n \"ab\\n\"\n ]\n }\n ],\n \"source\": [\n \"x = 'ab'\\n\",\n \"\\n\",\n \"print(x.upper())\\n\",\n \"\\n\",\n \"print(x)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 43,\n \"id\": \"b3a22f15\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"['ab', 'cd']\\n\"\n ]\n }\n ],\n \"source\": [\n \"x = ['ab', 'cd']\\n\",\n \"\\n\",\n \"for i in x:\\n\",\n \" i.upper() # 'ab'.upper() -> 'AB'\\n\",\n \" \\n\",\n \"print(x)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 44,\n \"id\": \"4e0873d9\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"AB\\n\",\n \"CD\\n\",\n \"['ab', 'cd']\\n\"\n ]\n }\n ],\n \"source\": [\n \"x = ['ab', 'cd']\\n\",\n \"\\n\",\n \"for i in x:\\n\",\n \" print(i.upper()) # 'ab'.upper() -> 'AB'\\n\",\n \" \\n\",\n \"print(x)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 45,\n \"id\": \"77e96be5\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"a\\n\",\n \"b\\n\",\n \"c\\n\",\n \"d\\n\"\n ]\n }\n ],\n \"source\": [\n \"x = 'abcd'\\n\",\n \"\\n\",\n \"for i in x:\\n\",\n \" print(i)\\n\",\n \" x.upper()\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 46,\n \"id\": \"d6c07e4d\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"0\\n\",\n \"1\\n\",\n \"2\\n\",\n \"3\\n\",\n \"4\\n\"\n ]\n }\n ],\n \"source\": [\n \"# i\\n\",\n \"# 0,1,2,3,4\\n\",\n \"for i in range(5):\\n\",\n \" print(i)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 47,\n \"id\": \"bba46637\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"5\\n\",\n \"6\\n\",\n \"7\\n\",\n \"8\\n\",\n \"9\\n\"\n ]\n }\n ],\n \"source\": [\n \"for i in range(5,10):\\n\",\n \" print(i)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 48,\n \"id\": \"fdb6483c\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"ename\": \"TypeError\",\n \"evalue\": \"'str' object cannot be interpreted as an integer\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[1;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[1;31mTypeError\\u001b[0m Traceback (most recent call last)\",\n \"Input \\u001b[1;32mIn [48]\\u001b[0m, in \\u001b[0;36m\\u001b[1;34m()\\u001b[0m\\n\\u001b[0;32m 1\\u001b[0m x \\u001b[38;5;241m=\\u001b[39m \\u001b[38;5;124m'\\u001b[39m\\u001b[38;5;124mabcd\\u001b[39m\\u001b[38;5;124m'\\u001b[39m\\n\\u001b[1;32m----> 3\\u001b[0m \\u001b[38;5;28;01mfor\\u001b[39;00m i \\u001b[38;5;129;01min\\u001b[39;00m \\u001b[38;5;28;43mrange\\u001b[39;49m\\u001b[43m(\\u001b[49m\\u001b[43mx\\u001b[49m\\u001b[43m)\\u001b[49m:\\n\\u001b[0;32m 4\\u001b[0m \\u001b[38;5;28mprint\\u001b[39m(i)\\n\",\n \"\\u001b[1;31mTypeError\\u001b[0m: 'str' object cannot be interpreted as an integer\"\n ]\n }\n ],\n \"source\": [\n \"x = 'abcd'\\n\",\n \"\\n\",\n \"for i in range(x):\\n\",\n \" print(i)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 49,\n \"id\": \"dac1b7e1\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"0\\n\",\n \"1\\n\",\n \"2\\n\",\n \"3\\n\"\n ]\n }\n ],\n \"source\": [\n \"x = 'abcd'\\n\",\n \"\\n\",\n \"# 0,1,2,3\\n\",\n \"for i in range(len(x)):\\n\",\n \" print(i)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 50,\n \"id\": \"ce020a17\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"ename\": \"AttributeError\",\n \"evalue\": \"'int' object has no attribute 'upper'\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[1;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[1;31mAttributeError\\u001b[0m Traceback (most recent call last)\",\n \"Input \\u001b[1;32mIn [50]\\u001b[0m, in \\u001b[0;36m\\u001b[1;34m()\\u001b[0m\\n\\u001b[0;32m 3\\u001b[0m \\u001b[38;5;66;03m# 0,1,2,3\\u001b[39;00m\\n\\u001b[0;32m 4\\u001b[0m \\u001b[38;5;28;01mfor\\u001b[39;00m i \\u001b[38;5;129;01min\\u001b[39;00m \\u001b[38;5;28mrange\\u001b[39m(\\u001b[38;5;28mlen\\u001b[39m(x)):\\n\\u001b[1;32m----> 5\\u001b[0m \\u001b[38;5;28mprint\\u001b[39m(\\u001b[43mi\\u001b[49m\\u001b[38;5;241;43m.\\u001b[39;49m\\u001b[43mupper\\u001b[49m())\\n\",\n \"\\u001b[1;31mAttributeError\\u001b[0m: 'int' object has no attribute 'upper'\"\n ]\n }\n ],\n \"source\": [\n \"x = 'abcd'\\n\",\n \"\\n\",\n \"# 0,1,2,3\\n\",\n \"for i in range(len(x)):\\n\",\n \" print(i.upper())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 51,\n \"id\": \"e4b348f0\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"abcd\\n\"\n ]\n }\n ],\n \"source\": [\n \"x = 'abcd'\\n\",\n \"\\n\",\n \"# 0,1,2,3\\n\",\n \"for i in range(len(x)):\\n\",\n \" x[i].upper()\\n\",\n \"\\n\",\n \"print(x)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 52,\n \"id\": \"e4afa49b\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"A\\n\",\n \"B\\n\",\n \"C\\n\",\n \"D\\n\",\n \"abcd\\n\"\n ]\n }\n ],\n \"source\": [\n \"x = 'abcd'\\n\",\n \"\\n\",\n \"# 0,1,2,3\\n\",\n \"for i in range(len(x)):\\n\",\n \" print(x[i].upper())\\n\",\n \"\\n\",\n \"print(x)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 53,\n \"id\": \"9d691550\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"a\\n\",\n \"a\\n\",\n \"a\\n\",\n \"a\\n\"\n ]\n }\n ],\n \"source\": [\n \"x = 'abcd'\\n\",\n \"\\n\",\n \"# 0,1,2,3\\n\",\n \"for i in range(len(x)):\\n\",\n \" x = 'a' # x = 'abcd', 'a', 'a'\\n\",\n \" print(x)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"9fe9971b\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.12\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n" }, { "path": "July Cohort 2023/1_Python Programming/05_Dictionary Tuples and Set.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"id\": \"0d853ca0\",\n \"metadata\": {},\n \"source\": [\n \"# Dictionary\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"28ba2c85\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# { key : value }\\n\",\n \"\\n\",\n \"unordered data structures\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"id\": \"de03adfa\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"{'ravi': 24, 'shyam': 21, 'roy': 41}\\n\"\n ]\n }\n ],\n \"source\": [\n \"ages = {\\\"ravi\\\":24, \\\"shyam\\\": 21, \\\"roy\\\": 41}\\n\",\n \"\\n\",\n \"print(ages)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"id\": \"1507411b\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(type(ages))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"id\": \"3a6e0c6d\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"24\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(ages['ravi'])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"id\": \"9c495fe7\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"41\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(ages['roy'])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"id\": \"dc9434fb\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[255, 0, 0]\\n\"\n ]\n }\n ],\n \"source\": [\n \"color = {\\n\",\n \" \\\"red\\\": [255,0,0],\\n\",\n \" \\\"green\\\": [0,255,0],\\n\",\n \" \\\"blue\\\": [0,0,255]\\n\",\n \"}\\n\",\n \"print(color['red'])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"id\": \"28854643\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"ename\": \"KeyError\",\n \"evalue\": \"'yellow'\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[1;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[1;31mKeyError\\u001b[0m Traceback (most recent call last)\",\n \"Input \\u001b[1;32mIn [7]\\u001b[0m, in \\u001b[0;36m\\u001b[1;34m()\\u001b[0m\\n\\u001b[1;32m----> 1\\u001b[0m \\u001b[38;5;28mprint\\u001b[39m(\\u001b[43mcolor\\u001b[49m\\u001b[43m[\\u001b[49m\\u001b[38;5;124;43m'\\u001b[39;49m\\u001b[38;5;124;43myellow\\u001b[39;49m\\u001b[38;5;124;43m'\\u001b[39;49m\\u001b[43m]\\u001b[49m)\\n\",\n \"\\u001b[1;31mKeyError\\u001b[0m: 'yellow'\"\n ]\n }\n ],\n \"source\": [\n \"print(color['yellow'])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"id\": \"f12d89ad\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"{}\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"d = {}\\n\",\n \"print(d)\\n\",\n \"print(type(d))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"id\": \"0d0ceafe\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"ename\": \"TypeError\",\n \"evalue\": \"unhashable type: 'list'\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[1;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[1;31mTypeError\\u001b[0m Traceback (most recent call last)\",\n \"Input \\u001b[1;32mIn [9]\\u001b[0m, in \\u001b[0;36m\\u001b[1;34m()\\u001b[0m\\n\\u001b[1;32m----> 1\\u001b[0m d \\u001b[38;5;241m=\\u001b[39m {[\\u001b[38;5;241m1\\u001b[39m,\\u001b[38;5;241m2\\u001b[39m]:\\u001b[38;5;241m10\\u001b[39m, \\u001b[38;5;241m2\\u001b[39m:\\u001b[38;5;241m2\\u001b[39m, \\u001b[38;5;241m3\\u001b[39m:\\u001b[38;5;241m3\\u001b[39m}\\n\\u001b[0;32m 2\\u001b[0m \\u001b[38;5;28mprint\\u001b[39m(d)\\n\",\n \"\\u001b[1;31mTypeError\\u001b[0m: unhashable type: 'list'\"\n ]\n }\n ],\n \"source\": [\n \"d = {[1,2]:10, 2:2, 3:3}\\n\",\n \"print(d)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"id\": \"8a6b2ca9\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[100, 20]\\n\"\n ]\n }\n ],\n \"source\": [\n \"# list are mutable objects\\n\",\n \"\\n\",\n \"l = [10,20]\\n\",\n \"l[0] = 100\\n\",\n \"print(l)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"id\": \"bc14edbb\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"ename\": \"TypeError\",\n \"evalue\": \"'str' object does not support item assignment\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[1;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[1;31mTypeError\\u001b[0m Traceback (most recent call last)\",\n \"Input \\u001b[1;32mIn [11]\\u001b[0m, in \\u001b[0;36m\\u001b[1;34m()\\u001b[0m\\n\\u001b[0;32m 1\\u001b[0m \\u001b[38;5;66;03m# strings are immutable objects\\u001b[39;00m\\n\\u001b[0;32m 3\\u001b[0m s \\u001b[38;5;241m=\\u001b[39m \\u001b[38;5;124m'\\u001b[39m\\u001b[38;5;124mindore\\u001b[39m\\u001b[38;5;124m'\\u001b[39m\\n\\u001b[1;32m----> 4\\u001b[0m s[\\u001b[38;5;241m0\\u001b[39m] \\u001b[38;5;241m=\\u001b[39m \\u001b[38;5;124m\\\"\\u001b[39m\\u001b[38;5;124mA\\u001b[39m\\u001b[38;5;124m\\\"\\u001b[39m\\n\\u001b[0;32m 5\\u001b[0m \\u001b[38;5;28mprint\\u001b[39m(s)\\n\",\n \"\\u001b[1;31mTypeError\\u001b[0m: 'str' object does not support item assignment\"\n ]\n }\n ],\n \"source\": [\n \"# strings are immutable objects\\n\",\n \"\\n\",\n \"s = 'indore'\\n\",\n \"s[0] = \\\"A\\\"\\n\",\n \"print(s)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"498704a8\",\n \"metadata\": {},\n \"source\": [\n \"# Dictionary Functions\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"id\": \"a88930aa\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"4\\n\"\n ]\n }\n ],\n \"source\": [\n \"d = {1:11, 2:41, 13:9, 14:16}\\n\",\n \"\\n\",\n \"print(len(d))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"id\": \"71aca243\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"30\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(sum(d))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"id\": \"a9c23606\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"ename\": \"TypeError\",\n \"evalue\": \"unsupported operand type(s) for +: 'int' and 'str'\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[1;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[1;31mTypeError\\u001b[0m Traceback (most recent call last)\",\n \"Input \\u001b[1;32mIn [14]\\u001b[0m, in \\u001b[0;36m\\u001b[1;34m()\\u001b[0m\\n\\u001b[0;32m 1\\u001b[0m a \\u001b[38;5;241m=\\u001b[39m {\\u001b[38;5;241m1\\u001b[39m:\\u001b[38;5;241m2\\u001b[39m, \\u001b[38;5;241m3\\u001b[39m:\\u001b[38;5;241m4\\u001b[39m, \\u001b[38;5;124m\\\"\\u001b[39m\\u001b[38;5;124mhi\\u001b[39m\\u001b[38;5;124m\\\"\\u001b[39m:\\u001b[38;5;241m16\\u001b[39m}\\n\\u001b[1;32m----> 3\\u001b[0m \\u001b[38;5;28mprint\\u001b[39m(\\u001b[38;5;28;43msum\\u001b[39;49m\\u001b[43m(\\u001b[49m\\u001b[43ma\\u001b[49m\\u001b[43m)\\u001b[49m)\\n\",\n \"\\u001b[1;31mTypeError\\u001b[0m: unsupported operand type(s) for +: 'int' and 'str'\"\n ]\n }\n ],\n \"source\": [\n \"a = {1:2, 3:4, \\\"hi\\\":16}\\n\",\n \"\\n\",\n \"print(sum(a))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"6afd220b\",\n \"metadata\": {},\n \"source\": [\n \"# Dictionary Methods\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"id\": \"4d319b95\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"{1: 'Apple', 'orange': [2, 3, 4], '2': '23'}\\n\"\n ]\n }\n ],\n \"source\": [\n \"pair = {1:\\\"Apple\\\", \\\"orange\\\": [2,3,4], \\\"2\\\":\\\"23\\\"}\\n\",\n \"\\n\",\n \"print(pair)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"id\": \"b53ea8e9\",\n \"metadata\": {\n \"scrolled\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[2, 3, 4]\\n\"\n ]\n }\n ],\n \"source\": [\n \"# get\\n\",\n \"# help(dict)\\n\",\n \"\\n\",\n \"print(pair.get(\\\"orange\\\"))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"id\": \"e3b011f8\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"None\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(pair.get(7))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"80c5249a\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"None ==> NULL ==> nothing\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"id\": \"8ece5b51\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Apple\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(pair.get(1,\\\"Element not found\\\"))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"id\": \"af462093\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Element not found\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(pair.get(10,\\\"Element not found\\\"))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"id\": \"61e47d6e\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"{1: 'Apple', 'orange': [2, 3, 4], '2': '23'}\\n\",\n \"{}\\n\"\n ]\n }\n ],\n \"source\": [\n \"# clear\\n\",\n \"\\n\",\n \"p = {1:\\\"Apple\\\", \\\"orange\\\": [2,3,4], \\\"2\\\":\\\"23\\\"}\\n\",\n \"\\n\",\n \"print(p)\\n\",\n \"p.clear()\\n\",\n \"print(p)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"id\": \"bcf864f7\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"dict_keys([1, 2, 3])\\n\"\n ]\n }\n ],\n \"source\": [\n \"# keys\\n\",\n \"\\n\",\n \"r = {1:\\\"A\\\", 2:'Zara', 3:\\\"Maza\\\"}\\n\",\n \"\\n\",\n \"print(r.keys())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"id\": \"4d8f44be\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[1, 2, 3]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(list(r.keys()))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"id\": \"13318e6e\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"dict_values(['A', 'Zara', 'Maza'])\\n\"\n ]\n }\n ],\n \"source\": [\n \"# values\\n\",\n \"\\n\",\n \"r = {1:\\\"A\\\", 2:'Zara', 3:\\\"Maza\\\"}\\n\",\n \"\\n\",\n \"print(r.values())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"id\": \"e29dc425\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"['A', 'Zara', 'Maza']\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(list(r.values()))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 28,\n \"id\": \"4e7dbf29\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"{3: 'Maza', 2: 'Zara'}\\n\",\n \"2\\n\"\n ]\n }\n ],\n \"source\": [\n \"r = {3:\\\"A\\\", 2:'Zara', 3:\\\"Maza\\\"}\\n\",\n \"\\n\",\n \"print(r)\\n\",\n \"\\n\",\n \"print(len(r))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"ec79a1b8\",\n \"metadata\": {},\n \"source\": [\n \"# Question\\n\",\n \"\\n\",\n \"print the following series\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"e205896b\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"{1:1, 2:4, 3:9, 4:16, .........10:100}\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 29,\n \"id\": \"5abc247e\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"{1: 2, 3: 'Hi'}\\n\"\n ]\n }\n ],\n \"source\": [\n \"a = {1:2}\\n\",\n \"a[3] = \\\"Hi\\\"\\n\",\n \"print(a)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 30,\n \"id\": \"7aea4934\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"{1: 1, 2: 4, 3: 9, 4: 16, 5: 25, 6: 36, 7: 49, 8: 64, 9: 81, 10: 100}\\n\"\n ]\n }\n ],\n \"source\": [\n \"d = {}\\n\",\n \"\\n\",\n \"for i in range(1,11):\\n\",\n \" d[i] = i * i\\n\",\n \"\\n\",\n \"print(d)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"323a152e\",\n \"metadata\": {},\n \"source\": [\n \"# Tuples\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 31,\n \"id\": \"5a6a2fc7\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"(4, 3, 5, 6)\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"t = (4,3,5,6)\\n\",\n \"# 0 1 2 3\\n\",\n \"\\n\",\n \"print(t)\\n\",\n \"\\n\",\n \"print(type(t))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 32,\n \"id\": \"d5b5852c\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"4\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(t[0])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 33,\n \"id\": \"519de1e2\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"ename\": \"TypeError\",\n \"evalue\": \"'tuple' object does not support item assignment\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[1;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[1;31mTypeError\\u001b[0m Traceback (most recent call last)\",\n \"Input \\u001b[1;32mIn [33]\\u001b[0m, in \\u001b[0;36m\\u001b[1;34m()\\u001b[0m\\n\\u001b[1;32m----> 1\\u001b[0m t[\\u001b[38;5;241m0\\u001b[39m] \\u001b[38;5;241m=\\u001b[39m \\u001b[38;5;241m11\\u001b[39m\\n\",\n \"\\u001b[1;31mTypeError\\u001b[0m: 'tuple' object does not support item assignment\"\n ]\n }\n ],\n \"source\": [\n \"t[0] = 11 # tuple are immutable objects\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 34,\n \"id\": \"d44d0e73\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"('one', 'two', 'three')\\n\"\n ]\n }\n ],\n \"source\": [\n \"t = \\\"one\\\", \\\"two\\\", \\\"three\\\"\\n\",\n \"\\n\",\n \"print(t)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 35,\n \"id\": \"85ca1c88\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(type(t))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 36,\n \"id\": \"88548f84\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"10\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"t = (10)\\n\",\n \"print(t)\\n\",\n \"print(type(t))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 37,\n \"id\": \"0605b55a\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"(10,)\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"t = (10,)\\n\",\n \"print(t)\\n\",\n \"print(type(t))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"954aa392\",\n \"metadata\": {},\n \"source\": [\n \"# Tuple unpacking\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 38,\n \"id\": \"a77b3c9c\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"1\\n\",\n \"2\\n\",\n \"3\\n\"\n ]\n }\n ],\n \"source\": [\n \"n = (1,2,3)\\n\",\n \"\\n\",\n \"a,b,c = n\\n\",\n \"\\n\",\n \"print(a)\\n\",\n \"print(b)\\n\",\n \"print(c)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"bda16a06\",\n \"metadata\": {},\n \"source\": [\n \"# Tuple Slicing\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 39,\n \"id\": \"1c2a8096\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"(11, 22, 33, 44, 55, 66, 77, 88, 99, 100)\\n\"\n ]\n }\n ],\n \"source\": [\n \"# -1\\n\",\n \"t = (11,22,33,44,55,66,77,88,99,100)\\n\",\n \"# 0 1 \\n\",\n \"\\n\",\n \"print(t)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 40,\n \"id\": \"14c17710\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"(44, 55)\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(t[3:5])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 41,\n \"id\": \"febf2d84\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"(11, 22, 33, 44, 55, 66)\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(t[:6])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 42,\n \"id\": \"e98f6a1e\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"(33, 44, 55, 66)\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(t[-8:-4])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 43,\n \"id\": \"c1de1350\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"()\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(t[-4:-8])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 44,\n \"id\": \"3f3523ca\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"(100, 99, 88, 77, 66, 55, 44, 33, 22, 11)\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(t[::-1])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"9119181e\",\n \"metadata\": {},\n \"source\": [\n \"# Tuple Methods\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 45,\n \"id\": \"da140ca3\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"3\\n\"\n ]\n }\n ],\n \"source\": [\n \"# count\\n\",\n \"\\n\",\n \"t = (2,3,4,5,5,6,6,4,4,)\\n\",\n \"\\n\",\n \"print(t.count(4))\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 46,\n \"id\": \"c35b8ce9\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"2\\n\"\n ]\n }\n ],\n \"source\": [\n \"# index\\n\",\n \"\\n\",\n \"t = (2,3,4,5,5,6,6,4,4,)\\n\",\n \"\\n\",\n \"print(t.index(4))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 47,\n \"id\": \"1e8caefa\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Help on built-in function index:\\n\",\n \"\\n\",\n \"index(value, start=0, stop=9223372036854775807, /) method of builtins.tuple instance\\n\",\n \" Return first index of value.\\n\",\n \" \\n\",\n \" Raises ValueError if the value is not present.\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"help(t.index)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 48,\n \"id\": \"15dafa64\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"7\\n\"\n ]\n }\n ],\n \"source\": [\n \"# index\\n\",\n \"\\n\",\n \"t = (2,3,4,5,5,6,6,4,4,)\\n\",\n \"\\n\",\n \"print(t.index(4, 5))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"a9ef4027\",\n \"metadata\": {},\n \"source\": [\n \"# Tuple functions\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 49,\n \"id\": \"22c1684e\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"5\\n\"\n ]\n }\n ],\n \"source\": [\n \"t = (2,3,4,5,6)\\n\",\n \"\\n\",\n \"print(len(t))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 50,\n \"id\": \"9f1bfd43\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"20\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(sum(t))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 51,\n \"id\": \"89a4ca0e\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"6\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(max(t))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"505a27fe\",\n \"metadata\": {},\n \"source\": [\n \"# Question\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"f36b7876\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# remove i from this tuple\\n\",\n \"\\n\",\n \"t = ('a', 'e','i', 'o', 'u')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"90775705\",\n \"metadata\": {},\n \"source\": [\n \"# Sets\\n\",\n \"\\n\",\n \"unordered and unindex\\n\",\n \"\\n\",\n \"sets are mutable\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 52,\n \"id\": \"7ee80027\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"{1, 2, 3, 4, 5, 6}\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"n = {1,2,3,4,5,6,6,6}\\n\",\n \"\\n\",\n \"print(n)\\n\",\n \"\\n\",\n \"print(type(n))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 53,\n \"id\": \"6fef6ded\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"s = set()\\n\",\n \"\\n\",\n \"print(type(s))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 54,\n \"id\": \"a23d23ac\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"{10, 'a', 'b'}\\n\"\n ]\n }\n ],\n \"source\": [\n \"n = set(['a','b',10])\\n\",\n \"print(n)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 55,\n \"id\": \"1a660809\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"True\\n\"\n ]\n }\n ],\n \"source\": [\n \"s = ['a','b',10]\\n\",\n \"\\n\",\n \"print(10 in s)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 56,\n \"id\": \"6997ba95\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"10\\n\",\n \"a\\n\",\n \"b\\n\"\n ]\n }\n ],\n \"source\": [\n \"for i in n:\\n\",\n \" print(i)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"1f38bc03\",\n \"metadata\": {},\n \"source\": [\n \"# Set operations\\n\",\n \"\\n\",\n \"#### union\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 57,\n \"id\": \"cde829c8\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"{1, 2, 3, 4, 5, 6, 7, 8, 9}\\n\"\n ]\n }\n ],\n \"source\": [\n \"f = {1,2,3,4,5,6}\\n\",\n \"s = {4,5,6,7,8,9}\\n\",\n \"\\n\",\n \"print(f | s) # union\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"4f9ef43c\",\n \"metadata\": {},\n \"source\": [\n \"#### intersection\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 58,\n \"id\": \"58b28ca7\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"{4, 5, 6}\\n\"\n ]\n }\n ],\n \"source\": [\n \"f = {1,2,3,4,5,6}\\n\",\n \"s = {4,5,6,7,8,9}\\n\",\n \"\\n\",\n \"print(f & s) # intersection\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"69c8e095\",\n \"metadata\": {},\n \"source\": [\n \"#### difference\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 59,\n \"id\": \"81b857d2\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"{1, 2, 3}\\n\"\n ]\n }\n ],\n \"source\": [\n \"f = {1,2,3,4,5,6}\\n\",\n \"s = {4,5,6,7,8,9}\\n\",\n \"\\n\",\n \"print(f - s) # difference\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 60,\n \"id\": \"d9eaa143\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"{8, 9, 7}\\n\"\n ]\n }\n ],\n \"source\": [\n \"f = {1,2,3,4,5,6}\\n\",\n \"s = {4,5,6,7,8,9}\\n\",\n \"\\n\",\n \"print(s - f) # difference\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"17db78c6\",\n \"metadata\": {},\n \"source\": [\n \"#### symmetric\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 61,\n \"id\": \"0200b5d0\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"{1, 2, 3, 7, 8, 9}\\n\"\n ]\n }\n ],\n \"source\": [\n \"f = {1,2,3,4,5,6}\\n\",\n \"s = {4,5,6,7,8,9}\\n\",\n \"\\n\",\n \"print(f ^ s) # symmetric\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"64ddb35d\",\n \"metadata\": {},\n \"source\": [\n \"# set functions\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 62,\n \"id\": \"000b5af4\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"6\\n\",\n \"21\\n\",\n \"6\\n\"\n ]\n }\n ],\n \"source\": [\n \"s = {1,2,3,4,5,6}\\n\",\n \"\\n\",\n \"print(len(s))\\n\",\n \"print(sum(s))\\n\",\n \"print(max(s))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"d07deba1\",\n \"metadata\": {},\n \"source\": [\n \"# set methods\\n\",\n \"\\n\",\n \"#### add\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 66,\n \"id\": \"784b2205\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"{1, 2, 3, 4, 5, 6}\\n\",\n \"{1, 2, 3, 4, 5, 6, -7}\\n\"\n ]\n }\n ],\n \"source\": [\n \"n = {1,2,1,3,4,5,6}\\n\",\n \"\\n\",\n \"print(n)\\n\",\n \"\\n\",\n \"n.add(-7)\\n\",\n \"\\n\",\n \"print(n)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"e78cb535\",\n \"metadata\": {},\n \"source\": [\n \"#### clear\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 67,\n \"id\": \"c948c4ff\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"{1, 2, 3, 4, 5, 6, -7}\\n\",\n \"set()\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(n)\\n\",\n \"n.clear()\\n\",\n \"print(n)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 68,\n \"id\": \"a5ac2b2e\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"{1, 4, 5, 6}\\n\"\n ]\n }\n ],\n \"source\": [\n \"s = {1,2,3,4,5,6}\\n\",\n \"\\n\",\n \"s.remove(3)\\n\",\n \"s.remove(2)\\n\",\n \"\\n\",\n \"print(s)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"5fdb4232\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.12\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n" }, { "path": "March Cohort 2023/.ipynb_checkpoints/0_Python Basics-checkpoint.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"id\": \"1dd9932f\",\n \"metadata\": {},\n \"source\": [\n \"# Python Basics\\n\",\n \"\\n\",\n \"we are working on print function in python\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"id\": \"3ca45a0a\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Hello World\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(\\\"Hello World\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"id\": \"e2a86f94\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Hello\\n\",\n \"World\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(\\\"Hello\\\")\\n\",\n \"print(\\\"World\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"id\": \"07db254e\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Hello\\n\",\n \"World\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(\\\"Hello\\\\nWorld\\\") # \\\\n -> new line character\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"id\": \"46c947b9\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Hello\\tWorld\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(\\\"Hello\\\\tWorld\\\") # \\\\t -> horizontal tab\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"id\": \"fd07c92b\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# 8 spaces - \\\\t\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"960ceae0\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# binary language -> 1010110\\n\",\n \"\\n\",\n \"0 -> low voltage, no-charge\\n\",\n \"\\n\",\n \"1 -> high voltage, charge\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"10a75868\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"bits > numbers > character > instructions > program > software\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"459a661b\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"compiler\\n\",\n \"\\n\",\n \"interpreter -> python\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"991396e2\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"source code -------> Byte code -----> Output\\n\",\n \" .c compile .bak Run .exe\\n\",\n \" .cpp .obj\\n\",\n \" .java .javac\\n\",\n \" .py .pyc\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"9de76aa4\",\n \"metadata\": {},\n \"source\": [\n \"# Variables\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"id\": \"ca83f18c\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"10\\n\"\n ]\n }\n ],\n \"source\": [\n \"a = 10\\n\",\n \"\\n\",\n \"print(a)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"id\": \"3a7bac6d\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"45.13\\n\"\n ]\n }\n ],\n \"source\": [\n \"b = 45.13\\n\",\n \"\\n\",\n \"print(b)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"id\": \"124cb33b\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"India\\n\"\n ]\n }\n ],\n \"source\": [\n \"a = \\\"India\\\"\\n\",\n \"\\n\",\n \"print(a)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"406d111f\",\n \"metadata\": {},\n \"source\": [\n \"# Data type\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"id\": \"707a85a0\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"10\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"a = 10\\n\",\n \"\\n\",\n \"print(a)\\n\",\n \"\\n\",\n \"print(type(a))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"id\": \"bc9295ca\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"10.65\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"b = 10.65\\n\",\n \"\\n\",\n \"print(b)\\n\",\n \"\\n\",\n \"print(type(b))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"id\": \"5632508a\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Screen\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"c = \\\"Screen\\\"\\n\",\n \"\\n\",\n \"print(c)\\n\",\n \"\\n\",\n \"print(type(c))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"id\": \"644ec54c\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"meet\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"d = 'meet'\\n\",\n \"\\n\",\n \"print(d)\\n\",\n \"\\n\",\n \"print(type(d))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"id\": \"43f33389\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"it's an apple\\n\"\n ]\n }\n ],\n \"source\": [\n \"x = \\\"it's an apple\\\"\\n\",\n \"\\n\",\n \"print(x)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"e04549b7\",\n \"metadata\": {},\n \"source\": [\n \"# Type Conversion\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"id\": \"321b1438\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"10.56\\n\",\n \"\\n\",\n \"10\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"a = 10.56\\n\",\n \"\\n\",\n \"print(a)\\n\",\n \"\\n\",\n \"print(type(a))\\n\",\n \"\\n\",\n \"b = int(a)\\n\",\n \"\\n\",\n \"print(b)\\n\",\n \"\\n\",\n \"print(type(b))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"id\": \"873230fd\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"100\\n\",\n \"\\n\",\n \"100.0\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"a = \\\"100\\\"\\n\",\n \"\\n\",\n \"print(a)\\n\",\n \"\\n\",\n \"print(type(a))\\n\",\n \"\\n\",\n \"b = float(a)\\n\",\n \"\\n\",\n \"print(b)\\n\",\n \"\\n\",\n \"print(type(b))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"83db42ca\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"int()\\n\",\n \"float()\\n\",\n \"str()\\n\",\n \"bool()\\n\",\n \"list()\\n\",\n \"dict()\\n\",\n \"tuple()\\n\",\n \"set()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"a54b3b8f\",\n \"metadata\": {},\n \"source\": [\n \"# Input function\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"id\": \"127dd9ee\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"10\\n\",\n \"10\\n\"\n ]\n }\n ],\n \"source\": [\n \"a = input()\\n\",\n \"\\n\",\n \"print(a)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"id\": \"6c872d87\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Enter Your Name: Himanshu\\n\",\n \"Himanshu\\n\"\n ]\n }\n ],\n \"source\": [\n \"a = input(\\\"Enter Your Name: \\\")\\n\",\n \"\\n\",\n \"print(a)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"id\": \"f332e9d1\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Enter Your Name: Himanshu\\n\",\n \"Hello! How are you? Himanshu\\n\"\n ]\n }\n ],\n \"source\": [\n \"a = input(\\\"Enter Your Name: \\\")\\n\",\n \"\\n\",\n \"print(\\\"Hello! How are you?\\\", a)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"076ba3aa\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.12\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n" }, { "path": "March Cohort 2023/.ipynb_checkpoints/13_14_Object Orientation-checkpoint.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"id\": \"f956ae23\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"a = 10\\n\",\n \"\\n\",\n \"print(type(a))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"id\": \"a8e19eec\",\n \"metadata\": {\n \"scrolled\": false\n },\n \"outputs\": [],\n \"source\": [\n \"# help(int)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"02596f25\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"int a = 10; # 2 bytes - 16 bits\\n\",\n \"\\n\",\n \"struct car{\\n\",\n \" int cust_id; # 2 bytes\\n\",\n \" char name[10];# 10 bytes\\n\",\n \" float price; # 4 bytes\\n\",\n \"}\\n\",\n \"\\n\",\n \"struct car c1;\\n\",\n \" int a;\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"73d30a38\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"bit>numbers>characters>instructions>function(program)>class>modules>package>library>framework>software\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"8ae18de9\",\n \"metadata\": {},\n \"source\": [\n \"# Object Orientation\\n\",\n \"\\n\",\n \"7 properties of OOP\\n\",\n \"\\n\",\n \"1. Class\\n\",\n \" Updated version of structures (struct -- C programming)\\n\",\n \" collection of variables and methods\\n\",\n \" class is a blueprint.\\n\",\n \"2. Object\\n\",\n \" Run time or real time entity.\\n\",\n \" hash code/virtual code - ID(python)\\n\",\n \" \\n\",\n \"3. Abstraction and Encapsulation\\n\",\n \" Abstraction - showing only essential features without showing any background details.\\n\",\n \" Encapsulation - wrapping up of data in a single unit.\\n\",\n \" \\n\",\n \"4. Inheritance\\n\",\n \" Acquiring properties of one class into another.\\n\",\n \" code reusability\\n\",\n \" - single level\\n\",\n \" - multi level\\n\",\n \" - heirarchical\\n\",\n \" - multiple\\n\",\n \" - hybride\\n\",\n \" \\n\",\n \"5. Polymorphism\\n\",\n \" same name multiple functionalities\\n\",\n \" - method overloading\\n\",\n \" - method overriding\\n\",\n \" \\n\",\n \"6. Dynamic Memory Allocation\\n\",\n \" run time memory allocation\\n\",\n \" \\n\",\n \"7. Message Passing\\n\",\n \" Communication between objects\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"id\": \"d69e271e\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"2207057668688\\n\"\n ]\n }\n ],\n \"source\": [\n \"a = 10\\n\",\n \"\\n\",\n \"print(id(a))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"id\": \"cb5639c1\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"2207057669008\\n\",\n \"2207057669328\\n\"\n ]\n }\n ],\n \"source\": [\n \"b = 20\\n\",\n \"\\n\",\n \"c = a + b\\n\",\n \"print(id(b))\\n\",\n \"\\n\",\n \"print(id(c))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"id\": \"921fed62\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"ename\": \"TypeError\",\n \"evalue\": \"can only concatenate str (not \\\"int\\\") to str\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[1;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[1;31mTypeError\\u001b[0m Traceback (most recent call last)\",\n \"Input \\u001b[1;32mIn [11]\\u001b[0m, in \\u001b[0;36m\\u001b[1;34m()\\u001b[0m\\n\\u001b[1;32m----> 1\\u001b[0m s \\u001b[38;5;241m=\\u001b[39m \\u001b[38;5;124;43m\\\"\\u001b[39;49m\\u001b[38;5;124;43mA\\u001b[39;49m\\u001b[38;5;124;43m\\\"\\u001b[39;49m\\u001b[43m \\u001b[49m\\u001b[38;5;241;43m+\\u001b[39;49m\\u001b[43m \\u001b[49m\\u001b[38;5;241;43m10\\u001b[39;49m\\n\",\n \"\\u001b[1;31mTypeError\\u001b[0m: can only concatenate str (not \\\"int\\\") to str\"\n ]\n }\n ],\n \"source\": [\n \"s = \\\"A\\\" + 10\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"id\": \"0f26ad3e\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"AAAA\\n\"\n ]\n }\n ],\n \"source\": [\n \"s = \\\"A\\\" * 4\\n\",\n \"\\n\",\n \"print(s)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"id\": \"ba90ba2a\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"def hello():\\n\",\n \" return \\\"Hi\\\"\\n\",\n \"\\n\",\n \"print(type(hello))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"id\": \"41a3ec56\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"2207169212128\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(id(hello))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"06ce3374\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"class win7:\\n\",\n \"def start()\\n\",\n \"\\n\",\n \"def controlpanel():\\n\",\n \" \\n\",\n \"\\n\",\n \"add(x, y)\\n\",\n \"\\n\",\n \"add(a,b,c)\\n\",\n \"\\n\",\n \"class win10(win7):\\n\",\n \" \\n\",\n \" \\n\",\n \"product_id = wid10()\\n\",\n \"\\n\",\n \"product_id.controlpanel()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"53b1fb7c\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"add()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"id\": \"67f2a3da\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"30\"\n ]\n },\n \"execution_count\": 17,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"10 + 20\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"8420ca86\",\n \"metadata\": {},\n \"source\": [\n \"# Day 14 \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"id\": \"33e8ee69\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"class Cricket:\\n\",\n \" \\n\",\n \" def bat():\\n\",\n \" print(\\\"Hello from Bat\\\")\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"id\": \"aee1a50c\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"c = Cricket()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"id\": \"c2d4fa85\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"ename\": \"TypeError\",\n \"evalue\": \"bat() takes 0 positional arguments but 1 was given\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[1;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[1;31mTypeError\\u001b[0m Traceback (most recent call last)\",\n \"Input \\u001b[1;32mIn [3]\\u001b[0m, in \\u001b[0;36m\\u001b[1;34m()\\u001b[0m\\n\\u001b[1;32m----> 1\\u001b[0m \\u001b[43mc\\u001b[49m\\u001b[38;5;241;43m.\\u001b[39;49m\\u001b[43mbat\\u001b[49m\\u001b[43m(\\u001b[49m\\u001b[43m)\\u001b[49m\\n\",\n \"\\u001b[1;31mTypeError\\u001b[0m: bat() takes 0 positional arguments but 1 was given\"\n ]\n }\n ],\n \"source\": [\n \"c.bat()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"cf30bd7f\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# self --> current object\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"id\": \"e50eb7d9\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Hello from Bat\\n\"\n ]\n }\n ],\n \"source\": [\n \"class Cricket:\\n\",\n \" \\n\",\n \" def bat(self): # self = c\\n\",\n \" print(\\\"Hello from Bat\\\")\\n\",\n \"\\n\",\n \"c = Cricket()\\n\",\n \"\\n\",\n \"c.bat()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"id\": \"623c69db\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"<__main__.Cricket object at 0x0000020F34DB4A00>\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(c)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"id\": \"242b62af\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"2264334551552\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(id(c))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"id\": \"515da4b9\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"<__main__.Cricket object at 0x0000020F36BD6A90>\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(Cricket())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"b06677ce\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"TV blueprint - Class\\n\",\n \"- smart tv\\n\",\n \"- colors\\n\",\n \"- usb\\n\",\n \"- hdmi\\n\",\n \"\\n\",\n \"Actual TV - Object\\n\",\n \"product_id\\n\",\n \"\\n\",\n \"\\n\",\n \"remote - reference\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"id\": \"8d0184d8\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"class Football:\\n\",\n \" \\n\",\n \" def goal(self, x): # self = f, x = 4\\n\",\n \" self.a = x # a is class variable, x is local variable\\n\",\n \" # f.a = 4\\n\",\n \" \\n\",\n \" def display(self):\\n\",\n \" print(self.a)\\n\",\n \" # f.a\\n\",\n \" \\n\",\n \"f = Football()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"id\": \"1d10510d\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"f.goal(4)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"id\": \"3ddef9fb\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"4\\n\"\n ]\n }\n ],\n \"source\": [\n \"f.display()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"id\": \"6216ae9a\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"4\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(f.a)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"id\": \"63e17007\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"ename\": \"AttributeError\",\n \"evalue\": \"'Football' object has no attribute 'x'\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[1;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[1;31mAttributeError\\u001b[0m Traceback (most recent call last)\",\n \"Input \\u001b[1;32mIn [12]\\u001b[0m, in \\u001b[0;36m\\u001b[1;34m()\\u001b[0m\\n\\u001b[1;32m----> 1\\u001b[0m \\u001b[38;5;28mprint\\u001b[39m(\\u001b[43mf\\u001b[49m\\u001b[38;5;241;43m.\\u001b[39;49m\\u001b[43mx\\u001b[49m)\\n\",\n \"\\u001b[1;31mAttributeError\\u001b[0m: 'Football' object has no attribute 'x'\"\n ]\n }\n ],\n \"source\": [\n \"print(f.x)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"id\": \"a84e2e93\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(type(f))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"id\": \"0e68d116\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"2264334513824\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(id(f))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"id\": \"8145572b\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"10\\n\",\n \"\\n\",\n \"2264248117840\\n\",\n \"2264248117840\\n\"\n ]\n }\n ],\n \"source\": [\n \"a = 10\\n\",\n \"\\n\",\n \"print(a)\\n\",\n \"\\n\",\n \"print(type(a))\\n\",\n \"\\n\",\n \"print(id(a))\\n\",\n \"\\n\",\n \"print(id(10))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"id\": \"04a1cb6b\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"<__main__.Test object at 0x0000020F36BD6C70>\\n\",\n \"2264366148720\\n\",\n \"<__main__.Test object at 0x0000020F36BD6C70>\\n\",\n \"2264366148720\\n\",\n \"Finish\\n\"\n ]\n }\n ],\n \"source\": [\n \"class Test:\\n\",\n \" def add(self):\\n\",\n \" print(self)\\n\",\n \" print(id(self))\\n\",\n \" print(\\\"Finish\\\")\\n\",\n \" \\n\",\n \"t = Test()\\n\",\n \"print(t)\\n\",\n \"print(id(t))\\n\",\n \"t.add()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"id\": \"e76165f0\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"<__main__.Test object at 0x0000020F37274610>\\n\",\n \"2264373085712\\n\",\n \"<__main__.Test object at 0x0000020F37274610>\\n\",\n \"2264373085712\\n\",\n \"Finish\\n\"\n ]\n }\n ],\n \"source\": [\n \"t2 = Test()\\n\",\n \"print(t2)\\n\",\n \"print(id(t2))\\n\",\n \"t2.add()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"c78e8f64\",\n \"metadata\": {},\n \"source\": [\n \"# Constructor\\n\",\n \"\\n\",\n \"constructors are special member method of a class\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"18cd2b35\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"__init__() -> magic method or dunders\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"id\": \"0e5bc082\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"class Cat:\\n\",\n \" \\n\",\n \" def __init__(self):\\n\",\n \" print(\\\"Hello from constructor\\\") # write code that is for initializing \\n\",\n \"# coins = 0\\n\",\n \"# score = 0\\n\",\n \" \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"id\": \"90a7183d\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Hello from constructor\\n\"\n ]\n }\n ],\n \"source\": [\n \"f = Cat()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"id\": \"7c63050d\",\n \"metadata\": {\n \"scrolled\": false\n },\n \"outputs\": [],\n \"source\": [\n \"# help(str)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"a89195ce\",\n \"metadata\": {},\n \"source\": [\n \"### paramatrized constructor\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"id\": \"870a85c9\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"class Car:\\n\",\n \" def __init__(self, x):\\n\",\n \" self.y = x\\n\",\n \" \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"id\": \"3575a84e\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"f = Car(10)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"id\": \"862c1549\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"10\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(f.y)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"04a7f0d7\",\n \"metadata\": {},\n \"source\": [\n \"### Write a class that can calculate area of square, circle, rectangle.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"id\": \"7995dcda\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"class Area:\\n\",\n \" \\n\",\n \" def square(self, x):\\n\",\n \" return x*x\\n\",\n \" \\n\",\n \" def circle(self, r):\\n\",\n \" return 3.14 * r * r\\n\",\n \" \\n\",\n \" def rectangle(self, l, b):\\n\",\n \" return l * b\\n\",\n \" \\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"id\": \"c19e6fac\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"16\"\n ]\n },\n \"execution_count\": 27,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"ar = Area()\\n\",\n \"\\n\",\n \"ar.square(4)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"6528d7d2\",\n \"metadata\": {},\n \"source\": [\n \"### Write a program to create a class to reverse a string word by word.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"87016fca\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"\\\"this is an apple\\\"\\n\",\n \"\\n\",\n \"\\\"apple an is this\\\"\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"70e87437\",\n \"metadata\": {},\n \"source\": [\n \"# Inheritance\\n\",\n \"\\n\",\n \"\\n\",\n \"### single level\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"4213330d\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"A windows 7\\n\",\n \"| |\\n\",\n \"B windows 10\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 28,\n \"id\": \"3cbdb564\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"This is add from class A\\n\",\n \"This is add from class A\\n\"\n ]\n }\n ],\n \"source\": [\n \"class A:\\n\",\n \" def add(self):\\n\",\n \" print(\\\"This is add from class A\\\")\\n\",\n \" \\n\",\n \"\\n\",\n \"class B(A):\\n\",\n \" def sub(self):\\n\",\n \" print(\\\"Sub from class B\\\")\\n\",\n \" \\n\",\n \"x = A()\\n\",\n \"y = B()\\n\",\n \"\\n\",\n \"x.add()\\n\",\n \"y.add()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 29,\n \"id\": \"694d0edf\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"ename\": \"AttributeError\",\n \"evalue\": \"'A' object has no attribute 'sub'\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[1;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[1;31mAttributeError\\u001b[0m Traceback (most recent call last)\",\n \"Input \\u001b[1;32mIn [29]\\u001b[0m, in \\u001b[0;36m\\u001b[1;34m()\\u001b[0m\\n\\u001b[1;32m----> 1\\u001b[0m \\u001b[43mx\\u001b[49m\\u001b[38;5;241;43m.\\u001b[39;49m\\u001b[43msub\\u001b[49m()\\n\",\n \"\\u001b[1;31mAttributeError\\u001b[0m: 'A' object has no attribute 'sub'\"\n ]\n }\n ],\n \"source\": [\n \"x.sub()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 30,\n \"id\": \"da9ee247\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Sub from class B\\n\"\n ]\n }\n ],\n \"source\": [\n \"y.sub()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"61a935df\",\n \"metadata\": {},\n \"source\": [\n \"### multi level\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"a163bf53\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"A\\n\",\n \"|\\n\",\n \"B\\n\",\n \"|\\n\",\n \"C\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 31,\n \"id\": \"b4a8158e\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"show from class C\\n\",\n \"Sub from class B\\n\",\n \"This is add from class A\\n\"\n ]\n }\n ],\n \"source\": [\n \"class A:\\n\",\n \" def add(self):\\n\",\n \" print(\\\"This is add from class A\\\")\\n\",\n \" \\n\",\n \"class B(A):\\n\",\n \" def sub(self):\\n\",\n \" print(\\\"Sub from class B\\\")\\n\",\n \" \\n\",\n \"class C(B):\\n\",\n \" def show(self):\\n\",\n \" print(\\\"show from class C\\\")\\n\",\n \" \\n\",\n \"z = C()\\n\",\n \"z.show()\\n\",\n \"z.sub()\\n\",\n \"z.add()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"6d610afa\",\n \"metadata\": {},\n \"source\": [\n \"### hierarchical\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"56edc720\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \" A\\n\",\n \"B C\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 32,\n \"id\": \"94cdc6e5\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"show from class C\\n\"\n ]\n },\n {\n \"ename\": \"AttributeError\",\n \"evalue\": \"'C' object has no attribute 'sub'\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[1;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[1;31mAttributeError\\u001b[0m Traceback (most recent call last)\",\n \"Input \\u001b[1;32mIn [32]\\u001b[0m, in \\u001b[0;36m\\u001b[1;34m()\\u001b[0m\\n\\u001b[0;32m 13\\u001b[0m z \\u001b[38;5;241m=\\u001b[39m C()\\n\\u001b[0;32m 14\\u001b[0m z\\u001b[38;5;241m.\\u001b[39mshow()\\n\\u001b[1;32m---> 15\\u001b[0m \\u001b[43mz\\u001b[49m\\u001b[38;5;241;43m.\\u001b[39;49m\\u001b[43msub\\u001b[49m()\\n\\u001b[0;32m 16\\u001b[0m z\\u001b[38;5;241m.\\u001b[39madd()\\n\",\n \"\\u001b[1;31mAttributeError\\u001b[0m: 'C' object has no attribute 'sub'\"\n ]\n }\n ],\n \"source\": [\n \"class A:\\n\",\n \" def add(self):\\n\",\n \" print(\\\"This is add from class A\\\")\\n\",\n \" \\n\",\n \"class B(A):\\n\",\n \" def sub(self):\\n\",\n \" print(\\\"Sub from class B\\\")\\n\",\n \" \\n\",\n \"class C(A):\\n\",\n \" def show(self):\\n\",\n \" print(\\\"show from class C\\\")\\n\",\n \" \\n\",\n \"z = C()\\n\",\n \"z.show()\\n\",\n \"z.sub()\\n\",\n \"z.add()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"afa65f2f\",\n \"metadata\": {},\n \"source\": [\n \"### multiple\\n\",\n \"\\n\",\n \"C3 algorithm to solve ambiguty error\\n\",\n \"\\n\",\n \"MRO - Method Resolution Order\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"520d2d17\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"A B\\n\",\n \" C\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 33,\n \"id\": \"41a10af7\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Add from class A\\n\"\n ]\n }\n ],\n \"source\": [\n \"class A:\\n\",\n \" def add(self):\\n\",\n \" print(\\\"Add from class A\\\")\\n\",\n \" \\n\",\n \"class B:\\n\",\n \" def add(self):\\n\",\n \" print(\\\"Add from class B\\\")\\n\",\n \" \\n\",\n \"class C(A,B):\\n\",\n \" def show(self):\\n\",\n \" print(\\\"show from class C\\\")\\n\",\n \" \\n\",\n \"z = C()\\n\",\n \"z.add()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 34,\n \"id\": \"101bcde3\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"(, , , )\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(C.__mro__)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 36,\n \"id\": \"e31f4dfc\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Add from class B\\n\",\n \"(, , , )\\n\"\n ]\n }\n ],\n \"source\": [\n \"class A:\\n\",\n \" def add(self):\\n\",\n \" print(\\\"Add from class A\\\")\\n\",\n \" \\n\",\n \"class B:\\n\",\n \" def add(self):\\n\",\n \" print(\\\"Add from class B\\\")\\n\",\n \" \\n\",\n \"class C(B,A):\\n\",\n \" def show(self):\\n\",\n \" print(\\\"show from class C\\\")\\n\",\n \" \\n\",\n \"z = C()\\n\",\n \"z.add()\\n\",\n \"print(C.__mro__)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"9361ebca\",\n \"metadata\": {},\n \"source\": [\n \"### Hybrid\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"e1434f79\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \" A\\n\",\n \"B C\\n\",\n \" D\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.12\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n" }, { "path": "March Cohort 2023/.ipynb_checkpoints/1_While Loops and Logic Building-checkpoint.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"id\": \"dfbc28de\",\n \"metadata\": {},\n \"source\": [\n \"# While Loop\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"id\": \"c1f425e3\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Himanshu\\n\",\n \"Himanshu\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(\\\"Himanshu\\\")\\n\",\n \"print(\\\"Himanshu\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"id\": \"55a46f9f\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"True\\n\",\n \"False\\n\"\n ]\n }\n ],\n \"source\": [\n \"i = 1\\n\",\n \"\\n\",\n \"print(i <= 5)\\n\",\n \"\\n\",\n \"print(i >= 5)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"id\": \"7a204214\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Himanshu\\n\",\n \"Himanshu\\n\",\n \"Himanshu\\n\",\n \"Himanshu\\n\",\n \"Himanshu\\n\"\n ]\n }\n ],\n \"source\": [\n \"i = 1\\n\",\n \"\\n\",\n \"while i <= 5 :\\n\",\n \" print(\\\"Himanshu\\\")\\n\",\n \" i = i + 1 # i = 1, 2, 3, 4, 5, 6\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"id\": \"b5ef4f72\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Himanshu\\n\",\n \"Himanshu\\n\",\n \"Himanshu\\n\",\n \"Himanshu\\n\",\n \"Himanshu\\n\",\n \"Himanshu\\n\",\n \"Himanshu\\n\",\n \"Himanshu\\n\",\n \"Himanshu\\n\",\n \"Himanshu\\n\"\n ]\n }\n ],\n \"source\": [\n \"i = 1\\n\",\n \"\\n\",\n \"while i <= 10 :\\n\",\n \" print(\\\"Himanshu\\\")\\n\",\n \" i = i + 1\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"id\": \"a3fc06bd\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"1\\n\",\n \"2\\n\",\n \"3\\n\",\n \"4\\n\",\n \"5\\n\"\n ]\n }\n ],\n \"source\": [\n \"i = 1\\n\",\n \"\\n\",\n \"while i <= 5 :\\n\",\n \" print(i)\\n\",\n \" i = i + 1\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"2b2a54cb\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"1\\n\",\n \"2\\n\",\n \"3\\n\",\n \"4\\n\",\n \"5\\n\",\n \"6\\n\",\n \"7\\n\",\n \"8\\n\",\n \"9\\n\",\n \"10\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"id\": \"9b83ffab\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"1\\n\",\n \"2\\n\",\n \"3\\n\",\n \"4\\n\",\n \"5\\n\",\n \"6\\n\",\n \"7\\n\",\n \"8\\n\",\n \"9\\n\",\n \"10\\n\"\n ]\n }\n ],\n \"source\": [\n \"i = 1\\n\",\n \"\\n\",\n \"while i <= 10:\\n\",\n \" print(i)\\n\",\n \" i = i + 1\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"id\": \"9522e499\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Hello\\n\",\n \"World\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(\\\"Hello\\\")\\n\",\n \"print(\\\"World\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"id\": \"dd5d35e6\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Hello\\n\",\n \"World\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(\\\"Hello\\\", end='\\\\n')\\n\",\n \"print(\\\"World\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"id\": \"c54de4e2\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Hello+World\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(\\\"Hello\\\", end='+')\\n\",\n \"print(\\\"World\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"id\": \"54a31434\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Hello World\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(\\\"Hello\\\", end=' ')\\n\",\n \"print(\\\"World\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"id\": \"b4f03f6d\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"HelloWorld\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(\\\"Hello\\\", end='')\\n\",\n \"print(\\\"World\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"id\": \"2a48a921\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Hello@World\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(\\\"Hello\\\", end='@')\\n\",\n \"print(\\\"World\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"id\": \"1357e71d\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Hello@World$\"\n ]\n }\n ],\n \"source\": [\n \"print(\\\"Hello\\\", end='@')\\n\",\n \"print(\\\"World\\\", end='$')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"f48834ba\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"1 2 3 4 5 6 7 8 9 10\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"id\": \"1c74034c\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"1 2 3 4 5 6 7 8 9 10 \"\n ]\n }\n ],\n \"source\": [\n \"i = 1\\n\",\n \"\\n\",\n \"while i <= 10:\\n\",\n \" print(i, end=' ')\\n\",\n \" i = i + 1\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"0649f1ee\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"1 4 9 16 25 36 49 64 81 100\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"id\": \"9bb2cd28\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"30\\n\",\n \"10\\n\"\n ]\n }\n ],\n \"source\": [\n \"a = 10\\n\",\n \"\\n\",\n \"print(a*3)\\n\",\n \"\\n\",\n \"print(a)\\n\",\n \"\\n\",\n \"# a = a * 3\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"id\": \"67f5a9b8\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"1 4 9 16 25 36 49 64 81 100 \"\n ]\n }\n ],\n \"source\": [\n \"i = 1\\n\",\n \"\\n\",\n \"while i <= 10:\\n\",\n \" print(i*i, end=' ')\\n\",\n \" i = i + 1\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"2ad2e158\",\n \"metadata\": {},\n \"source\": [\n \"**Write a program to enter 5 values from user and print them.**\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"id\": \"aeee4fe6\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Enter your number: 10\\n\",\n \"10\\n\",\n \"Enter your number: 20\\n\",\n \"20\\n\",\n \"Enter your number: 30\\n\",\n \"30\\n\",\n \"Enter your number: 40\\n\",\n \"40\\n\",\n \"Enter your number: 50\\n\",\n \"50\\n\"\n ]\n }\n ],\n \"source\": [\n \"i = 1\\n\",\n \"\\n\",\n \"while i <= 5:\\n\",\n \" a = input(\\\"Enter your number: \\\")\\n\",\n \" \\n\",\n \" print(a)\\n\",\n \" \\n\",\n \" i = i + 1\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"3773b36f\",\n \"metadata\": {},\n \"source\": [\n \"**Write a program to enter a number from user and print reverse of it.**\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"34362203\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"153\\n\",\n \"\\n\",\n \"351\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"id\": \"3dcb3a2f\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"1\"\n ]\n },\n \"execution_count\": 20,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"7 % 2 # return reminder\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"id\": \"42cfdbf1\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"3.5\"\n ]\n },\n \"execution_count\": 21,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"7 / 2\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"id\": \"885d87d3\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"3\"\n ]\n },\n \"execution_count\": 22,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"7 // 2\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"id\": \"d7cbb652\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"3\"\n ]\n },\n \"execution_count\": 24,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"153 % 10\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"59f14345\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \" x % 10 = z\\n\",\n \"153 % 10 = 3\\n\",\n \"153 // 10 = 15\\n\",\n \"\\n\",\n \"15 % 10 = 5\\n\",\n \"15 // 10 = 1\\n\",\n \"\\n\",\n \"1 % 10 = 1\\n\",\n \"1 // 10 = 0\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"id\": \"f759b069\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Enter a number: 10\\n\",\n \"10\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"n = int(input(\\\"Enter a number: \\\"))\\n\",\n \"\\n\",\n \"print(n)\\n\",\n \"\\n\",\n \"print(type(n))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 28,\n \"id\": \"6fc34b04\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Enter a Number: 12345\\n\",\n \"54321\"\n ]\n }\n ],\n \"source\": [\n \"n = int(input(\\\"Enter a Number: \\\"))\\n\",\n \"\\n\",\n \"while n > 0:\\n\",\n \" r = n % 10\\n\",\n \" print(r, end='') # 351\\n\",\n \" n = n // 10 # n = 153, 15, 1\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"5d585434\",\n \"metadata\": {},\n \"source\": [\n \"**Write a program to enter a number from user and print sum of its individual digits**\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"aaffcb41\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"153\\n\",\n \"\\n\",\n \"1 + 5 + 3\\n\",\n \"\\n\",\n \"9\\n\",\n \"\\n\",\n \"\\n\",\n \"0 s\\n\",\n \"3 s + 3 = 3\\n\",\n \"5 s + 5 = 8\\n\",\n \"1 s + 1 = 9\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 30,\n \"id\": \"1b2f5fbb\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Enter a Number: 2345\\n\",\n \"Sum = 14\\n\"\n ]\n }\n ],\n \"source\": [\n \"n = int(input(\\\"Enter a Number: \\\"))\\n\",\n \"s = 0\\n\",\n \"\\n\",\n \"while n > 0:\\n\",\n \" r = n % 10\\n\",\n \" s = s + r # s = 0, 3, 8, 9\\n\",\n \" n = n // 10\\n\",\n \"\\n\",\n \"print(\\\"Sum =\\\", s)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"88e40510\",\n \"metadata\": {},\n \"source\": [\n \"**Write a program to enter a number from user and check if it palindrome or not**\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"643c209d\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"121\\n\",\n \"\\n\",\n \"121\\n\",\n \"\\n\",\n \"153 = 351 not a palindrome\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 34,\n \"id\": \"432cbedf\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Enter a Number: 153\\n\",\n \"351\\n\",\n \"702\\n\"\n ]\n }\n ],\n \"source\": [\n \"n = int(input(\\\"Enter a Number: \\\"))\\n\",\n \"s = 0\\n\",\n \"\\n\",\n \"while n > 0:\\n\",\n \" r = n % 10\\n\",\n \" s = s * 10 + r # s = 0, 3, 35, 351\\n\",\n \" n = n // 10\\n\",\n \"\\n\",\n \"print(s)\\n\",\n \"print(s * 2)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 40,\n \"id\": \"59333ae3\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Enter a Number: 121\\n\",\n \"Palindrome\\n\"\n ]\n }\n ],\n \"source\": [\n \"n = int(input(\\\"Enter a Number: \\\"))\\n\",\n \"t = n\\n\",\n \"s = 0\\n\",\n \"\\n\",\n \"while n > 0:\\n\",\n \" r = n % 10\\n\",\n \" s = s * 10 + r # s = 0, 3, 35, 351\\n\",\n \" n = n // 10\\n\",\n \"\\n\",\n \"if s == t:\\n\",\n \" print(\\\"Palindrome\\\")\\n\",\n \"else:\\n\",\n \" print(\\\"Not a Palindrome\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 38,\n \"id\": \"dc812c1e\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"False\"\n ]\n },\n \"execution_count\": 38,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"10 == 11\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"28edc5f6\",\n \"metadata\": {},\n \"source\": [\n \"**Write a program to enter a number from user and check if it is armstrong or not**\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"b78ef4cd\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"153\\n\",\n \"\\n\",\n \"cube(1) + cube(5) + cube(3) = 153\\n\",\n \"\\n\",\n \"370\\n\",\n \"\\n\",\n \"371\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 44,\n \"id\": \"de686ea3\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Enter a Number: 371\\n\",\n \"Armstrong Number\\n\"\n ]\n }\n ],\n \"source\": [\n \"n = int(input(\\\"Enter a Number: \\\"))\\n\",\n \"t = n\\n\",\n \"s = 0\\n\",\n \"\\n\",\n \"while n > 0:\\n\",\n \" r = n % 10\\n\",\n \" s = s + r*r*r\\n\",\n \" n = n // 10\\n\",\n \"\\n\",\n \"if s == t:\\n\",\n \" print(\\\"Armstrong Number\\\")\\n\",\n \"else:\\n\",\n \" print(\\\"Not an Armstrong Number\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"b4fc183f\",\n \"metadata\": {},\n \"source\": [\n \"**Write a program to enter a number from user and print its individual digits on seperate line.**\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"75092737\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"153\\n\",\n \"\\n\",\n \"1\\n\",\n \"5\\n\",\n \"3\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 45,\n \"id\": \"56d6ec0d\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Enter a Number: 153\\n\",\n \"3\\n\",\n \"5\\n\",\n \"1\\n\"\n ]\n }\n ],\n \"source\": [\n \"n = int(input(\\\"Enter a Number: \\\"))\\n\",\n \"s = 0\\n\",\n \"\\n\",\n \"while n > 0:\\n\",\n \" r = n % 10\\n\",\n \" print(r)\\n\",\n \" n = n // 10\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 47,\n \"id\": \"e13e7967\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Enter a Number: 12345\\n\",\n \"1\\n\",\n \"2\\n\",\n \"3\\n\",\n \"4\\n\",\n \"5\\n\"\n ]\n }\n ],\n \"source\": [\n \"n = int(input(\\\"Enter a Number: \\\"))\\n\",\n \"s = 0\\n\",\n \"\\n\",\n \"while n > 0:\\n\",\n \" r = n % 10\\n\",\n \" s = s * 10 + r\\n\",\n \" n = n // 10\\n\",\n \"\\n\",\n \"# s = 351\\n\",\n \"\\n\",\n \"while s > 0:\\n\",\n \" r = s % 10\\n\",\n \" print(r)\\n\",\n \" s = s // 10\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"4864ff19\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.12\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n" }, { "path": "March Cohort 2023/.ipynb_checkpoints/2_List and Strings-checkpoint.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"id\": \"f18f8832\",\n \"metadata\": {},\n \"source\": [\n \"# List\\n\",\n \"\\n\",\n \"collection of different types of elements\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"4a53537e\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"a = 10 # int\\n\",\n \"b = 20\\n\",\n \"c = 30\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"id\": \"8179aaea\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[10, 'A', 'India', 10.6]\\n\"\n ]\n }\n ],\n \"source\": [\n \"l = [10, \\\"A\\\", 'India', 10.6]\\n\",\n \"# 0 1 2 3\\n\",\n \"\\n\",\n \"print(l)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"id\": \"b073284b\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"10\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l[0])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"id\": \"0b18a752\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"10.6\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l[3])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"id\": \"562832f4\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"India\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l[2])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"id\": \"2ce1c1e2\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"ename\": \"IndexError\",\n \"evalue\": \"list index out of range\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[1;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[1;31mIndexError\\u001b[0m Traceback (most recent call last)\",\n \"Input \\u001b[1;32mIn [5]\\u001b[0m, in \\u001b[0;36m\\u001b[1;34m()\\u001b[0m\\n\\u001b[1;32m----> 1\\u001b[0m \\u001b[38;5;28mprint\\u001b[39m(\\u001b[43ml\\u001b[49m\\u001b[43m[\\u001b[49m\\u001b[38;5;241;43m5\\u001b[39;49m\\u001b[43m]\\u001b[49m)\\n\",\n \"\\u001b[1;31mIndexError\\u001b[0m: list index out of range\"\n ]\n }\n ],\n \"source\": [\n \"print(l[5])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"id\": \"b56a18f7\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[10, 'A', 'India', 10.6]\\n\"\n ]\n }\n ],\n \"source\": [\n \"l = [10, \\\"A\\\", 'India', 10.6]\\n\",\n \"# 0 1 2 3\\n\",\n \"\\n\",\n \"print(l)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"id\": \"56810d39\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"l[0] = 700\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"id\": \"cec85000\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[700, 'A', 'India', 10.6]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"id\": \"a839bab4\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"20\\n\"\n ]\n }\n ],\n \"source\": [\n \"a = 10\\n\",\n \"\\n\",\n \"a = 20\\n\",\n \"\\n\",\n \"print(a)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"id\": \"07f87123\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"ename\": \"IndexError\",\n \"evalue\": \"list assignment index out of range\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[1;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[1;31mIndexError\\u001b[0m Traceback (most recent call last)\",\n \"Input \\u001b[1;32mIn [10]\\u001b[0m, in \\u001b[0;36m\\u001b[1;34m()\\u001b[0m\\n\\u001b[1;32m----> 1\\u001b[0m l[\\u001b[38;5;241m5\\u001b[39m] \\u001b[38;5;241m=\\u001b[39m \\u001b[38;5;241m400\\u001b[39m\\n\",\n \"\\u001b[1;31mIndexError\\u001b[0m: list assignment index out of range\"\n ]\n }\n ],\n \"source\": [\n \"l[5] = 400\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"id\": \"d61e6a52\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[700, 10.6, 'India', 10.6]\\n\"\n ]\n }\n ],\n \"source\": [\n \"l[1] = l[3]\\n\",\n \"#l[1] = 10.6\\n\",\n \"\\n\",\n \"print(l)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"id\": \"cd0e6f72\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[10, 'A', 'India', 10.6]\\n\"\n ]\n }\n ],\n \"source\": [\n \"# -4 -3 -2 -1\\n\",\n \"l = [10, \\\"A\\\", 'India', 10.6]\\n\",\n \"# 0 1 2 3\\n\",\n \"\\n\",\n \"print(l)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"id\": \"28fe4466\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"10\\n\",\n \"10\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l[0])\\n\",\n \"\\n\",\n \"print(l[-4])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"id\": \"a4ddd5ef\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"10.6\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l[-1])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"id\": \"edb5d5de\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"India\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l[-2])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"e57f0121\",\n \"metadata\": {},\n \"source\": [\n \"# List slicing\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"id\": \"536c7f69\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[11, 22, 33, 10, 24, 44, 36, 68, 99, 81, 100]\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"l = [11,22,33,10,24,44,36,68,99,81,100]\\n\",\n \"# 0 1 2 3 4 5 6 7 8 9 10\\n\",\n \"\\n\",\n \"print(l)\\n\",\n \"print(type(l))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"id\": \"c4ed3b05\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[22, 33, 10, 24, 44, 36]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l[1:7]) # l[start : end]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"id\": \"44a00013\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[11, 22, 33, 10, 24, 44, 36, 68, 99, 81, 100]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"id\": \"a344e33f\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[24, 44, 36, 68, 99, 81]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l[4:10])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"id\": \"07f07f98\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[99, 81]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l[8:10])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"id\": \"47353b5f\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[11, 22, 33, 10, 24, 44, 36, 68, 99, 81, 100]\\n\"\n ]\n }\n ],\n \"source\": [\n \"l = [11,22,33,10,24,44,36,68,99,81,100]\\n\",\n \"# 0 1 2 3 4 5 6 7 8 9 10\\n\",\n \"\\n\",\n \"print(l)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"id\": \"3d8b979b\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[11]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l[0:1])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"id\": \"94eda01d\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[22, 33, 10, 24, 44, 36, 68, 99, 81, 100]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l[1:])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"id\": \"c5f21115\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[99, 81, 100]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l[8:])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"id\": \"0e399980\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[11, 22, 33, 10, 24, 44, 36, 68, 99, 81, 100]\\n\"\n ]\n }\n ],\n \"source\": [\n \"# -11-10 -9 -8 -7 -6 -5 -4 -3 -2 -1\\n\",\n \"l = [17,22,33,101,24,44,36,68,99,81,100]\\n\",\n \"# 0 1 2 3 4 5 6 7 8 9 10\\n\",\n \"\\n\",\n \"print(l[:])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"id\": \"9da33ce5\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[11, 22, 33, 10, 24]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l[:5])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"id\": \"858c11cb\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[17, 22, 33, 101, 24, 44, 36]\\n\"\n ]\n }\n ],\n \"source\": [\n \"# -11-10 -9 -8 -7 -6 -5 -4 -3 -2 -1\\n\",\n \"l = [17,22,33,101,24,44,36,68,99,81,100]\\n\",\n \"# 0 1 2 3 4 5 6 7 8 9 10\\n\",\n \"\\n\",\n \"print(l[0:-4])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 28,\n \"id\": \"9b6f435f\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[33, 101, 24, 44, 36, 68, 99]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l[2:-2])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 29,\n \"id\": \"556aceba\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l[2:0])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 30,\n \"id\": \"fcab2e6a\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[]\\n\"\n ]\n }\n ],\n \"source\": [\n \"# -11-10 -9 -8 -7 -6 -5 -4 -3 -2 -1\\n\",\n \"l = [17,22,33,101,24,44,36,68,99,81,100]\\n\",\n \"# 0 1 2 3 4 5 6 7 8 9 10\\n\",\n \"\\n\",\n \"print(l[0:0])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 31,\n \"id\": \"a34bdf8d\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[44, 36, 68, 99, 81, 100]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l[5:15])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 32,\n \"id\": \"69241fb4\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"ename\": \"IndexError\",\n \"evalue\": \"list index out of range\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[1;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[1;31mIndexError\\u001b[0m Traceback (most recent call last)\",\n \"Input \\u001b[1;32mIn [32]\\u001b[0m, in \\u001b[0;36m\\u001b[1;34m()\\u001b[0m\\n\\u001b[1;32m----> 1\\u001b[0m \\u001b[38;5;28mprint\\u001b[39m(\\u001b[43ml\\u001b[49m\\u001b[43m[\\u001b[49m\\u001b[38;5;241;43m15\\u001b[39;49m\\u001b[43m]\\u001b[49m)\\n\",\n \"\\u001b[1;31mIndexError\\u001b[0m: list index out of range\"\n ]\n }\n ],\n \"source\": [\n \"print(l[15])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 33,\n \"id\": \"b4f6a948\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[22, 33, 101]\\n\"\n ]\n }\n ],\n \"source\": [\n \"# -11-10 -9 -8 -7 -6 -5 -4 -3 -2 -1\\n\",\n \"l = [17,22,33,101,24,44,36,68,99,81,100]\\n\",\n \"# 0 1 2 3 4 5 6 7 8 9 10\\n\",\n \"\\n\",\n \"print(l[1:4])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 34,\n \"id\": \"2c46b2a1\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l[4:1])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 35,\n \"id\": \"bb318e72\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[68, 99, 81]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l[-4:-1])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 36,\n \"id\": \"05c49a41\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l[-1:-4])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 38,\n \"id\": \"bd75ab98\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[17, 33, 24, 36, 99, 100]\\n\"\n ]\n }\n ],\n \"source\": [\n \"# -11-10 -9 -8 -7 -6 -5 -4 -3 -2 -1\\n\",\n \"l = [17,22,33,101,24,44,36,68,99,81,100]\\n\",\n \"# 0 1 2 3 4 5 6 7 8 9 10\\n\",\n \"\\n\",\n \"print(l[0:11:2]) # l[start : end : steps]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 39,\n \"id\": \"ed0ebd7d\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[17, 101, 36, 81]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l[0:11:3])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 40,\n \"id\": \"591165f5\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[17, 22, 33, 101, 24, 44, 36, 68, 99, 81, 100]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l[::])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 42,\n \"id\": \"9325c782\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[17, 22, 33, 101, 24, 44, 36, 68, 99, 81, 100]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l[::1])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 41,\n \"id\": \"7c4d1a41\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[100, 81, 99, 68, 36, 44, 24, 101, 33, 22, 17]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l[::-1]) # reverse\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 43,\n \"id\": \"769b5e71\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[]\\n\"\n ]\n }\n ],\n \"source\": [\n \"# -11-10 -9 -8 -7 -6 -5 -4 -3 -2 -1\\n\",\n \"l = [17,22,33,101,24,44,36,68,99,81,100]\\n\",\n \"# 0 1 2 3 4 5 6 7 8 9 10\\n\",\n \"\\n\",\n \"print(l[4:8:-1])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 44,\n \"id\": \"61f07964\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[99, 68, 36, 44]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l[8:4:-1])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 45,\n \"id\": \"9ccf1239\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[100, 99, 36, 24, 33, 17]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l[::-2])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 46,\n \"id\": \"0dee2ab9\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"ename\": \"ValueError\",\n \"evalue\": \"slice step cannot be zero\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[1;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[1;31mValueError\\u001b[0m Traceback (most recent call last)\",\n \"Input \\u001b[1;32mIn [46]\\u001b[0m, in \\u001b[0;36m\\u001b[1;34m()\\u001b[0m\\n\\u001b[1;32m----> 1\\u001b[0m \\u001b[38;5;28mprint\\u001b[39m(\\u001b[43ml\\u001b[49m\\u001b[43m[\\u001b[49m\\u001b[43m:\\u001b[49m\\u001b[43m:\\u001b[49m\\u001b[38;5;241;43m0\\u001b[39;49m\\u001b[43m]\\u001b[49m)\\n\",\n \"\\u001b[1;31mValueError\\u001b[0m: slice step cannot be zero\"\n ]\n }\n ],\n \"source\": [\n \"print(l[::0])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"4f3a41ae\",\n \"metadata\": {},\n \"source\": [\n \"# List functions\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 47,\n \"id\": \"5d15c64c\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"5\\n\"\n ]\n }\n ],\n \"source\": [\n \"l = [10,20,30,40,50]\\n\",\n \"\\n\",\n \"print(len(l))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 48,\n \"id\": \"d0c326c5\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"4\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(len(l) - 1)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 49,\n \"id\": \"c33408b9\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"150\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(sum(l))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 50,\n \"id\": \"29a67088\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"50\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(max(l))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 51,\n \"id\": \"7ce163a3\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"10\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(min(l))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 52,\n \"id\": \"2a9052cb\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"4\\n\"\n ]\n },\n {\n \"ename\": \"TypeError\",\n \"evalue\": \"unsupported operand type(s) for +: 'int' and 'str'\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[1;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[1;31mTypeError\\u001b[0m Traceback (most recent call last)\",\n \"Input \\u001b[1;32mIn [52]\\u001b[0m, in \\u001b[0;36m\\u001b[1;34m()\\u001b[0m\\n\\u001b[0;32m 1\\u001b[0m l \\u001b[38;5;241m=\\u001b[39m [\\u001b[38;5;124m\\\"\\u001b[39m\\u001b[38;5;124mA\\u001b[39m\\u001b[38;5;124m\\\"\\u001b[39m, \\u001b[38;5;241m10\\u001b[39m, \\u001b[38;5;241m20\\u001b[39m, \\u001b[38;5;241m30\\u001b[39m]\\n\\u001b[0;32m 3\\u001b[0m \\u001b[38;5;28mprint\\u001b[39m(\\u001b[38;5;28mlen\\u001b[39m(l))\\n\\u001b[1;32m----> 4\\u001b[0m \\u001b[38;5;28mprint\\u001b[39m(\\u001b[38;5;28;43msum\\u001b[39;49m\\u001b[43m(\\u001b[49m\\u001b[43ml\\u001b[49m\\u001b[43m)\\u001b[49m)\\n\",\n \"\\u001b[1;31mTypeError\\u001b[0m: unsupported operand type(s) for +: 'int' and 'str'\"\n ]\n }\n ],\n \"source\": [\n \"l = [\\\"A\\\", 10, 20, 30]\\n\",\n \"\\n\",\n \"print(len(l))\\n\",\n \"print(sum(l))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 55,\n \"id\": \"e62fa754\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'ab'\"\n ]\n },\n \"execution_count\": 55,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"\\\"a\\\" + \\\"b\\\"\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 53,\n \"id\": \"5d8a7988\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"3\\n\"\n ]\n }\n ],\n \"source\": [\n \"l = [\\\"a\\\", \\\"b\\\", \\\"c\\\"]\\n\",\n \"\\n\",\n \"print(len(l))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 54,\n \"id\": \"99cb555e\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"ename\": \"TypeError\",\n \"evalue\": \"unsupported operand type(s) for +: 'int' and 'str'\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[1;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[1;31mTypeError\\u001b[0m Traceback (most recent call last)\",\n \"Input \\u001b[1;32mIn [54]\\u001b[0m, in \\u001b[0;36m\\u001b[1;34m()\\u001b[0m\\n\\u001b[1;32m----> 1\\u001b[0m \\u001b[38;5;28mprint\\u001b[39m(\\u001b[38;5;28;43msum\\u001b[39;49m\\u001b[43m(\\u001b[49m\\u001b[43ml\\u001b[49m\\u001b[43m)\\u001b[49m)\\n\",\n \"\\u001b[1;31mTypeError\\u001b[0m: unsupported operand type(s) for +: 'int' and 'str'\"\n ]\n }\n ],\n \"source\": [\n \"print(sum(l))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 56,\n \"id\": \"1eff053f\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"c\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(max(l))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 57,\n \"id\": \"c3f7d556\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"a\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(min(l))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"8cb11d63\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# ASCII\\n\",\n \"\\n\",\n \"A - 65/2 - binary\\n\",\n \"B - 66\\n\",\n \"Z - 90\\n\",\n \"\\n\",\n \"space - 32\\n\",\n \"\\n\",\n \"a - 97\\n\",\n \"z - 122\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 58,\n \"id\": \"eea43d51\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"False\"\n ]\n },\n \"execution_count\": 58,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"\\\"A\\\" > \\\"a\\\"\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 59,\n \"id\": \"6ffe5a48\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"True\"\n ]\n },\n \"execution_count\": 59,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"\\\"A\\\" < 'a'\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"55fc39d8\",\n \"metadata\": {},\n \"source\": [\n \"# List methods\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 65,\n \"id\": \"d0f38ad0\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# help(print)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 64,\n \"id\": \"d56335b3\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# help(sum)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 63,\n \"id\": \"4832e051\",\n \"metadata\": {\n \"scrolled\": false\n },\n \"outputs\": [],\n \"source\": [\n \"# help(list)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 66,\n \"id\": \"315f91a5\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"sum?\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 68,\n \"id\": \"1e64def7\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[10, 20, 30, 40]\\n\"\n ]\n }\n ],\n \"source\": [\n \"l = [10,20,30]\\n\",\n \"\\n\",\n \"l.append(40)\\n\",\n \"\\n\",\n \"print(l)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 69,\n \"id\": \"9b78984e\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"l.append(\\\"India\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 70,\n \"id\": \"86227543\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[10, 20, 30, 40, 'India']\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 71,\n \"id\": \"5a90e417\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"4\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l.index('India'))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 72,\n \"id\": \"43bf35e2\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[10, 20, 30, 40, 'India']\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 73,\n \"id\": \"df0bd6ca\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"l.reverse()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 74,\n \"id\": \"826857cc\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"['India', 40, 30, 20, 10]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 75,\n \"id\": \"42f82dab\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"India\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l[0])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 76,\n \"id\": \"5fd18a26\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"l.reverse() # l[::-1]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 77,\n \"id\": \"acf2c947\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[10, 20, 30, 40, 'India']\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"50f6bb18\",\n \"metadata\": {},\n \"source\": [\n \"# Strings\\n\",\n \"\\n\",\n \"set of characters\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 78,\n \"id\": \"2282691e\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"hello\\n\"\n ]\n }\n ],\n \"source\": [\n \"# -1\\n\",\n \"s = 'hello'\\n\",\n \"# 01234\\n\",\n \"\\n\",\n \"print(s)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 79,\n \"id\": \"30758f8a\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"h\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(s[0])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 80,\n \"id\": \"401c2af6\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"o\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(s[4])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 81,\n \"id\": \"3543dcff\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"l\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(s[3])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 82,\n \"id\": \"70efef69\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"o\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(s[-1])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 83,\n \"id\": \"8b776e03\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"l\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(s[-2])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 84,\n \"id\": \"7a00d157\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(type(s))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 85,\n \"id\": \"bafe5d33\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(type(s[0]))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"69fd97c5\",\n \"metadata\": {},\n \"source\": [\n \"# String slicing\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 86,\n \"id\": \"cd9dff16\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"commu\\n\"\n ]\n }\n ],\n \"source\": [\n \"s = 'communication'\\n\",\n \"# 0123456789\\n\",\n \"print(s[0:5])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 87,\n \"id\": \"0d50213f\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"noitacinummoc\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(s[::-1])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 88,\n \"id\": \"0d4f5af4\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"omm\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(s[1:4])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 89,\n \"id\": \"e4712f62\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(s[4:1])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 90,\n \"id\": \"2d5ffb11\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"a = ''\\n\",\n \"\\n\",\n \"print(type(a))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 91,\n \"id\": \"b1755966\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"communication\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(s)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 92,\n \"id\": \"e82d1c2b\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"noitacin\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(s[-1:-9:-1])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"918d1a64\",\n \"metadata\": {},\n \"source\": [\n \"# String functions\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 93,\n \"id\": \"87ad584b\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"13\\n\"\n ]\n }\n ],\n \"source\": [\n \"s = 'communication'\\n\",\n \"\\n\",\n \"print(len(s))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 94,\n \"id\": \"34ab8cff\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"ename\": \"TypeError\",\n \"evalue\": \"unsupported operand type(s) for +: 'int' and 'str'\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[1;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[1;31mTypeError\\u001b[0m Traceback (most recent call last)\",\n \"Input \\u001b[1;32mIn [94]\\u001b[0m, in \\u001b[0;36m\\u001b[1;34m()\\u001b[0m\\n\\u001b[1;32m----> 1\\u001b[0m \\u001b[38;5;28mprint\\u001b[39m(\\u001b[38;5;28;43msum\\u001b[39;49m\\u001b[43m(\\u001b[49m\\u001b[43ms\\u001b[49m\\u001b[43m)\\u001b[49m)\\n\",\n \"\\u001b[1;31mTypeError\\u001b[0m: unsupported operand type(s) for +: 'int' and 'str'\"\n ]\n }\n ],\n \"source\": [\n \"print(sum(s))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 95,\n \"id\": \"7378c889\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"u\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(max(s))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 96,\n \"id\": \"de330ab1\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"a\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(min(s))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"7bcef9c5\",\n \"metadata\": {},\n \"source\": [\n \"# string methods\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 97,\n \"id\": \"b0b078e8\",\n \"metadata\": {\n \"scrolled\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Help on class str in module builtins:\\n\",\n \"\\n\",\n \"class str(object)\\n\",\n \" | str(object='') -> str\\n\",\n \" | str(bytes_or_buffer[, encoding[, errors]]) -> str\\n\",\n \" | \\n\",\n \" | Create a new string object from the given object. If encoding or\\n\",\n \" | errors is specified, then the object must expose a data buffer\\n\",\n \" | that will be decoded using the given encoding and error handler.\\n\",\n \" | Otherwise, returns the result of object.__str__() (if defined)\\n\",\n \" | or repr(object).\\n\",\n \" | encoding defaults to sys.getdefaultencoding().\\n\",\n \" | errors defaults to 'strict'.\\n\",\n \" | \\n\",\n \" | Methods defined here:\\n\",\n \" | \\n\",\n \" | __add__(self, value, /)\\n\",\n \" | Return self+value.\\n\",\n \" | \\n\",\n \" | __contains__(self, key, /)\\n\",\n \" | Return key in self.\\n\",\n \" | \\n\",\n \" | __eq__(self, value, /)\\n\",\n \" | Return self==value.\\n\",\n \" | \\n\",\n \" | __format__(self, format_spec, /)\\n\",\n \" | Return a formatted version of the string as described by format_spec.\\n\",\n \" | \\n\",\n \" | __ge__(self, value, /)\\n\",\n \" | Return self>=value.\\n\",\n \" | \\n\",\n \" | __getattribute__(self, name, /)\\n\",\n \" | Return getattr(self, name).\\n\",\n \" | \\n\",\n \" | __getitem__(self, key, /)\\n\",\n \" | Return self[key].\\n\",\n \" | \\n\",\n \" | __getnewargs__(...)\\n\",\n \" | \\n\",\n \" | __gt__(self, value, /)\\n\",\n \" | Return self>value.\\n\",\n \" | \\n\",\n \" | __hash__(self, /)\\n\",\n \" | Return hash(self).\\n\",\n \" | \\n\",\n \" | __iter__(self, /)\\n\",\n \" | Implement iter(self).\\n\",\n \" | \\n\",\n \" | __le__(self, value, /)\\n\",\n \" | Return self<=value.\\n\",\n \" | \\n\",\n \" | __len__(self, /)\\n\",\n \" | Return len(self).\\n\",\n \" | \\n\",\n \" | __lt__(self, value, /)\\n\",\n \" | Return self int\\n\",\n \" | \\n\",\n \" | Return the number of non-overlapping occurrences of substring sub in\\n\",\n \" | string S[start:end]. Optional arguments start and end are\\n\",\n \" | interpreted as in slice notation.\\n\",\n \" | \\n\",\n \" | encode(self, /, encoding='utf-8', errors='strict')\\n\",\n \" | Encode the string using the codec registered for encoding.\\n\",\n \" | \\n\",\n \" | encoding\\n\",\n \" | The encoding in which to encode the string.\\n\",\n \" | errors\\n\",\n \" | The error handling scheme to use for encoding errors.\\n\",\n \" | The default is 'strict' meaning that encoding errors raise a\\n\",\n \" | UnicodeEncodeError. Other possible values are 'ignore', 'replace' and\\n\",\n \" | 'xmlcharrefreplace' as well as any other name registered with\\n\",\n \" | codecs.register_error that can handle UnicodeEncodeErrors.\\n\",\n \" | \\n\",\n \" | endswith(...)\\n\",\n \" | S.endswith(suffix[, start[, end]]) -> bool\\n\",\n \" | \\n\",\n \" | Return True if S ends with the specified suffix, False otherwise.\\n\",\n \" | With optional start, test S beginning at that position.\\n\",\n \" | With optional end, stop comparing S at that position.\\n\",\n \" | suffix can also be a tuple of strings to try.\\n\",\n \" | \\n\",\n \" | expandtabs(self, /, tabsize=8)\\n\",\n \" | Return a copy where all tab characters are expanded using spaces.\\n\",\n \" | \\n\",\n \" | If tabsize is not given, a tab size of 8 characters is assumed.\\n\",\n \" | \\n\",\n \" | find(...)\\n\",\n \" | S.find(sub[, start[, end]]) -> int\\n\",\n \" | \\n\",\n \" | Return the lowest index in S where substring sub is found,\\n\",\n \" | such that sub is contained within S[start:end]. Optional\\n\",\n \" | arguments start and end are interpreted as in slice notation.\\n\",\n \" | \\n\",\n \" | Return -1 on failure.\\n\",\n \" | \\n\",\n \" | format(...)\\n\",\n \" | S.format(*args, **kwargs) -> str\\n\",\n \" | \\n\",\n \" | Return a formatted version of S, using substitutions from args and kwargs.\\n\",\n \" | The substitutions are identified by braces ('{' and '}').\\n\",\n \" | \\n\",\n \" | format_map(...)\\n\",\n \" | S.format_map(mapping) -> str\\n\",\n \" | \\n\",\n \" | Return a formatted version of S, using substitutions from mapping.\\n\",\n \" | The substitutions are identified by braces ('{' and '}').\\n\",\n \" | \\n\",\n \" | index(...)\\n\",\n \" | S.index(sub[, start[, end]]) -> int\\n\",\n \" | \\n\",\n \" | Return the lowest index in S where substring sub is found,\\n\",\n \" | such that sub is contained within S[start:end]. Optional\\n\",\n \" | arguments start and end are interpreted as in slice notation.\\n\",\n \" | \\n\",\n \" | Raises ValueError when the substring is not found.\\n\",\n \" | \\n\",\n \" | isalnum(self, /)\\n\",\n \" | Return True if the string is an alpha-numeric string, False otherwise.\\n\",\n \" | \\n\",\n \" | A string is alpha-numeric if all characters in the string are alpha-numeric and\\n\",\n \" | there is at least one character in the string.\\n\",\n \" | \\n\",\n \" | isalpha(self, /)\\n\",\n \" | Return True if the string is an alphabetic string, False otherwise.\\n\",\n \" | \\n\",\n \" | A string is alphabetic if all characters in the string are alphabetic and there\\n\",\n \" | is at least one character in the string.\\n\",\n \" | \\n\",\n \" | isascii(self, /)\\n\",\n \" | Return True if all characters in the string are ASCII, False otherwise.\\n\",\n \" | \\n\",\n \" | ASCII characters have code points in the range U+0000-U+007F.\\n\",\n \" | Empty string is ASCII too.\\n\",\n \" | \\n\",\n \" | isdecimal(self, /)\\n\",\n \" | Return True if the string is a decimal string, False otherwise.\\n\",\n \" | \\n\",\n \" | A string is a decimal string if all characters in the string are decimal and\\n\",\n \" | there is at least one character in the string.\\n\",\n \" | \\n\",\n \" | isdigit(self, /)\\n\",\n \" | Return True if the string is a digit string, False otherwise.\\n\",\n \" | \\n\",\n \" | A string is a digit string if all characters in the string are digits and there\\n\",\n \" | is at least one character in the string.\\n\",\n \" | \\n\",\n \" | isidentifier(self, /)\\n\",\n \" | Return True if the string is a valid Python identifier, False otherwise.\\n\",\n \" | \\n\",\n \" | Call keyword.iskeyword(s) to test whether string s is a reserved identifier,\\n\",\n \" | such as \\\"def\\\" or \\\"class\\\".\\n\",\n \" | \\n\",\n \" | islower(self, /)\\n\",\n \" | Return True if the string is a lowercase string, False otherwise.\\n\",\n \" | \\n\",\n \" | A string is lowercase if all cased characters in the string are lowercase and\\n\",\n \" | there is at least one cased character in the string.\\n\",\n \" | \\n\",\n \" | isnumeric(self, /)\\n\",\n \" | Return True if the string is a numeric string, False otherwise.\\n\",\n \" | \\n\",\n \" | A string is numeric if all characters in the string are numeric and there is at\\n\",\n \" | least one character in the string.\\n\",\n \" | \\n\",\n \" | isprintable(self, /)\\n\",\n \" | Return True if the string is printable, False otherwise.\\n\",\n \" | \\n\",\n \" | A string is printable if all of its characters are considered printable in\\n\",\n \" | repr() or if it is empty.\\n\",\n \" | \\n\",\n \" | isspace(self, /)\\n\",\n \" | Return True if the string is a whitespace string, False otherwise.\\n\",\n \" | \\n\",\n \" | A string is whitespace if all characters in the string are whitespace and there\\n\",\n \" | is at least one character in the string.\\n\",\n \" | \\n\",\n \" | istitle(self, /)\\n\",\n \" | Return True if the string is a title-cased string, False otherwise.\\n\",\n \" | \\n\",\n \" | In a title-cased string, upper- and title-case characters may only\\n\",\n \" | follow uncased characters and lowercase characters only cased ones.\\n\",\n \" | \\n\",\n \" | isupper(self, /)\\n\",\n \" | Return True if the string is an uppercase string, False otherwise.\\n\",\n \" | \\n\",\n \" | A string is uppercase if all cased characters in the string are uppercase and\\n\",\n \" | there is at least one cased character in the string.\\n\",\n \" | \\n\",\n \" | join(self, iterable, /)\\n\",\n \" | Concatenate any number of strings.\\n\",\n \" | \\n\",\n \" | The string whose method is called is inserted in between each given string.\\n\",\n \" | The result is returned as a new string.\\n\",\n \" | \\n\",\n \" | Example: '.'.join(['ab', 'pq', 'rs']) -> 'ab.pq.rs'\\n\",\n \" | \\n\",\n \" | ljust(self, width, fillchar=' ', /)\\n\",\n \" | Return a left-justified string of length width.\\n\",\n \" | \\n\",\n \" | Padding is done using the specified fill character (default is a space).\\n\",\n \" | \\n\",\n \" | lower(self, /)\\n\",\n \" | Return a copy of the string converted to lowercase.\\n\",\n \" | \\n\",\n \" | lstrip(self, chars=None, /)\\n\",\n \" | Return a copy of the string with leading whitespace removed.\\n\",\n \" | \\n\",\n \" | If chars is given and not None, remove characters in chars instead.\\n\",\n \" | \\n\",\n \" | partition(self, sep, /)\\n\",\n \" | Partition the string into three parts using the given separator.\\n\",\n \" | \\n\",\n \" | This will search for the separator in the string. If the separator is found,\\n\",\n \" | returns a 3-tuple containing the part before the separator, the separator\\n\",\n \" | itself, and the part after it.\\n\",\n \" | \\n\",\n \" | If the separator is not found, returns a 3-tuple containing the original string\\n\",\n \" | and two empty strings.\\n\",\n \" | \\n\",\n \" | removeprefix(self, prefix, /)\\n\",\n \" | Return a str with the given prefix string removed if present.\\n\",\n \" | \\n\",\n \" | If the string starts with the prefix string, return string[len(prefix):].\\n\",\n \" | Otherwise, return a copy of the original string.\\n\",\n \" | \\n\",\n \" | removesuffix(self, suffix, /)\\n\",\n \" | Return a str with the given suffix string removed if present.\\n\",\n \" | \\n\",\n \" | If the string ends with the suffix string and that suffix is not empty,\\n\",\n \" | return string[:-len(suffix)]. Otherwise, return a copy of the original\\n\",\n \" | string.\\n\",\n \" | \\n\",\n \" | replace(self, old, new, count=-1, /)\\n\",\n \" | Return a copy with all occurrences of substring old replaced by new.\\n\",\n \" | \\n\",\n \" | count\\n\",\n \" | Maximum number of occurrences to replace.\\n\",\n \" | -1 (the default value) means replace all occurrences.\\n\",\n \" | \\n\",\n \" | If the optional argument count is given, only the first count occurrences are\\n\",\n \" | replaced.\\n\",\n \" | \\n\",\n \" | rfind(...)\\n\",\n \" | S.rfind(sub[, start[, end]]) -> int\\n\",\n \" | \\n\",\n \" | Return the highest index in S where substring sub is found,\\n\",\n \" | such that sub is contained within S[start:end]. Optional\\n\",\n \" | arguments start and end are interpreted as in slice notation.\\n\",\n \" | \\n\",\n \" | Return -1 on failure.\\n\",\n \" | \\n\",\n \" | rindex(...)\\n\",\n \" | S.rindex(sub[, start[, end]]) -> int\\n\",\n \" | \\n\",\n \" | Return the highest index in S where substring sub is found,\\n\",\n \" | such that sub is contained within S[start:end]. Optional\\n\",\n \" | arguments start and end are interpreted as in slice notation.\\n\",\n \" | \\n\",\n \" | Raises ValueError when the substring is not found.\\n\",\n \" | \\n\",\n \" | rjust(self, width, fillchar=' ', /)\\n\",\n \" | Return a right-justified string of length width.\\n\",\n \" | \\n\",\n \" | Padding is done using the specified fill character (default is a space).\\n\",\n \" | \\n\",\n \" | rpartition(self, sep, /)\\n\",\n \" | Partition the string into three parts using the given separator.\\n\",\n \" | \\n\",\n \" | This will search for the separator in the string, starting at the end. If\\n\",\n \" | the separator is found, returns a 3-tuple containing the part before the\\n\",\n \" | separator, the separator itself, and the part after it.\\n\",\n \" | \\n\",\n \" | If the separator is not found, returns a 3-tuple containing two empty strings\\n\",\n \" | and the original string.\\n\",\n \" | \\n\",\n \" | rsplit(self, /, sep=None, maxsplit=-1)\\n\",\n \" | Return a list of the words in the string, using sep as the delimiter string.\\n\",\n \" | \\n\",\n \" | sep\\n\",\n \" | The delimiter according which to split the string.\\n\",\n \" | None (the default value) means split according to any whitespace,\\n\",\n \" | and discard empty strings from the result.\\n\",\n \" | maxsplit\\n\",\n \" | Maximum number of splits to do.\\n\",\n \" | -1 (the default value) means no limit.\\n\",\n \" | \\n\",\n \" | Splits are done starting at the end of the string and working to the front.\\n\",\n \" | \\n\",\n \" | rstrip(self, chars=None, /)\\n\",\n \" | Return a copy of the string with trailing whitespace removed.\\n\",\n \" | \\n\",\n \" | If chars is given and not None, remove characters in chars instead.\\n\",\n \" | \\n\",\n \" | split(self, /, sep=None, maxsplit=-1)\\n\",\n \" | Return a list of the words in the string, using sep as the delimiter string.\\n\",\n \" | \\n\",\n \" | sep\\n\",\n \" | The delimiter according which to split the string.\\n\",\n \" | None (the default value) means split according to any whitespace,\\n\",\n \" | and discard empty strings from the result.\\n\",\n \" | maxsplit\\n\",\n \" | Maximum number of splits to do.\\n\",\n \" | -1 (the default value) means no limit.\\n\",\n \" | \\n\",\n \" | splitlines(self, /, keepends=False)\\n\",\n \" | Return a list of the lines in the string, breaking at line boundaries.\\n\",\n \" | \\n\",\n \" | Line breaks are not included in the resulting list unless keepends is given and\\n\",\n \" | true.\\n\",\n \" | \\n\",\n \" | startswith(...)\\n\",\n \" | S.startswith(prefix[, start[, end]]) -> bool\\n\",\n \" | \\n\",\n \" | Return True if S starts with the specified prefix, False otherwise.\\n\",\n \" | With optional start, test S beginning at that position.\\n\",\n \" | With optional end, stop comparing S at that position.\\n\",\n \" | prefix can also be a tuple of strings to try.\\n\",\n \" | \\n\",\n \" | strip(self, chars=None, /)\\n\",\n \" | Return a copy of the string with leading and trailing whitespace removed.\\n\",\n \" | \\n\",\n \" | If chars is given and not None, remove characters in chars instead.\\n\",\n \" | \\n\",\n \" | swapcase(self, /)\\n\",\n \" | Convert uppercase characters to lowercase and lowercase characters to uppercase.\\n\",\n \" | \\n\",\n \" | title(self, /)\\n\",\n \" | Return a version of the string where each word is titlecased.\\n\",\n \" | \\n\",\n \" | More specifically, words start with uppercased characters and all remaining\\n\",\n \" | cased characters have lower case.\\n\",\n \" | \\n\",\n \" | translate(self, table, /)\\n\",\n \" | Replace each character in the string using the given translation table.\\n\",\n \" | \\n\",\n \" | table\\n\",\n \" | Translation table, which must be a mapping of Unicode ordinals to\\n\",\n \" | Unicode ordinals, strings, or None.\\n\",\n \" | \\n\",\n \" | The table must implement lookup/indexing via __getitem__, for instance a\\n\",\n \" | dictionary or list. If this operation raises LookupError, the character is\\n\",\n \" | left untouched. Characters mapped to None are deleted.\\n\",\n \" | \\n\",\n \" | upper(self, /)\\n\",\n \" | Return a copy of the string converted to uppercase.\\n\",\n \" | \\n\",\n \" | zfill(self, width, /)\\n\",\n \" | Pad a numeric string with zeros on the left, to fill a field of the given width.\\n\",\n \" | \\n\",\n \" | The string is never truncated.\\n\",\n \" | \\n\",\n \" | ----------------------------------------------------------------------\\n\",\n \" | Static methods defined here:\\n\",\n \" | \\n\",\n \" | __new__(*args, **kwargs) from builtins.type\\n\",\n \" | Create and return a new object. See help(type) for accurate signature.\\n\",\n \" | \\n\",\n \" | maketrans(...)\\n\",\n \" | Return a translation table usable for str.translate().\\n\",\n \" | \\n\",\n \" | If there is only one argument, it must be a dictionary mapping Unicode\\n\",\n \" | ordinals (integers) or characters to Unicode ordinals, strings or None.\\n\",\n \" | Character keys will be then converted to ordinals.\\n\",\n \" | If there are two arguments, they must be strings of equal length, and\\n\",\n \" | in the resulting dictionary, each character in x will be mapped to the\\n\",\n \" | character at the same position in y. If there is a third argument, it\\n\",\n \" | must be a string, whose characters will be mapped to None in the result.\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"help(str)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 98,\n \"id\": \"a795fbdb\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"False\"\n ]\n },\n \"execution_count\": 98,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"'raj123' == 'RAJ123'\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 100,\n \"id\": \"78f9fd1d\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"raj123\\n\"\n ]\n }\n ],\n \"source\": [\n \"s = 'RAJ123'\\n\",\n \"\\n\",\n \"print(s.casefold())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 101,\n \"id\": \"d7d25e0d\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"INDIA\\n\"\n ]\n }\n ],\n \"source\": [\n \"s = 'India'\\n\",\n \"\\n\",\n \"print(s.upper())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 102,\n \"id\": \"4965c3b0\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"india\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(s.lower())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 103,\n \"id\": \"feb1f94f\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"['This', 'is', 'an', 'apple']\\n\"\n ]\n }\n ],\n \"source\": [\n \"# split\\n\",\n \"\\n\",\n \"s = \\\"This is an apple\\\"\\n\",\n \"\\n\",\n \"print(s.split(\\\" \\\"))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 105,\n \"id\": \"d1231581\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"['H', 'llo Us', 'r']\\n\"\n ]\n }\n ],\n \"source\": [\n \"s = \\\"Hello User\\\"\\n\",\n \"\\n\",\n \"print(s.split(\\\"e\\\"))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 106,\n \"id\": \"2deaf22c\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"This+is+an+apple\\n\"\n ]\n }\n ],\n \"source\": [\n \"l = ['This', 'is', 'an', 'apple']\\n\",\n \"\\n\",\n \"a = \\\"+\\\"\\n\",\n \"\\n\",\n \"print(a.join(l))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 107,\n \"id\": \"d7bf333b\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"This is an apple\\n\"\n ]\n }\n ],\n \"source\": [\n \"l = ['This', 'is', 'an', 'apple']\\n\",\n \"\\n\",\n \"a = \\\" \\\"\\n\",\n \"\\n\",\n \"print(a.join(l))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 108,\n \"id\": \"99c38b4d\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Thisisanapple\\n\"\n ]\n }\n ],\n \"source\": [\n \"s = \\\"This is an apple\\\"\\n\",\n \"\\n\",\n \"l = s.split(\\\" \\\")\\n\",\n \"\\n\",\n \"x = \\\"\\\".join(l)\\n\",\n \"\\n\",\n \"print(x)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"6c209c90\",\n \"metadata\": {},\n \"source\": [\n \"**WAP to enter a string from user and print the biggest word from the paragraph**\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"92b19368\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"text = input(\\\"Enter your para: \\\")\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 109,\n \"id\": \"78217b1a\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"False\"\n ]\n },\n \"execution_count\": 109,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"\\\"abc\\\" > \\\"def\\\"\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"16d89cc5\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.12\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n" }, { "path": "March Cohort 2023/.ipynb_checkpoints/3_For Loop Dictionary Tuple and Sets-checkpoint.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"id\": \"958dd5a9\",\n \"metadata\": {},\n \"source\": [\n \"# Range function\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"id\": \"ecc0f5d9\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"range(0, 5)\\n\"\n ]\n }\n ],\n \"source\": [\n \"n = range(5)\\n\",\n \"\\n\",\n \"print(n)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"id\": \"a414c992\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(type(n))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"id\": \"c50d39c0\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[0, 1, 2, 3, 4]\\n\"\n ]\n }\n ],\n \"source\": [\n \"n = list(range(5))\\n\",\n \"\\n\",\n \"print(n)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"id\": \"8ff4b4f0\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[3, 4, 5, 6, 7]\\n\"\n ]\n }\n ],\n \"source\": [\n \"n = list(range(3,8))\\n\",\n \"\\n\",\n \"print(n)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"id\": \"c82ff6db\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"True\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(range(20) == range(0,20))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"id\": \"14dbda38\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[5, 7, 9, 11, 13, 15, 17, 19]\\n\"\n ]\n }\n ],\n \"source\": [\n \"n = list(range(5,20,2))\\n\",\n \"\\n\",\n \"print(n)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"f4b1e93a\",\n \"metadata\": {},\n \"source\": [\n \"# for loop\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"id\": \"a1e6f836\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"False\\n\"\n ]\n }\n ],\n \"source\": [\n \"l = [\\\"A\\\", 'B', 'C', 'D']\\n\",\n \"\\n\",\n \"print(\\\"Z\\\" in l)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"id\": \"fe7eb160\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"A\\n\",\n \"B\\n\",\n \"C\\n\",\n \"D\\n\"\n ]\n }\n ],\n \"source\": [\n \"l = [\\\"A\\\", 'B', 'C', 'D']\\n\",\n \"# i\\n\",\n \"\\n\",\n \"# \\\"D\\\"in l\\n\",\n \"for i in l:\\n\",\n \" print(i)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"id\": \"c237efad\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"A\\n\",\n \"Hello\\n\",\n \"B\\n\",\n \"Hello\\n\",\n \"C\\n\",\n \"Hello\\n\",\n \"D\\n\",\n \"Hello\\n\"\n ]\n }\n ],\n \"source\": [\n \"l = [\\\"A\\\", 'B', 'C', 'D']\\n\",\n \"\\n\",\n \"for i in l:\\n\",\n \" print(i)\\n\",\n \" print(\\\"Hello\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"id\": \"6efc03f4\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"a\\n\",\n \"b\\n\",\n \"c\\n\",\n \"d\\n\"\n ]\n }\n ],\n \"source\": [\n \"s = \\\"abcd\\\"\\n\",\n \"# i\\n\",\n \"\\n\",\n \"for i in s:\\n\",\n \" print(i)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"id\": \"0d1d9074\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'AB'\"\n ]\n },\n \"execution_count\": 12,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"'ab'.upper()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"id\": \"754cc5de\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"['ab', 'cd']\\n\"\n ]\n }\n ],\n \"source\": [\n \"x = ['ab', 'cd']\\n\",\n \"# i\\n\",\n \"\\n\",\n \"for i in x:\\n\",\n \" i.upper() # 'ab'.upper() - 'AB'\\n\",\n \"\\n\",\n \"print(x)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"id\": \"8ba916b9\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"AB\\n\",\n \"CD\\n\",\n \"['ab', 'cd']\\n\"\n ]\n }\n ],\n \"source\": [\n \"x = ['ab', 'cd']\\n\",\n \"# i\\n\",\n \"\\n\",\n \"for i in x:\\n\",\n \" print(i.upper()) # 'AB', 'CD'\\n\",\n \"\\n\",\n \"print(x)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"id\": \"7835c572\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"a\\n\",\n \"b\\n\",\n \"c\\n\",\n \"d\\n\"\n ]\n }\n ],\n \"source\": [\n \"x = 'abcd'\\n\",\n \"\\n\",\n \"for i in x:\\n\",\n \" print(i)\\n\",\n \" x.upper()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"id\": \"0ce5284c\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"A\\n\",\n \"B\\n\",\n \"C\\n\",\n \"D\\n\"\n ]\n }\n ],\n \"source\": [\n \"x = 'abcd'\\n\",\n \"\\n\",\n \"for i in x:\\n\",\n \" print(i.upper())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"id\": \"4f9188d4\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"0\\n\",\n \"1\\n\",\n \"2\\n\",\n \"3\\n\",\n \"4\\n\"\n ]\n }\n ],\n \"source\": [\n \"# i\\n\",\n \"# 0,1,2,3,4\\n\",\n \"for i in range(5):\\n\",\n \" print(i)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"id\": \"fab18b0b\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"ename\": \"TypeError\",\n \"evalue\": \"'str' object cannot be interpreted as an integer\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[1;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[1;31mTypeError\\u001b[0m Traceback (most recent call last)\",\n \"Input \\u001b[1;32mIn [18]\\u001b[0m, in \\u001b[0;36m\\u001b[1;34m()\\u001b[0m\\n\\u001b[0;32m 1\\u001b[0m x \\u001b[38;5;241m=\\u001b[39m \\u001b[38;5;124m'\\u001b[39m\\u001b[38;5;124mabcd\\u001b[39m\\u001b[38;5;124m'\\u001b[39m\\n\\u001b[1;32m----> 3\\u001b[0m \\u001b[38;5;28;01mfor\\u001b[39;00m i \\u001b[38;5;129;01min\\u001b[39;00m \\u001b[38;5;28;43mrange\\u001b[39;49m\\u001b[43m(\\u001b[49m\\u001b[43mx\\u001b[49m\\u001b[43m)\\u001b[49m:\\n\\u001b[0;32m 4\\u001b[0m \\u001b[38;5;28mprint\\u001b[39m(i)\\n\",\n \"\\u001b[1;31mTypeError\\u001b[0m: 'str' object cannot be interpreted as an integer\"\n ]\n }\n ],\n \"source\": [\n \"x = 'abcd'\\n\",\n \"\\n\",\n \"for i in range(x):\\n\",\n \" print(i)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"id\": \"1044b515\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"ename\": \"AttributeError\",\n \"evalue\": \"'int' object has no attribute 'upper'\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[1;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[1;31mAttributeError\\u001b[0m Traceback (most recent call last)\",\n \"Input \\u001b[1;32mIn [19]\\u001b[0m, in \\u001b[0;36m\\u001b[1;34m()\\u001b[0m\\n\\u001b[0;32m 3\\u001b[0m \\u001b[38;5;66;03m# 0,1,2,3\\u001b[39;00m\\n\\u001b[0;32m 4\\u001b[0m \\u001b[38;5;28;01mfor\\u001b[39;00m i \\u001b[38;5;129;01min\\u001b[39;00m \\u001b[38;5;28mrange\\u001b[39m(\\u001b[38;5;28mlen\\u001b[39m(x)):\\n\\u001b[1;32m----> 5\\u001b[0m \\u001b[38;5;28mprint\\u001b[39m(\\u001b[43mi\\u001b[49m\\u001b[38;5;241;43m.\\u001b[39;49m\\u001b[43mupper\\u001b[49m())\\n\",\n \"\\u001b[1;31mAttributeError\\u001b[0m: 'int' object has no attribute 'upper'\"\n ]\n }\n ],\n \"source\": [\n \"x = 'abcd'\\n\",\n \"\\n\",\n \"# 0,1,2,3\\n\",\n \"for i in range(len(x)):\\n\",\n \" print(i.upper())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"id\": \"9594cd6a\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"0\\n\",\n \"1\\n\",\n \"2\\n\",\n \"3\\n\"\n ]\n }\n ],\n \"source\": [\n \"x = 'abcd'\\n\",\n \"\\n\",\n \"# 0,1,2,3\\n\",\n \"for i in range(len(x)):\\n\",\n \" print(i)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"id\": \"0f7f2436\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"2\\n\",\n \"3\\n\",\n \"4\\n\",\n \"5\\n\"\n ]\n }\n ],\n \"source\": [\n \"for i in range(2,6):\\n\",\n \" print(i)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"id\": \"cab0c2b5\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"abcd\\n\"\n ]\n }\n ],\n \"source\": [\n \"x = 'abcd'\\n\",\n \"\\n\",\n \"# 0,1,2,3\\n\",\n \"for i in range(len(x)):\\n\",\n \" x[i].upper()\\n\",\n \"\\n\",\n \"print(x)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"id\": \"e2525fbc\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"a\\n\",\n \"a\\n\",\n \"a\\n\",\n \"a\\n\"\n ]\n }\n ],\n \"source\": [\n \"x = 'abcd'\\n\",\n \"\\n\",\n \"# 0,1,2,3\\n\",\n \"for i in range(len(x)):\\n\",\n \" x = 'a'\\n\",\n \" print(x)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"id\": \"d53ac18d\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"abcd\\n\",\n \"a\\n\",\n \"a\\n\",\n \"a\\n\"\n ]\n }\n ],\n \"source\": [\n \"x = 'abcd'\\n\",\n \"\\n\",\n \"# 0,1,2,3\\n\",\n \"for i in range(len(x)):\\n\",\n \" print(x)\\n\",\n \" x = 'a'\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"6ab70d2a\",\n \"metadata\": {},\n \"source\": [\n \"# Dictionary\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"e22d4eef\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"l = [1,2,3,4,54]\\n\",\n \"# 0 1 2 3 4\\n\",\n \"\\n\",\n \"# hashing\\n\",\n \"\\n\",\n \"# [, , ,44 , 55,]\\n\",\n \"# 45\\n\",\n \"# 65\\n\",\n \"\\n\",\n \"55 % 10 = 5 ---> \\n\",\n \"44 % 10 = 4\\n\",\n \"45 % 10 = 5\\n\",\n \"65 % 10 = 5\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"c47a4099\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# JSON\\n\",\n \"\\n\",\n \"{\\n\",\n \" \\\"username\\\": \\\"hemansnation\\\",\\n\",\n \" \\\"password\\\": \\\"12334545\\\"\\n\",\n \"}\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"id\": \"e5b3ba3d\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"{'ravi': 24, 'viky': 21, 'roy': 36}\\n\"\n ]\n }\n ],\n \"source\": [\n \"# key: value\\n\",\n \"\\n\",\n \"d = {\\\"ravi\\\": 24, \\\"viky\\\": 21, \\\"roy\\\": 36}\\n\",\n \"\\n\",\n \"print(d)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"id\": \"c846e290\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(type(d))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"ea68592a\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# unordered data structures\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"id\": \"7a710f26\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"36\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(d['roy'])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 28,\n \"id\": \"b7cd33ff\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"24\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(d['ravi'])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 29,\n \"id\": \"53e9fcdd\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[255, 0, 0]\\n\"\n ]\n }\n ],\n \"source\": [\n \"color = {\\n\",\n \" \\\"red\\\": [255,0,0],\\n\",\n \" \\\"green\\\": [0,255,0],\\n\",\n \" \\\"blue\\\": [0,0,255]\\n\",\n \"}\\n\",\n \"print(color['red'])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 30,\n \"id\": \"1ddbc289\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"ename\": \"KeyError\",\n \"evalue\": \"'yellow'\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[1;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[1;31mKeyError\\u001b[0m Traceback (most recent call last)\",\n \"Input \\u001b[1;32mIn [30]\\u001b[0m, in \\u001b[0;36m\\u001b[1;34m()\\u001b[0m\\n\\u001b[1;32m----> 1\\u001b[0m \\u001b[38;5;28mprint\\u001b[39m(\\u001b[43mcolor\\u001b[49m\\u001b[43m[\\u001b[49m\\u001b[38;5;124;43m'\\u001b[39;49m\\u001b[38;5;124;43myellow\\u001b[39;49m\\u001b[38;5;124;43m'\\u001b[39;49m\\u001b[43m]\\u001b[49m)\\n\",\n \"\\u001b[1;31mKeyError\\u001b[0m: 'yellow'\"\n ]\n }\n ],\n \"source\": [\n \"print(color['yellow'])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 31,\n \"id\": \"a7eeec1c\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"{}\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"d = {}\\n\",\n \"\\n\",\n \"print(d)\\n\",\n \"\\n\",\n \"print(type(d))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 32,\n \"id\": \"c86ce0ee\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"1\\n\"\n ]\n }\n ],\n \"source\": [\n \"d = {1:1, 2:2, 3:4}\\n\",\n \"\\n\",\n \"print(d[1])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 33,\n \"id\": \"9f399123\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"ename\": \"TypeError\",\n \"evalue\": \"unhashable type: 'list'\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[1;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[1;31mTypeError\\u001b[0m Traceback (most recent call last)\",\n \"Input \\u001b[1;32mIn [33]\\u001b[0m, in \\u001b[0;36m\\u001b[1;34m()\\u001b[0m\\n\\u001b[1;32m----> 1\\u001b[0m d \\u001b[38;5;241m=\\u001b[39m {[\\u001b[38;5;241m1\\u001b[39m,\\u001b[38;5;241m2\\u001b[39m]:\\u001b[38;5;241m1\\u001b[39m, \\u001b[38;5;241m2\\u001b[39m:\\u001b[38;5;241m2\\u001b[39m, \\u001b[38;5;241m3\\u001b[39m:\\u001b[38;5;241m4\\u001b[39m}\\n\\u001b[0;32m 3\\u001b[0m \\u001b[38;5;28mprint\\u001b[39m(d[\\u001b[38;5;241m1\\u001b[39m,\\u001b[38;5;241m2\\u001b[39m])\\n\",\n \"\\u001b[1;31mTypeError\\u001b[0m: unhashable type: 'list'\"\n ]\n }\n ],\n \"source\": [\n \"d = {[1,2]:1, 2:2, 3:4}\\n\",\n \"\\n\",\n \"print(d[1,2])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 34,\n \"id\": \"d166c563\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[100, 20]\\n\"\n ]\n }\n ],\n \"source\": [\n \"# mutable object - list\\n\",\n \"\\n\",\n \"l = [10,20]\\n\",\n \"\\n\",\n \"l[0] = 100\\n\",\n \"\\n\",\n \"print(l)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 35,\n \"id\": \"a8d2e6d7\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"ename\": \"TypeError\",\n \"evalue\": \"'str' object does not support item assignment\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[1;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[1;31mTypeError\\u001b[0m Traceback (most recent call last)\",\n \"Input \\u001b[1;32mIn [35]\\u001b[0m, in \\u001b[0;36m\\u001b[1;34m()\\u001b[0m\\n\\u001b[0;32m 1\\u001b[0m s \\u001b[38;5;241m=\\u001b[39m \\u001b[38;5;124m\\\"\\u001b[39m\\u001b[38;5;124mIndia\\u001b[39m\\u001b[38;5;124m\\\"\\u001b[39m \\u001b[38;5;66;03m# iimutable objects\\u001b[39;00m\\n\\u001b[1;32m----> 3\\u001b[0m s[\\u001b[38;5;241m0\\u001b[39m] \\u001b[38;5;241m=\\u001b[39m \\u001b[38;5;124m\\\"\\u001b[39m\\u001b[38;5;124mA\\u001b[39m\\u001b[38;5;124m\\\"\\u001b[39m\\n\\u001b[0;32m 5\\u001b[0m \\u001b[38;5;28mprint\\u001b[39m(s)\\n\",\n \"\\u001b[1;31mTypeError\\u001b[0m: 'str' object does not support item assignment\"\n ]\n }\n ],\n \"source\": [\n \"s = \\\"India\\\" # immutable objects\\n\",\n \"\\n\",\n \"s[0] = \\\"A\\\"\\n\",\n \"\\n\",\n \"print(s)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"d78cdcd4\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# int are immutable\\n\",\n \"\\n\",\n \"1 + 1 = 2\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"d3d32bf3\",\n \"metadata\": {},\n \"source\": [\n \"# Dictionary Functions\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 36,\n \"id\": \"b57d9f4a\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"4\\n\"\n ]\n }\n ],\n \"source\": [\n \"d = { 1:1, 2:4, 3:9, 4:16}\\n\",\n \"\\n\",\n \"print(len(d))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 37,\n \"id\": \"788f5c87\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"10\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(sum(d))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 38,\n \"id\": \"63a2eb0e\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"ename\": \"TypeError\",\n \"evalue\": \"unsupported operand type(s) for +: 'int' and 'str'\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[1;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[1;31mTypeError\\u001b[0m Traceback (most recent call last)\",\n \"Input \\u001b[1;32mIn [38]\\u001b[0m, in \\u001b[0;36m\\u001b[1;34m()\\u001b[0m\\n\\u001b[0;32m 1\\u001b[0m a \\u001b[38;5;241m=\\u001b[39m {\\u001b[38;5;241m1\\u001b[39m:\\u001b[38;5;241m1\\u001b[39m,\\u001b[38;5;241m2\\u001b[39m:\\u001b[38;5;241m4\\u001b[39m, \\u001b[38;5;124m\\\"\\u001b[39m\\u001b[38;5;124mhi\\u001b[39m\\u001b[38;5;124m\\\"\\u001b[39m:\\u001b[38;5;241m16\\u001b[39m}\\n\\u001b[1;32m----> 3\\u001b[0m \\u001b[38;5;28mprint\\u001b[39m(\\u001b[38;5;28;43msum\\u001b[39;49m\\u001b[43m(\\u001b[49m\\u001b[43ma\\u001b[49m\\u001b[43m)\\u001b[49m)\\n\",\n \"\\u001b[1;31mTypeError\\u001b[0m: unsupported operand type(s) for +: 'int' and 'str'\"\n ]\n }\n ],\n \"source\": [\n \"a = {1:1,2:4, \\\"hi\\\":16}\\n\",\n \"\\n\",\n \"print(sum(a))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 39,\n \"id\": \"650cf24f\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"{1: 1, 2: 4, 3: 9, 4: 16}\"\n ]\n },\n \"execution_count\": 39,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"d\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 40,\n \"id\": \"d78c3139\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"4\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(max(d))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 41,\n \"id\": \"08fe0e6e\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"1\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(min(d))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"1a16abb5\",\n \"metadata\": {},\n \"source\": [\n \"# Dictionary Methods\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 43,\n \"id\": \"9a2f7d81\",\n \"metadata\": {\n \"scrolled\": false\n },\n \"outputs\": [],\n \"source\": [\n \"# help(dict)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 44,\n \"id\": \"5d682511\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"{1: 'Apple', 'orange': [2, 3, 4], '2': '3'}\\n\"\n ]\n }\n ],\n \"source\": [\n \"pair = { 1: \\\"Apple\\\", \\\"orange\\\": [2,3,4], \\\"2\\\": \\\"3\\\"}\\n\",\n \"\\n\",\n \"print(pair)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 45,\n \"id\": \"1374c33c\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[2, 3, 4]\\n\"\n ]\n }\n ],\n \"source\": [\n \"# get\\n\",\n \"\\n\",\n \"print(pair.get(\\\"orange\\\"))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 48,\n \"id\": \"49f1f231\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[2, 3, 4]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(pair[\\\"orange\\\"])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 49,\n \"id\": \"0538283b\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"None\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(pair.get(7))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"6a643576\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"None => NULL # nothing\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 50,\n \"id\": \"ab1ab099\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Apple\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(pair.get(1, \\\"Not Available\\\"))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 51,\n \"id\": \"26d164e1\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Not Available\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(pair.get(10, \\\"Not Available\\\"))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 52,\n \"id\": \"0632cda6\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"{1: 1, 2: 4, 3: 9, 4: 16}\"\n ]\n },\n \"execution_count\": 52,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# clear\\n\",\n \"\\n\",\n \"d\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 53,\n \"id\": \"cb797c19\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"{}\\n\"\n ]\n }\n ],\n \"source\": [\n \"d.clear()\\n\",\n \"\\n\",\n \"print(d)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 54,\n \"id\": \"406f5d55\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"ename\": \"IndexError\",\n \"evalue\": \"list assignment index out of range\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[1;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[1;31mIndexError\\u001b[0m Traceback (most recent call last)\",\n \"Input \\u001b[1;32mIn [54]\\u001b[0m, in \\u001b[0;36m\\u001b[1;34m()\\u001b[0m\\n\\u001b[0;32m 1\\u001b[0m l \\u001b[38;5;241m=\\u001b[39m [\\u001b[38;5;241m10\\u001b[39m,\\u001b[38;5;241m20\\u001b[39m,\\u001b[38;5;241m30\\u001b[39m]\\n\\u001b[1;32m----> 3\\u001b[0m l[\\u001b[38;5;241m3\\u001b[39m] \\u001b[38;5;241m=\\u001b[39m \\u001b[38;5;241m100\\u001b[39m\\n\\u001b[0;32m 5\\u001b[0m \\u001b[38;5;28mprint\\u001b[39m(l)\\n\",\n \"\\u001b[1;31mIndexError\\u001b[0m: list assignment index out of range\"\n ]\n }\n ],\n \"source\": [\n \"l = [10,20,30]\\n\",\n \"\\n\",\n \"l[3] = 100\\n\",\n \"\\n\",\n \"print(l)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 55,\n \"id\": \"45715900\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"{'A': 100, 'Z': 20}\\n\"\n ]\n }\n ],\n \"source\": [\n \"d = {\\\"A\\\": 10, \\\"Z\\\": 20}\\n\",\n \"\\n\",\n \"d[\\\"A\\\"] = 100\\n\",\n \"\\n\",\n \"print(d)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 56,\n \"id\": \"3ab37c57\",\n \"metadata\": {\n \"scrolled\": true\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"{'A': 100, 'Z': 20, 'goa': 200}\\n\"\n ]\n }\n ],\n \"source\": [\n \"d[\\\"goa\\\"] = 200\\n\",\n \"\\n\",\n \"print(d)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"f6f67a8d\",\n \"metadata\": {},\n \"source\": [\n \"# Question\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"f7c72ebe\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"{1:1, 2:4, 3:9, 4:16, 5:25 ...........10:100}\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"2435adc7\",\n \"metadata\": {},\n \"source\": [\n \"# Tuple\\n\",\n \"\\n\",\n \"tuple are immutable\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 57,\n \"id\": \"b3e4ebb7\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"(1, 2, 3, 4)\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"t = (1,2,3,4)\\n\",\n \"\\n\",\n \"print(t)\\n\",\n \"\\n\",\n \"print(type(t))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 58,\n \"id\": \"eed35b67\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"ename\": \"TypeError\",\n \"evalue\": \"'tuple' object does not support item assignment\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[1;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[1;31mTypeError\\u001b[0m Traceback (most recent call last)\",\n \"Input \\u001b[1;32mIn [58]\\u001b[0m, in \\u001b[0;36m\\u001b[1;34m()\\u001b[0m\\n\\u001b[1;32m----> 1\\u001b[0m t[\\u001b[38;5;241m0\\u001b[39m] \\u001b[38;5;241m=\\u001b[39m \\u001b[38;5;241m100\\u001b[39m\\n\",\n \"\\u001b[1;31mTypeError\\u001b[0m: 'tuple' object does not support item assignment\"\n ]\n }\n ],\n \"source\": [\n \"t[0] = 100\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 59,\n \"id\": \"b630c971\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"ename\": \"TypeError\",\n \"evalue\": \"'str' object does not support item assignment\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[1;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[1;31mTypeError\\u001b[0m Traceback (most recent call last)\",\n \"Input \\u001b[1;32mIn [59]\\u001b[0m, in \\u001b[0;36m\\u001b[1;34m()\\u001b[0m\\n\\u001b[0;32m 1\\u001b[0m s \\u001b[38;5;241m=\\u001b[39m \\u001b[38;5;124m'\\u001b[39m\\u001b[38;5;124mgoa\\u001b[39m\\u001b[38;5;124m'\\u001b[39m\\n\\u001b[1;32m----> 3\\u001b[0m s[\\u001b[38;5;241m0\\u001b[39m] \\u001b[38;5;241m=\\u001b[39m \\u001b[38;5;124m\\\"\\u001b[39m\\u001b[38;5;124mA\\u001b[39m\\u001b[38;5;124m\\\"\\u001b[39m\\n\",\n \"\\u001b[1;31mTypeError\\u001b[0m: 'str' object does not support item assignment\"\n ]\n }\n ],\n \"source\": [\n \"s = 'goa'\\n\",\n \"\\n\",\n \"s[0] = \\\"A\\\"\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 60,\n \"id\": \"cf15d6bb\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"(5, 6, 67)\\n\"\n ]\n }\n ],\n \"source\": [\n \"t = (2,3,4,5,6,67)\\n\",\n \"\\n\",\n \"print(t[3:])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 61,\n \"id\": \"05edf72b\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(2, 3, 4, 5, 6, 67)\"\n ]\n },\n \"execution_count\": 61,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"t\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 62,\n \"id\": \"0c3d2bc3\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"5\\n\"\n ]\n }\n ],\n \"source\": [\n \"t = (1,2,3,4,5)\\n\",\n \"\\n\",\n \"print(len(t))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 63,\n \"id\": \"54aa0e3f\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"15\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(sum(t))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 64,\n \"id\": \"f0052bd4\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"5\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(max(t))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 65,\n \"id\": \"28a92019\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"1\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(min(t))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 66,\n \"id\": \"7885f892\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# -1\\n\",\n \"t = (\\\"A\\\", 1,2,4)\\n\",\n \"# 0 1 2 3\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 67,\n \"id\": \"da09ee4f\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"('A', 1, 2, 4)\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(t)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 68,\n \"id\": \"b337cd33\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"A\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(t[0])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 69,\n \"id\": \"504bca81\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"4\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(t[-1])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 70,\n \"id\": \"3be05cc3\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"()\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"t = ()\\n\",\n \"\\n\",\n \"print(t)\\n\",\n \"\\n\",\n \"print(type(t))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 71,\n \"id\": \"915d9c00\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"10\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"t = (10)\\n\",\n \"\\n\",\n \"print(t)\\n\",\n \"\\n\",\n \"print(type(t))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 72,\n \"id\": \"54ec18a0\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"(10,)\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"t = (10,)\\n\",\n \"\\n\",\n \"print(t)\\n\",\n \"\\n\",\n \"print(type(t))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"2792fe85\",\n \"metadata\": {},\n \"source\": [\n \"# Tuple Methods\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 73,\n \"id\": \"5bd0d610\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"2\\n\"\n ]\n }\n ],\n \"source\": [\n \"t = (2,3,4,5,5,3,3,3,7)\\n\",\n \"\\n\",\n \"print(t.count(5))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 74,\n \"id\": \"79899432\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"1\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(t.count(7))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 75,\n \"id\": \"4b0c56f1\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"0\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(t.count(10))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 76,\n \"id\": \"b17ad603\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"(2, 3, 4, 5, 5, 3, 3, 3, 7)\\n\"\n ]\n }\n ],\n \"source\": [\n \"# index\\n\",\n \"\\n\",\n \"print(t)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 77,\n \"id\": \"7f10fbae\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"3\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(t.index(5))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 78,\n \"id\": \"e6811f4e\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Help on built-in function index:\\n\",\n \"\\n\",\n \"index(value, start=0, stop=9223372036854775807, /) method of builtins.tuple instance\\n\",\n \" Return first index of value.\\n\",\n \" \\n\",\n \" Raises ValueError if the value is not present.\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"help(t.index)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 79,\n \"id\": \"068bf8b3\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(2, 3, 4, 5, 5, 3, 3, 3, 7)\"\n ]\n },\n \"execution_count\": 79,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"t\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 81,\n \"id\": \"a65ca47a\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"4\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(t.index(5,4))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"df156bc0\",\n \"metadata\": {},\n \"source\": [\n \"# Questions \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"f623b5de\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"t = ('a','e','i','o','u')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"1f2328fc\",\n \"metadata\": {},\n \"source\": [\n \"# Sets\\n\",\n \"\\n\",\n \"unordered and unindex\\n\",\n \"\\n\",\n \"sets are mutable\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 82,\n \"id\": \"11b203a2\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"{1, 2, 3, 4, 5, 6}\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"n = {1,2,3,4,5,6}\\n\",\n \"\\n\",\n \"print(n)\\n\",\n \"\\n\",\n \"print(type(n))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 83,\n \"id\": \"615a8e7e\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"{1, 2, 3, 4, 5, 6}\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"n = {1,2,3,4,5,6,6,6,6,5,5}\\n\",\n \"\\n\",\n \"print(n)\\n\",\n \"\\n\",\n \"print(type(n))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 84,\n \"id\": \"d7bf4db2\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"set()\\n\"\n ]\n }\n ],\n \"source\": [\n \"s = set()\\n\",\n \"\\n\",\n \"print(s)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 86,\n \"id\": \"98a00a15\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"{'b', 4, 'a'}\\n\"\n ]\n }\n ],\n \"source\": [\n \"n = set(['a','b', 4])\\n\",\n \"\\n\",\n \"print(n)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 87,\n \"id\": \"8d03b0ed\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"1\\n\",\n \"2\\n\",\n \"3\\n\",\n \"55\\n\",\n \"44\\n\"\n ]\n }\n ],\n \"source\": [\n \"n = {1,2,3,44,55}\\n\",\n \"\\n\",\n \"for i in n:\\n\",\n \" print(i)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"dec2b22e\",\n \"metadata\": {},\n \"source\": [\n \"## set operations\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 88,\n \"id\": \"c961559b\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"{1, 2, 3, 4, 5, 6, 7, 8, 9}\\n\"\n ]\n }\n ],\n \"source\": [\n \"# union\\n\",\n \"\\n\",\n \"f = {1,2,3,4,5,6}\\n\",\n \"s = {4,5,6,7,8,9}\\n\",\n \"\\n\",\n \"print(f | s)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 89,\n \"id\": \"ab540fe2\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"{4, 5, 6}\\n\"\n ]\n }\n ],\n \"source\": [\n \"# intersection\\n\",\n \"\\n\",\n \"f = {1,2,3,4,5,6}\\n\",\n \"s = {4,5,6,7,8,9}\\n\",\n \"\\n\",\n \"print(f & s)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 90,\n \"id\": \"abf57b8a\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"{1, 2, 3}\\n\"\n ]\n }\n ],\n \"source\": [\n \"# difference\\n\",\n \"\\n\",\n \"f = {1,2,3,4,5,6}\\n\",\n \"s = {4,5,6,7,8,9}\\n\",\n \"\\n\",\n \"print(f - s)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 91,\n \"id\": \"9a9f6ea6\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"{8, 9, 7}\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(s - f)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 92,\n \"id\": \"88a67b87\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"{1, 2, 3, 7, 8, 9}\\n\"\n ]\n }\n ],\n \"source\": [\n \"# symmetry\\n\",\n \"\\n\",\n \"f = {1,2,3,4,5,6}\\n\",\n \"s = {4,5,6,7,8,9}\\n\",\n \"\\n\",\n \"print(f ^ s)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"2682d1f9\",\n \"metadata\": {},\n \"source\": [\n \"# set functions\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 93,\n \"id\": \"553d14f2\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"4\\n\",\n \"10\\n\",\n \"4\\n\",\n \"1\\n\"\n ]\n }\n ],\n \"source\": [\n \"s = {1,2,3,4}\\n\",\n \"\\n\",\n \"print(len(s))\\n\",\n \"print(sum(s))\\n\",\n \"print(max(s))\\n\",\n \"print(min(s))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"a144e8bf\",\n \"metadata\": {},\n \"source\": [\n \"# set methods\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 95,\n \"id\": \"ceb0c93f\",\n \"metadata\": {\n \"scrolled\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# help(set)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 96,\n \"id\": \"83fb99b3\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"{1, 2, 3, 4}\\n\",\n \"{1, 2, 3, 4, -7}\\n\"\n ]\n }\n ],\n \"source\": [\n \"# add\\n\",\n \"\\n\",\n \"n = {1,2,3,4}\\n\",\n \"\\n\",\n \"print(n)\\n\",\n \"\\n\",\n \"n.add(-7)\\n\",\n \"\\n\",\n \"print(n)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 97,\n \"id\": \"2e99b696\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"{1, 2, 3, 4, -7}\\n\",\n \"set()\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(n)\\n\",\n \"n.clear()\\n\",\n \"\\n\",\n \"print(n)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 98,\n \"id\": \"feade89d\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"set()\"\n ]\n },\n \"execution_count\": 98,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# remove\\n\",\n \"\\n\",\n \"n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 99,\n \"id\": \"b83f2433\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"{1, 2, 4}\\n\"\n ]\n }\n ],\n \"source\": [\n \"n = {1,2,3,4}\\n\",\n \"\\n\",\n \"n.remove(3)\\n\",\n \"print(n)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"3730faed\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.12\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n" }, { "path": "March Cohort 2023/.ipynb_checkpoints/4_Functions-checkpoint.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"id\": \"a98c255e\",\n \"metadata\": {},\n \"source\": [\n \"# Functions\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"id\": \"18360579\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def myfunction(): # definition of function\\n\",\n \" print(\\\"Hey\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"id\": \"22e39c3e\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Hey\\n\"\n ]\n }\n ],\n \"source\": [\n \"myfunction() # calling of function\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"id\": \"c24cd8e5\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"1\\n\",\n \"2\\n\",\n \"3\\n\"\n ]\n }\n ],\n \"source\": [\n \"def paint():\\n\",\n \" print(1)\\n\",\n \" print(2)\\n\",\n \" print(3)\\n\",\n \"\\n\",\n \"paint()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"id\": \"1cd655a2\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"1\\n\",\n \"2\\n\",\n \"3\\n\"\n ]\n }\n ],\n \"source\": [\n \"paint()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"id\": \"f6b4813a\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"ename\": \"NameError\",\n \"evalue\": \"name 'hello' is not defined\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[1;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[1;31mNameError\\u001b[0m Traceback (most recent call last)\",\n \"Input \\u001b[1;32mIn [5]\\u001b[0m, in \\u001b[0;36m\\u001b[1;34m()\\u001b[0m\\n\\u001b[1;32m----> 1\\u001b[0m \\u001b[43mhello\\u001b[49m()\\n\\u001b[0;32m 3\\u001b[0m \\u001b[38;5;28;01mdef\\u001b[39;00m \\u001b[38;5;21mhello\\u001b[39m():\\n\\u001b[0;32m 4\\u001b[0m \\u001b[38;5;28mprint\\u001b[39m(\\u001b[38;5;124m\\\"\\u001b[39m\\u001b[38;5;124mHello\\u001b[39m\\u001b[38;5;124m\\\"\\u001b[39m)\\n\",\n \"\\u001b[1;31mNameError\\u001b[0m: name 'hello' is not defined\"\n ]\n }\n ],\n \"source\": [\n \"hello()\\n\",\n \"\\n\",\n \"def hello():\\n\",\n \" print(\\\"Hello\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"id\": \"28ae369c\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"ename\": \"TypeError\",\n \"evalue\": \"indore() missing 1 required positional argument: 'a'\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[1;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[1;31mTypeError\\u001b[0m Traceback (most recent call last)\",\n \"Input \\u001b[1;32mIn [6]\\u001b[0m, in \\u001b[0;36m\\u001b[1;34m()\\u001b[0m\\n\\u001b[0;32m 1\\u001b[0m \\u001b[38;5;28;01mdef\\u001b[39;00m \\u001b[38;5;21mindore\\u001b[39m(a):\\n\\u001b[0;32m 2\\u001b[0m \\u001b[38;5;28mprint\\u001b[39m(a)\\n\\u001b[1;32m----> 4\\u001b[0m \\u001b[43mindore\\u001b[49m\\u001b[43m(\\u001b[49m\\u001b[43m)\\u001b[49m\\n\",\n \"\\u001b[1;31mTypeError\\u001b[0m: indore() missing 1 required positional argument: 'a'\"\n ]\n }\n ],\n \"source\": [\n \"def indore(a): # a = \\\"A\\\"\\n\",\n \" print(a)\\n\",\n \"\\n\",\n \"indore()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"id\": \"52bc36b9\",\n \"metadata\": {\n \"scrolled\": true\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"10\\n\"\n ]\n }\n ],\n \"source\": [\n \"indore(10)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"id\": \"527fe9e4\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"A\\n\"\n ]\n }\n ],\n \"source\": [\n \"indore(\\\"A\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"id\": \"4af5fdc0\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"right\\n\"\n ]\n }\n ],\n \"source\": [\n \"indore(\\\"right\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"id\": \"aa11eea7\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"1\\n\",\n \"One\\n\",\n \"2\\n\",\n \"Two\\n\",\n \"right\\n\",\n \"three\\n\"\n ]\n }\n ],\n \"source\": [\n \"indore(1)\\n\",\n \"print(\\\"One\\\")\\n\",\n \"indore(2)\\n\",\n \"print(\\\"Two\\\")\\n\",\n \"indore(\\\"right\\\")\\n\",\n \"print(\\\"three\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"id\": \"84a3b11a\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"9\\n\"\n ]\n }\n ],\n \"source\": [\n \"def add(x, y):\\n\",\n \" print(x + y)\\n\",\n \"\\n\",\n \"add(5,4)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"74774c31\",\n \"metadata\": {},\n \"source\": [\n \"# Return statement\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"id\": \"bd50418a\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"7\\n\"\n ]\n }\n ],\n \"source\": [\n \"def play(x, y):\\n\",\n \" if x >= y:\\n\",\n \" return x\\n\",\n \" else:\\n\",\n \" return y\\n\",\n \"\\n\",\n \"print(play(4,7))\\n\",\n \"# 7\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"id\": \"86b599e4\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"8\\n\"\n ]\n }\n ],\n \"source\": [\n \"z = play(8,5)\\n\",\n \"#z = 8\\n\",\n \"\\n\",\n \"print(z)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"id\": \"ad6e3060\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"9\\n\"\n ]\n }\n ],\n \"source\": [\n \"def sub(x, y):\\n\",\n \" t = x + y\\n\",\n \" return t\\n\",\n \" print(\\\"Hi\\\")\\n\",\n \"\\n\",\n \"print(sub(4,5))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"id\": \"8cc83a2f\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"1\\n\",\n \"2\\n\",\n \"hello\\n\"\n ]\n }\n ],\n \"source\": [\n \"def pre():\\n\",\n \" print(1)\\n\",\n \" print(2)\\n\",\n \" print(\\\"hello\\\")\\n\",\n \" return\\n\",\n \" print(3)\\n\",\n \" print(4)\\n\",\n \"\\n\",\n \"pre()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"id\": \"cb329c92\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"1\\n\",\n \"2\\n\",\n \"hello\\n\",\n \"None\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(pre())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"id\": \"a463dbf9\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"1\\n\",\n \"2\\n\",\n \"None\\n\"\n ]\n }\n ],\n \"source\": [\n \"r = pre()\\n\",\n \"\\n\",\n \"print(r)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"280c0539\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"None ==> NULL ==> nothing\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"id\": \"79b26fc0\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"None\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(None)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"id\": \"f781cf14\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Hi\\n\",\n \"None\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(print(\\\"Hi\\\"))\\n\",\n \"# print(None)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"d25d6edf\",\n \"metadata\": {},\n \"source\": [\n \"# Default Argument\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"id\": \"f6141d42\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"ename\": \"TypeError\",\n \"evalue\": \"can only concatenate str (not \\\"int\\\") to str\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[1;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[1;31mTypeError\\u001b[0m Traceback (most recent call last)\",\n \"Input \\u001b[1;32mIn [22]\\u001b[0m, in \\u001b[0;36m\\u001b[1;34m()\\u001b[0m\\n\\u001b[1;32m----> 1\\u001b[0m \\u001b[38;5;124;43m\\\"\\u001b[39;49m\\u001b[38;5;124;43mhi\\u001b[39;49m\\u001b[38;5;124;43m\\\"\\u001b[39;49m\\u001b[43m \\u001b[49m\\u001b[38;5;241;43m+\\u001b[39;49m\\u001b[43m \\u001b[49m\\u001b[38;5;241;43m6\\u001b[39;49m\\n\",\n \"\\u001b[1;31mTypeError\\u001b[0m: can only concatenate str (not \\\"int\\\") to str\"\n ]\n }\n ],\n \"source\": [\n \"\\\"hi\\\" + 6\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"id\": \"c3edf839\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'hihihi'\"\n ]\n },\n \"execution_count\": 23,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"\\\"hi\\\" * 3\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"id\": \"2456f1b9\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Hello\\n\"\n ]\n }\n ],\n \"source\": [\n \"def say(msg, time = 1):\\n\",\n \" print(msg * time)\\n\",\n \"\\n\",\n \"say(\\\"Hello\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"id\": \"4b65a1da\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"ByeByeByeByeBye\\n\"\n ]\n }\n ],\n \"source\": [\n \"say(\\\"Bye\\\", 5)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"id\": \"a6382071\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Hello+++Indore\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(\\\"Hello\\\", end='+++')\\n\",\n \"print(\\\"Indore\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 28,\n \"id\": \"d7115a71\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def said(a, b = 5, c = 10):\\n\",\n \" print(a)\\n\",\n \" print(b)\\n\",\n \" print(c)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 29,\n \"id\": \"811ff6b8\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"3\\n\",\n \"7\\n\",\n \"10\\n\"\n ]\n }\n ],\n \"source\": [\n \"said(3,7)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 30,\n \"id\": \"dbd412b0\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"25\\n\",\n \"5\\n\",\n \"24\\n\"\n ]\n }\n ],\n \"source\": [\n \"said(25, c = 24)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 31,\n \"id\": \"c29189f6\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"15\\n\",\n \"5\\n\",\n \"100\\n\"\n ]\n }\n ],\n \"source\": [\n \"said(c = 100, a = 15)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 32,\n \"id\": \"e9d0ccbd\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"ename\": \"SyntaxError\",\n \"evalue\": \"positional argument follows keyword argument (2634499495.py, line 1)\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[1;36m Input \\u001b[1;32mIn [32]\\u001b[1;36m\\u001b[0m\\n\\u001b[1;33m said(c = 19, 20, 30)\\u001b[0m\\n\\u001b[1;37m ^\\u001b[0m\\n\\u001b[1;31mSyntaxError\\u001b[0m\\u001b[1;31m:\\u001b[0m positional argument follows keyword argument\\n\"\n ]\n }\n ],\n \"source\": [\n \"said(c = 19, 20, 30)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 33,\n \"id\": \"25993e69\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"0\\n\"\n ]\n }\n ],\n \"source\": [\n \"i = 0 # i is a global variable\\n\",\n \"\\n\",\n \"def change(i): # i is a local variable\\n\",\n \" i += 1 # i = 2\\n\",\n \" return 1\\n\",\n \"\\n\",\n \"change(1) # 1\\n\",\n \"print(i)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 34,\n \"id\": \"30f844d2\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"8\\n\",\n \"3\\n\"\n ]\n }\n ],\n \"source\": [\n \"x = 3 # global\\n\",\n \"\\n\",\n \"def add(x): # local\\n\",\n \" x += 1 # x = x + 1\\n\",\n \" print(x)\\n\",\n \"\\n\",\n \"add(7)\\n\",\n \"print(x)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 35,\n \"id\": \"bdce8f2a\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"11\\n\"\n ]\n },\n {\n \"ename\": \"NameError\",\n \"evalue\": \"name 'v' is not defined\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[1;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[1;31mNameError\\u001b[0m Traceback (most recent call last)\",\n \"Input \\u001b[1;32mIn [35]\\u001b[0m, in \\u001b[0;36m\\u001b[1;34m()\\u001b[0m\\n\\u001b[0;32m 3\\u001b[0m \\u001b[38;5;28mprint\\u001b[39m(v)\\n\\u001b[0;32m 5\\u001b[0m add(\\u001b[38;5;241m10\\u001b[39m)\\n\\u001b[1;32m----> 6\\u001b[0m \\u001b[38;5;28mprint\\u001b[39m(\\u001b[43mv\\u001b[49m)\\n\",\n \"\\u001b[1;31mNameError\\u001b[0m: name 'v' is not defined\"\n ]\n }\n ],\n \"source\": [\n \"def add(v):\\n\",\n \" v += 1\\n\",\n \" print(v)\\n\",\n \"\\n\",\n \"add(10)\\n\",\n \"print(v)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"7578af9e\",\n \"metadata\": {},\n \"source\": [\n \"# Reference Function\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 36,\n \"id\": \"ead4ef51\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"10\\n\"\n ]\n }\n ],\n \"source\": [\n \"a = 10\\n\",\n \"\\n\",\n \"b = a\\n\",\n \"\\n\",\n \"print(b)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 37,\n \"id\": \"06f37e14\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"10\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(a)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 38,\n \"id\": \"6b6bb7d6\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(type(add))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 39,\n \"id\": \"fe8316b6\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def multiply(x, y):\\n\",\n \" return x * y\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 40,\n \"id\": \"08e7fd6a\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"200\\n\"\n ]\n }\n ],\n \"source\": [\n \"indore = multiply\\n\",\n \"\\n\",\n \"print(indore(10,20))\\n\",\n \"# multiply(10,20)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 41,\n \"id\": \"1578b9d0\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"15\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(multiply(3,5))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"9c39298b\",\n \"metadata\": {},\n \"source\": [\n \"# functions and an argument\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 42,\n \"id\": \"28047e15\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"30\\n\"\n ]\n }\n ],\n \"source\": [\n \"def sub(x, y):\\n\",\n \" return x + y\\n\",\n \"\\n\",\n \"def do(red, x, y): # red = sub, x = 5, y = 10\\n\",\n \" return red(red(x,y), red(x,y))\\n\",\n \"# sub(sub(5,10),sub(5,10))\\n\",\n \"# sub( 15 , 15 )\\n\",\n \"# 30\\n\",\n \"\\n\",\n \"a = 5\\n\",\n \"b = 10\\n\",\n \"\\n\",\n \"\\n\",\n \"print(do(sub, a, b))\\n\",\n \"# 30\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 43,\n \"id\": \"82b1bbd9\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"81\\n\"\n ]\n }\n ],\n \"source\": [\n \"def square(x):\\n\",\n \" return x * x\\n\",\n \"\\n\",\n \"def test(blue, x):\\n\",\n \" print(blue(x))\\n\",\n \"\\n\",\n \"test(square, 9)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 44,\n \"id\": \"f438ee68\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"3\\n\",\n \"1\\n\"\n ]\n }\n ],\n \"source\": [\n \"def green(red, a):\\n\",\n \" print(red(a))\\n\",\n \"\\n\",\n \"green(max, [1,2,3])\\n\",\n \"green(min, [1,2,3])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 45,\n \"id\": \"51c59408\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"0\\n\"\n ]\n }\n ],\n \"source\": [\n \"def printn(x):\\n\",\n \" for i in range(x):\\n\",\n \" print(i)\\n\",\n \" return\\n\",\n \"\\n\",\n \"printn(10)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"3e07954c\",\n \"metadata\": {},\n \"source\": [\n \"# doc string\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 46,\n \"id\": \"b6cc4e7d\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Help on built-in function max in module builtins:\\n\",\n \"\\n\",\n \"max(...)\\n\",\n \" max(iterable, *[, default=obj, key=func]) -> value\\n\",\n \" max(arg1, arg2, *args, *[, key=func]) -> value\\n\",\n \" \\n\",\n \" With a single iterable argument, return its biggest item. The\\n\",\n \" default keyword-only argument specifies an object to return if\\n\",\n \" the provided iterable is empty.\\n\",\n \" With two or more arguments, return the largest argument.\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"help(max)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 48,\n \"id\": \"6596e128\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"this \\n\",\n \"is an\\n\",\n \"apple.\\n\",\n \"\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"# this is a single line comment\\n\",\n \"\\n\",\n \"s = '''this \\n\",\n \"is an\\n\",\n \"apple.\\n\",\n \"'''\\n\",\n \"print(s)\\n\",\n \"print(type(s))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 50,\n \"id\": \"53161af0\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def function_one():\\n\",\n \" '''\\n\",\n \" this function print 10,\\n\",\n \" also this funtion will print 5\\n\",\n \" '''\\n\",\n \" print(10)\\n\",\n \" print(5)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 51,\n \"id\": \"2156a9ff\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"10\\n\",\n \"5\\n\"\n ]\n }\n ],\n \"source\": [\n \"function_one()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 52,\n \"id\": \"ad1302c3\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Help on function function_one in module __main__:\\n\",\n \"\\n\",\n \"function_one()\\n\",\n \" this function print 10,\\n\",\n \" also this funtion will print 5\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"help(function_one)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 53,\n \"id\": \"50e6b7cb\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\",\n \" this function print 10,\\n\",\n \" also this funtion will print 5\\n\",\n \" \\n\"\n ]\n }\n ],\n \"source\": [\n \"print(function_one.__doc__) # dunder\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"a1973a4e\",\n \"metadata\": {},\n \"source\": [\n \"# Map Function\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 54,\n \"id\": \"66ca8c7e\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Help on class map in module builtins:\\n\",\n \"\\n\",\n \"class map(object)\\n\",\n \" | map(func, *iterables) --> map object\\n\",\n \" | \\n\",\n \" | Make an iterator that computes the function using arguments from\\n\",\n \" | each of the iterables. Stops when the shortest iterable is exhausted.\\n\",\n \" | \\n\",\n \" | Methods defined here:\\n\",\n \" | \\n\",\n \" | __getattribute__(self, name, /)\\n\",\n \" | Return getattr(self, name).\\n\",\n \" | \\n\",\n \" | __iter__(self, /)\\n\",\n \" | Implement iter(self).\\n\",\n \" | \\n\",\n \" | __next__(self, /)\\n\",\n \" | Implement next(self).\\n\",\n \" | \\n\",\n \" | __reduce__(...)\\n\",\n \" | Return state information for pickling.\\n\",\n \" | \\n\",\n \" | ----------------------------------------------------------------------\\n\",\n \" | Static methods defined here:\\n\",\n \" | \\n\",\n \" | __new__(*args, **kwargs) from builtins.type\\n\",\n \" | Create and return a new object. See help(type) for accurate signature.\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"help(map)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 55,\n \"id\": \"f473bded\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[6, 7, 8, 9, 10]\\n\"\n ]\n }\n ],\n \"source\": [\n \"def add(x):\\n\",\n \" return x + 5\\n\",\n \"\\n\",\n \"n = [1,2,3,4,5]\\n\",\n \"\\n\",\n \"r = list(map(add, n))\\n\",\n \"# 6, 7, 8, 9, 10\\n\",\n \"\\n\",\n \"print(r)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 56,\n \"id\": \"aeb1a9de\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"ename\": \"TypeError\",\n \"evalue\": \"'int' object is not iterable\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[1;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[1;31mTypeError\\u001b[0m Traceback (most recent call last)\",\n \"Input \\u001b[1;32mIn [56]\\u001b[0m, in \\u001b[0;36m\\u001b[1;34m()\\u001b[0m\\n\\u001b[0;32m 2\\u001b[0m \\u001b[38;5;28;01mreturn\\u001b[39;00m x \\u001b[38;5;241m+\\u001b[39m \\u001b[38;5;241m5\\u001b[39m\\n\\u001b[0;32m 4\\u001b[0m n \\u001b[38;5;241m=\\u001b[39m \\u001b[38;5;241m7\\u001b[39m\\n\\u001b[1;32m----> 6\\u001b[0m r \\u001b[38;5;241m=\\u001b[39m \\u001b[38;5;28mlist\\u001b[39m(\\u001b[38;5;28;43mmap\\u001b[39;49m\\u001b[43m(\\u001b[49m\\u001b[43madd\\u001b[49m\\u001b[43m,\\u001b[49m\\u001b[43m \\u001b[49m\\u001b[43mn\\u001b[49m\\u001b[43m)\\u001b[49m)\\n\\u001b[0;32m 8\\u001b[0m \\u001b[38;5;28mprint\\u001b[39m(r)\\n\",\n \"\\u001b[1;31mTypeError\\u001b[0m: 'int' object is not iterable\"\n ]\n }\n ],\n \"source\": [\n \"def add(x):\\n\",\n \" return x + 5\\n\",\n \"\\n\",\n \"n = 7\\n\",\n \"\\n\",\n \"r = list(map(add, n))\\n\",\n \"\\n\",\n \"print(r)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"cbaca289\",\n \"metadata\": {},\n \"source\": [\n \"# lambda function\\n\",\n \"\\n\",\n \"anonymous function\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 57,\n \"id\": \"fef01377\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"343\\n\"\n ]\n }\n ],\n \"source\": [\n \"def cube(y):\\n\",\n \" return y * y * y\\n\",\n \"\\n\",\n \"\\n\",\n \"# lambda argument: expresion\\n\",\n \"\\n\",\n \"g = lambda x: x * x * x\\n\",\n \"\\n\",\n \"print(g(7))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 59,\n \"id\": \"477123f3\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"343\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(cube(7))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"5caf7279\",\n \"metadata\": {},\n \"source\": [\n \"# lambda with map function\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 60,\n \"id\": \"00915aaf\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[1, 4, 9, 16, 25]\\n\"\n ]\n }\n ],\n \"source\": [\n \"n = [1,2,3,4,5]\\n\",\n \"\\n\",\n \"r = list(map(lambda y: y * y, n))\\n\",\n \"# 1, 4, 9, 16, 25\\n\",\n \"\\n\",\n \"print(r)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"0bc6d570\",\n \"metadata\": {},\n \"source\": [\n \"# filter function\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 61,\n \"id\": \"74819624\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Help on class filter in module builtins:\\n\",\n \"\\n\",\n \"class filter(object)\\n\",\n \" | filter(function or None, iterable) --> filter object\\n\",\n \" | \\n\",\n \" | Return an iterator yielding those items of iterable for which function(item)\\n\",\n \" | is true. If function is None, return the items that are true.\\n\",\n \" | \\n\",\n \" | Methods defined here:\\n\",\n \" | \\n\",\n \" | __getattribute__(self, name, /)\\n\",\n \" | Return getattr(self, name).\\n\",\n \" | \\n\",\n \" | __iter__(self, /)\\n\",\n \" | Implement iter(self).\\n\",\n \" | \\n\",\n \" | __next__(self, /)\\n\",\n \" | Implement next(self).\\n\",\n \" | \\n\",\n \" | __reduce__(...)\\n\",\n \" | Return state information for pickling.\\n\",\n \" | \\n\",\n \" | ----------------------------------------------------------------------\\n\",\n \" | Static methods defined here:\\n\",\n \" | \\n\",\n \" | __new__(*args, **kwargs) from builtins.type\\n\",\n \" | Create and return a new object. See help(type) for accurate signature.\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"help(filter)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 62,\n \"id\": \"166df9b8\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[22, 44]\\n\"\n ]\n }\n ],\n \"source\": [\n \"n = [11,22,33,44,55]\\n\",\n \"\\n\",\n \"r = list(filter(lambda x: x%2 == 0 , n))\\n\",\n \"# 22, 44\\n\",\n \"\\n\",\n \"print(r)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"6bb659fc\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.12\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n" }, { "path": "March Cohort 2023/.ipynb_checkpoints/5_Modules and Packages-checkpoint.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"id\": \"507b0a7e\",\n \"metadata\": {},\n \"source\": [\n \"# OS\\n\",\n \"\\n\",\n \"operating system\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"b4a5e0c7\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# hello.py\\n\",\n \"# something.exe\\n\",\n \"# .pdf .jpg .png\\n\",\n \"\\n\",\n \"# os.py\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"id\": \"c43e9e98\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import os\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"id\": \"083fa6d1\",\n \"metadata\": {\n \"scrolled\": false\n },\n \"outputs\": [],\n \"source\": [\n \"# help(os)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"id\": \"3533b409\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# make directory\\n\",\n \"\\n\",\n \"os.mkdir('prime')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"id\": \"c73636df\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'C:\\\\\\\\Users\\\\\\\\himan\\\\\\\\Documents\\\\\\\\Himanshu Ramchandani Live\\\\\\\\Data-Analyst-Roadmap'\"\n ]\n },\n \"execution_count\": 5,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# get current working directory\\n\",\n \"\\n\",\n \"os.getcwd()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"id\": \"8a5e48f9\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"os.rmdir('prime')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"deb43505\",\n \"metadata\": {},\n \"source\": [\n \"# sys\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"id\": \"fc53dc5b\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"3\\n\"\n ]\n }\n ],\n \"source\": [\n \"import sys\\n\",\n \"\\n\",\n \"a = 5000\\n\",\n \"\\n\",\n \"print(sys.getrefcount(a)) # reference count of the object\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"id\": \"4973d1d1\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"432\\n\",\n \"431\\n\"\n ]\n }\n ],\n \"source\": [\n \"a = 10\\n\",\n \"\\n\",\n \"b = a\\n\",\n \"\\n\",\n \"print(sys.getrefcount(a))\\n\",\n \"\\n\",\n \"del a\\n\",\n \"\\n\",\n \"print(sys.getrefcount(b))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"id\": \"d26bf1fa\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"9223372036854775807\"\n ]\n },\n \"execution_count\": 17,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"sys.maxsize\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"id\": \"1a9e67d3\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"['C:\\\\\\\\Users\\\\\\\\himan\\\\\\\\Documents\\\\\\\\Himanshu Ramchandani Live\\\\\\\\Data-Analyst-Roadmap',\\n\",\n \" 'C:\\\\\\\\Users\\\\\\\\himan\\\\\\\\anaconda3\\\\\\\\python39.zip',\\n\",\n \" 'C:\\\\\\\\Users\\\\\\\\himan\\\\\\\\anaconda3\\\\\\\\DLLs',\\n\",\n \" 'C:\\\\\\\\Users\\\\\\\\himan\\\\\\\\anaconda3\\\\\\\\lib',\\n\",\n \" 'C:\\\\\\\\Users\\\\\\\\himan\\\\\\\\anaconda3',\\n\",\n \" '',\\n\",\n \" 'C:\\\\\\\\Users\\\\\\\\himan\\\\\\\\anaconda3\\\\\\\\lib\\\\\\\\site-packages',\\n\",\n \" 'C:\\\\\\\\Users\\\\\\\\himan\\\\\\\\anaconda3\\\\\\\\lib\\\\\\\\site-packages\\\\\\\\win32',\\n\",\n \" 'C:\\\\\\\\Users\\\\\\\\himan\\\\\\\\anaconda3\\\\\\\\lib\\\\\\\\site-packages\\\\\\\\win32\\\\\\\\lib',\\n\",\n \" 'C:\\\\\\\\Users\\\\\\\\himan\\\\\\\\anaconda3\\\\\\\\lib\\\\\\\\site-packages\\\\\\\\Pythonwin']\"\n ]\n },\n \"execution_count\": 18,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"sys.path\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"id\": \"e3a7ec79\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'3.9.12 (main, Apr 4 2022, 05:22:27) [MSC v.1916 64 bit (AMD64)]'\"\n ]\n },\n \"execution_count\": 19,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"sys.version\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"b9d075a2\",\n \"metadata\": {},\n \"source\": [\n \"# statistics\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"id\": \"7cab6f36\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# import statistics\\n\",\n \"\\n\",\n \"from statistics import *\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"id\": \"1b06511a\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"35.666666666666664\\n\"\n ]\n }\n ],\n \"source\": [\n \"ages = [20,10,40,33,45,66]\\n\",\n \"\\n\",\n \"print(mean(ages))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"id\": \"88b2b6d7\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"36.5\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(median(ages))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"id\": \"ea23370e\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"20\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(mode(ages))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"id\": \"63d2eb9d\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"19.684172999307506\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(stdev(ages))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"8aed4e64\",\n \"metadata\": {},\n \"source\": [\n \"# math\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"id\": \"bd9eaf48\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import math\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"id\": \"4cc7951a\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"3.141592653589793\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(math.pi)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 28,\n \"id\": \"d11c039f\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"2.0\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(math.sqrt(4))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 29,\n \"id\": \"d06915ac\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"8.0\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(math.pow(2,3))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 30,\n \"id\": \"1851237f\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"3\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(math.floor(3.4))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 31,\n \"id\": \"066f7ea3\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"5\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(math.floor(5.9))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 32,\n \"id\": \"ee095949\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"6\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(math.ceil(5.9))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 34,\n \"id\": \"bce4d491\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"4\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(math.ceil(3.14))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 36,\n \"id\": \"2fe20f12\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"3.141592653589793\\n\"\n ]\n }\n ],\n \"source\": [\n \"from math import pi, sqrt, pow, floor, ceil\\n\",\n \"\\n\",\n \"\\n\",\n \"print(pi)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"fffd6d73\",\n \"metadata\": {},\n \"source\": [\n \"# random\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 37,\n \"id\": \"3686b11a\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import random as r\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 41,\n \"id\": \"79f3ce91\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"0.36521596355895625\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(r.random()) # 0 to 1\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 48,\n \"id\": \"fab41c2c\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"14\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(r.randint(5,20))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 51,\n \"id\": \"9ac057b2\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"0.12067598483950182\\n\",\n \"1.2067598483950182\\n\",\n \"1\\n\"\n ]\n }\n ],\n \"source\": [\n \"x = r.random()\\n\",\n \"print(x)\\n\",\n \"\\n\",\n \"x = x * 10\\n\",\n \"print(x)\\n\",\n \"\\n\",\n \"print(math.floor(x))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"785f6932\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"s = ['a', 'b', 'c', 'd', 'e', '1','2','5']\\n\",\n \"# 0 1 2 3 4\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"19c71f93\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"flow of charges > electricity > bits > numbers > character > instructions > function > class > module > package > library > framework\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"daf99091\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"source code ------> byte code ------> output\\n\",\n \" compile run \\n\",\n \" .py .pyc\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 52,\n \"id\": \"3da5a878\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def color():\\n\",\n \" return \\\"red\\\"\\n\",\n \"\\n\",\n \"def flower():\\n\",\n \" return \\\"color of the flower is blue\\\"\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"3eff71a8\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.12\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n" }, { "path": "March Cohort 2023/0_Python Basics.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"id\": \"1dd9932f\",\n \"metadata\": {},\n \"source\": [\n \"# Python Basics\\n\",\n \"\\n\",\n \"we are working on print function in python\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"id\": \"3ca45a0a\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Hello World\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(\\\"Hello World\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"id\": \"e2a86f94\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Hello\\n\",\n \"World\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(\\\"Hello\\\")\\n\",\n \"print(\\\"World\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"id\": \"07db254e\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Hello\\n\",\n \"World\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(\\\"Hello\\\\nWorld\\\") # \\\\n -> new line character\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"id\": \"46c947b9\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Hello\\tWorld\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(\\\"Hello\\\\tWorld\\\") # \\\\t -> horizontal tab\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"id\": \"fd07c92b\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# 8 spaces - \\\\t\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"960ceae0\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# binary language -> 1010110\\n\",\n \"\\n\",\n \"0 -> low voltage, no-charge\\n\",\n \"\\n\",\n \"1 -> high voltage, charge\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"10a75868\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"bits > numbers > character > instructions > program > software\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"459a661b\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"compiler\\n\",\n \"\\n\",\n \"interpreter -> python\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"991396e2\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"source code -------> Byte code -----> Output\\n\",\n \" .c compile .bak Run .exe\\n\",\n \" .cpp .obj\\n\",\n \" .java .javac\\n\",\n \" .py .pyc\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"9de76aa4\",\n \"metadata\": {},\n \"source\": [\n \"# Variables\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"id\": \"ca83f18c\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"10\\n\"\n ]\n }\n ],\n \"source\": [\n \"a = 10\\n\",\n \"\\n\",\n \"print(a)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"id\": \"3a7bac6d\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"45.13\\n\"\n ]\n }\n ],\n \"source\": [\n \"b = 45.13\\n\",\n \"\\n\",\n \"print(b)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"id\": \"124cb33b\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"India\\n\"\n ]\n }\n ],\n \"source\": [\n \"a = \\\"India\\\"\\n\",\n \"\\n\",\n \"print(a)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"406d111f\",\n \"metadata\": {},\n \"source\": [\n \"# Data type\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"id\": \"707a85a0\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"10\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"a = 10\\n\",\n \"\\n\",\n \"print(a)\\n\",\n \"\\n\",\n \"print(type(a))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"id\": \"bc9295ca\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"10.65\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"b = 10.65\\n\",\n \"\\n\",\n \"print(b)\\n\",\n \"\\n\",\n \"print(type(b))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"id\": \"5632508a\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Screen\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"c = \\\"Screen\\\"\\n\",\n \"\\n\",\n \"print(c)\\n\",\n \"\\n\",\n \"print(type(c))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"id\": \"644ec54c\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"meet\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"d = 'meet'\\n\",\n \"\\n\",\n \"print(d)\\n\",\n \"\\n\",\n \"print(type(d))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"id\": \"43f33389\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"it's an apple\\n\"\n ]\n }\n ],\n \"source\": [\n \"x = \\\"it's an apple\\\"\\n\",\n \"\\n\",\n \"print(x)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"e04549b7\",\n \"metadata\": {},\n \"source\": [\n \"# Type Conversion\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"id\": \"321b1438\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"10.56\\n\",\n \"\\n\",\n \"10\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"a = 10.56\\n\",\n \"\\n\",\n \"print(a)\\n\",\n \"\\n\",\n \"print(type(a))\\n\",\n \"\\n\",\n \"b = int(a)\\n\",\n \"\\n\",\n \"print(b)\\n\",\n \"\\n\",\n \"print(type(b))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"id\": \"873230fd\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"100\\n\",\n \"\\n\",\n \"100.0\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"a = \\\"100\\\"\\n\",\n \"\\n\",\n \"print(a)\\n\",\n \"\\n\",\n \"print(type(a))\\n\",\n \"\\n\",\n \"b = float(a)\\n\",\n \"\\n\",\n \"print(b)\\n\",\n \"\\n\",\n \"print(type(b))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"83db42ca\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"int()\\n\",\n \"float()\\n\",\n \"str()\\n\",\n \"bool()\\n\",\n \"list()\\n\",\n \"dict()\\n\",\n \"tuple()\\n\",\n \"set()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"a54b3b8f\",\n \"metadata\": {},\n \"source\": [\n \"# Input function\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"id\": \"127dd9ee\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"10\\n\",\n \"10\\n\"\n ]\n }\n ],\n \"source\": [\n \"a = input()\\n\",\n \"\\n\",\n \"print(a)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"id\": \"6c872d87\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Enter Your Name: Himanshu\\n\",\n \"Himanshu\\n\"\n ]\n }\n ],\n \"source\": [\n \"a = input(\\\"Enter Your Name: \\\")\\n\",\n \"\\n\",\n \"print(a)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"id\": \"f332e9d1\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Enter Your Name: Himanshu\\n\",\n \"Hello! How are you? Himanshu\\n\"\n ]\n }\n ],\n \"source\": [\n \"a = input(\\\"Enter Your Name: \\\")\\n\",\n \"\\n\",\n \"print(\\\"Hello! How are you?\\\", a)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"076ba3aa\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.12\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n" }, { "path": "March Cohort 2023/10_11_EDA Project/.ipynb_checkpoints/10_11_EDA Project-checkpoint.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"id\": \"373ae802\",\n \"metadata\": {},\n \"source\": [\n \"# Titanic Survival Prediction Analysis\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"id\": \"8031a2ba\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import numpy as np\\n\",\n \"import pandas as pd\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"import seaborn as sns\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"id\": \"d86ba18d\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
PassengerIdSurvivedPclassNameGenderAgeSibSpParchTicketFareCabinEmbarked
0103Braund, Mr. Owen Harrismale22.010A/5 211717.2500NaNS
1211Cumings, Mrs. John Bradley (Florence Briggs Th...female38.010PC 1759971.2833C85C
2313Heikkinen, Miss. Lainafemale26.000STON/O2. 31012827.9250NaNS
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000C123S
4503Allen, Mr. William Henrymale35.0003734508.0500NaNS
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\"\n ],\n \"text/plain\": [\n \" PassengerId Survived Pclass \\\\\\n\",\n \"0 1 0 3 \\n\",\n \"1 2 1 1 \\n\",\n \"2 3 1 3 \\n\",\n \"3 4 1 1 \\n\",\n \"4 5 0 3 \\n\",\n \"\\n\",\n \" Name Gender Age SibSp \\\\\\n\",\n \"0 Braund, Mr. Owen Harris male 22.0 1 \\n\",\n \"1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \\n\",\n \"2 Heikkinen, Miss. Laina female 26.0 0 \\n\",\n \"3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \\n\",\n \"4 Allen, Mr. William Henry male 35.0 0 \\n\",\n \"\\n\",\n \" Parch Ticket Fare Cabin Embarked \\n\",\n \"0 0 A/5 21171 7.2500 NaN S \\n\",\n \"1 0 PC 17599 71.2833 C85 C \\n\",\n \"2 0 STON/O2. 3101282 7.9250 NaN S \\n\",\n \"3 0 113803 53.1000 C123 S \\n\",\n \"4 0 373450 8.0500 NaN S \"\n ]\n },\n \"execution_count\": 2,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"titanic = pd.read_csv('titanic.csv')\\n\",\n \"\\n\",\n \"titanic.head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"id\": \"f2b02ee2\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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PassengerIdSurvivedPclassNameGenderAgeSibSpParchTicketFareCabinEmbarked
0FalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalse
1FalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse
2FalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalse
3FalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse
4FalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalse
.......................................
886FalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalse
887FalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse
888FalseFalseFalseFalseFalseTrueFalseFalseFalseFalseTrueFalse
889FalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse
890FalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalse
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891 rows × 12 columns

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\"\n ],\n \"text/plain\": [\n \" PassengerId Survived Pclass Name Gender Age SibSp Parch \\\\\\n\",\n \"0 False False False False False False False False \\n\",\n \"1 False False False False False False False False \\n\",\n \"2 False False False False False False False False \\n\",\n \"3 False False False False False False False False \\n\",\n \"4 False False False False False False False False \\n\",\n \".. ... ... ... ... ... ... ... ... \\n\",\n \"886 False False False False False False False False \\n\",\n \"887 False False False False False False False False \\n\",\n \"888 False False False False False True False False \\n\",\n \"889 False False False False False False False False \\n\",\n \"890 False False False False False False False False \\n\",\n \"\\n\",\n \" Ticket Fare Cabin Embarked \\n\",\n \"0 False False True False \\n\",\n \"1 False False False False \\n\",\n \"2 False False True False \\n\",\n \"3 False False False False \\n\",\n \"4 False False True False \\n\",\n \".. ... ... ... ... \\n\",\n \"886 False False True False \\n\",\n \"887 False False False False \\n\",\n \"888 False False True False \\n\",\n \"889 False False False False \\n\",\n \"890 False False True False \\n\",\n \"\\n\",\n \"[891 rows x 12 columns]\"\n ]\n },\n \"execution_count\": 3,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"titanic.isnull()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"id\": \"ad6adb8c\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"PassengerId 0\\n\",\n \"Survived 0\\n\",\n \"Pclass 0\\n\",\n \"Name 0\\n\",\n \"Gender 0\\n\",\n \"Age 177\\n\",\n \"SibSp 0\\n\",\n \"Parch 0\\n\",\n \"Ticket 0\\n\",\n \"Fare 0\\n\",\n \"Cabin 687\\n\",\n \"Embarked 2\\n\",\n \"dtype: int64\"\n ]\n },\n \"execution_count\": 4,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"titanic.isnull().sum()\"\n ]\n },\n {\n \"cell_type\": 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{},\n \"source\": [\n \"# Exploratory Data Analysis\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"id\": \"0fdcc279\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 8,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.figure(figsize=(8,8))\\n\",\n \"\\n\",\n \"sns.heatmap(titanic.isnull(), yticklabels=False, cbar=False, cmap='viridis')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"id\": \"a78063eb\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 10,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"sns.countplot(x='Survived', data=titanic)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"id\": \"e59fc93c\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 11,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"sns.countplot(x='Survived', hue='Gender' ,data=titanic)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"id\": \"457581d7\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 12,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"sns.countplot(x='Survived', hue='Pclass' ,data=titanic)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"ffd065db\",\n \"metadata\": {},\n \"source\": [\n \"# Data Cleaning\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"id\": \"eb40c7c6\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
countmeanstdmin25%50%75%max
Pclass
1186.038.23344114.8028560.9227.037.049.080.0
2173.029.87763014.0010770.6723.029.036.070.0
3355.025.14062012.4953980.4218.024.032.074.0
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" count mean std min 25% 50% 75% max\\n\",\n \"Pclass \\n\",\n \"1 186.0 38.233441 14.802856 0.92 27.0 37.0 49.0 80.0\\n\",\n \"2 173.0 29.877630 14.001077 0.67 23.0 29.0 36.0 70.0\\n\",\n \"3 355.0 25.140620 12.495398 0.42 18.0 24.0 32.0 74.0\"\n ]\n },\n \"execution_count\": 13,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"titanic.groupby('Pclass')['Age'].describe()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"id\": \"555b4d07\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"10\\n\",\n \"20\\n\"\n ]\n }\n ],\n \"source\": [\n \"a,b = 10,20\\n\",\n \"\\n\",\n \"print(a)\\n\",\n \"print(b)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"id\": \"dbbf4d86\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
PassengerIdSurvivedPclassNameGenderAgeSibSpParchTicketFareCabinEmbarked
88688702Montvila, Rev. Juozasmale27.00021153613.00NaNS
88788811Graham, Miss. Margaret Edithfemale19.00011205330.00B42S
88888903Johnston, Miss. Catherine Helen \\\"Carrie\\\"femaleNaN12W./C. 660723.45NaNS
88989011Behr, Mr. Karl Howellmale26.00011136930.00C148C
89089103Dooley, Mr. Patrickmale32.0003703767.75NaNQ
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" PassengerId Survived Pclass Name \\\\\\n\",\n \"886 887 0 2 Montvila, Rev. Juozas \\n\",\n \"887 888 1 1 Graham, Miss. Margaret Edith \\n\",\n \"888 889 0 3 Johnston, Miss. Catherine Helen \\\"Carrie\\\" \\n\",\n \"889 890 1 1 Behr, Mr. Karl Howell \\n\",\n \"890 891 0 3 Dooley, Mr. Patrick \\n\",\n \"\\n\",\n \" Gender Age SibSp Parch Ticket Fare Cabin Embarked \\n\",\n \"886 male 27.0 0 0 211536 13.00 NaN S \\n\",\n \"887 female 19.0 0 0 112053 30.00 B42 S \\n\",\n \"888 female NaN 1 2 W./C. 6607 23.45 NaN S \\n\",\n \"889 male 26.0 0 0 111369 30.00 C148 C \\n\",\n \"890 male 32.0 0 0 370376 7.75 NaN Q \"\n ]\n },\n \"execution_count\": 16,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"titanic.tail()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"id\": \"5b935cd7\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def age_calculate(cols):\\n\",\n \" age_map = [38,30,25]\\n\",\n \" age, pclass = cols[0], cols[1]\\n\",\n \" \\n\",\n \" if pd.isnull(age):\\n\",\n \" return age_map[int(pclass - 1)]\\n\",\n \" else:\\n\",\n \" return age\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"id\": \"48f9d274\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"titanic['Age'] = titanic[['Age', 'Pclass']].apply(age_calculate, axis=1) # [NaN, 3]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"id\": \"fce00d9d\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 18,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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1VdbtM3S9LelSmruE7VNXLk1yche++u/uyqrpRktt09x8sWcsxq/s9zf5HkldU1c9nep1/bZJXHnVBdve/Oa1Cqur1cw3XS/LQqvqjTF2yNZXSn3NatZzjllX1y5nel25TVZck+dbu/raF6jnuOVX11CSfUFUPS/LNSX5iwXp+saq+qrv/04I1nOQ3quryJD+T6TX2DUletK+L7XXST1U9NMkP5Mz/gS9J8gNHLdAlVNVtkzwxU3dxJ3l5ku/q7jcvVdNxVfWJSW7d3b+7glqul2kMpZL8wVFPwYL13C/JjyS5QXffrqo+N8njuvtrFq5rVb+n5MoxsKvV3T94irV82jXU8pbTquW4uWfpAUl+obvvNj/3hu6+6xL1HKurktwqyWcm+fJMr6vLu/sFC9b0niQ3yXSj86GcudlZcu5HkmQeyvri+eFLuvt5e7vWvmfJzjP1jrpAX7H0TL01qqrfSPI1me7AfyfJnyV5cXc/6jw/tu+aLkzy1bnqIP+ptUxOqOk1Sb40yW8ce4N7fXd/9oI1nTRb76+SvL6733Ha9Zxkvgl7Vy88JX7urv697n7P/PjjM40/vWKhel7R3fesqt8+9np6XXdfskQ959T2mu7+vKXrOATzDdmnd/evVdWNk1x49Bq7ru1lHWZV3f3oI1MX7Fvnj1vOzy2mqu5UVb9eVW+YH39OVf2LJWtKcrPufneS+yd5+vyHsvTyiecn+aYkt8jUrX70saQrTpghu/S6qG9J8pOZJo9dmqnb7FFJXl5V/+i0i6mqx1TVZ85ff1xVvTDJHyb506pa+jX15CR/fezxe+fnlvLWqvrCJF1VN6iq70ny+wvWc9xvVdXnL13EsdfS3U/6WEF9D0vyH5I8dX7qU5P83L6ut68xzH99nu91plbCUn4i0wzCpyZJd/9uVf10kh9asKbrzbO7/kHOLE9Y2q0WHFu6Om+oqgcnubCqPj3Jd2aa+bmkjyS5c3f/aXLluswnZ+pVeUmm2din6euTPH7++iGZboovzjQR4rJMsxyXUsdbud39kbk7eymPyDQ886mZJkn9apI1jF8m0/KIb62qt2S6sVhqvPdRmQ5mPuk9fen38iT59kxrjV+RJN39X6vqk/Z1sX3Nkr1PVV2QaQH3y/dxjR3cuLtfOQ0TXOmKpYqZPS7J5Ule1t2vqqrbJ/mvC9f0y1X15d299Kzm474j0w3FBzIN8l+eM+GwlNseheXsHUnu1N3vrKolxjI/eCyU7pvkZ7r7w0l+f+FwSpI/qqrvzJlW5bcl+aMF6/n87r70+BNV9YgkT1monuO+cukCkqS7Hz5/vs/StVyND3T3B4/ez+fX+N56nfb2BzTfPf5Iki/Y1zWupT+vaWedo+naD0jy9vP/yH51988m+dljj/8o044oS/qtJM+bb3xWMcjf3e/LFJhraYUnyUvntWBH/35/P8lL5mUA71qgng9U1V0zTa+/T5LvOfa9Gy9Qz3GPSPKjSf5Fpr+/X8/UelnKv6yqD3T3C5Okqh6d6Xe2eGAeTYSaW0s3XLicVNUNM93g/O1M/3YvTfKU7v6fixY2LQ/6/iQ3qqq/m6nG5+/rYvueJfuDSX43yX9cesLBkbn19uOZdof4yyRvSnLpUjP15ppumGks7LNy7I9jwV00Mk/9/3uZJq8sPVnk+Tn/Lk2LzZKdZzTeP9MbSZL8RZJP6e5vX6iee2bqer040yYYj5+f/6ok/6i7H7RQXRcmuay7/+ES1z9JVf2NJL+YaYjmKzLNSv2Glcxy/ppM3aC3zNRr8WlJfr+7P2uhep6TaS3v0XK3ByX5xO5+4BL1HJlv6L8lZ88m3tvym30H5tFU5A8neX9W0EqpqgvnbdZukuSCfc2m+ihr+tkkb0zy4Ezds5dm+uN45II1XZ7kK7v7I0vVcKyWL5m/vH+ST87Zf7Rv7u7vX6Sw2by85cGZxqDflOS53f1vl6xpjebX1P26+4NL13JkbsH9WqYdyL556ZvDI1X1ukzjg7/W3XerqvskedBRF+kS9Zw7e3gNM4pr2u7xMcceX5hpH+5Lz/Nj19pexzR63o5uZd5UVb+Sabu+F17Tf3xK7tjdD6yqr50X5/90pvG5Jb0906LgX86xfT+XWFYy78ySqnp8d3/xsW89v6pectr1zLXcKdMi6QdlalU+O9MN6CrGemrafeixOdOF9rJMa1b/YsGy3pxp9vAv5OytDU/1NVVn9pWu+fMNktw+yQOqahVrC5N8qLv/oqouqKoLuvtFVfV/LljPb1fVvbr7t5IrezLWMD/lNlX1fd39f1TVDTINjextW9G9BubcXXVpktt19+Or6taZuqteuc/rXoPPyLT93Lcnedo8/vSs7n7ZgjUddQG9ax5/+pNM6x+X9Kb54wbzxxpcXFW3n8d4U9OOSBcvVMsbM43j3K+7/9tczz9ZqJaTPCvTLN2jsfBLM4X6kktL3jZ/XJAFlyit9Eb+XO+qqptm+jd8ZlW9IwtMTqwzuzRdP8k3VtUfz48/Lcl/Pu16TvDQTL+f78s0/vzL3f1/7+ti++6SfXKmafdf2t13rmkB9a929+Lri5IrF3Q/MdMY5mKnFNR0ssRzMx3r9fRMW3U9prsXn3ywJlX1FZnGn49mVt4201Zmp94ar2l3kW/INBb+K5kC6ie7+3anXctJ6oSF71X16u6+x1I1rc38b/jCo7W9VfUJSe7d3T+3YE236e4/noeM3p/p5uLSJDdL8szT7iGo9e7SdHwN6PUzLRN8eeZTgrr7tXu57p4D87Xdffda2U4a85jY12eauv2qJM/u7ucuWdPa1HScz6Nz1YlIi667qulMxc+cH76xuxc7Jmqu5yaZJkc9KNOY02VJnrf0cpx5hvqrkzxnfuoBST6ru8+7Zd6ea1rVa6qqfqe7P/ec5658r1pCHTuPs6qe291Lz5Y/y7mzdrv7jxeq43z7xfa+XlP7Xpf1oXkQ9mgJx8WZWpyLqao3Zdp+7jlJvrcXPKWgzj6L7yqWGC885pmZuvD+t0zLAR6Sacu+pX1ezmzXd0lVpbufsVQx8+vnmZm6hW6e6Qi7f5aFTuU5Z3zuUTmzccKFmXbZWSwws77X1Ek7nS29VvX4AvHbL1bFOa5u1m6mm59Td2yt/wO7+9nX+APXkX2/OH40yfOSfFJVPSHTXe7S29Bd0tM2dGuw5rGUW3T306rqkX3mOKQXL1lQVf37JHfIdMNzdMJMJ1ksMI/r7ndm6hp66jX9t3uswWtq3Kur6t9kOj+xM22Msch5vcf01Xy9tMcnuVfOmbW7ZEHzWv9vz5nzlvdu37Nkn1nThtlflunO6e919yJ7NVbVo7v7h5M8oeYz5o7rBQ4h7lM8MeJaOJqI9Paq+upMkzVutWA9yXQC/V3WMvV/jarqM7v7jXU1+3zua2xn0NpeU9+R5F9mnuGcqVdgkfWzx1xSVe+e67nR/HWy/JK8tc3aPfKCmvYAfnbOnnn9zqv/kWtv37Nkb56p+f4zx567/kILg4+C+tULXPu8quqyJI/s7nfNjz8xyb9ecuOCJD9UVTdL8t1JnpTkoiRLzwJ9Q6Z1mIvuzLRyJ+39efwGY8kx6FW9pubu9H+21PVPsuTkw2uwilm7Jzh6jzx+o9PZU3f2vif9vDnJrTPtqFNJPiHTm907kjysu0+9+6Oq7tbde1unc22cNNFg6ckHazQP9H9uklfm7LWhi56HuSZV9beS/HHPx+hV1UMyLS15c6azaPdy530NNd0w05jlHZO8PsnTunuxN9uq+rfd/Y/ranaQ8no6o6rumORvZhoGOT5r99OS/NIS7+FL2ndgPiXTjMHL58dfnmkLquckeWJ33/N8P7+nml6U5FMyLXB9Vnf/3mnXcK55V497d/dfzo9vnuk8zFM/57GqHnOeb3fPW60toc7s+HOWo40NmGZZJvk7PW3+/sWZlrt8R6YbjTt39wMWqOnZmbpjX5ppZvpbetldrN7d3Rd5PV2zeZ369/c5B9pX1T2SPLa777dMZWfVctckd8nZs3f3Mq9h34F5lXVfR8+dNKX7tNR0qPU/yLS05KJMy0oWO96rqr4xyfdnCvGea3tCd5/20VCpqu8+4embZNqv8RbdfdNTLuksdYqHxR6i48u2qurHkvxZd//A/HiRv7k6dsh3TadJvPJo6cQS9N6Mq6o3dPddr+Z7ix7ePtfw2CT3zhSY/ynTDdnL9nVjuO9Zsu+sqn+a6S43mQLqL+elJostL5m7q350bm0+OsljsuB5mN39jKp6dabxpUpy/+5eZBeN7r5y7KuqPj7JIzPtpvGsnP+c072r6bDYhye5eabZsp+a6WSJL1uyrpW5sKquN3d5flnOPg1kqSUTV85Z6O4r6uyj9ZZw8fmWdC28nGttzndSyo1OrYqr94AklyT57e5+aE3n0f7kvi627z+gB2da9/VzmYLgZfNzF2ZqRZ26qrpzpuB+QKY9QJ+VaRLCErWcO7bzlCXHdo7MXcKPyjRWcVmSux91Fy/sVA+LPVA/k2m5xp9nGnN6aXLlWNRfLVTT0czP5OzZn0vN/Lww025aiyf3AXhVVT2szzkBpKq+JcsvwUmS98/LS66oqosyzY/Z2/rVfS8r+fNM4ycn+W/7vPZ5PD3Tm8qXd/fbFqrhyGU5e2znzkm+a8mCqur/ynQqyI8n+ezu/usl6znHqR4We4i6+wlV9euZxul/9dgSnAty9X+L+65pbTM/397dj1u6iAPxXZnOxb00ZwLyHpn2l/66pYo65tU1bWn4E5nq++tMkwL3Yt9jmHfKdIDtbXMsnBfcCmuvR798tNY2tjPX8ZFMM1CvyNlhtPQ6sFTVD2c6lPkbM735f1uS/9zdazpQmpUzhvnRmzcqOBrL/L2eD91ek6q6bZKLzp2gdJ1eY8+B+bpMY0yvyZmdWbLkVOSajvb6ml7BmXx1bN/Ikx5ztjr7sNhkOix2b+MVfGyqqpsvsbyG/aiqowPcO9OEn+ft7Vp7DsyrnJiwtKp6apK7J1n0TL65lg8fq6EyDaK/Lytoza1JVX1tklt194/Nj1+Z6VivTvLo7v4PS9YHLKOq/t9Mc0CONsf5+iR/2N172bFp35N+nl9V35ZpP9njC82XvLtbxZl8ySrHdtbq0ZmO0jpyg0ybsN8005i0wIRt+pIkdz0aq593TXv9vi6278B8yPz5e489t7dti0asfP9WTnaD7n7rsccvm2+63lnT8VrANv1BktskOTqX89ZJ9jaGue9Zsqs4TPe4ee3lSdthLXrOI+f1iccfdPc/Pvbw4lOuBVjYsW0Nb5bk9+dhmk5yzyS/ua/r7nvz9RtnWs93m+5+eFV9epLP6O5f3Od1r8H3HPv6hpn22Vx87SPn9YqrWQv2rdnjFHJgtX5kiYvue9LPszPNkP3G7r5rVd0oyf+/1JZ4V6eqXtzdJ+4ryfLmzQl+LtM4+NHxVJ+X5OMyHRn3pwuVBqzAvGnB8aWLh3e8V5I7dPfXV9WDkqS7318L74s172Jz5IJMi3A/eaFyGNDd70jyhVX1pTlzwvsvrXEtGHB6qurhmQ63fn+m7VYre5wns+/A/ODcqjyawXSHHJstu5DX5MwY5hWZjj36lsWqYdgckEISOPK9ST5r3lVu7/YdmI9N8itJbl1Vz0zyRUm+ac/XPFFVfX6Stx5NRDrnnMBFNjoHYCd/mGnt+qnY6xhmklTVLZLcK1NT+bdO607ghDpWd04gANdeVd0t01rsV+Tstf7fuZfr7XnSzxcl+Z3ufm9V/cNMO+w8sbvfcg0/uo9aVndOIADX3ryc5GWZNiu48sjI7r5sH9fbd5fskzMd7XNJpr7mf5fkGZl2ZzhtazwnEIBr74ruvtqzTa9rF+z5f/+Kecuir03yo939xCy3Hd3ROYE/n/WcEwjAtfeiqnp4VX1KVd386GNfF9t3l+yLM036eWiSL07yZ5m6aD97bxc9fz33yplzAt87P3enJDft7tee94cBWJWqetMJT3d372VZyb4D85OTPDjJq7r7pVV1myT37u5n7O2iALAH++6SfU+mST4vnVtyn5szx7AAwEetqh597OsHnvO9f7Wv6+47MF+S5OOq6lOT/Hqmrtmf2vM1AfjYdvy4v+8753tfsa+L7jswq7vfl+T+SZ7U3V+XM1ubAcC1UVfz9UmPrzN7D8yq+oIklyb5pfk5hyYDsIu+mq9Penyd2ff6w0dmai4/r7t/r6pun+RFe74mAB/bLqmqd2dqTd5o/jrz4xvu66J73xoPAD4W7PsA6YuTPDrTuOWVqd/dX7rP6wLAdW3fY5jPTPLGJLdL8oOZTgZ51Z6vCQDXuX1vXPCa7v68qvrd7v6c+bkXd/cSe8kCwLW270k/H5o/v72qvjrJ25Lcas/XBIDr3L4D84eq6mZJvjvJk5JclOSf7PmaAHCd20uXbFXdMMkjktwx0zllT5uP1QKAg7SvwHx2pu7Ylyb5yiRv6e5HXucXAoBTsq/AfP3REV5Vdb0kr+zuu1/nFwKAU7KvZSVHk32iKxaAjwX7amF+OMl7jx4muVGS981fd3dfdJ1fFAD2yNZ4ADBg3zv9AMDHBIEJAAMEJgAMEJgAMOB/Aa490anrRUfIAAAAAElFTkSuQmCC\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.figure(figsize=(8,8))\\n\",\n \"\\n\",\n \"sns.heatmap(titanic.isnull(), yticklabels=False, cbar=False, cmap='viridis')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"id\": \"03c24704\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"titanic.drop('Cabin', axis=1, inplace=True)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"id\": \"5b9f1450\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 20,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.figure(figsize=(8,8))\\n\",\n \"\\n\",\n \"sns.heatmap(titanic.isnull(), yticklabels=False, cbar=False, cmap='viridis')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"id\": \"a8ea552f\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"titanic.dropna(inplace=True)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"id\": \"61e919dd\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 22,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n 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\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.figure(figsize=(8,8))\\n\",\n \"\\n\",\n \"sns.heatmap(titanic.isnull(), yticklabels=False, cbar=False, cmap='viridis')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"id\": \"7ad3c921\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\",\n \"Int64Index: 889 entries, 0 to 890\\n\",\n \"Data columns (total 11 columns):\\n\",\n \" # Column Non-Null Count Dtype \\n\",\n \"--- ------ -------------- ----- \\n\",\n \" 0 PassengerId 889 non-null int64 \\n\",\n \" 1 Survived 889 non-null int64 \\n\",\n \" 2 Pclass 889 non-null int64 \\n\",\n \" 3 Name 889 non-null object \\n\",\n \" 4 Gender 889 non-null object \\n\",\n \" 5 Age 889 non-null float64\\n\",\n \" 6 SibSp 889 non-null int64 \\n\",\n \" 7 Parch 889 non-null int64 \\n\",\n \" 8 Ticket 889 non-null object \\n\",\n \" 9 Fare 889 non-null float64\\n\",\n \" 10 Embarked 889 non-null object \\n\",\n \"dtypes: float64(2), int64(5), object(4)\\n\",\n \"memory usage: 83.3+ KB\\n\"\n ]\n }\n ],\n \"source\": [\n \"titanic.info()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"076d4a4c\",\n \"metadata\": {},\n \"source\": [\n \"# Feature Engineering\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"id\": \"f731908b\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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PassengerIdSurvivedPclassNameGenderAgeSibSpParchTicketFareEmbarked
0103Braund, Mr. Owen Harrismale22.010A/5 211717.2500S
1211Cumings, Mrs. John Bradley (Florence Briggs Th...female38.010PC 1759971.2833C
2313Heikkinen, Miss. Lainafemale26.000STON/O2. 31012827.9250S
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000S
4503Allen, Mr. William Henrymale35.0003734508.0500S
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\"\n ],\n \"text/plain\": [\n \" PassengerId Survived Pclass \\\\\\n\",\n \"0 1 0 3 \\n\",\n \"1 2 1 1 \\n\",\n \"2 3 1 3 \\n\",\n \"3 4 1 1 \\n\",\n \"4 5 0 3 \\n\",\n \"\\n\",\n \" Name Gender Age SibSp \\\\\\n\",\n \"0 Braund, Mr. Owen Harris male 22.0 1 \\n\",\n \"1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \\n\",\n \"2 Heikkinen, Miss. Laina female 26.0 0 \\n\",\n \"3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \\n\",\n \"4 Allen, Mr. William Henry male 35.0 0 \\n\",\n \"\\n\",\n \" Parch Ticket Fare Embarked \\n\",\n \"0 0 A/5 21171 7.2500 S \\n\",\n \"1 0 PC 17599 71.2833 C \\n\",\n \"2 0 STON/O2. 3101282 7.9250 S \\n\",\n \"3 0 113803 53.1000 S \\n\",\n \"4 0 373450 8.0500 S \"\n ]\n },\n \"execution_count\": 24,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"titanic.head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"id\": \"8dc409e6\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
male
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41
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\"\n ],\n \"text/plain\": [\n \" male\\n\",\n \"0 1\\n\",\n \"1 0\\n\",\n \"2 0\\n\",\n \"3 0\\n\",\n \"4 1\"\n ]\n },\n \"execution_count\": 25,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"gender = pd.get_dummies(titanic['Gender'], drop_first=True)\\n\",\n \"\\n\",\n \"# male female\\n\",\n \"# 1 0\\n\",\n \"# 0 1\\n\",\n \"# 1 0\\n\",\n \"gender.head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"id\": \"42fb2bd0\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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CQS
0001
1100
2001
3001
4001
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\"\n ],\n \"text/plain\": [\n \" C Q S\\n\",\n \"0 0 0 1\\n\",\n \"1 1 0 0\\n\",\n \"2 0 0 1\\n\",\n \"3 0 0 1\\n\",\n \"4 0 0 1\"\n ]\n },\n \"execution_count\": 26,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"embark = pd.get_dummies(titanic['Embarked'])\\n\",\n \"embark.head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"id\": \"9c908558\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
SibSp_0SibSp_1SibSp_2SibSp_3SibSp_4SibSp_5SibSp_8
00100000
10100000
21000000
30100000
41000000
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\"\n ],\n \"text/plain\": [\n \" SibSp_0 SibSp_1 SibSp_2 SibSp_3 SibSp_4 SibSp_5 SibSp_8\\n\",\n \"0 0 1 0 0 0 0 0\\n\",\n \"1 0 1 0 0 0 0 0\\n\",\n \"2 1 0 0 0 0 0 0\\n\",\n \"3 0 1 0 0 0 0 0\\n\",\n \"4 1 0 0 0 0 0 0\"\n ]\n },\n \"execution_count\": 27,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"sibsp = pd.get_dummies(titanic['SibSp'], prefix='SibSp')\\n\",\n \"sibsp.head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 28,\n \"id\": \"b4fca7e5\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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Parch_0Parch_1Parch_2Parch_3Parch_4Parch_5Parch_6
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\"\n ],\n \"text/plain\": [\n \" Parch_0 Parch_1 Parch_2 Parch_3 Parch_4 Parch_5 Parch_6\\n\",\n \"0 1 0 0 0 0 0 0\\n\",\n \"1 1 0 0 0 0 0 0\\n\",\n \"2 1 0 0 0 0 0 0\\n\",\n \"3 1 0 0 0 0 0 0\\n\",\n \"4 1 0 0 0 0 0 0\"\n ]\n },\n \"execution_count\": 28,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"parch = pd.get_dummies(titanic['Parch'], prefix='Parch')\\n\",\n \"parch.head()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"4beab0b0\",\n \"metadata\": {},\n \"source\": [\n \"# Combine all the features\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 29,\n \"id\": \"94019ddd\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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SurvivedPclassAgeFare
00322.07.2500
11138.071.2833
21326.07.9250
31135.053.1000
40335.08.0500
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\"\n ],\n \"text/plain\": [\n \" Survived Pclass Age Fare\\n\",\n \"0 0 3 22.0 7.2500\\n\",\n \"1 1 1 38.0 71.2833\\n\",\n \"2 1 3 26.0 7.9250\\n\",\n \"3 1 1 35.0 53.1000\\n\",\n \"4 0 3 35.0 8.0500\"\n ]\n },\n \"execution_count\": 29,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"titanic = titanic.drop(['PassengerId', 'Name', 'Gender', 'SibSp', 'Parch',\\n\",\n \" 'Ticket', 'Embarked'], axis = 1)\\n\",\n \"\\n\",\n \"titanic.head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 31,\n \"id\": \"ecbd3207\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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SurvivedPclassAgeFaremaleCQSSibSp_0SibSp_1...SibSp_4SibSp_5SibSp_8Parch_0Parch_1Parch_2Parch_3Parch_4Parch_5Parch_6
00322.07.2500100101...0001000000
11138.071.2833010001...0001000000
21326.07.9250000110...0001000000
31135.053.1000000101...0001000000
40335.08.0500100110...0001000000
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5 rows × 22 columns

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\"\n ],\n \"text/plain\": [\n \" Survived Pclass Age Fare male C Q S SibSp_0 SibSp_1 ... \\\\\\n\",\n \"0 0 3 22.0 7.2500 1 0 0 1 0 1 ... \\n\",\n \"1 1 1 38.0 71.2833 0 1 0 0 0 1 ... \\n\",\n \"2 1 3 26.0 7.9250 0 0 0 1 1 0 ... \\n\",\n \"3 1 1 35.0 53.1000 0 0 0 1 0 1 ... \\n\",\n \"4 0 3 35.0 8.0500 1 0 0 1 1 0 ... \\n\",\n \"\\n\",\n \" SibSp_4 SibSp_5 SibSp_8 Parch_0 Parch_1 Parch_2 Parch_3 Parch_4 \\\\\\n\",\n \"0 0 0 0 1 0 0 0 0 \\n\",\n \"1 0 0 0 1 0 0 0 0 \\n\",\n \"2 0 0 0 1 0 0 0 0 \\n\",\n \"3 0 0 0 1 0 0 0 0 \\n\",\n \"4 0 0 0 1 0 0 0 0 \\n\",\n \"\\n\",\n \" Parch_5 Parch_6 \\n\",\n \"0 0 0 \\n\",\n \"1 0 0 \\n\",\n \"2 0 0 \\n\",\n \"3 0 0 \\n\",\n \"4 0 0 \\n\",\n \"\\n\",\n \"[5 rows x 22 columns]\"\n ]\n },\n \"execution_count\": 31,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"titanic = pd.concat([titanic, gender, embark, sibsp, parch], axis=1)\\n\",\n \"\\n\",\n \"titanic.head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 32,\n \"id\": \"22e99298\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"titanic.to_csv('titanic_cleaned.csv', index=False)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"dc3462b9\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.12\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n" }, { "path": "March Cohort 2023/10_11_EDA Project/10_11_EDA Project.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"id\": \"373ae802\",\n \"metadata\": {},\n \"source\": [\n \"# Titanic Survival Prediction Analysis\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"id\": \"8031a2ba\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import numpy as np\\n\",\n \"import pandas as pd\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"import seaborn as sns\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"id\": \"d86ba18d\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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PassengerIdSurvivedPclassNameGenderAgeSibSpParchTicketFareCabinEmbarked
0103Braund, Mr. Owen Harrismale22.010A/5 211717.2500NaNS
1211Cumings, Mrs. John Bradley (Florence Briggs Th...female38.010PC 1759971.2833C85C
2313Heikkinen, Miss. Lainafemale26.000STON/O2. 31012827.9250NaNS
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000C123S
4503Allen, Mr. William Henrymale35.0003734508.0500NaNS
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PassengerIdSurvivedPclassNameGenderAgeSibSpParchTicketFareCabinEmbarked
0FalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalse
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3FalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse
4FalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalse
.......................................
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887FalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse
888FalseFalseFalseFalseFalseTrueFalseFalseFalseFalseTrueFalse
889FalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse
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891 rows × 12 columns

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\"\n ],\n \"text/plain\": [\n \" PassengerId Survived Pclass Name Gender Age SibSp Parch \\\\\\n\",\n \"0 False False False False False False False False \\n\",\n \"1 False False False False False False False False \\n\",\n \"2 False False False False False False False False \\n\",\n \"3 False False False False False False False False \\n\",\n \"4 False False False False False False False False \\n\",\n \".. ... ... ... ... ... ... ... ... \\n\",\n \"886 False False False False False False False False \\n\",\n \"887 False False False False False False False False \\n\",\n \"888 False False False False False True False False \\n\",\n \"889 False False False False False False False False \\n\",\n \"890 False False False False False False False False \\n\",\n \"\\n\",\n \" Ticket Fare Cabin Embarked \\n\",\n \"0 False False True False \\n\",\n \"1 False False False False \\n\",\n \"2 False False True False \\n\",\n \"3 False False False False \\n\",\n \"4 False False True False \\n\",\n 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{},\n \"source\": [\n \"# Exploratory Data Analysis\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"id\": \"0fdcc279\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 8,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.figure(figsize=(8,8))\\n\",\n \"\\n\",\n \"sns.heatmap(titanic.isnull(), yticklabels=False, cbar=False, cmap='viridis')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"id\": \"a78063eb\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 10,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"sns.countplot(x='Survived', data=titanic)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"id\": \"e59fc93c\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 11,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"sns.countplot(x='Survived', hue='Gender' ,data=titanic)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"id\": \"457581d7\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 12,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"sns.countplot(x='Survived', hue='Pclass' ,data=titanic)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"ffd065db\",\n \"metadata\": {},\n \"source\": [\n \"# Data Cleaning\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"id\": \"eb40c7c6\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
countmeanstdmin25%50%75%max
Pclass
1186.038.23344114.8028560.9227.037.049.080.0
2173.029.87763014.0010770.6723.029.036.070.0
3355.025.14062012.4953980.4218.024.032.074.0
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" count mean std min 25% 50% 75% max\\n\",\n \"Pclass \\n\",\n \"1 186.0 38.233441 14.802856 0.92 27.0 37.0 49.0 80.0\\n\",\n \"2 173.0 29.877630 14.001077 0.67 23.0 29.0 36.0 70.0\\n\",\n \"3 355.0 25.140620 12.495398 0.42 18.0 24.0 32.0 74.0\"\n ]\n },\n \"execution_count\": 13,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"titanic.groupby('Pclass')['Age'].describe()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"id\": \"555b4d07\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"10\\n\",\n \"20\\n\"\n ]\n }\n ],\n \"source\": [\n \"a,b = 10,20\\n\",\n \"\\n\",\n \"print(a)\\n\",\n \"print(b)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"id\": \"dbbf4d86\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
PassengerIdSurvivedPclassNameGenderAgeSibSpParchTicketFareCabinEmbarked
88688702Montvila, Rev. Juozasmale27.00021153613.00NaNS
88788811Graham, Miss. Margaret Edithfemale19.00011205330.00B42S
88888903Johnston, Miss. Catherine Helen \\\"Carrie\\\"femaleNaN12W./C. 660723.45NaNS
88989011Behr, Mr. Karl Howellmale26.00011136930.00C148C
89089103Dooley, Mr. Patrickmale32.0003703767.75NaNQ
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" PassengerId Survived Pclass Name \\\\\\n\",\n \"886 887 0 2 Montvila, Rev. Juozas \\n\",\n \"887 888 1 1 Graham, Miss. Margaret Edith \\n\",\n \"888 889 0 3 Johnston, Miss. Catherine Helen \\\"Carrie\\\" \\n\",\n \"889 890 1 1 Behr, Mr. Karl Howell \\n\",\n \"890 891 0 3 Dooley, Mr. Patrick \\n\",\n \"\\n\",\n \" Gender Age SibSp Parch Ticket Fare Cabin Embarked \\n\",\n \"886 male 27.0 0 0 211536 13.00 NaN S \\n\",\n \"887 female 19.0 0 0 112053 30.00 B42 S \\n\",\n \"888 female NaN 1 2 W./C. 6607 23.45 NaN S \\n\",\n \"889 male 26.0 0 0 111369 30.00 C148 C \\n\",\n \"890 male 32.0 0 0 370376 7.75 NaN Q \"\n ]\n },\n \"execution_count\": 16,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"titanic.tail()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"id\": \"5b935cd7\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def age_calculate(cols):\\n\",\n \" age_map = [38,30,25]\\n\",\n \" age, pclass = cols[0], cols[1]\\n\",\n \" \\n\",\n \" if pd.isnull(age):\\n\",\n \" return age_map[int(pclass - 1)]\\n\",\n \" else:\\n\",\n \" return age\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"id\": \"48f9d274\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"titanic['Age'] = titanic[['Age', 'Pclass']].apply(age_calculate, axis=1) # [NaN, 3]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"id\": \"fce00d9d\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 18,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.figure(figsize=(8,8))\\n\",\n \"\\n\",\n \"sns.heatmap(titanic.isnull(), yticklabels=False, cbar=False, cmap='viridis')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"id\": \"03c24704\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"titanic.drop('Cabin', axis=1, inplace=True)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"id\": \"5b9f1450\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 20,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.figure(figsize=(8,8))\\n\",\n \"\\n\",\n \"sns.heatmap(titanic.isnull(), yticklabels=False, cbar=False, cmap='viridis')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"id\": \"a8ea552f\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"titanic.dropna(inplace=True)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"id\": \"61e919dd\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 22,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.figure(figsize=(8,8))\\n\",\n \"\\n\",\n \"sns.heatmap(titanic.isnull(), yticklabels=False, cbar=False, cmap='viridis')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"id\": \"7ad3c921\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\",\n \"Int64Index: 889 entries, 0 to 890\\n\",\n \"Data columns (total 11 columns):\\n\",\n \" # Column Non-Null Count Dtype \\n\",\n \"--- ------ -------------- ----- \\n\",\n \" 0 PassengerId 889 non-null int64 \\n\",\n \" 1 Survived 889 non-null int64 \\n\",\n \" 2 Pclass 889 non-null int64 \\n\",\n \" 3 Name 889 non-null object \\n\",\n \" 4 Gender 889 non-null object \\n\",\n \" 5 Age 889 non-null float64\\n\",\n \" 6 SibSp 889 non-null int64 \\n\",\n \" 7 Parch 889 non-null int64 \\n\",\n \" 8 Ticket 889 non-null object \\n\",\n \" 9 Fare 889 non-null float64\\n\",\n \" 10 Embarked 889 non-null object \\n\",\n \"dtypes: float64(2), int64(5), object(4)\\n\",\n \"memory usage: 83.3+ KB\\n\"\n ]\n }\n ],\n \"source\": [\n \"titanic.info()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"076d4a4c\",\n \"metadata\": {},\n \"source\": [\n \"# Feature Engineering\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"id\": \"f731908b\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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PassengerIdSurvivedPclassNameGenderAgeSibSpParchTicketFareEmbarked
0103Braund, Mr. Owen Harrismale22.010A/5 211717.2500S
1211Cumings, Mrs. John Bradley (Florence Briggs Th...female38.010PC 1759971.2833C
2313Heikkinen, Miss. Lainafemale26.000STON/O2. 31012827.9250S
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000S
4503Allen, Mr. William Henrymale35.0003734508.0500S
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\"\n ],\n \"text/plain\": [\n \" PassengerId Survived Pclass \\\\\\n\",\n \"0 1 0 3 \\n\",\n \"1 2 1 1 \\n\",\n \"2 3 1 3 \\n\",\n \"3 4 1 1 \\n\",\n \"4 5 0 3 \\n\",\n \"\\n\",\n \" Name Gender Age SibSp \\\\\\n\",\n \"0 Braund, Mr. Owen Harris male 22.0 1 \\n\",\n \"1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \\n\",\n \"2 Heikkinen, Miss. Laina female 26.0 0 \\n\",\n \"3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \\n\",\n \"4 Allen, Mr. William Henry male 35.0 0 \\n\",\n \"\\n\",\n \" Parch Ticket Fare Embarked \\n\",\n \"0 0 A/5 21171 7.2500 S \\n\",\n \"1 0 PC 17599 71.2833 C \\n\",\n \"2 0 STON/O2. 3101282 7.9250 S \\n\",\n \"3 0 113803 53.1000 S \\n\",\n \"4 0 373450 8.0500 S \"\n ]\n },\n \"execution_count\": 24,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"titanic.head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"id\": \"8dc409e6\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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male
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30
41
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\"\n ],\n \"text/plain\": [\n \" male\\n\",\n \"0 1\\n\",\n \"1 0\\n\",\n \"2 0\\n\",\n \"3 0\\n\",\n \"4 1\"\n ]\n },\n \"execution_count\": 25,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"gender = pd.get_dummies(titanic['Gender'], drop_first=True)\\n\",\n \"\\n\",\n \"# male female\\n\",\n \"# 1 0\\n\",\n \"# 0 1\\n\",\n \"# 1 0\\n\",\n \"gender.head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"id\": \"42fb2bd0\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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CQS
0001
1100
2001
3001
4001
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\"\n ],\n \"text/plain\": [\n \" C Q S\\n\",\n \"0 0 0 1\\n\",\n \"1 1 0 0\\n\",\n \"2 0 0 1\\n\",\n \"3 0 0 1\\n\",\n \"4 0 0 1\"\n ]\n },\n \"execution_count\": 26,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"embark = pd.get_dummies(titanic['Embarked'])\\n\",\n \"embark.head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"id\": \"9c908558\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
SibSp_0SibSp_1SibSp_2SibSp_3SibSp_4SibSp_5SibSp_8
00100000
10100000
21000000
30100000
41000000
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\"\n ],\n \"text/plain\": [\n \" SibSp_0 SibSp_1 SibSp_2 SibSp_3 SibSp_4 SibSp_5 SibSp_8\\n\",\n \"0 0 1 0 0 0 0 0\\n\",\n \"1 0 1 0 0 0 0 0\\n\",\n \"2 1 0 0 0 0 0 0\\n\",\n \"3 0 1 0 0 0 0 0\\n\",\n \"4 1 0 0 0 0 0 0\"\n ]\n },\n \"execution_count\": 27,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"sibsp = pd.get_dummies(titanic['SibSp'], prefix='SibSp')\\n\",\n \"sibsp.head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 28,\n \"id\": \"b4fca7e5\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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Parch_0Parch_1Parch_2Parch_3Parch_4Parch_5Parch_6
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\"\n ],\n \"text/plain\": [\n \" Parch_0 Parch_1 Parch_2 Parch_3 Parch_4 Parch_5 Parch_6\\n\",\n \"0 1 0 0 0 0 0 0\\n\",\n \"1 1 0 0 0 0 0 0\\n\",\n \"2 1 0 0 0 0 0 0\\n\",\n \"3 1 0 0 0 0 0 0\\n\",\n \"4 1 0 0 0 0 0 0\"\n ]\n },\n \"execution_count\": 28,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"parch = pd.get_dummies(titanic['Parch'], prefix='Parch')\\n\",\n \"parch.head()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"4beab0b0\",\n \"metadata\": {},\n \"source\": [\n \"# Combine all the features\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 29,\n \"id\": \"94019ddd\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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SurvivedPclassAgeFare
00322.07.2500
11138.071.2833
21326.07.9250
31135.053.1000
40335.08.0500
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\"\n ],\n \"text/plain\": [\n \" Survived Pclass Age Fare\\n\",\n \"0 0 3 22.0 7.2500\\n\",\n \"1 1 1 38.0 71.2833\\n\",\n \"2 1 3 26.0 7.9250\\n\",\n \"3 1 1 35.0 53.1000\\n\",\n \"4 0 3 35.0 8.0500\"\n ]\n },\n \"execution_count\": 29,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"titanic = titanic.drop(['PassengerId', 'Name', 'Gender', 'SibSp', 'Parch',\\n\",\n \" 'Ticket', 'Embarked'], axis = 1)\\n\",\n \"\\n\",\n \"titanic.head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 31,\n \"id\": \"ecbd3207\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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SurvivedPclassAgeFaremaleCQSSibSp_0SibSp_1...SibSp_4SibSp_5SibSp_8Parch_0Parch_1Parch_2Parch_3Parch_4Parch_5Parch_6
00322.07.2500100101...0001000000
11138.071.2833010001...0001000000
21326.07.9250000110...0001000000
31135.053.1000000101...0001000000
40335.08.0500100110...0001000000
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5 rows × 22 columns

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\"\n ],\n \"text/plain\": [\n \" Survived Pclass Age Fare male C Q S SibSp_0 SibSp_1 ... \\\\\\n\",\n \"0 0 3 22.0 7.2500 1 0 0 1 0 1 ... \\n\",\n \"1 1 1 38.0 71.2833 0 1 0 0 0 1 ... \\n\",\n \"2 1 3 26.0 7.9250 0 0 0 1 1 0 ... \\n\",\n \"3 1 1 35.0 53.1000 0 0 0 1 0 1 ... \\n\",\n \"4 0 3 35.0 8.0500 1 0 0 1 1 0 ... \\n\",\n \"\\n\",\n \" SibSp_4 SibSp_5 SibSp_8 Parch_0 Parch_1 Parch_2 Parch_3 Parch_4 \\\\\\n\",\n \"0 0 0 0 1 0 0 0 0 \\n\",\n \"1 0 0 0 1 0 0 0 0 \\n\",\n \"2 0 0 0 1 0 0 0 0 \\n\",\n \"3 0 0 0 1 0 0 0 0 \\n\",\n \"4 0 0 0 1 0 0 0 0 \\n\",\n \"\\n\",\n \" Parch_5 Parch_6 \\n\",\n \"0 0 0 \\n\",\n \"1 0 0 \\n\",\n \"2 0 0 \\n\",\n \"3 0 0 \\n\",\n \"4 0 0 \\n\",\n \"\\n\",\n \"[5 rows x 22 columns]\"\n ]\n },\n \"execution_count\": 31,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"titanic = pd.concat([titanic, gender, embark, sibsp, parch], axis=1)\\n\",\n \"\\n\",\n \"titanic.head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 32,\n \"id\": \"22e99298\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"titanic.to_csv('titanic_cleaned.csv', index=False)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"dc3462b9\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.12\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n" }, { "path": "March Cohort 2023/10_11_EDA Project/titanic.csv", "content": "PassengerId,Survived,Pclass,Name,Gender,Age,SibSp,Parch,Ticket,Fare,Cabin,Embarked\n1,0,3,\"Braund, Mr. Owen Harris\",male,22,1,0,A/5 21171,7.25,,S\n2,1,1,\"Cumings, Mrs. John Bradley (Florence Briggs Thayer)\",female,38,1,0,PC 17599,71.2833,C85,C\n3,1,3,\"Heikkinen, Miss. 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rq2SS/x+gmXs+5ndFteP+zqo5v/LTSZAy+OnppkgdYfFnmF4E/Hz93K3BHkt8F/gF46rlPqqofJHkE+PKGTitNiT+0lQYa34v+IeDyqvrJZs8jnS1flikNkOTXgH8F/sLY68XKM3xJasIzfElqwuBLUhMGX5KaMPiS1ITBl6Qm/h8RIuZ7ygnOiwAAAABJRU5ErkJggg==\\n\",\n 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\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.title(\\\"Weather\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"id\": \"2332081c\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[]\"\n ]\n },\n \"execution_count\": 6,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.plot(x,y)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"id\": \"a36ae2d4\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[]\"\n ]\n },\n \"execution_count\": 7,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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5/SmtUi2aJNZi4MIsR3Rq5HceYgGEj/nI6dLSQ33ywjF++9iPRkWF8cNt5PHpZeyv6LhARRnRLYfG2g2zIPuJ2HGMChhX+cvhi5S4GPjWHqUt2cMcFzZh+T2+6pdZxO1ZIu6JLMhFhwqTM7W5HMSZgWOH3QvaR49z+ziJue2cxibHRfHxnLx64yJqqVQWJcdH0b53ElMXbrXGbMV6y+YkzUFUmL97BXz5bxbHCYn57YStG9WlqTdWqmIz0FGas2sOsNdlcaP17jDkrK/ynkbX/KL+fuoJ56/eS3rg2j1/V0W7+UUVd4Gnc9kFmlhV+Y7xghf8kJSXKW99t4Ykv1wLw6LB2XH9uY8Ksv06VFREexlVpybw8b5M1bjPGCzZncYIN2blk/Ps7Hvl0FempdZhxXx9u6JlqRT8AZKQnU1xSOjVnjDkzG/EDhcUlvDR3E+NmrqdaVDhPDu/ElWkNrb9OAGmaGEu31Np8kJnFbX2tcZsxZxLyI/6VOw5x2fgF/PPLtQxsm8TMMX25yjppBqSM9NLGbZnWuM2YMwrZwn+8sJh/fLGGy55bQE5uPi9el8bz13YlMS7a7WimgoZ0tMZtxngjJAv/wi37uWTcPF6YvZEruzRk5n19uai9NVULdNWjIri0UwOmLbfGbcacSUgV/tz8Iv708UqGv/gdBcUlvH1zd/45vBPx1SPdjmZ8JKNbCscKi/lsmTVuM+Z0HD24KyJbgCNAMVCkqukiUgeYCKQCW4AMVXV8Unb22mwenrqSnYeOcWPPVH57YStqWH+doNMlpRbNk2KZmJnFyO7WuM2YU/HHiL+fqnZW1XTP6weBr1W1BfC157VjDuQVMGbSUm58fSExkWF8eNt5PDKsnRX9ICUijEhPYcm2g6zfY43bjDkVN6Z6LgPe9Dx/E7jcqR19sXIXg56awydLd3J3/+ZMu6c3XRtbU7Vgd0VaQ0/jNjvIa8ypOF34FZghIotEZJRnWT1V3QXg+Zp0qhVFZJSIZIpIZk5OToV2vnZ3LvXjq/HJXedz/+BW1lQtRCTERjOgTRJTFu+wxm3GnILThb+XqqYBFwN3ikgfb1dU1ZdUNV1V0xMTEyu08zv6NWPqHT1p26BmhdY3gSsjPYV9eQV8vTrb7SjGVDmOFn5V3en5mg1MBboDe0SkPoDnq2P/Z0aGhxFhnTRDUt+WiSR5GrcZY/6XV1VRRBqLyEDP82oiEufFOjXKvk9EagCDgZXAJ8ANnm+7Afi4IsGNOZOI8DCu6prMN2uz2XP4uNtxjKlSzlr4ReRW4EPg355FycBHXmy7HjBfRJYBPwLTVPUL4HFgkIisBwZ5XhvjcxnpKZQoTF5sd+cy5kTenNN4J6VTND8AqOp6ETnlAdkTqeomoNMplu8DBpQzpzHl1iShBt2b1OGDzO3c3reZ9V8yxsObqZ58VS0oeyEiEZSerWNMlZeRnsLmvXks3GKN24wp403hnyMivweqicgg4APgU2djGeMbl3Q4h9joCGvcZswJvCn8vwNygBXAr4HpwB+cDGWMr5Q2bqvP9BW7OHK80O04xlQJZyz8IhIGrFDVl1V1uKpe7XluUz0mYGSkexq3Ld/ldhRjqoQzFn5VLQGWiYh1uzIBq3NKLVokxdp0jzEe3kz11Ad+EpGvReSTsofTwYzxFRFhRLcUlmYdZJ01bjPGq9M5H3U8hTEOu7xLQx7/fA2TFmbxh6Ft3Y5jjKvOWvhVdY4/ghjjpITYaAa2qcfUJTt44KLWREVYKw8Tury5cveIiBz2PI6LSLGIHPZHOGN8KaNbMvvyCpi1Zo/bUYxx1VkLv6rGqWpNzyMGuAoY73w0Y3yrT4tE6tWMZlKmtXAwoa3cf++q6kdAf99HMcZZEeFhXJWWzOy12ew+ZI3bTOjyZqrnyhMeV4vI41jLBhOgrHGbMd6N+C894XEhpTdPv8zJUMY4JTWhBj2a1OGDzCzsOkQTqrw5nfMVVV1w4gIR6YWDN1AxxkkZ6Snc/8Eyfty8nx5N67odxxi/82bE/6yXy4wJCJd0qF/auM3uzmVC1GlH/CJyHtATSBSRMSe8VROwu5abgFUtKpxLOzVg6pLtPDqsHXExkW5HMsavzjTijwJiKf3lEHfC4zBwtfPRjHHOiG4pHC8s4dNl1rjNhJ7Tjvg9V+zOEZE3VHWrHzMZ47hOyfG0rBfLxMwsrulhPQhNaPFmjv+oiPxTRKaLyKyyh+PJjHGQiJCRnsKyrIOs3W2N20xo8abwTwDWAE0obdi2BVjoYCZj/OKKLg2JDBcm2UFeE2K8Kfx1VfVVoFBV56jqr4BzHc5ljOPqntC4raCoxO04xviNN4W/7H51u0RkiIh0AZIdzGSM32Skp7A/r4CvV1vjNhM6vCn8fxWReOB+4DfAK8B9jqYyxk/6tEzknJoxNt1jQsrZ7rkbDrRQ1UOqulJV+6lqV1W1O3CZoBAeJlzdNZk563KscZsJGWe7524xMKwyOxCRcBFZIiKfeV53EpHvRGSFiHwqIjUrs31jKmt4erI1bjMhxZupnm9FZLyI9BaRtLJHOfZxL7D6hNevAA+qagdgKvDbcmzLGJ9rXLcG5zatw6TMLEpKrHGbCX7eFP6eQDvgz8CTnse/vNm4iCQDQygt9mVaAXM9z7+i9MYuxrgqIz2FrfuO8uOW/W5HMZVQUqKMn7WexdsOuB2lSvPmDlz9TvHw9kYsTwMPACeeK7eS/04fDQdSTrWiiIwSkUwRyczJyfFyd8ZUzMXt6xMXHcGkhXaQN5C9OHcj/5qxjvsnLaOo2E7RPR1vbsRST0ReFZHPPa/bisjNXqw3FMhW1UUnvfUr4E4RWURp75+CU62vqi+parqqpicmJp71gxhTGdWiwrm0cwOmr9zF4eOFZ1/BVDnfbtzLv75cS6t6cWzem8fUJTvcjlRleTPV8wbwJdDA83odMNqL9XoBw0RkC/A+0F9E3lHVNao6WFW7Au8BG8sb2hgnjEgva9y20+0oppz2HD7OPe8toUlCDSbf0ZP2DWsy7uv1dmHeaXhT+BNUdRKe6RpVLQKKz7aSqj6kqsmqmgqMBGap6nUikgQgImHAH4AXKxreGF/qmBxPq3pxNt0TYAqLS7jr3cUcLSjmxeu6Ehsdwf2DWrH9wDE+WGT/lqfiTeHPE5G6eO6zKyLnAocqsc9fiMg6Svv/7ARer8S2jPEZESGjWwrLth9ize7DbscxXnriizUs3HKAx67sQIt6cQBc0CqRLo1qMX7WBo4XnnWcGnK8KfxjgE+AZiKyAHgLuLs8O1HV2ao61PN8nKq29DweVLvxqalC/tO4baGd0x8IPl+xi5fnbeaX5zXmss4N/7NcRPjN4FbsOnSc937c5mLCqsmbs3oWA30pPa3z10A7VV3udDBj3FCnRhSD2tZj6pLtNj9cxW3KyeW3Hy6nU0otHh7S5mfv92xWlx5N6vDcNxs5VmCj/hN5c1ZPDHAP8BdK2zLf6VlmTFDKSE/hwNFCZlrjtirrWEExd0xYTGS48Py1aURH/PxusCLC/YNbsTc3n7e+2+L/kFWYN1M9b1F6AdezwHigLfC2k6GMcVPvFonUj7fGbVWVqvLwRytYu+cIT4/sQsNa1U77vd2b1KF3iwRenLOR3PwiP6as2rwp/K1U9WZV/cbzGAW0dDqYMW4pa9w2d10Ouw4dczuOOcl7P2YxZfEO7unfgr4tz36Nz/2DW3HgaCFvLNjsh3SBwZvCv8RzJg8AItIDWOBcJGPcN7xrSmnjtkV2kLcqWbH9EI988hO9WyRwz4AWXq3TOaUWA9sk8dLcTRw6ZhfngXeFvweljdq2eC7G+g7o6+muaQd5TVBqVLc65zWty6TM7da4rYo4eLSA2ycsIiE2inEjuxAeJl6ve9+glhw+XsSr8zY5mDBweFP4L6L0frt9PY8mwCXAUOBS56IZ466Mbsls23+UHzZb4za3lZQoYyYtY8/h4zx3bRp1akSVa/12DeK5pMM5vDp/M/vzTtklJqR4czrnVuAwEA/ULXuo6lbPe8YEpYvb1ycuJsIO8lYBL8zZyKw12fxxaFu6NKpdoW2MHtiSo4XF/HuudYnx5nTOvwDLgWcoZ1tmYwJZTGQ4wzo1YPoKa9zmpgUb9vLkjLUM69SA689tXOHttKwXx7BODXjz2y1kHwntu615M9WTATRT1Qsq0JbZmIA2olsK+UUlfLLUGre5Yfeh0uZrTRNjeezKDoh4P69/KvcOaEFhsfLC7NAe9XtT+FcCtRzOYUyV1KFhPK3PieMDm+7xu8LiEu58dzHHCot58bo0akRHVHqbTRNjubJLQyb8sC2kT9X1pvA/RukpnV+KyCdlD6eDGVMViAgZ6da4zQ2PTV/Doq0H+MdVHWmeFOez7d4zoAWqyvhZG3y2zUDjTeF/E/gH8Dj/neN/0slQxlQll3sat020ds1+M235Ll5bsJkbe6ZyaacGZ1+hHFLqVCcjPYVJmVlk7T/q020HCm8K/15VfcZz1e6csofjyYypIurUiGJw23P4aMkO8ous2ZfTNubk8sCHy+jSqBa/v+Tnzdd84a7+zRERnp213pHtV3XeFP5FIvKYiJwnImllD8eTGVOFZHTzNG5ble12lKB2tKCI299ZRHRkOM9dk0ZUhDclqvzqx1fj2h6NmLx4B5v35jmyj6rMm59qF+Bc4O/Y6ZwmRJ3fPIEG1rjNUarKw1NXsj47l3EjO9PgDM3XfOH2C5oRGS6Mm7nO0f1URd5cwNXvFA87ndOElP80blufw86DoXs2iJMm/LCNqUt2MHpAS3q3OHvztcpKiovhhp6pfLxsJ+v2HHF8f1WJNxdw1RORV0Xkc8/rtiJys/PRjKlaru6aglrjNkcs336QP3+6ir4tE7m7f3O/7ffXfZpRPTKcp0Ns1O/NVM8bwJdA2aH1dcBoh/IYU2U1qludns3qMmlRljVu86EDeQXc/s5iEuOieXpEZ8LK0XytsurUiOJX5zdh+ord/LSzMrcSDyynLfwiUna1RIKqTgJKAFS1CLBTG0xIykhPIWv/Mb7fvM/tKEGhpES5b9JSco7k8/y1adQuZ/M1X7ild1NqxkTw1FehM+o/04j/R8/XPBGpCyiApzd/6PxqNOYEF7U/p7Rxm53T7xPPfbOB2Wtz+OOlbemUUsuVDPHVIrm1d1Nmrs5madZBVzL425kKf9nfW2OAT4BmIrKA0lsx3u10MGOqopjIcC7r3IDPV+62m3pU0vz1exk7cx2Xd27AdT0auZrlpvObULt6JGNDZNR/psKfKCJjgAuAqcATwOfAy8BA56MZUzWNSG9U2rhtmTVuq6hdh45xz/tLaJEUy9990HytsmKjI7itbzPmrsth4Zbgv//CmQp/OBALxAE1gAjPsuqeZcaEpPYNa1rjtkooKCrhjgmLyS8s5oXrulI9qvLN13zhl+elkhAbzb++XItqcB+8P9NPfJeq/rmyOxCRcCAT2KGqQ0WkM/AiEAMUAXeo6o9n2IQxVYqIMKJbCo9+uorVuw7Tpn5NtyMFlL9PX82SbQd57po0miXGuh3nP6pFhXNnv2Y8+ukqvt24j17NE9yO5Bhv5vgr615g9QmvnwAeVdXOwJ88r40JKJd3bkhUeJg1biunT5ft5I1vt3BTr1SGdKzvdpyf+UX3RtSPj+HJGcE96j9T4R9Q2Y2LSDIwBHjlhMUKlA2R4gGbKDUBp3aNKAa1q8dHS3ewITvX7TgBYUN2Lg9OXk5ao1o8dLEzzdcqKyYynLv6N2fxtoPMXpfjdhzHnLbwq6ovjnA8DTyA5xoAj9HAP0Uki9KePw+dakURGSUimSKSmZMTvP8AJnDd3rcZqnDJuHk8980GCotLzr5SiMrLP6H52rXONV/zheFdU0iuXY2xM9YF7ajfsZ++iAwFslV10Ulv3Q7cp6opwH3Aq6daX1VfUtV0VU1PTHS+b4cx5dW+YTxfjenDwLZJ/PPLtVw2fgErd9glLidTVX4/dQUbcnJ5ZmQX6sc723ytsqIiwrhnQAtW7DjEjFV73I7jCCd/7fYChonIFuB9oL+IvAPcAEzxfM8HQHcHMxjjqKS4GJ6/tisvXpdG9pF8LntuAf/4Yg3HC+3i9jLvfL+Vj5fuZMzAlpzfIjAOmF7ZpSFNEmowdsa6oGzP4VjhV9WHVDVZVVOBkcAsVb2O0jn9vp5v6w+E5p0QTFC5qH19vh7Tlyu7NOSF2Ru5ZNy8kDgf/GyWZh3kz5+tol+rRO7s57/ma5UVER7G6IEtWLvnCNNW7HI7js+5MdF2K/CkiCyjtMf/KBcyGONz8dUj+efwTrx9c3cKiksY/uJ3/OnjleTmF7kdzRUH8gq4c8JikuJieMrPzdd8YWjHBrSsF8tTM9dRFGTHb/xS+FV1tqoO9Tyfr6pdVbWTqvY4xTEAYwJa7xaJfDm6Dzf2TOXt77dy4VNzmb02tO7cVVKijJ5Y2nzthevSqFXd/83XKis8TLhvYEs25eTx8dLgOvmw6h5aNyaA1YiO4JFh7fjwtvOIiQzjxtcXMmbSUg7kFbgdzS+enbWBOety+L9hbemYXMvtOBV2YbtzaFu/JuO+Xh9UZ21Z4TfGQV0b12HaPb25q19zPlm6k0FPzWH6il1Be5ogwNx1OTz99Tqu7NKQa7q723ytssLChPsHt2Tb/qN8GEQ34LHCb4zDYiLD+c2Frfj4rl6cEx/DHRMWc9s7i8g+fNztaD634+Ax7n1/CS2T4vjbFe43X/OF/q2T6JxSi2e/Xk9+UXCcrWWF3xg/adcgno/u6MWDF7dm9tocBo6dw6SFWUEz+i8oKuHOCYspLFZeuC6NalHhbkfyCZHSUf/OQ8eDpkWHFX5j/CgiPIzb+jbj83t70/qcmjwweTnXv/ojWfuPuh2t0v42bRVLsw7yxNUdaVqFmq/5wvnNE+ieWofxszYExTUaVviNcUHTxFjeH3Uuf7m8PUu2HWDwU3N5bf5migP0YqFPlu3kze+2cvP5TbikQ9VrvlZZIsKYwS3JPpLPO99vdTtOpVnhN8YlYWHC9ec2ZsaYvvRoWoc/f7aK4S9+y/o9R9yOVi7r9xzhwcnLSW9cmwcvbu12HMec27Qu5zdP4PnZG8kL8GszrPAb47KGtarx+o3deGpEJzbtzWPIM/N5NkBOH8zLL+L2CYupHhXO+GvSiAwP7pIyZnBL9ucV8Ma3W9yOUinB/a9kTIAQEa7okszMMX0Z1K4eT361jkufnc+K7VW36Zuq8uCUFWzyNF87Jz7G7UiOS2tUm/6tk3hp7iYOHw/cey5b4TemCkmIjea5a9L49/Vd2Z9XwGXPzeexz1dXyQOKb323lU+X7eT+wa3oGcR3qzrZmEEtOXSskFfnbXY7SoVZ4TemCrqw3Tl8NaYvGekp/HvOJi56ei7fb9rndqz/WLztAH+dtooBrZO4vW8zt+P4VfuG8VzYrh6vzd/MwaOBeSW2FX5jqqj4apE8flVHJtzSg2JVRr70PQ9PXcERl6cY9ucVcNeExZwTH8PYjMBrvuYL9w1qSW5BES/N3eR2lAqxwm9MFdereQJfju7Dzec34d0ftzH4qbl8s8adpm/FJcq97y9hb14BL1zblfjqka7kcFvrc2oytGMDXl+whb25+W7HKTcr/MYEgOpREfxxaFsm396T2OgIbnpjIaPfX8J+Pzd9e+br9cxbv5dHh7WjfcN4v+67qhk9sAX5RcW8OHuj21HKzQq/MQEkrVFtPrvnfO4Z0ILPlu9i0Ng5fLpsp1/aPsxem80zs9ZzVVoyI7ulOL6/qq5ZYixXdEnm7e+3sifA+i5Z4TcmwERHhDNmUEs+vft8Gtauxt3vLeHWtxax+5BzxWf7gaOMnriUVvXi+Ovl7YOi+Zov3DugBcUlynPfbHA7SrlY4TcmQLWpX5Mpt/fk4UvaMG99DoPGzuG9H7f5fPSfX1TMnRMWU1ysvHBd16BpvuYLjepWZ3h6Cu/9uI3tBwKn35IVfmMCWER4GLf2acqXo/vQtkFNHpqygmte/oGt+/J8to+/fraaZdsP8c/hHWmSUMNn2w0Wd/dvjiCMnxU4o34r/MYEgdSEGrx367n8/YoOrNhxiAufnssr8zZVuunbx0t38Pb3W7m1dxMuah98zdd8oUGtalzToxEfLNrOlr2++4XrJCv8xgSJsDDhmh6N+GpMH3o2S+Cv01Zz5QvfsnZ3xZq+rdtzhAcnr6Bbam0euCh4m6/5wh0XNCMiTHjm6/VuR/GKFX5jgkz9+Gq8ekM640Z2Jmv/UYY+O4+nZ66joMj7pm+5+UXc9s4iakRHhETztcpKqhnDL89rzEdLd7AhO9ftOGdl/5rGBCER4bLODfnqvj5c0qE+T89cz6XPzmdp1sGzrquq/G7ycrbszePZX3ShXs3gb77mC7f1bUZMZDhPz1zndpSzssJvTBCrGxvNuJFdeOWX6Rw6VsiVzy/gb9NWcazg9E3f3vh2C9OW7+I3F7bivGZ1/Zg2sNWNjeamXql8tnwXq3cddjvOGVnhNyYEDGxbjxlj+jCyeyNenreZC5+ey7cb9/7s+xZtPcDfpq1mYJt63NYntJqv+cKo3s2Ii4ngqa+q9qjf8cIvIuEiskREPvO8nigiSz2PLSKy1OkMxhioGRPJ36/owLu39kAErnn5Bx6asuI/feX35eZz17uLaVCrGk9mdArJ5muVFV89klvOb8qMVXtYvv2g23FOyx8j/nuB1WUvVHWEqnZW1c7AZGCKHzIYYzx6Nkvgi3v7MKpPUyYu3MagsXOY8dNu7n1/KfvyCnj+2jTiq4Vm8zVf+NX5qdSqHsnYKjzqd7Twi0gyMAR45RTvCZABvOdkBmPMz1WLCuf3l7Rhyh29qFUtilFvL2L+hr385TJrvlZZcTGR/LpPM2avzWHR1v1uxzklp0f8TwMPAKc6j6w3sEdVT3niq4iMEpFMEcnMyclxMKIxoatzSi0+vft8HrioFb8Z3JIR3Rq5HSko3NCzMQmxUTw5o2qO+h0r/CIyFMhW1UWn+ZZfcIbRvqq+pKrpqpqemJjoSEZjDERFhHHHBc25q38Lt6MEjepREdx+QXO+3bjvlAfR3ebkiL8XMExEtgDvA/1F5B0AEYkArgQmOrh/Y4xxzbU9GlGvZjRjZ6zzS9vs8nCs8KvqQ6qarKqpwEhglqpe53l7ILBGVbc7tX9jjHFTTGQ4d/VrTubWA8xdX7VG/W6dxz8SO6hrjAlyGd1SaFirGmNnrK1So36/FH5Vna2qQ094faOqvuiPfRtjjFuiI8K5Z0Bzlm0/xMzV7twn+VTsyl1jjHHQlWnJpNatztiv1lFSyTbZvmKF3xhjHBQZHsa9A1uwetdhvvhpt9txACv8xhjjuGGdGtI8KZaxX62r9M1xfMEKvzHGOCw8TLhvYEs2ZOfy6bKdbsexwm+MMf5wcftzaH1OHE/PXEdRsfc3xXGCFX5jjPGDsDDh/sGt2LLvKFMW73A3i6t7N8aYEDKwTRKdkuMZ9/X6ct0K09es8BtjjJ+ICPcNasmOg8eYmJnlWg4r/MYY40d9WyaS3rg242et53jh6W+B6SQr/MYY40ciwpjBLdlzOJ8JP2xzJYMVfmOM8bOezRI4r2ldXpi9gaMFRX7fvxV+Y4xxwf2DW7I3t4C3vtvq931b4TfGGBekp9ahb8tEXpyzkSOeG977ixV+Y4xxyf2DW3LwaCGvL9ji1/1a4TfGGJd0TK7FoLb1eHneJg4d9d+o3wq/Mca4aMyglhw5XsTL8zb5bZ9W+I0xxkVt6tdkSMf6vL5gM/ty8/2yTyv8xhjjsvsGtuBYYTH/nuufUb8VfmOMcVnzpDgu79yQt77bQvbh447vzwq/McZUAfcMaEFhsfL87I2O78sKvzHGVAGpCTW4Oi2Zd3/Yxs6DxxzdlxV+Y4ypIu4e0BxFGf/NBkf3Y4XfGGOqiOTa1RnZrRGTFmaxbd9Rx/Zjhd8YY6qQu/o3JzxMeGbWesf24XjhF5FwEVkiIp+dsOxuEVkrIj+JyBNOZzDGmEBRr2YM153bmCmLt7MxJ9eRffhjxH8vsLrshYj0Ay4DOqpqO+BffshgjDEB4/YLmhEdEc64mc6M+h0t/CKSDAwBXjlh8e3A46qaD6Cq2U5mMMaYQJMQG82NvVL5dPlO1u4+4vPtOz3ifxp4ADjxrsItgd4i8oOIzBGRbqdaUURGiUimiGTm5OQ4HNMYY6qWUb2bcn7zBAqLfX9TdscKv4gMBbJVddFJb0UAtYFzgd8Ck0RETl5fVV9S1XRVTU9MTHQqpjHGVEm1a0Tx9s09aN8w3ufbjvD5Fv+rFzBMRC4BYoCaIvIOsB2YoqoK/CgiJUACYMN6Y4zxA8dG/Kr6kKomq2oqMBKYparXAR8B/QFEpCUQBex1Kocxxpj/5eSI/3ReA14TkZVAAXCDZ/RvjDHGD/xS+FV1NjDb87wAuM4f+zXGGPNzduWuMcaEGCv8xhgTYqzwG2NMiLHCb4wxIUYC4YQaEckBtlZw9QSC53RR+yxVT7B8DrDPUlVV5rM0VtWfXQEbEIW/MkQkU1XT3c7hC/ZZqp5g+Rxgn6WqcuKz2FSPMcaEGCv8xhgTYkKh8L/kdgAfss9S9QTL5wD7LFWVzz9L0M/xG2OM+V+hMOI3xhhzAiv8xhgTYoK28IvIayKS7ekCGrBEJEVEvhGR1Z6b09/rdqaKEpEYEflRRJZ5PsujbmeqLBEJF5ElIvKZ21kqQ0S2iMgKEVkqIplu56koEaklIh+KyBrP/zPnuZ2pIkSkleffouxxWERG+2z7wTrHLyJ9gFzgLVVt73aeihKR+kB9VV0sInHAIuByVV3lcrRy89xprYaq5opIJDAfuFdVv3c5WoWJyBggHaipqkPdzlNRIrIFSFfVgL7oSUTeBOap6isiEgVUV9WDLseqFBEJB3YAPVS1ohey/o+gHfGr6lxgv9s5KktVd6nqYs/zI8BqoKG7qSpGS+V6XkZ6HgE78hCRZGAI8IrbWQyISE2gD/AqlLaAD/Si7zEA2Oirog9BXPiDkYikAl2AH1yOUmGeqZGlQDbwlaoG7GcBngYeAHx/N2z/U2CGiCwSkVFuh6mgppTewvV1z/TbKyJSw+1QPjASeM+XG7TCHyBEJBaYDIxW1cNu56koVS1W1c5AMtBdRAJyGk5EhgLZqrrI7Sw+0ktV04CLgTs9U6WBJgJIA15Q1S5AHvCgu5EqxzNdNQz4wJfbtcIfADzz4ZOBCao6xe08vuD5E3w2cJG7SSqsFzDMMzf+PtBfRN5xN1LFqepOz9dsYCrQ3d1EFbId2H7CX5EfUvqLIJBdDCxW1T2+3KgV/irOc0D0VWC1qo51O09liEiiiNTyPK8GDATWuBqqglT1IVVNVtVUSv8Un6WqAXlLURGp4TlxAM/UyGAg4M6GU9XdQJaItPIsGgAE3EkQJ/kFPp7mAXdutu4XIvIecAGQICLbgf9T1VfdTVUhvYDrgRWeuXGA36vqdPciVVh94E3PWQphwCRVDejTIINEPWBq6RiDCOBdVf3C3UgVdjcwwTNFsgm4yeU8FSYi1YFBwK99vu1gPZ3TGGPMqdlUjzHGhBgr/MYYE2Ks8BtjTIixwm+MMSHGCr8xxoSYoD2d05jKEpFiYAWlPYWKgDeBp1U1GFo0mBBmhd+Y0zvmaS+BiCQB7wLxwP+5GcqYyrKpHmO84GllMAq4S0qlisg8EVnsefQEEJG3ReSysvVEZIKIDHMrtzGnYhdwGXMaIpKrqrEnLTsAtAaOACWqelxEWgDvqWq6iPQF7lPVy0UkHlgKtFDVIn/nN+Z0bKrHmPIRz9dIYLyIdAaKgZYAqjpHRJ7zTA1dCUy2om+qGiv8xnhJRJpSWuSzKZ3n3wN0onTK9PgJ3/o2cC2lzdt+5eeYxpyVFX5jvCAiicCLwHhVVc80znZVLRGRG4DwE779DeBHYLeq/uT/tMacmRV+Y06vmqcjatnpnG8DZa2xnwcmi8hw4BtKb/oBgKruEZHVwEd+TWuMl+zgrjE+5mmnuwJIU9VDbucx5mR2OqcxPiQiZTeXedaKvqmqbMRvjDEhxkb8xhgTYqzwG2NMiLHCb4wxIcYKvzHGhBgr/MYYE2L+H0r2uZSENskOAAAAAElFTkSuQmCC\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.xlabel(\\\"Day\\\")\\n\",\n \"plt.ylabel(\\\"Temperature\\\")\\n\",\n \"plt.title(\\\"Weather\\\")\\n\",\n \"plt.plot(x,y)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"id\": \"c30385dc\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[]\"\n ]\n },\n \"execution_count\": 8,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.xlabel(\\\"Day\\\")\\n\",\n \"plt.ylabel(\\\"Temperature\\\")\\n\",\n \"plt.title(\\\"Weather\\\")\\n\",\n \"\\n\",\n \"plt.plot(x,y, color=\\\"red\\\", linewidth=2)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"id\": \"eeea6f84\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[]\"\n ]\n },\n \"execution_count\": 9,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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d096qPWjoq2+9UjDFZWePPgS6Tu9BpUif2HN3jc3uR/EV4t+W7zLl7DlVLVw1yurzv7oS7veYt2rB/A7P+nOVSImPCizX+HGhaoWm221pUasGqPqvoU79PRE6qFgzxxeK58VLvKZ5GLrVr+o3xh3WmHOhZpyfXXnJtltp5Bc/jw1s/5Ou7vqZ88chaMcwNvu7ktYnbjPGPNf4cEBGG3TyMgvky17ltX709q/uspkvtLjapWpC0qdqG0oVKn3x+ZfyVvNPyHQrkK+BiKmPCg33jmA1VPWMTr1iyIoNvGkypQqW49bJbgxfMAJlLUD7Y8EH2H9tPjzo9qBZXze1IxoQNUQ395WwTExM1KSkpKMdKSUvhxbkv8se+P/i47cdBOaYxxjhBRBarqtckV3bGf4qfN/9Mzy97suavNQC0q9aO2y+/3eVUxhgTWDbGDxw+fpiHpj9Eo5GNTjZ9gL5f97UvC40xeU7EN/7v139Pjfdr8NbCt1CyDnvtOLSDR7971KVkxhjjjIgd6tl/bD+PfvvoGa/9zheVj/hi8Wf9otcYY8JJRDb+KWun0GdaH7Yf2p7tPvXK1mPkLSOpVaZWEJOZQFmyfQkXFr3QlrA0xoeIGurZeWgn7T9vz22f3pZt04/NF8ug6wex4J4F1vTDzN6jexm8cDB1htah3gf1GJo01O1IxoSkiDjjV1U+Wv4RD01/iH3Hsv+ytsnFTRh28zAql6ocxHQmECatmUTHiR05nn78ZG3U0lE80+QZmzrDmNPk+T8Rmw5souXHLek2pVu2Tb9o/qIMaTWEmd1mWtMPUw3KNSAtIy1LbeOBjcz8c6ZLiYwJXY42fhHZICIrRGSpiCR5aueJyAwRWef5WdKJY2doBu8uepfq71Vn+u/Ts92vVeVWrO67mt6Jve3MMIxlN3Gbrc5ljLdgdLpmqppwyt1jTwI/qGpl4AfP84A7lnaM/y34H4eOH/K5vVTBUoy7fRxTO04lvli8ExFMkPlajH3ymsnsPbrXhTTGhC43TnFvAcZ4fh8D3OrEQQrFFGLYzcN8butQowNr+q7hrpp32WWaecjNVW/OMnEbQEp6Ch+vsKk3jDmV041fge9EZLGI9PLUyqjqdgDPz/N9vVBEeolIkogk7d69O0cHb3ZJM3rV7XXy+YVFL+SLDl8wvu144grH5eg9TejKH52fLrW6eNVHJts8/cacyunG30hV6wI3AX1FpLG/L1TVD1Q1UVUT4+Jy3qQH3TCI+GLx9Krbi9V9VtOmapscv5cJfb7m6U/ekUzy9mQX0hgTmhxt/Kq6zfNzFzAZaADsFJGyAJ6fu5zMUDy2OCvvW8nQm4dSPLa4k4cyIaDG+TVoUK6BV93O+o35h1+NX0QuFpHrPb8XFJGifrym8In9RKQw0BxYCXwJdPPs1g34IifBz4U1/Mji60vecSvGcSztmAtpjAk9Z238InIvMAE4cRtkPDDFj/cuA8wTkWXAImCaqk4HXgFuEJF1wA2e58YEzJ3V7zy5OtoJ+47tY8raKe4EMibE+HPG3xdoBBwEUNV1ZPOF7KlUdb2q1vY8qqvqS576HlW9TlUre37atXYmoIrHFueO6nd41e2afmMy+dP4U1T15H3wIpIPCP1lu0xE65Hg/SXvD+t/YMP+DcEPY0yI8afxzxGR/wAFReQG4HNgqrOxjMmdxhc3ptJ5lbLUFGX00tHuBDImhPjT+J8AdgMrgN7A18DTToYyJrdExOdZ/6ilo8jQDBcSGRM6ztj4RSQKWKGqw1T1DlVt5/ndhnpMyOtau6vX/EubDmzih/U/uJTImNBwxsavqhnAMhEpH6Q8xgRMuWLluKnSTV71T1Z+4kIaY0KHP/PxlwVWicgi4PCJoqraLbAm5PWo04Np66YRJVG0qNSCnnV60rpKa7djGeMqfxr/846nMMYhrau0ZtD1g7ir5l2UK1bO7TjGhISzNn5VnROMIMY4IX90fh5v9LjbMYwJKWdt/CLyN/9ct58fiAEOq2oxJ4MZY4xxhj9n/Fnm5RGRW8mcbM0YY0wYOufZOVV1CnBt4KMYY4wJBn+Gem4/5WkUkIhN2WCMMWHLn6t6bj7l9zRgA5nLJxoTtjI0g1l/zmLh1oX855r/uB3HmKDyp/EPV9X5pxZEpBEOL6BijBM27t/I6KWjGbV0FBsPbATgrpp3UaFEBXeDGRNE/ozxD/azZkxIU1VuHHsjA+YMONn0AUYlj3IxlTHBl23jF5ErReRRIE5EHjnlMQCIDlpCYwJEROhWu5tXfdTSUaRnpLuQyBh3nOmMPz9QhMzhoKKnPA4C7ZyPZkzgdUvo5jVx2+aDm/nhT5u4zUSObMf4PXfszhGR0aq6Mbv9jAknFxa9kJsq3cS0ddOy1Ecmj6T5pc1dSmVMcPkzxn9ERP5PRL4WkZknHo4nM8YhvhZjn7x2MnuO7HEhjTHB50/jHwesBS4hc8K2DcAvDmYyxlGtq7Tm/MJZl40+nn6ccSvGuZTImODyp/GXUtURQKqqzlHVHsAVDucyxjEx0TF0qdXFqz4ieQS2xpCJBP40/lTPz+0i0kpE6gDxDmYyxnE96ngvy7h853KWbF/iQhpjgsufxv+iiBQHHgUeA4YDDzuayhiHVYurxhXx3v9wHZk80oU0xgTX2dbcjQYqq+oBVV2pqs1UtZ6qfhmkfMY4xteXvONWjONo6lEX0hgTPGdbczcdyNUSiyISLSLJIvKV53ltEflZRFaIyFQRsXn9jSvaV29PoZhCWWoHUg4wee1klxKZ3NpzZA8TVk9gzoY5ZGiG23FClj9DPT+JyDsico2I1D3xOIdjPAisOeX5cOBJVa0JTAZseSTjimIFitG+enuv+ojkES6kMbm1atcqag+pzR2f30HTMU3pNqWbfVmfDX8a/1VAdeAF4HXP4zV/3lxE4oFWZDb7E6oCcz2/zwDa+hvWmEDzNdwz88+Z/LnvTxfSmJw6cOwAt316G1v/3nqyNnb5WKasneJeqBB21sbvGdc//eHvQixvAv2AU//NtZJ/ho/uAC7y9UIR6SUiSSKStHv3bj8PZ8y5aXRRI6qUquJVH7XUJm4LF6pKjy97sG7vOq9tz85+1oZ8fDhr4xeRMiIyQkS+8TyvJiLep0ner2sN7FLVxadt6gH0FZHFZM79c9zX61X1A1VNVNXEuLi4s34QY3JCROiR4H1p5+ilo23itjDxvwX/Y9KaST63rdy1ks9WfRbkRKHPn6Ge0cC3wIWe578BD/nxukZAGxHZAHwCXCsiY1V1rao2V9V6wHjgj3MNbUwgda3dlWjJOuFsTHQMmw9udimR8de8TfPoN6PfGfcZMHsAaRlpQUoUHvxp/KVV9TM8wzWqmgac9VRIVfuraryqVgA6ADNVtbOInA8gIlHA08CQnIY3JhDKFi1Ly8otic0XS+danZnVbRbr/r3OFmcJcTsP7aT95+1J1zO3o1/3/MrHKz4OUqrw4M8KXIdFpBSedXZF5ArgQC6O2VFE+np+nwTYYKpx3Vst3qJkwZKUiC3hdhTjh7SMNDpO7Mj2Q9u9thWKKcSR1CNZas/PeZ6ONToSEx0TrIghzZ8z/keAL4FLRWQ+8CHw73M5iKrOVtXWnt/fUtUqnseTatdbmRBwSclLrOmHkSOpR8gX5X3e2uiiRky7a5pXff2+9YxeOjoIycKDP1f1LAGakHlZZ2+guqoudzqYMcZkp1iBYnzT6RuebfwsggBwfuHz+bTdpzSt0JQWlVp4vWbg3IGkpKUEO2pI8ueqnljgAWAgmdMy9/XUjDHGNdFR0Tzf7Hm+7vQ1cYXiGN92POWKlQNgYLOBXvtvPriZYUuGBTtmSPJnqOdDMm/gGgy8A1QDPnIylDHG+KtFpRb8+eCfXHvJP7cXJV6YyC1Vbzn5PF9UPnrX652lFsn8+XK3qqrWPuX5LBFZ5lQgY4w5V4XzF/aqvdDsBaatm0aXWl14pvEzXFLyEheShSZ/Gn+yiFyhqgsARKQhMN/ZWMa4LyUthS9//ZKRS0fStVZXOtbs6HYkcw5qlanF5oc3c0GRC9yOEnL8afwNga4issnzvDywRkRWAKqqtRxLZ4wL/tz3J28tfIuxy8ey52jmOrwpaSnW+F2yevdqihUoRnyxc1//yZq+b/40fu+vx43Jw7b+vZW3Fr6VpTZrwyzW71tPxZIVXUoVmfYf20+b8W04kHKA8W3Hc33F692OlCf4cznnRuAgUBwodeKhqhs924zJU7KduC3Z7jUMJlWl+xfd+WPfH/x15C+af9ScF+e+aJOuBYA/l3MOBJYDb3OO0zIbE46ynbhtmU3cFkyv/fRalmmVFeWZWc/w6LePuhcqj/Dncs72wKWq2jQH0zIbE5Z8Tdy25eAWZqyf4VKiyDJ341z6/9Dfq140f1Huq39frt//0PFDvDLvFV6Z90qu3ysc+dP4VwIlHM5hTEg5MXHb6Wwxdudt/3s7d0640+fka6NvHe1zGM5fR1OP8sbPb1DxrYr0/6E/A+cOZOehnbmJG5b8afwvk3lJ57ci8uWJh9PBjHGbr9W5pqydwl9H/nIhTWRIy0ijw8QO7Di0w2vbo1c+yu2X357j905NT6Xm+zV59LtH2X0kc3GnI6lHeHX+qzl+z3DlT+MfA7wKvMI/Y/yvOxnKmFDQsnJLyhQuk6WWmpHKuOXjXEqU9z31w1PM3TjXq351+at5+bqXc/XeMdExtKnaxqv+ftL7bD241ccr8i5/Gv9fqvq2qs5S1TknHo4nM8ZlMdExdK3d1as+InmELeLtgC/WfsGgnwZ51csULsOn7T4NyJTKT179JIViCmWpHUs7xn9//G+u3zuc+NP4F4vIyyJypYjUPfFwPJkxIaBHHe+re1bsWkHStiQX0uRdv+/9nW5TunnVoySKT9p9woVFL/TxqnN3fuHzeaDBA171YUuGsXF/5Fyd7k/jrwNcAfwXu5zTRJjLSl/GVRdd5VW3L3kD52jqUdp91o4DKd7rO7107Us0rdA0oMd77KrHKJq/aJZaakYqA+d6z+iZV/lzA1czHw+7nNNEDF9f8n688mOvVZ5MzvT9ui/LdnrP+3hzlZvp1+jM6+nmRKlCpXjkyke86qOXjub3vb8H/HihyJ8buMqIyAgR+cbzvJqIeP9JMCaPuqPaHRSOyTr748GUg0xaM8mlRHnHiCUjGLXU+47oS0pcwphbxxAl/gxKnLuHr3iYkrEls9TSNZ3n5zzvyPFCjT//VUcD3wInBtl+Ax5yKI8xIadogaLcWf1Or/qI5BEupMk7krcn0/frvl71AtEFmNh+IiULlvTxqsAoHlucx656zKs+bvk41uxe49hxQ0W2jV9ETkzgVlpVPwMyAFQ1DbD71k1E6VnX+x+5szfM5o+9f7iQJvztP7afdp+3IyXdeynEd1q+Q52ydRzP8EDDByhdqHSWmqIMmDPA8WO77Uxn/Is8Pw+LSClAAUTkCsD7Wxhj8rAr46+kaqmqXnVfwxTm7PYf2+81fAZwd8LdPr9TcUKR/EV4stGTXvXPVn3Gsh15e62pMzV+8fx8BPgSuFRE5pO5FOO/nQ5mTCgREa+GVLdsXS4vfblLicJbhRIVWHDPgiz3SdQqU4t3W76LiJzhlYF1X/37KFukrFf9udnPBS2DGyS7G1FEZAvwhudpFFCAzL8MUoB0VX3D5wsdkJiYqElJdt20cdeOQztIGJLAndXvpHud7iRckOB2pLCnqgxbMoxnZj3D/B7zqXRepaBneGfRO/z7G+9z2UX3LKJ+ufpBzxNIIrJYVRO96mdo/NuB9/nnzD8LVQ3a19/W+E2oSM9IJzoq+uw7mnNy+Phhn+vmBkNKWgqVB1dm88HNWeotKrXgm07fuJIpULJr/GdagWu7qr4QgANHA0nAVlVtLSIJwBAgFkgD+qjqojO8hTEhw5q+M9xq+gAF8hXgmcbP0OurXlnq03+fzvxN82lUvpFLyZzjzxh/bj0InHp91CDgeVVNAJ71PDfGGNfcnXC3z2U1p/8+3YU0zjtT478ut28uIvFAK2D4KWUFinl+Lw5sy+1xjAkFB44dYOafM92OEXJ+3vwzB1MOuh3jjGKiY3iuyT9f6F53yXX81OMnBl6bN6dxyLbxq+reALz/m0A/PPcAeDwE/J+IbCZzzh/vZXYAEeklIkkikrR79+4ARDHGWY/PeJzrPryOe768h/3H9rsdJySs27OOFuNaUH9YfVbuWul2nDPqVLMTXWp1YVa3WXzf9XuuvOhKtyM5xpn7oQERaQ3sUtXFp226D3hYVS8CHgZ83v6oqh+oaqKqJsbFxTkV05iAmPnnTIYtGQZk3tFb7d1qWdaLjURHUo/Q7vN2HEw5yG97fqPh8IYhvZZBdFQ0H972YcAnhQtFjjV+oBHQRkQ2AJ8A14rIWKAbcGKSk8+BBg5mMMZxR1KPcO/Ue7PUth/azm2f3kb7z9tH5NJ+qkqfaX1YvnP5ydqR1CN0ntyZ4UuGn+GVJhgca/yq2l9V41W1AtABmKmqnckc02/i2e1aYJ1TGYwJhpHJI1m/b73PbZ+v/pxq71Xjo2UfRdTiLcOXDGfMsjFe9UtLXkq7au1cSGRO5eQZf3buBV4XkWVkzvHf6yz7GxPS+tTvw5BWQ7zmeD9h79G9dJ3SlZYft2TTgU1BThd8S7Yv8XlDVGy+WCa0n0CJ2BLBD2WyCErjV9XZqtra8/s8Va2nqrVVtaGP7wCMCStREkXvxN6s7ruaVpVbZbvf9N+nU/296ry76F0yNCPb/cLZvqP7aPtZW5+Tr73X8r2wvts5PSM9z/zF7cYZvzF5UnyxeKZ2nMq428dRqmApn/scOn6I+7+5nyajm/DrX78GOaGzMjSDrlO6smH/Bq9tPev0pHud7sEPFQAZmsGE1ROoNaQWLca2ID0j/CcntsZvTACJCHfVvIs1fdfQoUaHbPebt2ketYfU5pV5r5CanhrEhM55dd6rfPXbV171hAsSGHzTYBcS5Y6qMvXXqdQdWpc7Pr+D1btXs+avNYxfOd7taLlmjd8YB8QVjmN82/F80eGLbBcKT0lPof8P/Wk4vCHJ25ODnDCwZv05i6dnPe1VL16gOBPbT6RgTEEXUuXe6z+/7rUs5IDZA8L+L2tr/MY4qE3VNqzus5pedbO/hiF5RzL1h9Vn8prJQUwWOFsPbqXDxA4+v7f48LYPfU6FEA5EhIHNvO/c/WPfH3y47EMXEgWONX5jHFY8tjhDbx7KzK4zubTkpT73iSscF5Y3DqWmp3LnhDvZdXiX17YnGj1Bm6ptXEgVONdcfA3NL23uVX9h7gukpHl/gR0urPEbEyTNLmnG8vuW8+iVj3otIv5ey/ccXWPWKU9+/yTzN8/3qjet0JQXr33RhUSB5+usf9OBTWG95rI1fmOCqFBMIV5r/ho/9/yZGufXAKBdtXbcdvltLic7dxNXT+SNBd7rMZUtUpbxbceTL+pMs76HjwblGtC6Smuv+ks/vsTR1KMuJMo9a/zGuKBBuQYs7rWYF5u96NcVL6F2CeFve36j+xfel2dGSzSftvuUC4pc4EIq57zQ1Htpkm1/b2Po4qEupMk9a/zGuCR/dH6eavzUWZvkH3v/oMo7VZi4emKQkp3d/mP7KZK/iFf91etf5ZqLr3EhkbPqlK1D28vbetVfnvcyh48fdiFR7ljjNyaEqSr3Tr2X9fvW0+7zdrT9rC3b/97udiwalGtAcu9kmlVodrJ2++W388iVj7iYylnPN30eOW19ql2Hd/HOondcSpRz1viNCWHDlwxn1oZZJ59PWjOJau9VY/TS0a5P+lamSBm+6/Id/a/uT5VSVRjZZiQigVq4L/RUP786HWt29KoP+mlQyC80czpr/MaEqC0Ht/DYjMe86vuP7af7F925ceyNPqdHCKZ8Ufn473X/ZUmvJRSPLe5qlmB4rslzXldk7T26lzcXvOlOoByyxm9MiIqW6DNe2z9j/QxqvFeDtxe+7fqXv24ulh5MVUpVoVvtbl71N35+g31H97mQKGes8RsTosoWLcuUO6fwSdtPiCvkexW6w6mHeXD6gzQe3Zg1u9cEOWFkeqbxM16Xqh5IOcDrP7/uUqJzZ43fmBAmItxZ405W911Np5qdst3vp80/kTA0gZfmvhTweWR+WP9D2M9NE0iXlLyEnnV6etXfXPAmuw+Hx/rg1viNCQOlC5Vm7O1j+arjV8QXi/e5z/H04zw962nqD6vP4m2BWebih/U/0Hxsc6778LqQuJooVDzd+GkKRBfIUjucephB8we5lOjcWOM3Joy0qtKKVX1WcV/ifdnus2znMhoOb8iT3z+ZqztLtx7cSseJHcnQDH7c9CN1htZh9obZOX6/vCS+WDy96/XOUrus9GVcddFVLiU6N+L2JWH+SExM1KSkJLdjGBNS5myYwz1T7+H3vb9nu0/l8yozvM1wGl/c+JzeOzU9laZjmvLT5p+y1KMkilndZp3z++VFOw7toOJbFbmw6IUMaDqAjjU6Eh0V7XasLERksaomnl63M35jwlSTCk1Y/q/l9Luqn9clhies27uO+Zu8J1E7m34z+nk1fYAmFzcJm7Nap11Q5ALm3D2HNX3X0LlW55Br+mdijd+YMFYwpiCv3vAqC+9ZSM3za3ptT7gggceu8r4X4Ew+X/U5by5806ue1yZfC4T65eoTEx3jdoxzZo3fmDwg8cJEknolMbDZQPJH5wcy7wMY2WbkOTWmtX+tpceXPbzq0RLNZ3d8RpkiZQKW2bjHGr8xeUT+6Pw83fhpknsnc0X8FfRr1I86Zev4/frDxw/T7rN2HDp+yGvboBsGcXX5qwMZ17jI/s1mTB5TLa4a87rPI13PfjfvsMXDaFWlFWWLlKX3V71ZtXuV1z5tL2/Lw1c87ERU4xJr/MbkQdFR0URz5i8b52yYQ6+velG8QHFuuewWxq0Y57VP5fMqM/KWvD35mlMWblnInI1z6Neon9tRvDje+EUkGkgCtqpqaxH5FKjq2VwC2K+qCU7nMMb842jqUe6Zeg+QOd2Ar8XDC+YryMT2EylWoFiw44W1JduX8OysZ5m2bhqC0LJyy5OrrYWKYIzxPwicnEREVe9U1QRPs58ITApCBmPMKQbMHnDG6/8BhrYeSs0y3lcKmez1mdaHeh/UY9q6aQAoynOzn3M5lTdHG7+IxAOtgOE+tgnQHhjvZAZjTFaHjh9i7IqxZ9ynd73edKndJUiJ8o7LSl/mVZu0ZhJLti9xIU32nD7jfxPoB2T42HYNsFNV1/l6oYj0EpEkEUnavTs8Jj4yJhwUyV+E5f9aTtfaXX1ur1e2Hm+2eDO4ofKIXvV6+ZxL6dlZz7qQJnuONX4RaQ3sUtXsZovqyBnO9lX1A1VNVNXEuDjfU9IaY3KmVKFSjLl1DN90+obyxcufrF9S4hImtJ9AbL5YF9OFr9h8sTx9zdNe9WnrprFgywIXEvnm2Fw9IvIy0AVIA2KBYsAkVe0sIvmArUA9Vd1ytveyuXqMcc6R1CNM+20ax9OP07JyS0oWLOl2pLB2PP04Vd+p6rU62vUVr2dGlxlBzRL0uXpUtb+qxqtqBaADMFNVO3s2Xw+s9afpG2OcVSimEHdUv4NOtTpZ0w+A/NH5ebax99DO9+u/Z86GOS4k8ubWnbsdsC91jTF5VJfaXah8XmWv+jOzniEUZkQOSuNX1dmq2vqU53er6pBgHNsYY4ItX1Q+BjQd4FX/cdOPfL/+++AHOo3N1WOMMQ64s/qdVIur5lUPhbN+a/zGGOOA6KhoXmj6gld94daFJ2/wcos1fmOMcchtl99GwgUJXvVnZz1Lhvq6vSk4rPEbY4xDoiTK51l/8o5kJq+Z7EKiTNb4jTHGQa2rtKZBuQZe9edmP0d6xtmnznaCNX5jjHGQiDCw2UCv+qrdq/h01acuJLLGb4wxjruh4g0+VzB795d3XUhjjd8YYxwnIrzY7MWTz0vEluCla1/i287fupLHVuAyxpggaFKhCbdffjs14mrw8JUPUyK2hGtZrPEbY0yQTLhjQkgsY2lDPcYYEySh0PTBGr8xxkQca/zGGBNhrPEbY0yIOJp6NCjHscZvjDEu23JwC/d9dR+Xvn0pf6f87fjxrPEbY4xLdh7ayUPTH6LS25UYsngI2w9t5+2Fbzt+XGv8xhjjkqdmPsVbC98iJT3lZO21n19j/7H9jh7XGr8xxrik/9X9iZboLLX9x/bzxs9vOHpca/zGGOOSS8+7lB51enjV/7fgf/x15C/HjmuN3xhjXPR046fJH50/S+3Q8UP83/z/c+yY1viNMcZF5YuXp1fdXl71wYsGs+PQDkeOaY3fGGNc9p9r/kNsvtgstaNpR3ll3iuOHM8avzHGuKxs0bL0rd/Xq/5+0vtsObgl4Mezxm+MMSHgiUZPUDimcJba8fTjvDT3pYAfy/HGLyLRIpIsIl+dUvu3iPwqIqtEZJDTGYwxJtTFFY7jgYYPeNVHJI/gz31/BvRYwTjjfxBYc+KJiDQDbgFqqWp14LUgZDDGmJD32FWPUaxAsSy11IxUBi8aHNDjONr4RSQeaAUMP6V8H/CKqqYAqOouJzMYY0y4OK/geTxyxSMnn5ctUpZ3bnqHl697OaDHcfqM/02gH5BxSq0KcI2ILBSROSJS39cLRaSXiCSJSNLu3bsdjmmMMaHhoSse4rLSl/F689f544E/6NugLwXyFQjoMRxbelFEWgO7VHWxiDQ97ZglgSuA+sBnIlJRVfXU16vqB8AHAImJiVm2GWNMXlU8tjir+qwiSpw7L3dyzd1GQBsRaQnEAsVEZCywBZjkafSLRCQDKA3Yab0xxoCjTR8cHOpR1f6qGq+qFYAOwExV7QxMAa4FEJEqQH7AuUkpjDHGZOHkGX92RgIjRWQlcBzodvowjzHGGOcEpfGr6mxgtuf340DnYBzXGGOMN7tz1xhjIow1fmOMiTDW+I0xJsJY4zfGmAgj4XBBjYjsBjbm8OWlyTuXi9pnCT155XOAfZZQlZvPcrGqxp1eDIvGnxsikqSqiW7nCAT7LKEnr3wOsM8Sqpz4LDbUY4wxEcYavzHGRJhIaPwfuB0ggOyzhJ688jnAPkuoCvhnyfNj/MYYY7KKhDN+Y4wxp7DGb4wxESbPNn4RGSkiuzyzgIYtEblIRGaJyBrP4vQPup0pp0QkVkQWicgyz2d53u1MuSUi0SKSLCJfuZ0lN0Rkg4isEJGlIpLkdp6cEpESIjJBRNZ6/sxc6XamnBCRqp7/FyceB0XkoYC9f14d4xeRxsAh4ENVreF2npwSkbJAWVVdIiJFgcXAraq62uVo50xEBCisqodEJAaYBzyoqgtcjpZjIvIIkAgUU9XWbufJKRHZACSqaljf9CQiY4AfVXW4iOQHCqnqfpdj5YqIRANbgYaqmtMbWbPIs2f8qjoX2Ot2jtxS1e2qusTz+9/AGqCcu6lyRjMd8jyN8TzC9sxDROKBVsBwt7MYEJFiQGNgBGROAR/uTd/jOuCPQDV9yMONPy8SkQpAHWChy1FyzDM0shTYBcxQ1bD9LMCbQD8gw+UcgaDAdyKyWER6uR0mhyqSuYTrKM/w23ARKex2qADoAIwP5Bta4w8TIlIEmAg8pKoH3c6TU6qarqoJQDzQQETCchhORFoDu1R1sdtZAqSRqtYFbgL6eoZKw00+oC7wvqrWAQ4DT7obKXc8w1VtgM8D+b7W+MOAZzx8IjBOVSe5nScQPP8Enw20cDdJjjUC2njGxj8BrhWRse5GyjlV3eb5uQuYDDRwN1GObAG2nPKvyAlk/kUQzm4ClqjqzkC+qTX+EOf5QnQEsEZV33A7T26ISJyIlPD8XhC4HljraqgcUtX+qhqvqhXI/Kf4TFUNyyVFRaSw58IBPEMjzYGwuxpOVXcAm0Wkqqd0HRB2F0GcpiMBHuYBdxZbDwoRGQ80BUqLyBbgOVUd4W6qHGkEdAFWeMbGAf6jql+7FynHygJjPFcpRAGfqWpYXwaZR5QBJmeeY5AP+FhVp7sbKcf+DYzzDJGsB7q7nCfHRKQQcAPQO+DvnVcv5zTGGOObDfUYY0yEscZvjDERxhq/McZEGGv8xhgTYazxG2NMhMmzl3Mak1sikg6sIHNOoTRgDPCmquaFKRpMBLPGb0z2jnqml0BEzgc+BooDz7kZypjcsqEeY/zgmcqgF3C/ZKogIj+KyBLP4yoAEflIRG458ToRGScibdzKbYwvdgOXMdkQkUOqWuS02j7gMuBvIENVj4lIZWC8qiaKSBPgYVW9VUSKA0uByqqaFuz8xmTHhnqMOTfi+RkDvCMiCUA6UAVAVeeIyLueoaHbgYnW9E2oscZvjJ9EpCKZTX4XmeP8O4HaZA6ZHjtl14+ATmRO3tYjyDGNOStr/Mb4QUTigCHAO6qqnmGcLaqaISLdgOhTdh8NLAJ2qOqq4Kc15sys8RuTvYKeGVFPXM75EXBiauz3gIkicgcwi8xFPwBQ1Z0isgaYEtS0xvjJvtw1JsA80+muAOqq6gG38xhzOruc05gAEpETi8sMtqZvQpWd8RtjTISxM35jjIkw1viNMSbCWOM3xpgIY43fGGMijDV+Y4yJMP8PsWKhhR6bvrAAAAAASUVORK5CYII=\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.xlabel(\\\"Day\\\")\\n\",\n \"plt.ylabel(\\\"Temperature\\\")\\n\",\n \"plt.title(\\\"Weather\\\")\\n\",\n \"\\n\",\n \"plt.plot(x,y, color=\\\"green\\\", linewidth=5, linestyle='dashed')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"id\": \"60d6d89e\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[]\"\n ]\n },\n \"execution_count\": 12,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.xlabel(\\\"Day\\\")\\n\",\n \"plt.ylabel(\\\"Temperature\\\")\\n\",\n \"plt.title(\\\"Weather\\\")\\n\",\n \"\\n\",\n \"plt.plot(x,y, color=\\\"red\\\", linewidth=2, linestyle='dotted')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"af1765ba\",\n \"metadata\": {},\n \"source\": [\n \"## format strings\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"id\": \"dce734b8\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[]\"\n ]\n },\n \"execution_count\": 13,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.xlabel(\\\"Day\\\")\\n\",\n \"plt.ylabel(\\\"Temperature\\\")\\n\",\n \"plt.title(\\\"Weather\\\")\\n\",\n \"\\n\",\n \"plt.plot(x,y, 'r+--')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"id\": \"ced721bc\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[]\"\n ]\n },\n \"execution_count\": 14,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.xlabel(\\\"Day\\\")\\n\",\n \"plt.ylabel(\\\"Temperature\\\")\\n\",\n \"plt.title(\\\"Weather\\\")\\n\",\n \"\\n\",\n \"plt.plot(x,y, color=\\\"blue\\\", marker='*', linestyle='--')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"id\": \"31aae626\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[]\"\n ]\n },\n \"execution_count\": 15,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.xlabel(\\\"Day\\\")\\n\",\n \"plt.ylabel(\\\"Temperature\\\")\\n\",\n \"plt.title(\\\"Weather\\\")\\n\",\n \"\\n\",\n \"plt.plot(x,y, color=\\\"blue\\\", marker='*', linestyle='')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"id\": \"08a3557d\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[]\"\n ]\n },\n \"execution_count\": 19,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.xlabel(\\\"Day\\\")\\n\",\n \"plt.ylabel(\\\"Temperature\\\")\\n\",\n \"plt.title(\\\"Weather\\\")\\n\",\n \"\\n\",\n \"plt.plot(x,y, color=\\\"blue\\\", marker='^', linestyle='', markersize=10)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"id\": \"6f0a9738\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[]\"\n ]\n },\n \"execution_count\": 23,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.xlabel(\\\"Day\\\")\\n\",\n \"plt.ylabel(\\\"Temperature\\\")\\n\",\n \"plt.title(\\\"Weather\\\")\\n\",\n \"\\n\",\n \"plt.plot(x,y, \\\"g<\\\", alpha=0.6) # rgba\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"d6ba4604\",\n \"metadata\": {},\n \"source\": [\n \"## Label parameter\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"id\": \"007da045\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"days = [1,2,3,4,5,6,7]\\n\",\n \"max_t = [50,51,52,58,47,49,46]\\n\",\n \"min_t = [43,42,40,44,33,35,37]\\n\",\n \"avg_t = [45,48,48,46,40,42,41]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"id\": \"1bcc024a\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[]\"\n ]\n },\n \"execution_count\": 25,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.plot(days, max_t, label='max')\\n\",\n \"plt.plot(days, min_t, label='min')\\n\",\n \"plt.plot(days, avg_t, label='average')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"7cea201d\",\n \"metadata\": {},\n \"source\": [\n \"## legend\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"id\": \"49123f28\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 26,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.plot(days, max_t, label='max')\\n\",\n \"plt.plot(days, min_t, label='min')\\n\",\n \"plt.plot(days, avg_t, label='average')\\n\",\n \"\\n\",\n \"plt.legend(loc='best')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"id\": \"5b28f921\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 27,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.plot(days, max_t, label='max')\\n\",\n \"plt.plot(days, min_t, label='min')\\n\",\n \"plt.plot(days, avg_t, label='average')\\n\",\n \"\\n\",\n \"plt.legend(loc='upper left', shadow=True, fontsize='large')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"257dcdfc\",\n \"metadata\": {},\n \"source\": [\n \"# Bar chart\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 28,\n \"id\": \"d9c45b59\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import numpy as np\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 29,\n \"id\": \"f7826d22\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"company = ['Google', 'Amazon', 'MS', 'Facebook']\\n\",\n \"revenue = [90, 136, 89, 27]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 30,\n \"id\": \"0d42eea6\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([0, 1, 2, 3])\"\n ]\n },\n \"execution_count\": 30,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"xposition = np.arange(len(company))\\n\",\n \"xposition\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 31,\n \"id\": \"17763021\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 31,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.bar(xposition, revenue, label='Revenue')\\n\",\n \"plt.ylabel(\\\"Revenue(Bln)\\\")\\n\",\n \"plt.title(\\\"Technology Stocks\\\")\\n\",\n \"\\n\",\n \"plt.legend()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 32,\n \"id\": \"9fcd3b58\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 32,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.bar(xposition, revenue, label='Revenue')\\n\",\n \"\\n\",\n \"plt.xticks(xposition, company)\\n\",\n \"\\n\",\n \"plt.ylabel(\\\"Revenue(Bln)\\\")\\n\",\n \"plt.title(\\\"Technology Stocks\\\")\\n\",\n \"\\n\",\n \"plt.legend()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 33,\n \"id\": \"04ba079a\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"profit = [40,2,34,12]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 34,\n \"id\": \"7d281426\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 34,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.bar(xposition, revenue, label='Revenue')\\n\",\n \"plt.bar(xposition, profit, label='Profit')\\n\",\n \"\\n\",\n \"plt.xticks(xposition, company)\\n\",\n \"\\n\",\n \"plt.ylabel(\\\"Revenue(Bln)\\\")\\n\",\n \"plt.title(\\\"Technology Stocks\\\")\\n\",\n \"\\n\",\n \"plt.legend()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 35,\n \"id\": \"0b8cd1b2\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 35,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.bar(xposition-0.2, revenue, width=0.4 , label='Revenue')\\n\",\n \"plt.bar(xposition+0.2, profit, width=0.4 , label='Profit')\\n\",\n \"\\n\",\n \"plt.xticks(xposition, company)\\n\",\n \"\\n\",\n \"plt.ylabel(\\\"Revenue(Bln)\\\")\\n\",\n \"plt.title(\\\"Technology Stocks\\\")\\n\",\n \"\\n\",\n \"plt.legend()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 36,\n \"id\": \"22f3a2bc\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 36,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.bar(xposition-0.2, revenue, label='Revenue')\\n\",\n \"plt.bar(xposition+0.2, profit, label='Profit')\\n\",\n \"\\n\",\n \"plt.xticks(xposition, company)\\n\",\n \"\\n\",\n \"plt.ylabel(\\\"Revenue(Bln)\\\")\\n\",\n \"plt.title(\\\"Technology Stocks\\\")\\n\",\n \"\\n\",\n \"plt.legend()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"cfe556e6\",\n \"metadata\": {},\n \"source\": [\n \"# Pie chart\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 37,\n \"id\": \"4a9dd3c0\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"value = [1400, 600, 300, 410, 250]\\n\",\n \"\\n\",\n \"label = [\\\"Home Rent\\\", 'Food', 'Internet', 'Car', 'Others']\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 38,\n \"id\": \"dce1b13b\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"([,\\n\",\n \" ,\\n\",\n \" ,\\n\",\n \" ,\\n\",\n \" ],\\n\",\n \" [Text(0.09328656407206024, 1.0960372333838069, 'Home Rent'),\\n\",\n \" Text(-0.9822184890776084, -0.4952240298229684, 'Food'),\\n\",\n \" Text(-0.16284704617934698, -1.0878790555712807, 'Internet'),\\n\",\n \" Text(0.6256100334857941, -0.9047718419590123, 'Car'),\\n\",\n \" Text(1.0615045230766318, -0.28845822485734873, 'Others')])\"\n ]\n },\n \"execution_count\": 38,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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u3FVUkt/wNY3z5dFYGGuYvnOfb31V7IY8m3wZet9a+Dz0wuLI+JfK/bn45nX0VHMhlPSVtxyunvnrvnGlr7tlS0nq0IZdtWebry4m2J294yXL7vtECLY9UDDw3k/dsGSXdviGmNwa9vX3C7Je+Mvm8zY8vx6TezEebebbadgG9oSH3ifuHVNanREZ1veX/WF8t5bmqpzNjd74wfe7SWyuH7W94EWOa89l2jmlP3gvLLLfvCy3QvHjQwPMyfd+GMZn9UciG4nRL+UXrf3blFSvv3NvvxMGV+W4/Bw7i80FiuyGPJrcDu6zW4AP3DamsT4uMzPR9ewfLaGNpFp7yd/ZVz6j/xmUXND70iqRbfXsjCfDi/IXzulwwUETGiMhTIrJJRLaIyA9FpFREJovI+zpsFxWRW3Nb8sls9+Sgh+xn1Cyc+NWggef39P3H++ZmhL27ztq3etrcpTePHfXm0iUY47t1xMCdA/sMRESAJ4AnjTHnAROAAcB3gMnA+07/7syISMYzHXsh5H+xXYCX3Tt48Iq0SI/Xxt4z2P4z0EUmXTJx42/mzlz+teYBb+9chpWldHvsuW5sMw84YYz5BYBxLil+GfgX4G7gOhFpEJHr3O0niciLIrJVRG5s34mIfEJEVrrb/qQ90CJyVETuFJEVwHQRqRORRhFZKyLf76o4L4T8KfR6eadOiJz4zaABE3uzj42jxTM/274tR6ouW/3dWZNfu7+xuO1Eo+16umHT/IXzunOqcQGnjMAb56ilCbgLeMwYM9kY85j77YnAe4DLgG+KSImIhIHrgJnGmMk4M8N+3N2+P/C6MeZyoBG4FrjAGHORu/8zsh/yaHI/OgDXqf8YUrkiLb27PTVeLZ5bpG/I4Y0XzFl2SzjUFFuGSXt5boE/dXM7wZ0ht5uvx4wxzcaYA8A+nKW8/w6YCqwSkQb36/Ybn1LA793Pj+DcyfhzEfkA3ZhB1n7IHX+wXYDXnBB557cDB4R7u58No7u+BdYGATmn6ZlZs5fdVjr40IYlGJOXa/oZ+l03t1sPTOv4gogMAsbS+VztHS8vpnAWORFgkdvjTzbGnG+MibrbnHBPATDOz+kynNBfAzzbVXEaco+6e0jlyrRIr9dxPzRQqtLOZSBPKkm9U3HJa/fPvXR13faSliNrbNfTwQ66Pyj8V6BcRD4F/z049h/AwzgTpAzs5j4+JO7/cxEZIp3MFyAiA4AKY8wzwE04A3tn5I2QO5fSvPQ/2Kp3RI4/PnBA1tYyO1rGzmztK1cGHt05fvbLX51y/oZfr5B0ygtTLf22O5fOAIwzkHgt8GER2YTzMMsJ4GvACzgDbR0H3jrbRyNwB/BnEVmLMyDd2WXTgcDT7jZLcAb4zshLqzT+Hphiuwgv+N6QwauMyNxs7W/XUJKDPB9zx+jdyy4fsbf+RPz8Ty7ZN3zqZYiUWSrl15lsbIzZAfyfTr7VDFx6hvdd2OHzx4DHOtlmQIfPd+McrnebN3pyxyJ8utZUNh0XOfbEwP4XZHOfG8eIr9aeK0639bsw/gtneafj+2ws77Rp/sJ5vr5fvSPvhDya3AU8bbsM2747dPAqIzIsm/tcXy2V2dxfvpSdODh6+spvTa95/Sf5Xt5pUR7byjnvhNyx0HYBNh0TOfrUgP4XZXu/m0adeaoor6s6sPaSuUtvPmfMzuf/hjGHc9xcC/CzHLeRV14L+XP4eMK83vrO0MGvGHGWHs6mY2VSkZLO10fzC8EUT9j8+zmzXo6kBh1pyuXyTr+bv3Bej2e69SJvhdyZqvmntsuw4ajI208P6H9xrvZ/pNz7I+zdUdp6dOi0NffMnvLqvRv6tOZkeacf5WCfVnkr5I6HcA6ZCspdw4asNiKDc7X/HVUSqCmVK49sDc95aUHN+C1PvoxJZ+soZfX8hfMCt46790Lu3Ob6qO0y8ultkSPP9C+fnMs2EmMzf3rJD8bt+MuMOctuHZCl5Z3uz0pRHuO9kDu+SwFdTvv2sCFrjOR2BLyxOvvn+l7RJ9U8YPK6H8+9fNVdb/ZtPryqh7vZAvwqm3V5hTdDHk1uopObAoLoSJEk/9S//JJct7N5JCHT+cMSgdH/+J7QzOW3XxqOP7JK0m1NGb79rvkL53nx/vle6zLkIpJyb8lr/wj1pkERCYlId2Zp/TYF0JvfOXTIq4hU5LqdlhIpTxX5d2XOTIzcu+LSuUtvHjVid/0SjDnajbdsAX6Z67ps6U5P/k6HJ2MmG2Oacl0UANFkgoCfmyeLJPlcHnrxdocG+PsyWiaKTKp00obFc2fU33G0f9fLOwW2F4ceHq6781bVuzNT/EHcUeEzvD5VRF4TkeXA/Aya+iYBHmmPDhual168XdNZ8k6+2vKKfs2HR1y+6q4ZF7/2wNqiVHOik002EeBeHLoX8rIOh+rtj4Q+AnzFnZliHU4Yz/T6L4AbjTHTM6oummwCHszoPT6RLCo6/F/lZXl9ICc+Vkry2Z6XDD0Uv2ju0lsmjNv+3FJMuuOjtwuC3ItD5ofr14rT81QaY5a4318EzMng9cUZ1vgNCN5h5jeGDWlA8jtrS7xaqvLZntcIpmj8tj/Onv3SV4orDm9egjHPzV847ynbdeVaPh41Pd0UON0TTR4hWnELATo/P1xUdOj58rKp+W53+3DGGWgTbz1inHclbccrpzbcN6OtuO/F0GC7nJzL+JzcGJMEDonIbPelTwJLzvD6YSApIrPc1z9OpqLJXwHPZ/w+j7qjashriHRntpCsaiuW0tZiu1M0e8h9Nesb4raLyIee/kW/HlgoIuU4D5R8uovXPw08JCLH6d4Ut52ZD7wGlPbw/Z7wVlHRwSVlZdO63jI3Dg5i38hDjLfVvke8Adxpu4h8EV9NgR2t+Hfgq7bL6I3Pn1X14tLysitttf+lJ1NLZsZN1mad8alrw4n4k7aLyBdv3vF2enfh43WpDhYVHVha1u+0UwHlQ2O19LPZvgf8ppACDn4LeTR5HPgoJ09p6xu3Vw19HZH+NmuIj+3dPO4+txn4V9tF5Ju/Qg4QTTYAEdtlZOpAcdH+l8r6ZTQBXy7sGka1cWYSLTTNwEfCibiVBSBt8l/IHT8EnrFdRCa+VjW0EcnveuGdMSJFzSU02a7DglvDifirtouwwZ8hd2aQuQGf3CSzv7ho//J+ds/FO9pX6d3FFnLk9+FE/AHbRdjiz5BD++QSn8IHj09+tWqYJ3rxdptHSqvtGvJoG/AZ20XY5N+QA0STf8Hj5+f7iov3rejX1/q5eEeN1XYH//KoFfhoOBFP2i7EJn+HHCCavBv4ue0yTidSNTRucRWQTm0YI6Ns15Ant4UT8ZW2i7DN/yF3fA5nwThP2VNcvGdVv76X267jVHsHy2gDQR9l/kE4Ef+B7SK8IBghjybbgA8BnroXOVI1dAPizZtPjvcN9D3si4CbbRfhFcEIOUA0eRioBTyxqP3u4uLdq/v1vcJ2HaezZzCHbNeQI08Bnwkn4p4fkM2X4IQcIJrcBrwXOGy5Em4bPmwTIn1t13E6G0dLEEPwAnBdOBEP/NyAmQhWyAGiydXAu7EY9F19it9s6Fvq2V4coLE6/4+65tgrwPvDibgvb3nOpeCFHCCafAWLQb+tathmRDz9SOyGMTLWdg1ZFAfeW4i3rHZHMEMO1oK+s0/xrrUe78UBDg+QYWkCcefbVuDqcCJ+wHYhXhXckIOVoC8YPmyr13vxdkfLfL8I4gpgejgR9/u/I6eCHXJoD/psyP0lozf69Nn5eqn3e/F2u4bi5zvBngCuCifigVpmOBeCH3KAaPJ14HIgp3c/LRg+dBvin2mPN44RsV1DD90HfDiciBfcPPI9URghB4gm9wJXAo/nYvfb+/TZ0Vhamtm88patr87tIos5kAK+EE7Ebw4n4mnbxfhF4YQcIJp8B/gIUJftXS8YPqwJEV9NdbxplFTbriEDx4Brwon4j2wX4jeFFXJwnkWPJr+K8zz68WzscltJn+3x0hJf9eIAx8qkIiW+eCZ/NzA3nIg/bbsQPyq8kLeLJhcBU3Gmee6VBVXDdvitF293pJxdtmvowjPA5HAivtp2IX5VuCGH9pVTL8eZTqpHtpb02b7Bh714uzeGS3eW9rWhGfhSOBGv1RH03inskANEk81EkzcB/0APHm65dfiwHYgUZ72uPEmM8eQRyOvAZeFE/H7bhQSBhrxdNBkDLgK6fd63uaRk26YS//biAI3VzvLSHtGGM7f+1HAivtZ2MUHhrxVU8iVacQ3OIfwZR5+vGT3i5S2lpTPyUlOOlLaa44u/nyoTZ2FKm9YCnw4n4mss1xE42pN3Jpp8EggD38OZJ+x/2VBSsnVLSYlv7m47nZYSKU8VscNiCYeA24BpGvDc0JCfTjR5nGgyAlyM85zySRYMH7YHkUD8/A4NsHIZ7QRwDzA+nIjfE07EuzWDrIiMEJHfiMgWEWkUkWdEZEJuS/W3QPyS5lQ0GSeanIczvVQjQLy0ZMu2kj6+78XbNZ0l+bw9NA08DEwIJ+K3hRPxbs9QIyIC/AF40Rgz3hgzCfga0OXST+IoyN/3gvxH90g0+XugBvjU7VVD1welFweIj83b/fYx4OJwIv7pcCLek1OEq4BWY8zC9heMMQ3AqyLyVxFZIyLrROT9ACISEpG4iPwYWAME6Rn6btOBtx6oWVRTDPwTTi8y0XI5vXbObrOp7uHUeTlsYhlwezgR/1tvdiIiNwJnG2O+fMrrfYByY8wRERkG1APnAeNwnjefYYyp703bfubFa6Set+76dSlgcc2imkeBa4DPA/OwP0LdI9uHEzLQKpDNHv0o8EtgYTgR7/VdhV0Q4N9FZA7O6cBo/ucQfnshBxw05L2y7vp1aZznmp+oWVQzAfg3nHvih9isK1OpYilpLWZLaYrxWdjdWuBB4NEcTMe0Hmds5FQfB6qAqcaYVhFpAtqnwj6W5Rp8JzDnlbatu37dxnXXr7sFpxe5HlhuuaSMHBhEb24dPQEsBm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\"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.pie(value, labels=label)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 40,\n \"id\": \"ffd103ef\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"([,\\n\",\n \" ,\\n\",\n \" ,\\n\",\n \" ,\\n\",\n \" ],\\n\",\n \" [Text(0.16791581532970842, 1.9728670200908525, 'Home Rent'),\\n\",\n \" Text(-1.7679932803396952, -0.8914032536813431, 'Food'),\\n\",\n \" Text(-0.2931246831228246, -1.9581823000283056, 'Internet'),\\n\",\n \" Text(1.1260980602744295, -1.628589315526222, 'Car'),\\n\",\n \" Text(1.9107081415379372, -0.5192248047432277, 'Others')],\\n\",\n \" [Text(0.09159044472529551, 1.0761092836859194, '47.30%'),\\n\",\n \" Text(-0.9643599710943792, -0.4862199565534599, '20.27%'),\\n\",\n \" Text(-0.15988619079426794, -1.0680994363790757, '10.14%'),\\n\",\n \" Text(0.6142353056042342, -0.8883214448324848, '13.85%'),\\n\",\n \" Text(1.0422044408388749, -0.2832135298599424, '8.45%')])\"\n ]\n },\n \"execution_count\": 40,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.pie(value, labels=label, autopct= '%1.2f%%' , radius=1.8)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"fb77d24f\",\n \"metadata\": {},\n \"source\": [\n \"# Histogram\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 46,\n \"id\": \"45b87311\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"blood_sugar = [113,85,90,150,149,88,93,115,135,80,77,82,129]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 45,\n \"id\": \"e856a0f0\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[77, 80, 82, 85, 88, 90, 93, 113, 115, 129, 135, 149, 150]\\n\"\n ]\n }\n ],\n \"source\": [\n \"b = [113,85,90,150,149,88,93,115,135,80,77,82,129]\\n\",\n \"b.sort()\\n\",\n \"print(b)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 47,\n \"id\": \"48ff0682\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(array([3., 3., 1., 0., 1., 1., 0., 2., 0., 2.]),\\n\",\n \" array([ 77. , 84.3, 91.6, 98.9, 106.2, 113.5, 120.8, 128.1, 135.4,\\n\",\n \" 142.7, 150. ]),\\n\",\n \" )\"\n ]\n },\n \"execution_count\": 47,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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VGs3b+DfjjbnrWcj6L3gHdwST3dnn2JzmHGctaVd+tqp9V1c+Bf+UXp1WWnbOVQv82cGGSJyUJ8DLgCL1HEryuG/M64D+mlO8xkjyj+7kR+CPgZmY0a2dYtt3AFUken+QCel/ufnkK+UaZuZxJLgbeBryyqh5esGqmsibZvGD2lcBXu+mZyllVd1XVM6pqU1VtoleOW6rqf2ctax77aPFX0TsNC5PIeaa+lT4D3ya/m97/bIeAD9P7pvjpwOfoHQF/DnjatHN2WW8D7qZ3uuVl3bKZyErvH5fjwE/p/aX4i8Wy0fs19+v0HoV8yZRzvqqb/jHwXeAz0865SNaj9M6XHuheO6eddUjOj3d/p+4E/hNYP+2cw7L2rb+X7iqXGfxMPwzc1X2mu4FzJ5XTW/8lqRGtnHKRpF95FrokNcJCl6RGWOiS1AgLXZIaYaFLUiMsdElqxP8DJjkayXVWsHcAAAAASUVORK5CYII=\\n\",\n 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\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.hist(blood_sugar, rwidth=0.8)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 48,\n \"id\": \"5d821278\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(array([7., 1., 2., 3.]),\\n\",\n \" array([ 77. , 95.25, 113.5 , 131.75, 150. ]),\\n\",\n \" )\"\n ]\n },\n \"execution_count\": 48,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.hist(blood_sugar, rwidth=0.8, bins=4)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 49,\n \"id\": \"bfebb999\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(array([4.]), array([125, 150]), )\"\n ]\n },\n \"execution_count\": 49,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.xlabel(\\\"Sugar Level\\\")\\n\",\n \"plt.ylabel(\\\"Number of Patients\\\")\\n\",\n \"plt.title(\\\"Bloog sugar level\\\")\\n\",\n \"\\n\",\n \"plt.hist(blood_sugar, bins=[80,100], rwidth=0.95, color='g')\\n\",\n \"plt.hist(blood_sugar, bins=[100,125], rwidth=0.95, color='b')\\n\",\n \"plt.hist(blood_sugar, bins=[125,150], rwidth=0.95, color='r')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"c14df2f4\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.12\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n" }, { "path": "March Cohort 2023/12_15_16_17_18_Statistics/.ipynb_checkpoints/12_15_Statistics-checkpoint.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"id\": \"df89b0c5\",\n \"metadata\": {},\n \"source\": [\n \"# Statistics\\n\",\n \"\\n\",\n \"Data Science = Software Development + Statistics\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"id\": \"231d4e70\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"ename\": \"IndentationError\",\n \"evalue\": \"unindent does not match any outer indentation level (, line 3)\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[1;36m File \\u001b[1;32m:3\\u001b[1;36m\\u001b[0m\\n\\u001b[1;33m Univariate Hypothesis Testing\\u001b[0m\\n\\u001b[1;37m ^\\u001b[0m\\n\\u001b[1;31mIndentationError\\u001b[0m\\u001b[1;31m:\\u001b[0m unindent does not match any outer indentation level\\n\"\n ]\n }\n ],\n \"source\": [\n \" Stats\\n\",\n \" Descriptive Inferential\\n\",\n \" Univariate Hypothesis Testing\\n\",\n \" Bivariate Model Fitting\\n\",\n \" Multivariate\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"id\": \"fa6fd519\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"ename\": \"SyntaxError\",\n \"evalue\": \"invalid syntax (117687496.py, line 2)\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[1;36m Input \\u001b[1;32mIn [4]\\u001b[1;36m\\u001b[0m\\n\\u001b[1;33m 1. Measure of Frequency - count\\u001b[0m\\n\\u001b[1;37m ^\\u001b[0m\\n\\u001b[1;31mSyntaxError\\u001b[0m\\u001b[1;31m:\\u001b[0m invalid syntax\\n\"\n ]\n }\n ],\n \"source\": [\n \"- Univariate\\n\",\n \"1. Measure of Frequency - count\\n\",\n \"2. Central Tendency - mean, median, mode\\n\",\n \" - Geometric mean } ratio or rates\\n\",\n \" - Harmonic mean\\n\",\n \"3. Despersion\\n\",\n \" - range\\n\",\n \" - inter quartile range\\n\",\n \" - variance\\n\",\n \" - standard deviation\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"id\": \"94473ea3\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"ename\": \"IndentationError\",\n \"evalue\": \"unexpected indent (880973301.py, line 2)\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[1;36m Input \\u001b[1;32mIn [5]\\u001b[1;36m\\u001b[0m\\n\\u001b[1;33m Qualitative Quantitative\\u001b[0m\\n\\u001b[1;37m ^\\u001b[0m\\n\\u001b[1;31mIndentationError\\u001b[0m\\u001b[1;31m:\\u001b[0m unexpected indent\\n\"\n ]\n }\n ],\n \"source\": [\n \" Data\\n\",\n \" Qualitative Quantitative\\n\",\n \" \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"e7ee5068\",\n \"metadata\": {},\n \"source\": [\n \"# Central Tendancy\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"id\": \"acd1670e\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import statistics\\n\",\n \"\\n\",\n \"import numpy as np\\n\",\n \"import pandas as pd\\n\",\n \"\\n\",\n \"import seaborn as sns\\n\",\n \"import matplotlib.pyplot as plt\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"id\": \"27b667c2\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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RankCompanyCountrySalesProfitsAssetsMarket ValueSectorIndustry
01ICBCChina151.442.03473.2229.8FinancialsMajor Banks
12China Construction BankChina134.235.03016.6200.5FinancialsRegional Banks
23Berkshire HathawayUnited States222.924.1620.9409.9FinancialsInvestment Services
34JPMorgan ChaseUnited States102.524.22513.0306.6FinancialsMajor Banks
45Wells FargoUnited States97.621.91943.4274.4FinancialsMajor Banks
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\"\n ],\n \"text/plain\": [\n \" Rank Company Country Sales Profits Assets \\\\\\n\",\n \"0 1 ICBC China 151.4 42.0 3473.2 \\n\",\n \"1 2 China Construction Bank China 134.2 35.0 3016.6 \\n\",\n \"2 3 Berkshire Hathaway United States 222.9 24.1 620.9 \\n\",\n \"3 4 JPMorgan Chase United States 102.5 24.2 2513.0 \\n\",\n \"4 5 Wells Fargo United States 97.6 21.9 1943.4 \\n\",\n \"\\n\",\n \" Market Value Sector Industry \\n\",\n \"0 229.8 Financials Major Banks \\n\",\n \"1 200.5 Financials Regional Banks \\n\",\n \"2 409.9 Financials Investment Services \\n\",\n \"3 306.6 Financials Major Banks \\n\",\n \"4 274.4 Financials Major Banks \"\n ]\n },\n \"execution_count\": 7,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df = pd.read_csv('datasets/forbes.csv')\\n\",\n \"\\n\",\n \"df.head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"id\": \"40528971\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
RankCompanyCountrySalesProfitsAssetsMarket ValueSectorIndustry
0FalseFalseFalseFalseFalseFalseFalseFalseFalse
1FalseFalseFalseFalseFalseFalseFalseFalseFalse
2FalseFalseFalseFalseFalseFalseFalseFalseFalse
3FalseFalseFalseFalseFalseFalseFalseFalseFalse
4FalseFalseFalseFalseFalseFalseFalseFalseFalse
..............................
1995FalseFalseFalseFalseFalseFalseFalseFalseFalse
1996FalseFalseFalseFalseFalseFalseFalseFalseTrue
1997FalseFalseFalseFalseFalseFalseFalseFalseFalse
1998FalseFalseFalseFalseFalseFalseFalseFalseFalse
1999FalseFalseFalseFalseFalseFalseFalseFalseTrue
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2000 rows × 9 columns

\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" Rank Company Country Sales Profits Assets Market Value Sector \\\\\\n\",\n \"0 False False False False False False False False \\n\",\n \"1 False False False False False False False False \\n\",\n \"2 False False False False False False False False \\n\",\n \"3 False False False False False False False False \\n\",\n \"4 False False False False False False False False \\n\",\n \"... ... ... ... ... ... ... ... ... \\n\",\n \"1995 False False False False False False False False \\n\",\n \"1996 False False False False False False False False \\n\",\n \"1997 False False False False False False False False \\n\",\n \"1998 False False False False False False False False \\n\",\n \"1999 False False False False False False False False \\n\",\n \"\\n\",\n \" Industry \\n\",\n \"0 False \\n\",\n \"1 False \\n\",\n \"2 False \\n\",\n \"3 False \\n\",\n \"4 False \\n\",\n \"... ... \\n\",\n \"1995 False \\n\",\n \"1996 True \\n\",\n \"1997 False \\n\",\n \"1998 False \\n\",\n \"1999 True \\n\",\n \"\\n\",\n \"[2000 rows x 9 columns]\"\n ]\n },\n \"execution_count\": 8,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df.isnull()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"id\": \"db69da00\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Rank 0\\n\",\n \"Company 0\\n\",\n \"Country 0\\n\",\n \"Sales 0\\n\",\n \"Profits 0\\n\",\n \"Assets 0\\n\",\n \"Market Value 0\\n\",\n \"Sector 197\\n\",\n \"Industry 491\\n\",\n \"dtype: int64\"\n ]\n },\n \"execution_count\": 9,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df.isnull().sum()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"id\": \"2a980414\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Rank 0\\n\",\n \"Company 0\\n\",\n \"Country 0\\n\",\n \"Sales 0\\n\",\n \"Profits 0\\n\",\n \"Assets 0\\n\",\n \"Market Value 0\\n\",\n \"Sector 0\\n\",\n \"Industry 0\\n\",\n \"dtype: int64\"\n ]\n },\n \"execution_count\": 10,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df.dropna(inplace=True)\\n\",\n \"\\n\",\n \"df.isnull().sum()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"id\": \"b2de3fb4\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(1508, 9)\"\n ]\n },\n \"execution_count\": 11,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df.shape\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"id\": \"9aa68486\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"26.978494031830294\"\n ]\n },\n \"execution_count\": 12,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"market_value_mean = df['Market Value'].mean()\\n\",\n \"\\n\",\n \"market_value_mean\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"11d37231\",\n \"metadata\": {},\n \"source\": [\n \"## Mean Key Points\\n\",\n \"\\n\",\n \"- best represents the data\\n\",\n \"- mean consider all the data points\\n\",\n \"- extremly sensitive to the presence of outliers\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"id\": \"88065b85\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"13.1\"\n ]\n },\n \"execution_count\": 13,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"market_value_median = df['Market Value'].median()\\n\",\n \"\\n\",\n \"market_value_median\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"7594c091\",\n \"metadata\": {},\n \"source\": [\n \"## Median Key points\\n\",\n \"\\n\",\n \"- It is less sensitive to outliers\\n\",\n \"- 50% of the data is either side of it.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"id\": \"585fdb6d\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"0.24\"\n ]\n },\n \"execution_count\": 14,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df['Market Value'].min()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"id\": \"da70e1e2\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"752.0\"\n ]\n },\n \"execution_count\": 15,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df['Market Value'].max()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"id\": \"6f4c63c0\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"751.76\"\n ]\n },\n \"execution_count\": 16,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"range1 = df['Market Value'].max() - df['Market Value'].min()\\n\",\n \"\\n\",\n \"range1\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"id\": \"434e6e34\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
RankSalesProfitsAssetsMarket Value
count1508.0000001508.0000001508.0000001508.0000001508.000000
mean909.13527919.6230461.40743097.64701626.978494
std554.04445630.8758093.244201287.03615747.099541
min1.0000000.001000-13.0000001.3000000.240000
25%433.7500004.9000000.34975012.8000007.300000
50%883.50000010.3000000.69250025.90000013.100000
75%1354.25000019.9000001.42500060.32500026.900000
max1999.000000485.30000045.2000003473.200000752.000000
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" Rank Sales Profits Assets Market Value\\n\",\n \"count 1508.000000 1508.000000 1508.000000 1508.000000 1508.000000\\n\",\n \"mean 909.135279 19.623046 1.407430 97.647016 26.978494\\n\",\n \"std 554.044456 30.875809 3.244201 287.036157 47.099541\\n\",\n \"min 1.000000 0.001000 -13.000000 1.300000 0.240000\\n\",\n \"25% 433.750000 4.900000 0.349750 12.800000 7.300000\\n\",\n \"50% 883.500000 10.300000 0.692500 25.900000 13.100000\\n\",\n \"75% 1354.250000 19.900000 1.425000 60.325000 26.900000\\n\",\n \"max 1999.000000 485.300000 45.200000 3473.200000 752.000000\"\n ]\n },\n \"execution_count\": 17,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df.describe()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"id\": \"c4b36bb3\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Financials 474\\n\",\n \"Consumer Discretionary 209\\n\",\n \"Industrials 192\\n\",\n \"Materials 145\\n\",\n \"Consumer Staples 106\\n\",\n \"Information Technology 103\\n\",\n \"Utilities 86\\n\",\n \"Energy 79\\n\",\n \"Health Care 71\\n\",\n \"Telecommunication Services 43\\n\",\n \"Name: Sector, dtype: int64\"\n ]\n },\n \"execution_count\": 18,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# sector column is a categorical variable\\n\",\n \"\\n\",\n \"df['Sector'].value_counts()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"id\": \"b4d8b09e\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"0 Financials\\n\",\n \"Name: Sector, dtype: object\"\n ]\n },\n \"execution_count\": 19,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df['Sector'].mode()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"0f6db83f\",\n \"metadata\": {},\n \"source\": [\n \"## Mode\\n\",\n \"\\n\",\n \"- the value that occured most in the data\\n\",\n \"- vote in an election\\n\",\n \"- mode is not good for continuous data.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"214ca025\",\n \"metadata\": {},\n \"source\": [\n \"# box plot\\n\",\n \"\\n\",\n \"- mean\\n\",\n \"- median\\n\",\n \"- 25th percentile\\n\",\n \"- 75th percentile\\n\",\n \"- maximum values are where in the data\\n\",\n \"- outliers\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"id\": \"ab0791e2\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.figure(figsize=(10,8))\\n\",\n \"\\n\",\n \"sns.boxplot(data=df, y = 'Market Value', showmeans=True)\\n\",\n \"\\n\",\n \"plt.axhline(y=market_value_mean, color='r', linestyle='-')\\n\",\n \"plt.axhline(y=market_value_median, color='g', linestyle='-')\\n\",\n \"\\n\",\n \"plt.title('Forbes Data')\\n\",\n \"\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"id\": \"e11c59db\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.figure(figsize=(10,8))\\n\",\n \"\\n\",\n \"sns.boxplot(data=df, y = 'Market Value', showmeans=True)\\n\",\n \"\\n\",\n \"plt.ylim(0,80)\\n\",\n \"\\n\",\n \"plt.axhline(y=market_value_mean, color='r', linestyle='-')\\n\",\n \"plt.axhline(y=market_value_median, color='g', linestyle='-')\\n\",\n \"\\n\",\n \"plt.title('Forbes Data')\\n\",\n \"\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"3a8efbb2\",\n \"metadata\": {},\n \"source\": [\n \"# mode\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"id\": \"0afbc532\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
SectorRankCompanyCountrySalesProfitsAssetsMarket ValueIndustry
0Consumer Discretionary209209209209209209209209
1Consumer Staples106106106106106106106106
2Energy7979797979797979
3Financials474474474474474474474474
4Health Care7171717171717171
5Industrials192192192192192192192192
6Information Technology103103103103103103103103
7Materials145145145145145145145145
8Telecommunication Services4343434343434343
9Utilities8686868686868686
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" Sector Rank Company Country Sales Profits Assets \\\\\\n\",\n \"0 Consumer Discretionary 209 209 209 209 209 209 \\n\",\n \"1 Consumer Staples 106 106 106 106 106 106 \\n\",\n \"2 Energy 79 79 79 79 79 79 \\n\",\n \"3 Financials 474 474 474 474 474 474 \\n\",\n \"4 Health Care 71 71 71 71 71 71 \\n\",\n \"5 Industrials 192 192 192 192 192 192 \\n\",\n \"6 Information Technology 103 103 103 103 103 103 \\n\",\n \"7 Materials 145 145 145 145 145 145 \\n\",\n \"8 Telecommunication Services 43 43 43 43 43 43 \\n\",\n \"9 Utilities 86 86 86 86 86 86 \\n\",\n \"\\n\",\n \" Market Value Industry \\n\",\n \"0 209 209 \\n\",\n \"1 106 106 \\n\",\n \"2 79 79 \\n\",\n \"3 474 474 \\n\",\n \"4 71 71 \\n\",\n \"5 192 192 \\n\",\n \"6 103 103 \\n\",\n \"7 145 145 \\n\",\n \"8 43 43 \\n\",\n \"9 86 86 \"\n ]\n },\n \"execution_count\": 22,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"gb = df.groupby('Sector', as_index=False).count()\\n\",\n \"\\n\",\n \"gb\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"id\": \"635fa133\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
SectorRank
3Financials474
0Consumer Discretionary209
5Industrials192
7Materials145
1Consumer Staples106
6Information Technology103
9Utilities86
2Energy79
4Health Care71
8Telecommunication Services43
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" Sector Rank\\n\",\n \"3 Financials 474\\n\",\n \"0 Consumer Discretionary 209\\n\",\n \"5 Industrials 192\\n\",\n \"7 Materials 145\\n\",\n \"1 Consumer Staples 106\\n\",\n \"6 Information Technology 103\\n\",\n \"9 Utilities 86\\n\",\n \"2 Energy 79\\n\",\n \"4 Health Care 71\\n\",\n \"8 Telecommunication Services 43\"\n ]\n },\n \"execution_count\": 23,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"gb = gb[['Sector', 'Rank']]\\n\",\n \"\\n\",\n \"gb = gb.sort_values(by='Rank', ascending=False)\\n\",\n \"\\n\",\n \"gb\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"4efa0eb2\",\n \"metadata\": {},\n \"source\": [\n \"# pie chart\\n\",\n \"\\n\",\n \"- mode\\n\",\n \"- category wise display values\\n\",\n \"- percentage of values aquired by categories\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"id\": \"70799918\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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tgffasuVfZDqOUCh1tCVPRLy/NA3wHeIJGM3CHite49g2uebGnTlehwuBN4KvF+TP22w6ilAoubQlT0S0vbQywCud2/7AUYAAe8WfOdH+8KlzXU+3aLTjLIH05e9ZcXTxeqRiiLWEqOuWlpQA/Ab6OpTcTJSZ13SidrkKF1wLgoeL8GdtsB1FKXTgtwlT0yUu7DucusizbUW6s+cm2AtNfp6tQ4VSNs9rDU8X5M/y2wyil2k6LMBU98tJScQYsP2A7Sr11/gGLbqn9sU5XoWxYDDxYnD9ju+0gSqm20SJMRYe8tHHAS0B/21EaMobKkTV/qD1Fh3TbWVS7VAnMAn5bnD9DX8yVijI6MF9Ftrw0F3lp3wc+IcIKMHCmq/iu52WdrkLZkgw8A8zLnjW3u+0wSqnW0ZYwFbny0voAfwUutx3lfOqMe+/gmtm9DS59U6NsOgjcW5w/Y77tIEqpltE/Gioy5aXdCWwgwgswgDjx9bnLPV+nq1C29QT+nT1r7vezZ83V13alooC2hKnIkpeWDPwW+JztKK1xzHRcM7rmuUtt51AqYB5wX3H+jKO2gyilzk3fLanIkZfWD1hKlBVgABlSeukwKd5hO4dSAVcD67JnzY34lmSl2jNtCVORIS/tSmAOzrp5UWmNf+DC22p/pH/0VCTx4cwplq93TyoVebQlTNmXl/Zt4H2iuAADuES2j+pI+SnbOZRqwI2zpNe72bPmdrIdRil1Ji3ClD15aUnkpb0E/ALnj0VUEyHlcc+cdbZzKNWEa4Fl2bPmDrQdRCn1KS3ClB15aVk4M37fYztKMM10z+8n+HUpGRWJBuMUYpfZDqKUcmgRpsIvL20KsAq4xHaUYIsTX9873At1ugoVqboAH2bPmvsZ20GUUi0owkSkh4i8LCI7RGSziLwrIoPDES5URCRbRKpEZK2IFIrIChF5oMHjN4nIrBBe/5siktzg63dFJD1U14soeWkzcW6fz7AdJVQe98zx2M6g1HnEA3/NnjU3z3YQpdq7894dKSICLAFmG2OeC2wbCaQaYxaFJWEQiIjHGONt8HU28I4xZnjg6/7A68CvjTF/DtI13cYY3zkeKwZGG2OOBeNaLchyxvO3Ji/tW8AvAbEdJdSuq/nZjkKTNcB2DqWa8RLw+eL8GTW2gyjVHjXXEjYNqKsvwACMMeuMMYvE8QsR2SgiBSIyE0BEporIfBF5VUSKRORvgWIOEckPtKZtEJEnA9teEJE76s8vIuUNzrNARP4hIlsDx34m0GpVICIDAvt1FZHXRGRl4GNSYHueiPxBROYBL57vSRpjdgKPAl8PHPugiPw28Pmdgee4XkQWBra5ReTJQI4NIvK1wPZiEXlCRD4B7hSRq0VkqYisEZFXRKSDiHwd6AV8LCIfNzguI/D5o4HrbRSRbwa2ZQda7P4oIptEZJ6IJAUeeyjwvNcHvg/JDb6vTwWu8QsR2SYiXQOPuURke/01Qy4vTchLexJ4inZQgAH8JO5P+21nUKoF7sWZZT9mW6aVimTNFWHDgdXneOw2YCRwMXAlzh/6noHHLgG+CQzDWXR5koh0Bm4FLjLGjAB+0oJ8FwPfAHKBzwKDjTFjgeeBrwX2+TXwtDFmDHB74LF6o4CbjTH3tuBaa4ChTWx/ArjGGHMxcFNg25eAfsAlgefytwb7VxtjLgP+DXwfuNIYcynOGKhHjTHPAAeAacaYaQ0vJCKjcCYqHQeMBx4SkfpxU4OAZ40xFwEnA88V4HVjzJhAvkLgCw1OOThw/W/hrMFYPw7kSmB9WFri8tLiAtd+LOTXiiCXyrZRqVTodBUqGlyG3jmplBUXMjD/MuDvxhifMeYwsAAYE3hshTFmnzHGD6wDsoFSoBp4XkRuAypbcI2VxpiDxpgaYAfOWCKAgsA5wSkofisi64C3gI4ikhp47C1jTFULn8+5WmgWAy+IyEN8Oo3ClcBz9V18xpjjDfafE/h3PE4RujiQ7QEgq5kMlwFvGGMqjDHlOF2kkwOP7TLGrAt8vppPn/9wEVkkIgU4RdZFDc73SoMu0T8B9wc+/zwQlG7X88pLSwXm4rzbbld0ugoVZQYAC7Nnzc2xHUSp9qS5ImwTTmtSU87XrdRwfIEPqB+TNBZ4DbgFZ3JOAG99jkC3Zfw5zuNv8LUfqB/87AImGGNGBj56G2PKAo9VnCdjY5fgtCSdwRjzME6LVh9gnYh0wXnu5xpMV39NAT5okGuYMeYL5ziGBsecy1nf08DnLwBfNcbkAj8EEpvIgjFmL3BYRKbjtLS910yWC5OX1h2nML8qpNeJYHe7P87W6SpUFOkJLMieNXeE7SBKtRfNFWEfAQmBViAARGSMiEwBFgIzA+OjugKXAyvOdSIR6QCkGWPexemqHBl4qJhPC72bgbhWPod5wFcbXGfkuXc9Z7Zs4EngN008NsAYs9wY8wRwDKcYmwc8LCKewD5NzfS+DKcbdmBgn2T59K7SMiC1iWMWArcE9k3B6b5t7gaIVOCgiMTxaXfjuTyP0zX4j3PdNBAUeWm9cHLH3BQUrREnvqzb3YvO1Z2vVCTqCnycPWvuaNtBlGoPzluEGefWyVuBq8SZomITkIczpukNYAOwHqdYe9wYc+g8p0sF3hGRDTgtJN8KbP8jMEVEVuC00LSm9QqcwfSjAwPkNwMPt/C4ARKYogL4B/Cbc9wZ+YvAAPyNOEXSepxiZg+wQUTW00R3mzHmKPAg8PfAc17Gp2PO/gC8Vz8wv8Exa3BatlYAy4HnjTFrm3ke/x3Y9wOgqJl93wI6EMquSKcAm48zhq3de9zzss7Fp6JNZ5zB+hNtB1Eq1ukC3u2IiIzGuYlhcrM7t4UWYGcxBnNdbf6uItO3v+0sSrVSOXBDcf6MBbaDKBWr9F16OyHO5LOvAf8ZkgtoAdYkEeTHcX/aZzuHUm3QAXgve9bcdjuuU6lQ05YwdeG0ADsvYyjPrXneX05yR9tZlGqDGuCO4vwZ79gOolSs0ZYwdWG0AGuWCB2+4/lHc2P7lIpUCcDr2bPmXm07iFKxRosw1XZ5aT3RAqxF7nF/mA3a7KyiVhzwWvasuWNtB1EqlmgRptomL60TzlQdWoC1QLz4sm51fbLKdg6lLkAHYG72rLlNrSyilGoDLcJU6+WlJQFv4yxrpVpoVtzf28W6mSqmZQDzsmfNzbQdRKlYoEWYap28NA/OvGqTbEeJNt04OWqw7N1lO4dSF6gPTiHW1CTVSqlW0CJMtdYfgBtsh4hGznQVf95jO4dSQZCD0zWZYjuIUtFMizDVcnlpecDnbMeIZmOl6NIUqsqa31OpiDceeDV71tzWLjWnlArQIky1TF7a54Ef2I4R7URIfczzyhrbOZQKkmuBF7JnzdXxjkq1gRZhqnl5adcA/2c7Rqy4z/3vvjpdhYoh9wI/tB1CqWikRZg6v7y0wcAcwGM7SqyIF2+/m11LVtvOoVQQfT971tzbbIdQKtpoEabOLS8tFXgTSLOcJOb8Z9xLtiMoFUwCzM6eNVenrVGqFbQIU03LSxPgLzh3Qakg686JUYNkX7HtHEoFUQfgzexZczvZDqJUtNAiTJ3LE8DNtkPEKp2uQsWoAcDL2bPmum0HUSoaaBGmzpaXdhN6J2TIjZPCkSlUldvOoVSQXQ3k2w6hVDTQIkydKS8tB/grzhgPFUIidPyW51WdrkLFom9nz5p7r+0QSkU6MXqnvKqXl5YGrAAG247SXtQYz64hNbOzQbToVbGmCphUnD9jre0gSkUqbQlTDf0JLcDCKkG8/W50LdXWMBWLkoA3smfNTbcdRKlIpUWYcuSlPQToPD8W/FfcS37bGZQKkSzgOdshlIpUWoQpyEsbAvzKdoz2qgfHRw+Q/btt51AqRGZmz5r7WdshlIpEWoS1d3lp8cDfgWTbUdorEeTHnj8X286hVAj9NnvW3H62QygVabQIUz8FLrEdor2b4Nqs01WoWNYR+KvOH6bUmbQIa8/y0q4CHrUdQ4EIad/wvKYD9FUsmwh833YIpSKJTlHRXuWlZQAbgJ62oyhHjYnbMaTmhf46XYWKYT5gcnH+jKW2gygVCbQlrP36f2gBFlESpG7ADNdybQ1TscyN0y2ZajuIUpFAi7D2KC/tPuAm2zHU2b4X9zedrkLFuv7Ab2yHUCoSaBHW3jjdkE/bjqGa1pOSUf3kgC7srWLdA9mz5t5gO4RStmkR1v48DWTYDqGaJoLrJ54/77KdQ6kw+G32rLk6NY5q17QIa0/y0q4B7rMdQ53fRNemkclUV9jOoVSIZQF5tkMoZZMWYe1FXloKunxIVHCmq3hdB+ir9uBb2bPmjrAdQilbtAhrP34EZNsOoVrmAfe/etnOoFQYeIDnsmfN1WlZVLvksR1AhUFe2mjgG7ZjqJZLlLoB17mWr33PP05XMwgC463l0EvfxXjrwO8necgk0id/hoqiTzj1yUvUleylx/1PkdBz0LnP4fdxcPa38KR2odsdPwDg5MK/ULl9OYjgTk6ny/XfxJPaJVxPK1ZMAL4E/J/tIEqFm07WGuvy0jzAKuBi21FU6+w3XVZMqvnNWNs5YoExBlNXjSs+CePzcuhvj9P5ii/hSkgBEUr+9Vs6TfvCeYuw0hVvUHNoO6a28nQR5q+pxJXgjC0vXfUWdSV76HLNV8PynGLMSWBocf6Mw7aDKBVO2h0Z+76MFmBRqRclo7Pl4F7bOWKBiOCKTwLA+L3g94EIcRl9iOuS2ezx3tJjVO1cSYeLrz5je30BBmDqqgHtVWujdOAp2yGUCjctwmJZXloaevdR1BLB9WPPCztt54gVxu/jwJ+/xr7f3Edi9kgSeg1p8bEnPvwD6VM/jzSxotSJhS+y73cPUrF5PumT9ebjC3Bv9qy5V9kOoVQ4aREW276PzgkW1S5zFeh0FUEiLje9PvcbMr/yAjUHt1J7tLhFx1VuX4ErJZ2EHgObfLzT5feT+ZUXSBk2lbLV7wQxcbv02+xZc3Wssmo3tAiLVXlp/YGv246hLowIaV/zvLHado5Y4krsQGKfXKp2tmwWkJr9m6natpx9v/88R9/6X6p3b+DY20+etV/KsKlUbl0c7LjtzWCcQfpKtQtahMWu/wXibYdQF+5z7vd1uooL5Ks8hb+6HAB/XQ3Vu9e1aCwYQKcpD5L5yGwy/+NPdL3pcRKzRpBx47cBqDu+//R+lduXE9e5ZedU5/UDXeBbtRfa7BuL8tImA7fbjqGCI1HqBl7jWrH2X/6xOl1FG/nKj3Ns7tNg/GD8JA+dTPLAsVRuXcLxD/4PX9Upjrz6Q+K79aP7zB/jLSuh5P1n6H7nD8973pMLZlN3fB+IC0/HrnS+5pEwPaOY1g34Ls5wCqVimk5REWvy0gRYAYy2HUUFzz6TseKymmd0ugrVXlQBg4rzZ+xvdk+loph2R8ae+9ACLOb05tjoLDm0z3YOpcIkCb2zW7UDWoTFkry0OJzliVSMEcH1I88LO2znUCqMHsyeNXew7RBKhZIWYbHlAXR9yJg12bXh4iRqKm3nUCpMPOibShXjtAiLFc7yRP9lO4YKHZeQ/lXPmzpdhWpP7sqeNXek7RBKhYoWYbHjs0A/2yFUaH3B/W4P2xmUCiMBfmI7hFKhokVYLMhLcwPfsx1DhV6i1A262rVqne0cSoXRjOxZc0fYDqFUKGgRFhs+AwywHUKFx/c9f6m2nUGpMHvcdgClQkGLsGjntILppIbtSB85OqavHNbpKlR7MjN71ty+tkMoFWxahEW/e4BBtkOo8BHB/UPPC9tt51AqjDzAo7ZDKBVsOmN+NHNmx98E5NiOEgk+/88q3tnqpVuKsPErHQD474+q+ecWLy6BbinCC7ck0Sv17PceTy+t4fm1dQiQ293Fn29OItEjAPxmeS2/XVmLxwUzBnn436sSw/m0muQ3nBhW8+fEahKSbGdRKkwqgL7F+TOO2w6iVLBoS1h0uwYtwE57cGQc79+XfMa270xKYMN/dGDdwx24YbCHHy2oOeu4/aV+nllRy6qHUtj4lQ74/PDyxjoAPt7l5Z9b6tjwcAqbvtKBb0+MjDXRXUKnRzz/XGU7h1JhlAJ8xXYIpYJJi7Do9nXbASLJ5VkeOifJGds6Jnz6dUWtc797U7x+qPKC12+orON0a9nvV9Uy67IEEgKtYt1SIue/zBfd73a3nUGpMPta9qy59puilQqSyPmLolonL20QcK3tGNHgex9W0+fpMv5WUMePpiWc9Xjvji6+PSGevk+X0fOX5aQlwtUDPABsLfGzaLeXcc+XM+WFClbu94U7/jklSe3gK1yr19nOoVQYdQMetB1CqWDRIix6fZVzN+yoBv7nikT2fiuVz+TG8dsVtWc9fqLK8M8tXnZ9owMHHu1ARS38dYOzn9cPJ6ph2RdS+MVVidz1aiWRNI7yCZ2uQrU/j2XPmqt/u1RM0F/kaJSXloq+G2y1e3PjeK3Qe9b2f+/00i/dRdcUF3Fu4bYcD0v2Oi1emR2dr0WEsb3duASOVUZOEdZXjozpI0f2286hVBgNBG62HUKpYNAiLDo9CHS0HSIabCv5tPvwrS1ehmac/SvfN01Ytt9HZZ3BGMOHu3zkZLgBuGVoHB/tcgq3rSU+an2QkRw5DZA6XYVqpx6yHUCpYNApKqKNMy1FETDYdpRIc89rlcwv9nGs0tA9Rfjh1ATe3e5lyzE/LoGsdBfPzUikd0cXB8r8fPGtat79jHM35Q8+rmbOJi8eF1zS083zNyaS4BFqfYbP/7OadYd8xLvhyasTmd7PY/mZnslv5HhOzZ+Ta4jXAcuqvfADWcX5M3TSYhXVtAiLNnlp1wLv2Y6hIsuvvbd+8rT3zsts51AqjJ4ozp/xY9shlLoQ2h0ZfR62HUBFnofcc7vazqBUmH0ue9bcyBkboFQbaBEWTfLSMoDrbcdQkSdZaodMd63ZYDuHUmHUD5huO4RSF0KLsOhyNxBnO4SKTE94/lJhO4NSYfZF2wGUuhBahEWX+20HUJErSw6PyZSjB2znUCqMbs2eNbez7RBKtZUWYdEiL20IMMZ2DBW5RPDkeV7YajuHUmGUAHzGdgil2kqLsOihrWCqWdNd63ITqNVZ9FV78gXbAZRqKy3CooEzN5i+21PNconp8rD77VW2cygVRhdnz5p7ie0QSrWFFmHRYQqQZTuEig5f9ryTYTuDUmF2h+0ASrWFFmHRQbsiVYslS83Qqa51Ol2Fak9utR1AqbbQIizS5aXFAbfZjqGiyw88L+p0Fao9ycmeNXeo7RBKtZYWYZFvOpBmO4SKLtlyaExvjh60nUOpMNI3qyrqaBEW+W6xHUBFHxE8eXEv6nQVqj3RLkkVdXQB70jm3BW5H+hpO4qKPn4jx4bWvJBaS1yC7SxKhUnf4vwZe22HUKqltCUsgj2X3vHSvR6Pz3YOFZ1cYjK+rNNVqPZFW8NUVNEiLII92yn9tuv79Mock5W59ZvdMhasTEzYbECbLlWLPex5W5d0Ue2JjgtTUUW7IyNY7uzc9cCIhttcxhwaVlO77Z6y8sRrKipyEwyJluKpKPHZ2lkFi/wjcm3nUCoMfEDP4vwZR20HUaoltAiLULmzc/sAe867kzEVvby+jbeWl9fdVVqe09nv7xKedCqa7PD3XHpF7S8n2M6hVJh8oTh/xp9sh1CqJbQ7MnJd3+weIikH4jzjnu2UftmUvr07Terbu+CJjM4LtsbF7QpDPhUl+svBMT0pOWQ7h1JhcpXtAEq1lBZhkat1LyQirlK3O/eN1A5Tbs/s2e/S7D67vtS964IFSYkbfE4TvWqnRPD8IG72Fts5lAqTqbYDKNVS2h0ZgXJn5wpwBAjKGoBizPGBdXWFd5aWu28qrxieYkyHYJxXRQ+/kaNDa17oqNNVqHYipzh/RpHtEEo1R1vCItNwglSAARiRztvi4yf9NKPz+PFZmXHT+vRe/WTn9IUHPG6dUb2dcInp+pB7rk5XodqLqbYDKNUSWoRFpqkhO7NIwjGPe9TstI6XX9Ond8+xWZlFj3XLWLA2IV7fNca4//C81cl2BqXCZJrtAEq1hMd2ANWkqeG6UJXLNXReSvLQeSnJuIw5mFtTu+2e0rKUqyoqc+MhPlw5VOh1kOphl7kKNn7izx1uO4tSITbFdgClWkLHhEWYYI8HazNjyvp4vZtuLavw3VFWntPJ79dJP2PAdn+vJVfWPjnRdg6lwuCi4vwZm22HUOp8tCUs8gR1PFibiaTujYsb/0zndJ7plOZL8/s3XFlRdeKzpaVZA+q82bbjqbYZIAfG9OD44UN07m47i1IhNhXQIkxFNB0TFnkibyyDiPuU2z3itY4dptyS2Sv70uw+ux7u3nXBoqTEAj/4bcdTLSdC3BNxLxbazqFUGETea6lSjWh3ZITJnZ37D+BO2zlaSow5Nqi2bstdZeWeG8srhicbk2I7kzo/n5GjQ2tmp9Xh0TF/KpYdBboX58/QP3IqYmlLWOQZZTtAaxiRjK0J8ZN+ktF53LisTM8VfXqterpT+qJDbrfO0B6h3GK6fsH97krbOZQKsa7AQNshlDofbQmLILmzczsBx23nCApjTLIxRZdXVh25r7Ss58U1tYNtR1KfKjeJm4fX/GmY7RxKhdhdxfkzXrEdQqlz0ZawyHKp7QBBIyKVLlfO+x1SptzXq8fgkdl99t/fs9vC91OS19RBne147V0HqR420bVxk+0cSoXYSNsBlDofvTsyskRVV2Rr+ER6r01M7L02MRGMKe3r9a66razC3FFWPizN70+3na89+qFn9qmran9hO4ZSoTTSdgClzkeLsMgSOy1h5yPScU9c3IRfdU7nV53SvOl+//qrKypP3ldalt2vzptlO157MVD2j+nO8SOH6dzNdhalQuRi2wGUOh8dExZBcmfnbqOdDySN95sd46ur9953qixjXHX1MJd2mYfUXN+4BY/UfUNnF1exrGtx/oxjtkMo1RQtwiJE7uzcNOAEILazRAox5ujQ2rotM0vL4q+vqByeZEyy7UyxxmfkyJCa2Z28eOJsZ1EqRK4qzp/xb9shlGqKtjJEjkvQAuwMRqRrYUL8ZXldu4wdm5UpV/XptfKZTmmLjrjdR2xnixVuMd2+4H5Pp6tQsWyk7QBKnYsWYZFDpws4H5GkQx7PmD+mp02+ok+vruOzMjfN6tplwcb4+G22o0W7r3reTLOdQakQGmk7gFLnogPzI4fOo9VSIlIhctHcDinM7ZCCx5i9I6trdt1bWpY6rbIq16O/162SKlUXjXdt2rTMf9FFtrMoFQIjbQdQ6lx0TFiEyJ2d+y5wne0cUc+YU9l13k13lJVza3n5RR39Rlt5WmCLP3PxNbX/O8l2DqVCwAt0KM6fUWM7iFKNaXdk5BhkO0BMEEkrjo+b+GSXThMn9c1Mvrxv77U/7dxp4W6PZ6/taJFssOwb040TR23nUCoEPEBf2yGUaooWYREgd3ZuHJBtO0fMEYk74XZf8ve01Mtv6NOrz+iszG1f65axYHliwiYD2gTcgAjx/x33F51BX8WqbNsBlGqKjp2JDP3Qn0XI1bhcg+anJA+an5KMy5gjObW1W2aWlideV1E5PNGYJNv5bLvetTznW3jrdLoKFYOybQdQqinaEhYZdFB+mPlFum1KSJj8RNcuY8ZkZZqr+/Ra8Wx62ifH3K522yXnFtP9c+5/6XQVKhZl2w6gVFO0CIsMWoTZJJJ80OMZ+1yntMum9endZUJW5sbvZXResDk+boftaOH2Nc/rHW1nUCoEsm0HUKop2gUWGfrbDqACRFzlIsPfSu3AW6kd8Biz+9LqmuJ7S8vSplRWDY/16S86StXwsVK4eYXJ0XnrVCzJth1AqabE9B+UKNLLdgDVNK9I1oqkxKwVSYmIMSf71Xk331FWLreUlV+UakxMthr9KO6FE9fW/tx2DKWCKct2AKWaovOERYDc2blLgfG2c6hWMKaui99fcF15Rfm9peX9+3i9mbYjBYsx1I6p+d2pY6R3tZ1FqSDxA0nF+TNqbQdRqiEdExYZetgOoFpJJK7E7b70r2kdL7++T6/MMVmZW7/ZLWPBysSEzdE+/UVguorNtnMoFUQudK4wFYG0CIsMWoRFuWqXa/CHKclTPt+z+7CR2X0O39Oz+6K3OqSsrBGqbWdrixtcy4Z48NbZzqFUEGXbDqBUY1qEWZY7OzcdSLSdQwWPX6THxsSEyd/r2mXM6Kw+vmsyey1/Lr3jJ8ddrhLb2VrKLabHA+55q2znUCqIetsOoFRjWoTZ19N2ABVCIikH4jzjnu2UftmUvr07Terbu+CJjM4LtsbF7bIdrTlf97zewXYGpYKok+0ASjWmd0fap12R7YWIq9Ttzn0jtQNvpHYgzphdo6uq93ymtKzTZVXVF7nBbTtiQ2lSmTtGigpXmqE5trMoFQTptgMo1ZgWYfZpS1g7VSfSb2lyUr+lyUmIMccH1tUV3lla7r6pvGJ4ijER0Qr1o7gXSq6rzbcdQ6lgSLMdQKnGtAizr7PtAMo+I9J5W3z8pJ9mdOanXTrVZPj8q2dUVFTcW1o2qJfXZ61QHyp7xnTh1LES0jJsZVAqSNJtB1CqMR0TZl+q7QAqwogkHPO4R81O63j5NX169xyblVn0WLeMBWsT4ovCH4WE78f9dWO4r6tUCKTbDqBUY1qE2ZdiO4CKbFUu19B5KclT7u/VY+jF2X0O3tez+8K5KcmrayEsE0/e5Fo6xI3PG45rKRVC6bYDKNWYFmH2RcTYHxUd/CI91ycmXD6rW8aoUdl9aq7P7Lnsj2kdF59wuY6H6ppu8fe83z1vZajOr1SYpNsOoFRjWoTZp0WYahuR1L1xceOf6Zw+6fK+vdMu69t7Q16Xzgt2xHmKg32pb+p0FSr6pdsOoFRjOjDfPv3jpi6ciPuU2z3itY4deK2jM/3FWGf6i86Tqqovcl3gG640qci9VLYWrTGDhwYrslJhlm47gFKN6QLeluXOzn0buMF2DhW7xJhjg2rrttxVVu65sbxieLIxbRqHuMmf9cmM2p9dFux8SoWJH/AU58/QP3oqYmhLmH3aEqZCyohkbE2Iz/hJQmd+0qVTTTefb9UN5ZVV95SWDerh87V4suBhsnt0Z06VHCetSyjzKhUiLiABonM9VxWbdEyYfXp3pAofkYQjHs/oP6V3nHxVn17dx2VlFn6na5cF6xPitzZ/KInfj/ubTlehopn+zVMRRX8h7dOfgbJDRCpdrpz3O6RMua9Xj8Ejs/vsv79nt4XvpySvqYO6pg65ybVksE5XoaJYRC0NppR2R9qn4xNURPCJ9F6bmNh7bWIiGFPa1+tddVtZhbmjrHxYmt+fDuARf8/73P9eNtt3zXjLcZVqC33TqyKKFmH2aRGmIo9Ixz1xcRN+1TmdX3VK86b7/euvrqg8eV9pWfa3zKtJs33X2E6oVFtoS5iKKFqE2adFmIpsIp6TbvfF/+iYyj86pnLTOnfhHd02FB0c6Eve6h7qPujuneJzeeJsx1SqOVLaZC+7UtZoEWafFmEqKgzZawq/85qv+mDm7UnuKpfsMmWJt+c8KR06HBtzQDIPFzL84CZya3fRP+kEnXr5xZNpO7NSDZkOcX7bGZRqSIsw+7QIUxGt+wmz7/FXfbszjzHR74qr3t/rsrLak7/13pT2Nd+cdcknXEknjg/NWbT3qg7vTbha3jv9mlJhkk9tY8jujYw4uZUcOUCvTlUk90NE7whWtuhNJSqiaBFmnxZhKiIlV5tT3/inf+3InWa8QCbA9gG3rkBcU/zGe+poZfHau2XSpX/nk33r1l4/OSnp1O6cYQv3JSefnCCCK4XKtJGsHTGStafPacAcML33FDL8wCZya3bRP/E4XXr6cPdBRKw9WdVeaBGmIorOmG9Z7uzcxcBE2zmUquf2mbr7P/QvuWa1yXVB5/rtPldc9YLJT5Uirm7VJ56qTHKnlt7Y5z+61Ii34uWETw57xT8EICXl+I6cnIVHEpPKxovQosKqiqSybQwu3siIk1vI4QCZ6ZUkZyOSGqrnqdqluEPTRmohpiKGtoTZpy8IKmJcu8q/7P4P/T09fqY0fmx7/1uWI6767fFVvrIeZXXHl3SM7zJxZs0k78sJi3f4xD+goqLzgFWrbhmQmnp0y9CcRacSEyvGNnfdJKpSR7A+dwTrT28zYA6ZnnsLGX5gI7nVuxiQUEKXnj48fbXVTLWRz3YApRrSljDLcmfnzgWut51DtW8jdvoLvvWmn5Qacpt63OfyVC+Y/PQpxNUdoPrEUwB0S+y7aVrPey4CqKD68JyEJdV+MVkNj01LO7R56NBPKuMTqkYHI2s1CRXbGbxrIyNObGGY2U9megUp2Yh0DMb5I5nvyCFO5f83/uMlIELyDbeTfPu9Z+zjLy/j1E+/j//IQYzPR8pd95N03c0AHL3nelzJKeBygdtNl+desvE0bPEfmjZSp6hQEUVbwuwrsx0g1Pb9v32UrSvD09HDoP8ZBMChlw9Ruq4U8Qjx3eLJ/EIm7pQzXx9rDtaw93d7T39de7SWbrd2I+OaDA6/dpjStaWICO6ObjK/mElcJ50lobUyj5ri777qO9T9JOedfHVH/1tXIK7LAYzx1QFxAEeq91xU56/ZFOdKuCiFxO531E7Y/0r80n1GzOk7I0+d6jFs+fI76NRp/4YhQxf74+JqRl5I5kRqUoZTMHw4BWdsP2R67C/kov2byK3aycCEEjK6e/FkIRI7E3S63aQ+/Chxg3PwV1Zw/OF7iR81Dk/2gNO7VP3zH3iy+5P601/jP3mcYw/cSuKV1yNxzv+PTk/9AVdaJ1vPwCbtdVARR4sw+0ptBwi1Tpd1ossVXdj3x32nt6UMT6H7nd0Rt3DoH4c4OvcoPe46cy3phJ4JDPzxQACM37Dlm1voOMpp7Mi4PoPut3cHoOSDEo788wi9H+wdpmcU/TpWmJJH3/BtzNnLRIHs8+3rc3mq9/W+fEiDLaeLMIBNJ5ecGtl5mnNek9T79tpxu1+LX3bICGf8QE+c6D1i2dK76JKxe83gwUvjPJ66Jlvd2qoHh3r34FDvaXx4elsNCZU7zMBdGxlxvIhhZj990srpkI1IWjCvHS7uLl1xd+kKgCs5BU/ffviOHT2jCEPAVFZgjMFUVeFKTQO3NgABlbYDKNWYFmH2xXwRljIkhdqjtWdsSx3+6Xjr5AHJnFp56rznKN9cTny3eOIz4gFwJ336R8Vf49chQi0U5zXVD73vXzalwFwinD3uqyk7+t9yuhUMAOM/Y8bLradWjRnRacohl7h6AKSblKxbasfufCN+xVGEro3PV3Is69Klx7Lo1m3nyoGDlndwu705F/i0zimBmuRhbLpoGJvO2H7EdDtQyEX7NpFbtYNB8cfI6O4lLguRqKlWfIcOULd9Cx1zhp+xPemWuzn5/W9y7M6rMZUVpD3xc8QVaAwU4cR3vgIiJN14O8k33G4huTUltgMo1ZgWYfbFfBHWnBMLT5A27vwNE6eWnyJt/Jn7HH71MCeWnMCd5Kbfd/uFMmL0M8bcstQsmbnQn+02TG3pYU4r2JTBjbaeUYQZ/HF7K4qKsjoMO93y1cWk9r+5dsy2f8av9CA02fd15Ej/MUeO9KNHz63L+/df3dnt9g1q1XO6AN040qsbR3pN4ePT22qIr9ppBu7aRO7xIob599K3YzmpWYhEXN+dv6qSkz/4Nqlf+TaulA5nPFa7cglxA4bQ6Zd/wHdgLye+8x90yb0EV0oHOj/zZ9wZ3fCfOM6J7zyMp0828RePsvQswk6LMBVxtAizr10XYUfeOgJuSJtw7iLM7/VTtraMHnec2V3Z/Y7udL+jO0ffOUrJhyV0v7V7qONGpXFF/jWPvONPTqxjUmuP3XHmHZEBvrPG1qwt+XB435ScGhFJqN/W1XQcdEPtqMJ34le7EM7xAxYOHRwy7tDBwf7evQuXZPdb29Pl8lupqBOoTcph87AcNp+x/ZjJOFTIRXs3kVu5ncFxx+javY64bFutZsZbx6kffJvEK68j8fIrznq86v23SLnnc4gInt59cffojW9PMa6c4bgzugHg6tSZhMumU1e0SYswpSzSIsy+mB+Yfy4nPjlB2foy+j3e77zdieUbyknMSsST1vSva9r4NHY/vVuLsEb6HTLbv/uK72Tnctp0V6JfPDX7ek8Z0ni7MWcXYTX+yoyTtUc+6ZTQ/bKG23uY9Jxr60YWvB+3rh9Ch8bHfUpc+/cPm7h//1Bfn74bP+nbd0OWy2X6tCV3sGVwrMdkFvSYzILT22qJq95lBmzdRG5JIRf56lvNjLg6n+dUF8wYQ+kvfoinbz9S7vxsk/u4u/Wgds0K4kdciu94Cb69xbh79cZUVWGMH1dyCqaqitpVS0m5/0uhjBtptAhTEUeLMPvaZUtY2YYyjr17jH6z+uFKOP/Na6eWnSJ9fPoZ22oO1ZDQw2l0KVtbRkLPhCaObJ86l5rD33nNt7X/ISYKtLm1Zkf/m5ed3QoG4G/yLrPVJfO6Xtnr7MIg098l96q6Ees+iNswBCHp/Fd1uffuGXHZvr3D67Ky1y3KzNw8UMT0bNMTCKF46hKHUJQzhCLgldPbS0yXw0UM27uRERXbGeQ5Svf6VrOgvNbWbVxH9Qdz8fQfRMlDMwHo8IWv4jtyCIDkm+4k5bMPUfrzH1DyhTsxxtDhS9/AldYJ74F9nHriUQCMz0fiFdeRMLbVjaPRLChFmIh0gdN3f/TAmXvsaODrscaY2gb7vgC8Y4x5NRjXtk1EegHPGGPuaMOxDwLzjDEHAl8/DzxljNl83gObP2934P8BfXBuGCo2xgRl2icRWWKMCelk6jpPmGW5s3OnQ4PbuWLQ3t/vpaKoAm+5F09HD91u6caxucfwe/14Upy/TUkDkuj9YG/qTtSx/8/7yX40G3AG3W95dAuDfzEYd/Kn9cSe3+yh5lANCMR3iafXg73a/RQVCbWm4pF3/KvGbTGjBS5ofUa/eGrmX/70ccR1VgHk9x7eUVv2twFNHXdL36+vT3AnXdzUYztch1Z9HLdpBEJ8S3OI+Gr691+9vGevLTnSxCD/aODFU7uL/rs2kXuskIu8e8hKLaNjXyOuDNvZ2pn/PjRt5E+CeUIRyQPKjTFPnuPxF4ihIuxCiMh84NvGmFVBPu//AZuNMb8OfD3CGLOhhccKTh1kbWF3LcIsy52dOwwa3bqlVCu4/MY3c6F/yc3LzBCXoVswzrltwO0L9/aZfnlTj/m9B7bUlr18VjclwIDUkctGZ1xzzjnHitz7l3/iKRqFtK4V3uXyVg4YuGJF9+47RogQ0i6/cDlBp6NFDNuzkRHl2xnsOUL3brXEZyPSvt9NhM5XDk0b+ftgnrC+CAM+Bp4COgDHgAeNMQcbFmEiMuoc+wwEngO64rSq3QnsBP4XuA5nfeGfGGPmiMhU4IfAYWAk8DpQAHwDSAJuMcbsCFy3ChgKZAGfAx4AJgDLjTEPBvKXG2M6BD6/A7jBGPNg4PhSYDROa9/jgeeQHXg+w8UZE/lz4JpAxj8aY34jIk8ANwbyLAG+DNwOvADsD+SaALxHoCgTkXuA/wIEmGuM+W59PuDXwA2B4242xhxu9DN4C5htjHmtiZ/Pd4C7gATgDWPMDwLP4b3Az2wC8CaQYox5PHDMg8AoY8zXGn1/Hgc+C/iB94wxs0RkAPBs4GdXCTxkjCkSkTuBHwR+nqeMMU2+loKF7siGT6qF+0/F+UHd0IZrfRP4gzGmyflhWtIcGqrqvYGDITqvagembPCvfOh9f+d4H5ODdU6/eGr2Zk49952KxnfOpV92lq0fc2mXK/e5xJ3Z1ONDfb3HefEtXebZNhZpeVep3+9J3rZ14tSdO8aUDRq0dEFG190j5ZyD/aNDJ050ncDirhNYfHqbF3fdbtNv6yZyj27mIu8esjuUktbXiCsqWwEjzOHmd2kTAX6DUyAcFZGZwP8Anz+9g1NYn2ufvwH5xpg3RCQRcAG34RRZFwMZwEoRWRg43cVADnAcp1h73hgzVkS+AXwN+GZgv07AdOAm4G1gEvDFwLlGGmPWNfO8egKX4RRybwGNW/O+BPQDLjHGeEWk/s3Rb40xPwo877/gFHavishXafC3tH4ccKCL8+fAKOAEME9EbjHGvInTor/MGPM9Eflf4CGgcWvms8CcwPn/DfzZGHNARK4GBgFjcX5Gb4nI5cAeYAjwOWPMV0SkK7AUeDxwvvqfzWkich1wCzDOGFPZ4Ln+AXjYGLNNRMYBv8P5nj8BXGOM2S8i6ef7Jsf6mLBvAn+liUn6RMRtjPli2BM1UvBAwYnc2bnVQKLtLCp6DNlrCr/zmq+6YxVjgn3uHf1vWn7GvGCNGM5dhBmMe1f5xh0DUi9usggDGO7rO8GL75NVnp2TaOEC3/V8vrjUoqLLp3i215wcPHjJgs5d9o2S8w74jy4efHED2D54ANsH38Qbp7efNOlHt5CzdyMjyrYxxH2Y7t1qSchGpMVdu4r9ITpvAjAc+CBQWLg5+831kKb2EWeB+t7GmDcAjDHVACJyGfB3Y4wPOCwiC4AxOK1TK40xBwP77QDmBa5RAExrcM23jTFGRAqAw8aYgsAxm3AmaF7XzPN6M9BNtzkw7qqxK4HnjDHeQPbjge3TAq1GyUBnnJ6et89znTHAfGPM0UC+vwGX47RQ1QLvBPZbDVzV+GBjzL9EpD9wLU7L4VoRGQ5cHfhYG9i1A05RtgfYbYxZFjj+qIjsFJHxwDacn9XiRpe5Eqe4q6x/riLSAZgIvNLgxrL6wcmLgRdE5B84rZXnZK0IC7Rw5eE0yw7H+QbfF/iluRb4VeCxNQ2OyaNB/7uIbMRppjwK/APIxPnl/jHQHegFfCwix4wx0wJNm0/hNJ8+JiI/4dPm0N/j/DIkAa8aY37QKK8bZ/DfaJym1z8ZY54O0rfjEM3MWq4UQPcTZt/jr/p2Zx5jotC6AqYlmm0FAzDe8y6CvOH4/Iv7dxhRKSLJ59pnpK/fZbXiW7jBs/ucxd75eL0J6Zs3T5sSF1d1bMjQxavT0w+OlWYH/UevdE52HcfSruNYenqbD5d3t8netonco4UMr9tNdsop0vuYwPqe6iyhKsIE2GSMmdDafeTc652e7/92TYPP/Q2+9nPm3/SaJvZpvF/D8UiNGwIaHtNUHml0PIGWvN8Bo40xewN/s5trYDjfc60zn46Z8nGOmiVQAL4EvCQi7+AUcQL8zBjzf40yZgMVjU4xB6fbsgin27LxOK2znitOi+VJY8zIJvI8HGgZmwGsC7Q8NnljiO2WsEuAi4ADOJXjJBFZBfwRp0lvO843pznXAgeMMTMARCTNGHNKRB4FphljjgX2SwE2GmOeCOzX8BzfC1S3buDDJgb3jcR5xzI8cGx6W57wOexDizB1HsnV5tQ3/ulfO3KnGS/Om42Q2NH/xuWIu5nC6NwtYQC1/ur0kpoDCzMSe5/3PGO9Ay/34luw2bOvRTP3N6WuLiljY8GVU+LjKw8PzVm0omPHIxOkFQP/o5kbv6c/Owf1Z+egG/nn6e2lpmPJFnL2bGRE6VaGuA/TM6OGhH40mMOtHfLjvNkNhRqgq4hMMMYsDXQ9DjbGNBzru+Vc+4jIvvrut8A8e25gIfBlEZmN05p0OfAdnK7BYDosIjmBfLfSuimT5gEPi8j8Bt2R9QPcjwVaiu7g027MMiC1ifMsB34tIhk43ZH34HTdtoiITMfpsqwMtCwOwGntKgN+LCJ/M8aUi0hvoO4cp3kd+B6wG/juOZ7rEyLyUn13ZKBe2CUidxpjXgkM8h9hjFkvIgOMMcuB5SJyI86dmxFZhK0wxuwDEJF1OIVIObDLGLMtsP2vOH3P51MAPCkiP8cZNLjoHPv5gLMG7wXcJSJfwvme9ASGAQ2LsJ1AfxH5DTCXT5uAg2Fv87uo9sjtM3X3f+hfcs1qk+ui5TPdt4Vf3LV7M6c1P2u98TZ7J9Hqknm9r+71oBE5/3pSE71DptSKd/5296GpLU96ttra5O4b1l/TPTGxbP/QnEU7O3QomSCtHPwfKzpS2mUMy7uMYfnpbT5c3r0ma8cmhh8uZHhdMf2ST5Hexy/uHuc5VSw5fGjayFAt4O3HKTaeEWdNUg9OT87pIswYUxsY+N7UPp8F/k9EfoRTJNwJvIEzaHw9TgvM48aYQyIS7CJsFk53315gI7Sqa/95YDCwQUTqcAbm/1ZE/ojzN7kYWNlg/xeA50SkfmA+AIGbE/4TZ6C8AO8aY/5Jy40CfisiXpzWqeeNMSsBAgXm0sDLUDlwH04dcAZjzAkR2QwMM8asaOLx90VkJLBKRGqBd3FuJPgM8HsR+T7O9Bgv4/zMfiEigwLP58PAtiaF/e7I+oH5jQfci8hvgVU4/dS/NsZMCWy/CfiSMeaGwBOtNcb8b+Cx7cCVxpjiQBV+PfAwzlwkPxKRYpxm0WMNr90gy3zg2zgV6gfAmMAP4wWcPuoXpMHA/EBlfw3wIHDUGHN64OWFyJ2d+3M+HRSoFADXrvIvu/9Df0+Pn6xwXG97/1sW7ul7VbPdg96aDcu9lf8e19x+N/V5ZE2Sp8OlLbn2v+M2zC92H53akn1bIinp1O6cYQv3JSefnCDC+Seia8dKST2+lZw9m8gt3cpQOUTPjGoS++F0K8WSVYemjQz6+EmlLlQkvlMsAvoFmvN24DRN1ivGGQOGiFyKc2dG/d0Vx40xfw2M+3owsH998+cxzq8jTh/xqcAAxOuA+Q13CDSV1hpjXgsMhnyhjc+vKdoSpk4bsdNf8K03/aTUcM6pHoLNL+7aPX2mD2zRzsbXojl1NpxY4BvXdUaLTnll3Yip77F2wX738TZ3TTZUVZWWtWb1jVkpKcd35OQsPJKYVDZeWnkTQHvQkbLOo1nReTSfvvn34/LtM312bmL44c0Mry2mf9JJOmX6xd3LYtQLdcB2AKWaEnFFmDGmOtAtOFdEjgGf4AzcB6cr8f5A1+VKYGtgey5O858fpzn3PwLb/wC8JyIHjTEN7xppfM31IrIWp2l4J2ffGQHQG/iziNS/q/7Ptj7HJmwP4rlUlMo8aoq/+6rvUPeT4Su+6u3sd+NyxN3CaS68LWo+Ly7fOHp0xrXFbnFnt2T/6+oumfK2rFp42HWqTYP1m1JR0XnAqlW3DEhNPbplaM6iU4mJFWODde5Y5cLv7svu/n3Z3f865p7eXm46nNzK0N2byD21haFykF5dqknK5jw3YESQfbYDKNUUnaw1AuTOzs0GdtnOoezoWGFKHn3DtzFnLxPFGVcQVn5x186//OljtLClw1u19BNv9dLLmt8TLul8xYLBaaNb3LplMObN+JWflLjKgjbvWUNpaYc2Dxn6SVVCQlW7WbU6lPyIfz+Zuzcz/PBmcmt20T/pBJ16+8XT23a2Rr5xaNrIZ2yHUKoxLcIiQO7sXMHpDo3ZW+zV2eK8pvqh9/3LphSYSwR7E49u73/zoj19r25x0VNX9ckiX/WKFu3vkfiy27K+ac5zK/5ZDMb/WvyypSddlSFb2LBTp/0bhgxd7I+LqxkZqmu0ZxUknwq0mp3cQo4cpHfnKqfV7IKW07oAVx2aNvLflq6t1DlpERYhcmfnrsOZCVnFOmPMLUvNkpkL/dlug9UWA7+4axdMfvqocblbnKOucsFCX83qFncZTutx94JuSVmtGuvlx+99JX7pqjJXdUi7Zrtk7F4zePDSOI+nLjeU11FgwByg957NDD+4mdyanQxIPEHnXj7cmY3nCwqBXoemjdTVSVTE0SIsQuTOzn0ZZ7kEFcPGFfnXPPKOPzmxLujz/bTJ9v43L9zT9+pWjcGqq/xwga9mfYuLqtS4zruv6/3FPg3GU7aID3/tnIQl6yulJuR3tXXrtnPlwEHLO7jd3pxQX0udqYqksq0MKQ60mpkDZHauJDkLZ86nYDhxaNrImFhvVMWeiBuY344V2Q6gQqffIbP9u6/4TnYuZ7TtLPX84qrbm3nFgFYfaHyteudWVnc8q9JXtiLF07FVg+LduOLvqpmQ+3LC4jXVUteiqS7a6siR/mOOHOlHj55bl/fvv7qz2+1rfr40FRRJVKVezLrcixusomPAHDK99mzmooObyK3eyYCE42T08uHu04ZWs8KgBlYqiLQIixxbbAdQwde51Bz+zmu+rf0PMVFo+YLV4bAr+4ZlxtXSOyI/Zc6e67BZ60o+ipvU/ZZWH+fBnTizZtLQlxM+WV8j3hB31wuHDg4Zd+jgYH/v3oVLsvut7ely+fuF9pqqKQLSkwN9e3Kg7xV8cHp7FYnl283g4k3knihimDlAZqcKUrI4/5jDTed5TCmrtAiLHNoSFkMSak3FI+/4V43bYkYLhOROvwvhF1fdnj5Xtq3AML5Wj9/ZV7nlEp+/brvbFdeyucgaiMOdPLNmUv+/J3yyqU58F7X2+NYT1/79wybu3z/U16fvxk/69t2Q5XKZPqG/7tl8PsNXvrKfjC4e/uenZ05uP2fOST76sPz0fnv21PHqa1l07OjmF784wvJllaSnu3n+/1mJHhJJVHfIZcPw3DMWM4FDpse+Qi46sIncqp0MjC8ho6cXT1+cLvDNdtIq1TwtwiJHEc5yChHVWqJax+U3vpkL/UtuXmaGuAxBmXg0FNraCuZo2TxhjW0pXXVwWPqEVhdhAPF4UmfWTOr9csInW7ziH9KWc7Sey713z4jL9u0dXpeVvW5RZubmgSKmZ3iu7Xjj9VP07RtHZcXZ3/KZM9OZOTMdgKVLKnjttVN07Oi8fFxzTSq33JzGz39+JJxxrenBocweHMqcxoent9WQULnDDNy1l6w1rViKUKmw0uU8IkTBAwVV6Du2qDZlg3/li0/6im9daia7DN1s5zmXC2oFAzAtmjD/LJtPLhltjDnR1ssmEpc+s2ZShtu4drT1HG1hjCuueNelkxd/ck/nA/uHLDSGo+G47tGjXpYvr+T665uf3eOjj8uZNv3TZf9GjEgitWP7fnlPoCZ5GJsuuoZ3tTtSRaz2/b808qxsfhcVaYbsNYXP/8q79pG5/jHxPlo/0D3MdmXPWGZc7sy2n8HbptcNn/EmHazauaH5Pc8tifgud9VMTHUZKb6Q87SFMe6EHTvGXr5k8T0phw4NWGAMx0N5vd89W8JDX+pCc8PQq6v9rFpZxeTJtqbgimh7rpi+o8R2CKXORYuwyKJFWBTpfsLs++UfvYt/9Fff0I5VXGI7T0s4rWBXXdBgc9PGljCANSUfDDTGtH5kfwMpJHS7s3ZCvBixshSN3+9J3rZ14pSlS+6OO3oka4ExnAr2NZYtrSC9k5vBgxOa3Xfp0kouuijxdFekOsNa2wGUOh8dExZZtAiLAsnV5tQ3/ulfO3KnGS9wAS1K4Vecdf0FjAWr1/qB+fUqvKd6l3tPLk2N6zThQhKkmqRet9eO2/Na/LKDRgjrOK16Pl9calHR5VM822tODh68ZEHnLvtGidCh+SObt3FTDUuXVLBieSW1tYbKSj8/++kR/vO/zu7lnt+oK1KdYY3tAEqdj7aERZYNQI3tEKppbp+p+9w834I/Pe3zXbLTTBVItJ2pNfziqtvd9+rsCz+T74JeN9aU/Dso/WbpJqXvrbXjqgnTGK1z8XoT0jdvnjZl+bI7qk+c6LnAGKou9Jxf/GJnXp6Txd9e6sv3vt+NkSOTmizAysv9bNhQzcSJ0bCGthXaEqYimhZhEaTggYI6YL3tHOps167yL/vLk74D1602U1wQlbNvF2ddt8y43Bc+X8EFdEcCHKraOaLOXxuUCTQ7mw79bq4dc5IQj89qibq6pIyNBVdOWbH89tJTp7otMIbaYF/j7bdLefvt0tNfL/6kglGjkkhKOvOl/H9+cpivf+0Ae/fWcffM3bz3bmnjU7UXq20HUOp8dNmiCJM7O/e3wCO2cyjHiJ3+gm+96SelhqheW9AvLu+Cyb86GIwirObUn5Ya/8kL6k4clj7hk9xOl192oVnqHZKThe/Er+6F2FsIvbHExLL9Q3MW7ezQoWSCiA79sGDHFdN3tGlKFKXCRVvCIo+OC4sAmUdN8W9+7132/Tn+3GgvwCCIrWCAwX/BI8CLTq4YY4w/aN2IPUx6zrV1I/dgKA/WOS9UdXVq73Vrr5+8etVN+ysq0hcbw4U1IarW+sh2AKWao0VY5FlkO0B71rHClOT91bvgl8/7enc/yXjbeYLBLy7v7r7XZAXthMbf5oH59fz4EvZVbgvq/E2Z/i65V9WN2E4QxmQFU1VVWtaa1TdOWrtmxq6qytSlxqDdD+HxYfO7KGWXFmERpuCBgp1Ase0c7U2c11R/5R3f/D8+4/MM28sUgTjbmYKlOOvaoLWCOfxBed1YU/LvYcaYumCcq16Wv+vIaXXDN2Mi7waXiorOA1atumXC+nXXbq2uTllhO0+MM8DHtkMo1RwtwiKTNqOHizHmliX+xS8+6SuZWmCmCpEzpigYAq1gfYN81qCMb6r2lXcrrSsJejEywN991GRvznoMQS3wgqWsrOuQlStuG7th/VWba2qSdOB4aGy6YvqO9rFmk4pqWoRFJi3CwmBckX/Ni7/0bbl3gX+S29Dbdp5Q2N332mXG5QlyEWaC9rqxumRep2Cdq6Ehvl5jJ3gHr8JwQRPDhtKpUz2GrVh+x6iNBdM31NUlrLOdJ8boa6iKCnrHTmTSF5AQ6nfIbP/uK76TncsZbTtLKPnF5S3OuiaI3ZAB5sIH5tc7Wr13WK2vuiDenRj0mx8u8vWZUIdv8SrPjglI5L7hPHGi94hlS++iS8butYMHL/V4PHVRfyNIBNDxYCoqROwLU3tW8EDBQSAo8yipT3UuNYd/9mfvovw/+/rFegEGsLvvNcuMyxO8AfmnmaC+edt0cnHI7mgc6cueNNKXvThU5w+mkmNZlyxdcnfulqJJK30+j/7/bzsfsMB2CKVaQouwyKXv5IIkodZUPPq6b8Hvn/V1GHCIyQIxv8ieX1y+4qxrg9wNWc8E9fu3rXT1GL/xHwzmORsa7R0weZg3M2r+KB850n/MksV352zbNna5z+feZjtPFFpzxfQdQV/PU6lQ0CIscmmX5AVy+Y3vnvm+RbOf8lWM32KmCARluZxosLvv1SEYC1YvuEWYwXj2VGzeEsxzNjbRO2TKIG/P+aG8RnAJhw4OGbdk8T0Ddu4YtcTvd+2ynSiK6Guniho6JixyfQx40Z9Rm0zZ4F/50Pv+zvE+LnCx6uhjEF9x1nWhXFg86NN3rCv5ODcr5aJqEQnZepxTvMOm1ol3frH76NRQXSP4xLV//7CJ+/cP9fXpu3Fx374b+rpcJvjj/GKL9iKoqKEtYRGq4IGCk8B8yzGizpC9pvD5X3nXPjLXPybexwDbeWwI3ViwesFtCQOo8Vd2OVF7eFWwz9vYlXUjpmb6Os8P9XWCz+Xeu2fEpCWL7+2xd89FC42RkHXfRrla4BPbIZRqKS3CItubtgNEi+4nzL5f/tG7+Ed/9Q3tWMUltvPYYhDfruyQtoIR7IH59VYfm9ctFOdt7Nq6S6Z296dFzRixhoxxxRUXX3r54k/u6Xxg/5CFxhC0pZ9ixIIrpu+IqBUTlDofLcIi25ugS5ycT3K1OfWfc3zzn3nOl9HnGJMELnhJnWi2u+/VS0PbCgZAfChOerz24OAaX+W6UJy7sRtqR12e4U+N2iXCjHEn7Ngx9vIli+9JOXRowAJjOG47U4R4zXYApVpDi7AIVvBAwX4g5F000cjtM3Wfm+db8Kenfb5LdpqpAiEbSxQtnFaw60PcCgaEcJzihhMLw7LckCByc+2YSen+lKiYvuJc/H5P8ratE6csXXJ33JEj2fONoT3fFegH3rAdQqnW0CIs8r1pO0CkuXaVf9lfnvQduG61meKCzrbzRIrAHZHZYbhUyNbV3Fm2fozf+PaF6vwNCeK6rXbc+FR/0rJwXC+UfL641C1Fk6cuW3qXKTmWOd8YQjb3WgRbpEsVqWijRVjk03d2ASN2+gv+/JS34PMf+Md7/IS6yy2qBFrBwrX0UigXN3ftKivYEcLzn3kxxH1n7fhRKSZhZbiuGUpeb0L65s3Tpi5fdmfNiRM9FxhDexofpV2RKupoERbhCh4oKARCOodSpMs8aop/83vvsu/P8eem1KBLujRhd9+rwtIKZowv5Itirz8xf6QxpiLU16nnwhV3V83EEYkmbk24rhlqdXWJXTYWXDllxfLbS0+d6rbAGGptZwoxgxZhKgppERYd2mVrWMcKU5L3V++CXz7v6939JONt54lUTivYjF7huZo/5EVYnb8mraRm/+pQX6chN66EmTWThiYYz/pwXjfUamuTu29Yf82UVStvOVpW1mWRMXhtZwqRJVdM33HAdgilWkuLsOjwV9sBwinOa6q/8o5v/h+f8XmG7WWKhLb7K+rt6XPlcuPy9AvLxYwvLH/EVx2bl2mMCeudwXG4k2fWTOofZ9ybwnndcKiuTu29bu31k1evuml/RUX6YmPw284UZC/ZDqBUW2gRFgUKHijYBIS1ZcAKY8wtS/yLX3zSVzK1wEwVSLMdKdIZxLez34ye4buiLyzdWqfqjvav9lWE/Xc+Hk/q3TWTMj3GVRTua4dDVVVa1prVN05au+aG4srK1KXGxMQUOF7gH8E4kYiUN/r6QRH5bRvPNVVE3mnw+cQGj70gIne04Bw9RORlEdkhIptF5F0RGdyWPCoy6ZI40eNFYJTtEKEyrsi/5pF3/MmJdUyynSWa7OlzxTLjigvj98wftu6s9cc/Zny3G8N1udMSiEubWTPJ+3LC4u0+8Q+8kHP96le/IiEhARHB5XLxpS996YzHN2zYwOLFziwZ8fHxzJgxgx49epx+3O/388c//pHU1FTuvffeC4lyhoqKTv1Xr7qlf4fUY1tzchaeTEysGBu0k4ffvCum7zhmO0QzpgLlwJKWHiAigjMUZbYx5u7AtpFAd2BrC48XY0ystXrGFC3CosdLwJPEWNdcv0Nm+3df8Z3sXM5o21mijUH8O/vdEKaxYIFrGp8vXNfaXbF51Bhz3S63hKmrtYEk4rvcVTPRNydhcbFfTPaFnOuBBx4gOTm5ycc6derEgw8+SFJSEtu2beOdd97hi1/84unHly9fTkZGBjU1oZk+rbwsY/DKFbeRlnZo85Chn1QlJFRF4xu9sHRFikhX4Dmgb2DTN40xi0VkLPArIAmoAj5njNnS4Lhs4GHAJyL3AV8LPHS5iDwK9AAeN8a82uiS04A6Y8xz9RuMMesC5+wA/BPohPM34fvGmH8GrvUeztrDE4BbROQu4C4gAXjDGPODC/9uqGDR7sgoUfBAwTHgXds5gqVzqTn8sz97F+X/2ddPC7C2CbSChblACf3dkQ3I9tK1e8N4vTOkkNDtzpoJCWIkZPOW9enTh6SkJAAyMzMpLS09/VhpaSnbtm3j0ksvDdXlTzt1qsewFcvvGLWxYPqGurqEdSG/YPBUEty5FJNEZF39B/CjBo/9GnjaGDMGuB14PrC9CLjcGHMJ8ATw04YnNMYU4xRvTxtjRhpj6ldq6AlcBtwA5DeRZTjnHoZSDdxqjLkUp1j7ZaDlC2AI8GIgzxBgEDAWGAmMEpHLm/0uqLDRlrDo8iJws+0QFyKh1lQ88o5/1bgtZrTAZNt5olWgFaxH83sG+8LhawkD2Hhi0aWDO44+JSJWxgemktTzjtrxe1+NX3rQCK0eeyci/OUvf0FEGDVqFKNGnbuhae3atQwc+Gnv5/vvv8+VV15JbW34Zpc4caL3iGVL76JLxu61gwcv9Xg8dZE+JcxrV0zfEczpTKqMMSPrvxCRB+H0m8QrgWGf1jp0FJFUnLGrs0VkEM5UGS3trXgz0FW4WUS6tzKnAD8NFFR+oDdONyXAbmNM/QTEVwc+1ga+7oBTlC1s5fVUiGgRFl3eAY4ThbPEu/zGN3Ohf8nNy8wQl2GK7TzRbk+f6cuMK25i83sGmzesRZjX1HU4Ur17QfekbGu/M2kmuc+tteN2vRG//IgRWrXI+Oc//3lSU1OpqKjgL3/5CxkZGWRlnT3P8K5du1i7di2f+9znANi6dSspKSn06tWL4uLioDyP1ig5lnXJ0mNZdOu2c9XAQctT3G5vTthDtEybBs23kQuYYIw5YwJcEfkN8LEx5tZAd+D8Fp6vYR9zU2vebgLONXj/M0BXYJQxpk5Eivl06baGRakAPzPG/F8LM6kw0+7IKFLwQEEtUXgr9pQN/pUvPukrvnWpmewyrfsjps7mtILdGP5WMAjbFBUNrTo2r7/twcWdTYd+N9eOKcVQ0prjUlNTAUhJSWHo0KHs37//rH0OHz7M22+/zd1333167NiePXvYsmULv/rVr3j11VfZtWsXr7/+ehCeSescOdJ/9JLF9+Rs2zZuuc/n3hb2AOe38orpO1aE8XrzgK/WfxEYJA9OS1j9D/bBcxxbBqS28nofAQki8lCDa44RkSmBax4JFGDT4JwriPwL+HxgDBki0ltE9DU4gmhLWPR5jgYvBJFsyF5T+J3XfNUdqxhjO0ss2Zs5zVIrGBjC2x0JUO490afSV7oixZNm9Q6+DNNx4I21o4rejl/tQZqfPqW2thZjDAkJCdTW1rJjxw6mTDmzQe/UqVPMmTOHW2+9lS5dupzefuWVV3LllVcCUFxczJIlS7jtttuC/Ixa7tDBweMOHRzk7927cEl2v7U9XS5/2G+WaMJvwny9rwPPisgGnL+dC3EG3P8vTnfkoziFU1PeBl4VkZv5dGD+eRljjIjcCvxKRGbhjAMrBr6J00r2toisAtbhjEtr6hzzRCQHWBroRi0H7gN0jc0IIWGeD1EFQe7s3A+B6bZznEv3E2bf46/6dmceY6I03cyu2sgg/gWXP73L74obYOP6vtrt6+sq3ro43NftnTx47WXdb70k3NdtygHX8U3vxq3ti5y/ZePEiRPMmTMHcKaaGD58OJdffjmrVq0CYPTo0bz11lsUFhaSlubUdE1NY1FfhAVziooL4/f16btxWd++G/q6XKaPpRBHgL5XTN8RmttGlQoTLcKiUO7s3FuIwKWMkqvNqW/807925E4zXj4dn6CCaE/m9CXbB95upRUMwFe7ZXVdxVwr0xjcnvXoNo8rbpCNaze2x3Vs/by49YMQmp57oh0Q8ddlZa1bmtln8yARE8YJgwH4nyum7/h+mK+pVNDpmLDo9BZOs3REcPtM3efm+Rb86Wmf75KdZqoWYKHhjAW7qbV3UQU5hM/a2Kwtp1YesnXtxvr6My6eXje8EEO7bYkxxhVXXHzp5Ys/uafz/v1DFhgTti4uL/D7MF1LqZDSIiwKFTxQ4Ad+ZzsHwLWr/Mv+8qTvwHWrzRRXFN61GU32Zk5b7nfb6YasZ/BaazovPLV0tDHmuK3rN9bf333U5d6c9RjCOXdaxDHGnbBzx9gpSxbf0+HQoQELjCHUP6M3r5i+4+w7HJSKQlqERa/ncSYqtGLETn/Bn5/yFnz+A/94j/+cd+aoIAm0gtm/qynM84Q15DPepINVOzbYun5TBvt6jZ3gHbwKg7XvS6Tw+z3J27ZOnLJ0yd1xR45kzzeGUyG6VLgH5CsVMlqERamCBwpOAH8L93Uzj5ri3/zeu+z7c/y5KTVE+kSOMWNf5lTrrWAOr9WpIlaXfDDEGBP2aTLO5yJfnwljvAOXYdA1+gCfLy51S9HkqcuW3mVKjmXON4by5o9qsQ1XTN+hE42qmKFTVES3Z4CHmt0rCDpWmJJH3/BtzNnLRIHscFwzFP5y4jivnDyJAe5MS+f+zmf3oK6orOBnR47gNYZObjcv9s3iYF0d/3nwIMd8XgS4Kz2dz3YKT++rAbOj301dw3Kx5hi7DT6V3tKe5d4TS1PjOk+wGqSRi31Zk+rEu2idu/gyRO8IBvB6E9I3b542NS6uumTI0E9Wp6cfHCtC0gWeNpyTsyoVctoSFsUKHijYiDOLfsjEeU31V97xzf/jMz7PsL1MkSheQHxbTQ2vnDzJnKxs3sjux/yKcoobLQlT6vPxo8OHebZ3Jm/368/TvXoD4BHh8W7deKdff17OyuKlEyfYHqJFlRvb13vqMr87fmDze4aesdwSBrCm5N8dbGdoymjvgMkX+fpoK00jdXWJXTYWXDllxfLbS0+d6rbQGNq6DpOV1n+lQkmLsOj345Cc1RhzyxL/4hef9JVMLTBTheYnp4x0O2pruDgpiSSXC48IY5KS+bCs7Ix95paWclWHVHrFObVmF4/TWNzV42FYonPTZ4rLTf+EBI54Q98rZsDs6H9zZLSCARif9TltDlXtyq3z1xbaztGUCd7BUwZ7e863nSMS1dYmd9+w/prLV6285WhZWZdFxtDa/0C/v2L6DmvjYJUKBS3ColzBAwUrcJbTCJpxRf41L/7St+XeBf5JbkPvYJ7bpkHxCayqrOSkz0eV38/CinIOes+8sa24rpZSv48H9uzmjuJd/PPU2WOL99fVUlhdzYjE0M/Esa/3lIhpBXNExnCswpNLI+YuycYu9w6b2s/XbYHtHJGqujq197q1109eveqm/RUV6YtNy8bSlQFPhTqbUuGmY8Jiw4+Bqy/0JP0Ome3ffcV3snM5o4OQKeIMSEjgi5278IW9e0h2uRiSkIhHzhy+4zOGTdXV/KlPX2r8fu7Zs5uLk5LIjo8HoMLv5xv79/Of3brTwe0OaV6nFeyWjJBepLUszhPW0JZTK8bkdpp8RMRl/47RJlxRlzvlfdbN3+cumWo7S6SqqkrLWrP6xqyUlBM7h+YsOJyUVDZezj2e7rdXTN/RqnU7lYoG2hIWAwoeKPgEmN/W4zuXmsM/+7N3Uf6fff1itQCrd3t6Oq9l9+MvfbNIc7vJios/4/HunjguS0kh2eWik8fD6ORkimqqAagzhm/u388NHdO4KrW1a/G23r7eU5b73fERMUN8vUiZicGPP35f5daI7JKsd23dyKk9/OnaItaMiopO/VevumXCunXXbauuTlnexC7lwC/DnUupcNAiLHa0emxYQq2pePR134LfP+vrMOAQkwVC27QTAUoC47gO1NXx7/Iyru/Y8YzHp3fowOqqKrzGUOX3s6GqigHxCRhj+O9DB+mfEM+DTdxRGWyBVrAuze8ZZsYXMXf+rSn59zBjTFsHeYfFjNpLL8/wpy6ynSMalJdlDF654rZxG9ZftbmmJml1g4e0FUzFLF07Mobkzs5dDDS7rqDLb3wzF/qX3LzMDHEZIrI7J1Tu27Obkz4fcSI83rUbE1JSePnkCQDuTu8EwP87XsIbp07hAu4ITGOxurKSz+7dw+D4BOp7ML+Z0ZUpHUJzo96+Xpcv2zp45viQnPwC1Ja/Od9ft3Oq7Rz1run9+cXp8V0n2c5xPgbjfz1++dITroqIzhlpOnXav2HwkCWV8fHVN2gRpmKVFmExJHd27tXAv863z5QN/pUPve/vHO8jAib+VE0xYBZMfnp7pHVFAtSWvT7f7y2eajtHvYyEzMIren0mx3aO5vgxvlfjl64odVVF1PxmUeAneXl5/207hFKhot2RMaTggYJ5wMdNPTZkryl8/lfetY/M9Y/RAiyy7e81OeLGgn3KG1GvGcdq9uXU+qoLbOdojgtx31E7fnSKSVhhO0sUKQF+YTuEUqEUUS+oKigeB043b3Y/Yfb98o/exT/6q29oxyousZhLtYABs33AbRG7ELoxEXFz5Bk2nvwkmMvihIwLV9xdNRMvTjLxq5vfWwE/y8vLK7UdQqlQ0ikqYkzBAwWrcmfnvppcba7+xj/9a0fuNOMFMm3nUi0TaAWLuLFgn4qcgfn1tpeuGTOy8/SDLnH1bM1xE35/FynxSbhdbtwuN+8+8MczHn9u+d95Y/MHAHj9PraX7Gbd196iU1JHPt65nLwPn8Hn93PPxTN4ZPx9LbqmG1fCzJqJw/6esHhdjdSNbE3edmYv8KztEEqFmhZhMWh4sX/W91/2T3YZptrOolrOaQW7tZPtHOfni7jWc4Px7C7ftKVfam6rijCAf9zzazonpzf52MPj7uHhcfcA8MH2xTy/8h90SuqIz+/j+x88zUszn6JnaldumP0lrhp4GYMzslt0TQ/upLtrJg58KeGTjXXiG97azO1EXl5eXrXtEEqFWsS9oKoL9/cfbNrpMrxkO4dqnf29Jq/wuxOG2M5xXhHYHQmw7vjHI4wxVaE6/z83f8jNOVc61zpYSHZ6b7LSexHvjuOmnCuYt+2TVp0vDk+Hu2sm9fEYd0TPdWZJITDbdgilwkGLsNj1I+CY7RCqZQKtYOm2czTPH5GvGbX+qs4nag+tas0xIvCZfzzG9S98kb+te+uc+1XVVTN/13KuGzIFgENlx+jV8dOZXXqmduVQ+dFWZ04gLm1mzcTubuPa1uqDY9t38vLyImNWYKVCLCJfUNWFyykqPAX8wHYO1TIHel4W+a1ggMEfsRP6rjr2rx6t2f/1z/yO9x78f7x45y+YveYNlu1d1+R+H2xfzJjeuXRKcib2NZw9rc+5V9s5vyTiO8+smZjuMrKrTSeIPa/l5eXNtR1CqXDRIiy2/R+wyXYIdX4GzLaBt6XbztEixh9xA/Prnag9PKjaV7m2pfv3SHWW5cxI6cS1gyez7kDTPYNvFX7ETcOuOP11z9SuHCg9cvrrg2VH6d6h7Ut8JpPQ9c6aCYliZG+bTxIbSoGv2w6hVDhpERbDcooKfcCjtnOo8zvQc9LKaGgFc0Rmd2S9guML6lqyX2VtFeU1lac/X7hrJUO69j9rv9KacpbtXcc1Ay87ve3inkMpPrGPPScPUOur463CD7lq4IVNhp9KUs87aseLGA5c0Imi2/fz8vLa8/NX7ZDeHRnjcooK5xUOzXkNuN12FnU2A2wbeHua7Rwt54/o14yd5RtGj8q4eo9L3H3Pt9/RyhM89Pr3APD5fdw87Eqm9R/HX9b+E4DPXnIzAO9vXcTl2WNIjk86fazH5eHHV32T+/7xbXzGz8zc6xnStd8FZ08zyZm31Y4rfj1++REj7Ws5MWAVOiWFaod02aJ2oHBoTndgMxCxk4C2Vwd6TlxeNOQz42znaKnqk88WYGpybec4n1FdrlowsOOlU2znaKtjUrr9zfiVnRAibwH30PABY/Ly8lrclaxUrIjorgUVHDlFhYeBb9nOoc62deAdUdQKBpjIHZhfb8OJhSONMVExi35TMkzHgTfWjj6G4ZTtLGHyjBZgqr3SIqydyCkqfBF413YO9akDPSeu8LsThtrO0TomorsjAer8NWnHavatsZ3jQnQ3aUOur7tkH4Yy21lCbC/whO0QStmiRVj78mWcO5BUBNg28I5U2xlaz0R8SxjAqmPz+pgoH2vRy9/5oqvrLt6JodJ2lhD6Wl5eXtS2Wip1obQIa0dyigr34SzwrSw70GPCCp87Icd2jtaLjiKstO5YvypfedQvlN3Xn3Hx9LrhRRhicQmff+bl5f3TdgilbNIirP35A/CR7RDt3bZBd0ZhKxgAcbYDtNT64x/bjhAU/f3dL728blgBhhZNvxElyoGv2g6hlG1ahLUzOUWFBngIYrqLI6Id6DF+ZXS2gkG0tIQB7KkoHOUz3p22cwTDYH/PMRO9Q1ZjiJXlfJ7Iy8vbZzuEUrZF/CBbFXw5RYU7C4fmfA942naW9mjbwDs7tOW4wyf38qd///j01yWlB5kx+kGmjfh0CjhjDK8ueZZNe5YT70ngs1Mfp0/XwdR5a/nVW9/E66vDZ3xc0u9yZox5sA0pIn9gfgOyrXTNvqFpY8+ehTUKDfNljvfiW7zCs30CEtVvoFcAz9gOoVQk0HnC2qnCoTkuYBEw0XaW9uRgj/ErCod+duyFnsfv9/G9v87kO7c+S+fU7qe3b9qznAUb3+A/rvsZxUcKeXXJs3zn1mcxxlDrrSYhLgmfz8tTb32DOyY+Qr/uw1p13eoTT5UCHVtzTFVtHf9YtYFDp8oQ4K4xF5Od0emMx19avo6TlVX4jWHKkP6M7deHOp+P3328FK/Pj98YRmT25Jrhg1uV1y1xFbdnfcsrItE1Fch5rPbsXLTWveuyNi9YaVcZMDIvLy8mWiiVulDR9K5WBVFOUaG/cGjOF4B1QILlOO3G1ja2gjW2Zf9aunbsdUYBBrCheDFjB1+NiNCv+zCqaso5VVFCWkoXEuKcWd99fi8+v7etf8Nb/Zrx5tpNDO3RlQcmjsLr81PnO7NHbcn23XTv2IEvTB5DeXUNP39/AZf27Y3H5eLhKeNJiPPg8/v57UdLGdqzK1ldOp3jSmfzmbqUw9XFC3ok9YvayVsbG+XtP7kO78KNnr2X287SBl/RAkypT0Vzk7a6QDlFhUXAt23naC8O9hi30udJbF3T0zms3vExowZOP2v7yYpjdErpevrr9JSunKw8BjitZz979UvMevF2hvYeRXb3Ng1La9XA/Oq6OnYeO87Yfn0A8LhdJMU3OoVAjdeLMYYar4/k+DhcLkFESIhzaj6f3+D3+9uSl9XH5g0wxsTKWCoAxnsHXz7E22uB7Ryt9Ne8vLy/2g6hVCTRIqydyykq/C0wx3aO9mDrwLtSgnEer6+Ogt1LuKR/yxpC6lu8XC43/3nHH/jJfXPYfbSIA8d3teXyrSrCSsor6ZAQz5yVG3hq3iL+sXIDNV7vGftMGpjNkdJyfvT2h/xy3kJuHjkMlziZ/X7DU/MWkffWBwzqntGqVrB65d6TmRXeU6tafWCEm+zNmdLf122+7RwttAP4iu0QSkUaLcIUwBeBItshYtnB7sFrBdu8dwV9MgbRMfnspUDTUzI4UXH09NcnK46SlnzmEoTJCR0Y1HMkm/eubNV1jfG1eooEvzHsP1HKhAF9efTqycR73HxcuOOMfbYcOkqv9DSeuPEKHr1qMm+s3UR1nXMpl0t49OrJ/PcNV7D3+EkOnmrbBPJrj38Yk13u0+typ/bxdZlvO0cz6oB78vLyYn32f6VaTYswRU5RYTlwO1BhO0us2jooOK1gAKu2f8SoAWd3RQLkZk1kxdZ5GGPYdXgzSfEppKV0oazqJJU1zsTktd4atuxfTff0Pq28sr/VRVhaUiJpSYmnW7BGZPZk38kzl0RcWbyX3MweiAgZqSl0TknmSOmZv4pJ8XEM6NaFLQePtDYCAAcqt4/0+uu2tOngCHdN3cipPX3pkdw1+V95eXmtq/iVaie0CFMA5BQVbga+ZDtHLDrYfWzQWsFq66op2reakf0uO71t0ea3WbT5bQAu6juOLqk9+eHLn+WlhU9x1+RvAFBaWcIzbz/GT1/5Ir94/SsMzRxFbtaE1l3c+LzN73SmjkmJpCcncqTUKQC3HT5G945nzlPbKTmJbYedcWtl1TUcLSunS4dkyqtrqKp16r46r49th4/RrWPb72soOrW8bRVcFJhRN2pKV3/HRbZzNOGNvLy8J22HUCpS6RQV6gyFQ3OeRcduBNWCy57c5PMkXWQ7x4Uy/opjNaf+L6O1x+0/cYpXVhXg8/vpnJLMzLEXs27PAQAmDsziVFU1c1asp7S6BmMM03MGMCorkwMnS3l5xXqMMfiN4eI+vbj6okFtzu8Sd/UdWY9ViEiX5veOPgZjXo9fvviEq+Ky5vcOix3AqLy8vFPN7qlUO6VFmDpD4dCceOATYIztLLHgUPcxqzbnPDjado5gMP6yQzWn/tjDdo4LcVm32+b3Thk01XaOUPFjfK/GL11R6qpqZTNn0FUDE/Ly8tZZzqFURNPuSHWGnKLCWuBO4LjtLLFgy6CZSbYzBIsxvqif5mFNyQdDjDGt7laNFi7EfUft+NEpJnGF5Shf0wJMqeZpEabOklNUuBu4D9Bm0gtwqNuYVbHQDfmp1t8dGWkqfWU9y+qO2y5QQsqFK+6umgkXJ5n41ZYizM7Ly3ve0rWViipahKkm5RQVvgf8j+0c0WzL4NhpBQMgBlrCANaU/LtVyy5FIzeuhJk1E4clmLh1Yb70IuDLwTyhiPhEZF2Dj1nBPL9SNmkRps7nB8D7tkNEo0PdRq+OrVYwAG9MFGGHq4uH1/lrNtvOEWoe3El310wcGG88BWG6ZCFwc15eXk2Qz1tljBnZ4CP/Qk8oIrpkn4oIWoSpc8opKvTjjA+z1a0RtbYMvjv2JgdtwxQVkWrzyaUnbGcIhzg8HWbWTOzrMe7CEF/qEHBdXl5e2L6vIlIsIj8UkTUiUiAiQwPbU0TkTyKyUkTWisjNge0PisgrIvI2ME9EkkXkHyKyQUTmiMhyERktIl8QkacbXOchEXkqXM9LtS9ahKnzCkzkej3O7eaqBQ53G7XK50kabjtHsBliozsSYMuplWON8R+2nSMcEohLu7tmYg+3cW0L0SXKgRl5eXm7Q3T+pEbdkTMbPHbMGHMp8Hs+XQf3e8BHxpgxwDTgFyJSP1nyBOABY8x0nKl4ThhjRgA/BkYF9nkZuElE6pfo+hzw5xA9N9XOaRGmmpVTVHgEuBaI2ckugykmW8EAjLdtK2hHIIM/bm/FlnazVFci8Z1m1kxMdxlp04Kh5+EF7szLy1sT5PM21Lg7suFat68H/l0NZAc+vxqYJSLrgPlAItA38NgHxpj6O78vwym4MMZsBDYEPq8APgJuCLSuxRljwtWlq9oZLcJUi+QUFW4HZqBLG53X4a6jVns9ybm2c4RG7HRHAqw9/uEwY0ywxy9FrGQSut5VMzHRZWRPEE/7cF5ens1xo/U/Px9QP85LgNsbFG19jTH13bENX7/kPOd9HngQbQVTIaZFmGqxnKLCVcAdOO9+VRO2DL473naGkDG+mGkJA6j2VXQ9VXu0Xa1p2IHEnnfUjneJ4UAQTvfjvLy8/xeE8wTbv4CviYgAiMgl59jvE+CuwD7DgNNvnowxy4E+wL3A30OaVrVrWoSpVskpKnwf+ILtHJHocNdL13jjYrUVDAzemJs3blXJvFYvwxTtOprkzNtqx9eJ4ULGxM3Oy8t7Imihzq/xmLDm7o78MRAHbBCRjYGvm/I7oKuIbAC+i9Md2XCJpX8Ai40x7eImDmWH3qarWi2nqPDFwqE5vYCf2c4SSbYMvieu+b2iWIzME9ZQSc3+oTW+qg0J7qQRtrOEUyeTknVL7dgdb8SvcCO0thD9AHgoFLmaYoxxn2N7doPPVwFTA59X0cRcZcaYF4AXGmyqBu4zxlSLyADgQ6DhzQWXAU+jVAhpS5hqk5yiwnzgN7ZzRIpYbwVzxM7A/IY2nvikXY5z7GJSB9xUO7oEw8lWHLYeuD0vLy/qV08AkoFPRGQ98AbwH8aYWhFJF5GtODcEfGg3oop1WoSpC/FN4BXbISLBlsH3xH6rsom5hjAAdpStHes3/v22c9jQzaQNmVF76QEMpS3YvRi4Pi8vryzEscLCGFNmjBltjLnYGDPCGPNeYPtJY8xgY8ydtjOq2KdFmGqzwGSun8UZCNtuHel6yRpvXHLMd2eZGG0JMxh3cfnGUM2hFfF6mk7Drqm7uBhz3jufdwFT8/LygjGgXykVoEWYuiA5RYU1wM3Au7az2FLUHlrBAIwv5gbm11t//OORxphK2zls6ePPGHFFXe4WDNVNPLwTpwAL1WSsSrVbWoSpCxYoxG4F3radJdyOZIxc441LiflWMEfszkxS669OP157sF0vz9XP3+3SKXXDCjDUNti8A5iSl5cXzLnFlFIBWoSpoMgpKqwFbgfetBwlrIqG3NvknVsxKcbmCWts9bF5PW1nsG2Qv+eYSd4hazB4gW04Bdg+27mUilVahKmgySkqrMNZ8PtV21nC4UjGyLXeuJSLbecIF0NsDsyvd6L28MBqX0Uol9+JCjm+zPFjvQPfwumCbJc3LCgVLlqEqaDKKSr0AncDs21nCbWiIfe2r/8/xne+ZV5iwobjC2K70myZtSN8WQ/rIHylQq99/RFRYZFTVOjDWXPtGdtZQuVIxsXr2lMrmCP2ZsxvbFd5wSi/8bXnAeiLgGmZ+ZOP2g6iVHugRZgKiZyiQpNTVPgN4Ie2s4RC0ZDPxHyr0FlMTA8Jq+faUbau2HYIS94DrsnMn3yq2T2VUkGhRZgKqZyiwjycSV1jphXlaMaIdjUW7FPedvF6UXB84SXGmJiYkLQV5gA3Z+ZPrrIdRKn2pF28qCq7cooKfw08ANTYzhIM7bIVDDDtoyWMOlPb8Wj13rW2c4TRH4B7M/Mnx8JSREpFFS3CVFjkFBX+BZgGHLad5UIc7TJiXV1ch5G2c9gR+wPz660umdfXGBMzrbfn4AO+k5k/+cuZ+ZPbR4WtVITRIkyFTU5R4VJgDBC1rQxFQ9tnK5jD125eL0rrSrKrfGWrbOcIoePAdZn5k5+0HUSp9qzdvKiqyJBTVLgXuIwoXPj7aJfc9XVxHdrhWLCAdtIdWW/d8Y9j9fWxABiTmT/5A9tBlGrvYvVFJqKJiBGRvzT42iMiR0XknWaOGyki17fher1E5LwTqIpItohsbO252yKnqLASmAn8gCgasF805L6oyRoa/nb1erG3omiUz+/dYTtHkL0KTMjMn7zTdhCllBZhtlQAw0UkKfD1VUBLZqYeCbSqCBMRjzHmgDHmjtZFDK3AFBY/Au7A+X5EtKNdctfVxbfXsWD12lcRBrC1dFWszBjvB76XmT/5zsz8yRH//02p9qLdvahGkPeAGYHP7wH+Xv+AiIwVkSUisjbw7xARiQd+BMwUkXUiMlNEUkTkTyKyMrDvzYHjHxSRV0TkbWBew1auwOeLRGRN4GNi42AicpGIrAhcZ4OIDArVNyGnqPB1YBIQ0QsEFw25z3YE64xpf0XYppOLRxtjTtrOcYFOAjdm5k/+qe0gSqkztbsX1QjyMnC3iCQCI4DlDR4rAi43xlwCPAH81BhTG/h8jjFmpDFmDvA94CNjzBicOw9/ISIpgXNMAB4wxkxvdN0jwFXGmEtxugSbmtX+YeDXxpiRwGggpAv45hQVrscZsL84lNdpq2Ndhq/XVjBojy1hPuNNPlS1a53tHBdgMzA2M3/yu7aDKKXO1u5eVCOFMWYDkI3TCtb4BTINeCXQevU0cNE5TnM1MEtE1gHzgUSgb+CxD4wxx5s4Jg74o4gU4AyOH9bEPkuB/xKR7wJZxpiQT+CYU1R4BJgOPB/qa7VWYbsfC1bP77adwIbVJR8MNMZE45qSbwLjM/Mnb7MdRCnVNC3C7HoLeJIGXZEBPwY+NsYMB27EKa6aIsDtgZaxkcaYvsaYwsBj5xr38S2cubouxmnlim+8gzHmJeAmoAr4l4g0bk0LiZyiwtqcosKHcFroToTjms051vmi9XXxqSNt54gMpl0WYRXek5kV3lMrbedoBQPkAbdl5k9ubzP/KxVVtAiz60/Aj4wxBY22p/HpQP0HG2wvA1IbfP0v4GsiIgAickkLrpkGHDTO9OefBc76wyoi/YGdxphncArFES04b9DkFBX+A8gFPgzndZtSOPSz7WtehvMx7bMlDGBNyb+Tmt8rIpwCbsnMn/zDzPzJ2oKrVITTIswiY8w+Y8yvm3jof4GfichiziySPgaG1Q/Mx2kxiwM2BLouf9yCy/4OeEBElgGDabrFbCawMdDNORR4saXPKVhyigr349w1+hiWljsKtIK1pLBtJ9pnSxjAwaodF3v9tVts52jGv4HhmfmT37IdRCnVMhL7K3OoaFc4NGcE8DdgeDivu2hi/pq6+NRLw3nNSFZ94tc7wDfAdg5bLkqf+MnwTpMvs52jCVXAd4HfauuXUtFFW8JUxMspKtyAM37tV4RpcteSzsM2aAHWWPttCQMoPLl8jDHmmO0cjawALsnMn/wbLcCUij5ahKmokFNUWJNTVPgtnDtCQz6B5uahn43Gu+FCzWM7gE1+fAn7K7eFZVWJFqjDmbJmYmb+5EjvJlVKnYMWYSqq5BQV/hvnRoHzLsN0IUo65Wyoi++oY8HOYtp1EQawpuSDHGNMneUYa4FxmfmTf5yZP1nfLCgVxbQIU1Enp6jweE5R4Z3AZ4BDwT7/5pz7vcE+Z2zQIqzKV969rK7E1nQV9WO/xmbmT15rKYNSKoi0CFNRK6eo8CVgCM6EtkEpnEo65RTUxXfUsWBNi7MdIBKsLvkgzcJl5wMjMvMn/29m/mR9k6BUjNAiTEW1nKLC0pyiwkdxFjeff6HnK8y533ZXUyTTIgw4Ur3nojp/zaYwXe4U8GVgemb+5O1huqZSKky0CFMxIaeocFNOUeE0nGWg2jRwv6RTTkGttoKdjxZhAZtOLD4V4ksY4K/AsMz8yX/QOx+Vik1ahKmYklNU+DLOBLO/wLmDrMUKh362NiShYocWYQFbS1eP8Rt/0McjBswDLs3Mn/zZzPzJB0J0DaVUBNAiTMWcnKLC8pyiwsdx7qL8d0uOOd5paEFtQtqo0CaLXsb4dBxSAwZ/3N6KoqIgn3YtcFVm/uRrMvMnrwvyuZVSEUiLMBWzcooKi3KKCq8C7gT2nm/fzUPv11aw8/Lr96eRtSUfDjfGBGNJrWKcO31HZeZPbtGbBqVUbNAiTMW8nKLCV3HuovwOcNaM58fTh2zUVrBmGJ/esNBIjb8y42TtkQuZrqIEeBQYkpk/+SUd96VU+6NFmGoXcooKq3KKCp8E+gH/DZysf2xzzgNWFgiPLtod2ZTVJfO6tuGwKiAfGJCZP/npzPzJ2sqoVDvV7idfVO1LTlFhOfCTwqE5vwW+XdIp5+rahLQxtnNFPr+2hDWhpObAkBpf1foEd9LFLdjdB8wGfpCZP3lfiKMppaKAtoSpdimnqPBkTlHh99df/NXrgP8BSm1nimQ6MP/cCk4srGrBbm8DF2fmT/5CuAswEckWkY2NtuWJyLdF5EER6dVg+/MiMizwebGIZAQ+X9LgXPc22H+0iDwTnmeiVOzRIky1a488N73kkeemfx/IwlkQ+bjlSBHKp2sUnsPOsvVj/MbXVGFVC7wA5GbmT74pM39yuCZ4bY0HgdNFmDHmi8aYzY13MsZMDHyaDdzbYPsqY8zXQ5xRqZilRZhSwCPPTT/5yHPTf4xTjH0d2GY5UmTRlrBzMhj3rvKNOxpsOg78FMjOzJ/8ucz8yRvPcWgkGA38TUTWiUiSiMwXkdGNdxKR8sCn+cDkwP7fEpGpIvJOYJ8UEfmTiKwUkbUicnNg+0UisiJwzAYRGRS2Z6dUhNMiTKkGHnluevkjz03/Dc7dlDOAf+HMXt7OaRF2PhuOz7/YGP9G4GtA38z8yd/LzJ980HauFlgFfMYYM9IY05Ju1VnAosD+Tzd67HvAR8aYMcA04BcikgI8DPzaGDMSp+jT8XBKBejAfKWa8Mhz0w3wLvDusw9/NBTnj+sDQIrVYLYYr3ZHNs0A/671V//2H8W/eOexOe/4bQdqwrneRAT7zcXVwE0i8u3A14lAX2Ap8D0RyQReN8ZoK7NSAVqEKdWMR56bXgQ88uzDH/0X8AXgqzhTXbQbRseENXYKeBF49rE572yxHaYZJUCnRts6A7uCfB0BbjfGNP5+FIrIcgItyyLyRWPMR0G+tlJRSbsjlWqhR56bfuqR56Y/BQwApuMMui6zGipcjBZhOFNMvIezSHyPx+a88/UoKMAwxpQDB0XkCgAR6QxcC3yC8/ub2orTnW//fwFfExEJXOeSwL/9gZ3GmGeAt3CWE1NKoS1hSrVaoKvyY+DjZx/+6BHgVuB+4Epi9o2NNxK72cKlAKfV62+PzXknGsZ5NeV+4FkR+WXg6x8aY3aIyAvAcyJSBUxowXk2AF4RWY/zJmRtg8d+DPwK2BAoxIqBG4CZwH0iUgccAn50wc9GqRghxuiYY6WC4dmHP+qFswbg/cBwy3GCylezeWVd5fvtaVLbo8BLwOzH5ryztrmdlVKqLbQIUyoEnn34o5HAbcBNQEtmU49o3pqC5d7KD8bZzhFie4F/Bj7mPzbnHb0jVCkVUlqEKRVizz78URZOMXYTMAWIs5uo9bzVa5d5qz4ebztHCGzAKbrefGzOO2tsh1FKtS9ahCkVRs8+/FEacB1OQXY9kGY3Uct4q1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\"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.figure(figsize=(10,8))\\n\",\n \"\\n\",\n \"plt.pie(gb['Rank'], labels=gb['Sector'], autopct='%.2f')\\n\",\n \"\\n\",\n \"plt.suptitle(\\\"Sector Vs Rank\\\")\\n\",\n \"\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"d4812687\",\n \"metadata\": {},\n \"source\": [\n \"# Range\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"id\": \"fee0db23\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
RankCompanyCountrySalesProfitsAssetsMarket ValueSectorIndustry
01ICBCChina151.442.03473.2229.8FinancialsMajor Banks
12China Construction BankChina134.235.03016.6200.5FinancialsRegional Banks
23Berkshire HathawayUnited States222.924.1620.9409.9FinancialsInvestment Services
34JPMorgan ChaseUnited States102.524.22513.0306.6FinancialsMajor Banks
45Wells FargoUnited States97.621.91943.4274.4FinancialsMajor Banks
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" Rank Company Country Sales Profits Assets \\\\\\n\",\n \"0 1 ICBC China 151.4 42.0 3473.2 \\n\",\n \"1 2 China Construction Bank China 134.2 35.0 3016.6 \\n\",\n \"2 3 Berkshire Hathaway United States 222.9 24.1 620.9 \\n\",\n \"3 4 JPMorgan Chase United States 102.5 24.2 2513.0 \\n\",\n \"4 5 Wells Fargo United States 97.6 21.9 1943.4 \\n\",\n \"\\n\",\n \" Market Value Sector Industry \\n\",\n \"0 229.8 Financials Major Banks \\n\",\n \"1 200.5 Financials Regional Banks \\n\",\n \"2 409.9 Financials Investment Services \\n\",\n \"3 306.6 Financials Major Banks \\n\",\n \"4 274.4 Financials Major Banks \"\n ]\n },\n \"execution_count\": 25,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df.head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"id\": \"ad69c17b\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"45.2\"\n ]\n },\n \"execution_count\": 26,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"profit_max = df['Profits'].max()\\n\",\n \"\\n\",\n \"profit_max\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"id\": \"4a88e4a1\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"-13.0\"\n ]\n },\n \"execution_count\": 27,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"profit_min = df['Profits'].min()\\n\",\n \"\\n\",\n \"profit_min\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 28,\n \"id\": \"29b12a1f\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"58.2\"\n ]\n },\n \"execution_count\": 28,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"profit_range = profit_max - profit_min\\n\",\n \"\\n\",\n \"profit_range\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 29,\n \"id\": \"356b1be1\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.figure(figsize=(10,8))\\n\",\n \"\\n\",\n \"sns.boxplot(data = df, y = 'Profits', showmeans=True)\\n\",\n \"\\n\",\n \"plt.title('Forbes Data')\\n\",\n \"\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 30,\n \"id\": \"8be191bb\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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YbGiUBeqOAAd2yBwxoRQEDumUPGNCKAgZ064orrhiZr7zyykZJgN4oYEC37r777mVngHFRwIBuzc7Ojsx79uxplATojQIGdGt6enpk3rhxY6MkQG8UMKBbp59++sj8nOc8p1ESoDcKGNCt9773vSPzNddc0ygJ0BsFDOhWrXXZGWBcFDCgW6WUZWeAcVHAgG5t2rRpZH7ta1/bKAnQGwUM6Naxx07ceuutjZIAvVHAgG4dPHhw2RlgXBQwoFsTExPLzgDjooAB3XrTm940Mr/5zW9ulATojQIGdOvYPWC33XZboyRAb5oXsFLK6lLKH5VSfqN1FqAv8/Pzy84A49K8gCW5PMk9rUMA/ZmcnFx2BhiXpgWslPK8JP86yXuP91yAx9rmzZtH5pmZmUZJgN60XgH7xST/OcnhxjmADu3atWtk3rlzZ6MkQG+aFbBSymuT/E2tdd9xnveWUsreUsre/fv3D5QO6IE9YEArLVfAzklyXillPsmvJfm+UsoNxz6p1nptrXV9rXX9unXrhs4IrGD2gAGtNCtgtdafrLU+r9Y6meT1SX671npRqzxAf172speNzN/+7d/eKAnQm9Z7wACa+chHPjIyf+hDH2qUBOjN4+K+G7XWO5Lc0TgGAMAgrIABAAxMAQMAGJgCBgAwMAUMAGBgChjQrbPPPntk3rBhQ6MkQG8UMKBbn/jEJ0bmu+66q1ESoDcKGNCthYWFZWeAcVHAgG6tWbNm2RlgXBQwoFsXX3zxyHzJJZc0SgL0RgEDurV79+6R+aabbmqUBOiNAgZ0a35+ftkZYFwUMKBbk5OTy84A46KAAd0655xzRmbngAFDUcCAbn3wgx8cma+//vpGSYDeKGBAt2qty84A46KAAd0qpSw7A4yLAgZ060UvetHI/NKXvrRREqA3ChjQLfeCBFpRwAAABqaAAQAMTAEDuvW6171uZP7BH/zBRkmA3ihgQLcuu+yykfmtb31royRAbxQwoGtnn312EqfgA8NSwICuHfkmpG9AAkNSwIBu7d27NwsLC0mShYWF7Nu3r3EioBcKGNCtt7/97SPzVVdd1SYI0B0FDOjWkdWvR5sBxkUBA7q1Zs2aZWeAcVHAgG696lWvGplf/epXN0oC9EYBA7q1e/fukfnDH/5woyRAbxQwoFu11mVngHFRwIBulVKWnQHGRQEDuvW2t71tZL7iiivaBAG6M9E6ADzRbd++PXNzc61j8BiYnZ3N7Oxs6xicpKmpqWzZsqV1DDgpVsCArp1yyilJkjPOOKNxEqAnVsDgn8i/eT+xXX755UmSq6++unESoCdWwAAABqaAAQAMTAEDABiYAgYAMDAFDABgYCdUwEopl5dSnlYWva+Uclcp5VXHfyUAAMc60RWwH6m13p/kVUnWJXlTkp8dWyoAgBXsRAvYkRukvSbJr9ZaP37UNQAATsKJFrB9pZTbs1jAfquU8tQkh8cXCwBg5TrRk/D/fZKXJPlMrfXBUsraLH4MCQDASTrRFbA9tda7aq1fSpJa64Ek7x5bKgCAFWzZFbBSypOTnJbk2aWUZ+Yf9n09Lclzx5wNAGBFOt5HkJckeVsWy9ZdR12/P8l7xpQJAGBFW7aA1VqvTnJ1KWVLrXX7QJkAAFa0430E+X211t9Ocl8p5XXHPl5r/cjYkgEArFDH+whyQ5LfTnLuIzxWkyhgAAAn6XgF7G+X/vN9tdbfG3cYAIAeHO8YiiNnff33cQcBAOjF8VbA7imlzCdZV0r5xFHXS5Jaa/22sSUDAFihjvctyAtLKd+Y5LeSnDdMJACAle24tyKqtX4+yYtLKU9K8oKly5+utX5trMkAAFaoE7oXZCnle5LsSjKfxY8fv6mUMlNrvXOM2QAAVqQTvRn3u5K8qtb66SQppbwgyY1JXj6uYAAAK9WJ3oz7lCPlK0lqrX+a5JTxRAIAWNlOdAVsXynlfUmuX5rfkGTfeCIBAKxsJ1rAfjTJW5NclsU9YHcm+aVxhQIAWMmOW8BKKauS7Ku1vjCLe8EAAPgnOO4esFrr4SQfL6X88wHyAACseCf6EeRzkvxxKeUPk3zlyMVaq8NZAQBO0okWsHeMNQUAQEeWLWCllCdncQP+VJJPJnlfrfXgEMEAAFaq4+0B25lkfRbL16uTvHPsiQAAVrjjfQT5rbXWFyXJ0jlgfzj+SAAAK9vxVsAevuG2jx4BAB4bx1sBe3Ep5f6lP5ckpy7NJUmttT7t633jUso3ZfEG39+Y5HCSa2utV3+9vw8A4Ili2QJWa109xvc+mOTKWutdpZSnZvF2R3tqrX8yxvcEAGjuRG/G/Zirtf5VrfWupT8/kOSeJGe0ygMAMJRmBexopZTJJC9N8geNowAAjF3zAlZKWZPkpiRvq7Xe/wiPv6WUsreUsnf//v3DBwQAeIw1LWCllFOyWL4+UGv9yCM9p9Z6ba11fa11/bp164YNCAAwBs0KWCmlJHlfkntqre9qlQMAYGgtV8DOSfLDSb6vlHL30s9rGuYBABjEid6M+zFXa/29LJ4nBgDQleab8AEAeqOAAQAMTAEDABiYAgYAMDAFDABgYAoYAMDAFDAAgIEpYAAAA1PAAAAGpoABAAxMAQMAGJgCBgAwMAUMAGBgChgAwMAUMACAgSlgQNe+OvHV7Hv+vnzhoS+0jgJ0ZKJ1ABZt3749c3NzrWNAdz7+tI9nYc1CLnrPRTnzL89sHQe6MjU1lS1btrSO0YQC9jgxNzeXuz91Tw6d9qzWUaAbh5/8tSx820JSkvue+bnc/7GnZNVXT2kdC7qw+sEvto7QlAL2OHLotGflobNe0zoGdOPgc/Ykq1YnOZSsWpWvnH1aJv5qY+tY0IVT7/1o6whN2QMGdKlOLOTwMz+VrDq0eGHVoRx+5qdSJxbaBgO6oIABXTq07veT1GOu1qXrAOOlgAFdOnzaff+w+nXEqkOL1wHGzB4woEtP+rM3to4AdMwKGADAwBQwAICBKWAAAANTwAAABqaAAQAMTAEDABiYAgYAMDAFDABgYAoYAMDAFDAAgIEpYAAAA1PAAAAGpoABAAxMAQMAGJgCBgAwMAUMAGBgChgAwMAUMACAgSlgAAADU8AAAAamgAEADEwBAwAY2ETrACy67777svrBL+fUez/aOgoAjN3qBw/kvvsOto7RjBUwAICBWQF7nDjjjDPy+a9O5KGzXtM6CgCM3an3fjRnnHF66xjNWAEDABiYAgYAMDAFDABgYAoYAMDAFDAAgIEpYAAAA1PAAAAGpoABAAxMAQMAGJgCBgAwMAUMAGBg7gX5OLL6wS/m1Hs/2joGdGXV392fJDn85Kc1TgJ9Wf3gF5P0ey9IBexxYmpqqnUE6NLc3ANJkqlv6fcfBNDG6V3/s08Be5zYsmVL6wjQpcsvvzxJcvXVVzdOAvTEHjAAgIEpYAAAA1PAAAAGpoABAAxMAQMAGJgCBgAwMAUMAGBgChgAwMCaFrBSyqZSyqdLKXOllJ9omQUAYCjNClgpZXWS9yR5dZJvTXJhKeVbW+UBABhKyxWw70gyV2v9TK3175P8WpLvb5gHAGAQLQvYGUn+8qj5s0vXAABWtJYFrDzCtfqPnlTKW0ope0spe/fv3z9ALACA8WpZwD6b5JuOmp+X5HPHPqnWem2tdX2tdf26desGCwcAMC4tC9j/TfL8Usq/KKU8Kcnrk9zcMA8AwCAmWr1xrfVgKeXSJL+VZHWS/1Fr/eNWeQAAhtKsgCVJrfWjST7aMgMAwNCchA8AMDAFDABgYAoYAMDAFDAAgIEpYAAAA1PAAAAGpoABAAxMAQMAGJgCBgAwMAUMAGBgChgAwMAUMACAgSlgAAADU8AAAAamgAEADEwBAwAYmAIGADAwBQwAYGAKGADAwBQwAICBKWAAAANTwAAABqaAAQAMTAEDABiYAgYAMDAFDABgYAoYAMDAFDAAgIEpYAAAA1PAAAAGpoABAAxMAQMAGJgCBgAwMAUMAGBgChgAwMAUMACAgSlgAAADm2gdAJ7otm/fnrm5udYx+Dod+f/u8ssvb5yEr9fU1FS2bNnSOgacFAUM6Nqpp57aOgLQIQUM/on8mzcAJ8seMACAgSlgAAADU8AAAAamgAEADEwBAwAYmAIGADAwBQzo2oEDB3LZZZflwIEDraMAHVHAgK7t3Lkzn/zkJ7Nr167WUYCOKGBAtw4cOJDbbrsttdbcdtttVsGAwShgQLd27tyZw4cPJ0kOHTpkFQwYjAIGdGt2djYHDx5Mkhw8eDB79uxpnAjohQIGdGt6enpk3rhxY6MkQG8UMKBbT33qU0fmpz/96Y2SAL1RwIBufeADHxiZ7QEDhqKAAQAMTAEDABiYAgZ06w1veMPIvHnz5kZJgN4oYEC3HnjggZH5y1/+cqMkQG8UMKBbs7OzI7NzwIChKGBAt5wDBrSigAHd2rBhw7IzwLgoYEC3duzYMTJv3769URKgNwoY0K35+fllZ4BxUcCAbk1OTi47A4yLAgZ0a+vWrcvOAOOigAHdmpqaenjVa3JyMlNTU20DAd1QwICuHTn9fmZmpnESoCcKGNC1Xbt2JUl27tzZOAnQEwUM6Nbc3NzD33ycn5/P3Nxc20BANxQwoFvbtm1bdgYYFwUM6JZzwIBWmhSwUsrPl1LuLaV8opSyu5TyjBY5gL45BwxopdUK2J4kL6y1fluSP03yk41yAB07//zzR+YLLrigURKgN00KWK319lrrwaXxY0me1yIH0LfrrrtuZL7mmmsaJQF683jYA/YjSW59tAdLKW8ppewtpezdv3//gLGAlW5hYWHZGWBcxlbASimzpZRPPcLP9x/1nJ9OcjDJBx7t99Rar621rq+1rl+3bt244gIdWrNmzbIzwLhMjOsX11qnl3u8lDKT5LVJXllrrePKAfBoLr744rz73e9+eL7kkksapgF60upbkJuS/HiS82qtD7bIALB79+6R+aabbmqUBOhNqz1gO5I8NcmeUsrdpZRfaZQD6JhzwIBWxvYR5HJqrVMt3hfgaJOTkyOlyzlgwFAeD9+CBGji0ksvHZm3bNnSKAnQGwUM6Nadd9657AwwLgoY0K3Z2dmRec+ePY2SAL1RwIBuTU9Pp5SSJCmlZOPGjY0TAb1QwIBunXfeeTlyDGGtNeeee27jREAvFDCgWzfffPPICtgtt9zSOBHQCwUM6Nbs7OzICpg9YMBQFDCgW9PTo3dMswcMGIoCBnRrw4YNy84A46KAAd3asWPHyLx9+/ZGSYDeKGBAt9wLEmhFAQO6dey9H90LEhiKAgZ0y70ggVYUMKBb7gUJtKKAAd1yL0igFQUM6Nb09HQmJiaSJBMTE84BAwajgAHdmpmZyapVi38Nrl69Ops3b26cCOiFAgZ0a+3atfne7/3eJMkrXvGKrF27tnEioBcKGNC1I/eCBBiSAgZ068CBA7njjjuSJHfccUcOHDjQNhDQDQUM6NbOnTtz+PDhJMmhQ4eya9euxomAXihgQLdmZ2dz8ODBJMnBgwcdQwEMRgEDujU9PZ1SSpKklOIYCmAwChjQrfPOO+/hTfi11px77rmNEwG9UMCAbt18880jK2C33HJL40RALxQwoFuzs7MjK2D2gAFDUcCAbtkDBrSigAHdsgcMaEUBA7plDxjQigIGdMseMKAVBQzo1vT0dCYmJpIkExMT9oABg1HAgG7NzMxk1arFvwZXr16dzZs3N04E9EIBA7q1du3abNq0KaWUbNq0KWvXrm0dCehEObL/4YmglLI/yZ+3zgGsHKtWrTr1tNNOO+vBBx+89/Dhww+1zgOsKN9ca133SA88oQoYwDiUUvbWWte3zgH0w0eQAAADU8AAAAamgAEk17YOAPTFHjAAgIFZAQMAGJgCBgAwMAUMAGBgChgAwMAUMACAgf1/0+OvARuDRbMAAAAASUVORK5CYII=\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.figure(figsize=(10,8))\\n\",\n \"\\n\",\n \"sns.boxplot(data = df, y = 'Profits', showmeans=True)\\n\",\n \"\\n\",\n \"plt.ylim(-3,8)\\n\",\n \"\\n\",\n \"plt.title('Forbes Data')\\n\",\n \"\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"60b33559\",\n \"metadata\": {},\n \"source\": [\n \"# Range\\n\",\n \"\\n\",\n \"- range is sensitive to outliers\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"57270870\",\n \"metadata\": {},\n \"source\": [\n \"# Inter Quartile Range\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 31,\n \"id\": \"55f9d714\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"0.34975\"\n ]\n },\n \"execution_count\": 31,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# 25th percentile\\n\",\n \"\\n\",\n \"Q1 = np.quantile(df['Profits'], 0.25)\\n\",\n \"\\n\",\n \"Q1\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 32,\n \"id\": \"7dfaa0d9\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"1.4249999999999998\"\n ]\n },\n \"execution_count\": 32,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# 75th percentile\\n\",\n \"\\n\",\n \"Q3 = np.quantile(df['Profits'], 0.75)\\n\",\n \"\\n\",\n \"Q3\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 33,\n \"id\": \"e1aab44b\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"1.0752499999999998\"\n ]\n },\n \"execution_count\": 33,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"IQR = Q3 - Q1\\n\",\n \"\\n\",\n \"IQR\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"194f9cf6\",\n \"metadata\": {},\n \"source\": [\n \"# IQR\\n\",\n \"\\n\",\n \"- resistant to outliers\\n\",\n \"- better than range\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"d30aaeac\",\n \"metadata\": {},\n \"source\": [\n \"# Standard Deviation and Variance\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 34,\n \"id\": \"94231206\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"3.244201278603241\"\n ]\n },\n \"execution_count\": 34,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"profit_std = df['Profits'].std()\\n\",\n \"\\n\",\n \"profit_std\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 36,\n \"id\": \"054b2250\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"1.4074303713527847\"\n ]\n },\n \"execution_count\": 36,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"profit_mean = df['Profits'].mean()\\n\",\n \"\\n\",\n \"profit_mean\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"7e91b587\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"cricket\\n\",\n \"\\n\",\n \"virat kohli rohit sharma\\n\",\n \"40 0\\n\",\n \"50 100\\n\",\n \"30 10\\n\",\n \"20 15\\n\",\n \"45 0\\n\",\n \"67 101\\n\",\n \"40 200\\n\",\n \"\\n\",\n \"mean mean\\n\",\n \"35 60\\n\",\n \"\\n\",\n \"5 60\\n\",\n \"15 40\\n\",\n \"5 50\\n\",\n \"15 60\\n\",\n \"25 41\\n\",\n \" 140\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 37,\n \"id\": \"86d516fd\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"10.524841936090901\"\n ]\n },\n \"execution_count\": 37,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"profit_var = df['Profits'].var()\\n\",\n \"\\n\",\n \"profit_var\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"f5f846b7\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"appl msoft\\n\",\n \"100 100\\n\",\n \"500 120\\n\",\n \"-200 130\\n\",\n \"600 125\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 38,\n \"id\": \"0c7d3fa2\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"10.524841936090903\"\n ]\n },\n \"execution_count\": 38,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"profit_std ** 2 # variance\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"27a5b442\",\n \"metadata\": {},\n \"source\": [\n \"# Summary Stats\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 39,\n \"id\": \"3d683610\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
RankSalesProfitsAssetsMarket Value
count1508.0000001508.0000001508.0000001508.0000001508.000000
mean909.13527919.6230461.40743097.64701626.978494
std554.04445630.8758093.244201287.03615747.099541
min1.0000000.001000-13.0000001.3000000.240000
25%433.7500004.9000000.34975012.8000007.300000
50%883.50000010.3000000.69250025.90000013.100000
75%1354.25000019.9000001.42500060.32500026.900000
max1999.000000485.30000045.2000003473.200000752.000000
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" Rank Sales Profits Assets Market Value\\n\",\n \"count 1508.000000 1508.000000 1508.000000 1508.000000 1508.000000\\n\",\n \"mean 909.135279 19.623046 1.407430 97.647016 26.978494\\n\",\n \"std 554.044456 30.875809 3.244201 287.036157 47.099541\\n\",\n \"min 1.000000 0.001000 -13.000000 1.300000 0.240000\\n\",\n \"25% 433.750000 4.900000 0.349750 12.800000 7.300000\\n\",\n \"50% 883.500000 10.300000 0.692500 25.900000 13.100000\\n\",\n \"75% 1354.250000 19.900000 1.425000 60.325000 26.900000\\n\",\n \"max 1999.000000 485.300000 45.200000 3473.200000 752.000000\"\n ]\n },\n \"execution_count\": 39,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df.describe()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"45cddf9c\",\n \"metadata\": {},\n \"source\": [\n \"# Noraml Distribution\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 40,\n \"id\": \"eeec52ce\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([-0.21471611, 0.61754921, -0.60251843, 0.14196059, 0.60611692,\\n\",\n \" 0.33409508, 0.93308273, -0.83391941, -2.49212651, -0.64909842])\"\n ]\n },\n \"execution_count\": 40,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# mean = 0\\n\",\n \"# std = 1\\n\",\n \"\\n\",\n \"normally_distributed_data = np.random.normal(loc=0, scale=1, size=100000)\\n\",\n \"\\n\",\n \"normally_distributed_data[0:10]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"91a281e8\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# mean == median # normal distribution\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 42,\n \"id\": \"3524740c\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"-0.00010732710555090066\"\n ]\n },\n \"execution_count\": 42,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"sample_mean = np.mean(normally_distributed_data)\\n\",\n \"\\n\",\n \"sample_mean\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 43,\n \"id\": \"c995dc03\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"-0.0014516077341794609\"\n ]\n },\n \"execution_count\": 43,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"sample_median = np.median(normally_distributed_data)\\n\",\n \"\\n\",\n \"sample_median\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 44,\n \"id\": \"9dc19349\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"1.0014081381099627\"\n ]\n },\n \"execution_count\": 44,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"sample_std = np.std(normally_distributed_data)\\n\",\n \"\\n\",\n \"sample_std\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"81360486\",\n \"metadata\": {},\n \"source\": [\n \"## plot normal distribution\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 46,\n \"id\": \"908217a4\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.figure(figsize=(12,8))\\n\",\n \"\\n\",\n \"sns.histplot(x = normally_distributed_data)\\n\",\n \"\\n\",\n \"plt.title(\\\"Normal Distribution\\\", fontsize='15')\\n\",\n \"\\n\",\n \"plt.axvline(x = sample_mean, color='r', linestyle='-')\\n\",\n \"plt.axvline(x = sample_median, color='g', linestyle='-')\\n\",\n \"\\n\",\n \"plt.xlabel('Values', fontsize='15')\\n\",\n \"plt.ylabel('probability', fontsize='15')\\n\",\n \"\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"91fcee90\",\n \"metadata\": {},\n \"source\": [\n \"# Probability'\\n\",\n \"\\n\",\n \"an event is likely to occur\\n\",\n \"\\n\",\n \"favorable cases / total number of cases\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"04e37d20\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"1/2\\n\",\n \"\\n\",\n \" H T\\n\",\n \"0 - 0.5 , 0.5 - 1\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"ef7d552b\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \" 0 1 2\\n\",\n \"0-0.1 0.1 - 0.2 0.2 - 0.3\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"7556d169\",\n \"metadata\": {},\n \"source\": [\n \"# Coin\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 49,\n \"id\": \"c3a058c9\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import random\\n\",\n \"import scipy.stats\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 50,\n \"id\": \"977c6909\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def flip(n):\\n\",\n \" \\n\",\n \" result = []\\n\",\n \" \\n\",\n \" for i in range(1,n+1):\\n\",\n \" result.append(random.choice(['Head', 'Tails']))\\n\",\n \" \\n\",\n \" return result\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 51,\n \"id\": \"0c567150\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"['Head',\\n\",\n \" 'Tails',\\n\",\n \" 'Tails',\\n\",\n \" 'Tails',\\n\",\n \" 'Head',\\n\",\n \" 'Head',\\n\",\n \" 'Tails',\\n\",\n \" 'Tails',\\n\",\n \" 'Tails',\\n\",\n \" 'Head']\"\n ]\n },\n \"execution_count\": 51,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"flip(10)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 52,\n \"id\": \"a45cb78e\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": \"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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"f = flip(10)\\n\",\n \"\\n\",\n \"plt.figure(figsize=(12,8))\\n\",\n \"\\n\",\n \"sns.countplot(x = f)\\n\",\n \"\\n\",\n \"plt.xlabel('Coin Flips')\\n\",\n \"plt.title('Counts')\\n\",\n \"\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 53,\n \"id\": \"3f563d5f\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"f = flip(100)\\n\",\n \"\\n\",\n \"plt.figure(figsize=(12,8))\\n\",\n \"\\n\",\n \"sns.countplot(x = f)\\n\",\n \"\\n\",\n \"plt.xlabel('Coin Flips')\\n\",\n \"plt.title('Counts')\\n\",\n \"\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 54,\n \"id\": \"85907d8a\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"f = flip(100000)\\n\",\n \"\\n\",\n \"plt.figure(figsize=(12,8))\\n\",\n \"\\n\",\n \"sns.countplot(x = f)\\n\",\n \"\\n\",\n \"plt.xlabel('Coin Flips')\\n\",\n \"plt.title('Counts')\\n\",\n \"\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"1081d8ad\",\n \"metadata\": {},\n \"source\": [\n \"# Dice\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 55,\n \"id\": \"b8e72228\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def roll(n):\\n\",\n \" \\n\",\n \" result = []\\n\",\n \" \\n\",\n \" for i in range(1,n+1):\\n\",\n \" result.append(random.choice([1,2,3,4,5,6]))\\n\",\n \" \\n\",\n \" return result\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 56,\n \"id\": \"acb6b988\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[4, 5, 4, 5, 3, 3, 3, 3, 4, 6]\"\n ]\n },\n \"execution_count\": 56,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"roll(10)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 57,\n \"id\": \"ca45a0e6\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"f = roll(10)\\n\",\n \"\\n\",\n \"plt.figure(figsize=(12,8))\\n\",\n \"\\n\",\n \"sns.countplot(x = f)\\n\",\n \"\\n\",\n \"plt.xlabel('Dice Roll')\\n\",\n \"plt.title('Counts')\\n\",\n \"\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 58,\n \"id\": \"d9bd67b6\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"f = roll(100)\\n\",\n \"\\n\",\n \"plt.figure(figsize=(12,8))\\n\",\n \"\\n\",\n \"sns.countplot(x = f)\\n\",\n \"\\n\",\n \"plt.xlabel('Dice Roll')\\n\",\n \"plt.title('Counts')\\n\",\n \"\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 59,\n \"id\": \"a0b6328e\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"f = roll(100000)\\n\",\n \"\\n\",\n \"plt.figure(figsize=(12,8))\\n\",\n \"\\n\",\n \"sns.countplot(x = f)\\n\",\n \"\\n\",\n \"plt.xlabel('Dice Roll')\\n\",\n \"plt.title('Counts')\\n\",\n \"\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 60,\n \"id\": \"3cf86622\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
DateOpenHighLowCloseAdjCloseVolume
031-Dec-1986243.37244.03241.28242.17242.1713,92,00,000
12-Jan-1987242.17246.45242.17246.45246.459,18,80,000
25-Jan-1987246.45252.57246.45252.19252.1918,19,00,000
36-Jan-1987252.20253.99252.14252.78252.7818,93,00,000
47-Jan-1987252.78255.72252.65255.33255.3319,09,00,000
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" Date Open High Low Close AdjClose Volume\\n\",\n \"0 31-Dec-1986 243.37 244.03 241.28 242.17 242.17 13,92,00,000\\n\",\n \"1 2-Jan-1987 242.17 246.45 242.17 246.45 246.45 9,18,80,000\\n\",\n \"2 5-Jan-1987 246.45 252.57 246.45 252.19 252.19 18,19,00,000\\n\",\n \"3 6-Jan-1987 252.20 253.99 252.14 252.78 252.78 18,93,00,000\\n\",\n \"4 7-Jan-1987 252.78 255.72 252.65 255.33 255.33 19,09,00,000\"\n ]\n },\n \"execution_count\": 60,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"data = pd.read_csv('datasets/SP_500_1987.csv')\\n\",\n \"\\n\",\n \"data.head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 61,\n \"id\": \"9d2c64dc\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
OpenHighLowCloseAdjClose
count253.000000253.000000253.000000253.000000253.000000
mean286.964466289.328063284.412095286.978617286.978617
std28.92368328.24114229.53552828.88905328.889053
min223.980000225.770000216.460000223.920000223.920000
25%267.840000270.400000264.310000267.840000267.840000
50%290.520000292.470000288.340000290.520000290.520000
75%308.940000310.270000307.420000308.960000308.960000
max336.770000337.890000334.460000336.770000336.770000
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" Open High Low Close AdjClose\\n\",\n \"count 253.000000 253.000000 253.000000 253.000000 253.000000\\n\",\n \"mean 286.964466 289.328063 284.412095 286.978617 286.978617\\n\",\n \"std 28.923683 28.241142 29.535528 28.889053 28.889053\\n\",\n \"min 223.980000 225.770000 216.460000 223.920000 223.920000\\n\",\n \"25% 267.840000 270.400000 264.310000 267.840000 267.840000\\n\",\n \"50% 290.520000 292.470000 288.340000 290.520000 290.520000\\n\",\n \"75% 308.940000 310.270000 307.420000 308.960000 308.960000\\n\",\n \"max 336.770000 337.890000 334.460000 336.770000 336.770000\"\n ]\n },\n \"execution_count\": 61,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"data.describe()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 62,\n \"id\": \"52bb2007\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Date object\\n\",\n \"Open float64\\n\",\n \"High float64\\n\",\n \"Low float64\\n\",\n \"Close float64\\n\",\n \"AdjClose float64\\n\",\n \"Volume object\\n\",\n \"dtype: object\"\n ]\n },\n \"execution_count\": 62,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"data.dtypes\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 63,\n \"id\": \"3e57630e\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Date datetime64[ns]\\n\",\n \"Open float64\\n\",\n \"High float64\\n\",\n \"Low float64\\n\",\n \"Close float64\\n\",\n \"AdjClose float64\\n\",\n \"Volume object\\n\",\n \"dtype: object\"\n ]\n },\n \"execution_count\": 63,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"data['Date'] = pd.to_datetime(data['Date'])\\n\",\n \"\\n\",\n \"data.dtypes\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 64,\n \"id\": \"ad432bf2\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Text(0.5, 1.0, 'S&P 500 1987')\"\n ]\n },\n \"execution_count\": 64,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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btYNMmn5+6lC1d6ltdlCj1iAK1iIjUHy++6KsVHnRQsUeyq3JZ3CXdCnUIhRuTSJ4pUIuISP2wfTuMGgWnnlqc6fJqU06BukkTD85Vad0atm2DLVsKOy6RPIrhvygiIiJ5MGaMT+cWx3YPKK9A3bHjziskpoqCtvqopYwoUIuISP3w4ovQsGH8psuLlFOgrq5/GhSopSwpUIuISP0wcqRPl9emTbFHUrVyCtTV9U9DMlDrwkQpIwrUIiJS/pYsgQkT4tvuAeUTqJcurTlQt2rlW1WopYwoUIuISPl76SXfxjlQN2kCe+wB8+fn/tw7duT+nJHXXoN33/XbIaRfoVagljKiQC0iIuXv+eehS5d4TpeX6uij4T//yd35KirgF7+A5s29Qp9ra9fC2WfDN77h99ev99k7FKilnlGgFhGR8jZtGjzzDFx4YfUzT8TFKafAzJkwe3b251qxAk47DX7+c9i6FSZNyv6clf3tbx6MP/oIPvkkOQd1OhclqodayogCtYiIlLcbboAWLeCaa4o9ktqdcopvoxUd6+r99+GQQ2D0aLj9dt8XrWCYK5s2wR//6K8D8PTTtS/qAqpQS1lSoBYRkfI1fjw89RRcdRW0b1/s0dSub1/o1Su7QH3nnXDMMT5F4H//Cz/4AbRsmftA/c9/eoC+7TY49FAP1NFr1BSomzf3hXUUqKWMKFCLiEj5ii5G/MEPijuOdJl5lfq117xNI1OTJsHll/tc2xMmwKBBvr9Tp2T1OBe2b4ff/Q6OOAKOPRa++EVfOOcf//DHawrUZsnlx0XKhAK1iIiUrzlzoHNnnz2jVJx6KmzYAG+/nflzZ8707c037zzfdseOua1QP/qo/2yvu84D8jnn+PbFF2HYMP+Z10SBWsqMArWIiJSv2bNhr72KPYrMnHSSt0U89VTmz1282Ldduuy8v1OnugXqEOBf//KAn7rvN7+B/v09PAPssw98+KFfCPn8895uUpPWrXVRopQVBWoRESlfs2dD797FHkVmWrTwKvVTT2U+f/TixR5mO3TYeX9dA/Wbb8LFF8Nf/pLc98ILMGUKXHut90JHBgxI/zcBqlBLmclboDazZmY2xswmmdlUM/tFYv/vzOxjM5tsZk+b2R4pz7nOzGaa2Sdmdkq+xiYiIvXAtm2+SEqpVajBWygWL4b33svseYsXe7tFg0r/vXfq5NXj7dszO1/Ug/7kk74NAW65BXr2hOHDMztXqlatFKilrOSzQr0FOD6EcCBwEHCqmR0BvALsH0IYCEwHrgMws/7AcGAAcCrwVzOr5XdGIiIi1Zg3zyu8pVahBm+laNw4GWTTtWjRru0e4IE6BA/VmRg50rdjx/rP8+23feaQq6/28dWVKtRSZvIWqIOLmq4aJ75CCGFUCCH6iPwe0C1x+0zgkRDClhDCHGAmcFi+xiciImVuzhzflmKFevfdvZf62Wcze97ixdUHasis7WPRIp81JFoF8cEHvc2jQwf4+tczG1dlCtRSZvLaQ21mDc1sIrAMeCWE8H6lQ74OJD7+sicwP+WxBYl9lc95qZmNM7Nxy5cvz8OoRUSkLESrDZZioAYYPNi/hy1b0n9OdYE6msYuk0A9apRvL78cBg6E66/3FpTbb4fddkv/PFXRRYlSZvIaqEMIFSGEg/Aq9GFmtn/0mJldD2wHHop2VXWKKs55Vwjh0BDCoR0qX3QhIiISmTPH2xK6di32SOpm7729TSOqtNdm2zZYvrzmCnUmc1GPHOnnGjjQL0xs0gQefxwuuCD9c1QnCtSZXnQpElMFmeUjhLAGGI33RmNmFwPDgAtDCFFoXgB0T3laN2BRIcYnIiJlaPZsX3Wwtinc4mrvvX07a1Z6x0fV56o+QNSl5WPyZDjqKJ9f+oc/9P7rs89O//k1iZYfV5VaykQ+Z/noEM3gYWbNgROBj83sVOAa4IwQwmcpT3kOGG5mTc2sN9AXGJOv8YmISJkrxSnzUkWBOmpdqU11c1CDB9imTTML1OvWJReHMfOZOXJl/8QvrO+4I3fnFCmifFaouwCvm9lkYCzeQz0CuBNoBbxiZhPN7P8AQghTgceAacBLwGUhhIo8jk9ERErV1Knws5/VXOGcM6d0+6fB+55btEi/Qr0o8UvdqgK1WeZzUa9bl9sQnerkk7115Be/8CXLRUpco3ydOIQwGTi4iv19anjOzcDN+RqTiIiUgRUr4LTTfBq3Rx6BJ56AAw7Y+Zi1a2HlytKuUJv5B4J0A3VNFWrILFDv2OGrI0atGfnwl7/AG2/4xY6vvJK/1xEpAK2UKCIipSMEuPBCWLIE7rzTq6iHHw7//OfOx33wgW8rB+1Ss/femQXqqBJdlUwCdbTUeL4q1OCrKh53XPrfn0iMKVCLiEjpmDTJp3O75Ra47DIPzkccAZdc4vMlb9rkx72fmKX1sBJfzmDvvb11JZ3ZMBYv9jmiG1Xzy+eOHdMP1NEc0fmsUAN06wYLF2q2Dyl5CtQiIlI6xo3z7Re+4NvOnb1d4Kc/hfvug899DrZu9UDdpw+0a1e8sebC3nvD5s3Jdo6aLF5c8xSBXbv6tHlbt9Z+rqg3vRCBeuvWzFdwFIkZBWoRESkd48d7yItmwACfFu+mm7ztY8IEeOklX4Dk8MOLNsycyWTqvOoWdYnstx9UVMCMGbWfK6pQ57PlAzxQAyxYkN/XEckzBWoRESkd48fDIYdAgyr++7rgAmjfHn77Ww+X5RCoo1lK0gnUCxfWXKHu39+3U6fWfq5CtnyAArWUPAVqEREpDdu2+WIjgwZV/XjjxvDlL8N//+v3yyFQ9+zpFfhPPqn5uPXr/ULNPtVOpAX77OMfRNIJ1IVs+QAFail5CtQiIlIapk6FLVuqD9TgM4CAL5N94IGFGVc+NW4MQ4fCXXf5suLVmT7dt/36VX9M8+Ze8Z42rfbXLVTLR8eOfhGlArWUOAVqEREpDePH+7amQH3EEd53PGiQrwxYDv70J68YX3tt9cdEgXqffWo+14AB8apQN2jgbSoK1FLiFKhFRKQ0TJjgFdOa2hrMYMQIuP/+wo0r3/r3hyuv9FlM3nmn6mOmT/fvPfVizerONWNG7TN9FKpCDd72oUAtJU6BWkRESsOECXDwwVVfkJhq332hb9/CjKlQbrgBuneH730Ptm/f9fHp073fulmzms8zYIA/v7aZPtat8wp/kyZ1H3O6FKilDChQi4hIafjkk+RMFfVNy5be+jF5sq8QWdn06TX3T0ein19tfdTr1+e/3SMSBeoQCvN6InmgQC0iIvG3ciWsXl1+ledMnHUWnHaaV6sXLkzuDyH9QL3vvt4acv/98JOfwJ//nOxNT7VuXWHaPcAD9aZNsGZNYV5PJA+qWZ9UREQkS1OnQtu2NS82kq6ZM31bU/90uTODO+7wto2rroJHHvH9y5Z5AE4nUDdvDgccAC+8ACNH+pLfTZr4vN1t2yaPW7eusBVq8Cp1mzZVH1NR4duGDQszJpEMqUItIiK5t2MHnHACDB+em/NFPb/1uUINftHhT34Cjz4Kr77q+9KZMi/V6NHw6ac+r/frr/sFis8+u/MxhW75gJr7qL/7XTj77MKMR6QOFKhFRCT3pk2DpUvhzTdh3Ljszzdjhl+MGK0cWJ/9+Mdeqb/sMp+XO9NA3aYN9OjhP88hQ6BXL3j88Z2PKXTLB8D8+dUfM3Fi7YvbiBSRArWIiOTe66/7tlkzuO227M83Y4aHwHKZWzobzZrBX/7iQfrGG+Ghh/zn0qNH5ucyg3PP9Wr36tXJ/YVs+eja1dtOoraeqixZkpwbWySGFKhFRCT3Xn/dK5+XXQaPPVZz9TEdM2ao3SPVySd7EL71VnjrLZ8BpK79xeed5+0fzz2X3FfIlo+GDf29ra4CHYL/tkOBWmJMgVpERHJrxw544w1fMvv73/d9d9xR9/OFoEBdlT/9yZdaf+st+Pa3636ewYN9DuvUto9CtnyAr/D48cdVP7Zmjfd5b9igqfUkthSoRUQktz78EFatguOO8zaE886Du+6qe4Vx5UpYu1aBurKuXeHBB3259WxEbR+jRnl43b7dp7ErVIUafDq/2bO9Ul7ZkiW+DQE2bizcmEQyoEAtIiK5NXq0b4cO9e2VV3rF895763Y+zfCRf+eem2z7iD74FDpQb98Os2bt+lgUqCG5JLpIzChQi4hIbv33v95C0L273x88GI45xlsUqlo2uzZRoK7Pc1Dn2+GH+/v1+OPJ0Frolg+ouo86NVCrj1piSoFaRERya+xYOOywnfddeSXMnQtPP535+T74wGex6N07J8OTKqS2fUTzQReyQh0F6qr6qBWopQQoUIuISO4sXw5z5uwaqL/wBa8w12UKvZEjvR+7SZOcDFGqcd55fvHfgw/6/UIG6t13h86dq65QL12avK1ALTGlQC0iIrkzdqxvBw/eeX/DhnDFFfDee94Skq45czxkff7zORuiVOPww32RlYcf9vuFbPkA76NWhVpKlAK1iIjkztixvgLfoEG7Pva1r8Eee/iMH+kaOdK3p56ai9FJTRo08LaPtWv9fiEr1JCcOq/y1HhLlngFGxSoJbYUqEVEJHfGjIH99oOWLXd9rEULn+Jt4sT0zzdypPdOp7ustmTnvPOStwtdod5vP1+tMfotR2TJkuQMLwrUElMK1CIikr277oK//a3qCxJTDRjgVciKitrPuXEj/Oc/3u5hlruxSvWOOAL23NNvF7pCfcEFPjvMWWclL4yEnQO1ps2TmFKgFhGR7OzYAT/8IXzve35RYuX+6VQDBsCWLVXPN5xq/Xo4/XRfYGT48NyOV6rXoAF8+cvQvHnVv2XIpw4dYMQIXxFx2DDfVlT4n6m99/YPVapQS0wpUIuISHYWLIDPPoOLL4bzz4cvfrH6YwcM8O3UqdUfs2YNnHwyvP02PPSQz2EthXPTTX7xaKNGhX/t/ff3ubCnTPE/S0uX+ge2Ll084CtQS0wpUIuIlJoQdr1wq5iimRkuuQT+/W+f/qw6/fv7trpAvXIlnHACjB/vwer883M7VqndbrvBwIHFe/1TToE//9mr1Zdc4vs6d/YWFAVqiakifPwUEZGsXHwxfPopvPFGsUfiokC97761H9uypffJVhWoly6FE0/0lRGfeQZOOy2nw5QS8t3vwvTpcPvtfr9TJ79IUoFaYkoVahGRUjJmDDzwALz5JkyYUOzRuI8/9unwOnZM7/gBA3YN1Bs2wJAhMHs2vPCCwrTA73/vCwKBL4uuQC0xpkAtIlIqQoBrr/WLt5o1g3vuST722mvwr38VZ1wff+xTnqU7E8eAAb5Yy/btyX3PPOP7Hn/cWz5EGjaERx/138T06OGBWrN8SEwpUIuIlIpRo+D11+GGG3wBjoce8osBAW680WfZ2Lat8OP6+OP02j0iAwb4EtdTpiT3PfGET9emBVwkVfPmcOyxflsVaokxBWoRkVKwY4dXp3v3hm9/G771La/WPfGET0M3bpzP2/z++7l93XnzfOo6gA8+gD/+cecq4dq1sHhxZoH6oIN8e/DBPmf1ggXw0ktwzjk+bZtIVRSoJcb0L5eISCl49FFfYfCmm6BJE59KrkcPePJJ76XeutWPe/XV3L3mkiVeTf7CF/z8w4fDlVd6qP/tbz3Af/KJH5tJoD7wQB/nL3/pYz/2WP9QcM45uRu7lB8FaokxBWoRkbjbuhV++lOfyiyaRs4MzjgDXnklGaL32mvnQF1RAQ8+6AutPP545q97001+seBrr/lCG9Onw+9+56vpXXutv94tt/ixmQRq8D7pG27wUD1njs/icPTRmY9R6g8FaokxBWoRkbi7+26f/eKWW3ZuiTjjDG/H+NOfPNx++cve8rFunVeuBw6Eiy7ydpCRIzN7zZkzfTnxSy+Fww/34H7iiXDVVT4LxzvvePX6mWegcWOvWtfFNdf4h4SrrvKL0ESq07q1f7iMfhsjEiOah1pEJM42bPAq7rHHwuc/v/NjQ4Z41W7lSp9m7sQTPXQPGOB9yfvuC489Brfd5vNWZ+JPf/KV8n7+c1ixwucFvv325EweRx0F//mPXyS5fr2H6rpo2NAXgxGpTatWvl2/Htq1K+5YRCpRoBYRibM//QmWLYNnn911WromTXxWjMcf94B71FEeNJo08Sn0LrjAA+tTT/n81ZkYM8ZbO7p08a+33676uKFD6/Z9iWQqCtTr1ilQS+yo5UNEJK4qKuBvf/PQfMQRVR9z3nketI87zuemnjXLp7G76KJkC0WvXjB/vs8Uko7t22HyZJ+FQyQuUivUIjGjQC0iElevvQYLF8I3vlH9Meee6+0c0UWBu+++a/tFz54+P/Xixem97scfw+bNcMghdRu3SD4oUEuMKVCLiMTVP/8Jbdokl1+uipkvy1yTnj19O3dueq/7wQe+VYVa4kSBWmJMgVpEJI7WroWnn/YZMJo2ze5cvXr5Nt0LEz/4wNtH9tknu9cVySUFaokxBWoRkTi67z5vu7j44uzP1aOHb6NAHYL3XB9zDNxzj4f3VB984IuvNNJ16xIjCtQSYwrUIiJxs3Yt3HyzT4N32GHZn69FC2jfPtnysXQpvPGGX3j4rW9B584+I8ioUX5B4gcfqN1D4qd1a9+uW1fccYhUQeUHEZE4CQF+8xufW/o3v8ndeXv2TFaoJ0/27TPPwG67wf33wyOPwMMPQ8eOHugVqCVuUqfNE4kZVahFROLi2mu9d/k3v/He6UGDcnfuXr2SgfrDD317wAG+CuJf/+ozgDz+uFfE27bV/NISP40bQ8uWsHp1sUcisgtVqEVE4mD7dl/qe9AguPBC+OpXc3v+nj3hxRe9Aj55si/W0r598vGmTX0KvnPPze3riuRSmzYK1BJLCtQiInHw7rseFK68Mj+htmdP2LQJli/3CvUBB+T+NUTyTYFaYkotHyIicfDCCz6rxskn5+f8ffr49t13Ydo0GDgwP68jkk8K1BJTCtQiInEwYgQce2xyJoNcO/FE6NoVrr4atmxRhVpKU5s2sGpVsUchsgsFahGRYps7F6ZOhWHD8vcaTZrA5ZfDzJl+XxVqKUWqUEtMKVCLiBRTRQVcc43fzmegBvj2t31O6oYNYb/98vtaIvmgQC0xpYsSRUSKJQT4znfgscfg1luhb9/8vl6bNnDddTBxYvbLmYsUQ5s2fnHtli36MyyxokAtIlIMIXg/8z33wPXXw49+VJjXvf76wryOSD60bevb1at9hU+RmFDLh0gp2r692COQbN10E9x2m/c133RTsUcjUhratPGt2j4kZhSoRUpJCHDVVV6Z2by52KORuvrTn+BnP4OLL4bbbwezYo9IpDQoUEtMKVCL5MuoUbDXXvD++7k7Z1TVXLkSFi3K3XmlcF57Da64As4+29s9GuifYZG0KVBLTOlfcpF8eOUVOOMMmDPHq5G58Oc/e1VzwAC/v3Rpbs4rhfXaa76Ay4MP+lZE0qdALTGlQC2Saxs2wPDh0K8fnH8+PP00rFkDL74IkybV7ZwPPgjf/z6cdRbcd5/vU6AuTdOm+WwezZsXeyQipUeBWmIqb4HazJqZ2Rgzm2RmU83sF4n9bc3sFTObkdi2SXnOdWY208w+MbNT8jU2kbz6v//zlbzuuguuvNJ7nb/+dZ9j+Oc/z/x8zz8PX/saHH88PPwwdOvm+5csyeWopVCmTk3+lkFEMrPHHr5VoJaYyWeFegtwfAjhQOAg4FQzOwK4FngthNAXeC1xHzPrDwwHBgCnAn81s4Z5HJ9I7m3eDH/4g4ffI46AQYNg//29Sh0CLFyY2fnGj4fzzoNDDoFnnoFmzaBDB39MFerSs2kTzJ4N/fsXeyQipalxY2jZUoFaYidvgTq4DYm7jRNfATgTuD+x/37grMTtM4FHQghbQghzgJnAYfkan0he/OMfXjmO5vo1gxtvhCFD4PTTYfHizM43YoQvYPDii9Cqle9r3BjatVOgLkXTp8OOHQrUItlo08Z/CygSI3ntoTazhmY2EVgGvBJCeB/oFEJYDJDYdkwcvicwP+XpCxL7RErDtm2+2t3hh8PQocn9550Ho0fDwIEetnfsSP+cq1b5rzjbt995f6dOCtSlaOpU36rlQ6TutPy4xFBeA3UIoSKEcBDQDTjMzPav4fCqJmINuxxkdqmZjTOzccuXL8/RSEVy4OGHYe5cr05XNa9w166+IMuKFemfc9Wq5MpgqTp1Ug91KZo2DRo2zP8S4yLlTIFaYqggs3yEENYAo/He6KVm1gUgsV2WOGwB0D3lad2AXSbaDSHcFUI4NIRwaIeol1Sk2HbsgFtu8Sr0sGFVH9Oli28zafuoLlB37qwKdSmaNg369IGmTYs9EpHS1batArXETj5n+ehgZnskbjcHTgQ+Bp4DLk4cdjHwbOL2c8BwM2tqZr2BvsCYfI1PJKeefho+/hiuu676Ve+iQJ3Jgiw1VagVqEvPtGlq9xDJlirUEkP5XFWgC3B/YqaOBsBjIYQRZvYu8JiZfQOYB5wHEEKYamaPAdOA7cBlIYSKPI5PJDdCgJtv9l/jn3de9cd17erbTCvUvXrtur9TJ5/veuNGaNEio+FKkWzaBDNn1vxnRERqp0AtMZS3QB1CmAwcXMX+lcAJ1TznZuDmfI1JJC9efhk++ADuvdf7Y6vTubNvc1WhBq9S77VX+ueT4nn+eaiogGOPLfZIREpbmzb+AXXLFrVPSWxopUSRbN16K3TvDl/5Ss3HNWvm4TjdCvWOHTX3UIPaPkrJP/7hi/Icf3yxRyJS2rRaosSQArVINkKAMWPgi1+EJk1qP75Ll/Qr1OvXe6iurUIt8bdwIYwaBRdfXPNvMUSkdrvv7tu1a4s7DpEUCtQi2VizxvuYe/ZM7/iuXdOvUEcLF9QUqDV1XvyFAH/7m384+trXij0akdLXrJlvt2wp7jhEUihQi2Rj3jzf9uiR3vGZVKhrCtQdE+shzZ0L117rW4mf997zVRFvvhlOOcWnzBOR7ESBevPm4o5DJEU+Z/kQKX+ZBuquXb2qHEL10+tFagrUjRv7/ttu8xUaO3SAq65Kf9ySf7NmwRe+AC1bev/0l75U7BGJlAcFaokhBWqRbNSlQr1tG6xcuety4pXVFKjBL0yMjlm4ML3Xl8LYvNkX+Nmxw3untTKiSO4oUEsMKVCLZGPePL8YMWrBqE00F/WiRdkH6i99yfu3n3lGgTpu/v1vX+hnxAiFaZFcU6CWGFKgFsnGvHk+ZV6DNC9HSF0tceDAmo9dudK30RRRlf3sZ74dPx4WLEjv9SX/QoDbb4cDDoDTTiv2aETKjwK1xJAuShTJxrx56bd7AOy9t28//rj2Y1et8v7b2qbj69ZNgTpOXn8dPvwQrrii9j55EcmcArXEkAK1SDbmz/cKdbo6d4Y994SxY2s/trpFXSrr1s0r3jt2pD+OYgvBl+F+7jlfPbCc/PGPfpHoBRcUeyQi5UmBWmJIgVqkrrZv997lTCrUAIMH5zZQ77mnj2XZsszGUUzHH++9xWeeCXfcUezR5M6MGd43/d3vJv/TF5Hc0jzUEkMK1CJ1FVWF6xKoZ8zwRWFqkkmFGkqn7WP7dhg9Gs49F04+GW64ITlbSqk55xz4+teT9++4w6c0/O53izcmkXKnCrXEkAK1SF1lOmVeZPBg344bV/Nx5Rqoo9lLhgyBv//d2z9+8IPijqkuVq+GZ5+FRx/1/9jXrPH5ps8/31t7RCQ/mjb1rQK1xIgCtUhd1TVQH3qob2tr+yjXQL1ihW87dIBeveDqq33qv1KrUo8a5f3fn30Gb74J997r0xiW4ocDkVLSqJF/KVBLjChQi9RVFAAzuSgRfBq8Pn1qDtQhpB+o27f3mUBKZS7q5ct9G83DffHFvn344Zqft3WrT0fXsSP8+td5G17aRozw96dZM7+48s9/hmOPhUMOKfbIRMpfs2YK1BIrCtQidfXf/3p1uGXLzJ87eHDNLR8bN/qKiukE6gYNfMGYUqxQA+y1Fxx5JDz0UPXPCQEuvBB++EN//qRJ+R9nTSoqYORIn2f6+OO9deXTT32qPBHJPwVqiRkFapG6WL7cA9X559ft+Yce6lPuLV1a9ePR/uoWdamslOairlyhBg/LH34IkydX/Zz774cnnoBf/QqOOCLZh10sY8b4wjvDhsHpp/uFlr16wRlnFHdcIvWFArXEjAK1SF088oiHqK9+tW7Pjy5MrK7t45lnfHv00emdr5QCdVShTg3UX/qS90Tedtuuxy9aBJdf7hcxXnutV+2LHajfesu3J53kobpxY7jqKmjYsLjjEqkvmjZVoJZYUaAWqYt//QsOPhj2379uzz/kEG/VqCpQhwD33edtEPvtl975unXzHuoQ6jaeysaN8+9t7drcnC/V8uXQuvXOK0B26OAXJ95/P9xzz87HP/00bNgAf/ubB9Y4BOpFi6BVKx9Ljx4wdy5cdllxxyRSn6hCLTGjQC2SiYoKvzBu3Li6V6cBWrSA/v2rDtRjx8K0aXDJJemfr29f2LQJrr8+NysPjhkDU6f6aoa5tmLFztXpyK9+5fNSX3YZvP9+cv/LL3ufdfThIg6BesmSnafG69pVy4yLFJICtcSMArVIJs4/3y+M+/zn4RvfyO5c0YqJlavK994LzZvDl7+c/rkuvhi+9S245Rb4f/8vu3FBMrBG/c65tHx58oLEVA0b+kwfe+7pC6YsWeIze7z+ugftSNu2sG6dX7RZLIsXQ5cuxXt9kfpOgVpiRoFaJF0hwPPPe+X4hRf8V/7ZGDzYq7WffprcN2aMB+qvfMXbItLVtCncdZf3Ij/zTPatH6tX+zYfgbq6CjV4WH76aQ/0X/qSz++8YQOccsrOx0DtK03mU+UKtYgUlgK1xIwCtUi6Vqzwf8APOig3v96vfGHixo0epLt2hd/+tm7nPOYYD3vZzkkdVaijCwhzacWKqivUkQMP9D7qt97y2T8aNvSp6SJRoC5m28eSJapQixSTArXEjAK1SLrmz/dtpgu5VGfgQL8wLwrUf/sbzJjhF+alO11eZbXNHpKufFWoQ/BzVlehjlxwAVx5JSxb5hdnplbrix2oP/vMW05UoRYpHgVqiRkFapHI5s3ebrF1a9WP5zpQN2ni1dgo/L73Huy9NwwdWvdzHnigTz83Zkx2Y8tXD/Vnn/nPubZADV6lv/RS71lPVexAvWSJbxWoRYpHgVpiRoFa6rfPPksGpNtug29+Ex58sOpjo3mecxWowRd4GT8eduyADz7wqfiy0azZziG9rvJVoY7OV1PLR6RRI1+B8Oyzd95f7EC9eLFv1fIhUjwK1BIzCtRS3jZv9mnX/vCHqv/x/fGPoV8/eOMNuPVW31fdEtjz5/sCHumEwXQNHgzr13tFefbs7AN1dM6xYz2k11W+KtRVLeqSqXwG6hde8MVaapp/WxVqkeJToJaYUaCW8rVjh4fLU0/1RUN+/etdj3nvPQ+0Q4d6X+y55/o0bVVd1Dd/vi+g0iCHf22inud77/VtrgL1unXej11XUYU61xclZlKhrs7uu/tFofkI1A88AK++Ct/7XvUzpShQixRfs2awZUuxRyHyPwrUUr4+/BCmTIFf/hKGD/ee3NSQuX27P37aadCypS/U8utfe5B6+OFdzzd/fm7bPcAXK2nRIvl6uQjUhx3m27q2fWze7IvEQDwr1A0beqjOdaAOwWcWad0a/v1vX0p85Mhki0dk8WIfQzbfg4hkRxVqiRkFailfo0f79uKL4Y9/9H+A/9//S1YeP/nEKxznn+9zQd9zj684ePjhXjGu/I91PgJ1w4a+DPnGjV7xzEXVMwrpdb0wMapOd+7st3O5gEoUqLNtm8nHaomffupLit90E5x1lv+ZOe00n8awc2f/Tccrr3iFumNHf+9EpDiaNfNVYbdvL/ZIRAAFailnr7/uS1b36OGB6KabYNQoePJJf3ziRN8edJBPU9eokd//yU/g44/ha19L9iHv2OFtILkO1JBs+zjooNycLwrpda1QR4F6n318u3JlbsYFXvGOKszZaNs2Oc5ceftt3w4Z4ovLrFnjvfV/+pOvjDl5sq9GuWiRLkgUKbZmzXyrKrXEhAK1lKcdO3yVveOOS+773vc8tF5xhfdNT5zoU9dFwTFyxhneHvLoo/DTn/q+Zcu8UpvPQJ2Ldo/IYYf5rCHVTQFYk6jy26+fb3PVR711K7z7rlens10YJ5cV6oqKnds99t/f9+++Oxx7LHz/+/CPf8BvfuNV7DfeUP+0SLEpUEvMNCr2AETyYvJkr2CmzuncqBH89a9w1FHeVz1xooenxo13ff6PfgQzZ8Itt3iV+8ADfX+3brkf6zHHeIvGiSfm7pyDB3s7y5QpXq3ORFT5jQJ1LvqoN270Cz5Hj4Y77sj+fG3bwpw52Z8H/Oe+bp0H9KOPrr6V46yz/D/xzz5ToBYpNgVqiRkFailPUf/0kCE77z/ySJ9r+vbb/R/kL32p6uebwV/+4hXJ73zHFxiB/FSo99zTA10uZw9JvTAx00BduUKdbaBevRqGDfMZVe6+23/+2cpVhXrx4uSfFfCWjuq0bu3fxxNPqOVDpNgUqCVm1PIh5WnMGO+drioA33KLh6MNG5KV56o0bgyPPw79+/uy4JCfQA25DdMAvXpBu3Z1uzCxcg91poF62zafJWPDBr+A77jjPNg/+mhuwjQke6izmWsb/CJDgMceg0sugYsuqvn488/3rSrUIsWlQC0xo0At5Wn+fA+VVWnfPrmIyxFH1Hye1q19sY8uXWC33TyklgKz5AIvmVq1yp/fu7ffz7SH+umn4cILvV/9c5/z1pkXXvCWj1xp29bD9Lp12Z1n1Cjv6T7nHLjvvto/MA0b5tPpnXFGdq8rItlRoJaYUaCW8rRwobdSVOcb3/A5qaPWiJp07+5tAY88kv3FdIV02GEwdar3L2di9WrYYw+/YLNt28wr1LNm+XbbNg/nr77qqw/mUi5WS9yxwyvUJ5+c/m8ImjSB3//ef/shIsXTtKlvFaglJtRDLeUnBJ/arLYLCPv0Sf+c/fole4pLxeDBHhonTPALH9O1alUysHbokHmgnjvXfwvw8cf+n12bNpk9Px2pgXqvvep2jkmTfPaWk0/O3bhEpDBUoZaYUYVays/KlT7DRU0V6vogmo4v07aP1auTIbh9+7oF6t69oXnz/IRpSFaIv/c9nx6xLkaN8m2uq+cikn8K1BIzCtRSfhYu9G19D9SdOnnwrOrCxFmz4J13kqtGpkqtUO+7rwfW3/2u6mOrMmdO9f3ruXLggXD//f6biCFDvKd52rTMzvHyyzBwoGbsEClFCtQSMwrUUn4UqJOquzDxG9/wCwYHDvSp7D77LPlYaoX6ttt8/uUf/9gv2qvNjh0+1WC+AzXAV78K06f7rC1vvAEHHODTG6azVPrGjb4yoto9REqTArXEjAK1lJ8FC3yrQO2BevbsnWfq2LEDxo1LLmJy6aXeb37NNR6GUyvUrVv71IEtW8KHH9b+ekuW+IqIhQjU4DOvXHutV9y/+U3/cBC1ctTkjTc8eJ9ySv7HKCK5p0AtMaNALeVn4UKfjUO/yk/OYjJuXHLfjBleof3mN3158jffhBNOgD/8wS/wW7EiGajBf5adOsHSpbW/3ty5vo2m3CuU9u09WEN6Pd8vv+z/IX/uc/kdl4jkhwK1xIwCtZSfhQs9AFa1pHh9M2iQB+LUto8PPvDtwQf7Y8cc41Xo2bO9taNXr12nE0w3UEfLgReqQp0qalOJFqapyahR3nsd/acsIqUl+ru7ZUtxxyGSUOu0eWZmwIXAXiGEX5pZD6BzCKEOS7CJAB995KGue3dfjnrCBF+V78tfhv32y/78tc1BXZ+0bu0/29RAPWGCz6fcv//Ox/bo4f3It9yy63k6dYJPPqn99aIKdc+edR5ynbVu7R8Qapubet48n9IvWk5eREqPKtQSM+nMQ/1XYAdwPPBLYD3wJDA4j+OScnb++T4HcKRbN1+W+v/+zwNZtlXDhQsL33IQZ336+MqRkQ8+8Av4Mqngd+qU3vR0c+f6sbvtlvEws9aggVepa6tQRz3WuiBRpHQ1auR/5xWoJSbSafk4PIRwGbAZIISwGmiS11FJ+Zoxw8P0ddfBiBEewObP99X0li6Ff/0r+9dQhXpn7dr53NzgU9998AEcckhm5+jUyc9R2wwahZgyryZt2tReoR41yv98VK7Qi0jpMPPiiwK1xEQ6gXqbmTUEAoCZdcAr1iKZe+IJ3373u3D66cnWgKFD4dBDfVnnioq6n3/TJg9UCtRJqYF6/ny/ffDBmZ2jUyff1nbB39y5xQ/UNVWoKyr8w9vJJ5fWMvIisisFaomRdAL1HcDTQEczuxl4G/h1Xkcl5evxx+GII7x/OpWZXxA3YwY8+2zdz685qHfVrp3PM715c/KCxEwr1J07+7amCxO3bPH+5GK227RtW3OgHjfOH1e7h0jpU6CWGKk1UIcQHgJ+DNwCLAbOCiE8nu+BSRmaNcsD3XnnVf342Wf7tG2//e2uq/KF4KviAWzf7tXtX/1q1+OiQN2tW27HXsqiKfBWrfL3AKBfv8zOEVWoawrU48d7S8jgIl5eUVvLx6hR/uHtxBMLNyYRyQ8FaomRWgO1me0NzAkh/AWYApxkZnvke2BShp580rfnnFP14w0bwtVX+1LZlS+Ae/BBr2qPHQsjR8KLL8INN8Dw4d7mEXnpJb9QRf2xSe3a+XblSg/ETZrAHntkdo50AvU77/j26KMzHmLO1Fahfvlln0qwffvCjUlE8kOBWmIknZaPJ4EKM+sD3AP0Bv6d11FJeXr8ca9e1jSl2te+Bh06wK23JveF4Etg79jh+++7Dzp29OndHn/c5xNevNiD9d13w5lnQteuef92SkblQN2pU+b9w+kG6j59kscWQ9RDXfk3FwBr1/o0jWr3ECkPCtQSI+kE6h0hhO3A2cCfQgg/BLQEnWRm7lzvX62u3SPSvDl8//tegY6Wun7nHZg40dsUnnwSnn8eLr7YV8Z7+mmYNs2D+g03eGi8/PJ8fzelpapAnamWLX0qvOoCdQjw3/8WtzoNHqgrKmD9+l0fe/11f0zLjYuUBwVqiZF0Z/k4H/gqMCKxT0vQSWai2T3OPbf2Y7/3PQ9vv/+937/jDm9RePFFnzu5ogIuucQfO/NMD9wNGvjS2fvvD8cdl4/voHTlIlBDzaslzpjhM4AUO1Cn9otX9vLL/sHgiCMKOyYRyQ8FaomRdAL1JcCRwM0hhDlm1ht4ML/DkrLzxBPeu5rODBBt28K3vgX//re3ejz5JHzzm7D33t5jXXlFxQMP9L7rL38Zfvc7TYdWWWqgXrIkOWNHpjp18udX5e23fVvsQF3T8uOjRvn0jE00jb5IWWjaVIFaYiOdWT6mAVcDH5rZ/sCCEMJv8j4yKR/z5sH776dXnY5ceaW3EVx1FRx1FPz8577/5pvhkUd2Pb5zZ99/6qk5GXJZad7cv1asgGXL8lOhHjHCPwjtu2/dx5kLUYW6cqCeNQtmz1a7h0g5UYVaYiSdWT6OA2YAf8GXIZ9uZsfmd1hSVqLZPTIJ1D16wBVXwAkneFhr0SIvQ6s32rXztoyKitwH6sce8172yy/31ptiiirUlVs+tNy4SPlp0gS2bi32KEQAaJTGMX8ATg4hfAJgZv2Ah4FB+RyYlJEnnoCDDvIZIDIR9VBL9tq184s3IbtAvWKFzwMegl80+v77cP31cNhhvi226lo+Xn/dp13M9M+giMSXArXESDqBunEUpgFCCNPNTBclSnoWLPDZH26+udgjqd/atYPJk/12NoE6BO+T/vDD5PzfvXr5POGNY/DPQlUXJYbg85qfdJL660XKSdOmCtQSG+kE6nFmdi/wQOL+hcD4/A1JYmfFCl90Jar+ZeKpp3ybSbuH5F67dsm5mesaqA891M/TsCF8+9s+W8bhh/u84nEJqrvt5sE+tUI9Y4a3qhxzTPHGJSK516QJbNlS7FGIAOkF6u8ClwHfBwx4E++llvrilFP8orZoJodMPP44HHBA5ktdS25FlVuoe6A+/HD/cBVnZsnFXSLRqpvH6tIPkbKilg+JkXRm+dgSQrgthHB2COGLIYQ/hhBq/UhoZt3N7HUz+8jMpprZDxL7DzKz98xsopmNM7PDUp5znZnNNLNPzEyX48fBrFkwYYLP9TxuXGbPXbTIn1fbYi6Sf9HUeY0b1+03DaWkbdudWz7efNNX39xnn+KNSURyTy0fEiPVVqjN7EOgivV7XQhhYC3n3g5cFUKYYGatgPFm9gpwK/CLEMJIMzstcf84M+sPDAcGAF2BV82sXwihIrNvSXLqmWd826wZ/PnPcP/96T/36ae9zUDtHsUXBeq6LDteaipXqN96y6vT5f59i9Q3UctHCPr7LUVXU8vHsGxOHEJYDCxO3F5vZh8Be+IhvXXisN2BRYnbZwKPJKrfc8xsJnAY8G4245AsPfOMz9Bx9NFw992+cErHjuk99/HHYcCAnRdhkeJIDdTlrm1b/+0I+Bzoc+fCD39Y1CGJSB5EizRt3x6Pi6KlXqup5aMx0C2E8GnqF9CD9Hqv/8fMegEHA+8DVwC/M7P5wO+B6xKH7QnMT3nagsQ+KZalS71l46yzfI7hbdt8oZVQ7S8udn7um2+qOh0X9SlQp1ao33rLt+qfFik/TZv6Vm0fEgM1BerbgfVV7N+UeCwtZtYSeBK4IoSwDr/I8YchhO7AD4F7o0OrePouyc3MLk30Xo9bvnx5usOQunjxRQ/PZ53l/ac/+5lPj3bPPek/95xz8j5MSUN9C9QrVyany2vd2i+MFZHyElWoNdOHxEBNgbpXCGFy5Z0hhHFAr3ROnpiv+kngoRBCYv40Lgai24/jbR3gFenuKU/vRrIdJPX17wohHBpCOLRDhw7pDEPqasoUn4ZsYKJd/qc/9bl8L78cPvig5ufOnOnTq/Xvn/9xSu3qU6A+5BBYvx7GjvVA/bnP+Z9FESkvUaBWhVpioKZA3ayGx5rXdmIzM7z6/FEI4baUhxYBQxK3j8eXNQd4DhhuZk3NrDfQFxhT2+tIHs2eDb17Jy/2aNgQHnoI2rf3mTvWrq3+ufPnQ7duCjJx0bmz/3q0PqwUeOaZ3k95553w8cdq9xApV2r5kBipqRd6rJl9K4Rwd+pOM/sG6S3scjRwEfChmU1M7PsJ8C3gT2bWCNgMXAoQQphqZo8B0/AZQi7TDB9FNmeOB+pUHTrAY4/BkCFwySXw5JNVX109bx706FGYcUrtWrf2cLlnPbgsoU0bnzv9gcRaVArUIuVJLR8SIzUF6iuAp80sdWXEQ4EmwBdrO3EI4W2q7osGGFTNc24GtEZ1HITgFeohQ3Z97Kij4Le/9QsU//hHuPLKXY+ZN8+Pk/jo1avYIyicL30JRozwBYkGVfnPjYiUOrV8SIxUG6hDCEuBo8xsKLB/YvcLIYT/FGRkUlyrVnkfauUKdeSHP/SVE6+5BoYOhYMPTj5WUQELFkD37lU/VyTfzjzTfx185JHJ/3RFpLyo5UNipKaFXVonZuWYlPiK9rfFZ99Yp5aMMjZ7tm/32qvqx818to927bwSmBqoly71KfbU8iHF0ro1/POf0LNnsUciIvmilg+JkZpaPv6NL+4ynuT0daktHC3N7O4Qwk/yNTgpojlzfFtdhRp8AY0ePeCTT/z+fxK/vNhtN98qUEsxDR9e7BGISD6p5UNipKaWj2GJbZWJyswaAlPwCw2l3EQV6poCNcC++/rFbgDf/77/w/arX/l9BWoREckXtXxIjNTU8nFITU8MIUwAtKZ0uZo922f0aNmy5uP22Qf+8Q/47DP46CPYsQPee88fU6AWEZF8UcuHxEhNLR9/SGyb4bN7TMJbPgbiS4h/Lr9Dk6KaM6f6/ulU++4LGzbASy95mAb497+9h3X33fM7RhERqb/U8iExUlPLx1AAM3sEuDSE8GHi/v7A1YUZnhTcrbf6P1KzZ8Nhh9V+/D77+Pbhh33buLFflLj//tU/R0REJFtq+ZAYqalCHdk3CtMAIYQpZnZQ/oYkRfPWWz4NXiSdi7r23de3I0b4RYpHHAEvvqh2DxERyS+1fEiM1LT0eOQjM7vHzI4zsyFmdje+mqGUk+3b4bLLPAh/5zu+r2/f2p/Xtav3WW/e7FPnDR3q+xWoRUQkn9TyITGSToX6EuC7wPfxHuoJQK88jknyKYSqlwq/80748EN46ik46yw491w4+ujaz2fmbR/jx8Mhh8Bxx/l+LeoiIiL5pJYPiZFaK9QhhM3A68BM4BDgBOCDPI9L8uHTT/1iwZEjd96/aBHceCN8/vMeps3ghBOgWbP0zhu1fRx8sH/dcIPmABYRkfxSy4fESE3T5vUDhgPnAyuBRwFCCMcVZGSSe88+6zNy/OEPHp4jV1/tn/DvuKPq6nVtUgN1w4bwy1/mZrwiIiLVUYVaYqSmlo+PgbeAL4QQZgKY2Q8LMirJjxdf9O1rr8H06dCvH7z+us/QceON0KdP3c57ySXQqlVyxg8REZF8Uw+1xEhNLR/nAEuA183sbjM7gZ2XHpdSsnEjjB4NF1zgU9v93//5P0KXXearIV57bd3Pveee8IMf1K26LSIiUhcNG0KDBmr5kFioaR7qp4GnzawFcBbwQ6CTmf0NeDqEMKowQ5Sc+M9//B+dr38dKirgb3+D99/31Q2ffx6aNy/2CEVERDLTtKkq1BIL6VyUuDGE8FAIYRjQDZgIZFHOlJxbswZOPhnmzq3+mBdf9OntPvc576EePhymTYPzzoNhwwo1UhERkdxp0kSBWmIhnXmo/yeEsCqE8PcQwvH5GpDUwcSJ8MorXoWuzujRPqVd06beovGPf8DKlfDoowUapIiISI41aaKWD4mFjAK1xNSKFb6trkIdgj/Wr9/O+xs0UN+ziIiULrV8SEwoUJeD2gL1ypW+kqFWLxQRkXKiCrXEhAJ1OagtUM+b51utXigiIuVEPdQSEwrU5aC2QD1/vm9VoRYRkXKilg+JCQXqchAF6oULq/6HRRVqEREpR2r5kJhQoC4HUaDesQMWLNj18Xnz/FN8hw6FHZeIiEg+qeVDYkKBuhwsXw4tWvjtqto+5s+Hbt18Vg8REZFyoZYPiQklrHKwYgUMGuS3qwrU8+apf1pERMqPWj4kJhSoy8GKFXDQQV6Brq5Crf5pEREpN2r5kJhQoC51n33mX126eFtH5UC9fTssWqQKtYiIlB+1fEhMKFCXupUrfdu+PfTqtWugXrTIL1ZUhVpERMqNWj4kJhSoS100w0cUqKdPT06TB8nbqlCLiEi5UcuHxIQCdalLDdRHHw1Ll0LPnvC5z8Gdd8K4cf64KtQiIlJu1PIhMdGo2AOQLKUG6ksvheOPh0cf9a/LL08ep0AtIiLlRi0fEhOqUJeaBQvgpJPg/vshhJ0DNUCfPnD99TB5MkydCjfeCNddB61bF2/MIiIi+aCWD4kJVahLza23wquv+tfTT8PAgWAGbdrsemz//vCLXxR+jCIiIoWglg+JCQXqUrJ8OdxzD3z1q36R4a9+5bN6tGsHDRsWe3QiIiKF1aQJVFT4l/4flCJSy0cpufNO2LQJrr0WrrkGWraESZOS7R4iIiL1SZMmvlWVWopMgbpUbNgAf/4znHUW7Lefh+kLL/THFKhFRKQ+atrUtwrUUmQK1KXi7rth9WqvTEcuvdS3CtQiIlIfRRVqzfQhRaYe6lKwdSv84Q8wZAgccURy/yGHwPDhcMwxxRubiIhIsajlQ2JCgboUPPQQLFzoFyRW9vDDhR+PiIhIHKjlQ2JCLR9xt2OHT5V34IFwyinFHo2IiEh8qOVDYkIV6rh77jn4+GOvRJsVezQiIiLxoZYPiQlVqOMsBLjlFujdG849t9ijERERiRe1fEhMqEIdZy+9BGPGwF/+Ao30VomIiOxELR8SE6pQx9XmzXD55dCvH3zjG8UejYiISPyo5UNiQmXPuLr1Vpg1C0aNSv5KS0RERJLU8iExoQp1HIUAt98OZ54JJ51U7NGIiIjEk1o+JCYUqONo4UJfFVFhWkREpHpq+ZCYUKCOoylTfLv//sUdh4iISJyp5UNiQoE6jqJAPWBAccchIiISZ2r5kJhQoI6jKVOgc2do377YIxEREYkvtXxITChQx9GUKXDAAcUehYiISLyp5UNiQoE6bioqYNo09U+LiIjURi0fEhMK1HEzZw5s2qRALSIiUhu1fEhMKFDHjWb4EBERSU/jxr5VoJYiU6COmyhQ9+9f3HGIiIjEnZlXqdXyIUWmQB03U6ZA797QsmWxRyIiIhJ/TZqoQi1Fp0AdN1OmqN1DREQkXU2bKlBL0SlQx8nWrfDJJwrUIiIi6VLLh8SAAnWcfPIJbN+uQC0iIpIutXxIDChQx0l0QaIWdREREUmPWj4kBhSo42TKFGjUCPbZp9gjERERKQ1q+ZAYyFugNrPuZva6mX1kZlPN7Acpj11uZp8k9t+asv86M5uZeOyUfI0ttqZMgX79khPVi4iISM3U8iEx0CiP594OXBVCmGBmrYDxZvYK0Ak4ExgYQthiZh0BzKw/MBwYAHQFXjWzfiGEijyOMR7WrIEWLTxQH3posUcjIiJSOpo2VYVaii5vFeoQwuIQwoTE7fXAR8CewHeB34QQtiQeW5Z4ypnAIyGELSGEOcBM4LB8jS82Jk/2eacPPhhmz9YFiSIiIplQhVpioCA91GbWCzgYeB/oBxxjZu+b2RtmNjhx2J7A/JSnLUjsK19z5sApp8Buu8GyxOcKBWoREZH0KVBLDOSz5QMAM2sJPAlcEUJYZ2aNgDbAEcBg4DEz2wuwKp4eqjjfpcClAD169MjbuPNu2TI4+WT/NdVbb0HbtvDoo3D66cUemYiISOlo2hRWrCj2KKSey2uF2swa42H6oRDCU4ndC4CnghsD7ADaJ/Z3T3l6N2BR5XOGEO4KIRwaQji0Q4cO+Rx+/qxbB5//PCxcCC+8AAMGQJcucMUVuiBRREQkE6pQSwzkc5YPA+4FPgoh3Jby0DPA8Ylj+gFNgBXAc8BwM2tqZr2BvsCYfI2vaLZsgS9+ESZNgieegCOPLPaIRERESpfmoZYYyGfLx9HARcCHZjYxse8nwH3AfWY2BdgKXBxCCMBUM3sMmIbPEHJZWc7w8Y1vwH/+A//6F5x2WrFHIyIiUto0D7XEQN4CdQjhbaruiwb4SjXPuRm4OV9jKrpPP4WHHoJrr4WLLir2aEREREpfdS0fW7fCyJFw5pmFH5PUO1opsZCefNK33/xmccchIiJSLqpr+Rg5Es46C6ZNK/iQpP5RoC6kJ57w+ab33rvYIxERESkP1bV8bNjg29WrCzseqZcUqAtl/nx4910499xij0RERKR8VNfysW2bb6NgLZJHCtSF8lRi1kAFahERkdxp2tTDc6i0dEUUshWopQAUqAtl5EjYd1/o16/YIxERESkf0foNlavU0f316ws7HqmXFKgLYft2eOcdGDq02CMREREpL9UFarV8SAEpUBfCBx/4X+ghQ4o9EhERkfLStKlvq6tQK1BLAShQF8Ibb/j22GOLOw4REZFyE1WoK8/0oUAtBaRAXQhvvgl9+0KXLsUeiYiISHkpZMvHvHm5O5eUFQXqfKuogLfeUruHiIhIPhSq5WPUKOjZEyZMyM35pKwoUOfblCmwZo0CtYiISD4UquXj3nt9O2NGbs4nZUWBOt/GjvXtkUcWdxwiIiLlqBAtH2vXwnPP+e3Fi7M/n5QdBep8mzgRWreG3r2LPRIREZHyU4iWjyefhM2b/bYCtVRBgTrfJk6EAw+EBvpRi4iI5FwhWj4eeMAnF+jRA5YsSe5ftQq+/GVYsSL715CSppSXTzt2wKRJcNBBxR6JiIhIecp3y8e8eTB6NFx0kc/WlVqhfvddeOyx5PS4Um8pUOfT7Nn+F1mBWkREJD/y3fLx73/79sILoXPnnQP1smW+1XR69Z4CdT5NnOhbBWoREZH8yGfLRwje7nH00bDXXrtWqBWoJUGBOp8mToRGjaB//2KPREREpDyl0/IRQt3OPXEiTJsGX/mK3+/SBVauTL6WAnX21q/3NTtKnAJ1Pk2cCPvuC82aFXskIiIi5am2lo+Kil2r1+l64AFo3Bi+9CW/H614vHSpbxWos7NjB+yzD9xwQ7FHkjUF6nzZtAnGjVO7h4iISD7V1vIBdWv72L7d+6dPPx3atvV9UaCO2j4UqLOzcKH/LP/6V9i4sdijyYoCdT6EAJde6p9go18TiYiISO7V1vIB3laQqdde8//HL7ooua9zZ99GgTq1Ur1pU+avUd9Nn+7btWvhoYeKO5YsKVDnw9//Dg8+CDfdBKecUuzRiIiIlK/aWj6gbhXqBx6APfbwCnWkqgp18+Z+e/78zF+jvosCdffucOedde91jwEF6ny4/34YNAiuv77YIxERESlvNbV8RNcwZRqoN2yAp5/23ukosAN06gRmHqh37IDly5OtnWr7yNz06bDbbt5D/eGHMGZMsUdUZwrUubZhA4wdC6ee6n/pREREJH9qavmIep8zDdTvvAOffQbnnbfz/kaNoEMHXy1xzRrvsx482B9ToM7c9Om+AuU550DDhvD888UeUZ0pUOfaO+/4FcXHHVfskYiIiJS/Bg086FbV8lHXQB0tJd69+66PRYu7RBckHnywF9AUqDM3fTr06+fv01FHwYgRxR5RnSlQ59ro0f4X+8gjiz0SERGR+qFJk6pbPtq08duZBurVq30bPT9VtLhLFKi7dfN9CtSZ2boV5szxafMAhg2DSZNgwYLijquOFKhzbfRoOOwwaNGi2CMRERGpH5o0yW3Lx6pVvq0uUC9YkAzUHTtCjx4K1JmaM8d/o9+vn98fNsy3L7xQvDFlQYE6l6L+abV7iIiIFE7Tprlt+Vi9Glq29EVdKjv4YO+hHjvW7ytQ1000w0cUqPfbD3r1Ktm2DwXqXFL/tIiISOFV1/Kxxx5+uy6BuqrqNMDxx/v20Ue9d7p9e9hrL5g7t+4rMtZHUaDu29e3Zl6lfu21kpzTW4E6l6L+6aOOKvZIRERE6o/qWj6aNvUWzLq0fETV7coGDPCZPj79FNq18//3DznEX2/KlLqNv77ZuhVeftk/jKT+nIcN8zD9+uvFG1sdKVDn0htvqH9aRESk0Cq3fITg95s08daNXFaozZJV6o4dfXvIIb4dPz6z16lvtmzx4uMpp8Arr8B11+38+JAhnqFKsO1DgTpXov7pIUOKPRIREZH6pXLLx/btvm3cuG6BuqYKNcDQob6NAvVee8Huu8OECZm9Tn0weTL8/ve+PkebNv6ze+89X4nyyit3PrZZMzjxRL8wscRWTWxU7AGUjf/+1/8Cq39aRESksCq3fGzbltyf6wo17FqhNvMqtSrUO3v1VTjpJL/dvz9861semIcMgdatq37OsGHw7LPePnPAAYUba5YUqHNF/dMiIiLFUbnlI7qdr0Ddp48H6KjVA2DQILjjDg/zVc0OUh/NmuXbSZNg4MD0nnPaab594YWSCtRq+ciV0aN9+dGWLYs9EhERkfqlcstHFKjr0vKxaRNs3lxzy4cZjBsH11yT3DdokL/u1KnVP++pp3yZ7foiWnEymhovHV27+geVEuujVqDOBc0/LSIiUjw1tXy0auWtGH36pBfSalolMZXZzvfTuTDx1Vc9VC9fXvs4StWiRTBzpt9escI/0DRrltk5hg2Dd9+FlStzP748UaDOBfVPi4iIFE/TplVXqJs0gR/9CL77XZg/3wNtbaJVEmuqUFelTx8P7zVdmBidu6Yqdqn74Q/h3HP99ooVPjVepoYNgx074KWXdt4/axZ85zvwySfZjzPHFKhzYfRoaNhQ/dMiIiLFULlCndrycdhh8Oc/+5LhUaCtSboV6soaNKj9wsSo4jptWmbnLiWffupf4N9vXQL1oEHQqdOuv1GYNAn+/vfMe+ILQIE6F954Q/3TIiIixVJTy0ekbdv8BmrwQD1pUnLavsrqQ4V66VJYs8b70Fes8MVvMtWggV+c+NJLO/8sK6+uGCMK1NnauBHGjFG7h4iISLHU1PIRSTdQ17XlA7yyunkzfPRRzecu1wp1CLBkid9etqzuLR/gbR9r1nhbbWTGDK9cVzflXhEpUGdL/dMiIiLFVVPLR6Rt22T1uSbZVqih+raP6lo+Nm2C66+H55/P/DXjZP16/0ABHqyzCdQnneTvX2rbx4wZmc0YUkAK1NmK+qePPrrYIxEREamfctnysWqVz+Cx++6Zj6NfP186u6oLE7dt88DZpk2yegswZ45fg/XrX8Odd2b2eqtW+RLecbF0afL2vHn+/dY1ULdq5QvAvPBCct/06bFs9wAF6uy99ZZ/IlX/tIiISHFk0vJR25LWq1fDHnt4H2+mGjaEgw+uukIdhfnPfc6306bByJHeJjJ3rq8kOGdOZq/3l7/AySfvHGSLKXUcUZ94XXqoI8OG+c9p9mxYt87Pr0BdhrZt84ndNbuHiIhI8UQV6igsV9Xy0aaNt2jWNkNEbask1uaQQ2DiRKio2Hl/FKiPOca311wDp58OPXp4lhg2zGfH2LEj/deKpo8bO7bu482l1EA9ZYpv61qhBv+ZgFepo7mt1fJRhiZN8r6nI48s9khERETqryZNPExHIba6lg+ove1j1aq6XZAYGTQIPvts17mSo/7pgQO9neG99+ArX/FrsfbeG3r18g8CixbB4sXw7LO1v1YUMseMqft4cykK1A0b5iZQ77037LOP91HHeIYPgEbFHkBJe/dd3ypQi4iIFE/Tpr7dsgUaNaq+5QM8MPfsWf25sq1QDxrk2/HjvY0jEgX59u3hd7/zMV98cXLFxd69fTtnjq+meMcdHsyj760qUaCOS4V6yRL/fvr29QsIIbtADb5U+29+47N7gC+gE0OqUGfj3Xd9zfnu3Ys9EhERkforCs5RkK5ulg+ofaaPbCvU++wDzZvvemFi6nR83/42fO1rOy9fnhqoP/jAWz+WLav+dVav9qp3w4YeqGvrDS+EpUs9QHfrlvxtQbaB+vLL/f194AHPW82bZz/OPFCgzsa773p1OvUvhIiIiBRW5UCdTctHthXqRo3goIN2vTAxavmoLqxHVfM5c7wHG2oO1LNm+fakk/zcmV7QmA9Ll3olOaomQ3YXJQJ07gyXXuq3Y9ruAQrUdbdkiV+Vq3YPERGR4kpt+YDaWz5qsm5d9guHHHJIssocWbXKq8nVnbtZM/+t9+uvw9q1vq+mQB21e5x/vm/j0PYRBerOnf1+q1Y7vwd19aMf+XlSW2hiRoG6rtQ/LSIiEg/ptHxEVeeaAvX27f7cFi2yG8+gQT6bSNRHDF5Fbtu25t9q9+7t0/FG0gnUZ57pHyjiGKizbfeIdOvmuevGG3NzvjxQoK6rd9/1v6jRqkgiIiJSHFGFuqaWj+bN/biaAvVnn/l2t92yG0/qhYmRVatqb3/o3dur2lHori1Q77mnL0AzaNDOS3QXQ7TseOfOuQ/U4HmrQ4fcnS/HFKjr6t13/c1t1qzYIxEREanfouBcU8uHWe2rJW7c6NtsK9T77efhPTVQRxXqmkQXJvbv7/mipgVbZs5MzngxZIhXqGubYzufNmzwqYTzUaEuAQrUdbF1qxZ0ERERiYt0Wj7AA21Ns3xEFepsA3XjxnDggTvP9JFuhRp8tcVOnWqvUEeBeuhQb1d5++3sxp2NKPynBupsL0gsIQrUdTFpEmzerP5pERGROKiu5aNRpeU20q1QZ9vyAckVEyOZVKgPPhg6dqw+UK9Y4QE2CtRHH+0h/vXXsx52nVUVqFWhlhrpgkQREZH4qKrlo0mTXS8ALFTLB8Bee8GaNckZO9KpUB96KJx9Nnzxi9UH6rfegsGDoUEDOPZY37fbbnD44cUN1NHKkJ06+c/54IN9TPWEAnVdvPuuX3HarVuxRyIiIiJVtXxUbveA2gN1rlo+AHr08O38+R70N26svULdsiU8+aRXqisH6i1b4Mc/9n7pBg3gzTd3bj0dOtR7tqMAX0iffupT2w0cCPvv7+ObMAGGDy/8WIpEgbouogVdREREpPiqavmoav7jNm0K1/IRraI8f37yNTPpKY56qEPwVtPBg33J8m99y+8fffTOxw8d6jOEpE67VwgheHDevt0/DFT1QaYeUKDO1MaN/hfymGOKPRIRERGB6ls+Kmvb1qvQ0XGV5bJCHQXqefN2XnY8XR07+geDKVPgiCM8XI8YAX//u1eyKzvySP9gMXp01kPPyOzZ8N578MtfJnu666FGtR8iO2nRwlc/EhERkXjIpOUDfKaP6MK5VLnsoe7SxVdGnD/fV1YGnzc6XR07+vbf//aJEN5915c0r06zZh6qC91HPWaMb4cMKezrxowq1CIiIlLa0m35qG358Vy2fDRq5EuJz58P06b5vkyWzo4C9eOPe6vIwIG1P2foUC/61TQ1YK6NHethfsCAwr1mDClQi4iISGnLpOUDqg/UuWz5AL8wMQrUXboklz9PRxSoZ83y2TwapBHZhg71nuY336zbeOti7Fif0aOe9k5H8haozay7mb1uZh+Z2VQz+0Glx682s2Bm7VP2XWdmM83sEzM7JV9jExERkTKSactHTRVqs9ytgty9u/dQT52aWXUa/KLESDQ9Xm0OO8yXWC9U28f27T6zyGGHFeb1YiyfFertwFUhhP2AI4DLzKw/eNgGTgLmRQcnHhsODABOBf5qZg3zOD4REREpB5nM8gE1B+rddtt1/uq66t4dFiyAjz7KPFCnLoqSbn9y06Y++0ehAvW0ab7c+ODBhXm9GMtboA4hLA4hTEjcXg98BETd+H8EfgyElKecCTwSQtgSQpgDzAT0kUdERERqlsuWj1y1e4C3fGzZAhs2ZB6oGzXy3undd0+vfzoyZAhMngzr1mX2enUxdqxvFagL00NtZr2Ag4H3zewMYGEIYVKlw/YE5qfcX0AygIuIiIhUrWFDryrX1vLRurX3Ild30V5Uoc6VaOo8yDxQg6+2eMIJ/v2lK7o4cMaMzF8vU2PGwB571Ovp8iJ5nzbPzFoCTwJX4G0g1wMnV3VoFfvCLgeZXQpcCtAjWoVIRERE6i8zb3dIbfmoKhg3aFDz4i4bN+a2Qp1toH7uuWQ7S7r69vXtzJkwaFDmr5mJsWN9ufR0Lpgsc3n9CZhZYzxMPxRCeArYG+gNTDKzuUA3YIKZdcYr0il/8ugGLKp8zhDCXSGEQ0MIh3bo0CGfwxcREZFS0aTJzi0f1c06UdPy4/lo+QCfsSO1JzpdnTtnNjMIeFUb8l+h3rQJPvxQ7R4J+Zzlw4B7gY9CCLcBhBA+DCF0DCH0CiH0wkP0ISGEJcBzwHAza2pmvYG+wJh8jU9ERETKSJMmO7d8VNVDDTUH6ly3fLRr5zOG1KU6XVe77QbdunmFOhdCqLofe+JEn+VDM3wA+a1QHw1cBBxvZhMTX6dVd3AIYSrwGDANeAm4LIRQkcfxiYiISLmo3PJRXaCuqeUj1xVqM/jiF+Gss3J3znT06ZO7CvXzz/sUfitW7LxfFyTuJG891CGEt6m6Lzr1mF6V7t8M3JyvMYmIiEiZyqTlY/r0qh/LdQ81+NLhhda3Lzz7bG7ONWGCL30+d+7ObStjx/piNZksp17G1EUuIiIipS+Tlo9CzfJRLH36wLJluZk6b15iyZClS3feP2aM2j1SKFCLiIhI6Uu35aNtW1izBiqq6CrNdctHsUQzfeSi7ePTT32bGqjXrPEqv9o9/keBWkREREpfJi0fIcDatbs+Vk4VasjNhYlVBerx432rQP0/CtQiIiJS+jJp+YBdL0ysqPBAXg4V6r339m22FeodO2B+Ys291EA9JjEJ26GHZnf+MqJALSIiIqUvk5YP2DVQf/aZb8shUO+2m18smG2FeunS5M80NVCPHetV8OhnKQrUIiIiUgailo8Qam75iBZKqRyoN270bTm0fID3UWdboY7aPWDXCrXaPXaiQC0iIiKlL2r52LrVQ3Xz5lUfF1VVK8/0EQXqcqhQg1eQs61QR4G6X79koF68GBYu1AwflShQi4iISOmLWj6i1o3qKs31oeUDvEKd7dR5UaAePDgZqLWgS5UUqEVERKT0RS0ftQXj+tLykYuZPubNg91393C+cqX3po8ZAw0bwsEH52acZUKBWkREREpf1PJRW4W6cWNo1ap+VKghuz7qTz+Fnj196XGA5cu9Qr3//uXzwSNHFKhFRESk9DVt6hXqdCrNbdtWX6Eul0AdTZ2XTYW6cqBessQDtdo9dqFALSIiIqUv3Qo1eNtHubd8RFPnZVOhnjcPevRIBurXXvOLOY86KjdjLCMK1CIiIlL6MgnUbdvuOstHubV8QHYzfaxf76tJpgbqBx/07Ukn5WZ8ZUSBWkREREpf1PKRTjCuqeWjXCrUkN1c1MuX+7Zjx2SgnjwZ9tsPunXLzfjKiAK1iIiIlL4mTXz58PXr/X5976EGr1DXdeq8FSt82749tGyZ/HmefHLuxldGFKhFRESk9EVLja9d69t0AnUIyX1RZbu6BWFKUTTTR13aPlIDNSSr1ArUVVKgFhERkdLXtKlvo97o2gL1tm3JqjT47d12A7P8jbHQqpuLetu22p9bVaBu3BiGDMnd+MqIArWIiIiUvqhCnW6ghmSVevx47w8up3YPSE6dl9pHvXQpdOgATz9d83MrB+oTToCLLiq/n1GONCr2AERERESyFgXqNWt8W1PrRrRa4urVMGcOHHec3z/hhHyNrjhatICuXXeuUD/1lLfFTJ9e83NXrvQVEXff3e//6lf5G2cZUKAWERGR0pfa8lFb60ZqhXruXL/97rtw+OF5HWJRVJ7p44knfBv1mldnxQpo1668WmDySC0fIiIiUvpSK9S1TX2XGqiXLfPbBxxQnuGxb99khXrZMhg92m+nE6ijdg+plQK1iIiIlL5sAnWLFuXbG9ynj/dNr1sHzzwDO3b4xYUK1Dmllg8REREpfZVbPmpSOVB37JjfsRVTNHXerFnw5JMesFu2rH1u6hUrYN998z++MqEKtYiIiJS+1Fk+agvUzZt7AK8PgTqaOm/SJG/3OOMMv9BQFeqcUqAWERGR0pe6sEtt7RtmPtPH6tXlH6ijqfPuvhu2boVTT609UIfgs3woUKdNgVpERERKX9TyEULtFWpIrpZY7oE6mjrvv//1yvwxx0Dr1jUH6rVrfRl3Beq0KVCLiIhI6Ysq1JB+oF6xovwDNST7qIcOhWbNaq9QV17URWqlQC0iIiKlry6BevZs2L69/AN11Ed96qm+3X13vygxhKqPV6DOmAK1iIiIlL6o5QPSD9Tz5/vtcg/U0WwdqYG6ogI++6zq46NA3a5d/sdWJjRtnoiIiJS+1Ap1OnNKR1PnQfkH6m99yxeuiVo/ouXEq7uAUxXqjKlCLSIiIqUv05aPNm2Stzt1yv144mT33eGUU5L3W7f2bXV91CtX+laBOm0K1CIiIlL66tLyESn3CnVlqRXqqqxY4asptmpVuDGVOAVqERERKX11uSgRfE7q+tYrnE6gbt/efzaSFgVqERERKX11DdTt2kGjenZJWRSoq1t+XKskZkyBWkREREpfXS9KrG/tHlBzhToEGD8+OdWepEWBWkREREqfmff9QmYV6voYqGu6KPHDD306wdNPL+yYSpwCtYiIiJSH6MJEBeqatWrlH0CqCtQvvODb004r7JhKnAK1iIiIlIeo7SOdQN26NTRoUD8DdYMGHqqrCtQjRsChh0KXLoUfVwlToBYREZHykEmgbtAA7rgDvvnN/I4prqLlx1OtWAHvvqt2jzqoZ5e1ioiISNnKpOUD4LLL8jeWuNt9910r1CNH+kWJw4YVZ0wlTBVqERERKQ9RhTqdWT7qu9atdw3UI0ZA585wyCHFGVMJU6AWERGR8pBJy0d9V7lCvW0bvPyyt3s0UDzMlH5iIiIiUh4ybfmozyoH6nfe8ftq96gTBWoREREpD1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\"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.figure(figsize=(12,8))\\n\",\n \"\\n\",\n \"sns.lineplot(data=data, x = 'Date', y='AdjClose', color='red')\\n\",\n \"\\n\",\n \"plt.title(\\\"S&P 500 1987\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 67,\n \"id\": \"2cecaa85\",\n \"metadata\": {\n \"scrolled\": false\n },\n \"outputs\": [],\n \"source\": [\n \"# help(data['AdjClose'].pct_change())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 65,\n \"id\": \"15c92d9e\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
DateOpenHighLowCloseAdjCloseVolumeReturns
01986-12-31243.37244.03241.28242.17242.1713,92,00,000NaN
11987-01-02242.17246.45242.17246.45246.459,18,80,0000.017674
21987-01-05246.45252.57246.45252.19252.1918,19,00,0000.023291
31987-01-06252.20253.99252.14252.78252.7818,93,00,0000.002340
41987-01-07252.78255.72252.65255.33255.3319,09,00,0000.010088
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" Date Open High Low Close AdjClose Volume Returns\\n\",\n \"0 1986-12-31 243.37 244.03 241.28 242.17 242.17 13,92,00,000 NaN\\n\",\n \"1 1987-01-02 242.17 246.45 242.17 246.45 246.45 9,18,80,000 0.017674\\n\",\n \"2 1987-01-05 246.45 252.57 246.45 252.19 252.19 18,19,00,000 0.023291\\n\",\n \"3 1987-01-06 252.20 253.99 252.14 252.78 252.78 18,93,00,000 0.002340\\n\",\n \"4 1987-01-07 252.78 255.72 252.65 255.33 255.33 19,09,00,000 0.010088\"\n ]\n },\n \"execution_count\": 65,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"data['Returns'] = data['AdjClose'].pct_change()\\n\",\n \"\\n\",\n \"data.head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 68,\n \"id\": \"2d892d6c\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
DateOpenHighLowCloseAdjCloseVolumeReturns
11987-01-02242.17246.45242.17246.45246.459,18,80,0000.017674
21987-01-05246.45252.57246.45252.19252.1918,19,00,0000.023291
31987-01-06252.20253.99252.14252.78252.7818,93,00,0000.002340
41987-01-07252.78255.72252.65255.33255.3319,09,00,0000.010088
51987-01-08255.36257.28254.97257.28257.2819,45,00,0000.007637
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" Date Open High Low Close AdjClose Volume Returns\\n\",\n \"1 1987-01-02 242.17 246.45 242.17 246.45 246.45 9,18,80,000 0.017674\\n\",\n \"2 1987-01-05 246.45 252.57 246.45 252.19 252.19 18,19,00,000 0.023291\\n\",\n \"3 1987-01-06 252.20 253.99 252.14 252.78 252.78 18,93,00,000 0.002340\\n\",\n \"4 1987-01-07 252.78 255.72 252.65 255.33 255.33 19,09,00,000 0.010088\\n\",\n \"5 1987-01-08 255.36 257.28 254.97 257.28 257.28 19,45,00,000 0.007637\"\n ]\n },\n \"execution_count\": 68,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"data = data.dropna()\\n\",\n \"data.head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 69,\n \"id\": \"c23e43a2\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(252, 8)\"\n ]\n },\n \"execution_count\": 69,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"data.shape\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"44c9342e\",\n \"metadata\": {},\n \"source\": [\n \"# Skewness\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 70,\n \"id\": \"86c6f635\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.figure(figsize=(12,8))\\n\",\n \"\\n\",\n \"sns.histplot(data=data, x = 'Returns')\\n\",\n \"\\n\",\n \"plt.title(\\\"S&P\\\")\\n\",\n \"\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"915bfd73\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.12\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n" }, { "path": "March Cohort 2023/12_15_16_17_18_Statistics/.ipynb_checkpoints/16_17_Statistics and Regression-checkpoint.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"id\": \"103790af\",\n \"metadata\": {},\n \"source\": [\n \"# Multiple Linear Regression\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"id\": \"6dd89ea3\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import numpy as np\\n\",\n \"import pandas as pd\\n\",\n \"\\n\",\n \"import statsmodels.api as sm\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"id\": \"73c667a7\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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Sl.NO.PLAYER NAMEAGECOUNTRYTEAMPLAYING ROLET-RUNST-WKTSODI-RUNS-SODI-SR-B...SR-BSIXERSRUNS-CWKTSAVE-BLECONSR-BLAUCTION YEARBASE PRICESOLD PRICE
01Abdulla, YA2SAKXIPAllrounder0000.00...0.0003071520.478.9013.9320095000050000
12Abdur Razzak2BANRCBBowler2141865771.41...0.0002900.0014.500.0020085000050000
23Agarkar, AB2INDKKRBowler57158126980.62...121.01510592936.528.8124.902008200000350000
34Ashwin, R1INDCSKBowler2843124184.56...76.32011254922.966.2322.142011100000850000
45Badrinath, S2INDCSKBatsman6307945.93...120.7128000.000.000.002011100000800000
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5 rows × 26 columns

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\"\n ],\n \"text/plain\": [\n \" Sl.NO. PLAYER NAME AGE COUNTRY TEAM PLAYING ROLE T-RUNS T-WKTS \\\\\\n\",\n \"0 1 Abdulla, YA 2 SA KXIP Allrounder 0 0 \\n\",\n \"1 2 Abdur Razzak 2 BAN RCB Bowler 214 18 \\n\",\n \"2 3 Agarkar, AB 2 IND KKR Bowler 571 58 \\n\",\n \"3 4 Ashwin, R 1 IND CSK Bowler 284 31 \\n\",\n \"4 5 Badrinath, S 2 IND CSK Batsman 63 0 \\n\",\n \"\\n\",\n \" ODI-RUNS-S ODI-SR-B ... SR-B SIXERS RUNS-C WKTS AVE-BL ECON \\\\\\n\",\n \"0 0 0.00 ... 0.00 0 307 15 20.47 8.90 \\n\",\n \"1 657 71.41 ... 0.00 0 29 0 0.00 14.50 \\n\",\n \"2 1269 80.62 ... 121.01 5 1059 29 36.52 8.81 \\n\",\n \"3 241 84.56 ... 76.32 0 1125 49 22.96 6.23 \\n\",\n \"4 79 45.93 ... 120.71 28 0 0 0.00 0.00 \\n\",\n \"\\n\",\n \" SR-BL AUCTION YEAR BASE PRICE SOLD PRICE \\n\",\n \"0 13.93 2009 50000 50000 \\n\",\n \"1 0.00 2008 50000 50000 \\n\",\n \"2 24.90 2008 200000 350000 \\n\",\n \"3 22.14 2011 100000 850000 \\n\",\n \"4 0.00 2011 100000 800000 \\n\",\n \"\\n\",\n \"[5 rows x 26 columns]\"\n ]\n },\n \"execution_count\": 2,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df = pd.read_csv('datasets/IPL2013.csv')\\n\",\n \"\\n\",\n \"df.head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"id\": \"89f7bb2b\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\",\n \"RangeIndex: 130 entries, 0 to 129\\n\",\n \"Data columns (total 26 columns):\\n\",\n \" # Column Non-Null Count Dtype \\n\",\n \"--- ------ -------------- ----- \\n\",\n \" 0 Sl.NO. 130 non-null int64 \\n\",\n \" 1 PLAYER NAME 130 non-null object \\n\",\n \" 2 AGE 130 non-null int64 \\n\",\n \" 3 COUNTRY 130 non-null object \\n\",\n \" 4 TEAM 130 non-null object \\n\",\n \" 5 PLAYING ROLE 130 non-null object \\n\",\n \" 6 T-RUNS 130 non-null int64 \\n\",\n \" 7 T-WKTS 130 non-null int64 \\n\",\n \" 8 ODI-RUNS-S 130 non-null int64 \\n\",\n \" 9 ODI-SR-B 130 non-null float64\\n\",\n \" 10 ODI-WKTS 130 non-null int64 \\n\",\n \" 11 ODI-SR-BL 130 non-null float64\\n\",\n \" 12 CAPTAINCY EXP 130 non-null int64 \\n\",\n \" 13 RUNS-S 130 non-null int64 \\n\",\n \" 14 HS 130 non-null int64 \\n\",\n \" 15 AVE 130 non-null float64\\n\",\n \" 16 SR-B 130 non-null float64\\n\",\n \" 17 SIXERS 130 non-null int64 \\n\",\n \" 18 RUNS-C 130 non-null int64 \\n\",\n \" 19 WKTS 130 non-null int64 \\n\",\n \" 20 AVE-BL 130 non-null float64\\n\",\n \" 21 ECON 130 non-null float64\\n\",\n \" 22 SR-BL 130 non-null float64\\n\",\n \" 23 AUCTION YEAR 130 non-null int64 \\n\",\n \" 24 BASE PRICE 130 non-null int64 \\n\",\n \" 25 SOLD PRICE 130 non-null int64 \\n\",\n \"dtypes: float64(7), int64(15), object(4)\\n\",\n \"memory usage: 26.5+ KB\\n\"\n ]\n }\n ],\n \"source\": [\n \"df.info()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"id\": \"983b5a17\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Index(['Sl.NO.', 'PLAYER NAME', 'AGE', 'COUNTRY', 'TEAM', 'PLAYING ROLE',\\n\",\n \" 'T-RUNS', 'T-WKTS', 'ODI-RUNS-S', 'ODI-SR-B', 'ODI-WKTS', 'ODI-SR-BL',\\n\",\n \" 'CAPTAINCY EXP', 'RUNS-S', 'HS', 'AVE', 'SR-B', 'SIXERS', 'RUNS-C',\\n\",\n \" 'WKTS', 'AVE-BL', 'ECON', 'SR-BL', 'AUCTION YEAR', 'BASE PRICE',\\n\",\n \" 'SOLD PRICE'],\\n\",\n \" dtype='object')\"\n ]\n },\n \"execution_count\": 4,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"xfeatures = df.columns\\n\",\n \"xfeatures\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"id\": \"beac70ae\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"xfeatures = ['AGE', 'COUNTRY', 'PLAYING ROLE',\\n\",\n \" 'T-RUNS', 'T-WKTS', 'ODI-RUNS-S', 'ODI-SR-B', 'ODI-WKTS', 'ODI-SR-BL',\\n\",\n \" 'CAPTAINCY EXP', 'RUNS-S', 'HS', 'AVE', 'SR-B', 'SIXERS', 'RUNS-C',\\n\",\n \" 'WKTS', 'AVE-BL', 'ECON', 'SR-BL']\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"489a7f42\",\n \"metadata\": {},\n \"source\": [\n \"# Feature encoding on categorical columns\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"id\": \"9f8602c5\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array(['Allrounder', 'Bowler', 'Batsman', 'W. Keeper'], dtype=object)\"\n ]\n },\n \"execution_count\": 6,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df['PLAYING ROLE'].unique()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"id\": \"4b4f0c3e\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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T-RUNST-WKTSODI-RUNS-SODI-SR-BODI-WKTSODI-SR-BLRUNS-SHSAVESR-B...COUNTRY_NZCOUNTRY_PAKCOUNTRY_SACOUNTRY_SLCOUNTRY_WICOUNTRY_ZIMPLAYING ROLE_BatsmanPLAYING ROLE_BowlerPLAYING ROLE_W. KeeperCAPTAINCY EXP_1
00000.0000.0000.000.00...0010000000
12141865771.4118537.6000.000.00...0000000100
257158126980.6228832.91673918.56121.01...0000000100
32843124184.565136.858115.8076.32...0000000100
46307945.9300.013177132.93120.71...0000001000
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5 rows × 31 columns

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\"\n ],\n \"text/plain\": [\n \" T-RUNS T-WKTS ODI-RUNS-S ODI-SR-B ODI-WKTS ODI-SR-BL RUNS-S HS \\\\\\n\",\n \"0 0 0 0 0.00 0 0.0 0 0 \\n\",\n \"1 214 18 657 71.41 185 37.6 0 0 \\n\",\n \"2 571 58 1269 80.62 288 32.9 167 39 \\n\",\n \"3 284 31 241 84.56 51 36.8 58 11 \\n\",\n \"4 63 0 79 45.93 0 0.0 1317 71 \\n\",\n \"\\n\",\n \" AVE SR-B ... COUNTRY_NZ COUNTRY_PAK COUNTRY_SA COUNTRY_SL \\\\\\n\",\n \"0 0.00 0.00 ... 0 0 1 0 \\n\",\n \"1 0.00 0.00 ... 0 0 0 0 \\n\",\n \"2 18.56 121.01 ... 0 0 0 0 \\n\",\n \"3 5.80 76.32 ... 0 0 0 0 \\n\",\n \"4 32.93 120.71 ... 0 0 0 0 \\n\",\n \"\\n\",\n \" COUNTRY_WI COUNTRY_ZIM PLAYING ROLE_Batsman PLAYING ROLE_Bowler \\\\\\n\",\n \"0 0 0 0 0 \\n\",\n \"1 0 0 0 1 \\n\",\n \"2 0 0 0 1 \\n\",\n \"3 0 0 0 1 \\n\",\n \"4 0 0 1 0 \\n\",\n \"\\n\",\n \" PLAYING ROLE_W. Keeper CAPTAINCY EXP_1 \\n\",\n \"0 0 0 \\n\",\n \"1 0 0 \\n\",\n \"2 0 0 \\n\",\n \"3 0 0 \\n\",\n \"4 0 0 \\n\",\n \"\\n\",\n \"[5 rows x 31 columns]\"\n ]\n },\n \"execution_count\": 9,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"xfeatures = encoded_df.columns\\n\",\n \"\\n\",\n \"encoded_df.head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"id\": \"8af55ff4\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from sklearn.model_selection import train_test_split\\n\",\n \"\\n\",\n \"x = sm.add_constant(encoded_df)\\n\",\n \"\\n\",\n \"y = df['SOLD PRICE']\\n\",\n \"\\n\",\n \"xtrain, xtest, ytrain, ytest = train_test_split(x, y,\\n\",\n \" train_size=0.8,\\n\",\n \" random_state=101)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"id\": \"d5cbd477\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"
Model: OLS Adj. R-squared: 0.367
Dependent Variable: SOLD PRICE AIC: 2952.4618
Date: 2023-04-24 21:08 BIC: 3034.4379
No. Observations: 104 Log-Likelihood: -1445.2
Df Model: 30 F-statistic: 2.989
Df Residuals: 73 Prob (F-statistic): 7.43e-05
R-squared: 0.551 Scale: 9.8071e+10
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Coef. Std.Err. t P>|t| [0.025 0.975]
const 321792.1559 214961.4272 1.4970 0.1387 -106625.3506 750209.6624
T-RUNS -15.3217 31.7900 -0.4820 0.6313 -78.6790 48.0356
T-WKTS -92.1211 532.7482 -0.1729 0.8632 -1153.8869 969.6446
ODI-RUNS-S 46.4615 32.7116 1.4203 0.1598 -18.7326 111.6555
ODI-SR-B 1254.6514 1524.3920 0.8231 0.4132 -1783.4575 4292.7602
ODI-WKTS 1034.2327 705.8108 1.4653 0.1471 -372.4462 2440.9116
ODI-SR-BL -1951.6835 1570.2876 -1.2429 0.2179 -5081.2621 1177.8952
RUNS-S 79.5617 164.1206 0.4848 0.6293 -247.5303 406.6536
HS -2845.5968 2659.2674 -1.0701 0.2881 -8145.5090 2454.3155
AVE 16170.0170 7116.7912 2.2721 0.0260 1986.2726 30353.7615
SR-B -1412.9785 1268.6500 -1.1138 0.2690 -3941.3943 1115.4373
SIXERS 2933.8502 3250.6472 0.9025 0.3697 -3544.6804 9412.3808
RUNS-C 90.3731 250.6836 0.3605 0.7195 -409.2387 589.9848
WKTS 1023.5083 6199.6413 0.1651 0.8693 -11332.3589 13379.3755
AVE-BL 10484.2466 10614.6012 0.9877 0.3266 -10670.6230 31639.1163
ECON 1582.7941 10799.9290 0.1466 0.8839 -19941.4334 23107.0217
SR-BL -16112.2676 14683.6651 -1.0973 0.2761 -45376.7699 13152.2347
AGE_2 -204982.5473 122684.6949 -1.6708 0.0990 -449492.7915 39527.6970
AGE_3 -314011.4605 166581.1800 -1.8850 0.0634 -646007.2734 17984.3525
COUNTRY_BAN -0.0000 0.0000 -0.4054 0.6864 -0.0000 0.0000
COUNTRY_ENG 568937.2590 223554.3597 2.5450 0.0130 123394.0628 1014480.4552
COUNTRY_IND 105574.3654 116110.3502 0.9093 0.3662 -125833.2288 336981.9597
COUNTRY_NZ 120771.0634 169814.2346 0.7112 0.4792 -217668.2181 459210.3449
COUNTRY_PAK -18339.1812 162409.9584 -0.1129 0.9104 -342021.7614 305343.3991
COUNTRY_SA -48587.6579 135853.6982 -0.3576 0.7216 -319343.6880 222168.3722
COUNTRY_SL -132932.0463 156295.0094 -0.8505 0.3978 -444427.5507 178563.4581
COUNTRY_WI -292568.4951 188284.0245 -1.5539 0.1245 -667818.0146 82681.0244
COUNTRY_ZIM -70610.1889 377372.9760 -0.1871 0.8521 -822713.4453 681493.0676
PLAYING ROLE_Batsman 45944.5780 131655.5496 0.3490 0.7281 -216444.5538 308333.7098
PLAYING ROLE_Bowler 103751.1060 110824.6388 0.9362 0.3523 -117122.0806 324624.2926
PLAYING ROLE_W. Keeper -89731.6479 187093.4020 -0.4796 0.6329 -462608.2602 283144.9645
CAPTAINCY EXP_1 71431.5146 116029.1349 0.6156 0.5401 -159814.2177 302677.2470
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"
Omnibus: 6.983 Durbin-Watson: 1.857
Prob(Omnibus): 0.030 Jarque-Bera (JB): 6.499
Skew: 0.583 Prob(JB): 0.039
Kurtosis: 3.376 Condition No.: 12332195260360820
\"\n ],\n \"text/plain\": [\n \"\\n\",\n \"\\\"\\\"\\\"\\n\",\n \" Results: Ordinary least squares\\n\",\n \"========================================================================================\\n\",\n \"Model: OLS Adj. R-squared: 0.367 \\n\",\n \"Dependent Variable: SOLD PRICE AIC: 2952.4618 \\n\",\n \"Date: 2023-04-24 21:08 BIC: 3034.4379 \\n\",\n \"No. Observations: 104 Log-Likelihood: -1445.2 \\n\",\n \"Df Model: 30 F-statistic: 2.989 \\n\",\n \"Df Residuals: 73 Prob (F-statistic): 7.43e-05 \\n\",\n \"R-squared: 0.551 Scale: 9.8071e+10\\n\",\n \"----------------------------------------------------------------------------------------\\n\",\n \" Coef. Std.Err. t P>|t| [0.025 0.975] \\n\",\n \"----------------------------------------------------------------------------------------\\n\",\n \"const 321792.1559 214961.4272 1.4970 0.1387 -106625.3506 750209.6624\\n\",\n \"T-RUNS -15.3217 31.7900 -0.4820 0.6313 -78.6790 48.0356\\n\",\n \"T-WKTS -92.1211 532.7482 -0.1729 0.8632 -1153.8869 969.6446\\n\",\n \"ODI-RUNS-S 46.4615 32.7116 1.4203 0.1598 -18.7326 111.6555\\n\",\n \"ODI-SR-B 1254.6514 1524.3920 0.8231 0.4132 -1783.4575 4292.7602\\n\",\n \"ODI-WKTS 1034.2327 705.8108 1.4653 0.1471 -372.4462 2440.9116\\n\",\n \"ODI-SR-BL -1951.6835 1570.2876 -1.2429 0.2179 -5081.2621 1177.8952\\n\",\n \"RUNS-S 79.5617 164.1206 0.4848 0.6293 -247.5303 406.6536\\n\",\n \"HS -2845.5968 2659.2674 -1.0701 0.2881 -8145.5090 2454.3155\\n\",\n \"AVE 16170.0170 7116.7912 2.2721 0.0260 1986.2726 30353.7615\\n\",\n \"SR-B -1412.9785 1268.6500 -1.1138 0.2690 -3941.3943 1115.4373\\n\",\n \"SIXERS 2933.8502 3250.6472 0.9025 0.3697 -3544.6804 9412.3808\\n\",\n \"RUNS-C 90.3731 250.6836 0.3605 0.7195 -409.2387 589.9848\\n\",\n \"WKTS 1023.5083 6199.6413 0.1651 0.8693 -11332.3589 13379.3755\\n\",\n \"AVE-BL 10484.2466 10614.6012 0.9877 0.3266 -10670.6230 31639.1163\\n\",\n \"ECON 1582.7941 10799.9290 0.1466 0.8839 -19941.4334 23107.0217\\n\",\n \"SR-BL -16112.2676 14683.6651 -1.0973 0.2761 -45376.7699 13152.2347\\n\",\n \"AGE_2 -204982.5473 122684.6949 -1.6708 0.0990 -449492.7915 39527.6970\\n\",\n \"AGE_3 -314011.4605 166581.1800 -1.8850 0.0634 -646007.2734 17984.3525\\n\",\n \"COUNTRY_BAN -0.0000 0.0000 -0.4054 0.6864 -0.0000 0.0000\\n\",\n \"COUNTRY_ENG 568937.2590 223554.3597 2.5450 0.0130 123394.0628 1014480.4552\\n\",\n \"COUNTRY_IND 105574.3654 116110.3502 0.9093 0.3662 -125833.2288 336981.9597\\n\",\n \"COUNTRY_NZ 120771.0634 169814.2346 0.7112 0.4792 -217668.2181 459210.3449\\n\",\n \"COUNTRY_PAK -18339.1812 162409.9584 -0.1129 0.9104 -342021.7614 305343.3991\\n\",\n \"COUNTRY_SA -48587.6579 135853.6982 -0.3576 0.7216 -319343.6880 222168.3722\\n\",\n \"COUNTRY_SL -132932.0463 156295.0094 -0.8505 0.3978 -444427.5507 178563.4581\\n\",\n \"COUNTRY_WI -292568.4951 188284.0245 -1.5539 0.1245 -667818.0146 82681.0244\\n\",\n \"COUNTRY_ZIM -70610.1889 377372.9760 -0.1871 0.8521 -822713.4453 681493.0676\\n\",\n \"PLAYING ROLE_Batsman 45944.5780 131655.5496 0.3490 0.7281 -216444.5538 308333.7098\\n\",\n \"PLAYING ROLE_Bowler 103751.1060 110824.6388 0.9362 0.3523 -117122.0806 324624.2926\\n\",\n \"PLAYING ROLE_W. Keeper -89731.6479 187093.4020 -0.4796 0.6329 -462608.2602 283144.9645\\n\",\n \"CAPTAINCY EXP_1 71431.5146 116029.1349 0.6156 0.5401 -159814.2177 302677.2470\\n\",\n \"----------------------------------------------------------------------------------------\\n\",\n \"Omnibus: 6.983 Durbin-Watson: 1.857 \\n\",\n \"Prob(Omnibus): 0.030 Jarque-Bera (JB): 6.499 \\n\",\n \"Skew: 0.583 Prob(JB): 0.039 \\n\",\n \"Kurtosis: 3.376 Condition No.: 12332195260360820\\n\",\n \"========================================================================================\\n\",\n \"* The condition number is large (1e+16). This might indicate strong\\n\",\n \"multicollinearity or other numerical problems.\\n\",\n \"\\\"\\\"\\\"\"\n ]\n },\n \"execution_count\": 11,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"model1 = sm.OLS(ytrain, xtrain).fit()\\n\",\n \"\\n\",\n \"model1.summary2()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"id\": \"ad35c43b\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"ename\": \"SyntaxError\",\n \"evalue\": \"invalid syntax (1120071531.py, line 3)\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[1;36m Input \\u001b[1;32mIn [12]\\u001b[1;36m\\u001b[0m\\n\\u001b[1;33m 95% 5%\\u001b[0m\\n\\u001b[1;37m ^\\u001b[0m\\n\\u001b[1;31mSyntaxError\\u001b[0m\\u001b[1;31m:\\u001b[0m invalid syntax\\n\"\n ]\n }\n ],\n \"source\": [\n \"p-value\\n\",\n \"\\n\",\n \"95% 5%\\n\",\n \"0.95 0.05\\n\",\n \"\\n\",\n \"\\n\",\n \"# VIF (Variance Inflation Factor)\\n\",\n \"\\n\",\n \"\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"id\": \"81de17e2\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from statsmodels.stats.outliers_influence import variance_inflation_factor\\n\",\n \"\\n\",\n \"def get_vif_score(x):\\n\",\n \" \\n\",\n \" xmatrix = x.values\\n\",\n \" vif = [variance_inflation_factor(xmatrix, i) for i in range(xmatrix.shape[1])]\\n\",\n \" \\n\",\n \" vif_factor = pd.DataFrame()\\n\",\n \" vif_factor['Column'] = x.columns\\n\",\n \" vif_factor['VIF'] = vif\\n\",\n \" \\n\",\n \" return vif_factor\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"id\": \"4368e988\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
ColumnVIF
0T-RUNS12.612694
1T-WKTS7.679284
2ODI-RUNS-S16.426209
3ODI-SR-B13.829376
4ODI-WKTS9.951800
5ODI-SR-BL4.426818
6RUNS-S16.135407
7HS22.781017
8AVE25.226566
9SR-B21.576204
10SIXERS9.547268
11RUNS-C38.229691
12WKTS33.366067
13AVE-BL100.198105
14ECON7.650140
15SR-BL103.723846
16AGE_26.996226
17AGE_33.855003
18COUNTRY_BAN1.469017
19COUNTRY_ENG1.391524
20COUNTRY_IND4.568898
21COUNTRY_NZ1.497856
22COUNTRY_PAK1.796355
23COUNTRY_SA1.886555
24COUNTRY_SL1.984902
25COUNTRY_WI1.531847
26COUNTRY_ZIM1.312168
27PLAYING ROLE_Batsman4.843136
28PLAYING ROLE_Bowler3.795864
29PLAYING ROLE_W. Keeper3.132044
30CAPTAINCY EXP_14.245128
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" Column VIF\\n\",\n \"0 T-RUNS 12.612694\\n\",\n \"1 T-WKTS 7.679284\\n\",\n \"2 ODI-RUNS-S 16.426209\\n\",\n \"3 ODI-SR-B 13.829376\\n\",\n \"4 ODI-WKTS 9.951800\\n\",\n \"5 ODI-SR-BL 4.426818\\n\",\n \"6 RUNS-S 16.135407\\n\",\n \"7 HS 22.781017\\n\",\n \"8 AVE 25.226566\\n\",\n \"9 SR-B 21.576204\\n\",\n \"10 SIXERS 9.547268\\n\",\n \"11 RUNS-C 38.229691\\n\",\n \"12 WKTS 33.366067\\n\",\n \"13 AVE-BL 100.198105\\n\",\n \"14 ECON 7.650140\\n\",\n \"15 SR-BL 103.723846\\n\",\n \"16 AGE_2 6.996226\\n\",\n \"17 AGE_3 3.855003\\n\",\n \"18 COUNTRY_BAN 1.469017\\n\",\n \"19 COUNTRY_ENG 1.391524\\n\",\n \"20 COUNTRY_IND 4.568898\\n\",\n \"21 COUNTRY_NZ 1.497856\\n\",\n \"22 COUNTRY_PAK 1.796355\\n\",\n \"23 COUNTRY_SA 1.886555\\n\",\n \"24 COUNTRY_SL 1.984902\\n\",\n \"25 COUNTRY_WI 1.531847\\n\",\n \"26 COUNTRY_ZIM 1.312168\\n\",\n \"27 PLAYING ROLE_Batsman 4.843136\\n\",\n \"28 PLAYING ROLE_Bowler 3.795864\\n\",\n \"29 PLAYING ROLE_W. Keeper 3.132044\\n\",\n \"30 CAPTAINCY EXP_1 4.245128\"\n ]\n },\n \"execution_count\": 14,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"vif_factor = get_vif_score(x[xfeatures])\\n\",\n \"\\n\",\n \"vif_factor\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"id\": \"21f1c866\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"columns_with_large_vif = vif_factor[vif_factor.VIF > 4].Column\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"id\": \"f6f77c94\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" 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T-RUNST-WKTSODI-RUNS-SODI-SR-BODI-WKTSODI-SR-BLRUNS-SHSAVESR-BSIXERSRUNS-CWKTSAVE-BLECONSR-BLAGE_2COUNTRY_INDPLAYING ROLE_BatsmanCAPTAINCY EXP_1
T-RUNS1.0000000.0262850.8928230.2314110.0455050.0677000.4101430.4112090.3740460.1142980.216571-0.253083-0.277157-0.298999-0.329022-0.309105-0.266649-0.1792780.3844220.690647
T-WKTS0.0262851.000000-0.0882760.0120520.8229400.060641-0.218544-0.268432-0.265540-0.147752-0.1980360.2973020.2897350.1624560.1175300.205208-0.178391-0.147490-0.2764500.088782
ODI-RUNS-S0.892823-0.0882761.0000000.3192640.0565540.1267950.5239550.4957650.4462800.1941110.376012-0.268950-0.302745-0.225109-0.249795-0.237517-0.242422-0.1682240.3679950.714058
ODI-SR-B0.2314110.0120520.3192641.0000000.1601140.2845840.3063380.3600860.3403270.3753710.3200410.004855-0.0225020.018958-0.0190670.017522-0.011977-0.1686090.0834850.291373
ODI-WKTS0.0455050.8229400.0565540.1601141.0000000.124361-0.198101-0.209109-0.222062-0.030404-0.1487220.3271220.3024660.2173020.2302210.259800-0.125708-0.187669-0.3588240.077536
ODI-SR-BL0.0677000.0606410.1267950.2845840.1243611.000000-0.027772-0.067615-0.053766-0.051684-0.0336160.1968870.1515940.4166270.3132950.402495-0.133908-0.022494-0.0146890.103315
RUNS-S0.410143-0.2185440.5239550.306338-0.198101-0.0277721.0000000.8346310.7670230.3800220.866213-0.160762-0.220959-0.101818-0.179459-0.118567-0.0857620.2092920.4185550.352422
HS0.411209-0.2684320.4957650.360086-0.209109-0.0676150.8346311.0000000.8762490.5310280.788439-0.235327-0.291494-0.176049-0.254494-0.192990-0.022883-0.0340290.4088260.392749
AVE0.374046-0.2655400.4462800.340327-0.222062-0.0537660.7670230.8762491.0000000.5835790.705365-0.278482-0.343958-0.120430-0.202301-0.1408090.031758-0.0378090.4324850.378271
SR-B0.114298-0.1477520.1941110.375371-0.030404-0.0516840.3800220.5310280.5835791.0000000.425394-0.063179-0.069823-0.057895-0.076228-0.0682480.181181-0.0490310.1579030.176919
SIXERS0.216571-0.1980360.3760120.320041-0.148722-0.0336160.8662130.7884390.7053650.4253941.000000-0.080295-0.144558-0.012210-0.101027-0.021412-0.0435160.1320990.2948960.254991
RUNS-C-0.2530830.297302-0.2689500.0048550.3271220.196887-0.160762-0.235327-0.278482-0.063179-0.0802951.0000000.9592100.4095050.4105020.4712240.0257220.266465-0.436411-0.277903
WKTS-0.2771570.289735-0.302745-0.0225020.3024660.151594-0.220959-0.291494-0.343958-0.069823-0.1445580.9592101.0000000.2978220.3748750.3682640.0444670.216827-0.422958-0.300074
AVE-BL-0.2989990.162456-0.2251090.0189580.2173020.416627-0.101818-0.176049-0.120430-0.057895-0.0122100.4095050.2978221.0000000.7227580.983415-0.0723860.149050-0.366341-0.183775
ECON-0.3290220.117530-0.249795-0.0190670.2302210.313295-0.179459-0.254494-0.202301-0.076228-0.1010270.4105020.3748750.7227581.0000000.7264800.0049420.065552-0.288471-0.248974
SR-BL-0.3091050.205208-0.2375170.0175220.2598000.402495-0.118567-0.192990-0.140809-0.068248-0.0214120.4712240.3682640.9834150.7264801.000000-0.0902490.126109-0.395188-0.198688
AGE_2-0.266649-0.178391-0.242422-0.011977-0.125708-0.133908-0.085762-0.0228830.0317580.181181-0.0435160.0257220.044467-0.0723860.004942-0.0902491.000000-0.002036-0.170277-0.109261
COUNTRY_IND-0.179278-0.147490-0.168224-0.168609-0.187669-0.0224940.209292-0.034029-0.037809-0.0490310.1320990.2664650.2168270.1490500.0655520.126109-0.0020361.0000000.071734-0.192539
PLAYING ROLE_Batsman0.384422-0.2764500.3679950.083485-0.358824-0.0146890.4185550.4088260.4324850.1579030.294896-0.436411-0.422958-0.366341-0.288471-0.395188-0.1702770.0717341.0000000.314284
CAPTAINCY EXP_10.6906470.0887820.7140580.2913730.0775360.1033150.3524220.3927490.3782710.1769190.254991-0.277903-0.300074-0.183775-0.248974-0.198688-0.109261-0.1925390.3142841.000000
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" T-RUNS T-WKTS ODI-RUNS-S ODI-SR-B ODI-WKTS \\\\\\n\",\n \"T-RUNS 1.000000 0.026285 0.892823 0.231411 0.045505 \\n\",\n \"T-WKTS 0.026285 1.000000 -0.088276 0.012052 0.822940 \\n\",\n \"ODI-RUNS-S 0.892823 -0.088276 1.000000 0.319264 0.056554 \\n\",\n \"ODI-SR-B 0.231411 0.012052 0.319264 1.000000 0.160114 \\n\",\n \"ODI-WKTS 0.045505 0.822940 0.056554 0.160114 1.000000 \\n\",\n \"ODI-SR-BL 0.067700 0.060641 0.126795 0.284584 0.124361 \\n\",\n \"RUNS-S 0.410143 -0.218544 0.523955 0.306338 -0.198101 \\n\",\n \"HS 0.411209 -0.268432 0.495765 0.360086 -0.209109 \\n\",\n \"AVE 0.374046 -0.265540 0.446280 0.340327 -0.222062 \\n\",\n \"SR-B 0.114298 -0.147752 0.194111 0.375371 -0.030404 \\n\",\n \"SIXERS 0.216571 -0.198036 0.376012 0.320041 -0.148722 \\n\",\n \"RUNS-C -0.253083 0.297302 -0.268950 0.004855 0.327122 \\n\",\n \"WKTS -0.277157 0.289735 -0.302745 -0.022502 0.302466 \\n\",\n \"AVE-BL -0.298999 0.162456 -0.225109 0.018958 0.217302 \\n\",\n \"ECON -0.329022 0.117530 -0.249795 -0.019067 0.230221 \\n\",\n \"SR-BL -0.309105 0.205208 -0.237517 0.017522 0.259800 \\n\",\n \"AGE_2 -0.266649 -0.178391 -0.242422 -0.011977 -0.125708 \\n\",\n \"COUNTRY_IND -0.179278 -0.147490 -0.168224 -0.168609 -0.187669 \\n\",\n \"PLAYING ROLE_Batsman 0.384422 -0.276450 0.367995 0.083485 -0.358824 \\n\",\n \"CAPTAINCY EXP_1 0.690647 0.088782 0.714058 0.291373 0.077536 \\n\",\n \"\\n\",\n \" ODI-SR-BL RUNS-S HS AVE SR-B \\\\\\n\",\n \"T-RUNS 0.067700 0.410143 0.411209 0.374046 0.114298 \\n\",\n \"T-WKTS 0.060641 -0.218544 -0.268432 -0.265540 -0.147752 \\n\",\n \"ODI-RUNS-S 0.126795 0.523955 0.495765 0.446280 0.194111 \\n\",\n \"ODI-SR-B 0.284584 0.306338 0.360086 0.340327 0.375371 \\n\",\n \"ODI-WKTS 0.124361 -0.198101 -0.209109 -0.222062 -0.030404 \\n\",\n \"ODI-SR-BL 1.000000 -0.027772 -0.067615 -0.053766 -0.051684 \\n\",\n \"RUNS-S -0.027772 1.000000 0.834631 0.767023 0.380022 \\n\",\n \"HS -0.067615 0.834631 1.000000 0.876249 0.531028 \\n\",\n \"AVE -0.053766 0.767023 0.876249 1.000000 0.583579 \\n\",\n \"SR-B -0.051684 0.380022 0.531028 0.583579 1.000000 \\n\",\n \"SIXERS -0.033616 0.866213 0.788439 0.705365 0.425394 \\n\",\n \"RUNS-C 0.196887 -0.160762 -0.235327 -0.278482 -0.063179 \\n\",\n \"WKTS 0.151594 -0.220959 -0.291494 -0.343958 -0.069823 \\n\",\n \"AVE-BL 0.416627 -0.101818 -0.176049 -0.120430 -0.057895 \\n\",\n \"ECON 0.313295 -0.179459 -0.254494 -0.202301 -0.076228 \\n\",\n \"SR-BL 0.402495 -0.118567 -0.192990 -0.140809 -0.068248 \\n\",\n \"AGE_2 -0.133908 -0.085762 -0.022883 0.031758 0.181181 \\n\",\n \"COUNTRY_IND -0.022494 0.209292 -0.034029 -0.037809 -0.049031 \\n\",\n \"PLAYING ROLE_Batsman -0.014689 0.418555 0.408826 0.432485 0.157903 \\n\",\n \"CAPTAINCY EXP_1 0.103315 0.352422 0.392749 0.378271 0.176919 \\n\",\n \"\\n\",\n \" SIXERS RUNS-C WKTS AVE-BL ECON \\\\\\n\",\n \"T-RUNS 0.216571 -0.253083 -0.277157 -0.298999 -0.329022 \\n\",\n \"T-WKTS -0.198036 0.297302 0.289735 0.162456 0.117530 \\n\",\n \"ODI-RUNS-S 0.376012 -0.268950 -0.302745 -0.225109 -0.249795 \\n\",\n \"ODI-SR-B 0.320041 0.004855 -0.022502 0.018958 -0.019067 \\n\",\n \"ODI-WKTS -0.148722 0.327122 0.302466 0.217302 0.230221 \\n\",\n \"ODI-SR-BL -0.033616 0.196887 0.151594 0.416627 0.313295 \\n\",\n \"RUNS-S 0.866213 -0.160762 -0.220959 -0.101818 -0.179459 \\n\",\n \"HS 0.788439 -0.235327 -0.291494 -0.176049 -0.254494 \\n\",\n \"AVE 0.705365 -0.278482 -0.343958 -0.120430 -0.202301 \\n\",\n \"SR-B 0.425394 -0.063179 -0.069823 -0.057895 -0.076228 \\n\",\n \"SIXERS 1.000000 -0.080295 -0.144558 -0.012210 -0.101027 \\n\",\n \"RUNS-C -0.080295 1.000000 0.959210 0.409505 0.410502 \\n\",\n \"WKTS -0.144558 0.959210 1.000000 0.297822 0.374875 \\n\",\n \"AVE-BL -0.012210 0.409505 0.297822 1.000000 0.722758 \\n\",\n \"ECON -0.101027 0.410502 0.374875 0.722758 1.000000 \\n\",\n \"SR-BL -0.021412 0.471224 0.368264 0.983415 0.726480 \\n\",\n \"AGE_2 -0.043516 0.025722 0.044467 -0.072386 0.004942 \\n\",\n \"COUNTRY_IND 0.132099 0.266465 0.216827 0.149050 0.065552 \\n\",\n \"PLAYING ROLE_Batsman 0.294896 -0.436411 -0.422958 -0.366341 -0.288471 \\n\",\n \"CAPTAINCY EXP_1 0.254991 -0.277903 -0.300074 -0.183775 -0.248974 \\n\",\n \"\\n\",\n \" SR-BL AGE_2 COUNTRY_IND PLAYING ROLE_Batsman \\\\\\n\",\n \"T-RUNS -0.309105 -0.266649 -0.179278 0.384422 \\n\",\n \"T-WKTS 0.205208 -0.178391 -0.147490 -0.276450 \\n\",\n \"ODI-RUNS-S -0.237517 -0.242422 -0.168224 0.367995 \\n\",\n \"ODI-SR-B 0.017522 -0.011977 -0.168609 0.083485 \\n\",\n \"ODI-WKTS 0.259800 -0.125708 -0.187669 -0.358824 \\n\",\n \"ODI-SR-BL 0.402495 -0.133908 -0.022494 -0.014689 \\n\",\n \"RUNS-S -0.118567 -0.085762 0.209292 0.418555 \\n\",\n \"HS -0.192990 -0.022883 -0.034029 0.408826 \\n\",\n \"AVE -0.140809 0.031758 -0.037809 0.432485 \\n\",\n \"SR-B -0.068248 0.181181 -0.049031 0.157903 \\n\",\n \"SIXERS -0.021412 -0.043516 0.132099 0.294896 \\n\",\n \"RUNS-C 0.471224 0.025722 0.266465 -0.436411 \\n\",\n \"WKTS 0.368264 0.044467 0.216827 -0.422958 \\n\",\n \"AVE-BL 0.983415 -0.072386 0.149050 -0.366341 \\n\",\n \"ECON 0.726480 0.004942 0.065552 -0.288471 \\n\",\n \"SR-BL 1.000000 -0.090249 0.126109 -0.395188 \\n\",\n \"AGE_2 -0.090249 1.000000 -0.002036 -0.170277 \\n\",\n \"COUNTRY_IND 0.126109 -0.002036 1.000000 0.071734 \\n\",\n \"PLAYING ROLE_Batsman -0.395188 -0.170277 0.071734 1.000000 \\n\",\n \"CAPTAINCY EXP_1 -0.198688 -0.109261 -0.192539 0.314284 \\n\",\n \"\\n\",\n \" CAPTAINCY EXP_1 \\n\",\n \"T-RUNS 0.690647 \\n\",\n \"T-WKTS 0.088782 \\n\",\n \"ODI-RUNS-S 0.714058 \\n\",\n \"ODI-SR-B 0.291373 \\n\",\n \"ODI-WKTS 0.077536 \\n\",\n \"ODI-SR-BL 0.103315 \\n\",\n \"RUNS-S 0.352422 \\n\",\n \"HS 0.392749 \\n\",\n \"AVE 0.378271 \\n\",\n \"SR-B 0.176919 \\n\",\n \"SIXERS 0.254991 \\n\",\n \"RUNS-C -0.277903 \\n\",\n \"WKTS -0.300074 \\n\",\n \"AVE-BL -0.183775 \\n\",\n \"ECON -0.248974 \\n\",\n \"SR-BL -0.198688 \\n\",\n \"AGE_2 -0.109261 \\n\",\n \"COUNTRY_IND -0.192539 \\n\",\n \"PLAYING ROLE_Batsman 0.314284 \\n\",\n \"CAPTAINCY EXP_1 1.000000 \"\n ]\n },\n \"execution_count\": 16,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"x[columns_with_large_vif].corr()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"id\": \"8eb9de04\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Text(0.5, 1.0, 'Correlation between Features')\"\n ]\n },\n \"execution_count\": 17,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# correlation matrix\\n\",\n \"\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"import seaborn as sns\\n\",\n \"\\n\",\n \"plt.figure(figsize=(12,10))\\n\",\n \"\\n\",\n \"sns.heatmap(x[columns_with_large_vif].corr(), annot=True)\\n\",\n \"\\n\",\n \"plt.title(\\\"Correlation between Features\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"id\": \"b369ed31\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"['ODI-WKTS',\\n\",\n \" 'COUNTRY_IND',\\n\",\n \" 'COUNTRY_PAK',\\n\",\n \" 'PLAYING ROLE_Batsman',\\n\",\n \" 'COUNTRY_ENG',\\n\",\n \" 'COUNTRY_NZ',\\n\",\n \" 'COUNTRY_SL',\\n\",\n \" 'COUNTRY_SA',\\n\",\n \" 'PLAYING ROLE_Bowler',\\n\",\n \" 'SIXERS',\\n\",\n \" 'COUNTRY_ZIM',\\n\",\n \" 'WKTS',\\n\",\n \" 'PLAYING ROLE_W. Keeper',\\n\",\n \" 'AGE_3',\\n\",\n \" 'CAPTAINCY EXP_1',\\n\",\n \" 'ODI-SR-BL',\\n\",\n \" 'COUNTRY_WI',\\n\",\n \" 'COUNTRY_BAN']\"\n ]\n },\n \"execution_count\": 19,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"columns_to_be_removed = [\\n\",\n \" 'T-RUNS', 'T-WKTS', 'RUNS-S', 'HS', 'AVE', 'RUNS-C', 'SR-B',\\n\",\n \" 'AVE-BL', 'ECON', 'ODI-SR-B', 'ODI-RUNS-S', 'AGE_2', 'SR-BL'\\n\",\n \"]\\n\",\n \"\\n\",\n \"xnewfeatures = list( set(xfeatures) - set(columns_to_be_removed) )\\n\",\n \"xnewfeatures\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"id\": \"a03c4574\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
ColumnVIF
0ODI-WKTS2.742889
1COUNTRY_IND3.144668
2COUNTRY_PAK1.334773
3PLAYING ROLE_Batsman2.680207
4COUNTRY_ENG1.131869
5COUNTRY_NZ1.173418
6COUNTRY_SL1.519752
7COUNTRY_SA1.416657
8PLAYING ROLE_Bowler3.060168
9SIXERS2.397409
10COUNTRY_ZIM1.205305
11WKTS2.883101
12PLAYING ROLE_W. Keeper1.900941
13AGE_31.779861
14CAPTAINCY EXP_12.458745
15ODI-SR-BL2.822148
16COUNTRY_WI1.194093
17COUNTRY_BAN1.094293
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" Column VIF\\n\",\n \"0 ODI-WKTS 2.742889\\n\",\n \"1 COUNTRY_IND 3.144668\\n\",\n \"2 COUNTRY_PAK 1.334773\\n\",\n \"3 PLAYING ROLE_Batsman 2.680207\\n\",\n \"4 COUNTRY_ENG 1.131869\\n\",\n \"5 COUNTRY_NZ 1.173418\\n\",\n \"6 COUNTRY_SL 1.519752\\n\",\n \"7 COUNTRY_SA 1.416657\\n\",\n \"8 PLAYING ROLE_Bowler 3.060168\\n\",\n \"9 SIXERS 2.397409\\n\",\n \"10 COUNTRY_ZIM 1.205305\\n\",\n \"11 WKTS 2.883101\\n\",\n \"12 PLAYING ROLE_W. Keeper 1.900941\\n\",\n \"13 AGE_3 1.779861\\n\",\n \"14 CAPTAINCY EXP_1 2.458745\\n\",\n \"15 ODI-SR-BL 2.822148\\n\",\n \"16 COUNTRY_WI 1.194093\\n\",\n \"17 COUNTRY_BAN 1.094293\"\n ]\n },\n \"execution_count\": 20,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"get_vif_score(x[xnewfeatures])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"id\": \"d36ca194\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"C:\\\\Users\\\\himan\\\\anaconda3\\\\lib\\\\site-packages\\\\statsmodels\\\\regression\\\\linear_model.py:1918: RuntimeWarning: divide by zero encountered in double_scalars\\n\",\n \" return np.sqrt(eigvals[0]/eigvals[-1])\\n\"\n ]\n },\n {\n \"data\": {\n \"text/html\": [\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"
Model: OLS Adj. R-squared (uncentered): 0.746
Dependent Variable: SOLD PRICE AIC: 2953.6299
Date: 2023-04-24 21:26 BIC: 2998.5846
No. Observations: 104 Log-Likelihood: -1459.8
Df Model: 17 F-statistic: 19.01
Df Residuals: 87 Prob (F-statistic): 2.46e-22
R-squared (uncentered): 0.788 Scale: 1.0893e+11
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"
Coef. Std.Err. t P>|t| [0.025 0.975]
ODI-WKTS 1000.2719 383.6520 2.6072 0.0107 237.7220 1762.8218
COUNTRY_IND 241916.0736 90265.6898 2.6800 0.0088 62503.2409 421328.9062
COUNTRY_PAK 60298.7726 141684.1935 0.4256 0.6715 -221313.9038 341911.4489
PLAYING ROLE_Batsman 177457.3060 100597.5501 1.7640 0.0812 -22491.2177 377405.8296
COUNTRY_ENG 678083.6712 208244.3343 3.2562 0.0016 264175.5079 1091991.8346
COUNTRY_NZ 147178.4532 150707.9524 0.9766 0.3315 -152369.9212 446726.8275
COUNTRY_SL -24754.1190 143387.0044 -0.1726 0.8633 -309751.3164 260243.0784
COUNTRY_SA 81761.5043 118244.2325 0.6915 0.4911 -153261.7115 316784.7202
PLAYING ROLE_Bowler 70814.4129 98253.6432 0.7207 0.4730 -124475.3419 266104.1678
SIXERS 6898.3644 1658.7173 4.1589 0.0001 3601.4841 10195.2446
COUNTRY_ZIM -138202.6253 374302.3654 -0.3692 0.7129 -882169.1065 605763.8559
WKTS 1815.9081 1930.2590 0.9408 0.3494 -2020.6907 5652.5069
PLAYING ROLE_W. Keeper 28857.7846 150328.1606 0.1920 0.8482 -269935.7123 327651.2816
AGE_3 -56497.7482 101704.1167 -0.5555 0.5800 -258645.6927 145650.1963
CAPTAINCY EXP_1 198466.1639 93102.8927 2.1317 0.0359 13414.0834 383518.2445
ODI-SR-BL 806.6222 1262.5348 0.6389 0.5246 -1702.8024 3316.0468
COUNTRY_WI -204026.7967 164456.0856 -1.2406 0.2181 -530901.0743 122847.4810
COUNTRY_BAN 0.0000 0.0000 nan nan 0.0000 0.0000
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"
Omnibus: 16.784 Durbin-Watson: 2.074
Prob(Omnibus): 0.000 Jarque-Bera (JB): 19.711
Skew: 0.911 Prob(JB): 0.000
Kurtosis: 4.107 Condition No.: inf
\"\n ],\n \"text/plain\": [\n \"\\n\",\n \"\\\"\\\"\\\"\\n\",\n \" Results: Ordinary least squares\\n\",\n \"========================================================================================\\n\",\n \"Model: OLS Adj. R-squared (uncentered): 0.746 \\n\",\n \"Dependent Variable: SOLD PRICE AIC: 2953.6299 \\n\",\n \"Date: 2023-04-24 21:26 BIC: 2998.5846 \\n\",\n \"No. Observations: 104 Log-Likelihood: -1459.8 \\n\",\n \"Df Model: 17 F-statistic: 19.01 \\n\",\n \"Df Residuals: 87 Prob (F-statistic): 2.46e-22 \\n\",\n \"R-squared (uncentered): 0.788 Scale: 1.0893e+11\\n\",\n \"----------------------------------------------------------------------------------------\\n\",\n \" Coef. Std.Err. t P>|t| [0.025 0.975] \\n\",\n \"----------------------------------------------------------------------------------------\\n\",\n \"ODI-WKTS 1000.2719 383.6520 2.6072 0.0107 237.7220 1762.8218\\n\",\n \"COUNTRY_IND 241916.0736 90265.6898 2.6800 0.0088 62503.2409 421328.9062\\n\",\n \"COUNTRY_PAK 60298.7726 141684.1935 0.4256 0.6715 -221313.9038 341911.4489\\n\",\n \"PLAYING ROLE_Batsman 177457.3060 100597.5501 1.7640 0.0812 -22491.2177 377405.8296\\n\",\n \"COUNTRY_ENG 678083.6712 208244.3343 3.2562 0.0016 264175.5079 1091991.8346\\n\",\n \"COUNTRY_NZ 147178.4532 150707.9524 0.9766 0.3315 -152369.9212 446726.8275\\n\",\n \"COUNTRY_SL -24754.1190 143387.0044 -0.1726 0.8633 -309751.3164 260243.0784\\n\",\n \"COUNTRY_SA 81761.5043 118244.2325 0.6915 0.4911 -153261.7115 316784.7202\\n\",\n \"PLAYING ROLE_Bowler 70814.4129 98253.6432 0.7207 0.4730 -124475.3419 266104.1678\\n\",\n \"SIXERS 6898.3644 1658.7173 4.1589 0.0001 3601.4841 10195.2446\\n\",\n \"COUNTRY_ZIM -138202.6253 374302.3654 -0.3692 0.7129 -882169.1065 605763.8559\\n\",\n \"WKTS 1815.9081 1930.2590 0.9408 0.3494 -2020.6907 5652.5069\\n\",\n \"PLAYING ROLE_W. Keeper 28857.7846 150328.1606 0.1920 0.8482 -269935.7123 327651.2816\\n\",\n \"AGE_3 -56497.7482 101704.1167 -0.5555 0.5800 -258645.6927 145650.1963\\n\",\n \"CAPTAINCY EXP_1 198466.1639 93102.8927 2.1317 0.0359 13414.0834 383518.2445\\n\",\n \"ODI-SR-BL 806.6222 1262.5348 0.6389 0.5246 -1702.8024 3316.0468\\n\",\n \"COUNTRY_WI -204026.7967 164456.0856 -1.2406 0.2181 -530901.0743 122847.4810\\n\",\n \"COUNTRY_BAN 0.0000 0.0000 nan nan 0.0000 0.0000\\n\",\n \"----------------------------------------------------------------------------------------\\n\",\n \"Omnibus: 16.784 Durbin-Watson: 2.074 \\n\",\n \"Prob(Omnibus): 0.000 Jarque-Bera (JB): 19.711\\n\",\n \"Skew: 0.911 Prob(JB): 0.000 \\n\",\n \"Kurtosis: 4.107 Condition No.: inf \\n\",\n \"========================================================================================\\n\",\n \"* The condition number is large (inf). This might indicate strong\\n\",\n \"multicollinearity or other numerical problems.\\n\",\n \"\\\"\\\"\\\"\"\n ]\n },\n \"execution_count\": 21,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"xtrain = xtrain[xnewfeatures]\\n\",\n \"\\n\",\n \"model2 = sm.OLS(ytrain, xtrain).fit()\\n\",\n \"\\n\",\n \"model2.summary2()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"id\": \"1260dd00\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"
Model: OLS Adj. R-squared (uncentered): 0.690
Dependent Variable: SOLD PRICE AIC: 2963.0980
Date: 2023-04-24 21:31 BIC: 2973.6756
No. Observations: 104 Log-Likelihood: -1477.5
Df Model: 4 F-statistic: 58.80
Df Residuals: 100 Prob (F-statistic): 1.95e-25
R-squared (uncentered): 0.702 Scale: 1.3328e+11
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"
Coef. Std.Err. t P>|t| [0.025 0.975]
COUNTRY_IND 412178.1281 60936.7223 6.7640 0.0000 291281.4066 533074.8497
COUNTRY_ENG 737378.1579 215586.2418 3.4203 0.0009 309661.1943 1165095.1214
SIXERS 7185.4933 1509.6537 4.7597 0.0000 4190.3833 10180.6033
CAPTAINCY EXP_1 375372.1235 74692.9719 5.0255 0.0000 227183.3947 523560.8523
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"
Omnibus: 2.870 Durbin-Watson: 1.830
Prob(Omnibus): 0.238 Jarque-Bera (JB): 2.549
Skew: 0.383 Prob(JB): 0.280
Kurtosis: 3.036 Condition No.: 176
\"\n ],\n \"text/plain\": [\n \"\\n\",\n \"\\\"\\\"\\\"\\n\",\n \" Results: Ordinary least squares\\n\",\n \"================================================================================\\n\",\n \"Model: OLS Adj. R-squared (uncentered): 0.690 \\n\",\n \"Dependent Variable: SOLD PRICE AIC: 2963.0980 \\n\",\n \"Date: 2023-04-24 21:31 BIC: 2973.6756 \\n\",\n \"No. Observations: 104 Log-Likelihood: -1477.5 \\n\",\n \"Df Model: 4 F-statistic: 58.80 \\n\",\n \"Df Residuals: 100 Prob (F-statistic): 1.95e-25 \\n\",\n \"R-squared (uncentered): 0.702 Scale: 1.3328e+11\\n\",\n \"--------------------------------------------------------------------------------\\n\",\n \" Coef. Std.Err. t P>|t| [0.025 0.975] \\n\",\n \"--------------------------------------------------------------------------------\\n\",\n \"COUNTRY_IND 412178.1281 60936.7223 6.7640 0.0000 291281.4066 533074.8497\\n\",\n \"COUNTRY_ENG 737378.1579 215586.2418 3.4203 0.0009 309661.1943 1165095.1214\\n\",\n \"SIXERS 7185.4933 1509.6537 4.7597 0.0000 4190.3833 10180.6033\\n\",\n \"CAPTAINCY EXP_1 375372.1235 74692.9719 5.0255 0.0000 227183.3947 523560.8523\\n\",\n \"--------------------------------------------------------------------------------\\n\",\n \"Omnibus: 2.870 Durbin-Watson: 1.830\\n\",\n \"Prob(Omnibus): 0.238 Jarque-Bera (JB): 2.549\\n\",\n \"Skew: 0.383 Prob(JB): 0.280\\n\",\n \"Kurtosis: 3.036 Condition No.: 176 \\n\",\n \"================================================================================\\n\",\n \"\\n\",\n \"\\\"\\\"\\\"\"\n ]\n },\n \"execution_count\": 22,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"significant_variables = ['COUNTRY_IND','COUNTRY_ENG','SIXERS','CAPTAINCY EXP_1']\\n\",\n \"\\n\",\n \"model2 = sm.OLS(ytrain, xtrain[significant_variables]).fit()\\n\",\n \"\\n\",\n \"model2.summary2()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"56344941\",\n \"metadata\": {},\n \"source\": [\n \"## hypothesis\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"e4f353f4\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"1. Null Hypothesis H0: it is considered ture till it is proven false.\\n\",\n \" \\n\",\n \"2. Alternative Hypothesis: it is the negation of the Null Hypothesis.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"7f705a97\",\n \"metadata\": {},\n \"source\": [\n \"## Select a test\\n\",\n \"\\n\",\n \"it is used to accept or reject the alternative hypothesis.\\n\",\n \"\\n\",\n \"Result: Test Statistics\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"72ffcc59\",\n \"metadata\": {},\n \"source\": [\n \"## p-value\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"1dad8cfb\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"you have to convert this test statistics into p-value(0-1)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"af38612c\",\n \"metadata\": {},\n \"source\": [\n \"## significance level\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"b3f51f20\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"5% or 0.05\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"e4a4bde6\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"a large p-value indicates that the results are due to luck.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"6dd95570\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.12\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n" }, { "path": "March Cohort 2023/12_15_16_17_18_Statistics/.ipynb_checkpoints/18_Hypothesis Testing-checkpoint.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"id\": \"c585a495\",\n \"metadata\": {},\n \"source\": [\n \"# Hypothesis Testing\\n\",\n \"Proposed Explanation\\n\",\n \"\\n\",\n \"1. Null Hypothesis H0: It is considered true till it proven false.\\n\",\n \"2. Alternative Hypothesis: It is the negation of the null hypothesis.\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"8c03609b\",\n \"metadata\": {},\n \"source\": [\n \"Select a test: and apply it -> test statistics\\n\",\n \"\\n\",\n \"we need to convert this test statistics to a p-value\\n\",\n \"\\n\",\n \"#### significance level\\n\",\n \"threshold for luck -> 5%\\n\",\n \"\\n\",\n \"p-value < 0.05 -> we will reject the null hypothesis and accept the alternative hypothesis.\\n\",\n \"\\n\",\n \"#### a large p-value indicates that the results are due to luck.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"id\": \"1262f0c8\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Lady tasting Tea\\n\",\n \"\\n\",\n \"#is it possible for you to tell weather the tea added before or after the milk?\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"4ebc5491\",\n \"metadata\": {},\n \"source\": [\n \"H0: the lady cannot tell if the milk was poured first.\\n\",\n \" (NH possess no relationship and no difference)\\n\",\n \"H1: the lady can tell if the milk was poured first.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"c46c9066\",\n \"metadata\": {},\n \"source\": [\n \"#### select a test\\n\",\n \"\\n\",\n \"8 cups of tea, 4 of each type\\n\",\n \"\\n\",\n \"Lady got all 8 correct -> test statistics\\n\",\n \"\\n\",\n \"8C4 -> 70 is the number of combinations\\n\",\n \"\\n\",\n \"p-value = 1/70 = 1.4%\\n\",\n \"\\n\",\n \"1.4% < 5% => Reject the H0\\n\",\n \"\\n\",\n \"\\n\",\n \"### Errors in Hypothesis Testing\\n\",\n \"\\n\",\n \"Type I error\\n\",\n \"When you reject the null hypothesis when it was actually true.\\n\",\n \"\\n\",\n \"Type II error\\n\",\n \"When we don't reject the null hypothesis and accept the alternative hypothesis.\\n\",\n \"\\n\",\n \"\\n\",\n \"### Power (Recall)\\n\",\n \"power of statistical test\\n\",\n \"\\n\",\n \"probability of rejecting H0 when H1 is true.\\n\",\n \"\\n\",\n \"If the value of power is high than it is good.\\n\",\n \"\\n\",\n \"if it is high that means the low probability of Type-II error\\n\",\n \"\\n\",\n \"\\n\",\n \"### alpha\\n\",\n \"\\n\",\n \"alpha is the probability of rejecting H0 when H0 is true.\\n\",\n \"\\n\",\n \"alpha = probability of commiting a Type-I error.\\n\",\n \"\\n\",\n \"if the test has high alpha value that is not good.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"0cf026df\",\n \"metadata\": {},\n \"source\": [\n \"# Chi-Square Test\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"id\": \"a36e5cfa\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import scipy.stats as stats\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"id\": \"08662308\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import seaborn as sns\\n\",\n \"import pandas as pd\\n\",\n \"import numpy as np\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"id\": \"8316d566\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
total_billtipsexsmokerdaytimesize
016.991.01FemaleNoSunDinner2
110.341.66MaleNoSunDinner3
221.013.50MaleNoSunDinner3
323.683.31MaleNoSunDinner2
424.593.61FemaleNoSunDinner4
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" total_bill tip sex smoker day time size\\n\",\n \"0 16.99 1.01 Female No Sun Dinner 2\\n\",\n \"1 10.34 1.66 Male No Sun Dinner 3\\n\",\n \"2 21.01 3.50 Male No Sun Dinner 3\\n\",\n \"3 23.68 3.31 Male No Sun Dinner 2\\n\",\n \"4 24.59 3.61 Female No Sun Dinner 4\"\n ]\n },\n \"execution_count\": 8,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"dataset = sns.load_dataset('tips')\\n\",\n \"\\n\",\n \"dataset.head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"id\": \"4dd3d2da\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"smoker Yes No\\n\",\n \"sex \\n\",\n \"Male 60 97\\n\",\n \"Female 33 54\\n\"\n ]\n }\n ],\n \"source\": [\n \"dataset_table = pd.crosstab(dataset['sex'], dataset['smoker'])\\n\",\n \"\\n\",\n \"print(dataset_table)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"id\": \"2464bd7a\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[60, 97],\\n\",\n \" [33, 54]], dtype=int64)\"\n ]\n },\n \"execution_count\": 13,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"observed_values = dataset_table.values\\n\",\n \"observed_values\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"id\": \"3a55191e\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(0.0,\\n\",\n \" 1.0,\\n\",\n \" 1,\\n\",\n \" array([[59.84016393, 97.15983607],\\n\",\n \" [33.15983607, 53.84016393]]))\"\n ]\n },\n \"execution_count\": 11,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"val = stats.chi2_contingency(dataset_table)\\n\",\n \"\\n\",\n \"val\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"id\": \"5c52c6d3\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"expected_values = val[3]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"id\": \"55db1242\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from scipy.stats import chi2\\n\",\n \"\\n\",\n \"chi_square = sum([(o-e)**2./e for o, e in zip(observed_values, expected_values)])\\n\",\n \"\\n\",\n \"chi_square_stats = chi_square[0] + chi_square[1]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"id\": \"f1f1261b\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"0.001934818536627623\"\n ]\n },\n \"execution_count\": 15,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"chi_square_stats\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"id\": \"9ae6c359\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Degree of Freedom: 1\\n\"\n ]\n }\n ],\n \"source\": [\n \"no_of_rows = len(dataset_table.iloc[0:2,0])\\n\",\n \"no_of_columns = len(dataset_table.iloc[0,0:2])\\n\",\n \"ddof = (no_of_rows - 1) * (no_of_columns - 1)\\n\",\n \"\\n\",\n \"print(\\\"Degree of Freedom:\\\",ddof)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"id\": \"5d7188ed\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"alpha = 0.05\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"id\": \"6aa1816a\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Critical Value: 3.841458820694124\\n\"\n ]\n }\n ],\n \"source\": [\n \"critical_value = chi2.ppf(q=1-alpha, df=ddof)\\n\",\n \"\\n\",\n \"print(\\\"Critical Value:\\\", critical_value)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"id\": \"0dc7e08d\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"p-value: 0.964915107315732\\n\",\n \"Significance Level: 0.05\\n\",\n \"Degree of Freedom: 1\\n\"\n ]\n }\n ],\n \"source\": [\n \"# p-value\\n\",\n \"\\n\",\n \"p_value = 1 - chi2.cdf(x=chi_square_stats, df=ddof)\\n\",\n \"\\n\",\n \"print(\\\"p-value:\\\", p_value)\\n\",\n \"print(\\\"Significance Level:\\\", alpha)\\n\",\n \"print(\\\"Degree of Freedom:\\\", ddof)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"id\": \"cbcc9460\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Accepting H0, there is no relationship between 2 categorical variables\\n\"\n ]\n }\n ],\n \"source\": [\n \"if chi_square_stats >= critical_value:\\n\",\n \" print(\\\"Reject H0, there is a relationship between 2 categorical variables\\\")\\n\",\n \"else:\\n\",\n \" print(\\\"Accepting H0, there is no relationship between 2 categorical variables\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"id\": \"822ee03e\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Accepting H0, there is no relationship between 2 categorical variables\\n\"\n ]\n }\n ],\n \"source\": [\n \"if p_value <= alpha:\\n\",\n \" print(\\\"Reject H0, there is a relationship between 2 categorical variables\\\")\\n\",\n \"else:\\n\",\n \" print(\\\"Accepting H0, there is no relationship between 2 categorical variables\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"d4ee4b89\",\n \"metadata\": {},\n \"source\": [\n \"# t-test\\n\",\n \"\\n\",\n \"if there is a significant difference between the means of two groups\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"id\": \"497d7ce4\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"ages = [10,20,35,50,28,40,55,18,16,55,30,25,43,18,30,28,14,24,16,17,32,3,26,27,66,60]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"id\": \"c1d8bb90\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"26\"\n ]\n },\n \"execution_count\": 26,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"len(ages)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"id\": \"adcb366f\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"ages_mean = np.mean(ages)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 28,\n \"id\": \"3e10989c\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"30.23076923076923\"\n ]\n },\n \"execution_count\": 28,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"ages_mean\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 29,\n \"id\": \"320ad6c6\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([60, 18, 66, 32, 17, 55, 43, 17, 16, 55])\"\n ]\n },\n \"execution_count\": 29,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"sample_size = 10\\n\",\n \"\\n\",\n \"age_sample = np.random.choice(ages, sample_size)\\n\",\n \"\\n\",\n \"age_sample\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 30,\n \"id\": \"19a1f903\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"0.24751746592331406\\n\"\n ]\n }\n ],\n \"source\": [\n \"from scipy.stats import ttest_1samp\\n\",\n \"\\n\",\n \"ttest, p_value = ttest_1samp(age_sample, 30)\\n\",\n \"\\n\",\n \"print(p_value)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 31,\n \"id\": \"33a09441\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"We are accepting the Null Hypothesis\\n\"\n ]\n }\n ],\n \"source\": [\n \"if p_value < 0.05:\\n\",\n \" print(\\\"We are rejecting null hypothesis\\\")\\n\",\n \"else:\\n\",\n \" print(\\\"We are accepting the Null Hypothesis\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"d11fd400\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.12\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n" }, { "path": "March Cohort 2023/12_15_16_17_18_Statistics/12_15_Statistics.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"id\": \"df89b0c5\",\n \"metadata\": {},\n \"source\": [\n \"# Statistics\\n\",\n \"\\n\",\n \"Data Science = Software Development + Statistics\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"id\": \"231d4e70\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"ename\": \"IndentationError\",\n \"evalue\": \"unindent does not match any outer indentation level (, line 3)\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[1;36m File \\u001b[1;32m:3\\u001b[1;36m\\u001b[0m\\n\\u001b[1;33m Univariate Hypothesis Testing\\u001b[0m\\n\\u001b[1;37m ^\\u001b[0m\\n\\u001b[1;31mIndentationError\\u001b[0m\\u001b[1;31m:\\u001b[0m unindent does not match any outer indentation level\\n\"\n ]\n }\n ],\n \"source\": [\n \" Stats\\n\",\n \" Descriptive Inferential\\n\",\n \" Univariate Hypothesis Testing\\n\",\n \" Bivariate Model Fitting\\n\",\n \" Multivariate\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"id\": \"fa6fd519\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"ename\": \"SyntaxError\",\n \"evalue\": \"invalid syntax (117687496.py, line 2)\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[1;36m Input \\u001b[1;32mIn [4]\\u001b[1;36m\\u001b[0m\\n\\u001b[1;33m 1. Measure of Frequency - count\\u001b[0m\\n\\u001b[1;37m ^\\u001b[0m\\n\\u001b[1;31mSyntaxError\\u001b[0m\\u001b[1;31m:\\u001b[0m invalid syntax\\n\"\n ]\n }\n ],\n \"source\": [\n \"- Univariate\\n\",\n \"1. Measure of Frequency - count\\n\",\n \"2. Central Tendency - mean, median, mode\\n\",\n \" - Geometric mean } ratio or rates\\n\",\n \" - Harmonic mean\\n\",\n \"3. Despersion\\n\",\n \" - range\\n\",\n \" - inter quartile range\\n\",\n \" - variance\\n\",\n \" - standard deviation\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"id\": \"94473ea3\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"ename\": \"IndentationError\",\n \"evalue\": \"unexpected indent (880973301.py, line 2)\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[1;36m Input \\u001b[1;32mIn [5]\\u001b[1;36m\\u001b[0m\\n\\u001b[1;33m Qualitative Quantitative\\u001b[0m\\n\\u001b[1;37m ^\\u001b[0m\\n\\u001b[1;31mIndentationError\\u001b[0m\\u001b[1;31m:\\u001b[0m unexpected indent\\n\"\n ]\n }\n ],\n \"source\": [\n \" Data\\n\",\n \" Qualitative Quantitative\\n\",\n \" \"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"e7ee5068\",\n \"metadata\": {},\n \"source\": [\n \"# Central Tendancy\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"id\": \"acd1670e\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import statistics\\n\",\n \"\\n\",\n \"import numpy as np\\n\",\n \"import pandas as pd\\n\",\n \"\\n\",\n \"import seaborn as sns\\n\",\n \"import matplotlib.pyplot as plt\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"id\": \"27b667c2\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
RankCompanyCountrySalesProfitsAssetsMarket ValueSectorIndustry
01ICBCChina151.442.03473.2229.8FinancialsMajor Banks
12China Construction BankChina134.235.03016.6200.5FinancialsRegional Banks
23Berkshire HathawayUnited States222.924.1620.9409.9FinancialsInvestment Services
34JPMorgan ChaseUnited States102.524.22513.0306.6FinancialsMajor Banks
45Wells FargoUnited States97.621.91943.4274.4FinancialsMajor Banks
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" Rank Company Country Sales Profits Assets \\\\\\n\",\n \"0 1 ICBC China 151.4 42.0 3473.2 \\n\",\n \"1 2 China Construction Bank China 134.2 35.0 3016.6 \\n\",\n \"2 3 Berkshire Hathaway United States 222.9 24.1 620.9 \\n\",\n \"3 4 JPMorgan Chase United States 102.5 24.2 2513.0 \\n\",\n \"4 5 Wells Fargo United States 97.6 21.9 1943.4 \\n\",\n \"\\n\",\n \" Market Value Sector Industry \\n\",\n \"0 229.8 Financials Major Banks \\n\",\n \"1 200.5 Financials Regional Banks \\n\",\n \"2 409.9 Financials Investment Services \\n\",\n \"3 306.6 Financials Major Banks \\n\",\n \"4 274.4 Financials Major Banks \"\n ]\n },\n \"execution_count\": 7,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df = pd.read_csv('datasets/forbes.csv')\\n\",\n \"\\n\",\n \"df.head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"id\": \"40528971\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
RankCompanyCountrySalesProfitsAssetsMarket ValueSectorIndustry
0FalseFalseFalseFalseFalseFalseFalseFalseFalse
1FalseFalseFalseFalseFalseFalseFalseFalseFalse
2FalseFalseFalseFalseFalseFalseFalseFalseFalse
3FalseFalseFalseFalseFalseFalseFalseFalseFalse
4FalseFalseFalseFalseFalseFalseFalseFalseFalse
..............................
1995FalseFalseFalseFalseFalseFalseFalseFalseFalse
1996FalseFalseFalseFalseFalseFalseFalseFalseTrue
1997FalseFalseFalseFalseFalseFalseFalseFalseFalse
1998FalseFalseFalseFalseFalseFalseFalseFalseFalse
1999FalseFalseFalseFalseFalseFalseFalseFalseTrue
\\n\",\n \"

2000 rows × 9 columns

\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" Rank Company Country Sales Profits Assets Market Value Sector \\\\\\n\",\n \"0 False False False False False False False False \\n\",\n \"1 False False False False False False False False \\n\",\n \"2 False False False False False False False False \\n\",\n \"3 False False False False False False False False \\n\",\n \"4 False False False False False False False False \\n\",\n \"... ... ... ... ... ... ... ... ... \\n\",\n \"1995 False False False False False False False False \\n\",\n \"1996 False False False False False False False False \\n\",\n \"1997 False False False False False False False False \\n\",\n \"1998 False False False False False False False False \\n\",\n \"1999 False False False False False False False False \\n\",\n \"\\n\",\n \" Industry \\n\",\n \"0 False \\n\",\n \"1 False \\n\",\n \"2 False \\n\",\n \"3 False \\n\",\n \"4 False \\n\",\n \"... ... \\n\",\n \"1995 False \\n\",\n \"1996 True \\n\",\n \"1997 False \\n\",\n \"1998 False \\n\",\n \"1999 True \\n\",\n \"\\n\",\n \"[2000 rows x 9 columns]\"\n ]\n },\n \"execution_count\": 8,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df.isnull()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"id\": \"db69da00\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Rank 0\\n\",\n \"Company 0\\n\",\n \"Country 0\\n\",\n \"Sales 0\\n\",\n \"Profits 0\\n\",\n \"Assets 0\\n\",\n \"Market Value 0\\n\",\n \"Sector 197\\n\",\n \"Industry 491\\n\",\n \"dtype: int64\"\n ]\n },\n \"execution_count\": 9,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df.isnull().sum()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"id\": \"2a980414\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Rank 0\\n\",\n \"Company 0\\n\",\n \"Country 0\\n\",\n \"Sales 0\\n\",\n \"Profits 0\\n\",\n \"Assets 0\\n\",\n \"Market Value 0\\n\",\n \"Sector 0\\n\",\n \"Industry 0\\n\",\n \"dtype: int64\"\n ]\n },\n \"execution_count\": 10,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df.dropna(inplace=True)\\n\",\n \"\\n\",\n \"df.isnull().sum()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"id\": \"b2de3fb4\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(1508, 9)\"\n ]\n },\n \"execution_count\": 11,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df.shape\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"id\": \"9aa68486\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"26.978494031830294\"\n ]\n },\n \"execution_count\": 12,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"market_value_mean = df['Market Value'].mean()\\n\",\n \"\\n\",\n \"market_value_mean\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"11d37231\",\n \"metadata\": {},\n \"source\": [\n \"## Mean Key Points\\n\",\n \"\\n\",\n \"- best represents the data\\n\",\n \"- mean consider all the data points\\n\",\n \"- extremly sensitive to the presence of outliers\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"id\": \"88065b85\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"13.1\"\n ]\n },\n \"execution_count\": 13,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"market_value_median = df['Market Value'].median()\\n\",\n \"\\n\",\n \"market_value_median\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"7594c091\",\n \"metadata\": {},\n \"source\": [\n \"## Median Key points\\n\",\n \"\\n\",\n \"- It is less sensitive to outliers\\n\",\n \"- 50% of the data is either side of it.\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"id\": \"585fdb6d\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"0.24\"\n ]\n },\n \"execution_count\": 14,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df['Market Value'].min()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"id\": \"da70e1e2\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"752.0\"\n ]\n },\n \"execution_count\": 15,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df['Market Value'].max()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"id\": \"6f4c63c0\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"751.76\"\n ]\n },\n \"execution_count\": 16,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"range1 = df['Market Value'].max() - df['Market Value'].min()\\n\",\n \"\\n\",\n \"range1\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"id\": \"434e6e34\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
RankSalesProfitsAssetsMarket Value
count1508.0000001508.0000001508.0000001508.0000001508.000000
mean909.13527919.6230461.40743097.64701626.978494
std554.04445630.8758093.244201287.03615747.099541
min1.0000000.001000-13.0000001.3000000.240000
25%433.7500004.9000000.34975012.8000007.300000
50%883.50000010.3000000.69250025.90000013.100000
75%1354.25000019.9000001.42500060.32500026.900000
max1999.000000485.30000045.2000003473.200000752.000000
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" Rank Sales Profits Assets Market Value\\n\",\n \"count 1508.000000 1508.000000 1508.000000 1508.000000 1508.000000\\n\",\n \"mean 909.135279 19.623046 1.407430 97.647016 26.978494\\n\",\n \"std 554.044456 30.875809 3.244201 287.036157 47.099541\\n\",\n \"min 1.000000 0.001000 -13.000000 1.300000 0.240000\\n\",\n \"25% 433.750000 4.900000 0.349750 12.800000 7.300000\\n\",\n \"50% 883.500000 10.300000 0.692500 25.900000 13.100000\\n\",\n \"75% 1354.250000 19.900000 1.425000 60.325000 26.900000\\n\",\n \"max 1999.000000 485.300000 45.200000 3473.200000 752.000000\"\n ]\n },\n \"execution_count\": 17,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df.describe()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"id\": \"c4b36bb3\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Financials 474\\n\",\n \"Consumer Discretionary 209\\n\",\n \"Industrials 192\\n\",\n \"Materials 145\\n\",\n \"Consumer Staples 106\\n\",\n \"Information Technology 103\\n\",\n \"Utilities 86\\n\",\n \"Energy 79\\n\",\n \"Health Care 71\\n\",\n \"Telecommunication Services 43\\n\",\n \"Name: Sector, dtype: int64\"\n ]\n },\n \"execution_count\": 18,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# sector column is a categorical variable\\n\",\n \"\\n\",\n \"df['Sector'].value_counts()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"id\": \"b4d8b09e\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"0 Financials\\n\",\n \"Name: Sector, dtype: object\"\n ]\n },\n \"execution_count\": 19,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df['Sector'].mode()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"0f6db83f\",\n \"metadata\": {},\n \"source\": [\n \"## Mode\\n\",\n \"\\n\",\n \"- the value that occured most in the data\\n\",\n \"- vote in an election\\n\",\n \"- mode is not good for continuous data.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"214ca025\",\n \"metadata\": {},\n \"source\": [\n \"# box plot\\n\",\n \"\\n\",\n \"- mean\\n\",\n \"- median\\n\",\n \"- 25th percentile\\n\",\n \"- 75th percentile\\n\",\n \"- maximum values are where in the data\\n\",\n \"- outliers\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"id\": \"ab0791e2\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.figure(figsize=(10,8))\\n\",\n \"\\n\",\n \"sns.boxplot(data=df, y = 'Market Value', showmeans=True)\\n\",\n \"\\n\",\n \"plt.axhline(y=market_value_mean, color='r', linestyle='-')\\n\",\n \"plt.axhline(y=market_value_median, color='g', linestyle='-')\\n\",\n \"\\n\",\n \"plt.title('Forbes Data')\\n\",\n \"\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"id\": \"e11c59db\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.figure(figsize=(10,8))\\n\",\n \"\\n\",\n \"sns.boxplot(data=df, y = 'Market Value', showmeans=True)\\n\",\n \"\\n\",\n \"plt.ylim(0,80)\\n\",\n \"\\n\",\n \"plt.axhline(y=market_value_mean, color='r', linestyle='-')\\n\",\n \"plt.axhline(y=market_value_median, color='g', linestyle='-')\\n\",\n \"\\n\",\n \"plt.title('Forbes Data')\\n\",\n \"\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"3a8efbb2\",\n \"metadata\": {},\n \"source\": [\n \"# mode\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"id\": \"0afbc532\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
SectorRankCompanyCountrySalesProfitsAssetsMarket ValueIndustry
0Consumer Discretionary209209209209209209209209
1Consumer Staples106106106106106106106106
2Energy7979797979797979
3Financials474474474474474474474474
4Health Care7171717171717171
5Industrials192192192192192192192192
6Information Technology103103103103103103103103
7Materials145145145145145145145145
8Telecommunication Services4343434343434343
9Utilities8686868686868686
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" Sector Rank Company Country Sales Profits Assets \\\\\\n\",\n \"0 Consumer Discretionary 209 209 209 209 209 209 \\n\",\n \"1 Consumer Staples 106 106 106 106 106 106 \\n\",\n \"2 Energy 79 79 79 79 79 79 \\n\",\n \"3 Financials 474 474 474 474 474 474 \\n\",\n \"4 Health Care 71 71 71 71 71 71 \\n\",\n \"5 Industrials 192 192 192 192 192 192 \\n\",\n \"6 Information Technology 103 103 103 103 103 103 \\n\",\n \"7 Materials 145 145 145 145 145 145 \\n\",\n \"8 Telecommunication Services 43 43 43 43 43 43 \\n\",\n \"9 Utilities 86 86 86 86 86 86 \\n\",\n \"\\n\",\n \" Market Value Industry \\n\",\n \"0 209 209 \\n\",\n \"1 106 106 \\n\",\n \"2 79 79 \\n\",\n \"3 474 474 \\n\",\n \"4 71 71 \\n\",\n \"5 192 192 \\n\",\n \"6 103 103 \\n\",\n \"7 145 145 \\n\",\n \"8 43 43 \\n\",\n \"9 86 86 \"\n ]\n },\n \"execution_count\": 22,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"gb = df.groupby('Sector', as_index=False).count()\\n\",\n \"\\n\",\n \"gb\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"id\": \"635fa133\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
SectorRank
3Financials474
0Consumer Discretionary209
5Industrials192
7Materials145
1Consumer Staples106
6Information Technology103
9Utilities86
2Energy79
4Health Care71
8Telecommunication Services43
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" Sector Rank\\n\",\n \"3 Financials 474\\n\",\n \"0 Consumer Discretionary 209\\n\",\n \"5 Industrials 192\\n\",\n \"7 Materials 145\\n\",\n \"1 Consumer Staples 106\\n\",\n \"6 Information Technology 103\\n\",\n \"9 Utilities 86\\n\",\n \"2 Energy 79\\n\",\n \"4 Health Care 71\\n\",\n \"8 Telecommunication Services 43\"\n ]\n },\n \"execution_count\": 23,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"gb = gb[['Sector', 'Rank']]\\n\",\n \"\\n\",\n \"gb = gb.sort_values(by='Rank', ascending=False)\\n\",\n \"\\n\",\n \"gb\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"4efa0eb2\",\n \"metadata\": {},\n \"source\": [\n \"# pie chart\\n\",\n \"\\n\",\n \"- mode\\n\",\n \"- category wise display values\\n\",\n \"- percentage of values aquired by categories\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"id\": \"70799918\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.figure(figsize=(10,8))\\n\",\n \"\\n\",\n \"plt.pie(gb['Rank'], labels=gb['Sector'], autopct='%.2f')\\n\",\n \"\\n\",\n \"plt.suptitle(\\\"Sector Vs Rank\\\")\\n\",\n \"\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"d4812687\",\n \"metadata\": {},\n \"source\": [\n \"# Range\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"id\": \"fee0db23\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
RankCompanyCountrySalesProfitsAssetsMarket ValueSectorIndustry
01ICBCChina151.442.03473.2229.8FinancialsMajor Banks
12China Construction BankChina134.235.03016.6200.5FinancialsRegional Banks
23Berkshire HathawayUnited States222.924.1620.9409.9FinancialsInvestment Services
34JPMorgan ChaseUnited States102.524.22513.0306.6FinancialsMajor Banks
45Wells FargoUnited States97.621.91943.4274.4FinancialsMajor Banks
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" Rank Company Country Sales Profits Assets \\\\\\n\",\n \"0 1 ICBC China 151.4 42.0 3473.2 \\n\",\n \"1 2 China Construction Bank China 134.2 35.0 3016.6 \\n\",\n \"2 3 Berkshire Hathaway United States 222.9 24.1 620.9 \\n\",\n \"3 4 JPMorgan Chase United States 102.5 24.2 2513.0 \\n\",\n \"4 5 Wells Fargo United States 97.6 21.9 1943.4 \\n\",\n \"\\n\",\n \" Market Value Sector Industry \\n\",\n \"0 229.8 Financials Major Banks \\n\",\n \"1 200.5 Financials Regional Banks \\n\",\n \"2 409.9 Financials Investment Services \\n\",\n \"3 306.6 Financials Major Banks \\n\",\n \"4 274.4 Financials Major Banks \"\n ]\n },\n \"execution_count\": 25,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df.head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"id\": \"ad69c17b\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"45.2\"\n ]\n },\n \"execution_count\": 26,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"profit_max = df['Profits'].max()\\n\",\n \"\\n\",\n \"profit_max\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"id\": \"4a88e4a1\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"-13.0\"\n ]\n },\n \"execution_count\": 27,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"profit_min = df['Profits'].min()\\n\",\n \"\\n\",\n \"profit_min\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 28,\n \"id\": \"29b12a1f\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"58.2\"\n ]\n },\n \"execution_count\": 28,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"profit_range = profit_max - profit_min\\n\",\n \"\\n\",\n \"profit_range\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 29,\n \"id\": \"356b1be1\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.figure(figsize=(10,8))\\n\",\n \"\\n\",\n \"sns.boxplot(data = df, y = 'Profits', showmeans=True)\\n\",\n \"\\n\",\n \"plt.title('Forbes Data')\\n\",\n \"\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 30,\n \"id\": \"8be191bb\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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YbGiUBeqOAAd2yBwxoRQEDumUPGNCKAgZ064orrhiZr7zyykZJgN4oYEC37r777mVngHFRwIBuzc7Ojsx79uxplATojQIGdGt6enpk3rhxY6MkQG8UMKBbp59++sj8nOc8p1ESoDcKGNCt9773vSPzNddc0ygJ0BsFDOhWrXXZGWBcFDCgW6WUZWeAcVHAgG5t2rRpZH7ta1/bKAnQGwUM6Naxx07ceuutjZIAvVHAgG4dPHhw2RlgXBQwoFsTExPLzgDjooAB3XrTm940Mr/5zW9ulATojQIGdOvYPWC33XZboyRAb5oXsFLK6lLKH5VSfqN1FqAv8/Pzy84A49K8gCW5PMk9rUMA/ZmcnFx2BhiXpgWslPK8JP86yXuP91yAx9rmzZtH5pmZmUZJgN60XgH7xST/OcnhxjmADu3atWtk3rlzZ6MkQG+aFbBSymuT/E2tdd9xnveWUsreUsre/fv3D5QO6IE9YEArLVfAzklyXillPsmvJfm+UsoNxz6p1nptrXV9rXX9unXrhs4IrGD2gAGtNCtgtdafrLU+r9Y6meT1SX671npRqzxAf172speNzN/+7d/eKAnQm9Z7wACa+chHPjIyf+hDH2qUBOjN4+K+G7XWO5Lc0TgGAMAgrIABAAxMAQMAGJgCBgAwMAUMAGBgChjQrbPPPntk3rBhQ6MkQG8UMKBbn/jEJ0bmu+66q1ESoDcKGNCthYWFZWeAcVHAgG6tWbNm2RlgXBQwoFsXX3zxyHzJJZc0SgL0RgEDurV79+6R+aabbmqUBOiNAgZ0a35+ftkZYFwUMKBbk5OTy84A46KAAd0655xzRmbngAFDUcCAbn3wgx8cma+//vpGSYDeKGBAt2qty84A46KAAd0qpSw7A4yLAgZ060UvetHI/NKXvrRREqA3ChjQLfeCBFpRwAAABqaAAQAMTAEDuvW6171uZP7BH/zBRkmA3ihgQLcuu+yykfmtb31royRAbxQwoGtnn312EqfgA8NSwICuHfkmpG9AAkNSwIBu7d27NwsLC0mShYWF7Nu3r3EioBcKGNCtt7/97SPzVVdd1SYI0B0FDOjWkdWvR5sBxkUBA7q1Zs2aZWeAcVHAgG696lWvGplf/epXN0oC9EYBA7q1e/fukfnDH/5woyRAbxQwoFu11mVngHFRwIBulVKWnQHGRQEDuvW2t71tZL7iiivaBAG6M9E6ADzRbd++PXNzc61j8BiYnZ3N7Oxs6xicpKmpqWzZsqV1DDgpVsCArp1yyilJkjPOOKNxEqAnVsDgn8i/eT+xXX755UmSq6++unESoCdWwAAABqaAAQAMTAEDABiYAgYAMDAFDABgYCdUwEopl5dSnlYWva+Uclcp5VXHfyUAAMc60RWwH6m13p/kVUnWJXlTkp8dWyoAgBXsRAvYkRukvSbJr9ZaP37UNQAATsKJFrB9pZTbs1jAfquU8tQkh8cXCwBg5TrRk/D/fZKXJPlMrfXBUsraLH4MCQDASTrRFbA9tda7aq1fSpJa64Ek7x5bKgCAFWzZFbBSypOTnJbk2aWUZ+Yf9n09Lclzx5wNAGBFOt5HkJckeVsWy9ZdR12/P8l7xpQJAGBFW7aA1VqvTnJ1KWVLrXX7QJkAAFa0430E+X211t9Ocl8p5XXHPl5r/cjYkgEArFDH+whyQ5LfTnLuIzxWkyhgAAAn6XgF7G+X/vN9tdbfG3cYAIAeHO8YiiNnff33cQcBAOjF8VbA7imlzCdZV0r5xFHXS5Jaa/22sSUDAFihjvctyAtLKd+Y5LeSnDdMJACAle24tyKqtX4+yYtLKU9K8oKly5+utX5trMkAAFaoE7oXZCnle5LsSjKfxY8fv6mUMlNrvXOM2QAAVqQTvRn3u5K8qtb66SQppbwgyY1JXj6uYAAAK9WJ3oz7lCPlK0lqrX+a5JTxRAIAWNlOdAVsXynlfUmuX5rfkGTfeCIBAKxsJ1rAfjTJW5NclsU9YHcm+aVxhQIAWMmOW8BKKauS7Ku1vjCLe8EAAPgnOO4esFrr4SQfL6X88wHyAACseCf6EeRzkvxxKeUPk3zlyMVaq8NZAQBO0okWsHeMNQUAQEeWLWCllCdncQP+VJJPJnlfrfXgEMEAAFaq4+0B25lkfRbL16uTvHPsiQAAVrjjfQT5rbXWFyXJ0jlgfzj+SAAAK9vxVsAevuG2jx4BAB4bx1sBe3Ep5f6lP5ckpy7NJUmttT7t633jUso3ZfEG39+Y5HCSa2utV3+9vw8A4Ili2QJWa109xvc+mOTKWutdpZSnZvF2R3tqrX8yxvcEAGjuRG/G/Zirtf5VrfWupT8/kOSeJGe0ygMAMJRmBexopZTJJC9N8geNowAAjF3zAlZKWZPkpiRvq7Xe/wiPv6WUsreUsnf//v3DBwQAeIw1LWCllFOyWL4+UGv9yCM9p9Z6ba11fa11/bp164YNCAAwBs0KWCmlJHlfkntqre9qlQMAYGgtV8DOSfLDSb6vlHL30s9rGuYBABjEid6M+zFXa/29LJ4nBgDQleab8AEAeqOAAQAMTAEDABiYAgYAMDAFDABgYAoYAMDAFDAAgIEpYAAAA1PAAAAGpoABAAxMAQMAGJgCBgAwMAUMAGBgChgAwMAUMACAgSlgQNe+OvHV7Hv+vnzhoS+0jgJ0ZKJ1ABZt3749c3NzrWNAdz7+tI9nYc1CLnrPRTnzL89sHQe6MjU1lS1btrSO0YQC9jgxNzeXuz91Tw6d9qzWUaAbh5/8tSx820JSkvue+bnc/7GnZNVXT2kdC7qw+sEvto7QlAL2OHLotGflobNe0zoGdOPgc/Ykq1YnOZSsWpWvnH1aJv5qY+tY0IVT7/1o6whN2QMGdKlOLOTwMz+VrDq0eGHVoRx+5qdSJxbaBgO6oIABXTq07veT1GOu1qXrAOOlgAFdOnzaff+w+nXEqkOL1wHGzB4woEtP+rM3to4AdMwKGADAwBQwAICBKWAAAANTwAAABqaAAQAMTAEDABiYAgYAMDAFDABgYAoYAMDAFDAAgIEpYAAAA1PAAAAGpoABAAxMAQMAGJgCBgAwMAUMAGBgChgAwMAUMACAgSlgAAADU8AAAAamgAEADEwBAwAY2ETrACy67777svrBL+fUez/aOgoAjN3qBw/kvvsOto7RjBUwAICBWQF7nDjjjDPy+a9O5KGzXtM6CgCM3an3fjRnnHF66xjNWAEDABiYAgYAMDAFDABgYAoYAMDAFDAAgIEpYAAAA1PAAAAGpoABAAxMAQMAGJgCBgAwMAUMAGBg7gX5OLL6wS/m1Hs/2joGdGXV392fJDn85Kc1TgJ9Wf3gF5P0ey9IBexxYmpqqnUE6NLc3ANJkqlv6fcfBNDG6V3/s08Be5zYsmVL6wjQpcsvvzxJcvXVVzdOAvTEHjAAgIEpYAAAA1PAAAAGpoABAAxMAQMAGJgCBgAwMAUMAGBgChgAwMCaFrBSyqZSyqdLKXOllJ9omQUAYCjNClgpZXWS9yR5dZJvTXJhKeVbW+UBABhKyxWw70gyV2v9TK3175P8WpLvb5gHAGAQLQvYGUn+8qj5s0vXAABWtJYFrDzCtfqPnlTKW0ope0spe/fv3z9ALACA8WpZwD6b5JuOmp+X5HPHPqnWem2tdX2tdf26desGCwcAMC4tC9j/TfL8Usq/KKU8Kcnrk9zcMA8AwCAmWr1xrfVgKeXSJL+VZHWS/1Fr/eNWeQAAhtKsgCVJrfWjST7aMgMAwNCchA8AMDAFDABgYAoYAMDAFDAAgIEpYAAAA1PAAAAGpoABAAxMAQMAGJgCBgAwMAUMAGBgChgAwMAUMACAgSlgAAADU8AAAAamgAEADEwBAwAYmAIGADAwBQwAYGAKGADAwBQwAICBKWAAAANTwAAABqaAAQAMTAEDABiYAgYAMDAFDABgYAoYAMDAFDAAgIEpYAAAA1PAAAAGpoABAAxMAQMAGJgCBgAwMAUMAGBgChgAwMAUMACAgSlgAAADm2gdAJ7otm/fnrm5udYx+Dod+f/u8ssvb5yEr9fU1FS2bNnSOgacFAUM6Nqpp57aOgLQIQUM/on8mzcAJ8seMACAgSlgAAADU8AAAAamgAEADEwBAwAYmAIGADAwBQzo2oEDB3LZZZflwIEDraMAHVHAgK7t3Lkzn/zkJ7Nr167WUYCOKGBAtw4cOJDbbrsttdbcdtttVsGAwShgQLd27tyZw4cPJ0kOHTpkFQwYjAIGdGt2djYHDx5Mkhw8eDB79uxpnAjohQIGdGt6enpk3rhxY6MkQG8UMKBbT33qU0fmpz/96Y2SAL1RwIBufeADHxiZ7QEDhqKAAQAMTAEDABiYAgZ06w1veMPIvHnz5kZJgN4oYEC3HnjggZH5y1/+cqMkQG8UMKBbs7OzI7NzwIChKGBAt5wDBrSigAHd2rBhw7IzwLgoYEC3duzYMTJv3769URKgNwoY0K35+fllZ4BxUcCAbk1OTi47A4yLAgZ0a+vWrcvOAOOigAHdmpqaenjVa3JyMlNTU20DAd1QwICuHTn9fmZmpnESoCcKGNC1Xbt2JUl27tzZOAnQEwUM6Nbc3NzD33ycn5/P3Nxc20BANxQwoFvbtm1bdgYYFwUM6JZzwIBWmhSwUsrPl1LuLaV8opSyu5TyjBY5gL45BwxopdUK2J4kL6y1fluSP03yk41yAB07//zzR+YLLrigURKgN00KWK319lrrwaXxY0me1yIH0LfrrrtuZL7mmmsaJQF683jYA/YjSW59tAdLKW8ppewtpezdv3//gLGAlW5hYWHZGWBcxlbASimzpZRPPcLP9x/1nJ9OcjDJBx7t99Rar621rq+1rl+3bt244gIdWrNmzbIzwLhMjOsX11qnl3u8lDKT5LVJXllrrePKAfBoLr744rz73e9+eL7kkksapgF60upbkJuS/HiS82qtD7bIALB79+6R+aabbmqUBOhNqz1gO5I8NcmeUsrdpZRfaZQD6JhzwIBWxvYR5HJqrVMt3hfgaJOTkyOlyzlgwFAeD9+CBGji0ksvHZm3bNnSKAnQGwUM6Nadd9657AwwLgoY0K3Z2dmRec+ePY2SAL1RwIBuTU9Pp5SSJCmlZOPGjY0TAb1QwIBunXfeeTlyDGGtNeeee27jREAvFDCgWzfffPPICtgtt9zSOBHQCwUM6Nbs7OzICpg9YMBQFDCgW9PTo3dMswcMGIoCBnRrw4YNy84A46KAAd3asWPHyLx9+/ZGSYDeKGBAt9wLEmhFAQO6dey9H90LEhiKAgZ0y70ggVYUMKBb7gUJtKKAAd1yL0igFQUM6Nb09HQmJiaSJBMTE84BAwajgAHdmpmZyapVi38Nrl69Ops3b26cCOiFAgZ0a+3atfne7/3eJMkrXvGKrF27tnEioBcKGNC1I/eCBBiSAgZ068CBA7njjjuSJHfccUcOHDjQNhDQDQUM6NbOnTtz+PDhJMmhQ4eya9euxomAXihgQLdmZ2dz8ODBJMnBgwcdQwEMRgEDujU9PZ1SSpKklOIYCmAwChjQrfPOO+/hTfi11px77rmNEwG9UMCAbt18880jK2C33HJL40RALxQwoFuzs7MjK2D2gAFDUcCAbtkDBrSigAHdsgcMaEUBA7plDxjQigIGdMseMKAVBQzo1vT0dCYmJpIkExMT9oABg1HAgG7NzMxk1arFvwZXr16dzZs3N04E9EIBA7q1du3abNq0KaWUbNq0KWvXrm0dCehEObL/4YmglLI/yZ+3zgGsHKtWrTr1tNNOO+vBBx+89/Dhww+1zgOsKN9ca133SA88oQoYwDiUUvbWWte3zgH0w0eQAAADU8AAAAamgAEk17YOAPTFHjAAgIFZAQMAGJgCBgAwMAUMAGBgChgAwMAUMACAgf1/0+OvARuDRbMAAAAASUVORK5CYII=\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.figure(figsize=(10,8))\\n\",\n \"\\n\",\n \"sns.boxplot(data = df, y = 'Profits', showmeans=True)\\n\",\n \"\\n\",\n \"plt.ylim(-3,8)\\n\",\n \"\\n\",\n \"plt.title('Forbes Data')\\n\",\n \"\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"60b33559\",\n \"metadata\": {},\n \"source\": [\n \"# Range\\n\",\n \"\\n\",\n \"- range is sensitive to outliers\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"57270870\",\n \"metadata\": {},\n \"source\": [\n \"# Inter Quartile Range\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 31,\n \"id\": \"55f9d714\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"0.34975\"\n ]\n },\n \"execution_count\": 31,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# 25th percentile\\n\",\n \"\\n\",\n \"Q1 = np.quantile(df['Profits'], 0.25)\\n\",\n \"\\n\",\n \"Q1\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 32,\n \"id\": \"7dfaa0d9\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"1.4249999999999998\"\n ]\n },\n \"execution_count\": 32,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# 75th percentile\\n\",\n \"\\n\",\n \"Q3 = np.quantile(df['Profits'], 0.75)\\n\",\n \"\\n\",\n \"Q3\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 33,\n \"id\": \"e1aab44b\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"1.0752499999999998\"\n ]\n },\n \"execution_count\": 33,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"IQR = Q3 - Q1\\n\",\n \"\\n\",\n \"IQR\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"194f9cf6\",\n \"metadata\": {},\n \"source\": [\n \"# IQR\\n\",\n \"\\n\",\n \"- resistant to outliers\\n\",\n \"- better than range\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"d30aaeac\",\n \"metadata\": {},\n \"source\": [\n \"# Standard Deviation and Variance\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 34,\n \"id\": \"94231206\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"3.244201278603241\"\n ]\n },\n \"execution_count\": 34,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"profit_std = df['Profits'].std()\\n\",\n \"\\n\",\n \"profit_std\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 36,\n \"id\": \"054b2250\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"1.4074303713527847\"\n ]\n },\n \"execution_count\": 36,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"profit_mean = df['Profits'].mean()\\n\",\n \"\\n\",\n \"profit_mean\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"7e91b587\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"cricket\\n\",\n \"\\n\",\n \"virat kohli rohit sharma\\n\",\n \"40 0\\n\",\n \"50 100\\n\",\n \"30 10\\n\",\n \"20 15\\n\",\n \"45 0\\n\",\n \"67 101\\n\",\n \"40 200\\n\",\n \"\\n\",\n \"mean mean\\n\",\n \"35 60\\n\",\n \"\\n\",\n \"5 60\\n\",\n \"15 40\\n\",\n \"5 50\\n\",\n \"15 60\\n\",\n \"25 41\\n\",\n \" 140\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 37,\n \"id\": \"86d516fd\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"10.524841936090901\"\n ]\n },\n \"execution_count\": 37,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"profit_var = df['Profits'].var()\\n\",\n \"\\n\",\n \"profit_var\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"f5f846b7\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"appl msoft\\n\",\n \"100 100\\n\",\n \"500 120\\n\",\n \"-200 130\\n\",\n \"600 125\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 38,\n \"id\": \"0c7d3fa2\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"10.524841936090903\"\n ]\n },\n \"execution_count\": 38,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"profit_std ** 2 # variance\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"27a5b442\",\n \"metadata\": {},\n \"source\": [\n \"# Summary Stats\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 39,\n \"id\": \"3d683610\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
RankSalesProfitsAssetsMarket Value
count1508.0000001508.0000001508.0000001508.0000001508.000000
mean909.13527919.6230461.40743097.64701626.978494
std554.04445630.8758093.244201287.03615747.099541
min1.0000000.001000-13.0000001.3000000.240000
25%433.7500004.9000000.34975012.8000007.300000
50%883.50000010.3000000.69250025.90000013.100000
75%1354.25000019.9000001.42500060.32500026.900000
max1999.000000485.30000045.2000003473.200000752.000000
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" Rank Sales Profits Assets Market Value\\n\",\n \"count 1508.000000 1508.000000 1508.000000 1508.000000 1508.000000\\n\",\n \"mean 909.135279 19.623046 1.407430 97.647016 26.978494\\n\",\n \"std 554.044456 30.875809 3.244201 287.036157 47.099541\\n\",\n \"min 1.000000 0.001000 -13.000000 1.300000 0.240000\\n\",\n \"25% 433.750000 4.900000 0.349750 12.800000 7.300000\\n\",\n \"50% 883.500000 10.300000 0.692500 25.900000 13.100000\\n\",\n \"75% 1354.250000 19.900000 1.425000 60.325000 26.900000\\n\",\n \"max 1999.000000 485.300000 45.200000 3473.200000 752.000000\"\n ]\n },\n \"execution_count\": 39,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df.describe()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"45cddf9c\",\n \"metadata\": {},\n \"source\": [\n \"# Noraml Distribution\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 40,\n \"id\": \"eeec52ce\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([-0.21471611, 0.61754921, -0.60251843, 0.14196059, 0.60611692,\\n\",\n \" 0.33409508, 0.93308273, -0.83391941, -2.49212651, -0.64909842])\"\n ]\n },\n \"execution_count\": 40,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# mean = 0\\n\",\n \"# std = 1\\n\",\n \"\\n\",\n \"normally_distributed_data = np.random.normal(loc=0, scale=1, size=100000)\\n\",\n \"\\n\",\n \"normally_distributed_data[0:10]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"91a281e8\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# mean == median # normal distribution\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 42,\n \"id\": \"3524740c\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"-0.00010732710555090066\"\n ]\n },\n \"execution_count\": 42,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"sample_mean = np.mean(normally_distributed_data)\\n\",\n \"\\n\",\n \"sample_mean\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 43,\n \"id\": \"c995dc03\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"-0.0014516077341794609\"\n ]\n },\n \"execution_count\": 43,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"sample_median = np.median(normally_distributed_data)\\n\",\n \"\\n\",\n \"sample_median\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 44,\n \"id\": \"9dc19349\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"1.0014081381099627\"\n ]\n },\n \"execution_count\": 44,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"sample_std = np.std(normally_distributed_data)\\n\",\n \"\\n\",\n \"sample_std\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"81360486\",\n \"metadata\": {},\n \"source\": [\n \"## plot normal distribution\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 46,\n \"id\": \"908217a4\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.figure(figsize=(12,8))\\n\",\n \"\\n\",\n \"sns.histplot(x = normally_distributed_data)\\n\",\n \"\\n\",\n \"plt.title(\\\"Normal Distribution\\\", fontsize='15')\\n\",\n \"\\n\",\n \"plt.axvline(x = sample_mean, color='r', linestyle='-')\\n\",\n \"plt.axvline(x = sample_median, color='g', linestyle='-')\\n\",\n \"\\n\",\n \"plt.xlabel('Values', fontsize='15')\\n\",\n \"plt.ylabel('probability', fontsize='15')\\n\",\n \"\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"91fcee90\",\n \"metadata\": {},\n \"source\": [\n \"# Probability'\\n\",\n \"\\n\",\n \"an event is likely to occur\\n\",\n \"\\n\",\n \"favorable cases / total number of cases\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"04e37d20\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"1/2\\n\",\n \"\\n\",\n \" H T\\n\",\n \"0 - 0.5 , 0.5 - 1\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"ef7d552b\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \" 0 1 2\\n\",\n \"0-0.1 0.1 - 0.2 0.2 - 0.3\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"7556d169\",\n \"metadata\": {},\n \"source\": [\n \"# Coin\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 49,\n \"id\": \"c3a058c9\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import random\\n\",\n \"import scipy.stats\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 50,\n \"id\": \"977c6909\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def flip(n):\\n\",\n \" \\n\",\n \" result = []\\n\",\n \" \\n\",\n \" for i in range(1,n+1):\\n\",\n \" result.append(random.choice(['Head', 'Tails']))\\n\",\n \" \\n\",\n \" return result\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 51,\n \"id\": \"0c567150\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"['Head',\\n\",\n \" 'Tails',\\n\",\n \" 'Tails',\\n\",\n \" 'Tails',\\n\",\n \" 'Head',\\n\",\n \" 'Head',\\n\",\n \" 'Tails',\\n\",\n \" 'Tails',\\n\",\n \" 'Tails',\\n\",\n \" 'Head']\"\n ]\n },\n \"execution_count\": 51,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"flip(10)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 52,\n \"id\": \"a45cb78e\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": \"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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"f = flip(10)\\n\",\n \"\\n\",\n \"plt.figure(figsize=(12,8))\\n\",\n \"\\n\",\n \"sns.countplot(x = f)\\n\",\n \"\\n\",\n \"plt.xlabel('Coin Flips')\\n\",\n \"plt.title('Counts')\\n\",\n \"\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 53,\n \"id\": \"3f563d5f\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"f = flip(100)\\n\",\n \"\\n\",\n \"plt.figure(figsize=(12,8))\\n\",\n \"\\n\",\n \"sns.countplot(x = f)\\n\",\n \"\\n\",\n \"plt.xlabel('Coin Flips')\\n\",\n \"plt.title('Counts')\\n\",\n \"\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 54,\n \"id\": \"85907d8a\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"f = flip(100000)\\n\",\n \"\\n\",\n \"plt.figure(figsize=(12,8))\\n\",\n \"\\n\",\n \"sns.countplot(x = f)\\n\",\n \"\\n\",\n \"plt.xlabel('Coin Flips')\\n\",\n \"plt.title('Counts')\\n\",\n \"\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"1081d8ad\",\n \"metadata\": {},\n \"source\": [\n \"# Dice\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 55,\n \"id\": \"b8e72228\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def roll(n):\\n\",\n \" \\n\",\n \" result = []\\n\",\n \" \\n\",\n \" for i in range(1,n+1):\\n\",\n \" result.append(random.choice([1,2,3,4,5,6]))\\n\",\n \" \\n\",\n \" return result\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 56,\n \"id\": \"acb6b988\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[4, 5, 4, 5, 3, 3, 3, 3, 4, 6]\"\n ]\n },\n \"execution_count\": 56,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"roll(10)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 57,\n \"id\": \"ca45a0e6\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"f = roll(10)\\n\",\n \"\\n\",\n \"plt.figure(figsize=(12,8))\\n\",\n \"\\n\",\n \"sns.countplot(x = f)\\n\",\n \"\\n\",\n \"plt.xlabel('Dice Roll')\\n\",\n \"plt.title('Counts')\\n\",\n \"\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 58,\n \"id\": \"d9bd67b6\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"f = roll(100)\\n\",\n \"\\n\",\n \"plt.figure(figsize=(12,8))\\n\",\n \"\\n\",\n \"sns.countplot(x = f)\\n\",\n \"\\n\",\n \"plt.xlabel('Dice Roll')\\n\",\n \"plt.title('Counts')\\n\",\n \"\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 59,\n \"id\": \"a0b6328e\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"f = roll(100000)\\n\",\n \"\\n\",\n \"plt.figure(figsize=(12,8))\\n\",\n \"\\n\",\n \"sns.countplot(x = f)\\n\",\n \"\\n\",\n \"plt.xlabel('Dice Roll')\\n\",\n \"plt.title('Counts')\\n\",\n \"\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 60,\n \"id\": \"3cf86622\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
DateOpenHighLowCloseAdjCloseVolume
031-Dec-1986243.37244.03241.28242.17242.1713,92,00,000
12-Jan-1987242.17246.45242.17246.45246.459,18,80,000
25-Jan-1987246.45252.57246.45252.19252.1918,19,00,000
36-Jan-1987252.20253.99252.14252.78252.7818,93,00,000
47-Jan-1987252.78255.72252.65255.33255.3319,09,00,000
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" Date Open High Low Close AdjClose Volume\\n\",\n \"0 31-Dec-1986 243.37 244.03 241.28 242.17 242.17 13,92,00,000\\n\",\n \"1 2-Jan-1987 242.17 246.45 242.17 246.45 246.45 9,18,80,000\\n\",\n \"2 5-Jan-1987 246.45 252.57 246.45 252.19 252.19 18,19,00,000\\n\",\n \"3 6-Jan-1987 252.20 253.99 252.14 252.78 252.78 18,93,00,000\\n\",\n \"4 7-Jan-1987 252.78 255.72 252.65 255.33 255.33 19,09,00,000\"\n ]\n },\n \"execution_count\": 60,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"data = pd.read_csv('datasets/SP_500_1987.csv')\\n\",\n \"\\n\",\n \"data.head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 61,\n \"id\": \"9d2c64dc\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
OpenHighLowCloseAdjClose
count253.000000253.000000253.000000253.000000253.000000
mean286.964466289.328063284.412095286.978617286.978617
std28.92368328.24114229.53552828.88905328.889053
min223.980000225.770000216.460000223.920000223.920000
25%267.840000270.400000264.310000267.840000267.840000
50%290.520000292.470000288.340000290.520000290.520000
75%308.940000310.270000307.420000308.960000308.960000
max336.770000337.890000334.460000336.770000336.770000
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" Open High Low Close AdjClose\\n\",\n \"count 253.000000 253.000000 253.000000 253.000000 253.000000\\n\",\n \"mean 286.964466 289.328063 284.412095 286.978617 286.978617\\n\",\n \"std 28.923683 28.241142 29.535528 28.889053 28.889053\\n\",\n \"min 223.980000 225.770000 216.460000 223.920000 223.920000\\n\",\n \"25% 267.840000 270.400000 264.310000 267.840000 267.840000\\n\",\n \"50% 290.520000 292.470000 288.340000 290.520000 290.520000\\n\",\n \"75% 308.940000 310.270000 307.420000 308.960000 308.960000\\n\",\n \"max 336.770000 337.890000 334.460000 336.770000 336.770000\"\n ]\n },\n \"execution_count\": 61,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"data.describe()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 62,\n \"id\": \"52bb2007\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Date object\\n\",\n \"Open float64\\n\",\n \"High float64\\n\",\n \"Low float64\\n\",\n \"Close float64\\n\",\n \"AdjClose float64\\n\",\n \"Volume object\\n\",\n \"dtype: object\"\n ]\n },\n \"execution_count\": 62,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"data.dtypes\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 63,\n \"id\": \"3e57630e\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Date datetime64[ns]\\n\",\n \"Open float64\\n\",\n \"High float64\\n\",\n \"Low float64\\n\",\n \"Close float64\\n\",\n \"AdjClose float64\\n\",\n \"Volume object\\n\",\n \"dtype: object\"\n ]\n },\n \"execution_count\": 63,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"data['Date'] = pd.to_datetime(data['Date'])\\n\",\n \"\\n\",\n \"data.dtypes\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 64,\n \"id\": \"ad432bf2\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Text(0.5, 1.0, 'S&P 500 1987')\"\n ]\n },\n \"execution_count\": 64,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.figure(figsize=(12,8))\\n\",\n \"\\n\",\n \"sns.lineplot(data=data, x = 'Date', y='AdjClose', color='red')\\n\",\n \"\\n\",\n \"plt.title(\\\"S&P 500 1987\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 67,\n \"id\": \"2cecaa85\",\n \"metadata\": {\n \"scrolled\": false\n },\n \"outputs\": [],\n \"source\": [\n \"# help(data['AdjClose'].pct_change())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 65,\n \"id\": \"15c92d9e\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
DateOpenHighLowCloseAdjCloseVolumeReturns
01986-12-31243.37244.03241.28242.17242.1713,92,00,000NaN
11987-01-02242.17246.45242.17246.45246.459,18,80,0000.017674
21987-01-05246.45252.57246.45252.19252.1918,19,00,0000.023291
31987-01-06252.20253.99252.14252.78252.7818,93,00,0000.002340
41987-01-07252.78255.72252.65255.33255.3319,09,00,0000.010088
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" Date Open High Low Close AdjClose Volume Returns\\n\",\n \"0 1986-12-31 243.37 244.03 241.28 242.17 242.17 13,92,00,000 NaN\\n\",\n \"1 1987-01-02 242.17 246.45 242.17 246.45 246.45 9,18,80,000 0.017674\\n\",\n \"2 1987-01-05 246.45 252.57 246.45 252.19 252.19 18,19,00,000 0.023291\\n\",\n \"3 1987-01-06 252.20 253.99 252.14 252.78 252.78 18,93,00,000 0.002340\\n\",\n \"4 1987-01-07 252.78 255.72 252.65 255.33 255.33 19,09,00,000 0.010088\"\n ]\n },\n \"execution_count\": 65,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"data['Returns'] = data['AdjClose'].pct_change()\\n\",\n \"\\n\",\n \"data.head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 68,\n \"id\": \"2d892d6c\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
DateOpenHighLowCloseAdjCloseVolumeReturns
11987-01-02242.17246.45242.17246.45246.459,18,80,0000.017674
21987-01-05246.45252.57246.45252.19252.1918,19,00,0000.023291
31987-01-06252.20253.99252.14252.78252.7818,93,00,0000.002340
41987-01-07252.78255.72252.65255.33255.3319,09,00,0000.010088
51987-01-08255.36257.28254.97257.28257.2819,45,00,0000.007637
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" Date Open High Low Close AdjClose Volume Returns\\n\",\n \"1 1987-01-02 242.17 246.45 242.17 246.45 246.45 9,18,80,000 0.017674\\n\",\n \"2 1987-01-05 246.45 252.57 246.45 252.19 252.19 18,19,00,000 0.023291\\n\",\n \"3 1987-01-06 252.20 253.99 252.14 252.78 252.78 18,93,00,000 0.002340\\n\",\n \"4 1987-01-07 252.78 255.72 252.65 255.33 255.33 19,09,00,000 0.010088\\n\",\n \"5 1987-01-08 255.36 257.28 254.97 257.28 257.28 19,45,00,000 0.007637\"\n ]\n },\n \"execution_count\": 68,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"data = data.dropna()\\n\",\n \"data.head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 69,\n \"id\": \"c23e43a2\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(252, 8)\"\n ]\n },\n \"execution_count\": 69,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"data.shape\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"44c9342e\",\n \"metadata\": {},\n \"source\": [\n \"# Skewness\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 70,\n \"id\": \"86c6f635\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.figure(figsize=(12,8))\\n\",\n \"\\n\",\n \"sns.histplot(data=data, x = 'Returns')\\n\",\n \"\\n\",\n \"plt.title(\\\"S&P\\\")\\n\",\n \"\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"915bfd73\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.12\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n" }, { "path": "March Cohort 2023/12_15_16_17_18_Statistics/16_17_Statistics and Regression.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"id\": \"103790af\",\n \"metadata\": {},\n \"source\": [\n \"# Multiple Linear Regression\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \"\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"id\": \"6dd89ea3\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import numpy as np\\n\",\n \"import pandas as pd\\n\",\n \"\\n\",\n \"import statsmodels.api as sm\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"id\": \"73c667a7\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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Sl.NO.PLAYER NAMEAGECOUNTRYTEAMPLAYING ROLET-RUNST-WKTSODI-RUNS-SODI-SR-B...SR-BSIXERSRUNS-CWKTSAVE-BLECONSR-BLAUCTION YEARBASE PRICESOLD PRICE
01Abdulla, YA2SAKXIPAllrounder0000.00...0.0003071520.478.9013.9320095000050000
12Abdur Razzak2BANRCBBowler2141865771.41...0.0002900.0014.500.0020085000050000
23Agarkar, AB2INDKKRBowler57158126980.62...121.01510592936.528.8124.902008200000350000
34Ashwin, R1INDCSKBowler2843124184.56...76.32011254922.966.2322.142011100000850000
45Badrinath, S2INDCSKBatsman6307945.93...120.7128000.000.000.002011100000800000
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5 rows × 26 columns

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\"\n ],\n \"text/plain\": [\n \" Sl.NO. PLAYER NAME AGE COUNTRY TEAM PLAYING ROLE T-RUNS T-WKTS \\\\\\n\",\n \"0 1 Abdulla, YA 2 SA KXIP Allrounder 0 0 \\n\",\n \"1 2 Abdur Razzak 2 BAN RCB Bowler 214 18 \\n\",\n \"2 3 Agarkar, AB 2 IND KKR Bowler 571 58 \\n\",\n \"3 4 Ashwin, R 1 IND CSK Bowler 284 31 \\n\",\n \"4 5 Badrinath, S 2 IND CSK Batsman 63 0 \\n\",\n \"\\n\",\n \" ODI-RUNS-S ODI-SR-B ... SR-B SIXERS RUNS-C WKTS AVE-BL ECON \\\\\\n\",\n \"0 0 0.00 ... 0.00 0 307 15 20.47 8.90 \\n\",\n \"1 657 71.41 ... 0.00 0 29 0 0.00 14.50 \\n\",\n \"2 1269 80.62 ... 121.01 5 1059 29 36.52 8.81 \\n\",\n \"3 241 84.56 ... 76.32 0 1125 49 22.96 6.23 \\n\",\n \"4 79 45.93 ... 120.71 28 0 0 0.00 0.00 \\n\",\n \"\\n\",\n \" SR-BL AUCTION YEAR BASE PRICE SOLD PRICE \\n\",\n \"0 13.93 2009 50000 50000 \\n\",\n \"1 0.00 2008 50000 50000 \\n\",\n \"2 24.90 2008 200000 350000 \\n\",\n \"3 22.14 2011 100000 850000 \\n\",\n \"4 0.00 2011 100000 800000 \\n\",\n \"\\n\",\n \"[5 rows x 26 columns]\"\n ]\n },\n \"execution_count\": 2,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df = pd.read_csv('datasets/IPL2013.csv')\\n\",\n \"\\n\",\n \"df.head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"id\": \"89f7bb2b\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\",\n \"RangeIndex: 130 entries, 0 to 129\\n\",\n \"Data columns (total 26 columns):\\n\",\n \" # Column Non-Null Count Dtype \\n\",\n \"--- ------ -------------- ----- \\n\",\n \" 0 Sl.NO. 130 non-null int64 \\n\",\n \" 1 PLAYER NAME 130 non-null object \\n\",\n \" 2 AGE 130 non-null int64 \\n\",\n \" 3 COUNTRY 130 non-null object \\n\",\n \" 4 TEAM 130 non-null object \\n\",\n \" 5 PLAYING ROLE 130 non-null object \\n\",\n \" 6 T-RUNS 130 non-null int64 \\n\",\n \" 7 T-WKTS 130 non-null int64 \\n\",\n \" 8 ODI-RUNS-S 130 non-null int64 \\n\",\n \" 9 ODI-SR-B 130 non-null float64\\n\",\n \" 10 ODI-WKTS 130 non-null int64 \\n\",\n \" 11 ODI-SR-BL 130 non-null float64\\n\",\n \" 12 CAPTAINCY EXP 130 non-null int64 \\n\",\n \" 13 RUNS-S 130 non-null int64 \\n\",\n \" 14 HS 130 non-null int64 \\n\",\n \" 15 AVE 130 non-null float64\\n\",\n \" 16 SR-B 130 non-null float64\\n\",\n \" 17 SIXERS 130 non-null int64 \\n\",\n \" 18 RUNS-C 130 non-null int64 \\n\",\n \" 19 WKTS 130 non-null int64 \\n\",\n \" 20 AVE-BL 130 non-null float64\\n\",\n \" 21 ECON 130 non-null float64\\n\",\n \" 22 SR-BL 130 non-null float64\\n\",\n \" 23 AUCTION YEAR 130 non-null int64 \\n\",\n \" 24 BASE PRICE 130 non-null int64 \\n\",\n \" 25 SOLD PRICE 130 non-null int64 \\n\",\n \"dtypes: float64(7), int64(15), object(4)\\n\",\n \"memory usage: 26.5+ KB\\n\"\n ]\n }\n ],\n \"source\": [\n \"df.info()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"id\": \"983b5a17\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Index(['Sl.NO.', 'PLAYER NAME', 'AGE', 'COUNTRY', 'TEAM', 'PLAYING ROLE',\\n\",\n \" 'T-RUNS', 'T-WKTS', 'ODI-RUNS-S', 'ODI-SR-B', 'ODI-WKTS', 'ODI-SR-BL',\\n\",\n \" 'CAPTAINCY EXP', 'RUNS-S', 'HS', 'AVE', 'SR-B', 'SIXERS', 'RUNS-C',\\n\",\n \" 'WKTS', 'AVE-BL', 'ECON', 'SR-BL', 'AUCTION YEAR', 'BASE PRICE',\\n\",\n \" 'SOLD PRICE'],\\n\",\n \" dtype='object')\"\n ]\n },\n \"execution_count\": 4,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"xfeatures = df.columns\\n\",\n \"xfeatures\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"id\": \"beac70ae\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"xfeatures = ['AGE', 'COUNTRY', 'PLAYING ROLE',\\n\",\n \" 'T-RUNS', 'T-WKTS', 'ODI-RUNS-S', 'ODI-SR-B', 'ODI-WKTS', 'ODI-SR-BL',\\n\",\n \" 'CAPTAINCY EXP', 'RUNS-S', 'HS', 'AVE', 'SR-B', 'SIXERS', 'RUNS-C',\\n\",\n \" 'WKTS', 'AVE-BL', 'ECON', 'SR-BL']\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"489a7f42\",\n \"metadata\": {},\n \"source\": [\n \"# Feature encoding on categorical columns\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"id\": \"9f8602c5\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array(['Allrounder', 'Bowler', 'Batsman', 'W. Keeper'], dtype=object)\"\n ]\n },\n \"execution_count\": 6,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df['PLAYING ROLE'].unique()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"id\": \"4b4f0c3e\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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T-RUNST-WKTSODI-RUNS-SODI-SR-BODI-WKTSODI-SR-BLRUNS-SHSAVESR-B...COUNTRY_NZCOUNTRY_PAKCOUNTRY_SACOUNTRY_SLCOUNTRY_WICOUNTRY_ZIMPLAYING ROLE_BatsmanPLAYING ROLE_BowlerPLAYING ROLE_W. KeeperCAPTAINCY EXP_1
00000.0000.0000.000.00...0010000000
12141865771.4118537.6000.000.00...0000000100
257158126980.6228832.91673918.56121.01...0000000100
32843124184.565136.858115.8076.32...0000000100
46307945.9300.013177132.93120.71...0000001000
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5 rows × 31 columns

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\"\n ],\n \"text/plain\": [\n \" T-RUNS T-WKTS ODI-RUNS-S ODI-SR-B ODI-WKTS ODI-SR-BL RUNS-S HS \\\\\\n\",\n \"0 0 0 0 0.00 0 0.0 0 0 \\n\",\n \"1 214 18 657 71.41 185 37.6 0 0 \\n\",\n \"2 571 58 1269 80.62 288 32.9 167 39 \\n\",\n \"3 284 31 241 84.56 51 36.8 58 11 \\n\",\n \"4 63 0 79 45.93 0 0.0 1317 71 \\n\",\n \"\\n\",\n \" AVE SR-B ... COUNTRY_NZ COUNTRY_PAK COUNTRY_SA COUNTRY_SL \\\\\\n\",\n \"0 0.00 0.00 ... 0 0 1 0 \\n\",\n \"1 0.00 0.00 ... 0 0 0 0 \\n\",\n \"2 18.56 121.01 ... 0 0 0 0 \\n\",\n \"3 5.80 76.32 ... 0 0 0 0 \\n\",\n \"4 32.93 120.71 ... 0 0 0 0 \\n\",\n \"\\n\",\n \" COUNTRY_WI COUNTRY_ZIM PLAYING ROLE_Batsman PLAYING ROLE_Bowler \\\\\\n\",\n \"0 0 0 0 0 \\n\",\n \"1 0 0 0 1 \\n\",\n \"2 0 0 0 1 \\n\",\n \"3 0 0 0 1 \\n\",\n \"4 0 0 1 0 \\n\",\n \"\\n\",\n \" PLAYING ROLE_W. Keeper CAPTAINCY EXP_1 \\n\",\n \"0 0 0 \\n\",\n \"1 0 0 \\n\",\n \"2 0 0 \\n\",\n \"3 0 0 \\n\",\n \"4 0 0 \\n\",\n \"\\n\",\n \"[5 rows x 31 columns]\"\n ]\n },\n \"execution_count\": 9,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"xfeatures = encoded_df.columns\\n\",\n \"\\n\",\n \"encoded_df.head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"id\": \"8af55ff4\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from sklearn.model_selection import train_test_split\\n\",\n \"\\n\",\n \"x = sm.add_constant(encoded_df)\\n\",\n \"\\n\",\n \"y = df['SOLD PRICE']\\n\",\n \"\\n\",\n \"xtrain, xtest, ytrain, ytest = train_test_split(x, y,\\n\",\n \" train_size=0.8,\\n\",\n \" random_state=101)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"id\": \"d5cbd477\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"
Model: OLS Adj. R-squared: 0.367
Dependent Variable: SOLD PRICE AIC: 2952.4618
Date: 2023-04-24 21:08 BIC: 3034.4379
No. Observations: 104 Log-Likelihood: -1445.2
Df Model: 30 F-statistic: 2.989
Df Residuals: 73 Prob (F-statistic): 7.43e-05
R-squared: 0.551 Scale: 9.8071e+10
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Coef. Std.Err. t P>|t| [0.025 0.975]
const 321792.1559 214961.4272 1.4970 0.1387 -106625.3506 750209.6624
T-RUNS -15.3217 31.7900 -0.4820 0.6313 -78.6790 48.0356
T-WKTS -92.1211 532.7482 -0.1729 0.8632 -1153.8869 969.6446
ODI-RUNS-S 46.4615 32.7116 1.4203 0.1598 -18.7326 111.6555
ODI-SR-B 1254.6514 1524.3920 0.8231 0.4132 -1783.4575 4292.7602
ODI-WKTS 1034.2327 705.8108 1.4653 0.1471 -372.4462 2440.9116
ODI-SR-BL -1951.6835 1570.2876 -1.2429 0.2179 -5081.2621 1177.8952
RUNS-S 79.5617 164.1206 0.4848 0.6293 -247.5303 406.6536
HS -2845.5968 2659.2674 -1.0701 0.2881 -8145.5090 2454.3155
AVE 16170.0170 7116.7912 2.2721 0.0260 1986.2726 30353.7615
SR-B -1412.9785 1268.6500 -1.1138 0.2690 -3941.3943 1115.4373
SIXERS 2933.8502 3250.6472 0.9025 0.3697 -3544.6804 9412.3808
RUNS-C 90.3731 250.6836 0.3605 0.7195 -409.2387 589.9848
WKTS 1023.5083 6199.6413 0.1651 0.8693 -11332.3589 13379.3755
AVE-BL 10484.2466 10614.6012 0.9877 0.3266 -10670.6230 31639.1163
ECON 1582.7941 10799.9290 0.1466 0.8839 -19941.4334 23107.0217
SR-BL -16112.2676 14683.6651 -1.0973 0.2761 -45376.7699 13152.2347
AGE_2 -204982.5473 122684.6949 -1.6708 0.0990 -449492.7915 39527.6970
AGE_3 -314011.4605 166581.1800 -1.8850 0.0634 -646007.2734 17984.3525
COUNTRY_BAN -0.0000 0.0000 -0.4054 0.6864 -0.0000 0.0000
COUNTRY_ENG 568937.2590 223554.3597 2.5450 0.0130 123394.0628 1014480.4552
COUNTRY_IND 105574.3654 116110.3502 0.9093 0.3662 -125833.2288 336981.9597
COUNTRY_NZ 120771.0634 169814.2346 0.7112 0.4792 -217668.2181 459210.3449
COUNTRY_PAK -18339.1812 162409.9584 -0.1129 0.9104 -342021.7614 305343.3991
COUNTRY_SA -48587.6579 135853.6982 -0.3576 0.7216 -319343.6880 222168.3722
COUNTRY_SL -132932.0463 156295.0094 -0.8505 0.3978 -444427.5507 178563.4581
COUNTRY_WI -292568.4951 188284.0245 -1.5539 0.1245 -667818.0146 82681.0244
COUNTRY_ZIM -70610.1889 377372.9760 -0.1871 0.8521 -822713.4453 681493.0676
PLAYING ROLE_Batsman 45944.5780 131655.5496 0.3490 0.7281 -216444.5538 308333.7098
PLAYING ROLE_Bowler 103751.1060 110824.6388 0.9362 0.3523 -117122.0806 324624.2926
PLAYING ROLE_W. Keeper -89731.6479 187093.4020 -0.4796 0.6329 -462608.2602 283144.9645
CAPTAINCY EXP_1 71431.5146 116029.1349 0.6156 0.5401 -159814.2177 302677.2470
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"
Omnibus: 6.983 Durbin-Watson: 1.857
Prob(Omnibus): 0.030 Jarque-Bera (JB): 6.499
Skew: 0.583 Prob(JB): 0.039
Kurtosis: 3.376 Condition No.: 12332195260360820
\"\n ],\n \"text/plain\": [\n \"\\n\",\n \"\\\"\\\"\\\"\\n\",\n \" Results: Ordinary least squares\\n\",\n \"========================================================================================\\n\",\n \"Model: OLS Adj. R-squared: 0.367 \\n\",\n \"Dependent Variable: SOLD PRICE AIC: 2952.4618 \\n\",\n \"Date: 2023-04-24 21:08 BIC: 3034.4379 \\n\",\n \"No. Observations: 104 Log-Likelihood: -1445.2 \\n\",\n \"Df Model: 30 F-statistic: 2.989 \\n\",\n \"Df Residuals: 73 Prob (F-statistic): 7.43e-05 \\n\",\n \"R-squared: 0.551 Scale: 9.8071e+10\\n\",\n \"----------------------------------------------------------------------------------------\\n\",\n \" Coef. Std.Err. t P>|t| [0.025 0.975] \\n\",\n \"----------------------------------------------------------------------------------------\\n\",\n \"const 321792.1559 214961.4272 1.4970 0.1387 -106625.3506 750209.6624\\n\",\n \"T-RUNS -15.3217 31.7900 -0.4820 0.6313 -78.6790 48.0356\\n\",\n \"T-WKTS -92.1211 532.7482 -0.1729 0.8632 -1153.8869 969.6446\\n\",\n \"ODI-RUNS-S 46.4615 32.7116 1.4203 0.1598 -18.7326 111.6555\\n\",\n \"ODI-SR-B 1254.6514 1524.3920 0.8231 0.4132 -1783.4575 4292.7602\\n\",\n \"ODI-WKTS 1034.2327 705.8108 1.4653 0.1471 -372.4462 2440.9116\\n\",\n \"ODI-SR-BL -1951.6835 1570.2876 -1.2429 0.2179 -5081.2621 1177.8952\\n\",\n \"RUNS-S 79.5617 164.1206 0.4848 0.6293 -247.5303 406.6536\\n\",\n \"HS -2845.5968 2659.2674 -1.0701 0.2881 -8145.5090 2454.3155\\n\",\n \"AVE 16170.0170 7116.7912 2.2721 0.0260 1986.2726 30353.7615\\n\",\n \"SR-B -1412.9785 1268.6500 -1.1138 0.2690 -3941.3943 1115.4373\\n\",\n \"SIXERS 2933.8502 3250.6472 0.9025 0.3697 -3544.6804 9412.3808\\n\",\n \"RUNS-C 90.3731 250.6836 0.3605 0.7195 -409.2387 589.9848\\n\",\n \"WKTS 1023.5083 6199.6413 0.1651 0.8693 -11332.3589 13379.3755\\n\",\n \"AVE-BL 10484.2466 10614.6012 0.9877 0.3266 -10670.6230 31639.1163\\n\",\n \"ECON 1582.7941 10799.9290 0.1466 0.8839 -19941.4334 23107.0217\\n\",\n \"SR-BL -16112.2676 14683.6651 -1.0973 0.2761 -45376.7699 13152.2347\\n\",\n \"AGE_2 -204982.5473 122684.6949 -1.6708 0.0990 -449492.7915 39527.6970\\n\",\n \"AGE_3 -314011.4605 166581.1800 -1.8850 0.0634 -646007.2734 17984.3525\\n\",\n \"COUNTRY_BAN -0.0000 0.0000 -0.4054 0.6864 -0.0000 0.0000\\n\",\n \"COUNTRY_ENG 568937.2590 223554.3597 2.5450 0.0130 123394.0628 1014480.4552\\n\",\n \"COUNTRY_IND 105574.3654 116110.3502 0.9093 0.3662 -125833.2288 336981.9597\\n\",\n \"COUNTRY_NZ 120771.0634 169814.2346 0.7112 0.4792 -217668.2181 459210.3449\\n\",\n \"COUNTRY_PAK -18339.1812 162409.9584 -0.1129 0.9104 -342021.7614 305343.3991\\n\",\n \"COUNTRY_SA -48587.6579 135853.6982 -0.3576 0.7216 -319343.6880 222168.3722\\n\",\n \"COUNTRY_SL -132932.0463 156295.0094 -0.8505 0.3978 -444427.5507 178563.4581\\n\",\n \"COUNTRY_WI -292568.4951 188284.0245 -1.5539 0.1245 -667818.0146 82681.0244\\n\",\n \"COUNTRY_ZIM -70610.1889 377372.9760 -0.1871 0.8521 -822713.4453 681493.0676\\n\",\n \"PLAYING ROLE_Batsman 45944.5780 131655.5496 0.3490 0.7281 -216444.5538 308333.7098\\n\",\n \"PLAYING ROLE_Bowler 103751.1060 110824.6388 0.9362 0.3523 -117122.0806 324624.2926\\n\",\n \"PLAYING ROLE_W. Keeper -89731.6479 187093.4020 -0.4796 0.6329 -462608.2602 283144.9645\\n\",\n \"CAPTAINCY EXP_1 71431.5146 116029.1349 0.6156 0.5401 -159814.2177 302677.2470\\n\",\n \"----------------------------------------------------------------------------------------\\n\",\n \"Omnibus: 6.983 Durbin-Watson: 1.857 \\n\",\n \"Prob(Omnibus): 0.030 Jarque-Bera (JB): 6.499 \\n\",\n \"Skew: 0.583 Prob(JB): 0.039 \\n\",\n \"Kurtosis: 3.376 Condition No.: 12332195260360820\\n\",\n \"========================================================================================\\n\",\n \"* The condition number is large (1e+16). This might indicate strong\\n\",\n \"multicollinearity or other numerical problems.\\n\",\n \"\\\"\\\"\\\"\"\n ]\n },\n \"execution_count\": 11,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"model1 = sm.OLS(ytrain, xtrain).fit()\\n\",\n \"\\n\",\n \"model1.summary2()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"id\": \"ad35c43b\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"ename\": \"SyntaxError\",\n \"evalue\": \"invalid syntax (1120071531.py, line 3)\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[1;36m Input \\u001b[1;32mIn [12]\\u001b[1;36m\\u001b[0m\\n\\u001b[1;33m 95% 5%\\u001b[0m\\n\\u001b[1;37m ^\\u001b[0m\\n\\u001b[1;31mSyntaxError\\u001b[0m\\u001b[1;31m:\\u001b[0m invalid syntax\\n\"\n ]\n }\n ],\n \"source\": [\n \"p-value\\n\",\n \"\\n\",\n \"95% 5%\\n\",\n \"0.95 0.05\\n\",\n \"\\n\",\n \"\\n\",\n \"# VIF (Variance Inflation Factor)\\n\",\n \"\\n\",\n \"\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"id\": \"81de17e2\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from statsmodels.stats.outliers_influence import variance_inflation_factor\\n\",\n \"\\n\",\n \"def get_vif_score(x):\\n\",\n \" \\n\",\n \" xmatrix = x.values\\n\",\n \" vif = [variance_inflation_factor(xmatrix, i) for i in range(xmatrix.shape[1])]\\n\",\n \" \\n\",\n \" vif_factor = pd.DataFrame()\\n\",\n \" vif_factor['Column'] = x.columns\\n\",\n \" vif_factor['VIF'] = vif\\n\",\n \" \\n\",\n \" return vif_factor\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"id\": \"4368e988\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
ColumnVIF
0T-RUNS12.612694
1T-WKTS7.679284
2ODI-RUNS-S16.426209
3ODI-SR-B13.829376
4ODI-WKTS9.951800
5ODI-SR-BL4.426818
6RUNS-S16.135407
7HS22.781017
8AVE25.226566
9SR-B21.576204
10SIXERS9.547268
11RUNS-C38.229691
12WKTS33.366067
13AVE-BL100.198105
14ECON7.650140
15SR-BL103.723846
16AGE_26.996226
17AGE_33.855003
18COUNTRY_BAN1.469017
19COUNTRY_ENG1.391524
20COUNTRY_IND4.568898
21COUNTRY_NZ1.497856
22COUNTRY_PAK1.796355
23COUNTRY_SA1.886555
24COUNTRY_SL1.984902
25COUNTRY_WI1.531847
26COUNTRY_ZIM1.312168
27PLAYING ROLE_Batsman4.843136
28PLAYING ROLE_Bowler3.795864
29PLAYING ROLE_W. Keeper3.132044
30CAPTAINCY EXP_14.245128
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" Column VIF\\n\",\n \"0 T-RUNS 12.612694\\n\",\n \"1 T-WKTS 7.679284\\n\",\n \"2 ODI-RUNS-S 16.426209\\n\",\n \"3 ODI-SR-B 13.829376\\n\",\n \"4 ODI-WKTS 9.951800\\n\",\n \"5 ODI-SR-BL 4.426818\\n\",\n \"6 RUNS-S 16.135407\\n\",\n \"7 HS 22.781017\\n\",\n \"8 AVE 25.226566\\n\",\n \"9 SR-B 21.576204\\n\",\n \"10 SIXERS 9.547268\\n\",\n \"11 RUNS-C 38.229691\\n\",\n \"12 WKTS 33.366067\\n\",\n \"13 AVE-BL 100.198105\\n\",\n \"14 ECON 7.650140\\n\",\n \"15 SR-BL 103.723846\\n\",\n \"16 AGE_2 6.996226\\n\",\n \"17 AGE_3 3.855003\\n\",\n \"18 COUNTRY_BAN 1.469017\\n\",\n \"19 COUNTRY_ENG 1.391524\\n\",\n \"20 COUNTRY_IND 4.568898\\n\",\n \"21 COUNTRY_NZ 1.497856\\n\",\n \"22 COUNTRY_PAK 1.796355\\n\",\n \"23 COUNTRY_SA 1.886555\\n\",\n \"24 COUNTRY_SL 1.984902\\n\",\n \"25 COUNTRY_WI 1.531847\\n\",\n \"26 COUNTRY_ZIM 1.312168\\n\",\n \"27 PLAYING ROLE_Batsman 4.843136\\n\",\n \"28 PLAYING ROLE_Bowler 3.795864\\n\",\n \"29 PLAYING ROLE_W. Keeper 3.132044\\n\",\n \"30 CAPTAINCY EXP_1 4.245128\"\n ]\n },\n \"execution_count\": 14,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"vif_factor = get_vif_score(x[xfeatures])\\n\",\n \"\\n\",\n \"vif_factor\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"id\": \"21f1c866\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"columns_with_large_vif = vif_factor[vif_factor.VIF > 4].Column\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"id\": \"f6f77c94\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" 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T-RUNST-WKTSODI-RUNS-SODI-SR-BODI-WKTSODI-SR-BLRUNS-SHSAVESR-BSIXERSRUNS-CWKTSAVE-BLECONSR-BLAGE_2COUNTRY_INDPLAYING ROLE_BatsmanCAPTAINCY EXP_1
T-RUNS1.0000000.0262850.8928230.2314110.0455050.0677000.4101430.4112090.3740460.1142980.216571-0.253083-0.277157-0.298999-0.329022-0.309105-0.266649-0.1792780.3844220.690647
T-WKTS0.0262851.000000-0.0882760.0120520.8229400.060641-0.218544-0.268432-0.265540-0.147752-0.1980360.2973020.2897350.1624560.1175300.205208-0.178391-0.147490-0.2764500.088782
ODI-RUNS-S0.892823-0.0882761.0000000.3192640.0565540.1267950.5239550.4957650.4462800.1941110.376012-0.268950-0.302745-0.225109-0.249795-0.237517-0.242422-0.1682240.3679950.714058
ODI-SR-B0.2314110.0120520.3192641.0000000.1601140.2845840.3063380.3600860.3403270.3753710.3200410.004855-0.0225020.018958-0.0190670.017522-0.011977-0.1686090.0834850.291373
ODI-WKTS0.0455050.8229400.0565540.1601141.0000000.124361-0.198101-0.209109-0.222062-0.030404-0.1487220.3271220.3024660.2173020.2302210.259800-0.125708-0.187669-0.3588240.077536
ODI-SR-BL0.0677000.0606410.1267950.2845840.1243611.000000-0.027772-0.067615-0.053766-0.051684-0.0336160.1968870.1515940.4166270.3132950.402495-0.133908-0.022494-0.0146890.103315
RUNS-S0.410143-0.2185440.5239550.306338-0.198101-0.0277721.0000000.8346310.7670230.3800220.866213-0.160762-0.220959-0.101818-0.179459-0.118567-0.0857620.2092920.4185550.352422
HS0.411209-0.2684320.4957650.360086-0.209109-0.0676150.8346311.0000000.8762490.5310280.788439-0.235327-0.291494-0.176049-0.254494-0.192990-0.022883-0.0340290.4088260.392749
AVE0.374046-0.2655400.4462800.340327-0.222062-0.0537660.7670230.8762491.0000000.5835790.705365-0.278482-0.343958-0.120430-0.202301-0.1408090.031758-0.0378090.4324850.378271
SR-B0.114298-0.1477520.1941110.375371-0.030404-0.0516840.3800220.5310280.5835791.0000000.425394-0.063179-0.069823-0.057895-0.076228-0.0682480.181181-0.0490310.1579030.176919
SIXERS0.216571-0.1980360.3760120.320041-0.148722-0.0336160.8662130.7884390.7053650.4253941.000000-0.080295-0.144558-0.012210-0.101027-0.021412-0.0435160.1320990.2948960.254991
RUNS-C-0.2530830.297302-0.2689500.0048550.3271220.196887-0.160762-0.235327-0.278482-0.063179-0.0802951.0000000.9592100.4095050.4105020.4712240.0257220.266465-0.436411-0.277903
WKTS-0.2771570.289735-0.302745-0.0225020.3024660.151594-0.220959-0.291494-0.343958-0.069823-0.1445580.9592101.0000000.2978220.3748750.3682640.0444670.216827-0.422958-0.300074
AVE-BL-0.2989990.162456-0.2251090.0189580.2173020.416627-0.101818-0.176049-0.120430-0.057895-0.0122100.4095050.2978221.0000000.7227580.983415-0.0723860.149050-0.366341-0.183775
ECON-0.3290220.117530-0.249795-0.0190670.2302210.313295-0.179459-0.254494-0.202301-0.076228-0.1010270.4105020.3748750.7227581.0000000.7264800.0049420.065552-0.288471-0.248974
SR-BL-0.3091050.205208-0.2375170.0175220.2598000.402495-0.118567-0.192990-0.140809-0.068248-0.0214120.4712240.3682640.9834150.7264801.000000-0.0902490.126109-0.395188-0.198688
AGE_2-0.266649-0.178391-0.242422-0.011977-0.125708-0.133908-0.085762-0.0228830.0317580.181181-0.0435160.0257220.044467-0.0723860.004942-0.0902491.000000-0.002036-0.170277-0.109261
COUNTRY_IND-0.179278-0.147490-0.168224-0.168609-0.187669-0.0224940.209292-0.034029-0.037809-0.0490310.1320990.2664650.2168270.1490500.0655520.126109-0.0020361.0000000.071734-0.192539
PLAYING ROLE_Batsman0.384422-0.2764500.3679950.083485-0.358824-0.0146890.4185550.4088260.4324850.1579030.294896-0.436411-0.422958-0.366341-0.288471-0.395188-0.1702770.0717341.0000000.314284
CAPTAINCY EXP_10.6906470.0887820.7140580.2913730.0775360.1033150.3524220.3927490.3782710.1769190.254991-0.277903-0.300074-0.183775-0.248974-0.198688-0.109261-0.1925390.3142841.000000
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" T-RUNS T-WKTS ODI-RUNS-S ODI-SR-B ODI-WKTS \\\\\\n\",\n \"T-RUNS 1.000000 0.026285 0.892823 0.231411 0.045505 \\n\",\n \"T-WKTS 0.026285 1.000000 -0.088276 0.012052 0.822940 \\n\",\n \"ODI-RUNS-S 0.892823 -0.088276 1.000000 0.319264 0.056554 \\n\",\n \"ODI-SR-B 0.231411 0.012052 0.319264 1.000000 0.160114 \\n\",\n \"ODI-WKTS 0.045505 0.822940 0.056554 0.160114 1.000000 \\n\",\n \"ODI-SR-BL 0.067700 0.060641 0.126795 0.284584 0.124361 \\n\",\n \"RUNS-S 0.410143 -0.218544 0.523955 0.306338 -0.198101 \\n\",\n \"HS 0.411209 -0.268432 0.495765 0.360086 -0.209109 \\n\",\n \"AVE 0.374046 -0.265540 0.446280 0.340327 -0.222062 \\n\",\n \"SR-B 0.114298 -0.147752 0.194111 0.375371 -0.030404 \\n\",\n \"SIXERS 0.216571 -0.198036 0.376012 0.320041 -0.148722 \\n\",\n \"RUNS-C -0.253083 0.297302 -0.268950 0.004855 0.327122 \\n\",\n \"WKTS -0.277157 0.289735 -0.302745 -0.022502 0.302466 \\n\",\n \"AVE-BL -0.298999 0.162456 -0.225109 0.018958 0.217302 \\n\",\n \"ECON -0.329022 0.117530 -0.249795 -0.019067 0.230221 \\n\",\n \"SR-BL -0.309105 0.205208 -0.237517 0.017522 0.259800 \\n\",\n \"AGE_2 -0.266649 -0.178391 -0.242422 -0.011977 -0.125708 \\n\",\n \"COUNTRY_IND -0.179278 -0.147490 -0.168224 -0.168609 -0.187669 \\n\",\n \"PLAYING ROLE_Batsman 0.384422 -0.276450 0.367995 0.083485 -0.358824 \\n\",\n \"CAPTAINCY EXP_1 0.690647 0.088782 0.714058 0.291373 0.077536 \\n\",\n \"\\n\",\n \" ODI-SR-BL RUNS-S HS AVE SR-B \\\\\\n\",\n \"T-RUNS 0.067700 0.410143 0.411209 0.374046 0.114298 \\n\",\n \"T-WKTS 0.060641 -0.218544 -0.268432 -0.265540 -0.147752 \\n\",\n \"ODI-RUNS-S 0.126795 0.523955 0.495765 0.446280 0.194111 \\n\",\n \"ODI-SR-B 0.284584 0.306338 0.360086 0.340327 0.375371 \\n\",\n \"ODI-WKTS 0.124361 -0.198101 -0.209109 -0.222062 -0.030404 \\n\",\n \"ODI-SR-BL 1.000000 -0.027772 -0.067615 -0.053766 -0.051684 \\n\",\n \"RUNS-S -0.027772 1.000000 0.834631 0.767023 0.380022 \\n\",\n \"HS -0.067615 0.834631 1.000000 0.876249 0.531028 \\n\",\n \"AVE -0.053766 0.767023 0.876249 1.000000 0.583579 \\n\",\n \"SR-B -0.051684 0.380022 0.531028 0.583579 1.000000 \\n\",\n \"SIXERS -0.033616 0.866213 0.788439 0.705365 0.425394 \\n\",\n \"RUNS-C 0.196887 -0.160762 -0.235327 -0.278482 -0.063179 \\n\",\n \"WKTS 0.151594 -0.220959 -0.291494 -0.343958 -0.069823 \\n\",\n \"AVE-BL 0.416627 -0.101818 -0.176049 -0.120430 -0.057895 \\n\",\n \"ECON 0.313295 -0.179459 -0.254494 -0.202301 -0.076228 \\n\",\n \"SR-BL 0.402495 -0.118567 -0.192990 -0.140809 -0.068248 \\n\",\n \"AGE_2 -0.133908 -0.085762 -0.022883 0.031758 0.181181 \\n\",\n \"COUNTRY_IND -0.022494 0.209292 -0.034029 -0.037809 -0.049031 \\n\",\n \"PLAYING ROLE_Batsman -0.014689 0.418555 0.408826 0.432485 0.157903 \\n\",\n \"CAPTAINCY EXP_1 0.103315 0.352422 0.392749 0.378271 0.176919 \\n\",\n \"\\n\",\n \" SIXERS RUNS-C WKTS AVE-BL ECON \\\\\\n\",\n \"T-RUNS 0.216571 -0.253083 -0.277157 -0.298999 -0.329022 \\n\",\n \"T-WKTS -0.198036 0.297302 0.289735 0.162456 0.117530 \\n\",\n \"ODI-RUNS-S 0.376012 -0.268950 -0.302745 -0.225109 -0.249795 \\n\",\n \"ODI-SR-B 0.320041 0.004855 -0.022502 0.018958 -0.019067 \\n\",\n \"ODI-WKTS -0.148722 0.327122 0.302466 0.217302 0.230221 \\n\",\n \"ODI-SR-BL -0.033616 0.196887 0.151594 0.416627 0.313295 \\n\",\n \"RUNS-S 0.866213 -0.160762 -0.220959 -0.101818 -0.179459 \\n\",\n \"HS 0.788439 -0.235327 -0.291494 -0.176049 -0.254494 \\n\",\n \"AVE 0.705365 -0.278482 -0.343958 -0.120430 -0.202301 \\n\",\n \"SR-B 0.425394 -0.063179 -0.069823 -0.057895 -0.076228 \\n\",\n \"SIXERS 1.000000 -0.080295 -0.144558 -0.012210 -0.101027 \\n\",\n \"RUNS-C -0.080295 1.000000 0.959210 0.409505 0.410502 \\n\",\n \"WKTS -0.144558 0.959210 1.000000 0.297822 0.374875 \\n\",\n \"AVE-BL -0.012210 0.409505 0.297822 1.000000 0.722758 \\n\",\n \"ECON -0.101027 0.410502 0.374875 0.722758 1.000000 \\n\",\n \"SR-BL -0.021412 0.471224 0.368264 0.983415 0.726480 \\n\",\n \"AGE_2 -0.043516 0.025722 0.044467 -0.072386 0.004942 \\n\",\n \"COUNTRY_IND 0.132099 0.266465 0.216827 0.149050 0.065552 \\n\",\n \"PLAYING ROLE_Batsman 0.294896 -0.436411 -0.422958 -0.366341 -0.288471 \\n\",\n \"CAPTAINCY EXP_1 0.254991 -0.277903 -0.300074 -0.183775 -0.248974 \\n\",\n \"\\n\",\n \" SR-BL AGE_2 COUNTRY_IND PLAYING ROLE_Batsman \\\\\\n\",\n \"T-RUNS -0.309105 -0.266649 -0.179278 0.384422 \\n\",\n \"T-WKTS 0.205208 -0.178391 -0.147490 -0.276450 \\n\",\n \"ODI-RUNS-S -0.237517 -0.242422 -0.168224 0.367995 \\n\",\n \"ODI-SR-B 0.017522 -0.011977 -0.168609 0.083485 \\n\",\n \"ODI-WKTS 0.259800 -0.125708 -0.187669 -0.358824 \\n\",\n \"ODI-SR-BL 0.402495 -0.133908 -0.022494 -0.014689 \\n\",\n \"RUNS-S -0.118567 -0.085762 0.209292 0.418555 \\n\",\n \"HS -0.192990 -0.022883 -0.034029 0.408826 \\n\",\n \"AVE -0.140809 0.031758 -0.037809 0.432485 \\n\",\n \"SR-B -0.068248 0.181181 -0.049031 0.157903 \\n\",\n \"SIXERS -0.021412 -0.043516 0.132099 0.294896 \\n\",\n \"RUNS-C 0.471224 0.025722 0.266465 -0.436411 \\n\",\n \"WKTS 0.368264 0.044467 0.216827 -0.422958 \\n\",\n \"AVE-BL 0.983415 -0.072386 0.149050 -0.366341 \\n\",\n \"ECON 0.726480 0.004942 0.065552 -0.288471 \\n\",\n \"SR-BL 1.000000 -0.090249 0.126109 -0.395188 \\n\",\n \"AGE_2 -0.090249 1.000000 -0.002036 -0.170277 \\n\",\n \"COUNTRY_IND 0.126109 -0.002036 1.000000 0.071734 \\n\",\n \"PLAYING ROLE_Batsman -0.395188 -0.170277 0.071734 1.000000 \\n\",\n \"CAPTAINCY EXP_1 -0.198688 -0.109261 -0.192539 0.314284 \\n\",\n \"\\n\",\n \" CAPTAINCY EXP_1 \\n\",\n \"T-RUNS 0.690647 \\n\",\n \"T-WKTS 0.088782 \\n\",\n \"ODI-RUNS-S 0.714058 \\n\",\n \"ODI-SR-B 0.291373 \\n\",\n \"ODI-WKTS 0.077536 \\n\",\n \"ODI-SR-BL 0.103315 \\n\",\n \"RUNS-S 0.352422 \\n\",\n \"HS 0.392749 \\n\",\n \"AVE 0.378271 \\n\",\n \"SR-B 0.176919 \\n\",\n \"SIXERS 0.254991 \\n\",\n \"RUNS-C -0.277903 \\n\",\n \"WKTS -0.300074 \\n\",\n \"AVE-BL -0.183775 \\n\",\n \"ECON -0.248974 \\n\",\n \"SR-BL -0.198688 \\n\",\n \"AGE_2 -0.109261 \\n\",\n \"COUNTRY_IND -0.192539 \\n\",\n \"PLAYING ROLE_Batsman 0.314284 \\n\",\n \"CAPTAINCY EXP_1 1.000000 \"\n ]\n },\n \"execution_count\": 16,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"x[columns_with_large_vif].corr()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"id\": \"8eb9de04\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Text(0.5, 1.0, 'Correlation between Features')\"\n ]\n },\n \"execution_count\": 17,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"# correlation matrix\\n\",\n \"\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"import seaborn as sns\\n\",\n \"\\n\",\n \"plt.figure(figsize=(12,10))\\n\",\n \"\\n\",\n \"sns.heatmap(x[columns_with_large_vif].corr(), annot=True)\\n\",\n \"\\n\",\n \"plt.title(\\\"Correlation between Features\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"id\": \"b369ed31\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"['ODI-WKTS',\\n\",\n \" 'COUNTRY_IND',\\n\",\n \" 'COUNTRY_PAK',\\n\",\n \" 'PLAYING ROLE_Batsman',\\n\",\n \" 'COUNTRY_ENG',\\n\",\n \" 'COUNTRY_NZ',\\n\",\n \" 'COUNTRY_SL',\\n\",\n \" 'COUNTRY_SA',\\n\",\n \" 'PLAYING ROLE_Bowler',\\n\",\n \" 'SIXERS',\\n\",\n \" 'COUNTRY_ZIM',\\n\",\n \" 'WKTS',\\n\",\n \" 'PLAYING ROLE_W. Keeper',\\n\",\n \" 'AGE_3',\\n\",\n \" 'CAPTAINCY EXP_1',\\n\",\n \" 'ODI-SR-BL',\\n\",\n \" 'COUNTRY_WI',\\n\",\n \" 'COUNTRY_BAN']\"\n ]\n },\n \"execution_count\": 19,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"columns_to_be_removed = [\\n\",\n \" 'T-RUNS', 'T-WKTS', 'RUNS-S', 'HS', 'AVE', 'RUNS-C', 'SR-B',\\n\",\n \" 'AVE-BL', 'ECON', 'ODI-SR-B', 'ODI-RUNS-S', 'AGE_2', 'SR-BL'\\n\",\n \"]\\n\",\n \"\\n\",\n \"xnewfeatures = list( set(xfeatures) - set(columns_to_be_removed) )\\n\",\n \"xnewfeatures\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"id\": \"a03c4574\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
ColumnVIF
0ODI-WKTS2.742889
1COUNTRY_IND3.144668
2COUNTRY_PAK1.334773
3PLAYING ROLE_Batsman2.680207
4COUNTRY_ENG1.131869
5COUNTRY_NZ1.173418
6COUNTRY_SL1.519752
7COUNTRY_SA1.416657
8PLAYING ROLE_Bowler3.060168
9SIXERS2.397409
10COUNTRY_ZIM1.205305
11WKTS2.883101
12PLAYING ROLE_W. Keeper1.900941
13AGE_31.779861
14CAPTAINCY EXP_12.458745
15ODI-SR-BL2.822148
16COUNTRY_WI1.194093
17COUNTRY_BAN1.094293
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" Column VIF\\n\",\n \"0 ODI-WKTS 2.742889\\n\",\n \"1 COUNTRY_IND 3.144668\\n\",\n \"2 COUNTRY_PAK 1.334773\\n\",\n \"3 PLAYING ROLE_Batsman 2.680207\\n\",\n \"4 COUNTRY_ENG 1.131869\\n\",\n \"5 COUNTRY_NZ 1.173418\\n\",\n \"6 COUNTRY_SL 1.519752\\n\",\n \"7 COUNTRY_SA 1.416657\\n\",\n \"8 PLAYING ROLE_Bowler 3.060168\\n\",\n \"9 SIXERS 2.397409\\n\",\n \"10 COUNTRY_ZIM 1.205305\\n\",\n \"11 WKTS 2.883101\\n\",\n \"12 PLAYING ROLE_W. Keeper 1.900941\\n\",\n \"13 AGE_3 1.779861\\n\",\n \"14 CAPTAINCY EXP_1 2.458745\\n\",\n \"15 ODI-SR-BL 2.822148\\n\",\n \"16 COUNTRY_WI 1.194093\\n\",\n \"17 COUNTRY_BAN 1.094293\"\n ]\n },\n \"execution_count\": 20,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"get_vif_score(x[xnewfeatures])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"id\": \"d36ca194\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"C:\\\\Users\\\\himan\\\\anaconda3\\\\lib\\\\site-packages\\\\statsmodels\\\\regression\\\\linear_model.py:1918: RuntimeWarning: divide by zero encountered in double_scalars\\n\",\n \" return np.sqrt(eigvals[0]/eigvals[-1])\\n\"\n ]\n },\n {\n \"data\": {\n \"text/html\": [\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"
Model: OLS Adj. R-squared (uncentered): 0.746
Dependent Variable: SOLD PRICE AIC: 2953.6299
Date: 2023-04-24 21:26 BIC: 2998.5846
No. Observations: 104 Log-Likelihood: -1459.8
Df Model: 17 F-statistic: 19.01
Df Residuals: 87 Prob (F-statistic): 2.46e-22
R-squared (uncentered): 0.788 Scale: 1.0893e+11
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"
Coef. Std.Err. t P>|t| [0.025 0.975]
ODI-WKTS 1000.2719 383.6520 2.6072 0.0107 237.7220 1762.8218
COUNTRY_IND 241916.0736 90265.6898 2.6800 0.0088 62503.2409 421328.9062
COUNTRY_PAK 60298.7726 141684.1935 0.4256 0.6715 -221313.9038 341911.4489
PLAYING ROLE_Batsman 177457.3060 100597.5501 1.7640 0.0812 -22491.2177 377405.8296
COUNTRY_ENG 678083.6712 208244.3343 3.2562 0.0016 264175.5079 1091991.8346
COUNTRY_NZ 147178.4532 150707.9524 0.9766 0.3315 -152369.9212 446726.8275
COUNTRY_SL -24754.1190 143387.0044 -0.1726 0.8633 -309751.3164 260243.0784
COUNTRY_SA 81761.5043 118244.2325 0.6915 0.4911 -153261.7115 316784.7202
PLAYING ROLE_Bowler 70814.4129 98253.6432 0.7207 0.4730 -124475.3419 266104.1678
SIXERS 6898.3644 1658.7173 4.1589 0.0001 3601.4841 10195.2446
COUNTRY_ZIM -138202.6253 374302.3654 -0.3692 0.7129 -882169.1065 605763.8559
WKTS 1815.9081 1930.2590 0.9408 0.3494 -2020.6907 5652.5069
PLAYING ROLE_W. Keeper 28857.7846 150328.1606 0.1920 0.8482 -269935.7123 327651.2816
AGE_3 -56497.7482 101704.1167 -0.5555 0.5800 -258645.6927 145650.1963
CAPTAINCY EXP_1 198466.1639 93102.8927 2.1317 0.0359 13414.0834 383518.2445
ODI-SR-BL 806.6222 1262.5348 0.6389 0.5246 -1702.8024 3316.0468
COUNTRY_WI -204026.7967 164456.0856 -1.2406 0.2181 -530901.0743 122847.4810
COUNTRY_BAN 0.0000 0.0000 nan nan 0.0000 0.0000
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"
Omnibus: 16.784 Durbin-Watson: 2.074
Prob(Omnibus): 0.000 Jarque-Bera (JB): 19.711
Skew: 0.911 Prob(JB): 0.000
Kurtosis: 4.107 Condition No.: inf
\"\n ],\n \"text/plain\": [\n \"\\n\",\n \"\\\"\\\"\\\"\\n\",\n \" Results: Ordinary least squares\\n\",\n \"========================================================================================\\n\",\n \"Model: OLS Adj. R-squared (uncentered): 0.746 \\n\",\n \"Dependent Variable: SOLD PRICE AIC: 2953.6299 \\n\",\n \"Date: 2023-04-24 21:26 BIC: 2998.5846 \\n\",\n \"No. Observations: 104 Log-Likelihood: -1459.8 \\n\",\n \"Df Model: 17 F-statistic: 19.01 \\n\",\n \"Df Residuals: 87 Prob (F-statistic): 2.46e-22 \\n\",\n \"R-squared (uncentered): 0.788 Scale: 1.0893e+11\\n\",\n \"----------------------------------------------------------------------------------------\\n\",\n \" Coef. Std.Err. t P>|t| [0.025 0.975] \\n\",\n \"----------------------------------------------------------------------------------------\\n\",\n \"ODI-WKTS 1000.2719 383.6520 2.6072 0.0107 237.7220 1762.8218\\n\",\n \"COUNTRY_IND 241916.0736 90265.6898 2.6800 0.0088 62503.2409 421328.9062\\n\",\n \"COUNTRY_PAK 60298.7726 141684.1935 0.4256 0.6715 -221313.9038 341911.4489\\n\",\n \"PLAYING ROLE_Batsman 177457.3060 100597.5501 1.7640 0.0812 -22491.2177 377405.8296\\n\",\n \"COUNTRY_ENG 678083.6712 208244.3343 3.2562 0.0016 264175.5079 1091991.8346\\n\",\n \"COUNTRY_NZ 147178.4532 150707.9524 0.9766 0.3315 -152369.9212 446726.8275\\n\",\n \"COUNTRY_SL -24754.1190 143387.0044 -0.1726 0.8633 -309751.3164 260243.0784\\n\",\n \"COUNTRY_SA 81761.5043 118244.2325 0.6915 0.4911 -153261.7115 316784.7202\\n\",\n \"PLAYING ROLE_Bowler 70814.4129 98253.6432 0.7207 0.4730 -124475.3419 266104.1678\\n\",\n \"SIXERS 6898.3644 1658.7173 4.1589 0.0001 3601.4841 10195.2446\\n\",\n \"COUNTRY_ZIM -138202.6253 374302.3654 -0.3692 0.7129 -882169.1065 605763.8559\\n\",\n \"WKTS 1815.9081 1930.2590 0.9408 0.3494 -2020.6907 5652.5069\\n\",\n \"PLAYING ROLE_W. Keeper 28857.7846 150328.1606 0.1920 0.8482 -269935.7123 327651.2816\\n\",\n \"AGE_3 -56497.7482 101704.1167 -0.5555 0.5800 -258645.6927 145650.1963\\n\",\n \"CAPTAINCY EXP_1 198466.1639 93102.8927 2.1317 0.0359 13414.0834 383518.2445\\n\",\n \"ODI-SR-BL 806.6222 1262.5348 0.6389 0.5246 -1702.8024 3316.0468\\n\",\n \"COUNTRY_WI -204026.7967 164456.0856 -1.2406 0.2181 -530901.0743 122847.4810\\n\",\n \"COUNTRY_BAN 0.0000 0.0000 nan nan 0.0000 0.0000\\n\",\n \"----------------------------------------------------------------------------------------\\n\",\n \"Omnibus: 16.784 Durbin-Watson: 2.074 \\n\",\n \"Prob(Omnibus): 0.000 Jarque-Bera (JB): 19.711\\n\",\n \"Skew: 0.911 Prob(JB): 0.000 \\n\",\n \"Kurtosis: 4.107 Condition No.: inf \\n\",\n \"========================================================================================\\n\",\n \"* The condition number is large (inf). This might indicate strong\\n\",\n \"multicollinearity or other numerical problems.\\n\",\n \"\\\"\\\"\\\"\"\n ]\n },\n \"execution_count\": 21,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"xtrain = xtrain[xnewfeatures]\\n\",\n \"\\n\",\n \"model2 = sm.OLS(ytrain, xtrain).fit()\\n\",\n \"\\n\",\n \"model2.summary2()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"id\": \"1260dd00\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"
Model: OLS Adj. R-squared (uncentered): 0.690
Dependent Variable: SOLD PRICE AIC: 2963.0980
Date: 2023-04-24 21:31 BIC: 2973.6756
No. Observations: 104 Log-Likelihood: -1477.5
Df Model: 4 F-statistic: 58.80
Df Residuals: 100 Prob (F-statistic): 1.95e-25
R-squared (uncentered): 0.702 Scale: 1.3328e+11
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"
Coef. Std.Err. t P>|t| [0.025 0.975]
COUNTRY_IND 412178.1281 60936.7223 6.7640 0.0000 291281.4066 533074.8497
COUNTRY_ENG 737378.1579 215586.2418 3.4203 0.0009 309661.1943 1165095.1214
SIXERS 7185.4933 1509.6537 4.7597 0.0000 4190.3833 10180.6033
CAPTAINCY EXP_1 375372.1235 74692.9719 5.0255 0.0000 227183.3947 523560.8523
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"
Omnibus: 2.870 Durbin-Watson: 1.830
Prob(Omnibus): 0.238 Jarque-Bera (JB): 2.549
Skew: 0.383 Prob(JB): 0.280
Kurtosis: 3.036 Condition No.: 176
\"\n ],\n \"text/plain\": [\n \"\\n\",\n \"\\\"\\\"\\\"\\n\",\n \" Results: Ordinary least squares\\n\",\n \"================================================================================\\n\",\n \"Model: OLS Adj. R-squared (uncentered): 0.690 \\n\",\n \"Dependent Variable: SOLD PRICE AIC: 2963.0980 \\n\",\n \"Date: 2023-04-24 21:31 BIC: 2973.6756 \\n\",\n \"No. Observations: 104 Log-Likelihood: -1477.5 \\n\",\n \"Df Model: 4 F-statistic: 58.80 \\n\",\n \"Df Residuals: 100 Prob (F-statistic): 1.95e-25 \\n\",\n \"R-squared (uncentered): 0.702 Scale: 1.3328e+11\\n\",\n \"--------------------------------------------------------------------------------\\n\",\n \" Coef. Std.Err. t P>|t| [0.025 0.975] \\n\",\n \"--------------------------------------------------------------------------------\\n\",\n \"COUNTRY_IND 412178.1281 60936.7223 6.7640 0.0000 291281.4066 533074.8497\\n\",\n \"COUNTRY_ENG 737378.1579 215586.2418 3.4203 0.0009 309661.1943 1165095.1214\\n\",\n \"SIXERS 7185.4933 1509.6537 4.7597 0.0000 4190.3833 10180.6033\\n\",\n \"CAPTAINCY EXP_1 375372.1235 74692.9719 5.0255 0.0000 227183.3947 523560.8523\\n\",\n \"--------------------------------------------------------------------------------\\n\",\n \"Omnibus: 2.870 Durbin-Watson: 1.830\\n\",\n \"Prob(Omnibus): 0.238 Jarque-Bera (JB): 2.549\\n\",\n \"Skew: 0.383 Prob(JB): 0.280\\n\",\n \"Kurtosis: 3.036 Condition No.: 176 \\n\",\n \"================================================================================\\n\",\n \"\\n\",\n \"\\\"\\\"\\\"\"\n ]\n },\n \"execution_count\": 22,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"significant_variables = ['COUNTRY_IND','COUNTRY_ENG','SIXERS','CAPTAINCY EXP_1']\\n\",\n \"\\n\",\n \"model2 = sm.OLS(ytrain, xtrain[significant_variables]).fit()\\n\",\n \"\\n\",\n \"model2.summary2()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"56344941\",\n \"metadata\": {},\n \"source\": [\n \"## hypothesis\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"e4f353f4\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"1. Null Hypothesis H0: it is considered ture till it is proven false.\\n\",\n \" \\n\",\n \"2. Alternative Hypothesis: it is the negation of the Null Hypothesis.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"7f705a97\",\n \"metadata\": {},\n \"source\": [\n \"## Select a test\\n\",\n \"\\n\",\n \"it is used to accept or reject the alternative hypothesis.\\n\",\n \"\\n\",\n \"Result: Test Statistics\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"72ffcc59\",\n \"metadata\": {},\n \"source\": [\n \"## p-value\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"1dad8cfb\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"you have to convert this test statistics into p-value(0-1)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"af38612c\",\n \"metadata\": {},\n \"source\": [\n \"## significance level\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"b3f51f20\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"5% or 0.05\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"e4a4bde6\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"a large p-value indicates that the results are due to luck.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"6dd95570\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.12\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n" }, { "path": "March Cohort 2023/12_15_16_17_18_Statistics/18_Hypothesis Testing.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"id\": \"c585a495\",\n \"metadata\": {},\n \"source\": [\n \"# Hypothesis Testing\\n\",\n \"Proposed Explanation\\n\",\n \"\\n\",\n \"1. Null Hypothesis H0: It is considered true till it proven false.\\n\",\n \"2. Alternative Hypothesis: It is the negation of the null hypothesis.\\n\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"8c03609b\",\n \"metadata\": {},\n \"source\": [\n \"Select a test: and apply it -> test statistics\\n\",\n \"\\n\",\n \"we need to convert this test statistics to a p-value\\n\",\n \"\\n\",\n \"#### significance level\\n\",\n \"threshold for luck -> 5%\\n\",\n \"\\n\",\n \"p-value < 0.05 -> we will reject the null hypothesis and accept the alternative hypothesis.\\n\",\n \"\\n\",\n \"#### a large p-value indicates that the results are due to luck.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"id\": \"1262f0c8\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# Lady tasting Tea\\n\",\n \"\\n\",\n \"#is it possible for you to tell weather the tea added before or after the milk?\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"4ebc5491\",\n \"metadata\": {},\n \"source\": [\n \"H0: the lady cannot tell if the milk was poured first.\\n\",\n \" (NH possess no relationship and no difference)\\n\",\n \"H1: the lady can tell if the milk was poured first.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"c46c9066\",\n \"metadata\": {},\n \"source\": [\n \"#### select a test\\n\",\n \"\\n\",\n \"8 cups of tea, 4 of each type\\n\",\n \"\\n\",\n \"Lady got all 8 correct -> test statistics\\n\",\n \"\\n\",\n \"8C4 -> 70 is the number of combinations\\n\",\n \"\\n\",\n \"p-value = 1/70 = 1.4%\\n\",\n \"\\n\",\n \"1.4% < 5% => Reject the H0\\n\",\n \"\\n\",\n \"\\n\",\n \"### Errors in Hypothesis Testing\\n\",\n \"\\n\",\n \"Type I error\\n\",\n \"When you reject the null hypothesis when it was actually true.\\n\",\n \"\\n\",\n \"Type II error\\n\",\n \"When we don't reject the null hypothesis and accept the alternative hypothesis.\\n\",\n \"\\n\",\n \"\\n\",\n \"### Power (Recall)\\n\",\n \"power of statistical test\\n\",\n \"\\n\",\n \"probability of rejecting H0 when H1 is true.\\n\",\n \"\\n\",\n \"If the value of power is high than it is good.\\n\",\n \"\\n\",\n \"if it is high that means the low probability of Type-II error\\n\",\n \"\\n\",\n \"\\n\",\n \"### alpha\\n\",\n \"\\n\",\n \"alpha is the probability of rejecting H0 when H0 is true.\\n\",\n \"\\n\",\n \"alpha = probability of commiting a Type-I error.\\n\",\n \"\\n\",\n \"if the test has high alpha value that is not good.\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"0cf026df\",\n \"metadata\": {},\n \"source\": [\n \"# Chi-Square Test\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"id\": \"a36e5cfa\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import scipy.stats as stats\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"id\": \"08662308\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import seaborn as sns\\n\",\n \"import pandas as pd\\n\",\n \"import numpy as np\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"id\": \"8316d566\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
total_billtipsexsmokerdaytimesize
016.991.01FemaleNoSunDinner2
110.341.66MaleNoSunDinner3
221.013.50MaleNoSunDinner3
323.683.31MaleNoSunDinner2
424.593.61FemaleNoSunDinner4
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" total_bill tip sex smoker day time size\\n\",\n \"0 16.99 1.01 Female No Sun Dinner 2\\n\",\n \"1 10.34 1.66 Male No Sun Dinner 3\\n\",\n \"2 21.01 3.50 Male No Sun Dinner 3\\n\",\n \"3 23.68 3.31 Male No Sun Dinner 2\\n\",\n \"4 24.59 3.61 Female No Sun Dinner 4\"\n ]\n },\n \"execution_count\": 8,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"dataset = sns.load_dataset('tips')\\n\",\n \"\\n\",\n \"dataset.head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"id\": \"4dd3d2da\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"smoker Yes No\\n\",\n \"sex \\n\",\n \"Male 60 97\\n\",\n \"Female 33 54\\n\"\n ]\n }\n ],\n \"source\": [\n \"dataset_table = pd.crosstab(dataset['sex'], dataset['smoker'])\\n\",\n \"\\n\",\n \"print(dataset_table)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"id\": \"2464bd7a\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[60, 97],\\n\",\n \" [33, 54]], dtype=int64)\"\n ]\n },\n \"execution_count\": 13,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"observed_values = dataset_table.values\\n\",\n \"observed_values\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"id\": \"3a55191e\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(0.0,\\n\",\n \" 1.0,\\n\",\n \" 1,\\n\",\n \" array([[59.84016393, 97.15983607],\\n\",\n \" [33.15983607, 53.84016393]]))\"\n ]\n },\n \"execution_count\": 11,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"val = stats.chi2_contingency(dataset_table)\\n\",\n \"\\n\",\n \"val\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"id\": \"5c52c6d3\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"expected_values = val[3]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"id\": \"55db1242\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from scipy.stats import chi2\\n\",\n \"\\n\",\n \"chi_square = sum([(o-e)**2./e for o, e in zip(observed_values, expected_values)])\\n\",\n \"\\n\",\n \"chi_square_stats = chi_square[0] + chi_square[1]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"id\": \"f1f1261b\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"0.001934818536627623\"\n ]\n },\n \"execution_count\": 15,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"chi_square_stats\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"id\": \"9ae6c359\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Degree of Freedom: 1\\n\"\n ]\n }\n ],\n \"source\": [\n \"no_of_rows = len(dataset_table.iloc[0:2,0])\\n\",\n \"no_of_columns = len(dataset_table.iloc[0,0:2])\\n\",\n \"ddof = (no_of_rows - 1) * (no_of_columns - 1)\\n\",\n \"\\n\",\n \"print(\\\"Degree of Freedom:\\\",ddof)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"id\": \"5d7188ed\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"alpha = 0.05\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"id\": \"6aa1816a\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Critical Value: 3.841458820694124\\n\"\n ]\n }\n ],\n \"source\": [\n \"critical_value = chi2.ppf(q=1-alpha, df=ddof)\\n\",\n \"\\n\",\n \"print(\\\"Critical Value:\\\", critical_value)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"id\": \"0dc7e08d\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"p-value: 0.964915107315732\\n\",\n \"Significance Level: 0.05\\n\",\n \"Degree of Freedom: 1\\n\"\n ]\n }\n ],\n \"source\": [\n \"# p-value\\n\",\n \"\\n\",\n \"p_value = 1 - chi2.cdf(x=chi_square_stats, df=ddof)\\n\",\n \"\\n\",\n \"print(\\\"p-value:\\\", p_value)\\n\",\n \"print(\\\"Significance Level:\\\", alpha)\\n\",\n \"print(\\\"Degree of Freedom:\\\", ddof)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"id\": \"cbcc9460\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Accepting H0, there is no relationship between 2 categorical variables\\n\"\n ]\n }\n ],\n \"source\": [\n \"if chi_square_stats >= critical_value:\\n\",\n \" print(\\\"Reject H0, there is a relationship between 2 categorical variables\\\")\\n\",\n \"else:\\n\",\n \" print(\\\"Accepting H0, there is no relationship between 2 categorical variables\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"id\": \"822ee03e\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Accepting H0, there is no relationship between 2 categorical variables\\n\"\n ]\n }\n ],\n \"source\": [\n \"if p_value <= alpha:\\n\",\n \" print(\\\"Reject H0, there is a relationship between 2 categorical variables\\\")\\n\",\n \"else:\\n\",\n \" print(\\\"Accepting H0, there is no relationship between 2 categorical variables\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"d4ee4b89\",\n \"metadata\": {},\n \"source\": [\n \"# t-test\\n\",\n \"\\n\",\n \"if there is a significant difference between the means of two groups\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"id\": \"497d7ce4\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"ages = [10,20,35,50,28,40,55,18,16,55,30,25,43,18,30,28,14,24,16,17,32,3,26,27,66,60]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"id\": \"c1d8bb90\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"26\"\n ]\n },\n \"execution_count\": 26,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"len(ages)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"id\": \"adcb366f\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"ages_mean = np.mean(ages)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 28,\n \"id\": \"3e10989c\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"30.23076923076923\"\n ]\n },\n \"execution_count\": 28,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"ages_mean\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 29,\n \"id\": \"320ad6c6\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([60, 18, 66, 32, 17, 55, 43, 17, 16, 55])\"\n ]\n },\n \"execution_count\": 29,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"sample_size = 10\\n\",\n \"\\n\",\n \"age_sample = np.random.choice(ages, sample_size)\\n\",\n \"\\n\",\n \"age_sample\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 30,\n \"id\": \"19a1f903\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"0.24751746592331406\\n\"\n ]\n }\n ],\n \"source\": [\n \"from scipy.stats import ttest_1samp\\n\",\n \"\\n\",\n \"ttest, p_value = ttest_1samp(age_sample, 30)\\n\",\n \"\\n\",\n \"print(p_value)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 31,\n \"id\": \"33a09441\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"We are accepting the Null Hypothesis\\n\"\n ]\n }\n ],\n \"source\": [\n \"if p_value < 0.05:\\n\",\n \" print(\\\"We are rejecting null hypothesis\\\")\\n\",\n \"else:\\n\",\n \" print(\\\"We are accepting the Null Hypothesis\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"d11fd400\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.12\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n" }, { "path": "March Cohort 2023/12_15_16_17_18_Statistics/datasets/Birthweight_reduced_kg_R.csv", "content": "ID,Length,Birthweight,Headcirc,Gestation,smoker,mage,mnocig,mheight,mppwt,fage,fedyrs,fnocig,fheight,lowbwt,mage35\n1360,56,4.55,34,44,0,20,0,162,57,23,10,35,179,0,0\n1016,53,4.32,36,40,0,19,0,171,62,19,12,0,183,0,0\n462,58,4.1,39,41,0,35,0,172,58,31,16,25,185,0,1\n1187,53,4.07,38,44,0,20,0,174,68,26,14,25,189,0,0\n553,54,3.94,37,42,0,24,0,175,66,30,12,0,184,0,0\n1636,51,3.93,38,38,0,29,0,165,61,31,16,0,180,0,0\n820,52,3.77,34,40,0,24,0,157,50,31,16,0,173,0,0\n1191,53,3.65,33,42,0,21,0,165,61,21,10,25,185,0,0\n1081,54,3.63,38,38,0,18,0,172,50,20,12,7,172,0,0\n822,50,3.42,35,38,0,20,0,157,48,22,14,0,179,0,0\n1683,53,3.35,33,41,0,27,0,164,62,37,14,0,170,0,0\n1088,51,3.27,36,40,0,24,0,168,53,29,16,0,181,0,0\n1107,52,3.23,36,38,0,31,0,164,57,35,16,0,183,0,0\n755,53,3.2,33,41,0,21,0,155,55,25,14,25,183,0,0\n1058,53,3.15,34,40,0,29,0,167,60,30,16,25,182,0,0\n321,48,3.11,33,37,0,28,0,158,54,39,10,0,171,0,0\n697,48,3.03,35,39,0,27,0,162,62,27,14,0,178,0,0\n808,48,2.92,33,34,0,26,0,167,64,25,12,25,175,0,0\n1600,53,2.9,34,39,0,19,0,165,57,23,14,2,193,0,0\n1313,43,2.65,32,33,0,24,0,149,45,26,16,0,169,1,0\n792,53,3.64,38,40,1,20,2,170,59,24,12,12,185,0,0\n1388,51,3.14,33,41,1,22,7,160,53,24,16,12,176,0,0\n575,50,2.78,30,37,1,19,7,165,60,20,14,0,183,0,0\n569,50,2.51,35,39,1,22,7,159,52,23,14,25,200,1,0\n1363,48,2.37,30,37,1,20,7,163,47,20,10,35,185,1,0\n300,46,2.05,32,35,1,41,7,166,57,37,14,25,173,1,1\n431,48,1.92,30,33,1,20,7,161,50,20,10,35,180,1,0\n1764,58,4.57,39,41,1,32,12,173,70,38,14,25,180,0,0\n532,53,3.59,34,40,1,31,12,163,49,41,12,50,191,0,0\n752,49,3.32,36,40,1,27,12,152,48,37,12,25,170,0,0\n1023,52,3,35,38,1,30,12,165,64,38,14,50,180,0,0\n57,51,3.32,38,39,1,23,17,157,48,32,12,25,169,0,0\n1522,50,2.74,33,39,1,21,17,156,53,24,12,7,179,0,0\n223,50,3.87,33,45,1,28,25,163,54,30,16,0,183,0,0\n272,52,3.86,36,39,1,30,25,170,78,40,16,50,178,0,0\n27,53,3.55,37,41,1,37,25,161,66,46,16,0,175,0,1\n365,52,3.53,37,40,1,26,25,170,62,30,10,25,181,0,0\n619,52,3.41,33,39,1,23,25,181,69,23,16,2,181,0,0\n1369,49,3.18,34,38,1,31,25,162,57,32,16,50,194,0,0\n1262,53,3.19,34,41,1,27,35,163,51,31,16,25,185,0,0\n516,47,2.66,33,35,1,20,35,170,57,23,12,50,186,1,0\n1272,53,2.75,32,40,1,37,50,168,61,31,16,0,173,0,1\n" }, { "path": "March Cohort 2023/12_15_16_17_18_Statistics/datasets/CarPrice_Assignment.csv", "content": "car_ID,symboling,CarName,fueltype,aspiration,doornumber,carbody,drivewheel,enginelocation,wheelbase,carlength,carwidth,carheight,curbweight,enginetype,cylindernumber,enginesize,fuelsystem,boreratio,stroke,compressionratio,horsepower,peakrpm,citympg,highwaympg,price\n1,3,alfa-romero giulia,gas,std,two,convertible,rwd,front,88.6,168.8,64.1,48.8,2548,dohc,four,130,mpfi,3.47,2.68,9,111,5000,21,27,13495\n2,3,alfa-romero stelvio,gas,std,two,convertible,rwd,front,88.6,168.8,64.1,48.8,2548,dohc,four,130,mpfi,3.47,2.68,9,111,5000,21,27,16500\n3,1,alfa-romero Quadrifoglio,gas,std,two,hatchback,rwd,front,94.5,171.2,65.5,52.4,2823,ohcv,six,152,mpfi,2.68,3.47,9,154,5000,19,26,16500\n4,2,audi 100 ls,gas,std,four,sedan,fwd,front,99.8,176.6,66.2,54.3,2337,ohc,four,109,mpfi,3.19,3.4,10,102,5500,24,30,13950\n5,2,audi 100ls,gas,std,four,sedan,4wd,front,99.4,176.6,66.4,54.3,2824,ohc,five,136,mpfi,3.19,3.4,8,115,5500,18,22,17450\n6,2,audi fox,gas,std,two,sedan,fwd,front,99.8,177.3,66.3,53.1,2507,ohc,five,136,mpfi,3.19,3.4,8.5,110,5500,19,25,15250\n7,1,audi 100ls,gas,std,four,sedan,fwd,front,105.8,192.7,71.4,55.7,2844,ohc,five,136,mpfi,3.19,3.4,8.5,110,5500,19,25,17710\n8,1,audi 5000,gas,std,four,wagon,fwd,front,105.8,192.7,71.4,55.7,2954,ohc,five,136,mpfi,3.19,3.4,8.5,110,5500,19,25,18920\n9,1,audi 4000,gas,turbo,four,sedan,fwd,front,105.8,192.7,71.4,55.9,3086,ohc,five,131,mpfi,3.13,3.4,8.3,140,5500,17,20,23875\n10,0,audi 5000s (diesel),gas,turbo,two,hatchback,4wd,front,99.5,178.2,67.9,52,3053,ohc,five,131,mpfi,3.13,3.4,7,160,5500,16,22,17859.167\n11,2,bmw 320i,gas,std,two,sedan,rwd,front,101.2,176.8,64.8,54.3,2395,ohc,four,108,mpfi,3.5,2.8,8.8,101,5800,23,29,16430\n12,0,bmw 320i,gas,std,four,sedan,rwd,front,101.2,176.8,64.8,54.3,2395,ohc,four,108,mpfi,3.5,2.8,8.8,101,5800,23,29,16925\n13,0,bmw x1,gas,std,two,sedan,rwd,front,101.2,176.8,64.8,54.3,2710,ohc,six,164,mpfi,3.31,3.19,9,121,4250,21,28,20970\n14,0,bmw x3,gas,std,four,sedan,rwd,front,101.2,176.8,64.8,54.3,2765,ohc,six,164,mpfi,3.31,3.19,9,121,4250,21,28,21105\n15,1,bmw z4,gas,std,four,sedan,rwd,front,103.5,189,66.9,55.7,3055,ohc,six,164,mpfi,3.31,3.19,9,121,4250,20,25,24565\n16,0,bmw x4,gas,std,four,sedan,rwd,front,103.5,189,66.9,55.7,3230,ohc,six,209,mpfi,3.62,3.39,8,182,5400,16,22,30760\n17,0,bmw x5,gas,std,two,sedan,rwd,front,103.5,193.8,67.9,53.7,3380,ohc,six,209,mpfi,3.62,3.39,8,182,5400,16,22,41315\n18,0,bmw x3,gas,std,four,sedan,rwd,front,110,197,70.9,56.3,3505,ohc,six,209,mpfi,3.62,3.39,8,182,5400,15,20,36880\n19,2,chevrolet impala,gas,std,two,hatchback,fwd,front,88.4,141.1,60.3,53.2,1488,l,three,61,2bbl,2.91,3.03,9.5,48,5100,47,53,5151\n20,1,chevrolet monte carlo,gas,std,two,hatchback,fwd,front,94.5,155.9,63.6,52,1874,ohc,four,90,2bbl,3.03,3.11,9.6,70,5400,38,43,6295\n21,0,chevrolet vega 2300,gas,std,four,sedan,fwd,front,94.5,158.8,63.6,52,1909,ohc,four,90,2bbl,3.03,3.11,9.6,70,5400,38,43,6575\n22,1,dodge rampage,gas,std,two,hatchback,fwd,front,93.7,157.3,63.8,50.8,1876,ohc,four,90,2bbl,2.97,3.23,9.41,68,5500,37,41,5572\n23,1,dodge challenger se,gas,std,two,hatchback,fwd,front,93.7,157.3,63.8,50.8,1876,ohc,four,90,2bbl,2.97,3.23,9.4,68,5500,31,38,6377\n24,1,dodge d200,gas,turbo,two,hatchback,fwd,front,93.7,157.3,63.8,50.8,2128,ohc,four,98,mpfi,3.03,3.39,7.6,102,5500,24,30,7957\n25,1,dodge monaco (sw),gas,std,four,hatchback,fwd,front,93.7,157.3,63.8,50.6,1967,ohc,four,90,2bbl,2.97,3.23,9.4,68,5500,31,38,6229\n26,1,dodge colt hardtop,gas,std,four,sedan,fwd,front,93.7,157.3,63.8,50.6,1989,ohc,four,90,2bbl,2.97,3.23,9.4,68,5500,31,38,6692\n27,1,dodge colt (sw),gas,std,four,sedan,fwd,front,93.7,157.3,63.8,50.6,1989,ohc,four,90,2bbl,2.97,3.23,9.4,68,5500,31,38,7609\n28,1,dodge coronet custom,gas,turbo,two,sedan,fwd,front,93.7,157.3,63.8,50.6,2191,ohc,four,98,mpfi,3.03,3.39,7.6,102,5500,24,30,8558\n29,-1,dodge dart custom,gas,std,four,wagon,fwd,front,103.3,174.6,64.6,59.8,2535,ohc,four,122,2bbl,3.34,3.46,8.5,88,5000,24,30,8921\n30,3,dodge coronet custom (sw),gas,turbo,two,hatchback,fwd,front,95.9,173.2,66.3,50.2,2811,ohc,four,156,mfi,3.6,3.9,7,145,5000,19,24,12964\n31,2,honda civic,gas,std,two,hatchback,fwd,front,86.6,144.6,63.9,50.8,1713,ohc,four,92,1bbl,2.91,3.41,9.6,58,4800,49,54,6479\n32,2,honda civic cvcc,gas,std,two,hatchback,fwd,front,86.6,144.6,63.9,50.8,1819,ohc,four,92,1bbl,2.91,3.41,9.2,76,6000,31,38,6855\n33,1,honda civic,gas,std,two,hatchback,fwd,front,93.7,150,64,52.6,1837,ohc,four,79,1bbl,2.91,3.07,10.1,60,5500,38,42,5399\n34,1,honda accord cvcc,gas,std,two,hatchback,fwd,front,93.7,150,64,52.6,1940,ohc,four,92,1bbl,2.91,3.41,9.2,76,6000,30,34,6529\n35,1,honda civic cvcc,gas,std,two,hatchback,fwd,front,93.7,150,64,52.6,1956,ohc,four,92,1bbl,2.91,3.41,9.2,76,6000,30,34,7129\n36,0,honda accord lx,gas,std,four,sedan,fwd,front,96.5,163.4,64,54.5,2010,ohc,four,92,1bbl,2.91,3.41,9.2,76,6000,30,34,7295\n37,0,honda civic 1500 gl,gas,std,four,wagon,fwd,front,96.5,157.1,63.9,58.3,2024,ohc,four,92,1bbl,2.92,3.41,9.2,76,6000,30,34,7295\n38,0,honda accord,gas,std,two,hatchback,fwd,front,96.5,167.5,65.2,53.3,2236,ohc,four,110,1bbl,3.15,3.58,9,86,5800,27,33,7895\n39,0,honda civic 1300,gas,std,two,hatchback,fwd,front,96.5,167.5,65.2,53.3,2289,ohc,four,110,1bbl,3.15,3.58,9,86,5800,27,33,9095\n40,0,honda prelude,gas,std,four,sedan,fwd,front,96.5,175.4,65.2,54.1,2304,ohc,four,110,1bbl,3.15,3.58,9,86,5800,27,33,8845\n41,0,honda accord,gas,std,four,sedan,fwd,front,96.5,175.4,62.5,54.1,2372,ohc,four,110,1bbl,3.15,3.58,9,86,5800,27,33,10295\n42,0,honda civic,gas,std,four,sedan,fwd,front,96.5,175.4,65.2,54.1,2465,ohc,four,110,mpfi,3.15,3.58,9,101,5800,24,28,12945\n43,1,honda civic (auto),gas,std,two,sedan,fwd,front,96.5,169.1,66,51,2293,ohc,four,110,2bbl,3.15,3.58,9.1,100,5500,25,31,10345\n44,0,isuzu MU-X,gas,std,four,sedan,rwd,front,94.3,170.7,61.8,53.5,2337,ohc,four,111,2bbl,3.31,3.23,8.5,78,4800,24,29,6785\n45,1,isuzu D-Max ,gas,std,two,sedan,fwd,front,94.5,155.9,63.6,52,1874,ohc,four,90,2bbl,3.03,3.11,9.6,70,5400,38,43,8916.5\n46,0,isuzu D-Max V-Cross,gas,std,four,sedan,fwd,front,94.5,155.9,63.6,52,1909,ohc,four,90,2bbl,3.03,3.11,9.6,70,5400,38,43,8916.5\n47,2,isuzu D-Max ,gas,std,two,hatchback,rwd,front,96,172.6,65.2,51.4,2734,ohc,four,119,spfi,3.43,3.23,9.2,90,5000,24,29,11048\n48,0,jaguar xj,gas,std,four,sedan,rwd,front,113,199.6,69.6,52.8,4066,dohc,six,258,mpfi,3.63,4.17,8.1,176,4750,15,19,32250\n49,0,jaguar xf,gas,std,four,sedan,rwd,front,113,199.6,69.6,52.8,4066,dohc,six,258,mpfi,3.63,4.17,8.1,176,4750,15,19,35550\n50,0,jaguar xk,gas,std,two,sedan,rwd,front,102,191.7,70.6,47.8,3950,ohcv,twelve,326,mpfi,3.54,2.76,11.5,262,5000,13,17,36000\n51,1,maxda rx3,gas,std,two,hatchback,fwd,front,93.1,159.1,64.2,54.1,1890,ohc,four,91,2bbl,3.03,3.15,9,68,5000,30,31,5195\n52,1,maxda glc deluxe,gas,std,two,hatchback,fwd,front,93.1,159.1,64.2,54.1,1900,ohc,four,91,2bbl,3.03,3.15,9,68,5000,31,38,6095\n53,1,mazda rx2 coupe,gas,std,two,hatchback,fwd,front,93.1,159.1,64.2,54.1,1905,ohc,four,91,2bbl,3.03,3.15,9,68,5000,31,38,6795\n54,1,mazda rx-4,gas,std,four,sedan,fwd,front,93.1,166.8,64.2,54.1,1945,ohc,four,91,2bbl,3.03,3.15,9,68,5000,31,38,6695\n55,1,mazda glc deluxe,gas,std,four,sedan,fwd,front,93.1,166.8,64.2,54.1,1950,ohc,four,91,2bbl,3.08,3.15,9,68,5000,31,38,7395\n56,3,mazda 626,gas,std,two,hatchback,rwd,front,95.3,169,65.7,49.6,2380,rotor,two,70,4bbl,3.33,3.255,9.4,101,6000,17,23,10945\n57,3,mazda glc,gas,std,two,hatchback,rwd,front,95.3,169,65.7,49.6,2380,rotor,two,70,4bbl,3.33,3.255,9.4,101,6000,17,23,11845\n58,3,mazda rx-7 gs,gas,std,two,hatchback,rwd,front,95.3,169,65.7,49.6,2385,rotor,two,70,4bbl,3.33,3.255,9.4,101,6000,17,23,13645\n59,3,mazda glc 4,gas,std,two,hatchback,rwd,front,95.3,169,65.7,49.6,2500,rotor,two,80,mpfi,3.33,3.255,9.4,135,6000,16,23,15645\n60,1,mazda 626,gas,std,two,hatchback,fwd,front,98.8,177.8,66.5,53.7,2385,ohc,four,122,2bbl,3.39,3.39,8.6,84,4800,26,32,8845\n61,0,mazda glc custom l,gas,std,four,sedan,fwd,front,98.8,177.8,66.5,55.5,2410,ohc,four,122,2bbl,3.39,3.39,8.6,84,4800,26,32,8495\n62,1,mazda glc custom,gas,std,two,hatchback,fwd,front,98.8,177.8,66.5,53.7,2385,ohc,four,122,2bbl,3.39,3.39,8.6,84,4800,26,32,10595\n63,0,mazda rx-4,gas,std,four,sedan,fwd,front,98.8,177.8,66.5,55.5,2410,ohc,four,122,2bbl,3.39,3.39,8.6,84,4800,26,32,10245\n64,0,mazda glc deluxe,diesel,std,four,sedan,fwd,front,98.8,177.8,66.5,55.5,2443,ohc,four,122,idi,3.39,3.39,22.7,64,4650,36,42,10795\n65,0,mazda 626,gas,std,four,hatchback,fwd,front,98.8,177.8,66.5,55.5,2425,ohc,four,122,2bbl,3.39,3.39,8.6,84,4800,26,32,11245\n66,0,mazda glc,gas,std,four,sedan,rwd,front,104.9,175,66.1,54.4,2670,ohc,four,140,mpfi,3.76,3.16,8,120,5000,19,27,18280\n67,0,mazda rx-7 gs,diesel,std,four,sedan,rwd,front,104.9,175,66.1,54.4,2700,ohc,four,134,idi,3.43,3.64,22,72,4200,31,39,18344\n68,-1,buick electra 225 custom,diesel,turbo,four,sedan,rwd,front,110,190.9,70.3,56.5,3515,ohc,five,183,idi,3.58,3.64,21.5,123,4350,22,25,25552\n69,-1,buick century luxus (sw),diesel,turbo,four,wagon,rwd,front,110,190.9,70.3,58.7,3750,ohc,five,183,idi,3.58,3.64,21.5,123,4350,22,25,28248\n70,0,buick century,diesel,turbo,two,hardtop,rwd,front,106.7,187.5,70.3,54.9,3495,ohc,five,183,idi,3.58,3.64,21.5,123,4350,22,25,28176\n71,-1,buick skyhawk,diesel,turbo,four,sedan,rwd,front,115.6,202.6,71.7,56.3,3770,ohc,five,183,idi,3.58,3.64,21.5,123,4350,22,25,31600\n72,-1,buick opel isuzu deluxe,gas,std,four,sedan,rwd,front,115.6,202.6,71.7,56.5,3740,ohcv,eight,234,mpfi,3.46,3.1,8.3,155,4750,16,18,34184\n73,3,buick skylark,gas,std,two,convertible,rwd,front,96.6,180.3,70.5,50.8,3685,ohcv,eight,234,mpfi,3.46,3.1,8.3,155,4750,16,18,35056\n74,0,buick century special,gas,std,four,sedan,rwd,front,120.9,208.1,71.7,56.7,3900,ohcv,eight,308,mpfi,3.8,3.35,8,184,4500,14,16,40960\n75,1,buick regal sport coupe (turbo),gas,std,two,hardtop,rwd,front,112,199.2,72,55.4,3715,ohcv,eight,304,mpfi,3.8,3.35,8,184,4500,14,16,45400\n76,1,mercury cougar,gas,turbo,two,hatchback,rwd,front,102.7,178.4,68,54.8,2910,ohc,four,140,mpfi,3.78,3.12,8,175,5000,19,24,16503\n77,2,mitsubishi mirage,gas,std,two,hatchback,fwd,front,93.7,157.3,64.4,50.8,1918,ohc,four,92,2bbl,2.97,3.23,9.4,68,5500,37,41,5389\n78,2,mitsubishi lancer,gas,std,two,hatchback,fwd,front,93.7,157.3,64.4,50.8,1944,ohc,four,92,2bbl,2.97,3.23,9.4,68,5500,31,38,6189\n79,2,mitsubishi outlander,gas,std,two,hatchback,fwd,front,93.7,157.3,64.4,50.8,2004,ohc,four,92,2bbl,2.97,3.23,9.4,68,5500,31,38,6669\n80,1,mitsubishi g4,gas,turbo,two,hatchback,fwd,front,93,157.3,63.8,50.8,2145,ohc,four,98,spdi,3.03,3.39,7.6,102,5500,24,30,7689\n81,3,mitsubishi mirage g4,gas,turbo,two,hatchback,fwd,front,96.3,173,65.4,49.4,2370,ohc,four,110,spdi,3.17,3.46,7.5,116,5500,23,30,9959\n82,3,mitsubishi g4,gas,std,two,hatchback,fwd,front,96.3,173,65.4,49.4,2328,ohc,four,122,2bbl,3.35,3.46,8.5,88,5000,25,32,8499\n83,3,mitsubishi outlander,gas,turbo,two,hatchback,fwd,front,95.9,173.2,66.3,50.2,2833,ohc,four,156,spdi,3.58,3.86,7,145,5000,19,24,12629\n84,3,mitsubishi g4,gas,turbo,two,hatchback,fwd,front,95.9,173.2,66.3,50.2,2921,ohc,four,156,spdi,3.59,3.86,7,145,5000,19,24,14869\n85,3,mitsubishi mirage g4,gas,turbo,two,hatchback,fwd,front,95.9,173.2,66.3,50.2,2926,ohc,four,156,spdi,3.59,3.86,7,145,5000,19,24,14489\n86,1,mitsubishi montero,gas,std,four,sedan,fwd,front,96.3,172.4,65.4,51.6,2365,ohc,four,122,2bbl,3.35,3.46,8.5,88,5000,25,32,6989\n87,1,mitsubishi pajero,gas,std,four,sedan,fwd,front,96.3,172.4,65.4,51.6,2405,ohc,four,122,2bbl,3.35,3.46,8.5,88,5000,25,32,8189\n88,1,mitsubishi outlander,gas,turbo,four,sedan,fwd,front,96.3,172.4,65.4,51.6,2403,ohc,four,110,spdi,3.17,3.46,7.5,116,5500,23,30,9279\n89,-1,mitsubishi mirage g4,gas,std,four,sedan,fwd,front,96.3,172.4,65.4,51.6,2403,ohc,four,110,spdi,3.17,3.46,7.5,116,5500,23,30,9279\n90,1,Nissan versa,gas,std,two,sedan,fwd,front,94.5,165.3,63.8,54.5,1889,ohc,four,97,2bbl,3.15,3.29,9.4,69,5200,31,37,5499\n91,1,nissan gt-r,diesel,std,two,sedan,fwd,front,94.5,165.3,63.8,54.5,2017,ohc,four,103,idi,2.99,3.47,21.9,55,4800,45,50,7099\n92,1,nissan rogue,gas,std,two,sedan,fwd,front,94.5,165.3,63.8,54.5,1918,ohc,four,97,2bbl,3.15,3.29,9.4,69,5200,31,37,6649\n93,1,nissan latio,gas,std,four,sedan,fwd,front,94.5,165.3,63.8,54.5,1938,ohc,four,97,2bbl,3.15,3.29,9.4,69,5200,31,37,6849\n94,1,nissan titan,gas,std,four,wagon,fwd,front,94.5,170.2,63.8,53.5,2024,ohc,four,97,2bbl,3.15,3.29,9.4,69,5200,31,37,7349\n95,1,nissan leaf,gas,std,two,sedan,fwd,front,94.5,165.3,63.8,54.5,1951,ohc,four,97,2bbl,3.15,3.29,9.4,69,5200,31,37,7299\n96,1,nissan juke,gas,std,two,hatchback,fwd,front,94.5,165.6,63.8,53.3,2028,ohc,four,97,2bbl,3.15,3.29,9.4,69,5200,31,37,7799\n97,1,nissan latio,gas,std,four,sedan,fwd,front,94.5,165.3,63.8,54.5,1971,ohc,four,97,2bbl,3.15,3.29,9.4,69,5200,31,37,7499\n98,1,nissan note,gas,std,four,wagon,fwd,front,94.5,170.2,63.8,53.5,2037,ohc,four,97,2bbl,3.15,3.29,9.4,69,5200,31,37,7999\n99,2,nissan clipper,gas,std,two,hardtop,fwd,front,95.1,162.4,63.8,53.3,2008,ohc,four,97,2bbl,3.15,3.29,9.4,69,5200,31,37,8249\n100,0,nissan rogue,gas,std,four,hatchback,fwd,front,97.2,173.4,65.2,54.7,2324,ohc,four,120,2bbl,3.33,3.47,8.5,97,5200,27,34,8949\n101,0,nissan nv200,gas,std,four,sedan,fwd,front,97.2,173.4,65.2,54.7,2302,ohc,four,120,2bbl,3.33,3.47,8.5,97,5200,27,34,9549\n102,0,nissan dayz,gas,std,four,sedan,fwd,front,100.4,181.7,66.5,55.1,3095,ohcv,six,181,mpfi,3.43,3.27,9,152,5200,17,22,13499\n103,0,nissan fuga,gas,std,four,wagon,fwd,front,100.4,184.6,66.5,56.1,3296,ohcv,six,181,mpfi,3.43,3.27,9,152,5200,17,22,14399\n104,0,nissan otti,gas,std,four,sedan,fwd,front,100.4,184.6,66.5,55.1,3060,ohcv,six,181,mpfi,3.43,3.27,9,152,5200,19,25,13499\n105,3,nissan teana,gas,std,two,hatchback,rwd,front,91.3,170.7,67.9,49.7,3071,ohcv,six,181,mpfi,3.43,3.27,9,160,5200,19,25,17199\n106,3,nissan kicks,gas,turbo,two,hatchback,rwd,front,91.3,170.7,67.9,49.7,3139,ohcv,six,181,mpfi,3.43,3.27,7.8,200,5200,17,23,19699\n107,1,nissan clipper,gas,std,two,hatchback,rwd,front,99.2,178.5,67.9,49.7,3139,ohcv,six,181,mpfi,3.43,3.27,9,160,5200,19,25,18399\n108,0,peugeot 504,gas,std,four,sedan,rwd,front,107.9,186.7,68.4,56.7,3020,l,four,120,mpfi,3.46,3.19,8.4,97,5000,19,24,11900\n109,0,peugeot 304,diesel,turbo,four,sedan,rwd,front,107.9,186.7,68.4,56.7,3197,l,four,152,idi,3.7,3.52,21,95,4150,28,33,13200\n110,0,peugeot 504 (sw),gas,std,four,wagon,rwd,front,114.2,198.9,68.4,58.7,3230,l,four,120,mpfi,3.46,3.19,8.4,97,5000,19,24,12440\n111,0,peugeot 504,diesel,turbo,four,wagon,rwd,front,114.2,198.9,68.4,58.7,3430,l,four,152,idi,3.7,3.52,21,95,4150,25,25,13860\n112,0,peugeot 504,gas,std,four,sedan,rwd,front,107.9,186.7,68.4,56.7,3075,l,four,120,mpfi,3.46,2.19,8.4,95,5000,19,24,15580\n113,0,peugeot 604sl,diesel,turbo,four,sedan,rwd,front,107.9,186.7,68.4,56.7,3252,l,four,152,idi,3.7,3.52,21,95,4150,28,33,16900\n114,0,peugeot 504,gas,std,four,wagon,rwd,front,114.2,198.9,68.4,56.7,3285,l,four,120,mpfi,3.46,2.19,8.4,95,5000,19,24,16695\n115,0,peugeot 505s turbo diesel,diesel,turbo,four,wagon,rwd,front,114.2,198.9,68.4,58.7,3485,l,four,152,idi,3.7,3.52,21,95,4150,25,25,17075\n116,0,peugeot 504,gas,std,four,sedan,rwd,front,107.9,186.7,68.4,56.7,3075,l,four,120,mpfi,3.46,3.19,8.4,97,5000,19,24,16630\n117,0,peugeot 504,diesel,turbo,four,sedan,rwd,front,107.9,186.7,68.4,56.7,3252,l,four,152,idi,3.7,3.52,21,95,4150,28,33,17950\n118,0,peugeot 604sl,gas,turbo,four,sedan,rwd,front,108,186.7,68.3,56,3130,l,four,134,mpfi,3.61,3.21,7,142,5600,18,24,18150\n119,1,plymouth fury iii,gas,std,two,hatchback,fwd,front,93.7,157.3,63.8,50.8,1918,ohc,four,90,2bbl,2.97,3.23,9.4,68,5500,37,41,5572\n120,1,plymouth cricket,gas,turbo,two,hatchback,fwd,front,93.7,157.3,63.8,50.8,2128,ohc,four,98,spdi,3.03,3.39,7.6,102,5500,24,30,7957\n121,1,plymouth fury iii,gas,std,four,hatchback,fwd,front,93.7,157.3,63.8,50.6,1967,ohc,four,90,2bbl,2.97,3.23,9.4,68,5500,31,38,6229\n122,1,plymouth satellite custom (sw),gas,std,four,sedan,fwd,front,93.7,167.3,63.8,50.8,1989,ohc,four,90,2bbl,2.97,3.23,9.4,68,5500,31,38,6692\n123,1,plymouth fury gran sedan,gas,std,four,sedan,fwd,front,93.7,167.3,63.8,50.8,2191,ohc,four,98,2bbl,2.97,3.23,9.4,68,5500,31,38,7609\n124,-1,plymouth valiant,gas,std,four,wagon,fwd,front,103.3,174.6,64.6,59.8,2535,ohc,four,122,2bbl,3.35,3.46,8.5,88,5000,24,30,8921\n125,3,plymouth duster,gas,turbo,two,hatchback,rwd,front,95.9,173.2,66.3,50.2,2818,ohc,four,156,spdi,3.59,3.86,7,145,5000,19,24,12764\n126,3,porsche macan,gas,std,two,hatchback,rwd,front,94.5,168.9,68.3,50.2,2778,ohc,four,151,mpfi,3.94,3.11,9.5,143,5500,19,27,22018\n127,3,porcshce panamera,gas,std,two,hardtop,rwd,rear,89.5,168.9,65,51.6,2756,ohcf,six,194,mpfi,3.74,2.9,9.5,207,5900,17,25,32528\n128,3,porsche cayenne,gas,std,two,hardtop,rwd,rear,89.5,168.9,65,51.6,2756,ohcf,six,194,mpfi,3.74,2.9,9.5,207,5900,17,25,34028\n129,3,porsche boxter,gas,std,two,convertible,rwd,rear,89.5,168.9,65,51.6,2800,ohcf,six,194,mpfi,3.74,2.9,9.5,207,5900,17,25,37028\n130,1,porsche cayenne,gas,std,two,hatchback,rwd,front,98.4,175.7,72.3,50.5,3366,dohcv,eight,203,mpfi,3.94,3.11,10,288,5750,17,28,31400.5\n131,0,renault 12tl,gas,std,four,wagon,fwd,front,96.1,181.5,66.5,55.2,2579,ohc,four,132,mpfi,3.46,3.9,8.7,90,5100,23,31,9295\n132,2,renault 5 gtl,gas,std,two,hatchback,fwd,front,96.1,176.8,66.6,50.5,2460,ohc,four,132,mpfi,3.46,3.9,8.7,90,5100,23,31,9895\n133,3,saab 99e,gas,std,two,hatchback,fwd,front,99.1,186.6,66.5,56.1,2658,ohc,four,121,mpfi,3.54,3.07,9.31,110,5250,21,28,11850\n134,2,saab 99le,gas,std,four,sedan,fwd,front,99.1,186.6,66.5,56.1,2695,ohc,four,121,mpfi,3.54,3.07,9.3,110,5250,21,28,12170\n135,3,saab 99le,gas,std,two,hatchback,fwd,front,99.1,186.6,66.5,56.1,2707,ohc,four,121,mpfi,2.54,2.07,9.3,110,5250,21,28,15040\n136,2,saab 99gle,gas,std,four,sedan,fwd,front,99.1,186.6,66.5,56.1,2758,ohc,four,121,mpfi,3.54,3.07,9.3,110,5250,21,28,15510\n137,3,saab 99gle,gas,turbo,two,hatchback,fwd,front,99.1,186.6,66.5,56.1,2808,dohc,four,121,mpfi,3.54,3.07,9,160,5500,19,26,18150\n138,2,saab 99e,gas,turbo,four,sedan,fwd,front,99.1,186.6,66.5,56.1,2847,dohc,four,121,mpfi,3.54,3.07,9,160,5500,19,26,18620\n139,2,subaru,gas,std,two,hatchback,fwd,front,93.7,156.9,63.4,53.7,2050,ohcf,four,97,2bbl,3.62,2.36,9,69,4900,31,36,5118\n140,2,subaru dl,gas,std,two,hatchback,fwd,front,93.7,157.9,63.6,53.7,2120,ohcf,four,108,2bbl,3.62,2.64,8.7,73,4400,26,31,7053\n141,2,subaru dl,gas,std,two,hatchback,4wd,front,93.3,157.3,63.8,55.7,2240,ohcf,four,108,2bbl,3.62,2.64,8.7,73,4400,26,31,7603\n142,0,subaru,gas,std,four,sedan,fwd,front,97.2,172,65.4,52.5,2145,ohcf,four,108,2bbl,3.62,2.64,9.5,82,4800,32,37,7126\n143,0,subaru brz,gas,std,four,sedan,fwd,front,97.2,172,65.4,52.5,2190,ohcf,four,108,2bbl,3.62,2.64,9.5,82,4400,28,33,7775\n144,0,subaru baja,gas,std,four,sedan,fwd,front,97.2,172,65.4,52.5,2340,ohcf,four,108,mpfi,3.62,2.64,9,94,5200,26,32,9960\n145,0,subaru r1,gas,std,four,sedan,4wd,front,97,172,65.4,54.3,2385,ohcf,four,108,2bbl,3.62,2.64,9,82,4800,24,25,9233\n146,0,subaru r2,gas,turbo,four,sedan,4wd,front,97,172,65.4,54.3,2510,ohcf,four,108,mpfi,3.62,2.64,7.7,111,4800,24,29,11259\n147,0,subaru trezia,gas,std,four,wagon,fwd,front,97,173.5,65.4,53,2290,ohcf,four,108,2bbl,3.62,2.64,9,82,4800,28,32,7463\n148,0,subaru tribeca,gas,std,four,wagon,fwd,front,97,173.5,65.4,53,2455,ohcf,four,108,mpfi,3.62,2.64,9,94,5200,25,31,10198\n149,0,subaru dl,gas,std,four,wagon,4wd,front,96.9,173.6,65.4,54.9,2420,ohcf,four,108,2bbl,3.62,2.64,9,82,4800,23,29,8013\n150,0,subaru dl,gas,turbo,four,wagon,4wd,front,96.9,173.6,65.4,54.9,2650,ohcf,four,108,mpfi,3.62,2.64,7.7,111,4800,23,23,11694\n151,1,toyota corona mark ii,gas,std,two,hatchback,fwd,front,95.7,158.7,63.6,54.5,1985,ohc,four,92,2bbl,3.05,3.03,9,62,4800,35,39,5348\n152,1,toyota corona,gas,std,two,hatchback,fwd,front,95.7,158.7,63.6,54.5,2040,ohc,four,92,2bbl,3.05,3.03,9,62,4800,31,38,6338\n153,1,toyota corolla 1200,gas,std,four,hatchback,fwd,front,95.7,158.7,63.6,54.5,2015,ohc,four,92,2bbl,3.05,3.03,9,62,4800,31,38,6488\n154,0,toyota corona hardtop,gas,std,four,wagon,fwd,front,95.7,169.7,63.6,59.1,2280,ohc,four,92,2bbl,3.05,3.03,9,62,4800,31,37,6918\n155,0,toyota corolla 1600 (sw),gas,std,four,wagon,4wd,front,95.7,169.7,63.6,59.1,2290,ohc,four,92,2bbl,3.05,3.03,9,62,4800,27,32,7898\n156,0,toyota carina,gas,std,four,wagon,4wd,front,95.7,169.7,63.6,59.1,3110,ohc,four,92,2bbl,3.05,3.03,9,62,4800,27,32,8778\n157,0,toyota mark ii,gas,std,four,sedan,fwd,front,95.7,166.3,64.4,53,2081,ohc,four,98,2bbl,3.19,3.03,9,70,4800,30,37,6938\n158,0,toyota corolla 1200,gas,std,four,hatchback,fwd,front,95.7,166.3,64.4,52.8,2109,ohc,four,98,2bbl,3.19,3.03,9,70,4800,30,37,7198\n159,0,toyota corona,diesel,std,four,sedan,fwd,front,95.7,166.3,64.4,53,2275,ohc,four,110,idi,3.27,3.35,22.5,56,4500,34,36,7898\n160,0,toyota corolla,diesel,std,four,hatchback,fwd,front,95.7,166.3,64.4,52.8,2275,ohc,four,110,idi,3.27,3.35,22.5,56,4500,38,47,7788\n161,0,toyota corona,gas,std,four,sedan,fwd,front,95.7,166.3,64.4,53,2094,ohc,four,98,2bbl,3.19,3.03,9,70,4800,38,47,7738\n162,0,toyota corolla,gas,std,four,hatchback,fwd,front,95.7,166.3,64.4,52.8,2122,ohc,four,98,2bbl,3.19,3.03,9,70,4800,28,34,8358\n163,0,toyota mark ii,gas,std,four,sedan,fwd,front,95.7,166.3,64.4,52.8,2140,ohc,four,98,2bbl,3.19,3.03,9,70,4800,28,34,9258\n164,1,toyota corolla liftback,gas,std,two,sedan,rwd,front,94.5,168.7,64,52.6,2169,ohc,four,98,2bbl,3.19,3.03,9,70,4800,29,34,8058\n165,1,toyota corona,gas,std,two,hatchback,rwd,front,94.5,168.7,64,52.6,2204,ohc,four,98,2bbl,3.19,3.03,9,70,4800,29,34,8238\n166,1,toyota celica gt liftback,gas,std,two,sedan,rwd,front,94.5,168.7,64,52.6,2265,dohc,four,98,mpfi,3.24,3.08,9.4,112,6600,26,29,9298\n167,1,toyota corolla tercel,gas,std,two,hatchback,rwd,front,94.5,168.7,64,52.6,2300,dohc,four,98,mpfi,3.24,3.08,9.4,112,6600,26,29,9538\n168,2,toyota corona liftback,gas,std,two,hardtop,rwd,front,98.4,176.2,65.6,52,2540,ohc,four,146,mpfi,3.62,3.5,9.3,116,4800,24,30,8449\n169,2,toyota corolla,gas,std,two,hardtop,rwd,front,98.4,176.2,65.6,52,2536,ohc,four,146,mpfi,3.62,3.5,9.3,116,4800,24,30,9639\n170,2,toyota starlet,gas,std,two,hatchback,rwd,front,98.4,176.2,65.6,52,2551,ohc,four,146,mpfi,3.62,3.5,9.3,116,4800,24,30,9989\n171,2,toyota tercel,gas,std,two,hardtop,rwd,front,98.4,176.2,65.6,52,2679,ohc,four,146,mpfi,3.62,3.5,9.3,116,4800,24,30,11199\n172,2,toyota corolla,gas,std,two,hatchback,rwd,front,98.4,176.2,65.6,52,2714,ohc,four,146,mpfi,3.62,3.5,9.3,116,4800,24,30,11549\n173,2,toyota cressida,gas,std,two,convertible,rwd,front,98.4,176.2,65.6,53,2975,ohc,four,146,mpfi,3.62,3.5,9.3,116,4800,24,30,17669\n174,-1,toyota corolla,gas,std,four,sedan,fwd,front,102.4,175.6,66.5,54.9,2326,ohc,four,122,mpfi,3.31,3.54,8.7,92,4200,29,34,8948\n175,-1,toyota celica gt,diesel,turbo,four,sedan,fwd,front,102.4,175.6,66.5,54.9,2480,ohc,four,110,idi,3.27,3.35,22.5,73,4500,30,33,10698\n176,-1,toyota corona,gas,std,four,hatchback,fwd,front,102.4,175.6,66.5,53.9,2414,ohc,four,122,mpfi,3.31,3.54,8.7,92,4200,27,32,9988\n177,-1,toyota corolla,gas,std,four,sedan,fwd,front,102.4,175.6,66.5,54.9,2414,ohc,four,122,mpfi,3.31,3.54,8.7,92,4200,27,32,10898\n178,-1,toyota mark ii,gas,std,four,hatchback,fwd,front,102.4,175.6,66.5,53.9,2458,ohc,four,122,mpfi,3.31,3.54,8.7,92,4200,27,32,11248\n179,3,toyota corolla liftback,gas,std,two,hatchback,rwd,front,102.9,183.5,67.7,52,2976,dohc,six,171,mpfi,3.27,3.35,9.3,161,5200,20,24,16558\n180,3,toyota corona,gas,std,two,hatchback,rwd,front,102.9,183.5,67.7,52,3016,dohc,six,171,mpfi,3.27,3.35,9.3,161,5200,19,24,15998\n181,-1,toyota starlet,gas,std,four,sedan,rwd,front,104.5,187.8,66.5,54.1,3131,dohc,six,171,mpfi,3.27,3.35,9.2,156,5200,20,24,15690\n182,-1,toyouta tercel,gas,std,four,wagon,rwd,front,104.5,187.8,66.5,54.1,3151,dohc,six,161,mpfi,3.27,3.35,9.2,156,5200,19,24,15750\n183,2,vokswagen rabbit,diesel,std,two,sedan,fwd,front,97.3,171.7,65.5,55.7,2261,ohc,four,97,idi,3.01,3.4,23,52,4800,37,46,7775\n184,2,volkswagen 1131 deluxe sedan,gas,std,two,sedan,fwd,front,97.3,171.7,65.5,55.7,2209,ohc,four,109,mpfi,3.19,3.4,9,85,5250,27,34,7975\n185,2,volkswagen model 111,diesel,std,four,sedan,fwd,front,97.3,171.7,65.5,55.7,2264,ohc,four,97,idi,3.01,3.4,23,52,4800,37,46,7995\n186,2,volkswagen type 3,gas,std,four,sedan,fwd,front,97.3,171.7,65.5,55.7,2212,ohc,four,109,mpfi,3.19,3.4,9,85,5250,27,34,8195\n187,2,volkswagen 411 (sw),gas,std,four,sedan,fwd,front,97.3,171.7,65.5,55.7,2275,ohc,four,109,mpfi,3.19,3.4,9,85,5250,27,34,8495\n188,2,volkswagen super beetle,diesel,turbo,four,sedan,fwd,front,97.3,171.7,65.5,55.7,2319,ohc,four,97,idi,3.01,3.4,23,68,4500,37,42,9495\n189,2,volkswagen dasher,gas,std,four,sedan,fwd,front,97.3,171.7,65.5,55.7,2300,ohc,four,109,mpfi,3.19,3.4,10,100,5500,26,32,9995\n190,3,vw dasher,gas,std,two,convertible,fwd,front,94.5,159.3,64.2,55.6,2254,ohc,four,109,mpfi,3.19,3.4,8.5,90,5500,24,29,11595\n191,3,vw rabbit,gas,std,two,hatchback,fwd,front,94.5,165.7,64,51.4,2221,ohc,four,109,mpfi,3.19,3.4,8.5,90,5500,24,29,9980\n192,0,volkswagen rabbit,gas,std,four,sedan,fwd,front,100.4,180.2,66.9,55.1,2661,ohc,five,136,mpfi,3.19,3.4,8.5,110,5500,19,24,13295\n193,0,volkswagen rabbit custom,diesel,turbo,four,sedan,fwd,front,100.4,180.2,66.9,55.1,2579,ohc,four,97,idi,3.01,3.4,23,68,4500,33,38,13845\n194,0,volkswagen dasher,gas,std,four,wagon,fwd,front,100.4,183.1,66.9,55.1,2563,ohc,four,109,mpfi,3.19,3.4,9,88,5500,25,31,12290\n195,-2,volvo 145e (sw),gas,std,four,sedan,rwd,front,104.3,188.8,67.2,56.2,2912,ohc,four,141,mpfi,3.78,3.15,9.5,114,5400,23,28,12940\n196,-1,volvo 144ea,gas,std,four,wagon,rwd,front,104.3,188.8,67.2,57.5,3034,ohc,four,141,mpfi,3.78,3.15,9.5,114,5400,23,28,13415\n197,-2,volvo 244dl,gas,std,four,sedan,rwd,front,104.3,188.8,67.2,56.2,2935,ohc,four,141,mpfi,3.78,3.15,9.5,114,5400,24,28,15985\n198,-1,volvo 245,gas,std,four,wagon,rwd,front,104.3,188.8,67.2,57.5,3042,ohc,four,141,mpfi,3.78,3.15,9.5,114,5400,24,28,16515\n199,-2,volvo 264gl,gas,turbo,four,sedan,rwd,front,104.3,188.8,67.2,56.2,3045,ohc,four,130,mpfi,3.62,3.15,7.5,162,5100,17,22,18420\n200,-1,volvo diesel,gas,turbo,four,wagon,rwd,front,104.3,188.8,67.2,57.5,3157,ohc,four,130,mpfi,3.62,3.15,7.5,162,5100,17,22,18950\n201,-1,volvo 145e (sw),gas,std,four,sedan,rwd,front,109.1,188.8,68.9,55.5,2952,ohc,four,141,mpfi,3.78,3.15,9.5,114,5400,23,28,16845\n202,-1,volvo 144ea,gas,turbo,four,sedan,rwd,front,109.1,188.8,68.8,55.5,3049,ohc,four,141,mpfi,3.78,3.15,8.7,160,5300,19,25,19045\n203,-1,volvo 244dl,gas,std,four,sedan,rwd,front,109.1,188.8,68.9,55.5,3012,ohcv,six,173,mpfi,3.58,2.87,8.8,134,5500,18,23,21485\n204,-1,volvo 246,diesel,turbo,four,sedan,rwd,front,109.1,188.8,68.9,55.5,3217,ohc,six,145,idi,3.01,3.4,23,106,4800,26,27,22470\n205,-1,volvo 264gl,gas,turbo,four,sedan,rwd,front,109.1,188.8,68.9,55.5,3062,ohc,four,141,mpfi,3.78,3.15,9.5,114,5400,19,25,22625\n" }, { "path": "March Cohort 2023/12_15_16_17_18_Statistics/datasets/Crime_R.csv", "content": "CrimeRate,Youth,Southern,Education,ExpenditureYear0,LabourForce,Males,MoreMales,StateSize,YouthUnemployment,MatureUnemployment,HighYouthUnemploy,Wage,BelowWage,CrimeRate10,Youth10,Education10,ExpenditureYear10,LabourForce10,Males10,MoreMales10,StateSize10,YouthUnemploy10,MatureUnemploy10,HighYouthUnemploy10,Wage10,BelowWage10\n45.5,135,0,12.4,69,540,965,0,6,80,22,1,564,139,26.5,135,12.5,71,564,974,0,6,82,20,1,632,142\n52.3,140,0,10.9,55,535,1045,1,6,135,40,1,453,200,35.9,135,10.9,54,540,1039,1,7,138,39,1,521,210\n56.6,157,1,11.2,47,512,962,0,22,97,34,0,288,276,37.1,153,11,44,529,959,0,24,98,33,0,359,256\n60.3,139,1,11.9,46,480,968,0,19,135,53,0,457,249,42.7,139,11.8,41,497,983,0,20,131,50,0,510,235\n64.2,126,0,12.2,106,599,989,0,40,78,25,1,593,171,46.7,125,12.2,97,602,989,0,42,79,24,1,660,162\n67.6,128,0,13.5,67,624,972,0,28,77,25,1,507,206,47.9,128,13.8,60,621,983,0,28,81,24,1,571,199\n70.5,130,0,14.1,63,641,984,0,14,70,21,1,486,196,50.6,153,14.1,57,641,993,0,14,71,23,1,556,176\n73.2,143,0,12.9,66,537,977,0,10,114,35,1,487,166,55.9,143,13,63,549,973,0,11,119,36,1,561,168\n75,141,0,12.9,56,523,968,0,4,107,37,0,489,170,61.8,153,12.9,54,538,968,0,5,110,36,1,550,126\n78.1,133,0,11.4,51,599,1024,1,7,99,27,1,425,225,65.4,134,11.2,47,600,1024,1,7,97,28,1,499,215\n79.8,142,1,12.9,45,533,969,0,18,94,33,0,318,250,71.4,142,13.1,44,552,969,0,19,93,36,0,378,247\n82.3,123,0,12.5,97,526,948,0,113,124,50,0,572,158,75.4,134,12.4,87,529,949,0,117,125,49,0,639,146\n83.1,135,0,13.6,62,595,986,0,22,77,27,0,529,190,77.3,137,13.7,61,599,993,0,23,80,28,0,591,189\n84.9,121,0,13.2,118,547,964,0,25,84,29,0,689,126,78.6,132,13.3,115,538,968,0,25,82,30,0,742,127\n85.6,166,1,11.4,58,521,973,0,46,72,26,0,396,237,80.6,153,11.2,54,543,983,0,47,76,25,1,568,246\n88,140,0,12.9,71,632,1029,1,7,100,24,1,526,174,82.2,130,12.9,68,620,1024,1,8,104,25,1,570,182\n92.3,126,0,12.7,74,602,984,0,34,102,33,1,557,195,87.5,134,12.9,67,599,982,0,33,107,34,1,621,199\n94.3,130,0,13.3,128,536,934,0,51,78,34,0,627,135,92.9,127,13.3,128,530,949,0,52,79,33,0,692,140\n95.3,125,0,12,90,586,964,0,97,105,43,0,617,163,94.1,134,11.9,81,571,971,0,99,106,41,0,679,162\n96.8,151,1,10,58,510,950,0,33,108,41,0,394,261,96.2,161,10.1,56,515,1001,1,32,110,40,0,465,254\n97.4,152,1,10.8,57,530,986,0,30,92,43,0,405,264,97.8,152,11,53,541,989,0,30,92,41,0,470,243\n98.7,162,1,12.1,75,522,996,0,40,73,27,0,496,224,99.9,162,12,70,533,992,0,41,80,28,0,562,229\n99.9,149,1,10.7,61,515,953,0,36,86,35,0,395,251,101.4,150,10.7,54,520,952,0,35,84,32,0,476,249\n103,177,1,11,58,638,974,0,24,76,28,0,382,254,103.5,164,10.9,56,638,978,0,25,79,28,0,456,257\n104.3,134,0,12.5,75,595,972,0,47,83,31,0,580,172,104.5,133,12.7,71,599,982,0,50,87,32,0,649,182\n105.9,130,0,13.4,90,623,1049,1,3,113,40,0,588,160,106.4,153,13.4,91,622,1050,1,3,119,41,0,649,159\n106.6,157,1,11.1,65,553,955,0,39,81,28,0,421,239,107.8,156,11.2,62,562,956,0,39,85,29,0,499,243\n107.2,148,0,13.7,72,601,998,0,9,84,20,1,590,144,110.1,134,13.9,66,602,999,0,9,87,15,0,656,151\n108.3,126,0,13.8,97,542,990,0,18,102,35,0,589,166,110.5,126,13.8,97,549,993,0,19,103,34,1,659,160\n109.4,135,1,11.4,123,537,978,0,31,89,34,0,631,165,113.5,134,11.3,115,529,978,0,32,93,35,0,703,175\n112.1,142,1,10.9,81,497,956,0,33,116,47,0,427,247,116.3,147,10.7,77,501,962,0,33,117,44,0,500,256\n114.3,127,1,12.8,82,519,982,0,4,97,38,0,620,168,119.7,125,12.9,79,510,945,0,4,99,39,0,696,170\n115.1,131,0,13.7,78,574,1038,1,7,142,42,1,540,176,124.5,134,13.6,73,581,1029,1,7,143,41,1,615,177\n117.2,136,0,12.9,95,574,1012,1,29,111,37,1,622,162,127.8,140,13,96,581,1011,1,29,115,36,1,691,169\n119.7,119,0,11.9,166,521,938,0,168,92,36,0,637,154,129.8,120,11.9,157,524,935,0,180,93,27,1,698,169\n121.6,147,1,13.9,63,560,972,0,23,76,24,1,462,233,130.7,139,14,64,571,970,0,24,78,24,1,511,220\n123.4,145,1,11.7,82,560,981,0,96,88,31,0,488,228,132.5,154,11.8,74,563,980,0,99,89,29,1,550,230\n127.2,132,0,10.4,87,564,953,0,43,83,32,0,513,227,134.6,135,10.2,83,560,948,0,44,83,32,0,589,234\n132.4,152,0,12,82,571,1018,1,10,103,28,1,537,215,137.5,151,12.1,76,567,1079,1,11,105,27,1,617,204\n135.5,125,0,12.5,113,567,985,0,78,130,58,0,626,166,140.5,140,12.5,105,571,993,0,77,131,59,0,684,174\n137.8,141,0,14.2,109,591,985,0,18,91,20,1,578,174,145.7,142,14.2,101,590,987,0,19,94,19,1,649,180\n140.8,150,0,12,109,531,964,0,9,87,38,0,559,153,150.6,153,12,98,539,982,0,10,88,36,0,635,151\n145.4,131,1,12.2,115,542,969,0,50,79,35,0,472,206,157.3,131,12.1,109,548,976,0,52,82,34,0,539,219\n149.3,143,0,12.3,103,583,1012,1,13,96,36,0,557,194,162.7,142,12.2,95,612,1003,1,13,97,36,0,625,196\n154.3,124,0,12.3,121,580,966,0,101,77,35,0,657,170,169.6,134,12.2,116,580,987,0,104,79,36,0,719,172\n157.7,136,0,15.1,149,577,994,0,157,102,39,0,673,167,177.2,140,15.2,141,578,995,0,160,110,40,0,739,169\n161.8,131,0,13.2,160,631,1071,1,3,102,41,0,674,152,178.2,132,13.2,143,632,1058,1,4,100,40,0,748,150\n" }, { "path": "March Cohort 2023/12_15_16_17_18_Statistics/datasets/IPL2013.csv", "content": "Sl.NO.,PLAYER NAME,AGE,COUNTRY,TEAM,PLAYING ROLE,T-RUNS,T-WKTS,ODI-RUNS-S,ODI-SR-B,ODI-WKTS,ODI-SR-BL,CAPTAINCY EXP,RUNS-S,HS,AVE,SR-B,SIXERS,RUNS-C,WKTS,AVE-BL,ECON,SR-BL,AUCTION YEAR,BASE PRICE,SOLD PRICE\n1,\"Abdulla, YA\",2,SA,KXIP,Allrounder,0,0,0,0,0,0,0,0,0,0,0,0,307,15,20.47,8.9,13.93,2009,50000,50000\n2,Abdur Razzak,2,BAN,RCB,Bowler,214,18,657,71.41,185,37.6,0,0,0,0,0,0,29,0,0,14.5,0,2008,50000,50000\n3,\"Agarkar, AB\",2,IND,KKR,Bowler,571,58,1269,80.62,288,32.9,0,167,39,18.56,121.01,5,1059,29,36.52,8.81,24.9,2008,200000,350000\n4,\"Ashwin, R\",1,IND,CSK,Bowler,284,31,241,84.56,51,36.8,0,58,11,5.8,76.32,0,1125,49,22.96,6.23,22.14,2011,100000,850000\n5,\"Badrinath, S\",2,IND,CSK,Batsman,63,0,79,45.93,0,0,0,1317,71,32.93,120.71,28,0,0,0,0,0,2011,100000,800000\n6,\"Bailey, GJ\",2,AUS,CSK,Batsman,0,0,172,72.26,0,0,1,63,48,21,95.45,0,0,0,0,0,0,2009,50000,50000\n7,\"Balaji, L\",2,IND,CSK+,Bowler,51,27,120,78.94,34,42.5,0,26,15,4.33,72.22,1,1342,52,25.81,7.98,19.4,2011,100000,500000\n8,\"Bollinger, DE\",2,AUS,CSK,Bowler,54,50,50,92.59,62,31.3,0,21,16,21,165.88,1,693,37,18.73,7.22,15.57,2011,200000,700000\n9,\"Botha, J\",2,SA,RR,Allrounder,83,17,609,85.77,72,53,1,335,67,30.45,114.73,3,610,19,32.11,6.85,28.11,2011,200000,950000\n10,\"Boucher, MV\",2,SA,RCB+,W. Keeper,5515,1,4686,84.76,0,0,1,394,50,28.14,127.51,13,0,0,0,0,0,2008,200000,450000\n11,\"Bravo, DJ\",2,WI,MI+,Allrounder,2200,86,2004,81.39,142,34.1,0,839,70,27.97,127.12,38,1338,47,28.47,8.12,21.11,2011,200000,200000\n12,\"Chanderpaul, S\",3,WI,RCB,Batsman,9918,9,8778,70.74,14,52.8,1,25,16,8.33,80.64,0,0,0,0,0,0,2008,200000,200000\n13,\"Chawla, PP\",1,IND,KXIP,Allrounder,5,3,38,65.51,32,41,0,337,24,13.48,113.09,9,1819,73,126.3,38.11,100.2,2008,125000,400000\n14,\"de Villiers, AB\",2,SA,DD+,W. Keeper,5457,2,4998,93.19,0,0,1,1302,105,34.26,128.53,42,0,0,0,0,0,2008,200000,300000\n15,\"Dhawan, S\",2,IND,MI+,Batsman,0,0,69,56.09,0,0,0,1540,95,31.43,122.32,36,66,4,16.5,8.25,12,2011,100000,300000\n16,\"Dhoni, MS\",2,IND,CSK,W. Keeper,3509,0,6773,88.19,1,12,1,1782,70,37.13,136.45,64,0,0,0,0,0,2008,400000,1500000\n17,\"Dilshan, TM\",2,SL,DD+,Allrounder,4722,32,6455,86.8,67,58.3,1,1077,76,28.34,117.83,24,356,5,71.2,8.07,53,2008,150000,250000\n18,\"Dinda, AB\",2,IND,KKR+,Bowler,0,0,18,60,5,61.4,0,6,2,1,33.33,0,926,36,25.72,7.29,21.19,2011,100000,375000\n19,\"Dravid, RS\",3,IND,RCB+,Batsman,13288,1,10889,71.24,4,46.5,1,1703,75,27.92,116.88,23,0,0,0,0,0,2011,400000,500000\n20,\"Duminy, J-P\",2,SA,MI+,Batsman,654,11,2536,84,25,47.6,0,978,74,36.22,119.27,35,377,10,37.7,7.11,31.8,2009,300000,300000\n21,\"Edwards, FH\",2,WI,DC,Bowler,380,157,73,45.62,60,35.6,0,4,3,4,80,0,154,5,30.8,6.6,28,2009,150000,150000\n22,\"Fernando, CRD\",2,SL,MI,Bowler,249,97,239,60.96,187,34.7,0,4,2,0,133.33,0,298,17,17.53,7.64,13.76,2008,150000,150000\n23,\"Fleming, SP\",3,NZ,CSK,Batsman,7172,0,8037,71.49,1,29,1,196,45,21.77,118.78,3,0,0,0,0,0,2008,350000,350000\n24,\"Flintoff, A\",2,ENG,CSK,Allrounder,3845,226,3394,88.82,169,33.2,1,62,24,31,116.98,2,105,2,52.5,9.55,33,2009,950000,1550000\n25,\"Gambhir, G\",2,IND,DD+,Batsman,3712,0,4819,86.17,0,0,1,2065,93,33.31,128.9,32,0,0,0,0,0,2008,220000,725000\n26,\"Ganguly, SC\",3,IND,KKR+,Batsman,7212,32,11363,73.7,100,45.6,1,1349,91,25.45,106.81,42,363,10,36.3,7.89,27.6,2011,200000,400000\n27,\"Gayle, CH\",2,WI,KKR+,Allrounder,6373,72,8087,83.95,156,44.4,1,1804,128,50.11,161.79,129,606,13,46.62,8.05,34.85,2008,250000,800000\n28,\"Gibbs, HH\",3,SA,DC,Batsman,6167,0,8094,83.26,0,0,1,886,69,27.69,109.79,31,0,0,0,0,0,2008,250000,575000\n29,\"Gilchrist, AC\",3,AUS,DC+,W. 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WPUJC\",3,SL,DC,Bowler,3089,355,2025,72.52,400,39.4,0,81,20,10.13,110.96,3,355,18,19.72,7.55,15.67,2008,200000,200000\n119,Van der Merwe,2,SA,RCB+,Allrounder,0,0,39,95.12,17,41.4,0,137,35,15.22,118.1,8,427,18,23.72,6.83,20.94,2011,50000,50000\n120,\"Venugopal Rao, Y\",2,IND,DC+,Batsman,0,0,218,60.05,0,0,0,914,71,22.29,118.24,37,321,6,53.5,9.44,34,2011,100000,700000\n121,\"Vettori, DL\",2,NZ,DD+,Allrounder,4486,359,2105,81.93,282,45.7,1,121,29,15.13,107.08,2,878,28,31.36,6.81,27.75,2008,250000,625000\n122,\"Vinay Kumar, R\",2,IND,RCB+,Bowler,11,1,43,43.87,28,35.3,0,217,25,9.43,104.83,5,1664,61,27.28,8.24,19.87,2011,100000,475000\n123,\"Warne, SK\",3,AUS,RR,Bowler,3154,708,1018,72.04,293,36.3,1,198,34,9.9,92.52,6,1447,57,25.39,7.27,20.95,2008,450000,450000\n124,\"Warner, DA\",1,AUS,DD,Batsman,483,2,876,85.79,0,0,0,1025,109,27.7,135.76,44,0,0,0,0,0,2011,200000,750000\n125,\"White, CL\",2,AUS,RCB+,Batsman,146,5,2037,80.48,12,27.5,1,745,78,31.04,132.09,29,70,0,0,14,0,2008,100000,500000\n126,\"Yadav, AS\",2,IND,DC,Batsman,0,0,0,0,0,0,0,49,16,9.8,125.64,2,0,0,0,0,0,2010,50000,750000\n127,Younis Khan,2,PAK,RR,Batsman,6398,7,6814,75.78,3,86.6,1,3,3,3,42.85,0,0,0,0,0,0,2008,225000,225000\n128,Yuvraj Singh,2,IND,KXIP+,Batsman,1775,9,8051,87.58,109,44.3,1,1237,66,26.32,131.88,67,569,23,24.74,7.02,21.13,2011,400000,1800000\n129,Zaheer Khan,2,IND,MI+,Bowler,1114,288,790,73.55,278,35.4,0,99,23,9.9,91.67,1,1783,65,27.43,7.75,21.26,2008,200000,450000\n130,\"Zoysa, DNT\",2,SL,DC,Bowler,288,64,343,95.81,108,39.4,0,11,10,11,122.22,0,99,2,49.5,9,33,2008,100000,110000" }, { "path": "March Cohort 2023/12_15_16_17_18_Statistics/datasets/SP_500_1987.csv", "content": "Date,Open,High,Low,Close,AdjClose,Volume\n31-Dec-1986,243.37,244.03,241.28,242.17,242.17,\"13,92,00,000\"\n2-Jan-1987,242.17,246.45,242.17,246.45,246.45,\"9,18,80,000\"\n5-Jan-1987,246.45,252.57,246.45,252.19,252.19,\"18,19,00,000\"\n6-Jan-1987,252.2,253.99,252.14,252.78,252.78,\"18,93,00,000\"\n7-Jan-1987,252.78,255.72,252.65,255.33,255.33,\"19,09,00,000\"\n8-Jan-1987,255.36,257.28,254.97,257.28,257.28,\"19,45,00,000\"\n9-Jan-1987,257.26,259.2,256.11,258.73,258.73,\"19,30,00,000\"\n12-Jan-1987,258.72,261.36,257.92,260.3,260.3,\"18,42,00,000\"\n13-Jan-1987,260.3,260.45,259.21,259.95,259.95,\"17,09,00,000\"\n14-Jan-1987,259.95,262.72,259.62,262.64,262.64,\"21,42,00,000\"\n15-Jan-1987,262.65,266.68,262.64,265.49,265.49,\"25,31,00,000\"\n16-Jan-1987,265.46,267.24,264.31,266.28,266.28,\"21,84,00,000\"\n19-Jan-1987,266.26,269.34,264,269.34,269.34,\"16,28,00,000\"\n20-Jan-1987,269.34,271.03,267.65,269.04,269.04,\"22,48,00,000\"\n21-Jan-1987,269.04,270.87,267.35,267.84,267.84,\"18,42,00,000\"\n22-Jan-1987,267.84,274.05,267.32,273.91,273.91,\"18,87,00,000\"\n23-Jan-1987,273.91,280.96,268.41,270.1,270.1,\"30,24,00,000\"\n26-Jan-1987,270.1,270.4,267.73,269.61,269.61,\"13,89,00,000\"\n27-Jan-1987,269.61,274.31,269.61,273.75,273.75,\"19,23,00,000\"\n28-Jan-1987,273.75,275.71,273.03,275.4,275.4,\"19,58,00,000\"\n29-Jan-1987,275.4,276.85,272.54,274.24,274.24,\"20,53,00,000\"\n30-Jan-1987,274.24,274.24,271.38,274.08,274.08,\"16,34,00,000\"\n2-Feb-1987,274.08,277.35,273.16,276.45,276.45,\"17,74,00,000\"\n3-Feb-1987,276.45,277.83,275.84,275.99,275.99,\"19,81,00,000\"\n4-Feb-1987,275.99,279.65,275.35,279.64,279.64,\"22,24,00,000\"\n5-Feb-1987,279.64,282.26,278.66,281.16,281.16,\"25,67,00,000\"\n6-Feb-1987,281.16,281.79,279.87,280.04,280.04,\"18,41,00,000\"\n09-Feb-1987,280.04,280.04,277.24,278.16,278.16,\"14,33,00,000\"\n10-Feb-1987,278.16,278.16,273.49,275.07,275.07,\"16,83,00,000\"\n11-Feb-1987,275.07,277.71,274.71,277.54,277.54,\"17,24,00,000\"\n12-Feb-1987,277.54,278.04,273.89,275.62,275.62,\"20,04,00,000\"\n13-Feb-1987,275.62,280.91,275.01,279.7,279.7,\"18,44,00,000\"\n17-Feb-1987,279.7,285.49,279.7,285.49,285.49,\"18,78,00,000\"\n18-Feb-1987,285.49,287.55,282.97,285.42,285.42,\"21,82,00,000\"\n19-Feb-1987,285.42,286.24,283.84,285.57,285.57,\"18,15,00,000\"\n20-Feb-1987,285.57,285.98,284.31,285.48,285.48,\"17,58,00,000\"\n23-Feb-1987,285.48,285.5,279.37,282.38,282.38,\"17,05,00,000\"\n24-Feb-1987,282.38,283.33,281.45,282.88,282.88,\"15,13,00,000\"\n25-Feb-1987,282.88,285.35,282.14,284,284,\"18,41,00,000\"\n26-Feb-1987,284,284.4,280.73,282.96,282.96,\"16,58,00,000\"\n27-Feb-1987,282.96,284.55,282.77,284.2,284.2,\"14,28,00,000\"\n2-Mar-1987,284.17,284.83,282.3,283,283,\"15,67,00,000\"\n3-Mar-1987,283,284.19,282.92,284.12,284.12,\"14,92,00,000\"\n4-Mar-1987,284.12,288.62,284.12,288.62,288.62,\"19,84,00,000\"\n5-Mar-1987,288.62,291.24,288.6,290.52,290.52,\"20,54,00,000\"\n6-Mar-1987,290.52,290.67,288.77,290.66,290.66,\"18,16,00,000\"\n9-Mar-1987,290.66,290.66,287.12,288.3,288.3,\"16,54,00,000\"\n10-Mar-1987,288.3,290.87,287.89,290.86,290.86,\"17,48,00,000\"\n11-Mar-1987,290.87,292.51,289.33,290.31,290.31,\"18,69,00,000\"\n12-Mar-1987,290.33,291.91,289.66,291.22,291.22,\"17,45,00,000\"\n13-Mar-1987,291.22,291.79,289.88,289.89,289.89,\"15,09,00,000\"\n16-Mar-1987,289.88,289.89,286.64,288.23,288.23,\"13,49,00,000\"\n17-Mar-1987,288.09,292.47,287.96,292.47,292.47,\"17,73,00,000\"\n18-Mar-1987,292.49,294.58,290.87,292.78,292.78,\"19,81,00,000\"\n19-Mar-1987,292.73,294.46,292.26,294.08,294.08,\"16,61,00,000\"\n20-Mar-1987,294.08,298.17,294.08,298.17,298.17,\"23,40,00,000\"\n23-Mar-1987,298.16,301.17,297.5,301.16,301.16,\"18,91,00,000\"\n24-Mar-1987,301.17,301.92,300.14,301.64,301.64,\"18,99,00,000\"\n25-Mar-1987,301.52,301.85,299.36,300.38,300.38,\"17,13,00,000\"\n26-Mar-1987,300.39,302.72,300.38,300.93,300.93,\"19,60,00,000\"\n27-Mar-1987,300.96,301.41,296.06,296.13,296.13,\"18,44,00,000\"\n30-Mar-1987,296.1,296.13,286.69,289.2,289.2,\"20,84,00,000\"\n31-Mar-1987,289.21,291.87,289.07,291.7,291.7,\"17,18,00,000\"\n1-Apr-1987,291.59,292.38,288.34,292.38,292.38,\"18,26,00,000\"\n2-Apr-1987,292.41,294.47,292.02,293.63,293.63,\"18,30,00,000\"\n3-Apr-1987,293.64,301.3,292.3,300.41,300.41,\"21,34,00,000\"\n6-Apr-1987,300.46,302.21,300.41,301.95,301.95,\"17,37,00,000\"\n7-Apr-1987,301.94,303.65,296.67,296.69,296.69,\"18,64,00,000\"\n8-Apr-1987,296.72,299.2,295.18,297.26,297.26,\"17,98,00,000\"\n9-Apr-1987,297.25,297.71,291.5,292.86,292.86,\"18,03,00,000\"\n10-April-1987,292.82,293.74,290.94,292.49,292.49,\"16,95,00,000\"\n13-April-1987,292.48,293.36,285.62,285.62,285.62,\"18,10,00,000\"\n14-April-1987,285.61,285.62,275.67,279.16,279.16,\"26,65,00,000\"\n15-April-1987,279.17,285.14,279.16,284.44,284.44,\"19,82,00,000\"\n16-April-1987,284.45,289.57,284.44,286.91,286.91,\"18,96,00,000\"\n20-April-1987,286.91,288.36,284.55,286.09,286.09,\"13,91,00,000\"\n21-April-1987,285.88,293.07,282.89,293.07,293.07,\"19,13,00,000\"\n22-April-1987,293.05,293.46,286.98,287.19,287.19,\"18,59,00,000\"\n23-April-1987,287.19,289.12,284.28,286.82,286.82,\"17,39,00,000\"\n24-April-1987,286.81,286.82,281.18,281.52,281.52,\"17,80,00,000\"\n27-April-1987,281.52,284.45,276.22,281.83,281.83,\"22,27,00,000\"\n28-April-1987,281.83,285.95,281.83,282.51,282.51,\"18,01,00,000\"\n29-April-1987,282.58,286.42,282.58,284.57,284.57,\"17,36,00,000\"\n30-April-1987,284.58,290.08,284.57,288.36,288.36,\"18,31,00,000\"\n1-May-1987,286.99,289.71,286.52,288.03,288.03,\"16,01,00,000\"\n4-May-1987,288.02,289.99,286.39,289.36,289.36,\"14,06,00,000\"\n5-May-1987,289.36,295.4,289.34,295.34,295.34,\"19,23,00,000\"\n6-May-1987,295.35,296.19,293.6,295.47,295.47,\"19,66,00,000\"\n7-May-1987,295.45,296.8,294.07,294.71,294.71,\"21,52,00,000\"\n8-May-1987,294.73,296.18,291.73,293.37,293.37,\"16,19,00,000\"\n11-May-1987,293.37,298.69,291.55,291.57,291.57,\"20,37,00,000\"\n12-May-1987,291.57,293.3,290.18,293.3,293.3,\"15,53,00,000\"\n13-May-1987,293.31,294.54,290.74,293.98,293.98,\"17,10,00,000\"\n14-May-1987,293.98,295.1,292.95,294.24,294.24,\"15,20,00,000\"\n15-May-1987,294.23,294.24,287.11,287.43,287.43,\"18,08,00,000\"\n18-May-1987,287.43,287.43,282.57,286.65,286.65,\"17,42,00,000\"\n19-May-1987,286.66,287.39,278.83,279.62,279.62,\"17,54,00,000\"\n20-May-1987,279.62,280.89,277.01,278.21,278.21,\"20,68,00,000\"\n21-May-1987,278.23,282.31,278.21,280.17,280.17,\"16,48,00,000\"\n22-May-1987,280.17,283.33,280.17,282.16,282.16,\"13,58,00,000\"\n26-May-1987,282.16,289.11,282.16,289.11,289.11,\"15,25,00,000\"\n27-May-1987,289.07,290.78,288.19,288.73,288.73,\"17,14,00,000\"\n28-May-1987,288.73,291.5,286.33,290.76,290.76,\"15,38,00,000\"\n29-May-1987,290.77,292.87,289.7,290.1,290.1,\"15,35,00,000\"\n1-Jun-1987,290.12,291.96,289.23,289.83,289.83,\"14,93,00,000\"\n2-Jun-1987,289.82,290.94,286.93,288.46,288.46,\"15,34,00,000\"\n3-Jun-1987,288.56,293.47,288.56,293.47,293.47,\"16,42,00,000\"\n4-Jun-1987,293.46,295.09,292.76,295.09,295.09,\"14,03,00,000\"\n5-Jun-1987,295.11,295.11,292.8,293.45,293.45,\"12,91,00,000\"\n8-Jun-1987,293.46,297.03,291.55,296.72,296.72,\"13,64,00,000\"\n9-Jun-1987,296.72,297.59,295.9,297.28,297.28,\"16,42,00,000\"\n10-Jun-1987,297.28,300.81,295.66,297.47,297.47,\"19,74,00,000\"\n11-Jun-1987,297.5,298.94,297.47,298.73,298.73,\"13,89,00,000\"\n12-Jun-1987,298.77,302.26,298.73,301.62,301.62,\"17,51,00,000\"\n15-Jun-1987,301.62,304.11,301.62,303.14,303.14,\"15,69,00,000\"\n16-Jun-1987,303.12,304.86,302.6,304.76,304.76,\"15,78,00,000\"\n17-Jun-1987,304.77,305.74,304.03,304.81,304.81,\"18,47,00,000\"\n18-Jun-1987,304.78,306.13,303.38,305.69,305.69,\"16,86,00,000\"\n19-Jun-1987,305.71,306.97,305.55,306.97,306.97,\"22,05,00,000\"\n22-Jun-1987,306.98,310.2,306.97,309.65,309.65,\"17,82,00,000\"\n23-Jun-1987,309.66,310.27,307.48,308.43,308.43,\"19,42,00,000\"\n24-Jun-1987,308.44,308.91,306.32,306.86,306.86,\"15,38,00,000\"\n25-Jun-1987,306.87,309.44,306.86,308.96,308.96,\"17,35,00,000\"\n26-Jun-1987,308.94,308.96,306.36,307.16,307.16,\"15,05,00,000\"\n29-Jun-1987,307.15,308.15,306.75,307.9,307.9,\"14,25,00,000\"\n30-Jun-1987,307.89,308,303.01,304,304,\"16,55,00,000\"\n1-Jul-1987,303.99,304,302.53,302.94,302.94,\"15,70,00,000\"\n2-Jul-1987,302.96,306.34,302.94,305.63,305.63,\"15,49,00,000\"\n6-Jul-1987,305.64,306.75,304.23,304.92,304.92,\"15,50,00,000\"\n7-Jul-1987,304.91,308.63,304.73,307.4,307.4,\"20,07,00,000\"\n8-Jul-1987,307.41,308.48,306.01,308.29,308.29,\"20,75,00,000\"\n9-Jul-1987,308.3,309.56,307.42,307.52,307.52,\"19,54,00,000\"\n10-Jul-1987,307.55,308.4,306.96,308.37,308.37,\"17,21,00,000\"\n13-Jul-1987,308.41,308.41,305.49,307.63,307.63,\"15,25,00,000\"\n14-Jul-1987,307.67,310.69,307.46,310.68,310.68,\"18,59,00,000\"\n15-Jul-1987,310.67,312.08,309.07,310.42,310.42,\"20,23,00,000\"\n16-Jul-1987,311,312.83,310.42,312.7,312.7,\"21,09,00,000\"\n17-Jul-1987,312.71,314.59,312.38,314.59,314.59,\"21,00,00,000\"\n20-Jul-1987,314.56,314.59,311.24,311.39,311.39,\"16,81,00,000\"\n21-Jul-1987,311.36,312.41,307.51,308.55,308.55,\"18,66,00,000\"\n22-Jul-1987,308.56,309.12,307.22,308.47,308.47,\"17,47,00,000\"\n23-Jul-1987,308.5,309.63,306.1,307.81,307.81,\"16,37,00,000\"\n24-Jul-1987,307.82,309.28,307.78,309.27,309.27,\"15,84,00,000\"\n27-Jul-1987,309.3,310.7,308.61,310.65,310.65,\"15,20,00,000\"\n28-Jul-1987,310.65,312.33,310.28,312.33,312.33,\"17,26,00,000\"\n29-Jul-1987,312.34,315.65,311.73,315.65,315.65,\"19,62,00,000\"\n30-Jul-1987,315.69,318.53,315.65,318.05,318.05,\"20,80,00,000\"\n31-Jul-1987,318.05,318.85,317.56,318.66,318.66,\"18,19,00,000\"\n3-Aug-1987,318.62,320.26,316.52,317.57,317.57,\"20,78,00,000\"\n4-Aug-1987,317.59,318.25,314.51,316.23,316.23,\"16,65,00,000\"\n5-Aug-1987,316.25,319.74,316.23,318.45,318.45,\"19,27,00,000\"\n6-Aug-1987,318.49,322.09,317.5,322.09,322.09,\"19,20,00,000\"\n7-Aug-1987,322.1,324.15,321.82,323,323,\"21,27,00,000\"\n10-Aug-1987,322.98,328,322.95,328,328,\"18,72,00,000\"\n11-Aug-1987,328.02,333.4,328,333.33,333.33,\"27,81,00,000\"\n12-Aug-1987,333.32,334.57,331.06,332.39,332.39,\"23,58,00,000\"\n13-Aug-1987,332.38,335.52,332.38,334.65,334.65,\"21,71,00,000\"\n14-Aug-1987,334.63,336.08,332.63,333.99,333.99,\"19,61,00,000\"\n17-Aug-1987,333.98,335.43,332.88,334.11,334.11,\"16,61,00,000\"\n18-Aug-1987,334.1,334.11,326.43,329.25,329.25,\"19,84,00,000\"\n19-Aug-1987,329.26,329.89,326.54,329.83,329.83,\"18,09,00,000\"\n20-Aug-1987,331.49,335.19,329.83,334.84,334.84,\"19,66,00,000\"\n21-Aug-1987,334.85,336.37,334.3,335.9,335.9,\"18,96,00,000\"\n24-Aug-1987,335.89,335.9,331.92,333.33,333.33,\"14,94,00,000\"\n25-Aug-1987,333.37,337.89,333.33,336.77,336.77,\"21,35,00,000\"\n26-Aug-1987,336.77,337.39,334.46,334.57,334.57,\"19,62,00,000\"\n27-Aug-1987,334.56,334.57,331.1,331.38,331.38,\"16,36,00,000\"\n28-Aug-1987,331.37,331.38,327.03,327.04,327.04,\"15,63,00,000\"\n31-Aug-1987,327.03,330.09,326.99,329.8,329.8,\"16,58,00,000\"\n1-Sep-1987,329.81,332.18,322.83,323.4,323.4,\"19,35,00,000\"\n2-Sep-1987,323.4,324.53,318.76,321.68,321.68,\"19,99,00,000\"\n3-Sep-1987,321.47,324.29,317.39,320.21,320.21,\"16,52,00,000\"\n4-Sep-1987,320.21,322.03,316.53,316.7,316.7,\"12,91,00,000\"\n8-Sep-1987,316.68,316.7,308.56,313.56,313.56,\"24,29,00,000\"\n9-Sep-1987,313.6,315.41,312.29,313.92,313.92,\"16,49,00,000\"\n10-Sep-1987,313.92,317.59,313.92,317.13,317.13,\"17,98,00,000\"\n11-Sep-1987,317.14,322.45,317.13,321.98,321.98,\"17,80,00,000\"\n14-Sep-1987,322.02,323.81,320.4,323.08,323.08,\"15,44,00,000\"\n15-Sep-1987,323.07,323.08,317.63,317.74,317.74,\"13,62,00,000\"\n16-Sep-1987,317.75,319.5,314.61,314.86,314.86,\"19,57,00,000\"\n17-Sep-1987,314.94,316.08,313.45,314.93,314.93,\"15,07,00,000\"\n18-Sep-1987,314.98,316.99,314.86,314.86,314.86,\"18,81,00,000\"\n21-Sep-1987,314.92,317.66,310.12,310.54,310.54,\"17,01,00,000\"\n22-Sep-1987,310.54,319.51,308.69,319.5,319.5,\"20,95,00,000\"\n23-Sep-1987,319.49,321.83,319.12,321.19,321.19,\"22,03,00,000\"\n24-Sep-1987,321.09,322.01,319.12,319.72,319.72,\"16,22,00,000\"\n25-Sep-1987,319.72,320.55,318.1,320.16,320.16,\"13,80,00,000\"\n28-Sep-1987,320.16,325.33,320.16,323.2,323.2,\"18,81,00,000\"\n29-Sep-1987,323.2,324.63,320.27,321.69,321.69,\"17,35,00,000\"\n30-Sep-1987,321.69,322.53,320.16,321.83,321.83,\"18,31,00,000\"\n1-Oct-1987,321.83,327.34,321.83,327.33,327.33,\"19,32,00,000\"\n2-Oct-1987,327.33,328.94,327.22,328.07,328.07,\"18,91,00,000\"\n5-Oct-1987,328.07,328.57,326.09,328.08,328.08,\"15,97,00,000\"\n6-Oct-1987,328.08,328.08,319.17,319.22,319.22,\"17,56,00,000\"\n7-Oct-1987,319.22,319.39,315.78,318.54,318.54,\"18,63,00,000\"\n8-Oct-1987,318.54,319.34,312.02,314.16,314.16,\"19,87,00,000\"\n9-Oct-1987,314.16,315.04,310.97,311.07,311.07,\"15,83,00,000\"\n12-Oct-1987,311.07,311.07,306.76,309.39,309.39,\"14,19,00,000\"\n13-Oct-1987,309.39,314.53,309.39,314.52,314.52,\"17,29,00,000\"\n14-Oct-1987,314.52,314.52,304.78,305.23,305.23,\"20,74,00,000\"\n15-Oct-1987,305.21,305.23,298.07,298.08,298.08,\"26,32,00,000\"\n16-Oct-1987,298.08,298.92,281.52,282.7,282.7,\"33,85,00,000\"\n19-Oct-1987,282.7,282.7,224.83,224.84,224.84,\"60,43,00,000\"\n20-Oct-1987,225.06,245.62,216.46,236.83,236.83,\"60,81,00,000\"\n21-Oct-1987,236.83,259.27,236.83,258.38,258.38,\"44,96,00,000\"\n22-Oct-1987,258.24,258.38,242.99,248.25,248.25,\"39,22,00,000\"\n23-Oct-1987,248.29,250.7,242.76,248.22,248.22,\"24,56,00,000\"\n26-Oct-1987,248.2,248.22,227.26,227.67,227.67,\"30,88,00,000\"\n27-Oct-1987,227.67,237.81,227.67,233.19,233.19,\"26,02,00,000\"\n28-Oct-1987,233.19,238.58,226.26,233.28,233.28,\"27,94,00,000\"\n29-Oct-1987,233.31,246.69,233.28,244.77,244.77,\"25,81,00,000\"\n30-Oct-1987,244.77,254.04,244.77,251.79,251.79,\"30,34,00,000\"\n2-Nov-1987,251.73,255.75,249.15,255.75,255.75,\"17,60,00,000\"\n3-Nov-1987,255.75,255.75,242.78,250.82,250.82,\"22,78,00,000\"\n4-Nov-1987,250.81,251,246.34,248.96,248.96,\"20,25,00,000\"\n5-Nov-1987,248.93,256.09,247.72,254.48,254.48,\"22,60,00,000\"\n6-Nov-1987,254.49,257.21,249.68,250.41,250.41,\"22,82,90,000\"\n9-Nov-1987,250.41,250.41,243.01,243.17,243.17,\"16,06,90,000\"\n10-Nov-1987,243.14,243.17,237.64,239,239,\"18,43,10,000\"\n11-Nov-1987,239.01,243.86,239,241.9,241.9,\"14,78,50,000\"\n12-Nov-1987,241.93,249.9,241.9,248.52,248.52,\"20,62,80,000\"\n13-Nov-1987,248.54,249.42,245.64,245.64,245.64,\"17,49,20,000\"\n16-Nov-1987,245.69,249.54,244.98,246.76,246.76,\"16,43,40,000\"\n17-Nov-1987,246.73,246.76,240.81,243.04,243.04,\"14,82,40,000\"\n18-Nov-1987,243.09,245.55,240.67,245.55,245.55,\"15,82,70,000\"\n19-Nov-1987,245.54,245.55,239.7,240.05,240.05,\"15,71,40,000\"\n20-Nov-1987,240.04,242.01,235.89,242,242,\"18,91,70,000\"\n23-Nov-1987,242,242.99,240.5,242.99,242.99,\"14,31,60,000\"\n24-Nov-1987,242.98,247.9,242.98,246.39,246.39,\"19,95,20,000\"\n25-Nov-1987,246.42,246.54,244.08,244.1,244.1,\"13,97,80,000\"\n27-Nov-1987,244.11,244.12,240.34,240.34,240.34,\"8,63,60,000\"\n30-Nov-1987,240.27,240.34,225.75,230.3,230.3,\"26,89,10,000\"\n1-Dec-1987,230.32,234.02,230.3,232,232,\"14,98,70,000\"\n2-Dec-1987,232.01,234.56,230.31,233.45,233.45,\"14,88,90,000\"\n3-Dec-1987,233.46,233.9,225.21,225.21,225.21,\"20,41,60,000\"\n4-Dec-1987,225.2,225.77,221.24,223.92,223.92,\"18,48,00,000\"\n7-Dec-1987,223.98,228.77,223.92,228.76,228.76,\"14,66,60,000\"\n8-Dec-1987,228.77,234.92,228.69,234.91,234.91,\"22,73,10,000\"\n9-Dec-1987,234.91,240.09,233.83,238.89,238.89,\"23,14,30,000\"\n10-Dec-1987,238.89,240.05,233.4,233.57,233.57,\"18,89,60,000\"\n11-Dec-1987,233.6,235.48,233.35,235.32,235.32,\"15,16,80,000\"\n14-Dec-1987,235.3,242.34,235.04,242.19,242.19,\"18,76,80,000\"\n15-Dec-1987,242.19,245.59,241.31,242.81,242.81,\"21,49,70,000\"\n16-Dec-1987,242.81,248.11,242.8,248.08,248.08,\"19,38,20,000\"\n17-Dec-1987,248.08,248.6,242.96,242.98,242.98,\"19,17,80,000\"\n18-Dec-1987,243.01,249.18,243.01,249.16,249.16,\"27,62,20,000\"\n21-Dec-1987,249.14,250.25,248.3,249.54,249.54,\"16,17,90,000\"\n22-Dec-1987,249.56,249.97,247.01,249.95,249.95,\"19,26,50,000\"\n23-Dec-1987,249.96,253.35,249.95,253.16,253.16,\"20,31,10,000\"\n24-Dec-1987,253.13,253.16,251.68,252.03,252.03,\"10,88,00,000\"\n28-Dec-1987,252.01,252.02,244.19,245.57,245.57,\"13,12,20,000\"\n29-Dec-1987,245.58,245.88,244.28,244.59,244.59,\"11,15,80,000\"\n30-Dec-1987,244.63,248.06,244.59,247.86,247.86,\"14,92,30,000\"" }, { "path": "March Cohort 2023/12_15_16_17_18_Statistics/datasets/data_loan.csv", "content": "ID,Default,Loan_type,Gender,Age,Degree,Income,Credit_score,Loan_length,Signers,Citizenship\n1,0,Car ,Female,30,HS ,114885,641,0,2,Citizen \n2,0,Home,Female,43,HS ,95770,534,7,2,Citizen \n3,0,Home,Male ,39,HS ,94220,558,5,2,Citizen \n4,0,Home,Male ,39,College ,58946,622,7,2,Citizen \n5,0,Car ,Female,42,HS ,79754,702,2,1,Citizen \n6,0,Car ,Female,57,Graduate,119627,624,3,2,Non-citizen\n7,0,Car ,Male ,41,HS ,78765,594,3,2,Citizen \n8,0,Car ,Female,53,HS ,123167,652,2,2,Non-citizen\n9,0,Car ,Female,44,HS ,116175,694,3,2,Citizen \n10,0,Car ,Female,57,College ,111085,586,3,2,Citizen \n11,0,Car ,Male ,50,HS ,54387,593,2,2,Citizen \n12,0,Car ,Male ,39,HS ,89468,600,0,2,Citizen \n13,0,Car ,Male ,52,HS ,118613,622,0,2,Citizen \n14,0,Car ,Male ,37,HS ,84429,615,0,2,Citizen \n15,0,Car ,Female,46,College ,113281,577,3,2,Citizen \n16,0,Home,Female,45,College ,93253,580,10,1,Citizen \n17,0,Car ,Male ,45,College ,89304,665,3,1,Citizen \n18,0,Car ,Male ,45,College ,126260,638,1,2,Citizen \n19,0,Car ,Male ,42,HS ,77616,655,3,2,Citizen \n20,0,Car ,Female,34,HS ,88568,630,0,2,Citizen \n21,0,Car ,Male ,47,HS ,118155,730,3,1,Citizen \n22,0,Car ,Female,50,College ,132630,625,3,2,Citizen \n23,0,Home,Male ,46,HS ,99880,551,8,2,Citizen \n24,1,Home,Female,53,Graduate,118848,701,8,1,Citizen \n25,1,Home,Female,36,College ,109327,682,8,1,Citizen \n26,0,Car ,Female,41,HS ,90653,641,0,2,Citizen \n27,0,Car ,Female,44,Graduate,122276,709,3,1,Non-citizen\n28,0,Car ,Male ,36,College ,97064,564,2,2,Citizen \n29,0,Car ,Female,41,College ,107481,632,1,2,Citizen \n30,0,Car ,Female,45,College ,113738,625,2,1,Citizen \n31,0,Car ,Male ,46,HS ,90207,663,3,2,Citizen \n32,0,Car ,Female,55,Graduate,120783,585,3,1,Citizen \n33,0,Home,Male ,41,HS ,104234,586,7,2,Citizen \n34,1,Car ,Female,46,College ,106650,697,3,2,Citizen \n35,0,Home,Male ,56,College ,109333,586,11,2,Citizen \n36,0,Car ,Male ,41,HS ,114009,780,3,2,Citizen \n37,0,Car ,Female,33,College ,73802,551,1,2,Citizen \n38,0,Car ,Male ,44,HS ,93651,639,1,2,Citizen \n39,1,Home,Male ,36,HS ,90710,667,5,2,Citizen \n40,0,Home,Female,43,HS ,75956,592,6,2,Citizen \n41,0,Home,Male ,41,HS ,80480,689,5,2,Citizen \n42,0,Home,Male ,50,HS ,113200,573,6,2,Citizen \n43,0,Car ,Female,51,HS ,94323,632,0,2,Citizen \n44,0,Car ,Female,39,College ,122986,629,3,1,Citizen \n45,0,Car ,Female,50,HS ,118155,705,0,2,Citizen \n46,0,Car ,Male ,41,HS ,92730,614,1,2,Citizen \n47,0,Car ,Male ,39,HS ,106330,650,3,2,Citizen \n48,0,Car ,Male ,47,HS ,92742,668,1,1,Citizen \n49,0,Car ,Female,40,HS ,90204,714,0,1,Citizen \n50,0,Car ,Male ,57,College ,105074,678,2,2,Citizen \n51,0,Car ,Female,44,HS ,67887,546,0,2,Citizen \n52,0,Car ,Female,52,College ,117994,753,0,2,Citizen \n53,0,Car ,Female,47,HS ,115866,669,2,2,Citizen \n54,0,Car ,Male ,39,HS ,86162,457,1,2,Citizen \n55,0,Car ,Male ,45,HS ,97152,534,0,2,Citizen \n56,0,Home,Female,50,HS ,127891,596,8,1,Citizen \n57,0,Home,Female,40,HS ,86997,571,6,2,Citizen \n58,0,Car ,Female,44,HS ,94050,620,0,2,Citizen \n59,0,Car ,Male ,47,College ,84924,644,2,2,Citizen \n60,0,Car ,Male ,48,HS ,92904,605,3,2,Citizen \n61,0,Car ,Male ,40,HS ,94239,563,3,2,Citizen \n62,0,Car ,Male ,46,HS ,117596,602,0,2,Citizen \n63,0,Home,Male ,49,HS ,98343,686,10,2,Citizen \n64,0,Car ,Female,36,HS ,70946,637,0,2,Citizen \n65,0,Home,Male ,34,College ,120250,590,6,2,Citizen \n66,0,Car ,Female,37,College ,94604,535,1,2,Citizen \n67,0,Home,Male ,40,HS ,86004,524,7,2,Citizen \n68,0,Car ,Male ,37,HS ,111413,592,0,2,Citizen \n69,0,Home,Male ,48,HS ,98027,655,5,2,Citizen \n70,0,Home,Male ,36,HS ,64901,483,6,2,Citizen \n71,0,Car ,Female,42,HS ,96461,591,0,2,Citizen \n72,1,Car ,Male ,48,HS ,112781,680,2,2,Citizen \n73,0,Car ,Female,42,HS ,105276,690,2,1,Citizen \n74,0,Home,Male ,36,HS ,89483,663,12,2,Citizen \n75,0,Car ,Female,41,College ,107629,650,1,2,Citizen \n76,0,Car ,Male ,50,HS ,88480,582,0,2,Citizen \n77,0,Car ,Female,48,HS ,63332,597,1,2,Citizen \n78,0,Car ,Female,47,College ,110486,605,2,2,Citizen \n79,0,Car ,Male ,36,HS ,91003,584,1,2,Citizen \n80,0,Car ,Female,46,Graduate,87658,688,2,2,Citizen \n81,0,Home,Male ,35,HS ,67801,514,6,2,Citizen \n82,0,Car ,Female,44,HS ,107880,654,0,2,Citizen \n83,0,Car ,Female,42,College ,126326,608,3,1,Citizen \n84,0,Car ,Female,43,HS ,94438,687,1,2,Citizen \n85,0,Home,Male ,33,HS ,95915,557,5,2,Citizen \n86,0,Car ,Male ,47,College ,75815,582,2,2,Citizen \n87,0,Car ,Female,44,HS ,109771,596,2,2,Citizen \n88,0,Home,Male ,41,HS ,99144,621,13,2,Citizen \n89,0,Car ,Female,42,Graduate,118537,623,2,2,Citizen \n90,0,Car ,Female,44,HS ,132693,660,2,1,Citizen \n91,0,Car ,Male ,40,HS ,108362,703,1,2,Citizen \n92,0,Car ,Female,40,HS ,104512,701,0,1,Citizen \n93,0,Car ,Female,39,College ,113403,657,1,2,Non-citizen\n94,0,Car ,Male ,41,HS ,82365,572,0,2,Citizen \n95,0,Car ,Female,36,HS ,113265,596,1,2,Citizen \n96,0,Car ,Female,38,HS ,84750,598,0,2,Citizen \n97,0,Car ,Male ,49,HS ,100032,584,2,2,Citizen \n98,0,Car ,Male ,32,College ,94471,468,3,2,Citizen \n99,0,Car ,Male ,37,HS ,61508,562,2,2,Citizen \n100,0,Car ,Female,55,College ,128124,691,3,2,Citizen \n101,0,Car ,Male ,48,HS ,104622,626,2,1,Citizen \n102,0,Car ,Male ,41,HS ,101887,526,1,2,Citizen \n103,0,Car ,Male ,37,College ,94400,637,2,2,Citizen \n104,0,Car ,Female,50,College ,102471,571,1,2,Citizen \n105,0,Car ,Male ,54,Graduate,118278,471,3,2,Citizen \n106,0,Car ,Female,62,College ,98850,622,1,2,Citizen \n107,0,Car ,Female,51,College ,121978,644,3,2,Citizen \n108,1,Home,Male ,47,HS ,116594,650,7,2,Citizen \n109,0,Car ,Female,47,HS ,106577,712,2,1,Citizen \n110,0,Home,Male ,42,HS ,82115,578,7,2,Citizen \n111,0,Home,Female,37,HS ,88622,523,5,2,Citizen \n112,0,Car ,Male ,41,College ,106759,580,3,1,Citizen \n113,0,Home,Female,26,College ,95496,649,5,2,Citizen \n114,0,Car ,Female,46,College ,78871,583,1,2,Citizen \n115,0,Home,Female,37,College ,72233,583,6,2,Citizen \n116,0,Car ,Male ,44,HS ,96670,720,1,2,Citizen \n117,1,Car ,Female,56,College ,137669,689,1,1,Non-citizen\n118,1,Car ,Female,50,College ,114114,696,2,2,Non-citizen\n119,0,Car ,Female,41,College ,86496,604,2,2,Citizen \n120,0,Car ,Female,31,HS ,97583,506,0,2,Citizen \n121,0,Car ,Male ,37,HS ,120005,502,2,2,Citizen \n122,0,Home,Male ,33,HS ,104788,535,6,2,Citizen \n123,1,Car ,Female,54,HS ,91587,742,0,2,Citizen \n124,0,Home,Male ,46,College ,113556,613,10,2,Citizen \n125,0,Car ,Male ,52,Graduate,101998,636,3,2,Non-citizen\n126,0,Car ,Male ,40,College ,111874,529,3,2,Citizen \n127,0,Car ,Female,32,HS ,122053,540,0,2,Citizen \n128,0,Home,Male ,44,HS ,83691,521,5,2,Citizen \n129,0,Car ,Female,38,HS ,106560,558,0,2,Citizen \n130,0,Car ,Male ,41,College ,98747,596,1,2,Citizen \n131,0,Car ,Male ,37,HS ,79054,632,0,2,Citizen \n132,0,Home,Male ,42,HS ,70309,572,5,2,Citizen \n133,0,Home,Male ,37,HS ,79672,552,3,2,Citizen \n134,0,Car ,Female,41,HS ,96418,657,1,1,Citizen \n135,0,Home,Male ,32,HS ,105689,525,6,2,Citizen \n136,0,Home,Female,41,College ,105493,718,8,2,Citizen \n137,1,Car ,Male ,48,HS ,129293,706,2,2,Citizen \n138,0,Car ,Male ,38,HS ,93754,588,0,2,Citizen \n139,0,Car ,Female,51,HS ,105797,580,2,2,Citizen \n140,0,Car ,Male ,41,College ,112033,563,2,2,Citizen \n141,0,Car ,Male ,48,College ,94165,583,2,2,Citizen \n142,0,Home,Male ,30,HS ,71571,566,1,2,Citizen \n143,1,Car ,Female,44,College ,104553,688,2,2,Citizen \n144,0,Home,Male ,38,Graduate,107839,682,12,2,Citizen \n145,0,Car ,Female,42,HS ,86878,487,2,2,Citizen \n146,0,Car ,Male ,56,College ,114940,633,2,2,Citizen \n147,0,Car ,Male ,47,College ,85479,653,1,2,Citizen \n148,0,Home,Female,42,HS ,84707,627,6,2,Citizen \n149,0,Home,Male ,47,College ,65487,504,9,2,Citizen \n150,0,Car ,Female,35,HS ,95957,625,0,2,Citizen \n151,0,Car ,Male ,35,HS ,121765,568,3,2,Citizen \n152,0,Car ,Male ,46,College ,94219,701,1,2,Citizen \n153,0,Car ,Female,41,HS ,95467,639,1,2,Non-citizen\n154,0,Car ,Male ,40,HS ,81158,625,1,2,Citizen \n155,0,Home,Male ,30,HS ,55276,535,5,2,Citizen \n156,1,Car ,Female,39,College ,92009,738,1,2,Citizen \n157,0,Car ,Female,31,HS ,99295,572,0,2,Citizen \n158,0,Home,Male ,36,College ,105141,593,8,2,Citizen \n159,0,Car ,Male ,46,HS ,83935,767,1,2,Citizen \n160,0,Home,Male ,39,HS ,112444,537,3,2,Citizen \n161,0,Car ,Male ,49,HS ,109448,601,2,2,Citizen \n162,0,Car ,Female,37,HS ,88295,601,0,2,Citizen \n163,0,Car ,Male ,52,HS ,101542,552,2,2,Citizen \n164,0,Home,Male ,49,HS ,105387,585,12,2,Citizen \n165,0,Car ,Male ,36,HS ,105836,505,1,2,Citizen \n166,0,Car ,Male ,47,HS ,98693,662,2,2,Citizen \n167,0,Home,Male ,45,HS ,103574,521,10,2,Citizen \n168,0,Home,Male ,44,HS ,85816,605,9,2,Citizen \n169,0,Car ,Male ,52,HS ,105639,822,0,2,Citizen \n170,0,Home,Female,44,HS ,110446,621,6,2,Citizen \n171,0,Car ,Male ,40,College ,96149,560,2,2,Citizen \n172,0,Car ,Female,56,HS ,129233,659,0,2,Citizen \n173,0,Car ,Male ,48,College ,142083,683,2,2,Citizen \n174,0,Car ,Female,37,HS ,96187,600,1,2,Citizen \n175,0,Home,Female,43,College ,115134,578,10,2,Citizen \n176,0,Home,Male ,36,HS ,53709,541,7,2,Citizen \n177,0,Car ,Female,55,HS ,117622,575,0,2,Non-citizen\n178,0,Car ,Male ,50,College ,128119,822,3,2,Citizen \n179,0,Car ,Female,41,HS ,90013,595,2,2,Citizen \n180,0,Car ,Male ,31,HS ,61786,533,0,2,Citizen \n181,0,Home,Female,34,College ,64024,693,5,2,Citizen \n182,0,Car ,Male ,45,College ,75261,602,2,2,Citizen \n183,0,Home,Female,42,College ,106206,570,4,2,Citizen \n184,0,Car ,Female,43,College ,120510,595,0,2,Citizen \n185,0,Home,Male ,33,HS ,116700,549,1,2,Citizen \n186,0,Home,Male ,32,HS ,87202,575,3,2,Citizen \n187,0,Car ,Male ,40,HS ,83125,569,0,2,Citizen \n188,0,Car ,Male ,42,HS ,83844,650,0,2,Citizen \n189,0,Car ,Male ,43,HS ,100112,453,2,2,Citizen \n190,0,Car ,Male ,49,Graduate,126511,716,3,2,Citizen \n191,0,Car ,Male ,50,HS ,106383,535,1,2,Non-citizen\n192,0,Car ,Male ,46,HS ,102451,559,2,2,Citizen \n193,1,Home,Female,38,Graduate,87650,641,5,2,Citizen \n194,0,Home,Male ,46,HS ,105334,534,10,2,Citizen \n195,0,Car ,Male ,39,HS ,101608,522,0,2,Citizen \n196,0,Home,Male ,42,HS ,108430,629,13,2,Citizen \n197,0,Car ,Female,48,HS ,115372,591,2,2,Citizen \n198,0,Car ,Female,41,HS ,101821,655,2,2,Citizen \n199,0,Car ,Male ,39,HS ,101002,629,3,2,Citizen \n200,0,Car ,Female,48,College ,108378,688,0,2,Non-citizen\n201,0,Car ,Male ,44,Graduate,87408,537,3,2,Citizen \n202,0,Home,Male ,53,College ,112383,671,8,2,Citizen \n203,1,Home,Male ,31,HS ,108061,614,9,2,Citizen \n204,0,Car ,Female,39,HS ,101420,701,2,2,Citizen \n205,0,Car ,Male ,35,College ,93252,517,1,2,Citizen \n206,0,Car ,Male ,45,HS ,79590,629,2,2,Citizen \n207,0,Car ,Female,53,HS ,106569,701,1,2,Citizen \n208,0,Home,Male ,45,College ,109824,708,9,2,Non-citizen\n209,0,Home,Female,36,HS ,112640,649,4,2,Citizen \n210,0,Car ,Female,47,HS ,71524,644,0,2,Citizen \n211,0,Car ,Female,29,HS ,45103,585,0,2,Citizen \n212,0,Car ,Female,51,HS ,106947,700,1,2,Citizen \n213,0,Home,Female,47,HS ,90422,598,3,2,Citizen \n214,1,Car ,Male ,46,HS ,120777,658,2,2,Citizen \n215,0,Home,Male ,42,HS ,113592,588,5,2,Citizen \n216,0,Home,Male ,36,HS ,97782,638,6,2,Citizen \n217,0,Car ,Female,43,HS ,109092,649,0,2,Citizen \n218,0,Home,Male ,44,HS ,88268,608,7,1,Citizen \n219,1,Car ,Male ,50,HS ,97334,626,0,2,Citizen \n220,0,Car ,Female,39,HS ,95519,563,1,2,Citizen \n221,0,Home,Male ,48,College ,57502,579,9,2,Citizen \n222,0,Home,Male ,33,HS ,98858,616,7,2,Citizen \n223,0,Car ,Male ,44,HS ,77614,614,0,2,Citizen \n224,0,Car ,Male ,46,College ,89245,649,2,2,Citizen \n225,0,Car ,Female,40,College ,95368,638,1,2,Citizen \n226,0,Car ,Female,42,HS ,123815,684,3,2,Citizen \n227,0,Home,Male ,50,HS ,106085,664,2,2,Citizen \n228,0,Car ,Male ,50,HS ,118184,645,1,2,Citizen \n229,0,Car ,Male ,31,HS ,75153,522,0,2,Citizen \n230,0,Car ,Female,38,HS ,102509,512,3,2,Citizen \n231,0,Car ,Male ,47,HS ,110604,576,1,2,Citizen \n232,1,Home,Female,51,Graduate,110699,683,3,2,Citizen \n233,0,Home,Female,43,HS ,72684,625,4,2,Citizen \n234,0,Car ,Male ,42,HS ,81629,690,0,2,Citizen \n235,0,Home,Male ,41,HS ,47859,517,1,2,Citizen \n236,0,Car ,Male ,38,HS ,97805,561,0,2,Citizen \n237,0,Car ,Female,48,College ,95118,696,1,1,Citizen \n238,0,Home,Male ,35,HS ,85182,603,4,2,Citizen \n239,0,Car ,Female,41,HS ,69221,697,0,2,Citizen \n240,0,Home,Male ,38,College ,84649,578,6,2,Citizen \n241,0,Car ,Female,49,College ,101001,719,1,2,Citizen \n242,0,Home,Male ,39,HS ,87312,571,3,2,Citizen \n243,1,Car ,Female,44,College ,100978,685,2,2,Citizen \n244,0,Home,Female,45,HS ,98420,592,3,2,Citizen \n245,0,Car ,Male ,35,HS ,76690,517,0,2,Citizen \n246,0,Car ,Male ,49,HS ,105512,669,0,2,Citizen \n247,0,Car ,Male ,38,HS ,83456,512,2,2,Citizen \n248,0,Car ,Male ,49,College ,107705,695,3,2,Citizen \n249,0,Car ,Female,45,HS ,102789,549,1,2,Citizen \n250,0,Car ,Male ,37,College ,65698,620,1,2,Citizen \n251,0,Car ,Male ,49,HS ,84677,566,0,2,Citizen \n252,0,Car ,Male ,53,HS ,126542,659,3,2,Citizen \n253,0,Car ,Female,46,HS ,117076,584,2,2,Citizen \n254,0,Car ,Male ,53,HS ,105099,583,0,2,Citizen \n255,0,Car ,Male ,51,College ,98294,558,3,2,Citizen \n256,0,Car ,Male ,38,HS ,93933,637,1,2,Citizen \n258,0,Home,Female,34,HS ,82406,601,3,2,Citizen \n259,0,Car ,Male ,47,HS ,96505,635,1,2,Citizen \n260,0,Car ,Male ,43,College ,102415,688,2,2,Citizen \n261,0,Car ,Female,45,HS ,94892,643,0,2,Citizen \n262,0,Car ,Female,42,HS ,80752,616,0,2,Citizen \n263,0,Home,Female,34,HS ,107424,539,1,2,Citizen \n264,0,Car ,Female,45,Graduate,127969,662,3,2,Citizen \n265,0,Home,Female,50,HS ,107040,584,4,2,Citizen \n266,0,Home,Male ,42,HS ,56255,630,6,2,Citizen \n267,0,Car ,Male ,40,College ,85385,678,3,2,Citizen \n268,0,Home,Male ,42,HS ,78929,579,1,2,Citizen \n269,0,Car ,Female,48,HS ,101432,598,1,2,Citizen \n270,0,Car ,Female,44,College ,138385,558,2,2,Citizen \n271,0,Car ,Female,57,Graduate,93078,707,3,1,Citizen \n272,0,Home,Male ,38,College ,95156,619,7,2,Citizen \n273,0,Home,Male ,42,HS ,96701,588,8,1,Citizen \n274,0,Home,Male ,44,HS ,100606,579,9,1,Citizen \n275,0,Home,Male ,29,HS ,94702,464,4,2,Citizen \n276,0,Car ,Male ,44,College ,116089,608,1,2,Citizen \n277,0,Car ,Female,44,HS ,91502,726,0,2,Citizen \n278,0,Home,Male ,41,HS ,79165,634,3,2,Citizen \n279,0,Car ,Male ,40,HS ,62904,666,0,2,Citizen \n280,0,Home,Male ,40,HS ,111836,616,3,2,Non-citizen\n281,0,Car ,Female,37,HS ,108773,579,0,2,Citizen \n282,0,Car ,Male ,44,College ,127623,642,2,2,Citizen \n283,1,Car ,Male ,47,HS ,90080,698,0,2,Citizen \n284,1,Home,Female,43,HS ,112638,564,6,2,Citizen \n285,0,Car ,Male ,40,HS ,69589,630,0,2,Citizen \n286,0,Home,Male ,30,HS ,89490,565,2,2,Citizen \n287,0,Car ,Male ,35,HS ,92995,463,0,2,Citizen \n288,0,Car ,Male ,44,College ,92371,624,3,2,Citizen \n289,0,Car ,Male ,36,HS ,114615,684,2,2,Citizen \n290,0,Home,Male ,39,HS ,103997,590,6,2,Citizen \n291,0,Home,Female,38,College ,108771,592,13,2,Citizen \n292,0,Home,Male ,33,HS ,92762,524,10,2,Citizen \n293,0,Car ,Female,49,College ,93237,666,1,2,Citizen \n294,0,Car ,Male ,37,HS ,91143,571,0,2,Citizen \n295,0,Car ,Female,43,HS ,97926,510,0,2,Citizen \n296,0,Home,Female,50,HS ,139022,682,10,1,Non-citizen\n297,0,Car ,Male ,32,HS ,64108,629,0,2,Citizen \n298,0,Home,Male ,39,HS ,91205,519,1,2,Citizen \n299,0,Car ,Male ,41,HS ,98098,679,1,2,Citizen \n300,0,Car ,Male ,33,HS ,73509,510,0,2,Citizen \n301,0,Car ,Female,52,HS ,134028,625,2,2,Citizen \n302,0,Home,Male ,31,HS ,94993,654,5,2,Citizen \n303,0,Car ,Male ,37,HS ,66753,504,0,2,Citizen \n304,0,Home,Female,40,HS ,97553,631,1,2,Citizen \n305,0,Home,Male ,37,HS ,47752,512,7,2,Citizen \n306,0,Home,Male ,38,HS ,94635,629,9,2,Citizen \n307,0,Home,Female,44,HS ,104991,506,6,2,Citizen \n308,0,Car ,Female,42,HS ,111666,479,0,2,Citizen \n309,0,Car ,Male ,45,College ,90679,617,1,2,Citizen \n310,0,Car ,Male ,36,College ,90156,659,2,2,Citizen \n311,0,Home,Male ,45,HS ,111981,715,7,2,Citizen \n312,0,Car ,Male ,41,College ,123681,575,1,2,Citizen \n313,0,Car ,Male ,43,HS ,106828,673,0,2,Citizen \n314,0,Car ,Male ,44,HS ,126459,647,3,2,Citizen \n315,0,Car ,Male ,45,HS ,102523,592,0,2,Citizen \n316,0,Car ,Male ,33,HS ,60002,728,1,2,Citizen \n317,0,Home,Male ,37,HS ,90529,596,8,2,Citizen \n318,0,Car ,Female,41,HS ,90085,616,0,2,Citizen \n319,0,Home,Male ,35,HS ,94422,577,4,2,Citizen \n320,0,Car ,Male ,47,HS ,107142,591,1,2,Citizen \n321,0,Home,Male ,30,HS ,117138,535,6,2,Citizen \n322,0,Car ,Female,35,HS ,74330,638,1,1,Citizen \n323,0,Car ,Female,45,HS ,113073,599,0,2,Citizen \n324,0,Home,Male ,35,HS ,73092,523,6,2,Citizen \n325,0,Car ,Male ,52,HS ,109324,582,2,2,Citizen \n326,0,Car ,Male ,38,College ,105928,614,2,2,Citizen \n327,0,Car ,Female,37,College ,107889,597,0,2,Citizen \n328,0,Home,Male ,37,HS ,93045,443,6,2,Citizen \n329,0,Car ,Female,46,HS ,92836,624,0,1,Citizen \n330,0,Car ,Male ,45,HS ,85088,622,1,2,Citizen \n331,0,Home,Male ,34,HS ,61951,494,4,2,Citizen \n332,0,Car ,Male ,49,HS ,104406,643,1,2,Citizen \n333,0,Car ,Male ,42,HS ,77268,634,2,2,Citizen \n334,0,Home,Female,40,College ,99521,595,7,2,Citizen \n335,0,Home,Male ,47,HS ,111971,560,2,2,Citizen \n336,0,Car ,Male ,42,HS ,85432,530,1,2,Citizen \n337,0,Car ,Male ,38,HS ,108023,626,0,2,Citizen \n338,0,Car ,Male ,43,HS ,89821,631,1,2,Citizen \n339,1,Car ,Male ,56,Graduate,116286,701,2,2,Non-citizen\n340,0,Car ,Female,44,HS ,86820,744,0,1,Citizen \n341,0,Home,Male ,37,HS ,92839,718,8,2,Citizen \n342,0,Car ,Female,43,College ,98519,637,2,2,Citizen \n343,0,Car ,Male ,42,HS ,72996,553,0,2,Citizen \n344,0,Car ,Female,51,College ,109867,635,3,2,Citizen \n345,0,Car ,Female,51,College ,104754,627,2,2,Citizen \n346,0,Car ,Male ,36,HS ,109294,509,1,2,Citizen \n347,0,Car ,Male ,58,HS ,111420,633,2,2,Citizen \n348,1,Car ,Female,45,College ,111484,644,2,1,Non-citizen\n349,0,Home,Male ,33,College ,95112,666,6,2,Citizen \n350,0,Car ,Female,42,HS ,100902,564,0,2,Citizen \n351,0,Home,Female,48,Graduate,86260,668,11,2,Citizen \n352,0,Home,Male ,44,HS ,76710,542,6,2,Citizen \n353,0,Car ,Male ,40,College ,103332,614,1,2,Citizen \n354,0,Home,Male ,47,HS ,104901,518,9,2,Citizen \n355,0,Car ,Female,42,HS ,100738,653,0,2,Citizen \n356,0,Home,Female,39,HS ,109824,650,7,2,Citizen \n357,0,Car ,Male ,35,HS ,105831,540,1,2,Citizen \n358,0,Car ,Female,45,College ,98573,656,0,2,Citizen \n359,0,Car ,Female,41,College ,108167,574,2,2,Citizen \n360,0,Home,Male ,38,HS ,99921,499,10,2,Citizen \n361,0,Home,Male ,33,HS ,78738,433,5,2,Citizen \n362,0,Home,Female,37,Graduate,106738,592,8,2,Citizen \n363,0,Car ,Male ,40,HS ,95502,567,0,2,Citizen \n364,0,Car ,Male ,39,HS ,91658,403,1,2,Citizen \n365,0,Car ,Female,43,HS ,76332,674,0,2,Citizen \n366,1,Car ,Female,51,Graduate,124592,796,3,2,Non-citizen\n367,0,Car ,Male ,49,College ,84298,589,3,2,Citizen \n368,0,Home,Male ,43,HS ,119009,587,8,2,Citizen \n369,0,Car ,Male ,40,HS ,90963,627,2,2,Citizen \n370,0,Car ,Female,40,HS ,115422,672,0,1,Citizen \n371,0,Car ,Male ,27,HS ,82144,550,0,2,Citizen \n372,0,Home,Male ,39,HS ,90919,527,4,2,Citizen \n373,1,Car ,Female,53,College ,101185,709,3,1,Citizen \n374,0,Car ,Male ,38,HS ,104208,667,0,2,Citizen \n375,0,Home,Male ,44,HS ,84069,720,4,2,Citizen \n376,0,Home,Male ,26,HS ,79546,546,4,2,Citizen \n377,0,Car ,Male ,42,College ,81982,454,1,2,Citizen \n378,0,Car ,Female,51,HS ,118404,655,2,1,Citizen \n379,0,Car ,Male ,46,College ,83902,641,2,2,Citizen \n380,0,Car ,Male ,53,HS ,132712,647,2,2,Citizen \n381,0,Car ,Female,49,Graduate,105788,682,3,2,Citizen \n382,0,Car ,Female,51,HS ,70941,634,1,2,Citizen \n383,0,Home,Male ,38,HS ,100456,455,8,2,Citizen \n384,0,Home,Female,37,HS ,98387,532,2,2,Citizen \n385,0,Home,Male ,32,HS ,78638,501,5,2,Citizen \n386,1,Car ,Male ,42,College ,133041,641,0,2,Citizen \n387,0,Home,Male ,44,HS ,106028,586,4,2,Citizen \n388,1,Car ,Male ,41,College ,118404,690,0,2,Citizen \n389,0,Home,Female,44,HS ,73558,641,8,2,Citizen \n390,0,Car ,Male ,51,HS ,121742,675,1,2,Citizen \n391,0,Car ,Female,44,HS ,123021,693,1,2,Citizen \n392,0,Car ,Male ,49,HS ,121560,664,3,2,Citizen \n393,0,Home,Male ,44,HS ,127522,595,7,2,Citizen \n394,0,Home,Female,45,HS ,79765,514,3,2,Citizen \n395,0,Home,Female,35,HS ,110407,636,2,2,Citizen \n396,0,Car ,Male ,40,HS ,106680,711,1,2,Citizen \n397,0,Car ,Female,51,Graduate,102979,693,3,1,Non-citizen\n398,0,Car ,Male ,46,HS ,123397,700,2,1,Non-citizen\n399,0,Home,Male ,26,HS ,80687,502,4,2,Citizen \n400,0,Car ,Female,35,College ,96089,586,1,1,Citizen \n401,0,Car ,Female,44,College ,110755,749,2,2,Citizen \n402,0,Car ,Female,47,Graduate,99830,675,3,2,Non-citizen\n403,0,Home,Female,37,HS ,121760,687,5,2,Citizen \n404,0,Home,Male ,41,HS ,99238,518,7,2,Citizen \n405,0,Car ,Male ,38,HS ,98333,600,1,1,Citizen \n406,0,Car ,Female,53,College ,95259,685,2,2,Citizen \n407,0,Car ,Female,50,Graduate,120905,641,2,2,Citizen \n408,0,Home,Male ,44,HS ,53413,505,3,2,Citizen \n409,0,Home,Female,46,HS ,81750,579,7,2,Citizen \n410,0,Car ,Male ,32,HS ,83209,576,0,2,Citizen \n411,0,Car ,Female,29,College ,84191,588,0,2,Citizen \n412,0,Car ,Female,42,HS ,108036,605,0,2,Citizen \n413,1,Car ,Female,41,College ,99189,540,0,2,Non-citizen\n414,0,Home,Female,44,HS ,79854,684,2,2,Citizen \n415,0,Car ,Male ,52,HS ,70678,570,0,2,Citizen \n416,0,Car ,Male ,49,Graduate,107097,651,2,2,Citizen \n417,0,Car ,Male ,56,College ,120219,639,1,2,Citizen \n418,0,Car ,Female,43,HS ,118860,539,1,2,Citizen \n419,0,Car ,Male ,47,HS ,111913,683,0,2,Citizen \n420,0,Home,Male ,46,HS ,72652,569,2,2,Citizen \n421,0,Home,Male ,40,College ,116710,501,5,2,Citizen \n422,1,Car ,Female,46,College ,115253,695,3,2,Citizen \n423,0,Car ,Female,54,HS ,74634,618,0,2,Citizen \n424,0,Car ,Female,56,HS ,101975,659,1,2,Citizen \n425,0,Car ,Male ,35,College ,127232,583,2,2,Citizen \n426,0,Home,Male ,41,HS ,84763,623,2,2,Citizen \n427,0,Car ,Male ,44,HS ,140787,573,1,2,Citizen \n428,0,Car ,Female,45,HS ,94827,530,0,2,Citizen \n429,0,Car ,Female,40,HS ,89780,625,0,2,Citizen \n430,0,Car ,Male ,38,HS ,85635,527,0,2,Citizen \n431,0,Car ,Male ,39,HS ,81346,631,0,2,Citizen \n432,0,Car ,Male ,39,HS ,106550,560,2,2,Citizen \n433,0,Car ,Male ,49,HS ,79303,702,0,2,Citizen \n434,0,Car ,Female,48,Graduate,89068,595,3,2,Citizen \n435,0,Home,Female,44,College ,111674,701,10,2,Citizen \n436,0,Car ,Male ,57,HS ,101010,659,3,2,Citizen \n437,0,Home,Male ,40,HS ,66717,645,0,2,Citizen \n438,0,Car ,Male ,39,College ,102409,625,2,2,Citizen \n439,0,Car ,Male ,49,College ,83150,607,3,2,Citizen \n440,0,Car ,Male ,44,HS ,93250,532,2,2,Citizen \n441,0,Home,Female,30,HS ,119535,614,5,2,Citizen \n442,0,Car ,Male ,36,College ,131647,596,1,2,Citizen \n443,0,Car ,Male ,43,HS ,91123,613,2,2,Citizen \n444,0,Car ,Female,36,HS ,106879,623,2,2,Citizen \n445,0,Car ,Female,41,HS ,123004,699,0,2,Non-citizen\n446,0,Car ,Female,50,College ,90253,615,3,2,Citizen \n447,0,Car ,Female,41,HS ,112445,572,1,2,Citizen \n448,0,Home,Female,33,HS ,87944,533,5,2,Citizen \n449,0,Car ,Male ,43,College ,108624,611,2,2,Citizen \n450,0,Car ,Female,55,College ,125573,597,3,2,Citizen \n451,0,Car ,Female,37,HS ,72853,452,0,2,Citizen \n452,0,Car ,Male ,42,HS ,122876,593,2,2,Citizen \n453,0,Car ,Female,42,Graduate,76813,599,1,2,Citizen \n454,0,Car ,Male ,38,College ,114388,552,0,2,Citizen \n455,0,Car ,Male ,44,HS ,74075,586,3,2,Citizen \n456,0,Car ,Female,40,HS ,116857,616,0,2,Citizen \n457,0,Car ,Female,39,HS ,113036,626,3,2,Citizen \n458,0,Home,Male ,29,HS ,100284,543,9,2,Citizen \n459,0,Car ,Female,51,HS ,92992,650,2,1,Citizen \n460,0,Home,Male ,33,HS ,77608,563,2,2,Citizen \n461,0,Home,Male ,32,HS ,79725,462,10,2,Citizen \n462,0,Car ,Male ,48,HS ,67437,642,3,2,Citizen \n463,0,Car ,Female,33,HS ,93955,581,0,2,Citizen \n464,0,Home,Male ,55,HS ,102176,607,13,2,Citizen \n465,0,Car ,Male ,41,College ,101409,665,2,2,Citizen \n466,0,Car ,Male ,38,HS ,75265,604,0,2,Citizen \n467,0,Car ,Female,40,HS ,69944,582,1,2,Citizen \n468,0,Car ,Male ,39,HS ,75964,559,2,2,Citizen \n469,0,Car ,Female,33,HS ,71606,563,0,1,Citizen \n470,0,Home,Male ,43,HS ,109159,566,8,2,Citizen \n471,0,Home,Male ,42,HS ,119276,640,7,2,Citizen \n472,0,Home,Female,45,HS ,80671,699,7,2,Citizen \n473,1,Car ,Female,42,Graduate,102688,656,2,2,Citizen \n474,0,Car ,Male ,47,HS ,97613,598,1,2,Citizen \n475,0,Car ,Male ,34,College ,114805,544,1,2,Citizen \n476,0,Car ,Female,48,HS ,88976,572,1,2,Citizen \n477,0,Home,Male ,47,College ,88143,630,8,2,Citizen \n478,0,Home,Female,36,HS ,55916,563,6,2,Citizen \n479,0,Car ,Male ,45,HS ,68525,644,0,2,Citizen \n480,0,Car ,Male ,30,HS ,95725,653,0,2,Citizen \n481,0,Home,Male ,46,HS ,101531,578,11,2,Citizen \n482,0,Car ,Male ,47,HS ,110843,607,2,2,Citizen \n483,0,Car ,Male ,31,HS ,65063,477,0,2,Citizen \n484,0,Car ,Female,35,HS ,89168,632,1,2,Citizen \n485,0,Car ,Female,47,HS ,120479,710,2,2,Citizen \n486,0,Car ,Male ,34,HS ,92419,560,1,2,Citizen \n487,0,Car ,Male ,51,HS ,80782,562,0,2,Citizen \n488,0,Home,Male ,45,HS ,90216,581,6,2,Citizen \n489,0,Car ,Male ,38,HS ,82131,600,0,2,Citizen \n490,0,Car ,Female,44,Graduate,114328,556,3,2,Citizen \n491,0,Car ,Male ,48,HS ,106250,621,0,2,Citizen \n492,0,Home,Female,44,College ,85907,491,6,2,Citizen \n493,0,Home,Female,36,HS ,113961,659,4,2,Citizen \n494,0,Car ,Male ,54,HS ,80983,591,1,2,Citizen \n495,0,Car ,Male ,24,HS ,64642,652,0,2,Citizen \n496,0,Home,Male ,33,HS ,97980,658,7,2,Citizen \n497,0,Home,Female,40,College ,69611,609,7,2,Citizen \n498,0,Home,Male ,41,HS ,93844,638,6,2,Citizen \n499,0,Car ,Male ,34,HS ,88094,618,0,2,Citizen \n500,0,Car ,Male ,45,College ,97515,585,3,2,Citizen \n501,0,Car ,Female,47,HS ,127611,597,2,1,Citizen \n502,0,Car ,Male ,33,HS ,120653,638,2,2,Citizen \n503,0,Car ,Female,48,HS ,87623,584,0,2,Citizen \n504,0,Car ,Male ,37,HS ,76163,578,2,2,Citizen \n505,0,Car ,Male ,47,College ,93307,571,1,2,Citizen \n506,0,Home,Male ,35,HS ,96804,630,5,1,Citizen \n507,0,Home,Male ,47,College ,81461,775,6,2,Citizen \n508,0,Car ,Female,45,College ,119436,639,0,2,Citizen \n509,0,Car ,Male ,45,HS ,92921,674,1,2,Citizen \n510,0,Car ,Female,43,HS ,109026,593,1,2,Citizen \n511,0,Car ,Female,46,College ,103601,658,2,2,Citizen \n512,0,Car ,Male ,50,College ,94578,585,3,2,Citizen \n513,0,Car ,Male ,50,HS ,96042,614,2,2,Citizen \n514,0,Home,Male ,35,HS ,91465,540,5,2,Citizen \n515,0,Home,Male ,45,HS ,87212,684,5,2,Citizen \n516,1,Car ,Male ,41,HS ,97202,785,1,2,Citizen \n517,0,Car ,Female,38,HS ,95068,558,1,2,Citizen \n518,0,Home,Male ,40,College ,77838,601,8,1,Citizen \n519,0,Car ,Female,51,HS ,113921,677,0,2,Citizen \n520,0,Car ,Male ,49,College ,87883,720,3,2,Citizen \n521,0,Home,Male ,51,HS ,114891,617,8,2,Citizen \n522,0,Car ,Male ,51,College ,112162,602,3,2,Citizen \n523,0,Car ,Male ,42,College ,68536,654,0,2,Citizen \n524,1,Car ,Male ,53,HS ,118509,691,0,2,Citizen \n525,0,Home,Male ,40,HS ,70753,521,7,2,Citizen \n526,0,Car ,Female,49,College ,112219,664,1,2,Non-citizen\n527,0,Car ,Male ,42,College ,90458,471,2,2,Citizen \n528,0,Car ,Male ,42,HS ,101512,576,3,2,Citizen \n529,0,Car ,Male ,44,College ,83725,588,2,2,Citizen \n530,0,Home,Male ,46,HS ,85747,662,3,2,Citizen \n531,0,Car ,Male ,50,HS ,115657,631,2,2,Citizen \n532,0,Car ,Female,42,HS ,112556,637,2,1,Citizen \n533,1,Car ,Female,52,Graduate,123444,690,2,1,Non-citizen\n534,0,Home,Female,39,HS ,122509,627,6,2,Citizen \n535,0,Car ,Male ,34,HS ,70708,654,1,2,Citizen \n536,1,Car ,Male ,38,College ,83865,566,0,2,Citizen \n537,0,Car ,Male ,45,HS ,109564,616,1,2,Citizen \n538,0,Car ,Female,51,Graduate,105208,733,3,2,Citizen \n539,0,Car ,Male ,40,College ,81726,553,0,2,Citizen \n540,0,Car ,Male ,33,HS ,81146,702,0,2,Citizen \n541,0,Car ,Male ,42,HS ,84029,618,1,2,Citizen \n542,1,Home,Female,39,HS ,121291,556,6,2,Citizen \n543,0,Car ,Male ,43,College ,120473,613,2,2,Citizen \n544,0,Home,Male ,36,HS ,110647,585,8,2,Citizen \n545,0,Car ,Male ,43,HS ,103793,680,3,1,Citizen \n546,0,Car ,Male ,47,HS ,84822,655,3,1,Citizen \n547,1,Car ,Female,46,Graduate,93990,741,3,2,Non-citizen\n548,0,Home,Male ,44,HS ,99915,488,3,2,Citizen \n549,0,Car ,Female,35,Graduate,74995,635,0,2,Non-citizen\n550,0,Car ,Female,51,HS ,117071,609,0,1,Citizen \n551,0,Car ,Male ,29,HS ,121462,566,0,2,Citizen \n552,0,Car ,Female,53,Graduate,104113,682,2,2,Citizen \n553,0,Home,Male ,41,HS ,57556,505,3,2,Citizen \n554,0,Car ,Female,41,HS ,89158,563,0,2,Citizen \n555,0,Car ,Female,33,HS ,83195,481,0,2,Citizen \n556,0,Car ,Female,41,HS ,90136,518,2,2,Citizen \n557,0,Car ,Male ,35,HS ,94961,526,0,2,Citizen \n558,0,Car ,Male ,46,HS ,97995,516,0,2,Citizen \n559,0,Home,Female,40,HS ,109967,642,7,2,Citizen \n560,0,Car ,Female,41,HS ,120044,746,1,2,Citizen \n561,0,Car ,Male ,36,HS ,101401,581,0,2,Citizen \n562,0,Car ,Male ,36,HS ,103486,620,2,2,Citizen \n563,0,Home,Male ,50,HS ,96958,596,7,2,Citizen \n564,0,Home,Male ,55,HS ,99347,687,7,2,Citizen \n565,0,Car ,Female,45,HS ,106629,655,2,2,Citizen \n566,1,Car ,Female,53,College ,107720,743,3,1,Citizen \n567,0,Car ,Male ,45,College ,59721,560,0,2,Citizen \n568,0,Car ,Female,47,College ,132663,724,2,1,Non-citizen\n569,0,Home,Male ,46,College ,119322,648,7,2,Citizen \n570,0,Car ,Female,39,College ,105644,582,2,2,Citizen \n571,0,Home,Male ,41,HS ,114628,599,0,2,Citizen \n572,0,Car ,Female,33,HS ,114313,635,0,2,Citizen \n573,0,Home,Female,46,HS ,109345,628,11,2,Citizen \n574,0,Car ,Female,53,HS ,94195,525,1,2,Citizen \n575,0,Home,Female,44,HS ,114406,679,5,2,Citizen \n576,0,Home,Male ,43,HS ,116973,664,8,2,Citizen \n577,0,Home,Female,47,College ,122232,678,7,2,Citizen \n578,1,Car ,Female,45,Graduate,123065,642,2,2,Citizen \n579,0,Home,Male ,54,HS ,95154,593,9,1,Citizen \n580,0,Car ,Male ,44,HS ,101525,535,0,2,Citizen \n581,0,Car ,Female,45,College ,98490,717,1,2,Citizen \n582,0,Car ,Male ,37,College ,97314,617,3,1,Citizen \n583,0,Home,Female,48,HS ,83052,604,5,1,Citizen \n584,0,Car ,Female,39,HS ,118404,630,0,1,Citizen \n585,0,Car ,Male ,46,HS ,101158,632,3,2,Citizen \n586,0,Home,Male ,38,HS ,91920,584,7,2,Citizen \n587,0,Home,Male ,43,HS ,82312,477,1,2,Citizen \n588,0,Home,Male ,35,HS ,78448,609,8,2,Citizen \n589,0,Home,Male ,35,HS ,84759,615,4,2,Citizen \n590,0,Home,Male ,44,HS ,99072,777,7,2,Citizen \n591,0,Home,Female,42,College ,93705,587,6,2,Citizen \n592,0,Car ,Male ,35,HS ,108584,630,0,2,Citizen \n593,0,Car ,Female,39,Graduate,98791,634,1,2,Non-citizen\n594,1,Car ,Male ,46,HS ,101439,661,0,2,Citizen \n595,0,Home,Male ,43,HS ,121200,550,8,2,Citizen \n596,0,Car ,Male ,26,HS ,56397,538,0,2,Citizen \n597,0,Car ,Female,38,HS ,83498,694,0,1,Citizen \n598,0,Car ,Female,41,HS ,98914,589,1,2,Citizen \n599,0,Car ,Male ,41,HS ,105853,555,1,2,Citizen \n600,0,Home,Female,48,HS ,94949,630,5,2,Citizen \n601,0,Car ,Female,34,HS ,103728,656,2,1,Citizen \n602,0,Car ,Female,48,College ,115287,651,2,2,Citizen \n603,0,Car ,Male ,44,HS ,102137,594,1,2,Citizen \n604,0,Home,Female,32,College ,95647,559,7,2,Citizen \n605,0,Car ,Male ,46,HS ,75777,551,0,2,Citizen \n606,0,Home,Male ,44,College ,104945,627,14,2,Citizen \n607,0,Car ,Male ,29,HS ,99674,577,0,2,Citizen \n608,0,Home,Male ,41,College ,83289,558,8,2,Citizen \n609,0,Car ,Female,48,HS ,109876,650,2,2,Citizen \n610,0,Home,Male ,32,HS ,61820,547,2,2,Citizen \n611,0,Car ,Male ,47,HS ,99958,522,1,2,Citizen \n612,0,Home,Female,43,HS ,93071,606,3,2,Citizen \n613,0,Car ,Male ,34,HS ,91068,627,1,2,Citizen \n614,0,Home,Male ,33,HS ,115094,714,9,2,Citizen \n615,1,Home,Female,34,HS ,85744,703,5,2,Citizen \n616,0,Car ,Female,57,Graduate,103925,676,1,2,Non-citizen\n617,0,Car ,Male ,43,HS ,69818,611,0,2,Citizen \n618,0,Car ,Female,53,College ,119650,589,1,2,Non-citizen\n619,0,Car ,Female,44,College ,115049,569,2,2,Citizen \n620,0,Car ,Female,49,College ,94946,650,1,2,Citizen \n621,0,Home,Female,38,HS ,87019,637,3,2,Citizen \n622,0,Car ,Male ,44,College ,92800,549,3,2,Citizen \n623,0,Car ,Female,35,HS ,80559,701,0,2,Citizen \n624,0,Car ,Male ,41,HS ,97224,624,1,1,Citizen \n625,0,Car ,Female,47,HS ,90720,609,3,2,Citizen \n626,0,Car ,Female,47,HS ,90947,664,2,1,Citizen \n627,0,Car ,Male ,46,College ,87839,543,1,2,Citizen \n628,0,Home,Male ,41,HS ,79163,536,6,2,Citizen \n629,0,Car ,Male ,43,HS ,107491,576,0,1,Citizen \n630,0,Car ,Male ,37,HS ,70410,609,0,2,Citizen \n631,0,Home,Female,45,HS ,124777,723,7,2,Citizen \n632,0,Car ,Female,51,HS ,92203,673,0,1,Citizen \n633,0,Car ,Male ,47,HS ,85847,605,0,2,Citizen \n634,0,Home,Male ,39,HS ,92451,588,5,2,Citizen \n635,0,Car ,Female,43,HS ,111023,641,1,2,Citizen \n636,0,Car ,Female,45,HS ,109479,615,1,2,Citizen \n637,0,Home,Male ,38,HS ,75416,585,5,2,Citizen \n638,0,Car ,Male ,44,Graduate,76821,568,2,2,Citizen \n639,0,Home,Female,37,College ,108077,603,9,2,Citizen \n640,1,Car ,Female,47,College ,89303,643,2,1,Citizen \n641,0,Car ,Female,48,HS ,104973,709,1,2,Citizen \n642,0,Car ,Female,45,College ,112422,613,2,2,Citizen \n643,0,Car ,Male ,40,HS ,83503,564,0,2,Citizen \n644,0,Car ,Male ,42,HS ,107131,608,0,2,Citizen \n645,0,Home,Female,36,HS ,85450,610,4,2,Citizen \n646,0,Car ,Female,42,HS ,84303,656,0,2,Citizen \n647,0,Car ,Female,41,College ,96638,671,2,2,Citizen \n648,0,Car ,Female,36,HS ,78946,557,0,2,Citizen \n649,0,Car ,Male ,43,College ,67920,632,0,2,Citizen \n650,1,Car ,Female,45,HS ,97505,618,0,2,Citizen \n651,0,Home,Male ,49,HS ,89720,602,1,2,Citizen \n652,0,Home,Male ,35,HS ,122706,583,6,2,Citizen \n653,0,Car ,Male ,46,HS ,109763,638,0,2,Citizen \n654,0,Home,Male ,32,HS ,86462,566,6,2,Citizen \n655,1,Car ,Female,56,Graduate,135156,673,2,2,Citizen \n656,0,Home,Female,50,HS ,99471,718,0,2,Citizen \n657,0,Car ,Female,41,HS ,100161,652,1,2,Citizen \n658,0,Car ,Female,41,HS ,93052,612,0,2,Citizen \n659,0,Car ,Male ,43,HS ,75819,570,2,2,Citizen \n660,0,Car ,Female,38,HS ,96671,695,2,2,Citizen \n661,0,Home,Male ,36,HS ,101256,597,2,2,Citizen \n662,1,Car ,Female,34,Graduate,113531,671,2,2,Citizen \n663,0,Car ,Male ,46,HS ,84402,619,0,2,Citizen \n664,0,Home,Male ,36,HS ,101790,569,6,2,Citizen \n665,0,Car ,Female,46,HS ,102701,569,3,1,Citizen \n666,0,Car ,Female,47,College ,140220,735,2,2,Citizen \n667,0,Car ,Male ,41,HS ,102863,590,0,2,Citizen \n668,0,Car ,Male ,32,HS ,87778,616,2,2,Citizen \n669,0,Home,Female,46,College ,86501,629,12,1,Citizen \n670,0,Home,Female,43,HS ,102367,589,8,2,Citizen \n671,0,Car ,Male ,34,College ,90661,576,0,2,Citizen \n672,0,Home,Female,49,HS ,97635,714,5,2,Citizen \n673,0,Home,Male ,37,HS ,89572,598,2,2,Citizen \n674,0,Home,Male ,42,HS ,108829,699,7,1,Citizen \n675,0,Car ,Male ,41,HS ,93533,581,0,2,Citizen \n676,1,Home,Male ,41,College ,105459,608,13,2,Citizen \n677,0,Car ,Female,44,College ,104436,625,0,2,Citizen \n678,0,Home,Male ,46,HS ,107246,654,10,2,Citizen \n679,0,Car ,Male ,41,HS ,79444,587,0,2,Citizen \n680,0,Car ,Female,51,College ,106328,749,2,2,Non-citizen\n681,0,Car ,Male ,39,HS ,105976,661,1,1,Citizen \n682,0,Home,Female,44,Graduate,93994,683,11,2,Citizen \n683,0,Home,Female,38,HS ,88894,628,5,2,Citizen \n684,0,Car ,Male ,35,HS ,61728,534,0,2,Citizen \n685,0,Home,Male ,46,HS ,99711,662,12,1,Citizen \n686,0,Car ,Female,40,College ,86159,643,0,2,Citizen \n687,0,Car ,Female,42,HS ,87053,624,1,2,Citizen \n688,0,Home,Male ,49,HS ,103573,604,10,2,Citizen \n689,1,Car ,Male ,44,HS ,65005,713,0,2,Citizen \n690,0,Home,Male ,39,HS ,85973,540,3,2,Citizen \n691,0,Car ,Male ,40,HS ,64613,618,0,2,Citizen \n692,0,Car ,Female,44,HS ,95595,568,0,2,Citizen \n693,0,Car ,Male ,41,HS ,61649,592,0,2,Citizen \n694,0,Home,Male ,46,HS ,92772,740,4,2,Citizen \n695,0,Car ,Female,44,HS ,86942,568,2,2,Citizen \n696,0,Car ,Female,46,HS ,63190,702,1,2,Citizen \n697,0,Home,Male ,47,HS ,104779,696,7,2,Citizen \n698,0,Car ,Male ,53,College ,95542,556,2,2,Citizen \n699,0,Car ,Female,37,College ,88263,587,1,1,Citizen \n700,1,Car ,Male ,40,HS ,85449,536,0,2,Citizen \n701,0,Home,Male ,43,HS ,89154,591,4,2,Citizen \n702,0,Home,Male ,40,HS ,98471,662,4,2,Citizen \n703,0,Car ,Male ,49,College ,98863,643,2,2,Citizen \n704,0,Home,Female,45,HS ,102348,546,5,2,Citizen \n705,0,Home,Male ,38,HS ,64813,468,7,2,Citizen \n706,0,Car ,Male ,37,HS ,79426,579,1,2,Citizen \n707,0,Home,Male ,46,Graduate,90416,640,11,2,Citizen \n708,0,Car ,Female,41,College ,84974,602,1,2,Citizen \n709,0,Car ,Male ,42,College ,108786,705,2,2,Citizen \n710,0,Home,Male ,42,HS ,88753,558,6,2,Citizen \n711,0,Home,Male ,33,HS ,70548,535,5,2,Citizen \n712,0,Car ,Male ,42,HS ,113174,644,0,2,Citizen \n713,0,Home,Male ,47,HS ,84607,652,9,2,Citizen \n714,0,Car ,Female,39,HS ,108313,681,2,2,Citizen \n715,0,Car ,Female,41,HS ,119686,600,1,1,Citizen \n716,0,Car ,Male ,37,HS ,83496,507,0,2,Citizen \n717,0,Car ,Male ,30,HS ,72949,499,0,2,Citizen \n718,0,Car ,Male ,48,HS ,100749,512,1,2,Citizen \n719,0,Car ,Male ,34,College ,94148,625,1,1,Citizen \n720,0,Car ,Female,51,College ,124110,664,1,1,Citizen \n721,0,Home,Male ,48,HS ,75398,600,11,1,Citizen \n722,0,Car ,Male ,38,College ,97842,557,2,2,Citizen \n723,0,Car ,Female,46,College ,107163,711,1,1,Citizen \n724,0,Car ,Male ,41,HS ,111052,611,1,2,Citizen \n725,0,Car ,Female,38,College ,102906,627,2,1,Citizen \n726,0,Car ,Female,44,College ,119756,606,2,2,Citizen \n727,0,Car ,Male ,38,College ,80667,524,1,2,Citizen \n728,0,Car ,Female,42,HS ,99314,659,0,2,Citizen \n729,0,Car ,Male ,52,College ,106805,631,0,2,Non-citizen\n730,0,Car ,Male ,33,HS ,89059,560,1,2,Citizen \n731,0,Car ,Male ,43,HS ,102051,635,0,2,Citizen \n732,0,Car ,Female,37,HS ,112473,497,0,2,Citizen \n733,0,Car ,Female,40,HS ,110347,605,1,2,Citizen \n734,0,Home,Male ,46,HS ,113547,562,8,2,Citizen \n735,0,Car ,Male ,52,HS ,112583,658,3,1,Citizen \n736,0,Home,Male ,30,HS ,66511,514,5,2,Citizen \n737,0,Car ,Female,51,HS ,106488,650,1,2,Citizen \n738,0,Car ,Male ,43,HS ,71642,561,0,2,Citizen \n739,0,Car ,Male ,31,HS ,79613,624,0,2,Citizen \n740,0,Car ,Female,44,HS ,97169,633,3,1,Citizen \n741,0,Home,Female,34,HS ,120747,586,5,2,Citizen \n742,0,Car ,Male ,46,HS ,89105,550,3,2,Citizen \n743,0,Car ,Female,49,HS ,97799,658,1,2,Non-citizen\n744,0,Car ,Female,43,HS ,66134,564,0,2,Citizen \n745,0,Car ,Female,38,HS ,100078,644,0,2,Citizen \n746,1,Car ,Female,38,HS ,127735,612,2,2,Citizen \n747,1,Car ,Male ,39,College ,82112,713,3,2,Citizen \n748,0,Home,Male ,37,HS ,77376,556,2,2,Citizen \n749,0,Car ,Female,40,HS ,107645,660,2,2,Citizen \n750,0,Car ,Male ,40,HS ,81750,496,2,2,Citizen \n751,0,Home,Female,36,HS ,66473,514,5,2,Citizen \n752,0,Car ,Male ,35,HS ,75283,538,1,2,Citizen \n753,0,Home,Female,41,HS ,110422,693,2,2,Citizen \n754,1,Car ,Male ,51,College ,101498,688,3,1,Citizen \n755,0,Home,Male ,42,HS ,69180,590,2,2,Citizen \n756,0,Car ,Male ,45,HS ,74423,574,2,2,Citizen \n757,0,Car ,Male ,50,HS ,100783,653,1,1,Citizen \n758,0,Car ,Female,46,HS ,90736,690,0,1,Citizen \n759,0,Home,Male ,38,HS ,97007,563,0,2,Citizen \n760,0,Home,Male ,52,HS ,108001,561,10,2,Citizen \n761,0,Home,Male ,40,College ,89546,704,7,2,Citizen \n762,0,Home,Male ,42,HS ,112497,554,8,2,Citizen \n763,0,Car ,Male ,32,HS ,63293,586,0,2,Citizen \n764,1,Car ,Female,34,HS ,81891,683,0,2,Citizen \n765,0,Car ,Male ,47,College ,85524,590,1,2,Citizen \n766,1,Car ,Female,42,HS ,107848,657,2,2,Citizen \n767,0,Car ,Male ,48,HS ,98357,599,1,2,Citizen \n768,0,Home,Male ,38,HS ,113308,600,10,2,Citizen \n769,0,Car ,Male ,49,HS ,90435,778,1,2,Citizen \n770,0,Car ,Female,36,HS ,95659,557,1,2,Citizen \n771,0,Car ,Female,49,HS ,101074,632,0,2,Citizen \n772,0,Home,Male ,28,HS ,48991,571,3,2,Citizen \n773,0,Car ,Male ,36,College ,76197,496,1,2,Citizen \n774,0,Car ,Female,45,HS ,86833,548,0,2,Citizen \n775,0,Car ,Male ,34,HS ,61076,483,2,2,Citizen \n776,0,Home,Male ,35,HS ,113555,563,4,2,Citizen \n777,0,Car ,Male ,52,College ,131046,628,2,2,Citizen \n778,1,Car ,Female,45,HS ,91765,606,1,2,Citizen \n779,0,Car ,Female,49,HS ,127703,600,1,2,Citizen \n780,0,Home,Male ,36,HS ,79281,523,8,2,Citizen \n781,0,Car ,Female,48,HS ,112970,556,1,2,Citizen \n782,0,Home,Male ,44,HS ,92037,553,7,2,Citizen \n783,0,Car ,Female,54,HS ,109130,695,0,1,Citizen \n784,0,Car ,Female,47,HS ,100167,669,0,2,Citizen \n785,0,Car ,Female,47,HS ,102549,643,3,1,Citizen \n786,0,Car ,Female,42,HS ,89811,712,0,1,Citizen \n787,0,Car ,Male ,32,HS ,94539,618,1,2,Citizen \n788,0,Car ,Male ,42,HS ,77790,556,1,2,Citizen \n789,0,Home,Male ,37,HS ,75418,567,4,2,Citizen \n790,0,Car ,Female,40,HS ,110722,603,0,2,Citizen \n791,0,Car ,Female,49,HS ,116579,625,0,2,Citizen \n792,0,Car ,Female,43,HS ,108353,628,1,1,Citizen \n793,0,Car ,Female,40,HS ,95586,585,0,2,Citizen \n794,0,Car ,Female,43,HS ,98705,600,0,2,Citizen \n795,0,Home,Male ,38,HS ,77307,585,5,2,Citizen \n796,0,Car ,Male ,35,HS ,94008,693,2,2,Citizen \n797,0,Home,Female,48,HS ,115138,503,7,2,Citizen \n798,0,Car ,Female,46,College ,106310,682,0,2,Citizen \n799,0,Car ,Male ,32,HS ,89526,528,0,2,Citizen \n800,0,Home,Male ,35,HS ,96173,651,1,2,Citizen \n801,0,Home,Male ,47,HS ,87035,637,3,2,Citizen \n802,0,Car ,Male ,42,HS ,98652,659,2,2,Citizen \n803,0,Home,Female,43,HS ,97974,670,6,2,Citizen \n804,0,Car ,Male ,42,HS ,102945,683,2,2,Citizen \n805,0,Car ,Male ,41,Graduate,87601,683,2,2,Citizen \n806,0,Car ,Female,37,HS ,106338,633,0,2,Citizen \n807,0,Car ,Male ,40,HS ,97956,622,0,2,Citizen \n808,1,Car ,Male ,62,College ,147798,771,3,1,Citizen \n809,0,Home,Male ,50,HS ,102943,604,5,2,Citizen \n810,0,Car ,Male ,42,HS ,113958,597,2,2,Citizen \n811,0,Car ,Female,42,HS ,111679,573,2,1,Citizen \n812,1,Car ,Female,43,HS ,139702,605,1,2,Citizen \n813,0,Car ,Female,44,College ,106871,599,3,2,Citizen \n814,0,Car ,Female,38,Graduate,81722,577,1,2,Citizen \n815,0,Home,Female,42,College ,110901,552,8,2,Citizen \n816,0,Car ,Male ,37,HS ,107701,608,2,2,Citizen \n817,0,Car ,Female,42,HS ,106891,627,0,2,Citizen \n818,0,Home,Female,56,College ,116817,754,13,1,Citizen \n819,0,Car ,Male ,43,Graduate,97351,572,3,2,Citizen \n820,0,Car ,Male ,35,HS ,88425,562,0,2,Citizen \n821,1,Car ,Female,49,College ,114163,648,0,2,Citizen \n822,0,Car ,Female,48,HS ,106237,663,1,1,Citizen \n823,0,Home,Male ,44,HS ,99157,555,5,2,Citizen \n824,0,Car ,Female,49,College ,95537,713,2,1,Non-citizen\n825,1,Car ,Female,42,HS ,100775,613,2,2,Citizen \n826,0,Car ,Female,41,College ,65252,548,1,2,Citizen \n827,0,Home,Male ,39,HS ,81820,586,3,2,Citizen \n828,0,Car ,Male ,52,HS ,116967,632,3,2,Citizen \n829,0,Car ,Male ,38,HS ,100467,528,2,2,Citizen \n830,0,Car ,Female,46,Graduate,74597,700,2,2,Citizen \n831,0,Car ,Male ,39,HS ,103638,576,3,2,Citizen \n832,0,Car ,Male ,49,HS ,117767,649,2,1,Citizen \n833,0,Car ,Male ,43,College ,86011,630,0,2,Citizen \n834,0,Car ,Female,51,HS ,91355,580,0,2,Citizen \n835,0,Car ,Male ,35,HS ,110027,579,0,2,Citizen \n836,0,Home,Female,39,HS ,71096,576,5,2,Citizen \n837,0,Home,Female,43,College ,123215,567,10,2,Citizen \n838,0,Car ,Male ,37,HS ,116645,710,2,2,Citizen \n839,0,Car ,Female,48,College ,80794,553,3,2,Citizen \n840,0,Car ,Female,41,College ,102818,633,0,2,Citizen \n841,0,Car ,Male ,50,HS ,103719,676,2,1,Citizen \n842,0,Home,Female,46,HS ,128328,606,11,2,Citizen \n843,0,Car ,Female,43,College ,125766,594,2,2,Citizen \n844,0,Car ,Female,45,Graduate,90558,631,3,2,Citizen \n845,0,Home,Male ,30,HS ,103907,532,4,2,Citizen \n846,0,Home,Male ,40,HS ,84951,619,4,2,Citizen \n847,0,Home,Male ,40,HS ,109889,602,3,2,Citizen \n848,0,Car ,Male ,38,HS ,93495,560,1,1,Citizen \n849,1,Car ,Female,44,HS ,102875,707,0,2,Citizen \n850,0,Car ,Male ,47,HS ,108857,667,1,2,Citizen \n851,0,Home,Male ,45,HS ,109930,573,4,2,Citizen \n852,0,Car ,Male ,51,College ,97097,605,2,2,Citizen \n853,0,Car ,Male ,46,College ,150094,561,2,2,Citizen \n854,0,Car ,Female,37,Graduate,85341,529,3,2,Citizen \n855,0,Home,Female,31,HS ,85237,592,0,2,Citizen \n856,0,Home,Male ,46,HS ,120429,650,5,2,Citizen \n857,0,Home,Male ,32,HS ,84159,581,4,2,Citizen \n858,0,Home,Male ,36,HS ,84172,543,0,2,Citizen \n859,0,Car ,Female,42,College ,98314,658,1,2,Citizen \n860,0,Car ,Female,42,College ,74910,691,0,2,Citizen \n861,0,Home,Male ,41,HS ,55401,510,6,2,Citizen \n862,0,Car ,Male ,44,HS ,98643,615,1,2,Citizen \n863,0,Home,Male ,43,HS ,85795,584,2,2,Citizen \n864,0,Home,Female,47,HS ,119765,530,6,2,Citizen \n865,0,Car ,Male ,46,HS ,98234,589,2,2,Citizen \n866,0,Car ,Female,37,College ,79413,563,2,2,Citizen \n867,0,Home,Female,41,College ,84233,620,5,2,Citizen \n868,0,Car ,Female,38,HS ,104259,618,2,2,Citizen \n869,0,Car ,Male ,37,HS ,108333,523,0,2,Citizen \n870,0,Home,Male ,41,HS ,86777,656,1,2,Citizen \n871,0,Car ,Female,41,HS ,86638,651,2,2,Citizen \n872,0,Car ,Female,47,HS ,100230,555,1,1,Non-citizen\n873,0,Car ,Male ,28,HS ,86377,588,2,2,Citizen \n874,0,Home,Male ,29,HS ,93095,511,6,2,Citizen \n875,0,Car ,Female,46,College ,117194,628,3,2,Citizen \n876,0,Car ,Female,42,HS ,96519,639,0,1,Citizen \n877,0,Car ,Female,44,HS ,74997,615,1,2,Citizen \n878,0,Car ,Male ,43,College ,84505,611,1,2,Citizen \n879,0,Home,Male ,45,College ,84038,655,6,2,Citizen \n880,1,Car ,Female,48,College ,87896,763,1,1,Citizen \n881,0,Car ,Male ,43,College ,74134,626,2,2,Citizen \n882,0,Home,Male ,44,HS ,83217,580,8,2,Citizen \n883,0,Car ,Male ,51,HS ,116792,768,0,1,Citizen \n884,0,Car ,Male ,40,HS ,91575,552,0,2,Citizen \n885,0,Car ,Male ,49,HS ,103860,616,0,2,Citizen \n886,0,Car ,Female,43,HS ,100386,620,2,2,Citizen \n887,0,Car ,Male ,43,HS ,80689,564,2,2,Citizen \n888,1,Car ,Female,36,HS ,110339,609,2,2,Citizen \n889,0,Car ,Male ,40,HS ,82254,607,2,2,Citizen \n890,0,Home,Female,38,HS ,106506,554,5,2,Citizen \n891,0,Car ,Male ,47,HS ,90707,555,1,2,Citizen \n892,0,Car ,Female,48,HS ,89779,612,0,2,Citizen \n893,0,Car ,Male ,31,HS ,88428,511,2,2,Citizen \n894,0,Car ,Male ,48,Graduate,111332,661,3,2,Citizen \n895,0,Home,Male ,35,College ,54989,476,2,2,Citizen \n896,0,Car ,Female,44,HS ,74430,690,1,2,Citizen \n897,0,Car ,Female,44,HS ,102439,641,1,2,Citizen \n898,0,Home,Male ,48,HS ,98443,582,9,2,Citizen \n899,0,Car ,Male ,48,HS ,106882,602,2,2,Citizen \n900,0,Home,Male ,33,HS ,82247,464,10,2,Citizen \n901,1,Car ,Female,55,College ,112841,759,0,2,Citizen \n902,0,Car ,Male ,49,HS ,104857,711,2,1,Citizen \n903,0,Car ,Male ,29,College ,75558,623,2,2,Citizen \n904,1,Car ,Female,35,College ,83220,620,2,2,Citizen \n905,0,Car ,Male ,37,College ,89435,588,0,2,Citizen \n906,0,Home,Male ,52,HS ,59505,588,2,2,Citizen \n907,0,Car ,Female,46,College ,84313,525,2,2,Non-citizen\n908,0,Car ,Female,41,HS ,90017,591,1,1,Non-citizen\n909,0,Car ,Male ,56,College ,99733,521,2,2,Citizen \n910,0,Car ,Female,43,Graduate,108116,744,1,2,Citizen \n911,0,Home,Female,43,HS ,81237,577,8,2,Citizen \n912,0,Car ,Female,41,HS ,110597,641,3,1,Citizen \n913,0,Home,Male ,41,HS ,84825,636,1,2,Citizen \n914,0,Car ,Male ,48,College ,81185,583,2,2,Citizen \n915,0,Car ,Female,47,HS ,69053,545,0,2,Citizen \n916,0,Car ,Male ,44,HS ,110452,678,0,2,Citizen \n917,0,Car ,Male ,37,HS ,90420,596,2,2,Citizen \n918,0,Home,Male ,50,HS ,88827,705,9,2,Citizen \n919,0,Car ,Female,42,HS ,70323,640,0,2,Citizen \n920,0,Car ,Male ,55,College ,103035,702,3,1,Citizen \n921,0,Home,Male ,41,HS ,95695,542,6,2,Citizen \n922,0,Car ,Female,51,College ,128301,665,3,1,Citizen \n923,0,Home,Male ,39,HS ,65422,543,8,2,Citizen \n924,0,Home,Male ,47,HS ,111349,520,6,2,Citizen \n925,0,Car ,Male ,50,HS ,98316,615,2,2,Citizen \n926,0,Car ,Male ,35,HS ,91955,501,0,2,Citizen \n927,0,Home,Male ,35,HS ,63712,568,2,2,Citizen \n928,0,Car ,Female,46,HS ,124000,601,1,2,Citizen \n929,0,Car ,Female,36,HS ,91644,554,1,2,Citizen \n930,0,Car ,Male ,51,HS ,119669,618,2,2,Citizen \n931,0,Home,Male ,49,College ,84993,621,11,2,Citizen \n932,0,Home,Female,46,HS ,83739,598,7,2,Citizen \n933,0,Home,Male ,37,HS ,85549,520,7,2,Citizen \n934,0,Car ,Male ,41,HS ,86935,595,2,2,Citizen \n935,0,Car ,Female,47,College ,107596,652,3,1,Non-citizen\n936,0,Car ,Male ,31,HS ,71264,590,0,2,Citizen \n937,0,Car ,Male ,29,HS ,102032,506,1,2,Citizen \n938,0,Home,Female,37,HS ,115304,421,7,2,Citizen \n939,0,Car ,Male ,36,College ,83802,667,3,2,Citizen \n940,0,Car ,Male ,55,HS ,112159,612,1,2,Citizen \n941,0,Car ,Female,31,HS ,98078,478,0,2,Citizen \n942,0,Car ,Male ,47,HS ,96520,592,2,2,Citizen \n943,0,Car ,Female,43,Graduate,106981,695,1,2,Citizen \n944,0,Home,Male ,46,HS ,65328,530,5,2,Citizen \n945,0,Car ,Male ,39,HS ,98570,598,2,2,Citizen \n946,0,Car ,Male ,36,HS ,96850,566,2,2,Citizen \n947,1,Car ,Male ,44,College ,103305,624,3,2,Citizen \n948,0,Car ,Female,44,College ,112642,651,2,1,Non-citizen\n949,0,Home,Female,48,HS ,96491,612,4,2,Citizen \n950,0,Car ,Female,51,HS ,126740,688,2,2,Non-citizen\n951,0,Home,Male ,41,HS ,74628,510,2,2,Citizen \n952,0,Car ,Female,43,HS ,91442,553,2,2,Citizen \n953,0,Car ,Male ,45,HS ,76664,635,0,2,Citizen \n954,0,Home,Male ,33,HS ,105360,671,5,2,Citizen \n955,0,Home,Male ,44,HS ,110435,563,4,1,Citizen \n956,0,Car ,Male ,52,College ,116523,512,2,2,Citizen \n957,0,Car ,Male ,48,HS ,92426,670,2,2,Citizen \n958,0,Car ,Male ,37,HS ,91139,618,0,2,Citizen \n959,0,Car ,Male ,40,HS ,98178,604,2,2,Citizen \n960,0,Car ,Male ,27,HS ,85700,661,0,2,Citizen \n961,0,Car ,Male ,44,College ,98725,618,2,2,Citizen \n962,0,Car ,Female,40,HS ,87841,699,2,2,Citizen \n963,0,Car ,Male ,46,HS ,106169,565,2,2,Citizen \n964,0,Home,Male ,41,HS ,92829,682,2,2,Citizen \n965,0,Car ,Male ,25,HS ,81741,523,0,2,Citizen \n966,0,Car ,Male ,36,HS ,78837,574,2,1,Citizen \n967,0,Car ,Female,43,HS ,121743,517,1,2,Citizen \n968,0,Home,Male ,37,HS ,90407,608,4,2,Citizen \n969,0,Car ,Female,44,Graduate,121406,622,2,2,Citizen \n970,0,Car ,Male ,38,HS ,83333,595,0,2,Citizen \n971,0,Home,Female,45,HS ,77352,612,4,2,Citizen \n972,0,Home,Male ,42,HS ,91238,645,12,1,Citizen \n973,0,Car ,Female,28,HS ,95128,586,0,2,Citizen \n974,0,Car ,Female,48,College ,123328,641,2,2,Citizen \n975,0,Car ,Male ,37,HS ,75214,488,2,2,Citizen \n976,0,Car ,Female,45,HS ,102647,630,1,2,Citizen \n977,0,Car ,Female,47,HS ,102850,650,2,2,Citizen \n978,0,Car ,Female,58,HS ,98552,661,2,2,Citizen \n979,0,Car ,Male ,47,College ,99017,527,2,2,Citizen \n980,0,Home,Female,39,Graduate,106841,635,13,2,Citizen \n981,0,Car ,Female,38,HS ,92303,560,0,2,Citizen \n982,0,Home,Male ,43,HS ,76301,537,3,2,Citizen \n983,0,Car ,Female,45,College ,90783,667,2,2,Citizen \n984,0,Home,Male ,39,HS ,76488,672,8,2,Citizen \n985,0,Car ,Male ,42,HS ,85484,590,1,2,Citizen \n986,0,Car ,Male ,47,HS ,77810,553,1,2,Citizen \n987,0,Home,Male ,28,HS ,64063,583,6,2,Citizen \n988,0,Home,Male ,39,HS ,112930,617,5,2,Citizen \n989,0,Car ,Male ,42,HS ,106976,552,0,2,Citizen \n990,0,Car ,Female,44,HS ,71407,664,1,2,Citizen \n991,0,Car ,Female,40,HS ,95594,661,0,2,Citizen \n992,0,Car ,Female,46,College ,87508,655,1,2,Citizen \n993,0,Car ,Male ,44,HS ,107724,648,1,2,Citizen \n994,1,Car ,Female,54,College ,91540,650,0,2,Non-citizen\n995,0,Home,Female,54,HS ,88485,614,7,1,Citizen \n996,0,Car ,Female,38,HS ,108412,622,0,2,Citizen \n997,0,Car ,Female,52,College ,117000,602,3,2,Citizen \n998,0,Home,Male ,36,HS ,79377,524,3,2,Citizen \n999,0,Car ,Male ,42,HS ,104840,655,0,2,Citizen \n1000,0,Home,Male ,34,HS ,74649,658,4,2,Citizen \n1001,0,Home,Male ,45,HS ,103598,552,7,2,Citizen \n1002,0,Car ,Female,47,HS ,77196,635,0,2,Citizen \n1003,0,Car ,Male ,52,HS ,73936,600,1,2,Citizen \n1004,0,Home,Male ,27,HS ,79007,560,3,2,Citizen \n1005,0,Car ,Female,50,College ,111821,641,1,2,Citizen \n1006,0,Car ,Male ,37,HS ,102417,590,1,2,Citizen \n1007,0,Car ,Male ,34,HS ,92043,578,2,2,Citizen \n1008,0,Car ,Female,44,College ,141178,612,0,2,Citizen \n1009,0,Car ,Female,50,College ,136811,595,2,1,Non-citizen\n1010,0,Home,Male ,31,HS ,98991,640,7,2,Citizen \n1011,0,Car ,Male ,33,HS ,59393,528,0,2,Citizen \n1012,0,Home,Male ,29,HS ,101886,632,2,2,Citizen \n1013,0,Home,Male ,50,College ,100375,578,10,2,Citizen \n1014,0,Car ,Male ,22,HS ,55649,459,0,2,Citizen \n1016,0,Home,Male ,44,HS ,77047,664,5,2,Citizen \n1017,0,Car ,Female,38,Graduate,135378,720,1,2,Non-citizen\n1018,0,Car ,Male ,39,HS ,89861,606,0,2,Citizen \n1019,0,Car ,Male ,42,HS ,112390,625,1,2,Citizen \n1020,0,Home,Female,40,HS ,82273,590,6,2,Citizen \n1021,0,Car ,Male ,31,HS ,110933,750,0,2,Citizen \n1022,0,Home,Male ,51,HS ,107237,642,6,2,Citizen \n1023,1,Car ,Female,48,HS ,97481,631,0,2,Citizen \n1024,0,Car ,Male ,44,HS ,101614,678,3,2,Citizen \n1025,0,Car ,Female,49,College ,131574,651,2,1,Non-citizen\n1026,0,Car ,Female,36,HS ,81568,626,0,2,Citizen \n1027,0,Home,Male ,35,HS ,87989,678,5,2,Citizen \n1028,0,Car ,Female,49,HS ,65788,743,0,2,Non-citizen\n1029,0,Car ,Male ,38,HS ,63725,574,0,2,Citizen \n1030,0,Home,Male ,40,College ,101335,691,6,2,Citizen \n1031,0,Home,Female,37,HS ,91958,649,8,2,Citizen \n1032,0,Car ,Female,36,HS ,101202,672,0,2,Citizen \n1033,0,Car ,Female,47,HS ,109354,640,3,2,Citizen \n1034,0,Car ,Female,36,HS ,85825,575,0,2,Citizen \n1035,0,Car ,Female,52,HS ,122941,588,0,2,Citizen \n1036,0,Car ,Male ,44,College ,98837,624,1,2,Citizen \n1037,0,Home,Male ,45,HS ,101268,625,7,2,Citizen \n1038,0,Car ,Female,53,Graduate,142959,671,2,1,Non-citizen\n1039,0,Car ,Male ,44,Graduate,93865,637,2,2,Citizen \n1040,0,Home,Male ,44,HS ,73868,577,9,2,Citizen \n1041,0,Home,Male ,60,HS ,109354,631,11,2,Citizen \n1042,0,Home,Female,35,HS ,113976,582,8,1,Citizen \n1043,0,Home,Male ,37,HS ,102125,625,3,2,Citizen \n1044,0,Car ,Male ,38,College ,65554,471,2,2,Citizen \n1045,1,Home,Female,42,HS ,91239,726,8,2,Citizen \n1046,0,Car ,Male ,48,HS ,87479,637,2,2,Citizen \n1047,0,Home,Male ,52,College ,75713,552,9,2,Citizen \n1048,0,Car ,Male ,31,HS ,68898,555,0,2,Citizen \n1049,0,Car ,Male ,48,College ,66514,558,0,2,Citizen \n1050,1,Car ,Female,45,HS ,119518,671,0,2,Citizen \n1051,0,Home,Female,38,College ,100125,604,6,1,Citizen \n1052,0,Home,Male ,43,HS ,98160,592,9,2,Citizen \n1053,0,Home,Female,37,College ,98135,607,8,2,Citizen \n1054,0,Home,Female,40,College ,92532,682,5,2,Citizen \n1055,0,Car ,Female,39,HS ,121981,568,0,2,Citizen \n1056,0,Car ,Male ,42,HS ,95048,716,0,1,Citizen \n1057,0,Car ,Female,57,College ,95454,648,1,2,Citizen \n1058,0,Car ,Female,43,HS ,125406,633,3,2,Citizen \n1059,0,Home,Male ,37,HS ,74451,661,2,2,Citizen \n1060,0,Car ,Male ,49,HS ,102846,629,0,2,Citizen \n1061,1,Home,Female,37,HS ,118777,633,6,2,Citizen \n1063,0,Car ,Male ,38,HS ,73642,516,0,2,Citizen \n1064,0,Home,Male ,40,HS ,90926,543,5,2,Citizen \n1065,0,Car ,Female,49,HS ,116367,680,0,2,Citizen \n1066,0,Home,Male ,33,HS ,96148,674,6,2,Citizen \n1067,0,Home,Male ,34,HS ,105258,590,6,2,Citizen \n1068,0,Car ,Male ,38,HS ,74672,544,0,2,Citizen \n1069,0,Car ,Female,35,HS ,77939,630,0,2,Citizen \n1070,0,Home,Male ,46,HS ,78055,587,5,2,Citizen \n1071,0,Car ,Male ,39,HS ,125171,551,2,2,Citizen \n1072,0,Car ,Female,38,HS ,64211,615,2,2,Citizen \n1073,0,Home,Male ,38,HS ,102998,540,7,2,Citizen \n1074,0,Home,Male ,38,HS ,62399,544,5,2,Citizen \n1075,0,Car ,Male ,43,College ,91217,705,0,2,Citizen \n1076,0,Home,Male ,48,HS ,113063,692,7,2,Citizen \n1077,0,Home,Male ,37,HS ,105306,605,6,2,Citizen \n1078,0,Home,Male ,45,HS ,93313,625,4,2,Citizen \n1079,0,Home,Male ,47,HS ,99015,694,5,2,Citizen \n1080,0,Home,Male ,39,HS ,93493,599,6,2,Citizen \n1081,0,Home,Male ,40,HS ,106081,635,8,2,Citizen \n1082,0,Car ,Male ,45,HS ,85585,590,2,2,Citizen \n1083,0,Car ,Male ,45,College ,119690,593,0,2,Citizen \n1084,0,Car ,Male ,43,HS ,107134,616,0,2,Citizen \n1085,0,Home,Male ,42,HS ,91392,655,11,2,Citizen \n1086,0,Car ,Male ,39,HS ,100892,648,1,2,Citizen \n1087,0,Car ,Male ,42,HS ,107847,600,0,2,Citizen \n1088,0,Car ,Male ,50,HS ,69296,637,2,2,Citizen \n1089,0,Home,Male ,45,HS ,81952,581,7,2,Citizen \n1090,0,Car ,Female,50,College ,98775,694,2,2,Citizen \n1091,1,Car ,Female,40,College ,112136,716,2,2,Citizen \n1092,0,Car ,Female,36,HS ,91909,636,0,2,Citizen \n1093,0,Car ,Female,39,HS ,117820,579,2,2,Citizen \n1094,0,Car ,Male ,44,HS ,83852,607,2,2,Citizen \n1095,0,Car ,Female,45,HS ,105645,576,0,2,Citizen \n1096,1,Home,Male ,45,College ,80338,667,3,2,Citizen \n1097,0,Home,Male ,33,College ,89253,619,6,2,Citizen \n1098,0,Home,Male ,43,HS ,88793,597,2,2,Citizen \n1099,1,Home,Male ,48,HS ,97653,614,2,2,Citizen \n1100,0,Car ,Female,53,College ,116541,716,2,1,Citizen \n1101,0,Car ,Female,45,College ,85153,603,0,2,Citizen \n1102,0,Home,Female,38,HS ,93469,654,6,2,Citizen \n1103,1,Car ,Female,49,College ,129954,744,3,2,Citizen \n1104,0,Car ,Female,45,HS ,120386,592,0,2,Non-citizen\n1105,0,Home,Male ,32,HS ,55979,587,1,2,Citizen \n1106,0,Car ,Female,46,HS ,110429,724,2,1,Citizen \n1107,0,Car ,Male ,50,HS ,105758,626,2,1,Citizen \n1108,0,Home,Male ,42,HS ,82291,516,3,2,Citizen \n1109,0,Car ,Male ,42,HS ,115772,598,1,2,Citizen \n1110,0,Home,Male ,37,HS ,106550,561,9,2,Citizen \n1111,0,Car ,Female,44,HS ,82678,582,0,2,Citizen \n1112,0,Home,Male ,36,HS ,96528,528,5,2,Citizen \n1113,1,Car ,Female,50,College ,123119,646,2,1,Citizen \n1114,0,Home,Male ,33,HS ,82841,467,13,2,Citizen \n1115,0,Home,Male ,45,HS ,112001,552,5,2,Citizen \n1116,0,Home,Male ,34,HS ,85679,572,7,2,Citizen \n1117,0,Home,Male ,46,HS ,116594,675,10,1,Citizen \n1118,0,Car ,Female,46,HS ,127430,609,1,2,Citizen \n1119,0,Car ,Female,40,HS ,92327,557,0,2,Citizen \n1120,0,Car ,Male ,38,College ,59505,527,0,2,Citizen \n1121,0,Car ,Female,48,College ,111026,589,2,2,Citizen \n1122,0,Car ,Female,45,College ,93763,625,3,2,Citizen \n1123,0,Home,Male ,38,HS ,69758,489,0,2,Citizen \n1124,0,Car ,Female,52,College ,121456,615,3,2,Citizen \n1125,0,Home,Male ,47,College ,121503,608,9,2,Citizen \n1126,0,Car ,Male ,45,HS ,79233,667,0,2,Citizen \n1127,0,Car ,Male ,31,HS ,97882,685,2,2,Citizen \n1128,0,Car ,Male ,36,HS ,102534,645,0,2,Citizen \n1129,0,Car ,Female,52,College ,112908,660,0,1,Citizen \n1130,0,Car ,Female,47,HS ,100588,632,2,2,Citizen \n1131,0,Home,Male ,40,College ,100211,634,13,2,Citizen \n1132,1,Car ,Male ,44,HS ,99806,560,0,2,Citizen \n1133,0,Car ,Male ,45,College ,102694,625,1,2,Citizen \n1134,1,Car ,Female,47,HS ,96529,642,2,2,Citizen \n1135,0,Car ,Female,38,College ,116576,610,2,2,Citizen \n1136,0,Car ,Female,45,HS ,88567,573,1,2,Citizen \n1137,0,Car ,Female,33,HS ,91145,519,0,2,Citizen \n1138,0,Home,Male ,44,College ,70608,645,7,2,Citizen \n1139,1,Home,Female,43,HS ,98644,618,7,2,Citizen \n1140,0,Car ,Female,55,College ,113409,680,2,2,Citizen \n1141,0,Car ,Female,45,HS ,111282,654,0,2,Non-citizen\n1142,0,Car ,Female,50,College ,72220,578,0,2,Citizen \n1143,0,Car ,Female,39,HS ,139700,636,1,2,Citizen \n1144,0,Car ,Male ,41,HS ,101906,538,2,2,Citizen \n1145,0,Car ,Male ,46,HS ,77471,556,3,2,Citizen \n1146,0,Home,Female,45,HS ,109313,572,7,2,Citizen \n1147,0,Car ,Male ,36,HS ,105238,605,0,2,Citizen \n1148,0,Car ,Female,48,HS ,105018,710,2,2,Citizen \n1149,0,Car ,Female,47,College ,125485,626,2,1,Citizen \n1150,0,Car ,Male ,44,College ,104119,577,2,2,Citizen \n1151,0,Home,Female,42,HS ,93970,693,5,2,Citizen \n1152,0,Home,Male ,42,HS ,77979,646,6,2,Citizen \n1153,0,Car ,Female,50,HS ,113666,672,0,2,Citizen \n1154,0,Home,Male ,44,College ,96173,615,9,2,Citizen \n1155,0,Home,Male ,36,HS ,83498,518,4,2,Citizen \n1156,0,Home,Male ,46,HS ,119246,691,7,2,Citizen \n1157,0,Home,Male ,42,HS ,74800,536,5,2,Citizen \n1158,0,Home,Male ,42,HS ,93466,623,2,2,Citizen \n1160,0,Home,Female,47,College ,112921,689,10,2,Citizen \n1161,0,Car ,Female,44,College ,106839,657,2,2,Citizen \n1162,0,Car ,Female,38,HS ,82795,645,0,2,Citizen \n1163,0,Home,Male ,40,HS ,92513,536,2,1,Citizen \n1164,0,Car ,Male ,37,HS ,84324,665,0,2,Citizen \n1165,0,Car ,Female,46,HS ,115588,653,1,2,Citizen \n1166,0,Home,Male ,35,HS ,94711,598,7,2,Citizen \n1167,0,Car ,Male ,42,HS ,58819,636,0,2,Citizen \n1168,0,Home,Male ,47,HS ,90402,658,7,2,Citizen \n1169,0,Car ,Female,35,HS ,88892,738,1,2,Citizen \n1171,0,Car ,Male ,35,College ,67457,664,2,2,Citizen \n1172,0,Car ,Female,39,HS ,101589,636,0,2,Citizen \n1173,0,Car ,Male ,49,HS ,86160,582,2,2,Citizen \n1174,0,Home,Male ,37,College ,91784,625,5,2,Citizen \n1175,0,Home,Male ,36,HS ,95911,662,5,2,Citizen \n1176,0,Home,Male ,48,HS ,110571,590,2,2,Citizen \n1177,0,Car ,Female,37,College ,93485,642,1,2,Citizen \n1178,0,Home,Male ,39,HS ,93650,693,5,2,Citizen \n1179,0,Car ,Female,42,HS ,106090,644,0,2,Citizen \n1180,0,Car ,Female,52,HS ,83575,725,2,1,Citizen \n1181,0,Home,Male ,36,HS ,100746,613,1,2,Citizen \n1182,0,Home,Male ,36,HS ,82775,502,6,2,Citizen \n1183,0,Car ,Male ,42,College ,118592,594,2,2,Citizen \n1184,0,Car ,Female,42,HS ,111033,607,3,2,Citizen \n1185,0,Car ,Male ,53,College ,112571,676,2,2,Citizen \n1186,0,Home,Male ,41,College ,90298,607,8,2,Citizen \n1187,0,Car ,Male ,40,HS ,98249,571,0,2,Citizen \n1188,0,Car ,Female,35,College ,98057,615,3,1,Citizen \n1189,0,Car ,Female,36,HS ,114245,534,0,2,Citizen \n1190,0,Car ,Female,53,HS ,94735,637,2,1,Citizen \n1191,0,Car ,Male ,44,HS ,108989,612,2,2,Citizen \n1192,0,Car ,Male ,31,HS ,95416,582,0,2,Citizen \n1193,1,Car ,Female,41,HS ,117052,638,2,2,Citizen \n1194,0,Car ,Male ,47,HS ,122781,653,2,2,Citizen \n1195,0,Car ,Female,43,College ,92476,551,1,2,Citizen \n1196,0,Home,Male ,35,HS ,78251,570,5,2,Citizen \n1197,0,Home,Male ,44,HS ,106567,601,11,1,Citizen \n1198,0,Car ,Male ,53,College ,119536,560,2,2,Citizen \n1199,1,Car ,Male ,53,HS ,90420,681,1,2,Citizen \n1200,1,Home,Female,57,College ,118825,710,12,1,Citizen \n1201,0,Car ,Male ,45,College ,121857,638,3,2,Citizen \n1202,0,Car ,Male ,50,HS ,81695,589,0,2,Citizen \n1203,0,Car ,Male ,40,HS ,99874,554,1,2,Citizen \n1204,0,Home,Female,30,HS ,65436,589,0,2,Citizen \n1205,0,Home,Male ,46,HS ,102649,581,6,1,Citizen \n1206,0,Car ,Male ,36,HS ,103601,636,3,2,Citizen \n1207,1,Car ,Female,51,HS ,126963,686,2,2,Citizen \n1208,0,Home,Female,38,HS ,104127,666,4,1,Citizen \n1209,0,Home,Female,38,HS ,76570,708,5,2,Citizen \n1210,0,Home,Male ,39,HS ,85507,535,5,2,Citizen \n1211,0,Car ,Male ,42,HS ,66386,454,0,2,Citizen \n1212,0,Car ,Male ,43,Graduate,102841,675,0,2,Citizen \n1213,0,Car ,Male ,39,HS ,108303,645,1,2,Citizen \n1214,0,Home,Female,43,College ,73032,585,7,2,Citizen \n1215,0,Car ,Male ,31,HS ,86023,559,1,2,Citizen \n1216,0,Home,Male ,47,College ,97255,613,12,2,Citizen \n1217,0,Home,Male ,35,HS ,70638,535,2,2,Citizen \n1218,0,Home,Female,41,HS ,132971,628,5,2,Citizen \n1219,0,Home,Male ,50,HS ,60219,547,6,2,Citizen \n1220,0,Home,Male ,41,HS ,92658,541,7,2,Citizen \n1221,0,Home,Male ,44,HS ,87121,528,5,2,Citizen \n1222,1,Home,Female,54,College ,105355,698,3,2,Citizen \n1223,0,Home,Male ,40,College ,74887,578,5,2,Citizen \n1224,0,Car ,Male ,37,College ,70524,593,0,2,Citizen \n1225,0,Car ,Male ,35,HS ,61022,532,2,2,Citizen \n1226,0,Car ,Male ,45,HS ,85810,562,0,2,Citizen \n1227,0,Home,Male ,45,HS ,112330,692,4,2,Citizen \n1228,0,Home,Male ,39,HS ,81765,658,8,1,Citizen \n1229,0,Car ,Female,48,HS ,92723,605,0,2,Citizen \n1230,0,Home,Male ,38,HS ,79850,569,7,2,Citizen \n1231,0,Car ,Male ,43,HS ,67235,701,2,2,Citizen \n1232,0,Car ,Female,50,College ,100680,599,3,1,Citizen \n1233,0,Home,Male ,38,HS ,103340,560,8,2,Citizen \n1234,0,Car ,Female,41,College ,106651,669,1,2,Non-citizen\n1235,0,Car ,Female,44,HS ,124451,723,0,2,Citizen \n1236,0,Car ,Female,34,HS ,117835,620,1,2,Citizen \n1237,0,Car ,Female,40,HS ,103534,631,1,2,Citizen \n1238,0,Home,Male ,24,HS ,71836,675,5,2,Citizen \n1239,0,Car ,Male ,37,HS ,97335,647,1,2,Citizen \n1240,1,Car ,Male ,41,Graduate,134819,655,2,2,Citizen \n1241,0,Car ,Male ,37,HS ,107560,621,1,2,Citizen \n1242,0,Home,Male ,39,HS ,72243,725,5,2,Citizen \n1243,0,Car ,Female,38,HS ,102463,547,0,2,Citizen \n1244,0,Car ,Male ,45,College ,104142,522,2,2,Citizen \n1245,0,Home,Female,40,College ,115813,685,10,1,Citizen \n1246,0,Home,Male ,29,HS ,48601,562,2,2,Citizen \n1247,0,Home,Male ,42,HS ,100293,600,2,2,Citizen \n1248,0,Car ,Male ,47,HS ,129402,684,2,2,Citizen \n1249,0,Car ,Male ,41,Graduate,68241,506,1,2,Citizen \n1250,0,Home,Female,39,HS ,111124,671,5,2,Citizen \n1251,0,Car ,Female,54,HS ,109932,649,0,2,Citizen \n1252,0,Car ,Male ,52,HS ,93400,681,2,1,Citizen \n1253,0,Car ,Male ,34,HS ,81283,614,2,2,Citizen \n1254,0,Car ,Male ,49,College ,122325,815,3,1,Citizen \n1255,0,Car ,Male ,42,HS ,98947,574,0,2,Citizen \n1256,1,Car ,Male ,48,College ,129023,718,3,2,Citizen \n1257,0,Car ,Female,38,HS ,72236,625,0,2,Citizen \n1258,0,Home,Male ,44,College ,100239,581,7,2,Citizen \n1259,0,Home,Male ,32,HS ,89534,531,7,2,Citizen \n1260,0,Home,Male ,38,College ,105069,559,8,2,Citizen \n1261,0,Car ,Female,40,HS ,93418,526,1,2,Citizen \n1262,0,Car ,Male ,37,HS ,94374,601,1,2,Citizen \n1263,1,Car ,Female,44,College ,113661,753,3,1,Citizen \n1264,0,Car ,Female,45,HS ,78848,661,2,1,Citizen \n1265,0,Car ,Male ,35,HS ,83072,630,2,2,Citizen \n1266,0,Car ,Male ,42,HS ,98761,631,2,2,Citizen \n1267,0,Car ,Male ,49,College ,93148,653,1,2,Citizen \n1268,0,Car ,Male ,39,HS ,102320,644,1,2,Citizen \n1269,0,Car ,Female,49,HS ,110658,594,0,1,Citizen \n1270,0,Car ,Male ,54,HS ,94028,587,1,2,Citizen \n1271,0,Car ,Female,52,HS ,85075,657,1,2,Citizen \n1272,0,Car ,Male ,39,HS ,76516,525,0,2,Citizen \n1273,0,Home,Male ,45,HS ,78650,606,6,2,Citizen \n1274,0,Home,Female,43,HS ,103136,594,7,2,Citizen \n1275,0,Home,Female,35,HS ,72272,662,5,1,Citizen \n1276,0,Car ,Female,47,College ,111690,604,2,2,Citizen \n1277,0,Car ,Female,46,HS ,102067,505,0,2,Citizen \n1278,0,Home,Male ,32,HS ,71611,586,7,2,Citizen \n1279,0,Car ,Male ,29,HS ,95687,623,0,2,Citizen \n1280,0,Car ,Male ,42,HS ,95740,591,3,2,Citizen \n1281,1,Car ,Female,41,Graduate,76918,645,0,2,Citizen \n1282,0,Home,Male ,43,HS ,100152,523,4,2,Citizen \n1283,0,Home,Female,48,HS ,93852,564,8,2,Citizen \n1284,0,Car ,Female,42,HS ,111015,616,1,2,Citizen \n1285,0,Home,Female,44,HS ,74457,661,7,2,Citizen \n1286,0,Car ,Male ,58,Graduate,104720,670,2,2,Non-citizen\n1287,0,Car ,Male ,42,College ,91711,608,2,2,Non-citizen\n1288,0,Car ,Male ,37,College ,81712,594,1,2,Citizen \n1289,0,Car ,Male ,42,College ,103246,711,2,2,Citizen \n1290,0,Home,Male ,34,HS ,71177,557,8,2,Citizen \n1291,0,Home,Male ,32,HS ,81877,652,7,2,Citizen \n1292,0,Car ,Female,45,HS ,91868,590,1,2,Citizen \n1293,0,Car ,Male ,42,HS ,118095,783,3,1,Citizen \n1294,1,Car ,Female,50,College ,123149,668,2,2,Citizen \n1295,0,Car ,Male ,43,HS ,73429,592,0,2,Citizen \n1296,0,Car ,Female,50,College ,133490,664,1,2,Citizen \n1297,0,Car ,Female,52,College ,109154,677,0,2,Citizen \n1298,0,Car ,Female,46,College ,102072,697,2,2,Citizen \n1299,0,Car ,Male ,39,HS ,108771,627,3,2,Citizen \n1300,0,Car ,Male ,54,Graduate,94095,747,1,2,Non-citizen\n1301,0,Car ,Female,48,Graduate,103509,701,0,2,Citizen \n1302,1,Car ,Female,58,Graduate,110395,707,1,2,Non-citizen\n1303,0,Car ,Male ,39,College ,81210,590,1,2,Non-citizen\n1304,0,Car ,Female,36,HS ,95604,581,0,2,Citizen \n1305,0,Home,Male ,36,HS ,99023,580,4,2,Citizen \n1306,0,Car ,Female,39,HS ,66437,631,0,2,Citizen \n1307,0,Car ,Female,46,HS ,108527,617,2,1,Citizen \n1308,0,Car ,Male ,31,HS ,72903,546,2,2,Citizen \n1309,0,Car ,Male ,54,Graduate,91534,631,2,2,Citizen \n1310,0,Car ,Male ,38,HS ,85303,579,2,2,Citizen \n1311,0,Car ,Female,53,HS ,97180,702,1,2,Citizen \n1312,0,Car ,Male ,42,HS ,67681,581,1,2,Citizen \n1314,1,Car ,Female,47,Graduate,122899,620,2,2,Citizen \n1315,1,Car ,Male ,37,College ,132393,648,0,2,Citizen \n1316,0,Car ,Male ,40,HS ,94832,548,0,2,Citizen \n1317,1,Car ,Female,44,College ,121667,620,2,2,Citizen \n1318,0,Car ,Male ,47,HS ,119588,614,0,2,Citizen \n1319,0,Car ,Male ,49,HS ,98805,644,1,2,Citizen \n1320,0,Home,Male ,33,HS ,79937,541,0,2,Citizen \n1321,0,Car ,Male ,42,HS ,70726,702,0,2,Citizen \n1322,1,Car ,Female,42,College ,91830,521,2,2,Citizen \n1323,0,Car ,Female,49,HS ,103814,765,2,2,Citizen \n1324,0,Car ,Male ,45,HS ,105343,613,1,2,Citizen \n1325,1,Car ,Male ,38,College ,115190,577,1,2,Citizen \n1326,0,Car ,Male ,55,HS ,115171,736,2,2,Citizen \n1327,0,Car ,Male ,37,College ,85113,596,2,2,Citizen \n1328,0,Car ,Male ,51,HS ,113810,638,2,2,Citizen \n1329,0,Car ,Female,47,HS ,95159,621,0,2,Citizen \n1330,1,Car ,Male ,38,HS ,89529,654,0,2,Citizen \n1331,0,Car ,Male ,46,HS ,103748,652,1,2,Citizen \n1332,0,Car ,Male ,42,HS ,111654,558,3,2,Citizen \n1333,0,Car ,Male ,31,HS ,73590,537,0,2,Citizen \n1334,0,Home,Male ,41,HS ,97031,691,7,2,Citizen \n1335,0,Home,Male ,44,College ,102112,725,6,2,Citizen \n1336,0,Car ,Female,30,HS ,96754,567,0,2,Citizen \n1337,0,Car ,Male ,50,College ,84917,627,2,2,Citizen \n1338,1,Car ,Male ,44,College ,119778,666,1,2,Citizen \n1339,0,Car ,Female,42,College ,93672,636,2,2,Citizen \n1340,0,Car ,Male ,49,HS ,110257,551,1,2,Citizen \n1341,0,Home,Male ,39,HS ,99049,552,3,2,Citizen \n1342,1,Car ,Male ,53,College ,86532,589,1,2,Citizen \n1343,0,Home,Male ,33,HS ,87421,606,3,2,Citizen \n1344,0,Home,Female,49,HS ,101868,727,5,2,Citizen \n1345,0,Car ,Female,35,College ,122956,501,2,2,Citizen \n1346,0,Home,Male ,39,HS ,80743,585,3,2,Citizen \n1347,0,Home,Female,40,HS ,102569,662,6,2,Citizen \n1348,1,Car ,Female,47,College ,140368,736,0,2,Citizen \n1349,0,Car ,Male ,44,HS ,108300,617,0,2,Citizen \n1350,0,Car ,Female,51,Graduate,101050,681,1,2,Citizen \n1351,0,Home,Male ,43,HS ,99254,545,9,2,Citizen \n1352,0,Home,Male ,34,HS ,95940,520,4,2,Citizen \n1353,0,Car ,Male ,44,HS ,99668,581,2,2,Citizen \n1354,0,Car ,Male ,44,HS ,74273,571,2,2,Citizen \n1355,0,Car ,Male ,40,College ,72785,534,2,2,Citizen \n1356,0,Home,Male ,40,HS ,92779,623,3,2,Citizen \n1357,0,Car ,Male ,37,HS ,111631,590,0,2,Citizen \n1358,0,Car ,Male ,46,HS ,84324,586,1,2,Citizen \n1359,0,Car ,Female,48,Graduate,74595,681,2,2,Citizen \n1360,0,Home,Male ,36,HS ,80699,672,3,2,Citizen \n1361,0,Car ,Male ,41,HS ,80962,613,2,2,Citizen \n1362,0,Car ,Female,40,College ,51881,611,0,2,Citizen \n1363,0,Car ,Female,53,College ,119421,615,3,2,Citizen \n1364,0,Car ,Male ,30,HS ,72742,516,0,2,Citizen \n1365,1,Home,Male ,38,HS ,116684,682,7,2,Citizen \n1366,0,Car ,Male ,41,College ,93605,567,3,1,Citizen \n1367,0,Car ,Male ,49,College ,95036,655,2,2,Citizen \n1368,0,Home,Male ,37,HS ,83528,527,6,2,Citizen \n1369,0,Home,Female,44,HS ,123102,631,8,2,Citizen \n1370,0,Car ,Female,45,HS ,86537,688,3,2,Citizen \n1371,0,Car ,Female,41,Graduate,96592,530,0,2,Non-citizen\n1372,0,Car ,Female,47,College ,95819,570,0,2,Citizen \n1373,0,Car ,Female,41,HS ,80582,576,1,2,Citizen \n1374,1,Car ,Female,55,College ,136333,668,3,2,Citizen \n1375,0,Home,Female,37,HS ,88174,699,5,2,Citizen \n1376,0,Car ,Female,42,College ,149116,752,0,2,Citizen \n1377,0,Home,Female,27,HS ,108001,626,4,2,Citizen \n1378,1,Car ,Male ,40,HS ,101049,607,0,2,Citizen \n1379,0,Car ,Male ,49,College ,112216,635,3,2,Citizen \n1380,0,Car ,Female,48,College ,108506,570,2,2,Citizen \n1381,0,Car ,Female,38,HS ,95433,550,1,1,Citizen \n1382,1,Car ,Male ,35,HS ,108275,598,0,2,Citizen \n1383,0,Car ,Female,43,College ,90908,583,0,2,Citizen \n1384,0,Car ,Male ,48,College ,98321,687,2,2,Citizen \n1385,0,Car ,Female,43,College ,85530,652,0,2,Citizen \n1386,0,Car ,Male ,35,HS ,104126,585,2,2,Citizen \n1387,0,Home,Female,44,HS ,122168,657,10,2,Citizen \n1388,0,Car ,Female,41,HS ,79997,561,0,2,Citizen \n1389,0,Home,Female,49,HS ,126765,615,12,1,Citizen \n1390,0,Car ,Male ,44,HS ,73410,630,0,2,Citizen \n1391,0,Home,Female,46,HS ,92940,524,3,2,Citizen \n1392,0,Car ,Female,46,HS ,108096,725,3,1,Citizen \n1393,0,Car ,Male ,54,HS ,61898,560,0,2,Citizen \n1394,0,Car ,Female,46,College ,122937,651,1,1,Citizen \n1395,0,Car ,Female,54,College ,110492,610,2,2,Citizen \n1396,0,Car ,Male ,39,College ,85797,646,3,2,Citizen \n1397,0,Car ,Male ,44,College ,81826,590,0,2,Citizen \n1398,0,Car ,Female,52,HS ,82476,707,0,2,Citizen \n1399,0,Car ,Female,40,College ,112338,590,2,2,Citizen \n1400,0,Car ,Male ,50,HS ,99492,653,1,2,Citizen \n1401,0,Home,Female,49,HS ,127681,625,9,2,Citizen \n1402,0,Car ,Female,40,HS ,82284,668,1,2,Citizen \n1403,0,Car ,Male ,30,HS ,105668,473,2,2,Citizen \n1404,0,Car ,Male ,34,HS ,100246,615,1,2,Citizen \n1405,0,Home,Male ,39,HS ,56204,607,12,2,Citizen \n1406,0,Car ,Male ,37,College ,86573,552,0,2,Citizen \n1407,0,Home,Female,48,HS ,99007,623,10,2,Citizen \n1408,0,Car ,Male ,43,HS ,92606,669,1,2,Citizen \n1409,0,Car ,Female,40,HS ,102575,722,0,2,Citizen \n1410,0,Home,Male ,40,HS ,79030,687,5,1,Citizen \n1411,0,Car ,Male ,20,HS ,59066,536,0,1,Citizen \n1412,0,Home,Female,38,HS ,86835,629,5,2,Citizen \n1413,1,Car ,Female,47,College ,104642,685,3,2,Citizen \n1414,0,Home,Male ,33,College ,100385,658,7,2,Citizen \n1415,0,Home,Male ,38,HS ,74705,668,5,2,Citizen \n1416,0,Home,Male ,34,HS ,102108,632,2,2,Citizen \n1417,0,Car ,Male ,38,HS ,90868,528,0,2,Citizen \n1418,0,Car ,Male ,41,HS ,53187,508,0,2,Citizen \n1419,0,Home,Male ,45,HS ,117222,622,6,2,Citizen \n1420,0,Car ,Male ,36,HS ,88281,554,0,2,Citizen \n1421,0,Home,Male ,45,HS ,78971,584,9,2,Citizen \n1422,0,Car ,Male ,40,HS ,95799,499,0,2,Citizen \n1423,0,Car ,Female,45,HS ,100986,642,1,2,Citizen \n1424,0,Car ,Female,40,HS ,92813,617,1,2,Citizen \n1425,0,Car ,Male ,41,College ,97520,553,1,2,Citizen \n1426,0,Car ,Male ,41,HS ,70988,632,0,2,Citizen \n1427,0,Car ,Female,43,College ,111384,563,1,2,Citizen \n1428,0,Car ,Male ,49,HS ,80549,664,2,2,Citizen \n1429,0,Car ,Male ,35,College ,69649,625,3,2,Citizen \n1430,0,Home,Male ,46,HS ,83874,565,6,2,Citizen \n1431,0,Home,Female,39,College ,88400,570,12,2,Citizen \n1432,0,Car ,Male ,41,HS ,66530,583,1,2,Citizen \n1433,0,Car ,Female,34,HS ,98385,602,1,2,Citizen \n1434,0,Car ,Male ,56,College ,103480,670,3,2,Citizen \n1435,0,Car ,Female,30,HS ,109920,557,1,2,Citizen \n1436,0,Car ,Female,50,College ,102080,647,2,2,Citizen \n1437,0,Car ,Female,39,HS ,94158,625,1,2,Citizen \n1438,0,Home,Male ,38,HS ,74191,590,4,2,Citizen \n1439,0,Car ,Female,56,HS ,91160,609,2,2,Citizen \n1440,0,Car ,Female,48,College ,115603,716,1,1,Citizen \n1441,0,Home,Male ,36,HS ,95741,561,8,2,Citizen \n1442,0,Home,Female,47,College ,140500,596,8,2,Citizen \n1443,0,Home,Male ,41,HS ,48845,585,4,2,Citizen \n1444,0,Home,Female,41,HS ,96136,698,5,2,Citizen \n1445,0,Car ,Female,48,College ,98837,630,0,2,Citizen \n1446,0,Home,Male ,34,HS ,96090,653,6,2,Citizen \n1447,0,Home,Female,39,College ,102706,672,9,2,Citizen \n1448,0,Car ,Male ,51,HS ,99280,644,2,2,Citizen \n1449,0,Home,Male ,40,HS ,97336,625,7,1,Citizen \n1450,0,Home,Male ,45,HS ,89471,556,8,2,Citizen \n1451,1,Car ,Male ,43,HS ,118176,662,0,2,Citizen \n1452,0,Home,Male ,30,HS ,77826,504,1,2,Citizen \n1453,0,Car ,Female,38,HS ,100483,565,1,2,Citizen \n1454,0,Car ,Female,38,College ,99351,720,1,1,Citizen \n1455,0,Home,Female,52,College ,88594,664,12,2,Citizen \n1456,0,Home,Male ,32,College ,104079,620,6,2,Citizen \n1457,0,Car ,Female,47,College ,125746,666,3,2,Citizen \n1458,0,Car ,Female,45,HS ,83183,557,0,2,Citizen \n1459,0,Home,Male ,47,HS ,91547,558,6,2,Citizen \n1460,0,Car ,Male ,41,College ,78343,592,2,2,Citizen \n1461,0,Home,Male ,41,HS ,96956,647,5,2,Citizen \n1462,0,Home,Male ,32,Graduate,89250,590,10,2,Citizen \n1463,0,Car ,Male ,48,College ,104672,547,3,2,Citizen \n1464,0,Car ,Male ,39,HS ,98653,607,0,2,Citizen \n1465,0,Car ,Female,49,HS ,100434,803,0,1,Citizen \n1466,0,Car ,Male ,35,HS ,94903,532,0,2,Citizen \n1467,1,Car ,Female,44,Graduate,109812,645,1,2,Citizen \n1468,0,Home,Male ,37,College ,103659,667,11,2,Citizen \n1469,0,Car ,Male ,36,HS ,77129,583,1,1,Citizen \n1470,0,Car ,Female,46,College ,122099,730,3,2,Citizen \n1471,0,Car ,Female,58,Graduate,103473,611,3,2,Citizen \n1472,0,Home,Male ,47,College ,119675,679,9,2,Citizen \n1473,0,Car ,Female,41,HS ,98479,643,0,2,Citizen \n1474,0,Home,Male ,39,HS ,102854,548,12,1,Citizen \n1475,0,Home,Male ,38,HS ,88140,643,5,2,Citizen \n1476,0,Home,Male ,40,College ,92335,746,7,2,Citizen \n1477,0,Car ,Male ,52,College ,95952,533,1,2,Citizen \n1478,0,Car ,Male ,45,HS ,88037,560,0,2,Citizen \n1479,0,Car ,Male ,56,HS ,129745,704,3,1,Citizen \n1480,0,Home,Female,41,HS ,75871,523,5,2,Citizen \n1481,0,Car ,Male ,37,College ,89146,569,1,2,Citizen \n1482,0,Car ,Male ,32,HS ,95925,647,0,2,Citizen \n1483,0,Car ,Male ,39,College ,71351,469,2,2,Citizen \n1484,0,Car ,Male ,43,HS ,113472,658,3,1,Citizen \n1485,1,Car ,Male ,28,HS ,104058,565,0,2,Citizen \n1486,0,Car ,Male ,48,College ,80817,624,2,2,Citizen \n1487,0,Home,Female,43,HS ,106131,630,7,2,Citizen \n1488,0,Car ,Female,47,Graduate,104433,642,3,1,Non-citizen\n1489,0,Car ,Male ,41,HS ,109859,695,0,2,Citizen \n1490,0,Home,Female,32,HS ,87367,574,2,2,Citizen \n1491,0,Car ,Female,48,HS ,88659,670,1,2,Citizen \n1492,0,Home,Male ,35,HS ,73591,606,9,2,Citizen \n1493,0,Home,Male ,30,HS ,64623,548,1,2,Citizen \n1494,0,Home,Male ,46,HS ,92017,755,6,2,Citizen \n1495,0,Car ,Female,41,HS ,76749,659,0,2,Citizen \n1496,0,Car ,Female,49,HS ,102137,556,0,2,Citizen \n1497,0,Home,Female,45,Graduate,72079,680,9,2,Citizen \n1498,0,Car ,Female,43,College ,74082,625,2,2,Citizen \n1499,0,Home,Female,38,College ,82836,636,8,2,Citizen \n1500,0,Car ,Male ,42,HS ,94505,596,1,1,Citizen \n1501,0,Car ,Female,46,HS ,114567,732,0,2,Non-citizen\n1502,0,Home,Female,30,HS ,96044,685,7,2,Citizen \n1503,0,Car ,Female,48,HS ,105119,657,1,2,Citizen \n1504,0,Home,Male ,49,HS ,97621,567,8,2,Citizen \n1505,0,Home,Female,38,College ,100057,636,3,2,Citizen \n1506,0,Car ,Male ,43,HS ,90159,647,1,2,Citizen \n1507,0,Home,Male ,50,College ,97325,650,10,2,Citizen \n1508,0,Car ,Male ,43,HS ,107715,582,0,2,Citizen \n1509,0,Car ,Female,46,HS ,95771,611,0,2,Citizen \n1510,0,Home,Female,45,HS ,106319,548,6,2,Citizen \n1511,0,Car ,Male ,42,HS ,100389,568,2,2,Citizen \n1512,0,Car ,Female,29,HS ,114142,554,1,2,Citizen \n1513,1,Car ,Female,46,HS ,90209,771,2,1,Citizen \n1514,0,Car ,Male ,44,HS ,79919,656,0,2,Citizen \n1515,0,Car ,Male ,52,HS ,110066,674,0,1,Citizen \n1516,0,Car ,Male ,36,College ,90277,624,1,2,Citizen \n1517,0,Car ,Male ,35,HS ,66957,610,0,2,Citizen \n1518,0,Car ,Male ,32,HS ,67593,550,0,2,Citizen \n1519,0,Car ,Male ,39,HS ,81073,709,0,2,Citizen \n1520,0,Home,Male ,35,HS ,91087,632,6,2,Citizen \n1521,0,Car ,Male ,50,HS ,133800,734,2,1,Citizen \n1522,1,Car ,Male ,43,College ,96699,669,2,2,Citizen \n1523,0,Car ,Female,45,Graduate,76754,807,0,2,Citizen \n1524,0,Car ,Female,35,HS ,92225,530,0,2,Citizen \n1525,0,Home,Male ,40,HS ,88871,592,1,2,Citizen \n1526,0,Car ,Female,38,College ,89829,615,1,2,Citizen \n1527,0,Car ,Female,48,College ,119714,632,1,1,Citizen \n1528,1,Car ,Female,43,Graduate,82369,587,2,2,Citizen \n1529,0,Car ,Female,54,College ,119961,700,3,1,Citizen \n1530,0,Car ,Female,45,HS ,121139,665,0,2,Citizen \n1531,0,Car ,Male ,39,HS ,105730,558,1,2,Citizen \n1532,0,Home,Male ,41,College ,83603,606,8,2,Citizen \n1533,0,Car ,Female,39,HS ,93060,578,0,2,Citizen \n1534,0,Car ,Female,32,HS ,64940,604,0,2,Citizen \n1535,0,Home,Male ,46,HS ,76618,532,8,2,Citizen \n1536,0,Car ,Male ,45,College ,117169,646,2,2,Citizen \n1537,0,Car ,Male ,49,College ,93156,654,2,1,Non-citizen\n1538,0,Home,Female,49,HS ,96732,558,7,2,Citizen \n1539,0,Home,Male ,39,HS ,78664,578,11,2,Citizen \n1540,1,Car ,Male ,44,HS ,90906,718,1,2,Citizen \n1541,0,Car ,Male ,38,College ,98914,710,2,2,Citizen \n1542,0,Car ,Male ,35,HS ,98805,582,2,2,Citizen \n1543,0,Car ,Female,48,HS ,104461,629,1,2,Citizen \n1544,1,Car ,Female,55,College ,120422,770,2,2,Citizen \n1545,0,Home,Male ,38,HS ,83760,609,3,2,Citizen \n1546,0,Car ,Male ,39,College ,87377,498,0,2,Citizen \n1547,0,Home,Male ,37,HS ,82267,486,6,2,Citizen \n1548,0,Car ,Male ,38,HS ,87259,544,0,2,Citizen \n1549,0,Car ,Male ,49,HS ,99188,647,0,2,Citizen \n1550,0,Home,Male ,41,HS ,107508,588,9,2,Citizen \n1551,0,Car ,Male ,40,HS ,95976,591,2,2,Citizen \n1552,0,Home,Male ,46,HS ,108251,677,8,2,Citizen \n1553,0,Home,Male ,44,HS ,90107,576,12,2,Citizen \n1554,0,Car ,Female,38,HS ,124918,502,2,2,Citizen \n1555,0,Home,Female,28,HS ,81821,671,4,2,Citizen \n1556,0,Home,Male ,36,College ,76718,572,4,2,Citizen \n1557,0,Car ,Female,48,HS ,83503,713,0,1,Citizen \n1558,1,Car ,Female,41,College ,118979,672,2,2,Citizen \n1559,0,Car ,Female,31,College ,112651,537,0,2,Citizen \n1560,0,Car ,Male ,45,College ,118572,614,3,2,Citizen \n1561,0,Car ,Female,33,College ,89101,598,1,2,Citizen \n1562,0,Home,Male ,45,HS ,89332,501,6,2,Citizen \n1563,0,Home,Male ,32,HS ,105637,493,7,2,Citizen \n1564,0,Car ,Female,36,HS ,109389,611,0,2,Citizen \n1565,1,Car ,Male ,41,Graduate,93460,661,2,2,Citizen \n1566,0,Car ,Female,44,HS ,99565,640,0,2,Citizen \n1567,0,Car ,Female,40,HS ,81595,571,1,2,Citizen \n1568,1,Home,Male ,34,College ,111179,568,10,2,Citizen \n1569,0,Car ,Female,41,HS ,89810,482,0,1,Citizen \n1570,0,Home,Male ,38,HS ,81105,629,3,2,Citizen \n1571,0,Car ,Female,44,College ,96835,657,2,2,Citizen \n1572,0,Car ,Male ,36,HS ,80037,530,1,2,Citizen \n1574,0,Car ,Male ,32,HS ,85075,499,2,2,Citizen \n1575,0,Car ,Male ,36,HS ,74640,484,3,2,Citizen \n1576,0,Home,Female,40,HS ,105960,610,3,2,Citizen \n1577,0,Car ,Male ,44,HS ,85190,585,2,2,Citizen \n1578,0,Car ,Male ,45,Graduate,105227,630,2,2,Citizen \n1579,0,Home,Male ,50,HS ,78480,654,6,2,Citizen \n1580,0,Car ,Female,40,HS ,73927,610,0,1,Citizen \n1581,0,Home,Female,39,College ,82744,631,2,2,Citizen \n1582,0,Home,Male ,48,HS ,107996,662,9,1,Citizen \n1583,0,Car ,Female,45,HS ,115538,565,1,2,Citizen \n1584,0,Home,Female,31,HS ,56456,485,4,2,Citizen \n1585,0,Car ,Female,44,Graduate,128996,631,3,1,Citizen \n1586,0,Car ,Female,51,College ,104009,671,3,2,Citizen \n1587,0,Car ,Female,54,College ,105630,729,2,2,Citizen \n1588,0,Car ,Male ,34,College ,87161,573,0,2,Citizen \n1589,0,Car ,Female,35,HS ,123648,610,2,2,Citizen \n1590,0,Home,Male ,31,HS ,101457,648,4,2,Citizen \n1591,0,Car ,Male ,47,HS ,93375,642,2,2,Citizen \n1592,0,Home,Male ,50,College ,105507,669,6,2,Citizen \n1593,1,Car ,Female,42,Graduate,122608,625,2,2,Non-citizen\n1594,0,Car ,Female,46,HS ,98037,649,1,2,Citizen \n1595,0,Home,Female,43,HS ,82209,713,9,1,Citizen \n1596,0,Car ,Male ,44,HS ,77005,597,0,2,Citizen \n1597,1,Car ,Male ,43,HS ,99622,677,0,2,Citizen \n1598,0,Car ,Female,46,HS ,96656,597,2,2,Citizen \n1599,0,Home,Male ,28,HS ,94195,601,10,2,Citizen \n1600,0,Car ,Male ,49,College ,82949,603,1,2,Citizen \n1601,0,Car ,Male ,41,HS ,82770,692,2,2,Citizen \n1602,0,Car ,Male ,38,HS ,67675,643,0,2,Citizen \n1603,0,Home,Male ,35,HS ,69371,442,5,2,Citizen \n1604,0,Car ,Male ,51,Graduate,95163,599,2,2,Citizen \n1605,1,Car ,Male ,35,HS ,104294,573,2,2,Citizen \n1606,0,Car ,Female,48,College ,109865,601,2,2,Citizen \n1607,0,Home,Male ,42,HS ,82195,555,5,2,Citizen \n1608,0,Car ,Male ,45,College ,82905,620,3,2,Citizen \n1609,1,Car ,Male ,37,HS ,114525,670,2,2,Non-citizen\n1610,0,Home,Male ,44,HS ,94067,627,6,2,Citizen \n1611,0,Home,Female,29,HS ,95390,563,4,2,Citizen \n1612,0,Car ,Male ,30,HS ,72457,619,2,2,Citizen \n1613,0,Home,Male ,43,HS ,84328,561,7,2,Citizen \n1614,0,Car ,Male ,44,College ,99815,646,1,2,Citizen \n1615,0,Car ,Female,50,HS ,89066,623,1,2,Citizen \n1616,0,Car ,Male ,35,HS ,92274,505,2,2,Citizen \n1617,0,Car ,Male ,31,College ,94679,611,1,2,Citizen \n1618,0,Home,Male ,42,HS ,75713,544,6,2,Citizen \n1619,0,Car ,Female,46,Graduate,88604,660,2,1,Citizen \n1620,0,Home,Male ,43,HS ,80099,595,2,2,Citizen \n1621,0,Home,Male ,46,HS ,83367,583,3,2,Citizen \n1622,0,Home,Male ,28,HS ,47993,540,6,2,Citizen \n1623,0,Home,Male ,41,HS ,62884,550,10,2,Citizen \n1624,0,Car ,Male ,37,College ,99426,613,2,2,Citizen \n1625,0,Home,Male ,48,HS ,110506,648,9,2,Citizen \n1626,0,Car ,Female,44,HS ,93393,621,1,2,Non-citizen\n1627,0,Car ,Female,43,College ,104777,684,0,2,Non-citizen\n1628,0,Car ,Female,41,HS ,93728,541,0,2,Citizen \n1629,0,Home,Male ,31,HS ,108350,596,5,2,Citizen \n1630,0,Home,Male ,39,HS ,86363,575,4,2,Citizen \n1631,0,Car ,Male ,39,College ,106875,524,3,2,Citizen \n1632,0,Car ,Female,42,HS ,102575,639,0,2,Citizen \n1633,0,Car ,Male ,45,HS ,124210,569,2,2,Citizen \n1634,0,Car ,Female,48,HS ,107807,598,2,2,Citizen \n1635,0,Car ,Female,51,College ,89052,558,2,2,Citizen \n1636,0,Car ,Female,49,College ,121354,610,2,2,Citizen \n1637,0,Home,Male ,50,HS ,103021,626,5,2,Citizen \n1638,0,Car ,Female,48,HS ,131116,663,2,1,Citizen \n1639,0,Car ,Male ,35,HS ,84424,573,0,2,Citizen \n1640,0,Car ,Male ,38,HS ,90043,478,2,2,Citizen \n1641,1,Car ,Female,43,College ,91930,688,2,2,Citizen \n1642,0,Home,Male ,38,College ,98967,638,7,2,Citizen \n1643,0,Car ,Male ,42,HS ,77620,548,0,2,Citizen \n1644,0,Car ,Male ,41,HS ,93377,621,0,2,Citizen \n1645,0,Car ,Male ,52,HS ,68108,640,2,2,Citizen \n1646,0,Car ,Male ,47,College ,84802,688,0,2,Citizen \n1647,0,Car ,Male ,45,College ,85276,543,1,2,Citizen \n1648,0,Home,Male ,47,College ,106771,582,12,2,Citizen \n1649,1,Car ,Male ,43,HS ,121809,659,1,2,Citizen \n1650,0,Car ,Female,53,HS ,114260,588,1,2,Citizen \n1651,0,Car ,Female,38,College ,116818,635,0,2,Citizen \n1652,0,Car ,Male ,49,HS ,110881,588,0,2,Citizen \n1653,0,Car ,Female,43,College ,113168,632,0,2,Citizen \n1654,0,Car ,Male ,29,HS ,81855,683,0,2,Citizen \n1655,0,Car ,Female,48,College ,98551,632,0,2,Citizen \n1656,0,Car ,Male ,41,HS ,56519,584,0,2,Citizen \n1657,0,Car ,Female,31,HS ,87574,565,2,2,Citizen \n1658,0,Car ,Male ,40,College ,89498,586,2,2,Citizen \n1659,0,Home,Male ,49,HS ,87332,528,8,2,Citizen \n1660,0,Car ,Male ,43,HS ,84456,559,0,2,Citizen \n1661,0,Car ,Female,35,College ,103033,564,2,2,Citizen \n1662,1,Car ,Female,50,College ,118983,727,2,2,Non-citizen\n1663,1,Car ,Female,46,Graduate,111791,755,0,2,Citizen \n1664,1,Car ,Female,42,College ,100525,623,1,2,Citizen \n1665,0,Car ,Male ,38,HS ,73485,521,2,2,Citizen \n1666,0,Home,Male ,36,HS ,108650,534,6,2,Citizen \n1667,1,Home,Female,42,College ,115102,631,4,2,Citizen \n1668,0,Car ,Male ,45,HS ,114505,485,1,2,Citizen \n1669,0,Home,Female,41,HS ,90726,605,3,2,Citizen \n1670,0,Home,Male ,31,HS ,78283,533,4,2,Citizen \n1671,0,Car ,Female,47,College ,98294,608,0,2,Citizen \n1672,0,Car ,Male ,51,Graduate,114126,689,2,2,Citizen \n1673,0,Car ,Male ,38,HS ,97889,671,1,2,Citizen \n1674,0,Car ,Female,53,College ,118144,577,2,2,Citizen \n1675,0,Car ,Male ,41,College ,93172,641,3,2,Citizen \n1676,0,Home,Male ,33,HS ,74023,542,4,2,Citizen \n1677,0,Home,Female,35,HS ,74771,474,0,2,Citizen \n1678,0,Home,Male ,43,HS ,86215,512,4,2,Citizen \n1679,0,Home,Male ,46,HS ,87789,500,9,2,Citizen \n1680,0,Home,Female,36,College ,97590,567,5,2,Citizen \n1681,0,Home,Female,31,HS ,90975,555,4,2,Citizen \n1682,0,Home,Male ,33,HS ,81043,596,1,2,Citizen \n1683,0,Car ,Male ,39,College ,67291,517,0,2,Citizen \n1684,0,Car ,Male ,44,HS ,96634,599,3,2,Citizen \n1685,0,Car ,Female,53,HS ,78733,632,1,1,Citizen \n1686,0,Car ,Female,40,HS ,78850,573,1,2,Citizen \n1687,0,Car ,Male ,33,College ,100338,546,1,2,Citizen \n1688,0,Car ,Male ,45,HS ,100326,665,2,2,Citizen \n1689,0,Home,Male ,42,HS ,76288,592,6,2,Citizen \n1690,0,Car ,Male ,46,HS ,83441,575,2,2,Citizen \n1691,0,Car ,Female,40,HS ,83243,542,0,2,Citizen \n1692,0,Home,Male ,44,HS ,91509,607,2,2,Citizen \n1693,0,Home,Male ,35,HS ,51474,550,5,2,Citizen \n1694,0,Home,Male ,29,HS ,84992,520,4,2,Citizen \n1695,0,Car ,Female,42,Graduate,117096,580,2,2,Citizen \n1696,0,Home,Female,37,HS ,96354,570,6,2,Citizen \n1697,0,Car ,Female,35,Graduate,86397,734,0,2,Non-citizen\n1698,1,Car ,Female,40,HS ,117167,599,0,2,Citizen \n1699,0,Home,Male ,36,HS ,72600,630,4,2,Citizen \n1700,0,Home,Male ,44,HS ,87353,682,4,2,Citizen \n1701,0,Car ,Male ,50,HS ,103637,595,1,2,Citizen \n1702,0,Car ,Male ,54,HS ,109762,632,2,2,Citizen \n1703,0,Car ,Female,44,College ,79593,656,1,2,Citizen \n1705,0,Car ,Male ,47,HS ,93565,677,2,1,Citizen \n1706,0,Car ,Female,35,HS ,91910,583,1,2,Citizen \n1707,0,Car ,Male ,47,College ,94717,570,2,2,Citizen \n1708,0,Car ,Male ,47,College ,88681,627,2,1,Citizen \n1709,0,Home,Male ,37,HS ,74640,592,5,2,Citizen \n1710,0,Car ,Female,45,HS ,76359,545,2,2,Citizen \n1711,0,Home,Female,43,HS ,103942,579,3,2,Citizen \n1712,0,Home,Male ,49,College ,113663,603,11,2,Citizen \n1713,0,Home,Female,48,HS ,100789,695,6,2,Citizen \n1714,0,Home,Female,34,HS ,87558,616,4,2,Citizen \n1715,0,Home,Male ,40,HS ,84428,596,5,2,Citizen \n1716,0,Car ,Female,49,HS ,105644,533,0,2,Citizen \n1717,0,Home,Female,57,HS ,102853,679,5,2,Citizen \n1718,0,Home,Male ,49,HS ,74179,531,6,2,Citizen \n1719,0,Home,Male ,39,HS ,92994,576,7,2,Citizen \n1720,0,Car ,Female,51,College ,111811,712,1,1,Non-citizen\n1721,0,Car ,Male ,45,HS ,105904,584,0,2,Citizen \n1722,0,Car ,Female,45,College ,86517,691,1,2,Citizen \n1723,0,Car ,Female,48,College ,100247,702,0,2,Citizen \n1724,0,Home,Male ,40,HS ,91295,673,7,2,Citizen \n1725,0,Car ,Male ,38,HS ,80159,638,2,2,Citizen \n1726,0,Car ,Female,44,HS ,74338,568,1,2,Citizen \n1727,0,Home,Male ,53,HS ,92428,602,6,2,Citizen \n1728,0,Home,Male ,37,HS ,103558,629,7,2,Citizen \n1729,1,Car ,Female,49,College ,87829,679,3,2,Citizen \n1730,0,Home,Female,40,HS ,107633,564,6,2,Citizen \n1731,0,Car ,Male ,45,College ,82533,573,1,2,Citizen \n1732,0,Home,Male ,36,HS ,86592,538,7,2,Citizen \n1733,0,Car ,Male ,47,HS ,101888,620,0,2,Citizen \n1734,0,Home,Male ,58,HS ,102651,571,5,2,Citizen \n1735,0,Car ,Female,33,College ,102805,713,0,1,Citizen \n1736,0,Car ,Male ,38,HS ,86680,588,0,2,Citizen \n1737,0,Car ,Male ,42,HS ,79449,665,0,2,Citizen \n1738,0,Car ,Male ,51,HS ,103838,573,3,2,Citizen \n1739,0,Car ,Female,35,College ,107172,626,0,2,Citizen \n1740,0,Home,Male ,38,College ,119864,568,10,2,Citizen \n1741,0,Home,Female,50,College ,107202,499,8,2,Citizen \n1742,0,Home,Male ,30,HS ,84913,528,4,2,Citizen \n1743,0,Home,Female,44,HS ,105181,671,11,2,Citizen \n1744,1,Car ,Male ,42,HS ,70100,618,0,2,Citizen \n1745,0,Home,Male ,31,HS ,99851,611,3,2,Citizen \n1746,1,Car ,Male ,50,College ,135228,617,1,2,Citizen \n1747,0,Car ,Female,44,Graduate,111580,697,1,2,Non-citizen\n1748,0,Car ,Male ,51,College ,105432,512,2,2,Citizen \n1749,1,Car ,Male ,41,HS ,97953,711,2,2,Citizen \n1750,0,Home,Female,40,HS ,93427,573,5,2,Citizen \n1751,0,Car ,Male ,53,HS ,96759,642,3,2,Citizen \n1752,1,Car ,Male ,45,College ,151256,664,2,2,Non-citizen\n1753,0,Car ,Female,37,HS ,101531,593,2,2,Citizen \n1754,0,Home,Male ,34,HS ,64524,529,2,2,Citizen \n1755,0,Home,Male ,47,HS ,66407,620,11,2,Citizen \n1756,0,Car ,Female,51,College ,111284,752,3,1,Citizen \n1757,0,Home,Female,60,College ,84774,611,10,2,Citizen \n1758,1,Car ,Male ,34,HS ,77093,617,0,2,Citizen \n1759,0,Car ,Male ,44,HS ,101146,635,0,2,Citizen \n1760,0,Car ,Female,44,HS ,90215,676,1,2,Citizen \n1761,0,Car ,Male ,41,HS ,105565,558,1,2,Citizen \n1762,0,Car ,Female,47,HS ,119917,609,0,2,Citizen \n1763,0,Home,Female,29,HS ,96724,631,3,2,Citizen \n1764,0,Car ,Male ,36,HS ,101002,478,0,2,Citizen \n1765,0,Home,Female,42,College ,99114,679,9,2,Citizen \n1766,0,Car ,Female,48,HS ,96197,604,3,2,Citizen \n1767,0,Car ,Female,48,HS ,96705,638,1,2,Citizen \n1768,0,Home,Male ,39,HS ,99951,566,1,2,Citizen \n1769,0,Car ,Male ,42,HS ,87793,643,0,2,Citizen \n1770,0,Car ,Female,45,HS ,99817,641,0,2,Citizen \n1771,0,Car ,Female,38,HS ,104703,610,0,2,Citizen \n1772,0,Home,Male ,42,HS ,79232,658,10,2,Citizen \n1773,0,Car ,Male ,47,College ,84761,682,1,2,Citizen \n1774,0,Car ,Male ,43,HS ,90232,596,0,2,Citizen \n1775,0,Car ,Male ,36,College ,82784,566,3,2,Citizen \n1776,0,Car ,Female,53,HS ,133991,638,2,2,Citizen \n1777,0,Car ,Female,42,College ,62840,568,1,2,Citizen \n1778,0,Home,Male ,40,HS ,94869,675,5,2,Citizen \n1779,0,Home,Female,46,College ,105734,601,5,2,Citizen \n1780,0,Car ,Female,48,Graduate,106017,671,2,2,Citizen \n1781,0,Car ,Male ,36,College ,93780,557,1,2,Citizen \n1782,1,Car ,Female,49,Graduate,124194,684,2,2,Non-citizen\n1783,0,Car ,Male ,40,HS ,94936,538,0,2,Citizen \n1784,0,Car ,Female,45,College ,119962,697,1,2,Citizen \n1785,0,Home,Male ,28,HS ,79889,536,0,2,Citizen \n1786,0,Car ,Male ,48,HS ,94249,678,2,2,Citizen \n1787,0,Car ,Female,47,HS ,70257,570,0,2,Citizen \n1788,0,Car ,Male ,43,HS ,92998,593,2,2,Citizen \n1789,0,Car ,Male ,46,College ,96985,651,2,1,Citizen \n1790,0,Car ,Male ,43,HS ,86817,505,2,2,Citizen \n1791,0,Car ,Male ,39,HS ,116109,615,0,2,Citizen \n1792,0,Home,Male ,48,HS ,73439,610,4,2,Citizen \n1793,0,Car ,Male ,39,HS ,75689,646,0,2,Citizen \n1794,0,Home,Male ,37,HS ,71075,544,6,2,Citizen \n1795,0,Car ,Female,48,College ,82144,712,2,1,Citizen \n1797,0,Car ,Male ,38,HS ,116117,570,0,2,Citizen \n1798,0,Car ,Male ,46,HS ,96381,694,2,2,Citizen \n1799,0,Car ,Male ,37,HS ,92082,462,1,2,Citizen \n1800,0,Home,Male ,44,HS ,63415,523,10,2,Citizen \n1801,0,Car ,Female,46,College ,84926,539,1,2,Citizen \n1802,0,Home,Male ,43,HS ,76149,583,1,2,Citizen \n1803,0,Car ,Female,57,College ,148406,697,2,1,Citizen \n1804,0,Home,Male ,48,HS ,105869,691,9,2,Citizen \n1805,0,Home,Female,45,HS ,95250,638,4,2,Citizen \n1806,0,Car ,Male ,44,HS ,120244,604,0,2,Citizen \n1807,0,Car ,Female,35,College ,111488,635,1,2,Non-citizen\n1808,1,Car ,Female,51,HS ,99260,679,2,2,Citizen \n1809,0,Car ,Female,43,Graduate,125587,633,2,2,Citizen \n1810,0,Car ,Male ,44,HS ,103917,614,0,2,Citizen \n1811,1,Car ,Male ,46,HS ,113640,634,1,2,Citizen \n1812,0,Car ,Male ,41,HS ,84355,601,0,2,Citizen \n1813,0,Car ,Male ,40,HS ,115403,476,3,1,Citizen \n1814,0,Car ,Male ,35,HS ,117315,575,2,2,Citizen \n1815,0,Car ,Female,41,HS ,81854,598,2,2,Citizen \n1816,0,Car ,Male ,26,HS ,87296,569,0,2,Citizen \n1817,0,Car ,Female,38,HS ,91375,576,1,2,Citizen \n1818,1,Car ,Female,45,HS ,104437,621,0,2,Citizen \n1819,0,Car ,Female,42,College ,77153,598,2,2,Citizen \n1820,0,Home,Male ,26,HS ,77848,545,1,2,Citizen \n1821,0,Car ,Male ,36,HS ,92837,539,2,2,Citizen \n1822,0,Car ,Male ,46,HS ,91892,674,2,2,Citizen \n1823,0,Car ,Male ,43,Graduate,95967,572,3,2,Citizen \n1824,0,Home,Male ,33,HS ,94166,466,5,2,Citizen \n1825,0,Car ,Female,43,College ,85255,668,3,2,Citizen \n1826,0,Car ,Male ,38,HS ,87960,517,1,2,Citizen \n1827,0,Car ,Male ,42,HS ,90294,754,0,2,Citizen \n1828,0,Home,Male ,38,HS ,99277,644,4,2,Citizen \n1829,0,Car ,Female,32,HS ,74965,563,0,2,Citizen \n1830,0,Car ,Male ,44,HS ,91084,546,0,2,Citizen \n1831,0,Car ,Female,46,HS ,117215,674,0,2,Citizen \n1832,0,Car ,Male ,36,College ,58971,524,1,2,Citizen \n1833,0,Car ,Male ,46,College ,102699,629,2,2,Citizen \n1834,0,Car ,Male ,39,College ,88758,655,0,2,Citizen \n1835,0,Home,Male ,40,HS ,72266,621,3,1,Citizen \n1836,0,Car ,Male ,46,College ,82378,573,3,2,Citizen \n1837,0,Car ,Male ,40,HS ,87183,646,0,2,Citizen \n1838,0,Car ,Female,46,HS ,70316,601,0,2,Citizen \n1839,0,Car ,Female,49,HS ,77309,661,1,2,Citizen \n1840,0,Car ,Female,42,HS ,76455,575,0,2,Citizen \n1841,0,Car ,Male ,37,College ,90718,580,1,2,Citizen \n1842,0,Car ,Male ,34,HS ,99355,663,2,2,Citizen \n1843,0,Car ,Male ,34,HS ,83518,513,1,2,Citizen \n1844,0,Car ,Male ,42,College ,110614,623,3,2,Citizen \n1845,0,Car ,Male ,51,HS ,116019,525,2,2,Citizen \n1846,0,Car ,Male ,52,HS ,117720,733,2,1,Citizen \n1847,0,Car ,Female,47,College ,115197,684,0,2,Citizen \n1848,0,Car ,Male ,42,HS ,83865,581,0,2,Citizen \n1849,0,Car ,Female,47,Graduate,105696,622,0,2,Non-citizen\n1850,0,Car ,Female,47,HS ,107610,624,0,2,Citizen \n1851,0,Car ,Male ,49,HS ,110934,697,1,2,Citizen \n1852,0,Car ,Male ,42,HS ,84947,588,0,2,Citizen \n1853,0,Car ,Female,52,HS ,110049,567,3,2,Citizen \n1854,0,Home,Male ,48,HS ,107662,590,5,2,Citizen \n1855,0,Car ,Male ,40,HS ,84749,530,0,2,Citizen \n1856,0,Car ,Male ,36,HS ,99174,581,1,2,Citizen \n1857,0,Car ,Male ,47,HS ,114180,591,1,2,Citizen \n1858,0,Home,Female,39,HS ,103308,591,6,2,Citizen \n1859,0,Home,Male ,43,College ,93121,536,5,2,Citizen \n1860,0,Car ,Male ,41,HS ,92854,603,0,2,Citizen \n1861,0,Car ,Female,41,HS ,86773,575,0,2,Citizen \n1862,0,Car ,Female,52,College ,102523,581,2,2,Citizen \n1863,0,Home,Male ,43,HS ,94033,573,1,2,Citizen \n1864,0,Home,Male ,43,HS ,87937,667,4,2,Citizen \n1865,0,Car ,Female,32,HS ,98324,582,0,2,Citizen \n1866,0,Car ,Female,44,HS ,128883,663,2,1,Citizen \n1867,0,Home,Female,41,HS ,87043,601,1,2,Citizen \n1868,0,Car ,Female,49,HS ,96596,564,1,2,Citizen \n1869,0,Car ,Female,52,Graduate,104816,732,2,1,Citizen \n1870,0,Car ,Male ,43,HS ,83456,546,2,2,Citizen \n1871,0,Home,Female,49,College ,108097,542,6,2,Citizen \n1872,0,Car ,Male ,41,HS ,107492,547,2,2,Citizen \n1873,0,Car ,Male ,42,HS ,104870,482,0,2,Citizen \n1874,0,Car ,Male ,44,HS ,95066,531,1,2,Citizen \n1875,0,Car ,Female,38,HS ,112433,708,0,2,Citizen \n1876,0,Home,Male ,37,HS ,98868,688,7,2,Citizen \n1877,0,Home,Female,40,HS ,90340,645,5,2,Citizen \n1879,0,Home,Male ,31,HS ,75384,559,4,2,Citizen \n1880,0,Car ,Male ,36,HS ,135633,634,1,2,Citizen \n1881,0,Car ,Female,49,HS ,66243,535,0,2,Citizen \n1882,0,Car ,Male ,44,HS ,79150,624,0,2,Citizen \n1883,0,Car ,Male ,42,HS ,96152,616,2,2,Citizen \n1884,0,Car ,Female,40,College ,108721,546,0,2,Citizen \n1885,0,Home,Male ,46,College ,95117,595,9,2,Citizen \n1886,0,Car ,Female,50,HS ,109206,693,2,2,Citizen \n1887,0,Car ,Male ,45,HS ,58189,485,0,2,Citizen \n1888,0,Car ,Female,42,College ,102827,567,2,2,Citizen \n1889,0,Car ,Female,42,College ,104965,587,2,2,Citizen \n1890,0,Car ,Female,52,College ,98444,588,3,1,Citizen \n1891,0,Car ,Male ,47,College ,93764,575,2,2,Citizen \n1892,0,Car ,Male ,39,HS ,81373,644,0,2,Citizen \n1893,0,Car ,Male ,47,HS ,96402,562,2,2,Citizen \n1894,0,Car ,Female,43,HS ,113919,541,0,2,Citizen \n1895,0,Car ,Female,56,HS ,65179,585,0,1,Citizen \n1896,0,Car ,Male ,39,HS ,108898,591,2,2,Citizen \n1897,0,Car ,Female,49,College ,99212,681,1,2,Citizen \n1898,0,Car ,Male ,50,College ,123897,558,2,2,Citizen \n1899,0,Car ,Male ,46,HS ,137353,668,0,2,Citizen \n1900,0,Car ,Female,59,HS ,79888,578,3,1,Citizen \n1901,0,Car ,Female,47,College ,99661,672,1,1,Citizen \n1902,0,Car ,Male ,43,HS ,107596,485,0,2,Citizen \n1903,0,Car ,Male ,41,HS ,104846,579,0,2,Citizen \n1904,0,Home,Male ,34,HS ,97426,550,6,2,Citizen \n1905,1,Home,Male ,38,HS ,113722,688,4,2,Citizen \n1906,0,Car ,Male ,42,HS ,61320,567,0,2,Citizen \n1907,0,Car ,Male ,50,Graduate,94601,683,0,2,Non-citizen\n1908,0,Car ,Male ,44,HS ,76656,540,0,2,Citizen \n1909,0,Car ,Male ,46,HS ,74701,668,0,2,Citizen \n1910,0,Home,Male ,38,HS ,109773,635,6,2,Citizen \n1911,0,Home,Male ,39,HS ,104538,651,7,2,Citizen \n1912,0,Home,Male ,41,HS ,102680,621,8,2,Citizen \n1913,0,Car ,Female,52,College ,123323,720,0,2,Citizen \n1914,0,Home,Male ,33,HS ,78528,530,3,2,Citizen \n1915,0,Car ,Male ,45,College ,91214,629,3,1,Citizen \n1916,0,Car ,Female,41,HS ,80524,584,2,2,Citizen \n1917,0,Car ,Female,48,HS ,105954,593,0,2,Citizen \n1918,0,Home,Female,52,College ,92294,665,8,2,Citizen \n1919,0,Car ,Female,49,College ,106616,628,3,1,Citizen \n1920,0,Home,Male ,43,HS ,63398,600,2,2,Citizen \n1921,0,Home,Male ,53,HS ,95627,554,5,1,Citizen \n1922,0,Car ,Male ,37,HS ,68380,523,2,2,Citizen \n1923,0,Car ,Male ,39,HS ,106948,604,0,1,Citizen \n1924,0,Car ,Male ,41,College ,94146,700,2,2,Citizen \n1925,0,Home,Male ,38,HS ,99805,565,4,2,Citizen \n1926,0,Car ,Female,43,Graduate,109155,583,2,2,Citizen \n1927,0,Home,Male ,37,HS ,54748,547,0,2,Citizen \n1928,0,Car ,Male ,31,HS ,66020,508,0,2,Citizen \n1929,0,Car ,Male ,31,HS ,82449,618,0,2,Citizen \n1930,0,Home,Female,47,HS ,105250,633,10,2,Citizen \n1931,0,Home,Male ,40,College ,71938,591,4,2,Citizen \n1932,0,Car ,Female,48,HS ,118182,643,2,1,Citizen \n1933,0,Car ,Male ,37,College ,118936,636,0,2,Non-citizen\n1934,0,Home,Female,38,HS ,101441,709,5,2,Citizen \n1935,0,Car ,Male ,45,Graduate,116372,751,3,2,Citizen \n1936,0,Car ,Female,48,HS ,116976,689,1,2,Citizen \n1937,0,Home,Female,42,College ,91311,543,9,2,Citizen \n1938,0,Car ,Female,41,College ,97423,558,3,2,Citizen \n1939,0,Car ,Male ,41,HS ,118393,612,2,2,Citizen \n1940,1,Car ,Female,46,HS ,113656,589,1,2,Citizen \n1941,0,Car ,Female,44,College ,95662,602,2,2,Citizen \n1942,0,Car ,Female,45,College ,81405,711,2,1,Citizen \n1943,0,Car ,Male ,30,College ,72084,451,0,2,Citizen \n1944,0,Home,Male ,42,HS ,105285,712,5,2,Citizen \n1945,0,Home,Female,38,Graduate,111834,599,6,2,Non-citizen\n1946,0,Car ,Male ,48,HS ,114255,628,1,2,Citizen \n1947,0,Car ,Male ,42,HS ,90643,569,2,2,Citizen \n1948,0,Home,Male ,29,HS ,51774,539,6,2,Citizen \n1949,0,Home,Female,36,College ,97286,739,2,2,Citizen \n1950,0,Car ,Male ,49,HS ,104062,560,0,2,Citizen \n1951,0,Car ,Female,46,HS ,100459,627,1,2,Citizen \n1952,0,Home,Male ,30,HS ,78753,466,3,2,Citizen \n1953,0,Car ,Male ,41,HS ,91197,519,3,2,Citizen \n1954,0,Home,Male ,46,College ,103984,569,7,2,Citizen \n1955,0,Car ,Male ,36,HS ,101886,603,0,2,Citizen \n1956,0,Car ,Male ,43,HS ,84323,557,1,2,Citizen \n1957,0,Home,Female,36,HS ,99001,702,3,2,Citizen \n1958,0,Car ,Male ,49,Graduate,100156,719,3,2,Citizen \n1959,0,Car ,Male ,41,HS ,98350,637,2,2,Citizen \n1960,0,Home,Male ,30,HS ,88767,431,8,2,Citizen \n1961,0,Car ,Female,44,Graduate,84255,576,2,2,Non-citizen\n1962,0,Car ,Female,45,HS ,125380,683,2,1,Citizen \n1963,1,Car ,Male ,42,HS ,109047,633,0,2,Citizen \n1964,0,Car ,Male ,30,HS ,82337,522,0,2,Citizen \n1965,0,Car ,Female,43,HS ,96669,589,2,2,Citizen \n1966,0,Car ,Male ,37,HS ,111587,610,0,2,Citizen \n1967,0,Car ,Female,45,HS ,110725,544,0,2,Citizen \n1968,1,Car ,Male ,42,HS ,107627,584,1,2,Citizen \n1969,0,Car ,Male ,40,HS ,92030,592,0,2,Citizen \n1970,0,Home,Male ,34,HS ,102239,569,13,2,Citizen \n1971,0,Home,Male ,43,Graduate,117046,681,8,2,Citizen \n1972,0,Car ,Male ,43,HS ,97652,704,2,2,Citizen \n1973,0,Car ,Male ,45,HS ,89432,595,0,2,Citizen \n1974,0,Car ,Male ,44,College ,101880,655,2,2,Citizen \n1975,0,Home,Male ,29,HS ,87259,498,1,2,Citizen \n1976,0,Car ,Female,50,HS ,134043,623,0,2,Citizen \n1977,1,Car ,Female,50,College ,126264,684,1,2,Citizen \n1978,0,Car ,Female,49,HS ,98529,619,1,2,Non-citizen\n1979,0,Car ,Male ,43,HS ,116011,663,1,2,Citizen \n1980,0,Home,Male ,39,HS ,96679,563,6,2,Citizen \n1981,0,Car ,Female,35,HS ,76862,536,2,2,Citizen \n1982,0,Car ,Male ,40,HS ,75702,437,1,2,Citizen \n1983,0,Car ,Male ,45,HS ,75268,639,2,2,Citizen \n1984,0,Car ,Female,42,College ,100412,550,2,2,Citizen \n1985,0,Car ,Male ,47,HS ,108510,604,2,2,Citizen \n1986,0,Car ,Male ,36,College ,93979,542,1,2,Citizen \n1987,0,Car ,Male ,36,HS ,125154,532,0,2,Citizen \n1988,0,Home,Male ,27,HS ,72194,564,3,2,Citizen \n1989,1,Home,Male ,38,Graduate,122329,736,13,2,Citizen \n1990,0,Car ,Male ,48,HS ,91065,634,1,2,Citizen \n1991,0,Car ,Female,48,HS ,108428,633,1,2,Citizen \n1992,0,Car ,Male ,53,College ,110150,597,1,2,Citizen \n1993,0,Car ,Male ,45,College ,123503,646,1,2,Citizen \n1994,0,Car ,Female,40,College ,93824,643,1,2,Citizen \n1995,0,Car ,Female,46,HS ,89539,623,1,2,Citizen \n1996,0,Car ,Female,55,College ,115981,623,2,2,Citizen \n1997,0,Car ,Female,41,College ,86550,649,2,2,Citizen \n1998,0,Car ,Female,57,HS ,80870,672,0,2,Citizen \n1999,1,Car ,Male ,43,Graduate,114963,661,2,2,Citizen \n2000,0,Car ,Male ,41,College ,118291,606,1,2,Citizen \n2001,0,Car ,Female,49,College ,116450,606,1,1,Citizen \n2002,0,Car ,Male ,44,HS ,124023,736,3,1,Non-citizen\n2003,0,Car ,Female,42,HS ,73544,612,2,2,Citizen \n2004,0,Car ,Male ,36,HS ,83483,559,2,2,Citizen \n2005,0,Car ,Male ,41,HS ,88697,572,1,2,Citizen \n2006,0,Car ,Female,47,College ,105374,606,1,2,Citizen \n2007,0,Car ,Female,50,College ,119950,649,3,1,Citizen \n2008,0,Car ,Female,39,College ,94044,576,3,2,Citizen \n2009,0,Car ,Female,49,College ,86890,647,0,2,Citizen \n2010,0,Car ,Female,48,College ,116004,645,2,2,Citizen \n2011,0,Car ,Male ,42,HS ,105638,733,2,2,Citizen \n2012,0,Car ,Male ,51,HS ,111482,704,1,1,Citizen \n2013,0,Car ,Male ,39,HS ,92273,519,0,2,Citizen \n2014,0,Home,Male ,44,HS ,89949,572,4,2,Citizen \n2015,0,Car ,Male ,34,HS ,98413,608,0,2,Citizen \n2016,0,Car ,Male ,33,HS ,109580,643,0,2,Citizen \n2017,0,Home,Female,23,HS ,89663,562,5,2,Citizen \n2018,0,Car ,Male ,33,College ,90853,730,2,2,Citizen \n2019,0,Home,Male ,47,HS ,95646,615,5,2,Citizen \n2020,0,Car ,Male ,39,College ,94461,483,0,2,Citizen \n2021,0,Car ,Female,44,College ,111027,676,2,2,Non-citizen\n2022,0,Car ,Male ,47,HS ,96951,591,2,2,Citizen \n2023,0,Car ,Female,30,College ,99390,677,2,2,Citizen \n2024,0,Home,Male ,43,HS ,98185,642,6,2,Citizen \n2025,0,Home,Male ,36,HS ,73296,626,8,2,Citizen \n2026,0,Car ,Male ,46,HS ,93508,574,2,2,Citizen \n2027,0,Car ,Male ,37,HS ,85455,654,1,2,Citizen \n2028,0,Car ,Male ,43,Graduate,96485,542,3,2,Citizen \n2029,0,Car ,Male ,44,HS ,90993,614,0,2,Citizen \n2030,0,Home,Male ,41,HS ,94218,518,7,2,Citizen \n2031,0,Car ,Female,30,College ,94654,596,1,2,Citizen \n2032,0,Home,Female,39,HS ,117451,582,14,2,Citizen \n2034,0,Car ,Male ,46,HS ,105689,590,2,2,Citizen \n2035,0,Car ,Female,48,HS ,107408,656,0,2,Citizen \n2036,0,Home,Male ,49,HS ,102095,671,7,2,Citizen \n2037,0,Car ,Female,47,College ,106427,612,2,2,Citizen \n2038,0,Car ,Female,45,HS ,125103,589,2,1,Citizen \n2039,0,Car ,Male ,45,HS ,104882,555,1,2,Citizen \n2040,0,Car ,Male ,38,HS ,102891,542,0,2,Citizen \n2041,0,Car ,Male ,45,HS ,106479,658,2,1,Citizen \n2042,0,Car ,Male ,48,College ,82874,634,1,2,Citizen \n2043,0,Car ,Male ,49,College ,85080,720,1,2,Citizen \n2044,1,Car ,Male ,37,College ,97907,547,1,2,Citizen \n2045,0,Home,Male ,34,HS ,101090,587,10,2,Citizen \n2046,0,Home,Male ,42,HS ,79110,507,10,2,Citizen \n2047,0,Car ,Female,50,Graduate,95026,642,1,2,Citizen \n2048,0,Car ,Male ,42,HS ,112039,592,2,2,Citizen \n2049,1,Home,Female,38,HS ,80441,581,4,2,Citizen \n2050,0,Home,Male ,36,HS ,96591,587,6,2,Citizen \n2051,0,Car ,Female,44,HS ,75868,614,1,2,Citizen \n2052,1,Car ,Male ,46,HS ,122637,683,3,2,Citizen \n2053,1,Car ,Female,40,College ,92853,622,1,1,Citizen \n2054,0,Car ,Male ,49,HS ,99932,625,0,2,Citizen \n2055,0,Car ,Male ,42,College ,110395,631,1,2,Citizen \n2056,0,Home,Male ,38,College ,74354,562,7,2,Citizen \n2057,0,Home,Female,43,HS ,97450,704,0,2,Citizen \n2058,0,Home,Male ,41,HS ,118385,638,4,2,Citizen \n2059,0,Car ,Female,47,HS ,117891,713,0,2,Non-citizen\n2060,0,Home,Female,44,HS ,102061,684,10,1,Non-citizen\n2061,0,Car ,Male ,44,HS ,80951,743,0,2,Citizen \n2062,0,Car ,Female,44,HS ,94199,484,0,2,Citizen \n2063,0,Home,Male ,34,HS ,92983,635,5,2,Citizen \n2064,0,Home,Female,43,HS ,101812,676,7,2,Citizen \n2065,0,Home,Male ,35,HS ,106304,626,8,2,Citizen \n2066,0,Car ,Female,45,HS ,106561,549,2,2,Citizen \n2067,0,Car ,Male ,42,College ,105438,590,2,2,Citizen \n2068,0,Car ,Male ,45,HS ,92335,614,0,2,Citizen \n2069,0,Home,Male ,43,HS ,72346,589,3,2,Citizen \n2070,0,Car ,Female,45,Graduate,102455,551,1,2,Non-citizen\n2071,0,Home,Male ,34,HS ,80993,561,3,2,Citizen \n2072,0,Car ,Female,46,College ,108346,656,2,2,Citizen \n2073,0,Home,Male ,38,HS ,98316,570,3,2,Citizen \n2074,0,Car ,Male ,40,HS ,76163,548,1,2,Citizen \n2075,0,Car ,Female,42,College ,99517,660,0,2,Citizen \n2076,1,Car ,Female,48,College ,131874,596,2,2,Citizen \n2077,0,Car ,Male ,49,HS ,110572,645,0,2,Citizen \n2078,0,Car ,Female,49,College ,89147,686,2,2,Citizen \n2079,0,Home,Male ,36,HS ,61574,435,2,2,Citizen \n2080,0,Car ,Male ,36,HS ,75010,587,0,2,Citizen \n2081,0,Home,Male ,41,HS ,104701,491,5,2,Citizen \n2082,0,Car ,Male ,49,HS ,91816,472,0,2,Citizen \n2083,0,Home,Male ,31,HS ,97984,588,3,2,Citizen \n2084,0,Home,Male ,42,HS ,93230,620,7,2,Citizen \n2085,0,Car ,Female,43,HS ,100175,635,0,2,Citizen \n2086,0,Car ,Female,58,College ,90008,676,3,2,Citizen \n2087,0,Home,Male ,39,HS ,113988,591,3,2,Citizen \n2088,0,Home,Female,39,College ,93373,540,9,2,Citizen \n2089,0,Car ,Female,51,College ,105179,581,3,2,Citizen \n2090,0,Car ,Male ,47,HS ,92653,625,0,2,Citizen \n2091,0,Home,Male ,44,HS ,89844,610,14,2,Citizen \n2092,0,Car ,Female,43,College ,130407,736,0,2,Citizen \n2093,0,Car ,Female,53,College ,126074,631,2,2,Citizen \n2094,0,Home,Male ,43,College ,106714,662,13,2,Citizen \n2095,0,Car ,Male ,38,HS ,67615,611,2,2,Citizen \n2096,0,Car ,Female,49,HS ,117353,606,2,2,Citizen \n2097,0,Car ,Female,50,College ,75567,622,2,2,Citizen \n2098,0,Car ,Male ,38,HS ,97633,621,0,2,Citizen \n2099,0,Car ,Male ,30,College ,94383,572,0,2,Citizen \n2100,1,Car ,Male ,43,College ,82840,527,2,2,Citizen \n2101,0,Car ,Male ,36,HS ,84242,658,0,2,Citizen \n2102,0,Car ,Male ,38,HS ,72736,634,0,2,Citizen \n2103,0,Car ,Male ,48,HS ,72342,581,0,2,Citizen \n2104,0,Car ,Male ,48,HS ,93788,647,2,2,Citizen \n2105,0,Car ,Female,60,HS ,137542,674,2,1,Citizen \n2106,0,Home,Male ,35,HS ,95753,733,1,2,Citizen \n2107,0,Home,Female,40,HS ,101725,577,1,2,Citizen \n2108,0,Home,Male ,39,College ,103283,484,7,2,Citizen \n2109,0,Car ,Male ,49,College ,105402,612,3,1,Citizen \n2110,0,Home,Male ,39,HS ,95779,627,4,2,Citizen \n2111,0,Home,Male ,40,HS ,122225,524,6,2,Citizen \n2112,0,Home,Female,44,HS ,80277,708,5,2,Citizen \n2113,0,Home,Male ,33,HS ,73986,594,6,2,Citizen \n2114,1,Car ,Female,40,College ,116457,637,1,2,Citizen \n2115,0,Car ,Male ,44,HS ,117401,605,1,2,Citizen \n2116,0,Car ,Male ,36,HS ,89574,527,0,2,Citizen \n2117,0,Home,Male ,34,HS ,68400,472,5,2,Citizen \n2118,0,Car ,Female,49,HS ,109886,726,1,2,Citizen \n2119,0,Car ,Female,44,College ,102246,694,1,2,Citizen \n2120,0,Car ,Male ,50,HS ,82350,633,3,2,Citizen \n2121,0,Car ,Male ,44,HS ,93973,612,1,2,Citizen \n2122,0,Car ,Male ,35,HS ,112929,576,0,2,Citizen \n2123,0,Car ,Female,50,HS ,113819,610,0,2,Citizen \n2124,0,Car ,Male ,42,HS ,85757,594,1,2,Citizen \n2125,0,Car ,Male ,44,HS ,137820,631,1,2,Citizen \n2126,0,Car ,Male ,34,HS ,83562,598,0,2,Citizen \n2127,0,Car ,Male ,44,HS ,79181,674,0,2,Citizen \n2128,0,Home,Male ,38,HS ,112880,585,7,2,Citizen \n2129,0,Home,Male ,45,HS ,114062,572,8,2,Citizen \n2130,0,Home,Female,43,College ,84280,612,10,2,Citizen \n2131,0,Home,Male ,51,HS ,83576,585,8,2,Non-citizen\n2132,0,Car ,Female,54,Graduate,86118,722,2,2,Citizen \n2133,0,Car ,Male ,48,College ,106875,627,3,2,Citizen \n2134,0,Home,Male ,32,College ,66924,588,6,2,Citizen \n2135,0,Car ,Female,38,HS ,79563,576,1,2,Citizen \n2136,0,Car ,Female,47,College ,131633,577,2,2,Non-citizen\n2137,0,Car ,Male ,50,Graduate,112209,622,2,2,Non-citizen\n2138,0,Car ,Male ,40,HS ,106178,606,0,2,Citizen \n2139,0,Car ,Female,46,HS ,70112,633,0,2,Citizen \n2140,0,Home,Female,47,HS ,118490,632,7,2,Citizen \n2141,0,Car ,Female,38,HS ,80410,612,0,2,Citizen \n2142,0,Car ,Female,41,HS ,115643,596,1,2,Citizen \n2143,0,Car ,Female,35,HS ,57037,653,0,1,Citizen \n2144,0,Car ,Male ,43,HS ,88719,563,3,1,Citizen \n2145,0,Car ,Male ,41,HS ,87832,619,0,1,Citizen \n2146,0,Home,Female,42,HS ,103818,513,2,2,Citizen \n2147,0,Car ,Male ,51,College ,121405,698,2,1,Non-citizen\n2148,0,Car ,Female,45,College ,111183,721,3,1,Citizen \n2149,0,Home,Male ,46,HS ,105757,750,7,1,Citizen \n2150,0,Car ,Female,38,HS ,119368,605,0,2,Citizen \n2151,0,Home,Female,47,College ,123429,721,11,2,Citizen \n2152,0,Car ,Male ,32,HS ,93180,635,1,2,Citizen \n2153,0,Car ,Male ,48,Graduate,81623,590,2,2,Citizen \n2154,1,Car ,Female,48,College ,113525,687,2,1,Non-citizen\n2155,0,Car ,Male ,48,HS ,91582,575,2,2,Citizen \n2156,0,Home,Female,39,College ,84634,675,9,2,Citizen \n2157,0,Car ,Female,47,College ,77055,560,2,2,Non-citizen\n2158,0,Car ,Female,42,College ,111672,644,1,2,Citizen \n2159,0,Car ,Male ,51,Graduate,72540,552,3,2,Citizen \n2160,0,Home,Male ,35,HS ,80938,557,0,2,Citizen \n2161,0,Car ,Male ,44,HS ,104768,668,0,2,Citizen \n2162,0,Car ,Male ,40,College ,101308,657,2,2,Citizen \n2163,0,Home,Female,32,HS ,87119,580,0,2,Citizen \n2164,0,Car ,Male ,56,HS ,126647,702,2,2,Citizen \n2165,0,Car ,Male ,48,HS ,130460,707,3,2,Citizen \n2166,0,Car ,Male ,45,HS ,92539,664,0,2,Citizen \n2167,0,Car ,Male ,40,HS ,92114,634,2,2,Citizen \n2168,0,Home,Male ,25,HS ,80766,521,2,2,Citizen \n2169,0,Car ,Female,40,Graduate,88334,519,2,2,Citizen \n2170,0,Car ,Male ,45,College ,70010,646,1,2,Citizen \n2171,0,Car ,Female,43,HS ,80957,652,0,2,Citizen \n2172,0,Car ,Male ,46,HS ,95683,674,0,2,Citizen \n2173,0,Home,Male ,34,HS ,78124,583,4,2,Citizen \n2174,0,Home,Female,50,Graduate,101345,615,6,2,Citizen \n2175,0,Home,Male ,27,HS ,74024,610,5,2,Citizen \n2176,0,Car ,Male ,31,HS ,72812,456,1,2,Citizen \n2177,0,Car ,Male ,38,HS ,101407,597,2,2,Citizen \n2178,0,Home,Male ,25,College ,109189,690,7,2,Citizen \n2179,0,Car ,Male ,36,HS ,75655,551,2,2,Citizen \n2180,0,Home,Male ,51,HS ,84474,591,8,2,Citizen \n2181,0,Home,Male ,49,College ,77398,585,8,2,Citizen \n2182,0,Home,Male ,42,HS ,81352,616,8,2,Citizen \n2183,0,Car ,Female,47,HS ,89454,650,0,2,Citizen \n2184,0,Car ,Female,42,HS ,107675,643,1,2,Citizen \n2185,0,Home,Male ,49,HS ,101891,675,7,2,Citizen \n2186,0,Home,Male ,29,HS ,77903,475,6,2,Citizen \n2187,0,Car ,Female,44,HS ,120121,631,1,2,Citizen \n2188,0,Car ,Male ,41,College ,50005,550,0,2,Citizen \n2189,0,Car ,Male ,42,HS ,100845,597,0,2,Citizen \n2190,0,Home,Male ,45,College ,58058,678,0,2,Citizen \n2191,0,Car ,Male ,42,HS ,94761,563,0,2,Citizen \n2192,0,Car ,Female,48,College ,98927,609,0,1,Citizen \n2193,0,Car ,Male ,40,College ,117787,571,2,2,Citizen \n2194,0,Car ,Male ,48,HS ,78518,672,1,2,Citizen \n2195,0,Car ,Male ,50,College ,95046,556,1,2,Citizen \n2196,0,Car ,Male ,38,College ,63133,587,0,2,Citizen \n2197,0,Car ,Male ,41,College ,88738,596,3,2,Citizen \n2198,1,Home,Male ,40,HS ,103613,652,6,2,Citizen \n2199,0,Car ,Male ,34,College ,82750,595,0,2,Citizen \n2200,0,Car ,Male ,48,HS ,127964,711,0,1,Non-citizen\n2201,1,Car ,Male ,41,HS ,115877,650,0,2,Citizen \n2202,0,Car ,Male ,45,HS ,111012,631,1,2,Citizen \n2203,0,Home,Male ,32,HS ,82359,459,5,2,Citizen \n2204,0,Car ,Female,56,HS ,88170,691,1,2,Citizen \n2205,0,Car ,Female,52,HS ,66893,671,0,2,Citizen \n2206,0,Home,Female,42,HS ,88512,619,3,2,Citizen \n2207,0,Car ,Male ,47,HS ,108399,495,0,2,Citizen \n2208,0,Car ,Female,36,HS ,96174,591,0,2,Citizen \n2209,0,Car ,Male ,51,HS ,92168,543,2,2,Citizen \n2210,0,Home,Male ,39,HS ,67080,498,5,2,Citizen \n2211,0,Car ,Female,50,College ,116809,598,2,1,Citizen \n2212,0,Car ,Male ,46,HS ,93992,564,0,2,Citizen \n2213,0,Car ,Female,45,HS ,96083,589,0,2,Citizen \n2214,0,Car ,Male ,43,HS ,107934,597,0,2,Citizen \n2215,0,Home,Male ,39,HS ,83364,577,5,2,Citizen \n2216,0,Car ,Female,44,HS ,108501,674,3,2,Citizen \n2217,1,Car ,Female,54,HS ,116084,753,1,1,Citizen \n2218,0,Car ,Female,47,HS ,88511,636,0,2,Citizen \n2219,0,Home,Female,47,HS ,125712,634,9,1,Citizen \n2220,0,Home,Male ,41,HS ,99367,658,7,2,Citizen \n2221,0,Car ,Male ,40,HS ,101934,610,2,2,Citizen \n2222,1,Car ,Female,46,College ,112731,771,0,2,Citizen \n2223,0,Home,Male ,45,HS ,96448,669,4,2,Citizen \n2224,0,Home,Female,41,HS ,86819,557,7,2,Citizen \n2225,0,Car ,Female,45,HS ,111710,657,0,1,Citizen \n2226,0,Car ,Female,44,College ,135851,682,3,2,Citizen \n2227,0,Home,Female,45,HS ,86613,641,3,2,Citizen \n2228,0,Home,Male ,35,HS ,85079,621,4,2,Citizen \n2229,0,Car ,Female,57,HS ,130119,686,3,1,Non-citizen\n2230,0,Car ,Male ,38,College ,107538,548,0,2,Citizen \n2231,0,Home,Male ,40,HS ,124326,647,9,2,Citizen \n2232,0,Car ,Female,39,College ,108902,629,3,2,Citizen \n2233,0,Home,Male ,53,HS ,84456,628,9,2,Citizen \n2234,0,Car ,Male ,45,HS ,70520,549,1,2,Citizen \n2235,0,Car ,Male ,45,HS ,85890,556,1,2,Citizen \n2236,0,Car ,Female,41,HS ,96726,645,0,2,Citizen \n2237,0,Home,Male ,41,HS ,96883,606,4,2,Citizen \n2238,0,Home,Male ,47,HS ,64104,607,6,2,Citizen \n2239,0,Car ,Female,46,HS ,88807,557,1,2,Citizen \n2240,0,Car ,Female,43,HS ,108321,657,2,1,Citizen \n2241,0,Home,Male ,39,HS ,68238,662,6,2,Citizen \n2242,0,Home,Female,43,College ,113097,679,11,2,Citizen \n2243,0,Car ,Male ,38,College ,85125,625,0,2,Citizen \n2244,0,Home,Male ,38,HS ,80815,520,0,2,Citizen \n2245,0,Home,Male ,40,HS ,83071,595,5,2,Citizen \n2246,0,Home,Male ,44,HS ,85313,677,4,2,Citizen \n2247,0,Car ,Male ,50,HS ,141194,659,3,2,Citizen \n2248,0,Car ,Female,41,College ,100333,525,0,2,Citizen \n2249,0,Car ,Female,43,HS ,100260,571,1,2,Citizen \n2250,1,Car ,Female,54,HS ,124041,733,3,1,Citizen \n2251,0,Car ,Male ,43,HS ,102750,536,0,2,Citizen \n2252,0,Car ,Male ,46,HS ,94890,622,1,2,Citizen \n2253,0,Car ,Male ,40,HS ,82202,576,0,2,Citizen \n2254,0,Home,Female,53,College ,109575,637,7,2,Citizen \n2255,0,Home,Male ,42,HS ,102059,594,4,2,Citizen \n2256,0,Car ,Female,35,HS ,89206,630,2,1,Citizen \n2257,0,Car ,Male ,49,HS ,110573,695,1,1,Citizen \n2258,0,Car ,Female,55,College ,92919,574,2,2,Citizen \n2259,0,Car ,Male ,42,HS ,102114,639,0,2,Citizen \n2260,0,Home,Male ,38,HS ,87612,563,10,2,Citizen \n2261,0,Car ,Female,36,College ,74600,518,0,2,Citizen \n2262,0,Car ,Male ,48,HS ,113742,694,0,2,Citizen \n2263,1,Home,Female,45,College ,101127,604,6,2,Citizen \n2264,0,Car ,Female,38,College ,90889,698,0,2,Citizen \n2265,0,Car ,Female,42,College ,97098,663,2,2,Citizen \n2266,0,Car ,Male ,50,HS ,80563,590,2,2,Citizen \n2267,0,Car ,Female,40,College ,99363,674,2,1,Citizen \n2268,0,Car ,Male ,37,Graduate,127284,564,3,2,Non-citizen\n2269,0,Car ,Female,56,College ,99285,757,2,1,Citizen \n2270,0,Car ,Female,52,HS ,116843,653,3,2,Citizen \n2271,0,Car ,Male ,43,HS ,84973,563,0,2,Citizen \n2272,0,Home,Male ,40,HS ,83161,537,6,2,Citizen \n2273,0,Car ,Female,39,HS ,79964,582,0,2,Citizen \n2274,0,Car ,Male ,38,College ,108986,641,1,2,Citizen \n2275,0,Car ,Female,42,College ,92859,740,0,2,Citizen \n2276,0,Car ,Female,45,HS ,76783,667,1,1,Citizen \n2277,0,Car ,Female,42,HS ,100737,618,2,1,Citizen \n2278,0,Car ,Male ,42,HS ,90737,703,3,2,Citizen \n2279,0,Car ,Male ,40,College ,82646,558,2,2,Citizen \n2280,0,Car ,Female,46,HS ,89155,640,0,2,Citizen \n2281,0,Car ,Female,50,HS ,89631,683,1,2,Citizen \n2282,0,Car ,Male ,33,HS ,105723,618,3,2,Citizen \n2283,0,Home,Male ,38,College ,93312,602,10,2,Non-citizen\n2284,1,Car ,Female,39,HS ,111760,566,0,2,Citizen \n2285,0,Home,Male ,38,HS ,98325,572,6,2,Citizen \n2286,0,Home,Male ,33,HS ,80389,560,3,2,Citizen \n2287,0,Home,Male ,44,HS ,100386,607,7,2,Citizen \n2288,0,Car ,Female,50,HS ,83569,642,2,2,Citizen \n2289,0,Car ,Male ,38,College ,73692,517,0,2,Citizen \n2290,0,Car ,Female,47,HS ,103733,624,0,1,Non-citizen\n2291,0,Car ,Male ,36,Graduate,97728,579,2,2,Citizen \n2292,0,Car ,Female,40,HS ,91103,566,0,2,Citizen \n2293,0,Car ,Female,38,HS ,76898,491,0,2,Citizen \n2294,0,Car ,Female,42,HS ,128550,629,3,1,Citizen \n2295,0,Home,Male ,23,HS ,87837,553,6,2,Citizen \n2296,0,Car ,Female,45,HS ,99654,787,0,2,Citizen \n2297,0,Home,Male ,49,HS ,85240,657,4,2,Citizen \n2298,0,Car ,Male ,44,College ,95839,670,1,2,Citizen \n2299,0,Home,Female,39,HS ,97264,542,5,2,Citizen \n2300,0,Car ,Female,46,College ,99001,629,2,2,Citizen \n2301,0,Home,Male ,29,HS ,63099,584,0,2,Citizen \n2302,0,Car ,Female,51,College ,112972,612,1,2,Citizen \n2303,0,Home,Male ,39,HS ,82308,494,5,2,Citizen \n2304,0,Car ,Female,49,College ,100183,685,3,1,Non-citizen\n2305,0,Car ,Female,33,Graduate,103581,611,2,2,Citizen \n2307,0,Home,Male ,41,HS ,103362,666,7,2,Citizen \n2308,0,Car ,Female,51,College ,94602,654,1,2,Citizen \n2309,0,Car ,Male ,46,Graduate,115596,609,3,1,Non-citizen\n2310,0,Car ,Male ,46,HS ,92958,665,1,2,Citizen \n2311,0,Car ,Male ,42,HS ,78563,577,1,2,Citizen \n2312,0,Car ,Female,40,College ,132989,608,2,2,Citizen \n2313,0,Car ,Female,49,HS ,97256,641,1,2,Citizen \n2314,0,Car ,Male ,47,HS ,116197,559,0,2,Citizen \n2315,1,Car ,Female,47,HS ,94003,634,0,2,Citizen \n2316,0,Car ,Female,53,Graduate,134927,690,2,2,Citizen \n2317,0,Car ,Male ,46,HS ,82488,622,1,2,Citizen \n2318,0,Car ,Female,44,HS ,110310,559,0,2,Citizen \n2319,0,Car ,Female,42,HS ,87482,629,0,2,Citizen \n2320,0,Car ,Male ,44,HS ,98757,626,0,2,Citizen \n2321,0,Car ,Female,47,HS ,100369,640,3,1,Citizen \n2322,0,Car ,Male ,43,College ,82022,557,0,2,Citizen \n2323,0,Car ,Female,48,Graduate,125065,680,3,2,Non-citizen\n2324,0,Car ,Male ,47,HS ,71155,504,3,2,Citizen \n2325,0,Home,Male ,44,HS ,104377,644,2,2,Citizen \n2326,0,Car ,Female,43,HS ,98807,584,2,2,Citizen \n2328,0,Car ,Male ,51,HS ,103840,538,2,2,Citizen \n2329,0,Car ,Male ,46,College ,85831,565,2,2,Citizen \n2330,0,Car ,Male ,45,HS ,82055,550,1,2,Citizen \n2331,0,Car ,Male ,40,College ,95017,510,2,2,Citizen \n2332,0,Car ,Male ,45,HS ,108221,500,0,2,Citizen \n2333,0,Home,Male ,35,HS ,64874,537,8,2,Citizen \n2334,0,Car ,Male ,42,HS ,115197,713,0,2,Citizen \n2335,0,Car ,Female,43,College ,90901,516,3,2,Citizen \n2336,0,Car ,Female,34,HS ,77383,596,0,2,Citizen \n2337,0,Car ,Male ,44,HS ,79331,576,1,2,Citizen \n2338,0,Car ,Male ,52,HS ,74923,702,1,1,Citizen \n2339,0,Home,Male ,38,HS ,58017,544,4,2,Citizen \n2340,0,Car ,Male ,45,HS ,84253,670,0,2,Citizen \n2341,0,Car ,Male ,34,College ,81218,553,2,2,Citizen \n2342,0,Car ,Male ,46,HS ,84606,664,0,2,Citizen \n2343,0,Car ,Female,45,College ,113363,666,3,1,Citizen \n2344,0,Car ,Male ,47,College ,113728,654,1,2,Citizen \n2345,0,Home,Male ,37,HS ,117095,504,4,2,Citizen \n2346,0,Car ,Female,50,HS ,104352,702,2,1,Citizen \n2347,0,Car ,Female,50,College ,95885,659,3,1,Citizen \n2348,0,Car ,Female,49,HS ,118582,542,0,2,Non-citizen\n2349,0,Home,Female,50,College ,116150,554,11,2,Citizen \n2350,0,Car ,Male ,46,College ,117192,587,2,2,Citizen \n2351,0,Home,Male ,38,HS ,74121,629,5,2,Citizen \n2352,0,Car ,Male ,43,HS ,112376,634,1,2,Citizen \n2353,1,Car ,Female,53,College ,134348,696,3,1,Non-citizen\n2354,1,Car ,Female,49,College ,136674,682,2,1,Citizen \n2355,0,Car ,Female,43,HS ,96708,590,2,2,Citizen \n2356,0,Car ,Female,44,HS ,101831,644,0,1,Citizen \n2357,0,Home,Male ,46,HS ,122394,605,7,2,Citizen \n2358,0,Home,Male ,50,College ,113623,712,8,2,Citizen \n2359,0,Home,Male ,29,HS ,87534,470,1,2,Citizen \n2360,0,Home,Male ,41,HS ,123121,750,5,2,Citizen \n2361,0,Home,Female,44,HS ,107852,608,5,2,Citizen \n2362,0,Car ,Male ,38,HS ,89852,601,0,2,Citizen \n2363,0,Car ,Female,33,College ,105714,721,0,2,Citizen \n2364,0,Car ,Female,36,College ,102690,590,2,2,Citizen \n2365,0,Car ,Male ,37,HS ,88175,560,0,2,Citizen \n2366,0,Car ,Female,45,HS ,108757,672,1,2,Non-citizen\n2367,0,Car ,Male ,39,College ,122589,590,0,2,Citizen \n2368,0,Home,Male ,33,HS ,71706,590,3,2,Citizen \n2369,0,Car ,Male ,34,HS ,62854,499,0,2,Citizen \n2370,0,Home,Female,41,HS ,112108,483,7,2,Citizen \n2371,0,Car ,Male ,43,HS ,113035,700,0,2,Citizen \n2372,0,Car ,Male ,43,College ,99644,658,1,2,Citizen \n2373,0,Car ,Male ,49,College ,140584,620,3,2,Citizen \n2374,0,Car ,Male ,36,HS ,81316,491,0,2,Citizen \n2375,0,Car ,Male ,47,College ,121056,602,2,2,Citizen \n2376,1,Home,Female,46,HS ,116998,624,5,2,Citizen \n2377,0,Home,Male ,45,HS ,72471,607,9,1,Citizen \n2378,0,Car ,Male ,41,HS ,81511,577,1,2,Citizen \n2379,0,Home,Male ,36,HS ,81161,591,8,2,Citizen \n2380,0,Car ,Male ,42,HS ,90492,587,2,2,Citizen \n2381,0,Home,Female,35,HS ,105122,611,6,2,Citizen \n2382,0,Car ,Male ,42,HS ,85079,615,0,2,Citizen \n2383,0,Home,Male ,40,HS ,88214,611,6,1,Citizen \n2384,0,Car ,Male ,31,HS ,110861,571,1,2,Citizen \n2385,0,Car ,Female,46,College ,128397,650,3,1,Non-citizen\n2386,0,Car ,Female,43,HS ,90441,573,1,1,Citizen \n2387,0,Home,Male ,37,College ,77979,618,5,2,Citizen \n2388,0,Car ,Female,48,HS ,127361,635,2,2,Citizen \n2389,0,Home,Male ,37,HS ,62035,586,9,2,Citizen \n2390,1,Car ,Female,34,Graduate,94246,615,1,2,Citizen \n2391,0,Car ,Male ,43,HS ,77710,513,0,2,Citizen \n2392,0,Car ,Male ,37,HS ,86301,508,0,2,Citizen \n2393,0,Car ,Male ,39,HS ,113144,591,2,2,Citizen \n2394,0,Car ,Female,28,HS ,115263,582,1,2,Citizen \n2395,0,Home,Male ,39,HS ,81608,619,2,2,Citizen \n2396,0,Home,Male ,48,HS ,102002,648,5,2,Citizen \n2397,0,Car ,Male ,55,HS ,136330,668,3,1,Citizen \n2398,0,Car ,Female,41,HS ,102519,608,3,2,Citizen \n2399,0,Car ,Female,50,HS ,97890,676,2,2,Citizen \n2400,0,Car ,Male ,44,HS ,100824,587,1,2,Citizen \n2401,0,Home,Male ,31,HS ,78575,489,4,2,Citizen \n2402,0,Car ,Male ,54,College ,75293,546,1,2,Citizen \n2403,0,Home,Female,31,HS ,98141,547,5,2,Citizen \n2404,1,Home,Male ,46,HS ,124899,675,10,2,Citizen \n2405,0,Car ,Male ,49,College ,68452,558,2,2,Citizen \n2406,0,Car ,Male ,35,College ,89585,611,2,2,Citizen \n2407,0,Home,Male ,44,HS ,119452,589,2,2,Citizen \n2408,0,Home,Male ,43,HS ,75766,551,5,2,Citizen \n2409,0,Car ,Female,46,College ,126423,707,2,2,Non-citizen\n2410,0,Car ,Female,46,Graduate,117971,670,1,2,Non-citizen\n2411,0,Car ,Male ,38,HS ,78974,554,1,2,Citizen \n2412,0,Car ,Female,39,HS ,88759,670,2,2,Citizen \n2413,0,Home,Female,39,HS ,80063,639,6,2,Citizen \n2414,0,Car ,Male ,46,College ,91579,712,3,1,Citizen \n2415,0,Home,Female,52,HS ,112822,623,7,2,Citizen \n2416,0,Car ,Female,50,Graduate,120626,770,1,2,Citizen \n2417,0,Home,Male ,50,HS ,86847,624,8,2,Citizen \n2418,0,Car ,Female,49,HS ,123857,624,2,1,Citizen \n2419,0,Home,Female,49,HS ,89712,567,5,2,Citizen \n2420,0,Car ,Male ,43,College ,91267,566,2,2,Citizen \n2421,0,Car ,Female,44,HS ,74939,681,2,2,Citizen \n2422,1,Car ,Female,47,College ,78962,617,0,2,Citizen \n2423,0,Car ,Female,35,HS ,103146,659,0,2,Citizen \n2424,0,Home,Female,32,HS ,82405,561,2,2,Citizen \n2425,0,Car ,Male ,43,College ,86237,544,2,2,Citizen \n2426,0,Car ,Male ,41,HS ,105386,601,0,2,Citizen \n2427,0,Home,Male ,41,HS ,78163,652,5,2,Citizen \n2428,0,Home,Male ,44,HS ,96672,641,8,2,Citizen \n2429,1,Car ,Female,45,College ,116579,605,1,2,Citizen \n2430,0,Home,Male ,41,HS ,98445,599,4,2,Citizen \n2431,0,Car ,Female,50,Graduate,127261,738,3,2,Citizen \n2432,0,Home,Male ,46,HS ,91912,629,6,2,Citizen \n2433,0,Car ,Male ,37,HS ,101134,707,2,1,Citizen \n2434,0,Car ,Female,38,HS ,109375,586,0,2,Citizen \n2435,0,Car ,Female,48,College ,83515,659,0,2,Citizen \n2436,0,Car ,Female,46,HS ,100416,659,1,2,Citizen \n2437,0,Car ,Male ,42,College ,92485,506,1,2,Citizen \n2438,0,Car ,Male ,43,HS ,86023,576,0,2,Citizen \n2439,0,Home,Female,47,College ,104339,647,8,2,Citizen \n2440,0,Car ,Male ,53,College ,95073,676,3,1,Citizen \n2441,0,Car ,Female,34,College ,72717,618,2,2,Citizen \n2442,0,Car ,Female,52,College ,106712,597,2,2,Citizen \n2443,0,Car ,Female,46,College ,111842,598,2,2,Citizen \n2444,0,Car ,Male ,50,College ,118988,515,1,2,Non-citizen\n2445,0,Car ,Male ,41,HS ,86532,702,1,2,Citizen \n2446,0,Car ,Male ,43,HS ,100232,630,1,2,Citizen \n2447,0,Car ,Female,38,Graduate,88841,525,1,2,Citizen \n2448,0,Home,Female,43,HS ,81931,590,6,2,Citizen \n2449,0,Car ,Female,41,HS ,101867,647,0,2,Citizen \n2450,0,Car ,Male ,47,College ,104242,671,2,1,Non-citizen\n2451,0,Car ,Male ,47,HS ,109443,594,2,2,Citizen \n2452,1,Car ,Female,54,College ,122463,693,2,2,Citizen \n2453,0,Car ,Male ,43,HS ,95357,616,1,2,Citizen \n2454,0,Home,Male ,28,HS ,73594,562,4,2,Citizen \n2455,0,Home,Male ,42,HS ,57024,644,6,2,Citizen \n2456,0,Car ,Male ,44,College ,111920,660,2,2,Citizen \n2457,0,Car ,Male ,38,HS ,107195,625,0,2,Citizen \n2458,0,Car ,Male ,30,HS ,67180,585,1,2,Citizen \n2459,0,Car ,Female,44,College ,115461,648,0,1,Citizen \n2460,0,Car ,Female,47,College ,77526,627,1,1,Non-citizen\n2461,0,Car ,Female,46,College ,67967,658,0,2,Citizen \n2462,0,Car ,Male ,49,HS ,113309,582,2,2,Citizen \n2463,0,Home,Male ,29,HS ,65257,454,2,2,Citizen \n2464,0,Home,Male ,29,HS ,76010,466,4,2,Citizen \n2465,0,Car ,Male ,42,HS ,116440,558,0,2,Citizen \n2466,0,Car ,Male ,43,HS ,124830,635,3,2,Citizen \n2467,0,Home,Male ,43,HS ,96145,612,7,2,Citizen \n2468,0,Car ,Female,46,Graduate,120584,654,3,1,Non-citizen\n2469,0,Home,Female,35,HS ,89819,597,3,2,Citizen \n2470,0,Home,Male ,38,HS ,93552,635,7,2,Citizen \n2471,0,Car ,Male ,34,HS ,106129,642,1,2,Citizen \n2472,0,Car ,Female,42,HS ,93307,662,2,2,Citizen \n2473,0,Home,Female,28,HS ,73110,542,3,2,Citizen \n2474,0,Car ,Female,47,HS ,74288,561,1,2,Citizen \n2475,0,Car ,Female,51,HS ,120378,682,1,1,Non-citizen\n2476,0,Car ,Female,43,College ,120263,692,3,1,Citizen \n2477,0,Car ,Female,41,HS ,98166,528,1,2,Citizen \n2478,0,Car ,Male ,45,HS ,107337,633,3,1,Citizen \n2479,0,Car ,Male ,37,HS ,75168,587,1,2,Citizen \n2480,0,Car ,Male ,35,HS ,68848,469,1,2,Citizen \n2481,0,Car ,Male ,38,College ,80988,648,2,2,Citizen \n2482,0,Home,Male ,33,HS ,78731,599,3,2,Citizen \n2483,0,Home,Male ,40,College ,97335,711,6,2,Citizen \n2484,0,Car ,Male ,46,HS ,71650,530,0,2,Citizen \n2485,0,Car ,Female,34,College ,92663,598,2,2,Citizen \n2486,0,Home,Male ,37,HS ,74825,625,3,2,Citizen \n2487,0,Car ,Female,34,HS ,112788,679,3,2,Citizen \n2488,0,Car ,Male ,44,HS ,81470,557,2,2,Citizen \n2489,0,Car ,Male ,34,HS ,96566,625,0,2,Citizen \n2490,0,Home,Female,39,College ,74885,538,7,2,Citizen \n2491,0,Car ,Female,45,HS ,105407,587,2,2,Citizen \n2492,0,Home,Male ,42,HS ,86305,573,4,2,Citizen \n2493,0,Home,Male ,35,HS ,59535,553,7,2,Citizen \n2494,0,Car ,Male ,51,HS ,108558,702,2,2,Citizen \n2495,0,Home,Female,45,HS ,106380,625,5,2,Citizen \n2496,0,Car ,Male ,45,HS ,85981,601,1,2,Citizen \n2497,0,Car ,Male ,42,HS ,86015,743,0,2,Non-citizen\n2498,0,Car ,Female,42,HS ,85865,584,0,2,Citizen \n2499,0,Car ,Male ,54,HS ,121933,677,2,2,Citizen \n2500,0,Home,Female,39,HS ,103224,600,6,2,Citizen \n2501,0,Car ,Male ,35,College ,102455,690,2,1,Citizen \n2502,0,Home,Male ,49,HS ,87873,615,7,2,Citizen \n2503,0,Home,Male ,39,HS ,92474,655,5,2,Citizen \n2504,0,Car ,Male ,42,College ,98204,587,1,2,Citizen \n2505,0,Car ,Male ,45,HS ,104240,706,0,2,Citizen \n2506,0,Car ,Male ,41,HS ,87852,514,0,2,Citizen \n2507,0,Car ,Female,44,HS ,80220,614,1,2,Citizen \n2508,0,Home,Male ,46,HS ,85608,567,7,2,Citizen \n2509,0,Car ,Male ,37,HS ,110359,520,0,2,Citizen \n2510,1,Car ,Female,44,HS ,123945,578,1,2,Citizen \n2511,0,Car ,Male ,46,HS ,76120,563,0,2,Citizen \n2512,0,Car ,Male ,40,College ,94692,681,2,2,Citizen \n2513,0,Car ,Male ,43,HS ,103613,615,1,2,Citizen \n2514,0,Car ,Female,42,College ,108977,620,0,2,Citizen \n2515,0,Car ,Female,54,HS ,95616,620,0,2,Citizen \n2516,0,Car ,Female,41,College ,72475,613,2,2,Citizen \n2517,0,Car ,Female,38,HS ,95636,605,0,2,Citizen \n2518,0,Car ,Female,39,HS ,72953,585,1,2,Citizen \n2519,0,Car ,Male ,53,HS ,120335,781,1,1,Non-citizen\n2520,0,Car ,Male ,43,HS ,89780,704,1,2,Citizen \n2521,0,Car ,Male ,34,HS ,59190,660,0,2,Citizen \n2522,0,Home,Male ,44,HS ,85079,654,4,2,Citizen \n2523,0,Home,Male ,45,HS ,109624,617,4,2,Citizen \n2524,0,Home,Female,49,College ,92377,760,4,2,Citizen \n2525,1,Car ,Male ,45,HS ,111802,698,2,2,Citizen \n2526,1,Car ,Male ,58,College ,113894,665,2,2,Non-citizen\n2527,0,Car ,Female,44,HS ,56841,536,0,2,Citizen \n2528,0,Car ,Male ,53,HS ,112156,596,0,2,Citizen \n2529,0,Car ,Male ,37,HS ,91703,673,3,2,Citizen \n2530,0,Car ,Female,48,Graduate,103015,757,3,2,Citizen \n2531,0,Car ,Male ,45,HS ,106660,539,0,2,Citizen \n2532,0,Home,Male ,46,HS ,102578,590,4,2,Citizen \n2533,0,Car ,Male ,46,HS ,98259,542,0,2,Citizen \n2534,0,Car ,Female,34,HS ,88498,582,0,2,Citizen \n2535,0,Car ,Female,39,Graduate,116456,477,2,2,Citizen \n2536,0,Car ,Female,48,HS ,105189,719,0,1,Citizen \n2537,0,Home,Male ,42,HS ,106561,628,12,1,Citizen \n2538,0,Car ,Female,44,HS ,81780,614,1,2,Citizen \n2539,0,Car ,Female,38,HS ,130541,665,0,2,Citizen \n2540,0,Car ,Female,40,HS ,112008,724,0,1,Citizen \n2541,1,Car ,Female,50,Graduate,96023,658,1,2,Citizen \n2542,1,Car ,Male ,37,College ,95134,650,1,2,Citizen \n2543,0,Car ,Male ,36,Graduate,127982,568,3,2,Non-citizen\n2544,0,Car ,Female,38,HS ,82564,532,1,2,Citizen \n2545,0,Home,Male ,34,HS ,82396,573,8,2,Citizen \n2546,0,Home,Female,51,College ,68717,603,8,2,Citizen \n2547,0,Car ,Male ,49,College ,90978,722,3,2,Citizen \n2548,0,Home,Female,39,College ,109053,531,7,2,Citizen \n2549,0,Car ,Male ,41,HS ,120126,661,1,2,Citizen \n2550,0,Car ,Female,61,College ,108540,664,3,1,Citizen \n2551,0,Home,Female,36,HS ,77719,629,5,2,Citizen \n2552,0,Car ,Female,54,College ,121507,693,2,1,Non-citizen\n2553,0,Car ,Male ,50,College ,131533,679,3,2,Citizen \n2554,0,Home,Female,42,HS ,70681,653,4,2,Citizen \n2555,0,Car ,Male ,51,HS ,63827,680,2,2,Citizen \n2556,0,Car ,Male ,43,HS ,81802,612,0,1,Citizen \n2557,0,Home,Male ,38,HS ,85709,644,3,2,Citizen \n2558,1,Car ,Female,41,College ,118115,706,1,2,Citizen \n2559,0,Home,Female,42,College ,108051,617,9,2,Citizen \n2560,0,Car ,Male ,39,HS ,81366,488,0,2,Citizen \n2561,0,Home,Male ,53,HS ,102170,779,7,2,Citizen \n2562,0,Home,Male ,43,HS ,78667,630,5,2,Citizen \n2563,0,Car ,Female,42,College ,84294,611,2,2,Citizen \n2564,0,Car ,Female,48,College ,107480,559,2,1,Citizen \n2565,0,Car ,Female,45,College ,97090,588,0,2,Citizen \n2566,0,Car ,Female,42,HS ,94293,725,0,2,Citizen \n2567,0,Car ,Male ,39,HS ,98485,526,0,2,Citizen \n2568,0,Car ,Male ,45,College ,109458,573,2,2,Citizen \n2569,0,Car ,Female,49,HS ,115798,602,2,2,Citizen \n2570,0,Car ,Female,35,College ,84203,560,0,2,Citizen \n2571,0,Car ,Male ,34,HS ,92180,538,1,2,Citizen \n2572,0,Home,Male ,42,HS ,130503,540,9,2,Citizen \n2573,0,Car ,Male ,41,HS ,70772,482,0,2,Citizen \n2574,0,Car ,Male ,47,HS ,80719,533,1,1,Citizen \n2575,0,Home,Female,51,College ,102291,598,13,2,Citizen \n2576,0,Home,Female,45,College ,76871,641,6,2,Citizen \n2577,0,Car ,Female,50,HS ,112264,678,1,2,Non-citizen\n2578,0,Car ,Male ,36,HS ,79859,540,0,2,Citizen \n2579,0,Car ,Male ,34,HS ,99209,529,1,2,Citizen \n2580,0,Home,Male ,31,HS ,65811,541,6,2,Citizen \n2581,0,Car ,Male ,41,College ,90020,616,0,2,Non-citizen\n2582,0,Car ,Female,47,College ,98411,702,1,2,Citizen \n2583,0,Home,Male ,41,HS ,103778,603,7,2,Citizen \n2584,0,Car ,Female,36,College ,107120,618,1,2,Citizen \n2585,0,Car ,Female,50,College ,119106,707,2,1,Non-citizen\n2586,0,Car ,Female,42,HS ,102102,639,1,2,Citizen \n2587,0,Car ,Female,55,HS ,81877,617,1,2,Citizen \n2588,0,Car ,Male ,46,College ,86886,574,1,2,Citizen \n2589,0,Car ,Male ,42,HS ,129967,633,1,2,Citizen \n2590,0,Car ,Male ,38,Graduate,83777,572,1,2,Citizen \n2591,1,Car ,Female,42,College ,118150,613,3,2,Citizen \n2592,0,Car ,Female,46,HS ,82934,588,1,2,Citizen \n2593,0,Car ,Male ,39,HS ,45649,559,0,2,Citizen \n2594,0,Home,Male ,44,HS ,92142,561,0,2,Citizen \n2595,1,Car ,Female,40,HS ,82681,648,3,1,Citizen \n2596,0,Car ,Female,43,HS ,82859,675,0,2,Citizen \n2597,0,Car ,Male ,46,HS ,109801,616,0,2,Citizen \n2598,0,Home,Female,51,HS ,74041,645,4,2,Citizen \n2599,0,Home,Male ,41,HS ,51561,512,2,2,Citizen \n2600,0,Home,Male ,35,College ,61825,533,9,2,Citizen \n2601,0,Home,Female,47,HS ,107298,552,1,2,Citizen \n2602,1,Car ,Female,47,College ,101675,625,1,2,Non-citizen\n2603,0,Car ,Female,48,College ,89329,568,1,2,Citizen \n2604,0,Car ,Female,40,College ,66755,660,0,2,Citizen \n2605,0,Car ,Male ,43,HS ,84954,594,1,2,Citizen \n2606,0,Car ,Female,38,College ,67975,531,2,2,Citizen \n2607,0,Car ,Male ,34,HS ,106489,636,1,2,Citizen \n2608,0,Car ,Male ,51,HS ,90379,542,1,2,Citizen \n2609,0,Car ,Female,52,HS ,108540,778,1,2,Citizen \n2610,0,Car ,Male ,37,HS ,68787,518,0,2,Citizen \n2611,0,Home,Male ,39,HS ,111623,576,6,2,Citizen \n2612,1,Car ,Male ,42,HS ,97723,628,0,2,Citizen \n2613,0,Car ,Male ,55,Graduate,88385,610,3,2,Non-citizen\n2614,0,Car ,Male ,44,College ,104452,565,2,2,Citizen \n2615,0,Car ,Male ,38,Graduate,99306,562,1,2,Citizen \n2616,0,Car ,Male ,40,HS ,97728,639,0,2,Citizen \n2617,0,Home,Male ,31,HS ,54442,511,5,2,Citizen \n2618,0,Car ,Female,53,Graduate,122538,680,2,2,Non-citizen\n2619,0,Car ,Female,44,College ,81030,617,1,2,Citizen \n2620,0,Car ,Female,41,HS ,98832,510,1,2,Citizen \n2621,0,Home,Male ,42,HS ,114359,583,5,2,Citizen \n2622,1,Car ,Female,56,College ,106979,682,1,2,Citizen \n2623,0,Home,Male ,44,HS ,97098,585,5,2,Citizen \n2624,0,Car ,Male ,40,HS ,94550,563,3,2,Citizen \n2625,0,Car ,Male ,33,HS ,63705,426,0,2,Citizen \n2626,0,Car ,Male ,47,HS ,128301,636,1,2,Citizen \n2627,0,Car ,Male ,31,HS ,84542,552,0,2,Citizen \n2628,0,Car ,Female,46,HS ,117024,645,0,1,Citizen \n2629,0,Car ,Female,44,HS ,96203,699,2,2,Citizen \n2630,0,Home,Male ,36,HS ,66685,576,6,2,Citizen \n2631,0,Home,Male ,39,HS ,59552,570,3,2,Citizen \n2632,0,Car ,Male ,56,College ,104106,738,2,2,Citizen \n2633,0,Car ,Male ,39,HS ,103451,653,0,2,Citizen \n2634,1,Car ,Female,53,Graduate,101102,703,1,2,Citizen \n2635,0,Home,Female,44,HS ,111681,626,9,1,Citizen \n2636,0,Car ,Male ,32,HS ,86973,565,0,2,Citizen \n2637,0,Car ,Male ,25,HS ,88745,578,0,2,Citizen \n2638,0,Home,Male ,40,HS ,108407,661,2,2,Citizen \n2639,0,Home,Female,45,HS ,98136,573,7,2,Citizen \n2640,0,Car ,Female,47,HS ,86146,646,3,2,Citizen \n2641,0,Home,Female,35,HS ,77892,540,6,2,Citizen \n2642,0,Home,Male ,38,HS ,89738,389,5,2,Citizen \n2643,0,Home,Male ,38,HS ,68502,633,3,2,Citizen \n2644,0,Car ,Female,39,HS ,85576,562,2,2,Citizen \n2645,0,Home,Male ,38,College ,113460,612,8,2,Citizen \n2646,0,Home,Female,39,College ,95210,556,3,2,Citizen \n2647,0,Car ,Female,56,College ,84013,720,1,1,Citizen \n2648,0,Car ,Male ,45,HS ,103125,623,1,2,Citizen \n2649,0,Car ,Male ,45,College ,101603,631,2,2,Non-citizen\n2650,0,Car ,Female,53,College ,132370,644,3,2,Citizen \n2651,0,Car ,Male ,39,HS ,95938,669,0,2,Citizen \n2652,0,Car ,Male ,43,HS ,86084,613,1,2,Citizen \n2653,0,Car ,Male ,41,HS ,107526,536,0,2,Citizen \n2654,0,Car ,Female,42,HS ,117212,600,0,2,Citizen \n2655,0,Car ,Male ,42,HS ,92597,612,1,2,Citizen \n2656,0,Home,Male ,35,HS ,114758,642,5,2,Citizen \n2657,0,Car ,Male ,52,HS ,92322,659,2,1,Citizen \n2658,0,Car ,Female,48,HS ,87859,614,0,2,Citizen \n2659,1,Car ,Male ,52,HS ,105417,687,2,2,Citizen \n2660,0,Car ,Female,47,College ,122785,573,2,2,Non-citizen\n2661,0,Home,Male ,34,HS ,64071,525,4,2,Citizen \n2662,0,Car ,Female,29,HS ,75852,552,1,2,Citizen \n2663,0,Car ,Female,42,HS ,87473,439,0,2,Citizen \n2664,0,Car ,Male ,39,HS ,81083,485,0,2,Citizen \n2665,0,Home,Male ,39,HS ,108416,600,7,2,Citizen \n2666,1,Home,Male ,39,HS ,105079,621,8,2,Citizen \n2667,0,Car ,Male ,39,College ,98779,582,0,2,Citizen \n2668,0,Car ,Male ,50,College ,130236,632,3,2,Citizen \n2669,0,Home,Male ,29,HS ,75371,557,6,2,Citizen \n2670,0,Car ,Male ,44,HS ,68495,721,2,2,Citizen \n2671,0,Car ,Male ,31,HS ,89909,560,0,2,Citizen \n2672,0,Home,Male ,40,HS ,86612,584,4,2,Citizen \n2673,0,Home,Female,38,College ,107620,599,8,2,Citizen \n2674,0,Car ,Male ,39,HS ,91647,645,1,2,Citizen \n2675,0,Car ,Female,41,HS ,104147,560,0,2,Citizen \n2676,0,Home,Male ,47,HS ,109206,621,2,2,Citizen \n2677,0,Home,Male ,43,HS ,93948,518,6,2,Citizen \n2678,0,Car ,Male ,44,College ,87300,648,2,2,Citizen \n2679,0,Car ,Male ,36,College ,72416,551,1,2,Citizen \n2680,0,Car ,Male ,51,College ,112142,648,2,2,Citizen \n2681,0,Car ,Male ,36,HS ,91626,554,1,2,Citizen \n2682,0,Car ,Male ,43,HS ,88598,604,0,2,Citizen \n2683,0,Car ,Male ,47,HS ,96430,580,2,2,Citizen \n2684,0,Car ,Male ,40,HS ,82106,560,0,2,Citizen \n2685,0,Car ,Male ,37,HS ,114252,638,1,2,Citizen \n2686,0,Car ,Male ,43,College ,115413,662,0,2,Citizen \n2687,0,Car ,Male ,45,HS ,83352,551,1,2,Citizen \n2688,0,Car ,Female,52,HS ,118732,569,1,2,Citizen \n2689,0,Car ,Female,47,HS ,113452,562,0,2,Citizen \n2690,0,Car ,Male ,43,HS ,104443,654,0,2,Citizen \n2691,0,Car ,Female,41,HS ,85201,622,0,2,Citizen \n2692,0,Car ,Male ,43,HS ,101932,698,0,2,Citizen \n2693,0,Car ,Male ,43,College ,75889,629,1,2,Citizen \n2694,0,Car ,Male ,41,HS ,94351,635,2,2,Citizen \n2695,0,Car ,Female,48,Graduate,126699,700,0,2,Non-citizen\n2696,0,Car ,Male ,34,College ,105239,582,0,2,Citizen \n2697,0,Car ,Male ,42,HS ,86807,562,1,2,Citizen \n2698,0,Car ,Female,46,HS ,68661,681,0,2,Citizen \n2699,0,Car ,Female,45,HS ,83237,543,0,2,Citizen \n2700,0,Car ,Female,49,College ,131640,682,2,2,Citizen \n2701,0,Car ,Male ,50,HS ,108037,568,2,2,Citizen \n2702,0,Car ,Male ,38,HS ,63009,606,0,2,Citizen \n2703,0,Car ,Male ,43,College ,111114,545,2,2,Citizen \n2704,0,Home,Male ,35,HS ,93652,609,5,2,Citizen \n2705,0,Car ,Male ,36,HS ,85762,547,0,2,Citizen \n2706,0,Car ,Male ,38,HS ,114928,534,2,2,Citizen \n2707,0,Car ,Female,50,HS ,124584,578,2,2,Citizen \n2708,0,Car ,Female,48,HS ,89105,682,1,1,Citizen \n2709,0,Car ,Male ,45,HS ,41568,510,0,2,Citizen \n2710,1,Car ,Female,46,HS ,88157,591,1,2,Citizen \n2711,0,Car ,Male ,32,HS ,79157,628,1,2,Citizen \n2712,0,Car ,Male ,42,College ,123185,626,1,2,Citizen \n2713,1,Car ,Female,53,Graduate,96860,746,2,2,Non-citizen\n2714,0,Home,Male ,51,HS ,100704,622,6,2,Citizen \n2715,0,Car ,Female,49,College ,80013,577,3,2,Citizen \n2716,0,Car ,Male ,37,HS ,94275,552,1,2,Citizen \n2717,0,Home,Male ,40,College ,126728,613,12,2,Citizen \n2718,0,Car ,Female,50,HS ,137984,665,3,2,Citizen \n2719,0,Home,Male ,42,HS ,106235,533,8,2,Citizen \n2720,0,Home,Female,45,HS ,115379,805,10,1,Citizen \n2721,0,Home,Male ,39,HS ,71710,550,5,2,Citizen \n2722,0,Car ,Male ,51,HS ,87879,635,2,2,Citizen \n2723,0,Car ,Female,43,HS ,95199,599,1,2,Citizen \n2724,1,Car ,Male ,55,College ,120898,594,1,2,Citizen \n2726,0,Car ,Male ,46,HS ,123932,654,1,2,Citizen \n2727,0,Car ,Male ,41,HS ,95391,588,0,2,Citizen \n2728,0,Home,Male ,40,HS ,43635,581,2,2,Citizen \n2729,0,Car ,Male ,51,HS ,94753,602,2,2,Citizen \n2730,0,Car ,Female,48,College ,88892,620,0,1,Non-citizen\n2731,0,Car ,Female,42,HS ,86824,660,1,1,Citizen \n2732,0,Car ,Male ,37,HS ,90987,588,0,2,Citizen \n2733,0,Car ,Male ,40,HS ,107008,633,3,2,Citizen \n2734,0,Car ,Male ,41,HS ,96818,620,3,2,Citizen \n2735,0,Car ,Male ,49,HS ,82365,596,3,2,Citizen \n2736,0,Car ,Male ,49,HS ,80904,666,0,2,Citizen \n2737,0,Home,Male ,41,College ,78637,471,8,2,Citizen \n2738,0,Home,Female,41,Graduate,119065,768,10,2,Citizen \n2739,0,Home,Male ,39,HS ,119698,614,8,2,Citizen \n2740,0,Car ,Female,46,College ,129168,696,3,2,Citizen \n2741,0,Car ,Male ,42,HS ,119100,606,2,2,Citizen \n2742,0,Car ,Male ,45,HS ,87813,557,2,2,Citizen \n2743,0,Car ,Male ,36,HS ,95251,641,0,2,Citizen \n2744,1,Car ,Male ,49,HS ,83903,718,0,2,Citizen \n2745,0,Car ,Female,53,Graduate,81774,650,2,2,Citizen \n2746,0,Car ,Male ,35,College ,88074,558,1,2,Citizen \n2747,1,Car ,Female,47,Graduate,109658,723,2,2,Citizen \n2748,0,Car ,Male ,32,College ,86725,658,1,2,Citizen \n2749,0,Car ,Female,56,HS ,137210,577,2,2,Citizen \n2750,0,Car ,Male ,45,HS ,118702,702,1,1,Citizen \n2751,0,Car ,Male ,50,HS ,77373,581,0,2,Citizen \n2752,0,Car ,Male ,46,College ,101932,684,2,2,Citizen \n2753,0,Home,Male ,51,College ,96921,669,13,2,Citizen \n2754,1,Car ,Male ,46,College ,111032,692,3,2,Citizen \n2755,0,Home,Male ,37,HS ,57147,514,5,2,Citizen \n2756,0,Home,Male ,37,HS ,84807,582,4,2,Citizen \n2757,0,Home,Male ,44,HS ,51949,531,2,2,Citizen \n2758,0,Home,Male ,33,HS ,85978,548,10,2,Citizen \n2759,0,Home,Female,33,HS ,58406,525,1,2,Citizen \n2760,0,Car ,Male ,46,HS ,81620,606,0,2,Citizen \n2761,0,Car ,Male ,36,HS ,84775,602,0,2,Citizen \n2762,1,Car ,Male ,40,HS ,102023,667,2,2,Citizen \n2763,0,Car ,Male ,48,College ,84142,597,1,2,Citizen \n2764,0,Car ,Male ,37,Graduate,90153,626,2,2,Citizen \n2765,0,Car ,Female,43,HS ,98270,511,2,2,Citizen \n2766,0,Home,Male ,29,College ,75730,510,8,2,Citizen \n2767,0,Car ,Female,42,HS ,101091,670,1,2,Citizen \n2768,0,Car ,Female,43,HS ,113027,659,0,1,Citizen \n2769,1,Car ,Female,52,HS ,107225,635,1,2,Citizen \n2770,0,Home,Male ,42,HS ,92684,651,2,2,Citizen \n2771,0,Car ,Female,46,HS ,86699,643,3,1,Citizen \n2772,0,Car ,Female,52,HS ,126475,600,0,2,Citizen \n2773,0,Car ,Female,44,College ,96768,651,2,1,Citizen \n2774,0,Car ,Male ,38,HS ,76436,632,1,2,Citizen \n2775,0,Home,Female,40,Graduate,103626,639,10,2,Citizen \n2776,0,Home,Male ,39,College ,97377,555,4,2,Citizen \n2777,0,Car ,Female,49,College ,125716,752,3,1,Citizen \n2778,0,Home,Male ,44,HS ,117753,683,3,2,Citizen \n2779,0,Home,Male ,38,HS ,86650,612,1,2,Citizen \n2780,0,Car ,Female,39,HS ,91710,492,0,2,Citizen \n2781,0,Car ,Female,47,College ,124534,654,2,2,Citizen \n2782,0,Car ,Male ,42,HS ,85219,585,0,2,Citizen \n2783,1,Car ,Male ,42,College ,86340,695,2,1,Citizen \n2784,0,Car ,Male ,36,HS ,80826,540,0,2,Citizen \n2785,0,Car ,Female,46,HS ,103610,680,1,2,Citizen \n2786,0,Car ,Male ,28,HS ,114473,633,2,1,Citizen \n2787,0,Car ,Female,54,College ,113089,620,3,2,Citizen \n2788,0,Home,Male ,39,HS ,137571,584,5,2,Citizen \n2789,0,Car ,Male ,45,HS ,111936,660,3,2,Citizen \n2790,0,Car ,Female,53,HS ,100482,777,1,1,Citizen \n2791,0,Car ,Female,50,College ,92861,542,2,2,Citizen \n2792,0,Car ,Female,57,Graduate,87294,746,2,2,Citizen \n2793,0,Car ,Male ,33,HS ,144859,553,0,2,Citizen \n2794,0,Home,Female,42,HS ,117013,685,5,2,Citizen \n2795,0,Car ,Male ,45,College ,66880,647,2,2,Citizen \n2796,0,Car ,Female,45,College ,87547,542,2,2,Citizen \n2797,0,Home,Female,38,HS ,90027,527,7,2,Citizen \n2798,0,Car ,Female,35,HS ,93282,538,2,1,Citizen \n2799,0,Car ,Female,44,HS ,87317,589,0,2,Citizen \n2800,0,Home,Male ,45,HS ,84784,667,6,2,Citizen \n2801,0,Car ,Female,46,HS ,102144,598,1,2,Citizen \n2802,0,Car ,Male ,50,HS ,89973,597,0,2,Citizen \n2803,0,Home,Male ,40,HS ,76837,618,6,2,Citizen \n2804,0,Car ,Male ,37,HS ,93548,643,0,2,Citizen \n2805,0,Home,Female,38,HS ,132080,628,5,2,Citizen \n2806,0,Home,Female,44,HS ,113276,657,7,1,Citizen \n2807,0,Car ,Female,39,HS ,100290,564,0,2,Citizen \n2808,0,Home,Male ,38,HS ,81396,633,5,2,Citizen \n2809,0,Car ,Female,36,HS ,98544,493,0,2,Citizen \n2810,0,Car ,Female,43,HS ,76466,566,0,2,Citizen \n2811,0,Car ,Male ,47,HS ,95405,676,0,2,Citizen \n2812,0,Car ,Male ,48,HS ,91110,588,0,2,Citizen \n2813,0,Home,Male ,28,HS ,62278,433,2,2,Citizen \n2814,0,Car ,Female,44,College ,115638,618,3,1,Citizen \n2815,0,Home,Male ,36,HS ,78086,510,9,2,Citizen \n2816,0,Home,Female,43,HS ,88683,496,9,2,Citizen \n2817,0,Car ,Male ,51,HS ,86799,551,2,2,Citizen \n2818,0,Car ,Male ,46,College ,100428,651,2,2,Citizen \n2819,0,Home,Male ,24,HS ,61086,594,7,2,Citizen \n2820,0,Car ,Male ,45,HS ,104838,568,0,2,Citizen \n2821,1,Car ,Male ,53,HS ,113409,637,0,2,Citizen \n2822,0,Home,Female,40,HS ,91359,656,9,2,Citizen \n2823,0,Home,Female,48,HS ,105852,619,2,2,Citizen \n2824,0,Car ,Female,51,HS ,99551,559,2,2,Citizen \n2825,0,Car ,Female,34,HS ,91110,627,0,2,Citizen \n2826,0,Car ,Female,48,HS ,95776,445,1,2,Citizen \n2827,0,Car ,Male ,37,College ,97905,533,1,2,Citizen \n2828,0,Car ,Male ,57,HS ,112035,620,3,2,Citizen \n2829,0,Car ,Male ,41,HS ,87676,522,0,2,Citizen \n2830,0,Car ,Male ,37,Graduate,108015,616,3,2,Citizen \n2831,0,Car ,Female,38,College ,115062,561,1,2,Citizen \n2832,0,Home,Male ,24,HS ,79495,464,7,2,Citizen \n2833,0,Car ,Female,39,HS ,108224,669,0,2,Citizen \n2834,0,Car ,Female,41,HS ,106160,605,1,2,Citizen \n2835,0,Car ,Male ,46,HS ,93438,522,0,2,Citizen \n2836,0,Car ,Male ,44,HS ,57882,522,0,2,Citizen \n2837,0,Car ,Female,35,College ,97906,588,2,2,Citizen \n2838,0,Home,Female,33,HS ,76587,540,5,2,Citizen \n2839,0,Home,Male ,37,HS ,107347,560,0,2,Citizen \n2840,0,Car ,Male ,48,HS ,88247,607,3,2,Citizen \n2841,0,Car ,Male ,51,HS ,103143,638,1,2,Citizen \n2842,0,Car ,Female,42,College ,96531,528,1,2,Citizen \n2843,0,Car ,Male ,35,HS ,99805,587,1,2,Citizen \n2844,0,Home,Male ,40,College ,85754,597,8,2,Citizen \n2845,0,Car ,Female,45,HS ,117309,564,1,1,Citizen \n2846,0,Car ,Female,41,HS ,98008,583,0,2,Citizen \n2847,0,Home,Female,43,HS ,83934,620,4,2,Citizen \n2848,0,Car ,Male ,50,HS ,113979,566,1,2,Citizen \n2849,0,Car ,Male ,39,HS ,90389,645,1,2,Citizen \n2850,0,Home,Male ,41,HS ,81808,693,7,2,Citizen \n2851,1,Car ,Female,48,HS ,120006,580,0,2,Citizen \n2852,0,Home,Male ,36,HS ,89092,635,2,2,Citizen \n2853,0,Car ,Male ,39,HS ,104401,712,1,2,Citizen \n2854,0,Car ,Female,40,College ,83297,535,0,2,Citizen \n2855,0,Car ,Male ,48,HS ,91776,576,2,2,Citizen \n2856,0,Car ,Female,38,College ,102361,566,2,2,Citizen \n2857,0,Home,Male ,51,HS ,109399,713,5,2,Citizen \n2858,0,Home,Female,46,College ,94489,541,9,2,Citizen \n2859,0,Car ,Male ,46,HS ,88507,509,2,2,Citizen \n2860,0,Car ,Male ,38,HS ,69593,517,1,2,Citizen \n2861,0,Car ,Female,45,HS ,113500,718,2,2,Citizen \n2862,0,Home,Male ,46,College ,113373,702,13,2,Citizen \n2863,0,Car ,Female,53,Graduate,73900,654,1,2,Citizen \n2864,0,Home,Female,42,HS ,83586,658,3,2,Citizen \n2865,0,Car ,Male ,40,HS ,78161,610,1,2,Citizen \n2866,0,Car ,Male ,38,HS ,76044,552,2,2,Citizen \n2867,0,Home,Male ,39,HS ,84708,658,6,2,Citizen \n2868,0,Home,Male ,34,College ,85824,625,1,2,Citizen \n2869,0,Car ,Female,45,College ,83492,632,2,2,Non-citizen\n2870,0,Car ,Female,45,HS ,114616,533,0,2,Citizen \n2871,0,Car ,Male ,47,HS ,87805,530,1,2,Citizen \n2872,0,Home,Male ,36,HS ,88249,690,5,2,Citizen \n2873,0,Car ,Female,44,HS ,97326,689,0,2,Citizen \n2874,0,Home,Male ,48,HS ,80940,621,5,2,Citizen \n2875,1,Home,Female,37,College ,80535,623,9,2,Citizen \n2876,0,Car ,Female,51,HS ,111877,641,0,1,Citizen \n2877,0,Car ,Male ,45,College ,110703,655,2,2,Citizen \n2878,0,Car ,Female,26,HS ,87355,455,0,2,Citizen \n2879,1,Car ,Female,36,HS ,115077,601,0,2,Citizen \n2880,0,Car ,Female,52,HS ,112518,695,0,2,Citizen \n2881,0,Car ,Female,47,College ,98502,598,1,2,Citizen \n2882,0,Home,Male ,40,HS ,93260,617,3,2,Citizen \n2883,0,Home,Female,39,College ,114814,667,6,2,Citizen \n2884,0,Car ,Male ,35,College ,95098,554,0,2,Citizen \n2885,0,Home,Male ,38,HS ,94995,522,3,2,Citizen \n2886,0,Car ,Female,52,HS ,103825,685,1,2,Citizen \n2887,0,Car ,Male ,55,HS ,83794,649,0,2,Citizen \n2888,0,Home,Female,29,HS ,91882,514,5,2,Citizen \n2889,0,Car ,Male ,27,HS ,97573,562,3,2,Citizen \n2890,0,Home,Male ,34,HS ,70678,568,9,2,Citizen \n2891,0,Car ,Female,43,College ,97531,612,2,2,Citizen \n2892,0,Car ,Male ,40,College ,87084,590,0,2,Citizen \n2893,0,Car ,Female,42,College ,72148,638,0,1,Non-citizen\n2894,0,Home,Female,43,HS ,91975,653,6,1,Citizen \n2895,0,Home,Male ,48,HS ,127233,648,8,2,Citizen \n2896,0,Home,Male ,43,HS ,89710,594,2,2,Citizen \n2897,0,Car ,Female,45,College ,82292,679,1,1,Non-citizen\n2898,0,Car ,Male ,42,College ,88411,566,1,2,Citizen \n2899,0,Car ,Female,42,HS ,97518,607,1,2,Citizen \n2900,0,Car ,Male ,45,HS ,122284,640,2,1,Citizen \n2901,1,Car ,Female,49,Graduate,103559,724,3,2,Citizen \n2902,0,Car ,Female,31,Graduate,86467,558,3,2,Citizen \n2903,0,Home,Male ,42,HS ,84665,551,3,2,Citizen \n2904,0,Home,Male ,40,HS ,90157,615,1,2,Citizen \n2905,0,Home,Male ,48,HS ,81626,530,5,2,Citizen \n2906,0,Car ,Male ,34,College ,107785,592,2,2,Citizen \n2907,0,Home,Male ,30,HS ,109754,605,0,2,Citizen \n2908,1,Car ,Female,41,College ,100964,653,2,2,Citizen \n2909,0,Car ,Male ,40,HS ,112755,593,2,2,Citizen \n2910,0,Car ,Female,42,College ,112425,637,1,2,Non-citizen\n2911,0,Home,Female,48,HS ,108944,621,7,2,Citizen \n2912,0,Car ,Male ,42,College ,93202,581,0,2,Citizen \n2913,0,Home,Male ,40,HS ,71359,666,8,2,Citizen \n2914,0,Car ,Male ,37,HS ,81162,555,1,2,Citizen \n2915,0,Car ,Female,51,Graduate,85372,551,3,2,Citizen \n2916,0,Car ,Male ,46,HS ,88487,533,0,2,Citizen \n2917,0,Car ,Male ,39,HS ,109359,530,0,2,Citizen \n2918,0,Car ,Female,40,HS ,85198,524,1,2,Citizen \n2919,1,Home,Male ,34,HS ,98777,668,0,2,Citizen \n2920,0,Home,Female,42,HS ,108881,634,6,2,Citizen \n2921,0,Car ,Male ,40,HS ,98510,537,0,2,Citizen \n2922,0,Car ,Male ,39,Graduate,84705,566,2,2,Non-citizen\n2923,0,Car ,Male ,39,HS ,100340,684,0,2,Citizen \n2924,0,Home,Male ,36,HS ,62578,548,4,2,Citizen \n2925,0,Home,Male ,40,HS ,89339,607,0,2,Citizen \n2926,0,Car ,Female,38,College ,88311,594,1,2,Citizen \n2927,0,Car ,Male ,49,College ,72407,553,0,2,Citizen \n2928,0,Home,Male ,48,HS ,74740,672,5,2,Citizen \n2929,0,Car ,Male ,40,College ,83127,566,2,2,Citizen \n2930,0,Car ,Male ,41,HS ,97265,664,0,2,Citizen \n2931,0,Car ,Male ,45,College ,94625,554,3,2,Citizen \n2932,0,Car ,Female,40,College ,93314,603,1,2,Citizen \n2933,0,Home,Female,34,HS ,118487,603,4,2,Citizen \n2934,0,Car ,Male ,47,HS ,90642,536,2,2,Citizen \n2935,0,Car ,Male ,49,HS ,94163,602,1,2,Citizen \n2936,0,Car ,Female,49,HS ,82903,631,3,2,Citizen \n2937,0,Car ,Male ,45,HS ,83084,703,1,2,Citizen \n2938,0,Car ,Female,49,HS ,124176,651,2,2,Citizen \n2939,0,Car ,Female,43,College ,100053,780,2,1,Citizen \n2940,0,Car ,Male ,46,HS ,97764,665,3,1,Citizen \n2941,0,Car ,Female,42,College ,97484,578,1,2,Citizen \n2942,0,Car ,Female,42,HS ,91693,521,0,2,Citizen \n2943,0,Car ,Female,42,College ,98799,594,1,2,Citizen \n2944,0,Car ,Female,56,HS ,138474,574,1,2,Citizen \n2945,0,Car ,Female,47,College ,97351,773,3,2,Citizen \n2946,0,Car ,Male ,35,HS ,91364,705,0,2,Citizen \n2947,0,Home,Male ,38,HS ,87921,482,6,2,Citizen \n2948,0,Car ,Female,56,Graduate,115941,698,2,2,Non-citizen\n2949,0,Home,Female,42,HS ,99837,729,1,2,Citizen \n2950,0,Car ,Male ,41,HS ,93920,634,1,2,Citizen \n2951,0,Car ,Female,36,HS ,92039,571,0,2,Citizen \n2952,0,Car ,Female,52,College ,109902,566,1,2,Citizen \n2953,0,Car ,Male ,49,Graduate,74801,542,3,2,Citizen \n2954,0,Car ,Female,49,College ,109323,636,2,2,Citizen \n2955,0,Car ,Male ,53,College ,120223,640,1,2,Citizen \n2956,1,Car ,Female,47,College ,122606,693,2,2,Citizen \n2957,0,Car ,Male ,35,HS ,106899,565,1,2,Citizen \n2958,0,Car ,Male ,44,HS ,105539,568,3,2,Citizen \n2959,0,Car ,Male ,46,HS ,81760,609,3,2,Citizen \n2960,0,Home,Male ,35,HS ,97750,548,7,2,Citizen \n2961,0,Home,Male ,34,HS ,74983,565,3,2,Citizen \n2962,0,Car ,Male ,37,College ,83525,540,1,2,Citizen \n2963,0,Car ,Female,42,HS ,80235,497,0,2,Citizen \n2964,0,Home,Male ,45,HS ,84161,561,4,2,Citizen \n2965,0,Car ,Male ,39,HS ,99138,609,1,2,Citizen \n2966,0,Car ,Male ,42,HS ,91918,589,1,2,Citizen \n2967,0,Home,Male ,35,HS ,55221,485,7,2,Citizen \n2968,0,Car ,Male ,44,College ,80046,606,1,2,Citizen \n2970,0,Car ,Male ,41,HS ,90935,591,1,2,Citizen \n2971,0,Home,Male ,44,HS ,74397,594,7,2,Citizen \n2972,1,Car ,Female,50,College ,115545,655,0,2,Citizen \n2973,0,Car ,Female,47,College ,114910,693,0,2,Citizen \n2974,0,Home,Male ,41,College ,77279,559,5,2,Citizen \n2975,0,Car ,Male ,50,HS ,85505,646,1,2,Citizen \n2976,0,Home,Male ,44,College ,95362,577,5,2,Citizen \n2977,0,Car ,Female,51,HS ,86068,651,0,2,Citizen \n2978,0,Car ,Male ,40,HS ,82218,659,1,1,Citizen \n2979,0,Home,Male ,57,HS ,113915,720,5,2,Citizen \n2980,0,Home,Female,37,HS ,126469,685,4,1,Citizen \n2981,0,Home,Female,41,HS ,91817,589,7,2,Citizen \n2982,0,Home,Male ,50,HS ,102791,625,10,2,Citizen \n2983,0,Car ,Female,56,College ,93382,623,3,2,Non-citizen\n2984,0,Car ,Male ,36,HS ,76429,558,2,2,Citizen \n2985,0,Home,Male ,46,HS ,107366,691,7,2,Citizen \n2986,0,Home,Male ,49,HS ,109913,615,7,2,Citizen \n2987,0,Car ,Female,48,College ,90237,602,2,2,Citizen \n2988,0,Home,Male ,35,HS ,70969,581,9,2,Citizen \n2989,0,Car ,Female,52,HS ,105849,641,1,1,Citizen \n2990,0,Home,Male ,32,HS ,97108,550,6,2,Citizen \n2991,0,Car ,Male ,42,HS ,97834,538,0,2,Citizen \n2992,1,Car ,Male ,52,HS ,120201,730,2,2,Citizen \n2993,0,Car ,Female,34,HS ,109159,583,0,2,Citizen \n2994,0,Car ,Male ,53,College ,120011,664,1,2,Citizen \n2995,0,Home,Female,36,HS ,92746,594,7,2,Citizen \n2996,0,Car ,Male ,36,HS ,97013,470,0,2,Citizen \n2997,0,Car ,Female,53,HS ,111729,635,0,2,Citizen \n2998,0,Car ,Male ,50,HS ,113431,669,1,2,Citizen \n2999,0,Home,Male ,33,HS ,95472,561,8,2,Citizen \n3000,0,Car ,Male ,42,HS ,70546,631,1,2,Citizen \n3001,0,Home,Male ,30,HS ,100516,479,2,2,Citizen \n3002,0,Home,Female,29,HS ,65475,608,0,2,Citizen \n3003,1,Car ,Male ,48,Graduate,114881,625,0,2,Citizen \n3004,0,Car ,Male ,41,HS ,98178,646,3,2,Citizen \n3005,0,Car ,Female,52,College ,117044,691,2,2,Non-citizen\n3006,0,Car ,Male ,44,HS ,92564,543,0,2,Citizen \n3007,0,Car ,Male ,40,HS ,98047,644,2,2,Citizen \n3008,0,Home,Female,48,HS ,114539,628,6,2,Citizen \n3009,0,Car ,Female,43,College ,103789,665,1,2,Citizen \n3010,0,Home,Male ,30,HS ,82930,412,5,2,Citizen \n3012,0,Home,Male ,40,HS ,58667,655,5,2,Citizen \n3013,0,Home,Male ,43,HS ,85684,569,3,2,Citizen \n3014,0,Car ,Male ,50,HS ,90965,555,2,2,Citizen \n3015,0,Car ,Male ,47,HS ,84944,598,1,2,Citizen \n3016,0,Car ,Female,43,HS ,96689,570,3,2,Citizen \n3017,0,Car ,Male ,41,HS ,84328,649,0,2,Citizen \n3018,0,Car ,Male ,43,HS ,69381,534,0,2,Citizen \n3019,1,Car ,Male ,40,HS ,122073,667,2,2,Citizen \n3020,0,Car ,Male ,44,HS ,81332,596,2,2,Citizen \n3021,0,Car ,Male ,33,HS ,73313,483,0,2,Citizen \n3022,0,Car ,Male ,42,HS ,94611,559,2,2,Citizen \n3023,0,Home,Male ,37,Graduate,78008,569,10,2,Citizen \n3024,0,Car ,Male ,48,HS ,112569,550,0,2,Citizen \n3025,1,Home,Male ,37,College ,109716,636,9,2,Citizen \n3026,0,Home,Male ,32,HS ,94942,604,3,2,Citizen \n3027,0,Car ,Male ,36,HS ,81947,523,0,2,Citizen \n3028,1,Home,Female,38,HS ,106972,641,6,1,Citizen \n3029,0,Car ,Male ,49,College ,110808,673,1,2,Citizen \n3030,0,Car ,Female,33,College ,92439,684,0,2,Citizen \n3031,0,Home,Male ,38,HS ,106576,578,8,2,Citizen \n3032,0,Car ,Female,49,HS ,111848,529,2,2,Citizen \n3033,0,Home,Male ,40,HS ,92231,541,8,2,Citizen \n3034,0,Car ,Male ,35,HS ,101679,670,0,2,Citizen \n3035,0,Home,Male ,31,HS ,56949,527,4,2,Citizen \n3036,0,Car ,Male ,37,HS ,93308,550,1,2,Citizen \n3037,0,Home,Male ,49,HS ,123456,608,8,2,Citizen \n3038,0,Home,Female,50,College ,110744,666,5,2,Citizen \n3039,0,Car ,Female,39,College ,74691,639,1,2,Citizen \n3040,0,Car ,Male ,45,HS ,75531,578,0,2,Citizen \n3041,0,Home,Male ,37,HS ,51109,577,5,2,Citizen \n3042,0,Car ,Male ,37,HS ,76402,619,1,2,Citizen \n3043,0,Car ,Female,36,College ,97707,631,2,2,Citizen \n3044,0,Home,Female,43,College ,94622,703,9,2,Citizen \n3045,0,Car ,Female,52,College ,90718,725,3,1,Citizen \n3046,0,Car ,Female,44,HS ,106935,591,0,2,Citizen \n3047,0,Car ,Female,46,College ,87156,531,2,2,Citizen \n3048,0,Car ,Female,37,HS ,78755,577,1,2,Citizen \n3049,0,Car ,Female,49,College ,69510,680,0,2,Citizen \n3050,0,Home,Male ,39,HS ,106421,702,9,2,Citizen \n3051,0,Home,Male ,38,HS ,95421,516,3,2,Citizen \n3052,0,Car ,Male ,38,HS ,77683,520,2,2,Citizen \n3053,0,Home,Male ,39,HS ,49861,542,6,2,Citizen \n3054,0,Car ,Female,37,College ,68424,596,2,2,Citizen \n3055,0,Home,Male ,41,HS ,106555,607,8,2,Citizen \n3056,0,Car ,Female,36,HS ,112709,687,2,2,Citizen \n3057,0,Car ,Female,45,HS ,65742,530,0,2,Citizen \n3058,0,Home,Male ,43,HS ,96432,560,6,2,Citizen \n3059,0,Car ,Female,41,HS ,87028,510,0,2,Citizen \n3060,0,Car ,Female,50,College ,102053,639,0,2,Citizen \n3061,0,Home,Male ,44,HS ,97070,660,4,2,Citizen \n3062,0,Car ,Male ,40,HS ,104094,641,0,2,Citizen \n3063,0,Home,Female,43,HS ,106819,599,12,2,Citizen \n3064,0,Car ,Male ,46,College ,90401,549,2,1,Citizen \n3065,0,Home,Female,44,HS ,132204,647,6,2,Citizen \n3066,0,Car ,Male ,31,HS ,62664,461,0,2,Citizen \n3067,0,Car ,Male ,43,HS ,82482,523,0,2,Citizen \n3068,0,Home,Male ,36,HS ,82036,660,5,2,Citizen \n3069,0,Home,Female,35,HS ,130780,683,5,2,Citizen \n3070,0,Car ,Male ,46,HS ,117432,554,0,2,Citizen \n3071,0,Home,Male ,32,HS ,58470,602,5,2,Citizen \n3072,0,Car ,Female,50,HS ,129491,700,2,1,Citizen \n3073,0,Car ,Female,41,HS ,86201,721,2,2,Citizen \n3074,0,Car ,Female,40,HS ,89051,620,3,2,Citizen \n3075,0,Car ,Male ,33,College ,81669,538,1,2,Citizen \n3076,1,Home,Male ,40,College ,116914,642,8,2,Citizen \n3077,0,Car ,Male ,46,HS ,67877,619,0,2,Citizen \n3078,1,Car ,Female,43,College ,83454,734,2,2,Citizen \n3079,0,Car ,Female,45,HS ,83438,676,0,2,Citizen \n3080,0,Car ,Male ,42,HS ,105833,608,1,2,Citizen \n3081,0,Home,Male ,41,HS ,87935,663,6,2,Citizen \n3082,0,Car ,Female,44,College ,124225,445,2,2,Citizen \n3083,0,Car ,Female,50,HS ,101921,623,0,2,Citizen \n3084,0,Home,Male ,47,HS ,59029,646,6,2,Citizen \n3085,0,Car ,Female,50,College ,70477,689,2,2,Citizen \n3086,0,Car ,Female,41,HS ,90625,667,0,1,Non-citizen\n3087,0,Home,Male ,36,HS ,77049,480,6,2,Citizen \n3088,0,Car ,Male ,39,HS ,69632,648,1,2,Citizen \n3089,0,Car ,Female,36,HS ,92083,704,0,1,Citizen \n3090,0,Car ,Male ,42,College ,88920,649,2,2,Citizen \n3091,0,Car ,Male ,42,HS ,115675,624,0,2,Citizen \n3092,0,Car ,Male ,33,College ,94818,646,0,2,Citizen \n3093,0,Car ,Male ,36,HS ,107262,645,1,2,Citizen \n3094,0,Car ,Male ,44,College ,111088,729,3,2,Citizen \n3095,0,Car ,Female,41,HS ,90785,664,0,2,Citizen \n3096,0,Home,Female,43,HS ,82159,661,4,2,Citizen \n3097,0,Home,Male ,44,HS ,97166,571,12,2,Citizen \n3098,0,Home,Male ,33,HS ,78070,544,3,2,Citizen \n3099,0,Car ,Male ,50,College ,106561,491,2,2,Citizen \n3100,1,Car ,Male ,50,HS ,111757,634,1,2,Citizen \n3101,0,Car ,Male ,41,HS ,94102,754,0,2,Citizen \n3102,0,Home,Female,50,HS ,107459,643,4,2,Citizen \n3103,0,Car ,Male ,40,HS ,78279,534,2,2,Citizen \n3104,0,Car ,Male ,45,College ,96796,573,2,2,Citizen \n3105,0,Car ,Male ,29,College ,105096,577,1,2,Citizen \n3106,0,Car ,Female,46,HS ,99588,625,2,1,Citizen \n3107,0,Car ,Female,42,HS ,110591,645,2,2,Citizen \n3108,0,Car ,Male ,40,HS ,93628,611,1,2,Citizen \n3110,0,Home,Male ,36,HS ,66004,513,1,2,Citizen \n3111,0,Car ,Female,43,College ,98483,604,0,2,Citizen \n3112,1,Car ,Female,50,Graduate,108890,720,3,2,Citizen \n3113,0,Car ,Male ,38,HS ,90704,494,0,2,Citizen \n3114,0,Car ,Male ,54,HS ,97345,556,1,2,Citizen \n3115,0,Home,Male ,37,HS ,93687,534,7,2,Citizen \n3116,1,Car ,Male ,37,HS ,126629,643,1,2,Citizen \n3117,0,Car ,Female,45,College ,104170,563,1,2,Citizen \n3118,1,Car ,Female,42,HS ,112700,649,2,2,Citizen \n3119,0,Car ,Female,43,HS ,95612,636,0,2,Citizen \n3120,0,Home,Male ,38,HS ,95508,604,3,2,Citizen \n3121,0,Home,Male ,37,HS ,86564,638,6,2,Citizen \n3122,0,Car ,Female,34,College ,66765,431,1,2,Citizen \n3123,1,Car ,Female,38,Graduate,110166,610,1,2,Citizen \n3124,1,Car ,Male ,43,College ,106866,620,0,2,Citizen \n3125,0,Home,Male ,41,HS ,108911,575,10,2,Citizen \n3126,0,Car ,Female,44,College ,96126,623,1,2,Citizen \n3127,1,Car ,Male ,49,HS ,122119,762,2,1,Citizen \n3128,0,Car ,Male ,49,HS ,95191,614,1,2,Citizen \n3129,0,Home,Male ,47,HS ,77066,513,8,2,Citizen \n3130,0,Car ,Female,41,College ,92875,549,0,2,Citizen \n3131,0,Home,Female,33,HS ,94529,684,8,2,Citizen \n3132,0,Car ,Male ,26,HS ,76374,517,2,2,Citizen \n3133,0,Home,Female,45,HS ,101116,631,8,2,Citizen \n3134,0,Home,Female,46,HS ,71631,608,2,2,Citizen \n3135,0,Home,Male ,38,HS ,76160,586,2,2,Citizen \n3136,0,Car ,Male ,46,HS ,66855,519,0,2,Citizen \n3137,1,Car ,Male ,37,College ,128810,680,3,2,Citizen \n3138,0,Car ,Female,50,HS ,115555,615,0,2,Citizen \n3139,0,Home,Female,46,HS ,68719,622,4,2,Citizen \n3140,0,Home,Male ,39,HS ,94930,631,6,2,Citizen \n3141,1,Car ,Male ,41,College ,101404,633,2,1,Citizen \n3142,0,Home,Female,33,HS ,46195,522,1,2,Citizen \n3143,1,Car ,Female,42,HS ,94362,634,0,2,Citizen \n3144,0,Home,Male ,46,HS ,86168,653,7,2,Citizen \n3145,0,Home,Male ,41,College ,72746,692,12,2,Citizen \n3146,0,Home,Male ,38,HS ,103870,543,4,2,Citizen \n3147,0,Car ,Male ,28,HS ,76584,566,1,2,Citizen \n3148,0,Car ,Male ,53,HS ,84885,693,2,1,Citizen \n3149,0,Home,Male ,38,HS ,61339,585,8,2,Citizen \n3150,0,Car ,Male ,46,HS ,69252,513,1,2,Citizen \n3151,0,Car ,Female,47,College ,87860,579,2,2,Citizen \n3152,0,Home,Male ,43,HS ,91259,594,3,2,Citizen \n3153,0,Car ,Male ,51,HS ,105550,712,1,1,Citizen \n3154,0,Car ,Female,52,HS ,98651,686,0,2,Citizen \n3155,0,Car ,Male ,43,HS ,89129,624,0,2,Citizen \n3156,0,Home,Male ,36,HS ,83205,622,7,2,Citizen \n3157,0,Home,Female,44,HS ,107184,639,7,1,Citizen \n3158,0,Car ,Female,42,HS ,108331,708,0,2,Citizen \n3159,0,Car ,Female,43,HS ,86639,601,1,2,Citizen \n3160,0,Home,Male ,45,HS ,99500,584,11,2,Citizen \n3161,0,Car ,Male ,30,HS ,70797,539,1,2,Citizen \n3162,0,Car ,Male ,46,Graduate,63274,666,2,2,Citizen \n3163,0,Car ,Male ,46,College ,83078,539,1,2,Citizen \n3164,0,Home,Male ,44,HS ,91063,645,6,2,Citizen \n3165,0,Car ,Male ,49,HS ,93318,574,3,1,Citizen \n3166,0,Car ,Male ,37,HS ,93219,659,2,2,Citizen \n3167,0,Car ,Female,47,HS ,77511,587,0,2,Citizen \n3168,1,Home,Male ,40,HS ,95361,657,5,2,Citizen \n3169,0,Home,Male ,36,HS ,82554,607,1,2,Citizen \n3170,0,Car ,Male ,55,HS ,83643,740,0,1,Citizen \n3171,0,Car ,Male ,39,HS ,86686,698,0,2,Citizen \n3172,0,Car ,Male ,46,HS ,92108,659,2,2,Citizen \n3173,0,Car ,Male ,32,HS ,93738,650,0,2,Citizen \n3174,0,Home,Male ,39,HS ,85753,674,0,2,Citizen \n3175,0,Car ,Female,39,College ,111368,643,2,1,Non-citizen\n3176,0,Home,Male ,46,College ,101307,660,9,2,Citizen \n3177,0,Car ,Female,47,HS ,80668,669,1,2,Citizen \n3178,0,Car ,Male ,48,HS ,112090,593,2,2,Citizen \n3179,0,Home,Male ,36,HS ,78279,562,3,2,Citizen \n3180,0,Car ,Female,50,HS ,107233,608,1,2,Citizen \n3181,1,Home,Female,49,College ,102462,635,10,2,Citizen \n3182,1,Car ,Female,49,Graduate,96922,708,2,1,Citizen \n3183,0,Car ,Female,40,HS ,98253,668,0,2,Citizen \n3184,1,Car ,Female,32,HS ,85800,599,0,2,Citizen \n3185,0,Home,Male ,44,HS ,94599,585,6,2,Citizen \n3186,0,Car ,Female,53,HS ,93125,550,0,2,Citizen \n3187,0,Car ,Male ,48,College ,112591,592,1,2,Citizen \n3188,0,Car ,Male ,39,HS ,67206,567,1,2,Citizen \n3189,0,Car ,Male ,40,HS ,88546,599,0,2,Citizen \n3190,0,Home,Male ,35,HS ,62990,617,7,2,Citizen \n3191,0,Home,Male ,51,College ,102101,559,10,2,Citizen \n3192,0,Car ,Male ,49,College ,96654,723,3,2,Citizen \n3193,0,Car ,Female,55,College ,119282,625,1,1,Non-citizen\n3194,0,Home,Female,47,HS ,70673,574,4,2,Citizen \n3195,0,Car ,Male ,41,HS ,103161,556,0,2,Non-citizen\n3196,0,Home,Male ,41,HS ,88528,603,8,2,Citizen \n3197,0,Car ,Male ,36,College ,117556,600,1,2,Citizen \n3198,0,Car ,Female,45,HS ,74650,616,0,2,Citizen \n3199,0,Home,Male ,42,HS ,91799,675,3,2,Citizen \n3200,0,Home,Male ,43,HS ,110969,528,7,2,Citizen \n3201,0,Car ,Female,44,HS ,103086,713,0,1,Citizen \n3202,0,Car ,Male ,39,HS ,100464,655,1,2,Citizen \n3203,0,Car ,Male ,41,HS ,79165,532,0,2,Citizen \n3204,0,Home,Male ,40,HS ,95060,571,1,2,Citizen \n3205,0,Car ,Male ,48,HS ,82777,582,0,2,Citizen \n3206,0,Car ,Female,41,College ,81044,661,1,2,Citizen \n3207,0,Home,Female,56,Graduate,119472,621,8,2,Citizen \n3208,0,Car ,Male ,44,College ,98452,556,2,2,Citizen \n3209,0,Home,Male ,43,HS ,117184,606,4,2,Citizen \n3210,0,Car ,Female,44,HS ,82459,607,1,2,Citizen \n3211,0,Car ,Female,40,HS ,100496,656,2,2,Citizen \n3212,0,Home,Male ,46,HS ,77816,685,4,2,Citizen \n3213,0,Home,Male ,38,HS ,101754,681,4,2,Citizen \n3214,0,Car ,Male ,41,HS ,69916,629,2,2,Citizen \n3215,0,Car ,Male ,48,HS ,113139,688,1,2,Citizen \n3216,0,Car ,Male ,47,HS ,110239,618,2,2,Citizen \n3217,1,Car ,Female,44,HS ,98853,654,0,2,Non-citizen\n3218,1,Car ,Female,39,College ,139527,551,0,2,Citizen \n3219,0,Car ,Female,51,HS ,101714,677,0,2,Citizen \n3220,0,Car ,Female,46,HS ,97722,620,1,2,Citizen \n3221,0,Home,Male ,39,HS ,135371,651,7,2,Citizen \n3222,0,Home,Female,32,College ,105446,629,6,2,Citizen \n3223,0,Home,Male ,43,HS ,94946,631,9,2,Citizen \n3224,0,Home,Male ,30,HS ,95809,586,5,2,Citizen \n3225,0,Home,Female,40,HS ,112566,680,2,2,Citizen \n3226,0,Home,Male ,42,HS ,99712,601,9,2,Citizen \n3227,1,Car ,Female,62,Graduate,107305,607,1,2,Citizen \n3228,0,Home,Male ,32,HS ,96354,486,3,2,Citizen \n3229,0,Car ,Female,48,Graduate,99720,653,2,2,Citizen \n3230,0,Car ,Female,29,HS ,76406,585,0,2,Citizen \n3231,0,Car ,Female,50,HS ,87718,611,2,1,Citizen \n3232,0,Car ,Female,41,HS ,99204,628,1,2,Citizen \n3233,0,Home,Male ,39,HS ,86985,533,5,2,Citizen \n3234,0,Home,Female,42,College ,113210,603,4,2,Citizen \n3235,0,Car ,Male ,40,HS ,81146,526,2,1,Citizen \n3236,0,Home,Female,36,HS ,65465,549,3,2,Citizen \n3237,0,Car ,Male ,36,HS ,98659,543,1,2,Citizen \n3238,0,Home,Male ,45,HS ,95962,597,10,2,Citizen \n3239,0,Home,Female,43,HS ,81348,635,6,2,Citizen \n3240,0,Car ,Male ,42,College ,88477,676,2,2,Citizen \n3241,0,Car ,Male ,46,HS ,125881,631,0,2,Citizen \n3242,0,Home,Male ,39,HS ,74349,571,2,2,Citizen \n3243,1,Car ,Female,43,HS ,94403,619,0,2,Citizen \n3244,0,Car ,Female,44,College ,94622,650,0,2,Citizen \n3245,0,Home,Female,44,College ,98878,525,7,2,Citizen \n3246,0,Car ,Male ,49,College ,87719,572,1,2,Citizen \n3247,0,Car ,Female,34,HS ,108520,643,0,2,Citizen \n3248,0,Home,Male ,45,HS ,98341,673,9,2,Citizen \n3249,0,Car ,Male ,36,College ,92253,523,3,2,Citizen \n3250,0,Car ,Female,47,HS ,88539,552,0,2,Citizen \n3251,0,Car ,Female,47,HS ,118047,552,0,2,Citizen \n3252,0,Car ,Male ,44,College ,84011,574,2,2,Citizen \n3253,0,Car ,Male ,44,HS ,92225,615,1,2,Citizen \n3254,0,Car ,Female,38,HS ,74768,581,0,2,Citizen \n3255,0,Car ,Female,42,College ,118106,557,0,2,Citizen \n3256,1,Home,Female,31,College ,110524,691,9,2,Citizen \n3257,0,Home,Male ,44,HS ,90887,599,11,2,Citizen \n3258,0,Car ,Male ,29,College ,92616,639,2,2,Citizen \n3259,0,Home,Female,30,HS ,93635,540,6,2,Citizen \n3260,0,Car ,Female,43,HS ,111207,647,0,2,Citizen \n3261,0,Car ,Female,31,HS ,104416,536,0,2,Citizen \n3262,0,Car ,Male ,33,HS ,88905,683,0,2,Citizen \n3263,0,Car ,Male ,40,College ,90591,674,1,2,Citizen \n3264,0,Car ,Male ,41,HS ,69769,538,1,2,Citizen \n3265,0,Home,Male ,38,HS ,46246,638,3,2,Citizen \n3266,0,Car ,Female,37,College ,83115,554,2,2,Citizen \n3267,0,Car ,Male ,36,HS ,95798,647,1,2,Citizen \n3268,0,Home,Male ,52,HS ,112931,652,12,2,Citizen \n3269,0,Car ,Male ,45,HS ,95613,503,2,2,Citizen \n3270,0,Car ,Female,51,College ,93078,686,1,2,Citizen \n3271,0,Home,Female,35,Graduate,82975,612,8,2,Citizen \n3272,0,Car ,Male ,51,HS ,93648,601,0,2,Citizen \n3273,0,Car ,Female,45,HS ,120763,692,1,2,Citizen \n3274,0,Car ,Male ,51,College ,95927,558,3,2,Citizen \n3275,0,Car ,Male ,44,HS ,83445,648,0,2,Citizen \n3276,0,Car ,Male ,43,HS ,72141,605,0,2,Citizen \n3277,0,Home,Male ,39,HS ,94057,585,8,2,Citizen \n3278,1,Home,Male ,37,HS ,153485,691,6,2,Citizen \n3279,0,Home,Female,42,HS ,99280,570,6,2,Citizen \n3280,0,Car ,Female,50,Graduate,94036,630,3,1,Non-citizen\n3281,0,Car ,Female,41,College ,103351,563,0,2,Citizen \n3282,0,Home,Female,41,HS ,116560,569,5,2,Citizen \n3283,0,Car ,Female,44,College ,112529,578,3,2,Citizen \n3284,0,Car ,Male ,44,HS ,73646,553,0,2,Citizen \n3285,1,Car ,Female,48,HS ,103369,650,0,1,Non-citizen\n3286,0,Car ,Male ,45,HS ,80528,653,2,1,Citizen \n3287,0,Car ,Male ,30,Graduate,95262,503,0,2,Citizen \n3288,0,Home,Male ,48,HS ,61304,535,4,2,Citizen \n3289,0,Car ,Male ,35,HS ,88000,553,1,2,Citizen \n3290,0,Car ,Female,48,Graduate,105270,631,2,1,Citizen \n3291,0,Car ,Female,47,HS ,65011,662,0,2,Citizen \n3292,0,Home,Female,41,College ,109859,717,8,2,Citizen \n3293,0,Home,Male ,44,HS ,94496,476,6,2,Citizen \n3294,0,Car ,Male ,38,College ,96910,580,1,2,Citizen \n3295,0,Home,Female,45,College ,84561,608,8,2,Citizen \n3296,0,Car ,Male ,46,College ,102012,522,2,2,Citizen \n3297,1,Car ,Female,43,HS ,119550,611,0,2,Citizen \n3298,0,Car ,Male ,36,HS ,53829,440,1,2,Citizen \n3299,0,Home,Female,41,HS ,109577,658,3,2,Citizen \n3300,0,Car ,Female,49,College ,107024,628,3,2,Citizen \n3301,0,Home,Female,41,College ,104782,627,6,2,Citizen \n3302,0,Car ,Male ,38,HS ,56664,440,0,2,Citizen \n3303,0,Home,Male ,47,HS ,86058,566,6,2,Citizen \n3304,0,Home,Male ,39,HS ,102830,528,5,2,Citizen \n3305,0,Home,Male ,41,HS ,100474,614,8,2,Citizen \n3306,0,Home,Male ,27,HS ,86671,621,3,2,Citizen \n3307,0,Car ,Male ,51,College ,107359,549,2,2,Citizen \n3308,0,Car ,Female,46,HS ,76293,672,1,2,Citizen \n3309,0,Home,Female,34,College ,117536,572,6,2,Citizen \n3310,0,Home,Male ,35,HS ,101662,571,4,2,Citizen \n3311,0,Car ,Female,56,Graduate,144703,605,2,2,Citizen \n3312,0,Car ,Female,40,HS ,86670,612,0,2,Citizen \n3313,0,Home,Male ,47,HS ,84198,645,4,2,Citizen \n3314,0,Car ,Male ,50,HS ,112980,590,1,2,Citizen \n3315,1,Car ,Female,52,College ,107519,713,3,2,Citizen \n3316,0,Home,Male ,43,HS ,102542,596,2,2,Citizen \n3317,0,Car ,Female,42,HS ,104381,591,0,2,Citizen \n3318,0,Car ,Female,56,College ,113135,716,3,2,Non-citizen\n3319,0,Car ,Female,40,HS ,95495,574,0,2,Citizen \n3320,0,Car ,Male ,44,College ,101618,650,3,2,Citizen \n3321,0,Home,Male ,32,HS ,86521,539,1,2,Citizen \n3322,0,Car ,Male ,36,College ,114529,662,0,2,Citizen \n3323,0,Home,Male ,35,College ,87281,583,3,2,Citizen \n3324,0,Home,Female,35,HS ,81510,583,7,2,Citizen \n3325,1,Car ,Female,54,Graduate,131918,723,2,2,Non-citizen\n3326,0,Car ,Male ,43,College ,94918,577,2,2,Citizen \n3327,0,Home,Female,40,HS ,127092,559,4,2,Citizen \n3328,0,Car ,Male ,54,HS ,108611,530,2,2,Citizen \n3329,0,Car ,Male ,40,College ,96462,640,1,2,Citizen \n3330,0,Home,Male ,53,HS ,119465,710,6,2,Citizen \n3331,0,Home,Male ,43,College ,93290,531,8,2,Citizen \n3332,0,Car ,Male ,46,College ,126705,683,2,2,Citizen \n3333,0,Car ,Male ,40,HS ,82623,600,0,2,Citizen \n3334,0,Home,Female,42,HS ,88446,531,1,2,Citizen \n3335,0,Car ,Female,52,HS ,108182,707,0,2,Citizen \n3336,0,Car ,Female,52,Graduate,115924,674,3,2,Citizen \n3337,0,Car ,Male ,31,HS ,79123,584,1,2,Citizen \n3338,0,Car ,Male ,43,HS ,76274,482,0,2,Citizen \n3339,0,Car ,Male ,34,HS ,59790,578,0,2,Citizen \n3340,0,Home,Male ,52,HS ,136464,559,10,2,Citizen \n3341,1,Car ,Female,45,Graduate,75042,574,2,2,Non-citizen\n3342,0,Car ,Male ,58,College ,110320,563,2,2,Citizen \n3343,0,Car ,Male ,43,HS ,87706,541,0,2,Citizen \n3344,0,Car ,Male ,53,HS ,91028,604,2,2,Citizen \n3345,0,Home,Male ,50,HS ,101161,677,5,2,Citizen \n3346,0,Car ,Male ,45,HS ,87196,702,0,2,Citizen \n3347,0,Car ,Male ,53,College ,121246,583,2,2,Citizen \n3348,0,Car ,Male ,39,College ,74547,534,3,2,Citizen \n3349,0,Car ,Male ,44,HS ,66957,478,0,2,Citizen \n3350,0,Car ,Male ,47,College ,92756,660,0,2,Citizen \n3351,0,Home,Female,36,HS ,99553,549,4,2,Citizen \n3352,0,Car ,Female,38,College ,81728,640,0,2,Citizen \n3353,0,Car ,Female,33,HS ,76935,600,0,2,Citizen \n3354,0,Car ,Female,50,College ,103079,611,3,2,Citizen \n3355,0,Car ,Male ,42,HS ,94311,590,1,2,Citizen \n3356,0,Home,Female,50,HS ,104906,626,8,1,Citizen \n3357,0,Home,Female,40,HS ,122257,608,8,1,Citizen \n3358,0,Car ,Female,48,HS ,121214,590,2,2,Citizen \n3359,0,Car ,Female,48,HS ,108397,617,3,2,Citizen \n3360,0,Home,Male ,46,HS ,83039,572,7,2,Citizen \n3361,0,Car ,Female,43,HS ,96342,647,1,2,Citizen \n3362,0,Car ,Male ,52,HS ,71495,731,1,2,Citizen \n3363,0,Home,Female,30,HS ,101006,672,1,2,Citizen \n3364,0,Car ,Male ,37,HS ,128348,588,2,2,Citizen \n3365,0,Car ,Female,40,HS ,104985,578,0,2,Citizen \n3366,0,Car ,Male ,42,HS ,98272,611,2,1,Citizen \n3367,0,Car ,Male ,41,College ,95433,690,1,2,Citizen \n3368,0,Car ,Female,44,College ,120063,589,3,1,Citizen \n3369,0,Car ,Male ,43,College ,93145,543,2,2,Citizen \n3370,0,Car ,Male ,47,College ,77161,673,2,2,Citizen \n3371,0,Home,Male ,43,HS ,102018,541,6,2,Citizen \n3372,0,Car ,Male ,45,HS ,118975,631,2,2,Citizen \n3373,0,Home,Female,41,HS ,97026,634,1,1,Citizen \n3374,0,Car ,Male ,41,HS ,91887,624,2,2,Citizen \n3375,0,Home,Female,43,College ,147138,613,7,2,Citizen \n3376,0,Home,Male ,33,College ,84820,641,10,2,Citizen \n3377,0,Home,Female,43,HS ,70114,589,5,2,Citizen \n3378,0,Car ,Male ,52,College ,103898,708,1,2,Citizen \n3379,0,Car ,Male ,44,HS ,104265,556,3,2,Citizen \n3380,0,Home,Female,38,HS ,81330,492,6,2,Citizen \n3381,0,Car ,Female,42,College ,92151,560,0,2,Citizen \n3382,0,Car ,Male ,43,HS ,85421,612,0,2,Citizen \n3383,0,Car ,Female,45,College ,78728,601,2,2,Citizen \n3384,0,Car ,Male ,44,College ,87225,538,0,2,Citizen \n3385,0,Home,Male ,47,College ,120864,582,10,2,Citizen \n3386,0,Car ,Male ,51,HS ,91656,600,1,2,Citizen \n3387,0,Home,Female,35,HS ,69686,560,0,2,Citizen \n3388,0,Home,Male ,38,HS ,98102,593,8,2,Citizen \n3389,0,Home,Male ,48,HS ,90982,567,6,2,Citizen \n3390,0,Home,Female,41,HS ,104068,539,0,2,Citizen \n3391,0,Car ,Male ,41,HS ,111564,525,1,2,Citizen \n3392,0,Car ,Male ,28,HS ,73260,504,1,2,Citizen \n3393,1,Car ,Female,49,HS ,115630,742,0,1,Non-citizen\n3394,0,Car ,Male ,39,HS ,57704,663,1,2,Citizen \n3395,0,Car ,Female,41,College ,105042,619,0,2,Citizen \n3396,0,Home,Male ,36,HS ,95271,667,8,2,Citizen \n3397,0,Car ,Female,43,College ,94000,620,1,2,Citizen \n3398,0,Car ,Female,33,College ,93076,656,0,2,Citizen \n3399,0,Car ,Female,53,Graduate,76856,603,3,2,Citizen \n3400,0,Home,Male ,30,HS ,46387,629,5,2,Citizen \n3401,0,Car ,Female,38,HS ,79166,538,0,2,Citizen \n3402,0,Car ,Female,37,HS ,76355,636,0,2,Citizen \n3403,0,Car ,Male ,43,College ,95022,591,3,2,Citizen \n3404,0,Home,Female,39,HS ,142057,601,3,2,Citizen \n3405,0,Car ,Female,41,College ,105750,604,2,2,Citizen \n3406,0,Home,Male ,44,HS ,81332,588,3,2,Citizen \n3407,0,Home,Male ,36,HS ,111169,644,8,2,Citizen \n3408,0,Home,Male ,40,HS ,91407,625,6,2,Citizen \n3409,0,Home,Female,44,HS ,71286,540,4,2,Citizen \n3410,0,Car ,Female,44,HS ,108686,741,1,1,Citizen \n3411,0,Car ,Male ,41,HS ,112274,593,2,1,Citizen \n3412,0,Home,Female,46,HS ,92515,694,3,2,Citizen \n3413,0,Car ,Male ,33,HS ,97059,543,0,2,Citizen \n3414,0,Car ,Female,49,HS ,83073,638,1,2,Citizen \n3415,0,Car ,Female,45,HS ,90041,602,0,2,Citizen \n3416,0,Home,Male ,47,Graduate,81733,528,4,2,Citizen \n3417,0,Car ,Female,42,HS ,76933,596,2,2,Citizen \n3418,0,Car ,Female,35,HS ,96130,608,1,2,Citizen \n3419,0,Home,Male ,31,HS ,90130,530,2,2,Citizen \n3420,0,Home,Female,40,College ,123478,586,9,2,Citizen \n3421,0,Car ,Male ,38,College ,78720,603,0,2,Citizen \n3422,0,Car ,Male ,39,HS ,78859,608,0,2,Citizen \n3423,1,Car ,Male ,40,HS ,108992,667,0,2,Citizen \n3424,0,Car ,Male ,43,HS ,98138,628,3,2,Citizen \n3425,0,Home,Female,39,HS ,114144,605,0,2,Citizen \n3426,0,Home,Female,44,HS ,117239,600,5,2,Citizen \n3427,0,Home,Male ,41,HS ,101617,660,8,2,Citizen \n3428,0,Home,Female,44,HS ,107880,698,8,1,Citizen \n3429,0,Car ,Female,48,College ,109322,604,3,2,Non-citizen\n3430,0,Car ,Female,42,HS ,98253,589,1,2,Citizen \n3431,0,Home,Male ,45,HS ,119034,581,9,2,Citizen \n3432,0,Car ,Female,50,HS ,117946,619,0,2,Citizen \n3433,0,Home,Female,37,HS ,97368,651,2,2,Citizen \n3434,0,Car ,Male ,36,College ,87931,548,1,2,Citizen \n3435,0,Car ,Male ,42,HS ,85624,634,2,2,Citizen \n3436,0,Home,Female,41,HS ,109173,614,7,1,Citizen \n3437,0,Car ,Female,42,College ,122580,760,0,2,Citizen \n3438,0,Car ,Male ,34,HS ,79291,573,0,2,Citizen \n3439,0,Home,Male ,42,HS ,100364,723,9,2,Citizen \n3440,0,Car ,Female,39,HS ,92078,681,0,1,Citizen \n3441,0,Car ,Female,44,HS ,129523,590,2,2,Citizen \n3442,0,Car ,Female,52,College ,125693,729,1,2,Non-citizen\n3443,1,Home,Female,44,College ,92999,529,6,2,Citizen \n3444,0,Home,Male ,49,HS ,97058,780,6,1,Citizen \n3445,0,Car ,Male ,50,HS ,103956,580,3,1,Citizen \n3446,0,Car ,Male ,54,HS ,77691,661,0,2,Citizen \n3447,0,Home,Female,32,HS ,86900,630,4,2,Citizen \n3448,1,Car ,Male ,44,HS ,100658,589,2,1,Citizen \n3449,0,Car ,Female,42,HS ,115386,695,1,2,Citizen \n3450,0,Car ,Male ,37,HS ,96389,511,1,2,Citizen \n3451,0,Car ,Female,44,HS ,91009,584,0,2,Citizen \n3452,0,Car ,Male ,50,HS ,96828,612,2,2,Citizen \n3453,0,Car ,Male ,31,HS ,93962,589,0,2,Citizen \n3454,0,Car ,Female,46,HS ,106829,604,1,1,Citizen \n3455,0,Home,Male ,35,HS ,78846,622,6,2,Citizen \n3456,0,Car ,Female,43,HS ,113003,592,3,2,Citizen \n3457,0,Car ,Male ,49,College ,111524,627,1,1,Citizen \n3458,0,Home,Male ,38,HS ,102779,545,5,2,Citizen \n3459,0,Car ,Female,43,HS ,103717,688,3,1,Citizen \n3460,0,Home,Female,53,College ,103686,610,13,2,Citizen \n3461,0,Home,Female,43,HS ,73471,627,1,2,Citizen \n3462,0,Car ,Female,43,Graduate,103180,694,1,2,Non-citizen\n3463,0,Car ,Male ,44,HS ,99344,712,0,1,Citizen \n3464,0,Home,Male ,43,HS ,97050,519,3,2,Citizen \n3465,0,Car ,Male ,42,HS ,94650,600,0,2,Citizen \n3466,1,Car ,Male ,48,HS ,103448,535,0,2,Citizen \n3467,1,Car ,Female,38,HS ,112239,665,0,2,Citizen \n3468,0,Car ,Male ,33,HS ,79224,627,1,2,Citizen \n3469,0,Car ,Male ,32,HS ,47656,491,0,2,Citizen \n3470,0,Car ,Male ,31,HS ,89989,585,0,2,Citizen \n3471,0,Car ,Male ,35,College ,65773,501,1,2,Citizen \n3472,0,Home,Male ,34,HS ,78680,637,2,2,Citizen \n3473,0,Car ,Male ,45,HS ,103583,640,0,1,Citizen \n3474,1,Car ,Female,44,HS ,100315,585,0,1,Citizen \n3475,0,Car ,Male ,26,HS ,85393,586,1,2,Citizen \n3476,0,Car ,Female,46,College ,122995,676,2,2,Citizen \n3477,0,Car ,Male ,40,HS ,53791,566,0,2,Citizen \n3478,0,Home,Female,43,HS ,94461,561,7,2,Citizen \n3479,0,Home,Female,33,HS ,119700,583,6,2,Citizen \n3480,0,Car ,Male ,45,College ,101132,534,3,2,Citizen \n3481,0,Car ,Male ,46,HS ,75843,585,0,2,Citizen \n3482,0,Home,Male ,38,HS ,104880,617,1,2,Citizen \n3483,0,Car ,Male ,46,HS ,94285,629,2,2,Citizen \n3484,0,Car ,Female,37,College ,104766,683,0,2,Citizen \n3485,0,Car ,Male ,32,HS ,81463,594,3,2,Citizen \n3486,0,Home,Male ,37,HS ,95598,574,8,2,Citizen \n3487,0,Car ,Male ,35,HS ,114043,686,0,2,Citizen \n3488,0,Car ,Male ,44,HS ,123902,528,0,2,Citizen \n3489,0,Home,Male ,34,Graduate,93302,572,10,2,Citizen \n3490,0,Car ,Female,47,HS ,123514,648,0,2,Citizen \n3491,0,Car ,Male ,41,HS ,101186,579,1,2,Citizen \n3492,0,Car ,Male ,43,Graduate,83214,564,3,2,Citizen \n3493,0,Car ,Female,51,HS ,91676,690,2,2,Citizen \n3494,0,Home,Male ,46,HS ,102146,587,4,2,Citizen \n3495,0,Home,Male ,33,HS ,78730,589,0,2,Citizen \n3496,0,Car ,Male ,46,HS ,84190,539,2,1,Citizen \n3497,0,Car ,Female,42,HS ,97056,603,2,2,Citizen \n3498,0,Home,Male ,43,HS ,110200,577,4,2,Citizen \n3499,0,Car ,Female,50,College ,125891,674,2,2,Citizen \n3500,0,Home,Male ,41,HS ,92081,616,6,2,Citizen \n3501,0,Car ,Male ,41,HS ,95781,699,0,2,Citizen \n3502,0,Home,Female,44,HS ,102151,661,7,2,Citizen \n3503,0,Car ,Male ,48,HS ,81659,635,0,2,Citizen \n3504,0,Home,Male ,42,HS ,90017,639,4,2,Citizen \n3505,0,Car ,Male ,40,HS ,90616,566,0,2,Citizen \n3506,0,Car ,Male ,41,College ,103786,631,3,2,Citizen \n3507,1,Car ,Female,50,HS ,106761,596,1,2,Citizen \n3508,1,Car ,Female,48,HS ,125971,703,1,2,Citizen \n3509,0,Car ,Male ,43,HS ,70378,577,2,2,Citizen \n3510,0,Car ,Male ,43,HS ,64185,578,1,1,Citizen \n3511,0,Home,Male ,38,HS ,97946,553,8,2,Citizen \n3512,0,Home,Male ,50,HS ,108498,562,6,2,Citizen \n3513,0,Car ,Male ,45,HS ,93972,616,2,2,Citizen \n3514,0,Home,Male ,34,HS ,79484,629,3,2,Citizen \n3515,0,Car ,Male ,35,HS ,72759,601,0,2,Citizen \n3516,1,Car ,Female,54,HS ,91396,702,1,2,Citizen \n3517,0,Car ,Female,39,Graduate,113322,628,2,2,Non-citizen\n3518,0,Car ,Female,50,College ,110400,691,2,2,Citizen \n3519,0,Car ,Female,51,HS ,112211,557,3,2,Citizen \n3520,0,Home,Male ,42,HS ,96670,531,6,2,Citizen \n3521,0,Home,Male ,36,College ,88404,563,6,2,Citizen \n3522,0,Car ,Female,49,HS ,88437,690,1,1,Citizen \n3523,0,Car ,Male ,48,College ,94181,618,0,2,Citizen \n3524,0,Car ,Female,51,College ,89959,653,2,2,Citizen \n3525,0,Car ,Male ,38,College ,103618,550,3,2,Citizen \n3526,1,Car ,Male ,46,HS ,135000,639,0,2,Citizen \n3527,0,Car ,Male ,57,HS ,118578,671,2,2,Citizen \n3528,0,Car ,Male ,50,HS ,103288,653,0,2,Citizen \n3529,0,Home,Female,43,HS ,77492,642,6,2,Citizen \n3530,0,Car ,Male ,47,HS ,103521,595,0,2,Citizen \n3531,0,Car ,Female,38,HS ,89551,558,0,2,Citizen \n3532,0,Home,Female,35,College ,98828,602,6,2,Citizen \n3533,1,Car ,Female,38,College ,134235,700,2,2,Citizen \n3534,0,Car ,Male ,31,HS ,90582,519,2,2,Citizen \n3535,0,Car ,Male ,47,College ,117757,613,2,2,Citizen \n3536,0,Car ,Female,46,HS ,106178,656,1,1,Citizen \n3537,0,Car ,Female,40,HS ,99188,683,0,2,Citizen \n3538,0,Car ,Female,41,College ,111634,629,0,2,Citizen \n3539,1,Car ,Male ,34,College ,77951,559,0,2,Citizen \n3540,0,Home,Female,44,HS ,104029,706,6,2,Citizen \n3541,0,Car ,Male ,39,College ,79805,599,2,2,Citizen \n3542,0,Home,Male ,54,HS ,101433,593,5,2,Citizen \n3543,0,Car ,Female,37,HS ,87715,649,0,2,Citizen \n3544,0,Car ,Male ,40,HS ,99528,609,2,2,Citizen \n3545,0,Car ,Male ,39,College ,126583,590,2,2,Citizen \n3546,0,Car ,Male ,36,HS ,74494,532,0,2,Citizen \n3547,0,Car ,Male ,39,HS ,93218,595,0,2,Citizen \n3548,0,Home,Male ,43,College ,142906,644,10,2,Citizen \n3549,0,Home,Male ,45,HS ,82279,536,7,2,Citizen \n3550,0,Car ,Male ,42,Graduate,110733,662,2,1,Citizen \n3551,0,Home,Male ,30,HS ,55569,463,3,2,Citizen \n3552,0,Car ,Female,42,College ,97375,694,0,2,Citizen \n3553,0,Car ,Male ,33,College ,102198,572,0,2,Citizen \n3554,0,Car ,Male ,43,College ,97538,679,2,2,Citizen \n3555,0,Car ,Male ,42,HS ,99751,562,0,2,Citizen \n3556,0,Car ,Female,48,HS ,88963,731,3,2,Citizen \n3557,0,Home,Male ,44,HS ,106682,666,5,2,Citizen \n3558,0,Car ,Male ,44,HS ,94434,604,1,2,Citizen \n3559,1,Car ,Female,47,College ,139708,647,2,2,Citizen \n3560,0,Home,Male ,42,HS ,82072,579,4,2,Citizen \n3561,1,Home,Male ,52,College ,122986,775,13,2,Citizen \n3562,0,Car ,Male ,39,HS ,69317,597,0,2,Citizen \n3563,0,Home,Male ,34,HS ,103254,505,10,2,Citizen \n3564,0,Car ,Female,37,HS ,92770,647,2,2,Citizen \n3565,0,Car ,Male ,42,HS ,108044,632,0,2,Citizen \n3566,0,Car ,Male ,39,HS ,106751,582,1,2,Citizen \n3567,0,Car ,Male ,42,HS ,79500,638,1,2,Citizen \n3568,0,Home,Male ,43,HS ,92359,583,7,2,Citizen \n3569,0,Home,Male ,44,College ,62250,500,5,2,Citizen \n3570,0,Car ,Female,40,Graduate,106862,651,3,1,Non-citizen\n3571,0,Car ,Female,49,HS ,100789,547,1,2,Citizen \n3572,0,Car ,Female,46,HS ,111854,596,2,2,Citizen \n3573,0,Car ,Male ,44,HS ,88313,656,0,2,Citizen \n3574,0,Car ,Female,45,College ,124601,577,3,2,Citizen \n3575,0,Car ,Male ,35,HS ,85908,542,0,2,Citizen \n3576,1,Home,Female,34,HS ,87618,773,8,2,Citizen \n3577,0,Car ,Male ,30,HS ,104587,671,0,2,Citizen \n3578,0,Car ,Male ,45,College ,101071,522,3,2,Citizen \n3579,0,Home,Male ,44,HS ,123210,599,11,2,Citizen \n3580,0,Home,Male ,44,HS ,99320,623,4,2,Citizen \n3581,0,Car ,Male ,39,HS ,93183,563,1,2,Citizen \n3582,0,Car ,Male ,41,HS ,96830,619,0,2,Citizen \n3583,0,Car ,Male ,41,HS ,71067,583,2,2,Citizen \n3584,0,Car ,Male ,61,College ,112149,717,2,2,Non-citizen\n3585,0,Car ,Female,47,College ,86800,628,0,2,Non-citizen\n3586,0,Home,Male ,41,HS ,110335,561,4,2,Citizen \n3587,0,Home,Male ,40,HS ,100152,526,2,2,Citizen \n3588,0,Car ,Male ,40,HS ,69411,563,0,2,Citizen \n3589,0,Home,Male ,34,HS ,78078,550,3,2,Citizen \n3590,0,Home,Male ,38,HS ,136050,585,8,2,Citizen \n3591,0,Home,Female,48,HS ,122548,646,4,2,Citizen \n3592,0,Car ,Female,49,College ,102784,729,2,2,Citizen \n3593,0,Home,Male ,41,HS ,73374,635,2,2,Citizen \n3594,0,Home,Male ,31,HS ,84459,683,10,2,Citizen \n3595,0,Car ,Male ,49,HS ,92143,592,3,2,Citizen \n3596,0,Car ,Male ,42,College ,83494,567,2,2,Citizen \n3597,0,Home,Male ,50,HS ,98009,725,10,2,Citizen \n3598,0,Car ,Male ,51,HS ,83824,498,1,2,Citizen \n3599,0,Home,Male ,33,HS ,102611,529,0,2,Citizen \n3600,0,Home,Male ,42,HS ,111534,669,11,1,Citizen \n3601,0,Car ,Female,36,College ,144433,630,0,2,Citizen \n3602,0,Car ,Male ,52,HS ,101841,564,0,2,Citizen \n3603,0,Home,Male ,45,HS ,75879,547,12,2,Citizen \n3604,0,Car ,Female,42,HS ,94866,549,0,2,Citizen \n3605,0,Car ,Male ,38,College ,102090,634,2,2,Citizen \n3606,0,Car ,Male ,45,HS ,99600,639,2,2,Citizen \n3607,0,Car ,Male ,37,HS ,62616,554,1,2,Citizen \n3608,0,Car ,Female,42,College ,96726,656,2,2,Citizen \n3609,0,Car ,Male ,34,Graduate,82234,603,0,2,Citizen \n3610,0,Home,Female,39,College ,122489,540,8,2,Citizen \n3611,0,Car ,Female,47,HS ,100300,674,0,2,Non-citizen\n3612,0,Home,Female,38,HS ,134099,664,6,2,Citizen \n3613,0,Car ,Female,44,College ,92824,665,2,2,Citizen \n3614,0,Car ,Female,50,College ,101084,625,3,2,Citizen \n3615,0,Car ,Female,31,HS ,96351,605,0,2,Citizen \n3616,0,Home,Male ,39,College ,103096,642,10,2,Citizen \n3617,0,Car ,Female,50,HS ,97864,531,0,1,Citizen \n3618,0,Home,Male ,45,HS ,101199,592,11,2,Citizen \n3619,0,Home,Male ,47,Graduate,113459,618,12,2,Citizen \n3620,0,Home,Female,38,College ,85501,584,6,2,Citizen \n3621,0,Home,Male ,38,HS ,102410,575,1,2,Citizen \n3622,0,Car ,Female,39,HS ,87841,633,1,2,Citizen \n3623,0,Car ,Male ,53,Graduate,104102,674,2,2,Citizen \n3624,0,Home,Female,42,HS ,95283,552,5,2,Citizen \n3625,0,Car ,Male ,44,HS ,124031,538,3,2,Citizen \n3626,0,Home,Male ,43,HS ,100836,454,7,2,Citizen \n3627,0,Home,Male ,34,HS ,78955,600,6,2,Citizen \n3628,0,Car ,Female,57,HS ,115441,734,2,2,Citizen \n3629,0,Home,Male ,33,HS ,110890,586,7,2,Citizen \n3630,0,Car ,Male ,41,HS ,95762,647,2,1,Citizen \n3631,0,Car ,Male ,39,HS ,100601,639,1,2,Citizen \n3632,0,Car ,Male ,49,HS ,82874,624,0,2,Citizen \n3633,0,Car ,Male ,44,HS ,84976,509,2,2,Citizen \n3634,0,Car ,Male ,39,HS ,82879,529,1,2,Citizen \n3635,0,Home,Male ,41,HS ,78023,545,4,2,Citizen \n3636,0,Car ,Female,53,HS ,124697,618,1,1,Citizen \n3637,1,Car ,Female,38,Graduate,111310,681,0,2,Non-citizen\n3638,0,Car ,Male ,37,HS ,84593,524,2,2,Citizen \n3639,0,Home,Female,45,HS ,94883,570,9,2,Citizen \n3640,0,Car ,Male ,38,HS ,87484,614,0,2,Citizen \n3641,0,Car ,Female,52,College ,74623,629,2,2,Citizen \n3642,0,Car ,Female,43,HS ,103574,581,0,2,Citizen \n3643,0,Home,Female,57,HS ,79427,617,3,2,Citizen \n3644,0,Home,Female,29,HS ,100888,583,6,2,Citizen \n3645,0,Car ,Male ,39,HS ,86468,551,2,2,Citizen \n3646,0,Car ,Male ,50,HS ,91833,583,0,2,Citizen \n3647,0,Car ,Female,47,College ,111797,552,2,2,Citizen \n3648,0,Home,Male ,39,HS ,61522,618,1,2,Citizen \n3649,0,Car ,Male ,44,HS ,91257,654,1,2,Citizen \n3650,0,Home,Male ,41,HS ,89726,585,2,2,Citizen \n3651,1,Car ,Male ,48,College ,126739,548,2,2,Citizen \n3652,0,Car ,Male ,40,HS ,60220,561,0,2,Citizen \n3653,0,Car ,Male ,39,HS ,55687,632,1,2,Citizen \n3654,0,Car ,Male ,52,College ,122286,686,2,2,Citizen \n3655,0,Home,Male ,34,HS ,103394,593,4,2,Citizen \n3656,0,Home,Male ,51,HS ,101124,659,7,2,Citizen \n3657,0,Car ,Male ,48,HS ,91212,530,2,2,Citizen \n3658,0,Car ,Female,40,College ,101593,677,2,2,Citizen \n3659,0,Home,Male ,49,HS ,97755,636,9,2,Citizen \n3660,0,Car ,Female,38,HS ,106527,692,2,2,Citizen \n3661,0,Home,Male ,37,College ,99089,672,9,2,Citizen \n3662,0,Car ,Female,34,HS ,70738,585,0,2,Citizen \n3663,0,Car ,Male ,52,HS ,99079,659,1,2,Citizen \n3664,0,Home,Male ,37,College ,96842,569,6,2,Citizen \n3665,0,Car ,Female,47,HS ,87754,628,1,2,Citizen \n3666,1,Car ,Female,38,College ,126170,578,2,2,Citizen \n3667,0,Home,Male ,43,HS ,89051,527,8,2,Citizen \n3668,0,Home,Male ,38,HS ,89754,611,10,2,Citizen \n3669,0,Car ,Male ,42,HS ,66967,580,1,2,Citizen \n3670,0,Car ,Female,43,College ,99890,625,0,2,Citizen \n3671,0,Home,Male ,45,HS ,98637,574,5,2,Citizen \n3672,0,Car ,Female,50,College ,111434,595,3,2,Citizen \n3673,0,Car ,Female,50,HS ,74315,631,0,1,Citizen \n3674,0,Car ,Female,39,HS ,85413,577,3,2,Citizen \n3675,1,Car ,Female,32,HS ,69210,558,1,2,Citizen \n3676,0,Car ,Male ,32,HS ,40217,546,2,2,Citizen \n3677,0,Home,Female,35,HS ,93961,637,9,2,Citizen \n3678,0,Car ,Male ,39,HS ,88874,600,0,2,Citizen \n3679,0,Car ,Female,37,HS ,96320,655,0,2,Citizen \n3680,0,Car ,Male ,54,HS ,79470,606,1,2,Citizen \n3681,0,Car ,Female,54,HS ,112583,671,0,2,Citizen \n3682,0,Home,Male ,44,HS ,97299,700,1,2,Citizen \n3683,0,Car ,Male ,45,College ,91942,608,3,1,Citizen \n3684,0,Car ,Female,39,Graduate,130305,677,3,2,Citizen \n3685,0,Home,Male ,49,HS ,93993,573,12,2,Citizen \n3686,0,Home,Female,36,HS ,108194,627,3,2,Citizen \n3687,0,Home,Male ,52,HS ,84340,747,7,2,Citizen \n3688,0,Home,Male ,41,HS ,86648,535,7,2,Citizen \n3689,0,Home,Male ,27,HS ,70528,480,0,2,Citizen \n3690,0,Car ,Male ,47,HS ,93918,642,1,2,Citizen \n3691,0,Home,Male ,39,HS ,90040,559,7,2,Citizen \n3692,0,Home,Male ,47,College ,91933,637,6,2,Citizen \n3693,0,Car ,Female,46,College ,105246,662,0,2,Citizen \n3694,0,Home,Male ,41,HS ,97352,558,3,2,Citizen \n3695,1,Car ,Male ,41,HS ,104835,681,0,2,Citizen \n3696,0,Home,Male ,29,HS ,92990,602,8,2,Citizen \n3697,0,Car ,Male ,40,HS ,78345,614,0,2,Citizen \n3698,0,Car ,Male ,44,HS ,104403,636,2,2,Citizen \n3699,0,Car ,Female,57,College ,118959,659,1,2,Citizen \n3700,0,Car ,Female,55,HS ,100709,722,2,2,Citizen \n3701,0,Car ,Male ,40,HS ,95811,507,0,2,Citizen \n3702,0,Car ,Female,37,HS ,94846,680,2,2,Citizen \n3703,0,Car ,Male ,41,HS ,126326,692,2,2,Citizen \n3704,0,Car ,Female,47,College ,87749,576,1,1,Citizen \n3705,0,Home,Male ,38,HS ,108516,621,5,2,Citizen \n3706,0,Car ,Male ,40,Graduate,88657,559,0,2,Citizen \n3707,0,Car ,Female,45,College ,116043,667,2,2,Citizen \n3708,0,Home,Male ,34,HS ,82593,569,6,2,Citizen \n3709,0,Home,Male ,33,HS ,87703,665,2,2,Citizen \n3710,0,Car ,Female,39,HS ,83371,657,0,2,Citizen \n3711,0,Car ,Male ,45,HS ,92651,567,1,2,Citizen \n3712,0,Car ,Male ,39,HS ,87277,479,0,2,Citizen \n3713,0,Home,Female,50,HS ,100421,576,7,2,Citizen \n3714,1,Car ,Female,48,College ,108474,695,3,1,Citizen \n3715,0,Car ,Male ,37,Graduate,105644,696,1,2,Citizen \n3716,0,Car ,Male ,44,College ,96250,568,0,2,Citizen \n3717,0,Car ,Male ,49,HS ,99253,531,0,2,Citizen \n3718,0,Car ,Female,44,College ,100085,627,2,2,Citizen \n3719,0,Home,Female,52,HS ,86300,698,4,2,Citizen \n3720,0,Car ,Male ,43,HS ,100195,631,0,2,Citizen \n3721,0,Car ,Male ,53,College ,86828,521,1,2,Citizen \n3722,0,Home,Male ,41,HS ,95891,606,5,2,Citizen \n3723,0,Car ,Male ,42,HS ,103042,622,1,2,Citizen \n3724,0,Car ,Male ,46,College ,67278,573,0,2,Citizen \n3725,0,Car ,Male ,52,Graduate,117905,685,3,2,Citizen \n3726,0,Car ,Male ,45,HS ,89395,606,3,2,Citizen \n3727,0,Car ,Male ,26,HS ,75512,578,2,2,Citizen \n3728,1,Car ,Male ,49,Graduate,125533,699,2,2,Citizen \n3729,0,Car ,Male ,56,College ,90210,642,3,1,Citizen \n3730,1,Car ,Female,57,College ,114900,698,1,2,Non-citizen\n3731,0,Car ,Male ,41,HS ,71194,633,1,2,Citizen \n3732,1,Car ,Male ,44,HS ,98941,784,2,2,Citizen \n3733,0,Car ,Male ,41,HS ,82874,670,0,2,Citizen \n3734,1,Home,Male ,44,College ,104556,686,6,2,Citizen \n3735,0,Car ,Female,47,HS ,95938,650,1,1,Citizen \n3736,0,Home,Male ,47,College ,85215,649,5,2,Citizen \n3737,0,Home,Male ,33,HS ,53948,519,5,2,Citizen \n3738,0,Car ,Male ,34,HS ,62751,525,0,2,Citizen \n3740,0,Home,Female,38,HS ,83048,602,7,1,Citizen \n3741,0,Car ,Male ,46,HS ,82124,567,2,2,Citizen \n3742,0,Home,Male ,38,HS ,87968,576,10,2,Citizen \n3743,0,Car ,Male ,44,College ,118258,570,2,2,Citizen \n3744,0,Car ,Female,37,HS ,85322,727,0,2,Non-citizen\n3745,0,Home,Female,36,College ,105131,694,5,2,Citizen \n3746,0,Car ,Female,45,HS ,96564,698,1,2,Citizen \n3747,0,Car ,Female,48,College ,103086,602,1,1,Non-citizen\n3748,0,Home,Female,42,College ,93558,666,7,2,Citizen \n3749,0,Car ,Male ,44,HS ,91430,496,1,2,Citizen \n3750,0,Car ,Male ,34,HS ,100477,575,3,2,Citizen \n3751,0,Car ,Male ,41,HS ,83776,581,2,2,Citizen \n3752,0,Car ,Male ,35,HS ,89571,580,2,2,Citizen \n3753,0,Car ,Male ,41,HS ,86775,573,2,2,Citizen \n3754,0,Car ,Female,40,HS ,126433,545,0,2,Citizen \n3755,0,Car ,Female,38,HS ,133424,720,3,2,Citizen \n3756,0,Car ,Male ,46,HS ,119394,605,2,2,Citizen \n3757,1,Home,Female,48,College ,81945,669,8,2,Citizen \n3758,0,Car ,Female,35,College ,105057,650,2,2,Citizen \n3759,1,Car ,Female,38,HS ,123956,738,2,2,Citizen \n3760,0,Car ,Male ,46,HS ,115785,689,1,2,Citizen \n3761,0,Car ,Male ,30,HS ,63239,528,0,2,Citizen \n3762,0,Home,Male ,31,HS ,72275,653,6,2,Citizen \n3763,0,Car ,Male ,28,HS ,86167,514,0,2,Citizen \n3764,0,Home,Female,32,Graduate,96715,584,9,2,Citizen \n3765,0,Car ,Female,53,College ,101092,682,3,2,Non-citizen\n3766,0,Home,Male ,37,Graduate,99074,551,5,2,Citizen \n3767,0,Home,Male ,39,HS ,64177,630,4,2,Citizen \n3768,0,Home,Male ,50,HS ,72215,624,4,2,Citizen \n3769,0,Car ,Female,36,HS ,90778,640,0,1,Citizen \n3770,0,Home,Male ,37,HS ,105785,507,9,2,Citizen \n3771,0,Car ,Male ,52,HS ,124292,654,3,2,Citizen \n3772,0,Car ,Male ,43,HS ,112604,600,1,2,Citizen \n3773,0,Car ,Male ,42,HS ,93396,650,0,2,Citizen \n3774,0,Car ,Male ,45,College ,93560,541,1,2,Citizen \n3775,0,Home,Male ,40,HS ,123798,583,3,2,Citizen \n3776,0,Home,Male ,44,College ,91117,632,9,2,Citizen \n3777,0,Car ,Female,44,HS ,89588,695,0,1,Citizen \n3778,0,Home,Male ,44,HS ,77102,600,5,2,Citizen \n3779,0,Home,Female,44,HS ,118117,556,6,2,Citizen \n3780,0,Car ,Male ,50,HS ,75888,591,2,1,Citizen \n3781,0,Home,Female,46,College ,107226,692,6,2,Citizen \n3782,0,Car ,Female,39,Graduate,86290,666,3,1,Citizen \n3783,0,Home,Female,48,HS ,108216,680,3,2,Citizen \n3784,0,Car ,Male ,39,HS ,118883,593,3,1,Citizen \n3785,0,Car ,Female,46,HS ,124516,598,1,1,Non-citizen\n3786,0,Home,Female,49,HS ,80068,566,4,2,Citizen \n3787,0,Home,Male ,41,HS ,89338,526,4,2,Citizen \n3789,0,Home,Male ,33,HS ,84225,659,5,2,Citizen \n3790,0,Car ,Female,47,Graduate,112143,611,2,2,Citizen \n3791,0,Car ,Male ,35,HS ,112917,626,1,1,Citizen \n3792,0,Car ,Female,48,College ,99671,691,1,2,Citizen \n3793,0,Home,Female,53,Graduate,111387,599,8,2,Citizen \n3794,0,Home,Male ,44,HS ,94203,556,10,2,Citizen \n3795,0,Home,Male ,48,HS ,97284,575,5,2,Citizen \n3796,0,Home,Male ,45,College ,57305,545,7,2,Citizen \n3797,0,Home,Female,47,HS ,117470,567,8,2,Citizen \n3798,0,Car ,Male ,43,HS ,85083,595,0,2,Citizen \n3799,1,Car ,Female,50,College ,124216,629,2,2,Citizen \n3800,0,Home,Male ,34,HS ,81889,557,6,2,Citizen \n3801,1,Car ,Male ,48,Graduate,119365,734,3,2,Citizen \n3802,0,Car ,Male ,47,HS ,95760,667,0,2,Citizen \n3803,0,Car ,Female,55,HS ,125615,707,2,1,Non-citizen\n3804,0,Car ,Female,45,HS ,89893,595,1,2,Citizen \n3805,0,Home,Female,48,HS ,99447,585,10,1,Citizen \n3806,0,Car ,Female,40,College ,89705,582,0,2,Citizen \n3807,0,Home,Female,44,HS ,71416,573,0,2,Citizen \n3808,0,Car ,Female,37,HS ,97149,550,0,2,Citizen \n3809,0,Home,Male ,37,College ,108486,715,4,2,Citizen \n3810,0,Home,Male ,37,HS ,90889,511,7,2,Citizen \n3811,0,Home,Male ,42,HS ,69276,557,2,2,Citizen \n3812,0,Home,Female,41,HS ,113279,642,5,2,Citizen \n3813,0,Home,Male ,41,HS ,125767,612,7,2,Citizen \n3814,0,Car ,Female,45,College ,87470,642,1,2,Citizen \n3815,0,Car ,Female,37,HS ,82576,599,1,2,Citizen \n3816,0,Car ,Male ,43,HS ,95790,636,2,2,Citizen \n3817,0,Car ,Male ,49,College ,106811,501,2,2,Citizen \n3818,0,Home,Female,54,HS ,98113,595,5,2,Citizen \n3819,0,Car ,Male ,37,HS ,99191,614,0,2,Citizen \n3820,0,Home,Male ,42,HS ,105995,632,5,2,Citizen \n3821,0,Home,Male ,42,HS ,76175,551,3,2,Citizen \n3822,0,Car ,Male ,37,HS ,80386,482,0,2,Citizen \n3823,0,Car ,Male ,48,HS ,126703,585,1,2,Citizen \n3824,0,Home,Female,43,HS ,80114,621,3,2,Citizen \n3825,0,Car ,Male ,39,HS ,91593,620,2,1,Citizen \n3826,0,Home,Female,49,HS ,103634,600,12,2,Citizen \n3827,0,Car ,Male ,39,HS ,71529,544,0,2,Citizen \n3828,0,Home,Male ,46,HS ,96482,654,4,2,Citizen \n3829,0,Home,Male ,33,HS ,96412,560,11,2,Citizen \n3830,0,Car ,Female,49,College ,116774,673,1,2,Citizen \n3831,0,Home,Male ,38,HS ,75157,575,6,2,Citizen \n3832,0,Car ,Male ,40,HS ,47046,633,0,2,Citizen \n3833,0,Home,Male ,45,HS ,77073,669,10,2,Citizen \n3834,0,Car ,Male ,46,HS ,103632,612,2,1,Citizen \n3835,0,Car ,Female,49,College ,101513,701,2,2,Citizen \n3836,0,Car ,Female,46,College ,87056,637,2,2,Citizen \n3837,0,Home,Male ,44,HS ,97428,610,6,2,Citizen \n3838,0,Car ,Male ,48,College ,112267,554,3,2,Citizen \n3839,0,Car ,Female,43,HS ,104486,651,1,2,Citizen \n3840,0,Car ,Male ,41,HS ,46979,557,0,2,Citizen \n3841,0,Car ,Female,36,HS ,77397,655,2,2,Citizen \n3842,0,Home,Female,44,HS ,89735,558,5,2,Citizen \n3843,0,Car ,Female,49,College ,117707,727,3,1,Non-citizen\n3844,0,Car ,Female,39,HS ,115423,648,0,2,Citizen \n3845,0,Home,Female,39,Graduate,112118,623,7,2,Citizen \n3846,0,Car ,Female,50,College ,118183,623,3,2,Citizen \n3847,0,Car ,Male ,48,HS ,91450,651,0,2,Citizen \n3848,0,Home,Male ,48,College ,118158,674,8,2,Citizen \n3849,0,Car ,Male ,42,HS ,86134,535,1,2,Citizen \n3850,0,Car ,Male ,38,HS ,72217,564,1,2,Citizen \n3851,0,Home,Male ,40,HS ,102174,676,7,2,Citizen \n3852,0,Car ,Male ,41,HS ,96468,591,0,2,Citizen \n3853,0,Car ,Female,51,HS ,89743,628,3,2,Citizen \n3854,0,Home,Male ,42,College ,95105,612,10,2,Citizen \n3855,0,Home,Male ,49,HS ,122796,665,8,2,Citizen \n3856,0,Car ,Male ,43,HS ,111696,527,0,2,Citizen \n3857,0,Car ,Female,49,HS ,84319,657,0,2,Citizen \n3858,0,Home,Male ,30,HS ,88932,513,4,2,Citizen \n3859,1,Car ,Female,45,College ,137277,602,3,2,Citizen \n3860,0,Home,Female,47,College ,140947,814,10,1,Non-citizen\n3861,0,Car ,Male ,43,HS ,127540,622,2,2,Citizen \n3862,0,Car ,Female,42,HS ,99267,568,1,2,Citizen \n3863,0,Car ,Male ,46,HS ,107985,634,2,2,Citizen \n3864,0,Car ,Male ,37,College ,83334,538,0,2,Citizen \n3865,0,Home,Male ,38,HS ,68714,476,5,2,Citizen \n3866,0,Car ,Male ,38,HS ,72820,541,2,2,Citizen \n3867,0,Home,Female,48,Graduate,82821,590,7,2,Citizen \n3868,0,Car ,Male ,38,HS ,103578,541,1,2,Citizen \n3869,0,Car ,Male ,44,College ,105486,621,3,1,Citizen \n3870,1,Car ,Female,45,Graduate,120861,720,3,2,Citizen \n3871,1,Car ,Female,39,HS ,105997,625,0,2,Citizen \n3872,0,Car ,Female,35,College ,133405,761,3,1,Non-citizen\n3873,0,Car ,Male ,48,College ,101161,646,2,2,Citizen \n3874,0,Car ,Female,40,HS ,91253,523,1,2,Citizen \n3875,0,Car ,Female,48,College ,92616,484,3,2,Non-citizen\n3876,0,Car ,Male ,39,HS ,52426,703,2,2,Citizen \n3877,0,Car ,Female,36,HS ,112905,626,1,2,Citizen \n3878,0,Car ,Male ,35,HS ,112368,615,0,2,Citizen \n3879,0,Car ,Male ,47,College ,95152,634,1,2,Non-citizen\n3880,0,Home,Female,38,HS ,103590,639,7,2,Citizen \n3881,0,Car ,Male ,37,HS ,64410,472,0,2,Citizen \n3882,0,Car ,Female,51,College ,103095,614,3,2,Citizen \n3883,0,Car ,Female,46,College ,106368,660,3,1,Non-citizen\n3884,0,Car ,Female,41,Graduate,108673,729,1,2,Citizen \n3885,0,Home,Male ,38,College ,83167,568,9,2,Citizen \n3886,0,Car ,Female,45,HS ,99383,717,1,2,Citizen \n3887,0,Car ,Male ,41,HS ,82104,606,2,2,Citizen \n3888,0,Car ,Female,48,College ,132547,643,3,2,Citizen \n3889,0,Home,Female,23,College ,106872,494,0,2,Citizen \n3890,0,Car ,Female,48,College ,125187,637,1,2,Citizen \n3891,0,Car ,Female,50,Graduate,65323,646,2,2,Citizen \n3892,0,Car ,Male ,44,HS ,72554,712,2,2,Citizen \n3893,0,Car ,Female,46,College ,94549,596,2,2,Citizen \n3894,0,Home,Male ,38,HS ,106082,632,0,2,Citizen \n3895,0,Car ,Male ,48,HS ,111839,596,1,2,Citizen \n3896,0,Car ,Male ,39,HS ,83731,580,0,2,Citizen \n3897,0,Car ,Male ,30,HS ,91294,582,0,2,Citizen \n3898,0,Home,Male ,40,HS ,76465,556,2,2,Citizen \n3899,0,Home,Male ,34,HS ,91433,627,2,2,Citizen \n3900,1,Car ,Female,46,College ,118529,614,0,2,Citizen \n3901,0,Car ,Male ,46,HS ,100821,602,1,2,Citizen \n3902,0,Home,Male ,37,HS ,84846,641,3,2,Citizen \n3903,0,Car ,Male ,49,Graduate,109691,638,2,1,Citizen \n3904,0,Home,Female,41,HS ,100796,605,8,2,Citizen \n3905,0,Car ,Male ,41,HS ,81688,549,0,2,Citizen \n3906,0,Car ,Female,35,HS ,75343,535,0,2,Citizen \n3907,0,Car ,Female,53,College ,116700,794,3,1,Citizen \n3908,0,Car ,Male ,41,College ,81617,600,2,2,Citizen \n3909,0,Home,Male ,38,HS ,74778,630,3,2,Citizen \n3910,0,Car ,Female,41,HS ,80014,696,0,1,Citizen \n3911,0,Car ,Female,44,College ,116936,648,3,2,Citizen \n3912,0,Home,Female,42,HS ,88953,637,8,2,Citizen \n3913,0,Home,Male ,47,HS ,111007,683,4,2,Citizen \n3914,0,Car ,Female,45,College ,108386,669,1,1,Citizen \n3915,0,Car ,Female,45,HS ,108380,644,2,2,Citizen \n3916,0,Car ,Male ,30,College ,61958,569,1,2,Citizen \n3917,0,Home,Male ,46,HS ,100603,647,8,2,Citizen \n3918,0,Home,Female,44,HS ,81728,545,2,2,Citizen \n3919,0,Car ,Male ,45,HS ,91544,571,2,2,Citizen \n3920,0,Car ,Female,39,HS ,100172,659,3,2,Citizen \n3921,0,Home,Female,55,HS ,88742,498,7,2,Citizen \n3922,0,Car ,Male ,40,HS ,87180,622,1,1,Citizen \n3923,0,Car ,Female,39,Graduate,102633,710,2,2,Citizen \n3924,0,Home,Male ,31,HS ,75548,523,5,2,Citizen \n3925,0,Car ,Male ,45,College ,86333,624,1,2,Citizen \n3926,0,Home,Male ,37,HS ,103363,593,5,2,Citizen \n3927,0,Car ,Male ,40,Graduate,74766,621,3,2,Citizen \n3928,0,Car ,Male ,43,College ,76820,589,0,2,Citizen \n3929,0,Car ,Female,51,College ,101468,592,2,2,Citizen \n3930,0,Car ,Male ,30,HS ,43232,538,0,2,Citizen \n3931,0,Car ,Male ,45,HS ,65565,602,0,2,Citizen \n3932,0,Car ,Male ,40,College ,93704,608,2,2,Citizen \n3933,0,Car ,Female,59,College ,130213,626,2,2,Citizen \n3934,0,Car ,Female,41,HS ,112628,669,0,2,Citizen \n3935,0,Car ,Male ,44,HS ,76309,493,2,2,Citizen \n3936,0,Car ,Female,44,HS ,94892,674,2,2,Citizen \n3937,0,Home,Female,40,HS ,78786,591,4,2,Citizen \n3938,0,Car ,Male ,43,HS ,94852,628,0,2,Citizen \n3939,0,Car ,Male ,43,HS ,87832,689,1,2,Citizen \n3940,0,Car ,Male ,56,College ,110967,610,1,2,Citizen \n3941,0,Car ,Female,51,HS ,98527,464,0,2,Citizen \n3942,0,Car ,Male ,36,HS ,69548,568,2,1,Citizen \n3943,0,Car ,Male ,46,HS ,100127,625,2,2,Citizen \n3944,0,Car ,Male ,32,HS ,83833,550,0,2,Non-citizen\n3945,0,Home,Female,42,HS ,101818,564,8,2,Citizen \n3946,0,Home,Male ,35,HS ,69739,677,5,2,Citizen \n3947,0,Car ,Female,45,College ,117492,662,3,2,Citizen \n3948,0,Car ,Male ,49,HS ,78322,704,2,1,Citizen \n3949,0,Car ,Male ,43,College ,83948,540,0,2,Citizen \n3950,0,Home,Male ,35,HS ,88590,569,0,2,Citizen \n3951,0,Car ,Male ,29,HS ,97160,519,0,2,Citizen \n3952,0,Home,Male ,43,College ,85325,638,7,2,Citizen \n3953,0,Home,Male ,41,HS ,88851,514,4,2,Citizen \n3954,0,Car ,Male ,41,HS ,107743,664,0,2,Citizen \n3955,0,Car ,Male ,39,HS ,70234,559,0,2,Citizen \n3956,1,Car ,Female,42,HS ,103181,594,2,2,Citizen \n3957,0,Home,Male ,35,College ,61717,517,3,2,Citizen \n3958,0,Car ,Male ,39,HS ,103955,664,2,2,Citizen \n3959,0,Home,Male ,35,HS ,105291,603,6,2,Citizen \n3960,0,Car ,Male ,40,HS ,82518,610,2,2,Citizen \n3961,0,Car ,Female,47,HS ,106284,576,1,2,Citizen \n3962,0,Car ,Female,45,College ,97824,632,3,2,Non-citizen\n3963,0,Car ,Male ,52,HS ,102608,624,1,2,Citizen \n3964,1,Car ,Male ,47,HS ,124004,695,2,2,Citizen \n3965,0,Car ,Male ,43,HS ,77569,646,1,2,Citizen \n3966,0,Car ,Male ,44,HS ,93975,609,0,2,Citizen \n3967,0,Car ,Male ,43,HS ,106168,642,2,2,Citizen \n3968,0,Home,Male ,41,HS ,88837,630,6,2,Citizen \n3969,0,Home,Male ,32,College ,101453,648,6,2,Citizen \n3970,0,Car ,Male ,42,College ,84780,684,2,2,Non-citizen\n3971,0,Car ,Female,37,College ,123480,642,0,2,Citizen \n3972,0,Car ,Male ,51,HS ,126575,586,1,2,Citizen \n3973,0,Home,Male ,39,College ,76202,536,5,2,Citizen \n3974,0,Car ,Male ,34,HS ,91106,574,2,2,Citizen \n3975,0,Car ,Male ,39,HS ,107755,625,1,2,Citizen \n3976,0,Car ,Male ,45,Graduate,109439,563,3,2,Citizen \n3977,0,Car ,Male ,43,HS ,96221,506,1,2,Citizen \n3978,0,Home,Male ,37,College ,46846,533,8,2,Citizen \n3979,0,Home,Male ,31,HS ,76992,582,6,2,Citizen \n3980,0,Car ,Male ,46,HS ,106195,707,2,2,Citizen \n3981,0,Car ,Female,39,HS ,108999,662,1,2,Citizen \n3982,1,Car ,Female,40,College ,125431,705,2,1,Citizen \n3983,0,Car ,Female,51,HS ,105912,573,0,2,Citizen \n3984,0,Home,Female,46,College ,86557,625,7,2,Citizen \n3985,1,Home,Female,47,College ,98839,637,9,2,Citizen \n3986,1,Car ,Female,48,College ,103292,816,1,1,Non-citizen\n3987,0,Home,Male ,34,HS ,78921,586,0,2,Citizen \n3988,0,Car ,Male ,33,HS ,113547,539,2,2,Citizen \n3989,0,Car ,Female,41,HS ,97547,556,2,2,Citizen \n3990,0,Car ,Female,51,College ,109908,655,3,1,Citizen \n3991,1,Car ,Female,47,College ,113317,779,2,2,Citizen \n3992,0,Home,Female,44,HS ,93316,646,7,1,Citizen \n3993,0,Car ,Female,51,Graduate,89943,643,2,2,Citizen \n3994,0,Car ,Male ,39,Graduate,102569,604,3,2,Citizen \n3995,0,Home,Female,47,HS ,78176,653,5,2,Citizen \n3996,0,Car ,Male ,42,HS ,87663,594,2,2,Citizen \n3997,0,Home,Male ,31,HS ,62355,515,5,2,Citizen \n3998,0,Car ,Female,54,College ,105815,638,2,2,Citizen \n3999,0,Car ,Female,46,HS ,121929,529,0,2,Citizen \n4000,0,Car ,Male ,45,HS ,93001,657,2,2,Citizen \n4001,1,Home,Female,51,HS ,112653,727,9,1,Citizen \n4002,0,Home,Male ,33,HS ,80330,509,6,2,Citizen \n4003,1,Car ,Female,46,HS ,123803,687,1,2,Citizen \n4004,0,Car ,Female,49,HS ,111708,613,2,2,Citizen \n4005,0,Car ,Male ,40,College ,86710,601,0,2,Citizen \n4006,1,Car ,Male ,38,College ,93549,554,2,2,Citizen \n4007,0,Home,Female,39,HS ,84923,638,6,2,Citizen \n4008,0,Car ,Male ,50,HS ,115182,681,3,2,Citizen \n4009,0,Car ,Male ,45,HS ,108556,622,1,2,Citizen \n4010,0,Home,Male ,40,HS ,87531,676,6,2,Citizen \n4011,0,Car ,Female,41,HS ,93353,515,2,2,Citizen \n4012,1,Home,Female,35,College ,76240,664,5,2,Non-citizen\n4013,0,Car ,Female,39,HS ,94437,611,1,2,Citizen \n4014,0,Home,Male ,38,HS ,79816,635,5,1,Citizen \n4015,1,Car ,Female,46,HS ,134283,748,0,2,Citizen \n4016,0,Car ,Female,43,HS ,84981,621,0,2,Citizen \n4017,0,Car ,Female,52,College ,109886,660,0,2,Citizen \n4018,0,Car ,Male ,52,HS ,118391,660,0,2,Citizen \n4019,0,Car ,Male ,33,Graduate,92917,473,3,2,Citizen \n4020,0,Car ,Male ,41,HS ,108082,575,0,2,Citizen \n4021,0,Car ,Male ,33,HS ,78993,510,0,2,Citizen \n4022,0,Home,Male ,44,HS ,69194,617,1,2,Citizen \n4023,0,Car ,Female,52,HS ,111167,576,0,2,Citizen \n4024,0,Car ,Male ,48,College ,80096,618,0,2,Citizen \n4025,1,Car ,Male ,53,College ,105272,734,1,2,Citizen \n4026,0,Home,Male ,33,HS ,74831,632,7,2,Citizen \n4027,0,Home,Male ,42,HS ,109229,656,8,2,Citizen \n4028,0,Home,Male ,50,HS ,78033,613,8,2,Citizen \n4029,0,Home,Male ,45,College ,96278,574,11,2,Citizen \n4030,0,Car ,Male ,48,HS ,103384,536,0,2,Citizen \n4031,0,Car ,Female,38,HS ,107765,681,1,2,Citizen \n4032,0,Car ,Female,35,HS ,74366,594,0,2,Citizen \n4033,0,Home,Male ,42,HS ,86075,584,4,2,Citizen \n4034,0,Car ,Female,48,College ,108081,617,2,2,Citizen \n4035,0,Car ,Male ,56,HS ,94831,675,1,1,Citizen \n4036,0,Car ,Male ,51,HS ,85545,608,1,2,Citizen \n4037,0,Car ,Male ,51,College ,100328,691,2,2,Citizen \n4038,1,Home,Female,52,HS ,124766,651,5,1,Non-citizen\n4039,0,Home,Female,45,HS ,112533,634,7,2,Citizen \n4040,0,Home,Female,46,HS ,103406,679,6,1,Citizen \n4041,0,Car ,Female,49,HS ,81446,568,2,2,Citizen \n4042,0,Car ,Male ,46,College ,103667,665,2,2,Citizen \n4043,0,Car ,Female,52,College ,97185,606,3,2,Citizen \n4044,0,Car ,Female,44,College ,100545,724,0,2,Citizen \n4045,0,Car ,Female,53,HS ,115793,504,0,2,Citizen \n4046,0,Home,Male ,43,HS ,80111,576,5,2,Citizen \n4047,0,Home,Male ,34,HS ,112809,668,3,2,Citizen \n4048,0,Car ,Female,44,Graduate,113643,561,2,2,Citizen \n4049,0,Home,Male ,38,HS ,73682,606,2,2,Citizen \n4050,0,Home,Female,39,HS ,114416,614,2,2,Citizen \n4051,0,Car ,Male ,51,College ,118953,631,2,2,Citizen \n4052,0,Car ,Female,50,College ,94940,632,2,1,Citizen \n4053,0,Car ,Male ,39,HS ,87434,610,0,2,Citizen \n4054,0,Car ,Female,44,College ,105999,577,2,2,Citizen \n4055,0,Home,Male ,31,HS ,91724,589,1,2,Citizen \n4056,0,Home,Male ,36,College ,120913,617,13,2,Citizen \n4057,0,Home,Male ,37,HS ,74970,596,8,2,Citizen \n4058,0,Home,Female,50,College ,100260,650,6,2,Citizen \n4059,0,Home,Male ,43,HS ,74827,548,10,2,Citizen \n4060,0,Car ,Female,36,College ,111422,633,1,2,Citizen \n4061,0,Car ,Female,41,HS ,86104,545,0,2,Citizen \n4062,0,Home,Male ,41,HS ,113113,548,9,2,Citizen \n4063,0,Car ,Female,41,HS ,106656,659,0,2,Citizen \n4064,1,Car ,Female,39,Graduate,123493,798,0,2,Non-citizen\n4065,0,Car ,Female,37,College ,78129,644,3,2,Citizen \n4066,0,Car ,Male ,41,College ,77614,669,1,2,Citizen \n4067,0,Car ,Male ,42,HS ,112966,606,2,2,Citizen \n4068,0,Car ,Female,41,HS ,73691,593,0,2,Citizen \n4069,0,Home,Male ,37,HS ,103658,600,10,2,Citizen \n4070,0,Car ,Male ,50,HS ,95095,678,1,2,Citizen \n4071,0,Home,Female,46,HS ,107208,614,8,2,Citizen \n4072,0,Car ,Male ,42,HS ,115419,617,2,1,Citizen \n4073,0,Car ,Female,42,Graduate,126308,682,2,2,Non-citizen\n4074,0,Home,Male ,36,HS ,103205,702,4,2,Citizen \n4075,0,Car ,Male ,47,College ,111910,619,2,2,Citizen \n4076,0,Car ,Male ,35,HS ,97153,591,0,2,Citizen \n4077,0,Home,Male ,33,HS ,88818,422,3,2,Citizen \n4078,0,Car ,Male ,44,HS ,110218,694,3,2,Citizen \n4079,0,Car ,Male ,58,HS ,99057,639,0,1,Citizen \n4080,0,Car ,Female,40,College ,94641,554,3,2,Citizen \n4081,0,Home,Male ,34,HS ,110183,589,4,1,Citizen \n4082,0,Car ,Male ,36,College ,98639,685,1,2,Citizen \n4083,0,Car ,Male ,40,HS ,99665,623,1,2,Citizen \n4084,0,Car ,Male ,49,HS ,144418,727,1,2,Citizen \n4085,0,Car ,Female,41,College ,113281,658,2,2,Citizen \n4086,0,Car ,Female,33,College ,90520,582,0,2,Citizen \n4087,0,Car ,Female,50,College ,108316,695,2,2,Citizen \n4088,0,Car ,Male ,38,HS ,117226,540,2,2,Citizen \n4089,0,Car ,Male ,42,HS ,112289,607,2,2,Citizen \n4090,0,Home,Male ,34,College ,93864,597,4,2,Citizen \n4091,0,Home,Male ,36,College ,87684,519,3,2,Citizen \n4092,0,Car ,Female,39,HS ,106004,678,0,2,Citizen \n4093,0,Car ,Male ,35,HS ,75703,544,0,2,Citizen \n4094,0,Car ,Male ,43,Graduate,86937,721,3,1,Citizen \n4095,0,Car ,Male ,42,HS ,89144,659,0,2,Citizen \n4096,0,Car ,Female,47,Graduate,77577,615,2,2,Citizen \n4097,0,Car ,Male ,36,College ,73045,515,0,2,Citizen \n4098,0,Home,Male ,55,HS ,98377,730,4,2,Citizen \n4099,0,Home,Female,34,College ,66984,663,4,1,Citizen \n4100,0,Car ,Female,37,HS ,104330,523,1,2,Citizen \n4101,0,Home,Male ,33,HS ,80088,484,5,2,Citizen \n4102,0,Home,Female,40,HS ,114603,671,10,2,Citizen \n4103,0,Home,Male ,38,HS ,70327,570,3,2,Citizen \n4104,0,Car ,Male ,39,HS ,104640,577,0,2,Citizen \n4105,0,Car ,Male ,46,HS ,91178,515,1,2,Citizen \n4106,0,Car ,Male ,50,HS ,102734,655,1,2,Citizen \n4107,0,Home,Female,41,College ,89500,638,10,2,Citizen \n4108,0,Car ,Male ,42,HS ,94175,522,1,2,Citizen \n4109,0,Home,Male ,45,College ,94231,651,7,2,Citizen \n4110,0,Car ,Female,44,HS ,62157,630,1,1,Citizen \n4111,0,Car ,Female,51,College ,135858,758,2,2,Non-citizen\n4112,0,Car ,Male ,41,HS ,64427,491,1,2,Citizen \n4113,0,Car ,Male ,45,College ,100258,587,3,2,Citizen \n4114,0,Car ,Female,31,College ,95625,679,0,2,Citizen \n4115,0,Car ,Male ,51,Graduate,87090,531,3,2,Citizen \n4116,0,Car ,Male ,45,HS ,78112,552,0,2,Citizen \n4117,0,Car ,Female,49,College ,130963,637,3,2,Citizen \n4118,1,Car ,Female,48,College ,119099,622,2,2,Citizen \n4119,0,Home,Male ,44,HS ,109565,520,2,2,Citizen \n4120,0,Home,Female,41,HS ,127588,632,4,2,Citizen \n4121,0,Car ,Male ,32,HS ,101480,689,2,2,Citizen \n4122,0,Car ,Female,48,HS ,105260,685,2,2,Citizen \n4123,0,Car ,Male ,47,HS ,77881,458,0,2,Citizen \n4124,0,Car ,Female,44,Graduate,87956,729,3,2,Citizen \n4125,0,Car ,Male ,40,College ,125736,604,3,2,Citizen \n4126,0,Car ,Female,43,HS ,93753,632,0,2,Citizen \n4127,0,Car ,Male ,38,HS ,97391,593,0,2,Citizen \n4128,0,Home,Female,44,HS ,74806,517,8,2,Citizen \n4129,0,Car ,Female,51,HS ,89388,689,0,2,Citizen \n4130,0,Home,Male ,38,HS ,105208,720,6,2,Citizen \n4131,0,Home,Female,39,HS ,74125,558,3,2,Citizen \n4132,0,Car ,Female,48,HS ,120752,627,1,1,Non-citizen\n4134,0,Car ,Male ,33,HS ,77536,506,2,2,Citizen \n4135,0,Home,Male ,34,HS ,114299,639,7,2,Citizen \n4136,0,Car ,Male ,41,HS ,87283,558,0,2,Citizen \n4137,0,Car ,Male ,41,College ,102417,708,2,2,Citizen \n4138,0,Home,Female,37,College ,113605,502,9,2,Citizen \n4139,0,Home,Male ,44,HS ,91088,547,7,2,Citizen \n4140,0,Car ,Female,35,HS ,110532,565,0,2,Citizen \n4141,0,Car ,Female,42,HS ,96913,573,2,1,Citizen \n4142,0,Car ,Female,47,College ,109532,663,0,2,Citizen \n4143,0,Car ,Male ,38,College ,99184,607,2,2,Citizen \n4144,0,Car ,Female,33,HS ,87615,481,2,2,Citizen \n4145,0,Car ,Female,41,College ,71433,603,0,2,Citizen \n4146,0,Car ,Male ,48,College ,86105,548,0,2,Citizen \n4147,0,Home,Male ,45,HS ,98440,605,5,2,Citizen \n4148,0,Home,Male ,60,HS ,92043,709,5,2,Citizen \n4149,0,Car ,Female,42,HS ,88951,568,0,2,Citizen \n4150,0,Car ,Male ,43,HS ,92531,693,2,2,Citizen \n4151,0,Car ,Male ,44,Graduate,97332,584,2,2,Citizen \n4152,0,Car ,Male ,43,HS ,78999,488,2,2,Citizen \n4153,0,Car ,Male ,47,College ,112257,675,2,2,Citizen \n4154,0,Car ,Female,43,HS ,93340,659,1,2,Citizen \n4155,0,Car ,Male ,42,College ,65241,488,2,2,Citizen \n4156,0,Car ,Male ,54,HS ,115416,685,2,2,Citizen \n4157,0,Home,Male ,44,HS ,85542,646,9,2,Citizen \n4158,0,Home,Male ,39,HS ,72189,552,2,2,Citizen \n4159,1,Car ,Male ,52,College ,100687,583,2,2,Citizen \n4160,0,Car ,Female,40,HS ,97157,638,0,2,Citizen \n4161,0,Home,Female,37,HS ,86052,533,10,2,Citizen \n4162,0,Car ,Male ,48,College ,113947,564,2,2,Citizen \n4163,1,Car ,Female,50,College ,125988,608,1,2,Citizen \n4164,0,Car ,Female,35,HS ,111865,568,3,2,Citizen \n4165,0,Car ,Male ,43,College ,94990,677,3,2,Citizen \n4166,1,Car ,Female,47,College ,101642,692,2,2,Citizen \n4167,0,Car ,Male ,48,HS ,77928,573,0,2,Citizen \n4168,1,Home,Male ,52,HS ,113996,642,3,2,Citizen \n4169,1,Car ,Female,45,Graduate,114196,664,3,2,Citizen \n4170,0,Car ,Male ,43,HS ,71026,580,0,2,Citizen \n4171,0,Car ,Female,46,HS ,68912,639,0,2,Citizen \n4172,0,Home,Male ,31,HS ,91966,539,3,2,Citizen \n4173,0,Car ,Male ,35,HS ,92721,502,0,2,Citizen \n4174,1,Car ,Male ,45,College ,94922,668,1,2,Citizen \n4175,0,Home,Male ,33,HS ,82603,528,1,2,Citizen \n4176,0,Car ,Male ,38,HS ,69189,599,0,2,Citizen \n4177,0,Home,Male ,40,HS ,54885,519,7,2,Citizen \n4178,0,Car ,Male ,44,HS ,105223,624,2,2,Citizen \n4179,0,Car ,Female,45,Graduate,104783,710,0,2,Citizen \n4180,0,Home,Male ,40,HS ,74250,585,0,2,Citizen \n4181,0,Home,Male ,46,HS ,109811,586,9,2,Citizen \n4182,0,Car ,Male ,40,College ,90968,564,0,2,Citizen \n4183,0,Home,Male ,38,HS ,78890,581,3,2,Citizen \n4184,0,Car ,Female,57,Graduate,100398,687,3,2,Citizen \n4185,0,Home,Male ,45,College ,101462,569,8,2,Citizen \n4186,0,Car ,Female,45,Graduate,102743,667,3,2,Non-citizen\n4187,0,Home,Female,48,HS ,126747,727,7,2,Citizen \n4188,0,Car ,Female,54,HS ,117815,726,2,1,Non-citizen\n4189,0,Car ,Female,50,College ,116940,769,2,1,Citizen \n4190,0,Car ,Female,48,College ,113270,647,2,2,Citizen \n4191,0,Home,Male ,43,College ,100380,534,8,2,Citizen \n4192,0,Car ,Female,39,HS ,100498,633,0,2,Citizen \n4193,0,Car ,Female,42,College ,109464,646,2,2,Citizen \n4194,0,Car ,Male ,52,HS ,68124,594,2,2,Citizen \n4195,0,Car ,Female,43,College ,64636,550,0,2,Citizen \n4196,0,Home,Male ,43,HS ,89854,672,6,1,Citizen \n4197,0,Home,Male ,44,HS ,86829,603,9,2,Citizen \n4198,0,Home,Female,31,HS ,54687,564,2,2,Citizen \n4199,1,Car ,Male ,36,College ,95288,580,2,2,Citizen \n4200,0,Car ,Female,38,HS ,121538,518,0,2,Citizen \n4201,0,Home,Male ,43,HS ,58963,599,3,2,Citizen \n4202,0,Home,Male ,43,College ,109095,618,13,2,Citizen \n4203,0,Car ,Male ,43,HS ,92747,515,0,2,Citizen \n4204,0,Car ,Male ,50,HS ,106163,483,2,1,Citizen \n4205,0,Car ,Male ,38,College ,119064,677,2,2,Citizen \n4206,0,Home,Female,48,HS ,106373,609,7,2,Citizen \n4207,0,Home,Male ,35,HS ,80675,569,7,2,Citizen \n4208,0,Car ,Male ,47,HS ,95629,634,2,2,Citizen \n4209,0,Car ,Male ,45,College ,86656,609,2,2,Citizen \n4210,0,Car ,Male ,43,HS ,96256,614,2,2,Citizen \n4211,0,Car ,Female,30,College ,77165,569,0,2,Citizen \n4212,1,Car ,Male ,40,College ,90413,596,1,2,Citizen \n4213,0,Car ,Male ,35,HS ,97350,610,2,1,Citizen \n4214,0,Car ,Female,48,College ,101683,671,2,1,Citizen \n4215,0,Car ,Male ,50,HS ,92432,526,2,2,Citizen \n4216,0,Home,Male ,35,HS ,109580,574,11,2,Citizen \n4217,0,Car ,Female,45,HS ,103176,692,0,1,Citizen \n4218,0,Car ,Male ,34,HS ,81474,586,1,2,Citizen \n4219,1,Car ,Female,45,HS ,107278,771,0,2,Citizen \n4220,0,Home,Female,43,HS ,105519,677,4,2,Citizen \n4221,0,Car ,Female,48,College ,92211,715,1,2,Citizen \n4222,0,Car ,Male ,37,HS ,79221,568,2,2,Citizen \n4223,0,Home,Female,50,HS ,92733,725,11,2,Citizen \n4224,0,Car ,Male ,45,HS ,73079,602,0,2,Citizen \n4225,0,Car ,Male ,44,College ,105427,633,1,2,Citizen \n4226,0,Home,Female,38,College ,92884,631,2,2,Citizen \n4227,0,Car ,Male ,42,College ,97460,575,2,2,Citizen \n4228,0,Home,Female,45,HS ,91823,595,6,2,Citizen \n4229,0,Car ,Male ,51,College ,89476,515,3,2,Citizen \n4230,0,Home,Female,37,HS ,72996,590,5,2,Citizen \n4231,1,Car ,Male ,46,College ,75736,600,0,2,Citizen \n4232,0,Home,Female,29,HS ,82630,710,1,2,Citizen \n4233,0,Home,Male ,40,HS ,104323,592,11,2,Citizen \n4234,0,Car ,Male ,40,College ,81439,576,0,2,Citizen \n4235,0,Home,Female,47,HS ,106456,569,4,2,Citizen \n4236,0,Car ,Female,50,HS ,115411,771,0,2,Citizen \n4237,0,Car ,Female,50,HS ,108483,691,2,2,Citizen \n4238,0,Car ,Male ,46,HS ,68861,601,2,2,Citizen \n4239,0,Car ,Male ,39,HS ,84244,633,0,2,Citizen \n4240,0,Car ,Male ,46,College ,86142,622,0,2,Citizen \n4241,1,Car ,Female,52,Graduate,139008,792,1,2,Non-citizen\n4242,0,Car ,Male ,46,HS ,104185,608,0,2,Citizen \n4243,1,Home,Female,46,HS ,120706,681,8,2,Citizen \n4244,0,Car ,Female,47,HS ,103851,729,2,1,Citizen \n4245,0,Car ,Female,43,HS ,90018,622,0,2,Citizen \n4246,0,Home,Male ,32,HS ,115887,596,6,2,Citizen \n4247,0,Car ,Female,48,College ,85377,691,1,2,Citizen \n4248,0,Car ,Male ,26,HS ,79845,595,0,2,Citizen \n4249,0,Car ,Female,30,Graduate,94619,694,2,2,Citizen \n4250,0,Car ,Female,46,College ,109803,670,2,2,Citizen \n4251,0,Car ,Male ,42,HS ,87394,601,0,2,Citizen \n4252,0,Car ,Female,36,HS ,121274,558,0,2,Citizen \n4253,0,Car ,Male ,42,HS ,68529,558,1,2,Citizen \n4254,0,Car ,Male ,47,College ,102738,624,3,2,Citizen \n4255,0,Car ,Male ,44,HS ,106798,478,2,2,Citizen \n4256,0,Home,Female,44,HS ,87484,633,4,2,Citizen \n4257,0,Home,Male ,48,HS ,85902,672,5,1,Citizen \n4258,0,Car ,Male ,52,HS ,114109,688,1,2,Citizen \n4259,1,Car ,Male ,57,College ,129862,662,2,2,Citizen \n4260,0,Car ,Female,46,HS ,113718,653,0,2,Citizen \n4261,0,Home,Male ,45,HS ,84520,587,7,2,Citizen \n4262,0,Car ,Female,42,HS ,98440,566,0,2,Citizen \n4263,0,Car ,Male ,35,HS ,73367,584,0,2,Citizen \n4264,1,Home,Female,37,HS ,140418,703,2,2,Citizen \n4265,0,Car ,Male ,56,HS ,68986,592,1,2,Citizen \n4266,0,Home,Female,29,HS ,117694,544,6,2,Citizen \n4267,0,Car ,Female,54,HS ,155953,652,3,1,Citizen \n4268,0,Car ,Male ,46,HS ,124511,508,2,2,Citizen \n4269,0,Home,Male ,39,HS ,109334,543,5,2,Citizen \n4270,0,Home,Male ,43,HS ,78798,641,0,2,Citizen \n4271,1,Home,Female,54,HS ,94632,729,10,1,Citizen \n4272,0,Car ,Female,45,HS ,105837,668,0,2,Citizen \n4273,0,Car ,Male ,37,HS ,86600,579,0,2,Citizen \n4274,0,Car ,Female,43,College ,96735,550,1,2,Citizen \n4275,0,Car ,Female,36,HS ,104510,565,2,2,Citizen \n4276,0,Car ,Female,39,HS ,72601,574,0,2,Citizen \n4277,0,Home,Male ,31,HS ,73523,645,4,2,Citizen \n4278,0,Home,Male ,47,HS ,105347,665,6,2,Citizen \n4279,0,Home,Male ,42,HS ,132522,741,8,2,Citizen \n4280,0,Home,Female,41,HS ,101384,601,0,2,Citizen \n4281,0,Home,Male ,43,HS ,76369,551,2,2,Citizen \n4282,0,Car ,Male ,33,HS ,71053,579,0,2,Citizen \n4283,0,Car ,Female,54,HS ,94157,643,0,1,Citizen \n4284,0,Car ,Male ,48,HS ,141616,554,3,2,Citizen \n4285,0,Home,Female,50,HS ,102406,676,4,2,Citizen \n4286,0,Car ,Female,39,HS ,126294,684,0,2,Citizen \n4287,0,Car ,Male ,47,HS ,127201,681,1,2,Citizen \n4288,0,Home,Male ,43,HS ,89165,787,6,2,Citizen \n4289,0,Car ,Female,32,HS ,100398,711,0,2,Citizen \n4290,0,Home,Male ,43,College ,130079,559,9,2,Citizen \n4291,0,Home,Male ,40,HS ,105433,606,5,2,Citizen \n4292,0,Car ,Male ,42,HS ,70969,632,0,2,Citizen \n4293,0,Car ,Male ,43,HS ,107030,585,2,2,Citizen \n4294,0,Car ,Female,53,HS ,121814,586,3,2,Citizen \n4295,0,Car ,Female,43,HS ,100217,575,2,2,Citizen \n4296,0,Car ,Male ,41,College ,71636,607,2,2,Citizen \n4297,0,Car ,Male ,42,HS ,61144,558,0,2,Citizen \n4298,0,Car ,Female,57,HS ,85407,769,2,2,Citizen \n4299,0,Car ,Female,38,College ,91555,576,0,2,Citizen \n4300,0,Car ,Male ,47,HS ,112050,715,3,2,Citizen \n4301,0,Car ,Female,40,HS ,107635,598,0,2,Citizen \n4302,0,Car ,Female,42,HS ,81075,560,3,2,Citizen \n4303,0,Car ,Female,41,HS ,76185,573,0,2,Citizen \n4304,0,Home,Female,41,HS ,124591,603,5,2,Citizen \n4305,0,Home,Male ,44,HS ,116536,631,11,2,Citizen \n4306,0,Home,Male ,36,HS ,89547,620,6,2,Citizen \n4307,0,Car ,Male ,44,College ,106059,688,1,2,Citizen \n4308,0,Car ,Female,41,HS ,107135,668,2,1,Non-citizen\n4309,0,Car ,Male ,42,HS ,96315,687,2,2,Citizen \n4310,0,Car ,Male ,25,HS ,62105,453,0,2,Citizen \n4311,0,Home,Male ,49,HS ,92534,588,7,2,Citizen \n4312,0,Car ,Male ,37,HS ,102801,595,1,2,Citizen \n4313,0,Home,Male ,32,HS ,76153,604,3,2,Citizen \n4314,0,Car ,Female,52,HS ,89526,536,0,2,Citizen \n4315,0,Home,Male ,42,HS ,102301,577,6,2,Citizen \n4316,0,Car ,Female,43,HS ,107459,660,1,2,Citizen \n4317,0,Home,Male ,26,HS ,87524,550,8,2,Citizen \n4318,0,Home,Male ,42,HS ,137442,598,5,2,Citizen \n4319,0,Car ,Female,40,College ,104028,710,2,2,Citizen \n4320,1,Car ,Male ,45,College ,128576,710,1,2,Citizen \n4321,0,Car ,Female,44,HS ,100396,592,1,2,Citizen \n4322,0,Home,Male ,33,HS ,94745,597,1,2,Citizen \n4323,0,Home,Female,53,HS ,115266,589,5,1,Citizen \n4324,0,Home,Male ,43,HS ,85315,515,5,2,Citizen \n4325,0,Home,Female,47,HS ,135915,650,11,2,Citizen \n4326,0,Home,Female,38,HS ,79057,558,5,2,Citizen \n4327,0,Home,Male ,38,HS ,96622,575,5,2,Citizen \n4328,0,Home,Female,44,HS ,108746,547,8,2,Citizen \n4329,0,Car ,Female,35,HS ,76218,540,0,2,Citizen \n4330,0,Car ,Female,36,College ,86890,608,2,2,Citizen \n4331,0,Car ,Female,48,College ,109879,659,3,1,Citizen \n4332,0,Car ,Male ,44,HS ,82191,546,1,2,Citizen \n4333,0,Car ,Male ,40,College ,101265,747,2,2,Citizen \n4334,0,Car ,Male ,33,HS ,100170,586,1,2,Citizen \n4335,0,Home,Male ,43,HS ,90942,619,7,2,Citizen \n4336,0,Car ,Female,45,College ,108406,611,2,2,Citizen \n4337,0,Home,Male ,34,HS ,89536,610,6,2,Citizen \n4338,0,Car ,Male ,43,Graduate,99401,654,2,2,Citizen \n4339,0,Home,Male ,49,HS ,112824,556,5,2,Citizen \n4340,0,Car ,Female,36,College ,95384,571,0,2,Citizen \n4341,0,Car ,Male ,40,HS ,107593,505,0,2,Citizen \n4342,0,Car ,Female,46,College ,86351,600,0,2,Citizen \n4343,1,Home,Male ,54,College ,80685,611,6,2,Citizen \n4344,0,Car ,Male ,40,College ,98374,549,0,2,Citizen \n4345,0,Car ,Male ,47,HS ,100781,639,0,1,Citizen \n4346,0,Car ,Male ,45,HS ,99581,589,0,2,Citizen \n4347,0,Car ,Male ,42,HS ,61637,513,1,2,Citizen \n4348,0,Home,Male ,33,HS ,74852,560,5,2,Citizen \n4349,0,Car ,Male ,38,HS ,80880,618,1,2,Citizen \n4350,0,Car ,Male ,33,HS ,108926,709,1,2,Citizen \n4351,0,Home,Male ,39,HS ,119078,657,5,2,Citizen \n4352,0,Car ,Male ,38,College ,88484,558,1,2,Citizen \n4353,0,Car ,Female,40,HS ,97092,716,0,1,Citizen \n4354,0,Home,Female,46,College ,107100,677,2,2,Citizen \n4355,0,Car ,Male ,37,HS ,107586,687,1,2,Citizen \n4356,1,Car ,Male ,53,HS ,137569,652,2,2,Citizen \n4357,0,Car ,Male ,36,HS ,60038,604,0,2,Citizen \n4358,0,Car ,Male ,44,HS ,97654,570,3,2,Citizen \n4359,0,Home,Male ,43,HS ,103978,696,4,2,Citizen \n4360,0,Home,Female,33,HS ,83560,604,1,2,Citizen \n4361,0,Home,Female,49,HS ,75149,622,5,2,Citizen \n4362,0,Home,Male ,41,HS ,84918,659,5,2,Citizen \n4363,0,Car ,Female,30,HS ,68454,540,0,2,Citizen \n4364,0,Car ,Female,47,HS ,123862,648,1,2,Citizen \n4365,0,Car ,Female,48,HS ,72965,545,0,2,Citizen \n4366,0,Car ,Male ,41,HS ,81326,635,2,2,Citizen \n4367,0,Car ,Female,47,HS ,82698,583,2,1,Citizen \n4368,0,Car ,Male ,30,HS ,106459,530,0,2,Citizen " }, { "path": "March Cohort 2023/12_15_16_17_18_Statistics/datasets/forbes.csv", "content": "Rank,Company,Country,Sales,Profits,Assets,Market Value,Sector,Industry\n1,ICBC,China,151.4,42,3473.2,229.8,Financials,Major Banks\n2,China Construction Bank,China,134.2,35,3016.6,200.5,Financials,Regional Banks\n3,Berkshire Hathaway,United States,222.9,24.1,620.9,409.9,Financials,Investment Services\n4,JPMorgan Chase,United States,102.5,24.2,2513,306.6,Financials,Major Banks\n5,Wells Fargo,United States,97.6,21.9,1943.4,274.4,Financials,Major Banks\n6,Agricultural Bank of China,China,115.7,27.8,2816,149.2,Financials,Regional Banks\n7,Bank of America,United States,92.2,16.6,2196.8,231.9,Financials,Major Banks\n8,Bank of China,China,113.1,24.9,2611.5,141.3,Financials,Major Banks\n9,Apple,United States,217.5,45.2,331.1,752,Information Technology,Computer Hardware\n10,Toyota Motor,Japan,249.9,17.1,412.5,171.9,Consumer Discretionary,Auto & Truck Manufacturers\n11,AT&T,United States,163.8,13,403.8,249.3,Telecommunication Services,Telecommunications services\n12,Citigroup,United States,84,14.7,1795.1,164.3,Financials,Major Banks\n13,Exxon Mobil,United States,197.5,7.8,330.3,343.2,Energy,\n14,General Electric,United States,119.7,10,365.2,261.2,Industrials,Conglomerates\n15,Samsung Electronics,South Korea,174,19.3,217.1,254.3,Information Technology,Semiconductors\n16,Ping An Insurance Group,China,106.6,9.5,801,100.8,Financials,Diversified Insurance\n17,Wal-Mart Stores,United States,485.3,13.6,198.8,221.1,Consumer Discretionary,Discount Stores\n18,Verizon Communications,United States,126,13.1,244.2,198.4,Telecommunication Services,Telecommunications services\n19,Microsoft,United States,85.3,16.8,224.6,507.5,Information Technology,Software & Programming\n20,Royal Dutch Shell,Netherlands,234.8,4.7,411.3,228.8,Energy,Oil & Gas Operations\n21,Allianz,Germany,115.7,7.6,935.9,83.7,Financials,Diversified Insurance\n22,China Mobile,Hong Kong,106.8,16.4,218.9,225.3,Telecommunication Services,Telecommunications services\n23,BNP Paribas,France,74.7,8.4,2190.7,80.5,Financials,Major Banks\n24,Alphabet,United States,89.9,19.5,167.5,579.5,Information Technology,\n25,China Petroleum & Chemical,China,255.7,7,216.7,105.1,Energy,\n26,Total,France,128.1,6.2,231,128.1,Energy,Oil & Gas Operations\n27,AXA Group,France,132.2,6.2,925.9,60.8,Financials,Diversified Insurance\n28,Daimler,Germany,169.5,9.4,256.3,76.1,Consumer Discretionary,Auto & Truck Manufacturers\n29,Volkswagen Group,Germany,240.3,5.7,458.7,72.9,Consumer Discretionary,Auto & Truck Manufacturers\n30,Mitsubishi UFJ Financial,Japan,49.2,8.2,2589.8,83.9,Financials,Major Banks\n31,Comcast,United States,80.4,8.7,180.5,193.5,Consumer Discretionary,Broadcasting & Cable\n32,Johnson & Johnson,United States,71.9,16.4,141.2,338.6,Health Care,Medical Equipment & Supplies\n33,Banco Santander,Spain,48.3,6.9,1412.4,89.4,Financials,Major Banks\n34,Bank of Communications,China,53,10.1,1209.2,62.2,Financials,Major Banks\n35,Nestle,Switzerland,90.8,8.7,129.8,229.5,Consumer Staples,Food Processing\n36,UnitedHealth Group,United States,184.9,7.2,125,160.2,Health Care,Managed Health Care\n37,Nippon Telegraph & Tel,Japan,105,7.4,180.3,92.2,Telecommunication Services,Telecommunications services\n38,Itaú Unibanco Holding,Brazil,61.3,6.7,419.9,79.2,Financials,\n39,Softbank,Japan,82.1,8.3,213.2,78.6,Telecommunication Services,Telecommunications services\n40,Gazprom,Russia,91.4,12.1,265.4,51.8,Energy,Oil & Gas Operations\n41,General Motors,United States,166.4,9.4,221.7,50.8,Consumer Discretionary,Auto & Truck Manufacturers\n42,China Merchants Bank,China,44.5,9.4,855.1,66.4,Financials,Major Banks\n43,IBM,United States,79.9,11.9,117.5,162.4,Information Technology,Computer Services\n44,Royal Bank of Canada,Canada,35.3,8.3,891.1,107.2,Financials,Major Banks\n45,Japan Post Holdings,Japan,123.7,3.1,2522.1,55.1,Consumer Discretionary,\n46,Procter & Gamble,United States,65.2,9.5,117,228.1,Consumer Staples,Household/Personal Care\n47,Pfizer,United States,52.8,7.1,171.6,203.1,Health Care,Pharmaceuticals\n48,HSBC Holdings,United Kingdom,62.1,2.5,2375,162.6,Financials,Major Banks\n49,Goldman Sachs Group,United States,36.9,7.4,860.2,90.6,Financials,Investment Services\n50,Siemens,Germany,88.4,6.4,133.1,109.8,Industrials,Conglomerates\n51,BMW Group,Germany,104.2,7.6,210.3,57.7,Consumer Discretionary,Auto & Truck Manufacturers\n52,China Life Insurance,China,82.8,2.9,388.7,98.1,Financials,Life & Health Insurance\n53,ING Group,Netherlands,53.6,5.1,891.3,57.9,Financials,Life & Health Insurance\n54,Intel,United States,59.4,10.3,113.3,170.4,Information Technology,Semiconductors\n55,Postal Savings Bank Of China,China,48,6,1189.4,55.2,Financials,\n56,Sberbank,Russia,43,8.1,415.6,63.9,Financials,Regional Banks\n57,TD Bank Group,Canada,31.9,6.9,910.4,92,Financials,Major Banks\n58,Cisco Systems,United States,48.6,9.8,126.2,165.1,Information Technology,Communications Equipment\n59,Commonwealth Bank,Australia,30.9,7.1,703.6,109.9,Financials,Major Banks\n60,Morgan Stanley,United States,37.9,6,816,78.3,Financials,Investment Services\n61,Novartis,Switzerland,48.5,6.7,130.4,193.2,Health Care,Pharmaceuticals\n62,Banco Bradesco,Brazil,70.2,4.3,362.4,53.5,Financials,Regional Banks\n63,Industrial Bank,China,44.3,8.1,872.1,45.1,Financials,Regional Banks\n64,Ford Motor,United States,151.8,4.6,238,44.7,Consumer Discretionary,Auto & Truck Manufacturers\n65,Shanghai Pudong Development,China,40.1,8,842.8,50.5,Financials,Regional Banks\n66,CVS Health,United States,177.5,5.3,94.5,79.8,Financials,\n67,Walt Disney,United States,54.9,9,91.6,178,Consumer Discretionary,Broadcasting & Cable\n68,Prudential,United Kingdom,96.9,2.6,569,53.7,Financials,Life & Health Insurance\n69,Prudential Financial,United States,56.3,4.3,784,45.6,Financials,Life & Health Insurance\n70,Oracle,United States,37.4,8.9,125.4,182.2,Information Technology,Software & Programming\n71,China State Construction Engineering,China,140.8,4.9,201.4,43.2,Industrials,\n72,Citic Pacific,Hong Kong,49.1,5.6,933.6,41.4,Materials,Iron & Steel\n73,Boeing,United States,94.6,4.9,90,108.8,Industrials,Aerospace & Defense\n74,Honda Motor,Japan,127.9,3.9,161.8,51.4,Consumer Discretionary,Auto & Truck Manufacturers\n75,China Minsheng Banking,China,40.2,7.2,848.7,43.7,Financials,Regional Banks\n76,Westpac Banking Group,Australia,27.8,5.5,642.7,86.1,Financials,Major Banks\n77,Deutsche Telekom,Germany,80.9,3,156.6,80,Telecommunication Services,Telecommunications services\n78,China Citic Bank,China,39.7,6.3,853.5,43.1,Financials,Regional Banks\n79,Roche Holding,Switzerland,51.3,9.7,75.6,219.3,Health Care,Pharmaceuticals\n80,UBS,Switzerland,38,3.4,920,60,Financials,Investment Services\n81,Bank of Nova Scotia,Canada,26.7,5.5,680.3,70.5,Financials,Major Banks\n82,Rosneft,Russia,74.9,2.7,193.2,62.4,Energy,Oil & Gas Operations\n83,Amazon.com,United States,136,2.4,86,427,Consumer Discretionary,Internet & Catalog Retail\n84,PepsiCo,United States,62.8,6.3,74.1,159.4,Consumer Staples,Beverages\n85,Sumitomo Mitsui Financial,Japan,30.5,5.2,1648.8,48.1,Financials,Major Banks\n86,Coca-Cola,United States,41.5,6.5,87.3,182.9,Consumer Staples,Beverages\n87,United Technologies,United States,57.4,5,89.7,90.6,Industrials,Conglomerates\n88,Sanofi,France,37.4,5.2,110.4,116.1,Health Care,Pharmaceuticals\n89,Bayer,Germany,51.8,5,90.1,94.4,Materials,Diversified Chemicals\n90,Mizuho Financial,Japan,29,6,1752.3,44.4,Financials,Major Banks\n91,Zurich Insurance Group,Switzerland,67.3,3.2,364.3,40,Financials,Diversified Insurance\n92,ANZ,Australia,26.5,4.2,701,68.8,Financials,Major Banks\n93,BASF,Germany,63.7,4.5,83.9,91,Materials,Diversified Chemicals\n94,Walgreens Boots Alliance,United States,116.1,4.2,72.5,89,Consumer Discretionary,\n95,Nissan Motor,Japan,105.9,4.5,157.3,38.4,Consumer Discretionary,Auto & Truck Manufacturers\n96,US Bancorp,United States,22,5.9,451,86.6,Financials,Major Banks\n97,American Express,United States,34,5.4,158.9,70.1,Financials,Consumer Financial Services\n98,Hon Hai Precision,Taiwan,135.2,4.6,80.4,54.4,Information Technology,Electronics\n99,Enel,Italy,75.9,2.8,164.1,47.5,Utilities,Electric Utilities\n100,Merck,United States,39.6,3.9,95.4,173.3,Health Care,Pharmaceuticals\n101,National Australia Bank,Australia,24.2,4.3,595.3,65,Financials,Major Banks\n102,PetroChina,China,214.8,1.2,344.9,204.5,Energy,Oil & Gas Operations\n103,Unilever,Netherlands,58.3,5.7,59.5,143.9,Consumer Staples,Food Processing\n104,Hyundai Motor,South Korea,80.7,4.7,148.1,34.2,Consumer Discretionary,Auto & Truck Manufacturers\n105,BBVA-Banco Bilbao Vizcaya,Spain,27.3,3.8,771.9,49.6,Financials,Major Banks\n106,Reliance Industries,India,41.8,4.3,97.9,71.2,Energy,Oil & Gas Operations\n107,Charter Communications,United States,29,3.5,153.2,101.6,Consumer Discretionary,Broadcasting & Cable\n108,SAIC Motor,China,112.7,4.8,85,43.5,Consumer Discretionary,Auto & Truck Manufacturers\n109,Chubb,Switzerland,31.9,4.1,159.8,64,Financials,Property & Casualty Insurance\n110,Telefónica,Spain,57.6,2.6,130.4,55.9,Telecommunication Services,\n111,AIA Group,Hong Kong,26.5,4.2,183,77.1,Financials,Life & Health Insurance\n112,Société Générale,France,28,4,1457.9,39.9,,\n113,Dow Chemical,United States,48.1,4.3,79.5,77,Materials,Diversified Chemicals\n114,Home Depot,United States,94.6,8,43,176.7,Consumer Discretionary,Home Improvement Retail\n115,Lloyds Banking Group,United Kingdom,23.4,2.8,1010.5,55.9,Financials,Major Banks\n116,Medtronic,Ireland,29.4,4,97.6,110,Health Care,Medical Equipment & Supplies\n117,Kraft Heinz Company,United States,26.5,3.6,120.5,110.4,Consumer Staples,\n118,Saudi Basic Industries,Saudi Arabia,35.4,4.8,84.5,80.7,Materials,Diversified Chemicals\n119,Facebook,United States,27.6,9.5,65,407.3,Information Technology,Computer Services\n120,Munich Re,Germany,68.8,2.9,286.9,30.5,Financials,Diversified Insurance\n121,Intesa Sanpaolo,Italy,24.8,3.4,764.8,45.3,Financials,Major Banks\n122,Barclays,United Kingdom,29.2,2.4,1499,45.4,Financials,Major Banks\n123,Rio Tinto,United Kingdom,33.9,4.7,89.3,72.9,Materials,Diversified Metals & Mining\n124,BHP Billiton,Australia,34,2.4,119.5,98.3,Materials,Diversified Metals & Mining\n125,CK Hutchison,Hong Kong,33.5,4.3,130.7,47.4,Energy,\n126,Anheuser-Busch InBev,Belgium,45.6,1.2,261.1,213.1,Consumer Staples,Beverages\n127,Taiwan Semiconductor,Taiwan,29.4,10.4,58.5,161.7,Information Technology,Semiconductors\n128,Capital One Financial,United States,27.9,3.7,357,40.3,Financials,Consumer Financial Services\n129,LukOil,Russia,74.6,3.1,83.2,44.6,Energy,Oil & Gas Operations\n130,Swiss Re,Switzerland,43.6,3.6,207.6,32.1,Financials,Diversified Insurance\n131,Amgen,United States,22.7,7.7,77.6,120.3,Health Care,Biotechs\n132,Banco do Brasil,Brazil,57.3,2.3,430.6,29,Financials,Major Banks\n133,Bank of Montreal,Canada,19.7,3.8,531.1,48.6,Financials,Major Banks\n134,Gilead Sciences,United States,30.3,13.5,57,87,Health Care,Biotechs\n135,China Communications Construction,China,64.1,2.5,115.3,38.8,Industrials,Construction Services\n136,Honeywell International,United States,39.3,4.7,54.1,94.6,Industrials,Conglomerates\n137,Manulife,Canada,38.9,2.2,511.3,34.4,Consumer Discretionary,\n138,Korea Electric Power,South Korea,51.9,6.1,147.2,25.5,Utilities,Electric Utilities\n139,Generali Group,Italy,90.5,2.3,553,23.7,Financials,Diversified Insurance\n140,Alibaba,China,21.5,5.7,70.7,264.9,Telecommunication Services,\n141,KDDI,Japan,43.2,5.1,52.3,68.3,Telecommunication Services,Telecommunications services\n142,China Telecom,China,53,2.7,93.9,40.4,Telecommunication Services,Telecommunications services\n143,AbbVie,United States,25.6,5.9,66.1,104.5,Health Care,Pharmaceuticals\n144,Tokio Marine Holdings,Japan,44.6,2.8,189.5,30.6,Financials,Property & Casualty Insurance\n145,Iberdrola,Spain,32.3,3,112.5,46.4,Utilities,Electric Utilities\n146,EDF,France,78.8,2.5,297.1,23.3,Utilities,Electric Utilities\n147,PNC Financial Services,United States,16.1,3.9,369.4,57.9,Financials,Regional Banks\n148,Tencent Holdings,China,22.8,6.2,57,277.1,Information Technology,Computer Services\n149,Allergan,Ireland,15,15,129,80.1,Health Care,Pharmaceuticals\n150,China Everbright Bank,China,26.1,4.6,578.5,26.8,Financials,Regional Banks\n151,Credit Agricole,France,18.6,3.5,1607.7,36.5,Financials,Major Banks\n152,Lockheed Martin,United States,49.9,3.8,47.8,78.3,Industrials,Aerospace & Defense\n153,Time Warner,United States,29.3,3.9,66,76.2,Consumer Discretionary,Broadcasting & Cable\n154,Duke Energy,United States,22.7,2.7,132.8,57.6,Utilities,Electric Utilities\n155,Anthem,United States,85.2,2.5,65.1,44,,\n156,Vale,Brazil,27.1,3.8,99.1,45.4,Materials,Iron & Steel\n157,Nordea Bank,Sweden,15.1,4.2,649.6,45.4,Financials,Regional Banks\n158,Express Scripts,United States,100.5,3.4,51.7,39.9,Health Care,Healthcare Services\n159,United Parcel Service,United States,60.5,3.4,40.4,91.3,Industrials,Air Courier\n160,Aetna,United States,63.2,2.3,69.1,45.1,Health Care,Managed Health Care\n161,China Shenhua Energy,China,26.5,3.4,82.3,54.1,Materials,Diversified Metals & Mining\n162,VINCI,France,42.6,2.8,72.9,43.6,Industrials,Construction Services\n163,Bank of New York Mellon,United States,15.6,3.5,333.5,48.8,Financials,Major Banks\n164,Renault,France,56.7,3.8,107.7,24.6,Consumer Discretionary,Auto & Truck Manufacturers\n165,China Railway Group,China,95.4,1.9,108.6,29.6,Industrials,Construction Services\n166,EADS,Netherlands,73.7,1.1,126.2,59.5,Industrials,Aerospace & Defense\n167,China Railway Construction,China,93.2,2.1,109.3,26.5,Industrials,Construction Services\n168,China Vanke,China,33.7,3.1,119.5,30.5,Financials,Real Estate\n169,Jardine Matheson,Hong Kong,37.1,2.5,71.5,47.6,Industrials,Conglomerates\n170,Qualcomm,United States,23.8,4.9,52.4,83.2,Information Technology,Semiconductors\n171,Hewlett Packard Enterprise,United States,48.8,3.2,76.7,30.4,Information Technology,\n172,British American Tobacco,United Kingdom,19.9,6.3,49.1,123.8,Consumer Staples,Tobacco\n173,Visa,United States,16,6,63.4,206.4,Financials,Consumer Financial Services\n174,China Pacific Insurance,China,40.3,1.8,144,33.5,Financials,Life & Health Insurance\n175,MetLife,United States,64.7,0.8,905.2,57.2,Financials,Diversified Insurance\n176,AstraZeneca,United Kingdom,23,3.7,62.5,75.2,Health Care,Pharmaceuticals\n177,Altria Group,United States,19.3,14.2,45.9,138.8,Consumer Staples,Tobacco\n178,SAP,Germany,24.4,4,46.7,119.7,Information Technology,Software & Programming\n179,Costco Wholesale,United States,121.2,2.4,35.6,74.9,Consumer Discretionary,Discount Stores\n180,Travelers,United States,27.7,3,101.7,33.6,,\n181,Philip Morris International,United States,26.7,6.9,36.9,176.2,Consumer Staples,Tobacco\n182,Union Pacific,United States,19.9,4.2,56.8,86.8,Industrials,Railroads\n183,Delta Air Lines,United States,39.6,4.4,58.9,33,Industrials,Airline\n184,Southern Company,United States,19.9,2.5,109.7,49.3,,\n185,Lowe's,United States,65,3.1,34.4,71.1,Energy,\n186,National Grid,United Kingdom,21.9,2.9,81.7,48,Utilities,Natural Gas Utilities\n187,Legal & General Group,United Kingdom,105.5,1.7,571.2,18.2,Financials,Life & Health Insurance\n188,McKesson,United States,196.5,2,57.9,30.5,Health Care,Pharmaceuticals\n189,Christian Dior,France,43.7,2,68.3,42.7,Consumer Discretionary,Apparel/Accessories\n190,PTT PCL,Thailand,48.7,2.6,63.4,32.4,Energy,Oil & Gas Operations\n191,Dai-ichi Life Insurance,Japan,55.7,1.7,438.8,19.9,Financials,Life & Health Insurance\n192,Twenty-First Century Fox,United States,28.1,3.1,49.2,57.5,,\n193,Canadian Imperial Bank,Canada,14.6,3.6,393.7,34.5,Financials,Major Banks\n194,FedEx,United States,57.6,1.9,46.8,52.3,Industrials,Air Courier\n195,GlaxoSmithKline,United Kingdom,37.6,1.2,73,99.8,Health Care,Pharmaceuticals\n196,Deutsche Post,Germany,63.4,2.9,40.6,41,Industrials,Air Courier\n197,3M,United States,30.1,5.1,32.9,113.5,Industrials,Conglomerates\n198,L'Oréal Group,France,28.6,3.4,37.6,107.5,Industrials,\n199,Aflac,United States,22.7,2.7,129.8,29.2,Financials,Life & Health Insurance\n200,Allstate,United States,36.3,1.9,110.4,29.6,Financials,Diversified Insurance\n201,Hitachi,Japan,85.8,1.8,82.7,25.6,Information Technology,Electronics\n202,Brookfield Asset Management,Canada,24.9,1.6,159.8,35.4,Financials,Real Estate\n203,BT Group,United Kingdom,31.7,3.1,54,38.5,Telecommunication Services,Telecommunications services\n204,NextEra Energy,United States,15.2,2.9,90,60.4,Utilities,Electric Utilities\n205,BlackRock,United States,11.2,3.2,220.2,62.6,Financials,Investment Services\n206,Continental,Germany,44.9,3.1,39.4,42.7,Consumer Discretionary,Auto & Truck Parts\n207,MS&AD Insurance,Japan,47.8,2,178.3,19.2,Financials,Property & Casualty Insurance\n208,Aviva,United Kingdom,74.3,0.949,511.1,25.9,Financials,Life & Health Insurance\n209,Cigna,United States,39.7,1.9,60.3,39,Health Care,Managed Health Care\n210,Phillips 66,United States,71.2,1.5,51.7,39.9,Energy,Oil & Gas Operations\n211,Valero Energy,United States,75.7,2.3,46.8,29.4,Energy,Oil & Gas Operations\n212,Enbridge,Canada,26.1,1.6,64,69.3,Energy,Oil Services & Equipment\n213,Mondelēz International,United States,25.9,1.6,61.5,67.4,Consumer Discretionary,\n214,People's Insurance Company,China,64.6,2.1,129.7,17.5,Financials,Property & Casualty Insurance\n215,McDonald's,United States,24.6,4.7,31,106.4,Consumer Discretionary,Restaurants\n216,ArcelorMittal,Luxembourg,56.8,1.8,75.1,25.1,Materials,Iron & Steel\n217,East Japan Railway,Japan,26.5,2.3,66.1,34.2,Industrials,Railroads\n218,Itochu,Japan,44,2.4,72,22.4,Industrials,Trading Companies\n219,Japan Tobacco,Japan,19.7,3.9,40.7,66.4,Consumer Staples,Tobacco\n220,EI du Pont de Nemours,United States,24.6,2.5,40,69.2,Materials,Diversified Chemicals\n221,Eli Lilly,United States,21.2,2.7,38.8,94.1,Health Care,\n222,Panasonic,Japan,66.6,1.9,52.7,26.2,Consumer Discretionary,Consumer Electronics\n223,Sun Hung Kai Properties,Hong Kong,13.4,5,80.1,43.6,Financials,Real Estate\n224,Fiat Chrysler Automobiles,United Kingdom,122.8,2,110.1,15.5,,\n225,LafargeHolcim,Switzerland,27.3,1.8,68.5,35.4,Consumer Discretionary,\n226,Qatar National Bank,Qatar,11.7,3.4,197.6,37.7,Financials,Regional Banks\n227,Target,United States,69.5,2.7,37.4,29.4,Consumer Discretionary,Discount Stores\n228,Fresenius,Germany,32.2,1.8,49,43.9,Health Care,Medical Equipment & Supplies\n229,Danaher,United States,19.9,2.6,45.3,59.7,Industrials,Conglomerates\n230,Danske Bank,Denmark,11.7,2.9,494.2,31.9,Financials,Major Banks\n231,General Dynamics,United States,31.4,3,32.9,57,Industrials,Aerospace & Defense\n232,Reynolds American,United States,12.5,6.1,51.1,90.6,Consumer Staples,Tobacco\n233,Denso,Japan,41.1,2.2,43.6,33.6,Consumer Discretionary,Auto & Truck Parts\n234,Bristol-Myers Squibb,United States,19.4,4.5,33.7,88,Health Care,Pharmaceuticals\n235,CRRC,China,34.2,1.7,48.7,42.6,,\n236,ABB,Switzerland,33.8,2,39.7,49.9,Industrials,Conglomerates\n237,Exelon,United States,32.2,1.1,114.9,33.3,Utilities,Electric Utilities\n238,Cathay Financial,Taiwan,29,1.5,252.2,19.8,Financials,Life & Health Insurance\n239,Sun Life Financial,Canada,19.9,1.9,188.7,21.9,Financials,Life & Health Insurance\n240,Repsol,Spain,38.4,1.9,68.4,23.8,Energy,\n241,Kroger,United States,115.3,1.9,37.5,27.3,Consumer Staples,Food Retail\n242,Abbott Laboratories,United States,20.9,1.4,55,75.7,Health Care,Pharmaceuticals\n243,HCA Holdings,United States,41.5,2.9,33.8,32.6,Health Care,Healthcare Services\n244,State Bank of India,India,43.7,0.667,493.3,36.5,Financials,Regional Banks\n245,DBS Group,Singapore,10.3,3.1,333.5,34.4,Financials,Major Banks\n246,Oil & Natural Gas,India,19.9,2.2,57.7,37.2,Energy,Oil & Gas Operations\n247,American Airlines Group,United States,40.2,2.7,51.3,21.1,Financials,\n248,BB&T,United States,11.3,2.4,221.2,35.6,Financials,Regional Banks\n249,Nike,United States,33.8,4.1,23.2,91.2,Financials,\n250,Schneider Electric,France,27.3,1.9,44.1,41.9,Industrials,Electrical Equipment\n251,Thermo Fisher Scientific,United States,18.3,2,45.9,59.9,Utilities,Precision Healthcare Equipment\n252,Danone,France,24.3,1.9,46.4,42,Consumer Staples,Food Processing\n253,Samsung Life Insurance,South Korea,24.1,1.8,219.1,18.2,Financials,Life & Health Insurance\n254,Air Liquide,France,20.1,2,46.5,45,Materials,Specialized Chemicals\n255,Huaxia Bank,China,15.9,3,340.9,17.6,Financials,Regional Banks\n256,Orix,Japan,22.8,2.4,95.5,19.8,Financials,Consumer Financial Services\n257,Bank of Beijing,China,13.4,2.7,306.3,21.5,Financials,Regional Banks\n258,HDFC Bank,India,11.4,2,110.3,57.4,Financials,Regional Banks\n259,KBC Group,Belgium,11.3,2.7,290.3,27.6,Financials,Major Banks\n260,Deere & Company,United States,26.7,1.5,56.3,34.8,Consumer Staples,\n261,Diageo,United Kingdom,15.2,3.2,37.4,71.2,Consumer Staples,Beverages\n262,Central Japan Railway,Japan,16.1,3.4,51,32.6,Industrials,Railroads\n263,Liberty Global,United Kingdom,20,1.7,68.7,31.2,Consumer Discretionary,Broadcasting & Cable\n264,Indian Oil,India,54.1,1.7,37.3,30,Energy,Oil & Gas Operations\n265,Shinhan Financial Group,South Korea,14.2,2.4,327.6,20,Financials,Investment Services\n266,Canon,Japan,31.3,1.4,44.1,41.6,Industrials,Business Products & Supplies\n267,Synchrony Financial,United States,15.1,2.3,90.2,26.9,Financials,\n268,Saint-Gobain,France,43.2,1.5,46.2,28,Materials,Construction Materials\n269,United Continental Holdings,United States,36.6,2.3,45.4,22.3,Industrials,Airline\n270,Carnival,United States,16.5,3,38.7,43.9,Consumer Discretionary,Hotels & Motels\n271,HP,United States,48.7,2.5,28.2,29.4,Information Technology,\n272,Accenture,Ireland,35.7,3.8,20.1,77.8,Information Technology,Computer Services\n273,Country Garden Holdings,Hong Kong,23,1.7,85.1,20.3,Financials,Real Estate\n274,State Street,United States,10.6,2.1,242.7,30.1,Financials,Investment Services\n275,Volvo Group,Sweden,35.3,1.5,44.7,28.6,Industrials,Heavy Equipment\n276,Inditex,Spain,25.7,3.5,21.2,112.1,Consumer Discretionary,Apparel/Footwear Retail\n277,Seven & I Holdings,Japan,53.9,0.894,49.2,37,Consumer Staples,Food Retail\n278,Mitsubishi Electric,Japan,39.1,1.9,34.1,29.9,Industrials,Electrical Equipment\n279,Bridgestone,Japan,30.7,2.4,31.9,31.7,Consumer Discretionary,Auto & Truck Parts\n280,PG&E,United States,17.7,1.4,70.9,34,Utilities,Electric Utilities\n281,Cardinal Health,United States,127.2,1.4,35,26,Health Care,Pharmaceuticals\n282,Telstra,Australia,18.6,4.1,30.2,40.7,Telecommunication Services,Telecommunications services\n283,Dominion Resources,United States,11.4,2.1,71.6,48.7,Utilities,Electric Utilities\n284,Nomura,Japan,14.8,1.5,369.1,21.2,Financials,\n285,Raytheon,United States,24.1,2.2,30.1,44.9,Industrials,Aerospace & Defense\n286,Henkel,Germany,20.7,2.3,30,52,Consumer Staples,Household/Personal Care\n287,Marathon Petroleum,United States,56,1.2,44.4,26.4,Energy,Oil & Gas Operations\n288,Merck,Germany,16.6,1.8,41.1,49.1,Health Care,Pharmaceuticals\n289,Peugeot,France,59.8,1.9,48.2,16.9,Consumer Discretionary,Auto & Truck Manufacturers\n290,Tata Motors,India,41.2,1.7,39.5,24.9,Consumer Discretionary,Auto & Truck Manufacturers\n291,Archer Daniels Midland,United States,62.4,1.3,39.8,25.6,Consumer Staples,Food Processing\n292,CNP Assurances,France,30.7,1.2,428.1,13.8,Financials,Diversified Insurance\n293,Natixis,France,14.6,1.5,556.7,19.1,Financials,Major Banks\n294,China Huarong Asset Management,China,13.5,3,203.2,16.6,Financials,\n295,Imperial Tobacco Group,United Kingdom,39.2,0.896,42.5,46.5,Consumer Staples,Tobacco\n296,BCE,Canada,16.4,2.3,37.4,40.5,Telecommunication Services,Telecommunications services\n297,Northrop Grumman,United States,24.5,2.2,28,41.7,Industrials,Aerospace & Defense\n298,LyondellBasell Industries,United Kingdom,29.2,3.8,23.4,35.8,Materials,Diversified Chemicals\n299,Posco,South Korea,45.8,1.1,66,19.2,Materials,Iron & Steel\n300,Telecom Italia,Italy,21,2,74.3,18.1,Telecommunication Services,Telecommunications services\n301,Oversea-Chinese Banking,Singapore,8.6,2.5,283.7,28.8,Financials,Regional Banks\n302,KB Financial Group,South Korea,13.5,1.8,312,17.9,Financials,Investment Services\n303,Charles Schwab,United States,7.6,1.9,223.4,53.3,Financials,Investment Services\n304,Orange,France,45.3,0.552,101.5,40.4,Financials,\n305,Surgutneftegas,Russia,13.9,7.8,60.9,18.5,Energy,Oil & Gas Operations\n306,Sompo,Japan,29.4,1.7,91.1,14.4,Financials,\n307,Gas Natural Fenosa,Spain,25.6,1.5,49.7,21.7,Utilities,Natural Gas Utilities\n308,WPP,United Kingdom,19.4,1.9,42.7,27.4,Consumer Discretionary,Advertising\n309,Bank Of Shanghai,China,10.9,2.1,236.3,20.7,Financials,\n310,ICICI Bank,India,11.9,1.6,138.9,25.1,Financials,Regional Banks\n311,Poly Real Estate,China,22.4,2,68.3,16.6,Financials,Real Estate\n312,Hyundai Mobis,South Korea,33,2.6,34.5,18.9,Consumer Discretionary,Auto & Truck Parts\n313,DNB,Norway,8.7,2.2,308.2,25.6,Financials,Regional Banks\n314,Philips,Netherlands,27.1,1.6,34.1,28.9,Industrials,Conglomerates\n315,AmerisourceBergen,United States,148.3,1.3,33.9,19,Health Care,Pharmaceuticals\n316,Heineken Holding,Netherlands,23,1.7,42.1,23,Consumer Staples,Beverages\n317,CRH,Ireland,30.1,1.4,33.3,29,Materials,Construction Materials\n318,Discover Financial Services,United States,10.5,2.4,92.8,25.3,Financials,Consumer Financial Services\n319,Safran,France,18.2,2.1,32.8,32.2,Industrials,Aerospace & Defense\n320,Etisalat,United Arab Emirates,14.3,2.3,33.4,42.7,Telecommunication Services,Telecommunications services\n321,CaixaBank,Spain,11,1.2,367,25.7,Financials,Regional Banks\n322,Eaton,Ireland,19.7,1.9,30.4,33.4,Industrials,Other Industrial Equipment\n323,Fubon Financial,Taiwan,15.4,1.5,196.5,16.1,Financials,Investment Services\n324,Sumitomo,Japan,38.2,1.1,67.7,16.8,Financials,\n325,SunTrust Banks,United States,8.9,1.9,204.9,27.2,Financials,Regional Banks\n326,Greenland Holdings Group,China,32.8,1.2,99.6,14.3,Financials,\n327,China Cinda Asset Management,China,13.4,2.3,169,14.7,Financials,\n328,Anglo American,United Kingdom,21.5,1.6,50.1,19.8,Materials,Diversified Metals & Mining\n329,China Resources Land,Hong Kong,14.5,2.5,55.1,19.8,Financials,Real Estate\n330,Royal Ahold Delhaize N.V.,Netherlands,55,0.918,38.3,26,Utilities,\n331,SingTel,Singapore,11.9,2.8,33,45.2,Telecommunication Services,Telecommunications services\n332,United Overseas Bank,Singapore,8.2,2.2,235.4,25.6,Financials,Regional Banks\n333,Subaru,Japan,29.8,2.8,22.7,27.3,Financials,\n334,Automatic Data Processing,United States,12.1,1.7,40,45.9,Consumer Discretionary,\n335,Midea Group,China,23.9,2.2,24.5,31.5,Utilities,\n336,Linde,Germany,18.7,1.3,39,31.3,Materials,Diversified Chemicals\n337,KIA Motors,South Korea,45.4,2.4,42.1,12.6,Consumer Discretionary,Auto & Truck Manufacturers\n338,Hartford Financial Services,United States,18.1,0.896,225.2,17.9,Financials,Diversified Insurance\n339,Southwest Airlines,United States,20.4,2.2,24.8,33.6,Industrials,Airline\n340,América Móvil,Mexico,52.2,0.463,73.6,47.3,Financials,\n341,Principal Financial Group,United States,12.5,1.3,228,18,Financials,Life & Health Insurance\n342,Fannie Mae,United States,108.3,12.3,3288,2.8,Financials,Investment Services\n343,Ameriprise Financial,United States,11.7,1.3,139.8,19.9,Financials,Investment Services\n344,Macquarie Group,Australia,10.6,1.5,147.8,21.2,Financials,Investment Services\n345,Carrefour,France,87.1,0.825,51.5,16.6,Consumer Staples,Food Retail\n346,Celgene,United States,11,2,28.1,97.7,Health Care,Biotechs\n347,China Evergrande Group,China,32.4,0.764,194.4,14.6,Health Care,\n348,Takeda Pharmaceutical,Japan,15.9,1.2,35.5,36.9,Health Care,Pharmaceuticals\n349,SK Hynix,South Korea,14.8,2.5,26.7,32,Information Technology,Semiconductors\n350,Saudi Telecom,Saudi Arabia,13.8,2.3,27.1,37.3,Telecommunication Services,Telecommunications services\n351,Tyson Foods,United States,36.9,1.9,22.3,24.2,Consumer Staples,Food Processing\n352,Sinohydro Group,China,34.6,0.862,71.2,16.5,Industrials,Construction Services\n353,Reckitt Benckiser Group,United Kingdom,13.3,2.5,22.3,64.1,Consumer Staples,Household/Personal Care\n354,CSX,United States,11.1,1.7,36,43.8,Industrials,Railroads\n355,Freddie Mac,United States,69.8,7.8,2023.4,1.5,Financials,Investment Services\n356,Lincoln National,United States,13.4,1.2,263.5,14.8,Financials,Life & Health Insurance\n357,Kinder Morgan,United States,12.9,0.704,80.3,48.1,Energy,Oil Services & Equipment\n358,Power Corp of Canada,Canada,35.4,0.856,311.2,10.8,Financials,Life & Health Insurance\n359,BP,United Kingdom,183.8,0.115,263.3,114.7,Energy,Oil & Gas Operations\n360,Chevron,United States,110.5,-0.497,260.1,206.1,Energy,Oil & Gas Operations\n361,Michelin Group,France,23.1,1.8,26.7,21.6,Consumer Discretionary,Auto & Truck Parts\n362,Cheung Kong Property Holdings,Hong Kong,9,2.5,51.2,26.2,Financials,\n363,SK Holdings,South Korea,72.1,0.66,85.3,14.7,Energy,Oil & Gas Operations\n364,Gree Electric Appliances,China,15.3,2.1,27.2,27.7,Consumer Discretionary,Household Appliances\n365,BAE Systems,United Kingdom,24,1.2,29.1,25.6,Industrials,Aerospace & Defense\n366,Bank Of Jiangsu,China,9.7,1.6,230,16.6,Financials,\n367,Edison International,United States,11.9,1.3,51.3,26.1,Utilities,Electric Utilities\n368,Kansai Electric Power,Japan,28.3,1.6,58.5,11.3,Utilities,Electric Utilities\n369,New China Life Insurance,China,22,0.751,100.5,17.6,Financials,Life & Health Insurance\n370,Starbucks,United States,21.7,2.9,14,84.6,Consumer Discretionary,Restaurants\n371,Centrica,United Kingdom,36.6,2.3,27.1,14.8,Utilities,Natural Gas Utilities\n372,Biogen,United States,10.5,3.7,22.9,58,Health Care,\n373,Canadian National Railway,Canada,9.1,2.7,28,55.9,Industrials,Railroads\n374,HDFC,India,8.1,1.6,64.9,36.7,Financials,Consumer Financial Services\n375,Novo Nordisk,Denmark,16.6,5.6,13.8,88.2,Health Care,Pharmaceuticals\n376,Swedbank,Sweden,6.3,2.3,237.1,25.1,Financials,Major Banks\n377,Tata Consultancy Services,India,17.4,3.9,14.5,74.5,Information Technology,Computer Services\n378,Monsanto,United States,14.5,1.9,21.4,50.9,Materials,Specialized Chemicals\n379,Mastercard,United States,10.8,4.1,18.7,121.3,Information Technology,\n380,Femsa,Mexico,20.9,1.1,26.5,33.1,Consumer Staples,Beverages\n381,PayPal,United States,10.8,1.4,33.1,51.7,Consumer Discretionary,\n382,Progressive,United States,23.5,1,34.3,22.8,Financials,Property & Casualty Insurance\n383,Svenska Handelsbanken,Sweden,6.4,1.9,289.2,25.9,Financials,Major Banks\n384,Mitsui Fudosan,Japan,14.9,1.2,46.7,21.1,Financials,Real Estate\n385,SEB,Sweden,7.7,1.2,288.5,23.8,Financials,Major Banks\n386,Bank Rakyat Indonesia,Indonesia,8.3,2,74.5,24.2,Financials,Regional Banks\n387,Texas Instruments,United States,13.4,3.4,16.4,80.5,Information Technology,Semiconductors\n388,Wilmar International,Singapore,41.4,0.984,37,16.1,Consumer Staples,Food Processing\n389,Daiwa House Industry,Japan,31.9,1.2,29.6,19.1,Industrials,Construction Services\n390,Baidu,China,10.6,1.7,26.2,59.9,Information Technology,Computer Services\n391,Consolidated Edison,United States,12.1,1.2,48.3,23.8,Utilities,Electric Utilities\n392,Maybank,Malaysia,7.6,1.6,164.1,20.7,Financials,Regional Banks\n393,DISH Network,United States,15.1,1.4,28.2,29.1,Consumer Discretionary,Broadcasting & Cable\n394,Citic Securities,China,7.6,1.6,86,27.9,Financials,Investment Services\n395,American Electric,United States,16.2,0.613,66.2,33.1,Utilities,Electric Utilities\n396,Suzuki Motor,Japan,28.3,1.4,25.9,19.9,Consumer Discretionary,Auto & Truck Manufacturers\n397,VTB Bank,Russia,19.5,0.699,206.2,14.9,Financials,Regional Banks\n398,Metallurgical Corp of China,China,32.4,0.806,54.3,15.4,Industrials,Construction Services\n399,Petrobras,Brazil,81.1,-4.3,247.3,61.3,Energy,Oil & Gas Operations\n400,Norfolk Southern,United States,9.9,1.7,34.9,36,Industrials,Railroads\n401,TJX Cos,United States,33.2,2.3,12.9,49.1,Consumer Discretionary,Department Stores\n402,Investor AB,Sweden,7.3,3.9,40.7,32.2,Financials,\n403,American International Group,United States,52.4,-0.849,498.3,60.1,Financials,Diversified Insurance\n404,Sempra Energy,United States,10.2,1.3,47.8,27.5,Utilities,Natural Gas Utilities\n405,International Airlines,United Kingdom,25,2.2,28.9,13.7,Industrials,Airline\n406,SSE,United Kingdom,37.2,1.1,27.8,18.7,Utilities,Electric Utilities\n407,eBay,United States,9,7.3,23.8,36.6,Consumer Discretionary,Internet & Catalog Retail\n408,NTPC,India,12.2,1.6,38.7,21.5,Utilities,Electric Utilities\n409,Old Mutual,United Kingdom,22.1,0.769,208.1,11.7,Financials,Investment Services\n410,Magna International,Canada,36.4,2,22.6,15.3,Consumer Discretionary,Auto & Truck Parts\n411,China Yangtze Power,China,6.5,2.8,45.3,42.3,Utilities,Electric Utilities\n412,NN Group,Netherlands,15.6,1.3,177.5,10.3,Energy,\n413,Sumitomo Mitsui Trust,Japan,9.2,1.5,548.2,12.8,Financials,Major Banks\n414,General Mills,United States,15.7,1.6,21.7,33.2,Consumer Staples,Food Processing\n415,Talanx,Germany,34.7,1,158.2,8.9,Financials,Diversified Insurance\n416,The Priceline Group,United States,10.7,2.1,19.8,87,Financials,\n417,EXOR,Italy,152.6,0.483,193,11.9,Financials,Investment Services\n418,Glencore International,Switzerland,153.7,-0.453,124.6,57.3,Materials,Diversified Metals & Mining\n419,Vodafone,United Kingdom,60.8,-9.3,175.7,67,Telecommunication Services,Telecommunications services\n420,Huaneng Power International,China,16.9,1.3,44.5,14.4,Utilities,Electric Utilities\n421,Standard Bank Group,South Africa,8.6,1.5,142.9,15.9,Financials,Major Banks\n422,Swiss Life Holding,Switzerland,19.5,0.936,197,10.2,Financials,Life & Health Insurance\n423,FirstRand,South Africa,8.4,1.7,86.3,18.2,Financials,Major Banks\n424,Sysco,United States,53.1,1,17.6,28,Consumer Staples,Food Retail\n425,International Paper,United States,21.1,0.904,33.3,20.8,Materials,Paper & Paper Products\n426,Vivendi,France,12,1.4,34.1,24.2,Telecommunication Services,Telecommunications services\n427,Kimberly-Clark,United States,18.2,2.2,14.6,46.7,Consumer Staples,Household/Personal Care\n428,National Commercial Bank,Saudi Arabia,6.1,2.5,117.7,20.6,Financials,\n429,Erste Group Bank,Austria,9.2,1.4,220.8,13.5,Financials,Major Banks\n430,Baoshan Iron & Steel,China,26.8,0.667,40,21.4,Materials,Iron & Steel\n431,Eni,Italy,61.6,-1.6,135.9,58.2,Energy,Oil & Gas Operations\n432,Saudi Electricity,Saudi Arabia,13.3,0.561,107.4,26.6,Utilities,Electric Utilities\n433,Corning,United States,9.4,3.7,27.9,24.9,Information Technology,Communications Equipment\n434,Daikin Industries,Japan,18.5,1.4,20.6,28.4,Materials,Construction Materials\n435,Mitsubishi Chemical,Japan,31.8,1.4,38.1,11.1,Materials,Diversified Chemicals\n436,Becton Dickinson,United States,12.4,1.3,24.3,39.2,Health Care,\n437,SK Innovation,South Korea,34.1,1.4,27,13.5,Energy,Oil & Gas Operations\n438,Humana,United States,54.4,0.594,25.8,30.4,Health Care,Managed Health Care\n439,Emerson Electric,United States,17.2,1.5,17.5,38.3,Industrials,Electrical Equipment\n440,Nippon Steel & Sumitomo Metal,Japan,41.6,0.477,54.6,20.2,Materials,Iron & Steel\n441,Deutsche Bank,Germany,44.3,-1.9,1686.6,34,Financials,Major Banks\n442,Toyota Industries,Japan,20,1.1,38.6,14.8,Consumer Discretionary,Auto & Truck Parts\n443,Pernod Ricard,France,9.7,1.4,33.5,31.4,Consumer Staples,Beverages\n444,Aon,United Kingdom,11.6,1.4,26.6,31.4,Financials,Insurance Brokers\n445,IntercontinentalExchange,United States,6,1.4,82,36.2,Financials,Investment Services\n446,Stryker,United States,11.3,1.6,20.4,48.8,Health Care,Medical Equipment & Supplies\n447,Fujifilm Holdings,Japan,21.7,1.1,28.6,18.1,Consumer Discretionary,Consumer Electronics\n448,Fosun International,China,11.1,1.5,70,13.1,Materials,Iron & Steel\n449,Sony,Japan,69.2,-0.393,151.7,40.4,Consumer Discretionary,Consumer Electronics\n450,Chubu Electric Power,Japan,24.2,1.5,45.6,10,Utilities,Electric Utilities\n451,Las Vegas Sands,United States,11.4,1.7,20.5,44.6,Consumer Discretionary,Casinos & Gaming\n452,Xcel Energy,United States,11.1,1.1,41.2,22.5,Utilities,Electric Utilities\n453,Fifth Third Bancorp,United States,6.7,1.6,142.2,18.8,Financials,Regional Banks\n454,Bouygues,France,35.1,0.81,36.8,14.5,Industrials,Construction Services\n455,ENGIE,France,73.7,-0.459,167.9,34.8,,\n456,Steinhoff International,Netherlands,14.5,1.3,27.5,21.1,Consumer Discretionary,Furniture & Fixtures\n457,Wheelock,Hong Kong,7.8,2.1,67.1,16.7,,\n458,Marsh & McLennan,United States,13.2,1.8,18.2,37.8,Financials,Insurance Brokers\n459,CBS,United States,13.2,1.3,24.2,29.5,Consumer Discretionary,Broadcasting & Cable\n460,Fujitsu,Japan,41.7,1.2,27.1,12.5,Information Technology,Computer Hardware\n461,Statoil,Norway,45.7,-2.9,104.5,56.2,Energy,Oil & Gas Operations\n462,Wesfarmers,Australia,50,0.439,30,37.8,Consumer Staples,Food Retail\n463,Axis Bank,India,7.8,1.3,80.4,18.8,Financials,Regional Banks\n464,HeidelbergCement,Germany,16.8,0.782,40.4,18.5,Materials,Construction Materials\n465,Illinois Tool Works,United States,13.6,2,15.2,45.5,Industrials,Other Industrial Equipment\n466,Formosa Petrochemical,Taiwan,16.9,2.3,14,33.6,Energy,Oil & Gas Operations\n467,Marriott International,United States,17.1,0.78,24.1,34.9,Consumer Discretionary,Hotels & Motels\n468,Adidas,Germany,21.3,1.1,16,39.6,Consumer Discretionary,Apparel/Accessories\n469,CLP Holdings,Hong Kong,10.2,1.6,26.6,26.8,Utilities,Electric Utilities\n470,Thomson Reuters,United States,11.2,1.1,27.9,31.4,Consumer Discretionary,Printing & Publishing\n471,Kering,France,13.7,0.913,25.5,33.7,Consumer Discretionary,Apparel/Accessories\n472,Colgate-Palmolive,United States,15.2,2.4,12.1,64.9,Consumer Staples,Household/Personal Care\n473,Richemont,Switzerland,11.5,1.1,22.6,44.3,Consumer Discretionary,Specialty Stores\n474,Shin-Etsu Chemical,Japan,11.3,1.5,21,36.2,Materials,Diversified Chemicals\n475,Mapfre,Spain,23.3,0.858,67.4,10.5,Financials,Diversified Insurance\n476,Poste Italiane,Italy,36.6,0.688,203.8,8.6,Consumer Discretionary,\n477,Aisin Seiki,Japan,31.7,1.1,27.7,13.2,Consumer Discretionary,Auto & Truck Parts\n478,Mitsubishi,Japan,57,-0.164,129.1,34.7,Utilities,\n479,Mitsubishi Estate,Japan,10.3,0.869,46.3,25.4,Financials,Real Estate\n480,PPL,United States,7.5,1.9,38.3,25.4,Utilities,Electric Utilities\n481,Waste Management,United States,13.6,1.2,21.3,32.3,Industrials,Environmental & Waste\n482,Micron Technology,United States,14.7,0.689,32.4,31.6,Information Technology,Semiconductors\n483,Larsen & Toubro,India,15.7,0.814,29.8,24.5,Industrials,Construction Services\n484,Aegon,Netherlands,36.6,0.501,437.4,10,Financials,Diversified Insurance\n485,Teva Pharmaceutical,Israel,21.9,0.337,92.9,32.4,Health Care,\n486,Compass Group,United Kingdom,27.8,1.4,13.7,31.1,Consumer Discretionary,Restaurants\n487,Suncor Energy,Canada,20.2,0.328,66.1,51.7,Energy,Oil & Gas Operations\n488,EDP-Energias de Portugal,Portugal,16.1,1.1,46.5,12.3,Utilities,Electric Utilities\n489,Syngenta,Switzerland,12.8,1.2,19.5,40.9,Materials,Specialized Chemicals\n490,Tenaga Nasional,Malaysia,10.9,1.7,30.9,17.4,Utilities,Electric Utilities\n491,Credit Suisse Group,Switzerland,29.9,-2.8,806.7,30.1,Financials,Investment Services\n492,Hana Financial Group,South Korea,11.1,1.1,288.3,9.7,Financials,Regional Banks\n493,Sampo,Finland,6.9,1.8,39.8,26.5,Financials,Diversified Insurance\n494,Bank Mandiri,Indonesia,7.5,1,77.1,21.4,Financials,Regional Banks\n495,Schlumberger,United States,27.8,-1.7,78,109.2,Energy,Oil Services & Equipment\n496,UniCredit Group,Italy,24.4,-13,906.6,33.3,Financials,Major Banks\n497,M&T Bank,United States,5.7,1.3,123.4,23.5,Financials,Regional Banks\n498,Thales,France,16.5,1,23.9,20.6,Industrials,Aerospace & Defense\n499,Applied Materials,United States,11.8,2.1,15.2,42,Information Technology,Semiconductors\n500,Royal Bank of Scotland,United Kingdom,21.6,-7.1,986.9,35,Financials,Major Banks\n501,SK Telecom,South Korea,14.7,1.4,25.9,16.3,Telecommunication Services,Telecommunications services\n502,TE Connectivity,Switzerland,12.5,2.1,17.6,26,Information Technology,Electronics\n503,Caterpillar,United States,38.5,-0.67,74.7,56,Industrials,Heavy Equipment\n504,China Merchants Shekou Industrial Zone Holdings,China,8.6,1.4,36.1,20.8,Financials,\n505,Ecolab,United States,13,1.2,18.3,36.3,Materials,Specialized Chemicals\n506,H&M,Sweden,22.7,2.2,10.9,39.9,Consumer Discretionary,Apparel/Footwear Retail\n507,Omnicom Group,United States,15.5,1.1,23.2,20.1,Consumer Discretionary,Advertising\n508,Chinatrust Financial,Taiwan,11,0.866,150.2,11.8,Financials,Consumer Financial Services\n509,Komatsu,Japan,15.7,0.939,23,24.3,Industrials,Heavy Equipment\n510,Haitong Securities,China,6.3,1.2,80.7,22.9,Financials,Investment Services\n511,Grupo ACS,Spain,36.3,0.831,35.2,11.2,Industrials,Construction Services\n512,Resona Holdings,Japan,6.7,1.7,410.7,11.7,Financials,Regional Banks\n513,Bharti Airtel,India,14.7,0.702,34.8,21.4,Telecommunication Services,Telecommunications services\n514,Deutsche Lufthansa,Germany,35,2,37.8,7.4,Industrials,Airline\n515,Al Rajhi Bank,Saudi Arabia,4.5,2.2,90.5,27.6,Financials,Regional Banks\n516,Swisscom,Switzerland,11.8,1.6,21.1,22.5,Telecommunication Services,Telecommunications services\n517,Couche Tard,Canada,35.7,1.1,13.8,25.9,Consumer Discretionary,Specialty Stores\n518,Loews,United States,13,0.654,76.6,15.9,Financials,Diversified Insurance\n519,ConocoPhillips,United States,23.8,-3.6,89.8,61.3,Energy,Oil & Gas Operations\n520,Baxter International,United States,10.2,4.9,15.5,28.6,Health Care,Medical Equipment & Supplies\n521,Praxair,United States,10.5,1.5,19.3,33.9,Materials,Specialized Chemicals\n522,Kyocera,Japan,12.9,1.1,26.5,19.9,Information Technology,Electronics\n523,Garanti Bank,Turkey,9.2,1.7,88.1,10.2,Financials,Regional Banks\n524,China Unicom,Hong Kong,41.3,0.102,88.9,33.8,Telecommunication Services,Telecommunications services\n525,CIC Group,France,11.7,1.3,304.8,7.9,Financials,Major Banks\n526,Kubota,Japan,14.7,1.2,22.9,18.5,Industrials,Heavy Equipment\n527,Isbank,Turkey,11.7,1.8,118,7.9,Financials,Regional Banks\n528,Astellas Pharma,Japan,12.1,1.9,15.9,28,Health Care,Pharmaceuticals\n529,CNOOC,Hong Kong,22,0.96,91.8,54.8,Energy,Oil & Gas Operations\n530,Simon Property Group,United States,5.4,1.8,31.1,53,Financials,Real Estate\n531,ASML Holding,Netherlands,7.5,1.7,19.7,56.8,Information Technology,Semiconductors\n532,British Sky Broadcasting,United Kingdom,17.1,0.847,22.9,20.6,Consumer Discretionary,Broadcasting & Cable\n533,Cummins,United States,17.5,1.4,15,25.1,Industrials,Heavy Equipment\n534,China Zheshang Bank,China,8.1,1.5,195,9.8,Utilities,\n535,Standard Chartered,United Kingdom,19.4,-0.247,646.7,30.2,Financials,Major Banks\n536,Tokyo Electric Power,Japan,50.2,1,105.5,6.1,Utilities,Electric Utilities\n537,Cognizant Technology,United States,13.5,1.6,14.3,35.6,Information Technology,Computer Services\n538,Viacom,United States,12.7,1.4,23.3,18,Consumer Discretionary,Broadcasting & Cable\n539,Sasol,South Africa,11.8,0.993,27.6,19.8,Materials,Diversified Chemicals\n540,Public Service Enterprise Group,United States,8.8,0.887,40.1,22.6,Energy,\n541,Coal India,India,11,1.6,17.8,27.4,Materials,Diversified Metals & Mining\n542,Novatek,Russia,8,3.8,15.8,39,Energy,Oil & Gas Operations\n543,Kirin Holdings,Japan,19.1,1.1,20.1,17.4,Consumer Staples,Beverages\n544,T&D Holdings,Japan,16.6,0.703,128.6,9.2,Financials,Life & Health Insurance\n545,Mitsui,Japan,39.2,0.115,100,26.2,Financials,\n546,Regions Financial,United States,5.8,1.2,126.7,17.4,Financials,Regional Banks\n547,Ingersoll Rand,Ireland,13.5,1.5,17.4,21.4,,\n548,CenturyLink,United States,17.5,0.626,47,13.4,Telecommunication Services,Telecommunications services\n549,Bank of Nanjing,China,7.8,1.3,153.4,10.5,Financials,Regional Banks\n550,Dongfeng Motor Group,China,18.4,2,26.6,9.4,Consumer Discretionary,Auto & Truck Manufacturers\n551,Citizens Financial Group,United States,5.8,1,150.6,17.6,Financials,\n552,Swire Pacific,Hong Kong,8,1.2,48.2,15.1,Industrials,Conglomerates\n553,RELX Group,United Kingdom,9.3,1.6,16.5,40.9,Information Technology,\n554,Samsung Fire & Marine,South Korea,17.2,0.74,55.5,10.4,Financials,Property & Casualty Insurance\n555,Associated British Foods,United Kingdom,19,1.2,14.8,24.4,Consumer Staples,Food Processing\n556,Standard Life,United Kingdom,25.2,0.497,228.7,9,Financials,Life & Health Insurance\n557,Ally Financial,United States,10.1,1.1,163.7,9.1,Financials,\n558,Møller-Maersk,Denmark,35.5,-2,62.6,35,,\n559,Ecopetrol,Colombia,15.6,0.513,40.4,19.7,Energy,Oil & Gas Operations\n560,National Bank of Canada,Canada,6.2,1.1,179.6,14.3,Financials,Major Banks\n561,Sumitomo Electric,Japan,25.6,0.905,24.6,12.5,Consumer Discretionary,Auto & Truck Parts\n562,Shire,Ireland,11.6,0.342,67,51.3,Health Care,Pharmaceuticals\n563,Akzo Nobel,Netherlands,15.7,1.1,17.4,21.4,Materials,Diversified Chemicals\n564,Bank Central Asia,Indonesia,4.8,1.5,50.2,32.1,Financials,Regional Banks\n565,Marubeni,Japan,63.5,0.429,64.3,10.7,Industrials,Trading Companies\n566,DTE Energy,United States,10.7,0.868,32,18.5,Utilities,Electric Utilities\n567,Koç Holding,Turkey,23.5,1.1,25.1,10.4,Financials,\n568,Unum,United States,11.1,0.931,63.6,10.6,Financials,\n569,Best Buy,United States,39.4,1.2,14.2,14.9,Information Technology,Computer & Electronics Retail\n570,Atlas Copco,Sweden,12.1,1.4,13.2,42.9,Industrials,Other Industrial Equipment\n571,MTR,Hong Kong,5.8,1.3,33.2,33.3,Industrials,Railroads\n572,Suncorp Group,Australia,10.3,0.777,70.1,12.6,Financials,Diversified Insurance\n573,Emirates NBD,United Arab Emirates,5.5,1.8,122,12.5,Financials,Regional Banks\n574,LG Chem,South Korea,17.8,1.1,17,17.7,Materials,Specialized Chemicals\n575,QBE Insurance Group,Australia,11.6,0.84,42.3,13.3,Financials,Property & Casualty Insurance\n576,Northern Trust,United States,5.2,1,123.9,19.8,Financials,Regional Banks\n577,CME Group,United States,3.6,1.5,69.4,40.1,Financials,Investment Services\n578,Otsuka Holding,Japan,11,0.852,22.7,25.1,Health Care,Pharmaceuticals\n579,Altice,Netherlands,20.6,-1.7,89.3,30.9,Telecommunication Services,\n580,American Tower,United States,5.8,0.956,30.9,52.2,Financials,Real Estate\n581,Sinopharm Group,China,38.9,0.701,22.7,12.7,Health Care,Pharmaceuticals\n582,Falabella,Chile,12.7,0.901,20.8,21.3,Consumer Discretionary,Department Stores\n583,Bharat Petroleum,India,28.8,1.2,14.4,15.2,Energy,Oil & Gas Operations\n584,Newell Brands,United States,13.3,0.527,33.8,22.3,Consumer Discretionary,\n585,Akbank,Turkey,7.9,1.5,91.6,9.2,Financials,Regional Banks\n586,Norilsk Nickel,Russia,8.2,2.5,16.5,24.9,Materials,Diversified Metals & Mining\n587,Atlantia,Italy,6,1.2,41.5,21.1,Industrials,Other Transportation\n588,Tesco,United Kingdom,78.4,0.232,61.8,19.2,Consumer Staples,Food Retail\n589,MGM Resorts,United States,9.5,1.1,28.9,16.1,Consumer Discretionary,Casinos & Gaming\n590,Dollar Tree,United States,20.7,0.896,16,18.3,Consumer Discretionary,Discount Stores\n591,GFNorte,Mexico,6.4,1,61.9,16.2,Financials,Regional Banks\n592,Broadcom,Singapore,15.6,-1.9,49.6,87.6,Information Technology,Semiconductors\n593,Woori Bank,South Korea,9.5,0.909,257.2,8.2,Financials,\n594,Guotai Junan Securities,China,4.9,1.5,59.2,20.3,,\n595,Constellation Brands,United States,7.3,1.5,18.6,33.4,Consumer Staples,Beverages\n596,Royal Caribbean Cruises,United States,8.5,1.3,22.3,20.9,Consumer Discretionary,Hotels & Motels\n597,Air Products & Chemicals,United States,9.1,1.5,16,29.4,Materials,Specialized Chemicals\n598,Infosys,India,10.1,2.1,11.9,34.9,Information Technology,Computer Services\n599,BYD,China,15.3,0.759,20.9,18.4,Consumer Discretionary,Auto & Truck Manufacturers\n600,KeyCorp,United States,5.4,0.791,137.3,18.9,Financials,Regional Banks\n601,Johnson Controls International,Ireland,22.1,-0.457,48.8,38.5,,\n602,Asahi Kasei,Japan,17,1,20.1,13.1,Materials,Diversified Chemicals\n603,New World Development,Hong Kong,7.1,1.2,54.2,12.1,Financials,Real Estate\n604,Siam Cement,Thailand,12,1.6,15.1,19.1,Materials,Specialized Chemicals\n605,Toray Industries,Japan,18.6,0.836,20.7,14.2,Materials,Diversified Chemicals\n606,Grupo Mexico,Mexico,8.2,1.1,22.8,23.7,Materials,Diversified Metals & Mining\n607,E.ON,Germany,59.8,-9.3,70.4,16.9,Utilities,Electric Utilities\n608,Dell Technologies,United States,61.9,-3.7,118.2,13.5,Information Technology,\n609,Nokia,Finland,26.1,-0.749,49.2,30.3,Information Technology,Communications Equipment\n610,Eletrobrás,Brazil,17.4,0.983,52.4,7.2,Utilities,\n611,Legend Holding,China,44.3,0.732,46.4,7,Financials,\n612,Dollar General,United States,22,1.3,11.9,19.1,Consumer Discretionary,Discount Stores\n613,Banco de Sabadell,Spain,7.8,0.786,224.1,10,Financials,Regional Banks\n614,Eversource Energy,United States,7.6,0.942,32.1,18.8,Energy,\n615,China Taiping Insurance,Hong Kong,21.6,0.582,64.4,8.6,Financials,Diversified Insurance\n616,Hyundai Heavy Industries,South Korea,33.9,0.452,40.8,11,Industrials,Heavy Equipment\n617,Sekisui House,Japan,18.7,1.1,19.4,11.3,Industrials,Construction Services\n618,Siam Commercial Bank,Thailand,4.9,1.3,81.3,16.1,Financials,Regional Banks\n619,China Eastern Airlines,China,14.8,0.694,30.2,12.9,Industrials,Airline\n620,Cemex,Mexico,13.7,0.751,29.1,13.1,Materials,Construction Materials\n621,Sumitomo Realty,Japan,9,0.948,41.1,12.3,Financials,Real Estate\n622,Murata Manufacturing,Japan,10.4,1.4,13.9,28.4,Information Technology,Electronics\n623,WEC Energy Group,United States,7.5,0.94,31.3,19,Energy,\n624,JXTG Holdings,Japan,72,-0.65,58,16.6,Financials,\n625,China Fortune Land Development,China,7.6,0.977,36,15.5,,\n626,Evonik,Germany,14.1,0.827,22.1,14.9,,\n627,Henderson Land,Hong Kong,3.3,2.8,45.9,22.9,Financials,Real Estate\n628,China Southern Airlines,China,17.2,0.77,28.8,10.4,Industrials,Airline\n629,ThyssenKrupp Group,Germany,44,0.362,36.9,13.5,Industrials,Conglomerates\n630,Whirlpool,United States,20.7,0.888,19.2,12.4,Consumer Discretionary,Household Appliances\n631,Paccar,United States,17,0.522,20.6,23.6,Industrials,Heavy Equipment\n632,Public Bank,Malaysia,4.5,1.3,84.7,17.4,Financials,Regional Banks\n633,Bank of Ningbo,China,6.1,1.2,125.6,10.3,Financials,Regional Banks\n634,Valeo,France,18.3,1,15.2,15.4,Consumer Discretionary,Auto & Truck Parts\n635,Asahi Group Holdings,Japan,15.7,0.821,17,17.8,Consumer Staples,Beverages\n636,Unibail-Rodamco,France,2.4,2.7,43,23.6,Financials,Real Estate\n637,Delphi Automotive,United Kingdom,16.7,1.2,12.3,20.2,Consumer Discretionary,Auto & Truck Parts\n638,Bunge,United States,42.7,0.743,19.2,10.6,Consumer Staples,Food Processing\n639,Capgemini,France,13.9,1,17.4,15.7,Information Technology,Computer Services\n640,Mylan,United Kingdom,11.1,0.48,34.7,21.2,Health Care,Pharmaceuticals\n641,Sodexo,France,22.5,0.707,15.8,17.5,Consumer Discretionary,Business & Personal Services\n642,Kasikornbank,Thailand,5.4,1.1,79.5,13.4,Financials,Major Banks\n643,TELUS,Canada,9.6,0.923,20.7,19.5,Telecommunication Services,Telecommunications services\n644,Formosa Chemicals,Taiwan,9.9,1.4,16.9,17.9,Materials,Specialized Chemicals\n645,Kao Corp,Japan,13.4,1.2,11.5,27.3,Consumer Staples,Household/Personal Care\n646,VMware,United States,7.1,1.2,16.6,37.8,Information Technology,Software & Programming\n647,Kweichow Moutai,China,5,2.5,15.8,71.5,Consumer Staples,Beverages\n648,SCA,Sweden,13.7,0.651,18.2,21.6,Consumer Staples,Household/Personal Care\n649,Fidelity National Information,United States,9.3,0.568,26,26.3,Consumer Discretionary,Business & Personal Services\n650,Aeon,Japan,75.5,0.057,76.1,12.4,Consumer Discretionary,Discount Stores\n651,Centene,United States,40.6,0.558,20.2,12.2,Health Care,\n652,Great Wall Motor,China,14.2,1.6,13.3,15.2,Consumer Discretionary,Auto & Truck Manufacturers\n653,Telkom Indonesia,Indonesia,8.7,1.5,13.3,30.4,Telecommunication Services,\n654,Molson Coors Brewing,United States,4.9,2,29.3,20.5,Consumer Staples,Beverages\n655,Naspers,South Africa,5.9,0.994,18.2,75.4,Consumer Discretionary,Broadcasting & Cable\n656,Stanley Black & Decker,United States,11.4,0.965,15.6,20.2,Consumer Staples,Household/Personal Care\n657,Nucor,United States,16.2,0.731,15.2,19.4,Materials,Iron & Steel\n658,Transneft,Russia,12.7,3.5,45.6,4.7,Energy,Oil Services & Equipment\n659,JD.com,China,39,-0.576,23.1,45.7,Consumer Discretionary,\n660,National Bank of Abu Dhabi,United Arab Emirates,4,1.4,114.5,16.1,Financials,Regional Banks\n661,West Japan Railway,Japan,13.3,0.742,24.5,12.8,Industrials,Railroads\n662,DaVita,United States,14.7,0.88,18.7,13,Health Care,Healthcare Services\n663,Commerzbank,Germany,15.9,0.295,513.1,11,Financials,Major Banks\n664,Fortescue Metals Group,Australia,8.2,1.9,19,14.3,Materials,Iron & Steel\n665,Nan Ya Plastics,Taiwan,8.5,1.5,16.1,18.5,Materials,Diversified Chemicals\n666,Solvay,Belgium,13.4,0.687,25.5,12.9,Materials,Diversified Chemicals\n667,Longfor Properties,China,8.2,1.4,32.4,10.1,Financials,Real Estate\n668,Rogers Communications,Canada,10.3,0.644,21.1,22.9,Telecommunication Services,Telecommunications services\n669,Barrick Gold,Canada,8.6,0.649,25.3,22.3,Materials,Diversified Metals & Mining\n670,China Reinsurance Group,China,13.7,0.776,29.7,9.9,,\n671,CIMB Group Holdings,Malaysia,6,0.86,108.3,10.9,Financials,Regional Banks\n672,Krung Thai Bank,Thailand,4.8,0.915,75.1,17,Financials,Regional Banks\n673,Tatneft,Russia,8.7,1.6,17.9,14.6,Energy,Oil & Gas Operations\n674,WH Group,Hong Kong,21.5,1,13.6,12.8,,\n675,Kellogg,United States,13,0.694,15.1,25.4,Consumer Staples,Food Processing\n676,Recruit Holdings,Japan,16.1,0.778,11.2,29,Financials,\n677,Fast Retailing,Japan,16.4,0.639,12.4,31.8,Consumer Discretionary,Apparel/Footwear Retail\n678,Estée Lauder,United States,11.4,1.1,11.2,30.8,,\n679,Allied Irish Banks,Ireland,4,1.5,100.9,14.6,Financials,Regional Banks\n680,PKN Orlen,Poland,20.2,1.3,13.3,11.2,Energy,Oil & Gas Operations\n681,BAIC Motor,China,17.5,0.955,24.3,8.3,Consumer Discretionary,\n682,Mazda Motor,Japan,29.5,0.837,22,8.1,Consumer Discretionary,Auto & Truck Manufacturers\n683,Weyerhaeuser,United States,6.8,0.986,19.2,26.1,Financials,Real Estate\n684,Woolworths,Australia,41.1,0.344,17.1,25.7,Consumer Staples,Food Retail\n685,Dong Energy,Denmark,8.5,1.1,19.4,16.4,Energy,\n686,Level 3 Communications,United States,8.2,0.666,24.9,21.1,Telecommunication Services,Telecommunications services\n687,Franklin Resources,United States,6.5,1.7,16.2,23.6,Financials,Investment Services\n688,Nidec,Japan,10.6,0.944,12.8,28.1,Industrials,Electrical Equipment\n689,George Weston,Canada,36.2,0.415,28.3,10.9,Consumer Staples,Food Retail\n690,Lenovo Group,Hong Kong,42.6,0.608,27.2,7.7,Information Technology,Computer Hardware\n691,LG Display,South Korea,22.8,0.782,20.6,9.4,Information Technology,Electronics\n692,Activision Blizzard,United States,6.6,0.911,17.5,37.3,,\n693,Grupo Aval,Colombia,8.5,0.701,74.7,9.2,Financials,Investment Services\n694,All Nippon Airways,Japan,16.1,0.841,19.4,10.4,Industrials,Airline\n695,Reinsurance Group of America,United States,11.5,0.701,53.1,8.1,Financials,Diversified Insurance\n696,Tesoro,United States,24.6,0.734,20.4,9.4,Energy,Oil & Gas Operations\n697,Samsung C&T,South Korea,24.2,0.093,36.8,21.2,Industrials,Trading Companies\n698,RWE Group,Germany,48.2,-6.3,85.4,10.3,Utilities,Electric Utilities\n699,Guosen Securities,China,6,1,28.3,17.6,Financials,\n700,TransCanada,Canada,9.6,0.176,65.7,40.6,Energy,Oil Services & Equipment\n701,Adobe Systems,United States,6.1,1.3,13,64.4,Information Technology,Software & Programming\n702,Teliasonera,Sweden,9.8,0.544,27.9,17,Telecommunication Services,Telecommunications services\n703,Dentsu,Japan,7.7,0.769,27.1,15.5,Consumer Discretionary,Advertising\n704,Deutsche Boerse,Germany,3.2,0.799,172.8,17.9,Financials,Investment Services\n705,Daiwa Securities,Japan,5.2,0.948,176,9.9,Financials,Investment Services\n706,GF Securities,China,4.1,1.2,51.8,18.4,Financials,Investment Services\n707,Textron,United States,13.8,0.962,15.4,12.9,Industrials,Conglomerates\n708,Mitsubishi Heavy Industries,Japan,36,-0.007,48.9,13.4,Industrials,Heavy Equipment\n709,Republic Services,United States,9.4,0.613,20.6,21.3,Industrials,Environmental & Waste\n710,Ericsson,Sweden,26,0.2,31.6,21.4,Information Technology,Communications Equipment\n711,Isuzu Motors,Japan,17.4,0.957,15.1,10.8,Consumer Discretionary,Auto & Truck Manufacturers\n712,Anhui Conch Cement,China,8.3,1.3,15.8,16.9,Materials,Construction Materials\n713,First Data,United States,11.6,0.42,40.3,14,Information Technology,\n714,Vonovia,Germany,3.9,2.5,34.3,16.8,,\n715,Telenor,Norway,15.5,0.337,24,24.4,Telecommunication Services,Telecommunications services\n716,Halliburton,United States,15.9,-5.8,27,43.1,Energy,Oil Services & Equipment\n717,PPG,United States,15.1,0.456,15.8,27.3,Materials,\n718,CGN Power,China,5.1,1.1,41.4,14.2,Energy,\n719,Bankia,Spain,3.7,0.962,200.6,13.2,Financials,Regional Banks\n720,Chunghwa Telecom,Taiwan,7.1,1.2,13.9,26.5,Telecommunication Services,Telecommunications services\n721,Veolia Environnement,France,27,0.348,40,10.2,Utilities,Diversified Utilities\n722,Occidental Petroleum,United States,10.1,-0.574,43.1,48.2,Energy,Oil & Gas Operations\n723,J Sainsbury,United Kingdom,33.7,0.69,25.8,7.1,Consumer Staples,Food Retail\n724,Bancolombia,Colombia,6.7,0.939,65.4,8.9,Financials,Regional Banks\n725,Sherwin-Williams,United States,11.9,1.1,6.8,29.2,Consumer Discretionary,Home Improvement Retail\n726,NVIDIA,United States,6.9,1.6,9.8,59.1,Information Technology,Semiconductors\n727,HAL Trust,Netherlands,8.9,0.964,17.5,15.1,Financials,Investment Services\n728,VF,United States,12.1,1.2,9.7,22.5,Consumer Discretionary,Apparel/Accessories\n729,Yum Brands,United States,11,1.6,5.5,22.7,Consumer Discretionary,Restaurants\n730,Qingdao Haier,China,17,0.718,18.1,10.6,Consumer Discretionary,Household Appliances\n731,Prologis,United States,2.5,1.2,30.3,28.4,Financials,Real Estate\n732,Goodyear,United States,15.2,1.3,16.5,8.9,Consumer Discretionary,Auto & Truck Parts\n733,Liberty Interactive,United States,10.6,1.2,20.9,9.4,Consumer Discretionary,Internet & Catalog Retail\n734,Coca-Cola European Partners,United Kingdom,10.1,0.614,19.6,17.9,,\n735,Ross Stores,United States,12.9,1.1,5.5,24.7,Consumer Discretionary,Apparel/Footwear Retail\n736,CNH Industrial,United Kingdom,25.3,-0.373,47.8,13.5,Materials,\n737,Macy's,United States,25.8,0.619,21.2,8.9,Consumer Discretionary,Department Stores\n738,XL Group,Bermuda,10.4,0.441,58.4,10.6,Financials,Diversified Insurance\n739,Parker-Hannifin,United States,11.2,0.88,11.7,21.5,Industrials,Other Industrial Equipment\n740,Sabanci Holding,Turkey,11.9,0.88,87.9,5.4,Industrials,Conglomerates\n741,Annaly Capital Management,United States,2,1.4,87.9,11.5,Financials,Real Estate\n742,Industrial Bank of Korea,South Korea,7.8,0.998,212.7,6,Financials,Regional Banks\n743,Hanwha,South Korea,40.6,0.424,128.2,2.2,,\n744,Essilor International,France,7.9,0.899,13.9,25.7,Health Care,Medical Equipment & Supplies\n745,Kotak Mahindra Bank,India,4.8,0.683,37.4,25.1,Financials,Regional Banks\n746,Kyushu Electric Power,Japan,16.8,0.977,38.1,5.1,Utilities,Electric Utilities\n747,Scor,France,14.6,0.667,44.2,6.8,Financials,Diversified Insurance\n748,Chongqing Changan Auto,China,10.6,1.7,15.1,10.2,Consumer Discretionary,Auto & Truck Manufacturers\n749,Husky Energy,Canada,9.8,0.696,24.9,11.4,Energy,Oil & Gas Operations\n750,China Energy Engineering,China,33.4,0.645,42.1,5.6,Energy,\n751,Abertis,Spain,5.3,0.88,32.9,16,Industrials,Other Transportation\n752,Huatai Securities,China,3.7,0.944,57.8,16.9,Financials,Investment Services\n753,Luxottica Group,Italy,10.1,0.941,10.9,25.9,Consumer Discretionary,Specialty Stores\n754,Teck Resources,Canada,7,0.785,26.6,13.6,Materials,Diversified Metals & Mining\n755,Lotte Chemical,South Korea,11.4,1.6,13.1,11.1,Materials,Specialized Chemicals\n756,Tohoku Electric Power,Japan,18.2,0.658,34.9,6.8,Utilities,Electric Utilities\n757,Western Digital,United States,15.9,-0.408,29,24.5,Information Technology,Computer Storage Devices\n758,National Bank of Kuwait,Kuwait,3.2,0.931,79.2,14,Financials,Regional Banks\n759,Rolls-Royce Holdings,United Kingdom,20.2,-5.4,31.6,18.2,Industrials,Aerospace & Defense\n760,Japan Airlines,Japan,11.9,1.3,13.9,10.6,Industrials,Airline\n761,Credicorp,Peru,4.5,1,46.7,12.7,Financials,Regional Banks\n762,UPM-Kymmene,Finland,10.9,0.972,14.7,12.7,Materials,Paper & Paper Products\n763,Guangdong Wens Foodstuffs Group,China,8.9,1.9,6,20.9,Consumer Staples,\n764,Toyota Tsusho,Japan,71,-0.14,34.8,10.6,Industrials,Trading Companies\n765,Wipro,India,8.1,1.3,11.8,19.4,Information Technology,Computer Services\n766,Hilton,United States,11.7,0.348,26.2,18.7,,\n767,Daqin Railway,China,6.7,1,17.9,16.6,Industrials,Railroads\n768,Lennar,United States,11.3,0.891,15.4,11.9,Industrials,Construction Services\n769,Ferrovial,Spain,11.9,0.416,24.7,14.9,Industrials,Other Transportation\n770,Anadarko Petroleum,United States,8.5,-3.1,45.6,34.9,Energy,Oil & Gas Operations\n771,Scentre Group,Australia,1.9,2.2,24.6,17.6,,\n772,AutoZone,United States,10.8,1.3,8.9,19.9,Consumer Discretionary,Specialty Stores\n773,Agrium,Canada,13.6,0.578,17,13,Materials,Specialized Chemicals\n774,Vestas Wind Systems,Denmark,11.3,1.1,10.5,17.5,Industrials,Electrical Equipment\n775,China Resources Power,Hong Kong,8.5,0.993,25.8,8.8,Utilities,Electric Utilities\n776,Freeport-mcmoran,United States,14.9,-4,37.3,18.2,,\n777,Samba Financial Group,Saudi Arabia,2.4,1.3,61.7,11.3,Financials,Regional Banks\n778,ROSSETI,Russia,13.6,1.1,34.2,3.3,Energy,\n779,Schaeffler,Germany,14.8,0.95,12.5,11.1,Industrials,\n780,GD Power Development,China,8.3,0.638,38.4,9.5,Utilities,Electric Utilities\n781,Adecco,Switzerland,25.1,0.799,10.7,11.8,Consumer Discretionary,Business & Personal Services\n782,Discovery Communications,United States,6.5,1.2,15.8,16.8,Consumer Discretionary,Broadcasting & Cable\n783,Canadian Natural Resources,Canada,7.9,-0.154,44.9,37.1,Energy,Oil & Gas Operations\n784,Ryanair Holdings,Ireland,7.3,1.5,11.5,19.6,Industrials,Airline\n785,Huntington Bancshares,United States,3.8,0.712,99.7,14.3,Financials,Regional Banks\n786,Hong Kong Exchanges,Hong Kong,1.3,0.743,31.9,30.9,Financials,Investment Services\n787,Carlsberg,Denmark,9.3,0.667,18.3,14.1,Consumer Staples,Beverages\n788,Porsche Automobil Holding,Germany,0.001,1.5,30,16.1,Consumer Discretionary,Auto & Truck Manufacturers\n789,Kone,Finland,9.7,1.1,8.4,23.7,Industrials,Construction Services\n790,CarMax,United States,15.9,0.627,16.3,10.5,Consumer Discretionary,Specialty Stores\n791,Itaúsa,Brazil,1.3,2.4,18.1,23,,\n792,Sime Darby,Malaysia,10.7,0.697,15.4,14.3,Industrials,Conglomerates\n793,Wolseley,Switzerland,21.5,0.624,10.7,15.5,Materials,Construction Materials\n794,Air France-KLM,France,27.5,0.876,24.2,2.2,Industrials,Airline\n795,Willis Towers Watson,United Kingdom,7.9,0.42,30.3,17.4,,\n796,Welltower,United States,4.3,0.714,28.9,26.2,,\n797,ITC,India,6,1.5,7.8,51.6,Consumer Staples,Tobacco\n798,KT Corp,South Korea,19.6,0.613,25.3,7.1,Telecommunication Services,Telecommunications services\n799,OMV Group,Austria,21.3,-0.446,33.9,13.6,Energy,Oil & Gas Operations\n800,Sumitomo Chemical,Japan,17.6,0.48,24.3,8.8,Materials,Diversified Chemicals\n801,Emaar Properties,United Arab Emirates,4.3,1.4,26.2,14.6,Financials,Real Estate\n802,VEON,Netherlands,8.9,2.5,21,6.8,Telecommunication Services,\n803,Eiffage,France,15.8,0.525,31.1,7.3,Industrials,Construction Services\n804,Bangkok Bank,Thailand,4.3,0.902,82.2,10.1,Financials,Regional Banks\n805,Metro Group,Germany,64.5,0.222,30.8,10.1,Consumer Staples,Food Retail\n806,CSL,Australia,6.4,1.3,8.1,43.9,Health Care,Biotechs\n807,Hindustan Petroleum,India,28.5,0.752,13.7,8.5,Energy,Oil & Gas Operations\n808,DR Horton,United States,12.6,0.935,11.7,12.4,Industrials,Construction Services\n809,Leonardo,Italy,13.3,0.559,26.8,8.3,,\n810,Shenwan Hongyuan Group,China,3.2,0.814,39.6,18.3,,\n811,LG Electronics,South Korea,47.7,0.066,31.3,10.2,Consumer Discretionary,Consumer Electronics\n812,Kuehne + Nagel,Switzerland,16.8,0.729,6.2,16.9,Industrials,Other Transportation\n813,FirstEnergy,United States,14.4,-6.2,43.1,13.9,Utilities,Electric Utilities\n814,Magnit,Russia,16,0.809,7.5,15.6,Consumer Staples,Food Retail\n815,S&P Global,United States,5.7,2.1,8.7,33.6,,\n816,Chongqing Rural Bank,Hong Kong,5.3,1.2,115.6,6.5,Financials,Regional Banks\n817,Toshiba,Japan,47.2,-3.8,47.7,8.3,Information Technology,Electronics\n818,General Growth Properties,United States,2.3,1.3,22.7,20.4,Financials,Real Estate\n819,JFE Holdings,Japan,29.4,0.273,36.1,9.4,Materials,Iron & Steel\n820,Netease,China,5.7,1.7,8.4,36.8,Information Technology,Computer Services\n821,LabCorp,United States,9.6,0.722,14.2,14.7,Health Care,Healthcare Services\n822,ATOS,France,13,0.64,14.1,12.8,Information Technology,Computer Services\n823,Fanuc,Japan,4.8,1.2,13,39.6,Industrials,Other Industrial Equipment\n824,SM Investments,Philippines,7.3,0.65,17.3,17.8,Consumer Discretionary,Department Stores\n825,Hyundai Steel,South Korea,14.4,0.706,26.8,6.7,Materials,Iron & Steel\n826,Eastman Chemical,United States,8.9,0.866,15.5,11.7,Materials,Specialized Chemicals\n827,O'Reilly Automotive,United States,8.6,1,7.4,23.2,Consumer Discretionary,Specialty Stores\n828,L Brands,United States,12.6,1.2,8.2,13.5,Consumer Discretionary,Apparel/Footwear Retail\n829,First Republic Bank,United States,2.4,0.665,73.4,14.2,Financials,Regional Banks\n830,S-Oil,South Korea,14.1,1,11.6,9.9,Energy,Oil & Gas Operations\n831,EOG Resources,United States,7.5,-1.1,29.5,56.2,Energy,Oil & Gas Operations\n832,Secom,Japan,8.5,0.748,13.6,16.7,Industrials,Security Systems\n833,LG Corp,South Korea,8.6,0.926,16.1,10.7,Consumer Discretionary,Household Appliances\n834,Abu Dhabi Commercial Bank,United Arab Emirates,3.1,1.1,70.3,9.8,Financials,Regional Banks\n835,Hermès International,France,5.8,1.2,5.8,50.3,,\n836,Link REIT,Hong Kong,1.2,1.8,21.8,16,Financials,Real Estate\n837,TD Ameritrade Holding,United States,3.4,0.846,28.8,20.3,Financials,Investment Services\n838,Sanlam,South Africa,5.8,0.655,48.8,9.5,Financials,Life & Health Insurance\n839,Daiichi Sankyo,Japan,8.9,0.55,17,15.5,Health Care,Pharmaceuticals\n840,Norsk Hydro,Norway,9.8,0.761,15.2,11.9,Materials,Aluminum\n841,Williams,United States,7.5,-0.424,47.9,25.1,,\n842,Guangzhou Automobile Group,China,7.2,0.953,11.8,19.2,,\n843,American Financial Group,United States,6.4,0.649,55.7,8.2,Financials,Diversified Insurance\n844,NXP Semiconductors,Netherlands,9.5,0.2,24.9,36.2,Information Technology,Semiconductors\n845,China National Nuclear Power,China,4.2,0.627,41.4,17,Energy,\n846,Athene Holding,Bermuda,4.1,0.805,86.7,9.8,Energy,\n847,Woodside Petroleum,Australia,4.1,0.866,24.8,20.9,Energy,Oil & Gas Operations\n848,Boston Scientific,United States,8.4,0.347,18.1,33.7,Health Care,Medical Equipment & Supplies\n849,Raiffeisen Bank International,Austria,6.8,0.513,118,7.3,Financials,Major Banks\n850,AMP,Australia,9.7,-0.256,101.4,11.4,Financials,Investment Services\n851,Formosa Plastics,Taiwan,5.6,1.2,13.5,19,Materials,Specialized Chemicals\n852,China Steel,Taiwan,9.1,0.497,21,12.8,Materials,Iron & Steel\n853,Bank of Ireland,Ireland,4.3,0.797,129.9,8.4,Financials,Major Banks\n854,China Gezhouba,China,14.9,0.511,21.8,8.5,Industrials,Construction Services\n855,Bâloise Group,Switzerland,9.1,0.543,78.9,6.3,,\n856,Rakuten,Japan,7.2,0.35,39.5,15.2,Consumer Discretionary,Internet & Catalog Retail\n857,Ameren,United States,6.1,0.653,24.7,13.2,Utilities,Electric Utilities\n858,Symantec,United States,3.8,2,16.8,19.1,Information Technology,Software & Programming\n859,Yara International,Norway,11.3,0.757,14,10.7,Materials,Specialized Chemicals\n860,Assa Abloy,Sweden,8.4,0.777,10.5,22.6,Industrials,Other Industrial Equipment\n861,Shengjing Bank,China,5.9,1,130.3,5.2,Financials,\n862,Aena,Spain,4.1,1.3,16.4,24.3,Industrials,\n863,Mega Financial Holding,Taiwan,2.5,0.696,103,10.6,Financials,Investment Services\n864,Shanghai International Port,China,4.7,1,16.8,20.2,Industrials,Other Transportation\n865,Tokyo Gas,Japan,15,0.465,18.2,10.1,Utilities,Natural Gas Utilities\n866,Mohawk Industries,United States,9,0.93,10.2,17,Consumer Discretionary,Furniture & Fixtures\n867,Ooredoo Telecom,Qatar,8.9,0.602,24.9,9.1,Telecommunication Services,Telecommunications services\n868,China Galaxy Securities,China,2.7,0.774,35.4,16.1,Financials,\n869,Unipol Gruppo,Italy,18.4,0.365,96,2.9,Financials,Diversified Insurance\n870,Quanta Computer,Taiwan,27.7,0.469,18.2,7.9,Information Technology,Computer Hardware\n871,Lam Research,United States,6.4,0.999,11.5,21.1,Materials,\n872,China Merchants Securities,China,2.8,0.812,35,15.1,Financials,Investment Services\n873,Bank of Jinzhou,China,4.1,1.2,77.6,7.9,Financials,\n874,Entergy,United States,10.8,-0.564,45.9,13.7,Utilities,Electric Utilities\n875,Canadian Pacific Railway,Canada,4.7,1.2,14.5,22,Industrials,Railroads\n876,JM Smucker,United States,7.4,0.67,15.8,15.1,Consumer Staples,Food Processing\n877,NEC,Japan,24.5,0.603,21.3,6.2,Information Technology,Electronics\n878,Kingfisher,United Kingdom,15,0.813,12.9,9,Consumer Discretionary,Home Improvement Retail\n879,Lear,United States,18.6,0.975,9.9,9.2,Consumer Discretionary,Auto & Truck Parts\n880,Taisei,Japan,13.4,0.915,13.7,8.4,Industrials,Construction Services\n881,Kajima,Japan,16.1,0.875,15.7,6.9,Industrials,Construction Services\n882,Sandvik,Sweden,9.5,0.643,11.5,18.5,Industrials,Other Industrial Equipment\n883,DP World,United Arab Emirates,4.2,1,20.8,18,Industrials,Other Transportation\n884,Pegatron,Taiwan,35.9,0.6,13.8,7.5,Information Technology,Electronics\n885,Power Grid of India,India,3.2,0.919,27.8,15.9,Utilities,Electric Utilities\n886,Obayashi,Japan,16.7,0.767,17.3,6.7,Industrials,Construction Services\n887,Zimmer Biomet,United States,7.7,0.3,26.7,24.1,Industrials,\n888,Devon Energy,United States,10.5,-3.3,25.9,22.3,Energy,Oil & Gas Operations\n889,Hormel Foods,United States,9.5,0.89,6.4,18.1,Consumer Staples,Food Processing\n890,Julius Baer Group,Switzerland,3.3,0.629,94.7,11.1,Financials,Investment Services\n891,S.f. Holding,China,8.6,0.629,6.4,34.4,Financials,\n892,Schindler Holding,Switzerland,9.8,0.776,8.2,19.8,Industrials,Other Industrial Equipment\n893,Avalonbay Communities,United States,2,1,17.9,25.4,Financials,Real Estate\n894,Cencosud,Chile,15.3,0.573,15.6,9,Consumer Staples,Food Retail\n895,JBS,Brazil,48.9,0.108,31.6,8.2,Consumer Staples,Food Processing\n896,Uniper,Germany,74.4,-3.6,63.2,6.1,Energy,\n897,Suning Appliance,China,22.3,0.106,19.7,14.8,Consumer Discretionary,Specialty Stores\n898,Yahoo!,United States,5.2,-0.214,48.1,44.4,Information Technology,\n899,L3 Technologies,United States,10.5,0.71,11.9,12.9,Information Technology,\n900,CEZ Group,Czech Republic,8.3,0.584,24.6,9.2,Utilities,Electric Utilities\n901,PKO Bank Polski,Poland,3.8,0.729,68.4,10.5,Financials,Regional Banks\n902,Alliance Data Systems,United States,7.1,0.432,25.5,14,Consumer Discretionary,Business & Personal Services\n903,AerCap Holdings,Ireland,5,1,41.6,7.6,Financials,\n904,Genuine Parts,United States,15.3,0.687,8.9,13.3,Consumer Discretionary,Specialty Stores\n905,Fortis (Canada),Canada,5.2,0.498,36.7,13.6,Utilities,Electric Utilities\n906,Infineon Technologies,Germany,7.3,0.832,10.1,22.5,Information Technology,Semiconductors\n907,Klepierre,France,1.5,1.3,25.7,12.4,,\n908,Randstad Holding,Netherlands,22.9,0.65,10,10.3,Consumer Discretionary,Business & Personal Services\n909,Skanska,Sweden,17,0.668,12,9.3,Industrials,Construction Services\n910,Raymond James Financial,United States,5.7,0.568,31.7,10.9,Financials,Investment Services\n911,Gemdale,China,7.9,0.948,22.1,7.3,Financials,Real Estate\n912,Invesco,United States,4.8,0.829,25.7,12.3,Financials,Investment Services\n913,DSM,Netherlands,8.8,0.718,13.8,12.1,Materials,Diversified Chemicals\n914,Ageas,Belgium,12.9,0.03,107.7,8,Financials,Diversified Insurance\n915,Zhejiang Zheneng Electric Power,China,6,1.1,15.8,11.4,Utilities,Electric Utilities\n916,Mahindra & Mahindra,India,10.7,0.491,16.4,11.8,Consumer Discretionary,Auto & Truck Manufacturers\n917,Swatch Group,Switzerland,7.7,0.583,12.9,19.3,Consumer Discretionary,Apparel/Accessories\n918,Huishang Bank,China,5.3,1,108.6,5.3,Financials,\n919,Vornado Realty,United States,2.6,0.907,20.8,19.2,Financials,Real Estate\n920,Everest Re Group,Bermuda,5.8,0.986,21.3,9.5,Financials,Diversified Insurance\n921,Bank Hapoalim,Israel,4.4,0.685,116.4,7.9,Financials,Major Banks\n922,Shimao Property Holdings,Hong Kong,9.1,0.781,37.7,5.6,Financials,Real Estate\n923,Saudi British Bank,Saudi Arabia,2.2,1,49.6,8.8,Financials,Regional Banks\n924,Bank Negara Indonesia,Indonesia,4.4,0.853,44.8,9.1,Financials,Regional Banks\n925,Inner Mongolia Yili,China,9.1,0.852,5.7,16.4,Consumer Staples,Food Processing\n926,Asr Nederland,Netherlands,7.5,0.729,59.4,4.3,,\n927,Beiersdorf,Germany,7.5,0.784,8,24.1,Consumer Staples,Household/Personal Care\n928,Sharp,Japan,18.5,-1.7,15.2,20.5,Consumer Discretionary,Consumer Electronics\n929,Salesforce.com,United States,8.4,0.18,17.6,59.7,Information Technology,Software & Programming\n930,Aluminum Corp of China,China,21.4,0.059,27.4,9.9,Materials,Aluminum\n931,Mercantil Servicios,Venezuela,0.37,2,148.7,2.7,Financials,Regional Banks\n932,Halkbank,Turkey,7,0.731,71,3.5,Financials,Regional Banks\n933,Guangzhou R&F,China,8.1,1,32.6,5.4,Financials,Real Estate\n934,Tesla,United States,7,-0.675,22.7,49.3,Consumer Discretionary,\n935,Tokyu,Japan,10.3,0.573,18.2,9,Industrials,Railroads\n936,Neste Oil,Finland,12.9,1,7.9,10,Energy,Oil & Gas Operations\n937,CMS Energy,United States,6.4,0.551,21.6,12.6,Utilities,Electric Utilities\n938,Conagra Brands,United States,9.6,0.608,10.5,17.2,,\n939,China Shipbuilding Industry,China,8.2,-0.204,28.6,20.4,Industrials,Heavy Equipment\n940,Jeronimo Martins,Portugal,16.2,0.656,6,11.2,Consumer Staples,Food Retail\n941,Fairfax Financial,Canada,10.3,-0.526,43.4,11.2,Financials,Property & Casualty Insurance\n942,Westfield,Australia,1.1,1.4,18.8,14,,\n943,Keyence,Japan,3.7,1.3,10.4,48.3,Information Technology,Electronics\n944,Banque Saudi Fransi,Saudi Arabia,2.2,0.936,54.2,8.4,Financials,Regional Banks\n945,Towngas,Hong Kong,3.7,0.946,15.1,26,Energy,\n946,Galaxy Entertainment,Hong Kong,6.8,0.81,8.5,23.8,Consumer Discretionary,Casinos & Gaming\n947,WR Berkley,United States,7.7,0.602,23.4,8.6,Financials,Property & Casualty Insurance\n948,CapitaLand,Singapore,3.9,0.862,31.7,11.4,Financials,Real Estate\n949,Shimizu,Japan,14.5,0.767,14,7.2,Industrials,Construction Services\n950,Snam,Italy,2.7,0.952,21.2,15,Energy,\n951,VakifBank,Turkey,7,0.688,69,3.7,Financials,Regional Banks\n952,Baker Hughes,United States,9.8,-2.7,19,26.1,Energy,Oil Services & Equipment\n953,Onex,Canada,22.5,-0.653,42.9,7.3,Financials,Investment Services\n954,Publicis Groupe,France,10.6,-0.583,26.3,15.4,Consumer Discretionary,Advertising\n955,St. James's Place,United Kingdom,15.3,0.151,92.6,6.7,,\n956,Hershey,United States,7.4,0.072,5.5,22.7,Consumer Staples,Food Processing\n957,CenterPoint Energy,United States,7.5,0.432,22.3,12,Utilities,Natural Gas Utilities\n958,HCL Technologies,India,6.3,1.1,6.4,18.7,Information Technology,Software & Programming\n959,Riyad Bank,Saudi Arabia,2.6,0.891,58,8.3,Financials,Regional Banks\n960,TUI,Germany,18.8,0.501,14.7,7.9,Consumer Discretionary,Business & Personal Services\n961,BOE Technology Group,China,8.8,-0.036,26.3,18.4,Information Technology,Electronics\n962,UC Rusal,Russia,8,1.2,14.6,7.9,Materials,Aluminum\n963,Idemitsu Kosan,Japan,27.9,0.475,22.5,5.6,Energy,Oil & Gas Operations\n964,WestRock,United States,14.4,0.138,22.9,12.8,,\n965,Deutsche Wohnen,Germany,1.3,1.8,17.7,12,,\n966,Public Storage,United States,2.6,1.4,10.1,38.8,Financials,Real Estate\n967,Mebuki Financial Group,Japan,1.4,1.5,137.5,4.5,Financials,\n968,Comerica,United States,2.9,0.473,73.2,12,Financials,Regional Banks\n969,Voya Financial,United States,10.7,-0.428,216.9,7,Financials,\n970,Arch Capital Group,Bermuda,4.3,0.693,29.4,11.5,Financials,Property & Casualty Insurance\n971,Daito Trust Construction,Japan,13.5,0.749,6,10.4,Industrials,Construction Services\n972,Fiserv,United States,5.5,0.93,9.7,24.9,Information Technology,Software & Programming\n973,Ventas,United States,3.4,0.551,23.2,23,Financials,Real Estate\n974,CBRE Group,United States,13.2,0.572,10.8,11.1,Financials,Real Estate\n975,Huadian Power International,China,9.4,0.511,30.2,6.8,Utilities,Electric Utilities\n976,Markel,United States,5.6,0.44,25.9,13.6,Financials,Diversified Insurance\n977,Fidelity National Financial,United States,8.2,0.65,14.6,10.4,Financials,Property & Casualty Insurance\n978,Inpex,Japan,7.6,-0.025,37.5,14.9,Energy,Oil & Gas Operations\n979,Leucadia National,United States,10.9,0.127,45.3,9.5,Industrials,Conglomerates\n980,China Coal Energy,China,8.8,0.302,34.8,10.3,Materials,Diversified Metals & Mining\n981,Ultrapar Participacoes,Brazil,22.2,0.448,7.4,12.5,,\n982,Bank Of Hangzhou,China,4.1,0.589,85.3,8.6,Financials,\n983,Electronic Arts,United States,4.6,1.3,7.7,27.4,Information Technology,\n984,Fortive,United States,6.2,0.872,8.2,20.9,Industrials,\n985,CP All,Thailand,12.3,0.473,9.8,16,Consumer Discretionary,Specialty Stores\n986,Amphenol,United States,6.3,0.823,8.5,21.5,Information Technology,Electronics\n987,Regeneron Pharmaceuticals,United States,4.9,0.88,7,40.4,Health Care,Biotechs\n988,Mediobanca,Italy,2.6,0.776,77.5,7.6,Financials,Regional Banks\n989,Asahi Glass,Japan,11.8,0.437,17,9.2,Materials,Construction Materials\n990,Universal Health,United States,9.8,0.702,10.3,11.9,Health Care,Healthcare Services\n991,Mediatek,Taiwan,8.5,0.735,11.5,11.1,Information Technology,Semiconductors\n992,Singapore Airlines,Singapore,10.8,0.524,17,8.4,Industrials,Airline\n993,Banco BTG Pactual Participations,Bermuda,5.3,0.922,59.4,5.7,,\n994,Suntory Beverage & Food,Japan,13,0.424,11.7,13.3,Consumer Staples,\n995,Shanghai Pharmaceuticals,China,18.1,0.481,11.9,9.1,Health Care,Drug Retail\n996,Netflix,United States,8.8,0.187,13.6,61.6,Consumer Discretionary,Internet & Catalog Retail\n997,CGI Group,Canada,8.1,0.835,8.6,14.1,Information Technology,Computer Services\n998,Gap,United States,15.5,0.676,7.6,9.6,Consumer Discretionary,Apparel/Footwear Retail\n999,Hankyu Hanshin Holdings,Japan,6.9,0.719,19.8,8.2,Industrials,Railroads\n1000,Dr Pepper Snapple Group,United States,6.4,0.847,9.8,17.7,Consumer Staples,Beverages\n1001,Cincinnati Financial,United States,5.5,0.591,20.8,11.6,Financials,Diversified Insurance\n1002,Dongbu Insurance,South Korea,12.4,0.451,36.5,4.1,Financials,Property & Casualty Insurance\n1003,Power Assets Holdings,Hong Kong,0.166,0.827,16.7,18.9,Utilities,Electric Utilities\n1004,Bank of East Asia,Hong Kong,3.3,0.416,98.8,11.2,Financials,Regional Banks\n1005,Harris,United States,7.3,0.668,11.6,13.7,Information Technology,Communications Equipment\n1006,YPF,Argentina,14.2,-1.9,26.6,9.9,Energy,\n1007,Finatis,France,40.7,-2.5,46,0.292,Consumer Staples,Food Retail\n1008,Nielsen,United States,6.3,0.502,15.7,14.9,,\n1009,Novolipetsk Steel,Russia,7.6,0.907,10.2,11.6,Materials,Iron & Steel\n1010,Kuwait Finance House,Kuwait,3.1,0.547,54,10.1,Financials,Regional Banks\n1011,China Grand Automotive Services,China,18,0.421,16.3,7.4,Consumer Discretionary,\n1012,EnBW-Energie Baden,Germany,21.4,-2,43.1,6.5,Utilities,Electric Utilities\n1013,Dubai Islamic Bank,United Arab Emirates,2.3,0.843,47.6,7.8,Financials,Regional Banks\n1014,Hikvision,China,4.5,1.1,5.5,28.9,Information Technology,Electronics\n1015,Navient,United States,5,0.682,121.1,4.3,Financials,\n1016,South32,Australia,6,0.747,14.1,11.6,Materials,\n1017,Tokyo Electron,Japan,6.5,0.797,7.6,18.1,Information Technology,Semiconductors\n1018,BDO Unibank,Philippines,2.6,0.549,46.8,10.2,Financials,\n1019,Kohl's,United States,18.7,0.555,13.6,6.8,Consumer Discretionary,Department Stores\n1020,MTN Group,South Africa,10.1,-0.178,19.6,16.6,Telecommunication Services,Telecommunications services\n1021,WW Grainger,United States,10.1,0.601,5.7,13.5,Industrials,Electrical Equipment\n1022,Severstal,Russia,5.9,1.6,6.7,12.3,Materials,Iron & Steel\n1023,Sun Pharma Industries,India,4.6,1.1,9.1,24.9,Health Care,Pharmaceuticals\n1024,Weichai Power,China,13.9,0.366,24.1,7,Industrials,Heavy Equipment\n1025,Arrow Electronics,United States,23.8,0.523,14.2,6.3,Information Technology,Electronics\n1026,Grupa PZU,Poland,6.4,0.492,29.8,8.1,Financials,Property & Casualty Insurance\n1027,PTT Global Chemical,Thailand,9.9,0.726,11,9.5,Materials,Diversified Chemicals\n1028,Bank Leumi,Israel,3.9,0.727,114,6.7,Financials,Major Banks\n1029,China Resources Pharmaceutical Group,Hong Kong,20.2,0.364,17.3,7.3,Health Care,\n1030,Geely Automobile Holdings,Hong Kong,8,0.767,9.7,12.7,,\n1031,Insurance Australia Group,Australia,6.7,0.45,21.2,10.6,Financials,Property & Casualty Insurance\n1032,E-Trade Financial,United States,2,0.552,49.5,9.6,Financials,Investment Services\n1033,Nintendo,Japan,3.6,0.726,12.5,32.8,Consumer Discretionary,Consumer Electronics\n1034,Stora Enso,Finland,10.8,0.512,13,9.5,Materials,Paper & Paper Products\n1035,BBMG,China,6.9,0.407,30,8.9,Materials,Construction Materials\n1036,Seagate Technology,Ireland,10.9,0.513,8.6,13.8,Information Technology,Computer Storage Devices\n1037,TDK,Japan,10.7,0.603,14.4,7.7,Information Technology,Electronics\n1038,Uni-President,Taiwan,12.8,0.45,11.7,10.4,Consumer Staples,Food Processing\n1039,Rockwell Automation,United States,5.9,0.758,7.1,20,Industrials,Other Industrial Equipment\n1040,Henry Schein,United States,11.6,0.507,6.7,13.3,Health Care,Medical Equipment & Supplies\n1041,Meiji Holdings,Japan,11.4,0.531,7.6,12.7,Consumer Staples,Food Processing\n1042,Intuit,United States,4.9,0.793,4.4,30.3,Information Technology,Software & Programming\n1043,Lixil Group,Japan,16.8,0.33,18.5,7.7,Materials,Construction Materials\n1044,London Stock Exchange,United Kingdom,2.2,0.205,697.9,14,Financials,Investment Services\n1045,Amadeus IT Group,Spain,4.9,0.913,8.3,21.8,Information Technology,\n1046,Arconic,United States,12.4,-0.941,20,11.6,Materials,\n1047,Zoetis,United States,4.9,0.821,7.6,25.8,Health Care,Pharmaceuticals\n1048,Helvetia Holding,Switzerland,9.5,0.382,54.5,5.4,Financials,Diversified Insurance\n1049,Advanced Semiconductor,Taiwan,8.5,0.692,11.1,10,Information Technology,Semiconductors\n1050,Yamaha Motor,Japan,13.8,0.581,11.3,8,Consumer Discretionary,Recreational Products\n1051,Alleghany,United States,6.1,0.457,24.1,9.4,Financials,Property & Casualty Insurance\n1052,Crown Castle International,United States,3.9,0.357,22.7,34.1,Telecommunication Services,Telecommunications services\n1053,ZTE,China,15.1,-0.329,20.4,9.9,Information Technology,Communications Equipment\n1054,Grupo Inbursa,Mexico,3.3,0.665,25.8,11.2,Financials,Regional Banks\n1055,Inter Rao,Russia,13,0.907,9.3,7.5,Utilities,Electric Utilities\n1056,Newmont Mining,United States,7.7,-0.027,21,18.2,Materials,Diversified Metals & Mining\n1057,AES,United States,13.7,-1.1,36.1,7.3,Utilities,Electric Utilities\n1058,Oneok,United States,8.9,0.352,16.1,11.9,Energy,Oil Services & Equipment\n1059,Schroders,United Kingdom,3.1,0.661,25.9,10.9,Financials,Investment Services\n1060,C.H. Robinson,United States,13.1,0.513,3.7,10.7,Consumer Discretionary,\n1061,Quest Diagnostics,United States,7.5,0.641,10.1,13.4,Health Care,Healthcare Services\n1062,Mitsubishi UFJ Lease,Japan,7.6,0.492,44.2,4.3,Financials,Rental & Leasing\n1063,Expedia,United States,8.8,0.269,15.8,18.9,,\n1064,Boston Properties,United States,2.6,0.511,18.9,20.7,Financials,Real Estate\n1065,Beijing Enterprises,Hong Kong,7.2,0.803,18.7,6.6,Utilities,Natural Gas Utilities\n1066,Restaurant Brands International,Canada,4.1,0.675,19.1,13.1,,\n1067,Analog Devices,United States,3.6,0.914,10.2,29.2,Information Technology,Semiconductors\n1068,Liberty Expedia Holdings,United States,1.6,2.3,34.6,2.6,,\n1069,KPN,Netherlands,7.5,0.399,15.5,12.6,Telecommunication Services,Telecommunications services\n1070,GS Holdings,South Korea,11.6,0.695,16.8,4.8,Energy,Oil & Gas Operations\n1071,OTP Bank,Hungary,3.7,0.718,38.6,7.9,Financials,Regional Banks\n1072,Roper Industries,United States,3.8,0.659,14.3,21.2,Industrials,Other Industrial Equipment\n1073,Host Hotels & Resorts,United States,5.4,0.762,11.4,13.5,Financials,Real Estate\n1074,Asustek Computer,Taiwan,14.5,0.595,11.3,7.3,Information Technology,Computer Hardware\n1075,Interpublic Group,United States,7.8,0.609,12.5,9.5,Consumer Discretionary,Advertising\n1076,QuintilesIMS,United States,6.9,0.115,21.2,18.7,,\n1077,Tata Steel,India,16.1,-0.444,25.7,7.5,Materials,Iron & Steel\n1078,Bankinter,Spain,2.2,0.542,70.9,7.6,Financials,Regional Banks\n1079,Campbell Soup,United States,7.9,0.497,7.6,17.2,Consumer Staples,Food Processing\n1080,Osaka Gas,Japan,10.9,0.459,14.9,8,Utilities,Natural Gas Utilities\n1081,First Financial Holding,Taiwan,1.8,0.538,78.8,7.3,Financials,Investment Services\n1082,Lotte Shopping,South Korea,25.5,0.134,34.7,5.7,Consumer Discretionary,Department Stores\n1083,Vienna Insurance Group,Austria,10.4,0.321,51.8,3.1,Financials,Diversified Insurance\n1084,SMC,Japan,4.3,0.938,10.2,19.1,Financials,\n1085,Investec,United Kingdom,4.8,0.539,68.2,6.3,Financials,Investment Services\n1086,Power Finance,India,4.3,0.945,37.3,6.3,Consumer Discretionary,Business & Personal Services\n1087,Voestalpine,Austria,11.9,0.477,15.1,7.1,Materials,Iron & Steel\n1088,Hydro One,Canada,4.9,0.559,18.9,10.8,Utilities,\n1089,Whole Foods Market,United States,15.8,0.445,6.4,9.9,Consumer Staples,Food Retail\n1090,Japan Exchange Group,Japan,1,0.379,346.8,7.8,Financials,Investment Services\n1091,Fortum,Finland,4,0.55,23.2,12.5,Utilities,Electric Utilities\n1092,Grupo Bimbo,Mexico,13.4,0.316,11.9,11.8,Consumer Staples,Food Processing\n1093,Alrosa,Russia,4.7,2,7.7,11.4,Materials,Diversified Metals & Mining\n1094,China National Building,China,15.3,0.157,49,3.7,Materials,Construction Materials\n1095,Saputo,Canada,8.4,0.532,5.7,13.2,Consumer Staples,Food Processing\n1096,Shanghai Construction,China,19.8,0.315,25.1,5.5,Industrials,Construction Services\n1097,Mondi,United Kingdom,7.4,0.7,7.7,11.7,Materials,Paper & Paper Products\n1098,Shin Kong Financial,Taiwan,10.9,0.149,98,2.8,Financials,Life & Health Insurance\n1099,SDIC Power Holdings,China,4.4,0.658,29.8,7.7,Energy,Oil & Gas Operations\n1100,Land Securities Group,United Kingdom,1.4,0.758,19.3,10.8,Financials,Real Estate\n1101,China Hongqiao Group,China,7.3,0.649,19.1,6.6,Materials,Diversified Metals & Mining\n1102,Clorox,United States,5.9,0.655,4.6,17.1,Consumer Staples,Household/Personal Care\n1103,Cielo,Brazil,3.5,1.1,9.4,20.9,Consumer Discretionary,Business & Personal Services\n1104,Assurant,United States,7.2,0.565,29.7,5.2,Financials,Diversified Insurance\n1105,TCL Corp,China,16.5,0.312,21.3,6.4,Consumer Discretionary,Consumer Electronics\n1106,Wuliangye Yibin,China,3.4,1,8.9,23.5,Consumer Staples,Beverages\n1107,Thai Beverage,Thailand,5.3,0.757,5.2,17.1,Consumer Staples,Beverages\n1108,Zions Bancorp,United States,2.5,0.455,63.4,8.4,Financials,Regional Banks\n1109,National Oilwell Varco,United States,7.2,-2.4,21.1,14.7,Energy,Oil Services & Equipment\n1110,Flextronics International,Singapore,23.8,0.294,12.8,8.8,Information Technology,Electronics\n1111,Hainan Airlines,China,6.1,0.477,21.3,8.4,Industrials,Airline\n1112,Yanzhou Coal Mining,China,15,0.308,21,6.8,Materials,Diversified Metals & Mining\n1113,Shanghai Electric Group,China,11.8,0.312,25.3,6.7,Industrials,Heavy Equipment\n1114,Xiamen C&D,China,19.3,0.359,20.3,4.6,Industrials,Trading Companies\n1115,Lend Lease Group,Australia,11.7,0.55,13.7,6.6,Financials,Real Estate\n1116,T Rowe Price,United States,4.2,1.2,6.4,16.9,Financials,Investment Services\n1117,Pgnig Group,Poland,8.4,0.596,11.9,8.6,Energy,Oil & Gas Operations\n1118,Galp Energia,Portugal,14.5,0.198,13.1,12.8,Energy,Oil & Gas Operations\n1119,Shenzhen Overseas,China,4.3,0.775,20.4,8.9,Consumer Discretionary,Hotels & Motels\n1120,Kerry Group,Ireland,6.8,0.59,7.8,14.2,Consumer Staples,Food Processing\n1121,Celanese,United States,5.4,0.9,8.4,13,Materials,Diversified Chemicals\n1122,Experian,Ireland,4.5,0.829,7.7,18.9,Consumer Discretionary,Business & Personal Services\n1123,Legrand,France,5.6,0.695,8.6,16,Industrials,Electrical Equipment\n1124,Datang International Power,China,8.8,-0.39,33.6,8.3,Utilities,Electric Utilities\n1125,HCP,United States,2.5,0.627,15.8,14.9,Financials,Real Estate\n1126,SES,Luxembourg,2.3,1.1,14.4,10.6,Consumer Discretionary,Broadcasting & Cable\n1127,AGNC Investment,United States,1.5,0.623,56.9,6.6,Financials,\n1128,Bank of Tianjin,China,4,0.683,94.6,4.3,Financials,\n1129,Phoenix Group Holdings,United Kingdom,9.9,-0.136,93.2,3.5,Financials,Life & Health Insurance\n1130,Harbin Bank,China,3.8,0.734,77.6,3.4,Financials,Regional Banks\n1131,Alaska Air Group,United States,5.9,0.814,10,11,Industrials,Airline\n1132,Cenovus Energy,Canada,9.2,-0.411,19.3,11.2,Energy,Oil & Gas Operations\n1133,Apache,United States,5.3,-1.5,22.5,20,Energy,Oil & Gas Operations\n1134,Delta Electronics,Taiwan,6.6,0.583,7.3,13.9,Information Technology,Electronics\n1135,Electrolux Group,Sweden,14.1,0.525,9.5,8,Consumer Discretionary,Household Appliances\n1136,Hyundai Marine & Fire,South Korea,10.9,0.353,29.8,2.4,Financials,Property & Casualty Insurance\n1137,Korea Gas,South Korea,18.2,-0.581,33.1,3.6,Utilities,Natural Gas Utilities\n1138,Keppel,Singapore,4.9,0.568,20.3,8.9,,\n1139,Wm Morrison Supermarkets,United Kingdom,21.7,0.407,11.6,6.8,Consumer Staples,Food Retail\n1140,AntarChile,Chile,16.7,0.327,21.9,5.3,Financials,Investment Services\n1141,SGS,Switzerland,6.1,0.551,5.3,16.4,Consumer Discretionary,Business & Personal Services\n1142,Hyundai Engineering,South Korea,16.2,0.423,16.5,4.9,Industrials,Construction Services\n1143,Toppan Printing,Japan,13.2,0.349,16.4,7,Consumer Discretionary,Printing & Publishing\n1144,Actelion,Switzerland,2.5,0.707,2.1,30.5,Health Care,Pharmaceuticals\n1145,Bank of Baroda,India,7.9,-0.774,104.4,6.1,Financials,Regional Banks\n1146,Canadian Tire,Canada,9.6,0.505,11.4,8.5,Consumer Discretionary,Specialty Stores\n1147,Intuitive Surgical,United States,2.7,0.736,6.5,28.2,Health Care,Medical Equipment & Supplies\n1148,Punjab National Bank,India,8.8,-0.564,107.7,4.9,Financials,Regional Banks\n1149,Charoen Pokphand Foods,Thailand,13.2,0.417,16.3,6.3,Consumer Staples,Food Processing\n1150,Renesas Electronics,Japan,5.8,0.569,7.1,16.4,Information Technology,Semiconductors\n1151,JG Summit Holdings,Philippines,4.9,0.634,13.4,11.6,Financials,\n1152,Ball,United States,9.1,0.225,16.2,12.6,Materials,Containers & Packaging\n1153,Olympus,Japan,6.9,0.56,8.6,12.8,Consumer Discretionary,Consumer Electronics\n1154,Sun Art Retail Group,Hong Kong,15.1,0.388,8.7,9.6,Consumer Discretionary,Specialty Stores\n1155,BCI-Banco Credito,Chile,3,0.503,46.2,7.4,Financials,Regional Banks\n1156,CDW,United States,14,0.424,6.9,9.2,Information Technology,\n1157,Hesteel,China,10.8,0.153,26.8,7.4,Materials,\n1158,Brown-Forman,United States,3,1,4.6,17.6,Consumer Staples,Beverages\n1159,Sika,Switzerland,5.8,0.572,5,15.4,Materials,Construction Materials\n1160,Hoya,Japan,4.4,0.797,5.6,18.1,Information Technology,Electronics\n1161,SVB Financial Group,United States,1.6,0.383,44.8,9.5,Financials,Regional Banks\n1162,Genworth Financial,United States,8.4,-0.273,104.7,2,Financials,Diversified Insurance\n1163,Cerner,United States,4.8,0.637,5.6,19.7,Health Care,Healthcare Services\n1164,Foot Locker,United States,7.8,0.664,3.8,9.5,Consumer Discretionary,Apparel/Footwear Retail\n1165,Mitsubishi Materials,Japan,12.1,0.498,15.8,3.8,Materials,Diversified Metals & Mining\n1166,Interactive Brokers Group,United States,1.4,0.084,54.7,14.3,Financials,Investment Services\n1167,Monster Beverage,United States,3,0.713,4.2,26,Consumer Staples,Beverages\n1168,Dover,United States,6.8,0.509,10.1,12.5,Industrials,Conglomerates\n1169,Intact Financial,Canada,6.7,0.408,16.8,9.3,Financials,Property & Casualty Insurance\n1170,Global Logistic Properties,Singapore,0.881,0.669,20.5,9.4,Financials,Real Estate\n1171,Skyworks Solutions,United States,3.3,0.898,4.1,18.8,Utilities,\n1172,Terna,Italy,2.3,0.7,17.6,10,Utilities,Electric Utilities\n1173,Tokyo Century,Japan,8.8,0.378,29.7,3.5,,\n1174,Autoliv,Sweden,10.1,0.567,8.2,8.5,Consumer Discretionary,Auto & Truck Parts\n1175,Hindalco Industries,India,15.2,0.031,22,6.8,Materials,Aluminum\n1176,Advance Auto Parts,United States,9.6,0.458,8.3,10.5,Consumer Discretionary,Specialty Stores\n1177,Ayala,Philippines,4.2,0.548,18.4,10.7,Industrials,Conglomerates\n1178,Paychex,United States,3.1,0.8,7.3,20.9,Consumer Discretionary,Business & Personal Services\n1179,Rockwell Collins,United States,5.3,0.738,7.7,12.9,Industrials,Aerospace & Defense\n1180,Oriental Land,Japan,4.3,0.699,7.1,19.6,Consumer Discretionary,Hotels & Motels\n1181,Industrial Alliance Insurance,Canada,6.4,0.418,41.7,4.5,Financials,Life & Health Insurance\n1182,Attijariwafa Bank,Morocco,3.3,0.485,42.4,7.5,Financials,Regional Banks\n1183,American Water Works,United States,3.3,0.468,18.5,13.8,Utilities,Diversified Utilities\n1184,Motorola Solutions,United States,6,0.559,8.5,13.9,Information Technology,Communications Equipment\n1185,United Rentals,United States,5.8,0.566,12,10.4,Financials,Rental & Leasing\n1186,Norwegian Cruise Line Holdings,United States,4.9,0.633,13,11,Financials,\n1187,Harley-Davidson,United States,6,0.692,9.9,10.7,Consumer Discretionary,Recreational Products\n1188,Hess,United States,4.8,-6.1,28.6,15.2,Energy,Oil & Gas Operations\n1189,Masraf Al Rayan,Qatar,0.755,0.568,25.1,8.8,Financials,Regional Banks\n1190,Jiangsu Yanghe Brewery,China,2.6,0.879,5.3,18.6,Consumer Staples,Beverages\n1191,Grifols,Spain,4.5,0.603,10.7,16.8,Health Care,Biotechs\n1192,Orient Securities,China,1.8,0.349,30.6,12.2,,\n1193,Mitsubishi Motors,Japan,17.9,-2,13.3,9,Consumer Discretionary,Auto & Truck Manufacturers\n1194,Origin Energy,Australia,9,-1.5,19.7,9.6,Utilities,Electric Utilities\n1195,Qantas Airways,Australia,11.8,0.636,12.5,5.3,Industrials,Airline\n1196,Advanced Info Service,Thailand,4.3,0.869,7.7,15,Telecommunication Services,Telecommunications services\n1197,Pentair,United Kingdom,5.7,0.522,11.5,11.4,Industrials,Other Industrial Equipment\n1198,Industries Qatar,Qatar,1.3,0.812,9.7,18.4,Materials,Specialized Chemicals\n1199,Nitto Denko,Japan,6.8,0.529,7.5,12.4,Materials,Diversified Chemicals\n1200,Arab National Bank,Saudi Arabia,2.2,0.761,45.3,5.3,Financials,Regional Banks\n1201,Zijin Mining Group,China,11.7,0.276,12.8,10.4,Materials,Diversified Metals & Mining\n1202,Ricoh,Japan,18.7,0.144,24.3,5.8,Industrials,Business Products & Supplies\n1203,Xerox,United States,15.6,-0.477,18.1,7.2,Industrials,Business Products & Supplies\n1204,Tenet Healthcare,United States,19.6,-0.192,24.7,1.7,Health Care,Healthcare Services\n1205,SCANA,United States,4.2,0.594,18.7,9.3,Utilities,Electric Utilities\n1206,Jiangxi Copper,China,30.3,0.12,12.6,7.7,Materials,Diversified Metals & Mining\n1207,Givaudan,Switzerland,4.7,0.654,6.4,16.6,Materials,Specialized Chemicals\n1208,Liberty Broadband,United States,0.031,0.917,9.6,15.7,,\n1209,Chiba Bank,Japan,1.9,0.493,119.9,5.2,Financials,Major Banks\n1210,Everbright Securities,China,1.9,0.454,25.6,9.9,Financials,\n1211,Smith & Nephew,United Kingdom,4.7,0.801,7.3,13.4,Health Care,Medical Equipment & Supplies\n1212,UCB,Belgium,4.6,0.601,10.8,14.6,Health Care,Pharmaceuticals\n1213,Industrivarden,Sweden,2,1.9,10.5,9.3,Financials,Investment Services\n1214,PVH,United States,8.2,0.549,11.1,8,Consumer Discretionary,Apparel/Accessories\n1215,ALFA,Mexico,15.7,0.144,16.9,7.3,Industrials,Conglomerates\n1216,Avnet,United States,20.8,0.392,13.3,5.7,Information Technology,Electronics\n1217,Lundbergs,Sweden,2.3,1.4,13.4,8.1,Financials,Real Estate\n1218,NRG Energy,United States,13.1,-0.774,30.4,5.9,Utilities,Electric Utilities\n1219,Shionogi,Japan,3.1,0.852,5.5,17.4,Health Care,\n1220,Csc Financial,China,2.5,0.749,26.1,6.8,Financials,\n1221,New York Community,United States,1.9,0.492,49.1,6.6,Financials,Thrifts & Mortgage Finance\n1222,Naver,South Korea,3.5,0.646,5.3,24,Information Technology,\n1223,LARGAN Precision,Taiwan,1.5,0.705,3,20.6,,\n1224,Yum China Holdings,United States,6.8,0.502,3.7,12,Financials,\n1225,Banco De Venezuela,Venezuela,7,2.5,2.6,2.5,Financials,\n1226,CJ Corp,South Korea,20.6,0.192,22.4,4.4,Consumer Staples,Food Processing\n1227,CA,United States,4,0.781,11,13.3,Information Technology,Software & Programming\n1228,Top Frontier Investment Holdings,Philippines,14.2,0.021,28.8,1.9,Financials,\n1229,Qatar Islamic Bank,Qatar,1.1,0.555,38.4,6.6,Financials,Regional Banks\n1230,Canara Bank,India,7.5,-0.398,85.1,2.8,Financials,Major Banks\n1231,Mosaic,United States,7.2,0.298,16.8,10.1,Materials,Specialized Chemicals\n1232,Shinsei Bank,Japan,3,0.517,79,4.6,Financials,Regional Banks\n1233,Braskem,Brazil,13.8,-0.136,15.9,7.9,Materials,Specialized Chemicals\n1234,Rural Electrification,India,3.7,0.869,32.2,5.7,Financials,Investment Services\n1235,Alexion Pharmaceuticals,United States,3,0.394,13.3,26,Health Care,Biotechs\n1236,Aramark,United States,14.4,0.32,10.4,9,,\n1237,Bank of Zhengzhou,China,2.5,0.603,52.7,3.4,Financials,\n1238,Manpower,United States,19.7,0.444,7.6,6.6,Consumer Discretionary,Business & Personal Services\n1239,Yes Bank,India,2.5,0.386,25,11,Financials,\n1240,Dexia,Belgium,0.393,0.417,224.4,0.373,Financials,Major Banks\n1241,Masco,United States,7.4,0.485,5.1,10.7,Materials,Construction Materials\n1242,Torchmark,United States,3.9,0.549,21.4,9,Financials,Life & Health Insurance\n1243,Proximus,Belgium,6.4,0.579,8.6,10.4,Telecommunication Services,\n1244,X5 Retail Group,Netherlands,15.8,0.339,7.9,8.7,Consumer Staples,Food Retail\n1245,RSA Insurance Group,United Kingdom,9.3,0.76,23.3,7.5,Financials,Property & Casualty Insurance\n1246,Air Canada,Canada,11.1,0.661,11.3,2.7,Industrials,Airline\n1247,KLA-Tencor,United States,3.3,0.864,5.1,15.1,Information Technology,Semiconductors\n1248,Valeant Pharmaceuticals,Canada,9.7,-2.4,43.5,3.3,Health Care,Pharmaceuticals\n1249,3i Group,United Kingdom,0.644,2.4,8.4,9.1,,\n1250,Bank of India,India,7,-0.948,93.1,2.3,Financials,Regional Banks\n1251,Check Point Software,Israel,1.7,0.725,5.2,18,Information Technology,Software & Programming\n1252,Taiwan Cooperative Financial,Taiwan,2,0.427,103.7,5.9,Financials,Consumer Financial Services\n1253,Community Health Systems,United States,18.4,-1.7,21.9,1,Health Care,\n1254,Huntington Ingalls Industries,United States,7.1,0.573,6.9,9.4,Industrials,\n1255,PGE Polska Grupa Energetyczna,Poland,7.1,0.651,16.2,5.3,,\n1256,Sekisui Chemical,Japan,9.8,0.517,8.1,7.8,Industrials,Construction Services\n1257,Brenntag,Germany,11.6,0.399,7.8,8.8,Materials,Specialized Chemicals\n1258,Bombardier,Canada,16.3,-1,22.8,3.6,Industrials,Aerospace & Defense\n1259,Coty,United States,5.4,-0.011,21.9,12.7,Industrials,\n1260,Genting,Malaysia,4.4,0.518,20.6,7.9,Consumer Discretionary,Casinos & Gaming\n1261,Bank Of Guiyang,China,1.8,0.554,53.6,5.7,Financials,\n1262,PotashCorp,Canada,4.5,0.335,17.3,14.2,,\n1263,W&W-Wüstenrot,Germany,7.1,0.258,75.9,1.8,,\n1264,Sojitz,Japan,13.9,0.317,18.1,3.1,Industrials,Trading Companies\n1265,Darden Restaurants,United States,7,0.495,4.7,10.3,Consumer Discretionary,Restaurants\n1266,Sunac China Holdings,China,5.5,0.367,42.2,5.9,,\n1267,Unicharm,Japan,6.5,0.406,6.1,14.5,Consumer Staples,Household/Personal Care\n1268,Ctrip.com International,China,2.9,-0.223,20.8,25.1,,\n1269,Jyske Bank,Denmark,2.3,0.46,83.2,4.8,Financials,Regional Banks\n1270,China Life Insurance (Taiwan),Taiwan,7.6,0.294,41.1,3.4,Financials,Life & Health Insurance\n1271,Dai Nippon Printing,Japan,13,0.292,14.4,7.2,Consumer Discretionary,Printing & Publishing\n1272,CIT Group,United States,4.4,-0.861,64.2,8.8,Financials,Consumer Financial Services\n1273,IndusInd Bank,India,2.3,0.349,21.2,13.1,Financials,\n1274,Banca MPS,Italy,5.9,-3.6,161.8,15,Financials,Major Banks\n1275,Bed Bath & Beyond,United States,12.2,0.685,6.8,5.8,Consumer Discretionary,Specialty Stores\n1276,Bank of Chongqing,China,2.7,0.529,53.7,2.7,Financials,Regional Banks\n1277,Marathon Oil,United States,4.1,-2.1,31.1,13.8,Energy,Oil & Gas Operations\n1278,East West Bancorp,United States,1.3,0.432,34.8,7.4,Financials,Regional Banks\n1279,Nitori Holdings,Japan,4.7,0.554,4.4,15.1,Financials,\n1280,Yamato Holdings,Japan,13.3,0.341,10,8.3,Industrials,Trucking\n1281,Cosco Shipping,China,10.7,-1.5,17.2,7.9,,\n1282,SKF Group,Sweden,8.5,0.465,9.5,8.9,Industrials,Other Industrial Equipment\n1283,GAIL India,India,8.3,0.344,11,10.2,Utilities,Natural Gas Utilities\n1284,PulteGroup,United States,7.6,0.598,10.2,7.4,Industrials,Construction Services\n1285,Signature Bank,United States,1.4,0.396,39.2,7.6,Financials,Regional Banks\n1286,AGL Energy,Australia,8.6,0.272,10.7,13.8,Utilities,Electric Utilities\n1287,Latam Airlines,Chile,9,0.072,19.2,8,Industrials,Airline\n1288,AutoNation,United States,21.6,0.431,10.3,4.1,Consumer Discretionary,Specialty Stores\n1289,Doosan,South Korea,14.3,-0.147,23.7,1.6,Industrials,Construction Services\n1290,Transocean,Switzerland,4.2,0.765,26.9,4.9,Energy,Oil Services & Equipment\n1291,Hua Nan Financial,Taiwan,1.8,0.437,79,5.8,Financials,Consumer Financial Services\n1292,KT&G,South Korea,3.9,1.1,8.1,11.2,Consumer Staples,Tobacco\n1293,LKQ,United States,8.9,0.458,8.3,8.6,Consumer Discretionary,Auto & Truck Parts\n1294,Equity Residential,United States,2.4,0.232,20.7,23.1,Financials,Real Estate\n1295,Amorepacific,South Korea,4.9,0.551,4.3,14.1,,\n1296,Iida Group Holdings,Japan,11.1,0.715,9.6,4.3,,\n1297,Gecina,France,0.668,0.9,13,8.7,Financials,Real Estate\n1298,Edwards Lifesciences,United States,3,0.57,4.5,19.9,Health Care,Medical Equipment & Supplies\n1299,Medipal Holdings,Japan,28.2,0.296,13.5,3.7,Health Care,Pharmaceuticals\n1300,Bunzl,United Kingdom,10,0.359,5.6,9.8,Consumer Discretionary,Business & Personal Services\n1301,El Puerto de Liverpool,Mexico,5.4,0.545,7.2,11.8,Consumer Discretionary,Department Stores\n1302,Vicinity Centres,Australia,1,1.1,11.7,8.6,,\n1303,Delta Lloyd,Netherlands,6.5,0.256,80,2.6,Financials,Diversified Insurance\n1304,Eisai,Japan,4.9,0.507,8.9,14.9,Health Care,Pharmaceuticals\n1305,Marks & Spencer,United Kingdom,15,0.356,10.7,6.8,Consumer Discretionary,Department Stores\n1306,Pioneer Natural Resources,United States,4,-0.557,16.5,32.2,Energy,Oil & Gas Operations\n1307,Arab Bank,Jordan,2.4,0.522,47.5,5.7,Financials,Regional Banks\n1308,Foncière des Régions,France,1.1,0.866,20.6,6.3,,\n1309,Sino-Ocean Land Holdings,China,5.4,0.572,21.8,3.7,Financials,Real Estate\n1310,Nippon Express,Japan,17.1,0.341,13.2,4.8,Industrials,Trucking\n1311,Dxc Technology,United States,7.5,-0.101,8.3,21.5,Information Technology,\n1312,Nordstrom,United States,14.8,0.354,7.9,7.4,Consumer Discretionary,Department Stores\n1313,Pandora,Denmark,3,0.895,2.1,11.8,Consumer Staples,Household/Personal Care\n1314,GKN,United Kingdom,11.9,0.327,11.1,7.6,Consumer Discretionary,Auto & Truck Parts\n1315,Ajinomoto,Japan,10.1,0.258,11.4,10.9,Consumer Staples,Food Processing\n1316,BNK Financial Group,South Korea,3.3,0.425,77.4,2.6,Financials,\n1317,Founder Securities,China,1.2,0.387,21.9,10.1,,\n1318,Vedanta Resources,United Kingdom,9.9,-1.5,30,2.8,Materials,Diversified Metals & Mining\n1319,Petronas Chemicals,Malaysia,3.3,0.708,7.3,14,Materials,Specialized Chemicals\n1320,Magnitogorsk Iron & Steel,Russia,5.6,1.1,6.2,7,Materials,Iron & Steel\n1321,Cintas,United States,5.1,0.501,4.3,12.9,,\n1322,Dassault Systemes,France,3.4,0.495,7.3,22.2,Information Technology,Software & Programming\n1323,Quinenco,Chile,6.8,0.117,54.6,4.3,Industrials,Conglomerates\n1324,Nippon Yusen,Japan,17.7,-2.1,17.8,3.5,Industrials,Other Transportation\n1325,BRF,Brazil,9.7,-0.107,13.8,9.3,Consumer Staples,\n1326,Hasbro,United States,5,0.551,5.1,12,Consumer Discretionary,\n1327,Illumina,United States,2.4,0.464,4.3,25.2,Health Care,Biotechs\n1328,Kawasaki Heavy Industries,Japan,14,0.317,15.4,4.9,Industrials,Heavy Equipment\n1329,Digital Realty Trust,United States,2.1,0.426,12.2,17.3,,\n1330,Alcoa,United States,15.1,0.02,16.7,6.2,Materials,Aluminum\n1331,Chugoku Electric Power,Japan,11,0.246,25.8,4,Utilities,Electric Utilities\n1332,Koito Manufacturing,Japan,7.6,0.52,5.4,8.1,Industrials,\n1333,AmTrust Financial Services,United States,5.6,0.485,23.1,3.2,Financials,\n1334,Juniper Networks,United States,5,0.601,9.7,10.6,Information Technology,Communications Equipment\n1335,United Utilities,United Kingdom,2.4,0.608,15.9,8.5,Utilities,Diversified Utilities\n1336,Red Eléctrica,Spain,2.1,0.705,11.1,10.7,Utilities,\n1337,RusHydro,Russia,5.6,0.6,16.3,6,Utilities,Electric Utilities\n1338,Kobe Steel,Japan,15.5,-0.407,18.9,3.2,Materials,Iron & Steel\n1339,Wyndham Worldwide,United States,5.6,0.612,9.8,8.9,Consumer Discretionary,Hotels & Motels\n1340,MOL Hungarian Oil,Hungary,12.5,-0.74,14.7,7.3,Energy,Oil & Gas Operations\n1341,Cathay Pacific Airways,Hong Kong,11.9,-0.74,22.9,5.7,Industrials,Airline\n1342,JetBlue Airways,United States,6.6,0.75,9.5,7,Industrials,\n1343,Agilent Technologies,United States,4.2,0.507,7.9,17,Information Technology,Electronics\n1344,Wolters Kluwer,Netherlands,4.8,0.541,9.9,11.9,Consumer Discretionary,Printing & Publishing\n1345,Arkema,France,8.3,0.472,9.7,7.7,Materials,Diversified Chemicals\n1346,Ezdan Holding Group,Qatar,0.538,0.498,13.7,11.5,Financials,Real Estate\n1347,JSW Steel,India,7.3,0.407,12.8,7.5,Materials,Iron & Steel\n1348,Electric Power Development,Japan,6.7,0.411,21.4,4.2,Utilities,Electric Utilities\n1349,Hexagon,Sweden,3.5,0.635,8.3,14.7,Industrials,Other Industrial Equipment\n1350,Ahli United Bank,Bahrain,1.6,0.543,31.3,5.9,Financials,Regional Banks\n1351,Kerry Properties,Hong Kong,1.8,0.842,22.3,5.4,,\n1352,Yuanta Financial Holding,Taiwan,3.3,0.416,67.1,5.2,Financials,Investment Services\n1353,Airports of Thailand,Thailand,1.5,0.568,4.8,16.4,Industrials,Other Transportation\n1354,CR Bard,United States,3.7,0.529,5.3,18,Health Care,Medical Equipment & Supplies\n1355,Hanwha Chemical,South Korea,8,0.651,11.4,3.8,Materials,Diversified Chemicals\n1356,Noble Group,Hong Kong,46.5,-0.012,12.4,1.8,Industrials,Conglomerates\n1357,Frontier Communications,United States,8.9,-0.376,30.2,2.4,Telecommunication Services,Telecommunications services\n1358,Ulta Salon Cosmetcs & Fragrance,United States,4.9,0.41,2.6,17.7,Consumer Discretionary,\n1359,Crown Holdings,United States,8.3,0.496,9.6,7.4,Materials,Containers & Packaging\n1360,Kintetsu,Japan,11.2,0.186,16.2,6.9,Industrials,Railroads\n1361,Starwood Property Trust,United States,0.786,0.363,77.3,5.9,,\n1362,Arthur J. Gallagher,United States,5.6,0.414,11.5,10.1,,\n1363,Johnson Matthey,United Kingdom,15,0.328,5.6,7.3,Materials,Diversified Chemicals\n1364,UGI,United States,5.8,0.481,11.3,8.6,Utilities,Natural Gas Utilities\n1365,Grupo Televisa,Mexico,5.2,0.199,14.9,15.8,Consumer Discretionary,Broadcasting & Cable\n1366,JB Hunt Transport,United States,6.6,0.432,3.9,10.1,Industrials,Trucking\n1367,Iliad,France,5.2,0.444,8.7,13.1,Information Technology,Computer Services\n1368,Next,United Kingdom,5.5,0.847,3,7.3,Consumer Discretionary,Apparel/Footwear Retail\n1369,Arca Continental,Mexico,5,0.483,6.8,12.5,Consumer Staples,Beverages\n1370,Uralkali,Russia,2.3,1.4,8.7,8.3,Materials,Specialized Chemicals\n1371,Xilinx,United States,2.3,0.614,4.8,14.1,Information Technology,Semiconductors\n1372,AAC Technologies Holdings,China,2.3,0.602,3.5,14.4,,\n1373,Gudang Garam,Indonesia,5.7,0.502,4.7,9.9,Consumer Staples,Tobacco\n1374,Banco Davivienda,Colombia,3.3,0.563,31.2,4.8,Financials,Regional Banks\n1375,Mead Johnson Nutrition,United States,3.7,0.543,4.1,16.4,Consumer Staples,Household/Personal Care\n1376,Essex Property Trust,United States,1.3,0.415,12.2,15.3,,\n1377,Amcor,Australia,9.3,0.22,8.6,13.3,Materials,Containers & Packaging\n1378,Financiere de l'Odet,France,11.1,0.252,22.2,5.4,Industrials,Conglomerates\n1379,DCC,Ireland,15.8,0.287,6.6,7.8,Industrials,Conglomerates\n1380,Geberit,Switzerland,2.9,0.557,3.5,15.2,Materials,Construction Materials\n1381,Alfresa Holdings,Japan,23.5,0.306,11.3,4,Health Care,Pharmaceuticals\n1382,Mitsui OSK Lines,Japan,13.6,-1.5,18.8,3.7,Industrials,Other Transportation\n1383,ICA Gruppen,Sweden,12.1,0.399,8.4,6.8,Consumer Discretionary,Printing & Publishing\n1384,E.Sun Financial,Taiwan,1.7,0.407,58.5,5.3,Financials,Consumer Financial Services\n1385,Fluor,United States,19,0.281,9.5,7.3,Industrials,Construction Services\n1386,Saudi Arabian Mining,Saudi Arabia,2.5,0.107,25.9,12.7,Materials,Diversified Metals & Mining\n1387,Partners Group Holding,Switzerland,1.1,0.567,1.9,14.7,,\n1388,Mashreq Bank,United Arab Emirates,2.4,0.525,33.4,3.9,Financials,Regional Banks\n1389,Noble Energy,United States,3.4,-0.998,21.4,15.1,Energy,Oil & Gas Operations\n1390,Caltex Australia,Australia,13.1,0.453,3.8,6,Energy,Oil & Gas Operations\n1391,Bank Audi,Lebanon,4,0.446,44.3,2.8,Financials,Regional Banks\n1392,LG Household & Health Care,South Korea,5.3,0.49,3.7,10.8,Health Care,\n1393,Colruyt,Belgium,10.3,0.417,4.8,7.1,Consumer Staples,Food Retail\n1394,Focus Media Information Technology,China,1.3,0.539,1.5,15.4,Information Technology,\n1395,OMRON,Japan,7.3,0.405,5.9,9.3,Information Technology,Electronics\n1396,Tenaris,Luxembourg,4.5,0.056,14,20.3,Energy,Oil Services & Equipment\n1397,Old Republic International,United States,5.9,0.467,19.1,5.2,Financials,Property & Casualty Insurance\n1398,Fukuoka Financial Group,Japan,2.1,0.309,156,3.7,Financials,Regional Banks\n1399,Baiyin Nonferrous Group,China,8.7,0.018,6.3,13.4,,\n1400,Dentsply Sirona,United States,3.7,0.43,11.7,14.3,,\n1401,Tractor Supply,United States,6.8,0.437,2.8,9.1,Consumer Discretionary,Specialty Stores\n1402,Grupo Carso,Mexico,5.1,0.51,5.3,10.5,Industrials,Conglomerates\n1403,Materials Industry Zhongda Group,China,29.2,0.221,12.6,4.5,,\n1404,Almarai,Saudi Arabia,3.9,0.537,7.7,15.1,Consumer Staples,Food Processing\n1405,Open Text,Canada,2,1.1,6.9,8.7,,\n1406,RHB Bank,Malaysia,2.6,0.403,52.8,4.8,Financials,\n1407,Targa Resources,United States,6.7,-0.187,13,11.6,Energy,\n1408,Taylor Wimpey,United Kingdom,5,0.795,5.9,7.9,Industrials,\n1409,Banca Mediolanum,Italy,1.7,0.435,44.2,5.4,,\n1410,Expeditors International,United States,6.1,0.431,2.8,10.1,Industrials,Air Courier\n1411,Inner Mongolia Baotou Steel,Mongolia,3.8,-0.267,21.1,14.6,Materials,Iron & Steel\n1412,Metro,Canada,9.7,0.43,4.3,7,,\n1413,Stockland Australia,Australia,1.8,0.665,12.6,8.6,Financials,Real Estate\n1414,Olam International,Singapore,14.9,0.255,16.2,3.8,Consumer Staples,Food Processing\n1415,Pinnacle West,United States,3.5,0.442,16,9.4,Utilities,Electric Utilities\n1416,Chang Hwa Bank,Taiwan,1.3,0.375,62.2,5.5,Financials,Regional Banks\n1417,Samsung SDS,South Korea,7.1,0.4,5.7,9.4,Information Technology,\n1418,Brambles,Australia,5.5,0.424,7.6,11.2,Consumer Discretionary,Business & Personal Services\n1419,Verisk Analytics,United States,2,0.589,4.6,13.2,Consumer Discretionary,Business & Personal Services\n1420,Sealed Air,United States,6.8,0.473,7.4,8.3,Materials,Containers & Packaging\n1421,Union Bank of India,India,5.5,0.207,61.8,1.6,Financials,Regional Banks\n1422,British Land,United Kingdom,0.851,0.474,17.3,8.3,Financials,Real Estate\n1423,Seiko Epson,Japan,9.4,0.348,8.4,8.3,Industrials,Business Products & Supplies\n1424,Barratt Developments,United Kingdom,5.6,0.771,7.6,7,,\n1425,NSK,Japan,8.6,0.434,9,7.3,Industrials,Other Industrial Equipment\n1426,Equifax,United States,3.2,0.489,6.7,16.3,Consumer Discretionary,Business & Personal Services\n1427,Transurban Group,Australia,1.9,0.095,17.1,18.2,,\n1428,Perrigo,Ireland,5.6,-1.5,17.5,9.9,Health Care,Pharmaceuticals\n1429,Rite Aid,United States,32.6,0.091,11.7,4.8,Consumer Staples,Drug Retail\n1430,Coloplast,Denmark,2.2,0.485,1.8,16.3,Health Care,Medical Equipment & Supplies\n1431,Newcrest Mining,Australia,3.6,0.439,11.2,14,Materials,Diversified Metals & Mining\n1432,China Merchants Holdings,Hong Kong,1,0.708,13.3,7.6,Industrials,Other Transportation\n1433,Dassault Aviation,France,4,0.419,14.4,10.4,Industrials,Aerospace & Defense\n1434,UBI Banca,Italy,4.6,-0.918,118.5,3.6,Financials,Regional Banks\n1435,Bajaj Auto,India,3.5,0.578,3.1,12.8,Consumer Discretionary,Recreational Products\n1436,Sabesp,Brazil,4,0.846,11.6,7.4,Utilities,Diversified Utilities\n1437,Steel Dynamics,United States,7.8,0.382,6.4,8.5,Materials,\n1438,Ramsay Health Care,Australia,6.6,0.358,5.9,10.5,Health Care,\n1439,Equinix,United States,3.6,0.124,12.6,31.1,,\n1440,Coach,United States,4.5,0.511,4.7,11.1,Consumer Discretionary,Apparel/Accessories\n1441,Cosmo Energy Holdings,Japan,19.7,0.201,13.5,1.4,,\n1442,ISS,Denmark,11.8,0.329,7,7.2,,\n1443,Coca-Cola HBC,Switzerland,6.9,0.383,6.9,9.4,Consumer Staples,Beverages\n1444,Goodman Group,Australia,1.1,0.678,9.3,10.8,,\n1445,Aeroports de Paris,France,3.3,0.481,11.2,11.9,Industrials,Other Transportation\n1446,E-mart,South Korea,12.7,0.324,12.8,5.5,Consumer Discretionary,Discount Stores\n1447,RiseSun Real Estate Development,China,4.1,0.448,19.2,7.3,Materials,\n1448,China National Chemical,China,8.4,0.368,12.5,6.5,Industrials,Construction Services\n1449,Acciona,Spain,6.6,0.389,18.4,4.6,Industrials,Construction Services\n1450,Fastenal,United States,4,0.5,2.7,14.5,Consumer Discretionary,Home Improvement Retail\n1451,Hyundai Glovis,South Korea,13.2,0.436,6.6,4.7,Industrials,Other Transportation\n1452,Hanesbrands,United States,6,0.539,6.9,7.8,Consumer Discretionary,Apparel/Accessories\n1453,Penske Automotive,United States,20.1,0.343,8.9,3.8,Consumer Discretionary,Specialty Stores\n1454,Shoprite Holdings,South Africa,9.4,0.344,4,7.9,Consumer Staples,Food Retail\n1455,Gjensidige Forsikring,Norway,3.4,0.556,15.7,7.4,Financials,Property & Casualty Insurance\n1456,McCormick,United States,4.4,0.472,4.8,12.4,Consumer Staples,\n1457,Shanghai Commercial & Savings Bank,Taiwan,1.3,0.364,51.1,4.1,Financials,\n1458,Shiseido,Japan,7.8,0.295,8.1,10.5,Consumer Staples,Household/Personal Care\n1459,AVIC Capita,China,1.4,0.35,23,8.1,,\n1460,Melco International Development,Hong Kong,3.1,1.3,13.4,2.8,,\n1461,Citrix Systems,United States,3.4,0.536,6.4,13,Information Technology,Software & Programming\n1462,Goldcorp,Canada,3.5,0.167,21.5,12.6,Materials,Diversified Metals & Mining\n1463,Realty Income,United States,1.1,0.316,13.2,16.3,,\n1464,AECOM Technology,United States,17.5,0.164,13.5,5.1,Industrials,\n1465,ITV,United Kingdom,4.1,0.598,4.4,10.8,Consumer Discretionary,Broadcasting & Cable\n1466,Cheniere Energy,United States,1.3,-0.61,23.7,11.3,Energy,\n1467,Banco Popular Espanol,Spain,4.3,-3.9,156,3.6,Financials,Regional Banks\n1468,Agile Property Holdings,China,7.1,0.341,19,3.5,Financials,Real Estate\n1469,NCC Group,Sweden,6.2,0.936,2.9,2.6,Industrials,Construction Services\n1470,Oji Holdings,Japan,13,0.093,16.1,4.3,Materials,Paper & Paper Products\n1471,Korean Air,South Korea,10.1,-0.487,19.8,2.6,Industrials,Airline\n1472,Hong Leong Financial,Malaysia,2,0.371,47.5,4.2,Financials,Investment Services\n1473,Smurfit Kappa Group,Ireland,9,0.49,9.6,6,Materials,Paper & Paper Products\n1474,Abu Dhabi Islamic Bank,United Arab Emirates,1.8,0.449,33.3,3.4,Financials,Regional Banks\n1475,Hyosung,South Korea,10.3,0.393,11.7,3.9,Materials,Diversified Metals & Mining\n1476,Aozora Bank,Japan,1.1,0.405,41.1,4.1,Financials,Major Banks\n1477,Ashtead Group,United Kingdom,4,0.644,7.5,10.5,,\n1478,Storebrand,Norway,5.1,0.252,60.4,2.9,Financials,Diversified Insurance\n1479,Persimmon,United Kingdom,4.2,0.844,5.1,8.2,,\n1480,Ingredion,United States,5.7,0.482,5.8,8.6,Consumer Staples,Food Processing\n1481,KB Insurance,South Korea,8.2,0.258,23.7,1.6,,\n1482,Sistema,Russia,10.8,-0.045,18.1,3.7,Telecommunication Services,Telecommunications services\n1483,Taishin Financial Holdings,Taiwan,1.4,0.354,48.9,3.9,Financials,Investment Services\n1484,Yamaguchi Financial,Japan,1.5,0.299,87.5,2.6,Financials,Regional Banks\n1485,Mizrahi Tefahot Bank,Israel,1.9,0.33,59.9,3.9,Financials,Regional Banks\n1486,Meritz Financial Group,South Korea,6.7,0.218,30.9,1.5,Financials,\n1487,Kangmei Pharmaceutical,China,3.2,0.496,7.9,13.4,Health Care,\n1488,IHI,Japan,14,0.245,14.7,5.1,Industrials,Heavy Equipment\n1489,Scripps Networks Interactive,United States,3.4,0.674,6.4,10,Consumer Discretionary,Broadcasting & Cable\n1490,Hang Lung Group,Hong Kong,1.9,0.478,24,6.1,Financials,Real Estate\n1491,Waters,United States,2.2,0.522,4.7,12.6,,Precision Healthcare Equipment\n1492,SEIBU,Japan,4.7,0.724,13.6,5.8,Consumer Discretionary,\n1493,China Longyuan Power,China,3.4,0.516,20,6.4,Utilities,Diversified Utilities\n1494,Adient,Ireland,16.6,-1.5,11.3,6.3,Energy,\n1495,Blom Bank,Lebanon,2.4,0.458,29.5,2.6,Financials,Regional Banks\n1496,Hokuhoku Financial Group,Japan,1.7,0.279,104.9,2,Financials,Regional Banks\n1497,Jiangsu Hengrui Medicine,China,1.6,0.39,2.1,18.7,Financials,\n1498,Ametek,United States,3.8,0.512,7.1,12.5,Industrials,Electrical Equipment\n1499,STMicroelectronics,Switzerland,7,0.167,8,13.7,Information Technology,Semiconductors\n1500,Dangote Cement,Nigeria,2.4,0.745,4.9,9.1,Materials,Construction Materials\n1501,SDIC Essence,China,1.6,0.386,18.5,8.2,,\n1502,Wärtsilä,Finland,5.3,0.389,5.7,10.8,,\n1503,Oi,Brazil,7.5,-2,25.2,0.952,Telecommunication Services,Telecommunications services\n1504,Maxim Integrated Products,United States,2.2,0.5,3.9,12.7,Information Technology,Semiconductors\n1505,Chow Tai Fook Jewellery,Hong Kong,6.4,0.336,6.9,10.1,Consumer Discretionary,Apparel/Accessories\n1506,Shimano,Japan,3,0.469,3.8,13.6,Consumer Discretionary,Recreational Products\n1507,Bank Muscat,Oman,1.4,0.459,28.1,2.8,Financials,Regional Banks\n1508,Concordia Financial Group,Japan,0.303,0.058,164.1,5.7,Financials,\n1509,Thai Oil,Thailand,7.8,0.602,6.1,4.4,Energy,Oil & Gas Operations\n1510,Axiata Group,Malaysia,5.2,0.122,15.7,10.2,Information Technology,Communications Equipment\n1511,Turkish Airlines,Turkey,9.7,-0.015,18.5,2,Industrials,Airline\n1512,Moscow Exchange,Russia,0.652,0.376,40,4.4,Financials,\n1513,Adani Ports & Special Economic Zone,India,1.2,0.549,6,11.2,,\n1514,News Corp,United States,8.1,-0.363,14.5,7.3,Consumer Discretionary,Broadcasting & Cable\n1515,Metalurgica Gerdau,Brazil,10.8,-0.395,16.8,1.4,Materials,Iron & Steel\n1516,Packaging Corp of America,United States,5.8,0.445,5.8,8.6,,\n1517,Oceanwide Holdings,China,3.4,0.41,21.7,6.9,Financials,\n1518,China Communications Services,China,13.3,0.383,9,4.3,Telecommunication Services,Telecommunications services\n1519,Hertz Global Holdings,United States,9.1,-0.492,19.2,1.3,Financials,Rental & Leasing\n1520,Fresnillo,Mexico,1.9,0.432,4.3,14.8,Materials,\n1521,DSV,Denmark,10.1,0.248,5.7,9.3,Industrials,Trucking\n1522,Alpha Bank,Greece,4.6,0.046,68.4,2.9,Financials,Regional Banks\n1523,FleetCor Technologies,United States,1.8,0.449,9.6,13.4,Energy,\n1524,IDBI Bank,India,4.8,-0.548,56.6,2.4,Financials,Regional Banks\n1525,EMS-Chemie Holding,Switzerland,2,0.456,2,13.6,Materials,Specialized Chemicals\n1526,Whitbread,United Kingdom,4.4,0.57,6,9.1,Consumer Discretionary,Hotels & Motels\n1527,Albemarle,United States,2.9,0.509,8.2,11.7,Consumer Discretionary,\n1528,Taiwan Mobile,Taiwan,3.6,0.475,4.7,12.7,Telecommunication Services,Telecommunications services\n1529,NEX Group,United Kingdom,0.694,0.107,146.3,2.7,Telecommunication Services,\n1530,Suruga Bank,Japan,1.3,0.371,38.5,4.4,Financials,Regional Banks\n1531,Compal Electronics,Taiwan,23.8,0.252,10.8,2.8,Information Technology,Computer Hardware\n1532,Metropolitan Bank & Trust,Philippines,2,0.375,37.8,5.5,Financials,Regional Banks\n1533,Mitsui Chemicals,Japan,10.9,0.362,11,4.8,Materials,Specialized Chemicals\n1534,TransDigm Group,United States,3.3,0.457,10,12.1,,\n1535,NiSource,United States,4.5,0.324,18.7,7.7,Utilities,Natural Gas Utilities\n1536,Banco BPI,Portugal,0.819,0.347,43.5,1.6,Financials,Regional Banks\n1537,Taiheiyo Cement,Japan,7.4,0.616,8.9,4.1,Materials,Construction Materials\n1538,Alstom,France,7.9,-1.1,16.3,6.6,Industrials,Conglomerates\n1539,Liberty Media Corporation Series A Liberty Formula One,United States,5.3,0.679,3,6.8,,\n1540,Lindt & Sprungli,Switzerland,4,0.426,6.4,14.6,Consumer Staples,Food Processing\n1541,Continental Resources,United States,2.1,-0.4,14.4,17.3,Energy,Oil & Gas Operations\n1542,Vulcan Materials,United States,3.6,0.395,8.8,16.4,Materials,\n1543,MISC,Malaysia,2.3,0.623,12.5,7.4,Industrials,Other Transportation\n1544,Uniqa,Austria,5.7,0.164,35.1,2.4,Financials,Diversified Insurance\n1545,Companhia Brasileira de Distribuicao,Brazil,12,-0.139,13.9,5.9,,\n1546,Grupo Bolivar,Colombia,3.8,0.394,33.7,1.4,,\n1547,Hiroshima Bank,Japan,1.3,0.282,76.4,2.6,Financials,Regional Banks\n1548,Yunnan Baiyao Group,China,3.4,0.457,3.4,13.1,Health Care,Pharmaceuticals\n1549,Alinma Bank,Saudi Arabia,1.1,0.401,27.9,6,Financials,Regional Banks\n1550,Ferrari,Italy,3.4,0.441,4.1,13.6,Consumer Discretionary,\n1551,LG Uplus,South Korea,9.9,0.425,9.9,5.7,Telecommunication Services,Telecommunications services\n1552,Ensco,United Kingdom,2.8,0.874,14.4,2.8,Energy,Oil Services & Equipment\n1553,Assured Guaranty,Bermuda,1.4,0.881,14.2,4.8,Financials,Diversified Insurance\n1554,Banco BPM SpA,Italy,3.2,-1.9,123.8,4.3,,\n1555,EFG International,Switzerland,1.1,0.344,41.6,1.7,,\n1556,Rajesh Exports,India,36.3,0.173,3.5,2.8,Materials,\n1557,Wendel,France,9.2,-0.449,17.2,5.8,Financials,Investment Services\n1558,Spirit AeroSystems,United States,6.8,0.469,5.4,6.8,,\n1559,Terumo,Japan,4.6,0.359,9.1,13.1,Health Care,Medical Equipment & Supplies\n1560,Office Depot,United States,12.3,0.38,5.5,2.5,,\n1561,Shizuoka Bank,Japan,1.9,0.262,102.2,5.1,Financials,Regional Banks\n1562,Mercuries & Associates,Taiwan,6.1,0.064,30.1,0.56,Consumer Discretionary,Department Stores\n1563,Bank Otkritie,Russia,4.9,0.206,48.3,4.1,Financials,\n1564,Foshan Haitian Flavouring and Food Company,China,1.9,0.428,1.9,14,Consumer Staples,\n1565,Staples,United States,19.8,-1.4,8.3,6.4,Consumer Discretionary,Specialty Stores\n1566,Zhengzhou Yutong Bus,China,5.4,0.609,5.1,6.9,,\n1567,Industrial Securities,China,1.3,0.412,17.4,7.5,Financials,\n1568,Pacific Exploration & Production,Canada,1.4,2.5,2.7,1.6,,\n1569,Ihs Markit,United Kingdom,2.7,0.153,14,17.2,Information Technology,\n1570,Bendigo & Adelaide Bank,Australia,2.2,0.309,51.4,4.2,Financials,Regional Banks\n1571,US Foods,United States,22.9,0.21,8.9,6.1,Consumer Staples,\n1572,CCR,Brazil,2.9,0.492,7.5,11.5,Industrials,Other Transportation\n1573,Church & Dwight,United States,3.5,0.459,4.4,12.6,Consumer Staples,Household/Personal Care\n1574,Yamada Denki,Japan,14.4,0.305,10.5,4.8,Information Technology,Computer & Electronics Retail\n1575,BCV Group,Switzerland,1.2,0.314,43.4,5.9,Financials,Regional Banks\n1576,Hankook Tire,South Korea,5.7,0.752,8,5.9,Consumer Discretionary,\n1577,Martin Marietta Materials,United States,3.8,0.423,7.6,14,Materials,\n1578,Intelsat,Luxembourg,2.2,0.99,12.9,0.504,,\n1579,Royal Mail,United Kingdom,13.4,0.302,11,5.1,Telecommunication Services,\n1580,Orkla,Norway,4.5,0.511,6.5,9.1,Industrials,Conglomerates\n1581,Saipem,Italy,11,-2.3,15.1,4.5,,\n1582,Aeroflot-Russian Airlines,Russia,7.4,0.559,4.9,3.2,Materials,\n1583,Westlake Chemical,United States,5.1,0.397,10.9,8.7,Materials,Diversified Chemicals\n1584,Canadian Utilities,Canada,2.6,0.468,14,7.9,Utilities,\n1585,Suzuken,Japan,20.1,0.244,10.6,3.2,Health Care,Pharmaceuticals\n1586,Concho Resources,United States,1.6,-1.5,12.5,19.1,Energy,Oil & Gas Operations\n1587,Hero Motocorp,India,4.3,0.473,1.9,10,Consumer Discretionary,Recreational Products\n1588,Pembina Pipeline,Canada,3.2,0.352,11.2,13,Energy,Oil & Gas Operations\n1589,Union National Bank,United Arab Emirates,1.4,0.409,28.3,3.4,Financials,Regional Banks\n1590,Gunma Bank,Japan,1.3,0.276,68.2,2.3,Financials,Regional Banks\n1591,Novozymes,Denmark,2.1,0.453,2.8,12.2,Health Care,Biotechs\n1592,Avis Budget Group,United States,8.7,0.163,17.8,2.3,Financials,Rental & Leasing\n1593,Sumitomo Metal Mining,Japan,7,-0.404,13.9,7.7,Materials,Diversified Metals & Mining\n1594,China International Marine,China,7.6,0.077,17.9,6,Industrials,Heavy Equipment\n1595,ONO Pharmaceutical,Japan,2.2,0.444,5,12.3,Health Care,\n1596,Maxis,Malaysia,2.1,0.486,4.4,10.8,Telecommunication Services,Telecommunications services\n1597,BM&F Bovespa,Brazil,0.666,0.415,9.7,12.8,Financials,Investment Services\n1598,CNO Financial Group,United States,4,0.358,32,3.6,Financials,Life & Health Insurance\n1599,Piraeus Bank,Greece,3.4,-0.039,86,1.6,Financials,Regional Banks\n1600,World Fuel Services,United States,26.9,0.127,5.4,2.5,Consumer Discretionary,Specialty Stores\n1601,CJ Cheiljedang,South Korea,12.6,0.238,13.1,4,Consumer Staples,Food Processing\n1602,Tiffany & Co.,United States,4,0.446,5.1,11.6,Consumer Discretionary,\n1603,XPO Logistics,United States,14.6,0.063,11.7,5.3,Consumer Discretionary,\n1604,BBVA Banco Continental,Peru,1.8,0.397,23.5,6.2,,\n1605,Tech Data,United States,26.2,0.195,7.9,3.4,Information Technology,Electronics\n1606,National Bank of Greece,Greece,3.1,0.252,82.8,2.4,Financials,Major Banks\n1607,EasyJet,United Kingdom,6.6,0.606,7.2,5.1,Industrials,Airline\n1608,HRG Group,United States,5.2,0.037,35.7,3.9,,\n1609,Kyushu Financial Group,Japan,1.5,0.125,82.6,2.8,Financials,\n1610,Boliden,Sweden,4.7,0.495,6,8.5,Materials,\n1611,NVR,United States,5.8,0.425,2.6,7.7,,\n1612,Alliant Energy,United States,3.3,0.382,14,9,Utilities,Electric Utilities\n1613,Nishi-nippon Financial Holdings,Japan,1.3,0.223,81.4,1.7,Energy,\n1614,Fortune Brands Home & Security,United States,5,0.408,5.1,9.5,,\n1615,NetApp,United States,5.4,0.311,9.1,11.1,Information Technology,Computer Storage Devices\n1616,Snap-on,United States,3.7,0.546,4.7,9.5,,\n1617,Bank of Qingdao,China,1.6,0.316,40,3.7,Financials,\n1618,Far East Horizon,Hong Kong,2.1,0.434,24,3.6,,\n1619,Garmin,Switzerland,3,0.511,4.5,10,Consumer Discretionary,Consumer Electronics\n1620,People's United Financial,United States,1.5,0.28,40.9,6.1,Financials,Thrifts & Mortgage Finance\n1621,North Pacific Bank,Japan,1,0.174,77.1,1.4,Financials,Regional Banks\n1622,Grasim Industries,India,5.3,0.461,9.1,7.7,Materials,Construction Materials\n1623,Banco Comercial Portugues,Portugal,3.1,0.27,75.2,2.9,Financials,Regional Banks\n1624,Moscovskiy Oblastnoi Bank,Russia,2.6,1.7,4.5,0.363,Financials,\n1625,Rexel,France,14.6,0.153,11.5,5.3,Information Technology,Electronics\n1626,Axis Capital Holdings,Bermuda,4,0.512,18.5,5.7,Financials,Diversified Insurance\n1627,Bank of Kyoto,Japan,0.947,0.156,75,2.7,Financials,Regional Banks\n1628,Hachijuni Bank,Japan,1.8,0.252,74,2.7,Financials,Regional Banks\n1629,Guangdong Investment,Hong Kong,1.3,0.543,6.7,9.4,Utilities,Diversified Utilities\n1630,Chugoku Bank,Japan,1.2,0.219,73.3,2.7,Financials,Regional Banks\n1631,77 Bank,Japan,0.9,0.088,72.5,1.6,Financials,Regional Banks\n1632,STRABAG,Austria,14,0.225,11.5,3.8,Industrials,Construction Services\n1633,SBI Holdings,Japan,2.5,0.359,29.8,2.9,Financials,Investment Services\n1634,Vietin Bank,Vietnam,2.7,0.304,41.7,3,Financials,Regional Banks\n1635,GPT Group,Australia,0.549,0.857,8.6,7,Financials,Real Estate\n1636,Shandong Gold Mining,China,7.5,0.195,4.1,9.7,Utilities,\n1637,Jabil Circuit,United States,18.3,0.152,10,5.2,Information Technology,Electronics\n1638,Sears Holdings,United States,22.1,-2.2,9.4,1.2,Consumer Discretionary,Department Stores\n1639,Smiths Group,United Kingdom,4.3,0.576,5.8,8,Industrials,Conglomerates\n1640,Eurobank Ergasias,Greece,2.2,0.254,70,1.4,,\n1641,Tsinghua Tongfang,China,4.2,0.887,8.9,6.5,,\n1642,BPER Banca,Italy,2.7,0.016,68.5,2.3,Financials,\n1643,China Jinmao,Hong Kong,4.2,0.382,24,3.5,Financials,\n1644,Vertex Pharmaceuticals,United States,1.7,-0.112,2.9,28.8,Health Care,Biotechs\n1645,Dixons Carphone,United Kingdom,14.2,0.302,9.4,4.5,,\n1646,Incyte,United States,1.1,0.104,1.6,28.6,Health Care,\n1647,President Chain Store,Taiwan,6.7,0.305,3,8.8,Consumer Staples,Food Retail\n1648,New Residential Investment,United States,1.2,0.505,18.4,5.2,Financials,\n1649,Deutsche Pfandbriefbank,Germany,2.2,0.218,66.1,1.7,,\n1650,MAp,Australia,1,0.421,8.9,11.6,,\n1651,BorgWarner,United States,9.1,0.119,8.8,8.1,Consumer Discretionary,Auto & Truck Parts\n1652,Chimera Investment,United States,0.942,0.552,16.7,3.8,Financials,\n1653,ProSiebenSat1 Media,Germany,4.2,0.445,7.1,9.9,Consumer Discretionary,Broadcasting & Cable\n1654,Shanghai Lujianzi,China,1.9,0.395,11.5,9.6,,\n1655,Tosoh,Japan,6.4,0.564,6.5,5.6,Materials,Diversified Chemicals\n1656,Dana Holding,United States,5.8,0.64,4.9,2.6,Financials,\n1657,Joint Stock Commercial Bank for Foreign Trade of Vietnam,Vietnam,2.1,0.305,34.6,5.8,Financials,\n1658,SL Green Realty,United States,1.7,0.261,15.9,10.8,Financials,Real Estate\n1659,Beijing Shougang,China,6.3,0.06,18.3,6.3,,\n1660,Sysmex,Japan,2.3,0.378,2.3,12.9,,\n1661,Want Want China,China,3,0.03,3.9,9.1,Consumer Staples,Food Processing\n1662,WellCare Health Plans,United States,14.2,0.241,6.2,6.3,Health Care,\n1663,Catcher Technology,Taiwan,2.5,0.683,5.6,7.6,Information Technology,\n1664,Central Bank of India,India,4.3,-0.167,46.4,3,Financials,Regional Banks\n1665,Iyo Bank,Japan,0.987,0.194,59.6,2.1,Financials,Regional Banks\n1666,Mettler-Toledo International,United States,2.5,0.384,2.2,12.5,,\n1667,New Hope Liuhe,China,9.1,0.39,5.6,5,Consumer Staples,Food Processing\n1668,Wistron,Taiwan,19.2,0.043,9.4,2.3,Information Technology,Computer Hardware\n1669,American Equity Investment,United States,2.1,0.083,58.3,2.1,Financials,Life & Health Insurance\n1670,Cnpc Capital,China,0.83,-0.039,0.289,25.7,,\n1671,Michael Kors Holdings,United Kingdom,4.6,0.756,2.8,6.1,Consumer Discretionary,Apparel/Accessories\n1672,Ternium,Luxembourg,6,0.596,8.3,5.1,Materials,Iron & Steel\n1673,Israel Discount Bank,Israel,2.4,0.236,57.1,2.8,Financials,Regional Banks\n1674,Signet Jewelers,Bermuda,6.4,0.543,6.6,4.7,Consumer Discretionary,Specialty Stores\n1675,Empire,Canada,18.4,-0.618,6.7,4.1,Consumer Staples,Food Retail\n1676,PacWest Bancorp,United States,1.1,0.348,22,6.3,,\n1677,Techtronic Industries,Hong Kong,5.5,0.409,5.1,7.6,Utilities,\n1678,Tokyu Fudosan,Japan,7.1,0.245,18.3,3.3,,\n1679,Hengan International Group,China,2.9,0.501,4.9,9.2,Consumer Staples,Household/Personal Care\n1680,Moody's,United States,3.6,0.267,5.3,21.6,Consumer Discretionary,Business & Personal Services\n1681,Juroku Bank,Japan,1,0.103,54.7,1.2,Financials,Regional Banks\n1682,AU Optronics,Taiwan,10.2,0.242,13.3,3.8,Information Technology,Electronics\n1683,Commercial Bank For Investment & Development Of Vietnam,Vietnam,3.3,0.275,44.2,2.6,Financials,\n1684,Shaw Communications,Canada,3.8,0.357,11.4,10.1,Consumer Discretionary,Broadcasting & Cable\n1685,Haseko,Japan,7.1,0.477,5,3.1,,\n1686,Bureau Veritas Registre International de Classific,France,5,0.353,6.4,9.4,,\n1687,DSME,South Korea,11,-2.3,12.4,1.1,Industrials,Heavy Equipment\n1688,CST Brands,United States,11.1,0.324,4.4,3.7,Consumer Discretionary,Specialty Stores\n1689,Molina Healthcare,United States,17.8,0.052,7.4,2.7,Health Care,\n1690,Brilliance China Automotive Holdings,Hong Kong,0.771,0.554,4.5,8.4,,\n1691,Toho Bank,Japan,0.665,0.074,53.3,0.912,Financials,Regional Banks\n1692,Cattolica Assicurazioni,Italy,5.5,0.084,24.8,1.4,Financials,Diversified Insurance\n1693,EQT,United States,1.8,-0.453,15.5,10.9,Energy,Oil & Gas Operations\n1694,Snap,United States,0.404,-0.515,1.7,24.4,Financials,\n1695,Tongyang Life Insurance,South Korea,6,0.01,22.1,0.945,Utilities,\n1696,Samsung Heavy Industries,South Korea,9,-0.104,14.3,3.4,Industrials,Heavy Equipment\n1697,SinoPac Financial,Taiwan,1.5,0.257,52.1,3.3,Financials,Consumer Financial Services\n1698,INTL FCStone,United States,17.4,0.051,6.3,0.681,,\n1699,Commercial International Bank,Egypt,2.2,0.597,14.8,5,Financials,Regional Banks\n1700,Cybg,United Kingdom,1.8,-0.281,51.9,3.1,Financials,\n1701,Orient,Japan,2,0.27,44.8,3,Financials,Consumer Financial Services\n1702,Wynn Resorts,United States,4.5,0.242,12,11.9,Consumer Discretionary,Casinos & Gaming\n1703,Hopson Development Holdings,Hong Kong,2.1,0.498,17.2,2.1,Financials,Real Estate\n1704,JTEKT,Japan,12.1,0.295,9.7,5.3,Consumer Discretionary,Auto & Truck Parts\n1705,Nippon Paint,Japan,5.8,0.25,7.1,11.6,Materials,\n1706,Calpine,United States,6.6,0.092,19.3,3.8,Utilities,Electric Utilities\n1707,BayWa,Germany,17,0.034,6.8,1.1,Consumer Discretionary,Specialty Stores\n1708,Migdal Insurance,Israel,4.5,0.053,34.8,1,Financials,\n1709,Changjiang Securities,China,0.894,0.332,15.4,8.1,Financials,\n1710,Aareal Bank,Germany,1.3,0.22,51.1,2.2,Financials,Thrifts & Mortgage Finance\n1711,Kansas City Southern,United States,2.4,0.478,9,9.3,Industrials,Railroads\n1712,Dufry,Switzerland,7.9,0.003,9.8,8,Utilities,\n1713,Interconexion Electrica,Colombia,4,0.7,12.8,4.5,Financials,\n1714,San-In Godo Bank,Japan,0.83,0.14,50.8,1.2,Financials,Regional Banks\n1715,Nanto Bank,Japan,0.71,0.13,50.7,0.967,Financials,Regional Banks\n1716,Li & Fung,Hong Kong,16.8,0.223,7.7,3.6,Industrials,Trading Companies\n1717,Lupin,India,2.5,0.444,3.6,9.9,Health Care,\n1718,Encana,Canada,3.2,-0.949,14.7,11.3,Energy,Oil & Gas Operations\n1719,City Developments,Singapore,2.8,0.473,13.7,6.7,Financials,Real Estate\n1720,WPG Holdings,Taiwan,16.6,0.165,5.6,2.2,Information Technology,Electronics\n1721,KWG Property Holding,Hong Kong,1.3,0.52,15.9,2.3,,\n1722,Huntsman,United States,9.7,0.326,9.2,5.9,Materials,Diversified Chemicals\n1723,Sino Land,Hong Kong,2.2,0.439,19.3,5.2,Financials,Real Estate\n1724,Bohai Financial Investment Holding,China,3.1,0.272,29.3,6.4,Financials,\n1725,Tokyo TY Financial Group,Japan,0.731,0.25,48.9,0.853,Materials,\n1726,Nippon Steel Trading,Japan,16.4,0.169,5.8,1.3,Materials,Diversified Metals & Mining\n1727,Bank of Greece,Greece,1.9,1.2,10,0.24,Financials,Major Banks\n1728,Performance Food Group,United States,16.4,0.074,3.6,2.4,,\n1729,Ogaki Kyoritsu Bank,Japan,0.914,0.119,48.5,1.2,Financials,Regional Banks\n1730,Hyakugo Bank,Japan,0.61,0.078,48.2,0.994,Financials,Regional Banks\n1731,Daishi Bank,Japan,0.656,0.105,48,1.3,Financials,Regional Banks\n1732,Senshu Ikeda Holdings,Japan,0.899,0.115,47.9,1.1,Financials,Regional Banks\n1733,Supervalu,United States,16,0.103,4.5,0.996,Consumer Staples,Food Retail\n1734,NASDAQ OMX Group,United States,3.7,0.109,14.2,11.5,Financials,Investment Services\n1735,CPFL Energia,Brazil,5.4,0.258,13,8.4,Utilities,Electric Utilities\n1736,Severn Trent,United Kingdom,2.6,0.534,11.2,7,Utilities,Diversified Utilities\n1737,PBF Energy,United States,15.9,0.171,7.6,2.3,Energy,Oil & Gas Operations\n1738,Crown Resorts,Australia,2.6,0.82,6.5,6.5,Consumer Discretionary,Casinos & Gaming\n1739,Mirae Asset Daewoo,South Korea,1.3,0.034,47.4,5.1,,\n1740,Shiga Bank,Japan,0.647,0.133,47.2,1.3,Financials,Regional Banks\n1741,SK Networks,South Korea,15.9,-0.7,7.2,1.7,Industrials,Trading Companies\n1742,Banco de Chile,Chile,3.8,0.131,47.1,4.5,,\n1743,Showa Shell Sekiyu,Japan,15.9,0.156,8.4,3.8,Energy,Oil & Gas Operations\n1744,Turkcell,Turkey,4.7,0.508,9,7.1,Telecommunication Services,Telecommunications services\n1745,Syndicate Bank,India,3.9,-0.232,46.7,0.985,Financials,Regional Banks\n1746,Affiliated Managers Group,United States,2.2,0.471,8.7,9.1,Financials,Investment Services\n1747,Vipshop Holdings,China,8.4,0.3,3.6,6.8,,\n1748,Isetan Mitsukoshi Holdings,Japan,11.5,0.203,11.2,4.2,Consumer Discretionary,Department Stores\n1749,Taiwan Business Bank,Taiwan,0.934,0.161,46.5,1.6,Financials,Regional Banks\n1750,CalAtlantic Group,United States,6.5,0.484,8.7,4.3,Industrials,\n1751,Waste Connections,Canada,3.3,0.198,11.2,15.4,,\n1752,Nürnberger,Germany,4.6,0.064,30.7,0.837,,\n1753,KION Group,Germany,6.2,0.272,12.5,7.1,Materials,\n1754,International Flavors & Fragrances,United States,3.1,0.404,4,10.4,Utilities,\n1755,China National Materials,China,7.5,0.088,15.6,1.3,Industrials,Other Industrial Equipment\n1756,Prada,Italy,3.9,0.365,5.1,11.4,Consumer Discretionary,Apparel/Accessories\n1757,Indorama Ventures,Thailand,7.2,0.429,7.3,4.9,,\n1758,Korea Zinc,South Korea,5,0.511,5.4,6.5,Materials,\n1759,Nomura Research Institute,Japan,3.8,0.47,5.3,9,,\n1760,Tingyi Holding,China,8.4,0.177,8.1,7.6,Consumer Staples,Food Processing\n1761,Tenneco,United States,8.6,0.363,4.3,3.2,,\n1762,Cullen/Frost Bankers,United States,1.1,0.303,30.2,5.7,,\n1763,First Pacific,Hong Kong,6.8,0.103,17.2,3.3,Financials,Investment Services\n1764,Logan Property Holdings,China,3.1,0.671,12.4,3.1,Financials,\n1765,DGB Financial Group,South Korea,2.5,0.248,44.3,1.7,Financials,Consumer Financial Services\n1766,Shanghai Fosun Pharmaceutical (group),China,2.2,0.423,6.3,9.8,Health Care,\n1767,Central Puerto,Argentina,0.36,0.12,0.811,20.2,,\n1768,Xishui Strong Year Ltd. Inner Mongolia,China,3.8,-0.029,44.2,2.4,,\n1769,China Mengniu Dairy,Hong Kong,8.1,-0.113,7.1,7.7,Consumer Staples,Food Processing\n1770,Securitas,Sweden,10.3,0.309,5.3,5.8,Consumer Discretionary,Business & Personal Services\n1771,AmBank Group,Malaysia,1.7,0.306,28.8,3.3,Financials,Regional Banks\n1772,Ningbo Port,China,2.4,0.378,8.4,10.8,,\n1773,Penn Virginia,United States,0.132,1,0.292,0.732,,\n1774,Chongqing New Century Cruise,China,0.267,0.12,1.2,20,,\n1775,Jacobs Engineering,United States,10.7,0.224,7.1,6.6,Industrials,Construction Services\n1776,Red Star Macalline Group,China,1.4,0.609,13.2,4.1,,\n1777,Virgin Money Holdings,United Kingdom,1.4,0.189,43.3,1.7,,\n1778,Mattel,United States,5.5,0.318,6.5,8.5,Consumer Staples,Household/Personal Care\n1779,Credit Saison,Japan,3.1,0.345,23.8,3.3,Financials,Consumer Financial Services\n1780,Mirvac Group,Australia,1.9,0.794,8.6,6.3,,\n1781,Hyakujushi Bank,Japan,0.728,0.083,42.9,0.961,Financials,Regional Banks\n1782,GameStop,United States,8.6,0.353,5,2.2,,\n1783,Asian Paints,India,2.3,0.281,1.7,16.1,Materials,\n1784,Frasers Centrepoint,Singapore,2.7,0.45,17.1,3.7,,\n1785,Xiamen Xiangyu,China,14.6,0.076,5.2,1.9,,\n1786,Credito Emiliano,Italy,1.6,0.146,41.7,2.2,Financials,Regional Banks\n1787,Remgro,South Africa,1.9,0.494,8.5,8.4,Industrials,Conglomerates\n1788,Core-Mark Holding Company,United States,14.5,0.054,1.5,1.4,,\n1789,Kiyo Bank,Japan,0.665,0.11,41.7,1,Financials,\n1790,Shenzhou International Group Holdings,China,2.3,0.444,3.1,9.1,,\n1791,SYNNEX,United States,14.5,0.248,5.1,4.2,,\n1792,Damac Properties,United Arab Emirates,1.9,1,6.7,4.7,,\n1793,Aboitiz Equity Ventures,Philippines,2.5,0.487,8.1,8.4,Industrials,Conglomerates\n1794,HD Supply Holdings,United States,7.4,0.202,6,8.1,Financials,\n1795,RioCan Real Estate Investment Trust,Canada,0.885,0.627,10.6,6.4,,\n1796,Emera,Canada,3.2,0.193,21.8,7.4,Energy,\n1797,Grupo Zuliano,Venezuela,1,0.999,1.5,0.072,,\n1798,InterContinental Hotels,United Kingdom,1.7,0.415,2.9,9.6,Consumer Discretionary,Hotels & Motels\n1799,Impulsora del Desarrollo y el Empleo en America Latina SAB Class B,Mexico,0.847,0.997,5.3,4.9,,\n1800,Accor,France,1.8,0.252,12.5,12.1,Consumer Discretionary,Hotels & Motels\n1801,Liberbank,Spain,1.2,0.143,40.4,1.1,Financials,Regional Banks\n1802,Bandai Namco Holdings,Japan,5.6,0.428,4.2,6.5,,\n1803,Zenith Bank,Nigeria,2.1,0.499,15,1.5,Financials,Regional Banks\n1804,Fullshare Holdings,Hong Kong,0.645,0.468,6.9,8.6,,\n1805,Hologic,United States,2.9,0.332,7.3,11.9,,\n1806,Keiyo Bank,Japan,0.627,0.125,40,1.1,Financials,Regional Banks\n1807,Banque Centrale Populaire,Morocco,2.4,0.27,34.8,4.7,Financials,Regional Banks\n1808,Owens Corning,United States,5.7,0.393,7.7,6.8,Industrials,\n1809,Autodesk,United States,2,-0.584,4.8,18.7,Information Technology,\n1810,Yonghui Superstores,China,7.4,0.187,4.2,8,Consumer Discretionary,\n1811,Banca Popolare di Sondrio,Italy,1.3,0.109,39.2,1.5,Financials,Regional Banks\n1812,VeriSign,United States,1.1,0.441,2.3,8.9,Information Technology,Software & Programming\n1813,Antofagasta,United Kingdom,3.7,0.113,13.7,10.6,Materials,Diversified Metals & Mining\n1814,Fuyao Glass Industry Group,China,2.5,0.472,4.3,8.4,Materials,\n1815,Bank of Queensland,Australia,1.7,0.247,39.2,3.5,Financials,Regional Banks\n1816,Angang Steel,China,8.7,0.241,12.7,5.3,Materials,Iron & Steel\n1817,Kingboard Chemical Holdings,Hong Kong,4.6,0.648,9.9,4,,\n1818,Wanhua Chemical Group,China,3.9,0.409,7.7,9.5,Materials,Specialized Chemicals\n1819,Habib Bank,Pakistan,1.6,0.325,24.1,3.7,Financials,\n1820,Umicore,Belgium,11.6,0.145,4.4,6,Materials,Diversified Metals & Mining\n1821,Popular,Puerto Rico,1.9,0.217,38.7,4.2,Financials,Regional Banks\n1822,Chesapeake Energy,United States,8.4,-4.2,13,5.6,Energy,Oil & Gas Operations\n1823,Golden Agri-Resources,Singapore,7.2,0.397,8.3,3.3,Consumer Staples,Food Processing\n1824,Far Eastern New Century,Taiwan,6.7,0.196,15.9,4.5,Materials,Diversified Chemicals\n1825,Musashino Bank,Japan,0.609,0.106,38,0.952,Financials,Regional Banks\n1826,JB Financial Group,South Korea,1.8,0.114,37.9,0.799,Financials,\n1827,Basler Kantonalbank,Switzerland,0.732,0.124,37.9,3,Financials,Regional Banks\n1828,Indian Overseas Bank,India,3.5,-0.552,37.8,1,Financials,Regional Banks\n1829,Bid Corp.,South Africa,9.4,0.255,3.8,6.7,,\n1830,Marine Harvest ASA,Norway,3.9,0.594,5.1,7,,\n1831,Nippon Paper Industries,Japan,9,-0.166,12,2.1,Materials,\n1832,TAQA,United Arab Emirates,4.4,-5.2,28.4,0.941,Utilities,Electric Utilities\n1833,United Aircraft,Russia,6.2,-0.037,17.2,4.4,Industrials,\n1834,Inventec,Taiwan,13.3,0.175,5.7,2.6,Information Technology,Computer Hardware\n1835,Bluescope Steel,Australia,7.4,0.381,6.8,5.1,Materials,\n1836,Hanwa,Japan,13.2,0.249,5.9,1.4,Materials,Diversified Metals & Mining\n1837,Makita,Japan,3.7,0.4,5,9.5,Consumer Staples,Household/Personal Care\n1838,China Railway Signal & Communication,China,4.4,0.46,7.2,7.3,Telecommunication Services,\n1839,Shanghai Industrial Holdings,Hong Kong,2.9,0.374,19.5,3.3,Financials,\n1840,Xiamen International Trade Group,China,13.1,0.151,8.6,2.2,,\n1841,Hokkoku Bank,Japan,0.607,0.136,36.5,1.1,Financials,Regional Banks\n1842,Dali Foods Group,China,2.7,0.473,2.4,8,Financials,\n1843,Commercial Bank of Qatar,Qatar,1.7,0.077,35.8,3.4,Financials,Regional Banks\n1844,Industrias Peñoles,Mexico,4.4,0.307,6.9,10.4,,\n1845,Allahabad Bank,India,3.2,-0.11,35.7,0.838,Financials,Regional Banks\n1846,Avery Dennison,United States,6.1,0.321,4.4,7.1,,\n1847,Merlin Properties SOCIMI S.A,Spain,0.401,0.645,11.5,5.4,,\n1848,Petro Rabigh,Saudi Arabia,6.7,0.01,15.5,3,Materials,Diversified Chemicals\n1849,Phosagro,Russia,2.8,0.894,3.8,5.5,Materials,\n1850,UCO Bank,India,2.9,-0.443,35.7,0.905,Financials,Regional Banks\n1851,China Molybdenum,China,1,0.15,12.7,11,Materials,\n1852,NH Investment & Securities,South Korea,1.5,0.204,35.6,3,,\n1853,Japan Securities,Japan,0.212,0.029,35.6,0.514,Financials,Investment Services\n1854,Corporation Bank,India,3.2,-0.077,35.5,0.98,Financials,Regional Banks\n1855,Public Power,Greece,5.8,0.075,18.1,0.754,Utilities,Electric Utilities\n1856,Tongling Nonferrous Metals,China,12.8,-0.115,6.1,4.9,Materials,Diversified Metals & Mining\n1857,Zhongsheng Group Holdings,China,10.8,0.279,5.7,3.1,,\n1858,Alawwal Bank,Saudi Arabia,1.4,0.284,28,3.4,Financials,\n1859,Oriental Bank of Commerce,India,3.2,0.022,34.9,0.769,Financials,Regional Banks\n1860,Paramount Res,Canada,0.186,0.88,1.5,1.4,,\n1861,Toyota Boshoku,Japan,12.5,0.084,5.6,4,Consumer Discretionary,Auto & Truck Parts\n1862,JC Penney,United States,12.5,0.001,9.3,1.8,Consumer Discretionary,Department Stores\n1863,Spectrum Brands Holdings,United States,5.1,0.331,7,8.1,,\n1864,G4S,United Kingdom,10.2,0.267,6.9,6,Industrials,Security Systems\n1865,Jabal Omar Development,Saudi Arabia,0.45,0.196,6.1,16.9,,\n1866,Guoco Group,Hong Kong,1.8,0.465,14.9,3.8,,\n1867,Two Harbors Investment,United States,0.741,0.353,20.1,3.4,Financials,Real Estate\n1868,Beijing Capital Development,China,4.3,0.266,26.1,4.9,Financials,\n1869,Hellenic Petroleum,Greece,7.4,0.365,7.6,1.7,Energy,Oil & Gas Operations\n1870,Enka,Turkey,3.5,0.588,7.6,6.8,Industrials,Construction Services\n1871,Jilin Jiutai Rural Commercial Bank,China,1.5,0.284,27.6,2.7,Financials,\n1872,Innolux,Taiwan,8.9,0.058,11.5,4.2,Information Technology,Computer Hardware\n1873,Luzerner Kantonalbank,Switzerland,0.62,0.189,34.3,3.6,Financials,Regional Banks\n1874,China Resources Gas Group,Hong Kong,4.2,0.424,7.7,8,Materials,\n1875,First Quantum Minerals,Canada,2.7,-0.057,19.5,7.4,Materials,Diversified Metals & Mining\n1876,ST Engineering,Singapore,4.8,0.351,5.8,8,Industrials,Aerospace & Defense\n1877,Delek Group,Israel,1.5,0.163,34.2,2.8,Financials,Investment Services\n1878,Lanxess,Germany,8.5,0.212,10.6,6.2,Materials,Diversified Chemicals\n1879,Extra Space Storage,United States,0.973,0.365,7.1,9.8,,\n1880,Financial Street Holdings,China,2.8,0.422,16.2,5,Financials,Real Estate\n1881,Burberry Group,United Kingdom,3.6,0.372,2.9,9.6,Consumer Discretionary,Apparel/Accessories\n1882,Petronas Gas,Malaysia,1.1,0.42,3.7,8.8,Utilities,Natural Gas Utilities\n1883,Workday,United States,1.6,-0.0408,3.2,16.6,,\n1884,GS Engineering,South Korea,9.5,-0.024,11.1,2,Industrials,Construction Services\n1885,Hokkaido Electric Power,Japan,6.5,0.058,15.5,1.5,Utilities,Electric Utilities\n1886,Lonza Group,Switzerland,4.2,0.306,7.1,10.9,Materials,\n1887,FIBI Holdings,Israel,1.1,0.066,33.6,0.732,Financials,Regional Banks\n1888,GEA Group,Germany,5,0.315,6.5,8.6,Industrials,Conglomerates\n1889,Mid-america Apartment Communities,United States,1.1,0.212,11.6,11.6,,\n1890,Vietnam Dairy Products,Vietnam,2.1,0.396,1.3,9.1,Consumer Staples,\n1891,Sumitomo Rubber,Japan,7,0.381,7.7,4.5,Consumer Discretionary,Auto & Truck Parts\n1892,Areva,France,0.011,-0.736,33.3,1.7,Utilities,Diversified Utilities\n1893,Leidos,United States,7,0.24,9.1,7.6,,\n1894,Reliance Steel,United States,8.6,0.304,7.4,5.8,Materials,Iron & Steel\n1895,Kroton Educacional,Brazil,1.5,0.535,5.4,7.1,,\n1896,Laurentian Bank,Canada,1.2,0.12,33.1,1.5,Financials,Regional Banks\n1897,Babcock International Group,United Kingdom,6.1,0.433,7.6,5.5,Consumer Discretionary,Business & Personal Services\n1898,Fabege AB,Sweden,0.246,0.83,5.5,2.7,,\n1899,First Citizens BancShares,United States,1.5,0.226,33,3.9,,\n1900,Hella Kgaa Hueck &,Germany,7.1,0.367,5.5,4.9,,\n1901,BOK Financial,United States,1.5,0.23,33,5,Financials,Regional Banks\n1902,Jazz Pharmaceuticals,Ireland,1.5,0.389,4.8,9.2,Health Care,\n1903,Toho Holdings,Japan,11.8,0.199,5.6,1.6,Financials,Pharmaceuticals\n1904,Microchip Technology,United States,3.1,0.094,7.1,16,Information Technology,Semiconductors\n1905,Shanghai International Airport,China,1,0.422,3.9,8.6,Industrials,\n1906,Atmos Energy,United States,3.2,0.372,10.6,8.4,Energy,\n1907,Amdocs,United States,3.8,0.403,5.2,9,Information Technology,Software & Programming\n1908,Kuwait Projects,Kuwait,2,0.127,32.7,2.1,Financials,Investment Services\n1909,China Aviation Oil,Singapore,11.7,0.089,1.3,1,Industrials,Trading Companies\n1910,Hakuhodo DY Holdings,Japan,11.4,0.259,5.7,4.4,Financials,\n1911,MMI Holdings,South Africa,3.7,0.11,32.5,2.5,Financials,Life & Health Insurance\n1912,Chinese Estates,Hong Kong,0.893,0.819,5.9,3,Financials,Real Estate\n1913,TP ICAP,United Kingdom,1.2,0.058,32.1,3.1,,\n1914,Technipfmc,United Kingdom,1,0,0.001,15.6,Energy,\n1915,Murphy USA,United States,11.6,0.222,2.1,2.7,Consumer Discretionary,Specialty Stores\n1916,Bank of Nagoya,Japan,0.4,0.049,32,0.669,Financials,Regional Banks\n1917,Emirates Integrated Telecom,United Arab Emirates,3.5,0.477,5,7.5,Telecommunication Services,Telecommunications services\n1918,St Galler Kantonalbank,Switzerland,0.585,0.148,31.7,2.3,Financials,Regional Banks\n1919,Ares Capital,United States,1.1,0.474,9.2,7.5,Financials,\n1920,Aroundtown Property Holdings,Germany,1.1,0.806,8.5,3.4,Financials,\n1921,GOME Electrical Appliances Holding,Hong Kong,11.5,0.049,8.9,3.1,,\n1922,Greentown China Holdings,China,4.5,0.226,24.4,2.1,Financials,Real Estate\n1923,Jiangsu Expressway,China,1.4,0.503,5.2,7.2,Industrials,\n1924,Premier,United States,1.3,0.805,2.6,4.5,Consumer Discretionary,\n1925,China Agri-Industries,Hong Kong,11.5,0.183,9.3,2.7,Materials,Diversified Chemicals\n1926,Pou Chen,Taiwan,8.5,0.31,9.3,4.1,Consumer Discretionary,Apparel/Accessories\n1927,Aldar Properties,United Arab Emirates,1.7,0.758,9.7,4.9,Financials,Real Estate\n1928,Tomony Holdings,Japan,0.605,0.234,31.3,0.809,Financials,Regional Banks\n1929,Vereit,United States,1.4,-0.195,15.6,8.5,,\n1930,BioMarin Pharmaceutical,United States,1.1,-0.642,4,15.2,Health Care,\n1931,Steel Authority of India,India,5.9,-0.656,16.2,4.3,Materials,Iron & Steel\n1932,Doha Bank,Qatar,1.1,0.289,24.8,2.2,Financials,Regional Banks\n1933,Jaccs,Japan,1.1,0.084,31.1,0.703,Financials,Consumer Financial Services\n1934,Indiabulls Housing Finance,India,1.7,0.408,13.7,6.3,Financials,\n1935,Red Hat,United States,2.4,0.254,4.5,15.1,Information Technology,\n1936,Korea Investment Holdings,South Korea,1.3,0.241,30.9,2.4,Financials,\n1937,Indian Bank,India,2.8,0.115,30.8,2,Financials,Regional Banks\n1938,Kesko,Finland,11.3,0.108,4.6,4.5,Consumer Staples,Food Retail\n1939,Minato Bank,Japan,0.561,0.066,30.8,0.734,,\n1940,Andhra Bank,India,3,0.085,30.7,0.616,Financials,Regional Banks\n1941,US Steel,United States,10.3,-0.44,9.2,5.9,Materials,Iron & Steel\n1942,Verbund,Austria,3.1,0.47,12.2,6.1,Utilities,Electric Utilities\n1943,Saudi Investment Bank,Saudi Arabia,1,0.281,25.2,2.6,Financials,Investment Services\n1944,BMCE Bank,Morocco,1.9,0.208,30.2,3.5,Financials,Regional Banks\n1945,NH Foods,Japan,11.1,0.223,6.4,5.6,,\n1946,Thomas Cook Group,United Kingdom,11.1,0.017,9,1.6,Consumer Discretionary,Business & Personal Services\n1947,Manila Electric,Philippines,5.4,0.404,6,6.3,Utilities,Electric Utilities\n1948,Arab Banking,Bahrain,1.7,0.183,30.1,1.1,Financials,Regional Banks\n1949,Synovus Financial,United States,1.3,0.247,30.1,5,Financials,Regional Banks\n1950,Western Union,United States,5.4,0.253,9.4,9.2,Consumer Discretionary,Business & Personal Services\n1951,Pearson,United Kingdom,6.1,-3.2,12.4,6.5,Consumer Discretionary,Printing & Publishing\n1952,Banca IFIS,Italy,1.2,0.761,9.2,2.1,Financials,\n1953,Motherson Sumi Systems,India,6.1,0.222,3.9,8.3,,\n1954,SBA Communications,United States,1.6,0.076,7.4,14.7,Telecommunication Services,\n1955,Advanced Micro Devices,United States,4.3,-0.497,3.3,12.7,Utilities,\n1956,Hudson's Bay,Canada,11,-0.392,9.4,1.5,,\n1957,Total System Services,United States,4.2,0.318,6.4,9.7,Financials,\n1958,Sinopec Oilfield Service,China,6.4,-2.4,10.7,7,Energy,\n1959,Westar Energy,United States,2.6,0.346,11.5,7.7,Energy,\n1960,Group 1 Automotive,United States,10.9,0.141,4.5,1.4,Consumer Discretionary,\n1961,Dollarama,Canada,2.3,0.338,1.5,9.7,Consumer Discretionary,\n1962,Shanghai RAAS Blood Products,China,0.326,0.231,2,14.5,,\n1963,Associated Banc-Corp,United States,1.2,0.198,29.3,3.6,Financials,\n1964,DVB Bank,Germany,1.4,-0.154,29.2,1.2,Financials,Regional Banks\n1965,RenaissanceRe Holdings,Bermuda,1.8,0.497,12.4,5.7,Financials,\n1966,Global Payments,United States,3.8,0.214,10.7,11.9,Consumer Discretionary,\n1967,Commerce Bancshares,United States,1.2,0.272,25.6,5.6,Financials,\n1968,ServiceNow,United States,1.4,-0.452,2,14.3,Information Technology,\n1969,Samsung SDI,South Korea,4.5,-0.647,12.3,8.5,Information Technology,\n1970,China Great Wall Computer,China,10.8,0.02,6,4.4,Information Technology,\n1971,Tian An China Investments,Hong Kong,0.244,0.736,4.3,1.2,Financials,\n1972,HollyFrontier,United States,10.6,-0.261,9.6,4.7,Energy,Oil & Gas Operations\n1973,Dah Sing Financial Holdings,Hong Kong,0.868,0.244,28.8,2.6,Financials,\n1974,Nanya Technology,Taiwan,1.3,0.736,4.3,4.3,Information Technology,\n1975,Shanxi Taigang Stainless,China,8.5,-0.322,11,4.1,Materials,Iron & Steel\n1976,First Horizon National,United States,1.4,0.227,28.7,4.3,,\n1977,Brother Industries,Japan,6,0.408,5.8,5.2,Materials,\n1978,Chicago Bridge & Iron,Netherlands,10.7,-0.313,7.8,3.1,Industrials,Construction Services\n1979,Belle International Holdings,Hong Kong,6.3,0.384,4.7,5.4,Consumer Discretionary,Apparel/Footwear Retail\n1980,F5 Networks,United States,2,0.37,2.4,8.9,,\n1981,Valspar,United States,4.2,0.341,4.2,8.8,Information Technology,\n1982,DKSH Holding,Switzerland,10.7,0.212,4.3,5.1,Materials,\n1983,Grupo Galicia,Argentina,3.7,0.407,15.3,5.1,,\n1984,Bank of Iwate,Japan,0.427,0.097,28.3,0.721,Financials,Regional Banks\n1985,Konica Minolta Business Solutions,Japan,8.9,0.28,8.8,4.3,Financials,\n1986,Synnex Technology Intl,Taiwan,10.6,0.151,4,1.8,Information Technology,\n1987,Eurazeo,France,3.4,0.575,10.9,4.5,Financials,Investment Services\n1988,Bankunited,United States,1.2,0.217,28.1,3.9,Financials,\n1989,Barry Callebaut,Switzerland,6.8,0.221,5.8,7.3,Consumer Discretionary,\n1990,Inchcape,United Kingdom,10.6,0.249,5.4,4.4,Consumer Discretionary,Specialty Stores\n1991,Yamanashi Chuo Bank,Japan,0.522,0.062,28,0.739,Financials,Regional Banks\n1992,Guangxi Guiguan Electric Power,China,2,0.643,6,6,Energy,\n1993,Live Nation Entertainment,United States,8.4,-0.047,6.8,6.4,Energy,\n1994,Shaanxi Coal Industry,China,3.8,-0.024,13.6,9,Materials,Diversified Metals & Mining\n1995,Aurubis,Germany,10.6,0.249,4.5,3.1,Materials,Diversified Metals & Mining\n1996,BEKB-BCBE,Switzerland,0.555,0.131,27.9,1.7,Financials,Regional Banks\n1997,Fastighets Balder,Sweden,0.63,0.639,10.2,3.8,Materials,\n1998,Akamai Technologies,United States,2.3,0.316,4.4,10.1,Information Technology,Computer Services\n1999,Oita Bank,Japan,0.523,0.071,27.9,0.595,Financials,Regional Banks\n2000,Tech Mahindra,India,4.2,0.469,3.6,6.7,Information Technology," }, { "path": "March Cohort 2023/12_15_16_17_18_Statistics/sample_submission.csv", "content": 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}, { "path": "March Cohort 2023/13_14_Object Orientation.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"id\": \"f956ae23\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"a = 10\\n\",\n \"\\n\",\n \"print(type(a))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"id\": \"a8e19eec\",\n \"metadata\": {\n \"scrolled\": false\n },\n \"outputs\": [],\n \"source\": [\n \"# help(int)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"02596f25\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"int a = 10; # 2 bytes - 16 bits\\n\",\n \"\\n\",\n \"struct car{\\n\",\n \" int cust_id; # 2 bytes\\n\",\n \" char name[10];# 10 bytes\\n\",\n \" float price; # 4 bytes\\n\",\n \"}\\n\",\n \"\\n\",\n \"struct car c1;\\n\",\n \" int a;\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"73d30a38\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"bit>numbers>characters>instructions>function(program)>class>modules>package>library>framework>software\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"8ae18de9\",\n \"metadata\": {},\n \"source\": [\n \"# Object Orientation\\n\",\n \"\\n\",\n \"7 properties of OOP\\n\",\n \"\\n\",\n \"1. Class\\n\",\n \" Updated version of structures (struct -- C programming)\\n\",\n \" collection of variables and methods\\n\",\n \" class is a blueprint.\\n\",\n \"2. Object\\n\",\n \" Run time or real time entity.\\n\",\n \" hash code/virtual code - ID(python)\\n\",\n \" \\n\",\n \"3. Abstraction and Encapsulation\\n\",\n \" Abstraction - showing only essential features without showing any background details.\\n\",\n \" Encapsulation - wrapping up of data in a single unit.\\n\",\n \" \\n\",\n \"4. Inheritance\\n\",\n \" Acquiring properties of one class into another.\\n\",\n \" code reusability\\n\",\n \" - single level\\n\",\n \" - multi level\\n\",\n \" - heirarchical\\n\",\n \" - multiple\\n\",\n \" - hybride\\n\",\n \" \\n\",\n \"5. Polymorphism\\n\",\n \" same name multiple functionalities\\n\",\n \" - method overloading\\n\",\n \" - method overriding\\n\",\n \" \\n\",\n \"6. Dynamic Memory Allocation\\n\",\n \" run time memory allocation\\n\",\n \" \\n\",\n \"7. Message Passing\\n\",\n \" Communication between objects\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"id\": \"d69e271e\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"2207057668688\\n\"\n ]\n }\n ],\n \"source\": [\n \"a = 10\\n\",\n \"\\n\",\n \"print(id(a))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"id\": \"cb5639c1\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"2207057669008\\n\",\n \"2207057669328\\n\"\n ]\n }\n ],\n \"source\": [\n \"b = 20\\n\",\n \"\\n\",\n \"c = a + b\\n\",\n \"print(id(b))\\n\",\n \"\\n\",\n \"print(id(c))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"id\": \"921fed62\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"ename\": \"TypeError\",\n \"evalue\": \"can only concatenate str (not \\\"int\\\") to str\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[1;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[1;31mTypeError\\u001b[0m Traceback (most recent call last)\",\n \"Input \\u001b[1;32mIn [11]\\u001b[0m, in \\u001b[0;36m\\u001b[1;34m()\\u001b[0m\\n\\u001b[1;32m----> 1\\u001b[0m s \\u001b[38;5;241m=\\u001b[39m \\u001b[38;5;124;43m\\\"\\u001b[39;49m\\u001b[38;5;124;43mA\\u001b[39;49m\\u001b[38;5;124;43m\\\"\\u001b[39;49m\\u001b[43m \\u001b[49m\\u001b[38;5;241;43m+\\u001b[39;49m\\u001b[43m \\u001b[49m\\u001b[38;5;241;43m10\\u001b[39;49m\\n\",\n \"\\u001b[1;31mTypeError\\u001b[0m: can only concatenate str (not \\\"int\\\") to str\"\n ]\n }\n ],\n \"source\": [\n \"s = \\\"A\\\" + 10\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"id\": \"0f26ad3e\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"AAAA\\n\"\n ]\n }\n ],\n \"source\": [\n \"s = \\\"A\\\" * 4\\n\",\n \"\\n\",\n \"print(s)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"id\": \"ba90ba2a\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"def hello():\\n\",\n \" return \\\"Hi\\\"\\n\",\n \"\\n\",\n \"print(type(hello))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"id\": \"41a3ec56\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"2207169212128\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(id(hello))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"06ce3374\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"class win7:\\n\",\n \"def start()\\n\",\n \"\\n\",\n \"def controlpanel():\\n\",\n \" \\n\",\n \"\\n\",\n \"add(x, y)\\n\",\n \"\\n\",\n \"add(a,b,c)\\n\",\n \"\\n\",\n \"class win10(win7):\\n\",\n \" \\n\",\n \" \\n\",\n \"product_id = wid10()\\n\",\n \"\\n\",\n \"product_id.controlpanel()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"53b1fb7c\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"add()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"id\": \"67f2a3da\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"30\"\n ]\n },\n \"execution_count\": 17,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"10 + 20\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"8420ca86\",\n \"metadata\": {},\n \"source\": [\n \"# Day 14 \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"id\": \"33e8ee69\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"class Cricket:\\n\",\n \" \\n\",\n \" def bat():\\n\",\n \" print(\\\"Hello from Bat\\\")\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"id\": \"aee1a50c\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"c = Cricket()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"id\": \"c2d4fa85\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"ename\": \"TypeError\",\n \"evalue\": \"bat() takes 0 positional arguments but 1 was given\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[1;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[1;31mTypeError\\u001b[0m Traceback (most recent call last)\",\n \"Input \\u001b[1;32mIn [3]\\u001b[0m, in \\u001b[0;36m\\u001b[1;34m()\\u001b[0m\\n\\u001b[1;32m----> 1\\u001b[0m \\u001b[43mc\\u001b[49m\\u001b[38;5;241;43m.\\u001b[39;49m\\u001b[43mbat\\u001b[49m\\u001b[43m(\\u001b[49m\\u001b[43m)\\u001b[49m\\n\",\n \"\\u001b[1;31mTypeError\\u001b[0m: bat() takes 0 positional arguments but 1 was given\"\n ]\n }\n ],\n \"source\": [\n \"c.bat()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"cf30bd7f\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# self --> current object\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"id\": \"e50eb7d9\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Hello from Bat\\n\"\n ]\n }\n ],\n \"source\": [\n \"class Cricket:\\n\",\n \" \\n\",\n \" def bat(self): # self = c\\n\",\n \" print(\\\"Hello from Bat\\\")\\n\",\n \"\\n\",\n \"c = Cricket()\\n\",\n \"\\n\",\n \"c.bat()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"id\": \"623c69db\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"<__main__.Cricket object at 0x0000020F34DB4A00>\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(c)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"id\": \"242b62af\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"2264334551552\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(id(c))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"id\": \"515da4b9\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"<__main__.Cricket object at 0x0000020F36BD6A90>\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(Cricket())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"b06677ce\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"TV blueprint - Class\\n\",\n \"- smart tv\\n\",\n \"- colors\\n\",\n \"- usb\\n\",\n \"- hdmi\\n\",\n \"\\n\",\n \"Actual TV - Object\\n\",\n \"product_id\\n\",\n \"\\n\",\n \"\\n\",\n \"remote - reference\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"id\": \"8d0184d8\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"class Football:\\n\",\n \" \\n\",\n \" def goal(self, x): # self = f, x = 4\\n\",\n \" self.a = x # a is class variable, x is local variable\\n\",\n \" # f.a = 4\\n\",\n \" \\n\",\n \" def display(self):\\n\",\n \" print(self.a)\\n\",\n \" # f.a\\n\",\n \" \\n\",\n \"f = Football()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"id\": \"1d10510d\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"f.goal(4)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"id\": \"3ddef9fb\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"4\\n\"\n ]\n }\n ],\n \"source\": [\n \"f.display()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"id\": \"6216ae9a\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"4\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(f.a)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"id\": \"63e17007\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"ename\": \"AttributeError\",\n \"evalue\": \"'Football' object has no attribute 'x'\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[1;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[1;31mAttributeError\\u001b[0m Traceback (most recent call last)\",\n \"Input \\u001b[1;32mIn [12]\\u001b[0m, in \\u001b[0;36m\\u001b[1;34m()\\u001b[0m\\n\\u001b[1;32m----> 1\\u001b[0m \\u001b[38;5;28mprint\\u001b[39m(\\u001b[43mf\\u001b[49m\\u001b[38;5;241;43m.\\u001b[39;49m\\u001b[43mx\\u001b[49m)\\n\",\n \"\\u001b[1;31mAttributeError\\u001b[0m: 'Football' object has no attribute 'x'\"\n ]\n }\n ],\n \"source\": [\n \"print(f.x)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"id\": \"a84e2e93\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(type(f))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"id\": \"0e68d116\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"2264334513824\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(id(f))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"id\": \"8145572b\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"10\\n\",\n \"\\n\",\n \"2264248117840\\n\",\n \"2264248117840\\n\"\n ]\n }\n ],\n \"source\": [\n \"a = 10\\n\",\n \"\\n\",\n \"print(a)\\n\",\n \"\\n\",\n \"print(type(a))\\n\",\n \"\\n\",\n \"print(id(a))\\n\",\n \"\\n\",\n \"print(id(10))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"id\": \"04a1cb6b\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"<__main__.Test object at 0x0000020F36BD6C70>\\n\",\n \"2264366148720\\n\",\n \"<__main__.Test object at 0x0000020F36BD6C70>\\n\",\n \"2264366148720\\n\",\n \"Finish\\n\"\n ]\n }\n ],\n \"source\": [\n \"class Test:\\n\",\n \" def add(self):\\n\",\n \" print(self)\\n\",\n \" print(id(self))\\n\",\n \" print(\\\"Finish\\\")\\n\",\n \" \\n\",\n \"t = Test()\\n\",\n \"print(t)\\n\",\n \"print(id(t))\\n\",\n \"t.add()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"id\": \"e76165f0\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"<__main__.Test object at 0x0000020F37274610>\\n\",\n \"2264373085712\\n\",\n \"<__main__.Test object at 0x0000020F37274610>\\n\",\n \"2264373085712\\n\",\n \"Finish\\n\"\n ]\n }\n ],\n \"source\": [\n \"t2 = Test()\\n\",\n \"print(t2)\\n\",\n \"print(id(t2))\\n\",\n \"t2.add()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"c78e8f64\",\n \"metadata\": {},\n \"source\": [\n \"# Constructor\\n\",\n \"\\n\",\n \"constructors are special member method of a class\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"18cd2b35\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"__init__() -> magic method or dunders\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"id\": \"0e5bc082\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"class Cat:\\n\",\n \" \\n\",\n \" def __init__(self):\\n\",\n \" print(\\\"Hello from constructor\\\") # write code that is for initializing \\n\",\n \"# coins = 0\\n\",\n \"# score = 0\\n\",\n \" \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"id\": \"90a7183d\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Hello from constructor\\n\"\n ]\n }\n ],\n \"source\": [\n \"f = Cat()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"id\": \"7c63050d\",\n \"metadata\": {\n \"scrolled\": false\n },\n \"outputs\": [],\n \"source\": [\n \"# help(str)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"a89195ce\",\n \"metadata\": {},\n \"source\": [\n \"### paramatrized constructor\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"id\": \"870a85c9\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"class Car:\\n\",\n \" def __init__(self, x):\\n\",\n \" self.y = x\\n\",\n \" \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"id\": \"3575a84e\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"f = Car(10)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"id\": \"862c1549\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"10\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(f.y)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"04a7f0d7\",\n \"metadata\": {},\n \"source\": [\n \"### Write a class that can calculate area of square, circle, rectangle.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"id\": \"7995dcda\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"class Area:\\n\",\n \" \\n\",\n \" def square(self, x):\\n\",\n \" return x*x\\n\",\n \" \\n\",\n \" def circle(self, r):\\n\",\n \" return 3.14 * r * r\\n\",\n \" \\n\",\n \" def rectangle(self, l, b):\\n\",\n \" return l * b\\n\",\n \" \\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"id\": \"c19e6fac\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"16\"\n ]\n },\n \"execution_count\": 27,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"ar = Area()\\n\",\n \"\\n\",\n \"ar.square(4)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"6528d7d2\",\n \"metadata\": {},\n \"source\": [\n \"### Write a program to create a class to reverse a string word by word.\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"87016fca\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"\\\"this is an apple\\\"\\n\",\n \"\\n\",\n \"\\\"apple an is this\\\"\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"70e87437\",\n \"metadata\": {},\n \"source\": [\n \"# Inheritance\\n\",\n \"\\n\",\n \"\\n\",\n \"### single level\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"4213330d\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"A windows 7\\n\",\n \"| |\\n\",\n \"B windows 10\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 28,\n \"id\": \"3cbdb564\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"This is add from class A\\n\",\n \"This is add from class A\\n\"\n ]\n }\n ],\n \"source\": [\n \"class A:\\n\",\n \" def add(self):\\n\",\n \" print(\\\"This is add from class A\\\")\\n\",\n \" \\n\",\n \"\\n\",\n \"class B(A):\\n\",\n \" def sub(self):\\n\",\n \" print(\\\"Sub from class B\\\")\\n\",\n \" \\n\",\n \"x = A()\\n\",\n \"y = B()\\n\",\n \"\\n\",\n \"x.add()\\n\",\n \"y.add()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 29,\n \"id\": \"694d0edf\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"ename\": \"AttributeError\",\n \"evalue\": \"'A' object has no attribute 'sub'\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[1;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[1;31mAttributeError\\u001b[0m Traceback (most recent call last)\",\n \"Input \\u001b[1;32mIn [29]\\u001b[0m, in \\u001b[0;36m\\u001b[1;34m()\\u001b[0m\\n\\u001b[1;32m----> 1\\u001b[0m \\u001b[43mx\\u001b[49m\\u001b[38;5;241;43m.\\u001b[39;49m\\u001b[43msub\\u001b[49m()\\n\",\n \"\\u001b[1;31mAttributeError\\u001b[0m: 'A' object has no attribute 'sub'\"\n ]\n }\n ],\n \"source\": [\n \"x.sub()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 30,\n \"id\": \"da9ee247\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Sub from class B\\n\"\n ]\n }\n ],\n \"source\": [\n \"y.sub()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"61a935df\",\n \"metadata\": {},\n \"source\": [\n \"### multi level\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"a163bf53\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"A\\n\",\n \"|\\n\",\n \"B\\n\",\n \"|\\n\",\n \"C\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 31,\n \"id\": \"b4a8158e\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"show from class C\\n\",\n \"Sub from class B\\n\",\n \"This is add from class A\\n\"\n ]\n }\n ],\n \"source\": [\n \"class A:\\n\",\n \" def add(self):\\n\",\n \" print(\\\"This is add from class A\\\")\\n\",\n \" \\n\",\n \"class B(A):\\n\",\n \" def sub(self):\\n\",\n \" print(\\\"Sub from class B\\\")\\n\",\n \" \\n\",\n \"class C(B):\\n\",\n \" def show(self):\\n\",\n \" print(\\\"show from class C\\\")\\n\",\n \" \\n\",\n \"z = C()\\n\",\n \"z.show()\\n\",\n \"z.sub()\\n\",\n \"z.add()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"6d610afa\",\n \"metadata\": {},\n \"source\": [\n \"### hierarchical\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"56edc720\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \" A\\n\",\n \"B C\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 32,\n \"id\": \"94cdc6e5\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"show from class C\\n\"\n ]\n },\n {\n \"ename\": \"AttributeError\",\n \"evalue\": \"'C' object has no attribute 'sub'\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[1;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[1;31mAttributeError\\u001b[0m Traceback (most recent call last)\",\n \"Input \\u001b[1;32mIn [32]\\u001b[0m, in \\u001b[0;36m\\u001b[1;34m()\\u001b[0m\\n\\u001b[0;32m 13\\u001b[0m z \\u001b[38;5;241m=\\u001b[39m C()\\n\\u001b[0;32m 14\\u001b[0m z\\u001b[38;5;241m.\\u001b[39mshow()\\n\\u001b[1;32m---> 15\\u001b[0m \\u001b[43mz\\u001b[49m\\u001b[38;5;241;43m.\\u001b[39;49m\\u001b[43msub\\u001b[49m()\\n\\u001b[0;32m 16\\u001b[0m z\\u001b[38;5;241m.\\u001b[39madd()\\n\",\n \"\\u001b[1;31mAttributeError\\u001b[0m: 'C' object has no attribute 'sub'\"\n ]\n }\n ],\n \"source\": [\n \"class A:\\n\",\n \" def add(self):\\n\",\n \" print(\\\"This is add from class A\\\")\\n\",\n \" \\n\",\n \"class B(A):\\n\",\n \" def sub(self):\\n\",\n \" print(\\\"Sub from class B\\\")\\n\",\n \" \\n\",\n \"class C(A):\\n\",\n \" def show(self):\\n\",\n \" print(\\\"show from class C\\\")\\n\",\n \" \\n\",\n \"z = C()\\n\",\n \"z.show()\\n\",\n \"z.sub()\\n\",\n \"z.add()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"afa65f2f\",\n \"metadata\": {},\n \"source\": [\n \"### multiple\\n\",\n \"\\n\",\n \"C3 algorithm to solve ambiguty error\\n\",\n \"\\n\",\n \"MRO - Method Resolution Order\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"520d2d17\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"A B\\n\",\n \" C\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 33,\n \"id\": \"41a10af7\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Add from class A\\n\"\n ]\n }\n ],\n \"source\": [\n \"class A:\\n\",\n \" def add(self):\\n\",\n \" print(\\\"Add from class A\\\")\\n\",\n \" \\n\",\n \"class B:\\n\",\n \" def add(self):\\n\",\n \" print(\\\"Add from class B\\\")\\n\",\n \" \\n\",\n \"class C(A,B):\\n\",\n \" def show(self):\\n\",\n \" print(\\\"show from class C\\\")\\n\",\n \" \\n\",\n \"z = C()\\n\",\n \"z.add()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 34,\n \"id\": \"101bcde3\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"(, , , )\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(C.__mro__)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 36,\n \"id\": \"e31f4dfc\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Add from class B\\n\",\n \"(, , , )\\n\"\n ]\n }\n ],\n \"source\": [\n \"class A:\\n\",\n \" def add(self):\\n\",\n \" print(\\\"Add from class A\\\")\\n\",\n \" \\n\",\n \"class B:\\n\",\n \" def add(self):\\n\",\n \" print(\\\"Add from class B\\\")\\n\",\n \" \\n\",\n \"class C(B,A):\\n\",\n \" def show(self):\\n\",\n \" print(\\\"show from class C\\\")\\n\",\n \" \\n\",\n \"z = C()\\n\",\n \"z.add()\\n\",\n \"print(C.__mro__)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"9361ebca\",\n \"metadata\": {},\n \"source\": [\n \"### Hybrid\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"e1434f79\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \" A\\n\",\n \"B C\\n\",\n \" D\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.12\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n" }, { "path": "March Cohort 2023/19_20_Machine Learning/.ipynb_checkpoints/19_Machine Learning and Linear Regression with One variable-checkpoint.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"id\": \"7242d236\",\n \"metadata\": {},\n \"source\": [\n \"# Machine Learning\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"f822ef63\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"Supervised Unsupervised Reinforcement\\n\",\n \"\\n\",\n \"Regression - clusturing - markov decision model\\n\",\n \"- Linear Regression - dimension reduction - game theory\\n\",\n \"Classification - anomaly detection\\n\",\n \"- Logistic Regression\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"59019bc8\",\n \"metadata\": {},\n \"source\": [\n \"# Linear Regression with One variable\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"id\": \"a0ff1cca\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# linear - arranged in straight line\\n\",\n \"# regression - a technique to determine the statistical relationship \\n\",\n \"# between 2 or more variables\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"1f3259c7\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \" x y\\n\",\n \"area(sq ft) price($)\\n\",\n \"2500 800000\\n\",\n \"3000 950000\\n\",\n \"3200 1000000\\n\",\n \"3600 1200000\\n\",\n \"4000 1500000\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"38b295a5\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"y = mx + c\\n\",\n \"y = b1x + b0\\n\",\n \"\\n\",\n \"800000 = m(2500) + c\\n\",\n \"950000 = m(3000) + c\\n\",\n \"\\n\",\n \"m = 3000\\n\",\n \"c = 4000\\n\",\n \"\\n\",\n \"y = 3000(x) + 4000 --> principle equation\\n\",\n \"y = 3000(1200) + 4000 --> prediction\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"id\": \"7d739803\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import pandas as pd\\n\",\n \"import numpy as np\\n\",\n \"import matplotlib.pyplot as plt\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"id\": \"ddd8d883\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
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\"\n ],\n \"text/plain\": [\n \" area price\\n\",\n \"0 2600 550000\\n\",\n \"1 3000 565000\\n\",\n \"2 3200 610000\\n\",\n \"3 3600 680000\\n\",\n \"4 4000 725000\"\n ]\n },\n \"execution_count\": 4,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df = pd.read_csv(\\\"homeprices.csv\\\")\\n\",\n \"df.head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"id\": \"f2481b31\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 5,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.xlabel('Area')\\n\",\n \"plt.ylabel('Price')\\n\",\n \"\\n\",\n \"plt.scatter(df.area, df.price, color='red', marker='+')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"id\": \"eba2ddd6\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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\"\n ],\n \"text/plain\": [\n \" area\\n\",\n \"0 2600\\n\",\n \"1 3000\\n\",\n \"2 3200\\n\",\n \"3 3600\\n\",\n \"4 4000\"\n ]\n },\n \"execution_count\": 6,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"x = df[['area']]\\n\",\n \"x\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"id\": \"e67faccd\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"0 550000\\n\",\n \"1 565000\\n\",\n \"2 610000\\n\",\n \"3 680000\\n\",\n \"4 725000\\n\",\n \"Name: price, dtype: int64\"\n ]\n },\n \"execution_count\": 7,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"y = df.price\\n\",\n \"y\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"id\": \"23e39b32\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from sklearn.linear_model import LinearRegression\\n\",\n \"\\n\",\n \"model = LinearRegression()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"id\": \"e9cc482f\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"LinearRegression()\"\n ]\n },\n \"execution_count\": 9,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"model\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"id\": \"40d56579\",\n \"metadata\": {\n \"scrolled\": false\n },\n \"outputs\": [],\n \"source\": [\n \"# help(model)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"id\": \"949d6f6a\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"LinearRegression()\"\n ]\n },\n \"execution_count\": 12,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"model.fit(x,y)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"id\": \"d1ba4c61\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([135.78767123])\"\n ]\n },\n \"execution_count\": 13,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# m = slope = gradient = 135.78\\n\",\n \"model.coef_\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"id\": \"3696cb1a\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"180616.43835616432\"\n ]\n },\n \"execution_count\": 14,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# c = intercept = 180616.43\\n\",\n \"model.intercept_\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"id\": \"6eeccf2a\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"C:\\\\Users\\\\himan\\\\anaconda3\\\\lib\\\\site-packages\\\\sklearn\\\\base.py:450: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\\n\",\n \" warnings.warn(\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"array([601558.21917808])\"\n ]\n },\n \"execution_count\": 15,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"model.predict([[3100]])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"a6a89a8e\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# y = mx + c\\n\",\n \"y = 135.78(x) + 180616.43\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"ad003525\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"x = 3100\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"id\": \"7211c8b8\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"601534.4299999999\"\n ]\n },\n \"execution_count\": 16,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"y = 135.78*(3100) + 180616.43\\n\",\n \"y\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"82249914\",\n \"metadata\": {},\n \"source\": [\n \"# Boston House Price Data\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"3f497053\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"y1 = m1x1 + m2x2 + ..... + c + e\\n\",\n \"y2 = m1x1 + m2x2 + ..... + c + e\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"id\": \"a20860cc\",\n \"metadata\": {\n \"scrolled\": false\n },\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"C:\\\\Users\\\\himan\\\\anaconda3\\\\lib\\\\site-packages\\\\sklearn\\\\utils\\\\deprecation.py:87: FutureWarning: Function load_boston is deprecated; `load_boston` is deprecated in 1.0 and will be removed in 1.2.\\n\",\n \"\\n\",\n \" The Boston housing prices dataset has an ethical problem. You can refer to\\n\",\n \" the documentation of this function for further details.\\n\",\n \"\\n\",\n \" The scikit-learn maintainers therefore strongly discourage the use of this\\n\",\n \" dataset unless the purpose of the code is to study and educate about\\n\",\n \" ethical issues in data science and machine learning.\\n\",\n \"\\n\",\n \" In this special case, you can fetch the dataset from the original\\n\",\n \" source::\\n\",\n \"\\n\",\n \" import pandas as pd\\n\",\n \" import numpy as np\\n\",\n \"\\n\",\n \"\\n\",\n \" data_url = \\\"http://lib.stat.cmu.edu/datasets/boston\\\"\\n\",\n \" raw_df = pd.read_csv(data_url, sep=\\\"\\\\s+\\\", skiprows=22, header=None)\\n\",\n \" data = np.hstack([raw_df.values[::2, :], raw_df.values[1::2, :2]])\\n\",\n \" target = raw_df.values[1::2, 2]\\n\",\n \"\\n\",\n \" Alternative datasets include the California housing dataset (i.e.\\n\",\n \" :func:`~sklearn.datasets.fetch_california_housing`) and the Ames housing\\n\",\n \" dataset. You can load the datasets as follows::\\n\",\n \"\\n\",\n \" from sklearn.datasets import fetch_california_housing\\n\",\n \" housing = fetch_california_housing()\\n\",\n \"\\n\",\n \" for the California housing dataset and::\\n\",\n \"\\n\",\n \" from sklearn.datasets import fetch_openml\\n\",\n \" housing = fetch_openml(name=\\\"house_prices\\\", as_frame=True)\\n\",\n \"\\n\",\n \" for the Ames housing dataset.\\n\",\n \" \\n\",\n \" warnings.warn(msg, category=FutureWarning)\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"{'data': array([[6.3200e-03, 1.8000e+01, 2.3100e+00, ..., 1.5300e+01, 3.9690e+02,\\n\",\n \" 4.9800e+00],\\n\",\n \" [2.7310e-02, 0.0000e+00, 7.0700e+00, ..., 1.7800e+01, 3.9690e+02,\\n\",\n \" 9.1400e+00],\\n\",\n \" [2.7290e-02, 0.0000e+00, 7.0700e+00, ..., 1.7800e+01, 3.9283e+02,\\n\",\n \" 4.0300e+00],\\n\",\n \" ...,\\n\",\n \" [6.0760e-02, 0.0000e+00, 1.1930e+01, ..., 2.1000e+01, 3.9690e+02,\\n\",\n \" 5.6400e+00],\\n\",\n \" [1.0959e-01, 0.0000e+00, 1.1930e+01, ..., 2.1000e+01, 3.9345e+02,\\n\",\n \" 6.4800e+00],\\n\",\n \" [4.7410e-02, 0.0000e+00, 1.1930e+01, ..., 2.1000e+01, 3.9690e+02,\\n\",\n \" 7.8800e+00]]),\\n\",\n \" 'target': array([24. , 21.6, 34.7, 33.4, 36.2, 28.7, 22.9, 27.1, 16.5, 18.9, 15. ,\\n\",\n \" 18.9, 21.7, 20.4, 18.2, 19.9, 23.1, 17.5, 20.2, 18.2, 13.6, 19.6,\\n\",\n \" 15.2, 14.5, 15.6, 13.9, 16.6, 14.8, 18.4, 21. , 12.7, 14.5, 13.2,\\n\",\n \" 13.1, 13.5, 18.9, 20. , 21. , 24.7, 30.8, 34.9, 26.6, 25.3, 24.7,\\n\",\n \" 21.2, 19.3, 20. , 16.6, 14.4, 19.4, 19.7, 20.5, 25. , 23.4, 18.9,\\n\",\n \" 35.4, 24.7, 31.6, 23.3, 19.6, 18.7, 16. , 22.2, 25. , 33. , 23.5,\\n\",\n \" 19.4, 22. , 17.4, 20.9, 24.2, 21.7, 22.8, 23.4, 24.1, 21.4, 20. ,\\n\",\n \" 20.8, 21.2, 20.3, 28. , 23.9, 24.8, 22.9, 23.9, 26.6, 22.5, 22.2,\\n\",\n \" 23.6, 28.7, 22.6, 22. , 22.9, 25. , 20.6, 28.4, 21.4, 38.7, 43.8,\\n\",\n \" 33.2, 27.5, 26.5, 18.6, 19.3, 20.1, 19.5, 19.5, 20.4, 19.8, 19.4,\\n\",\n \" 21.7, 22.8, 18.8, 18.7, 18.5, 18.3, 21.2, 19.2, 20.4, 19.3, 22. ,\\n\",\n \" 20.3, 20.5, 17.3, 18.8, 21.4, 15.7, 16.2, 18. , 14.3, 19.2, 19.6,\\n\",\n \" 23. , 18.4, 15.6, 18.1, 17.4, 17.1, 13.3, 17.8, 14. , 14.4, 13.4,\\n\",\n \" 15.6, 11.8, 13.8, 15.6, 14.6, 17.8, 15.4, 21.5, 19.6, 15.3, 19.4,\\n\",\n \" 17. , 15.6, 13.1, 41.3, 24.3, 23.3, 27. , 50. , 50. , 50. , 22.7,\\n\",\n \" 25. , 50. , 23.8, 23.8, 22.3, 17.4, 19.1, 23.1, 23.6, 22.6, 29.4,\\n\",\n \" 23.2, 24.6, 29.9, 37.2, 39.8, 36.2, 37.9, 32.5, 26.4, 29.6, 50. ,\\n\",\n \" 32. , 29.8, 34.9, 37. , 30.5, 36.4, 31.1, 29.1, 50. , 33.3, 30.3,\\n\",\n \" 34.6, 34.9, 32.9, 24.1, 42.3, 48.5, 50. , 22.6, 24.4, 22.5, 24.4,\\n\",\n \" 20. , 21.7, 19.3, 22.4, 28.1, 23.7, 25. , 23.3, 28.7, 21.5, 23. ,\\n\",\n \" 26.7, 21.7, 27.5, 30.1, 44.8, 50. , 37.6, 31.6, 46.7, 31.5, 24.3,\\n\",\n \" 31.7, 41.7, 48.3, 29. , 24. , 25.1, 31.5, 23.7, 23.3, 22. , 20.1,\\n\",\n \" 22.2, 23.7, 17.6, 18.5, 24.3, 20.5, 24.5, 26.2, 24.4, 24.8, 29.6,\\n\",\n \" 42.8, 21.9, 20.9, 44. , 50. , 36. , 30.1, 33.8, 43.1, 48.8, 31. ,\\n\",\n \" 36.5, 22.8, 30.7, 50. , 43.5, 20.7, 21.1, 25.2, 24.4, 35.2, 32.4,\\n\",\n \" 32. , 33.2, 33.1, 29.1, 35.1, 45.4, 35.4, 46. , 50. , 32.2, 22. ,\\n\",\n \" 20.1, 23.2, 22.3, 24.8, 28.5, 37.3, 27.9, 23.9, 21.7, 28.6, 27.1,\\n\",\n \" 20.3, 22.5, 29. , 24.8, 22. , 26.4, 33.1, 36.1, 28.4, 33.4, 28.2,\\n\",\n \" 22.8, 20.3, 16.1, 22.1, 19.4, 21.6, 23.8, 16.2, 17.8, 19.8, 23.1,\\n\",\n \" 21. , 23.8, 23.1, 20.4, 18.5, 25. , 24.6, 23. , 22.2, 19.3, 22.6,\\n\",\n \" 19.8, 17.1, 19.4, 22.2, 20.7, 21.1, 19.5, 18.5, 20.6, 19. , 18.7,\\n\",\n \" 32.7, 16.5, 23.9, 31.2, 17.5, 17.2, 23.1, 24.5, 26.6, 22.9, 24.1,\\n\",\n \" 18.6, 30.1, 18.2, 20.6, 17.8, 21.7, 22.7, 22.6, 25. , 19.9, 20.8,\\n\",\n \" 16.8, 21.9, 27.5, 21.9, 23.1, 50. , 50. , 50. , 50. , 50. , 13.8,\\n\",\n \" 13.8, 15. , 13.9, 13.3, 13.1, 10.2, 10.4, 10.9, 11.3, 12.3, 8.8,\\n\",\n \" 7.2, 10.5, 7.4, 10.2, 11.5, 15.1, 23.2, 9.7, 13.8, 12.7, 13.1,\\n\",\n \" 12.5, 8.5, 5. , 6.3, 5.6, 7.2, 12.1, 8.3, 8.5, 5. , 11.9,\\n\",\n \" 27.9, 17.2, 27.5, 15. , 17.2, 17.9, 16.3, 7. , 7.2, 7.5, 10.4,\\n\",\n \" 8.8, 8.4, 16.7, 14.2, 20.8, 13.4, 11.7, 8.3, 10.2, 10.9, 11. ,\\n\",\n \" 9.5, 14.5, 14.1, 16.1, 14.3, 11.7, 13.4, 9.6, 8.7, 8.4, 12.8,\\n\",\n \" 10.5, 17.1, 18.4, 15.4, 10.8, 11.8, 14.9, 12.6, 14.1, 13. , 13.4,\\n\",\n \" 15.2, 16.1, 17.8, 14.9, 14.1, 12.7, 13.5, 14.9, 20. , 16.4, 17.7,\\n\",\n \" 19.5, 20.2, 21.4, 19.9, 19. , 19.1, 19.1, 20.1, 19.9, 19.6, 23.2,\\n\",\n \" 29.8, 13.8, 13.3, 16.7, 12. , 14.6, 21.4, 23. , 23.7, 25. , 21.8,\\n\",\n \" 20.6, 21.2, 19.1, 20.6, 15.2, 7. , 8.1, 13.6, 20.1, 21.8, 24.5,\\n\",\n \" 23.1, 19.7, 18.3, 21.2, 17.5, 16.8, 22.4, 20.6, 23.9, 22. , 11.9]),\\n\",\n \" 'feature_names': array(['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD',\\n\",\n \" 'TAX', 'PTRATIO', 'B', 'LSTAT'], dtype='\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" 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CRIMZNINDUSCHASNOXRMAGEDISRADTAXPTRATIOBLSTAT
00.0063218.02.310.00.5386.57565.24.09001.0296.015.3396.904.98
10.027310.07.070.00.4696.42178.94.96712.0242.017.8396.909.14
20.027290.07.070.00.4697.18561.14.96712.0242.017.8392.834.03
30.032370.02.180.00.4586.99845.86.06223.0222.018.7394.632.94
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\\n\",\n \"\"\n ],\n \"text/plain\": [\n \" CRIM ZN INDUS CHAS NOX RM AGE DIS RAD TAX \\\\\\n\",\n \"0 0.00632 18.0 2.31 0.0 0.538 6.575 65.2 4.0900 1.0 296.0 \\n\",\n \"1 0.02731 0.0 7.07 0.0 0.469 6.421 78.9 4.9671 2.0 242.0 \\n\",\n \"2 0.02729 0.0 7.07 0.0 0.469 7.185 61.1 4.9671 2.0 242.0 \\n\",\n \"3 0.03237 0.0 2.18 0.0 0.458 6.998 45.8 6.0622 3.0 222.0 \\n\",\n \"4 0.06905 0.0 2.18 0.0 0.458 7.147 54.2 6.0622 3.0 222.0 \\n\",\n \"\\n\",\n \" PTRATIO B LSTAT \\n\",\n \"0 15.3 396.90 4.98 \\n\",\n \"1 17.8 396.90 9.14 \\n\",\n \"2 17.8 392.83 4.03 \\n\",\n \"3 18.7 394.63 2.94 \\n\",\n \"4 18.7 396.90 5.33 \"\n ]\n },\n \"execution_count\": 23,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df = pd.DataFrame(boston.data)\\n\",\n \"\\n\",\n \"df.columns = boston.feature_names\\n\",\n \"\\n\",\n \"df.head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"id\": \"d3ecb421\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(506,)\"\n ]\n },\n \"execution_count\": 24,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"boston.target.shape\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"id\": \"619db92b\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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CRIMZNINDUSCHASNOXRMAGEDISRADTAXPTRATIOBLSTATPrice
00.0063218.02.310.00.5386.57565.24.09001.0296.015.3396.904.9824.0
10.027310.07.070.00.4696.42178.94.96712.0242.017.8396.909.1421.6
20.027290.07.070.00.4697.18561.14.96712.0242.017.8392.834.0334.7
30.032370.02.180.00.4586.99845.86.06223.0222.018.7394.632.9433.4
40.069050.02.180.00.4587.14754.26.06223.0222.018.7396.905.3336.2
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\"\n ],\n \"text/plain\": [\n \" CRIM ZN INDUS CHAS NOX RM AGE DIS RAD TAX \\\\\\n\",\n \"0 0.00632 18.0 2.31 0.0 0.538 6.575 65.2 4.0900 1.0 296.0 \\n\",\n \"1 0.02731 0.0 7.07 0.0 0.469 6.421 78.9 4.9671 2.0 242.0 \\n\",\n \"2 0.02729 0.0 7.07 0.0 0.469 7.185 61.1 4.9671 2.0 242.0 \\n\",\n \"3 0.03237 0.0 2.18 0.0 0.458 6.998 45.8 6.0622 3.0 222.0 \\n\",\n \"4 0.06905 0.0 2.18 0.0 0.458 7.147 54.2 6.0622 3.0 222.0 \\n\",\n \"\\n\",\n \" PTRATIO B LSTAT Price \\n\",\n \"0 15.3 396.90 4.98 24.0 \\n\",\n \"1 17.8 396.90 9.14 21.6 \\n\",\n \"2 17.8 392.83 4.03 34.7 \\n\",\n \"3 18.7 394.63 2.94 33.4 \\n\",\n \"4 18.7 396.90 5.33 36.2 \"\n ]\n },\n \"execution_count\": 25,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df['Price'] = boston.target\\n\",\n \"\\n\",\n \"df.head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"id\": \"821bee3c\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
CRIMZNINDUSCHASNOXRMAGEDISRADTAXPTRATIOBLSTATPrice
count506.000000506.000000506.000000506.000000506.000000506.000000506.000000506.000000506.000000506.000000506.000000506.000000506.000000506.000000
mean3.61352411.36363611.1367790.0691700.5546956.28463468.5749013.7950439.549407408.23715418.455534356.67403212.65306322.532806
std8.60154523.3224536.8603530.2539940.1158780.70261728.1488612.1057108.707259168.5371162.16494691.2948647.1410629.197104
min0.0063200.0000000.4600000.0000000.3850003.5610002.9000001.1296001.000000187.00000012.6000000.3200001.7300005.000000
25%0.0820450.0000005.1900000.0000000.4490005.88550045.0250002.1001754.000000279.00000017.400000375.3775006.95000017.025000
50%0.2565100.0000009.6900000.0000000.5380006.20850077.5000003.2074505.000000330.00000019.050000391.44000011.36000021.200000
75%3.67708312.50000018.1000000.0000000.6240006.62350094.0750005.18842524.000000666.00000020.200000396.22500016.95500025.000000
max88.976200100.00000027.7400001.0000000.8710008.780000100.00000012.12650024.000000711.00000022.000000396.90000037.97000050.000000
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" CRIM ZN INDUS CHAS NOX RM \\\\\\n\",\n \"count 506.000000 506.000000 506.000000 506.000000 506.000000 506.000000 \\n\",\n \"mean 3.613524 11.363636 11.136779 0.069170 0.554695 6.284634 \\n\",\n \"std 8.601545 23.322453 6.860353 0.253994 0.115878 0.702617 \\n\",\n \"min 0.006320 0.000000 0.460000 0.000000 0.385000 3.561000 \\n\",\n \"25% 0.082045 0.000000 5.190000 0.000000 0.449000 5.885500 \\n\",\n \"50% 0.256510 0.000000 9.690000 0.000000 0.538000 6.208500 \\n\",\n \"75% 3.677083 12.500000 18.100000 0.000000 0.624000 6.623500 \\n\",\n \"max 88.976200 100.000000 27.740000 1.000000 0.871000 8.780000 \\n\",\n \"\\n\",\n \" AGE DIS RAD TAX PTRATIO B \\\\\\n\",\n \"count 506.000000 506.000000 506.000000 506.000000 506.000000 506.000000 \\n\",\n \"mean 68.574901 3.795043 9.549407 408.237154 18.455534 356.674032 \\n\",\n \"std 28.148861 2.105710 8.707259 168.537116 2.164946 91.294864 \\n\",\n \"min 2.900000 1.129600 1.000000 187.000000 12.600000 0.320000 \\n\",\n \"25% 45.025000 2.100175 4.000000 279.000000 17.400000 375.377500 \\n\",\n \"50% 77.500000 3.207450 5.000000 330.000000 19.050000 391.440000 \\n\",\n \"75% 94.075000 5.188425 24.000000 666.000000 20.200000 396.225000 \\n\",\n \"max 100.000000 12.126500 24.000000 711.000000 22.000000 396.900000 \\n\",\n \"\\n\",\n \" LSTAT Price \\n\",\n \"count 506.000000 506.000000 \\n\",\n \"mean 12.653063 22.532806 \\n\",\n \"std 7.141062 9.197104 \\n\",\n \"min 1.730000 5.000000 \\n\",\n \"25% 6.950000 17.025000 \\n\",\n \"50% 11.360000 21.200000 \\n\",\n \"75% 16.955000 25.000000 \\n\",\n \"max 37.970000 50.000000 \"\n ]\n },\n \"execution_count\": 26,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df.describe()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"id\": \"f124baa6\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\",\n \"RangeIndex: 506 entries, 0 to 505\\n\",\n \"Data columns (total 14 columns):\\n\",\n \" # Column Non-Null Count Dtype \\n\",\n \"--- ------ -------------- ----- \\n\",\n \" 0 CRIM 506 non-null float64\\n\",\n \" 1 ZN 506 non-null float64\\n\",\n \" 2 INDUS 506 non-null float64\\n\",\n \" 3 CHAS 506 non-null float64\\n\",\n \" 4 NOX 506 non-null float64\\n\",\n \" 5 RM 506 non-null float64\\n\",\n \" 6 AGE 506 non-null float64\\n\",\n \" 7 DIS 506 non-null float64\\n\",\n \" 8 RAD 506 non-null float64\\n\",\n \" 9 TAX 506 non-null float64\\n\",\n \" 10 PTRATIO 506 non-null float64\\n\",\n \" 11 B 506 non-null float64\\n\",\n \" 12 LSTAT 506 non-null float64\\n\",\n \" 13 Price 506 non-null float64\\n\",\n \"dtypes: float64(14)\\n\",\n \"memory usage: 55.5 KB\\n\"\n ]\n }\n ],\n \"source\": [\n \"df.info()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 28,\n \"id\": \"cf7dc760\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from sklearn.model_selection import train_test_split\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 29,\n \"id\": \"32764bf6\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"xtrain shape: (404, 13)\\n\",\n \"xtest shape: (102, 13)\\n\",\n \"ytrain shape: (404,)\\n\",\n \"ytest shape: (102,)\\n\"\n ]\n }\n ],\n \"source\": [\n \"x = boston.data\\n\",\n \"\\n\",\n \"y = boston.target\\n\",\n \"\\n\",\n \"xtrain, xtest, ytrain, ytest = train_test_split(x, y, test_size=0.2,\\n\",\n \" random_state=100)\\n\",\n \"\\n\",\n \"print(\\\"xtrain shape:\\\", xtrain.shape)\\n\",\n \"print(\\\"xtest shape:\\\", xtest.shape)\\n\",\n \"print(\\\"ytrain shape:\\\", ytrain.shape)\\n\",\n \"print(\\\"ytest shape:\\\", ytest.shape)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 31,\n \"id\": \"c68d3cf3\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"LinearRegression()\"\n ]\n },\n \"execution_count\": 31,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"from sklearn.linear_model import LinearRegression\\n\",\n \"\\n\",\n \"model = LinearRegression()\\n\",\n \"\\n\",\n \"model.fit(xtrain, ytrain)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 32,\n \"id\": \"a4f1e3ec\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([34.4081095 , 31.18524626, 22.31286141, 17.88613877, 20.43572131,\\n\",\n \" 26.14444413, 26.21920244, 23.57978445, 22.41577853, 19.51182817,\\n\",\n \" 26.86691495, 17.20411302, 20.68511041, 15.67921778, 41.69912781,\\n\",\n \" 20.2946735 , 28.99258631, 19.06413492, 32.48035595, 41.13627224,\\n\",\n \" 34.64732462, 16.38997909, 20.42215729, 18.05324255, 13.38743087,\\n\",\n \" 12.64800748, 27.45930237, 20.30199107, 18.78954741, 20.24950994,\\n\",\n \" 15.60161419, 24.38040555, 38.95651978, 24.7184131 , 31.26791961,\\n\",\n \" 28.26279775, 15.84707127, 14.76661568, 16.79024244, 23.23674899,\\n\",\n \" 22.85417065, 23.48976177, 14.16818173, 21.42613087, 32.38362329,\\n\",\n \" 26.7881669 , 19.37574824, 15.27894103, 17.21175121, 12.91591919,\\n\",\n \" 21.84063224, 20.25050371, 23.65622638, 23.9608324 , 11.94749102,\\n\",\n \" 14.49718052, 24.69872363, 34.18169066, 10.30437821, 21.04686616,\\n\",\n \" 17.96204214, 19.76593459, 17.45231513, 29.982971 , 20.73183476,\\n\",\n \" 25.24657823, 15.81416285, 24.96705225, 22.1298931 , 20.77593563,\\n\",\n \" 18.69600904, 24.2201495 , 4.37889874, 15.95687399, 28.03130587,\\n\",\n \" 9.28438308, 24.76810967, 35.14238234, 11.61172029, 27.04175401,\\n\",\n \" 34.84290485, 40.44603313, 13.93219791, 15.95544402, 19.26007763,\\n\",\n \" 12.76037799, 20.90536105, 23.85659356, 13.17899179, 14.76828889,\\n\",\n \" 32.31355353, 22.93635318, 24.63095357, 23.51981407, 19.34136578,\\n\",\n \" 22.83582245, 21.72263765, 36.18508621, 18.01097012, 23.18226475,\\n\",\n \" 13.77270991, 14.43864146])\"\n ]\n },\n \"execution_count\": 32,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"ypred = model.predict(xtest)\\n\",\n \"ypred\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 33,\n \"id\": \"811d7567\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([34.6, 31.5, 20.6, 14.5, 16.2, 22.3, 22.8, 24.4, 19.8, 16.7, 23.2,\\n\",\n \" 15.1, 21. , 18.9, 48.5, 24.1, 22.5, 16.1, 30.3, 50. , 33.8, 10.4,\\n\",\n \" 20.4, 14.2, 10.5, 12.7, 24.5, 16.7, 20.1, 17.1, 18.4, 24.3, 50. ,\\n\",\n \" 23.8, 30.7, 33.4, 15. , 10.9, 19.1, 21. , 22.9, 22.4, 19. , 19.3,\\n\",\n \" 27. , 27.1, 19.1, 15.4, 19.1, 13.5, 20.9, 19.3, 20.5, 26.2, 12. ,\\n\",\n \" 19.7, 29.6, 34.9, 8.3, 21.2, 14.5, 27.5, 19.4, 32.7, 21.1, 28.1,\\n\",\n \" 17.4, 30.1, 21.4, 20.9, 10.9, 21.7, 8.8, 15.2, 26.6, 8.7, 24.7,\\n\",\n \" 38.7, 6.3, 22.6, 43.8, 50. , 14.5, 11.7, 12.5, 13.6, 21.5, 19.1,\\n\",\n \" 9.5, 17.2, 29.8, 22.6, 21.6, 22.2, 27.1, 17. , 22. , 50. , 7.2,\\n\",\n \" 50. , 14. , 11. ])\"\n ]\n },\n \"execution_count\": 33,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"ytest\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"17b0418a\",\n \"metadata\": {},\n \"source\": [\n \"# Evaluation\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 39,\n \"id\": \"40e55958\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"0.7555033086871303\"\n ]\n },\n \"execution_count\": 39,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"model.score(xtest, ytest)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"733d9d23\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.12\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n" }, { "path": "March Cohort 2023/19_20_Machine Learning/.ipynb_checkpoints/20_Logistic Regression-checkpoint.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"id\": \"c71657c4\",\n \"metadata\": {},\n \"source\": [\n \"# Logistic Regression\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"f3aa2921\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"y = mx + c\\n\",\n \"\\n\",\n \"s(y) = 1/ 1+ e**(-y)\\n\",\n \"\\n\",\n \"s(mx + c) = 1/ 1+ e**(-(mx + c)) => 0 to 1\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"6513a10e\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \" male female\\n\",\n \" 0 - 0.5 0.5 - 1\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"bed78bb3\",\n \"metadata\": {},\n \"source\": [\n \"## Insurance Prediction using Logistic Regression\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"id\": \"9c2cd6a9\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import pandas as pd\\n\",\n \"import matplotlib.pyplot as plt\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"id\": \"074396bc\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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agebought_insurance
0220
1250
2471
3520
4461
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\"\n ],\n \"text/plain\": [\n \" age bought_insurance\\n\",\n \"0 22 0\\n\",\n \"1 25 0\\n\",\n \"2 47 1\\n\",\n \"3 52 0\\n\",\n \"4 46 1\"\n ]\n },\n \"execution_count\": 2,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df = pd.read_csv(\\\"insurance_data.csv\\\")\\n\",\n \"\\n\",\n \"df.head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"id\": \"706150aa\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 3,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAXQAAAD4CAYAAAD8Zh1EAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAOW0lEQVR4nO3df4zkd13H8eeLOxrll4fcQurd4ZXk+HEhtNTxwOCPA4TeAfFiIkmLIjaaS5MewUTTVhM1SgzxDw02FC5NrUhQLwSq1HJSSbVFQtDOaWm5lsPNtfSWQ7sVwQQSm4O3f8wcDHtzM7Pb2ZvtZ56PZLLz+THf73s/O/faT78700lVIUl66nvarAuQJE2HgS5JjTDQJakRBrokNcJAl6RGbJ7Vibdu3Vo7d+6c1ekl6Snp2LFjj1fVwrCxmQX6zp076Xa7szq9JD0lJfny+ca85CJJjTDQJakRBrokNcJAl6RGGOiS1IixgZ7k1iSPJfnCecaT5MYki0nuT3L59MtszN69vZs2po3y8xlVx1rH1uN807aRvu+1Pm5GaznJDv2DwL4R4/uBXf3bQeADT74sSdJqjX0delV9OsnOEVMOAB+q3v+H93NJtiS5uKq+Oq0im3H2t/I993x/++67Z1CMzrFRfj6j6ljr2Hqcb9o20ve9kepchWlcQ98GnBpoL/X7zpHkYJJuku7y8vIUTi1JOiuTfMBFf4d+R1W9fMjYJ4D3VNVn+u27gOuq6tioY3Y6nZrbd4q6M9/YNsrPZ1Qdax1bj/NN20b6vtf6uHVcyyTHqqozbGwaO/QlYMdAeztwegrHlSStwjR26G8GDgFvAl4F3FhVe8Ydc6536JK0RqN26GP/KJrkr4G9wNYkS8DvAU8HqKrDwFF6Yb4IfAu4ejplS5JWY5JXuVw1ZryAa6dWkSRpTXynqCQ1wkCXpEYY6JLUCANdkhphoEtSIwx0SWqEgS5JjTDQJakRBrokNcJAl6RGGOiS1AgDXZIaYaBLUiMMdElqhIEuSY0w0CWpEQa6JDXCQJekRhjoktQIA12SGmGgS1IjDHRJaoSBLkmNMNAlqREGuiQ1wkCXpEYY6JLUCANdkhphoEtSIwx0SWqEgS5JjZgo0JPsS3IiyWKSG4aM/1CSv0vy+STHk1w9/VIlSaOMDfQkm4CbgP3AbuCqJLtXTLsWeLCqLgX2An+c5KIp1ypJGmGSHfoeYLGqTlbVE8AR4MCKOQU8O0mAZwFfA85MtVJJ0kiTBPo24NRAe6nfN+h9wMuA08ADwLuq6jsrD5TkYJJuku7y8vIaS5YkDTNJoGdIX61oXwHcB/wIcBnwviTPOedBVTdXVaeqOgsLC6ssVZI0yiSBvgTsGGhvp7cTH3Q1cFv1LAIPAy+dTomSpElMEuj3AruSXNL/Q+eVwO0r5jwKvB4gyQuAlwAnp1moJGm0zeMmVNWZJIeAO4FNwK1VdTzJNf3xw8C7gQ8meYDeJZrrq+rxdaxbkrTC2EAHqKqjwNEVfYcH7p8G3jjd0iRJq+E7RSWpEQa6JDXCQJekRhjoktQIA12SGmGgS1IjDHRJaoSBLkmNMNAlqREGuiQ1wkCXpEYY6JLUCANdkhphoEtSIwx0SWqEgS5JjTDQJakRBrokNcJAl6RGGOiS1AgDXZIaYaBLUiMMdElqhIEuSY0w0CWpEQa6JDXCQJekRhjoktQIA12SGmGgS1IjJgr0JPuSnEiymOSG88zZm+S+JMeT3DPdMiVJ42weNyHJJuAm4A3AEnBvktur6sGBOVuA9wP7qurRJM9fp3olSecxyQ59D7BYVSer6gngCHBgxZy3AbdV1aMAVfXYdMuUJI0zSaBvA04NtJf6fYNeDDw3yd1JjiX55WEHSnIwSTdJd3l5eW0VS5KGmiTQM6SvVrQ3Az8GvBm4AvidJC8+50FVN1dVp6o6CwsLqy5WknR+Y6+h09uR7xhobwdOD5nzeFV9E/hmkk8DlwJfmkqVkqSxJtmh3wvsSnJJkouAK4HbV8z5OPBTSTYneQbwKuCh6ZYqSRpl7A69qs4kOQTcCWwCbq2q40mu6Y8frqqHknwSuB/4DnBLVX1hPQuXJH2/VK28HH5hdDqd6na7Mzm3JD1VJTlWVZ1hY75TVJIaYaBLUiMMdElqhIEuSY0w0CWpEQa6JDXCQJekRhjoktQIA12SGmGgS1IjDHRJaoSBLkmNMNAlqREGuiQ1wkCXpEYY6JLUCANdkhphoEtSIwx0SWqEgS5JjTDQJakRBrokNcJAl6RGGOiS1AgDXZIaYaBLUiMMdElqhIEuSY0w0CWpEQa6JDXCQJekRkwU6En2JTmRZDHJDSPm/XiSbyf5hemVKEmaxNhAT7IJuAnYD+wGrkqy+zzz/gi4c9pFSpLGm2SHvgdYrKqTVfUEcAQ4MGTeO4GPAY9NsT5J0oQmCfRtwKmB9lK/77uSbAN+Hjg86kBJDibpJukuLy+vtlZJ0giTBHqG9NWK9nuB66vq26MOVFU3V1WnqjoLCwsTlihJmsTmCeYsATsG2tuB0yvmdIAjSQC2Am9Kcqaq/nYaRUqSxpsk0O8FdiW5BPgKcCXwtsEJVXXJ2ftJPgjcYZhL0oU1NtCr6kySQ/RevbIJuLWqjie5pj8+8rq5JOnCmGSHTlUdBY6u6Bsa5FX1K0++LEnSavlOUUlqhIEuSY0w0CWpEQa6JDXCQJekRhjoktQIA12SGmGgS1IjDHRJaoSBLkmNMNAlqREGuiQ1wkCXpEYY6JLUCANdkhphoEtSIwx0SWqEgS5JjTDQJakRBrokNcJAl6RGGOiS1AgDXZIaYaBLUiMMdElqhIEuSY0w0CWpEQa6JDXCQJekRhjoktSIiQI9yb4kJ5IsJrlhyPgvJrm/f/tskkunX6okaZSxgZ5kE3ATsB/YDVyVZPeKaQ8DP1NVrwDeDdw87UIlSaNNskPfAyxW1cmqegI4AhwYnFBVn62q/+k3Pwdsn26ZkqRxJgn0bcCpgfZSv+98fhX4+2EDSQ4m6SbpLi8vT16lJGmsSQI9Q/pq6MTktfQC/fph41V1c1V1qqqzsLAweZWSpLE2TzBnCdgx0N4OnF45KckrgFuA/VX139MpT5I0qUl26PcCu5JckuQi4Erg9sEJSV4I3Aa8vaq+NP0yJUnjjN2hV9WZJIeAO4FNwK1VdTzJNf3xw8DvAs8D3p8E4ExVddavbEnSSqkaejl83XU6nep2uzM5tyQ9VSU5dr4Ns+8UlaRGGOiS1AgDXZIaYaBLUiMMdElqhIEuSY0w0CWpEQa6JDXCQJekRhjoktQIA12SGmGgS1IjDHRJaoSBLkmNMNAlqREGuiQ1wkCXpEYY6JLUCANdkhphoEtSIwx0SWqEgS5JjTDQJakRBrokNcJAl6RGGOiS1AgDXZIaYaBLUiMMdElqhIEuSY0w0CWpERMFepJ9SU4kWUxyw5DxJLmxP35/ksunX2rfli292zB79/ZuqzXqcWsdu9DnW486pSdr2s89n8sjjQ30JJuAm4D9wG7gqiS7V0zbD+zq3w4CH5hynZKkMTZPMGcPsFhVJwGSHAEOAA8OzDkAfKiqCvhcki1JLq6qr06t0rO78m984/vbX//6935j33NP7+vZ9t13jz7mqMetdexCn2896pSerGk/93wuT2SSSy7bgFMD7aV+32rnkORgkm6S7vLy8mprlSSNkN6mesSE5K3AFVX1a/3224E9VfXOgTmfAN5TVZ/pt+8CrquqY+c7bqfTqW63u/qKB3fmK631t/aox6117EKfbz3qlJ6saT/3fC6T5FhVdYaNTbJDXwJ2DLS3A6fXMEeStI4m2aFvBr4EvB74CnAv8LaqOj4w583AIeBNwKuAG6tqz6jjrnmHLklzbNQOfewfRavqTJJDwJ3AJuDWqjqe5Jr++GHgKL0wXwS+BVw9reIlSZOZ5FUuVNVReqE92Hd44H4B1063NEnSavhOUUlqhIEuSY0w0CWpEQa6JDVi7MsW1+3EyTLw5Qt82q3A4xf4nBudazKc63Iu1+Rcs1iTH62qhWEDMwv0WUjSPd/rN+eVazKc63Iu1+RcG21NvOQiSY0w0CWpEfMW6DfPuoANyDUZznU5l2tyrg21JnN1DV2SWjZvO3RJapaBLkmNaDbQk+xI8k9JHkpyPMm7+v0/nORTSf6j//W5s671QknyA0n+Ncnn+2vy+/3+uV2Ts5JsSvLvSe7ot+d6TZI8kuSBJPcl6fb75n1NtiT5aJIv9nPlJzbamjQb6MAZ4Deq6mXAq4Fr+x9ufQNwV1XtAu7qt+fF/wGvq6pLgcuAfUlezXyvyVnvAh4aaLsm8NqqumzgddbzviZ/Cnyyql4KXErv+bKx1qSq5uIGfBx4A3ACuLjfdzFwYta1zWg9ngH8G70PJJnrNaH3CVt3Aa8D7uj3zfuaPAJsXdE3t2sCPAd4mP4LSTbqmrS8Q/+uJDuBVwL/Arygqr4K0P/6/BmWdsH1Ly3cBzwGfKqq5n5NgPcC1wHfGeib9zUp4B+SHEtysN83z2vyImAZ+PP+pblbkjyTDbYmzQd6kmcBHwN+var+d9b1zFpVfbuqLqO3K92T5OUzLmmmkrwFeKxGfKD5nHpNVV0O7Kd3ufKnZ13QjG0GLgc+UFWvBL7JrC+vDNF0oCd5Or0w/8uquq3f/V9JLu6PX0xvpzp3qurrwN3APuZ7TV4D/FySR4AjwOuSfJj5XhOq6nT/62PA3wB7mO81WQKW+v9FC/BRegG/odak2UBPEuDPgIeq6k8Ghm4H3tG//w5619bnQpKFJFv6938Q+Fngi8zxmlTVb1XV9qraCVwJ/GNV/RJzvCZJnpnk2WfvA28EvsAcr0lV/SdwKslL+l2vBx5kg61Js+8UTfKTwD8DD/C9a6O/Te86+keAFwKPAm+tqq/NpMgLLMkrgL+g92HfTwM+UlV/kOR5zOmaDEqyF/jNqnrLPK9JkhfR25VD71LDX1XVH87zmgAkuQy4Bb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\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.scatter(df.age, df.bought_insurance, marker='+', color='red')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"id\": \"30fcfbb7\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(27, 2)\"\n ]\n },\n \"execution_count\": 4,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df.shape\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"id\": \"edd0315f\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from sklearn.model_selection import train_test_split\\n\",\n \"\\n\",\n \"xtrain, xtest, ytrain, ytest = train_test_split(df[['age']],\\n\",\n \" df.bought_insurance,\\n\",\n \" train_size=0.9)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"id\": \"bd2f6713\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"LogisticRegression()\"\n ]\n },\n \"execution_count\": 6,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"from sklearn.linear_model import LogisticRegression\\n\",\n \"\\n\",\n \"model = LogisticRegression()\\n\",\n \"\\n\",\n \"model.fit(xtrain, ytrain)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"id\": \"742d8875\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
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\"\n ],\n \"text/plain\": [\n \" age\\n\",\n \"24 50\\n\",\n \"3 52\\n\",\n \"11 28\"\n ]\n },\n \"execution_count\": 7,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"xtest\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"id\": \"1d225ec1\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([1, 1, 0], dtype=int64)\"\n ]\n },\n \"execution_count\": 8,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"ypredictions = model.predict(xtest)\\n\",\n \"\\n\",\n \"ypredictions\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"id\": \"58338281\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[0.10748428, 0.89251572],\\n\",\n \" [0.08108033, 0.91891967],\\n\",\n \" [0.78666634, 0.21333366]])\"\n ]\n },\n \"execution_count\": 9,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"model.predict_proba(xtest)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"id\": \"104a52c5\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from sklearn.metrics import accuracy_score\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"id\": \"ad16e9c5\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"0.6666666666666666\"\n ]\n },\n \"execution_count\": 13,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"accuracy_score(ytest,ypredictions)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"995595c0\",\n \"metadata\": {},\n \"source\": [\n \"# Multi Class Classification\\n\",\n \"\\n\",\n \"## Hand-written Digit Dataset\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"id\": \"9502f54d\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from sklearn.datasets import load_digits\\n\",\n \"\\n\",\n \"digits = load_digits()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"id\": \"664ee90a\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(1797, 64)\"\n ]\n },\n \"execution_count\": 15,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"digits.data.shape\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"id\": \"9f0a2e82\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([0, 1, 2, ..., 8, 9, 8])\"\n ]\n },\n \"execution_count\": 16,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"digits.target\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"id\": \"5aebeeed\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[ 0., 0., 5., ..., 0., 0., 0.],\\n\",\n \" [ 0., 0., 0., ..., 10., 0., 0.],\\n\",\n \" [ 0., 0., 0., ..., 16., 9., 0.],\\n\",\n \" ...,\\n\",\n \" [ 0., 0., 1., ..., 6., 0., 0.],\\n\",\n \" [ 0., 0., 2., ..., 12., 0., 0.],\\n\",\n \" [ 0., 0., 10., ..., 12., 1., 0.]])\"\n ]\n },\n \"execution_count\": 17,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"digits.data\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"id\": \"8fad6b0c\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import numpy as np\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"id\": \"42d489f4\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n 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\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.figure(figsize=(20,4))\\n\",\n \"\\n\",\n \"for index, (image, label) in enumerate(zip(digits.data[0:5], digits.target[0:5])):\\n\",\n \" plt.subplot(1,5, index + 1)\\n\",\n \" plt.imshow(np.reshape(image, (8,8)), cmap=plt.cm.gray)\\n\",\n \" plt.title('%i\\\\n'%label, fontsize=20)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"id\": \"a0e9ee80\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"xtrain, xtest, ytrain, ytest = train_test_split(digits.data,\\n\",\n \" digits.target,\\n\",\n \" train_size=0.75,\\n\",\n \" random_state=101)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"id\": \"89a33411\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"C:\\\\Users\\\\himan\\\\anaconda3\\\\lib\\\\site-packages\\\\sklearn\\\\linear_model\\\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\\n\",\n \"STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\\n\",\n \"\\n\",\n \"Increase the number of iterations (max_iter) or scale the data as shown in:\\n\",\n \" https://scikit-learn.org/stable/modules/preprocessing.html\\n\",\n \"Please also refer to the documentation for alternative solver options:\\n\",\n \" https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\\n\",\n \" n_iter_i = _check_optimize_result(\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"LogisticRegression()\"\n ]\n },\n \"execution_count\": 21,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"model = LogisticRegression()\\n\",\n \"model.fit(xtrain, ytrain)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"id\": \"eb0a5220\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([7])\"\n ]\n },\n \"execution_count\": 22,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"model.predict(xtest[0].reshape(1,-1))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"id\": \"b61ba7da\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([7, 4, 7, 9, 1, 7, 5, 7, 1, 0])\"\n ]\n },\n \"execution_count\": 23,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"model.predict(xtest[0:10])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"id\": \"d4fffa9b\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(450,)\"\n ]\n },\n \"execution_count\": 24,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"predictions = model.predict(xtest)\\n\",\n \"\\n\",\n \"predictions.shape\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"id\": \"4fcfc55f\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"0.9711111111111111\"\n ]\n },\n \"execution_count\": 25,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"model.score(xtest, ytest)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"id\": \"0abeddc9\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \" precision recall f1-score support\\n\",\n \"\\n\",\n \" 0 0.98 1.00 0.99 44\\n\",\n \" 1 0.91 1.00 0.95 52\\n\",\n \" 2 1.00 0.97 0.99 36\\n\",\n \" 3 1.00 0.95 0.97 41\\n\",\n \" 4 1.00 0.94 0.97 53\\n\",\n \" 5 0.96 1.00 0.98 51\\n\",\n \" 6 1.00 0.95 0.97 39\\n\",\n \" 7 1.00 0.98 0.99 46\\n\",\n \" 8 0.98 0.94 0.96 49\\n\",\n \" 9 0.90 0.97 0.94 39\\n\",\n \"\\n\",\n \" accuracy 0.97 450\\n\",\n \" macro avg 0.97 0.97 0.97 450\\n\",\n \"weighted avg 0.97 0.97 0.97 450\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"from sklearn.metrics import classification_report\\n\",\n \"\\n\",\n \"print(classification_report(ytest, predictions))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"e4d6dea7\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"0 - 0.1 0.1 - 0.2 0.2 - 0.3\\n\",\n \" 0 1 2\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 28,\n \"id\": \"3261d864\",\n \"metadata\": {\n \"scrolled\": true\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[[44 0 0 0 0 0 0 0 0 0]\\n\",\n \" [ 0 52 0 0 0 0 0 0 0 0]\\n\",\n \" [ 0 1 35 0 0 0 0 0 0 0]\\n\",\n \" [ 0 0 0 39 0 2 0 0 0 0]\\n\",\n \" [ 0 1 0 0 50 0 0 0 0 2]\\n\",\n \" [ 0 0 0 0 0 51 0 0 0 0]\\n\",\n \" [ 0 1 0 0 0 0 37 0 1 0]\\n\",\n \" [ 0 0 0 0 0 0 0 45 0 1]\\n\",\n \" [ 0 2 0 0 0 0 0 0 46 1]\\n\",\n \" [ 1 0 0 0 0 0 0 0 0 38]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"from sklearn.metrics import confusion_matrix\\n\",\n \"\\n\",\n \"print(confusion_matrix(ytest, predictions))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"776ca3c3\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"gender\\tage\\tincome\\tspending\\n\",\n \"M\\t25\\t100000\\t50000\\n\",\n \"M\\t30\\t150000\\t60000\\n\",\n \"F\\t32\\t165000\\t90000\\n\",\n \"F\\t21\\t200000\\t110000\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 30,\n \"id\": \"4ca7d100\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
genderageincomespending
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\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" gender age income spending\\n\",\n \"0 M 24 50000 30000\\n\",\n \"1 F 25 100000 50000\\n\",\n \"2 M 27 1200000 600000\\n\",\n \"3 F 35 1500000 750000\"\n ]\n },\n \"execution_count\": 30,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df = pd.read_excel(\\\"spend.xlsx\\\")\\n\",\n \"\\n\",\n \"df\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 33,\n \"id\": \"c598a10e\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 33,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"import seaborn as sns\\n\",\n \"\\n\",\n \"sns.countplot(x = 'spending', hue='gender', data=df)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 34,\n \"id\": \"e2d9fe0f\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[]\"\n ]\n },\n \"execution_count\": 34,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.plot(df['gender'], df['spending'], label='male')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 35,\n \"id\": \"aa1d58ba\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 35,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.bar(df['gender'], df['spending'], label='spending')\\n\",\n \"plt.bar(df['gender'], df['income'], label='income')\\n\",\n \"\\n\",\n \"plt.legend()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"ffd472db\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.12\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n" }, { "path": "March Cohort 2023/19_20_Machine Learning/19_Machine Learning and Linear Regression with One variable.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"id\": \"7242d236\",\n \"metadata\": {},\n \"source\": [\n \"# Machine Learning\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"f822ef63\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"Supervised Unsupervised Reinforcement\\n\",\n \"\\n\",\n \"Regression - clusturing - markov decision model\\n\",\n \"- Linear Regression - dimension reduction - game theory\\n\",\n \"Classification - anomaly detection\\n\",\n \"- Logistic Regression\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"59019bc8\",\n \"metadata\": {},\n \"source\": [\n \"# Linear Regression with One variable\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"id\": \"a0ff1cca\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# linear - arranged in straight line\\n\",\n \"# regression - a technique to determine the statistical relationship \\n\",\n \"# between 2 or more variables\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"1f3259c7\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \" x y\\n\",\n \"area(sq ft) price($)\\n\",\n \"2500 800000\\n\",\n \"3000 950000\\n\",\n \"3200 1000000\\n\",\n \"3600 1200000\\n\",\n \"4000 1500000\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"38b295a5\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"y = mx + c\\n\",\n \"y = b1x + b0\\n\",\n \"\\n\",\n \"800000 = m(2500) + c\\n\",\n \"950000 = m(3000) + c\\n\",\n \"\\n\",\n \"m = 3000\\n\",\n \"c = 4000\\n\",\n \"\\n\",\n \"y = 3000(x) + 4000 --> principle equation\\n\",\n \"y = 3000(1200) + 4000 --> prediction\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"id\": \"7d739803\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import pandas as pd\\n\",\n \"import numpy as np\\n\",\n \"import matplotlib.pyplot as plt\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"id\": \"ddd8d883\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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\"\n ],\n \"text/plain\": [\n \" area price\\n\",\n \"0 2600 550000\\n\",\n \"1 3000 565000\\n\",\n \"2 3200 610000\\n\",\n \"3 3600 680000\\n\",\n \"4 4000 725000\"\n ]\n },\n \"execution_count\": 4,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df = pd.read_csv(\\\"homeprices.csv\\\")\\n\",\n \"df.head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"id\": \"f2481b31\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 5,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.xlabel('Area')\\n\",\n \"plt.ylabel('Price')\\n\",\n \"\\n\",\n \"plt.scatter(df.area, df.price, color='red', marker='+')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"id\": \"eba2ddd6\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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\"\n ],\n \"text/plain\": [\n \" area\\n\",\n \"0 2600\\n\",\n \"1 3000\\n\",\n \"2 3200\\n\",\n \"3 3600\\n\",\n \"4 4000\"\n ]\n },\n \"execution_count\": 6,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"x = df[['area']]\\n\",\n \"x\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"id\": \"e67faccd\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"0 550000\\n\",\n \"1 565000\\n\",\n \"2 610000\\n\",\n \"3 680000\\n\",\n \"4 725000\\n\",\n \"Name: price, dtype: int64\"\n ]\n },\n \"execution_count\": 7,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"y = df.price\\n\",\n \"y\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"id\": \"23e39b32\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from sklearn.linear_model import LinearRegression\\n\",\n \"\\n\",\n \"model = LinearRegression()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"id\": \"e9cc482f\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"LinearRegression()\"\n ]\n },\n \"execution_count\": 9,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"model\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"id\": \"40d56579\",\n \"metadata\": {\n \"scrolled\": false\n },\n \"outputs\": [],\n \"source\": [\n \"# help(model)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"id\": \"949d6f6a\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"LinearRegression()\"\n ]\n },\n \"execution_count\": 12,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"model.fit(x,y)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"id\": \"d1ba4c61\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([135.78767123])\"\n ]\n },\n \"execution_count\": 13,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# m = slope = gradient = 135.78\\n\",\n \"model.coef_\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"id\": \"3696cb1a\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"180616.43835616432\"\n ]\n },\n \"execution_count\": 14,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# c = intercept = 180616.43\\n\",\n \"model.intercept_\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"id\": \"6eeccf2a\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"C:\\\\Users\\\\himan\\\\anaconda3\\\\lib\\\\site-packages\\\\sklearn\\\\base.py:450: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\\n\",\n \" warnings.warn(\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"array([601558.21917808])\"\n ]\n },\n \"execution_count\": 15,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"model.predict([[3100]])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"a6a89a8e\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# y = mx + c\\n\",\n \"y = 135.78(x) + 180616.43\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"ad003525\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"x = 3100\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"id\": \"7211c8b8\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"601534.4299999999\"\n ]\n },\n \"execution_count\": 16,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"y = 135.78*(3100) + 180616.43\\n\",\n \"y\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"82249914\",\n \"metadata\": {},\n \"source\": [\n \"# Boston House Price Data\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"3f497053\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"y1 = m1x1 + m2x2 + ..... + c + e\\n\",\n \"y2 = m1x1 + m2x2 + ..... + c + e\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"id\": \"a20860cc\",\n \"metadata\": {\n \"scrolled\": false\n },\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"C:\\\\Users\\\\himan\\\\anaconda3\\\\lib\\\\site-packages\\\\sklearn\\\\utils\\\\deprecation.py:87: FutureWarning: Function load_boston is deprecated; `load_boston` is deprecated in 1.0 and will be removed in 1.2.\\n\",\n \"\\n\",\n \" The Boston housing prices dataset has an ethical problem. You can refer to\\n\",\n \" the documentation of this function for further details.\\n\",\n \"\\n\",\n \" The scikit-learn maintainers therefore strongly discourage the use of this\\n\",\n \" dataset unless the purpose of the code is to study and educate about\\n\",\n \" ethical issues in data science and machine learning.\\n\",\n \"\\n\",\n \" In this special case, you can fetch the dataset from the original\\n\",\n \" source::\\n\",\n \"\\n\",\n \" import pandas as pd\\n\",\n \" import numpy as np\\n\",\n \"\\n\",\n \"\\n\",\n \" data_url = \\\"http://lib.stat.cmu.edu/datasets/boston\\\"\\n\",\n \" raw_df = pd.read_csv(data_url, sep=\\\"\\\\s+\\\", skiprows=22, header=None)\\n\",\n \" data = np.hstack([raw_df.values[::2, :], raw_df.values[1::2, :2]])\\n\",\n \" target = raw_df.values[1::2, 2]\\n\",\n \"\\n\",\n \" Alternative datasets include the California housing dataset (i.e.\\n\",\n \" :func:`~sklearn.datasets.fetch_california_housing`) and the Ames housing\\n\",\n \" dataset. You can load the datasets as follows::\\n\",\n \"\\n\",\n \" from sklearn.datasets import fetch_california_housing\\n\",\n \" housing = fetch_california_housing()\\n\",\n \"\\n\",\n \" for the California housing dataset and::\\n\",\n \"\\n\",\n \" from sklearn.datasets import fetch_openml\\n\",\n \" housing = fetch_openml(name=\\\"house_prices\\\", as_frame=True)\\n\",\n \"\\n\",\n \" for the Ames housing dataset.\\n\",\n \" \\n\",\n \" warnings.warn(msg, category=FutureWarning)\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"{'data': array([[6.3200e-03, 1.8000e+01, 2.3100e+00, ..., 1.5300e+01, 3.9690e+02,\\n\",\n \" 4.9800e+00],\\n\",\n \" [2.7310e-02, 0.0000e+00, 7.0700e+00, ..., 1.7800e+01, 3.9690e+02,\\n\",\n \" 9.1400e+00],\\n\",\n \" [2.7290e-02, 0.0000e+00, 7.0700e+00, ..., 1.7800e+01, 3.9283e+02,\\n\",\n \" 4.0300e+00],\\n\",\n \" ...,\\n\",\n \" [6.0760e-02, 0.0000e+00, 1.1930e+01, ..., 2.1000e+01, 3.9690e+02,\\n\",\n \" 5.6400e+00],\\n\",\n \" [1.0959e-01, 0.0000e+00, 1.1930e+01, ..., 2.1000e+01, 3.9345e+02,\\n\",\n \" 6.4800e+00],\\n\",\n \" [4.7410e-02, 0.0000e+00, 1.1930e+01, ..., 2.1000e+01, 3.9690e+02,\\n\",\n \" 7.8800e+00]]),\\n\",\n \" 'target': array([24. , 21.6, 34.7, 33.4, 36.2, 28.7, 22.9, 27.1, 16.5, 18.9, 15. ,\\n\",\n \" 18.9, 21.7, 20.4, 18.2, 19.9, 23.1, 17.5, 20.2, 18.2, 13.6, 19.6,\\n\",\n \" 15.2, 14.5, 15.6, 13.9, 16.6, 14.8, 18.4, 21. , 12.7, 14.5, 13.2,\\n\",\n \" 13.1, 13.5, 18.9, 20. , 21. , 24.7, 30.8, 34.9, 26.6, 25.3, 24.7,\\n\",\n \" 21.2, 19.3, 20. , 16.6, 14.4, 19.4, 19.7, 20.5, 25. , 23.4, 18.9,\\n\",\n \" 35.4, 24.7, 31.6, 23.3, 19.6, 18.7, 16. , 22.2, 25. , 33. , 23.5,\\n\",\n \" 19.4, 22. , 17.4, 20.9, 24.2, 21.7, 22.8, 23.4, 24.1, 21.4, 20. ,\\n\",\n \" 20.8, 21.2, 20.3, 28. , 23.9, 24.8, 22.9, 23.9, 26.6, 22.5, 22.2,\\n\",\n \" 23.6, 28.7, 22.6, 22. , 22.9, 25. , 20.6, 28.4, 21.4, 38.7, 43.8,\\n\",\n \" 33.2, 27.5, 26.5, 18.6, 19.3, 20.1, 19.5, 19.5, 20.4, 19.8, 19.4,\\n\",\n \" 21.7, 22.8, 18.8, 18.7, 18.5, 18.3, 21.2, 19.2, 20.4, 19.3, 22. ,\\n\",\n \" 20.3, 20.5, 17.3, 18.8, 21.4, 15.7, 16.2, 18. , 14.3, 19.2, 19.6,\\n\",\n \" 23. , 18.4, 15.6, 18.1, 17.4, 17.1, 13.3, 17.8, 14. , 14.4, 13.4,\\n\",\n \" 15.6, 11.8, 13.8, 15.6, 14.6, 17.8, 15.4, 21.5, 19.6, 15.3, 19.4,\\n\",\n \" 17. , 15.6, 13.1, 41.3, 24.3, 23.3, 27. , 50. , 50. , 50. , 22.7,\\n\",\n \" 25. , 50. , 23.8, 23.8, 22.3, 17.4, 19.1, 23.1, 23.6, 22.6, 29.4,\\n\",\n \" 23.2, 24.6, 29.9, 37.2, 39.8, 36.2, 37.9, 32.5, 26.4, 29.6, 50. ,\\n\",\n \" 32. , 29.8, 34.9, 37. , 30.5, 36.4, 31.1, 29.1, 50. , 33.3, 30.3,\\n\",\n \" 34.6, 34.9, 32.9, 24.1, 42.3, 48.5, 50. , 22.6, 24.4, 22.5, 24.4,\\n\",\n \" 20. , 21.7, 19.3, 22.4, 28.1, 23.7, 25. , 23.3, 28.7, 21.5, 23. ,\\n\",\n \" 26.7, 21.7, 27.5, 30.1, 44.8, 50. , 37.6, 31.6, 46.7, 31.5, 24.3,\\n\",\n \" 31.7, 41.7, 48.3, 29. , 24. , 25.1, 31.5, 23.7, 23.3, 22. , 20.1,\\n\",\n \" 22.2, 23.7, 17.6, 18.5, 24.3, 20.5, 24.5, 26.2, 24.4, 24.8, 29.6,\\n\",\n \" 42.8, 21.9, 20.9, 44. , 50. , 36. , 30.1, 33.8, 43.1, 48.8, 31. ,\\n\",\n \" 36.5, 22.8, 30.7, 50. , 43.5, 20.7, 21.1, 25.2, 24.4, 35.2, 32.4,\\n\",\n \" 32. , 33.2, 33.1, 29.1, 35.1, 45.4, 35.4, 46. , 50. , 32.2, 22. ,\\n\",\n \" 20.1, 23.2, 22.3, 24.8, 28.5, 37.3, 27.9, 23.9, 21.7, 28.6, 27.1,\\n\",\n \" 20.3, 22.5, 29. , 24.8, 22. , 26.4, 33.1, 36.1, 28.4, 33.4, 28.2,\\n\",\n \" 22.8, 20.3, 16.1, 22.1, 19.4, 21.6, 23.8, 16.2, 17.8, 19.8, 23.1,\\n\",\n \" 21. , 23.8, 23.1, 20.4, 18.5, 25. , 24.6, 23. , 22.2, 19.3, 22.6,\\n\",\n \" 19.8, 17.1, 19.4, 22.2, 20.7, 21.1, 19.5, 18.5, 20.6, 19. , 18.7,\\n\",\n \" 32.7, 16.5, 23.9, 31.2, 17.5, 17.2, 23.1, 24.5, 26.6, 22.9, 24.1,\\n\",\n \" 18.6, 30.1, 18.2, 20.6, 17.8, 21.7, 22.7, 22.6, 25. , 19.9, 20.8,\\n\",\n \" 16.8, 21.9, 27.5, 21.9, 23.1, 50. , 50. , 50. , 50. , 50. , 13.8,\\n\",\n \" 13.8, 15. , 13.9, 13.3, 13.1, 10.2, 10.4, 10.9, 11.3, 12.3, 8.8,\\n\",\n \" 7.2, 10.5, 7.4, 10.2, 11.5, 15.1, 23.2, 9.7, 13.8, 12.7, 13.1,\\n\",\n \" 12.5, 8.5, 5. , 6.3, 5.6, 7.2, 12.1, 8.3, 8.5, 5. , 11.9,\\n\",\n \" 27.9, 17.2, 27.5, 15. , 17.2, 17.9, 16.3, 7. , 7.2, 7.5, 10.4,\\n\",\n \" 8.8, 8.4, 16.7, 14.2, 20.8, 13.4, 11.7, 8.3, 10.2, 10.9, 11. ,\\n\",\n \" 9.5, 14.5, 14.1, 16.1, 14.3, 11.7, 13.4, 9.6, 8.7, 8.4, 12.8,\\n\",\n \" 10.5, 17.1, 18.4, 15.4, 10.8, 11.8, 14.9, 12.6, 14.1, 13. , 13.4,\\n\",\n \" 15.2, 16.1, 17.8, 14.9, 14.1, 12.7, 13.5, 14.9, 20. , 16.4, 17.7,\\n\",\n \" 19.5, 20.2, 21.4, 19.9, 19. , 19.1, 19.1, 20.1, 19.9, 19.6, 23.2,\\n\",\n \" 29.8, 13.8, 13.3, 16.7, 12. , 14.6, 21.4, 23. , 23.7, 25. , 21.8,\\n\",\n \" 20.6, 21.2, 19.1, 20.6, 15.2, 7. , 8.1, 13.6, 20.1, 21.8, 24.5,\\n\",\n \" 23.1, 19.7, 18.3, 21.2, 17.5, 16.8, 22.4, 20.6, 23.9, 22. , 11.9]),\\n\",\n \" 'feature_names': array(['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD',\\n\",\n \" 'TAX', 'PTRATIO', 'B', 'LSTAT'], dtype='\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" 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CRIMZNINDUSCHASNOXRMAGEDISRADTAXPTRATIOBLSTAT
00.0063218.02.310.00.5386.57565.24.09001.0296.015.3396.904.98
10.027310.07.070.00.4696.42178.94.96712.0242.017.8396.909.14
20.027290.07.070.00.4697.18561.14.96712.0242.017.8392.834.03
30.032370.02.180.00.4586.99845.86.06223.0222.018.7394.632.94
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\\n\",\n \"\"\n ],\n \"text/plain\": [\n \" CRIM ZN INDUS CHAS NOX RM AGE DIS RAD TAX \\\\\\n\",\n \"0 0.00632 18.0 2.31 0.0 0.538 6.575 65.2 4.0900 1.0 296.0 \\n\",\n \"1 0.02731 0.0 7.07 0.0 0.469 6.421 78.9 4.9671 2.0 242.0 \\n\",\n \"2 0.02729 0.0 7.07 0.0 0.469 7.185 61.1 4.9671 2.0 242.0 \\n\",\n \"3 0.03237 0.0 2.18 0.0 0.458 6.998 45.8 6.0622 3.0 222.0 \\n\",\n \"4 0.06905 0.0 2.18 0.0 0.458 7.147 54.2 6.0622 3.0 222.0 \\n\",\n \"\\n\",\n \" PTRATIO B LSTAT \\n\",\n \"0 15.3 396.90 4.98 \\n\",\n \"1 17.8 396.90 9.14 \\n\",\n \"2 17.8 392.83 4.03 \\n\",\n \"3 18.7 394.63 2.94 \\n\",\n \"4 18.7 396.90 5.33 \"\n ]\n },\n \"execution_count\": 23,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df = pd.DataFrame(boston.data)\\n\",\n \"\\n\",\n \"df.columns = boston.feature_names\\n\",\n \"\\n\",\n \"df.head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"id\": \"d3ecb421\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(506,)\"\n ]\n },\n \"execution_count\": 24,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"boston.target.shape\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"id\": \"619db92b\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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CRIMZNINDUSCHASNOXRMAGEDISRADTAXPTRATIOBLSTATPrice
00.0063218.02.310.00.5386.57565.24.09001.0296.015.3396.904.9824.0
10.027310.07.070.00.4696.42178.94.96712.0242.017.8396.909.1421.6
20.027290.07.070.00.4697.18561.14.96712.0242.017.8392.834.0334.7
30.032370.02.180.00.4586.99845.86.06223.0222.018.7394.632.9433.4
40.069050.02.180.00.4587.14754.26.06223.0222.018.7396.905.3336.2
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\"\n ],\n \"text/plain\": [\n \" CRIM ZN INDUS CHAS NOX RM AGE DIS RAD TAX \\\\\\n\",\n \"0 0.00632 18.0 2.31 0.0 0.538 6.575 65.2 4.0900 1.0 296.0 \\n\",\n \"1 0.02731 0.0 7.07 0.0 0.469 6.421 78.9 4.9671 2.0 242.0 \\n\",\n \"2 0.02729 0.0 7.07 0.0 0.469 7.185 61.1 4.9671 2.0 242.0 \\n\",\n \"3 0.03237 0.0 2.18 0.0 0.458 6.998 45.8 6.0622 3.0 222.0 \\n\",\n \"4 0.06905 0.0 2.18 0.0 0.458 7.147 54.2 6.0622 3.0 222.0 \\n\",\n \"\\n\",\n \" PTRATIO B LSTAT Price \\n\",\n \"0 15.3 396.90 4.98 24.0 \\n\",\n \"1 17.8 396.90 9.14 21.6 \\n\",\n \"2 17.8 392.83 4.03 34.7 \\n\",\n \"3 18.7 394.63 2.94 33.4 \\n\",\n \"4 18.7 396.90 5.33 36.2 \"\n ]\n },\n \"execution_count\": 25,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df['Price'] = boston.target\\n\",\n \"\\n\",\n \"df.head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"id\": \"821bee3c\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
CRIMZNINDUSCHASNOXRMAGEDISRADTAXPTRATIOBLSTATPrice
count506.000000506.000000506.000000506.000000506.000000506.000000506.000000506.000000506.000000506.000000506.000000506.000000506.000000506.000000
mean3.61352411.36363611.1367790.0691700.5546956.28463468.5749013.7950439.549407408.23715418.455534356.67403212.65306322.532806
std8.60154523.3224536.8603530.2539940.1158780.70261728.1488612.1057108.707259168.5371162.16494691.2948647.1410629.197104
min0.0063200.0000000.4600000.0000000.3850003.5610002.9000001.1296001.000000187.00000012.6000000.3200001.7300005.000000
25%0.0820450.0000005.1900000.0000000.4490005.88550045.0250002.1001754.000000279.00000017.400000375.3775006.95000017.025000
50%0.2565100.0000009.6900000.0000000.5380006.20850077.5000003.2074505.000000330.00000019.050000391.44000011.36000021.200000
75%3.67708312.50000018.1000000.0000000.6240006.62350094.0750005.18842524.000000666.00000020.200000396.22500016.95500025.000000
max88.976200100.00000027.7400001.0000000.8710008.780000100.00000012.12650024.000000711.00000022.000000396.90000037.97000050.000000
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" CRIM ZN INDUS CHAS NOX RM \\\\\\n\",\n \"count 506.000000 506.000000 506.000000 506.000000 506.000000 506.000000 \\n\",\n \"mean 3.613524 11.363636 11.136779 0.069170 0.554695 6.284634 \\n\",\n \"std 8.601545 23.322453 6.860353 0.253994 0.115878 0.702617 \\n\",\n \"min 0.006320 0.000000 0.460000 0.000000 0.385000 3.561000 \\n\",\n \"25% 0.082045 0.000000 5.190000 0.000000 0.449000 5.885500 \\n\",\n \"50% 0.256510 0.000000 9.690000 0.000000 0.538000 6.208500 \\n\",\n \"75% 3.677083 12.500000 18.100000 0.000000 0.624000 6.623500 \\n\",\n \"max 88.976200 100.000000 27.740000 1.000000 0.871000 8.780000 \\n\",\n \"\\n\",\n \" AGE DIS RAD TAX PTRATIO B \\\\\\n\",\n \"count 506.000000 506.000000 506.000000 506.000000 506.000000 506.000000 \\n\",\n \"mean 68.574901 3.795043 9.549407 408.237154 18.455534 356.674032 \\n\",\n \"std 28.148861 2.105710 8.707259 168.537116 2.164946 91.294864 \\n\",\n \"min 2.900000 1.129600 1.000000 187.000000 12.600000 0.320000 \\n\",\n \"25% 45.025000 2.100175 4.000000 279.000000 17.400000 375.377500 \\n\",\n \"50% 77.500000 3.207450 5.000000 330.000000 19.050000 391.440000 \\n\",\n \"75% 94.075000 5.188425 24.000000 666.000000 20.200000 396.225000 \\n\",\n \"max 100.000000 12.126500 24.000000 711.000000 22.000000 396.900000 \\n\",\n \"\\n\",\n \" LSTAT Price \\n\",\n \"count 506.000000 506.000000 \\n\",\n \"mean 12.653063 22.532806 \\n\",\n \"std 7.141062 9.197104 \\n\",\n \"min 1.730000 5.000000 \\n\",\n \"25% 6.950000 17.025000 \\n\",\n \"50% 11.360000 21.200000 \\n\",\n \"75% 16.955000 25.000000 \\n\",\n \"max 37.970000 50.000000 \"\n ]\n },\n \"execution_count\": 26,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df.describe()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"id\": \"f124baa6\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\",\n \"RangeIndex: 506 entries, 0 to 505\\n\",\n \"Data columns (total 14 columns):\\n\",\n \" # Column Non-Null Count Dtype \\n\",\n \"--- ------ -------------- ----- \\n\",\n \" 0 CRIM 506 non-null float64\\n\",\n \" 1 ZN 506 non-null float64\\n\",\n \" 2 INDUS 506 non-null float64\\n\",\n \" 3 CHAS 506 non-null float64\\n\",\n \" 4 NOX 506 non-null float64\\n\",\n \" 5 RM 506 non-null float64\\n\",\n \" 6 AGE 506 non-null float64\\n\",\n \" 7 DIS 506 non-null float64\\n\",\n \" 8 RAD 506 non-null float64\\n\",\n \" 9 TAX 506 non-null float64\\n\",\n \" 10 PTRATIO 506 non-null float64\\n\",\n \" 11 B 506 non-null float64\\n\",\n \" 12 LSTAT 506 non-null float64\\n\",\n \" 13 Price 506 non-null float64\\n\",\n \"dtypes: float64(14)\\n\",\n \"memory usage: 55.5 KB\\n\"\n ]\n }\n ],\n \"source\": [\n \"df.info()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 28,\n \"id\": \"cf7dc760\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from sklearn.model_selection import train_test_split\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 29,\n \"id\": \"32764bf6\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"xtrain shape: (404, 13)\\n\",\n \"xtest shape: (102, 13)\\n\",\n \"ytrain shape: (404,)\\n\",\n \"ytest shape: (102,)\\n\"\n ]\n }\n ],\n \"source\": [\n \"x = boston.data\\n\",\n \"\\n\",\n \"y = boston.target\\n\",\n \"\\n\",\n \"xtrain, xtest, ytrain, ytest = train_test_split(x, y, test_size=0.2,\\n\",\n \" random_state=100)\\n\",\n \"\\n\",\n \"print(\\\"xtrain shape:\\\", xtrain.shape)\\n\",\n \"print(\\\"xtest shape:\\\", xtest.shape)\\n\",\n \"print(\\\"ytrain shape:\\\", ytrain.shape)\\n\",\n \"print(\\\"ytest shape:\\\", ytest.shape)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 31,\n \"id\": \"c68d3cf3\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"LinearRegression()\"\n ]\n },\n \"execution_count\": 31,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"from sklearn.linear_model import LinearRegression\\n\",\n \"\\n\",\n \"model = LinearRegression()\\n\",\n \"\\n\",\n \"model.fit(xtrain, ytrain)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 32,\n \"id\": \"a4f1e3ec\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([34.4081095 , 31.18524626, 22.31286141, 17.88613877, 20.43572131,\\n\",\n \" 26.14444413, 26.21920244, 23.57978445, 22.41577853, 19.51182817,\\n\",\n \" 26.86691495, 17.20411302, 20.68511041, 15.67921778, 41.69912781,\\n\",\n \" 20.2946735 , 28.99258631, 19.06413492, 32.48035595, 41.13627224,\\n\",\n \" 34.64732462, 16.38997909, 20.42215729, 18.05324255, 13.38743087,\\n\",\n \" 12.64800748, 27.45930237, 20.30199107, 18.78954741, 20.24950994,\\n\",\n \" 15.60161419, 24.38040555, 38.95651978, 24.7184131 , 31.26791961,\\n\",\n \" 28.26279775, 15.84707127, 14.76661568, 16.79024244, 23.23674899,\\n\",\n \" 22.85417065, 23.48976177, 14.16818173, 21.42613087, 32.38362329,\\n\",\n \" 26.7881669 , 19.37574824, 15.27894103, 17.21175121, 12.91591919,\\n\",\n \" 21.84063224, 20.25050371, 23.65622638, 23.9608324 , 11.94749102,\\n\",\n \" 14.49718052, 24.69872363, 34.18169066, 10.30437821, 21.04686616,\\n\",\n \" 17.96204214, 19.76593459, 17.45231513, 29.982971 , 20.73183476,\\n\",\n \" 25.24657823, 15.81416285, 24.96705225, 22.1298931 , 20.77593563,\\n\",\n \" 18.69600904, 24.2201495 , 4.37889874, 15.95687399, 28.03130587,\\n\",\n \" 9.28438308, 24.76810967, 35.14238234, 11.61172029, 27.04175401,\\n\",\n \" 34.84290485, 40.44603313, 13.93219791, 15.95544402, 19.26007763,\\n\",\n \" 12.76037799, 20.90536105, 23.85659356, 13.17899179, 14.76828889,\\n\",\n \" 32.31355353, 22.93635318, 24.63095357, 23.51981407, 19.34136578,\\n\",\n \" 22.83582245, 21.72263765, 36.18508621, 18.01097012, 23.18226475,\\n\",\n \" 13.77270991, 14.43864146])\"\n ]\n },\n \"execution_count\": 32,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"ypred = model.predict(xtest)\\n\",\n \"ypred\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 33,\n \"id\": \"811d7567\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([34.6, 31.5, 20.6, 14.5, 16.2, 22.3, 22.8, 24.4, 19.8, 16.7, 23.2,\\n\",\n \" 15.1, 21. , 18.9, 48.5, 24.1, 22.5, 16.1, 30.3, 50. , 33.8, 10.4,\\n\",\n \" 20.4, 14.2, 10.5, 12.7, 24.5, 16.7, 20.1, 17.1, 18.4, 24.3, 50. ,\\n\",\n \" 23.8, 30.7, 33.4, 15. , 10.9, 19.1, 21. , 22.9, 22.4, 19. , 19.3,\\n\",\n \" 27. , 27.1, 19.1, 15.4, 19.1, 13.5, 20.9, 19.3, 20.5, 26.2, 12. ,\\n\",\n \" 19.7, 29.6, 34.9, 8.3, 21.2, 14.5, 27.5, 19.4, 32.7, 21.1, 28.1,\\n\",\n \" 17.4, 30.1, 21.4, 20.9, 10.9, 21.7, 8.8, 15.2, 26.6, 8.7, 24.7,\\n\",\n \" 38.7, 6.3, 22.6, 43.8, 50. , 14.5, 11.7, 12.5, 13.6, 21.5, 19.1,\\n\",\n \" 9.5, 17.2, 29.8, 22.6, 21.6, 22.2, 27.1, 17. , 22. , 50. , 7.2,\\n\",\n \" 50. , 14. , 11. ])\"\n ]\n },\n \"execution_count\": 33,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"ytest\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"17b0418a\",\n \"metadata\": {},\n \"source\": [\n \"# Evaluation\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 39,\n \"id\": \"40e55958\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"0.7555033086871303\"\n ]\n },\n \"execution_count\": 39,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"model.score(xtest, ytest)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"733d9d23\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.12\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n" }, { "path": "March Cohort 2023/19_20_Machine Learning/20_Logistic Regression.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"id\": \"c71657c4\",\n \"metadata\": {},\n \"source\": [\n \"# Logistic Regression\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"f3aa2921\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"y = mx + c\\n\",\n \"\\n\",\n \"s(y) = 1/ 1+ e**(-y)\\n\",\n \"\\n\",\n \"s(mx + c) = 1/ 1+ e**(-(mx + c)) => 0 to 1\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"6513a10e\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \" male female\\n\",\n \" 0 - 0.5 0.5 - 1\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"bed78bb3\",\n \"metadata\": {},\n \"source\": [\n \"## Insurance Prediction using Logistic Regression\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"id\": \"9c2cd6a9\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import pandas as pd\\n\",\n \"import matplotlib.pyplot as plt\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"id\": \"074396bc\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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agebought_insurance
0220
1250
2471
3520
4461
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\"\n ],\n \"text/plain\": [\n \" age bought_insurance\\n\",\n \"0 22 0\\n\",\n \"1 25 0\\n\",\n \"2 47 1\\n\",\n \"3 52 0\\n\",\n \"4 46 1\"\n ]\n },\n \"execution_count\": 2,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df = pd.read_csv(\\\"insurance_data.csv\\\")\\n\",\n \"\\n\",\n \"df.head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"id\": \"706150aa\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 3,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": \"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\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.scatter(df.age, df.bought_insurance, marker='+', color='red')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"id\": \"30fcfbb7\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(27, 2)\"\n ]\n },\n \"execution_count\": 4,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df.shape\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"id\": \"edd0315f\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from sklearn.model_selection import train_test_split\\n\",\n \"\\n\",\n \"xtrain, xtest, ytrain, ytest = train_test_split(df[['age']],\\n\",\n \" df.bought_insurance,\\n\",\n \" train_size=0.9)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"id\": \"bd2f6713\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"LogisticRegression()\"\n ]\n },\n \"execution_count\": 6,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"from sklearn.linear_model import LogisticRegression\\n\",\n \"\\n\",\n \"model = LogisticRegression()\\n\",\n \"\\n\",\n \"model.fit(xtrain, ytrain)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"id\": \"742d8875\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
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\"\n ],\n \"text/plain\": [\n \" age\\n\",\n \"24 50\\n\",\n \"3 52\\n\",\n \"11 28\"\n ]\n },\n \"execution_count\": 7,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"xtest\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"id\": \"1d225ec1\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([1, 1, 0], dtype=int64)\"\n ]\n },\n \"execution_count\": 8,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"ypredictions = model.predict(xtest)\\n\",\n \"\\n\",\n \"ypredictions\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"id\": \"58338281\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[0.10748428, 0.89251572],\\n\",\n \" [0.08108033, 0.91891967],\\n\",\n \" [0.78666634, 0.21333366]])\"\n ]\n },\n \"execution_count\": 9,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"model.predict_proba(xtest)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"id\": \"104a52c5\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from sklearn.metrics import accuracy_score\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"id\": \"ad16e9c5\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"0.6666666666666666\"\n ]\n },\n \"execution_count\": 13,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"accuracy_score(ytest,ypredictions)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"995595c0\",\n \"metadata\": {},\n \"source\": [\n \"# Multi Class Classification\\n\",\n \"\\n\",\n \"## Hand-written Digit Dataset\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"id\": \"9502f54d\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from sklearn.datasets import load_digits\\n\",\n \"\\n\",\n \"digits = load_digits()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"id\": \"664ee90a\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(1797, 64)\"\n ]\n },\n \"execution_count\": 15,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"digits.data.shape\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"id\": \"9f0a2e82\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([0, 1, 2, ..., 8, 9, 8])\"\n ]\n },\n \"execution_count\": 16,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"digits.target\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"id\": \"5aebeeed\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[ 0., 0., 5., ..., 0., 0., 0.],\\n\",\n \" [ 0., 0., 0., ..., 10., 0., 0.],\\n\",\n \" [ 0., 0., 0., ..., 16., 9., 0.],\\n\",\n \" ...,\\n\",\n \" [ 0., 0., 1., ..., 6., 0., 0.],\\n\",\n \" [ 0., 0., 2., ..., 12., 0., 0.],\\n\",\n \" [ 0., 0., 10., ..., 12., 1., 0.]])\"\n ]\n },\n \"execution_count\": 17,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"digits.data\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"id\": \"8fad6b0c\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import numpy as np\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"id\": \"42d489f4\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n 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\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.figure(figsize=(20,4))\\n\",\n \"\\n\",\n \"for index, (image, label) in enumerate(zip(digits.data[0:5], digits.target[0:5])):\\n\",\n \" plt.subplot(1,5, index + 1)\\n\",\n \" plt.imshow(np.reshape(image, (8,8)), cmap=plt.cm.gray)\\n\",\n \" plt.title('%i\\\\n'%label, fontsize=20)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"id\": \"a0e9ee80\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"xtrain, xtest, ytrain, ytest = train_test_split(digits.data,\\n\",\n \" digits.target,\\n\",\n \" train_size=0.75,\\n\",\n \" random_state=101)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"id\": \"89a33411\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"C:\\\\Users\\\\himan\\\\anaconda3\\\\lib\\\\site-packages\\\\sklearn\\\\linear_model\\\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\\n\",\n \"STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\\n\",\n \"\\n\",\n \"Increase the number of iterations (max_iter) or scale the data as shown in:\\n\",\n \" https://scikit-learn.org/stable/modules/preprocessing.html\\n\",\n \"Please also refer to the documentation for alternative solver options:\\n\",\n \" https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\\n\",\n \" n_iter_i = _check_optimize_result(\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"LogisticRegression()\"\n ]\n },\n \"execution_count\": 21,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"model = LogisticRegression()\\n\",\n \"model.fit(xtrain, ytrain)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"id\": \"eb0a5220\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([7])\"\n ]\n },\n \"execution_count\": 22,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"model.predict(xtest[0].reshape(1,-1))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"id\": \"b61ba7da\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([7, 4, 7, 9, 1, 7, 5, 7, 1, 0])\"\n ]\n },\n \"execution_count\": 23,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"model.predict(xtest[0:10])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"id\": \"d4fffa9b\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(450,)\"\n ]\n },\n \"execution_count\": 24,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"predictions = model.predict(xtest)\\n\",\n \"\\n\",\n \"predictions.shape\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"id\": \"4fcfc55f\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"0.9711111111111111\"\n ]\n },\n \"execution_count\": 25,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"model.score(xtest, ytest)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"id\": \"0abeddc9\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \" precision recall f1-score support\\n\",\n \"\\n\",\n \" 0 0.98 1.00 0.99 44\\n\",\n \" 1 0.91 1.00 0.95 52\\n\",\n \" 2 1.00 0.97 0.99 36\\n\",\n \" 3 1.00 0.95 0.97 41\\n\",\n \" 4 1.00 0.94 0.97 53\\n\",\n \" 5 0.96 1.00 0.98 51\\n\",\n \" 6 1.00 0.95 0.97 39\\n\",\n \" 7 1.00 0.98 0.99 46\\n\",\n \" 8 0.98 0.94 0.96 49\\n\",\n \" 9 0.90 0.97 0.94 39\\n\",\n \"\\n\",\n \" accuracy 0.97 450\\n\",\n \" macro avg 0.97 0.97 0.97 450\\n\",\n \"weighted avg 0.97 0.97 0.97 450\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"from sklearn.metrics import classification_report\\n\",\n \"\\n\",\n \"print(classification_report(ytest, predictions))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"e4d6dea7\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"0 - 0.1 0.1 - 0.2 0.2 - 0.3\\n\",\n \" 0 1 2\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 28,\n \"id\": \"3261d864\",\n \"metadata\": {\n \"scrolled\": true\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[[44 0 0 0 0 0 0 0 0 0]\\n\",\n \" [ 0 52 0 0 0 0 0 0 0 0]\\n\",\n \" [ 0 1 35 0 0 0 0 0 0 0]\\n\",\n \" [ 0 0 0 39 0 2 0 0 0 0]\\n\",\n \" [ 0 1 0 0 50 0 0 0 0 2]\\n\",\n \" [ 0 0 0 0 0 51 0 0 0 0]\\n\",\n \" [ 0 1 0 0 0 0 37 0 1 0]\\n\",\n \" [ 0 0 0 0 0 0 0 45 0 1]\\n\",\n \" [ 0 2 0 0 0 0 0 0 46 1]\\n\",\n \" [ 1 0 0 0 0 0 0 0 0 38]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"from sklearn.metrics import confusion_matrix\\n\",\n \"\\n\",\n \"print(confusion_matrix(ytest, predictions))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"776ca3c3\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"gender\\tage\\tincome\\tspending\\n\",\n \"M\\t25\\t100000\\t50000\\n\",\n \"M\\t30\\t150000\\t60000\\n\",\n \"F\\t32\\t165000\\t90000\\n\",\n \"F\\t21\\t200000\\t110000\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 30,\n \"id\": \"4ca7d100\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
genderageincomespending
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\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" gender age income spending\\n\",\n \"0 M 24 50000 30000\\n\",\n \"1 F 25 100000 50000\\n\",\n \"2 M 27 1200000 600000\\n\",\n \"3 F 35 1500000 750000\"\n ]\n },\n \"execution_count\": 30,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df = pd.read_excel(\\\"spend.xlsx\\\")\\n\",\n \"\\n\",\n \"df\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 33,\n \"id\": \"c598a10e\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 33,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"import seaborn as sns\\n\",\n \"\\n\",\n \"sns.countplot(x = 'spending', hue='gender', data=df)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 34,\n \"id\": \"e2d9fe0f\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[]\"\n ]\n },\n \"execution_count\": 34,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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kpFhQ9ZFflcZYVFvOX9/YSFeHipklZ3D49h6EJFhjGmPBiQdHD9hyqZVlhMS+9t5dIl3DjxEzumDGSFAsMY0yYsKA4R0oO1/JIYTF/encvES7hxkszuH3GSFIHxYWkP8YY010WFOdYWVUdjxQW8+L2clwi3HBpBnfMGMnwwRYYxpjeyYIiRMqq6nh0w05e3O6/C8k38jO4c2YuaRYYxphexoIixMqr63hsw05WbvMHxnUTMrhzxkgyPO4Q98wYY/wsKHqJiiP1PLZhJy+8U4ZPlesmpHPXzFwLDGNMyFlQ9DL7jtbz+IadPPdOGT6f8vVL0rh7Zh6ZSRYYxpjQsKDopfYfbeDxjTt5dmspXp/ytfFp3D0zl+wh8aHumjGmn7Gg6OUOHmvg8Y27+MOWElp8ytxxw7lnVh4jLDCMMeeIBUWYOFjTwPKNu/j9lhKaWnzMHZfG3bNyGZk8INRdM8b0cRYUYaayppHfvLGLZzaX0Nji5asXD+eeWbnkDh0Y6q4ZY/ooC4owdeh4e2DUN3v5ykXDWTQrl7wUCwxjzNllQRHmDh9v5LebdvP0W3uoa/ZyzYWpLJqVx+hhFhjGmLPDgqKPqKpt4olNu3jqzT3UNnm55sJhLCrI47xhCaHumjEmzFlQ9DFH6pp4YtNunnxzD8cbW5gz1h8YY4ZbYBhjTo8FRR91tK6ZJ97czZObdlPT2MLsMSksKsjjgrRBoe6aMSbMWFD0cUfrmvndm7v53Zu7qWlo4crzU1hckMeF6RYYxpju6SooXN1sYI+I7BCR90Vkm1PmEZE1IlLkvCYGbP+AiBSLyGcicnVA+QSnnWIRWSoi4pTHiMgLTvkWEckOqDPf+YwiEZl/mv8N+rRB7ij++apRbLpvFv985Si27j7MV5dtYsFT7/Bh+ZFQd88YE+a6FRSOmao6LiBx7gfWqWoesM75HREZA8wDxgJzgEdFJMKp8xiwEMhzfuY45QuAalXNBX4JPOS05QEeBCYBE4EHAwPJBBsUF8XiK/PYdP8svnfVKLaVVPMPy97kO09u5f2yI6HunjEmTJ1KUHQ0F1jhvF8BXBtQ/ryqNqrqbqAYmCgiqUCCqm5W//GupzvUaW3rRaDAmW1cDaxR1SpVrQbW0B4u5gQSYqO4pyCPTffN5PtXj+a9siNc+8ibzP/dVt4trQ5194wxYaa7QaHAf4vIdhFZ6JSlqOo+AOd1qFOeBpQF1C13ytKc9x3Lg+qoagtwFEjqoq0gIrJQRLaJyLbKyspufqW+b2BsFHfNzGXTfbP4wZzRfFh+hK8/+hbfemIL20uqQt09Y0yY6G5QTFHVS4AvAXeJyBVdbCudlGkX5adbp71Adbmq5qtqfnJychdd658GxERy5wx/YNz/pfP4uOIY//jYZr752y28s8cCwxjTtW4FhapWOK8HgZfwrxcccA4n4bwedDYvBzICqqcDFU55eiflQXVEJBIYBFR10ZY5DfExkdw+fSRv3DeTH15zHp/uP8b1j2/mn37zNlt2HQ5194wxvdRJg0JE4kVkYOt7YDbwd2AV0HoW0nzgZef9KmCecybTCPyL1ludw1M1InKZs/5wc4c6rW1dB6x31jFeA2aLSKKziD3bKTNnwB0dycIrRvLGD2bx4y+fz+cHjnPD8reZt3wzm3daYBhjgp30OgoRycE/iwCIBJ5V1Z+ISBKwEsgESoHrVbXKqfMj4BagBbhXVVc75fnAU0AcsBq4R1VVRGKBZ4Dx+GcS81R1l1PnFuCHzuf/RFWf7Kq//fE6ijNV3+Tl2a2lPL5xJ5U1jUwc4eHegjwmj0zCOYPZGNPH2QV3plsamr085wTGgWONXJqdyOKCUUzJtcAwpq+zoDCnpKHZywvvlPHYhp3sP9bAhKxEFhfkMS1viAWGMX2UBYU5LY0tXla+U8ajG3ay72gD4zMHs7ggj+mjki0wjOljLCjMGWls8fLHbeU8tmEne4/UMy7DHxgzRltgGNNXWFCYs6KpxceL28t5pLCYvUfquTh9EIsK8ph13lALDGPCnAWFOauaWnz8+d1ylhUWU15dz4Vp/sC48nwLDGPClQWF6RHNXh8vvbuXZYXFlFbVMXZ4AosK8pg9JsUCw5gwY0FhelSz18fL71ewbH0Rew7XcX5qAosLcpk9ZhgulwWGMeHAgsKcEy1eH6s+qGDZ+mJ2HarlvGEDWVSQx5yxFhjG9HYWFOac8vqUv35QwdL1ReyqrGV0ykDuKcjlmgtSLTCM6aUsKExIeH3KKx9W8PD6YooPHidv6ADuKcjjyxemEmGBYUyvYkFhQsrrU17dsY+l64ooOnic3KEDuGdWLl+5aLgFhjG9hAWF6RV8PmX13/ezdF0Rnx2oISc5nntm5fLVi4YTGXEmD1s0xpwpCwrTq/h8ymsf7WfJuiI+3V/DiCHx3D0zl7njLDCMCRULCtMr+XzKf398gKXrivh43zGyk9zcNTOXr41Ps8Aw5hyzoDC9mqqy5uMDLFlXxEcVx8j0uLl7Zi5fuySNKAsMY84JCwoTFlSVdZ8cZMm6InbsPUqGJ467ZuTyjxPSLTCM6WEWFCasqCqFnx1kydoiPig/StrgOO6amct1E9KJjrTAMKYnWFCYsKSqbPi8kiVri3i/7Ahpg+O4Y8ZIrs9PJyYyItTdM6ZP6Soour17JiIRIvKeiLzi/O4RkTUiUuS8JgZs+4CIFIvIZyJydUD5BBHZ4fxtqTh3jhORGBF5wSnfIiLZAXXmO59RJCLzT+P7mzAlIswcPZSX7rycFbdMJCUhhh//5e/M/PkGntm8h8YWb6i7aEy/cCrz+MXAJwG/3w+sU9U8YJ3zOyIyBpgHjAXmAI+KSOvu32PAQiDP+ZnjlC8AqlU1F/gl8JDTlgd4EJgETAQeDAwk0z+ICNNHJfOnOy7nmQUTGT44jv/58kdM/9kGVry1h4ZmCwxjelK3gkJE0oEvA78NKJ4LrHDerwCuDSh/XlUbVXU3UAxMFJFUIEFVN6v/eNfTHeq0tvUiUODMNq4G1qhqlapWA2toDxfTz4gI0/KS+ePtk/nDrZPI9Lh5cNVHTP95IU++udsCw5ge0t0Zxa+AHwC+gLIUVd0H4LwOdcrTgLKA7cqdsjTnfcfyoDqq2gIcBZK6aCuIiCwUkW0isq2ysrKbX8mEKxFhSu4QXvjuZTx72ySyk+L5t79+zBU/K+SJTRYYxpxtJw0KEfkKcFBVt3ezzc5u3qNdlJ9unfYC1eWqmq+q+cnJyd3spgl3IsLlI4fwwncn89xtl5GTHM9/vPIxUx8q5Ldv7KK+yQLDmLOhOzOKKcA/iMge4Hlgloj8HjjgHE7CeT3obF8OZATUTwcqnPL0TsqD6ohIJDAIqOqiLWOCTB6ZxPMLJ/PCwssYlTKA//x/nzDtZ+tZ/vpO6ppaQt09Y8LaSYNCVR9Q1XRVzca/SL1eVb8JrAJaz0KaD7zsvF8FzHPOZBqBf9F6q3N4qkZELnPWH27uUKe1reucz1DgNWC2iCQ6i9iznTJjOjUpJ4lnb7uMP94+mfOGJfC/X/2UaQ8V8vjGndQ2WmAYczoiz6DuT4GVIrIAKAWuB1DVj0RkJfAx0ALcpaqtxwDuAJ4C4oDVzg/AE8AzIlKMfyYxz2mrSkT+A3jH2e7fVbXqDPps+olLsz38/tZJbC+p4ldri/jp6k9Z/voubp02gpsnZzMg5kz+6RvTv9gFd6ZfeLe0miVri9j4eSWJ7ihunZbDzZOzGBgbFequGdMr2JXZxjjeLzvCkrWfU/hZJYPdUSyYMoJvT8m2wDD9ngWFMR18UHaEpeuKWPfpQRJiI1kwNYfvTM0mwQLD9FMWFMacwI7yoyxZV8TaTw4wMDaSW6aM4JapIxgUZ4Fh+hcLCmNO4u97j7J0XRH//bE/ML4zZQQLpoxgkNsCw/QPFhTGdNPHFcdYuq6Iv320n4ExkXx7SjYLpo5gsDs61F0zpkdZUBhzij7Zd4yH1xfx6o79DIiJZP7lWdw6NYfEeAsM0zdZUBhzmj7bX8PS9UW8umMf7qgIbr48m9um5eCxwDB9jAWFMWeo6EANS9cX88qHFcRFRfCtyVksnJZD0oCYUHfNmLPCgsKYs6T4YA0Pry/mrx9UEBPpD4zbpuWQPNACw4Q3CwpjzrKdlcdZtr6Yl9/fS3Ski29OymLh9ByGDowNddeMOS0WFMb0kF2Vx1lWWMzL71cQ6RJumpTF7dNzGJpggWHCiwWFMT1sz6FalhUW89J7e4l0CTdOzOSOGSNJscAwYcKCwphzpORwLY8UFvPnd/ficgk3XprB7TNGkjooLtRdM6ZLFhTGnGNlVXU8UljMi9vLcYlww6UZ3DFjJMMHW2CY3smCwpgQKauq49ENO3lxu//R79/Iz+DOmbmkWWCYXsaCwpgQ23uknkcLi1m5zR8Y103I4M4ZI8nwuEPcM2P8LCiM6SUqjtTz2IadvPBOGT5VrpuQzl0zcy0wTMhZUBjTy+w7Ws/jG3by3Dtl+HzK1y9J4+6ZeWQmWWCY0LCgMKaXOnCsgcc27OS5raW0+JSvjU/j7pm5ZA+JD3XXTD/TVVC4ulE5VkS2isgHIvKRiPybU+4RkTUiUuS8JgbUeUBEikXkMxG5OqB8gojscP62VETEKY8RkRec8i0ikh1QZ77zGUUiMv8M/jsY0+ukJMTyr/8wljd+MJP5k7P56wcVFPxiI/+y8n12H6oNdfeMAboxo3AG83hVPS4iUcAmYDHwdaBKVX8qIvcDiap6n4iMAZ4DJgLDgbXAKFX1ishWp+7bwKvAUlVdLSJ3Ahep6u0iMg/4mqreICIeYBuQDyiwHZigqtUn6q/NKEw4O1jTwPKNu/j9lhKaWnzMHZfG3bNyGZk8INRdM33cGc0o1O+482uU86PAXGCFU74CuNZ5Pxd4XlUbVXU3UAxMFJFUIEFVN6s/nZ7uUKe1rReBAiegrgbWqGqVEw5rgDnd+9rGhJ+hA2P58VfG8MYPZnHrtBz+9vf9XPWLjSx+/j2KD9aEunumnzppUACISISIvA8cxD9wbwFSVHUfgPM61Nk8DSgLqF7ulKU57zuWB9VR1RbgKJDURVsd+7dQRLaJyLbKysrufCVjerXkgTH88JrzeeO+mdx2RQ5rPj7AVb98nXuee4+iAxYY5tzqVlCoqldVxwHp+GcHF3SxuXTWRBflp1snsH/LVTVfVfOTk5O76Jox4WXIgBge+NL5bLpvFrdPH8n6Tw4w+1evc9ez7/LZfgsMc250KyhaqeoRYAP+wz8HnMNJOK8Hnc3KgYyAaulAhVOe3kl5UB0RiQQGAVVdtGVMv+KJj+a+Oeex6b5Z3DljJBs/q+TqX73OnX/Yzqf7j4W6e6aP685ZT8kiMth5HwdcCXwKrAJaz0KaD7zsvF8FzHPOZBoB5AFbncNTNSJymbP+cHOHOq1tXQesd9YxXgNmi0iic1bVbKfMmH4pMT6a7199Hpvum8k9s3J54/NDzPnVG9z+zHY+rrDAMD0jshvbpAIrRCQCf7CsVNVXRGQzsFJEFgClwPUAqvqRiKwEPgZagLtU1eu0dQfwFBAHrHZ+AJ4AnhGRYvwziXlOW1Ui8h/AO852/66qVWfyhY3pCwa7o/ne7NHcOjWHJ97czZNv7uZvH+1n9pgUFhXkcUHaoFB30fQhdsGdMX3A0fpmnnxzN7/btJtjDS1ceX4KiwvyuDDdAsN0j12ZbUw/cayhmafe3MMTm3ZztL6ZgvOGsvjKPC5KHxzqrplezoLCmH6mpqGZFW/t4bebdnOkrpmZo5NZfOUoxmUMDnXXTC9lQWFMP3W8scUfGG/sorqumemjkll8ZR6XZCaevLLpVywojOnnjje28MzmEn7zxi6qapuYljeEe6/MY0KWJ9RdM72EBYUxBoDaxhZ+/3YJy1/fxeHaJqbmDmHxlXlcmm2B0d9ZUBhjgtQ1tfCHt0v59es7OXS8ictHJrG4II9JOUmh7poJEQsKY0yn6pu8/GFLCb9+fReVNY1cluNhccEoJo+0wOhvLCiMMV1qaPby7JZSHt+4k4M1jUwc4eHegjwmj0zCeWyM6eMsKIwx3dLQ7OX5raU8tnEnB441cml2IosLRjEl1wKjr7OgMMackoZmLyu3lfFo4U72H2tgQlYiiwvymJY3xAKjj7KgMMaclsYWLyu3lfNYYTEVRxsYnzmYxQV5TB+VbIHRx1hQGGPOSGOLlxe3l/No4U72HqlnXIY/MGaMtsDoKywojDFnRVOLjz+9W84jhcWUV9dzcfogFhXkMeu8oRYYYc6CwhhzVjV7ffz53XKWFRZTVlXPhWn+wLjyfAuMcGVBYYzpEc1eHy+9t5dHCospOVzH2OEJLCrIY/aYFAuMMGNBYYzpUS1eH395v4Jl64vYc7iO81MTWFyQy+wxw3C5LDDCgQWFMeacaPH6WPVBBcvWF7PrUC3nDRvIooI85oy1wOjtLCiMMeeU16f89YMKlq4vYldlLaNTBnJPQS7XXJBqgdFLdRUUrm5UzhCRQhH5REQ+EpHFTrlHRNaISJHzmhhQ5wERKRaRz0Tk6oDyCSKyw/nbUnEOYopIjIi84JRvEZHsgDrznc8oEpH5Z/DfwRhzjkS4hGvHp7Hmn6ezZN44vKrc/ex7XP2r11n1QQVeX9/aQe3rThoUQAvwPVU9H7gMuEtExgD3A+tUNQ9Y5/yO87d5wFhgDvCoiEQ4bT0GLATynJ85TvkCoFpVc4FfAg85bXmAB4FJwETgwcBAMsb0bhEuYe64NF679woevnE8AIue8wfGy+/vtcAIEycNClXdp6rvOu9rgE+ANGAusMLZbAVwrfN+LvC8qjaq6m6gGJgoIqlAgqpuVv/xrqc71Glt60WgwJltXA2sUdUqVa0G1tAeLsaYMBHhEr568XBeu/cKHvmnS4gQYfHz73PVLzfy0nvltHh9oe6i6UJ3ZhRtnENC44EtQIqq7gN/mABDnc3SgLKAauVOWZrzvmN5UB1VbQGOAkldtNWxXwtFZJuIbKusrDyVr2SMOYdcLuHLF6WyevE0HrvpEqIjXPzzCx9w1S9f50/bLTB6q24HhYgMAP4E3Kuqx7ratJMy7aL8dOu0F6guV9V8Vc1PTk7uomvGmN7A5RK+dGEqry6axuPfnEBcVATf++MHXPmLjfxxW5kFRi/TraAQkSj8IfEHVf2zU3zAOZyE83rQKS8HMgKqpwMVTnl6J+VBdUQkEhgEVHXRljGmD3C5hDkXDOP/LZrK8m9NID4mku+/+CGz/u9GVr5TRrMFRq/QnbOeBHgC+ERVfxHwp1VA61lI84GXA8rnOWcyjcC/aL3VOTxVIyKXOW3e3KFOa1vXAeuddYzXgNkikugsYs92yowxfYiIMHvsMF65Zyq/vTmfQXFR/OBPHzLr/27g+a2lFhghdtLrKERkKvAGsANo/V/rh/jXKVYCmUApcL2qVjl1fgTcgv+MqXtVdbVTng88BcQBq4F7VFVFJBZ4Bv/6RxUwT1V3OXVucT4P4Ceq+mRX/bXrKIwJf6pK4WcHWbK2iA/Kj5I2OI67ZuZy3YR0oiNPaWnVdJNdcGeMCUuqyobPK1mytoj3y46QNjiOO2aM5Pr8dGIiI07egOk2CwpjTFhTVV4vOsSStZ/zbukRhg+K5Y4ZI/nGpRkWGGeJBYUxpk9QVTYVH2LJ2iK2lVQzLMEfGDdcmkFslAXGmbCgMMb0KarKWzsPs2RtEVv3VJGSEMPt00dy48RMC4zTZEFhjOmTVJXNu/yBsWV3FUMHxvDd6SO5aZIFxqmyoDDG9Hmbdx5mybrPeXtXFUMGxHD79BxumpRFXLQFRndYUBhj+o0tuw6zZF0Rb+08zJAB0Sy8IodvXpaFOzoy1F3r1SwojDH9zjt7qliytohNxYdIio/mtity+NZlWcTHWGB0xoLCGNNvbS+p4ldri3ij6BCe+GhunTaCmydnM8ACI4gFhTGm33u3tJola4vY+Hklie4obp2Ww82TsxgYGxXqrvUKFhTGGON4v+wIS9Z+TuFnlQx2R7Fgygi+PSW73weGBYUxxnTwQdkRlq4rYt2nB0mIjWTB1By+MzWbhH4aGBYUxhhzAjvKj7JkXRFrPznAwNhIbpkyglumjmBQXP8KDAsKY4w5ib/vPcrSdUX898f+wPjOlBEsmDKCQe7+ERgWFMYY000fVxxj6boi/vbRfgbGRPLtKdksmDqCwe7oUHetR1lQGGPMKfpk3zEeXl/Eqzv2MyAmkvmXZ3Hr1BwS4/tmYFhQGGPMafpsfw1L1xfx6o59uKMiuPnybG6bloOnjwWGBYUxxpyhogM1LF1fzCsfVhAXFcG3JmexcFoOSQNiQt21s8KCwhhjzpLigzU8vL6Yv35QQUykPzBum5ZD8sDwDgwLCmOMOct2Vh5n2fpiXn5/L9GRLr45KYuF03MYOjA21F07LV0FxUmfUi4ivxORgyLy94Ayj4isEZEi5zUx4G8PiEixiHwmIlcHlE8QkR3O35aKiDjlMSLyglO+RUSyA+rMdz6jSETmn+b3N8aYs25k8gB+ecM41v7LdK65MJUn39rDtIcK+fe/fszBYw2h7t5ZddKgAJ4C5nQoux9Yp6p5wDrnd0RkDDAPGOvUeVREWm8G/xiwEMhzflrbXABUq2ou8EvgIactD/AgMAmYCDwYGEjGGNMb5CQP4BffGMe6f5nOVy8ezorNe5j2s0L+ddVHHOgjgXHSoFDV14GqDsVzgRXO+xXAtQHlz6tqo6ruBoqBiSKSCiSo6mb1H+t6ukOd1rZeBAqc2cbVwBpVrVLVamANXwwsY4zpFbKHxPNf11/M+u9NZ+644fz+7RKm/ayQB1/+O/uO1oe6e2ekOzOKzqSo6j4A53WoU54GlAVsV+6UpTnvO5YH1VHVFuAokNRFW18gIgtFZJuIbKusrDzNr2SMMWcuKymen113MYX/YwZfH5/GH7aUMv1nG/iff/k7FUfCMzBONyhORDop0y7KT7dOcKHqclXNV9X85OTkbnXUGGN6UobHzU//8SIK/8cM/nFCOs+/U8r0nxfyo5d2sDfMAuN0g+KAczgJ5/WgU14OZARslw5UOOXpnZQH1RGRSGAQ/kNdJ2rLGGPCRobHzf/5+oVs+P5MvpGfwcptZcz4eSEP/HkHZVV1oe5et5xuUKwCWs9Cmg+8HFA+zzmTaQT+ReutzuGpGhG5zFl/uLlDnda2rgPWO+sYrwGzRSTRWcSe7ZQZY0zYSRscx0++diEbvz+TeZdm8qft5cz8rw3c/6cPe31gnPQ6ChF5DpgBDAEO4D8T6S/ASiATKAWuV9UqZ/sfAbcALcC9qrraKc/HfwZVHLAauEdVVURigWeA8fhnEvNUdZdT5xbgh05XfqKqT57sC9l1FMaYcLDvaD2/3riLZ7eW4vMpX78kjbtn5pGZ5A5Jf+yCO2OM6aUOHGvg8Y07eXZLKS0+5Wvj07h7Zi7ZQ+LPaT8sKIwxppc7eKyBX7++iz9sKaHZq8wdN5x7ZuUx4hwFhgWFMcaEiYM1Dfzm9V0883YJTS0+5o5L4+5ZuYxMHtCjn2tBYYwxYebQ8UZ+8/ount5cQmOLl69ePJx7ZuWSO3Rgj3yeBYUxxoSpw8cb+c0bu3l68x7qm7185aLhLJqVS17K2Q0MCwpjjAlzVbVN/PaNXax4aw91zV6uuTCVRbPyGD3s7ASGBYUxxvQR1bVNPLFpN0+9tYfjjS1cc+EwFhXkcd6whDNqt6ugiDyjlo0xxpwxr09p9vrw+pQWr9Ls8wWVNXuVFp+PFq/S4lNmjE7mgrRB/OaNXby6Yz+v7tjPnLHD+MGc0eT0wKK3BYUxptdT9Q+QLQEDZutg2jp4tnh9NHvVP7C2Dar+19ZBt8XXvm3H9lp87fVbArYNHKy9bZ/tf9/sDW4r+LMD2+lQ1vre5//72Tiw87eP9hPhEh656ZIzb6wDCwpj+gjfCQep4MGsbc81YNuOe66BA2PQYPiFQfbEg2GzT/F2GIDbPsfZJmgA7/DZgZ/p9Z37Q+SRLiEyQohyuYiIECJdLqIihAiXEBXhIsIlRAa8j3K2iYmKJNIlRDjbR0a4/G057UW6XM6r/29RzrZBZa2f43I+J+KLn9O6bWBZT11zYUFh+g1VZ2+xiyl9+8DYvrfX4j3BINhhDzRwb9br7TgYBrbXebuBhx5atw3ciw3+bGeADfjMc73c6BK+MOgFDpxt5QEDbGSEi5goF25X6wDp3z4ycGCMEKIC2ovsMDC2lQUMwIGDaOug2zrIBg7ugX3qbNBubS/CJTgP4TRYUJgOfK17jZ1NwzscOw0cvAL3Ejub0n9xb7Z9m6A916C92Q6f0+FwQqdT+g4DbcfPPNc63YvsbHAKGgyF+KhIZ4+1vSx4MAwcgF3te5kR7QNddwbG1sE6sB9tn9Nh2457sy6XDaT9hQXFKQrcK+3qWGfHQarjsdPAgbF90Otw/PIkU/rAY7SBU/rAwwVf3JvtcLy2Q//P9VgqQtuAFDil77hH2T4Y+geq6EgX7m5O6QP3ZqNc4uyxBgx8J5jSdzXIt+25fmFv1hmgba/U9CEWFI7q2iau//XmTs80aB2UWwfrc62zY6HBe5SB0/32vUR3dGTQANfdKX3g8dfgY6bte66dDYyRQX/rxp6ry2V7pcaEAQsKR1Ski1EpA/yD4Rf2YgP3KJ3BrptT+sDjtSea0nd26CGwPdsrNcaEkgWFY0BMJI/eNCHU3TDGmF7nbD8z2xhjTB9jQWGMMaZLFhTGGGO6FBZBISJzROQzESkWkftD3R9jjOlPen1QiEgE8AjwJWAMcKOIjAltr4wxpv/o9UEBTASKVXWXqjYBzwNzQ9wnY4zpN8IhKNKAsoDfy52yNiKyUES2ici2ysrKc9o5Y4zp68IhKDq72izo8mhVXa6q+aqan5ycfI66ZYwx/UM4XHBXDmQE/J4OVJxo4+3btx8SkZIz+LwhwKEzqG+MMaFyJuNX1on+0OsfhSoikcDnQAGwF3gH+CdV/aiHPm/biR4HaIwxvVlPjV+9fkahqi0icjfwGhAB/K6nQsIYY8wX9fqgAFDVV4FXQ90PY4zpj8JhMftcWx7qDhhjzGnqkfGr169RGGOMCS2bURhjjOmSBYUxxpgu9fugEBEVkWcCfo8UkUoReSWU/TLGmO4SEa+IvB/wk3022w+Ls556WC1wgYjEqWo9cBX+6zWMMSZc1KvquJ5qvN/PKByrgS87728EngthX4wxplexoPB7HpgnIrHARcCWEPfHGGNORVzAYaeXznbjdugJUNUPnWN6N2IX9hljwk+PHnqyoGi3CvgvYAaQFNquGGNM72FB0e53wFFV3SEiM0LcF2OM6TUsKByqWg4sCXU/jDGmt7FbeBhjjOmSnfVkjDGmSxYUxhhjumRBYYwxpksWFMYYY7pkQWGMMaZLFhTGGGO6ZEFhjDGmS/8fB/88XJW7kbwAAAAASUVORK5CYII=\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.plot(df['gender'], df['spending'], label='male')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 35,\n \"id\": \"aa1d58ba\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 35,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.bar(df['gender'], df['spending'])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 38,\n \"id\": \"33238bde\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 38,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.bar(df['gender'], df['spending'], label='spending')\\n\",\n \"plt.bar(df['gender'], df['income'], label='income')\\n\",\n \"\\n\",\n \"plt.legend()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"ffd472db\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.12\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n" }, { "path": "March Cohort 2023/19_20_Machine Learning/areas.csv", "content": "area\n1000\n1500\n2300\n3540\n4120\n4560\n5490\n3460\n4750\n2300\n9000\n8600\n7100\n" }, { "path": "March Cohort 2023/19_20_Machine Learning/homeprices.csv", "content": "area,price\n2600,550000\n3000,565000\n3200,610000\n3600,680000\n4000,725000\n" }, { "path": "March Cohort 2023/19_20_Machine Learning/insurance_data.csv", "content": "age,bought_insurance\n22,0\n25,0\n47,1\n52,0\n46,1\n56,1\n55,0\n60,1\n62,1\n61,1\n18,0\n28,0\n27,0\n29,0\n49,1\n55,1\n25,1\n58,1\n19,0\n18,0\n21,0\n26,0\n40,1\n45,1\n50,1\n54,1\n23,0" }, { "path": "March Cohort 2023/1_While Loops and Logic Building.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"id\": \"dfbc28de\",\n \"metadata\": {},\n \"source\": [\n \"# While Loop\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"id\": \"c1f425e3\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Himanshu\\n\",\n \"Himanshu\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(\\\"Himanshu\\\")\\n\",\n \"print(\\\"Himanshu\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"id\": \"55a46f9f\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"True\\n\",\n \"False\\n\"\n ]\n }\n ],\n \"source\": [\n \"i = 1\\n\",\n \"\\n\",\n \"print(i <= 5)\\n\",\n \"\\n\",\n \"print(i >= 5)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"id\": \"7a204214\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Himanshu\\n\",\n \"Himanshu\\n\",\n \"Himanshu\\n\",\n \"Himanshu\\n\",\n \"Himanshu\\n\"\n ]\n }\n ],\n \"source\": [\n \"i = 1\\n\",\n \"\\n\",\n \"while i <= 5 :\\n\",\n \" print(\\\"Himanshu\\\")\\n\",\n \" i = i + 1 # i = 1, 2, 3, 4, 5, 6\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"id\": \"b5ef4f72\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Himanshu\\n\",\n \"Himanshu\\n\",\n \"Himanshu\\n\",\n \"Himanshu\\n\",\n \"Himanshu\\n\",\n \"Himanshu\\n\",\n \"Himanshu\\n\",\n \"Himanshu\\n\",\n \"Himanshu\\n\",\n \"Himanshu\\n\"\n ]\n }\n ],\n \"source\": [\n \"i = 1\\n\",\n \"\\n\",\n \"while i <= 10 :\\n\",\n \" print(\\\"Himanshu\\\")\\n\",\n \" i = i + 1\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"id\": \"a3fc06bd\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"1\\n\",\n \"2\\n\",\n \"3\\n\",\n \"4\\n\",\n \"5\\n\"\n ]\n }\n ],\n \"source\": [\n \"i = 1\\n\",\n \"\\n\",\n \"while i <= 5 :\\n\",\n \" print(i)\\n\",\n \" i = i + 1\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"2b2a54cb\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"1\\n\",\n \"2\\n\",\n \"3\\n\",\n \"4\\n\",\n \"5\\n\",\n \"6\\n\",\n \"7\\n\",\n \"8\\n\",\n \"9\\n\",\n \"10\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"id\": \"9b83ffab\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"1\\n\",\n \"2\\n\",\n \"3\\n\",\n \"4\\n\",\n \"5\\n\",\n \"6\\n\",\n \"7\\n\",\n \"8\\n\",\n \"9\\n\",\n \"10\\n\"\n ]\n }\n ],\n \"source\": [\n \"i = 1\\n\",\n \"\\n\",\n \"while i <= 10:\\n\",\n \" print(i)\\n\",\n \" i = i + 1\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"id\": \"9522e499\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Hello\\n\",\n \"World\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(\\\"Hello\\\")\\n\",\n \"print(\\\"World\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"id\": \"dd5d35e6\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Hello\\n\",\n \"World\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(\\\"Hello\\\", end='\\\\n')\\n\",\n \"print(\\\"World\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"id\": \"c54de4e2\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Hello+World\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(\\\"Hello\\\", end='+')\\n\",\n \"print(\\\"World\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"id\": \"54a31434\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Hello World\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(\\\"Hello\\\", end=' ')\\n\",\n \"print(\\\"World\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"id\": \"b4f03f6d\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"HelloWorld\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(\\\"Hello\\\", end='')\\n\",\n \"print(\\\"World\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"id\": \"2a48a921\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Hello@World\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(\\\"Hello\\\", end='@')\\n\",\n \"print(\\\"World\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"id\": \"1357e71d\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Hello@World$\"\n ]\n }\n ],\n \"source\": [\n \"print(\\\"Hello\\\", end='@')\\n\",\n \"print(\\\"World\\\", end='$')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"f48834ba\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"1 2 3 4 5 6 7 8 9 10\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"id\": \"1c74034c\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"1 2 3 4 5 6 7 8 9 10 \"\n ]\n }\n ],\n \"source\": [\n \"i = 1\\n\",\n \"\\n\",\n \"while i <= 10:\\n\",\n \" print(i, end=' ')\\n\",\n \" i = i + 1\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"0649f1ee\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"1 4 9 16 25 36 49 64 81 100\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"id\": \"9bb2cd28\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"30\\n\",\n \"10\\n\"\n ]\n }\n ],\n \"source\": [\n \"a = 10\\n\",\n \"\\n\",\n \"print(a*3)\\n\",\n \"\\n\",\n \"print(a)\\n\",\n \"\\n\",\n \"# a = a * 3\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"id\": \"67f5a9b8\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"1 4 9 16 25 36 49 64 81 100 \"\n ]\n }\n ],\n \"source\": [\n \"i = 1\\n\",\n \"\\n\",\n \"while i <= 10:\\n\",\n \" print(i*i, end=' ')\\n\",\n \" i = i + 1\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"2ad2e158\",\n \"metadata\": {},\n \"source\": [\n \"**Write a program to enter 5 values from user and print them.**\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"id\": \"aeee4fe6\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Enter your number: 10\\n\",\n \"10\\n\",\n \"Enter your number: 20\\n\",\n \"20\\n\",\n \"Enter your number: 30\\n\",\n \"30\\n\",\n \"Enter your number: 40\\n\",\n \"40\\n\",\n \"Enter your number: 50\\n\",\n \"50\\n\"\n ]\n }\n ],\n \"source\": [\n \"i = 1\\n\",\n \"\\n\",\n \"while i <= 5:\\n\",\n \" a = input(\\\"Enter your number: \\\")\\n\",\n \" \\n\",\n \" print(a)\\n\",\n \" \\n\",\n \" i = i + 1\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"3773b36f\",\n \"metadata\": {},\n \"source\": [\n \"**Write a program to enter a number from user and print reverse of it.**\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"34362203\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"153\\n\",\n \"\\n\",\n \"351\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"id\": \"3dcb3a2f\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"1\"\n ]\n },\n \"execution_count\": 20,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"7 % 2 # return reminder\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"id\": \"42cfdbf1\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"3.5\"\n ]\n },\n \"execution_count\": 21,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"7 / 2\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"id\": \"885d87d3\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"3\"\n ]\n },\n \"execution_count\": 22,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"7 // 2\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"id\": \"d7cbb652\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"3\"\n ]\n },\n \"execution_count\": 24,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"153 % 10\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"59f14345\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \" x % 10 = z\\n\",\n \"153 % 10 = 3\\n\",\n \"153 // 10 = 15\\n\",\n \"\\n\",\n \"15 % 10 = 5\\n\",\n \"15 // 10 = 1\\n\",\n \"\\n\",\n \"1 % 10 = 1\\n\",\n \"1 // 10 = 0\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"id\": \"f759b069\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Enter a number: 10\\n\",\n \"10\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"n = int(input(\\\"Enter a number: \\\"))\\n\",\n \"\\n\",\n \"print(n)\\n\",\n \"\\n\",\n \"print(type(n))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 28,\n \"id\": \"6fc34b04\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Enter a Number: 12345\\n\",\n \"54321\"\n ]\n }\n ],\n \"source\": [\n \"n = int(input(\\\"Enter a Number: \\\"))\\n\",\n \"\\n\",\n \"while n > 0:\\n\",\n \" r = n % 10\\n\",\n \" print(r, end='') # 351\\n\",\n \" n = n // 10 # n = 153, 15, 1\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"5d585434\",\n \"metadata\": {},\n \"source\": [\n \"**Write a program to enter a number from user and print sum of its individual digits**\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"aaffcb41\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"153\\n\",\n \"\\n\",\n \"1 + 5 + 3\\n\",\n \"\\n\",\n \"9\\n\",\n \"\\n\",\n \"\\n\",\n \"0 s\\n\",\n \"3 s + 3 = 3\\n\",\n \"5 s + 5 = 8\\n\",\n \"1 s + 1 = 9\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 30,\n \"id\": \"1b2f5fbb\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Enter a Number: 2345\\n\",\n \"Sum = 14\\n\"\n ]\n }\n ],\n \"source\": [\n \"n = int(input(\\\"Enter a Number: \\\"))\\n\",\n \"s = 0\\n\",\n \"\\n\",\n \"while n > 0:\\n\",\n \" r = n % 10\\n\",\n \" s = s + r # s = 0, 3, 8, 9\\n\",\n \" n = n // 10\\n\",\n \"\\n\",\n \"print(\\\"Sum =\\\", s)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"88e40510\",\n \"metadata\": {},\n \"source\": [\n \"**Write a program to enter a number from user and check if it palindrome or not**\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"643c209d\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"121\\n\",\n \"\\n\",\n \"121\\n\",\n \"\\n\",\n \"153 = 351 not a palindrome\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 34,\n \"id\": \"432cbedf\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Enter a Number: 153\\n\",\n \"351\\n\",\n \"702\\n\"\n ]\n }\n ],\n \"source\": [\n \"n = int(input(\\\"Enter a Number: \\\"))\\n\",\n \"s = 0\\n\",\n \"\\n\",\n \"while n > 0:\\n\",\n \" r = n % 10\\n\",\n \" s = s * 10 + r # s = 0, 3, 35, 351\\n\",\n \" n = n // 10\\n\",\n \"\\n\",\n \"print(s)\\n\",\n \"print(s * 2)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 40,\n \"id\": \"59333ae3\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Enter a Number: 121\\n\",\n \"Palindrome\\n\"\n ]\n }\n ],\n \"source\": [\n \"n = int(input(\\\"Enter a Number: \\\"))\\n\",\n \"t = n\\n\",\n \"s = 0\\n\",\n \"\\n\",\n \"while n > 0:\\n\",\n \" r = n % 10\\n\",\n \" s = s * 10 + r # s = 0, 3, 35, 351\\n\",\n \" n = n // 10\\n\",\n \"\\n\",\n \"if s == t:\\n\",\n \" print(\\\"Palindrome\\\")\\n\",\n \"else:\\n\",\n \" print(\\\"Not a Palindrome\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 38,\n \"id\": \"dc812c1e\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"False\"\n ]\n },\n \"execution_count\": 38,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"10 == 11\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"28edc5f6\",\n \"metadata\": {},\n \"source\": [\n \"**Write a program to enter a number from user and check if it is armstrong or not**\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"b78ef4cd\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"153\\n\",\n \"\\n\",\n \"cube(1) + cube(5) + cube(3) = 153\\n\",\n \"\\n\",\n \"370\\n\",\n \"\\n\",\n \"371\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 44,\n \"id\": \"de686ea3\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Enter a Number: 371\\n\",\n \"Armstrong Number\\n\"\n ]\n }\n ],\n \"source\": [\n \"n = int(input(\\\"Enter a Number: \\\"))\\n\",\n \"t = n\\n\",\n \"s = 0\\n\",\n \"\\n\",\n \"while n > 0:\\n\",\n \" r = n % 10\\n\",\n \" s = s + r*r*r\\n\",\n \" n = n // 10\\n\",\n \"\\n\",\n \"if s == t:\\n\",\n \" print(\\\"Armstrong Number\\\")\\n\",\n \"else:\\n\",\n \" print(\\\"Not an Armstrong Number\\\")\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"b4fc183f\",\n \"metadata\": {},\n \"source\": [\n \"**Write a program to enter a number from user and print its individual digits on seperate line.**\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"75092737\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"153\\n\",\n \"\\n\",\n \"1\\n\",\n \"5\\n\",\n \"3\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 45,\n \"id\": \"56d6ec0d\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Enter a Number: 153\\n\",\n \"3\\n\",\n \"5\\n\",\n \"1\\n\"\n ]\n }\n ],\n \"source\": [\n \"n = int(input(\\\"Enter a Number: \\\"))\\n\",\n \"s = 0\\n\",\n \"\\n\",\n \"while n > 0:\\n\",\n \" r = n % 10\\n\",\n \" print(r)\\n\",\n \" n = n // 10\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 47,\n \"id\": \"e13e7967\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Enter a Number: 12345\\n\",\n \"1\\n\",\n \"2\\n\",\n \"3\\n\",\n \"4\\n\",\n \"5\\n\"\n ]\n }\n ],\n \"source\": [\n \"n = int(input(\\\"Enter a Number: \\\"))\\n\",\n \"s = 0\\n\",\n \"\\n\",\n \"while n > 0:\\n\",\n \" r = n % 10\\n\",\n \" s = s * 10 + r\\n\",\n \" n = n // 10\\n\",\n \"\\n\",\n \"# s = 351\\n\",\n \"\\n\",\n \"while s > 0:\\n\",\n \" r = s % 10\\n\",\n \" print(r)\\n\",\n \" s = s // 10\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"4864ff19\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.12\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n" }, { "path": "March Cohort 2023/21_EDA and Data Viz/.ipynb_checkpoints/21_EDA and Data Visualization-checkpoint.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"id\": \"343be340\",\n \"metadata\": {},\n \"source\": [\n \"# EDA\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"id\": \"6d58db2b\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import numpy as np\\n\",\n \"import pandas as pd\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"id\": \"cb96b2f2\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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TrackComposerMillisecondsBytesUnitPriceGenreAlbumArtist
0For Those About To Rock (We Salute You)Angus Young, Malcolm Young, Brian Johnson343719111703340.99RockFor Those About To Rock We Salute YouAC/DC
1Put The Finger On YouAngus Young, Malcolm Young, Brian Johnson20566267134510.99RockFor Those About To Rock We Salute YouAC/DC
2Let's Get It UpAngus Young, Malcolm Young, Brian Johnson23392676365610.99RockFor Those About To Rock We Salute YouAC/DC
3Inject The VenomAngus Young, Malcolm Young, Brian Johnson21083468528600.99RockFor Those About To Rock We Salute YouAC/DC
4SnowballedAngus Young, Malcolm Young, Brian Johnson20310265994240.99RockFor Those About To Rock We Salute YouAC/DC
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" Track \\\\\\n\",\n \"0 For Those About To Rock (We Salute You) \\n\",\n \"1 Put The Finger On You \\n\",\n \"2 Let's Get It Up \\n\",\n \"3 Inject The Venom \\n\",\n \"4 Snowballed \\n\",\n \"\\n\",\n \" Composer Milliseconds Bytes \\\\\\n\",\n \"0 Angus Young, Malcolm Young, Brian Johnson 343719 11170334 \\n\",\n \"1 Angus Young, Malcolm Young, Brian Johnson 205662 6713451 \\n\",\n \"2 Angus Young, Malcolm Young, Brian Johnson 233926 7636561 \\n\",\n \"3 Angus Young, Malcolm Young, Brian Johnson 210834 6852860 \\n\",\n \"4 Angus Young, Malcolm Young, Brian Johnson 203102 6599424 \\n\",\n \"\\n\",\n \" UnitPrice Genre Album Artist \\n\",\n \"0 0.99 Rock For Those About To Rock We Salute You AC/DC \\n\",\n \"1 0.99 Rock For Those About To Rock We Salute You AC/DC \\n\",\n \"2 0.99 Rock For Those About To Rock We Salute You AC/DC \\n\",\n \"3 0.99 Rock For Those About To Rock We Salute You AC/DC \\n\",\n \"4 0.99 Rock For Those About To Rock We Salute You AC/DC \"\n ]\n },\n \"execution_count\": 2,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df = pd.read_csv('itunes_data.csv')\\n\",\n \"df.head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"id\": \"c1388561\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\",\n \"RangeIndex: 3503 entries, 0 to 3502\\n\",\n \"Data columns (total 8 columns):\\n\",\n \" # Column Non-Null Count Dtype \\n\",\n \"--- ------ -------------- ----- \\n\",\n \" 0 Track 3503 non-null object \\n\",\n \" 1 Composer 2525 non-null object \\n\",\n \" 2 Milliseconds 3503 non-null int64 \\n\",\n \" 3 Bytes 3503 non-null int64 \\n\",\n \" 4 UnitPrice 3503 non-null float64\\n\",\n \" 5 Genre 3503 non-null object \\n\",\n \" 6 Album 3503 non-null object \\n\",\n \" 7 Artist 3503 non-null object \\n\",\n \"dtypes: float64(1), int64(2), object(5)\\n\",\n \"memory usage: 219.1+ KB\\n\"\n ]\n }\n ],\n \"source\": [\n \"df.info()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"id\": \"a0737e50\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Track 3257\\n\",\n \"Composer 852\\n\",\n \"Milliseconds 3080\\n\",\n \"Bytes 3501\\n\",\n \"UnitPrice 2\\n\",\n \"Genre 25\\n\",\n \"Album 347\\n\",\n \"Artist 204\\n\",\n \"dtype: int64\"\n ]\n },\n \"execution_count\": 5,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df.nunique(axis=0)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"d715d6f0\",\n \"metadata\": {},\n \"source\": [\n \"#### Dimensional Analysis\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"id\": \"23233e4a\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"df['Minutes'] = df['Milliseconds']/(1000*60)\\n\",\n \"\\n\",\n \"df['MB'] = df['Bytes'] / 1000000\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"id\": \"db4ccd76\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"df.drop(['Milliseconds', 'Bytes'], axis=1, inplace=True)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"id\": \"4c65b688\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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TrackComposerUnitPriceGenreAlbumArtistMinutesMB
0For Those About To Rock (We Salute You)Angus Young, Malcolm Young, Brian Johnson0.99RockFor Those About To Rock We Salute YouAC/DC5.72865011.170334
1Put The Finger On YouAngus Young, Malcolm Young, Brian Johnson0.99RockFor Those About To Rock We Salute YouAC/DC3.4277006.713451
2Let's Get It UpAngus Young, Malcolm Young, Brian Johnson0.99RockFor Those About To Rock We Salute YouAC/DC3.8987677.636561
3Inject The VenomAngus Young, Malcolm Young, Brian Johnson0.99RockFor Those About To Rock We Salute YouAC/DC3.5139006.852860
4SnowballedAngus Young, Malcolm Young, Brian Johnson0.99RockFor Those About To Rock We Salute YouAC/DC3.3850336.599424
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" Track \\\\\\n\",\n \"0 For Those About To Rock (We Salute You) \\n\",\n \"1 Put The Finger On You \\n\",\n \"2 Let's Get It Up \\n\",\n \"3 Inject The Venom \\n\",\n \"4 Snowballed \\n\",\n \"\\n\",\n \" Composer UnitPrice Genre \\\\\\n\",\n \"0 Angus Young, Malcolm Young, Brian Johnson 0.99 Rock \\n\",\n \"1 Angus Young, Malcolm Young, Brian Johnson 0.99 Rock \\n\",\n \"2 Angus Young, Malcolm Young, Brian Johnson 0.99 Rock \\n\",\n \"3 Angus Young, Malcolm Young, Brian Johnson 0.99 Rock \\n\",\n \"4 Angus Young, Malcolm Young, Brian Johnson 0.99 Rock \\n\",\n \"\\n\",\n \" Album Artist Minutes MB \\n\",\n \"0 For Those About To Rock We Salute You AC/DC 5.728650 11.170334 \\n\",\n \"1 For Those About To Rock We Salute You AC/DC 3.427700 6.713451 \\n\",\n \"2 For Those About To Rock We Salute You AC/DC 3.898767 7.636561 \\n\",\n \"3 For Those About To Rock We Salute You AC/DC 3.513900 6.852860 \\n\",\n \"4 For Those About To Rock We Salute You AC/DC 3.385033 6.599424 \"\n ]\n },\n \"execution_count\": 8,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df.head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"id\": \"d12453da\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import matplotlib.pyplot as plt\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"id\": \"f89c2753\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"df['Minutes'].plot.box()\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"id\": \"97e62aa5\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import seaborn as sns\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"id\": \"e6063135\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 13,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"sns.boxenplot(y = df['Minutes'])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"id\": \"6e7b8457\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 14,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n 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UnitPriceMinutesMB
count3503.0000003503.0000003503.000000
mean1.0508056.55998733.510207
std0.2390068.916757105.392534
min0.9900000.0178500.038747
25%0.9900003.4546836.342566
50%0.9900004.2605678.107896
75%0.9900005.36075010.266789
max1.99000088.1158831059.546140
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" UnitPrice Minutes MB\\n\",\n \"count 3503.000000 3503.000000 3503.000000\\n\",\n \"mean 1.050805 6.559987 33.510207\\n\",\n \"std 0.239006 8.916757 105.392534\\n\",\n \"min 0.990000 0.017850 0.038747\\n\",\n \"25% 0.990000 3.454683 6.342566\\n\",\n \"50% 0.990000 4.260567 8.107896\\n\",\n \"75% 0.990000 5.360750 10.266789\\n\",\n \"max 1.990000 88.115883 1059.546140\"\n ]\n },\n \"execution_count\": 16,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df.describe()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"id\": \"4ec7f0a5\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 17,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"sns.histplot(x=df['Minutes'], kde=True) # kernel density estimation\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"id\": \"19abf450\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 18,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"sns.violinplot(data=df, x='Minutes')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"id\": \"64555428\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"top_5_genres = df['Genre'].value_counts().index[:5]\\n\",\n \"\\n\",\n \"top_5_data = df[df['Genre'].isin(top_5_genres)]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"id\": \"6b25e96f\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 20,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n 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\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"sns.violinplot(data=top_5_data, x='Minutes', y='Genre')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"id\": \"f0044743\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 21,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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zCez/9Ve5/8oWLAr+qOk2FtFR4pl8i0811f0nAr7zven57642d6tqiNDg8wo4/+SYTs6zyPpe/nqTFwtAvGac7npvB4RE+se8F6sxBJy1pDYd+RFwH/BHwI8B54NHM/FxEXAk8CawFvgP8m8x8vfjMLuAe4Bzwscw82FTvpRaYfHH2X76njy8dHjHwtSw1U9N/G/iNzPxx4Bbgvoi4AdgJHMrM9cCh4jXFe9uAjcDtwCMRMf1iqlIbVNcQGBkbJ6mUar749VMX1hSQlpuGQz8zz2bmN4rn3weOAf3AFmBvsdleYGvxfAvwRGa+mZkngRPAzY1+v9QKew4enxLwnuBrOWvJ6J2IWAtsAp4DrsnMs1A5MABXF5v1Ay/XfOx00SZ1jGsFqGyaDv2IuAL4EnB/Zv7TTJvWaat7UhUR90bEUEQMjY6ONttFaVrTrRUw88BWaelqKvQjoptK4H8xM/cXza9ExLXF+9cCrxbtp4Hraj6+BjhT7+dm5qOZOZCZA319fc10UZrRjs0b6Om++NJST3cXv3rL9RfuSp7tALBqZfcC9U5qvWZG7wTwe8CxzPxMzVsHgO3A7uLx6Zr2P46IzwCrgfXA841+v9QK1eGrM02tUB3dM914fEf5aCmJbPBfbET8LPBXwBEqQzYBfotKXX8fcD1wCrgrM18rPvMA8O+pjPy5PzP/YrbvGRgYyKGhoYb6KLXSup1frluPDODk7jva3R1pRhFxODMHJrc3fKafmX/N9H/53jbNZx4CHmr0O6VOmm7uItcQ1lLi3DvSHE1X/3dyOi0lTsMgzdFc6v/SYmfoS/Pg3EVa6izvSFKJGPqSVCKGviSViKEvSSVi6EtSiRj6klQihr4klYihL0kl4s1ZLTB5jVXv0pS0WBn6TaqusVpdcm9kbJxd+48AGPySFh3LO02qt8bq+MQ59hw83qEeSdL0DP0mTbfGqmuvSlqMDP0mTTeXunOsS1qMDP0mOce6pKXEC7lNco51SUuJoT+N+QzDdI51SUuFoV+HwzDnz3sVpKXBmn4dDsOcn+pBcmRsnOSdg+Tg8EinuyZpEkO/Dodhzo8HSWnpsLxTx+reHkbqBPxiHoZZW17pXdlNJnxvfOJCqQUW7mKzB0lp6TD069ixecNFNX3o3DDMweERHvjTI/zgrYvPpPtrgnvyNYjX35i4sN3I2Dg7nvomJEyczwttrbxGsRQPklJZGfp1tGoYZr2z77HxCboiOJdJf28Pa3+4h6/9n9fm3cfa4K5XXqk1cS6ntFXLL60I/cV0kJQ0s8icGgiLycDAQA4NDbXkZ7V6hEn159U7y22X/t4ezhQXUOcrgJO772hJPxy9Iy0uEXE4Mwcmt5fmTL/RYZjve+gZXvn+W23pYyOqIdvIgaeV5RfvVZCWhtKE/nQjTD79Z0e5/8kXOtOpFqieVU8ur9Tq7oqLavpg+UUqq1KE/nse+Ar/r05dGy6+6LnUVIN78jWIdo/ekbR0tL2mHxG3A58DuoDHMnP3TNs3UtNf7CWZVug3uCXNYFHU9COiC/gfwAeB08DfRsSBzHyxVd+xnAN/1cpuPvXhjQa9pIa1u7xzM3AiM78NEBFPAFuAloX+Ug38W//5lXzn/44zMjZ+0ZBOz+YltVK7Q78feLnm9WngfZM3ioh7gXsBrr/++vb0rEO6Lwn23HWTwS6pLdo9907UaZtyUSEzH83Mgcwc6Ovra0O32qOn+xIuv/SdBVd6e7oNfElt1e4z/dPAdTWv1wBnWvkF1/yzSztW4unt6ebBj1hzl7R4tTv0/xZYHxHrgBFgG/ArrfyC5x744Lwv5q6/+nKe+fX3t7IbkrQotTX0M/PtiPhPwEEqQzZ/PzOPtvp7nnvgg63+kZK0LLT95qzM/ArwlXZ/ryTJRVQkqVQMfUkqEUNfkkrE0JekEln0i6hExCjw3QY/fhXwjy3sznLgPpnKfTKV+2SqpbZPfjQzp9zduuhDvxkRMVRvlrkyc59M5T6Zyn0y1XLZJ5Z3JKlEDH1JKpHlHvqPdroDi5D7ZCr3yVTuk6mWxT5Z1jV9SdLFlvuZviSphqEvSSWyLEM/Im6PiOMRcSIidna6P50QEddFxP+MiGMRcTQiPl60XxkRz0TES8Xjqk73td0ioisihiPiz4vX7pOI3oh4KiL+vvg389Nl3i8R8Yni9+ZbEfF4RFy2XPbHsgv9msXXfwG4AfjliLihs73qiLeB38jMHwduAe4r9sNO4FBmrgcOFa/L5uPAsZrX7hP4HPCXmfke4CYq+6eU+yUi+oGPAQOZ+RNUpoHfxjLZH8su9KlZfD0z3wKqi6+XSmaezcxvFM+/T+WXuJ/KvthbbLYX2NqRDnZIRKwB7gAeq2ku+z55N/BzwO8BZOZbmTlGuffLCqAnIlYAK6ms8Lcs9sdyDP16i6+Xev3CiFgLbAKeA67JzLNQOTAAV3ewa53wWeA3gfM1bWXfJz8GjAJ/UJS9HouIyynpfsnMEeB3gFPAWeB7mflVlsn+WI6hP6fF18siIq4AvgTcn5n/1On+dFJEfAh4NTMPd7ovi8wK4CeBz2fmJuAHLNHSRSsUtfotwDpgNXB5RNzd2V61znIM/QVffH2piIhuKoH/xczcXzS/EhHXFu9fC7zaqf51wK3ARyLiO1TKfh+IiC9Q7n0Cld+Z05n5XPH6KSoHgbLul58HTmbmaGZOAPuBn2GZ7I/lGPoXFl+PiEupXIA50OE+tV1EBJUa7bHM/EzNWweA7cXz7cDT7e5bp2Tmrsxck5lrqfy7eDYz76bE+wQgM/8BeDkiNhRNtwEvUt79cgq4JSJWFr9Ht1G5JrYs9seyvCM3In6RSu22uvj6Q53tUftFxM8CfwUc4Z369W9RqevvA66n8o/7rsx8rSOd7KCIeD/wXzLzQxHxw5R8n0TEe6lc3L4U+Dbwa1ROCku5XyLi08BHqYyCGwb+A3AFy2B/LMvQlyTVtxzLO5KkaRj6klQihr4klYihL0klYuhLUokY+pJUIoa+JJXI/wfZ4YR6QfFQHgAAAABJRU5ErkJggg==\\n\",\n 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\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.scatter(df['Minutes'], df['MB'])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"id\": \"b71d4f0d\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Text(0, 0.5, 'MB')\"\n ]\n },\n \"execution_count\": 22,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.scatter(df['Minutes'], df['MB'])\\n\",\n \"plt.xlabel('minutes')\\n\",\n \"plt.ylabel('MB')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"id\": \"45dd51d8\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 23,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"sns.scatterplot(data=df, x='Minutes', y='MB')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"id\": \"4422dc87\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 24,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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ejAr+iKIqTUYFfURTFyTjNzV1FUc6clJK9JQb2lBhwd3VhdJQfQ0N9+rtZSjepHn83LV++HCEEBw4caC/Lz89n5MiRQN9m4nz++edpaWlpf33xxRdTX1/f7XpNJhOXX345o0aNYsyYMeTl5Z1w37i4OEaNGsXo0aOZO3cu5eVn93T0e++9x9133322TVZ6SEZBHVe9tplHlu/lD59kcfUbmzlY3tDfzVK6SQX+blqyZAnTpk3jo48+6nL72QR+q/XsHmv/eeD/8ssv8fPzO6u6jvfxxx+j1+vZs2cPP/zwAwEBASfdf+3atezevZv09HT+/ve/d/v8Sv+wWO28uT4Ps+1Yct26Fgs/Hup+tlylfzlN4DesWkXOrNnsTxlOzqzZGFat6nadTU1NbNy4kXfeeafLwN9VCuauUjWDo4e7aNEi5s+fz9y5c3nvvfe48sorufDCCxk6dCgPPvhge7133nkn6enpjBgxgj/96U8AvPjii5SWljJz5kxmzpwJOHrf1dXVPPTQQ7z66qvtxz/xxBP861//AuCf//wn48ePJzU1tb2un3N3d6ekpAQpJf7+/qf9YTJ9+nQOHz7c4S8ggGeffZYnnngCgBkzZvDQQw8xYcIEkpKS+OmnnzrVs3r1aiZPnkx1dfVpnVfpGRa7nZL61k7l5QaVS2qgc4rAb1i1irLHHsdaWgpSYi0tpeyxx7sd/D///HMuvPBCkpKSCAgIYOfOnR22u7u7d0rB3FWq5ubmZgA2b97M4sWL+eGHHwDHXwtLly5lz549LF26lKKiIsCR7nnHjh1kZWXx448/kpWVxW9/+1siIiJYu3Yta9eu7dCOa6+9lqVLl7a//vjjj1m0aBFr1qwhJyeHbdu2kZmZSUZGBuvXr+90nQkJCWRkZPDwww+f0c/niy++6JS/qCtWq5Vt27bx/PPP8+c//7nDtuXLl/PUU0/x5ZdfEhQUdEbnV7rHy92VmybHdiqfNSy0H1qj9CSnCPyV/34e+bOMl9JopPLfz3er3iVLlnDttdcCjuC6ZMmSUx6zZs0annrqKdLS0pgxY0aHVM1z5szpMIwye/Zs9Ho9Wq2W4cOHU1BQADgC99ixYxkzZgzZ2dns27fvpOccM2YMlZWVlJaWsnv3bvz9/YmJiWHNmjWsWbOGMWPGMHbsWA4cOEBOTk6HY1tbW7nlllvIzs4mMzOT559/HnDcP8jOzu7yfDNnziQtLY2GhobT+rC48sorARg3bhz5+fnt5WvXruXpp59m9erV+PurpZl7g9lqI6eikf1lDTSbOg8xXpASyuOXDifEx4PYQC9evs6Re0cZ2JxiVo+1rOyMyk9HTU0NP/zwA3v37kUIgc1mQwjBM888c9LjjqZqTk5O7lC+detWdDpdhzIPD4/27zUaDVarlSNHjvDss8+yfft2/P39ueWWW04rjfPChQv59NNPKS8vb/+wklLy8MMPc/vtt5/wuD179hAcHExERATLli3jggsuQAhBfX09w4cP7/KYtWvXduidNzU1YbcfGyf+eXuPXufRazwqISGBvLw8Dh06RHp6p3QjSjdVN5p4Y30u727Mx2aXzEkJ5f8uTSE28NjvYZC3B7dOi2f+6HBcXVzw17n3Y4uVnuIUPX7X8PAzKj8dn376Kb/4xS8oKCggPz+foqIi4uPj2bBhQ4f9fp6C+USpmk9XQ0MDOp0OvV5PRUUFX3311QnPdbyjK4V9+umnLFy4sL0t7777Lk1NTQCUlJRQWdlxXZyhQ4dy4MABsrOz0el0vPPOOzzwwAMsWLAAIcRptTk0NJTKykpqamowmUx88cUXp3VcbGwsn332Gb/4xS9O+NeFcva25tfy1k9HsNkdv4vf7q9g+a6SLvcN9tGqoD+IOEXgD/n97xDajmmZhVZLyO9/d9Z1LlmyhCuuuKJD2VVXXcWHH37YoeznKZhPlKr5dI0ePZoxY8YwYsQIbr31VqZOndq+7bbbbuOiiy5qv7l7vBEjRtDY2EhkZCThbR94c+fO5frrr2fy5MmMGjWKhQsXdvrg8Pf3Z/Hixdx0002MGTOG3/zmN3zwwQe8/fbbbNq06bTa7ObmxuOPP87EiRO59NJLGTZs2Glfb3JyMh988AGLFi0iNzf31Acop21bXk2nstVZZTQZ1Qpwg53TpGU2rFpF5b+fx1pWhmt4OCG//x36+fN7uqlKH1Jpmbvno22F/PGzPR3Krp8Qw18uH4nG5fT+mlPObU6fllk/f74K9IpynKmJgYyL9SOjoB6AML0Hv5gcq4K+E3CawK8oSkfRATreuCmdQxWNWKx2EkN9iPTz7O9mKX1ABX5FcWJB3h4EeXucekdlUHGKm7uKoijKMSrwK4qiOBkV+BVFUZyMCvzd4O3tfdr7rlu3rsO899dff53//ve/vdEsRVGUk1I3d/vIunXr8Pb2ZsqUKQDccccd/dwiRVGcldME/kNby9m8IpemWhPeAR5MvmwISRPDevw8q1at4q9//Stms5nAwEA++OADWltbef3119FoNPzvf//jpZde4vvvv8fb25s//OEPzJgxg4kTJ7J27Vrq6+t55513OO+883q8bYqiKOAkQz2Htpaz9oMDNNWaAGiqNbH2gwMc2np2q0OdzLRp09iyZQu7du3i2muv5ZlnniEuLo477riD3//+92RmZnYZ1E+WmlhRFKUnOUWPf/OKXKxme4cyq9nO5hW5Pd7rLy4u5pprrqGsrAyz2Ux8fPxpHXei1MSKoig9zSl6/Ed7+qdb3h333HMPd999N3v27OGNN944rZTJcOLUxIqiKD3NKXr83gEeXQZ574Cef2LRYDAQGRkJwOLFi9vLfXx8aGhQi1Qrp6eh1UJ2qYHiulbC9FpGRuhVWmSlx/Raj18IoRVCbBNC7BZCZAsh/txWHiCE+FYIkdP2tdeXVpp82RBc3Tteqqu7C5MvG9KteltaWoiKimr/99xzz/HEE0+waNEizjvvvA6LkcyfP5/ly5eTlpbW5bqyilJmaGV3UT3FtS38d3M+1721lQc+zeKmd7bx/HeHaOpihSxFORu9lpZZOFbp0Ekpm4QQbsAG4F7gSqBWSvmUEOKPgL+U8qGT1dUTaZn7alaP0ncGU1rmLbk13PPRLqoaTdw/N4kXvsvBau/4f3PFXVMZHe3XPw1UBqQ+T8ssHZ8oTW0v3dr+SeAyYEZb+WJgHXDSwN8TkiaGqUCvnJNKK6u468NMaprNAJit9k5BH6BRLZCi9JBeHeMXQmiADCAReEVKuVUIESqlLAOQUpYJIUJOcOxtwG0AMTExvdlMRekzNU0m9pY2UFDTjNVqZ1KcL+aS7PagD1DVaCI20IuCmpb2Mn8vtw5r4SpKd/TqrB4ppU1KmQZEAROEECPP4Ng3pZTpUsr04ODgE+3TMw1VBpyB+N7bbHZ+yqnm8RV7eWJlNusPV7M+z4CLly9e7pr2/T7bWcJNk2KZlhiIxkUwLtaf//xyPNEBXv3YemUw6ZNZPVLKeiHEOuBCoEIIEd7W2w8HKk9+dNe0Wi01NTUEBgae9qLfyuAgpaSmpgbtz9ZRPtftLjbwh092tw/jrDtYhcVmJyXNi6dmevP7bxuw2SUWux1XrLxy/VgaTVb0nm74aN36ufXKYNJrgV8IEQxY2oK+J3AB8DSwErgZeKrt64qzqT8qKori4mKqqqp6qsnKAKLVaomKiurvZpy2hlYL+TXNncbuNx6u4faJwVy88RaS5txHqVVPqI8HiaPC8PByR++lpnAqPa83e/zhwOK2cX4X4GMp5RdCiM3Ax0KIXwGFwKKzqdzNze20n4pVlD7XUAbGesxe4fxUZOKZrw+wcFx0p90Cde4E671x1UcwbMvDDEu+GNLuB51vPzRacRa9OasnCxjTRXkNMLu3zqso/cpuh9zvYeXdNPsmsn3iS/x6yX6khOzSBqYMCWRTbk377o9ekkJKTChc9xGYGsArCFxVL1/pXb02j78ndTWPX1HOSZX74Y3zaImbx/pRf6Xa6EJ1k4kvsso4XNnEVWMjGRrqQ6DOncQQb4aH++Lhpjl1vYpyFvp8Hr+i9LfGVgt2JHrPPuxB1x4BrZ61KU9w/7IDGC12NC6C26cnoBGCZTtLGBvjx/u/mojOQ/33U/qHUyRpU5xLs8nKl3vKWPjGJq54ZRPLMoowtJpPfWBP0AVSNOpuHll9BKPFkRHWZpe8/mMuF6eG4yLgvjlJKugr/UoFfmXQ2VFQx28+2MnB8ibyqpu5/5MsNuTUnPrAsySlZH9ZAysyS/jWEE1p1MUYWjs+ZWuXjhu5H902iUkJgb3WFkU5HSftdgghtMA1QB2wCngQOA/IBf4ipazu9RYqyhlalVnSqez9LflcOCIUjaZn+jomixVTZS5uxiqy7EO46b1MzDZHD//+OUkEebtT3XTsrwyNi2BoiDcT4lXQV/rfqf4X/BeYC9yKI6dODPAy0Ai815sNU5SzFezT+cGuUF8tLi4986BfaV0zDbuW47t4JvYtb/LPNYfagz7AWxvy+MtlI/Hzcjx0pXVz4Z8LUxkb2+uJaBXltJxqoHG4lHKkEMIVKJZSnt9W/rUQYncvt01RTqnB3EB2dTb5DfmE68IZGTSSi0eFsXhzPi1mGwDuGhd+MTm2R57wllJSX7yf4WvuBquRFu8YCo50HNZpaLVS1WRk1d3TqGgwEqBzJz5Ip54wV84Zpwr8ZgAppVUIUfqzbbbeaZKinB6L3cLSA0t5cdeL7WXz4ubxp0l/4tM7prA9vxar3c74uABGRep75Jx1LRZaa4rB6lhZLTD/SxamzOO17R0X+kkK9SU6wEvl11HOSacK/FFCiBcBcdz3tL2O7NWWKcopFDUU8eruVzuUfZP/DdcPu56xEWMZHtG9p18rG4wU1bXiq3UlLkiHm8YFL3cNVq8Q0LiDzYxLXS7XJ+/AMHoCH+8xoPd04/8uSWF0VM980ChKbzhV4H/guO9//gSVeqJK6VdGmxGrvfOqVC2Wli72PjN7Swzc+UEGRbWtuLoI7p+bxE2T4vDWuuIdMYyi8/5J9E8PgM1M9I5/8Niij7hz3gzcXV0I9R1YyeMU53PSwC+lXHyy7YrSnyK9IxkROILsmuz2Mr2Hnlh97FnXKaWksdXCEyuzKaptBcBqlzz99UHGxvgzMSGQEVEB5LtfQWnkaDxN1Xj4R+IVnky0i3oCVxkYTjWdc+XJtkspF/RscxTl9Ok99Pxt2t94M+tN1hevZ2TgSO4ddy/RPp2ToZ2OzKJ6PtpWwJyUUHYU1HXaXlLf2v59XIgeQjqlolKUAeFUQz2TgSJgCbAVx9i+opwzhvgN4S9T/0KdsQ4fdx+83M7uZuqBsgbeWp9HcpgPJfVGhof7sK+sscM+gd4qeZoyOJwq8IcBc4DrgOuB1cASKWX2SY9SlD7krnEnVBfarTrya1rQe7rx3LeHcNMIHrk4hRe+z6G+xTFV86qxkUT5efZEcxWl351qjN8GfI1j3r4Hjg+AdUKIJ6WUL/VFAxXnZLXZKalvReMiiPTz7PU58B6uLnySUQSAxSb597eHuG5CDAlBOsobTVitNqLU1ExlkDhlpqi2gH8JjqAfB7wIfNa7zVKcWbnByDsbjrB4Uz6uGsG9s4dyzfho/HpoNaqCmmbyq5vx0boxNNQbH60bvp6uWGzHUpQ3GK28sT6Pe2cPZX9ZAw/MS8bDVd28VQaHU93cXQyMBL4C/iyl3NsnrVKcltFi4+u9Zbz1Ux4AZhv846sDxAfpmDsirNv1ZxTU8cv/bKPB6JgGek16NA9elExKuC9zhofy7b6K9n0Dde6MjPBlQVo4Q4J9un1uRTlXnKrHfxPQDCQBvz3uz20BSCmlWh9O6RF2u2R7fi1rD1ay8XDnTJrfH6joduAvqW9lV2Edl6SGszKzlGazjaU7irh4VDjnJwfzf5ekkBLuwxe7yxgd7cf1E6JJDvPF11MtdK4MLqca41dpm5U+sa/MwI3vbCUxxJu4QB17SgwdtieFdK/HvbOgjgc+3U1uVTORfp7cPzeZl9ceprbZTHmDY5pmbKCO31+QxK+mJeDlrsGthzJ5Ksq5Rv1mK/2qwtDKroI6iuscwXd/WSNjYvzx9TzWJ4ny0zIjOeSsz1FmaOX2/2WQW9UMOHr+/1pzkGvHO+b7h/h4tO8rhEDv6aaCvjKoqWWAlH6TWVjHv9YcZFNeLcPCfNqnUL74fQ6/nBqHh6sLCcHejIrUn1WyM0OrGSEEJXWtVDV2TKLWbLahcRH8alo8ISrFguJkVOBX+kVhbTOPfr6X7NIGALJLGyioOcTNU+J4Ze1hXvrhMK/eMJaLR4Wfdp0mi42DFY2U1Dfh6W6l1lzBoVLB5Ng43DUuHXLmCwGJId5k5NcSF6jr8etTlHOZCvxKn2potZBRUEeLxUaDsWMe+yaTlUg/LffMSmTykEDGRPuddr12UxP1R/bSZPZjd6GFRrNkfEIELj4r2FkKD12YzF9W72/f/5dT4rDY7Py/6Qlq/VvF6ajfeKXPlNa38srawyzdXoS7qwvXT4ght6qZtQcr2/fx83Tn+olnmGTNYsK0ZxW57mO4ZemR9p79ku1FvHrjZeS4bKGqxpsVd02lpL6VMF9t+/x9RXFGKvArfeaLrFI+2FoIgNVs4+0NR7h/bhI/HqrELmHeiLAzzqFf3FiMvbaeLyoTyW0ydhjOsUv4LKOc8aNsmDUaRkf7MfoM/opQlMFKBX6lTzSbrCzL6LwIenFtK08sGEGIjwepkXoi/E98E7esvpWaZjNBPu60mGxkFdexr7ye+AAfml1cMXaRh7/ZbCPYI5aJw7uXy0dRBhMV+JU+4eHqwvAIHw5WdMx46eflxqhIX8bEBJz0+J9yqrhv6W7mjQjlklFhfH+girc3HGnfPiE+gBsnxrB6T1mH4xaOi2Covzsjwv167FoUZaBTk5WVXtFotHCoopHSthz2rhoXbp0aj6/2WF8jLtCLi0aGnTLo59c0c+f/dnLF2AgSgnVszqvlvU35HfbZdqSW8gYjj1ycwoT4AMbG+PHc1aOZlhjMiPDup3pQlMFE9fiVHneoopFHl+9le34t/l5u/OXykcwdHsqoKD8+v2sqB8sbcXd1YViYL5H+p051XFLXis1uZ3piMDuL6kkK8yE2UEduVVOH/ZpMVl5dm8vc4aHcPSuRlHDfXs/qqSgDkZBSnnqvs6lYiGjgvzhy+tuBN6WULwghAoClODJ95gNXSyk7L3d0nPT0dLljh1ridyBoMVm584MMfjxU3V4mBKy4ayqpUX6nXc/RtMxIR0DfXlDL0m1F7C9vxF3jwi+nxpFVbGBzniOvT5C3O/++NgWbzZXEEF+iTnKvQFGchRAiQ0qZ/vPy3uzxW4H7pZQ7hRA+QIYQ4lvgFuB7KeVTQog/An8EHurFdih9oLbZxJ62QJwa5UdsoI7/bi4AQEo4Ut182oH/cGUT+0oN/GfjEfaVNXL/3CS25tWyv9xxf8Bss/PG+jz+ccUo8muaSY3S8+vzYhgbE4imj9e9LWooYm/NXhpMDQwLGMbwwOG4adQ0UeXc1muBX0pZBpS1fd8ohNgPRAKXATPadlsMrEMF/gGtoKaZ7/ZXcKSqmXA/TzYerqbFbOOKMZEs3+WYyRPk7XGKWhwzf77IKuUfXx0gOdSba8bH4O7qQlFtCxsOV3fav6rJyDNXpTI6Wo+vZ98vi1jUUMQd391BYaNjiqpA8PLsl5keNb3P26IoZ6JPbu4KIeKAMTjW7Q1t+1A4+uHQZfYtIcRtQogdQogdVVVVfdFM5SyUG4zc9cFO/vLFfv63tZB/fnOQUZF6qhpNxAU6hlsuGx3BsPCTZ9c0W21sOFzFQ8v2cMPEWLy1bjy8fA9/+WIf3lpXLhnV+QbtkGBvzksK7rOgb7PbKGgoIK8+D5PVxN6ave1BH0Ai+XfGvzGYDCepRVH6X6/f3BVCeAPLgN9JKRtO92ablPJN4E1wjPH3XguV7jhQ1sDetnw7R/1vSwHXT4wlNtCLJf9vIsPCfPDXnbzHv7vYwKbcWlLCfSisaeb7/Y6neetaLPzli/28fuNYvjtQSUOrYwGVBaPDGRWh752L6kKdsY6lB5fyVtZbWOwWFgxZwIigEZ32q26txmQzdVGDopw7ejXwCyHccAT9D6SUR5drrBBChEspy4QQ4UDliWtQzkVGi5WCmlaEgBazrdP2VosNX60r42IDTiurZmF1E59lFBPs68GkhEA+2VHcaZ+SuhZun55AXKAOP083Rkb5oe/DBVJ2Vu7klcxX2l+vyF3BtMhpuAgX7PLY08JXJ19NsGdwn7VLUc5GrwV+4ejavwPsl1I+d9ymlcDNwFNtX1f0VhuUnldS18rz3x3i052O4PzswtHo3DU0H/cBMG9EGHNHhp4w6LearOwtMXCkphlPdxeajDasdkmzyYbdLokJ8GJfWce/Ivx1HoyL9Se2nzJpbivb1qlsZe5KXpr5Es/vfJ6q1iquTr6ahUMXqimkyjmvN3v8U3Es3bhHCJHZVvYIjoD/sRDiV0AhsKgX26D0sM251XyScaxH/uQX+3jh2jQ+2FrIwfJGLkuL4Orx0cQHeXd5fH1lM18fquSPX+xrLxsb48etU+O5/5PdXD8hhjtnDOH+j3e3590ZF+PHpIRAIvxOPee/tyT6J3YqC9AGMCVyCmkhaRhtRoI9g1XQVwaEXpvH35PUPP7+1dRqYVt+LVuO1OKrdSUhSMcjn++hvsUx3j4zKZjXbhpHs8lKgM696+BnacVYW8uO/VbuWX+QupaOKZkfnJeMxkWQV92Mv6eGqUMDKTU04e+pZXR0EKH9vFhKYUMhv1/3ew7VHQLAz8OPN+e8SUpgSr+2S1FOpj/m8SuDQHFdFRkFJu79aHd7WaDOnb9dPoq7PtwFQHqcP1o3DVq3rufQG4tzKK2tYbc1DKu3CzdNjuU/G/JpNFmP7WO18eL3h4nQa4kJ9OKWaQmcN/TcSbUQ4xvDq7Nf5XD9Ycw2M4l+iUT7Rvd3sxTlrKjAr5zQ4eoyDtQc5tW1Hf8qrGk2U1zXis7dlXC9lgtPsEpWs9FKca2BepMf96wqoLIxGwBfrSv3zB7K3790LIziphGMjNCTFu3HyAhfFo6LIMy3/4Z1TiRUF0qoTmX5VAY+FfiVEzpQtx+D0USTqfNUTLPVzts3jyU+2LvLIH24somiOgOuLu5UNBhJjdLzXdsUzQajlX2lBkaE+2C2SX45NQ69VwOPXhROlJ8n4QGBvX5tiuLMVHZOpQOjxUpGQS2fZhRRb6phWf5bLEqP6LCPxkUwJsaPyUOCuwz6JouN+pYWdhU0sbOgjsLaFiYPCeSO8xPa98mvaeH6ibEMC/MhSJpJMjQyJiSE8IDTX2NXUZSzo3r8SruKxnoO1xaRWVpGVZ03I2ODyanP4ZLog/xhXgqfZZTi7+XO7ecnMD7Wv8s6mo/kUtZqp8Tkyc7C+vZUC4E6d568fAT+Xm7UtVi4JDWcrJJarhoewNg4f3z9hvblpSqKU1OBXwFgX1kNPxwoJ7O4kWGRXnj67cGVcdyVeh+vZf+FMK8w7rz4XlKDkkgO6XzTVdbm05BfReWSz9gy/1ZMwtwhv05Ns5mPthZyeVokQsC4eLhhUjJebiqLpqL0NRX4FfaWGHho2T6y21IvfJcNF6WOQJOwg8O5Y3hp+vsgzET7RhKlD+p0fFFZPmUtZgI276QmbSKf761iypDOi6vsK2vk3jlDabQVoHMNU0FfUfqJGuN3cgcqqll3sLI96B/19R4Dvq7R+OlsBHlEMTl6dJdBf1teFbctLeDqt3L4rSkZS8ooDpQ3kBjaOSnbjORghEsj4Z6JJIepG7iK0l9Uj99JldTXsb+qmCqDBout86+BlKB3C2Rh2hASQzo+hVtU28LGnAoOljcR6KMlLsib/eVN7Kts5r4vj/DA7CGs2l3Kbecl8O7GI1jtkrRoP+44L46hau1bRel3KvA7oczCOrbl11LZqGFUhB4X0UxcoBf5NS3t+8xOCWJkWBgjQo8lHKtvMVNe28SjK/eTUVjfXn7N+GiGh/uyr6yBqiYT0bKFyToLh1tNvHBtKt5aV4aH+xHs079P3yqK4qACv5M5WFHLfR/vJq/6WJB//NIUFo2LorCulUPljUxNDGRBWghJocfG6XcV1vHI8iwuHBHeIegDLMso5s4ZQ9hX1oCLADd/mBUomR6uxWTyIDpAj74fFkpRFKVrKvA7CYvdQkZ5BgeLdB2CPsA7G/J57NJh2IWZRWOHkRjig5/uWKDOKTNw238zqGoyMXd457qtdsnR9Dy/nZGAf5gGi8aLhIB4XF3Ur5iinGvU/0onUNVoZFNeBRvz7AwL6fzAVU2zCRtGzh8uSA0NxGa1U11koKmqAbu3J5n1LVQ1ORYXabXYCPb2aH8NMCspiFHuJt6bE4ZvXCCjQrtcVO2MVDZXklOfg9VuJcEvgWgflRdHUXqKCvyD3P5SA//ZVMDHO4oAeGBeCK4uAqv9WP6dy0ZHEu3vRWpoAna7ZN/mUn768BBSghAwflEiv5wUw3+2FLJ4Uz73zUliZ2E9ORWNzEvUc7Esx7a/kqbUcSRGdP1g15koaijivh/v40DtAcCR/viNC95gWOCwbtetKIqazjmo5VY2siWvlk8yitrLlmwr5JGLUxgd5Uuwtwe3TY/npskxpAQFUZFv4EhuHRuW5nA0W7eUsGNZLuODfLl+Qgwmq51/fHWAkRG+XJASTGiIL8YxY3G/7FImThyObw+sirWlfEt70AeoNdby4YEPsdqtJzlKUZTTpXr8g1Btk5GtR+rYXVxPgM6dO88fwpvr87DaJcV1rfzjq/08dW0YMf7BpEcmYzHayPiqALcwLa12id3aMRun3SYxNVlJCvXht7MTSQr1xMfLTqPRRnKojqHBPZuxMqc2p1PZnuo9mGwmdc9AUXqA6vEPMiV1Law7WE12WQM+WjdW7i5lVVYpt0yNa98n1FdLUlAQaRFDsJjt1DcbCRvjTos3VFgtuGs75tV389Bg1bqQXWagsrEFX98a4vx9uHTkMIYG93zO/AnhEzqVXRR/ETq3/ll2UVEGG9V9GkRqm038ZfV+vt5b3l72uwuGsmRbIRF+nsQGejE6yo/bpieQEupLUUE9GrsBW2UNpuxs4g/uoezy24m/LJbCL4tobbSg9XZjzKIh3PPDfu6bM5SIkAYifEKJ9e+9J2/HhYzjjtQ7eGfvO1jtVi5OuJhL4i/ptfMpirNRSy8OAlJKciubyKls4s4PdnbY5qt15erx0QR7exCu1zIiwpdIf08OllTi7eVJQY25bf69wE8jGVmZw1ceMWilhiA3V/z8tWwqrUdg5xfj/UmKjumTa7LarZQ0lmCVViK9I9G6qoe/FOVMqaUXB6n6FjMbD1fz0fYipiV2zqXTYLSic3clNUpPQrCOhnojRTUt2PHgSLWZh5ZlUdNsRuMiuGlSLN5RSfzy8DYOjpyC9PLCFckVwaEE+3oSHdj1Auon0mJpIc+QR62xlkjvSOL18biI0xtddHVxJVYfe0bnUxTl9KjAP4BVGFrZnFfL75ZmApAe64+Hqwsmq719nzExfpw3NJAIL3dsNbVY3TxpNVlpsdh5+usD1DSbAbDZJe9tyid2/nDsOm/8inLZG5rI0BBP0qKDcNWc2e2gZksz7+19j9ezXgfAzcWN52c+z/So6T1z8YqinDV1c3eAOlTeQEZBHSt3l7aXfbC1kIcuHEZCkA4hYPawEJ6aPwIfG9QaLWytlyzeWsxz3x6itsVMTmVTp3qNFhuuFjPB0aFMTXIlPS7ktIN+fYuZTbnVrMwsYVfZ/vagD44nhx/f+DgVzRXdv3hFUbpF9fgHGCklmw7XkFvdhNZVw7TEQNYerERKqGw08fTXB3jowmTGxPgT7eNBebOJWpsNN1zYW9LAzsI6ovy9sNlhbIwfO3+WdycpWIe9UhI6Ihnhdnpz8htbLdS3mnlz/RHe31IAwO8WmDrtV2Osod5UrxYsV5R+pgL/AGIwNrIzv4Xb39+J2eYYzkkJ9+GXU+J4d2M+4MibMyLClzCXRuqMgvzaVn7YX4ne041QXy0VDSYOVTSxNa+GF65N48Fle6htNuMi4J5ZQxnhbiTw8stOK+hXNRjZnFfDnhIDQ4K9qW8xt29zl0EIBJJjkwfifOMI9gzuqipFUfqQCvwDxKGafA7UHOTd773bgz7A/rJGbpwYy5VjI3ERguvHReJqsFJQZkPjYaDGamb1njJMVjs6dw13zkjk2TUHaTbbyKtq5u9XjMRqlwR7ezA62hftaQT8FksLFqvgue9yWLLt2FPBF48KY3JCIJvzali2xcKd0x9jcc6/aLY0E6GL4O/T/k6AZ+eVuRRF6Vsq8A8A+6vy2FG1kcI6A0W1yZ22lxlauTwtnCRaaWiw8+2b+zja0fbydefNq1K5eXkmzWYbDUYLnm4aWi02/HTuhPp4EBeow9/b45TtyKkporqlAbPVhsXi0SHoA3y5p5zfXzCUzXk15FYZ2bA7mvev/AiTvYkwXRhBnp1nHSmK0vdU4D/HtVhaWJn3CYcNhylrqmB+2vm8t7Gwwz4zkoIJlCZatAHs+uQAx42u0NJgxqvWwoVJIXx9qBKbXaJxEUT5ezIi3JsRkadOqnagKpeMqq3k1B8i0jsSu92NFN+JXe579Ebw2Bg/Hr04haEB+rO/eEVReoUK/OewuhYzdcZaNpRsIC00jS1lW5g7LIuF44azfFcZv06P4YrkEKSHC/kGDV4mI6bmzonMTE0W5sQE8H1uFSMjfAmfncj5ycEMDfU96fkLapopbazg31kPs79uf3v5DSk3kF2/iclDhrM5t7a9PDrAkwlxAfxw//kEe3vg0wMJ245XWt9KXYuZUB8tQT6n/gtFUZSu9VrgF0K8C1wKVEopR7aVBQBLgTggH7haSlnXW20YqKoajazOKuPdjfkEebtz1fhH8fQuZq3HWt468BQjAlL5/vZ/IVpcaaw3U20w4eLnzt7aJkZOC2fHyiPtdQnhGO7xcxe8c3M6iX6uRIREnfDcNruk3NCKzS55eFkWV01r6RD0AZYdWsYvR/6Se+cFEfCTO1uP1DI+NoBbpsYxKsoXrVvP/lrZ7ZK1Byt58FPHw2bRAZ68cM0YxsZ2PwW0ojij3uzxvwe8DPz3uLI/At9LKZ8SQvyx7fVDvdiGAafZaGH5rhL+/qUjLXFhbQuZRfDolf5cm3wtPm4+XOg3j7ydzez8sqD9uHFXJtDqYkUTo2fsvFgOZ1Tg4eVG0oRQCrKrGXPVEALdmtGFnHiRlMyKbLaWZtBotDIycDRHalow2TpPyzTZTPi4+dJibeb+i4LwdhuB3tMND1dNF7V2X25VE3f+79hMpqLaVn7zwU5W3D2VUF+VykFRzlSvBX4p5XohRNzPii8DZrR9vxhYhwr87bKK6yirN7J4U0GHcrsEU3Mk04dEEagNp7HWws6vOu6za+URJt02nM2VDZyX5MdIPzdqC5uoKm1i/IJ4IqL0QOfxdpPVRGlzKY3mRp7Y/Di5hlwAtBotd8x+liD3EHzcfGi0NLYfMz1qOgk+w/D39CQhoPtpFcxWG3XNZrRuGvRendfmLa5r7TCTCaC8wUi5wagCv6Kchb4e4w+VUpYBSCnLhBAn7H4KIW4DbgOIiembxGD9xWy18XV2BU+uyiY9LgBfT1dK6o9tv2NqHFfEhCHNYEFyuLihww1cALtVYjVaEUCmoYkh4Trih+rx89UQ4Nt1jp3ihmJe3f0qX+R9gYfGg4VJC4nwjuCnkp8w2owcbl3LjkPz+cuUl/j8yH85bDjE7OjZzIyaw/CAYXh6dH+cfUd+LUt3FJFb2czUxEDOGxrE+LgAxNFFfIFA784fBjp3DXqvnr2HoCjO4py9uSulfBN4ExzZOfu5Ob1qf1kj9360Cylh7YFK7p+bzIHy/QwP8+Gd+SPY/UMZy7/ciZuHhrQ5MaQk+FKk1WA22trr8NK7U2q2EuHnSZC3B4E6NxIiO/fwy5rKKWmoorTGjf3N37IqbxUARpuR/+3/H/eMuYcNJRuQSAzmaqYNDWLx2nJ+fd4fCU91YUhgMC4uZ5fpw2Kzk1vVRFm9kUg/T6x2O5tza6huNLOrqI6dhXVUNJgI8HInMdSn/bihId78YW4Sz645BICLgL9fOYq4QJWfX1HORl8H/gohRHhbbz8cqOzj859TsorryS5pwGyztS91aLLa+TSjiP/dMA7/Bis522s4vMPxYzIbbWxbdYQLfpnCeTcNY9vyXBqrjehDPQmbE8l2QxPz4yJI8Lbg5efX4Vx1rXXk1Ofw0q6XyKzK5KqhV7GjonOq66LGIkK8QqhoqSA9cB7TYoK4YWL3hnPKDUYyi+rIq2pGSsitbiIx2Jt3NhyhptlMeqw/D85L5plvDvL5rhIWpIV3CPye7q7cOi2eaYnBVDYaifL3JDHE5yRnVBTlZPo68K8Ebgaeavu6oo/Pf87YVVjHbf/NoKrJxH1zkjpsW3ztKFqqbNTVt5KXWdXp2MqCRjwDPKifHIC/m4Zhsf6gFVyX5EecXoCHX/u+RquRypZKcupyeGfvO+yp3gNATl0OMT4xFDR0vFcQ5hWG3kPPbSPvZnzIFGK72auuaTLy0bZClmwvpKLBcaP439eM5r6Pd7d/2O0oqMNN48K0xCC259fi3kVSOC93V9Ji/DqVK4py5npzOucSHDdyg4QQxcCfcAT8j4UQvwIKgUW9df5zVaPRwoHyBn44UEVVkyMQ5lU1M3lIIPOGaLkgKobdq0qpLWsiIS2YUTOj2PxZboc6dH4eWDWwZHcxV42LIt3HlSidBh/9sV5wdmUORxoPsuTAEqx2K9elXIeH5tiYfFZ1Fg+Of5DdVbtpMDcAkOyfzIXxF3Lj8BvRe3T/was9xfW8/mMuB8qbmDciDBcheG9TfnvP/3ib82q4e1Yiw8J8GBJ8Znn/FUU5M705q+e6E2ya3VvnPNfZ7JKPtxexbGdxh57055klbLtnBOYmPV+9tgdzq+MhrIyyAlKmhBOeqKfssAGAgAgd+mBPWrSCl68fS7ReQ4j/sSBdbmilqqWanMY9/Gnzn9rLH9v4GP838f/IrMrEanfU/2rmq/xj6nPUGRsI0nmR5J90Rpkza5pMNJusBPto8XTvOJUzr6qJG97eSoPRca7cqiYuHhXGmGi/LtM8R+i1jIvxJz7Ii8DTSB+hKMrZO2dv7g42tU0mCmtbqG4yUd9i4cZJQXy1t5wr08K5f3wchgoLrYam9qB/1IEt5Vx6TyqGilbctBrc9e4Y3QTeejd8vSSBOsfTt1ll+eyu2c7u6gyuSV7IxtKNndqwpmANacFp7WP7UyOmoSWMefHj8XI//V8Fu12y4XA1/9l0mNQ4K6F6dyZGJzIk6Ng6vDkVTe1B/6iv95bzm5mJmC02ZiYHs/agYxhL4yL46xUjmTnsxM8YKIrSc1Tg72V2aWdryS5W532D0WZkZMh07pwZT1l9K1vunU5LRSsNFUa8fN1psnaevOTq5oLVLil2syN0GnQaO6NCtPj5+mC328ms2MNhw0HMNjNuri5sK9/ERfFz8HLz6lSXj7sPVw69kpnRMwnThTHMfxgx+pNPlbXZJfXNJnKrm8mpaMLTXcOQYB2rsw8RGf8Tiws+wiZtTK06n4fGP0C8v+NGsJtr5169h6uGaH9PEkO8uWJsFL86r5X6FgvxgTqGhZ88fYSiKD1HBf5eZLLa2FVcyo6yYsw2C98WreAbuZy/pr/OhREjKN5bx94fi2moNhIa70PanFj0IZ4YKlvb6xg9J5o9ZiP6QC2JwV6EujVj13qyrXgvRS0HqW6tptXayn/3/RdXF1cenvAwLdYWUoNS+erIV+1P3mqEhoVDF+Kn9WNM8Bh8PE49K+ZgeQOf7ChiRISeP63Mbu/Bxwd58dSieO766VNs0jGldGPZj6w6ksJv/e8CYFiYD8mh3hysOLbK192zErkiLRJ3N8ewUGKIGstXlP4g5M/vsp2D0tPT5Y4dnacenstqm028tf4Ib6zPxS4hJdyLy6c2kKaPRFscyo7V+dgsdoZNDsPUYuXQtgrOvyEZARiqWmk2mAmL96XZ3xWtvzsJgR64alzYX1NIi72O+9f/llar4wMi1CuUK4Zeweu7X2du7Fyazc3cOPxGao217K/dj0AwNXIak8InonE5cVoFo8VGRkEdW/NqGBWl50B1Pj6+5WzfF8nqrI4zbx++JA6r97oOyysm6BP44OIP8HZ3BPSC6mY259VwuKqJyQmBjIv1x6+LJ3MVRekdQogMKWX6z8tVj7+XZBYZeO3HY7NxcqtauTL8fMoPN7F+6aH28j3rShg7LxZ3rYb9G0sZMy+WyADHzU3p44aHmyRMa+FI82G+PPIl2dXZ+Hr4tgd9gIqWClosLfi4+VDSVIK/1p+XM1/l8fFPMdR3FKG6YAJ0XfeuDS1miupaEUjM1FNtyyEm2oSfPo4tR14jRiZwqLxzMrTDVU3MiIzrUJYWnIbW9VgKhdggHbFB6iErRTnXqMDfSw6WH8tt882dk/AyCRprLO2zc45XfKCWsCF69MGeeGhdqaptQRfmRaiPJMd4hA/2fU64dzgfHviQmdEzKWkq6VRHrbEWvYeei+IvoralmZkjLyApKKrLGTRFtc2U1BvRCMFfvthHVokBD1cX7pkdz0Hrt6wr/YpAbSC3jryVr/O/5oKR88n5oePC7MMiBIJjaRWCPYO5PuV6XF3Ur5SinOvU/9JeEh/kRZC3OyuuTWf3V0UcyazG08eNlCnhnfbV+Xvg4elKXGoQrn5uBAX5UWnJ5Uh9Jc/ueJpAbSCVrY6hlp2VO7ki8QoO1R3qUMdQv6FEeUcxOXQm8X5RnW6uWqx2zDYbVU1GDlaWcbjSzI4jrWSVOD6ITFY7z36Ty4vX3clPZWswmBzlc+PmEu/lSlVDCMt3VuHh6sINU/zw1VeT5J/E23PfxmKzkOCXQIR3RG/8KBVF6WEq8PeScUE+fLIgjcZqIy0GxyLkrY0WXN01ePm609LgKHPz0DDivEjcvTQYXQVWVyNLct/C1UXDUL+hVLRUYLVbGR0yGgCDyUCdsY5FSYvak6vdkXonacFjSQlMas+jY7LaKKlrxU0jsNqtHKluxmyF7fl11LY2sCAthOfXFHZqd361hUlh0xkbNpIlB5dQ3FgMwKVxl/HqLVeQU38Iva6akSHjSPRPJJHEvvhxKorSg1Tg72Fmi42qwka+f3cfjTVGEJA8MQxPH3fys6rJ+LqAaQsTMbVakVLiH+dLnc4Ff08NG8vX8fTOR9rrumfMPQDUGGvQe+iJ9I6kpKmEFbkrGB2cxmuz3sXH1Q834c/eEgO5JWVE+XtS32qh1Wzjma8PUtts5sbJMRTUtPDlnnJmJAcT5efNV7ubGBbmw56Shg7td9O4MESXQlFjUXvQB/gifwUjQ1K4ftRFasF0RRngzi7NotKl+pIaqvIbKM+px9TS9vCShINbygmNd8xTt1nsGJstCCHwH+KLyVeDr1bw3NosPsp5q0N9+YZ8UoNSAXg7621mRM/gkYmP8OasD/h/if+krj6YoipX9pcZMFpslNYb2XKklk92FPPo53u5cXIsWjcNr63LIzHEG62bC+sOVuGjdWNNdgX3zh6Kt8exz/5Zw0LQae0MCQjlUG3HoSSAfTX7VNBXlEFA9fh7SEVBLQe31LB/YylanRtj58ZweGcl1UWOm6IWow0hHCkXbDY7PiGe+AZ78FzWM4wNmEugzp3slpYOda7KW8Xfp/2diYaJNJgaGB4wnGjtOJZsqaKysZAKQysPzhuGwdDAxtxqvjvciNFi59LUcFLCfXn+20PcPCWOV9flsjWvltFRfmw9Usv+8gYi/T0x2pr559XJ5JRbEICbm4VCy7dM0aYwK2YW+2r3dWjP5PDJffXjVBSlF6ke/xlqNDWyr2YfufW5WGwWDDUtlOTVkbezlj1ri7Ga7TTVmdiyIo/Eccfy3viFejL75uFMvyGZ0NRAVprfZ9bKyXyRv5ynMu8lKtjMhVHXtu+vERpmRV2Mj4hnauiF3Jx8NyN8ZtDS6o6hxUp1k4nbpkQw3rKNRfvu4oXWR1l9QS0z4z35IquMSfEBNJttuLg4Zt5E+XtS3ZYUbmiIDw1GC16eRv6x9xfUua/G7LWBr2sfY2ne61jtVi5JuITzo84HQCC4MvFKJoRP6MOftKIovUX1+M9AviGfP2/+MzsqduDn4cfyC76kPNtAZUEjhftqOu3f2mjGzUNDYnoIPqFeGLBj83XhrvXXUdh47Maq2W6m1naIkpJh3Jr8IFurV/P/kv9Ea6uezNxGRkV4se9QAf/LrMdf587c4WE8u+YgSaZsvL+8ub2eIeW/4ZFZb/FTgTc2KXHXuOAiwMfDldQoP5btLGF0lJ6kMHciwq3EB3sxNWIqn+a9016HVqMlwS+BKJ8onp7+NIUNhWiEhhjfmA5z9BVFGbhU4D9NNruNpQeXsqNiB5fEXMLdsQ9hrLaw6bNcIpP80Ok9aK43dzjGJ0DLBbcOR6N3w+QGnu71vLTvdVxE5z+0gnSeFJg9SdTOYu7Y+dQYGtlWbOBXic3odj2LrjKDCxMu5juPOTy3Lpd7Zw8ltuQfneqJy/uQWcmP0Gyy8seLhuHr6cpbv0intrWBBy7TYHLZxxt5TyCRLEj5mN+k/YZAbSBf5H1Bgj6Be8bcQ6KfY6aOzk1HSmBK7/xAFUXpNyrwn6ZGcyNrC9ey4rxluLQEUFfUjM7PHYvJRmF2LVOuSqS6qAm73ZECwzfIk4BoHWYvgUYruON/mfz72uHsrdrLjSm/4B/b/9Ze91B9Mkne09El2RnhWUPInvcYkb+G9PF34bXiEWh2ZLEMrnmJy+IP8WPE3bhqBGb3zjnzbVp/bp84hGazFbPVjpe7KwE+Rh7afgu1ptr2/Z6Y/ASBnoEEEsh96fdxy4hb8HLz6jK5m6Iog4sK/KdJi5Z3Rn9AQ4WJirxq9m8uI3liGL5BnjRUt5L5XSET5sdjtdrx9vfAN8QTF19Xfv9JJlkljqd4s0tN/CH9YX4o+Ib353xOg6kFs2yguTGSG9/K5OqRPsxv+Tvaks0AeBly2oP+UV5HvmHBtNvIMdnICZ7HRNf3weoYu8fFFXv6LbS47iNcH8bQgKHtx70+53W+PvI1RY1FXJJwCePDxrdvcxEuBHkF9fJPUFGUc4UK/KehvKiW/Zur2Le2BCT4BmmZfNkQtqzMY8oVQ8jeWEptSTN7fizhvGsS0YdpOWho5YbnN7fXoXPX4OnqQpDLSGYGD6W8RrL2YBMuLj4MC4PYQB1zw5rR/nTsGLoYEsLFlWA/b3LqJWW6FAov/4yQyk14uEjEkFl4RY7lvC4SsaUEpqhhG0VRABX4T6rV0kpJtYnGMhP7fjiWH6eh2kjB3hrCEvRsXHaYi+4YRWujGZ2fBzo/DQFhvmzdU96+f4Rey58vG4HdLlmVVcbqrDIi/Dy5eFQ4r/2Yy9LtRTx8cQp2c07HBtQXQngalGW2Fxkn3E2VWzApkS5E+QOe8WhHTuvdH4SiKIOKCvwnUFRRQUWdC5UWCwEGc6ft5UcMjDgvktrSJiQSnxhvAnxsNOLB5rwaJsQH8O8FI2ltMDMizp+XthwhyMeDj7YXAVBqMLKnxMDdsxL515pDbMmroUofxMTEBfgcXuk4SeaH2Ob8FevYX0HVAWpChvBuw16+O/ArnpzyJMPDpvflj0RRlEFCBf4uZBXV8FNOI//69iB2Ca/O6jxEEhLri7HZzKxbUnAPdaOixUZRtY11B/P57+YC/n1BCo3flNBYa2S7Vxl3XDWEBzZ2fBrWZLVjsdkBsNok20qMLI29jWvmXYBnyUas0eOpj5hEqfDmlZof2bFvNXZp59KESxkROKJPfhaKogw+KvAfpyGvhHKrjUONgn+uOdhe/k1ZHbMnhlC01ZEhU+fnzrhL4vDwdgFvDx76bA9JYT74ebrx1k95/Co9loqvi2mqc9x0NbVY2f7hIe68MpYHv93f4Zwa4XjAav5YH0pa91Jtr+GhpiwenvMYwbpgwly1hAF/1v+ZgoYCdG46EvQJ+HqopQoVRTk7KvC3ySouY3txPYX1dpJCOgbVFfvLqY4L4IE7R9DUbME/xJOYREfOmmU7i1l3qIpRUXpaLDbsEmI83WloC/pH2W2SaHe3DmXxgV54a135+6IoPi78K/vrsgB4dOKjROujO+wb5RNFlE9UT1+2oihOyOkDf1NZNRXlJqrKTAS7atlRXUugTouLAPtxq1Lm1rZQ7wEe3lp2VzUivFwZHqFnb7Ejb72ri8BosePj4YrBZsfD0xVzq7XDuQxIHpyXTIPRQpifC1pdMfktn3JB5CzGWEcS6RPCBbEXMDFsYl/+CBRFcTJOu+auyWIlt6yB+p017Pq6qL08ZkooP7iaiAvx5tV1uZisdgJ07jy3MJXyxlb++Fk2N06MoaC2meeuTmPrkVru/nAXIyJ8OW9oEME+HizfWcLvhkVx6PN8ZNunR/ql8WhH+tJgr+PTI6/wU+mPjAoaxaMTHyUpIKlHr01RFAVOvOauUwb+A8U1vL6hiHH+Oho/L+LnP4KAK2J4NbOQRy9JoabJTKS/Jw98spuqJsfsHg9XF341LZ45w0OJ8PPk+e8O8dH2IsbHBbBgdDhR/l74eGgIla5YGsx46d0JCNfh1pYCubKlkiZzEyFeIe0LkyuKovQ0tdg6IKWkKKeANUVmPs8sYdL0pE5BH8DVKhkX68s4f1dyta5c+/a2DtstNjtuGoG3hyuhvloev3Q4N02KxWKTxAV6ofdyP2k7QrxCCPEK6clLUxRFOW1OFfgbDhyhqMHMd/urAThiNBHh79E++wbA08eNkHBPfj1GT3BEMC01zQTo3KltPjaXf87wMKIDvIgL0jmOcXeM9yuKogwEgz7wS6sVc2Eh0mTCWlJFkc2HlDAfsooNvLeziH9dmIJ2SxXV+Y2Exngz6fJYQocG4ubq+NHEBup4/1cTWLwpn6xiA3NHhDJ9aDDDwnxx06jlDBRFGXgG9Rh/Y85hjIXFcGAfwlUDQ0exq96INSmFx1dkU9VowkXADeOiuWlsNH5uNkKiu05WZrNLjBYbOo9B/1mpKMogcU6N8QshLgReADTA21LKp3qyfrtdsm1/CW9urKDCqOH6qNGkbVhF+LS5DMn6kp9cvfnzghHUNZtxcREMDfYiNsIHj5MEdY2LUEFfUZRBoc8jmRBCA7wCzAGKge1CiJVSyn0nP/L07Smu56YPs7DYHH/NPFoKT0y7jBnP/Bn97x/h0opiquuKsev9CQvxwz9cpSRWFMV59EcXdgJwWEqZByCE+Ai4DOixwL+rsK496B/1dk4rk4am4JO1Ff+rFxGoVcsIKorinPrj7mQkUHTc6+K2sg6EELcJIXYIIXZUVVX9fPNJebh3/jzzchN4JSTgNXECLiroK4rixPoj8IsuyjrdYZZSvimlTJdSpgcHB5/RCcbF+OPr2TH43zsumKBRw/BMTj6juhRFUQab/hjqKQaOz0AWBZT25AmSwnxYettk1h2ooKrByMwEP8bE+uPlq56SVRRF6Y/Avx0YKoSIB0qAa4Hre/okKeG+pISr1MWKoig/1+eBX0ppFULcDXyDYzrnu1LK7L5uh6IoirPql4npUsovgS/749yKoijOTuUcUBRFcTIq8CuKojgZFfgVRVGcjAr8iqIoTmZAZOcUQlQBBWd5eBBQ3YPNORcN9msc7NcH6hoHg3Px+mKllJ2egB0Qgb87hBA7ukpLOpgM9msc7NcH6hoHg4F0fWqoR1EUxcmowK8oiuJknCHwv9nfDegDg/0aB/v1gbrGwWDAXN+gH+NXFEVROnKGHr+iKIpyHBX4FUVRnMygDfxCiAuFEAeFEIeFEH/s7/b0BiFEvhBijxAiUwixo7/b0xOEEO8KISqFEHuPKwsQQnwrhMhp++rfn23srhNc4xNCiJK29zJTCHFxf7axO4QQ0UKItUKI/UKIbCHEvW3lg+Z9PMk1Doj3cVCO8bct6H6I4xZ0B67ryQXdzwVCiHwgXUp5rj00ctaEENOBJuC/UsqRbWXPALVSyqfaPsT9pZQP9Wc7u+ME1/gE0CSlfLY/29YThBDhQLiUcqcQwgfIAC4HbmGQvI8nucarGQDv42Dt8bcv6C6lNANHF3RXznFSyvVA7c+KLwMWt32/GMd/sAHrBNc4aEgpy6SUO9u+bwT241hXe9C8jye5xgFhsAb+01rQfRCQwBohRIYQ4rb+bkwvCpVSloHjPxwQ0s/t6S13CyGy2oaCBuwwyPGEEHHAGGArg/R9/Nk1wgB4Hwdr4D+tBd0HgalSyrHARcBdbUMIysD0GjAESAPKgH/1a2t6gBDCG1gG/E5K2dDf7ekNXVzjgHgfB2vg7/UF3c8FUsrStq+VwHIcQ1yDUUXbmOrRsdXKfm5Pj5NSVkgpbVJKO/AWA/y9FEK44QiIH0gpP2srHlTvY1fXOFDex8Ea+NsXdBdCuONY0H1lP7epRwkhdG03lRBC6IC5wN6THzVgrQRubvv+ZmBFP7alVxwNiG2uYAC/l0IIAbwD7JdSPnfcpkHzPp7oGgfK+zgoZ/UAtE2jep5jC7r/rX9b1LOEEAk4evngWDv5w8FwjUKIJcAMHCluK4A/AZ8DHwMxQCGwSEo5YG+OnuAaZ+AYHpBAPnD70fHwgUYIMQ34CdgD2NuKH8ExBj4o3seTXON1DID3cdAGfkVRFKVrg3WoR1EURTkBFfgVRVGcjAr8iqIoTkYFfkVRFCejAr+iKIqTUYFfcUpCCCmEeP+4165CiCohxBdtrxd0J6urEOJ3QgivnmirovQ0FfgVZ9UMjBRCeLa9ngOUHN0opVwppXyqG/X/DlCBXzknqcCvOLOvgEvavr8OWHJ0gxDiFiHEy23fvyeEeFEIsUkIkSeEWNhWPuPoXwhtr19uO+63QASwVgixtm3bXCHEZiHETiHEJ205XhBCPCWE2NeW1OucTuWrDB4q8CvO7CPgWiGEFkjlWHbFroQD04BLgZP+JSClfBFHbqiZUsqZQogg4P+AC9qS6u0A7hNCBOB4rH+ElDIV+Gt3L0hRTodrfzdAUfqLlDKrLaXudcCXp9j987bEW/uEEKFneKpJwHBgoyPFC+7AZqABMAJvCyFWA1+csAZF6UEq8CvObiXwLI5cOYEn2c903PdH035b6fhXs/YExwrgWynldZ02CDEBmI0jkeDdwKzTarWidIMa6lGc3bvAk1LKPWdxbAEwXAjhIYTQ4wjgRzUCPm3fbwGmCiESAYQQXkKIpLZxfr2U8kscN4PTzvIaFOWMqB6/4tSklMXAC2d5bJEQ4mMgC8gBdh23+U3gKyFEWds4/y3AEiGER9v2/8Px4bCi7R6DAH5/lpehKGdEZedUFEVxMmqoR1EUxcmowK8oiuJkVOBXFEVxMirwK4qiOBkV+BVFUZyMCvyKoihORgV+RVEUJ/P/AcxYq7RbgL2gAAAAAElFTkSuQmCC\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"sns.scatterplot(data=top_5_data, x='Minutes', y='MB', hue='Genre')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"id\": \"faedb015\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 25,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"sns.pairplot(data=df)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"id\": \"04e26de3\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 26,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"sns.heatmap(df.corr(), annot=True)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"id\": \"ad2d66de\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 27,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n 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\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"sns.heatmap(df.corr(method='spearman'), annot=True)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"696def26\",\n \"metadata\": {},\n \"source\": [\n \"### missing values through plots\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 28,\n \"id\": \"d445d93b\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Collecting missingno\\n\",\n \" Downloading missingno-0.5.2-py3-none-any.whl (8.7 kB)\\n\",\n \"Requirement already satisfied: matplotlib in c:\\\\users\\\\himan\\\\anaconda3\\\\lib\\\\site-packages (from missingno) (3.5.1)\\n\",\n \"Requirement already satisfied: numpy in c:\\\\users\\\\himan\\\\anaconda3\\\\lib\\\\site-packages (from missingno) (1.21.5)\\n\",\n \"Requirement already satisfied: seaborn in c:\\\\users\\\\himan\\\\anaconda3\\\\lib\\\\site-packages (from missingno) (0.11.2)\\n\",\n \"Requirement already satisfied: scipy in c:\\\\users\\\\himan\\\\anaconda3\\\\lib\\\\site-packages (from missingno) (1.7.3)\\n\",\n \"Requirement already satisfied: fonttools>=4.22.0 in c:\\\\users\\\\himan\\\\anaconda3\\\\lib\\\\site-packages (from matplotlib->missingno) (4.25.0)\\n\",\n \"Requirement already satisfied: packaging>=20.0 in c:\\\\users\\\\himan\\\\anaconda3\\\\lib\\\\site-packages (from matplotlib->missingno) (21.3)\\n\",\n \"Requirement already satisfied: pillow>=6.2.0 in c:\\\\users\\\\himan\\\\anaconda3\\\\lib\\\\site-packages (from matplotlib->missingno) (9.0.1)\\n\",\n \"Requirement already satisfied: pyparsing>=2.2.1 in c:\\\\users\\\\himan\\\\anaconda3\\\\lib\\\\site-packages (from matplotlib->missingno) (3.0.4)\\n\",\n \"Requirement already satisfied: kiwisolver>=1.0.1 in c:\\\\users\\\\himan\\\\anaconda3\\\\lib\\\\site-packages (from matplotlib->missingno) (1.3.2)\\n\",\n \"Requirement already satisfied: python-dateutil>=2.7 in c:\\\\users\\\\himan\\\\anaconda3\\\\lib\\\\site-packages (from matplotlib->missingno) (2.8.2)\\n\",\n \"Requirement already satisfied: cycler>=0.10 in c:\\\\users\\\\himan\\\\anaconda3\\\\lib\\\\site-packages (from matplotlib->missingno) (0.11.0)\\n\",\n \"Requirement already satisfied: six>=1.5 in c:\\\\users\\\\himan\\\\anaconda3\\\\lib\\\\site-packages (from python-dateutil>=2.7->matplotlib->missingno) (1.16.0)\\n\",\n \"Requirement already satisfied: pandas>=0.23 in c:\\\\users\\\\himan\\\\anaconda3\\\\lib\\\\site-packages (from seaborn->missingno) (1.4.2)\\n\",\n \"Requirement already satisfied: pytz>=2020.1 in c:\\\\users\\\\himan\\\\anaconda3\\\\lib\\\\site-packages (from pandas>=0.23->seaborn->missingno) (2021.3)\\n\",\n \"Installing collected packages: missingno\\n\",\n \"Successfully installed missingno-0.5.2\\n\"\n ]\n }\n ],\n \"source\": [\n \"!pip install missingno\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 29,\n \"id\": \"5c722f58\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import missingno as msno\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 30,\n \"id\": \"8b34e69c\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 30,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n 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\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"msno.matrix(df)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"f086e84b\",\n \"metadata\": {},\n \"source\": [\n \"## EDA summary using Pandas Profiling\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 32,\n \"id\": \"a2454c15\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Collecting pandas_profiling\\n\",\n \" Downloading pandas_profiling-3.6.6-py2.py3-none-any.whl (324 kB)\\n\",\n \"Collecting ydata-profiling\\n\",\n \" Downloading ydata_profiling-4.1.2-py2.py3-none-any.whl (345 kB)\\n\",\n \"Collecting phik<0.13,>=0.11.1\\n\",\n \" Downloading phik-0.12.3-cp39-cp39-win_amd64.whl (663 kB)\\n\",\n \"Requirement already satisfied: requests<2.29,>=2.24.0 in c:\\\\users\\\\himan\\\\anaconda3\\\\lib\\\\site-packages (from ydata-profiling->pandas_profiling) 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typing_extensions-4.5.0-py3-none-any.whl (27 kB)\\n\",\n \"Requirement already satisfied: six>=1.5 in c:\\\\users\\\\himan\\\\anaconda3\\\\lib\\\\site-packages (from python-dateutil>=2.7->matplotlib<3.7,>=3.2->ydata-profiling->pandas_profiling) (1.16.0)\\n\",\n \"Requirement already satisfied: charset-normalizer~=2.0.0 in c:\\\\users\\\\himan\\\\anaconda3\\\\lib\\\\site-packages (from requests<2.29,>=2.24.0->ydata-profiling->pandas_profiling) (2.0.4)\\n\",\n \"Requirement already satisfied: certifi>=2017.4.17 in c:\\\\users\\\\himan\\\\anaconda3\\\\lib\\\\site-packages (from requests<2.29,>=2.24.0->ydata-profiling->pandas_profiling) (2021.10.8)\\n\",\n \"Requirement already satisfied: idna<4,>=2.5 in c:\\\\users\\\\himan\\\\anaconda3\\\\lib\\\\site-packages (from requests<2.29,>=2.24.0->ydata-profiling->pandas_profiling) (3.3)\\n\",\n \"Requirement already satisfied: urllib3<1.27,>=1.21.1 in c:\\\\users\\\\himan\\\\anaconda3\\\\lib\\\\site-packages (from requests<2.29,>=2.24.0->ydata-profiling->pandas_profiling) (1.26.9)\\n\",\n \"Requirement already satisfied: patsy>=0.5.2 in c:\\\\users\\\\himan\\\\anaconda3\\\\lib\\\\site-packages (from statsmodels<0.14,>=0.13.2->ydata-profiling->pandas_profiling) (0.5.2)\\n\",\n \"Requirement already satisfied: colorama in c:\\\\users\\\\himan\\\\anaconda3\\\\lib\\\\site-packages (from tqdm<4.65,>=4.48.2->ydata-profiling->pandas_profiling) (0.4.4)\\n\",\n \"Building wheels for collected packages: htmlmin\\n\",\n \" Building wheel for htmlmin (setup.py): started\\n\",\n \" Building wheel for htmlmin (setup.py): finished with status 'done'\\n\",\n \" Created wheel for htmlmin: filename=htmlmin-0.1.12-py3-none-any.whl size=27096 sha256=ff0bca2339091901a96f2090fdd14cc31223f1dd8c30643f8e17e6051a73d77b\\n\",\n \" Stored in directory: c:\\\\users\\\\himan\\\\appdata\\\\local\\\\pip\\\\cache\\\\wheels\\\\1d\\\\05\\\\04\\\\c6d7d3b66539d9e659ac6dfe81e2d0fd4c1a8316cc5a403300\\n\",\n \"Successfully built htmlmin\\n\",\n \"Installing collected packages: tangled-up-in-unicode, multimethod, visions, typing-extensions, imagehash, typeguard, pydantic, phik, htmlmin, ydata-profiling, pandas-profiling\\n\",\n \" Attempting uninstall: typing-extensions\\n\",\n \" Found existing installation: typing-extensions 4.1.1\\n\",\n \" Uninstalling typing-extensions-4.1.1:\\n\",\n \" Successfully uninstalled typing-extensions-4.1.1\\n\",\n \"Successfully installed htmlmin-0.1.12 imagehash-4.3.1 multimethod-1.9.1 pandas-profiling-3.6.6 phik-0.12.3 pydantic-1.10.7 tangled-up-in-unicode-0.2.0 typeguard-2.13.3 typing-extensions-4.5.0 visions-0.7.5 ydata-profiling-4.1.2\\n\"\n ]\n }\n ],\n \"source\": [\n \"!pip install pandas_profiling\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 33,\n \"id\": \"0a63bbf8\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"C:\\\\Users\\\\himan\\\\AppData\\\\Local\\\\Temp\\\\ipykernel_20104\\\\2274191625.py:1: DeprecationWarning: `import pandas_profiling` is going to be deprecated by April 1st. Please use `import ydata_profiling` instead.\\n\",\n \" from pandas_profiling import ProfileReport\\n\"\n ]\n }\n ],\n \"source\": [\n \"from pandas_profiling import ProfileReport\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 34,\n \"id\": \"5f6244cc\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"application/vnd.jupyter.widget-view+json\": {\n \"model_id\": \"8648e29203de451d91ae1293b1a9479a\",\n \"version_major\": 2,\n \"version_minor\": 0\n },\n \"text/plain\": [\n \"Summarize dataset: 0%| | 0/5 [00:00\"\n ],\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"data\": {\n \"text/plain\": []\n },\n \"execution_count\": 34,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"report = ProfileReport(df)\\n\",\n \"\\n\",\n \"report\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"31bd522b\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.12\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n" }, { "path": "March Cohort 2023/21_EDA and Data Viz/21_EDA and Data Visualization.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"id\": \"343be340\",\n \"metadata\": {},\n \"source\": [\n \"# EDA\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"id\": \"6d58db2b\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import numpy as np\\n\",\n \"import pandas as pd\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"id\": \"cb96b2f2\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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TrackComposerMillisecondsBytesUnitPriceGenreAlbumArtist
0For Those About To Rock (We Salute You)Angus Young, Malcolm Young, Brian Johnson343719111703340.99RockFor Those About To Rock We Salute YouAC/DC
1Put The Finger On YouAngus Young, Malcolm Young, Brian Johnson20566267134510.99RockFor Those About To Rock We Salute YouAC/DC
2Let's Get It UpAngus Young, Malcolm Young, Brian Johnson23392676365610.99RockFor Those About To Rock We Salute YouAC/DC
3Inject The VenomAngus Young, Malcolm Young, Brian Johnson21083468528600.99RockFor Those About To Rock We Salute YouAC/DC
4SnowballedAngus Young, Malcolm Young, Brian Johnson20310265994240.99RockFor Those About To Rock We Salute YouAC/DC
\\n\",\n \"
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\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
TrackComposerUnitPriceGenreAlbumArtistMinutesMB
0For Those About To Rock (We Salute You)Angus Young, Malcolm Young, Brian Johnson0.99RockFor Those About To Rock We Salute YouAC/DC5.72865011.170334
1Put The Finger On YouAngus Young, Malcolm Young, Brian Johnson0.99RockFor Those About To Rock We Salute YouAC/DC3.4277006.713451
2Let's Get It UpAngus Young, Malcolm Young, Brian Johnson0.99RockFor Those About To Rock We Salute YouAC/DC3.8987677.636561
3Inject The VenomAngus Young, Malcolm Young, Brian Johnson0.99RockFor Those About To Rock We Salute YouAC/DC3.5139006.852860
4SnowballedAngus Young, Malcolm Young, Brian Johnson0.99RockFor Those About To Rock We Salute YouAC/DC3.3850336.599424
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" Track \\\\\\n\",\n \"0 For Those About To Rock (We Salute You) \\n\",\n \"1 Put The Finger On You \\n\",\n \"2 Let's Get It Up \\n\",\n \"3 Inject The Venom \\n\",\n \"4 Snowballed \\n\",\n \"\\n\",\n \" Composer UnitPrice Genre \\\\\\n\",\n \"0 Angus Young, Malcolm Young, Brian Johnson 0.99 Rock \\n\",\n \"1 Angus Young, Malcolm Young, Brian Johnson 0.99 Rock \\n\",\n \"2 Angus Young, Malcolm Young, Brian Johnson 0.99 Rock \\n\",\n \"3 Angus Young, Malcolm Young, Brian Johnson 0.99 Rock \\n\",\n \"4 Angus Young, Malcolm Young, Brian Johnson 0.99 Rock \\n\",\n \"\\n\",\n \" Album Artist Minutes MB \\n\",\n \"0 For Those About To Rock We Salute You AC/DC 5.728650 11.170334 \\n\",\n \"1 For Those About To Rock We Salute You AC/DC 3.427700 6.713451 \\n\",\n \"2 For Those About To Rock We Salute You AC/DC 3.898767 7.636561 \\n\",\n \"3 For Those About To Rock We Salute You AC/DC 3.513900 6.852860 \\n\",\n \"4 For Those About To Rock We Salute You AC/DC 3.385033 6.599424 \"\n ]\n },\n \"execution_count\": 8,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df.head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"id\": \"d12453da\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import matplotlib.pyplot as plt\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"id\": \"f89c2753\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"df['Minutes'].plot.box()\\n\",\n \"plt.show()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"id\": \"97e62aa5\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import seaborn as sns\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"id\": \"e6063135\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 13,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"sns.boxenplot(y = df['Minutes'])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"id\": \"6e7b8457\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 14,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n 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UnitPriceMinutesMB
count3503.0000003503.0000003503.000000
mean1.0508056.55998733.510207
std0.2390068.916757105.392534
min0.9900000.0178500.038747
25%0.9900003.4546836.342566
50%0.9900004.2605678.107896
75%0.9900005.36075010.266789
max1.99000088.1158831059.546140
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" UnitPrice Minutes MB\\n\",\n \"count 3503.000000 3503.000000 3503.000000\\n\",\n \"mean 1.050805 6.559987 33.510207\\n\",\n \"std 0.239006 8.916757 105.392534\\n\",\n \"min 0.990000 0.017850 0.038747\\n\",\n \"25% 0.990000 3.454683 6.342566\\n\",\n \"50% 0.990000 4.260567 8.107896\\n\",\n \"75% 0.990000 5.360750 10.266789\\n\",\n \"max 1.990000 88.115883 1059.546140\"\n ]\n },\n \"execution_count\": 16,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df.describe()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"id\": \"4ec7f0a5\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 17,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"sns.histplot(x=df['Minutes'], kde=True) # kernel density estimation\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"id\": \"19abf450\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 18,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"sns.violinplot(data=df, x='Minutes')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"id\": \"64555428\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"top_5_genres = df['Genre'].value_counts().index[:5]\\n\",\n \"\\n\",\n \"top_5_data = df[df['Genre'].isin(top_5_genres)]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"id\": \"6b25e96f\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 20,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n 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\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"sns.violinplot(data=top_5_data, x='Minutes', y='Genre')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"id\": \"f0044743\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 21,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n 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\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.scatter(df['Minutes'], df['MB'])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"id\": \"b71d4f0d\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"Text(0, 0.5, 'MB')\"\n ]\n },\n \"execution_count\": 22,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.scatter(df['Minutes'], df['MB'])\\n\",\n \"plt.xlabel('minutes')\\n\",\n \"plt.ylabel('MB')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"id\": \"45dd51d8\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 23,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"sns.scatterplot(data=df, x='Minutes', y='MB')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"id\": \"4422dc87\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 24,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"sns.scatterplot(data=top_5_data, x='Minutes', y='MB', hue='Genre')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"id\": \"faedb015\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 25,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"sns.pairplot(data=df)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"id\": \"04e26de3\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 26,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"sns.heatmap(df.corr(), annot=True)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"id\": \"ad2d66de\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 27,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n 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\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"sns.heatmap(df.corr(method='spearman'), annot=True)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"696def26\",\n \"metadata\": {},\n \"source\": [\n \"### missing values through plots\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 28,\n \"id\": \"d445d93b\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Collecting missingno\\n\",\n \" Downloading missingno-0.5.2-py3-none-any.whl (8.7 kB)\\n\",\n \"Requirement already satisfied: matplotlib in c:\\\\users\\\\himan\\\\anaconda3\\\\lib\\\\site-packages (from missingno) (3.5.1)\\n\",\n \"Requirement already satisfied: numpy in c:\\\\users\\\\himan\\\\anaconda3\\\\lib\\\\site-packages (from missingno) (1.21.5)\\n\",\n \"Requirement already satisfied: seaborn in c:\\\\users\\\\himan\\\\anaconda3\\\\lib\\\\site-packages (from missingno) (0.11.2)\\n\",\n \"Requirement already satisfied: scipy in c:\\\\users\\\\himan\\\\anaconda3\\\\lib\\\\site-packages (from missingno) (1.7.3)\\n\",\n \"Requirement already satisfied: fonttools>=4.22.0 in c:\\\\users\\\\himan\\\\anaconda3\\\\lib\\\\site-packages (from matplotlib->missingno) (4.25.0)\\n\",\n \"Requirement already satisfied: packaging>=20.0 in c:\\\\users\\\\himan\\\\anaconda3\\\\lib\\\\site-packages (from matplotlib->missingno) (21.3)\\n\",\n \"Requirement already satisfied: pillow>=6.2.0 in c:\\\\users\\\\himan\\\\anaconda3\\\\lib\\\\site-packages (from matplotlib->missingno) (9.0.1)\\n\",\n \"Requirement already satisfied: pyparsing>=2.2.1 in c:\\\\users\\\\himan\\\\anaconda3\\\\lib\\\\site-packages (from matplotlib->missingno) (3.0.4)\\n\",\n \"Requirement already satisfied: kiwisolver>=1.0.1 in c:\\\\users\\\\himan\\\\anaconda3\\\\lib\\\\site-packages (from matplotlib->missingno) (1.3.2)\\n\",\n \"Requirement already satisfied: python-dateutil>=2.7 in c:\\\\users\\\\himan\\\\anaconda3\\\\lib\\\\site-packages (from matplotlib->missingno) (2.8.2)\\n\",\n \"Requirement already satisfied: cycler>=0.10 in c:\\\\users\\\\himan\\\\anaconda3\\\\lib\\\\site-packages (from matplotlib->missingno) (0.11.0)\\n\",\n \"Requirement already satisfied: six>=1.5 in c:\\\\users\\\\himan\\\\anaconda3\\\\lib\\\\site-packages (from python-dateutil>=2.7->matplotlib->missingno) (1.16.0)\\n\",\n \"Requirement already satisfied: pandas>=0.23 in c:\\\\users\\\\himan\\\\anaconda3\\\\lib\\\\site-packages (from seaborn->missingno) (1.4.2)\\n\",\n \"Requirement already satisfied: pytz>=2020.1 in c:\\\\users\\\\himan\\\\anaconda3\\\\lib\\\\site-packages (from pandas>=0.23->seaborn->missingno) (2021.3)\\n\",\n \"Installing collected packages: missingno\\n\",\n \"Successfully installed missingno-0.5.2\\n\"\n ]\n }\n ],\n \"source\": [\n \"!pip install missingno\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 29,\n \"id\": \"5c722f58\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import missingno as msno\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 30,\n \"id\": \"8b34e69c\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 30,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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S/kryslJL5oXit9Sdd1Mz2q/2MO7mUcniSY5OsTPKGuZDRwBq2lfY8Yq411HiaizHr0pzb9mutvyqlrCilzNZaP19KOSvN4IeDkhyX5PRa66862wGAEaAHH9dJ7YiN/0ry1ST/UEp5RttLcjbJXmkv5sz10W2/dMyNboNFa4+3QZJXJdktyW2TPLF9bU07cjxJ3t++fo/2NSdWbLZSyo5JvpXmJH1VrfXnbYuepOkduS5N+4rUWk9M8vo0bQM+VEp5aru8Ou7YUmXehIallL9N05bnjHYyw4+l+bz7vyT/W2t9fK310ran6YvS3MHwZccdW6OUcss0/1OfneQGbb/csba92GlJHpfmbpkXlVKe12WtbL/m5uJozw1umSaQvDDJE0opc9/n5gbbwJJpL8TMnbcmyQfS/I/9QJK7JHlfKWXf9kJgL0lqrT+utX6p1npUrfV/heEA257wkOusWut0kjeknUyulPKX7UszaVoKzAVBs/NueTRbPFtt/gWV9mJLqbV+LcmD0ty6+LxSyoOS9aF4L00vwMuS/K6Lmtnu7Z9klyRr0owGT20mcV2RZiKlS5JcWv4wgeaP0oTin0kzgaYJDtkq80aG3yvNCfp709wRkyTvS9OruZfkV6WU+5ZSHpvkn9O07XlSrfUXy18126OFgWN7F8xLkxyd5AGllCe1x+NgXij+50n2TPL0Usruy14027X2gt/cBbt9aq1fqLXeOM3n105JXjn3/7Ntx9hrL8oIx1m0eRebP1RKeUeSq2qtP6+1vjrJR9IMdPjnUsre7Xe+nUspLyul3LPLugFGjZYpXOfMv32x1npMKeWNSV6d5J3t6LQVaUZ3HJhkh/whBN8zyTmllCP1BGRLtSfh60opK9PchdCrtZ6TJLXW75dSHpJm9vd3llL+OU0geY8kL04zyebx3VTO9qzWenop5S+S/GOaFgFjtdb31FqvLqW8IM3I8Y/OnbC3bVF+VEp5a5rRbv0Oy2c7V0p5XZIjk5yT5HO11qvaY3BdKeVNadqTPT/JM5P8Ksm5aSb4+nFXNbP9mXfx5YVJflBr/b9a63dLKevSXPj7j1LKlbXWz7UtoMbbz8bDklxda72ky/rZviyYF+HdSW7StiD7fK31+FLKk5N8PMkr2vZj/5lkLM1n4aVpLgbCUihJXpjk4lLKh2qtv661vqq9sflpST5SSplK8sgkT05yt84qBRhBJtXkOmXBl9iDa63Ht48PTfL3SSaS3DjJp5LcLskgzQjKlWm+zP5VrfWUDkpnOzZ3a2Pby/mLSW6T5tj6bK31BfPed1iaHrvjSX6aps/uxUme1o4YN4Emm62UsrLWurZ9fNckU2n61b+q/f2GJI+ttX6h/GFS4TJv5NEOtdY1HZXPEGjvRJhOcs8kn0zyN7XW1Qvec7Mk10vy+ySra62XLXedbP9KKXdJ8sM0rfBeO+/73SFJXpvkIUke04bivTQXpdd1VS/bpwX/Iz+d5u6XdyY5utZ6/rzve4ekaQ21Ls33ut2T/EWSiVrrqR2Vz5BYcBz+Q5K/S/KaJB+stf66Xf6aJM9Kcv00/18fVWs9qaOSAUaSQJzrjAVh+PvSjL79dK31qHbZfdLcYnuvJC+stX5kwRcOYSRbrQ2GvpTmwsrXkhyQ5KlJPp/kGbXWS9v33StNz+efJDmy1np0u1w4ybVqg549klzW9o5MKeWQWuuxpZS7pRkpfrs0J0j/XxuGm/SLRVt4HM27yDKe5NtpbuGeTPLhdqT4uECSpVRKWZXm7qqvJXldrfW4dvlcKP6AJE+ptX6qsyIZCu0dLk9J04++336m7ZhmEuAVbd/6Q9KMBt8nyRVpWkH9qLOi2W4tOIfttb3p1//PbUeBPz9/HIrfPc3FmDNqrb/spnqA0aVlCtcZ875IfCp/aEVx4rzXv1tKqWluaXxX+0Xj3+dtQmDEFlkQEO2R5hh6Va31B+1o8ePTnCx9tJTy1FrrpbXW75VSDk/TPuUlpZTft7d/C8PZHH+W5ElJ3pTklFLK/ya5XinlT2utP2xbCvxjklunGSWehSdWsKUWnKzfKs2Ehb9JMtu2R7lfkuOSHJWkllI+0gZIjju22MIBCu1Fl9l29Pdjk3y2Xf66Wutx7QXB16QJhv6llPKlJJdrf8e1KaXsluRetdavzFu2a5oJDD9eaz22XXZAkrcluVGSsVLKn7fH3WPStGK8stb6++XfA4bBvP+v/5Dk1PZ/6Lp5re4mS9Of/u+TzJZSPlprPb/W+oNOCwcYcUaIc51SSnlGktcleXqSb7W3NZZkgwlKDk3TUuChaUYSfbyjctmOLegZvm+S/ZI8vdb6jHnvuV6SJyT5hyT/k6Y1ytxI8Qekaa9yVpK/rrV+f7n3ge1LezJ0YJLPpenNfFGSm6Vpi/LDee+bGym+b5K311r/uV0unGSLLbiT6v1JDk+yY5q2T0+otZ7fvjaeJhTfP83dWB+vtV7ZTdUMg1LKE9N8l7ug/fwbtN/rVqUJxb+c5I3zQsu7J/lNrfXc7qpme9HecfVvadrc3X/ujpZSyg5pWvOsSXPnwWFpRuZ+P8l5Se6T5vPvUT7jWCqllJ2S/DrN97sj0tzlvK49TmuSP0lz3rBfkg8keVet9Xdd1QtA0uu6AFjgjmkmtDlx7gS+/T03cWba4PGdaUKlH25sI3BN2oBoXRt4f6f9+VaSJ5VSHjz3vrZX7n+mmRDnsCRfLKXs0gaT30yyKs0X3F8t+06w3WiPmb1rrbO11n6SR6RpyXP3JH8/F4aXUnrtsfnDNMfceUleWEp5UdKMFO9mD9hetZ9Vc2H4VJI/TfJPSf4jTZuAY0spd06SNky6R5KfpTlZf1wXNTMcSinPTDNx4ctLKTduR1DOfcZ9LsnzkjwsyQtKKfdNklrrD4ThbK72f+Jbkzyk/U531/aldWkuuNw0yf8meXyavvUPqLU+NcmxScaE4SyV9q6YK9MMcrgsyduTPK4dfDOojQuSnJ/k6iTP7bBcAFoCca5rbpkmA59J1o/+WB8ElVKe0D7/ZpK/qLWe0VGdbKfaL621Ha32pSRXppnM8M1pbpt9fillYu79bSj+qTSji9akua12roXF15LcrNZ6zvLuBduLtjf9V5K8bu7zLMmtklyYZoT4EaWUu7Yh0SDtxb82FJ9MclWSp5VS9lz24tnuzfvfuXeSvZK8uNb6jlrri5M8M03blC8uCMXvneYi4bHdVM0wqLV+KMm/J3liklfOC8Xn2jV+Pc3x94Qkf9v2d4bNMu/84Ixa6xVtu7EftK1QZpP8a5oe4g9N8uRa61va9fZIc4fMOaWUFXN3ocKWaM8h1ptrmVJrvSTNJNVXpAnFHz9vnZumOed4cJJbGx3O5vI5BduOlilcJ8ybgOSlafqr/VWt9ZML3nNAmpOrf6y1fnr+beCwOeaOmfbEe580tzS+t9Z6Svv6M9Pcfnt0mgm/+vPW3TlNGF4XTJTjOOQalVIeneQbtdbLSil71FpnSin7pGmJ8rE0/ZyfkeSE9nOwpBm9tq6Ucqckl7rowtYqpXwgzQn6VUmeWGs9s13eS3JomguCeyd5+PzPPNhcC3uGL3jtw2nmTviPJG+tf5hM7tA0Yfk3kpxZaz1tmcplyLSt7w5I8pY0AfgTaq3/tZH33THJ36W5u+/etdafLGuhDIUFc3K8JM13uZLkTbXW37bL90hzUfl6ab7n/TDJI5PcK8l9aq3uLOVatd/TekmuV2u9uOt6YBgJxFl213LidIc0vZp/lSaQ/Hy7fO8kr0/T9+/B1UzcbKW2T+7RaVqg/CrJIbXW380Ly5+e5ENJ/jvNMfijBesLwNksC4+VUspb07ShuE+t9bz2WLxbko+mCcWfmqSf5sTqjUm+X2v9wrIXztBoT6aekWYS1z3SBEFfnddGpaQ5QX9HkokkB9daT+6kWLZLpZSVtda17ePDktwgTdudi+a+q5VSPpImqPxCkjek+Yx7VZr2An+qHRSbo/08e0SSHWutn2qXfTTJqbXWt7YtU16XphXP42qtn5m37kvSjMy9VZJHL/xuB1uqlPLpNHdUXZLmDqxLkzwtzXe3NaWU3dO0jTo4zZ0xv07yJMcem6M0kwP/Y5q5h26S5n/nv8/NlQAsjfFrfwssnQVX1Z+TZvKuG6YZOXRCrfXUUsqfJ/l8kveVUp6c5gvGrZPcKclhwnAWox11+900I8T3T3KjJL9L0y5lba31w6WUmuZuhBuWUp5Va/3ZvPWF4WyWjRwrFyUZJPlMKeVxtdZzSyknpLmt+yNJPpEmHL9dkien+cyDzbbwIkx7x8En0/wf/UCS5yc5Lckv2tdrKeV7SV6e5u4sPXW5Vu0dUw9O8vW5PsztcfbwJLu2b5supby/1vqpWuvTSinvSXNB5ilpjr89kjxQGM4W2CnJY5Mc1s4B84g08x68K0lqrSeUUl7Tvve/2v+znynNJJs7JzkhyXNrrT/voHa2cwvOYR+a5oLeo5KcleZ84p1JPpmmzd10rfWSUspj0nyn2yHJObXW33RSPNuVUspuSX6Qpq3YqWkGcL0/zTnEBzssDYaOEeIsmwVtJj6R5lbt09Nc9bx+miDoH9vRundOM+HSoWlGTp6U5lZbtzeyRTY1oruU8rw0AdDFaSZkOnfBSLfnpBnpcagTdrbU/ONuwePnJXlRmnD8Me1xtyLNhML/lmYSsN/GKCK20IKT9R2SjNdaL2+fr0jy52lC8a8neWGt9Rfz1i1pRl0KxLlWpZTPJHl0mj70H0kzCfDzkrw0ySlJDknygjSteF5Ra/1ou95D04x26yX5r1rrWctfPduzUspeSb6Y5LZp5nV5cK31xAWff/NHij++bbM4nqYV2Zquamf7teAc9q/TtEm5eZJn1Vqvbv+H3ipN7/rbpbnj75u11qs7KpntVBuG99NcOH56rfUXpZRdknw6zZ0Id5t/XurOZVgcgTjLrpTy3vxhkptjSil/l+aWoN8m+XCSt9VaL2pP4Nd/4G+qzQpsSmlmd1/XTn6zU/tzea31ivb1v03y4jSh+KPmwsm5L7Dz2qj0qlCczbSRYLLOXWhplz0/zYSZ60Pxdnkvzajw86rJltgCC465N6UJJG+Q5Ow0rSl+1p60Pyl/CMUn54492BLtyfn30gxoOCLJA5L8JM33t7nj8L5pWvWsTPI3+tOzGHPf59rHn0lzN8LFafo2v3cj77lrkiPT9G1+TK31c91UzvaqvQvhFbXWV85bdsckc23FPlJrfcaCdW6dJhS/VZLnJPmS8wc2VyllpzQXlc9KczfMFfM+0/4pyX5J/itN27Gf1FpPaF8TisNW6nVdAKOlPUE6OMlz2jD8lWl6lz45yYlpRhcdUUrZsw0lB+3JlS8TbJE2IFrX9mD7ZJLvJ/l5mnYVj0+SWut70lyM2TPJ50op+7Wh0fi87RRfZtlc7cWTuUDonUm+kuQHpZS/KKXsmyS11n9KM5Hh9ZN8tpSyX7t8UGvtC8PZUvOOuU+nubPlx0mOSXNL97eTPLW9yPxfSf46zRwKH5o7JuHalFJ2ak/W0955cGiadmNvT9OH/pRa62x7nKXW+p00/1/vnuQWC7ZVlrN2tm/t97C5UOiNac4JHpWmJ/OL28ENcy3xxtvHJyR5TZLPJjmji7rZ7r0kyUQpZce5BbXWHyd5UJILkxxeSrnP/BXaFot/meaz8R1pBuLA5vrzNP8ve7XWS+d97u2aZjDhPdLMMfSxNG2hnplo5wmLIRBnmyml9NqRueufp7nq+ZUkx5ZSnpDmy8Yza62fTPKkJL9M8pgkbyyl7D73Ae+Dni3RnjzNtqPYTkjThuLLSf45zReNT7SjdFNrfXeavn+7JzmmlHLjuS8gtdXJTrDdmX/xpO2n+/g0J0WXpmkr8JJSyq2SDULx6yX5Zilln06KZmi0t3HfJc3/0hfWWp+Tpm3FDdLMmZD2s+0zadr23LajUtnOtN/lvpBk7tbttHda3S3JuWn+rz6hlLJze1F5LhT/f2nu/rv//O35v8rmagc3zLUce3+a/6vvrbV+LU3bnouTvGhBKL5LKeWRaYLwJ9VaT+uofLZD8wbF/EOaCVivKqU8de71Wus30wzk2iXJa9q7ETLv9TPTHJsPmWtbBteklDLe5iRHp7lz+d6llK/Oe8txaS7C/FmaedXun+b84YhSys2Xt1oYLgJxllzbImButOPcqLUvpukxeUmSN9RaL0kzuuPLSf67XXVtktVpRus+KM0EJLDF2jYnJclrk8wmeWKt9eW11penmdDrc0n+sTQTuM6NFP9AkmOT/L6bqtmeLegTfss0J0qPSfIXtdb7JDkqTTj5ovaW2rlQ/INJLkvTVgA221wwOW+07YFJLkjy49pMpnmrNP9f/zPJ2+eCyrZ9z8eT3L7Wel4HpbP9KWn6l65NctyCUPw+aS48PybNnQhzoXivlHKbNP+DteZhq8w7j9gpzaS/f5/ku+1rv0hz3F2c5IWllBe1d728I8m7k1xfD2e2RNsm5X/bdp7r2jD8SUk+3F6QSZLUWv8nTUuLeyR560ZC8bPqvHk6YFPmjrk0k55flqblziuTPKCU8rVSyilpWiw+sdZ6Qq11tr0D6wVJDkjTyx7YSgJxllRpJoL4SSnlb+Yte3aSg5L8KE3f+qvb28/ukGTXWutl7Vv3TjOT8iFJ7l9r/e3yVs/2qpRym1LKS0op/1lKuX8b+tQ0E9v8otZ69tzdCrXWHyR5W5r+bE+euxWy1vq2JH/ejiwf29TfgjntKLQXlFKuPy8Mf1uSt6YZkfvTud7htdYj00zy9ew0J+5zI8XfnuSBtdazO9kJtitt24qXlVK+kuTf5h97aU6MLq/NHBy3SXJ8kv9J8te11itLKS9PcyGw1FrX1lov7Wg32I60baDWJflQklckuWGS4+eF4lcmuV+SM5O8Jc0dfnsl+dM0dwHumD8MfIAtVkp5d5qg6NFJzp13J9ZYrfWcNKH4b5K8Ic1Iyj9P0zf8V91UzPaovbj8njQX+Y5M8hftsm8leW+SRy8Ixb+e5ti7R5I3lVLusfxVsz1bcMz9fZr5NlanGaT18jTnsbdK8tha6zkLzk93SPO59+vlrRqGy/i1vwU2T/nDrMhnpelTOtcz/G5pTqS+04aNJc2Ioa8neWJ7Ff6kJM9s33uJHrpsrlLKoWl6qZ2d5oRp/ojHXv7QKmC2tBMu1VqPL6V8Ickzkuyc5Kr2PXWu3cqy7gTbq79I0/Zkr1LKO9KMXntoklumCcMvTpJSyso2gHxdO5j3NUl2LaW8th1FdHE35bM9aUcR/U+SmuYk6MPZ8E6qL6c5KX9mmmByLgxfXZo+9QemuQtrpyRXLGPpbMfauw3G2sEM/9kufnuaUPzgWuvltdYr2jDo/9JMGPzYNC3wfp3kAW0LAdhsc3ddtQHQ6WlaLt42yR7t6+snTW+Doscm+f+S7Jrkc445tlR7vB2TZmDWJUn+KcmOtdZ3lVJe377tyaWU1Fr/pl3n66WURyf5WpIrSylPqLWu6WQH2O5s5Jh7Vzuo692llH9Lcx77uiT/nuRh8wZt7ZrkgWkuRBtACItQtPFjKZRmsoeTk/w0yV/VWn9ZSnlFkjeluVX2iFrrp9uRRnMjO+7avn5YmpP0y5M8otb6o052gu1OKeXgJN9Mc8HlPbXWny54/dVprrAfkeQDc18k2t/vTHLfJPdtR7jBFmkvAv5dmoD7H2utR5RSdk/yiTTB+FFJXtcGSSvnRou3o8ifk+TWtdYLOiqf7Uj7P/aHSc5P85n2o7njad57bp+mPcrtk3y31npYu3yvNMfiYWl6mv5sOWtn+9OO/n5RkmNrrd9Y8NqKJE9Mc6fV75McPNcnt21r8T9J7plm8tb/bEe7wTW6lmNuZZpj7nVJrk5zzF087/vc+nMLWIxSygFJvpPmvGKQ5GVJXtQGlDdOM3L8yUk+PReKt+s9IMmvaq0/6aBstmObOOZeWGv9p/a731+n+Q73zVrrw9p1/i3J45IcWpuJXoGtJBBn0doP6++mmbjwkPkn26WUryV5cJpJIp7ZfoGdH4rfLM1oyj2THKefKZur/WL6uTQjh17c9qVf2Mt5jzTH5vWTvKnW+s/t8tsk+VSSU2qtT93I5mGztJ9/L05zq+NUrfXFbVD+uTS3Or47yTs3Eorf0J0wbI7STPD10SQ3SfKMufY6cyMk28fPSNOD8pZJ/iXNJMFTaf633i7JvdK05nHBmWvUjj77bJJHtos+lOTLtZkgc+49OyR5Qpr2UAtD8V3SHItPrrX+fDlrZ/u0hcfcW9L0DL9nrfWSuVB8uWtm+Mydn5ZSjkjyvCRPTXMh5i/ThOL/tCAU/89a63O7q5jt3TUcc89Kc267MBT/Vpo7op+W5N611pM6Kh2Ghh7iLEob/Jyc5lbsq5M8fa6vZJLUWv80yTfSfMl9Will17nbb9vXf1Fr/Wat9bPCcLbQfmn6zn92LgxPmtvPkvW9JWeSPCDJ75K8pZRyWinlG0m+mGaSsGe27y2BzTT/eGlHP74zyeuTTJZS3tn2Zn5MkjPSTHrzkja8XNuOrkxM3srm2yvNnBufTjs54by+zmlPpD6Y5n/xmWnaVfx3mn71hyX5VZJ7CcPZTDV/uAX72DR9mz9TSvm/UsojSik3a1sCfDLNxcAbZ8Oe4penCSuF4WyuLTnmXpKmj/0xpZTdq3lf2EqllB1KKddv70BImvOCpBlI8/s0kxW+LM1x985SyvNrM7/VG9JcpH52KeUfl7lstmNbcMz9Z/5wzM3vKf6QNBdohOGwRATibLU2DD8hTehziyTHJPmrJK9qr2YmWR+K/1+SNyZ5ZhuKz5ZSHH8sxqFpTsT/b2MvzusZfmGaCzLvTDOa/NI0Pe7v1vafHJ8L0WFTSik7t7fEru81P/faRkLxt7cXYx6T5GdJ/ibJa9pj7eq5bSz3PrDdumuai87/MzcSct5dVm9L03rsrWnC8hOSrK21PifJRK31HkmeX2s9o5PK2a60d1gN0tzxcl6az6/90ozKvVGau/2+UJo+9devtX48yXPT3JHw81LKzonPNzbfVh5zz0tyvSQ/LaXsZoQ4W6q9gHdumklY31ZKmZj3//XYJD9J8sp2gMNzk3w8G4bib0nyjiTv62QH2O4s8phbnaaP+N8luZ0wHJaOQJKt0o7GOC9NP9O/rrWem2ZU2vfTXLl85YJQ/H5pTtSPSjOK/Hr6/bFI65KMpZ1UbhMXWOZOkvZOM6nrY9LM1P3q+ofJmNYtT7lsr9rw+z1J/qeUsiq5xlD8jUleVEp5bq31ojQj3S5OE47vvuzFMwzGkqxN01ty/Wdd+/uGSZ5ea31FmskML0hySinlJmkmGU7+8DkI12je59qlaSaJe1KaljuvTjPp18vTzPnyb2lCyiOTfDvNJJu/TPO/FjbbIo65d6QJl27URd1s956S5ti5WZrj7fhSyjtLM0Fmkrwqydr2u9zqNMfhx9LcbfrSdv6Xl7vYzBZY7DF3SZL3uvsKlpZAnK3SXtG8T5oekb9sb9+eTfLnueZQ/Lg0PXWfpE0Fi3RikvEkf5s0IyYXHlPtidauaW5tXD+6d97rgiKuVXvMvCXNSfj75768biIUf0+a1havKaXcvNb6+zRtKx7aPoYtdXqaMPyvkvWfdb1a66DW+sw0t9am1vrNNBMz/TjJ7+YuOhuty5aojSvSnIiPpbmIPKi1XlxrfVuSq5JcmOb/76vTHG/7JvlTJ+psDcccHfhUmtYnNclpaXo23y3Ju0opRye5S5oWFndLklrrb5K8IsmX0rTB29PALrbQUhxzvs/BEhOIs9VqrT+qtZ7fPh60o22vzjWH4g9M8pUk3/ahziL9PM1J0dNKKX+WrA8oF36u3TXNF4wfLnN9DJFa60/TfHn9Sa45FL8wzYSa10uyR7tsptb6i2UvmmHx6zQXAJ9USvn/kj+E4vMel1LKfmlGjH8nSXXRmcWotX4nTUD53FLK/klSSvlUkoOSPKL9eUCS7yX5UHtHDGw1xxzLpdZ6cZJ/SDNI6+/SfGd7QprJMvdK8pwk90hzV/O923V+m6aVxUHt+rDZHHNw3TTedQEMj9pObFNrvbqU8udpRkn+ZZKUUo5qR0+m1vrwLutkONRaf1dK+cs0o3Zf1x57R8/rrTueZnKSN6dpHfCFzoplKNRazyqlPD3Jh9OE4qm1/r82FB+bd8fBDmlGs122iU3BZqu1zpRS/jbNfAl/X0oZ1Fq/sGB02vWTvCZNi4EHtRenYbG+luZk/S9KKXdNct80J/AntJ9356UJJ2GpOOZYFrXWS0opb0hzV8J7ktyw1vr6UsqhSR6a5KQkE0l+N2+d321sW7A5HHNw3VMM0mWpzQVDpZQVaULxuyf5TJJXzYXisFRKKYenOc4uS9M64ENpvmjcL8kTk+ycZgLNq+faDHRWLEOhlHKLNKH47ZI8u9b62Xmv3TjJvyS5QZL/r+35B4vWftZ9Js2J0kfSnEytS9OS5zFJHpbksFprv6saGT6llP9JMyr3vCR/keR72o2xLTnmWE6llN3SXFB+YZI31VqPnPfaHrWZJB2WjGMOrjsE4mwTC0Lxr6WZMf7Qtp0ALKlSykFJptJcfFnR/vSTnJLkWbWZQHO8mkCTJdKG4h9Kc3vjS5McneTWSZ6e5rbue9daf9xZgQyldsTkvyY5MMnVSUqSX6WZXO75jjmWSimltHe/PCHNxZfP1lr/puu6GF6OObrSBpR/n+RFSV5fa31tu9xAGrYJxxxcNwjE2WbmheLjSfaqtZ7XdU0Mr/aLxY2SHJBkNsnJSS5oT66E4Sy5UspNkxyV5PFJ1iS5OE2/+qfWWk/usjaGVynlhklumeTOaQLx7yf5pX66bAullL2SfDfJRUkeUGu9vOOSGHKOObqwIKD8+1rrGzsuiSHnmIPuCcTZphb01YVl50o721IpZYckd0rT8+/MJKe1M8MDDIVSyhOTfDzJ42utn+66HoafY44utAHlq5IckeTltda3dVwSQ84xB90yqSbblDCcrgnD2ZZqrWuS/KD9ARhG307ywzRtyGA5OOZYdrXWS0spRyVZm+SLXdfD8HPMQbeMEAcAADaplLJDewEQloVjjq64u5Tl5piDbgjEAQAAAAAYCb2uCwAAAAAAgOWw3QfipZTHllL+qZTy3VLKpaWUWkr5j67rAgAAAADgumUYJtV8dZKDkqxOcl6S23ZbDgAAAAAA10Xb/QjxJC9McpskuyV5Tse1AAAAAABwHbXdjxCvtX5r7nEppctSAAAAAAC4DhuGEeIAAAAAAHCtBOIAAAAAAIyE7b5lylK4//3vX7uugdExNTWVJJmcnOy0DkaL447l5phjuTnmWG6OOZabY44uOO7owvT09DD2RO48ezz55JPzjne8I7U2pWxp6+k1a9bkt7/9bZ73vOflsY997LYocUss6hgRiAMAAAAADLEPfOAD+eUvf7no7Zx11llLUE23BOIAAAAAAEPsH//xH/PrX//6Gt9Ta02tNYPBYP3P3PNLL700L3/5y3PrW996mSredgTiAAAAAABDbMWKFbnpTW+61evPzMwsXTEdM6kmAAAAAAAjQSAOAAAAAMBI2O5bppRSHpXkUe3Tvdrf9yylfLh9/Lta60uWuSwAAAAAAK5jtvtAPMlEkqctWHaL9idJfpFEIA4AAAAAMOK2+0C81vraJK/tuAwAAAAAgOuks88+O2eeeWYGg0FqrRkMBut/5j9f+NrcsiuuuKLrXVgy230gDtujiYmJTE9Pd10GI6Tf73ddAgAAANCRN77xjTnrrLMWtY3x8fHss88+S1RRdwTiAAAAAABDbO3atbnFLW6RhzzkIRkbG8vY2FjGx8fX/yxcNvd4/rIddtgh++23X9e7smgCcehAv9/P5ORk12UwQqamprouAQAAAOjI9a53vZx++ul53/vet6jtvPrVr84DH/jAJaqqGwJxAAAAAIAh9va3vz2//e1vMzs7e409wzfVQ/yyyy7LW97yllx66aVd78qiCcQBAAAAAIbYLrvskv3333+r15+ZmVm6YjrW67oAAAAAAABYDgJxAAAAAABGgkAcAAAAAICRIBAHAAAAAGAkCMQBAAAAABgJ410XAKNoYmIi09PTXZfBCOn3+12XAAAAAHTkQx/6UI499tgMBoPUWjMYDP7o8caezy2bnZ1NkvR62//4aoE4AAAAAMAQ+9a3vpWrrroqt7nNbTI2NpZSSkop6x/3er30er1rXLZixYrc+9737npXFk0gDgAAAAAw5O50pzvlyCOP7LqMzgnEAQAAAACG3Lp167J69eqNjgCf+z0KBOLQgX6/n8nJya7LYIRMTU11XQIAAADQkbGxsXznO9/Jd77znU2+59raqKxcuTKvfvWrc9BBBy1j5UtPIA4AAAAAMMQe/ehH54tf/GLWrVuXdevWZXZ2NrOzs+sfL1w+GAw2up2zzjpLIA5suYmJiUxPT3ddBiOk3+93XQIAAADQkaOPPjpnnXXWVq9fSsmKFSuy3377LWFV3RCIAwAAAAAMsbVr1+bQQw/N85///A36hs//WbhsYduUYSEQhw7oIc5y00McAAAARtuJJ56YF73oRZsVgG/s+cqVK/PsZz87N7vZzbrelUURiAMAAAAADLEnP/nJOemkkzIYDDIYDFJr3eTj2dnZDZYNBoNcddVVOfHEE3PwwQcLxAEAAAAAuO46/PDDc/jhh2/1+jMzM1m1atUSVtSdXtcFAAAAAADAchCIAwAAAAAwEgTiAAAAAACMBD3EoQMTExOZnp7uugxGSL/f77oEAAAAoCPHHXdcTjnllM2eSHPhsjVr1nS9C0tGIA4AAAAAMMTe+9735pe//GVWrFiRXq+XUkp6vd4GPwuXLXx+y1veMre73e263pVFE4gDAAAAAAyxWmse8IAH5Mgjj+y6lM7pIQ4AAAAAwEgQiAMAAAAAMBIE4gAAAAAAjAQ9xKED/X4/k5OTXZfBCJmamuq6BAAAAKAjpZR897vfzapVqzY5aebCZQtfX7lyZZ7//Ofnlre8Zde7sygCcQAAAACAIfaMZzwjJ598cgaDQQaDQWqtmZ2dTa11g2Wben3t2rU56aSTcvLJJwvEAQAAAAC47jrssMNy2GGHbfX6MzMzWbVq1RJW1B2BOAAAAADAELv00kvzq1/9arNHiC9ctnr16q53YckIxKEDExMTmZ6e7roMRki/3++6BAAAAKAjL3vZy/KTn/xk0dvZfffdl6CabgnEAQAAAACG2OrVq7P33nvnLne5S8bGxjI2Npbx8fH1PwuXbew9u+yySw4++OCud2XRBOLQgX6/n8nJya7LYIRMTU11XQIAAADQkd122y2nnXZavvSlLy1qO6961avyoAc9aImq6oZAHAAAAABgiL397W/Pb37zm432Cl/YR3xjvcUvvfTSvOUtb8lll13W9a4smkAcAAAAAGCI7bzzztl///23ev2ZmZmlK6Zjva4LAAAAAACA5WCEOAAAAADAEDvzzDNz5plnbrQdysYeL1x2xRVXdL0LS0YgDh2YmJjI9PR012UwQvr9ftclAAAAAB056qijcvbZZ2/1+r1eLzvuuGNuetObLmFV3RCIQwf6/X4mJye7LoMRMjU11XUJAAAAQEfWrVu3ydfGxsYyPj6+we+5x+Pj4+n1eun1elm5cmV23XXXZax62xCIAwAAAAAMsec85zk55ZRTNmiFck3tUxYuW7NmTY455picdtppOeCAA7renUURiAMAAAAADLF73vOeuec977nV68/MzGTVqlVLWFF3el0XAAAAAAAAy0EgDgAAAADASNAyBQAAAABgiM3Ozuayyy7b6h7il156ade7sGQE4tCBiYmJTE9Pd10GI6Tf73ddAgAAANCRI444IieddNKit7PTTjstQTXdEogDAAAAAAyxCy+8MLe85S3zkIc8JGNjYxkbG8v4+HjGx8fT6/XW/5RSNng8Nja2ftnKlStzxzvesetdWTSBOHSg3+9ncnKy6zIYIVNTU12XAAAAAHRkbGwsP//5z/Mv//IvG319UyH4/GUrVqzIq171qtzpTnda5uqXlkAcAAAAAGCIvfjFL87pp59+rX3DN9VH/Morr8zXvva1/PznPxeIAwAAAABw3XXggQfmwAMP3Or1Z2Zm8rWvfW0JK+pOr+sCAAAAAABgOQjEAQAAAAAYCQJxAAAAAABGgkAcAAAAAICRIBAHAAAAAGAkCMQBAAAAABgJAnEAAAAAAEaCQBwAAAAAgJEgEAcAAAAAYCSMd10AAAAAAADbzuWXX54LLrggg8Egg8EgtdYNHs/Ozl7jstWrV3e9C0tGIA4dmJiYyPT0dNdlMEL6/X7XJQAAAAAdOeKII3L66acveju77bbbElTTLYE4AAAAAMAQu+yyy7LbbrvlZje7WcbHxzM2NpaxsbGMj49v8vn83+Pj49lll11yn/vcp+tdWTSBOHSg3+9ncnKy6zIYIVNTU12XAAAAAHTkJje5Sc4777yccsopi9rOrrvumoc+9KFLVFU3BOIAAAAAAEPszW9+c9auXbvVPcQvueSSvPCFL8xVV13V9a4smkAcAAAAAGCI9Xq97Ljjjlu9/szMzNIV07Fe1wUAAAAAAMByMEIcAAAAAGCIDQaDRbdMGRYCcQAAAACAIXb44Yfn6quvXvR2rrjiiiWoplsCcejAxMREpqenuy6DEdLv97suAQAAAOjIUoThSfKd73wnT37yk5dkW10RiEMH+v1+Jicnuy6DETI1NdV1CQAAAMB2bu+99+66hEUTiAMAAAAADLEvfOELOe6441JrTZKUUrZo/dWrV2dqaioHHXTQtihvWQnEAQAAAACG2K677poHPvCBW73+zMzM0Nx93uu6AAAAAAAAWA4CcQAAAAAARoJAHAAAAACAkSAQBwAAAABgJAjEAQAAAAAYCeNdFwCjaGJiItPT012XwQjp9/tdlwAAAAB05Ctf+UpOPPHEDAaDDAaD1Fo3eDw7O3uNy66++uqud2HJCMQBAAAAAIbYJz7xifzud7/LDW5wg/R6vfR6vZRS1j/e2LJSSsbHx1NKyU477ZRDDz00d73rXbvelUUTiAMAAAAADLnrX//6mZiYyNjY2B8F3xtbtjAsX7lyZfbcc8+ud2PRBOLQgX6/n8nJya7LYIRMTU11XQIAAADQkTvf+c455phjcswxx2zQGuWa2qfUWv9oOytXrswjH/nIDvZg6QjEAQAAAACG2Ite9KItXmcuKK+15uKLL87jHve4zM7OboPqlpdAHAAAAACADcy1UkmSFStWdFzN0hGIAwAAAAAMsd/+9rf5xS9+ca1tUja17PLLL+96F5aMQBw6MDExkenp6a7LYIT0+/2uSwAAAAA68upXvzo/+9nPFr2dG93oRktQTbcE4gAAAAAAQ+zKK6/MXe961zzzmc9Mr9dLKSW9Xm+Dn4XLFj5fsWJFdt111653ZdEE4gAAAAAAQ2733XfP7W9/+67L6Fyv6wIAAAAAAGA5CMQBAAAAABgJAnEAAAAAAEaCQBwAAAAAgJFgUk0AAAAAgCF39tln5yMf+Uh6vV5KKen1ehv8zF+2sddXrlyZQw45JDvuuGPXu7IoAnHoQL/fz+TkZNdlMEKmpqa6LgEAAADoyM1vfvP83//9X84+++xFbeclL3lJHv7why9RVd0QiAMAAAAADLE3vOENqbVmMBis/73w8caezy2bmZnJc5/73Kxdu7brXVk0gTgAAAAAwJArpWRsbGyr1t1pp52WuJrumFQTAAAAAICRIBAHAAAAAGAkaJkCAAAAADDErrrqqlx44YUb7RVea83s7Ow1Lrvsssu63oUlIxCHDkxMTGR6errrMhgh/X6/6xIAAACAjrzkJS/Jqaeeuujt7LrrrktQTbcE4gAAAAAAQ+ySSy7JHe5whzz60Y9Or9dLKSW9Xm+Dn4XLFj5fsWJFbnGLW3S9K4smEAcAAAAAGHJ/8id/kgc84AFdl9E5k2oCAAAAADASjBAHAAAAABhyp512Wt785jdv0BJlbGxsg9Yo17Rs5cqVedjDHpbdd9+9611ZFIE4dKDf72dycrLrMhghU1NTXZcAAAAAdOTud797jjvuuPzoRz9KrTWDwSCDwWCDxxt7Pres1pok2WWXXfLIRz6y471ZHIE4AAAAAMAQe8ELXrCo9S+++OI8+tGPzuzs7BJV1B09xAEAAAAA2KRSStclLBmBOAAAAAAAI0EgDgAAAADASBCIAwAAAAAwEgTiAAAAAACMBIE4AAAAAAAjQSAOAAAAAMBIEIgDAAAAADASBOIAAAAAAIyE8a4LAAAAAACge7XWDAaD9b/nHl9xxRVdl7ZkBOLQgYmJiUxPT3ddBiOk3+93XQIAAADQkbe97W35/ve/v0HIPf/x7Oxsaq2ptV7jdsbHt/84efvfAwAAAAAANumUU07JrrvumoMPPji9Xi+llPR6vQ1+Fi5b+HzFihU57LDDut6VRdNDHAAAAABgyJVS1v+e/7O5y+aWb++MEIcO9Pv9TE5Odl0GI2RqaqrrEgAAAICO3OEOd8j09HQ+//nPZ3Z2dqu30+v18ohHPGIJK1t+AnHogB7iLDc9xAEAAGB0zczMZM2aNYvejh7iAAAAAABcp51//vnZe++9c5e73CVjY2MZGxvL+Pj4+p+Fyzb2nh122CF3v/vdu96VRROIQwe0TGG5aZkCAAAAo2vlypU566yz8qUvfWmrt9Hr9fLmN785Bx988BJWtvwE4gAAAAAAQ+w1r3lNzjzzzMzOzmbdunXrf69bty6DwWD94429Pjs7myuuuCLf/OY3c/7553e9K4smEIcO6CHOctNDHAAAAEbXBz7wgXzve99b9HZWrly5BNV0SyAOAAAAADDEfvGLX2T//ffP/e53v4yNjaWUkl6vl16v90ePN/b62NhYVqxYkUMOOaTrXVk0gTh0QA9xlpse4gAAADC6Sik555xzcv75529W+L2xwHzlypXZe++9c8ABB3S9O4siEAcAAAAAGGLPec5z8uMf/zizs7OptWYwGGQwGGzweGPP55atWbMm3//+93PaaacJxAEAAAAAuO665z3vmXve855bvf7MzExWrVq1hBV1p9d1AQAAAAAAsBwE4gAAAAAAjASBOAAAAAAAI0EgDgAAAADASBCIAwAAAAAwEgTiAAAAAACMhPGuCwAAAAAAYNs5/vjjc8opp2QwGGQwGKTWmtnZ2dRaN1g293jhsjVr1nS9C0tGIA4dmJiYyPT0dNdlMEL6/X7XJQAAAAAdee9735tzzz034+Pj6fV66fV6KaWsf7yxZaWUjI2NrV92i1vcIre73e263pVFE4gDAAAAAAyxwWCQW9ziFjnssMOuNfjeVFi+cuXK7L///l3vyqIJxKED/X4/k5OTXZfBCJmamuq6BAAAAKAj++23X4455picddZZi9rOEUcckYc97GFLVFU3BOIAAAAAAEPsTW960yb7g2+sp/jC12ZmZvK3f/u3Q9FLXCAOAAAAADDE5tqijI2NbdX6u+yyyxJX1B2BOAAAAADAEDv//PNz9tln/9Ho72sbKT637PLLL+96F5aMQBw6MDExkenp6a7LYIT0+/2uSwAAAAA68prXvCY///nPF7WNUkr22muvJaqoOwJxAAAAAIAhtmbNmtziFrfIQx7ykPWtU8bHx9f/LFw293j+sp133jn77LNP17uyaAJx6EC/38/k5GTXZTBCpqamui4BAAAA6MjOO++cn/70p3nf+963qO289rWvzf3ud78lqqobAnEAAAAAgCH21re+Neeff/619gqf31t8/rLVq1fnXe96Vy666KKud2XRBOIAAAAAAENsjz32yB577LHV68/MzORd73rX0hXUoV7XBQAAAAAAwHIQiAMAAAAAMBIE4gAAAAAAjASBOAAAAAAAI0EgDgAAAADASBCIAwAAAAAwEgTiAAAAAACMhPGuC4BRNDExkenp6a7LYIT0+/2uSwAAAAA68u1vfzv9fj+DwSCDwSC11szOzqbWusGyTb2+du3arndhyQjEAQAAAACG2Lvf/e5cdNFFi97O7W9/+yWopltapgAAAAAADLGlCMOT5JJLLlmS7XTJCHHoQL/fz+TkZNdlMEKmpqa6LgEAAADoyH777Zdf/vKXi97O9a9//SWoplsCcQAAAACAIfasZz0rP/rRj/6oV/jGnq9bty6zs7NZt27d+sdr1qzJT37yk5xyyim51a1u1fXuLIpAHDpgUk2Wm0k1AQAAYHQde+yx+f73v3+tgfi1GR/f/uPk7X8PAAAAAADYpB//+MfZbbfdcve73z1jY2MppaTX66XX66WUstFlc4/nflasWJHDDjus611ZNIE4AAAAAMCQu+ENb5h73etemwy8Fy5b+HzlypXZddddu96NRROIQwdMqslyM6kmAAAAjK6dd945/X5/0S1VX/va1+Z+97vf0hTVEYE4AAAAAMAQe8tb3pLzzjvvWnuI11ozOzv7R8tWr16dd7/73bnooou63pVFE4gDAAAAAAyxPffcM3vuuedWrz8zM5N3v/vdS1hRdwTiAAAAAABD7KKLLsq55567qBHiw0IgDh2YmJjI9PR012UwQhbbIwwAAADYfr385S/Pz372s0Vv5wY3uMESVNMtgTgAAAAAwBC78sorc+c73zlPfepTMzY2llJKer1eer1eSikbXTb3eO5nxYoVi2q7cl0hEAcAAAAAGHJ77rlnJiYmui6jcwJxAAAAAIAhNzs7m6uuuuqPRoGXUroubVkJxKED/X4/k5OTXZfBCJmamuq6BAAAAKAjY2Nj+fa3v51vf/vbf/TaplqlLGylsnLlyrzqVa/KgQce2MEeLB2BOAAAAADAEHvhC1+Y008/PYPBIIPBILXWDR7Pzs5e47KrrroqX//613PmmWcKxAEAAAAAuO466KCDctBBB231+jMzM/n617++hBV1p9d1AQAAAAAAsByMEIcOTExMZHp6uusyGCH9fr/rEgAAAICOfOYzn8kPf/jDjbZGmXu8sedzy9atW9f1LiwZgTgAAAAAwBA7+uijc8EFF2SXXXbJ+Ph4xsbG1v+e/3jhsrmfsbGxHHjggTnkkEO63pVFE4hDB/r9fiYnJ7sugxEyNTXVdQkAAABAR1auXJl169blkksu2eptjI+P5wEPeED23nvvJaxs+QnEAQAAAACG2Ktf/eqceeaZ19omZVMtUy6//PJ86lOfyvnnn9/1riyaQBwAAAAAYIjtv//+2X///bd6/ZmZmXzqU59awoq60+u6AAAAAAAAWA4CcQAAAAAARoJAHAAAAACAkSAQBwAAAABgJAjEAQAAAAAYCQJxAAAAAABGgkAcAAAAAICRIBAHAAAAAGAkjHddAAAAAAAA285ZZ52Vr33taxkMBkmSUkpqrdf4eL41a9YsX7HbmEAcAAAAAGCIPetZz1qS7Zx33nlLsp0uaZkCAAAAADDExsbGui7hOkMgDgAAAAAwxGZnZ5dkO5dddtmSbKdLWqZAByYmJjI9Pd11GYyQfr/fdQkAAADAdu5xj3tc1yUsmkAcAAAAAGCI7bLLLrn88stz+9vffqu3cbOb3Sz77bffElbVDYE4AAAAAMAQu/zyy5Mkp5122lZv47TTTss97nGP3O9+91uqsjohEIcO9Pv9TE5Odl0GI2RqaqrrEgAAAICOPPzhD8+XvvSlRW/nkksuWYJquiUQBwAAAAAYYi95yUvykpe8ZIvWGQwGqbVmMBjk4osvzuMf//glm5yzSwJxAAAAAAA20Ov1kiRjY2NZuXJlx9UsHYE4AAAAAMAQ++1vf5ujjz46tdYkSSlli9a/8sort0VZnRCIAwAAAAAMsVe84hU566yzFr2d008/PatWrVqCirojEAcAAAAAGGJve9vb8tWvfnWrR4hfccUV+cQnPpHb3e5226K8ZSUQBwAAAAAYYje4wQ3y5Cc/eavXn5mZySc+8YklrKg7va4LAAAAAACA5SAQBwAAAABgJAjEAQAAAAAYCQJxAAAAAABGgkAcAAAAAICRIBAHAAAAAGAkCMQBAAAAABgJAnEAAAAAAEaCQBwAAAAAgJEgEAcAAAAAYCQIxAEAAAAAGAkCcQAAAAAARoJAHAAAAACAkSAQBwAAAABgJAjEAQAAAAAYCQJxAAAAAABGgkAcAAAAAICRIBAHAAAAAGAkjHddAAAAAAAA3au1ZjAYrP899/iKK67ourQlIxCHDkxMTGR6errrMhgh/X6/6xIAAACAjrz1rW/NV7/61UVvZ3x8+4+Tt/89gO1Qv9/P5ORk12UwQqamprouAQAAAOjIUoThSfLrX/96SbbTJYE4dMAIcZabEeIAAADAYh144IFdl7BoAnEAAAAAgCG27777Zs8998wTnvCEjI+PZ2xsLGNjYxkfH1//M3/Z/N+llPR6vYyPj2ennXbqelcWTSAOAAAAADDEzjvvvJx33nk55ZRTFrWdqampHHTQQUtUVTd6XRcAAAAAAMB1369+9auuS1g0I8ShAybVZLmZVBMAAABG113veteccMIJi95OrXUJqumWQBwAAAAAYIi94Q1vyPnnn5/Z2dmsW7du/e+5x5taPvd79erV+ehHP5o1a9Z0vSuLJhCHDkxMTGR6errrMhgh/X6/6xIAAACAjhxxxBE59dRTF72dXXfddQmq6ZZAHAAAAABgiF1yySW5wx3ukMc85jHp9Xrp9XoppWRsbCyllM1atmLFiuy///5d78qiCcShA3qIs9z0EAcAAIDRds455+QTn/jEBkH33OONLVsYjq9cuTLPeMYzss8++3S9K4siEAcAAAAAGGKPfvSjc+KJJ2YwGGQwGKTWmtnZ2dRa1y+bnZ1d/3j++waDQa6++uqce+65ucMd7pBVq1Z1vTuLIhAHAAAAABhiq1atWlSQPTMzs90H4XN6XRcAAAAAAADLQSAOAAAAAMBIEIgDAAAAADASBOIAAAAAAIwEgTgAAAAAACNBIA4AAAAAwEgQiAMAAAAAMBIE4gAAAAAAjASBOAAAAAAAI0EgDgAAAADASBCIAwAAAAAwEgTiAAAAAACMBIE4AAAAAAAjQSAOAAAAAMBIEIgDAAAAADASBOIAAAAAAIwEgTgAAAAAACNBIA4AAAAAwEgQiAMAAAAAMBIE4gAAAAAAjASBOAAAAAAAI2G86wJgFE1MTGR6errrMhgh/X6/6xIAAACAjnzjG9/ISSedlMFgkMFgkFrrJh/Pzs7+0bK1a9d2vQtLRiAOAAAAADDEPvaxj+W3v/1t9thjj/R6vZRS0uv11j8eGxv7o2Vzj3u9XlasWJG73/3umZiY6HpXFk0gDgAAAAAwxGqt2XnnnbPvvvtuMvS+tucrVqzIrrvu2vWuLJpAHDrQ7/czOTnZdRmMkKmpqa5LAAAAADrykIc8JD/4wQ9y1VVXXWNrlE21Tpmdnc3FF1+cm970pnnUox7V9e4sikAcAAAAAGCIPeUpT8lTnvKUrV5/ZmYmq1atSq11CavqRq/rAgAAAAAAYDkYIQ4AAAAAMMTWrVuXSy+99BrbpGzs+dyySy+9tOtdWDICcejAxMREpqenuy6DEdLv97suAQAAAOjIS1/60px00kmL3s7OO++8BNV0SyAOAAAAADDELrzwwhxwwAF5+MMfnl6vl16vl1JKxsbGUkrZrGUrVqzIHe5wh653ZdEE4gAAAAAAQ26fffbJIx7xiK7L6JxJNQEAAAAAGAlGiEMH+v1+Jicnuy6DETI1NdV1CQAAAEBHSin53ve+l8c//vEbtEKZe7w5z1euXJnnPOc52X///bvenUURiAMAAAAADLGnPOUp6ff7GQwGGQwGqbVmdnY2tdYNlm3q9bVr1+ZHP/pR7nnPewrEAQAAAAC47nrwgx+cBz/4wVu9/szMTFatWrWEFXVHD3EAAAAAAEaCEeIAAAAAAEPsjDPOyP/7f/8vg8EgSdNTfEusWbNmW5TVCYE4AAAAAMAQe/e7353TTjtt0ds577zzlqCabgnEAQAAAACG2Gte85r0+/3UWpM0I8Sv6fHcz9wEm6tXr8773//+7Lvvvp3tw1IRiAMAAAAADLGTTz45J598cmZnZ9cH3XNh98YeL1y2du3arndhyQjEoQMTExOZnp7uugxGSL/f77oEAAAAoCMf/ehH88tf/nKjr42NjWV8fHyD33OP5y8/8MADMzExsbyFbwMCcehAv9/P5ORk12UwQqamprouAQAAAOjIypUrN/na7OxsZmdnr3UbpZRccMEF2X///ZeytGUnEAcAAAAAGGKvf/3rc84552zQCuWa2qcsXHb55Zfn3//933PBBRd0vSuLJhAHAAAAABhiN7nJTXKTm9xkq9efmZnJv//7vy9hRd3pdV0AAAAAAAAsB4E4AAAAAAAjQSAOAAAAAMBIEIgDAAAAADASBOIAAAAAAIwEgTgAAAAAACNBIA4AAAAAwEgQiAMAAAAAMBIE4gAAAAAAjASBOAAAAAAAI0EgDgAAAADASBCIAwAAAAAwEgTiAAAAAACMBIE4AAAAAAAjQSAOAAAAAMBIEIgDAAAAADASBOIAAAAAAIwEgTgAAAAAACNBIA4AAAAAwEgQiAMAAAAAMBIE4gAAAAAAjASBOAAAAAAAI0EgDgAAAADASBCIAwAAAAAwEgTiAAAAAACMBIE4AAAAAAAjQSAOAAAAAMBIEIgDAAAAADASBOIAAAAAAIwEgTgAAAAAACNhvOsCYBRNTExkenq66zIYIf1+v+sSAAAAgI68733vy7HHHpvBYJDBYJBaa2ZnZ1Nr3WDZ3OONLUuSXm/7H18tEAcAAAAAGGLf+973snbt2tzhDndIr9dLr9dLKWX9440tK6VkbGxs/bKVK1fmvve9b9e7smgCcQAAAACAIXfHO94xRx55ZNdldG77H+MOAAAAAACbwQhxAAAAAIAhd8455+TjH//4olqm3P3ud88OO+zQ9a4sikAcOtDv9zM5Odl1GYyQqamprksAAAAAOrLvvvvm2GOPzVlnnbWo7RxxxBF52MMetkRVdUMgDgAAAAAwxI466qisW7cug8Egg8EgtdYNHs/Ozl7jsksuuSQveMELsmbNmq53ZdEE4gAAAAAAQ6yUkhUrVmz1+jMzM0tXTMdMqgkAAAAAwEgwQhwAAAAAYIhdcskl+dWvfrXVLVNWr17d9S4sGYE4dGBiYiLT09Ndl8EI6ff7XZcAAAAAdORlL3tZzjjjjEVvZ4899lh8MR0TiAMAAAAADLHLL788Bx10UJ70pCel1+ullJJer7fBz8JlC5+vWLEie+21V9e7smgCcehAv9/P5ORk12UwQqamprouAQAAAOjQGWeckampqc0OwBc+X7lyZZ797GfnZje7Wde7sigCcQAAAACAIfbkJz85J5100iZ7iF9TP/HBYJCrrroqJ554Yg4++GCBOAAAAAAA112HH354Dj/88K1ef2ZmJqtWrVrCirrT67oAAAAAAABYDgJxAAAAAABGgkAcAAAAAICRIBAHAAAAAGAkCMQBAAAAABgJAnEAAAAAAEaCQBwAAAAAgJEgEAcAAAAAYCQIxAEAAAAAGAkCcQAAAAAARoJAHAAAAACAkSAQBwAAAABgJAjEAQAAAAAYCQJxAAAAAABGgkAcAAAAAICRIBAHAAAAAGAkCMQBAAAAABgJ410XAAAAAADAtnPRRRflvPPOy2AwyGAwSK01s7OzqbVusGzu8cJlq1ev7noXloxAHDowMTGR6enprstghPT7/a5LAAAAADryile8Ij/96U8XvZ3rX//6S1BNtwTiAAAAAABD7Iorrsid73znPOUpT0mv10uv10spZf3jjS0rpWRsbGz9spUrVwrEga3T7/czOTnZdRmMkKmpqa5LAAAAADpSSsmpp56ao446aoOQ+9pC8PnLVqxYkec973nZf//9u96dRRGIAwAAAAAMsac+9anp9/sb7RW+OX3E165dmxNOOCH9fl8gDgAAAADAddeDHvSgPOhBD9rq9WdmZrJq1aolrKg7va4LAAAAAACA5SAQBwAAAABgJAjEAQAAAAAYCQJxAAAAAABGgkAcAAAAAICRIBAHAAAAAGAkCMQBAAAAABgJAnEAAAAAAEaCQBwAAAAAgJEgEAcAAAAAYCQIxAEAAAAAGAnjXRcAo2hiYiLT09Ndl8EI6ff7XZcAAAAAdOTDH/5wjjvuuAwGg9RaMxgM1v/Mf77wtblls7OzSZJSSsd7sngCcQAAAACAIfa///u/ueKKK3LrW986Y2NjKaWk1+ul1+v90eONvT42NpYVK1bk3ve+d9e7smgCcQAAAACAITcYDHLVVVdda/i9qddnZ2fXjxTfngnEoQP9fj+Tk5Ndl8EImZqa6roEAAAAoCMPfehD84Mf/CCDwSBXX331+jYom2qXsvD12dnZ/O53v8v++++fRz3qUV3vzqIIxAEAAAAAhtiTnvSkPOlJT9rq9WdmZrJq1arUWpewqm70ui4AAAAAAACWg0AcAAAAAICRIBAHAAAAAGAkCMQBAAAAABgJAnEAAAAAAEaCQBwAAAAAgJEgEAcAAAAAYCQIxAEAAAAAGAnjXRcAo2hiYiLT09Ndl8EI6ff7XZcAAAAAdOSTn/xkjj/++AwGgwwGg9RaMzs7m1rrBss29frs7GySpJTS8Z4snkAcOtDv9zM5Odl1GYyQqamprksAAAAAOvLlL38555133qK3c8Mb3nAJqumWQBwAAAAAYIg9+9nPzimnnHKNo8I39nxu2Zo1a3Lcccflwgsv7HpXFk0gDgAAAAAwxO51r3vlXve611avPzMzk1WrVi1hRd0xqSYAAAAAACNBIA4AAAAAwEgQiAMAAAAAMBIE4gAAAAAAjASBOAAAAAAAI0EgDgAAAADASBCIAwAAAAAwEgTiAAAAAACMBIE4AAAAAAAjQSAOAAAAAMBIEIgDAAAAADASBOIAAAAAAIwEgTgAAAAAACNBIA4AAAAAwE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\\n\",\n 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\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"msno.matrix(df)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"f086e84b\",\n \"metadata\": {},\n \"source\": [\n \"## EDA summary using Pandas Profiling\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 32,\n \"id\": \"a2454c15\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Collecting pandas_profiling\\n\",\n \" Downloading pandas_profiling-3.6.6-py2.py3-none-any.whl (324 kB)\\n\",\n \"Collecting ydata-profiling\\n\",\n \" Downloading ydata_profiling-4.1.2-py2.py3-none-any.whl (345 kB)\\n\",\n \"Collecting phik<0.13,>=0.11.1\\n\",\n \" Downloading phik-0.12.3-cp39-cp39-win_amd64.whl (663 kB)\\n\",\n \"Requirement already satisfied: requests<2.29,>=2.24.0 in c:\\\\users\\\\himan\\\\anaconda3\\\\lib\\\\site-packages (from ydata-profiling->pandas_profiling) (2.27.1)\\n\",\n \"Collecting imagehash==4.3.1\\n\",\n \" Downloading ImageHash-4.3.1-py2.py3-none-any.whl (296 kB)\\n\",\n \"Collecting multimethod<1.10,>=1.4\\n\",\n \" Downloading multimethod-1.9.1-py3-none-any.whl (10 kB)\\n\",\n \"Requirement already satisfied: numpy<1.24,>=1.16.0 in c:\\\\users\\\\himan\\\\anaconda3\\\\lib\\\\site-packages (from ydata-profiling->pandas_profiling) (1.21.5)\\n\",\n \"Requirement already satisfied: PyYAML<6.1,>=5.0.0 in c:\\\\users\\\\himan\\\\anaconda3\\\\lib\\\\site-packages (from ydata-profiling->pandas_profiling) (6.0)\\n\",\n \"Requirement already satisfied: statsmodels<0.14,>=0.13.2 in c:\\\\users\\\\himan\\\\anaconda3\\\\lib\\\\site-packages (from ydata-profiling->pandas_profiling) (0.13.2)\\n\",\n \"Collecting typeguard<2.14,>=2.13.2\\n\",\n \" Downloading typeguard-2.13.3-py3-none-any.whl (17 kB)\\n\",\n \"Requirement already satisfied: pandas!=1.4.0,<1.6,>1.1 in c:\\\\users\\\\himan\\\\anaconda3\\\\lib\\\\site-packages (from ydata-profiling->pandas_profiling) (1.4.2)\\n\",\n \"Collecting pydantic<1.11,>=1.8.1\\n\",\n \" Downloading pydantic-1.10.7-cp39-cp39-win_amd64.whl (2.2 MB)\\n\",\n \"Collecting htmlmin==0.1.12\\n\",\n \" Downloading htmlmin-0.1.12.tar.gz (19 kB)\\n\",\n \"Requirement already satisfied: seaborn<0.13,>=0.10.1 in c:\\\\users\\\\himan\\\\anaconda3\\\\lib\\\\site-packages (from ydata-profiling->pandas_profiling) (0.11.2)\\n\",\n \"Collecting visions[type_image_path]==0.7.5\\n\",\n \" Downloading visions-0.7.5-py3-none-any.whl (102 kB)\\n\",\n \"Requirement already satisfied: tqdm<4.65,>=4.48.2 in c:\\\\users\\\\himan\\\\anaconda3\\\\lib\\\\site-packages (from ydata-profiling->pandas_profiling) (4.64.0)\\n\",\n \"Requirement already satisfied: jinja2<3.2,>=2.11.1 in c:\\\\users\\\\himan\\\\anaconda3\\\\lib\\\\site-packages (from ydata-profiling->pandas_profiling) (2.11.3)\\n\",\n \"Requirement already satisfied: matplotlib<3.7,>=3.2 in c:\\\\users\\\\himan\\\\anaconda3\\\\lib\\\\site-packages (from ydata-profiling->pandas_profiling) (3.5.1)\\n\",\n \"Requirement already satisfied: scipy<1.10,>=1.4.1 in c:\\\\users\\\\himan\\\\anaconda3\\\\lib\\\\site-packages (from ydata-profiling->pandas_profiling) (1.7.3)\\n\",\n \"Requirement already satisfied: PyWavelets in c:\\\\users\\\\himan\\\\anaconda3\\\\lib\\\\site-packages (from imagehash==4.3.1->ydata-profiling->pandas_profiling) (1.3.0)\\n\",\n \"Requirement already satisfied: pillow in c:\\\\users\\\\himan\\\\anaconda3\\\\lib\\\\site-packages (from imagehash==4.3.1->ydata-profiling->pandas_profiling) (9.0.1)\\n\",\n \"Requirement already satisfied: networkx>=2.4 in c:\\\\users\\\\himan\\\\anaconda3\\\\lib\\\\site-packages (from visions[type_image_path]==0.7.5->ydata-profiling->pandas_profiling) (2.7.1)\\n\",\n \"Requirement already satisfied: attrs>=19.3.0 in c:\\\\users\\\\himan\\\\anaconda3\\\\lib\\\\site-packages (from visions[type_image_path]==0.7.5->ydata-profiling->pandas_profiling) (21.4.0)\\n\",\n \"Collecting tangled-up-in-unicode>=0.0.4\\n\",\n \" Downloading tangled_up_in_unicode-0.2.0-py3-none-any.whl (4.7 MB)\\n\",\n \"Requirement already satisfied: MarkupSafe>=0.23 in c:\\\\users\\\\himan\\\\anaconda3\\\\lib\\\\site-packages (from jinja2<3.2,>=2.11.1->ydata-profiling->pandas_profiling) (2.0.1)\\n\",\n \"Requirement already satisfied: kiwisolver>=1.0.1 in c:\\\\users\\\\himan\\\\anaconda3\\\\lib\\\\site-packages (from matplotlib<3.7,>=3.2->ydata-profiling->pandas_profiling) (1.3.2)\\n\",\n \"Requirement already satisfied: cycler>=0.10 in c:\\\\users\\\\himan\\\\anaconda3\\\\lib\\\\site-packages (from matplotlib<3.7,>=3.2->ydata-profiling->pandas_profiling) (0.11.0)\\n\",\n \"Requirement already satisfied: python-dateutil>=2.7 in c:\\\\users\\\\himan\\\\anaconda3\\\\lib\\\\site-packages (from matplotlib<3.7,>=3.2->ydata-profiling->pandas_profiling) (2.8.2)\\n\",\n \"Requirement already satisfied: pyparsing>=2.2.1 in c:\\\\users\\\\himan\\\\anaconda3\\\\lib\\\\site-packages (from matplotlib<3.7,>=3.2->ydata-profiling->pandas_profiling) (3.0.4)\\n\",\n \"Requirement already satisfied: fonttools>=4.22.0 in c:\\\\users\\\\himan\\\\anaconda3\\\\lib\\\\site-packages (from matplotlib<3.7,>=3.2->ydata-profiling->pandas_profiling) (4.25.0)\\n\",\n \"Requirement already satisfied: packaging>=20.0 in c:\\\\users\\\\himan\\\\anaconda3\\\\lib\\\\site-packages (from matplotlib<3.7,>=3.2->ydata-profiling->pandas_profiling) (21.3)\\n\",\n \"Requirement already satisfied: pytz>=2020.1 in c:\\\\users\\\\himan\\\\anaconda3\\\\lib\\\\site-packages (from pandas!=1.4.0,<1.6,>1.1->ydata-profiling->pandas_profiling) (2021.3)\\n\",\n \"Requirement already satisfied: joblib>=0.14.1 in c:\\\\users\\\\himan\\\\anaconda3\\\\lib\\\\site-packages (from phik<0.13,>=0.11.1->ydata-profiling->pandas_profiling) (1.1.0)\\n\",\n \"Collecting typing-extensions>=4.2.0\\n\",\n \" Downloading typing_extensions-4.5.0-py3-none-any.whl (27 kB)\\n\",\n \"Requirement already satisfied: six>=1.5 in c:\\\\users\\\\himan\\\\anaconda3\\\\lib\\\\site-packages (from python-dateutil>=2.7->matplotlib<3.7,>=3.2->ydata-profiling->pandas_profiling) (1.16.0)\\n\",\n \"Requirement already satisfied: charset-normalizer~=2.0.0 in c:\\\\users\\\\himan\\\\anaconda3\\\\lib\\\\site-packages (from requests<2.29,>=2.24.0->ydata-profiling->pandas_profiling) (2.0.4)\\n\",\n \"Requirement already satisfied: certifi>=2017.4.17 in c:\\\\users\\\\himan\\\\anaconda3\\\\lib\\\\site-packages (from requests<2.29,>=2.24.0->ydata-profiling->pandas_profiling) (2021.10.8)\\n\",\n \"Requirement already satisfied: idna<4,>=2.5 in c:\\\\users\\\\himan\\\\anaconda3\\\\lib\\\\site-packages (from requests<2.29,>=2.24.0->ydata-profiling->pandas_profiling) (3.3)\\n\",\n \"Requirement already satisfied: urllib3<1.27,>=1.21.1 in c:\\\\users\\\\himan\\\\anaconda3\\\\lib\\\\site-packages (from requests<2.29,>=2.24.0->ydata-profiling->pandas_profiling) (1.26.9)\\n\",\n \"Requirement already satisfied: patsy>=0.5.2 in c:\\\\users\\\\himan\\\\anaconda3\\\\lib\\\\site-packages (from statsmodels<0.14,>=0.13.2->ydata-profiling->pandas_profiling) (0.5.2)\\n\",\n \"Requirement already satisfied: colorama in c:\\\\users\\\\himan\\\\anaconda3\\\\lib\\\\site-packages (from tqdm<4.65,>=4.48.2->ydata-profiling->pandas_profiling) (0.4.4)\\n\",\n \"Building wheels for collected packages: htmlmin\\n\",\n \" Building wheel for htmlmin (setup.py): started\\n\",\n \" Building wheel for htmlmin (setup.py): finished with status 'done'\\n\",\n \" Created wheel for htmlmin: filename=htmlmin-0.1.12-py3-none-any.whl size=27096 sha256=ff0bca2339091901a96f2090fdd14cc31223f1dd8c30643f8e17e6051a73d77b\\n\",\n \" Stored in directory: c:\\\\users\\\\himan\\\\appdata\\\\local\\\\pip\\\\cache\\\\wheels\\\\1d\\\\05\\\\04\\\\c6d7d3b66539d9e659ac6dfe81e2d0fd4c1a8316cc5a403300\\n\",\n \"Successfully built htmlmin\\n\",\n \"Installing collected packages: tangled-up-in-unicode, multimethod, visions, typing-extensions, imagehash, typeguard, pydantic, phik, htmlmin, ydata-profiling, pandas-profiling\\n\",\n \" Attempting uninstall: typing-extensions\\n\",\n \" Found existing installation: typing-extensions 4.1.1\\n\",\n \" Uninstalling typing-extensions-4.1.1:\\n\",\n \" Successfully uninstalled typing-extensions-4.1.1\\n\",\n \"Successfully installed htmlmin-0.1.12 imagehash-4.3.1 multimethod-1.9.1 pandas-profiling-3.6.6 phik-0.12.3 pydantic-1.10.7 tangled-up-in-unicode-0.2.0 typeguard-2.13.3 typing-extensions-4.5.0 visions-0.7.5 ydata-profiling-4.1.2\\n\"\n ]\n }\n ],\n \"source\": [\n \"!pip install pandas_profiling\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 33,\n \"id\": \"0a63bbf8\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"C:\\\\Users\\\\himan\\\\AppData\\\\Local\\\\Temp\\\\ipykernel_20104\\\\2274191625.py:1: DeprecationWarning: `import pandas_profiling` is going to be deprecated by April 1st. Please use `import ydata_profiling` instead.\\n\",\n \" from pandas_profiling import ProfileReport\\n\"\n ]\n }\n ],\n \"source\": [\n \"from pandas_profiling import ProfileReport\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 34,\n \"id\": \"5f6244cc\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"application/vnd.jupyter.widget-view+json\": {\n \"model_id\": \"8648e29203de451d91ae1293b1a9479a\",\n \"version_major\": 2,\n \"version_minor\": 0\n },\n \"text/plain\": [\n \"Summarize dataset: 0%| | 0/5 [00:00\"\n ],\n \"text/plain\": [\n \"\"\n ]\n },\n \"metadata\": {},\n \"output_type\": \"display_data\"\n },\n {\n \"data\": {\n \"text/plain\": []\n },\n \"execution_count\": 34,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"report = ProfileReport(df)\\n\",\n \"\\n\",\n \"report\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"31bd522b\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.12\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n" }, { "path": "March Cohort 2023/21_EDA and Data Viz/itunes_data.csv", "content": "Track,Composer,Milliseconds,Bytes,UnitPrice,Genre,Album,Artist\nFor Those About To Rock (We Salute You),\"Angus Young, Malcolm Young, Brian Johnson\",343719,11170334,0.99,Rock,For Those About To Rock We Salute You,AC/DC\nPut The Finger On You,\"Angus Young, Malcolm Young, Brian Johnson\",205662,6713451,0.99,Rock,For Those About To Rock We Salute You,AC/DC\nLet's Get It Up,\"Angus Young, Malcolm Young, Brian Johnson\",233926,7636561,0.99,Rock,For Those About To Rock We Salute You,AC/DC\nInject The Venom,\"Angus Young, Malcolm Young, Brian Johnson\",210834,6852860,0.99,Rock,For Those About To Rock We Salute You,AC/DC\nSnowballed,\"Angus Young, Malcolm Young, Brian Johnson\",203102,6599424,0.99,Rock,For Those About To Rock We Salute You,AC/DC\nEvil Walks,\"Angus Young, Malcolm Young, Brian Johnson\",263497,8611245,0.99,Rock,For Those About To Rock We Salute You,AC/DC\nC.O.D.,\"Angus Young, Malcolm Young, Brian Johnson\",199836,6566314,0.99,Rock,For Those About To Rock We Salute You,AC/DC\nBreaking The Rules,\"Angus Young, Malcolm Young, Brian Johnson\",263288,8596840,0.99,Rock,For Those About To Rock We Salute You,AC/DC\nNight Of The Long Knives,\"Angus Young, Malcolm Young, Brian Johnson\",205688,6706347,0.99,Rock,For Those About To Rock We Salute You,AC/DC\nSpellbound,\"Angus Young, Malcolm Young, Brian Johnson\",270863,8817038,0.99,Rock,For Those About To Rock We Salute You,AC/DC\nBalls to the Wall,,342562,5510424,0.99,Rock,Balls to the Wall,Accept\nFast As a Shark,\"F. Baltes, S. Kaufman, U. Dirkscneider & W. Hoffman\",230619,3990994,0.99,Rock,Restless and Wild,Accept\nRestless and Wild,\"F. Baltes, R.A. Smith-Diesel, S. Kaufman, U. Dirkscneider & W. Hoffman\",252051,4331779,0.99,Rock,Restless and Wild,Accept\nPrincess of the Dawn,Deaffy & R.A. Smith-Diesel,375418,6290521,0.99,Rock,Restless and Wild,Accept\nGo Down,AC/DC,331180,10847611,0.99,Rock,Let There Be Rock,AC/DC\nDog Eat Dog,AC/DC,215196,7032162,0.99,Rock,Let There Be Rock,AC/DC\nLet There Be Rock,AC/DC,366654,12021261,0.99,Rock,Let There Be Rock,AC/DC\nBad Boy Boogie,AC/DC,267728,8776140,0.99,Rock,Let There Be Rock,AC/DC\nProblem Child,AC/DC,325041,10617116,0.99,Rock,Let There Be Rock,AC/DC\nOverdose,AC/DC,369319,12066294,0.99,Rock,Let There Be Rock,AC/DC\nHell Ain't A Bad Place To Be,AC/DC,254380,8331286,0.99,Rock,Let There Be Rock,AC/DC\nWhole Lotta Rosie,AC/DC,323761,10547154,0.99,Rock,Let There Be Rock,AC/DC\nWalk On Water,\"Steven Tyler, Joe Perry, Jack Blades, Tommy Shaw\",295680,9719579,0.99,Rock,Big Ones,Aerosmith\nLove In An Elevator,\"Steven Tyler, Joe Perry\",321828,10552051,0.99,Rock,Big Ones,Aerosmith\nRag Doll,\"Steven Tyler, Joe Perry, Jim Vallance, Holly Knight\",264698,8675345,0.99,Rock,Big Ones,Aerosmith\nWhat It Takes,\"Steven Tyler, Joe Perry, Desmond Child\",310622,10144730,0.99,Rock,Big Ones,Aerosmith\nDude (Looks Like A Lady),\"Steven Tyler, Joe Perry, Desmond Child\",264855,8679940,0.99,Rock,Big Ones,Aerosmith\nJanie's Got A Gun,\"Steven Tyler, Tom Hamilton\",330736,10869391,0.99,Rock,Big Ones,Aerosmith\nCryin',\"Steven Tyler, Joe Perry, Taylor Rhodes\",309263,10056995,0.99,Rock,Big Ones,Aerosmith\nAmazing,\"Steven Tyler, Richie Supa\",356519,11616195,0.99,Rock,Big Ones,Aerosmith\nBlind Man,\"Steven Tyler, Joe Perry, Taylor Rhodes\",240718,7877453,0.99,Rock,Big Ones,Aerosmith\nDeuces Are Wild,\"Steven Tyler, Jim Vallance\",215875,7074167,0.99,Rock,Big Ones,Aerosmith\nThe Other Side,\"Steven Tyler, Jim Vallance\",244375,7983270,0.99,Rock,Big Ones,Aerosmith\nCrazy,\"Steven Tyler, Joe Perry, Desmond Child\",316656,10402398,0.99,Rock,Big Ones,Aerosmith\nEat The Rich,\"Steven Tyler, Joe Perry, Jim Vallance\",251036,8262039,0.99,Rock,Big Ones,Aerosmith\nAngel,\"Steven Tyler, Desmond Child\",307617,9989331,0.99,Rock,Big Ones,Aerosmith\nLivin' On The Edge,\"Steven Tyler, Joe Perry, Mark Hudson\",381231,12374569,0.99,Rock,Big Ones,Aerosmith\nAll I Really Want,Alanis Morissette & Glenn Ballard,284891,9375567,0.99,Rock,Jagged Little Pill,Alanis Morissette\nYou Oughta Know,Alanis Morissette & Glenn Ballard,249234,8196916,0.99,Rock,Jagged Little Pill,Alanis Morissette\nPerfect,Alanis Morissette & Glenn Ballard,188133,6145404,0.99,Rock,Jagged Little Pill,Alanis Morissette\nHand In My Pocket,Alanis Morissette & Glenn Ballard,221570,7224246,0.99,Rock,Jagged Little Pill,Alanis Morissette\nRight Through You,Alanis Morissette & Glenn Ballard,176117,5793082,0.99,Rock,Jagged Little Pill,Alanis Morissette\nForgiven,Alanis Morissette & Glenn Ballard,300355,9753256,0.99,Rock,Jagged Little Pill,Alanis Morissette\nYou Learn,Alanis Morissette & Glenn Ballard,239699,7824837,0.99,Rock,Jagged Little Pill,Alanis Morissette\nHead Over Feet,Alanis Morissette & Glenn Ballard,267493,8758008,0.99,Rock,Jagged Little Pill,Alanis Morissette\nMary Jane,Alanis Morissette & Glenn Ballard,280607,9163588,0.99,Rock,Jagged Little Pill,Alanis Morissette\nIronic,Alanis Morissette & Glenn Ballard,229825,7598866,0.99,Rock,Jagged Little Pill,Alanis Morissette\nNot The Doctor,Alanis Morissette & Glenn Ballard,227631,7604601,0.99,Rock,Jagged Little Pill,Alanis Morissette\nWake Up,Alanis Morissette & Glenn Ballard,293485,9703359,0.99,Rock,Jagged Little Pill,Alanis Morissette\nYou Oughta Know (Alternate),Alanis Morissette & Glenn Ballard,491885,16008629,0.99,Rock,Jagged Little Pill,Alanis Morissette\nWe Die Young,Jerry Cantrell,152084,4925362,0.99,Rock,Facelift,Alice In Chains\nMan In The Box,\"Jerry Cantrell, Layne Staley\",286641,9310272,0.99,Rock,Facelift,Alice In Chains\nSea Of Sorrow,Jerry Cantrell,349831,11316328,0.99,Rock,Facelift,Alice In Chains\nBleed The Freak,Jerry Cantrell,241946,7847716,0.99,Rock,Facelift,Alice In Chains\nI Can't Remember,\"Jerry Cantrell, Layne Staley\",222955,7302550,0.99,Rock,Facelift,Alice In Chains\n\"Love, Hate, Love\",\"Jerry Cantrell, Layne Staley\",387134,12575396,0.99,Rock,Facelift,Alice In Chains\nIt Ain't Like That,\"Jerry Cantrell, Michael Starr, Sean Kinney\",277577,8993793,0.99,Rock,Facelift,Alice In Chains\nSunshine,Jerry Cantrell,284969,9216057,0.99,Rock,Facelift,Alice In Chains\nPut You Down,Jerry Cantrell,196231,6420530,0.99,Rock,Facelift,Alice In Chains\nConfusion,\"Jerry Cantrell, Michael Starr, Layne Staley\",344163,11183647,0.99,Rock,Facelift,Alice In Chains\nI Know Somethin (Bout You),Jerry Cantrell,261955,8497788,0.99,Rock,Facelift,Alice In Chains\nReal Thing,\"Jerry Cantrell, Layne Staley\",243879,7937731,0.99,Rock,Facelift,Alice In Chains\nDesafinado,,185338,5990473,0.99,Jazz,Warner 25 Anos,Antônio Carlos Jobim\nGarota De Ipanema,,285048,9348428,0.99,Jazz,Warner 25 Anos,Antônio Carlos Jobim\nSamba De Uma Nota Só (One Note Samba),,137273,4535401,0.99,Jazz,Warner 25 Anos,Antônio Carlos Jobim\nPor Causa De Você,,169900,5536496,0.99,Jazz,Warner 25 Anos,Antônio Carlos Jobim\nLigia,,251977,8226934,0.99,Jazz,Warner 25 Anos,Antônio Carlos Jobim\nFotografia,,129227,4198774,0.99,Jazz,Warner 25 Anos,Antônio Carlos Jobim\nDindi (Dindi),,253178,8149148,0.99,Jazz,Warner 25 Anos,Antônio Carlos Jobim\nSe Todos Fossem Iguais A Você (Instrumental),,134948,4393377,0.99,Jazz,Warner 25 Anos,Antônio Carlos Jobim\nFalando De Amor,,219663,7121735,0.99,Jazz,Warner 25 Anos,Antônio Carlos Jobim\nAngela,,169508,5574957,0.99,Jazz,Warner 25 Anos,Antônio Carlos Jobim\nCorcovado (Quiet Nights Of Quiet Stars),,205662,6687994,0.99,Jazz,Warner 25 Anos,Antônio Carlos Jobim\nOutra Vez,,126511,4110053,0.99,Jazz,Warner 25 Anos,Antônio Carlos Jobim\nO Boto (Bôto),,366837,12089673,0.99,Jazz,Warner 25 Anos,Antônio Carlos Jobim\n\"Canta, Canta Mais\",,271856,8719426,0.99,Jazz,Warner 25 Anos,Antônio Carlos Jobim\nEnter Sandman,Apocalyptica,221701,7286305,0.99,Metal,Plays Metallica By Four Cellos,Apocalyptica\nMaster Of Puppets,Apocalyptica,436453,14375310,0.99,Metal,Plays Metallica By Four Cellos,Apocalyptica\nHarvester Of Sorrow,Apocalyptica,374543,12372536,0.99,Metal,Plays Metallica By Four Cellos,Apocalyptica\nThe Unforgiven,Apocalyptica,322925,10422447,0.99,Metal,Plays Metallica By Four Cellos,Apocalyptica\nSad But True,Apocalyptica,288208,9405526,0.99,Metal,Plays Metallica By Four Cellos,Apocalyptica\nCreeping Death,Apocalyptica,308035,10110980,0.99,Metal,Plays Metallica By Four Cellos,Apocalyptica\nWherever I May Roam,Apocalyptica,369345,12033110,0.99,Metal,Plays Metallica By Four Cellos,Apocalyptica\nWelcome Home (Sanitarium),Apocalyptica,350197,11406431,0.99,Metal,Plays Metallica By Four Cellos,Apocalyptica\nCochise,Audioslave/Chris Cornell,222380,5339931,0.99,Rock,Audioslave,Audioslave\nShow Me How to Live,Audioslave/Chris Cornell,277890,6672176,0.99,Rock,Audioslave,Audioslave\nGasoline,Audioslave/Chris Cornell,279457,6709793,0.99,Rock,Audioslave,Audioslave\nWhat You Are,Audioslave/Chris Cornell,249391,5988186,0.99,Rock,Audioslave,Audioslave\nLike a Stone,Audioslave/Chris Cornell,294034,7059624,0.99,Rock,Audioslave,Audioslave\nSet It Off,Audioslave/Chris Cornell,263262,6321091,0.99,Rock,Audioslave,Audioslave\nShadow on the Sun,Audioslave/Chris Cornell,343457,8245793,0.99,Rock,Audioslave,Audioslave\nI am the Highway,Audioslave/Chris Cornell,334942,8041411,0.99,Rock,Audioslave,Audioslave\nExploder,Audioslave/Chris Cornell,206053,4948095,0.99,Rock,Audioslave,Audioslave\nHypnotize,Audioslave/Chris Cornell,206628,4961887,0.99,Rock,Audioslave,Audioslave\nBring'em Back Alive,Audioslave/Chris Cornell,329534,7911634,0.99,Rock,Audioslave,Audioslave\nLight My Way,Audioslave/Chris Cornell,303595,7289084,0.99,Rock,Audioslave,Audioslave\nGetaway Car,Audioslave/Chris Cornell,299598,7193162,0.99,Rock,Audioslave,Audioslave\nThe Last Remaining Light,Audioslave/Chris Cornell,317492,7622615,0.99,Rock,Audioslave,Audioslave\nYour Time Has Come,\"Cornell, Commerford, Morello, Wilk\",255529,8273592,0.99,Alternative & Punk,Out Of Exile,Audioslave\nOut Of Exile,\"Cornell, Commerford, Morello, Wilk\",291291,9506571,0.99,Alternative & Punk,Out Of Exile,Audioslave\nBe Yourself,\"Cornell, Commerford, Morello, Wilk\",279484,9106160,0.99,Alternative & Punk,Out Of Exile,Audioslave\nDoesn't Remind Me,\"Cornell, Commerford, Morello, Wilk\",255869,8357387,0.99,Alternative & Punk,Out Of Exile,Audioslave\nDrown Me Slowly,\"Cornell, Commerford, Morello, Wilk\",233691,7609178,0.99,Alternative & Punk,Out Of Exile,Audioslave\nHeaven's Dead,\"Cornell, Commerford, Morello, Wilk\",276688,9006158,0.99,Alternative & Punk,Out Of Exile,Audioslave\nThe Worm,\"Cornell, Commerford, Morello, Wilk\",237714,7710800,0.99,Alternative & Punk,Out Of Exile,Audioslave\nMan Or Animal,\"Cornell, Commerford, Morello, Wilk\",233195,7542942,0.99,Alternative & Punk,Out Of Exile,Audioslave\nYesterday To Tomorrow,\"Cornell, Commerford, Morello, Wilk\",273763,8944205,0.99,Alternative & Punk,Out Of Exile,Audioslave\nDandelion,\"Cornell, Commerford, Morello, Wilk\",278125,9003592,0.99,Alternative & Punk,Out Of Exile,Audioslave\n#1 Zero,\"Cornell, Commerford, Morello, Wilk\",299102,9731988,0.99,Alternative & Punk,Out Of Exile,Audioslave\nThe Curse,\"Cornell, Commerford, Morello, Wilk\",309786,10029406,0.99,Alternative & Punk,Out Of Exile,Audioslave\nMoney,\"Berry Gordy, Jr./Janie Bradford\",147591,2365897,0.99,Rock And Roll,BackBeat Soundtrack,BackBeat\nLong Tall Sally,\"Enotris Johnson/Little Richard/Robert \"\"Bumps\"\" Blackwell\",106396,1707084,0.99,Rock And Roll,BackBeat Soundtrack,BackBeat\nBad Boy,Larry Williams,116088,1862126,0.99,Rock And Roll,BackBeat Soundtrack,BackBeat\nTwist And Shout,Bert Russell/Phil Medley,161123,2582553,0.99,Rock And Roll,BackBeat Soundtrack,BackBeat\nPlease Mr. Postman,Brian Holland/Freddie Gorman/Georgia Dobbins/Robert Bateman/William Garrett,137639,2206986,0.99,Rock And Roll,BackBeat Soundtrack,BackBeat\nC'Mon Everybody,Eddie Cochran/Jerry Capehart,140199,2247846,0.99,Rock And Roll,BackBeat Soundtrack,BackBeat\nRock 'N' Roll Music,Chuck Berry,141923,2276788,0.99,Rock And Roll,BackBeat Soundtrack,BackBeat\nSlow Down,Larry Williams,163265,2616981,0.99,Rock And Roll,BackBeat Soundtrack,BackBeat\nRoadrunner,Bo Diddley,143595,2301989,0.99,Rock And Roll,BackBeat Soundtrack,BackBeat\nCarol,Chuck Berry,143830,2306019,0.99,Rock And Roll,BackBeat Soundtrack,BackBeat\nGood Golly Miss Molly,Little Richard,106266,1704918,0.99,Rock And Roll,BackBeat Soundtrack,BackBeat\n20 Flight Rock,Ned Fairchild,107807,1299960,0.99,Rock And Roll,BackBeat Soundtrack,BackBeat\nQuadrant,Billy Cobham,261851,8538199,0.99,Jazz,The Best Of Billy Cobham,Billy Cobham\nSnoopy's search-Red baron,Billy Cobham,456071,15075616,0.99,Jazz,The Best Of Billy Cobham,Billy Cobham\n\"Spanish moss-\"\"A sound portrait\"\"-Spanish moss\",Billy Cobham,248084,8217867,0.99,Jazz,The Best Of Billy Cobham,Billy Cobham\nMoon germs,Billy Cobham,294060,9714812,0.99,Jazz,The Best Of Billy Cobham,Billy Cobham\nStratus,Billy Cobham,582086,19115680,0.99,Jazz,The Best Of Billy Cobham,Billy Cobham\nThe pleasant pheasant,Billy Cobham,318066,10630578,0.99,Jazz,The Best Of Billy Cobham,Billy Cobham\nSolo-Panhandler,Billy Cobham,246151,8230661,0.99,Jazz,The Best Of Billy Cobham,Billy Cobham\nDo what cha wanna,George Duke,274155,9018565,0.99,Jazz,The Best Of Billy Cobham,Billy Cobham\nIntro/ Low Down,,323683,10642901,0.99,Metal,Alcohol Fueled Brewtality Live! [Disc 1],Black Label Society\n13 Years Of Grief,,246987,8137421,0.99,Metal,Alcohol Fueled Brewtality Live! [Disc 1],Black Label Society\nStronger Than Death,,300747,9869647,0.99,Metal,Alcohol Fueled Brewtality Live! [Disc 1],Black Label Society\nAll For You,,235833,7726948,0.99,Metal,Alcohol Fueled Brewtality Live! [Disc 1],Black Label Society\nSuper Terrorizer,,319373,10513905,0.99,Metal,Alcohol Fueled Brewtality Live! [Disc 1],Black Label Society\nPhoney Smile Fake Hellos,,273606,9011701,0.99,Metal,Alcohol Fueled Brewtality Live! [Disc 1],Black Label Society\nLost My Better Half,,284081,9355309,0.99,Metal,Alcohol Fueled Brewtality Live! [Disc 1],Black Label Society\nBored To Tears,,247327,8130090,0.99,Metal,Alcohol Fueled Brewtality Live! [Disc 1],Black Label Society\nA.N.D.R.O.T.A.Z.,,266266,8574746,0.99,Metal,Alcohol Fueled Brewtality Live! [Disc 1],Black Label Society\nBorn To Booze,,282122,9257358,0.99,Metal,Alcohol Fueled Brewtality Live! [Disc 1],Black Label Society\nWorld Of Trouble,,359157,11820932,0.99,Metal,Alcohol Fueled Brewtality Live! [Disc 1],Black Label Society\nNo More Tears,,555075,18041629,0.99,Metal,Alcohol Fueled Brewtality Live! [Disc 1],Black Label Society\nThe Begining... At Last,,365662,11965109,0.99,Metal,Alcohol Fueled Brewtality Live! [Disc 1],Black Label Society\nHeart Of Gold,,194873,6417460,0.99,Metal,Alcohol Fueled Brewtality Live! [Disc 2],Black Label Society\nSnowblind,,420022,13842549,0.99,Metal,Alcohol Fueled Brewtality Live! [Disc 2],Black Label Society\nLike A Bird,,276532,9115657,0.99,Metal,Alcohol Fueled Brewtality Live! [Disc 2],Black Label Society\nBlood In The Wall,,284368,9359475,0.99,Metal,Alcohol Fueled Brewtality Live! [Disc 2],Black Label Society\nThe Beginning...At Last,,271960,8975814,0.99,Metal,Alcohol Fueled Brewtality Live! [Disc 2],Black Label Society\nBlack Sabbath,,382066,12440200,0.99,Metal,Black Sabbath,Black Sabbath\nThe Wizard,,264829,8646737,0.99,Metal,Black Sabbath,Black Sabbath\nBehind The Wall Of Sleep,,217573,7169049,0.99,Metal,Black Sabbath,Black Sabbath\nN.I.B.,,368770,12029390,0.99,Metal,Black Sabbath,Black Sabbath\nEvil Woman,,204930,6655170,0.99,Metal,Black Sabbath,Black Sabbath\nSleeping Village,,644571,21128525,0.99,Metal,Black Sabbath,Black Sabbath\nWarning,,212062,6893363,0.99,Metal,Black Sabbath,Black Sabbath\nWheels Of Confusion / The Straightener,\"Tony Iommi, Bill Ward, Geezer Butler, Ozzy Osbourne\",494524,16065830,0.99,Metal,Black Sabbath Vol. 4 (Remaster),Black Sabbath\nTomorrow's Dream,\"Tony Iommi, Bill Ward, Geezer Butler, Ozzy Osbourne\",192496,6252071,0.99,Metal,Black Sabbath Vol. 4 (Remaster),Black Sabbath\nChanges,\"Tony Iommi, Bill Ward, Geezer Butler, Ozzy Osbourne\",286275,9175517,0.99,Metal,Black Sabbath Vol. 4 (Remaster),Black Sabbath\nFX,\"Tony Iommi, Bill Ward, Geezer Butler, Ozzy Osbourne\",103157,3331776,0.99,Metal,Black Sabbath Vol. 4 (Remaster),Black Sabbath\nSupernaut,\"Tony Iommi, Bill Ward, Geezer Butler, Ozzy Osbourne\",285779,9245971,0.99,Metal,Black Sabbath Vol. 4 (Remaster),Black Sabbath\nSnowblind,\"Tony Iommi, Bill Ward, Geezer Butler, Ozzy Osbourne\",331676,10813386,0.99,Metal,Black Sabbath Vol. 4 (Remaster),Black Sabbath\nCornucopia,\"Tony Iommi, Bill Ward, Geezer Butler, Ozzy Osbourne\",234814,7653880,0.99,Metal,Black Sabbath Vol. 4 (Remaster),Black Sabbath\nLaguna Sunrise,\"Tony Iommi, Bill Ward, Geezer Butler, Ozzy Osbourne\",173087,5671374,0.99,Metal,Black Sabbath Vol. 4 (Remaster),Black Sabbath\nSt. Vitus Dance,\"Tony Iommi, Bill Ward, Geezer Butler, Ozzy Osbourne\",149655,4884969,0.99,Metal,Black Sabbath Vol. 4 (Remaster),Black Sabbath\nUnder The Sun/Every Day Comes and Goes,\"Tony Iommi, Bill Ward, Geezer Butler, Ozzy Osbourne\",350458,11360486,0.99,Metal,Black Sabbath Vol. 4 (Remaster),Black Sabbath\nSmoked Pork,,47333,1549074,0.99,Alternative & Punk,Body Count,Body Count\nBody Count's In The House,,204251,6715413,0.99,Alternative & Punk,Body Count,Body Count\nNow Sports,,4884,161266,0.99,Alternative & Punk,Body Count,Body Count\nBody Count,,317936,10489139,0.99,Alternative & Punk,Body Count,Body Count\nA Statistic,,6373,211997,0.99,Alternative & Punk,Body Count,Body Count\nBowels Of The Devil,,223216,7324125,0.99,Alternative & Punk,Body Count,Body Count\nThe Real Problem,,11650,387360,0.99,Alternative & Punk,Body Count,Body Count\nKKK Bitch,,173008,5709631,0.99,Alternative & Punk,Body Count,Body Count\nD Note,,95738,3067064,0.99,Alternative & Punk,Body Count,Body Count\nVoodoo,,300721,9875962,0.99,Alternative & Punk,Body Count,Body Count\nThe Winner Loses,,392254,12843821,0.99,Alternative & Punk,Body Count,Body Count\nThere Goes The Neighborhood,,350171,11443471,0.99,Alternative & Punk,Body Count,Body Count\nOprah,,6635,224313,0.99,Alternative & Punk,Body Count,Body Count\nEvil Dick,,239020,7828873,0.99,Alternative & Punk,Body Count,Body Count\nBody Count Anthem,,166426,5463690,0.99,Alternative & Punk,Body Count,Body Count\nMomma's Gotta Die Tonight,,371539,12122946,0.99,Alternative & Punk,Body Count,Body Count\nFreedom Of Speech,,281234,9337917,0.99,Alternative & Punk,Body Count,Body Count\nKing In Crimson,Roy Z,283167,9218499,0.99,Metal,Chemical Wedding,Bruce Dickinson\nChemical Wedding,Roy Z,246177,8022764,0.99,Metal,Chemical Wedding,Bruce Dickinson\nThe Tower,Roy Z,285257,9435693,0.99,Metal,Chemical Wedding,Bruce Dickinson\nKilling Floor,Adrian Smith,269557,8854240,0.99,Metal,Chemical Wedding,Bruce Dickinson\nBook Of Thel,Eddie Casillas/Roy Z,494393,16034404,0.99,Metal,Chemical Wedding,Bruce Dickinson\nGates Of Urizen,Roy Z,265351,8627004,0.99,Metal,Chemical Wedding,Bruce Dickinson\nJerusalem,Roy Z,402390,13194463,0.99,Metal,Chemical Wedding,Bruce Dickinson\nTrupets Of Jericho,Roy Z,359131,11820908,0.99,Metal,Chemical Wedding,Bruce Dickinson\nMachine Men,Adrian Smith,341655,11138147,0.99,Metal,Chemical Wedding,Bruce Dickinson\nThe Alchemist,Roy Z,509413,16545657,0.99,Metal,Chemical Wedding,Bruce Dickinson\nRealword,Roy Z,237531,7802095,0.99,Metal,Chemical Wedding,Bruce Dickinson\nFirst Time I Met The Blues,Eurreal Montgomery,140434,4604995,0.99,Blues,The Best Of Buddy Guy - The Millenium Collection,Buddy Guy\nLet Me Love You Baby,Willie Dixon,175386,5716994,0.99,Blues,The Best Of Buddy Guy - The Millenium Collection,Buddy Guy\nStone Crazy,Buddy Guy,433397,14184984,0.99,Blues,The Best Of Buddy Guy - The Millenium Collection,Buddy Guy\nPretty Baby,Willie Dixon,237662,7848282,0.99,Blues,The Best Of Buddy Guy - The Millenium Collection,Buddy Guy\nWhen My Left Eye Jumps,Al Perkins/Willie Dixon,235311,7685363,0.99,Blues,The Best Of Buddy Guy - The Millenium Collection,Buddy Guy\nLeave My Girl Alone,Buddy Guy,204721,6859518,0.99,Blues,The Best Of Buddy Guy - The Millenium Collection,Buddy Guy\nShe Suits Me To A Tee,Buddy Guy,136803,4456321,0.99,Blues,The Best Of Buddy Guy - The Millenium Collection,Buddy Guy\nKeep It To Myself (Aka Keep It To Yourself),Sonny Boy Williamson [I],166060,5487056,0.99,Blues,The Best Of Buddy Guy - The Millenium Collection,Buddy Guy\nMy Time After Awhile,Robert Geddins/Ron Badger/Sheldon Feinberg,182491,6022698,0.99,Blues,The Best Of Buddy Guy - The Millenium Collection,Buddy Guy\nToo Many Ways (Alternate),Willie Dixon,135053,4459946,0.99,Blues,The Best Of Buddy Guy - The Millenium Collection,Buddy Guy\nTalkin' 'Bout Women Obviously,Amos Blakemore/Buddy Guy,589531,19161377,0.99,Blues,The Best Of Buddy Guy - The Millenium Collection,Buddy Guy\nJorge Da Capadócia,Jorge Ben,177397,5842196,0.99,Latin,Prenda Minha,Caetano Veloso\nPrenda Minha,Tradicional,99369,3225364,0.99,Latin,Prenda Minha,Caetano Veloso\nMeditação,Tom Jobim - Newton Mendoça,148793,4865597,0.99,Latin,Prenda Minha,Caetano Veloso\nTerra,Caetano Veloso,482429,15889054,0.99,Latin,Prenda Minha,Caetano Veloso\nEclipse Oculto,Caetano Veloso,221936,7382703,0.99,Latin,Prenda Minha,Caetano Veloso\n\"Texto \"\"Verdade Tropical\"\"\",Caetano Veloso,84088,2752161,0.99,Latin,Prenda Minha,Caetano Veloso\nBem Devagar,Gilberto Gil,133172,4333651,0.99,Latin,Prenda Minha,Caetano Veloso\nDrão,Gilberto Gil,156264,5065932,0.99,Latin,Prenda Minha,Caetano Veloso\nSaudosismo,Caetano Veloso,144326,4726981,0.99,Latin,Prenda Minha,Caetano Veloso\nCarolina,Chico Buarque,181812,5924159,0.99,Latin,Prenda Minha,Caetano Veloso\nSozinho,Peninha,190589,6253200,0.99,Latin,Prenda Minha,Caetano Veloso\nEsse Cara,Caetano Veloso,223111,7217126,0.99,Latin,Prenda Minha,Caetano Veloso\nMel,Caetano Veloso - Waly Salomão,294765,9854062,0.99,Latin,Prenda Minha,Caetano Veloso\nLinha Do Equador,Caetano Veloso - Djavan,299337,10003747,0.99,Latin,Prenda Minha,Caetano Veloso\nOdara,Caetano Veloso,141270,4704104,0.99,Latin,Prenda Minha,Caetano Veloso\nA Luz De Tieta,Caetano Veloso,251742,8507446,0.99,Latin,Prenda Minha,Caetano Veloso\nAtrás Da Verd-E-Rosa Só Não Vai Quem Já Morreu,David Corrêa - Paulinho Carvalho - Carlos Sena - Bira do Ponto,307252,10364247,0.99,Latin,Prenda Minha,Caetano Veloso\nVida Boa,Fausto Nilo - Armandinho,281730,9411272,0.99,Latin,Prenda Minha,Caetano Veloso\nSozinho (Hitmakers Classic Mix),,436636,14462072,0.99,Latin,Sozinho Remix Ao Vivo,Caetano Veloso\nSozinho (Hitmakers Classic Radio Edit),,195004,6455134,0.99,Latin,Sozinho Remix Ao Vivo,Caetano Veloso\nSozinho (Caêdrum 'n' Bass),,328071,10975007,0.99,Latin,Sozinho Remix Ao Vivo,Caetano Veloso\nCarolina,,163056,5375395,0.99,Latin,Minha Historia,Chico Buarque\nEssa Moça Ta Diferente,,167235,5568574,0.99,Latin,Minha Historia,Chico Buarque\nVai Passar,,369763,12359161,0.99,Latin,Minha Historia,Chico Buarque\nSamba De Orly,,162429,5431854,0.99,Latin,Minha Historia,Chico Buarque\n\"Bye, Bye Brasil\",,283402,9499590,0.99,Latin,Minha Historia,Chico Buarque\nAtras Da Porta,,189675,6132843,0.99,Latin,Minha Historia,Chico Buarque\nTatuagem,,172120,5645703,0.99,Latin,Minha Historia,Chico Buarque\nO Que Será (À Flor Da Terra),,167288,5574848,0.99,Latin,Minha Historia,Chico Buarque\nMorena De Angola,,186801,6373932,0.99,Latin,Minha Historia,Chico Buarque\nApesar De Você,,234501,7886937,0.99,Latin,Minha Historia,Chico Buarque\nA Banda,,132493,4349539,0.99,Latin,Minha Historia,Chico Buarque\nMinha Historia,,182256,6029673,0.99,Latin,Minha Historia,Chico Buarque\nCom Açúcar E Com Afeto,,175386,5846442,0.99,Latin,Minha Historia,Chico Buarque\nBrejo Da Cruz,,214099,7270749,0.99,Latin,Minha Historia,Chico Buarque\nMeu Caro Amigo,,260257,8778172,0.99,Latin,Minha Historia,Chico Buarque\nGeni E O Zepelim,,317570,10342226,0.99,Latin,Minha Historia,Chico Buarque\nTrocando Em Miúdos,,169717,5461468,0.99,Latin,Minha Historia,Chico Buarque\nVai Trabalhar Vagabundo,,139154,4693941,0.99,Latin,Minha Historia,Chico Buarque\nGota D'água,,153208,5074189,0.99,Latin,Minha Historia,Chico Buarque\nConstrução / Deus Lhe Pague,,383059,12675305,0.99,Latin,Minha Historia,Chico Buarque\nMeia-Lua Inteira,,222093,7466288,0.99,Latin,Minha Historia,Chico Buarque\nVoce e Linda,,242938,8050268,0.99,Latin,Minha Historia,Chico Buarque\nUm Indio,,195944,6453213,0.99,Latin,Minha Historia,Chico Buarque\nPodres Poderes,,259761,8622495,0.99,Latin,Minha Historia,Chico Buarque\nVoce Nao Entende Nada - Cotidiano,,421982,13885612,0.99,Latin,Minha Historia,Chico Buarque\nO Estrangeiro,,374700,12472890,0.99,Latin,Minha Historia,Chico Buarque\nMenino Do Rio,,147670,4862277,0.99,Latin,Minha Historia,Chico Buarque\nQualquer Coisa,,193410,6372433,0.99,Latin,Minha Historia,Chico Buarque\nSampa,,185051,6151831,0.99,Latin,Minha Historia,Chico Buarque\nQueixa,,299676,9953962,0.99,Latin,Minha Historia,Chico Buarque\nO Leaozinho,,184398,6098150,0.99,Latin,Minha Historia,Chico Buarque\nFora Da Ordem,,354011,11746781,0.99,Latin,Minha Historia,Chico Buarque\nTerra,,401319,13224055,0.99,Latin,Minha Historia,Chico Buarque\n\"Alegria, Alegria\",,169221,5497025,0.99,Latin,Minha Historia,Chico Buarque\nMateus Enter,Chico Science,33149,1103013,0.99,Latin,Afrociberdelia,Chico Science & Nação Zumbi\nO Cidadão Do Mundo,Chico Science,200933,6724966,0.99,Latin,Afrociberdelia,Chico Science & Nação Zumbi\nEtnia,Chico Science,152555,5061413,0.99,Latin,Afrociberdelia,Chico Science & Nação Zumbi\nQuilombo Groove [Instrumental],Chico Science,151823,5042447,0.99,Latin,Afrociberdelia,Chico Science & Nação Zumbi\nMacô,Chico Science,249600,8253934,0.99,Latin,Afrociberdelia,Chico Science & Nação Zumbi\nUm Passeio No Mundo Livre,Chico Science,240091,7984291,0.99,Latin,Afrociberdelia,Chico Science & Nação Zumbi\nSamba Do Lado,Chico Science,227317,7541688,0.99,Latin,Afrociberdelia,Chico Science & Nação Zumbi\nMaracatu Atômico,Chico Science,284264,9670057,0.99,Latin,Afrociberdelia,Chico Science & Nação Zumbi\nO Encontro De Isaac Asimov Com Santos Dumont No Céu,Chico Science,99108,3240816,0.99,Latin,Afrociberdelia,Chico Science & Nação Zumbi\nCorpo De Lama,Chico Science,232672,7714954,0.99,Latin,Afrociberdelia,Chico Science & Nação Zumbi\nSobremesa,Chico Science,240091,7960868,0.99,Latin,Afrociberdelia,Chico Science & Nação Zumbi\nManguetown,Chico Science,194560,6475159,0.99,Latin,Afrociberdelia,Chico Science & Nação Zumbi\nUm Satélite Na Cabeça,Chico Science,126615,4272821,0.99,Latin,Afrociberdelia,Chico Science & Nação Zumbi\nBaião Ambiental [Instrumental],Chico Science,152659,5198539,0.99,Latin,Afrociberdelia,Chico Science & Nação Zumbi\nSangue De Bairro,Chico Science,132231,4415557,0.99,Latin,Afrociberdelia,Chico Science & Nação Zumbi\nEnquanto O Mundo Explode,Chico Science,88764,2968650,0.99,Latin,Afrociberdelia,Chico Science & Nação Zumbi\nInterlude Zumbi,Chico Science,71627,2408550,0.99,Latin,Afrociberdelia,Chico Science & Nação Zumbi\nCriança De Domingo,Chico Science,208222,6984813,0.99,Latin,Afrociberdelia,Chico Science & Nação Zumbi\nAmor De Muito,Chico Science,175333,5881293,0.99,Latin,Afrociberdelia,Chico Science & Nação Zumbi\nSamidarish [Instrumental],Chico Science,272431,8911641,0.99,Latin,Afrociberdelia,Chico Science & Nação Zumbi\nMaracatu Atômico [Atomic Version],Chico Science,273084,9019677,0.99,Latin,Afrociberdelia,Chico Science & Nação Zumbi\nMaracatu Atômico [Ragga Mix],Chico Science,210155,6986421,0.99,Latin,Afrociberdelia,Chico Science & Nação Zumbi\nMaracatu Atômico [Trip Hop],Chico Science,221492,7380787,0.99,Latin,Afrociberdelia,Chico Science & Nação Zumbi\nBanditismo Por Uma Questa,,307095,10251097,0.99,Latin,Da Lama Ao Caos,Chico Science & Nação Zumbi\nBanditismo Por Uma Questa,,243644,8147224,0.99,Latin,Da Lama Ao Caos,Chico Science & Nação Zumbi\nRios Pontes & Overdrives,,286720,9659152,0.99,Latin,Da Lama Ao Caos,Chico Science & Nação Zumbi\nCidade,,216346,7241817,0.99,Latin,Da Lama Ao Caos,Chico Science & Nação Zumbi\nPraiera,,183640,6172781,0.99,Latin,Da Lama Ao Caos,Chico Science & Nação Zumbi\nSamba Makossa,,271856,9095410,0.99,Latin,Da Lama Ao Caos,Chico Science & Nação Zumbi\nDa Lama Ao Caos,,251559,8378065,0.99,Latin,Da Lama Ao Caos,Chico Science & Nação Zumbi\nMaracatu De Tiro Certeiro,,88868,2901397,0.99,Latin,Da Lama Ao Caos,Chico Science & Nação Zumbi\nSalustiano Song,,215405,7183969,0.99,Latin,Da Lama Ao Caos,Chico Science & Nação Zumbi\nAntene Se,,248372,8253618,0.99,Latin,Da Lama Ao Caos,Chico Science & Nação Zumbi\nRisoflora,,105586,3536938,0.99,Latin,Da Lama Ao Caos,Chico Science & Nação Zumbi\nLixo Do Mangue,,193253,6534200,0.99,Latin,Da Lama Ao Caos,Chico Science & Nação Zumbi\nComputadores Fazem Arte,,404323,13702771,0.99,Latin,Da Lama Ao Caos,Chico Science & Nação Zumbi\nGirassol,Bino Farias/Da Gama/Lazão/Pedro Luis/Toni Garrido,249808,8327676,0.99,Reggae,Acústico MTV [Live],Cidade Negra\nA Sombra Da Maldade,Da Gama/Toni Garrido,230922,7697230,0.99,Reggae,Acústico MTV [Live],Cidade Negra\nJohnny B. Goode,Chuck Berry,254615,8505985,0.99,Reggae,Acústico MTV [Live],Cidade Negra\nSoldado Da Paz,Herbert Vianna,194220,6455080,0.99,Reggae,Acústico MTV [Live],Cidade Negra\nFirmamento,Bino Farias/Da Gama/Henry Lawes/Lazão/Toni Garrido/Winston Foser-Vers,222145,7402658,0.99,Reggae,Acústico MTV [Live],Cidade Negra\nExtra,Gilberto Gil,304352,10078050,0.99,Reggae,Acústico MTV [Live],Cidade Negra\nO Erê,Bernardo Vilhena/Bino Farias/Da Gama/Lazão/Toni Garrido,236382,7866924,0.99,Reggae,Acústico MTV [Live],Cidade Negra\nPodes Crer,Bino Farias/Da Gama/Lazão/Toni Garrido,232280,7747747,0.99,Reggae,Acústico MTV [Live],Cidade Negra\nA Estrada,Bino Farias/Da Gama/Lazão/Toni Garrido,248842,8275673,0.99,Reggae,Acústico MTV [Live],Cidade Negra\nBerlim,Da Gama/Toni Garrido,207542,6920424,0.99,Reggae,Acústico MTV [Live],Cidade Negra\nJá Foi,Bino Farias/Da Gama/Lazão/Toni Garrido,221544,7388466,0.99,Reggae,Acústico MTV [Live],Cidade Negra\nOnde Você Mora?,Marisa Monte/Nando Reis,256026,8502588,0.99,Reggae,Acústico MTV [Live],Cidade Negra\nPensamento,Bino Farias/Da Gamma/Lazão/Rás Bernard,173008,5748424,0.99,Reggae,Acústico MTV [Live],Cidade Negra\nConciliação,Da Gama/Lazão/Rás Bernardo,257619,8552474,0.99,Reggae,Acústico MTV [Live],Cidade Negra\nRealidade Virtual,Bino Farias/Da Gama/Lazão/Toni Garrido,195239,6503533,0.99,Reggae,Acústico MTV [Live],Cidade Negra\nMensagem,Bino Farias/Da Gama/Lazão/Rás Bernardo,225332,7488852,0.99,Reggae,Acústico MTV [Live],Cidade Negra\nA Cor Do Sol,Bernardo Vilhena/Da Gama/Lazão,231392,7663348,0.99,Reggae,Acústico MTV [Live],Cidade Negra\nOnde Você Mora?,Marisa Monte/Nando Reis,298396,10056970,0.99,Reggae,Cidade Negra - Hits,Cidade Negra\nO Erê,Bernardo Vilhena/Bino/Da Gama/Lazao/Toni Garrido,206942,6950332,0.99,Reggae,Cidade Negra - Hits,Cidade Negra\nA Sombra Da Maldade,Da Gama/Toni Garrido,285231,9544383,0.99,Reggae,Cidade Negra - Hits,Cidade Negra\nA Estrada,Da Gama/Lazao/Toni Garrido,282174,9344477,0.99,Reggae,Cidade Negra - Hits,Cidade Negra\nFalar A Verdade,Bino/Da Gama/Ras Bernardo,244950,8189093,0.99,Reggae,Cidade Negra - Hits,Cidade Negra\nFirmamento,Harry Lawes/Winston Foster-Vers,225488,7507866,0.99,Reggae,Cidade Negra - Hits,Cidade Negra\nPensamento,Bino/Da Gama/Ras Bernardo,192391,6399761,0.99,Reggae,Cidade Negra - Hits,Cidade Negra\nRealidade Virtual,Bino/Da Gamma/Lazao/Toni Garrido,240300,8069934,0.99,Reggae,Cidade Negra - Hits,Cidade Negra\nDoutor,Bino/Da Gama/Toni Garrido,178155,5950952,0.99,Reggae,Cidade Negra - Hits,Cidade Negra\nNa Frente Da TV,Bino/Da Gama/Lazao/Ras Bernardo,289750,9633659,0.99,Reggae,Cidade Negra - Hits,Cidade Negra\nDowntown,Cidade Negra,239725,8024386,0.99,Reggae,Cidade Negra - Hits,Cidade Negra\nSábado A Noite,Lulu Santos,267363,8895073,0.99,Reggae,Cidade Negra - Hits,Cidade Negra\nA Cor Do Sol,Bernardo Vilhena/Da Gama/Lazao,273031,9142937,0.99,Reggae,Cidade Negra - Hits,Cidade Negra\nEu Também Quero Beijar,Fausto Nilo/Moraes Moreira/Pepeu Gomes,211147,7029400,0.99,Reggae,Cidade Negra - Hits,Cidade Negra\nNoite Do Prazer,,311353,10309980,0.99,Latin,Na Pista,Cláudio Zoli\nÀ Francesa,,244532,8150846,0.99,Latin,Na Pista,Cláudio Zoli\nCada Um Cada Um (A Namoradeira),,253492,8441034,0.99,Latin,Na Pista,Cláudio Zoli\nLinha Do Equador,,244715,8123466,0.99,Latin,Na Pista,Cláudio Zoli\nAmor Demais,,254040,8420093,0.99,Latin,Na Pista,Cláudio Zoli\nFérias,,264202,8731945,0.99,Latin,Na Pista,Cláudio Zoli\nGostava Tanto De Você,,230452,7685326,0.99,Latin,Na Pista,Cláudio Zoli\nFlor Do Futuro,,275748,9205941,0.99,Latin,Na Pista,Cláudio Zoli\nFelicidade Urgente,,266605,8873358,0.99,Latin,Na Pista,Cláudio Zoli\nLivre Pra Viver,,214595,7111596,0.99,Latin,Na Pista,Cláudio Zoli\n\"Dig-Dig, Lambe-Lambe (Ao Vivo)\",Cassiano Costa/Cintia Maviane/J.F./Lucas Costa,205479,6892516,0.99,Pop,Axé Bahia 2001,Various Artists\nPererê,Augusto Conceição/Chiclete Com Banana,198661,6643207,0.99,Pop,Axé Bahia 2001,Various Artists\nTriboTchan,Cal Adan/Paulo Levi,194194,6507950,0.99,Pop,Axé Bahia 2001,Various Artists\n\"Tapa Aqui, Descobre Ali\",Paulo Levi/W. Rangel,188630,6327391,0.99,Pop,Axé Bahia 2001,Various Artists\nDaniela,Jorge Cardoso/Pierre Onasis,230791,7748006,0.99,Pop,Axé Bahia 2001,Various Artists\nBate Lata,Fábio Nolasco/Gal Sales/Ivan Brasil,206733,7034985,0.99,Pop,Axé Bahia 2001,Various Artists\nGarotas do Brasil,\"Garay, Ricardo Engels/Luca Predabom/Ludwig, Carlos Henrique/Maurício Vieira\",210155,6973625,0.99,Pop,Axé Bahia 2001,Various Artists\nLevada do Amor (Ailoviu),Luiz Wanderley/Paulo Levi,190093,6457752,0.99,Pop,Axé Bahia 2001,Various Artists\nLavadeira,\"Do Vale, Valverde/Gal Oliveira/Luciano Pinto\",214256,7254147,0.99,Pop,Axé Bahia 2001,Various Artists\nReboladeira,Cal Adan/Ferrugem/Julinho Carioca/Tríona Ní Dhomhnaill,210599,7027525,0.99,Pop,Axé Bahia 2001,Various Artists\nÉ que Nessa Encarnação Eu Nasci Manga,Lucina/Luli,196519,6568081,0.99,Pop,Axé Bahia 2001,Various Artists\nReggae Tchan,\"Cal Adan/Del Rey, Tension/Edu Casanova\",206654,6931328,0.99,Pop,Axé Bahia 2001,Various Artists\nMy Love,Jauperi/Zeu Góes,203493,6772813,0.99,Pop,Axé Bahia 2001,Various Artists\nLatinha de Cerveja,Adriano Bernandes/Edmar Neves,166687,5532564,0.99,Pop,Axé Bahia 2001,Various Artists\nYou Shook Me,J B Lenoir/Willie Dixon,315951,10249958,0.99,Rock,BBC Sessions [Disc 1] [Live],Led Zeppelin\nI Can't Quit You Baby,Willie Dixon,263836,8581414,0.99,Rock,BBC Sessions [Disc 1] [Live],Led Zeppelin\nCommunication Breakdown,Jimmy Page/John Bonham/John Paul Jones,192653,6287257,0.99,Rock,BBC Sessions [Disc 1] [Live],Led Zeppelin\nDazed and Confused,Jimmy Page,401920,13035765,0.99,Rock,BBC Sessions [Disc 1] [Live],Led Zeppelin\nThe Girl I Love She Got Long Black Wavy Hair,Jimmy Page/John Bonham/John Estes/John Paul Jones/Robert Plant,183327,5995686,0.99,Rock,BBC Sessions [Disc 1] [Live],Led Zeppelin\nWhat is and Should Never Be,Jimmy Page/Robert Plant,260675,8497116,0.99,Rock,BBC Sessions [Disc 1] [Live],Led Zeppelin\nCommunication Breakdown(2),Jimmy Page/John Bonham/John Paul Jones,161149,5261022,0.99,Rock,BBC Sessions [Disc 1] [Live],Led Zeppelin\nTravelling Riverside Blues,Jimmy Page/Robert Johnson/Robert Plant,312032,10232581,0.99,Rock,BBC Sessions [Disc 1] [Live],Led Zeppelin\nWhole Lotta Love,Jimmy Page/John Bonham/John Paul Jones/Robert Plant/Willie Dixon,373394,12258175,0.99,Rock,BBC Sessions [Disc 1] [Live],Led Zeppelin\nSomethin' Else,Bob Cochran/Sharon Sheeley,127869,4165650,0.99,Rock,BBC Sessions [Disc 1] [Live],Led Zeppelin\nCommunication Breakdown(3),Jimmy Page/John Bonham/John Paul Jones,185260,6041133,0.99,Rock,BBC Sessions [Disc 1] [Live],Led Zeppelin\nI Can't Quit You Baby(2),Willie Dixon,380551,12377615,0.99,Rock,BBC Sessions [Disc 1] [Live],Led Zeppelin\nYou Shook Me(2),J B Lenoir/Willie Dixon,619467,20138673,0.99,Rock,BBC Sessions [Disc 1] [Live],Led Zeppelin\nHow Many More Times,Chester Burnett/Jimmy Page/John Bonham/John Paul Jones/Robert Plant,711836,23092953,0.99,Rock,BBC Sessions [Disc 1] [Live],Led Zeppelin\nDebra Kadabra,Frank Zappa,234553,7649679,0.99,Rock,Bongo Fury,Frank Zappa & Captain Beefheart\nCarolina Hard-Core Ecstasy,Frank Zappa,359680,11731061,0.99,Rock,Bongo Fury,Frank Zappa & Captain Beefheart\nSam With The Showing Scalp Flat Top,Don Van Vliet,171284,5572993,0.99,Rock,Bongo Fury,Frank Zappa & Captain Beefheart\nPoofter's Froth Wyoming Plans Ahead,Frank Zappa,183902,6007019,0.99,Rock,Bongo Fury,Frank Zappa & Captain Beefheart\n200 Years Old,Frank Zappa,272561,8912465,0.99,Rock,Bongo Fury,Frank Zappa & Captain Beefheart\nCucamonga,Frank Zappa,144483,4728586,0.99,Rock,Bongo Fury,Frank Zappa & Captain Beefheart\nAdvance Romance,Frank Zappa,677694,22080051,0.99,Rock,Bongo Fury,Frank Zappa & Captain Beefheart\nMan With The Woman Head,Don Van Vliet,88894,2922044,0.99,Rock,Bongo Fury,Frank Zappa & Captain Beefheart\nMuffin Man,Frank Zappa,332878,10891682,0.99,Rock,Bongo Fury,Frank Zappa & Captain Beefheart\nVai-Vai 2001,,276349,9402241,0.99,Soundtrack,Carnaval 2001,Various Artists\nX-9 2001,,273920,9310370,0.99,Soundtrack,Carnaval 2001,Various Artists\nGavioes 2001,,282723,9616640,0.99,Soundtrack,Carnaval 2001,Various Artists\nNene 2001,,284969,9694508,0.99,Soundtrack,Carnaval 2001,Various Artists\nRosas De Ouro 2001,,284342,9721084,0.99,Soundtrack,Carnaval 2001,Various Artists\nMocidade Alegre 2001,,282488,9599937,0.99,Soundtrack,Carnaval 2001,Various Artists\nCamisa Verde 2001,,283454,9633755,0.99,Soundtrack,Carnaval 2001,Various Artists\nLeandro De Itaquera 2001,,274808,9451845,0.99,Soundtrack,Carnaval 2001,Various Artists\nTucuruvi 2001,,287921,9883335,0.99,Soundtrack,Carnaval 2001,Various Artists\nAguia De Ouro 2001,,284160,9698729,0.99,Soundtrack,Carnaval 2001,Various Artists\nIpiranga 2001,,248293,8522591,0.99,Soundtrack,Carnaval 2001,Various Artists\nMorro Da Casa Verde 2001,,284708,9718778,0.99,Soundtrack,Carnaval 2001,Various Artists\nPerola Negra 2001,,281626,9619196,0.99,Soundtrack,Carnaval 2001,Various Artists\nSao Lucas 2001,,296254,10020122,0.99,Soundtrack,Carnaval 2001,Various Artists\nGuanabara,Marcos Valle,247614,8499591,0.99,Latin,Chill: Brazil (Disc 1),Marcos Valle\nMas Que Nada,Jorge Ben,248398,8255254,0.99,Latin,Chill: Brazil (Disc 1),Marcos Valle\nVôo Sobre o Horizonte,J.r.Bertami/Parana,225097,7528825,0.99,Latin,Chill: Brazil (Disc 1),Marcos Valle\nA Paz,Donato/Gilberto Gil,263183,8619173,0.99,Latin,Chill: Brazil (Disc 1),Marcos Valle\nWave (Vou te Contar),Antonio Carlos Jobim,271647,9057557,0.99,Latin,Chill: Brazil (Disc 1),Marcos Valle\nÁgua de Beber,Antonio Carlos Jobim/Vinicius de Moraes,146677,4866476,0.99,Latin,Chill: Brazil (Disc 1),Marcos Valle\nSamba da Bençaco,Baden Powell/Vinicius de Moraes,282200,9440676,0.99,Latin,Chill: Brazil (Disc 1),Marcos Valle\nPode Parar,Jorge Vercilo/Jota Maranhao,179408,6046678,0.99,Latin,Chill: Brazil (Disc 1),Marcos Valle\nMenino do Rio,Caetano Veloso,262713,8737489,0.99,Latin,Chill: Brazil (Disc 1),Marcos Valle\nAndo Meio Desligado,Caetano Veloso,195813,6547648,0.99,Latin,Chill: Brazil (Disc 1),Marcos Valle\nMistério da Raça,Luiz Melodia/Ricardo Augusto,184320,6191752,0.99,Latin,Chill: Brazil (Disc 1),Marcos Valle\nAll Star,Nando Reis,176326,5891697,0.99,Latin,Chill: Brazil (Disc 1),Marcos Valle\nMenina Bonita,Alexandre Brazil/Pedro Luis/Rodrigo Cabelo,237087,7938246,0.99,Latin,Chill: Brazil (Disc 1),Marcos Valle\nPescador de Ilusões,Macelo Yuka/O Rappa,245524,8267067,0.99,Latin,Chill: Brazil (Disc 1),Marcos Valle\nÀ Vontade (Live Mix),Bombom/Ed Motta,180636,5972430,0.99,Latin,Chill: Brazil (Disc 1),Marcos Valle\nMaria Fumaça,Luiz Carlos/Oberdan,141008,4743149,0.99,Latin,Chill: Brazil (Disc 1),Marcos Valle\nSambassim (dj patife remix),Alba Carvalho/Fernando Porto,213655,7243166,0.99,Latin,Chill: Brazil (Disc 1),Marcos Valle\nGarota De Ipanema,Vários,279536,9141343,0.99,Latin,Chill: Brazil (Disc 2),Antônio Carlos Jobim\nTim Tim Por Tim Tim,Vários,213237,7143328,0.99,Latin,Chill: Brazil (Disc 2),Antônio Carlos Jobim\nTarde Em Itapoã,Vários,313704,10344491,0.99,Latin,Chill: Brazil (Disc 2),Antônio Carlos Jobim\nTanto Tempo,Vários,170292,5572240,0.99,Latin,Chill: Brazil (Disc 2),Antônio Carlos Jobim\nEu Vim Da Bahia - Live,Vários,157988,5115428,0.99,Latin,Chill: Brazil (Disc 2),Antônio Carlos Jobim\nAlô Alô Marciano,Vários,238106,8013065,0.99,Latin,Chill: Brazil (Disc 2),Antônio Carlos Jobim\nLinha Do Horizonte,Vários,279484,9275929,0.99,Latin,Chill: Brazil (Disc 2),Antônio Carlos Jobim\nOnly A Dream In Rio,Vários,371356,12192989,0.99,Latin,Chill: Brazil (Disc 2),Antônio Carlos Jobim\nAbrir A Porta,Vários,271960,8991141,0.99,Latin,Chill: Brazil (Disc 2),Antônio Carlos Jobim\nAlice,Vários,165982,5594341,0.99,Latin,Chill: Brazil (Disc 2),Antônio Carlos Jobim\nMomentos Que Marcam,Vários,280137,9313740,0.99,Latin,Chill: Brazil (Disc 2),Antônio Carlos Jobim\nUm Jantar Pra Dois,Vários,237714,7819755,0.99,Latin,Chill: Brazil (Disc 2),Antônio Carlos Jobim\nBumbo Da Mangueira,Vários,270158,9073350,0.99,Latin,Chill: Brazil (Disc 2),Antônio Carlos Jobim\nMr Funk Samba,Vários,213890,7102545,0.99,Latin,Chill: Brazil (Disc 2),Antônio Carlos Jobim\nSanto Antonio,Vários,162716,5492069,0.99,Latin,Chill: Brazil (Disc 2),Antônio Carlos Jobim\nPor Você,Vários,205557,6792493,0.99,Latin,Chill: Brazil (Disc 2),Antônio Carlos Jobim\nSó Tinha De Ser Com Você,Vários,389642,13085596,0.99,Latin,Chill: Brazil (Disc 2),Antônio Carlos Jobim\nFree Speech For The Dumb,Molaney/Morris/Roberts/Wainwright,155428,5076048,0.99,Metal,Garage Inc. (Disc 1),Metallica\nIt's Electric,Harris/Tatler,213995,6978601,0.99,Metal,Garage Inc. (Disc 1),Metallica\nSabbra Cadabra,Black Sabbath,380342,12418147,0.99,Metal,Garage Inc. (Disc 1),Metallica\nTurn The Page,Seger,366524,11946327,0.99,Metal,Garage Inc. (Disc 1),Metallica\nDie Die My Darling,Danzig,149315,4867667,0.99,Metal,Garage Inc. (Disc 1),Metallica\nLoverman,Cave,472764,15446975,0.99,Metal,Garage Inc. (Disc 1),Metallica\nMercyful Fate,Diamond/Shermann,671712,21942829,0.99,Metal,Garage Inc. (Disc 1),Metallica\nAstronomy,A.Bouchard/J.Bouchard/S.Pearlman,397531,13065612,0.99,Metal,Garage Inc. (Disc 1),Metallica\nWhiskey In The Jar,Traditional,305005,9943129,0.99,Metal,Garage Inc. (Disc 1),Metallica\nTuesday's Gone,Collins/Van Zandt,545750,17900787,0.99,Metal,Garage Inc. (Disc 1),Metallica\nThe More I See,Molaney/Morris/Roberts/Wainwright,287973,9378873,0.99,Metal,Garage Inc. (Disc 1),Metallica\nA Kind Of Magic,Roger Taylor,262608,8689618,0.99,Rock,Greatest Hits II,Queen\nUnder Pressure,Queen & David Bowie,236617,7739042,0.99,Rock,Greatest Hits II,Queen\nRadio GA GA,Roger Taylor,343745,11358573,0.99,Rock,Greatest Hits II,Queen\nI Want It All,Queen,241684,7876564,0.99,Rock,Greatest Hits II,Queen\nI Want To Break Free,John Deacon,259108,8552861,0.99,Rock,Greatest Hits II,Queen\nInnuendo,Queen,387761,12664591,0.99,Rock,Greatest Hits II,Queen\nIt's A Hard Life,Freddie Mercury,249417,8112242,0.99,Rock,Greatest Hits II,Queen\nBreakthru,Queen,249234,8150479,0.99,Rock,Greatest Hits II,Queen\nWho Wants To Live Forever,Brian May,297691,9577577,0.99,Rock,Greatest Hits II,Queen\nHeadlong,Queen,273057,8921404,0.99,Rock,Greatest Hits II,Queen\nThe Miracle,Queen,294974,9671923,0.99,Rock,Greatest Hits II,Queen\nI'm Going Slightly Mad,Queen,248032,8192339,0.99,Rock,Greatest Hits II,Queen\nThe Invisible Man,Queen,238994,7920353,0.99,Rock,Greatest Hits II,Queen\nHammer To Fall,Brian May,220316,7255404,0.99,Rock,Greatest Hits II,Queen\nFriends Will Be Friends,Freddie Mercury & John Deacon,248920,8114582,0.99,Rock,Greatest Hits II,Queen\nThe Show Must Go On,Queen,263784,8526760,0.99,Rock,Greatest Hits II,Queen\nOne Vision,Queen,242599,7936928,0.99,Rock,Greatest Hits II,Queen\nDetroit Rock City,\"Paul Stanley, B. Ezrin\",218880,7146372,0.99,Rock,Greatest Kiss,Kiss\nBlack Diamond,Paul Stanley,314148,10266007,0.99,Rock,Greatest Kiss,Kiss\nHard Luck Woman,Paul Stanley,216032,7109267,0.99,Rock,Greatest Kiss,Kiss\nSure Know Something,\"Paul Stanley, Vincent Poncia\",242468,7939886,0.99,Rock,Greatest Kiss,Kiss\nLove Gun,Paul Stanley,196257,6424915,0.99,Rock,Greatest Kiss,Kiss\nDeuce,Gene Simmons,185077,6097210,0.99,Rock,Greatest Kiss,Kiss\nGoin' Blind,\"Gene Simmons, S. Coronel\",216215,7045314,0.99,Rock,Greatest Kiss,Kiss\nShock Me,Ace Frehley,227291,7529336,0.99,Rock,Greatest Kiss,Kiss\nDo You Love Me,\"Paul Stanley, B. Ezrin, K. Fowley\",214987,6976194,0.99,Rock,Greatest Kiss,Kiss\nShe,\"Gene Simmons, S. Coronel\",248346,8229734,0.99,Rock,Greatest Kiss,Kiss\nI Was Made For Loving You,\"Paul Stanley, Vincent Poncia, Desmond Child\",271360,9018078,0.99,Rock,Greatest Kiss,Kiss\nShout It Out Loud,\"Paul Stanley, Gene Simmons, B. Ezrin\",219742,7194424,0.99,Rock,Greatest Kiss,Kiss\nGod Of Thunder,Paul Stanley,255791,8309077,0.99,Rock,Greatest Kiss,Kiss\nCalling Dr. Love,Gene Simmons,225332,7395034,0.99,Rock,Greatest Kiss,Kiss\nBeth,\"S. Penridge, Bob Ezrin, Peter Criss\",166974,5360574,0.99,Rock,Greatest Kiss,Kiss\nStrutter,\"Paul Stanley, Gene Simmons\",192496,6317021,0.99,Rock,Greatest Kiss,Kiss\nRock And Roll All Nite,\"Paul Stanley, Gene Simmons\",173609,5735902,0.99,Rock,Greatest Kiss,Kiss\nCold Gin,Ace Frehley,262243,8609783,0.99,Rock,Greatest Kiss,Kiss\nPlaster Caster,Gene Simmons,207333,6801116,0.99,Rock,Greatest Kiss,Kiss\nGod Gave Rock 'n' Roll To You,\"Paul Stanley, Gene Simmons, Rus Ballard, Bob Ezrin\",320444,10441590,0.99,Rock,Greatest Kiss,Kiss\nHeart of the Night,,273737,9098263,0.99,Jazz,Heart of the Night,Spyro Gyra\nDe La Luz,,315219,10518284,0.99,Jazz,Heart of the Night,Spyro Gyra\nWestwood Moon,,295627,9765802,0.99,Jazz,Heart of the Night,Spyro Gyra\nMidnight,,266866,8851060,0.99,Jazz,Heart of the Night,Spyro Gyra\nPlaytime,,273580,9070880,0.99,Jazz,Heart of the Night,Spyro Gyra\nSurrender,,287634,9422926,0.99,Jazz,Heart of the Night,Spyro Gyra\nValentino's,,296124,9848545,0.99,Jazz,Heart of the Night,Spyro Gyra\nBelieve,,310778,10317185,0.99,Jazz,Heart of the Night,Spyro Gyra\nAs We Sleep,,316865,10429398,0.99,Jazz,Heart of the Night,Spyro Gyra\nWhen Evening Falls,,298135,9863942,0.99,Jazz,Heart of the Night,Spyro Gyra\nJ Squared,,288757,9480777,0.99,Jazz,Heart of the Night,Spyro Gyra\nBest Thing,,274259,9069394,0.99,Jazz,Heart of the Night,Spyro Gyra\nMaria,Billie Joe Armstrong -Words Green Day -Music,167262,5484747,0.99,Alternative & Punk,International Superhits,Green Day\nPoprocks And Coke,Billie Joe Armstrong -Words Green Day -Music,158354,5243078,0.99,Alternative & Punk,International Superhits,Green Day\nLongview,Billie Joe Armstrong -Words Green Day -Music,234083,7714939,0.99,Alternative & Punk,International Superhits,Green Day\nWelcome To Paradise,Billie Joe Armstrong -Words Green Day -Music,224208,7406008,0.99,Alternative & Punk,International Superhits,Green Day\nBasket Case,Billie Joe Armstrong -Words Green Day -Music,181629,5951736,0.99,Alternative & Punk,International Superhits,Green Day\nWhen I Come Around,Billie Joe Armstrong -Words Green Day -Music,178364,5839426,0.99,Alternative & Punk,International Superhits,Green Day\nShe,Billie Joe Armstrong -Words Green Day -Music,134164,4425128,0.99,Alternative & Punk,International Superhits,Green Day\nJ.A.R. (Jason Andrew Relva),Mike Dirnt -Words Green Day -Music,170997,5645755,0.99,Alternative & Punk,International Superhits,Green Day\nGeek Stink Breath,Billie Joe Armstrong -Words Green Day -Music,135888,4408983,0.99,Alternative & Punk,International Superhits,Green Day\nBrain Stew,Billie Joe Armstrong -Words Green Day -Music,193149,6305550,0.99,Alternative & Punk,International Superhits,Green Day\nJaded,Billie Joe Armstrong -Words Green Day -Music,90331,2950224,0.99,Alternative & Punk,International Superhits,Green Day\nWalking Contradiction,Billie Joe Armstrong -Words Green Day -Music,151170,4932366,0.99,Alternative & Punk,International Superhits,Green Day\nStuck With Me,Billie Joe Armstrong -Words Green Day -Music,135523,4431357,0.99,Alternative & Punk,International Superhits,Green Day\nHitchin' A Ride,Billie Joe Armstrong -Words Green Day -Music,171546,5616891,0.99,Alternative & Punk,International Superhits,Green Day\nGood Riddance (Time Of Your Life),Billie Joe Armstrong -Words Green Day -Music,153600,5075241,0.99,Alternative & Punk,International Superhits,Green Day\nRedundant,Billie Joe Armstrong -Words Green Day -Music,198164,6481753,0.99,Alternative & Punk,International Superhits,Green Day\nNice Guys Finish Last,Billie Joe Armstrong -Words Green Day -Music,170187,5604618,0.99,Alternative & Punk,International Superhits,Green Day\nMinority,Billie Joe Armstrong -Words Green Day -Music,168803,5535061,0.99,Alternative & Punk,International Superhits,Green Day\nWarning,Billie Joe Armstrong -Words Green Day -Music,221910,7343176,0.99,Alternative & Punk,International Superhits,Green Day\nWaiting,Billie Joe Armstrong -Words Green Day -Music,192757,6316430,0.99,Alternative & Punk,International Superhits,Green Day\nMacy's Day Parade,Billie Joe Armstrong -Words Green Day -Music,213420,7075573,0.99,Alternative & Punk,International Superhits,Green Day\nInto The Light,David Coverdale,76303,2452653,0.99,Rock,Into The Light,David Coverdale\nRiver Song,David Coverdale,439510,14359478,0.99,Rock,Into The Light,David Coverdale\nShe Give Me ...,David Coverdale,252551,8385478,0.99,Rock,Into The Light,David Coverdale\nDon't You Cry,David Coverdale,347036,11269612,0.99,Rock,Into The Light,David Coverdale\nLove Is Blind,David Coverdale/Earl Slick,344999,11409720,0.99,Rock,Into The Light,David Coverdale\nSlave,David Coverdale/Earl Slick,291892,9425200,0.99,Rock,Into The Light,David Coverdale\nCry For Love,Bossi/David Coverdale/Earl Slick,293015,9567075,0.99,Rock,Into The Light,David Coverdale\nLiving On Love,Bossi/David Coverdale/Earl Slick,391549,12785876,0.99,Rock,Into The Light,David Coverdale\nMidnight Blue,David Coverdale/Earl Slick,298631,9750990,0.99,Rock,Into The Light,David Coverdale\nToo Many Tears,Adrian Vanderberg/David Coverdale,359497,11810238,0.99,Rock,Into The Light,David Coverdale\nDon't Lie To Me,David Coverdale/Earl Slick,283585,9288007,0.99,Rock,Into The Light,David Coverdale\nWherever You May Go,David Coverdale,239699,7803074,0.99,Rock,Into The Light,David Coverdale\nGrito De Alerta,Gonzaga Jr.,202213,6539422,0.99,Latin,Meus Momentos,Gonzaguinha\nNão Dá Mais Pra Segurar (Explode Coração),,219768,7083012,0.99,Latin,Meus Momentos,Gonzaguinha\nComeçaria Tudo Outra Vez,,196545,6473395,0.99,Latin,Meus Momentos,Gonzaguinha\nO Que É O Que É ?,,259291,8650647,0.99,Latin,Meus Momentos,Gonzaguinha\nSangrando,Gonzaga Jr/Gonzaguinha,169717,5494406,0.99,Latin,Meus Momentos,Gonzaguinha\n\"Diga Lá, Coração\",,255921,8280636,0.99,Latin,Meus Momentos,Gonzaguinha\nLindo Lago Do Amor,Gonzaga Jr.,249678,8353191,0.99,Latin,Meus Momentos,Gonzaguinha\nEu Apenas Queria Que Voçê Soubesse,,155637,5130056,0.99,Latin,Meus Momentos,Gonzaguinha\nCom A Perna No Mundo,Gonzaga Jr.,227448,7747108,0.99,Latin,Meus Momentos,Gonzaguinha\nE Vamos À Luta,,222406,7585112,0.99,Latin,Meus Momentos,Gonzaguinha\nUm Homem Também Chora (Guerreiro Menino),,207229,6854219,0.99,Latin,Meus Momentos,Gonzaguinha\nComportamento Geral,Gonzaga Jr,181577,5997444,0.99,Latin,Meus Momentos,Gonzaguinha\nPonto De Interrogação,,180950,5946265,0.99,Latin,Meus Momentos,Gonzaguinha\n\"Espere Por Mim, Morena\",Gonzaguinha,207072,6796523,0.99,Latin,Meus Momentos,Gonzaguinha\nBalada Do Louco,Arnaldo Baptista - Rita Lee,241057,7852328,0.99,Alternative & Punk,Minha História,Os Mutantes\nAndo Meio Desligado,Arnaldo Baptista - Rita Lee - Sérgio Dias,287817,9484504,0.99,Alternative & Punk,Minha História,Os Mutantes\nTop Top,Os Mutantes - Arnolpho Lima Filho,146938,4875374,0.99,Alternative & Punk,Minha História,Os Mutantes\nBaby,Caetano Veloso,177188,5798202,0.99,Alternative & Punk,Minha História,Os Mutantes\nA E O Z,Mutantes,518556,16873005,0.99,Alternative & Punk,Minha História,Os Mutantes\nPanis Et Circenses,Caetano Veloso - Gilberto Gil,125152,4069688,0.99,Alternative & Punk,Minha História,Os Mutantes\nChão De Estrelas,Orestes Barbosa-Sílvio Caldas,284813,9433620,0.99,Alternative & Punk,Minha História,Os Mutantes\nVida De Cachorro,Rita Lee - Arnaldo Baptista - Sérgio Baptista,195186,6411149,0.99,Alternative & Punk,Minha História,Os Mutantes\nBat Macumba,Gilberto Gil - Caetano Veloso,187794,6295223,0.99,Alternative & Punk,Minha História,Os Mutantes\nDesculpe Babe,Arnaldo Baptista - Rita Lee,170422,5637959,0.99,Alternative & Punk,Minha História,Os Mutantes\nRita Lee,Arnaldo Baptista/Rita Lee/Sérgio Dias,189257,6270503,0.99,Alternative & Punk,Minha História,Os Mutantes\n\"Posso Perder Minha Mulher, Minha Mãe, Desde Que Eu Tenha O Rock And Roll\",Arnaldo Baptista - Rita Lee - Arnolpho Lima Filho,222955,7346254,0.99,Alternative & Punk,Minha História,Os Mutantes\nBanho De Lua,B. de Filippi - F. Migliaci - Versão: Fred Jorge,221831,7232123,0.99,Alternative & Punk,Minha História,Os Mutantes\nMeu Refrigerador Não Funciona,Arnaldo Baptista - Rita Lee - Sérgio Dias,382981,12495906,0.99,Alternative & Punk,Minha História,Os Mutantes\nBurn,Coverdale/Lord/Paice,453955,14775708,0.99,Rock,MK III The Final Concerts [Disc 1],Deep Purple\nStormbringer,Coverdale,277133,9050022,0.99,Rock,MK III The Final Concerts [Disc 1],Deep Purple\nGypsy,Coverdale/Hughes/Lord/Paice,339173,11046952,0.99,Rock,MK III The Final Concerts [Disc 1],Deep Purple\nLady Double Dealer,Coverdale,233586,7608759,0.99,Rock,MK III The Final Concerts [Disc 1],Deep Purple\nMistreated,Coverdale,758648,24596235,0.99,Rock,MK III The Final Concerts [Disc 1],Deep Purple\nSmoke On The Water,Gillan/Glover/Lord/Paice,618031,20103125,0.99,Rock,MK III The Final Concerts [Disc 1],Deep Purple\nYou Fool No One,Coverdale/Lord/Paice,804101,26369966,0.99,Rock,MK III The Final Concerts [Disc 1],Deep Purple\nCustard Pie,Jimmy Page/Robert Plant,253962,8348257,0.99,Rock,Physical Graffiti [Disc 1],Led Zeppelin\nThe Rover,Jimmy Page/Robert Plant,337084,11011286,0.99,Rock,Physical Graffiti [Disc 1],Led Zeppelin\nIn My Time Of Dying,John Bonham/John Paul Jones,666017,21676727,0.99,Rock,Physical Graffiti [Disc 1],Led Zeppelin\nHouses Of The Holy,Jimmy Page/Robert Plant,242494,7972503,0.99,Rock,Physical Graffiti [Disc 1],Led Zeppelin\nTrampled Under Foot,John Paul Jones,336692,11154468,0.99,Rock,Physical Graffiti [Disc 1],Led Zeppelin\nKashmir,John Bonham,508604,16686580,0.99,Rock,Physical Graffiti [Disc 1],Led Zeppelin\nImperatriz,Guga/Marquinho Lessa/Tuninho Professor,339173,11348710,0.99,Latin,Sambas De Enredo 2001,Various Artists\nBeija-Flor,Caruso/Cleber/Deo/Osmar,327000,10991159,0.99,Latin,Sambas De Enredo 2001,Various Artists\nViradouro,Dadinho/Gilbreto Gomes/Gustavo/P.C. Portugal/R. Mocoto,344320,11484362,0.99,Latin,Sambas De Enredo 2001,Various Artists\nMocidade,\"Domenil/J. Brito/Joaozinho/Rap, Marcelo Do\",261720,8817757,0.99,Latin,Sambas De Enredo 2001,Various Artists\nUnidos Da Tijuca,\"Douglas/Neves, Vicente Das/Silva, Gilmar L./Toninho Gentil/Wantuir\",338834,11440689,0.99,Latin,Sambas De Enredo 2001,Various Artists\nSalgueiro,\"Augusto/Craig Negoescu/Rocco Filho/Saara, Ze Carlos Da\",305920,10294741,0.99,Latin,Sambas De Enredo 2001,Various Artists\nMangueira,Bizuca/Clóvis Pê/Gilson Bernini/Marelo D'Aguia,298318,9999506,0.99,Latin,Sambas De Enredo 2001,Various Artists\nUnião Da Ilha,\"Dito/Djalma Falcao/Ilha, Almir Da/Márcio André\",330945,11100945,0.99,Latin,Sambas De Enredo 2001,Various Artists\nGrande Rio,Carlos Santos/Ciro/Claudio Russo/Zé Luiz,307252,10251428,0.99,Latin,Sambas De Enredo 2001,Various Artists\nPortela,Flavio Bororo/Paulo Apparicio/Wagner Alves/Zeca Sereno,319608,10712216,0.99,Latin,Sambas De Enredo 2001,Various Artists\nCaprichosos,Gule/Jorge 101/Lequinho/Luiz Piao,351320,11870956,0.99,Latin,Sambas De Enredo 2001,Various Artists\nTradição,Adalto Magalha/Lourenco,269165,9114880,0.99,Latin,Sambas De Enredo 2001,Various Artists\nImpério Serrano,Arlindo Cruz/Carlos Sena/Elmo Caetano/Mauricao,334942,11161196,0.99,Latin,Sambas De Enredo 2001,Various Artists\nTuiuti,\"Claudio Martins/David Lima/Kleber Rodrigues/Livre, Cesare Som\",259657,8749492,0.99,Latin,Sambas De Enredo 2001,Various Artists\n(Da Le) Yaleo,Santana,353488,11769507,0.99,Rock,Supernatural,Santana\nLove Of My Life,Carlos Santana & Dave Matthews,347820,11634337,0.99,Rock,Supernatural,Santana\nPut Your Lights On,E. Shrody,285178,9394769,0.99,Rock,Supernatural,Santana\nAfrica Bamba,\"I. Toure, S. Tidiane Toure, Carlos Santana & K. Perazzo\",282827,9492487,0.99,Rock,Supernatural,Santana\nSmooth,M. Itaal Shur & Rob Thomas,298161,9867455,0.99,Rock,Supernatural,Santana\nDo You Like The Way,L. Hill,354899,11741062,0.99,Rock,Supernatural,Santana\nMaria Maria,\"W. Jean, J. Duplessis, Carlos Santana, K. Perazzo & R. Rekow\",262635,8664601,0.99,Rock,Supernatural,Santana\nMigra,\"R. Taha, Carlos Santana & T. Lindsay\",329064,10963305,0.99,Rock,Supernatural,Santana\nCorazon Espinado,F. Olivera,276114,9206802,0.99,Rock,Supernatural,Santana\nWishing It Was,\"Eale-Eye Cherry, M. Simpson, J. King & M. Nishita\",292832,9771348,0.99,Rock,Supernatural,Santana\nEl Farol,Carlos Santana & KC Porter,291160,9599353,0.99,Rock,Supernatural,Santana\nPrimavera,KC Porter & JB Eckl,378618,12504234,0.99,Rock,Supernatural,Santana\nThe Calling,Carlos Santana & C. Thompson,747755,24703884,0.99,Rock,Supernatural,Santana\nSolução,,247431,8100449,0.99,Latin,The Best of Ed Motta,Ed Motta\nManuel,,230269,7677671,0.99,Latin,The Best of Ed Motta,Ed Motta\nEntre E Ouça,,286302,9391004,0.99,Latin,The Best of Ed Motta,Ed Motta\nUm Contrato Com Deus,,202501,6636465,0.99,Latin,The Best of Ed Motta,Ed Motta\nUm Jantar Pra Dois,,244009,8021589,0.99,Latin,The Best of Ed Motta,Ed Motta\nVamos Dançar,,226194,7617432,0.99,Latin,The Best of Ed Motta,Ed Motta\nUm Love,,181603,6095524,0.99,Latin,The Best of Ed Motta,Ed Motta\nSeis Da Tarde,,238445,7935898,0.99,Latin,The Best of Ed Motta,Ed Motta\nBaixo Rio,,198008,6521676,0.99,Latin,The Best of Ed Motta,Ed Motta\nSombras Do Meu Destino,,280685,9161539,0.99,Latin,The Best of Ed Motta,Ed Motta\nDo You Have Other Loves?,,295235,9604273,0.99,Latin,The Best of Ed Motta,Ed Motta\nAgora Que O Dia Acordou,,323213,10572752,0.99,Latin,The Best of Ed Motta,Ed Motta\nJá!!!,,217782,7103608,0.99,Latin,The Best of Ed Motta,Ed Motta\nA Rua,,238027,7930264,0.99,Latin,The Best of Ed Motta,Ed Motta\nNow's The Time,Miles Davis,197459,6358868,0.99,Jazz,The Essential Miles Davis [Disc 1],Miles Davis\nJeru,Miles Davis,193410,6222536,0.99,Jazz,The Essential Miles Davis [Disc 1],Miles Davis\nCompulsion,Miles Davis,345025,11254474,0.99,Jazz,The Essential Miles Davis [Disc 1],Miles Davis\nTempus Fugit,Miles Davis,231784,7548434,0.99,Jazz,The Essential Miles Davis [Disc 1],Miles Davis\nWalkin',Miles Davis,807392,26411634,0.99,Jazz,The Essential Miles Davis [Disc 1],Miles Davis\n'Round Midnight,Miles Davis,357459,11590284,0.99,Jazz,The Essential Miles Davis [Disc 1],Miles Davis\nBye Bye Blackbird,Miles Davis,476003,15549224,0.99,Jazz,The Essential Miles Davis [Disc 1],Miles Davis\nNew Rhumba,Miles Davis,277968,9018024,0.99,Jazz,The Essential Miles Davis [Disc 1],Miles Davis\nGenerique,Miles Davis,168777,5437017,0.99,Jazz,The Essential Miles Davis [Disc 1],Miles Davis\nSummertime,Miles Davis,200437,6461370,0.99,Jazz,The Essential Miles Davis [Disc 1],Miles Davis\nSo What,Miles Davis,564009,18360449,0.99,Jazz,The Essential Miles Davis [Disc 1],Miles Davis\nThe Pan Piper,Miles Davis,233769,7593713,0.99,Jazz,The Essential Miles Davis [Disc 1],Miles Davis\nSomeday My Prince Will Come,Miles Davis,544078,17890773,0.99,Jazz,The Essential Miles Davis [Disc 1],Miles Davis\nMy Funny Valentine (Live),Miles Davis,907520,29416781,0.99,Jazz,The Essential Miles Davis [Disc 2],Miles Davis\nE.S.P.,Miles Davis,330684,11079866,0.99,Jazz,The Essential Miles Davis [Disc 2],Miles Davis\nNefertiti,Miles Davis,473495,15478450,0.99,Jazz,The Essential Miles Davis [Disc 2],Miles Davis\nPetits Machins (Little Stuff),Miles Davis,487392,16131272,0.99,Jazz,The Essential Miles Davis [Disc 2],Miles Davis\nMiles Runs The Voodoo Down,Miles Davis,843964,27967919,0.99,Jazz,The Essential Miles Davis [Disc 2],Miles Davis\nLittle Church (Live),Miles Davis,196101,6273225,0.99,Jazz,The Essential Miles Davis [Disc 2],Miles Davis\nBlack Satin,Miles Davis,316682,10529483,0.99,Jazz,The Essential Miles Davis [Disc 2],Miles Davis\nJean Pierre (Live),Miles Davis,243461,7955114,0.99,Jazz,The Essential Miles Davis [Disc 2],Miles Davis\nTime After Time,Miles Davis,220734,7292197,0.99,Jazz,The Essential Miles Davis [Disc 2],Miles Davis\nPortia,Miles Davis,378775,12520126,0.99,Jazz,The Essential Miles Davis [Disc 2],Miles Davis\nSpace Truckin',Blackmore/Gillan/Glover/Lord/Paice,1196094,39267613,0.99,Rock,The Final Concerts (Disc 2),Deep Purple\nGoing Down / Highway Star,Gillan/Glover/Lord/Nix - Blackmore/Paice,913658,29846063,0.99,Rock,The Final Concerts (Disc 2),Deep Purple\nMistreated (Alternate Version),Blackmore/Coverdale,854700,27775442,0.99,Rock,The Final Concerts (Disc 2),Deep Purple\nYou Fool No One (Alternate Version),Blackmore/Coverdale/Lord/Paice,763924,24887209,0.99,Rock,The Final Concerts (Disc 2),Deep Purple\nJeepers Creepers,,185965,5991903,0.99,Jazz,Up An' Atom,Gene Krupa\nBlue Rythm Fantasy,,348212,11204006,0.99,Jazz,Up An' Atom,Gene Krupa\nDrum Boogie,,191555,6185636,0.99,Jazz,Up An' Atom,Gene Krupa\nLet Me Off Uptown,,187637,6034685,0.99,Jazz,Up An' Atom,Gene Krupa\nLeave Us Leap,,182726,5898810,0.99,Jazz,Up An' Atom,Gene Krupa\nOpus No.1,,179800,5846041,0.99,Jazz,Up An' Atom,Gene Krupa\nBoogie Blues,,204199,6603153,0.99,Jazz,Up An' Atom,Gene Krupa\nHow High The Moon,,201430,6529487,0.99,Jazz,Up An' Atom,Gene Krupa\nDisc Jockey Jump,,193149,6260820,0.99,Jazz,Up An' Atom,Gene Krupa\nUp An' Atom,,179565,5822645,0.99,Jazz,Up An' Atom,Gene Krupa\nBop Boogie,,189596,6093124,0.99,Jazz,Up An' Atom,Gene Krupa\nLemon Drop,,194089,6287531,0.99,Jazz,Up An' Atom,Gene Krupa\nCoronation Drop,,176222,5899898,0.99,Jazz,Up An' Atom,Gene Krupa\nOvertime,,163030,5432236,0.99,Jazz,Up An' Atom,Gene Krupa\nImagination,,289306,9444385,0.99,Jazz,Up An' Atom,Gene Krupa\nDon't Take Your Love From Me,,282331,9244238,0.99,Jazz,Up An' Atom,Gene Krupa\nMidget,,217025,7257663,0.99,Jazz,Up An' Atom,Gene Krupa\nI'm Coming Virginia,,280163,9209827,0.99,Jazz,Up An' Atom,Gene Krupa\nPayin' Them Dues Blues,,198556,6536918,0.99,Jazz,Up An' Atom,Gene Krupa\nJungle Drums,,199627,6546063,0.99,Jazz,Up An' Atom,Gene Krupa\nShowcase,,201560,6697510,0.99,Jazz,Up An' Atom,Gene Krupa\nSwedish Schnapps,,191268,6359750,0.99,Jazz,Up An' Atom,Gene Krupa\nSamba Da Bênção,,409965,13490008,0.99,Bossa Nova,Vinícius De Moraes - Sem Limite,Toquinho & Vinícius\nPot-Pourri N.º 4,,392437,13125975,0.99,Bossa Nova,Vinícius De Moraes - Sem Limite,Toquinho & Vinícius\nOnde Anda Você,,168437,5550356,0.99,Bossa Nova,Vinícius De Moraes - Sem Limite,Toquinho & Vinícius\nSamba Da Volta,,170631,5676090,0.99,Bossa Nova,Vinícius De Moraes - Sem Limite,Toquinho & Vinícius\nCanto De Ossanha,,204956,6771624,0.99,Bossa Nova,Vinícius De Moraes - Sem Limite,Toquinho & Vinícius\nPot-Pourri N.º 5,,219898,7117769,0.99,Bossa Nova,Vinícius De Moraes - Sem Limite,Toquinho & Vinícius\nFormosa,,137482,4560873,0.99,Bossa Nova,Vinícius De Moraes - Sem Limite,Toquinho & Vinícius\nComo É Duro Trabalhar,,226168,7541177,0.99,Bossa Nova,Vinícius De Moraes - Sem Limite,Toquinho & Vinícius\nMinha Namorada,,244297,7927967,0.99,Bossa Nova,Vinícius De Moraes - Sem Limite,Toquinho & Vinícius\nPor Que Será,,162142,5371483,0.99,Bossa Nova,Vinícius De Moraes - Sem Limite,Toquinho & Vinícius\nBerimbau,,190667,6335548,0.99,Bossa Nova,Vinícius De Moraes - Sem Limite,Toquinho & Vinícius\nDeixa,,179826,5932799,0.99,Bossa Nova,Vinícius De Moraes - Sem Limite,Toquinho & Vinícius\nPot-Pourri N.º 2,,211748,6878359,0.99,Bossa Nova,Vinícius De Moraes - Sem Limite,Toquinho & Vinícius\nSamba Em Prelúdio,,212636,6923473,0.99,Bossa Nova,Vinícius De Moraes - Sem Limite,Toquinho & Vinícius\nCarta Ao Tom 74,,162560,5382354,0.99,Bossa Nova,Vinícius De Moraes - Sem Limite,Toquinho & Vinícius\nLinha de Passe (João Bosco),,230948,7902328,0.99,Latin,Vozes do MPB,Various Artists\nPela Luz dos Olhos Teus (Miúcha e Tom Jobim),,163970,5399626,0.99,Latin,Vozes do MPB,Various Artists\nChão de Giz (Elba Ramalho),,274834,9016916,0.99,Latin,Vozes do MPB,Various Artists\nMarina (Dorival Caymmi),,172643,5523628,0.99,Latin,Vozes do MPB,Various Artists\nAquarela (Toquinho),,259944,8480140,0.99,Latin,Vozes do MPB,Various Artists\nCoração do Agreste (Fafá de Belém),,258194,8380320,0.99,Latin,Vozes do MPB,Various Artists\nDona (Roupa Nova),,243356,7991295,0.99,Latin,Vozes do MPB,Various Artists\nComeçaria Tudo Outra Vez (Maria Creuza),,206994,6851151,0.99,Latin,Vozes do MPB,Various Artists\nCaçador de Mim (Sá & Guarabyra),,238341,7751360,0.99,Latin,Vozes do MPB,Various Artists\nRomaria (Renato Teixeira),,244793,8033885,0.99,Latin,Vozes do MPB,Various Artists\nAs Rosas Não Falam (Beth Carvalho),,116767,3836641,0.99,Latin,Vozes do MPB,Various Artists\nWave (Os Cariocas),,130063,4298006,0.99,Latin,Vozes do MPB,Various Artists\nGarota de Ipanema (Dick Farney),,174367,5767474,0.99,Latin,Vozes do MPB,Various Artists\nPreciso Apender a Viver Só (Maysa),,143464,4642359,0.99,Latin,Vozes do MPB,Various Artists\nSusie Q,Hawkins-Lewis-Broadwater,275565,9043825,0.99,Rock,\"Chronicle, Vol. 1\",Creedence Clearwater Revival\nI Put A Spell On You,Jay Hawkins,272091,8943000,0.99,Rock,\"Chronicle, Vol. 1\",Creedence Clearwater Revival\nProud Mary,J. C. Fogerty,189022,6229590,0.99,Rock,\"Chronicle, Vol. 1\",Creedence Clearwater Revival\nBad Moon Rising,J. C. Fogerty,140146,4609835,0.99,Rock,\"Chronicle, Vol. 1\",Creedence Clearwater Revival\nLodi,J. C. Fogerty,191451,6260214,0.99,Rock,\"Chronicle, Vol. 1\",Creedence Clearwater Revival\nGreen River,J. C. Fogerty,154279,5105874,0.99,Rock,\"Chronicle, Vol. 1\",Creedence Clearwater Revival\nCommotion,J. C. Fogerty,162899,5354252,0.99,Rock,\"Chronicle, Vol. 1\",Creedence Clearwater Revival\nDown On The Corner,J. C. Fogerty,164858,5521804,0.99,Rock,\"Chronicle, Vol. 1\",Creedence Clearwater Revival\nFortunate Son,J. C. Fogerty,140329,4617559,0.99,Rock,\"Chronicle, Vol. 1\",Creedence Clearwater Revival\nTravelin' Band,J. C. Fogerty,129358,4270414,0.99,Rock,\"Chronicle, Vol. 1\",Creedence Clearwater Revival\nWho'll Stop The Rain,J. C. Fogerty,149394,4899579,0.99,Rock,\"Chronicle, Vol. 1\",Creedence Clearwater Revival\nUp Around The Bend,J. C. Fogerty,162429,5368701,0.99,Rock,\"Chronicle, Vol. 1\",Creedence Clearwater Revival\nRun Through The Jungle,J. C. Fogerty,186044,6156567,0.99,Rock,\"Chronicle, Vol. 1\",Creedence Clearwater Revival\nLookin' Out My Back Door,J. C. Fogerty,152946,5034670,0.99,Rock,\"Chronicle, Vol. 1\",Creedence Clearwater Revival\nLong As I Can See The Light,J. C. Fogerty,213237,6924024,0.99,Rock,\"Chronicle, Vol. 1\",Creedence Clearwater Revival\nI Heard It Through The Grapevine,Whitfield-Strong,664894,21947845,0.99,Rock,\"Chronicle, Vol. 1\",Creedence Clearwater Revival\nHave You Ever Seen The Rain?,J. C. Fogerty,160052,5263675,0.99,Rock,\"Chronicle, Vol. 1\",Creedence Clearwater Revival\nHey Tonight,J. C. Fogerty,162847,5343807,0.99,Rock,\"Chronicle, Vol. 1\",Creedence Clearwater Revival\nSweet Hitch-Hiker,J. C. Fogerty,175490,5716603,0.99,Rock,\"Chronicle, Vol. 1\",Creedence Clearwater Revival\nSomeday Never Comes,J. C. Fogerty,239360,7945235,0.99,Rock,\"Chronicle, Vol. 1\",Creedence Clearwater Revival\nWalking On The Water,J.C. Fogerty,281286,9302129,0.99,Rock,\"Chronicle, Vol. 2\",Creedence Clearwater Revival\n\"Suzie-Q, Pt. 2\",J.C. Fogerty,244114,7986637,0.99,Rock,\"Chronicle, Vol. 2\",Creedence Clearwater Revival\nBorn On The Bayou,J.C. Fogerty,316630,10361866,0.99,Rock,\"Chronicle, Vol. 2\",Creedence Clearwater Revival\nGood Golly Miss Molly,J.C. Fogerty,163604,5348175,0.99,Rock,\"Chronicle, Vol. 2\",Creedence Clearwater Revival\nTombstone Shadow,J.C. Fogerty,218880,7209080,0.99,Rock,\"Chronicle, Vol. 2\",Creedence Clearwater Revival\nWrote A Song For Everyone,J.C. Fogerty,296385,9675875,0.99,Rock,\"Chronicle, Vol. 2\",Creedence Clearwater Revival\nNight Time Is The Right Time,J.C. Fogerty,190119,6211173,0.99,Rock,\"Chronicle, Vol. 2\",Creedence Clearwater Revival\nCotton Fields,J.C. Fogerty,178181,5919224,0.99,Rock,\"Chronicle, Vol. 2\",Creedence Clearwater Revival\nIt Came Out Of The Sky,J.C. Fogerty,176718,5807474,0.99,Rock,\"Chronicle, Vol. 2\",Creedence Clearwater Revival\nDon't Look Now,J.C. Fogerty,131918,4366455,0.99,Rock,\"Chronicle, Vol. 2\",Creedence Clearwater Revival\nThe Midnight Special,J.C. Fogerty,253596,8297482,0.99,Rock,\"Chronicle, Vol. 2\",Creedence Clearwater Revival\nBefore You Accuse Me,J.C. Fogerty,207804,6815126,0.99,Rock,\"Chronicle, Vol. 2\",Creedence Clearwater Revival\nMy Baby Left Me,J.C. Fogerty,140460,4633440,0.99,Rock,\"Chronicle, Vol. 2\",Creedence Clearwater Revival\nPagan Baby,J.C. Fogerty,385619,12713813,0.99,Rock,\"Chronicle, Vol. 2\",Creedence Clearwater Revival\n(Wish I Could) Hideaway,J.C. Fogerty,228466,7432978,0.99,Rock,\"Chronicle, Vol. 2\",Creedence Clearwater Revival\nIt's Just A Thought,J.C. Fogerty,237374,7778319,0.99,Rock,\"Chronicle, Vol. 2\",Creedence Clearwater Revival\nMolina,J.C. Fogerty,163239,5390811,0.99,Rock,\"Chronicle, Vol. 2\",Creedence Clearwater Revival\nBorn To Move,J.C. Fogerty,342804,11260814,0.99,Rock,\"Chronicle, Vol. 2\",Creedence Clearwater Revival\nLookin' For A Reason,J.C. Fogerty,209789,6933135,0.99,Rock,\"Chronicle, Vol. 2\",Creedence Clearwater Revival\nHello Mary Lou,J.C. Fogerty,132832,4476563,0.99,Rock,\"Chronicle, Vol. 2\",Creedence Clearwater Revival\nGatas Extraordinárias,,212506,7095702,0.99,Latin,Cássia Eller - Coleção Sem Limite [Disc 2],Cássia Eller\nBrasil,,243696,7911683,0.99,Latin,Cássia Eller - Coleção Sem Limite [Disc 2],Cássia Eller\nEu Sou Neguinha (Ao Vivo),,251768,8376000,0.99,Latin,Cássia Eller - Coleção Sem Limite [Disc 2],Cássia Eller\nGeração Coca-Cola (Ao Vivo),,228153,7573301,0.99,Latin,Cássia Eller - Coleção Sem Limite [Disc 2],Cássia Eller\nLanterna Dos Afogados,,204538,6714582,0.99,Latin,Cássia Eller - Coleção Sem Limite [Disc 2],Cássia Eller\nCoroné Antonio Bento,,200437,6713066,0.99,Latin,Cássia Eller - Coleção Sem Limite [Disc 2],Cássia Eller\n\"Você Passa, Eu Acho Graça (Ao Vivo)\",,206733,6943576,0.99,Latin,Cássia Eller - Coleção Sem Limite [Disc 2],Cássia Eller\nMeu Mundo Fica Completo (Com Você),,247771,8322240,0.99,Latin,Cássia Eller - Coleção Sem Limite [Disc 2],Cássia Eller\n1° De Julho,,270262,9017535,0.99,Latin,Cássia Eller - Coleção Sem Limite [Disc 2],Cássia Eller\nMúsica Urbana 2,,194899,6383472,0.99,Latin,Cássia Eller - Coleção Sem Limite [Disc 2],Cássia Eller\nVida Bandida (Ao Vivo),,192626,6360785,0.99,Latin,Cássia Eller - Coleção Sem Limite [Disc 2],Cássia Eller\nPalavras Ao Vento,,212453,7048676,0.99,Latin,Cássia Eller - Coleção Sem Limite [Disc 2],Cássia Eller\nNão Sei O Que Eu Quero Da Vida,,151849,5024963,0.99,Latin,Cássia Eller - Coleção Sem Limite [Disc 2],Cássia Eller\nWoman Is The Nigger Of The World (Ao Vivo),,298919,9724145,0.99,Latin,Cássia Eller - Coleção Sem Limite [Disc 2],Cássia Eller\nJuventude Transviada (Ao Vivo),,278622,9183808,0.99,Latin,Cássia Eller - Coleção Sem Limite [Disc 2],Cássia Eller\nMalandragem,,247588,8165048,0.99,Latin,Cássia Eller - Sem Limite [Disc 1],Cássia Eller\nO Segundo Sol,,252133,8335629,0.99,Latin,Cássia Eller - Sem Limite [Disc 1],Cássia Eller\nSmells Like Teen Spirit (Ao Vivo),,316865,10384506,0.99,Latin,Cássia Eller - Sem Limite [Disc 1],Cássia Eller\nE.C.T.,,227500,7571834,0.99,Latin,Cássia Eller - Sem Limite [Disc 1],Cássia Eller\nTodo Amor Que Houver Nesta Vida,,227160,7420347,0.99,Latin,Cássia Eller - Sem Limite [Disc 1],Cássia Eller\nMetrô. Linha 743,,174654,5837495,0.99,Latin,Cássia Eller - Sem Limite [Disc 1],Cássia Eller\nNós (Ao Vivo),,193828,6498661,0.99,Latin,Cássia Eller - Sem Limite [Disc 1],Cássia Eller\nNa Cadência Do Samba,,196075,6483952,0.99,Latin,Cássia Eller - Sem Limite [Disc 1],Cássia Eller\nAdmirável Gado Novo,,274390,9144031,0.99,Latin,Cássia Eller - Sem Limite [Disc 1],Cássia Eller\nEleanor Rigby,,189466,6303205,0.99,Latin,Cássia Eller - Sem Limite [Disc 1],Cássia Eller\nSocorro,,258586,8549393,0.99,Latin,Cássia Eller - Sem Limite [Disc 1],Cássia Eller\nBlues Da Piedade,,257123,8472964,0.99,Latin,Cássia Eller - Sem Limite [Disc 1],Cássia Eller\nRubens,,211853,7026317,0.99,Latin,Cássia Eller - Sem Limite [Disc 1],Cássia Eller\nNão Deixe O Samba Morrer - Cassia Eller e Alcione,,268173,8936345,0.99,Latin,Cássia Eller - Sem Limite [Disc 1],Cássia Eller\nMis Penas Lloraba Yo (Ao Vivo) Soy Gitano (Tangos),,188473,6195854,0.99,Latin,Cássia Eller - Sem Limite [Disc 1],Cássia Eller\nComin' Home,Bolin/Coverdale/Paice,235781,7644604,0.99,Rock,Come Taste The Band,Deep Purple\nLady Luck,Cook/Coverdale,168202,5501379,0.99,Rock,Come Taste The Band,Deep Purple\nGettin' Tighter,Bolin/Hughes,218044,7176909,0.99,Rock,Come Taste The Band,Deep Purple\nDealer,Bolin/Coverdale,230922,7591066,0.99,Rock,Come Taste The Band,Deep Purple\nI Need Love,Bolin/Coverdale,263836,8701064,0.99,Rock,Come Taste The Band,Deep Purple\nDrifter,Bolin/Coverdale,242834,8001505,0.99,Rock,Come Taste The Band,Deep Purple\nLove Child,Bolin/Coverdale,188160,6173806,0.99,Rock,Come Taste The Band,Deep Purple\nThis Time Around / Owed to 'G' [Instrumental],Bolin/Hughes/Lord,370102,11995679,0.99,Rock,Come Taste The Band,Deep Purple\nYou Keep On Moving,Coverdale/Hughes,319111,10447868,0.99,Rock,Come Taste The Band,Deep Purple\nSpeed King,\"Blackmore, Gillan, Glover, Lord, Paice\",264385,8587578,0.99,Rock,Deep Purple In Rock,Deep Purple\nBloodsucker,\"Blackmore, Gillan, Glover, Lord, Paice\",256261,8344405,0.99,Rock,Deep Purple In Rock,Deep Purple\nChild In Time,\"Blackmore, Gillan, Glover, Lord, Paice\",620460,20230089,0.99,Rock,Deep Purple In Rock,Deep Purple\nFlight Of The Rat,\"Blackmore, Gillan, Glover, Lord, Paice\",478302,15563967,0.99,Rock,Deep Purple In Rock,Deep Purple\nInto The Fire,\"Blackmore, Gillan, Glover, Lord, Paice\",210259,6849310,0.99,Rock,Deep Purple In Rock,Deep Purple\nLiving Wreck,\"Blackmore, Gillan, Glover, Lord, Paice\",274886,8993056,0.99,Rock,Deep Purple In Rock,Deep Purple\nHard Lovin' Man,\"Blackmore, Gillan, Glover, Lord, Paice\",431203,13931179,0.99,Rock,Deep Purple In Rock,Deep Purple\nFireball,\"Ritchie Blackmore, Ian Gillan, Roger Glover, Jon Lord, Ian Paice\",204721,6714807,0.99,Rock,Fireball,Deep Purple\nNo No No,\"Ritchie Blackmore, Ian Gillan, Roger Glover, Jon Lord, Ian Paice\",414902,13646606,0.99,Rock,Fireball,Deep Purple\nStrange Kind Of Woman,\"Ritchie Blackmore, Ian Gillan, Roger Glover, Jon Lord, Ian Paice\",247092,8072036,0.99,Rock,Fireball,Deep Purple\nAnyone's Daughter,\"Ritchie Blackmore, Ian Gillan, Roger Glover, Jon Lord, Ian Paice\",284682,9354480,0.99,Rock,Fireball,Deep Purple\nThe Mule,\"Ritchie Blackmore, Ian Gillan, Roger Glover, Jon Lord, Ian Paice\",322063,10638390,0.99,Rock,Fireball,Deep Purple\nFools,\"Ritchie Blackmore, Ian Gillan, Roger Glover, Jon Lord, Ian Paice\",500427,16279366,0.99,Rock,Fireball,Deep Purple\nNo One Came,\"Ritchie Blackmore, Ian Gillan, Roger Glover, Jon Lord, Ian Paice\",385880,12643813,0.99,Rock,Fireball,Deep Purple\nKnocking At Your Back Door,\"Richie Blackmore, Ian Gillian, Roger Glover\",424829,13779332,0.99,Rock,Knocking at Your Back Door: The Best Of Deep Purple in the 80's,Deep Purple\nBad Attitude,\"Richie Blackmore, Ian Gillian, Roger Glover, Jon Lord\",307905,10035180,0.99,Rock,Knocking at Your Back Door: The Best Of Deep Purple in the 80's,Deep Purple\nChild In Time (Son Of Aleric - Instrumental),\"Richie Blackmore, Ian Gillian, Roger Glover, Jon Lord, Ian Paice\",602880,19712753,0.99,Rock,Knocking at Your Back Door: The Best Of Deep Purple in the 80's,Deep Purple\nNobody's Home,\"Richie Blackmore, Ian Gillian, Roger Glover, Jon Lord, Ian Paice\",243017,7929493,0.99,Rock,Knocking at Your Back Door: The Best Of Deep Purple in the 80's,Deep Purple\nBlack Night,\"Richie Blackmore, Ian Gillian, Roger Glover, Jon Lord, Ian Paice\",368770,12058906,0.99,Rock,Knocking at Your Back Door: The Best Of Deep Purple in the 80's,Deep Purple\nPerfect Strangers,\"Richie Blackmore, Ian Gillian, Roger Glover\",321149,10445353,0.99,Rock,Knocking at Your Back Door: The Best Of Deep Purple in the 80's,Deep Purple\nThe Unwritten Law,\"Richie Blackmore, Ian Gillian, Roger Glover, Ian Paice\",295053,9740361,0.99,Rock,Knocking at Your Back Door: The Best Of Deep Purple in the 80's,Deep Purple\nCall Of The Wild,\"Richie Blackmore, Ian Gillian, Roger Glover, Jon Lord\",293851,9575295,0.99,Rock,Knocking at Your Back Door: The Best Of Deep Purple in the 80's,Deep Purple\nHush,South,213054,6944928,0.99,Rock,Knocking at Your Back Door: The Best Of Deep Purple in the 80's,Deep Purple\nSmoke On The Water,\"Richie Blackmore, Ian Gillian, Roger Glover, Jon Lord, Ian Paice\",464378,15180849,0.99,Rock,Knocking at Your Back Door: The Best Of Deep Purple in the 80's,Deep Purple\nSpace Trucking,\"Richie Blackmore, Ian Gillian, Roger Glover, Jon Lord, Ian Paice\",341185,11122183,0.99,Rock,Knocking at Your Back Door: The Best Of Deep Purple in the 80's,Deep Purple\nHighway Star,Ian Gillan/Ian Paice/Jon Lord/Ritchie Blckmore/Roger Glover,368770,12012452,0.99,Rock,Machine Head,Deep Purple\nMaybe I'm A Leo,Ian Gillan/Ian Paice/Jon Lord/Ritchie Blckmore/Roger Glover,290455,9502646,0.99,Rock,Machine Head,Deep Purple\nPictures Of Home,Ian Gillan/Ian Paice/Jon Lord/Ritchie Blckmore/Roger Glover,303777,9903835,0.99,Rock,Machine Head,Deep Purple\nNever Before,Ian Gillan/Ian Paice/Jon Lord/Ritchie Blckmore/Roger Glover,239830,7832790,0.99,Rock,Machine Head,Deep Purple\nSmoke On The Water,Ian Gillan/Ian Paice/Jon Lord/Ritchie Blckmore/Roger Glover,340871,11246496,0.99,Rock,Machine Head,Deep Purple\nLazy,Ian Gillan/Ian Paice/Jon Lord/Ritchie Blckmore/Roger Glover,442096,14397671,0.99,Rock,Machine Head,Deep Purple\nSpace Truckin',Ian Gillan/Ian Paice/Jon Lord/Ritchie Blckmore/Roger Glover,272796,8981030,0.99,Rock,Machine Head,Deep Purple\nVavoom : Ted The Mechanic,\"Ian Gillan, Roger Glover, Jon Lord, Steve Morse, Ian Paice\",257384,8510755,0.99,Rock,Purpendicular,Deep Purple\nLoosen My Strings,\"Ian Gillan, Roger Glover, Jon Lord, Steve Morse, Ian Paice\",359680,11702232,0.99,Rock,Purpendicular,Deep Purple\nSoon Forgotten,\"Ian Gillan, Roger Glover, Jon Lord, Steve Morse, Ian Paice\",287791,9401383,0.99,Rock,Purpendicular,Deep Purple\nSometimes I Feel Like Screaming,\"Ian Gillan, Roger Glover, Jon Lord, Steve Morse, Ian Paice\",451840,14789410,0.99,Rock,Purpendicular,Deep Purple\nCascades : I'm Not Your Lover,\"Ian Gillan, Roger Glover, Jon Lord, Steve Morse, Ian Paice\",283689,9209693,0.99,Rock,Purpendicular,Deep Purple\nThe Aviator,\"Ian Gillan, Roger Glover, Jon Lord, Steve Morse, Ian Paice\",320992,10532053,0.99,Rock,Purpendicular,Deep Purple\nRosa's Cantina,\"Ian Gillan, Roger Glover, Jon Lord, Steve Morse, Ian Paice\",312372,10323804,0.99,Rock,Purpendicular,Deep Purple\nA Castle Full Of Rascals,\"Ian Gillan, Roger Glover, Jon Lord, Steve Morse, Ian Paice\",311693,10159566,0.99,Rock,Purpendicular,Deep Purple\nA Touch Away,\"Ian Gillan, Roger Glover, Jon Lord, Steve Morse, Ian Paice\",276323,9098561,0.99,Rock,Purpendicular,Deep Purple\nHey Cisco,\"Ian Gillan, Roger Glover, Jon Lord, Steve Morse, Ian Paice\",354089,11600029,0.99,Rock,Purpendicular,Deep Purple\nSomebody Stole My Guitar,\"Ian Gillan, Roger Glover, Jon Lord, Steve Morse, Ian Paice\",249443,8180421,0.99,Rock,Purpendicular,Deep Purple\nThe Purpendicular Waltz,\"Ian Gillan, Roger Glover, Jon Lord, Steve Morse, Ian Paice\",283924,9299131,0.99,Rock,Purpendicular,Deep Purple\nKing Of Dreams,\"Blackmore, Glover, Turner\",328385,10733847,0.99,Rock,Slaves And Masters,Deep Purple\nThe Cut Runs Deep,\"Blackmore, Glover, Turner, Lord, Paice\",342752,11191650,0.99,Rock,Slaves And Masters,Deep Purple\nFire In The Basement,\"Blackmore, Glover, Turner, Lord, Paice\",283977,9267550,0.99,Rock,Slaves And Masters,Deep Purple\nTruth Hurts,\"Blackmore, Glover, Turner\",314827,10224612,0.99,Rock,Slaves And Masters,Deep Purple\nBreakfast In Bed,\"Blackmore, Glover, Turner\",317126,10323804,0.99,Rock,Slaves And Masters,Deep Purple\nLove Conquers All,\"Blackmore, Glover, Turner\",227186,7328516,0.99,Rock,Slaves And Masters,Deep Purple\nFortuneteller,\"Blackmore, Glover, Turner, Lord, Paice\",349335,11369671,0.99,Rock,Slaves And Masters,Deep Purple\nToo Much Is Not Enough,\"Turner, Held, Greenwood\",257724,8382800,0.99,Rock,Slaves And Masters,Deep Purple\nWicked Ways,\"Blackmore, Glover, Turner, Lord, Paice\",393691,12826582,0.99,Rock,Slaves And Masters,Deep Purple\nStormbringer,D.Coverdale/R.Blackmore/Ritchie Blackmore,246413,8044864,0.99,Rock,Stormbringer,Deep Purple\nLove Don't Mean a Thing,D.Coverdale/G.Hughes/Glenn Hughes/I.Paice/Ian Paice/J.Lord/John Lord/R.Blackmore/Ritchie Blackmore,263862,8675026,0.99,Rock,Stormbringer,Deep Purple\nHoly Man,D.Coverdale/G.Hughes/Glenn Hughes/J.Lord/John Lord,270236,8818093,0.99,Rock,Stormbringer,Deep Purple\nHold On,D.Coverdal/G.Hughes/Glenn Hughes/I.Paice/Ian Paice/J.Lord/John Lord,306860,10022428,0.99,Rock,Stormbringer,Deep Purple\nLady Double Dealer,D.Coverdale/R.Blackmore/Ritchie Blackmore,201482,6554330,0.99,Rock,Stormbringer,Deep Purple\nYou Can't Do it Right (With the One You Love),D.Coverdale/G.Hughes/Glenn Hughes/R.Blackmore/Ritchie Blackmore,203755,6709579,0.99,Rock,Stormbringer,Deep Purple\nHigh Ball Shooter,D.Coverdale/G.Hughes/Glenn Hughes/I.Paice/Ian Paice/J.Lord/John Lord/R.Blackmore/Ritchie Blackmore,267833,8772471,0.99,Rock,Stormbringer,Deep Purple\nThe Gypsy,D.Coverdale/G.Hughes/Glenn Hughes/I.Paice/Ian Paice/J.Lord/John Lord/R.Blackmore/Ritchie Blackmore,242886,7946614,0.99,Rock,Stormbringer,Deep Purple\nSoldier Of Fortune,D.Coverdale/R.Blackmore/Ritchie Blackmore,193750,6315321,0.99,Rock,Stormbringer,Deep Purple\nThe Battle Rages On,ian paice/jon lord,356963,11626228,0.99,Rock,The Battle Rages On,Deep Purple\nLick It Up,roger glover,240274,7792604,0.99,Rock,The Battle Rages On,Deep Purple\nAnya,jon lord/roger glover,392437,12754921,0.99,Rock,The Battle Rages On,Deep Purple\nTalk About Love,roger glover,247823,8072171,0.99,Rock,The Battle Rages On,Deep Purple\nTime To Kill,roger glover,351033,11354742,0.99,Rock,The Battle Rages On,Deep Purple\nRamshackle Man,roger glover,334445,10874679,0.99,Rock,The Battle Rages On,Deep Purple\nA Twist In The Tail,roger glover,257462,8413103,0.99,Rock,The Battle Rages On,Deep Purple\nNasty Piece Of Work,jon lord/roger glover,276662,9076997,0.99,Rock,The Battle Rages On,Deep Purple\nSolitaire,roger glover,282226,9157021,0.99,Rock,The Battle Rages On,Deep Purple\nOne Man's Meat,roger glover,278804,9068960,0.99,Rock,The Battle Rages On,Deep Purple\nPour Some Sugar On Me,,292519,9518842,0.99,Rock,Vault: Def Leppard's Greatest Hits,Def Leppard\nPhotograph,,248633,8108507,0.99,Rock,Vault: Def Leppard's Greatest Hits,Def Leppard\nLove Bites,,346853,11305791,0.99,Rock,Vault: Def Leppard's Greatest Hits,Def Leppard\nLet's Get Rocked,,296019,9724150,0.99,Rock,Vault: Def Leppard's Greatest Hits,Def Leppard\nTwo Steps Behind [Acoustic Version],,259787,8523388,0.99,Rock,Vault: Def Leppard's Greatest Hits,Def Leppard\nAnimal,,244741,7985133,0.99,Rock,Vault: Def Leppard's Greatest Hits,Def Leppard\nHeaven Is,,214021,6988128,0.99,Rock,Vault: Def Leppard's Greatest Hits,Def Leppard\nRocket,,247248,8092463,0.99,Rock,Vault: Def Leppard's Greatest Hits,Def Leppard\nWhen Love & Hate Collide,,257280,8364633,0.99,Rock,Vault: Def Leppard's Greatest Hits,Def Leppard\nAction,,220604,7130830,0.99,Rock,Vault: Def Leppard's Greatest Hits,Def Leppard\nMake Love Like A Man,,255660,8309725,0.99,Rock,Vault: Def Leppard's Greatest Hits,Def Leppard\nArmageddon It,,322455,10522352,0.99,Rock,Vault: Def Leppard's Greatest Hits,Def Leppard\nHave You Ever Needed Someone So Bad,,319320,10400020,0.99,Rock,Vault: Def Leppard's Greatest Hits,Def Leppard\nRock Of Ages,,248424,8150318,0.99,Rock,Vault: Def Leppard's Greatest Hits,Def Leppard\nHysteria,,355056,11622738,0.99,Rock,Vault: Def Leppard's Greatest Hits,Def Leppard\nBringin' On The Heartbreak,,272457,8853324,0.99,Rock,Vault: Def Leppard's Greatest Hits,Def Leppard\nRoll Call,Jim Beard,321358,10653494,0.99,Jazz,Outbreak,Dennis Chambers\nOtay,\"John Scofield, Robert Aries, Milton Chambers and Gary Grainger\",423653,14176083,0.99,Jazz,Outbreak,Dennis Chambers\nGroovus Interruptus,Jim Beard,319373,10602166,0.99,Jazz,Outbreak,Dennis Chambers\nParis On Mine,Jon Herington,368875,12059507,0.99,Jazz,Outbreak,Dennis Chambers\nIn Time,Sylvester Stewart,368953,12287103,0.99,Jazz,Outbreak,Dennis Chambers\nPlan B,\"Dean Brown, Dennis Chambers & Jim Beard\",272039,9032315,0.99,Jazz,Outbreak,Dennis Chambers\nOutbreak,Jim Beard & Jon Herington,659226,21685807,0.99,Jazz,Outbreak,Dennis Chambers\n\"Baltimore, DC\",John Scofield,346932,11394473,0.99,Jazz,Outbreak,Dennis Chambers\nTalkin Loud and Saying Nothin,James Brown & Bobby Byrd,360411,11994859,0.99,Jazz,Outbreak,Dennis Chambers\nPétala,,270080,8856165,0.99,Latin,Djavan Ao Vivo - Vol. 02,Djavan\nMeu Bem-Querer,,255608,8330047,0.99,Latin,Djavan Ao Vivo - Vol. 02,Djavan\nCigano,,304692,10037362,0.99,Latin,Djavan Ao Vivo - Vol. 02,Djavan\nBoa Noite,,338755,11283582,0.99,Latin,Djavan Ao Vivo - Vol. 02,Djavan\nFato Consumado,,211565,7018586,0.99,Latin,Djavan Ao Vivo - Vol. 02,Djavan\nFaltando Um Pedaço,,267728,8788760,0.99,Latin,Djavan Ao Vivo - Vol. 02,Djavan\nÁlibi,,213237,6928434,0.99,Latin,Djavan Ao Vivo - Vol. 02,Djavan\nEsquinas,,280999,9096726,0.99,Latin,Djavan Ao Vivo - Vol. 02,Djavan\nSe...,,286432,9413777,0.99,Latin,Djavan Ao Vivo - Vol. 02,Djavan\nEu Te Devoro,,311614,10312775,0.99,Latin,Djavan Ao Vivo - Vol. 02,Djavan\nLilás,,274181,9049542,0.99,Latin,Djavan Ao Vivo - Vol. 02,Djavan\nAcelerou,,284081,9396942,0.99,Latin,Djavan Ao Vivo - Vol. 02,Djavan\nUm Amor Puro,,327784,10687311,0.99,Latin,Djavan Ao Vivo - Vol. 02,Djavan\nSamurai,Djavan,330997,10872787,0.99,Latin,Djavan Ao Vivo - Vol. 1,Djavan\nNem Um Dia,Djavan,337423,11181446,0.99,Latin,Djavan Ao Vivo - Vol. 1,Djavan\nOceano,Djavan,217338,7026441,0.99,Latin,Djavan Ao Vivo - Vol. 1,Djavan\nAçai,Djavan,270968,8893682,0.99,Latin,Djavan Ao Vivo - Vol. 1,Djavan\nSerrado,Djavan,295314,9842240,0.99,Latin,Djavan Ao Vivo - Vol. 1,Djavan\nFlor De Lis,Djavan,236355,7801108,0.99,Latin,Djavan Ao Vivo - Vol. 1,Djavan\nAmar É Tudo,Djavan,211617,7073899,0.99,Latin,Djavan Ao Vivo - Vol. 1,Djavan\nAzul,Djavan,253962,8381029,0.99,Latin,Djavan Ao Vivo - Vol. 1,Djavan\nSeduzir,Djavan,277524,9163253,0.99,Latin,Djavan Ao Vivo - Vol. 1,Djavan\nA Carta,\"Djavan - Gabriel, O Pensador\",347297,11493463,0.99,Latin,Djavan Ao Vivo - Vol. 1,Djavan\nSina,Djavan,268173,8906539,0.99,Latin,Djavan Ao Vivo - Vol. 1,Djavan\nAcelerou,Djavan,284133,9391439,0.99,Latin,Djavan Ao Vivo - Vol. 1,Djavan\nUm Amor Puro,Djavan,327105,10664698,0.99,Latin,Djavan Ao Vivo - Vol. 1,Djavan\nO Bêbado e a Equilibrista,,223059,7306143,0.99,Latin,Elis Regina-Minha História,Elis Regina\nO Mestre-Sala dos Mares,,186226,6180414,0.99,Latin,Elis Regina-Minha História,Elis Regina\nAtrás da Porta,,166608,5432518,0.99,Latin,Elis Regina-Minha História,Elis Regina\n\"Dois Pra Lá, Dois Pra Cá\",,263026,8684639,0.99,Latin,Elis Regina-Minha História,Elis Regina\nCasa no Campo,,170788,5531841,0.99,Latin,Elis Regina-Minha História,Elis Regina\nRomaria,,242834,7968525,0.99,Latin,Elis Regina-Minha História,Elis Regina\n\"Alô, Alô, Marciano\",,241397,8137254,0.99,Latin,Elis Regina-Minha História,Elis Regina\nMe Deixas Louca,,214831,6888030,0.99,Latin,Elis Regina-Minha História,Elis Regina\nFascinação,,180793,5793959,0.99,Latin,Elis Regina-Minha História,Elis Regina\nSaudosa Maloca,,278125,9059416,0.99,Latin,Elis Regina-Minha História,Elis Regina\nAs Aparências Enganam,,247379,8014346,0.99,Latin,Elis Regina-Minha História,Elis Regina\nMadalena,,157387,5243721,0.99,Latin,Elis Regina-Minha História,Elis Regina\nMaria Rosa,,232803,7592504,0.99,Latin,Elis Regina-Minha História,Elis Regina\nAprendendo A Jogar,,290664,9391041,0.99,Latin,Elis Regina-Minha História,Elis Regina\nLayla,Clapton/Gordon,430733,14115792,0.99,Blues,The Cream Of Clapton,Eric Clapton\nBadge,Clapton/Harrison,163552,5322942,0.99,Blues,The Cream Of Clapton,Eric Clapton\nI Feel Free,Bruce/Clapton,174576,5725684,0.99,Blues,The Cream Of Clapton,Eric Clapton\nSunshine Of Your Love,Bruce/Clapton,252891,8225889,0.99,Blues,The Cream Of Clapton,Eric Clapton\nCrossroads,Clapton/Robert Johnson Arr: Eric Clapton,253335,8273540,0.99,Blues,The Cream Of Clapton,Eric Clapton\nStrange Brew,Clapton/Collins/Pappalardi,167810,5489787,0.99,Blues,The Cream Of Clapton,Eric Clapton\nWhite Room,Bruce/Clapton,301583,9872606,0.99,Blues,The Cream Of Clapton,Eric Clapton\nBell Bottom Blues,Clapton,304744,9946681,0.99,Blues,The Cream Of Clapton,Eric Clapton\nCocaine,Cale/Clapton,215928,7138399,0.99,Blues,The Cream Of Clapton,Eric Clapton\nI Shot The Sheriff,Marley,263862,8738973,0.99,Blues,The Cream Of Clapton,Eric Clapton\nAfter Midnight,Clapton/J. J. Cale,191320,6460941,0.99,Blues,The Cream Of Clapton,Eric Clapton\nSwing Low Sweet Chariot,Clapton/Trad. Arr. Clapton,208143,6896288,0.99,Blues,The Cream Of Clapton,Eric Clapton\nLay Down Sally,Clapton/Levy,231732,7774207,0.99,Blues,The Cream Of Clapton,Eric Clapton\nKnockin On Heavens Door,Clapton/Dylan,264411,8758819,0.99,Blues,The Cream Of Clapton,Eric Clapton\nWonderful Tonight,Clapton,221387,7326923,0.99,Blues,The Cream Of Clapton,Eric Clapton\nLet It Grow,Clapton,297064,9742568,0.99,Blues,The Cream Of Clapton,Eric Clapton\nPromises,Clapton/F.eldman/Linn,180401,6006154,0.99,Blues,The Cream Of Clapton,Eric Clapton\nI Can't Stand It,Clapton,249730,8271980,0.99,Blues,The Cream Of Clapton,Eric Clapton\nSigne,Eric Clapton,193515,6475042,0.99,Blues,Unplugged,Eric Clapton\nBefore You Accuse Me,Eugene McDaniel,224339,7456807,0.99,Blues,Unplugged,Eric Clapton\nHey Hey,Big Bill Broonzy,196466,6543487,0.99,Blues,Unplugged,Eric Clapton\nTears In Heaven,\"Eric Clapton, Will Jennings\",274729,9032835,0.99,Blues,Unplugged,Eric Clapton\nLonely Stranger,Eric Clapton,328724,10894406,0.99,Blues,Unplugged,Eric Clapton\nNobody Knows You When You're Down & Out,Jimmy Cox,231836,7669922,0.99,Blues,Unplugged,Eric Clapton\nLayla,\"Eric Clapton, Jim Gordon\",285387,9490542,0.99,Blues,Unplugged,Eric Clapton\nRunning On Faith,Jerry Lynn Williams,378984,12536275,0.99,Blues,Unplugged,Eric Clapton\nWalkin' Blues,Robert Johnson,226429,7435192,0.99,Blues,Unplugged,Eric Clapton\nAlberta,Traditional,222406,7412975,0.99,Blues,Unplugged,Eric Clapton\nSan Francisco Bay Blues,Jesse Fuller,203363,6724021,0.99,Blues,Unplugged,Eric Clapton\nMalted Milk,Robert Johnson,216528,7096781,0.99,Blues,Unplugged,Eric Clapton\nOld Love,\"Eric Clapton, Robert Cray\",472920,15780747,0.99,Blues,Unplugged,Eric Clapton\nRollin' And Tumblin',McKinley Morgenfield (Muddy Waters),251768,8407355,0.99,Blues,Unplugged,Eric Clapton\nA Novidade,Gilberto Gil,324780,10765600,0.99,Latin,Unplugged,Eric Clapton\nTenho Sede,Gilberto Gil,261616,8708114,0.99,Latin,Unplugged,Eric Clapton\nRefazenda,Gilberto Gil,218305,7237784,0.99,Latin,Unplugged,Eric Clapton\nRealce,Gilberto Gil,264489,8847612,0.99,Latin,Unplugged,Eric Clapton\nEsotérico,Gilberto Gil,317779,10530533,0.99,Latin,Unplugged,Eric Clapton\nDrão,Gilberto Gil,301453,9931950,0.99,Latin,Unplugged,Eric Clapton\nA Paz,Gilberto Gil,293093,9593064,0.99,Latin,Unplugged,Eric Clapton\nBeira Mar,Gilberto Gil,295444,9597994,0.99,Latin,Unplugged,Eric Clapton\nSampa,Gilberto Gil,225697,7469905,0.99,Latin,Unplugged,Eric Clapton\nParabolicamará,Gilberto Gil,284943,9543435,0.99,Latin,Unplugged,Eric Clapton\nTempo Rei,Gilberto Gil,302733,10019269,0.99,Latin,Unplugged,Eric Clapton\nExpresso 2222,Gilberto Gil,284760,9690577,0.99,Latin,Unplugged,Eric Clapton\nAquele Abraço,Gilberto Gil,263993,8805003,0.99,Latin,Unplugged,Eric Clapton\nPalco,Gilberto Gil,270550,9049901,0.99,Latin,Unplugged,Eric Clapton\nToda Menina Baiana,Gilberto Gil,278177,9351000,0.99,Latin,Unplugged,Eric Clapton\nSítio Do Pica-Pau Amarelo,Gilberto Gil,218070,7217955,0.99,Latin,Unplugged,Eric Clapton\nCollision,Jon Hudson/Mike Patton,204303,6656596,0.99,Alternative & Punk,Album Of The Year,Faith No More\nStripsearch,Jon Hudson/Mike Bordin/Mike Patton,270106,8861119,0.99,Alternative & Punk,Album Of The Year,Faith No More\nLast Cup Of Sorrow,Bill Gould/Mike Patton,251663,8221247,0.99,Alternative & Punk,Album Of The Year,Faith No More\nNaked In Front Of The Computer,Mike Patton,128757,4225077,0.99,Alternative & Punk,Album Of The Year,Faith No More\nHelpless,Bill Gould/Mike Bordin/Mike Patton,326217,10753135,0.99,Alternative & Punk,Album Of The Year,Faith No More\nMouth To Mouth,Bill Gould/Jon Hudson/Mike Bordin/Mike Patton,228493,7505887,0.99,Alternative & Punk,Album Of The Year,Faith No More\nAshes To Ashes,Bill Gould/Jon Hudson/Mike Bordin/Mike Patton/Roddy Bottum,217391,7093746,0.99,Alternative & Punk,Album Of The Year,Faith No More\nShe Loves Me Not,Bill Gould/Mike Bordin/Mike Patton,209867,6887544,0.99,Alternative & Punk,Album Of The Year,Faith No More\nGot That Feeling,Mike Patton,140852,4643227,0.99,Alternative & Punk,Album Of The Year,Faith No More\nPaths Of Glory,Bill Gould/Jon Hudson/Mike Bordin/Mike Patton/Roddy Bottum,257253,8436300,0.99,Alternative & Punk,Album Of The Year,Faith No More\nHome Sick Home,Mike Patton,119040,3898976,0.99,Alternative & Punk,Album Of The Year,Faith No More\nPristina,Bill Gould/Mike Patton,232698,7497361,0.99,Alternative & Punk,Album Of The Year,Faith No More\nLand Of Sunshine,,223921,7353567,0.99,Alternative & Punk,Angel Dust,Faith No More\nCaffeine,,267937,8747367,0.99,Alternative & Punk,Angel Dust,Faith No More\nMidlife Crisis,,263235,8628841,0.99,Alternative & Punk,Angel Dust,Faith No More\nRV,,223242,7288162,0.99,Alternative & Punk,Angel Dust,Faith No More\nSmaller And Smaller,,310831,10180103,0.99,Alternative & Punk,Angel Dust,Faith No More\nEverything's Ruined,,273658,9010917,0.99,Alternative & Punk,Angel Dust,Faith No More\nMalpractice,,241371,7900683,0.99,Alternative & Punk,Angel Dust,Faith No More\nKindergarten,,270680,8853647,0.99,Alternative & Punk,Angel Dust,Faith No More\nBe Aggressive,,222432,7298027,0.99,Alternative & Punk,Angel Dust,Faith No More\nA Small Victory,,297168,9733572,0.99,Alternative & Punk,Angel Dust,Faith No More\nCrack Hitler,,279144,9162435,0.99,Alternative & Punk,Angel Dust,Faith No More\nJizzlobber,,398341,12926140,0.99,Alternative & Punk,Angel Dust,Faith No More\nMidnight Cowboy,,251924,8242626,0.99,Alternative & Punk,Angel Dust,Faith No More\nEasy,,185835,6073008,0.99,Alternative & Punk,Angel Dust,Faith No More\nGet Out,\"Mike Bordin, Billy Gould, Mike Patton\",137482,4524972,0.99,Rock,King For A Day Fool For A Lifetime,Faith No More\nRicochet,\"Mike Bordin, Billy Gould, Mike Patton\",269400,8808812,0.99,Rock,King For A Day Fool For A Lifetime,Faith No More\nEvidence,\"Mike Bordin, Billy Gould, Mike Patton, Trey Spruance\",293590,9626136,0.99,Rock,King For A Day Fool For A Lifetime,Faith No More\nThe Gentle Art Of Making Enemies,\"Mike Bordin, Billy Gould, Mike Patton\",209319,6908609,0.99,Rock,King For A Day Fool For A Lifetime,Faith No More\nStar A.D.,\"Mike Bordin, Billy Gould, Mike Patton\",203807,6747658,0.99,Rock,King For A Day Fool For A Lifetime,Faith No More\nCuckoo For Caca,\"Mike Bordin, Billy Gould, Mike Patton, Trey Spruance\",222902,7388369,0.99,Rock,King For A Day Fool For A Lifetime,Faith No More\nCaralho Voador,\"Mike Bordin, Billy Gould, Mike Patton, Trey Spruance\",242102,8029054,0.99,Rock,King For A Day Fool For A Lifetime,Faith No More\nUgly In The Morning,\"Mike Bordin, Billy Gould, Mike Patton\",186435,6224997,0.99,Rock,King For A Day Fool For A Lifetime,Faith No More\nDigging The Grave,\"Mike Bordin, Billy Gould, Mike Patton\",185129,6109259,0.99,Rock,King For A Day Fool For A Lifetime,Faith No More\nTake This Bottle,\"Mike Bordin, Billy Gould, Mike Patton, Trey Spruance\",298997,9779971,0.99,Rock,King For A Day Fool For A Lifetime,Faith No More\nKing For A Day,\"Mike Bordin, Billy Gould, Mike Patton, Trey Spruance\",395859,13163733,0.99,Rock,King For A Day Fool For A Lifetime,Faith No More\nWhat A Day,\"Mike Bordin, Billy Gould, Mike Patton\",158275,5203430,0.99,Rock,King For A Day Fool For A Lifetime,Faith No More\nThe Last To Know,\"Mike Bordin, Billy Gould, Mike Patton\",267833,8736776,0.99,Rock,King For A Day Fool For A Lifetime,Faith No More\nJust A Man,\"Mike Bordin, Billy Gould, Mike Patton\",336666,11031254,0.99,Rock,King For A Day Fool For A Lifetime,Faith No More\nAbsolute Zero,\"Mike Bordin, Billy Gould, Mike Patton\",181995,5929427,0.99,Rock,King For A Day Fool For A Lifetime,Faith No More\nFrom Out Of Nowhere,Faith No More,202527,6587802,0.99,Alternative & Punk,The Real Thing,Faith No More\nEpic,Faith No More,294008,9631296,0.99,Alternative & Punk,The Real Thing,Faith No More\nFalling To Pieces,Faith No More,316055,10333123,0.99,Alternative & Punk,The Real Thing,Faith No More\nSurprise! You're Dead!,Faith No More,147226,4823036,0.99,Alternative & Punk,The Real Thing,Faith No More\nZombie Eaters,Faith No More,360881,11835367,0.99,Alternative & Punk,The Real Thing,Faith No More\nThe Real Thing,Faith No More,493635,16233080,0.99,Alternative & Punk,The Real Thing,Faith No More\nUnderwater Love,Faith No More,231993,7634387,0.99,Alternative & Punk,The Real Thing,Faith No More\nThe Morning After,Faith No More,223764,7355898,0.99,Alternative & Punk,The Real Thing,Faith No More\nWoodpecker From Mars,Faith No More,340532,11174250,0.99,Alternative & Punk,The Real Thing,Faith No More\nWar Pigs,\"Tony Iommi, Bill Ward, Geezer Butler, Ozzy Osbourne\",464770,15267802,0.99,Alternative & Punk,The Real Thing,Faith No More\nEdge Of The World,Faith No More,250357,8235607,0.99,Alternative & Punk,The Real Thing,Faith No More\nDeixa Entrar,,33619,1095012,0.99,Latin,Deixa Entrar,Falamansa\nFalamansa Song,,237165,7921313,0.99,Latin,Deixa Entrar,Falamansa\nXote Dos Milagres,,269557,8897778,0.99,Latin,Deixa Entrar,Falamansa\nRindo À Toa,,222066,7365321,0.99,Latin,Deixa Entrar,Falamansa\nConfidência,,222197,7460829,0.99,Latin,Deixa Entrar,Falamansa\nForró De Tóquio,,169273,5588756,0.99,Latin,Deixa Entrar,Falamansa\nZeca Violeiro,,143673,4781949,0.99,Latin,Deixa Entrar,Falamansa\nAvisa,,355030,11844320,0.99,Latin,Deixa Entrar,Falamansa\nPrincipiando/Decolagem,,116767,3923789,0.99,Latin,Deixa Entrar,Falamansa\nAsas,,231915,7711669,0.99,Latin,Deixa Entrar,Falamansa\nMedo De Escuro,,213760,7056323,0.99,Latin,Deixa Entrar,Falamansa\nOração,,271072,9003882,0.99,Latin,Deixa Entrar,Falamansa\nMinha Gata,,181838,6039502,0.99,Latin,Deixa Entrar,Falamansa\nDesaforo,,174524,5853561,0.99,Latin,Deixa Entrar,Falamansa\nIn Your Honor,\"Dave Grohl, Taylor Hawkins, Nate Mendel, Chris Shiflett\",230191,7468463,0.99,Rock,In Your Honor [Disc 1],Foo Fighters\nNo Way Back,\"Dave Grohl, Taylor Hawkins, Nate Mendel, Chris Shiflett\",196675,6421400,0.99,Rock,In Your Honor [Disc 1],Foo Fighters\nBest Of You,\"Dave Grohl, Taylor Hawkins, Nate Mendel, Chris Shiflett\",255712,8363467,0.99,Rock,In Your Honor [Disc 1],Foo Fighters\nDOA,\"Dave Grohl, Taylor Hawkins, Nate Mendel, Chris Shiflett\",252186,8232342,0.99,Rock,In Your Honor [Disc 1],Foo Fighters\nHell,\"Dave Grohl, Taylor Hawkins, Nate Mendel, Chris Shiflett\",117080,3819255,0.99,Rock,In Your Honor [Disc 1],Foo Fighters\nThe Last Song,\"Dave Grohl, Taylor Hawkins, Nate Mendel, Chris Shiflett\",199523,6496742,0.99,Rock,In Your Honor [Disc 1],Foo Fighters\nFree Me,\"Dave Grohl, Taylor Hawkins, Nate Mendel, Chris Shiflett\",278700,9109340,0.99,Rock,In Your Honor [Disc 1],Foo Fighters\nResolve,\"Dave Grohl, Taylor Hawkins, Nate Mendel, Chris Shiflett\",288731,9416186,0.99,Rock,In Your Honor [Disc 1],Foo Fighters\nThe Deepest Blues Are Black,\"Dave Grohl, Taylor Hawkins, Nate Mendel, Chris Shiflett\",238419,7735473,0.99,Rock,In Your Honor [Disc 1],Foo Fighters\nEnd Over End,\"Dave Grohl, Taylor Hawkins, Nate Mendel, Chris Shiflett\",352078,11395296,0.99,Rock,In Your Honor [Disc 1],Foo Fighters\nStill,\"Dave Grohl, Taylor Hawkins, Nate Mendel, Chris Shiflett/FOO FIGHTERS\",313182,10323157,0.99,Rock,In Your Honor [Disc 2],Foo Fighters\nWhat If I Do?,\"Dave Grohl, Taylor Hawkins, Nate Mendel, Chris Shiflett/FOO FIGHTERS\",302994,9929799,0.99,Rock,In Your Honor [Disc 2],Foo Fighters\nMiracle,\"Dave Grohl, Taylor Hawkins, Nate Mendel, Chris Shiflett/FOO FIGHTERS\",209684,6877994,0.99,Rock,In Your Honor [Disc 2],Foo Fighters\nAnother Round,\"Dave Grohl, Taylor Hawkins, Nate Mendel, Chris Shiflett/FOO FIGHTERS\",265848,8752670,0.99,Rock,In Your Honor [Disc 2],Foo Fighters\nFriend Of A Friend,\"Dave Grohl, Taylor Hawkins, Nate Mendel, Chris Shiflett/FOO FIGHTERS\",193280,6355088,0.99,Rock,In Your Honor [Disc 2],Foo Fighters\nOver And Out,\"Dave Grohl, Taylor Hawkins, Nate Mendel, Chris Shiflett/FOO FIGHTERS\",316264,10428382,0.99,Rock,In Your Honor [Disc 2],Foo Fighters\nOn The Mend,\"Dave Grohl, Taylor Hawkins, Nate Mendel, Chris Shiflett/FOO FIGHTERS\",271908,9071997,0.99,Rock,In Your Honor [Disc 2],Foo Fighters\nVirginia Moon,\"Dave Grohl, Taylor Hawkins, Nate Mendel, Chris Shiflett/FOO FIGHTERS\",229198,7494639,0.99,Rock,In Your Honor [Disc 2],Foo Fighters\nCold Day In The Sun,\"Dave Grohl, Taylor Hawkins, Nate Mendel, Chris Shiflett/FOO FIGHTERS\",200724,6596617,0.99,Rock,In Your Honor [Disc 2],Foo Fighters\nRazor,\"Dave Grohl, Taylor Hawkins, Nate Mendel, Chris Shiflett/FOO FIGHTERS\",293276,9721373,0.99,Rock,In Your Honor [Disc 2],Foo Fighters\nAll My Life,Foo Fighters,263653,8665545,0.99,Alternative & Punk,One By One,Foo Fighters\nLow,Foo Fighters,268120,8847196,0.99,Alternative & Punk,One By One,Foo Fighters\nHave It All,Foo Fighters,298057,9729292,0.99,Alternative & Punk,One By One,Foo Fighters\nTimes Like These,Foo Fighters,266370,8624691,0.99,Alternative & Punk,One By One,Foo Fighters\nDisenchanted Lullaby,Foo Fighters,273528,8919111,0.99,Alternative & Punk,One By One,Foo Fighters\nTired Of You,Foo Fighters,311353,10094743,0.99,Alternative & Punk,One By One,Foo Fighters\nHalo,Foo Fighters,306442,10026371,0.99,Alternative & Punk,One By One,Foo Fighters\nLonely As You,Foo Fighters,277185,9022628,0.99,Alternative & Punk,One By One,Foo Fighters\nOverdrive,Foo Fighters,270550,8793187,0.99,Alternative & Punk,One By One,Foo Fighters\nBurn Away,Foo Fighters,298396,9678073,0.99,Alternative & Punk,One By One,Foo Fighters\nCome Back,Foo Fighters,469968,15371980,0.99,Alternative & Punk,One By One,Foo Fighters\nDoll,\"Dave, Taylor, Nate, Chris\",83487,2702572,0.99,Rock,The Colour And The Shape,Foo Fighters\nMonkey Wrench,\"Dave, Taylor, Nate, Chris\",231523,7527531,0.99,Rock,The Colour And The Shape,Foo Fighters\n\"Hey, Johnny Park!\",\"Dave, Taylor, Nate, Chris\",248528,8079480,0.99,Rock,The Colour And The Shape,Foo Fighters\nMy Poor Brain,\"Dave, Taylor, Nate, Chris\",213446,6973746,0.99,Rock,The Colour And The Shape,Foo Fighters\nWind Up,\"Dave, Taylor, Nate, Chris\",152163,4950667,0.99,Rock,The Colour And The Shape,Foo Fighters\nUp In Arms,\"Dave, Taylor, Nate, Chris\",135732,4406227,0.99,Rock,The Colour And The Shape,Foo Fighters\nMy Hero,\"Dave, Taylor, Nate, Chris\",260101,8472365,0.99,Rock,The Colour And The Shape,Foo Fighters\nSee You,\"Dave, Taylor, Nate, Chris\",146782,4888173,0.99,Rock,The Colour And The Shape,Foo Fighters\nEnough Space,Dave Grohl,157387,5169280,0.99,Rock,The Colour And The Shape,Foo Fighters\nFebruary Stars,\"Dave, Taylor, Nate, Chris\",289306,9344875,0.99,Rock,The Colour And The Shape,Foo Fighters\nEverlong,Dave Grohl,250749,8270816,0.99,Rock,The Colour And The Shape,Foo Fighters\nWalking After You,Dave Grohl,303856,9898992,0.99,Rock,The Colour And The Shape,Foo Fighters\nNew Way Home,\"Dave, Taylor, Nate, Chris\",342230,11205664,0.99,Rock,The Colour And The Shape,Foo Fighters\nMy Way,claude françois/gilles thibault/jacques revaux/paul anka,275879,8928684,0.99,Easy Listening,My Way: The Best Of Frank Sinatra [Disc 1],Frank Sinatra\nStrangers In The Night,berthold kaempfert/charles singleton/eddie snyder,155794,5055295,0.99,Easy Listening,My Way: The Best Of Frank Sinatra [Disc 1],Frank Sinatra\n\"New York, New York\",fred ebb/john kander,206001,6707993,0.99,Easy Listening,My Way: The Best Of Frank Sinatra [Disc 1],Frank Sinatra\nI Get A Kick Out Of You,cole porter,194429,6332441,0.99,Easy Listening,My Way: The Best Of Frank Sinatra [Disc 1],Frank Sinatra\nSomething Stupid,carson c. parks,158615,5210643,0.99,Easy Listening,My Way: The Best Of Frank Sinatra [Disc 1],Frank Sinatra\nMoon River,henry mancini/johnny mercer,198922,6395808,0.99,Easy Listening,My Way: The Best Of Frank Sinatra [Disc 1],Frank Sinatra\nWhat Now My Love,carl sigman/gilbert becaud/pierre leroyer,149995,4913383,0.99,Easy Listening,My Way: The Best Of Frank Sinatra [Disc 1],Frank Sinatra\nSummer Love,hans bradtke/heinz meier/johnny mercer,174994,5693242,0.99,Easy Listening,My Way: The Best Of Frank Sinatra [Disc 1],Frank Sinatra\nFor Once In My Life,orlando murden/ronald miller,171154,5557537,0.99,Easy Listening,My Way: The Best Of Frank Sinatra [Disc 1],Frank Sinatra\nLove And Marriage,jimmy van heusen/sammy cahn,89730,2930596,0.99,Easy Listening,My Way: The Best Of Frank Sinatra [Disc 1],Frank Sinatra\nThey Can't Take That Away From Me,george gershwin/ira gershwin,161227,5240043,0.99,Easy Listening,My Way: The Best Of Frank Sinatra [Disc 1],Frank Sinatra\nMy Kind Of Town,jimmy van heusen/sammy cahn,188499,6119915,0.99,Easy Listening,My Way: The Best Of Frank Sinatra [Disc 1],Frank Sinatra\nFly Me To The Moon,bart howard,149263,4856954,0.99,Easy Listening,My Way: The Best Of Frank Sinatra [Disc 1],Frank Sinatra\nI've Got You Under My Skin,cole porter,210808,6883787,0.99,Easy Listening,My Way: The Best Of Frank Sinatra [Disc 1],Frank Sinatra\nThe Best Is Yet To Come,carolyn leigh/cy coleman,173583,5633730,0.99,Easy Listening,My Way: The Best Of Frank Sinatra [Disc 1],Frank Sinatra\nIt Was A Very Good Year,ervin drake,266605,8554066,0.99,Easy Listening,My Way: The Best Of Frank Sinatra [Disc 1],Frank Sinatra\nCome Fly With Me,jimmy van heusen/sammy cahn,190458,6231029,0.99,Easy Listening,My Way: The Best Of Frank Sinatra [Disc 1],Frank Sinatra\nThat's Life,dean kay thompson/kelly gordon,187010,6095727,0.99,Easy Listening,My Way: The Best Of Frank Sinatra [Disc 1],Frank Sinatra\nThe Girl From Ipanema,antonio carlos jobim/norman gimbel/vinicius de moraes,193750,6410674,0.99,Easy Listening,My Way: The Best Of Frank Sinatra [Disc 1],Frank Sinatra\nThe Lady Is A Tramp,lorenz hart/richard rodgers,184111,5987372,0.99,Easy Listening,My Way: The Best Of Frank Sinatra [Disc 1],Frank Sinatra\n\"Bad, Bad Leroy Brown\",jim croce,169900,5548581,0.99,Easy Listening,My Way: The Best Of Frank Sinatra [Disc 1],Frank Sinatra\nMack The Knife,bert brecht/kurt weill/marc blitzstein,292075,9541052,0.99,Easy Listening,My Way: The Best Of Frank Sinatra [Disc 1],Frank Sinatra\nLoves Been Good To Me,rod mckuen,203964,6645365,0.99,Easy Listening,My Way: The Best Of Frank Sinatra [Disc 1],Frank Sinatra\nL.A. Is My Lady,alan bergman/marilyn bergman/peggy lipton jones/quincy jones,193175,6378511,0.99,Easy Listening,My Way: The Best Of Frank Sinatra [Disc 1],Frank Sinatra\nEntrando Na Sua (Intro),,179252,5840027,0.99,Latin,Roda De Funk,Funk Como Le Gusta\nNervosa,,229537,7680421,0.99,Latin,Roda De Funk,Funk Como Le Gusta\nFunk De Bamba (Com Fernanda Abreu),,237191,7866165,0.99,Latin,Roda De Funk,Funk Como Le Gusta\nCall Me At Cleo´s,,236617,7920510,0.99,Latin,Roda De Funk,Funk Como Le Gusta\nOlhos Coloridos (Com Sandra De Sá),,321332,10567404,0.99,Latin,Roda De Funk,Funk Como Le Gusta\nZambação,,301113,10030604,0.99,Latin,Roda De Funk,Funk Como Le Gusta\nFunk Hum,,244453,8084475,0.99,Latin,Roda De Funk,Funk Como Le Gusta\nForty Days (Com DJ Hum),,221727,7347172,0.99,Latin,Roda De Funk,Funk Como Le Gusta\nBalada Da Paula,Emerson Villani,322821,10603717,0.99,Latin,Roda De Funk,Funk Como Le Gusta\nDujji,,324597,10833935,0.99,Latin,Roda De Funk,Funk Como Le Gusta\nMeu Guarda-Chuva,,248528,8216625,0.99,Latin,Roda De Funk,Funk Como Le Gusta\nMotéis,,213498,7041077,0.99,Latin,Roda De Funk,Funk Como Le Gusta\nWhistle Stop,,526132,17533664,0.99,Latin,Roda De Funk,Funk Como Le Gusta\n16 Toneladas,,191634,6390885,0.99,Latin,Roda De Funk,Funk Como Le Gusta\nDivirta-Se (Saindo Da Sua),,74919,2439206,0.99,Latin,Roda De Funk,Funk Como Le Gusta\nForty Days Instrumental,,292493,9584317,0.99,Latin,Roda De Funk,Funk Como Le Gusta\nÓia Eu Aqui De Novo,,219454,7469735,0.99,Soundtrack,As Canções de Eu Tu Eles,Gilberto Gil\nBaião Da Penha,,247928,8393047,0.99,Soundtrack,As Canções de Eu Tu Eles,Gilberto Gil\nEsperando Na Janela,Manuca/Raimundinho DoAcordion/Targino Godim,261041,8660617,0.99,Soundtrack,As Canções de Eu Tu Eles,Gilberto Gil\nJuazeiro,Humberto Teixeira/Luiz Gonzaga,222275,7349779,0.99,Soundtrack,As Canções de Eu Tu Eles,Gilberto Gil\nÚltimo Pau-De-Arara,Corumbá/José Gumarães/Venancio,200437,6638563,0.99,Soundtrack,As Canções de Eu Tu Eles,Gilberto Gil\nAsa Branca,Humberto Teixeira/Luiz Gonzaga,217051,7387183,0.99,Soundtrack,As Canções de Eu Tu Eles,Gilberto Gil\nQui Nem Jiló,Humberto Teixeira/Luiz Gonzaga,204695,6937472,0.99,Soundtrack,As Canções de Eu Tu Eles,Gilberto Gil\nAssum Preto,Humberto Teixeira/Luiz Gonzaga,199653,6625000,0.99,Soundtrack,As Canções de Eu Tu Eles,Gilberto Gil\nPau-De-Arara,\"Guio De Morais E Seus \"\"Parentes\"\"/Luiz Gonzaga\",191660,6340649,0.99,Soundtrack,As Canções de Eu Tu Eles,Gilberto Gil\nA Volta Da Asa Branca,Luiz Gonzaga/Zé Dantas,271020,9098093,0.99,Soundtrack,As Canções de Eu Tu Eles,Gilberto Gil\nO Amor Daqui De Casa,Gilberto Gil,148636,4888292,0.99,Soundtrack,As Canções de Eu Tu Eles,Gilberto Gil\nAs Pegadas Do Amor,Gilberto Gil,209136,6899062,0.99,Soundtrack,As Canções de Eu Tu Eles,Gilberto Gil\nLamento Sertanejo,Dominguinhos/Gilberto Gil,260963,8518290,0.99,Soundtrack,As Canções de Eu Tu Eles,Gilberto Gil\nCasinha Feliz,Gilberto Gil,32287,1039615,0.99,Soundtrack,As Canções de Eu Tu Eles,Gilberto Gil\nIntrodução (Live),,154096,5227579,0.99,Latin,Quanta Gente Veio Ver (Live),Gilberto Gil\nPalco (Live),,238315,8026622,0.99,Latin,Quanta Gente Veio Ver (Live),Gilberto Gil\nIs This Love (Live),,295262,9819759,0.99,Latin,Quanta Gente Veio Ver (Live),Gilberto Gil\nStir It Up (Live),,282409,9594738,0.99,Latin,Quanta Gente Veio Ver (Live),Gilberto Gil\nRefavela (Live),,236695,7985305,0.99,Latin,Quanta Gente Veio Ver (Live),Gilberto Gil\nVendedor De Caranguejo (Live),,248842,8358128,0.99,Latin,Quanta Gente Veio Ver (Live),Gilberto Gil\nQuanta (Live),,357485,11774865,0.99,Latin,Quanta Gente Veio Ver (Live),Gilberto Gil\nEstrela (Live),,285309,9436411,0.99,Latin,Quanta Gente Veio Ver (Live),Gilberto Gil\nPela Internet (Live),,263471,8804401,0.99,Latin,Quanta Gente Veio Ver (Live),Gilberto Gil\nCérebro Eletrônico (Live),,231627,7805352,0.99,Latin,Quanta Gente Veio Ver (Live),Gilberto Gil\nOpachorô (Live),,259526,8596384,0.99,Latin,Quanta Gente Veio Ver (Live),Gilberto Gil\nCopacabana (Live),,289671,9673672,0.99,Latin,Quanta Gente Veio Ver (Live),Gilberto Gil\nA Novidade (Live),,316969,10508000,0.99,Latin,Quanta Gente Veio Ver (Live),Gilberto Gil\nGhandi (Live),,222458,7481950,0.99,Latin,Quanta Gente Veio Ver (Live),Gilberto Gil\nDe Ouro E Marfim (Live),,234971,7838453,0.99,Latin,Quanta Gente Veio Ver (Live),Gilberto Gil\nDoce De Carnaval (Candy All),,356101,11998470,0.99,Jazz,Quanta Gente Veio ver--Bônus De Carnaval,Gilberto Gil\nLamento De Carnaval,,294530,9819276,0.99,Jazz,Quanta Gente Veio ver--Bônus De Carnaval,Gilberto Gil\nPretinha,,265273,8914579,0.99,Jazz,Quanta Gente Veio ver--Bônus De Carnaval,Gilberto Gil\nStraight Out Of Line,Sully Erna,259213,8511877,0.99,Metal,Faceless,Godsmack\nFaceless,Sully Erna,216006,6992417,0.99,Metal,Faceless,Godsmack\nChanges,Sully Erna; Tony Rombola,260022,8455835,0.99,Metal,Faceless,Godsmack\nMake Me Believe,Sully Erna,248607,8075050,0.99,Metal,Faceless,Godsmack\nI Stand Alone,Sully Erna,246125,8017041,0.99,Metal,Faceless,Godsmack\nRe-Align,Sully Erna,260884,8513891,0.99,Metal,Faceless,Godsmack\nI Fucking Hate You,Sully Erna,247170,8059642,0.99,Metal,Faceless,Godsmack\nReleasing The Demons,Sully Erna,252760,8276372,0.99,Metal,Faceless,Godsmack\nDead And Broken,Sully Erna,251454,8206611,0.99,Metal,Faceless,Godsmack\nI Am,Sully Erna,239516,7803270,0.99,Metal,Faceless,Godsmack\nThe Awakening,Sully Erna,89547,3035251,0.99,Metal,Faceless,Godsmack\nSerenity,Sully Erna; Tony Rombola,274834,9172976,0.99,Metal,Faceless,Godsmack\nAmerican Idiot,\"Billie Joe Armstrong, Mike Dirnt, Tré Cool\",174419,5705793,0.99,Alternative & Punk,American Idiot,Green Day\nJesus Of Suburbia / City Of The Damned / I Don't Care / Dearly Beloved / Tales Of Another Broken Home,Billie Joe Armstrong/Green Day,548336,17875209,0.99,Alternative & Punk,American Idiot,Green Day\nHoliday,\"Billie Joe Armstrong, Mike Dirnt, Tré Cool\",232724,7599602,0.99,Alternative & Punk,American Idiot,Green Day\nBoulevard Of Broken Dreams,\"Mike Dint, Billie Joe, Tré Cool\",260858,8485122,0.99,Alternative & Punk,American Idiot,Green Day\nAre We The Waiting,Green Day,163004,5328329,0.99,Alternative & Punk,American Idiot,Green Day\nSt. Jimmy,Green Day,175307,5716589,0.99,Alternative & Punk,American Idiot,Green Day\nGive Me Novacaine,Green Day,205871,6752485,0.99,Alternative & Punk,American Idiot,Green Day\nShe's A Rebel,Green Day,120528,3901226,0.99,Alternative & Punk,American Idiot,Green Day\nExtraordinary Girl,Green Day,214021,6975177,0.99,Alternative & Punk,American Idiot,Green Day\nLetterbomb,Green Day,246151,7980902,0.99,Alternative & Punk,American Idiot,Green Day\nWake Me Up When September Ends,\"Mike Dint, Billie Joe, Tré Cool\",285753,9325597,0.99,Alternative & Punk,American Idiot,Green Day\nHomecoming / The Death Of St. Jimmy / East 12th St. / Nobody Likes You / Rock And Roll Girlfriend / We're Coming Home Again,Mike Dirnt/Tré Cool,558602,18139840,0.99,Alternative & Punk,American Idiot,Green Day\nWhatsername,Green Day,252316,8244843,0.99,Alternative & Punk,American Idiot,Green Day\nWelcome to the Jungle,,273552,4538451,0.99,Rock,Appetite for Destruction,Guns N' Roses\nIt's So Easy,,202824,3394019,0.99,Rock,Appetite for Destruction,Guns N' Roses\nNightrain,,268537,4457283,0.99,Rock,Appetite for Destruction,Guns N' Roses\nOut Ta Get Me,,263893,4382147,0.99,Rock,Appetite for Destruction,Guns N' Roses\nMr. Brownstone,,228924,3816323,0.99,Rock,Appetite for Destruction,Guns N' Roses\nParadise City,,406347,6687123,0.99,Rock,Appetite for Destruction,Guns N' Roses\nMy Michelle,,219961,3671299,0.99,Rock,Appetite for Destruction,Guns N' Roses\nThink About You,,231640,3860275,0.99,Rock,Appetite for Destruction,Guns N' Roses\nSweet Child O' Mine,,356424,5879347,0.99,Rock,Appetite for Destruction,Guns N' Roses\nYou're Crazy,,197135,3301971,0.99,Rock,Appetite for Destruction,Guns N' Roses\nAnything Goes,,206400,3451891,0.99,Rock,Appetite for Destruction,Guns N' Roses\nRocket Queen,,375349,6185539,0.99,Rock,Appetite for Destruction,Guns N' Roses\nRight Next Door to Hell,,182321,3175950,0.99,Rock,Use Your Illusion I,Guns N' Roses\nDust N' Bones,,298374,5053742,0.99,Rock,Use Your Illusion I,Guns N' Roses\nLive and Let Die,,184016,3203390,0.99,Rock,Use Your Illusion I,Guns N' Roses\nDon't Cry (Original),,284744,4833259,0.99,Rock,Use Your Illusion I,Guns N' Roses\nPerfect Crime,,143637,2550030,0.99,Rock,Use Your Illusion I,Guns N' Roses\nYou Ain't the First,,156268,2754414,0.99,Rock,Use Your Illusion I,Guns N' Roses\nBad Obsession,,328282,5537678,0.99,Rock,Use Your Illusion I,Guns N' Roses\nBack off Bitch,,303436,5135662,0.99,Rock,Use Your Illusion I,Guns N' Roses\nDouble Talkin' Jive,,203637,3520862,0.99,Rock,Use Your Illusion I,Guns N' Roses\nNovember Rain,,537540,8923566,0.99,Rock,Use Your Illusion I,Guns N' Roses\nThe Garden,,322175,5438862,0.99,Rock,Use Your Illusion I,Guns N' Roses\nGarden of Eden,,161539,2839694,0.99,Rock,Use Your Illusion I,Guns N' Roses\nDon't Damn Me,,318901,5385886,0.99,Rock,Use Your Illusion I,Guns N' Roses\nBad Apples,,268351,4567966,0.99,Rock,Use Your Illusion I,Guns N' Roses\nDead Horse,,257600,4394014,0.99,Rock,Use Your Illusion I,Guns N' Roses\nComa,,616511,10201342,0.99,Rock,Use Your Illusion I,Guns N' Roses\nCivil War,Duff McKagan/Slash/W. Axl Rose,461165,15046579,0.99,Metal,Use Your Illusion II,Guns N' Roses\n14 Years,Izzy Stradlin'/W. Axl Rose,261355,8543664,0.99,Metal,Use Your Illusion II,Guns N' Roses\nYesterdays,Billy/Del James/W. Axl Rose/West Arkeen,196205,6398489,0.99,Metal,Use Your Illusion II,Guns N' Roses\nKnockin' On Heaven's Door,Bob Dylan,336457,10986716,0.99,Metal,Use Your Illusion II,Guns N' Roses\nGet In The Ring,Duff McKagan/Slash/W. Axl Rose,341054,11134105,0.99,Metal,Use Your Illusion II,Guns N' Roses\nShotgun Blues,W. Axl Rose,203206,6623916,0.99,Metal,Use Your Illusion II,Guns N' Roses\nBreakdown,W. Axl Rose,424960,13978284,0.99,Metal,Use Your Illusion II,Guns N' Roses\nPretty Tied Up,Izzy Stradlin',287477,9408754,0.99,Metal,Use Your Illusion II,Guns N' Roses\nLocomotive,Slash/W. Axl Rose,522396,17236842,0.99,Metal,Use Your Illusion II,Guns N' Roses\nSo Fine,Duff McKagan,246491,8039484,0.99,Metal,Use Your Illusion II,Guns N' Roses\nEstranged,W. Axl Rose,563800,18343787,0.99,Metal,Use Your Illusion II,Guns N' Roses\nYou Could Be Mine,Izzy Stradlin'/W. Axl Rose,343875,11207355,0.99,Metal,Use Your Illusion II,Guns N' Roses\nDon't Cry,Izzy Stradlin'/W. Axl Rose,284238,9222458,0.99,Metal,Use Your Illusion II,Guns N' Roses\nMy World,W. Axl Rose,84532,2764045,0.99,Metal,Use Your Illusion II,Guns N' Roses\nColibri,Richard Bull,361012,12055329,0.99,Jazz,Blue Moods,Incognito\nLove Is The Colour,R. Carless,251585,8419165,0.99,Jazz,Blue Moods,Incognito\nMagnetic Ocean,Patrick Claher/Richard Bull,321123,10720741,0.99,Jazz,Blue Moods,Incognito\nDeep Waters,Richard Bull,396460,13075359,0.99,Jazz,Blue Moods,Incognito\nL'Arc En Ciel De Miles,Kevin Robinson/Richard Bull,242390,8053997,0.99,Jazz,Blue Moods,Incognito\nGypsy,Kevin Robinson,330997,11083374,0.99,Jazz,Blue Moods,Incognito\nJourney Into Sunlight,Jean Paul Maunick,249756,8241177,0.99,Jazz,Blue Moods,Incognito\nSunchild,Graham Harvey,259970,8593143,0.99,Jazz,Blue Moods,Incognito\nMillenium,Maxton Gig Beesley Jnr.,379167,12511939,0.99,Jazz,Blue Moods,Incognito\nThinking 'Bout Tomorrow,Fayyaz Virgi/Richard Bull,355395,11865384,0.99,Jazz,Blue Moods,Incognito\nJacob's Ladder,Julian Crampton,367647,12201595,0.99,Jazz,Blue Moods,Incognito\nShe Wears Black,G Harvey/R Hope-Taylor,528666,17617944,0.99,Jazz,Blue Moods,Incognito\nDark Side Of The Cog,Jean Paul Maunick,377155,12491122,0.99,Jazz,Blue Moods,Incognito\nDifferent World,,258692,4383764,0.99,Rock,A Matter of Life and Death,Iron Maiden\nThese Colours Don't Run,,412152,6883500,0.99,Rock,A Matter of Life and Death,Iron Maiden\nBrighter Than a Thousand Suns,,526255,8721490,0.99,Rock,A Matter of Life and Death,Iron Maiden\nThe Pilgrim,,307593,5172144,0.99,Rock,A Matter of Life and Death,Iron Maiden\nThe Longest Day,,467810,7785748,0.99,Rock,A Matter of Life and Death,Iron Maiden\nOut of the Shadows,,336896,5647303,0.99,Rock,A Matter of Life and Death,Iron Maiden\nThe Reincarnation of Benjamin Breeg,,442106,7367736,0.99,Rock,A Matter of Life and Death,Iron Maiden\nFor the Greater Good of God,,564893,9367328,0.99,Rock,A Matter of Life and Death,Iron Maiden\nLord of Light,,444614,7393698,0.99,Rock,A Matter of Life and Death,Iron Maiden\nThe Legacy,,562966,9314287,0.99,Rock,A Matter of Life and Death,Iron Maiden\nHallowed Be Thy Name (Live) [Non Album Bonus Track],,431262,7205816,0.99,Rock,A Matter of Life and Death,Iron Maiden\nThe Number Of The Beast,Steve Harris,294635,4718897,0.99,Metal,A Real Dead One,Iron Maiden\nThe Trooper,Steve Harris,235311,3766272,0.99,Metal,A Real Dead One,Iron Maiden\nProwler,Steve Harris,255634,4091904,0.99,Metal,A Real Dead One,Iron Maiden\nTransylvania,Steve Harris,265874,4255744,0.99,Metal,A Real Dead One,Iron Maiden\nRemember Tomorrow,Paul Di'Anno/Steve Harris,352731,5648438,0.99,Metal,A Real Dead One,Iron Maiden\nWhere Eagles Dare,Steve Harris,289358,4630528,0.99,Metal,A Real Dead One,Iron Maiden\nSanctuary,David Murray/Paul Di'Anno/Steve Harris,293250,4694016,0.99,Metal,A Real Dead One,Iron Maiden\nRunning Free,Paul Di'Anno/Steve Harris,228937,3663872,0.99,Metal,A Real Dead One,Iron Maiden\nRun To The Hilss,Steve Harris,237557,3803136,0.99,Metal,A Real Dead One,Iron Maiden\n2 Minutes To Midnight,Adrian Smith/Bruce Dickinson,337423,5400576,0.99,Metal,A Real Dead One,Iron Maiden\nIron Maiden,Steve Harris,324623,5195776,0.99,Metal,A Real Dead One,Iron Maiden\nHallowed Be Thy Name,Steve Harris,471849,7550976,0.99,Metal,A Real Dead One,Iron Maiden\nBe Quick Or Be Dead,Bruce Dickinson/Janick Gers,196911,3151872,0.99,Metal,A Real Live One,Iron Maiden\nFrom Here To Eternity,Steve Harris,259866,4159488,0.99,Metal,A Real Live One,Iron Maiden\nCan I Play With Madness,Adrian Smith/Bruce Dickinson/Steve Harris,282488,4521984,0.99,Metal,A Real Live One,Iron Maiden\nWasting Love,Bruce Dickinson/Janick Gers,347846,5566464,0.99,Metal,A Real Live One,Iron Maiden\nTailgunner,Bruce Dickinson/Steve Harris,249469,3993600,0.99,Metal,A Real Live One,Iron Maiden\nThe Evil That Men Do,Adrian Smith/Bruce Dickinson/Steve Harris,325929,5216256,0.99,Metal,A Real Live One,Iron Maiden\nAfraid To Shoot Strangers,Steve Harris,407980,6529024,0.99,Metal,A Real Live One,Iron Maiden\nBring Your Daughter... To The Slaughter,Bruce Dickinson,317727,5085184,0.99,Metal,A Real Live One,Iron Maiden\nHeaven Can Wait,Steve Harris,448574,7178240,0.99,Metal,A Real Live One,Iron Maiden\nThe Clairvoyant,Steve Harris,269871,4319232,0.99,Metal,A Real Live One,Iron Maiden\nFear Of The Dark,Steve Harris,431333,6906078,0.99,Metal,A Real Live One,Iron Maiden\nThe Wicker Man,Adrian Smith/Bruce Dickinson/Steve Harris,275539,11022464,0.99,Rock,Brave New World,Iron Maiden\nGhost Of The Navigator,Bruce Dickinson/Janick Gers/Steve Harris,410070,16404608,0.99,Rock,Brave New World,Iron Maiden\nBrave New World,Bruce Dickinson/David Murray/Steve Harris,378984,15161472,0.99,Rock,Brave New World,Iron Maiden\nBlood Brothers,Steve Harris,434442,17379456,0.99,Rock,Brave New World,Iron Maiden\nThe Mercenary,Janick Gers/Steve Harris,282488,11300992,0.99,Rock,Brave New World,Iron Maiden\nDream Of Mirrors,Janick Gers/Steve Harris,561162,22448256,0.99,Rock,Brave New World,Iron Maiden\nThe Fallen Angel,Adrian Smith/Steve Harris,240718,9629824,0.99,Rock,Brave New World,Iron Maiden\nThe Nomad,David Murray/Steve Harris,546115,21846144,0.99,Rock,Brave New World,Iron Maiden\nOut Of The Silent Planet,Bruce Dickinson/Janick Gers/Steve Harris,385541,15423616,0.99,Rock,Brave New World,Iron Maiden\nThe Thin Line Between Love & Hate,David Murray/Steve Harris,506801,20273280,0.99,Rock,Brave New World,Iron Maiden\nWildest Dreams,Adrian Smith/Steve Harris,232777,9312384,0.99,Heavy Metal,Dance Of Death,Iron Maiden\nRainmaker,Bruce Dickinson/David Murray/Steve Harris,228623,9146496,0.99,Heavy Metal,Dance Of Death,Iron Maiden\nNo More Lies,Steve Harris,441782,17672320,0.99,Heavy Metal,Dance Of Death,Iron Maiden\nMontsegur,Bruce Dickinson/Janick Gers/Steve Harris,350484,14020736,0.99,Heavy Metal,Dance Of Death,Iron Maiden\nDance Of Death,Janick Gers/Steve Harris,516649,20670727,0.99,Heavy Metal,Dance Of Death,Iron Maiden\nGates Of Tomorrow,Bruce Dickinson/Janick Gers/Steve Harris,312032,12482688,0.99,Heavy Metal,Dance Of Death,Iron Maiden\nNew Frontier,Adrian Smith/Bruce Dickinson/Nicko McBrain,304509,12181632,0.99,Heavy Metal,Dance Of Death,Iron Maiden\nPaschendale,Adrian Smith/Steve Harris,508107,20326528,0.99,Heavy Metal,Dance Of Death,Iron Maiden\nFace In The Sand,Adrian Smith/Bruce Dickinson/Steve Harris,391105,15648948,0.99,Heavy Metal,Dance Of Death,Iron Maiden\nAge Of Innocence,David Murray/Steve Harris,370468,14823478,0.99,Heavy Metal,Dance Of Death,Iron Maiden\nJourneyman,Bruce Dickinson/David Murray/Steve Harris,427023,17082496,0.99,Heavy Metal,Dance Of Death,Iron Maiden\nBe Quick Or Be Dead,Bruce Dickinson/Janick Gers,204512,8181888,0.99,Rock,Fear Of The Dark,Iron Maiden\nFrom Here To Eternity,Steve Harris,218357,8739038,0.99,Rock,Fear Of The Dark,Iron Maiden\nAfraid To Shoot Strangers,Steve Harris,416496,16664589,0.99,Rock,Fear Of The Dark,Iron Maiden\nFear Is The Key,Bruce Dickinson/Janick Gers,335307,13414528,0.99,Rock,Fear Of The Dark,Iron Maiden\nChildhood's End,Steve Harris,280607,11225216,0.99,Rock,Fear Of The Dark,Iron Maiden\nWasting Love,Bruce Dickinson/Janick Gers,350981,14041216,0.99,Rock,Fear Of The Dark,Iron Maiden\nThe Fugitive,Steve Harris,294112,11765888,0.99,Rock,Fear Of The Dark,Iron Maiden\nChains Of Misery,Bruce Dickinson/David Murray,217443,8700032,0.99,Rock,Fear Of The Dark,Iron Maiden\nThe Apparition,Janick Gers/Steve Harris,234605,9386112,0.99,Rock,Fear Of The Dark,Iron Maiden\nJudas Be My Guide,Bruce Dickinson/David Murray,188786,7553152,0.99,Rock,Fear Of The Dark,Iron Maiden\nWeekend Warrior,Janick Gers/Steve Harris,339748,13594678,0.99,Rock,Fear Of The Dark,Iron Maiden\nFear Of The Dark,Steve Harris,436976,17483789,0.99,Rock,Fear Of The Dark,Iron Maiden\n01 - Prowler,Steve Harris,236173,5668992,0.99,Blues,Iron Maiden,Iron Maiden\n02 - Sanctuary,David Murray/Paul Di'Anno/Steve Harris,196284,4712576,0.99,Blues,Iron Maiden,Iron Maiden\n03 - Remember Tomorrow,Harris/Paul Di´Anno,328620,7889024,0.99,Blues,Iron Maiden,Iron Maiden\n04 - Running Free,Harris/Paul Di´Anno,197276,4739122,0.99,Blues,Iron Maiden,Iron Maiden\n05 - Phantom of the Opera,Steve Harris,428016,10276872,0.99,Blues,Iron Maiden,Iron Maiden\n06 - Transylvania,Steve Harris,259343,6226048,0.99,Blues,Iron Maiden,Iron Maiden\n07 - Strange World,Steve Harris,332460,7981184,0.99,Blues,Iron Maiden,Iron Maiden\n08 - Charlotte the Harlot,Murray Dave,252708,6066304,0.99,Blues,Iron Maiden,Iron Maiden\n09 - Iron Maiden,Steve Harris,216058,5189891,0.99,Blues,Iron Maiden,Iron Maiden\nThe Ides Of March,Steve Harris,105926,2543744,0.99,Heavy Metal,Killers,Iron Maiden\nWrathchild,Steve Harris,174471,4188288,0.99,Heavy Metal,Killers,Iron Maiden\nMurders In The Rue Morgue,Steve Harris,258377,6205786,0.99,Heavy Metal,Killers,Iron Maiden\nAnother Life,Steve Harris,203049,4874368,0.99,Heavy Metal,Killers,Iron Maiden\nGenghis Khan,Steve Harris,187141,4493440,0.99,Heavy Metal,Killers,Iron Maiden\nInnocent Exile,Di´Anno/Harris,232515,5584861,0.99,Heavy Metal,Killers,Iron Maiden\nKillers,Steve Harris,300956,7227440,0.99,Heavy Metal,Killers,Iron Maiden\nProdigal Son,Steve Harris,372349,8937600,0.99,Heavy Metal,Killers,Iron Maiden\nPurgatory,Steve Harris,200150,4804736,0.99,Heavy Metal,Killers,Iron Maiden\nDrifter,Steve Harris,288757,6934660,0.99,Heavy Metal,Killers,Iron Maiden\nIntro- Churchill S Speech,,48013,1154488,0.99,Heavy Metal,Live After Death,Iron Maiden\nAces High,,276375,6635187,0.99,Heavy Metal,Live After Death,Iron Maiden\n2 Minutes To Midnight,Smith/Dickinson,366550,8799380,0.99,Metal,Live After Death,Iron Maiden\nThe Trooper,Harris,268878,6455255,0.99,Metal,Live After Death,Iron Maiden\nRevelations,Dickinson,371826,8926021,0.99,Metal,Live After Death,Iron Maiden\nFlight Of Icarus,Smith/Dickinson,229982,5521744,0.99,Metal,Live After Death,Iron Maiden\nRime Of The Ancient Mariner,,789472,18949518,0.99,Metal,Live After Death,Iron Maiden\nPowerslave,,454974,10921567,0.99,Metal,Live After Death,Iron Maiden\nThe Number Of The Beast,Harris,275121,6605094,0.99,Metal,Live After Death,Iron Maiden\nHallowed Be Thy Name,Harris,451422,10836304,0.99,Metal,Live After Death,Iron Maiden\nIron Maiden,Harris,261955,6289117,0.99,Metal,Live After Death,Iron Maiden\nRun To The Hills,Harris,231627,5561241,0.99,Metal,Live After Death,Iron Maiden\nRunning Free,Harris/Di Anno,204617,4912986,0.99,Metal,Live After Death,Iron Maiden\nWrathchild,Steve Harris,183666,4410181,0.99,Heavy Metal,Live After Death,Iron Maiden\nAcacia Avenue,,379872,9119118,0.99,Heavy Metal,Live After Death,Iron Maiden\nChildren Of The Damned,Steve Harris,278177,6678446,0.99,Heavy Metal,Live After Death,Iron Maiden\nDie With Your Boots On,Adrian Smith/Bruce Dickinson/Steve Harris,314174,7542367,0.99,Heavy Metal,Live After Death,Iron Maiden\nPhantom Of The Opera,Steve Harris,441155,10589917,0.99,Heavy Metal,Live After Death,Iron Maiden\nBe Quick Or Be Dead,,233142,5599853,0.99,Rock,Live At Donington 1992 (Disc 1),Iron Maiden\nThe Number Of The Beast,,294008,7060625,0.99,Rock,Live At Donington 1992 (Disc 1),Iron Maiden\nWrathchild,,174106,4182963,0.99,Rock,Live At Donington 1992 (Disc 1),Iron Maiden\nFrom Here To Eternity,,284447,6831163,0.99,Rock,Live At Donington 1992 (Disc 1),Iron Maiden\nCan I Play With Madness,,213106,5118995,0.99,Rock,Live At Donington 1992 (Disc 1),Iron Maiden\nWasting Love,,336953,8091301,0.99,Rock,Live At Donington 1992 (Disc 1),Iron Maiden\nTailgunner,,247640,5947795,0.99,Rock,Live At Donington 1992 (Disc 1),Iron Maiden\nThe Evil That Men Do,,478145,11479913,0.99,Rock,Live At Donington 1992 (Disc 1),Iron Maiden\nAfraid To Shoot Strangers,,412525,9905048,0.99,Rock,Live At Donington 1992 (Disc 1),Iron Maiden\nFear Of The Dark,,431542,10361452,0.99,Rock,Live At Donington 1992 (Disc 1),Iron Maiden\nBring Your Daughter... To The Slaughter...,,376711,9045532,0.99,Rock,Live At Donington 1992 (Disc 2),Iron Maiden\nThe Clairvoyant,,262426,6302648,0.99,Rock,Live At Donington 1992 (Disc 2),Iron Maiden\nHeaven Can Wait,,440555,10577743,0.99,Rock,Live At Donington 1992 (Disc 2),Iron Maiden\nRun To The Hills,,235859,5665052,0.99,Rock,Live At Donington 1992 (Disc 2),Iron Maiden\n2 Minutes To Midnight,Adrian Smith/Bruce Dickinson,338233,8122030,0.99,Rock,Live At Donington 1992 (Disc 2),Iron Maiden\nIron Maiden,,494602,11874875,0.99,Rock,Live At Donington 1992 (Disc 2),Iron Maiden\nHallowed Be Thy Name,,447791,10751410,0.99,Rock,Live At Donington 1992 (Disc 2),Iron Maiden\nThe Trooper,,232672,5588560,0.99,Rock,Live At Donington 1992 (Disc 2),Iron Maiden\nSanctuary,,318511,7648679,0.99,Rock,Live At Donington 1992 (Disc 2),Iron Maiden\nRunning Free,,474017,11380851,0.99,Rock,Live At Donington 1992 (Disc 2),Iron Maiden\nTailgunner,Bruce Dickinson/Steve Harris,255582,4089856,0.99,Metal,No Prayer For The Dying,Iron Maiden\nHoly Smoke,Bruce Dickinson/Steve Harris,229459,3672064,0.99,Metal,No Prayer For The Dying,Iron Maiden\nNo Prayer For The Dying,Steve Harris,263941,4225024,0.99,Metal,No Prayer For The Dying,Iron Maiden\nPublic Enema Number One,Bruce Dickinson/David Murray,254197,4071587,0.99,Metal,No Prayer For The Dying,Iron Maiden\nFates Warning,David Murray/Steve Harris,250853,4018088,0.99,Metal,No Prayer For The Dying,Iron Maiden\nThe Assassin,Steve Harris,258768,4141056,0.99,Metal,No Prayer For The Dying,Iron Maiden\nRun Silent Run Deep,Bruce Dickinson/Steve Harris,275408,4407296,0.99,Metal,No Prayer For The Dying,Iron Maiden\nHooks In You,Adrian Smith/Bruce Dickinson,247510,3960832,0.99,Metal,No Prayer For The Dying,Iron Maiden\nBring Your Daughter... ...To The Slaughter,Bruce Dickinson,284238,4548608,0.99,Metal,No Prayer For The Dying,Iron Maiden\nMother Russia,Steve Harris,332617,5322752,0.99,Metal,No Prayer For The Dying,Iron Maiden\nWhere Eagles Dare,Steve Harris,369554,5914624,0.99,Metal,Piece Of Mind,Iron Maiden\nRevelations,Bruce Dickinson,408607,6539264,0.99,Metal,Piece Of Mind,Iron Maiden\nFlight Of The Icarus,Adrian Smith/Bruce Dickinson,230269,3686400,0.99,Metal,Piece Of Mind,Iron Maiden\nDie With Your Boots On,Adrian Smith/Bruce Dickinson/Steve Harris,325694,5212160,0.99,Metal,Piece Of Mind,Iron Maiden\nThe Trooper,Steve Harris,251454,4024320,0.99,Metal,Piece Of Mind,Iron Maiden\nStill Life,David Murray/Steve Harris,294347,4710400,0.99,Metal,Piece Of Mind,Iron Maiden\nQuest For Fire,Steve Harris,221309,3543040,0.99,Metal,Piece Of Mind,Iron Maiden\nSun And Steel,Adrian Smith/Bruce Dickinson,206367,3306324,0.99,Metal,Piece Of Mind,Iron Maiden\nTo Tame A Land,Steve Harris,445283,7129264,0.99,Metal,Piece Of Mind,Iron Maiden\nAces High,Harris,269531,6472088,0.99,Metal,Powerslave,Iron Maiden\n2 Minutes To Midnight,Smith/Dickinson,359810,8638809,0.99,Metal,Powerslave,Iron Maiden\nLosfer Words,Steve Harris,252891,6074756,0.99,Metal,Powerslave,Iron Maiden\nFlash of The Blade,Dickinson,242729,5828861,0.99,Metal,Powerslave,Iron Maiden\nDuelists,Steve Harris,366471,8800686,0.99,Metal,Powerslave,Iron Maiden\nBack in the Village,Dickinson/Smith,320548,7696518,0.99,Metal,Powerslave,Iron Maiden\nPowerslave,Dickinson,407823,9791106,0.99,Metal,Powerslave,Iron Maiden\nRime of the Ancient Mariner,Harris,816509,19599577,0.99,Metal,Powerslave,Iron Maiden\nIntro,,115931,4638848,0.99,Metal,Rock In Rio [CD1],Iron Maiden\nThe Wicker Man,Adrian Smith/Bruce Dickinson/Steve Harris,281782,11272320,0.99,Metal,Rock In Rio [CD1],Iron Maiden\nGhost Of The Navigator,Bruce Dickinson/Janick Gers/Steve Harris,408607,16345216,0.99,Metal,Rock In Rio [CD1],Iron Maiden\nBrave New World,Bruce Dickinson/David Murray/Steve Harris,366785,14676148,0.99,Metal,Rock In Rio [CD1],Iron Maiden\nWrathchild,Steve Harris,185808,7434368,0.99,Metal,Rock In Rio [CD1],Iron Maiden\n2 Minutes To Midnight,Adrian Smith/Bruce Dickinson,386821,15474816,0.99,Metal,Rock In Rio [CD1],Iron Maiden\nBlood Brothers,Steve Harris,435513,17422464,0.99,Metal,Rock In Rio [CD1],Iron Maiden\nSign Of The Cross,Steve Harris,649116,25966720,0.99,Metal,Rock In Rio [CD1],Iron Maiden\nThe Mercenary,Janick Gers/Steve Harris,282697,11309184,0.99,Metal,Rock In Rio [CD1],Iron Maiden\nThe Trooper,Steve Harris,273528,10942592,0.99,Metal,Rock In Rio [CD1],Iron Maiden\nDream Of Mirrors,Janick Gers/Steve Harris,578324,23134336,0.99,Rock,Rock In Rio [CD2],Iron Maiden\nThe Clansman,Steve Harris,559203,22370432,0.99,Rock,Rock In Rio [CD2],Iron Maiden\nThe Evil That Men Do,Adrian Smith/Bruce Dickinson/Steve Harris,280737,11231360,0.99,Metal,Rock In Rio [CD2],Iron Maiden\nFear Of The Dark,Steve Harris,460695,18430080,0.99,Rock,Rock In Rio [CD2],Iron Maiden\nIron Maiden,Steve Harris,351869,14076032,0.99,Rock,Rock In Rio [CD2],Iron Maiden\nThe Number Of The Beast,Steve Harris,300434,12022107,0.99,Rock,Rock In Rio [CD2],Iron Maiden\nHallowed Be Thy Name,Steve Harris,443977,17760384,0.99,Rock,Rock In Rio [CD2],Iron Maiden\nSanctuary,David Murray/Paul Di'Anno/Steve Harris,317335,12695680,0.99,Rock,Rock In Rio [CD2],Iron Maiden\nRun To The Hills,Steve Harris,292179,11688064,0.99,Rock,Rock In Rio [CD2],Iron Maiden\nMoonchild,Adrian Smith; Bruce Dickinson,340767,8179151,0.99,Metal,Seventh Son of a Seventh Son,Iron Maiden\nInfinite Dreams,Steve Harris,369005,8858669,0.99,Metal,Seventh Son of a Seventh Son,Iron Maiden\nCan I Play With Madness,Adrian Smith; Bruce Dickinson; Steve Harris,211043,5067867,0.99,Metal,Seventh Son of a Seventh Son,Iron Maiden\nThe Evil That Men Do,Adrian Smith; Bruce Dickinson; Steve Harris,273998,6578930,0.99,Metal,Seventh Son of a Seventh Son,Iron Maiden\nSeventh Son of a Seventh Son,Steve Harris,593580,14249000,0.99,Metal,Seventh Son of a Seventh Son,Iron Maiden\nThe Prophecy,Dave Murray; Steve Harris,305475,7334450,0.99,Metal,Seventh Son of a Seventh Son,Iron Maiden\nThe Clairvoyant,Adrian Smith; Bruce Dickinson; Steve Harris,267023,6411510,0.99,Metal,Seventh Son of a Seventh Son,Iron Maiden\nOnly the Good Die Young,Bruce Dickinson; Harris,280894,6744431,0.99,Metal,Seventh Son of a Seventh Son,Iron Maiden\nCaught Somewhere in Time,Steve Harris,445779,10701149,0.99,Metal,Somewhere in Time,Iron Maiden\nWasted Years,Adrian Smith,307565,7384358,0.99,Metal,Somewhere in Time,Iron Maiden\nSea of Madness,Adrian Smith,341995,8210695,0.99,Metal,Somewhere in Time,Iron Maiden\nHeaven Can Wait,Steve Harris,441417,10596431,0.99,Metal,Somewhere in Time,Iron Maiden\nStranger in a Strange Land,Adrian Smith,344502,8270899,0.99,Metal,Somewhere in Time,Iron Maiden\nAlexander the Great,Steve Harris,515631,12377742,0.99,Metal,Somewhere in Time,Iron Maiden\nDe Ja Vu,David Murray/Steve Harris,296176,7113035,0.99,Metal,Somewhere in Time,Iron Maiden\nThe Loneliness of the Long Dis,Steve Harris,391314,9393598,0.99,Metal,Somewhere in Time,Iron Maiden\n22 Acacia Avenue,Adrian Smith/Steve Harris,395572,5542516,0.99,Metal,The Number of The Beast,Iron Maiden\nChildren of the Damned,Steve Harris,274364,3845631,0.99,Metal,The Number of The Beast,Iron Maiden\nGangland,Adrian Smith/Clive Burr/Steve Harris,228440,3202866,0.99,Metal,The Number of The Beast,Iron Maiden\nHallowed Be Thy Name,Steve Harris,428669,6006107,0.99,Metal,The Number of The Beast,Iron Maiden\nInvaders,Steve Harris,203180,2849181,0.99,Metal,The Number of The Beast,Iron Maiden\nRun to the Hills,Steve Harris,228884,3209124,0.99,Metal,The Number of The Beast,Iron Maiden\nThe Number Of The Beast,Steve Harris,293407,11737216,0.99,Rock,The Number of The Beast,Iron Maiden\nThe Prisoner,Adrian Smith/Steve Harris,361299,5062906,0.99,Metal,The Number of The Beast,Iron Maiden\nSign Of The Cross,Steve Harris,678008,27121792,0.99,Rock,The X Factor,Iron Maiden\nLord Of The Flies,Janick Gers/Steve Harris,303699,12148864,0.99,Rock,The X Factor,Iron Maiden\nMan On The Edge,Blaze Bayley/Janick Gers,253413,10137728,0.99,Rock,The X Factor,Iron Maiden\nFortunes Of War,Steve Harris,443977,17760384,0.99,Rock,The X Factor,Iron Maiden\nLook For The Truth,Blaze Bayley/Janick Gers/Steve Harris,310230,12411008,0.99,Rock,The X Factor,Iron Maiden\nThe Aftermath,Blaze Bayley/Janick Gers/Steve Harris,380786,15233152,0.99,Rock,The X Factor,Iron Maiden\nJudgement Of Heaven,Steve Harris,312476,12501120,0.99,Rock,The X Factor,Iron Maiden\nBlood On The World's Hands,Steve Harris,357799,14313600,0.99,Rock,The X Factor,Iron Maiden\nThe Edge Of Darkness,Blaze Bayley/Janick Gers/Steve Harris,399333,15974528,0.99,Rock,The X Factor,Iron Maiden\n2 A.M.,Blaze Bayley/Janick Gers/Steve Harris,337658,13511087,0.99,Rock,The X Factor,Iron Maiden\nThe Unbeliever,Janick Gers/Steve Harris,490422,19617920,0.99,Rock,The X Factor,Iron Maiden\nFutureal,Blaze Bayley/Steve Harris,175777,7032960,0.99,Rock,Virtual XI,Iron Maiden\nThe Angel And The Gambler,Steve Harris,592744,23711872,0.99,Rock,Virtual XI,Iron Maiden\nLightning Strikes Twice,David Murray/Steve Harris,290377,11616384,0.99,Rock,Virtual XI,Iron Maiden\nThe Clansman,Steve Harris,539689,21592327,0.99,Rock,Virtual XI,Iron Maiden\nWhen Two Worlds Collide,Blaze Bayley/David Murray/Steve Harris,377312,15093888,0.99,Rock,Virtual XI,Iron Maiden\nThe Educated Fool,Steve Harris,404767,16191616,0.99,Rock,Virtual XI,Iron Maiden\nDon't Look To The Eyes Of A Stranger,Steve Harris,483657,19347584,0.99,Rock,Virtual XI,Iron Maiden\nComo Estais Amigos,Blaze Bayley/Janick Gers,330292,13213824,0.99,Rock,Virtual XI,Iron Maiden\nPlease Please Please,James Brown/Johnny Terry,165067,5394585,0.99,R&B/Soul,Sex Machine,James Brown\nThink,Lowman Pauling,166739,5513208,0.99,R&B/Soul,Sex Machine,James Brown\nNight Train,Jimmy Forrest/Lewis C. Simpkins/Oscar Washington,212401,7027377,0.99,R&B/Soul,Sex Machine,James Brown\nOut Of Sight,Ted Wright,143725,4711323,0.99,R&B/Soul,Sex Machine,James Brown\nPapa's Got A Brand New Bag Pt.1,James Brown,127399,4174420,0.99,R&B/Soul,Sex Machine,James Brown\nI Got You (I Feel Good),James Brown,167392,5468472,0.99,R&B/Soul,Sex Machine,James Brown\nIt's A Man's Man's Man's World,Betty Newsome/James Brown,168228,5541611,0.99,R&B/Soul,Sex Machine,James Brown\nCold Sweat,Alfred Ellis/James Brown,172408,5643213,0.99,R&B/Soul,Sex Machine,James Brown\n\"Say It Loud, I'm Black And I'm Proud Pt.1\",Alfred Ellis/James Brown,167392,5478117,0.99,R&B/Soul,Sex Machine,James Brown\nGet Up (I Feel Like Being A) Sex Machine,Bobby Byrd/James Brown/Ron Lenhoff,316551,10498031,0.99,R&B/Soul,Sex Machine,James Brown\nHey America,Addie William Jones/Nat Jones,218226,7187857,0.99,R&B/Soul,Sex Machine,James Brown\nMake It Funky Pt.1,Charles Bobbitt/James Brown,196231,6507782,0.99,R&B/Soul,Sex Machine,James Brown\nI'm A Greedy Man Pt.1,Charles Bobbitt/James Brown,217730,7251211,0.99,R&B/Soul,Sex Machine,James Brown\nGet On The Good Foot,Fred Wesley/James Brown/Joseph Mims,215902,7182736,0.99,R&B/Soul,Sex Machine,James Brown\nGet Up Offa That Thing,Deanna Brown/Deidra Jenkins/Yamma Brown,250723,8355989,0.99,R&B/Soul,Sex Machine,James Brown\nIt's Too Funky In Here,Brad Shapiro/George Jackson/Robert Miller/Walter Shaw,239072,7973979,0.99,R&B/Soul,Sex Machine,James Brown\nLiving In America,Charlie Midnight/Dan Hartman,282880,9432346,0.99,R&B/Soul,Sex Machine,James Brown\nI'm Real,Full Force/James Brown,334236,11183457,0.99,R&B/Soul,Sex Machine,James Brown\nHot Pants Pt.1,Fred Wesley/James Brown,188212,6295110,0.99,R&B/Soul,Sex Machine,James Brown\nSoul Power (Live),James Brown,260728,8593206,0.99,R&B/Soul,Sex Machine,James Brown\nWhen You Gonna Learn (Digeridoo),\"Jay Kay/Kay, Jay\",230635,7655482,0.99,Rock,Emergency On Planet Earth,Jamiroquai\nToo Young To Die,\"Smith, Toby\",365818,12391660,0.99,Rock,Emergency On Planet Earth,Jamiroquai\nHooked Up,\"Smith, Toby\",275879,9301687,0.99,Rock,Emergency On Planet Earth,Jamiroquai\n\"If I Like It, I Do It\",\"Gelder, Nick van\",293093,9848207,0.99,Rock,Emergency On Planet Earth,Jamiroquai\nMusic Of The Wind,\"Smith, Toby\",383033,12870239,0.99,Rock,Emergency On Planet Earth,Jamiroquai\nEmergency On Planet Earth,\"Smith, Toby\",245263,8117218,0.99,Rock,Emergency On Planet Earth,Jamiroquai\n\"Whatever It Is, I Just Can't Stop\",\"Jay Kay/Kay, Jay\",247222,8249453,0.99,Rock,Emergency On Planet Earth,Jamiroquai\nBlow Your Mind,\"Smith, Toby\",512339,17089176,0.99,Rock,Emergency On Planet Earth,Jamiroquai\nRevolution 1993,\"Smith, Toby\",616829,20816872,0.99,Rock,Emergency On Planet Earth,Jamiroquai\nDidgin' Out,\"Buchanan, Wallis\",157100,5263555,0.99,Rock,Emergency On Planet Earth,Jamiroquai\nCanned Heat,Jay Kay,331964,11042037,0.99,R&B/Soul,Synkronized,Jamiroquai\nPlanet Home,Jay Kay/Toby Smith,284447,9566237,0.99,R&B/Soul,Synkronized,Jamiroquai\nBlack Capricorn Day,Jay Kay,341629,11477231,0.99,R&B/Soul,Synkronized,Jamiroquai\nSoul Education,Jay Kay/Toby Smith,255477,8575435,0.99,R&B/Soul,Synkronized,Jamiroquai\nFailling,Jay Kay/Toby Smith,225227,7503999,0.99,R&B/Soul,Synkronized,Jamiroquai\nDestitute Illusions,Derrick McKenzie/Jay Kay/Toby Smith,340218,11452651,0.99,R&B/Soul,Synkronized,Jamiroquai\nSupersonic,Jay Kay,315872,10699265,0.99,R&B/Soul,Synkronized,Jamiroquai\nButterfly,Jay Kay/Toby Smith,268852,8947356,0.99,R&B/Soul,Synkronized,Jamiroquai\nWere Do We Go From Here,Jay Kay,313626,10504158,0.99,R&B/Soul,Synkronized,Jamiroquai\nKing For A Day,Jay Kay/Toby Smith,221544,7335693,0.99,R&B/Soul,Synkronized,Jamiroquai\nDeeper Underground,Toby Smith,281808,9351277,0.99,R&B/Soul,Synkronized,Jamiroquai\nJust Another Story,Toby Smith,529684,17582818,0.99,Electronica/Dance,The Return Of The Space Cowboy,Jamiroquai\nStillness In Time,Toby Smith,257097,8644290,0.99,Electronica/Dance,The Return Of The Space Cowboy,Jamiroquai\nHalf The Man,Toby Smith,289854,9577679,0.99,Electronica/Dance,The Return Of The Space Cowboy,Jamiroquai\nLight Years,Toby Smith,354560,11796244,0.99,Electronica/Dance,The Return Of The Space Cowboy,Jamiroquai\nManifest Destiny,Toby Smith,382197,12676962,0.99,Electronica/Dance,The Return Of The Space Cowboy,Jamiroquai\nThe Kids,Toby Smith,309995,10334529,0.99,Electronica/Dance,The Return Of The Space Cowboy,Jamiroquai\nMr. Moon,Stuard Zender/Toby Smith,329534,11043559,0.99,Electronica/Dance,The Return Of The Space Cowboy,Jamiroquai\nScam,Stuart Zender,422321,14019705,0.99,Electronica/Dance,The Return Of The Space Cowboy,Jamiroquai\nJourney To Arnhemland,Toby Smith/Wallis Buchanan,322455,10843832,0.99,Electronica/Dance,The Return Of The Space Cowboy,Jamiroquai\nMorning Glory,J. Kay/Jay Kay,384130,12777210,0.99,Electronica/Dance,The Return Of The Space Cowboy,Jamiroquai\nSpace Cowboy,J. Kay/Jay Kay,385697,12906520,0.99,Electronica/Dance,The Return Of The Space Cowboy,Jamiroquai\nLast Chance,C. Cester/C. Muncey,112352,3683130,0.99,Alternative & Punk,Get Born,JET\nAre You Gonna Be My Girl,C. Muncey/N. Cester,213890,6992324,0.99,Alternative & Punk,Get Born,JET\nRollover D.J.,C. Cester/N. Cester,196702,6406517,0.99,Alternative & Punk,Get Born,JET\nLook What You've Done,N. Cester,230974,7517083,0.99,Alternative & Punk,Get Born,JET\nGet What You Need,C. Cester/C. Muncey/N. Cester,247719,8043765,0.99,Alternative & Punk,Get Born,JET\nMove On,C. Cester/N. Cester,260623,8519353,0.99,Alternative & Punk,Get Born,JET\nRadio Song,C. Cester/C. Muncey/N. Cester,272117,8871509,0.99,Alternative & Punk,Get Born,JET\nGet Me Outta Here,C. Cester/N. Cester,176274,5729098,0.99,Alternative & Punk,Get Born,JET\nCold Hard Bitch,C. Cester/C. Muncey/N. Cester,243278,7929610,0.99,Alternative & Punk,Get Born,JET\nCome Around Again,C. Muncey/N. Cester,270497,8872405,0.99,Alternative & Punk,Get Born,JET\nTake It Or Leave It,C. Muncey/N. Cester,142889,4643370,0.99,Alternative & Punk,Get Born,JET\nLazy Gun,C. Cester/N. Cester,282174,9186285,0.99,Alternative & Punk,Get Born,JET\nTimothy,C. Cester,270341,8856507,0.99,Alternative & Punk,Get Born,JET\nFoxy Lady,Jimi Hendrix,199340,6480896,0.99,Rock,Are You Experienced?,Jimi Hendrix\nManic Depression,Jimi Hendrix,222302,7289272,0.99,Rock,Are You Experienced?,Jimi Hendrix\nRed House,Jimi Hendrix,224130,7285851,0.99,Rock,Are You Experienced?,Jimi Hendrix\nCan You See Me,Jimi Hendrix,153077,4987068,0.99,Rock,Are You Experienced?,Jimi Hendrix\nLove Or Confusion,Jimi Hendrix,193123,6329408,0.99,Rock,Are You Experienced?,Jimi Hendrix\nI Don't Live Today,Jimi Hendrix,235311,7661214,0.99,Rock,Are You Experienced?,Jimi Hendrix\nMay This Be Love,Jimi Hendrix,191216,6240028,0.99,Rock,Are You Experienced?,Jimi Hendrix\nFire,Jimi Hendrix,164989,5383075,0.99,Rock,Are You Experienced?,Jimi Hendrix\nThird Stone From The Sun,Jimi Hendrix,404453,13186975,0.99,Rock,Are You Experienced?,Jimi Hendrix\nRemember,Jimi Hendrix,168150,5509613,0.99,Rock,Are You Experienced?,Jimi Hendrix\nAre You Experienced?,Jimi Hendrix,254537,8292497,0.99,Rock,Are You Experienced?,Jimi Hendrix\nHey Joe,Billy Roberts,210259,6870054,0.99,Rock,Are You Experienced?,Jimi Hendrix\nStone Free,Jimi Hendrix,216293,7002331,0.99,Rock,Are You Experienced?,Jimi Hendrix\nPurple Haze,Jimi Hendrix,171572,5597056,0.99,Rock,Are You Experienced?,Jimi Hendrix\n51st Anniversary,Jimi Hendrix,196388,6398044,0.99,Rock,Are You Experienced?,Jimi Hendrix\nThe Wind Cries Mary,Jimi Hendrix,200463,6540638,0.99,Rock,Are You Experienced?,Jimi Hendrix\nHighway Chile,Jimi Hendrix,212453,6887949,0.99,Rock,Are You Experienced?,Jimi Hendrix\nSurfing with the Alien,,263707,4418504,0.99,Rock,Surfing with the Alien (Remastered),Joe Satriani\nIce 9,,239721,4036215,0.99,Rock,Surfing with the Alien (Remastered),Joe Satriani\nCrushing Day,,314768,5232158,0.99,Rock,Surfing with the Alien (Remastered),Joe Satriani\n\"Always With Me, Always With You\",,202035,3435777,0.99,Rock,Surfing with the Alien (Remastered),Joe Satriani\nSatch Boogie,,193560,3300654,0.99,Rock,Surfing with the Alien (Remastered),Joe Satriani\nHill of the Skull,J. Satriani,108435,1944738,0.99,Rock,Surfing with the Alien (Remastered),Joe Satriani\nCircles,,209071,3548553,0.99,Rock,Surfing with the Alien (Remastered),Joe Satriani\nLords of Karma,J. Satriani,288227,4809279,0.99,Rock,Surfing with the Alien (Remastered),Joe Satriani\nMidnight,J. Satriani,102630,1851753,0.99,Rock,Surfing with the Alien (Remastered),Joe Satriani\nEcho,J. Satriani,337570,5595557,0.99,Rock,Surfing with the Alien (Remastered),Joe Satriani\nEngenho De Dentro,,310073,10211473,0.99,Latin,Jorge Ben Jor 25 Anos,Jorge Ben\nAlcohol,,355239,12010478,0.99,Latin,Jorge Ben Jor 25 Anos,Jorge Ben\nMama Africa,,283062,9488316,0.99,Latin,Jorge Ben Jor 25 Anos,Jorge Ben\nSalve Simpatia,,343484,11314756,0.99,Latin,Jorge Ben Jor 25 Anos,Jorge Ben\nW/Brasil (Chama O Síndico),,317100,10599953,0.99,Latin,Jorge Ben Jor 25 Anos,Jorge Ben\nPaís Tropical,,452519,14946972,0.99,Latin,Jorge Ben Jor 25 Anos,Jorge Ben\nOs Alquimistas Estão Chegando,,367281,12304520,0.99,Latin,Jorge Ben Jor 25 Anos,Jorge Ben\nCharles Anjo 45,,389276,13022833,0.99,Latin,Jorge Ben Jor 25 Anos,Jorge Ben\nSelassiê,,326321,10724982,0.99,Latin,Jorge Ben Jor 25 Anos,Jorge Ben\nMenina Sarará,,191477,6393818,0.99,Latin,Jorge Ben Jor 25 Anos,Jorge Ben\nQue Maravilha,,338076,10996656,0.99,Latin,Jorge Ben Jor 25 Anos,Jorge Ben\nSanta Clara Clareou,,380081,12524725,0.99,Latin,Jorge Ben Jor 25 Anos,Jorge Ben\nFilho Maravilha,,227526,7498259,0.99,Latin,Jorge Ben Jor 25 Anos,Jorge Ben\nTaj Mahal,,289750,9502898,0.99,Latin,Jorge Ben Jor 25 Anos,Jorge Ben\nRapidamente,,252238,8470107,0.99,Latin,Jota Quest-1995,Jota Quest\nAs Dores do Mundo,Hyldon,255477,8537092,0.99,Latin,Jota Quest-1995,Jota Quest\nVou Pra Ai,,300878,10053718,0.99,Latin,Jota Quest-1995,Jota Quest\nMy Brother,,253231,8431821,0.99,Latin,Jota Quest-1995,Jota Quest\nHá Quanto Tempo,,270027,9004470,0.99,Latin,Jota Quest-1995,Jota Quest\nVício,,269897,8887216,0.99,Latin,Jota Quest-1995,Jota Quest\nEncontrar Alguém,Marco Tulio Lara/Rogerio Flausino,224078,7437935,0.99,Latin,Jota Quest-1995,Jota Quest\nDance Enquanto é Tempo,,229093,7583799,0.99,Latin,Jota Quest-1995,Jota Quest\nA Tarde,,266919,8836127,0.99,Latin,Jota Quest-1995,Jota Quest\nAlways Be All Right,,128078,4299676,0.99,Latin,Jota Quest-1995,Jota Quest\nSem Sentido,,250462,8292108,0.99,Latin,Jota Quest-1995,Jota Quest\nOnibusfobia,,315977,10474904,0.99,Latin,Jota Quest-1995,Jota Quest\nPura Elegancia,João Suplicy,284107,9632269,0.99,World,Cafezinho,João Suplicy\nChoramingando,João Suplicy,190484,6400532,0.99,World,Cafezinho,João Suplicy\nPor Merecer,João Suplicy,230582,7764601,0.99,World,Cafezinho,João Suplicy\nNo Futuro,João Suplicy,182308,6056200,0.99,World,Cafezinho,João Suplicy\nVoce Inteira,João Suplicy,241084,8077282,0.99,World,Cafezinho,João Suplicy\nCuando A Noite Vai Chegando,João Suplicy,270628,9081874,0.99,World,Cafezinho,João Suplicy\nNaquele Dia,João Suplicy,251768,8452654,0.99,World,Cafezinho,João Suplicy\nEquinocio,João Suplicy,269008,8871455,0.99,World,Cafezinho,João Suplicy\nPapelão,João Suplicy,213263,7257390,0.99,World,Cafezinho,João Suplicy\nCuando Eu For Pro Ceu,João Suplicy,118804,3948371,0.99,World,Cafezinho,João Suplicy\nDo Nosso Amor,João Suplicy,203415,6774566,0.99,World,Cafezinho,João Suplicy\nBorogodo,João Suplicy,208457,7104588,0.99,World,Cafezinho,João Suplicy\nCafezinho,João Suplicy,180924,6031174,0.99,World,Cafezinho,João Suplicy\nEnquanto O Dia Não Vem,João Suplicy,220891,7248336,0.99,World,Cafezinho,João Suplicy\nThe Green Manalishi,,205792,6720789,0.99,Metal,Living After Midnight,Judas Priest\nLiving After Midnight,,213289,7056785,0.99,Metal,Living After Midnight,Judas Priest\nBreaking The Law (Live),,144195,4728246,0.99,Metal,Living After Midnight,Judas Priest\nHot Rockin',,197328,6509179,0.99,Metal,Living After Midnight,Judas Priest\nHeading Out To The Highway (Live),,276427,9006022,0.99,Metal,Living After Midnight,Judas Priest\nThe Hellion,,41900,1351993,0.99,Metal,Living After Midnight,Judas Priest\nElectric Eye,,222197,7231368,0.99,Metal,Living After Midnight,Judas Priest\nYou've Got Another Thing Comin',,305162,9962558,0.99,Metal,Living After Midnight,Judas Priest\nTurbo Lover,,335542,11068866,0.99,Metal,Living After Midnight,Judas Priest\nFreewheel Burning,,265952,8713599,0.99,Metal,Living After Midnight,Judas Priest\nSome Heads Are Gonna Roll,,249939,8198617,0.99,Metal,Living After Midnight,Judas Priest\nMetal Meltdown,,290664,9390646,0.99,Metal,Living After Midnight,Judas Priest\nRam It Down,,292179,9554023,0.99,Metal,Living After Midnight,Judas Priest\nDiamonds And Rust (Live),,219350,7163147,0.99,Metal,Living After Midnight,Judas Priest\nVictim Of Change (Live),,430942,14067512,0.99,Metal,Living After Midnight,Judas Priest\nTyrant (Live),,282253,9190536,0.99,Metal,Living After Midnight,Judas Priest\nComin' Home,\"Paul Stanley, Ace Frehley\",172068,5661120,0.99,Rock,Unplugged [Live],Kiss\nPlaster Caster,Gene Simmons,198060,6528719,0.99,Rock,Unplugged [Live],Kiss\nGoin' Blind,\"Gene Simmons, Stephen Coronel\",217652,7167523,0.99,Rock,Unplugged [Live],Kiss\nDo You Love Me,\"Paul Stanley, Bob Ezrin, Kim Fowley\",193619,6343111,0.99,Rock,Unplugged [Live],Kiss\nDomino,Gene Simmons,226377,7488191,0.99,Rock,Unplugged [Live],Kiss\nSure Know Something,\"Paul Stanley, Vincent Poncia\",254354,8375190,0.99,Rock,Unplugged [Live],Kiss\nA World Without Heroes,\"Paul Stanley, Gene Simmons, Bob Ezrin, Lewis Reed\",177815,5832524,0.99,Rock,Unplugged [Live],Kiss\nRock Bottom,\"Paul Stanley, Ace Frehley\",200594,6560818,0.99,Rock,Unplugged [Live],Kiss\nSee You Tonight,Gene Simmons,146494,4817521,0.99,Rock,Unplugged [Live],Kiss\nI Still Love You,Paul Stanley,369815,12086145,0.99,Rock,Unplugged [Live],Kiss\nEvery Time I Look At You,\"Paul Stanley, Vincent Cusano\",283898,9290948,0.99,Rock,Unplugged [Live],Kiss\n\"2,000 Man\",\"Mick Jagger, Keith Richard\",312450,10292829,0.99,Rock,Unplugged [Live],Kiss\nBeth,\"Peter Criss, Stan Penridge, Bob Ezrin\",170187,5577807,0.99,Rock,Unplugged [Live],Kiss\nNothin' To Lose,Gene Simmons,222354,7351460,0.99,Rock,Unplugged [Live],Kiss\nRock And Roll All Nite,\"Paul Stanley, Gene Simmons\",259631,8549296,0.99,Rock,Unplugged [Live],Kiss\nImmigrant Song,Robert Plant,201247,6457766,0.99,Rock,BBC Sessions [Disc 2] [Live],Led Zeppelin\nHeartbreaker,John Bonham/John Paul Jones/Robert Plant,316081,10179657,0.99,Rock,BBC Sessions [Disc 2] [Live],Led Zeppelin\nSince I've Been Loving You,John Paul Jones/Robert Plant,416365,13471959,0.99,Rock,BBC Sessions [Disc 2] [Live],Led Zeppelin\nBlack Dog,John Paul Jones/Robert Plant,317622,10267572,0.99,Rock,BBC Sessions [Disc 2] [Live],Led Zeppelin\nDazed And Confused,Jimmy Page/Led Zeppelin,1116734,36052247,0.99,Rock,BBC Sessions [Disc 2] [Live],Led Zeppelin\nStairway To Heaven,Robert Plant,529658,17050485,0.99,Rock,BBC Sessions [Disc 2] [Live],Led Zeppelin\nGoing To California,Robert Plant,234605,7646749,0.99,Rock,BBC Sessions [Disc 2] [Live],Led Zeppelin\nThat's The Way,Robert Plant,343431,11248455,0.99,Rock,BBC Sessions [Disc 2] [Live],Led Zeppelin\nWhole Lotta Love (Medley),Arthur Crudup/Bernard Besman/Bukka White/Doc Pomus/John Bonham/John Lee Hooker/John Paul Jones/Mort Shuman/Robert Plant/Willie Dixon,825103,26742545,0.99,Rock,BBC Sessions [Disc 2] [Live],Led Zeppelin\nThank You,Robert Plant,398262,12831826,0.99,Rock,BBC Sessions [Disc 2] [Live],Led Zeppelin\nWe're Gonna Groove,Ben E.King/James Bethea,157570,5180975,0.99,Rock,Coda,Led Zeppelin\nPoor Tom,Jimmy Page/Robert Plant,182491,6016220,0.99,Rock,Coda,Led Zeppelin\nI Can't Quit You Baby,Willie Dixon,258168,8437098,0.99,Rock,Coda,Led Zeppelin\nWalter's Walk,\"Jimmy Page, Robert Plant\",270785,8712499,0.99,Rock,Coda,Led Zeppelin\nOzone Baby,\"Jimmy Page, Robert Plant\",215954,7079588,0.99,Rock,Coda,Led Zeppelin\nDarlene,\"Jimmy Page, Robert Plant, John Bonham, John Paul Jones\",307226,10078197,0.99,Rock,Coda,Led Zeppelin\nBonzo's Montreux,John Bonham,258925,8557447,0.99,Rock,Coda,Led Zeppelin\nWearing And Tearing,\"Jimmy Page, Robert Plant\",330004,10701590,0.99,Rock,Coda,Led Zeppelin\nThe Song Remains The Same,Jimmy Page/Jimmy Page & Robert Plant/Robert Plant,330004,10708950,0.99,Rock,Houses Of The Holy,Led Zeppelin\nThe Rain Song,Jimmy Page/Jimmy Page & Robert Plant/Robert Plant,459180,15029875,0.99,Rock,Houses Of The Holy,Led Zeppelin\nOver The Hills And Far Away,Jimmy Page/Jimmy Page & Robert Plant/Robert Plant,290089,9552829,0.99,Rock,Houses Of The Holy,Led Zeppelin\nThe Crunge,John Bonham/John Paul Jones,197407,6460212,0.99,Rock,Houses Of The Holy,Led Zeppelin\nDancing Days,Jimmy Page/Jimmy Page & Robert Plant/Robert Plant,223216,7250104,0.99,Rock,Houses Of The Holy,Led Zeppelin\nD'Yer Mak'er,John Bonham/John Paul Jones,262948,8645935,0.99,Rock,Houses Of The Holy,Led Zeppelin\nNo Quarter,John Paul Jones,420493,13656517,0.99,Rock,Houses Of The Holy,Led Zeppelin\nThe Ocean,John Bonham/John Paul Jones,271098,8846469,0.99,Rock,Houses Of The Holy,Led Zeppelin\nIn The Evening,\"Jimmy Page, Robert Plant & John Paul Jones\",410566,13399734,0.99,Rock,In Through The Out Door,Led Zeppelin\nSouth Bound Saurez,John Paul Jones & Robert Plant,254406,8420427,0.99,Rock,In Through The Out Door,Led Zeppelin\nFool In The Rain,\"Jimmy Page, Robert Plant & John Paul Jones\",372950,12371433,0.99,Rock,In Through The Out Door,Led Zeppelin\nHot Dog,Jimmy Page & Robert Plant,197198,6536167,0.99,Rock,In Through The Out Door,Led Zeppelin\nCarouselambra,\"John Paul Jones, Jimmy Page & Robert Plant\",634435,20858315,0.99,Rock,In Through The Out Door,Led Zeppelin\nAll My Love,Robert Plant & John Paul Jones,356284,11684862,0.99,Rock,In Through The Out Door,Led Zeppelin\nI'm Gonna Crawl,\"Jimmy Page, Robert Plant & John Paul Jones\",329639,10737665,0.99,Rock,In Through The Out Door,Led Zeppelin\nBlack Dog,\"Jimmy Page, Robert Plant, John Paul Jones\",296672,9660588,0.99,Rock,IV,Led Zeppelin\nRock & Roll,\"Jimmy Page, Robert Plant, John Paul Jones, John Bonham\",220917,7142127,0.99,Rock,IV,Led Zeppelin\nThe Battle Of Evermore,\"Jimmy Page, Robert Plant\",351555,11525689,0.99,Rock,IV,Led Zeppelin\nStairway To Heaven,\"Jimmy Page, Robert Plant\",481619,15706767,0.99,Rock,IV,Led Zeppelin\nMisty Mountain Hop,\"Jimmy Page, Robert Plant, John Paul Jones\",278857,9092799,0.99,Rock,IV,Led Zeppelin\nFour Sticks,\"Jimmy Page, Robert Plant\",284447,9481301,0.99,Rock,IV,Led Zeppelin\nGoing To California,\"Jimmy Page, Robert Plant\",215693,7068737,0.99,Rock,IV,Led Zeppelin\nWhen The Levee Breaks,\"Jimmy Page, Robert Plant, John Paul Jones, John Bonham, Memphis Minnie\",427702,13912107,0.99,Rock,IV,Led Zeppelin\nGood Times Bad Times,Jimmy Page/John Bonham/John Paul Jones,166164,5464077,0.99,Rock,Led Zeppelin I,Led Zeppelin\nBabe I'm Gonna Leave You,Jimmy Page/Robert Plant,401475,13189312,0.99,Rock,Led Zeppelin I,Led Zeppelin\nYou Shook Me,J. B. Lenoir/Willie Dixon,388179,12643067,0.99,Rock,Led Zeppelin I,Led Zeppelin\nDazed and Confused,Jimmy Page,386063,12610326,0.99,Rock,Led Zeppelin I,Led Zeppelin\nYour Time Is Gonna Come,Jimmy Page/John Paul Jones,274860,9011653,0.99,Rock,Led Zeppelin I,Led Zeppelin\nBlack Mountain Side,Jimmy Page,132702,4440602,0.99,Rock,Led Zeppelin I,Led Zeppelin\nCommunication Breakdown,Jimmy Page/John Bonham/John Paul Jones,150230,4899554,0.99,Rock,Led Zeppelin I,Led Zeppelin\nI Can't Quit You Baby,Willie Dixon,282671,9252733,0.99,Rock,Led Zeppelin I,Led Zeppelin\nHow Many More Times,Jimmy Page/John Bonham/John Paul Jones,508055,16541364,0.99,Rock,Led Zeppelin I,Led Zeppelin\nWhole Lotta Love,\"Jimmy Page, Robert Plant, John Paul Jones, John Bonham\",334471,11026243,0.99,Rock,Led Zeppelin II,Led Zeppelin\nWhat Is And What Should Never Be,\"Jimmy Page, Robert Plant\",287973,9369385,0.99,Rock,Led Zeppelin II,Led Zeppelin\nThe Lemon Song,\"Jimmy Page, Robert Plant, John Paul Jones, John Bonham\",379141,12463496,0.99,Rock,Led Zeppelin II,Led Zeppelin\nThank You,\"Jimmy Page, Robert Plant\",287791,9337392,0.99,Rock,Led Zeppelin II,Led Zeppelin\nHeartbreaker,\"Jimmy Page, Robert Plant, John Paul Jones, John Bonham\",253988,8387560,0.99,Rock,Led Zeppelin II,Led Zeppelin\nLiving Loving Maid (She's Just A Woman),\"Jimmy Page, Robert Plant\",159216,5219819,0.99,Rock,Led Zeppelin II,Led Zeppelin\nRamble On,\"Jimmy Page, Robert Plant\",275591,9199710,0.99,Rock,Led Zeppelin II,Led Zeppelin\nMoby Dick,\"John Bonham, John Paul Jones, Jimmy Page\",260728,8664210,0.99,Rock,Led Zeppelin II,Led Zeppelin\nBring It On Home,\"Jimmy Page, Robert Plant\",259970,8494731,0.99,Rock,Led Zeppelin II,Led Zeppelin\nImmigrant Song,\"Jimmy Page, Robert Plant\",144875,4786461,0.99,Rock,Led Zeppelin III,Led Zeppelin\nFriends,\"Jimmy Page, Robert Plant\",233560,7694220,0.99,Rock,Led Zeppelin III,Led Zeppelin\nCelebration Day,\"Jimmy Page, Robert Plant, John Paul Jones\",209528,6871078,0.99,Rock,Led Zeppelin III,Led Zeppelin\nSince I've Been Loving You,\"Jimmy Page, Robert Plant, John Paul Jones\",444055,14482460,0.99,Rock,Led Zeppelin III,Led Zeppelin\nOut On The Tiles,\"Jimmy Page, Robert Plant, John Bonham\",246047,8060350,0.99,Rock,Led Zeppelin III,Led Zeppelin\nGallows Pole,Traditional,296228,9757151,0.99,Rock,Led Zeppelin III,Led Zeppelin\nTangerine,Jimmy Page,189675,6200893,0.99,Rock,Led Zeppelin III,Led Zeppelin\nThat's The Way,\"Jimmy Page, Robert Plant\",337345,11202499,0.99,Rock,Led Zeppelin III,Led Zeppelin\nBron-Y-Aur Stomp,\"Jimmy Page, Robert Plant, John Paul Jones\",259500,8674508,0.99,Rock,Led Zeppelin III,Led Zeppelin\nHats Off To (Roy) Harper,Traditional,219376,7236640,0.99,Rock,Led Zeppelin III,Led Zeppelin\nIn The Light,John Paul Jones/Robert Plant,526785,17033046,0.99,Rock,Physical Graffiti [Disc 2],Led Zeppelin\nBron-Yr-Aur,Jimmy Page,126641,4150746,0.99,Rock,Physical Graffiti [Disc 2],Led Zeppelin\nDown By The Seaside,Robert Plant,316186,10371282,0.99,Rock,Physical Graffiti [Disc 2],Led Zeppelin\nTen Years Gone,Robert Plant,393116,12756366,0.99,Rock,Physical Graffiti [Disc 2],Led Zeppelin\nNight Flight,John Paul Jones/Robert Plant,217547,7160647,0.99,Rock,Physical Graffiti [Disc 2],Led Zeppelin\nThe Wanton Song,Robert Plant,249887,8180988,0.99,Rock,Physical Graffiti [Disc 2],Led Zeppelin\nBoogie With Stu,Ian Stewart/John Bonham/John Paul Jones/Mrs. Valens/Robert Plant,233273,7657086,0.99,Rock,Physical Graffiti [Disc 2],Led Zeppelin\nBlack Country Woman,Robert Plant,273084,8951732,0.99,Rock,Physical Graffiti [Disc 2],Led Zeppelin\nSick Again,Robert Plant,283036,9279263,0.99,Rock,Physical Graffiti [Disc 2],Led Zeppelin\nAchilles Last Stand,Jimmy Page/Robert Plant,625502,20593955,0.99,Rock,Presence,Led Zeppelin\nFor Your Life,Jimmy Page/Robert Plant,384391,12633382,0.99,Rock,Presence,Led Zeppelin\nRoyal Orleans,John Bonham/John Paul Jones,179591,5930027,0.99,Rock,Presence,Led Zeppelin\nNobody's Fault But Mine,Jimmy Page/Robert Plant,376215,12237859,0.99,Rock,Presence,Led Zeppelin\nCandy Store Rock,Jimmy Page/Robert Plant,252055,8397423,0.99,Rock,Presence,Led Zeppelin\nHots On For Nowhere,Jimmy Page/Robert Plant,284107,9342342,0.99,Rock,Presence,Led Zeppelin\nTea For One,Jimmy Page/Robert Plant,566752,18475264,0.99,Rock,Presence,Led Zeppelin\nRock & Roll,John Bonham/John Paul Jones/Robert Plant,242442,7897065,0.99,Rock,The Song Remains The Same (Disc 1),Led Zeppelin\nCelebration Day,John Paul Jones/Robert Plant,230034,7478487,0.99,Rock,The Song Remains The Same (Disc 1),Led Zeppelin\nThe Song Remains The Same,Robert Plant,353358,11465033,0.99,Rock,The Song Remains The Same (Disc 1),Led Zeppelin\nRain Song,Robert Plant,505808,16273705,0.99,Rock,The Song Remains The Same (Disc 1),Led Zeppelin\nDazed And Confused,Jimmy Page,1612329,52490554,0.99,Rock,The Song Remains The Same (Disc 1),Led Zeppelin\nNo Quarter,John Paul Jones/Robert Plant,749897,24399285,0.99,Rock,The Song Remains The Same (Disc 2),Led Zeppelin\nStairway To Heaven,Robert Plant,657293,21354766,0.99,Rock,The Song Remains The Same (Disc 2),Led Zeppelin\nMoby Dick,John Bonham/John Paul Jones,766354,25345841,0.99,Rock,The Song Remains The Same (Disc 2),Led Zeppelin\nWhole Lotta Love,John Bonham/John Paul Jones/Robert Plant/Willie Dixon,863895,28191437,0.99,Rock,The Song Remains The Same (Disc 2),Led Zeppelin\nNatália,Renato Russo,235728,7640230,0.99,Latin,A TempestadeTempestade Ou O Livro Dos Dias,Legião Urbana\nL'Avventura,Renato Russo,278256,9165769,0.99,Latin,A TempestadeTempestade Ou O Livro Dos Dias,Legião Urbana\nMúsica De Trabalho,Renato Russo,260231,8590671,0.99,Latin,A TempestadeTempestade Ou O Livro Dos Dias,Legião Urbana\nLonge Do Meu Lado,Renato Russo - Marcelo Bonfá,266161,8655249,0.99,Latin,A TempestadeTempestade Ou O Livro Dos Dias,Legião Urbana\nA Via Láctea,Renato Russo,280084,9234879,0.99,Latin,A TempestadeTempestade Ou O Livro Dos Dias,Legião Urbana\nMúsica Ambiente,Renato Russo,247614,8234388,0.99,Latin,A TempestadeTempestade Ou O Livro Dos Dias,Legião Urbana\nAloha,Renato Russo,325955,10793301,0.99,Latin,A TempestadeTempestade Ou O Livro Dos Dias,Legião Urbana\nSoul Parsifal,Renato Russo - Marisa Monte,295053,9853589,0.99,Latin,A TempestadeTempestade Ou O Livro Dos Dias,Legião Urbana\nDezesseis,Renato Russo,323918,10573515,0.99,Latin,A TempestadeTempestade Ou O Livro Dos Dias,Legião Urbana\nMil Pedaços,Renato Russo,203337,6643291,0.99,Latin,A TempestadeTempestade Ou O Livro Dos Dias,Legião Urbana\nLeila,Renato Russo,323056,10608239,0.99,Latin,A TempestadeTempestade Ou O Livro Dos Dias,Legião Urbana\n1º De Julho,Renato Russo,290298,9619257,0.99,Latin,A TempestadeTempestade Ou O Livro Dos Dias,Legião Urbana\nEsperando Por Mim,Renato Russo,261668,8844133,0.99,Latin,A TempestadeTempestade Ou O Livro Dos Dias,Legião Urbana\nQuando Você Voltar,Renato Russo,173897,5781046,0.99,Latin,A TempestadeTempestade Ou O Livro Dos Dias,Legião Urbana\nO Livro Dos Dias,Renato Russo,257253,8570929,0.99,Latin,A TempestadeTempestade Ou O Livro Dos Dias,Legião Urbana\nSerá,Dado Villa-Lobos/Marcelo Bonfá,148401,4826528,0.99,Latin,Mais Do Mesmo,Legião Urbana\nAinda É Cedo,Dado Villa-Lobos/Ico Ouro-Preto/Marcelo Bonfá,236826,7796400,0.99,Latin,Mais Do Mesmo,Legião Urbana\nGeração Coca-Cola,Renato Russo,141453,4625731,0.99,Latin,Mais Do Mesmo,Legião Urbana\nEduardo E Mônica,Renato Russo,271229,9026691,0.99,Latin,Mais Do Mesmo,Legião Urbana\nTempo Perdido,Renato Russo,302158,9963914,0.99,Latin,Mais Do Mesmo,Legião Urbana\nIndios,Renato Russo,258168,8610226,0.99,Latin,Mais Do Mesmo,Legião Urbana\nQue País É Este,Renato Russo,177606,5822124,0.99,Latin,Mais Do Mesmo,Legião Urbana\nFaroeste Caboclo,Renato Russo,543007,18092739,0.99,Latin,Mais Do Mesmo,Legião Urbana\nHá Tempos,Dado Villa-Lobos/Marcelo Bonfá,197146,6432922,0.99,Latin,Mais Do Mesmo,Legião Urbana\nPais E Filhos,Dado Villa-Lobos/Marcelo Bonfá,308401,10130685,0.99,Latin,Mais Do Mesmo,Legião Urbana\nMeninos E Meninas,Dado Villa-Lobos/Marcelo Bonfá,203781,6667802,0.99,Latin,Mais Do Mesmo,Legião Urbana\nVento No Litoral,Dado Villa-Lobos/Marcelo Bonfá,366445,12063806,0.99,Latin,Mais Do Mesmo,Legião Urbana\nPerfeição,Dado Villa-Lobos/Marcelo Bonfá,276558,9258489,0.99,Latin,Mais Do Mesmo,Legião Urbana\nGiz,Dado Villa-Lobos/Marcelo Bonfá,202213,6677671,0.99,Latin,Mais Do Mesmo,Legião Urbana\nDezesseis,Dado Villa-Lobos/Marcelo Bonfá,321724,10501773,0.99,Latin,Mais Do Mesmo,Legião Urbana\nAntes Das Seis,Dado Villa-Lobos,189231,6296531,0.99,Latin,Mais Do Mesmo,Legião Urbana\nAre You Gonna Go My Way,Craig Ross/Lenny Kravitz,211591,6905135,0.99,Rock,Greatest Hits,Lenny Kravitz\nFly Away,Lenny Kravitz,221962,7322085,0.99,Rock,Greatest Hits,Lenny Kravitz\nRock And Roll Is Dead,Lenny Kravitz,204199,6680312,0.99,Rock,Greatest Hits,Lenny Kravitz\nAgain,Lenny Kravitz,228989,7490476,0.99,Rock,Greatest Hits,Lenny Kravitz\nIt Ain't Over 'Til It's Over,Lenny Kravitz,242703,8078936,0.99,Rock,Greatest Hits,Lenny Kravitz\nCan't Get You Off My Mind,Lenny Kravitz,273815,8937150,0.99,Rock,Greatest Hits,Lenny Kravitz\nMr. Cab Driver,Lenny Kravitz,230321,7668084,0.99,Rock,Greatest Hits,Lenny Kravitz\nAmerican Woman,B. Cummings/G. Peterson/M.J. Kale/R. Bachman,261773,8538023,0.99,Rock,Greatest Hits,Lenny Kravitz\nStand By My Woman,Henry Kirssch/Lenny Kravitz/S. Pasch A. Krizan,259683,8447611,0.99,Rock,Greatest Hits,Lenny Kravitz\nAlways On The Run,Lenny Kravitz/Slash,232515,7593397,0.99,Rock,Greatest Hits,Lenny Kravitz\nHeaven Help,Gerry DeVeaux/Terry Britten,190354,6222092,0.99,Rock,Greatest Hits,Lenny Kravitz\nI Belong To You,Lenny Kravitz,257123,8477980,0.99,Rock,Greatest Hits,Lenny Kravitz\nBelieve,Henry Hirsch/Lenny Kravitz,295131,9661978,0.99,Rock,Greatest Hits,Lenny Kravitz\nLet Love Rule,Lenny Kravitz,342648,11298085,0.99,Rock,Greatest Hits,Lenny Kravitz\nBlack Velveteen,Lenny Kravitz,290899,9531301,0.99,Rock,Greatest Hits,Lenny Kravitz\nJohnny B. Goode,,243200,8092024,0.99,Reggae,Greatest Hits,Lenny Kravitz\nDon't Look Back,,221100,7344023,0.99,Reggae,Greatest Hits,Lenny Kravitz\nJah Seh No,,276871,9134476,0.99,Reggae,Greatest Hits,Lenny Kravitz\nI'm The Toughest,,230191,7657594,0.99,Reggae,Greatest Hits,Lenny Kravitz\nNothing But Love,,221570,7335228,0.99,Reggae,Greatest Hits,Lenny Kravitz\nBuk-In-Hamm Palace,,265665,8964369,0.99,Reggae,Greatest Hits,Lenny Kravitz\nBush Doctor,,239751,7942299,0.99,Reggae,Greatest Hits,Lenny Kravitz\nWanted Dread And Alive,,260310,8670933,0.99,Reggae,Greatest Hits,Lenny Kravitz\nMystic Man,,353671,11812170,0.99,Reggae,Greatest Hits,Lenny Kravitz\nComing In Hot,,213054,7109414,0.99,Reggae,Greatest Hits,Lenny Kravitz\nPick Myself Up,,234684,7788255,0.99,Reggae,Greatest Hits,Lenny Kravitz\nCrystal Ball,,309733,10319296,0.99,Reggae,Greatest Hits,Lenny Kravitz\nEqual Rights Downpresser Man,,366733,12086524,0.99,Reggae,Greatest Hits,Lenny Kravitz\nHolding Back The Years,Mick Hucknall and Neil Moss,270053,8833220,0.99,Rock,Greatest Hits,Lenny Kravitz\nMoney's Too Tight To Mention,John and William Valentine,268408,8861921,0.99,Rock,Greatest Hits,Lenny Kravitz\nThe Right Thing,Mick Hucknall,262687,8624063,0.99,Rock,Greatest Hits,Lenny Kravitz\nIt's Only Love,Jimmy and Vella Cameron,232594,7659017,0.99,Rock,Greatest Hits,Lenny Kravitz\nA New Flame,Mick Hucknall,237662,7822875,0.99,Rock,Greatest Hits,Lenny Kravitz\nYou've Got It,Mick Hucknall and Lamont Dozier,235232,7712845,0.99,Rock,Greatest Hits,Lenny Kravitz\nIf You Don't Know Me By Now,Kenny Gamble and Leon Huff,206524,6712634,0.99,Rock,Greatest Hits,Lenny Kravitz\nStars,Mick Hucknall,248137,8194906,0.99,Rock,Greatest Hits,Lenny Kravitz\nSomething Got Me Started,Mick Hucknall and Fritz McIntyre,239595,7997139,0.99,Rock,Greatest Hits,Lenny Kravitz\nThrill Me,Mick Hucknall and Fritz McIntyre,303934,10034711,0.99,Rock,Greatest Hits,Lenny Kravitz\nYour Mirror,Mick Hucknall,240666,7893821,0.99,Rock,Greatest Hits,Lenny Kravitz\nFor Your Babies,Mick Hucknall,256992,8408803,0.99,Rock,Greatest Hits,Lenny Kravitz\nSo Beautiful,Mick Hucknall,298083,9837832,0.99,Rock,Greatest Hits,Lenny Kravitz\nAngel,Carolyn Franklin and Sonny Saunders,240561,7880256,0.99,Rock,Greatest Hits,Lenny Kravitz\nFairground,Mick Hucknall,263888,8793094,0.99,Rock,Greatest Hits,Lenny Kravitz\nStill Of The Night,Sykes,398210,13043817,0.99,Metal,Greatest Hits,Lenny Kravitz\nHere I Go Again,Marsden,233874,7652473,0.99,Metal,Greatest Hits,Lenny Kravitz\nIs This Love,Sykes,283924,9262360,0.99,Metal,Greatest Hits,Lenny Kravitz\nLove Ain't No Stranger,Galley,259395,8490428,0.99,Metal,Greatest Hits,Lenny Kravitz\nLooking For Love,Sykes,391941,12769847,0.99,Metal,Greatest Hits,Lenny Kravitz\nNow You're Gone,Vandenberg,251141,8162193,0.99,Metal,Greatest Hits,Lenny Kravitz\nSlide It In,Coverdale,202475,6615152,0.99,Metal,Greatest Hits,Lenny Kravitz\nSlow An' Easy,Moody,367255,11961332,0.99,Metal,Greatest Hits,Lenny Kravitz\nJudgement Day,Vandenberg,317074,10326997,0.99,Metal,Greatest Hits,Lenny Kravitz\nYou're Gonna Break My Hart Again,Sykes,250853,8176847,0.99,Metal,Greatest Hits,Lenny Kravitz\nThe Deeper The Love,Vandenberg,262791,8606504,0.99,Metal,Greatest Hits,Lenny Kravitz\nCrying In The Rain,Coverdale,337005,10931921,0.99,Metal,Greatest Hits,Lenny Kravitz\nFool For Your Loving,Marsden/Moody,250801,8129820,0.99,Metal,Greatest Hits,Lenny Kravitz\nSweet Lady Luck,Vandenberg,273737,8919163,0.99,Metal,Greatest Hits,Lenny Kravitz\nAssim Caminha A Humanidade,,210755,6993763,0.99,Latin,Lulu Santos - RCA 100 Anos De Música - Álbum 01,Lulu Santos\nUm Pro Outro,,236382,7825215,0.99,Latin,Lulu Santos - RCA 100 Anos De Música - Álbum 01,Lulu Santos\nCasa,,307591,10107269,0.99,Latin,Lulu Santos - RCA 100 Anos De Música - Álbum 01,Lulu Santos\nCondição,,263549,8778465,0.99,Latin,Lulu Santos - RCA 100 Anos De Música - Álbum 01,Lulu Santos\nSatisfação,,208065,6901681,0.99,Latin,Lulu Santos - RCA 100 Anos De Música - Álbum 01,Lulu Santos\nBrumário,,216241,7243499,0.99,Latin,Lulu Santos - RCA 100 Anos De Música - Álbum 01,Lulu Santos\nSábado À Noite,,193854,6435114,0.99,Latin,Lulu Santos - RCA 100 Anos De Música - Álbum 01,Lulu Santos\nA Cura,,280920,9260588,0.99,Latin,Lulu Santos - RCA 100 Anos De Música - Álbum 01,Lulu Santos\nAtrás Do Trio Elétrico,,149080,4917615,0.99,Latin,Lulu Santos - RCA 100 Anos De Música - Álbum 01,Lulu Santos\nTudo Bem,,196101,6419139,0.99,Latin,Lulu Santos - RCA 100 Anos De Música - Álbum 01,Lulu Santos\nToda Forma De Amor,,227813,7496584,0.99,Latin,Lulu Santos - RCA 100 Anos De Música - Álbum 01,Lulu Santos\nSereia,,278047,9121087,0.99,Latin,Lulu Santos - RCA 100 Anos De Música - Álbum 01,Lulu Santos\nSe Você Pensa,,195996,6552490,0.99,Latin,Lulu Santos - RCA 100 Anos De Música - Álbum 01,Lulu Santos\nLá Vem O Sol (Here Comes The Sun),,189492,6229645,0.99,Latin,Lulu Santos - RCA 100 Anos De Música - Álbum 01,Lulu Santos\nHonolulu,,261433,8558481,0.99,Latin,Lulu Santos - RCA 100 Anos De Música - Álbum 02,Lulu Santos\nDancin´Days,,237400,7875347,0.99,Latin,Lulu Santos - RCA 100 Anos De Música - Álbum 02,Lulu Santos\nAviso Aos Navegantes,,242808,8058651,0.99,Latin,Lulu Santos - RCA 100 Anos De Música - Álbum 02,Lulu Santos\nHyperconectividade,,180636,5948039,0.99,Latin,Lulu Santos - RCA 100 Anos De Música - Álbum 02,Lulu Santos\nO Descobridor Dos Sete Mares,,225854,7475780,0.99,Latin,Lulu Santos - RCA 100 Anos De Música - Álbum 02,Lulu Santos\nUm Certo Alguém,,194063,6430939,0.99,Latin,Lulu Santos - RCA 100 Anos De Música - Álbum 02,Lulu Santos\nFullgás,,346070,11505484,0.99,Latin,Lulu Santos - RCA 100 Anos De Música - Álbum 02,Lulu Santos\nAquilo,,246073,8167819,0.99,Latin,Lulu Santos - RCA 100 Anos De Música - Álbum 02,Lulu Santos\nSenta A Pua,,217547,7205844,0.99,Latin,Lulu Santos - RCA 100 Anos De Música - Álbum 02,Lulu Santos\nRo-Que-Se-Da-Ne,,146703,4805897,0.99,Latin,Lulu Santos - RCA 100 Anos De Música - Álbum 02,Lulu Santos\nTudo Igual,,276035,9201645,0.99,Latin,Lulu Santos - RCA 100 Anos De Música - Álbum 02,Lulu Santos\nFogo De Palha,,246804,8133732,0.99,Latin,Lulu Santos - RCA 100 Anos De Música - Álbum 02,Lulu Santos\nAssaltaram A Gramática,,261041,8698959,0.99,Latin,Lulu Santos - RCA 100 Anos De Música - Álbum 02,Lulu Santos\nO Último Romântico (Ao Vivo),,231993,7692697,0.99,Latin,Lulu Santos - RCA 100 Anos De Música - Álbum 02,Lulu Santos\nPseudo Silk Kimono,\"Kelly, Mosley, Rothery, Trewaves\",134739,4334038,0.99,Rock,Misplaced Childhood,Marillion\nKayleigh,\"Kelly, Mosley, Rothery, Trewaves\",234605,7716005,0.99,Rock,Misplaced Childhood,Marillion\nLavender,\"Kelly, Mosley, Rothery, Trewaves\",153417,4999814,0.99,Rock,Misplaced Childhood,Marillion\nBitter Suite: Brief Encounter / Lost Weekend / Blue Angel,\"Kelly, Mosley, Rothery, Trewaves\",356493,11791068,0.99,Rock,Misplaced Childhood,Marillion\nHeart Of Lothian: Wide Boy / Curtain Call,\"Kelly, Mosley, Rothery, Trewaves\",366053,11893723,0.99,Rock,Misplaced Childhood,Marillion\nWaterhole (Expresso Bongo),\"Kelly, Mosley, Rothery, Trewaves\",133093,4378835,0.99,Rock,Misplaced Childhood,Marillion\nLords Of The Backstage,\"Kelly, Mosley, Rothery, Trewaves\",112875,3741319,0.99,Rock,Misplaced Childhood,Marillion\nBlind Curve: Vocal Under A Bloodlight / Passing Strangers / Mylo / Perimeter Walk / Threshold,\"Kelly, Mosley, Rothery, Trewaves\",569704,18578995,0.99,Rock,Misplaced Childhood,Marillion\nChildhoods End?,\"Kelly, Mosley, Rothery, Trewaves\",272796,9015366,0.99,Rock,Misplaced Childhood,Marillion\nWhite Feather,\"Kelly, Mosley, Rothery, Trewaves\",143595,4711776,0.99,Rock,Misplaced Childhood,Marillion\nArrepio,Carlinhos Brown,136254,4511390,0.99,Latin,Barulhinho Bom,Marisa Monte\nMagamalabares,Carlinhos Brown,215875,7183757,0.99,Latin,Barulhinho Bom,Marisa Monte\nChuva No Brejo,Morais,145606,4857761,0.99,Latin,Barulhinho Bom,Marisa Monte\nCérebro Eletrônico,Gilberto Gil,172800,5760864,0.99,Latin,Barulhinho Bom,Marisa Monte\nTempos Modernos,Lulu Santos,183066,6066234,0.99,Latin,Barulhinho Bom,Marisa Monte\nMaraçá,Carlinhos Brown,230008,7621482,0.99,Latin,Barulhinho Bom,Marisa Monte\nBlanco,Marisa Monte/poema de Octavio Paz/versão: Haroldo de Campos,45191,1454532,0.99,Latin,Barulhinho Bom,Marisa Monte\nPanis Et Circenses,Caetano Veloso e Gilberto Gil,192339,6318373,0.99,Latin,Barulhinho Bom,Marisa Monte\nDe Noite Na Cama,Caetano Veloso e Gilberto Gil,209005,7012658,0.99,Latin,Barulhinho Bom,Marisa Monte\nBeija Eu,Caetano Veloso e Gilberto Gil,197276,6512544,0.99,Latin,Barulhinho Bom,Marisa Monte\nGive Me Love,Caetano Veloso e Gilberto Gil,249808,8196331,0.99,Latin,Barulhinho Bom,Marisa Monte\nAinda Lembro,Caetano Veloso e Gilberto Gil,218801,7211247,0.99,Latin,Barulhinho Bom,Marisa Monte\nA Menina Dança,Caetano Veloso e Gilberto Gil,129410,4326918,0.99,Latin,Barulhinho Bom,Marisa Monte\nDança Da Solidão,Caetano Veloso e Gilberto Gil,203520,6699368,0.99,Latin,Barulhinho Bom,Marisa Monte\nAo Meu Redor,Caetano Veloso e Gilberto Gil,275591,9158834,0.99,Latin,Barulhinho Bom,Marisa Monte\nBem Leve,Caetano Veloso e Gilberto Gil,159190,5246835,0.99,Latin,Barulhinho Bom,Marisa Monte\nSegue O Seco,Caetano Veloso e Gilberto Gil,178207,5922018,0.99,Latin,Barulhinho Bom,Marisa Monte\nO Xote Das Meninas,Caetano Veloso e Gilberto Gil,291866,9553228,0.99,Latin,Barulhinho Bom,Marisa Monte\nWherever I Lay My Hat,,136986,4477321,0.99,R&B/Soul,Seek And Shall Find: More Of The Best (1963-1981),Marvin Gaye\nGet My Hands On Some Lovin',,149054,4860380,0.99,R&B/Soul,Seek And Shall Find: More Of The Best (1963-1981),Marvin Gaye\nNo Good Without You,\"William \"\"Mickey\"\" Stevenson\",161410,5259218,0.99,R&B/Soul,Seek And Shall Find: More Of The Best (1963-1981),Marvin Gaye\nYou've Been A Long Time Coming,Brian Holland/Eddie Holland/Lamont Dozier,137221,4437949,0.99,R&B/Soul,Seek And Shall Find: More Of The Best (1963-1981),Marvin Gaye\nWhen I Had Your Love,\"Robert Rogers/Warren \"\"Pete\"\" Moore/William \"\"Mickey\"\" Stevenson\",152424,4972815,0.99,R&B/Soul,Seek And Shall Find: More Of The Best (1963-1981),Marvin Gaye\nYou're What's Happening (In The World Today),Allen Story/George Gordy/Robert Gordy,142027,4631104,0.99,R&B/Soul,Seek And Shall Find: More Of The Best (1963-1981),Marvin Gaye\nLoving You Is Sweeter Than Ever,Ivy Hunter/Stevie Wonder,166295,5377546,0.99,R&B/Soul,Seek And Shall Find: More Of The Best (1963-1981),Marvin Gaye\nIt's A Bitter Pill To Swallow,\"Smokey Robinson/Warren \"\"Pete\"\" Moore\",194821,6477882,0.99,R&B/Soul,Seek And Shall Find: More Of The Best (1963-1981),Marvin Gaye\nSeek And You Shall Find,\"Ivy Hunter/William \"\"Mickey\"\" Stevenson\",223451,7306719,0.99,R&B/Soul,Seek And Shall Find: More Of The Best (1963-1981),Marvin Gaye\nGonna Keep On Tryin' Till I Win Your Love,Barrett Strong/Norman Whitfield,176404,5789945,0.99,R&B/Soul,Seek And Shall Find: More Of The Best (1963-1981),Marvin Gaye\nGonna Give Her All The Love I've Got,Barrett Strong/Norman Whitfield,210886,6893603,0.99,R&B/Soul,Seek And Shall Find: More Of The Best (1963-1981),Marvin Gaye\nI Wish It Would Rain,Barrett Strong/Norman Whitfield/Roger Penzabene,172486,5647327,0.99,R&B/Soul,Seek And Shall Find: More Of The Best (1963-1981),Marvin Gaye\n\"Abraham, Martin And John\",Dick Holler,273057,8888206,0.99,R&B/Soul,Seek And Shall Find: More Of The Best (1963-1981),Marvin Gaye\nSave The Children,Al Cleveland/Marvin Gaye/Renaldo Benson,194821,6342021,0.99,R&B/Soul,Seek And Shall Find: More Of The Best (1963-1981),Marvin Gaye\nYou Sure Love To Ball,Marvin Gaye,218540,7217872,0.99,R&B/Soul,Seek And Shall Find: More Of The Best (1963-1981),Marvin Gaye\nEgo Tripping Out,Marvin Gaye,314514,10383887,0.99,R&B/Soul,Seek And Shall Find: More Of The Best (1963-1981),Marvin Gaye\nPraise,Marvin Gaye,235833,7839179,0.99,R&B/Soul,Seek And Shall Find: More Of The Best (1963-1981),Marvin Gaye\nHeavy Love Affair,Marvin Gaye,227892,7522232,0.99,R&B/Soul,Seek And Shall Find: More Of The Best (1963-1981),Marvin Gaye\nDown Under,,222171,7366142,0.99,Rock,The Best Of Men At Work,Men At Work\nOverkill,,225410,7408652,0.99,Rock,The Best Of Men At Work,Men At Work\nBe Good Johnny,,216320,7139814,0.99,Rock,The Best Of Men At Work,Men At Work\nEverything I Need,,216476,7107625,0.99,Rock,The Best Of Men At Work,Men At Work\nDown by the Sea,,408163,13314900,0.99,Rock,The Best Of Men At Work,Men At Work\nWho Can It Be Now?,,202396,6682850,0.99,Rock,The Best Of Men At Work,Men At Work\nIt's a Mistake,,273371,8979965,0.99,Rock,The Best Of Men At Work,Men At Work\nDr. Heckyll & Mr. Jive,,278465,9110403,0.99,Rock,The Best Of Men At Work,Men At Work\nShakes and Ladders,,198008,6560753,0.99,Rock,The Best Of Men At Work,Men At Work\nNo Sign of Yesterday,,362004,11829011,0.99,Rock,The Best Of Men At Work,Men At Work\nEnter Sandman,\"James Hetfield, Lars Ulrich and Kirk Hammett\",332251,10852002,0.99,Metal,Black Album,Metallica\nSad But True,Ulrich,324754,10541258,0.99,Metal,Black Album,Metallica\nHolier Than Thou,Ulrich,227892,7462011,0.99,Metal,Black Album,Metallica\nThe Unforgiven,\"James Hetfield, Lars Ulrich and Kirk Hammett\",387082,12646886,0.99,Metal,Black Album,Metallica\nWherever I May Roam,Ulrich,404323,13161169,0.99,Metal,Black Album,Metallica\nDon't Tread On Me,Ulrich,240483,7827907,0.99,Metal,Black Album,Metallica\nThrough The Never,\"James Hetfield, Lars Ulrich and Kirk Hammett\",244375,8024047,0.99,Metal,Black Album,Metallica\nNothing Else Matters,Ulrich,388832,12606241,0.99,Metal,Black Album,Metallica\nOf Wolf And Man,\"James Hetfield, Lars Ulrich and Kirk Hammett\",256835,8339785,0.99,Metal,Black Album,Metallica\nThe God That Failed,Ulrich,308610,10055959,0.99,Metal,Black Album,Metallica\nMy Friend Of Misery,\"James Hetfield, Lars Ulrich and Jason Newsted\",409547,13293515,0.99,Metal,Black Album,Metallica\nThe Struggle Within,Ulrich,234240,7654052,0.99,Metal,Black Album,Metallica\nHelpless,Harris/Tatler,398315,12977902,0.99,Metal,Garage Inc. (Disc 2),Metallica\nThe Small Hours,Holocaust,403435,13215133,0.99,Metal,Garage Inc. (Disc 2),Metallica\nThe Wait,Killing Joke,295418,9688418,0.99,Metal,Garage Inc. (Disc 2),Metallica\nCrash Course In Brain Surgery,Bourge/Phillips/Shelley,190406,6233729,0.99,Metal,Garage Inc. (Disc 2),Metallica\nLast Caress/Green Hell,Danzig,209972,6854313,0.99,Metal,Garage Inc. (Disc 2),Metallica\nAm I Evil?,Harris/Tatler,470256,15387219,0.99,Metal,Garage Inc. (Disc 2),Metallica\nBlitzkrieg,Jones/Sirotto/Smith,216685,7090018,0.99,Metal,Garage Inc. (Disc 2),Metallica\nBreadfan,Bourge/Phillips/Shelley,341551,11100130,0.99,Metal,Garage Inc. (Disc 2),Metallica\nThe Prince,Harris/Tatler,265769,8624492,0.99,Metal,Garage Inc. (Disc 2),Metallica\nStone Cold Crazy,Deacon/May/Mercury/Taylor,137717,4514830,0.99,Metal,Garage Inc. (Disc 2),Metallica\nSo What,Culmer/Exalt,189152,6162894,0.99,Metal,Garage Inc. (Disc 2),Metallica\nKilling Time,Sweet Savage,183693,6021197,0.99,Metal,Garage Inc. (Disc 2),Metallica\nOverkill,Clarke/Kilmister/Tayler,245133,7971330,0.99,Metal,Garage Inc. (Disc 2),Metallica\nDamage Case,Clarke/Farren/Kilmister/Tayler,220212,7212997,0.99,Metal,Garage Inc. (Disc 2),Metallica\nStone Dead Forever,Clarke/Kilmister/Tayler,292127,9556060,0.99,Metal,Garage Inc. (Disc 2),Metallica\nToo Late Too Late,Clarke/Kilmister/Tayler,192052,6276291,0.99,Metal,Garage Inc. (Disc 2),Metallica\nHit The Lights,\"James Hetfield, Lars Ulrich\",257541,8357088,0.99,Metal,Kill 'Em All,Metallica\nThe Four Horsemen,\"James Hetfield, Lars Ulrich, Dave Mustaine\",433188,14178138,0.99,Metal,Kill 'Em All,Metallica\nMotorbreath,James Hetfield,188395,6153933,0.99,Metal,Kill 'Em All,Metallica\nJump In The Fire,\"James Hetfield, Lars Ulrich, Dave Mustaine\",281573,9135755,0.99,Metal,Kill 'Em All,Metallica\n(Anesthesia) Pulling Teeth,Cliff Burton,254955,8234710,0.99,Metal,Kill 'Em All,Metallica\nWhiplash,\"James Hetfield, Lars Ulrich\",249208,8102839,0.99,Metal,Kill 'Em All,Metallica\nPhantom Lord,\"James Hetfield, Lars Ulrich, Dave Mustaine\",302053,9817143,0.99,Metal,Kill 'Em All,Metallica\nNo Remorse,\"James Hetfield, Lars Ulrich\",386795,12672166,0.99,Metal,Kill 'Em All,Metallica\nSeek & Destroy,\"James Hetfield, Lars Ulrich\",415817,13452301,0.99,Metal,Kill 'Em All,Metallica\nMetal Militia,\"James Hetfield, Lars Ulrich, Dave Mustaine\",311327,10141785,0.99,Metal,Kill 'Em All,Metallica\nAin't My Bitch,\"James Hetfield, Lars Ulrich\",304457,9931015,0.99,Metal,Load,Metallica\n2 X 4,\"James Hetfield, Lars Ulrich, Kirk Hammett\",328254,10732251,0.99,Metal,Load,Metallica\nThe House Jack Built,\"James Hetfield, Lars Ulrich, Kirk Hammett\",398942,13005152,0.99,Metal,Load,Metallica\nUntil It Sleeps,\"James Hetfield, Lars Ulrich\",269740,8837394,0.99,Metal,Load,Metallica\nKing Nothing,\"James Hetfield, Lars Ulrich, Kirk Hammett\",328097,10681477,0.99,Metal,Load,Metallica\nHero Of The Day,\"James Hetfield, Lars Ulrich, Kirk Hammett\",261982,8540298,0.99,Metal,Load,Metallica\nBleeding Me,\"James Hetfield, Lars Ulrich, Kirk Hammett\",497998,16249420,0.99,Metal,Load,Metallica\nCure,\"James Hetfield, Lars Ulrich\",294347,9648615,0.99,Metal,Load,Metallica\nPoor Twisted Me,\"James Hetfield, Lars Ulrich\",240065,7854349,0.99,Metal,Load,Metallica\nWasted My Hate,\"James Hetfield, Lars Ulrich, Kirk Hammett\",237296,7762300,0.99,Metal,Load,Metallica\nMama Said,\"James Hetfield, Lars Ulrich\",319764,10508310,0.99,Metal,Load,Metallica\nThorn Within,\"James Hetfield, Lars Ulrich, Kirk Hammett\",351738,11486686,0.99,Metal,Load,Metallica\nRonnie,\"James Hetfield, Lars Ulrich\",317204,10390947,0.99,Metal,Load,Metallica\nThe Outlaw Torn,\"James Hetfield, Lars Ulrich\",588721,19286261,0.99,Metal,Load,Metallica\nBattery,J.Hetfield/L.Ulrich,312424,10229577,0.99,Metal,Master Of Puppets,Metallica\nMaster Of Puppets,K.Hammett,515239,16893720,0.99,Metal,Master Of Puppets,Metallica\nThe Thing That Should Not Be,K.Hammett,396199,12952368,0.99,Metal,Master Of Puppets,Metallica\nWelcome Home (Sanitarium),K.Hammett,387186,12679965,0.99,Metal,Master Of Puppets,Metallica\nDisposable Heroes,J.Hetfield/L.Ulrich,496718,16135560,0.99,Metal,Master Of Puppets,Metallica\nLeper Messiah,C.Burton,347428,11310434,0.99,Metal,Master Of Puppets,Metallica\nOrion,K.Hammett,500062,16378477,0.99,Metal,Master Of Puppets,Metallica\nDamage Inc.,K.Hammett,330919,10725029,0.99,Metal,Master Of Puppets,Metallica\nFuel,\"Hetfield, Ulrich, Hammett\",269557,8876811,0.99,Metal,ReLoad,Metallica\nThe Memory Remains,\"Hetfield, Ulrich\",279353,9110730,0.99,Metal,ReLoad,Metallica\nDevil's Dance,\"Hetfield, Ulrich\",318955,10414832,0.99,Metal,ReLoad,Metallica\nThe Unforgiven II,\"Hetfield, Ulrich, Hammett\",395520,12886474,0.99,Metal,ReLoad,Metallica\nBetter Than You,\"Hetfield, Ulrich\",322899,10549070,0.99,Metal,ReLoad,Metallica\nSlither,\"Hetfield, Ulrich, Hammett\",313103,10199789,0.99,Metal,ReLoad,Metallica\nCarpe Diem Baby,\"Hetfield, Ulrich, Hammett\",372480,12170693,0.99,Metal,ReLoad,Metallica\nBad Seed,\"Hetfield, Ulrich, Hammett\",245394,8019586,0.99,Metal,ReLoad,Metallica\nWhere The Wild Things Are,\"Hetfield, Ulrich, Newsted\",414380,13571280,0.99,Metal,ReLoad,Metallica\nPrince Charming,\"Hetfield, Ulrich\",365061,12009412,0.99,Metal,ReLoad,Metallica\nLow Man's Lyric,\"Hetfield, Ulrich\",457639,14855583,0.99,Metal,ReLoad,Metallica\nAttitude,\"Hetfield, Ulrich\",315898,10335734,0.99,Metal,ReLoad,Metallica\nFixxxer,\"Hetfield, Ulrich, Hammett\",496065,16190041,0.99,Metal,ReLoad,Metallica\nFight Fire With Fire,Metallica,285753,9420856,0.99,Metal,Ride The Lightning,Metallica\nRide The Lightning,Metallica,397740,13055884,0.99,Metal,Ride The Lightning,Metallica\nFor Whom The Bell Tolls,Metallica,311719,10159725,0.99,Metal,Ride The Lightning,Metallica\nFade To Black,Metallica,414824,13531954,0.99,Metal,Ride The Lightning,Metallica\nTrapped Under Ice,Metallica,244532,7975942,0.99,Metal,Ride The Lightning,Metallica\nEscape,Metallica,264359,8652332,0.99,Metal,Ride The Lightning,Metallica\nCreeping Death,Metallica,396878,12955593,0.99,Metal,Ride The Lightning,Metallica\nThe Call Of Ktulu,Metallica,534883,17486240,0.99,Metal,Ride The Lightning,Metallica\nFrantic,Bob Rock/James Hetfield/Kirk Hammett/Lars Ulrich,350458,11510849,0.99,Metal,St. Anger,Metallica\nSt. Anger,Bob Rock/James Hetfield/Kirk Hammett/Lars Ulrich,441234,14363779,0.99,Metal,St. Anger,Metallica\nSome Kind Of Monster,Bob Rock/James Hetfield/Kirk Hammett/Lars Ulrich,505626,16557497,0.99,Metal,St. Anger,Metallica\nDirty Window,Bob Rock/James Hetfield/Kirk Hammett/Lars Ulrich,324989,10670604,0.99,Metal,St. Anger,Metallica\nInvisible Kid,Bob Rock/James Hetfield/Kirk Hammett/Lars Ulrich,510197,16591800,0.99,Metal,St. Anger,Metallica\nMy World,Bob Rock/James Hetfield/Kirk Hammett/Lars Ulrich,345626,11253756,0.99,Metal,St. Anger,Metallica\nShoot Me Again,Bob Rock/James Hetfield/Kirk Hammett/Lars Ulrich,430210,14093551,0.99,Metal,St. Anger,Metallica\nSweet Amber,Bob Rock/James Hetfield/Kirk Hammett/Lars Ulrich,327235,10616595,0.99,Metal,St. Anger,Metallica\nThe Unnamed Feeling,Bob Rock/James Hetfield/Kirk Hammett/Lars Ulrich,429479,14014582,0.99,Metal,St. Anger,Metallica\nPurify,Bob Rock/James Hetfield/Kirk Hammett/Lars Ulrich,314017,10232537,0.99,Metal,St. Anger,Metallica\nAll Within My Hands,Bob Rock/James Hetfield/Kirk Hammett/Lars Ulrich,527986,17162741,0.99,Metal,St. Anger,Metallica\nBlackened,\"James Hetfield, Lars Ulrich & Jason Newsted\",403382,13254874,0.99,Metal,...And Justice For All,Metallica\n...And Justice For All,\"James Hetfield, Lars Ulrich & Kirk Hammett\",585769,19262088,0.99,Metal,...And Justice For All,Metallica\nEye Of The Beholder,\"James Hetfield, Lars Ulrich & Kirk Hammett\",385828,12747894,0.99,Metal,...And Justice For All,Metallica\nOne,James Hetfield & Lars Ulrich,446484,14695721,0.99,Metal,...And Justice For All,Metallica\nThe Shortest Straw,James Hetfield and Lars Ulrich,395389,13013990,0.99,Metal,...And Justice For All,Metallica\nHarvester Of Sorrow,James Hetfield and Lars Ulrich,345547,11377339,0.99,Metal,...And Justice For All,Metallica\nThe Frayed Ends Of Sanity,\"James Hetfield, Lars Ulrich and Kirk Hammett\",464039,15198986,0.99,Metal,...And Justice For All,Metallica\nTo Live Is To Die,\"James Hetfield, Lars Ulrich and Cliff Burton\",588564,19243795,0.99,Metal,...And Justice For All,Metallica\nDyers Eve,\"James Hetfield, Lars Ulrich and Kirk Hammett\",313991,10302828,0.99,Metal,...And Justice For All,Metallica\nSpringsville,J. Carisi,207725,6776219,0.99,Jazz,Miles Ahead,Miles Davis\nThe Maids Of Cadiz,L. Delibes,233534,7505275,0.99,Jazz,Miles Ahead,Miles Davis\nThe Duke,Dave Brubeck,214961,6977626,0.99,Jazz,Miles Ahead,Miles Davis\nMy Ship,\"Ira Gershwin, Kurt Weill\",268016,8581144,0.99,Jazz,Miles Ahead,Miles Davis\nMiles Ahead,\"Miles Davis, Gil Evans\",209893,6807707,0.99,Jazz,Miles Ahead,Miles Davis\nBlues For Pablo,Gil Evans,318328,10218398,0.99,Jazz,Miles Ahead,Miles Davis\nNew Rhumba,A. Jamal,276871,8980400,0.99,Jazz,Miles Ahead,Miles Davis\nThe Meaning Of The Blues,\"R. Troup, L. Worth\",168594,5395412,0.99,Jazz,Miles Ahead,Miles Davis\nLament,J.J. Johnson,134191,4293394,0.99,Jazz,Miles Ahead,Miles Davis\nI Don't Wanna Be Kissed (By Anyone But You),\"H. Spina, J. Elliott\",191320,6219487,0.99,Jazz,Miles Ahead,Miles Davis\nSpringsville (Alternate Take),J. Carisi,196388,6382079,0.99,Jazz,Miles Ahead,Miles Davis\nBlues For Pablo (Alternate Take),Gil Evans,212558,6900619,0.99,Jazz,Miles Ahead,Miles Davis\nThe Meaning Of The Blues/Lament (Alternate Take),\"J.J. Johnson/R. Troup, L. Worth\",309786,9912387,0.99,Jazz,Miles Ahead,Miles Davis\nI Don't Wanna Be Kissed (By Anyone But You) (Alternate Take),\"H. Spina, J. Elliott\",192078,6254796,0.99,Jazz,Miles Ahead,Miles Davis\nCoração De Estudante,\"Wagner Tiso, Milton Nascimento\",238550,7797308,0.99,Latin,Milton Nascimento Ao Vivo,Milton Nascimento\nA Noite Do Meu Bem,Dolores Duran,220081,7125225,0.99,Latin,Milton Nascimento Ao Vivo,Milton Nascimento\nPaisagem Na Janela,\"Lô Borges, Fernando Brant\",197694,6523547,0.99,Latin,Milton Nascimento Ao Vivo,Milton Nascimento\nCuitelinho,Folclore,209397,6803970,0.99,Latin,Milton Nascimento Ao Vivo,Milton Nascimento\nCaxangá,\"Milton Nascimento, Fernando Brant\",245551,8144179,0.99,Latin,Milton Nascimento Ao Vivo,Milton Nascimento\nNos Bailes Da Vida,\"Milton Nascimento, Fernando Brant\",275748,9126170,0.99,Latin,Milton Nascimento Ao Vivo,Milton Nascimento\nMenestrel Das Alagoas,\"Milton Nascimento, Fernando Brant\",199758,6542289,0.99,Latin,Milton Nascimento Ao Vivo,Milton Nascimento\nBrasil,\"Milton Nascimento, Fernando Brant\",155428,5252560,0.99,Latin,Milton Nascimento Ao Vivo,Milton Nascimento\nCanção Do Novo Mundo,\"Beto Guedes, Ronaldo Bastos\",215353,7032626,0.99,Latin,Milton Nascimento Ao Vivo,Milton Nascimento\nUm Gosto De Sol,\"Milton Nascimento, Ronaldo Bastos\",307200,9893875,0.99,Latin,Milton Nascimento Ao Vivo,Milton Nascimento\nSolar,\"Milton Nascimento, Fernando Brant\",156212,5098288,0.99,Latin,Milton Nascimento Ao Vivo,Milton Nascimento\nPara Lennon E McCartney,\"Lô Borges, Márcio Borges, Fernando Brant\",321828,10626920,0.99,Latin,Milton Nascimento Ao Vivo,Milton Nascimento\n\"Maria, Maria\",\"Milton Nascimento, Fernando Brant\",72463,2371543,0.99,Latin,Milton Nascimento Ao Vivo,Milton Nascimento\nMinas,\"Milton Nascimento, Caetano Veloso\",152293,4921056,0.99,Latin,Minas,Milton Nascimento\n\"Fé Cega, Faca Amolada\",\"Milton Nascimento, Ronaldo Bastos\",278099,9258649,0.99,Latin,Minas,Milton Nascimento\nBeijo Partido,Toninho Horta,229564,7506969,0.99,Latin,Minas,Milton Nascimento\nSaudade Dos Aviões Da Panair (Conversando No Bar),\"Milton Nascimento, Fernando Brant\",268721,8805088,0.99,Latin,Minas,Milton Nascimento\nGran Circo,\"Milton Nascimento, Márcio Borges\",251297,8237026,0.99,Latin,Minas,Milton Nascimento\nPonta de Areia,\"Milton Nascimento, Fernando Brant\",272796,8874285,0.99,Latin,Minas,Milton Nascimento\nTrastevere,\"Milton Nascimento, Ronaldo Bastos\",265665,8708399,0.99,Latin,Minas,Milton Nascimento\nIdolatrada,\"Milton Nascimento, Fernando Brant\",286249,9426153,0.99,Latin,Minas,Milton Nascimento\nLeila (Venha Ser Feliz),Milton Nascimento,209737,6898507,0.99,Latin,Minas,Milton Nascimento\nPaula E Bebeto,\"Milton Nascimento, Caetano Veloso\",135732,4583956,0.99,Latin,Minas,Milton Nascimento\nSimples,Nelson Angelo,133093,4326333,0.99,Latin,Minas,Milton Nascimento\nNorwegian Wood,\"John Lennon, Paul McCartney\",413910,13520382,0.99,Latin,Minas,Milton Nascimento\nCaso Você Queira Saber,\"Beto Guedes, Márcio Borges\",205688,6787901,0.99,Latin,Minas,Milton Nascimento\nAce Of Spades,Clarke/Kilmister/Taylor,169926,5523552,0.99,Metal,Ace Of Spades,Motörhead\nLove Me Like A Reptile,Clarke/Kilmister/Taylor,203546,6616389,0.99,Metal,Ace Of Spades,Motörhead\nShoot You In The Back,Clarke/Kilmister/Taylor,160026,5175327,0.99,Metal,Ace Of Spades,Motörhead\nLive To Win,Clarke/Kilmister/Taylor,217626,7102182,0.99,Metal,Ace Of Spades,Motörhead\nFast And Loose,Clarke/Kilmister/Taylor,203337,6643350,0.99,Metal,Ace Of Spades,Motörhead\n(We Are) The Road Crew,Clarke/Kilmister/Taylor,192600,6283035,0.99,Metal,Ace Of Spades,Motörhead\nFire Fire,Clarke/Kilmister/Taylor,164675,5416114,0.99,Metal,Ace Of Spades,Motörhead\nJailbait,Clarke/Kilmister/Taylor,213916,6983609,0.99,Metal,Ace Of Spades,Motörhead\nDance,Clarke/Kilmister/Taylor,158432,5155099,0.99,Metal,Ace Of Spades,Motörhead\nBite The Bullet,Clarke/Kilmister/Taylor,98115,3195536,0.99,Metal,Ace Of Spades,Motörhead\nThe Chase Is Better Than The Catch,Clarke/Kilmister/Taylor,258403,8393310,0.99,Metal,Ace Of Spades,Motörhead\nThe Hammer,Clarke/Kilmister/Taylor,168071,5543267,0.99,Metal,Ace Of Spades,Motörhead\nDirty Love,Clarke/Kilmister/Taylor,176457,5805241,0.99,Metal,Ace Of Spades,Motörhead\nPlease Don't Touch,Heath/Robinson,169926,5557002,0.99,Metal,Ace Of Spades,Motörhead\nEmergency,Dufort/Johnson/McAuliffe/Williams,180427,5828728,0.99,Metal,Ace Of Spades,Motörhead\nKir Royal,Mônica Marianno,234788,7706552,0.99,World,Demorou...,Mônica Marianno\nO Que Vai Em Meu Coração,Mônica Marianno,255373,8366846,0.99,World,Demorou...,Mônica Marianno\nAos Leões,Mônica Marianno,234684,7790574,0.99,World,Demorou...,Mônica Marianno\nDois Índios,Mônica Marianno,219271,7213072,0.99,World,Demorou...,Mônica Marianno\nNoite Negra,Mônica Marianno,206811,6819584,0.99,World,Demorou...,Mônica Marianno\nBeijo do Olhar,Mônica Marianno,252682,8369029,0.99,World,Demorou...,Mônica Marianno\nÉ Fogo,Mônica Marianno,194873,6501520,0.99,World,Demorou...,Mônica Marianno\nJá Foi,Mônica Marianno,245681,8094872,0.99,World,Demorou...,Mônica Marianno\nSó Se For Pelo Cabelo,Mônica Marianno,238288,8006345,0.99,World,Demorou...,Mônica Marianno\nNo Clima,Mônica Marianno,249495,8362040,0.99,World,Demorou...,Mônica Marianno\nA Moça e a Chuva,Mônica Marianno,274625,8929357,0.99,World,Demorou...,Mônica Marianno\nDemorou!,Mônica Marianno,39131,1287083,0.99,World,Demorou...,Mônica Marianno\nBitter Pill,Mick Mars/Nikki Sixx/Tommy Lee/Vince Neil,266814,8666786,0.99,Metal,Motley Crue Greatest Hits,Mötley Crüe\nEnslaved,Mick Mars/Nikki Sixx/Tommy Lee,269844,8789966,0.99,Metal,Motley Crue Greatest Hits,Mötley Crüe\n\"Girls, Girls, Girls\",Mick Mars/Nikki Sixx/Tommy Lee,270288,8874814,0.99,Metal,Motley Crue Greatest Hits,Mötley Crüe\nKickstart My Heart,Nikki Sixx,283559,9237736,0.99,Metal,Motley Crue Greatest Hits,Mötley Crüe\nWild Side,Nikki Sixx/Tommy Lee/Vince Neil,276767,9116997,0.99,Metal,Motley Crue Greatest Hits,Mötley Crüe\nGlitter,Bryan Adams/Nikki Sixx/Scott Humphrey,340114,11184094,0.99,Metal,Motley Crue Greatest Hits,Mötley Crüe\nDr. Feelgood,Mick Mars/Nikki Sixx,282618,9281875,0.99,Metal,Motley Crue Greatest Hits,Mötley Crüe\nSame Ol' Situation,Mick Mars/Nikki Sixx/Tommy Lee/Vince Neil,254511,8283958,0.99,Metal,Motley Crue Greatest Hits,Mötley Crüe\nHome Sweet Home,Nikki Sixx/Tommy Lee/Vince Neil,236904,7697538,0.99,Metal,Motley Crue Greatest Hits,Mötley Crüe\nAfraid,Nikki Sixx,248006,8077464,0.99,Metal,Motley Crue Greatest Hits,Mötley Crüe\nDon't Go Away Mad (Just Go Away),Mick Mars/Nikki Sixx,279980,9188156,0.99,Metal,Motley Crue Greatest Hits,Mötley Crüe\nWithout You,Mick Mars/Nikki Sixx,268956,8738371,0.99,Metal,Motley Crue Greatest Hits,Mötley Crüe\nSmokin' in The Boys Room,Cub Coda/Michael Lutz,206837,6735408,0.99,Metal,Motley Crue Greatest Hits,Mötley Crüe\nPrimal Scream,Mick Mars/Nikki Sixx/Tommy Lee/Vince Neil,286197,9421164,0.99,Metal,Motley Crue Greatest Hits,Mötley Crüe\nToo Fast For Love,Nikki Sixx,200829,6580542,0.99,Metal,Motley Crue Greatest Hits,Mötley Crüe\nLooks That Kill,Nikki Sixx,240979,7831122,0.99,Metal,Motley Crue Greatest Hits,Mötley Crüe\nShout At The Devil,Nikki Sixx,221962,7281974,0.99,Metal,Motley Crue Greatest Hits,Mötley Crüe\nIntro,Kurt Cobain,52218,1688527,0.99,Rock,From The Muddy Banks Of The Wishkah [Live],Nirvana\nSchool,Kurt Cobain,160235,5234885,0.99,Rock,From The Muddy Banks Of The Wishkah [Live],Nirvana\nDrain You,Kurt Cobain,215196,7013175,0.99,Rock,From The Muddy Banks Of The Wishkah [Live],Nirvana\nAneurysm,Nirvana,271516,8862545,0.99,Rock,From The Muddy Banks Of The Wishkah [Live],Nirvana\nSmells Like Teen Spirit,Nirvana,287190,9425215,0.99,Rock,From The Muddy Banks Of The Wishkah [Live],Nirvana\nBeen A Son,Kurt Cobain,127555,4170369,0.99,Rock,From The Muddy Banks Of The Wishkah [Live],Nirvana\nLithium,Kurt Cobain,250017,8148800,0.99,Rock,From The Muddy Banks Of The Wishkah [Live],Nirvana\nSliver,Kurt Cobain,116218,3784567,0.99,Rock,From The Muddy Banks Of The Wishkah [Live],Nirvana\nSpank Thru,Kurt Cobain,190354,6186487,0.99,Rock,From The Muddy Banks Of The Wishkah [Live],Nirvana\nScentless Apprentice,Nirvana,211200,6898177,0.99,Rock,From The Muddy Banks Of The Wishkah [Live],Nirvana\nHeart-Shaped Box,Kurt Cobain,281887,9210982,0.99,Rock,From The Muddy Banks Of The Wishkah [Live],Nirvana\nMilk It,Kurt Cobain,225724,7406945,0.99,Rock,From The Muddy Banks Of The Wishkah [Live],Nirvana\nNegative Creep,Kurt Cobain,163761,5354854,0.99,Rock,From The Muddy Banks Of The Wishkah [Live],Nirvana\nPolly,Kurt Cobain,149995,4885331,0.99,Rock,From The Muddy Banks Of The Wishkah [Live],Nirvana\nBreed,Kurt Cobain,208378,6759080,0.99,Rock,From The Muddy Banks Of The Wishkah [Live],Nirvana\nTourette's,Kurt Cobain,115591,3753246,0.99,Rock,From The Muddy Banks Of The Wishkah [Live],Nirvana\nBlew,Kurt Cobain,216346,7096936,0.99,Rock,From The Muddy Banks Of The Wishkah [Live],Nirvana\nSmells Like Teen Spirit,Kurt Cobain,301296,9823847,0.99,Rock,Nevermind,Nirvana\nIn Bloom,Kurt Cobain,254928,8327077,0.99,Rock,Nevermind,Nirvana\nCome As You Are,Kurt Cobain,219219,7123357,0.99,Rock,Nevermind,Nirvana\nBreed,Kurt Cobain,183928,5984812,0.99,Rock,Nevermind,Nirvana\nLithium,Kurt Cobain,256992,8404745,0.99,Rock,Nevermind,Nirvana\nPolly,Kurt Cobain,177031,5788407,0.99,Rock,Nevermind,Nirvana\nTerritorial Pissings,Kurt Cobain,143281,4613880,0.99,Rock,Nevermind,Nirvana\nDrain You,Kurt Cobain,223973,7273440,0.99,Rock,Nevermind,Nirvana\nLounge Act,Kurt Cobain,156786,5093635,0.99,Rock,Nevermind,Nirvana\nStay Away,Kurt Cobain,212636,6956404,0.99,Rock,Nevermind,Nirvana\nOn A Plain,Kurt Cobain,196440,6390635,0.99,Rock,Nevermind,Nirvana\nSomething In The Way,Kurt Cobain,230556,7472168,0.99,Rock,Nevermind,Nirvana\nTime,,96888,3124455,0.99,Rock,Compositores,O Terço\nP.S.Apareça,,209188,6842244,0.99,Rock,Compositores,O Terço\nSangue Latino,,223033,7354184,0.99,Rock,Compositores,O Terço\nFolhas Secas,,161253,5284522,0.99,Rock,Compositores,O Terço\nPoeira,,267075,8784141,0.99,Rock,Compositores,O Terço\nMágica,,233743,7627348,0.99,Rock,Compositores,O Terço\nQuem Mata A Mulher Mata O Melhor,,262791,8640121,0.99,Rock,Compositores,O Terço\nMundaréu,,217521,7158975,0.99,Rock,Compositores,O Terço\nO Braço Da Minha Guitarra,,258351,8469531,0.99,Rock,Compositores,O Terço\nDeus,,284160,9188110,0.99,Rock,Compositores,O Terço\nMãe Terra,,306625,9949269,0.99,Rock,Compositores,O Terço\nÀs Vezes,,330292,10706614,0.99,Rock,Compositores,O Terço\nMenino De Rua,,329795,10784595,0.99,Rock,Compositores,O Terço\nPrazer E Fé,,214831,7031383,0.99,Rock,Compositores,O Terço\nElza,,199105,6517629,0.99,Rock,Compositores,O Terço\nRequebra,,240744,8010811,0.99,Latin,Olodum,Olodum\nNossa Gente (Avisa Là),,188212,6233201,0.99,Latin,Olodum,Olodum\nOlodum - Alegria Geral,,233404,7754245,0.99,Latin,Olodum,Olodum\nMadagáscar Olodum,,252264,8270584,0.99,Latin,Olodum,Olodum\nFaraó Divindade Do Egito,,228571,7523278,0.99,Latin,Olodum,Olodum\nTodo Amor (Asas Da Liberdade),,245133,8121434,0.99,Latin,Olodum,Olodum\nDenúncia,,159555,5327433,0.99,Latin,Olodum,Olodum\n\"Olodum, A Banda Do Pelô\",,146599,4900121,0.99,Latin,Olodum,Olodum\nCartao Postal,,211565,7082301,0.99,Latin,Olodum,Olodum\nJeito Faceiro,,217286,7233608,0.99,Latin,Olodum,Olodum\nRevolta Olodum,,230191,7557065,0.99,Latin,Olodum,Olodum\nReggae Odoyá,,224470,7499807,0.99,Latin,Olodum,Olodum\nProtesto Do Olodum (Ao Vivo),,206001,6766104,0.99,Latin,Olodum,Olodum\nOlodum - Smile (Instrumental),,235833,7871409,0.99,Latin,Olodum,Olodum\nVulcão Dub - Fui Eu,Bi Ribeira/Herbert Vianna/João Barone,287059,9495202,0.99,Latin,Acústico MTV,Os Paralamas Do Sucesso\nO Trem Da Juventude,Herbert Vianna,225880,7507655,0.99,Latin,Acústico MTV,Os Paralamas Do Sucesso\nManguetown,Chico Science/Dengue/Lúcio Maia,162925,5382018,0.99,Latin,Acústico MTV,Os Paralamas Do Sucesso\n\"Um Amor, Um Lugar\",Herbert Vianna,184555,6090334,0.99,Latin,Acústico MTV,Os Paralamas Do Sucesso\nBora-Bora,Herbert Vianna,182987,6036046,0.99,Latin,Acústico MTV,Os Paralamas Do Sucesso\nVai Valer,Herbert Vianna,206524,6899778,0.99,Latin,Acústico MTV,Os Paralamas Do Sucesso\nI Feel Good (I Got You) - Sossego,James Brown/Tim Maia,244976,8091302,0.99,Latin,Acústico MTV,Os Paralamas Do Sucesso\nUns Dias,Herbert Vianna,240796,7931552,0.99,Latin,Acústico MTV,Os Paralamas Do Sucesso\nSincero Breu,C. A./C.A./Celso Alvim/Herbert Vianna/Mário Moura/Pedro Luís/Sidon Silva,208013,6921669,0.99,Latin,Acústico MTV,Os Paralamas Do Sucesso\nMeu Erro,Herbert Vianna,188577,6192791,0.99,Latin,Acústico MTV,Os Paralamas Do Sucesso\nSelvagem,Bi Ribeiro/Herbert Vianna/João Barone,148558,4942831,0.99,Latin,Acústico MTV,Os Paralamas Do Sucesso\nBrasília 5:31,Herbert Vianna,178337,5857116,0.99,Latin,Acústico MTV,Os Paralamas Do Sucesso\nTendo A Lua,Herbert Vianna/Tet Tillett,198922,6568180,0.99,Latin,Acústico MTV,Os Paralamas Do Sucesso\nQue País É Este,Renato Russo,216685,7137865,0.99,Latin,Acústico MTV,Os Paralamas Do Sucesso\nNavegar Impreciso,Herbert Vianna,262870,8761283,0.99,Latin,Acústico MTV,Os Paralamas Do Sucesso\nFeira Moderna,Beto Guedes/Fernando Brant/L Borges,182517,6001793,0.99,Latin,Acústico MTV,Os Paralamas Do Sucesso\nTequila - Lourinha Bombril (Parate Y Mira),Bahiano/Chuck Rio/Diego Blanco/Herbert Vianna,255738,8514961,0.99,Latin,Acústico MTV,Os Paralamas Do Sucesso\nVamo Batê Lata,Herbert Vianna,228754,7585707,0.99,Latin,Acústico MTV,Os Paralamas Do Sucesso\nLife During Wartime,Chris Frantz/David Byrne/Jerry Harrison/Tina Weymouth,259186,8543439,0.99,Latin,Acústico MTV,Os Paralamas Do Sucesso\nNebulosa Do Amor,Herbert Vianna,203415,6732496,0.99,Latin,Acústico MTV,Os Paralamas Do Sucesso\nCaleidoscópio,Herbert Vianna,256522,8484597,0.99,Latin,Acústico MTV,Os Paralamas Do Sucesso\nTrac Trac,Fito Paez/Herbert Vianna,231653,7638256,0.99,Latin,Arquivo II,Os Paralamas Do Sucesso\nTendo A Lua,Herbert Vianna/Tetê Tillet,219585,7342776,0.99,Latin,Arquivo II,Os Paralamas Do Sucesso\nMensagen De Amor (2000),Herbert Vianna,183588,6061324,0.99,Latin,Arquivo II,Os Paralamas Do Sucesso\nLourinha Bombril,Bahiano/Diego Blanco/Herbert Vianna,159895,5301882,0.99,Latin,Arquivo II,Os Paralamas Do Sucesso\nLa Bella Luna,Herbert Vianna,192653,6428598,0.99,Latin,Arquivo II,Os Paralamas Do Sucesso\nBusca Vida,Herbert Vianna,176431,5798663,0.99,Latin,Arquivo II,Os Paralamas Do Sucesso\nUma Brasileira,Carlinhos Brown/Herbert Vianna,217573,7280574,0.99,Latin,Arquivo II,Os Paralamas Do Sucesso\nLuis Inacio (300 Picaretas),Herbert Vianna,198191,6576790,0.99,Latin,Arquivo II,Os Paralamas Do Sucesso\nSaber Amar,Herbert Vianna,202788,6723733,0.99,Latin,Arquivo II,Os Paralamas Do Sucesso\nEla Disse Adeus,Herbert Vianna,226298,7608999,0.99,Latin,Arquivo II,Os Paralamas Do Sucesso\nO Amor Nao Sabe Esperar,Herbert Vianna,241084,8042534,0.99,Latin,Arquivo II,Os Paralamas Do Sucesso\nAonde Quer Que Eu Va,Herbert Vianna/Paulo Sérgio Valle,258089,8470121,0.99,Latin,Arquivo II,Os Paralamas Do Sucesso\nCaleidoscópio,,211330,7000017,0.99,Latin,Arquivo Os Paralamas Do Sucesso,Os Paralamas Do Sucesso\nÓculos,,219271,7262419,0.99,Latin,Arquivo Os Paralamas Do Sucesso,Os Paralamas Do Sucesso\nCinema Mudo,,227918,7612168,0.99,Latin,Arquivo Os Paralamas Do Sucesso,Os Paralamas Do Sucesso\nAlagados,,302393,10255463,0.99,Latin,Arquivo Os Paralamas Do Sucesso,Os Paralamas Do Sucesso\nLanterna Dos Afogados,,190197,6264318,0.99,Latin,Arquivo Os Paralamas Do Sucesso,Os Paralamas Do Sucesso\nMelô Do Marinheiro,,208352,6905668,0.99,Latin,Arquivo Os Paralamas Do Sucesso,Os Paralamas Do Sucesso\nVital E Sua Moto,,210207,6902878,0.99,Latin,Arquivo Os Paralamas Do Sucesso,Os Paralamas Do Sucesso\nO Beco,,189178,6293184,0.99,Latin,Arquivo Os Paralamas Do Sucesso,Os Paralamas Do Sucesso\nMeu Erro,,208431,6893533,0.99,Latin,Arquivo Os Paralamas Do Sucesso,Os Paralamas Do Sucesso\nPerplexo,,161175,5355013,0.99,Latin,Arquivo Os Paralamas Do Sucesso,Os Paralamas Do Sucesso\nMe Liga,,229590,7565912,0.99,Latin,Arquivo Os Paralamas Do Sucesso,Os Paralamas Do Sucesso\nQuase Um Segundo,,275644,8971355,0.99,Latin,Arquivo Os Paralamas Do Sucesso,Os Paralamas Do Sucesso\nSelvagem,,245890,8141084,0.99,Latin,Arquivo Os Paralamas Do Sucesso,Os Paralamas Do Sucesso\nRomance Ideal,,250070,8260477,0.99,Latin,Arquivo Os Paralamas Do Sucesso,Os Paralamas Do Sucesso\nSerá Que Vai Chover?,,337057,11133830,0.99,Latin,Arquivo Os Paralamas Do Sucesso,Os Paralamas Do Sucesso\nSKA,,148871,4943540,0.99,Latin,Arquivo Os Paralamas Do Sucesso,Os Paralamas Do Sucesso\nBark at the Moon,O. Osbourne,257252,4601224,0.99,Rock,Bark at the Moon (Remastered),Ozzy Osbourne\nI Don't Know,\"B. Daisley, O. Osbourne & R. Rhoads\",312980,5525339,0.99,Rock,Blizzard of Ozz,Ozzy Osbourne\nCrazy Train,\"B. Daisley, O. Osbourne & R. Rhoads\",295960,5255083,0.99,Rock,Blizzard of Ozz,Ozzy Osbourne\nFlying High Again,\"L. Kerslake, O. Osbourne, R. Daisley & R. Rhoads\",290851,5179599,0.99,Rock,Diary of a Madman (Remastered),Ozzy Osbourne\n\"Mama, I'm Coming Home\",\"L. Kilmister, O. Osbourne & Z. Wylde\",251586,4302390,0.99,Rock,No More Tears (Remastered),Ozzy Osbourne\nNo More Tears,\"J. Purdell, M. Inez, O. Osbourne, R. Castillo & Z. Wylde\",444358,7362964,0.99,Rock,No More Tears (Remastered),Ozzy Osbourne\nI Don't Know,\"O. Osbourne, R. Daisley, R. Rhoads\",283088,9207869,0.99,Metal,Tribute,Ozzy Osbourne\nCrazy Train,\"O. Osbourne, R. Daisley, R. Rhoads\",322716,10517408,0.99,Metal,Tribute,Ozzy Osbourne\nBeliever,\"O. Osbourne, R. Daisley, R. Rhoads\",308897,10003794,0.99,Metal,Tribute,Ozzy Osbourne\nMr. Crowley,\"O. Osbourne, R. Daisley, R. Rhoads\",344241,11184130,0.99,Metal,Tribute,Ozzy Osbourne\nFlying High Again,\"O. Osbourne, R. Daisley, R. Rhoads, L. Kerslake\",261224,8481822,0.99,Metal,Tribute,Ozzy Osbourne\nRelvelation (Mother Earth),\"O. Osbourne, R. Daisley, R. Rhoads\",349440,11367866,0.99,Metal,Tribute,Ozzy Osbourne\nSteal Away (The Night),\"O. Osbourne, R. Daisley, R. Rhoads\",485720,15945806,0.99,Metal,Tribute,Ozzy Osbourne\nSuicide Solution (With Guitar Solo),\"O. Osbourne, R. Daisley, R. Rhoads\",467069,15119938,0.99,Metal,Tribute,Ozzy Osbourne\nIron Man,\"A. F. Iommi, W. Ward, T. Butler, J. Osbourne\",172120,5609799,0.99,Metal,Tribute,Ozzy Osbourne\nChildren Of The Grave,\"A. F. Iommi, W. Ward, T. Butler, J. Osbourne\",357067,11626740,0.99,Metal,Tribute,Ozzy Osbourne\nParanoid,\"A. F. Iommi, W. Ward, T. Butler, J. Osbourne\",176352,5729813,0.99,Metal,Tribute,Ozzy Osbourne\nGoodbye To Romance,\"O. Osbourne, R. Daisley, R. Rhoads\",334393,10841337,0.99,Metal,Tribute,Ozzy Osbourne\nNo Bone Movies,\"O. Osbourne, R. Daisley, R. Rhoads\",249208,8095199,0.99,Metal,Tribute,Ozzy Osbourne\nDee,R. Rhoads,261302,8555963,0.99,Metal,Tribute,Ozzy Osbourne\nShining In The Light,\"Jimmy Page, Robert Plant, Charlie Jones, Michael Lee\",240796,7951688,0.99,Rock,Walking Into Clarksdale,Page & Plant\nWhen The World Was Young,\"Jimmy Page, Robert Plant, Charlie Jones, Michael Lee\",373394,12198930,0.99,Rock,Walking Into Clarksdale,Page & Plant\nUpon A Golden Horse,\"Jimmy Page, Robert Plant, Charlie Jones, Michael Lee\",232359,7594829,0.99,Rock,Walking Into Clarksdale,Page & Plant\nBlue Train,\"Jimmy Page, Robert Plant, Charlie Jones, Michael Lee\",405028,13170391,0.99,Rock,Walking Into Clarksdale,Page & Plant\nPlease Read The Letter,\"Jimmy Page, Robert Plant, Charlie Jones, Michael Lee\",262112,8603372,0.99,Rock,Walking Into Clarksdale,Page & Plant\nMost High,\"Jimmy Page, Robert Plant, Charlie Jones, Michael Lee\",336535,10999203,0.99,Rock,Walking Into Clarksdale,Page & Plant\nHeart In Your Hand,\"Jimmy Page, Robert Plant, Charlie Jones, Michael Lee\",230896,7598019,0.99,Rock,Walking Into Clarksdale,Page & Plant\nWalking Into Clarksdale,\"Jimmy Page, Robert Plant, Charlie Jones, Michael Lee\",318511,10396315,0.99,Rock,Walking Into Clarksdale,Page & Plant\nBurning Up,\"Jimmy Page, Robert Plant, Charlie Jones, Michael Lee\",321619,10525136,0.99,Rock,Walking Into Clarksdale,Page & Plant\nWhen I Was A Child,\"Jimmy Page, Robert Plant, Charlie Jones, Michael Lee\",345626,11249456,0.99,Rock,Walking Into Clarksdale,Page & Plant\nHouse Of Love,\"Jimmy Page, Robert Plant, Charlie Jones, Michael Lee\",335699,10990880,0.99,Rock,Walking Into Clarksdale,Page & Plant\nSons Of Freedom,\"Jimmy Page, Robert Plant, Charlie Jones, Michael Lee\",246465,8087944,0.99,Rock,Walking Into Clarksdale,Page & Plant\nUnited Colours,\"Brian Eno, Bono, Adam Clayton, The Edge & Larry Mullen Jnr.\",330266,10939131,0.99,Soundtrack,Original Soundtracks 1,Passengers\nSlug,\"Brian Eno, Bono, Adam Clayton, The Edge & Larry Mullen Jnr.\",281469,9295950,0.99,Soundtrack,Original Soundtracks 1,Passengers\nYour Blue Room,\"Brian Eno, Bono, Adam Clayton, The Edge & Larry Mullen Jnr.\",328228,10867860,0.99,Soundtrack,Original Soundtracks 1,Passengers\nAlways Forever Now,\"Brian Eno, Bono, Adam Clayton, The Edge & Larry Mullen Jnr.\",383764,12727928,0.99,Soundtrack,Original Soundtracks 1,Passengers\nA Different Kind Of Blue,\"Brian Eno, Bono, Adam Clayton, The Edge & Larry Mullen Jnr.\",120816,3884133,0.99,Soundtrack,Original Soundtracks 1,Passengers\nBeach Sequence,\"Brian Eno, Bono, Adam Clayton, The Edge & Larry Mullen Jnr.\",212297,6928259,0.99,Soundtrack,Original Soundtracks 1,Passengers\nMiss Sarajevo,\"Brian Eno, Bono, Adam Clayton, The Edge & Larry Mullen Jnr.\",340767,11064884,0.99,Soundtrack,Original Soundtracks 1,Passengers\nIto Okashi,\"Brian Eno, Bono, Adam Clayton, The Edge & Larry Mullen Jnr.\",205087,6572813,0.99,Soundtrack,Original Soundtracks 1,Passengers\nOne Minute Warning,\"Brian Eno, Bono, Adam Clayton, The Edge & Larry Mullen Jnr.\",279693,9335453,0.99,Soundtrack,Original Soundtracks 1,Passengers\nCorpse (These Chains Are Way Too Long),\"Brian Eno, Bono, Adam Clayton, The Edge & Larry Mullen Jnr.\",214909,6920451,0.99,Soundtrack,Original Soundtracks 1,Passengers\nElvis Ate America,\"Brian Eno, Bono, Adam Clayton, The Edge & Larry Mullen Jnr.\",180166,5851053,0.99,Soundtrack,Original Soundtracks 1,Passengers\nPlot 180,\"Brian Eno, Bono, Adam Clayton, The Edge & Larry Mullen Jnr.\",221596,7253729,0.99,Soundtrack,Original Soundtracks 1,Passengers\nTheme From The Swan,\"Brian Eno, Bono, Adam Clayton, The Edge & Larry Mullen Jnr.\",203911,6638076,0.99,Soundtrack,Original Soundtracks 1,Passengers\nTheme From Let's Go Native,\"Brian Eno, Bono, Adam Clayton, The Edge & Larry Mullen Jnr.\",186723,6179777,0.99,Soundtrack,Original Soundtracks 1,Passengers\nWrathchild,Steve Harris,170396,5499390,0.99,Rock,The Beast Live,Paul D'Ianno\nKillers,Paul Di'Anno/Steve Harris,309995,10009697,0.99,Rock,The Beast Live,Paul D'Ianno\nProwler,Steve Harris,240274,7782963,0.99,Rock,The Beast Live,Paul D'Ianno\nMurders In The Rue Morgue,Steve Harris,258638,8360999,0.99,Rock,The Beast Live,Paul D'Ianno\nWomen In Uniform,Greg Macainsh,189936,6139651,0.99,Rock,The Beast Live,Paul D'Ianno\nRemember Tomorrow,Paul Di'Anno/Steve Harris,326426,10577976,0.99,Rock,The Beast Live,Paul D'Ianno\nSanctuary,David Murray/Paul Di'Anno/Steve Harris,198844,6423543,0.99,Rock,The Beast Live,Paul D'Ianno\nRunning Free,Paul Di'Anno/Steve Harris,199706,6483496,0.99,Rock,The Beast Live,Paul D'Ianno\nPhantom Of The Opera,Steve Harris,418168,13585530,0.99,Rock,The Beast Live,Paul D'Ianno\nIron Maiden,Steve Harris,235232,7600077,0.99,Rock,The Beast Live,Paul D'Ianno\nCorduroy,Pearl Jam & Eddie Vedder,305293,9991106,0.99,Rock,Live On Two Legs [Live],Pearl Jam\nGiven To Fly,Eddie Vedder & Mike McCready,233613,7678347,0.99,Rock,Live On Two Legs [Live],Pearl Jam\n\"Hail, Hail\",Stone Gossard & Eddie Vedder & Jeff Ament & Mike McCready,223764,7364206,0.99,Rock,Live On Two Legs [Live],Pearl Jam\nDaughter,Dave Abbruzzese & Jeff Ament & Stone Gossard & Mike McCready & Eddie Vedder,407484,13420697,0.99,Rock,Live On Two Legs [Live],Pearl Jam\nElderly Woman Behind The Counter In A Small Town,Dave Abbruzzese & Jeff Ament & Stone Gossard & Mike McCready & Eddie Vedder,229328,7509304,0.99,Rock,Live On Two Legs [Live],Pearl Jam\nUntitled,Pearl Jam,122801,3957141,0.99,Rock,Live On Two Legs [Live],Pearl Jam\nMFC,Eddie Vedder,148192,4817665,0.99,Rock,Live On Two Legs [Live],Pearl Jam\nGo,Dave Abbruzzese & Jeff Ament & Stone Gossard & Mike McCready & Eddie Vedder,161541,5290810,0.99,Rock,Live On Two Legs [Live],Pearl Jam\nRed Mosquito,Jeff Ament & Stone Gossard & Jack Irons & Mike McCready & Eddie Vedder,242991,7944923,0.99,Rock,Live On Two Legs [Live],Pearl Jam\nEven Flow,Stone Gossard & Eddie Vedder,317100,10394239,0.99,Rock,Live On Two Legs [Live],Pearl Jam\nOff He Goes,Eddie Vedder,343222,11245109,0.99,Rock,Live On Two Legs [Live],Pearl Jam\nNothingman,Jeff Ament & Eddie Vedder,278595,9107017,0.99,Rock,Live On Two Legs [Live],Pearl Jam\nDo The Evolution,Eddie Vedder & Stone Gossard,225462,7377286,0.99,Rock,Live On Two Legs [Live],Pearl Jam\nBetter Man,Eddie Vedder,246204,8019563,0.99,Rock,Live On Two Legs [Live],Pearl Jam\nBlack,Stone Gossard & Eddie Vedder,415712,13580009,0.99,Rock,Live On Two Legs [Live],Pearl Jam\nF*Ckin' Up,Neil Young,377652,12360893,0.99,Rock,Live On Two Legs [Live],Pearl Jam\nLife Wasted,Stone Gossard,234344,7610169,0.99,Alternative & Punk,Pearl Jam,Pearl Jam\nWorld Wide Suicide,Eddie Vedder,209188,6885908,0.99,Alternative & Punk,Pearl Jam,Pearl Jam\nComatose,Mike McCready & Stone Gossard,139990,4574516,0.99,Alternative & Punk,Pearl Jam,Pearl Jam\nSevered Hand,Eddie Vedder,270341,8817438,0.99,Alternative & Punk,Pearl Jam,Pearl Jam\nMarker In The Sand,Mike McCready,263235,8656578,0.99,Alternative & Punk,Pearl Jam,Pearl Jam\nParachutes,Stone Gossard,216555,7074973,0.99,Alternative & Punk,Pearl Jam,Pearl Jam\nUnemployable,Matt Cameron & Mike McCready,184398,6066542,0.99,Alternative & Punk,Pearl Jam,Pearl Jam\nBig Wave,Jeff Ament,178573,5858788,0.99,Alternative & Punk,Pearl Jam,Pearl Jam\nGone,Eddie Vedder,249547,8158204,0.99,Alternative & Punk,Pearl Jam,Pearl Jam\nWasted Reprise,Stone Gossard,53733,1731020,0.99,Alternative & Punk,Pearl Jam,Pearl Jam\nArmy Reserve,Jeff Ament,225567,7393771,0.99,Alternative & Punk,Pearl Jam,Pearl Jam\nCome Back,Eddie Vedder & Mike McCready,329743,10768701,0.99,Alternative & Punk,Pearl Jam,Pearl Jam\nInside Job,Eddie Vedder & Mike McCready,428643,14006924,0.99,Alternative & Punk,Pearl Jam,Pearl Jam\nCan't Keep,Eddie Vedder,219428,7215713,0.99,Rock,Riot Act,Pearl Jam\nSave You,Eddie Vedder/Jeff Ament/Matt Cameron/Mike McCready/Stone Gossard,230112,7609110,0.99,Rock,Riot Act,Pearl Jam\nLove Boat Captain,Eddie Vedder,276453,9016789,0.99,Rock,Riot Act,Pearl Jam\nCropduster,Matt Cameron,231888,7588928,0.99,Rock,Riot Act,Pearl Jam\nGhost,Jeff Ament,195108,6383772,0.99,Rock,Riot Act,Pearl Jam\nI Am Mine,Eddie Vedder,215719,7086901,0.99,Rock,Riot Act,Pearl Jam\nThumbing My Way,Eddie Vedder,250226,8201437,0.99,Rock,Riot Act,Pearl Jam\nYou Are,Matt Cameron,270863,8938409,0.99,Rock,Riot Act,Pearl Jam\nGet Right,Matt Cameron,158589,5223345,0.99,Rock,Riot Act,Pearl Jam\nGreen Disease,Eddie Vedder,161253,5375818,0.99,Rock,Riot Act,Pearl Jam\nHelp Help,Jeff Ament,215092,7033002,0.99,Rock,Riot Act,Pearl Jam\nBushleager,Stone Gossard,237479,7849757,0.99,Rock,Riot Act,Pearl Jam\n1/2 Full,Jeff Ament,251010,8197219,0.99,Rock,Riot Act,Pearl Jam\nArc,Pearl Jam,65593,2099421,0.99,Rock,Riot Act,Pearl Jam\nAll or None,Stone Gossard,277655,9104728,0.99,Rock,Riot Act,Pearl Jam\nOnce,Stone Gossard,231758,7561555,0.99,Rock,Ten,Pearl Jam\nEvenflow,Stone Gossard,293720,9622017,0.99,Rock,Ten,Pearl Jam\nAlive,Stone Gossard,341080,11176623,0.99,Rock,Ten,Pearl Jam\nWhy Go,Jeff Ament,200254,6539287,0.99,Rock,Ten,Pearl Jam\nBlack,Dave Krusen/Stone Gossard,343823,11213314,0.99,Rock,Ten,Pearl Jam\nJeremy,Jeff Ament,318981,10447222,0.99,Rock,Ten,Pearl Jam\nOceans,Jeff Ament/Stone Gossard,162194,5282368,0.99,Rock,Ten,Pearl Jam\nPorch,Eddie Vedder,210520,6877475,0.99,Rock,Ten,Pearl Jam\nGarden,Jeff Ament/Stone Gossard,299154,9740738,0.99,Rock,Ten,Pearl Jam\nDeep,Jeff Ament/Stone Gossard,258324,8432497,0.99,Rock,Ten,Pearl Jam\nRelease,Jeff Ament/Mike McCready/Stone Gossard,546063,17802673,0.99,Rock,Ten,Pearl Jam\nGo,Dave Abbruzzese/Eddie Vedder/Jeff Ament/Mike McCready/Stone Gossard,193123,6351920,0.99,Rock,Vs.,Pearl Jam\nAnimal,Dave Abbruzzese/Eddie Vedder/Jeff Ament/Mike McCready/Stone Gossard,169325,5503459,0.99,Rock,Vs.,Pearl Jam\nDaughter,Dave Abbruzzese/Eddie Vedder/Jeff Ament/Mike McCready/Stone Gossard,235598,7824586,0.99,Rock,Vs.,Pearl Jam\nGlorified G,Dave Abbruzzese/Eddie Vedder/Jeff Ament/Mike McCready/Stone Gossard,206968,6772116,0.99,Rock,Vs.,Pearl Jam\nDissident,Dave Abbruzzese/Eddie Vedder/Jeff Ament/Mike McCready/Stone Gossard,215510,7034500,0.99,Rock,Vs.,Pearl Jam\nW.M.A.,Dave Abbruzzese/Eddie Vedder/Jeff Ament/Mike McCready/Stone Gossard,359262,12037261,0.99,Rock,Vs.,Pearl Jam\nBlood,Dave Abbruzzese/Eddie Vedder/Jeff Ament/Mike McCready/Stone Gossard,170631,5551478,0.99,Rock,Vs.,Pearl Jam\nRearviewmirror,Dave Abbruzzese/Eddie Vedder/Jeff Ament/Mike McCready/Stone Gossard,284186,9321053,0.99,Rock,Vs.,Pearl Jam\nRats,Dave Abbruzzese/Eddie Vedder/Jeff Ament/Mike McCready/Stone Gossard,255425,8341934,0.99,Rock,Vs.,Pearl Jam\nElderly Woman Behind The Counter In A Small Town,Dave Abbruzzese/Eddie Vedder/Jeff Ament/Mike McCready/Stone Gossard,196336,6499398,0.99,Rock,Vs.,Pearl Jam\nLeash,Dave Abbruzzese/Eddie Vedder/Jeff Ament/Mike McCready/Stone Gossard,189257,6191560,0.99,Rock,Vs.,Pearl Jam\nIndifference,Dave Abbruzzese/Eddie Vedder/Jeff Ament/Mike McCready/Stone Gossard,302053,9756133,0.99,Rock,Vs.,Pearl Jam\nSpeak To Me/Breathe,\"Mason/Waters, Gilmour, Wright\",234213,7631305,0.99,Rock,Dark Side Of The Moon,Pink Floyd\nOn The Run,\"Gilmour, Waters\",214595,7206300,0.99,Rock,Dark Side Of The Moon,Pink Floyd\nTime,\"Mason, Waters, Wright, Gilmour\",425195,13955426,0.99,Rock,Dark Side Of The Moon,Pink Floyd\nThe Great Gig In The Sky,\"Wright, Waters\",284055,9147563,0.99,Rock,Dark Side Of The Moon,Pink Floyd\nMoney,Waters,391888,12930070,0.99,Rock,Dark Side Of The Moon,Pink Floyd\nUs And Them,\"Waters, Wright\",461035,15000299,0.99,Rock,Dark Side Of The Moon,Pink Floyd\nAny Colour You Like,\"Gilmour, Mason, Wright, Waters\",205740,6707989,0.99,Rock,Dark Side Of The Moon,Pink Floyd\nBrain Damage,Waters,230556,7497655,0.99,Rock,Dark Side Of The Moon,Pink Floyd\nEclipse,Waters,125361,4065299,0.99,Rock,Dark Side Of The Moon,Pink Floyd\nZeroVinteUm,,315637,10426550,0.99,Hip Hop/Rap,Os Cães Ladram Mas A Caravana Não Pára,Planet Hemp\nQueimando Tudo,,172591,5723677,0.99,Hip Hop/Rap,Os Cães Ladram Mas A Caravana Não Pára,Planet Hemp\nHip Hop Rio,,151536,4991935,0.99,Hip Hop/Rap,Os Cães Ladram Mas A Caravana Não Pára,Planet Hemp\nBossa,,29048,967098,0.99,Hip Hop/Rap,Os Cães Ladram Mas A Caravana Não Pára,Planet Hemp\n100% HardCore,,165146,5407744,0.99,Hip Hop/Rap,Os Cães Ladram Mas A Caravana Não Pára,Planet Hemp\nBiruta,,213263,7108200,0.99,Hip Hop/Rap,Os Cães Ladram Mas A Caravana Não Pára,Planet Hemp\nMão Na Cabeça,,202631,6642753,0.99,Hip Hop/Rap,Os Cães Ladram Mas A Caravana Não Pára,Planet Hemp\nO Bicho Tá Pregando,,171964,5683369,0.99,Hip Hop/Rap,Os Cães Ladram Mas A Caravana Não Pára,Planet Hemp\nAdoled (Ocean),,185103,6009946,0.99,Hip Hop/Rap,Os Cães Ladram Mas A Caravana Não Pára,Planet Hemp\nSeus Amigos,,100858,3304738,0.99,Hip Hop/Rap,Os Cães Ladram Mas A Caravana Não Pára,Planet Hemp\nPaga Pau,,197485,6529041,0.99,Hip Hop/Rap,Os Cães Ladram Mas A Caravana Não Pára,Planet Hemp\nRappers Reais,,202004,6684160,0.99,Hip Hop/Rap,Os Cães Ladram Mas A Caravana Não Pára,Planet Hemp\nNega Do Cabelo Duro,,121808,4116536,0.99,Hip Hop/Rap,Os Cães Ladram Mas A Caravana Não Pára,Planet Hemp\nHemp Family,,205923,6806900,0.99,Hip Hop/Rap,Os Cães Ladram Mas A Caravana Não Pára,Planet Hemp\nQuem Me Cobrou?,,121704,3947664,0.99,Hip Hop/Rap,Os Cães Ladram Mas A Caravana Não Pára,Planet Hemp\nSe Liga,,410409,13559173,0.99,Hip Hop/Rap,Os Cães Ladram Mas A Caravana Não Pára,Planet Hemp\nBohemian Rhapsody,\"Mercury, Freddie\",358948,11619868,0.99,Rock,Greatest Hits I,Queen\nAnother One Bites The Dust,\"Deacon, John\",216946,7172355,0.99,Rock,Greatest Hits I,Queen\nKiller Queen,\"Mercury, Freddie\",182099,5967749,0.99,Rock,Greatest Hits I,Queen\nFat Bottomed Girls,\"May, Brian\",204695,6630041,0.99,Rock,Greatest Hits I,Queen\nBicycle Race,\"Mercury, Freddie\",183823,6012409,0.99,Rock,Greatest Hits I,Queen\nYou're My Best Friend,\"Deacon, John\",172225,5602173,0.99,Rock,Greatest Hits I,Queen\nDon't Stop Me Now,\"Mercury, Freddie\",211826,6896666,0.99,Rock,Greatest Hits I,Queen\nSave Me,\"May, Brian\",228832,7444624,0.99,Rock,Greatest Hits I,Queen\nCrazy Little Thing Called Love,\"Mercury, Freddie\",164231,5435501,0.99,Rock,Greatest Hits I,Queen\nSomebody To Love,\"Mercury, Freddie\",297351,9650520,0.99,Rock,Greatest Hits I,Queen\nNow I'm Here,\"May, Brian\",255346,8328312,0.99,Rock,Greatest Hits I,Queen\nGood Old-Fashioned Lover Boy,\"Mercury, Freddie\",175960,5747506,0.99,Rock,Greatest Hits I,Queen\nPlay The Game,\"Mercury, Freddie\",213368,6915832,0.99,Rock,Greatest Hits I,Queen\nFlash,\"May, Brian\",168489,5464986,0.99,Rock,Greatest Hits I,Queen\nSeven Seas Of Rhye,\"Mercury, Freddie\",170553,5539957,0.99,Rock,Greatest Hits I,Queen\nWe Will Rock You,\"Deacon, John/May, Brian\",122880,4026955,0.99,Rock,Greatest Hits I,Queen\nWe Are The Champions,\"Mercury, Freddie\",180950,5880231,0.99,Rock,Greatest Hits I,Queen\nWe Will Rock You,May,122671,4026815,0.99,Rock,News Of The World,Queen\nWe Are The Champions,Mercury,182883,5939794,0.99,Rock,News Of The World,Queen\nSheer Heart Attack,Taylor,207386,6642685,0.99,Rock,News Of The World,Queen\n\"All Dead, All Dead\",May,190119,6144878,0.99,Rock,News Of The World,Queen\nSpread Your Wings,Deacon,275356,8936992,0.99,Rock,News Of The World,Queen\nFight From The Inside,Taylor,184737,6078001,0.99,Rock,News Of The World,Queen\n\"Get Down, Make Love\",Mercury,231235,7509333,0.99,Rock,News Of The World,Queen\nSleep On The Sidewalk,May,187428,6099840,0.99,Rock,News Of The World,Queen\nWho Needs You,Deacon,186958,6292969,0.99,Rock,News Of The World,Queen\nIt's Late,May,386194,12519388,0.99,Rock,News Of The World,Queen\nMy Melancholy Blues,Mercury,206471,6691838,0.99,Rock,News Of The World,Queen\nShiny Happy People,Bill Berry/Michael Stipe/Mike Mills/Peter Buck,226298,7475323,0.99,Alternative & Punk,Out Of Time,R.E.M. Feat. Kate Pearson\nMe In Honey,Bill Berry/Michael Stipe/Mike Mills/Peter Buck,246674,8194751,0.99,Alternative & Punk,Out Of Time,R.E.M. Feat. Kate Pearson\nRadio Song,Bill Berry/Michael Stipe/Mike Mills/Peter Buck,255477,8421172,0.99,Alternative & Punk,Out Of Time,R.E.M. Feat. Kate Pearson\nLosing My Religion,Bill Berry/Michael Stipe/Mike Mills/Peter Buck,269035,8885672,0.99,Alternative & Punk,Out Of Time,R.E.M. Feat. Kate Pearson\nLow,Bill Berry/Michael Stipe/Mike Mills/Peter Buck,296777,9633860,0.99,Alternative & Punk,Out Of Time,R.E.M. Feat. Kate Pearson\nNear Wild Heaven,Bill Berry/Michael Stipe/Mike Mills/Peter Buck,199862,6610009,0.99,Alternative & Punk,Out Of Time,R.E.M. Feat. Kate Pearson\nEndgame,Bill Berry/Michael Stipe/Mike Mills/Peter Buck,230687,7664479,0.99,Alternative & Punk,Out Of Time,R.E.M. Feat. Kate Pearson\nBelong,Bill Berry/Michael Stipe/Mike Mills/Peter Buck,247013,8219375,0.99,Alternative & Punk,Out Of Time,R.E.M. Feat. Kate Pearson\nHalf A World Away,Bill Berry/Michael Stipe/Mike Mills/Peter Buck,208431,6837283,0.99,Alternative & Punk,Out Of Time,R.E.M. Feat. Kate Pearson\nTexarkana,Bill Berry/Michael Stipe/Mike Mills/Peter Buck,220081,7260681,0.99,Alternative & Punk,Out Of Time,R.E.M. Feat. Kate Pearson\nCountry Feedback,Bill Berry/Michael Stipe/Mike Mills/Peter Buck,249782,8178943,0.99,Alternative & Punk,Out Of Time,R.E.M. Feat. Kate Pearson\nPop Song 89,Bill Berry-Peter Buck-Mike Mills-Michael Stipe,185730,6132218,0.99,Alternative & Punk,Green,R.E.M.\nGet Up,Bill Berry-Peter Buck-Mike Mills-Michael Stipe,160235,5264376,0.99,Alternative & Punk,Green,R.E.M.\nYou Are The Everything,Bill Berry-Peter Buck-Mike Mills-Michael Stipe,226298,7373181,0.99,Alternative & Punk,Green,R.E.M.\nStand,Bill Berry-Peter Buck-Mike Mills-Michael Stipe,192862,6349090,0.99,Alternative & Punk,Green,R.E.M.\nWorld Leader Pretend,Bill Berry-Peter Buck-Mike Mills-Michael Stipe,259761,8537282,0.99,Alternative & Punk,Green,R.E.M.\nThe Wrong Child,Bill Berry-Peter Buck-Mike Mills-Michael Stipe,216633,7065060,0.99,Alternative & Punk,Green,R.E.M.\nOrange Crush,Bill Berry-Peter Buck-Mike Mills-Michael Stipe,231706,7742894,0.99,Alternative & Punk,Green,R.E.M.\nTurn You Inside-Out,Bill Berry-Peter Buck-Mike Mills-Michael Stipe,257358,8395671,0.99,Alternative & Punk,Green,R.E.M.\nHairshirt,Bill Berry-Peter Buck-Mike Mills-Michael Stipe,235911,7753807,0.99,Alternative & Punk,Green,R.E.M.\nI Remember California,Bill Berry-Peter Buck-Mike Mills-Michael Stipe,304013,9950311,0.99,Alternative & Punk,Green,R.E.M.\nUntitled,Bill Berry-Peter Buck-Mike Mills-Michael Stipe,191503,6332426,0.99,Alternative & Punk,Green,R.E.M.\nHow The West Was Won And Where It Got Us,Bill Berry-Peter Buck-Mike Mills-Michael Stipe,271151,8994291,0.99,Rock,New Adventures In Hi-Fi,R.E.M.\nThe Wake-Up Bomb,Bill Berry-Peter Buck-Mike Mills-Michael Stipe,308532,10077337,0.99,Rock,New Adventures In Hi-Fi,R.E.M.\nNew Test Leper,Bill Berry-Peter Buck-Mike Mills-Michael Stipe,326791,10866447,0.99,Rock,New Adventures In Hi-Fi,R.E.M.\nUndertow,Bill Berry-Peter Buck-Mike Mills-Michael Stipe,309498,10131005,0.99,Rock,New Adventures In Hi-Fi,R.E.M.\nE-Bow The Letter,Bill Berry-Peter Buck-Mike Mills-Michael Stipe,324963,10714576,0.99,Rock,New Adventures In Hi-Fi,R.E.M.\nLeave,Bill Berry-Peter Buck-Mike Mills-Michael Stipe,437968,14433365,0.99,Rock,New Adventures In Hi-Fi,R.E.M.\nDeparture,Bill Berry-Peter Buck-Mike Mills-Michael Stipe,209423,6818425,0.99,Rock,New Adventures In Hi-Fi,R.E.M.\nBittersweet Me,Bill Berry-Peter Buck-Mike Mills-Michael Stipe,245812,8114718,0.99,Rock,New Adventures In Hi-Fi,R.E.M.\nBe Mine,Bill Berry-Peter Buck-Mike Mills-Michael Stipe,333087,10790541,0.99,Rock,New Adventures In Hi-Fi,R.E.M.\nBinky The Doormat,Bill Berry-Peter Buck-Mike Mills-Michael Stipe,301688,9950320,0.99,Rock,New Adventures In Hi-Fi,R.E.M.\nZither,Bill Berry-Peter Buck-Mike Mills-Michael Stipe,154148,5032962,0.99,Rock,New Adventures In Hi-Fi,R.E.M.\n\"So Fast, So Numb\",Bill Berry-Peter Buck-Mike Mills-Michael Stipe,252682,8341223,0.99,Rock,New Adventures In Hi-Fi,R.E.M.\nLow Desert,Bill Berry-Peter Buck-Mike Mills-Michael Stipe,212062,6989288,0.99,Rock,New Adventures In Hi-Fi,R.E.M.\nElectrolite,Bill Berry-Peter Buck-Mike Mills-Michael Stipe,245315,8051199,0.99,Rock,New Adventures In Hi-Fi,R.E.M.\nCarnival Of Sorts,R.E.M.,233482,7669658,0.99,Alternative & Punk,The Best Of R.E.M.: The IRS Years,R.E.M.\nRadio Free Aurope,R.E.M.,245315,8163490,0.99,Alternative & Punk,The Best Of R.E.M.: The IRS Years,R.E.M.\nPerfect Circle,R.E.M.,208509,6898067,0.99,Alternative & Punk,The Best Of R.E.M.: The IRS Years,R.E.M.\nTalk About The Passion,R.E.M.,203206,6725435,0.99,Alternative & Punk,The Best Of R.E.M.: The IRS Years,R.E.M.\nSo Central Rain,R.E.M.,194768,6414550,0.99,Alternative & Punk,The Best Of R.E.M.: The IRS Years,R.E.M.\nDon't Go Back To Rockville,R.E.M.,272352,9010715,0.99,Alternative & Punk,The Best Of R.E.M.: The IRS Years,R.E.M.\nPretty Persuasion,R.E.M.,229929,7577754,0.99,Alternative & Punk,The Best Of R.E.M.: The IRS Years,R.E.M.\nGreen Grow The Rushes,R.E.M.,225671,7422425,0.99,Alternative & Punk,The Best Of R.E.M.: The IRS Years,R.E.M.\nCan't Get There From Here,R.E.M.,220630,7285936,0.99,Alternative & Punk,The Best Of R.E.M.: The IRS Years,R.E.M.\nDriver 8,R.E.M.,204747,6779076,0.99,Alternative & Punk,The Best Of R.E.M.: The IRS Years,R.E.M.\nFall On Me,R.E.M.,172016,5676811,0.99,Alternative & Punk,The Best Of R.E.M.: The IRS Years,R.E.M.\nI Believe,R.E.M.,227709,7542929,0.99,Alternative & Punk,The Best Of R.E.M.: The IRS Years,R.E.M.\nCuyahoga,R.E.M.,260623,8591057,0.99,Alternative & Punk,The Best Of R.E.M.: The IRS Years,R.E.M.\nThe One I Love,R.E.M.,197355,6495125,0.99,Alternative & Punk,The Best Of R.E.M.: The IRS Years,R.E.M.\nThe Finest Worksong,R.E.M.,229276,7574856,0.99,Alternative & Punk,The Best Of R.E.M.: The IRS Years,R.E.M.\nIt's The End Of The World As We Know It (And I Feel Fine),R.E.M.,244819,7998987,0.99,Alternative & Punk,The Best Of R.E.M.: The IRS Years,R.E.M.\nInfeliz Natal,Rodolfo,138266,4503299,0.99,Alternative & Punk,Cesta Básica,Raimundos\nA Sua,Rodolfo,142132,4622064,0.99,Alternative & Punk,Cesta Básica,Raimundos\nPapeau Nuky Doe,Rodolfo,121652,3995022,0.99,Alternative & Punk,Cesta Básica,Raimundos\nMerry Christmas,Rodolfo,126040,4166652,0.99,Alternative & Punk,Cesta Básica,Raimundos\nBodies,Rodolfo,180035,5873778,0.99,Alternative & Punk,Cesta Básica,Raimundos\nPuteiro Em João Pessoa,Rodolfo,195578,6395490,0.99,Alternative & Punk,Cesta Básica,Raimundos\nEsporrei Na Manivela,Rodolfo,293276,9618499,0.99,Alternative & Punk,Cesta Básica,Raimundos\nBê-a-Bá,Rodolfo,249051,8130636,0.99,Alternative & Punk,Cesta Básica,Raimundos\nCajueiro,Rodolfo,158589,5164837,0.99,Alternative & Punk,Cesta Básica,Raimundos\nPalhas Do Coqueiro,Rodolfo,133851,4396466,0.99,Alternative & Punk,Cesta Básica,Raimundos\nMaluco Beleza,,203206,6628067,0.99,Rock,Raul Seixas,Raul Seixas\nO Dia Em Que A Terra Parou,,261720,8586678,0.99,Rock,Raul Seixas,Raul Seixas\nNo Fundo Do Quintal Da Escola,,177606,5836953,0.99,Rock,Raul Seixas,Raul Seixas\nO Segredo Do Universo,,192679,6315187,0.99,Rock,Raul Seixas,Raul Seixas\nAs Profecias,,232515,7657732,0.99,Rock,Raul Seixas,Raul Seixas\nMata Virgem,,142602,4690029,0.99,Rock,Raul Seixas,Raul Seixas\nSapato 36,,196702,6507301,0.99,Rock,Raul Seixas,Raul Seixas\nTodo Mundo Explica,,134896,4449772,0.99,Rock,Raul Seixas,Raul Seixas\nQue Luz É Essa,,165067,5620058,0.99,Rock,Raul Seixas,Raul Seixas\nDiamante De Mendigo,,206053,6775101,0.99,Rock,Raul Seixas,Raul Seixas\nNegócio É,,175464,5826775,0.99,Rock,Raul Seixas,Raul Seixas\n\"Muita Estrela, Pouca Constelação\",,268068,8781021,0.99,Rock,Raul Seixas,Raul Seixas\nSéculo XXI,,244897,8040563,0.99,Rock,Raul Seixas,Raul Seixas\nRock Das Aranhas (Ao Vivo) (Live),,231836,7591945,0.99,Rock,Raul Seixas,Raul Seixas\nThe Power Of Equality,Anthony Kiedis/Chad Smith/Flea/John Frusciante,243591,8148266,0.99,Alternative & Punk,Blood Sugar Sex Magik,Red Hot Chili Peppers\nIf You Have To Ask,Anthony Kiedis/Chad Smith/Flea/John Frusciante,216790,7199175,0.99,Alternative & Punk,Blood Sugar Sex Magik,Red Hot Chili Peppers\nBreaking The Girl,Anthony Kiedis/Chad Smith/Flea/John Frusciante,295497,9805526,0.99,Alternative & Punk,Blood Sugar Sex Magik,Red Hot Chili Peppers\nFunky Monks,Anthony Kiedis/Chad Smith/Flea/John Frusciante,323395,10708168,0.99,Alternative & Punk,Blood Sugar Sex Magik,Red Hot Chili Peppers\nSuck My Kiss,Anthony Kiedis/Chad Smith/Flea/John Frusciante,217234,7129137,0.99,Alternative & Punk,Blood Sugar Sex Magik,Red Hot Chili Peppers\nI Could Have Lied,Anthony Kiedis/Chad Smith/Flea/John Frusciante,244506,8088244,0.99,Alternative & Punk,Blood Sugar Sex Magik,Red Hot Chili Peppers\nMellowship Slinky In B Major,Anthony Kiedis/Chad Smith/Flea/John Frusciante,240091,7971384,0.99,Alternative & Punk,Blood Sugar Sex Magik,Red Hot Chili Peppers\nThe Righteous & The Wicked,Anthony Kiedis/Chad Smith/Flea/John Frusciante,248084,8134096,0.99,Alternative & Punk,Blood Sugar Sex Magik,Red Hot Chili Peppers\nGive It Away,Anthony Kiedis/Chad Smith/Flea/John Frusciante,283010,9308997,0.99,Alternative & Punk,Blood Sugar Sex Magik,Red Hot Chili Peppers\nBlood Sugar Sex Magik,Anthony Kiedis/Chad Smith/Flea/John Frusciante,271229,8940573,0.99,Alternative & Punk,Blood Sugar Sex Magik,Red Hot Chili Peppers\nUnder The Bridge,Anthony Kiedis/Chad Smith/Flea/John Frusciante,264359,8682716,0.99,Alternative & Punk,Blood Sugar Sex Magik,Red Hot Chili Peppers\nNaked In The Rain,Anthony Kiedis/Chad Smith/Flea/John Frusciante,265717,8724674,0.99,Alternative & Punk,Blood Sugar Sex Magik,Red Hot Chili Peppers\nApache Rose Peacock,Anthony Kiedis/Chad Smith/Flea/John Frusciante,282226,9312588,0.99,Alternative & Punk,Blood Sugar Sex Magik,Red Hot Chili Peppers\nThe Greeting Song,Anthony Kiedis/Chad Smith/Flea/John Frusciante,193593,6346507,0.99,Alternative & Punk,Blood Sugar Sex Magik,Red Hot Chili Peppers\nMy Lovely Man,Anthony Kiedis/Chad Smith/Flea/John Frusciante,279118,9220114,0.99,Alternative & Punk,Blood Sugar Sex Magik,Red Hot Chili Peppers\nSir Psycho Sexy,Anthony Kiedis/Chad Smith/Flea/John Frusciante,496692,16354362,0.99,Alternative & Punk,Blood Sugar Sex Magik,Red Hot Chili Peppers\nThey're Red Hot,Robert Johnson,71941,2382220,0.99,Alternative & Punk,Blood Sugar Sex Magik,Red Hot Chili Peppers\nBy The Way,\"Anthony Kiedis, Flea, John Frusciante, and Chad Smith\",218017,7197430,0.99,Rock,By The Way,Red Hot Chili Peppers\nUniversally Speaking,\"Anthony Kiedis, Flea, John Frusciante, and Chad Smith\",259213,8501904,0.99,Rock,By The Way,Red Hot Chili Peppers\nThis Is The Place,\"Anthony Kiedis, Flea, John Frusciante, and Chad Smith\",257906,8469765,0.99,Rock,By The Way,Red Hot Chili Peppers\nDosed,\"Anthony Kiedis, Flea, John Frusciante, and Chad Smith\",312058,10235611,0.99,Rock,By The Way,Red Hot Chili Peppers\nDon't Forget Me,\"Anthony Kiedis, Flea, John Frusciante, and Chad Smith\",277995,9107071,0.99,Rock,By The Way,Red Hot Chili Peppers\nThe Zephyr Song,\"Anthony Kiedis, Flea, John Frusciante, and Chad Smith\",232960,7690312,0.99,Rock,By The Way,Red Hot Chili Peppers\nCan't Stop,\"Anthony Kiedis, Flea, John Frusciante, and Chad Smith\",269400,8872479,0.99,Rock,By The Way,Red Hot Chili Peppers\nI Could Die For You,\"Anthony Kiedis, Flea, John Frusciante, and Chad Smith\",193906,6333311,0.99,Rock,By The Way,Red Hot Chili Peppers\nMidnight,\"Anthony Kiedis, Flea, John Frusciante, and Chad Smith\",295810,9702450,0.99,Rock,By The Way,Red Hot Chili Peppers\nThrow Away Your Television,\"Anthony Kiedis, Flea, John Frusciante, and Chad Smith\",224574,7483526,0.99,Rock,By The Way,Red Hot Chili Peppers\nCabron,\"Anthony Kiedis, Flea, John Frusciante, and Chad Smith\",218592,7458864,0.99,Rock,By The Way,Red Hot Chili Peppers\nTear,\"Anthony Kiedis, Flea, John Frusciante, and Chad Smith\",317413,10395500,0.99,Rock,By The Way,Red Hot Chili Peppers\nOn Mercury,\"Anthony Kiedis, Flea, John Frusciante, and Chad Smith\",208509,6834762,0.99,Rock,By The Way,Red Hot Chili Peppers\nMinor Thing,\"Anthony Kiedis, Flea, John Frusciante, and Chad Smith\",217835,7148115,0.99,Rock,By The Way,Red Hot Chili Peppers\nWarm Tape,\"Anthony Kiedis, Flea, John Frusciante, and Chad Smith\",256653,8358200,0.99,Rock,By The Way,Red Hot Chili Peppers\nVenice Queen,\"Anthony Kiedis, Flea, John Frusciante, and Chad Smith\",369110,12280381,0.99,Rock,By The Way,Red Hot Chili Peppers\nAround The World,Anthony Kiedis/Chad Smith/Flea/John Frusciante,238837,7859167,0.99,Rock,Californication,Red Hot Chili Peppers\nParallel Universe,Red Hot Chili Peppers,270654,8958519,0.99,Rock,Californication,Red Hot Chili Peppers\nScar Tissue,Red Hot Chili Peppers,217469,7153744,0.99,Rock,Californication,Red Hot Chili Peppers\nOtherside,Red Hot Chili Peppers,255973,8357989,0.99,Rock,Californication,Red Hot Chili Peppers\nGet On Top,Red Hot Chili Peppers,198164,6587883,0.99,Rock,Californication,Red Hot Chili Peppers\nCalifornication,Red Hot Chili Peppers,321671,10568999,0.99,Rock,Californication,Red Hot Chili Peppers\nEasily,Red Hot Chili Peppers,231418,7504534,0.99,Rock,Californication,Red Hot Chili Peppers\nPorcelain,Anthony Kiedis/Chad Smith/Flea/John Frusciante,163787,5278793,0.99,Rock,Californication,Red Hot Chili Peppers\nEmit Remmus,Red Hot Chili Peppers,240300,7901717,0.99,Rock,Californication,Red Hot Chili Peppers\nI Like Dirt,Red Hot Chili Peppers,157727,5225917,0.99,Rock,Californication,Red Hot Chili Peppers\nThis Velvet Glove,Red Hot Chili Peppers,225280,7480537,0.99,Rock,Californication,Red Hot Chili Peppers\nSavior,Anthony Kiedis/Chad Smith/Flea/John Frusciante,292493,9551885,0.99,Rock,Californication,Red Hot Chili Peppers\nPurple Stain,Red Hot Chili Peppers,253440,8359971,0.99,Rock,Californication,Red Hot Chili Peppers\nRight On Time,Red Hot Chili Peppers,112613,3722219,0.99,Rock,Californication,Red Hot Chili Peppers\nRoad Trippin',Red Hot Chili Peppers,205635,6685831,0.99,Rock,Californication,Red Hot Chili Peppers\nThe Spirit Of Radio,Geddy Lee And Alex Lifeson/Geddy Lee And Neil Peart/Rush,299154,9862012,0.99,Rock,Retrospective I (1974-1980),Rush\nThe Trees,Geddy Lee And Alex Lifeson/Geddy Lee And Neil Peart/Rush,285126,9345473,0.99,Rock,Retrospective I (1974-1980),Rush\nSomething For Nothing,Geddy Lee And Alex Lifeson/Geddy Lee And Neil Peart/Rush,240770,7898395,0.99,Rock,Retrospective I (1974-1980),Rush\nFreewill,Geddy Lee And Alex Lifeson/Geddy Lee And Neil Peart/Rush,324362,10694110,0.99,Rock,Retrospective I (1974-1980),Rush\nXanadu,Geddy Lee And Alex Lifeson/Geddy Lee And Neil Peart/Rush,667428,21753168,0.99,Rock,Retrospective I (1974-1980),Rush\nBastille Day,Geddy Lee And Alex Lifeson/Geddy Lee And Neil Peart/Rush,280528,9264769,0.99,Rock,Retrospective I (1974-1980),Rush\nBy-Tor And The Snow Dog,Geddy Lee And Alex Lifeson/Geddy Lee And Neil Peart/Rush,519888,17076397,0.99,Rock,Retrospective I (1974-1980),Rush\nAnthem,Geddy Lee And Alex Lifeson/Geddy Lee And Neil Peart/Rush,264515,8693343,0.99,Rock,Retrospective I (1974-1980),Rush\nCloser To The Heart,Geddy Lee And Alex Lifeson/Geddy Lee And Neil Peart/Rush,175412,5767005,0.99,Rock,Retrospective I (1974-1980),Rush\n2112 Overture,Geddy Lee And Alex Lifeson/Geddy Lee And Neil Peart/Rush,272718,8898066,0.99,Rock,Retrospective I (1974-1980),Rush\nThe Temples Of Syrinx,Geddy Lee And Alex Lifeson/Geddy Lee And Neil Peart/Rush,133459,4360163,0.99,Rock,Retrospective I (1974-1980),Rush\nLa Villa Strangiato,Geddy Lee And Alex Lifeson/Geddy Lee And Neil Peart/Rush,577488,19137855,0.99,Rock,Retrospective I (1974-1980),Rush\nFly By Night,Geddy Lee And Alex Lifeson/Geddy Lee And Neil Peart/Rush,202318,6683061,0.99,Rock,Retrospective I (1974-1980),Rush\nFinding My Way,Geddy Lee And Alex Lifeson/Geddy Lee And Neil Peart/Rush,305528,9985701,0.99,Rock,Retrospective I (1974-1980),Rush\nJingo,M.Babatunde Olantunji,592953,19736495,0.99,Rock,Santana - As Years Go By,Santana\nEl Corazon Manda,E.Weiss,713534,23519583,0.99,Rock,Santana - As Years Go By,Santana\nLa Puesta Del Sol,E.Weiss,628062,20614621,0.99,Rock,Santana - As Years Go By,Santana\nPersuasion,Carlos Santana,318432,10354751,0.99,Rock,Santana - As Years Go By,Santana\nAs The Years Go by,Albert King,233064,7566829,0.99,Rock,Santana - As Years Go By,Santana\nSoul Sacrifice,Carlos Santana,296437,9801120,0.99,Rock,Santana - As Years Go By,Santana\nFried Neckbones And Home Fries,W.Correa,638563,20939646,0.99,Rock,Santana - As Years Go By,Santana\nSantana Jam,Carlos Santana,882834,29207100,0.99,Rock,Santana - As Years Go By,Santana\nEvil Ways,,475402,15289235,0.99,Rock,Santana Live,Santana\nWe've Got To Get Together/Jingo,,1070027,34618222,0.99,Rock,Santana Live,Santana\nRock Me,,94720,3037596,0.99,Rock,Santana Live,Santana\nJust Ain't Good Enough,,850259,27489067,0.99,Rock,Santana Live,Santana\nFunky Piano,,934791,30200730,0.99,Rock,Santana Live,Santana\nThe Way You Do To Mer,,618344,20028702,0.99,Rock,Santana Live,Santana\nÁgua E Fogo,Chico Amaral/Edgard Scandurra/Samuel Rosa,278987,9272272,0.99,Rock,Maquinarama,Skank\nTrês Lados,Chico Amaral/Samuel Rosa,233665,7699609,0.99,Rock,Maquinarama,Skank\nEla Desapareceu,Chico Amaral/Samuel Rosa,250122,8289200,0.99,Rock,Maquinarama,Skank\nBalada Do Amor Inabalável,Fausto Fawcett/Samuel Rosa,240613,8025816,0.99,Rock,Maquinarama,Skank\nCanção Noturna,Chico Amaral/Lelo Zanettik,238628,7874774,0.99,Rock,Maquinarama,Skank\nMuçulmano,\"Leão, Rodrigo F./Samuel Rosa\",249600,8270613,0.99,Rock,Maquinarama,Skank\nMaquinarama,Chico Amaral/Samuel Rosa,245629,8213710,0.99,Rock,Maquinarama,Skank\nRebelião,Chico Amaral/Samuel Rosa,298527,9817847,0.99,Rock,Maquinarama,Skank\nA Última Guerra,\"Leão, Rodrigo F./Lô Borges/Samuel Rosa\",314723,10480391,0.99,Rock,Maquinarama,Skank\nFica,Chico Amaral/Samuel Rosa,272169,8980972,0.99,Rock,Maquinarama,Skank\nAli,Nando Reis/Samuel Rosa,306390,10110351,0.99,Rock,Maquinarama,Skank\nPreto Damião,Chico Amaral/Samuel Rosa,264568,8697658,0.99,Rock,Maquinarama,Skank\nÉ Uma Partida De Futebol,Samuel Rosa,1071,38747,0.99,Rock,O Samba Poconé,Skank\nEu Disse A Ela,Samuel Rosa,254223,8479463,0.99,Rock,O Samba Poconé,Skank\nZé Trindade,Samuel Rosa,247954,8331310,0.99,Rock,O Samba Poconé,Skank\nGarota Nacional,Samuel Rosa,317492,10511239,0.99,Rock,O Samba Poconé,Skank\nTão Seu,Samuel Rosa,243748,8133126,0.99,Rock,O Samba Poconé,Skank\nSem Terra,Samuel Rosa,279353,9196411,0.99,Rock,O Samba Poconé,Skank\nOs Exilados,Samuel Rosa,245551,8222095,0.99,Rock,O Samba Poconé,Skank\nUm Dia Qualquer,Samuel Rosa,292414,9805570,0.99,Rock,O Samba Poconé,Skank\nLos Pretos,Samuel Rosa,239229,8025667,0.99,Rock,O Samba Poconé,Skank\nSul Da América,Samuel Rosa,254928,8484871,0.99,Rock,O Samba Poconé,Skank\nPoconé,Samuel Rosa,318406,10771610,0.99,Rock,O Samba Poconé,Skank\nLucky 13,Billy Corgan,189387,6200617,0.99,Alternative & Punk,Judas 0: B-Sides and Rarities,Smashing Pumpkins\nAeroplane Flies High,Billy Corgan,473391,15408329,0.99,Alternative & Punk,Judas 0: B-Sides and Rarities,Smashing Pumpkins\nBecause You Are,Billy Corgan,226403,7405137,0.99,Alternative & Punk,Judas 0: B-Sides and Rarities,Smashing Pumpkins\nSlow Dawn,Billy Corgan,192339,6269057,0.99,Alternative & Punk,Judas 0: B-Sides and Rarities,Smashing Pumpkins\nBelieve,James Iha,192940,6320652,0.99,Alternative & Punk,Judas 0: B-Sides and Rarities,Smashing Pumpkins\nMy Mistake,Billy Corgan,240901,7843477,0.99,Alternative & Punk,Judas 0: B-Sides and Rarities,Smashing Pumpkins\nMarquis In Spades,Billy Corgan,192731,6304789,0.99,Alternative & Punk,Judas 0: B-Sides and Rarities,Smashing Pumpkins\nHere's To The Atom Bomb,Billy Corgan,266893,8763140,0.99,Alternative & Punk,Judas 0: B-Sides and Rarities,Smashing Pumpkins\nSparrow,Billy Corgan,176822,5696989,0.99,Alternative & Punk,Judas 0: B-Sides and Rarities,Smashing Pumpkins\nWaiting,Billy Corgan,228336,7627641,0.99,Alternative & Punk,Judas 0: B-Sides and Rarities,Smashing Pumpkins\nSaturnine,Billy Corgan,229877,7523502,0.99,Alternative & Punk,Judas 0: B-Sides and Rarities,Smashing Pumpkins\nRock On,David Cook,366471,12133825,0.99,Alternative & Punk,Judas 0: B-Sides and Rarities,Smashing Pumpkins\nSet The Ray To Jerry,Billy Corgan,249364,8215184,0.99,Alternative & Punk,Judas 0: B-Sides and Rarities,Smashing Pumpkins\nWinterlong,Billy Corgan,299389,9670616,0.99,Alternative & Punk,Judas 0: B-Sides and Rarities,Smashing Pumpkins\nSoot & Stars,Billy Corgan,399986,12866557,0.99,Alternative & Punk,Judas 0: B-Sides and Rarities,Smashing Pumpkins\nBlissed & Gone,Billy Corgan,286302,9305998,0.99,Alternative & Punk,Judas 0: B-Sides and Rarities,Smashing Pumpkins\nSiva,Billy Corgan,261172,8576622,0.99,Alternative & Punk,Rotten Apples: Greatest Hits,Smashing Pumpkins\nRhinocerous,Billy Corgan,353462,11526684,0.99,Alternative & Punk,Rotten Apples: Greatest Hits,Smashing Pumpkins\nDrown,Billy Corgan,270497,8883496,0.99,Alternative & Punk,Rotten Apples: Greatest Hits,Smashing Pumpkins\nCherub Rock,Billy Corgan,299389,9786739,0.99,Alternative & Punk,Rotten Apples: Greatest Hits,Smashing Pumpkins\nToday,Billy Corgan,202213,6596933,0.99,Alternative & Punk,Rotten Apples: Greatest Hits,Smashing Pumpkins\nDisarm,Billy Corgan,198556,6508249,0.99,Alternative & Punk,Rotten Apples: Greatest Hits,Smashing Pumpkins\nLandslide,Stevie Nicks,190275,6187754,0.99,Alternative & Punk,Rotten Apples: Greatest Hits,Smashing Pumpkins\nBullet With Butterfly Wings,Billy Corgan,257306,8431747,0.99,Alternative & Punk,Rotten Apples: Greatest Hits,Smashing Pumpkins\n1979,Billy Corgan,263653,8728470,0.99,Alternative & Punk,Rotten Apples: Greatest Hits,Smashing Pumpkins\nZero,Billy Corgan,161123,5267176,0.99,Alternative & Punk,Rotten Apples: Greatest Hits,Smashing Pumpkins\n\"Tonight, Tonight\",Billy Corgan,255686,8351543,0.99,Alternative & Punk,Rotten Apples: Greatest Hits,Smashing Pumpkins\nEye,Billy Corgan,294530,9784201,0.99,Alternative & Punk,Rotten Apples: Greatest Hits,Smashing Pumpkins\nAva Adore,Billy Corgan,261433,8590412,0.99,Alternative & Punk,Rotten Apples: Greatest Hits,Smashing Pumpkins\nPerfect,Billy Corgan,203023,6734636,0.99,Alternative & Punk,Rotten Apples: Greatest Hits,Smashing Pumpkins\nThe Everlasting Gaze,Billy Corgan,242155,7844404,0.99,Alternative & Punk,Rotten Apples: Greatest Hits,Smashing Pumpkins\nStand Inside Your Love,Billy Corgan,253753,8270113,0.99,Alternative & Punk,Rotten Apples: Greatest Hits,Smashing Pumpkins\nReal Love,Billy Corgan,250697,8025896,0.99,Alternative & Punk,Rotten Apples: Greatest Hits,Smashing Pumpkins\n[Untitled],Billy Corgan,231784,7689713,0.99,Alternative & Punk,Rotten Apples: Greatest Hits,Smashing Pumpkins\nNothing To Say,Chris Cornell/Kim Thayil,238027,7744833,0.99,Rock,A-Sides,Soundgarden\nFlower,Chris Cornell/Kim Thayil,208822,6830732,0.99,Rock,A-Sides,Soundgarden\nLoud Love,Chris Cornell,297456,9660953,0.99,Rock,A-Sides,Soundgarden\nHands All Over,Chris Cornell/Kim Thayil,362475,11893108,0.99,Rock,A-Sides,Soundgarden\nGet On The Snake,Chris Cornell/Kim Thayil,225123,7313744,0.99,Rock,A-Sides,Soundgarden\nJesus Christ Pose,Ben Shepherd/Chris Cornell/Kim Thayil/Matt Cameron,352966,11739886,0.99,Rock,A-Sides,Soundgarden\nOutshined,Chris Cornell,312476,10274629,0.99,Rock,A-Sides,Soundgarden\nRusty Cage,Chris Cornell,267728,8779485,0.99,Rock,A-Sides,Soundgarden\nSpoonman,Chris Cornell,248476,8289906,0.99,Rock,A-Sides,Soundgarden\nThe Day I Tried To Live,Chris Cornell,321175,10507137,0.99,Rock,A-Sides,Soundgarden\nBlack Hole Sun,Soundgarden,320365,10425229,0.99,Rock,A-Sides,Soundgarden\nFell On Black Days,Chris Cornell,282331,9256082,0.99,Rock,A-Sides,Soundgarden\nPretty Noose,Chris Cornell,253570,8317931,0.99,Rock,A-Sides,Soundgarden\nBurden In My Hand,Chris Cornell,292153,9659911,0.99,Rock,A-Sides,Soundgarden\nBlow Up The Outside World,Chris Cornell,347898,11379527,0.99,Rock,A-Sides,Soundgarden\nTy Cobb,Ben Shepherd/Chris Cornell,188786,6233136,0.99,Rock,A-Sides,Soundgarden\nBleed Together,Chris Cornell,232202,7597074,0.99,Rock,A-Sides,Soundgarden\nMorning Dance,Jay Beckenstein,238759,8101979,0.99,Jazz,Morning Dance,Spyro Gyra\nJubilee,Jeremy Wall,275147,9151846,0.99,Jazz,Morning Dance,Spyro Gyra\nRasul,Jeremy Wall,238315,7854737,0.99,Jazz,Morning Dance,Spyro Gyra\nSong For Lorraine,Jay Beckenstein,240091,8101723,0.99,Jazz,Morning Dance,Spyro Gyra\nStarburst,Jeremy Wall,291500,9768399,0.99,Jazz,Morning Dance,Spyro Gyra\nHeliopolis,Jay Beckenstein,338729,11365655,0.99,Jazz,Morning Dance,Spyro Gyra\nIt Doesn't Matter,Chet Catallo,270027,9034177,0.99,Jazz,Morning Dance,Spyro Gyra\nLittle Linda,Jeremy Wall,264019,8958743,0.99,Jazz,Morning Dance,Spyro Gyra\nEnd Of Romanticism,Rick Strauss,320078,10553155,0.99,Jazz,Morning Dance,Spyro Gyra\nThe House Is Rockin',Doyle Bramhall/Stevie Ray Vaughan,144352,4706253,0.99,Blues,In Step,Stevie Ray Vaughan & Double Trouble\nCrossfire,B. Carter/C. Layton/R. Ellsworth/R. Wynans/T. Shannon,251219,8238033,0.99,Blues,In Step,Stevie Ray Vaughan & Double Trouble\nTightrope,Doyle Bramhall/Stevie Ray Vaughan,281155,9254906,0.99,Blues,In Step,Stevie Ray Vaughan & Double Trouble\nLet Me Love You Baby,Willie Dixon,164127,5378455,0.99,Blues,In Step,Stevie Ray Vaughan & Double Trouble\nLeave My Girl Alone,B. Guy,256365,8438021,0.99,Blues,In Step,Stevie Ray Vaughan & Double Trouble\nTravis Walk,Stevie Ray Vaughan,140826,4650979,0.99,Blues,In Step,Stevie Ray Vaughan & Double Trouble\nWall Of Denial,Doyle Bramhall/Stevie Ray Vaughan,336927,11085915,0.99,Blues,In Step,Stevie Ray Vaughan & Double Trouble\nScratch-N-Sniff,Doyle Bramhall/Stevie Ray Vaughan,163422,5353627,0.99,Blues,In Step,Stevie Ray Vaughan & Double Trouble\nLove Me Darlin',C. Burnett,201586,6650869,0.99,Blues,In Step,Stevie Ray Vaughan & Double Trouble\nRiviera Paradise,Stevie Ray Vaughan,528692,17232776,0.99,Blues,In Step,Stevie Ray Vaughan & Double Trouble\nDead And Bloated,R. DeLeo/Weiland,310386,10170433,0.99,Rock,Core,Stone Temple Pilots\nSex Type Thing,D. DeLeo/Kretz/Weiland,218723,7102064,0.99,Rock,Core,Stone Temple Pilots\nWicked Garden,D. DeLeo/R. DeLeo/Weiland,245368,7989505,0.99,Rock,Core,Stone Temple Pilots\nNo Memory,Dean Deleo,80613,2660859,0.99,Rock,Core,Stone Temple Pilots\nSin,R. DeLeo/Weiland,364800,12018823,0.99,Rock,Core,Stone Temple Pilots\nNaked Sunday,D. DeLeo/Kretz/R. DeLeo/Weiland,229720,7444201,0.99,Rock,Core,Stone Temple Pilots\nCreep,R. DeLeo/Weiland,333191,10894988,0.99,Rock,Core,Stone Temple Pilots\nPiece Of Pie,R. DeLeo/Weiland,324623,10605231,0.99,Rock,Core,Stone Temple Pilots\nPlush,R. DeLeo/Weiland,314017,10229848,0.99,Rock,Core,Stone Temple Pilots\nWet My Bed,R. DeLeo/Weiland,96914,3198627,0.99,Rock,Core,Stone Temple Pilots\nCrackerman,Kretz/R. DeLeo/Weiland,194403,6317361,0.99,Rock,Core,Stone Temple Pilots\nWhere The River Goes,D. DeLeo/Kretz/Weiland,505991,16468904,0.99,Rock,Core,Stone Temple Pilots\nSoldier Side - Intro,\"Dolmayan, John/Malakian, Daron/Odadjian, Shavo\",63764,2056079,0.99,Metal,Mezmerize,System Of A Down\nB.Y.O.B.,\"Tankian, Serj\",255555,8407935,0.99,Metal,Mezmerize,System Of A Down\nRevenga,\"Tankian, Serj\",228127,7503805,0.99,Metal,Mezmerize,System Of A Down\nCigaro,\"Tankian, Serj\",131787,4321705,0.99,Metal,Mezmerize,System Of A Down\nRadio/Video,\"Dolmayan, John/Malakian, Daron/Odadjian, Shavo\",249312,8224917,0.99,Metal,Mezmerize,System Of A Down\nThis Cocaine Makes Me Feel Like I'm On This Song,\"Tankian, Serj\",128339,4185193,0.99,Metal,Mezmerize,System Of A Down\nViolent Pornography,\"Dolmayan, John/Malakian, Daron/Odadjian, Shavo\",211435,6985960,0.99,Metal,Mezmerize,System Of A Down\nQuestion!,\"Tankian, Serj\",200698,6616398,0.99,Metal,Mezmerize,System Of A Down\nSad Statue,\"Tankian, Serj\",205897,6733449,0.99,Metal,Mezmerize,System Of A Down\nOld School Hollywood,\"Dolmayan, John/Malakian, Daron/Odadjian, Shavo\",176953,5830258,0.99,Metal,Mezmerize,System Of A Down\nLost in Hollywood,\"Tankian, Serj\",320783,10535158,0.99,Metal,Mezmerize,System Of A Down\nThe Sun Road,\"Terry Bozzio, Steve Stevens, Tony Levin\",880640,29008407,0.99,Rock,[1997] Black Light Syndrome,\"Terry Bozzio, Tony Levin & Steve Stevens\"\nDark Corners,\"Terry Bozzio, Steve Stevens, Tony Levin\",513541,16839223,0.99,Rock,[1997] Black Light Syndrome,\"Terry Bozzio, Tony Levin & Steve Stevens\"\nDuende,\"Terry Bozzio, Steve Stevens, Tony Levin\",447582,14956771,0.99,Rock,[1997] Black Light Syndrome,\"Terry Bozzio, Tony Levin & Steve Stevens\"\nBlack Light Syndrome,\"Terry Bozzio, Steve Stevens, Tony Levin\",526471,17300835,0.99,Rock,[1997] Black Light Syndrome,\"Terry Bozzio, Tony Levin & Steve Stevens\"\nFalling in Circles,\"Terry Bozzio, Steve Stevens, Tony Levin\",549093,18263248,0.99,Rock,[1997] Black Light Syndrome,\"Terry Bozzio, Tony Levin & Steve Stevens\"\nBook of Hours,\"Terry Bozzio, Steve Stevens, Tony Levin\",583366,19464726,0.99,Rock,[1997] Black Light Syndrome,\"Terry Bozzio, Tony Levin & Steve Stevens\"\nChaos-Control,\"Terry Bozzio, Steve Stevens, Tony Levin\",529841,17455568,0.99,Rock,[1997] Black Light Syndrome,\"Terry Bozzio, Tony Levin & Steve Stevens\"\nMidnight From The Inside Out,Chris Robinson/Rich Robinson,286981,9442157,0.99,Blues,Live [Disc 1],The Black Crowes\nSting Me,Chris Robinson/Rich Robinson,268094,8813561,0.99,Blues,Live [Disc 1],The Black Crowes\nThick & Thin,Chris Robinson/Rich Robinson,222720,7284377,0.99,Blues,Live [Disc 1],The Black Crowes\nGreasy Grass River,Chris Robinson/Rich Robinson,218749,7157045,0.99,Blues,Live [Disc 1],The Black Crowes\nSometimes Salvation,Chris Robinson/Rich Robinson,389146,12749424,0.99,Blues,Live [Disc 1],The Black Crowes\nCursed Diamonds,Chris Robinson/Rich Robinson,368300,12047978,0.99,Blues,Live [Disc 1],The Black Crowes\nMiracle To Me,Chris Robinson/Rich Robinson,372636,12222116,0.99,Blues,Live [Disc 1],The Black Crowes\nWiser Time,Chris Robinson/Rich Robinson,459990,15161907,0.99,Blues,Live [Disc 1],The Black Crowes\nGirl From A Pawnshop,Chris Robinson/Rich Robinson,404688,13250848,0.99,Blues,Live [Disc 1],The Black Crowes\nCosmic Fiend,Chris Robinson/Rich Robinson,308401,10115556,0.99,Blues,Live [Disc 1],The Black Crowes\nBlack Moon Creeping,Chris Robinson/Rich Robinson,359314,11740886,0.99,Blues,Live [Disc 2],The Black Crowes\nHigh Head Blues,Chris Robinson/Rich Robinson,371879,12227998,0.99,Blues,Live [Disc 2],The Black Crowes\nTitle Song,Chris Robinson/Rich Robinson,505521,16501316,0.99,Blues,Live [Disc 2],The Black Crowes\nShe Talks To Angels,Chris Robinson/Rich Robinson,361978,11837342,0.99,Blues,Live [Disc 2],The Black Crowes\nTwice As Hard,Chris Robinson/Rich Robinson,275565,9008067,0.99,Blues,Live [Disc 2],The Black Crowes\nLickin',Chris Robinson/Rich Robinson,314409,10331216,0.99,Blues,Live [Disc 2],The Black Crowes\nSoul Singing,Chris Robinson/Rich Robinson,233639,7672489,0.99,Blues,Live [Disc 2],The Black Crowes\nHard To Handle,A.Isbell/A.Jones/O.Redding,206994,6786304,0.99,Blues,Live [Disc 2],The Black Crowes\nRemedy,Chris Robinson/Rich Robinson,337084,11049098,0.99,Blues,Live [Disc 2],The Black Crowes\nWhite Riot,Joe Strummer/Mick Jones,118726,3922819,0.99,Alternative & Punk,The Singles,The Clash\nRemote Control,Joe Strummer/Mick Jones,180297,5949647,0.99,Alternative & Punk,The Singles,The Clash\nComplete Control,Joe Strummer/Mick Jones,192653,6272081,0.99,Alternative & Punk,The Singles,The Clash\nClash City Rockers,Joe Strummer/Mick Jones,227500,7555054,0.99,Alternative & Punk,The Singles,The Clash\n(White Man) In Hammersmith Palais,Joe Strummer/Mick Jones,240640,7883532,0.99,Alternative & Punk,The Singles,The Clash\nTommy Gun,Joe Strummer/Mick Jones,195526,6399872,0.99,Alternative & Punk,The Singles,The Clash\nEnglish Civil War,Mick Jones/Traditional arr. Joe Strummer,156708,5111226,0.99,Alternative & Punk,The Singles,The Clash\nI Fought The Law,Sonny Curtis,159764,5245258,0.99,Alternative & Punk,The Singles,The Clash\nLondon Calling,Joe Strummer/Mick Jones,199706,6569007,0.99,Alternative & Punk,The Singles,The Clash\nTrain In Vain,Joe Strummer/Mick Jones,189675,6329877,0.99,Alternative & Punk,The Singles,The Clash\nBankrobber,Joe Strummer/Mick Jones,272431,9067323,0.99,Alternative & Punk,The Singles,The Clash\nThe Call Up,The Clash,324336,10746937,0.99,Alternative & Punk,The Singles,The Clash\nHitsville UK,The Clash,261433,8606887,0.99,Alternative & Punk,The Singles,The Clash\nThe Magnificent Seven,The Clash,268486,8889821,0.99,Alternative & Punk,The Singles,The Clash\nThis Is Radio Clash,The Clash,249756,8366573,0.99,Alternative & Punk,The Singles,The Clash\nKnow Your Rights,The Clash,217678,7195726,0.99,Alternative & Punk,The Singles,The Clash\nRock The Casbah,The Clash,222145,7361500,0.99,Alternative & Punk,The Singles,The Clash\nShould I Stay Or Should I Go,The Clash,187219,6188688,0.99,Alternative & Punk,The Singles,The Clash\nWar (The Process),Billy Duffy/Ian Astbury,252630,8254842,0.99,Rock,Beyond Good And Evil,The Cult\nThe Saint,Billy Duffy/Ian Astbury,216215,7061584,0.99,Rock,Beyond Good And Evil,The Cult\nRise,Billy Duffy/Ian Astbury,219088,7106195,0.99,Rock,Beyond Good And Evil,The Cult\nTake The Power,Billy Duffy/Ian Astbury,235755,7650012,0.99,Rock,Beyond Good And Evil,The Cult\nBreathe,Billy Duffy/Ian Astbury/Marti Frederiksen/Mick Jones,299781,9742361,0.99,Rock,Beyond Good And Evil,The Cult\nNico,Billy Duffy/Ian Astbury,289488,9412323,0.99,Rock,Beyond Good And Evil,The Cult\nAmerican Gothic,Billy Duffy/Ian Astbury,236878,7739840,0.99,Rock,Beyond Good And Evil,The Cult\nAshes And Ghosts,Billy Duffy/Bob Rock/Ian Astbury,300591,9787692,0.99,Rock,Beyond Good And Evil,The Cult\nShape The Sky,Billy Duffy/Ian Astbury,209789,6885647,0.99,Rock,Beyond Good And Evil,The Cult\nSpeed Of Light,Billy Duffy/Bob Rock/Ian Astbury,262817,8563352,0.99,Rock,Beyond Good And Evil,The Cult\nTrue Believers,Billy Duffy/Ian Astbury,308009,9981359,0.99,Rock,Beyond Good And Evil,The Cult\nMy Bridges Burn,Billy Duffy/Ian Astbury,231862,7571370,0.99,Rock,Beyond Good And Evil,The Cult\nShe Sells Sanctuary,,253727,8368634,0.99,Rock,\"Pure Cult: The Best Of The Cult (For Rockers, Ravers, Lovers & Sinners) [UK]\",The Cult\nFire Woman,,312790,10196995,0.99,Rock,\"Pure Cult: The Best Of The Cult (For Rockers, Ravers, Lovers & Sinners) [UK]\",The Cult\nLil' Evil,,165825,5419655,0.99,Rock,\"Pure Cult: The Best Of The Cult (For Rockers, Ravers, Lovers & Sinners) [UK]\",The Cult\nSpirit Walker,,230060,7555897,0.99,Rock,\"Pure Cult: The Best Of The Cult (For Rockers, Ravers, Lovers & Sinners) [UK]\",The Cult\nThe Witch,,258768,8725403,0.99,Rock,\"Pure Cult: The Best Of The Cult (For Rockers, Ravers, Lovers & Sinners) [UK]\",The Cult\nRevolution,,256026,8371254,0.99,Rock,\"Pure Cult: The Best Of The Cult (For Rockers, Ravers, Lovers & Sinners) [UK]\",The Cult\nWild Hearted Son,,266893,8670550,0.99,Rock,\"Pure Cult: The Best Of The Cult (For Rockers, Ravers, Lovers & Sinners) [UK]\",The Cult\nLove Removal Machine,,257619,8412167,0.99,Rock,\"Pure Cult: The Best Of The Cult (For Rockers, Ravers, Lovers & Sinners) [UK]\",The Cult\nRain,,236669,7788461,0.99,Rock,\"Pure Cult: The Best Of The Cult (For Rockers, Ravers, Lovers & Sinners) [UK]\",The Cult\nEdie (Ciao Baby),,241632,7846177,0.99,Rock,\"Pure Cult: The Best Of The Cult (For Rockers, Ravers, Lovers & Sinners) [UK]\",The Cult\nHeart Of Soul,,274207,8967257,0.99,Rock,\"Pure Cult: The Best Of The Cult (For Rockers, Ravers, Lovers & Sinners) [UK]\",The Cult\nLove,,326739,10729824,0.99,Rock,\"Pure Cult: The Best Of The Cult (For Rockers, Ravers, Lovers & Sinners) [UK]\",The Cult\nWild Flower,,215536,7084321,0.99,Rock,\"Pure Cult: The Best Of The Cult (For Rockers, Ravers, Lovers & Sinners) [UK]\",The Cult\nGo West,,238158,7777749,0.99,Rock,\"Pure Cult: The Best Of The Cult (For Rockers, Ravers, Lovers & Sinners) [UK]\",The Cult\nResurrection Joe,,255451,8532840,0.99,Rock,\"Pure Cult: The Best Of The Cult (For Rockers, Ravers, Lovers & Sinners) [UK]\",The Cult\nSun King,,368431,12010865,0.99,Rock,\"Pure Cult: The Best Of The Cult (For Rockers, Ravers, Lovers & Sinners) [UK]\",The Cult\nSweet Soul Sister,,212009,6889883,0.99,Rock,\"Pure Cult: The Best Of The Cult (For Rockers, Ravers, Lovers & Sinners) [UK]\",The Cult\nEarth Mofo,,282200,9204581,0.99,Rock,\"Pure Cult: The Best Of The Cult (For Rockers, Ravers, Lovers & Sinners) [UK]\",The Cult\nBreak on Through,\"Robby Krieger, Ray Manzarek, John Densmore, Jim Morrison\",149342,4943144,0.99,Rock,The Doors,The Doors\nSoul Kitchen,\"Robby Krieger, Ray Manzarek, John Densmore, Jim Morrison\",215066,7040865,0.99,Rock,The Doors,The Doors\nThe Crystal Ship,\"Robby Krieger, Ray Manzarek, John Densmore, Jim Morrison\",154853,5052658,0.99,Rock,The Doors,The Doors\nTwentienth Century Fox,\"Robby Krieger, Ray Manzarek, John Densmore, Jim Morrison\",153913,5069211,0.99,Rock,The Doors,The Doors\nAlabama Song,Weill-Brecht,200097,6563411,0.99,Rock,The Doors,The Doors\nLight My Fire,\"Robby Krieger, Ray Manzarek, John Densmore, Jim Morrison\",428329,13963351,0.99,Rock,The Doors,The Doors\nBack Door Man,\"Willie Dixon, C. Burnett\",214360,7035636,0.99,Rock,The Doors,The Doors\nI Looked At You,\"Robby Krieger, Ray Manzarek, John Densmore, Jim Morrison\",142080,4663988,0.99,Rock,The Doors,The Doors\nEnd Of The Night,\"Robby Krieger, Ray Manzarek, John Densmore, Jim Morrison\",172695,5589732,0.99,Rock,The Doors,The Doors\nTake It As It Comes,\"Robby Krieger, Ray Manzarek, John Densmore, Jim Morrison\",137168,4512656,0.99,Rock,The Doors,The Doors\nThe End,\"Robby Krieger, Ray Manzarek, John Densmore, Jim Morrison\",701831,22927336,0.99,Rock,The Doors,The Doors\nRoxanne,G M Sumner,192992,6330159,0.99,Rock,The Police Greatest Hits,The Police\nCan't Stand Losing You,G M Sumner,181159,5971983,0.99,Rock,The Police Greatest Hits,The Police\nMessage in a Bottle,G M Sumner,291474,9647829,0.99,Rock,The Police Greatest Hits,The Police\nWalking on the Moon,G M Sumner,302080,10019861,0.99,Rock,The Police Greatest Hits,The Police\nDon't Stand so Close to Me,G M Sumner,241031,7956658,0.99,Rock,The Police Greatest Hits,The Police\n\"De Do Do Do, De Da Da Da\",G M Sumner,247196,8227075,0.99,Rock,The Police Greatest Hits,The Police\nEvery Little Thing She Does is Magic,G M Sumner,261120,8646853,0.99,Rock,The Police Greatest Hits,The Police\nInvisible Sun,G M Sumner,225593,7304320,0.99,Rock,The Police Greatest Hits,The Police\nSpirit's in the Material World,G M Sumner,181133,5986622,0.99,Rock,The Police Greatest Hits,The Police\nEvery Breath You Take,G M Sumner,254615,8364520,0.99,Rock,The Police Greatest Hits,The Police\nKing Of Pain,G M Sumner,300512,9880303,0.99,Rock,The Police Greatest Hits,The Police\nWrapped Around Your Finger,G M Sumner,315454,10361490,0.99,Rock,The Police Greatest Hits,The Police\nDon't Stand So Close to Me '86,G M Sumner,293590,9636683,0.99,Rock,The Police Greatest Hits,The Police\nMessage in a Bottle (new classic rock mix),G M Sumner,290951,9640349,0.99,Rock,The Police Greatest Hits,The Police\nTime Is On My Side,Jerry Ragavoy,179983,5855836,0.99,Rock,\"Hot Rocks, 1964-1971 (Disc 1)\",The Rolling Stones\nHeart Of Stone,Jagger/Richards,164493,5329538,0.99,Rock,\"Hot Rocks, 1964-1971 (Disc 1)\",The Rolling Stones\nPlay With Fire,Nanker Phelge,132022,4265297,0.99,Rock,\"Hot Rocks, 1964-1971 (Disc 1)\",The Rolling Stones\nSatisfaction,Jagger/Richards,226612,7398766,0.99,Rock,\"Hot Rocks, 1964-1971 (Disc 1)\",The Rolling Stones\nAs Tears Go By,Jagger/Richards/Oldham,164284,5357350,0.99,Rock,\"Hot Rocks, 1964-1971 (Disc 1)\",The Rolling Stones\nGet Off Of My Cloud,Jagger/Richards,176013,5719514,0.99,Rock,\"Hot Rocks, 1964-1971 (Disc 1)\",The Rolling Stones\nMother's Little Helper,Jagger/Richards,167549,5422434,0.99,Rock,\"Hot Rocks, 1964-1971 (Disc 1)\",The Rolling Stones\n19th Nervous Breakdown,Jagger/Richards,237923,7742984,0.99,Rock,\"Hot Rocks, 1964-1971 (Disc 1)\",The Rolling Stones\nPaint It Black,Jagger/Richards,226063,7442888,0.99,Rock,\"Hot Rocks, 1964-1971 (Disc 1)\",The Rolling Stones\nUnder My Thumb,Jagger/Richards,221387,7371799,0.99,Rock,\"Hot Rocks, 1964-1971 (Disc 1)\",The Rolling Stones\nRuby Tuesday,Jagger/Richards,197459,6433467,0.99,Rock,\"Hot Rocks, 1964-1971 (Disc 1)\",The Rolling Stones\nLet's Spend The Night Together,Jagger/Richards,217495,7137048,0.99,Rock,\"Hot Rocks, 1964-1971 (Disc 1)\",The Rolling Stones\nIntro,Jagger/Richards,49737,1618591,0.99,Rock,No Security,The Rolling Stones\nYou Got Me Rocking,Jagger/Richards,205766,6734385,0.99,Rock,No Security,The Rolling Stones\nGimmie Shelters,Jagger/Richards,382119,12528764,0.99,Rock,No Security,The Rolling Stones\nFlip The Switch,Jagger/Richards,252421,8336591,0.99,Rock,No Security,The Rolling Stones\nMemory Motel,Jagger/Richards,365844,11982431,0.99,Rock,No Security,The Rolling Stones\nCorinna,Jesse Ed Davis III/Taj Mahal,257488,8449471,0.99,Rock,No Security,The Rolling Stones\nSaint Of Me,Jagger/Richards,325694,10725160,0.99,Rock,No Security,The Rolling Stones\nWainting On A Friend,Jagger/Richards,302497,9978046,0.99,Rock,No Security,The Rolling Stones\nSister Morphine,Faithfull/Jagger/Richards,376215,12345289,0.99,Rock,No Security,The Rolling Stones\nLive With Me,Jagger/Richards,234893,7709006,0.99,Rock,No Security,The Rolling Stones\nRespectable,Jagger/Richards,215693,7099669,0.99,Rock,No Security,The Rolling Stones\nThief In The Night,De Beauport/Jagger/Richards,337266,10952756,0.99,Rock,No Security,The Rolling Stones\nThe Last Time,Jagger/Richards,287294,9498758,0.99,Rock,No Security,The Rolling Stones\nOut Of Control,Jagger/Richards,479242,15749289,0.99,Rock,No Security,The Rolling Stones\nLove Is Strong,Jagger/Richards,230896,7639774,0.99,Rock,Voodoo Lounge,The Rolling Stones\nYou Got Me Rocking,Jagger/Richards,215928,7162159,0.99,Rock,Voodoo Lounge,The Rolling Stones\nSparks Will Fly,Jagger/Richards,196466,6492847,0.99,Rock,Voodoo Lounge,The Rolling Stones\nThe Worst,Jagger/Richards,144613,4750094,0.99,Rock,Voodoo Lounge,The Rolling Stones\nNew Faces,Jagger/Richards,172146,5689122,0.99,Rock,Voodoo Lounge,The Rolling Stones\nMoon Is Up,Jagger/Richards,222119,7366316,0.99,Rock,Voodoo Lounge,The Rolling Stones\nOut Of Tears,Jagger/Richards,327418,10677236,0.99,Rock,Voodoo Lounge,The Rolling Stones\nI Go Wild,Jagger/Richards,264019,8630833,0.99,Rock,Voodoo Lounge,The Rolling Stones\nBrand New Car,Jagger/Richards,256052,8459344,0.99,Rock,Voodoo Lounge,The Rolling Stones\nSweethearts Together,Jagger/Richards,285492,9550459,0.99,Rock,Voodoo Lounge,The Rolling Stones\nSuck On The Jugular,Jagger/Richards,268225,8920566,0.99,Rock,Voodoo Lounge,The Rolling Stones\nBlinded By Rainbows,Jagger/Richards,273946,8971343,0.99,Rock,Voodoo Lounge,The Rolling Stones\nBaby Break It Down,Jagger/Richards,249417,8197309,0.99,Rock,Voodoo Lounge,The Rolling Stones\nThru And Thru,Jagger/Richards,375092,12175406,0.99,Rock,Voodoo Lounge,The Rolling Stones\nMean Disposition,Jagger/Richards,249155,8273602,0.99,Rock,Voodoo Lounge,The Rolling Stones\nWalking Wounded,The Tea Party,277968,9184345,0.99,Alternative & Punk,Tangents,The Tea Party\nTemptation,The Tea Party,205087,6711943,0.99,Alternative & Punk,Tangents,The Tea Party\nThe Messenger,Daniel Lanois,212062,6975437,0.99,Alternative & Punk,Tangents,The Tea Party\nPsychopomp,The Tea Party,315559,10295199,0.99,Alternative & Punk,Tangents,The Tea Party\nSister Awake,The Tea Party,343875,11299407,0.99,Alternative & Punk,Tangents,The Tea Party\nThe Bazaar,The Tea Party,222458,7245691,0.99,Alternative & Punk,Tangents,The Tea Party\nSave Me (Remix),The Tea Party,396303,13053839,0.99,Alternative & Punk,Tangents,The Tea Party\nFire In The Head,The Tea Party,306337,10005675,0.99,Alternative & Punk,Tangents,The Tea Party\nRelease,The Tea Party,244114,8014606,0.99,Alternative & Punk,Tangents,The Tea Party\nHeaven Coming Down,The Tea Party,241867,7846459,0.99,Alternative & Punk,Tangents,The Tea Party\nThe River (Remix),The Tea Party,343170,11193268,0.99,Alternative & Punk,Tangents,The Tea Party\nBabylon,The Tea Party,169795,5568808,0.99,Alternative & Punk,Tangents,The Tea Party\nWaiting On A Sign,The Tea Party,261903,8558590,0.99,Alternative & Punk,Tangents,The Tea Party\nLife Line,The Tea Party,277786,9082773,0.99,Alternative & Punk,Tangents,The Tea Party\nPaint It Black,Keith Richards/Mick Jagger,214752,7101572,0.99,Alternative & Punk,Tangents,The Tea Party\nTemptation,The Tea Party,205244,6719465,0.99,Alternative & Punk,Transmission,The Tea Party\nArmy Ants,The Tea Party,215405,7075838,0.99,Alternative & Punk,Transmission,The Tea Party\nPsychopomp,The Tea Party,317231,10351778,0.99,Alternative & Punk,Transmission,The Tea Party\nGyroscope,The Tea Party,177711,5810323,0.99,Alternative & Punk,Transmission,The Tea Party\nAlarum,The Tea Party,298187,9712545,0.99,Alternative & Punk,Transmission,The Tea Party\nRelease,The Tea Party,266292,8725824,0.99,Alternative & Punk,Transmission,The Tea Party\nTransmission,The Tea Party,317257,10351152,0.99,Alternative & Punk,Transmission,The Tea Party\nBabylon,The Tea Party,292466,9601786,0.99,Alternative & Punk,Transmission,The Tea Party\nPulse,The Tea Party,250253,8183872,0.99,Alternative & Punk,Transmission,The Tea Party\nEmerald,The Tea Party,289750,9543789,0.99,Alternative & Punk,Transmission,The Tea Party\nAftermath,The Tea Party,343745,11085607,0.99,Alternative & Punk,Transmission,The Tea Party\nI Can't Explain,Pete Townshend,125152,4082896,0.99,Rock,My Generation - The Very Best Of The Who,The Who\n\"Anyway, Anyhow, Anywhere\",\"Pete Townshend, Roger Daltrey\",161253,5234173,0.99,Rock,My Generation - The Very Best Of The Who,The Who\nMy Generation,John Entwistle/Pete Townshend,197825,6446634,0.99,Rock,My Generation - The Very Best Of The Who,The Who\nSubstitute,Pete Townshend,228022,7409995,0.99,Rock,My Generation - The Very Best Of The Who,The Who\nI'm A Boy,Pete Townshend,157126,5120605,0.99,Rock,My Generation - The Very Best Of The Who,The Who\nBoris The Spider,John Entwistle,149472,4835202,0.99,Rock,My Generation - The Very Best Of The Who,The Who\nHappy Jack,Pete Townshend,132310,4353063,0.99,Rock,My Generation - The Very Best Of The Who,The Who\nPictures Of Lily,Pete Townshend,164414,5329751,0.99,Rock,My Generation - The Very Best Of The Who,The Who\nI Can See For Miles,Pete Townshend,262791,8604989,0.99,Rock,My Generation - The Very Best Of The Who,The Who\nMagic Bus,Pete Townshend,197224,6452700,0.99,Rock,My Generation - The Very Best Of The Who,The Who\nPinball Wizard,John Entwistle/Pete Townshend,181890,6055580,0.99,Rock,My Generation - The Very Best Of The Who,The Who\nThe Seeker,Pete Townshend,204643,6736866,0.99,Rock,My Generation - The Very Best Of The Who,The Who\nBaba O'Riley,John Entwistle/Pete Townshend,309472,10141660,0.99,Rock,My Generation - The Very Best Of The Who,The Who\nWon't Get Fooled Again (Full Length Version),John Entwistle/Pete Townshend,513750,16855521,0.99,Rock,My Generation - The Very Best Of The Who,The Who\nLet's See Action,Pete Townshend,243513,8078418,0.99,Rock,My Generation - The Very Best Of The Who,The Who\n5.15,Pete Townshend,289619,9458549,0.99,Rock,My Generation - The Very Best Of The Who,The Who\nJoin Together,Pete Townshend,262556,8602485,0.99,Rock,My Generation - The Very Best Of The Who,The Who\nSqueeze Box,Pete Townshend,161280,5256508,0.99,Rock,My Generation - The Very Best Of The Who,The Who\nWho Are You (Single Edit Version),John Entwistle/Pete Townshend,299232,9900469,0.99,Rock,My Generation - The Very Best Of The Who,The Who\nYou Better You Bet,Pete Townshend,338520,11160877,0.99,Rock,My Generation - The Very Best Of The Who,The Who\nPrimavera,Genival Cassiano/Silvio Rochael,126615,4152604,0.99,Latin,Serie Sem Limite (Disc 1),Tim Maia\nChocolate,Tim Maia,194690,6411587,0.99,Latin,Serie Sem Limite (Disc 1),Tim Maia\nAzul Da Cor Do Mar,Tim Maia,197955,6475007,0.99,Latin,Serie Sem Limite (Disc 1),Tim Maia\nO Descobridor Dos Sete Mares,Gilson Mendonça/Michel,262974,8749583,0.99,Latin,Serie Sem Limite (Disc 1),Tim Maia\nAté Que Enfim Encontrei Você,Tim Maia,105064,3477751,0.99,Latin,Serie Sem Limite (Disc 1),Tim Maia\nCoroné Antonio Bento,\"Do Vale, João/Luiz Wanderley\",131317,4340326,0.99,Latin,Serie Sem Limite (Disc 1),Tim Maia\nNew Love,Tim Maia,237897,7786824,0.99,Latin,Serie Sem Limite (Disc 1),Tim Maia\nNão Vou Ficar,Tim Maia,172068,5642919,0.99,Latin,Serie Sem Limite (Disc 1),Tim Maia\nMúsica No Ar,Tim Maia,158511,5184891,0.99,Latin,Serie Sem Limite (Disc 1),Tim Maia\nSalve Nossa Senhora,Carlos Imperial/Edardo Araújo,115461,3827629,0.99,Latin,Serie Sem Limite (Disc 1),Tim Maia\nVocê Fugiu,Genival Cassiano,238367,7971147,0.99,Latin,Serie Sem Limite (Disc 1),Tim Maia\nCristina Nº 2,Carlos Imperial/Tim Maia,90148,2978589,0.99,Latin,Serie Sem Limite (Disc 1),Tim Maia\nCompadre,Tim Maia,171389,5631446,0.99,Latin,Serie Sem Limite (Disc 1),Tim Maia\nOver Again,Tim Maia,200489,6612634,0.99,Latin,Serie Sem Limite (Disc 1),Tim Maia\nRéu Confesso,Tim Maia,217391,7189874,0.99,Latin,Serie Sem Limite (Disc 1),Tim Maia\nO Que Me Importa,,153155,4977852,0.99,Latin,Serie Sem Limite (Disc 2),Tim Maia\nGostava Tanto De Você,,253805,8380077,0.99,Latin,Serie Sem Limite (Disc 2),Tim Maia\nVocê,,242599,7911702,0.99,Latin,Serie Sem Limite (Disc 2),Tim Maia\nNão Quero Dinheiro,,152607,5031797,0.99,Latin,Serie Sem Limite (Disc 2),Tim Maia\nEu Amo Você,,242782,7914628,0.99,Latin,Serie Sem Limite (Disc 2),Tim Maia\nA Festa Do Santo Reis,,159791,5204995,0.99,Latin,Serie Sem Limite (Disc 2),Tim Maia\nI Don't Know What To Do With Myself,,221387,7251478,0.99,Latin,Serie Sem Limite (Disc 2),Tim Maia\nPadre Cícero,,139598,4581685,0.99,Latin,Serie Sem Limite (Disc 2),Tim Maia\nNosso Adeus,,206471,6793270,0.99,Latin,Serie Sem Limite (Disc 2),Tim Maia\nCanário Do Reino,,139337,4552858,0.99,Latin,Serie Sem Limite (Disc 2),Tim Maia\nPreciso Ser Amado,,174001,5618895,0.99,Latin,Serie Sem Limite (Disc 2),Tim Maia\nBalanço,,209737,6890327,0.99,Latin,Serie Sem Limite (Disc 2),Tim Maia\nPreciso Aprender A Ser Só,,162220,5213894,0.99,Latin,Serie Sem Limite (Disc 2),Tim Maia\nEsta É A Canção,,184450,6069933,0.99,Latin,Serie Sem Limite (Disc 2),Tim Maia\nFormigueiro,,252943,8455132,0.99,Latin,Serie Sem Limite (Disc 2),Tim Maia\nComida,Titãs,322612,10786578,0.99,Alternative & Punk,Acústico,Titãs\nGo Back,Titãs,230504,7668899,0.99,Alternative & Punk,Acústico,Titãs\nPrá Dizer Adeus,Titãs,222484,7382048,0.99,Alternative & Punk,Acústico,Titãs\nFamília,Titãs,218331,7267458,0.99,Alternative & Punk,Acústico,Titãs\nOs Cegos Do Castelo,Titãs,296829,9868187,0.99,Alternative & Punk,Acústico,Titãs\nO Pulso,Titãs,199131,6566998,0.99,Alternative & Punk,Acústico,Titãs\nMarvin,Titãs,264359,8741444,0.99,Alternative & Punk,Acústico,Titãs\nNem 5 Minutos Guardados,Titãs,245995,8143797,0.99,Alternative & Punk,Acústico,Titãs\nFlores,Titãs,215510,7148017,0.99,Alternative & Punk,Acústico,Titãs\nPalavras,Titãs,158458,5285715,0.99,Alternative & Punk,Acústico,Titãs\nHereditário,Titãs,151693,5020547,0.99,Alternative & Punk,Acústico,Titãs\nA Melhor Forma,Titãs,191503,6349938,0.99,Alternative & Punk,Acústico,Titãs\nCabeça Dinossauro,Titãs,37120,1220930,0.99,Alternative & Punk,Acústico,Titãs\n32 Dentes,Titãs,184946,6157904,0.99,Alternative & Punk,Acústico,Titãs\nBichos Escrotos (Vinheta),Titãs,104986,3503755,0.99,Alternative & Punk,Acústico,Titãs\nNão Vou Lutar,Titãs,189988,6308613,0.99,Alternative & Punk,Acústico,Titãs\nHomem Primata (Vinheta),Titãs,34168,1124909,0.99,Alternative & Punk,Acústico,Titãs\nHomem Primata,Titãs,195500,6486470,0.99,Alternative & Punk,Acústico,Titãs\nPolícia (Vinheta),Titãs,56111,1824213,0.99,Alternative & Punk,Acústico,Titãs\nQuerem Meu Sangue,Titãs,212401,7069773,0.99,Alternative & Punk,Acústico,Titãs\nDiversão,Titãs,285936,9531268,0.99,Alternative & Punk,Acústico,Titãs\nTelevisão,Titãs,293668,9776548,0.99,Alternative & Punk,Acústico,Titãs\nSonifera Ilha,Branco Mello/Carlos Barmack/Ciro Pessoa/Marcelo Fromer/Toni Belloto,170684,5678290,0.99,Alternative & Punk,Volume Dois,Titãs\nLugar Nenhum,Arnaldo Antunes/Charles Gavin/Marcelo Fromer/Sérgio Britto/Toni Bellotto,195840,6472780,0.99,Alternative & Punk,Volume Dois,Titãs\nSua Impossivel Chance,Nando Reis,246622,8073248,0.99,Alternative & Punk,Volume Dois,Titãs\nDesordem,Charles Gavin/Marcelo Fromer/Sérgio Britto,213289,7067340,0.99,Alternative & Punk,Volume Dois,Titãs\nNão Vou Me Adaptar,Arnaldo Antunes,221831,7304656,0.99,Alternative & Punk,Volume Dois,Titãs\nDomingo,Sérgio Britto/Toni Bellotto,208613,6883180,0.99,Alternative & Punk,Volume Dois,Titãs\nAmanhã Não Se Sabe,Sérgio Britto,189440,6243967,0.99,Alternative & Punk,Volume Dois,Titãs\nCaras Como Eu,Toni Bellotto,183092,5999048,0.99,Alternative & Punk,Volume Dois,Titãs\nSenhora E Senhor,Arnaldo Anutnes/Marcelo Fromer/Paulo Miklos,203702,6733733,0.99,Alternative & Punk,Volume Dois,Titãs\nEra Uma Vez,Arnaldo Anutnes/Branco Mello/Marcelo Fromer/Sergio Brotto/Toni Bellotto,224261,7453156,0.99,Alternative & Punk,Volume Dois,Titãs\nMiséria,\"Arnaldo Antunes/Britto, SergioMiklos, Paulo\",262191,8727645,0.99,Alternative & Punk,Volume Dois,Titãs\nInsensível,Sérgio Britto,207830,6893664,0.99,Alternative & Punk,Volume Dois,Titãs\nEu E Ela,Nando Reis,276035,9138846,0.99,Alternative & Punk,Volume Dois,Titãs\nToda Cor,Ciro Pressoa/Marcelo Fromer,209084,6939176,0.99,Alternative & Punk,Volume Dois,Titãs\nÉ Preciso Saber Viver,Erasmo Carlos/Roberto Carlos,251115,8271418,0.99,Alternative & Punk,Volume Dois,Titãs\nSenhor Delegado/Eu Não Aguento,Antonio Lopes,156656,5277983,0.99,Alternative & Punk,Volume Dois,Titãs\nBattlestar Galactica: The Story So Far,,2622250,490750393,1.99,Science Fiction,Battlestar Galactica: The Story So Far,Battlestar Galactica\nOccupation / Precipice,,5286953,1054423946,1.99,TV Shows,\"Battlestar Galactica, Season 3\",Battlestar Galactica\n\"Exodus, Pt. 1\",,2621708,475079441,1.99,TV Shows,\"Battlestar Galactica, Season 3\",Battlestar Galactica\n\"Exodus, Pt. 2\",,2618000,466820021,1.99,TV Shows,\"Battlestar Galactica, Season 3\",Battlestar Galactica\nCollaborators,,2626626,483484911,1.99,TV Shows,\"Battlestar Galactica, Season 3\",Battlestar Galactica\nTorn,,2631291,495262585,1.99,TV Shows,\"Battlestar Galactica, Season 3\",Battlestar Galactica\nA Measure of Salvation,,2563938,489715554,1.99,Science Fiction,\"Battlestar Galactica, Season 3\",Battlestar Galactica\nHero,,2713755,506896959,1.99,Science Fiction,\"Battlestar Galactica, Season 3\",Battlestar Galactica\nUnfinished Business,,2622038,528499160,1.99,Science Fiction,\"Battlestar Galactica, Season 3\",Battlestar Galactica\nThe Passage,,2623875,490375760,1.99,Science Fiction,\"Battlestar Galactica, Season 3\",Battlestar Galactica\nThe Eye of Jupiter,,2618750,517909587,1.99,Science Fiction,\"Battlestar Galactica, Season 3\",Battlestar Galactica\nRapture,,2624541,508406153,1.99,Science Fiction,\"Battlestar Galactica, Season 3\",Battlestar Galactica\nTaking a Break from All Your Worries,,2624207,492700163,1.99,Science Fiction,\"Battlestar Galactica, Season 3\",Battlestar Galactica\nThe Woman King,,2626376,552893447,1.99,Science Fiction,\"Battlestar Galactica, Season 3\",Battlestar Galactica\nA Day In the Life,,2620245,462818231,1.99,Science Fiction,\"Battlestar Galactica, Season 3\",Battlestar Galactica\nDirty Hands,,2627961,537648614,1.99,Science Fiction,\"Battlestar Galactica, Season 3\",Battlestar Galactica\nMaelstrom,,2622372,514154275,1.99,Science Fiction,\"Battlestar Galactica, Season 3\",Battlestar Galactica\nThe Son Also Rises,,2621830,499258498,1.99,Science Fiction,\"Battlestar Galactica, Season 3\",Battlestar Galactica\n\"Crossroads, Pt. 1\",,2622622,486233524,1.99,Sci Fi & Fantasy,\"Battlestar Galactica, Season 3\",Battlestar Galactica\n\"Crossroads, Pt. 2\",,2869953,497335706,1.99,Sci Fi & Fantasy,\"Battlestar Galactica, Season 3\",Battlestar Galactica\nGenesis,,2611986,515671080,1.99,TV Shows,\"Heroes, Season 1\",Heroes\nDon't Look Back,,2571154,493628775,1.99,Drama,\"Heroes, Season 1\",Heroes\nOne Giant Leap,,2607649,521616246,1.99,Drama,\"Heroes, Season 1\",Heroes\nCollision,,2605480,526182322,1.99,Drama,\"Heroes, Season 1\",Heroes\nHiros,,2533575,488835454,1.99,Drama,\"Heroes, Season 1\",Heroes\nBetter Halves,,2573031,549353481,1.99,Drama,\"Heroes, Season 1\",Heroes\nNothing to Hide,,2605647,510058181,1.99,TV Shows,\"Heroes, Season 1\",Heroes\nSeven Minutes to Midnight,,2613988,515590682,1.99,Drama,\"Heroes, Season 1\",Heroes\nHomecoming,,2601351,516015339,1.99,Drama,\"Heroes, Season 1\",Heroes\nSix Months Ago,,2602852,505133869,1.99,TV Shows,\"Heroes, Season 1\",Heroes\nFallout,,2594761,501145440,1.99,Drama,\"Heroes, Season 1\",Heroes\nThe Fix,,2600266,507026323,1.99,Drama,\"Heroes, Season 1\",Heroes\nDistractions,,2590382,537111289,1.99,Drama,\"Heroes, Season 1\",Heroes\nRun!,,2602602,542936677,1.99,Drama,\"Heroes, Season 1\",Heroes\nUnexpected,,2598139,511777758,1.99,Drama,\"Heroes, Season 1\",Heroes\nCompany Man,,2601226,493168135,1.99,Drama,\"Heroes, Season 1\",Heroes\nCompany Man,,2601101,503786316,1.99,Drama,\"Heroes, Season 1\",Heroes\nParasite,,2602727,487461520,1.99,Drama,\"Heroes, Season 1\",Heroes\n.07%,,2585794,541715199,1.99,Drama,\"Heroes, Season 1\",Heroes\nFive Years Gone,,2587712,530551890,1.99,Drama,\"Heroes, Season 1\",Heroes\nThe Hard Part,,2601017,475996611,1.99,Drama,\"Heroes, Season 1\",Heroes\nLandslide,,2600725,518677861,1.99,Drama,\"Heroes, Season 1\",Heroes\nHow to Stop an Exploding Man,,2687103,487881159,1.99,Drama,\"Heroes, Season 1\",Heroes\nA Tale of Two Cities,,2636970,513691652,1.99,TV Shows,\"Lost, Season 3\",Lost\nThe Glass Ballerina,,2637458,535729216,1.99,Drama,\"Lost, Season 3\",Lost\nFurther Instructions,,2563980,502041019,1.99,TV Shows,\"Lost, Season 3\",Lost\nEvery Man for Himself,,2637387,513803546,1.99,Drama,\"Lost, Season 3\",Lost\nThe Cost of Living,,2637500,505647192,1.99,TV Shows,\"Lost, Season 3\",Lost\nI Do,,2627791,504676825,1.99,TV Shows,\"Lost, Season 3\",Lost\nNot In Portland,,2637303,499061234,1.99,Drama,\"Lost, Season 3\",Lost\nNot In Portland,,2637345,510546847,1.99,Drama,\"Lost, Season 3\",Lost\nFlashes Before Your Eyes,,2636636,537760755,1.99,Drama,\"Lost, Season 3\",Lost\nLost Survival Guide,,2632590,486675063,1.99,Drama,\"Lost, Season 3\",Lost\nStranger In a Strange Land,,2636428,505056021,1.99,Drama,\"Lost, Season 3\",Lost\nTricia Tanaka Is Dead,,2635010,548197162,1.99,Drama,\"Lost, Season 3\",Lost\nEnter 77,,2629796,517521422,1.99,Drama,\"Lost, Season 3\",Lost\nPar Avion,,2629879,517079642,1.99,Drama,\"Lost, Season 3\",Lost\nThe Man from Tallahassee,,2637637,550893556,1.99,Drama,\"Lost, Season 3\",Lost\nExposé,,2593760,511338017,1.99,Drama,\"Lost, Season 3\",Lost\nLeft Behind,,2635343,538491964,1.99,Drama,\"Lost, Season 3\",Lost\nOne of Us,,2638096,502387276,1.99,Drama,\"Lost, Season 3\",Lost\nCatch-22,,2561394,489773399,1.99,Drama,\"Lost, Season 3\",Lost\nD.O.C.,,2616032,518556641,1.99,Drama,\"Lost, Season 3\",Lost\nThe Brig,,2617325,488919543,1.99,Drama,\"Lost, Season 3\",Lost\nThe Man Behind the Curtain,,2615990,493951081,1.99,Drama,\"Lost, Season 3\",Lost\nGreatest Hits,,2617117,522102916,1.99,Drama,\"Lost, Season 3\",Lost\nThrough a Looking Glass,,5088838,1059546140,1.99,Drama,\"Lost, Season 3\",Lost\n\"Through the Looking Glass, Pt. 2\",,2617117,550943353,1.99,Drama,\"Lost, Season 3\",Lost\n\"Through the Looking Glass, Pt. 1\",,2610860,493211809,1.99,Drama,\"Lost, Season 3\",Lost\n\"Lost (Pilot, Part 1) [Premiere]\",,2548875,217124866,1.99,TV Shows,\"Lost, Season 1\",Lost\n\"Lost (Pilot, Part 2)\",,2436583,204995876,1.99,TV Shows,\"Lost, Season 1\",Lost\nTabula Rasa,,2627105,210526410,1.99,TV Shows,\"Lost, Season 1\",Lost\nWalkabout,,2587370,207748198,1.99,TV Shows,\"Lost, Season 1\",Lost\nWhite Rabbit,,2571965,201654606,1.99,TV Shows,\"Lost, Season 1\",Lost\nHouse of the Rising Sun,,2590032,210379525,1.99,TV Shows,\"Lost, Season 1\",Lost\nThe Moth,,2631327,228896396,1.99,TV Shows,\"Lost, Season 1\",Lost\nConfidence Man,,2615244,223756475,1.99,TV Shows,\"Lost, Season 1\",Lost\nSolitary,,2612894,207045178,1.99,TV Shows,\"Lost, Season 1\",Lost\nRaised By Another,,2590459,223623810,1.99,TV Shows,\"Lost, Season 1\",Lost\nAll the Best Cowboys Have Daddy Issues,,2555492,211743651,1.99,TV Shows,\"Lost, Season 1\",Lost\nWhatever the Case May Be,,2616410,183867185,1.99,TV Shows,\"Lost, Season 1\",Lost\nHearts and Minds,,2619462,207607466,1.99,TV Shows,\"Lost, Season 1\",Lost\nSpecial,,2618530,219961967,1.99,TV Shows,\"Lost, Season 1\",Lost\nHomecoming,,2515882,210675221,1.99,TV Shows,\"Lost, Season 1\",Lost\nOutlaws,,2619887,206500939,1.99,TV Shows,\"Lost, Season 1\",Lost\n...In Translation,,2604575,215441983,1.99,TV Shows,\"Lost, Season 1\",Lost\nNumbers,,2609772,214709143,1.99,TV Shows,\"Lost, Season 1\",Lost\nDeus Ex Machina,,2582009,214996732,1.99,TV Shows,\"Lost, Season 1\",Lost\nDo No Harm,,2618487,212039309,1.99,TV Shows,\"Lost, Season 1\",Lost\nThe Greater Good,,2617784,214130273,1.99,TV Shows,\"Lost, Season 1\",Lost\nBorn to Run,,2618619,213772057,1.99,TV Shows,\"Lost, Season 1\",Lost\nExodus (Part 1),,2620747,213107744,1.99,TV Shows,\"Lost, Season 1\",Lost\nExodus (Part 2) [Season Finale],,2605557,208667059,1.99,TV Shows,\"Lost, Season 1\",Lost\nExodus (Part 3) [Season Finale],,2619869,197937785,1.99,TV Shows,\"Lost, Season 1\",Lost\n\"Man of Science, Man of Faith (Premiere)\",,2612250,543342028,1.99,TV Shows,\"Lost, Season 2\",Lost\nAdrift,,2564958,502663995,1.99,TV Shows,\"Lost, Season 2\",Lost\nOrientation,,2609083,500600434,1.99,TV Shows,\"Lost, Season 2\",Lost\nEverybody Hates Hugo,,2609192,498163145,1.99,TV Shows,\"Lost, Season 2\",Lost\n...And Found,,2563833,500330548,1.99,TV Shows,\"Lost, Season 2\",Lost\nAbandoned,,2587041,537348711,1.99,TV Shows,\"Lost, Season 2\",Lost\nThe Other 48 Days,,2610625,535256753,1.99,TV Shows,\"Lost, Season 2\",Lost\nCollision,,2564916,475656544,1.99,TV Shows,\"Lost, Season 2\",Lost\nWhat Kate Did,,2610250,484583988,1.99,TV Shows,\"Lost, Season 2\",Lost\nThe 23rd Psalm,,2610416,487401604,1.99,TV Shows,\"Lost, Season 2\",Lost\nThe Hunting Party,,2611333,520350364,1.99,Drama,\"Lost, Season 2\",Lost\nFire + Water,,2600333,488458695,1.99,Drama,\"Lost, Season 2\",Lost\nThe Long Con,,2679583,518376636,1.99,TV Shows,\"Lost, Season 2\",Lost\nOne of Them,,2698791,542332389,1.99,Drama,\"Lost, Season 2\",Lost\nMaternity Leave,,2780416,555244214,1.99,Drama,\"Lost, Season 2\",Lost\nThe Whole Truth,,2610125,495487014,1.99,Drama,\"Lost, Season 2\",Lost\nLockdown,,2610250,543886056,1.99,Drama,\"Lost, Season 2\",Lost\nDave,,2825166,574325829,1.99,TV Shows,\"Lost, Season 2\",Lost\nS.O.S.,,2639541,517979269,1.99,TV Shows,\"Lost, Season 2\",Lost\nTwo for the Road,,2610958,502404558,1.99,Drama,\"Lost, Season 2\",Lost\n\"\"\"?\"\"\",,2782333,528227089,1.99,TV Shows,\"Lost, Season 2\",Lost\nThree Minutes,,2763666,531556853,1.99,TV Shows,\"Lost, Season 2\",Lost\n\"Live Together, Die Alone, Pt. 1\",,2478041,457364940,1.99,Drama,\"Lost, Season 2\",Lost\n\"Live Together, Die Alone, Pt. 2\",,2656531,503619265,1.99,TV Shows,\"Lost, Season 2\",Lost\nZoo Station,U2,276349,9056902,0.99,Rock,Achtung Baby,U2\nEven Better Than The Real Thing,U2,221361,7279392,0.99,Rock,Achtung Baby,U2\nOne,U2,276192,9158892,0.99,Rock,Achtung Baby,U2\nUntil The End Of The World,U2,278700,9132485,0.99,Rock,Achtung Baby,U2\nWho's Gonna Ride Your Wild Horses,U2,316551,10304369,0.99,Rock,Achtung Baby,U2\nSo Cruel,U2,349492,11527614,0.99,Rock,Achtung Baby,U2\nThe Fly,U2,268982,8825399,0.99,Rock,Achtung Baby,U2\nMysterious Ways,U2,243826,8062057,0.99,Rock,Achtung Baby,U2\nTryin' To Throw Your Arms Around The World,U2,232463,7612124,0.99,Rock,Achtung Baby,U2\nUltraviolet (Light My Way),U2,330788,10754631,0.99,Rock,Achtung Baby,U2\nAcrobat,U2,270288,8824723,0.99,Rock,Achtung Baby,U2\nLove Is Blindness,U2,263497,8531766,0.99,Rock,Achtung Baby,U2\nBeautiful Day,\"Adam Clayton, Bono, Larry Mullen, The Edge\",248163,8056723,0.99,Rock,All That You Can't Leave Behind,U2\nStuck In A Moment You Can't Get Out Of,\"Adam Clayton, Bono, Larry Mullen, The Edge\",272378,8997366,0.99,Rock,All That You Can't Leave Behind,U2\nElevation,\"Adam Clayton, Bono, Larry Mullen, The Edge\",227552,7479414,0.99,Rock,All That You Can't Leave Behind,U2\nWalk On,\"Adam Clayton, Bono, Larry Mullen, The Edge\",296280,9800861,0.99,Rock,All That You Can't Leave Behind,U2\nKite,\"Adam Clayton, Bono, Larry Mullen, The Edge\",266893,8765761,0.99,Rock,All That You Can't Leave Behind,U2\nIn A Little While,\"Adam Clayton, Bono, Larry Mullen, The Edge\",219271,7189647,0.99,Rock,All That You Can't Leave Behind,U2\nWild Honey,\"Adam Clayton, Bono, Larry Mullen, The Edge\",226768,7466069,0.99,Rock,All That You Can't Leave Behind,U2\nPeace On Earth,\"Adam Clayton, Bono, Larry Mullen, The Edge\",288496,9476171,0.99,Rock,All That You Can't Leave Behind,U2\nWhen I Look At The World,\"Adam Clayton, Bono, Larry Mullen, The Edge\",257776,8500491,0.99,Rock,All That You Can't Leave Behind,U2\nNew York,\"Adam Clayton, Bono, Larry Mullen, The Edge\",330370,10862323,0.99,Rock,All That You Can't Leave Behind,U2\nGrace,\"Adam Clayton, Bono, Larry Mullen, The Edge\",330657,10877148,0.99,Rock,All That You Can't Leave Behind,U2\nThe Three Sunrises,U2,234788,7717990,0.99,Rock,B-Sides 1980-1990,U2\nSpanish Eyes,U2,196702,6392710,0.99,Rock,B-Sides 1980-1990,U2\nSweetest Thing,U2,185103,6154896,0.99,Rock,B-Sides 1980-1990,U2\nLove Comes Tumbling,U2,282671,9328802,0.99,Rock,B-Sides 1980-1990,U2\nBass Trap,U2,213289,6834107,0.99,Rock,B-Sides 1980-1990,U2\nDancing Barefoot,Ivan Kral/Patti Smith,287895,9488294,0.99,Rock,B-Sides 1980-1990,U2\nEverlasting Love,Buzz Cason/Mac Gayden,202631,6708932,0.99,Rock,B-Sides 1980-1990,U2\nUnchained Melody,Alex North/Hy Zaret,294164,9597568,0.99,Rock,B-Sides 1980-1990,U2\nWalk To The Water,U2,289253,9523336,0.99,Rock,B-Sides 1980-1990,U2\nLuminous Times (Hold On To Love),Brian Eno/U2,277760,9015513,0.99,Rock,B-Sides 1980-1990,U2\nHallelujah Here She Comes,U2,242364,8027028,0.99,Rock,B-Sides 1980-1990,U2\nSilver And Gold,Bono,279875,9199746,0.99,Rock,B-Sides 1980-1990,U2\nEndless Deep,U2,179879,5899070,0.99,Rock,B-Sides 1980-1990,U2\nA Room At The Heartbreak Hotel,U2,274546,9015416,0.99,Rock,B-Sides 1980-1990,U2\n\"Trash, Trampoline And The Party Girl\",U2,153965,5083523,0.99,Rock,B-Sides 1980-1990,U2\nVertigo,\"Adam Clayton, Bono, Larry Mullen & The Edge\",194612,6329502,0.99,Rock,How To Dismantle An Atomic Bomb,U2\nMiracle Drug,\"Adam Clayton, Bono, Larry Mullen & The Edge\",239124,7760916,0.99,Rock,How To Dismantle An Atomic Bomb,U2\nSometimes You Can't Make It On Your Own,\"Adam Clayton, Bono, Larry Mullen & The Edge\",308976,10112863,0.99,Rock,How To Dismantle An Atomic Bomb,U2\nLove And Peace Or Else,\"Adam Clayton, Bono, Larry Mullen & The Edge\",290690,9476723,0.99,Rock,How To Dismantle An Atomic Bomb,U2\nCity Of Blinding Lights,\"Adam Clayton, Bono, Larry Mullen & The Edge\",347951,11432026,0.99,Rock,How To Dismantle An Atomic Bomb,U2\nAll Because Of You,\"Adam Clayton, Bono, Larry Mullen & The Edge\",219141,7198014,0.99,Rock,How To Dismantle An Atomic Bomb,U2\nA Man And A Woman,\"Adam Clayton, Bono, Larry Mullen & The Edge\",270132,8938285,0.99,Rock,How To Dismantle An Atomic Bomb,U2\nCrumbs From Your Table,\"Adam Clayton, Bono, Larry Mullen & The Edge\",303568,9892349,0.99,Rock,How To Dismantle An Atomic Bomb,U2\nOne Step Closer,\"Adam Clayton, Bono, Larry Mullen & The Edge\",231680,7512912,0.99,Rock,How To Dismantle An Atomic Bomb,U2\nOriginal Of The Species,\"Adam Clayton, Bono, Larry Mullen & The Edge\",281443,9230041,0.99,Rock,How To Dismantle An Atomic Bomb,U2\nYahweh,\"Adam Clayton, Bono, Larry Mullen & The Edge\",262034,8636998,0.99,Rock,How To Dismantle An Atomic Bomb,U2\nDiscotheque,\"Bono, The Edge, Adam Clayton, and Larry Mullen\",319582,10442206,0.99,Rock,Pop,U2\nDo You Feel Loved,\"Bono, The Edge, Adam Clayton, and Larry Mullen\",307539,10122694,0.99,Rock,Pop,U2\nMofo,\"Bono, The Edge, Adam Clayton, and Larry Mullen\",349178,11583042,0.99,Rock,Pop,U2\nIf God Will Send His Angels,\"Bono, The Edge, Adam Clayton, and Larry Mullen\",322533,10563329,0.99,Rock,Pop,U2\nStaring At The Sun,\"Bono, The Edge, Adam Clayton, and Larry Mullen\",276924,9082838,0.99,Rock,Pop,U2\nLast Night On Earth,\"Bono, The Edge, Adam Clayton, and Larry Mullen\",285753,9401017,0.99,Rock,Pop,U2\nGone,\"Bono, The Edge, Adam Clayton, and Larry Mullen\",266866,8746301,0.99,Rock,Pop,U2\nMiami,\"Bono, The Edge, Adam Clayton, and Larry Mullen\",293041,9741603,0.99,Rock,Pop,U2\nThe Playboy Mansion,\"Bono, The Edge, Adam Clayton, and Larry Mullen\",280555,9274144,0.99,Rock,Pop,U2\nIf You Wear That Velvet Dress,\"Bono, The Edge, Adam Clayton, and Larry Mullen\",315167,10227333,0.99,Rock,Pop,U2\nPlease,\"Bono, The Edge, Adam Clayton, and Larry Mullen\",302602,9909484,0.99,Rock,Pop,U2\nWake Up Dead Man,\"Bono, The Edge, Adam Clayton, and Larry Mullen\",292832,9515903,0.99,Rock,Pop,U2\nHelter Skelter,\"Lennon, John/McCartney, Paul\",187350,6097636,0.99,Rock,Rattle And Hum,U2\nVan Diemen's Land,\"Bono/Clayton, Adam/Mullen Jr., Larry/The Edge\",186044,5990280,0.99,Rock,Rattle And Hum,U2\nDesire,\"Bono/Clayton, Adam/Mullen Jr., Larry/The Edge\",179226,5874535,0.99,Rock,Rattle And Hum,U2\nHawkmoon 269,\"Bono/Clayton, Adam/Mullen Jr., Larry/The Edge\",382458,12494987,0.99,Rock,Rattle And Hum,U2\nAll Along The Watchtower,\"Dylan, Bob\",264568,8623572,0.99,Rock,Rattle And Hum,U2\nI Still Haven't Found What I'm Looking for,\"Bono/Clayton, Adam/Mullen Jr., Larry/The Edge\",353567,11542247,0.99,Rock,Rattle And Hum,U2\nFreedom For My People,\"Mabins, Macie/Magee, Sterling/Robinson, Bobby\",38164,1249764,0.99,Rock,Rattle And Hum,U2\nSilver And Gold,\"Bono/Clayton, Adam/Mullen Jr., Larry/The Edge\",349831,11450194,0.99,Rock,Rattle And Hum,U2\nPride (In The Name Of Love),\"Bono/Clayton, Adam/Mullen Jr., Larry/The Edge\",267807,8806361,0.99,Rock,Rattle And Hum,U2\nAngel Of Harlem,\"Bono/Clayton, Adam/Mullen Jr., Larry/The Edge\",229276,7498022,0.99,Rock,Rattle And Hum,U2\nLove Rescue Me,\"Bono/Clayton, Adam/Dylan, Bob/Mullen Jr., Larry/The Edge\",384522,12508716,0.99,Rock,Rattle And Hum,U2\nWhen Love Comes To Town,\"Bono/Clayton, Adam/Mullen Jr., Larry/The Edge\",255869,8340954,0.99,Rock,Rattle And Hum,U2\nHeartland,\"Bono/Clayton, Adam/Mullen Jr., Larry/The Edge\",303360,9867748,0.99,Rock,Rattle And Hum,U2\nGod Part II,\"Bono/Clayton, Adam/Mullen Jr., Larry/The Edge\",195604,6497570,0.99,Rock,Rattle And Hum,U2\nThe Star Spangled Banner,\"Hendrix, Jimi\",43232,1385810,0.99,Rock,Rattle And Hum,U2\nBullet The Blue Sky,\"Bono/Clayton, Adam/Mullen Jr., Larry/The Edge\",337005,10993607,0.99,Rock,Rattle And Hum,U2\nAll I Want Is You,\"Bono/Clayton, Adam/Mullen Jr., Larry/The Edge\",390243,12729820,0.99,Rock,Rattle And Hum,U2\nPride (In The Name Of Love),U2,230243,7549085,0.99,Rock,The Best Of 1980-1990,U2\nNew Year's Day,U2,258925,8491818,0.99,Rock,The Best Of 1980-1990,U2\nWith Or Without You,U2,299023,9765188,0.99,Rock,The Best Of 1980-1990,U2\nI Still Haven't Found What I'm Looking For,U2,280764,9306737,0.99,Rock,The Best Of 1980-1990,U2\nSunday Bloody Sunday,U2,282174,9269668,0.99,Rock,The Best Of 1980-1990,U2\nBad,U2,351817,11628058,0.99,Rock,The Best Of 1980-1990,U2\nWhere The Streets Have No Name,U2,276218,9042305,0.99,Rock,The Best Of 1980-1990,U2\nI Will Follow,U2,218253,7184825,0.99,Rock,The Best Of 1980-1990,U2\nThe Unforgettable Fire,U2,295183,9684664,0.99,Rock,The Best Of 1980-1990,U2\nSweetest Thing,U2 & Daragh O'Toole,183066,6071385,0.99,Rock,The Best Of 1980-1990,U2\nDesire,U2,179853,5893206,0.99,Rock,The Best Of 1980-1990,U2\nWhen Love Comes To Town,U2,258194,8479525,0.99,Rock,The Best Of 1980-1990,U2\nAngel Of Harlem,U2,230217,7527339,0.99,Rock,The Best Of 1980-1990,U2\nAll I Want Is You,U2 & Van Dyke Parks,591986,19202252,0.99,Rock,The Best Of 1980-1990,U2\nSunday Bloody Sunday,U2,278204,9140849,0.99,Rock,War,U2\nSeconds,U2,191582,6352121,0.99,Rock,War,U2\nNew Year's Day,U2,336274,11054732,0.99,Rock,War,U2\nLike A Song...,U2,287294,9365379,0.99,Rock,War,U2\nDrowning Man,U2,254458,8457066,0.99,Rock,War,U2\nThe Refugee,U2,221283,7374043,0.99,Rock,War,U2\nTwo Hearts Beat As One,U2,243487,7998323,0.99,Rock,War,U2\nRed Light,U2,225854,7453704,0.99,Rock,War,U2\nSurrender,U2,333505,11221406,0.99,Rock,War,U2\n\"\"\"40\"\"\",U2,157962,5251767,0.99,Rock,War,U2\nZooropa,U2; Bono,392359,12807979,0.99,Rock,Zooropa,U2\nBabyface,U2; Bono,241998,7942573,0.99,Rock,Zooropa,U2\nNumb,\"U2; Edge, The\",260284,8577861,0.99,Rock,Zooropa,U2\nLemon,U2; Bono,418324,13988878,0.99,Rock,Zooropa,U2\n\"Stay (Faraway, So Close!)\",U2; Bono,298475,9785480,0.99,Rock,Zooropa,U2\nDaddy's Gonna Pay For Your Crashed Car,U2; Bono,320287,10609581,0.99,Rock,Zooropa,U2\nSome Days Are Better Than Others,U2; Bono,257436,8417690,0.99,Rock,Zooropa,U2\nThe First Time,U2; Bono,225697,7247651,0.99,Rock,Zooropa,U2\nDirty Day,\"U2; Bono & Edge, The\",324440,10652877,0.99,Rock,Zooropa,U2\nThe Wanderer,U2; Bono,283951,9258717,0.99,Rock,Zooropa,U2\nBreakfast In Bed,,196179,6513325,0.99,Reggae,UB40 The Best Of - Volume Two [UK],UB40\nWhere Did I Go Wrong,,226742,7485054,0.99,Reggae,UB40 The Best Of - Volume Two [UK],UB40\nI Would Do For You,,334524,11193602,0.99,Reggae,UB40 The Best Of - Volume Two [UK],UB40\nHomely Girl,,203833,6790788,0.99,Reggae,UB40 The Best Of - Volume Two [UK],UB40\nHere I Am (Come And Take Me),,242102,8106249,0.99,Reggae,UB40 The Best Of - Volume Two [UK],UB40\nKingston Town,,226951,7638236,0.99,Reggae,UB40 The Best Of - Volume Two [UK],UB40\nWear You To The Ball,,213342,7159527,0.99,Reggae,UB40 The Best Of - Volume Two [UK],UB40\n(I Can't Help) Falling In Love With You,,207568,6905623,0.99,Reggae,UB40 The Best Of - Volume Two [UK],UB40\nHigher Ground,,260179,8665244,0.99,Reggae,UB40 The Best Of - Volume Two [UK],UB40\nBring Me Your Cup,,341498,11346114,0.99,Reggae,UB40 The Best Of - Volume Two [UK],UB40\nC'est La Vie,,270053,9031661,0.99,Reggae,UB40 The Best Of - Volume Two [UK],UB40\nReggae Music,,245106,8203931,0.99,Reggae,UB40 The Best Of - Volume Two [UK],UB40\nSuperstition,,319582,10728099,0.99,Reggae,UB40 The Best Of - Volume Two [UK],UB40\nUntil My Dying Day,,235807,7886195,0.99,Reggae,UB40 The Best Of - Volume Two [UK],UB40\nWhere Have All The Good Times Gone?,Ray Davies,186723,6063937,0.99,Rock,Diver Down,Van Halen\nHang 'Em High,Alex Van Halen/David Lee Roth/Edward Van Halen/Michael Anthony,210259,6872314,0.99,Rock,Diver Down,Van Halen\nCathedral,Alex Van Halen/David Lee Roth/Edward Van Halen/Michael Anthony,82860,2650998,0.99,Rock,Diver Down,Van Halen\nSecrets,Alex Van Halen/David Lee Roth/Edward Van Halen/Michael Anthony,206968,6803255,0.99,Rock,Diver Down,Van Halen\nIntruder,Alex Van Halen/David Lee Roth/Edward Van Halen/Michael Anthony,100153,3282142,0.99,Rock,Diver Down,Van Halen\n(Oh) Pretty Woman,Bill Dees/Roy Orbison,174680,5665828,0.99,Rock,Diver Down,Van Halen\nDancing In The Street,Ivy Jo Hunter/Marvin Gaye/William Stevenson,225985,7461499,0.99,Rock,Diver Down,Van Halen\nLittle Guitars (Intro),Alex Van Halen/David Lee Roth/Edward Van Halen/Michael Anthony,42240,1439530,0.99,Rock,Diver Down,Van Halen\nLittle Guitars,Alex Van Halen/David Lee Roth/Edward Van Halen/Michael Anthony,228806,7453043,0.99,Rock,Diver Down,Van Halen\nBig Bad Bill (Is Sweet William Now),Jack Yellen/Milton Ager,165146,5489609,0.99,Rock,Diver Down,Van Halen\nThe Full Bug,Alex Van Halen/David Lee Roth/Edward Van Halen/Michael Anthony,201116,6551013,0.99,Rock,Diver Down,Van Halen\nHappy Trails,Dale Evans,65488,2111141,0.99,Rock,Diver Down,Van Halen\nEruption,\"Edward Van Halen, Alex Van Halen, David Lee Roth, Michael Anthony\",102164,3272891,0.99,Rock,\"The Best Of Van Halen, Vol. I\",Van Halen\nAin't Talkin' 'bout Love,\"Edward Van Halen, Alex Van Halen, David Lee Roth, Michael Anthony\",228336,7569506,0.99,Rock,\"The Best Of Van Halen, Vol. I\",Van Halen\nRunnin' With The Devil,\"Edward Van Halen, Alex Van Halen, David Lee Roth, Michael Anthony\",215902,7061901,0.99,Rock,\"The Best Of Van Halen, Vol. I\",Van Halen\nDance the Night Away,\"Edward Van Halen, Alex Van Halen, David Lee Roth, Michael Anthony\",185965,6087433,0.99,Rock,\"The Best Of Van Halen, Vol. I\",Van Halen\nAnd the Cradle Will Rock...,\"Edward Van Halen, Alex Van Halen, David Lee Roth, Michael Anthony\",213968,7011402,0.99,Rock,\"The Best Of Van Halen, Vol. I\",Van Halen\nUnchained,\"Edward Van Halen, Alex Van Halen, David Lee Roth, Michael Anthony\",208953,6777078,0.99,Rock,\"The Best Of Van Halen, Vol. I\",Van Halen\nJump,\"Edward Van Halen, Alex Van Halen, David Lee Roth\",241711,7911090,0.99,Rock,\"The Best Of Van Halen, Vol. I\",Van Halen\nPanama,\"Edward Van Halen, Alex Van Halen, David Lee Roth\",211853,6921784,0.99,Rock,\"The Best Of Van Halen, Vol. I\",Van Halen\nWhy Can't This Be Love,Van Halen,227761,7457655,0.99,Rock,\"The Best Of Van Halen, Vol. I\",Van Halen\nDreams,\"Edward Van Halen, Alex Van Halen, Michael Anthony,/Edward Van Halen, Alex Van Halen, Michael Anthony, Sammy Hagar\",291813,9504119,0.99,Rock,\"The Best Of Van Halen, Vol. I\",Van Halen\nWhen It's Love,\"Edward Van Halen, Alex Van Halen, Michael Anthony,/Edward Van Halen, Alex Van Halen, Michael Anthony, Sammy Hagar\",338991,11049966,0.99,Rock,\"The Best Of Van Halen, Vol. I\",Van Halen\nPoundcake,\"Edward Van Halen, Alex Van Halen, Michael Anthony,/Edward Van Halen, Alex Van Halen, Michael Anthony, Sammy Hagar\",321854,10366978,0.99,Rock,\"The Best Of Van Halen, Vol. I\",Van Halen\nRight Now,Van Halen,321828,10503352,0.99,Rock,\"The Best Of Van Halen, Vol. I\",Van Halen\nCan't Stop Loving You,Van Halen,248502,8107896,0.99,Rock,\"The Best Of Van Halen, Vol. I\",Van Halen\nHumans Being,\"Edward Van Halen, Alex Van Halen, Michael Anthony,/Edward Van Halen, Alex Van Halen, Michael Anthony, Sammy Hagar\",308950,10014683,0.99,Rock,\"The Best Of Van Halen, Vol. I\",Van Halen\nCan't Get This Stuff No More,\"Edward Van Halen, Alex Van Halen, Michael Anthony,/Edward Van Halen, Alex Van Halen, Michael Anthony, David Lee Roth\",315376,10355753,0.99,Rock,\"The Best Of Van Halen, Vol. I\",Van Halen\nMe Wise Magic,\"Edward Van Halen, Alex Van Halen, Michael Anthony,/Edward Van Halen, Alex Van Halen, Michael Anthony, David Lee Roth\",366053,12013467,0.99,Rock,\"The Best Of Van Halen, Vol. I\",Van Halen\nRunnin' With The Devil,\"Edward Van Halen, Alex Van Halen, Michael Anthony and David Lee Roth\",216032,7056863,0.99,Rock,Van Halen,Van Halen\nEruption,\"Edward Van Halen, Alex Van Halen, Michael Anthony and David Lee Roth\",102556,3286026,0.99,Rock,Van Halen,Van Halen\nYou Really Got Me,Ray Davies,158589,5194092,0.99,Rock,Van Halen,Van Halen\nAin't Talkin' 'Bout Love,\"Edward Van Halen, Alex Van Halen, Michael Anthony and David Lee Roth\",230060,7617284,0.99,Rock,Van Halen,Van Halen\nI'm The One,\"Edward Van Halen, Alex Van Halen, Michael Anthony and David Lee Roth\",226507,7373922,0.99,Rock,Van Halen,Van Halen\nJamie's Cryin',\"Edward Van Halen, Alex Van Halen, Michael Anthony and David Lee Roth\",210546,6946086,0.99,Rock,Van Halen,Van Halen\nAtomic Punk,\"Edward Van Halen, Alex Van Halen, Michael Anthony and David Lee Roth\",182073,5908861,0.99,Rock,Van Halen,Van Halen\nFeel Your Love Tonight,\"Edward Van Halen, Alex Van Halen, Michael Anthony and David Lee Roth\",222850,7293608,0.99,Rock,Van Halen,Van Halen\nLittle Dreamer,\"Edward Van Halen, Alex Van Halen, Michael Anthony and David Lee Roth\",203258,6648122,0.99,Rock,Van Halen,Van Halen\nIce Cream Man,John Brim,200306,6573145,0.99,Rock,Van Halen,Van Halen\nOn Fire,\"Edward Van Halen, Alex Van Halen, Michael Anthony and David Lee Roth\",180636,5879235,0.99,Rock,Van Halen,Van Halen\nNeworld,Van Halen,105639,3495897,0.99,Rock,Van Halen III,Van Halen\nWithout You,Van Halen,390295,12619558,0.99,Rock,Van Halen III,Van Halen\nOne I Want,Van Halen,330788,10743970,0.99,Rock,Van Halen III,Van Halen\nFrom Afar,Van Halen,324414,10524554,0.99,Rock,Van Halen III,Van Halen\nDirty Water Dog,Van Halen,327392,10709202,0.99,Rock,Van Halen III,Van Halen\nOnce,Van Halen,462837,15378082,0.99,Rock,Van Halen III,Van Halen\nFire in the Hole,Van Halen,331728,10846768,0.99,Rock,Van Halen III,Van Halen\nJosephina,Van Halen,342491,11161521,0.99,Rock,Van Halen III,Van Halen\nYear to the Day,Van Halen,514612,16621333,0.99,Rock,Van Halen III,Van Halen\nPrimary,Van Halen,86987,2812555,0.99,Rock,Van Halen III,Van Halen\nBallot or the Bullet,Van Halen,342282,11212955,0.99,Rock,Van Halen III,Van Halen\nHow Many Say I,Van Halen,363937,11716855,0.99,Rock,Van Halen III,Van Halen\nSucker Train Blues,\"Dave Kushner, Duff, Matt Sorum, Scott Weiland & Slash\",267859,8738780,0.99,Rock,Contraband,Velvet Revolver\nDo It For The Kids,\"Dave Kushner, Duff, Matt Sorum, Scott Weiland & Slash\",235911,7693331,0.99,Rock,Contraband,Velvet Revolver\nBig Machine,\"Dave Kushner, Duff, Matt Sorum, Scott Weiland & Slash\",265613,8673442,0.99,Rock,Contraband,Velvet Revolver\nIllegal I Song,\"Dave Kushner, Duff, Matt Sorum, Scott Weiland & Slash\",257750,8483347,0.99,Rock,Contraband,Velvet Revolver\nSpectacle,\"Dave Kushner, Duff, Matt Sorum, Scott Weiland & Slash\",221701,7252876,0.99,Rock,Contraband,Velvet Revolver\nFall To Pieces,\"Dave Kushner, Duff, Matt Sorum, Scott Weiland & Slash\",270889,8823096,0.99,Rock,Contraband,Velvet Revolver\nHeadspace,\"Dave Kushner, Duff, Matt Sorum, Scott Weiland & Slash\",223033,7237986,0.99,Rock,Contraband,Velvet Revolver\nSuperhuman,\"Dave Kushner, Duff, Matt Sorum, Scott Weiland & Slash\",255921,8365328,0.99,Rock,Contraband,Velvet Revolver\nSet Me Free,\"Dave Kushner, Duff, Matt Sorum, Scott Weiland & Slash\",247954,8053388,0.99,Rock,Contraband,Velvet Revolver\nYou Got No Right,\"Dave Kushner, Duff, Matt Sorum, Scott Weiland & Slash\",335412,10991094,0.99,Rock,Contraband,Velvet Revolver\nSlither,\"Dave Kushner, Duff, Matt Sorum, Scott Weiland & Slash\",248398,8118785,0.99,Rock,Contraband,Velvet Revolver\nDirty Little Thing,\"Dave Kushner, Duff, Keith Nelson, Matt Sorum, Scott Weiland & Slash\",237844,7732982,0.99,Rock,Contraband,Velvet Revolver\nLoving The Alien,\"Dave Kushner, Duff, Matt Sorum, Scott Weiland & Slash\",348786,11412762,0.99,Rock,Contraband,Velvet Revolver\nPela Luz Dos Olhos Teus,,119196,3905715,0.99,Latin,Vinicius De Moraes,Vinícius De Moraes\nA Bencao E Outros,,421093,14234427,0.99,Latin,Vinicius De Moraes,Vinícius De Moraes\nTudo Na Mais Santa Paz,,222406,7426757,0.99,Latin,Vinicius De Moraes,Vinícius De Moraes\nO Velho E Aflor,,275121,9126828,0.99,Latin,Vinicius De Moraes,Vinícius De Moraes\nCotidiano N 2,,55902,1805797,0.99,Latin,Vinicius De Moraes,Vinícius De Moraes\nAdeus,,221884,7259351,0.99,Latin,Vinicius De Moraes,Vinícius De Moraes\nSamba Pra Endrigo,,259265,8823551,0.99,Latin,Vinicius De Moraes,Vinícius De Moraes\nSo Por Amor,,236591,7745764,0.99,Latin,Vinicius De Moraes,Vinícius De Moraes\nMeu Pranto Rolou,,181760,6003345,0.99,Latin,Vinicius De Moraes,Vinícius De Moraes\nMulher Carioca,,191686,6395048,0.99,Latin,Vinicius De Moraes,Vinícius De Moraes\nUm Homem Chamado Alfredo,,151640,4976227,0.99,Latin,Vinicius De Moraes,Vinícius De Moraes\nSamba Do Jato,,220813,7357840,0.99,Latin,Vinicius De Moraes,Vinícius De Moraes\n\"Oi, La\",,167053,5562700,0.99,Latin,Vinicius De Moraes,Vinícius De Moraes\n\"Vinicius, Poeta Do Encontro\",,336431,10858776,0.99,Latin,Vinicius De Moraes,Vinícius De Moraes\nSoneto Da Separacao,,193880,6277511,0.99,Latin,Vinicius De Moraes,Vinícius De Moraes\nFaixa Amarela,Beto Gogo/Jessé Pai/Luiz Carlos/Zeca Pagodinho,240692,8082036,0.99,Latin,Ao Vivo [IMPORT],Zeca Pagodinho\nPosso Até Me Apaixonar,Dudu Nobre,200698,6735526,0.99,Latin,Ao Vivo [IMPORT],Zeca Pagodinho\nNão Sou Mais Disso,Jorge Aragão/Zeca Pagodinho,225985,7613817,0.99,Latin,Ao Vivo [IMPORT],Zeca Pagodinho\nVivo Isolado Do Mundo,Alcides Dias Lopes,180035,6073995,0.99,Latin,Ao Vivo [IMPORT],Zeca Pagodinho\nCoração Em Desalinho,Mauro Diniz/Ratino Sigem,185208,6225948,0.99,Latin,Ao Vivo [IMPORT],Zeca Pagodinho\nSeu Balancê,Paulinho Rezende/Toninho Geraes,219454,7311219,0.99,Latin,Ao Vivo [IMPORT],Zeca Pagodinho\nVai Adiar,Alcino Corrêa/Monarco,270393,9134882,0.99,Latin,Ao Vivo [IMPORT],Zeca Pagodinho\nRugas,Augusto Garcez/Nelson Cavaquinho,140930,4703182,0.99,Latin,Ao Vivo [IMPORT],Zeca Pagodinho\nFeirinha da Pavuna/Luz do Repente/Bagaço da Laranja,\"Arlindo Cruz/Franco/Marquinhos PQD/Negro, Jovelina Pérolo/Zeca Pagodinho\",107206,3593684,0.99,Latin,Ao Vivo [IMPORT],Zeca Pagodinho\nSem Essa de Malandro Agulha,Aldir Blanc/Jayme Vignoli,158484,5332668,0.99,Latin,Ao Vivo [IMPORT],Zeca Pagodinho\nChico Não Vai na Corimba,Dudu Nobre/Zeca Pagodinho,269374,9122188,0.99,Latin,Ao Vivo [IMPORT],Zeca Pagodinho\nPapel Principal,Almir Guineto/Dedé Paraiso/Luverci Ernesto,217495,7325302,0.99,Latin,Ao Vivo [IMPORT],Zeca Pagodinho\nSaudade Louca,Acyr Marques/Arlindo Cruz/Franco,243591,8136475,0.99,Latin,Ao Vivo [IMPORT],Zeca Pagodinho\nCamarão que Dorme e Onda Leva,\"Acyi Marques/Arlindo Bruz/Braço, Beto Sem/Zeca Pagodinho\",299102,10012231,0.99,Latin,Ao Vivo [IMPORT],Zeca Pagodinho\nSapopemba e Maxambomba,Nei Lopes/Wilson Moreira,245394,8268712,0.99,Latin,Ao Vivo [IMPORT],Zeca Pagodinho\nMinha Fé,Murilão,206994,6981474,0.99,Latin,Ao Vivo [IMPORT],Zeca Pagodinho\nLua de Ogum,Ratinho/Zeca Pagodinho,168463,5719129,0.99,Latin,Ao Vivo [IMPORT],Zeca Pagodinho\nSamba pras moças,Grazielle/Roque Ferreira,152816,5121366,0.99,Latin,Ao Vivo [IMPORT],Zeca Pagodinho\nVerdade,Carlinhos Santana/Nelson Rufino,332826,11120708,0.99,Latin,Ao Vivo [IMPORT],Zeca Pagodinho\nThe Office: An American Workplace (Pilot),,1380833,290482361,1.99,TV Shows,\"The Office, Season 1\",The Office\nDiversity Day,,1306416,257879716,1.99,TV Shows,\"The Office, Season 1\",The Office\nHealth Care,,1321791,260493577,1.99,TV Shows,\"The Office, Season 1\",The Office\nThe Alliance,,1317125,266203162,1.99,TV Shows,\"The Office, Season 1\",The Office\nBasketball,,1323541,267464180,1.99,TV Shows,\"The Office, Season 1\",The Office\nHot Girl,,1325458,267836576,1.99,TV Shows,\"The Office, Season 1\",The Office\nThe Dundies,,1253541,246845576,1.99,TV Shows,\"The Office, Season 2\",The Office\nSexual Harassment,,1294541,273069146,1.99,TV Shows,\"The Office, Season 2\",The Office\nOffice Olympics,,1290458,256247623,1.99,TV Shows,\"The Office, Season 2\",The Office\nThe Fire,,1288166,266856017,1.99,TV Shows,\"The Office, Season 2\",The Office\nHalloween,,1315333,249205209,1.99,TV Shows,\"The Office, Season 2\",The Office\nThe Fight,,1320028,277149457,1.99,TV Shows,\"The Office, Season 2\",The Office\nThe Client,,1299341,253836788,1.99,TV Shows,\"The Office, Season 2\",The Office\nPerformance Review,,1292458,256143822,1.99,TV Shows,\"The Office, Season 2\",The Office\nEmail Surveillance,,1328870,265101113,1.99,TV Shows,\"The Office, Season 2\",The Office\nChristmas Party,,1282115,260891300,1.99,TV Shows,\"The Office, Season 2\",The Office\nBooze Cruise,,1267958,252518021,1.99,TV Shows,\"The Office, Season 2\",The Office\nThe Injury,,1275275,253912762,1.99,TV Shows,\"The Office, Season 2\",The Office\nThe Secret,,1264875,253143200,1.99,TV Shows,\"The Office, Season 2\",The Office\nThe Carpet,,1264375,256477011,1.99,TV Shows,\"The Office, Season 2\",The Office\nBoys and Girls,,1278333,255245729,1.99,TV Shows,\"The Office, Season 2\",The Office\nValentine's Day,,1270375,253552710,1.99,TV Shows,\"The Office, Season 2\",The Office\nDwight's Speech,,1278041,255001728,1.99,TV Shows,\"The Office, Season 2\",The Office\nTake Your Daughter to Work Day,,1268333,253451012,1.99,TV Shows,\"The Office, Season 2\",The Office\nMichael's Birthday,,1237791,247238398,1.99,TV Shows,\"The Office, Season 2\",The Office\nDrug Testing,,1278625,244626927,1.99,TV Shows,\"The Office, Season 2\",The Office\nConflict Resolution,,1274583,253808658,1.99,TV Shows,\"The Office, Season 2\",The Office\nCasino Night - Season Finale,,1712791,327642458,1.99,TV Shows,\"The Office, Season 2\",The Office\nGay Witch Hunt,,1326534,276942637,1.99,TV Shows,\"The Office, Season 3\",The Office\nThe Convention,,1297213,255117055,1.99,TV Shows,\"The Office, Season 3\",The Office\nThe Coup,,1276526,267205501,1.99,TV Shows,\"The Office, Season 3\",The Office\nGrief Counseling,,1282615,256912833,1.99,TV Shows,\"The Office, Season 3\",The Office\nThe Initiation,,1280113,251728257,1.99,TV Shows,\"The Office, Season 3\",The Office\nDiwali,,1279904,252726644,1.99,TV Shows,\"The Office, Season 3\",The Office\nBranch Closing,,1822781,358761786,1.99,TV Shows,\"The Office, Season 3\",The Office\nThe Merger,,1801926,345960631,1.99,TV Shows,\"The Office, Season 3\",The Office\nThe Convict,,1273064,248863427,1.99,Comedy,\"The Office, Season 3\",The Office\n\"A Benihana Christmas, Pts. 1 & 2\",,2519436,515301752,1.99,Comedy,\"The Office, Season 3\",The Office\nBack from Vacation,,1271688,245378749,1.99,Comedy,\"The Office, Season 3\",The Office\nTraveling Salesmen,,1289039,250822697,1.99,Comedy,\"The Office, Season 3\",The Office\nProducer's Cut: The Return,,1700241,337219980,1.99,Comedy,\"The Office, Season 3\",The Office\nBen Franklin,,1271938,264168080,1.99,Comedy,\"The Office, Season 3\",The Office\nPhyllis's Wedding,,1271521,258561054,1.99,Comedy,\"The Office, Season 3\",The Office\nBusiness School,,1302093,254402605,1.99,Comedy,\"The Office, Season 3\",The Office\nCocktails,,1272522,259011909,1.99,Comedy,\"The Office, Season 3\",The Office\nThe Negotiation,,1767851,371663719,1.99,Comedy,\"The Office, Season 3\",The Office\nSafety Training,,1271229,253054534,1.99,Comedy,\"The Office, Season 3\",The Office\nProduct Recall,,1268268,251208610,1.99,Comedy,\"The Office, Season 3\",The Office\nWomen's Appreciation,,1732649,338778844,1.99,Comedy,\"The Office, Season 3\",The Office\nBeach Games,,1676134,333671149,1.99,Comedy,\"The Office, Season 3\",The Office\nThe Job,,2541875,501060138,1.99,Comedy,\"The Office, Season 3\",The Office\nBranch Closing,,1814855,360331351,1.99,Comedy,\"The Office, Season 3\",The Office\nThe Return,,1705080,343877320,1.99,Comedy,\"The Office, Season 3\",The Office\nYour Time Is Gonna Come,\"Page, Jones\",310774,5126563,0.99,Rock,Un-Led-Ed,Dread Zeppelin\n\"Battlestar Galactica, Pt. 1\",,2952702,541359437,1.99,Sci Fi & Fantasy,\"Battlestar Galactica (Classic), Season 1\",Battlestar Galactica (Classic)\n\"Battlestar Galactica, Pt. 2\",,2956081,521387924,1.99,Sci Fi & Fantasy,\"Battlestar Galactica (Classic), Season 1\",Battlestar Galactica (Classic)\n\"Battlestar Galactica, Pt. 3\",,2927802,554509033,1.99,Sci Fi & Fantasy,\"Battlestar Galactica (Classic), Season 1\",Battlestar Galactica (Classic)\n\"Lost Planet of the Gods, Pt. 1\",,2922547,537812711,1.99,Sci Fi & Fantasy,\"Battlestar Galactica (Classic), Season 1\",Battlestar Galactica (Classic)\n\"Lost Planet of the Gods, Pt. 2\",,2914664,534343985,1.99,Sci Fi & Fantasy,\"Battlestar Galactica (Classic), Season 1\",Battlestar Galactica (Classic)\nThe Lost Warrior,,2920045,558872190,1.99,Sci Fi & Fantasy,\"Battlestar Galactica (Classic), Season 1\",Battlestar Galactica (Classic)\nThe Long Patrol,,2925008,513122217,1.99,Sci Fi & Fantasy,\"Battlestar Galactica (Classic), Season 1\",Battlestar Galactica (Classic)\n\"The Gun On Ice Planet Zero, Pt. 1\",,2907615,540980196,1.99,Sci Fi & Fantasy,\"Battlestar Galactica (Classic), Season 1\",Battlestar Galactica (Classic)\n\"The Gun On Ice Planet Zero, Pt. 2\",,2924341,546542281,1.99,Sci Fi & Fantasy,\"Battlestar Galactica (Classic), Season 1\",Battlestar Galactica (Classic)\nThe Magnificent Warriors,,2924716,570152232,1.99,Sci Fi & Fantasy,\"Battlestar Galactica (Classic), Season 1\",Battlestar Galactica (Classic)\nThe Young Lords,,2863571,587051735,1.99,Sci Fi & Fantasy,\"Battlestar Galactica (Classic), Season 1\",Battlestar Galactica (Classic)\n\"The Living Legend, Pt. 1\",,2924507,503641007,1.99,Sci Fi & Fantasy,\"Battlestar Galactica (Classic), Season 1\",Battlestar Galactica (Classic)\n\"The Living Legend, Pt. 2\",,2923298,515632754,1.99,Sci Fi & Fantasy,\"Battlestar Galactica (Classic), Season 1\",Battlestar Galactica (Classic)\nFire In Space,,2926593,536784757,1.99,Sci Fi & Fantasy,\"Battlestar Galactica (Classic), Season 1\",Battlestar Galactica (Classic)\n\"War of the Gods, Pt. 1\",,2922630,505761343,1.99,Sci Fi & Fantasy,\"Battlestar Galactica (Classic), Season 1\",Battlestar Galactica (Classic)\n\"War of the Gods, Pt. 2\",,2923381,487899692,1.99,Sci Fi & Fantasy,\"Battlestar Galactica (Classic), Season 1\",Battlestar Galactica (Classic)\nThe Man With Nine Lives,,2956998,577829804,1.99,Sci Fi & Fantasy,\"Battlestar Galactica (Classic), Season 1\",Battlestar Galactica (Classic)\nMurder On the Rising Star,,2935894,551759986,1.99,Sci Fi & Fantasy,\"Battlestar Galactica (Classic), Season 1\",Battlestar Galactica (Classic)\n\"Greetings from Earth, Pt. 1\",,2960293,536824558,1.99,Sci Fi & Fantasy,\"Battlestar Galactica (Classic), Season 1\",Battlestar Galactica (Classic)\n\"Greetings from Earth, Pt. 2\",,2903778,527842860,1.99,Sci Fi & Fantasy,\"Battlestar Galactica (Classic), Season 1\",Battlestar Galactica (Classic)\nBaltar's Escape,,2922088,525564224,1.99,Sci Fi & Fantasy,\"Battlestar Galactica (Classic), Season 1\",Battlestar Galactica (Classic)\nExperiment In Terra,,2923548,547982556,1.99,Sci Fi & Fantasy,\"Battlestar Galactica (Classic), Season 1\",Battlestar Galactica (Classic)\nTake the Celestra,,2927677,512381289,1.99,Sci Fi & Fantasy,\"Battlestar Galactica (Classic), Season 1\",Battlestar Galactica (Classic)\nThe Hand of God,,2924007,536583079,1.99,Sci Fi & Fantasy,\"Battlestar Galactica (Classic), Season 1\",Battlestar Galactica (Classic)\nPilot,,2484567,492670102,1.99,TV Shows,Aquaman,Aquaman\nInstant Karma,,193188,3150090,0.99,Pop,Instant Karma: The Amnesty International Campaign to Save Darfur,U2\n#9 Dream,,278312,4506425,0.99,Pop,Instant Karma: The Amnesty International Campaign to Save Darfur,U2\nMother,,287740,4656660,0.99,Pop,Instant Karma: The Amnesty International Campaign to Save Darfur,U2\nGive Peace a Chance,,274644,4448025,0.99,Pop,Instant Karma: The Amnesty International Campaign to Save Darfur,U2\nCold Turkey,,281424,4556003,0.99,Pop,Instant Karma: The Amnesty International Campaign to Save Darfur,U2\nWhatever Gets You Thru the Night,,215084,3499018,0.99,Pop,Instant Karma: The Amnesty International Campaign to Save Darfur,U2\nI'm Losing You,,240719,3907467,0.99,Pop,Instant Karma: The Amnesty International Campaign to Save Darfur,U2\nGimme Some Truth,,232778,3780807,0.99,Pop,Instant Karma: The Amnesty International Campaign to Save Darfur,U2\n\"Oh, My Love\",,159473,2612788,0.99,Pop,Instant Karma: The Amnesty International Campaign to Save Darfur,U2\nImagine,,192329,3136271,0.99,Pop,Instant Karma: The Amnesty International Campaign to Save Darfur,U2\nNobody Told Me,,210348,3423395,0.99,Pop,Instant Karma: The Amnesty International Campaign to Save Darfur,U2\nJealous Guy,,239094,3881620,0.99,Pop,Instant Karma: The Amnesty International Campaign to Save Darfur,U2\nWorking Class Hero,,265449,4301430,0.99,Pop,Instant Karma: The Amnesty International Campaign to Save Darfur,U2\nPower to the People,,213018,3466029,0.99,Pop,Instant Karma: The Amnesty International Campaign to Save Darfur,U2\nImagine,,219078,3562542,0.99,Pop,Instant Karma: The Amnesty International Campaign to Save Darfur,U2\nBeautiful Boy,,227995,3704642,0.99,Pop,Instant Karma: The Amnesty International Campaign to Save Darfur,U2\nIsolation,,156059,2558399,0.99,Pop,Instant Karma: The Amnesty International Campaign to Save Darfur,U2\nWatching the Wheels,,198645,3237063,0.99,Pop,Instant Karma: The Amnesty International Campaign to Save Darfur,U2\nGrow Old With Me,,149093,2447453,0.99,Pop,Instant Karma: The Amnesty International Campaign to Save Darfur,U2\nGimme Some Truth,,187546,3060083,0.99,Pop,Instant Karma: The Amnesty International Campaign to Save Darfur,U2\n[Just Like] Starting Over,,215549,3506308,0.99,Pop,Instant Karma: The Amnesty International Campaign to Save Darfur,U2\nGod,,260410,4221135,0.99,Pop,Instant Karma: The Amnesty International Campaign to Save Darfur,U2\nReal Love,,236911,3846658,0.99,Pop,Instant Karma: The Amnesty International Campaign to Save Darfur,U2\nSympton of the Universe,,340890,5489313,0.99,Rock,Speak of the Devil,Ozzy Osbourne\nSnowblind,,295960,4773171,0.99,Rock,Speak of the Devil,Ozzy Osbourne\nBlack Sabbath,,364180,5860455,0.99,Rock,Speak of the Devil,Ozzy Osbourne\nFairies Wear Boots,,392764,6315916,0.99,Rock,Speak of the Devil,Ozzy Osbourne\nWar Pigs,,515435,8270194,0.99,Rock,Speak of the Devil,Ozzy Osbourne\nThe Wizard,,282678,4561796,0.99,Rock,Speak of the Devil,Ozzy Osbourne\nN.I.B.,,335248,5399456,0.99,Rock,Speak of the Devil,Ozzy Osbourne\nSweet Leaf,,354706,5709700,0.99,Rock,Speak of the Devil,Ozzy Osbourne\nNever Say Die,,258343,4173799,0.99,Rock,Speak of the Devil,Ozzy Osbourne\n\"Sabbath, Bloody Sabbath\",,333622,5373633,0.99,Rock,Speak of the Devil,Ozzy Osbourne\nIron Man/Children of the Grave,,552308,8858616,0.99,Rock,Speak of the Devil,Ozzy Osbourne\nParanoid,,189171,3071042,0.99,Rock,Speak of the Devil,Ozzy Osbourne\nRock You Like a Hurricane,,255766,4300973,0.99,Rock,20th Century Masters - The Millennium Collection: The Best of Scorpions,Scorpions\nNo One Like You,,240325,4050259,0.99,Rock,20th Century Masters - The Millennium Collection: The Best of Scorpions,Scorpions\nThe Zoo,,332740,5550779,0.99,Rock,20th Century Masters - The Millennium Collection: The Best of Scorpions,Scorpions\nLoving You Sunday Morning,,339125,5654493,0.99,Rock,20th Century Masters - The Millennium Collection: The Best of Scorpions,Scorpions\nStill Loving You,,390674,6491444,0.99,Rock,20th Century Masters - The Millennium Collection: The Best of Scorpions,Scorpions\nBig City Nights,,251865,4237651,0.99,Rock,20th Century Masters - The Millennium Collection: The Best of Scorpions,Scorpions\nBelieve in Love,,325774,5437651,0.99,Rock,20th Century Masters - The Millennium Collection: The Best of Scorpions,Scorpions\nRhythm of Love,,231246,3902834,0.99,Rock,20th Century Masters - The Millennium Collection: The Best of Scorpions,Scorpions\nI Can't Explain,,205332,3482099,0.99,Rock,20th Century Masters - The Millennium Collection: The Best of Scorpions,Scorpions\nTease Me Please Me,,287229,4811894,0.99,Rock,20th Century Masters - The Millennium Collection: The Best of Scorpions,Scorpions\nWind of Change,,315325,5268002,0.99,Rock,20th Century Masters - The Millennium Collection: The Best of Scorpions,Scorpions\nSend Me an Angel,,273041,4581492,0.99,Rock,20th Century Masters - The Millennium Collection: The Best of Scorpions,Scorpions\nJump Around,E. Schrody/L. Muggerud,217835,8715653,0.99,Hip Hop/Rap,House of Pain,House Of Pain\nSalutations,E. Schrody/L. Dimant,69120,2767047,0.99,Hip Hop/Rap,House of Pain,House Of Pain\nPut Your Head Out,E. Schrody/L. Freese/L. Muggerud,182230,7291473,0.99,Hip Hop/Rap,House of Pain,House Of Pain\nTop O' The Morning To Ya,E. Schrody/L. Dimant,216633,8667599,0.99,Hip Hop/Rap,House of Pain,House Of Pain\nCommercial 1,L. Muggerud,7941,319888,0.99,Hip Hop/Rap,House of Pain,House Of Pain\nHouse And The Rising Sun,E. Schrody/J. Vasquez/L. Dimant,219402,8778369,0.99,Hip Hop/Rap,House of Pain,House Of Pain\nShamrocks And Shenanigans,E. Schrody/L. Dimant,218331,8735518,0.99,Hip Hop/Rap,House of Pain,House Of Pain\nHouse Of Pain Anthem,E. Schrody/L. Dimant,155611,6226713,0.99,Hip Hop/Rap,House of Pain,House Of Pain\n\"Danny Boy, Danny Boy\",E. Schrody/L. Muggerud,114520,4583091,0.99,Hip Hop/Rap,House of Pain,House Of Pain\nGuess Who's Back,E. Schrody/L. Muggerud,238393,9537994,0.99,Hip Hop/Rap,House of Pain,House Of Pain\nCommercial 2,L. Muggerud,21211,850698,0.99,Hip Hop/Rap,House of Pain,House Of Pain\nPut On Your Shit Kickers,E. Schrody/L. Muggerud,190432,7619569,0.99,Hip Hop/Rap,House of Pain,House Of Pain\nCome And Get Some Of This,E. Schrody/L. Muggerud/R. Medrano,170475,6821279,0.99,Hip Hop/Rap,House of Pain,House Of Pain\nLife Goes On,E. Schrody/R. Medrano,163030,6523458,0.99,Hip Hop/Rap,House of Pain,House Of Pain\nOne For The Road,E. Schrody/L. Dimant/L. Muggerud,170213,6810820,0.99,Hip Hop/Rap,House of Pain,House Of Pain\nFeel It,E. Schrody/R. Medrano,239908,9598588,0.99,Hip Hop/Rap,House of Pain,House Of Pain\nAll My Love,E. Schrody/L. Dimant,200620,8027065,0.99,Hip Hop/Rap,House of Pain,House Of Pain\nJump Around (Pete Rock Remix),E. Schrody/L. Muggerud,236120,9447101,0.99,Hip Hop/Rap,House of Pain,House Of Pain\nShamrocks And Shenanigans (Boom Shalock Lock Boom/Butch Vig Mix),E. Schrody/L. Dimant,237035,9483705,0.99,Hip Hop/Rap,House of Pain,House Of Pain\nInstinto Colectivo,,300564,12024875,0.99,Electronica/Dance,Radio Brasil (O Som da Jovem Vanguarda) - Seleccao de Henrique Amaro,O Rappa\nChapa o Coco,,143830,5755478,0.99,Electronica/Dance,Radio Brasil (O Som da Jovem Vanguarda) - Seleccao de Henrique Amaro,O Rappa\nProstituta,,359000,14362307,0.99,Electronica/Dance,Radio Brasil (O Som da Jovem Vanguarda) - Seleccao de Henrique Amaro,O Rappa\nEu So Queria Sumir,,269740,10791921,0.99,Electronica/Dance,Radio Brasil (O Som da Jovem Vanguarda) - Seleccao de Henrique Amaro,O Rappa\nTres Reis,,304143,12168015,0.99,Electronica/Dance,Radio Brasil (O Som da Jovem Vanguarda) - Seleccao de Henrique Amaro,O Rappa\nUm Lugar ao Sol,,212323,8495217,0.99,Electronica/Dance,Radio Brasil (O Som da Jovem Vanguarda) - Seleccao de Henrique Amaro,O Rappa\nBatalha Naval,,285727,11431382,0.99,Electronica/Dance,Radio Brasil (O Som da Jovem Vanguarda) - Seleccao de Henrique Amaro,O Rappa\nTodo o Carnaval tem seu Fim,,237426,9499371,0.99,Electronica/Dance,Radio Brasil (O Som da Jovem Vanguarda) - Seleccao de Henrique Amaro,O Rappa\nO Misterio do Samba,,226142,9047970,0.99,Electronica/Dance,Radio Brasil (O Som da Jovem Vanguarda) - Seleccao de Henrique Amaro,O Rappa\nArmadura,,232881,9317533,0.99,Electronica/Dance,Radio Brasil (O Som da Jovem Vanguarda) - Seleccao de Henrique Amaro,O Rappa\nNa Ladeira,,221570,8865099,0.99,Electronica/Dance,Radio Brasil (O Som da Jovem Vanguarda) - Seleccao de Henrique Amaro,O Rappa\nCarimbo,,328751,13152314,0.99,Electronica/Dance,Radio Brasil (O Som da Jovem Vanguarda) - Seleccao de Henrique Amaro,O Rappa\nCatimbo,,254484,10181692,0.99,Electronica/Dance,Radio Brasil (O Som da Jovem Vanguarda) - Seleccao de Henrique Amaro,O Rappa\nFunk de Bamba,,237322,9495184,0.99,Electronica/Dance,Radio Brasil (O Som da Jovem Vanguarda) - Seleccao de Henrique Amaro,O Rappa\nChega no Suingue,,221805,8874509,0.99,Electronica/Dance,Radio Brasil (O Som da Jovem Vanguarda) - Seleccao de Henrique Amaro,O Rappa\nMun-Ra,,274651,10988338,0.99,Electronica/Dance,Radio Brasil (O Som da Jovem Vanguarda) - Seleccao de Henrique Amaro,O Rappa\nFreestyle Love,,318484,12741680,0.99,Electronica/Dance,Radio Brasil (O Som da Jovem Vanguarda) - Seleccao de Henrique Amaro,O Rappa\nWar Pigs,,234013,8052374,0.99,Alternative,Cake: B-Sides and Rarities,Cake\n\"Past, Present, and Future\",,2492867,490796184,1.99,Drama,\"LOST, Season 4\",Lost\nThe Beginning of the End,,2611903,526865050,1.99,Drama,\"LOST, Season 4\",Lost\nLOST Season 4 Trailer,,112712,20831818,1.99,Drama,\"LOST, Season 4\",Lost\nLOST In 8:15,,497163,98460675,1.99,Drama,\"LOST, Season 4\",Lost\nConfirmed Dead,,2611986,512168460,1.99,Drama,\"LOST, Season 4\",Lost\nThe Economist,,2609025,516934914,1.99,Drama,\"LOST, Season 4\",Lost\nEggtown,,2608817,501061240,1.99,TV Shows,\"LOST, Season 4\",Lost\nThe Constant,,2611569,520209363,1.99,Drama,\"LOST, Season 4\",Lost\nThe Other Woman,,2605021,513246663,1.99,Drama,\"LOST, Season 4\",Lost\nJi Yeon,,2588797,506458858,1.99,TV Shows,\"LOST, Season 4\",Lost\nMeet Kevin Johnson,,2612028,504132981,1.99,TV Shows,\"LOST, Season 4\",Lost\nThe Shape of Things to Come,,2591299,502284266,1.99,Drama,\"LOST, Season 4\",Lost\nSomething Nice Back Home,,2612779,484711353,1.99,Drama,\"LOST, Season 4\",Lost\nCabin Fever,,2612028,477733942,1.99,Drama,\"LOST, Season 4\",Lost\n\"There's No Place Like Home, Pt. 1\",,2609526,522919189,1.99,Drama,\"LOST, Season 4\",Lost\n\"There's No Place Like Home, Pt. 2\",,2497956,523748920,1.99,Drama,\"LOST, Season 4\",Lost\n\"There's No Place Like Home, Pt. 3\",,2582957,486161766,1.99,Drama,\"LOST, Season 4\",Lost\nAmanda,Luca Gusella,246503,4011615,0.99,Jazz,Quiet Songs,Aisha Duo\nDespertar,Andrea Dulbecco,307385,4821485,0.99,Jazz,Quiet Songs,Aisha Duo\nDin Din Wo (Little Child),Habib Koité,285837,4615841,0.99,World,Muso Ko,Habib Koité and Bamada\nI Ka Barra (Your Work),Habib Koité,300605,4855457,0.99,World,Muso Ko,Habib Koité and Bamada\nDistance,Karsh Kale/Vishal Vaid,327122,5327463,0.99,Electronica/Dance,Realize,Karsh Kale\nOne Step Beyond,Karsh Kale,366085,6034098,0.99,Electronica/Dance,Realize,Karsh Kale\nI Guess You're Right,\"Darius \"\"Take One\"\" Minwalla/Jon Auer/Ken Stringfellow/Matt Harris\",212044,3453849,0.99,Rock,Every Kind of Light,The Posies\nLove Comes,\"Darius \"\"Take One\"\" Minwalla/Jon Auer/Ken Stringfellow/Matt Harris\",199923,3240609,0.99,Rock,Every Kind of Light,The Posies\nMuita Bobeira,Luciana Souza,172710,2775071,0.99,Latin,Duos II,Luciana Souza/Romero Lubambo\nOAM's Blues,Aaron Goldberg,266936,4292028,0.99,Jazz,Worlds,Aaron Goldberg\n\"Symphony No. 3 in E-flat major, Op. 55, \"\"Eroica\"\" - Scherzo: Allegro Vivace\",Ludwig van Beethoven,356426,5817216,0.99,Classical,The Best of Beethoven,Nicolaus Esterhazy Sinfonia\nSay Hello 2 Heaven,,384497,6477217,0.99,Alternative,Temple of the Dog,Temple of the Dog\nReach Down,,672773,11157785,0.99,Alternative,Temple of the Dog,Temple of the Dog\nHunger Strike,,246292,4233212,0.99,Alternative,Temple of the Dog,Temple of the Dog\nPushin Forward Back,,225278,3892066,0.99,Alternative,Temple of the Dog,Temple of the Dog\nCall Me a Dog,,304458,5177612,0.99,Alternative,Temple of the Dog,Temple of the Dog\nTimes of Trouble,,342539,5795951,0.99,Alternative,Temple of the Dog,Temple of the Dog\nWooden Jesus,,250565,4302603,0.99,Alternative,Temple of the Dog,Temple of the Dog\nYour Savior,,244226,4199626,0.99,Alternative,Temple of the Dog,Temple of the Dog\nFour Walled World,,414474,6964048,0.99,Alternative,Temple of the Dog,Temple of the Dog\nAll Night Thing,,231803,3997982,0.99,Alternative,Temple of the Dog,Temple of the Dog\nNo Such Thing,Chris Cornell,224837,3691272,0.99,Alternative,Carry On,Chris Cornell\nPoison Eye,Chris Cornell,237120,3890037,0.99,Alternative,Carry On,Chris Cornell\nArms Around Your Love,Chris Cornell,214016,3516224,0.99,Alternative,Carry On,Chris Cornell\nSafe and Sound,Chris Cornell,256764,4207769,0.99,Alternative,Carry On,Chris Cornell\nShe'll Never Be Your Man,Chris Cornell,204078,3355715,0.99,Alternative,Carry On,Chris Cornell\nGhosts,Chris Cornell,231547,3799745,0.99,Alternative,Carry On,Chris Cornell\nKilling Birds,Chris Cornell,218498,3588776,0.99,Alternative,Carry On,Chris Cornell\nBillie Jean,Michael Jackson,281401,4606408,0.99,Alternative,Carry On,Chris Cornell\nScar On the Sky,Chris Cornell,220193,3616618,0.99,Alternative,Carry On,Chris Cornell\nYour Soul Today,Chris Cornell,205959,3385722,0.99,Alternative,Carry On,Chris Cornell\nFinally Forever,Chris Cornell,217035,3565098,0.99,Alternative,Carry On,Chris Cornell\nSilence the Voices,Chris Cornell,267376,4379597,0.99,Alternative,Carry On,Chris Cornell\nDisappearing Act,Chris Cornell,273320,4476203,0.99,Alternative,Carry On,Chris Cornell\nYou Know My Name,Chris Cornell,240255,3940651,0.99,Alternative,Carry On,Chris Cornell\nRevelations,,252376,4111051,0.99,Alternative,Revelations,Audioslave\nOne and the Same,,217732,3559040,0.99,Alternative,Revelations,Audioslave\nSound of a Gun,,260154,4234990,0.99,Alternative,Revelations,Audioslave\nUntil We Fall,,230758,3766605,0.99,Alternative,Revelations,Audioslave\nOriginal Fire,,218916,3577821,0.99,Alternative,Revelations,Audioslave\nBroken City,,228366,3728955,0.99,Alternative,Revelations,Audioslave\nSomedays,,213831,3497176,0.99,Alternative,Revelations,Audioslave\nShape of Things to Come,,274597,4465399,0.99,Alternative,Revelations,Audioslave\nJewel of the Summertime,,233242,3806103,0.99,Alternative,Revelations,Audioslave\nWide Awake,,266308,4333050,0.99,Alternative,Revelations,Audioslave\nNothing Left to Say But Goodbye,,213041,3484335,0.99,Alternative,Revelations,Audioslave\nMoth,,298049,4838884,0.99,Alternative,Revelations,Audioslave\nShow Me How to Live (Live at the Quart Festival),,301974,4901540,0.99,Alternative,Revelations,Audioslave\n\"Band Members Discuss Tracks from \"\"Revelations\"\"\",,294294,61118891,0.99,Alternative,Revelations,Audioslave\nIntoitus: Adorate Deum,Anonymous,245317,4123531,0.99,Classical,Adorate Deum: Gregorian Chant from the Proper of the Mass,Alberto Turco & Nova Schola Gregoriana\n\"Miserere mei, Deus\",Gregorio Allegri,501503,8285941,0.99,Classical,Allegri: Miserere,\"Richard Marlow & The Choir of Trinity College, Cambridge\"\nCanon and Gigue in D Major: I. Canon,Johann Pachelbel,271788,4438393,0.99,Classical,Pachelbel: Canon & Gigue,English Concert & Trevor Pinnock\n\"Concerto No. 1 in E Major, RV 269 \"\"Spring\"\": I. Allegro\",Antonio Vivaldi,199086,3347810,0.99,Classical,Vivaldi: The Four Seasons,\"Anne-Sophie Mutter, Herbert Von Karajan & Wiener Philharmoniker\"\n\"Concerto for 2 Violins in D Minor, BWV 1043: I. Vivace\",Johann Sebastian Bach,193722,3192890,0.99,Classical,Bach: Violin Concertos,\"Hilary Hahn, Jeffrey Kahane, Los Angeles Chamber Orchestra & Margaret Batjer\"\n\"Aria Mit 30 Veränderungen, BWV 988 \"\"Goldberg Variations\"\": Aria\",Johann Sebastian Bach,120463,2081895,0.99,Classical,Bach: Goldberg Variations,Wilhelm Kempff\n\"Suite for Solo Cello No. 1 in G Major, BWV 1007: I. Prélude\",Johann Sebastian Bach,143288,2315495,0.99,Classical,Bach: The Cello Suites,Yo-Yo Ma\n\"The Messiah: Behold, I Tell You a Mystery... The Trumpet Shall Sound\",George Frideric Handel,582029,9553140,0.99,Classical,Handel: The Messiah (Highlights),Scholars Baroque Ensemble\nSolomon HWV 67: The Arrival of the Queen of Sheba,George Frideric Handel,197135,3247914,0.99,Classical,The World of Classical Favourites,Academy of St. Martin in the Fields & Sir Neville Marriner\nFantasia On Greensleeves,Ralph Vaughan Williams,268066,4513190,0.99,Classical,The World of Classical Favourites,Academy of St. Martin in the Fields & Sir Neville Marriner\n\"\"\"Eine Kleine Nachtmusik\"\" Serenade In G, K. 525: I. Allegro\",Wolfgang Amadeus Mozart,348971,5760129,0.99,Classical,Sir Neville Marriner: A Celebration,Academy of St. Martin in the Fields Chamber Ensemble & Sir Neville Marriner\n\"Concerto for Clarinet in A Major, K. 622: II. Adagio\",Wolfgang Amadeus Mozart,394482,6474980,0.99,Classical,Mozart: Wind Concertos,\"Berliner Philharmoniker, Claudio Abbado & Sabine Meyer\"\n\"Symphony No. 104 in D Major \"\"London\"\": IV. Finale: Spiritoso\",Franz Joseph Haydn,306687,10085867,0.99,Classical,Haydn: Symphonies 99 - 104,Royal Philharmonic Orchestra & Sir Thomas Beecham\nSymphony No.5 in C Minor: I. Allegro con brio,Ludwig van Beethoven,392462,6419730,0.99,Classical,Beethoven: Symhonies Nos. 5 & 6,Orchestre Révolutionnaire et Romantique & John Eliot Gardiner\nAve Maria,Franz Schubert,338243,5605648,0.99,Classical,A Soprano Inspired,\"Britten Sinfonia, Ivor Bolton & Lesley Garrett\"\n\"Nabucco: Chorus, \"\"Va, Pensiero, Sull'ali Dorate\"\"\",Giuseppe Verdi,274504,4498583,0.99,Classical,Great Opera Choruses,\"Chicago Symphony Chorus, Chicago Symphony Orchestra & Sir Georg Solti\"\nDie Walküre: The Ride of the Valkyries,Richard Wagner,189008,3114209,0.99,Classical,Wagner: Favourite Overtures,Sir Georg Solti & Wiener Philharmoniker\n\"Requiem, Op.48: 4. Pie Jesu\",Gabriel Fauré,258924,4314850,0.99,Classical,\"Fauré: Requiem, Ravel: Pavane & Others\",\"Academy of St. Martin in the Fields, John Birch, Sir Neville Marriner & Sylvia McNair\"\n\"The Nutcracker, Op. 71a, Act II: Scene 14: Pas de deux: Dance of the Prince & the Sugar-Plum Fairy\",Peter Ilyich Tchaikovsky,304226,5184289,0.99,Classical,Tchaikovsky: The Nutcracker,London Symphony Orchestra & Sir Charles Mackerras\n\"Nimrod (Adagio) from Variations On an Original Theme, Op. 36 \"\"Enigma\"\"\",Edward Elgar,250031,4124707,0.99,Classical,The Last Night of the Proms,Barry Wordsworth & BBC Concert Orchestra\nMadama Butterfly: Un Bel Dì Vedremo,Giacomo Puccini,277639,4588197,0.99,Classical,Puccini: Madama Butterfly - Highlights,\"Herbert Von Karajan, Mirella Freni & Wiener Philharmoniker\"\n\"Jupiter, the Bringer of Jollity\",Gustav Holst,522099,8547876,0.99,Classical,\"Holst: The Planets, Op. 32 & Vaughan Williams: Fantasies\",Eugene Ormandy\n\"Turandot, Act III, Nessun dorma!\",Giacomo Puccini,176911,2920890,0.99,Classical,Pavarotti's Opera Made Easy,Luciano Pavarotti\n\"Adagio for Strings from the String Quartet, Op. 11\",Samuel Barber,596519,9585597,0.99,Classical,Great Performances - Barber's Adagio and Other Romantic Favorites for Strings,Leonard Bernstein & New York Philharmonic\nCarmina Burana: O Fortuna,Carl Orff,156710,2630293,0.99,Classical,Carmina Burana,Boston Symphony Orchestra & Seiji Ozawa\nFanfare for the Common Man,Aaron Copland,198064,3211245,0.99,Classical,\"A Copland Celebration, Vol. I\",Aaron Copland & London Symphony Orchestra\n\"Toccata and Fugue in D Minor, BWV 565: I. Toccata\",Johann Sebastian Bach,153901,2649938,0.99,Classical,Bach: Toccata & Fugue in D Minor,Ton Koopman\n\"Symphony No.1 in D Major, Op.25 \"\"Classical\"\", Allegro Con Brio\",Sergei Prokofiev,254001,4195542,0.99,Classical,Prokofiev: Symphony No.1,Sergei Prokofiev & Yuri Temirkanov\n\"Scheherazade, Op. 35: I. The Sea and Sindbad's Ship\",Nikolai Rimsky-Korsakov,545203,8916313,0.99,Classical,Scheherazade,Chicago Symphony Orchestra & Fritz Reiner\n\"Concerto No.2 in F Major, BWV1047, I. Allegro\",Johann Sebastian Bach,307244,5064553,0.99,Classical,Bach: The Brandenburg Concertos,Orchestra of The Age of Enlightenment\n\"Concerto for Piano No. 2 in F Minor, Op. 21: II. Larghetto\",Frédéric Chopin,560342,9160082,0.99,Classical,Chopin: Piano Concertos Nos. 1 & 2,\"Emanuel Ax, Eugene Ormandy & Philadelphia Orchestra\"\nCavalleria Rusticana \\ Act \\ Intermezzo Sinfonico,Pietro Mascagni,243436,4001276,0.99,Classical,Mascagni: Cavalleria Rusticana,James Levine\n\"Karelia Suite, Op.11: 2. Ballade (Tempo Di Menuetto)\",Jean Sibelius,406000,5908455,0.99,Classical,Sibelius: Finlandia,Berliner Philharmoniker & Hans Rosbaud\n\"Piano Sonata No. 14 in C Sharp Minor, Op. 27, No. 2, \"\"Moonlight\"\": I. Adagio sostenuto\",Ludwig van Beethoven,391000,6318740,0.99,Classical,Beethoven Piano Sonatas: Moonlight & Pastorale,Maurizio Pollini\n\"Das Lied Von Der Erde, Von Der Jugend\",Gustav Mahler,223583,3700206,0.99,Classical,Great Recordings of the Century - Mahler: Das Lied von der Erde,Gustav Mahler\n\"Concerto for Cello and Orchestra in E minor, Op. 85: I. Adagio - Moderato\",Edward Elgar,483133,7865479,0.99,Classical,Elgar: Cello Concerto & Vaughan Williams: Fantasias,\"Felix Schmidt, London Symphony Orchestra & Rafael Frühbeck de Burgos\"\nTwo Fanfares for Orchestra: II. Short Ride in a Fast Machine,John Adams,254930,4310896,0.99,Classical,\"Adams, John: The Chairman Dances\",Edo de Waart & San Francisco Symphony\n\"Wellington's Victory or the Battle Symphony, Op.91: 2. Symphony of Triumph\",Ludwig van Beethoven,412000,6965201,0.99,Classical,\"Tchaikovsky: 1812 Festival Overture, Op.49, Capriccio Italien & Beethoven: Wellington's Victory\",Antal Doráti & London Symphony Orchestra\nMissa Papae Marcelli: Kyrie,Giovanni Pierluigi da Palestrina,240666,4244149,0.99,Classical,Palestrina: Missa Papae Marcelli & Allegri: Miserere,Choir Of Westminster Abbey & Simon Preston\nRomeo et Juliette: No. 11 - Danse des Chevaliers,,275015,4519239,0.99,Classical,Prokofiev: Romeo & Juliet,Michael Tilson Thomas & San Francisco Symphony\nOn the Beautiful Blue Danube,Johann Strauss II,526696,8610225,0.99,Classical,Strauss: Waltzes,Eugene Ormandy\n\"Symphonie Fantastique, Op. 14: V. Songe d'une nuit du sabbat\",Hector Berlioz,561967,9173344,0.99,Classical,Berlioz: Symphonie Fantastique,Michael Tilson Thomas & San Francisco Symphony\nCarmen: Overture,Georges Bizet,132932,2189002,0.99,Classical,Bizet: Carmen Highlights,\"Chor der Wiener Staatsoper, Herbert Von Karajan & Wiener Philharmoniker\"\n\"Lamentations of Jeremiah, First Set \\ Incipit Lamentatio\",Thomas Tallis,69194,1208080,0.99,Classical,English Renaissance,The King's Singers\nSing Joyfully,William Byrd,133768,2256484,0.99,Classical,English Renaissance,The King's Singers\n\"Music for the Royal Fireworks, HWV351 (1749): La Réjouissance\",George Frideric Handel,120000,2193734,0.99,Classical,Handel: Music for the Royal Fireworks (Original Version 1749),English Concert & Trevor Pinnock\n\"Peer Gynt Suite No.1, Op.46: 1. Morning Mood\",Edvard Grieg,253422,4298769,0.99,Classical,Grieg: Peer Gynt Suites & Sibelius: Pelléas et Mélisande,Berliner Philharmoniker & Herbert Von Karajan\n\"Die Zauberflöte, K.620: \"\"Der Hölle Rache Kocht in Meinem Herze\"\"\",Wolfgang Amadeus Mozart,174813,2861468,0.99,Opera,Mozart Gala: Famous Arias,\"Sir Georg Solti, Sumi Jo & Wiener Philharmoniker\"\n\"SCRIABIN: Prelude in B Major, Op. 11, No. 11\",,101293,3819535,0.99,Classical,SCRIABIN: Vers la flamme,Christopher O'Riley\n\"Pavan, Lachrimae Antiquae\",John Dowland,253281,4211495,0.99,Classical,Armada: Music from the Courts of England and Spain,Fretwork\n\"Symphony No. 41 in C Major, K. 551, \"\"Jupiter\"\": IV. Molto allegro\",Wolfgang Amadeus Mozart,362933,6173269,0.99,Classical,Mozart: Symphonies Nos. 40 & 41,Berliner Philharmoniker & Herbert Von Karajan\nRehab,,213240,3416878,0.99,R&B/Soul,Back to Black,Amy Winehouse\nYou Know I'm No Good,,256946,4133694,0.99,R&B/Soul,Back to Black,Amy Winehouse\nMe & Mr. Jones,,151706,2449438,0.99,R&B/Soul,Back to Black,Amy Winehouse\nJust Friends,,191933,3098906,0.99,R&B/Soul,Back to Black,Amy Winehouse\nBack to Black,Mark Ronson,240320,3852953,0.99,R&B/Soul,Back to Black,Amy Winehouse\nLove Is a Losing Game,,154386,2509409,0.99,R&B/Soul,Back to Black,Amy Winehouse\nTears Dry On Their Own,Nickolas Ashford & Valerie Simpson,185293,2996598,0.99,R&B/Soul,Back to Black,Amy Winehouse\nWake Up Alone,Paul O'duffy,221413,3576773,0.99,R&B/Soul,Back to Black,Amy Winehouse\nSome Unholy War,,141520,2304465,0.99,R&B/Soul,Back to Black,Amy Winehouse\nHe Can Only Hold Her,Richard Poindexter & Robert Poindexter,166680,2666531,0.99,R&B/Soul,Back to Black,Amy Winehouse\nYou Know I'm No Good (feat. Ghostface Killah),,202320,3260658,0.99,R&B/Soul,Back to Black,Amy Winehouse\nRehab (Hot Chip Remix),,418293,6670600,0.99,R&B/Soul,Back to Black,Amy Winehouse\nIntro / Stronger Than Me,,234200,3832165,0.99,Pop,Frank,Amy Winehouse\nYou Sent Me Flying / Cherry,,409906,6657517,0.99,Pop,Frank,Amy Winehouse\nF**k Me Pumps,Salaam Remi,200253,3324343,0.99,Pop,Frank,Amy Winehouse\nI Heard Love Is Blind,,129666,2190831,0.99,Pop,Frank,Amy Winehouse\n(There Is) No Greater Love (Teo Licks),Isham Jones & Marty Symes,167933,2773507,0.99,Pop,Frank,Amy Winehouse\nIn My Bed,Salaam Remi,315960,5211774,0.99,Pop,Frank,Amy Winehouse\nTake the Box,Luke Smith,199160,3281526,0.99,Pop,Frank,Amy Winehouse\nOctober Song,Matt Rowe & Stefan Skarbek,204846,3358125,0.99,Pop,Frank,Amy Winehouse\nWhat Is It About Men,\"Delroy \"\"Chris\"\" Cooper, Donovan Jackson, Earl Chinna Smith, Felix Howard, Gordon Williams, Luke Smith, Paul Watson & Wilburn Squiddley Cole\",209573,3426106,0.99,Pop,Frank,Amy Winehouse\nHelp Yourself,\"Freddy James, Jimmy hogarth & Larry Stock\",300884,5029266,0.99,Pop,Frank,Amy Winehouse\nAmy Amy Amy (Outro),\"Astor Campbell, Delroy \"\"Chris\"\" Cooper, Donovan Jackson, Dorothy Fields, Earl Chinna Smith, Felix Howard, Gordon Williams, James Moody, Jimmy McHugh, Matt Rowe, Salaam Remi & Stefan Skarbek\",663426,10564704,0.99,Pop,Frank,Amy Winehouse\nSlowness,,215386,3644793,0.99,Alternative,Carried to Dust (Bonus Track Version),Calexico\n\"Prometheus Overture, Op. 43\",Ludwig van Beethoven,339567,10887931,0.99,Classical,Beethoven: Symphony No. 6 'Pastoral' Etc.,Otto Klemperer & Philharmonia Orchestra\nSonata for Solo Violin: IV: Presto,Béla Bartók,299350,9785346,0.99,Classical,Bartok: Violin & Viola Concertos,Yehudi Menuhin\n\"A Midsummer Night's Dream, Op.61 Incidental Music: No.7 Notturno\",,387826,6497867,0.99,Classical,Mendelssohn: A Midsummer Night's Dream,Philharmonia Orchestra & Sir Neville Marriner\n\"Suite No. 3 in D, BWV 1068: III. Gavotte I & II\",Johann Sebastian Bach,225933,3847164,0.99,Classical,Bach: Orchestral Suites Nos. 1 - 4,\"Academy of St. Martin in the Fields, Sir Neville Marriner & Thurston Dart\"\n\"Concert pour 4 Parties de V**les, H. 545: I. Prelude\",Marc-Antoine Charpentier,110266,1973559,0.99,Classical,\"Charpentier: Divertissements, Airs & Concerts\",Les Arts Florissants & William Christie\nAdios nonino,Astor Piazzolla,289388,4781384,0.99,Classical,South American Getaway,The 12 Cellists of The Berlin Philharmonic\n\"Symphony No. 3 Op. 36 for Orchestra and Soprano \"\"Symfonia Piesni Zalosnych\"\" \\ Lento E Largo - Tranquillissimo\",Henryk Górecki,567494,9273123,0.99,Classical,Górecki: Symphony No. 3,Adrian Leaper & Doreen de Feis\n\"Act IV, Symphony\",Henry Purcell,364296,5987695,0.99,Classical,Purcell: The Fairy Queen,\"Roger Norrington, London Classical Players\"\n\"3 Gymnopédies: No.1 - Lent Et Grave, No.3 - Lent Et Douloureux\",Erik Satie,385506,6458501,0.99,Classical,The Ultimate Relexation Album,Charles Dutoit & L'Orchestre Symphonique de Montréal\n\"Music for the Funeral of Queen Mary: VI. \"\"Thou Knowest, Lord, the Secrets of Our Hearts\"\"\",Henry Purcell,142081,2365930,0.99,Classical,Purcell: Music for the Queen Mary,\"Equale Brass Ensemble, John Eliot Gardiner & Munich Monteverdi Orchestra and Choir\"\nSymphony No. 2: III. Allegro vivace,Kurt Weill,376510,6129146,0.99,Classical,Weill: The Seven Deadly Sins,Kent Nagano and Orchestre de l'Opéra de Lyon\n\"Partita in E Major, BWV 1006A: I. Prelude\",Johann Sebastian Bach,285673,4744929,0.99,Classical,\"J.S. Bach: Chaconne, Suite in E Minor, Partita in E Major & Prelude, Fugue and Allegro\",Julian Bream\nLe Sacre Du Printemps: I.iv. Spring Rounds,Igor Stravinsky,234746,4072205,0.99,Classical,Prokofiev: Symphony No.5 & Stravinksy: Le Sacre Du Printemps,Berliner Philharmoniker & Herbert Von Karajan\n\"Metopes, Op. 29: Calypso\",Karol Szymanowski,333669,5548755,0.99,Classical,\"Szymanowski: Piano Works, Vol. 1\",Martin Roscoe\n\"Symphony No. 2, Op. 16 - \"\"The Four Temperaments\"\": II. Allegro Comodo e Flemmatico\",Carl Nielsen,286998,4834785,0.99,Classical,Nielsen: The Six Symphonies,Göteborgs Symfoniker & Neeme Järvi\n\"24 Caprices, Op. 1, No. 24, for Solo Violin, in A Minor\",Niccolò Paganini,265541,4371533,0.99,Classical,Great Recordings of the Century: Paganini's 24 Caprices,Itzhak Perlman\n\"Étude 1, In C Major - Preludio (Presto) - Liszt\",,51780,2229617,0.99,Classical,Liszt - 12 Études D'Execution Transcendante,Michele Campanella\n\"Erlkonig, D.328\",,261849,4307907,0.99,Classical,\"Great Recordings of the Century - Shubert: Schwanengesang, 4 Lieder\",Gerald Moore\n\"Concerto for Violin, Strings and Continuo in G Major, Op. 3, No. 9: I. Allegro\",Pietro Antonio Locatelli,493573,16454937,0.99,Classical,\"Locatelli: Concertos for Violin, Strings and Continuo, Vol. 3\",\"Mela Tenenbaum, Pro Musica Prague & Richard Kapp\"\nPini Di Roma (Pinien Von Rom) \\ I Pini Della Via Appia,,286741,4718950,0.99,Classical,Respighi:Pines of Rome,Eugene Ormandy\n\"String Quartet No. 12 in C Minor, D. 703 \"\"Quartettsatz\"\": II. Andante - Allegro assai\",Franz Schubert,139200,2283131,0.99,Classical,Schubert: The Late String Quartets & String Quintet (3 CD's),Emerson String Quartet\n\"L'orfeo, Act 3, Sinfonia (Orchestra)\",Claudio Monteverdi,66639,1189062,0.99,Classical,Monteverdi: L'Orfeo,\"C. Monteverdi, Nigel Rogers - Chiaroscuro; London Baroque; London Cornett & Sackbu\"\n\"Quintet for Horn, Violin, 2 Violas, and Cello in E Flat Major, K. 407/386c: III. Allegro\",Wolfgang Amadeus Mozart,221331,3665114,0.99,Classical,Mozart: Chamber Music,Nash Ensemble\nKoyaanisqatsi,Philip Glass,206005,3305164,0.99,Soundtrack,Koyaanisqatsi (Soundtrack from the Motion Picture),Philip Glass Ensemble\n" }, { "path": "March Cohort 2023/22_23_Time Series Forecasting/.ipynb_checkpoints/22_Forecasting-checkpoint.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"id\": \"12c7b612\",\n \"metadata\": {},\n \"source\": [\n \"# Time series data and forecasting\\n\",\n \"\\n\",\n \"components\\n\",\n \"- trend\\n\",\n \"- seasonality\\n\",\n \"- cyclical \\n\",\n \"- random\\n\",\n \"\\n\",\n \"Models/ forecasting techniques\\n\",\n \"- AR\\n\",\n \"- MA\\n\",\n \"- ARMA\\n\",\n \"- ARIMA\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"861324fd\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"time series data -> random variable\\n\",\n \"- single variable (demand of the product at time t) univariate time series data\\n\",\n \"- multivariate time series data -> \\n\",\n \"\\n\",\n \"4 components\\n\",\n \"- trend\\n\",\n \"- seasonal\\n\",\n \"- cyclical\\n\",\n \"- irregular\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"c3f8bc6b\",\n \"metadata\": {},\n \"source\": [\n \"## Moving Average\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"id\": \"dacb8d91\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
MonthSale QuantityPromotion ExpensesCompetition Promotion
0130026661051
1244015531450
2332052791181
3442453491300
453001940981
5643777661560
672798343981
7843036681440
8929581851121
91036233861200
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" Month Sale Quantity Promotion Expenses Competition Promotion\\n\",\n \"0 1 3002666 105 1\\n\",\n \"1 2 4401553 145 0\\n\",\n \"2 3 3205279 118 1\\n\",\n \"3 4 4245349 130 0\\n\",\n \"4 5 3001940 98 1\\n\",\n \"5 6 4377766 156 0\\n\",\n \"6 7 2798343 98 1\\n\",\n \"7 8 4303668 144 0\\n\",\n \"8 9 2958185 112 1\\n\",\n \"9 10 3623386 120 0\"\n ]\n },\n \"execution_count\": 2,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"import pandas as pd\\n\",\n \"\\n\",\n \"df = pd.read_csv('wsb.csv')\\n\",\n \"\\n\",\n \"df.head(10)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"id\": \"fecf8a63\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import matplotlib.pyplot as plt\\n\",\n \"import seaborn as sns\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"id\": \"b1c6bf16\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[]\"\n ]\n },\n \"execution_count\": 4,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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Sale Quantitymoving_avg_12
03002666NaN
14401553NaN
23205279NaN
34245349NaN
43001940NaN
54377766NaN
62798343NaN
74303668NaN
82958185NaN
93623386NaN
103279115NaN
112843766NaN
1244475813503418.00
1336753053623827.58
1434771563563306.92
1537207943585963.33
1638340863542250.42
1738889133611595.92
1838713423570858.17
1936798623660274.75
2033582423608290.92
2133614883641629.00
2236703623619804.17
2331239663652408.08
2446340473675758.08
2537728793691296.92
2631871103699428.08
2730936833675257.58
2845573633622998.33
2938169563683271.42
3044108873677275.00
3136947133722237.08
3238226693723474.67
3336892863762176.92
3437286543789493.42
3547326773794351.08
3632164833928410.33
3734532393810280.00
3854316513783643.33
3942418513970688.42
4039098874066369.08
4132164384012412.75
4242220053962369.58
4336210343946629.42
4451622013940489.50
4546271774052117.17
4646239454130274.75
4745993684204882.33
\\n\",\n \"
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Sale Quantitymoving_avg_12
3632164833928410.33
3734532393810280.00
3854316513783643.33
3942418513970688.42
4039098874066369.08
4132164384012412.75
4242220053962369.58
4336210343946629.42
4451622013940489.50
4546271774052117.17
4646239454130274.75
4745993684204882.33
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" Sale Quantity moving_avg_12\\n\",\n \"36 3216483 3928410.33\\n\",\n \"37 3453239 3810280.00\\n\",\n \"38 5431651 3783643.33\\n\",\n \"39 4241851 3970688.42\\n\",\n \"40 3909887 4066369.08\\n\",\n \"41 3216438 4012412.75\\n\",\n \"42 4222005 3962369.58\\n\",\n \"43 3621034 3946629.42\\n\",\n \"44 5162201 3940489.50\\n\",\n \"45 4627177 4052117.17\\n\",\n \"46 4623945 4130274.75\\n\",\n \"47 4599368 4204882.33\"\n ]\n },\n \"execution_count\": 8,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df[['Sale Quantity', 'moving_avg_12']][36:]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"id\": \"ad26c086\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"No artists with labels found to put in legend. 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.figure(figsize=(10,4))\\n\",\n \"\\n\",\n \"plt.xlabel('Months')\\n\",\n \"plt.ylabel('Quantity')\\n\",\n \"\\n\",\n \"plt.plot(df['Sale Quantity'][12:])\\n\",\n \"plt.plot(df['moving_avg_12'][12:], '.')\\n\",\n \"\\n\",\n \"plt.legend()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"id\": \"a018f130\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# there is an increasing trend in sales quantity over the months\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"613775c8\",\n \"metadata\": {},\n \"source\": [\n \"## Forecast Accurary\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"d985e8e1\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"- RMSE\\n\",\n \"- MAPE\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"94146992\",\n \"metadata\": {},\n \"source\": [\n \"### Mean Absolute Percentage Error\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"id\": \"dafa7a48\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import numpy as np\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"id\": \"183268aa\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def get_MAPE(actual, predicted):\\n\",\n \" ytrue, ypred = np.array(actual), np.array(predicted)\\n\",\n \" \\n\",\n \" return np.round(np.mean(np.abs((actual - predicted) / actual))*100, 2)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"id\": \"43515dac\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"14.04\"\n ]\n },\n \"execution_count\": 14,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"get_MAPE(df['Sale Quantity'][36:].values, df['moving_avg_12'][36:].values)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"id\": \"2cd8e001\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"734725.8359239782\"\n ]\n },\n \"execution_count\": 15,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"from sklearn.metrics import mean_squared_error\\n\",\n \"\\n\",\n \"np.sqrt(mean_squared_error(df['Sale Quantity'][36:].values,\\n\",\n \" df['moving_avg_12'][36:].values))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"88910119\",\n \"metadata\": {},\n \"source\": [\n \"## Exponential Smoothing\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"id\": \"06de766f\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"df['ewm'] = df['Sale Quantity'].ewm(alpha=0.2).mean()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"id\": \"d9ad437b\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"pd.options.display.float_format = '{:.2f}'.format\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"id\": \"a1a0f4a2\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
MonthSale QuantityPromotion ExpensesCompetition Promotionmoving_avg_12ewm
3637321648312113928410.333828234.64
3738345323912803810280.003753219.93
3839543165117003783643.334088961.93
3940424185116003970688.424119543.81
4041390988715114066369.084077607.99
4142321643812014012412.753905359.34
4243422200515203962369.583968692.78
4344362103412503946629.423899157.24
4445516220117003940489.504151776.99
4546462717716004052117.174246860.31
4647462394516804130274.754322279.35
4748459936816604204882.334377698.31
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" Month Sale Quantity Promotion Expenses Competition Promotion \\\\\\n\",\n \"36 37 3216483 121 1 \\n\",\n \"37 38 3453239 128 0 \\n\",\n \"38 39 5431651 170 0 \\n\",\n \"39 40 4241851 160 0 \\n\",\n \"40 41 3909887 151 1 \\n\",\n \"41 42 3216438 120 1 \\n\",\n \"42 43 4222005 152 0 \\n\",\n \"43 44 3621034 125 0 \\n\",\n \"44 45 5162201 170 0 \\n\",\n \"45 46 4627177 160 0 \\n\",\n \"46 47 4623945 168 0 \\n\",\n \"47 48 4599368 166 0 \\n\",\n \"\\n\",\n \" moving_avg_12 ewm \\n\",\n \"36 3928410.33 3828234.64 \\n\",\n \"37 3810280.00 3753219.93 \\n\",\n \"38 3783643.33 4088961.93 \\n\",\n \"39 3970688.42 4119543.81 \\n\",\n \"40 4066369.08 4077607.99 \\n\",\n \"41 4012412.75 3905359.34 \\n\",\n \"42 3962369.58 3968692.78 \\n\",\n \"43 3946629.42 3899157.24 \\n\",\n \"44 3940489.50 4151776.99 \\n\",\n \"45 4052117.17 4246860.31 \\n\",\n \"46 4130274.75 4322279.35 \\n\",\n \"47 4204882.33 4377698.31 \"\n ]\n },\n \"execution_count\": 19,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df[36:]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"id\": \"875b1af2\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"11.15\"\n ]\n },\n \"execution_count\": 20,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"get_MAPE(df[['Sale Quantity']][36:].values,\\n\",\n \" df[['ewm']][36:].values)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"id\": \"1630eed7\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 21,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.figure(figsize=(10,4))\\n\",\n \"\\n\",\n \"plt.xlabel('Months')\\n\",\n \"plt.ylabel('Quantity')\\n\",\n \"\\n\",\n \"plt.plot(df['Sale Quantity'][12:])\\n\",\n \"plt.plot(df['ewm'][12:], '.')\\n\",\n \"\\n\",\n \"plt.legend()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"id\": \"0f326133\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"from statsmodels.tsa.seasonal import seasonal_decompose\\n\",\n \"\\n\",\n \"ts_decompose = seasonal_decompose(np.array(df['Sale Quantity']),\\n\",\n \" model='multiplicative',\\n\",\n \" period = 12)\\n\",\n \"\\n\",\n \"ts_plot = ts_decompose.plot()\\n\",\n \"\\n\",\n \"df['seasonal'] = ts_decompose.seasonal\\n\",\n \"df['trend'] = ts_decompose.trend\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"1a68c1b4\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.12\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n" }, { "path": "March Cohort 2023/22_23_Time Series Forecasting/.ipynb_checkpoints/23_ARIMA-checkpoint.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"id\": \"5e5ca069\",\n \"metadata\": {},\n \"source\": [\n \"# Auto Regressive Models\\n\",\n \"\\n\",\n \"AR and MA = ARMA = ARIMA\\n\",\n \"\\n\",\n \"\\n\",\n \"### Auto-Regressive (AR) Models\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"b389bf58\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"- auto-correlation function\\n\",\n \"- partial auto-correlation function\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"id\": \"91a0231c\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import pandas as pd\\n\",\n \"import numpy as np\\n\",\n \"import matplotlib.pyplot as plt\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"id\": \"8e78f456\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
Monthdemand
01457
12439
23404
34392
45403
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" Month demand\\n\",\n \"0 1 457\\n\",\n \"1 2 439\\n\",\n \"2 3 404\\n\",\n \"3 4 392\\n\",\n \"4 5 403\"\n ]\n },\n \"execution_count\": 2,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df = pd.read_csv('vimana.csv')\\n\",\n \"\\n\",\n \"df.head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"id\": \"1e17a7a3\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\",\n \"RangeIndex: 37 entries, 0 to 36\\n\",\n \"Data columns (total 2 columns):\\n\",\n \" # Column Non-Null Count Dtype\\n\",\n \"--- ------ -------------- -----\\n\",\n \" 0 Month 37 non-null int64\\n\",\n \" 1 demand 37 non-null int64\\n\",\n \"dtypes: int64(2)\\n\",\n \"memory usage: 720.0 bytes\\n\"\n ]\n }\n ],\n \"source\": [\n \"df.info()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"id\": \"49e834a8\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"from statsmodels.graphics.tsaplots import plot_acf, plot_pacf\\n\",\n \"\\n\",\n \"acf_plot = plot_acf(df.demand, lags=20)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"id\": \"05dffa14\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"pacf_plot = plot_pacf(df.demand, lags=17)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"538cb705\",\n \"metadata\": {},\n \"source\": [\n \"1. partial auto-correlation is significant for first p lags\\n\",\n \"2. there is a sign of stationary\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"1b96e85a\",\n \"metadata\": {},\n \"source\": [\n \"### building AR model\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"id\": \"9b68728d\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from statsmodels.tsa.arima.model import ARIMA\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"id\": \"3a318c0a\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"arima = ARIMA(df.demand[0:30].to_numpy(),\\n\",\n \" order=(1,0,0))\\n\",\n \"\\n\",\n \"model = arima.fit()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"id\": \"d7c7e91f\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"
SARIMAX Results
Dep. Variable: y No. Observations: 30
Model: ARIMA(1, 0, 0) Log Likelihood -182.765
Date: Tue, 16 May 2023 AIC 371.530
Time: 21:40:39 BIC 375.734
Sample: 0 HQIC 372.875
- 30
Covariance Type: opg
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"
coef std err z P>|z| [0.025 0.975]
const 513.4426 43.815 11.718 0.000 427.567 599.318
ar.L1 0.4726 0.181 2.617 0.009 0.119 0.827
sigma2 1.136e+04 3299.003 3.444 0.001 4896.110 1.78e+04
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"
Ljung-Box (L1) (Q): 0.55 Jarque-Bera (JB): 1.72
Prob(Q): 0.46 Prob(JB): 0.42
Heteroskedasticity (H): 2.72 Skew: 0.59
Prob(H) (two-sided): 0.13 Kurtosis: 2.96


Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).\"\n ],\n \"text/plain\": [\n \"\\n\",\n \"\\\"\\\"\\\"\\n\",\n \" SARIMAX Results \\n\",\n \"==============================================================================\\n\",\n \"Dep. Variable: y No. Observations: 30\\n\",\n \"Model: ARIMA(1, 0, 0) Log Likelihood -182.765\\n\",\n \"Date: Tue, 16 May 2023 AIC 371.530\\n\",\n \"Time: 21:40:39 BIC 375.734\\n\",\n \"Sample: 0 HQIC 372.875\\n\",\n \" - 30 \\n\",\n \"Covariance Type: opg \\n\",\n \"==============================================================================\\n\",\n \" coef std err z P>|z| [0.025 0.975]\\n\",\n \"------------------------------------------------------------------------------\\n\",\n \"const 513.4426 43.815 11.718 0.000 427.567 599.318\\n\",\n \"ar.L1 0.4726 0.181 2.617 0.009 0.119 0.827\\n\",\n \"sigma2 1.136e+04 3299.003 3.444 0.001 4896.110 1.78e+04\\n\",\n \"===================================================================================\\n\",\n \"Ljung-Box (L1) (Q): 0.55 Jarque-Bera (JB): 1.72\\n\",\n \"Prob(Q): 0.46 Prob(JB): 0.42\\n\",\n \"Heteroskedasticity (H): 2.72 Skew: 0.59\\n\",\n \"Prob(H) (two-sided): 0.13 Kurtosis: 2.96\\n\",\n \"===================================================================================\\n\",\n \"\\n\",\n \"Warnings:\\n\",\n \"[1] Covariance matrix calculated using the outer product of gradients (complex-step).\\n\",\n \"\\\"\\\"\\\"\"\n ]\n },\n \"execution_count\": 27,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"model.summary()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 28,\n \"id\": \"a5289c6f\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# AR with lag 1 is significant variable in the model\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"86fea566\",\n \"metadata\": {},\n \"source\": [\n \"#### forecast and measure accuracy\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 29,\n \"id\": \"e6d3acb1\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([480.15274444, 497.71044684, 506.00789345, 509.92911382,\\n\",\n \" 511.78221031, 512.65794956, 513.0718078 ])\"\n ]\n },\n \"execution_count\": 29,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"forecast_31_37 = model.predict(30,36)\\n\",\n \"\\n\",\n \"forecast_31_37\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 30,\n \"id\": \"7a473526\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def get_MAPE(actual, predicted):\\n\",\n \" ytrue, ypred = np.array(actual), np.array(predicted)\\n\",\n \" \\n\",\n \" return np.round(np.mean(np.abs((actual - predicted) / actual))*100, 2)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 31,\n \"id\": \"f5df3b3a\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"19.12\"\n ]\n },\n \"execution_count\": 31,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"get_MAPE(df.demand[30:], forecast_31_37)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"8f8bcf16\",\n \"metadata\": {},\n \"source\": [\n \"### moving average (MA) \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 32,\n \"id\": \"59388c6b\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"
SARIMAX Results
Dep. Variable: y No. Observations: 30
Model: ARIMA(0, 0, 1) Log Likelihood -184.297
Date: Tue, 16 May 2023 AIC 374.595
Time: 21:47:29 BIC 378.799
Sample: 0 HQIC 375.940
- 30
Covariance Type: opg
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"
coef std err z P>|z| [0.025 0.975]
const 517.0213 31.024 16.665 0.000 456.215 577.827
ma.L1 0.3173 0.224 1.414 0.157 -0.122 0.757
sigma2 1.265e+04 4209.483 3.004 0.003 4395.561 2.09e+04
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"
Ljung-Box (L1) (Q): 0.41 Jarque-Bera (JB): 2.21
Prob(Q): 0.52 Prob(JB): 0.33
Heteroskedasticity (H): 2.25 Skew: 0.66
Prob(H) (two-sided): 0.22 Kurtosis: 2.90


Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).\"\n ],\n \"text/plain\": [\n \"\\n\",\n \"\\\"\\\"\\\"\\n\",\n \" SARIMAX Results \\n\",\n \"==============================================================================\\n\",\n \"Dep. Variable: y No. Observations: 30\\n\",\n \"Model: ARIMA(0, 0, 1) Log Likelihood -184.297\\n\",\n \"Date: Tue, 16 May 2023 AIC 374.595\\n\",\n \"Time: 21:47:29 BIC 378.799\\n\",\n \"Sample: 0 HQIC 375.940\\n\",\n \" - 30 \\n\",\n \"Covariance Type: opg \\n\",\n \"==============================================================================\\n\",\n \" coef std err z P>|z| [0.025 0.975]\\n\",\n \"------------------------------------------------------------------------------\\n\",\n \"const 517.0213 31.024 16.665 0.000 456.215 577.827\\n\",\n \"ma.L1 0.3173 0.224 1.414 0.157 -0.122 0.757\\n\",\n \"sigma2 1.265e+04 4209.483 3.004 0.003 4395.561 2.09e+04\\n\",\n \"===================================================================================\\n\",\n \"Ljung-Box (L1) (Q): 0.41 Jarque-Bera (JB): 2.21\\n\",\n \"Prob(Q): 0.52 Prob(JB): 0.33\\n\",\n \"Heteroskedasticity (H): 2.25 Skew: 0.66\\n\",\n \"Prob(H) (two-sided): 0.22 Kurtosis: 2.90\\n\",\n \"===================================================================================\\n\",\n \"\\n\",\n \"Warnings:\\n\",\n \"[1] Covariance matrix calculated using the outer product of gradients (complex-step).\\n\",\n \"\\\"\\\"\\\"\"\n ]\n },\n \"execution_count\": 32,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"arima = ARIMA(df.demand[0:30].to_numpy(),\\n\",\n \" order=(0,0,1))\\n\",\n \"\\n\",\n \"ma_model = arima.fit()\\n\",\n \"\\n\",\n \"ma_model.summary()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 33,\n \"id\": \"3803c499\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"17.78\"\n ]\n },\n \"execution_count\": 33,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"forecast_31_37 = ma_model.predict(30,36)\\n\",\n \"\\n\",\n \"get_MAPE(df.demand[30:], forecast_31_37)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 34,\n \"id\": \"878fd131\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"
SARIMAX Results
Dep. Variable: y No. Observations: 30
Model: ARIMA(1, 0, 1) Log Likelihood -181.846
Date: Tue, 16 May 2023 AIC 371.692
Time: 21:50:14 BIC 377.296
Sample: 0 HQIC 373.485
- 30
Covariance Type: opg
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"
coef std err z P>|z| [0.025 0.975]
const 508.3844 59.146 8.595 0.000 392.461 624.308
ar.L1 0.7421 0.316 2.348 0.019 0.123 1.362
ma.L1 -0.3395 0.539 -0.629 0.529 -1.397 0.718
sigma2 1.065e+04 3040.079 3.505 0.000 4696.418 1.66e+04
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"
Ljung-Box (L1) (Q): 0.13 Jarque-Bera (JB): 2.46
Prob(Q): 0.72 Prob(JB): 0.29
Heteroskedasticity (H): 3.07 Skew: 0.67
Prob(H) (two-sided): 0.09 Kurtosis: 3.38


Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).\"\n ],\n \"text/plain\": [\n \"\\n\",\n \"\\\"\\\"\\\"\\n\",\n \" SARIMAX Results \\n\",\n \"==============================================================================\\n\",\n \"Dep. Variable: y No. Observations: 30\\n\",\n \"Model: ARIMA(1, 0, 1) Log Likelihood -181.846\\n\",\n \"Date: Tue, 16 May 2023 AIC 371.692\\n\",\n \"Time: 21:50:14 BIC 377.296\\n\",\n \"Sample: 0 HQIC 373.485\\n\",\n \" - 30 \\n\",\n \"Covariance Type: opg \\n\",\n \"==============================================================================\\n\",\n \" coef std err z P>|z| [0.025 0.975]\\n\",\n \"------------------------------------------------------------------------------\\n\",\n \"const 508.3844 59.146 8.595 0.000 392.461 624.308\\n\",\n \"ar.L1 0.7421 0.316 2.348 0.019 0.123 1.362\\n\",\n \"ma.L1 -0.3395 0.539 -0.629 0.529 -1.397 0.718\\n\",\n \"sigma2 1.065e+04 3040.079 3.505 0.000 4696.418 1.66e+04\\n\",\n \"===================================================================================\\n\",\n \"Ljung-Box (L1) (Q): 0.13 Jarque-Bera (JB): 2.46\\n\",\n \"Prob(Q): 0.72 Prob(JB): 0.29\\n\",\n \"Heteroskedasticity (H): 3.07 Skew: 0.67\\n\",\n \"Prob(H) (two-sided): 0.09 Kurtosis: 3.38\\n\",\n \"===================================================================================\\n\",\n \"\\n\",\n \"Warnings:\\n\",\n \"[1] Covariance matrix calculated using the outer product of gradients (complex-step).\\n\",\n \"\\\"\\\"\\\"\"\n ]\n },\n \"execution_count\": 34,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"arima = ARIMA(df.demand[0:30].to_numpy(),\\n\",\n \" order=(1,0,1))\\n\",\n \"\\n\",\n \"arma_model = arima.fit()\\n\",\n \"\\n\",\n \"arma_model.summary()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 35,\n \"id\": \"6f5703b7\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"20.27\"\n ]\n },\n \"execution_count\": 35,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"forecast_31_37 = arma_model.predict(30,36)\\n\",\n \"\\n\",\n \"get_MAPE(df.demand[30:], forecast_31_37)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"30adbf67\",\n \"metadata\": {},\n \"source\": [\n \"## ARIMA\\n\",\n \"time-series data is stationary - ARMA\\n\",\n \"\\n\",\n \"\\n\",\n \" non-stationary - ARIMA\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 36,\n \"id\": \"aba6b783\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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Datedemand
02014-10-0115
12014-10-027
22014-10-038
32014-10-0410
42014-10-0513
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" Date demand\\n\",\n \"0 2014-10-01 15\\n\",\n \"1 2014-10-02 7\\n\",\n \"2 2014-10-03 8\\n\",\n \"3 2014-10-04 10\\n\",\n \"4 2014-10-05 13\"\n ]\n },\n \"execution_count\": 36,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"store_df = pd.read_excel('store.xls')\\n\",\n \"\\n\",\n \"store_df.head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 37,\n \"id\": \"c1deb62f\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\",\n \"RangeIndex: 115 entries, 0 to 114\\n\",\n \"Data columns (total 2 columns):\\n\",\n \" # Column Non-Null Count Dtype \\n\",\n \"--- ------ -------------- ----- \\n\",\n \" 0 Date 115 non-null datetime64[ns]\\n\",\n \" 1 demand 115 non-null int64 \\n\",\n \"dtypes: datetime64[ns](1), int64(1)\\n\",\n \"memory usage: 1.9 KB\\n\"\n ]\n }\n ],\n \"source\": [\n \"store_df.info()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 38,\n \"id\": \"ecff6ab2\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
demand
Date
2014-10-0115
2014-10-027
2014-10-038
2014-10-0410
2014-10-0513
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" demand\\n\",\n \"Date \\n\",\n \"2014-10-01 15\\n\",\n \"2014-10-02 7\\n\",\n \"2014-10-03 8\\n\",\n \"2014-10-04 10\\n\",\n \"2014-10-05 13\"\n ]\n },\n \"execution_count\": 38,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"store_df.set_index(pd.to_datetime(store_df.Date), inplace=True)\\n\",\n \"\\n\",\n \"store_df.drop('Date', axis=1, inplace=True)\\n\",\n \"\\n\",\n \"store_df[:5]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 39,\n \"id\": \"6bd71b19\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[]\"\n ]\n },\n \"execution_count\": 39,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.figure(figsize=(10,4))\\n\",\n \"plt.xlabel('Date')\\n\",\n \"plt.ylabel('Demand')\\n\",\n \"\\n\",\n \"plt.plot(store_df.demand)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 40,\n \"id\": \"3aeb1f33\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"acf_plot = plot_acf(store_df.demand, lags=20)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 41,\n \"id\": \"362258a9\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from statsmodels.tsa.stattools import adfuller\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 42,\n \"id\": \"9c78933a\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Test Statistics -1.649533\\n\",\n \"p-value 0.457284\\n\",\n \"Lags Used 13.000000\\n\",\n \"Number of Observations Used 101.000000\\n\",\n \"dtype: float64\\n\"\n ]\n }\n ],\n \"source\": [\n \"def adfuller_test(ts):\\n\",\n \" adfuller_result = adfuller(ts, autolag=None)\\n\",\n \" adfuller_out = pd.Series(adfuller_result[0:4],\\n\",\n \" index=['Test Statistics',\\n\",\n \" 'p-value',\\n\",\n \" 'Lags Used',\\n\",\n \" 'Number of Observations Used'])\\n\",\n \" print(adfuller_out)\\n\",\n \" \\n\",\n \"adfuller_test(store_df.demand)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"2f7b1ad8\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# p-value > 0.05 that means we cannot reject the null hypothesis, \\n\",\n \"# as series is not stationary\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"eb42a0fa\",\n \"metadata\": {},\n \"source\": [\n \"#### differencing\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 43,\n \"id\": \"5a1055ea\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
demanddemand_diff
Date
2014-10-0115NaN
2014-10-027-8.0
2014-10-0381.0
2014-10-04102.0
2014-10-05133.0
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" demand demand_diff\\n\",\n \"Date \\n\",\n \"2014-10-01 15 NaN\\n\",\n \"2014-10-02 7 -8.0\\n\",\n \"2014-10-03 8 1.0\\n\",\n \"2014-10-04 10 2.0\\n\",\n \"2014-10-05 13 3.0\"\n ]\n },\n \"execution_count\": 43,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"store_df['demand_diff'] = store_df.demand - store_df.demand.shift(1)\\n\",\n \"\\n\",\n \"store_df.head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 44,\n \"id\": \"78378db1\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"store_diff_df = store_df.dropna()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 45,\n \"id\": \"e4c4e738\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[]\"\n ]\n },\n \"execution_count\": 45,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.figure(figsize=(10,4))\\n\",\n \"plt.xlabel('Date')\\n\",\n \"plt.ylabel('First Order Differencing')\\n\",\n \"\\n\",\n \"plt.plot(store_diff_df.demand_diff)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 46,\n \"id\": \"9ac6af67\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"C:\\\\Users\\\\himan\\\\anaconda3\\\\lib\\\\site-packages\\\\statsmodels\\\\graphics\\\\tsaplots.py:348: FutureWarning: The default method 'yw' can produce PACF values outside of the [-1,1] interval. After 0.13, the default will change tounadjusted Yule-Walker ('ywm'). You can use this method now by setting method='ywm'.\\n\",\n \" warnings.warn(\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n 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\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"pacf_plot = plot_pacf(store_df.demand_diff.dropna(),\\n\",\n \" lags=10)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 47,\n \"id\": \"41d50dfe\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"store_train = store_df[0:100]\\n\",\n \"store_test = store_df[100:0]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 48,\n \"id\": \"78743a66\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"C:\\\\Users\\\\himan\\\\anaconda3\\\\lib\\\\site-packages\\\\statsmodels\\\\tsa\\\\statespace\\\\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.\\n\",\n \" warn('Non-invertible starting MA parameters found.'\\n\"\n ]\n },\n {\n \"data\": {\n \"text/html\": [\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"
SARIMAX Results
Dep. Variable: y No. Observations: 100
Model: ARIMA(1, 1, 1) Log Likelihood -256.910
Date: Tue, 16 May 2023 AIC 519.821
Time: 22:15:55 BIC 527.606
Sample: 0 HQIC 522.971
- 100
Covariance Type: opg
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"
coef std err z P>|z| [0.025 0.975]
ar.L1 0.4091 0.311 1.315 0.189 -0.201 1.019
ma.L1 -0.7179 0.236 -3.039 0.002 -1.181 -0.255
sigma2 10.4865 1.026 10.223 0.000 8.476 12.497
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"
Ljung-Box (L1) (Q): 0.06 Jarque-Bera (JB): 75.18
Prob(Q): 0.81 Prob(JB): 0.00
Heteroskedasticity (H): 0.76 Skew: -0.79
Prob(H) (two-sided): 0.44 Kurtosis: 6.97


Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).\"\n ],\n \"text/plain\": [\n \"\\n\",\n \"\\\"\\\"\\\"\\n\",\n \" SARIMAX Results \\n\",\n \"==============================================================================\\n\",\n \"Dep. Variable: y No. Observations: 100\\n\",\n \"Model: ARIMA(1, 1, 1) Log Likelihood -256.910\\n\",\n \"Date: Tue, 16 May 2023 AIC 519.821\\n\",\n \"Time: 22:15:55 BIC 527.606\\n\",\n \"Sample: 0 HQIC 522.971\\n\",\n \" - 100 \\n\",\n \"Covariance Type: opg \\n\",\n \"==============================================================================\\n\",\n \" coef std err z P>|z| [0.025 0.975]\\n\",\n \"------------------------------------------------------------------------------\\n\",\n \"ar.L1 0.4091 0.311 1.315 0.189 -0.201 1.019\\n\",\n \"ma.L1 -0.7179 0.236 -3.039 0.002 -1.181 -0.255\\n\",\n \"sigma2 10.4865 1.026 10.223 0.000 8.476 12.497\\n\",\n \"===================================================================================\\n\",\n \"Ljung-Box (L1) (Q): 0.06 Jarque-Bera (JB): 75.18\\n\",\n \"Prob(Q): 0.81 Prob(JB): 0.00\\n\",\n \"Heteroskedasticity (H): 0.76 Skew: -0.79\\n\",\n \"Prob(H) (two-sided): 0.44 Kurtosis: 6.97\\n\",\n \"===================================================================================\\n\",\n \"\\n\",\n \"Warnings:\\n\",\n \"[1] Covariance matrix calculated using the outer product of gradients (complex-step).\\n\",\n \"\\\"\\\"\\\"\"\n ]\n },\n \"execution_count\": 48,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"arima = ARIMA(store_train.demand.to_numpy(),\\n\",\n \" order=(1,1,1))\\n\",\n \"\\n\",\n \"arima_model = arima.fit()\\n\",\n \"\\n\",\n \"arima_model.summary()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"35dce0cf\",\n \"metadata\": {},\n \"source\": [\n \"### forecasting and accuracy\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 50,\n \"id\": \"bd238d6f\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([17.25076347, 16.12611976, 15.66605623, 15.47785577, 15.40086769,\\n\",\n \" 15.36937379, 15.35649043, 15.35122017, 15.34906424, 15.3481823 ,\\n\",\n \" 15.34782152, 15.34767394, 15.34761356, 15.34758887, 15.34757876])\"\n ]\n },\n \"execution_count\": 50,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"store_predict = arima_model.forecast(steps=15)\\n\",\n \"\\n\",\n \"store_predict\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 51,\n \"id\": \"6cf83df8\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"24.12\"\n ]\n },\n \"execution_count\": 51,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"get_MAPE(store_df.demand[100:], store_predict)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"3d8e9051\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.12\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n" }, { "path": "March Cohort 2023/22_23_Time Series Forecasting/22_Forecasting.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"id\": \"12c7b612\",\n \"metadata\": {},\n \"source\": [\n \"# Time series data and forecasting\\n\",\n \"\\n\",\n \"components\\n\",\n \"- trend\\n\",\n \"- seasonality\\n\",\n \"- cyclical \\n\",\n \"- random\\n\",\n \"\\n\",\n \"Models/ forecasting techniques\\n\",\n \"- AR\\n\",\n \"- MA\\n\",\n \"- ARMA\\n\",\n \"- ARIMA\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"861324fd\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"time series data -> random variable\\n\",\n \"- single variable (demand of the product at time t) univariate time series data\\n\",\n \"- multivariate time series data -> \\n\",\n \"\\n\",\n \"4 components\\n\",\n \"- trend\\n\",\n \"- seasonal\\n\",\n \"- cyclical\\n\",\n \"- irregular\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"c3f8bc6b\",\n \"metadata\": {},\n \"source\": [\n \"## Moving Average\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"id\": \"dacb8d91\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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MonthSale QuantityPromotion ExpensesCompetition Promotion
0130026661051
1244015531450
2332052791181
3442453491300
453001940981
5643777661560
672798343981
7843036681440
8929581851121
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\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" Month Sale Quantity Promotion Expenses Competition Promotion\\n\",\n \"0 1 3002666 105 1\\n\",\n \"1 2 4401553 145 0\\n\",\n \"2 3 3205279 118 1\\n\",\n \"3 4 4245349 130 0\\n\",\n \"4 5 3001940 98 1\\n\",\n \"5 6 4377766 156 0\\n\",\n \"6 7 2798343 98 1\\n\",\n \"7 8 4303668 144 0\\n\",\n \"8 9 2958185 112 1\\n\",\n \"9 10 3623386 120 0\"\n ]\n },\n \"execution_count\": 2,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"import pandas as pd\\n\",\n \"\\n\",\n \"df = pd.read_csv('wsb.csv')\\n\",\n \"\\n\",\n \"df.head(10)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"id\": \"fecf8a63\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import matplotlib.pyplot as plt\\n\",\n \"import seaborn as sns\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"id\": \"b1c6bf16\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[]\"\n ]\n },\n \"execution_count\": 4,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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Sale Quantitymoving_avg_12
03002666NaN
14401553NaN
23205279NaN
34245349NaN
43001940NaN
54377766NaN
62798343NaN
74303668NaN
82958185NaN
93623386NaN
103279115NaN
112843766NaN
1244475813503418.00
1336753053623827.58
1434771563563306.92
1537207943585963.33
1638340863542250.42
1738889133611595.92
1838713423570858.17
1936798623660274.75
2033582423608290.92
2133614883641629.00
2236703623619804.17
2331239663652408.08
2446340473675758.08
2537728793691296.92
2631871103699428.08
2730936833675257.58
2845573633622998.33
2938169563683271.42
3044108873677275.00
3136947133722237.08
3238226693723474.67
3336892863762176.92
3437286543789493.42
3547326773794351.08
3632164833928410.33
3734532393810280.00
3854316513783643.33
3942418513970688.42
4039098874066369.08
4132164384012412.75
4242220053962369.58
4336210343946629.42
4451622013940489.50
4546271774052117.17
4646239454130274.75
4745993684204882.33
\\n\",\n \"
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Sale Quantitymoving_avg_12
3632164833928410.33
3734532393810280.00
3854316513783643.33
3942418513970688.42
4039098874066369.08
4132164384012412.75
4242220053962369.58
4336210343946629.42
4451622013940489.50
4546271774052117.17
4646239454130274.75
4745993684204882.33
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" Sale Quantity moving_avg_12\\n\",\n \"36 3216483 3928410.33\\n\",\n \"37 3453239 3810280.00\\n\",\n \"38 5431651 3783643.33\\n\",\n \"39 4241851 3970688.42\\n\",\n \"40 3909887 4066369.08\\n\",\n \"41 3216438 4012412.75\\n\",\n \"42 4222005 3962369.58\\n\",\n \"43 3621034 3946629.42\\n\",\n \"44 5162201 3940489.50\\n\",\n \"45 4627177 4052117.17\\n\",\n \"46 4623945 4130274.75\\n\",\n \"47 4599368 4204882.33\"\n ]\n },\n \"execution_count\": 8,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df[['Sale Quantity', 'moving_avg_12']][36:]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"id\": \"ad26c086\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"No artists with labels found to put in legend. 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.figure(figsize=(10,4))\\n\",\n \"\\n\",\n \"plt.xlabel('Months')\\n\",\n \"plt.ylabel('Quantity')\\n\",\n \"\\n\",\n \"plt.plot(df['Sale Quantity'][12:])\\n\",\n \"plt.plot(df['moving_avg_12'][12:], '.')\\n\",\n \"\\n\",\n \"plt.legend()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"id\": \"a018f130\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# there is an increasing trend in sales quantity over the months\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"613775c8\",\n \"metadata\": {},\n \"source\": [\n \"## Forecast Accurary\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"d985e8e1\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"- RMSE\\n\",\n \"- MAPE\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"94146992\",\n \"metadata\": {},\n \"source\": [\n \"### Mean Absolute Percentage Error\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"id\": \"dafa7a48\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import numpy as np\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"id\": \"183268aa\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def get_MAPE(actual, predicted):\\n\",\n \" ytrue, ypred = np.array(actual), np.array(predicted)\\n\",\n \" \\n\",\n \" return np.round(np.mean(np.abs((actual - predicted) / actual))*100, 2)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"id\": \"43515dac\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"14.04\"\n ]\n },\n \"execution_count\": 14,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"get_MAPE(df['Sale Quantity'][36:].values, df['moving_avg_12'][36:].values)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"id\": \"2cd8e001\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"734725.8359239782\"\n ]\n },\n \"execution_count\": 15,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"from sklearn.metrics import mean_squared_error\\n\",\n \"\\n\",\n \"np.sqrt(mean_squared_error(df['Sale Quantity'][36:].values,\\n\",\n \" df['moving_avg_12'][36:].values))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"88910119\",\n \"metadata\": {},\n \"source\": [\n \"## Exponential Smoothing\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"id\": \"06de766f\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"df['ewm'] = df['Sale Quantity'].ewm(alpha=0.2).mean()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"id\": \"d9ad437b\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"pd.options.display.float_format = '{:.2f}'.format\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"id\": \"a1a0f4a2\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
MonthSale QuantityPromotion ExpensesCompetition Promotionmoving_avg_12ewm
3637321648312113928410.333828234.64
3738345323912803810280.003753219.93
3839543165117003783643.334088961.93
3940424185116003970688.424119543.81
4041390988715114066369.084077607.99
4142321643812014012412.753905359.34
4243422200515203962369.583968692.78
4344362103412503946629.423899157.24
4445516220117003940489.504151776.99
4546462717716004052117.174246860.31
4647462394516804130274.754322279.35
4748459936816604204882.334377698.31
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" Month Sale Quantity Promotion Expenses Competition Promotion \\\\\\n\",\n \"36 37 3216483 121 1 \\n\",\n \"37 38 3453239 128 0 \\n\",\n \"38 39 5431651 170 0 \\n\",\n \"39 40 4241851 160 0 \\n\",\n \"40 41 3909887 151 1 \\n\",\n \"41 42 3216438 120 1 \\n\",\n \"42 43 4222005 152 0 \\n\",\n \"43 44 3621034 125 0 \\n\",\n \"44 45 5162201 170 0 \\n\",\n \"45 46 4627177 160 0 \\n\",\n \"46 47 4623945 168 0 \\n\",\n \"47 48 4599368 166 0 \\n\",\n \"\\n\",\n \" moving_avg_12 ewm \\n\",\n \"36 3928410.33 3828234.64 \\n\",\n \"37 3810280.00 3753219.93 \\n\",\n \"38 3783643.33 4088961.93 \\n\",\n \"39 3970688.42 4119543.81 \\n\",\n \"40 4066369.08 4077607.99 \\n\",\n \"41 4012412.75 3905359.34 \\n\",\n \"42 3962369.58 3968692.78 \\n\",\n \"43 3946629.42 3899157.24 \\n\",\n \"44 3940489.50 4151776.99 \\n\",\n \"45 4052117.17 4246860.31 \\n\",\n \"46 4130274.75 4322279.35 \\n\",\n \"47 4204882.33 4377698.31 \"\n ]\n },\n \"execution_count\": 19,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df[36:]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"id\": \"875b1af2\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"11.15\"\n ]\n },\n \"execution_count\": 20,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"get_MAPE(df[['Sale Quantity']][36:].values,\\n\",\n \" df[['ewm']][36:].values)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"id\": \"1630eed7\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\\n\"\n ]\n },\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 21,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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laOKYiKimotS7OjLC5rl1eAwB4Jk+XJLkIY4wxlrCuQTvWNJUDyKy+sGmfH7ap6biCWiU1JXrotRxTEYkU1BrP4d3htFYVo72mJG/T87kIY4wxlpCxSQ+G7G5c2VYF4HxuVCYYldLyE1iOVKkoeJA3F2HzSSaoda5rV9TizZ5RjATDdfOJRukBMMaS1zVox7v/81X8/Y7LsKi6WOnhsDzRFWzKX9VoRHmhFpYM6gmTXtAT6QkDAjEVXUP2VA4ppyQT1DrXdctrce/zJ/Gdx49hSW0ptGqCRkXQqFXBz1XQqAlatQpqFV1wmUGrRlNFIUoTjMpQEhdhjOWAI+fGMeXx4fW3RrgIY2kjFWHttSWoMxoyqjFfCmpNZHckEDi+6PkTg/D6/NDEmTOWD5IJap1raV0JLmo04m8HLPgbLAnfTkWRDs0VhTBXFqGlogjNwT/NlYUJZ5nJjYswxnKA9Avx8LlxhUfC8knXoAMleg1qS/WoM+ozaoebdVKaCUusCGupLMS0T6B/3IUFpsJUDi0nJBvUGoqI8NfbL8W0T8Dr9wf+9Pnh9YvAh88/8zWvT2Da54fPL2Yuc7i86B2dQq91Et0jk3jtlBWP7Ds36z4qinQwVxbBXFEEs1SoVRbBXFmE4gLlSiEuwhjLAZbgi9/hc/l9GC5Lr85BO9prSkBEqCvTY0/vmNJDmnF+Jiyx5cjmiuAOyZFJLsLCSDaodS4igk5D0KWoVd3p8aF3dBI9I5PosU6hZyRQoL16agR/2Xd+xvYDHQvwvZtWpeQ+E8FFGGM5QJoJOzloh2vaB71WrfCIWK4TQqBr0I63ragDANQZDRh3TmPK40WhTvmXlmGHGzq1CiUJznJIMRU91klcgapUDi0nJBvUKjeDTo0ltaVYUlt6wdemPF70WgMzZ9VJ5pwlixe6GcsBFpsTOo0KXr/AiQFuJmbyG3a4YZuaRntNoAexPpgXZcmQmAqrw4OKYh2IKKHvry4pQKFOjW7eIRmWxZZ8UKtSCnUaLK0rxXUr6rA2GK+iFC7CGMsBFpsTly+qBJDbfWFTHi/+a+cpeLx+pYeS97qCSflttSUAAjNhQObEVFgd7oSXIoHA8lgzx1SElaqgVsZFGGNZb9LtxYTLiw6zCWWFWhzpy90i7IUTQ7j76U7s7BxSeih5rzO4M7KtJlCE1UtFWKbMhE16EgpqDdVSWYgeKx9dNNfgRODfONmgVsZFGGNZbyavp0yPlQ3GnJ4JkzYg7M2gBvB8dXLQjooi3czuwxpj4M9MObrI6kjsyKJQ5ooinB2dgtfHM6+hpB7UuhQEteY72YswIlIT0X4iejzM1zYT0TgRHQh+fEPu8TCWa6QenDqjASsajOgKNufnIulnzaRdePmqc9A+MwsGAAUaNSqLCzJiJkwIgeEED+8OZa4sgtcv0DeWGYVlppDe+PFMWPLSMRN2J4DjEb7+shBidfDj22kYD2M5JfQX4soGI7x+gc4cbc6XcqgO943nbKGZDYQQ6Bqwo61mdjBwfZk+I2bCHG4vPF5/Uj1hAB/kPZ+ZmbAM3h2ZLWQtwoioEcDbAfxCzvthLJ9ZbC4QBZKrVzYYAeRuc77F5kSBRgWPz5+zP2M2OGdzYtLjm2nKl9QZ9TMv0EqayQhLsiesuSKQD9bLzfmz9NucKCnQKBpymivkngm7B8BXAERaUL+YiA4S0VNEtDzcFYjoViLaQ0R7hoeH5RgnY1mrf9yJquICaNUqNJYbYDRocSRHCxSLzYmtS6oBAHt6eElSKScHAzsj22vmFmEG9NucEEIoMawZUlp+sjNhVcUFKNKpuTl/jv5xF++MTJGYijAi+gsRvZ2IYi7aiOgGAENCiL0RrrYPQLMQ4iIA9wH4a7grCSF+JoToEEJ0VFVxaB5joQK/EAPLAkSUs835Ux4vxqamsaLBiNbKIuztHVV6SHlL2hm5eE4RVl+mx6THhwmXV4lhzRgJzoQl25hPRDBXFnFW2BwDEy7UclN+SsRaVN0P4B8AnCSiu4hoSQzfcymAdxJRD4DfA9hKRL8JvYIQYkII4Qh+/iQALRFVxjx6xhgsNifqQxpklzeUomvQDrc3t3qmpKb8hjIDOszl2NM7Br9f2RmXfNU1YEdtqR5Gw+xzAzMlK2zEkdy5kaHMlUXo4Z6wWSw216zfOSxxMRVhQojnhBAfArAWQA+AZ4noNSK6hYjCnt4phPiaEKJRCGEGcDOAF4QQHw69DhHVUjDOmIg2BMdjTfinSQG7axrbjw7AGvxPzFgmEyJwwHDoVvGVDUZM+8RMmGaukOIp6ssM6Gg2wTY1jdMjufUzZouuIfsF/WDA+dR8pXdISj1hpqLkliMBoKWiCH1jTkxzTAWA80GtqTw3Mp/Fs7xYAeBjAD4BYD+AnyBQlD0bzx0S0W1EdFvwrzcBOEJEBwHcC+BmoXAzQa91Cp/69V68+paitSBjMZlwejHl8c28+AHI2eb880WYHuvMgaNGuC8s/Xx+gZODjpnjikJJbwaU3iFpdbhRqtdAp0m+7dlcWQSfX+DsKPeFAeeDWut5OTIlYu0JewTAywAKAbxDCPFOIcQfhBCfAXDh/8Q5hBA7hRA3BD9/QAjxQPDznwohlgshLhJCbBJCvJb4j5Ia7bUl0GtV2Mc5RCwLWGbiKc7/QmwyFaJUr8nJIkxFQE2pHq2VRTAV6TgvTAFnRqfg9vov6AcDAuctqkj5mbCRyeSDWiUtlYEdkrwkGSDtfuWZsNSIdX/pL4I9WzOIqEAI4RZCdMgwLsVo1SqsaizD/jP8y51lvtC0fAkRYUWDMed2SJ6zuVBTqodWHXjvuK65HHt60tOcPzgRuG+GmQy6uTsjAUCjVqGmVPmsMKvDnbIizFwRzAob4ZkwgINaUy3Wudp/D3PZ66kcSCZZ21SOo5YJDoNkGU9qVq+fE5q4ssGIzgF7Th10bbE5Z/2cHc3l6LFOYdgub//mjhND2PT/nsfx/glZ7ydbnJzZGRl+EaTOqFd8Jszq8CQdTyExFelQUqDhg7yDOKg1tSIWYcHG+XUADES0hojWBj82I7A0mZPWNpXB6xc5N5PAck//uBMaFV3wrn9FgxEenx9dg7mTnG8Zn1OEBfvC5D5H8ukjAxACePXUiKz3ky06B+1oMhWiUBd+IaWuzICBCYWXIx3ulBVhUkwFL0cG5FRQ69ndwMs/DPypkGgzYdcC+AGARgA/AvDD4McXAHxd3qEpZ21z4Jf7Pl6SZBnOElyiU6to1uW51pzv9wv021yzll1XNBih06hkzQsTQmBH5xAAYHc355IBQNfghccVhao36mFRMLDV6/NjbGo66bT8UFyEnZczQa1ndwMPvxN44T8CfypUiEUswoQQDwshtgD4mBBiS8jHO4UQj6RpjGlXWVyAJlMh9vXalB4KYxEFlugu/IXYXFGIkhxqzh+ZdMPj86MhZCasQKPGRY1GvCnjDslj/RMYsgd22r3ZM6p4ErzSPF4/Tg9Pzjq4e65aowFub6AQUsLolBTUmpqZMABoqSjEuTFnTi3vJ6p/PEeCWnteBnweQPgCf/a8rMgwoi1HSrleZiL6wtyPNIxPMWuayrDvzFje/9JlmW1uRpiEiLCiPnea82d63+b8rOuaTThqke8w752dgWPSPnXlQoxNTePUUH7nkvVYJ+H1C7SHyQiTSCGeUqRIus2cG5mixnwgMBPmF4GdofmufzxHglrNlwNqHUDqwJ/myxUZRrTlyKLgn8UASuZ8RI2myGZrm8oxZHfjnEK/SBiLxu8XGIiwNLCy0YgT/bnRnB8a1Bqqo7kc0z6Bg2dtstzvjhNDWNlgxPUr6wAAu9O0GzNTSTsjF1fPX4RJDdtKHeRtTdGRRaHMlYGXwnxvznd7fbkT1LpgA/DRvwNb/yXw54INigwjYmedEOK/g58+J4R4NfRrRHSpbKPKAGubAn1h+8/Y0Fies3sQWBazTnrg8fnnDU0Mbc5fEewRy1ZSEdZQNncmLBja2juGja0VKb1P25QH+86M4Y4ti2CuKERVSQHe7B7FhzY2p/R+sknXoB1qFaG1qmje60izJEodXSQdWZSqxnwgkJoPcFbY0ETgsc2ooNazuwNLiebL4y+kFmxQrPiSxBpRcV+Ml+WMJXXB0FZuzmcZKlpej9ScnwtLkudsThTp1Cg1zH7fWF6kw6LqYlnywl46OQK/ADYvqQYRYYPZlPfN+V2DdpgrCqHXque9TmVxAbRqmllCTreZcyNT2JhfXqSD0aDN+4O8pTdDGTMTliHN9cmIOBNGRBcDuARA1ZwesFIA8/8vzAFSaOu+Mzalh8JYWPNlhEmaTYUoKQg059+czoHJQMoICx41O0tHczmePNwPv19Apbrw64naeWII5YVaXNRYBgBYby7HE4f70Tc2lbez412DDiyJ0A8GACoVoaZUr9hMmHXSA42KLijYk2WuKMz7mTApeiTcZiBFhGuuV3hmK17RZsJ0CPR+aTC7H2wCgXMfc9rapnIck7Hpl7FkRJsJU6kIyxtKc2ImzGJzzVtsrmsux4TLi1PDqWua9/sFXuwaxpVtVTPxHxtaAsudb+ZpX5hr2ocea+SdkZJ6o0GxwFZrMCMsXMGeDHNlEXryPDVfeuOXMbsjM6S5PhnResJeBPAiET0khOhN05gyxpqmMkz7AqGtHWaT0sNhbJb+cRcKNCqYiubvfVnZYMTDr/di2uefOe4nG1lsznn72tYH/2/u6RmLqUCIxeFz47BOerC5vXrmsvbaEpToNdjdPYb3rGlMyf1kk1NDDgiBiDsjJXVletlDdOdjdaTu3MhQ5ooi/P2gBa5pX8Tl2Fw2MO5EiT6Dglql5vpEe8IyQKy/lQuI6GdE9AwRvSB9yDqyDCA153NfGMtEFpsTdUZ9xHf8KxqM8HizOznfNe2DddKDhnmWQJorClFZrEtpX9iOziEQAVe0Vc1cplYROprLsbvbmrL7ySbScyiWQrfOaMDghAt+f/ojfkYmPSmNp5C0VBZBCOBsHsdUBCJxMmQpUrJgA3D5F7OyAANiL8L+BGA/gH8F8OWQj5xWVVKABSYD9nNfGMtA82WEhcqF5vz54ikkRBQ4zDuFMy87OoexekHZBbOM61tMeGt4ElaHvOdVZqLOQTt0ahXMFdH74erL9Jj2iZkm+XQasbtRGWF2OFFSTEU+N+fH8jsnYRlwhJASYi3CvEKI+4UQu4UQe6UPWUeWIdY2lXNoK8tI/TZn1ONDzBVFKC7I7uT8aBsQAKCj2YQzo1MYsiffhzTicONQnw1bQpYiJRtbAkufcqb0Z6quATtaq4qgiWFZW3qhtqQ5K0wIAetk6s6NDMUxFTLOhOXALsdExVqEPUZEnyaiOiIySR+yjixDrG0qx+CEO+2/TBiLxOcXGLS7o+b1qFSE5fWlOHxuIk0jS735MsJCzRzmnYLi6KWuYQiBsEXYyoYyFGhUeRlV0TXoiKkfDDi/WaQ/zWHXUx4fXNN+WZYjjYValBdq0TmQn6cmSEGtssyEZcgRQkqItQj7KALLj68B2Bv82CPXoDLJmqYyAMA+hZpMGQtnyO6Czy9iOkh3ZYMRx/snMO3LzuT8czYniICa0vl/1uX1RhRoVCmZodrROYzK4gIsry+94Gs6jQqrF5Tl3Q5Ju2sa52zOmDc+SLOW6X7zOnNkkQzLkQBwzbIaPHbIgoEseFPu94uUnvgiBbXKMhOWA7scExVTESaEaAnz0Sr34DLB0rpSDm1lGWe+sxTDWdkYaM7P1nMPLTYnqksKoNPM/+tKp1HhogVl2NubXHHk9fnxUtcwNrdXzZs5tqElcF6lw+1N6r6yycngcyfWIqy8UIsCjSrtM2Ejk8Gg1pLUz4QBwGe2LoYQAve9cFKW20+lxw5ZcOXdO9CbouVTaUY6ljd+ccuQI4SUEPOedSJaQUTvJ6KPSB9yDixTaNUqrGoo4+Z8llGkjLBIfVISKdohW/vCLOPOmH7O9eZyHLVMwOlJPNfvwFkbxp3TYZciJRtaTPCL/JodPxncGdkeYxFGRKgvM6T9/MgRe+rT8kMtMBXi5vVN+MObZzN+l+SBszZ4/QIvnBhKye1JQa2y7Y7M8l2OiYqpCCOif0PgmKL7AGwBcDeAd8o4royyprkMRzm0lWUQKQgzlnelLcHm/GzdIRkpqDVUR7MJXr/AgSQO897ROQS1inDZ4sp5r7O2qRxqFeVVX1jngAMGrRqN5bH3A9UZ9bCkOTXfOhlcjpShMV9yx9ZFUKsIP3k+s2fDpEiRnZ3DKbm9jAtqzRGxzoTdBOAqAANCiFsAXARAnrcaGWhtUzmmfQJHLdn5IsZyzzmbE8UFGpTqtVGvq1IRltWXZuVMmBCBvpZITfkSKdcvmbywnZ3DWNdUDqNh/se1qECD5fWl2J1HfWFdg3YsrimO61ioOgVS86XokEgBxsmqKdXjHzc145F9fXgrhac0pFrXYGBsu05bUzKBkHFBrTki1iLMKYTwA/ASUSmAIQB50RMGhDbn2xQdB2OS/nFnXMsCUnO+N8ua862THni8ftTH8LMaC7VoqylOOC9scMKFo5YJbF5SFfW6G8wmHDhrg9ubH7PjnYP2uE8jqC/TY8juSutzbsThQUmBRvZE+9s2L4Req8Y9z2XmbNjopAfDdjcuX1wJt9eP108nHzBsiSWeIk+zvpIRaxG2h4jKAPwcgZ2R+wDkzaNcXaJHY7mBm/NZxugfd6EuhtkhycoGI1zT/pSer5gO/TFkhIXqMJuw78wYfAkktb8YXLaJ1A8mWd9igsfrx6G+7JtdjNdY8AU91n4wSZ3RAL8AhuzpC2y1Tnpka8oPVVlcgFsuNeOxgxYc78+8+BdpKfLDm5qh16pmntvJGIgW1JrHWV/JiHV35KeFEDYhxAMArgHw0eCyZN7g0FaWSSw2V0yzQ5IVDYG4hcNZVjSci5KWP1dHcznsLm9CxzTt6BxCbakeS2LIwpLOq8yHvjDpsVxcUxzX90n9iv1p7AuzOtyyxVPMdevlC1Gi1+BHz3al5f7iIW2kWNVoxMWtFdjZmXxzftTZ9zzO+kpGrI35V0gfAJoAlAU/zxtrm8owOOFO+24fxuZKJDSxpbIYhTp11jXnxxLUGqqjOXiYd5xLktM+P14+OYItS6oinsUpMRXpsKi6OC/ywqQiLNagVon0gm1JY1/YiEOetPwZIcttxkItbr28Fc8eG8TBJDaDyKFz0I4SvQa1pXpsbq9Gj3UKPUkctxT4neOJ/Dsnj7O+khHrcmToeZH/F8BjAL4p05gy0tpmPsybZYbB8WBoYhx5PeqZ5PzsK8IMWjXKCqNvQACABSYDqkoKsDfO4mhPzxgcbi82x7AUKdnQYsLensSWPrNJ16Bj5gU9HtILdnpnwuQ5vBtA2OW2Wy5rQXmhFj/MsNmwrgEH2mpKQETY3B7ocUxmNmzmd06kmbA8zvpKRqzLke8I+bgGwAoAg/IOLbPMhLZycz5TmLTtP5ag1lArGow4lmXN+YGMMH1Ms1NAIJ9qvTn+w7x3dg5BqyZcumj+aIq5NphNsLu9GdkTlEpSU36s/waSUr0GRTp12mbCfH6B0SmPLId3Awi73FZcoME/b16Il7qGM2ZpWgiBrqHzGymaK4rQUlmEnV2J94VJhXTUN355mvWVjJjDWufoQ6AQyxtSaCvPhDGlxfwLcQ6pOf+t4ew5gPhcjBlhodY1m9A35ozraJkdnUNYbzbFtf1+/cxh3pnx4isHIQS6EtgZCQQK4royQ9pmwsamPBAC0WfCEt3BN89y2z9uMqOqpAA/eKYzI3qGh+1u2Kam0RbSw3dlWxVefyvxqAqpDUe2oNY8FmtP2H1EdG/w46cAXgFwUN6hZZ41TRzaypQXz5FFoVZmYXK+JcaMsFAdwdaBPTEeYXTO5kTXoCOmXZGhGsoMaCgz5HQRJr2gt8fZlC+pM+rT1kcrnRtZGakIS2YH3zzLbQadGp/Zugi7u0fxyqmR+e83TdENUj5Y6G7Wze1VcHv92JVgVIX0b8hBrakX60zYCQCngh+vA/iKEOLDso0qQ63h0FaWAfrHnSgr1MKgiy8LqbUqu5rz3V4fhu3uuGfCltWXwqBVY0+Mh3lLvTJbYsgHm2tDiwm7u0czYgZEDtILelucTfmSeqMhbcuRI8Gg1oiN+cnu4Jtnue0D6xegocyAHzzTdeFzIc3RDZ3BjRSh/2abWitQoFElnJ7PQa3yiViEEZGWiO4B8B0AHwNwCwJHFl0e/PoamceXUdY2lwEAnyPJFNVvi5LXMw+1irCsLnua86XlxHiLMK1ahdULyrA3xr6wHSeG0VhuwMKq+Gd71ptNGHF40J3EzrNMNvOCnsByJBBYMh9xuNMSaisVYZWRijCZdvAVaNT47FWLcPCsDc8fn9MAn+bohq4BO0xFulkzgnqtGhcvrMCLCfaFWcZdcc+8s9hEmwn7IYBiAM1CiLVCiDUAlgJoJaL7ATwi9wAzCYe2skwQ+IWYWG/GigYjjlkmsmJH3/mMsPh/1g5zOY71T2DS7Y14PbfXh1dPjWBLe3XcjecAsKElsPSZq0uSXQN2VMx5QY+H9MIt7a6Tk7QcWRHp8G4Zd/DduLYR5opC/PDZLvhD/3+lOboh0JR/4RuKzW1V6B6ZRK81/jcMA+Mu1HI/mCyiFWHXA/ikEGIm+VAIMQHgnwHcDOCDMo4tI61tKucdkkxR/ePOuJvyJSsbjHBO+zL6zDuJtIwVb08YAKxrLocvhsO8d3ePwjntS2gpEgAWVhXDVKTD7u7cfGMWussuEdLzNB0HeVsn3VCrKOK5nwBk28GnVavwuavbcLx/Ak8dGZh9f2mKbhBCoGvAHvZ0Ayl+JZElyf7gLmWWetGKML8I0+wghPABGBZC7Ip2B0SkJqL9RPR4mK9RsNn/FBEdIqK1sQ9dGWuayjAw4ZoJkWQsnZweH2xT0wktRwLAysZgc34WJOdL/8cSeQe+trkcRIjaF7bjxDB0GhUubo09miKUFImxuyf5s/kyjfSCHm5WJVbpzAqzOjyoKNLFdch4qr3jonq01RTjR892zp5tTlN0wzmbE5MeHxaHKcLMlUUwVxTGnRcmBbXWlvJypByiFWHHiOgjcy8kog8DOB7jfdwZ4bpvA7A4+HErgPtjvE3FrG3i0FamnJmMsATflS6sKoZBq86KvjCLzYmqkgIUaOI/jLlUr0V7TUnUHZI7O4dwcWtF3JscQq03m3B2NL5IjGwgvaAn2pQPnH+epqM5f0TOoNYYqVWEL1zThreGJ/HX/efSfv8npZ2R8/ybbW6vxuun44uqSCQcmsUuWhF2O4DbiWgnEf2QiH5ARC8C+CyAT0e7cSJqBPB2AL+Y5yrvAvArEbALgeOQ6uIYf9otrStFgYZDW5kypAOtE50JU6sIy+pLs2KH5DmbM+6m/FAd5nLsP2Obt/+tZ2QSp0cmZxLFE7UhmBe2O8f6wmaOK0piObJQp4HRoE3LTNiIwx25KT9Nrl1ei+X1pbjn+S5MpzkYeWYjRXX4f7Mr26vgmvbjjTiCZWdyCbknTBYRizAhxDkhxEYA3wbQA+AMgG8LITYIIWIp8+8B8BUA8z0TGwCcDfl7X/CyWYjoViLaQ0R7hoeTPw0+GTqNCqsajdh/lmfCWPpJM2GJ9ElJVgaT8zO9OT+QEZb4L/6OZhMcbi9ODIRPtJ+JpogzH2yuZXWlKNKpsbs7t5YkpXiKcEtbF4iQg1Vn1M+8eZCTdTJ9h3dHQkT40rZ2nB114k97+tJ6312DdtSUFsA4zzFfF89EVcS+JHk+qJWXI+UQ67FFLwgh7hNC3CuEeD6W7yGiGwAMCSH2RrpauLsLc/8/E0J0CCE6qqqSe9eaCmubynH03ERatl0zFspic4IIqInzHL9QKxqMmPL40D2Suc35QghYbMlti+8wB1oH5ouq2NE5jJbKIpgrixK+DwDQqFVY21yON3OsOb9rwI7aUn30RvcoOVj1ZQZY0rBUK+u5kXHa3F6FtU1luO+Fk2kN9551ukGYwlivVWNTawVejKM5/3xQK8+EySHRY4ticSmAdxJRD4DfA9hKRL+Zc50+AAtC/t4IwCLjmFJiTVM5PD4/jpzL7TPjWObpt7lQWVwAnSbx/7rZkJxvm5qGc9qX1HJkQ5kBtaX6sM35To8Pr5+2Jr0UKdlgNqFz0A7blCclt5cJOgftsfWDRcnBCqTmy7scOeXxYsrjSzhKI9WICF+6th394y78bveZtNynzy9wctARWD6OUBhvbq/C6ZFJnLFOxXS7/RzUKivZijAhxNeEEI1CCDMCcRYvhEnZ/zuAjwR3SW4CMC6E6JdrTKmytqkMALCfm/NZmlnGnQlnhEkWVhVBr1XhcF/mvok4nxGWeBFGRFhnLseeML1au05b4fH6k16KlEh9YbGm9Gc6n1/g1JAjtuOKouRg1ZcZAkW1R74ZoZmMsAzoCZNcsrASlyyswH/uOIUpT+S8ulQ4OzoFt9cfmAmLUBjPRFV0xbYk2c9BrbKScyYsLCK6jYhuC/71SQCnETgO6eeIodk/E1SX6tFQxqGtLP36xxNLyw+lUauwrC6zm/OleIpket+AwDmSlvELI2V2dA7BoFXPFE/JumhBGXRqVc40558JvqDH1A8WJQdLauiWczYsprR8BXxxWxtGHB48/Fqv7Pc167iiCIVxS2URmisKY84L6x938lKkjNIyvyiE2AlgZ/DzB0IuFwjswMw6gR6Q3PiFy7KDEAL9NicuX5xYplWolQ1G/HlvH/x+oWiu0nwsSaTlh1pvDs5Q9Y7hncGCTgiBF04M4dJFFdBrE4+mCKXXqrGq0YjdOfI7oXMgzp2RCzbMm4F1PivMhdYEjoaKRUxp+QpY12zClvYqPPDiW/jQpiaU6qP01yWhK/hvtri6GCgIFsY9LwcKsDn/NpvbqvCHPWfhmvZF/T8wMO6aaWFgqZf2mbBcsTYY2pqOrdeMAcCEy4tJjy8lSwMrGoyY9PhwOkPPPLSMu1CgUcGU5G63JbUlKNSpsTdkhuqt4Un0jTlnlmVSZX2LCUfOjadl6UluJ4OzKouTCGqVnM8Kk+93pXUyhsO7FfLFbe0Yd07jwVe6E7uBCDtPQ3UNOdBYbkCR1LsVISB2c3s1XNP+qG8aOKhVflyEJWgmtJXzwliazOT1pCA0UUrOz9QlyXM2JxrKDAmd5xhKo1ZhTVMZ3gzp1ZK256eqKV+yocUEr19g/xlbSm9XCZ2DdiwwGVCoS36xRNrJ2y/jDsmR4ExYpjTmh1rRYMTbVtTiFy93w+6aju+bo+w8DTXfcUXhbGqtgE6jmn9JMlj4jZ14FQAHtcqJi7AEzYS25mFfmM8v0rrtmgUkG9QaalFVcaA5P0OLMIst8fMx5+poNuHEwAQcwcO8d3QOYXF1MRrLC2O7gRhnItYFj0rKhSXJrsHYX9Cj0WvVqCjSybpqYHV4UFygSdnycqp95GIzHG4v3jgd53Mjys5TybTPj9MjjphPNzDoAlEVYfPCQgq/qkffh7XUxUGtMuI9pwnSaVRY2WDMmyLMNe3DKydH8PTRATx3fBC2qWmUFWpRU6JHjVGPmpIC1JTqUVMq/Rn4qCzWQaPmWj8Vkj2yKJRGrcLSutKMLsKuWJyamaoOczn8IrCbeU1TOXZ3j+KWS1ti+2bpBcnnCTQ4RziAuVSvxdLaUryZ5c35Hq8fp4cncfXSmpTdZl2ZXtaji6yT7oxcipSsaSqDTqPCG91WXL0sjsdVarCXnn9zdp5KekYmMe0TcZ3zubmtCt9+/BjOWKfQVBHyhiSk8CMfsEl1nINaZcRFWBLWNpfjoVd74Pb6EjrfLtPZXdPY0TmM7UcHsOPEEKY8PpToNbhmaQ1aKoswZHdjcMKFwQkXugbsGHa4L0hhV1FgiUAq0JpMRbh9y8KMCVXMJv02F9QqQnVJat6Vrmww4pF95zKuOd/j9WPI7k4qniLUmqZyqIKHeU95fJj2idiXIsPNREQ4hHlDiwm/f/MMPF5/UlluSuqxTsLrF/OeP5iIOqMBvVb5+g9HHJmRlj8fvVaN1QvK4jouCMD5nafzNNhLZnZGxjF7ubm9Ct9+PBBV8ZGLzee/EFL4+UiDXf6luJ1nwmTDRVgS1jaV4WcvBUJb1zWXKz2clBid9OC5Y4N4+ugAXjk5Ao/Pj8piHd69pgHXLa+d6SUIx+cXsDrcGJwIFmd2FwbHXYG/2104Z3Phxa5h7O0dxW8/uel8AymLiWXciZqSAqhTVDCtaDDiV6/3ots6iYUy7VpLxOCEC0IkH08hKS7QYEltKfb0jmLI7kJxgQYdzTFGU8Q4EyHZ0GLCQ6/14IhlfKZvNNt0zuyyS10RVm/UY9db8h3rZHV4sMAU4/KyQja1VuCnL5zEhGs6vl2SEXaeSroGHVAR4vp/3FJZhCZTIKpiVhEWUvg9dLYBp7rK+Xe1jPiRTYL0S3b/mbGsLsL6x5145uggnj4ygDe6rfAHXwD/8eJmXLeiFmubymN64VerCNWlelSX6rES4bc0P3tsEJ/69R7c/tt9+PlHOqDlpcqY9dtcqEtRYQKcT84/cm48o4qwVAS1zrXeXI4/7e3DW0OTuGxRZeyzVDHORJy/n0Bx92b3aOD3w9ndMX/vLIl+Xwp0DdqhVhFaq5I7zilUXZkBdrcXdtc0SmSIaRhxeLAmGKKdqTa1mHCvAPb2jGHLktTuzO0asMNcURRXTxwRYXN7Ff4YLqoiWPjt/tUe1BljS9ZnieEiLAlSaGs27oZyenz49a4ePHl4AAfO2gAAi6qL8enNi3Ddilosry9NemdaONcsq8F/vGclvvbIYXztkcP4/k2rZLmfXNQ/7sSKFOb1LKouhk6jwuG+cbxrdUPKbjdZqcoIC7XObMLDr/diyuPDliVx9prFMBMhqSopQEtlEd7sGcWnWq0x95PNEkcfmhy6Bu0wVxSmtMn9fGCra3YRloJi0+8XGJ10Z+TOyFBrmsqhVRN2dVtTX4SFnhkZh83tVfjV673Y3T2KK9ou/H/RP566DTIsPC7CkrS2OfyxKJls12krvvqXQ+i1TmFVoxFfvrYd1y6vxaLq9MyGfHBDEwbGXfjJ8ydRW6rHl65tT8v9ZjMhBPrHXdi2vDZlt6nN0OZ8iwwzYR0hM9Wpzgeba4PZhKePDsDffRiqOPrJZvS8DOHzgIQv8Ges3ydJsrDpGnRgSQr7wYDz/5YWm3P2AdMpKDZtzmn4BTK6JwwI7EhcvaAMu+LdIRmFa9qHHuskblhVF/f3XtxaORNVEa4I46BW+fFaUJLWLChD/3h2hLZOur34xt+O4Oaf7QIA/P7WTfj7HZfh9i2L0laAST539WJ8cMMC/HTHKfx6l/xHemS70UkP3F5/yreKr2woxVHLBPxzNlQo6ZzNhYoiXUpnYurLDGgoM2BpXelMbpVc1reYMO6cxpnStRHPVJzPsYKL4BZqeIUKbqHGQPn62O88jlypcKQX9ERmVSIJnQmbEWP8QjTSkUXZsNlnY0sFjpwbn4lLSYW3hh3wC8QcTxHKoFNjY4sp7DmSUlAr74yUFxdhSVrbnB2hra+eGsG197yEX+/qxT9d2oKn7rwcm1orFBsPEeE771qBq5dW4xt/O4Knj2T8ue2Kkl68Uv0LcWWDEQ63Fz0y7lyLl8XmTOksmOSH778Id924MuW3O9eGYF/Yy86WiGcqzuX3C/zXzlN4x189+IL+2zjUdgc+gW9g25+deOHEYGx3nkxhc3Y3bM/chTXoSunOSCAQ2EoE9Iem5kc5+DtW54uwzJ4JA4CNrSb4/AJ7e1MXbdQ1GOcRU3Nsbq/G6eFJnB2d3fs1OB54XPncSHlxEZakZRke2mp3TeNrjxzGh37xBnRqFf70qYvxjXcsS0kSdrI0ahXu++BarF5Qhs/+/kBOhFzK5VyKDrSeS+oxy6QlyUARlvpf/JtaK3DRgrKU3+5cC0wG1JbqsbtnLOLRMaGsDjdueehN3P10J65bUYvvfe4TWPuh7+C7n/k4GssL8U8P7cGPnum8IALmAokWNsEZtOo3f4D/1X0Xq0RnjD9tbLRqFapLCmAJnQmLcvB3rKwZnJY/17rmcmhUhDdOp26naNegA1o1wVyZ2EYKKa5lbnDrTC4hz4TJiouwJEmhrfszsAh7sWsY1/74JfzhzTO49YpWPHnn5egwx7g1P00MOjX+56Pr0VhmwCcefnPmXR2bTZpBSHWTbFtNCXQaVcYcXySEkG0mLF2ICOtbTNjdbYUQ0Zd53zhtxfX3vozXT1vx7+9egZ9+cM1M83pTRSEe+fQleN+6Rtz7wil87Je7MTbpmf/GEi1sgjNoKvihhRd1Y3ti+7441BkNF7ZtxFikRmKVZsIyvCcMAAp1GqxqNGJXKouwATtaK4sT3mneWlmEBSbDBUcYDQQLZp4JkxcXYSmwtrkcR85NwO3NjKN8xp3T+MqfD+KjD+5GYYEGf/nnS/D165dm7JEepiIdHv6nDSjQqvHRB3dnRX9duvWPu6DTqFL+QqNVq7CsrhT7MmSH74QzcEh5qmf80m2DuRyDE26cHZ3/uezzC9z3/El88Oe7UKjT4NFPX4IPb2q+YLewXqvG3Tetwv+7cSXeOD2KG+57BYf6bPPfeSKFTXAGzQcVvKSBuvWK2L83RvVl+pmjt1IieJyUbmAPVASUF2Z+EQYAG1srcKgvdQe9dw3ZE+oHkxARNrdV47W3rLOOo5NmwvjIInlxEZYCaxaUwePz46hlQumh4IUTg9j24xfxl33n8OnNC/H4Zy7DmiwIjVxgKsRDt6yH3eXFxx58E+POOA+6zXGWcRfqjHpZ4jzeX2vBpnMPwX7y1ZTfdrzkyAibEeMZkKmwviUw47x7np3Tw3Y3Pvrgbvzw2S6846J6PPaZy7C8fv5daESED25owp9uuxgAcNP9r+N3u8/ENNMWk+AM2s81/4D7m38sSyRGndEAy7gzNWMO2YBw05HbcWVhT0ad+hDJxuBB76noI550e3F21Im2JDdWbW6vgnPaN+vIrYFxF0r1Gg5qlRkXYSlwvjlfuSVJ25QHX/jDAfzTQ3tQZtDh0U9fgq9ctyTzZr8ivBAurzfiZ/+4DqdHHPjkr/bwIeEh+m3OyO9IEy0wzu7GzcfuwOfVf4LhdzempUCJJGo8RRI/ZzK7BuPVVl0Co0GL3d0XLju9dmoE19/7Mt7sGcVdN67EPR9YjeIYX+guWlCGxz5zGTa2mvC1Rw7jK38+lLL/J/aqNbjLcT105k0pub256ox6uKb9sE2l4A1WyAYEtZjGFdoTyd9mmnSYTVCrKCVLkieHHAAS2xkZ6uKFFdCpVbOWJC02F++MTAMuwlKgRuHQ1u1HB3DNj1/C3w9a8NmrFuOxz1yGVY1liowlohheCC9ZVIkfvn81dneP4vN/OHC+ETmNsxiZqH/cNX+DbDIFRs/LIP80NOQH+ROPCUiViIeUJ/lzpiIOIVYqFWG9uRxv9px/Y+bzC/z42S586H/eQKleg7/dcSlu3tAU9+ymqUiHh27ZgM9sXYQ/7e3De+9/DWesyaWaD4y78Mc9fQDiO38wHlJhPSumIlEhGxC80KCnZE3yt5kmxQUarGgw4o0wBXq8kt0ZKSnUabCx1TSrOX9ggoNa04HnGVNkTVNZSrcdx+o/njiGn7/cjaV1pXjolvURlzRSJtEwyBgPQ37nRfUYmnDh3584jm8/dhTfXDMJ+tW7FEsQV5rPLzAw4Zr/F2Kch0zPYr4cpNbB53VjGhqg6TIoOXd6zuaETq1CZVGYnW5J/pzxnAGZCuvNJjx3fAhDdhcggDt/fwCvn7bixrUN+M67ViS1zKNWEb64rR2rF5Th8384gBvuexk/uXlNTEnsPr9A54Ade3tHsad3DHt6xmaWgUv1Gly0QJ7fIeezwpxYVl+a3I2FHCf1+VcLoTFlTxEGBI4w+uWrPRceFxSnrgE7CjSqlJybeWVbFf79ieM4OzqFBaZC9Ns4qDUduAhLkbVN5Xj8UH/gmIc0TeE+e2wQP3+5G/+wsQnfeufy9JzDmEzKdRwvhJ+4vBUD4y784pVuvM32KjYl+uKbA4btbvj8Yv7nVTIFRvDFrOv1J/Ev+434FyzGutQMOyEWW6DYDNvfk4KfM53nMUp9Yf+14y08fsgCh9uL79+0Cu/rWJCy+7hqaQ0e/8zl+NRv9uKWh97EZ69ajDuvWjzrrFeH24sDZ2zY0zuKvb1j2H/GNhMWWl1SgA5zOW651IwOswnL6kpjP1czTjOp+amYCQNmjpN66bnteH8WxFOE2thqwn+/dBr7zozhkoWVCd9O56Adi2uKYzrbN5rN7dX49yeOY2fXMN63rhHWSQ5qTQcuwlJE6gvbf8aGupXyP3GH7C589S+HsKyuFN98R5oKMCC52Yg4Xwi/fv1SDNnduPtQFf5o0AaerGmaxcgkEZfogOQLjAUbUF+5BgcPPIsXTgxhXbNyMSYWm3P+ZdcU/JzpLN5X1Bth0Krx0Gs9WFxdjN99chMWy7DU11RRiEc/fQn+5dEjuPf5kzhw1ob3rm3A3uAs14mBCfgFQBRYtnrX6np0mMvR0WxCY7khbWe3VhYXQKOi2YGtSXJN++Bwe7MiqDVUh9kEFQG7To8mVYSdHHTgkkWpCd1eWFWExnIDXuwcwhWLA2PieAr5cRGWItI7yH29Y7h+ZfxneMVDCIGv/vkQJt1e/OTm1bK9cw0r2WWdOF4IVSrCD953EW6ZdOOD3V/D9zom0NpxXV7NggGY2dYf8V1pkgWG0aDFuuZy7DgxjC9fuyTh20mWxeaM/KKU5kIqGTqNCp+4vAUTzml89W1LZA1I1mvV+MH7VmFtcxm+9fdjeKlrGIU6NdY0leGOLYuwzmzCmqYylIYenp1mahWhplSfmp6wICktvzLLirBSvRbL641JhbaOT01jYMKVsh4+IsLm9io8su8ceoM9hhzUKj8uwlJEp1FhVYMRL5wYwuevaZN1W+9v3jiDHZ3D+NY7lyf3zjqR3q40L+voNCo88OF1eN8DHrz/qAcvvX0tku9+kJfdNY0HX+nBJy5vScnzoD9NydVbl1TjrqdOYGDcpcg74GmfH4MTLjTkUDPwF7el73B6IsKHNjbjisVVGHdOY0ltCTTpmiGPUX2ZfmYHbCpIafkV4XoIM9zGFhN+tas34b6wrqHUNOWH2txWjd/sOoO/H7QA4JmwdMis/6FZ7vati9BjncSdv98f/XiRBJ0acuA/njiGK9uq8JGLmxO/oWR2mqUg5ToeJXot/uM9KzDicOOXr/bEfwNp3ln5k+dO4sfPdeGx4C+yZFlsLhTq1Cg1yPueaWuwqXvHnONL0mVwwgW/kCkjLI8sMBViRYMx4wowQErNT91MmHUye86NnGtTawU8Xj8OnLUl9P3SzsjFNcllhIW6ZFEgquLxQ4HfXRzUKr/M+1+axba0V+Ob71yO544P4TuPH0v57Xu8fnzuD/tRqNPg+zetSq6XI81b9pO1rtmEq5fW4IGdb0U+tmWuNOdDnbFO4eHXewAAzxyL8dDlKAKbPeQJag21uLoYDWUGvHAixiIsxcWtJbjsykVY7qoz6jEw7oI/RW9SR7Lo3Mi51reYQAS8cTqxM3O7Buwo0qlTerpEoU6DDS0muKb9HNSaJlyEpdhHLjbjE5e14KHXevDgK90pve17nuvCkXMT+H83rkR1aZLvUBI96FdBX762HQ6PF/e/+Fbs35TmYvN7209Ao1LhhlV1eOXkyMwutGRYxl1pKUyICFuWVOHVUyPRj+CSobiNGtTKsl6dUQ+Pzw9rPG+kIphZjszCmTCjQYultaUJ54V1DTrQVluS8jdn0oHe/P8wPbgIk8HXr1+Ka5fX4DtPHMP2owMpuc3d3aO4/8W3cPP6Bbh2ee35LyQ6G5HoQb8Kaq8twY1rGvHQaz2xny+ZxmJzb+8YnjjUj39b7cBXi57ACv8JvDjnUNxERE3LT6GtS6ox5fFFf3cuQ3F7/sgiXgLJVXUzga2p6QuzOtwo1Kll3fQgp42tJuztHUvo3OGuQTvaqlO/21YqwrgfLD24CEulYEGkOvcm7vnAGqxqLMOdv9+Pgwmu+UsmXNP4/B8OoMlUiP97w7LZ95fMbESae7tS4fPXLAYEcM+zJ2P7hjQVm0IIfPfJ49ha1IMPHL8DjQd+jN8WfBdde59P6nY9Xj+GHe605fVc3FqJAo0q+pKkDMWtxeZEeaE2a19QWXTS5hJLig7yHnG4s3IWTLKptQJurx+H+sbj+r4RhxvWSU/SxxWFs7CqGMvrSzPz1JUcxEVYOInMLs0piAyDe/GLj3SgsrgAH3/4TZwdTfxYkW/+7SgGJlz48QdWz16jz7K+rlRoLC/Ehzc14097z+JU8Ny0qBItNuN4Hjx9ZAB7e8fwuUWDIJ8HJHzQwgvqfQUerz+++w0xOOGCEOmbHTLo1LhkYQV2dA5FPmhZhuLWYnPyEkiOk059SNlM2KQnK3dGSjaYA5l88UZVpOq4onCICI/dcRm+cE1bym+bXYiLsLkSnV0KUxBVlRTgoVvWw+P145aH3sS4M8LBtfO84D920IJH9p/DZ7Yuwtqm8tnfk4V9Xalw+5aFKNRp8IPtnfLdSRzPA4/Xj7uePoH2mhIsv+Tts/5NXnK3J3VQr9Qnlc7k6q1LqtFrncLpkcnIV0zxTKrFlp7eN6aciiIddBpVynZIjjg8WZcRFqq8SIcltSV4ozu+5vyugUAR1pbCnZGhwp5YwWTBRdhcic4uzVMQLaouwX//Ywd6rZO47dd7w8+KzPOCb7E58S+PHsbqBYHAxQtkYV9XKlQUF+CTl7fi6aMDCW/vjiqO58Gvd/Wi1zqFr799KdTNG2f+Tbwf/iuOa5bimWOJ9wVKL1bpLE6k8wd3xLpLMkUsNmdKd3qxzENEqDOmLivM6nBn5c7IUJtaK7CnZwzTvthnzDsHHSgr1KKqJLt/dsZF2IUSnV2KUBBdvLAC33vvKrx+2oqvPXL4wmWeMC/4fr/AF/94EF6/wD0fWD1/5k8W9nWlwscvb0FFkQ7fe+pE5GWzRMX4PBifmsa9z5/E5YsrcWVboKFV+jcpaLkYV7ZV4ZmjgwlvyY96ZJEMGssL0VZTHHtURQpMuKZhd3u5KT8P1BlTk5rv9wuMTnqyuicMCIS2Oqd9cfWFnRy0o60m9TsjWfpxETZXMrNLEQqiG9c24nNXL8Zf9vXhvhdOzf5imBf8/3mlG6+ftuLf3rEM5sqiJH+o3FNcoMEdWxfh9dNWvHxyJPV3EOPz4Kc7TmLCNY2vX7807NevXVGDIbsbB/tsCQ2j3+aC0ZD+ZvUtS6qxu3sUdleEJfQU6ueMsLxRbzSk5PzIcec0vH6R1T1hALAheNB7rFEVQgh0DtplW4pk6cVFWDgyzS7dedVi3Li2AT96tguP7u+bfX8hL/jH1Evw/e2d2LasBu/vWJDSMeSSf9jYhMZyA+7efiJl4Y+zRHkenLFO4eHXevG+dY1YWlca9jpb22ugVlHCwa1SUGu6bW2vhtcv8IocBW4YnBGWP+rK9Bi0u5M+VSSb0/JDVRQXYHF1MXbFGNo6OOGG3eWVpSmfpZ9sRRgR6YloNxEdJKKjRPStMNfZTETjRHQg+PENucaTCYgId924Che3VuArfz40u2E7+ILvql2Hz/1hP4yFWtz13iRT8XNcgUaNL1zThiPnJvDE4f603//d209ArSJ84Zr5zwc0FmqxqdWUcF6cUs3q65rLUarXyLckOWcjipQRxj1hua/OaIDPLzBkT25JMpvT8ufa1FqBvT2j8MbQF9Y5KDXlcxGWC+ScCXMD2CqEuAjAagDXEdGmMNd7WQixOvjxbRnHkxGkA6mbK4pw66/2XBCzcPfTnegadOD7N62CqSi73+Glw7tWN2BJbQl++ExnXI2tydp3ZgyPH+rHJ69ojRpqeO3yWpwenowtUmNOcaLUTJhGrcIVbVXY2TWc+lnGMBtRLDYntGpCVQ68oLLI6mdiKpIrwrI5LX+uja0mTHp8OGKZiHrd8zsjuQjLBbIVYSJAetXRBj/kOdU6yxgLtfjlx9ZDp1Hhlod2Y8QRmFZ/+eQwHny1Gx+7xIzN7dUKjzI7qFWEL1/bjh7rFP7w5tm03KcQAt994jgqiwvwqStao17/6qU1ABB9l+Sc4sR9+nWMTU0rtkS3dUk1hu1uHI3hhSEuYTaiWGxO1Br1vDU+D0hxK/1JBrZKy5G5MBM20xcWQ5xN16AdVSUFKOc36TlB1p4wIlIT0QEAQwCeFUK8EeZqFweXLJ8iouVyjieTLDAV4hcfXY9huxufeHgP+sed+NKfDmJRdTH+z9uWKD28rLJ1STXWm8vxk+dPwumJ//iPeG0/OoA9vWP44ra2mA64rS8zYFWjEduPRukLm1OcTHbtBABFZsIA4Mq2KhAh9UuSYTaiWGyumTR1ltukf+dkA1tHHB4QAeWF2V+MVJfosbCqKKZMwa5BO/eD5RBZizAhhE8IsRpAI4ANRLRizlX2AWgOLlneB+Cv4W6HiG4loj1EtGd4OPmz+DLF6gVl+MnNa3Cwz4ZtP3oJo5Me3POB1dBr1UoPLasQEb563RIM29148NXUHpo+l8frx11PnUBbTTHet64x5u+7dnktDp61YSDSEsyc4qTPuA5AeoNaQ1UUF+CixjK80JniIizMztNznBGWN0oNGhTq1EkfXTTicMNUqIM6R2ZPNwbzwiJtWPD7BboGHVjMOyNzRlp2RwohbAB2ArhuzuUT0pKlEOJJAFoiqgzz/T8TQnQIITqqqqrSMOL0uXZ5Lf717ctgd3vxxW3tWNFgVHpIWanDbMLVS6vxwItvwTblke1+/veNXvRYp/C165fOn90WxrZlgSXJZ49HmA2bU5x0agOxF0pmZ21dUo1DfbaZJfOUCdl56vMLDExwWn6+kAJbk50Js2b5uZFzbWwxwe724liE5f9zNiec0z6eCcshcu6OrCKisuDnBgBXAzgx5zq1FNz+R0QbguNJ/IyXLPXxy1rw8le2xNRfxOb3pWvb4XB7cf/Ot2S5/XHnNH7y/ElctqgSm9viezOwqLoYLZVFeCbaLsmQ4kRqXI7W+C+nrUuqIQSws1O+Geghuws+v+AiLI/UlxlgSUFjfrZnhIXa1FoBABGXJDulpnwZDu5mypBzJqwOwA4iOgTgTQR6wh4notuI6LbgdW4CcISIDgK4F8DNQpb488y3wFTIcRRJWlJbivesacBDr/Wk7IDgUP+54xTGnYFg1nj/rYgI25bX4PW3rJHPEA3RP+5EZbEOBRrllqeX15eiuqRA1iOMzmeEcVp+vqgz6pMKbB2acKFz0K5Yv6Qcakr1aKksihjaKsVTLK7m5chcIefuyENCiDVCiFVCiBVS/IQQ4gEhxAPBz38qhFguhLhICLFJCPGaXONh+eHzV7dBCOAnz51M6e2eHZ3CQ6/24Ka1jVhWHz6YNZpty2rh9QvsjLHHymJzKdYPJiEibGmvxktdw7JFgJwL9gZxT1j+qDMaMOxwhz9LNwq/X+DzfzwAr0/g0+HO1M1iG1tM2N09Om9f2MlBOxrKDCjRa9M8MiYXTsxnOWWBqRAf2tSEP+45G1suV4zu3t4JlQr44rb5g1mjWbOgDFUlBTEHtyqVETbXliXVsLu92NMzJsvtSzNhdVyE5Y36Mj2EAAYn4l+S/O+XTuPVU4Ej3Rbl2IzQxlYTJlxeHO8P3xfWyU35OYeLMJZzbt+yCAatGj96tjMlt7f/zBgeO2jBrZdHD2aNRKUiXLOsBjs7h+Gajh6l0a9QWv5cly2uhFZN2JHqXZJBFpsTRoMWxTHEfbDcUDsTUxFfEXbgrA0/fKYTb19Zhw+sz70j3Ta2BPrC3ui+8Agjr8+Pt4Yd3JSfY7gIYzmnsrgAn7yiFU8eHsDBs7akbksIge8+GQhmvfXKhUmPbduyGkx5fHjtrchnMk64pmF3ezNiJqy4QIONLRWyHWFksTkzothk6VNvlFLzY+8Ls7umcefv96OmVI/v3rgyJ3to68sMaDIVhg1t7R2dgsfr56T8HMNFGMtJn7i8FaYiHe7efiL6lSPYfnQQb/aM4QvXtKVkpubihRUoLtBg+5HIwa1SmnimLNFtWVKNU0MOnB2dSvltn7O50MBN+XlFel7HkxX2jb8dxdnRKfzk5tUwGnK3J2pjiwm7e0YvOC6MjyvKTVyEsZxUXKDBHVsW4dVTVrx8MrF4hUAw63Esri7G+ztiD2aNpECjxpYl1Xju+GDEUEZLcIagPgNmwoBAVAUgQ3o+eCYsHxUXaFCi18Q8E/bo/j48uv8c7ryqDR1mk8yjU9bG1grYpqZndkJKugYdIELO9cHlO27CYDnrQ5ua8D+vdON7T59AoU4Nr0/A6xeY9vmDn/sxHfxT+prXd/6yzgEHeqxT+OXH1scVzBrNtmU1eOygBfvOjGH9PC8o0kxYphQnLZVFaKkswo7OIXz0EnPKbtfh9mLcqdz5mEw59UZDTDNhvdZJ/OujR7DBbMIdW3NrN2Q4G0POkVxad34ndtegHc2mQhh0fKJKLuEijOWsAo0aX9zWhi/88SDee//rCd3G21fWYXN7ak9p2NxeBZ1ahWeODsxfhI07oSKguiRzwii3tFfjf9/ohdPjS9kLQf9MRhgXYfmmrix6ar7H68dnf7cfahXhxzevzpkjiiJZYCpEQ5kBb3SP4mOXtsxc3jlox2Jeisw5XISxnPaeNQ2oNeox7RPQqghqFUGjVkGrJmhUwT/VKmhUBE2Yywo0qpQ3AJfotbhkUQW2Hx2cN/jVYnOhplSf0hm4ZG1dUo0HX+3Ga2+N4KqlNSm5zXPBIox7wvJPndGAQ33jEa/zo2e7cLBvHPd/aG1e5chtbDVhZ+cwhBAgIri9PvSMTOK65bVKD42lGBdhLKcRES5ZeMFxpIrbtqwWX3/0MDoH7VhSe2H4a6ZkhIVa31KOQp0aL5wYSlkRZsmwZVeWPvVGPUYnPXBN+6DXXjiz+uqpEfz3S2/hgxua8LaVdQqMUDmbWirwyL5zODnkQFtNCbpHJuH1Cz6uKAdlzttsxvLI1cuqQQQ8czT8Lsn+cVfG7IyUFGjUuGxRJXacGEKqThez2JxQqwjVJZlVcDL5Sc/vcFlhVocbn//DASysKsY3bliW7qEpTjpHUoqqmDkzkoNacw4XYYwpoLpEj7VN5WHT84UQgR2DGTYTBgSWJC3jrgt2biXKYnOitlSfF70+bLaZrLA5Z0gKIfDlPx+CzTmNe29ek5eN6AtMBtQZ9dgVDG09OeiARkVoreQiLNdwEcaYQrYtq8FRywT6xmZnb41NTcPt9St+bmQ4W1IcVXHO5syrXh923nwzYQ+/1oMXTgzh629bkvA5rdmOiLCxxYQ3TlshhEDnoB0tlUXQafglO9fwvyhjCtkWbLJ99tjsJUnLzI7BzJsJqynVY3l9KXakqAizjDsz8udk8qsLk5p/zDKB7z51AlctqU5pFEo22tRagRGHB28NT+LkoJ1DWnMUF2GMKaSlsghtNcUXLElKMwOZOBMGBJYk9/aOwTblSep2fH6BgfHMOB+TpZ9eq4apSAdL8Pnu9Pjwmd/tQ5lBi7tvWpWTxxLFY2OwL2xn5xB6R6e4CMtRXIQxpqBty2qxu3sUY5PnCxppZqAuQ2eItiyphl8AL3YldhKBZMThxrRPcBGWx+qM+pmesG8/fgynRybx4w+sRkVx5uTjKcVcUYjqkgL89o0zEAJor+V+sFzERRhjCtq2vAZ+ATwfsrxnsbmgVRMqizLzheiixjKYinRJL0mezwjjIixf1RkN6B934anD/fjd7jO47cqFuHRR5kXKKIGIsLG1AqdHJgGAg1pzFBdhjCloZYMRdUb9rCXJ/nEnao16qDJ0x6BaRdjcVoUXu4Yjnn8ZjYXT8vNefZkevdYpfPUvh3DRgjJ84Zo2pYeUUTa1Bk7U0GlUaDYVKjwaJgcuwhhTEBFh27IavHxyGE6PD0Dg3MhM7QeTbFlSjbGpaRw4O5bwbWTyBgSWHnVGA5zTPvgFcO/Nq6HNoBMiMsHGlkBf2KKq4ow6PYOlDv+rMqawbctr4Zr246WTgR4ry3hmZoSFuqKtCmoVJRVVYbG5UKLXoESvTeHIWDZpqQzM7nzn3cvRXFGk8Ggyz8KqItQb9VjZYFR6KEwmXIQxprANLSYYDVpsPzows2Mw09Ly5zIatFjXXI4dJxJvzueMMHbNslps/9wVeM+aRqWHkpGICH/+50vw9bcvVXooTCZchDGmMK1ahauWVOP540MYmHDB6xcZPxMGBKIqjvVP4PnjgwkdY2SxObkfLM+pVYR2Pg8xovoyA4wGni3OVVyEMZYBti2vwbhzGn87cA5A5maEhXrPmgY0VxTi4w/vwYf/5w0cOTce1/dbbJl3SDljjKUTF2GMZYAr2qpQoFHhN6/3AsiOHYM1pXo8+/kr8W/vWIZjlgnccN8ruPP3+3F2dCrq9055vBibms6Kn5MxxuTCRRhjGaBQp8Hli6tm0sOzZcegTqPCLZe24MWvbMHtWxbi6SMDuOqHL+I7jx+bFUA7l8UW+Dm5J4wxls+4CGMsQ2xbXgMAMGjVWdcDUqrX4svXLsHOL2/Gu9fU45evduOK7+/A/Tvfgmvad8H1OSOMMca4CGMsY1y1pBoqChxXlK3n5tUZDbj7povw1J1XYL3ZhO89fQJbfrATf9pzdlawK2eEMcYYF2GMZYyK4gJsXVKN5fXZnwnUXluCBz+2Hr/75CZUlxTgy38+hLff+zJ2dA5BCAGLzQkVBfrKGGMsX1EiW8uV1NHRIfbs2aP0MBiThdfnh4ooY48sSoQQAk8c7sfdT3fizOgULllYAZ9f4MzoFF7/2lVKD48xxmRFRHuFEB3hvqZJ92AYY/PLxaNJiAg3rKrHtmW1+O0bvbj3hVMYnfRgXXO50kNjjDFFcRHGGEsLnUaFj13agveua8SvXu9Few2HdDLG8hsXYYyxtCrRa3H7lkVKD4MxxhSXe2sfjDHGGGNZgIswxhhjjDEFcBHGGGOMMaYA2YowItIT0W4iOkhER4noW2GuQ0R0LxGdIqJDRLRWrvEwxhhjjGUSORvz3QC2CiEcRKQF8AoRPSWE2BVynbcBWBz82Ajg/uCfjDHGGGM5TbaZMBHgCP5VG/yYmwz7LgC/Cl53F4AyIqqTa0yMMcYYY5lC1p4wIlIT0QEAQwCeFUK8MecqDQDOhvy9L3jZ3Nu5lYj2ENGe4eFh2cbLGGOMMZYushZhQgifEGI1gEYAG4hoxZyrhDub5YJzlIQQPxNCdAghOqqqqmQYKWOMMcZYeqUlrFUIYSOinQCuA3Ak5Et9ABaE/L0RgCXSbe3du3eEiHpTPsjsVglgROlBZDh+jCLjxycyfnyi48coMn58osvVx6h5vi/IVoQRURWA6WABZgBwNYDvzbna3wHcQUS/R6Ahf1wI0R/pdoUQPBU2BxHtme9wUBbAj1Fk/PhExo9PdPwYRcaPT3T5+BjJORNWB+BhIlIjsOz5RyHE40R0GwAIIR4A8CSA6wGcAjAF4BYZx8MYY4wxljFkK8KEEIcArAlz+QMhnwsAt8s1BsYYY4yxTMWJ+bnhZ0oPIAvwYxQZPz6R8eMTHT9GkfHjE13ePUYUmIxijDHGGGPpxDNhjDHGGGMK4CKMMcYYY0wBXIRlGSJ6kIiGiOhIyGXfJKJzRHQg+HG9kmNUEhEtIKIdRHQ8eHD8ncHLTUT0LBGdDP5ZrvRYlRDh8eHnUBAR6YloNxEdDD5G3wpezs8hRHx8+DkUInhizH4iejz4d37+zBHmMcq75xD3hGUZIroCgAOBMzdXBC/7JgCHEOIHSo4tEwTPHq0TQuwjohIAewG8G8DHAIwKIe4iov8DoFwI8VXlRqqMCI/P+8HPIQAAERGAIiGEg4i0AF4BcCeAG8HPoUiPz3Xg59AMIvoCgA4ApUKIG4jobvDzZ5Ywj9E3kWfPIZ4JyzJCiJcAjCo9jkwlhOgXQuwLfm4HcByB80jfBeDh4NUeRqDwyDsRHh8WJAIcwb9qgx8C/BwCEPHxYUFE1Ajg7QB+EXIxP39CzPMY5R0uwnLHHUR0KLhcmffT3ABARGYEsureAFAjncYQ/LNawaFlhDmPD8DPoRnBZZIDAIYAPCuE4OdQiHkeH4CfQ5J7AHwFgD/kMn7+zHYPLnyMgDx7DnERlhvuB7AQwGoA/QB+qOhoMgARFQP4C4DPCSEmlB5Ppgnz+PBzKIQQwieEWI3AebYbiGiFwkPKKPM8PvwcAkBENwAYEkLsVXosmSrCY5R3zyEuwnKAEGIw+EvRD+DnADYoPSYlBftU/gLgf4UQjwQvHgz2Q0l9UUNKjU9p4R4ffg6FJ4SwAdiJQL8TP4fmCH18+Dk041IA7ySiHgC/B7CViH4Dfv6ECvsY5eNziIuwHCD9xw56D4Aj81031wWbhv8HwHEhxI9CvvR3AB8Nfv5RAH9L99gywXyPDz+HziOiKiIqC35uAHA1gBPg5xCA+R8ffg4FCCG+JoRoFEKYAdwM4AUhxIfBz58Z8z1G+fgckvMAbyYDIvodgM0AKomoD8C/AdhMRKsRaI7tAfAppcaXAS4F8I8ADgd7VgDg6wDuAvBHIvo4gDMA3qfM8BQ33+PzQX4OzagD8DARqRF4o/pHIcTjRPQ6+DkEzP/4/JqfQxHx76Do7s635xBHVDDGGGOMKYCXIxljjDHGFMBFGGOMMcaYArgIY4wxxhhTABdhjDHGGGMK4CKMMcYYY0wBXIQxxrIeEQki+nXI3zVENExEjyd4e2VE9OmQv29O9LYYY2w+XIQxxnLBJIAVwfBQALgGwLkkbq8MwKejXYkxxpLBRRhjLFc8BeDtwc8/COB30heIyEREfw0eDLyLiFYFL/9m8KDgnUR0mog+G/yWuwAsJKIDRPT94GXFRPRnIjpBRP8bPH0ARHQXER0L3vYP0vOjMsZyASfmM8Zyxe8BfCO4bLgKwIMALg9+7VsA9gsh3k1EWwH8CoFDggFgCYAtAEoAdBLR/QD+D4AVwUOqQUSbAawBsByABcCrAC4lomMIHK+yRAghpON8GGMsFjwTxhjLCUKIQwDMCMyCPTnny5cB+HXwei8AqCAiY/BrTwgh3EKIEQQOVa6Z5y52CyH6gocLHwje1wQAF4BfENGNAKZS9gMxxnIeF2GMsVzydwA/QMhSZBCFua50Zps75DIf5l8huOB6QggvgA0A/gLg3QCejnO8jLE8xkUYYyyXPAjg20KIw3MufwnAh4CZpcURIcREhNuxI7A8GRERFQMwCiGeBPA5nF/iZIyxqLgnjDGWM4QQfQB+EuZL3wTwSyI6hMCS4Uej3I6ViF4loiMINPw/Mc9VSwD8jYj0CMy2fT7RsTPG8g8JIaJfizHGGGOMpRQvRzLGGGOMKYCLMMYYY4wxBXARxhhjjDGmAC7CGGOMMcYUwEUYY4wxxpgCuAhjjDHGGFMAF2GMMcYYYwr4/6oX4eww0zoPAAAAAElFTkSuQmCC\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.figure(figsize=(10,4))\\n\",\n \"\\n\",\n \"plt.xlabel('Months')\\n\",\n \"plt.ylabel('Quantity')\\n\",\n \"\\n\",\n \"plt.plot(df['Sale Quantity'][12:])\\n\",\n \"plt.plot(df['ewm'][12:], '.')\\n\",\n \"\\n\",\n \"plt.legend()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"id\": \"0f326133\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n 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\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"from statsmodels.tsa.seasonal import seasonal_decompose\\n\",\n \"\\n\",\n \"ts_decompose = seasonal_decompose(np.array(df['Sale Quantity']),\\n\",\n \" model='multiplicative',\\n\",\n \" period = 12)\\n\",\n \"\\n\",\n \"ts_plot = ts_decompose.plot()\\n\",\n \"\\n\",\n \"df['seasonal'] = ts_decompose.seasonal\\n\",\n \"df['trend'] = ts_decompose.trend\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"1a68c1b4\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.12\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n" }, { "path": "March Cohort 2023/22_23_Time Series Forecasting/23_ARIMA.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"id\": \"5e5ca069\",\n \"metadata\": {},\n \"source\": [\n \"# Auto Regressive Models\\n\",\n \"\\n\",\n \"AR and MA = ARMA = ARIMA\\n\",\n \"\\n\",\n \"\\n\",\n \"### Auto-Regressive (AR) Models\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"b389bf58\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"- auto-correlation function\\n\",\n \"- partial auto-correlation function\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"id\": \"91a0231c\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import pandas as pd\\n\",\n \"import numpy as np\\n\",\n \"import matplotlib.pyplot as plt\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"id\": \"8e78f456\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
Monthdemand
01457
12439
23404
34392
45403
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\"\n ],\n \"text/plain\": [\n \" Month demand\\n\",\n \"0 1 457\\n\",\n \"1 2 439\\n\",\n \"2 3 404\\n\",\n \"3 4 392\\n\",\n \"4 5 403\"\n ]\n },\n \"execution_count\": 2,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df = pd.read_csv('vimana.csv')\\n\",\n \"\\n\",\n \"df.head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"id\": \"1e17a7a3\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\",\n \"RangeIndex: 37 entries, 0 to 36\\n\",\n \"Data columns (total 2 columns):\\n\",\n \" # Column Non-Null Count Dtype\\n\",\n \"--- ------ -------------- -----\\n\",\n \" 0 Month 37 non-null int64\\n\",\n \" 1 demand 37 non-null int64\\n\",\n \"dtypes: int64(2)\\n\",\n \"memory usage: 720.0 bytes\\n\"\n ]\n }\n ],\n \"source\": [\n \"df.info()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"id\": \"49e834a8\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"from statsmodels.graphics.tsaplots import plot_acf, plot_pacf\\n\",\n \"\\n\",\n \"acf_plot = plot_acf(df.demand, lags=20)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"id\": \"05dffa14\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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NZv5o2eTSnjRuB0v+OI9NrBNMmj25pXGbVZJEINgOTy9ZPADorK0n6Y+B7wOyIePO2xIjoTH9uA+4h6WoyG9CGDBGfn3UGk0YPY+yIdv7b+6f4QrHlVhaJ4ElgiqRTJLUDlwCLyytIOhG4G7gsIn5bVn60pJGlZeCDwKoMYjJruSFDxMijhnLcyCM556QxTgKWWw0PH42IA5LmAQ+SDB9dGBGrJV2Vbp8PXA8cC/xvJefKpWGi44F70rKhwB0R8UCjMeWdx5ebWZ5kch9BRCwBllSUzS9b/iTwySrtngfOyiKGgcLjy80sbzzFRJN5fLmZ5Y0TQZN5fLmZ5Y0TQZN5fLmZ5Y0TQZN5fLmZ5Y1nH22y0vjya+5eyb43urjinad41JBZwbV6JKETQQuUxpePPGoo55w0ptXhmFkL5WEkobuGzMxaKA8jCZ0IzMxaKA8jCZ0IzMxaKA8jCZ0IzMxaKA8jCZ0IzMxaKA8z1XrUkJlZi7V6JKETwQDV6nHHZjZ4OBEMQHkYd2xmg4evEQxAeRh3bGaDhxPBAJSHccdmNng4EQxAeRh3bGaDhxPBAJSHccdmNnhkkggkXShpraT1kq6tsl2Sbk63r5R0Tr1t7VB5GHdsA0d3d7B84yvcvXwzyze+Qnd3tDoky5mGRw1JagNuAWYCm4EnJS2OiGfLqs0CpqSP6cB3gOl1trUqWj3u2AYGjzCzeiiisU8Hks4DboiIP0nX/xogIv6+rM4/Ao9GxJ3p+lpgBnByb22rOeakM2Lm5xf2OdZnt+wCYOqEUT3W2/X6Gz1u37hjDwAnHTu8zzHk7TkGozwd21b/jV59/QAv7NxL+b+5BJNGD2PkUX3/HNjq32cwq/fYjjrqiMPex0+ueudTEdFRWZ7FfQSTgE1l65tJPvX3VmdSnW0BkDQXmAswYsKphxVobwmgXln8E+TlOWDwvXHm6dhm8TyNHJPX3+ii8rNeBOx7o+uwEkGrf5+snycvz5FF+0ZkkQiqnV9WnmbUqlNP26QwYgGwAKCjoyN+/F/O60uMffKr53b023Pn0ZfuWw3A9R86s6XPkeXzDCaNHJPlG1/h5n9Zx76y4cZHDh3CFe88pWVdinl6reTlOfrivFOPPey2P7mqenkWF4s3A5PL1k8AOuusU09bMztMpRFmRw4dgvAIM6suizOCJ4Epkk4BXgAuAf6yos5iYJ6ku0i6fn4fEVskba+jrZkdptIIM89LZT1pOBFExAFJ84AHgTZgYUSslnRVun0+sAS4CFgP7AE+0VPbRmMysz8YMkScc9IYjy6zmjKZdC4ilpC82ZeXzS9bDuDT9bY1M7Pm8Z3FZmYF50RgZk3T3R28+voBtr+6z3c554gTgZk1Reku5xd27uWl3fu5+V/W8eX71zgZ5IATgZk1Rel7NEo3uPl7NPLDicDMmsLfo5FfTgRm1hT+Ho38ciIws6bwXc755S+vN7Om8F3O+eVEUHCl4Xyvv9HF8o2v+B/T+pXvcs4ndw0VWN6G83mMuVlrOBEUWJ6G8+UtKZkViRNBgeVpOF+ekpJZ0TgRFFiehvPlKSmZFY0TQYHlaThfnpKSWdF41FCB5Wk4Xykprd+2m/0Humn3GHOzpnEiKLi8DOfLU1IyKxonAsuNvCQls6LxNQIzs4JrKBFIOkbSw5LWpT8P+SgnabKkf5W0RtJqSX9Vtu0GSS9IWpE+LmokHjMz67tGzwiuBZZGxBRgabpe6QDwuYg4A3gH8GlJU8u2fz0ipqUPf3exmVmTNZoIZgOL0uVFwMWVFSJiS0QsT5dfBdYAkxrcr5mZZaTRRDA+IrZA8oYPjOupsqSTgbOBJ8qK50laKWlhta6lsrZzJS2TtGz79u0Nhm1mZiW9JgJJj0haVeUxuy87kjQC+BlwdUTsSou/A5wKTAO2ADfVah8RCyKiIyI6xo4d25ddm5lZD3odPhoRH6i1TdJWSRMiYoukCcC2GvWOIEkCP4qIu8uee2tZne8C9/UleDMza1yjXUOLgTnp8hzg3soKkgTcCqyJiK9VbJtQtvpRYFWD8ZgNKp6a25qh0URwIzBT0jpgZrqOpImSSiOA3gVcBry/yjDRr0h6RtJK4Hzgsw3GYzZoeGpua5aG7iyOiB3ABVXKO4GL0uVfAFXnCYiIyxrZv+WDv+Wsf/Q0NbfvvrYs+c5ia4g/tfYfT81tzeJEYA3xF8r0H0/Nbc3iSeeqOO/UY1sdwoDx5IaXq35q7Y7wcWzQuaccwy+fe4kVm3ayd38Xw9rbmDZ5NFfNOJU2d70x6qgjgMb+X/PyHK3mRGANOXPiKIa1t7Fnf9ebZcPa25g6cVQLoxoc2oaI26+czqNrt/Fs5y6mThzFjNPHOQlY5pwIrCEzTh/HtMmjD/nUOuP0Hm8ytzq1DREXnDGeC84Y3+pQBp2u7uCVPfvZs6+LpWu2FjrJOhFYQ/yp1Qairu7gslufYP223XQHfObOXzNt8mhuv3J6IV+7TgTWMH9qtYHm0bXbWLFpJ6XBbXv2d7Fi004eXbutkK9jjxoys8JZ3bmLvWXXtQD27u/i2c5dNVoMbk4EZlY4pUEO5Yo8yMGJwMwKpzTIYXh7GwKGF3yQg68RmFnheJDDwZwIzKyQPMjhD9w1ZGZWcE4EZmYF50RgZlZwTgRmNuCUpod44ZW9LF2zla4WTXuelzga5URgZgNK+fQQm3fu5TN3/prLbn2i6W/CeYkjC04EZjag9DQ9RBHjyEJDiUDSMZIelrQu/Vn1+/MkbUi/m3iFpGV9bW9mVpKX6SHyEkcWGj0juBZYGhFTgKXpei3nR8S0iOg4zPZmZrmZHiIvcWSh0UQwG1iULi8CLm5yezMrmLxMD5GXOLKgiMO/sCFpZ0SMLlt/JSIO6d6R9DvgFSCAf4yIBX1pn26bC8wFOPHEE9++cePGw47bzAa2ru7IxfQQeYmjXpKequiVScp7SwSSHgGOr7LpOmBRnYlgYkR0ShoHPAx8JiIe60siKNfR0RHLli3rrZqZmZWplQh6nWsoIj7Qw5NulTQhIrZImgBUvVweEZ3pz22S7gHOBR4D6mpvZmb9p9FrBIuBOenyHODeygqSjpY0srQMfBBYVW97MzPrX40mghuBmZLWATPTdSRNlLQkrTMe+IWkp4H/B/xzRDzQU3szM2uehqahjogdwAVVyjuBi9Ll54Gz+tLezMyax3cWm5kVnBOBmVnBORGYmRWcE4GZWcE5EZiZFZwTgZlZwTkRmJkVnBOBmVnBORGYmRWcE4GZWcE5EZiZFZwTgZlZwTkRmJkVnBOBmVnBORGYmRWcE4GZWcE5EZiZFZwTgZlZwTWUCCQdI+lhSevSn2Oq1Dld0oqyxy5JV6fbbpD0Qtm2ixqJx8zM+q7RM4JrgaURMQVYmq4fJCLWRsS0iJgGvB3YA9xTVuXrpe0RsaSyvZmZ9a9GE8FsYFG6vAi4uJf6FwDPRcTGBvdrZmYZaTQRjI+ILQDpz3G91L8EuLOibJ6klZIWVutaMjOz/tVrIpD0iKRVVR6z+7IjSe3AR4CflhV/BzgVmAZsAW7qof1cScskLdu+fXtfdm1mZj0Y2luFiPhArW2StkqaEBFbJE0AtvXwVLOA5RGxtey531yW9F3gvh7iWAAsAOjo6Ije4jYzs/o02jW0GJiTLs8B7u2h7qVUdAulyaPko8CqBuMxM7M+ajQR3AjMlLQOmJmuI2mipDdHAEkanm6/u6L9VyQ9I2klcD7w2QbjMTOzPuq1a6gnEbGDZCRQZXkncFHZ+h7g2Cr1Lmtk/2Zm1jjfWWxmVnBOBGZmBedEYGZWcE4EZmYF50RgZlZwTgRmZgXnRGBmVnBOBGZmBedEYGZWcE4EZmYF50RgZlZwTgRmZgXnRGBmVnBOBGZmBedEYGZWcE4EZmYF50RgZlZwTgRmZgXnRGBmVnANJQJJ/1HSakndkjp6qHehpLWS1ku6tqz8GEkPS1qX/hzTSDxmZtZ3jZ4RrAL+DHisVgVJbcAtwCxgKnCppKnp5muBpRExBViarpuZWRM1lAgiYk1ErO2l2rnA+oh4PiL2A3cBs9Nts4FF6fIi4OJG4jEzs74b2oR9TAI2la1vBqany+MjYgtARGyRNK7Wk0iaC8xNV3dL6i0B1XIc8NJhtm2FgRTvQIoVBla8AylWGFjxDqRYobF4T6pW2GsikPQIcHyVTddFxL117FhVyqKOdgc3iFgALOhru0OCkZZFRM3rGXkzkOIdSLHCwIp3IMUKAyvegRQr9E+8vSaCiPhAg/vYDEwuWz8B6EyXt0qakJ4NTAC2NbgvMzPro2YMH30SmCLpFEntwCXA4nTbYmBOujwHqOcMw8zMMtTo8NGPStoMnAf8s6QH0/KJkpYARMQBYB7wILAG+ElErE6f4kZgpqR1wMx0vb813L3UZAMp3oEUKwyseAdSrDCw4h1IsUI/xKuIPnfXm5nZIOI7i83MCs6JwMys4AZtIqg1rUXZdkm6Od2+UtI5LYpzsqR/lbQmna7jr6rUmSHp95JWpI/rWxFrWTwbJD2TxrKsyva8HNvTy47ZCkm7JF1dUaelx1bSQknbJK0qK6tr6pXeXuNNjPerkn6T/q3vkTS6RtseXzdNivUGSS+U/b0vqtG2qce2Rqw/Lotzg6QVNdo2flwjYtA9gDbgOeCtQDvwNDC1os5FwP0k9zm8A3iiRbFOAM5Jl0cCv60S6wzgvlYf17J4NgDH9bA9F8e2ymviReCkPB1b4L3AOcCqsrKvANemy9cC/1Dj9+nxNd7EeD8IDE2X/6FavPW8bpoU6w3Af6/jtdLUY1st1ortNwHX99dxHaxnBD1Na1EyG/hBJB4HRqf3MjRVRGyJiOXp8qskI6smNTuOjOXi2Fa4AHguIja2OI6DRMRjwMsVxfVMvVLPazxz1eKNiIciGR0I8DjJvUItV+PY1qPpx7anWCUJ+BhwZ3/tf7AmgmrTWlS+udZTp6kknQycDTxRZfN5kp6WdL+kM5sb2SECeEjSU+nUH5Vyd2xJ7l+p9Y+Up2MLFVOvANWmXsnjMQb4TyRng9X09rpplnlpN9bCGt1ueTu27wG2RsS6GtsbPq6DNRHUM61FJlNfZEXSCOBnwNURsati83KSLo2zgG8BP29yeJXeFRHnkMwo+2lJ763Ynrdj2w58BPhplc15O7b1ytUxBpB0HXAA+FGNKr29bprhO8CpwDRgC0mXS6W8HdtL6flsoOHjOlgTQU/TWvSlTlNIOoIkCfwoIu6u3B4RuyJid7q8BDhC0nFNDrM8ns705zbgHpJT6XK5ObapWcDyiNhauSFvxza1tdSVptpTr+TqGEuaA3wI+HikHdeV6njd9LuI2BoRXRHRDXy3Rgy5ObaShpJM9f/jWnWyOK6DNRH0NK1FyWLg8nSEyzuA35dOx5sp7f+7FVgTEV+rUef4tB6SziX5u+1oXpQHxXK0pJGlZZILhasqquXi2Jap+YkqT8e2TD1Tr9TzGm8KSRcC1wAfiYg9NerU87rpdxXXqj5aI4bcHFvgA8BvImJztY2ZHdf+vBLeygfJyJXfklz9vy4tuwq4Kl0WyRfmPAc8A3S0KM53k5x2rgRWpI+LKmKdB6wmGb3wOPDOFh7Xt6ZxPJ3GlNtjm8YynOSN/S1lZbk5tiQJagvwBskn0SuBY0m+qGld+vOYtO5EYElPr/EWxbuepE+99PqdXxlvrddNC2K9PX1NriR5c5+Qh2NbLda0/LbSa7WsbubH1VNMmJkV3GDtGjIzszo5EZiZFZwTgZlZwTkRmJkVnBOBmVnBORGYmRWcE4GZWcH9f0ALNr6sOI01AAAAAElFTkSuQmCC\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"pacf_plot = plot_pacf(df.demand, lags=17)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"538cb705\",\n \"metadata\": {},\n \"source\": [\n \"1. partial auto-correlation is significant for first p lags\\n\",\n \"2. there is a sign of stationary\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"1b96e85a\",\n \"metadata\": {},\n \"source\": [\n \"### building AR model\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"id\": \"9b68728d\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from statsmodels.tsa.arima.model import ARIMA\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"id\": \"3a318c0a\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"arima = ARIMA(df.demand[0:30].to_numpy(),\\n\",\n \" order=(1,0,0))\\n\",\n \"\\n\",\n \"model = arima.fit()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"id\": \"d7c7e91f\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"
SARIMAX Results
Dep. Variable: y No. Observations: 30
Model: ARIMA(1, 0, 0) Log Likelihood -182.765
Date: Tue, 16 May 2023 AIC 371.530
Time: 21:40:39 BIC 375.734
Sample: 0 HQIC 372.875
- 30
Covariance Type: opg
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"
coef std err z P>|z| [0.025 0.975]
const 513.4426 43.815 11.718 0.000 427.567 599.318
ar.L1 0.4726 0.181 2.617 0.009 0.119 0.827
sigma2 1.136e+04 3299.003 3.444 0.001 4896.110 1.78e+04
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"
Ljung-Box (L1) (Q): 0.55 Jarque-Bera (JB): 1.72
Prob(Q): 0.46 Prob(JB): 0.42
Heteroskedasticity (H): 2.72 Skew: 0.59
Prob(H) (two-sided): 0.13 Kurtosis: 2.96


Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).\"\n ],\n \"text/plain\": [\n \"\\n\",\n \"\\\"\\\"\\\"\\n\",\n \" SARIMAX Results \\n\",\n \"==============================================================================\\n\",\n \"Dep. Variable: y No. Observations: 30\\n\",\n \"Model: ARIMA(1, 0, 0) Log Likelihood -182.765\\n\",\n \"Date: Tue, 16 May 2023 AIC 371.530\\n\",\n \"Time: 21:40:39 BIC 375.734\\n\",\n \"Sample: 0 HQIC 372.875\\n\",\n \" - 30 \\n\",\n \"Covariance Type: opg \\n\",\n \"==============================================================================\\n\",\n \" coef std err z P>|z| [0.025 0.975]\\n\",\n \"------------------------------------------------------------------------------\\n\",\n \"const 513.4426 43.815 11.718 0.000 427.567 599.318\\n\",\n \"ar.L1 0.4726 0.181 2.617 0.009 0.119 0.827\\n\",\n \"sigma2 1.136e+04 3299.003 3.444 0.001 4896.110 1.78e+04\\n\",\n \"===================================================================================\\n\",\n \"Ljung-Box (L1) (Q): 0.55 Jarque-Bera (JB): 1.72\\n\",\n \"Prob(Q): 0.46 Prob(JB): 0.42\\n\",\n \"Heteroskedasticity (H): 2.72 Skew: 0.59\\n\",\n \"Prob(H) (two-sided): 0.13 Kurtosis: 2.96\\n\",\n \"===================================================================================\\n\",\n \"\\n\",\n \"Warnings:\\n\",\n \"[1] Covariance matrix calculated using the outer product of gradients (complex-step).\\n\",\n \"\\\"\\\"\\\"\"\n ]\n },\n \"execution_count\": 27,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"model.summary()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 28,\n \"id\": \"a5289c6f\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# AR with lag 1 is significant variable in the model\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"86fea566\",\n \"metadata\": {},\n \"source\": [\n \"#### forecast and measure accuracy\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 29,\n \"id\": \"e6d3acb1\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([480.15274444, 497.71044684, 506.00789345, 509.92911382,\\n\",\n \" 511.78221031, 512.65794956, 513.0718078 ])\"\n ]\n },\n \"execution_count\": 29,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"forecast_31_37 = model.predict(30,36)\\n\",\n \"\\n\",\n \"forecast_31_37\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 30,\n \"id\": \"7a473526\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def get_MAPE(actual, predicted):\\n\",\n \" ytrue, ypred = np.array(actual), np.array(predicted)\\n\",\n \" \\n\",\n \" return np.round(np.mean(np.abs((actual - predicted) / actual))*100, 2)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 31,\n \"id\": \"f5df3b3a\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"19.12\"\n ]\n },\n \"execution_count\": 31,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"get_MAPE(df.demand[30:], forecast_31_37)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"8f8bcf16\",\n \"metadata\": {},\n \"source\": [\n \"### moving average (MA) \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 32,\n \"id\": \"59388c6b\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"
SARIMAX Results
Dep. Variable: y No. Observations: 30
Model: ARIMA(0, 0, 1) Log Likelihood -184.297
Date: Tue, 16 May 2023 AIC 374.595
Time: 21:47:29 BIC 378.799
Sample: 0 HQIC 375.940
- 30
Covariance Type: opg
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"
coef std err z P>|z| [0.025 0.975]
const 517.0213 31.024 16.665 0.000 456.215 577.827
ma.L1 0.3173 0.224 1.414 0.157 -0.122 0.757
sigma2 1.265e+04 4209.483 3.004 0.003 4395.561 2.09e+04
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"
Ljung-Box (L1) (Q): 0.41 Jarque-Bera (JB): 2.21
Prob(Q): 0.52 Prob(JB): 0.33
Heteroskedasticity (H): 2.25 Skew: 0.66
Prob(H) (two-sided): 0.22 Kurtosis: 2.90


Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).\"\n ],\n \"text/plain\": [\n \"\\n\",\n \"\\\"\\\"\\\"\\n\",\n \" SARIMAX Results \\n\",\n \"==============================================================================\\n\",\n \"Dep. Variable: y No. Observations: 30\\n\",\n \"Model: ARIMA(0, 0, 1) Log Likelihood -184.297\\n\",\n \"Date: Tue, 16 May 2023 AIC 374.595\\n\",\n \"Time: 21:47:29 BIC 378.799\\n\",\n \"Sample: 0 HQIC 375.940\\n\",\n \" - 30 \\n\",\n \"Covariance Type: opg \\n\",\n \"==============================================================================\\n\",\n \" coef std err z P>|z| [0.025 0.975]\\n\",\n \"------------------------------------------------------------------------------\\n\",\n \"const 517.0213 31.024 16.665 0.000 456.215 577.827\\n\",\n \"ma.L1 0.3173 0.224 1.414 0.157 -0.122 0.757\\n\",\n \"sigma2 1.265e+04 4209.483 3.004 0.003 4395.561 2.09e+04\\n\",\n \"===================================================================================\\n\",\n \"Ljung-Box (L1) (Q): 0.41 Jarque-Bera (JB): 2.21\\n\",\n \"Prob(Q): 0.52 Prob(JB): 0.33\\n\",\n \"Heteroskedasticity (H): 2.25 Skew: 0.66\\n\",\n \"Prob(H) (two-sided): 0.22 Kurtosis: 2.90\\n\",\n \"===================================================================================\\n\",\n \"\\n\",\n \"Warnings:\\n\",\n \"[1] Covariance matrix calculated using the outer product of gradients (complex-step).\\n\",\n \"\\\"\\\"\\\"\"\n ]\n },\n \"execution_count\": 32,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"arima = ARIMA(df.demand[0:30].to_numpy(),\\n\",\n \" order=(0,0,1))\\n\",\n \"\\n\",\n \"ma_model = arima.fit()\\n\",\n \"\\n\",\n \"ma_model.summary()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 33,\n \"id\": \"3803c499\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"17.78\"\n ]\n },\n \"execution_count\": 33,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"forecast_31_37 = ma_model.predict(30,36)\\n\",\n \"\\n\",\n \"get_MAPE(df.demand[30:], forecast_31_37)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 34,\n \"id\": \"878fd131\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"
SARIMAX Results
Dep. Variable: y No. Observations: 30
Model: ARIMA(1, 0, 1) Log Likelihood -181.846
Date: Tue, 16 May 2023 AIC 371.692
Time: 21:50:14 BIC 377.296
Sample: 0 HQIC 373.485
- 30
Covariance Type: opg
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"
coef std err z P>|z| [0.025 0.975]
const 508.3844 59.146 8.595 0.000 392.461 624.308
ar.L1 0.7421 0.316 2.348 0.019 0.123 1.362
ma.L1 -0.3395 0.539 -0.629 0.529 -1.397 0.718
sigma2 1.065e+04 3040.079 3.505 0.000 4696.418 1.66e+04
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"
Ljung-Box (L1) (Q): 0.13 Jarque-Bera (JB): 2.46
Prob(Q): 0.72 Prob(JB): 0.29
Heteroskedasticity (H): 3.07 Skew: 0.67
Prob(H) (two-sided): 0.09 Kurtosis: 3.38


Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).\"\n ],\n \"text/plain\": [\n \"\\n\",\n \"\\\"\\\"\\\"\\n\",\n \" SARIMAX Results \\n\",\n \"==============================================================================\\n\",\n \"Dep. Variable: y No. Observations: 30\\n\",\n \"Model: ARIMA(1, 0, 1) Log Likelihood -181.846\\n\",\n \"Date: Tue, 16 May 2023 AIC 371.692\\n\",\n \"Time: 21:50:14 BIC 377.296\\n\",\n \"Sample: 0 HQIC 373.485\\n\",\n \" - 30 \\n\",\n \"Covariance Type: opg \\n\",\n \"==============================================================================\\n\",\n \" coef std err z P>|z| [0.025 0.975]\\n\",\n \"------------------------------------------------------------------------------\\n\",\n \"const 508.3844 59.146 8.595 0.000 392.461 624.308\\n\",\n \"ar.L1 0.7421 0.316 2.348 0.019 0.123 1.362\\n\",\n \"ma.L1 -0.3395 0.539 -0.629 0.529 -1.397 0.718\\n\",\n \"sigma2 1.065e+04 3040.079 3.505 0.000 4696.418 1.66e+04\\n\",\n \"===================================================================================\\n\",\n \"Ljung-Box (L1) (Q): 0.13 Jarque-Bera (JB): 2.46\\n\",\n \"Prob(Q): 0.72 Prob(JB): 0.29\\n\",\n \"Heteroskedasticity (H): 3.07 Skew: 0.67\\n\",\n \"Prob(H) (two-sided): 0.09 Kurtosis: 3.38\\n\",\n \"===================================================================================\\n\",\n \"\\n\",\n \"Warnings:\\n\",\n \"[1] Covariance matrix calculated using the outer product of gradients (complex-step).\\n\",\n \"\\\"\\\"\\\"\"\n ]\n },\n \"execution_count\": 34,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"arima = ARIMA(df.demand[0:30].to_numpy(),\\n\",\n \" order=(1,0,1))\\n\",\n \"\\n\",\n \"arma_model = arima.fit()\\n\",\n \"\\n\",\n \"arma_model.summary()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 35,\n \"id\": \"6f5703b7\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"20.27\"\n ]\n },\n \"execution_count\": 35,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"forecast_31_37 = arma_model.predict(30,36)\\n\",\n \"\\n\",\n \"get_MAPE(df.demand[30:], forecast_31_37)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"30adbf67\",\n \"metadata\": {},\n \"source\": [\n \"## ARIMA\\n\",\n \"time-series data is stationary - ARMA\\n\",\n \"\\n\",\n \"\\n\",\n \" non-stationary - ARIMA\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 36,\n \"id\": \"aba6b783\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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Datedemand
02014-10-0115
12014-10-027
22014-10-038
32014-10-0410
42014-10-0513
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" Date demand\\n\",\n \"0 2014-10-01 15\\n\",\n \"1 2014-10-02 7\\n\",\n \"2 2014-10-03 8\\n\",\n \"3 2014-10-04 10\\n\",\n \"4 2014-10-05 13\"\n ]\n },\n \"execution_count\": 36,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"store_df = pd.read_excel('store.xls')\\n\",\n \"\\n\",\n \"store_df.head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 37,\n \"id\": \"c1deb62f\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\",\n \"RangeIndex: 115 entries, 0 to 114\\n\",\n \"Data columns (total 2 columns):\\n\",\n \" # Column Non-Null Count Dtype \\n\",\n \"--- ------ -------------- ----- \\n\",\n \" 0 Date 115 non-null datetime64[ns]\\n\",\n \" 1 demand 115 non-null int64 \\n\",\n \"dtypes: datetime64[ns](1), int64(1)\\n\",\n \"memory usage: 1.9 KB\\n\"\n ]\n }\n ],\n \"source\": [\n \"store_df.info()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 38,\n \"id\": \"ecff6ab2\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
demand
Date
2014-10-0115
2014-10-027
2014-10-038
2014-10-0410
2014-10-0513
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" demand\\n\",\n \"Date \\n\",\n \"2014-10-01 15\\n\",\n \"2014-10-02 7\\n\",\n \"2014-10-03 8\\n\",\n \"2014-10-04 10\\n\",\n \"2014-10-05 13\"\n ]\n },\n \"execution_count\": 38,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"store_df.set_index(pd.to_datetime(store_df.Date), inplace=True)\\n\",\n \"\\n\",\n \"store_df.drop('Date', axis=1, inplace=True)\\n\",\n \"\\n\",\n \"store_df[:5]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 39,\n \"id\": \"6bd71b19\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[]\"\n ]\n },\n \"execution_count\": 39,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.figure(figsize=(10,4))\\n\",\n \"plt.xlabel('Date')\\n\",\n \"plt.ylabel('Demand')\\n\",\n \"\\n\",\n \"plt.plot(store_df.demand)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 40,\n \"id\": \"3aeb1f33\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"image/png\": 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\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"acf_plot = plot_acf(store_df.demand, lags=20)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 41,\n \"id\": \"362258a9\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"from statsmodels.tsa.stattools import adfuller\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 42,\n \"id\": \"9c78933a\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Test Statistics -1.649533\\n\",\n \"p-value 0.457284\\n\",\n \"Lags Used 13.000000\\n\",\n \"Number of Observations Used 101.000000\\n\",\n \"dtype: float64\\n\"\n ]\n }\n ],\n \"source\": [\n \"def adfuller_test(ts):\\n\",\n \" adfuller_result = adfuller(ts, autolag=None)\\n\",\n \" adfuller_out = pd.Series(adfuller_result[0:4],\\n\",\n \" index=['Test Statistics',\\n\",\n \" 'p-value',\\n\",\n \" 'Lags Used',\\n\",\n \" 'Number of Observations Used'])\\n\",\n \" print(adfuller_out)\\n\",\n \" \\n\",\n \"adfuller_test(store_df.demand)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"2f7b1ad8\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# p-value > 0.05 that means we cannot reject the null hypothesis, \\n\",\n \"# as series is not stationary\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"eb42a0fa\",\n \"metadata\": {},\n \"source\": [\n \"#### differencing\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 43,\n \"id\": \"5a1055ea\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
demanddemand_diff
Date
2014-10-0115NaN
2014-10-027-8.0
2014-10-0381.0
2014-10-04102.0
2014-10-05133.0
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" demand demand_diff\\n\",\n \"Date \\n\",\n \"2014-10-01 15 NaN\\n\",\n \"2014-10-02 7 -8.0\\n\",\n \"2014-10-03 8 1.0\\n\",\n \"2014-10-04 10 2.0\\n\",\n \"2014-10-05 13 3.0\"\n ]\n },\n \"execution_count\": 43,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"store_df['demand_diff'] = store_df.demand - store_df.demand.shift(1)\\n\",\n \"\\n\",\n \"store_df.head()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 44,\n \"id\": \"78378db1\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"store_diff_df = store_df.dropna()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 45,\n \"id\": \"e4c4e738\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"[]\"\n ]\n },\n \"execution_count\": 45,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n \"data\": {\n \"image/png\": 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/6yJFGL6UmBGMppDNcuZBhjFluLJOYLAMAbqfNoCszV2MGNHdnZlHxP2PsTRo3LwN4QfYYIyoklszA65TSr71+J1440VwnZXXHDCAJoQEgFE+iy1db0XWrM70cRSrDcXgurGyURGmIUqLfnT0OHTZW8yA3GMvNEAPImZfZWeNGA6sRVC7EHMrJ0swGAHXGzAoXLAdOhRBPZXAqGGv0UmqCqNCIjBkAy41lCkaT6JLX53bYsBDW0ZilM8pnUSBMZo/OR3D5FnPXWWtKyZj9MYBvA3in/PUtAB8G8DBj7N0mrq3liSXT8MiCRamUaa6YttYIg0mxSYuMWbuIoWuJyAg08+DdRrEST8Eti4MB6crfjHmZopSXrzED0DLlLSMUTymvU8kaBk3Ufi1FkrDbJMNQK5QyJ2YkqcFSk3XP67GgDDBXB2bWGssUUk2WKe5jlqsxG+rywmlnONqEe2opgVkGwDjn/M2c8zcDOANAHMAlAD5m5uJanWgyrRxMvT4XEukMwk0020utOQFyswdEeYir12b13WkkoXiutxggfZ5qrzHLlvIEHUqWuPWP+WA0Baedwe2wqTRm5mbM1vdIHo9WyJjtm5ZmdrZKI4KSMVMFZgMBa41lUo9AMxzJlCosZdptDOt7fE1p4VRKYLaBc35K9f1pAFs55wsAWuMIbRDxZCYnYwY0l8lstktLDswoY1Yx4uq1GTeRRhNWDTAX9PidNR9kHszTVALZY78dLkZCsocbY0wp55qpMVuKJrGq0wOv026JfVE05yy1SmAmZ/56fbkZMyuNZQqqDJ2LGczmB2aAVM5sxovdUgKz3YyxXzLG3sMYew+AnwHYJY9KWjJ1dS1OLJmG15XNmAHWSNmXSjAmlZBE1k9ozCgwKx8lY9aEQtVGsxJLKRcFgl4TSpniQkT9XP42OubVJ0lRzjUzYxaMJtHtdUqzTxu8L3LOlcDMzPJtPRF7Trc/e6FhpbFMsWQaiVQma5fhsCGh0/Gv5WMGSF5mLy9EmkoiBJTm/P/nAN4M4HIADMC/AbiDS6/0KhPX1vLEUml45KCmx6QWfzMJRrNpZkB1kmqD7EGtERthM3YQNZqQVsbMZ47GrMPtUHRPQHuV70OqspLXaYfdxkwNUpajSXR5nXL2s7H74vRyDMFYCi6HDUsWCFpqwUIkAYeNKRfUQNZkdnYljj7Z16xR5HdBu532Ihkze8Htw70+hOIpLEaSTdVUVTQwkwOwn8hfRBlwzpHhyNnI1UQTafhcud5LVrhSKZXCuYHG2YN0huu+F1ZAXFUxVt81ZjIci5EkXHbJQyiSSCnHBVGclVgKa7q9ObdJGbMkMhkOW42OuZDKU0kQkKcN1Ftjlkpn4LDX1+0oGM2+fsYYOj0OUzNmS5Ekun1OyUqowYJ7kS07f303/vfIQk2Pq0Yh5mSq97sBYTJrgbFMWUNnWWMm+5hxzgv26HgqXeBjBgAjfX4AwNH55up2L/rJZoy9iTE2yRhbZowFGWMhxhg5YZbA3/78Jbzha3t0748lM/A4c0uZVvDrKZX8uYF+l35548CpEM74f/dY2kT1Hd96HP941766P29I9hM6Y00nAMqalcuKhvi/xyeVZGoZOOQf7wDgd0uf33pmzPZMzuHCf/gNnji8ULfnBHJd2AHJNsQsjVk8lUY0mUaXXMpsdMZsYkYS/l+yqQ8ZLo2ja3bUczIFVhrLlG/H5HbYkOFAKk//xjnX1ZiN9DWnl1kpl1yfB/B6znkX57yTcx7gnHeavbBmZ/9MCP/+2FEcntVv1Y2lsnYZAY9UImn0BlQOQZX5HyBlBv0uu2bGbO/JIOKpDO59aaaeSyyLiZkgHp6ar/vzCv3Meeu7AVBnZrmsaJQyFff/Gmag8zPEAOCw2+B12pWxUGaTznB86pd7sRxN4tO/2ltX7UwwL2PY6TUvYyY6H7u8Usas0fvi3ukg1vd6sU7OzC63gGWGek6mwEpjmfINzIWGLH9eZirDwTm0xf+9WS+zZqKUwOwU57z+aYQm53P3TCDDgXAirdvhEkuklYyZzcbQ7XU2XORaDqFoYWlHb3bg9LJkyrh7cq4uayuXdIZjKZrE1OkVJOs8UkqUr88f7gZAnZnloiX+N0OzqW7dV9Phqd8g8/95+jj2nwrhujNX47njy/jVC9N1eV5AI2PmdpqmMRO/t8vnQq/fhVA8pTvAuh5MTAcxPtip/P1bwTJjIZwoKO91ehyKpKLR5Bs6Cw1ZvpeZ+F5LY+Zx2rG6092SgdmTjLH/Yoy9XS5rvklnGgAh8+jBeTwwcRpr5auraFLbeyWWyjr/A2i6eZlBDbfzDrf2SWpmWZpX9uzLS6a22FfKcjQJziUH6UMGWU4zEH/zDX1+dHmdTWmI2CjiqbSm63evCYPMtTJmgNSNbKbWShBLpnHrfQdw7roufO0dF2BsMIDP37O/LgFLMp1BJJHOmXpgZsZMmLhK4n/pb7kUbczeGEumcXgujLGhTnT7WicwW4wkc1z/AXksU8AaY5myGjPpMyc0ZPleZnH5/KqlMQOAkV5/0xl3lxKYdQKIALgWwOvkr98xc1HNTCbD8Zm792Goy4M/vHwDAH1XcLXzP2COKaaZSJqb/IyZU1MIPb0cg8PGkM5wPHqw/uXCYqjf93rr4MRz9/pdGO5tTt+dRiGys/kBkziB1jIDHYwWasyA+mXMvvfwEUwvx3DzDeNw2G24+YYxHFuI4EePHzX9uVfyTpKAuRozEfh0e52qILsxwdCBUyFkOHDGUABdLZIxS2c4liKFGTPAOmOZshqzrF0GgILOzGzGTDucGe7ztZ7GjHP+hxpff1SPxTUjv3phGs8fX8ZHrt2Gvg7poNfatDnniCazpUxANsVsklKm4jGTV9oJuB1Y0disZ4IxXLyxF36XHbsnZ+u1zJJRv+/7ZuobmInn7vG7mnITaSTis+V3aWvMapUx45zrZsw63Nrl+1qyGE7g6w9O4VVjq3DZ5j4AwCu3DuAVm/vw5QemFI81s9CaE9rpcdYpYyYH2Q26aBUXamOD2YxZs49lCkaTyHAUZMwASWc2Z4FSZiiWgo0BftnrU5Qq873MRMZYy8cMkHRmp4JxxHQqV1ZENzBjjP21/O9XGGNfzv+q3xKbh0Qqgy/cux9jgwH87vlrlZNFRGPMUiKdAefICcx6/a6m6coM6WQq9EqZ08sxrO/x4bLNfZbUmYlN3+WwKaNX6vfcklWG32XHSK8PJxajSNVZ59asiOMwX2Pmc9nhcthqljGLJTNIZbi2xkznmK8lX/3tFMLxFD52w5hyG2MMt9wwjoVwArc9dMjU588KsdUZM+l1p01wiVcyZj5nw62E9k2H4HPZMdzra5mMmdacTEF/hzVKmVKziVOxxiieMSvUmAHN2ZlplDETgv8nATyl8UXk8cPHj+LYQgQ33zAGu41lzSc1Nu2YfHDlZMxkU8xmcCnWmhsIaIv/k+kM5lbiGOzyYMeWfhydj+DovLVq/iKzsn2kBxN1LmUuhhPo9kkb0EifD6kMx8mlWF3X0KyIz1YgT2PGGENvDbv5gnnjx9R0mOzn9fJCBP/26BH83oXrsXV1IOe+s9d14fXnrsG39xzCzLJ5x0x+WQnI+kuZkS1cVj1f1kqocRmzbYMB2GwMXqcdLrutYXq3WqE1J1MwELDGWKZQLJVTOtfVmMnfu3R8/ZqxM1PXxZJz/gv53+/XbznNSzCWxFcemMLlW/rwyq0DAACffLLQ0piJtGqOxswveS8FYynlysyqBA0yZvkas9OhODgHhro8uGhjLwCpO1OY/9Waw3NhfOKnL+Crb79Ac+PRQlxBXrapD48cnMd8HZ2vF1Raj+Fe6T05thDBsHylVw6PHJzDd/ccxlffcUFO0N+qrOhkzADppFOrDLTehQggl+9NzJh96f5J2G0Mf/XqrZr333TdNtz94jS+9tspfOqNZ5myhqCmxswh35dEl6+2+9Wy3PFttzF0m9DIUSqcc0zMhPDac4YAyMa6XvO6UcshlkzjD777REEH5YUjPfj8m88xNMBVdK2apczsWKZGuv8Ho0nFwBlQZcz0ujJ1Splqk9lmoRSD2fsYY92q73sYY/eauqom5LaHDmIhnMDN148rqdcO2XwyrFHKVAIzR27GDGiOQebKiSpfYyaXN9RZP9GROdjlwaZ+P9Z2e7HHxHLm/ftO4eEpqTO2VBbDCXiddpw/3AMgayhZDxbDCeVvL9LulXZmPnZoAb/Zdxrff+RIrZZnaURAlN+VCQC9NdRsLke1L0SArPjfrEz3I1NzePUZgxjs8mjev77Xh8u39OOpo4umPD+gHo+TqzFT31dLlqNJRc/lctgQcDsaYiU0vRzDcjSJ8cFsprLL67CExuyJwwt44vAC1vf6cPbaLpy9tgvre334yVPHi9qoZHWthQF11mS2seeh/IyZW77QzO9CThQpZfb4nAi4HS1TyhQMcM6XxDec80UAq0xbURMysxzDd/YcxuvPXYOz13Upt/sNM2bSwSSGmAPmmGKaRVDnRNXhdoDzXF2d8DAb6vKCMYado/14+OCcaToqoRErp8lgISzNUhsfCsi/o37lTHXGbLDTA5fDVrGXWUQ+1r7226mWmelnhMjOambMaljK1LsQAYAOtxPpDFc+07VkOZLEyeUYzhgy9vTe0OfHMROHNeebfUr/lzNm0dpnC5ciiZyqQaOshCbkRqBx1fvf7XNZQmO2e3IWLrsNt73rQnz57efjy28/H9/7PxdhbDCAL9xrbKMiMsl6GjMADfcyExozgbtIKVOvK5MxhuG+5up2LyUwSzPGhsU3jLERANYXQdWRf77vANIZjpuu25Zzu5h3WGopU/HraYITqpHGDMjV1Qnti7ji3zHaj1AsheeOL5uyNhFU7ZmaK1knIVyw+zrcGAi469oAIM2sk95Hm41hfY+34k0kkpRmxoXiKXztt1O1XKYlUewy3IUBU6/fVbOLnKCG+F0gjvmQCe7/okN4bChg+LjhXh9W4inTdFiidNeR4/wvzwk1K2PmzQYNPT5nQ+Zlin1gW07GzGmRwGwOF23sybm4t9uYYqPyQwMblaVIAm6HLcdHUzBgkbFM+YbGLr1SpnxBpOdjBkiViFbLmH0cwB7G2A8YYz8AsAvALeYuq3k4cCqEHz/1Mt596Qas783VBIk233C8sJQZ1ShlNtO8TD0xtCgpqcXQ08sx+Fx25aR2+eZ+MFZeRqtUkukMpk6vYKjLg7mVRMnWFwuqcuL4UGfdMmZi4oBa6zHS58fRCjeRSDyFoS4P3nLBOnz/kaN4uYk2o0pYiSdht7GcCxxBj5zZqEXXYDGNGWCOCF40ohTLmCkCZ5P+3qGYNPbKrtItZTVmJmTMoklLZMz2yaOY1Jmbbq+z4aXM08EYJmZC2LFloOA+YaPylQemdMvMwvU/fxg4YJ2xTMG8yTKV+pgBknb3+GLElA5iMyjFx+weABcA+C8A/w3gQs45acxkPnf3BPxuBz549ZaC+xx2GzxOG8IaA2+VjJkr18cMaBaNmfCYyQ3MAjoZs8Euj7IJ9PhdOGdtlym2GYdmw0ikM4q5b6latkVVOXF8MFC30Uxi4oC6SWG414dj8+GKylLhRBpepx0fvnYrGANuve9ALZdrOVbkgEHrBNPrd4Hz2lgbZEv32nYZgHb3dbXsmw6h1+/CqoCxCFuxBDCpXJM/JxPIBqlmZMyC0dyGgkaZb++bDmJsMDcotoL4X+ydO0f7C+7LtVE5qPnzi5GEpocZYI2xTJkMx0oidxazMpJJ18dMv9lppM+HZJpjWtY7W51SMmYA4AawAGAZwBmMsSvMW1Lz8Nihedw/cRrvv3Kzbvef36XdsaXYZagyZh1uB5x21iQasyQ63I6Czp8Od2EL/fRyFIOducLlnaMDpoxnEpmuV25dhW2rAyUHf/kZs3qNZlK7/guGe30IJ9KYr+BEFE2k4Xc7MNTlxR/t2IifPnsCL54wp2RsBUIaA8wFtZyXGYol4bRrZ+a0yve1YmImiLHBgGbgqWa9yZYAoVjh1IMOkzRmnHMsRTQyZnXeF8UopvG8bGW3T5pu0kivwT1Tc+jzu3QzqcJG5Tt7DmvaqGjNyRQwxtDf4Wqol1kongLnudIB0XUZT5anMQOyGeVmmUNcSlfm5wA8DKmkeZP89VGT12V5OOf4zN0TGOry4I8u36j7OL/bUbLGjDFWU8GymUgdM6VlD0TGTM3O0X5TxjPtmwnCZbdh04AfO0b78cSRBUQ1umLVJNMZhGIpZaMSep6JOkwAULqjckqZlZ9kw4kUfHIW9v1Xbka314nP3TNRg5Vak3Bc240fUM3LrMEJPd/sUk2HSaXMdIZj/6lQQWCghcdpx2Cnx7Q5q8Fo4fvstNvgc9lrnjGLJNJIZXhOYNbrdyGSSNfVvV2MYlJ3ZAJQ1mVGCbcUMhmO3ZNz2DHab2iJcdN125DOcPyzRtZ8MZLtetViIOBuaFemlnRA+JTp2WUYaczMLvXXmlIyZm8EsI1z/lrO+evkr9ebvC7Lc8+LM3ju5SX81au3GvpFSYGZvl2GWrgJSCfo/Cv8p44u4KfPnChrfWLgsVG2IJHK4Nb7DmDZQC8RS6Zx66/3F2S28jtmBPmlzHSG41QojqG8wOz84R74XHbsOmCsM7vnxWk8VOQxavZNh7BlVQecdht2jvYjkcrgiSMLhj8j9CIiw7J5oAMuuw17NXRmdzx1HE8fq50tgVbGLOtUXf5JNppIK4FZp8eJD1w9it2Tc0X1fLfvOoj9VVqEcM7xzYcOVn1Vmkpn8KXfTOKmHz+X86UlZl4xzJhJx+d8mSeYxw7NF3zeJCGy9vOUWsqMp9L45/sOlJxZOzIfRiyZwdigsfBfMNznMy0jEIonNS/EAh5HzbPeS6o5mYKeGgbZpTIhC/+1MmaAfpPWt3YdwtTp6j5L6QzHt3Yd0tSITsyEMLcSx87RQn2ZmvW9Prz70g348VMv48Cp3PUYZcwA2f2/TqXMXQdmcXeevYdW139RHzODwGxNtxdOO2uazsxSArNDAKztdtoAdk3OocvrxJsvWGf4OL/Lbpwxy/NeyZ+XGUmk8Kf//jT+6r+fLSuD8509h/Hl+ydxl4GfzWOH5vHl+ydxx9PHdR/z672n8OUHpvCzvBNVUOdE5VeyB9LmOr8SRzrDMdjlzXmcy2HDZZv6sGdKv9SYyXB8/M4X8alf7tV9TD4T00FlI71kYx9cdhv2FAlKxPstMixOuw1bVnUoG7PgxRPL+MiPn8Of/fvTRbNwpbKkmpMpWNfjA2PAsfny9RDhRCpH9/euS4fR53fhjqf0/8ZH58P4x7smcNNPnqvK7XtiJoTP3j2B7+ypbjzQfz35Mv75Nwewa3IWD0/N4eGpOdzz4gz+/hd7C8S7K7GUplUGIJ2YfC47fvHcybKe/9u7D+MjP34OB2dXlNskIbL2NlhqKfP548v40v2T+NmzpV1kibJ8KRkzABjp9ZmWEdDKmAHmzMtcVs3JFPQq+tv6abv2TgfhddqVbIvAaCzTcjSJT9+1Dx/98fNVWZf89JkT+PRd+/DXPyn8PeIia8eWQn1ZPh+8egv8bgc+d3c2a55KZ7AcTepqzADgjDWdmJgJ1qUJ6lO/3Iv/e+cLOXuPlj0NYwwuh63ABkTJmOk4/wNSt+q6Hl9FF7uNoJTALALgWcbYbTQrM0swlkSf35XTpaSF3+3QFP9HNUYyAWJeZjYw+/buw5gNxeFx2HM+XEbMr8TxjQcl0afRB0sEekbZlN1ytmpXnlZL70Tll011xUlK8TDrLDTH3DkqjWfSu8rfOx3EfDiBqdMrJYk251fiOB2KK15kXpcdF23sKaozW1DGk2Rfz9hQoOC9+9w9E/C57JgJxvC9Rw4XXU8pKH5Cqk2ymrJUJJ7OycK6HXbsHO03tA4Rf9vnjy8XNaY0QhxH1TR1hOMp/MtvJrF9pAeP3fIqPCJ//e3rz0Q8lcHhudz3xEhj1ulx4r07N+FXL0zjmTKynCtxqZPzC/fszz5PntmlGq1OZC1EML/7QGnvz8R0CHYbw5ZVHSU9fqTPh9lQvGYXDWq0NGaAORkzEfCoxf+NyJipRzGp6ZJtPJY0AjOh53r25SXc/eJMRc8bS6bxxV/vh89lx6OH5vFgXsVgz9Qctq7u0DUcVtPjd+H9V27G/ROn8dih+Zx1G2XM/njHRgTcDny2xHNOpUwvRzF5egWLkSReOpndb/Umy7gdNk0fM7fDVlSHOdzbPF5mpQRmPwfwKQCPgGZlKoRiKQRKGJvUUURjlp9+leZlSh+cuZU4bnvoIK4/cxAfumYUv90/i0cOFt/Uv/LAFCKJFNb3eg0d7IVHz2OHFgoOdkAqTYmM1mMH53O6FPVOVG6HXfHRArKBmdYmslMeXbV7SjswVGfTSjnZi9eqzjDs2DKAiZkQTgf15wguapQTzxjqxOlQHPOyAHbXgVnsnpzDR67dhmvGV+Ebvz1YE1H5YiQBj9NWUNKWOjPL30Qisvhfzc7RAcytJHSPhT2Ts1jb7S3JmNII8Tc6NBfG8cXKNkBxIXLLa8ZzNlpRzsvPGq/E9DVmAPDeKzahv8OFz9w9UXIGYyWeAmPAPS/N4KmjUhk8GEtqeqUB0mfYaWdFM2biM/9IiebK+6aD2DzgL3m01nBfdpxXLeFcGhOnmTHzmpAxk+dQ5mvMgPrNyxSjmLSylYrGTCMwExeQfpcdn79noqLO7u8/cgQnl2P45rsuxEifD5+9a0LJFMeSaTx+eKFoGVPNH12+EUNdHuUzYDQnU9Dtc+EDV2/BQwdm8YhBVaNa1F3z6vOAnj2NFJgV+pgZ6csEI3KpvxlmUZdil/F9SDYZj3HOvy++zF+atQlGk7qaEzU+l11bYyZH+flXY71+F5YiCaQzHF++fxKxVAY3Xb8N73nFBqzt9uKzd08YlpuOzofxw8eP4vcvWo+rt63CxHRQ9/H7poPwu+yIJtN4+uhSwf0HZ1cwvRzDztF+hOIpPPdy9jFBnStoQJ4dKG/WYhxTvsYMADb1+7Gmy6ObQdg9OYttqwMYCLhLCsxEhkutyRHt5EY/v5BXypR+h7QhT8yEkMlIjR7rerx416XD+Nj1YwgnUvjqA9UbuC6EE5rz6kb6yi9LpTMc0WRWYybYobwHhQFwKp3BI1PzuGJrP255zXhRY0o9Ysk0nji8oLzflYzcmluJ4/Zd0oXIhSM9OfeNru6Aw8YKsphGGjNAujD6y2u24onDC7h/X2kjulZiKbxqbDVWBdz4x7ukk5lRxowxhg7VMa+HOKEEYyk8X0Kn7MRMqMCqwYgRpTOztuWaaDKNdIbraMxqbx0hMmbdqs+FCCLqlTFTRjFpGPsalTJFxuyW14zjyHwE//HEsbKedymSwNd+O4Wrtg3giq0DuOm6bdh/KoT/keUmTxxeQCKV0bTJ0MPjtOPDr96K515ewl0vzBjOyVTzB5dJ55zPFDnnVMPuyTn0d7gxNhjIOQ+IY6owY2bX9DHTG8ekZrjXh1A81XAPulIopSvzdQCeBXCP/P15jLGfm7wuy6OX2s9HrysznsxoXgn3+FzIcOC540v40ePH8PaL12PzQIfy4Xr++DJ+aVBu+sK9++Gw2fCha7ZifKgT4UQaxxcLy4CJVAYHZ1fw5gvXwWFjmiftXfIH5WPXj8HGsiWvTIZjJW4ghvZkLUKmgzG47DbNtLk0nmlAczxTNJHG/x5exBVb+6VS3ORs0c1h33QIqwLunMG7Zwx1os/vMizXiitI9YlAPZrpp8+ewL7pIG66bhvcDjtGVwfw1u3r8YPHjlQttpZc/wvfm+FeqSwV0SiD6yFMi/MDs9WdHl3rkOeOLyEUT2Hn6ACuGO3H5Vv68OX7J8suT/3vkQXEUxn80eUbMdjpqaicKS5E/vr6bQX3uR12bB7I1f2lMxyRRFqxaNHjbRetx6Z+Pz53z0RJmaqVeAqrOt340DVb8dTRRfx67ylDjRmQe8zroe4oLFbOXI4kcWIpWrK+DFA3jdQ2Y6Y3fg2Q7AxqnTFb0tCYiUaAemXMtEYxCcS6tE7w08sxMAb8/kXrcemmXnzpN5Nlda1+7bdTCMVT+NgNYwCA1549hHPXdeGLvz6AWDKtjGG6ZGNfWa/nTResw9hgAJ+/dwKnZFG/1pxMNR6nHR+5diteOLGMXzxfnk6zFDIZqSJzxWg/rtg6gCePLij7XUgpZRZmzBIaPmZGwn+BMsy8CTozSyllfhLAxQCWAIBz/iwAfX+IGsEYu54xtp8xNsUYu9ns5ysXvdR+Ph2yxiw/fRpNpDU9kUQA84k7X4TLYcNfvmqrct8bz1+L8aFOfOHeCc3S43MvL+GXz0/jT3ZuxOpOD8bkTUWru/Dg7AqSaY7tG3px/nC35ol09+QsNvX7cdbaLpyzrlsR0a8kJI8ZXTF0TsYs11w2n51btcczPX54Hol0Rg4YBgo0CFrsmw4qr1lgszHsGO3Hnql53cBuISx5sqnT4WI007MvL+GLvz6As9d24XXnrFHu/6tXb4XdxvBPv96v9StLRj0nU00lZSmxqflchcflTtk6JN9uYNeBOdgY8IrNfYox5WIkiW8+qG1MqcfuyTnphLGpV9G0leOyfXgujB89fgzvuHgYmwa0NVX5uj9lgHmRz6HTbsNfX78Nk6dX8BODJghBKJZCwO3AW7evw+YBPz539wTCibThhViHu3hJLyZnzNZ0eYp2yU6UOIpJTZfXiYDHUXMdjdHUg06vE8FYsqbloeWoNM3Br7rAcNht6PI662YlpDWKSeBySDYhWhmzU8EYBjrccNptuOWGccyHE7h9V2nNMC8vRPD9R47iLResUzKljDHc8ppxzARj+O7Dh7F7cg7bN/QUSB+KYbcxfOyGMRydjyifbSONmeCN50nnnH/69X7Nc0417J0OYiGcwE754juZ5nj8cFY64HHaCkqULodN08fMrXEuzWfYpIyyGZQSmKU45/l5d1OLtIwxO4CvAbgBwBkA3s4YO8PM5yyXUEy7fTwfv9uBDEfBgONYKq05p0xkT/ZOB3HjFZuUuWVAdg7aywtR/PCx3BQ55xz/eNc+9PlduPGKTQCAbasDYEzbj2ufMuolgJ2jA3jx5HLO1Wg8lcZjhxaUMtjO0X48+/ISlqPJ7EBjAzG0WmNmJFIV45nyS197Jufgcthw8cZeXC53H+0yOJmJUUxapYcdW/oxtxLX1ViJOZn5jA914lcvTOPEUhS33DCWU3Ze3enBn+zYhJ8/dxLPH1/SXVcxFsOJnEydYKQCw9BIXDtjBkh6vkQqgycO51qH7Jmaw9nrupU1nLW2C284TzKmLMcle9eBWVw40gOfy4Edo/1YjibxQhnGtl+4dwJuhw1/8apR3ceMD3Xi5HJM6doTgVnAoJQpuO7MQVww3I1b7ztgmIVMpDKIpzLocDvgsNvwsevHcEhuONA73sUaVorMyhQnlGvOWI1nipgr7ytxFJMaxlhFJfBi6I1fE7cl07xA91MNS9Ekur2FnnHS7NP6lKH2TQexrserG4zrjWVS73fnru/G75wzhG/vPoxTBhpXwa33HQBjwIev3Zpz+6Wb+vCqsVX42gNTmJgJlaUvU3Pl1gFctqlPuVA36soU2GwMt8jnnH9/rLyybDHEfn75ln5ctKEXLodNySTnz8kUaGrMUhnDjkxBM5nMFt/RgBcZY+8AYGeMjQL4C0iNAGZyMYApzvkhAGCM/SeANwAo3TfBRBKpDGLJTEknBHWXovoqJ5ZMa5YyRd2/v8ON9+7cVHD/FaP92LGlH195YFLS1sh718mlKB4/vIC/f8OZSibL67JjY59fszNzYiYEl8OGDX1+7Bztx633HcDDU3N43blSVujpo0uIJtPKJrBzdABfeWAKjx6cU1LCehmzgMeBk0vSRjSzHMN567t1358evwtnr+3C7slZ/OU12ZPy7sk5XLyhFx6nHR6nHeNDndgzOYc/v6pw9BWQHcU0rqHJEa9h9+QszlhTeL+ezmt8MIBdB2Zx5bYBvEKjNf19r9yEHz1xDJ+5awI/eu8lulnBaCKNZ15exCs2F/4O6bkL38dKRuyEDTJmF2/ohctuw+7JWVwhN10sR5N49uUl/NmVm3Me+9Frt+HuF2bwiTtfxHVnDSq3O+0M1585VHC1fjokze0TJUjRxr9nclbzb//YofmcTOBSJIG7XpjBX12zNedCJB+hHdw3E8Slm/qUrGyxjBkgBS3/9zXjeMs3H8V39xzGB67WDgDDeVm4V5+xGhdt6MH/HlksWso8HTI++YoTyqvPWI1/e/QoHjs4j2vPHNR87MRMCD0+Z9FRTPmM9Prx0kntgHhiJojn8zLTnR4Hrjtz0LCjTRngrnEhKk6ewWgyZz974fhySXNqrxgdKLhwW86bkyno8WlnzJ46uoCDRaZ0MABXja1S5kAWY5/KdkeLTp1B5jPLMeWzC0gmr/e+NIOP3/kirj1zte7vC8VSuPOZE3j/lZsxlGctBAAfu2EM1//LLgDaY5hKQcq+jeH1X30YPpe95KaSK7YOYOdoP776wCTecuE6zb8NICUH7tt7qqBbdftIj2YWfM/kHMYGA1gVkP7+l2zsVTLJWiPAAFljVtCVmTEcxyTwuuxYFXA3RSmzlMDsg5Bc/+MAfgTgXgD/YOaiAKwF8LLq++MALlE/gDF2I4AbAWB4eNjk5eSi5bGih/CUkq7Ss5tCNKl9MK3t8cLtkEov+d11gPThuvmGMbz1tkfx13c8n3Pf2GAAb784970YH+rEixob9b7pILatDsBht+Gcdd3o9Diwe3JWCcx2T87CYWO4dFMvAOD84W74XXbsnpxDr196HXpXkx1uSW/DOcdMMKYp/Fezc7Qf33zokNJQcCoYw/5TIbzpgrXKY64Y7cd3Hz6MSCKlGXgYaUIGuzzYNODHk0cX8T6N51/UKSdevLEX//rIEXzs+jHNdQc8TvzF1VvwyV/sxYMHZnHVtlWaj/v0XXvx748dw8M3X4213dlNN5nOIBhLaWrMun0u9PpdJQ9hB7J2DOJiQI2WdcijB+eRzvCCK/D1vT6875Wb8JUHpnD/RK5g/sHzZvGlt52fc9vDctfWFfLv6etw46y1ndg1OVcQAD16cB5v/9ZjBesb7vXhT3YaKyRE9mhiWg7M5AyVkfhfzfYNUpn1jqdP6AZmSnlU/p0ioPv92x7LOeHm0+F24NBscY2ZjUn+ej75s6QXmInAoJgFQD7DfT7c+9IMUukMHHlZhPf94CnNDOydf/YKnD/cU3C7QAixtTSl6kHmq+SPXjrD8Z7vPVGSHuyN563Bv+QdT8uR3DmZgl6/S7ngE8SSabzr208o+koj3nPZCP7uDWcVfZwYxfTas4d0H9Ptcyrdo2qml6PKnglIuqb37tyErz94EL/Zd8rwedd0efCnr9ysed/W1QG885IRPHjgdFlZ1HzOWdeNt1y4ruypJh+6Zive/I1H8OD+03jDeWs1H3NwdgU3/qDQsKHL68Sum67K+ZtGEik8eWQR/0eeaQxI54F/vGsCM8sx3ckybqetQLOdkBvpSmHbYACPTM3pJkasguGOJpcUf845vwZScFYvtHajnPIp5/x2ALcDwPbt2+va/6rnsaKFX8cVPJZMw6ujMXvub681PGjOWtuFR295VYGodFXAA2feZjw2GMCvXpgu6F7bNx3CVdukE6ld1mHtnpwD5xyMMeyenMMFwz1KlsBpt+GyzdJjRACi9/qFEHoxkkQilSnqt7NzdABf++1BPHpwHtedOaga0DuQ85jbdh3C44cWcNVYYQC0dzo7ikmLTf0dutqChXACWzSu6K4eW1X0b/GOS0bwvUeO4HN3T+CK0YECX7uDsyv4jyeka4y9J4M5gZkohehpPS7b1IeHp7J/k2KEE/qlTEB6Dz979wROB2NY1SnpnPwuO84f7i547Eeu3YZ3XjKCVCZbNvj+I0fwrd2H8d6dm3DW2i7l9t0H5tCbN7dv5+gAvrXrUM5xJ3W37sNQlwf/eeOlOe9Vf4e76EY5EHBLwaqs/wmVkTETjPT5DGeHhjQ+2+cP9+D5TxofB6WK/4WdzKWb+nR1ZmIU0zsuHjH8fVqM9PqQynBML8eU+ZmApKs5Oh/BTddtwxvOky6+TofieNPXH8He6aBhYKZIF3Q0ZgByyrIvybKIv3/Dmbha47MquOV/XtDUvy5Hk+jr0L5YydeZPnlkEdFkGre+9VxcvLG34GcEH/jRM5rPpYUyiskgAOryOgs89cLxFIKxVIGZ9l9fP4Z3XzZSVHPZ53cbasf+7vVnIpEeNxzDVAqfe/M5Zek/AamDHjBuvpgNSfd9+e3n4wJ5Tzm2EME7v/04vv7QFG65YVx57OOHF2QNcTb7J+33E9g9OSsPsS88BtwOGxbChaXMUi/O3v/KzXjHtx/HDx49ivdeUViRsgqGYSbnPA0gwhjrMnqcCRwHsF71/ToAtW8LqRAjMWw+InuRb5kRN4jYS4nku7xOrOvx5XxpebmIzUU9bmc2FMfcSjxHKL9jywCml2M4OLuChXACL55cVvRlgp2j/Ti2EFEycHoZww63EyuxlKJRKpYxu0Aez5Q1KJ1Ff4crx/Zi+4YeuB02XZ2ZehSTFiN9Phxb0Paw0euMZIwV/Vu4HDbcdN02TMxkW9rVfOGe/fA4bGCs0OxXa06mmp2j/TgVjGPq9Irm/flEDUqZ4vcBWeuQ3ZNzuGxzn+57NtjlyTm+PviqUfT6XfjM3fuU95Fzjt1Tc7h8S+7cvp1b+pHKcDymmoX6qxem8fzxZXzk2m0Y6fPn/O5SjnnGGMaHAsrVfjkaM4FwqtcTq2czZrnHdrH1BdzFuxPjqYzS8LNztB9HdMyVxSgmLb1kMYZ1OjNFR/Vrzh5S3vPz13cj4HEUdXfPasy0SpmF5rq7NZ5L6+vcdd04OBsuaEhZljVm+QjzbfXfbvfkrFRiP2vQ8LnOXtuFielQSU0KovM3v5FITbfXVaAxm5F1ZFr73VCX13B963p8RQX9Nlvx/agU7DZWku+Xmk6DTlSByCBuHsh+tl+xuR9vOn8dvvfwEZxYympWdx+QNMQXbcgG02ODAfR3SNZIeiPQXDo+ZqVmzF6xpR+v3DqAr/52ynAUYaMp5dXEALzAGPtOHZ3//xfAKGNsI2PMBeBtkIxuLYFR+3g+ImOW7/4fTaYLxjGZwZjK9kGQLftp+31JWZpCLYP4Xox50nv9AY8DiXRGOenkX0HmIzIIeyYld/qHp+awI+9E73HacfHGXl0bhonpoGEH20ifD7FkBqfz5r/FkmmEE+mSOpT0EC3tt953IOck89TRBdzz0gze98rNGOn1FZQPtOZkqhGBcf7UBT1E8O/XCczGByXrkD1Tczg6H8axhUhZQuJOjxMfvHoLHp6aV9a0/1QIs6F4wbFy4YYeeJw2xSQ4kcrgC/fux9hgAL97vnYppBTGBjux/1QI6QwvS2OmvAavEynZ700LpTxaxu8EpFJmPJUxNOdVl08U3aOGubLejMZSUCwB8gK+3QckE+ENqnIsYwzjg50Fo8fyCcVScNqZZhe5WmMm2HVgFmcMdRbVc40PdSKd4QUXHkuRhI7GzIV4KpPzt9s1Oac0nRR7rlA8pWkdlI8YxTTSq1+67vIVasxmDMy0mx27jSHgcWjq6gRa/nNAtpnh1l9nh6nvmZrFJRt7cwJNyT6pHw9PzWFZx55GW2OWLsnHTHDzDWMIxpL4+oPV+1CaRSmB2a8A/A2AXaiT8z/nPAXgA5D0bPsA/Dfn/CUzn7McytGYiRRrfl08lsxobnS1Zm23t+CqWJnBpxLKr+/1YWO/Xxl43elx4Jx13Tm/a2O/H2u7vThwStpIdUuZ8muelDfcYhkzIJtB+PXeGcytJDQDhitGBzTHM4lRTEbai2GdLkdlgHkJHUp6iJb26eUYvvfwEQBSJukzd01gIODGn+zciPGhTqUEJ1AcuHWee12PD5sG/EWtFQSi21DvytumKlmL4fHlConfeckIhnt9+Mxd+5DOcKWLKv/3uB12XLqpT8lw/vDxozi2EMHNN4wVHWNmxPhQJ2LJDI7Mhwv0YKUQ0MjwqFHKo2X8TiAbyGl5FgpiKu/CzQOSubKWEe++6WBZo5jUDHZ64LLbcsZ5pdIZPHpQMhHOL4mPDQUUE2U9hIebVjldnDxFVi0cT+HpY4vYubX4caV10ZjOcITiKc0ylpiXKS5oTodi2DcdLOniQuu59JiY0R7FpKbL60Q8lcm5EFPGz7VgYAZIr9koMNPynwOkc9AfXr4B//PMcew9GcTMcgwHTq1o7j07R/sxH05gPpzQmSxTOCszkS49YwZIe8ibL1iH7z2Sm8WzEpZ1/uec38U538o538w5/7TZz1cORu3j+Qi9T2Fgli7bi6YSlKtiVSlzYjqEwU5PQflu52g/Hj04j4cOzGLHaH/BCZQxhivkDdftsOlepagDM7uNldQJJTbXz8vzCTU/tFu1XfzFazNySR/R8QVTBpgXMVsshmhp//qDU1gMJ/Drvafw5NFF/NU1W+FzOTA22Ikj8+EcqwZl4oBBtm7nln48dmi+JA+hiIH4X/l9owOYW4njew8fwdpuLzb2a2vy9FCXbn/6zAnsmpzF6KoOzU6ynaMDODQbxsRMEF95YAqXb+nDK7dW1uovUDozp4NKEKWXIdRCK8OjRimPVpAxU/+8FpLGTNpyFXPlqUJz5YmZIDb1lz6KSY3dxrCu15tTIlWbCOczPtSJlXjK8ASlV1YCshYi4m/x+OF5JNNcaQQxYkOfHx6nLWdvCsWS4Lzw5A6o5mXK82Xzm06MyFoHGWcHOefYN609ikmNlvu/mHKyWmMucCvQrZElVLMcTcKR5z8n+LMrt6DL68Tn7plQLjS1jkf1YPaS7TKSmZJ8zNR8+NVbwQB8sUofSrMg5/8KCBm0j+eTzZjlnlijyfLSr9UwPhTIGc20V6fst3N0ANFkGqeCcd2rUHG70WsX2YPJUyGsDrhLypCIDMKhubDUQq2xuemNZ1IygAalzLXdXtgYcCyvAaBY1qocPnbDGMLxFL50/yQ+d88ENg/48dbt65S1cZ6r9RNXmN0aHWiCnaMDiCUzeOpo8SHcQvxvVCIXAe+hubBmBqUUXnv2EM5Z14V/+vV+eQyT9rFyhfxcf/bvT2MhnMDN149X9HxqRld3wC6PZhKNBeWIodVdhFqsVJgxK5aJAwrb+ndu7dccz1RKYGDESN6wZrWJcD4i0DUSxkvWBdrHqNdph93GlEB314E5uB22gpFaWthtDNtW55oGKwPMdTRmQPZiavfkHHp8TpypYYGTj9/twEivr2jGbCaoP4pJjVZgNr0cQ6/fZeluv2oomjGTbU60PuNdXic+cJU0e/MbDx5UxjDls6rTo9yuqzHTGMlUio+ZmjXdXvzh5Rtx5zMnsLeIcXkjsKzzv5UJRpNgDOgo4UpdaB/yM2Z6I5nMYEw1mkmMYtLa+C/d1KsEUTs0fLsA4RBvnFEQYuxDc+GS9RaMMUVTpffcjDHs3CKNZzo0u4LDc2Ecngvj6WOLGMgbxZSPy2HDmm5vgYdNKVmrUtkqj2r610eO4NBsGB+7fkyxLBDvt/qKfSGcgL+In9Clm/vkkVnFdWaReAo+l90wUFnd6cHW1VKJbMeWyrJXNtnoeHo5hrjB3L4tqzqwutONQ3NhvOG8NTh7XfU9RNJoJj8mpkNYiaUMs4NaaHURqhEDzPU6W/UQzQLFMmYeVclFmCvf9fy0ciy/dHIZJ5aiZTn+5zPcm9vosntyFueoTITVbBsMaDamqCk2J1Q9lmnP1Bwu2dRX8t4mlfiDylqVixWtjJkqMOOcY/fkHHaMDpQcmIvnMiJ7kWcc7ImLKbUYfmY5hsEWzZYBouFBvytTz39O8O7LRrCux4tDc2HsHNW/KBT7SVkaswrOpe+/cjO6vE589p6Jsn/WbCzp/G91grHSr9RdDhtcdhtWVCWsdIYjkc5oOv+bgdhk9k4HlVFMWlcrAY9TNgP057Taq+n2uXD++m7D8qTImJVilaHmStmG45Xb9AOGV26TxjNd/cWHcNU/PYir/ulB3PXCDM4q4ap5pM9XoDFTMmY1CMwAaVST12nHRRt68OozsoaS63q86HDnav30ukHVdLgduGCkpySdWSSZLiqCBqT32WFjuHxLefP21Lxicz+uHlsFt0Maw6QFYwyv3DoAl92Gj15bOP+yUsQJttgAcy20ugjVhOTPdrmZvayRtH5GIZaXMevxu3DOum58e89h5Vh+7Zf3AADOWlN5EDvc58dKPIWFcALL0SSeO76sGzz7XA5s7PMbNgAEo0kEDOaRBjzSWKaTS1FMnV5RMqWlMDYYwGIkqTTlKBkzLR8zObBcCCeyTSc6F3FajA914uhCxFAHaDSKSY1mKbMEz8ZmRjLV1X/vJIsL/ePE7bDjpuukfcBI0iDOA1rnGLfDhgyHUv7nnJc8KzMfkcXbdWDW0EKnEVjV+d/SBEscYC7wu+05m4GI+Osh/geAras7lNFM0aS0Dj2h/K2/fx6SRcarfOUdFyCd1o/N1SfLwU7jjkw11585iP9476U5Bo35vPbsIbjshTqD7RuKl06Ge32496Vck8eFsP4VeiWs7vTgl3+xA/0d7pyTO2MMY4OBnBOg3pzMfHZu6cetvzmA+ZW4YVZQZMyK8cGrt+B3zhnSzKCUwz+/9TwcW4gYBoM33zCO97xig26gXwljg5342bMnsWopio4yPodAcY1ZOJ4qy35DIN6DaEL/sxNPpuHJc/L/8tvOwzPHlnJu87rsulnjUlDGeS1EcDoY1zQRVjM2FDCcQ2uUMQMknVkollIaGcrp9BUXjfumg1jd6VFc47U+j51eJ2xMuqARTSf5lj5GjA3KcoJTIVyg49tWbBSToNsrfXbUGaSZ5RjONZhy0uwIU109X8WliLb/nJrXn7sGAwG34RD2V2zuw3+/7zJs1yiHCy1ZPCUZKKcyHBmOigIzQBrV9g+/2ocXTizneDM2Gqs6/1uaUIkDzAV+t0OZYwhkHdrrVcoUV8X7poOIJNJwOWy6om+1AaoexR6jDszKuYK02Rgu09DBqHHYbbjBwJHbiOFePxbCCYRUmplFuTU/3yW9GjbrDOEeH+rET589oWxsi+FESdq2nVsH8MX7DuDhg/N4/blrdB8XSaRLCswCHmdBx20ldPmcONtnvJn1+l01KROrEfqfF08s49JN5WX9xN9dL2O2Ek9pTtwohsh+GznQSz5muX+fkT6/0phSK9TjvP73yIKuibBgfLATd70wg7DOazfSmAFAwO1EMJrE7qk5rAq4lVJ5KYiGnX3TIVy5bZWhxsxuY+j2ubAQSeCZl5ewZVUH1pSwXwkUOcG0fmA2MRMybCIS5GfMYsk05sMJDLVwKbPL60QyLVnNaF2MLUeT2Kxj8C1gjGmOpst/jJ5ZsNCSxVMZ+N3ZMWfl+rIJtCoZVkD31TDGfiD/972c849zzi+Svz7BOS8+kbWFCUbLzJi5cl3BY/LBVK9SJiBtShMzIeybDmLr6o6aBiL5qD2grOTpI05Y6nLmQjiBHoP0ey0ZGwogFMt6KZWaMTt7bRe6vE7sPmBczowk0hUFFc2GOMGmMrzsUqbHaYPTzgw1ZuV6mAGAxyV9nowCM8nHzPwsuchOHluIyCbC/bomwkDWSFWrYzGVziCSSBvud51eyd9qz6TUzV1OGbjL58Tabq/i8bcsZ6D0mot6fE7MLMfkppPysorrerwIGJyEY8k0Ds2u4IwS9H0BjwOMZQOz00GpFGul/a7WiCymnsmsnv9cLRFSAFF1isuft0ob6bQqGVbAaJe4kDE2AuCPGGM9jLFe9Ve9FmhFiqX28/G77TkGs8L7ptwW32oYGwzg6HwEz728VNIVYTV4nXYI+Z2VNBfDvYWu6IuR4jqvWpHfALAYTpaUMbPbGHZsyY7M0iOcKK2U2eyskkczAeUbwTLGEPA4C8aZCYTGrFzERVYsYRyY1aMT2+O0Y7BT8kg7thBRLG70EBlIrfmJWiOq8gl4nJiaXcFiJFmSdUU+Y4PZzszlaBIep023mtArGyTHU5myn4sxJvu2aQdmk6dWkOHGjv8Cm42h05PtUsxOOSk9g9dsaOnqBEb+c7VElCyFl1lC1ppVWsoEZM3qTLCkqRD1wujVfBOSRcYYco1lnwLwpPlLsy7FUvv5+N2OHLuMepcygWxQEIylqmrFLwXGmHJys9IV5IjGuJqFcEIRFZuN8FLaNx1EPJXGSjxVcrZux2g/ZoLSyCw9oiWWMpsdcZULlG9rAUgNAEEdEfNKvDyZgsBTcimzPhdjw30+PHFkAYB+l7NAy4RaUIo1UKfHCXFOu7wCbdz4UCcOzoYRT6WLdvb1+FyIJTNw2plu04kRY/KkA62TcKkdmQK1r5cYx2Sl/a7WdBlkzIz852qJuLARJUxhnVFNkkNUMqxkNmv0an7BOR8H8F3O+SbO+UbVl3Wnf9YBI8NFLfwuh474v34nUXX7/XiRjqNaIDmFS4PVrULA40Sv35VTyiylM7JWCC+liZlgduJAic8tTq67DujbZoQTqbLMVpsZcfKsJIgyypitVJgxc9qlEqleYMY5zxnJZDaiAaAUE2Gj0UylmGmL+8aHOjEQKG4mnc/YUADpDMfkqRUsRZKKsF4LkSktZQyTFkajmcQopuESG1W6vE7lczzdwuOYBKLjUitjtmzQtFFLhJZMBGSKxsxe+ecq24BinXKmUWD2E/nfrfVYSLPAOc8Rj5eClDFTlzLrrzETV8VAaan6aulwO9Df4a5YlGkWksdT1mS2VJ1XrRgblEYzFZuTmc/6Xh829RuPZ4rE6zNNwgqIzbSijJnXoW8wG08VDDAvFY/TrmTD80mmq+seKxeRHS7VRFhvNJMIzIw1ZtJ95dhkqFGX+ItmzOTPSzmdn2qMRjOJUUyljgxTG67OLMcQ8DgqOh6bBfF30epo1hvHVGvE50fRmKXSObdXgjIVwkINAEavxsYY+1sAWxljH87/qtcCrUY4kUaGoyyNWYfbrriyA+pSZv2CFnFVvLrTXZdAJOBxWNJsUe1lFk2kEUtmauL6XyrjQ9JophPyFXs5z71ztB+PHVpAMq1tydAu4n8g61hfiVA/4NbOmGUyvGLxPyBdaMV0MmaxOmfJh+VOz1IDGL3RTLOyv5hRxkxUDyoNlsRopn3TQSkwMyjvC9lBJVo2QH80UzyVxt6TwaKO/2rUgdn0ctRSelozEPY6S9FCk1kj/7laUqAxS1VfylSmQuhoDxuB0Q70NgBvlB9jfu2rSRBXC+VkzHxyxkzYJNR7kxZ89Lpths7NteSvXr0VFtJSKoz0+vCL504ikcqoXP/r05UJSFfsnAOPHZqXn7v0wGzLqg5Ek2ksRhIFJeJ0Rmpjr2cWtpGcMdSJT7x2HNefOVj2z3Z6tTVmokGnEh8zQPIf0ytlZrUw9fn7vGpsFW66bhteNb6qpMerRzOJrs5MhuM7ew5jTZcHowYWGNeMr8bNN4wZ+g8aIUYzTcxIgdlZBlmX1527BjYbw1lrK8v6641m+sGjRxGMpfCaMqx48jNmgy0s/AcAv0sav6VVyjTyn6sl2a7M3FJmtU01Yzql/EahuwNxzvcD+Bxj7HnO+d11XJOlUcSwZQRmHW4HUhmu+Bg1opQJQNcbxgwqEQHXg/W9PmQ4cGIpqpSX65kxE8a+jxyUArOeMoJC0fG0HEkWBGYiICh3RFGzYrMx/MnOyqSuehozYWljSsYsKeaY1idL7nc78OdXbSn58WI008R0CNfJwe6vXpjG88eX8cXfO9fwxNfjd+FPX7m5qvWODXbivn2nEE2kDU/ug10e/PGO6iYCCusgwXI0ia/+dgo7R/vLyvoJ8T/nHDPBmOnd7o2GMYZula5OjZH/XC3J+pjlljKrlcyMD3Xi3r0ziCRSFWkXa43uChhj7+Kc/zuAMxhj4/n3c85vNXVlFqUUMWw+fln3E0mk5cCs/nYZhIQw8zw6H4ZN1t7UU2O2tlsyNBRDo8sJCo3a1SNytscKm4rV6fQ4EU6kkUpncvz8Kh1gLvA47YgmtcvMihbGohlNn8uBDbIJNSCt9/P3TmB8qBNvPH+t6c8/PhTAfz35MgDzT+5jg52456XsSfgbDx7EcjSJj10/Vtbv6fI6kc5wLMkjpVpZ+C/QG2QuKklG3bu1QO38D6gy0VUGZqKSceDUCs6zwPQGo1cjWnk6IJUy87/aEnGlXc4B6HPnDjJXrp4tukm3MmrLjMVIbedkloLNlrV6CHgchsaf+XQbBWayHUs72GVUi7ioyh84HqpFxkxH/C+y5PXKmFXCuMrj64ePHcPLC1HcfMNYyWL4alA3JJmtUxqXT8L7Z0I4uRTF9x4+jDeet7bskTyie3Ty9Ao4b+2OTEGnTmC2FEkY+s/Viqz4v3Y+ZkC2kmGVCQBGpczb5H//rn7LsT5Cm1KOXYa4Al/JC8zaRQ9kJVYF3PA4bTg6H8H6HkkTUi8fM8HYUABPHl0sO1Nn5CMUSYjAjDJmxehUustSOfNCRcasGo2ZEMvn0wiLnHIZG+zE3S/OYGY5hq88MIkdW/or7rQsl3FVGdDsjJnaHuGHjx8D58BHri3ffEAcR/vlYLYdArNun1PpKFezHDW2OakV+j5m1X2uRCXDKp2ZhmEmY+wqxtgdjLGX5K+fMMaurM/SrEkoVr74X3TKiXJTLJmB3cbKypYQtYExJltmRLAQScLGzE+/5yNODOVq27oNfITEsdUuGrNqEBmz/LFMtdCY6Yn/YzUquZjJ+FCnFKT8+FksRpK4+YaxssYrVUOXz4k1cmBjdmAm5iP+/LkTuOPp43jPK0awrqc07zI14vMo9Gqt3pUJ6JcylyLGNie1Iutjlqcxq/JcKioZVvEyM5qV+VoA3wXwSwDvAPBOAHcB+C5j7DX1WZ71CJYwoiQfoTFbkctN0WTa0iWNVme4149j8xEshhPo9rnqUqpRI0TC5WbMxMXAksbGGE5QKbNURONOQWBWC42ZbimzGTJmUon94al5vPG8NWWX9qpFXLB0m5zBFpMjHju0gECZTRJqRCCiBGadrd2VCcBQ/G92CRooLGXGa2CXIRgbClhmNJPRq7kJwBs559/jnD/HOX+Wc/5dSBYaH6vL6ixIMJaEy1FeLd2voTGz8gbd6oz0yRmzOg4wVzMmd8CVmzGz25g8TqhwY4yS+L9kxEVVKKatMQtUaDDrddl0uzLFCcTKn/t1PZIJtctuw0eu3Vb35xfmr/XIvIgg8M+v2lJxICjWeWAmBK/TXpa3ZbPS5XUiGEsWGBEXMwauFfk+Zlm7jOoDs/GhTsuMZjI6kgY558/l38g5f54xttrENVmaYDRVllUGoKUxy1h6g251Rvp8iCbT2H8qVNeOTIHf7cAfXDqCyzaXr9/p8jk1vejCJP4vGT0Hc5Exq7QcbFzKrL+pdLkwxvCeyzagx+9SvMzqyWvPXoPJUytY221+5ul3zhnC6VAM73nFhop/hyhlhuIpbOr3163s20i6fC5wLl3UqDNkxfznagVjDC6HrSBjVm0pE8hWMiamQxWVtmuJUWAWrvC+liYUS5Yl/AdUGrOcjJl1N+hWR8zCOzi7gmvGG3ON8XdvOKuin9PTeJBdRunoZcxW4kl4nfYcC41yEIGZMJJWk9WYWTtw/uh19c+UCc5Y04nb/2B7XZ7rkk19uGRTX1W/w+u0w2lnSKZ5Wwj/gVzLHnVgJs04rU/1wW235fiYuRy2mgTFopS/bzqIa85obO7JaBffzBj7ucbtDEDbDjEPxlIIlHkAiiyG0AFRKbOxCC8zzuvfkVkt3V6XTmDWXgaz1SAy2Fri/0qF/wDgcdnBORQjaTXZrky6IGsVGGPo8rowt9IeHmaAqjM8msAwpAvcRCqDaDJdl1ImIOnJ1F2ZtWqo8bsdGOnzFYzragRGu9AbDO77p1ovpFmoJGPmdtjgsLFsKTPVPqNzrMjabi9sDMjw+nqY1YIurxMnlws1ECLo91g8I2MFHHYb/C57ocYslqrYKgPI2t9oXXgpPmb0uW8purwOzK3E26IjE9DuDBf/766TXtftsGdnZaYzNc1Cjw92WsLLzMjH7KF6LqRZCEaTWFPmTDTGGPxuh6qUmaHArIG4HDYMdXlxYila1zmZtaDL59QV//tcdtjq3GHarHR6C9/HajNm4jMdTabRnXefMu2DurFbCqlxINzyczIFWtNHluWh5vWyHXI7zMmYAVIDyr17ZxBNpOFtoF6XdokyCcVSZVllCPwue9YuI0Eas0YjJgDUc05mLeiS29XzW7rDiTTpy8og4HEUasxiqYqtMgAoG7mWZUashloYwjqIQGWos00yZhom19mMWX32UpfDluNjVsvATHj57T/V2HImRQdlEowlK7oy8LsdWbuMFGnMGo0IzBrRlVkN3V4nUhmuaMoEkXiKOjLLoNPj1NaYVRGYiZKKVmdmPJkh78IWRAQq7aIx69TImIkgrW4as7yuzGoHmKsZVzozG1vOLOb8b2eMfaFei7E6iVQGsWSmIh2K3+1AWO6ci5NdRsMZ7pUaAJpRYwYUmsxGEmkKzMpAK2MWilVZynRlNWb5xOlirCXpbLPAzOO0w+O0aWrM6heYqTRmqUzV45jUiKkQjdaZGQZmnPM0gAsZ5d8BVDbAXOB325WMWZTsMhrO9g096PQ4MNIAv6ZqUMS3EQrMqqHTW5gxCydqI/6PJjIF98WSmZq4kxPW4oyhTmzo8zVdd3c1dHmdOfuPyJjVzS7DmWuXUctSps3GsG0wgMnTKzX7nZVQyi70DICfMcZ+DJV/Gef8f0xblUUJVTCOSeB3OTAXkkSSsWSauucazEUbevH8J69r9DLKRquUAEhBRTVluHYjP2PGOZc0ZjUS/+cTT9FnvhV560Xr8daL1jd6GXWl2+vCUjRrci32onqJ/112GxZUpcxa73u3vfvCugWZepTyinoBzAO4WnUbB9B2gZm4wi7X+R+QvJPCiRQ454glG9vxQTQv2a6oXPf/aCKNVQF3I5bUlHR6pK5MYQYbT2WQynDFDLoSvC7pyl0rMKNpH0SrkG9yvRxNIuBx1G3mcL6PWZ+/tpno/o7G76NFdyHO+R/WYyHNQDUZM59cykykM8hw8jMiKkN0PmllzKgrs3QCHqmJIpbMwKvyNKumlCk+0zGtrkySLxAtQqfXmTNPsl5zMgVm+phZhaI7BWNsK2PsfsbYi/L35zDGPmH+0qxHsIqUrdSVmVaNZqFNmiifLo12dUDKmJHGrHTEwGmRBRfmz2aVMmPJdEueQIj2o9vnxHIkt5RZL3NZQHRlmqMxswqlvKJvAbgFQBKQhpgDeJtZC2KMfZIxdoIx9qz89RqznqtcqsmYdbgcSKQzSgMBlTKJSvC77HDYWGHGLJ6uqgzXbgRkOYL4PIoB5h3uyk8wio+ZpsYsQxkzoiXIL2UuRRJ1zZi58gxma2mXYRVK2cl9nPMn8hozU3oPrhH/zDm33NinYBVdmT75pLkQlq40SAhMVII0n8+ZY5eRyXBEkzTmqxzEWLXlqLSVheLS+1mNkFh8pjUNZpPpmrb1E0Sj6PY6EU6kkUxn4LRL1hlDdZx84HbYEE9mxf/tmjGbY4xthiT4B2PsLQCmTV2VRQnGUmBMyn6VS4c8XHp+RQ7MaJMmKqTLl3vFKjI0NMC8dPQyZpVkwwU2G4PbYdP0MYvVeHQMQTSKrrx5mcvRykzXK8XtsCORNsfHzCqUslP8OYDbAIwxxk4A+BCA95u5KAAfYIw9zxj7LmOsx+TnKplgNIkOt6OieYSizDS3EgeQ7eAiiHLJ9xESxsVeEv+XTJeiMZPeO0VjVmU52Ouy69tltOAJhGg/1DpXznkDxP82pDMcyXSmZTVmpXRlHgJwDWPMD8DGOa96iBRj7DcABjXu+jiAbwD4FKQM3acAfBHAH2n8jhsB3AgAw8PD1S6pJEKxVEVWGYDkYwYA81TKJKqk2+vE3EpWfBuRZ7D6SbdYMgUZsxqI/wGpAUCrlCmNZKK/D9H8qAeZRxJpJNO8ruJ/oSmLxNPIcMnXrNXQ3YUYYx/WuR0AwDm/tdIn5ZxfU8rjGGPfAvBLnd9xO4DbAWD79u1c6zG1JhhLVlzqEBmzeTlj1orpV6I+dHmdODireD0rczPJLqN0xAVWUGjMYjXKmDm1M2bSfNzWO4EQ7YcIzILRZN3HMQFZRwOh+W7FiRpGu1BA/ncbgIsA/Fz+/nUAdpm1IMbYEOdcaNh+F8CLZj1XuYRiycozZgUas9Y7mIj60OV1YknVrh6RS5lkl1E6HqcNDhvLyZg57azqsojHaS/QmEllF052GURLILwUl6KJuo9jArJJDXEx1YqfK93AjHP+dwDAGPs1gAtECZMx9kkAPzZxTZ9njJ0HqZR5BMD7THyusghGU1jTXdmwWlHKnJNLmdRBR1RKl8+FUDyFdIbDbmMIJ0j8Xy6MsZx5mSsxaaRVtWOBtTRmwnOJLsaIVkApZUYskjFrR40ZgGEA6vkvCQAbTFkNAM75u8363dUSiicR8ASKP1CD/FImCYGJSunyOsG5lMHt9rkQVTJmVMosB/W8zJV4dXMyBV6nXclgCoSpNH3miVZAWM0sRZPKaLiuBmjMhOF7u/qY/QDAE4yxOyFlsX4XwPdNXZVFCUZTykFZLh1KYEZ2GUR1dKvEt90+F8JxoTGjY6ocxLxMQCqLVGMuK/A47UqDj0CUNiljRrQCDrsNAbcDyw3TmLVxKRMAmJTX/zcAdwPYKd/8h5zzZ8xemNXgnCMUSyrdXOXicdpgY8B8WLbLoMCMqBB1u/pIHxBJkvi/EnIzZsmq5mQKvK5CjZn4vhVPIER70uVz5pQyhe6sHojSZahdS5mcc84Y+ynn/EIAT9dpTZYknJBac8WMvXJhjMHvciAUF1F+6x1MRH3ozjN4jMRJ/F8JnR4nDs2tAJBKmasClelH1XidtgK7DDE+hjJmRKsgxjItRZKw21hdrXqyGjP5XNqCn6tSXtFjjLGLTF+JxRHReaUZMyCrM3M5bBWZ1BIEkOsjBEAR/1MWtjxyMmay+L9atOwylIwZ/X2IFqFbnj4izGWrbZophwKNWTv5mKm4CsCfMsaOAAgDYJCSaeeYuTCrIfyOKrXLALJdc3QCJapBCG3FvMxoIgWfy07Bfpl0erMas1qJ/z2apUwpY0ZZcqJV6PI6cSC4gqVosq5WGYCGxqwFz6el7EQ3mL6KJiCbMat88xYZMyppENWgNngEpIwZlTHLJ+BxIJxII5XOIBRL1UZj5rQjnsogk+FKoJy1y6C/EdEaiFJmsM5zMoFs6TIUb1ONmcwmAGdC6sjcyzn/rblLsibCM6Wag1B4mdEGTVSD22GHx2lTTGYj8RQJ/ytAZL8XI0nEU5malTIByelf/E0UuwwS/xMtQpfXheWIpDHr66if8B9QacyiravX1n1FjLG1jLHHAXwSUnC2BcAnGWNPMMbW1ml9lkGkTWuSMaMNmqiSbq8rK/6njFlFiM/y9HIUQPVzMgGpKxNATgMAGcwSrUaX14lEOoOZYKyuVhmASmMWa08fs68C+Abn/F/VNzLG/gDA1wG8wcR1WQ5RNqqFxsxDJ1GiSqSxTBSYVYPIfp9ckgOzGmTMRDZc3QBA4n+i1RCd4bOheOM1Zi2Y6DAKNc/ID8oAgHP+bwDGTFuRRQnWNGPWehE+UV+65K4oQJqV6a9BUNFuiM/yyaVYzvfVoJQyk+qMmShl0ueeaA3UWbJ6Z8wKfMxaMBNt9Io0w1DGmE3vvlYmGEvC5bBVpQ/rcJPGjKgNQnwLSBkz6vQtH5H9VkqZNXD+F3+HaCKj3JZ1/qe/EdEaqLNkXXU0lwXaXGMG4BeMsW8xxvziBvn/3wRwl+krsxihWKqqMiaQNQClkyhRLd2qwCxMGbOKEJ/nk8tSxqymGrOcUibZZRCtRWcDM2aMMbjsNiTS0ueqFX3MjF7RXwNYBnCUMfYUY+xJAEcABAF8tA5rsxTBaLLiOZmCDrLLIGqEOmMWJY1ZRYgpHmZrzOKpNBw2BkcLnkCI9qSRpUwge5Hjctjqam5bL3R3Is55EsBHGWN/A6kjkwGY4pxH6rU4KxGMpRCo8gD0UymTqBHdPiciiTQSqQzCcQrMKkEEYtOyxqyWdhnqrsxYMkOfeaKlEOL//P/XC7fThlC8dbPQRXciznkUwAt1WIulCcWqz5iJkydt0kS1ZAeZJxBNpsnHrAIcdhv8LjtOhWpfyozldWVSlpxoJTrcDthtDOkMb1DGzJ7zb6tBu0WJSKXM6g5AEv8TtUJoPKZlfZSwYiHKI+BxgnOAMcBXg8+lV9MuI9OyJxCiPWGMKQFZve0ygKx3WatmzFrzVZlAKJaqup2eRjIRtaJb7oQSHYVeyphVhNCZdbgcNZk1qlXKjKfSLdnST7Q3IjCr90gmIBuQtW1gxhi7v5TbWp1grPqZYGIkE3VlEtXSpZijyhkz0phVhMiC16KMCQAel7Sl5mfMaNoH0Wp0eZ3wOKuzkKoUtfi/FdHdjRhjHgA+AP2MsR5I4n8A6ASwpg5rswyJVAaxZKbqIceK8z8FZkSVdHtzPbhIY1YZIgteC+E/ILXu21i+wSxlzIjWo8vrbIi+DFBpzFr0XGq0G70PwIcgBWFPIRuYBQF8zdxlWYuVuGRkV23GbLDLgw19PmwbDNRiWUQbo2TMZI0ZdWVWhvhM1ypjxhiD12nP68pMU8aMaDkuGO6p2eemXBSNWYta0BjZZXwJwJcYYx/knH+ljmuyHL1+Fw7+42uQ4byq3+NzOfDgTVfVaFVEO6OI/2UPLhL/V0atM2aA1JkZzRvJ1OGnjCbRWvzlNaMNe25FY9aimehSXtUMYywAAIyxTzDG/ocxdoHJ67IcdhuDs0Wjc6L5sNsYAh6H0pXpddKJvxKExqwWczIFboe9YIg5yRcIonaIgKxtxf8A/oZzHmKM7QBwHYDvA/iGucsiCKIYXV4nTgXJLqMaAkL8X+OMWazALqM1TyAE0QjIxwwQO8xrAXyDc/4zAPWdWkoQRAHdPicycnWdxP+Vodhl1GCAuSBfYxZPUcaMIGqJmI/Zql2ZpbyqE4yx2wC8FcBdjDF3iT9HEISJqDuiSPxfGYEa22UAcmCWb5dBgRlB1AwqZUoB2b0AruecLwHoBXCTmYsiCKI43d5s4pq88SpDjFmr1gpHjcdlRzSZUb6PJckugyBqSasbzBruRowxG4AnOOdnids459MAps1eGEEQxojOTJ/LXhPX+nbEnIyZDaeWpYwZ5xzxFI1kIoha0uo+ZobhJuc8A+A5xthwndZDEESJdPuygRlRGYNdHtgYMNTlqdnvVJcy4ykpc0Zj2AiidghtmatFnRJKuUwcAvASY+wJAGFxI+f89aatiiCIonQpGTMS/lfK2m4vHrrpKqzr8dbsd6p9zOJySZMMZgmidrR1KVPm70xfBUEQZdPtpYxZLVjf66vp7/M47YjJXZmxlPQvacwIona0usFs0cCMc/5QPRZCEER5dFFgZklySpmUMSOImiO0Za2q3TQaYr6Hc76DMRYCoJ5FxABwznmn6asjCEIXEZj5a9hRSFSP12lHKsORTGeUjBnZZRBE7WhnH7N3AgDnPMA571R9BaoNyhhjv8cYe4kxlmGMbc+77xbG2BRjbD9j7LpqnocgWpkuWfxPVhnWwitnMKPJtDIBgMT/BFE72tnH7E7xH8bYHTV+3hcBvAnALvWNjLEzALwNwJkArgfwdcYYnXUIQgPKmFkTkR2LJdKIyaXMVi25EEQjaOeRTGpjpE21fFLO+T7O+X6Nu94A4D8553HO+WEAUwAuruVzE0Sr0O2TDGZJY2YtRAYzmkwjnqKMGUHUmlbvyjR6VVzn/2ayFsDLqu+Py7cRBJGH32WHy2FTTFIJa5BbyhQ+ZhQ8E0St8Lulz1OrXpQa1UDOZYwFIWXOvPL/gRLF/4yx3wAY1Ljr4/IgdM0f07hNMyhkjN0I4EYAGB4m/1ui/WCM4dt/sB2jqzsavRRChZIxS2Q1Zq16ZU8QjeD89T34598/F5ds6mv0UkxBNzDjnFcVinLOr6ngx44DWK/6fh2Akzq//3YAtwPA9u3b65XRIwhLccXWgUYvgcjDk1PKpIwZQdQam43hd89f1+hlmIbVLuN+DuBtjDE3Y2wjgFEATzR4TQRBECUjSpnxZCabMSONGUEQJdKQ3YIx9ruMseMALgPwK8bYvQDAOX8JwH8D2AvgHgB/zjlPN2KNBEEQlaAW/2ftMihjRhBEaTSkz55zfidUdhx5930awKfruyKCIIjaoNaYiVImacwIgigV2i0IgiBqiMclbavRZBrxZBqMZZ3KCYIgikG7BUEQRA0RGbNYMo1YKgOPww7GtBrOCYIgCqHAjCAIooZ48uwyyFyWIIhyoB2DIAiihjjtNjjtTC5lZlp2bAxBEOZAgRlBEESN8TjtUldmijJmBEGUB+0YBEEQNcbrtEsas2SarDIIgigLCswIgiBqjNdllzVmGbLKIAiiLGjHIAiCqDFeuZQZT6XhpowZQRBlQIEZQRBEjZE0ZhnEkhkqZRIEURYUmBEEQdQYr9OOmLDLoFImQRBlQDsGQRBEjfG6RCkzQ6VMgiDKggIzgiCIGqNozChjRhBEmdCOQRAEUWM8TrkrM0UaM4IgyoMCM4IgiBrjddkUHzOyyyAIohxoxyAIgqgxWbsMypgRBFEejkYvgCAIotXwOu2IJNIAQCOZCIIoC9oxCIIgaozHlc2SUcaMIIhyoMCMIAiixnhVwRhpzAiCKAfaMQiCIGqMOktGPmYEQZQDBWYEQRA1Rp0xo1ImQRDlQIEZQRBEjfFQKZMgiAqhHYMgCKLGeEn8TxBEhVBgRhAEUWNySpmUMSMIogxoxyAIgqgxpDEjCKJSKDAjCIKoMV5Xdmt1k8EsQRBlQDsGQRBEjfHklDIpY0YQROlQYEYQBFFjqJRJEESlUGBGEARRY3K7MmmbJQiidGjHIAiCqDHq8qWbSpkEQZQBBWYEQRA1xmZjirEsGcwSBFEOjkYvgCAIohXxuuzgkII0giCIUqHAjCAIwgS8TjvSGd7oZRAE0WQ0JMfOGPs9xthLjLEMY2y76vYNjLEoY+xZ+eubjVgfQRBEtXiddurIJAiibBqVMXsRwJsA3KZx30HO+Xn1XQ5BEERt8TjtSGYyjV4GQRBNRkMCM875PgBgjLQXBEG0Jl6XHck0BWYEQZSHFduFNjLGnmGMPcQY29noxRAEQVSC12mncUwEQZSNaRkzxthvAAxq3PVxzvnPdH5sGsAw53yeMXYhgJ8yxs7knAc1fv+NAG4EgOHh4VotmyAIoib88Y6NiCTSjV4GQRBNhmmBGef8mgp+Jg4gLv//KcbYQQBbATyp8djbAdwOANu3b6fWJ4IgLMVVY6savQSCIJoQS+XZGWMDjDG7/P9NAEYBHGrsqgiCIAiCIOpDo+wyfpcxdhzAZQB+xRi7V77rCgDPM8aeA/ATAH/KOV9oxBoJgiAIgiDqTaO6Mu8EcKfG7XcAuKP+KyIIgiAIgmg8liplEgRBEARBtDMUmBEEQRAEQVgECswIgiAIgiAsAgVmBEEQBEEQFoECM4IgCIIgCIvAOG9+b1bG2CyAozX+tf0A5mr8O5uFdn7tldDO71c7v/ZSaOf3p51feyW08/vVjq99hHM+oHVHSwRmZsAYe5Jzvr3R62gE7fzaK6Gd3692fu2l0M7vTzu/9kpo5/ernV+7FlTKJAiCIAiCsAgUmBEEQRAEQVgECsz0ub3RC2gg7fzaK6Gd3692fu2l0M7vTzu/9kpo5/ernV97AaQxIwiCIAiCsAiUMSMIgiAIgrAITROYMcbWM8Z+yxjbxxh7iTH2l/LtvYyx+xhjk/K/PfLtffLjVxhjX9X5nT9njL1o8JyfZoy9zBhbybvdzRj7L8bYFGPsccbYBp2f130cY+wextgSY+yXLfrar2CMPc0YSzHG3pJ3X5ox9qz89fNir79cLPZ+6b4PeY9rxWOl1Ndet2PFYu/PhxljexljzzPG7meMjej8fCseG6W+9pbYRxhjDzLG9qvWu0rnOS9kjL0g/62/zBhjxd6HvJ+33LFSx9fesGOl5nDOm+ILwBCAC+T/BwAcAHAGgM8DuFm+/WYAn5P/7wewA8CfAviqxu97E4AfAXjR4DkvlZ93Je/2PwPwTfn/bwPwXzo/r/s4AK8C8DoAv2zR174BwDkA/g3AW/LuWzF6vS12rOi+D21wrJT62ut2rFjs/bkKgE/+//sNPkuteGyU+trrdmyY+X4BeBDA9hKe8wkAlwFgAO4GcEOZnyXLHSt1fO0NO1Zqfuw1egEVLxz4GYBXA9gPYEh1MO3Pe9z/0ThQOgDskQ803U1F748K4F4Al8n/d0AyxmMaP2f4OABXlvIhacbXrnr8vzb6Q9LI98vofWj1Y6XU197IY8UK74983/kAHm63Y6PYa2/ksVHj9+tBFAlO5N81ofr+7QBuK/Y+NMGxUpfXbqVjpdqvpillqpHTs+cDeBzAas75NADI/2qmSfP4FIAvAohUuIS1AF6WnzMFYBlAXxWPK5kmeu1GeBhjTzLGHmOMvbHCdZSEBd6vUmnFY6UWmHasWOz9+WNIWQItWv3YMHrtRjTTPgIA35NLaX8jynR5rAVwXPX9cfm2crDisQLU57UbUbdjpRY0XWDGGOsAcAeAD3HOgxX8/HkAtnDO76xmGRq38SoeV9qTNtdrN2KYSy7P7wDwL4yxzVWsRxeLvF8lP53Gbc1+rNQCU44VK70/jLF3AdgO4At6D9G4rSWOjRJeuxFNsY/IvJNzfjaAnfLXu7WeSuO2cv/OljpWZOr12o2oy7FSK5oqMGOMOSEdJD/knP+PfPMpxtiQfP8QgNNFfs1lAC5kjB2BlIrfKosT7Spx4N8X+R3HAayXn9MBoAvAApOErs8yxp41elzprzhLE752XTjnJ+V/D0FKc59f7GfKxULvl9762uFY0VtfQ48VK70/jLFrAHwcwOs553H5trY4Nkp87bo00T4CzvkJ+d8QJF3exRrv13EA61Q/tg7AySLrs/qxUs/XbrQG04+VWtI0gZmc/vwOgH2c81tVd/0cwHvk/78HUi1cF875NzjnazjnGyCJFQ9wzq/knKc55+fJX/+vyHLUz/kWAA9wiY+L32H0uOKvNpcmfe16r6WHMeaW/98P4HIAe4s8Z1lY7P3S+93tcKzo/e6GHStWen8YY+cDuA1SYKKc4Nrh2Cjjtev9fNPsI4wxh7xGEez8DiRdXs77xaXSYIgxdqn83H9Q7Hdb/Vip82vXW4Ppx0rN4RYQupXyBWkD4ACeB/Cs/PUaSPXz+wFMyv/2qn7mCKSrhRVIEfkZeb9zA4w7ij4v/1xG/veT8u0eAD8GMAWpk2STzs/rPg7AbgCzAKLy776uxV77RfLPhQHMA3hJvv0VAF4A8Jz87x+3+LGi+T60ybFS6muv27FisffnNwBOqdbx8zY6Nkp97U2/j0DqWHxK/j0vAfgSALvOc24H8CKAgwC+Cigm8E25j9T5tTfsWKn1Fzn/EwRBEARBWISmKWUSBEEQBEG0OhSYEQRBEARBWAQKzAiCIAiCICwCBWYEQRAEQRAWgQIzgiAIgiAIi0CBGUEQbQVjLC0bU77EGHuOMfZhxpjhXsgY28AYe0e91kgQRPtCgRlBEO1GlEvGlGdCGsz8GgB/W+RnNkAa50IQBGEq5GNGEERbwRhb4Zx3qL7fBOB/AfQDGAHwA0jGmADwAc75I4yxxwCMAzgM4PsAvgzgswCuBOAG8DXO+W11exEEQbQsFJgRBNFW5Adm8m2LAMYAhABkOOcxxtgogP/gnG9njF0J4KOc89+RH38jgFWc83+Qx708DOD3OOeH6/laCIJoPRyNXgBBEIQFYPK/TgBfZYydByANYKvO468FcA5j7C3y910ARiFl1AiCICqGAjOCINoauZSZBnAaktbsFIBzIWlwY3o/BuCDnPN767JIgiDaBhL/EwTRtjDGBgB8E8BXuaTr6AIwzTnPAHg3ALv80BCAgOpH7wXwfsaYU/49WxljfhAEQVQJZcwIgmg3vIyxZyGVLVOQxP63yvd9HcAdjLHfA/BbAGH59ucBpBhjzwH4VwBfgtSp+TRjjAGYBfDG+iyfIIhWhsT/BEEQBEEQFoFKmQRBEARBEBaBAjOCIAiCIAiLQIEZQRAEQRCERaDAjCAIgiAIwiJQYEYQBEEQBGERKDAjCIIgCIKwCBSYEQRBEARBWAQKzAiCIAiCICzC/wen16Y/H2bZYQAAAABJRU5ErkJggg==\\n\",\n \"text/plain\": [\n \"
\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"plt.figure(figsize=(10,4))\\n\",\n \"plt.xlabel('Date')\\n\",\n \"plt.ylabel('First Order Differencing')\\n\",\n \"\\n\",\n \"plt.plot(store_diff_df.demand_diff)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 46,\n \"id\": \"9ac6af67\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"C:\\\\Users\\\\himan\\\\anaconda3\\\\lib\\\\site-packages\\\\statsmodels\\\\graphics\\\\tsaplots.py:348: FutureWarning: The default method 'yw' can produce PACF values outside of the [-1,1] interval. After 0.13, the default will change tounadjusted Yule-Walker ('ywm'). You can use this method now by setting method='ywm'.\\n\",\n \" warnings.warn(\\n\"\n ]\n },\n {\n \"data\": {\n \"image/png\": 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xJayWtlzS/zHxJujGf/4KkMypta2ZmPavqIJDUBNwMzAQmA5dImlxSbSYwKX/MBX7QjbZmZtaDajE0dCawPv8heiTdC8wCXiqqMwv4cUQE8GtJIySNASZW0PYgG1rf5tP/91fd7uhLm3cCHFJbgJ3vvHdI7cz6m1e27QbgG4+srnNPrNTwIw6r+WvWIgjGARuLpluAaRXUGVdhWwAkzSXbm2DomJMOqaOTxww/pHbVKPyHOuHowV6ml9lvltmbyypI5b2txzK7UosgKHc6QVRYp5K2WWHEAmABQHNzc/z0v57dnT7WxKH8eH1hi+qai06rdXeSXmZ7e/DVB1/gnff2cdG/Gdtr98NJ4b2tF3+GKlPNj9ffd0X58locLG4BJhRNjwc2VVinkrZmByjcD+fVHXt4fddebvzFOq57dA3t7WW3IcwO4s/QgWoRBEuBSZJOlDQImA0sKqmzCLgsP3voLODNiNhcYVuzAxTuhxP5/9neuh9Oe3vw1jtttL71Lite2Z7sl0YjqNdnqK+qOggiog2YBzwOrAHui4jVkq6QVNgRWQxsANYDPwT+srO21fbJGls97ofjLcjG4nsqHagmF5RFxGKyL/visluKngfwhUrbmnWmcD+cd4v+I/f0/XA624L0RVf9Tz0+Q32ZbzFh/U7hfjiHDxyA6J374dRrC9LDUT2jHp+hvsy3mLB+p3A/nN78Fa16bEEWD0dFwI2/WOcfiamRenyG+jIHgfVLAwaIM04Y2WvDMoUtyNJf7urJLUgPR/Ws3v4M9WUOArMK1GMLsrPhKH95WS05CMwq1NtbkCkd0CwcC3nnvX2seGV7ww7T9NX19MHiBuODi40jlQOaqZya25fX03sEDcQHFxtLKgc0UzkW0pfX03sEDcRXSzaewnDUp84YzxknjGy4EIB0Lu7qy+vpIGggffmDZtaRwrGQYo14LKQvr6eDoIH05Q+aWUdSORbSl9fTxwgaSD3OdTerVirHQvryejoIGkhf/qCZdSaVi7v66no6CBpMX/2gmVnf5WMEZmaJ8x5BNxzKT8QVfmi6mp+X6w9SWU+zRuQ9AqvavvZg++69vLp9D0vWbGFfH7hS0swq5yCwquxrDy699VnWb91Fy449fPGe57j01mcdBmb9iIOgB6Wwpfz02q2s3LiDwqrt3ruPlRt38PTarfXtmJlVrKogkHSUpCclrcv/HnSqiqQJkv5B0hpJqyV9qWjetZJelbQyf1xYTX/6klS2lFdv2smevfsOKNuzdx8vbdpZpx6ZWXdVu0cwH1gSEZOAJfl0qTbgKxFxKnAW8AVJk4vm3xARU/NHw/x2cSpbyqeNHc6Rg5oOKDtyUBOTxw6vU4/MrLuqDYJZwML8+ULg4tIKEbE5Ilbkz98C1gDjqlxun5fKlvK5p4xi6oQRDB7UhIDBg5qYOmEE554yqt5dM7MKVXv66OiI2AzZF76kTv/3S5oInA48W1Q8T9JlwDKyPYftHbSdC8wFOP7446vsds8rbCnvLgqDRtxSbhog7rx8Gk+v3cpLm3Yyeexwzj1lFE2+mtms31BE52PWkp4Cjisz62pgYUSMKKq7PSLKXtIqaSjwj8C3IuLBvGw08DoQwP8ExkTEf+6q083NzbFs2bKuqtVV4RjByo072LN3H0fmW8p3Xj7NX5JmVheSlkdEc2l5l3sEEXFBJy+6RdKYfG9gDFB2AFzSYcADwF2FEMhfe0tRnR8Cj3TVn/7CW8pm1l9UOzS0CJgDXJ//fbi0giQBtwJrIuI7JfPGFIaWgE8Cq6rsT5/SNECcf+pozj91dL27YmbWoWoPFl8PTJe0DpieTyNprKTCGUDnAJcCHylzmujfSnpR0gvAecCVVfbHzMy6qao9gojYBpxfpnwTcGH+/JdA2fGQiLi0muWbmVn1fGWxmVniHARmZolzEJiZJc5BYGaWOAeBmVniHARmZolzEJiZJc5BYGaWOAeBmVniHARmZolzEJiZJc5BYGaWOAeBmVniHARmZolzEJiZJc5BYGaWOAeBmVniHARmZomrKggkHSXpSUnr8r8jO6j3cv7bxCslLetuezMz6znV7hHMB5ZExCRgST7dkfMiYmpENB9iezMz6wHVBsEsYGH+fCFwcS+3NzOzKlUbBKMjYjNA/ndUB/UCeELScklzD6E9kuZKWiZpWWtra5XdNjOzgoFdVZD0FHBcmVlXd2M550TEJkmjgCcl/SYinulGeyJiAbAAoLm5ObrT1szMOtZlEETEBR3Nk7RF0piI2CxpDLC1g9fYlP/dKukh4EzgGaCi9mZm1nOqHRpaBMzJn88BHi6tIGmIpGGF58BHgVWVtjczs55VbRBcD0yXtA6Ynk8jaaykxXmd0cAvJT0P/D/g7yPisc7am5lZ7+lyaKgzEbENOL9M+Sbgwvz5BmBKd9qbmVnv8ZXFZmaJcxCYmSXOQWBmljgHgZlZ4hwEZmaJcxCYmSXOQWBmljgHgZlZ4hwEZmaJcxCYmSXOQWBmljgHgZlZ4hwEZmaJcxCYmSXOQWBmljgHgZlZ4hwEZmaJcxCYmSWuqiCQdJSkJyWty/+OLFPnFEkrix47JX05n3etpFeL5l1YTX/MzKz7qt0jmA8siYhJwJJ8+gARsTYipkbEVOCPgN3AQ0VVbijMj4jFpe3NzKxnVRsEs4CF+fOFwMVd1D8f+G1EvFLlcs3MrEaqDYLREbEZIP87qov6s4F7SsrmSXpB0m3lhpbMzKxndRkEkp6StKrMY1Z3FiRpEPAJ4GdFxT8ATgKmApuBb3fSfq6kZZKWtba2dmfRZmbWiYFdVYiICzqaJ2mLpDERsVnSGGBrJy81E1gREVuKXnv/c0k/BB7ppB8LgAUAzc3N0VW/zcysMtUODS0C5uTP5wAPd1L3EkqGhfLwKPgksKrK/piZWTdVGwTXA9MlrQOm59NIGitp/xlAkgbn8x8saf+3kl6U9AJwHnBllf0xM7Nu6nJoqDMRsY3sTKDS8k3AhUXTu4Gjy9S7tJrlm5lZ9XxlsZlZ4hwEZmaJcxCYmSXOQWBmljgHgZlZ4hwEZmaJcxCYmSXOQWBmljgHgZlZ4hwEZmaJcxCYmSXOQWBmljgHgZlZ4hwEZmaJcxCYmSXOQWBmljgHgZlZ4hwEZmaJcxCYmSWuqiCQ9GeSVktql9TcSb0ZktZKWi9pflH5UZKelLQu/zuymv6YmVn3VbtHsAr4FPBMRxUkNQE3AzOBycAlkibns+cDSyJiErAknzYzs15UVRBExJqIWNtFtTOB9RGxISL2AvcCs/J5s4CF+fOFwMXV9MfMzLpvYC8sYxywsWi6BZiWPx8dEZsBImKzpFEdvYikucDcfHKXpK4CqCPHAK8fYtv+xOvZWLyejaVe63lCucIug0DSU8BxZWZdHREPV7BglSmLCtod2CBiAbCgu+0O6oy0LCI6PJ7RKLyejcXr2Vj62np2GQQRcUGVy2gBJhRNjwc25c+3SBqT7w2MAbZWuSwzM+um3jh9dCkwSdKJkgYBs4FF+bxFwJz8+Rygkj0MMzOroWpPH/2kpBbgbODvJT2el4+VtBggItqAecDjwBrgvohYnb/E9cB0SeuA6fl0T6t6eKmf8Ho2Fq9nY+lT66mIbg/Xm5lZA/GVxWZmiXMQmJklLqkg6OhWF41E0gRJ/yBpTX77jy/Vu089RVKTpOckPVLvvvQkSSMk3S/pN/m/69n17lNPkHRl/pldJekeSUfUu0+1IOk2SVslrSoq61O310kmCLq41UUjaQO+EhGnAmcBX2jQ9QT4EtkJCI3ue8BjEfEBYAoNuM6SxgF/BTRHxAeBJrIzDBvBHcCMkrI+dXudZIKAzm910TAiYnNErMifv0X2pTGuvr2qPUnjgY8BP6p3X3qSpOHAnwC3AkTE3ojYUddO9ZyBwJGSBgKD+cP1Rv1aRDwDvFFS3Kdur5NSEJS71UXDfUEWkzQROB14ts5d6QnfBf470F7nfvS09wOtwO35MNiPJA2pd6dqLSJeBf438HtgM/BmRDxR3171qANurwN0eHud3pBSENTkVhf9haShwAPAlyNiZ737U0uSLgK2RsTyevelFwwEzgB+EBGnA2/TgHfpzcfIZwEnAmOBIZL+U317lY6UgqCzW100FEmHkYXAXRHxYL370wPOAT4h6WWyIb6PSPpJfbvUY1qAlogo7NXdTxYMjeYC4HcR0RoR7wEPAv++zn3qSVvy2+rQF26vk1IQdHari4YhSWTjyWsi4jv17k9PiIi/jojxETGR7N/xFxHRkFuPEfEasFHSKXnR+cBLdexST/k9cJakwfln+Hwa8KB4kT51e53euA11nxARbZIKt7poAm4rutVFIzkHuBR4UdLKvOyqiFhcvy5Zlb4I3JVvwGwAPlfn/tRcRDwr6X5gBdmZb8/Rx27DcKgk3QOcCxyT35Ln62S307lP0uVkIfhn9euhbzFhZpa8lIaGzMysDAeBmVniHARmZolzEJiZJc5BYGaWOAeBmVniHARmZon7//zYV/yH9ps5AAAAAElFTkSuQmCC\\n\",\n 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\"\n ]\n },\n \"metadata\": {\n \"needs_background\": \"light\"\n },\n \"output_type\": \"display_data\"\n }\n ],\n \"source\": [\n \"pacf_plot = plot_pacf(store_df.demand_diff.dropna(),\\n\",\n \" lags=10)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 47,\n \"id\": \"41d50dfe\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"store_train = store_df[0:100]\\n\",\n \"store_test = store_df[100:0]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 48,\n \"id\": \"78743a66\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n \"C:\\\\Users\\\\himan\\\\anaconda3\\\\lib\\\\site-packages\\\\statsmodels\\\\tsa\\\\statespace\\\\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.\\n\",\n \" warn('Non-invertible starting MA parameters found.'\\n\"\n ]\n },\n {\n \"data\": {\n \"text/html\": [\n \"\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"
SARIMAX Results
Dep. Variable: y No. Observations: 100
Model: ARIMA(1, 1, 1) Log Likelihood -256.910
Date: Tue, 16 May 2023 AIC 519.821
Time: 22:15:55 BIC 527.606
Sample: 0 HQIC 522.971
- 100
Covariance Type: opg
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"
coef std err z P>|z| [0.025 0.975]
ar.L1 0.4091 0.311 1.315 0.189 -0.201 1.019
ma.L1 -0.7179 0.236 -3.039 0.002 -1.181 -0.255
sigma2 10.4865 1.026 10.223 0.000 8.476 12.497
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \"\\n\",\n \"
Ljung-Box (L1) (Q): 0.06 Jarque-Bera (JB): 75.18
Prob(Q): 0.81 Prob(JB): 0.00
Heteroskedasticity (H): 0.76 Skew: -0.79
Prob(H) (two-sided): 0.44 Kurtosis: 6.97


Warnings:
[1] Covariance matrix calculated using the outer product of gradients (complex-step).\"\n ],\n \"text/plain\": [\n \"\\n\",\n \"\\\"\\\"\\\"\\n\",\n \" SARIMAX Results \\n\",\n \"==============================================================================\\n\",\n \"Dep. Variable: y No. Observations: 100\\n\",\n \"Model: ARIMA(1, 1, 1) Log Likelihood -256.910\\n\",\n \"Date: Tue, 16 May 2023 AIC 519.821\\n\",\n \"Time: 22:15:55 BIC 527.606\\n\",\n \"Sample: 0 HQIC 522.971\\n\",\n \" - 100 \\n\",\n \"Covariance Type: opg \\n\",\n \"==============================================================================\\n\",\n \" coef std err z P>|z| [0.025 0.975]\\n\",\n \"------------------------------------------------------------------------------\\n\",\n \"ar.L1 0.4091 0.311 1.315 0.189 -0.201 1.019\\n\",\n \"ma.L1 -0.7179 0.236 -3.039 0.002 -1.181 -0.255\\n\",\n \"sigma2 10.4865 1.026 10.223 0.000 8.476 12.497\\n\",\n \"===================================================================================\\n\",\n \"Ljung-Box (L1) (Q): 0.06 Jarque-Bera (JB): 75.18\\n\",\n \"Prob(Q): 0.81 Prob(JB): 0.00\\n\",\n \"Heteroskedasticity (H): 0.76 Skew: -0.79\\n\",\n \"Prob(H) (two-sided): 0.44 Kurtosis: 6.97\\n\",\n \"===================================================================================\\n\",\n \"\\n\",\n \"Warnings:\\n\",\n \"[1] Covariance matrix calculated using the outer product of gradients (complex-step).\\n\",\n \"\\\"\\\"\\\"\"\n ]\n },\n \"execution_count\": 48,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"arima = ARIMA(store_train.demand.to_numpy(),\\n\",\n \" order=(1,1,1))\\n\",\n \"\\n\",\n \"arima_model = arima.fit()\\n\",\n \"\\n\",\n \"arima_model.summary()\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"35dce0cf\",\n \"metadata\": {},\n \"source\": [\n \"### forecasting and accuracy\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 50,\n \"id\": \"bd238d6f\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([17.25076347, 16.12611976, 15.66605623, 15.47785577, 15.40086769,\\n\",\n \" 15.36937379, 15.35649043, 15.35122017, 15.34906424, 15.3481823 ,\\n\",\n \" 15.34782152, 15.34767394, 15.34761356, 15.34758887, 15.34757876])\"\n ]\n },\n \"execution_count\": 50,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"store_predict = arima_model.forecast(steps=15)\\n\",\n \"\\n\",\n \"store_predict\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 51,\n \"id\": \"6cf83df8\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"24.12\"\n ]\n },\n \"execution_count\": 51,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"get_MAPE(store_df.demand[100:], store_predict)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"3d8e9051\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.12\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n" }, { "path": "March Cohort 2023/22_23_Time Series Forecasting/vimana.csv", "content": "Month,demand\n1,457\n2,439\n3,404\n4,392\n5,403\n6,371\n7,382\n8,358\n9,594\n10,482\n11,574\n12,704\n13,486\n14,509\n15,537\n16,407\n17,523\n18,363\n19,479\n20,516\n21,656\n22,558\n23,647\n24,864\n25,610\n26,677\n27,609\n28,673\n29,400\n30,443\n31,503\n32,688\n33,602\n34,629\n35,823\n36,671\n37,487" }, { "path": "March Cohort 2023/22_23_Time Series Forecasting/wsb.csv", "content": "Month,Sale Quantity,Promotion Expenses,Competition Promotion\r1,3002666,105,1\r2,4401553,145,0\r3,3205279,118,1\r4,4245349,130,0\r5,3001940,98,1\r6,4377766,156,0\r7,2798343,98,1\r8,4303668,144,0\r9,2958185,112,1\r10,3623386,120,0\r11,3279115,125,0\r12,2843766,102,1\r13,4447581,160,0\r14,3675305,130,0\r15,3477156,130,0\r16,3720794,140,0\r17,3834086,167,1\r18,3888913,148,1\r19,3871342,150,1\r20,3679862,129,0\r21,3358242,120,0\r22,3361488,122,0\r23,3670362,135,0\r24,3123966,110,1\r25,4634047,165,0\r26,3772879,129,1\r27,3187110,120,1\r28,3093683,112,1\r29,4557363,162,0\r30,3816956,140,1\r31,4410887,160,0\r32,3694713,139,0\r33,3822669,141,1\r34,3689286,136,0\r35,3728654,130,1\r36,4732677,168,0\r37,3216483,121,1\r38,3453239,128,0\r39,5431651,170,0\r40,4241851,160,0\r41,3909887,151,1\r42,3216438,120,1\r43,4222005,152,0\r44,3621034,125,0\r45,5162201,170,0\r46,4627177,160,0\r47,4623945,168,0\r48,4599368,166,0" }, { "path": "March Cohort 2023/24_SQL/SQLqueriesQandA.sql", "content": "-- DROP DATABASE ORG;\n\nCREATE DATABASE ORG;\nSHOW DATABASES;\nUSE ORG;\n\n-- drop table Worker;\n\nCREATE TABLE Worker (\n\tWORKER_ID INT NOT NULL PRIMARY KEY AUTO_INCREMENT,\n\tFIRST_NAME CHAR(25),\n\tLAST_NAME CHAR(25),\n\tSALARY INT,\n\tJOINING_DATE DATETIME,\n\tDEPARTMENT CHAR(25)\n);\n\nINSERT INTO Worker \n\t(WORKER_ID, FIRST_NAME, LAST_NAME, SALARY, JOINING_DATE, DEPARTMENT) VALUES\n\t\t(001, 'Monika', 'Arora', 100000, '14-02-20 09.00.00', 'HR'),\n\t\t(002, 'Niharika', 'Verma', 80000, '14-06-11 09.00.00', 'Admin'),\n\t\t(003, 'Vishal', 'Singhal', 300000, '14-02-20 09.00.00', 'HR'),\n\t\t(004, 'Amitabh', 'Singh', 500000, '14-02-20 09.00.00', 'Admin'),\n\t\t(005, 'Vivek', 'Bhati', 500000, '14-06-11 09.00.00', 'Admin'),\n\t\t(006, 'Vipul', 'Diwan', 200000, '14-06-11 09.00.00', 'Account'),\n\t\t(007, 'Satish', 'Kumar', 75000, '14-01-20 09.00.00', 'Account'),\n\t\t(008, 'Geetika', 'Chauhan', 90000, '14-04-11 09.00.00', 'Admin');\n\nCREATE TABLE Bonus (\n\tWORKER_REF_ID INT,\n\tBONUS_AMOUNT INT,\n\tBONUS_DATE DATETIME,\n\tFOREIGN KEY (WORKER_REF_ID)\n\t\tREFERENCES Worker(WORKER_ID)\n ON DELETE CASCADE\n);\n\nINSERT INTO Bonus \n\t(WORKER_REF_ID, BONUS_AMOUNT, BONUS_DATE) VALUES\n\t\t(001, 5000, '16-02-20'),\n\t\t(002, 3000, '16-06-11'),\n\t\t(003, 4000, '16-02-20'),\n\t\t(001, 4500, '16-02-20'),\n\t\t(002, 3500, '16-06-11');\n \n\nCREATE TABLE Title (\n\tWORKER_REF_ID INT,\n\tWORKER_TITLE CHAR(25),\n\tAFFECTED_FROM DATETIME,\n\tFOREIGN KEY (WORKER_REF_ID)\n\t\tREFERENCES Worker(WORKER_ID)\n ON DELETE CASCADE\n);\n\nINSERT INTO Title \n\t(WORKER_REF_ID, WORKER_TITLE, AFFECTED_FROM) VALUES\n (001, 'Manager', '2016-02-20 00:00:00'),\n (002, 'Executive', '2016-06-11 00:00:00'),\n (008, 'Executive', '2016-06-11 00:00:00'),\n (005, 'Manager', '2016-06-11 00:00:00'),\n (004, 'Asst. Manager', '2016-06-11 00:00:00'),\n (007, 'Executive', '2016-06-11 00:00:00'),\n (006, 'Lead', '2016-06-11 00:00:00'),\n (003, 'Lead', '2016-06-11 00:00:00');\n \n \nselect * from Worker;\nselect * from bonus;\nselect * from title;\n \n -- Q-1. Write an SQL query to fetch “FIRST_NAME” from Worker table \n -- using the alias name as .\n \nSELECT FIRST_NAME AS WORKER_NAME FROM Worker;\n\n-- Q-2. Write an SQL query to fetch “FIRST_NAME” from Worker table \n-- in upper case.\n\nSELECT upper(FIRST_NAME) FROM Worker;\n\n\n-- Q-3. Write an SQL query to fetch unique values of DEPARTMENT \n-- from Worker table.\n\nSELECT distinct DEPARTMENT FROM Worker;\n\n-- Q-4. Write an SQL query to print the first three characters \n-- of FIRST_NAME from Worker table.\n\nSELECT substring(FIRST_NAME, 1, 3) FROM Worker;\n\n-- INITIALS OF FIRST NAME AND LAST NAME\n\nSELECT substring(FIRST_NAME, 1,1), substring(LAST_NAME, 1,1) FROM Worker;\n\nSELECT CONCAT(substring(FIRST_NAME, 1,1), substring(LAST_NAME, 1,1)) FROM Worker;\n\n-- Q-5. Write an SQL query to find the position of the \n-- alphabet (‘a’) in the first name column ‘Amitabh’ from Worker \n-- table.\n\nSELECT instr(FIRST_NAME, 'a') FROM Worker WHERE FIRST_NAME = 'Amitabh';\n\n-- CASE SENSITIVE/ BINARY DEPRECATED\n-- SELECT instr(FIRST_NAME, BINARY 'a') FROM Worker WHERE FIRST_NAME = 'Amitabh';\n-- SELECT INSTR(FIRST_NAME, BINARY 'a') FROM Worker WHERE FIRST_NAME='Amitabh';\n\n\n-- Q-6. Write an SQL query to print the FIRST_NAME from Worker \n-- table after removing white spaces from the right side.\n\nSELECT rtrim(FIRST_NAME) FROM Worker;\n\n-- Q-7. Write an SQL query to print the DEPARTMENT from Worker \n-- table after removing white spaces from the left side.\n\nSELECT ltrim(DEPARTMENT) FROM Worker;\n\n\n-- Q-8. Write an SQL query that fetches the unique values of \n-- DEPARTMENT from Worker table and prints its length.\n\nSELECT distinct length(DEPARTMENT) FROM Worker;\n\n-- Q-9. Write an SQL query to print the FIRST_NAME from \n-- Worker table after replacing ‘a’ with ‘A’.\n\nSELECT replace(FIRST_NAME, 'a', 'A') FROM Worker;\n\n-- Q-10. Write an SQL query to print the FIRST_NAME and \n-- LAST_NAME from Worker table into a single column \n-- COMPLETE_NAME. A space char should separate them.\n\n\n\n\n-- Q-11. Write an SQL query to print all Worker details \n-- from the Worker table order by FIRST_NAME Ascending.\n\n\n-- Q-12. Write an SQL query to print all Worker details \n-- from the Worker table order by FIRST_NAME Ascending \n-- and DEPARTMENT Descending.\n\n\n\n-- Q-13. Write an SQL query to print details for Workers \n-- with the first name as “Vipul” and “Satish” from Worker \n-- table.\n\n\n-- Q-14. Write an SQL query to print details of workers \n-- excluding first names, “Vipul” and “Satish” from Worker \n-- table.\n\n\n\n-- Q-15. Write an SQL query to print details of Workers \n-- with DEPARTMENT name as “Admin”.\n\n\n\n\n-- Q-16. Write an SQL query to print details of the Workers \n-- whose FIRST_NAME contains ‘a’.\n\n\n\n-- Q-17. Write an SQL query to print details of the Workers \n-- whose FIRST_NAME ends with ‘a’.\n\n\n\n-- Q-18. Write an SQL query to print details of the Workers \n-- whose FIRST_NAME ends with ‘h’ and contains six alphabets.\n\n\n\n-- Q-19. Write an SQL query to print details of the Workers \n-- whose SALARY lies between 100000 and 500000.\n\n\n-- Q-20. Write an SQL query to print details of the Workers \n-- who have joined in Feb’2014.\n\n\n\n-- Q-21. Write an SQL query to fetch the count of employees \n-- working in the department ‘Admin’.\n\n\n\n-- Q-22. Write an SQL query to fetch worker names with \n-- salaries >= 50000 and <= 100000.\n\n\n\n\n-- Q-23. Write an SQL query to fetch the no. of workers \n-- for each department in the descending order.\n\n\n\n-- Q-24. Write an SQL query to print details of the \n-- Workers who are also Managers.\n\n\n-- Q-25. Write an SQL query to fetch duplicate records \n-- having matching data in some fields of a table.\n\n\n\n-- Q-26. Write an SQL query to show only odd rows \n-- from a table.\n\n\n\n-- Q-27. Write an SQL query to show only even rows \n-- from a table.\n\n\n\n\n-- Q-28. Write an SQL query to clone a new table from \n-- another table.\n\n\n\n\n-- Q-29. Write an SQL query to fetch intersecting \n-- records of two tables.\n\n\n\n-- Q-30. Write an SQL query to show records from one \n-- table that another table does not have.\n\n\n\n-- Q-31. Write an SQL query to show the current date \n-- and time.\n\n\n\n\n-- Q-32. Write an SQL query to show the top n (say 10) \n-- records of a table.\n\n\n\n\n-- Q-33. Write an SQL query to determine the nth (say n=5) \n-- highest salary from a table.\n\n\n\n\n-- Q-34. Write an SQL query to determine the 5th highest \n-- salary without using TOP or limit method.\n\n\n\n\n\n-- Q-35. Write an SQL query to fetch the list of employees \n-- with the same salary.\n\n\n\n\n-- Q-36. Write an SQL query to show the second highest \n-- salary from a table.\n\n\n\n\n-- Q-37. Write an SQL query to show one row twice in \n-- results from a table.\n\n\n\n\n-- Q-38. Write an SQL query to fetch intersecting \n-- records of two tables.\n\n\n\n-- Q-39. Write an SQL query to fetch the first 50% \n-- records from a table.\n\n\n\n\n-- Q-40. Write an SQL query to fetch the departments \n-- that have less than five people in it.\n\n\n\n\n-- Q-41. Write an SQL query to show all departments \n-- along with the number of people in there.\n\n\n\n-- Q-42. Write an SQL query to show the last record \n-- from a table.\n\n\n\n-- Q-43. Write an SQL query to fetch the first row \n-- of a table.\n\n\n\n\n-- Q-44. Write an SQL query to fetch the last five \n-- records from a table.\n\n\n\n-- Q-45. Write an SQL query to print the name of employees \n-- having the highest salary in each department.\n\n\n\n\n-- Q-46. Write an SQL query to fetch three max salaries \n-- from a table.\n\n\n\n-- Q-47. Write an SQL query to fetch three min salaries \n-- from a table.\n\n\n\n\n-- Q-48. Write an SQL query to fetch nth max salaries \n-- from a table.\n\n\n\n-- Q-49. Write an SQL query to fetch departments along \n-- with the total salaries paid for each of them.\n\n\n\n-- Q-50. Write an SQL query to fetch the names of workers \n-- who earn the highest salary.\n" }, { "path": "March Cohort 2023/2_List and Strings.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"id\": \"f18f8832\",\n \"metadata\": {},\n \"source\": [\n \"# List\\n\",\n \"\\n\",\n \"collection of different types of elements\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"4a53537e\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"a = 10 # int\\n\",\n \"b = 20\\n\",\n \"c = 30\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"id\": \"8179aaea\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[10, 'A', 'India', 10.6]\\n\"\n ]\n }\n ],\n \"source\": [\n \"l = [10, \\\"A\\\", 'India', 10.6]\\n\",\n \"# 0 1 2 3\\n\",\n \"\\n\",\n \"print(l)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"id\": \"b073284b\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"10\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l[0])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"id\": \"0b18a752\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"10.6\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l[3])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"id\": \"562832f4\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"India\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l[2])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"id\": \"2ce1c1e2\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"ename\": \"IndexError\",\n \"evalue\": \"list index out of range\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[1;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[1;31mIndexError\\u001b[0m Traceback (most recent call last)\",\n \"Input \\u001b[1;32mIn [5]\\u001b[0m, in \\u001b[0;36m\\u001b[1;34m()\\u001b[0m\\n\\u001b[1;32m----> 1\\u001b[0m \\u001b[38;5;28mprint\\u001b[39m(\\u001b[43ml\\u001b[49m\\u001b[43m[\\u001b[49m\\u001b[38;5;241;43m5\\u001b[39;49m\\u001b[43m]\\u001b[49m)\\n\",\n \"\\u001b[1;31mIndexError\\u001b[0m: list index out of range\"\n ]\n }\n ],\n \"source\": [\n \"print(l[5])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"id\": \"b56a18f7\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[10, 'A', 'India', 10.6]\\n\"\n ]\n }\n ],\n \"source\": [\n \"l = [10, \\\"A\\\", 'India', 10.6]\\n\",\n \"# 0 1 2 3\\n\",\n \"\\n\",\n \"print(l)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"id\": \"56810d39\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"l[0] = 700\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"id\": \"cec85000\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[700, 'A', 'India', 10.6]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"id\": \"a839bab4\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"20\\n\"\n ]\n }\n ],\n \"source\": [\n \"a = 10\\n\",\n \"\\n\",\n \"a = 20\\n\",\n \"\\n\",\n \"print(a)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"id\": \"07f87123\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"ename\": \"IndexError\",\n \"evalue\": \"list assignment index out of range\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[1;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[1;31mIndexError\\u001b[0m Traceback (most recent call last)\",\n \"Input \\u001b[1;32mIn [10]\\u001b[0m, in \\u001b[0;36m\\u001b[1;34m()\\u001b[0m\\n\\u001b[1;32m----> 1\\u001b[0m l[\\u001b[38;5;241m5\\u001b[39m] \\u001b[38;5;241m=\\u001b[39m \\u001b[38;5;241m400\\u001b[39m\\n\",\n \"\\u001b[1;31mIndexError\\u001b[0m: list assignment index out of range\"\n ]\n }\n ],\n \"source\": [\n \"l[5] = 400\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"id\": \"d61e6a52\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[700, 10.6, 'India', 10.6]\\n\"\n ]\n }\n ],\n \"source\": [\n \"l[1] = l[3]\\n\",\n \"#l[1] = 10.6\\n\",\n \"\\n\",\n \"print(l)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"id\": \"cd0e6f72\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[10, 'A', 'India', 10.6]\\n\"\n ]\n }\n ],\n \"source\": [\n \"# -4 -3 -2 -1\\n\",\n \"l = [10, \\\"A\\\", 'India', 10.6]\\n\",\n \"# 0 1 2 3\\n\",\n \"\\n\",\n \"print(l)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"id\": \"28fe4466\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"10\\n\",\n \"10\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l[0])\\n\",\n \"\\n\",\n \"print(l[-4])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"id\": \"a4ddd5ef\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"10.6\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l[-1])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"id\": \"edb5d5de\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"India\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l[-2])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"e57f0121\",\n \"metadata\": {},\n \"source\": [\n \"# List slicing\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"id\": \"536c7f69\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[11, 22, 33, 10, 24, 44, 36, 68, 99, 81, 100]\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"l = [11,22,33,10,24,44,36,68,99,81,100]\\n\",\n \"# 0 1 2 3 4 5 6 7 8 9 10\\n\",\n \"\\n\",\n \"print(l)\\n\",\n \"print(type(l))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"id\": \"c4ed3b05\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[22, 33, 10, 24, 44, 36]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l[1:7]) # l[start : end]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"id\": \"44a00013\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[11, 22, 33, 10, 24, 44, 36, 68, 99, 81, 100]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"id\": \"a344e33f\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[24, 44, 36, 68, 99, 81]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l[4:10])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"id\": \"07f07f98\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[99, 81]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l[8:10])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"id\": \"47353b5f\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[11, 22, 33, 10, 24, 44, 36, 68, 99, 81, 100]\\n\"\n ]\n }\n ],\n \"source\": [\n \"l = [11,22,33,10,24,44,36,68,99,81,100]\\n\",\n \"# 0 1 2 3 4 5 6 7 8 9 10\\n\",\n \"\\n\",\n \"print(l)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"id\": \"3d8b979b\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[11]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l[0:1])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"id\": \"94eda01d\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[22, 33, 10, 24, 44, 36, 68, 99, 81, 100]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l[1:])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"id\": \"c5f21115\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[99, 81, 100]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l[8:])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"id\": \"0e399980\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[11, 22, 33, 10, 24, 44, 36, 68, 99, 81, 100]\\n\"\n ]\n }\n ],\n \"source\": [\n \"# -11-10 -9 -8 -7 -6 -5 -4 -3 -2 -1\\n\",\n \"l = [17,22,33,101,24,44,36,68,99,81,100]\\n\",\n \"# 0 1 2 3 4 5 6 7 8 9 10\\n\",\n \"\\n\",\n \"print(l[:])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"id\": \"9da33ce5\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[11, 22, 33, 10, 24]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l[:5])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"id\": \"858c11cb\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[17, 22, 33, 101, 24, 44, 36]\\n\"\n ]\n }\n ],\n \"source\": [\n \"# -11-10 -9 -8 -7 -6 -5 -4 -3 -2 -1\\n\",\n \"l = [17,22,33,101,24,44,36,68,99,81,100]\\n\",\n \"# 0 1 2 3 4 5 6 7 8 9 10\\n\",\n \"\\n\",\n \"print(l[0:-4])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 28,\n \"id\": \"9b6f435f\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[33, 101, 24, 44, 36, 68, 99]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l[2:-2])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 29,\n \"id\": \"556aceba\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l[2:0])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 30,\n \"id\": \"fcab2e6a\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[]\\n\"\n ]\n }\n ],\n \"source\": [\n \"# -11-10 -9 -8 -7 -6 -5 -4 -3 -2 -1\\n\",\n \"l = [17,22,33,101,24,44,36,68,99,81,100]\\n\",\n \"# 0 1 2 3 4 5 6 7 8 9 10\\n\",\n \"\\n\",\n \"print(l[0:0])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 31,\n \"id\": \"a34bdf8d\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[44, 36, 68, 99, 81, 100]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l[5:15])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 32,\n \"id\": \"69241fb4\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"ename\": \"IndexError\",\n \"evalue\": \"list index out of range\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[1;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[1;31mIndexError\\u001b[0m Traceback (most recent call last)\",\n \"Input \\u001b[1;32mIn [32]\\u001b[0m, in \\u001b[0;36m\\u001b[1;34m()\\u001b[0m\\n\\u001b[1;32m----> 1\\u001b[0m \\u001b[38;5;28mprint\\u001b[39m(\\u001b[43ml\\u001b[49m\\u001b[43m[\\u001b[49m\\u001b[38;5;241;43m15\\u001b[39;49m\\u001b[43m]\\u001b[49m)\\n\",\n \"\\u001b[1;31mIndexError\\u001b[0m: list index out of range\"\n ]\n }\n ],\n \"source\": [\n \"print(l[15])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 33,\n \"id\": \"b4f6a948\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[22, 33, 101]\\n\"\n ]\n }\n ],\n \"source\": [\n \"# -11-10 -9 -8 -7 -6 -5 -4 -3 -2 -1\\n\",\n \"l = [17,22,33,101,24,44,36,68,99,81,100]\\n\",\n \"# 0 1 2 3 4 5 6 7 8 9 10\\n\",\n \"\\n\",\n \"print(l[1:4])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 34,\n \"id\": \"2c46b2a1\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l[4:1])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 35,\n \"id\": \"bb318e72\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[68, 99, 81]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l[-4:-1])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 36,\n \"id\": \"05c49a41\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l[-1:-4])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 38,\n \"id\": \"bd75ab98\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[17, 33, 24, 36, 99, 100]\\n\"\n ]\n }\n ],\n \"source\": [\n \"# -11-10 -9 -8 -7 -6 -5 -4 -3 -2 -1\\n\",\n \"l = [17,22,33,101,24,44,36,68,99,81,100]\\n\",\n \"# 0 1 2 3 4 5 6 7 8 9 10\\n\",\n \"\\n\",\n \"print(l[0:11:2]) # l[start : end : steps]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 39,\n \"id\": \"ed0ebd7d\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[17, 101, 36, 81]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l[0:11:3])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 40,\n \"id\": \"591165f5\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[17, 22, 33, 101, 24, 44, 36, 68, 99, 81, 100]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l[::])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 42,\n \"id\": \"9325c782\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[17, 22, 33, 101, 24, 44, 36, 68, 99, 81, 100]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l[::1])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 41,\n \"id\": \"7c4d1a41\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[100, 81, 99, 68, 36, 44, 24, 101, 33, 22, 17]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l[::-1]) # reverse\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 43,\n \"id\": \"769b5e71\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[]\\n\"\n ]\n }\n ],\n \"source\": [\n \"# -11-10 -9 -8 -7 -6 -5 -4 -3 -2 -1\\n\",\n \"l = [17,22,33,101,24,44,36,68,99,81,100]\\n\",\n \"# 0 1 2 3 4 5 6 7 8 9 10\\n\",\n \"\\n\",\n \"print(l[4:8:-1])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 44,\n \"id\": \"61f07964\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[99, 68, 36, 44]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l[8:4:-1])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 45,\n \"id\": \"9ccf1239\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[100, 99, 36, 24, 33, 17]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l[::-2])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 46,\n \"id\": \"0dee2ab9\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"ename\": \"ValueError\",\n \"evalue\": \"slice step cannot be zero\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[1;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[1;31mValueError\\u001b[0m Traceback (most recent call last)\",\n \"Input \\u001b[1;32mIn [46]\\u001b[0m, in \\u001b[0;36m\\u001b[1;34m()\\u001b[0m\\n\\u001b[1;32m----> 1\\u001b[0m \\u001b[38;5;28mprint\\u001b[39m(\\u001b[43ml\\u001b[49m\\u001b[43m[\\u001b[49m\\u001b[43m:\\u001b[49m\\u001b[43m:\\u001b[49m\\u001b[38;5;241;43m0\\u001b[39;49m\\u001b[43m]\\u001b[49m)\\n\",\n \"\\u001b[1;31mValueError\\u001b[0m: slice step cannot be zero\"\n ]\n }\n ],\n \"source\": [\n \"print(l[::0])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"4f3a41ae\",\n \"metadata\": {},\n \"source\": [\n \"# List functions\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 47,\n \"id\": \"5d15c64c\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"5\\n\"\n ]\n }\n ],\n \"source\": [\n \"l = [10,20,30,40,50]\\n\",\n \"\\n\",\n \"print(len(l))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 48,\n \"id\": \"d0c326c5\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"4\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(len(l) - 1)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 49,\n \"id\": \"c33408b9\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"150\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(sum(l))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 50,\n \"id\": \"29a67088\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"50\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(max(l))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 51,\n \"id\": \"7ce163a3\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"10\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(min(l))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 52,\n \"id\": \"2a9052cb\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"4\\n\"\n ]\n },\n {\n \"ename\": \"TypeError\",\n \"evalue\": \"unsupported operand type(s) for +: 'int' and 'str'\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[1;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[1;31mTypeError\\u001b[0m Traceback (most recent call last)\",\n \"Input \\u001b[1;32mIn [52]\\u001b[0m, in \\u001b[0;36m\\u001b[1;34m()\\u001b[0m\\n\\u001b[0;32m 1\\u001b[0m l \\u001b[38;5;241m=\\u001b[39m [\\u001b[38;5;124m\\\"\\u001b[39m\\u001b[38;5;124mA\\u001b[39m\\u001b[38;5;124m\\\"\\u001b[39m, \\u001b[38;5;241m10\\u001b[39m, \\u001b[38;5;241m20\\u001b[39m, \\u001b[38;5;241m30\\u001b[39m]\\n\\u001b[0;32m 3\\u001b[0m \\u001b[38;5;28mprint\\u001b[39m(\\u001b[38;5;28mlen\\u001b[39m(l))\\n\\u001b[1;32m----> 4\\u001b[0m \\u001b[38;5;28mprint\\u001b[39m(\\u001b[38;5;28;43msum\\u001b[39;49m\\u001b[43m(\\u001b[49m\\u001b[43ml\\u001b[49m\\u001b[43m)\\u001b[49m)\\n\",\n \"\\u001b[1;31mTypeError\\u001b[0m: unsupported operand type(s) for +: 'int' and 'str'\"\n ]\n }\n ],\n \"source\": [\n \"l = [\\\"A\\\", 10, 20, 30]\\n\",\n \"\\n\",\n \"print(len(l))\\n\",\n \"print(sum(l))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 55,\n \"id\": \"e62fa754\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'ab'\"\n ]\n },\n \"execution_count\": 55,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"\\\"a\\\" + \\\"b\\\"\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 53,\n \"id\": \"5d8a7988\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"3\\n\"\n ]\n }\n ],\n \"source\": [\n \"l = [\\\"a\\\", \\\"b\\\", \\\"c\\\"]\\n\",\n \"\\n\",\n \"print(len(l))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 54,\n \"id\": \"99cb555e\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"ename\": \"TypeError\",\n \"evalue\": \"unsupported operand type(s) for +: 'int' and 'str'\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[1;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[1;31mTypeError\\u001b[0m Traceback (most recent call last)\",\n \"Input \\u001b[1;32mIn [54]\\u001b[0m, in \\u001b[0;36m\\u001b[1;34m()\\u001b[0m\\n\\u001b[1;32m----> 1\\u001b[0m \\u001b[38;5;28mprint\\u001b[39m(\\u001b[38;5;28;43msum\\u001b[39;49m\\u001b[43m(\\u001b[49m\\u001b[43ml\\u001b[49m\\u001b[43m)\\u001b[49m)\\n\",\n \"\\u001b[1;31mTypeError\\u001b[0m: unsupported operand type(s) for +: 'int' and 'str'\"\n ]\n }\n ],\n \"source\": [\n \"print(sum(l))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 56,\n \"id\": \"1eff053f\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"c\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(max(l))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 57,\n \"id\": \"c3f7d556\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"a\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(min(l))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"8cb11d63\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# ASCII\\n\",\n \"\\n\",\n \"A - 65/2 - binary\\n\",\n \"B - 66\\n\",\n \"Z - 90\\n\",\n \"\\n\",\n \"space - 32\\n\",\n \"\\n\",\n \"a - 97\\n\",\n \"z - 122\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 58,\n \"id\": \"eea43d51\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"False\"\n ]\n },\n \"execution_count\": 58,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"\\\"A\\\" > \\\"a\\\"\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 59,\n \"id\": \"6ffe5a48\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"True\"\n ]\n },\n \"execution_count\": 59,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"\\\"A\\\" < 'a'\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"55fc39d8\",\n \"metadata\": {},\n \"source\": [\n \"# List methods\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 65,\n \"id\": \"d0f38ad0\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# help(print)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 64,\n \"id\": \"d56335b3\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# help(sum)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 63,\n \"id\": \"4832e051\",\n \"metadata\": {\n \"scrolled\": false\n },\n \"outputs\": [],\n \"source\": [\n \"# help(list)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 66,\n \"id\": \"315f91a5\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"sum?\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 68,\n \"id\": \"1e64def7\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[10, 20, 30, 40]\\n\"\n ]\n }\n ],\n \"source\": [\n \"l = [10,20,30]\\n\",\n \"\\n\",\n \"l.append(40)\\n\",\n \"\\n\",\n \"print(l)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 69,\n \"id\": \"9b78984e\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"l.append(\\\"India\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 70,\n \"id\": \"86227543\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[10, 20, 30, 40, 'India']\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 71,\n \"id\": \"5a90e417\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"4\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l.index('India'))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 72,\n \"id\": \"43bf35e2\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[10, 20, 30, 40, 'India']\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 73,\n \"id\": \"df0bd6ca\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"l.reverse()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 74,\n \"id\": \"826857cc\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"['India', 40, 30, 20, 10]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 75,\n \"id\": \"42f82dab\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"India\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l[0])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 76,\n \"id\": \"5fd18a26\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"l.reverse() # l[::-1]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 77,\n \"id\": \"acf2c947\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[10, 20, 30, 40, 'India']\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(l)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"50f6bb18\",\n \"metadata\": {},\n \"source\": [\n \"# Strings\\n\",\n \"\\n\",\n \"set of characters\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 78,\n \"id\": \"2282691e\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"hello\\n\"\n ]\n }\n ],\n \"source\": [\n \"# -1\\n\",\n \"s = 'hello'\\n\",\n \"# 01234\\n\",\n \"\\n\",\n \"print(s)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 79,\n \"id\": \"30758f8a\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"h\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(s[0])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 80,\n \"id\": \"401c2af6\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"o\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(s[4])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 81,\n \"id\": \"3543dcff\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"l\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(s[3])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 82,\n \"id\": \"70efef69\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"o\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(s[-1])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 83,\n \"id\": \"8b776e03\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"l\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(s[-2])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 84,\n \"id\": \"7a00d157\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(type(s))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 85,\n \"id\": \"bafe5d33\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(type(s[0]))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"69fd97c5\",\n \"metadata\": {},\n \"source\": [\n \"# String slicing\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 86,\n \"id\": \"cd9dff16\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"commu\\n\"\n ]\n }\n ],\n \"source\": [\n \"s = 'communication'\\n\",\n \"# 0123456789\\n\",\n \"print(s[0:5])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 87,\n \"id\": \"0d50213f\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"noitacinummoc\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(s[::-1])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 88,\n \"id\": \"0d4f5af4\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"omm\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(s[1:4])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 89,\n \"id\": \"e4712f62\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(s[4:1])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 90,\n \"id\": \"2d5ffb11\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"a = ''\\n\",\n \"\\n\",\n \"print(type(a))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 91,\n \"id\": \"b1755966\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"communication\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(s)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 92,\n \"id\": \"e82d1c2b\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"noitacin\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(s[-1:-9:-1])\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"918d1a64\",\n \"metadata\": {},\n \"source\": [\n \"# String functions\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 93,\n \"id\": \"87ad584b\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"13\\n\"\n ]\n }\n ],\n \"source\": [\n \"s = 'communication'\\n\",\n \"\\n\",\n \"print(len(s))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 94,\n \"id\": \"34ab8cff\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"ename\": \"TypeError\",\n \"evalue\": \"unsupported operand type(s) for +: 'int' and 'str'\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[1;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[1;31mTypeError\\u001b[0m Traceback (most recent call last)\",\n \"Input \\u001b[1;32mIn [94]\\u001b[0m, in \\u001b[0;36m\\u001b[1;34m()\\u001b[0m\\n\\u001b[1;32m----> 1\\u001b[0m \\u001b[38;5;28mprint\\u001b[39m(\\u001b[38;5;28;43msum\\u001b[39;49m\\u001b[43m(\\u001b[49m\\u001b[43ms\\u001b[49m\\u001b[43m)\\u001b[49m)\\n\",\n \"\\u001b[1;31mTypeError\\u001b[0m: unsupported operand type(s) for +: 'int' and 'str'\"\n ]\n }\n ],\n \"source\": [\n \"print(sum(s))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 95,\n \"id\": \"7378c889\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"u\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(max(s))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 96,\n \"id\": \"de330ab1\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"a\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(min(s))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"7bcef9c5\",\n \"metadata\": {},\n \"source\": [\n \"# string methods\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 97,\n \"id\": \"b0b078e8\",\n \"metadata\": {\n \"scrolled\": false\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Help on class str in module builtins:\\n\",\n \"\\n\",\n \"class str(object)\\n\",\n \" | str(object='') -> str\\n\",\n \" | str(bytes_or_buffer[, encoding[, errors]]) -> str\\n\",\n \" | \\n\",\n \" | Create a new string object from the given object. If encoding or\\n\",\n \" | errors is specified, then the object must expose a data buffer\\n\",\n \" | that will be decoded using the given encoding and error handler.\\n\",\n \" | Otherwise, returns the result of object.__str__() (if defined)\\n\",\n \" | or repr(object).\\n\",\n \" | encoding defaults to sys.getdefaultencoding().\\n\",\n \" | errors defaults to 'strict'.\\n\",\n \" | \\n\",\n \" | Methods defined here:\\n\",\n \" | \\n\",\n \" | __add__(self, value, /)\\n\",\n \" | Return self+value.\\n\",\n \" | \\n\",\n \" | __contains__(self, key, /)\\n\",\n \" | Return key in self.\\n\",\n \" | \\n\",\n \" | __eq__(self, value, /)\\n\",\n \" | Return self==value.\\n\",\n \" | \\n\",\n \" | __format__(self, format_spec, /)\\n\",\n \" | Return a formatted version of the string as described by format_spec.\\n\",\n \" | \\n\",\n \" | __ge__(self, value, /)\\n\",\n \" | Return self>=value.\\n\",\n \" | \\n\",\n \" | __getattribute__(self, name, /)\\n\",\n \" | Return getattr(self, name).\\n\",\n \" | \\n\",\n \" | __getitem__(self, key, /)\\n\",\n \" | Return self[key].\\n\",\n \" | \\n\",\n \" | __getnewargs__(...)\\n\",\n \" | \\n\",\n \" | __gt__(self, value, /)\\n\",\n \" | Return self>value.\\n\",\n \" | \\n\",\n \" | __hash__(self, /)\\n\",\n \" | Return hash(self).\\n\",\n \" | \\n\",\n \" | __iter__(self, /)\\n\",\n \" | Implement iter(self).\\n\",\n \" | \\n\",\n \" | __le__(self, value, /)\\n\",\n \" | Return self<=value.\\n\",\n \" | \\n\",\n \" | __len__(self, /)\\n\",\n \" | Return len(self).\\n\",\n \" | \\n\",\n \" | __lt__(self, value, /)\\n\",\n \" | Return self int\\n\",\n \" | \\n\",\n \" | Return the number of non-overlapping occurrences of substring sub in\\n\",\n \" | string S[start:end]. Optional arguments start and end are\\n\",\n \" | interpreted as in slice notation.\\n\",\n \" | \\n\",\n \" | encode(self, /, encoding='utf-8', errors='strict')\\n\",\n \" | Encode the string using the codec registered for encoding.\\n\",\n \" | \\n\",\n \" | encoding\\n\",\n \" | The encoding in which to encode the string.\\n\",\n \" | errors\\n\",\n \" | The error handling scheme to use for encoding errors.\\n\",\n \" | The default is 'strict' meaning that encoding errors raise a\\n\",\n \" | UnicodeEncodeError. Other possible values are 'ignore', 'replace' and\\n\",\n \" | 'xmlcharrefreplace' as well as any other name registered with\\n\",\n \" | codecs.register_error that can handle UnicodeEncodeErrors.\\n\",\n \" | \\n\",\n \" | endswith(...)\\n\",\n \" | S.endswith(suffix[, start[, end]]) -> bool\\n\",\n \" | \\n\",\n \" | Return True if S ends with the specified suffix, False otherwise.\\n\",\n \" | With optional start, test S beginning at that position.\\n\",\n \" | With optional end, stop comparing S at that position.\\n\",\n \" | suffix can also be a tuple of strings to try.\\n\",\n \" | \\n\",\n \" | expandtabs(self, /, tabsize=8)\\n\",\n \" | Return a copy where all tab characters are expanded using spaces.\\n\",\n \" | \\n\",\n \" | If tabsize is not given, a tab size of 8 characters is assumed.\\n\",\n \" | \\n\",\n \" | find(...)\\n\",\n \" | S.find(sub[, start[, end]]) -> int\\n\",\n \" | \\n\",\n \" | Return the lowest index in S where substring sub is found,\\n\",\n \" | such that sub is contained within S[start:end]. Optional\\n\",\n \" | arguments start and end are interpreted as in slice notation.\\n\",\n \" | \\n\",\n \" | Return -1 on failure.\\n\",\n \" | \\n\",\n \" | format(...)\\n\",\n \" | S.format(*args, **kwargs) -> str\\n\",\n \" | \\n\",\n \" | Return a formatted version of S, using substitutions from args and kwargs.\\n\",\n \" | The substitutions are identified by braces ('{' and '}').\\n\",\n \" | \\n\",\n \" | format_map(...)\\n\",\n \" | S.format_map(mapping) -> str\\n\",\n \" | \\n\",\n \" | Return a formatted version of S, using substitutions from mapping.\\n\",\n \" | The substitutions are identified by braces ('{' and '}').\\n\",\n \" | \\n\",\n \" | index(...)\\n\",\n \" | S.index(sub[, start[, end]]) -> int\\n\",\n \" | \\n\",\n \" | Return the lowest index in S where substring sub is found,\\n\",\n \" | such that sub is contained within S[start:end]. Optional\\n\",\n \" | arguments start and end are interpreted as in slice notation.\\n\",\n \" | \\n\",\n \" | Raises ValueError when the substring is not found.\\n\",\n \" | \\n\",\n \" | isalnum(self, /)\\n\",\n \" | Return True if the string is an alpha-numeric string, False otherwise.\\n\",\n \" | \\n\",\n \" | A string is alpha-numeric if all characters in the string are alpha-numeric and\\n\",\n \" | there is at least one character in the string.\\n\",\n \" | \\n\",\n \" | isalpha(self, /)\\n\",\n \" | Return True if the string is an alphabetic string, False otherwise.\\n\",\n \" | \\n\",\n \" | A string is alphabetic if all characters in the string are alphabetic and there\\n\",\n \" | is at least one character in the string.\\n\",\n \" | \\n\",\n \" | isascii(self, /)\\n\",\n \" | Return True if all characters in the string are ASCII, False otherwise.\\n\",\n \" | \\n\",\n \" | ASCII characters have code points in the range U+0000-U+007F.\\n\",\n \" | Empty string is ASCII too.\\n\",\n \" | \\n\",\n \" | isdecimal(self, /)\\n\",\n \" | Return True if the string is a decimal string, False otherwise.\\n\",\n \" | \\n\",\n \" | A string is a decimal string if all characters in the string are decimal and\\n\",\n \" | there is at least one character in the string.\\n\",\n \" | \\n\",\n \" | isdigit(self, /)\\n\",\n \" | Return True if the string is a digit string, False otherwise.\\n\",\n \" | \\n\",\n \" | A string is a digit string if all characters in the string are digits and there\\n\",\n \" | is at least one character in the string.\\n\",\n \" | \\n\",\n \" | isidentifier(self, /)\\n\",\n \" | Return True if the string is a valid Python identifier, False otherwise.\\n\",\n \" | \\n\",\n \" | Call keyword.iskeyword(s) to test whether string s is a reserved identifier,\\n\",\n \" | such as \\\"def\\\" or \\\"class\\\".\\n\",\n \" | \\n\",\n \" | islower(self, /)\\n\",\n \" | Return True if the string is a lowercase string, False otherwise.\\n\",\n \" | \\n\",\n \" | A string is lowercase if all cased characters in the string are lowercase and\\n\",\n \" | there is at least one cased character in the string.\\n\",\n \" | \\n\",\n \" | isnumeric(self, /)\\n\",\n \" | Return True if the string is a numeric string, False otherwise.\\n\",\n \" | \\n\",\n \" | A string is numeric if all characters in the string are numeric and there is at\\n\",\n \" | least one character in the string.\\n\",\n \" | \\n\",\n \" | isprintable(self, /)\\n\",\n \" | Return True if the string is printable, False otherwise.\\n\",\n \" | \\n\",\n \" | A string is printable if all of its characters are considered printable in\\n\",\n \" | repr() or if it is empty.\\n\",\n \" | \\n\",\n \" | isspace(self, /)\\n\",\n \" | Return True if the string is a whitespace string, False otherwise.\\n\",\n \" | \\n\",\n \" | A string is whitespace if all characters in the string are whitespace and there\\n\",\n \" | is at least one character in the string.\\n\",\n \" | \\n\",\n \" | istitle(self, /)\\n\",\n \" | Return True if the string is a title-cased string, False otherwise.\\n\",\n \" | \\n\",\n \" | In a title-cased string, upper- and title-case characters may only\\n\",\n \" | follow uncased characters and lowercase characters only cased ones.\\n\",\n \" | \\n\",\n \" | isupper(self, /)\\n\",\n \" | Return True if the string is an uppercase string, False otherwise.\\n\",\n \" | \\n\",\n \" | A string is uppercase if all cased characters in the string are uppercase and\\n\",\n \" | there is at least one cased character in the string.\\n\",\n \" | \\n\",\n \" | join(self, iterable, /)\\n\",\n \" | Concatenate any number of strings.\\n\",\n \" | \\n\",\n \" | The string whose method is called is inserted in between each given string.\\n\",\n \" | The result is returned as a new string.\\n\",\n \" | \\n\",\n \" | Example: '.'.join(['ab', 'pq', 'rs']) -> 'ab.pq.rs'\\n\",\n \" | \\n\",\n \" | ljust(self, width, fillchar=' ', /)\\n\",\n \" | Return a left-justified string of length width.\\n\",\n \" | \\n\",\n \" | Padding is done using the specified fill character (default is a space).\\n\",\n \" | \\n\",\n \" | lower(self, /)\\n\",\n \" | Return a copy of the string converted to lowercase.\\n\",\n \" | \\n\",\n \" | lstrip(self, chars=None, /)\\n\",\n \" | Return a copy of the string with leading whitespace removed.\\n\",\n \" | \\n\",\n \" | If chars is given and not None, remove characters in chars instead.\\n\",\n \" | \\n\",\n \" | partition(self, sep, /)\\n\",\n \" | Partition the string into three parts using the given separator.\\n\",\n \" | \\n\",\n \" | This will search for the separator in the string. If the separator is found,\\n\",\n \" | returns a 3-tuple containing the part before the separator, the separator\\n\",\n \" | itself, and the part after it.\\n\",\n \" | \\n\",\n \" | If the separator is not found, returns a 3-tuple containing the original string\\n\",\n \" | and two empty strings.\\n\",\n \" | \\n\",\n \" | removeprefix(self, prefix, /)\\n\",\n \" | Return a str with the given prefix string removed if present.\\n\",\n \" | \\n\",\n \" | If the string starts with the prefix string, return string[len(prefix):].\\n\",\n \" | Otherwise, return a copy of the original string.\\n\",\n \" | \\n\",\n \" | removesuffix(self, suffix, /)\\n\",\n \" | Return a str with the given suffix string removed if present.\\n\",\n \" | \\n\",\n \" | If the string ends with the suffix string and that suffix is not empty,\\n\",\n \" | return string[:-len(suffix)]. Otherwise, return a copy of the original\\n\",\n \" | string.\\n\",\n \" | \\n\",\n \" | replace(self, old, new, count=-1, /)\\n\",\n \" | Return a copy with all occurrences of substring old replaced by new.\\n\",\n \" | \\n\",\n \" | count\\n\",\n \" | Maximum number of occurrences to replace.\\n\",\n \" | -1 (the default value) means replace all occurrences.\\n\",\n \" | \\n\",\n \" | If the optional argument count is given, only the first count occurrences are\\n\",\n \" | replaced.\\n\",\n \" | \\n\",\n \" | rfind(...)\\n\",\n \" | S.rfind(sub[, start[, end]]) -> int\\n\",\n \" | \\n\",\n \" | Return the highest index in S where substring sub is found,\\n\",\n \" | such that sub is contained within S[start:end]. Optional\\n\",\n \" | arguments start and end are interpreted as in slice notation.\\n\",\n \" | \\n\",\n \" | Return -1 on failure.\\n\",\n \" | \\n\",\n \" | rindex(...)\\n\",\n \" | S.rindex(sub[, start[, end]]) -> int\\n\",\n \" | \\n\",\n \" | Return the highest index in S where substring sub is found,\\n\",\n \" | such that sub is contained within S[start:end]. Optional\\n\",\n \" | arguments start and end are interpreted as in slice notation.\\n\",\n \" | \\n\",\n \" | Raises ValueError when the substring is not found.\\n\",\n \" | \\n\",\n \" | rjust(self, width, fillchar=' ', /)\\n\",\n \" | Return a right-justified string of length width.\\n\",\n \" | \\n\",\n \" | Padding is done using the specified fill character (default is a space).\\n\",\n \" | \\n\",\n \" | rpartition(self, sep, /)\\n\",\n \" | Partition the string into three parts using the given separator.\\n\",\n \" | \\n\",\n \" | This will search for the separator in the string, starting at the end. If\\n\",\n \" | the separator is found, returns a 3-tuple containing the part before the\\n\",\n \" | separator, the separator itself, and the part after it.\\n\",\n \" | \\n\",\n \" | If the separator is not found, returns a 3-tuple containing two empty strings\\n\",\n \" | and the original string.\\n\",\n \" | \\n\",\n \" | rsplit(self, /, sep=None, maxsplit=-1)\\n\",\n \" | Return a list of the words in the string, using sep as the delimiter string.\\n\",\n \" | \\n\",\n \" | sep\\n\",\n \" | The delimiter according which to split the string.\\n\",\n \" | None (the default value) means split according to any whitespace,\\n\",\n \" | and discard empty strings from the result.\\n\",\n \" | maxsplit\\n\",\n \" | Maximum number of splits to do.\\n\",\n \" | -1 (the default value) means no limit.\\n\",\n \" | \\n\",\n \" | Splits are done starting at the end of the string and working to the front.\\n\",\n \" | \\n\",\n \" | rstrip(self, chars=None, /)\\n\",\n \" | Return a copy of the string with trailing whitespace removed.\\n\",\n \" | \\n\",\n \" | If chars is given and not None, remove characters in chars instead.\\n\",\n \" | \\n\",\n \" | split(self, /, sep=None, maxsplit=-1)\\n\",\n \" | Return a list of the words in the string, using sep as the delimiter string.\\n\",\n \" | \\n\",\n \" | sep\\n\",\n \" | The delimiter according which to split the string.\\n\",\n \" | None (the default value) means split according to any whitespace,\\n\",\n \" | and discard empty strings from the result.\\n\",\n \" | maxsplit\\n\",\n \" | Maximum number of splits to do.\\n\",\n \" | -1 (the default value) means no limit.\\n\",\n \" | \\n\",\n \" | splitlines(self, /, keepends=False)\\n\",\n \" | Return a list of the lines in the string, breaking at line boundaries.\\n\",\n \" | \\n\",\n \" | Line breaks are not included in the resulting list unless keepends is given and\\n\",\n \" | true.\\n\",\n \" | \\n\",\n \" | startswith(...)\\n\",\n \" | S.startswith(prefix[, start[, end]]) -> bool\\n\",\n \" | \\n\",\n \" | Return True if S starts with the specified prefix, False otherwise.\\n\",\n \" | With optional start, test S beginning at that position.\\n\",\n \" | With optional end, stop comparing S at that position.\\n\",\n \" | prefix can also be a tuple of strings to try.\\n\",\n \" | \\n\",\n \" | strip(self, chars=None, /)\\n\",\n \" | Return a copy of the string with leading and trailing whitespace removed.\\n\",\n \" | \\n\",\n \" | If chars is given and not None, remove characters in chars instead.\\n\",\n \" | \\n\",\n \" | swapcase(self, /)\\n\",\n \" | Convert uppercase characters to lowercase and lowercase characters to uppercase.\\n\",\n \" | \\n\",\n \" | title(self, /)\\n\",\n \" | Return a version of the string where each word is titlecased.\\n\",\n \" | \\n\",\n \" | More specifically, words start with uppercased characters and all remaining\\n\",\n \" | cased characters have lower case.\\n\",\n \" | \\n\",\n \" | translate(self, table, /)\\n\",\n \" | Replace each character in the string using the given translation table.\\n\",\n \" | \\n\",\n \" | table\\n\",\n \" | Translation table, which must be a mapping of Unicode ordinals to\\n\",\n \" | Unicode ordinals, strings, or None.\\n\",\n \" | \\n\",\n \" | The table must implement lookup/indexing via __getitem__, for instance a\\n\",\n \" | dictionary or list. If this operation raises LookupError, the character is\\n\",\n \" | left untouched. Characters mapped to None are deleted.\\n\",\n \" | \\n\",\n \" | upper(self, /)\\n\",\n \" | Return a copy of the string converted to uppercase.\\n\",\n \" | \\n\",\n \" | zfill(self, width, /)\\n\",\n \" | Pad a numeric string with zeros on the left, to fill a field of the given width.\\n\",\n \" | \\n\",\n \" | The string is never truncated.\\n\",\n \" | \\n\",\n \" | ----------------------------------------------------------------------\\n\",\n \" | Static methods defined here:\\n\",\n \" | \\n\",\n \" | __new__(*args, **kwargs) from builtins.type\\n\",\n \" | Create and return a new object. See help(type) for accurate signature.\\n\",\n \" | \\n\",\n \" | maketrans(...)\\n\",\n \" | Return a translation table usable for str.translate().\\n\",\n \" | \\n\",\n \" | If there is only one argument, it must be a dictionary mapping Unicode\\n\",\n \" | ordinals (integers) or characters to Unicode ordinals, strings or None.\\n\",\n \" | Character keys will be then converted to ordinals.\\n\",\n \" | If there are two arguments, they must be strings of equal length, and\\n\",\n \" | in the resulting dictionary, each character in x will be mapped to the\\n\",\n \" | character at the same position in y. If there is a third argument, it\\n\",\n \" | must be a string, whose characters will be mapped to None in the result.\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"help(str)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 98,\n \"id\": \"a795fbdb\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"False\"\n ]\n },\n \"execution_count\": 98,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"'raj123' == 'RAJ123'\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 100,\n \"id\": \"78f9fd1d\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"raj123\\n\"\n ]\n }\n ],\n \"source\": [\n \"s = 'RAJ123'\\n\",\n \"\\n\",\n \"print(s.casefold())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 101,\n \"id\": \"d7d25e0d\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"INDIA\\n\"\n ]\n }\n ],\n \"source\": [\n \"s = 'India'\\n\",\n \"\\n\",\n \"print(s.upper())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 102,\n \"id\": \"4965c3b0\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"india\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(s.lower())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 103,\n \"id\": \"feb1f94f\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"['This', 'is', 'an', 'apple']\\n\"\n ]\n }\n ],\n \"source\": [\n \"# split\\n\",\n \"\\n\",\n \"s = \\\"This is an apple\\\"\\n\",\n \"\\n\",\n \"print(s.split(\\\" \\\"))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 105,\n \"id\": \"d1231581\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"['H', 'llo Us', 'r']\\n\"\n ]\n }\n ],\n \"source\": [\n \"s = \\\"Hello User\\\"\\n\",\n \"\\n\",\n \"print(s.split(\\\"e\\\"))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 106,\n \"id\": \"2deaf22c\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"This+is+an+apple\\n\"\n ]\n }\n ],\n \"source\": [\n \"l = ['This', 'is', 'an', 'apple']\\n\",\n \"\\n\",\n \"a = \\\"+\\\"\\n\",\n \"\\n\",\n \"print(a.join(l))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 107,\n \"id\": \"d7bf333b\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"This is an apple\\n\"\n ]\n }\n ],\n \"source\": [\n \"l = ['This', 'is', 'an', 'apple']\\n\",\n \"\\n\",\n \"a = \\\" \\\"\\n\",\n \"\\n\",\n \"print(a.join(l))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 108,\n \"id\": \"99c38b4d\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Thisisanapple\\n\"\n ]\n }\n ],\n \"source\": [\n \"s = \\\"This is an apple\\\"\\n\",\n \"\\n\",\n \"l = s.split(\\\" \\\")\\n\",\n \"\\n\",\n \"x = \\\"\\\".join(l)\\n\",\n \"\\n\",\n \"print(x)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"6c209c90\",\n \"metadata\": {},\n \"source\": [\n \"**WAP to enter a string from user and print the biggest word from the paragraph**\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"92b19368\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"text = input(\\\"Enter your para: \\\")\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 109,\n \"id\": \"78217b1a\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"False\"\n ]\n },\n \"execution_count\": 109,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"\\\"abc\\\" > \\\"def\\\"\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"16d89cc5\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.12\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n" }, { "path": "March Cohort 2023/3_For Loop Dictionary Tuple and Sets.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"id\": \"958dd5a9\",\n \"metadata\": {},\n \"source\": [\n \"# Range function\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"id\": \"ecc0f5d9\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"range(0, 5)\\n\"\n ]\n }\n ],\n \"source\": [\n \"n = range(5)\\n\",\n \"\\n\",\n \"print(n)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"id\": \"a414c992\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(type(n))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"id\": \"c50d39c0\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[0, 1, 2, 3, 4]\\n\"\n ]\n }\n ],\n \"source\": [\n \"n = list(range(5))\\n\",\n \"\\n\",\n \"print(n)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"id\": \"8ff4b4f0\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[3, 4, 5, 6, 7]\\n\"\n ]\n }\n ],\n \"source\": [\n \"n = list(range(3,8))\\n\",\n \"\\n\",\n \"print(n)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"id\": \"c82ff6db\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"True\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(range(20) == range(0,20))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"id\": \"14dbda38\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[5, 7, 9, 11, 13, 15, 17, 19]\\n\"\n ]\n }\n ],\n \"source\": [\n \"n = list(range(5,20,2))\\n\",\n \"\\n\",\n \"print(n)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"f4b1e93a\",\n \"metadata\": {},\n \"source\": [\n \"# for loop\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"id\": \"a1e6f836\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"False\\n\"\n ]\n }\n ],\n \"source\": [\n \"l = [\\\"A\\\", 'B', 'C', 'D']\\n\",\n \"\\n\",\n \"print(\\\"Z\\\" in l)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"id\": \"fe7eb160\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"A\\n\",\n \"B\\n\",\n \"C\\n\",\n \"D\\n\"\n ]\n }\n ],\n \"source\": [\n \"l = [\\\"A\\\", 'B', 'C', 'D']\\n\",\n \"# i\\n\",\n \"\\n\",\n \"# \\\"D\\\"in l\\n\",\n \"for i in l:\\n\",\n \" print(i)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"id\": \"c237efad\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"A\\n\",\n \"Hello\\n\",\n \"B\\n\",\n \"Hello\\n\",\n \"C\\n\",\n \"Hello\\n\",\n \"D\\n\",\n \"Hello\\n\"\n ]\n }\n ],\n \"source\": [\n \"l = [\\\"A\\\", 'B', 'C', 'D']\\n\",\n \"\\n\",\n \"for i in l:\\n\",\n \" print(i)\\n\",\n \" print(\\\"Hello\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"id\": \"6efc03f4\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"a\\n\",\n \"b\\n\",\n \"c\\n\",\n \"d\\n\"\n ]\n }\n ],\n \"source\": [\n \"s = \\\"abcd\\\"\\n\",\n \"# i\\n\",\n \"\\n\",\n \"for i in s:\\n\",\n \" print(i)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"id\": \"0d1d9074\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'AB'\"\n ]\n },\n \"execution_count\": 12,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"'ab'.upper()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"id\": \"754cc5de\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"['ab', 'cd']\\n\"\n ]\n }\n ],\n \"source\": [\n \"x = ['ab', 'cd']\\n\",\n \"# i\\n\",\n \"\\n\",\n \"for i in x:\\n\",\n \" i.upper() # 'ab'.upper() - 'AB'\\n\",\n \"\\n\",\n \"print(x)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"id\": \"8ba916b9\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"AB\\n\",\n \"CD\\n\",\n \"['ab', 'cd']\\n\"\n ]\n }\n ],\n \"source\": [\n \"x = ['ab', 'cd']\\n\",\n \"# i\\n\",\n \"\\n\",\n \"for i in x:\\n\",\n \" print(i.upper()) # 'AB', 'CD'\\n\",\n \"\\n\",\n \"print(x)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"id\": \"7835c572\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"a\\n\",\n \"b\\n\",\n \"c\\n\",\n \"d\\n\"\n ]\n }\n ],\n \"source\": [\n \"x = 'abcd'\\n\",\n \"\\n\",\n \"for i in x:\\n\",\n \" print(i)\\n\",\n \" x.upper()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"id\": \"0ce5284c\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"A\\n\",\n \"B\\n\",\n \"C\\n\",\n \"D\\n\"\n ]\n }\n ],\n \"source\": [\n \"x = 'abcd'\\n\",\n \"\\n\",\n \"for i in x:\\n\",\n \" print(i.upper())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"id\": \"4f9188d4\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"0\\n\",\n \"1\\n\",\n \"2\\n\",\n \"3\\n\",\n \"4\\n\"\n ]\n }\n ],\n \"source\": [\n \"# i\\n\",\n \"# 0,1,2,3,4\\n\",\n \"for i in range(5):\\n\",\n \" print(i)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"id\": \"fab18b0b\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"ename\": \"TypeError\",\n \"evalue\": \"'str' object cannot be interpreted as an integer\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[1;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[1;31mTypeError\\u001b[0m Traceback (most recent call last)\",\n \"Input \\u001b[1;32mIn [18]\\u001b[0m, in \\u001b[0;36m\\u001b[1;34m()\\u001b[0m\\n\\u001b[0;32m 1\\u001b[0m x \\u001b[38;5;241m=\\u001b[39m \\u001b[38;5;124m'\\u001b[39m\\u001b[38;5;124mabcd\\u001b[39m\\u001b[38;5;124m'\\u001b[39m\\n\\u001b[1;32m----> 3\\u001b[0m \\u001b[38;5;28;01mfor\\u001b[39;00m i \\u001b[38;5;129;01min\\u001b[39;00m \\u001b[38;5;28;43mrange\\u001b[39;49m\\u001b[43m(\\u001b[49m\\u001b[43mx\\u001b[49m\\u001b[43m)\\u001b[49m:\\n\\u001b[0;32m 4\\u001b[0m \\u001b[38;5;28mprint\\u001b[39m(i)\\n\",\n \"\\u001b[1;31mTypeError\\u001b[0m: 'str' object cannot be interpreted as an integer\"\n ]\n }\n ],\n \"source\": [\n \"x = 'abcd'\\n\",\n \"\\n\",\n \"for i in range(x):\\n\",\n \" print(i)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"id\": \"1044b515\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"ename\": \"AttributeError\",\n \"evalue\": \"'int' object has no attribute 'upper'\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[1;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[1;31mAttributeError\\u001b[0m Traceback (most recent call last)\",\n \"Input \\u001b[1;32mIn [19]\\u001b[0m, in \\u001b[0;36m\\u001b[1;34m()\\u001b[0m\\n\\u001b[0;32m 3\\u001b[0m \\u001b[38;5;66;03m# 0,1,2,3\\u001b[39;00m\\n\\u001b[0;32m 4\\u001b[0m \\u001b[38;5;28;01mfor\\u001b[39;00m i \\u001b[38;5;129;01min\\u001b[39;00m \\u001b[38;5;28mrange\\u001b[39m(\\u001b[38;5;28mlen\\u001b[39m(x)):\\n\\u001b[1;32m----> 5\\u001b[0m \\u001b[38;5;28mprint\\u001b[39m(\\u001b[43mi\\u001b[49m\\u001b[38;5;241;43m.\\u001b[39;49m\\u001b[43mupper\\u001b[49m())\\n\",\n \"\\u001b[1;31mAttributeError\\u001b[0m: 'int' object has no attribute 'upper'\"\n ]\n }\n ],\n \"source\": [\n \"x = 'abcd'\\n\",\n \"\\n\",\n \"# 0,1,2,3\\n\",\n \"for i in range(len(x)):\\n\",\n \" print(i.upper())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"id\": \"9594cd6a\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"0\\n\",\n \"1\\n\",\n \"2\\n\",\n \"3\\n\"\n ]\n }\n ],\n \"source\": [\n \"x = 'abcd'\\n\",\n \"\\n\",\n \"# 0,1,2,3\\n\",\n \"for i in range(len(x)):\\n\",\n \" print(i)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"id\": \"0f7f2436\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"2\\n\",\n \"3\\n\",\n \"4\\n\",\n \"5\\n\"\n ]\n }\n ],\n \"source\": [\n \"for i in range(2,6):\\n\",\n \" print(i)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"id\": \"cab0c2b5\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"abcd\\n\"\n ]\n }\n ],\n \"source\": [\n \"x = 'abcd'\\n\",\n \"\\n\",\n \"# 0,1,2,3\\n\",\n \"for i in range(len(x)):\\n\",\n \" x[i].upper()\\n\",\n \"\\n\",\n \"print(x)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"id\": \"e2525fbc\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"a\\n\",\n \"a\\n\",\n \"a\\n\",\n \"a\\n\"\n ]\n }\n ],\n \"source\": [\n \"x = 'abcd'\\n\",\n \"\\n\",\n \"# 0,1,2,3\\n\",\n \"for i in range(len(x)):\\n\",\n \" x = 'a'\\n\",\n \" print(x)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"id\": \"d53ac18d\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"abcd\\n\",\n \"a\\n\",\n \"a\\n\",\n \"a\\n\"\n ]\n }\n ],\n \"source\": [\n \"x = 'abcd'\\n\",\n \"\\n\",\n \"# 0,1,2,3\\n\",\n \"for i in range(len(x)):\\n\",\n \" print(x)\\n\",\n \" x = 'a'\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"6ab70d2a\",\n \"metadata\": {},\n \"source\": [\n \"# Dictionary\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"e22d4eef\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"l = [1,2,3,4,54]\\n\",\n \"# 0 1 2 3 4\\n\",\n \"\\n\",\n \"# hashing\\n\",\n \"\\n\",\n \"# [, , ,44 , 55,]\\n\",\n \"# 45\\n\",\n \"# 65\\n\",\n \"\\n\",\n \"55 % 10 = 5 ---> \\n\",\n \"44 % 10 = 4\\n\",\n \"45 % 10 = 5\\n\",\n \"65 % 10 = 5\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"c47a4099\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# JSON\\n\",\n \"\\n\",\n \"{\\n\",\n \" \\\"username\\\": \\\"hemansnation\\\",\\n\",\n \" \\\"password\\\": \\\"12334545\\\"\\n\",\n \"}\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"id\": \"e5b3ba3d\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"{'ravi': 24, 'viky': 21, 'roy': 36}\\n\"\n ]\n }\n ],\n \"source\": [\n \"# key: value\\n\",\n \"\\n\",\n \"d = {\\\"ravi\\\": 24, \\\"viky\\\": 21, \\\"roy\\\": 36}\\n\",\n \"\\n\",\n \"print(d)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"id\": \"c846e290\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(type(d))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"ea68592a\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# unordered data structures\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"id\": \"7a710f26\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"36\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(d['roy'])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 28,\n \"id\": \"b7cd33ff\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"24\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(d['ravi'])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 29,\n \"id\": \"53e9fcdd\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[255, 0, 0]\\n\"\n ]\n }\n ],\n \"source\": [\n \"color = {\\n\",\n \" \\\"red\\\": [255,0,0],\\n\",\n \" \\\"green\\\": [0,255,0],\\n\",\n \" \\\"blue\\\": [0,0,255]\\n\",\n \"}\\n\",\n \"print(color['red'])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 30,\n \"id\": \"1ddbc289\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"ename\": \"KeyError\",\n \"evalue\": \"'yellow'\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[1;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[1;31mKeyError\\u001b[0m Traceback (most recent call last)\",\n \"Input \\u001b[1;32mIn [30]\\u001b[0m, in \\u001b[0;36m\\u001b[1;34m()\\u001b[0m\\n\\u001b[1;32m----> 1\\u001b[0m \\u001b[38;5;28mprint\\u001b[39m(\\u001b[43mcolor\\u001b[49m\\u001b[43m[\\u001b[49m\\u001b[38;5;124;43m'\\u001b[39;49m\\u001b[38;5;124;43myellow\\u001b[39;49m\\u001b[38;5;124;43m'\\u001b[39;49m\\u001b[43m]\\u001b[49m)\\n\",\n \"\\u001b[1;31mKeyError\\u001b[0m: 'yellow'\"\n ]\n }\n ],\n \"source\": [\n \"print(color['yellow'])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 31,\n \"id\": \"a7eeec1c\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"{}\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"d = {}\\n\",\n \"\\n\",\n \"print(d)\\n\",\n \"\\n\",\n \"print(type(d))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 32,\n \"id\": \"c86ce0ee\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"1\\n\"\n ]\n }\n ],\n \"source\": [\n \"d = {1:1, 2:2, 3:4}\\n\",\n \"\\n\",\n \"print(d[1])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 33,\n \"id\": \"9f399123\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"ename\": \"TypeError\",\n \"evalue\": \"unhashable type: 'list'\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[1;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[1;31mTypeError\\u001b[0m Traceback (most recent call last)\",\n \"Input \\u001b[1;32mIn [33]\\u001b[0m, in \\u001b[0;36m\\u001b[1;34m()\\u001b[0m\\n\\u001b[1;32m----> 1\\u001b[0m d \\u001b[38;5;241m=\\u001b[39m {[\\u001b[38;5;241m1\\u001b[39m,\\u001b[38;5;241m2\\u001b[39m]:\\u001b[38;5;241m1\\u001b[39m, \\u001b[38;5;241m2\\u001b[39m:\\u001b[38;5;241m2\\u001b[39m, \\u001b[38;5;241m3\\u001b[39m:\\u001b[38;5;241m4\\u001b[39m}\\n\\u001b[0;32m 3\\u001b[0m \\u001b[38;5;28mprint\\u001b[39m(d[\\u001b[38;5;241m1\\u001b[39m,\\u001b[38;5;241m2\\u001b[39m])\\n\",\n \"\\u001b[1;31mTypeError\\u001b[0m: unhashable type: 'list'\"\n ]\n }\n ],\n \"source\": [\n \"d = {[1,2]:1, 2:2, 3:4}\\n\",\n \"\\n\",\n \"print(d[1,2])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 34,\n \"id\": \"d166c563\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[100, 20]\\n\"\n ]\n }\n ],\n \"source\": [\n \"# mutable object - list\\n\",\n \"\\n\",\n \"l = [10,20]\\n\",\n \"\\n\",\n \"l[0] = 100\\n\",\n \"\\n\",\n \"print(l)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 35,\n \"id\": \"a8d2e6d7\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"ename\": \"TypeError\",\n \"evalue\": \"'str' object does not support item assignment\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[1;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[1;31mTypeError\\u001b[0m Traceback (most recent call last)\",\n \"Input \\u001b[1;32mIn [35]\\u001b[0m, in \\u001b[0;36m\\u001b[1;34m()\\u001b[0m\\n\\u001b[0;32m 1\\u001b[0m s \\u001b[38;5;241m=\\u001b[39m \\u001b[38;5;124m\\\"\\u001b[39m\\u001b[38;5;124mIndia\\u001b[39m\\u001b[38;5;124m\\\"\\u001b[39m \\u001b[38;5;66;03m# iimutable objects\\u001b[39;00m\\n\\u001b[1;32m----> 3\\u001b[0m s[\\u001b[38;5;241m0\\u001b[39m] \\u001b[38;5;241m=\\u001b[39m \\u001b[38;5;124m\\\"\\u001b[39m\\u001b[38;5;124mA\\u001b[39m\\u001b[38;5;124m\\\"\\u001b[39m\\n\\u001b[0;32m 5\\u001b[0m \\u001b[38;5;28mprint\\u001b[39m(s)\\n\",\n \"\\u001b[1;31mTypeError\\u001b[0m: 'str' object does not support item assignment\"\n ]\n }\n ],\n \"source\": [\n \"s = \\\"India\\\" # immutable objects\\n\",\n \"\\n\",\n \"s[0] = \\\"A\\\"\\n\",\n \"\\n\",\n \"print(s)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"d78cdcd4\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# int are immutable\\n\",\n \"\\n\",\n \"1 + 1 = 2\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"d3d32bf3\",\n \"metadata\": {},\n \"source\": [\n \"# Dictionary Functions\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 36,\n \"id\": \"b57d9f4a\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"4\\n\"\n ]\n }\n ],\n \"source\": [\n \"d = { 1:1, 2:4, 3:9, 4:16}\\n\",\n \"\\n\",\n \"print(len(d))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 37,\n \"id\": \"788f5c87\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"10\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(sum(d))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 38,\n \"id\": \"63a2eb0e\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"ename\": \"TypeError\",\n \"evalue\": \"unsupported operand type(s) for +: 'int' and 'str'\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[1;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[1;31mTypeError\\u001b[0m Traceback (most recent call last)\",\n \"Input \\u001b[1;32mIn [38]\\u001b[0m, in \\u001b[0;36m\\u001b[1;34m()\\u001b[0m\\n\\u001b[0;32m 1\\u001b[0m a \\u001b[38;5;241m=\\u001b[39m {\\u001b[38;5;241m1\\u001b[39m:\\u001b[38;5;241m1\\u001b[39m,\\u001b[38;5;241m2\\u001b[39m:\\u001b[38;5;241m4\\u001b[39m, \\u001b[38;5;124m\\\"\\u001b[39m\\u001b[38;5;124mhi\\u001b[39m\\u001b[38;5;124m\\\"\\u001b[39m:\\u001b[38;5;241m16\\u001b[39m}\\n\\u001b[1;32m----> 3\\u001b[0m \\u001b[38;5;28mprint\\u001b[39m(\\u001b[38;5;28;43msum\\u001b[39;49m\\u001b[43m(\\u001b[49m\\u001b[43ma\\u001b[49m\\u001b[43m)\\u001b[49m)\\n\",\n \"\\u001b[1;31mTypeError\\u001b[0m: unsupported operand type(s) for +: 'int' and 'str'\"\n ]\n }\n ],\n \"source\": [\n \"a = {1:1,2:4, \\\"hi\\\":16}\\n\",\n \"\\n\",\n \"print(sum(a))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 39,\n \"id\": \"650cf24f\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"{1: 1, 2: 4, 3: 9, 4: 16}\"\n ]\n },\n \"execution_count\": 39,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"d\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 40,\n \"id\": \"d78c3139\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"4\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(max(d))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 41,\n \"id\": \"08fe0e6e\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"1\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(min(d))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"1a16abb5\",\n \"metadata\": {},\n \"source\": [\n \"# Dictionary Methods\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 43,\n \"id\": \"9a2f7d81\",\n \"metadata\": {\n \"scrolled\": false\n },\n \"outputs\": [],\n \"source\": [\n \"# help(dict)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 44,\n \"id\": \"5d682511\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"{1: 'Apple', 'orange': [2, 3, 4], '2': '3'}\\n\"\n ]\n }\n ],\n \"source\": [\n \"pair = { 1: \\\"Apple\\\", \\\"orange\\\": [2,3,4], \\\"2\\\": \\\"3\\\"}\\n\",\n \"\\n\",\n \"print(pair)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 45,\n \"id\": \"1374c33c\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[2, 3, 4]\\n\"\n ]\n }\n ],\n \"source\": [\n \"# get\\n\",\n \"\\n\",\n \"print(pair.get(\\\"orange\\\"))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 48,\n \"id\": \"49f1f231\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[2, 3, 4]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(pair[\\\"orange\\\"])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 49,\n \"id\": \"0538283b\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"None\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(pair.get(7))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"6a643576\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"None => NULL # nothing\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 50,\n \"id\": \"ab1ab099\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Apple\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(pair.get(1, \\\"Not Available\\\"))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 51,\n \"id\": \"26d164e1\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Not Available\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(pair.get(10, \\\"Not Available\\\"))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 52,\n \"id\": \"0632cda6\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"{1: 1, 2: 4, 3: 9, 4: 16}\"\n ]\n },\n \"execution_count\": 52,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# clear\\n\",\n \"\\n\",\n \"d\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 53,\n \"id\": \"cb797c19\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"{}\\n\"\n ]\n }\n ],\n \"source\": [\n \"d.clear()\\n\",\n \"\\n\",\n \"print(d)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 54,\n \"id\": \"406f5d55\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"ename\": \"IndexError\",\n \"evalue\": \"list assignment index out of range\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[1;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[1;31mIndexError\\u001b[0m Traceback (most recent call last)\",\n \"Input \\u001b[1;32mIn [54]\\u001b[0m, in \\u001b[0;36m\\u001b[1;34m()\\u001b[0m\\n\\u001b[0;32m 1\\u001b[0m l \\u001b[38;5;241m=\\u001b[39m [\\u001b[38;5;241m10\\u001b[39m,\\u001b[38;5;241m20\\u001b[39m,\\u001b[38;5;241m30\\u001b[39m]\\n\\u001b[1;32m----> 3\\u001b[0m l[\\u001b[38;5;241m3\\u001b[39m] \\u001b[38;5;241m=\\u001b[39m \\u001b[38;5;241m100\\u001b[39m\\n\\u001b[0;32m 5\\u001b[0m \\u001b[38;5;28mprint\\u001b[39m(l)\\n\",\n \"\\u001b[1;31mIndexError\\u001b[0m: list assignment index out of range\"\n ]\n }\n ],\n \"source\": [\n \"l = [10,20,30]\\n\",\n \"\\n\",\n \"l[3] = 100\\n\",\n \"\\n\",\n \"print(l)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 55,\n \"id\": \"45715900\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"{'A': 100, 'Z': 20}\\n\"\n ]\n }\n ],\n \"source\": [\n \"d = {\\\"A\\\": 10, \\\"Z\\\": 20}\\n\",\n \"\\n\",\n \"d[\\\"A\\\"] = 100\\n\",\n \"\\n\",\n \"print(d)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 56,\n \"id\": \"3ab37c57\",\n \"metadata\": {\n \"scrolled\": true\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"{'A': 100, 'Z': 20, 'goa': 200}\\n\"\n ]\n }\n ],\n \"source\": [\n \"d[\\\"goa\\\"] = 200\\n\",\n \"\\n\",\n \"print(d)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"f6f67a8d\",\n \"metadata\": {},\n \"source\": [\n \"# Question\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"f7c72ebe\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"{1:1, 2:4, 3:9, 4:16, 5:25 ...........10:100}\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"2435adc7\",\n \"metadata\": {},\n \"source\": [\n \"# Tuple\\n\",\n \"\\n\",\n \"tuple are immutable\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 57,\n \"id\": \"b3e4ebb7\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"(1, 2, 3, 4)\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"t = (1,2,3,4)\\n\",\n \"\\n\",\n \"print(t)\\n\",\n \"\\n\",\n \"print(type(t))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 58,\n \"id\": \"eed35b67\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"ename\": \"TypeError\",\n \"evalue\": \"'tuple' object does not support item assignment\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[1;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[1;31mTypeError\\u001b[0m Traceback (most recent call last)\",\n \"Input \\u001b[1;32mIn [58]\\u001b[0m, in \\u001b[0;36m\\u001b[1;34m()\\u001b[0m\\n\\u001b[1;32m----> 1\\u001b[0m t[\\u001b[38;5;241m0\\u001b[39m] \\u001b[38;5;241m=\\u001b[39m \\u001b[38;5;241m100\\u001b[39m\\n\",\n \"\\u001b[1;31mTypeError\\u001b[0m: 'tuple' object does not support item assignment\"\n ]\n }\n ],\n \"source\": [\n \"t[0] = 100\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 59,\n \"id\": \"b630c971\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"ename\": \"TypeError\",\n \"evalue\": \"'str' object does not support item assignment\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[1;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[1;31mTypeError\\u001b[0m Traceback (most recent call last)\",\n \"Input \\u001b[1;32mIn [59]\\u001b[0m, in \\u001b[0;36m\\u001b[1;34m()\\u001b[0m\\n\\u001b[0;32m 1\\u001b[0m s \\u001b[38;5;241m=\\u001b[39m \\u001b[38;5;124m'\\u001b[39m\\u001b[38;5;124mgoa\\u001b[39m\\u001b[38;5;124m'\\u001b[39m\\n\\u001b[1;32m----> 3\\u001b[0m s[\\u001b[38;5;241m0\\u001b[39m] \\u001b[38;5;241m=\\u001b[39m \\u001b[38;5;124m\\\"\\u001b[39m\\u001b[38;5;124mA\\u001b[39m\\u001b[38;5;124m\\\"\\u001b[39m\\n\",\n \"\\u001b[1;31mTypeError\\u001b[0m: 'str' object does not support item assignment\"\n ]\n }\n ],\n \"source\": [\n \"s = 'goa'\\n\",\n \"\\n\",\n \"s[0] = \\\"A\\\"\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 60,\n \"id\": \"cf15d6bb\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"(5, 6, 67)\\n\"\n ]\n }\n ],\n \"source\": [\n \"t = (2,3,4,5,6,67)\\n\",\n \"\\n\",\n \"print(t[3:])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 61,\n \"id\": \"05edf72b\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(2, 3, 4, 5, 6, 67)\"\n ]\n },\n \"execution_count\": 61,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"t\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 62,\n \"id\": \"0c3d2bc3\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"5\\n\"\n ]\n }\n ],\n \"source\": [\n \"t = (1,2,3,4,5)\\n\",\n \"\\n\",\n \"print(len(t))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 63,\n \"id\": \"54aa0e3f\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"15\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(sum(t))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 64,\n \"id\": \"f0052bd4\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"5\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(max(t))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 65,\n \"id\": \"28a92019\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"1\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(min(t))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 66,\n \"id\": \"7885f892\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# -1\\n\",\n \"t = (\\\"A\\\", 1,2,4)\\n\",\n \"# 0 1 2 3\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 67,\n \"id\": \"da09ee4f\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"('A', 1, 2, 4)\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(t)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 68,\n \"id\": \"b337cd33\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"A\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(t[0])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 69,\n \"id\": \"504bca81\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"4\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(t[-1])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 70,\n \"id\": \"3be05cc3\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"()\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"t = ()\\n\",\n \"\\n\",\n \"print(t)\\n\",\n \"\\n\",\n \"print(type(t))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 71,\n \"id\": \"915d9c00\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"10\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"t = (10)\\n\",\n \"\\n\",\n \"print(t)\\n\",\n \"\\n\",\n \"print(type(t))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 72,\n \"id\": \"54ec18a0\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"(10,)\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"t = (10,)\\n\",\n \"\\n\",\n \"print(t)\\n\",\n \"\\n\",\n \"print(type(t))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"2792fe85\",\n \"metadata\": {},\n \"source\": [\n \"# Tuple Methods\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 73,\n \"id\": \"5bd0d610\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"2\\n\"\n ]\n }\n ],\n \"source\": [\n \"t = (2,3,4,5,5,3,3,3,7)\\n\",\n \"\\n\",\n \"print(t.count(5))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 74,\n \"id\": \"79899432\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"1\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(t.count(7))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 75,\n \"id\": \"4b0c56f1\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"0\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(t.count(10))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 76,\n \"id\": \"b17ad603\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"(2, 3, 4, 5, 5, 3, 3, 3, 7)\\n\"\n ]\n }\n ],\n \"source\": [\n \"# index\\n\",\n \"\\n\",\n \"print(t)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 77,\n \"id\": \"7f10fbae\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"3\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(t.index(5))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 78,\n \"id\": \"e6811f4e\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Help on built-in function index:\\n\",\n \"\\n\",\n \"index(value, start=0, stop=9223372036854775807, /) method of builtins.tuple instance\\n\",\n \" Return first index of value.\\n\",\n \" \\n\",\n \" Raises ValueError if the value is not present.\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"help(t.index)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 79,\n \"id\": \"068bf8b3\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"(2, 3, 4, 5, 5, 3, 3, 3, 7)\"\n ]\n },\n \"execution_count\": 79,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"t\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 81,\n \"id\": \"a65ca47a\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"4\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(t.index(5,4))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"df156bc0\",\n \"metadata\": {},\n \"source\": [\n \"# Questions \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"f623b5de\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"t = ('a','e','i','o','u')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"1f2328fc\",\n \"metadata\": {},\n \"source\": [\n \"# Sets\\n\",\n \"\\n\",\n \"unordered and unindex\\n\",\n \"\\n\",\n \"sets are mutable\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 82,\n \"id\": \"11b203a2\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"{1, 2, 3, 4, 5, 6}\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"n = {1,2,3,4,5,6}\\n\",\n \"\\n\",\n \"print(n)\\n\",\n \"\\n\",\n \"print(type(n))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 83,\n \"id\": \"615a8e7e\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"{1, 2, 3, 4, 5, 6}\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"n = {1,2,3,4,5,6,6,6,6,5,5}\\n\",\n \"\\n\",\n \"print(n)\\n\",\n \"\\n\",\n \"print(type(n))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 84,\n \"id\": \"d7bf4db2\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"set()\\n\"\n ]\n }\n ],\n \"source\": [\n \"s = set()\\n\",\n \"\\n\",\n \"print(s)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 86,\n \"id\": \"98a00a15\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"{'b', 4, 'a'}\\n\"\n ]\n }\n ],\n \"source\": [\n \"n = set(['a','b', 4])\\n\",\n \"\\n\",\n \"print(n)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 87,\n \"id\": \"8d03b0ed\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"1\\n\",\n \"2\\n\",\n \"3\\n\",\n \"55\\n\",\n \"44\\n\"\n ]\n }\n ],\n \"source\": [\n \"n = {1,2,3,44,55}\\n\",\n \"\\n\",\n \"for i in n:\\n\",\n \" print(i)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"dec2b22e\",\n \"metadata\": {},\n \"source\": [\n \"## set operations\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 88,\n \"id\": \"c961559b\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"{1, 2, 3, 4, 5, 6, 7, 8, 9}\\n\"\n ]\n }\n ],\n \"source\": [\n \"# union\\n\",\n \"\\n\",\n \"f = {1,2,3,4,5,6}\\n\",\n \"s = {4,5,6,7,8,9}\\n\",\n \"\\n\",\n \"print(f | s)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 89,\n \"id\": \"ab540fe2\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"{4, 5, 6}\\n\"\n ]\n }\n ],\n \"source\": [\n \"# intersection\\n\",\n \"\\n\",\n \"f = {1,2,3,4,5,6}\\n\",\n \"s = {4,5,6,7,8,9}\\n\",\n \"\\n\",\n \"print(f & s)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 90,\n \"id\": \"abf57b8a\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"{1, 2, 3}\\n\"\n ]\n }\n ],\n \"source\": [\n \"# difference\\n\",\n \"\\n\",\n \"f = {1,2,3,4,5,6}\\n\",\n \"s = {4,5,6,7,8,9}\\n\",\n \"\\n\",\n \"print(f - s)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 91,\n \"id\": \"9a9f6ea6\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"{8, 9, 7}\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(s - f)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 92,\n \"id\": \"88a67b87\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"{1, 2, 3, 7, 8, 9}\\n\"\n ]\n }\n ],\n \"source\": [\n \"# symmetry\\n\",\n \"\\n\",\n \"f = {1,2,3,4,5,6}\\n\",\n \"s = {4,5,6,7,8,9}\\n\",\n \"\\n\",\n \"print(f ^ s)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"2682d1f9\",\n \"metadata\": {},\n \"source\": [\n \"# set functions\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 93,\n \"id\": \"553d14f2\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"4\\n\",\n \"10\\n\",\n \"4\\n\",\n \"1\\n\"\n ]\n }\n ],\n \"source\": [\n \"s = {1,2,3,4}\\n\",\n \"\\n\",\n \"print(len(s))\\n\",\n \"print(sum(s))\\n\",\n \"print(max(s))\\n\",\n \"print(min(s))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"a144e8bf\",\n \"metadata\": {},\n \"source\": [\n \"# set methods\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 95,\n \"id\": \"ceb0c93f\",\n \"metadata\": {\n \"scrolled\": true\n },\n \"outputs\": [],\n \"source\": [\n \"# help(set)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 96,\n \"id\": \"83fb99b3\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"{1, 2, 3, 4}\\n\",\n \"{1, 2, 3, 4, -7}\\n\"\n ]\n }\n ],\n \"source\": [\n \"# add\\n\",\n \"\\n\",\n \"n = {1,2,3,4}\\n\",\n \"\\n\",\n \"print(n)\\n\",\n \"\\n\",\n \"n.add(-7)\\n\",\n \"\\n\",\n \"print(n)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 97,\n \"id\": \"2e99b696\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"{1, 2, 3, 4, -7}\\n\",\n \"set()\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(n)\\n\",\n \"n.clear()\\n\",\n \"\\n\",\n \"print(n)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 98,\n \"id\": \"feade89d\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"set()\"\n ]\n },\n \"execution_count\": 98,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# remove\\n\",\n \"\\n\",\n \"n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 99,\n \"id\": \"b83f2433\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"{1, 2, 4}\\n\"\n ]\n }\n ],\n \"source\": [\n \"n = {1,2,3,4}\\n\",\n \"\\n\",\n \"n.remove(3)\\n\",\n \"print(n)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"3730faed\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.12\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n" }, { "path": "March Cohort 2023/4_Functions.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"id\": \"a98c255e\",\n \"metadata\": {},\n \"source\": [\n \"# Functions\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"id\": \"18360579\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def myfunction(): # definition of function\\n\",\n \" print(\\\"Hey\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"id\": \"22e39c3e\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Hey\\n\"\n ]\n }\n ],\n \"source\": [\n \"myfunction() # calling of function\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"id\": \"c24cd8e5\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"1\\n\",\n \"2\\n\",\n \"3\\n\"\n ]\n }\n ],\n \"source\": [\n \"def paint():\\n\",\n \" print(1)\\n\",\n \" print(2)\\n\",\n \" print(3)\\n\",\n \"\\n\",\n \"paint()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"id\": \"1cd655a2\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"1\\n\",\n \"2\\n\",\n \"3\\n\"\n ]\n }\n ],\n \"source\": [\n \"paint()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"id\": \"f6b4813a\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"ename\": \"NameError\",\n \"evalue\": \"name 'hello' is not defined\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[1;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[1;31mNameError\\u001b[0m Traceback (most recent call last)\",\n \"Input \\u001b[1;32mIn [5]\\u001b[0m, in \\u001b[0;36m\\u001b[1;34m()\\u001b[0m\\n\\u001b[1;32m----> 1\\u001b[0m \\u001b[43mhello\\u001b[49m()\\n\\u001b[0;32m 3\\u001b[0m \\u001b[38;5;28;01mdef\\u001b[39;00m \\u001b[38;5;21mhello\\u001b[39m():\\n\\u001b[0;32m 4\\u001b[0m \\u001b[38;5;28mprint\\u001b[39m(\\u001b[38;5;124m\\\"\\u001b[39m\\u001b[38;5;124mHello\\u001b[39m\\u001b[38;5;124m\\\"\\u001b[39m)\\n\",\n \"\\u001b[1;31mNameError\\u001b[0m: name 'hello' is not defined\"\n ]\n }\n ],\n \"source\": [\n \"hello()\\n\",\n \"\\n\",\n \"def hello():\\n\",\n \" print(\\\"Hello\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"id\": \"28ae369c\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"ename\": \"TypeError\",\n \"evalue\": \"indore() missing 1 required positional argument: 'a'\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[1;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[1;31mTypeError\\u001b[0m Traceback (most recent call last)\",\n \"Input \\u001b[1;32mIn [6]\\u001b[0m, in \\u001b[0;36m\\u001b[1;34m()\\u001b[0m\\n\\u001b[0;32m 1\\u001b[0m \\u001b[38;5;28;01mdef\\u001b[39;00m \\u001b[38;5;21mindore\\u001b[39m(a):\\n\\u001b[0;32m 2\\u001b[0m \\u001b[38;5;28mprint\\u001b[39m(a)\\n\\u001b[1;32m----> 4\\u001b[0m \\u001b[43mindore\\u001b[49m\\u001b[43m(\\u001b[49m\\u001b[43m)\\u001b[49m\\n\",\n \"\\u001b[1;31mTypeError\\u001b[0m: indore() missing 1 required positional argument: 'a'\"\n ]\n }\n ],\n \"source\": [\n \"def indore(a): # a = \\\"A\\\"\\n\",\n \" print(a)\\n\",\n \"\\n\",\n \"indore()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"id\": \"52bc36b9\",\n \"metadata\": {\n \"scrolled\": true\n },\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"10\\n\"\n ]\n }\n ],\n \"source\": [\n \"indore(10)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"id\": \"527fe9e4\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"A\\n\"\n ]\n }\n ],\n \"source\": [\n \"indore(\\\"A\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"id\": \"4af5fdc0\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"right\\n\"\n ]\n }\n ],\n \"source\": [\n \"indore(\\\"right\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"id\": \"aa11eea7\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"1\\n\",\n \"One\\n\",\n \"2\\n\",\n \"Two\\n\",\n \"right\\n\",\n \"three\\n\"\n ]\n }\n ],\n \"source\": [\n \"indore(1)\\n\",\n \"print(\\\"One\\\")\\n\",\n \"indore(2)\\n\",\n \"print(\\\"Two\\\")\\n\",\n \"indore(\\\"right\\\")\\n\",\n \"print(\\\"three\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"id\": \"84a3b11a\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"9\\n\"\n ]\n }\n ],\n \"source\": [\n \"def add(x, y):\\n\",\n \" print(x + y)\\n\",\n \"\\n\",\n \"add(5,4)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"74774c31\",\n \"metadata\": {},\n \"source\": [\n \"# Return statement\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"id\": \"bd50418a\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"7\\n\"\n ]\n }\n ],\n \"source\": [\n \"def play(x, y):\\n\",\n \" if x >= y:\\n\",\n \" return x\\n\",\n \" else:\\n\",\n \" return y\\n\",\n \"\\n\",\n \"print(play(4,7))\\n\",\n \"# 7\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"id\": \"86b599e4\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"8\\n\"\n ]\n }\n ],\n \"source\": [\n \"z = play(8,5)\\n\",\n \"#z = 8\\n\",\n \"\\n\",\n \"print(z)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"id\": \"ad6e3060\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"9\\n\"\n ]\n }\n ],\n \"source\": [\n \"def sub(x, y):\\n\",\n \" t = x + y\\n\",\n \" return t\\n\",\n \" print(\\\"Hi\\\")\\n\",\n \"\\n\",\n \"print(sub(4,5))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"id\": \"8cc83a2f\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"1\\n\",\n \"2\\n\",\n \"hello\\n\"\n ]\n }\n ],\n \"source\": [\n \"def pre():\\n\",\n \" print(1)\\n\",\n \" print(2)\\n\",\n \" print(\\\"hello\\\")\\n\",\n \" return\\n\",\n \" print(3)\\n\",\n \" print(4)\\n\",\n \"\\n\",\n \"pre()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"id\": \"cb329c92\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"1\\n\",\n \"2\\n\",\n \"hello\\n\",\n \"None\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(pre())\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"id\": \"a463dbf9\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"1\\n\",\n \"2\\n\",\n \"None\\n\"\n ]\n }\n ],\n \"source\": [\n \"r = pre()\\n\",\n \"\\n\",\n \"print(r)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"280c0539\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"None ==> NULL ==> nothing\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 20,\n \"id\": \"79b26fc0\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"None\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(None)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"id\": \"f781cf14\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Hi\\n\",\n \"None\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(print(\\\"Hi\\\"))\\n\",\n \"# print(None)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"d25d6edf\",\n \"metadata\": {},\n \"source\": [\n \"# Default Argument\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"id\": \"f6141d42\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"ename\": \"TypeError\",\n \"evalue\": \"can only concatenate str (not \\\"int\\\") to str\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[1;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[1;31mTypeError\\u001b[0m Traceback (most recent call last)\",\n \"Input \\u001b[1;32mIn [22]\\u001b[0m, in \\u001b[0;36m\\u001b[1;34m()\\u001b[0m\\n\\u001b[1;32m----> 1\\u001b[0m \\u001b[38;5;124;43m\\\"\\u001b[39;49m\\u001b[38;5;124;43mhi\\u001b[39;49m\\u001b[38;5;124;43m\\\"\\u001b[39;49m\\u001b[43m \\u001b[49m\\u001b[38;5;241;43m+\\u001b[39;49m\\u001b[43m \\u001b[49m\\u001b[38;5;241;43m6\\u001b[39;49m\\n\",\n \"\\u001b[1;31mTypeError\\u001b[0m: can only concatenate str (not \\\"int\\\") to str\"\n ]\n }\n ],\n \"source\": [\n \"\\\"hi\\\" + 6\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"id\": \"c3edf839\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'hihihi'\"\n ]\n },\n \"execution_count\": 23,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"\\\"hi\\\" * 3\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"id\": \"2456f1b9\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Hello\\n\"\n ]\n }\n ],\n \"source\": [\n \"def say(msg, time = 1):\\n\",\n \" print(msg * time)\\n\",\n \"\\n\",\n \"say(\\\"Hello\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"id\": \"4b65a1da\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"ByeByeByeByeBye\\n\"\n ]\n }\n ],\n \"source\": [\n \"say(\\\"Bye\\\", 5)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"id\": \"a6382071\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Hello+++Indore\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(\\\"Hello\\\", end='+++')\\n\",\n \"print(\\\"Indore\\\")\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 28,\n \"id\": \"d7115a71\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def said(a, b = 5, c = 10):\\n\",\n \" print(a)\\n\",\n \" print(b)\\n\",\n \" print(c)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 29,\n \"id\": \"811ff6b8\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"3\\n\",\n \"7\\n\",\n \"10\\n\"\n ]\n }\n ],\n \"source\": [\n \"said(3,7)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 30,\n \"id\": \"dbd412b0\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"25\\n\",\n \"5\\n\",\n \"24\\n\"\n ]\n }\n ],\n \"source\": [\n \"said(25, c = 24)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 31,\n \"id\": \"c29189f6\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"15\\n\",\n \"5\\n\",\n \"100\\n\"\n ]\n }\n ],\n \"source\": [\n \"said(c = 100, a = 15)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 32,\n \"id\": \"e9d0ccbd\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"ename\": \"SyntaxError\",\n \"evalue\": \"positional argument follows keyword argument (2634499495.py, line 1)\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[1;36m Input \\u001b[1;32mIn [32]\\u001b[1;36m\\u001b[0m\\n\\u001b[1;33m said(c = 19, 20, 30)\\u001b[0m\\n\\u001b[1;37m ^\\u001b[0m\\n\\u001b[1;31mSyntaxError\\u001b[0m\\u001b[1;31m:\\u001b[0m positional argument follows keyword argument\\n\"\n ]\n }\n ],\n \"source\": [\n \"said(c = 19, 20, 30)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 33,\n \"id\": \"25993e69\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"0\\n\"\n ]\n }\n ],\n \"source\": [\n \"i = 0 # i is a global variable\\n\",\n \"\\n\",\n \"def change(i): # i is a local variable\\n\",\n \" i += 1 # i = 2\\n\",\n \" return 1\\n\",\n \"\\n\",\n \"change(1) # 1\\n\",\n \"print(i)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 34,\n \"id\": \"30f844d2\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"8\\n\",\n \"3\\n\"\n ]\n }\n ],\n \"source\": [\n \"x = 3 # global\\n\",\n \"\\n\",\n \"def add(x): # local\\n\",\n \" x += 1 # x = x + 1\\n\",\n \" print(x)\\n\",\n \"\\n\",\n \"add(7)\\n\",\n \"print(x)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 35,\n \"id\": \"bdce8f2a\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"11\\n\"\n ]\n },\n {\n \"ename\": \"NameError\",\n \"evalue\": \"name 'v' is not defined\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[1;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[1;31mNameError\\u001b[0m Traceback (most recent call last)\",\n \"Input \\u001b[1;32mIn [35]\\u001b[0m, in \\u001b[0;36m\\u001b[1;34m()\\u001b[0m\\n\\u001b[0;32m 3\\u001b[0m \\u001b[38;5;28mprint\\u001b[39m(v)\\n\\u001b[0;32m 5\\u001b[0m add(\\u001b[38;5;241m10\\u001b[39m)\\n\\u001b[1;32m----> 6\\u001b[0m \\u001b[38;5;28mprint\\u001b[39m(\\u001b[43mv\\u001b[49m)\\n\",\n \"\\u001b[1;31mNameError\\u001b[0m: name 'v' is not defined\"\n ]\n }\n ],\n \"source\": [\n \"def add(v):\\n\",\n \" v += 1\\n\",\n \" print(v)\\n\",\n \"\\n\",\n \"add(10)\\n\",\n \"print(v)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"7578af9e\",\n \"metadata\": {},\n \"source\": [\n \"# Reference Function\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 36,\n \"id\": \"ead4ef51\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"10\\n\"\n ]\n }\n ],\n \"source\": [\n \"a = 10\\n\",\n \"\\n\",\n \"b = a\\n\",\n \"\\n\",\n \"print(b)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 37,\n \"id\": \"06f37e14\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"10\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(a)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 38,\n \"id\": \"6b6bb7d6\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(type(add))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 39,\n \"id\": \"fe8316b6\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def multiply(x, y):\\n\",\n \" return x * y\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 40,\n \"id\": \"08e7fd6a\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"200\\n\"\n ]\n }\n ],\n \"source\": [\n \"indore = multiply\\n\",\n \"\\n\",\n \"print(indore(10,20))\\n\",\n \"# multiply(10,20)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 41,\n \"id\": \"1578b9d0\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"15\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(multiply(3,5))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"9c39298b\",\n \"metadata\": {},\n \"source\": [\n \"# functions and an argument\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 42,\n \"id\": \"28047e15\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"30\\n\"\n ]\n }\n ],\n \"source\": [\n \"def sub(x, y):\\n\",\n \" return x + y\\n\",\n \"\\n\",\n \"def do(red, x, y): # red = sub, x = 5, y = 10\\n\",\n \" return red(red(x,y), red(x,y))\\n\",\n \"# sub(sub(5,10),sub(5,10))\\n\",\n \"# sub( 15 , 15 )\\n\",\n \"# 30\\n\",\n \"\\n\",\n \"a = 5\\n\",\n \"b = 10\\n\",\n \"\\n\",\n \"\\n\",\n \"print(do(sub, a, b))\\n\",\n \"# 30\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 43,\n \"id\": \"82b1bbd9\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"81\\n\"\n ]\n }\n ],\n \"source\": [\n \"def square(x):\\n\",\n \" return x * x\\n\",\n \"\\n\",\n \"def test(blue, x):\\n\",\n \" print(blue(x))\\n\",\n \"\\n\",\n \"test(square, 9)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 44,\n \"id\": \"f438ee68\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"3\\n\",\n \"1\\n\"\n ]\n }\n ],\n \"source\": [\n \"def green(red, a):\\n\",\n \" print(red(a))\\n\",\n \"\\n\",\n \"green(max, [1,2,3])\\n\",\n \"green(min, [1,2,3])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 45,\n \"id\": \"51c59408\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"0\\n\"\n ]\n }\n ],\n \"source\": [\n \"def printn(x):\\n\",\n \" for i in range(x):\\n\",\n \" print(i)\\n\",\n \" return\\n\",\n \"\\n\",\n \"printn(10)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"3e07954c\",\n \"metadata\": {},\n \"source\": [\n \"# doc string\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 46,\n \"id\": \"b6cc4e7d\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Help on built-in function max in module builtins:\\n\",\n \"\\n\",\n \"max(...)\\n\",\n \" max(iterable, *[, default=obj, key=func]) -> value\\n\",\n \" max(arg1, arg2, *args, *[, key=func]) -> value\\n\",\n \" \\n\",\n \" With a single iterable argument, return its biggest item. The\\n\",\n \" default keyword-only argument specifies an object to return if\\n\",\n \" the provided iterable is empty.\\n\",\n \" With two or more arguments, return the largest argument.\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"help(max)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 48,\n \"id\": \"6596e128\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"this \\n\",\n \"is an\\n\",\n \"apple.\\n\",\n \"\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"# this is a single line comment\\n\",\n \"\\n\",\n \"s = '''this \\n\",\n \"is an\\n\",\n \"apple.\\n\",\n \"'''\\n\",\n \"print(s)\\n\",\n \"print(type(s))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 50,\n \"id\": \"53161af0\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def function_one():\\n\",\n \" '''\\n\",\n \" this function print 10,\\n\",\n \" also this funtion will print 5\\n\",\n \" '''\\n\",\n \" print(10)\\n\",\n \" print(5)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 51,\n \"id\": \"2156a9ff\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"10\\n\",\n \"5\\n\"\n ]\n }\n ],\n \"source\": [\n \"function_one()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 52,\n \"id\": \"ad1302c3\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Help on function function_one in module __main__:\\n\",\n \"\\n\",\n \"function_one()\\n\",\n \" this function print 10,\\n\",\n \" also this funtion will print 5\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"help(function_one)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 53,\n \"id\": \"50e6b7cb\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\",\n \" this function print 10,\\n\",\n \" also this funtion will print 5\\n\",\n \" \\n\"\n ]\n }\n ],\n \"source\": [\n \"print(function_one.__doc__) # dunder\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"a1973a4e\",\n \"metadata\": {},\n \"source\": [\n \"# Map Function\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 54,\n \"id\": \"66ca8c7e\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Help on class map in module builtins:\\n\",\n \"\\n\",\n \"class map(object)\\n\",\n \" | map(func, *iterables) --> map object\\n\",\n \" | \\n\",\n \" | Make an iterator that computes the function using arguments from\\n\",\n \" | each of the iterables. Stops when the shortest iterable is exhausted.\\n\",\n \" | \\n\",\n \" | Methods defined here:\\n\",\n \" | \\n\",\n \" | __getattribute__(self, name, /)\\n\",\n \" | Return getattr(self, name).\\n\",\n \" | \\n\",\n \" | __iter__(self, /)\\n\",\n \" | Implement iter(self).\\n\",\n \" | \\n\",\n \" | __next__(self, /)\\n\",\n \" | Implement next(self).\\n\",\n \" | \\n\",\n \" | __reduce__(...)\\n\",\n \" | Return state information for pickling.\\n\",\n \" | \\n\",\n \" | ----------------------------------------------------------------------\\n\",\n \" | Static methods defined here:\\n\",\n \" | \\n\",\n \" | __new__(*args, **kwargs) from builtins.type\\n\",\n \" | Create and return a new object. See help(type) for accurate signature.\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"help(map)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 55,\n \"id\": \"f473bded\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[6, 7, 8, 9, 10]\\n\"\n ]\n }\n ],\n \"source\": [\n \"def add(x):\\n\",\n \" return x + 5\\n\",\n \"\\n\",\n \"n = [1,2,3,4,5]\\n\",\n \"\\n\",\n \"r = list(map(add, n))\\n\",\n \"# 6, 7, 8, 9, 10\\n\",\n \"\\n\",\n \"print(r)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 56,\n \"id\": \"aeb1a9de\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"ename\": \"TypeError\",\n \"evalue\": \"'int' object is not iterable\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[1;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[1;31mTypeError\\u001b[0m Traceback (most recent call last)\",\n \"Input \\u001b[1;32mIn [56]\\u001b[0m, in \\u001b[0;36m\\u001b[1;34m()\\u001b[0m\\n\\u001b[0;32m 2\\u001b[0m \\u001b[38;5;28;01mreturn\\u001b[39;00m x \\u001b[38;5;241m+\\u001b[39m \\u001b[38;5;241m5\\u001b[39m\\n\\u001b[0;32m 4\\u001b[0m n \\u001b[38;5;241m=\\u001b[39m \\u001b[38;5;241m7\\u001b[39m\\n\\u001b[1;32m----> 6\\u001b[0m r \\u001b[38;5;241m=\\u001b[39m \\u001b[38;5;28mlist\\u001b[39m(\\u001b[38;5;28;43mmap\\u001b[39;49m\\u001b[43m(\\u001b[49m\\u001b[43madd\\u001b[49m\\u001b[43m,\\u001b[49m\\u001b[43m \\u001b[49m\\u001b[43mn\\u001b[49m\\u001b[43m)\\u001b[49m)\\n\\u001b[0;32m 8\\u001b[0m \\u001b[38;5;28mprint\\u001b[39m(r)\\n\",\n \"\\u001b[1;31mTypeError\\u001b[0m: 'int' object is not iterable\"\n ]\n }\n ],\n \"source\": [\n \"def add(x):\\n\",\n \" return x + 5\\n\",\n \"\\n\",\n \"n = 7\\n\",\n \"\\n\",\n \"r = list(map(add, n))\\n\",\n \"\\n\",\n \"print(r)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"cbaca289\",\n \"metadata\": {},\n \"source\": [\n \"# lambda function\\n\",\n \"\\n\",\n \"anonymous function\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 57,\n \"id\": \"fef01377\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"343\\n\"\n ]\n }\n ],\n \"source\": [\n \"def cube(y):\\n\",\n \" return y * y * y\\n\",\n \"\\n\",\n \"\\n\",\n \"# lambda argument: expresion\\n\",\n \"\\n\",\n \"g = lambda x: x * x * x\\n\",\n \"\\n\",\n \"print(g(7))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 59,\n \"id\": \"477123f3\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"343\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(cube(7))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"5caf7279\",\n \"metadata\": {},\n \"source\": [\n \"# lambda with map function\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 60,\n \"id\": \"00915aaf\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[1, 4, 9, 16, 25]\\n\"\n ]\n }\n ],\n \"source\": [\n \"n = [1,2,3,4,5]\\n\",\n \"\\n\",\n \"r = list(map(lambda y: y * y, n))\\n\",\n \"# 1, 4, 9, 16, 25\\n\",\n \"\\n\",\n \"print(r)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"0bc6d570\",\n \"metadata\": {},\n \"source\": [\n \"# filter function\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 61,\n \"id\": \"74819624\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"Help on class filter in module builtins:\\n\",\n \"\\n\",\n \"class filter(object)\\n\",\n \" | filter(function or None, iterable) --> filter object\\n\",\n \" | \\n\",\n \" | Return an iterator yielding those items of iterable for which function(item)\\n\",\n \" | is true. If function is None, return the items that are true.\\n\",\n \" | \\n\",\n \" | Methods defined here:\\n\",\n \" | \\n\",\n \" | __getattribute__(self, name, /)\\n\",\n \" | Return getattr(self, name).\\n\",\n \" | \\n\",\n \" | __iter__(self, /)\\n\",\n \" | Implement iter(self).\\n\",\n \" | \\n\",\n \" | __next__(self, /)\\n\",\n \" | Implement next(self).\\n\",\n \" | \\n\",\n \" | __reduce__(...)\\n\",\n \" | Return state information for pickling.\\n\",\n \" | \\n\",\n \" | ----------------------------------------------------------------------\\n\",\n \" | Static methods defined here:\\n\",\n \" | \\n\",\n \" | __new__(*args, **kwargs) from builtins.type\\n\",\n \" | Create and return a new object. See help(type) for accurate signature.\\n\",\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"help(filter)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 62,\n \"id\": \"166df9b8\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[22, 44]\\n\"\n ]\n }\n ],\n \"source\": [\n \"n = [11,22,33,44,55]\\n\",\n \"\\n\",\n \"r = list(filter(lambda x: x%2 == 0 , n))\\n\",\n \"# 22, 44\\n\",\n \"\\n\",\n \"print(r)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"6bb659fc\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.12\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n" }, { "path": "March Cohort 2023/5_Modules and Packages.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"id\": \"507b0a7e\",\n \"metadata\": {},\n \"source\": [\n \"# OS\\n\",\n \"\\n\",\n \"operating system\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"b4a5e0c7\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# hello.py\\n\",\n \"# something.exe\\n\",\n \"# .pdf .jpg .png\\n\",\n \"\\n\",\n \"# os.py\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"id\": \"c43e9e98\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import os\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"id\": \"083fa6d1\",\n \"metadata\": {\n \"scrolled\": false\n },\n \"outputs\": [],\n \"source\": [\n \"# help(os)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"id\": \"3533b409\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# make directory\\n\",\n \"\\n\",\n \"os.mkdir('prime')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"id\": \"c73636df\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'C:\\\\\\\\Users\\\\\\\\himan\\\\\\\\Documents\\\\\\\\Himanshu Ramchandani Live\\\\\\\\Data-Analyst-Roadmap'\"\n ]\n },\n \"execution_count\": 5,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"# get current working directory\\n\",\n \"\\n\",\n \"os.getcwd()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"id\": \"8a5e48f9\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"os.rmdir('prime')\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"deb43505\",\n \"metadata\": {},\n \"source\": [\n \"# sys\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"id\": \"fc53dc5b\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"3\\n\"\n ]\n }\n ],\n \"source\": [\n \"import sys\\n\",\n \"\\n\",\n \"a = 5000\\n\",\n \"\\n\",\n \"print(sys.getrefcount(a)) # reference count of the object\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"id\": \"4973d1d1\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"432\\n\",\n \"431\\n\"\n ]\n }\n ],\n \"source\": [\n \"a = 10\\n\",\n \"\\n\",\n \"b = a\\n\",\n \"\\n\",\n \"print(sys.getrefcount(a))\\n\",\n \"\\n\",\n \"del a\\n\",\n \"\\n\",\n \"print(sys.getrefcount(b))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"id\": \"d26bf1fa\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"9223372036854775807\"\n ]\n },\n \"execution_count\": 17,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"sys.maxsize\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"id\": \"1a9e67d3\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"['C:\\\\\\\\Users\\\\\\\\himan\\\\\\\\Documents\\\\\\\\Himanshu Ramchandani Live\\\\\\\\Data-Analyst-Roadmap',\\n\",\n \" 'C:\\\\\\\\Users\\\\\\\\himan\\\\\\\\anaconda3\\\\\\\\python39.zip',\\n\",\n \" 'C:\\\\\\\\Users\\\\\\\\himan\\\\\\\\anaconda3\\\\\\\\DLLs',\\n\",\n \" 'C:\\\\\\\\Users\\\\\\\\himan\\\\\\\\anaconda3\\\\\\\\lib',\\n\",\n \" 'C:\\\\\\\\Users\\\\\\\\himan\\\\\\\\anaconda3',\\n\",\n \" '',\\n\",\n \" 'C:\\\\\\\\Users\\\\\\\\himan\\\\\\\\anaconda3\\\\\\\\lib\\\\\\\\site-packages',\\n\",\n \" 'C:\\\\\\\\Users\\\\\\\\himan\\\\\\\\anaconda3\\\\\\\\lib\\\\\\\\site-packages\\\\\\\\win32',\\n\",\n \" 'C:\\\\\\\\Users\\\\\\\\himan\\\\\\\\anaconda3\\\\\\\\lib\\\\\\\\site-packages\\\\\\\\win32\\\\\\\\lib',\\n\",\n \" 'C:\\\\\\\\Users\\\\\\\\himan\\\\\\\\anaconda3\\\\\\\\lib\\\\\\\\site-packages\\\\\\\\Pythonwin']\"\n ]\n },\n \"execution_count\": 18,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"sys.path\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 19,\n \"id\": \"e3a7ec79\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"'3.9.12 (main, Apr 4 2022, 05:22:27) [MSC v.1916 64 bit (AMD64)]'\"\n ]\n },\n \"execution_count\": 19,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"sys.version\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"b9d075a2\",\n \"metadata\": {},\n \"source\": [\n \"# statistics\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 21,\n \"id\": \"7cab6f36\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# import statistics\\n\",\n \"\\n\",\n \"from statistics import *\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 22,\n \"id\": \"1b06511a\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"35.666666666666664\\n\"\n ]\n }\n ],\n \"source\": [\n \"ages = [20,10,40,33,45,66]\\n\",\n \"\\n\",\n \"print(mean(ages))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 23,\n \"id\": \"88b2b6d7\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"36.5\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(median(ages))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 24,\n \"id\": \"ea23370e\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"20\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(mode(ages))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"id\": \"63d2eb9d\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"19.684172999307506\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(stdev(ages))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"8aed4e64\",\n \"metadata\": {},\n \"source\": [\n \"# math\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"id\": \"bd9eaf48\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import math\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"id\": \"4cc7951a\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"3.141592653589793\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(math.pi)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 28,\n \"id\": \"d11c039f\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"2.0\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(math.sqrt(4))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 29,\n \"id\": \"d06915ac\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"8.0\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(math.pow(2,3))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 30,\n \"id\": \"1851237f\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"3\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(math.floor(3.4))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 31,\n \"id\": \"066f7ea3\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"5\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(math.floor(5.9))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 32,\n \"id\": \"ee095949\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"6\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(math.ceil(5.9))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 34,\n \"id\": \"bce4d491\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"4\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(math.ceil(3.14))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 36,\n \"id\": \"2fe20f12\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"3.141592653589793\\n\"\n ]\n }\n ],\n \"source\": [\n \"from math import pi, sqrt, pow, floor, ceil\\n\",\n \"\\n\",\n \"\\n\",\n \"print(pi)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"fffd6d73\",\n \"metadata\": {},\n \"source\": [\n \"# random\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 37,\n \"id\": \"3686b11a\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import random as r\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 41,\n \"id\": \"79f3ce91\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"0.36521596355895625\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(r.random()) # 0 to 1\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 48,\n \"id\": \"fab41c2c\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"14\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(r.randint(5,20))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 51,\n \"id\": \"9ac057b2\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"0.12067598483950182\\n\",\n \"1.2067598483950182\\n\",\n \"1\\n\"\n ]\n }\n ],\n \"source\": [\n \"x = r.random()\\n\",\n \"print(x)\\n\",\n \"\\n\",\n \"x = x * 10\\n\",\n \"print(x)\\n\",\n \"\\n\",\n \"print(math.floor(x))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"785f6932\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"s = ['a', 'b', 'c', 'd', 'e', '1','2','5']\\n\",\n \"# 0 1 2 3 4\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"19c71f93\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"flow of charges > electricity > bits > numbers > character > instructions > function > class > module > package > library > framework\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"daf99091\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"source code ------> byte code ------> output\\n\",\n \" compile run \\n\",\n \" .py .pyc\\n\",\n \"\\n\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 52,\n \"id\": \"3da5a878\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"def color():\\n\",\n \" return \\\"red\\\"\\n\",\n \"\\n\",\n \"def flower():\\n\",\n \" return \\\"color of the flower is blue\\\"\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"3eff71a8\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.12\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n" }, { "path": "March Cohort 2023/5_Modules-Packages/main.py", "content": "# import rose\n\n# print(rose.pie)\n\n# print(rose.color())\n\nfrom statsmeme import descriptive\n\nprint(descriptive.mode())\n\nfrom statsmeme.inferential import models\n\nprint(models())\n\n# wrong statement\n\n# from statsmeme import inferential.models\n\n" }, { "path": "March Cohort 2023/5_Modules-Packages/rose.py", "content": "pie = 3000\n\ndef color():\n return \"red\"\n\ndef flower():\n return \"color of the flower is blue\"" }, { "path": "March Cohort 2023/5_Modules-Packages/statsmeme/__init__.py", "content": "" }, { "path": "March Cohort 2023/5_Modules-Packages/statsmeme/descriptive.py", "content": "def mean(l):\n m = sum(l)/len(l)\n return m\n\n\ndef mode():\n return \"this is mode function from descriptive stats\"\n\n" }, { "path": "March Cohort 2023/5_Modules-Packages/statsmeme/inferential.py", "content": "def models():\n return \"models from inferetial stats\"" }, { "path": "March Cohort 2023/6_VirtualEnvironmentAndFlask/application.py", "content": "from flask import Flask, render_template\nimport requests\nfrom bs4 import BeautifulSoup\n\napp = Flask(__name__) # __main__\n\nurl = 'https://archive.ics.uci.edu/ml/datasets.php'\n\nresponse = requests.get(url)\n\nstatus = response.status_code\n\n# print(status)\n\ncontent = response.content\n\nsoup = BeautifulSoup(content, 'html.parser')\n\n# print(soup.body)\nprint(soup.title)\nprint(soup.title.get_text())\n\n\n@app.route('/')\ndef hello():\n return \"Hello from index route\"\n\n@app.route('/home')\ndef home():\n return \"

Welcome User, this is Home

\"\n\n\n@app.route('/profile')\ndef profile():\n name = \"Himanshu\"\n\n return render_template('index.html', n = name)\n\n\nif __name__ == '__main__':\n app.run(port=5000)\n\n\n\n\n\n# https: //github.com /hemansnation\n# scheme DNS route\n# - file Domain Name System\n# - ftp IP\n# - http\n# ipv4, ipv6\n# API - Application Programming Interface (code/algorithm)\n# localhost - IP - 127.0.0.1:5000/hemansnation" }, { "path": "March Cohort 2023/6_VirtualEnvironmentAndFlask/templates/index.html", "content": "\n\n \n \n \n Document\n\n\n

Hello, \n {{ n }}\n

\n\n\n" }, { "path": "March Cohort 2023/7_NumPy/.ipynb_checkpoints/7_NumPy-checkpoint.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"id\": \"26f54c18\",\n \"metadata\": {},\n \"source\": [\n \"# NumPy\\n\",\n \"\\n\",\n \"Numeric Python\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"126668cf\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# !pip install numpy\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"id\": \"2bfbe2df\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import numpy as np\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"id\": \"22636407\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[1 2 3]\\n\"\n ]\n }\n ],\n \"source\": [\n \"v = np.array([1,2,3])\\n\",\n \"\\n\",\n \"print(v)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"id\": \"0cd6ad0d\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[[1]\\n\",\n \" [2]\\n\",\n \" [3]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"v = np.array([[1],[2],[3]])\\n\",\n \"\\n\",\n \"print(v)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"1acb7495\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"0th dimension -> .\\n\",\n \"1 D ._____.\\n\",\n \"\\n\",\n \"2 D .___|___.\\n\",\n \"\\n\",\n \"3 D .___|___.\\n\",\n \" /\\n\",\n \"4 D\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"61e93431\",\n \"metadata\": {},\n \"source\": [\n \"## Create a matrix\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"id\": \"d863cd16\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[[1 2 3]\\n\",\n \" [4 5 6]\\n\",\n \" [7 8 9]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"matrix = np.array([[1,2,3], [4,5,6], [7,8,9]])\\n\",\n \"\\n\",\n \"print(matrix)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"c0266ec6\",\n \"metadata\": {},\n \"source\": [\n \"## selecting elements\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"id\": \"34cf8022\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"3\\n\"\n ]\n }\n ],\n \"source\": [\n \"v = np.array([1,2,3,4,5,6,7])\\n\",\n \"# 0 1 2 3\\n\",\n \"print(v[2])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"id\": \"e2869c95\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([1, 2, 3, 4, 5, 6, 7])\"\n ]\n },\n \"execution_count\": 7,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"v[:]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"id\": \"9ea056c4\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([3, 4, 5])\"\n ]\n },\n \"execution_count\": 8,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"v[2:5]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"id\": \"ee5da0b4\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"7\"\n ]\n },\n \"execution_count\": 9,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"v[-1]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"id\": \"46c135df\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[[1 2 3]\\n\",\n \" [4 5 6]\\n\",\n \" [7 8 9]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"matrix = np.array([[1,2,3], [4,5,6], [7,8,9]])\\n\",\n \"\\n\",\n \"print(matrix)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"id\": \"3d0a3a64\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[[1 2 3]\\n\",\n \" [4 5 6]\\n\",\n \" [7 8 9]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"m = np.array([[1,2,3], [4,5,6], [7,8,9]])\\n\",\n \"\\n\",\n \"print(m)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"id\": \"a1b8f65f\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"1\"\n ]\n },\n \"execution_count\": 12,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"m[0,0]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"id\": \"93c8144a\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"5\"\n ]\n },\n \"execution_count\": 13,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"m[1,1]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"id\": \"1758d8de\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[1, 2],\\n\",\n \" [4, 5],\\n\",\n \" [7, 8]])\"\n ]\n },\n \"execution_count\": 14,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"m[:3,:2]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"id\": \"2782cb49\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[1, 2, 3],\\n\",\n \" [4, 5, 6],\\n\",\n \" [7, 8, 9]])\"\n ]\n },\n \"execution_count\": 15,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"m\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"id\": \"89140708\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[2],\\n\",\n \" [5],\\n\",\n \" [8]])\"\n ]\n },\n \"execution_count\": 16,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"m[:,1:2]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"id\": \"6cebac94\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[5]])\"\n ]\n },\n \"execution_count\": 17,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"m[1:2,1:2]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"id\": \"04b19a8a\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[4, 5, 6]])\"\n ]\n },\n \"execution_count\": 18,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"m[1:2,:]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"b98fde70\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"00 01 02\\n\",\n \"10 11 12\\n\",\n \"20 21 22\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"ab6051ab\",\n \"metadata\": {},\n \"source\": [\n \"## Describe a Matrix\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"id\": \"fc23b733\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[[ 1 2 3 4]\\n\",\n \" [ 4 5 6 4]\\n\",\n \" [ 7 8 9 13]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"matrix = np.array([[1,2,3,4], [4,5,6,4], [7,8,9,13]])\\n\",\n \"\\n\",\n \"print(matrix)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"id\": \"5605cd43\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"(3, 4)\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(matrix.shape)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"id\": \"f97c5f02\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"12\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(matrix.size)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 28,\n \"id\": \"d60657d9\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"2\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(matrix.ndim)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"48dee353\",\n \"metadata\": {},\n \"source\": [\n \"## max and min values\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 29,\n \"id\": \"9a5bd9cf\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[[1 2 3]\\n\",\n \" [4 5 6]\\n\",\n \" [7 8 9]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"matrix = np.array([[1,2,3], [4,5,6], [7,8,9]])\\n\",\n \"\\n\",\n \"print(matrix)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 30,\n \"id\": \"b7b433c7\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"9\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(np.max(matrix))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 31,\n \"id\": \"069def1a\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"1\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(np.min(matrix))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 32,\n \"id\": \"f6d3191c\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[7 8 9]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(np.max(matrix, axis=0)) # column\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 33,\n \"id\": \"e7f42b91\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[3 6 9]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(np.max(matrix, axis=1)) # row\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"bca10442\",\n \"metadata\": {},\n \"source\": [\n \"## Mean, Variance and Standard Deviation\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 34,\n \"id\": \"807c45c9\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[[1 2 3]\\n\",\n \" [4 5 6]\\n\",\n \" [7 8 9]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"matrix = np.array([[1,2,3], [4,5,6], [7,8,9]])\\n\",\n \"\\n\",\n \"print(matrix)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 35,\n \"id\": \"403da62b\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"5.0\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(np.mean(matrix)) # 45/9 = 5.0\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 36,\n \"id\": \"32f7f4eb\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"6.666666666666667\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(np.var(matrix))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 37,\n \"id\": \"f935ec11\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"2.581988897471611\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(np.std(matrix))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"a5ff96c9\",\n \"metadata\": {},\n \"source\": [\n \"## Reshaping Array\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 38,\n \"id\": \"d61f922b\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[[1 2 3]\\n\",\n \" [4 5 6]\\n\",\n \" [7 8 9]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"matrix = np.array([[1,2,3], [4,5,6], [7,8,9]])\\n\",\n \"\\n\",\n \"print(matrix)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 39,\n \"id\": \"5e4cb0aa\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"(3, 3)\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(matrix.shape)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 40,\n \"id\": \"c96a11df\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[[1]\\n\",\n \" [2]\\n\",\n \" [3]\\n\",\n \" [4]\\n\",\n \" [5]\\n\",\n \" [6]\\n\",\n \" [7]\\n\",\n \" [8]\\n\",\n \" [9]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(matrix.reshape(9,1))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 41,\n \"id\": \"8ba5abef\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"ename\": \"ValueError\",\n \"evalue\": \"cannot reshape array of size 9 into shape (9,2)\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[1;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[1;31mValueError\\u001b[0m Traceback (most recent call last)\",\n \"Input \\u001b[1;32mIn [41]\\u001b[0m, in \\u001b[0;36m\\u001b[1;34m()\\u001b[0m\\n\\u001b[1;32m----> 1\\u001b[0m \\u001b[38;5;28mprint\\u001b[39m(\\u001b[43mmatrix\\u001b[49m\\u001b[38;5;241;43m.\\u001b[39;49m\\u001b[43mreshape\\u001b[49m\\u001b[43m(\\u001b[49m\\u001b[38;5;241;43m9\\u001b[39;49m\\u001b[43m,\\u001b[49m\\u001b[38;5;241;43m2\\u001b[39;49m\\u001b[43m)\\u001b[49m)\\n\",\n \"\\u001b[1;31mValueError\\u001b[0m: cannot reshape array of size 9 into shape (9,2)\"\n ]\n }\n ],\n \"source\": [\n \"print(matrix.reshape(9,2))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 42,\n \"id\": \"31fd6936\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[1 2 3 4 5 6 7 8 9]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(matrix.reshape(9)) # 1 - d array\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 43,\n \"id\": \"a0fe4023\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[[1 2 3 4 5 6 7 8 9]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(matrix.reshape(1,-1))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 44,\n \"id\": \"c8e1142a\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[1 2 3 4 5 6 7 8 9]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(matrix.flatten())\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"fd8bffb7\",\n \"metadata\": {},\n \"source\": [\n \"## Transpose of matrix\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 45,\n \"id\": \"9abc8c81\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[[1 2 3]\\n\",\n \" [4 5 6]\\n\",\n \" [7 8 9]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"matrix = np.array([[1,2,3], [4,5,6], [7,8,9]])\\n\",\n \"\\n\",\n \"print(matrix)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 46,\n \"id\": \"e33de6cf\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[[1 4 7]\\n\",\n \" [2 5 8]\\n\",\n \" [3 6 9]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(matrix.T)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"a87bc2c8\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"720PX\\n\",\n \"1080px\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"af80fec7\",\n \"metadata\": {},\n \"source\": [\n \"## Determinant and Rank\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 47,\n \"id\": \"9e192e55\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[[1 2 3]\\n\",\n \" [4 5 6]\\n\",\n \" [7 8 9]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"matrix = np.array([[1,2,3], [4,5,6], [7,8,9]])\\n\",\n \"\\n\",\n \"print(matrix)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 48,\n \"id\": \"0d8ff2a2\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"-9.51619735392994e-16\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(np.linalg.det(matrix))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 50,\n \"id\": \"3879e071\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"2\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(np.linalg.matrix_rank(matrix)) # number of non zero rows\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"91c3d0b9\",\n \"metadata\": {},\n \"source\": [\n \"# Random Values\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 55,\n \"id\": \"ab89dd83\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[7 4 8]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(np.random.randint(0,11,3))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 60,\n \"id\": \"cda283bb\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[3 6 6]\\n\"\n ]\n }\n ],\n \"source\": [\n \"np.random.seed(5)\\n\",\n \"\\n\",\n \"print(np.random.randint(0,11,3))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 62,\n \"id\": \"b56ac918\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[ 2.82374728 -1.88768311 4.64888033 3.91525025 -0.8205163 -0.98003789\\n\",\n \" -0.63509625 -1.33655895 0.57844071 0.55140067]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(np.random.normal(1.0, 2.0, 10)) # normal distribution\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"5b17b378\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"-1 and 0 and 1\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.12\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n" }, { "path": "March Cohort 2023/7_NumPy/7_NumPy.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"id\": \"26f54c18\",\n \"metadata\": {},\n \"source\": [\n \"# NumPy\\n\",\n \"\\n\",\n \"Numeric Python\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"126668cf\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# !pip install numpy\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"id\": \"2bfbe2df\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import numpy as np\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"id\": \"22636407\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[1 2 3]\\n\"\n ]\n }\n ],\n \"source\": [\n \"v = np.array([1,2,3])\\n\",\n \"\\n\",\n \"print(v)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"id\": \"0cd6ad0d\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[[1]\\n\",\n \" [2]\\n\",\n \" [3]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"v = np.array([[1],[2],[3]])\\n\",\n \"\\n\",\n \"print(v)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"1acb7495\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"0th dimension -> .\\n\",\n \"1 D ._____.\\n\",\n \"\\n\",\n \"2 D .___|___.\\n\",\n \"\\n\",\n \"3 D .___|___.\\n\",\n \" /\\n\",\n \"4 D\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"61e93431\",\n \"metadata\": {},\n \"source\": [\n \"## Create a matrix\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"id\": \"d863cd16\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[[1 2 3]\\n\",\n \" [4 5 6]\\n\",\n \" [7 8 9]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"matrix = np.array([[1,2,3], [4,5,6], [7,8,9]])\\n\",\n \"\\n\",\n \"print(matrix)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"c0266ec6\",\n \"metadata\": {},\n \"source\": [\n \"## selecting elements\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"id\": \"34cf8022\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"3\\n\"\n ]\n }\n ],\n \"source\": [\n \"v = np.array([1,2,3,4,5,6,7])\\n\",\n \"# 0 1 2 3\\n\",\n \"print(v[2])\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"id\": \"e2869c95\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([1, 2, 3, 4, 5, 6, 7])\"\n ]\n },\n \"execution_count\": 7,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"v[:]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"id\": \"9ea056c4\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([3, 4, 5])\"\n ]\n },\n \"execution_count\": 8,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"v[2:5]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"id\": \"ee5da0b4\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"7\"\n ]\n },\n \"execution_count\": 9,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"v[-1]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"id\": \"46c135df\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[[1 2 3]\\n\",\n \" [4 5 6]\\n\",\n \" [7 8 9]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"matrix = np.array([[1,2,3], [4,5,6], [7,8,9]])\\n\",\n \"\\n\",\n \"print(matrix)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 11,\n \"id\": \"3d0a3a64\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[[1 2 3]\\n\",\n \" [4 5 6]\\n\",\n \" [7 8 9]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"m = np.array([[1,2,3], [4,5,6], [7,8,9]])\\n\",\n \"\\n\",\n \"print(m)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"id\": \"a1b8f65f\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"1\"\n ]\n },\n \"execution_count\": 12,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"m[0,0]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 13,\n \"id\": \"93c8144a\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"5\"\n ]\n },\n \"execution_count\": 13,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"m[1,1]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 14,\n \"id\": \"1758d8de\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[1, 2],\\n\",\n \" [4, 5],\\n\",\n \" [7, 8]])\"\n ]\n },\n \"execution_count\": 14,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"m[:3,:2]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 15,\n \"id\": \"2782cb49\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[1, 2, 3],\\n\",\n \" [4, 5, 6],\\n\",\n \" [7, 8, 9]])\"\n ]\n },\n \"execution_count\": 15,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"m\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 16,\n \"id\": \"89140708\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[2],\\n\",\n \" [5],\\n\",\n \" [8]])\"\n ]\n },\n \"execution_count\": 16,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"m[:,1:2]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 17,\n \"id\": \"6cebac94\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[5]])\"\n ]\n },\n \"execution_count\": 17,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"m[1:2,1:2]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 18,\n \"id\": \"04b19a8a\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"array([[4, 5, 6]])\"\n ]\n },\n \"execution_count\": 18,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"m[1:2,:]\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"b98fde70\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"00 01 02\\n\",\n \"10 11 12\\n\",\n \"20 21 22\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"ab6051ab\",\n \"metadata\": {},\n \"source\": [\n \"## Describe a Matrix\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 25,\n \"id\": \"fc23b733\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[[ 1 2 3 4]\\n\",\n \" [ 4 5 6 4]\\n\",\n \" [ 7 8 9 13]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"matrix = np.array([[1,2,3,4], [4,5,6,4], [7,8,9,13]])\\n\",\n \"\\n\",\n \"print(matrix)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 26,\n \"id\": \"5605cd43\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"(3, 4)\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(matrix.shape)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 27,\n \"id\": \"f97c5f02\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"12\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(matrix.size)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 28,\n \"id\": \"d60657d9\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"2\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(matrix.ndim)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"48dee353\",\n \"metadata\": {},\n \"source\": [\n \"## max and min values\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 29,\n \"id\": \"9a5bd9cf\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[[1 2 3]\\n\",\n \" [4 5 6]\\n\",\n \" [7 8 9]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"matrix = np.array([[1,2,3], [4,5,6], [7,8,9]])\\n\",\n \"\\n\",\n \"print(matrix)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 30,\n \"id\": \"b7b433c7\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"9\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(np.max(matrix))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 31,\n \"id\": \"069def1a\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"1\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(np.min(matrix))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 32,\n \"id\": \"f6d3191c\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[7 8 9]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(np.max(matrix, axis=0)) # column\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 33,\n \"id\": \"e7f42b91\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[3 6 9]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(np.max(matrix, axis=1)) # row\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"bca10442\",\n \"metadata\": {},\n \"source\": [\n \"## Mean, Variance and Standard Deviation\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 34,\n \"id\": \"807c45c9\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[[1 2 3]\\n\",\n \" [4 5 6]\\n\",\n \" [7 8 9]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"matrix = np.array([[1,2,3], [4,5,6], [7,8,9]])\\n\",\n \"\\n\",\n \"print(matrix)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 35,\n \"id\": \"403da62b\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"5.0\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(np.mean(matrix)) # 45/9 = 5.0\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 36,\n \"id\": \"32f7f4eb\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"6.666666666666667\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(np.var(matrix))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 37,\n \"id\": \"f935ec11\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"2.581988897471611\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(np.std(matrix))\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"a5ff96c9\",\n \"metadata\": {},\n \"source\": [\n \"## Reshaping Array\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 38,\n \"id\": \"d61f922b\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[[1 2 3]\\n\",\n \" [4 5 6]\\n\",\n \" [7 8 9]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"matrix = np.array([[1,2,3], [4,5,6], [7,8,9]])\\n\",\n \"\\n\",\n \"print(matrix)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 39,\n \"id\": \"5e4cb0aa\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"(3, 3)\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(matrix.shape)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 40,\n \"id\": \"c96a11df\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[[1]\\n\",\n \" [2]\\n\",\n \" [3]\\n\",\n \" [4]\\n\",\n \" [5]\\n\",\n \" [6]\\n\",\n \" [7]\\n\",\n \" [8]\\n\",\n \" [9]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(matrix.reshape(9,1))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 41,\n \"id\": \"8ba5abef\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"ename\": \"ValueError\",\n \"evalue\": \"cannot reshape array of size 9 into shape (9,2)\",\n \"output_type\": \"error\",\n \"traceback\": [\n \"\\u001b[1;31m---------------------------------------------------------------------------\\u001b[0m\",\n \"\\u001b[1;31mValueError\\u001b[0m Traceback (most recent call last)\",\n \"Input \\u001b[1;32mIn [41]\\u001b[0m, in \\u001b[0;36m\\u001b[1;34m()\\u001b[0m\\n\\u001b[1;32m----> 1\\u001b[0m \\u001b[38;5;28mprint\\u001b[39m(\\u001b[43mmatrix\\u001b[49m\\u001b[38;5;241;43m.\\u001b[39;49m\\u001b[43mreshape\\u001b[49m\\u001b[43m(\\u001b[49m\\u001b[38;5;241;43m9\\u001b[39;49m\\u001b[43m,\\u001b[49m\\u001b[38;5;241;43m2\\u001b[39;49m\\u001b[43m)\\u001b[49m)\\n\",\n \"\\u001b[1;31mValueError\\u001b[0m: cannot reshape array of size 9 into shape (9,2)\"\n ]\n }\n ],\n \"source\": [\n \"print(matrix.reshape(9,2))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 42,\n \"id\": \"31fd6936\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[1 2 3 4 5 6 7 8 9]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(matrix.reshape(9)) # 1 - d array\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 43,\n \"id\": \"a0fe4023\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[[1 2 3 4 5 6 7 8 9]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(matrix.reshape(1,-1))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 44,\n \"id\": \"c8e1142a\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[1 2 3 4 5 6 7 8 9]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(matrix.flatten())\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"fd8bffb7\",\n \"metadata\": {},\n \"source\": [\n \"## Transpose of matrix\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 45,\n \"id\": \"9abc8c81\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[[1 2 3]\\n\",\n \" [4 5 6]\\n\",\n \" [7 8 9]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"matrix = np.array([[1,2,3], [4,5,6], [7,8,9]])\\n\",\n \"\\n\",\n \"print(matrix)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 46,\n \"id\": \"e33de6cf\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[[1 4 7]\\n\",\n \" [2 5 8]\\n\",\n \" [3 6 9]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(matrix.T)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"a87bc2c8\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"720PX\\n\",\n \"1080px\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"af80fec7\",\n \"metadata\": {},\n \"source\": [\n \"## Determinant and Rank\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 47,\n \"id\": \"9e192e55\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[[1 2 3]\\n\",\n \" [4 5 6]\\n\",\n \" [7 8 9]]\\n\"\n ]\n }\n ],\n \"source\": [\n \"matrix = np.array([[1,2,3], [4,5,6], [7,8,9]])\\n\",\n \"\\n\",\n \"print(matrix)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 48,\n \"id\": \"0d8ff2a2\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"-9.51619735392994e-16\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(np.linalg.det(matrix))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 50,\n \"id\": \"3879e071\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"2\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(np.linalg.matrix_rank(matrix)) # number of non zero rows\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"91c3d0b9\",\n \"metadata\": {},\n \"source\": [\n \"# Random Values\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 55,\n \"id\": \"ab89dd83\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[7 4 8]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(np.random.randint(0,11,3))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 60,\n \"id\": \"cda283bb\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[3 6 6]\\n\"\n ]\n }\n ],\n \"source\": [\n \"np.random.seed(5)\\n\",\n \"\\n\",\n \"print(np.random.randint(0,11,3))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 62,\n \"id\": \"b56ac918\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"[ 2.82374728 -1.88768311 4.64888033 3.91525025 -0.8205163 -0.98003789\\n\",\n \" -0.63509625 -1.33655895 0.57844071 0.55140067]\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(np.random.normal(1.0, 2.0, 10)) # normal distribution\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"5b17b378\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"-1 and 0 and 1\"\n ]\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.12\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n" }, { "path": "March Cohort 2023/8_9_Pandas/.ipynb_checkpoints/8_9_Pandas-checkpoint.ipynb", "content": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"id\": \"a6a858e0\",\n \"metadata\": {},\n \"source\": [\n \"# Pandas\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"id\": \"848d64c8\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# !pip install pandas\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"id\": \"eabb0abf\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import pandas as pd\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"id\": \"4e949120\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" 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ESTTemperatureDewPointHumiditySea Level PressureInVisibilityMilesWindSpeedMPHPrecipitationInCloudCoverEventsWindDirDegrees
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daytemperature
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daytemperaturewindspeedevent
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daytemperaturewindspeedevent
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temperaturewindspeed
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mean30.3333334.666667
std3.8297082.338090
min24.0000002.000000
25%28.7500002.500000
50%31.5000005.000000
75%32.0000006.750000
max35.0000007.000000
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daytemperaturewindspeedevent
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temperaturewindspeedevent
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temperaturewindspeedevent
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daytemperaturewindspeedevent
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11/2/2023357Sunny
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31/4/2023244Rain
41/5/2023322Sunny
51/6/2023317Snow
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\"\n ],\n \"text/plain\": [\n \" day temperature windspeed event\\n\",\n \"0 1/1/2023 32 6 Rain\\n\",\n \"1 1/2/2023 35 7 Sunny\\n\",\n \"2 1/3/2023 28 2 Snow\\n\",\n \"3 1/4/2023 24 4 Rain\\n\",\n \"4 1/5/2023 32 2 Sunny\\n\",\n \"5 1/6/2023 31 7 Snow\"\n ]\n },\n \"execution_count\": 48,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 49,\n \"id\": \"6a2cd4ba\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
daytemperaturewindspeed
event
Rain1/1/2023326
Sunny1/2/2023357
Snow1/3/2023282
Rain1/4/2023244
Sunny1/5/2023322
Snow1/6/2023317
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daytemperaturewindspeed
event
Sunny1/2/2023357
Sunny1/5/2023322
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" day temperature windspeed\\n\",\n \"event \\n\",\n \"Sunny 1/2/2023 35 7\\n\",\n \"Sunny 1/5/2023 32 2\"\n ]\n },\n \"execution_count\": 50,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df.loc['Sunny']\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"8c16b66d\",\n \"metadata\": {},\n \"source\": [\n \"# Different ways to create dataframe\\n\",\n \"\\n\",\n \"### using excel\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 51,\n \"id\": \"36d8a05a\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
daytemperaturewindspeedevent
02017-01-01326Rain
12017-01-02357Sunny
22017-01-03282Snow
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" day temperature windspeed event\\n\",\n \"0 2017-01-01 32 6 Rain\\n\",\n \"1 2017-01-02 35 7 Sunny\\n\",\n \"2 2017-01-03 28 2 Snow\"\n ]\n },\n \"execution_count\": 51,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df = pd.read_excel('weather_data.xlsx', 'Sheet1')\\n\",\n \"df\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"4e1681da\",\n \"metadata\": {},\n \"source\": [\n \"### using tuples and list\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 52,\n \"id\": \"46ba1a56\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
0123
01/1/2023326Rain
11/2/2023356Sunny
21/3/2023322Snow
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" 0 1 2 3\\n\",\n \"0 1/1/2023 32 6 Rain\\n\",\n \"1 1/2/2023 35 6 Sunny\\n\",\n \"2 1/3/2023 32 2 Snow\"\n ]\n },\n \"execution_count\": 52,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"weather_data = [\\n\",\n \" ('1/1/2023', 32, 6, 'Rain'),\\n\",\n \" ('1/2/2023', 35, 6, 'Sunny'),\\n\",\n \" ('1/3/2023', 32, 2, 'Snow')\\n\",\n \"]\\n\",\n \"\\n\",\n \"df = pd.DataFrame(data=weather_data)\\n\",\n \"\\n\",\n \"df\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 53,\n \"id\": \"f6e5f754\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
daytemperaturewindspeedevent
01/1/2023326Rain
11/2/2023356Sunny
21/3/2023322Snow
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" day temperature windspeed event\\n\",\n \"0 1/1/2023 32 6 Rain\\n\",\n \"1 1/2/2023 35 6 Sunny\\n\",\n \"2 1/3/2023 32 2 Snow\"\n ]\n },\n \"execution_count\": 53,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"weather_data = [\\n\",\n \" ('1/1/2023', 32, 6, 'Rain'),\\n\",\n \" ('1/2/2023', 35, 6, 'Sunny'),\\n\",\n \" ('1/3/2023', 32, 2, 'Snow')\\n\",\n \"]\\n\",\n \"\\n\",\n \"df = pd.DataFrame(data=weather_data, columns=['day', 'temperature', 'windspeed', 'event'])\\n\",\n \"\\n\",\n \"df\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"f2ed69e8\",\n \"metadata\": {},\n \"source\": [\n \"### list of dictionary\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 54,\n \"id\": \"588425b3\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
daytemperaturewindspeedevent
01/1/2023326Rain
11/2/2023357Sunny
21/3/2023326Snow
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" day temperature windspeed event\\n\",\n \"0 1/1/2023 32 6 Rain\\n\",\n \"1 1/2/2023 35 7 Sunny\\n\",\n \"2 1/3/2023 32 6 Snow\"\n ]\n },\n \"execution_count\": 54,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"weather_data = [\\n\",\n \" {'day': '1/1/2023', 'temperature': 32, 'windspeed': 6, 'event': 'Rain'},\\n\",\n \" {'day': '1/2/2023', 'temperature': 35, 'windspeed': 7, 'event': 'Sunny'},\\n\",\n \" {'day': '1/3/2023', 'temperature': 32, 'windspeed': 6, 'event': 'Snow'}\\n\",\n \"]\\n\",\n \"\\n\",\n \"df = pd.DataFrame(data=weather_data)\\n\",\n \"df\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 55,\n \"id\": \"2e6b7433\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
daytemperaturewindspeedevent
01/1/2023326Rain
11/2/2023357Sunny
21/3/2023326Snow
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" day temperature windspeed event\\n\",\n \"0 1/1/2023 32 6 Rain\\n\",\n \"1 1/2/2023 35 7 Sunny\\n\",\n \"2 1/3/2023 32 6 Snow\"\n ]\n },\n \"execution_count\": 55,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"5636c651\",\n \"metadata\": {},\n \"source\": [\n \"### write to csv\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 56,\n \"id\": \"49a42525\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"df.to_csv('indore.csv', index=True)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"2076e420\",\n \"metadata\": {},\n \"source\": [\n \"# Day 9 - Pandas day 2\\n\",\n \"\\n\",\n \"### Handling missing values\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"id\": \"0bcbb714\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import pandas as pd\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"id\": \"f64c5df9\",\n \"metadata\": {\n \"scrolled\": false\n },\n \"outputs\": [],\n \"source\": [\n \"# help(pd.read_csv)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"id\": \"8b9179c5\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
daytemperaturewindspeedevent
02017-01-0132.06.0Rain
12017-01-04NaN9.0Sunny
22017-01-0528.0NaNSnow
32017-01-06NaN7.0NaN
42017-01-0732.0NaNRain
52017-01-08NaNNaNSunny
62017-01-09NaNNaNNaN
72017-01-1034.08.0Cloudy
82017-01-1140.012.0Sunny
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" day temperature windspeed event\\n\",\n \"0 2017-01-01 32.0 6.0 Rain\\n\",\n \"1 2017-01-04 NaN 9.0 Sunny\\n\",\n \"2 2017-01-05 28.0 NaN Snow\\n\",\n \"3 2017-01-06 NaN 7.0 NaN\\n\",\n \"4 2017-01-07 32.0 NaN Rain\\n\",\n \"5 2017-01-08 NaN NaN Sunny\\n\",\n \"6 2017-01-09 NaN NaN NaN\\n\",\n \"7 2017-01-10 34.0 8.0 Cloudy\\n\",\n \"8 2017-01-11 40.0 12.0 Sunny\"\n ]\n },\n \"execution_count\": 4,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df = pd.read_csv('weather_data2.csv', parse_dates=['day'])\\n\",\n \"df\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"id\": \"ed579b5c\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import numpy as np\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"id\": \"a7814651\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"nan\"\n ]\n },\n \"execution_count\": 6,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"np.nan # \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"id\": \"d96c216f\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(type(df.day[0]))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"id\": \"f6d09c15\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
temperaturewindspeedevent
day
2017-01-0132.06.0Rain
2017-01-04NaN9.0Sunny
2017-01-0528.0NaNSnow
2017-01-06NaN7.0NaN
2017-01-0732.0NaNRain
2017-01-08NaNNaNSunny
2017-01-09NaNNaNNaN
2017-01-1034.08.0Cloudy
2017-01-1140.012.0Sunny
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" temperature windspeed event\\n\",\n \"day \\n\",\n \"2017-01-01 32.0 6.0 Rain\\n\",\n \"2017-01-04 NaN 9.0 Sunny\\n\",\n \"2017-01-05 28.0 NaN Snow\\n\",\n \"2017-01-06 NaN 7.0 NaN\\n\",\n \"2017-01-07 32.0 NaN Rain\\n\",\n \"2017-01-08 NaN NaN Sunny\\n\",\n \"2017-01-09 NaN NaN NaN\\n\",\n \"2017-01-10 34.0 8.0 Cloudy\\n\",\n \"2017-01-11 40.0 12.0 Sunny\"\n ]\n },\n \"execution_count\": 8,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df.set_index('day', inplace=True)\\n\",\n \"df\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"c32e25ab\",\n \"metadata\": {},\n \"source\": [\n \"## fillna\\n\",\n \"\\n\",\n \"Fill Not Available\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"id\": \"6b9159d9\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
temperaturewindspeedevent
day
2017-01-0132.06.0Rain
2017-01-040.09.0Sunny
2017-01-0528.00.0Snow
2017-01-060.07.00
2017-01-0732.00.0Rain
2017-01-080.00.0Sunny
2017-01-090.00.00
2017-01-1034.08.0Cloudy
2017-01-1140.012.0Sunny
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" temperature windspeed event\\n\",\n \"day \\n\",\n \"2017-01-01 32.0 6.0 Rain\\n\",\n \"2017-01-04 0.0 9.0 Sunny\\n\",\n \"2017-01-05 28.0 0.0 Snow\\n\",\n \"2017-01-06 0.0 7.0 0\\n\",\n \"2017-01-07 32.0 0.0 Rain\\n\",\n \"2017-01-08 0.0 0.0 Sunny\\n\",\n \"2017-01-09 0.0 0.0 0\\n\",\n \"2017-01-10 34.0 8.0 Cloudy\\n\",\n \"2017-01-11 40.0 12.0 Sunny\"\n ]\n },\n \"execution_count\": 9,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"new_df = df.fillna(0)\\n\",\n \"new_df\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"id\": \"4f61e387\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
temperaturewindspeedevent
day
2017-01-0132.06.0Rain
2017-01-040.09.0Sunny
2017-01-0528.00.0Snow
2017-01-060.07.0No Event
2017-01-0732.00.0Rain
2017-01-080.00.0Sunny
2017-01-090.00.0No Event
2017-01-1034.08.0Cloudy
2017-01-1140.012.0Sunny
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temperaturewindspeedevent
day
2017-01-0132.06.0Rain
2017-01-0432.09.0Sunny
2017-01-0528.09.0Snow
2017-01-0628.07.0Snow
2017-01-0732.07.0Rain
2017-01-0832.07.0Sunny
2017-01-0932.07.0Sunny
2017-01-1034.08.0Cloudy
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temperaturewindspeedevent
day
2017-01-0132.06.0Rain
2017-01-0428.09.0Sunny
2017-01-0528.07.0Snow
2017-01-0632.07.0Rain
2017-01-0732.08.0Rain
2017-01-0834.08.0Sunny
2017-01-0934.08.0Cloudy
2017-01-1034.08.0Cloudy
2017-01-1140.012.0Sunny
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temperaturewindspeedevent
day
2017-01-0132.06.0Rain
2017-01-04NaN9.0Sunny
2017-01-0528.0NaNSnow
2017-01-06NaN7.0NaN
2017-01-0732.0NaNRain
2017-01-08NaNNaNSunny
2017-01-09NaNNaNNaN
2017-01-1034.08.0Cloudy
2017-01-1140.012.0Sunny
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temperaturewindspeedevent
day
2017-01-0132.0000006.00Rain
2017-01-0430.0000009.00Sunny
2017-01-0528.0000008.00Snow
2017-01-0630.0000007.00NaN
2017-01-0732.0000007.25Rain
2017-01-0832.6666677.50Sunny
2017-01-0933.3333337.75NaN
2017-01-1034.0000008.00Cloudy
2017-01-1140.00000012.00Sunny
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temperaturewindspeedevent
day
2017-01-0132.0000006.00Rain
2017-01-0429.0000009.00Sunny
2017-01-0528.0000008.00Snow
2017-01-0630.0000007.00NaN
2017-01-0732.0000007.25Rain
2017-01-0832.6666677.50Sunny
2017-01-0933.3333337.75NaN
2017-01-1034.0000008.00Cloudy
2017-01-1140.00000012.00Sunny
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temperaturewindspeedevent
day
2017-01-0132.06.0Rain
2017-01-04NaN9.0Sunny
2017-01-0528.0NaNSnow
2017-01-06NaN7.0NaN
2017-01-0732.0NaNRain
2017-01-08NaNNaNSunny
2017-01-09NaNNaNNaN
2017-01-1034.08.0Cloudy
2017-01-1140.012.0Sunny
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\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
temperaturewindspeedevent
day
2017-01-0132.06.0Rain
2017-01-1034.08.0Cloudy
2017-01-1140.012.0Sunny
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temperaturewindspeedevent
day
2017-01-0132.06.0Rain
2017-01-04NaN9.0Sunny
2017-01-0528.0NaNSnow
2017-01-06NaN7.0NaN
2017-01-0732.0NaNRain
2017-01-08NaNNaNSunny
2017-01-1034.08.0Cloudy
2017-01-1140.012.0Sunny
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temperaturewindspeedevent
day
2017-01-0132.06.0Rain
2017-01-04NaN9.0Sunny
2017-01-0528.0NaNSnow
2017-01-0732.0NaNRain
2017-01-1034.08.0Cloudy
2017-01-1140.012.0Sunny
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temperaturewindspeedevent
day
2017-01-0132.06.0Rain
2017-01-04NaN9.0Sunny
2017-01-0528.0NaNSnow
2017-01-06NaN7.0NaN
2017-01-0732.0NaNRain
2017-01-08NaNNaNSunny
2017-01-09NaNNaNNaN
2017-01-1034.08.0Cloudy
2017-01-1140.012.0Sunny
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temperaturewindspeedevent
2017-01-0132.06.0Rain
2017-01-02NaNNaNNaN
2017-01-03NaNNaNNaN
2017-01-04NaN9.0Sunny
2017-01-0528.0NaNSnow
2017-01-06NaN7.0NaN
2017-01-0732.0NaNRain
2017-01-08NaNNaNSunny
2017-01-09NaNNaNNaN
2017-01-1034.08.0Cloudy
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daytemperaturewindspeedevent
01/1/2017326Rain
11/2/2017-999997Sunny
21/3/201728-99999Snow
31/4/2017-9999970
41/5/201732-99999Rain
51/6/2017312Sunny
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daytemperaturewindspeedevent
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21/3/201728.0NaNSnow
31/4/2017NaN7.00
41/5/201732.0NaNRain
51/6/201731.02.0Sunny
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daytemperaturewindspeedevent
01/1/2017326Rain
11/2/201707Sunny
21/3/2017280Snow
31/4/2017070
41/5/2017320Rain
51/6/2017312Sunny
61/6/20173450
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daytemperaturewindspeedevent
01/1/201732.06.0Rain
11/2/2017NaN7.0Sunny
21/3/201728.0NaNSnow
31/4/2017NaN7.0NaN
41/5/201732.0NaNRain
51/6/201731.02.0Sunny
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daytemperaturewindspeedevent
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21/3/201728-99999Snow
31/4/2017-9999970
41/5/201732-99999Rain
51/6/2017312Sunny
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daytemperaturewindspeedevent
01/1/201732.06.0Sunny
11/2/2017NaN7.0Sunny
21/3/201728.0NaNSnow
31/4/2017NaN7.00
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daytemperaturewindspeedevent
01/1/2017326Rain
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21/3/201728-99999Snow
31/4/2017-9999970
41/5/201732-99999Rain
51/6/2017312Sunny
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daytemperaturewindspeedevent
01/1/2017326R
11/2/2017-999997S
21/3/201728-99999S
31/4/2017-9999970
41/5/201732-99999R
51/6/2017312S
61/6/20173450
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" day temperature windspeed event\\n\",\n \"0 1/1/2017 32 6 R\\n\",\n \"1 1/2/2017 -99999 7 S\\n\",\n \"2 1/3/2017 28 -99999 S\\n\",\n \"3 1/4/2017 -99999 7 0\\n\",\n \"4 1/5/2017 32 -99999 R\\n\",\n \"5 1/6/2017 31 2 S\\n\",\n \"6 1/6/2017 34 5 0\"\n ]\n },\n \"execution_count\": 31,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"new_df = df.replace({\\n\",\n \" 'temperature': '[A-Za-z]',\\n\",\n \" 'event': '[a-z]'\\n\",\n \"}, '', regex=True)\\n\",\n \"\\n\",\n \"new_df\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 32,\n \"id\": \"102ce591\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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daytemperaturewindspeedeventspeed
001-01-201732C6Rain45kmph
101-02-2017-999997Sunny40km
201-03-201728F-99999Snow30kmp
301-04-2017-999997056kmph
401-05-201732C-99999Rain65
501-06-201731c2Sunny76
601-06-201734f5080k
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" day temperature windspeed event speed\\n\",\n \"0 01-01-2017 32C 6 Rain 45kmph\\n\",\n \"1 01-02-2017 -99999 7 Sunny 40km\\n\",\n \"2 01-03-2017 28F -99999 Snow 30kmp\\n\",\n \"3 01-04-2017 -99999 7 0 56kmph\\n\",\n \"4 01-05-2017 32C -99999 Rain 65\\n\",\n \"5 01-06-2017 31c 2 Sunny 76\\n\",\n \"6 01-06-2017 34f 5 0 80k\"\n ]\n },\n \"execution_count\": 32,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df = pd.read_csv('weather_datamissing_regex.csv')\\n\",\n \"\\n\",\n \"df\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"65f7bcb5\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# '[!@#$%^&]'\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 34,\n \"id\": \"854610ff\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
daytemperaturewindspeedeventspeed
001-01-2017326Rain45
101-02-2017-999997Sunny40
201-03-201728-99999Snow30
301-04-2017-999997056
401-05-201732-99999Rain65
501-06-2017312Sunny76
601-06-2017345080
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" day temperature windspeed event speed\\n\",\n \"0 01-01-2017 32 6 Rain 45\\n\",\n \"1 01-02-2017 -99999 7 Sunny 40\\n\",\n \"2 01-03-2017 28 -99999 Snow 30\\n\",\n \"3 01-04-2017 -99999 7 0 56\\n\",\n \"4 01-05-2017 32 -99999 Rain 65\\n\",\n \"5 01-06-2017 31 2 Sunny 76\\n\",\n \"6 01-06-2017 34 5 0 80\"\n ]\n },\n \"execution_count\": 34,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"new_df = df.replace({\\n\",\n \" 'temperature': '[CcFf]',\\n\",\n \" 'speed': '[kmph]'\\n\",\n \"}, '', regex=True)\\n\",\n \"\\n\",\n \"new_df\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 36,\n \"id\": \"8b919c4a\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# new_df = df.replace({\\n\",\n \"# 'temperature': '[-100000-0]',\\n\",\n \"# 'windspeed': '[-100000-0]'\\n\",\n \"# }, '', regex=True)\\n\",\n \"\\n\",\n \"# new_df\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"cfbe57f1\",\n \"metadata\": {},\n \"source\": [\n \"# Group By\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 37,\n \"id\": \"5e885561\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
daycitytemperaturewindspeedevent
01/1/2017new york326Rain
11/2/2017new york367Sunny
21/3/2017new york2812Snow
31/4/2017new york337Sunny
41/1/2017mumbai905Sunny
51/2/2017mumbai8512Fog
61/3/2017mumbai8715Fog
71/4/2017mumbai925Rain
81/1/2017paris4520Sunny
91/2/2017paris5013Cloudy
101/3/2017paris548Cloudy
111/4/2017paris4210Cloudy
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" day city temperature windspeed event\\n\",\n \"0 1/1/2017 new york 32 6 Rain\\n\",\n \"1 1/2/2017 new york 36 7 Sunny\\n\",\n \"2 1/3/2017 new york 28 12 Snow\\n\",\n \"3 1/4/2017 new york 33 7 Sunny\\n\",\n \"4 1/1/2017 mumbai 90 5 Sunny\\n\",\n \"5 1/2/2017 mumbai 85 12 Fog\\n\",\n \"6 1/3/2017 mumbai 87 15 Fog\\n\",\n \"7 1/4/2017 mumbai 92 5 Rain\\n\",\n \"8 1/1/2017 paris 45 20 Sunny\\n\",\n \"9 1/2/2017 paris 50 13 Cloudy\\n\",\n \"10 1/3/2017 paris 54 8 Cloudy\\n\",\n \"11 1/4/2017 paris 42 10 Cloudy\"\n ]\n },\n \"execution_count\": 37,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df = pd.read_csv('weather_by_cities.csv')\\n\",\n \"df\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 38,\n \"id\": \"c9952665\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 38,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"gb = df.groupby('city')\\n\",\n \"gb\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 39,\n \"id\": \"517be304\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\",\n \"City: mumbai\\n\",\n \"\\n\",\n \"Data: \\n\",\n \" day city temperature windspeed event\\n\",\n \"4 1/1/2017 mumbai 90 5 Sunny\\n\",\n \"5 1/2/2017 mumbai 85 12 Fog\\n\",\n \"6 1/3/2017 mumbai 87 15 Fog\\n\",\n \"7 1/4/2017 mumbai 92 5 Rain\\n\",\n \"\\n\",\n \"City: new york\\n\",\n \"\\n\",\n \"Data: \\n\",\n \" day city temperature windspeed event\\n\",\n \"0 1/1/2017 new york 32 6 Rain\\n\",\n \"1 1/2/2017 new york 36 7 Sunny\\n\",\n \"2 1/3/2017 new york 28 12 Snow\\n\",\n \"3 1/4/2017 new york 33 7 Sunny\\n\",\n \"\\n\",\n \"City: paris\\n\",\n \"\\n\",\n \"Data: \\n\",\n \" day city temperature windspeed event\\n\",\n \"8 1/1/2017 paris 45 20 Sunny\\n\",\n \"9 1/2/2017 paris 50 13 Cloudy\\n\",\n \"10 1/3/2017 paris 54 8 Cloudy\\n\",\n \"11 1/4/2017 paris 42 10 Cloudy\\n\"\n ]\n }\n ],\n \"source\": [\n \"for city, data in gb:\\n\",\n \" print()\\n\",\n \" print(\\\"City: \\\", city)\\n\",\n \" print()\\n\",\n \" print(\\\"Data: \\\\n\\\", data)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 40,\n \"id\": \"f09dcab9\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
daycitytemperaturewindspeedevent
41/1/2017mumbai905Sunny
51/2/2017mumbai8512Fog
61/3/2017mumbai8715Fog
71/4/2017mumbai925Rain
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" day city temperature windspeed event\\n\",\n \"4 1/1/2017 mumbai 90 5 Sunny\\n\",\n \"5 1/2/2017 mumbai 85 12 Fog\\n\",\n \"6 1/3/2017 mumbai 87 15 Fog\\n\",\n \"7 1/4/2017 mumbai 92 5 Rain\"\n ]\n },\n \"execution_count\": 40,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"gb.get_group('mumbai')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 41,\n \"id\": \"b8c88700\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
daycitytemperaturewindspeedevent
81/1/2017paris4520Sunny
91/2/2017paris5013Cloudy
101/3/2017paris548Cloudy
111/4/2017paris4210Cloudy
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" day city temperature windspeed event\\n\",\n \"8 1/1/2017 paris 45 20 Sunny\\n\",\n \"9 1/2/2017 paris 50 13 Cloudy\\n\",\n \"10 1/3/2017 paris 54 8 Cloudy\\n\",\n \"11 1/4/2017 paris 42 10 Cloudy\"\n ]\n },\n \"execution_count\": 41,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"gb.get_group('paris')\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 42,\n \"id\": \"e4883217\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
temperaturewindspeed
city
mumbai88.509.25
new york32.258.00
paris47.7512.75
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" temperature windspeed\\n\",\n \"city \\n\",\n \"mumbai 88.50 9.25\\n\",\n \"new york 32.25 8.00\\n\",\n \"paris 47.75 12.75\"\n ]\n },\n \"execution_count\": 42,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"gb.mean()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 43,\n \"id\": \"fcab2c81\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
daytemperaturewindspeedevent
city
mumbai1/4/20179215Sunny
new york1/4/20173612Sunny
paris1/4/20175420Sunny
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" day temperature windspeed event\\n\",\n \"city \\n\",\n \"mumbai 1/4/2017 92 15 Sunny\\n\",\n \"new york 1/4/2017 36 12 Sunny\\n\",\n \"paris 1/4/2017 54 20 Sunny\"\n ]\n },\n \"execution_count\": 43,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"gb.max()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 44,\n \"id\": \"0ad8cca3\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
temperaturewindspeed
countmeanstdmin25%50%75%maxcountmeanstdmin25%50%75%max
city
mumbai4.088.503.10912685.086.5088.590.5092.04.09.255.0579975.05.008.512.7515.0
new york4.032.253.30403828.031.0032.533.7536.04.08.002.7080136.06.757.08.2512.0
paris4.047.755.31507342.044.2547.551.0054.04.012.755.2519848.09.5011.514.7520.0
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\"\n ],\n \"text/plain\": [\n \" temperature \\\\\\n\",\n \" count mean std min 25% 50% 75% max \\n\",\n \"city \\n\",\n \"mumbai 4.0 88.50 3.109126 85.0 86.50 88.5 90.50 92.0 \\n\",\n \"new york 4.0 32.25 3.304038 28.0 31.00 32.5 33.75 36.0 \\n\",\n \"paris 4.0 47.75 5.315073 42.0 44.25 47.5 51.00 54.0 \\n\",\n \"\\n\",\n \" windspeed \\n\",\n \" count mean std min 25% 50% 75% max \\n\",\n \"city \\n\",\n \"mumbai 4.0 9.25 5.057997 5.0 5.00 8.5 12.75 15.0 \\n\",\n \"new york 4.0 8.00 2.708013 6.0 6.75 7.0 8.25 12.0 \\n\",\n \"paris 4.0 12.75 5.251984 8.0 9.50 11.5 14.75 20.0 \"\n ]\n },\n \"execution_count\": 44,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"gb.describe()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 45,\n \"id\": \"559ef865\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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daytemperaturewindspeedevent
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citytemperaturehumidity
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citytemperaturehumidity
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citytemperaturehumidity
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citytemperaturehumidity
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citytemperaturehumidity
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citytemperaturehumidity
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citytemperaturehumidityevent
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ESTTemperatureDewPointHumiditySea Level PressureInVisibilityMilesWindSpeedMPHPrecipitationInCloudCoverEventsWindDirDegrees
01/1/201638235230.03108.005NaN281
11/2/201636184630.02107.003NaN275
21/3/201640214729.86108.001NaN277
31/4/20162594430.05109.003NaN345
41/5/201620-34130.57105.000NaN333
51/6/20163343530.50104.000NaN259
61/7/201639113330.28102.003NaN293
71/8/201639296430.20104.008NaN79
81/9/201644387730.1698.0T8Rain76
91/10/201650467129.594NaN1.87Rain109
101/11/20163383729.9210NaN01NaN289
111/12/201635155329.85106.0T4NaN235
121/13/20162644229.941010.000NaN284
131/14/201630124729.95105.0T7NaN266
141/15/201643316229.8295.0T2NaN101
151/16/201647377029.5287.00.247Rain340
161/17/201636236629.7886.00.056Fog-Snow345
171/18/20162565329.83912.0T2Snow293
181/19/20162234230.031011.001NaN293
191/20/201632154930.13106.002NaN302
201/21/201631114530.15106.001NaN312
211/22/20162664130.219NaN0.013Snow34
221/23/201626217829.77116.02.318Fog-Snow42
231/24/201628115329.9286.0T3Snow327
241/25/201634185430.25103.002NaN286
251/26/201643295630.03107.002NaN244
261/27/201641224530.03107.0T3Rain311
271/28/201637205129.90105.001NaN234
281/29/201636215029.58108.004NaN298
291/30/201634164630.01107.000NaN257
301/31/201646285229.90105.000NaN241
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\"\n ],\n \"text/plain\": [\n \" EST Temperature DewPoint Humidity Sea Level PressureIn \\\\\\n\",\n \"0 1/1/2016 38 23 52 30.03 \\n\",\n \"1 1/2/2016 36 18 46 30.02 \\n\",\n \"2 1/3/2016 40 21 47 29.86 \\n\",\n \"3 1/4/2016 25 9 44 30.05 \\n\",\n \"4 1/5/2016 20 -3 41 30.57 \\n\",\n \"5 1/6/2016 33 4 35 30.50 \\n\",\n \"6 1/7/2016 39 11 33 30.28 \\n\",\n \"7 1/8/2016 39 29 64 30.20 \\n\",\n \"8 1/9/2016 44 38 77 30.16 \\n\",\n \"9 1/10/2016 50 46 71 29.59 \\n\",\n \"10 1/11/2016 33 8 37 29.92 \\n\",\n \"11 1/12/2016 35 15 53 29.85 \\n\",\n \"12 1/13/2016 26 4 42 29.94 \\n\",\n \"13 1/14/2016 30 12 47 29.95 \\n\",\n \"14 1/15/2016 43 31 62 29.82 \\n\",\n \"15 1/16/2016 47 37 70 29.52 \\n\",\n \"16 1/17/2016 36 23 66 29.78 \\n\",\n \"17 1/18/2016 25 6 53 29.83 \\n\",\n \"18 1/19/2016 22 3 42 30.03 \\n\",\n \"19 1/20/2016 32 15 49 30.13 \\n\",\n \"20 1/21/2016 31 11 45 30.15 \\n\",\n \"21 1/22/2016 26 6 41 30.21 \\n\",\n \"22 1/23/2016 26 21 78 29.77 \\n\",\n \"23 1/24/2016 28 11 53 29.92 \\n\",\n \"24 1/25/2016 34 18 54 30.25 \\n\",\n \"25 1/26/2016 43 29 56 30.03 \\n\",\n \"26 1/27/2016 41 22 45 30.03 \\n\",\n \"27 1/28/2016 37 20 51 29.90 \\n\",\n \"28 1/29/2016 36 21 50 29.58 \\n\",\n \"29 1/30/2016 34 16 46 30.01 \\n\",\n \"30 1/31/2016 46 28 52 29.90 \\n\",\n \"\\n\",\n \" VisibilityMiles WindSpeedMPH PrecipitationIn CloudCover Events \\\\\\n\",\n \"0 10 8.0 0 5 NaN \\n\",\n \"1 10 7.0 0 3 NaN \\n\",\n \"2 10 8.0 0 1 NaN \\n\",\n \"3 10 9.0 0 3 NaN \\n\",\n \"4 10 5.0 0 0 NaN \\n\",\n \"5 10 4.0 0 0 NaN \\n\",\n \"6 10 2.0 0 3 NaN \\n\",\n \"7 10 4.0 0 8 NaN \\n\",\n \"8 9 8.0 T 8 Rain \\n\",\n \"9 4 NaN 1.8 7 Rain \\n\",\n \"10 10 NaN 0 1 NaN \\n\",\n \"11 10 6.0 T 4 NaN \\n\",\n \"12 10 10.0 0 0 NaN \\n\",\n \"13 10 5.0 T 7 NaN \\n\",\n \"14 9 5.0 T 2 NaN \\n\",\n \"15 8 7.0 0.24 7 Rain \\n\",\n \"16 8 6.0 0.05 6 Fog-Snow \\n\",\n \"17 9 12.0 T 2 Snow \\n\",\n \"18 10 11.0 0 1 NaN \\n\",\n \"19 10 6.0 0 2 NaN \\n\",\n \"20 10 6.0 0 1 NaN \\n\",\n \"21 9 NaN 0.01 3 Snow \\n\",\n \"22 1 16.0 2.31 8 Fog-Snow \\n\",\n \"23 8 6.0 T 3 Snow \\n\",\n \"24 10 3.0 0 2 NaN \\n\",\n \"25 10 7.0 0 2 NaN \\n\",\n \"26 10 7.0 T 3 Rain \\n\",\n \"27 10 5.0 0 1 NaN \\n\",\n \"28 10 8.0 0 4 NaN \\n\",\n \"29 10 7.0 0 0 NaN \\n\",\n \"30 10 5.0 0 0 NaN \\n\",\n \"\\n\",\n \" WindDirDegrees \\n\",\n \"0 281 \\n\",\n \"1 275 \\n\",\n \"2 277 \\n\",\n \"3 345 \\n\",\n \"4 333 \\n\",\n \"5 259 \\n\",\n \"6 293 \\n\",\n \"7 79 \\n\",\n \"8 76 \\n\",\n \"9 109 \\n\",\n \"10 289 \\n\",\n \"11 235 \\n\",\n \"12 284 \\n\",\n \"13 266 \\n\",\n \"14 101 \\n\",\n \"15 340 \\n\",\n \"16 345 \\n\",\n \"17 293 \\n\",\n \"18 293 \\n\",\n \"19 302 \\n\",\n \"20 312 \\n\",\n \"21 34 \\n\",\n \"22 42 \\n\",\n \"23 327 \\n\",\n \"24 286 \\n\",\n \"25 244 \\n\",\n \"26 311 \\n\",\n \"27 234 \\n\",\n \"28 298 \\n\",\n \"29 257 \\n\",\n \"30 241 \"\n ]\n },\n \"execution_count\": 3,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df = pd.read_csv('nyc_weather.csv')\\n\",\n \"df\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"id\": \"a345f8d9\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"pandas.core.frame.DataFrame\"\n ]\n },\n \"execution_count\": 4,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"type(df)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"id\": \"debc5617\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"0 38\\n\",\n \"1 36\\n\",\n \"2 40\\n\",\n \"3 25\\n\",\n \"4 20\\n\",\n \"5 33\\n\",\n \"6 39\\n\",\n \"7 39\\n\",\n \"8 44\\n\",\n \"9 50\\n\",\n \"10 33\\n\",\n \"11 35\\n\",\n \"12 26\\n\",\n \"13 30\\n\",\n \"14 43\\n\",\n \"15 47\\n\",\n \"16 36\\n\",\n \"17 25\\n\",\n \"18 22\\n\",\n \"19 32\\n\",\n \"20 31\\n\",\n \"21 26\\n\",\n \"22 26\\n\",\n \"23 28\\n\",\n \"24 34\\n\",\n \"25 43\\n\",\n \"26 41\\n\",\n \"27 37\\n\",\n \"28 36\\n\",\n \"29 34\\n\",\n \"30 46\\n\",\n \"Name: Temperature, dtype: int64\"\n ]\n },\n \"execution_count\": 5,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df['Temperature']\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"id\": \"02f6142e\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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Temperature
038
136
240
325
420
533
639
739
844
950
1033
1135
1226
1330
1443
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1636
1725
1822
1932
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2328
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2737
2836
2934
3046
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daytemperaturewindspeedevent
01/1/2023326Rain
11/2/2023357Sunny
21/3/2023282Snow
31/4/2023244Rain
41/5/2023322Sunny
51/6/2023317Snow
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temperaturewindspeedevent
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temperaturewindspeedevent
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daytemperaturewindspeedevent
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daytemperaturewindspeed
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Sunny1/2/2023357
Snow1/3/2023282
Rain1/4/2023244
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daytemperaturewindspeed
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Sunny1/2/2023357
Sunny1/5/2023322
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daytemperaturewindspeedevent
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12017-01-02357Sunny
22017-01-03282Snow
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\"\n ],\n \"text/plain\": [\n \" 0 1 2 3\\n\",\n \"0 1/1/2023 32 6 Rain\\n\",\n \"1 1/2/2023 35 6 Sunny\\n\",\n \"2 1/3/2023 32 2 Snow\"\n ]\n },\n \"execution_count\": 52,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"weather_data = [\\n\",\n \" ('1/1/2023', 32, 6, 'Rain'),\\n\",\n \" ('1/2/2023', 35, 6, 'Sunny'),\\n\",\n \" ('1/3/2023', 32, 2, 'Snow')\\n\",\n \"]\\n\",\n \"\\n\",\n \"df = pd.DataFrame(data=weather_data)\\n\",\n \"\\n\",\n \"df\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 53,\n \"id\": \"f6e5f754\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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daytemperaturewindspeedevent
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11/2/2023356Sunny
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\"\n ],\n \"text/plain\": [\n \" day temperature windspeed event\\n\",\n \"0 1/1/2023 32 6 Rain\\n\",\n \"1 1/2/2023 35 6 Sunny\\n\",\n \"2 1/3/2023 32 2 Snow\"\n ]\n },\n \"execution_count\": 53,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"weather_data = [\\n\",\n \" ('1/1/2023', 32, 6, 'Rain'),\\n\",\n \" ('1/2/2023', 35, 6, 'Sunny'),\\n\",\n \" ('1/3/2023', 32, 2, 'Snow')\\n\",\n \"]\\n\",\n \"\\n\",\n \"df = pd.DataFrame(data=weather_data, columns=['day', 'temperature', 'windspeed', 'event'])\\n\",\n \"\\n\",\n \"df\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"f2ed69e8\",\n \"metadata\": {},\n \"source\": [\n \"### list of dictionary\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 54,\n \"id\": \"588425b3\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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daytemperaturewindspeedevent
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11/2/2023357Sunny
21/3/2023326Snow
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daytemperaturewindspeedevent
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21/3/2023326Snow
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\"\n ],\n \"text/plain\": [\n \" day temperature windspeed event\\n\",\n \"0 1/1/2023 32 6 Rain\\n\",\n \"1 1/2/2023 35 7 Sunny\\n\",\n \"2 1/3/2023 32 6 Snow\"\n ]\n },\n \"execution_count\": 55,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"5636c651\",\n \"metadata\": {},\n \"source\": [\n \"### write to csv\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 56,\n \"id\": \"49a42525\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"df.to_csv('indore.csv', index=True)\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"2076e420\",\n \"metadata\": {},\n \"source\": [\n \"# Day 9 - Pandas day 2\\n\",\n \"\\n\",\n \"### Handling missing values\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"id\": \"0bcbb714\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import pandas as pd\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"id\": \"f64c5df9\",\n \"metadata\": {\n \"scrolled\": false\n },\n \"outputs\": [],\n \"source\": [\n \"# help(pd.read_csv)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"id\": \"8b9179c5\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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daytemperaturewindspeedevent
02017-01-0132.06.0Rain
12017-01-04NaN9.0Sunny
22017-01-0528.0NaNSnow
32017-01-06NaN7.0NaN
42017-01-0732.0NaNRain
52017-01-08NaNNaNSunny
62017-01-09NaNNaNNaN
72017-01-1034.08.0Cloudy
82017-01-1140.012.0Sunny
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\"\n ],\n \"text/plain\": [\n \" day temperature windspeed event\\n\",\n \"0 2017-01-01 32.0 6.0 Rain\\n\",\n \"1 2017-01-04 NaN 9.0 Sunny\\n\",\n \"2 2017-01-05 28.0 NaN Snow\\n\",\n \"3 2017-01-06 NaN 7.0 NaN\\n\",\n \"4 2017-01-07 32.0 NaN Rain\\n\",\n \"5 2017-01-08 NaN NaN Sunny\\n\",\n \"6 2017-01-09 NaN NaN NaN\\n\",\n \"7 2017-01-10 34.0 8.0 Cloudy\\n\",\n \"8 2017-01-11 40.0 12.0 Sunny\"\n ]\n },\n \"execution_count\": 4,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df = pd.read_csv('weather_data2.csv', parse_dates=['day'])\\n\",\n \"df\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"id\": \"ed579b5c\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"import numpy as np\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"id\": \"a7814651\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"nan\"\n ]\n },\n \"execution_count\": 6,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"np.nan # \"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"id\": \"d96c216f\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\"\n ]\n }\n ],\n \"source\": [\n \"print(type(df.day[0]))\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"id\": \"f6d09c15\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
temperaturewindspeedevent
day
2017-01-0132.06.0Rain
2017-01-04NaN9.0Sunny
2017-01-0528.0NaNSnow
2017-01-06NaN7.0NaN
2017-01-0732.0NaNRain
2017-01-08NaNNaNSunny
2017-01-09NaNNaNNaN
2017-01-1034.08.0Cloudy
2017-01-1140.012.0Sunny
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\"\n ],\n \"text/plain\": [\n \" temperature windspeed event\\n\",\n \"day \\n\",\n \"2017-01-01 32.0 6.0 Rain\\n\",\n \"2017-01-04 NaN 9.0 Sunny\\n\",\n \"2017-01-05 28.0 NaN Snow\\n\",\n \"2017-01-06 NaN 7.0 NaN\\n\",\n \"2017-01-07 32.0 NaN Rain\\n\",\n \"2017-01-08 NaN NaN Sunny\\n\",\n \"2017-01-09 NaN NaN NaN\\n\",\n \"2017-01-10 34.0 8.0 Cloudy\\n\",\n \"2017-01-11 40.0 12.0 Sunny\"\n ]\n },\n \"execution_count\": 8,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df.set_index('day', inplace=True)\\n\",\n \"df\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"c32e25ab\",\n \"metadata\": {},\n \"source\": [\n \"## fillna\\n\",\n \"\\n\",\n \"Fill Not Available\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 9,\n \"id\": \"6b9159d9\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
temperaturewindspeedevent
day
2017-01-0132.06.0Rain
2017-01-040.09.0Sunny
2017-01-0528.00.0Snow
2017-01-060.07.00
2017-01-0732.00.0Rain
2017-01-080.00.0Sunny
2017-01-090.00.00
2017-01-1034.08.0Cloudy
2017-01-1140.012.0Sunny
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\"\n ],\n \"text/plain\": [\n \" temperature windspeed event\\n\",\n \"day \\n\",\n \"2017-01-01 32.0 6.0 Rain\\n\",\n \"2017-01-04 0.0 9.0 Sunny\\n\",\n \"2017-01-05 28.0 0.0 Snow\\n\",\n \"2017-01-06 0.0 7.0 0\\n\",\n \"2017-01-07 32.0 0.0 Rain\\n\",\n \"2017-01-08 0.0 0.0 Sunny\\n\",\n \"2017-01-09 0.0 0.0 0\\n\",\n \"2017-01-10 34.0 8.0 Cloudy\\n\",\n \"2017-01-11 40.0 12.0 Sunny\"\n ]\n },\n \"execution_count\": 9,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"new_df = df.fillna(0)\\n\",\n \"new_df\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 10,\n \"id\": \"4f61e387\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
temperaturewindspeedevent
day
2017-01-0132.06.0Rain
2017-01-040.09.0Sunny
2017-01-0528.00.0Snow
2017-01-060.07.0No Event
2017-01-0732.00.0Rain
2017-01-080.00.0Sunny
2017-01-090.00.0No Event
2017-01-1034.08.0Cloudy
2017-01-1140.012.0Sunny
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\"\n ],\n \"text/plain\": [\n \" temperature windspeed event\\n\",\n \"day \\n\",\n \"2017-01-01 32.0 6.0 Rain\\n\",\n \"2017-01-04 0.0 9.0 Sunny\\n\",\n \"2017-01-05 28.0 0.0 Snow\\n\",\n \"2017-01-06 0.0 7.0 No Event\\n\",\n \"2017-01-07 32.0 0.0 Rain\\n\",\n \"2017-01-08 0.0 0.0 Sunny\\n\",\n \"2017-01-09 0.0 0.0 No Event\\n\",\n \"2017-01-10 34.0 8.0 Cloudy\\n\",\n \"2017-01-11 40.0 12.0 Sunny\"\n ]\n },\n \"execution_count\": 10,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"new_df = df.fillna({\\n\",\n \" 'temperature': 0,\\n\",\n \" 'windspeed': 0,\\n\",\n \" 'event': 'No Event'\\n\",\n \"})\\n\",\n \"\\n\",\n \"new_df\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"dbddb92f\",\n \"metadata\": {},\n \"source\": [\n \"## forward fill\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 12,\n \"id\": \"629fbc00\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
temperaturewindspeedevent
day
2017-01-0132.06.0Rain
2017-01-0432.09.0Sunny
2017-01-0528.09.0Snow
2017-01-0628.07.0Snow
2017-01-0732.07.0Rain
2017-01-0832.07.0Sunny
2017-01-0932.07.0Sunny
2017-01-1034.08.0Cloudy
2017-01-1140.012.0Sunny
\\n\",\n \"
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\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
temperaturewindspeedevent
day
2017-01-0132.06.0Rain
2017-01-0428.09.0Sunny
2017-01-0528.07.0Snow
2017-01-0632.07.0Rain
2017-01-0732.08.0Rain
2017-01-0834.08.0Sunny
2017-01-0934.08.0Cloudy
2017-01-1034.08.0Cloudy
2017-01-1140.012.0Sunny
\\n\",\n \"
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\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
temperaturewindspeedevent
day
2017-01-0132.06.0Rain
2017-01-04NaN9.0Sunny
2017-01-0528.0NaNSnow
2017-01-06NaN7.0NaN
2017-01-0732.0NaNRain
2017-01-08NaNNaNSunny
2017-01-09NaNNaNNaN
2017-01-1034.08.0Cloudy
2017-01-1140.012.0Sunny
\\n\",\n \"
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\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
temperaturewindspeedevent
day
2017-01-0132.0000006.00Rain
2017-01-0430.0000009.00Sunny
2017-01-0528.0000008.00Snow
2017-01-0630.0000007.00NaN
2017-01-0732.0000007.25Rain
2017-01-0832.6666677.50Sunny
2017-01-0933.3333337.75NaN
2017-01-1034.0000008.00Cloudy
2017-01-1140.00000012.00Sunny
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temperaturewindspeedevent
day
2017-01-0132.0000006.00Rain
2017-01-0429.0000009.00Sunny
2017-01-0528.0000008.00Snow
2017-01-0630.0000007.00NaN
2017-01-0732.0000007.25Rain
2017-01-0832.6666677.50Sunny
2017-01-0933.3333337.75NaN
2017-01-1034.0000008.00Cloudy
2017-01-1140.00000012.00Sunny
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temperaturewindspeedevent
day
2017-01-0132.06.0Rain
2017-01-04NaN9.0Sunny
2017-01-0528.0NaNSnow
2017-01-06NaN7.0NaN
2017-01-0732.0NaNRain
2017-01-08NaNNaNSunny
2017-01-09NaNNaNNaN
2017-01-1034.08.0Cloudy
2017-01-1140.012.0Sunny
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temperaturewindspeedevent
day
2017-01-0132.06.0Rain
2017-01-1034.08.0Cloudy
2017-01-1140.012.0Sunny
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temperaturewindspeedevent
day
2017-01-0132.06.0Rain
2017-01-04NaN9.0Sunny
2017-01-0528.0NaNSnow
2017-01-06NaN7.0NaN
2017-01-0732.0NaNRain
2017-01-08NaNNaNSunny
2017-01-1034.08.0Cloudy
2017-01-1140.012.0Sunny
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temperaturewindspeedevent
day
2017-01-0132.06.0Rain
2017-01-04NaN9.0Sunny
2017-01-0528.0NaNSnow
2017-01-0732.0NaNRain
2017-01-1034.08.0Cloudy
2017-01-1140.012.0Sunny
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temperaturewindspeedevent
day
2017-01-0132.06.0Rain
2017-01-04NaN9.0Sunny
2017-01-0528.0NaNSnow
2017-01-06NaN7.0NaN
2017-01-0732.0NaNRain
2017-01-08NaNNaNSunny
2017-01-09NaNNaNNaN
2017-01-1034.08.0Cloudy
2017-01-1140.012.0Sunny
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temperaturewindspeedevent
2017-01-0132.06.0Rain
2017-01-02NaNNaNNaN
2017-01-03NaNNaNNaN
2017-01-04NaN9.0Sunny
2017-01-0528.0NaNSnow
2017-01-06NaN7.0NaN
2017-01-0732.0NaNRain
2017-01-08NaNNaNSunny
2017-01-09NaNNaNNaN
2017-01-1034.08.0Cloudy
2017-01-1140.012.0Sunny
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daytemperaturewindspeedevent
01/1/2017326Rain
11/2/2017-999997Sunny
21/3/201728-99999Snow
31/4/2017-9999970
41/5/201732-99999Rain
51/6/2017312Sunny
61/6/20173450
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daytemperaturewindspeedevent
01/1/201732.06.0Rain
11/2/2017NaN7.0Sunny
21/3/201728.0NaNSnow
31/4/2017NaN7.00
41/5/201732.0NaNRain
51/6/201731.02.0Sunny
61/6/201734.05.00
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daytemperaturewindspeedevent
01/1/2017326Rain
11/2/201707Sunny
21/3/2017280Snow
31/4/2017070
41/5/2017320Rain
51/6/2017312Sunny
61/6/20173450
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daytemperaturewindspeedevent
01/1/201732.06.0Rain
11/2/2017NaN7.0Sunny
21/3/201728.0NaNSnow
31/4/2017NaN7.0NaN
41/5/201732.0NaNRain
51/6/201731.02.0Sunny
61/6/201734.05.0NaN
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\"\n ],\n \"text/plain\": [\n \" day temperature windspeed event\\n\",\n \"0 1/1/2017 32.0 6.0 Rain\\n\",\n \"1 1/2/2017 NaN 7.0 Sunny\\n\",\n \"2 1/3/2017 28.0 NaN Snow\\n\",\n \"3 1/4/2017 NaN 7.0 NaN\\n\",\n \"4 1/5/2017 32.0 NaN Rain\\n\",\n \"5 1/6/2017 31.0 2.0 Sunny\\n\",\n \"6 1/6/2017 34.0 5.0 NaN\"\n ]\n },\n \"execution_count\": 27,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"new_df = df.replace({\\n\",\n \" 'temperature': -99999,\\n\",\n \" 'windspeed': -99999,\\n\",\n \" 'event': '0'\\n\",\n \"}, np.nan)\\n\",\n \"\\n\",\n \"new_df\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 28,\n \"id\": \"d920f96d\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
daytemperaturewindspeedevent
01/1/2017326Rain
11/2/2017-999997Sunny
21/3/201728-99999Snow
31/4/2017-9999970
41/5/201732-99999Rain
51/6/2017312Sunny
61/6/20173450
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\"\n ],\n \"text/plain\": [\n \" day temperature windspeed event\\n\",\n \"0 1/1/2017 32 6 Rain\\n\",\n \"1 1/2/2017 -99999 7 Sunny\\n\",\n \"2 1/3/2017 28 -99999 Snow\\n\",\n \"3 1/4/2017 -99999 7 0\\n\",\n \"4 1/5/2017 32 -99999 Rain\\n\",\n \"5 1/6/2017 31 2 Sunny\\n\",\n \"6 1/6/2017 34 5 0\"\n ]\n },\n \"execution_count\": 28,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 29,\n \"id\": \"ef7b6054\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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daytemperaturewindspeedevent
01/1/201732.06.0Sunny
11/2/2017NaN7.0Sunny
21/3/201728.0NaNSnow
31/4/2017NaN7.00
41/5/201732.0NaNSunny
51/6/201731.02.0Sunny
61/6/201734.05.00
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\"\n ],\n \"text/plain\": [\n \" day temperature windspeed event\\n\",\n \"0 1/1/2017 32.0 6.0 Sunny\\n\",\n \"1 1/2/2017 NaN 7.0 Sunny\\n\",\n \"2 1/3/2017 28.0 NaN Snow\\n\",\n \"3 1/4/2017 NaN 7.0 0\\n\",\n \"4 1/5/2017 32.0 NaN Sunny\\n\",\n \"5 1/6/2017 31.0 2.0 Sunny\\n\",\n \"6 1/6/2017 34.0 5.0 0\"\n ]\n },\n \"execution_count\": 29,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"new_df = df.replace({\\n\",\n \" -99999: np.nan,\\n\",\n \" 'Rain': 'Sunny'\\n\",\n \"})\\n\",\n \"\\n\",\n \"new_df\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"891181f3\",\n \"metadata\": {},\n \"source\": [\n \"## RegEx\\n\",\n \"\\n\",\n \"Regular Expression\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"32f1c97e\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"y = mx + c\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 30,\n \"id\": \"a5f09b2b\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
daytemperaturewindspeedevent
01/1/2017326Rain
11/2/2017-999997Sunny
21/3/201728-99999Snow
31/4/2017-9999970
41/5/201732-99999Rain
51/6/2017312Sunny
61/6/20173450
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\"\n ],\n \"text/plain\": [\n \" day temperature windspeed event\\n\",\n \"0 1/1/2017 32 6 Rain\\n\",\n \"1 1/2/2017 -99999 7 Sunny\\n\",\n \"2 1/3/2017 28 -99999 Snow\\n\",\n \"3 1/4/2017 -99999 7 0\\n\",\n \"4 1/5/2017 32 -99999 Rain\\n\",\n \"5 1/6/2017 31 2 Sunny\\n\",\n \"6 1/6/2017 34 5 0\"\n ]\n },\n \"execution_count\": 30,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"2cc501a3\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"45km - 45 \\n\",\n \"45k\\n\",\n \"45kmp\\n\",\n \"45kmph\\n\",\n \"45kph\\n\",\n \"45\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 31,\n \"id\": \"22efab8c\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
daytemperaturewindspeedevent
01/1/2017326R
11/2/2017-999997S
21/3/201728-99999S
31/4/2017-9999970
41/5/201732-99999R
51/6/2017312S
61/6/20173450
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\"\n ],\n \"text/plain\": [\n \" day temperature windspeed event\\n\",\n \"0 1/1/2017 32 6 R\\n\",\n \"1 1/2/2017 -99999 7 S\\n\",\n \"2 1/3/2017 28 -99999 S\\n\",\n \"3 1/4/2017 -99999 7 0\\n\",\n \"4 1/5/2017 32 -99999 R\\n\",\n \"5 1/6/2017 31 2 S\\n\",\n \"6 1/6/2017 34 5 0\"\n ]\n },\n \"execution_count\": 31,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"new_df = df.replace({\\n\",\n \" 'temperature': '[A-Za-z]',\\n\",\n \" 'event': '[a-z]'\\n\",\n \"}, '', regex=True)\\n\",\n \"\\n\",\n \"new_df\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 32,\n \"id\": \"102ce591\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
daytemperaturewindspeedeventspeed
001-01-201732C6Rain45kmph
101-02-2017-999997Sunny40km
201-03-201728F-99999Snow30kmp
301-04-2017-999997056kmph
401-05-201732C-99999Rain65
501-06-201731c2Sunny76
601-06-201734f5080k
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\"\n ],\n \"text/plain\": [\n \" day temperature windspeed event speed\\n\",\n \"0 01-01-2017 32C 6 Rain 45kmph\\n\",\n \"1 01-02-2017 -99999 7 Sunny 40km\\n\",\n \"2 01-03-2017 28F -99999 Snow 30kmp\\n\",\n \"3 01-04-2017 -99999 7 0 56kmph\\n\",\n \"4 01-05-2017 32C -99999 Rain 65\\n\",\n \"5 01-06-2017 31c 2 Sunny 76\\n\",\n \"6 01-06-2017 34f 5 0 80k\"\n ]\n },\n \"execution_count\": 32,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df = pd.read_csv('weather_datamissing_regex.csv')\\n\",\n \"\\n\",\n \"df\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"65f7bcb5\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# '[!@#$%^&]'\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 34,\n \"id\": \"854610ff\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
daytemperaturewindspeedeventspeed
001-01-2017326Rain45
101-02-2017-999997Sunny40
201-03-201728-99999Snow30
301-04-2017-999997056
401-05-201732-99999Rain65
501-06-2017312Sunny76
601-06-2017345080
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" day temperature windspeed event speed\\n\",\n \"0 01-01-2017 32 6 Rain 45\\n\",\n \"1 01-02-2017 -99999 7 Sunny 40\\n\",\n \"2 01-03-2017 28 -99999 Snow 30\\n\",\n \"3 01-04-2017 -99999 7 0 56\\n\",\n \"4 01-05-2017 32 -99999 Rain 65\\n\",\n \"5 01-06-2017 31 2 Sunny 76\\n\",\n \"6 01-06-2017 34 5 0 80\"\n ]\n },\n \"execution_count\": 34,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"new_df = df.replace({\\n\",\n \" 'temperature': '[CcFf]',\\n\",\n \" 'speed': '[kmph]'\\n\",\n \"}, '', regex=True)\\n\",\n \"\\n\",\n \"new_df\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 36,\n \"id\": \"8b919c4a\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": [\n \"# new_df = df.replace({\\n\",\n \"# 'temperature': '[-100000-0]',\\n\",\n \"# 'windspeed': '[-100000-0]'\\n\",\n \"# }, '', regex=True)\\n\",\n \"\\n\",\n \"# new_df\"\n ]\n },\n {\n \"cell_type\": \"markdown\",\n \"id\": \"cfbe57f1\",\n \"metadata\": {},\n \"source\": [\n \"# Group By\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 37,\n \"id\": \"5e885561\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
daycitytemperaturewindspeedevent
01/1/2017new york326Rain
11/2/2017new york367Sunny
21/3/2017new york2812Snow
31/4/2017new york337Sunny
41/1/2017mumbai905Sunny
51/2/2017mumbai8512Fog
61/3/2017mumbai8715Fog
71/4/2017mumbai925Rain
81/1/2017paris4520Sunny
91/2/2017paris5013Cloudy
101/3/2017paris548Cloudy
111/4/2017paris4210Cloudy
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" day city temperature windspeed event\\n\",\n \"0 1/1/2017 new york 32 6 Rain\\n\",\n \"1 1/2/2017 new york 36 7 Sunny\\n\",\n \"2 1/3/2017 new york 28 12 Snow\\n\",\n \"3 1/4/2017 new york 33 7 Sunny\\n\",\n \"4 1/1/2017 mumbai 90 5 Sunny\\n\",\n \"5 1/2/2017 mumbai 85 12 Fog\\n\",\n \"6 1/3/2017 mumbai 87 15 Fog\\n\",\n \"7 1/4/2017 mumbai 92 5 Rain\\n\",\n \"8 1/1/2017 paris 45 20 Sunny\\n\",\n \"9 1/2/2017 paris 50 13 Cloudy\\n\",\n \"10 1/3/2017 paris 54 8 Cloudy\\n\",\n \"11 1/4/2017 paris 42 10 Cloudy\"\n ]\n },\n \"execution_count\": 37,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df = pd.read_csv('weather_by_cities.csv')\\n\",\n \"df\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 38,\n \"id\": \"c9952665\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n ]\n },\n \"execution_count\": 38,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"gb = df.groupby('city')\\n\",\n \"gb\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 39,\n \"id\": \"517be304\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"name\": \"stdout\",\n \"output_type\": \"stream\",\n \"text\": [\n \"\\n\",\n \"City: mumbai\\n\",\n \"\\n\",\n \"Data: \\n\",\n \" day city temperature windspeed event\\n\",\n \"4 1/1/2017 mumbai 90 5 Sunny\\n\",\n \"5 1/2/2017 mumbai 85 12 Fog\\n\",\n \"6 1/3/2017 mumbai 87 15 Fog\\n\",\n \"7 1/4/2017 mumbai 92 5 Rain\\n\",\n \"\\n\",\n \"City: new york\\n\",\n \"\\n\",\n \"Data: \\n\",\n \" day city temperature windspeed event\\n\",\n \"0 1/1/2017 new york 32 6 Rain\\n\",\n \"1 1/2/2017 new york 36 7 Sunny\\n\",\n \"2 1/3/2017 new york 28 12 Snow\\n\",\n \"3 1/4/2017 new york 33 7 Sunny\\n\",\n \"\\n\",\n \"City: paris\\n\",\n \"\\n\",\n \"Data: \\n\",\n \" day city temperature windspeed event\\n\",\n \"8 1/1/2017 paris 45 20 Sunny\\n\",\n \"9 1/2/2017 paris 50 13 Cloudy\\n\",\n \"10 1/3/2017 paris 54 8 Cloudy\\n\",\n \"11 1/4/2017 paris 42 10 Cloudy\\n\"\n ]\n }\n ],\n \"source\": [\n \"for city, data in gb:\\n\",\n \" print()\\n\",\n \" print(\\\"City: \\\", city)\\n\",\n \" print()\\n\",\n \" print(\\\"Data: \\\\n\\\", data)\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 40,\n \"id\": \"f09dcab9\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
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daycitytemperaturewindspeedevent
41/1/2017mumbai905Sunny
51/2/2017mumbai8512Fog
61/3/2017mumbai8715Fog
71/4/2017mumbai925Rain
\\n\",\n \"
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\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
daycitytemperaturewindspeedevent
81/1/2017paris4520Sunny
91/2/2017paris5013Cloudy
101/3/2017paris548Cloudy
111/4/2017paris4210Cloudy
\\n\",\n \"
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\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
temperaturewindspeed
city
mumbai88.509.25
new york32.258.00
paris47.7512.75
\\n\",\n \"
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\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
daytemperaturewindspeedevent
city
mumbai1/4/20179215Sunny
new york1/4/20173612Sunny
paris1/4/20175420Sunny
\\n\",\n \"
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\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
temperaturewindspeed
countmeanstdmin25%50%75%maxcountmeanstdmin25%50%75%max
city
mumbai4.088.503.10912685.086.5088.590.5092.04.09.255.0579975.05.008.512.7515.0
new york4.032.253.30403828.031.0032.533.7536.04.08.002.7080136.06.757.08.2512.0
paris4.047.755.31507342.044.2547.551.0054.04.012.755.2519848.09.5011.514.7520.0
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" temperature \\\\\\n\",\n \" count mean std min 25% 50% 75% max \\n\",\n \"city \\n\",\n \"mumbai 4.0 88.50 3.109126 85.0 86.50 88.5 90.50 92.0 \\n\",\n \"new york 4.0 32.25 3.304038 28.0 31.00 32.5 33.75 36.0 \\n\",\n \"paris 4.0 47.75 5.315073 42.0 44.25 47.5 51.00 54.0 \\n\",\n \"\\n\",\n \" windspeed \\n\",\n \" count mean std min 25% 50% 75% max \\n\",\n \"city \\n\",\n \"mumbai 4.0 9.25 5.057997 5.0 5.00 8.5 12.75 15.0 \\n\",\n \"new york 4.0 8.00 2.708013 6.0 6.75 7.0 8.25 12.0 \\n\",\n \"paris 4.0 12.75 5.251984 8.0 9.50 11.5 14.75 20.0 \"\n ]\n },\n \"execution_count\": 44,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"gb.describe()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 45,\n \"id\": \"559ef865\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
daytemperaturewindspeedevent
city
mumbai1/1/2017855Fog
new york1/1/2017286Rain
paris1/1/2017428Cloudy
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" day temperature windspeed event\\n\",\n \"city \\n\",\n \"mumbai 1/1/2017 85 5 Fog\\n\",\n \"new york 1/1/2017 28 6 Rain\\n\",\n \"paris 1/1/2017 42 8 Cloudy\"\n ]\n },\n \"execution_count\": 45,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"gb.min()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 46,\n \"id\": \"d36da53e\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"city\\n\",\n \"mumbai 4\\n\",\n \"new york 4\\n\",\n \"paris 4\\n\",\n \"dtype: int64\"\n ]\n },\n \"execution_count\": 46,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"gb.size()\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 47,\n \"id\": \"0bad1050\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
daytemperaturewindspeedevent
city
mumbai4444
new york4444
paris4444
\\n\",\n \"
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\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
citytemperaturehumidity
0mumbai3280
1delhi4560
2banglore3078
\\n\",\n \"
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\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
citytemperaturehumidity
0new york2168
1chicago1465
2orlando3575
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" city temperature humidity\\n\",\n \"0 new york 21 68\\n\",\n \"1 chicago 14 65\\n\",\n \"2 orlando 35 75\"\n ]\n },\n \"execution_count\": 49,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"us_weather = pd.DataFrame({\\n\",\n \" \\\"city\\\": ['new york', 'chicago', 'orlando'],\\n\",\n \" \\\"temperature\\\": [21,14,35],\\n\",\n \" \\\"humidity\\\": [68,65,75]\\n\",\n \"})\\n\",\n \"\\n\",\n \"us_weather\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 50,\n \"id\": \"aa5817cd\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
citytemperaturehumidity
0mumbai3280
1delhi4560
2banglore3078
0new york2168
1chicago1465
2orlando3575
\\n\",\n \"
\"\n ],\n \"text/plain\": [\n \" city temperature humidity\\n\",\n \"0 mumbai 32 80\\n\",\n \"1 delhi 45 60\\n\",\n \"2 banglore 30 78\\n\",\n \"0 new york 21 68\\n\",\n \"1 chicago 14 65\\n\",\n \"2 orlando 35 75\"\n ]\n },\n \"execution_count\": 50,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df = pd.concat([india_weather, us_weather])\\n\",\n \"\\n\",\n \"df\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 51,\n \"id\": \"f483201b\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
citytemperaturehumidity
0mumbai3280
1delhi4560
2banglore3078
3new york2168
4chicago1465
5orlando3575
\\n\",\n \"
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\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
citytemperaturehumidity
india0mumbai3280
1delhi4560
2banglore3078
us0new york2168
1chicago1465
2orlando3575
\\n\",\n \"
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\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
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\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
citytemperaturehumiditycitytemperaturehumidity
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\"\n ],\n \"text/plain\": [\n \" city temperature humidity city temperature humidity\\n\",\n \"0 mumbai 32 80 new york 21 68\\n\",\n \"1 delhi 45 60 chicago 14 65\\n\",\n \"2 banglore 30 78 orlando 35 75\"\n ]\n },\n \"execution_count\": 54,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df = pd.concat([india_weather, us_weather], axis=1) # column wise\\n\",\n \"\\n\",\n \"df\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 55,\n \"id\": \"eaaf6a69\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"0 Humid\\n\",\n \"1 Dry\\n\",\n \"2 Rain\\n\",\n \"Name: event, dtype: object\"\n ]\n },\n \"execution_count\": 55,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"s = pd.Series(['Humid', 'Dry', 'Rain'], name='event')\\n\",\n \"\\n\",\n \"s\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 56,\n \"id\": \"36063710\",\n \"metadata\": {},\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
\\n\",\n \"\\n\",\n \"\\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \" \\n\",\n \"
citytemperaturehumidityevent
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\"\n ],\n \"text/plain\": [\n \" city temperature humidity event\\n\",\n \"0 mumbai 32 80 Humid\\n\",\n \"1 delhi 45 60 Dry\\n\",\n \"2 banglore 30 78 Rain\"\n ]\n },\n \"execution_count\": 56,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n }\n ],\n \"source\": [\n \"df = pd.concat([india_weather, s], axis=1)\\n\",\n \"\\n\",\n \"df\"\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": null,\n \"id\": \"346ab668\",\n \"metadata\": {},\n \"outputs\": [],\n \"source\": []\n }\n ],\n \"metadata\": {\n \"kernelspec\": {\n \"display_name\": \"Python 3 (ipykernel)\",\n \"language\": \"python\",\n \"name\": \"python3\"\n },\n \"language_info\": {\n \"codemirror_mode\": {\n \"name\": \"ipython\",\n \"version\": 3\n },\n \"file_extension\": \".py\",\n \"mimetype\": \"text/x-python\",\n \"name\": \"python\",\n \"nbconvert_exporter\": \"python\",\n \"pygments_lexer\": \"ipython3\",\n \"version\": \"3.9.12\"\n }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 5\n}\n" }, { "path": "March Cohort 2023/8_9_Pandas/Data Cleaning With Pandas/Data Cleaning with Python and Pandas_ Detecting Missing Values.html", "content": "\n\n\n\t\n\t\t\t\n\t\n\t\n\n\t\t\n\n\t\n\n\tData Cleaning with Python and Pandas: Detecting Missing Values\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\t\t\n\t\t\n\t\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\t
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Data Cleaning with Python and Pandas: Detecting Missing Values

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\n\t\t\t\t\tData cleaning can be a tedious task.

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It’s the start of a new project and you’re excited to apply some machine learning models.

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You take a look at the data and quickly realize it’s an absolute mess.

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According to IBM Data Analytics you can expect to spend up to 80% of your time cleaning data.

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In this post we’ll walk through a number of different data cleaning tasks using Python’s Pandas library.  Specifically, we’ll focus on probably the biggest data cleaning task, missing values.

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After reading this post you’ll be able to more quickly clean data.  We all want to spend less time cleaning data, and more time exploring and modeling.\n\t\t\t\t

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\n\t\t\t\tClick here to get the FREE Data Cleaning Cheat Sheet\n\t\t\t
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Sources of Missing Values

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Before we dive into code, it’s important to understand the sources of missing data.  Here’s some typical reasons why data is missing:

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  • User forgot to fill in a field.
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  • Data was lost while transferring manually from a legacy database.
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  • There was a programming error.
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  • Users chose not to fill out a field tied to their beliefs about how the results would be used or interpreted.
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As you can see, some of these sources are just simple random mistakes.  Other times, there can be a deeper reason why data is missing.

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It’s important to understand these different types of missing data from a statistics point of view.  The type of missing data will influence how you deal with filling in the missing values.

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Today we’ll learn how to detect missing values, and do some basic imputation.  For a detailed statistical approach for dealing with missing data, check out these awesome slides from data scientist Matt Brems.

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Keep in mind, imputing with a median or mean value is usually a bad idea, so be sure to check out Matt’s slides for the correct approach.

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Getting Started

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Before you start cleaning a data set, it’s a good idea to just get a general feel for the data.  After that, you can put together a plan to clean the data.

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I like to start by asking the following questions:

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  • What are the features?
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  • What are the expected types (int, float, string, boolean)?
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  • Is there obvious missing data (values that Pandas can detect)?
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  • Is there other types of missing data that’s not so obvious (can’t easily detect with Pandas)?
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To show you what I mean, let’s start working through the example.

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The data we’re going to work with is a very small real estate dataset.  Head on over to our github page to grab a copy of the csv file so that you can code along.

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Here’s a quick look at the data:

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\"real

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This is a much smaller dataset than what you’ll typically work with.  Even though it’s a small dataset, it highlights a lot of real-world situations that you will encounter on projects.

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A good way to get a quick feel for the data is to take a look at the first few rows.  Here’s how you would do that in Pandas:

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# Importing libraries\nimport pandas as pd\nimport numpy as np\n\n# Read csv file into a pandas dataframe\ndf = pd.read_csv(\"property data.csv\")\n\n# Take a look at the first few rows\nprint df.head()\n
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Out:\n   ST_NUM    ST_NAME OWN_OCCUPIED  NUM_BEDROOMS\n0   104.0     PUTNAM            Y           3.0\n1   197.0  LEXINGTON            N           3.0\n2     NaN  LEXINGTON            N           3.0\n3   201.0   BERKELEY          NaN           1.0\n4   203.0   BERKELEY            Y           3.0\n
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I know that I said we’ll be working with Pandas, but you can see that I also imported Numpy. We’ll use this a little bit later on to rename some missing values, so we might as well import it now.

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After importing the libraries we read the csv file into a Pandas dataframe. You can think of the dataframe as a spreadsheet.

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With the .head()method, we can easily see the first few rows.

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Now I can answer my original question, what are my features?  It’s pretty easy to infer the following features from the column names:

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  • ST_NUM: Street number
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  • ST_NAME: Street name
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  • OWN_OCCUPIED: Is the residence owner occupied
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  • NUM_BEDROOMS: Number of bedrooms
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We can also answer, what are the expected types?

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  • ST_NUM: float or int… some sort of numeric type
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  • ST_NAME: string
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  • OWN_OCCUPIED: string… Y (“Yes”) or N (“No”)
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  • NUM_BEDROOMS: float or int, a numeric type
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To answer the next two questions, we’ll need to start getting more in-depth width Pandas. Let’s start looking at examples of how to detect missing values

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Standard Missing Values

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So what do I mean by “standard missing values”? These are missing values that Pandas can detect.

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Going back to our original data set, let’s take a look at the “Street Number” column.

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\"standard

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In the third column there’s an empty cell. In the seventh row there’s an “NA” value.

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Clearly these are both missing values. Let’s see how Pandas deals with these.

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# Looking at the ST_NUM column\nprint df['ST_NUM']\nprint df['ST_NUM'].isnull()\n
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# Looking at the ST_NUM column\nOut:\n0    104.0\n1    197.0\n2      NaN\n3    201.0\n4    203.0\n5    207.0\n6      NaN\n7    213.0\n8    215.0\n\nOut:\n0    False\n1    False\n2     True\n3    False\n4    False\n5    False\n6     True\n7    False\n8    False\n
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Taking a look at the column, we can see that Pandas filled in the blank space with “NA”. Using the isnull() method, we can confirm that both the missing value and “NA” were recognized as missing values. Both boolean responses are True.

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This is a simple example, but highlights an important point. Pandas will recognize both empty cells and “NA” types as missing values. In the next section, we’ll take a look at some types that Pandas won’t recognize.

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Non-Standard Missing Values

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Sometimes it might be the case where there’s missing values that have different formats.

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Let’s take a look at the “Number of Bedrooms” column to see what I mean.

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In this column, there’s four missing values.

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  • n/a
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  • NA
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  • —
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  • na
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From the previous section, we know that Pandas will recognize “NA” as a missing value, but what about the others? Let’s take a look.

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# Looking at the NUM_BEDROOMS column\nprint df['NUM_BEDROOMS']\nprint df['NUM_BEDROOMS'].isnull()\n
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\nOut:\n0      3\n1      3\n2    n/a\n3      1\n4      3\n5    NaN\n6      2\n7     --\n8     na\n\nOut:\n0    False\n1    False\n2    False\n3    False\n4    False\n5     True\n6    False\n7    False\n8    False\n
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Just like before, Pandas recognized the “NA” as a missing value. Unfortunately, the other types weren’t recognized.

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If there’s multiple users manually entering data, then this is a common problem. Maybe i like to use “n/a” but you like to use “na”.

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An easy way to detect these various formats is to put them in a list. Then when we import the data, Pandas will recognize them right away. Here’s an example of how we would do that.

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# Making a list of missing value types\nmissing_values = [\"n/a\", \"na\", \"--\"]\ndf = pd.read_csv(\"property data.csv\", na_values = missing_values)\n
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Now let’s take another look at this column and see what happens.

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# Looking at the NUM_BEDROOMS column\nprint df['NUM_BEDROOMS']\nprint df['NUM_BEDROOMS'].isnull()\n
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\nOut:\n0    3.0\n1    3.0\n2    NaN\n3    1.0\n4    3.0\n5    NaN\n6    2.0\n7    NaN\n8    NaN\n\nOut:\n0    False\n1    False\n2     True\n3    False\n4    False\n5     True\n6    False\n7     True\n8     True\n
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This time, all of the different formats were recognized as missing values.

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You might not be able to catch all of these right away. As you work through the data and see other types of missing values, you can add them to the list.

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It’s important to recognize these non-standard types of missing values for purposes of summarizing and transforming missing values. If you try and count the number of missing values before converting these non-standard types, you could end up missing a lot of missing values.

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In the next section we’ll take a look at a more complicated, but very common, type of missing value.

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Unexpected Missing Values

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So far we’ve seen standard missing values, and non-standard missing values. What if we an unexpected type?

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For example, if our feature is expected to be a string, but there’s a numeric type, then technically this is also a missing value.

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Let’s take a look at the “Owner Occupied” column to see what I’m talking about.

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\"unexpected

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From our previous examples, we know that Pandas will detect the empty cell in row seven as a missing value. Let’s confirm with some code.

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# Looking at the OWN_OCCUPIED column\nprint df['OWN_OCCUPIED']\nprint df['OWN_OCCUPIED'].isnull()\n
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# Looking at the ST_NUM column\nOut:\n0      Y\n1      N\n2      N\n3     12\n4      Y\n5      Y\n6    NaN\n7      Y\n8      Y\n\nOut:\n0    False\n1    False\n2    False\n3    False\n4    False\n5    False\n6     True\n7    False\n8    False\n
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In the fourth row, there’s the number 12. The response for Owner Occupied should clearly be a string (Y or N), so this numeric type should be a missing value.

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This example is a little more complicated so we’ll need to think through a strategy for detecting these types of missing values. There’s a number of different approaches, but here’s the way that I’m going to work thorugh this one.

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  1. Loop through the OWN_OCCUPIED column
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  2. Try and turn the entry into an integer
    3. \n
  3. If the entry can be changed into an integer, enter a missing value
    4. \n
  4. If the number can’t be an integer, we know it’s a string, so keep going
    5. \n
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Let’s take a look at the code and then we’ll go through it in detail.

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# Detecting numbers \ncnt=0\nfor row in df['OWN_OCCUPIED']:\n    try:\n        int(row)\n        df.loc[cnt, 'OWN_OCCUPIED']=np.nan\n    except ValueError:\n        pass\n    cnt+=1\n
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In the code we’re looping through each entry in the “Owner Occupied” column. To try and change the entry to an integer, we’re using int(row).

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If the value can be changed to an integer, we change the entry to a missing value using Numpy’s np.nan.

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On the other hand, if it can’t be changed to an integer, we pass and keep going.

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You’ll notice that I used try and except ValueError. This is called exception handling, and we use this to handle errors.

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If we were to try and change an entry into an integer and it couldn’t be changed, then a ValueError would be returned, and the code would stop. To deal with this, we use exception handling to recognize these errors, and keep going.

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Another important bit of the code is the .loc method. This is the preferred Pandas method for modfiying entries in place. For more info on this you can check out the Pandas documentation.

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Now that we’ve worked through the different ways of detecting missing values, we’ll take a look at summarizing, and replacing them.

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Summarizing Missing Values

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After we’ve cleaned the missing values, we will probably want to summarize them. For instance, we might want to look at the total number of missing values for each feature.

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# Total missing values for each feature\nprint df.isnull().sum()\n
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Out:\nST_NUM          2\nST_NAME         0\nOWN_OCCUPIED    2\nNUM_BEDROOMS    4\n
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Other times we might want to do a quick check to see if we have any missing values at all.

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# Any missing values?\nprint df.isnull().values.any()\n
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Out:\nTrue\n
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We might also want to get a total count of missing values.

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# Total number of missing values\nprint df.isnull().sum().sum()\n
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Out:\n8\n
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Now that we’ve summarized the number of missing values, let’s take a look at doing some simple replacements.

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Replacing

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Often times you’ll have to figure out how you want to handle missing values.

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Sometimes you’ll simply want to delete those rows, other times you’ll replace them.

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As I mentioned earlier, this shouldn’t be taken lightly. We’ll go over some basic imputations, but for a detailed statistical approach for dealing with missing data, check out these awesome slides from data scientist Matt Brems.

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That being said, maybe you just want to fill in missing values with a single value.

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# Replace missing values with a number\ndf['ST_NUM'].fillna(125, inplace=True)\n
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More likely, you might want to do a location based imputation. Here’s how you would do that.

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# Location based replacement\ndf.loc[2,'ST_NUM'] = 125\n
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\n

A very common way to replace missing values is using a median.

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# Replace using median \nmedian = df['NUM_BEDROOMS'].median()\ndf['NUM_BEDROOMS'].fillna(median, inplace=True)\n
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We’ve gone over a few simple ways to replace missing values, but be sure to check out Matt’s slides for the proper techniques.

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Conclusion

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Dealing with messy data is inevitable. Data cleaning is just part of the process on a data science project.

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In this article we went over some ways to detect, summarize, and replace missing values.

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For even more resources about data cleaning, check out these data science books.

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Armed with these techniques, you’ll spend less time data cleaning, and more time exploring and modeling.

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Leave a comment below and let me know which technique you find most useful!\n\t\t\t\t

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\n\t\t\t\tClick here to get the FREE Data Cleaning Cheat Sheet\n\t\t\t
\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\n\n
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13 Comments

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    \n\t\t\t\t\"Satish\"\t\t\t
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    \n\t\t\t\tSatish\t\t\t\t\n\t\t\t\ton July 12, 2018 at 11:19 am\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t
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    Thanks for the great tutorial.

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      \n\t\t\t\t\"DataOptimal\"\t\t\t
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      \n\t\t\t\tDataOptimal\t\t\t\t\n\t\t\t\ton July 12, 2018 at 1:40 pm\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t
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      Thanks Satish, glad you enjoyed it!

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    \n\t\t\t\tShoeb Mohammad\t\t\t\t\n\t\t\t\ton July 12, 2018 at 6:01 pm\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t
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    Literally It is the best easy tutorial to make beginners understand each and every word of the code. Thank you DataOptimal for this wonderful tutorial and it actually helped.

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    Thanks again. Keep up the great work.

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      \n\t\t\t\tDataOptimal\t\t\t\t\n\t\t\t\ton July 12, 2018 at 6:43 pm\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t
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      Thank you for the kind words Shoeb, I’m glad it helped!

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      We’ll be publishing plenty more posts, so be sure to check back often!

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    \n\t\t\t\tRavi\t\t\t\t\n\t\t\t\ton August 9, 2018 at 4:49 pm\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t
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    Wonderful and effective explanation with an example, Great work guys @DataOptimal.

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      \n\t\t\t\tDataOptimal\t\t\t\t\n\t\t\t\ton August 28, 2018 at 2:11 pm\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t
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      Thanks, glad you enjoyed it!

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    \n\t\t\t\t\"sasha\"\t\t\t
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    \n\t\t\t\tsasha\t\t\t\t\n\t\t\t\ton August 28, 2018 at 4:24 am\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t
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    \n\t\t\t\t\n\t\t\t\t
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    This was hands on the best explanation for using pandas, it was well documented and helped me a lot in my preparation. Thanks a lot. you should do more tutorials, call it data science made simple or something \"😉\"

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      \n\t\t\t\t\"DataOptimal\"\t\t\t
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      \n\t\t\t\tDataOptimal\t\t\t\t\n\t\t\t\ton August 28, 2018 at 2:12 pm\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t
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      I’m glad you enjoyed it Sasha! We’re working on plenty of more tutorials, so stay tuned!

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    \n\t\t\t\t\"KARN\t\t\t
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    \n\t\t\t\tKARN KUMAR\t\t\t\t\n\t\t\t\ton September 11, 2018 at 2:52 pm\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t
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    \n\t\t\t\t\n\t\t\t\t
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    This is awesome tutorial, would be great if you could create more videos or blogs.

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      \n\t\t\t\tDataOptimal\t\t\t\t\n\t\t\t\ton October 24, 2018 at 6:42 pm\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t
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      Thanks Karn! I’ll be posting more soon, so stay tuned!

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      • \n
    \n
    6. \n\t
  6. \n\t\t
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    \n\t\t\t\t\"Junior\"\t\t\t
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    \n\t\t\t\tJunior\t\t\t\t\n\t\t\t\ton October 11, 2018 at 8:06 am\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t
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    \n\t\t\t\t\n\t\t\t\t
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    This is a great resource, exactly what I was looking for! Thank you!

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      \n\t\t\t\tDataOptimal\t\t\t\t\n\t\t\t\ton October 24, 2018 at 6:42 pm\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t
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      You’re welcome Junior, glad you enjoyed it!

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    \n\t\t\t\t\"Jacobs\"\t\t\t
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    \n\t\t\t\tJacobs\t\t\t\t\n\t\t\t\ton October 12, 2018 at 7:13 am\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t
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    Do you have a similar example of data cleaning with R

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Data Cleaning with Python and Pandas: Detecting Missing Values

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\n\t\t\t\tFREE Data Cleaning Cheat Sheet\n\t\t\t
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Data cleaning can be a tedious task.

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It’s the start of a new project and you’re excited to apply some machine learning models.

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You take a look at the data and quickly realize it’s an absolute mess.

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According to IBM Data Analytics you can expect to spend up to 80% of your time cleaning data.

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In this post we’ll walk through a number of different data cleaning tasks using Python’s Pandas library.  Specifically, we’ll focus on probably the biggest data cleaning task, missing values.

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After reading this post you’ll be able to more quickly clean data.  We all want to spend less time cleaning data, and more time exploring and modeling.

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Before we jump into the code, let’s first understand what are some sources of missing values in messy datasets.

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Sources of Missing Values

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Before we dive into code, it’s important to understand the sources of missing data.  Here’s some typical reasons why data is missing:

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  • User forgot to fill in a field.
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  • Data was lost while transferring manually from a legacy database.
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  • There was a programming error.
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  • Users chose not to fill out a field tied to their beliefs about how the results would be used or interpreted.
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As you can see, some of these sources are just simple random mistakes.  Other times, there can be a deeper reason why data is missing.

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It’s important to understand these different types of missing data from a statistics point of view.  The type of missing data will influence how you deal with filling in the missing values.

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Today we’ll learn how to detect missing values, and do some basic imputation.  For a detailed statistical approach for dealing with missing data, check out these awesome slides from data scientist Matt Brems.

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Keep in mind, imputing with a median or mean value is usually a bad idea, so be sure to check out Matt’s slides for the correct approach.

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Getting Started

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Before you start cleaning a data set, it’s a good idea to just get a general feel for the data.  After that, you can put together a plan to clean the data.

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I like to start by asking the following questions:

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  • What are the features?
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  • What are the expected types (int, float, string, boolean)?
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  • Is there obvious missing data (values that Pandas can detect)?
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  • Is there other types of missing data that’s not so obvious (can’t easily detect with Pandas)?
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To show you what I mean, let’s start working through the example.

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The data we’re going to work with is a very small real estate dataset.  Head on over to our github page to grab a copy of the csv file so that you can code along.

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Here’s a quick look at the data:

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This is a much smaller dataset than what you’ll typically work with.  Even though it’s a small dataset, it highlights a lot of real-world situations that you will encounter on projects.

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A good way to get a quick feel for the data is to take a look at the first few rows.  Here’s how you would do that in Pandas:

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# Importing libraries\nimport pandas as pd\nimport numpy as np\n\n# Read csv file into a pandas dataframe\ndf = pd.read_csv(\"property data.csv\")\n\n# Take a look at the first few rows\nprint df.head()\n
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Out:\n   ST_NUM    ST_NAME OWN_OCCUPIED  NUM_BEDROOMS\n0   104.0     PUTNAM            Y           3.0\n1   197.0  LEXINGTON            N           3.0\n2     NaN  LEXINGTON            N           3.0\n3   201.0   BERKELEY          NaN           1.0\n4   203.0   BERKELEY            Y           3.0\n
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I know that I said we’ll be working with Pandas, but you can see that I also imported Numpy. We’ll use this a little bit later on to rename some missing values, so we might as well import it now.

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After importing the libraries we read the csv file into a Pandas dataframe. You can think of the dataframe as a spreadsheet.

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With the .head()method, we can easily see the first few rows.

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Now I can answer my original question, what are my features?  It’s pretty easy to infer the following features from the column names:

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  • ST_NUM: Street number
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  • ST_NAME: Street name
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  • OWN_OCCUPIED: Is the residence owner occupied
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  • NUM_BEDROOMS: Number of bedrooms
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We can also answer, what are the expected types?

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  • ST_NUM: float or int… some sort of numeric type
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  • ST_NAME: string
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  • OWN_OCCUPIED: string… Y (“Yes”) or N (“No”)
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  • NUM_BEDROOMS: float or int, a numeric type
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To answer the next two questions, we’ll need to start getting more in-depth width Pandas. Let’s start looking at examples of how to detect missing values

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Standard Missing Values

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So what do I mean by “standard missing values”? These are missing values that Pandas can detect.

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Going back to our original data set, let’s take a look at the “Street Number” column.

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In the third column there’s an empty cell. In the seventh row there’s an “NA” value.

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Clearly these are both missing values. Let’s see how Pandas deals with these.

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# Looking at the ST_NUM column\nprint df['ST_NUM']\nprint df['ST_NUM'].isnull()\n
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# Looking at the ST_NUM column\nOut:\n0    104.0\n1    197.0\n2      NaN\n3    201.0\n4    203.0\n5    207.0\n6      NaN\n7    213.0\n8    215.0\n\nOut:\n0    False\n1    False\n2     True\n3    False\n4    False\n5    False\n6     True\n7    False\n8    False\n
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Taking a look at the column, we can see that Pandas filled in the blank space with “NA”. Using the isnull() method, we can confirm that both the missing value and “NA” were recognized as missing values. Both boolean responses are True.

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This is a simple example, but highlights an important point. Pandas will recognize both empty cells and “NA” types as missing values. In the next section, we’ll take a look at some types that Pandas won’t recognize.

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Non-Standard Missing Values

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Sometimes it might be the case where there’s missing values that have different formats.

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Let’s take a look at the “Number of Bedrooms” column to see what I mean.

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In this column, there’s four missing values.

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  • n/a
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  • NA
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  • —
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  • na
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From the previous section, we know that Pandas will recognize “NA” as a missing value, but what about the others? Let’s take a look.

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# Looking at the NUM_BEDROOMS column\nprint df['NUM_BEDROOMS']\nprint df['NUM_BEDROOMS'].isnull()\n
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\nOut:\n0      3\n1      3\n2    n/a\n3      1\n4      3\n5    NaN\n6      2\n7     --\n8     na\n\nOut:\n0    False\n1    False\n2    False\n3    False\n4    False\n5     True\n6    False\n7    False\n8    False\n
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Just like before, Pandas recognized the “NA” as a missing value. Unfortunately, the other types weren’t recognized.

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If there’s multiple users manually entering data, then this is a common problem. Maybe i like to use “n/a” but you like to use “na”.

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An easy way to detect these various formats is to put them in a list. Then when we import the data, Pandas will recognize them right away. Here’s an example of how we would do that.

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# Making a list of missing value types\nmissing_values = [\"n/a\", \"na\", \"--\"]\ndf = pd.read_csv(\"property data.csv\", na_values = missing_values)\n
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Now let’s take another look at this column and see what happens.

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# Looking at the NUM_BEDROOMS column\nprint df['NUM_BEDROOMS']\nprint df['NUM_BEDROOMS'].isnull()\n
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\nOut:\n0    3.0\n1    3.0\n2    NaN\n3    1.0\n4    3.0\n5    NaN\n6    2.0\n7    NaN\n8    NaN\n\nOut:\n0    False\n1    False\n2     True\n3    False\n4    False\n5     True\n6    False\n7     True\n8     True\n
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This time, all of the different formats were recognized as missing values.

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You might not be able to catch all of these right away. As you work through the data and see other types of missing values, you can add them to the list.

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It’s important to recognize these non-standard types of missing values for purposes of summarizing and transforming missing values. If you try and count the number of missing values before converting these non-standard types, you could end up missing a lot of missing values.

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In the next section we’ll take a look at a more complicated, but very common, type of missing value.

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Unexpected Missing Values

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So far we’ve seen standard missing values, and non-standard missing values. What if we an unexpected type?

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For example, if our feature is expected to be a string, but there’s a numeric type, then technically this is also a missing value.

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Let’s take a look at the “Owner Occupied” column to see what I’m talking about.

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\"unexpected

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From our previous examples, we know that Pandas will detect the empty cell in row seven as a missing value. Let’s confirm with some code.

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# Looking at the OWN_OCCUPIED column\nprint df['OWN_OCCUPIED']\nprint df['OWN_OCCUPIED'].isnull()\n
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# Looking at the ST_NUM column\nOut:\n0      Y\n1      N\n2      N\n3     12\n4      Y\n5      Y\n6    NaN\n7      Y\n8      Y\n\nOut:\n0    False\n1    False\n2    False\n3    False\n4    False\n5    False\n6     True\n7    False\n8    False\n
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In the fourth row, there’s the number 12. The response for Owner Occupied should clearly be a string (Y or N), so this numeric type should be a missing value.

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This example is a little more complicated so we’ll need to think through a strategy for detecting these types of missing values. There’s a number of different approaches, but here’s the way that I’m going to work thorugh this one.

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  1. Loop through the OWN_OCCUPIED column
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  2. Try and turn the entry into an integer
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  3. If the entry can be changed into an integer, enter a missing value
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  4. If the number can’t be an integer, we know it’s a string, so keep going
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Let’s take a look at the code and then we’ll go through it in detail.

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# Detecting numbers \ncnt=0\nfor row in df['OWN_OCCUPIED']:\n    try:\n        int(row)\n        df.loc[cnt, 'OWN_OCCUPIED']=np.nan\n    except ValueError:\n        pass\n    cnt+=1\n
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In the code we’re looping through each entry in the “Owner Occupied” column. To try and change the entry to an integer, we’re using int(row).

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If the value can be changed to an integer, we change the entry to a missing value using Numpy’s np.nan.

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On the other hand, if it can’t be changed to an integer, we pass and keep going.

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You’ll notice that I used try and except ValueError. This is called exception handling, and we use this to handle errors.

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If we were to try and change an entry into an integer and it couldn’t be changed, then a ValueError would be returned, and the code would stop. To deal with this, we use exception handling to recognize these errors, and keep going.

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Another important bit of the code is the .loc method. This is the preferred Pandas method for modfiying entries in place. For more info on this you can check out the Pandas documentation.

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Now that we’ve worked through the different ways of detecting missing values, we’ll take a look at summarizing, and replacing them.

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Summarizing Missing Values

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After we’ve cleaned the missing values, we will probably want to summarize them. For instance, we might want to look at the total number of missing values for each feature.

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# Total missing values for each feature\nprint df.isnull().sum()\n
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Out:\nST_NUM          2\nST_NAME         0\nOWN_OCCUPIED    2\nNUM_BEDROOMS    4\n
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Other times we might want to do a quick check to see if we have any missing values at all.

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# Any missing values?\nprint df.isnull().values.any()\n
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Out:\nTrue\n
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We might also want to get a total count of missing values.

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# Total number of missing values\nprint df.isnull().sum().sum()\n
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Out:\n8\n
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Now that we’ve summarized the number of missing values, let’s take a look at doing some simple replacements.

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Replacing

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Often times you’ll have to figure out how you want to handle missing values.

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Sometimes you’ll simply want to delete those rows, other times you’ll replace them.

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As I mentioned earlier, this shouldn’t be taken lightly. We’ll go over some basic imputations, but for a detailed statistical approach for dealing with missing data, check out these awesome slides from data scientist Matt Brems.

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That being said, maybe you just want to fill in missing values with a single value.

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# Replace missing values with a number\ndf['ST_NUM'].fillna(125, inplace=True)\n
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More likely, you might want to do a location based imputation. Here’s how you would do that.

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# Location based replacement\ndf.loc[2,'ST_NUM'] = 125\n
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A very common way to replace missing values is using a median.

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# Replace using median \nmedian = df['NUM_BEDROOMS'].median()\ndf['NUM_BEDROOMS'].fillna(median, inplace=True)\n
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We’ve gone over a few simple ways to replace missing values, but be sure to check out Matt’s slides for the proper techniques.

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Conclusion

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Dealing with messy data is inevitable. Data cleaning is just part of the process on a data science project.

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In this article we went over some ways to detect, summarize, and replace missing values.

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For even more resources about data cleaning, check out these data science books.

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Armed with these techniques, you’ll spend less time data cleaning, and more time exploring and modeling.

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Leave a comment below and let me know which technique you find most useful!

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13 Comments

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    \n\t\t\t\t\"Satish\"\t\t\t
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    \n\t\t\t\tSatish\t\t\t\t\n\t\t\t\ton July 12, 2018 at 11:19 am\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t
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    Thanks for the great tutorial.

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      \n\t\t\t\t\"DataOptimal\"\t\t\t
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      \n\t\t\t\tDataOptimal\t\t\t\t\n\t\t\t\ton July 12, 2018 at 1:40 pm\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t
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      Thanks Satish, glad you enjoyed it!

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    \n\t\t\t\t\"Shoeb\t\t\t
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    \n\t\t\t\tShoeb Mohammad\t\t\t\t\n\t\t\t\ton July 12, 2018 at 6:01 pm\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t
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    Literally It is the best easy tutorial to make beginners understand each and every word of the code. Thank you DataOptimal for this wonderful tutorial and it actually helped.

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    Thanks again. Keep up the great work.

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      \n\t\t\t\t\"DataOptimal\"\t\t\t
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      \n\t\t\t\tDataOptimal\t\t\t\t\n\t\t\t\ton July 12, 2018 at 6:43 pm\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t
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      Thank you for the kind words Shoeb, I’m glad it helped!

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      We’ll be publishing plenty more posts, so be sure to check back often!

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    \n\t\t\t\t\"Ravi\"\t\t\t
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    \n\t\t\t\tRavi\t\t\t\t\n\t\t\t\ton August 9, 2018 at 4:49 pm\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t
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    \n\t\t\t\t\n\t\t\t\t
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    Wonderful and effective explanation with an example, Great work guys @DataOptimal.

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      \n\t\t\t\t\"DataOptimal\"\t\t\t
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      \n\t\t\t\tDataOptimal\t\t\t\t\n\t\t\t\ton August 28, 2018 at 2:11 pm\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t
      \n\n\t\t\t
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      Thanks, glad you enjoyed it!

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    \n\t\t\t\t\"sasha\"\t\t\t
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    \n\t\t\t\tsasha\t\t\t\t\n\t\t\t\ton August 28, 2018 at 4:24 am\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t
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    \n\t\t\t\t\n\t\t\t\t
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    This was hands on the best explanation for using pandas, it was well documented and helped me a lot in my preparation. Thanks a lot. you should do more tutorials, call it data science made simple or something \"😉\"

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      \n\t\t\t\t\"DataOptimal\"\t\t\t
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      \n\t\t\t\tDataOptimal\t\t\t\t\n\t\t\t\ton August 28, 2018 at 2:12 pm\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t
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      I’m glad you enjoyed it Sasha! We’re working on plenty of more tutorials, so stay tuned!

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    \n\t\t\t\t\"KARN\t\t\t
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    \n\t\t\t\tKARN KUMAR\t\t\t\t\n\t\t\t\ton September 11, 2018 at 2:52 pm\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t
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    This is awesome tutorial, would be great if you could create more videos or blogs.

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      \n\t\t\t\tDataOptimal\t\t\t\t\n\t\t\t\ton October 24, 2018 at 6:42 pm\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t
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      Thanks Karn! I’ll be posting more soon, so stay tuned!

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    \n\t\t\t\t\"Junior\"\t\t\t
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    \n\t\t\t\tJunior\t\t\t\t\n\t\t\t\ton October 11, 2018 at 8:06 am\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t
    \n\n\t\t\t
    \n\t\t\t\t\n\t\t\t\t
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    This is a great resource, exactly what I was looking for! Thank you!

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      \n\t\t\t\t\"DataOptimal\"\t\t\t
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      \n\t\t\t\tDataOptimal\t\t\t\t\n\t\t\t\ton October 24, 2018 at 6:42 pm\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t
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      You’re welcome Junior, glad you enjoyed it!

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    \n\t\t\t\t\"Jacobs\"\t\t\t
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    \n\t\t\t\tJacobs\t\t\t\t\n\t\t\t\ton October 12, 2018 at 7:13 am\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t
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    Do you have a similar example of data cleaning with R

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Learn Data Science Fast

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Learn how to get a data science job with techniques that I used to land a job in under 3 months.


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\t\n\t\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n" }, { "path": "March Cohort 2023/8_9_Pandas/Data Cleaning With Pandas/Data Cleaning with Python and Pandas_ Detecting Missing Values_files/analytics.js.download", "content": "(function(){var k=this,l=function(a,b){a=a.split(\".\");var c=k;a[0]in c||\"undefined\"==typeof c.execScript||c.execScript(\"var \"+a[0]);for(var d;a.length&&(d=a.shift());)a.length||void 0===b?c=c[d]&&c[d]!==Object.prototype[d]?c[d]:c[d]={}:c[d]=b};var m=function(a,b){for(var c in b)b.hasOwnProperty(c)&&(a[c]=b[c])},n=function(a){for(var b in a)if(a.hasOwnProperty(b))return!0;return!1};var q=/^(?:(?:https?|mailto|ftp):|[^:/?#]*(?:[/?#]|$))/i;var r=window,t=document,u=function(a,b){t.addEventListener?t.addEventListener(a,b,!1):t.attachEvent&&t.attachEvent(\"on\"+a,b)};var 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$current_popup_auto.find( '.et_bloom_custom_html_form' ) : $current_popup_auto.find( '.et_bloom_submit_subscription' ),\n\t\t\t\tpage_id = $data_holder.data( 'page_id' ),\n\t\t\t\toptin_id = $data_holder.data( 'optin_id' ),\n\t\t\t\tlist_id = $data_holder.data( 'list_id' );\n\n\t\t\tif ( ! $current_popup_auto.hasClass( 'et_bloom_animated' ) ) {\n\t\t\t\tvar $cookies_expire_auto = $current_popup_auto.data( 'cookie_duration' ) ? $current_popup_auto.data( 'cookie_duration' ) : false,\n\t\t\t\t\t$already_subscribed = checkCookieValue( 'et_bloom_subscribed_to_' + optin_id + list_id, 'true' );\n\n\t\t\t\tif ( ( ( false !== $cookies_expire_auto && ! checkCookieValue( 'etBloomCookie_' + optin_id, 'true' ) ) || false == $cookies_expire_auto ) && ! $already_subscribed ) {\n\t\t\t\t\tif ( false !== $cookies_expire_auto ) {\n\t\t\t\t\t\tmake_popup_visible ( $current_popup_auto, $delay, $cookies_expire_auto, 'etBloomCookie_' + optin_id + '=true' );\n\t\t\t\t\t} else {\n\t\t\t\t\t\tmake_popup_visible ( $current_popup_auto, $delay, '', '' );\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\n\t\tfunction scroll_trigger( $current_popup_bottom, is_bottom_trigger ) {\n\t\t\tvar triggered = 0,\n\t\t\t\t$data_holder = $current_popup_bottom.find( '.et_bloom_custom_html_form' ).length ? $current_popup_bottom.find( '.et_bloom_custom_html_form' ) : $current_popup_bottom.find( '.et_bloom_submit_subscription' ),\n\t\t\t\tpage_id = $data_holder.data( 'page_id' ),\n\t\t\t\toptin_id = $data_holder.data( 'optin_id' );\n\t\t\t\tlist_id = $data_holder.data( 'list_id' );\n\n\t\t\tif ( ! $current_popup_bottom.hasClass( 'et_bloom_animated' ) ) {\n\t\t\t\tvar\tcookies_expire_bottom = $current_popup_bottom.data( 'cookie_duration' ) ? $current_popup_bottom.data( 'cookie_duration' ) : false,\n\t\t\t\t\t$already_subscribed = checkCookieValue( 'et_bloom_subscribed_to_' + optin_id + list_id, 'true' );\n\n\t\t\t\tif ( true == is_bottom_trigger ) {\n\t\t\t\t\tvar scroll_trigger = $( '.et_bloom_bottom_trigger' ).length ? $( '.et_bloom_bottom_trigger' ).offset().top : $( document ).height() - 500;\n\t\t\t\t} else {\n\t\t\t\t\tvar scroll_pos = $current_popup_bottom.data( 'scroll_pos' ) > 100 ? 100 : $current_popup_bottom.data( 'scroll_pos' ),\n\t\t\t\t\t\tscroll_trigger = 100 == scroll_pos ? $( document ).height() - 50 : $( document ).height() * scroll_pos / 100;\n\t\t\t\t}\n\n\t\t\t\t$( window ).scroll( function(){\n\t\t\t\t\tif ( ( ( false !== cookies_expire_bottom && ! checkCookieValue( 'etBloomCookie_' + optin_id, 'true' ) ) || false == cookies_expire_bottom ) && ! $already_subscribed ) {\n\t\t\t\t\t\tif( $( window ).scrollTop() + $( window ).height() > scroll_trigger ) {\n\t\t\t\t\t\t\tif ( 0 == triggered ) {\n\t\t\t\t\t\t\t\tif ( false !== cookies_expire_bottom ) {\n\t\t\t\t\t\t\t\t\tmake_popup_visible ( $current_popup_bottom, 0, cookies_expire_bottom, 'etBloomCookie_' + optin_id + '=true' );\n\t\t\t\t\t\t\t\t} else {\n\t\t\t\t\t\t\t\t\tmake_popup_visible ( $current_popup_bottom, 0, '', '' );\n\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t\ttriggered++;\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t});\n\t\t\t}\n\t\t}\n\n\t\t$.fn.isInViewport = function() {\n\t\t\tvar elementTop = $( this ).offset().top;\n\t\t\tvar elementBottom = elementTop + $( this ).outerHeight();\n\t\t\tvar viewportTop = $( window ).scrollTop();\n\t\t\tvar viewportBottom = viewportTop + $( window ).height();\n\n\t\t\treturn elementBottom > viewportTop && elementTop < viewportBottom;\n\t\t};\n\n\t\tvar $inline_optins = $( '.et_bloom_inline_form .et_bloom_submit_subscription, .et_bloom_widget_content .et_bloom_submit_subscription, .et_bloom_custom_html_form' );\n\n\t\tif ( $inline_optins.length > 0 ) {\n\t\t\tvar imp_recorded_count = 0;\n\n\t\t\t$(window).on( 'scroll.et_bloom_impressions', function() {\n\t\t\t\t$inline_optins.each( function() {\n\t\t\t\t\tif ( ! $(this).hasClass( 'et_bloom_impression_recorded' ) && $(this).isInViewport() ) {\n\t\t\t\t\t\t$( this ).addClass( 'et_bloom_impression_recorded' );\n\n\t\t\t\t\t\timp_recorded_count++;\n\n\t\t\t\t\t\tupdate_stats_table( 'imp', $(this) );\n\t\t\t\t\t}\n\t\t\t\t} );\n\n\t\t\t\tif ( imp_recorded_count >= $inline_optins.length ) {\n\t\t\t\t\t$(window).off( 'scroll.et_bloom_impressions' );\n\t\t\t\t}\n\t\t\t} );\n\t\t}\n\n\t\t if( $( '.et_bloom_auto_popup' ).length ) {\n\t\t\t$( '.et_bloom_auto_popup:not(.et_bloom_visible)' ).each( function() {\n\t\t\t\tvar this_el = $( this ),\n\t\t\t\t\tdelay = '' !== this_el.data( 'delay' ) ? this_el.data( 'delay' ) * 1000 : 0;\n\t\t\t\tauto_popup( this_el, delay );\n\t\t\t});\n\t\t }\n\n\t\tif( $( '.et_bloom_trigger_bottom' ).length ) {\n\n\t\t\t$( '.et_bloom_trigger_bottom:not(.et_bloom_visible)' ).each( function(){\n\t\t\t\tscroll_trigger( $( this ), true );\n\t\t\t});\n\n\t\t}\n\n\t\tif( $( '.et_bloom_scroll' ).length ) {\n\n\t\t\t$( '.et_bloom_scroll:not(.et_bloom_visible)' ).each( function(){\n\t\t\t\tscroll_trigger( $( this ), false );\n\t\t\t});\n\t\t}\n\n\t\tif ( $( '.et_bloom_trigger_click' ).length ) {\n\t\t\t$( '.et_bloom_trigger_click:not(.et_bloom_visible)' ).each( function() {\n\t\t\t\tvar $this_el = $( this ),\n\t\t\t\t\tselector = $this_el.attr( 'data-trigger_click' );\n\n\t\t\t\tif ( typeof selector !== 'undefined' ) {\n\t\t\t\t\t$( 'body' ).on( 'click', selector, function() {\n\t\t\t\t\t\tmake_popup_visible ( $this_el, 0, '', '' );\n\t\t\t\t\t\treturn false;\n\t\t\t\t\t});\n\t\t\t\t}\n\t\t\t});\n\t\t}\n\n\t\tif( $( '.et_bloom_trigger_idle' ).length ) {\n\t\t\t$( '.et_bloom_trigger_idle:not(.et_bloom_visible)' ).each( function() {\n\t\t\t\tvar $this_el = $( this ),\n\t\t\t\t\t$data_holder = $this_el.find( '.et_bloom_custom_html_form' ).length ? $this_el.find( '.et_bloom_custom_html_form' ) : $this_el.find( '.et_bloom_submit_subscription' ),\n\t\t\t\t\tpage_id = $data_holder.data( 'page_id' ),\n\t\t\t\t\toptin_id = $data_holder.data( 'optin_id' ),\n\t\t\t\t\tlist_id = $data_holder.data( 'list_id' );\n\n\t\t\t\tif ( ! $this_el.hasClass( 'et_bloom_animated' ) ) {\n\t\t\t\t\tvar $cookies_expire_idle = $this_el.data( 'cookie_duration' ) ? $this_el.data( 'cookie_duration' ) : false,\n\t\t\t\t\t\t$already_subscribed = checkCookieValue( 'et_bloom_subscribed_to_' + optin_id + list_id, 'true' );\n\t\t\t\t\t\t$idle_timeout = '' !== $this_el.data( 'idle_timeout' ) ? $this_el.data( 'idle_timeout' ) * 1000 : 30000,\n\t\t\t\t\t\t$delay = 0;\n\n\t\t\t\t\tif ( ( ( false !== $cookies_expire_idle && ! checkCookieValue( 'etBloomCookie_' + optin_id, 'true' ) ) || false == $cookies_expire_idle ) && ! $already_subscribed ) {\n\t\t\t\t\t\t$( document ).idleTimer( $idle_timeout );\n\n\t\t\t\t\t\t$( document ).on( 'idle.idleTimer', function() {\n\t\t\t\t\t\t\tif ( false !== $cookies_expire_idle ) {\n\t\t\t\t\t\t\t\tmake_popup_visible ( $this_el, $delay, $cookies_expire_idle, 'etBloomCookie_' + optin_id + '=true' );\n\t\t\t\t\t\t\t} else {\n\t\t\t\t\t\t\t\tmake_popup_visible ( $this_el, $delay, '', '' );\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t});\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t});\n\t\t}\n\n\t\tif ( 'true' == get_url_parameter( 'et_bloom_popup' ) ) {\n\t\t\t$( '.et_bloom_after_comment' ).each( function() {\n\t\t\t\tauto_popup( $( this ), 0 );\n\t\t\t});\n\t\t}\n\n\t\tif ( $( '.et_bloom_after_order' ).length ) {\n\t\t\t$( '.et_bloom_after_purchase' ).each( function() {\n\t\t\t\tauto_popup( $( this ), 0 );\n\t\t\t});\n\t\t}\n\n\t\tif( $( '.et_bloom_locked_container' ).length ) {\n\t\t\tvar $i = 0;\n\n\t\t\t$( '.et_bloom_locked_container' ).each( function() {\n\t\t\t\tvar $this_el = $( this ),\n\t\t\t\t\tcontent = $this_el.find( '.et_bloom_locked_content' ),\n\t\t\t\t\tform = $this_el.find( '.et_bloom_locked_form' ),\n\t\t\t\t\tpage_id = $this_el.data( 'page_id' ),\n\t\t\t\t\toptin_id = $this_el.data( 'optin_id' );\n\n\t\t\t\t$this_el.data( 'container_id', $i );\n\t\t\t\t$locked_containers.push( content );\n\n\t\t\t\tif ( checkCookieValue( 'et_bloom_unlocked' + optin_id + page_id, 'true' ) ) {\n\t\t\t\t\tcontent.css( {'display' : 'block'} );\n\t\t\t\t\tform.remove();\n\t\t\t\t} else {\n\t\t\t\t\tcontent.remove();\n\t\t\t\t\tupdate_stats_table( 'imp', $this_el );\n\t\t\t\t}\n\n\t\t\t\t$i++;\n\t\t\t});\n\t\t}\n\n\t\t$( 'body' ).on( 'click', '.et_bloom_locked_container .et_bloom_submit_subscription', function(){\n\t\t\tvar $current_container = $( this ).closest( '.et_bloom_locked_container' ),\n\t\t\t\tcontainer_id = $current_container.data( 'container_id' ),\n\t\t\t\tpage_id = $current_container.data( 'page_id' ),\n\t\t\t\toptin_id = typeof $current_container.data( 'current_optin_id' ) !== 'undefined' ? $current_container.data( 'current_optin_id' ) : $current_container.data( 'optin_id' );\n\n\t\t\tperform_subscription( $( this ), $current_container, container_id, page_id, optin_id );\n\n\t\t\treturn false;\n\t\t});\n\n\t\t// unlock content immediately if custom HTML form is used.\n\t\t$( 'body' ).on( 'click', '.et_bloom_locked_container .et_bloom_custom_html_form input[type=\"submit\"], .et_bloom_locked_container .et_bloom_custom_html_form button[type=\"submit\"]', function() {\n\t\t\tvar current_container = $( this ).closest( '.et_bloom_locked_container' ),\n\t\t\t\tcontainer_id = current_container.data( 'container_id' ),\n\t\t\t\tpage_id = current_container.data( 'page_id' ),\n\t\t\t\toptin_id = current_container.data( 'optin_id' );\n\n\t\t\tunlock_content( current_container, container_id, page_id, optin_id );\n\t\t} );\n\n\t\tfunction unlock_content( current_container, container_id, locked_page_id, locked_optin_id ) {\n\t\t\tset_cookie( 365, 'et_bloom_unlocked' + locked_optin_id + locked_page_id + '=true' );\n\t\t\tcurrent_container.find( '.et_bloom_locked_form' ).replaceWith( $locked_containers[container_id] );\n\t\t\tcurrent_container.find( '.et_bloom_locked_content' ).css( { 'display' : 'block' } );\n\t\t}\n\n\t\t// Move inline forms into appropriate sections in Divi theme\n\t\tif( $( '.et_bloom_below_post' ).length ) {\n\t\t\tif ( $( 'body' ).hasClass( 'et_pb_pagebuilder_layout' ) ) {\n\t\t\t\tvar bottom_inline = $( '.et_bloom_below_post' ),\n\t\t\t\t\tdivi_container = '
';\n\n\t\t\t\tif ( bottom_inline.length ) {\n\t\t\t\t\t$( '.et_pb_section' ).not( '.et_pb_fullwidth_section' ).last().append( divi_container ).find( '.et_pb_row' ).last().find( '.et_pb_column' ).append( bottom_inline );\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\n\t\tfunction define_popup_position( $this_popup, $just_loaded, $message_space ) {\n\t\t\tvar this_popup = $this_popup.find( '.et_bloom_form_container' ),\n\t\t\t\tpopup_max_height = this_popup.hasClass( 'et_bloom_popup_container' ) ? $( window ).height() - 40 : $( window ).height() - 20,\n\t\t\t\treal_popup_height = 0,\n\t\t\t\tpercentage = this_popup.parent().hasClass( 'et_bloom_flyin' ) ? 0.03 : 0.05,\n\t\t\t\tpercentage = this_popup.hasClass( 'et_bloom_with_border' ) ? percentage + 0.03 : percentage,\n\t\t\t\tbreakout_offset = this_popup.hasClass( 'breakout_edge' ) ? 0.95 : 1,\n\t\t\t\tdashed_offset = this_popup.hasClass( 'et_bloom_border_dashed' ) ? 4 : 0,\n\t\t\t\tform_height = this_popup.find( 'form' ).innerHeight() + $message_space,\n\t\t\t\tform_add = true == $just_loaded ? 5 : 0;\n\n\t\t\tthis_popup.css( { 'max-height' : popup_max_height } );\n\n\t\t\tif ( this_popup.hasClass( 'et_bloom_popup_container' ) ) {\n\t\t\t\tvar top_position = $( window ).height() / 2 - this_popup.innerHeight() / 2;\n\t\t\t\tthis_popup.css( { 'top' : top_position + 'px' } );\n\t\t\t}\n\n\t\t\tthis_popup.find( '.et_bloom_form_container_wrapper' ).css( { 'max-height' : popup_max_height - 20 } );\n\n\n\t\t\tif ( ( 768 > $( 'body' ).outerWidth() + 15 ) || this_popup.hasClass( 'et_bloom_form_bottom' ) ) {\n\t\t\t\tif ( this_popup.hasClass( 'et_bloom_form_right' ) || this_popup.hasClass( 'et_bloom_form_left' ) ) {\n\t\t\t\t\tthis_popup.find( '.et_bloom_form_header' ).css( { 'height' : 'auto' } );\n\t\t\t\t}\n\n\t\t\t\treal_popup_height = this_popup.find( '.et_bloom_form_container_wrapper' ).height() + 30 + form_add;\n\n\t\t\t\tif ( this_popup.hasClass( 'et_bloom_form_right' ) || this_popup.hasClass( 'et_bloom_form_left' ) ) {\n\t\t\t\t\tthis_popup.find( '.et_bloom_form_container_wrapper' ).css( { 'height' : real_popup_height - 30 + dashed_offset } );\n\t\t\t\t}\n\t\t\t} else {\n\t\t\t\treal_popup_height = this_popup.find( '.et_bloom_form_container_wrapper' ).height() + $message_space;\n\n\t\t\t\t/**\n\t\t\t\t * Sometimes real_popup_height return 0 because the page is no loaded fully. This\n\t\t\t\t * is added to set the Bloom container height as 100% to avoid 0 height. Note, the\n\t\t\t\t * height will be set as 100% only when the real_popup_height is 0.\n\t\t\t\t */\n\t\t\t\tvar container_height = real_popup_height;\n\t\t\t\tif (parseInt(real_popup_height) === 0) {\n\t\t\t\t\tcontainer_height = '100%';\n\t\t\t\t}\n\n\t\t\t\tif ( this_popup.hasClass( 'et_bloom_form_right' ) || this_popup.hasClass( 'et_bloom_form_left' ) ) {\n\t\t\t\t\tthis_popup.find( '.et_bloom_form_header' ).css( { 'height' : real_popup_height * breakout_offset - dashed_offset } );\n\t\t\t\t\tthis_popup.find( '.et_bloom_form_content' ).css( { 'min-height' : real_popup_height - dashed_offset } );\n\t\t\t\t\tthis_popup.find( '.et_bloom_form_container_wrapper' ).css( { 'height' : container_height } );\n\t\t\t\t}\n\t\t\t}\n\n\t\t\tif ( real_popup_height > popup_max_height ) {\n\t\t\t\tthis_popup.find( '.et_bloom_form_container_wrapper' ).addClass( 'et_bloom_vertical_scroll' );\n\t\t\t} else {\n\t\t\t\tthis_popup.find( '.et_bloom_form_container_wrapper' ).removeClass( 'et_bloom_vertical_scroll' );\n\t\t\t}\n\n\t\t\tif ( $this_popup.hasClass( 'et_bloom_popup' ) ) {\n\t\t\t\t$( 'body' ).addClass( 'et_bloom_popup_active' );\n\t\t\t}\n\t\t}\n\n\t\t$( 'body' ).on( 'click', '.et_bloom_submit_subscription:not(.et_bloom_submit_subscription_locked)', function() {\n\t\t\tperform_subscription( $( this ), '', '', '', '' );\n\t\t\treturn false;\n\t\t});\n\n\t\tfunction perform_subscription(this_button, current_container, container_id, locked_page_id, locked_optin_id) {\n\t\t\tvar this_form = this_button.parents('form');\n\t\t\tvar list_id = this_button.data('list_id');\n\t\t\tvar account_name = this_button.data('account');\n\t\t\tvar service = this_button.data('service');\n\t\t\tvar name = this_form.find('.et_bloom_subscribe_name input').val();\n\t\t\tvar last_name = undefined !== this_form.find('.et_bloom_subscribe_last input').val() ? this_form.find('.et_bloom_subscribe_last input').val() : '';\n\t\t\tvar email = this_form.find('.et_bloom_subscribe_email input').val();\n\t\t\tvar page_id = this_button.data('page_id');\n\t\t\tvar optin_id = this_button.data('optin_id');\n\t\t\tvar $popup_container = this_form.closest('.et_bloom_optin');\n\t\t\tvar is_popup = $popup_container.hasClass('et_bloom_popup') || $popup_container.hasClass('et_bloom_flyin');\n\t\t\tvar $success_action_el = this_button.closest('.et_bloom_success_action');\n\t\t\tvar success_action_details = $success_action_el.length > 0 ? $success_action_el.parent().data('success_action_details').split('|') : [];\n\t\t\tvar success_action = 2 === success_action_details.length ? success_action_details[0] : '';\n\t\t\tvar success_action_url = '' !== success_action ? success_action_details[1] : '';\n\n\t\t\tvar ip_address = this_button.data('ip_address');\n\t\t\tvar $fields_container = this_form.find('.et_bloom_fields');\n\t\t\tvar $custom_fields = $fields_container.find('.et_bloom_custom_field input[type=text], .et_bloom_checkbox_handle, [data-field_type=\"radio\"], textarea, select');\n\t\t\tvar custom_fields = {};\n\t\t\tvar is_valid = true;\n\n\t\t\t// Email Validation\n\t\t\t// Use the regex defined in the HTML5 spec for input[type=email] validation\n\t\t\t// (see https://www.w3.org/TR/2016/REC-html51-20161101/sec-forms.html#email-state-typeemail)\n\t\t\tvar et_email_reg_html5 = /^[a-zA-Z0-9.!#$%&'*+\\/=?^_`{|}~-]+@[a-zA-Z0-9](?:[a-zA-Z0-9-]{0,61}[a-zA-Z0-9])?(?:\\.[a-zA-Z0-9](?:[a-zA-Z0-9-]{0,61}[a-zA-Z0-9])?)*$/;\n\n\t\t\tthis_form.find('.et_bloom_warn_field').removeClass('et_bloom_warn_field');\n\n\t\t\tif (this_form.length > 0 && typeof this_form[0].reportValidity === 'function') {\n\t\t\t\t// Checks HTML5 validation constraints\n\t\t\t\tis_valid = this_form[0].reportValidity();\n\t\t\t}\n\n\t\t\t$custom_fields.each( function() {\n\t\t\t\tvar $this_el = $(this);\n\t\t\t\tvar $this_wrapper = false;\n\t\t\t\tvar this_id = $this_el.attr('data-id');\n\t\t\t\tvar field_type = typeof $this_el.data( 'field_type' ) !== 'undefined' ? $this_el.data( 'field_type' ) : 'text';\n\n\t\t\t\tif ('checkbox' === field_type || 'radio' === field_type) {\n\t\t\t\t\t$this_wrapper = $this_el.closest('.et_bloom_custom_field');\n\t\t\t\t}\n\n\t\t\t\tvar this_val = $this_el.val();\n\t\t\t\tvar required_mark = typeof $this_el.data('required_mark') !== 'undefined' ? $this_el.data('required_mark') : 'not_required';\n\t\t\t\tvar original_id = typeof $this_el.data('original_id') !== 'undefined' ? $this_el.data('original_id') : '';\n\t\t\t\tvar unchecked = false;\n\t\t\t\tvar default_value;\n\n\t\t\t\t// radio field properties adjustment\n\t\t\t\tif ('radio' === field_type) {\n\t\t\t\t\tif (0 !== $this_wrapper.find('input[type=\"radio\"]').length) {\n\t\t\t\t\t\tvar $firstRadio = $this_wrapper.find('input[type=\"radio\"]:first');\n\n\t\t\t\t\t\trequired_mark = typeof $firstRadio.data('required_mark') !== 'undefined' ? $firstRadio.data('required_mark') : 'not_required';\n\n\t\t\t\t\t\tthis_val = '';\n\n\t\t\t\t\t\tif ($this_wrapper.find('input[type=\"radio\"]:checked')) {\n\t\t\t\t\t\t\tthis_val = $this_wrapper.find('input[type=\"radio\"]:checked').val();\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\n\t\t\t\t\tif (!$.isEmptyObject(this_val)) {\n\t\t\t\t\t\tcustom_fields[this_id] = this_val;\n\t\t\t\t\t}\n\n\t\t\t\t\tif (0 === $this_wrapper.find('input[type=\"radio\"]:checked').length) {\n\t\t\t\t\t\tunchecked = true;\n\t\t\t\t\t}\n\n\t\t\t\t\tif (this_val) {\n\t\t\t\t\t\tcustom_fields[this_id] = this_val;\n\t\t\t\t\t}\n\t\t\t\t} else if ('checkbox' === field_type) {\n\t\t\t\t\tthis_val = {};\n\n\t\t\t\t\tif ( 0 !== $this_wrapper.find('input[type=\"checkbox\"]').length) {\n\t\t\t\t\t\tvar $checkboxHandle = $this_wrapper.find('.et_bloom_checkbox_handle');\n\n\t\t\t\t\t\trequired_mark = typeof $checkboxHandle.data('required_mark') !== 'undefined' ? $checkboxHandle.data('required_mark') : 'not_required';\n\n\t\t\t\t\t\tif ($this_wrapper.find('input[type=\"checkbox\"]:checked').length > 0) {\n\t\t\t\t\t\t\t$this_wrapper.find('input[type=\"checkbox\"]:checked').each(function() {\n\t\t\t\t\t\t\t\tvar field_id = $(this).data('id');\n\t\t\t\t\t\t\t\tthis_val[field_id] = $(this).val();\n\t\t\t\t\t\t\t} );\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\n\t\t\t\t\tif (!$.isEmptyObject(this_val)) {\n\t\t\t\t\t\tcustom_fields[this_id] = this_val;\n\t\t\t\t\t}\n\n\t\t\t\t\tif (0 === $this_wrapper.find('input[type=\"checkbox\"]:checked').length) {\n\t\t\t\t\t\tunchecked = true;\n\t\t\t\t\t}\n\t\t\t\t} else if ('ontraport' === service && 'select' === field_type) {\n\t\t\t\t\t// Need to pass option ID as a value for dropdown menu in Ontraport\n\t\t\t\t\tvar $selected_option = $this_el.find(':selected');\n\t\t\t\t\tcustom_fields[this_id] = $selected_option.length > 0 ? $selected_option.data('id') : this_val;\n\t\t\t\t} else {\n\t\t\t\t\tcustom_fields[this_id] = this_val;\n\t\t\t\t}\n\n\t\t\t\t// add error message for the field if it is required and empty\n\t\t\t\tif ('required' === required_mark && ('' === this_val || true === unchecked)) {\n\t\t\t\t\tif (false === $this_wrapper) {\n\t\t\t\t\t\t$this_el.addClass('et_bloom_warn_field');\n\t\t\t\t\t} else {\n\t\t\t\t\t\t$this_wrapper.addClass('et_bloom_warn_field');\n\t\t\t\t\t}\n\n\t\t\t\t\tis_valid = false;\n\t\t\t\t}\n\n\t\t\t\tif ('email' === field_type) {\n\t\t\t\t\t// remove trailing/leading spaces and convert email to lowercase\n\t\t\t\t\tvar processed_email = this_val.trim().toLowerCase();\n\t\t\t\t\tvar is_valid_email = et_email_reg_html5.test(processed_email);\n\n\t\t\t\t\tif ('' !== processed_email && !is_valid_email) {\n\t\t\t\t\t\t$this_el.addClass('et_bloom_warn_field');\n\t\t\t\t\t\tis_valid = false;\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t} );\n\n\t\t\tif (! is_valid) {\n\t\t\t\treturn;\n\t\t\t}\n\n\t\t\tif ('' == email) {\n\t\t\t\tthis_form.find('.et_bloom_subscribe_email input').addClass('et_bloom_warn_field');\n\t\t\t} else {\n\t\t\t\tip_address = ip_address ? 'true' : 'false';\n\t\t\t\t$subscribe_data = JSON.stringify({ 'list_id' : list_id, 'account_name' : account_name, 'service' : service, 'name' : name, 'email' : email, 'page_id' : page_id, 'optin_id' : optin_id, 'last_name' : last_name, 'ip_address': ip_address });\n\t\t\t\t$.ajax({\n\t\t\t\t\ttype: 'POST',\n\t\t\t\t\tdataType: 'json',\n\t\t\t\t\turl: bloomSettings.ajaxurl,\n\t\t\t\t\tdata: {\n\t\t\t\t\t\taction : 'bloom_subscribe',\n\t\t\t\t\t\tsubscribe_data_array : $subscribe_data,\n\t\t\t\t\t\tcustom_fields: custom_fields,\n\t\t\t\t\t\tsubscribe_nonce : bloomSettings.subscribe_nonce\n\t\t\t\t\t},\n\t\t\t\t\tbeforeSend: function(data) {\n\t\t\t\t\t\tthis_button.addClass('et_bloom_button_text_loading');\n\t\t\t\t\t\tthis_button.find('.et_bloom_subscribe_loader').css({ 'display' : 'block' });\n\t\t\t\t\t},\n\t\t\t\t\tsuccess: function(data) {\n\t\t\t\t\t\tthis_button.removeClass('et_bloom_button_text_loading');\n\t\t\t\t\t\tthis_button.find('.et_bloom_subscribe_loader').css({ 'display' : 'none' });\n\t\t\t\t\t\tif (data) {\n\t\t\t\t\t\t\tif ('' !== current_container && (data.success || 'Invalid email' !== data.error)) {\n\t\t\t\t\t\t\t\tunlock_content(current_container, container_id, locked_page_id, locked_optin_id);\n\t\t\t\t\t\t\t} else {\n\t\t\t\t\t\t\t\tif (data.error) {\n\t\t\t\t\t\t\t\t\tthis_form.find('.et_bloom_error_message').remove();\n\t\t\t\t\t\t\t\t\tthis_form.prepend('

' + data.error + '

');\n\t\t\t\t\t\t\t\t\tthis_form.parent().parent().find('.et_bloom_form_header').addClass('et_bloom_with_error');\n\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t\tif (data.success && '' == current_container) {\n\t\t\t\t\t\t\t\t\tset_cookie(365, 'et_bloom_subscribed_to_' + optin_id + list_id + '=true');\n\n\t\t\t\t\t\t\t\t\tif ('' === success_action || '' === success_action_url) {\n\t\t\t\t\t\t\t\t\t this_form.parent().find('.et_bloom_success_message').addClass('et_bloom_animate_message');\n\t\t\t\t\t\t\t\t\t this_form.parent().find('.et_bloom_success_container').addClass('et_bloom_animate_success');\n\t\t\t\t\t\t\t\t\t this_form.remove();\n\t\t\t\t\t\t\t\t\t} else {\n\t\t\t\t\t\t\t\t\t\twindow.location = success_action_url;\n\t\t\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\t\t\t// auto close popup if enabled\n\t\t\t\t\t\t\t\t\tif (is_popup && $popup_container.hasClass('et_bloom_auto_close')) {\n\t\t\t\t\t\t\t\t\t\tsetTimeout(function() {\n\t\t\t\t\t\t\t\t\t\t\tperform_popup_closing($popup_container);\n\t\t\t\t\t\t\t\t\t\t},1400);\n\t\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t}\n\n\t\t\t\t\t\t\tif (is_popup) {\n\t\t\t\t\t\t\t\tdefine_popup_position($popup_container, false, 50);\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t});\n\t\t\t}\n\t\t}\n\n\t\t$( 'body' ).on( 'click', '.et_bloom_custom_html_form input[type=\"submit\"], .et_bloom_custom_html_form button[type=\"submit\"]', function() {\n\t\t\tvar this_button = $( this ),\n\t\t\t\tform_container = this_button.closest( '.et_bloom_custom_html_form' );\n\n\t\t\tupdate_stats_table( 'con', form_container );\n\t\t} );\n\n\t\t$( window ).resize( function(){\n\t\t\tif ( $( '.et_bloom_resize' ).length ) {\n\t\t\t\t$( '.et_bloom_resize' ).each( function() {\n\t\t\t\t\tdefine_popup_position( $( this ), false, 0 );\n\t\t\t\t});\n\t\t\t}\n\t\t});\n\t});\n})(jQuery)\n" }, { "path": "March Cohort 2023/8_9_Pandas/Data Cleaning With Pandas/Data Cleaning with Python and Pandas_ Detecting Missing Values_files/custom.js.download", "content": "(function($){\n\t$(document).ready(function() {\n\t\tvar all_networks_opened = 0;\n\n\t\t// fix the \"on media\" wrapper inside the Divi Gallery grid\n\t\tet_pb_fix_gallery_wrapper();\n\n\t\t$( 'body' ).on( 'click', '.et_social_share', function() {\n\t\t\tvar $this_el = $(this),\n\t\t\t\tsocial_type = $this_el.data( 'social_type' ),\n\t\t\t\tsocial_network = $this_el.data( 'social_name' ),\n\t\t\t\tshare_link = 'media' == social_type ? $this_el.data( 'social_link' ) : $this_el.prop( 'href' );\n\n\t\t\tupdate_stats_table( social_network, $this_el );\n\n\t\t\tif ( 'like' === social_network ) {\n\t\t\t\treturn false;\n\t\t\t}\n\n\t\t\tvar left = ( $( window ).width()/2 ) - ( 550/2 );\n\t\t\tvar top = ( $( window ).height()/2 ) - ( 450/2 );\n\t\t\tvar new_window = window.open( share_link, '', 'scrollbars=1, height=450, width=550, left=' + left + ', top=' + top );\n\n\t\t\tif ( window.focus ) {\n\t\t\t\tnew_window.focus();\n\t\t\t}\n\n\t\t\treturn false;\n\t\t});\n\n\t\t$( '.et_social_follow' ).click( function() {\n\t\t\tvar $this_el = $(this),\n\t\t\t\tsocial_network = $this_el.data( 'social_name' );\n\n\t\t\tupdate_stats_table( social_network, $this_el );\n\n\t\t\tif ( 'like' === social_network ) {\n\t\t\t\treturn false;\n\t\t\t}\n\t\t});\n\n\t\t$( 'body' ).on( 'click', '.et_social_share_pinterest', function() {\n\t\t\tif ( $( this ).hasClass( 'et_social_pin_all' ) ) {\n\t\t\t\tvar left = ( $( window ).width()/2 ) - ( 550/2 ),\n\t\t\t\t\ttop = ( $( window ).height()/2 ) - ( 450/2 ),\n\t\t\t\t\tshare_link = $( this ).attr( 'href' ),\n\t\t\t\t\tnew_window = window.open( share_link, '', 'scrollbars=1, height=450, width=550, left=' + left + ', top=' + top );\n\n\t\t\t\tif ( window.focus ) {\n\t\t\t\t\tnew_window.focus();\n\t\t\t\t}\n\t\t\t} else {\n\t\t\t\t$( '.et_social_pin_images_outer' ).fadeToggle( 400 );\n\t\t\t}\n\n\t\t\treturn false;\n\t\t});\n\n\t\tfunction get_url_parameter( param_name ) {\n\t\t\tvar page_url = window.location.search.substring(1);\n\t\t\tvar url_variables = page_url.split('&');\n\t\t\tfor ( var i = 0; i < url_variables.length; i++ ) {\n\t\t\t\t\tvar curr_param_name = url_variables[i].split( '=' );\n\t\t\t\tif ( curr_param_name[0] == param_name ) {\n\t\t\t\t\treturn curr_param_name[1];\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\n\t\tfunction update_stats_table( $social_network, $this_el ) {\n\t\t\tvar action = $this_el.data( 'social_type' ),\n\t\t\t\tmedia_url = 'media' == action ? $this_el.closest( '.et_social_media_wrapper' ).find( 'img' ).attr( 'src' ) : '',\n\t\t\t\tpost_id = $this_el.data( 'post_id' ),\n\t\t\t\tlocation = $this_el.data( 'location' ),\n\t\t\t\tstats_data = '';\n\n\t\t\tstats_data = JSON.stringify({\n\t\t\t\t'action' : action,\n\t\t\t\t'network' : $social_network,\n\t\t\t\t'media_url' : media_url,\n\t\t\t\t'post_id' : post_id,\n\t\t\t\t'location' : location\n\t\t\t});\n\n\t\t\t$.ajax({\n\t\t\t\ttype: 'POST',\n\t\t\t\turl: monarchSettings.ajaxurl,\n\t\t\t\tdata: {\n\t\t\t\t\taction : 'add_stats_record_db',\n\t\t\t\t\tstats_data_array : stats_data,\n\t\t\t\t\tadd_stats_nonce : monarchSettings.stats_nonce\n\t\t\t\t},\n\t\t\t\tsuccess: function( data ) {\n\t\t\t\t\tif ( true == data ){\n\t\t\t\t\t\tif ( 'like' === action ){\n\t\t\t\t\t\t\tupdate_single_shares( $this_el, '', post_id, $social_network, 'like' );\n\t\t\t\t\t\t}\n\t\t\t\t\t\tif ( 'media' === action ){\n\t\t\t\t\t\t\tupdate_total_media_shares( $this_el.closest( '.et_social_media_wrapper' ) );\n\t\t\t\t\t\t\tupdate_single_shares( $this_el, media_url, post_id, $social_network, 'media' );\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t});\n\t\t}\n\n\t\tfunction append_share_counts( $current_network ) {\n\t\t\tvar network = $current_network.data( 'social_name' ),\n\t\t\t\tmin_count = $current_network.data( 'min_count' ),\n\t\t\t\tpost_id = $current_network.data( 'post_id' ),\n\t\t\t\turl = monarchSettings.pageurl !== '' ? monarchSettings.pageurl : window.location.href,\n\t\t\t\tlabel_div = $current_network.find( '.et_social_network_label' ),\n\t\t\t\tappend_to = ( 0 != ( label_div.length ) ) ? label_div : $current_network;\n\n\t\t\t$share_count_data = JSON.stringify({ 'network' : network, 'min_count' : min_count, 'post_id' : post_id, 'url' : url });\n\n\t\t\t$.ajax({\n\t\t\t\ttype: 'POST',\n\t\t\t\turl: monarchSettings.ajaxurl,\n\t\t\t\tdata: {\n\t\t\t\t\t\taction : 'get_shares_count',\n\t\t\t\t\t\tshare_count_array : $share_count_data,\n\t\t\t\t\t\tget_share_counts_nonce : monarchSettings.share_counts\n\t\t\t\t\t},\n\t\t\t\t\tbeforeSend: function( data ){\n\t\t\t\t\t\tappend_to.append( '' );\n\t\t\t\t\t},\n\t\t\t\t\tsuccess: function( data ){\n\t\t\t\t\t\t$current_network.find( 'span.et_social_placeholder' ).remove();\n\t\t\t\t\t\tappend_to.append( data );\n\t\t\t\t\t}\n\t\t\t});\n\t\t}\n\n\t\tfunction append_total_shares( $current_area ) {\n\t\t\tvar post_id = $current_area.data( 'post_id' ),\n\t\t\t\turl = monarchSettings.pageurl !== '' ? monarchSettings.pageurl : window.location.href,\n\t\t\t\tappend_to = $current_area;\n\n\t\t\t$share_total_count_data = JSON.stringify({ 'post_id' : post_id, 'url' : url });\n\n\t\t\t$.ajax({\n\t\t\t\ttype: 'POST',\n\t\t\t\turl: monarchSettings.ajaxurl,\n\t\t\t\tdata: {\n\t\t\t\t\t\taction : 'get_total_shares',\n\t\t\t\t\t\tshare_total_count_array : $share_total_count_data,\n\t\t\t\t\t\tget_total_counts_nonce : monarchSettings.total_counts\n\t\t\t\t\t},\n\t\t\t\t\tbeforeSend: function( data ){\n\t\t\t\t\t\tappend_to.append( '' );\n\t\t\t\t\t},\n\t\t\t\t\tsuccess: function( data ){\n\t\t\t\t\t\tappend_to.find( 'span.et_social_placeholder' ).remove();\n\t\t\t\t\t\tappend_to.append( data );\n\t\t\t\t\t}\n\t\t\t});\n\t\t}\n\n\t\tfunction append_follow_counts( $current_area ) {\n\t\t\tvar network = $current_area.data( 'network' ),\n\t\t\t\tmin_count = $current_area.data( 'min_count' ),\n\t\t\t\tindex = $current_area.data( 'index' ),\n\t\t\t\tappend_to = $current_area;\n\n\t\t\t$follow_count_data = JSON.stringify({ 'network' : network, 'min_count' : min_count, 'index' : index });\n\t\t\t$.ajax({\n\t\t\t\ttype: 'POST',\n\t\t\t\turl: monarchSettings.ajaxurl,\n\t\t\t\tdata: {\n\t\t\t\t\t\taction : 'get_follow_counts',\n\t\t\t\t\t\tfollow_count_array : $follow_count_data,\n\t\t\t\t\t\tget_follow_counts_nonce : monarchSettings.follow_counts\n\t\t\t\t\t},\n\t\t\t\t\tbeforeSend: function( data ){\n\t\t\t\t\t\tappend_to.append( '' );\n\t\t\t\t\t},\n\t\t\t\t\tsuccess: function( data ){\n\t\t\t\t\t\t$current_area.find( 'span.et_social_placeholder' ).remove();\n\t\t\t\t\t\tappend_to.append( data );\n\t\t\t\t\t}\n\t\t\t});\n\t\t}\n\n\t\tfunction append_total_follows( $current_area ) {\n\t\t\tvar append_to = $current_area;\n\t\t\t$.ajax({\n\t\t\t\ttype: 'POST',\n\t\t\t\turl: monarchSettings.ajaxurl,\n\t\t\t\tdata: {\n\t\t\t\t\t\taction : 'get_follow_total',\n\t\t\t\t\t\tget_total_counts_nonce : monarchSettings.total_counts\n\t\t\t\t\t},\n\t\t\t\t\tbeforeSend: function( data ){\n\t\t\t\t\t\tappend_to.append( '' );\n\t\t\t\t\t},\n\t\t\t\t\tsuccess: function( data ){\n\t\t\t\t\t\tappend_to.find( 'span.et_social_placeholder' ).remove();\n\t\t\t\t\t\tappend_to.append( data );\n\t\t\t\t\t}\n\t\t\t});\n\t\t}\n\n\t\tif ( $( '.et_social_display_follow_counts' ).length ) {\n\t\t\t$( '.et_social_display_follow_counts' ).each( function(){\n\t\t\t\tappend_follow_counts( $( this) );\n\t\t\t});\n\t\t}\n\n\t\tif ( $( '.et_social_follow_total' ).length ) {\n\t\t\t$( '.et_social_follow_total' ).each( function(){\n\t\t\t\tappend_total_follows( $( this) );\n\t\t\t});\n\t\t}\n\n\n\t\tif ( $( '.et_social_total_share' ).length ) {\n\t\t\t$( '.et_social_total_share' ).each( function(){\n\t\t\t\tappend_total_shares( $( this) );\n\t\t\t});\n\t\t}\n\n\t\tif ( $( '.et_social_display_count' ).length ) {\n\t\t\t$( '.et_social_display_count' ).each( function(){\n\t\t\t\tappend_share_counts( $( this) );\n\t\t\t});\n\t\t}\n\n\t\tif ( $( '.et_social_media_wrapper' ).length && $( '.et_social_media_wrapper .et_social_totalcount' ).length ) {\n\n\t\t\t$( '.et_social_media_wrapper' ).each( function() {\n\t\t\t\t update_total_media_shares( $( this ) );\n\t\t\t});\n\t\t}\n\n\t\tif ( $( '.et_social_media_wrapper' ).length && $( '.et_social_media_wrapper .et_social_withcounts' ).length ) {\n\n\t\t\t\t$( '.et_social_media_wrapper .et_social_share' ).each( function() {\n\t\t\t\t\tvar this_el = $( this ),\n\t\t\t\t\t\tmedia_url = this_el.closest( '.et_social_media_wrapper' ).find('img').attr('src'),\n\t\t\t\t\t\tpost_id = this_el.data( 'post_id' ),\n\t\t\t\t\t\tsocial_network = this_el.data( 'social_name' );\n\n\t\t\t\t\tupdate_single_shares( this_el, media_url, post_id, social_network, 'media' );\n\n\t\t\t\t});\n\t\t}\n\n\t\tfunction update_total_media_shares( $element ) {\n\t\t\tif ( $( '.et_social_totalcount' ).length ) {\n\t\t\t\tvar this_el = $element,\n\t\t\t\t\tmedia_url = this_el.find( 'img' ).attr( 'src' ),\n\t\t\t\t\tpost_id = this_el.find( '.et_social_share' ).first().data( 'post_id' ),\n\t\t\t\t\tmedia_data = JSON.stringify({ 'media_url' : media_url, 'post_id' : post_id });\n\n\t\t\t\t$.ajax({\n\t\t\t\ttype: 'POST',\n\t\t\t\turl: monarchSettings.ajaxurl,\n\t\t\t\tdata: {\n\t\t\t\t\t\taction : 'get_media_shares_total',\n\t\t\t\t\t\tmedia_total : media_data,\n\t\t\t\t\t\tget_media_shares_total_nonce : monarchSettings.media_total\n\t\t\t\t\t},\n\t\t\t\t\tsuccess: function( data ){\n\t\t\t\t\t\tthis_el.find( '.et_social_totalcount_count' ).empty().append( data );\n\t\t\t\t\t}\n\t\t\t\t});\n\t\t\t}\n\t\t}\n\n\t\tfunction update_single_shares( $this_el, $media_url, $post_id, $network, $action ) {\n\t\t\tif( $( '.et_social_withcounts' ).length ) {\n\t\t\t\tvar media_data = JSON.stringify({ 'media_url' : $media_url, 'post_id' : $post_id, 'network' : $network, 'action' : $action });\n\t\t\t\t$.ajax({\n\t\t\t\ttype: 'POST',\n\t\t\t\turl: monarchSettings.ajaxurl,\n\t\t\t\tdata: {\n\t\t\t\t\t\taction : 'get_shares_single',\n\t\t\t\t\t\tmedia_single : media_data,\n\t\t\t\t\t\tget_media_shares_nonce : monarchSettings.media_single\n\t\t\t\t\t},\n\t\t\t\t\tsuccess: function( data ){\n\t\t\t\t\t\t$this_el.find( '.et_social_count span' ).not('.et_social_count_label').empty().append( data );\n\t\t\t\t\t}\n\t\t\t\t});\n\t\t\t}\n\t\t}\n\n\t\tfunction setCookieExpire(days) {\n\n\t\t\tvar ms = days*24*60*60*1000;\n\n\t\t\tvar date = new Date();\n\t\t\tdate.setTime(date.getTime() + ms);\n\n\t\t\treturn \"; expires=\" + date.toUTCString();\n\t\t}\n\n\t\tfunction checkCookieValue(cookieName, value) {\n\t\t\treturn parseCookies()[cookieName] === value;\n\t\t}\n\n\t\tfunction parseCookies() {\n\t\t\tvar cookies = document.cookie.split('; ');\n\n\t\t\tvar ret = {};\n\t\t\tfor (var i = cookies.length - 1; i >= 0; i--) {\n\t\t\t var el = cookies[i].split('=');\n\t\t\t ret[el[0]] = el[1];\n\t\t\t}\n\t\t\treturn ret;\n\t\t}\n\n\t\tfunction set_cookie( $expire ) {\n\t\t\tcookieExpire = setCookieExpire( $expire );\n\t\t\tdocument.cookie = 'etSocialCookie=true' + cookieExpire;\n\t\t}\n\n\t\t//separate function for the setTimeout to make it work properly within the loop.\n\t\tfunction make_popup_visible( $popup, $delay ){\n\t\t\tsetTimeout( function() {\n\t\t\t\t$popup.addClass( 'et_social_visible et_social_animated' );\n\t\t\t\tif ( $( '.et_social_resize' ).length ) {\n\t\t\t\t\t$( '.et_social_resize' ).each( function() {\n\t\t\t\t\t\tdefine_popup_position( $( this ) );\n\t\t\t\t\t});\n\t\t\t\t}\n\t\t\t}, $delay );\n\t\t}\n\n\t\tfunction auto_popup( this_el, delay ) {\n\t\t\tvar $current_popup_auto = this_el;\n\t\t\tif ( ! $current_popup_auto.hasClass( 'et_social_animated' ) ) {\n\t\t\t\tvar $cookies_expire_auto = $current_popup_auto.data( 'cookie_duration' ) ? $current_popup_auto.data( 'cookie_duration' ) : false,\n\t\t\t\t\t$delay = delay;\n\n\t\t\t\tif ( ( false !== $cookies_expire_auto && ! checkCookieValue( 'etSocialCookie', 'true' ) ) || false == $cookies_expire_auto ) {\n\n\t\t\t\t\tmake_popup_visible ( $current_popup_auto, $delay );\n\n\t\t\t\t\tif ( false !== $cookies_expire_auto ) {\n\t\t\t\t\t\tset_cookie( $cookies_expire_auto );\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\n\t\tif ( 'true' == get_url_parameter( 'et_monarch_popup' ) ) {\n\t\t\t$( '.et_social_after_comment' ).each( function() {\n\t\t\t\tvar $current_popup = $( this );\n\t\t\t\tauto_popup( $current_popup, 0 );\n\t\t\t});\n\t\t}\n\n\t\t if( $( '.et_social_auto_popup' ).length ) {\n\t\t\t$( '.et_social_auto_popup' ).each( function() {\n\t\t\t\tvar $current_popup_auto = $( this );\n\t\t\t\tauto_popup( $current_popup_auto, '' !== $current_popup_auto.data( 'delay' ) ? $current_popup_auto.data( 'delay' ) * 1000 : 0 );\n\t\t\t});\n\t\t }\n\n\t\t$( '.et_social_pinterest_window .et_social_close' ).on( 'click', function(){\n\t\t\t$( '.et_social_pin_images_outer' ).fadeToggle( 400 );\n\t\t});\n\n\t\t$ ( 'body' ).on( 'click', '.et_social_icon_cancel', function(){\n\t\t\tvar this_el = $( this );\n\t\t\tif ( this_el.parent().hasClass( 'et_social_flyin' ) ) {\n\t\t\t\t$popup = this_el.parent();\n\t\t\t} else {\n\t\t\t\t$popup = this_el.parent().parent();\n\n\t\t\t\tif ( $popup.hasClass( 'et_social_all_networks_popup' ) ) {\n\t\t\t\t\tall_networks_opened = 0;\n\t\t\t\t}\n\t\t\t}\n\n\t\t\t$popup.addClass( 'et_social_fadeout' );\n\n\t\t\tsetTimeout( function() {\n\t\t\t\t$popup.remove();\n\t\t\t}, 800 );\n\t\t\treturn false;\n\t\t});\n\n\t\tif ( $( '.et_monarch_after_order' ).length ) {\n\t\t\t$( '.et_social_after_purchase' ).each( function() {\n\t\t\t\tvar $current_popup = $( this );\n\t\t\t\tif ( ! $current_popup.hasClass( 'et_social_animated' ) ) {\n\t\t\t\t\tvar $cookies_expire = $current_popup.data( 'cookie_duration' ) ? $current_popup.data( 'cookie_duration' ) : false,\n\t\t\t\t\t\t$delay = 0;\n\n\t\t\t\t\tif ( ( false !== $cookies_expire && ! checkCookieValue( 'etSocialCookie', 'true' ) ) || false == $cookies_expire ) {\n\n\t\t\t\t\t\tmake_popup_visible ( $current_popup, $delay );\n\n\t\t\t\t\t\tif ( false !== $cookies_expire ) {\n\t\t\t\t\t\t\tset_cookie( $cookies_expire );\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t} );\n\t\t}\n\n\t\tif( $( '.et_social_trigger_bottom' ).length ) {\n\n\t\t\t$( '.et_social_trigger_bottom' ).each(function(){\n\t\t\t\tscroll_trigger( $( this ), true );\n\t\t\t});\n\n\t\t}\n\n\t\tif( $( '.et_social_scroll' ).length ) {\n\n\t\t\t$( '.et_social_scroll' ).each(function(){\n\t\t\t\tscroll_trigger( $( this ), false );\n\t\t\t});\n\n\t\t}\n\n\t\tfunction scroll_trigger( this_el, is_bottom_trigger ) {\n\t\t\tvar current_popup_bottom = this_el;\n\t\t\t\tif ( ! current_popup_bottom.hasClass( 'et_social_animated' ) ) {\n\t\t\t\t\tvar\tcookies_expire_bottom = current_popup_bottom.data( 'cookie_duration' ) ? current_popup_bottom.data( 'cookie_duration' ) : false;\n\n\t\t\t\t\tif ( true == is_bottom_trigger ) {\n\t\t\t\t\t\tvar scroll_trigger = $( '.et_social_bottom_trigger' ).length ? $( '.et_social_bottom_trigger' ).offset().top : $( document ).height() - 500;\n\t\t\t\t\t} else {\n\t\t\t\t\t\tvar scroll_pos = this_el.data( 'scroll_pos' ) > 100 ? 100 : this_el.data( 'scroll_pos' ),\n\t\t\t\t\t\t\tscroll_trigger = 100 == scroll_pos ? $( document ).height() - 10 : $( document ).height() * scroll_pos / 100;\n\t\t\t\t\t}\n\n\t\t\t\t\t$( window ).scroll( function(){\n\t\t\t\t\t\tif ( ( false !== cookies_expire_bottom && ! checkCookieValue( 'etSocialCookie', 'true' ) ) || false == cookies_expire_bottom ) {\n\t\t\t\t\t\t\tif( $( window ).scrollTop() + $( window ).height() > scroll_trigger ) {\n\t\t\t\t\t\t\t\tcurrent_popup_bottom.addClass( 'et_social_visible et_social_animated' );\n\t\t\t\t\t\t\t\tif ( $( '.et_social_resize' ).length ) {\n\t\t\t\t\t\t\t\t\t$( '.et_social_resize' ).each( function() {\n\t\t\t\t\t\t\t\t\t\tdefine_popup_position( $( this ) );\n\t\t\t\t\t\t\t\t\t});\n\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t\tif ( false !== cookies_expire_bottom ) {\n\t\t\t\t\t\t\t\t\tset_cookie( cookies_expire_bottom );\n\t\t\t\t\t\t\t\t}\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t}\n\t\t\t\t\t});\n\t\t\t\t}\n\t\t}\n\n\t\tif( $( '.et_social_trigger_idle' ).length ) {\n\t\t\t$( '.et_social_trigger_idle' ).each( function() {\n\t\t\t\tvar this_el = $( this );\n\n\t\t\t\t\tif ( ! this_el.hasClass( 'et_social_animated' ) ) {\n\t\t\t\t\t\tvar $cookies_expire_idle = this_el.data( 'cookie_duration' ) ? this_el.data( 'cookie_duration' ) : false,\n\t\t\t\t\t\t\t$idle_timeout = '' !== this_el.data( 'idle_timeout' ) ? this_el.data( 'idle_timeout' ) * 1000 : 30000;\n\n\t\t\t\t\t\tif ( ( false !== $cookies_expire_idle && ! checkCookieValue( 'etSocialCookie', 'true' ) ) || false == $cookies_expire_idle ) {\n\t\t\t\t\t\t\t$( document ).idleTimer( $idle_timeout );\n\n\t\t\t\t\t\t\t$( document ).on( \"idle.idleTimer\", function(){\n\t\t\t\t\t\t\t\tmake_popup_visible ( this_el, 0 );\n\t\t\t\t\t\t\t});\n\n\t\t\t\t\t\t\tif ( false !== $cookies_expire_idle ) {\n\t\t\t\t\t\t\t\tset_cookie( $cookies_expire_idle );\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t});\n\t\t}\n\n\t\t// open close the mobile sideabr on header click\n\t\t$( '.et_social_heading, .et_social_mobile_button' ).click( function(){\n\t\t\t$this_mobile_div = $( '.et_social_mobile' );\n\n\t\t\t$this_mobile_div.css( {'display' : 'block' } );\n\t\t\t$( '.et_social_mobile_button').removeClass( 'et_social_active_button' );\n\n\t\t\tif ( $this_mobile_div.hasClass( 'et_social_opened' ) ) {\n\t\t\t\t$this_mobile_div.find( '.et_social_networks' ).slideToggle( 600 );\n\t\t\t\t$this_mobile_div.removeClass( 'et_social_opened' ).addClass( 'et_social_closed' );\n\t\t\t\t$( '.et_social_mobile_overlay' ).removeClass( 'et_social_visible_overlay' );\n\t\t\t\t$( '.et_social_mobile_overlay' ).fadeToggle( 600 );\n\t\t\t} else {\n\t\t\t\t$this_mobile_div.removeClass( 'et_social_closed' ).addClass( 'et_social_opened' );\n\t\t\t\t$this_mobile_div.find( '.et_social_networks' ).slideToggle( 600 );\n\t\t\t\t$( '.et_social_mobile_overlay' ).addClass( 'et_social_visible_overlay' ).css({ 'display' : 'block' });\n\t\t\t}\n\t\t});\n\n\t\t//if close button clicked - hide the mobile sidebar from screen\n\t\t$( '.et_social_mobile .et_social_close' ).click( function(){\n\t\t\t$mobile_div = $( '.et_social_mobile' );\n\t\t\t$mobile_div.fadeToggle( 600 );\n\t\t\t$( '.et_social_mobile_button' ).addClass( 'et_social_active_button' );\n\n\t\t\tif ( $mobile_div.hasClass( 'et_social_opened' ) ) {\n\t\t\t\t$( '.et_social_mobile_overlay' ).fadeToggle( 600 );\n\t\t\t\t$mobile_div.removeClass( 'et_social_opened' );\n\t\t\t\t$mobile_div.find( '.et_social_networks' ).fadeToggle( 600 );\n\t\t\t}\n\n\t\t});\n\n\t\t// Move inline icons into appropriate sections in Divi theme\n\t\tif( $( '.et_social_inline' ).length ) {\n\t\t\tif ( $( 'body' ).hasClass( 'et_pb_pagebuilder_layout' ) ) {\n\t\t\t\tvar top_inline = $( '.et_social_inline_top' ),\n\t\t\t\t\tbottom_inline = $( '.et_social_inline_bottom' ),\n\t\t\t\t\tdivi_container = '
';\n\n\t\t\t\tif ( top_inline.length ) {\n\t\t\t\t\t$( '.et_pb_section' ).not( '.et_pb_fullwidth_section' ).first().prepend( divi_container ).find( '.et_pb_row' ).first().find( '.et_pb_column' ).append( top_inline );\n\t\t\t\t}\n\n\t\t\t\tif ( bottom_inline.length ) {\n\t\t\t\t\t$( '.et_pb_section' ).not( '.et_pb_fullwidth_section' ).last().append( divi_container ).find( '.et_pb_row' ).last().find( '.et_pb_column' ).append( bottom_inline );\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\n\t\tfunction define_popup_position( $this_popup ) {\n\t\t\tsetTimeout( function() { // make sure all css transitions are finished to calculate the heights correctly\n\t\t\t\tvar this_popup = $this_popup,\n\t\t\t\t\tnetworks_div = this_popup.find( '.et_social_networks' ),\n\t\t\t\t\theader_height = this_popup.find( '.et_social_header' ).outerHeight(),\n\t\t\t\t\ttotal_count_height = this_popup.find( '.et_social_totalcount' ).height(),\n\t\t\t\t\textra_height = 0 < total_count_height ? 20 : 0;\n\n\t\t\t\tthis_popup.height( this_popup.find( '.et_social_icons_container' ).innerHeight() + header_height + total_count_height + 20 + extra_height );\n\n\t\t\t\tvar\tpopup_max_height = this_popup.hasClass( 'et_social_popup_content' ) ? $( window ).height() : $( window ).height() - 60;\n\n\t\t\t\tif ( this_popup.hasClass( 'et_social_popup_content' ) && 768 < $( window ).width() ) {\n\t\t\t\t\tpopup_max_height = popup_max_height - 50;\n\t\t\t\t}\n\n\t\t\t\tthis_popup.css( { 'max-height' : popup_max_height } );\n\n\t\t\t\tif( this_popup.hasClass( 'et_social_popup_content' ) ) {\n\t\t\t\t\tvar top_position = $( window ).height() / 2 - this_popup.innerHeight() / 2;\n\t\t\t\t\tthis_popup.css( { 'top' : top_position + 'px' } );\n\t\t\t\t}\n\n\t\t\t\tvar networks_div_height = this_popup.height() - header_height + total_count_height - extra_height;\n\t\t\t\tnetworks_div.height( networks_div_height );\n\t\t\t}, 400 );\n\t\t}\n\n\t\tfunction set_mobile_sidebar_height() {\n\t\t\tsetTimeout( function() { // make sure all css transitions are finished to calculate the heights correctly\n\t\t\t\tvar\tmobile_div = $( '.et_social_mobile' );\n\n\t\t\t\tif ( !mobile_div.hasClass( 'et_social_opened' ) ){\n\t\t\t\t\t$('.et_social_mobile .et_social_networks').css({'display' : 'block'});\n\t\t\t\t}\n\t\t\t\tif( $('.et_social_active_button').length ) {\n\t\t\t\t\tmobile_div.css({'display' : 'block'});\n\t\t\t\t}\n\n\t\t\t\tvar inner_contatiner_height = mobile_div.find( '.et_social_icons_container' ).innerHeight() + 45;\n\n\t\t\t\tif ( !mobile_div.hasClass( 'et_social_opened' ) ){\n\t\t\t\t\t$('.et_social_mobile .et_social_networks').css({'display' : 'none'});\n\t\t\t\t}\n\t\t\t\tif( $('.et_social_active_button').length ) {\n\t\t\t\t\tmobile_div.css({'display' : 'none'});\n\t\t\t\t}\n\n\t\t\t\tmobile_div.find( '.et_social_networks' ).css( { 'max-height' : inner_contatiner_height, 'height' : inner_contatiner_height } );\n\t\t\t\tif ( $( window ).height() < inner_contatiner_height ) {\n\t\t\t\t\tvar inner_height = $( window ).height() - mobile_div.find( '.et_social_heading' ).innerHeight() + 10;\n\t\t\t\t\tmobile_div.find( '.et_social_networks' ).css({ 'height' : inner_height });\n\t\t\t\t}\n\t\t\t}, 400 );\n\t\t}\n\n\t\tfunction set_sidebar_position(){\n\t\t\tif( $( '.et_social_sidebar_networks' ).length ){\n\t\t\t\tvar this_sidebar = $( '.et_social_sidebar_networks' ),\n\t\t\t\t\ttop_position = $( window ).height() / 2 - this_sidebar.innerHeight() / 2;\n\t\t\t\t\ttop_position = 0 > top_position ? 0 : top_position;\n\t\t\t\tthis_sidebar.css( { 'top' : top_position + 'px' } );\n\t\t\t}\n\t\t}\n\n\t\tfunction set_media_wrapper_size() {\n\t\t\tif ( $( '.et_social_media_wrapper' ).length ) {\n\t\t\t\t$( '.et_social_media_wrapper' ).each( function(){\n\t\t\t\t\tvar this_wrapper = $( this ),\n\t\t\t\t\t\tthis_wrapper_media = this_wrapper.find( '.et_social_media' ),\n\t\t\t\t\t\tthis_image = this_wrapper.find( 'img' ),\n\t\t\t\t\t\timage_position = this_image.position(),\n\t\t\t\t\t\timage_left_offset = typeof image_position !== 'undefined' ? image_position.left : 0,\n\t\t\t\t\t\tthis_image_height = this_image.height(),\n\t\t\t\t\t\tthis_image_width = this_image.width(),\n\t\t\t\t\t\tthis_wrapper_networks_height = this_wrapper.find( '.et_social_networks' ).innerHeight();\n\n\t\t\t\t\tthis_wrapper.addClass( this_image.attr( 'class' ) );\n\t\t\t\t\tthis_wrapper_media.css( { 'max-height' : this_image_height } );\n\t\t\t\t\tthis_wrapper_media.css( { 'height' : this_wrapper_networks_height + 50 } );\n\t\t\t\t\tthis_wrapper_media.width( this_image_width - 80 );\n\n\t\t\t\t\t// adjust the icons position based on image alignement\n\t\t\t\t\tif ( 0 !== image_left_offset ) {\n\t\t\t\t\t\tthis_wrapper_media.css( { 'left' : image_left_offset + 20 } );\n\t\t\t\t\t}\n\t\t\t\t});\n\n\t\t\t\t// fix the media wrapper sizes in Divi Gallery Slider if needed\n\t\t\t\tet_pb_fix_gallery_slider_icons_size();\n\t\t\t}\n\t\t}\n\n\t\tfunction et_pb_fix_gallery_wrapper() {\n\t\t\tvar $media_wrappers_inside_gallery = $( '.et_pb_gallery_grid .et_social_media_wrapper' );\n\n\t\t\tif ( 0 > $media_wrappers_inside_gallery.length ) {\n\t\t\t\treturn;\n\t\t\t}\n\n\t\t\t$media_wrappers_inside_gallery.each( function() {\n\t\t\t\tvar $this_wrapper = $( this );\n\t\t\t\tvar $gallery_item = $this_wrapper.closest( '.et_pb_gallery_image' );\n\t\t\t\tvar $gallery_overlay = $gallery_item.find( '.et_overlay' );\n\n\t\t\t\tif ( 0 < $gallery_overlay.length ) {\n\t\t\t\t\t// move the gallery overlay inside media icons wrapper so it won't break the sharing functionality\n\t\t\t\t\t$this_wrapper.append( $gallery_overlay );\n\t\t\t\t}\n\t\t\t});\n\t\t}\n\n\t\tfunction et_pb_fix_gallery_slider_icons_size() {\n\t\t\tvar $gallery_slider_icons = $( '.et_pb_gallery_fullwidth .et_social_media_wrapper' );\n\n\t\t\tif ( 0 > $gallery_slider_icons.length ) {\n\t\t\t\treturn;\n\t\t\t}\n\n\t\t\tvar $all_galleries = $( '.et_pb_gallery_fullwidth' );\n\n\t\t\t$all_galleries.each( function() {\n\t\t\t\tvar $this_gallery = $( this );\n\t\t\t\tvar $this_gallery_media = $this_gallery.find( '.et_social_media_wrapper .et_social_media' );\n\n\t\t\t\tif ( 0 > $this_gallery_media.length ) {\n\t\t\t\t\treturn;\n\t\t\t\t}\n\n\t\t\t\tvar $first_icon_wrapper = $this_gallery_media.first();\n\t\t\t\tvar icons_width = $first_icon_wrapper.width();\n\t\t\t\tvar icons_height = $first_icon_wrapper.css( 'height' );\n\t\t\t\tvar icons_max_height = $first_icon_wrapper.css( 'max-height' );\n\n\t\t\t\t// use the dimensions from first slide and apply it to all the media icons in the slider\n\t\t\t\t$this_gallery_media.width( icons_width - 80 );\n\t\t\t\t$this_gallery_media.height( icons_height );\n\t\t\t\t$this_gallery_media.css( { 'max-height' : icons_max_height } );\n\t\t\t});\n\t\t}\n\n\t\t$ ( 'body' ).on( 'click', '.et_social_open_all', function() {\n\t\t\tall_networks_opened++;\n\t\t\tif ( 1 == all_networks_opened ) {\n\t\t\t\tvar this_button = $( this ),\n\t\t\t\t\tpage_id = this_button.data( 'page_id' ),\n\t\t\t\t\tpermalink = this_button.data( 'permalink' ),\n\t\t\t\t\ttitle = this_button.data( 'title' ),\n\t\t\t\t\tmedia = typeof this_button.data( 'media' ) !== 'undefined' ? this_button.data( 'media' ) : '',\n\t\t\t\t\tis_popup = 'popup' == this_button.data( 'location' ) ? 'true' : 'false';\n\n\t\t\t\t$.ajax({\n\t\t\t\t\ttype: 'POST',\n\t\t\t\t\turl: monarchSettings.ajaxurl,\n\t\t\t\t\tdata: {\n\t\t\t\t\t\t\taction : 'generate_all_networks_popup',\n\t\t\t\t\t\t\tall_networks_page_id : page_id,\n\t\t\t\t\t\t\tall_networks_link : permalink,\n\t\t\t\t\t\t\tall_networks_title : title,\n\t\t\t\t\t\t\tall_networks_media : media,\n\t\t\t\t\t\t\tis_popup : is_popup,\n\t\t\t\t\t\t\tgenerate_all_window_nonce : monarchSettings.generate_all_window_nonce\n\t\t\t\t\t\t},\n\t\t\t\t\t\tsuccess: function( data ) {\n\t\t\t\t\t\t\tif ( 'false' == is_popup ) {\n\t\t\t\t\t\t\t\t$( 'body' ).append( data );\n\t\t\t\t\t\t\t\tmake_popup_visible( $( '.et_social_all_networks_popup' ), 1 );\n\t\t\t\t\t\t\t} else {\n\t\t\t\t\t\t\t\tvar popup_container = this_button.parent().closest( '.et_social_popup_content' );\n\n\t\t\t\t\t\t\t\tthis_button.parent().replaceWith( data );\n\t\t\t\t\t\t\t\tdefine_popup_position( popup_container );\n\t\t\t\t\t\t\t\tall_networks_opened = 0;\n\t\t\t\t\t\t\t}\n\t\t\t\t\t\t}\n\t\t\t\t});\n\t\t\t}\n\n\t\t\treturn false;\n\t\t});\n\n\t\tset_mobile_sidebar_height();\n\n\t\tset_sidebar_position();\n\n\t\t$( window ).resize( function(){\n\t\t\tif ( $( '.et_social_resize' ).length ) {\n\t\t\t\t$( '.et_social_resize' ).each( function() {\n\t\t\t\t\tdefine_popup_position( $( this ) );\n\t\t\t\t});\n\t\t\t}\n\t\t\tif ( $('.et_social_mobile') ) {\n\t\t\t\tset_mobile_sidebar_height();\n\t\t\t}\n\t\t\tif( $( '.et_social_sidebar_networks' ).length ){\n\t\t\t\tset_sidebar_position();\n\t\t\t}\n\n\t\t\tset_media_wrapper_size();\n\t\t});\n\n\t\t$( '.et_social_hide_sidebar' ).click( function(){\n\t\t\t$( '.et_social_hide_sidebar' ).toggleClass( 'et_social_hidden_sidebar' );\n\t\t\t$( '.et_social_sidebar_networks' ).toggleClass( 'et_social_hidden_sidebar et_social_visible_sidebar' );\n\t\t});\n\n\t\t$( window ).load( function(){\n\t\t\tset_media_wrapper_size();\n\n\t\t\tif ( $( '.et_social_pin_images' ).length && ( $( '.et_social_all_button' ).length || $( '.et_social_pinterest' ).length ) ) {\n\t\t\t\tvar pin_container = $( '.et_social_pin_images' ),\n\t\t\t\t\tpermalink = pin_container.data( 'permalink' ),\n\t\t\t\t\ttitle = pin_container.data( 'title' ),\n\t\t\t\t\tpost_id = pin_container.data( 'post_id' ),\n\t\t\t\t\t$i = 0;\n\n\t\t\t\t$( 'img' ).each( function(){\n\t\t\t\t\t//do not include comment avatars into the Modal\n\t\t\t\t\tif ( ! $( this ).hasClass( 'avatar' ) ) {\n\t\t\t\t\t\tvar this_img = $( this ).attr( 'src' ),\n\t\t\t\t\t\t\tthis_alt = $( this ).attr( 'alt' );\n\n\t\t\t\t\t\tif ( '' != this_img ) {\n\t\t\t\t\t\t\tvar\tpin_link = 'http://www.pinterest.com/pin/create/button/?url=' + permalink + '&media=' + this_img + '&description=' + title,\n\t\t\t\t\t\t\t\tthis_img_container = '
\"'
';\n\t\t\t\t\t\t\t$( '.et_social_pin_images' ).append( this_img_container );\n\t\t\t\t\t\t\t$i++;\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t});\n\n\t\t\t\t//Append error message if no images found on page\n\t\t\t\tif ( 0 == $i ) {\n\t\t\t\t\t$( '.et_social_pin_images' ).append( monarchSettings.no_img_message );\n\t\t\t\t}\n\t\t\t}\n\t\t});\n\t});\n})(jQuery)" }, { "path": "March Cohort 2023/8_9_Pandas/Data Cleaning With Pandas/Data Cleaning with Python and Pandas_ Detecting Missing Values_files/custom.min.js.download", "content": "/*! ET et_shortcodes_frontend.js */\n!function($){$.fn.et_shortcodes_switcher=function(options){options=$.extend({slides:\">div\",activeClass:\"active\",linksNav:\"\",findParent:!0,lengthElement:\"li\",useArrows:!1,arrowLeft:\"a#prev-arrow\",arrowRight:\"a#next-arrow\",auto:!1,autoSpeed:5e3,slidePadding:\"\",pauseOnHover:!0,fx:\"fade\",sliderType:\"\"},options);return this.each(function(){var $activeSlide,$nextSlide,$et_shortcodes_mobile_controls,slidesContainer=jQuery(this).parent().css(\"position\",\"relative\"),$slides=jQuery(this).css({overflow:\"hidden\",position:\"relative\"}),$slides_wrapper_box=slidesContainer.find(\".et-tabs-content-wrapper\"),$slides_wrapper=$slides_wrapper_box.parent(),$slide=$slides.find(\".et-tabs-content-wrapper\"+options.slides),slidesNum=$slide.length,currentPosition=1,slides_wrapper_width=$slides_wrapper.width();if(\"slide\"===options.fx&&($slides_wrapper_box.width(200*(slidesNum+2)+\"%\"),$slide.css({width:slides_wrapper_width,visibility:\"visible\"}),$slides_wrapper_box.append($slide.filter(\":first\").clone().removeClass().addClass(\"et_slidecontent_cloned\")),$slides_wrapper_box.prepend($slide.filter(\":last\").clone().removeClass().addClass(\"et_slidecontent_cloned\")),$slides_wrapper_box.css(\"left\",-slides_wrapper_width)),$slide.filter(\":first\").css({display:\"block\"}).addClass(\"et_shortcode_slide_active\"),\"\"!=options.slidePadding&&$slide.css(\"padding\",options.slidePadding),\"\"!=options.linksNav){var linkSwitcher=jQuery(options.linksNav),linkSwitcherTab=\"\";(linkSwitcherTab=options.findParent?linkSwitcher.parent():linkSwitcher).filter(\".active\").length||linkSwitcherTab.filter(\":first\").addClass(\"active\"),linkSwitcher.click(function(){var orderNum;return gotoSlide(orderNum=(options.findParent?jQuery(this).parent():jQuery(this)).prevAll(options.lengthElement).length+1,currentPositiondiv\").css({width:$slides_wrapper.width()}),$slides_wrapper_box.css(\"left\",-$slides_wrapper.width()*currentPosition)}),function(){var et_shortcodes_slides_num=slidesContainer.find(\".et_slidecontent\").length,et_shortcodes_controllers_html=\"\";if(1
    \"),$et_shortcodes_mobile_controls=slidesContainer.find(\".et_shortcodes_controls\");for(var i=0;i';$et_shortcodes_mobile_controls.prepend(et_shortcodes_controllers_html),$et_shortcodes_mobile_controls.find(\"li:first\").addClass(\"et_shortcodes_active_control\"),$et_shortcodes_mobile_controls.find(\"a\").click(function(){var $this_control=$(this),$this_control_li=$this_control.parent(\"li\"),this_order=$this_control_li.prevAll().length+1;return this_order==currentPosition||gotoSlide(this_order,currentPosition
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