Repository: hemansnation/Data-Analyst-Roadmap Branch: main Commit: a4a08e040f94 Files: 110 Total size: 56.5 MB Directory structure: gitextract_mu6uxmva/ ├── July Cohort 2023/ │ └── 1_Python Programming/ │ ├── .ipynb_checkpoints/ │ │ ├── 01_Python Basics-checkpoint.ipynb │ │ ├── 02_03_While Loop and List-checkpoint.ipynb │ │ ├── 04_Strings and For loop-checkpoint.ipynb │ │ └── 05_Dictionary Tuples and Set-checkpoint.ipynb │ ├── 01_Python Basics.ipynb │ ├── 02_03_While Loop and List.ipynb │ ├── 04_Strings and For loop.ipynb │ └── 05_Dictionary Tuples and Set.ipynb ├── March Cohort 2023/ │ ├── .ipynb_checkpoints/ │ │ ├── 0_Python Basics-checkpoint.ipynb │ │ ├── 13_14_Object Orientation-checkpoint.ipynb │ │ ├── 1_While Loops and Logic Building-checkpoint.ipynb │ │ ├── 2_List and Strings-checkpoint.ipynb │ │ ├── 3_For Loop Dictionary Tuple and Sets-checkpoint.ipynb │ │ ├── 4_Functions-checkpoint.ipynb │ │ └── 5_Modules and Packages-checkpoint.ipynb │ ├── 0_Python Basics.ipynb │ ├── 10_11_EDA Project/ │ │ ├── .ipynb_checkpoints/ │ │ │ └── 10_11_EDA Project-checkpoint.ipynb │ │ ├── 10_11_EDA Project.ipynb │ │ ├── titanic.csv │ │ └── titanic_cleaned.csv │ ├── 10_Matplotlib/ │ │ ├── .ipynb_checkpoints/ │ │ │ └── 10_Matplotlib-checkpoint.ipynb │ │ └── 10_Matplotlib.ipynb │ ├── 12_15_16_17_18_Statistics/ │ │ ├── .ipynb_checkpoints/ │ │ │ ├── 12_15_Statistics-checkpoint.ipynb │ │ │ ├── 16_17_Statistics and Regression-checkpoint.ipynb │ │ │ └── 18_Hypothesis Testing-checkpoint.ipynb │ │ ├── 12_15_Statistics.ipynb │ │ ├── 16_17_Statistics and Regression.ipynb │ │ ├── 18_Hypothesis Testing.ipynb │ │ ├── datasets/ │ │ │ ├── Birthweight_reduced_kg_R.csv │ │ │ ├── CarPrice_Assignment.csv │ │ │ ├── Crime_R.csv │ │ │ ├── IPL2013.csv │ │ │ ├── SP_500_1987.csv │ │ │ ├── data_loan.csv │ │ │ └── forbes.csv │ │ ├── sample_submission.csv │ │ ├── test.csv │ │ └── train.csv │ ├── 13_14_Object Orientation.ipynb │ ├── 19_20_Machine Learning/ │ │ ├── .ipynb_checkpoints/ │ │ │ ├── 19_Machine Learning and Linear Regression with One variable-checkpoint.ipynb │ │ │ └── 20_Logistic Regression-checkpoint.ipynb │ │ ├── 19_Machine Learning and Linear Regression with One variable.ipynb │ │ ├── 20_Logistic Regression.ipynb │ │ ├── areas.csv │ │ ├── homeprices.csv │ │ ├── insurance_data.csv │ │ └── spend.xlsx │ ├── 1_While Loops and Logic Building.ipynb │ ├── 21_EDA and Data Viz/ │ │ ├── .ipynb_checkpoints/ │ │ │ └── 21_EDA and Data Visualization-checkpoint.ipynb │ │ ├── 21_EDA and Data Visualization.ipynb │ │ └── itunes_data.csv │ ├── 22_23_Time Series Forecasting/ │ │ ├── .ipynb_checkpoints/ │ │ │ ├── 22_Forecasting-checkpoint.ipynb │ │ │ └── 23_ARIMA-checkpoint.ipynb │ │ ├── 22_Forecasting.ipynb │ │ ├── 23_ARIMA.ipynb │ │ ├── store.xls │ │ ├── vimana.csv │ │ └── wsb.csv │ ├── 24_SQL/ │ │ └── SQLqueriesQandA.sql │ ├── 2_List and Strings.ipynb │ ├── 3_For Loop Dictionary Tuple and Sets.ipynb │ ├── 4_Functions.ipynb │ ├── 5_Modules and Packages.ipynb │ ├── 5_Modules-Packages/ │ │ ├── main.py │ │ ├── rose.py │ │ └── statsmeme/ │ │ ├── __init__.py │ │ ├── descriptive.py │ │ └── inferential.py │ ├── 6_VirtualEnvironmentAndFlask/ │ │ ├── application.py │ │ └── templates/ │ │ └── index.html │ ├── 7_NumPy/ │ │ ├── .ipynb_checkpoints/ │ │ │ └── 7_NumPy-checkpoint.ipynb │ │ └── 7_NumPy.ipynb │ └── 8_9_Pandas/ │ ├── .ipynb_checkpoints/ │ │ └── 8_9_Pandas-checkpoint.ipynb │ ├── 8_9_Pandas.ipynb │ ├── Data Cleaning With Pandas/ │ │ ├── Data Cleaning with Python and Pandas_ Detecting Missing Values.html │ │ └── Data Cleaning with Python and Pandas_ Detecting Missing Values_files/ │ │ ├── analytics.js.download │ │ ├── comment-reply.min.js.download │ │ ├── common.js.download │ │ ├── css │ │ ├── css(1) │ │ ├── css(2) │ │ ├── custom.js(1).download │ │ ├── custom.js.download │ │ ├── custom.min.js.download │ │ ├── form.js.download │ │ ├── frontend.min.js.download │ │ ├── idle-timer.min.js(1).download │ │ ├── idle-timer.min.js.download │ │ ├── jquery-migrate.min.js.download │ │ ├── jquery.js.download │ │ ├── jquery.uniform.min.js.download │ │ ├── linkid.js.download │ │ ├── prism-js.min.js.download │ │ ├── style(1).css │ │ ├── style(2).css │ │ ├── style(3).css │ │ ├── style.css │ │ ├── wp-embed.min.js.download │ │ └── wp-emoji-release.min.js.download │ ├── indore.csv │ ├── nyc_weather.csv │ ├── stock_data.csv │ ├── stocks_weather.xlsx │ ├── weather_by_cities.csv │ ├── weather_data.csv │ ├── weather_data.xlsx │ ├── weather_data2.csv │ ├── weather_datamissing.csv │ └── weather_datamissing_regex.csv └── README.md ================================================ FILE CONTENTS ================================================ ================================================ FILE: July Cohort 2023/1_Python Programming/.ipynb_checkpoints/01_Python Basics-checkpoint.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "id": "5effdff7", "metadata": {}, "source": [ "# Python Basics" ] }, { "cell_type": "code", "execution_count": 1, "id": "d04e5b22", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Hello User\n" ] } ], "source": [ "print(\"Hello User\")" ] }, { "cell_type": "code", "execution_count": 2, "id": "b0091bf1", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Hello\n", "User\n" ] } ], "source": [ "print(\"Hello\")\n", "print(\"User\")" ] }, { "cell_type": "code", "execution_count": 5, "id": "fb83814b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Hello\n", "User\n" ] } ], "source": [ "print(\"Hello\\nUser\") # \\n -> new line character" ] }, { "cell_type": "code", "execution_count": 6, "id": "791c342a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Hello\tUser\n" ] } ], "source": [ "print(\"Hello\\tUser\") # \\t -> horizontal tab - 8 spaces" ] }, { "cell_type": "code", "execution_count": null, "id": "9fad4ea5", "metadata": {}, "outputs": [], "source": [ "# binary language -> 101010111 -> bits\n", "\n", "0 -> low voltage, no-charge, false, off\n", "1 -> high voltage, charge, true, on" ] }, { "cell_type": "code", "execution_count": null, "id": "a1885e98", "metadata": {}, "outputs": [], "source": [ "bits > numbers > character > instructions > program > software" ] }, { "cell_type": "code", "execution_count": null, "id": "cbe99e7d", "metadata": {}, "outputs": [], "source": [ "source code ---------> Byte Code --------> output\n", " .c compile .bak run .exe\n", " .cpp .obj\n", " .java .javac\n", " .py .pyc" ] }, { "cell_type": "markdown", "id": "8a713554", "metadata": {}, "source": [ "# Variables" ] }, { "cell_type": "code", "execution_count": 8, "id": "5c20abe8", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10\n" ] } ], "source": [ "a = 10\n", "\n", "print(a)" ] }, { "cell_type": "code", "execution_count": 9, "id": "995009df", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "45.13\n" ] } ], "source": [ "b = 45.13\n", "\n", "print(b)" ] }, { "cell_type": "code", "execution_count": 10, "id": "17194c9e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "India\n" ] } ], "source": [ "a = \"India\"\n", "\n", "print(a)" ] }, { "cell_type": "markdown", "id": "9141a786", "metadata": {}, "source": [ "# Data Types" ] }, { "cell_type": "code", "execution_count": 11, "id": "6ba6bfd9", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10\n", "\n" ] } ], "source": [ "a = 10\n", "\n", "print(a)\n", "\n", "print(type(a))" ] }, { "cell_type": "code", "execution_count": 12, "id": "a6e1a232", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10.65\n", "\n" ] } ], "source": [ "b = 10.65\n", "\n", "print(b)\n", "\n", "print(type(b))" ] }, { "cell_type": "code", "execution_count": 13, "id": "1298d5d3", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "colors\n", "\n" ] } ], "source": [ "c = \"colors\"\n", "\n", "print(c)\n", "\n", "print(type(c))" ] }, { "cell_type": "code", "execution_count": 14, "id": "47c38fa9", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "colors\n", "\n" ] } ], "source": [ "c = 'colors'\n", "\n", "print(c)\n", "\n", "print(type(c))" ] }, { "cell_type": "code", "execution_count": 15, "id": "0acb14e3", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "it's an apple\n" ] } ], "source": [ "x = \"it's an apple\"\n", "\n", "print(x)" ] }, { "cell_type": "code", "execution_count": 17, "id": "fb2beb48", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "it\"s an apple\n" ] } ], "source": [ "x = 'it\"s an apple'\n", "\n", "print(x)" ] }, { "cell_type": "markdown", "id": "814feca5", "metadata": {}, "source": [ "# Input Function" ] }, { "cell_type": "code", "execution_count": 18, "id": "437ffd33", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10\n", "10\n" ] } ], "source": [ "a = input()\n", "\n", "print(a)" ] }, { "cell_type": "code", "execution_count": 19, "id": "8c71996c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Enter Your Name: Himanshu\n", "Himanshu\n" ] } ], "source": [ "a = input(\"Enter Your Name: \")\n", "\n", "print(a)" ] }, { "cell_type": "code", "execution_count": 20, "id": "792f782d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Enter Your Name: Himanshu\n", "Hello! How are you? Himanshu\n" ] } ], "source": [ "a = input(\"Enter Your Name: \")\n", "\n", "print(\"Hello! How are you?\", a)" ] }, { "cell_type": "code", "execution_count": 21, "id": "8ab7e6a7", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Hello Him\n" ] } ], "source": [ "print(\"Hello Him\")" ] }, { "cell_type": "markdown", "id": "84b26329", "metadata": {}, "source": [ "# Questions" ] }, { "cell_type": "code", "execution_count": null, "id": "376a1bc0", "metadata": {}, "outputs": [], "source": [ "Your Name\n", "------8-------College/Company\n", "------------16-------------Country" ] }, { "cell_type": "code", "execution_count": null, "id": "f60668a8", "metadata": {}, "outputs": [], "source": [ "*\n", "**\n", "***" ] }, { "cell_type": "code", "execution_count": null, "id": "7c2fa48a", "metadata": {}, "outputs": [], "source": [ "--4--*\n", "*----8----*\n", "--4--*" ] }, { "cell_type": "code", "execution_count": null, "id": "2650f9ed", "metadata": {}, "outputs": [], "source": [ " *\n", "* *\n", " *" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.12" } }, "nbformat": 4, "nbformat_minor": 5 } ================================================ FILE: July Cohort 2023/1_Python Programming/.ipynb_checkpoints/02_03_While Loop and List-checkpoint.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "id": "13c6ac1c", "metadata": {}, "source": [ "# While Loop" ] }, { "cell_type": "code", "execution_count": 1, "id": "0290b931", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Himanshu\n", "Himanshu\n" ] } ], "source": [ "print(\"Himanshu\")\n", "print(\"Himanshu\")" ] }, { "cell_type": "code", "execution_count": 2, "id": "aab5a9d5", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "True\n" ] } ], "source": [ "i = 1\n", "\n", "print(i <= 5)" ] }, { "cell_type": "code", "execution_count": 3, "id": "be002b98", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "False\n" ] } ], "source": [ "print(i >= 5)" ] }, { "cell_type": "code", "execution_count": 4, "id": "903683b3", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "True\n" ] } ], "source": [ "print(i >= 1)" ] }, { "cell_type": "code", "execution_count": 5, "id": "76da14cf", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Himanshu\n", "Himanshu\n", "Himanshu\n", "Himanshu\n", "Himanshu\n" ] } ], "source": [ "i = 1\n", "\n", "while i <= 5:\n", " print(\"Himanshu\")\n", " i = i + 1 # i = 1, 2, 3, 4, 5, 6" ] }, { "cell_type": "code", "execution_count": 6, "id": "145ad944", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1\n", "2\n", "3\n", "4\n", "5\n" ] } ], "source": [ "i = 1\n", "\n", "while i <= 5:\n", " print(i)\n", " i = i + 1" ] }, { "cell_type": "code", "execution_count": null, "id": "cdad3efc", "metadata": {}, "outputs": [], "source": [ "1\n", "2\n", "3\n", "4\n", "5\n", "6\n", "7\n", "8\n", "9\n", "10" ] }, { "cell_type": "code", "execution_count": 7, "id": "d7d2dfab", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1\n", "2\n", "3\n", "4\n", "5\n", "6\n", "7\n", "8\n", "9\n", "10\n" ] } ], "source": [ "i = 1\n", "\n", "while i <= 10:\n", " print(i)\n", " i = i + 1" ] }, { "cell_type": "code", "execution_count": 8, "id": "a103c74c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Hello\n", "World\n" ] } ], "source": [ "print(\"Hello\")\n", "print(\"World\")" ] }, { "cell_type": "code", "execution_count": 9, "id": "5df8d4f6", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Hello\n", "World\n" ] } ], "source": [ "print(\"Hello\", end=\"\\n\")\n", "print(\"World\")" ] }, { "cell_type": "code", "execution_count": 10, "id": "c89f0b40", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Hello+World\n" ] } ], "source": [ "print(\"Hello\", end=\"+\")\n", "print(\"World\")" ] }, { "cell_type": "code", "execution_count": 11, "id": "8a0e5ca8", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Hello World\n" ] } ], "source": [ "print(\"Hello\", end=\" \")\n", "print(\"World\")" ] }, { "cell_type": "code", "execution_count": 12, "id": "bce809e5", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Hello @@@ World\n" ] } ], "source": [ "print(\"Hello\", end=\" @@@ \")\n", "print(\"World\")" ] }, { "cell_type": "code", "execution_count": 13, "id": "54435a01", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Hello@World$" ] } ], "source": [ "print(\"Hello\", end=\"@\")\n", "print(\"World\", end=\"$\")" ] }, { "cell_type": "markdown", "id": "95e933ab", "metadata": {}, "source": [ "### print the following series of numbers" ] }, { "cell_type": "code", "execution_count": null, "id": "7a728f28", "metadata": {}, "outputs": [], "source": [ "1 2 3 4 5 6 7 8 9 10" ] }, { "cell_type": "code", "execution_count": 14, "id": "35896fd2", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1 2 3 4 5 6 7 8 9 10 " ] } ], "source": [ "i = 1\n", "\n", "while i <= 10:\n", " print(i, end=\" \")\n", " i = i + 1" ] }, { "cell_type": "code", "execution_count": 15, "id": "284eda1a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "100\n", "10\n" ] } ], "source": [ "a = 10\n", "\n", "print(a * a)\n", "\n", "print(a)" ] }, { "cell_type": "code", "execution_count": null, "id": "d1cabe31", "metadata": {}, "outputs": [], "source": [ "1 4 9 16 25 36 49 64 81 100" ] }, { "cell_type": "code", "execution_count": 16, "id": "32f564a1", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1 4 9 16 25 36 49 64 81 100 " ] } ], "source": [ "i = 1\n", "\n", "while i <= 10:\n", " print(i*i, end=\" \")\n", " i = i + 1" ] }, { "cell_type": "markdown", "id": "49b1b14e", "metadata": {}, "source": [ "**Write a program to enter 5 values from user and print them.**" ] }, { "cell_type": "code", "execution_count": 17, "id": "a1cd06ee", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Enter your number: 10\n", "10\n", "Enter your number: 20\n", "20\n", "Enter your number: 30\n", "30\n", "Enter your number: 40\n", "40\n", "Enter your number: 50\n", "50\n" ] } ], "source": [ "i = 1\n", "\n", "while i <= 5:\n", " a = input(\"Enter your number: \")\n", " print(a)\n", " i = i + 1\n", " # 1 + 1" ] }, { "cell_type": "code", "execution_count": 18, "id": "a1b92062", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "3.5" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "7 / 2" ] }, { "cell_type": "code", "execution_count": 19, "id": "b1e0fc38", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "7 % 2 # modulo - return the reminder" ] }, { "cell_type": "code", "execution_count": 20, "id": "7279d00f", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "10 % 2" ] }, { "cell_type": "code", "execution_count": 21, "id": "7ba7e050", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "3" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "11 % 4" ] }, { "cell_type": "code", "execution_count": 22, "id": "3c3ea69f", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "3" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "7 // 2 # floor division" ] }, { "cell_type": "code", "execution_count": 23, "id": "e378bbe4", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "15 // 7" ] }, { "cell_type": "code", "execution_count": 24, "id": "5947157a", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "3" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "153 % 10" ] }, { "cell_type": "code", "execution_count": null, "id": "07a156bd", "metadata": {}, "outputs": [], "source": [ "15)7(\n", " 7" ] }, { "cell_type": "markdown", "id": "d490f9d3", "metadata": {}, "source": [ "**Write a program to enter a number from user and print reverse of it.**" ] }, { "cell_type": "code", "execution_count": null, "id": "7959c4e2", "metadata": {}, "outputs": [], "source": [ "153\n", "\n", "351" ] }, { "cell_type": "code", "execution_count": null, "id": "960cfd0b", "metadata": {}, "outputs": [], "source": [ "153 % 10 = 3\n", "153 // 10 = 15\n", "\n", "15 % 10 = 5\n", "15 // 10 = 1\n", "\n", "1 % 10 = 1\n", "1 // 10 = 0" ] }, { "cell_type": "code", "execution_count": 25, "id": "3597feec", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Enter your number: 10\n", "10\n", "\n" ] } ], "source": [ "n = input(\"Enter your number: \")\n", "\n", "print(n)\n", "\n", "print(type(n))" ] }, { "cell_type": "code", "execution_count": 26, "id": "71b967e4", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Enter your number: 10\n", "10\n", "\n" ] } ], "source": [ "n = int(input(\"Enter your number: \"))\n", "\n", "print(n)\n", "\n", "print(type(n))" ] }, { "cell_type": "code", "execution_count": 31, "id": "5759b2ec", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Enter a number: 153\n", "351" ] } ], "source": [ "n = int(input(\"Enter a number: \"))\n", "\n", "while n > 0:\n", " r = n % 10 # r = 3, 5, 1\n", " print(r, end=\"\") # 351\n", " n = n // 10 # n = 153, 15, 1, 0" ] }, { "cell_type": "markdown", "id": "adf5586f", "metadata": {}, "source": [ "**Write a program to enter a number from user and print sum of its individual digits**" ] }, { "cell_type": "code", "execution_count": null, "id": "cbd3db08", "metadata": {}, "outputs": [], "source": [ "153\n", "\n", "1 + 5 + 3\n", "\n", "9" ] }, { "cell_type": "code", "execution_count": null, "id": "1d76dd47", "metadata": {}, "outputs": [], "source": [ "0 = s\n", "3 = s + 3 = s\n", "5 = s + 5 = s\n", "1" ] }, { "cell_type": "code", "execution_count": 32, "id": "52deeb6d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Enter a number: 153\n", "Sum = 9\n" ] } ], "source": [ "n = int(input(\"Enter a number: \"))\n", "s = 0\n", "\n", "while n > 0:\n", " r = n % 10\n", " s = s + r # s = 0, 3, 8, 9\n", " n = n // 10\n", "\n", "print(\"Sum =\", s)" ] }, { "cell_type": "markdown", "id": "c2bb6d4f", "metadata": {}, "source": [ "# Day 3" ] }, { "cell_type": "markdown", "id": "f4cb0aee", "metadata": {}, "source": [ "**Write a program to reverse a number**" ] }, { "cell_type": "code", "execution_count": null, "id": "85d9cb69", "metadata": {}, "outputs": [], "source": [ "153\n", "\n", "351 - this should be a whole number" ] }, { "cell_type": "code", "execution_count": 1, "id": "f31986e9", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Enter a number: 153\n", "351\n", "702\n" ] } ], "source": [ "n = int(input(\"Enter a number: \"))\n", "s = 0\n", "\n", "while n > 0:\n", " r = n % 10 # r = 3, 5, 1\n", " s = s*10 + r # s = 0, 3, 35, 351\n", " n = n // 10 # n = 153, 15, 1, 0\n", "\n", "print(s)\n", "print(s * 2)" ] }, { "cell_type": "markdown", "id": "949c7e7d", "metadata": {}, "source": [ "**Write a program to enter a number from user and check if it palindrome or not**" ] }, { "cell_type": "code", "execution_count": null, "id": "4fbfc3b5", "metadata": {}, "outputs": [], "source": [ "121\n", "\n", "121\n", "\n", "153 = 351 - not a palindrome number" ] }, { "cell_type": "code", "execution_count": 3, "id": "4855f13b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Enter a number: 121\n", "Not a Palindrome\n" ] } ], "source": [ "n = int(input(\"Enter a number: \"))\n", "s = 0\n", "\n", "while n > 0:\n", " r = n % 10 # r = 3, 5, 1\n", " s = s*10 + r # s = 0, 3, 35, 351\n", " n = n // 10 # n = 153, 15, 1, 0\n", "\n", "if s == n:\n", " print(\"Palindrome\")\n", "else:\n", " print(\"Not a Palindrome\")" ] }, { "cell_type": "code", "execution_count": 5, "id": "90712f12", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Enter a number: 1234321\n", "Palindrome\n" ] } ], "source": [ "n = int(input(\"Enter a number: \"))\n", "temp = n\n", "s = 0\n", "\n", "while n > 0:\n", " r = n % 10 # r = 3, 5, 1\n", " s = s*10 + r # s = 0, 3, 35, 351\n", " n = n // 10 # n = 153, 15, 1, 0\n", "\n", "if s == temp:\n", " print(\"Palindrome\")\n", "else:\n", " print(\"Not a Palindrome\")" ] }, { "cell_type": "markdown", "id": "e1a01ec1", "metadata": {}, "source": [ "**Write a program to enter a number from user and check if it is armstrong or not**" ] }, { "cell_type": "code", "execution_count": null, "id": "20722f78", "metadata": {}, "outputs": [], "source": [ "153\n", "\n", "cube(1) + cube(5) + cube(3)\n", "\n", "370\n", "\n", "371" ] }, { "cell_type": "code", "execution_count": 9, "id": "50334900", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Enter a number: 371\n", "Armstrong\n" ] } ], "source": [ "n = int(input(\"Enter a number: \"))\n", "temp = n\n", "s = 0\n", "\n", "while n > 0:\n", " r = n % 10 # r = 3, 5, 1\n", " s = s + (r*r*r) # s = 0, 27, 152, 153\n", " n = n // 10 # n = 153, 15, 1, 0\n", "\n", "if s == temp:\n", " print(\"Armstrong\")\n", "else:\n", " print(\"Not an Armstrong\")" ] }, { "cell_type": "markdown", "id": "f93f4400", "metadata": {}, "source": [ "**Write a program to enter a number from user and print its individual digits on seperate line**" ] }, { "cell_type": "code", "execution_count": null, "id": "5ef97f05", "metadata": {}, "outputs": [], "source": [ "153\n", "\n", "1\n", "5\n", "3" ] }, { "cell_type": "code", "execution_count": 10, "id": "e3d62d35", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Enter a number: 153\n", "3\n", "5\n", "1\n" ] } ], "source": [ "n = int(input(\"Enter a number: \"))\n", "s = 0\n", "\n", "while n > 0:\n", " r = n % 10 # r = 3, 5, 1\n", " print(r) # s = 0, 27, 152, 153\n", " n = n // 10 # n = 153, 15, 1, 0\n" ] }, { "cell_type": "code", "execution_count": 11, "id": "a5bbc5c8", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Enter a number: 153\n", "1\n", "5\n", "3\n" ] } ], "source": [ "n = int(input(\"Enter a number: \"))\n", "s = 0\n", "\n", "while n > 0:\n", " r = n % 10 # r = 3, 5, 1\n", " s = s*10 + r # s = 0, 27, 152, 153\n", " n = n // 10 # n = 153, 15, 1, 0\n", "\n", "while s > 0:\n", " r = s % 10\n", " print(r)\n", " s = s // 10" ] }, { "cell_type": "markdown", "id": "decf806e", "metadata": {}, "source": [ "# List\n", "\n", "collection of different type of elements" ] }, { "cell_type": "code", "execution_count": null, "id": "9b33c6ed", "metadata": {}, "outputs": [], "source": [ "a = 10 # int\n", "b = 20\n", "c = 30" ] }, { "cell_type": "code", "execution_count": 12, "id": "366b6a7a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[10, 'A', 'India', 10.6]\n", "\n" ] } ], "source": [ "l = [10, \"A\", \"India\", 10.6]\n", "\n", "print(l)\n", "\n", "print(type(l))" ] }, { "cell_type": "code", "execution_count": 13, "id": "1af4f02d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10\n" ] } ], "source": [ "l = [10, \"A\", \"India\", 10.6]\n", "# 0 1 2 3\n", "\n", "print(l[0])" ] }, { "cell_type": "code", "execution_count": 14, "id": "ced31696", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10.6\n" ] } ], "source": [ "print(l[3])" ] }, { "cell_type": "code", "execution_count": 15, "id": "0c0905a0", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "India\n" ] } ], "source": [ "print(l[2])" ] }, { "cell_type": "code", "execution_count": 16, "id": "1fe65d17", "metadata": {}, "outputs": [ { "ename": "IndexError", "evalue": "list index out of range", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mIndexError\u001b[0m Traceback (most recent call last)", "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", "\u001b[1;31mIndexError\u001b[0m: list index out of range" ] } ], "source": [ "print(l[5])" ] }, { "cell_type": "code", "execution_count": 17, "id": "c21b0555", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "l = [10, \"A\", \"India\", 10.6]\n", "# 0 1 2 3\n", "\n", "print(type(l[0]))" ] }, { "cell_type": "code", "execution_count": 18, "id": "e9319c1c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[200, 'A', 'India', 10.6]\n" ] } ], "source": [ "l[0] = 200\n", "\n", "print(l)" ] }, { "cell_type": "code", "execution_count": 19, "id": "f8fe43a4", "metadata": {}, "outputs": [ { "ename": "IndexError", "evalue": "list assignment index out of range", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mIndexError\u001b[0m Traceback (most recent call last)", "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", "\u001b[1;31mIndexError\u001b[0m: list assignment index out of range" ] } ], "source": [ "l[5] = 1000" ] }, { "cell_type": "markdown", "id": "2aec1fac", "metadata": {}, "source": [ "### negative index" ] }, { "cell_type": "code", "execution_count": 20, "id": "d86147ba", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10.6\n" ] } ], "source": [ "# -4 -3 -2 -1\n", "l = [10, \"A\", \"India\", 10.6]\n", "# 0 1 2 3\n", "\n", "print(l[-1])" ] }, { "cell_type": "code", "execution_count": 21, "id": "c3288372", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10.6\n" ] } ], "source": [ "print(l[3])" ] }, { "cell_type": "code", "execution_count": 22, "id": "393bd2e7", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10\n", "10\n" ] } ], "source": [ "print(l[0])\n", "\n", "print(l[-4])" ] }, { "cell_type": "code", "execution_count": 23, "id": "07f8d2b4", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "India\n" ] } ], "source": [ "print(l[-2])" ] }, { "cell_type": "markdown", "id": "18ef7bed", "metadata": {}, "source": [ "# List Slicing" ] }, { "cell_type": "code", "execution_count": 24, "id": "f556294d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[11, 22, 33, 44, 10, 24, 36, 68, 99, 81, 100]\n" ] } ], "source": [ "# -11-10 -9 -8 -7 -6 -5 -4 -3 -2 -1\n", "l = [11,22,33,44,10,24,36,68,99,81,100]\n", "# 0 1 2 3 4 5 6 7 8 9 10\n", "\n", "print(l)" ] }, { "cell_type": "code", "execution_count": 25, "id": "61499c7e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[22, 33, 44, 10, 24, 36]\n" ] } ], "source": [ "print(l[1:7]) # l[start : end]" ] }, { "cell_type": "code", "execution_count": 26, "id": "696ceede", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[11, 22, 33, 44, 10, 24, 36, 68, 99, 81, 100]\n" ] } ], "source": [ "print(l)" ] }, { "cell_type": "code", "execution_count": 27, "id": "9c790873", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[10, 24, 36, 68, 99, 81]\n" ] } ], "source": [ "# -11-10 -9 -8 -7 -6 -5 -4 -3 -2 -1\n", "l = [11,22,33,44,10,24,36,68,99,81,100]\n", "# 0 1 2 3 4 5 6 7 8 9 10\n", "\n", "print(l[4:10])" ] }, { "cell_type": "code", "execution_count": 28, "id": "88f74cab", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[99, 81]\n" ] } ], "source": [ "print(l[8:10])" ] }, { "cell_type": "code", "execution_count": 29, "id": "59a8810d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[11]\n" ] } ], "source": [ "print(l[0:1])" ] }, { "cell_type": "code", "execution_count": 30, "id": "da404e2d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[22, 33, 44, 10, 24, 36, 68, 99, 81, 100]\n" ] } ], "source": [ "print(l[1:])" ] }, { "cell_type": "code", "execution_count": 31, "id": "411185c6", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[99, 81, 100]\n" ] } ], "source": [ "print(l[8:])" ] }, { "cell_type": "code", "execution_count": 32, "id": "f3427d62", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[11, 22, 33, 44, 10, 24, 36, 68, 99, 81, 100]\n" ] } ], "source": [ "print(l[:])" ] }, { "cell_type": "code", "execution_count": 33, "id": "8cb04435", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[11, 22, 33, 44, 10]\n" ] } ], "source": [ "print(l[:5])" ] }, { "cell_type": "code", "execution_count": 34, "id": "07b3d8c8", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[11, 22, 33, 44, 10, 24, 36]\n" ] } ], "source": [ "# -11-10 -9 -8 -7 -6 -5 -4 -3 -2 -1\n", "l = [11,22,33,44,10,24,36,68,99,81,100]\n", "# 0 1 2 3 4 5 6 7 8 9 10\n", "\n", "print(l[0:-4])" ] }, { "cell_type": "code", "execution_count": 35, "id": "6b72269e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[33, 44, 10, 24, 36, 68, 99]\n" ] } ], "source": [ "print(l[2:-2])" ] }, { "cell_type": "code", "execution_count": 36, "id": "34eb2422", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[]\n" ] } ], "source": [ "print(l[2:0])" ] }, { "cell_type": "code", "execution_count": 37, "id": "ea9e9d22", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[]\n" ] } ], "source": [ "print(l[0:0])" ] }, { "cell_type": "code", "execution_count": 38, "id": "d4d6dc2c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[24, 36, 68, 99, 81, 100]\n" ] } ], "source": [ "print(l[5:15])" ] }, { "cell_type": "code", "execution_count": 39, "id": "327f5a53", "metadata": {}, "outputs": [ { "ename": "IndexError", "evalue": "list index out of range", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mIndexError\u001b[0m Traceback (most recent call last)", "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", "\u001b[1;31mIndexError\u001b[0m: list index out of range" ] } ], "source": [ "print(l[15])" ] }, { "cell_type": "code", "execution_count": 40, "id": "1654f679", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[22, 33, 44]\n" ] } ], "source": [ "# -11-10 -9 -8 -7 -6 -5 -4 -3 -2 -1\n", "l = [11,22,33,44,10,24,36,68,99,81,100]\n", "# 0 1 2 3 4 5 6 7 8 9 10\n", "\n", "print(l[1:4])" ] }, { "cell_type": "code", "execution_count": 41, "id": "ef550430", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[]\n" ] } ], "source": [ "print(l[4:1])" ] }, { "cell_type": "code", "execution_count": 42, "id": "64a79df5", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[68, 99, 81]\n" ] } ], "source": [ "print(l[-4:-1])" ] }, { "cell_type": "code", "execution_count": 43, "id": "70fecd4e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[]\n" ] } ], "source": [ "print(l[-1:-4])" ] }, { "cell_type": "markdown", "id": "a2b17316", "metadata": {}, "source": [ "## steps" ] }, { "cell_type": "code", "execution_count": 44, "id": "e9af568f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[11, 33, 10, 36, 99, 100]\n" ] } ], "source": [ "# -11-10 -9 -8 -7 -6 -5 -4 -3 -2 -1\n", "l = [11,22,33,44,10,24,36,68,99,81,100]\n", "# 0 1 2 3 4 5 6 7 8 9 10\n", "\n", "print(l[0:11:2]) # l[start : end : steps]" ] }, { "cell_type": "code", "execution_count": 45, "id": "d0127e99", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[11, 44, 36, 81]\n" ] } ], "source": [ "print(l[0:11:3])" ] }, { "cell_type": "code", "execution_count": 46, "id": "75e4fefa", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[11, 22, 33, 44, 10, 24, 36, 68, 99, 81, 100]\n" ] } ], "source": [ "print(l[::])" ] }, { "cell_type": "code", "execution_count": 47, "id": "3449b6af", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[11, 22, 33, 44, 10, 24, 36, 68, 99, 81, 100]\n" ] } ], "source": [ "print(l[::1])" ] }, { "cell_type": "code", "execution_count": 48, "id": "2e6345c0", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[100, 81, 99, 68, 36, 24, 10, 44, 33, 22, 11]\n" ] } ], "source": [ "print(l[::-1]) # reverse" ] }, { "cell_type": "code", "execution_count": 49, "id": "f284f488", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[]\n" ] } ], "source": [ "# -11-10 -9 -8 -7 -6 -5 -4 -3 -2 -1\n", "l = [11,22,33,44,10,24,36,68,99,81,100]\n", "# 0 1 2 3 4 5 6 7 8 9 10\n", "\n", "print(l[4:8:-1])" ] }, { "cell_type": "code", "execution_count": 50, "id": "1f16a88d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[99, 68, 36, 24]\n" ] } ], "source": [ "print(l[8:4:-1])" ] }, { "cell_type": "code", "execution_count": 51, "id": "11575dee", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[100, 99, 36, 10, 33, 11]\n" ] } ], "source": [ "print(l[::-2])" ] }, { "cell_type": "code", "execution_count": 52, "id": "1a639620", "metadata": {}, "outputs": [ { "ename": "ValueError", "evalue": "slice step cannot be zero", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", "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", "\u001b[1;31mValueError\u001b[0m: slice step cannot be zero" ] } ], "source": [ "print(l[::0])" ] }, { "cell_type": "markdown", "id": "20862888", "metadata": {}, "source": [ "## list functions" ] }, { "cell_type": "code", "execution_count": 53, "id": "8aa952c9", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "5\n" ] } ], "source": [ "l = [10,20,30,40,50]\n", "\n", "print(len(l))" ] }, { "cell_type": "code", "execution_count": 54, "id": "74856061", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "4\n" ] } ], "source": [ "print(len(l) - 1)" ] }, { "cell_type": "code", "execution_count": 55, "id": "250b458e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "150\n" ] } ], "source": [ "print(sum(l))" ] }, { "cell_type": "code", "execution_count": 56, "id": "7ea78614", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "50\n" ] } ], "source": [ "print(max(l))" ] }, { "cell_type": "code", "execution_count": 57, "id": "bad8aadc", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10\n" ] } ], "source": [ "print(min(l))" ] }, { "cell_type": "code", "execution_count": 58, "id": "27a21618", "metadata": {}, "outputs": [ { "ename": "TypeError", "evalue": "unsupported operand type(s) for +: 'int' and 'str'", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", "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", "\u001b[1;31mTypeError\u001b[0m: unsupported operand type(s) for +: 'int' and 'str'" ] } ], "source": [ "l = [\"A\", 10,20,30]\n", "\n", "print(sum(l))" ] }, { "cell_type": "code", "execution_count": 59, "id": "a176b651", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "4\n" ] } ], "source": [ "print(len(l))" ] }, { "cell_type": "markdown", "id": "1ee80d7b", "metadata": {}, "source": [ "# List methods" ] }, { "cell_type": "code", "execution_count": 61, "id": "e802bef5", "metadata": {}, "outputs": [], "source": [ "# help(print)" ] }, { "cell_type": "code", "execution_count": 63, "id": "5a500f31", "metadata": { "scrolled": false }, "outputs": [], "source": [ "# help(list)" ] }, { "cell_type": "code", "execution_count": 64, "id": "0538c1e8", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[10, 20, 30, 40]\n" ] } ], "source": [ "l = [10,20,30]\n", "\n", "l.append(40)\n", "\n", "print(l)" ] }, { "cell_type": "code", "execution_count": 65, "id": "ed712c2b", "metadata": {}, "outputs": [], "source": [ "l.append(\"India\")" ] }, { "cell_type": "code", "execution_count": 66, "id": "265b8be2", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[10, 20, 30, 40, 'India']\n" ] } ], "source": [ "print(l)" ] }, { "cell_type": "code", "execution_count": 67, "id": "ea15af1f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "4\n" ] } ], "source": [ "print(l.index('India'))" ] }, { "cell_type": "code", "execution_count": 68, "id": "c422ce83", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0\n" ] } ], "source": [ "print(l.index(10))" ] }, { "cell_type": "code", "execution_count": 69, "id": "0e88a9f3", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[10, 20, 30, 40, 'India']\n" ] } ], "source": [ "print(l)" ] }, { "cell_type": "code", "execution_count": 72, "id": "e3042d8a", "metadata": {}, "outputs": [], "source": [ "l.reverse()" ] }, { "cell_type": "code", "execution_count": 73, "id": "02a28ba4", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[10, 20, 30, 40, 'India']\n" ] } ], "source": [ "print(l)" ] }, { "cell_type": "code", "execution_count": 74, "id": "7c861f47", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['India', 40, 30, 20, 10]\n" ] } ], "source": [ "print(l[::-1])" ] }, { "cell_type": "code", "execution_count": 75, "id": "e1210f8f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[10, 20, 30, 40, 'India']\n" ] } ], "source": [ "print(l)" ] }, { "cell_type": "code", "execution_count": null, "id": "ef9c13af", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.12" } }, "nbformat": 4, "nbformat_minor": 5 } ================================================ FILE: July Cohort 2023/1_Python Programming/.ipynb_checkpoints/04_Strings and For loop-checkpoint.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "id": "89086545", "metadata": {}, "source": [ "# Strings\n", "\n", "set of characters\n" ] }, { "cell_type": "code", "execution_count": 1, "id": "663dd2fc", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "hello\n" ] } ], "source": [ "# -1\n", "s = 'hello'\n", "# 01234\n", "\n", "print(s)" ] }, { "cell_type": "code", "execution_count": 2, "id": "d1f891e2", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "h\n" ] } ], "source": [ "print(s[0])" ] }, { "cell_type": "code", "execution_count": 3, "id": "549cabfa", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "o\n" ] } ], "source": [ "print(s[4])" ] }, { "cell_type": "code", "execution_count": 4, "id": "e9b26f8e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "o\n" ] } ], "source": [ "print(s[-1])" ] }, { "cell_type": "code", "execution_count": 5, "id": "bef31594", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "l\n" ] } ], "source": [ "print(s[-2])" ] }, { "cell_type": "code", "execution_count": 6, "id": "00f3823f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "print(type(s))" ] }, { "cell_type": "code", "execution_count": 7, "id": "7326e7a9", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "print(type(s[0]))" ] }, { "cell_type": "markdown", "id": "4899cd30", "metadata": {}, "source": [ "# string slicing" ] }, { "cell_type": "code", "execution_count": 8, "id": "12c56ed2", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "commu\n" ] } ], "source": [ "s = 'communication'\n", "# 0123456789\n", "\n", "print(s[0:5])" ] }, { "cell_type": "code", "execution_count": 9, "id": "781ea5cd", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "noitacinummoc\n" ] } ], "source": [ "print(s[::-1])" ] }, { "cell_type": "code", "execution_count": 10, "id": "90aa93d3", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "omm\n" ] } ], "source": [ "print(s[1:4])" ] }, { "cell_type": "code", "execution_count": 11, "id": "5c24d8b3", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "s = 'communication'\n", "# 0123456789\n", "\n", "print(s[4:1])" ] }, { "cell_type": "code", "execution_count": 12, "id": "a8f6af2e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "s = ''\n", "\n", "print(type(s))" ] }, { "cell_type": "code", "execution_count": 13, "id": "2d3570c7", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "noitacin\n" ] } ], "source": [ "s = 'communication'\n", "# 0123456789\n", "\n", "print(s[-1:-9:-1])" ] }, { "cell_type": "markdown", "id": "ef70cbf4", "metadata": {}, "source": [ "# String Functions" ] }, { "cell_type": "code", "execution_count": 14, "id": "f12266cf", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "13\n" ] } ], "source": [ "s = 'communication'\n", "\n", "print(len(s))" ] }, { "cell_type": "code", "execution_count": 15, "id": "50320c72", "metadata": {}, "outputs": [ { "ename": "TypeError", "evalue": "unsupported operand type(s) for +: 'int' and 'str'", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", "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", "\u001b[1;31mTypeError\u001b[0m: unsupported operand type(s) for +: 'int' and 'str'" ] } ], "source": [ "print(sum(s)) # s = 0" ] }, { "cell_type": "code", "execution_count": 16, "id": "55b002c3", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "u\n" ] } ], "source": [ "print(max(s))" ] }, { "cell_type": "code", "execution_count": 17, "id": "7ccced34", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "a\n" ] } ], "source": [ "print(min(s))" ] }, { "cell_type": "code", "execution_count": null, "id": "8b30e14f", "metadata": {}, "outputs": [], "source": [ "# ASCII\n", "\n", "'A' -> 65\n", "'B' -> 66\n", "'Z' -> 90\n", "\n", "'a' -> 97\n", "'z' -> 122\n", "\n", "' ' -> 32 -> space" ] }, { "cell_type": "markdown", "id": "afa17626", "metadata": {}, "source": [ "## string methods\n" ] }, { "cell_type": "code", "execution_count": 19, "id": "e3ef73fb", "metadata": { "scrolled": true }, "outputs": [], "source": [ "# help(str)" ] }, { "cell_type": "code", "execution_count": 20, "id": "53f436a8", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "False" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "'raj123' == 'RAJ123'" ] }, { "cell_type": "code", "execution_count": 21, "id": "9a9f840d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "raj123\n" ] } ], "source": [ "s = 'RAJ123'\n", "\n", "print(s.casefold())" ] }, { "cell_type": "code", "execution_count": 22, "id": "4e6cd479", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "INDIA\n" ] } ], "source": [ "s = 'India'\n", "\n", "print(s.upper())" ] }, { "cell_type": "code", "execution_count": 23, "id": "76b04479", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "india\n" ] } ], "source": [ "print(s.lower())" ] }, { "cell_type": "code", "execution_count": 24, "id": "2e8f43e5", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['This', 'is', 'an', 'apple']\n" ] } ], "source": [ "# split\n", "\n", "s = 'This is an apple'\n", "\n", "print(s.split(\" \"))" ] }, { "cell_type": "code", "execution_count": 25, "id": "94237ec7", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['H', 'llo Us', 'r']\n" ] } ], "source": [ "s = \"Hello User\"\n", "\n", "print(s.split(\"e\"))" ] }, { "cell_type": "code", "execution_count": 26, "id": "42a71402", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "This+is+an+apple\n" ] } ], "source": [ "# join\n", "\n", "l = ['This', 'is', 'an', 'apple']\n", "\n", "a = \"+\"\n", "\n", "print(a.join(l))" ] }, { "cell_type": "code", "execution_count": 27, "id": "6ef43d06", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "This is an apple\n" ] } ], "source": [ "# join\n", "\n", "l = ['This', 'is', 'an', 'apple']\n", "\n", "a = \" \"\n", "\n", "print(a.join(l))\n", "# \" \".join(l)" ] }, { "cell_type": "code", "execution_count": 28, "id": "dae0d39a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "ThisisanApple\n" ] } ], "source": [ "# remove all the spaces from the string\n", "\n", "s = \"This is an Apple\"\n", "\n", "l = s.split(\" \")\n", "\n", "x = \"\".join(l)\n", "\n", "print(x)" ] }, { "cell_type": "markdown", "id": "5e18c7bc", "metadata": {}, "source": [ "**Write a program to enter a string from user and print the biggest word from the para**" ] }, { "cell_type": "code", "execution_count": null, "id": "fc9ffad3", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "id": "08b8681d", "metadata": {}, "source": [ "# range function" ] }, { "cell_type": "code", "execution_count": 29, "id": "cd95ff5d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "range(0, 5)\n" ] } ], "source": [ "n = range(5) # this will create a series of numbers from 0 to 4\n", "\n", "print(n)" ] }, { "cell_type": "code", "execution_count": 30, "id": "5faa24f2", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "print(type(n))" ] }, { "cell_type": "code", "execution_count": 31, "id": "b9b1b6fa", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0, 1, 2, 3, 4]\n" ] } ], "source": [ "n = list(range(5))\n", "\n", "print(n)" ] }, { "cell_type": "code", "execution_count": 32, "id": "00bf5fc9", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[3, 4, 5, 6, 7]\n" ] } ], "source": [ "n = list(range(3,8))\n", "\n", "print(n)" ] }, { "cell_type": "code", "execution_count": 33, "id": "85f51f76", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "True\n" ] } ], "source": [ "print(range(20) == range(0,20))" ] }, { "cell_type": "code", "execution_count": 34, "id": "cf56e7cc", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[5, 7, 9, 11, 13, 15, 17, 19]\n" ] } ], "source": [ "n = list(range(5,20,2)) # range(start, end, steps)\n", "\n", "print(n)" ] }, { "cell_type": "markdown", "id": "9a9e11b2", "metadata": {}, "source": [ "## for loop" ] }, { "cell_type": "code", "execution_count": 35, "id": "eb461d52", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "False\n" ] } ], "source": [ "l = [\"A\", \"B\", \"C\", \"D\"]\n", "\n", "print(\"Z\" in l)" ] }, { "cell_type": "code", "execution_count": 36, "id": "f3d37b0a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "A\n", "B\n", "C\n", "D\n" ] } ], "source": [ "l = [\"A\", \"B\", \"C\", \"D\"]\n", "# i\n", "\n", "# \"B\" in l\n", "for i in l:\n", " print(i)" ] }, { "cell_type": "code", "execution_count": 37, "id": "15a3f0ba", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "A\n", "Hi\n", "B\n", "Hi\n", "C\n", "Hi\n", "D\n", "Hi\n" ] } ], "source": [ "l = [\"A\", \"B\", \"C\", \"D\"]\n", "# i\n", "\n", "# \"B\" in l\n", "for i in l:\n", " print(i)\n", " print(\"Hi\")" ] }, { "cell_type": "code", "execution_count": 40, "id": "59f4f57f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "a\n", "b\n", "c\n", "d\n" ] } ], "source": [ "s = 'abcd'\n", "\n", "for i in s:\n", " print(i)" ] }, { "cell_type": "code", "execution_count": 42, "id": "be302d25", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "AB\n", "ab\n" ] } ], "source": [ "x = 'ab'\n", "\n", "print(x.upper())\n", "\n", "print(x)" ] }, { "cell_type": "code", "execution_count": 43, "id": "b3a22f15", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['ab', 'cd']\n" ] } ], "source": [ "x = ['ab', 'cd']\n", "\n", "for i in x:\n", " i.upper() # 'ab'.upper() -> 'AB'\n", " \n", "print(x)" ] }, { "cell_type": "code", "execution_count": 44, "id": "4e0873d9", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "AB\n", "CD\n", "['ab', 'cd']\n" ] } ], "source": [ "x = ['ab', 'cd']\n", "\n", "for i in x:\n", " print(i.upper()) # 'ab'.upper() -> 'AB'\n", " \n", "print(x)" ] }, { "cell_type": "code", "execution_count": 45, "id": "77e96be5", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "a\n", "b\n", "c\n", "d\n" ] } ], "source": [ "x = 'abcd'\n", "\n", "for i in x:\n", " print(i)\n", " x.upper()\n" ] }, { "cell_type": "code", "execution_count": 46, "id": "d6c07e4d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0\n", "1\n", "2\n", "3\n", "4\n" ] } ], "source": [ "# i\n", "# 0,1,2,3,4\n", "for i in range(5):\n", " print(i)" ] }, { "cell_type": "code", "execution_count": 47, "id": "bba46637", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "5\n", "6\n", "7\n", "8\n", "9\n" ] } ], "source": [ "for i in range(5,10):\n", " print(i)" ] }, { "cell_type": "code", "execution_count": 48, "id": "fdb6483c", "metadata": {}, "outputs": [ { "ename": "TypeError", "evalue": "'str' object cannot be interpreted as an integer", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", "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", "\u001b[1;31mTypeError\u001b[0m: 'str' object cannot be interpreted as an integer" ] } ], "source": [ "x = 'abcd'\n", "\n", "for i in range(x):\n", " print(i)" ] }, { "cell_type": "code", "execution_count": 49, "id": "dac1b7e1", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0\n", "1\n", "2\n", "3\n" ] } ], "source": [ "x = 'abcd'\n", "\n", "# 0,1,2,3\n", "for i in range(len(x)):\n", " print(i)" ] }, { "cell_type": "code", "execution_count": 50, "id": "ce020a17", "metadata": {}, "outputs": [ { "ename": "AttributeError", "evalue": "'int' object has no attribute 'upper'", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)", "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", "\u001b[1;31mAttributeError\u001b[0m: 'int' object has no attribute 'upper'" ] } ], "source": [ "x = 'abcd'\n", "\n", "# 0,1,2,3\n", "for i in range(len(x)):\n", " print(i.upper())" ] }, { "cell_type": "code", "execution_count": 51, "id": "e4b348f0", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "abcd\n" ] } ], "source": [ "x = 'abcd'\n", "\n", "# 0,1,2,3\n", "for i in range(len(x)):\n", " x[i].upper()\n", "\n", "print(x)" ] }, { "cell_type": "code", "execution_count": 52, "id": "e4afa49b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "A\n", "B\n", "C\n", "D\n", "abcd\n" ] } ], "source": [ "x = 'abcd'\n", "\n", "# 0,1,2,3\n", "for i in range(len(x)):\n", " print(x[i].upper())\n", "\n", "print(x)" ] }, { "cell_type": "code", "execution_count": 53, "id": "9d691550", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "a\n", "a\n", "a\n", "a\n" ] } ], "source": [ "x = 'abcd'\n", "\n", "# 0,1,2,3\n", "for i in range(len(x)):\n", " x = 'a' # x = 'abcd', 'a', 'a'\n", " print(x)" ] }, { "cell_type": "code", "execution_count": null, "id": "9fe9971b", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.12" } }, "nbformat": 4, "nbformat_minor": 5 } ================================================ FILE: July Cohort 2023/1_Python Programming/.ipynb_checkpoints/05_Dictionary Tuples and Set-checkpoint.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "id": "34a4e5e6", "metadata": {}, "source": [ "# Dictionary" ] }, { "cell_type": "code", "execution_count": null, "id": "cb73a69f", "metadata": {}, "outputs": [], "source": [ "# { key : value }\n", "\n", "unordered data structures" ] }, { "cell_type": "code", "execution_count": 1, "id": "9c5ff7b6", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'ravi': 24, 'shyam': 21, 'roy': 41}\n" ] } ], "source": [ "ages = {\"ravi\":24, \"shyam\": 21, \"roy\": 41}\n", "\n", "print(ages)" ] }, { "cell_type": "code", "execution_count": 2, "id": "fc51983d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "print(type(ages))" ] }, { "cell_type": "code", "execution_count": 3, "id": "45957197", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "24\n" ] } ], "source": [ "print(ages['ravi'])" ] }, { "cell_type": "code", "execution_count": 4, "id": "8587855f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "41\n" ] } ], "source": [ "print(ages['roy'])" ] }, { "cell_type": "code", "execution_count": 5, "id": "bcc42db7", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[255, 0, 0]\n" ] } ], "source": [ "color = {\n", " \"red\": [255,0,0],\n", " \"green\": [0,255,0],\n", " \"blue\": [0,0,255]\n", "}\n", "print(color['red'])" ] }, { "cell_type": "code", "execution_count": 7, "id": "f28a6d43", "metadata": {}, "outputs": [ { "ename": "KeyError", "evalue": "'yellow'", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", "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", "\u001b[1;31mKeyError\u001b[0m: 'yellow'" ] } ], "source": [ "print(color['yellow'])" ] }, { "cell_type": "code", "execution_count": 8, "id": "98fd771e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{}\n", "\n" ] } ], "source": [ "d = {}\n", "print(d)\n", "print(type(d))" ] }, { "cell_type": "code", "execution_count": 9, "id": "02b6f0ff", "metadata": {}, "outputs": [ { "ename": "TypeError", "evalue": "unhashable type: 'list'", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", "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", "\u001b[1;31mTypeError\u001b[0m: unhashable type: 'list'" ] } ], "source": [ "d = {[1,2]:10, 2:2, 3:3}\n", "print(d)" ] }, { "cell_type": "code", "execution_count": 10, "id": "080bd938", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[100, 20]\n" ] } ], "source": [ "# list are mutable objects\n", "\n", "l = [10,20]\n", "l[0] = 100\n", "print(l)" ] }, { "cell_type": "code", "execution_count": 11, "id": "0efe853a", "metadata": {}, "outputs": [ { "ename": "TypeError", "evalue": "'str' object does not support item assignment", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", "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", "\u001b[1;31mTypeError\u001b[0m: 'str' object does not support item assignment" ] } ], "source": [ "# strings are immutable objects\n", "\n", "s = 'indore'\n", "s[0] = \"A\"\n", "print(s)" ] }, { "cell_type": "markdown", "id": "6de43fed", "metadata": {}, "source": [ "# Dictionary Functions" ] }, { "cell_type": "code", "execution_count": 12, "id": "da7184d8", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "4\n" ] } ], "source": [ "d = {1:11, 2:41, 13:9, 14:16}\n", "\n", "print(len(d))" ] }, { "cell_type": "code", "execution_count": 13, "id": "2439c0d6", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "30\n" ] } ], "source": [ "print(sum(d))" ] }, { "cell_type": "code", "execution_count": 14, "id": "3842f621", "metadata": {}, "outputs": [ { "ename": "TypeError", "evalue": "unsupported operand type(s) for +: 'int' and 'str'", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", "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", "\u001b[1;31mTypeError\u001b[0m: unsupported operand type(s) for +: 'int' and 'str'" ] } ], "source": [ "a = {1:2, 3:4, \"hi\":16}\n", "\n", "print(sum(a))" ] }, { "cell_type": "markdown", "id": "72a7d21d", "metadata": {}, "source": [ "# Dictionary Methods" ] }, { "cell_type": "code", "execution_count": 15, "id": "dda87168", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{1: 'Apple', 'orange': [2, 3, 4], '2': '23'}\n" ] } ], "source": [ "pair = {1:\"Apple\", \"orange\": [2,3,4], \"2\":\"23\"}\n", "\n", "print(pair)" ] }, { "cell_type": "code", "execution_count": 18, "id": "031cb949", "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[2, 3, 4]\n" ] } ], "source": [ "# get\n", "# help(dict)\n", "\n", "print(pair.get(\"orange\"))" ] }, { "cell_type": "code", "execution_count": 19, "id": "cba8f060", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "None\n" ] } ], "source": [ "print(pair.get(7))" ] }, { "cell_type": "code", "execution_count": null, "id": "32604209", "metadata": {}, "outputs": [], "source": [ "None ==> NULL ==> nothing" ] }, { "cell_type": "code", "execution_count": 20, "id": "d7d265af", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Apple\n" ] } ], "source": [ "print(pair.get(1,\"Element not found\"))" ] }, { "cell_type": "code", "execution_count": 21, "id": "ff1c3065", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Element not found\n" ] } ], "source": [ "print(pair.get(10,\"Element not found\"))" ] }, { "cell_type": "code", "execution_count": 23, "id": "a75186b5", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{1: 'Apple', 'orange': [2, 3, 4], '2': '23'}\n", "{}\n" ] } ], "source": [ "# clear\n", "\n", "p = {1:\"Apple\", \"orange\": [2,3,4], \"2\":\"23\"}\n", "\n", "print(p)\n", "p.clear()\n", "print(p)" ] }, { "cell_type": "code", "execution_count": 24, "id": "d153a547", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "dict_keys([1, 2, 3])\n" ] } ], "source": [ "# keys\n", "\n", "r = {1:\"A\", 2:'Zara', 3:\"Maza\"}\n", "\n", "print(r.keys())" ] }, { "cell_type": "code", "execution_count": 25, "id": "b0c31ff7", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1, 2, 3]\n" ] } ], "source": [ "print(list(r.keys()))" ] }, { "cell_type": "code", "execution_count": 26, "id": "0afa95ad", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "dict_values(['A', 'Zara', 'Maza'])\n" ] } ], "source": [ "# values\n", "\n", "r = {1:\"A\", 2:'Zara', 3:\"Maza\"}\n", "\n", "print(r.values())" ] }, { "cell_type": "code", "execution_count": 27, "id": "9c024e2a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['A', 'Zara', 'Maza']\n" ] } ], "source": [ "print(list(r.values()))" ] }, { "cell_type": "code", "execution_count": 28, "id": "4d0c19d7", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{3: 'Maza', 2: 'Zara'}\n", "2\n" ] } ], "source": [ "r = {3:\"A\", 2:'Zara', 3:\"Maza\"}\n", "\n", "print(r)\n", "\n", "print(len(r))" ] }, { "cell_type": "markdown", "id": "104a3ab9", "metadata": {}, "source": [ "# Question\n", "\n", "print the following series" ] }, { "cell_type": "code", "execution_count": null, "id": "7f5a1e63", "metadata": {}, "outputs": [], "source": [ "{1:1, 2:4, 3:9, 4:16, .........10:100}" ] }, { "cell_type": "code", "execution_count": 29, "id": "7c6a9d9a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{1: 2, 3: 'Hi'}\n" ] } ], "source": [ "a = {1:2}\n", "a[3] = \"Hi\"\n", "print(a)" ] }, { "cell_type": "code", "execution_count": 30, "id": "e1ec63ea", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{1: 1, 2: 4, 3: 9, 4: 16, 5: 25, 6: 36, 7: 49, 8: 64, 9: 81, 10: 100}\n" ] } ], "source": [ "d = {}\n", "\n", "for i in range(1,11):\n", " d[i] = i * i\n", "\n", "print(d)" ] }, { "cell_type": "markdown", "id": "ae9ad8ce", "metadata": {}, "source": [ "# Tuples" ] }, { "cell_type": "code", "execution_count": 31, "id": "926d2c80", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(4, 3, 5, 6)\n", "\n" ] } ], "source": [ "t = (4,3,5,6)\n", "# 0 1 2 3\n", "\n", "print(t)\n", "\n", "print(type(t))" ] }, { "cell_type": "code", "execution_count": 32, "id": "0184a528", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "4\n" ] } ], "source": [ "print(t[0])" ] }, { "cell_type": "code", "execution_count": 33, "id": "67632ef3", "metadata": {}, "outputs": [ { "ename": "TypeError", "evalue": "'tuple' object does not support item assignment", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", "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", "\u001b[1;31mTypeError\u001b[0m: 'tuple' object does not support item assignment" ] } ], "source": [ "t[0] = 11 # tuple are immutable objects" ] }, { "cell_type": "code", "execution_count": 34, "id": "b6045c0f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "('one', 'two', 'three')\n" ] } ], "source": [ "t = \"one\", \"two\", \"three\"\n", "\n", "print(t)" ] }, { "cell_type": "code", "execution_count": 35, "id": "ffa2af8b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "print(type(t))" ] }, { "cell_type": "code", "execution_count": 36, "id": "2efc8510", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10\n", "\n" ] } ], "source": [ "t = (10)\n", "print(t)\n", "print(type(t))" ] }, { "cell_type": "code", "execution_count": 37, "id": "da1bb799", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(10,)\n", "\n" ] } ], "source": [ "t = (10,)\n", "print(t)\n", "print(type(t))" ] }, { "cell_type": "markdown", "id": "4cae5690", "metadata": {}, "source": [ "# Tuple unpacking" ] }, { "cell_type": "code", "execution_count": 38, "id": "58a607f3", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1\n", "2\n", "3\n" ] } ], "source": [ "n = (1,2,3)\n", "\n", "a,b,c = n\n", "\n", "print(a)\n", "print(b)\n", "print(c)" ] }, { "cell_type": "markdown", "id": "b276c6bf", "metadata": {}, "source": [ "# Tuple Slicing" ] }, { "cell_type": "code", "execution_count": 39, "id": "92e79ee4", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(11, 22, 33, 44, 55, 66, 77, 88, 99, 100)\n" ] } ], "source": [ "# -1\n", "t = (11,22,33,44,55,66,77,88,99,100)\n", "# 0 1 \n", "\n", "print(t)" ] }, { "cell_type": "code", "execution_count": 40, "id": "57630079", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(44, 55)\n" ] } ], "source": [ "print(t[3:5])" ] }, { "cell_type": "code", "execution_count": 41, "id": "fec889a9", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(11, 22, 33, 44, 55, 66)\n" ] } ], "source": [ "print(t[:6])" ] }, { "cell_type": "code", "execution_count": 42, "id": "503f8dba", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(33, 44, 55, 66)\n" ] } ], "source": [ "print(t[-8:-4])" ] }, { "cell_type": "code", "execution_count": 43, "id": "a0ba748f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "()\n" ] } ], "source": [ "print(t[-4:-8])" ] }, { "cell_type": "code", "execution_count": 44, "id": "fa4cc944", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(100, 99, 88, 77, 66, 55, 44, 33, 22, 11)\n" ] } ], "source": [ "print(t[::-1])" ] }, { "cell_type": "markdown", "id": "9b4af08a", "metadata": {}, "source": [ "# Tuple Methods" ] }, { "cell_type": "code", "execution_count": 45, "id": "7bbdf0aa", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "3\n" ] } ], "source": [ "# count\n", "\n", "t = (2,3,4,5,5,6,6,4,4,)\n", "\n", "print(t.count(4))\n" ] }, { "cell_type": "code", "execution_count": 46, "id": "ad80d733", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2\n" ] } ], "source": [ "# index\n", "\n", "t = (2,3,4,5,5,6,6,4,4,)\n", "\n", "print(t.index(4))" ] }, { "cell_type": "code", "execution_count": 47, "id": "df41de26", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Help on built-in function index:\n", "\n", "index(value, start=0, stop=9223372036854775807, /) method of builtins.tuple instance\n", " Return first index of value.\n", " \n", " Raises ValueError if the value is not present.\n", "\n" ] } ], "source": [ "help(t.index)" ] }, { "cell_type": "code", "execution_count": 48, "id": "86d6e3a9", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "7\n" ] } ], "source": [ "# index\n", "\n", "t = (2,3,4,5,5,6,6,4,4,)\n", "\n", "print(t.index(4, 5))" ] }, { "cell_type": "markdown", "id": "ff55ace0", "metadata": {}, "source": [ "# Tuple functions" ] }, { "cell_type": "code", "execution_count": 49, "id": "d2e05c64", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "5\n" ] } ], "source": [ "t = (2,3,4,5,6)\n", "\n", "print(len(t))" ] }, { "cell_type": "code", "execution_count": 50, "id": "2f71a91b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "20\n" ] } ], "source": [ "print(sum(t))" ] }, { "cell_type": "code", "execution_count": 51, "id": "f9c6d5a3", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "6\n" ] } ], "source": [ "print(max(t))" ] }, { "cell_type": "markdown", "id": "23dad4a2", "metadata": {}, "source": [ "# Question" ] }, { "cell_type": "code", "execution_count": null, "id": "c1ee0321", "metadata": {}, "outputs": [], "source": [ "# remove i from this tuple\n", "\n", "t = ('a', 'e','i', 'o', 'u')" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.12" } }, "nbformat": 4, "nbformat_minor": 5 } ================================================ FILE: July Cohort 2023/1_Python Programming/01_Python Basics.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "id": "5effdff7", "metadata": {}, "source": [ "# Python Basics" ] }, { "cell_type": "code", "execution_count": 1, "id": "d04e5b22", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Hello User\n" ] } ], "source": [ "print(\"Hello User\")" ] }, { "cell_type": "code", "execution_count": 2, "id": "b0091bf1", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Hello\n", "User\n" ] } ], "source": [ "print(\"Hello\")\n", "print(\"User\")" ] }, { "cell_type": "code", "execution_count": 5, "id": "fb83814b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Hello\n", "User\n" ] } ], "source": [ "print(\"Hello\\nUser\") # \\n -> new line character" ] }, { "cell_type": "code", "execution_count": 6, "id": "791c342a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Hello\tUser\n" ] } ], "source": [ "print(\"Hello\\tUser\") # \\t -> horizontal tab - 8 spaces" ] }, { "cell_type": "code", "execution_count": null, "id": "9fad4ea5", "metadata": {}, "outputs": [], "source": [ "# binary language -> 101010111 -> bits\n", "\n", "0 -> low voltage, no-charge, false, off\n", "1 -> high voltage, charge, true, on" ] }, { "cell_type": "code", "execution_count": null, "id": "a1885e98", "metadata": {}, "outputs": [], "source": [ "bits > numbers > character > instructions > program > software" ] }, { "cell_type": "code", "execution_count": null, "id": "cbe99e7d", "metadata": {}, "outputs": [], "source": [ "source code ---------> Byte Code --------> output\n", " .c compile .bak run .exe\n", " .cpp .obj\n", " .java .javac\n", " .py .pyc" ] }, { "cell_type": "markdown", "id": "8a713554", "metadata": {}, "source": [ "# Variables" ] }, { "cell_type": "code", "execution_count": 8, "id": "5c20abe8", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10\n" ] } ], "source": [ "a = 10\n", "\n", "print(a)" ] }, { "cell_type": "code", "execution_count": 9, "id": "995009df", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "45.13\n" ] } ], "source": [ "b = 45.13\n", "\n", "print(b)" ] }, { "cell_type": "code", "execution_count": 10, "id": "17194c9e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "India\n" ] } ], "source": [ "a = \"India\"\n", "\n", "print(a)" ] }, { "cell_type": "markdown", "id": "9141a786", "metadata": {}, "source": [ "# Data Types" ] }, { "cell_type": "code", "execution_count": 11, "id": "6ba6bfd9", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10\n", "\n" ] } ], "source": [ "a = 10\n", "\n", "print(a)\n", "\n", "print(type(a))" ] }, { "cell_type": "code", "execution_count": 12, "id": "a6e1a232", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10.65\n", "\n" ] } ], "source": [ "b = 10.65\n", "\n", "print(b)\n", "\n", "print(type(b))" ] }, { "cell_type": "code", "execution_count": 13, "id": "1298d5d3", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "colors\n", "\n" ] } ], "source": [ "c = \"colors\"\n", "\n", "print(c)\n", "\n", "print(type(c))" ] }, { "cell_type": "code", "execution_count": 14, "id": "47c38fa9", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "colors\n", "\n" ] } ], "source": [ "c = 'colors'\n", "\n", "print(c)\n", "\n", "print(type(c))" ] }, { "cell_type": "code", "execution_count": 15, "id": "0acb14e3", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "it's an apple\n" ] } ], "source": [ "x = \"it's an apple\"\n", "\n", "print(x)" ] }, { "cell_type": "code", "execution_count": 17, "id": "fb2beb48", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "it\"s an apple\n" ] } ], "source": [ "x = 'it\"s an apple'\n", "\n", "print(x)" ] }, { "cell_type": "markdown", "id": "814feca5", "metadata": {}, "source": [ "# Input Function" ] }, { "cell_type": "code", "execution_count": 18, "id": "437ffd33", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10\n", "10\n" ] } ], "source": [ "a = input()\n", "\n", "print(a)" ] }, { "cell_type": "code", "execution_count": 19, "id": "8c71996c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Enter Your Name: Himanshu\n", "Himanshu\n" ] } ], "source": [ "a = input(\"Enter Your Name: \")\n", "\n", "print(a)" ] }, { "cell_type": "code", "execution_count": 20, "id": "792f782d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Enter Your Name: Himanshu\n", "Hello! How are you? Himanshu\n" ] } ], "source": [ "a = input(\"Enter Your Name: \")\n", "\n", "print(\"Hello! How are you?\", a)" ] }, { "cell_type": "code", "execution_count": 21, "id": "8ab7e6a7", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Hello Him\n" ] } ], "source": [ "print(\"Hello Him\")" ] }, { "cell_type": "markdown", "id": "84b26329", "metadata": {}, "source": [ "# Questions" ] }, { "cell_type": "code", "execution_count": null, "id": "376a1bc0", "metadata": {}, "outputs": [], "source": [ "Your Name\n", "------8-------College/Company\n", "------------16-------------Country" ] }, { "cell_type": "code", "execution_count": null, "id": "f60668a8", "metadata": {}, "outputs": [], "source": [ "*\n", "**\n", "***" ] }, { "cell_type": "code", "execution_count": null, "id": "7c2fa48a", "metadata": {}, "outputs": [], "source": [ "--4--*\n", "*----8----*\n", "--4--*" ] }, { "cell_type": "code", "execution_count": null, "id": "2650f9ed", "metadata": {}, "outputs": [], "source": [ " *\n", "* *\n", " *" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.12" } }, "nbformat": 4, "nbformat_minor": 5 } ================================================ FILE: July Cohort 2023/1_Python Programming/02_03_While Loop and List.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "id": "13c6ac1c", "metadata": {}, "source": [ "# While Loop" ] }, { "cell_type": "code", "execution_count": 1, "id": "0290b931", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Himanshu\n", "Himanshu\n" ] } ], "source": [ "print(\"Himanshu\")\n", "print(\"Himanshu\")" ] }, { "cell_type": "code", "execution_count": 2, "id": "aab5a9d5", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "True\n" ] } ], "source": [ "i = 1\n", "\n", "print(i <= 5)" ] }, { "cell_type": "code", "execution_count": 3, "id": "be002b98", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "False\n" ] } ], "source": [ "print(i >= 5)" ] }, { "cell_type": "code", "execution_count": 4, "id": "903683b3", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "True\n" ] } ], "source": [ "print(i >= 1)" ] }, { "cell_type": "code", "execution_count": 5, "id": "76da14cf", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Himanshu\n", "Himanshu\n", "Himanshu\n", "Himanshu\n", "Himanshu\n" ] } ], "source": [ "i = 1\n", "\n", "while i <= 5:\n", " print(\"Himanshu\")\n", " i = i + 1 # i = 1, 2, 3, 4, 5, 6" ] }, { "cell_type": "code", "execution_count": 6, "id": "145ad944", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1\n", "2\n", "3\n", "4\n", "5\n" ] } ], "source": [ "i = 1\n", "\n", "while i <= 5:\n", " print(i)\n", " i = i + 1" ] }, { "cell_type": "code", "execution_count": null, "id": "cdad3efc", "metadata": {}, "outputs": [], "source": [ "1\n", "2\n", "3\n", "4\n", "5\n", "6\n", "7\n", "8\n", "9\n", "10" ] }, { "cell_type": "code", "execution_count": 7, "id": "d7d2dfab", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1\n", "2\n", "3\n", "4\n", "5\n", "6\n", "7\n", "8\n", "9\n", "10\n" ] } ], "source": [ "i = 1\n", "\n", "while i <= 10:\n", " print(i)\n", " i = i + 1" ] }, { "cell_type": "code", "execution_count": 8, "id": "a103c74c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Hello\n", "World\n" ] } ], "source": [ "print(\"Hello\")\n", "print(\"World\")" ] }, { "cell_type": "code", "execution_count": 9, "id": "5df8d4f6", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Hello\n", "World\n" ] } ], "source": [ "print(\"Hello\", end=\"\\n\")\n", "print(\"World\")" ] }, { "cell_type": "code", "execution_count": 10, "id": "c89f0b40", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Hello+World\n" ] } ], "source": [ "print(\"Hello\", end=\"+\")\n", "print(\"World\")" ] }, { "cell_type": "code", "execution_count": 11, "id": "8a0e5ca8", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Hello World\n" ] } ], "source": [ "print(\"Hello\", end=\" \")\n", "print(\"World\")" ] }, { "cell_type": "code", "execution_count": 12, "id": "bce809e5", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Hello @@@ World\n" ] } ], "source": [ "print(\"Hello\", end=\" @@@ \")\n", "print(\"World\")" ] }, { "cell_type": "code", "execution_count": 13, "id": "54435a01", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Hello@World$" ] } ], "source": [ "print(\"Hello\", end=\"@\")\n", "print(\"World\", end=\"$\")" ] }, { "cell_type": "markdown", "id": "95e933ab", "metadata": {}, "source": [ "### print the following series of numbers" ] }, { "cell_type": "code", "execution_count": null, "id": "7a728f28", "metadata": {}, "outputs": [], "source": [ "1 2 3 4 5 6 7 8 9 10" ] }, { "cell_type": "code", "execution_count": 14, "id": "35896fd2", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1 2 3 4 5 6 7 8 9 10 " ] } ], "source": [ "i = 1\n", "\n", "while i <= 10:\n", " print(i, end=\" \")\n", " i = i + 1" ] }, { "cell_type": "code", "execution_count": 15, "id": "284eda1a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "100\n", "10\n" ] } ], "source": [ "a = 10\n", "\n", "print(a * a)\n", "\n", "print(a)" ] }, { "cell_type": "code", "execution_count": null, "id": "d1cabe31", "metadata": {}, "outputs": [], "source": [ "1 4 9 16 25 36 49 64 81 100" ] }, { "cell_type": "code", "execution_count": 16, "id": "32f564a1", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1 4 9 16 25 36 49 64 81 100 " ] } ], "source": [ "i = 1\n", "\n", "while i <= 10:\n", " print(i*i, end=\" \")\n", " i = i + 1" ] }, { "cell_type": "markdown", "id": "49b1b14e", "metadata": {}, "source": [ "**Write a program to enter 5 values from user and print them.**" ] }, { "cell_type": "code", "execution_count": 17, "id": "a1cd06ee", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Enter your number: 10\n", "10\n", "Enter your number: 20\n", "20\n", "Enter your number: 30\n", "30\n", "Enter your number: 40\n", "40\n", "Enter your number: 50\n", "50\n" ] } ], "source": [ "i = 1\n", "\n", "while i <= 5:\n", " a = input(\"Enter your number: \")\n", " print(a)\n", " i = i + 1\n", " # 1 + 1" ] }, { "cell_type": "code", "execution_count": 18, "id": "a1b92062", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "3.5" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "7 / 2" ] }, { "cell_type": "code", "execution_count": 19, "id": "b1e0fc38", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "7 % 2 # modulo - return the reminder" ] }, { "cell_type": "code", "execution_count": 20, "id": "7279d00f", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "10 % 2" ] }, { "cell_type": "code", "execution_count": 21, "id": "7ba7e050", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "3" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "11 % 4" ] }, { "cell_type": "code", "execution_count": 22, "id": "3c3ea69f", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "3" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "7 // 2 # floor division" ] }, { "cell_type": "code", "execution_count": 23, "id": "e378bbe4", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "15 // 7" ] }, { "cell_type": "code", "execution_count": 24, "id": "5947157a", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "3" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "153 % 10" ] }, { "cell_type": "code", "execution_count": null, "id": "07a156bd", "metadata": {}, "outputs": [], "source": [ "15)7(\n", " 7" ] }, { "cell_type": "markdown", "id": "d490f9d3", "metadata": {}, "source": [ "**Write a program to enter a number from user and print reverse of it.**" ] }, { "cell_type": "code", "execution_count": null, "id": "7959c4e2", "metadata": {}, "outputs": [], "source": [ "153\n", "\n", "351" ] }, { "cell_type": "code", "execution_count": null, "id": "960cfd0b", "metadata": {}, "outputs": [], "source": [ "153 % 10 = 3\n", "153 // 10 = 15\n", "\n", "15 % 10 = 5\n", "15 // 10 = 1\n", "\n", "1 % 10 = 1\n", "1 // 10 = 0" ] }, { "cell_type": "code", "execution_count": 25, "id": "3597feec", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Enter your number: 10\n", "10\n", "\n" ] } ], "source": [ "n = input(\"Enter your number: \")\n", "\n", "print(n)\n", "\n", "print(type(n))" ] }, { "cell_type": "code", "execution_count": 26, "id": "71b967e4", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Enter your number: 10\n", "10\n", "\n" ] } ], "source": [ "n = int(input(\"Enter your number: \"))\n", "\n", "print(n)\n", "\n", "print(type(n))" ] }, { "cell_type": "code", "execution_count": 31, "id": "5759b2ec", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Enter a number: 153\n", "351" ] } ], "source": [ "n = int(input(\"Enter a number: \"))\n", "\n", "while n > 0:\n", " r = n % 10 # r = 3, 5, 1\n", " print(r, end=\"\") # 351\n", " n = n // 10 # n = 153, 15, 1, 0" ] }, { "cell_type": "markdown", "id": "adf5586f", "metadata": {}, "source": [ "**Write a program to enter a number from user and print sum of its individual digits**" ] }, { "cell_type": "code", "execution_count": null, "id": "cbd3db08", "metadata": {}, "outputs": [], "source": [ "153\n", "\n", "1 + 5 + 3\n", "\n", "9" ] }, { "cell_type": "code", "execution_count": null, "id": "1d76dd47", "metadata": {}, "outputs": [], "source": [ "0 = s\n", "3 = s + 3 = s\n", "5 = s + 5 = s\n", "1" ] }, { "cell_type": "code", "execution_count": 32, "id": "52deeb6d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Enter a number: 153\n", "Sum = 9\n" ] } ], "source": [ "n = int(input(\"Enter a number: \"))\n", "s = 0\n", "\n", "while n > 0:\n", " r = n % 10\n", " s = s + r # s = 0, 3, 8, 9\n", " n = n // 10\n", "\n", "print(\"Sum =\", s)" ] }, { "cell_type": "markdown", "id": "c2bb6d4f", "metadata": {}, "source": [ "# Day 3" ] }, { "cell_type": "markdown", "id": "f4cb0aee", "metadata": {}, "source": [ "**Write a program to reverse a number**" ] }, { "cell_type": "code", "execution_count": null, "id": "85d9cb69", "metadata": {}, "outputs": [], "source": [ "153\n", "\n", "351 - this should be a whole number" ] }, { "cell_type": "code", "execution_count": 1, "id": "f31986e9", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Enter a number: 153\n", "351\n", "702\n" ] } ], "source": [ "n = int(input(\"Enter a number: \"))\n", "s = 0\n", "\n", "while n > 0:\n", " r = n % 10 # r = 3, 5, 1\n", " s = s*10 + r # s = 0, 3, 35, 351\n", " n = n // 10 # n = 153, 15, 1, 0\n", "\n", "print(s)\n", "print(s * 2)" ] }, { "cell_type": "markdown", "id": "949c7e7d", "metadata": {}, "source": [ "**Write a program to enter a number from user and check if it palindrome or not**" ] }, { "cell_type": "code", "execution_count": null, "id": "4fbfc3b5", "metadata": {}, "outputs": [], "source": [ "121\n", "\n", "121\n", "\n", "153 = 351 - not a palindrome number" ] }, { "cell_type": "code", "execution_count": 3, "id": "4855f13b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Enter a number: 121\n", "Not a Palindrome\n" ] } ], "source": [ "n = int(input(\"Enter a number: \"))\n", "s = 0\n", "\n", "while n > 0:\n", " r = n % 10 # r = 3, 5, 1\n", " s = s*10 + r # s = 0, 3, 35, 351\n", " n = n // 10 # n = 153, 15, 1, 0\n", "\n", "if s == n:\n", " print(\"Palindrome\")\n", "else:\n", " print(\"Not a Palindrome\")" ] }, { "cell_type": "code", "execution_count": 5, "id": "90712f12", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Enter a number: 1234321\n", "Palindrome\n" ] } ], "source": [ "n = int(input(\"Enter a number: \"))\n", "temp = n\n", "s = 0\n", "\n", "while n > 0:\n", " r = n % 10 # r = 3, 5, 1\n", " s = s*10 + r # s = 0, 3, 35, 351\n", " n = n // 10 # n = 153, 15, 1, 0\n", "\n", "if s == temp:\n", " print(\"Palindrome\")\n", "else:\n", " print(\"Not a Palindrome\")" ] }, { "cell_type": "markdown", "id": "e1a01ec1", "metadata": {}, "source": [ "**Write a program to enter a number from user and check if it is armstrong or not**" ] }, { "cell_type": "code", "execution_count": null, "id": "20722f78", "metadata": {}, "outputs": [], "source": [ "153\n", "\n", "cube(1) + cube(5) + cube(3)\n", "\n", "370\n", "\n", "371" ] }, { "cell_type": "code", "execution_count": 9, "id": "50334900", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Enter a number: 371\n", "Armstrong\n" ] } ], "source": [ "n = int(input(\"Enter a number: \"))\n", "temp = n\n", "s = 0\n", "\n", "while n > 0:\n", " r = n % 10 # r = 3, 5, 1\n", " s = s + (r*r*r) # s = 0, 27, 152, 153\n", " n = n // 10 # n = 153, 15, 1, 0\n", "\n", "if s == temp:\n", " print(\"Armstrong\")\n", "else:\n", " print(\"Not an Armstrong\")" ] }, { "cell_type": "markdown", "id": "f93f4400", "metadata": {}, "source": [ "**Write a program to enter a number from user and print its individual digits on seperate line**" ] }, { "cell_type": "code", "execution_count": null, "id": "5ef97f05", "metadata": {}, "outputs": [], "source": [ "153\n", "\n", "1\n", "5\n", "3" ] }, { "cell_type": "code", "execution_count": 10, "id": "e3d62d35", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Enter a number: 153\n", "3\n", "5\n", "1\n" ] } ], "source": [ "n = int(input(\"Enter a number: \"))\n", "s = 0\n", "\n", "while n > 0:\n", " r = n % 10 # r = 3, 5, 1\n", " print(r) # s = 0, 27, 152, 153\n", " n = n // 10 # n = 153, 15, 1, 0\n" ] }, { "cell_type": "code", "execution_count": 11, "id": "a5bbc5c8", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Enter a number: 153\n", "1\n", "5\n", "3\n" ] } ], "source": [ "n = int(input(\"Enter a number: \"))\n", "s = 0\n", "\n", "while n > 0:\n", " r = n % 10 # r = 3, 5, 1\n", " s = s*10 + r # s = 0, 27, 152, 153\n", " n = n // 10 # n = 153, 15, 1, 0\n", "\n", "while s > 0:\n", " r = s % 10\n", " print(r)\n", " s = s // 10" ] }, { "cell_type": "markdown", "id": "decf806e", "metadata": {}, "source": [ "# List\n", "\n", "collection of different type of elements" ] }, { "cell_type": "code", "execution_count": null, "id": "9b33c6ed", "metadata": {}, "outputs": [], "source": [ "a = 10 # int\n", "b = 20\n", "c = 30" ] }, { "cell_type": "code", "execution_count": 12, "id": "366b6a7a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[10, 'A', 'India', 10.6]\n", "\n" ] } ], "source": [ "l = [10, \"A\", \"India\", 10.6]\n", "\n", "print(l)\n", "\n", "print(type(l))" ] }, { "cell_type": "code", "execution_count": 13, "id": "1af4f02d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10\n" ] } ], "source": [ "l = [10, \"A\", \"India\", 10.6]\n", "# 0 1 2 3\n", "\n", "print(l[0])" ] }, { "cell_type": "code", "execution_count": 14, "id": "ced31696", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10.6\n" ] } ], "source": [ "print(l[3])" ] }, { "cell_type": "code", "execution_count": 15, "id": "0c0905a0", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "India\n" ] } ], "source": [ "print(l[2])" ] }, { "cell_type": "code", "execution_count": 16, "id": "1fe65d17", "metadata": {}, "outputs": [ { "ename": "IndexError", "evalue": "list index out of range", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mIndexError\u001b[0m Traceback (most recent call last)", "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", "\u001b[1;31mIndexError\u001b[0m: list index out of range" ] } ], "source": [ "print(l[5])" ] }, { "cell_type": "code", "execution_count": 17, "id": "c21b0555", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "l = [10, \"A\", \"India\", 10.6]\n", "# 0 1 2 3\n", "\n", "print(type(l[0]))" ] }, { "cell_type": "code", "execution_count": 18, "id": "e9319c1c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[200, 'A', 'India', 10.6]\n" ] } ], "source": [ "l[0] = 200\n", "\n", "print(l)" ] }, { "cell_type": "code", "execution_count": 19, "id": "f8fe43a4", "metadata": {}, "outputs": [ { "ename": "IndexError", "evalue": "list assignment index out of range", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mIndexError\u001b[0m Traceback (most recent call last)", "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", "\u001b[1;31mIndexError\u001b[0m: list assignment index out of range" ] } ], "source": [ "l[5] = 1000" ] }, { "cell_type": "markdown", "id": "2aec1fac", "metadata": {}, "source": [ "### negative index" ] }, { "cell_type": "code", "execution_count": 20, "id": "d86147ba", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10.6\n" ] } ], "source": [ "# -4 -3 -2 -1\n", "l = [10, \"A\", \"India\", 10.6]\n", "# 0 1 2 3\n", "\n", "print(l[-1])" ] }, { "cell_type": "code", "execution_count": 21, "id": "c3288372", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10.6\n" ] } ], "source": [ "print(l[3])" ] }, { "cell_type": "code", "execution_count": 22, "id": "393bd2e7", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10\n", "10\n" ] } ], "source": [ "print(l[0])\n", "\n", "print(l[-4])" ] }, { "cell_type": "code", "execution_count": 23, "id": "07f8d2b4", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "India\n" ] } ], "source": [ "print(l[-2])" ] }, { "cell_type": "markdown", "id": "18ef7bed", "metadata": {}, "source": [ "# List Slicing" ] }, { "cell_type": "code", "execution_count": 24, "id": "f556294d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[11, 22, 33, 44, 10, 24, 36, 68, 99, 81, 100]\n" ] } ], "source": [ "# -11-10 -9 -8 -7 -6 -5 -4 -3 -2 -1\n", "l = [11,22,33,44,10,24,36,68,99,81,100]\n", "# 0 1 2 3 4 5 6 7 8 9 10\n", "\n", "print(l)" ] }, { "cell_type": "code", "execution_count": 25, "id": "61499c7e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[22, 33, 44, 10, 24, 36]\n" ] } ], "source": [ "print(l[1:7]) # l[start : end]" ] }, { "cell_type": "code", "execution_count": 26, "id": "696ceede", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[11, 22, 33, 44, 10, 24, 36, 68, 99, 81, 100]\n" ] } ], "source": [ "print(l)" ] }, { "cell_type": "code", "execution_count": 27, "id": "9c790873", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[10, 24, 36, 68, 99, 81]\n" ] } ], "source": [ "# -11-10 -9 -8 -7 -6 -5 -4 -3 -2 -1\n", "l = [11,22,33,44,10,24,36,68,99,81,100]\n", "# 0 1 2 3 4 5 6 7 8 9 10\n", "\n", "print(l[4:10])" ] }, { "cell_type": "code", "execution_count": 28, "id": "88f74cab", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[99, 81]\n" ] } ], "source": [ "print(l[8:10])" ] }, { "cell_type": "code", "execution_count": 29, "id": "59a8810d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[11]\n" ] } ], "source": [ "print(l[0:1])" ] }, { "cell_type": "code", "execution_count": 30, "id": "da404e2d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[22, 33, 44, 10, 24, 36, 68, 99, 81, 100]\n" ] } ], "source": [ "print(l[1:])" ] }, { "cell_type": "code", "execution_count": 31, "id": "411185c6", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[99, 81, 100]\n" ] } ], "source": [ "print(l[8:])" ] }, { "cell_type": "code", "execution_count": 32, "id": "f3427d62", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[11, 22, 33, 44, 10, 24, 36, 68, 99, 81, 100]\n" ] } ], "source": [ "print(l[:])" ] }, { "cell_type": "code", "execution_count": 33, "id": "8cb04435", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[11, 22, 33, 44, 10]\n" ] } ], "source": [ "print(l[:5])" ] }, { "cell_type": "code", "execution_count": 34, "id": "07b3d8c8", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[11, 22, 33, 44, 10, 24, 36]\n" ] } ], "source": [ "# -11-10 -9 -8 -7 -6 -5 -4 -3 -2 -1\n", "l = [11,22,33,44,10,24,36,68,99,81,100]\n", "# 0 1 2 3 4 5 6 7 8 9 10\n", "\n", "print(l[0:-4])" ] }, { "cell_type": "code", "execution_count": 35, "id": "6b72269e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[33, 44, 10, 24, 36, 68, 99]\n" ] } ], "source": [ "print(l[2:-2])" ] }, { "cell_type": "code", "execution_count": 36, "id": "34eb2422", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[]\n" ] } ], "source": [ "print(l[2:0])" ] }, { "cell_type": "code", "execution_count": 37, "id": "ea9e9d22", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[]\n" ] } ], "source": [ "print(l[0:0])" ] }, { "cell_type": "code", "execution_count": 38, "id": "d4d6dc2c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[24, 36, 68, 99, 81, 100]\n" ] } ], "source": [ "print(l[5:15])" ] }, { "cell_type": "code", "execution_count": 39, "id": "327f5a53", "metadata": {}, "outputs": [ { "ename": "IndexError", "evalue": "list index out of range", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mIndexError\u001b[0m Traceback (most recent call last)", "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", "\u001b[1;31mIndexError\u001b[0m: list index out of range" ] } ], "source": [ "print(l[15])" ] }, { "cell_type": "code", "execution_count": 40, "id": "1654f679", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[22, 33, 44]\n" ] } ], "source": [ "# -11-10 -9 -8 -7 -6 -5 -4 -3 -2 -1\n", "l = [11,22,33,44,10,24,36,68,99,81,100]\n", "# 0 1 2 3 4 5 6 7 8 9 10\n", "\n", "print(l[1:4])" ] }, { "cell_type": "code", "execution_count": 41, "id": "ef550430", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[]\n" ] } ], "source": [ "print(l[4:1])" ] }, { "cell_type": "code", "execution_count": 42, "id": "64a79df5", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[68, 99, 81]\n" ] } ], "source": [ "print(l[-4:-1])" ] }, { "cell_type": "code", "execution_count": 43, "id": "70fecd4e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[]\n" ] } ], "source": [ "print(l[-1:-4])" ] }, { "cell_type": "markdown", "id": "a2b17316", "metadata": {}, "source": [ "## steps" ] }, { "cell_type": "code", "execution_count": 44, "id": "e9af568f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[11, 33, 10, 36, 99, 100]\n" ] } ], "source": [ "# -11-10 -9 -8 -7 -6 -5 -4 -3 -2 -1\n", "l = [11,22,33,44,10,24,36,68,99,81,100]\n", "# 0 1 2 3 4 5 6 7 8 9 10\n", "\n", "print(l[0:11:2]) # l[start : end : steps]" ] }, { "cell_type": "code", "execution_count": 45, "id": "d0127e99", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[11, 44, 36, 81]\n" ] } ], "source": [ "print(l[0:11:3])" ] }, { "cell_type": "code", "execution_count": 46, "id": "75e4fefa", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[11, 22, 33, 44, 10, 24, 36, 68, 99, 81, 100]\n" ] } ], "source": [ "print(l[::])" ] }, { "cell_type": "code", "execution_count": 47, "id": "3449b6af", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[11, 22, 33, 44, 10, 24, 36, 68, 99, 81, 100]\n" ] } ], "source": [ "print(l[::1])" ] }, { "cell_type": "code", "execution_count": 48, "id": "2e6345c0", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[100, 81, 99, 68, 36, 24, 10, 44, 33, 22, 11]\n" ] } ], "source": [ "print(l[::-1]) # reverse" ] }, { "cell_type": "code", "execution_count": 49, "id": "f284f488", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[]\n" ] } ], "source": [ "# -11-10 -9 -8 -7 -6 -5 -4 -3 -2 -1\n", "l = [11,22,33,44,10,24,36,68,99,81,100]\n", "# 0 1 2 3 4 5 6 7 8 9 10\n", "\n", "print(l[4:8:-1])" ] }, { "cell_type": "code", "execution_count": 50, "id": "1f16a88d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[99, 68, 36, 24]\n" ] } ], "source": [ "print(l[8:4:-1])" ] }, { "cell_type": "code", "execution_count": 51, "id": "11575dee", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[100, 99, 36, 10, 33, 11]\n" ] } ], "source": [ "print(l[::-2])" ] }, { "cell_type": "code", "execution_count": 52, "id": "1a639620", "metadata": {}, "outputs": [ { "ename": "ValueError", "evalue": "slice step cannot be zero", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", "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", "\u001b[1;31mValueError\u001b[0m: slice step cannot be zero" ] } ], "source": [ "print(l[::0])" ] }, { "cell_type": "markdown", "id": "20862888", "metadata": {}, "source": [ "## list functions" ] }, { "cell_type": "code", "execution_count": 53, "id": "8aa952c9", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "5\n" ] } ], "source": [ "l = [10,20,30,40,50]\n", "\n", "print(len(l))" ] }, { "cell_type": "code", "execution_count": 54, "id": "74856061", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "4\n" ] } ], "source": [ "print(len(l) - 1)" ] }, { "cell_type": "code", "execution_count": 55, "id": "250b458e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "150\n" ] } ], "source": [ "print(sum(l))" ] }, { "cell_type": "code", "execution_count": 56, "id": "7ea78614", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "50\n" ] } ], "source": [ "print(max(l))" ] }, { "cell_type": "code", "execution_count": 57, "id": "bad8aadc", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10\n" ] } ], "source": [ "print(min(l))" ] }, { "cell_type": "code", "execution_count": 58, "id": "27a21618", "metadata": {}, "outputs": [ { "ename": "TypeError", "evalue": "unsupported operand type(s) for +: 'int' and 'str'", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", "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", "\u001b[1;31mTypeError\u001b[0m: unsupported operand type(s) for +: 'int' and 'str'" ] } ], "source": [ "l = [\"A\", 10,20,30]\n", "\n", "print(sum(l))" ] }, { "cell_type": "code", "execution_count": 59, "id": "a176b651", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "4\n" ] } ], "source": [ "print(len(l))" ] }, { "cell_type": "markdown", "id": "1ee80d7b", "metadata": {}, "source": [ "# List methods" ] }, { "cell_type": "code", "execution_count": 61, "id": "e802bef5", "metadata": {}, "outputs": [], "source": [ "# help(print)" ] }, { "cell_type": "code", "execution_count": 63, "id": "5a500f31", "metadata": { "scrolled": false }, "outputs": [], "source": [ "# help(list)" ] }, { "cell_type": "code", "execution_count": 64, "id": "0538c1e8", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[10, 20, 30, 40]\n" ] } ], "source": [ "l = [10,20,30]\n", "\n", "l.append(40)\n", "\n", "print(l)" ] }, { "cell_type": "code", "execution_count": 65, "id": "ed712c2b", "metadata": {}, "outputs": [], "source": [ "l.append(\"India\")" ] }, { "cell_type": "code", "execution_count": 66, "id": "265b8be2", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[10, 20, 30, 40, 'India']\n" ] } ], "source": [ "print(l)" ] }, { "cell_type": "code", "execution_count": 67, "id": "ea15af1f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "4\n" ] } ], "source": [ "print(l.index('India'))" ] }, { "cell_type": "code", "execution_count": 68, "id": "c422ce83", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0\n" ] } ], "source": [ "print(l.index(10))" ] }, { "cell_type": "code", "execution_count": 69, "id": "0e88a9f3", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[10, 20, 30, 40, 'India']\n" ] } ], "source": [ "print(l)" ] }, { "cell_type": "code", "execution_count": 72, "id": "e3042d8a", "metadata": {}, "outputs": [], "source": [ "l.reverse()" ] }, { "cell_type": "code", "execution_count": 73, "id": "02a28ba4", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[10, 20, 30, 40, 'India']\n" ] } ], "source": [ "print(l)" ] }, { "cell_type": "code", "execution_count": 74, "id": "7c861f47", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['India', 40, 30, 20, 10]\n" ] } ], "source": [ "print(l[::-1])" ] }, { "cell_type": "code", "execution_count": 75, "id": "e1210f8f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[10, 20, 30, 40, 'India']\n" ] } ], "source": [ "print(l)" ] }, { "cell_type": "code", "execution_count": null, "id": "ef9c13af", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.12" } }, "nbformat": 4, "nbformat_minor": 5 } ================================================ FILE: July Cohort 2023/1_Python Programming/04_Strings and For loop.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "id": "89086545", "metadata": {}, "source": [ "# Strings\n", "\n", "set of characters\n" ] }, { "cell_type": "code", "execution_count": 1, "id": "663dd2fc", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "hello\n" ] } ], "source": [ "# -1\n", "s = 'hello'\n", "# 01234\n", "\n", "print(s)" ] }, { "cell_type": "code", "execution_count": 2, "id": "d1f891e2", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "h\n" ] } ], "source": [ "print(s[0])" ] }, { "cell_type": "code", "execution_count": 3, "id": "549cabfa", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "o\n" ] } ], "source": [ "print(s[4])" ] }, { "cell_type": "code", "execution_count": 4, "id": "e9b26f8e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "o\n" ] } ], "source": [ "print(s[-1])" ] }, { "cell_type": "code", "execution_count": 5, "id": "bef31594", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "l\n" ] } ], "source": [ "print(s[-2])" ] }, { "cell_type": "code", "execution_count": 6, "id": "00f3823f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "print(type(s))" ] }, { "cell_type": "code", "execution_count": 7, "id": "7326e7a9", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "print(type(s[0]))" ] }, { "cell_type": "markdown", "id": "4899cd30", "metadata": {}, "source": [ "# string slicing" ] }, { "cell_type": "code", "execution_count": 8, "id": "12c56ed2", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "commu\n" ] } ], "source": [ "s = 'communication'\n", "# 0123456789\n", "\n", "print(s[0:5])" ] }, { "cell_type": "code", "execution_count": 9, "id": "781ea5cd", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "noitacinummoc\n" ] } ], "source": [ "print(s[::-1])" ] }, { "cell_type": "code", "execution_count": 10, "id": "90aa93d3", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "omm\n" ] } ], "source": [ "print(s[1:4])" ] }, { "cell_type": "code", "execution_count": 11, "id": "5c24d8b3", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "s = 'communication'\n", "# 0123456789\n", "\n", "print(s[4:1])" ] }, { "cell_type": "code", "execution_count": 12, "id": "a8f6af2e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "s = ''\n", "\n", "print(type(s))" ] }, { "cell_type": "code", "execution_count": 13, "id": "2d3570c7", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "noitacin\n" ] } ], "source": [ "s = 'communication'\n", "# 0123456789\n", "\n", "print(s[-1:-9:-1])" ] }, { "cell_type": "markdown", "id": "ef70cbf4", "metadata": {}, "source": [ "# String Functions" ] }, { "cell_type": "code", "execution_count": 14, "id": "f12266cf", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "13\n" ] } ], "source": [ "s = 'communication'\n", "\n", "print(len(s))" ] }, { "cell_type": "code", "execution_count": 15, "id": "50320c72", "metadata": {}, "outputs": [ { "ename": "TypeError", "evalue": "unsupported operand type(s) for +: 'int' and 'str'", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", "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", "\u001b[1;31mTypeError\u001b[0m: unsupported operand type(s) for +: 'int' and 'str'" ] } ], "source": [ "print(sum(s)) # s = 0" ] }, { "cell_type": "code", "execution_count": 16, "id": "55b002c3", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "u\n" ] } ], "source": [ "print(max(s))" ] }, { "cell_type": "code", "execution_count": 17, "id": "7ccced34", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "a\n" ] } ], "source": [ "print(min(s))" ] }, { "cell_type": "code", "execution_count": null, "id": "8b30e14f", "metadata": {}, "outputs": [], "source": [ "# ASCII\n", "\n", "'A' -> 65\n", "'B' -> 66\n", "'Z' -> 90\n", "\n", "'a' -> 97\n", "'z' -> 122\n", "\n", "' ' -> 32 -> space" ] }, { "cell_type": "markdown", "id": "afa17626", "metadata": {}, "source": [ "## string methods\n" ] }, { "cell_type": "code", "execution_count": 19, "id": "e3ef73fb", "metadata": { "scrolled": true }, "outputs": [], "source": [ "# help(str)" ] }, { "cell_type": "code", "execution_count": 20, "id": "53f436a8", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "False" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "'raj123' == 'RAJ123'" ] }, { "cell_type": "code", "execution_count": 21, "id": "9a9f840d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "raj123\n" ] } ], "source": [ "s = 'RAJ123'\n", "\n", "print(s.casefold())" ] }, { "cell_type": "code", "execution_count": 22, "id": "4e6cd479", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "INDIA\n" ] } ], "source": [ "s = 'India'\n", "\n", "print(s.upper())" ] }, { "cell_type": "code", "execution_count": 23, "id": "76b04479", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "india\n" ] } ], "source": [ "print(s.lower())" ] }, { "cell_type": "code", "execution_count": 24, "id": "2e8f43e5", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['This', 'is', 'an', 'apple']\n" ] } ], "source": [ "# split\n", "\n", "s = 'This is an apple'\n", "\n", "print(s.split(\" \"))" ] }, { "cell_type": "code", "execution_count": 25, "id": "94237ec7", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['H', 'llo Us', 'r']\n" ] } ], "source": [ "s = \"Hello User\"\n", "\n", "print(s.split(\"e\"))" ] }, { "cell_type": "code", "execution_count": 26, "id": "42a71402", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "This+is+an+apple\n" ] } ], "source": [ "# join\n", "\n", "l = ['This', 'is', 'an', 'apple']\n", "\n", "a = \"+\"\n", "\n", "print(a.join(l))" ] }, { "cell_type": "code", "execution_count": 27, "id": "6ef43d06", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "This is an apple\n" ] } ], "source": [ "# join\n", "\n", "l = ['This', 'is', 'an', 'apple']\n", "\n", "a = \" \"\n", "\n", "print(a.join(l))\n", "# \" \".join(l)" ] }, { "cell_type": "code", "execution_count": 28, "id": "dae0d39a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "ThisisanApple\n" ] } ], "source": [ "# remove all the spaces from the string\n", "\n", "s = \"This is an Apple\"\n", "\n", "l = s.split(\" \")\n", "\n", "x = \"\".join(l)\n", "\n", "print(x)" ] }, { "cell_type": "markdown", "id": "5e18c7bc", "metadata": {}, "source": [ "**Write a program to enter a string from user and print the biggest word from the para**" ] }, { "cell_type": "code", "execution_count": null, "id": "fc9ffad3", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "id": "08b8681d", "metadata": {}, "source": [ "# range function" ] }, { "cell_type": "code", "execution_count": 29, "id": "cd95ff5d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "range(0, 5)\n" ] } ], "source": [ "n = range(5) # this will create a series of numbers from 0 to 4\n", "\n", "print(n)" ] }, { "cell_type": "code", "execution_count": 30, "id": "5faa24f2", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "print(type(n))" ] }, { "cell_type": "code", "execution_count": 31, "id": "b9b1b6fa", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0, 1, 2, 3, 4]\n" ] } ], "source": [ "n = list(range(5))\n", "\n", "print(n)" ] }, { "cell_type": "code", "execution_count": 32, "id": "00bf5fc9", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[3, 4, 5, 6, 7]\n" ] } ], "source": [ "n = list(range(3,8))\n", "\n", "print(n)" ] }, { "cell_type": "code", "execution_count": 33, "id": "85f51f76", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "True\n" ] } ], "source": [ "print(range(20) == range(0,20))" ] }, { "cell_type": "code", "execution_count": 34, "id": "cf56e7cc", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[5, 7, 9, 11, 13, 15, 17, 19]\n" ] } ], "source": [ "n = list(range(5,20,2)) # range(start, end, steps)\n", "\n", "print(n)" ] }, { "cell_type": "markdown", "id": "9a9e11b2", "metadata": {}, "source": [ "## for loop" ] }, { "cell_type": "code", "execution_count": 35, "id": "eb461d52", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "False\n" ] } ], "source": [ "l = [\"A\", \"B\", \"C\", \"D\"]\n", "\n", "print(\"Z\" in l)" ] }, { "cell_type": "code", "execution_count": 36, "id": "f3d37b0a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "A\n", "B\n", "C\n", "D\n" ] } ], "source": [ "l = [\"A\", \"B\", \"C\", \"D\"]\n", "# i\n", "\n", "# \"B\" in l\n", "for i in l:\n", " print(i)" ] }, { "cell_type": "code", "execution_count": 37, "id": "15a3f0ba", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "A\n", "Hi\n", "B\n", "Hi\n", "C\n", "Hi\n", "D\n", "Hi\n" ] } ], "source": [ "l = [\"A\", \"B\", \"C\", \"D\"]\n", "# i\n", "\n", "# \"B\" in l\n", "for i in l:\n", " print(i)\n", " print(\"Hi\")" ] }, { "cell_type": "code", "execution_count": 40, "id": "59f4f57f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "a\n", "b\n", "c\n", "d\n" ] } ], "source": [ "s = 'abcd'\n", "\n", "for i in s:\n", " print(i)" ] }, { "cell_type": "code", "execution_count": 42, "id": "be302d25", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "AB\n", "ab\n" ] } ], "source": [ "x = 'ab'\n", "\n", "print(x.upper())\n", "\n", "print(x)" ] }, { "cell_type": "code", "execution_count": 43, "id": "b3a22f15", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['ab', 'cd']\n" ] } ], "source": [ "x = ['ab', 'cd']\n", "\n", "for i in x:\n", " i.upper() # 'ab'.upper() -> 'AB'\n", " \n", "print(x)" ] }, { "cell_type": "code", "execution_count": 44, "id": "4e0873d9", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "AB\n", "CD\n", "['ab', 'cd']\n" ] } ], "source": [ "x = ['ab', 'cd']\n", "\n", "for i in x:\n", " print(i.upper()) # 'ab'.upper() -> 'AB'\n", " \n", "print(x)" ] }, { "cell_type": "code", "execution_count": 45, "id": "77e96be5", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "a\n", "b\n", "c\n", "d\n" ] } ], "source": [ "x = 'abcd'\n", "\n", "for i in x:\n", " print(i)\n", " x.upper()\n" ] }, { "cell_type": "code", "execution_count": 46, "id": "d6c07e4d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0\n", "1\n", "2\n", "3\n", "4\n" ] } ], "source": [ "# i\n", "# 0,1,2,3,4\n", "for i in range(5):\n", " print(i)" ] }, { "cell_type": "code", "execution_count": 47, "id": "bba46637", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "5\n", "6\n", "7\n", "8\n", "9\n" ] } ], "source": [ "for i in range(5,10):\n", " print(i)" ] }, { "cell_type": "code", "execution_count": 48, "id": "fdb6483c", "metadata": {}, "outputs": [ { "ename": "TypeError", "evalue": "'str' object cannot be interpreted as an integer", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", "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", "\u001b[1;31mTypeError\u001b[0m: 'str' object cannot be interpreted as an integer" ] } ], "source": [ "x = 'abcd'\n", "\n", "for i in range(x):\n", " print(i)" ] }, { "cell_type": "code", "execution_count": 49, "id": "dac1b7e1", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0\n", "1\n", "2\n", "3\n" ] } ], "source": [ "x = 'abcd'\n", "\n", "# 0,1,2,3\n", "for i in range(len(x)):\n", " print(i)" ] }, { "cell_type": "code", "execution_count": 50, "id": "ce020a17", "metadata": {}, "outputs": [ { "ename": "AttributeError", "evalue": "'int' object has no attribute 'upper'", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)", "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", "\u001b[1;31mAttributeError\u001b[0m: 'int' object has no attribute 'upper'" ] } ], "source": [ "x = 'abcd'\n", "\n", "# 0,1,2,3\n", "for i in range(len(x)):\n", " print(i.upper())" ] }, { "cell_type": "code", "execution_count": 51, "id": "e4b348f0", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "abcd\n" ] } ], "source": [ "x = 'abcd'\n", "\n", "# 0,1,2,3\n", "for i in range(len(x)):\n", " x[i].upper()\n", "\n", "print(x)" ] }, { "cell_type": "code", "execution_count": 52, "id": "e4afa49b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "A\n", "B\n", "C\n", "D\n", "abcd\n" ] } ], "source": [ "x = 'abcd'\n", "\n", "# 0,1,2,3\n", "for i in range(len(x)):\n", " print(x[i].upper())\n", "\n", "print(x)" ] }, { "cell_type": "code", "execution_count": 53, "id": "9d691550", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "a\n", "a\n", "a\n", "a\n" ] } ], "source": [ "x = 'abcd'\n", "\n", "# 0,1,2,3\n", "for i in range(len(x)):\n", " x = 'a' # x = 'abcd', 'a', 'a'\n", " print(x)" ] }, { "cell_type": "code", "execution_count": null, "id": "9fe9971b", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.12" } }, "nbformat": 4, "nbformat_minor": 5 } ================================================ FILE: July Cohort 2023/1_Python Programming/05_Dictionary Tuples and Set.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "id": "0d853ca0", "metadata": {}, "source": [ "# Dictionary" ] }, { "cell_type": "code", "execution_count": null, "id": "28ba2c85", "metadata": {}, "outputs": [], "source": [ "# { key : value }\n", "\n", "unordered data structures" ] }, { "cell_type": "code", "execution_count": 1, "id": "de03adfa", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'ravi': 24, 'shyam': 21, 'roy': 41}\n" ] } ], "source": [ "ages = {\"ravi\":24, \"shyam\": 21, \"roy\": 41}\n", "\n", "print(ages)" ] }, { "cell_type": "code", "execution_count": 2, "id": "1507411b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "print(type(ages))" ] }, { "cell_type": "code", "execution_count": 3, "id": "3a6e0c6d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "24\n" ] } ], "source": [ "print(ages['ravi'])" ] }, { "cell_type": "code", "execution_count": 4, "id": "9c495fe7", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "41\n" ] } ], "source": [ "print(ages['roy'])" ] }, { "cell_type": "code", "execution_count": 5, "id": "dc9434fb", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[255, 0, 0]\n" ] } ], "source": [ "color = {\n", " \"red\": [255,0,0],\n", " \"green\": [0,255,0],\n", " \"blue\": [0,0,255]\n", "}\n", "print(color['red'])" ] }, { "cell_type": "code", "execution_count": 7, "id": "28854643", "metadata": {}, "outputs": [ { "ename": "KeyError", "evalue": "'yellow'", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", "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", "\u001b[1;31mKeyError\u001b[0m: 'yellow'" ] } ], "source": [ "print(color['yellow'])" ] }, { "cell_type": "code", "execution_count": 8, "id": "f12d89ad", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{}\n", "\n" ] } ], "source": [ "d = {}\n", "print(d)\n", "print(type(d))" ] }, { "cell_type": "code", "execution_count": 9, "id": "0d0ceafe", "metadata": {}, "outputs": [ { "ename": "TypeError", "evalue": "unhashable type: 'list'", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", "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", "\u001b[1;31mTypeError\u001b[0m: unhashable type: 'list'" ] } ], "source": [ "d = {[1,2]:10, 2:2, 3:3}\n", "print(d)" ] }, { "cell_type": "code", "execution_count": 10, "id": "8a6b2ca9", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[100, 20]\n" ] } ], "source": [ "# list are mutable objects\n", "\n", "l = [10,20]\n", "l[0] = 100\n", "print(l)" ] }, { "cell_type": "code", "execution_count": 11, "id": "bc14edbb", "metadata": {}, "outputs": [ { "ename": "TypeError", "evalue": "'str' object does not support item assignment", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", "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", "\u001b[1;31mTypeError\u001b[0m: 'str' object does not support item assignment" ] } ], "source": [ "# strings are immutable objects\n", "\n", "s = 'indore'\n", "s[0] = \"A\"\n", "print(s)" ] }, { "cell_type": "markdown", "id": "498704a8", "metadata": {}, "source": [ "# Dictionary Functions" ] }, { "cell_type": "code", "execution_count": 12, "id": "a88930aa", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "4\n" ] } ], "source": [ "d = {1:11, 2:41, 13:9, 14:16}\n", "\n", "print(len(d))" ] }, { "cell_type": "code", "execution_count": 13, "id": "71aca243", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "30\n" ] } ], "source": [ "print(sum(d))" ] }, { "cell_type": "code", "execution_count": 14, "id": "a9c23606", "metadata": {}, "outputs": [ { "ename": "TypeError", "evalue": "unsupported operand type(s) for +: 'int' and 'str'", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", "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", "\u001b[1;31mTypeError\u001b[0m: unsupported operand type(s) for +: 'int' and 'str'" ] } ], "source": [ "a = {1:2, 3:4, \"hi\":16}\n", "\n", "print(sum(a))" ] }, { "cell_type": "markdown", "id": "6afd220b", "metadata": {}, "source": [ "# Dictionary Methods" ] }, { "cell_type": "code", "execution_count": 15, "id": "4d319b95", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{1: 'Apple', 'orange': [2, 3, 4], '2': '23'}\n" ] } ], "source": [ "pair = {1:\"Apple\", \"orange\": [2,3,4], \"2\":\"23\"}\n", "\n", "print(pair)" ] }, { "cell_type": "code", "execution_count": 18, "id": "b53ea8e9", "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[2, 3, 4]\n" ] } ], "source": [ "# get\n", "# help(dict)\n", "\n", "print(pair.get(\"orange\"))" ] }, { "cell_type": "code", "execution_count": 19, "id": "e3b011f8", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "None\n" ] } ], "source": [ "print(pair.get(7))" ] }, { "cell_type": "code", "execution_count": null, "id": "80c5249a", "metadata": {}, "outputs": [], "source": [ "None ==> NULL ==> nothing" ] }, { "cell_type": "code", "execution_count": 20, "id": "8ece5b51", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Apple\n" ] } ], "source": [ "print(pair.get(1,\"Element not found\"))" ] }, { "cell_type": "code", "execution_count": 21, "id": "af462093", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Element not found\n" ] } ], "source": [ "print(pair.get(10,\"Element not found\"))" ] }, { "cell_type": "code", "execution_count": 23, "id": "61e47d6e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{1: 'Apple', 'orange': [2, 3, 4], '2': '23'}\n", "{}\n" ] } ], "source": [ "# clear\n", "\n", "p = {1:\"Apple\", \"orange\": [2,3,4], \"2\":\"23\"}\n", "\n", "print(p)\n", "p.clear()\n", "print(p)" ] }, { "cell_type": "code", "execution_count": 24, "id": "bcf864f7", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "dict_keys([1, 2, 3])\n" ] } ], "source": [ "# keys\n", "\n", "r = {1:\"A\", 2:'Zara', 3:\"Maza\"}\n", "\n", "print(r.keys())" ] }, { "cell_type": "code", "execution_count": 25, "id": "4d8f44be", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1, 2, 3]\n" ] } ], "source": [ "print(list(r.keys()))" ] }, { "cell_type": "code", "execution_count": 26, "id": "13318e6e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "dict_values(['A', 'Zara', 'Maza'])\n" ] } ], "source": [ "# values\n", "\n", "r = {1:\"A\", 2:'Zara', 3:\"Maza\"}\n", "\n", "print(r.values())" ] }, { "cell_type": "code", "execution_count": 27, "id": "e29dc425", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['A', 'Zara', 'Maza']\n" ] } ], "source": [ "print(list(r.values()))" ] }, { "cell_type": "code", "execution_count": 28, "id": "4e7dbf29", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{3: 'Maza', 2: 'Zara'}\n", "2\n" ] } ], "source": [ "r = {3:\"A\", 2:'Zara', 3:\"Maza\"}\n", "\n", "print(r)\n", "\n", "print(len(r))" ] }, { "cell_type": "markdown", "id": "ec79a1b8", "metadata": {}, "source": [ "# Question\n", "\n", "print the following series" ] }, { "cell_type": "code", "execution_count": null, "id": "e205896b", "metadata": {}, "outputs": [], "source": [ "{1:1, 2:4, 3:9, 4:16, .........10:100}" ] }, { "cell_type": "code", "execution_count": 29, "id": "5abc247e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{1: 2, 3: 'Hi'}\n" ] } ], "source": [ "a = {1:2}\n", "a[3] = \"Hi\"\n", "print(a)" ] }, { "cell_type": "code", "execution_count": 30, "id": "7aea4934", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{1: 1, 2: 4, 3: 9, 4: 16, 5: 25, 6: 36, 7: 49, 8: 64, 9: 81, 10: 100}\n" ] } ], "source": [ "d = {}\n", "\n", "for i in range(1,11):\n", " d[i] = i * i\n", "\n", "print(d)" ] }, { "cell_type": "markdown", "id": "323a152e", "metadata": {}, "source": [ "# Tuples" ] }, { "cell_type": "code", "execution_count": 31, "id": "5a6a2fc7", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(4, 3, 5, 6)\n", "\n" ] } ], "source": [ "t = (4,3,5,6)\n", "# 0 1 2 3\n", "\n", "print(t)\n", "\n", "print(type(t))" ] }, { "cell_type": "code", "execution_count": 32, "id": "d5b5852c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "4\n" ] } ], "source": [ "print(t[0])" ] }, { "cell_type": "code", "execution_count": 33, "id": "519de1e2", "metadata": {}, "outputs": [ { "ename": "TypeError", "evalue": "'tuple' object does not support item assignment", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", "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", "\u001b[1;31mTypeError\u001b[0m: 'tuple' object does not support item assignment" ] } ], "source": [ "t[0] = 11 # tuple are immutable objects" ] }, { "cell_type": "code", "execution_count": 34, "id": "d44d0e73", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "('one', 'two', 'three')\n" ] } ], "source": [ "t = \"one\", \"two\", \"three\"\n", "\n", "print(t)" ] }, { "cell_type": "code", "execution_count": 35, "id": "85ca1c88", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "print(type(t))" ] }, { "cell_type": "code", "execution_count": 36, "id": "88548f84", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10\n", "\n" ] } ], "source": [ "t = (10)\n", "print(t)\n", "print(type(t))" ] }, { "cell_type": "code", "execution_count": 37, "id": "0605b55a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(10,)\n", "\n" ] } ], "source": [ "t = (10,)\n", "print(t)\n", "print(type(t))" ] }, { "cell_type": "markdown", "id": "954aa392", "metadata": {}, "source": [ "# Tuple unpacking" ] }, { "cell_type": "code", "execution_count": 38, "id": "a77b3c9c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1\n", "2\n", "3\n" ] } ], "source": [ "n = (1,2,3)\n", "\n", "a,b,c = n\n", "\n", "print(a)\n", "print(b)\n", "print(c)" ] }, { "cell_type": "markdown", "id": "bda16a06", "metadata": {}, "source": [ "# Tuple Slicing" ] }, { "cell_type": "code", "execution_count": 39, "id": "1c2a8096", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(11, 22, 33, 44, 55, 66, 77, 88, 99, 100)\n" ] } ], "source": [ "# -1\n", "t = (11,22,33,44,55,66,77,88,99,100)\n", "# 0 1 \n", "\n", "print(t)" ] }, { "cell_type": "code", "execution_count": 40, "id": "14c17710", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(44, 55)\n" ] } ], "source": [ "print(t[3:5])" ] }, { "cell_type": "code", "execution_count": 41, "id": "febf2d84", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(11, 22, 33, 44, 55, 66)\n" ] } ], "source": [ "print(t[:6])" ] }, { "cell_type": "code", "execution_count": 42, "id": "e98f6a1e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(33, 44, 55, 66)\n" ] } ], "source": [ "print(t[-8:-4])" ] }, { "cell_type": "code", "execution_count": 43, "id": "c1de1350", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "()\n" ] } ], "source": [ "print(t[-4:-8])" ] }, { "cell_type": "code", "execution_count": 44, "id": "3f3523ca", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(100, 99, 88, 77, 66, 55, 44, 33, 22, 11)\n" ] } ], "source": [ "print(t[::-1])" ] }, { "cell_type": "markdown", "id": "9119181e", "metadata": {}, "source": [ "# Tuple Methods" ] }, { "cell_type": "code", "execution_count": 45, "id": "da140ca3", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "3\n" ] } ], "source": [ "# count\n", "\n", "t = (2,3,4,5,5,6,6,4,4,)\n", "\n", "print(t.count(4))\n" ] }, { "cell_type": "code", "execution_count": 46, "id": "c35b8ce9", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2\n" ] } ], "source": [ "# index\n", "\n", "t = (2,3,4,5,5,6,6,4,4,)\n", "\n", "print(t.index(4))" ] }, { "cell_type": "code", "execution_count": 47, "id": "1e8caefa", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Help on built-in function index:\n", "\n", "index(value, start=0, stop=9223372036854775807, /) method of builtins.tuple instance\n", " Return first index of value.\n", " \n", " Raises ValueError if the value is not present.\n", "\n" ] } ], "source": [ "help(t.index)" ] }, { "cell_type": "code", "execution_count": 48, "id": "15dafa64", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "7\n" ] } ], "source": [ "# index\n", "\n", "t = (2,3,4,5,5,6,6,4,4,)\n", "\n", "print(t.index(4, 5))" ] }, { "cell_type": "markdown", "id": "a9ef4027", "metadata": {}, "source": [ "# Tuple functions" ] }, { "cell_type": "code", "execution_count": 49, "id": "22c1684e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "5\n" ] } ], "source": [ "t = (2,3,4,5,6)\n", "\n", "print(len(t))" ] }, { "cell_type": "code", "execution_count": 50, "id": "9f1bfd43", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "20\n" ] } ], "source": [ "print(sum(t))" ] }, { "cell_type": "code", "execution_count": 51, "id": "89a4ca0e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "6\n" ] } ], "source": [ "print(max(t))" ] }, { "cell_type": "markdown", "id": "505a27fe", "metadata": {}, "source": [ "# Question" ] }, { "cell_type": "code", "execution_count": null, "id": "f36b7876", "metadata": {}, "outputs": [], "source": [ "# remove i from this tuple\n", "\n", "t = ('a', 'e','i', 'o', 'u')" ] }, { "cell_type": "markdown", "id": "90775705", "metadata": {}, "source": [ "# Sets\n", "\n", "unordered and unindex\n", "\n", "sets are mutable" ] }, { "cell_type": "code", "execution_count": 52, "id": "7ee80027", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{1, 2, 3, 4, 5, 6}\n", "\n" ] } ], "source": [ "n = {1,2,3,4,5,6,6,6}\n", "\n", "print(n)\n", "\n", "print(type(n))" ] }, { "cell_type": "code", "execution_count": 53, "id": "6fef6ded", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "s = set()\n", "\n", "print(type(s))" ] }, { "cell_type": "code", "execution_count": 54, "id": "a23d23ac", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{10, 'a', 'b'}\n" ] } ], "source": [ "n = set(['a','b',10])\n", "print(n)" ] }, { "cell_type": "code", "execution_count": 55, "id": "1a660809", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "True\n" ] } ], "source": [ "s = ['a','b',10]\n", "\n", "print(10 in s)" ] }, { "cell_type": "code", "execution_count": 56, "id": "6997ba95", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10\n", "a\n", "b\n" ] } ], "source": [ "for i in n:\n", " print(i)" ] }, { "cell_type": "markdown", "id": "1f38bc03", "metadata": {}, "source": [ "# Set operations\n", "\n", "#### union" ] }, { "cell_type": "code", "execution_count": 57, "id": "cde829c8", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{1, 2, 3, 4, 5, 6, 7, 8, 9}\n" ] } ], "source": [ "f = {1,2,3,4,5,6}\n", "s = {4,5,6,7,8,9}\n", "\n", "print(f | s) # union" ] }, { "cell_type": "markdown", "id": "4f9ef43c", "metadata": {}, "source": [ "#### intersection" ] }, { "cell_type": "code", "execution_count": 58, "id": "58b28ca7", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{4, 5, 6}\n" ] } ], "source": [ "f = {1,2,3,4,5,6}\n", "s = {4,5,6,7,8,9}\n", "\n", "print(f & s) # intersection" ] }, { "cell_type": "markdown", "id": "69c8e095", "metadata": {}, "source": [ "#### difference" ] }, { "cell_type": "code", "execution_count": 59, "id": "81b857d2", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{1, 2, 3}\n" ] } ], "source": [ "f = {1,2,3,4,5,6}\n", "s = {4,5,6,7,8,9}\n", "\n", "print(f - s) # difference" ] }, { "cell_type": "code", "execution_count": 60, "id": "d9eaa143", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{8, 9, 7}\n" ] } ], "source": [ "f = {1,2,3,4,5,6}\n", "s = {4,5,6,7,8,9}\n", "\n", "print(s - f) # difference" ] }, { "cell_type": "markdown", "id": "17db78c6", "metadata": {}, "source": [ "#### symmetric" ] }, { "cell_type": "code", "execution_count": 61, "id": "0200b5d0", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{1, 2, 3, 7, 8, 9}\n" ] } ], "source": [ "f = {1,2,3,4,5,6}\n", "s = {4,5,6,7,8,9}\n", "\n", "print(f ^ s) # symmetric" ] }, { "cell_type": "markdown", "id": "64ddb35d", "metadata": {}, "source": [ "# set functions" ] }, { "cell_type": "code", "execution_count": 62, "id": "000b5af4", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "6\n", "21\n", "6\n" ] } ], "source": [ "s = {1,2,3,4,5,6}\n", "\n", "print(len(s))\n", "print(sum(s))\n", "print(max(s))" ] }, { "cell_type": "markdown", "id": "d07deba1", "metadata": {}, "source": [ "# set methods\n", "\n", "#### add" ] }, { "cell_type": "code", "execution_count": 66, "id": "784b2205", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{1, 2, 3, 4, 5, 6}\n", "{1, 2, 3, 4, 5, 6, -7}\n" ] } ], "source": [ "n = {1,2,1,3,4,5,6}\n", "\n", "print(n)\n", "\n", "n.add(-7)\n", "\n", "print(n)" ] }, { "cell_type": "markdown", "id": "e78cb535", "metadata": {}, "source": [ "#### clear" ] }, { "cell_type": "code", "execution_count": 67, "id": "c948c4ff", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{1, 2, 3, 4, 5, 6, -7}\n", "set()\n" ] } ], "source": [ "print(n)\n", "n.clear()\n", "print(n)" ] }, { "cell_type": "code", "execution_count": 68, "id": "a5ac2b2e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{1, 4, 5, 6}\n" ] } ], "source": [ "s = {1,2,3,4,5,6}\n", "\n", "s.remove(3)\n", "s.remove(2)\n", "\n", "print(s)" ] }, { "cell_type": "code", "execution_count": null, "id": "5fdb4232", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.12" } }, "nbformat": 4, "nbformat_minor": 5 } ================================================ FILE: March Cohort 2023/.ipynb_checkpoints/0_Python Basics-checkpoint.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "id": "1dd9932f", "metadata": {}, "source": [ "# Python Basics\n", "\n", "we are working on print function in python" ] }, { "cell_type": "code", "execution_count": 1, "id": "3ca45a0a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Hello World\n" ] } ], "source": [ "print(\"Hello World\")" ] }, { "cell_type": "code", "execution_count": 2, "id": "e2a86f94", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Hello\n", "World\n" ] } ], "source": [ "print(\"Hello\")\n", "print(\"World\")" ] }, { "cell_type": "code", "execution_count": 3, "id": "07db254e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Hello\n", "World\n" ] } ], "source": [ "print(\"Hello\\nWorld\") # \\n -> new line character" ] }, { "cell_type": "code", "execution_count": 4, "id": "46c947b9", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Hello\tWorld\n" ] } ], "source": [ "print(\"Hello\\tWorld\") # \\t -> horizontal tab" ] }, { "cell_type": "code", "execution_count": 5, "id": "fd07c92b", "metadata": {}, "outputs": [], "source": [ "# 8 spaces - \\t" ] }, { "cell_type": "code", "execution_count": null, "id": "960ceae0", "metadata": {}, "outputs": [], "source": [ "# binary language -> 1010110\n", "\n", "0 -> low voltage, no-charge\n", "\n", "1 -> high voltage, charge\n" ] }, { "cell_type": "code", "execution_count": null, "id": "10a75868", "metadata": {}, "outputs": [], "source": [ "bits > numbers > character > instructions > program > software" ] }, { "cell_type": "code", "execution_count": null, "id": "459a661b", "metadata": {}, "outputs": [], "source": [ "compiler\n", "\n", "interpreter -> python" ] }, { "cell_type": "code", "execution_count": null, "id": "991396e2", "metadata": {}, "outputs": [], "source": [ "source code -------> Byte code -----> Output\n", " .c compile .bak Run .exe\n", " .cpp .obj\n", " .java .javac\n", " .py .pyc" ] }, { "cell_type": "markdown", "id": "9de76aa4", "metadata": {}, "source": [ "# Variables" ] }, { "cell_type": "code", "execution_count": 6, "id": "ca83f18c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10\n" ] } ], "source": [ "a = 10\n", "\n", "print(a)" ] }, { "cell_type": "code", "execution_count": 7, "id": "3a7bac6d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "45.13\n" ] } ], "source": [ "b = 45.13\n", "\n", "print(b)" ] }, { "cell_type": "code", "execution_count": 8, "id": "124cb33b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "India\n" ] } ], "source": [ "a = \"India\"\n", "\n", "print(a)" ] }, { "cell_type": "markdown", "id": "406d111f", "metadata": {}, "source": [ "# Data type" ] }, { "cell_type": "code", "execution_count": 9, "id": "707a85a0", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10\n", "\n" ] } ], "source": [ "a = 10\n", "\n", "print(a)\n", "\n", "print(type(a))" ] }, { "cell_type": "code", "execution_count": 10, "id": "bc9295ca", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10.65\n", "\n" ] } ], "source": [ "b = 10.65\n", "\n", "print(b)\n", "\n", "print(type(b))" ] }, { "cell_type": "code", "execution_count": 11, "id": "5632508a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Screen\n", "\n" ] } ], "source": [ "c = \"Screen\"\n", "\n", "print(c)\n", "\n", "print(type(c))" ] }, { "cell_type": "code", "execution_count": 12, "id": "644ec54c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "meet\n", "\n" ] } ], "source": [ "d = 'meet'\n", "\n", "print(d)\n", "\n", "print(type(d))" ] }, { "cell_type": "code", "execution_count": 13, "id": "43f33389", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "it's an apple\n" ] } ], "source": [ "x = \"it's an apple\"\n", "\n", "print(x)" ] }, { "cell_type": "markdown", "id": "e04549b7", "metadata": {}, "source": [ "# Type Conversion" ] }, { "cell_type": "code", "execution_count": 14, "id": "321b1438", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10.56\n", "\n", "10\n", "\n" ] } ], "source": [ "a = 10.56\n", "\n", "print(a)\n", "\n", "print(type(a))\n", "\n", "b = int(a)\n", "\n", "print(b)\n", "\n", "print(type(b))" ] }, { "cell_type": "code", "execution_count": 15, "id": "873230fd", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "100\n", "\n", "100.0\n", "\n" ] } ], "source": [ "a = \"100\"\n", "\n", "print(a)\n", "\n", "print(type(a))\n", "\n", "b = float(a)\n", "\n", "print(b)\n", "\n", "print(type(b))" ] }, { "cell_type": "code", "execution_count": null, "id": "83db42ca", "metadata": {}, "outputs": [], "source": [ "int()\n", "float()\n", "str()\n", "bool()\n", "list()\n", "dict()\n", "tuple()\n", "set()" ] }, { "cell_type": "markdown", "id": "a54b3b8f", "metadata": {}, "source": [ "# Input function" ] }, { "cell_type": "code", "execution_count": 16, "id": "127dd9ee", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10\n", "10\n" ] } ], "source": [ "a = input()\n", "\n", "print(a)" ] }, { "cell_type": "code", "execution_count": 17, "id": "6c872d87", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Enter Your Name: Himanshu\n", "Himanshu\n" ] } ], "source": [ "a = input(\"Enter Your Name: \")\n", "\n", "print(a)" ] }, { "cell_type": "code", "execution_count": 18, "id": "f332e9d1", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Enter Your Name: Himanshu\n", "Hello! How are you? Himanshu\n" ] } ], "source": [ "a = input(\"Enter Your Name: \")\n", "\n", "print(\"Hello! How are you?\", a)" ] }, { "cell_type": "code", "execution_count": null, "id": "076ba3aa", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.12" } }, "nbformat": 4, "nbformat_minor": 5 } ================================================ FILE: March Cohort 2023/.ipynb_checkpoints/13_14_Object Orientation-checkpoint.ipynb ================================================ { "cells": [ { "cell_type": "code", "execution_count": 1, "id": "f956ae23", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "a = 10\n", "\n", "print(type(a))" ] }, { "cell_type": "code", "execution_count": 14, "id": "a8e19eec", "metadata": { "scrolled": false }, "outputs": [], "source": [ "# help(int)" ] }, { "cell_type": "code", "execution_count": null, "id": "02596f25", "metadata": {}, "outputs": [], "source": [ "int a = 10; # 2 bytes - 16 bits\n", "\n", "struct car{\n", " int cust_id; # 2 bytes\n", " char name[10];# 10 bytes\n", " float price; # 4 bytes\n", "}\n", "\n", "struct car c1;\n", " int a;" ] }, { "cell_type": "code", "execution_count": null, "id": "73d30a38", "metadata": {}, "outputs": [], "source": [ "bit>numbers>characters>instructions>function(program)>class>modules>package>library>framework>software" ] }, { "cell_type": "markdown", "id": "8ae18de9", "metadata": {}, "source": [ "# Object Orientation\n", "\n", "7 properties of OOP\n", "\n", "1. Class\n", " Updated version of structures (struct -- C programming)\n", " collection of variables and methods\n", " class is a blueprint.\n", "2. Object\n", " Run time or real time entity.\n", " hash code/virtual code - ID(python)\n", " \n", "3. Abstraction and Encapsulation\n", " Abstraction - showing only essential features without showing any background details.\n", " Encapsulation - wrapping up of data in a single unit.\n", " \n", "4. Inheritance\n", " Acquiring properties of one class into another.\n", " code reusability\n", " - single level\n", " - multi level\n", " - heirarchical\n", " - multiple\n", " - hybride\n", " \n", "5. Polymorphism\n", " same name multiple functionalities\n", " - method overloading\n", " - method overriding\n", " \n", "6. Dynamic Memory Allocation\n", " run time memory allocation\n", " \n", "7. Message Passing\n", " Communication between objects" ] }, { "cell_type": "code", "execution_count": 8, "id": "d69e271e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2207057668688\n" ] } ], "source": [ "a = 10\n", "\n", "print(id(a))" ] }, { "cell_type": "code", "execution_count": 10, "id": "cb5639c1", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2207057669008\n", "2207057669328\n" ] } ], "source": [ "b = 20\n", "\n", "c = a + b\n", "print(id(b))\n", "\n", "print(id(c))" ] }, { "cell_type": "code", "execution_count": 11, "id": "921fed62", "metadata": {}, "outputs": [ { "ename": "TypeError", "evalue": "can only concatenate str (not \"int\") to str", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", "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", "\u001b[1;31mTypeError\u001b[0m: can only concatenate str (not \"int\") to str" ] } ], "source": [ "s = \"A\" + 10" ] }, { "cell_type": "code", "execution_count": 12, "id": "0f26ad3e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "AAAA\n" ] } ], "source": [ "s = \"A\" * 4\n", "\n", "print(s)" ] }, { "cell_type": "code", "execution_count": 15, "id": "ba90ba2a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "def hello():\n", " return \"Hi\"\n", "\n", "print(type(hello))" ] }, { "cell_type": "code", "execution_count": 16, "id": "41a3ec56", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2207169212128\n" ] } ], "source": [ "print(id(hello))" ] }, { "cell_type": "code", "execution_count": null, "id": "06ce3374", "metadata": {}, "outputs": [], "source": [ "class win7:\n", "def start()\n", "\n", "def controlpanel():\n", " \n", "\n", "add(x, y)\n", "\n", "add(a,b,c)\n", "\n", "class win10(win7):\n", " \n", " \n", "product_id = wid10()\n", "\n", "product_id.controlpanel()" ] }, { "cell_type": "code", "execution_count": null, "id": "53b1fb7c", "metadata": {}, "outputs": [], "source": [ "add()" ] }, { "cell_type": "code", "execution_count": 17, "id": "67f2a3da", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "30" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "10 + 20" ] }, { "cell_type": "markdown", "id": "8420ca86", "metadata": {}, "source": [ "# Day 14 " ] }, { "cell_type": "code", "execution_count": 1, "id": "33e8ee69", "metadata": {}, "outputs": [], "source": [ "class Cricket:\n", " \n", " def bat():\n", " print(\"Hello from Bat\")\n", "\n" ] }, { "cell_type": "code", "execution_count": 2, "id": "aee1a50c", "metadata": {}, "outputs": [], "source": [ "c = Cricket()" ] }, { "cell_type": "code", "execution_count": 3, "id": "c2d4fa85", "metadata": {}, "outputs": [ { "ename": "TypeError", "evalue": "bat() takes 0 positional arguments but 1 was given", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", "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", "\u001b[1;31mTypeError\u001b[0m: bat() takes 0 positional arguments but 1 was given" ] } ], "source": [ "c.bat()" ] }, { "cell_type": "code", "execution_count": null, "id": "cf30bd7f", "metadata": {}, "outputs": [], "source": [ "# self --> current object" ] }, { "cell_type": "code", "execution_count": 4, "id": "e50eb7d9", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Hello from Bat\n" ] } ], "source": [ "class Cricket:\n", " \n", " def bat(self): # self = c\n", " print(\"Hello from Bat\")\n", "\n", "c = Cricket()\n", "\n", "c.bat()" ] }, { "cell_type": "code", "execution_count": 5, "id": "623c69db", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<__main__.Cricket object at 0x0000020F34DB4A00>\n" ] } ], "source": [ "print(c)" ] }, { "cell_type": "code", "execution_count": 6, "id": "242b62af", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2264334551552\n" ] } ], "source": [ "print(id(c))" ] }, { "cell_type": "code", "execution_count": 7, "id": "515da4b9", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<__main__.Cricket object at 0x0000020F36BD6A90>\n" ] } ], "source": [ "print(Cricket())" ] }, { "cell_type": "code", "execution_count": null, "id": "b06677ce", "metadata": {}, "outputs": [], "source": [ "TV blueprint - Class\n", "- smart tv\n", "- colors\n", "- usb\n", "- hdmi\n", "\n", "Actual TV - Object\n", "product_id\n", "\n", "\n", "remote - reference" ] }, { "cell_type": "code", "execution_count": 8, "id": "8d0184d8", "metadata": {}, "outputs": [], "source": [ "class Football:\n", " \n", " def goal(self, x): # self = f, x = 4\n", " self.a = x # a is class variable, x is local variable\n", " # f.a = 4\n", " \n", " def display(self):\n", " print(self.a)\n", " # f.a\n", " \n", "f = Football()" ] }, { "cell_type": "code", "execution_count": 9, "id": "1d10510d", "metadata": {}, "outputs": [], "source": [ "f.goal(4)" ] }, { "cell_type": "code", "execution_count": 10, "id": "3ddef9fb", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "4\n" ] } ], "source": [ "f.display()" ] }, { "cell_type": "code", "execution_count": 11, "id": "6216ae9a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "4\n" ] } ], "source": [ "print(f.a)" ] }, { "cell_type": "code", "execution_count": 12, "id": "63e17007", "metadata": {}, "outputs": [ { "ename": "AttributeError", "evalue": "'Football' object has no attribute 'x'", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)", "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", "\u001b[1;31mAttributeError\u001b[0m: 'Football' object has no attribute 'x'" ] } ], "source": [ "print(f.x)" ] }, { "cell_type": "code", "execution_count": 13, "id": "a84e2e93", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "print(type(f))" ] }, { "cell_type": "code", "execution_count": 14, "id": "0e68d116", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2264334513824\n" ] } ], "source": [ "print(id(f))" ] }, { "cell_type": "code", "execution_count": 15, "id": "8145572b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10\n", "\n", "2264248117840\n", "2264248117840\n" ] } ], "source": [ "a = 10\n", "\n", "print(a)\n", "\n", "print(type(a))\n", "\n", "print(id(a))\n", "\n", "print(id(10))" ] }, { "cell_type": "code", "execution_count": 17, "id": "04a1cb6b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<__main__.Test object at 0x0000020F36BD6C70>\n", "2264366148720\n", "<__main__.Test object at 0x0000020F36BD6C70>\n", "2264366148720\n", "Finish\n" ] } ], "source": [ "class Test:\n", " def add(self):\n", " print(self)\n", " print(id(self))\n", " print(\"Finish\")\n", " \n", "t = Test()\n", "print(t)\n", "print(id(t))\n", "t.add()" ] }, { "cell_type": "code", "execution_count": 18, "id": "e76165f0", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<__main__.Test object at 0x0000020F37274610>\n", "2264373085712\n", "<__main__.Test object at 0x0000020F37274610>\n", "2264373085712\n", "Finish\n" ] } ], "source": [ "t2 = Test()\n", "print(t2)\n", "print(id(t2))\n", "t2.add()" ] }, { "cell_type": "markdown", "id": "c78e8f64", "metadata": {}, "source": [ "# Constructor\n", "\n", "constructors are special member method of a class" ] }, { "cell_type": "code", "execution_count": null, "id": "18cd2b35", "metadata": {}, "outputs": [], "source": [ "__init__() -> magic method or dunders" ] }, { "cell_type": "code", "execution_count": 19, "id": "0e5bc082", "metadata": {}, "outputs": [], "source": [ "class Cat:\n", " \n", " def __init__(self):\n", " print(\"Hello from constructor\") # write code that is for initializing \n", "# coins = 0\n", "# score = 0\n", " " ] }, { "cell_type": "code", "execution_count": 20, "id": "90a7183d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Hello from constructor\n" ] } ], "source": [ "f = Cat()" ] }, { "cell_type": "code", "execution_count": 22, "id": "7c63050d", "metadata": { "scrolled": false }, "outputs": [], "source": [ "# help(str)" ] }, { "cell_type": "markdown", "id": "a89195ce", "metadata": {}, "source": [ "### paramatrized constructor" ] }, { "cell_type": "code", "execution_count": 23, "id": "870a85c9", "metadata": {}, "outputs": [], "source": [ "class Car:\n", " def __init__(self, x):\n", " self.y = x\n", " " ] }, { "cell_type": "code", "execution_count": 24, "id": "3575a84e", "metadata": {}, "outputs": [], "source": [ "f = Car(10)" ] }, { "cell_type": "code", "execution_count": 25, "id": "862c1549", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10\n" ] } ], "source": [ "print(f.y)" ] }, { "cell_type": "markdown", "id": "04a7f0d7", "metadata": {}, "source": [ "### Write a class that can calculate area of square, circle, rectangle." ] }, { "cell_type": "code", "execution_count": 26, "id": "7995dcda", "metadata": {}, "outputs": [], "source": [ "class Area:\n", " \n", " def square(self, x):\n", " return x*x\n", " \n", " def circle(self, r):\n", " return 3.14 * r * r\n", " \n", " def rectangle(self, l, b):\n", " return l * b\n", " \n" ] }, { "cell_type": "code", "execution_count": 27, "id": "c19e6fac", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "16" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ar = Area()\n", "\n", "ar.square(4)" ] }, { "cell_type": "markdown", "id": "6528d7d2", "metadata": {}, "source": [ "### Write a program to create a class to reverse a string word by word." ] }, { "cell_type": "code", "execution_count": null, "id": "87016fca", "metadata": {}, "outputs": [], "source": [ "\"this is an apple\"\n", "\n", "\"apple an is this\"" ] }, { "cell_type": "markdown", "id": "70e87437", "metadata": {}, "source": [ "# Inheritance\n", "\n", "\n", "### single level" ] }, { "cell_type": "code", "execution_count": null, "id": "4213330d", "metadata": {}, "outputs": [], "source": [ "A windows 7\n", "| |\n", "B windows 10" ] }, { "cell_type": "code", "execution_count": 28, "id": "3cbdb564", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "This is add from class A\n", "This is add from class A\n" ] } ], "source": [ "class A:\n", " def add(self):\n", " print(\"This is add from class A\")\n", " \n", "\n", "class B(A):\n", " def sub(self):\n", " print(\"Sub from class B\")\n", " \n", "x = A()\n", "y = B()\n", "\n", "x.add()\n", "y.add()" ] }, { "cell_type": "code", "execution_count": 29, "id": "694d0edf", "metadata": {}, "outputs": [ { "ename": "AttributeError", "evalue": "'A' object has no attribute 'sub'", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)", "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", "\u001b[1;31mAttributeError\u001b[0m: 'A' object has no attribute 'sub'" ] } ], "source": [ "x.sub()" ] }, { "cell_type": "code", "execution_count": 30, "id": "da9ee247", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Sub from class B\n" ] } ], "source": [ "y.sub()" ] }, { "cell_type": "markdown", "id": "61a935df", "metadata": {}, "source": [ "### multi level" ] }, { "cell_type": "code", "execution_count": null, "id": "a163bf53", "metadata": {}, "outputs": [], "source": [ "A\n", "|\n", "B\n", "|\n", "C" ] }, { "cell_type": "code", "execution_count": 31, "id": "b4a8158e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "show from class C\n", "Sub from class B\n", "This is add from class A\n" ] } ], "source": [ "class A:\n", " def add(self):\n", " print(\"This is add from class A\")\n", " \n", "class B(A):\n", " def sub(self):\n", " print(\"Sub from class B\")\n", " \n", "class C(B):\n", " def show(self):\n", " print(\"show from class C\")\n", " \n", "z = C()\n", "z.show()\n", "z.sub()\n", "z.add()" ] }, { "cell_type": "markdown", "id": "6d610afa", "metadata": {}, "source": [ "### hierarchical" ] }, { "cell_type": "code", "execution_count": null, "id": "56edc720", "metadata": {}, "outputs": [], "source": [ " A\n", "B C" ] }, { "cell_type": "code", "execution_count": 32, "id": "94cdc6e5", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "show from class C\n" ] }, { "ename": "AttributeError", "evalue": "'C' object has no attribute 'sub'", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)", "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", "\u001b[1;31mAttributeError\u001b[0m: 'C' object has no attribute 'sub'" ] } ], "source": [ "class A:\n", " def add(self):\n", " print(\"This is add from class A\")\n", " \n", "class B(A):\n", " def sub(self):\n", " print(\"Sub from class B\")\n", " \n", "class C(A):\n", " def show(self):\n", " print(\"show from class C\")\n", " \n", "z = C()\n", "z.show()\n", "z.sub()\n", "z.add()" ] }, { "cell_type": "markdown", "id": "afa65f2f", "metadata": {}, "source": [ "### multiple\n", "\n", "C3 algorithm to solve ambiguty error\n", "\n", "MRO - Method Resolution Order" ] }, { "cell_type": "code", "execution_count": null, "id": "520d2d17", "metadata": {}, "outputs": [], "source": [ "A B\n", " C" ] }, { "cell_type": "code", "execution_count": 33, "id": "41a10af7", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Add from class A\n" ] } ], "source": [ "class A:\n", " def add(self):\n", " print(\"Add from class A\")\n", " \n", "class B:\n", " def add(self):\n", " print(\"Add from class B\")\n", " \n", "class C(A,B):\n", " def show(self):\n", " print(\"show from class C\")\n", " \n", "z = C()\n", "z.add()" ] }, { "cell_type": "code", "execution_count": 34, "id": "101bcde3", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(, , , )\n" ] } ], "source": [ "print(C.__mro__)" ] }, { "cell_type": "code", "execution_count": 36, "id": "e31f4dfc", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Add from class B\n", "(, , , )\n" ] } ], "source": [ "class A:\n", " def add(self):\n", " print(\"Add from class A\")\n", " \n", "class B:\n", " def add(self):\n", " print(\"Add from class B\")\n", " \n", "class C(B,A):\n", " def show(self):\n", " print(\"show from class C\")\n", " \n", "z = C()\n", "z.add()\n", "print(C.__mro__)" ] }, { "cell_type": "markdown", "id": "9361ebca", "metadata": {}, "source": [ "### Hybrid" ] }, { "cell_type": "code", "execution_count": null, "id": "e1434f79", "metadata": {}, "outputs": [], "source": [ " A\n", "B C\n", " D" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.12" } }, "nbformat": 4, "nbformat_minor": 5 } ================================================ FILE: March Cohort 2023/.ipynb_checkpoints/1_While Loops and Logic Building-checkpoint.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "id": "dfbc28de", "metadata": {}, "source": [ "# While Loop" ] }, { "cell_type": "code", "execution_count": 2, "id": "c1f425e3", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Himanshu\n", "Himanshu\n" ] } ], "source": [ "print(\"Himanshu\")\n", "print(\"Himanshu\")" ] }, { "cell_type": "code", "execution_count": 4, "id": "55a46f9f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "True\n", "False\n" ] } ], "source": [ "i = 1\n", "\n", "print(i <= 5)\n", "\n", "print(i >= 5)" ] }, { "cell_type": "code", "execution_count": 5, "id": "7a204214", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Himanshu\n", "Himanshu\n", "Himanshu\n", "Himanshu\n", "Himanshu\n" ] } ], "source": [ "i = 1\n", "\n", "while i <= 5 :\n", " print(\"Himanshu\")\n", " i = i + 1 # i = 1, 2, 3, 4, 5, 6" ] }, { "cell_type": "code", "execution_count": 6, "id": "b5ef4f72", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Himanshu\n", "Himanshu\n", "Himanshu\n", "Himanshu\n", "Himanshu\n", "Himanshu\n", "Himanshu\n", "Himanshu\n", "Himanshu\n", "Himanshu\n" ] } ], "source": [ "i = 1\n", "\n", "while i <= 10 :\n", " print(\"Himanshu\")\n", " i = i + 1" ] }, { "cell_type": "code", "execution_count": 7, "id": "a3fc06bd", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1\n", "2\n", "3\n", "4\n", "5\n" ] } ], "source": [ "i = 1\n", "\n", "while i <= 5 :\n", " print(i)\n", " i = i + 1" ] }, { "cell_type": "code", "execution_count": null, "id": "2b2a54cb", "metadata": {}, "outputs": [], "source": [ "1\n", "2\n", "3\n", "4\n", "5\n", "6\n", "7\n", "8\n", "9\n", "10" ] }, { "cell_type": "code", "execution_count": 8, "id": "9b83ffab", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1\n", "2\n", "3\n", "4\n", "5\n", "6\n", "7\n", "8\n", "9\n", "10\n" ] } ], "source": [ "i = 1\n", "\n", "while i <= 10:\n", " print(i)\n", " i = i + 1" ] }, { "cell_type": "code", "execution_count": 9, "id": "9522e499", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Hello\n", "World\n" ] } ], "source": [ "print(\"Hello\")\n", "print(\"World\")" ] }, { "cell_type": "code", "execution_count": 10, "id": "dd5d35e6", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Hello\n", "World\n" ] } ], "source": [ "print(\"Hello\", end='\\n')\n", "print(\"World\")" ] }, { "cell_type": "code", "execution_count": 11, "id": "c54de4e2", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Hello+World\n" ] } ], "source": [ "print(\"Hello\", end='+')\n", "print(\"World\")" ] }, { "cell_type": "code", "execution_count": 12, "id": "54a31434", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Hello World\n" ] } ], "source": [ "print(\"Hello\", end=' ')\n", "print(\"World\")" ] }, { "cell_type": "code", "execution_count": 13, "id": "b4f03f6d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "HelloWorld\n" ] } ], "source": [ "print(\"Hello\", end='')\n", "print(\"World\")" ] }, { "cell_type": "code", "execution_count": 14, "id": "2a48a921", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Hello@World\n" ] } ], "source": [ "print(\"Hello\", end='@')\n", "print(\"World\")" ] }, { "cell_type": "code", "execution_count": 15, "id": "1357e71d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Hello@World$" ] } ], "source": [ "print(\"Hello\", end='@')\n", "print(\"World\", end='$')" ] }, { "cell_type": "code", "execution_count": null, "id": "f48834ba", "metadata": {}, "outputs": [], "source": [ "1 2 3 4 5 6 7 8 9 10" ] }, { "cell_type": "code", "execution_count": 16, "id": "1c74034c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1 2 3 4 5 6 7 8 9 10 " ] } ], "source": [ "i = 1\n", "\n", "while i <= 10:\n", " print(i, end=' ')\n", " i = i + 1" ] }, { "cell_type": "code", "execution_count": null, "id": "0649f1ee", "metadata": {}, "outputs": [], "source": [ "1 4 9 16 25 36 49 64 81 100" ] }, { "cell_type": "code", "execution_count": 17, "id": "9bb2cd28", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "30\n", "10\n" ] } ], "source": [ "a = 10\n", "\n", "print(a*3)\n", "\n", "print(a)\n", "\n", "# a = a * 3" ] }, { "cell_type": "code", "execution_count": 18, "id": "67f5a9b8", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1 4 9 16 25 36 49 64 81 100 " ] } ], "source": [ "i = 1\n", "\n", "while i <= 10:\n", " print(i*i, end=' ')\n", " i = i + 1" ] }, { "cell_type": "markdown", "id": "2ad2e158", "metadata": {}, "source": [ "**Write a program to enter 5 values from user and print them.**" ] }, { "cell_type": "code", "execution_count": 19, "id": "aeee4fe6", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Enter your number: 10\n", "10\n", "Enter your number: 20\n", "20\n", "Enter your number: 30\n", "30\n", "Enter your number: 40\n", "40\n", "Enter your number: 50\n", "50\n" ] } ], "source": [ "i = 1\n", "\n", "while i <= 5:\n", " a = input(\"Enter your number: \")\n", " \n", " print(a)\n", " \n", " i = i + 1" ] }, { "cell_type": "markdown", "id": "3773b36f", "metadata": {}, "source": [ "**Write a program to enter a number from user and print reverse of it.**" ] }, { "cell_type": "code", "execution_count": null, "id": "34362203", "metadata": {}, "outputs": [], "source": [ "153\n", "\n", "351" ] }, { "cell_type": "code", "execution_count": 20, "id": "3dcb3a2f", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "7 % 2 # return reminder" ] }, { "cell_type": "code", "execution_count": 21, "id": "42cfdbf1", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "3.5" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "7 / 2" ] }, { "cell_type": "code", "execution_count": 22, "id": "885d87d3", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "3" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "7 // 2" ] }, { "cell_type": "code", "execution_count": 24, "id": "d7cbb652", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "3" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "153 % 10" ] }, { "cell_type": "code", "execution_count": null, "id": "59f14345", "metadata": {}, "outputs": [], "source": [ " x % 10 = z\n", "153 % 10 = 3\n", "153 // 10 = 15\n", "\n", "15 % 10 = 5\n", "15 // 10 = 1\n", "\n", "1 % 10 = 1\n", "1 // 10 = 0" ] }, { "cell_type": "code", "execution_count": 26, "id": "f759b069", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Enter a number: 10\n", "10\n", "\n" ] } ], "source": [ "n = int(input(\"Enter a number: \"))\n", "\n", "print(n)\n", "\n", "print(type(n))" ] }, { "cell_type": "code", "execution_count": 28, "id": "6fc34b04", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Enter a Number: 12345\n", "54321" ] } ], "source": [ "n = int(input(\"Enter a Number: \"))\n", "\n", "while n > 0:\n", " r = n % 10\n", " print(r, end='') # 351\n", " n = n // 10 # n = 153, 15, 1" ] }, { "cell_type": "markdown", "id": "5d585434", "metadata": {}, "source": [ "**Write a program to enter a number from user and print sum of its individual digits**" ] }, { "cell_type": "code", "execution_count": null, "id": "aaffcb41", "metadata": {}, "outputs": [], "source": [ "153\n", "\n", "1 + 5 + 3\n", "\n", "9\n", "\n", "\n", "0 s\n", "3 s + 3 = 3\n", "5 s + 5 = 8\n", "1 s + 1 = 9" ] }, { "cell_type": "code", "execution_count": 30, "id": "1b2f5fbb", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Enter a Number: 2345\n", "Sum = 14\n" ] } ], "source": [ "n = int(input(\"Enter a Number: \"))\n", "s = 0\n", "\n", "while n > 0:\n", " r = n % 10\n", " s = s + r # s = 0, 3, 8, 9\n", " n = n // 10\n", "\n", "print(\"Sum =\", s)" ] }, { "cell_type": "markdown", "id": "88e40510", "metadata": {}, "source": [ "**Write a program to enter a number from user and check if it palindrome or not**" ] }, { "cell_type": "code", "execution_count": null, "id": "643c209d", "metadata": {}, "outputs": [], "source": [ "121\n", "\n", "121\n", "\n", "153 = 351 not a palindrome" ] }, { "cell_type": "code", "execution_count": 34, "id": "432cbedf", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Enter a Number: 153\n", "351\n", "702\n" ] } ], "source": [ "n = int(input(\"Enter a Number: \"))\n", "s = 0\n", "\n", "while n > 0:\n", " r = n % 10\n", " s = s * 10 + r # s = 0, 3, 35, 351\n", " n = n // 10\n", "\n", "print(s)\n", "print(s * 2)" ] }, { "cell_type": "code", "execution_count": 40, "id": "59333ae3", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Enter a Number: 121\n", "Palindrome\n" ] } ], "source": [ "n = int(input(\"Enter a Number: \"))\n", "t = n\n", "s = 0\n", "\n", "while n > 0:\n", " r = n % 10\n", " s = s * 10 + r # s = 0, 3, 35, 351\n", " n = n // 10\n", "\n", "if s == t:\n", " print(\"Palindrome\")\n", "else:\n", " print(\"Not a Palindrome\")" ] }, { "cell_type": "code", "execution_count": 38, "id": "dc812c1e", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "False" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "10 == 11" ] }, { "cell_type": "markdown", "id": "28edc5f6", "metadata": {}, "source": [ "**Write a program to enter a number from user and check if it is armstrong or not**" ] }, { "cell_type": "code", "execution_count": null, "id": "b78ef4cd", "metadata": {}, "outputs": [], "source": [ "153\n", "\n", "cube(1) + cube(5) + cube(3) = 153\n", "\n", "370\n", "\n", "371" ] }, { "cell_type": "code", "execution_count": 44, "id": "de686ea3", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Enter a Number: 371\n", "Armstrong Number\n" ] } ], "source": [ "n = int(input(\"Enter a Number: \"))\n", "t = n\n", "s = 0\n", "\n", "while n > 0:\n", " r = n % 10\n", " s = s + r*r*r\n", " n = n // 10\n", "\n", "if s == t:\n", " print(\"Armstrong Number\")\n", "else:\n", " print(\"Not an Armstrong Number\")" ] }, { "cell_type": "markdown", "id": "b4fc183f", "metadata": {}, "source": [ "**Write a program to enter a number from user and print its individual digits on seperate line.**" ] }, { "cell_type": "code", "execution_count": null, "id": "75092737", "metadata": {}, "outputs": [], "source": [ "153\n", "\n", "1\n", "5\n", "3" ] }, { "cell_type": "code", "execution_count": 45, "id": "56d6ec0d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Enter a Number: 153\n", "3\n", "5\n", "1\n" ] } ], "source": [ "n = int(input(\"Enter a Number: \"))\n", "s = 0\n", "\n", "while n > 0:\n", " r = n % 10\n", " print(r)\n", " n = n // 10\n" ] }, { "cell_type": "code", "execution_count": 47, "id": "e13e7967", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Enter a Number: 12345\n", "1\n", "2\n", "3\n", "4\n", "5\n" ] } ], "source": [ "n = int(input(\"Enter a Number: \"))\n", "s = 0\n", "\n", "while n > 0:\n", " r = n % 10\n", " s = s * 10 + r\n", " n = n // 10\n", "\n", "# s = 351\n", "\n", "while s > 0:\n", " r = s % 10\n", " print(r)\n", " s = s // 10" ] }, { "cell_type": "code", "execution_count": null, "id": "4864ff19", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.12" } }, "nbformat": 4, "nbformat_minor": 5 } ================================================ FILE: March Cohort 2023/.ipynb_checkpoints/2_List and Strings-checkpoint.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "id": "f18f8832", "metadata": {}, "source": [ "# List\n", "\n", "collection of different types of elements" ] }, { "cell_type": "code", "execution_count": null, "id": "4a53537e", "metadata": {}, "outputs": [], "source": [ "a = 10 # int\n", "b = 20\n", "c = 30" ] }, { "cell_type": "code", "execution_count": 1, "id": "8179aaea", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[10, 'A', 'India', 10.6]\n" ] } ], "source": [ "l = [10, \"A\", 'India', 10.6]\n", "# 0 1 2 3\n", "\n", "print(l)" ] }, { "cell_type": "code", "execution_count": 2, "id": "b073284b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10\n" ] } ], "source": [ "print(l[0])" ] }, { "cell_type": "code", "execution_count": 3, "id": "0b18a752", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10.6\n" ] } ], "source": [ "print(l[3])" ] }, { "cell_type": "code", "execution_count": 4, "id": "562832f4", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "India\n" ] } ], "source": [ "print(l[2])" ] }, { "cell_type": "code", "execution_count": 5, "id": "2ce1c1e2", "metadata": {}, "outputs": [ { "ename": "IndexError", "evalue": "list index out of range", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mIndexError\u001b[0m Traceback (most recent call last)", "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", "\u001b[1;31mIndexError\u001b[0m: list index out of range" ] } ], "source": [ "print(l[5])" ] }, { "cell_type": "code", "execution_count": 6, "id": "b56a18f7", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[10, 'A', 'India', 10.6]\n" ] } ], "source": [ "l = [10, \"A\", 'India', 10.6]\n", "# 0 1 2 3\n", "\n", "print(l)" ] }, { "cell_type": "code", "execution_count": 7, "id": "56810d39", "metadata": {}, "outputs": [], "source": [ "l[0] = 700" ] }, { "cell_type": "code", "execution_count": 8, "id": "cec85000", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[700, 'A', 'India', 10.6]\n" ] } ], "source": [ "print(l)" ] }, { "cell_type": "code", "execution_count": 9, "id": "a839bab4", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "20\n" ] } ], "source": [ "a = 10\n", "\n", "a = 20\n", "\n", "print(a)" ] }, { "cell_type": "code", "execution_count": 10, "id": "07f87123", "metadata": {}, "outputs": [ { "ename": "IndexError", "evalue": "list assignment index out of range", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mIndexError\u001b[0m Traceback (most recent call last)", "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", "\u001b[1;31mIndexError\u001b[0m: list assignment index out of range" ] } ], "source": [ "l[5] = 400" ] }, { "cell_type": "code", "execution_count": 11, "id": "d61e6a52", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[700, 10.6, 'India', 10.6]\n" ] } ], "source": [ "l[1] = l[3]\n", "#l[1] = 10.6\n", "\n", "print(l)" ] }, { "cell_type": "code", "execution_count": 12, "id": "cd0e6f72", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[10, 'A', 'India', 10.6]\n" ] } ], "source": [ "# -4 -3 -2 -1\n", "l = [10, \"A\", 'India', 10.6]\n", "# 0 1 2 3\n", "\n", "print(l)" ] }, { "cell_type": "code", "execution_count": 13, "id": "28fe4466", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10\n", "10\n" ] } ], "source": [ "print(l[0])\n", "\n", "print(l[-4])" ] }, { "cell_type": "code", "execution_count": 14, "id": "a4ddd5ef", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10.6\n" ] } ], "source": [ "print(l[-1])" ] }, { "cell_type": "code", "execution_count": 15, "id": "edb5d5de", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "India\n" ] } ], "source": [ "print(l[-2])" ] }, { "cell_type": "markdown", "id": "e57f0121", "metadata": {}, "source": [ "# List slicing" ] }, { "cell_type": "code", "execution_count": 16, "id": "536c7f69", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[11, 22, 33, 10, 24, 44, 36, 68, 99, 81, 100]\n", "\n" ] } ], "source": [ "l = [11,22,33,10,24,44,36,68,99,81,100]\n", "# 0 1 2 3 4 5 6 7 8 9 10\n", "\n", "print(l)\n", "print(type(l))" ] }, { "cell_type": "code", "execution_count": 17, "id": "c4ed3b05", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[22, 33, 10, 24, 44, 36]\n" ] } ], "source": [ "print(l[1:7]) # l[start : end]" ] }, { "cell_type": "code", "execution_count": 18, "id": "44a00013", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[11, 22, 33, 10, 24, 44, 36, 68, 99, 81, 100]\n" ] } ], "source": [ "print(l)" ] }, { "cell_type": "code", "execution_count": 19, "id": "a344e33f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[24, 44, 36, 68, 99, 81]\n" ] } ], "source": [ "print(l[4:10])" ] }, { "cell_type": "code", "execution_count": 20, "id": "07f07f98", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[99, 81]\n" ] } ], "source": [ "print(l[8:10])" ] }, { "cell_type": "code", "execution_count": 21, "id": "47353b5f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[11, 22, 33, 10, 24, 44, 36, 68, 99, 81, 100]\n" ] } ], "source": [ "l = [11,22,33,10,24,44,36,68,99,81,100]\n", "# 0 1 2 3 4 5 6 7 8 9 10\n", "\n", "print(l)" ] }, { "cell_type": "code", "execution_count": 22, "id": "3d8b979b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[11]\n" ] } ], "source": [ "print(l[0:1])" ] }, { "cell_type": "code", "execution_count": 23, "id": "94eda01d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[22, 33, 10, 24, 44, 36, 68, 99, 81, 100]\n" ] } ], "source": [ "print(l[1:])" ] }, { "cell_type": "code", "execution_count": 24, "id": "c5f21115", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[99, 81, 100]\n" ] } ], "source": [ "print(l[8:])" ] }, { "cell_type": "code", "execution_count": 25, "id": "0e399980", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[11, 22, 33, 10, 24, 44, 36, 68, 99, 81, 100]\n" ] } ], "source": [ "# -11-10 -9 -8 -7 -6 -5 -4 -3 -2 -1\n", "l = [17,22,33,101,24,44,36,68,99,81,100]\n", "# 0 1 2 3 4 5 6 7 8 9 10\n", "\n", "print(l[:])" ] }, { "cell_type": "code", "execution_count": 26, "id": "9da33ce5", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[11, 22, 33, 10, 24]\n" ] } ], "source": [ "print(l[:5])" ] }, { "cell_type": "code", "execution_count": 27, "id": "858c11cb", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[17, 22, 33, 101, 24, 44, 36]\n" ] } ], "source": [ "# -11-10 -9 -8 -7 -6 -5 -4 -3 -2 -1\n", "l = [17,22,33,101,24,44,36,68,99,81,100]\n", "# 0 1 2 3 4 5 6 7 8 9 10\n", "\n", "print(l[0:-4])" ] }, { "cell_type": "code", "execution_count": 28, "id": "9b6f435f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[33, 101, 24, 44, 36, 68, 99]\n" ] } ], "source": [ "print(l[2:-2])" ] }, { "cell_type": "code", "execution_count": 29, "id": "556aceba", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[]\n" ] } ], "source": [ "print(l[2:0])" ] }, { "cell_type": "code", "execution_count": 30, "id": "fcab2e6a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[]\n" ] } ], "source": [ "# -11-10 -9 -8 -7 -6 -5 -4 -3 -2 -1\n", "l = [17,22,33,101,24,44,36,68,99,81,100]\n", "# 0 1 2 3 4 5 6 7 8 9 10\n", "\n", "print(l[0:0])" ] }, { "cell_type": "code", "execution_count": 31, "id": "a34bdf8d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[44, 36, 68, 99, 81, 100]\n" ] } ], "source": [ "print(l[5:15])" ] }, { "cell_type": "code", "execution_count": 32, "id": "69241fb4", "metadata": {}, "outputs": [ { "ename": "IndexError", "evalue": "list index out of range", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mIndexError\u001b[0m Traceback (most recent call last)", "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", "\u001b[1;31mIndexError\u001b[0m: list index out of range" ] } ], "source": [ "print(l[15])" ] }, { "cell_type": "code", "execution_count": 33, "id": "b4f6a948", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[22, 33, 101]\n" ] } ], "source": [ "# -11-10 -9 -8 -7 -6 -5 -4 -3 -2 -1\n", "l = [17,22,33,101,24,44,36,68,99,81,100]\n", "# 0 1 2 3 4 5 6 7 8 9 10\n", "\n", "print(l[1:4])" ] }, { "cell_type": "code", "execution_count": 34, "id": "2c46b2a1", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[]\n" ] } ], "source": [ "print(l[4:1])" ] }, { "cell_type": "code", "execution_count": 35, "id": "bb318e72", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[68, 99, 81]\n" ] } ], "source": [ "print(l[-4:-1])" ] }, { "cell_type": "code", "execution_count": 36, "id": "05c49a41", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[]\n" ] } ], "source": [ "print(l[-1:-4])" ] }, { "cell_type": "code", "execution_count": 38, "id": "bd75ab98", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[17, 33, 24, 36, 99, 100]\n" ] } ], "source": [ "# -11-10 -9 -8 -7 -6 -5 -4 -3 -2 -1\n", "l = [17,22,33,101,24,44,36,68,99,81,100]\n", "# 0 1 2 3 4 5 6 7 8 9 10\n", "\n", "print(l[0:11:2]) # l[start : end : steps]" ] }, { "cell_type": "code", "execution_count": 39, "id": "ed0ebd7d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[17, 101, 36, 81]\n" ] } ], "source": [ "print(l[0:11:3])" ] }, { "cell_type": "code", "execution_count": 40, "id": "591165f5", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[17, 22, 33, 101, 24, 44, 36, 68, 99, 81, 100]\n" ] } ], "source": [ "print(l[::])" ] }, { "cell_type": "code", "execution_count": 42, "id": "9325c782", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[17, 22, 33, 101, 24, 44, 36, 68, 99, 81, 100]\n" ] } ], "source": [ "print(l[::1])" ] }, { "cell_type": "code", "execution_count": 41, "id": "7c4d1a41", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[100, 81, 99, 68, 36, 44, 24, 101, 33, 22, 17]\n" ] } ], "source": [ "print(l[::-1]) # reverse" ] }, { "cell_type": "code", "execution_count": 43, "id": "769b5e71", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[]\n" ] } ], "source": [ "# -11-10 -9 -8 -7 -6 -5 -4 -3 -2 -1\n", "l = [17,22,33,101,24,44,36,68,99,81,100]\n", "# 0 1 2 3 4 5 6 7 8 9 10\n", "\n", "print(l[4:8:-1])" ] }, { "cell_type": "code", "execution_count": 44, "id": "61f07964", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[99, 68, 36, 44]\n" ] } ], "source": [ "print(l[8:4:-1])" ] }, { "cell_type": "code", "execution_count": 45, "id": "9ccf1239", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[100, 99, 36, 24, 33, 17]\n" ] } ], "source": [ "print(l[::-2])" ] }, { "cell_type": "code", "execution_count": 46, "id": "0dee2ab9", "metadata": {}, "outputs": [ { "ename": "ValueError", "evalue": "slice step cannot be zero", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", "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", "\u001b[1;31mValueError\u001b[0m: slice step cannot be zero" ] } ], "source": [ "print(l[::0])" ] }, { "cell_type": "markdown", "id": "4f3a41ae", "metadata": {}, "source": [ "# List functions" ] }, { "cell_type": "code", "execution_count": 47, "id": "5d15c64c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "5\n" ] } ], "source": [ "l = [10,20,30,40,50]\n", "\n", "print(len(l))" ] }, { "cell_type": "code", "execution_count": 48, "id": "d0c326c5", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "4\n" ] } ], "source": [ "print(len(l) - 1)" ] }, { "cell_type": "code", "execution_count": 49, "id": "c33408b9", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "150\n" ] } ], "source": [ "print(sum(l))" ] }, { "cell_type": "code", "execution_count": 50, "id": "29a67088", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "50\n" ] } ], "source": [ "print(max(l))" ] }, { "cell_type": "code", "execution_count": 51, "id": "7ce163a3", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10\n" ] } ], "source": [ "print(min(l))" ] }, { "cell_type": "code", "execution_count": 52, "id": "2a9052cb", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "4\n" ] }, { "ename": "TypeError", "evalue": "unsupported operand type(s) for +: 'int' and 'str'", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", "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", "\u001b[1;31mTypeError\u001b[0m: unsupported operand type(s) for +: 'int' and 'str'" ] } ], "source": [ "l = [\"A\", 10, 20, 30]\n", "\n", "print(len(l))\n", "print(sum(l))" ] }, { "cell_type": "code", "execution_count": 55, "id": "e62fa754", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'ab'" ] }, "execution_count": 55, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\"a\" + \"b\"" ] }, { "cell_type": "code", "execution_count": 53, "id": "5d8a7988", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "3\n" ] } ], "source": [ "l = [\"a\", \"b\", \"c\"]\n", "\n", "print(len(l))" ] }, { "cell_type": "code", "execution_count": 54, "id": "99cb555e", "metadata": {}, "outputs": [ { "ename": "TypeError", "evalue": "unsupported operand type(s) for +: 'int' and 'str'", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", "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", "\u001b[1;31mTypeError\u001b[0m: unsupported operand type(s) for +: 'int' and 'str'" ] } ], "source": [ "print(sum(l))" ] }, { "cell_type": "code", "execution_count": 56, "id": "1eff053f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "c\n" ] } ], "source": [ "print(max(l))" ] }, { "cell_type": "code", "execution_count": 57, "id": "c3f7d556", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "a\n" ] } ], "source": [ "print(min(l))" ] }, { "cell_type": "code", "execution_count": null, "id": "8cb11d63", "metadata": {}, "outputs": [], "source": [ "# ASCII\n", "\n", "A - 65/2 - binary\n", "B - 66\n", "Z - 90\n", "\n", "space - 32\n", "\n", "a - 97\n", "z - 122" ] }, { "cell_type": "code", "execution_count": 58, "id": "eea43d51", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "False" ] }, "execution_count": 58, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\"A\" > \"a\"" ] }, { "cell_type": "code", "execution_count": 59, "id": "6ffe5a48", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 59, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\"A\" < 'a'" ] }, { "cell_type": "markdown", "id": "55fc39d8", "metadata": {}, "source": [ "# List methods" ] }, { "cell_type": "code", "execution_count": 65, "id": "d0f38ad0", "metadata": {}, "outputs": [], "source": [ "# help(print)" ] }, { "cell_type": "code", "execution_count": 64, "id": "d56335b3", "metadata": {}, "outputs": [], "source": [ "# help(sum)" ] }, { "cell_type": "code", "execution_count": 63, "id": "4832e051", "metadata": { "scrolled": false }, "outputs": [], "source": [ "# help(list)" ] }, { "cell_type": "code", "execution_count": 66, "id": "315f91a5", "metadata": {}, "outputs": [], "source": [ "sum?" ] }, { "cell_type": "code", "execution_count": 68, "id": "1e64def7", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[10, 20, 30, 40]\n" ] } ], "source": [ "l = [10,20,30]\n", "\n", "l.append(40)\n", "\n", "print(l)" ] }, { "cell_type": "code", "execution_count": 69, "id": "9b78984e", "metadata": {}, "outputs": [], "source": [ "l.append(\"India\")" ] }, { "cell_type": "code", "execution_count": 70, "id": "86227543", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[10, 20, 30, 40, 'India']\n" ] } ], "source": [ "print(l)" ] }, { "cell_type": "code", "execution_count": 71, "id": "5a90e417", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "4\n" ] } ], "source": [ "print(l.index('India'))" ] }, { "cell_type": "code", "execution_count": 72, "id": "43bf35e2", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[10, 20, 30, 40, 'India']\n" ] } ], "source": [ "print(l)" ] }, { "cell_type": "code", "execution_count": 73, "id": "df0bd6ca", "metadata": {}, "outputs": [], "source": [ "l.reverse()" ] }, { "cell_type": "code", "execution_count": 74, "id": "826857cc", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['India', 40, 30, 20, 10]\n" ] } ], "source": [ "print(l)" ] }, { "cell_type": "code", "execution_count": 75, "id": "42f82dab", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "India\n" ] } ], "source": [ "print(l[0])" ] }, { "cell_type": "code", "execution_count": 76, "id": "5fd18a26", "metadata": {}, "outputs": [], "source": [ "l.reverse() # l[::-1]" ] }, { "cell_type": "code", "execution_count": 77, "id": "acf2c947", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[10, 20, 30, 40, 'India']\n" ] } ], "source": [ "print(l)" ] }, { "cell_type": "markdown", "id": "50f6bb18", "metadata": {}, "source": [ "# Strings\n", "\n", "set of characters" ] }, { "cell_type": "code", "execution_count": 78, "id": "2282691e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "hello\n" ] } ], "source": [ "# -1\n", "s = 'hello'\n", "# 01234\n", "\n", "print(s)" ] }, { "cell_type": "code", "execution_count": 79, "id": "30758f8a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "h\n" ] } ], "source": [ "print(s[0])" ] }, { "cell_type": "code", "execution_count": 80, "id": "401c2af6", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "o\n" ] } ], "source": [ "print(s[4])" ] }, { "cell_type": "code", "execution_count": 81, "id": "3543dcff", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "l\n" ] } ], "source": [ "print(s[3])" ] }, { "cell_type": "code", "execution_count": 82, "id": "70efef69", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "o\n" ] } ], "source": [ "print(s[-1])" ] }, { "cell_type": "code", "execution_count": 83, "id": "8b776e03", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "l\n" ] } ], "source": [ "print(s[-2])" ] }, { "cell_type": "code", "execution_count": 84, "id": "7a00d157", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "print(type(s))" ] }, { "cell_type": "code", "execution_count": 85, "id": "bafe5d33", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "print(type(s[0]))" ] }, { "cell_type": "markdown", "id": "69fd97c5", "metadata": {}, "source": [ "# String slicing" ] }, { "cell_type": "code", "execution_count": 86, "id": "cd9dff16", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "commu\n" ] } ], "source": [ "s = 'communication'\n", "# 0123456789\n", "print(s[0:5])" ] }, { "cell_type": "code", "execution_count": 87, "id": "0d50213f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "noitacinummoc\n" ] } ], "source": [ "print(s[::-1])" ] }, { "cell_type": "code", "execution_count": 88, "id": "0d4f5af4", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "omm\n" ] } ], "source": [ "print(s[1:4])" ] }, { "cell_type": "code", "execution_count": 89, "id": "e4712f62", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "print(s[4:1])" ] }, { "cell_type": "code", "execution_count": 90, "id": "2d5ffb11", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "a = ''\n", "\n", "print(type(a))" ] }, { "cell_type": "code", "execution_count": 91, "id": "b1755966", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "communication\n" ] } ], "source": [ "print(s)" ] }, { "cell_type": "code", "execution_count": 92, "id": "e82d1c2b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "noitacin\n" ] } ], "source": [ "print(s[-1:-9:-1])" ] }, { "cell_type": "markdown", "id": "918d1a64", "metadata": {}, "source": [ "# String functions" ] }, { "cell_type": "code", "execution_count": 93, "id": "87ad584b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "13\n" ] } ], "source": [ "s = 'communication'\n", "\n", "print(len(s))" ] }, { "cell_type": "code", "execution_count": 94, "id": "34ab8cff", "metadata": {}, "outputs": [ { "ename": "TypeError", "evalue": "unsupported operand type(s) for +: 'int' and 'str'", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", "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", "\u001b[1;31mTypeError\u001b[0m: unsupported operand type(s) for +: 'int' and 'str'" ] } ], "source": [ "print(sum(s))" ] }, { "cell_type": "code", "execution_count": 95, "id": "7378c889", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "u\n" ] } ], "source": [ "print(max(s))" ] }, { "cell_type": "code", "execution_count": 96, "id": "de330ab1", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "a\n" ] } ], "source": [ "print(min(s))" ] }, { "cell_type": "markdown", "id": "7bcef9c5", "metadata": {}, "source": [ "# string methods" ] }, { "cell_type": "code", "execution_count": 97, "id": "b0b078e8", "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Help on class str in module builtins:\n", "\n", "class str(object)\n", " | str(object='') -> str\n", " | str(bytes_or_buffer[, encoding[, errors]]) -> str\n", " | \n", " | Create a new string object from the given object. If encoding or\n", " | errors is specified, then the object must expose a data buffer\n", " | that will be decoded using the given encoding and error handler.\n", " | Otherwise, returns the result of object.__str__() (if defined)\n", " | or repr(object).\n", " | encoding defaults to sys.getdefaultencoding().\n", " | errors defaults to 'strict'.\n", " | \n", " | Methods defined here:\n", " | \n", " | __add__(self, value, /)\n", " | Return self+value.\n", " | \n", " | __contains__(self, key, /)\n", " | Return key in self.\n", " | \n", " | __eq__(self, value, /)\n", " | Return self==value.\n", " | \n", " | __format__(self, format_spec, /)\n", " | Return a formatted version of the string as described by format_spec.\n", " | \n", " | __ge__(self, value, /)\n", " | Return self>=value.\n", " | \n", " | __getattribute__(self, name, /)\n", " | Return getattr(self, name).\n", " | \n", " | __getitem__(self, key, /)\n", " | Return self[key].\n", " | \n", " | __getnewargs__(...)\n", " | \n", " | __gt__(self, value, /)\n", " | Return self>value.\n", " | \n", " | __hash__(self, /)\n", " | Return hash(self).\n", " | \n", " | __iter__(self, /)\n", " | Implement iter(self).\n", " | \n", " | __le__(self, value, /)\n", " | Return self<=value.\n", " | \n", " | __len__(self, /)\n", " | Return len(self).\n", " | \n", " | __lt__(self, value, /)\n", " | Return self int\n", " | \n", " | Return the number of non-overlapping occurrences of substring sub in\n", " | string S[start:end]. Optional arguments start and end are\n", " | interpreted as in slice notation.\n", " | \n", " | encode(self, /, encoding='utf-8', errors='strict')\n", " | Encode the string using the codec registered for encoding.\n", " | \n", " | encoding\n", " | The encoding in which to encode the string.\n", " | errors\n", " | The error handling scheme to use for encoding errors.\n", " | The default is 'strict' meaning that encoding errors raise a\n", " | UnicodeEncodeError. Other possible values are 'ignore', 'replace' and\n", " | 'xmlcharrefreplace' as well as any other name registered with\n", " | codecs.register_error that can handle UnicodeEncodeErrors.\n", " | \n", " | endswith(...)\n", " | S.endswith(suffix[, start[, end]]) -> bool\n", " | \n", " | Return True if S ends with the specified suffix, False otherwise.\n", " | With optional start, test S beginning at that position.\n", " | With optional end, stop comparing S at that position.\n", " | suffix can also be a tuple of strings to try.\n", " | \n", " | expandtabs(self, /, tabsize=8)\n", " | Return a copy where all tab characters are expanded using spaces.\n", " | \n", " | If tabsize is not given, a tab size of 8 characters is assumed.\n", " | \n", " | find(...)\n", " | S.find(sub[, start[, end]]) -> int\n", " | \n", " | Return the lowest index in S where substring sub is found,\n", " | such that sub is contained within S[start:end]. Optional\n", " | arguments start and end are interpreted as in slice notation.\n", " | \n", " | Return -1 on failure.\n", " | \n", " | format(...)\n", " | S.format(*args, **kwargs) -> str\n", " | \n", " | Return a formatted version of S, using substitutions from args and kwargs.\n", " | The substitutions are identified by braces ('{' and '}').\n", " | \n", " | format_map(...)\n", " | S.format_map(mapping) -> str\n", " | \n", " | Return a formatted version of S, using substitutions from mapping.\n", " | The substitutions are identified by braces ('{' and '}').\n", " | \n", " | index(...)\n", " | S.index(sub[, start[, end]]) -> int\n", " | \n", " | Return the lowest index in S where substring sub is found,\n", " | such that sub is contained within S[start:end]. Optional\n", " | arguments start and end are interpreted as in slice notation.\n", " | \n", " | Raises ValueError when the substring is not found.\n", " | \n", " | isalnum(self, /)\n", " | Return True if the string is an alpha-numeric string, False otherwise.\n", " | \n", " | A string is alpha-numeric if all characters in the string are alpha-numeric and\n", " | there is at least one character in the string.\n", " | \n", " | isalpha(self, /)\n", " | Return True if the string is an alphabetic string, False otherwise.\n", " | \n", " | A string is alphabetic if all characters in the string are alphabetic and there\n", " | is at least one character in the string.\n", " | \n", " | isascii(self, /)\n", " | Return True if all characters in the string are ASCII, False otherwise.\n", " | \n", " | ASCII characters have code points in the range U+0000-U+007F.\n", " | Empty string is ASCII too.\n", " | \n", " | isdecimal(self, /)\n", " | Return True if the string is a decimal string, False otherwise.\n", " | \n", " | A string is a decimal string if all characters in the string are decimal and\n", " | there is at least one character in the string.\n", " | \n", " | isdigit(self, /)\n", " | Return True if the string is a digit string, False otherwise.\n", " | \n", " | A string is a digit string if all characters in the string are digits and there\n", " | is at least one character in the string.\n", " | \n", " | isidentifier(self, /)\n", " | Return True if the string is a valid Python identifier, False otherwise.\n", " | \n", " | Call keyword.iskeyword(s) to test whether string s is a reserved identifier,\n", " | such as \"def\" or \"class\".\n", " | \n", " | islower(self, /)\n", " | Return True if the string is a lowercase string, False otherwise.\n", " | \n", " | A string is lowercase if all cased characters in the string are lowercase and\n", " | there is at least one cased character in the string.\n", " | \n", " | isnumeric(self, /)\n", " | Return True if the string is a numeric string, False otherwise.\n", " | \n", " | A string is numeric if all characters in the string are numeric and there is at\n", " | least one character in the string.\n", " | \n", " | isprintable(self, /)\n", " | Return True if the string is printable, False otherwise.\n", " | \n", " | A string is printable if all of its characters are considered printable in\n", " | repr() or if it is empty.\n", " | \n", " | isspace(self, /)\n", " | Return True if the string is a whitespace string, False otherwise.\n", " | \n", " | A string is whitespace if all characters in the string are whitespace and there\n", " | is at least one character in the string.\n", " | \n", " | istitle(self, /)\n", " | Return True if the string is a title-cased string, False otherwise.\n", " | \n", " | In a title-cased string, upper- and title-case characters may only\n", " | follow uncased characters and lowercase characters only cased ones.\n", " | \n", " | isupper(self, /)\n", " | Return True if the string is an uppercase string, False otherwise.\n", " | \n", " | A string is uppercase if all cased characters in the string are uppercase and\n", " | there is at least one cased character in the string.\n", " | \n", " | join(self, iterable, /)\n", " | Concatenate any number of strings.\n", " | \n", " | The string whose method is called is inserted in between each given string.\n", " | The result is returned as a new string.\n", " | \n", " | Example: '.'.join(['ab', 'pq', 'rs']) -> 'ab.pq.rs'\n", " | \n", " | ljust(self, width, fillchar=' ', /)\n", " | Return a left-justified string of length width.\n", " | \n", " | Padding is done using the specified fill character (default is a space).\n", " | \n", " | lower(self, /)\n", " | Return a copy of the string converted to lowercase.\n", " | \n", " | lstrip(self, chars=None, /)\n", " | Return a copy of the string with leading whitespace removed.\n", " | \n", " | If chars is given and not None, remove characters in chars instead.\n", " | \n", " | partition(self, sep, /)\n", " | Partition the string into three parts using the given separator.\n", " | \n", " | This will search for the separator in the string. If the separator is found,\n", " | returns a 3-tuple containing the part before the separator, the separator\n", " | itself, and the part after it.\n", " | \n", " | If the separator is not found, returns a 3-tuple containing the original string\n", " | and two empty strings.\n", " | \n", " | removeprefix(self, prefix, /)\n", " | Return a str with the given prefix string removed if present.\n", " | \n", " | If the string starts with the prefix string, return string[len(prefix):].\n", " | Otherwise, return a copy of the original string.\n", " | \n", " | removesuffix(self, suffix, /)\n", " | Return a str with the given suffix string removed if present.\n", " | \n", " | If the string ends with the suffix string and that suffix is not empty,\n", " | return string[:-len(suffix)]. Otherwise, return a copy of the original\n", " | string.\n", " | \n", " | replace(self, old, new, count=-1, /)\n", " | Return a copy with all occurrences of substring old replaced by new.\n", " | \n", " | count\n", " | Maximum number of occurrences to replace.\n", " | -1 (the default value) means replace all occurrences.\n", " | \n", " | If the optional argument count is given, only the first count occurrences are\n", " | replaced.\n", " | \n", " | rfind(...)\n", " | S.rfind(sub[, start[, end]]) -> int\n", " | \n", " | Return the highest index in S where substring sub is found,\n", " | such that sub is contained within S[start:end]. Optional\n", " | arguments start and end are interpreted as in slice notation.\n", " | \n", " | Return -1 on failure.\n", " | \n", " | rindex(...)\n", " | S.rindex(sub[, start[, end]]) -> int\n", " | \n", " | Return the highest index in S where substring sub is found,\n", " | such that sub is contained within S[start:end]. Optional\n", " | arguments start and end are interpreted as in slice notation.\n", " | \n", " | Raises ValueError when the substring is not found.\n", " | \n", " | rjust(self, width, fillchar=' ', /)\n", " | Return a right-justified string of length width.\n", " | \n", " | Padding is done using the specified fill character (default is a space).\n", " | \n", " | rpartition(self, sep, /)\n", " | Partition the string into three parts using the given separator.\n", " | \n", " | This will search for the separator in the string, starting at the end. If\n", " | the separator is found, returns a 3-tuple containing the part before the\n", " | separator, the separator itself, and the part after it.\n", " | \n", " | If the separator is not found, returns a 3-tuple containing two empty strings\n", " | and the original string.\n", " | \n", " | rsplit(self, /, sep=None, maxsplit=-1)\n", " | Return a list of the words in the string, using sep as the delimiter string.\n", " | \n", " | sep\n", " | The delimiter according which to split the string.\n", " | None (the default value) means split according to any whitespace,\n", " | and discard empty strings from the result.\n", " | maxsplit\n", " | Maximum number of splits to do.\n", " | -1 (the default value) means no limit.\n", " | \n", " | Splits are done starting at the end of the string and working to the front.\n", " | \n", " | rstrip(self, chars=None, /)\n", " | Return a copy of the string with trailing whitespace removed.\n", " | \n", " | If chars is given and not None, remove characters in chars instead.\n", " | \n", " | split(self, /, sep=None, maxsplit=-1)\n", " | Return a list of the words in the string, using sep as the delimiter string.\n", " | \n", " | sep\n", " | The delimiter according which to split the string.\n", " | None (the default value) means split according to any whitespace,\n", " | and discard empty strings from the result.\n", " | maxsplit\n", " | Maximum number of splits to do.\n", " | -1 (the default value) means no limit.\n", " | \n", " | splitlines(self, /, keepends=False)\n", " | Return a list of the lines in the string, breaking at line boundaries.\n", " | \n", " | Line breaks are not included in the resulting list unless keepends is given and\n", " | true.\n", " | \n", " | startswith(...)\n", " | S.startswith(prefix[, start[, end]]) -> bool\n", " | \n", " | Return True if S starts with the specified prefix, False otherwise.\n", " | With optional start, test S beginning at that position.\n", " | With optional end, stop comparing S at that position.\n", " | prefix can also be a tuple of strings to try.\n", " | \n", " | strip(self, chars=None, /)\n", " | Return a copy of the string with leading and trailing whitespace removed.\n", " | \n", " | If chars is given and not None, remove characters in chars instead.\n", " | \n", " | swapcase(self, /)\n", " | Convert uppercase characters to lowercase and lowercase characters to uppercase.\n", " | \n", " | title(self, /)\n", " | Return a version of the string where each word is titlecased.\n", " | \n", " | More specifically, words start with uppercased characters and all remaining\n", " | cased characters have lower case.\n", " | \n", " | translate(self, table, /)\n", " | Replace each character in the string using the given translation table.\n", " | \n", " | table\n", " | Translation table, which must be a mapping of Unicode ordinals to\n", " | Unicode ordinals, strings, or None.\n", " | \n", " | The table must implement lookup/indexing via __getitem__, for instance a\n", " | dictionary or list. If this operation raises LookupError, the character is\n", " | left untouched. Characters mapped to None are deleted.\n", " | \n", " | upper(self, /)\n", " | Return a copy of the string converted to uppercase.\n", " | \n", " | zfill(self, width, /)\n", " | Pad a numeric string with zeros on the left, to fill a field of the given width.\n", " | \n", " | The string is never truncated.\n", " | \n", " | ----------------------------------------------------------------------\n", " | Static methods defined here:\n", " | \n", " | __new__(*args, **kwargs) from builtins.type\n", " | Create and return a new object. See help(type) for accurate signature.\n", " | \n", " | maketrans(...)\n", " | Return a translation table usable for str.translate().\n", " | \n", " | If there is only one argument, it must be a dictionary mapping Unicode\n", " | ordinals (integers) or characters to Unicode ordinals, strings or None.\n", " | Character keys will be then converted to ordinals.\n", " | If there are two arguments, they must be strings of equal length, and\n", " | in the resulting dictionary, each character in x will be mapped to the\n", " | character at the same position in y. If there is a third argument, it\n", " | must be a string, whose characters will be mapped to None in the result.\n", "\n" ] } ], "source": [ "help(str)" ] }, { "cell_type": "code", "execution_count": 98, "id": "a795fbdb", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "False" ] }, "execution_count": 98, "metadata": {}, "output_type": "execute_result" } ], "source": [ "'raj123' == 'RAJ123'" ] }, { "cell_type": "code", "execution_count": 100, "id": "78f9fd1d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "raj123\n" ] } ], "source": [ "s = 'RAJ123'\n", "\n", "print(s.casefold())" ] }, { "cell_type": "code", "execution_count": 101, "id": "d7d25e0d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "INDIA\n" ] } ], "source": [ "s = 'India'\n", "\n", "print(s.upper())" ] }, { "cell_type": "code", "execution_count": 102, "id": "4965c3b0", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "india\n" ] } ], "source": [ "print(s.lower())" ] }, { "cell_type": "code", "execution_count": 103, "id": "feb1f94f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['This', 'is', 'an', 'apple']\n" ] } ], "source": [ "# split\n", "\n", "s = \"This is an apple\"\n", "\n", "print(s.split(\" \"))" ] }, { "cell_type": "code", "execution_count": 105, "id": "d1231581", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['H', 'llo Us', 'r']\n" ] } ], "source": [ "s = \"Hello User\"\n", "\n", "print(s.split(\"e\"))" ] }, { "cell_type": "code", "execution_count": 106, "id": "2deaf22c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "This+is+an+apple\n" ] } ], "source": [ "l = ['This', 'is', 'an', 'apple']\n", "\n", "a = \"+\"\n", "\n", "print(a.join(l))" ] }, { "cell_type": "code", "execution_count": 107, "id": "d7bf333b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "This is an apple\n" ] } ], "source": [ "l = ['This', 'is', 'an', 'apple']\n", "\n", "a = \" \"\n", "\n", "print(a.join(l))" ] }, { "cell_type": "code", "execution_count": 108, "id": "99c38b4d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Thisisanapple\n" ] } ], "source": [ "s = \"This is an apple\"\n", "\n", "l = s.split(\" \")\n", "\n", "x = \"\".join(l)\n", "\n", "print(x)" ] }, { "cell_type": "markdown", "id": "6c209c90", "metadata": {}, "source": [ "**WAP to enter a string from user and print the biggest word from the paragraph**" ] }, { "cell_type": "code", "execution_count": null, "id": "92b19368", "metadata": {}, "outputs": [], "source": [ "text = input(\"Enter your para: \")\n", "\n" ] }, { "cell_type": "code", "execution_count": 109, "id": "78217b1a", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "False" ] }, "execution_count": 109, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\"abc\" > \"def\"" ] }, { "cell_type": "code", "execution_count": null, "id": "16d89cc5", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.12" } }, "nbformat": 4, "nbformat_minor": 5 } ================================================ FILE: March Cohort 2023/.ipynb_checkpoints/3_For Loop Dictionary Tuple and Sets-checkpoint.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "id": "958dd5a9", "metadata": {}, "source": [ "# Range function" ] }, { "cell_type": "code", "execution_count": 1, "id": "ecc0f5d9", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "range(0, 5)\n" ] } ], "source": [ "n = range(5)\n", "\n", "print(n)" ] }, { "cell_type": "code", "execution_count": 2, "id": "a414c992", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "print(type(n))" ] }, { "cell_type": "code", "execution_count": 3, "id": "c50d39c0", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0, 1, 2, 3, 4]\n" ] } ], "source": [ "n = list(range(5))\n", "\n", "print(n)" ] }, { "cell_type": "code", "execution_count": 4, "id": "8ff4b4f0", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[3, 4, 5, 6, 7]\n" ] } ], "source": [ "n = list(range(3,8))\n", "\n", "print(n)" ] }, { "cell_type": "code", "execution_count": 5, "id": "c82ff6db", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "True\n" ] } ], "source": [ "print(range(20) == range(0,20))" ] }, { "cell_type": "code", "execution_count": 6, "id": "14dbda38", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[5, 7, 9, 11, 13, 15, 17, 19]\n" ] } ], "source": [ "n = list(range(5,20,2))\n", "\n", "print(n)" ] }, { "cell_type": "markdown", "id": "f4b1e93a", "metadata": {}, "source": [ "# for loop" ] }, { "cell_type": "code", "execution_count": 8, "id": "a1e6f836", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "False\n" ] } ], "source": [ "l = [\"A\", 'B', 'C', 'D']\n", "\n", "print(\"Z\" in l)" ] }, { "cell_type": "code", "execution_count": 9, "id": "fe7eb160", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "A\n", "B\n", "C\n", "D\n" ] } ], "source": [ "l = [\"A\", 'B', 'C', 'D']\n", "# i\n", "\n", "# \"D\"in l\n", "for i in l:\n", " print(i)" ] }, { "cell_type": "code", "execution_count": 10, "id": "c237efad", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "A\n", "Hello\n", "B\n", "Hello\n", "C\n", "Hello\n", "D\n", "Hello\n" ] } ], "source": [ "l = [\"A\", 'B', 'C', 'D']\n", "\n", "for i in l:\n", " print(i)\n", " print(\"Hello\")" ] }, { "cell_type": "code", "execution_count": 11, "id": "6efc03f4", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "a\n", "b\n", "c\n", "d\n" ] } ], "source": [ "s = \"abcd\"\n", "# i\n", "\n", "for i in s:\n", " print(i)" ] }, { "cell_type": "code", "execution_count": 12, "id": "0d1d9074", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'AB'" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "'ab'.upper()" ] }, { "cell_type": "code", "execution_count": 13, "id": "754cc5de", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['ab', 'cd']\n" ] } ], "source": [ "x = ['ab', 'cd']\n", "# i\n", "\n", "for i in x:\n", " i.upper() # 'ab'.upper() - 'AB'\n", "\n", "print(x)" ] }, { "cell_type": "code", "execution_count": 14, "id": "8ba916b9", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "AB\n", "CD\n", "['ab', 'cd']\n" ] } ], "source": [ "x = ['ab', 'cd']\n", "# i\n", "\n", "for i in x:\n", " print(i.upper()) # 'AB', 'CD'\n", "\n", "print(x)" ] }, { "cell_type": "code", "execution_count": 15, "id": "7835c572", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "a\n", "b\n", "c\n", "d\n" ] } ], "source": [ "x = 'abcd'\n", "\n", "for i in x:\n", " print(i)\n", " x.upper()" ] }, { "cell_type": "code", "execution_count": 16, "id": "0ce5284c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "A\n", "B\n", "C\n", "D\n" ] } ], "source": [ "x = 'abcd'\n", "\n", "for i in x:\n", " print(i.upper())" ] }, { "cell_type": "code", "execution_count": 17, "id": "4f9188d4", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0\n", "1\n", "2\n", "3\n", "4\n" ] } ], "source": [ "# i\n", "# 0,1,2,3,4\n", "for i in range(5):\n", " print(i)" ] }, { "cell_type": "code", "execution_count": 18, "id": "fab18b0b", "metadata": {}, "outputs": [ { "ename": "TypeError", "evalue": "'str' object cannot be interpreted as an integer", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", "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", "\u001b[1;31mTypeError\u001b[0m: 'str' object cannot be interpreted as an integer" ] } ], "source": [ "x = 'abcd'\n", "\n", "for i in range(x):\n", " print(i)" ] }, { "cell_type": "code", "execution_count": 19, "id": "1044b515", "metadata": {}, "outputs": [ { "ename": "AttributeError", "evalue": "'int' object has no attribute 'upper'", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)", "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", "\u001b[1;31mAttributeError\u001b[0m: 'int' object has no attribute 'upper'" ] } ], "source": [ "x = 'abcd'\n", "\n", "# 0,1,2,3\n", "for i in range(len(x)):\n", " print(i.upper())" ] }, { "cell_type": "code", "execution_count": 20, "id": "9594cd6a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0\n", "1\n", "2\n", "3\n" ] } ], "source": [ "x = 'abcd'\n", "\n", "# 0,1,2,3\n", "for i in range(len(x)):\n", " print(i)" ] }, { "cell_type": "code", "execution_count": 21, "id": "0f7f2436", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2\n", "3\n", "4\n", "5\n" ] } ], "source": [ "for i in range(2,6):\n", " print(i)" ] }, { "cell_type": "code", "execution_count": 22, "id": "cab0c2b5", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "abcd\n" ] } ], "source": [ "x = 'abcd'\n", "\n", "# 0,1,2,3\n", "for i in range(len(x)):\n", " x[i].upper()\n", "\n", "print(x)" ] }, { "cell_type": "code", "execution_count": 23, "id": "e2525fbc", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "a\n", "a\n", "a\n", "a\n" ] } ], "source": [ "x = 'abcd'\n", "\n", "# 0,1,2,3\n", "for i in range(len(x)):\n", " x = 'a'\n", " print(x)" ] }, { "cell_type": "code", "execution_count": 24, "id": "d53ac18d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "abcd\n", "a\n", "a\n", "a\n" ] } ], "source": [ "x = 'abcd'\n", "\n", "# 0,1,2,3\n", "for i in range(len(x)):\n", " print(x)\n", " x = 'a'" ] }, { "cell_type": "markdown", "id": "6ab70d2a", "metadata": {}, "source": [ "# Dictionary" ] }, { "cell_type": "code", "execution_count": null, "id": "e22d4eef", "metadata": {}, "outputs": [], "source": [ "l = [1,2,3,4,54]\n", "# 0 1 2 3 4\n", "\n", "# hashing\n", "\n", "# [, , ,44 , 55,]\n", "# 45\n", "# 65\n", "\n", "55 % 10 = 5 ---> \n", "44 % 10 = 4\n", "45 % 10 = 5\n", "65 % 10 = 5" ] }, { "cell_type": "code", "execution_count": null, "id": "c47a4099", "metadata": {}, "outputs": [], "source": [ "# JSON\n", "\n", "{\n", " \"username\": \"hemansnation\",\n", " \"password\": \"12334545\"\n", "}" ] }, { "cell_type": "code", "execution_count": 25, "id": "e5b3ba3d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'ravi': 24, 'viky': 21, 'roy': 36}\n" ] } ], "source": [ "# key: value\n", "\n", "d = {\"ravi\": 24, \"viky\": 21, \"roy\": 36}\n", "\n", "print(d)" ] }, { "cell_type": "code", "execution_count": 26, "id": "c846e290", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "print(type(d))" ] }, { "cell_type": "code", "execution_count": null, "id": "ea68592a", "metadata": {}, "outputs": [], "source": [ "# unordered data structures" ] }, { "cell_type": "code", "execution_count": 27, "id": "7a710f26", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "36\n" ] } ], "source": [ "print(d['roy'])" ] }, { "cell_type": "code", "execution_count": 28, "id": "b7cd33ff", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "24\n" ] } ], "source": [ "print(d['ravi'])" ] }, { "cell_type": "code", "execution_count": 29, "id": "53e9fcdd", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[255, 0, 0]\n" ] } ], "source": [ "color = {\n", " \"red\": [255,0,0],\n", " \"green\": [0,255,0],\n", " \"blue\": [0,0,255]\n", "}\n", "print(color['red'])" ] }, { "cell_type": "code", "execution_count": 30, "id": "1ddbc289", "metadata": {}, "outputs": [ { "ename": "KeyError", "evalue": "'yellow'", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", "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", "\u001b[1;31mKeyError\u001b[0m: 'yellow'" ] } ], "source": [ "print(color['yellow'])" ] }, { "cell_type": "code", "execution_count": 31, "id": "a7eeec1c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{}\n", "\n" ] } ], "source": [ "d = {}\n", "\n", "print(d)\n", "\n", "print(type(d))" ] }, { "cell_type": "code", "execution_count": 32, "id": "c86ce0ee", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1\n" ] } ], "source": [ "d = {1:1, 2:2, 3:4}\n", "\n", "print(d[1])" ] }, { "cell_type": "code", "execution_count": 33, "id": "9f399123", "metadata": {}, "outputs": [ { "ename": "TypeError", "evalue": "unhashable type: 'list'", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", "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", "\u001b[1;31mTypeError\u001b[0m: unhashable type: 'list'" ] } ], "source": [ "d = {[1,2]:1, 2:2, 3:4}\n", "\n", "print(d[1,2])" ] }, { "cell_type": "code", "execution_count": 34, "id": "d166c563", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[100, 20]\n" ] } ], "source": [ "# mutable object - list\n", "\n", "l = [10,20]\n", "\n", "l[0] = 100\n", "\n", "print(l)" ] }, { "cell_type": "code", "execution_count": 35, "id": "a8d2e6d7", "metadata": {}, "outputs": [ { "ename": "TypeError", "evalue": "'str' object does not support item assignment", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", "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", "\u001b[1;31mTypeError\u001b[0m: 'str' object does not support item assignment" ] } ], "source": [ "s = \"India\" # immutable objects\n", "\n", "s[0] = \"A\"\n", "\n", "print(s)" ] }, { "cell_type": "code", "execution_count": null, "id": "d78cdcd4", "metadata": {}, "outputs": [], "source": [ "# int are immutable\n", "\n", "1 + 1 = 2" ] }, { "cell_type": "markdown", "id": "d3d32bf3", "metadata": {}, "source": [ "# Dictionary Functions" ] }, { "cell_type": "code", "execution_count": 36, "id": "b57d9f4a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "4\n" ] } ], "source": [ "d = { 1:1, 2:4, 3:9, 4:16}\n", "\n", "print(len(d))" ] }, { "cell_type": "code", "execution_count": 37, "id": "788f5c87", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10\n" ] } ], "source": [ "print(sum(d))" ] }, { "cell_type": "code", "execution_count": 38, "id": "63a2eb0e", "metadata": {}, "outputs": [ { "ename": "TypeError", "evalue": "unsupported operand type(s) for +: 'int' and 'str'", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", "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", "\u001b[1;31mTypeError\u001b[0m: unsupported operand type(s) for +: 'int' and 'str'" ] } ], "source": [ "a = {1:1,2:4, \"hi\":16}\n", "\n", "print(sum(a))" ] }, { "cell_type": "code", "execution_count": 39, "id": "650cf24f", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{1: 1, 2: 4, 3: 9, 4: 16}" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d" ] }, { "cell_type": "code", "execution_count": 40, "id": "d78c3139", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "4\n" ] } ], "source": [ "print(max(d))" ] }, { "cell_type": "code", "execution_count": 41, "id": "08fe0e6e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1\n" ] } ], "source": [ "print(min(d))" ] }, { "cell_type": "markdown", "id": "1a16abb5", "metadata": {}, "source": [ "# Dictionary Methods" ] }, { "cell_type": "code", "execution_count": 43, "id": "9a2f7d81", "metadata": { "scrolled": false }, "outputs": [], "source": [ "# help(dict)" ] }, { "cell_type": "code", "execution_count": 44, "id": "5d682511", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{1: 'Apple', 'orange': [2, 3, 4], '2': '3'}\n" ] } ], "source": [ "pair = { 1: \"Apple\", \"orange\": [2,3,4], \"2\": \"3\"}\n", "\n", "print(pair)" ] }, { "cell_type": "code", "execution_count": 45, "id": "1374c33c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[2, 3, 4]\n" ] } ], "source": [ "# get\n", "\n", "print(pair.get(\"orange\"))" ] }, { "cell_type": "code", "execution_count": 48, "id": "49f1f231", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[2, 3, 4]\n" ] } ], "source": [ "print(pair[\"orange\"])" ] }, { "cell_type": "code", "execution_count": 49, "id": "0538283b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "None\n" ] } ], "source": [ "print(pair.get(7))" ] }, { "cell_type": "code", "execution_count": null, "id": "6a643576", "metadata": {}, "outputs": [], "source": [ "None => NULL # nothing" ] }, { "cell_type": "code", "execution_count": 50, "id": "ab1ab099", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Apple\n" ] } ], "source": [ "print(pair.get(1, \"Not Available\"))" ] }, { "cell_type": "code", "execution_count": 51, "id": "26d164e1", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Not Available\n" ] } ], "source": [ "print(pair.get(10, \"Not Available\"))" ] }, { "cell_type": "code", "execution_count": 52, "id": "0632cda6", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{1: 1, 2: 4, 3: 9, 4: 16}" ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# clear\n", "\n", "d" ] }, { "cell_type": "code", "execution_count": 53, "id": "cb797c19", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{}\n" ] } ], "source": [ "d.clear()\n", "\n", "print(d)" ] }, { "cell_type": "code", "execution_count": 54, "id": "406f5d55", "metadata": {}, "outputs": [ { "ename": "IndexError", "evalue": "list assignment index out of range", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mIndexError\u001b[0m Traceback (most recent call last)", "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", "\u001b[1;31mIndexError\u001b[0m: list assignment index out of range" ] } ], "source": [ "l = [10,20,30]\n", "\n", "l[3] = 100\n", "\n", "print(l)" ] }, { "cell_type": "code", "execution_count": 55, "id": "45715900", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'A': 100, 'Z': 20}\n" ] } ], "source": [ "d = {\"A\": 10, \"Z\": 20}\n", "\n", "d[\"A\"] = 100\n", "\n", "print(d)" ] }, { "cell_type": "code", "execution_count": 56, "id": "3ab37c57", "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'A': 100, 'Z': 20, 'goa': 200}\n" ] } ], "source": [ "d[\"goa\"] = 200\n", "\n", "print(d)" ] }, { "cell_type": "markdown", "id": "f6f67a8d", "metadata": {}, "source": [ "# Question" ] }, { "cell_type": "code", "execution_count": null, "id": "f7c72ebe", "metadata": {}, "outputs": [], "source": [ "{1:1, 2:4, 3:9, 4:16, 5:25 ...........10:100}" ] }, { "cell_type": "markdown", "id": "2435adc7", "metadata": {}, "source": [ "# Tuple\n", "\n", "tuple are immutable" ] }, { "cell_type": "code", "execution_count": 57, "id": "b3e4ebb7", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(1, 2, 3, 4)\n", "\n" ] } ], "source": [ "t = (1,2,3,4)\n", "\n", "print(t)\n", "\n", "print(type(t))" ] }, { "cell_type": "code", "execution_count": 58, "id": "eed35b67", "metadata": {}, "outputs": [ { "ename": "TypeError", "evalue": "'tuple' object does not support item assignment", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", "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", "\u001b[1;31mTypeError\u001b[0m: 'tuple' object does not support item assignment" ] } ], "source": [ "t[0] = 100" ] }, { "cell_type": "code", "execution_count": 59, "id": "b630c971", "metadata": {}, "outputs": [ { "ename": "TypeError", "evalue": "'str' object does not support item assignment", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", "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", "\u001b[1;31mTypeError\u001b[0m: 'str' object does not support item assignment" ] } ], "source": [ "s = 'goa'\n", "\n", "s[0] = \"A\"" ] }, { "cell_type": "code", "execution_count": 60, "id": "cf15d6bb", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(5, 6, 67)\n" ] } ], "source": [ "t = (2,3,4,5,6,67)\n", "\n", "print(t[3:])" ] }, { "cell_type": "code", "execution_count": 61, "id": "05edf72b", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(2, 3, 4, 5, 6, 67)" ] }, "execution_count": 61, "metadata": {}, "output_type": "execute_result" } ], "source": [ "t" ] }, { "cell_type": "code", "execution_count": 62, "id": "0c3d2bc3", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "5\n" ] } ], "source": [ "t = (1,2,3,4,5)\n", "\n", "print(len(t))" ] }, { "cell_type": "code", "execution_count": 63, "id": "54aa0e3f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "15\n" ] } ], "source": [ "print(sum(t))" ] }, { "cell_type": "code", "execution_count": 64, "id": "f0052bd4", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "5\n" ] } ], "source": [ "print(max(t))" ] }, { "cell_type": "code", "execution_count": 65, "id": "28a92019", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1\n" ] } ], "source": [ "print(min(t))" ] }, { "cell_type": "code", "execution_count": 66, "id": "7885f892", "metadata": {}, "outputs": [], "source": [ "# -1\n", "t = (\"A\", 1,2,4)\n", "# 0 1 2 3" ] }, { "cell_type": "code", "execution_count": 67, "id": "da09ee4f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "('A', 1, 2, 4)\n" ] } ], "source": [ "print(t)" ] }, { "cell_type": "code", "execution_count": 68, "id": "b337cd33", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "A\n" ] } ], "source": [ "print(t[0])" ] }, { "cell_type": "code", "execution_count": 69, "id": "504bca81", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "4\n" ] } ], "source": [ "print(t[-1])" ] }, { "cell_type": "code", "execution_count": 70, "id": "3be05cc3", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "()\n", "\n" ] } ], "source": [ "t = ()\n", "\n", "print(t)\n", "\n", "print(type(t))" ] }, { "cell_type": "code", "execution_count": 71, "id": "915d9c00", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10\n", "\n" ] } ], "source": [ "t = (10)\n", "\n", "print(t)\n", "\n", "print(type(t))" ] }, { "cell_type": "code", "execution_count": 72, "id": "54ec18a0", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(10,)\n", "\n" ] } ], "source": [ "t = (10,)\n", "\n", "print(t)\n", "\n", "print(type(t))" ] }, { "cell_type": "markdown", "id": "2792fe85", "metadata": {}, "source": [ "# Tuple Methods" ] }, { "cell_type": "code", "execution_count": 73, "id": "5bd0d610", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2\n" ] } ], "source": [ "t = (2,3,4,5,5,3,3,3,7)\n", "\n", "print(t.count(5))" ] }, { "cell_type": "code", "execution_count": 74, "id": "79899432", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1\n" ] } ], "source": [ "print(t.count(7))" ] }, { "cell_type": "code", "execution_count": 75, "id": "4b0c56f1", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0\n" ] } ], "source": [ "print(t.count(10))" ] }, { "cell_type": "code", "execution_count": 76, "id": "b17ad603", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(2, 3, 4, 5, 5, 3, 3, 3, 7)\n" ] } ], "source": [ "# index\n", "\n", "print(t)" ] }, { "cell_type": "code", "execution_count": 77, "id": "7f10fbae", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "3\n" ] } ], "source": [ "print(t.index(5))" ] }, { "cell_type": "code", "execution_count": 78, "id": "e6811f4e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Help on built-in function index:\n", "\n", "index(value, start=0, stop=9223372036854775807, /) method of builtins.tuple instance\n", " Return first index of value.\n", " \n", " Raises ValueError if the value is not present.\n", "\n" ] } ], "source": [ "help(t.index)" ] }, { "cell_type": "code", "execution_count": 79, "id": "068bf8b3", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(2, 3, 4, 5, 5, 3, 3, 3, 7)" ] }, "execution_count": 79, "metadata": {}, "output_type": "execute_result" } ], "source": [ "t" ] }, { "cell_type": "code", "execution_count": 81, "id": "a65ca47a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "4\n" ] } ], "source": [ "print(t.index(5,4))" ] }, { "cell_type": "markdown", "id": "df156bc0", "metadata": {}, "source": [ "# Questions " ] }, { "cell_type": "code", "execution_count": null, "id": "f623b5de", "metadata": {}, "outputs": [], "source": [ "t = ('a','e','i','o','u')" ] }, { "cell_type": "markdown", "id": "1f2328fc", "metadata": {}, "source": [ "# Sets\n", "\n", "unordered and unindex\n", "\n", "sets are mutable" ] }, { "cell_type": "code", "execution_count": 82, "id": "11b203a2", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{1, 2, 3, 4, 5, 6}\n", "\n" ] } ], "source": [ "n = {1,2,3,4,5,6}\n", "\n", "print(n)\n", "\n", "print(type(n))" ] }, { "cell_type": "code", "execution_count": 83, "id": "615a8e7e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{1, 2, 3, 4, 5, 6}\n", "\n" ] } ], "source": [ "n = {1,2,3,4,5,6,6,6,6,5,5}\n", "\n", "print(n)\n", "\n", "print(type(n))" ] }, { "cell_type": "code", "execution_count": 84, "id": "d7bf4db2", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "set()\n" ] } ], "source": [ "s = set()\n", "\n", "print(s)" ] }, { "cell_type": "code", "execution_count": 86, "id": "98a00a15", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'b', 4, 'a'}\n" ] } ], "source": [ "n = set(['a','b', 4])\n", "\n", "print(n)" ] }, { "cell_type": "code", "execution_count": 87, "id": "8d03b0ed", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1\n", "2\n", "3\n", "55\n", "44\n" ] } ], "source": [ "n = {1,2,3,44,55}\n", "\n", "for i in n:\n", " print(i)" ] }, { "cell_type": "markdown", "id": "dec2b22e", "metadata": {}, "source": [ "## set operations" ] }, { "cell_type": "code", "execution_count": 88, "id": "c961559b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{1, 2, 3, 4, 5, 6, 7, 8, 9}\n" ] } ], "source": [ "# union\n", "\n", "f = {1,2,3,4,5,6}\n", "s = {4,5,6,7,8,9}\n", "\n", "print(f | s)" ] }, { "cell_type": "code", "execution_count": 89, "id": "ab540fe2", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{4, 5, 6}\n" ] } ], "source": [ "# intersection\n", "\n", "f = {1,2,3,4,5,6}\n", "s = {4,5,6,7,8,9}\n", "\n", "print(f & s)" ] }, { "cell_type": "code", "execution_count": 90, "id": "abf57b8a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{1, 2, 3}\n" ] } ], "source": [ "# difference\n", "\n", "f = {1,2,3,4,5,6}\n", "s = {4,5,6,7,8,9}\n", "\n", "print(f - s)" ] }, { "cell_type": "code", "execution_count": 91, "id": "9a9f6ea6", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{8, 9, 7}\n" ] } ], "source": [ "print(s - f)" ] }, { "cell_type": "code", "execution_count": 92, "id": "88a67b87", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{1, 2, 3, 7, 8, 9}\n" ] } ], "source": [ "# symmetry\n", "\n", "f = {1,2,3,4,5,6}\n", "s = {4,5,6,7,8,9}\n", "\n", "print(f ^ s)" ] }, { "cell_type": "markdown", "id": "2682d1f9", "metadata": {}, "source": [ "# set functions" ] }, { "cell_type": "code", "execution_count": 93, "id": "553d14f2", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "4\n", "10\n", "4\n", "1\n" ] } ], "source": [ "s = {1,2,3,4}\n", "\n", "print(len(s))\n", "print(sum(s))\n", "print(max(s))\n", "print(min(s))" ] }, { "cell_type": "markdown", "id": "a144e8bf", "metadata": {}, "source": [ "# set methods" ] }, { "cell_type": "code", "execution_count": 95, "id": "ceb0c93f", "metadata": { "scrolled": true }, "outputs": [], "source": [ "# help(set)" ] }, { "cell_type": "code", "execution_count": 96, "id": "83fb99b3", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{1, 2, 3, 4}\n", "{1, 2, 3, 4, -7}\n" ] } ], "source": [ "# add\n", "\n", "n = {1,2,3,4}\n", "\n", "print(n)\n", "\n", "n.add(-7)\n", "\n", "print(n)" ] }, { "cell_type": "code", "execution_count": 97, "id": "2e99b696", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{1, 2, 3, 4, -7}\n", "set()\n" ] } ], "source": [ "print(n)\n", "n.clear()\n", "\n", "print(n)" ] }, { "cell_type": "code", "execution_count": 98, "id": "feade89d", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "set()" ] }, "execution_count": 98, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# remove\n", "\n", "n" ] }, { "cell_type": "code", "execution_count": 99, "id": "b83f2433", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{1, 2, 4}\n" ] } ], "source": [ "n = {1,2,3,4}\n", "\n", "n.remove(3)\n", "print(n)" ] }, { "cell_type": "code", "execution_count": null, "id": "3730faed", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.12" } }, "nbformat": 4, "nbformat_minor": 5 } ================================================ FILE: March Cohort 2023/.ipynb_checkpoints/4_Functions-checkpoint.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "id": "a98c255e", "metadata": {}, "source": [ "# Functions" ] }, { "cell_type": "code", "execution_count": 1, "id": "18360579", "metadata": {}, "outputs": [], "source": [ "def myfunction(): # definition of function\n", " print(\"Hey\")" ] }, { "cell_type": "code", "execution_count": 2, "id": "22e39c3e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Hey\n" ] } ], "source": [ "myfunction() # calling of function" ] }, { "cell_type": "code", "execution_count": 3, "id": "c24cd8e5", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1\n", "2\n", "3\n" ] } ], "source": [ "def paint():\n", " print(1)\n", " print(2)\n", " print(3)\n", "\n", "paint()" ] }, { "cell_type": "code", "execution_count": 4, "id": "1cd655a2", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1\n", "2\n", "3\n" ] } ], "source": [ "paint()" ] }, { "cell_type": "code", "execution_count": 5, "id": "f6b4813a", "metadata": {}, "outputs": [ { "ename": "NameError", "evalue": "name 'hello' is not defined", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", "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", "\u001b[1;31mNameError\u001b[0m: name 'hello' is not defined" ] } ], "source": [ "hello()\n", "\n", "def hello():\n", " print(\"Hello\")" ] }, { "cell_type": "code", "execution_count": 6, "id": "28ae369c", "metadata": {}, "outputs": [ { "ename": "TypeError", "evalue": "indore() missing 1 required positional argument: 'a'", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", "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", "\u001b[1;31mTypeError\u001b[0m: indore() missing 1 required positional argument: 'a'" ] } ], "source": [ "def indore(a): # a = \"A\"\n", " print(a)\n", "\n", "indore()" ] }, { "cell_type": "code", "execution_count": 7, "id": "52bc36b9", "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10\n" ] } ], "source": [ "indore(10)" ] }, { "cell_type": "code", "execution_count": 8, "id": "527fe9e4", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "A\n" ] } ], "source": [ "indore(\"A\")" ] }, { "cell_type": "code", "execution_count": 9, "id": "4af5fdc0", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "right\n" ] } ], "source": [ "indore(\"right\")" ] }, { "cell_type": "code", "execution_count": 10, "id": "aa11eea7", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1\n", "One\n", "2\n", "Two\n", "right\n", "three\n" ] } ], "source": [ "indore(1)\n", "print(\"One\")\n", "indore(2)\n", "print(\"Two\")\n", "indore(\"right\")\n", "print(\"three\")" ] }, { "cell_type": "code", "execution_count": 11, "id": "84a3b11a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "9\n" ] } ], "source": [ "def add(x, y):\n", " print(x + y)\n", "\n", "add(5,4)" ] }, { "cell_type": "markdown", "id": "74774c31", "metadata": {}, "source": [ "# Return statement" ] }, { "cell_type": "code", "execution_count": 12, "id": "bd50418a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "7\n" ] } ], "source": [ "def play(x, y):\n", " if x >= y:\n", " return x\n", " else:\n", " return y\n", "\n", "print(play(4,7))\n", "# 7" ] }, { "cell_type": "code", "execution_count": 13, "id": "86b599e4", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "8\n" ] } ], "source": [ "z = play(8,5)\n", "#z = 8\n", "\n", "print(z)" ] }, { "cell_type": "code", "execution_count": 14, "id": "ad6e3060", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "9\n" ] } ], "source": [ "def sub(x, y):\n", " t = x + y\n", " return t\n", " print(\"Hi\")\n", "\n", "print(sub(4,5))" ] }, { "cell_type": "code", "execution_count": 18, "id": "8cc83a2f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1\n", "2\n", "hello\n" ] } ], "source": [ "def pre():\n", " print(1)\n", " print(2)\n", " print(\"hello\")\n", " return\n", " print(3)\n", " print(4)\n", "\n", "pre()" ] }, { "cell_type": "code", "execution_count": 19, "id": "cb329c92", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1\n", "2\n", "hello\n", "None\n" ] } ], "source": [ "print(pre())" ] }, { "cell_type": "code", "execution_count": 17, "id": "a463dbf9", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1\n", "2\n", "None\n" ] } ], "source": [ "r = pre()\n", "\n", "print(r)" ] }, { "cell_type": "code", "execution_count": null, "id": "280c0539", "metadata": {}, "outputs": [], "source": [ "None ==> NULL ==> nothing" ] }, { "cell_type": "code", "execution_count": 20, "id": "79b26fc0", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "None\n" ] } ], "source": [ "print(None)" ] }, { "cell_type": "code", "execution_count": 21, "id": "f781cf14", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Hi\n", "None\n" ] } ], "source": [ "print(print(\"Hi\"))\n", "# print(None)" ] }, { "cell_type": "markdown", "id": "d25d6edf", "metadata": {}, "source": [ "# Default Argument" ] }, { "cell_type": "code", "execution_count": 22, "id": "f6141d42", "metadata": {}, "outputs": [ { "ename": "TypeError", "evalue": "can only concatenate str (not \"int\") to str", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", "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", "\u001b[1;31mTypeError\u001b[0m: can only concatenate str (not \"int\") to str" ] } ], "source": [ "\"hi\" + 6" ] }, { "cell_type": "code", "execution_count": 23, "id": "c3edf839", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'hihihi'" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\"hi\" * 3" ] }, { "cell_type": "code", "execution_count": 24, "id": "2456f1b9", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Hello\n" ] } ], "source": [ "def say(msg, time = 1):\n", " print(msg * time)\n", "\n", "say(\"Hello\")" ] }, { "cell_type": "code", "execution_count": 25, "id": "4b65a1da", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "ByeByeByeByeBye\n" ] } ], "source": [ "say(\"Bye\", 5)" ] }, { "cell_type": "code", "execution_count": 27, "id": "a6382071", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Hello+++Indore\n" ] } ], "source": [ "print(\"Hello\", end='+++')\n", "print(\"Indore\")" ] }, { "cell_type": "code", "execution_count": 28, "id": "d7115a71", "metadata": {}, "outputs": [], "source": [ "def said(a, b = 5, c = 10):\n", " print(a)\n", " print(b)\n", " print(c)" ] }, { "cell_type": "code", "execution_count": 29, "id": "811ff6b8", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "3\n", "7\n", "10\n" ] } ], "source": [ "said(3,7)" ] }, { "cell_type": "code", "execution_count": 30, "id": "dbd412b0", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "25\n", "5\n", "24\n" ] } ], "source": [ "said(25, c = 24)" ] }, { "cell_type": "code", "execution_count": 31, "id": "c29189f6", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "15\n", "5\n", "100\n" ] } ], "source": [ "said(c = 100, a = 15)" ] }, { "cell_type": "code", "execution_count": 32, "id": "e9d0ccbd", "metadata": {}, "outputs": [ { "ename": "SyntaxError", "evalue": "positional argument follows keyword argument (2634499495.py, line 1)", "output_type": "error", "traceback": [ "\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" ] } ], "source": [ "said(c = 19, 20, 30)" ] }, { "cell_type": "code", "execution_count": 33, "id": "25993e69", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0\n" ] } ], "source": [ "i = 0 # i is a global variable\n", "\n", "def change(i): # i is a local variable\n", " i += 1 # i = 2\n", " return 1\n", "\n", "change(1) # 1\n", "print(i)" ] }, { "cell_type": "code", "execution_count": 34, "id": "30f844d2", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "8\n", "3\n" ] } ], "source": [ "x = 3 # global\n", "\n", "def add(x): # local\n", " x += 1 # x = x + 1\n", " print(x)\n", "\n", "add(7)\n", "print(x)" ] }, { "cell_type": "code", "execution_count": 35, "id": "bdce8f2a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "11\n" ] }, { "ename": "NameError", "evalue": "name 'v' is not defined", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", "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", "\u001b[1;31mNameError\u001b[0m: name 'v' is not defined" ] } ], "source": [ "def add(v):\n", " v += 1\n", " print(v)\n", "\n", "add(10)\n", "print(v)" ] }, { "cell_type": "markdown", "id": "7578af9e", "metadata": {}, "source": [ "# Reference Function" ] }, { "cell_type": "code", "execution_count": 36, "id": "ead4ef51", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10\n" ] } ], "source": [ "a = 10\n", "\n", "b = a\n", "\n", "print(b)" ] }, { "cell_type": "code", "execution_count": 37, "id": "06f37e14", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10\n" ] } ], "source": [ "print(a)" ] }, { "cell_type": "code", "execution_count": 38, "id": "6b6bb7d6", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "print(type(add))" ] }, { "cell_type": "code", "execution_count": 39, "id": "fe8316b6", "metadata": {}, "outputs": [], "source": [ "def multiply(x, y):\n", " return x * y\n" ] }, { "cell_type": "code", "execution_count": 40, "id": "08e7fd6a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "200\n" ] } ], "source": [ "indore = multiply\n", "\n", "print(indore(10,20))\n", "# multiply(10,20)" ] }, { "cell_type": "code", "execution_count": 41, "id": "1578b9d0", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "15\n" ] } ], "source": [ "print(multiply(3,5))" ] }, { "cell_type": "markdown", "id": "9c39298b", "metadata": {}, "source": [ "# functions and an argument" ] }, { "cell_type": "code", "execution_count": 42, "id": "28047e15", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "30\n" ] } ], "source": [ "def sub(x, y):\n", " return x + y\n", "\n", "def do(red, x, y): # red = sub, x = 5, y = 10\n", " return red(red(x,y), red(x,y))\n", "# sub(sub(5,10),sub(5,10))\n", "# sub( 15 , 15 )\n", "# 30\n", "\n", "a = 5\n", "b = 10\n", "\n", "\n", "print(do(sub, a, b))\n", "# 30" ] }, { "cell_type": "code", "execution_count": 43, "id": "82b1bbd9", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "81\n" ] } ], "source": [ "def square(x):\n", " return x * x\n", "\n", "def test(blue, x):\n", " print(blue(x))\n", "\n", "test(square, 9)" ] }, { "cell_type": "code", "execution_count": 44, "id": "f438ee68", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "3\n", "1\n" ] } ], "source": [ "def green(red, a):\n", " print(red(a))\n", "\n", "green(max, [1,2,3])\n", "green(min, [1,2,3])" ] }, { "cell_type": "code", "execution_count": 45, "id": "51c59408", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0\n" ] } ], "source": [ "def printn(x):\n", " for i in range(x):\n", " print(i)\n", " return\n", "\n", "printn(10)" ] }, { "cell_type": "markdown", "id": "3e07954c", "metadata": {}, "source": [ "# doc string" ] }, { "cell_type": "code", "execution_count": 46, "id": "b6cc4e7d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Help on built-in function max in module builtins:\n", "\n", "max(...)\n", " max(iterable, *[, default=obj, key=func]) -> value\n", " max(arg1, arg2, *args, *[, key=func]) -> value\n", " \n", " With a single iterable argument, return its biggest item. The\n", " default keyword-only argument specifies an object to return if\n", " the provided iterable is empty.\n", " With two or more arguments, return the largest argument.\n", "\n" ] } ], "source": [ "help(max)" ] }, { "cell_type": "code", "execution_count": 48, "id": "6596e128", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "this \n", "is an\n", "apple.\n", "\n", "\n" ] } ], "source": [ "# this is a single line comment\n", "\n", "s = '''this \n", "is an\n", "apple.\n", "'''\n", "print(s)\n", "print(type(s))" ] }, { "cell_type": "code", "execution_count": 50, "id": "53161af0", "metadata": {}, "outputs": [], "source": [ "def function_one():\n", " '''\n", " this function print 10,\n", " also this funtion will print 5\n", " '''\n", " print(10)\n", " print(5)" ] }, { "cell_type": "code", "execution_count": 51, "id": "2156a9ff", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10\n", "5\n" ] } ], "source": [ "function_one()" ] }, { "cell_type": "code", "execution_count": 52, "id": "ad1302c3", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Help on function function_one in module __main__:\n", "\n", "function_one()\n", " this function print 10,\n", " also this funtion will print 5\n", "\n" ] } ], "source": [ "help(function_one)" ] }, { "cell_type": "code", "execution_count": 53, "id": "50e6b7cb", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", " this function print 10,\n", " also this funtion will print 5\n", " \n" ] } ], "source": [ "print(function_one.__doc__) # dunder" ] }, { "cell_type": "markdown", "id": "a1973a4e", "metadata": {}, "source": [ "# Map Function" ] }, { "cell_type": "code", "execution_count": 54, "id": "66ca8c7e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Help on class map in module builtins:\n", "\n", "class map(object)\n", " | map(func, *iterables) --> map object\n", " | \n", " | Make an iterator that computes the function using arguments from\n", " | each of the iterables. Stops when the shortest iterable is exhausted.\n", " | \n", " | Methods defined here:\n", " | \n", " | __getattribute__(self, name, /)\n", " | Return getattr(self, name).\n", " | \n", " | __iter__(self, /)\n", " | Implement iter(self).\n", " | \n", " | __next__(self, /)\n", " | Implement next(self).\n", " | \n", " | __reduce__(...)\n", " | Return state information for pickling.\n", " | \n", " | ----------------------------------------------------------------------\n", " | Static methods defined here:\n", " | \n", " | __new__(*args, **kwargs) from builtins.type\n", " | Create and return a new object. See help(type) for accurate signature.\n", "\n" ] } ], "source": [ "help(map)" ] }, { "cell_type": "code", "execution_count": 55, "id": "f473bded", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[6, 7, 8, 9, 10]\n" ] } ], "source": [ "def add(x):\n", " return x + 5\n", "\n", "n = [1,2,3,4,5]\n", "\n", "r = list(map(add, n))\n", "# 6, 7, 8, 9, 10\n", "\n", "print(r)" ] }, { "cell_type": "code", "execution_count": 56, "id": "aeb1a9de", "metadata": {}, "outputs": [ { "ename": "TypeError", "evalue": "'int' object is not iterable", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", "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", "\u001b[1;31mTypeError\u001b[0m: 'int' object is not iterable" ] } ], "source": [ "def add(x):\n", " return x + 5\n", "\n", "n = 7\n", "\n", "r = list(map(add, n))\n", "\n", "print(r)" ] }, { "cell_type": "markdown", "id": "cbaca289", "metadata": {}, "source": [ "# lambda function\n", "\n", "anonymous function" ] }, { "cell_type": "code", "execution_count": 57, "id": "fef01377", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "343\n" ] } ], "source": [ "def cube(y):\n", " return y * y * y\n", "\n", "\n", "# lambda argument: expresion\n", "\n", "g = lambda x: x * x * x\n", "\n", "print(g(7))" ] }, { "cell_type": "code", "execution_count": 59, "id": "477123f3", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "343\n" ] } ], "source": [ "print(cube(7))" ] }, { "cell_type": "markdown", "id": "5caf7279", "metadata": {}, "source": [ "# lambda with map function" ] }, { "cell_type": "code", "execution_count": 60, "id": "00915aaf", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1, 4, 9, 16, 25]\n" ] } ], "source": [ "n = [1,2,3,4,5]\n", "\n", "r = list(map(lambda y: y * y, n))\n", "# 1, 4, 9, 16, 25\n", "\n", "print(r)" ] }, { "cell_type": "markdown", "id": "0bc6d570", "metadata": {}, "source": [ "# filter function" ] }, { "cell_type": "code", "execution_count": 61, "id": "74819624", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Help on class filter in module builtins:\n", "\n", "class filter(object)\n", " | filter(function or None, iterable) --> filter object\n", " | \n", " | Return an iterator yielding those items of iterable for which function(item)\n", " | is true. If function is None, return the items that are true.\n", " | \n", " | Methods defined here:\n", " | \n", " | __getattribute__(self, name, /)\n", " | Return getattr(self, name).\n", " | \n", " | __iter__(self, /)\n", " | Implement iter(self).\n", " | \n", " | __next__(self, /)\n", " | Implement next(self).\n", " | \n", " | __reduce__(...)\n", " | Return state information for pickling.\n", " | \n", " | ----------------------------------------------------------------------\n", " | Static methods defined here:\n", " | \n", " | __new__(*args, **kwargs) from builtins.type\n", " | Create and return a new object. See help(type) for accurate signature.\n", "\n" ] } ], "source": [ "help(filter)" ] }, { "cell_type": "code", "execution_count": 62, "id": "166df9b8", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[22, 44]\n" ] } ], "source": [ "n = [11,22,33,44,55]\n", "\n", "r = list(filter(lambda x: x%2 == 0 , n))\n", "# 22, 44\n", "\n", "print(r)" ] }, { "cell_type": "code", "execution_count": null, "id": "6bb659fc", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.12" } }, "nbformat": 4, "nbformat_minor": 5 } ================================================ FILE: March Cohort 2023/.ipynb_checkpoints/5_Modules and Packages-checkpoint.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "id": "507b0a7e", "metadata": {}, "source": [ "# OS\n", "\n", "operating system" ] }, { "cell_type": "code", "execution_count": null, "id": "b4a5e0c7", "metadata": {}, "outputs": [], "source": [ "# hello.py\n", "# something.exe\n", "# .pdf .jpg .png\n", "\n", "# os.py" ] }, { "cell_type": "code", "execution_count": 7, "id": "c43e9e98", "metadata": {}, "outputs": [], "source": [ "import os" ] }, { "cell_type": "code", "execution_count": 3, "id": "083fa6d1", "metadata": { "scrolled": false }, "outputs": [], "source": [ "# help(os)" ] }, { "cell_type": "code", "execution_count": 8, "id": "3533b409", "metadata": {}, "outputs": [], "source": [ "# make directory\n", "\n", "os.mkdir('prime')" ] }, { "cell_type": "code", "execution_count": 5, "id": "c73636df", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'C:\\\\Users\\\\himan\\\\Documents\\\\Himanshu Ramchandani Live\\\\Data-Analyst-Roadmap'" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# get current working directory\n", "\n", "os.getcwd()" ] }, { "cell_type": "code", "execution_count": 9, "id": "8a5e48f9", "metadata": {}, "outputs": [], "source": [ "os.rmdir('prime')" ] }, { "cell_type": "markdown", "id": "deb43505", "metadata": {}, "source": [ "# sys" ] }, { "cell_type": "code", "execution_count": 13, "id": "fc53dc5b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "3\n" ] } ], "source": [ "import sys\n", "\n", "a = 5000\n", "\n", "print(sys.getrefcount(a)) # reference count of the object" ] }, { "cell_type": "code", "execution_count": 16, "id": "4973d1d1", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "432\n", "431\n" ] } ], "source": [ "a = 10\n", "\n", "b = a\n", "\n", "print(sys.getrefcount(a))\n", "\n", "del a\n", "\n", "print(sys.getrefcount(b))" ] }, { "cell_type": "code", "execution_count": 17, "id": "d26bf1fa", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "9223372036854775807" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sys.maxsize" ] }, { "cell_type": "code", "execution_count": 18, "id": "1a9e67d3", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['C:\\\\Users\\\\himan\\\\Documents\\\\Himanshu Ramchandani Live\\\\Data-Analyst-Roadmap',\n", " 'C:\\\\Users\\\\himan\\\\anaconda3\\\\python39.zip',\n", " 'C:\\\\Users\\\\himan\\\\anaconda3\\\\DLLs',\n", " 'C:\\\\Users\\\\himan\\\\anaconda3\\\\lib',\n", " 'C:\\\\Users\\\\himan\\\\anaconda3',\n", " '',\n", " 'C:\\\\Users\\\\himan\\\\anaconda3\\\\lib\\\\site-packages',\n", " 'C:\\\\Users\\\\himan\\\\anaconda3\\\\lib\\\\site-packages\\\\win32',\n", " 'C:\\\\Users\\\\himan\\\\anaconda3\\\\lib\\\\site-packages\\\\win32\\\\lib',\n", " 'C:\\\\Users\\\\himan\\\\anaconda3\\\\lib\\\\site-packages\\\\Pythonwin']" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sys.path" ] }, { "cell_type": "code", "execution_count": 19, "id": "e3a7ec79", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'3.9.12 (main, Apr 4 2022, 05:22:27) [MSC v.1916 64 bit (AMD64)]'" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sys.version" ] }, { "cell_type": "markdown", "id": "b9d075a2", "metadata": {}, "source": [ "# statistics" ] }, { "cell_type": "code", "execution_count": 21, "id": "7cab6f36", "metadata": {}, "outputs": [], "source": [ "# import statistics\n", "\n", "from statistics import *" ] }, { "cell_type": "code", "execution_count": 22, "id": "1b06511a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "35.666666666666664\n" ] } ], "source": [ "ages = [20,10,40,33,45,66]\n", "\n", "print(mean(ages))" ] }, { "cell_type": "code", "execution_count": 23, "id": "88b2b6d7", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "36.5\n" ] } ], "source": [ "print(median(ages))" ] }, { "cell_type": "code", "execution_count": 24, "id": "ea23370e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "20\n" ] } ], "source": [ "print(mode(ages))" ] }, { "cell_type": "code", "execution_count": 25, "id": "63d2eb9d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "19.684172999307506\n" ] } ], "source": [ "print(stdev(ages))" ] }, { "cell_type": "markdown", "id": "8aed4e64", "metadata": {}, "source": [ "# math" ] }, { "cell_type": "code", "execution_count": 26, "id": "bd9eaf48", "metadata": {}, "outputs": [], "source": [ "import math" ] }, { "cell_type": "code", "execution_count": 27, "id": "4cc7951a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "3.141592653589793\n" ] } ], "source": [ "print(math.pi)" ] }, { "cell_type": "code", "execution_count": 28, "id": "d11c039f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2.0\n" ] } ], "source": [ "print(math.sqrt(4))" ] }, { "cell_type": "code", "execution_count": 29, "id": "d06915ac", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "8.0\n" ] } ], "source": [ "print(math.pow(2,3))" ] }, { "cell_type": "code", "execution_count": 30, "id": "1851237f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "3\n" ] } ], "source": [ "print(math.floor(3.4))" ] }, { "cell_type": "code", "execution_count": 31, "id": "066f7ea3", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "5\n" ] } ], "source": [ "print(math.floor(5.9))" ] }, { "cell_type": "code", "execution_count": 32, "id": "ee095949", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "6\n" ] } ], "source": [ "print(math.ceil(5.9))" ] }, { "cell_type": "code", "execution_count": 34, "id": "bce4d491", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "4\n" ] } ], "source": [ "print(math.ceil(3.14))" ] }, { "cell_type": "code", "execution_count": 36, "id": "2fe20f12", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "3.141592653589793\n" ] } ], "source": [ "from math import pi, sqrt, pow, floor, ceil\n", "\n", "\n", "print(pi)" ] }, { "cell_type": "markdown", "id": "fffd6d73", "metadata": {}, "source": [ "# random" ] }, { "cell_type": "code", "execution_count": 37, "id": "3686b11a", "metadata": {}, "outputs": [], "source": [ "import random as r" ] }, { "cell_type": "code", "execution_count": 41, "id": "79f3ce91", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.36521596355895625\n" ] } ], "source": [ "print(r.random()) # 0 to 1" ] }, { "cell_type": "code", "execution_count": 48, "id": "fab41c2c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "14\n" ] } ], "source": [ "print(r.randint(5,20))" ] }, { "cell_type": "code", "execution_count": 51, "id": "9ac057b2", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.12067598483950182\n", "1.2067598483950182\n", "1\n" ] } ], "source": [ "x = r.random()\n", "print(x)\n", "\n", "x = x * 10\n", "print(x)\n", "\n", "print(math.floor(x))" ] }, { "cell_type": "code", "execution_count": null, "id": "785f6932", "metadata": {}, "outputs": [], "source": [ "s = ['a', 'b', 'c', 'd', 'e', '1','2','5']\n", "# 0 1 2 3 4" ] }, { "cell_type": "code", "execution_count": null, "id": "19c71f93", "metadata": {}, "outputs": [], "source": [ "flow of charges > electricity > bits > numbers > character > instructions > function > class > module > package > library > framework" ] }, { "cell_type": "code", "execution_count": null, "id": "daf99091", "metadata": {}, "outputs": [], "source": [ "source code ------> byte code ------> output\n", " compile run \n", " .py .pyc\n", "\n" ] }, { "cell_type": "code", "execution_count": 52, "id": "3da5a878", "metadata": {}, "outputs": [], "source": [ "def color():\n", " return \"red\"\n", "\n", "def flower():\n", " return \"color of the flower is blue\"" ] }, { "cell_type": "code", "execution_count": null, "id": "3eff71a8", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.12" } }, "nbformat": 4, "nbformat_minor": 5 } ================================================ FILE: March Cohort 2023/0_Python Basics.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "id": "1dd9932f", "metadata": {}, "source": [ "# Python Basics\n", "\n", "we are working on print function in python" ] }, { "cell_type": "code", "execution_count": 1, "id": "3ca45a0a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Hello World\n" ] } ], "source": [ "print(\"Hello World\")" ] }, { "cell_type": "code", "execution_count": 2, "id": "e2a86f94", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Hello\n", "World\n" ] } ], "source": [ "print(\"Hello\")\n", "print(\"World\")" ] }, { "cell_type": "code", "execution_count": 3, "id": "07db254e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Hello\n", "World\n" ] } ], "source": [ "print(\"Hello\\nWorld\") # \\n -> new line character" ] }, { "cell_type": "code", "execution_count": 4, "id": "46c947b9", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Hello\tWorld\n" ] } ], "source": [ "print(\"Hello\\tWorld\") # \\t -> horizontal tab" ] }, { "cell_type": "code", "execution_count": 5, "id": "fd07c92b", "metadata": {}, "outputs": [], "source": [ "# 8 spaces - \\t" ] }, { "cell_type": "code", "execution_count": null, "id": "960ceae0", "metadata": {}, "outputs": [], "source": [ "# binary language -> 1010110\n", "\n", "0 -> low voltage, no-charge\n", "\n", "1 -> high voltage, charge\n" ] }, { "cell_type": "code", "execution_count": null, "id": "10a75868", "metadata": {}, "outputs": [], "source": [ "bits > numbers > character > instructions > program > software" ] }, { "cell_type": "code", "execution_count": null, "id": "459a661b", "metadata": {}, "outputs": [], "source": [ "compiler\n", "\n", "interpreter -> python" ] }, { "cell_type": "code", "execution_count": null, "id": "991396e2", "metadata": {}, "outputs": [], "source": [ "source code -------> Byte code -----> Output\n", " .c compile .bak Run .exe\n", " .cpp .obj\n", " .java .javac\n", " .py .pyc" ] }, { "cell_type": "markdown", "id": "9de76aa4", "metadata": {}, "source": [ "# Variables" ] }, { "cell_type": "code", "execution_count": 6, "id": "ca83f18c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10\n" ] } ], "source": [ "a = 10\n", "\n", "print(a)" ] }, { "cell_type": "code", "execution_count": 7, "id": "3a7bac6d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "45.13\n" ] } ], "source": [ "b = 45.13\n", "\n", "print(b)" ] }, { "cell_type": "code", "execution_count": 8, "id": "124cb33b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "India\n" ] } ], "source": [ "a = \"India\"\n", "\n", "print(a)" ] }, { "cell_type": "markdown", "id": "406d111f", "metadata": {}, "source": [ "# Data type" ] }, { "cell_type": "code", "execution_count": 9, "id": "707a85a0", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10\n", "\n" ] } ], "source": [ "a = 10\n", "\n", "print(a)\n", "\n", "print(type(a))" ] }, { "cell_type": "code", "execution_count": 10, "id": "bc9295ca", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10.65\n", "\n" ] } ], "source": [ "b = 10.65\n", "\n", "print(b)\n", "\n", "print(type(b))" ] }, { "cell_type": "code", "execution_count": 11, "id": "5632508a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Screen\n", "\n" ] } ], "source": [ "c = \"Screen\"\n", "\n", "print(c)\n", "\n", "print(type(c))" ] }, { "cell_type": "code", "execution_count": 12, "id": "644ec54c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "meet\n", "\n" ] } ], "source": [ "d = 'meet'\n", "\n", "print(d)\n", "\n", "print(type(d))" ] }, { "cell_type": "code", "execution_count": 13, "id": "43f33389", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "it's an apple\n" ] } ], "source": [ "x = \"it's an apple\"\n", "\n", "print(x)" ] }, { "cell_type": "markdown", "id": "e04549b7", "metadata": {}, "source": [ "# Type Conversion" ] }, { "cell_type": "code", "execution_count": 14, "id": "321b1438", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10.56\n", "\n", "10\n", "\n" ] } ], "source": [ "a = 10.56\n", "\n", "print(a)\n", "\n", "print(type(a))\n", "\n", "b = int(a)\n", "\n", "print(b)\n", "\n", "print(type(b))" ] }, { "cell_type": "code", "execution_count": 15, "id": "873230fd", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "100\n", "\n", "100.0\n", "\n" ] } ], "source": [ "a = \"100\"\n", "\n", "print(a)\n", "\n", "print(type(a))\n", "\n", "b = float(a)\n", "\n", "print(b)\n", "\n", "print(type(b))" ] }, { "cell_type": "code", "execution_count": null, "id": "83db42ca", "metadata": {}, "outputs": [], "source": [ "int()\n", "float()\n", "str()\n", "bool()\n", "list()\n", "dict()\n", "tuple()\n", "set()" ] }, { "cell_type": "markdown", "id": "a54b3b8f", "metadata": {}, "source": [ "# Input function" ] }, { "cell_type": "code", "execution_count": 16, "id": "127dd9ee", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10\n", "10\n" ] } ], "source": [ "a = input()\n", "\n", "print(a)" ] }, { "cell_type": "code", "execution_count": 17, "id": "6c872d87", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Enter Your Name: Himanshu\n", "Himanshu\n" ] } ], "source": [ "a = input(\"Enter Your Name: \")\n", "\n", "print(a)" ] }, { "cell_type": "code", "execution_count": 18, "id": "f332e9d1", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Enter Your Name: Himanshu\n", "Hello! How are you? Himanshu\n" ] } ], "source": [ "a = input(\"Enter Your Name: \")\n", "\n", "print(\"Hello! How are you?\", a)" ] }, { "cell_type": "code", "execution_count": null, "id": "076ba3aa", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.12" } }, "nbformat": 4, "nbformat_minor": 5 } ================================================ FILE: March Cohort 2023/10_11_EDA Project/.ipynb_checkpoints/10_11_EDA Project-checkpoint.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "id": "373ae802", "metadata": {}, "source": [ "# Titanic Survival Prediction Analysis" ] }, { "cell_type": "code", "execution_count": 1, "id": "8031a2ba", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns" ] }, { "cell_type": "code", "execution_count": 2, "id": "d86ba18d", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " PassengerId Survived Pclass Name Gender Age SibSp Parch \\\n", "0 False False False False False False False False \n", "1 False False False False False False False False \n", "2 False False False False False False False False \n", "3 False False False False False False False False \n", "4 False False False False False False False False \n", ".. ... ... ... ... ... ... ... ... \n", "886 False False False False False False False False \n", "887 False False False False False False False False \n", "888 False False False False False True False False \n", "889 False False False False False False False False \n", "890 False False False False False False False False \n", "\n", " Ticket Fare Cabin Embarked \n", "0 False False True False \n", "1 False False False False \n", "2 False False True False \n", "3 False False False False \n", "4 False False True False \n", ".. ... ... ... ... \n", "886 False False True False \n", "887 False False False False \n", "888 False False True False \n", "889 False False False False \n", "890 False False True False \n", "\n", "[891 rows x 12 columns]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "titanic.isnull()" ] }, { "cell_type": "code", "execution_count": 4, "id": "ad6adb8c", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "PassengerId 0\n", "Survived 0\n", "Pclass 0\n", "Name 0\n", "Gender 0\n", "Age 177\n", "SibSp 0\n", "Parch 0\n", "Ticket 0\n", "Fare 0\n", "Cabin 687\n", "Embarked 2\n", "dtype: int64" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "titanic.isnull().sum()" ] }, { "cell_type": "code", "execution_count": 5, "id": "7f5c9fa4", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(891, 12)" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "titanic.shape" ] }, { "cell_type": "code", "execution_count": 6, "id": "f56ad5b8", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "10692" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "titanic.size" ] }, { "cell_type": "code", "execution_count": 7, "id": "a86f4013", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "titanic.ndim" ] }, { "cell_type": "markdown", "id": "8740ac70", "metadata": {}, "source": [ "# Exploratory Data Analysis" ] }, { "cell_type": "code", "execution_count": 8, "id": "0fdcc279", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(8,8))\n", "\n", "sns.heatmap(titanic.isnull(), yticklabels=False, cbar=False, cmap='viridis')" ] }, { "cell_type": "code", "execution_count": 10, "id": "a78063eb", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sns.countplot(x='Survived', data=titanic)" ] }, { "cell_type": "code", "execution_count": 11, "id": "e59fc93c", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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6VJVHCqWIGELzcw2PAZ/IzFehOTiAjxeLDQJebrFaUzG287ZmRsTyiFi+fv36SuuWpO6m8lCIiL7AXcClmfnnD1q0lbFdvt0tM+dnZkNmNgwYYE8+SWpPlYZCRPSiORBuzcxfFMOvRcTA4v2BwOvFeBNwRIvV64BXqqxPkvR+Vd59FMCNwDOZeV2LtxYDjcV0I3Bvi/EZEXFgRNQDQ4GlVdUnSdpVla0qJgD/HngyIh4vxr4JfB9YFBHnAeuA6QCZuToiFtH8LMQ24KLM3F5hfZKknVQWCpn5CK1fJwA4dTfrzMb2GZJUMx1y95Ekaf9gKEiSSoaCJKlkKEiSSoaCJKlkKEiSSoaCJKlkKEiSSoaCJKlkKEiSSoaCJKlkKEiSSoaCJKlkKEiSSoaCJKlkKEiSSoaCJKlkKEiSSoaCJKlkKEiSSoaCJKlkKEiSSoaCJKlkKEiSSoaCJKlkKEiSSoaCJKlkKEiSSoaCJKlkKEiSSoaCJKlkKEiSSoaCJKlkKEiSSoaCJKl0QFUbjoibgNOA1zNzRDF2CPBzYAiwFjgrM/+1eO8q4DxgO/CfM/PXVdUm7Q/GXn5LrUvoNO7uV+sKuo8qjxQWAFN2GrsSeCAzhwIPFPNExHBgBnBMsc4NEdGzwtokSa2oLBQy82HgzZ2GzwBuLqZvBqa1GL89M7dk5ovAGmB8VbVJklrX0dcUPpGZrwIUrx8vxgcBL7dYrqkYkyR1oM5yoTlaGctWF4yYGRHLI2L5+vXrKy5LkrqXjg6F1yJiIEDx+nox3gQc0WK5OuCV1jaQmfMzsyEzGwYMGFBpsZLU3XR0KCwGGovpRuDeFuMzIuLAiKgHhgJLO7g2Ser2qrwl9TZgInBYRDQBfwN8H1gUEecB64DpAJm5OiIWAU8D24CLMnN7VbVJklpXWShk5l/v5q1Td7P8bGB2VfVIkvass1xoliR1AoaCJKlkKEiSSpVdU9D+Z901I2tdQqdx5HeerHUJUk14pCBJKhkKkqSSoSBJKhkKkqSSoSBJKhkKkqSSoSBJKhkKkqSSoSBJKhkKkqSSoSBJKhkKkqSSoSBJKhkKkqSSoSBJKhkKkqSSoSBJKhkKkqSSoSBJKhkKkqSSoSBJKhkKkqSSoSBJKhkKkqSSoSBJKhkKkqSSoSBJKhkKkqSSoSBJKhkKkqSSoSBJKhkKkqRSpwuFiJgSEc9FxJqIuLLW9UhSd9KpQiEiegLXA38FDAf+OiKG17YqSeo+OlUoAOOBNZn5p8z8f8DtwBk1rkmSuo0Dal3ATgYBL7eYbwKOb7lARMwEZhazmyLiuQ6qrcsbDIcBb9S6jk7hb6LWFagF/zZbaJ+/zcG7e6OzhUJre5vvm8mcD8zvmHK6l4hYnpkNta5D2pl/mx2ns50+agKOaDFfB7xSo1okqdvpbKGwDBgaEfUR0RuYASyucU2S1G10qtNHmbktIv4T8GugJ3BTZq6ucVndiafl1Fn5t9lBIjP3vJQkqVvobKePJEk1ZChIkkqGgmwtok4rIm6KiNcj4qla19JdGArdnK1F1MktAKbUuojuxFCQrUXUaWXmw8Cbta6jOzEU1FprkUE1qkVSjRkK2mNrEUndh6EgW4tIKhkKsrWIpJKh0M1l5jZgR2uRZ4BFthZRZxERtwGPAkdFRFNEnFfrmro621xIkkoeKUiSSoaCJKlkKEiSSoaCJKlkKEiSSoaCBETEtyJidUSsiojHI+L4dtjm6e3VdTYiNrXHdqQ98ZZUdXsRcSJwHTAxM7dExGFA78zc45PdEXFA8axH1TVuysy+Vf8cySMFCQYCb2TmFoDMfCMzX4mItUVAEBENEbGkmL46IuZHxP3ALRHxWEQcs2NjEbEkIsZGxLkR8eOI6F9sq0fx/kER8XJE9IqIT0XEryJiRUT8c0QMK5apj4hHI2JZRFzbwb8PdWOGggT3A0dExB8j4oaI+HdtWGcscEZmfoXmduNnAUTEQODwzFyxY8HM3Ag8AezY7lTg15m5leYvpL84M8cClwE3FMv8EJiXmeOAf9nnPZTayFBQt5eZm2j+kJ8JrAd+HhHn7mG1xZn5f4vpRcD0Yvos4I5Wlv858OViekbxM/oCJwF3RMTjwE9pPmoBmADcVkz/44fZH2lfHFDrAqTOIDO3A0uAJRHxJNAIbOO9/3Hqs9Mqm1us+38iYkNEjKL5g/+CVn7EYuDvIuIQmgPoQeAjwFuZeezuytq7vZH2nkcK6vYi4qiIGNpi6FjgJWAtzR/gAF/cw2ZuB64A+mfmkzu/WRyNLKX5tNB9mbk9M/8MvBgR04s6IiJGF6v8nuYjCoCzP/ROSXvJUJCgL3BzRDwdEato/q7qq4G/BX4YEf8MbN/DNu6k+UN80Qcs83PgnOJ1h7OB8yLiCWA1730V6iXARRGxDOj/4XZH2nvekipJKnmkIEkqGQqSpJKhIEkqGQqSpJKhIEkqGQqSpJKhIEkq/X/pNyU2CDw8TgAAAABJRU5ErkJggg==\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sns.countplot(x='Survived', hue='Gender' ,data=titanic)" ] }, { "cell_type": "code", "execution_count": 12, "id": "457581d7", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sns.countplot(x='Survived', hue='Pclass' ,data=titanic)" ] }, { "cell_type": "markdown", "id": "ffd065db", "metadata": {}, "source": [ "# Data Cleaning" ] }, { "cell_type": "code", "execution_count": 13, "id": "eb40c7c6", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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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", "
" ], "text/plain": [ " count mean std min 25% 50% 75% max\n", "Pclass \n", "1 186.0 38.233441 14.802856 0.92 27.0 37.0 49.0 80.0\n", "2 173.0 29.877630 14.001077 0.67 23.0 29.0 36.0 70.0\n", "3 355.0 25.140620 12.495398 0.42 18.0 24.0 32.0 74.0" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "titanic.groupby('Pclass')['Age'].describe()" ] }, { "cell_type": "code", "execution_count": 14, "id": "555b4d07", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10\n", "20\n" ] } ], "source": [ "a,b = 10,20\n", "\n", "print(a)\n", "print(b)" ] }, { "cell_type": "code", "execution_count": 16, "id": "dbbf4d86", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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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", "
" ], "text/plain": [ " PassengerId Survived Pclass Name \\\n", "886 887 0 2 Montvila, Rev. Juozas \n", "887 888 1 1 Graham, Miss. Margaret Edith \n", "888 889 0 3 Johnston, Miss. Catherine Helen \"Carrie\" \n", "889 890 1 1 Behr, Mr. Karl Howell \n", "890 891 0 3 Dooley, Mr. Patrick \n", "\n", " Gender Age SibSp Parch Ticket Fare Cabin Embarked \n", "886 male 27.0 0 0 211536 13.00 NaN S \n", "887 female 19.0 0 0 112053 30.00 B42 S \n", "888 female NaN 1 2 W./C. 6607 23.45 NaN S \n", "889 male 26.0 0 0 111369 30.00 C148 C \n", "890 male 32.0 0 0 370376 7.75 NaN Q " ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "titanic.tail()" ] }, { "cell_type": "code", "execution_count": 15, "id": "5b935cd7", "metadata": {}, "outputs": [], "source": [ "def age_calculate(cols):\n", " age_map = [38,30,25]\n", " age, pclass = cols[0], cols[1]\n", " \n", " if pd.isnull(age):\n", " return age_map[int(pclass - 1)]\n", " else:\n", " return age" ] }, { "cell_type": "code", "execution_count": 17, "id": "48f9d274", "metadata": {}, "outputs": [], "source": [ "titanic['Age'] = titanic[['Age', 'Pclass']].apply(age_calculate, axis=1) # [NaN, 3]" ] }, { "cell_type": "code", "execution_count": 18, "id": "fce00d9d", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(8,8))\n", "\n", "sns.heatmap(titanic.isnull(), yticklabels=False, cbar=False, cmap='viridis')" ] }, { "cell_type": "code", "execution_count": 19, "id": "03c24704", "metadata": {}, "outputs": [], "source": [ "titanic.drop('Cabin', axis=1, inplace=True)" ] }, { "cell_type": "code", "execution_count": 20, "id": "5b9f1450", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(8,8))\n", "\n", "sns.heatmap(titanic.isnull(), yticklabels=False, cbar=False, cmap='viridis')" ] }, { "cell_type": "code", "execution_count": 21, "id": "a8ea552f", "metadata": {}, "outputs": [], "source": [ "titanic.dropna(inplace=True)" ] }, { "cell_type": "code", "execution_count": 22, "id": "61e919dd", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(8,8))\n", "\n", "sns.heatmap(titanic.isnull(), yticklabels=False, cbar=False, cmap='viridis')" ] }, { "cell_type": "code", "execution_count": 23, "id": "7ad3c921", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Int64Index: 889 entries, 0 to 890\n", "Data columns (total 11 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 PassengerId 889 non-null int64 \n", " 1 Survived 889 non-null int64 \n", " 2 Pclass 889 non-null int64 \n", " 3 Name 889 non-null object \n", " 4 Gender 889 non-null object \n", " 5 Age 889 non-null float64\n", " 6 SibSp 889 non-null int64 \n", " 7 Parch 889 non-null int64 \n", " 8 Ticket 889 non-null object \n", " 9 Fare 889 non-null float64\n", " 10 Embarked 889 non-null object \n", "dtypes: float64(2), int64(5), object(4)\n", "memory usage: 83.3+ KB\n" ] } ], "source": [ "titanic.info()" ] }, { "cell_type": "markdown", "id": "076d4a4c", "metadata": {}, "source": [ "# Feature Engineering" ] }, { "cell_type": "code", "execution_count": 24, "id": "f731908b", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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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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" ], "text/plain": [ " PassengerId Survived Pclass \\\n", "0 1 0 3 \n", "1 2 1 1 \n", "2 3 1 3 \n", "3 4 1 1 \n", "4 5 0 3 \n", "\n", " Name Gender Age SibSp \\\n", "0 Braund, Mr. Owen Harris male 22.0 1 \n", "1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n", "2 Heikkinen, Miss. Laina female 26.0 0 \n", "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n", "4 Allen, Mr. William Henry male 35.0 0 \n", "\n", " Parch Ticket Fare Embarked \n", "0 0 A/5 21171 7.2500 S \n", "1 0 PC 17599 71.2833 C \n", "2 0 STON/O2. 3101282 7.9250 S \n", "3 0 113803 53.1000 S \n", "4 0 373450 8.0500 S " ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "titanic.head()" ] }, { "cell_type": "code", "execution_count": 25, "id": "8dc409e6", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " male\n", "0 1\n", "1 0\n", "2 0\n", "3 0\n", "4 1" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gender = pd.get_dummies(titanic['Gender'], drop_first=True)\n", "\n", "# male female\n", "# 1 0\n", "# 0 1\n", "# 1 0\n", "gender.head()" ] }, { "cell_type": "code", "execution_count": 26, "id": "42fb2bd0", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " C Q S\n", "0 0 0 1\n", "1 1 0 0\n", "2 0 0 1\n", "3 0 0 1\n", "4 0 0 1" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "embark = pd.get_dummies(titanic['Embarked'])\n", "embark.head()" ] }, { "cell_type": "code", "execution_count": 27, "id": "9c908558", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " SibSp_0 SibSp_1 SibSp_2 SibSp_3 SibSp_4 SibSp_5 SibSp_8\n", "0 0 1 0 0 0 0 0\n", "1 0 1 0 0 0 0 0\n", "2 1 0 0 0 0 0 0\n", "3 0 1 0 0 0 0 0\n", "4 1 0 0 0 0 0 0" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sibsp = pd.get_dummies(titanic['SibSp'], prefix='SibSp')\n", "sibsp.head()" ] }, { "cell_type": "code", "execution_count": 28, "id": "b4fca7e5", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " Parch_0 Parch_1 Parch_2 Parch_3 Parch_4 Parch_5 Parch_6\n", "0 1 0 0 0 0 0 0\n", "1 1 0 0 0 0 0 0\n", "2 1 0 0 0 0 0 0\n", "3 1 0 0 0 0 0 0\n", "4 1 0 0 0 0 0 0" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "parch = pd.get_dummies(titanic['Parch'], prefix='Parch')\n", "parch.head()" ] }, { "cell_type": "markdown", "id": "4beab0b0", "metadata": {}, "source": [ "# Combine all the features" ] }, { "cell_type": "code", "execution_count": 29, "id": "94019ddd", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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SurvivedPclassAgeFare
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" ], "text/plain": [ " Survived Pclass Age Fare\n", "0 0 3 22.0 7.2500\n", "1 1 1 38.0 71.2833\n", "2 1 3 26.0 7.9250\n", "3 1 1 35.0 53.1000\n", "4 0 3 35.0 8.0500" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "titanic = titanic.drop(['PassengerId', 'Name', 'Gender', 'SibSp', 'Parch',\n", " 'Ticket', 'Embarked'], axis = 1)\n", "\n", "titanic.head()" ] }, { "cell_type": "code", "execution_count": 31, "id": "ecbd3207", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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SurvivedPclassAgeFaremaleCQSSibSp_0SibSp_1...SibSp_4SibSp_5SibSp_8Parch_0Parch_1Parch_2Parch_3Parch_4Parch_5Parch_6
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5 rows × 22 columns

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" ], "text/plain": [ " Survived Pclass Age Fare male C Q S SibSp_0 SibSp_1 ... \\\n", "0 0 3 22.0 7.2500 1 0 0 1 0 1 ... \n", "1 1 1 38.0 71.2833 0 1 0 0 0 1 ... \n", "2 1 3 26.0 7.9250 0 0 0 1 1 0 ... \n", "3 1 1 35.0 53.1000 0 0 0 1 0 1 ... \n", "4 0 3 35.0 8.0500 1 0 0 1 1 0 ... \n", "\n", " SibSp_4 SibSp_5 SibSp_8 Parch_0 Parch_1 Parch_2 Parch_3 Parch_4 \\\n", "0 0 0 0 1 0 0 0 0 \n", "1 0 0 0 1 0 0 0 0 \n", "2 0 0 0 1 0 0 0 0 \n", "3 0 0 0 1 0 0 0 0 \n", "4 0 0 0 1 0 0 0 0 \n", "\n", " Parch_5 Parch_6 \n", "0 0 0 \n", "1 0 0 \n", "2 0 0 \n", "3 0 0 \n", "4 0 0 \n", "\n", "[5 rows x 22 columns]" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "titanic = pd.concat([titanic, gender, embark, sibsp, parch], axis=1)\n", "\n", "titanic.head()" ] }, { "cell_type": "code", "execution_count": 32, "id": "22e99298", "metadata": {}, "outputs": [], "source": [ "titanic.to_csv('titanic_cleaned.csv', index=False)" ] }, { "cell_type": "code", "execution_count": null, "id": "dc3462b9", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.12" } }, "nbformat": 4, "nbformat_minor": 5 } ================================================ FILE: March Cohort 2023/10_11_EDA Project/10_11_EDA Project.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "id": "373ae802", "metadata": {}, "source": [ "# Titanic Survival Prediction Analysis" ] }, { "cell_type": "code", "execution_count": 1, "id": "8031a2ba", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns" ] }, { "cell_type": "code", "execution_count": 2, "id": "d86ba18d", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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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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" ], "text/plain": [ " PassengerId Survived Pclass \\\n", "0 1 0 3 \n", "1 2 1 1 \n", "2 3 1 3 \n", "3 4 1 1 \n", "4 5 0 3 \n", "\n", " Name Gender Age SibSp \\\n", "0 Braund, Mr. Owen Harris male 22.0 1 \n", "1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n", "2 Heikkinen, Miss. Laina female 26.0 0 \n", "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n", "4 Allen, Mr. William Henry male 35.0 0 \n", "\n", " Parch Ticket Fare Cabin Embarked \n", "0 0 A/5 21171 7.2500 NaN S \n", "1 0 PC 17599 71.2833 C85 C \n", "2 0 STON/O2. 3101282 7.9250 NaN S \n", "3 0 113803 53.1000 C123 S \n", "4 0 373450 8.0500 NaN S " ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "titanic = pd.read_csv('titanic.csv')\n", "\n", "titanic.head()" ] }, { "cell_type": "code", "execution_count": 3, "id": "f2b02ee2", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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PassengerIdSurvivedPclassNameGenderAgeSibSpParchTicketFareCabinEmbarked
0FalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalse
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3FalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse
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.......................................
886FalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalse
887FalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse
888FalseFalseFalseFalseFalseTrueFalseFalseFalseFalseTrueFalse
889FalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse
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891 rows × 12 columns

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" ], "text/plain": [ " PassengerId Survived Pclass Name Gender Age SibSp Parch \\\n", "0 False False False False False False False False \n", "1 False False False False False False False False \n", "2 False False False False False False False False \n", "3 False False False False False False False False \n", "4 False False False False False False False False \n", ".. ... ... ... ... ... ... ... ... \n", "886 False False False False False False False False \n", "887 False False False False False False False False \n", "888 False False False False False True False False \n", "889 False False False False False False False False \n", "890 False False False False False False False False \n", "\n", " Ticket Fare Cabin Embarked \n", "0 False False True False \n", "1 False False False False \n", "2 False False True False \n", "3 False False False False \n", "4 False False True False \n", ".. ... ... ... ... \n", "886 False False True False \n", "887 False False False False \n", "888 False False True False \n", "889 False False False False \n", "890 False False True False \n", "\n", "[891 rows x 12 columns]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "titanic.isnull()" ] }, { "cell_type": "code", "execution_count": 4, "id": "ad6adb8c", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "PassengerId 0\n", "Survived 0\n", "Pclass 0\n", "Name 0\n", "Gender 0\n", "Age 177\n", "SibSp 0\n", "Parch 0\n", "Ticket 0\n", "Fare 0\n", "Cabin 687\n", "Embarked 2\n", "dtype: int64" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "titanic.isnull().sum()" ] }, { "cell_type": "code", "execution_count": 5, "id": "7f5c9fa4", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(891, 12)" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "titanic.shape" ] }, { "cell_type": "code", "execution_count": 6, "id": "f56ad5b8", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "10692" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "titanic.size" ] }, { "cell_type": "code", "execution_count": 7, "id": "a86f4013", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "titanic.ndim" ] }, { "cell_type": "markdown", "id": "8740ac70", "metadata": {}, "source": [ "# Exploratory Data Analysis" ] }, { "cell_type": "code", "execution_count": 8, "id": "0fdcc279", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(8,8))\n", "\n", "sns.heatmap(titanic.isnull(), yticklabels=False, cbar=False, cmap='viridis')" ] }, { "cell_type": "code", "execution_count": 10, "id": "a78063eb", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sns.countplot(x='Survived', data=titanic)" ] }, { "cell_type": "code", "execution_count": 11, "id": "e59fc93c", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sns.countplot(x='Survived', hue='Gender' ,data=titanic)" ] }, { "cell_type": "code", "execution_count": 12, "id": "457581d7", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sns.countplot(x='Survived', hue='Pclass' ,data=titanic)" ] }, { "cell_type": "markdown", "id": "ffd065db", "metadata": {}, "source": [ "# Data Cleaning" ] }, { "cell_type": "code", "execution_count": 13, "id": "eb40c7c6", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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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", "
" ], "text/plain": [ " count mean std min 25% 50% 75% max\n", "Pclass \n", "1 186.0 38.233441 14.802856 0.92 27.0 37.0 49.0 80.0\n", "2 173.0 29.877630 14.001077 0.67 23.0 29.0 36.0 70.0\n", "3 355.0 25.140620 12.495398 0.42 18.0 24.0 32.0 74.0" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "titanic.groupby('Pclass')['Age'].describe()" ] }, { "cell_type": "code", "execution_count": 14, "id": "555b4d07", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10\n", "20\n" ] } ], "source": [ "a,b = 10,20\n", "\n", "print(a)\n", "print(b)" ] }, { "cell_type": "code", "execution_count": 16, "id": "dbbf4d86", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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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", "
" ], "text/plain": [ " PassengerId Survived Pclass Name \\\n", "886 887 0 2 Montvila, Rev. Juozas \n", "887 888 1 1 Graham, Miss. Margaret Edith \n", "888 889 0 3 Johnston, Miss. Catherine Helen \"Carrie\" \n", "889 890 1 1 Behr, Mr. Karl Howell \n", "890 891 0 3 Dooley, Mr. Patrick \n", "\n", " Gender Age SibSp Parch Ticket Fare Cabin Embarked \n", "886 male 27.0 0 0 211536 13.00 NaN S \n", "887 female 19.0 0 0 112053 30.00 B42 S \n", "888 female NaN 1 2 W./C. 6607 23.45 NaN S \n", "889 male 26.0 0 0 111369 30.00 C148 C \n", "890 male 32.0 0 0 370376 7.75 NaN Q " ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "titanic.tail()" ] }, { "cell_type": "code", "execution_count": 15, "id": "5b935cd7", "metadata": {}, "outputs": [], "source": [ "def age_calculate(cols):\n", " age_map = [38,30,25]\n", " age, pclass = cols[0], cols[1]\n", " \n", " if pd.isnull(age):\n", " return age_map[int(pclass - 1)]\n", " else:\n", " return age" ] }, { "cell_type": "code", "execution_count": 17, "id": "48f9d274", "metadata": {}, "outputs": [], "source": [ "titanic['Age'] = titanic[['Age', 'Pclass']].apply(age_calculate, axis=1) # [NaN, 3]" ] }, { "cell_type": "code", "execution_count": 18, "id": "fce00d9d", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(8,8))\n", "\n", "sns.heatmap(titanic.isnull(), yticklabels=False, cbar=False, cmap='viridis')" ] }, { "cell_type": "code", "execution_count": 19, "id": "03c24704", "metadata": {}, "outputs": [], "source": [ "titanic.drop('Cabin', axis=1, inplace=True)" ] }, { "cell_type": "code", "execution_count": 20, "id": "5b9f1450", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(8,8))\n", "\n", "sns.heatmap(titanic.isnull(), yticklabels=False, cbar=False, cmap='viridis')" ] }, { "cell_type": "code", "execution_count": 21, "id": "a8ea552f", "metadata": {}, "outputs": [], "source": [ "titanic.dropna(inplace=True)" ] }, { "cell_type": "code", "execution_count": 22, "id": "61e919dd", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(8,8))\n", "\n", "sns.heatmap(titanic.isnull(), yticklabels=False, cbar=False, cmap='viridis')" ] }, { "cell_type": "code", "execution_count": 23, "id": "7ad3c921", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Int64Index: 889 entries, 0 to 890\n", "Data columns (total 11 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 PassengerId 889 non-null int64 \n", " 1 Survived 889 non-null int64 \n", " 2 Pclass 889 non-null int64 \n", " 3 Name 889 non-null object \n", " 4 Gender 889 non-null object \n", " 5 Age 889 non-null float64\n", " 6 SibSp 889 non-null int64 \n", " 7 Parch 889 non-null int64 \n", " 8 Ticket 889 non-null object \n", " 9 Fare 889 non-null float64\n", " 10 Embarked 889 non-null object \n", "dtypes: float64(2), int64(5), object(4)\n", "memory usage: 83.3+ KB\n" ] } ], "source": [ "titanic.info()" ] }, { "cell_type": "markdown", "id": "076d4a4c", "metadata": {}, "source": [ "# Feature Engineering" ] }, { "cell_type": "code", "execution_count": 24, "id": "f731908b", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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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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" ], "text/plain": [ " PassengerId Survived Pclass \\\n", "0 1 0 3 \n", "1 2 1 1 \n", "2 3 1 3 \n", "3 4 1 1 \n", "4 5 0 3 \n", "\n", " Name Gender Age SibSp \\\n", "0 Braund, Mr. Owen Harris male 22.0 1 \n", "1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n", "2 Heikkinen, Miss. Laina female 26.0 0 \n", "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n", "4 Allen, Mr. William Henry male 35.0 0 \n", "\n", " Parch Ticket Fare Embarked \n", "0 0 A/5 21171 7.2500 S \n", "1 0 PC 17599 71.2833 C \n", "2 0 STON/O2. 3101282 7.9250 S \n", "3 0 113803 53.1000 S \n", "4 0 373450 8.0500 S " ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "titanic.head()" ] }, { "cell_type": "code", "execution_count": 25, "id": "8dc409e6", "metadata": {}, "outputs": [ { "data": { "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", "
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" ], "text/plain": [ " male\n", "0 1\n", "1 0\n", "2 0\n", "3 0\n", "4 1" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gender = pd.get_dummies(titanic['Gender'], drop_first=True)\n", "\n", "# male female\n", "# 1 0\n", "# 0 1\n", "# 1 0\n", "gender.head()" ] }, { "cell_type": "code", "execution_count": 26, "id": "42fb2bd0", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " C Q S\n", "0 0 0 1\n", "1 1 0 0\n", "2 0 0 1\n", "3 0 0 1\n", "4 0 0 1" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "embark = pd.get_dummies(titanic['Embarked'])\n", "embark.head()" ] }, { "cell_type": "code", "execution_count": 27, "id": "9c908558", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " SibSp_0 SibSp_1 SibSp_2 SibSp_3 SibSp_4 SibSp_5 SibSp_8\n", "0 0 1 0 0 0 0 0\n", "1 0 1 0 0 0 0 0\n", "2 1 0 0 0 0 0 0\n", "3 0 1 0 0 0 0 0\n", "4 1 0 0 0 0 0 0" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sibsp = pd.get_dummies(titanic['SibSp'], prefix='SibSp')\n", "sibsp.head()" ] }, { "cell_type": "code", "execution_count": 28, "id": "b4fca7e5", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Parch_0Parch_1Parch_2Parch_3Parch_4Parch_5Parch_6
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" ], "text/plain": [ " Parch_0 Parch_1 Parch_2 Parch_3 Parch_4 Parch_5 Parch_6\n", "0 1 0 0 0 0 0 0\n", "1 1 0 0 0 0 0 0\n", "2 1 0 0 0 0 0 0\n", "3 1 0 0 0 0 0 0\n", "4 1 0 0 0 0 0 0" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "parch = pd.get_dummies(titanic['Parch'], prefix='Parch')\n", "parch.head()" ] }, { "cell_type": "markdown", "id": "4beab0b0", "metadata": {}, "source": [ "# Combine all the features" ] }, { "cell_type": "code", "execution_count": 29, "id": "94019ddd", "metadata": {}, "outputs": [ { "data": { "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", "
SurvivedPclassAgeFare
00322.07.2500
11138.071.2833
21326.07.9250
31135.053.1000
40335.08.0500
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" ], "text/plain": [ " Survived Pclass Age Fare\n", "0 0 3 22.0 7.2500\n", "1 1 1 38.0 71.2833\n", "2 1 3 26.0 7.9250\n", "3 1 1 35.0 53.1000\n", "4 0 3 35.0 8.0500" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "titanic = titanic.drop(['PassengerId', 'Name', 'Gender', 'SibSp', 'Parch',\n", " 'Ticket', 'Embarked'], axis = 1)\n", "\n", "titanic.head()" ] }, { "cell_type": "code", "execution_count": 31, "id": "ecbd3207", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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SurvivedPclassAgeFaremaleCQSSibSp_0SibSp_1...SibSp_4SibSp_5SibSp_8Parch_0Parch_1Parch_2Parch_3Parch_4Parch_5Parch_6
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5 rows × 22 columns

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" ], "text/plain": [ " Survived Pclass Age Fare male C Q S SibSp_0 SibSp_1 ... \\\n", "0 0 3 22.0 7.2500 1 0 0 1 0 1 ... \n", "1 1 1 38.0 71.2833 0 1 0 0 0 1 ... \n", "2 1 3 26.0 7.9250 0 0 0 1 1 0 ... \n", "3 1 1 35.0 53.1000 0 0 0 1 0 1 ... \n", "4 0 3 35.0 8.0500 1 0 0 1 1 0 ... \n", "\n", " SibSp_4 SibSp_5 SibSp_8 Parch_0 Parch_1 Parch_2 Parch_3 Parch_4 \\\n", "0 0 0 0 1 0 0 0 0 \n", "1 0 0 0 1 0 0 0 0 \n", "2 0 0 0 1 0 0 0 0 \n", "3 0 0 0 1 0 0 0 0 \n", "4 0 0 0 1 0 0 0 0 \n", "\n", " Parch_5 Parch_6 \n", "0 0 0 \n", "1 0 0 \n", "2 0 0 \n", "3 0 0 \n", "4 0 0 \n", "\n", "[5 rows x 22 columns]" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "titanic = pd.concat([titanic, gender, embark, sibsp, parch], axis=1)\n", "\n", "titanic.head()" ] }, { "cell_type": "code", "execution_count": 32, "id": "22e99298", "metadata": {}, "outputs": [], "source": [ "titanic.to_csv('titanic_cleaned.csv', index=False)" ] }, { "cell_type": "code", "execution_count": null, "id": "dc3462b9", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.12" } }, "nbformat": 4, "nbformat_minor": 5 } ================================================ FILE: March Cohort 2023/10_11_EDA Project/titanic.csv ================================================ PassengerId,Survived,Pclass,Name,Gender,Age,SibSp,Parch,Ticket,Fare,Cabin,Embarked 1,0,3,"Braund, Mr. Owen Harris",male,22,1,0,A/5 21171,7.25,,S 2,1,1,"Cumings, Mrs. John Bradley (Florence Briggs Thayer)",female,38,1,0,PC 17599,71.2833,C85,C 3,1,3,"Heikkinen, Miss. Laina",female,26,0,0,STON/O2. 3101282,7.925,,S 4,1,1,"Futrelle, Mrs. Jacques Heath (Lily May Peel)",female,35,1,0,113803,53.1,C123,S 5,0,3,"Allen, Mr. William Henry",male,35,0,0,373450,8.05,,S 6,0,3,"Moran, Mr. James",male,,0,0,330877,8.4583,,Q 7,0,1,"McCarthy, Mr. Timothy J",male,54,0,0,17463,51.8625,E46,S 8,0,3,"Palsson, Master. Gosta Leonard",male,2,3,1,349909,21.075,,S 9,1,3,"Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)",female,27,0,2,347742,11.1333,,S 10,1,2,"Nasser, Mrs. Nicholas (Adele Achem)",female,14,1,0,237736,30.0708,,C 11,1,3,"Sandstrom, Miss. Marguerite Rut",female,4,1,1,PP 9549,16.7,G6,S 12,1,1,"Bonnell, Miss. Elizabeth",female,58,0,0,113783,26.55,C103,S 13,0,3,"Saundercock, Mr. William Henry",male,20,0,0,A/5. 2151,8.05,,S 14,0,3,"Andersson, Mr. Anders Johan",male,39,1,5,347082,31.275,,S 15,0,3,"Vestrom, Miss. Hulda Amanda Adolfina",female,14,0,0,350406,7.8542,,S 16,1,2,"Hewlett, Mrs. (Mary D Kingcome) ",female,55,0,0,248706,16,,S 17,0,3,"Rice, Master. Eugene",male,2,4,1,382652,29.125,,Q 18,1,2,"Williams, Mr. Charles Eugene",male,,0,0,244373,13,,S 19,0,3,"Vander Planke, Mrs. Julius (Emelia Maria Vandemoortele)",female,31,1,0,345763,18,,S 20,1,3,"Masselmani, Mrs. Fatima",female,,0,0,2649,7.225,,C 21,0,2,"Fynney, Mr. Joseph J",male,35,0,0,239865,26,,S 22,1,2,"Beesley, Mr. Lawrence",male,34,0,0,248698,13,D56,S 23,1,3,"McGowan, Miss. Anna ""Annie""",female,15,0,0,330923,8.0292,,Q 24,1,1,"Sloper, Mr. William Thompson",male,28,0,0,113788,35.5,A6,S 25,0,3,"Palsson, Miss. Torborg Danira",female,8,3,1,349909,21.075,,S 26,1,3,"Asplund, Mrs. Carl Oscar (Selma Augusta Emilia Johansson)",female,38,1,5,347077,31.3875,,S 27,0,3,"Emir, Mr. Farred Chehab",male,,0,0,2631,7.225,,C 28,0,1,"Fortune, Mr. Charles Alexander",male,19,3,2,19950,263,C23 C25 C27,S 29,1,3,"O'Dwyer, Miss. Ellen ""Nellie""",female,,0,0,330959,7.8792,,Q 30,0,3,"Todoroff, Mr. Lalio",male,,0,0,349216,7.8958,,S 31,0,1,"Uruchurtu, Don. Manuel E",male,40,0,0,PC 17601,27.7208,,C 32,1,1,"Spencer, Mrs. William Augustus (Marie Eugenie)",female,,1,0,PC 17569,146.5208,B78,C 33,1,3,"Glynn, Miss. Mary Agatha",female,,0,0,335677,7.75,,Q 34,0,2,"Wheadon, Mr. Edward H",male,66,0,0,C.A. 24579,10.5,,S 35,0,1,"Meyer, Mr. Edgar Joseph",male,28,1,0,PC 17604,82.1708,,C 36,0,1,"Holverson, Mr. Alexander Oskar",male,42,1,0,113789,52,,S 37,1,3,"Mamee, Mr. Hanna",male,,0,0,2677,7.2292,,C 38,0,3,"Cann, Mr. Ernest Charles",male,21,0,0,A./5. 2152,8.05,,S 39,0,3,"Vander Planke, Miss. Augusta Maria",female,18,2,0,345764,18,,S 40,1,3,"Nicola-Yarred, Miss. Jamila",female,14,1,0,2651,11.2417,,C 41,0,3,"Ahlin, Mrs. Johan (Johanna Persdotter Larsson)",female,40,1,0,7546,9.475,,S 42,0,2,"Turpin, Mrs. William John Robert (Dorothy Ann Wonnacott)",female,27,1,0,11668,21,,S 43,0,3,"Kraeff, Mr. Theodor",male,,0,0,349253,7.8958,,C 44,1,2,"Laroche, Miss. Simonne Marie Anne Andree",female,3,1,2,SC/Paris 2123,41.5792,,C 45,1,3,"Devaney, Miss. Margaret Delia",female,19,0,0,330958,7.8792,,Q 46,0,3,"Rogers, Mr. William John",male,,0,0,S.C./A.4. 23567,8.05,,S 47,0,3,"Lennon, Mr. Denis",male,,1,0,370371,15.5,,Q 48,1,3,"O'Driscoll, Miss. Bridget",female,,0,0,14311,7.75,,Q 49,0,3,"Samaan, Mr. Youssef",male,,2,0,2662,21.6792,,C 50,0,3,"Arnold-Franchi, Mrs. Josef (Josefine Franchi)",female,18,1,0,349237,17.8,,S 51,0,3,"Panula, Master. Juha Niilo",male,7,4,1,3101295,39.6875,,S 52,0,3,"Nosworthy, Mr. Richard Cater",male,21,0,0,A/4. 39886,7.8,,S 53,1,1,"Harper, Mrs. Henry Sleeper (Myna Haxtun)",female,49,1,0,PC 17572,76.7292,D33,C 54,1,2,"Faunthorpe, Mrs. Lizzie (Elizabeth Anne Wilkinson)",female,29,1,0,2926,26,,S 55,0,1,"Ostby, Mr. Engelhart Cornelius",male,65,0,1,113509,61.9792,B30,C 56,1,1,"Woolner, Mr. Hugh",male,,0,0,19947,35.5,C52,S 57,1,2,"Rugg, Miss. Emily",female,21,0,0,C.A. 31026,10.5,,S 58,0,3,"Novel, Mr. Mansouer",male,28.5,0,0,2697,7.2292,,C 59,1,2,"West, Miss. Constance Mirium",female,5,1,2,C.A. 34651,27.75,,S 60,0,3,"Goodwin, Master. William Frederick",male,11,5,2,CA 2144,46.9,,S 61,0,3,"Sirayanian, Mr. Orsen",male,22,0,0,2669,7.2292,,C 62,1,1,"Icard, Miss. Amelie",female,38,0,0,113572,80,B28, 63,0,1,"Harris, Mr. Henry Birkhardt",male,45,1,0,36973,83.475,C83,S 64,0,3,"Skoog, Master. Harald",male,4,3,2,347088,27.9,,S 65,0,1,"Stewart, Mr. Albert A",male,,0,0,PC 17605,27.7208,,C 66,1,3,"Moubarek, Master. Gerios",male,,1,1,2661,15.2458,,C 67,1,2,"Nye, Mrs. (Elizabeth Ramell)",female,29,0,0,C.A. 29395,10.5,F33,S 68,0,3,"Crease, Mr. Ernest James",male,19,0,0,S.P. 3464,8.1583,,S 69,1,3,"Andersson, Miss. Erna Alexandra",female,17,4,2,3101281,7.925,,S 70,0,3,"Kink, Mr. Vincenz",male,26,2,0,315151,8.6625,,S 71,0,2,"Jenkin, Mr. Stephen Curnow",male,32,0,0,C.A. 33111,10.5,,S 72,0,3,"Goodwin, Miss. 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0,3,39.0,29.125,0,0,1,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0 0,2,27.0,13.0,1,0,0,1,1,0,0,0,0,0,0,1,0,0,0,0,0,0 1,1,19.0,30.0,0,0,0,1,1,0,0,0,0,0,0,1,0,0,0,0,0,0 0,3,25.0,23.45,0,0,0,1,0,1,0,0,0,0,0,0,0,1,0,0,0,0 1,1,26.0,30.0,1,1,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0 0,3,32.0,7.75,1,0,1,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0 ================================================ FILE: March Cohort 2023/10_Matplotlib/.ipynb_checkpoints/10_Matplotlib-checkpoint.ipynb ================================================ { "cells": [], "metadata": {}, "nbformat": 4, "nbformat_minor": 5 } ================================================ FILE: March Cohort 2023/10_Matplotlib/10_Matplotlib.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "id": "d32aa7c4", "metadata": {}, "source": [ "# Matplotlib Basics\n" ] }, { "cell_type": "code", "execution_count": 1, "id": "796acdfb", "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 2, "id": "de34720f", "metadata": {}, "outputs": [], "source": [ "x = [1,2,3,4,5,6,7]\n", "y = [50,51,52,48,47,49,46]" ] }, { "cell_type": "code", "execution_count": 3, "id": "e73607da", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 0, 'Day')" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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q2SS/x+gmXs+5ndFteP+zqo5v/LTSZAy+OnppkgdYfFnmF4E/Hz93K3BHkt8F/gF46rlPqqofJHkE+PKGTitNiT+0lQYa34v+IeDyqvrJZs8jnS1flikNkOTXgH8F/sLY68XKM3xJasIzfElqwuBLUhMGX5KaMPiS1ITBl6Qm/h8RIuZ7ygnOiwAAAABJRU5ErkJggg==\n", 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.ylabel(\"Temperature\")" ] }, { "cell_type": "code", "execution_count": 5, "id": "87639bf2", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 1.0, 'Weather')" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.xlabel(\"Day\")\n", "plt.ylabel(\"Temperature\")\n", "plt.title(\"Weather\")\n", "plt.plot(x,y)" ] }, { "cell_type": "code", "execution_count": 8, "id": "c30385dc", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.xlabel(\"Day\")\n", "plt.ylabel(\"Temperature\")\n", "plt.title(\"Weather\")\n", "\n", "plt.plot(x,y, color=\"red\", linewidth=2)" ] }, { "cell_type": "code", "execution_count": 9, "id": "eeea6f84", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.xlabel(\"Day\")\n", "plt.ylabel(\"Temperature\")\n", "plt.title(\"Weather\")\n", "\n", "plt.plot(x,y, color=\"green\", linewidth=5, linestyle='dashed')" ] }, { "cell_type": "code", "execution_count": 12, "id": "60d6d89e", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.xlabel(\"Day\")\n", "plt.ylabel(\"Temperature\")\n", "plt.title(\"Weather\")\n", "\n", "plt.plot(x,y, color=\"red\", linewidth=2, linestyle='dotted')" ] }, { "cell_type": "markdown", "id": "af1765ba", "metadata": {}, "source": [ "## format strings" ] }, { "cell_type": "code", "execution_count": 13, "id": "dce734b8", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.xlabel(\"Day\")\n", "plt.ylabel(\"Temperature\")\n", "plt.title(\"Weather\")\n", "\n", "plt.plot(x,y, 'r+--')" ] }, { "cell_type": "code", "execution_count": 14, "id": "ced721bc", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.xlabel(\"Day\")\n", "plt.ylabel(\"Temperature\")\n", "plt.title(\"Weather\")\n", "\n", "plt.plot(x,y, color=\"blue\", marker='*', linestyle='--')" ] }, { "cell_type": "code", "execution_count": 15, "id": "31aae626", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.xlabel(\"Day\")\n", "plt.ylabel(\"Temperature\")\n", "plt.title(\"Weather\")\n", "\n", "plt.plot(x,y, color=\"blue\", marker='*', linestyle='')" ] }, { "cell_type": "code", "execution_count": 19, "id": "08a3557d", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.xlabel(\"Day\")\n", "plt.ylabel(\"Temperature\")\n", "plt.title(\"Weather\")\n", "\n", "plt.plot(x,y, color=\"blue\", marker='^', linestyle='', markersize=10)" ] }, { "cell_type": "code", "execution_count": 23, "id": "6f0a9738", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.xlabel(\"Day\")\n", "plt.ylabel(\"Temperature\")\n", "plt.title(\"Weather\")\n", "\n", "plt.plot(x,y, \"g<\", alpha=0.6) # rgba" ] }, { "cell_type": "markdown", "id": "d6ba4604", "metadata": {}, "source": [ "## Label parameter" ] }, { "cell_type": "code", "execution_count": 24, "id": "007da045", "metadata": {}, "outputs": [], "source": [ "days = [1,2,3,4,5,6,7]\n", "max_t = [50,51,52,58,47,49,46]\n", "min_t = [43,42,40,44,33,35,37]\n", "avg_t = [45,48,48,46,40,42,41]" ] }, { "cell_type": "code", "execution_count": 25, "id": "1bcc024a", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.plot(days, max_t, label='max')\n", "plt.plot(days, min_t, label='min')\n", "plt.plot(days, avg_t, label='average')" ] }, { "cell_type": "markdown", "id": "7cea201d", "metadata": {}, "source": [ "## legend" ] }, { "cell_type": "code", "execution_count": 26, "id": "49123f28", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.plot(days, max_t, label='max')\n", "plt.plot(days, min_t, label='min')\n", "plt.plot(days, avg_t, label='average')\n", "\n", "plt.legend(loc='best')" ] }, { "cell_type": "code", "execution_count": 27, "id": "5b28f921", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.plot(days, max_t, label='max')\n", "plt.plot(days, min_t, label='min')\n", "plt.plot(days, avg_t, label='average')\n", "\n", "plt.legend(loc='upper left', shadow=True, fontsize='large')" ] }, { "cell_type": "markdown", "id": "257dcdfc", "metadata": {}, "source": [ "# Bar chart" ] }, { "cell_type": "code", "execution_count": 28, "id": "d9c45b59", "metadata": {}, "outputs": [], "source": [ "import numpy as np" ] }, { "cell_type": "code", "execution_count": 29, "id": "f7826d22", "metadata": {}, "outputs": [], "source": [ "company = ['Google', 'Amazon', 'MS', 'Facebook']\n", "revenue = [90, 136, 89, 27]" ] }, { "cell_type": "code", "execution_count": 30, "id": "0d42eea6", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([0, 1, 2, 3])" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "xposition = np.arange(len(company))\n", "xposition" ] }, { "cell_type": "code", "execution_count": 31, "id": "17763021", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.bar(xposition, revenue, label='Revenue')\n", "plt.ylabel(\"Revenue(Bln)\")\n", "plt.title(\"Technology Stocks\")\n", "\n", "plt.legend()" ] }, { "cell_type": "code", "execution_count": 32, "id": "9fcd3b58", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.bar(xposition, revenue, label='Revenue')\n", "\n", "plt.xticks(xposition, company)\n", "\n", "plt.ylabel(\"Revenue(Bln)\")\n", "plt.title(\"Technology Stocks\")\n", "\n", "plt.legend()" ] }, { "cell_type": "code", "execution_count": 33, "id": "04ba079a", "metadata": {}, "outputs": [], "source": [ "profit = [40,2,34,12]" ] }, { "cell_type": "code", "execution_count": 34, "id": "7d281426", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.bar(xposition, revenue, label='Revenue')\n", "plt.bar(xposition, profit, label='Profit')\n", "\n", "plt.xticks(xposition, company)\n", "\n", "plt.ylabel(\"Revenue(Bln)\")\n", "plt.title(\"Technology Stocks\")\n", "\n", "plt.legend()" ] }, { "cell_type": "code", "execution_count": 35, "id": "0b8cd1b2", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.bar(xposition-0.2, revenue, width=0.4 , label='Revenue')\n", "plt.bar(xposition+0.2, profit, width=0.4 , label='Profit')\n", "\n", "plt.xticks(xposition, company)\n", "\n", "plt.ylabel(\"Revenue(Bln)\")\n", "plt.title(\"Technology Stocks\")\n", "\n", "plt.legend()" ] }, { "cell_type": "code", "execution_count": 36, "id": "22f3a2bc", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.bar(xposition-0.2, revenue, label='Revenue')\n", "plt.bar(xposition+0.2, profit, label='Profit')\n", "\n", "plt.xticks(xposition, company)\n", "\n", "plt.ylabel(\"Revenue(Bln)\")\n", "plt.title(\"Technology Stocks\")\n", "\n", "plt.legend()" ] }, { "cell_type": "markdown", "id": "cfe556e6", "metadata": {}, "source": [ "# Pie chart" ] }, { "cell_type": "code", "execution_count": 37, "id": "4a9dd3c0", "metadata": {}, "outputs": [], "source": [ "value = [1400, 600, 300, 410, 250]\n", "\n", "label = [\"Home Rent\", 'Food', 'Internet', 'Car', 'Others']" ] }, { "cell_type": "code", "execution_count": 38, "id": "dce1b13b", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "([,\n", " ,\n", " ,\n", " ,\n", " ],\n", " [Text(0.09328656407206024, 1.0960372333838069, 'Home Rent'),\n", " Text(-0.9822184890776084, -0.4952240298229684, 'Food'),\n", " Text(-0.16284704617934698, -1.0878790555712807, 'Internet'),\n", " Text(0.6256100334857941, -0.9047718419590123, 'Car'),\n", " Text(1.0615045230766318, -0.28845822485734873, 'Others')])" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.pie(value, labels=label)" ] }, { "cell_type": "code", "execution_count": 40, "id": "ffd103ef", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "([,\n", " ,\n", " ,\n", " ,\n", " ],\n", " [Text(0.16791581532970842, 1.9728670200908525, 'Home Rent'),\n", " Text(-1.7679932803396952, -0.8914032536813431, 'Food'),\n", " Text(-0.2931246831228246, -1.9581823000283056, 'Internet'),\n", " Text(1.1260980602744295, -1.628589315526222, 'Car'),\n", " Text(1.9107081415379372, -0.5192248047432277, 'Others')],\n", " [Text(0.09159044472529551, 1.0761092836859194, '47.30%'),\n", " Text(-0.9643599710943792, -0.4862199565534599, '20.27%'),\n", " Text(-0.15988619079426794, -1.0680994363790757, '10.14%'),\n", " Text(0.6142353056042342, -0.8883214448324848, '13.85%'),\n", " Text(1.0422044408388749, -0.2832135298599424, '8.45%')])" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.pie(value, labels=label, autopct= '%1.2f%%' , radius=1.8)" ] }, { "cell_type": "markdown", "id": "fb77d24f", "metadata": {}, "source": [ "# Histogram" ] }, { "cell_type": "code", "execution_count": 46, "id": "45b87311", "metadata": {}, "outputs": [], "source": [ "blood_sugar = [113,85,90,150,149,88,93,115,135,80,77,82,129]" ] }, { "cell_type": "code", "execution_count": 45, "id": "e856a0f0", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[77, 80, 82, 85, 88, 90, 93, 113, 115, 129, 135, 149, 150]\n" ] } ], "source": [ "b = [113,85,90,150,149,88,93,115,135,80,77,82,129]\n", "b.sort()\n", "print(b)" ] }, { "cell_type": "code", "execution_count": 47, "id": "48ff0682", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(array([3., 3., 1., 0., 1., 1., 0., 2., 0., 2.]),\n", " array([ 77. , 84.3, 91.6, 98.9, 106.2, 113.5, 120.8, 128.1, 135.4,\n", " 142.7, 150. ]),\n", " )" ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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Gs3b+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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.hist(blood_sugar, rwidth=0.8)" ] }, { "cell_type": "code", "execution_count": 48, "id": "5d821278", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(array([7., 1., 2., 3.]),\n", " array([ 77. , 95.25, 113.5 , 131.75, 150. ]),\n", " )" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.hist(blood_sugar, rwidth=0.8, bins=4)" ] }, { "cell_type": "code", "execution_count": 49, "id": "bfebb999", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(array([4.]), array([125, 150]), )" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.xlabel(\"Sugar Level\")\n", "plt.ylabel(\"Number of Patients\")\n", "plt.title(\"Bloog sugar level\")\n", "\n", "plt.hist(blood_sugar, bins=[80,100], rwidth=0.95, color='g')\n", "plt.hist(blood_sugar, bins=[100,125], rwidth=0.95, color='b')\n", "plt.hist(blood_sugar, bins=[125,150], rwidth=0.95, color='r')" ] }, { "cell_type": "code", "execution_count": null, "id": "c14df2f4", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.12" } }, "nbformat": 4, "nbformat_minor": 5 } ================================================ FILE: March Cohort 2023/12_15_16_17_18_Statistics/.ipynb_checkpoints/12_15_Statistics-checkpoint.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "id": "df89b0c5", "metadata": {}, "source": [ "# Statistics\n", "\n", "Data Science = Software Development + Statistics" ] }, { "cell_type": "code", "execution_count": 2, "id": "231d4e70", "metadata": {}, "outputs": [ { "ename": "IndentationError", "evalue": "unindent does not match any outer indentation level (, line 3)", "output_type": "error", "traceback": [ "\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" ] } ], "source": [ " Stats\n", " Descriptive Inferential\n", " Univariate Hypothesis Testing\n", " Bivariate Model Fitting\n", " Multivariate" ] }, { "cell_type": "code", "execution_count": 4, "id": "fa6fd519", "metadata": {}, "outputs": [ { "ename": "SyntaxError", "evalue": "invalid syntax (117687496.py, line 2)", "output_type": "error", "traceback": [ "\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" ] } ], "source": [ "- Univariate\n", "1. Measure of Frequency - count\n", "2. Central Tendency - mean, median, mode\n", " - Geometric mean } ratio or rates\n", " - Harmonic mean\n", "3. Despersion\n", " - range\n", " - inter quartile range\n", " - variance\n", " - standard deviation" ] }, { "cell_type": "code", "execution_count": 5, "id": "94473ea3", "metadata": {}, "outputs": [ { "ename": "IndentationError", "evalue": "unexpected indent (880973301.py, line 2)", "output_type": "error", "traceback": [ "\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" ] } ], "source": [ " Data\n", " Qualitative Quantitative\n", " " ] }, { "cell_type": "markdown", "id": "e7ee5068", "metadata": {}, "source": [ "# Central Tendancy" ] }, { "cell_type": "code", "execution_count": 6, "id": "acd1670e", "metadata": {}, "outputs": [], "source": [ "import statistics\n", "\n", "import numpy as np\n", "import pandas as pd\n", "\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 7, "id": "27b667c2", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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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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" ], "text/plain": [ " Rank Company Country Sales Profits Assets \\\n", "0 1 ICBC China 151.4 42.0 3473.2 \n", "1 2 China Construction Bank China 134.2 35.0 3016.6 \n", "2 3 Berkshire Hathaway United States 222.9 24.1 620.9 \n", "3 4 JPMorgan Chase United States 102.5 24.2 2513.0 \n", "4 5 Wells Fargo United States 97.6 21.9 1943.4 \n", "\n", " Market Value Sector Industry \n", "0 229.8 Financials Major Banks \n", "1 200.5 Financials Regional Banks \n", "2 409.9 Financials Investment Services \n", "3 306.6 Financials Major Banks \n", "4 274.4 Financials Major Banks " ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_csv('datasets/forbes.csv')\n", "\n", "df.head()" ] }, { "cell_type": "code", "execution_count": 8, "id": "40528971", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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1996FalseFalseFalseFalseFalseFalseFalseFalseTrue
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1998FalseFalseFalseFalseFalseFalseFalseFalseFalse
1999FalseFalseFalseFalseFalseFalseFalseFalseTrue
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" ], "text/plain": [ " Rank Company Country Sales Profits Assets Market Value Sector \\\n", "0 False False False False False False False False \n", "1 False False False False False False False False \n", "2 False False False False False False False False \n", "3 False False False False False False False False \n", "4 False False False False False False False False \n", "... ... ... ... ... ... ... ... ... \n", "1995 False False False False False False False False \n", "1996 False False False False False False False False \n", "1997 False False False False False False False False \n", "1998 False False False False False False False False \n", "1999 False False False False False False False False \n", "\n", " Industry \n", "0 False \n", "1 False \n", "2 False \n", "3 False \n", "4 False \n", "... ... \n", "1995 False \n", "1996 True \n", "1997 False \n", "1998 False \n", "1999 True \n", "\n", "[2000 rows x 9 columns]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.isnull()" ] }, { "cell_type": "code", "execution_count": 9, "id": "db69da00", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Rank 0\n", "Company 0\n", "Country 0\n", "Sales 0\n", "Profits 0\n", "Assets 0\n", "Market Value 0\n", "Sector 197\n", "Industry 491\n", "dtype: int64" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.isnull().sum()" ] }, { "cell_type": "code", "execution_count": 10, "id": "2a980414", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Rank 0\n", "Company 0\n", "Country 0\n", "Sales 0\n", "Profits 0\n", "Assets 0\n", "Market Value 0\n", "Sector 0\n", "Industry 0\n", "dtype: int64" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.dropna(inplace=True)\n", "\n", "df.isnull().sum()" ] }, { "cell_type": "code", "execution_count": 11, "id": "b2de3fb4", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(1508, 9)" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.shape" ] }, { "cell_type": "code", "execution_count": 12, "id": "9aa68486", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "26.978494031830294" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "market_value_mean = df['Market Value'].mean()\n", "\n", "market_value_mean" ] }, { "cell_type": "markdown", "id": "11d37231", "metadata": {}, "source": [ "## Mean Key Points\n", "\n", "- best represents the data\n", "- mean consider all the data points\n", "- extremly sensitive to the presence of outliers\n" ] }, { "cell_type": "code", "execution_count": 13, "id": "88065b85", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "13.1" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "market_value_median = df['Market Value'].median()\n", "\n", "market_value_median" ] }, { "cell_type": "markdown", "id": "7594c091", "metadata": {}, "source": [ "## Median Key points\n", "\n", "- It is less sensitive to outliers\n", "- 50% of the data is either side of it.\n" ] }, { "cell_type": "code", "execution_count": 14, "id": "585fdb6d", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.24" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['Market Value'].min()" ] }, { "cell_type": "code", "execution_count": 15, "id": "da70e1e2", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "752.0" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['Market Value'].max()" ] }, { "cell_type": "code", "execution_count": 16, "id": "6f4c63c0", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "751.76" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "range1 = df['Market Value'].max() - df['Market Value'].min()\n", "\n", "range1" ] }, { "cell_type": "code", "execution_count": 17, "id": "434e6e34", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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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
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" ], "text/plain": [ " Rank Sales Profits Assets Market Value\n", "count 1508.000000 1508.000000 1508.000000 1508.000000 1508.000000\n", "mean 909.135279 19.623046 1.407430 97.647016 26.978494\n", "std 554.044456 30.875809 3.244201 287.036157 47.099541\n", "min 1.000000 0.001000 -13.000000 1.300000 0.240000\n", "25% 433.750000 4.900000 0.349750 12.800000 7.300000\n", "50% 883.500000 10.300000 0.692500 25.900000 13.100000\n", "75% 1354.250000 19.900000 1.425000 60.325000 26.900000\n", "max 1999.000000 485.300000 45.200000 3473.200000 752.000000" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.describe()" ] }, { "cell_type": "code", "execution_count": 18, "id": "c4b36bb3", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Financials 474\n", "Consumer Discretionary 209\n", "Industrials 192\n", "Materials 145\n", "Consumer Staples 106\n", "Information Technology 103\n", "Utilities 86\n", "Energy 79\n", "Health Care 71\n", "Telecommunication Services 43\n", "Name: Sector, dtype: int64" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# sector column is a categorical variable\n", "\n", "df['Sector'].value_counts()" ] }, { "cell_type": "code", "execution_count": 19, "id": "b4d8b09e", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 Financials\n", "Name: Sector, dtype: object" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['Sector'].mode()" ] }, { "cell_type": "markdown", "id": "0f6db83f", "metadata": {}, "source": [ "## Mode\n", "\n", "- the value that occured most in the data\n", "- vote in an election\n", "- mode is not good for continuous data." ] }, { "cell_type": "markdown", "id": "214ca025", "metadata": {}, "source": [ "# box plot\n", "\n", "- mean\n", "- median\n", "- 25th percentile\n", "- 75th percentile\n", "- maximum values are where in the data\n", "- outliers" ] }, { "cell_type": "code", "execution_count": 20, "id": "ab0791e2", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(10,8))\n", "\n", "sns.boxplot(data=df, y = 'Market Value', showmeans=True)\n", "\n", "plt.axhline(y=market_value_mean, color='r', linestyle='-')\n", "plt.axhline(y=market_value_median, color='g', linestyle='-')\n", "\n", "plt.title('Forbes Data')\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 21, "id": "e11c59db", "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(10,8))\n", "\n", "sns.boxplot(data=df, y = 'Market Value', showmeans=True)\n", "\n", "plt.ylim(0,80)\n", "\n", "plt.axhline(y=market_value_mean, color='r', linestyle='-')\n", "plt.axhline(y=market_value_median, color='g', linestyle='-')\n", "\n", "plt.title('Forbes Data')\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "3a8efbb2", "metadata": {}, "source": [ "# mode" ] }, { "cell_type": "code", "execution_count": 22, "id": "0afbc532", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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SectorRankCompanyCountrySalesProfitsAssetsMarket ValueIndustry
0Consumer Discretionary209209209209209209209209
1Consumer Staples106106106106106106106106
2Energy7979797979797979
3Financials474474474474474474474474
4Health Care7171717171717171
5Industrials192192192192192192192192
6Information Technology103103103103103103103103
7Materials145145145145145145145145
8Telecommunication Services4343434343434343
9Utilities8686868686868686
\n", "
" ], "text/plain": [ " Sector Rank Company Country Sales Profits Assets \\\n", "0 Consumer Discretionary 209 209 209 209 209 209 \n", "1 Consumer Staples 106 106 106 106 106 106 \n", "2 Energy 79 79 79 79 79 79 \n", "3 Financials 474 474 474 474 474 474 \n", "4 Health Care 71 71 71 71 71 71 \n", "5 Industrials 192 192 192 192 192 192 \n", "6 Information Technology 103 103 103 103 103 103 \n", "7 Materials 145 145 145 145 145 145 \n", "8 Telecommunication Services 43 43 43 43 43 43 \n", "9 Utilities 86 86 86 86 86 86 \n", "\n", " Market Value Industry \n", "0 209 209 \n", "1 106 106 \n", "2 79 79 \n", "3 474 474 \n", "4 71 71 \n", "5 192 192 \n", "6 103 103 \n", "7 145 145 \n", "8 43 43 \n", "9 86 86 " ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gb = df.groupby('Sector', as_index=False).count()\n", "\n", "gb" ] }, { "cell_type": "code", "execution_count": 23, "id": "635fa133", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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SectorRank
3Financials474
0Consumer Discretionary209
5Industrials192
7Materials145
1Consumer Staples106
6Information Technology103
9Utilities86
2Energy79
4Health Care71
8Telecommunication Services43
\n", "
" ], "text/plain": [ " Sector Rank\n", "3 Financials 474\n", "0 Consumer Discretionary 209\n", "5 Industrials 192\n", "7 Materials 145\n", "1 Consumer Staples 106\n", "6 Information Technology 103\n", "9 Utilities 86\n", "2 Energy 79\n", "4 Health Care 71\n", "8 Telecommunication Services 43" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gb = gb[['Sector', 'Rank']]\n", "\n", "gb = gb.sort_values(by='Rank', ascending=False)\n", "\n", "gb" ] }, { "cell_type": "markdown", "id": "4efa0eb2", "metadata": {}, "source": [ "# pie chart\n", "\n", "- mode\n", "- category wise display values\n", "- percentage of values aquired by categories" ] }, { "cell_type": "code", "execution_count": 24, "id": "70799918", "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(10,8))\n", "\n", "plt.pie(gb['Rank'], labels=gb['Sector'], autopct='%.2f')\n", "\n", "plt.suptitle(\"Sector Vs Rank\")\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "d4812687", "metadata": {}, "source": [ "# Range" ] }, { "cell_type": "code", "execution_count": 25, "id": "fee0db23", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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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
\n", "
" ], "text/plain": [ " Rank Company Country Sales Profits Assets \\\n", "0 1 ICBC China 151.4 42.0 3473.2 \n", "1 2 China Construction Bank China 134.2 35.0 3016.6 \n", "2 3 Berkshire Hathaway United States 222.9 24.1 620.9 \n", "3 4 JPMorgan Chase United States 102.5 24.2 2513.0 \n", "4 5 Wells Fargo United States 97.6 21.9 1943.4 \n", "\n", " Market Value Sector Industry \n", "0 229.8 Financials Major Banks \n", "1 200.5 Financials Regional Banks \n", "2 409.9 Financials Investment Services \n", "3 306.6 Financials Major Banks \n", "4 274.4 Financials Major Banks " ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "code", "execution_count": 26, "id": "ad69c17b", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "45.2" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "profit_max = df['Profits'].max()\n", "\n", "profit_max" ] }, { "cell_type": "code", "execution_count": 27, "id": "4a88e4a1", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "-13.0" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "profit_min = df['Profits'].min()\n", "\n", "profit_min" ] }, { "cell_type": "code", "execution_count": 28, "id": "29b12a1f", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "58.2" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "profit_range = profit_max - profit_min\n", "\n", "profit_range" ] }, { "cell_type": "code", "execution_count": 29, "id": "356b1be1", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(10,8))\n", "\n", "sns.boxplot(data = df, y = 'Profits', showmeans=True)\n", "\n", "plt.title('Forbes Data')\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 30, "id": "8be191bb", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(10,8))\n", "\n", "sns.boxplot(data = df, y = 'Profits', showmeans=True)\n", "\n", "plt.ylim(-3,8)\n", "\n", "plt.title('Forbes Data')\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "60b33559", "metadata": {}, "source": [ "# Range\n", "\n", "- range is sensitive to outliers" ] }, { "cell_type": "markdown", "id": "57270870", "metadata": {}, "source": [ "# Inter Quartile Range" ] }, { "cell_type": "code", "execution_count": 31, "id": "55f9d714", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.34975" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 25th percentile\n", "\n", "Q1 = np.quantile(df['Profits'], 0.25)\n", "\n", "Q1" ] }, { "cell_type": "code", "execution_count": 32, "id": "7dfaa0d9", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1.4249999999999998" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 75th percentile\n", "\n", "Q3 = np.quantile(df['Profits'], 0.75)\n", "\n", "Q3" ] }, { "cell_type": "code", "execution_count": 33, "id": "e1aab44b", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1.0752499999999998" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "IQR = Q3 - Q1\n", "\n", "IQR" ] }, { "cell_type": "markdown", "id": "194f9cf6", "metadata": {}, "source": [ "# IQR\n", "\n", "- resistant to outliers\n", "- better than range" ] }, { "cell_type": "markdown", "id": "d30aaeac", "metadata": {}, "source": [ "# Standard Deviation and Variance" ] }, { "cell_type": "code", "execution_count": 34, "id": "94231206", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "3.244201278603241" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "profit_std = df['Profits'].std()\n", "\n", "profit_std" ] }, { "cell_type": "code", "execution_count": 36, "id": "054b2250", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1.4074303713527847" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "profit_mean = df['Profits'].mean()\n", "\n", "profit_mean" ] }, { "cell_type": "code", "execution_count": null, "id": "7e91b587", "metadata": {}, "outputs": [], "source": [ "cricket\n", "\n", "virat kohli rohit sharma\n", "40 0\n", "50 100\n", "30 10\n", "20 15\n", "45 0\n", "67 101\n", "40 200\n", "\n", "mean mean\n", "35 60\n", "\n", "5 60\n", "15 40\n", "5 50\n", "15 60\n", "25 41\n", " 140" ] }, { "cell_type": "code", "execution_count": 37, "id": "86d516fd", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "10.524841936090901" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "profit_var = df['Profits'].var()\n", "\n", "profit_var" ] }, { "cell_type": "code", "execution_count": null, "id": "f5f846b7", "metadata": {}, "outputs": [], "source": [ "appl msoft\n", "100 100\n", "500 120\n", "-200 130\n", "600 125" ] }, { "cell_type": "code", "execution_count": 38, "id": "0c7d3fa2", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "10.524841936090903" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "profit_std ** 2 # variance" ] }, { "cell_type": "markdown", "id": "27a5b442", "metadata": {}, "source": [ "# Summary Stats" ] }, { "cell_type": "code", "execution_count": 39, "id": "3d683610", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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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", "
" ], "text/plain": [ " Rank Sales Profits Assets Market Value\n", "count 1508.000000 1508.000000 1508.000000 1508.000000 1508.000000\n", "mean 909.135279 19.623046 1.407430 97.647016 26.978494\n", "std 554.044456 30.875809 3.244201 287.036157 47.099541\n", "min 1.000000 0.001000 -13.000000 1.300000 0.240000\n", "25% 433.750000 4.900000 0.349750 12.800000 7.300000\n", "50% 883.500000 10.300000 0.692500 25.900000 13.100000\n", "75% 1354.250000 19.900000 1.425000 60.325000 26.900000\n", "max 1999.000000 485.300000 45.200000 3473.200000 752.000000" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.describe()" ] }, { "cell_type": "markdown", "id": "45cddf9c", "metadata": {}, "source": [ "# Noraml Distribution" ] }, { "cell_type": "code", "execution_count": 40, "id": "eeec52ce", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([-0.21471611, 0.61754921, -0.60251843, 0.14196059, 0.60611692,\n", " 0.33409508, 0.93308273, -0.83391941, -2.49212651, -0.64909842])" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# mean = 0\n", "# std = 1\n", "\n", "normally_distributed_data = np.random.normal(loc=0, scale=1, size=100000)\n", "\n", "normally_distributed_data[0:10]" ] }, { "cell_type": "code", "execution_count": null, "id": "91a281e8", "metadata": {}, "outputs": [], "source": [ "# mean == median # normal distribution" ] }, { "cell_type": "code", "execution_count": 42, "id": "3524740c", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "-0.00010732710555090066" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sample_mean = np.mean(normally_distributed_data)\n", "\n", "sample_mean" ] }, { "cell_type": "code", "execution_count": 43, "id": "c995dc03", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "-0.0014516077341794609" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sample_median = np.median(normally_distributed_data)\n", "\n", "sample_median" ] }, { "cell_type": "code", "execution_count": 44, "id": "9dc19349", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1.0014081381099627" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sample_std = np.std(normally_distributed_data)\n", "\n", "sample_std" ] }, { "cell_type": "markdown", "id": "81360486", "metadata": {}, "source": [ "## plot normal distribution" ] }, { "cell_type": "code", "execution_count": 46, "id": "908217a4", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(12,8))\n", "\n", "sns.histplot(x = normally_distributed_data)\n", "\n", "plt.title(\"Normal Distribution\", fontsize='15')\n", "\n", "plt.axvline(x = sample_mean, color='r', linestyle='-')\n", "plt.axvline(x = sample_median, color='g', linestyle='-')\n", "\n", "plt.xlabel('Values', fontsize='15')\n", "plt.ylabel('probability', fontsize='15')\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "91fcee90", "metadata": {}, "source": [ "# Probability'\n", "\n", "an event is likely to occur\n", "\n", "favorable cases / total number of cases\n" ] }, { "cell_type": "code", "execution_count": null, "id": "04e37d20", "metadata": {}, "outputs": [], "source": [ "1/2\n", "\n", " H T\n", "0 - 0.5 , 0.5 - 1" ] }, { "cell_type": "code", "execution_count": null, "id": "ef7d552b", "metadata": {}, "outputs": [], "source": [ " 0 1 2\n", "0-0.1 0.1 - 0.2 0.2 - 0.3" ] }, { "cell_type": "markdown", "id": "7556d169", "metadata": {}, "source": [ "# Coin" ] }, { "cell_type": "code", "execution_count": 49, "id": "c3a058c9", "metadata": {}, "outputs": [], "source": [ "import random\n", "import scipy.stats" ] }, { "cell_type": "code", "execution_count": 50, "id": "977c6909", "metadata": {}, "outputs": [], "source": [ "def flip(n):\n", " \n", " result = []\n", " \n", " for i in range(1,n+1):\n", " result.append(random.choice(['Head', 'Tails']))\n", " \n", " return result" ] }, { "cell_type": "code", "execution_count": 51, "id": "0c567150", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['Head',\n", " 'Tails',\n", " 'Tails',\n", " 'Tails',\n", " 'Head',\n", " 'Head',\n", " 'Tails',\n", " 'Tails',\n", " 'Tails',\n", " 'Head']" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ "flip(10)" ] }, { "cell_type": "code", "execution_count": 52, "id": "a45cb78e", "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "f = flip(10)\n", "\n", "plt.figure(figsize=(12,8))\n", "\n", "sns.countplot(x = f)\n", "\n", "plt.xlabel('Coin Flips')\n", "plt.title('Counts')\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 53, "id": "3f563d5f", "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "f = flip(100)\n", "\n", "plt.figure(figsize=(12,8))\n", "\n", "sns.countplot(x = f)\n", "\n", "plt.xlabel('Coin Flips')\n", "plt.title('Counts')\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 54, "id": "85907d8a", "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "f = flip(100000)\n", "\n", "plt.figure(figsize=(12,8))\n", "\n", "sns.countplot(x = f)\n", "\n", "plt.xlabel('Coin Flips')\n", "plt.title('Counts')\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "1081d8ad", "metadata": {}, "source": [ "# Dice" ] }, { "cell_type": "code", "execution_count": 55, "id": "b8e72228", "metadata": {}, "outputs": [], "source": [ "def roll(n):\n", " \n", " result = []\n", " \n", " for i in range(1,n+1):\n", " result.append(random.choice([1,2,3,4,5,6]))\n", " \n", " return result" ] }, { "cell_type": "code", "execution_count": 56, "id": "acb6b988", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[4, 5, 4, 5, 3, 3, 3, 3, 4, 6]" ] }, "execution_count": 56, "metadata": {}, "output_type": "execute_result" } ], "source": [ "roll(10)" ] }, { "cell_type": "code", "execution_count": 57, "id": "ca45a0e6", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "f = roll(10)\n", "\n", "plt.figure(figsize=(12,8))\n", "\n", "sns.countplot(x = f)\n", "\n", "plt.xlabel('Dice Roll')\n", "plt.title('Counts')\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 58, "id": "d9bd67b6", "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "f = roll(100)\n", "\n", "plt.figure(figsize=(12,8))\n", "\n", "sns.countplot(x = f)\n", "\n", "plt.xlabel('Dice Roll')\n", "plt.title('Counts')\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 59, "id": "a0b6328e", "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "f = roll(100000)\n", "\n", "plt.figure(figsize=(12,8))\n", "\n", "sns.countplot(x = f)\n", "\n", "plt.xlabel('Dice Roll')\n", "plt.title('Counts')\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 60, "id": "3cf86622", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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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
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" ], "text/plain": [ " Date Open High Low Close AdjClose Volume\n", "0 31-Dec-1986 243.37 244.03 241.28 242.17 242.17 13,92,00,000\n", "1 2-Jan-1987 242.17 246.45 242.17 246.45 246.45 9,18,80,000\n", "2 5-Jan-1987 246.45 252.57 246.45 252.19 252.19 18,19,00,000\n", "3 6-Jan-1987 252.20 253.99 252.14 252.78 252.78 18,93,00,000\n", "4 7-Jan-1987 252.78 255.72 252.65 255.33 255.33 19,09,00,000" ] }, "execution_count": 60, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = pd.read_csv('datasets/SP_500_1987.csv')\n", "\n", "data.head()" ] }, { "cell_type": "code", "execution_count": 61, "id": "9d2c64dc", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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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
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" ], "text/plain": [ " Open High Low Close AdjClose\n", "count 253.000000 253.000000 253.000000 253.000000 253.000000\n", "mean 286.964466 289.328063 284.412095 286.978617 286.978617\n", "std 28.923683 28.241142 29.535528 28.889053 28.889053\n", "min 223.980000 225.770000 216.460000 223.920000 223.920000\n", "25% 267.840000 270.400000 264.310000 267.840000 267.840000\n", "50% 290.520000 292.470000 288.340000 290.520000 290.520000\n", "75% 308.940000 310.270000 307.420000 308.960000 308.960000\n", "max 336.770000 337.890000 334.460000 336.770000 336.770000" ] }, "execution_count": 61, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.describe()" ] }, { "cell_type": "code", "execution_count": 62, "id": "52bb2007", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Date object\n", "Open float64\n", "High float64\n", "Low float64\n", "Close float64\n", "AdjClose float64\n", "Volume object\n", "dtype: object" ] }, "execution_count": 62, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.dtypes" ] }, { "cell_type": "code", "execution_count": 63, "id": "3e57630e", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Date datetime64[ns]\n", "Open float64\n", "High float64\n", "Low float64\n", "Close float64\n", "AdjClose float64\n", "Volume object\n", "dtype: object" ] }, "execution_count": 63, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data['Date'] = pd.to_datetime(data['Date'])\n", "\n", "data.dtypes" ] }, { "cell_type": "code", "execution_count": 64, "id": "ad432bf2", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 1.0, 'S&P 500 1987')" ] }, "execution_count": 64, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(12,8))\n", "\n", "sns.lineplot(data=data, x = 'Date', y='AdjClose', color='red')\n", "\n", "plt.title(\"S&P 500 1987\")" ] }, { "cell_type": "code", "execution_count": 67, "id": "2cecaa85", "metadata": { "scrolled": false }, "outputs": [], "source": [ "# help(data['AdjClose'].pct_change())" ] }, { "cell_type": "code", "execution_count": 65, "id": "15c92d9e", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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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
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" ], "text/plain": [ " Date Open High Low Close AdjClose Volume Returns\n", "0 1986-12-31 243.37 244.03 241.28 242.17 242.17 13,92,00,000 NaN\n", "1 1987-01-02 242.17 246.45 242.17 246.45 246.45 9,18,80,000 0.017674\n", "2 1987-01-05 246.45 252.57 246.45 252.19 252.19 18,19,00,000 0.023291\n", "3 1987-01-06 252.20 253.99 252.14 252.78 252.78 18,93,00,000 0.002340\n", "4 1987-01-07 252.78 255.72 252.65 255.33 255.33 19,09,00,000 0.010088" ] }, "execution_count": 65, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data['Returns'] = data['AdjClose'].pct_change()\n", "\n", "data.head()" ] }, { "cell_type": "code", "execution_count": 68, "id": "2d892d6c", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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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", "
" ], "text/plain": [ " Date Open High Low Close AdjClose Volume Returns\n", "1 1987-01-02 242.17 246.45 242.17 246.45 246.45 9,18,80,000 0.017674\n", "2 1987-01-05 246.45 252.57 246.45 252.19 252.19 18,19,00,000 0.023291\n", "3 1987-01-06 252.20 253.99 252.14 252.78 252.78 18,93,00,000 0.002340\n", "4 1987-01-07 252.78 255.72 252.65 255.33 255.33 19,09,00,000 0.010088\n", "5 1987-01-08 255.36 257.28 254.97 257.28 257.28 19,45,00,000 0.007637" ] }, "execution_count": 68, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = data.dropna()\n", "data.head()" ] }, { "cell_type": "code", "execution_count": 69, "id": "c23e43a2", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(252, 8)" ] }, "execution_count": 69, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.shape" ] }, { "cell_type": "markdown", "id": "44c9342e", "metadata": {}, "source": [ "# Skewness" ] }, { "cell_type": "code", "execution_count": 70, "id": "86c6f635", "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(12,8))\n", "\n", "sns.histplot(data=data, x = 'Returns')\n", "\n", "plt.title(\"S&P\")\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "id": "915bfd73", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.12" } }, "nbformat": 4, "nbformat_minor": 5 } ================================================ FILE: March Cohort 2023/12_15_16_17_18_Statistics/.ipynb_checkpoints/16_17_Statistics and Regression-checkpoint.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "id": "103790af", "metadata": {}, "source": [ "# Multiple Linear Regression\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "\n", "" ] }, { "cell_type": "code", "execution_count": 1, "id": "6dd89ea3", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "\n", "import statsmodels.api as sm" ] }, { "cell_type": "code", "execution_count": 2, "id": "73c667a7", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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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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" ], "text/plain": [ " Sl.NO. PLAYER NAME AGE COUNTRY TEAM PLAYING ROLE T-RUNS T-WKTS \\\n", "0 1 Abdulla, YA 2 SA KXIP Allrounder 0 0 \n", "1 2 Abdur Razzak 2 BAN RCB Bowler 214 18 \n", "2 3 Agarkar, AB 2 IND KKR Bowler 571 58 \n", "3 4 Ashwin, R 1 IND CSK Bowler 284 31 \n", "4 5 Badrinath, S 2 IND CSK Batsman 63 0 \n", "\n", " ODI-RUNS-S ODI-SR-B ... SR-B SIXERS RUNS-C WKTS AVE-BL ECON \\\n", "0 0 0.00 ... 0.00 0 307 15 20.47 8.90 \n", "1 657 71.41 ... 0.00 0 29 0 0.00 14.50 \n", "2 1269 80.62 ... 121.01 5 1059 29 36.52 8.81 \n", "3 241 84.56 ... 76.32 0 1125 49 22.96 6.23 \n", "4 79 45.93 ... 120.71 28 0 0 0.00 0.00 \n", "\n", " SR-BL AUCTION YEAR BASE PRICE SOLD PRICE \n", "0 13.93 2009 50000 50000 \n", "1 0.00 2008 50000 50000 \n", "2 24.90 2008 200000 350000 \n", "3 22.14 2011 100000 850000 \n", "4 0.00 2011 100000 800000 \n", "\n", "[5 rows x 26 columns]" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_csv('datasets/IPL2013.csv')\n", "\n", "df.head()" ] }, { "cell_type": "code", "execution_count": 3, "id": "89f7bb2b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 130 entries, 0 to 129\n", "Data columns (total 26 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 Sl.NO. 130 non-null int64 \n", " 1 PLAYER NAME 130 non-null object \n", " 2 AGE 130 non-null int64 \n", " 3 COUNTRY 130 non-null object \n", " 4 TEAM 130 non-null object \n", " 5 PLAYING ROLE 130 non-null object \n", " 6 T-RUNS 130 non-null int64 \n", " 7 T-WKTS 130 non-null int64 \n", " 8 ODI-RUNS-S 130 non-null int64 \n", " 9 ODI-SR-B 130 non-null float64\n", " 10 ODI-WKTS 130 non-null int64 \n", " 11 ODI-SR-BL 130 non-null float64\n", " 12 CAPTAINCY EXP 130 non-null int64 \n", " 13 RUNS-S 130 non-null int64 \n", " 14 HS 130 non-null int64 \n", " 15 AVE 130 non-null float64\n", " 16 SR-B 130 non-null float64\n", " 17 SIXERS 130 non-null int64 \n", " 18 RUNS-C 130 non-null int64 \n", " 19 WKTS 130 non-null int64 \n", " 20 AVE-BL 130 non-null float64\n", " 21 ECON 130 non-null float64\n", " 22 SR-BL 130 non-null float64\n", " 23 AUCTION YEAR 130 non-null int64 \n", " 24 BASE PRICE 130 non-null int64 \n", " 25 SOLD PRICE 130 non-null int64 \n", "dtypes: float64(7), int64(15), object(4)\n", "memory usage: 26.5+ KB\n" ] } ], "source": [ "df.info()" ] }, { "cell_type": "code", "execution_count": 4, "id": "983b5a17", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['Sl.NO.', 'PLAYER NAME', 'AGE', 'COUNTRY', 'TEAM', 'PLAYING ROLE',\n", " 'T-RUNS', 'T-WKTS', 'ODI-RUNS-S', 'ODI-SR-B', 'ODI-WKTS', 'ODI-SR-BL',\n", " 'CAPTAINCY EXP', 'RUNS-S', 'HS', 'AVE', 'SR-B', 'SIXERS', 'RUNS-C',\n", " 'WKTS', 'AVE-BL', 'ECON', 'SR-BL', 'AUCTION YEAR', 'BASE PRICE',\n", " 'SOLD PRICE'],\n", " dtype='object')" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "xfeatures = df.columns\n", "xfeatures" ] }, { "cell_type": "code", "execution_count": 5, "id": "beac70ae", "metadata": {}, "outputs": [], "source": [ "xfeatures = ['AGE', 'COUNTRY', 'PLAYING ROLE',\n", " 'T-RUNS', 'T-WKTS', 'ODI-RUNS-S', 'ODI-SR-B', 'ODI-WKTS', 'ODI-SR-BL',\n", " 'CAPTAINCY EXP', 'RUNS-S', 'HS', 'AVE', 'SR-B', 'SIXERS', 'RUNS-C',\n", " 'WKTS', 'AVE-BL', 'ECON', 'SR-BL']" ] }, { "cell_type": "markdown", "id": "489a7f42", "metadata": {}, "source": [ "# Feature encoding on categorical columns" ] }, { "cell_type": "code", "execution_count": 6, "id": "9f8602c5", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array(['Allrounder', 'Bowler', 'Batsman', 'W. Keeper'], dtype=object)" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['PLAYING ROLE'].unique()" ] }, { "cell_type": "code", "execution_count": 7, "id": "4b4f0c3e", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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AllrounderBatsmanBowlerW. Keeper
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" ], "text/plain": [ " Allrounder Batsman Bowler W. Keeper\n", "0 1 0 0 0\n", "1 0 0 1 0\n", "2 0 0 1 0\n", "3 0 0 1 0\n", "4 0 1 0 0" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.get_dummies(df['PLAYING ROLE'])[0:5]" ] }, { "cell_type": "code", "execution_count": 8, "id": "704e7c42", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['T-RUNS', 'T-WKTS', 'ODI-RUNS-S', 'ODI-SR-B', 'ODI-WKTS', 'ODI-SR-BL',\n", " 'RUNS-S', 'HS', 'AVE', 'SR-B', 'SIXERS', 'RUNS-C', 'WKTS', 'AVE-BL',\n", " 'ECON', 'SR-BL', 'AGE_2', 'AGE_3', 'COUNTRY_BAN', 'COUNTRY_ENG',\n", " 'COUNTRY_IND', 'COUNTRY_NZ', 'COUNTRY_PAK', 'COUNTRY_SA', 'COUNTRY_SL',\n", " 'COUNTRY_WI', 'COUNTRY_ZIM', 'PLAYING ROLE_Batsman',\n", " 'PLAYING ROLE_Bowler', 'PLAYING ROLE_W. Keeper', 'CAPTAINCY EXP_1'],\n", " dtype='object')" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "categorical_features = ['AGE', 'COUNTRY', 'PLAYING ROLE', 'CAPTAINCY EXP']\n", "\n", "encoded_df = pd.get_dummies(df[xfeatures],\n", " columns = categorical_features,\n", " drop_first=True)\n", "\n", "encoded_df.columns" ] }, { "cell_type": "code", "execution_count": 9, "id": "7dea8fb2", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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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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" ], "text/plain": [ " T-RUNS T-WKTS ODI-RUNS-S ODI-SR-B ODI-WKTS ODI-SR-BL RUNS-S HS \\\n", "0 0 0 0 0.00 0 0.0 0 0 \n", "1 214 18 657 71.41 185 37.6 0 0 \n", "2 571 58 1269 80.62 288 32.9 167 39 \n", "3 284 31 241 84.56 51 36.8 58 11 \n", "4 63 0 79 45.93 0 0.0 1317 71 \n", "\n", " AVE SR-B ... COUNTRY_NZ COUNTRY_PAK COUNTRY_SA COUNTRY_SL \\\n", "0 0.00 0.00 ... 0 0 1 0 \n", "1 0.00 0.00 ... 0 0 0 0 \n", "2 18.56 121.01 ... 0 0 0 0 \n", "3 5.80 76.32 ... 0 0 0 0 \n", "4 32.93 120.71 ... 0 0 0 0 \n", "\n", " COUNTRY_WI COUNTRY_ZIM PLAYING ROLE_Batsman PLAYING ROLE_Bowler \\\n", "0 0 0 0 0 \n", "1 0 0 0 1 \n", "2 0 0 0 1 \n", "3 0 0 0 1 \n", "4 0 0 1 0 \n", "\n", " PLAYING ROLE_W. Keeper CAPTAINCY EXP_1 \n", "0 0 0 \n", "1 0 0 \n", "2 0 0 \n", "3 0 0 \n", "4 0 0 \n", "\n", "[5 rows x 31 columns]" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "xfeatures = encoded_df.columns\n", "\n", "encoded_df.head()" ] }, { "cell_type": "code", "execution_count": 10, "id": "8af55ff4", "metadata": {}, "outputs": [], "source": [ "from sklearn.model_selection import train_test_split\n", "\n", "x = sm.add_constant(encoded_df)\n", "\n", "y = df['SOLD PRICE']\n", "\n", "xtrain, xtest, ytrain, ytest = train_test_split(x, y,\n", " train_size=0.8,\n", " random_state=101)" ] }, { "cell_type": "code", "execution_count": 11, "id": "d5cbd477", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\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
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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
" ], "text/plain": [ "\n", "\"\"\"\n", " Results: Ordinary least squares\n", "========================================================================================\n", "Model: OLS Adj. R-squared: 0.367 \n", "Dependent Variable: SOLD PRICE AIC: 2952.4618 \n", "Date: 2023-04-24 21:08 BIC: 3034.4379 \n", "No. Observations: 104 Log-Likelihood: -1445.2 \n", "Df Model: 30 F-statistic: 2.989 \n", "Df Residuals: 73 Prob (F-statistic): 7.43e-05 \n", "R-squared: 0.551 Scale: 9.8071e+10\n", "----------------------------------------------------------------------------------------\n", " Coef. Std.Err. t P>|t| [0.025 0.975] \n", "----------------------------------------------------------------------------------------\n", "const 321792.1559 214961.4272 1.4970 0.1387 -106625.3506 750209.6624\n", "T-RUNS -15.3217 31.7900 -0.4820 0.6313 -78.6790 48.0356\n", "T-WKTS -92.1211 532.7482 -0.1729 0.8632 -1153.8869 969.6446\n", "ODI-RUNS-S 46.4615 32.7116 1.4203 0.1598 -18.7326 111.6555\n", "ODI-SR-B 1254.6514 1524.3920 0.8231 0.4132 -1783.4575 4292.7602\n", "ODI-WKTS 1034.2327 705.8108 1.4653 0.1471 -372.4462 2440.9116\n", "ODI-SR-BL -1951.6835 1570.2876 -1.2429 0.2179 -5081.2621 1177.8952\n", "RUNS-S 79.5617 164.1206 0.4848 0.6293 -247.5303 406.6536\n", "HS -2845.5968 2659.2674 -1.0701 0.2881 -8145.5090 2454.3155\n", "AVE 16170.0170 7116.7912 2.2721 0.0260 1986.2726 30353.7615\n", "SR-B -1412.9785 1268.6500 -1.1138 0.2690 -3941.3943 1115.4373\n", "SIXERS 2933.8502 3250.6472 0.9025 0.3697 -3544.6804 9412.3808\n", "RUNS-C 90.3731 250.6836 0.3605 0.7195 -409.2387 589.9848\n", "WKTS 1023.5083 6199.6413 0.1651 0.8693 -11332.3589 13379.3755\n", "AVE-BL 10484.2466 10614.6012 0.9877 0.3266 -10670.6230 31639.1163\n", "ECON 1582.7941 10799.9290 0.1466 0.8839 -19941.4334 23107.0217\n", "SR-BL -16112.2676 14683.6651 -1.0973 0.2761 -45376.7699 13152.2347\n", "AGE_2 -204982.5473 122684.6949 -1.6708 0.0990 -449492.7915 39527.6970\n", "AGE_3 -314011.4605 166581.1800 -1.8850 0.0634 -646007.2734 17984.3525\n", "COUNTRY_BAN -0.0000 0.0000 -0.4054 0.6864 -0.0000 0.0000\n", "COUNTRY_ENG 568937.2590 223554.3597 2.5450 0.0130 123394.0628 1014480.4552\n", "COUNTRY_IND 105574.3654 116110.3502 0.9093 0.3662 -125833.2288 336981.9597\n", "COUNTRY_NZ 120771.0634 169814.2346 0.7112 0.4792 -217668.2181 459210.3449\n", "COUNTRY_PAK -18339.1812 162409.9584 -0.1129 0.9104 -342021.7614 305343.3991\n", "COUNTRY_SA -48587.6579 135853.6982 -0.3576 0.7216 -319343.6880 222168.3722\n", "COUNTRY_SL -132932.0463 156295.0094 -0.8505 0.3978 -444427.5507 178563.4581\n", "COUNTRY_WI -292568.4951 188284.0245 -1.5539 0.1245 -667818.0146 82681.0244\n", "COUNTRY_ZIM -70610.1889 377372.9760 -0.1871 0.8521 -822713.4453 681493.0676\n", "PLAYING ROLE_Batsman 45944.5780 131655.5496 0.3490 0.7281 -216444.5538 308333.7098\n", "PLAYING ROLE_Bowler 103751.1060 110824.6388 0.9362 0.3523 -117122.0806 324624.2926\n", "PLAYING ROLE_W. Keeper -89731.6479 187093.4020 -0.4796 0.6329 -462608.2602 283144.9645\n", "CAPTAINCY EXP_1 71431.5146 116029.1349 0.6156 0.5401 -159814.2177 302677.2470\n", "----------------------------------------------------------------------------------------\n", "Omnibus: 6.983 Durbin-Watson: 1.857 \n", "Prob(Omnibus): 0.030 Jarque-Bera (JB): 6.499 \n", "Skew: 0.583 Prob(JB): 0.039 \n", "Kurtosis: 3.376 Condition No.: 12332195260360820\n", "========================================================================================\n", "* The condition number is large (1e+16). This might indicate strong\n", "multicollinearity or other numerical problems.\n", "\"\"\"" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model1 = sm.OLS(ytrain, xtrain).fit()\n", "\n", "model1.summary2()" ] }, { "cell_type": "code", "execution_count": 12, "id": "ad35c43b", "metadata": {}, "outputs": [ { "ename": "SyntaxError", "evalue": "invalid syntax (1120071531.py, line 3)", "output_type": "error", "traceback": [ "\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" ] } ], "source": [ "p-value\n", "\n", "95% 5%\n", "0.95 0.05\n", "\n", "\n", "# VIF (Variance Inflation Factor)\n", "\n", "" ] }, { "cell_type": "code", "execution_count": 13, "id": "81de17e2", "metadata": {}, "outputs": [], "source": [ "from statsmodels.stats.outliers_influence import variance_inflation_factor\n", "\n", "def get_vif_score(x):\n", " \n", " xmatrix = x.values\n", " vif = [variance_inflation_factor(xmatrix, i) for i in range(xmatrix.shape[1])]\n", " \n", " vif_factor = pd.DataFrame()\n", " vif_factor['Column'] = x.columns\n", " vif_factor['VIF'] = vif\n", " \n", " return vif_factor" ] }, { "cell_type": "code", "execution_count": 14, "id": "4368e988", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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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", "
" ], "text/plain": [ " Column VIF\n", "0 T-RUNS 12.612694\n", "1 T-WKTS 7.679284\n", "2 ODI-RUNS-S 16.426209\n", "3 ODI-SR-B 13.829376\n", "4 ODI-WKTS 9.951800\n", "5 ODI-SR-BL 4.426818\n", "6 RUNS-S 16.135407\n", "7 HS 22.781017\n", "8 AVE 25.226566\n", "9 SR-B 21.576204\n", "10 SIXERS 9.547268\n", "11 RUNS-C 38.229691\n", "12 WKTS 33.366067\n", "13 AVE-BL 100.198105\n", "14 ECON 7.650140\n", "15 SR-BL 103.723846\n", "16 AGE_2 6.996226\n", "17 AGE_3 3.855003\n", "18 COUNTRY_BAN 1.469017\n", "19 COUNTRY_ENG 1.391524\n", "20 COUNTRY_IND 4.568898\n", "21 COUNTRY_NZ 1.497856\n", "22 COUNTRY_PAK 1.796355\n", "23 COUNTRY_SA 1.886555\n", "24 COUNTRY_SL 1.984902\n", "25 COUNTRY_WI 1.531847\n", "26 COUNTRY_ZIM 1.312168\n", "27 PLAYING ROLE_Batsman 4.843136\n", "28 PLAYING ROLE_Bowler 3.795864\n", "29 PLAYING ROLE_W. Keeper 3.132044\n", "30 CAPTAINCY EXP_1 4.245128" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "vif_factor = get_vif_score(x[xfeatures])\n", "\n", "vif_factor" ] }, { "cell_type": "code", "execution_count": 15, "id": "21f1c866", "metadata": {}, "outputs": [], "source": [ "columns_with_large_vif = vif_factor[vif_factor.VIF > 4].Column" ] }, { "cell_type": "code", "execution_count": 16, "id": "f6f77c94", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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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", "
" ], "text/plain": [ " T-RUNS T-WKTS ODI-RUNS-S ODI-SR-B ODI-WKTS \\\n", "T-RUNS 1.000000 0.026285 0.892823 0.231411 0.045505 \n", "T-WKTS 0.026285 1.000000 -0.088276 0.012052 0.822940 \n", "ODI-RUNS-S 0.892823 -0.088276 1.000000 0.319264 0.056554 \n", "ODI-SR-B 0.231411 0.012052 0.319264 1.000000 0.160114 \n", "ODI-WKTS 0.045505 0.822940 0.056554 0.160114 1.000000 \n", "ODI-SR-BL 0.067700 0.060641 0.126795 0.284584 0.124361 \n", "RUNS-S 0.410143 -0.218544 0.523955 0.306338 -0.198101 \n", "HS 0.411209 -0.268432 0.495765 0.360086 -0.209109 \n", "AVE 0.374046 -0.265540 0.446280 0.340327 -0.222062 \n", "SR-B 0.114298 -0.147752 0.194111 0.375371 -0.030404 \n", "SIXERS 0.216571 -0.198036 0.376012 0.320041 -0.148722 \n", "RUNS-C -0.253083 0.297302 -0.268950 0.004855 0.327122 \n", "WKTS -0.277157 0.289735 -0.302745 -0.022502 0.302466 \n", "AVE-BL -0.298999 0.162456 -0.225109 0.018958 0.217302 \n", "ECON -0.329022 0.117530 -0.249795 -0.019067 0.230221 \n", "SR-BL -0.309105 0.205208 -0.237517 0.017522 0.259800 \n", "AGE_2 -0.266649 -0.178391 -0.242422 -0.011977 -0.125708 \n", "COUNTRY_IND -0.179278 -0.147490 -0.168224 -0.168609 -0.187669 \n", "PLAYING ROLE_Batsman 0.384422 -0.276450 0.367995 0.083485 -0.358824 \n", "CAPTAINCY EXP_1 0.690647 0.088782 0.714058 0.291373 0.077536 \n", "\n", " ODI-SR-BL RUNS-S HS AVE SR-B \\\n", "T-RUNS 0.067700 0.410143 0.411209 0.374046 0.114298 \n", "T-WKTS 0.060641 -0.218544 -0.268432 -0.265540 -0.147752 \n", "ODI-RUNS-S 0.126795 0.523955 0.495765 0.446280 0.194111 \n", "ODI-SR-B 0.284584 0.306338 0.360086 0.340327 0.375371 \n", "ODI-WKTS 0.124361 -0.198101 -0.209109 -0.222062 -0.030404 \n", "ODI-SR-BL 1.000000 -0.027772 -0.067615 -0.053766 -0.051684 \n", "RUNS-S -0.027772 1.000000 0.834631 0.767023 0.380022 \n", "HS -0.067615 0.834631 1.000000 0.876249 0.531028 \n", "AVE -0.053766 0.767023 0.876249 1.000000 0.583579 \n", "SR-B -0.051684 0.380022 0.531028 0.583579 1.000000 \n", "SIXERS -0.033616 0.866213 0.788439 0.705365 0.425394 \n", "RUNS-C 0.196887 -0.160762 -0.235327 -0.278482 -0.063179 \n", "WKTS 0.151594 -0.220959 -0.291494 -0.343958 -0.069823 \n", "AVE-BL 0.416627 -0.101818 -0.176049 -0.120430 -0.057895 \n", "ECON 0.313295 -0.179459 -0.254494 -0.202301 -0.076228 \n", "SR-BL 0.402495 -0.118567 -0.192990 -0.140809 -0.068248 \n", "AGE_2 -0.133908 -0.085762 -0.022883 0.031758 0.181181 \n", "COUNTRY_IND -0.022494 0.209292 -0.034029 -0.037809 -0.049031 \n", "PLAYING ROLE_Batsman -0.014689 0.418555 0.408826 0.432485 0.157903 \n", "CAPTAINCY EXP_1 0.103315 0.352422 0.392749 0.378271 0.176919 \n", "\n", " SIXERS RUNS-C WKTS AVE-BL ECON \\\n", "T-RUNS 0.216571 -0.253083 -0.277157 -0.298999 -0.329022 \n", "T-WKTS -0.198036 0.297302 0.289735 0.162456 0.117530 \n", "ODI-RUNS-S 0.376012 -0.268950 -0.302745 -0.225109 -0.249795 \n", "ODI-SR-B 0.320041 0.004855 -0.022502 0.018958 -0.019067 \n", "ODI-WKTS -0.148722 0.327122 0.302466 0.217302 0.230221 \n", "ODI-SR-BL -0.033616 0.196887 0.151594 0.416627 0.313295 \n", "RUNS-S 0.866213 -0.160762 -0.220959 -0.101818 -0.179459 \n", "HS 0.788439 -0.235327 -0.291494 -0.176049 -0.254494 \n", "AVE 0.705365 -0.278482 -0.343958 -0.120430 -0.202301 \n", "SR-B 0.425394 -0.063179 -0.069823 -0.057895 -0.076228 \n", "SIXERS 1.000000 -0.080295 -0.144558 -0.012210 -0.101027 \n", "RUNS-C -0.080295 1.000000 0.959210 0.409505 0.410502 \n", "WKTS -0.144558 0.959210 1.000000 0.297822 0.374875 \n", "AVE-BL -0.012210 0.409505 0.297822 1.000000 0.722758 \n", "ECON -0.101027 0.410502 0.374875 0.722758 1.000000 \n", "SR-BL -0.021412 0.471224 0.368264 0.983415 0.726480 \n", "AGE_2 -0.043516 0.025722 0.044467 -0.072386 0.004942 \n", "COUNTRY_IND 0.132099 0.266465 0.216827 0.149050 0.065552 \n", "PLAYING ROLE_Batsman 0.294896 -0.436411 -0.422958 -0.366341 -0.288471 \n", "CAPTAINCY EXP_1 0.254991 -0.277903 -0.300074 -0.183775 -0.248974 \n", "\n", " SR-BL AGE_2 COUNTRY_IND PLAYING ROLE_Batsman \\\n", "T-RUNS -0.309105 -0.266649 -0.179278 0.384422 \n", "T-WKTS 0.205208 -0.178391 -0.147490 -0.276450 \n", "ODI-RUNS-S -0.237517 -0.242422 -0.168224 0.367995 \n", "ODI-SR-B 0.017522 -0.011977 -0.168609 0.083485 \n", "ODI-WKTS 0.259800 -0.125708 -0.187669 -0.358824 \n", "ODI-SR-BL 0.402495 -0.133908 -0.022494 -0.014689 \n", "RUNS-S -0.118567 -0.085762 0.209292 0.418555 \n", "HS -0.192990 -0.022883 -0.034029 0.408826 \n", "AVE -0.140809 0.031758 -0.037809 0.432485 \n", "SR-B -0.068248 0.181181 -0.049031 0.157903 \n", "SIXERS -0.021412 -0.043516 0.132099 0.294896 \n", "RUNS-C 0.471224 0.025722 0.266465 -0.436411 \n", "WKTS 0.368264 0.044467 0.216827 -0.422958 \n", "AVE-BL 0.983415 -0.072386 0.149050 -0.366341 \n", "ECON 0.726480 0.004942 0.065552 -0.288471 \n", "SR-BL 1.000000 -0.090249 0.126109 -0.395188 \n", "AGE_2 -0.090249 1.000000 -0.002036 -0.170277 \n", "COUNTRY_IND 0.126109 -0.002036 1.000000 0.071734 \n", "PLAYING ROLE_Batsman -0.395188 -0.170277 0.071734 1.000000 \n", "CAPTAINCY EXP_1 -0.198688 -0.109261 -0.192539 0.314284 \n", "\n", " CAPTAINCY EXP_1 \n", "T-RUNS 0.690647 \n", "T-WKTS 0.088782 \n", "ODI-RUNS-S 0.714058 \n", "ODI-SR-B 0.291373 \n", "ODI-WKTS 0.077536 \n", "ODI-SR-BL 0.103315 \n", "RUNS-S 0.352422 \n", "HS 0.392749 \n", "AVE 0.378271 \n", "SR-B 0.176919 \n", "SIXERS 0.254991 \n", "RUNS-C -0.277903 \n", "WKTS -0.300074 \n", "AVE-BL -0.183775 \n", "ECON -0.248974 \n", "SR-BL -0.198688 \n", "AGE_2 -0.109261 \n", "COUNTRY_IND -0.192539 \n", "PLAYING ROLE_Batsman 0.314284 \n", "CAPTAINCY EXP_1 1.000000 " ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x[columns_with_large_vif].corr()" ] }, { "cell_type": "code", "execution_count": 17, "id": "8eb9de04", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 1.0, 'Correlation between Features')" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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UwW3scFCpSN68g4Q164zO2wU0xGXoQNDpULRa4j9bTvrFKwA49e6OQ+d2gELmnfvEzFwEGZkW8+/Yg2gWH7qBTqfQpXIRBtUpaWJz9nEsiw/dRKPT4WpXiFVv1gHg15CH/HnlCYoC3SoXoU/N4hZ1JP8eshNQgCiKEgNUB/MN+hwcURSlgxDCDggRQmxSFOVYPiTSgW5CiPmKokTnOvcV8LmiKFsM+lX+Vz/M0aVdS3p378Sk2ebceTHtZg/g5z7zSQiP5b2ts7m57zxRt7MbgGWDquFe0pevAsZQpEYZ2s8ZyMou09FpdeyZs5awKw8o5GDLkG1zuHf0ClG3n1KiQUXKt6zFN20mos3Q4OBh/Kp3rU7HvB/+5NvJQ/DxcKH3xC8IrF2J0kV8s2zW7z5GqSI+LJ3wLrEJSXT+eAHtm9TkQWgUG/efYu28kVhbqRk273ua1KxAcT8v8w6qBOUWvEvIW3NID42h9u75RO0+S8qtbB9TH0ZyvssMNM+ScW9WnXKfvc+5tpPRpWcS0m0m2pR0hJWamn/NIubABRLO3c4KK1SCNrMH8KshDwdtnc3tfeeJzpGHpQ15+E3AGPxrlKHNnIGs7jI963ydQW2IvhOKjaNd1rHiDSryRstafG/IQ3sPZ4RK0GJOf37vs4DEsFje+WsWd/eeI+Z29ntLSgZVw62EL6uajsGvRmlazh3A2s4zAGg2oy/3gy+xdehXqKzVWNvZABAwqRcnvviT+8GXKBlUjYBJvWDMEUP+qXAZO5KYkePQRkbhtWoFaUeOo3nwMFc+q3Ae9j7pp84YHU7ZsYvkPzbhOu3FjVdUKpxHjyR2lF7Lc+UK0o+a13L64H3STxtrpe7YRfLGTbhOyZ+W+8QPiRg6AU1ENP5rl5Fy6ASZ9x5lmaSdCiE0+AQA1mVL4r1oCk+7vovm4RNC3x6aFU/RPb+RfMByNSFUgkZz+rOj9wKSw2Lpsn0WD/ecIz7HdUuPT+b4tJ8p0bqWSfhbvx/m6uq9BH4xJB9+CSosGMS5t+aSFhpD/d3ziNp9jmSj8h7FmS6z0DxLxrNZdSp99j6n2k5B0Wi5Of1nEi8/QO1gS/2984k5dMkobE4aN29AsVJF6NzgbarUrMSkhWPp1850fGPnn3uYPHwmAPO/mUHXPh35fc1mAEJOXWRk3/EvdKtJ84YUL1mUtvV7ULVWZaYtGk+vtu+a2G3buJsJw/T31uIVs+nepzPr1/xJr0E9uHvzPsP7jsXNw5XtxzawfeMuMjM1eeoGtGhE8VJFaVG3C9VrVWbWoon0aNPfxG7elCUkJSUDMHHWKN55922++2r1C/0KatGEEqWL07R2e2rUrsrcz6bQuWUfE7tZkxeRlKiPf+qccQwY3JvlX67i2OGT7N15EIDyFd9g+Q+f0qx+J7NagS0aU6JUMZrV7Uz1WlWYvXgS3Vr3M7GbM+XTLF8mzx5Dv3d7suKrH3kW/4xZkxbSsm1Qnj41bt6A4qWK0qHBm1StWYkpC8fTp91gE7vtf+5m4vAZACz8Zibd+nRiw5pNrF6+ltXL1wIQ0LIxfYe8bbEDUDeoDoVLFqZ/k4FUqFGekfM+5MNOI03srp69ysn9p/hswyKj4536d+Lh7UdMHTQdF3cXfjy0iv2bDqDJXS5UKtwmfETk8PFoI6Lw/Wk5KYdPoLmfXTelnT5P6qHjAFiXKYXngqmE9RiI2ssTp7e7EvbWIJT0DDzmT8WhVTOSt+3GHFqdwoKD1/mmWy18HG3p89tJAkp5UdrDMcsmMS2TeQev83WXmvg52xGbkg7AnehE/rzyhJ971sdaLRi+6TyNS3pS3M3BrNa/htwTIPcE/JdRFCUVuAAUzmcQDfrXUo8yc84P/Rvnnsd9+e+mLye1q1fBxdnpfworrGyIfRBB3OMotJlarvx1knItjRsh5VrW4uJGfYPwScgdbJ3tcfR2JSkynjDDCHNGchpRd0Jx8nEDoM47zTm6fCvaDH1FmhxjXIFfufOIoj4eFPHxwNrKijYNaxB85qpx2hCkpKajKAopaem4ONqjVqm4/zSSqmWLYWdTCCu1mloVS3PgtOUsda5ZhpT74aQ9jETJ1BK5+ThebeoY2SScvYXmmf5HL+HcbWz9PLLOaQ2Vq7BWo7JSg2L8tnD/6qWJfRBB/OModJlarv11kjdy5eEbLWtxyZCHoTnyEMDJ150yzapzYd1BozA132nO8Rx5mBKTgG/10sQ9iODZI73Wjb9OUrqVsVaZVrW4uvEoAGEhd7FxdsDB25VCjnYUqVuOy+uCAdBlaklPSAFAURQKOek7IDZO9iRFxGXFZ12xPJonoWhDw0CjIXXfAWybNDLJZ4ceXUk9eARdXLzR8YwLl9AlmP8Bz411hfJoc2nZNDbVsu/elbRDZrQuXkLJp5ZN5XJoHoeieRoOGg3Ju4OxD2xoZKOkpmV9VtnZ5r70ANjWq0HmkzC0YZEWtbyqlybhQQSJhut2d8tJiue6bmkxCURfvIdOY/oSnfBTN0mPT8qXXy6G8p5qKO/hm4/j3aa2kc2zHOU9/txtbPzcAciIjCfx8gNAPyOWfPspNr7uFrUCWjdm24ZdAFw+fxUnZyc8vT1M7I7uP5H1+UrIdbz9vPPlS06atWnK1t/1sxKXzl2xqHVk//Gsz5dDruLjr9dSFHBwtAfA3sGOZ/EJaMzkdW5atAlg8/rtAFw4dwUnF0e8fDxN7J43mgFsbW1N6glLtGoXxMZ1+pmakLOXcHZ2wttc/Ik547dBMcSfc7bB3sEOBcu6LdoGsGnDNoMvl3F2ccqHL9laMdFxXAq5hkaTd8cpqHVT/tpguFbnr+Lk7PjCcnE55Do+ZspF264t2blpr0Wthq0asHfjPgCuh9zA0dkBd2/TMnvn6l0inkSYRqAo2BsGX+wcbEmMT0RrplwUqlQezeOnaJ/q66aUPQexD7BcXwi7XGVArUbY2IBahcrWFm1U7rHCbK6EP6Ooiz1FXOyxVqto/YYvwXeN65edN8NoXsYbP2d92t3t9QM692OTqeLrip21GiuVilpF3Dh413LdJPn3kJ2A/zBCCDegLHD4JYJ9DfQRQrjkOv45cEAIsVMIMUoI4fqKkvn3UVmREBaT9TUhLBZnXzcjE2dfdxJCc9iEx+LsY2zjWsQTv0rFeXrhLgAeJf0oVrc8gzfPZMD6KfhXLWVkHxn7DF8P16zv3h4uRMQ9M7Lp2aYR955G0GLoTHqM/ZTxA7qgUqkoU9SXczfuEZ+YTGp6BkdDrhMeE2/RRRtfd9JzpD89NCbPho1f72bEHMgxna4S1Nm/iMZXVxJ76DIJ542nmp183UnMlYdOufLQyUwePu8wtZzelwPzfkPRGf94P8/DAZtn8s76KfhVLYWTrxuJobFZNklh2fE8x9HXzSg9ieGxOPq64VLMi5TYRNp89j59d8yh1cLBWTMBB2f+QsCkXrx/8ksCpvTiyMLsZTFqL0+0Edk/ItqoKNRexg0HlacntgFNSNlsvOzkZVF7eaKNzNbSWdJq+gq0vD3RhEdlfddERKP2Nm0Q2Qc1ovCmVXgvnUPMDNPZNofWgSTvPGhy3MjGz42ksOzrlhwei4OfWx4h/ndsfd1Jy1HW0kJj8yzvhXsHEX3ggmk8Rb1wqlyCZ+dNl1Y8x9vPi/DQ7OsVERaJt6UZOfRLO9r3aM3xg6eyjlWtVZn1+1ez7NdPKVXOdMmDkdbT7EZcRFgkPi/Q6tijLUcPnATg11W/U+qNkgRf2s7m4F+ZP+XzrMZtXvj4eRMWmq0bHhqJj6953QVfTefE1T2UKluCn1auN2uTG18/b8KehueIPwJfC52kT5fN5tyNYEqXLcmP3/+adbx1+2YcOLmV1eu+ZtyH016J1qKvZnD62j5KlS3BmpXrzNpYQl8ucl6rqBeWi4492nDs4Emj47Z2NjQKqs/e7cEWw3r6ehIVmn0fR4VF4+lr2uGwxObVWylWphjrz/7K93u/Zfn0b8yWC7W3J9qIHPVFZJTZ+sIusBF+f/yI1xdziZmlry+0UdEk/vI7/tt+o/Cu39ElJZF26pzFNEUmp+HjZJv13cfJlqjkdCObh3EpJKRpGPz7GXr/eoK/rulnFUt7OnL+aRzxqRmkZmo5ej+a8MQ0/nMouoL7+48iOwH/TZoIIS4B4ej3BDyvMS39WmQdVxQlAfgJ+MjIQFF+BCoAv6N/K91JIYRN7oiEEO8LIc4KIc6u/Mn8OuiCwKQCFHnbFLK34a0VH7Nr1s+kJ+lHpVRWKuxcHFjZZTp75/3Km8s/zBXeNM7cMscv3qR8icLsWzGdDYvGMP+HTSSlpFGqiA8DOzVjyJxvGTbve94o7o+VWm3ZIWHqgKXRMtdGlfDvHcSd2WuzD+oUzjQfz/HqQ3GuWRqH8kUta2X5Zxy/mSSgKAplmtUgJeaZ0f6ArDBWKmxdHFjdZToH5v1Kt+UfmvfF5HKZFUNlpcancgku/Lyfn9tNITM1nbrDOgJQvW9zDs5ay3f1RxI8ay2tF79nFOOL/HP5eDgJy7/9+1O85jPK6KvzyOEkrigYLYCUg8d42vVdIkfNwHXYAOOTVlbYBzQgee+hF4mZ0cp3Sl8OM1KWxNwaVaRw7yBuz/7V6Lja3obqq0Zxc+oatEmW17ULs+XRsmMTF4zl/MmLhJy6CMCNSzdpV7s7bzcfwLpVG/n8x/mWtcyWQ4vmTF04nnMnL3D+1AUAGgfV58aVWwRWbU/3Zn2ZPH8sDo4vXiLxMj5+8tFMGlVpw91b92nfpeUL4zYImInfvOnYEVOpU7EZd27do2PXNlnHd28/QLP6nRj8zkjGThyRh1T+fRn/0QzqV27F3Vv36dCl1QucyK1jeiyvcjF5wTjDtbpodDygVWMunLlkcSnQ/6KVm9oBtbh77S5v1+7NkDbDGDF7OPaGGaMXYkYnNfgYYT0GEj12Gq5DB+jT6OSIXUBDQjv14WmbtxB2dti3bZFHvC+W1ioK1yMTWNqlBl93rcX3p+/xMC6ZUu6ODKhdgg/+PMfwzed4w8sJK5XZSkHyLyM7Af8yQoiuQogLhr/n8+VHFEWpClQBPhBCVDccjwFyD905AfG5jn0BvAsY/booihKqKMoPiqJ0Rr90qHLu9CiK8p2iKLUVRak9uF+eL7B7deg0OOdY+uLs505iRLyRSUJYLM7+OWx83UmM1NuorNS8teJjLm8+xvVdZ43CXN+lX6/99OI9FJ2CvXv2kiUfDxej0fvImGd4uxlPoGwJPkPzulUQQlDM15PC3u7cN4w6dmtWj/ULR/PjzOG4ONpTzNd0ROY56WEx2ORIv42/BxnhcSZ2DhWLUWHJEC71X4wmznTphSYhhbhj13APqm50PDE8FqdceZiUjzxMioynSO03KNuiFsOPfkHXpSMo0bAinb74QB9vWCw3DHkYasjDjOQ0nPyzR3Ud/dxJijT2JXd6nHz16UkMiyUxLJZww2zNrR2n8alcAoBK3Ztwe6de6+a2U/hWK50VXhsVhdone7RQ7eWFLjp7pBn0m3TdZk3De+Nv2AYF4DL2Y2ybmi7jeRHayCjU3tlaKi8vtLm1ypXDdcY0vH7/DdvAAJzHfIyNmeVJL9SKiMIqx4iulY8n2qgYi/bp5y9jVdQPlWv2/ha7xnXIuHEHXWx8nlrJYbE4+mVfNwdfd5LNlMFXQVpYLLY5ypqtvzvpZrQcKxaj0pIhXOj/KZk5yruwUlPth9GEbTxK5I4zJuHeGtiNdftWs27faqLCo/H1z75ePn7eRIWbX+bw/piBuHm48tn0r7KOJSelkJqi72Qc3X8CK2srXN2z64FeA3uwcf/PbNz/M1ERUfgW9jHSiswxk5OTD8a8i5uHGwunfZF1rEvPDlkjyo8ePOHpo1BKlTW/YbLPoDfZevBXth78lYjwKPz8s3V9/b2JjLC8lEOn07Fjyx5ad2hu0abfuz3Zeeh3dh76ncjwSPwKZ++F8vX3ISLc8vINnU7HX5t2066jaSPy9IlzFCtZBDd316xjfQe9xbaD69h2cJ3eFxMt83n4XGvb5j206WjZl+e8PbA7G/atYcO+NYZykfNaeVksF0PHDMLNw5XF0780Odems/mlQJ36d2TFruWs2LWcmIhYvPyz72MvP09iImJNwliizVutOLJTv58n9EEo4Y/DKVrGdKBHGxmN2idHfeHtlXd9EXIZqyL+qFycsa1bE01oOLr4Z6DVknrwCDZVK1oM6+1oS0SO0fuIxDS8HGxMbBoW98DO2go3u0LULOzGrahEALpWLsJvfRrww5t1cbG1pphrPjs1BYlOW3B//1FkJ+BfRlGUTYqiVDf8nc117hYwH5hgOHQY6CSEcAIQQnQDLiqKos0VLhbYgL4jgMG2jRDC2vDZF/AAzO+0K2AUTToeJX1xLeqF2lpN5Y71ubnXeJry5r7zVOveBIAiNcqQnphKkqET0HnRe0TfecqJlcZPELmx5xwlG+orOY+SvqitrUiJTcw6X6l0UR6FR/MkMoZMjYZdx0MIqF3JKA5fT1dOXdFvwI2JT+RBaCRFDGs9Y57p4wqLjmP/6Uu0bVTDoo+JIXexL+WHbTEvhLUa7y4Nid5tdLmxKexBlR/GcnX4MlLvhWUdt/ZwwspZX4GqbK1xb1qFlDvGly704j3cS/riUtQLlbWaih3rcytXHt7ed56qhjz0z5GHwYvWs7T+h3zd+GM2fbiMB8evsfXjbwC4teccJQx56G7Iw4dHruCWQ6t8x/rc3XveSOvu3vNU6t4YAL8apUlPTCE5Mp6UqGckhsXiVsoPgOKNKhFj2LycFBFH0foVACjWqBJxD7KXDGRev4FVkcKo/XzBygq7Fs1IO3rcSDOyR28iu/cisnsv0g4e4tmnX5B2OD/76Y3JvHEDdVFjrfRjxlpRb/Um6s1eRL3Zi7TgQyR89gXpR15eK/3qTayKFcbKX6/l0DqQlEMnjGysivpnfS5UvgzC2hpdjlFJxzZBJO/KeykQQNTFeziX9MXJcN1Kd67Po1zX7VWREHIX+1K+2BnKu2+XhkTuNi6PtoU9qP7DaC4P/5qUHOUdoNLnQ0i+/ZSHOZ5OlJMNP/5JzxYD6NliAAd3HabDW/oR6So1K5GUmER0pGnDqGvvjjQMrMfED6YbjdJ6eGV3jCrVqIAQgvjY7GWBv/34B92b96V7877s33mYTm+2BfRLiCxpde/TiUZB9Rk3dKqRVtjTcOo3qZ2lW6J0MR4/NF8Nr/3hdzoF9aZTUG/27Qymy9vtAaheqzKJCUlEmekEFCtZJOtzUKum3L39wGzcAD+tWkfbgDdpG/Amu7cfoHtP/UbeGrWrkpiQZLaTUbxkdsO0RZsA7ty+b3K8ctUKFLK2Ji5Hp/TnHzbQIagnHYJ6snfHQbq+1cHgSxWLvuSMs3nrvH15zvofN/JWi/681aI/B3YdpuNbhmtVsxKJiclmr1W33h1pGFifCbnKBYCjkwO1G9Tg4G7TFblb1/zF0DbDGNpmGMd2H6dld32HqEKN8iQnphAbmf9OQGRoFDUbVQfA1dOVoqWLEPYwzMQu49oNrIsWRm2oL+xbBZF62LhusiqSXV9YlysL1tboniWgDY+kUOUK+j0BgE2dmmQ+eIQlKvk68yg+hafPUsjU6th9K5zA0sbLtgJLexESGo9GpyM1U8uV8HhKuuvHHp9vEg5LSOXAnQjalPPLd35ICg75dKD/PiuAsUKIkoqiXBJCLAOOGh4HGgmYPu5Az2dAzjnZVsCXQojnXftxOZYZ/W3GTV/AmZBLxMcn0LzLOwx7ty/dO7bOd/gd01bT96cJCLWKkA2HiLr9lNp99CM/Z9fu5/aBC5QNqs5Hh5eQmZrBlrHfAlCs9htU696EiOuPGLpjHgD7F6/n9sGLhGwIpvPi9xm2ZwHaTA2bxxg/utRKrWbioG58MO87/SPQAutSpqgvG/bqK9W3Wjbk/W4tmfrNOrqPXYyiwMd9OuDmrH86wpgla3iWmIKVWsWkQd1wzmP6VtHquDXxB6qvm4xQqwj97SDJN5/g308/XR/6015KjumBtZsj5RbqL6mi0XK29UQK+bhR8avhCLUKVILILSeIydV4U7Q6dk9bTa+fJqBSq7i44RDRt59S05CH59fu586BC5QOqs4wQx5uM+RhXlzYEEyHxe/z3p4F6DI1bB2zAkWrY//UNXT/eTwqtYrL6w8Rc+sp1d5pBsDFXw5w78AFSgZVY/CRz8hMzWDX2O+y4tw/bQ3tv/oAtbUV8Y8is87t+WQVQTP0j4nVpmey95NVZI39aXU8W/IVHp8vArWKlG070dx/gH0X/VKilM2mjyjNievMKdjUqI7K1QWfzRtIXLmalG3mG5hodSQs+Qr3JYtApSJ1u0Grs0Frywu0ZkyhUHW9lvefG0hctZrU7Za1Yhcsw+eb+fpHhG7ZTebdhzj10DeSEv/Yhn3zJjh2bAEaLbq0dKLGz8kKLmxtsK1fi+g5X+SZJtCXkeNT19B27XiESsXN9YeIu/WUCobrdv2XA9h5udBlx2wKOdqh6HRUHtyGP4ImkJmUStCy4fg3qICtuyO9znzF+c82cnOd+SVIilbHjYk/UnPdJIRaxVNDeS/ST99QevLTPkqN6Y61myMVFuofo6potJxqPRnXuuXwf6spidceUn//AgDuzFtH9P4LZrWO7jtB4+YN2HpyA2mpacz4eF7WuaVrP2XW6AVERUQzadFYwp5EsGabvrw9fxRoi45BvNm/K1qNhrS0DCYOnW5WB+DwvmM0bd6Qnac2kpaaxpSRs7POfbP2c6aNnktURDTTFk0g9Ek4v25fCcC+7cF8s2QVK5b8wNyvprEpeC1CCJbM/tqow2GJ4L1HCWjRiP2nt5CamsYnH83IOvf9b18y+ePZREXGsGjZTBwdHRECbly9zfRxlpc25eTA3iMEtWzKkXM7SE1NY+yI7EfPrl6/nAkjpxMZEc3ny+fi6KSP/9qVW0weq/e/XceWdO/ZkcxMDWlp6Qx/d5xFrYN7jxLYojEHz2wlLTWN8Tl8+eG3pXwyahZREdEsXjYLJycHEIIbV28xdaz+unp6e7Bl31ocnRxQdAoDh/ShdcPukGY82npk33GaNG/I9pO/k5aaztSPs++br9d+xozR84mKiGbKovGEPQnnZ0O52L/jEN8u+QGAZu0COH7oFKkpea9nP3XgNHWb1eGnoz+SnprO4jGfZZ2bu2Y2S8Z/TkxELF0GdubtD97E3cud7/au4PSB0ywZ/wW/fLmWcUvG8v3eFSAE389blfX4UCO0OmIXL8V76UJQq0jeupPMew9x7K6vL5I2bsOueVMc2rUEjQYlPYOYifprlHH1Bqn7D+O7dgWKVkvmzTsk/bndok9WKhUTgsozbNN5dIpC50qFKe3hyO+XHgPwZtWilHJ3pGFxD9765QQqAV0rFaGMp362fey2i8SnZWKlEnwSVAFnW+s88/Bf4T+8Vr+gEC+zbk3yepEZfa/ACsfcWlMLSopPthTMMqfjrX8qEB2AEwVYwdooBbe2s3eRgpmsEgU4J5qeVHBjL3ujfV5s9Ioo/oLHXL5KJojHBaKToRScTxm6gtNK12YUmJaVqmDKu6OV7YuNXhHe1s4vNnpF/OBTcOXC813Ly4NeNfYfLP3XNwlkPDhbYG2cQiVq/+v+mkMuB5JIJBKJRCKRSF4z5HIgiUQikUgkEsnrhXxZmJwJkEgkEolEIpFIXjfkTIBEIpFIJBKJ5LVCkRuD5UyARCKRSCQSiUTyuiFnAiQSiUQikUgkrxdyT4CcCZBIJBKJRCKRSF435EyARCKRSCQSieT1Qu4JkDMBEolEIpFIJBLJ64acCZBYpCDf4jv53OwC0xpc2/Ir7V8lRexsCkQHYMqqgALTWjrocIFpdX9YMDqJmtSCEQJUouBeHFm8UMGVwUTrtALT+kHlWSA6SoG9TxRuaxwLTCvBpuDKYOECepP0Euu4AtEBsBMF94b2to/jC0xr6LyCy8MPPygwKcvotP92Cv515EyARCKRSCQSiUTymiFnAiQSiUQikUgkrxdyT4CcCZBIJBKJRCKRSF435EyARCKRSCQSieT1Qr4nQM4ESCQSiUQikUgkrxtyJkAikUgkEolE8noh9wTImQCJRCKRSCQSieR1Q3YCJBKJRCKRSCSS1wy5HKiAEUJ4APsNX30BLRBl+F4XqACsURSlusG+F7AKcFEUJVMIUQVYqyhKVSFEMDBWUZSzQogSwF5AA6RbiHsc0NtwXAcMURTlVF7pLRNQlTbT+6JSqzi/Lpij3/xlYtN2Rj/KBlUjMzWDzWO/JezKA5z93On6+Qc4ermg6BTO/XqAUz/uzgpTd0Ar6vZriU6r4/aBC/nOP4Ap85Zw+Nhp3N1c2fzLipcK+5w+0wdRLagmGakZfD92KQ+v3jex8SzizbBlo3BwceLh1Xt8O+ortJka7JzsGfL5SDwKe6JWq9n5/RaO/H4QgNbvdiDg7RYoikLMzSdc2nqcNhN7o1KrOLv+IIfN5F/76f0oF1SdzNQMNo5dQejVBwB0W/Q+5ZrVIDkmga9aT8iybzOxN+Vb1ESboSH2UQQbx31rEuexq/dZ9PtBdIpC14aVGdS6ntH5xNR0Jv+4g/C4RDQ6Hf1a1KZLg8qExyYwZc0uYhKSESpB90ZV6dOspkn8JQOq0nx6X4RaxaV1wZwy41fzGX0pFVSdzNR0do79jogrer9snO1ps3Awnm8UARR2jvue0PN3KNeuLo1GdcOjjD8/d5pO+GXTawIwataHNGxWj7TUNGaPWsitK7dNbGYsnUz5am+gydRy/cINFkz4DK1GS6uuLeg7rCcAqSmpLJr4BXeu3TWrM3HuaJo0b0BaajqTP5rN9cs3TWwWLJ9JpWrl0Wg0XAm5xsyxC9BotAwc1of23VsDoLZSU6psCZpUbEtCfIJZrQlzRtGkeUPSUtOYOnI21y/fMrGZ//WMLK3LIdeZPU6vNWBYH9p1awWAlZWakmVLEFCpnUWt92e+T62g2qSnpvPlmC+4e8XU//b9O9Dp3U74l/CnT7XeJMTp46pcvwpTVk4h4nEEACd2HWfdl+vM6uTmw1nDqd+sLmmp6SwYtYjbV+6Y2ExeOpFyVd9Am6nh+oWbfPbJ52g1L/dCH6eAGhSZ8R5CrSJm3V4ilm80Ou/Ssi5+Y/ug6HSg1fFk5kqSz1zPV9zOgfq4UauI+c00brcuAfgO6waANjmNx5O+IfX6AwC8BnXAs3crQBD92x6iVpneMznxDapKjVn6e+zer8HcWGZs71TGj7qfD8GtSgkuL9jAzRU7ss5ZO9tT57P3cClfBBSF06O+I+acaX4/p0hgVRrM1Gvd/C2Yi18ba7mU9iNgyft4Vi7BmUW/c/lbvZaDnzuBXw7FzssFdArXfz3I1VW7zUlk4RlUjYpz+iPUKh6vPcC9pVuNzvt3b0SpEZ0MeZjOlfErSbz2CIAqXwzBu2VNMqITOBLw4pc+Dp/5AXWb1SU9NY1Foz/jjpky17l/J7oN7krhEv50q/pmVll3cLLnky8n4F3YG7Vaze/f/cHuDXssahXkfTVx7miaNm9Iamqaxbpp4fKZVKpWwVBfXGPm2PloNFocnRxYuHwmfoV9UavV/PjNWjav2wZAscCqNJ2hLwfXfgvm3HLTMtp0Zl+KN6uOJjWdfaO/I8pQr1cf3IaKPQMBhZgbT9g35ju06Zl4VihG4PyBWDvYkvg4it0ffUNmUsG9nDFP5MZgORNQ0CiKEqMoSnVDI38F8Pnz74qiZACXgeJCCCdDkIbADaBGju/HcsYphCgC7AbGKIpSwVzcQC2gA1BTUZSqQAvgcV5pFSpBu9kDWNt/EV+3GE/lTg3wKlvYyKZsUDXcS/ryVcAY/pq4ivZzBgKg0+rYM2ctXzcfz8ou06nbr2VW2BINKlK+ZS2+aTOR5S0ncPy77S+Vh13atWTFkjkvFSYnVQNr4lvSj/GBI/hx0jf0n/u+Wbu3P+nL7lXbmBA0guRnSQS83RyA5n3bEHrnMVPbjmF+z2n0nNwftbUVbj7utBzQjukdxzO59ShUKhVdF7zHmgGL+LLlOKp2aohXGeP8eyOwOp4lfVkSOJrNk1bSae6grHPn/zjMmv4LTdJ15+hlvmo1nqVtPyH6fhgBwzoZndfqdMxfv5+vR3Tjz6kD2HX2JnfDYoxs1h+6QCk/DzZM7sfKj99iycZDZGq0qNUqxnQPYNP0gfw8rjfrD18wCStUghaz+/N7/0WsajGeCp3q41HW38imVFA13Er68n3AGHZPXEXLOQOyzjWf3pf7hy6xqvl4fmwziZg7oQBE3XrC5iFf8viU6Q/acxo0q0fRkoV5s/E7LJjwGePnjzJrt3vTPno27c87zQdRyLYQnXq3ByDscRjDenxM35aD+eGLn/lk4Riz4Zs0b0CxkkVpV/9NZoydz9RF483abd+4i46N3qZrQB9sbG3o3qczAD8uX0uP5v3o0bwfX8z9hrMnQiw2yhs3b0DxUkXp0OBNZo1dwJSFFrT+3E2nxj3pFvgOtraF6NZHf91XL1/LWy3681aL/nw5dwXn8tCqFVQb/xL+DGn6Pl9/sowP5g4za3f97DWm9s5ulOTk2pmrjGz7ESPbfpTvDkC9ZnUpUrIwfRr357MJnzNq/kizdvs27adfwEAGtngPG9tCtO/VLl/xZ6FSUXTOEO72n8n15iNw69QE27JFjUwSj13iRuuR3Gw7iodjv6LYwhEvFfedfjO53mwEbp1N4854HMGtNydxvdVIwr9cT7GFwwGwLVcMz96tuNFhLNdbj8SleR1sSvhZlBIqQa15AzjcZxG7AsZTvEsDnN8wrjsy4pIJmfITN1eY1p81Zvcl/OBFdjYZx+7mE0m4HZqnVqM5/dnVdxF/BI2ndOf6uOa6n9Pjkzk+7WcufbvD6LhOq+PkrF/5I2gCWzrNoFL/FiZhjVAJKi0YxJneCzjcZAz+XRvhmMuvlIdRnOwyi6NBE7iz5E+qfJZdPz9Zd4gzPedbjj8HdYPqULhkYfo3GcjnE75k5LwPzdpdPXuV8b0+IfxxuNHxTv078fD2I4a0/oAxb41jyNT3sbI2P25akPdVk+YNKV6yKG3r92DG2AVMs1A3bdu4mw6N3qJLQG9sc9RNvQb14O7N+3Rr9g4Dun3A+BkfYW1thUqlInBOf7b2W8TaZuN5o3N93HJdy+JB1XAt6cvPTcZwYMIqAucNAMDB142qA1uxvsNUfm0xEaFSUbZTfQCaLR7M8QXr+a3lRO7uPkvNoe0t+iYpeGQn4D+Goig64AzwfOi2FvA1+sY/hv/HcwTxBfYAUxRFMR5SMcYPiFYUJd2gE60oiuVfBqBw9dLEPogg7nEU2kwtV/46SbmWtYxsyrWsxcWNRwB4EnIHW2d7HL1dSYqMJ8wwQpCRnEbUnVCcfNwAqPNOc44u34o2Q/86+eQY8w0WS9SuXgUXZ6cXG1qgZqs6HPvzEAB3Q25j7+SAi5eriV2FhpU5s+MEAEc3BlOzVV3DGQVbBzsAbOxtSY5PQmcYrVSp1RSyLYRKrcLJx41noTHEPY5Em6nl0l8nqNDKOP8qtKpFyJ/6/HsccgdbJ3ucDGl5cPoGKc+STNJ158hldFpdVhhnXw+j81cehFPUy5Uinq5YW6lpXascwReNR8AEkJyWgaIopKZn4uJgi1qlwsvFkQrFfABwsC1EKV93IuMTjcL6VS9N/IMInj2OQpep5fpfJymTq1yUaVmLqxuPAhAWchdbZwccvF0p5GhHkXrluLQuGABdppb0hBQAYu+EEnsvzMTfnDRt3Yidf+hH466ev46jiwMe3u4mdicOZE9wXb9wA28/LwAun71KoiFPr56/hrefp1mdoDZN2fq7vsFz6dxVnJwd8fT2MLE7sv9E1ufLIdfw8fc2sWnXtSU7Nu216FNQ66b8tWGnXuu8Za2jRlrX8fEz1WrbtSU789Cq36oeBzYeAOBmyE0cnB1w83Yzsbt39R6RTyItxvOyNGrVkN1/6NN17fx1HJ0dcTdz3U4dOJ31+fqFm3hZuD6WsK9elvQH4WQ8ikDJ1BD31xFcsu5bPbqUtKzPKntbUJR8xe2QO+6tpnEnn7uB9lmy/nPITaz99NfRtkwRks/fQknLAK2OpFNXcG1T36KWe43SJD6IIPmR/h57tOUkhVsb32PpMQnEXryHLtN4psTK0Q6v+uW592uw3t9MLZmGe8wcXtVLk/AggkSD1t0tJymeq55Ki0kg+uK9rHruOamR8cQY6vnM5DTibofi4Gt6XZ/jWrMMKffDSX0YiZKpJWzzcXza1DayiT97C40hD+PO3cbWLzu+uJM3yIxPthh/Thq2asDejfsAuB5yA0dnB7Nl7s7Vu0Q8MW2UoyjYO+rreTsHWxLjEy3OShXkfdWsTVO2/m6oL85dwcnZyULdlN1MuBxyNatuUhRwcLQHwN7BjmfxCWg0WqrUrEj8gwgSDOXg1taTlMpVDkq1qsV1Q70eEXIXG2cH7L1dAVBZqbGyLYRQq7CyK0RyRBwAbqX8CD15A4DHh69Qpm2dv+X/q0RRtAX2919FdgL+mxwHGgohHNAv2wnGuBOQcybgJ2CZoii/vyDOPUBRIcQtIcRyIUTAixLh7OtOQo5R4ISwWJx93UxtQnPYhMfi7GNs41rEE79KxXl6QT896lHSj2J1yzN480wGrJ+Cf9VSL0rKK8XNx52Y0Ois77HhMbjlakg7ujmRkpCc1diOC4vBzUf/A7JvzU78yxThy9Mrmbt7CWtn/oCiKMRFxLLz+60sOb6CL0+vRFEUwq49zIozISwWFx/jHyFnHzeehcZm24Sb5nFe1HozkFvBF4yORcYn4euW3UnycXMiMldnomdgDe6Hx9Jy4rf0mLuGcT2CUKmEkc3TmGfceBxJlVwjlo6+biSGZac5MSwWp1xpdvJ1MyoXieGxOPm44VrMi9SYRNp++j79d8yhzcLBWNvZ5NtfL19PIkKzf0SjwqLx8rXcUFRbqWnTvSUnD542OdexZztOmDkO4OPnRfjTbJ2IsEh8DB0Jc1hZqenYoy1HD5wwOm5rZ0PjoPrs3XbQYlhvPy/CQ7MbIRFhUVmdFstabTh28KSJVqOg+uzdHmwxrIevB9Fh2WU/JjwGD1/TBkRelKtZnq92LWXGmhkUe6NYvsJ4+XoSFRqV9T0qLOqF161V9xacDj7zUmkr5OtBRo57OyMsBmsfU/9cWtenwoGvKb16Kg/HLc1X3Na54s4Mi8E6j7zz6NmShIPnAUi7+QjHehVRuzohbAvhHFQLa3/L/tv5upP6NPv+SQmLxS6f9YJjcW/SYxKp+8UQWu2ZS51PB6PO4x5z8HMjKcf9nBwei4Nf/uugLN0innhWLk5kiPnldQC2vu6k5agXUkNjscmj01C0dxBRL7lc9DmeJmUuGs+XKOubV2+lWJlirD/7K9/v/Zbl079BsdBhLMj7ytvPi/CnOeuL/NZN+vri11W/U+qNkgRf2s7m4F+ZP+VzFEXBx9ebpBy/RUlhsTjmKnMOvm4k5bh+z22Sw+MI+XYHA05+ybvnlpGRmMLjw1f0eXHzMSVb6ZeUlulQD0d/y9dbUvDITsB/k2PoG/t1gTOKotwFygghvABHRVHu5bDdB/QVQtjnFaGiKEnoZxXeR79PYL0QYkBuOyHE+0KIs0KIs/fSwnOfNq0EhYmJkU0hexveWvExu2b9TLphHaDKSoWdiwMru0xn77xfeXO5+WnafwxhNtG5TExtnvtVuWl1Hl27z8i6g5nabix9Zw3G1tEOe2cHarasw9gmw/i43ntY2VjjXtzHbBx56+TPjcDhndFptVzcbLQ6DAXTCHKrHL/2gHJFvdg7fwjrJ/ZlwYb9JKWmZ51PSctg7HdbGdcjCMdcDQhh5qKbpNlC/qnUanwql+DCL/tZ024KGSnp1BvWMW9HXxivZfNx8z7mwqlLXDx92eh4zYbV6dirHV/P+868jFkfLQtNWTiecydDOH/qotHxwFZNCDlz2eLyHLBUHC1rTV4wjnMnL5hoBbRqzIUzl/LUMnfD5qWVm7tX7vBug0F81OZD/lq9jcnfT8lfwDzuJ3OMmjeSS6cucfn0lXynTa9j5pgZnWe7T3K92XDuDZ6H/9g+rzRuAMcGVfB8uwVP560BIO3OEyKW/0nZX2dS5pcZpF57gKLNYz3yS2iZBLVS4ValBHfW7GNPq8loUtOp8GFe95i5ApgvqSys7G1o8d1ITsz4Je/13ub8siDm3qgiRXsHcWP2ry+XmOdSL3lf5aZ2QC3uXrvL27V7M6TNMEbMHo69o6Wf2YK7r/JV/+Zg6sLxhvriAgCNg+pz48otAqu2p3uzvkyePxYHRwcLv+W5tC3cxzYu9pRsVZM1DUfxQ+0Psba3oVzXRgDsH/s9Vfu35O3tsynkYIs2U2M5sQWNoiu4v/8ocmPwfwAhRFdguuHrYOAkUAdoDDwfWnwC9MR4KRDAIuAd4HchRGdFUSzeYYp+TioYCBZCXAb6A6tz2XwHfAewsut0xdkvezTD2c+dxIh4ozgTwmJx9s9h4+tOYqTeRmWl5q0VH3N58zGu7zprFOb6Lv0I39OL91B0CgjVP3qjqGydUdnqR8fjI+7i4e/J8+2k7r4exEXEGtknxiZg7+yASq1Cp9Xh5udBfKR+erPJm83Y/s0mACIfhhP1OBL/0oXxKOxF1ONIEmP1jbBbwRep9WbTrDid/dxJMMTxnGfhsbjkGBlx9nUnMcLYxhw1ujehXPOa/NB7rsk5H1cnwuOyl/BExCXi5eJoZLPlxBUGta6LEIJi3m4U9nDhfkQsVUr4kanVMub7rbSrW4HmNcqaxJ8YHotTjil6Jz93knKlOdFQLp4+t/F1JykyHhSFxLBYwgyzQrd2nH5hJ6B7/y506qNfR3r9wg2jJTdefp5ER0SbDTdoVD9cPVyZOHia0fHSFUoxcfFYRvf9JGtjHkDPgd3p8Y5+3eyVC9fxLZyt4+PnTWS4eZ0PxryLm4crM8cuMDnXtksLdmwy3Uz49sDudDes6b964Tq+/tmdRR8/L6IsaA0dMwg3D1dmjZtocq5NZ/NLgdr1a0/rXvpNyrcv3cYzxxIbD18PYnOV/bxIzdHAO3fwLOo5H+Ds5myUj8/p0r8THXrr1/TfuHgLL//s0UovPy+iI2JMwgD0H9UXV3cXpk74PN/pek5GWAyFcoywF/LzIDPSsn/Jp69RqJgvajcntHGJFu1AP/KfM25rPw8yzeSdXfniFF88nDt9Z6HNsZQuZv0+Ytbrl6f4T3iHjDDz/gOkhsViVzi7XrX3cyc1V91rMWxoLKlhscQaRuQfbztNhRGW77HksFgcc9zPDr7uJIe/uA56jrBS0/K7kdzddJwHO8/maZsWFottjt8LO3930s1oOVUsRpUlQzjbawGZcaZLIi3RqX9H2vVqC8AtkzLnScxLlPU2b7Xit+UbAAh9EEr443CKlinKzQv6PUsFeV/1GtgjR910Dd/COesLbyLDozCHvm5yY8bY7AdLdOnZgZVLfwLg0YMnPH0USqmyxYkIizQapXf0c89a0vOcpLBYHHNcP71NPEUbVybhcRRpsfryfnfnWXxrl+XmpmPE3Q1jSx/93jbXkr6UaF493/ki+eeRMwH/ARRF2ZRjc/BZRVES0W/aHUB2J+AE8DGmnQCAUUACsEqY66oDQohyQoicLbrqwENzts8JvXgPj5K+uBb1Qm2tpnLH+tzce87I5ua+81Tr3gSAIjXKkJ6Yqm/sAZ0XvUf0naecWLnTKMyNPeco2bAiAB4lfVFbW/3jPWVdWgKa+Kdo4p9yfs9pGnXTr4YqXaMsqYkpPIuKNwlz/cQV6rRrAEDj7oGc36NfOhIbGk3FRlUAcPZ0wa+UP5GPIogJjaZMjTcoZFsIALeinljb2uBWRJ9/VTs24Eau/Lux9xw1uunzr6gh/xLNpCUnZQOq0nRoR34e/CmZaRkm5ysV9+VRZDxPo5+RqdGy+9xNAqqWNrLxc3fm1A39EzdiEpJ5EBFHEU8XFEVh5s97KOnrQd/mtU3iBgi7eA+3kr64FPVCZa2mQsf63Nl73sjmzr7zVOreWK9VozTpiSkkR8aTHPWMhLBY3EvplxgVb1SJmNtPTTRysnHNZvq3eo/+rd7j8O5jtO2hfxJOpZoVSE5IJsZMI69jr3bUD6zD9OGzjUbkfPy9WfD9LGaNnM/je0+Mwqz7cWPWZt4DOw/R6U19A7ZqrUokJSYRHWnaaOvepxONguoxfug0k5E/RycHajeowcFdh03Crf9xY9Zm3gO7DtPxLX3DpWrNSiQmJpvV6ta7Iw0D6zPhg+mWtXabau34aXvWhsOTu0/QrHszAMrVKEdKYgpxkflv8Lnm2DtTttobqFTCbAcAYPOarQxuPZTBrYdydNcxWvdoCUDFmhVITkwm1sx1a9+rLXUCajNrxNyXGkl9TsrF29iU9KNQUW+EtRVuHZvwbK/xkq9CxX2zPttVLoUoZPXCDgBA8sXb2JTIEXcn07it/T0p+f1EHoz8gvT7xluurDxcsmxc2zQgbovptXpO7IV7OJX0xcFwjxXrXJ+nu89ZtM9JWtQzUkJjcCqtv8d8Glci4Zbleyzq4j2cS/riZNAq3bk+j3Ldz3kR8Olg4u6Ecvn7nS+0fRZyF4dSvtgV80JYq/Hr0pCIXH7ZFvag5g+juTj8a5JfsEcoN1vX/MXQNsMY2mYYx3Yfp2X3FgBUqFGe5MQUs2XOEpGhUdRsVB0AV09XipYuQtjD7PQU5H31249/0L15X7o378v+nYfp9KahvqhV+QV1U33GDZ1qdC+FPQ2nfhN93e7h5U6J0sV4/PApV0Ku41rCF2dDOXijU33u5yoH9/eep4KhXvepUZqMxBRSIuNJfBqDb40yWBl++4o0qkScoV6383DWBxaCOh915vIv+/nPoNMV3N9/FPG/VLSSV4MQYgaQpCjKp2bOfQ10VhSliOF7IHAQaKgoygnDsWCyHxFaCNgGXFQUZVzuuIUQtYClgCv6x4jeAd5XFMX8kCMwo3gfpWxQNdpM0z8yLGTDIY4s20LtPvqn5Jxdq7+Z280eQJmAqmSmZrBl7LeEXr5PsdpvMGjjdCKuP9KP9AP7F6/n9sGLqK3VdF78Pr4Vi6PN1LBn7q/0Xtor3/k2bvoCzoRcIj4+AQ93V4a925fuHVvnO/zg2uPoO2swVQNqkJ6azspxX/Pgsn7UbPSPk/lhwnLiI+PwKurDsKWjcHB15OHV+3w76ks0GRpcvd1479MRuHi7IYRg+zebOL5Z/4PeddTb1O3QCJ1GS9TVh1zZeYq2E/sg1CrObwgm+Ost1DXk32lD/nWcNYCyAdXITE3nz3Hf8tTwaMy3vhpBqfoVsHdzIin6Gfs/38i5DcGMDl6CupA1qYZRxschd6jie8/IxyNX7rH4j2B0Oh2dG1Tmvbb1+f2wfvnIm02rERmfxLSfdhGdkIyiKAxqVZf29SoScucJA5esp6y/J8KwR+DDTo1pUjl738bSQYcpFVSNZtPeQahVXN5wiJPLtlK9j/5H8MJa/Qa5FrP7UzKgKprUDHaO/S7rkZ/eFYvRZuFgVNZWPHsUyY6x35GekELZ1rVpMbMfdu5OpCekEHntIb17mz5FZuzckdQLrEN6ajpzRi/kxiX94zQ/+2k+88d9SnREDEce7iP8STgpyfoRtkM7jvDDFz8xcfFYAts1zVpTq9VoGdRuKIka0yUMk+ePpXGz+qSmpjF15ByuXtRvblu+dgnTR88jKiKaC0+PEvYknOQk/cbLfduDWbHkBwA6v92exs3qM27IVKN4VWb66ZPmj6VRUD3SUtOZ+vEcrhm0vl77GTNGzycqIprzT44Yae3fcYhvDVqd3m5Ho6D6TBhqPOtRvJDpuuShs4dSM7CW/lGGY7/gziX9pvHpq2ewdMJXxEbE0nFgR7oN7Y6blxvxMfGcO3CWpROW0r5/B9r1bYtWoyM9LZ1Vs1dy45w+rYm6NBOtnIyc8yF1A+uQnpbOwtGLuWm4bgt+msvicUuIiYhh/4PdhD+JIDVZ7+PhnUf56YtfTOL6XOVgUcc5qBaFp7+rf0To+v1ELPsdj3faABDzyy68P+iGe/cglEwNSloGT+ettviI0Nw/j85BtSgyIzvu8KW/42mIO/qXXRRbNALXtg3IMOwnUbQ6brbXP4HqjY3zULs6o2g0PJ31A4nHLhnFfVtjPFvn16xa9iNC1x3i+pdbKN1PX3fc/Wk/tl4utNw1B2snOxSdDk1yOjsDxqNJSsW1UnHqfKa/x5IeRXL642/JfJa9OThBbVwGizarRoMZ7yBUKm6uP8SFpVup8I7+fr7+ywHsvFzosmM2hRz1Wpkp6fwRNAH3CkXptGkaMdcfgaGeP7NwA48PZC9VK5xr+YdX8+pUnN0f1Cqe/HaQu19splg/fWP90U/7qLLkfXzb1yX1if6nSdFoOdZ6MgDVV3yIe8OKFHJ3Ij3qGbcX/8GTX/X7bZYUMm10fzhnOHUC9Y/tXDzmM25d0s//zl0zmyXjPycmIpYuAzvz9gdv4u7lTlxMPKcPnGbJ+C/w8HFn3JKx+gcPCMG6r9ezf5O+brMT1iZa/9R9dS/ddJR/yvxxNGpWn7TUNKaMnJ1VN32z9nOmjZ5LVEQ0F58eI/RJOCk56qZvlqzCy8eTuV9Nw8vHAyEEK7/6iW0bdwEwv3UPmsx4B5VaxbX1hzi7dCuVDeXgyi963wPm9Kd4oP73fv+Y74i8pK/X643uRtmO9dFptURdecj+8SvRZWioNqg1Vfrrr++9nWc5vmC9/to8/sXsgGVBknZ+a4E1gG1rdvrX/TWH7ARILDKjeJ8CKxyTz80uKCkG137x86VfBUXI/4bXv8uUVYEFprV0kOURzFfNJk3eswSvCnOdgH8Kc52AfwpznYB/ihd1Al4leXUCXiUF+fOYuxPwT5K7E/BPkrsT8E9hrhPwT2GuE/BPYa4T8E8xtJDp8s9/iv9EJ+Dc5oLrBNTq8q/7aw65HEgikUgkEolEInnNkBuDJRKJRCKRSCSvF7r/7vP7Cwo5EyCRSCQSiUQikbxmyJkAiUQikUgkEsnrxX/4+f0FhZwJkEgkEolEIpFIXjPkTIBEIpFIJBKJ5PXiP/z8/oJCzgRIJBKJRCKRSCSvGbITIJFIJBKJRCKRvGbI5UASiUQikUgkktcLuTFYdgIklvlkS68C0yqot/gCrDy7uEB0btb9qEB0AL4swLf4+hTMC0AB2N2mYKooXYpdgegAaGIL7ofnxEWnAtOq4pteYFodwiIKRCdVm1EgOgBehZwLTCtDW3A3sVpdMAsOFE3Bvd5ZbVVwb3fe5ltw5cL3Y/8C05L8N5CdAIlEIpFIJBLJ64XcGCz3BEgkEolEIpFIJK8bciZAIpFIJBKJRPJ6IWcC5EyARCKRSCQSiUTyuiFnAiQSiUQikUgkrxWKov23k/CvI2cCJBKJRCKRSCSS1ww5EyCRSCQSiUQieb2QewLkTIBEIpFIJBKJRPK6IWcCLCCEKAJ8DVRE31naBowDGgJbgHuAPRABLFIUZZsh3AwgSVGUT83EqQUuo8/3+0BfRVHihRCBwFhFUTrksF0NbFMU5Q8hRDDgqChKbcO52sCniqIECiHsge+BqoAA4oE2iqIk5dL2AVYBRQFr4IGiKO1eJk+OXbjBwtWb0el0dG1Wj3e7NDc6n5iSyqSlvxIeHYdGp6N/h0C6BNUFYO2Ow2zcfwoFhe7N6vNO+6Ym8feZPohqQTXJSM3g+7FLeXj1vomNZxFvhi0bhYOLEw+v3uPbUV+hzdRg52TPkM9H4lHYE7Vazc7vt3Dk94MAtH63AwFvt0BRFJ7cfGTIpvy9WGbKvCUcPnYadzdXNv+y4mWyywTHpjXxm/Y+qFTEbdhD9Io/jM47taiHz+h3UHQKaLWEzf6elLPXsPbzpPCno7HycgOdjrh1u4lZvdUk/pIBVWkxvS8qtYqL64I5+c1fJjYtZvSldFB1MlPT2T72OyKuPADgg6Ofk56chqLVodNqWdNxGgCdl43AvZQfALbO9qQlpLCj1WSjOAsHVqXerL4IlYpbvwVz+WtjXZfSfjT+/H08Kpfg/MLfufLtDgDUNta03TgFtY0VQq3mwfbTXPjsT4v5p65cG9tewxBCRcaRnWTsXG98vlxV7EfMQhcdDkDm+aNk/PULws0Lu8HjEc7uoOjIPLyDjH2bLOrkxqpaHez6jQCVmoyD20nf+pvx+QrVcBg7B12kXjfjzBHS//wp3/Fb166Lw9APEWoVaTu3k7rhV/PpeKM8Ll8sJ3HeTDKOHgLAbc06lNRU0GlRtFqefTgkTy2voGpUnt0PoVbxaO1B7iwzLkeOZfyp9sUQXKqU5MaC9dz7ZnvWuZKD21DsnWYIIXj4ywHuf78zTy27hrVxHz8MVCqSNu3k2Y/G18susAFuwwaAoqBotMQuXk76hasAOL/TDceubUFRyLj9gJjpi1EyMi1qTZw7mibNG5CWms7kj2Zz/fJNE5sFy2dSqVp5NBoNV0KuMXPsAjQaLQOH9aF999YAqK3UlCpbgiYV25IQn5CnfwDT5o0jsEVjUlPTGP/hdK5eumFiM/+LaVSpXhEhBPfvPmT8h9NJSU59YdwAo2d/SINm9UlPTWP2qAXcvHzbxGbmssmUr1YOTaaWaxeus2D8Z2g1Wpq0bsSQcYPQKQpajZYvpi/j4unLFrXGzR5J4+YNSEtNY/rH87hx+ZaJzZyvp1Gxqj4Pr4ZcZ+74RWg0Wmo1qMGS1fMJfRQGwIEdh/j+89UWtcbM/ohGzeqTlprOzFHzuWlGa/ayqVSoVg5NpoarF64zb/ynaDXZa7grVivPD9u+YdLQGRzYfsisztjZI2nUXK8z4+N55nW+nprLp8UmOj9uX8GkITPYvz3Yok8fzBxK3WZ1SEtN57PRn3Hnyl0Tm079O9J1cBf8S/jzZtW3SYjTl7EeQ7rTrGsQoC+DRcsU5e3qPUmMTzKJw65hbdwnDEOoVCRu2smzH4zvK/vABrgNH5D1OxKzeDnpIVexLl4Er0VTsuysi/gSt3wNCWst14XH7kWwaN9ldDroWq0Ygxq8YXR+9anb7Lj6BACtTuF+TCIHP2qLi10hpm8P4fDdcNztbdg4uJlFjX8V+cZg2QkwhxBCAH8C3yiK0lkIoQa+A+YC24EjzxvsQojqwGYhRKqiKPtfEHWqoijVDeHWAMMNceYHbyFEW0VRcv/yjgQiFEWpYoi3HGDu13IWsFdRlC8NdlXzqQuAVqdj3g9/8u3kIfh4uNB74hcE1q5E6SK+WTbrdx+jVBEflk54l9iEJDp/vID2TWryIDSKjftPsXbeSKyt1Ayb9z1NalaguJ9XVtiqgTXxLenH+MARlK5Rlv5z32dWl4km6Xj7k77sXrWNU38do//c9wl4uzkHftlN875tCL3zmC8Gz8fJ3ZkFB77i+OYjOLs703JAOya2+JjM9AyGLxuDsHFASTetXM3RpV1LenfvxKTZJn26l0Olwn/mB9zvNwVNeAylNn9O4r5TpN95nGWSfPwid/adAsCmfAmKLZ3A7ZYfoGi0hM9bRdrVu6gc7Ci99QuSjoYYhRUqQavZ/VnXZwGJ4bEM2DqL2/vOEXM7NMumVFA13Er68m3AGPxrlKb1nAH81GVG1vnfes4lNc44X7aMWJb1udmU3qQnpBidFypB/bn92d1rASlhsXTcMYtHe87xLIduenwyp6b+TLE2tYzCatMz2fXWPDQp6QgrNe03TeXpwYtEnTf98USosOvzIcmfTUCJi8Zh6jI0F06gC3tkZKa5fZnUr6Yah9VpSVv/LbpHd8DWDoepy9FcPWcS1ixChd3AkSTPG4cuJgqnuSvIPHcc3dOHxro3LpO8eNKL48uNSoXj8I95NnEMuugoXJd+S8bJY2gfPTSxs393CJnnzphE8Wz8xygJz/KhJagyfyAn35pHalgMTXbNJXzPOZJuPc0yyYhP4sqUNfi1qW0U1Kl8EYq904yjbaegy9BQ77dPiNwXQvL9cIt+uU/8kIihE9BEROO/dhkph06QeS87z9NOhRAafAIA67Il8V40hadd30Xt7YFTry6EdhuMkp6B16IpOLQJImnrHrNSTZo3oFjJorSr/yZVa1Vi6qLx9G77rond9o27+GTYdAAWrZhF9z6dWb/mT35cvpYfl68FIKBVY/oN6ZmvDkBgi0aUKFWMZnU7U71WFWYtnkj31v1N7OZO+YykpGQAJs0eTd933+bbr1a/MP4GzepRtGQR3mzUh0o1KzJ+/ije7TDMxG7Xn/uYPkL/MzJr+VQ6927Pnz9t5eyR8xzZfQyAMhVKMefbGfRs2s+sVqNm9SlWqiidG/akSs1KTFwwlv7t3zex27lxD1OGzwJg3vIZdOndkT9+2gzAhVMXGdlvwgv9atisPsVKFqFbo95UrlmRT+aPZmCHoaZaf+5l6ojZAMxZPo0uvTuw8actAKhUKkZMHsrJYNP7IadPRUsVoWvDXlSuWZGJC8YwoL1pJ3nXxr1MHa7Xmbt8Ol16d2SjwSeVSsWHU4ZyMvh0nj7VCapD4ZL+DGzyLuVrlOfDeSMY2WmUid3Vs9c4tf8UizYsMjr+x7cb+ePbjQDUa1GPboO7mO0AoFLhMelDwocY7qtfl5ESbHxfpZ4KISXnfbV4Ck+7vEvmwyeEvj00K56ie38j5cAxiz5pdQrz91xiRc+G+DjZ0Wf1IQLK+lLaM/sNxgPqlWVAvbIAHLodzi9n7uJiVwiATlWK0rNWSaZsO59n3kn+XeRyIPM0A9IURfkRQNFvIR8FDEI/+p+FoigX0DewR7ykxgmg8EvYLwammDnuB2T9iiuKclNRlHQLdk9y2F16CW2u3HlEUR8Pivh4YG1lRZuGNQg+c9XIRiBISU1HURRS0tJxcbRHrVJx/2kkVcsWw86mEFZqNbUqluZArhGpmq3qcOxP/WjO3ZDb2Ds54OLlapKOCg0rc2aHvoI7ujGYmq3qPvcIWwc7AGzsbUmOT0JnGM1RqdUUsi2ESq2ikF0h0OX/iQC1q1fBxdkp3/aWsKv2BukPw8h8HIGSqeHZtsM4taxvZKNLScv6rLKzRTFMVmii4ki7qm8Y65JTSb/zGCtfD6OwftVLE/cggmePo9Blarn210nKtjRudJdtWYsrG48CEBpyFxtnBxy8XfPtQ/n29bi29YTRMc8apUl8EEHSI73uvS0nKdbaWDctJoHoi/fQZZrmuyZFX1RVVmpU1lZZPudGXaocushQlOhw0GrIPB2MVY2G+Uq38ixW3wEASEtFF/YI4eaZr7DqMuXRhYeiiwwDrYaMEwewrt0oX2Hzg1W5CmhDn6ILDwONhvTgAxRq0NjEzrZzNzKOHkIXH/c/a7nVKEPy/XBSHkWiZGoJ3XwC39bGjf2M6ASeXbiXde88x7FsYeLO3UabmoGi1RFz4jq+7epY1LKpXA7N41A0T8NBoyF5dzD2gcbXS0k1X94BhFqNsLEBtQpha4MmKsaiVlCbpmz9XT+7dOncVZycHfH09jCxO7I/u+xeDrmGj7+3iU27ri3ZsWmvRa2ctGgbyKYN2wC4cO4yzi5OePmYlqvnHQAAW1sbFEuFPBdNWzdixx+7Abh6/hqOLo54eLub2J04cCrr87WQ63gbBldSU7JnG2ztbbF4cwGBbZqw7fddAFw+bzkPjx04mfX56gXzefgiAlo3ZrvBryvnr+Hk4oiHGa3jObVy+AXw9qDuHNxxiLhoy/dDQJvG7DD4dOX8NZyczesY+3QdH/8cOu9258D2Q8RGx+fpU4NW9dm3UT8GeCPkBg7Ojrh7u5nY3b16l4gnkXnGFdQ5gOAt5mc2bCqXIzPnfbXrxfeVuUlvu3o10DwOQxNmOS1XwuIo6uZAEVcHrNUqWlcsTPBtC51+YOf1J7SpmN2kqVXME2fbQhbt/xPodAX39x9FdgLMUwk4l/OAoigJwCOgjBn780D5/EZumFloDpiu6bDMCSBdCBGU6/gPwAQhxAkhxBwhRFkL4b8GVgkhDgohJgsh/F9Cm8jYZ/h6uGZ99/ZwISLOePSxZ5tG3HsaQYuhM+kx9lPGD+iCSqWiTFFfzt24R3xiMqnpGRwNuU54TLxRWDcfd2JCo7O+x4bH4Jaroevo5kRKQjI6rf6GiguLwc1H/6O4b81O/MsU4cvTK5m7ewlrZ/6AoijERcSy8/utLDm+gi9PryQlMQUlM39T8a8Sa18PMsOisr5rwqKx9jH9QXJq1YCye7+h+KrpPJ3wpWk8hb2xrVSK1AvGyx2cfN1IDIvN+p4YFouTr5upTWh2YyoxPBYnH72NgsLbv3zCgG2zqdYrdxGDonXLkRz9jLgHEUbH7X3dSA7N1k0Ji8XB1/THzxJCJei0Zy69Li0n9PBlokPMzAIAwtUTXWx2/ilx0ahcTRtc6tIVcZixAvuP56LyL24aj4cP6mJl0N4zXbZhDpWbJ7qY7B9KXUwUKjMdCHXZijgtWInDhAWoipTIV9wAKg9PdFE54o+OQuXpaWJj07AJadvNVxcu8z7Fddl32LTtmKeWrZ8bqTmuf1pYDLZ++btWiTce41G/AtZujqjtCuHdvDp2/qbl9zlqb0804TnKe0Q0am/TfLMPakThTavwXjqHmBn62TZtZAzPfvqDIrvWUnTvenRJyaSdOGcS9jk+fl6EP83Ow4iwSHxyNBhzY2WlpmOPthw9YNyhtbWzoXFQffZuO2gxrLGuN6FPs++H8NBIfC3oLvxqBqeu7aV02RL8tHK9WZvcePl6ERmanYeRoVF4+Vr2S22lpm2PVpw4mD1qHdCmMesO/8RnPy1gzuiFFsN6+3oSEZqdh5FhkXj5We4oW1mpadejNccPZjegq9SqzLp9q1m69lNKvVEyD79yaYVG4e1rWUtt0Hrul5evJ4Ftm2TNCljW8SI8NGe5iMI7D5/UWT6dyqHT9IU6AJ6+HkTl+P2KDovGIw+fLGFja0PtwNoc3XnUfBq9PdHmuK+0kdFYmel42jdrROHNq/BZNofo6aaz2A5tAknalXc5j0xMw9fJLuu7j5MdkYlpZm1TMzUcvxdJi3Iv1ayQ/AeQnQDzWFo0ntfx/GAnhLgAxADuwPMhJ0tDNLmPzyHXbIBhJqIU+pkCd+CMEKKCSUSKsttg9z36DkuIEMLkF0UI8b4Q4qwQ4uyqjbtyhDdNXG6nj1+8SfkShdm3YjobFo1h/g+bSEpJo1QRHwZ2asaQOd8ybN73vFHcHyu1OrewGe+VXCamNs9H1So3rc6ja/cZWXcwU9uNpe+swdg62mHv7EDNlnUY22QYH9d7Dxt7W4SNo6nWv4GZTE3cc4LbLT/g0ZA5+Ix+x+icyt6WYssnET77e3RJuTsy5vIvt4nl/Pul2yxWt5/Chv6LqdWvBUXrljOyq9CpAddzzQLoozQXp2lSLKHoFLa2msyG2h/hWaM0ruWKmDc0Vz5yOah9eIek8X1InjGUjP1bsBsx09jcxhb7YdNIW/cNpBkva7JIPnQ1D26T8GFPEj8ZTPruTTiMnp2/uC3Fnyv/HIZ+SPKqb82OJj0bNZz4Ee/xbPJ47Dp1wapyHqv88qFliaTbodxZtpUG6ydR79dPSLj6CEWTx4xaPu5ngJSDx3ja9V0iR83AddgAAFROjtgHNuBJ+748btUTlZ0tDu2am4TNkjJT9vMabZ+ycDznToZw/tRFo+OBrZoQcuZyvpYCQb5dBGDCRzNoULk1d27dp32XVn8jfst+jZ8/ipCTl4zW/R/adZSeTfsxYdAUhow3XSKVt5hl808WjCHk5EVCTuknlG9cvkn7Oj3o2WIA61b9wZIf5+Uh9XLX65P5owk5eZELp/Vao2d+yNK5K9C9YHT1pXUWjOH8yQtcMPg0ZtZHLJ3zzQt1DGIvpWWJ+i3rcfXMNfNLgV5CJ+XAMZ52eZfIj2fgNnyA8UkrK+wDGpC8x/xsQ1a8ZgqApYbO4TsRVC/snrUU6P8Miq7g/v6jyD0B5rkKdM95QAjhjH5TrbmhyhrA9Vz2RYHnOyRXKIqyAsOeACGEC/qNxsOBr9B3CnIPybkD0TkPKIpyQAgxG6if63gS+j0MfwohdEA7IUQz4D2DSTtFUUIVRYkFfgV+FUJsA5oCG3PF9R36/Q+kXdiWVQv4eLgYjd5HxjzD283FKMFbgs8wqLN+42AxX08Ke7tzPzSSKmWK0a1ZPbo1qwfAV7/twMfdBbVncdQexQCI33waD39Pnm97c/f1IC4i1ij+xNgE7J0dUKlV6LQ63Pw8iI/UTwc3ebMZ27/Rb3CKfBhO1ONI/EsXxqOwF1GPI0mM1f+wn9t1kupNyuV7T8CrIjM8Busco4RWfp5kRsZatE85c5VCxXxRuzmjjUsAKzVFl08ifmswCbtNG+OJ4bE4+WUvFXDycycxwniqPDEsFqcco7dOvu4kRcYDZP1PiUng1u5z+FUvzePT+tkGoVZRrk0dVnfItdYeSA6LxcE/W9fez52UXLr5ISMhhfDj1ykSWJX4m09MzitxUajcs/NPuHmii8+1RCRHw15z+TS273yIcHRGSUoAtRr7YdPJPHUAzXnzo2zm0MVGofLIXvKg8vBCF5dLNzWH7oVTiEEfI5ycURJf3JjURUeh8soRv6cXuhij2x6rN8rhNFG/UVvl4kKhuvVJ0mrJOHEUXaw+LcqzeDKOHcG6fAU0V8yv9EsLjTUavbf18yAtPP/X6vFvwTz+LRiA8hPfJjXMcvnVRkRhlWPU2srHE20eS3rSz1/GqqgfKldnbOtUR/M0HJ1hpjF5/1FsqlckeUf2lqueA7vT453OAFy5cB3fwtl56OPnTWS4cR4+54Mx7+Lm4crMsQtMzrXt0oIdm8zvO3jOO4Pe4u2+XQG4fOEq/oV9sqaMff29icgxSpsbnU7H9s17eG9Efzb+Zn5Wp/uALnTuo38+xPULN/DOsTTF29+L6Ajzfr07uj+uHq4sGG96jwJcOHWJwsX9cXF34VmsPl/fGtCNrn30s0dXL143Wtrj7edNlIU8fH/0QNw8XBk7LvsBAclJ2ffAsQMnmbhgDK7uLsQbtN4c0JUuBr+uXbhhrOXvRVSE+bIxePQAXD1cmTc+e+yrQrXyzP1Gv7fD1d2Fhs3ro9VqCd55xKCj9+naxRv4+nvzvKvn4+dFVLh5nfdGD8DNw5V54xbn0CnHvBUzsnQaNa+PRqvl0K4jAHTs34G2vdoAcOviLbz8s0fkPf08ibXgU14EdAogeGuwxfPaiCjUOe4rtbcn2kjLOmk57iudoXNr37gOGTfuoIuNzzMtPk52hCdmDzZFJKbi5WRr1nbXtSe0qWhhAEfyn0bOBJhnP2AvhOgHWct3PgNWA0ZDiIYNtlPRL7fJQlGUx4qiVDf8rch17hnwETBWCGEN3Ab8n4/gCyGKA9WAC2bSNhcYn0O/kRDCzfC5EPqnGT1UFOXrHPqhQohmhicJIYRwAkqjX96ULyqVLsqj8GieRMaQqdGw63gIAbUrGdn4erpy6oq+GR8Tn8iD0EiKGNawxjxLBCAsOo79py/RtlENtNEPybh5hIybRzi/5zSNugUAULpGWVITU3gWFW+SjusnrlCnXQMAGncP5Pwe/RRxbGg0FRtVAcDZ0wW/Uv5EPoogJjSaMjXeoJBhbWLFRlVAa/kpI/8UqZduYVPCH+siPghrK1w6NCVx3ykjm0LF/bI+21YqjbC21ncAgMILRpJ+9zExqzabjT/s4j3cS/riUtQLlbWaih3rc2ev8YasO/vOU7m7fr25f43SpCemkBwZj7WdDYUc9JW7tZ0NJZpWJipHQ7xE48rE3A0lMdy00Rd94R7OJX1xNOiW6lyfx3vytxHMxt2JQs76LTZqW2v8mlQm/m6oWVvt/ZuofAojPH1BbYV13UA0F4w7Q8I5ux+tKlkOhErfAQBsB4xBG/aIjD1Gfd4Xor17A5VvYVReet1CDZqRee64sa5Ltq66dHkQIl8dAADNzRuoCxdB5eMLVlbYBDYj46TxZr24/j2z/tKPHCJp6edknDgKNrYIO8N0vY0t1rXqoHlg+kSt58RfuItDKV/sinkhrNX4d2lA+B7Ly2xyU8iwIdCusAd+7eoQuum4Rdv0qzexKlYYK3+9Xw6tA0k5ZHy9rIpmLx0oVL4MwtoaXXwCmrBIbKpWQNja6PXq1TDa+Aiw7seN9Gjejx7N+3Fg5yE6val/0FnVWpVISkwi2kzDqHufTjQKqsf4odNMRk8dnRyo3aAGB3cdzjMPfvlhAx2DetExqBd7dgTT9S19w7Z6rSokJiQRZaaRXrxk0azPzVs35d5ty9do4+rN9Gs5mH4tB3No11Ha9dA/tahSzYokJSQTY2bgoFPv9tQLrMO0YbOM/CpSInt9drkqZbGytsrqAABsWP0nvVoOpFfLgQTvPEKHN/UN2io1Ledhl94daBBYl0kfzDDS8vDKHgioVL0CQqXK6gAA/L56E31avkuflu8SvOsI7Q1+Vc7yy1Src+/2NAisy5RhM420utR/m8719H8Hth1i4cQlHNp1NIfOIPq0HETwziO0M/hUuWZFkhKTLOh0oH5gXSbn8qlzvbfpVPctOtV9i/3bDrHwkyVZHQCAv9ZsY1ibEQxrM4Lju0/Qort+tqp8jfKkJCYTG/lygyH2TvZUrV+F42YGeZ6TfvUm1sUKY1XYcF+1yf999RyHtkEk7XzxkrdKfq48ik3maXwymVodu689JaCMr4ldYlom5x7HEFTW9Jzkv4+cCTCDoiiKEKIrsFwIMRV9Z2kHMAloADQRQoSg3yQcCXyUjycD5dYIEUJcBHoqivKzEOId4EchhC36p/sMNnQWcofbIYTIOdxUGvjG8EQjFfqnF5lr6dQClgkhNAa7lYqiWH60Qi6s1GomDurGB/O+Q6dT6BJYlzJFfdmwV98QeKtlQ97v1pKp36yj+9jFKAp83KcDbs76pTdjlqzhWWIKVmoVkwZ1w9nRaH81Fw+ep2pQTRYf+pr01HRWjsvuU43+cTI/TFhOfGQcGxb8wrClo+g+phcPr97n8AZ9tm/56nfe+3QEc3YtQQjBhgW/kBSXSFJcImd2nmDm9k/RabQ8vHofXVr+GmgA46Yv4EzIJeLjE2je5R2GvduX7h1b5zt8FlodoTNWUGLNLIRKRdzve0m//Qi33m0BiPt1J85tGuLatRmKRouSlsHjj/RreO1rV8StWzPSbtyn9LavAIj49CeSgs9mRa9odeyZtoa3fxqPUKu4tOEQ0befUr2P/tFsF9Ye4O6BC5QKqsaQw5+RmZrBjrHf6eP3dKb7dx8DIKzUXNtynPuHskeTK3asb7IhOKfuySlraPXreIRKxe31h4i/9ZRyffW6N38+gJ2XCx13zsba0Q5Fp6Pie23YFDgBex9XmnwxBKFSIVSC+3+d4sm+C+bzT6cjbe0y7EfNR6hUZBzdjS70IdYB+kZY5qFtWNVuSqHADvrHZWZkkPqt/okp6jKVKNSwJdrH97Caru+Pp//5A5rLeT/x47lu6uqvcJi4CFQqMoJ3onvygEIt9KONGfv+wrpeADYtO4NWi5KRTvJXL7EcSKcl6esvcJn3KahUpO3ZgfbhA2zbdwKwuA8AQOXmhvP0OfovajXpB/eRedayT4pWx5VJq6n/20SEWsXj34JJuvmE4v1aAPDwp33YeLnQZPdcrJzsQKdQ6r22BDcdhyYpldorR1HI3RFdppbLE38k81myRS20OmIXLMPnm/n6R4Ru2U3m3Yc49dBfr8Q/tmHfvAmOHVuARosuLZ2o8XpfMq7cIGXfEfx/W46i1ZJx4y6JG3dYlDq87zhNmjdk56k/SE1NY+rIOVnnlq9dwvTR84iKiGbqovGEPQln7fbvAdi3PZgVS34AoHm7QI4fOk1qivk1z+YI3nuUwBaNOXBmC2mpaUz4aEbWuVW/fcXEUbOIiohh8bKZODo5IITg+tVbTBs7P1/xH99/kobN6/HH8bWkpaYzZ1T2mv4lPy9g3tjFREfEMH7BaMKfhPP9X8v16dpxmB8+/4mg9k1p26MVGo2W9NR0pn4wy6LW0f0naNy8AVtOrCctNY0Zo7KX83z1y2JmjVlAdEQMkxaOJexJBKv/+hbIfhRoiw6B9OjfFa1GS3paOhOHTreodWz/SRo1b8Cm47+RlprOrFHZ+fHFz4uYM3Yh0RExfLJgDOFPIvjhr28AOLjjMCs/X5OvvNPrnKBR8/psPrGOtNQ0ZubQ+fKXRcweo9eZuPC5zoocOqvzrQNw+sAZ6jSrw49HfyA9NY3PxnyedW72mll8Pv4LYiNi6TywE29+8CbuXm6s2Luc0wfO8MV4/d6vRm0acu7wedJTzT3Xw4BWR8z8Zfga7qvEzYb76k3DffX7Nhxa6O8rJVOLkp5O5Pjs+0HY2mBXvxbRs794oU9WKhWftKrKB+tPoFMUOlctRhkvZ34P0Xdi36yh3/dx4FYYDUp6Y1fIuDn5yZaznH0UTXxqBq2+3s0HjcvTtZrpPq1/lf/wht2CQvwv69Ykrwc5lwP90wzp8mNBSbHy7OIXG70Cbtb9qEB0ALZpXQtMy0dTYFJ0bxPxYqNXgC6l4JzSxBbcD8+JiwW3Ua+Kr/nlI/8EHcISC0QnVZtRIDoAXoWcX2z0ishQCq68q0XBLDgoyLaMh1XB7Stb4V5w18r34xoFpmU3cFF+91L+Y6TuWV5ghcau1bB/3V9zyOVAEolEIpFIJJLXi//YxmAhRBshxE0hxB0hxCdmzrsIIf4SQlwUQlwVQgz8u1kgOwESiUQikUgkEsm/hGHv6ddAW/R7O3sJISrmMhsOXFMUpRoQCHxm2Av6PyP3BEgkEolEIpFIXi/+W3sC6gJ3FEW5ByCEWAd0Bq7lsFEAJ8MeUEcgFvhb68XkTIBEIpFIJBKJRPIPkfMdTIa/93OZFAYe5/j+xHAsJ8uACkAocBkYqSh/7yUEciZAIpFIJBKJRPJ6UYAzATnfwWSBF7+ZElqjf3R8M/RPhtwrhDiiKEr+H3mYCzkTIJFIJBKJRCKR/Hs8Qf9C2ucUQT/in5OBwJ+KnjvAfaD83xGVnQCJRCKRSCQSyevFf+vpQGeAskKIkobNvj2B3C+KeQQ0BxBC+ADlgHt/JwvkciCJRCKRSCQSieRfQlEUjRBiBLAbUAM/KIpyVQgx1HB+BTAbWC2EuIx++dAERVH+1gtaZCdAIpFIJBKJRPJ68d96OhCKouwAduQ6tiLH51Cg1avUlJ0AiUWOt/6pwLSK2NkUmFZBvcm33OmvCkQH4GqVqQWmVVqdXGBaD/bbFoiOg2PBvRlWoym4VZgF+RbfpISCu4c72nkViE4aBddIcEJdYFpJaAtMy76AVh2nmeyh/Oe4rI0vMK2tcT4FptVk2s0C06r5t19zJXkVyE6ARCKRSCQSieT14u89XfP/C+TGYIlEIpFIJBKJ5DVDzgRIJBKJRCKRSF4v/mN7Av4N5EyARCKRSCQSiUTymiE7ARKJRCKRSCQSyWuGXA4kkUgkEolEInm9kBuD5UyARCKRSCQSiUTyuiFnAiQSiUQikUgkrxdyY7DsBPwTCCGKAF8DFdHPtmwDxgENgS3APcAeiAAWKYqyzRBuBpCkKMqnZuKcDPQGtIAOGKIoyikhRDDgB6QBGcB7iqJcMBNeCzx/1bQWGKEoyvGX8cs9qBpl5wxEqFWErd3Pw6VbjM77dG9M8RGdAdAmp3Fz/EqSrj1EZWNNzS0zEYWsEGo1UdtOcn/x70ZhywZUpf20fqjUKs6uP8jhb/4y0W8/vR/lgqqTmZrBxrErCL36AIBui96nXLMaJMck8FXrCVn2bSb2pnyLmmgzNMQ+imDjuG9JS0gxitOxaU38pr0PKhVxG/YQveIPo/NOLerhM/odFJ0CWi1hs78n5ew1rP08KfzpaKy83ECnI27dbmJWb32Z7DRhyrwlHD52Gnc3Vzb/suLFAfLAN6gqNWb1RahV3Ps1mBvLjPPTqYwfdT8fgluVElxesIGbK7JfUmjtbE+dz97DpXwRUBROj/qOmHN3LGo5B9ag2MzBoFYR/dtewr/+0+i8e9em+A7rBoAuOY2HE1eQev0BAD6DO+LZqyUoCik3HvJgzFKU9EyzOo4BNSk87T1Qq4hdv5eob4yvlXPLeviM7gOKgqLREjprJSlnrwFQZNFHODergybmGbdaj3hh/tk1qo3nJ0MRajUJG3cSv2qD0Xn7oAa4f9gPdAqKVkvMghWkhVwFQOXkgNfMURQqUwJQiJy6hPSL1y1q2Teuhc/koaBS8eyPXcR+b3xvODarj+fIfig6HWi1RM77jtTzV7MNVCqK//EVmshong6dkbdfDWvjPn4YqFQkbdrJsx/XG58PbIDbsAFZeRi7eDnpF/Razu90w7FrW1AUMm4/IGb6YpQM89cKXv29lTO+gPWHOGSmjuhoqCMyUjP4I0cd8UZAVToY6pcz6w+ahG3yXnvaTe7D7BpDSIlLRG2tpsu8wfhXKYmiKEQ/CMe/QnEyU9P5bew3PDHEmxP3Il70WzYSexcHnlx9wNpRy9BmaqnZuRHNh3YCID0lnT+mrCT0+iOsbKwZsX46VjbWqNUqru88w+Pzt2k3TX/fnl8fzBEzPrab3o+yQdXITM1g09hvCTOkpcui93jDUA9+3fqTLHufCsXoNHcQhextiX8SxR8fLycpKQmArtP7UyGoBhkGv55a8KtvDr9+NfjlXdqfnouHUqRSSXZ8up7g77dl5+fAttTv2Qwh4NGZm5SsXxGVWsW5POr2N3LU7c996pqjbl+ao273rVicznMHYWVjjU6jY+vUH7l7UV9HdZ7enwqGMrA+D5/eWfYRdi4OPL36gN9GfY02U4tXaX/eXjyEIpVKsvPT9Rz6fjsALn7u9FoyDCcvVxSdwqa129j8g/Hv3wczh1K3WR3SUtP5bPRn3Lly10S3U/+OdB3cBf8S/rxZ9W0S4hIA6DGkO826BgGgtlJTtExR3q7ek8R4/XUqHlCVgBn6cnF1XTBnl5vmYcDMvpQIqo4mNZ09Y74j6ore7xrvtqFSr0AURSHmxhP2jv0ObXom9cf0oHSrmig6hZSYBPaO+RbCE43idA6sQZEZ+jo35re9RCzfaHTerUtAVt2uTU7j8aRvsup2r0Ed8OzdChBE/7aHqFWmaZb8+8jlQK8YIYQA/gQ2K4pSFngDcATmGkyOKIpSQ1GUcsBHwDIhRPMXxNkA6ADUVBSlKtACeJzDpI+iKNWA5cBiC9GkKopS3WA3EZj/Uo6pBOUWvMvF3vM41WQU3l0bYf9GYWOBh5Gc7zKD00HjuL9kI+U+ex8AXXomId1mcqbZeM40H497s+o41yprFHfHWQNZM2ARX7YcR9VODfEqYxz3G4HV8Szpy5LA0WyetJJOcwdlnTv/x2HW9F9okuQ7Ry/zVavxLG37CdH3wwgY1imXTyr8Z37Ag4HTudN6GC4dA7ApU9TIJPn4Re60+5C7HT7iyYQvKTz/QwAUjZbweau40+oD7nUfi3vf9iZhX5Yu7VqyYsmcvxUHgFAJas0bwOE+i9gVMJ7iXRrgnOtaZcQlEzLlJ26u2G4SvsbsvoQfvMjOJuPY3XwiCbdDLYupVBSbM4RbfWdxNehD3Ds3wbZsESOT9EcR3OwxmWstPyb0yw0UXzQMAGtfd7wHdeBa+7FcbTESoVbj3qmJRZ3Cs4Zyf8AMbrUcjmunpib5nXTsIrfbfsTtdiN5Mv4riiz8MOtc3B/7ud9/Rh65ZqzlNWU4YR9M4VGn93BsF4R1qWJGJqknQ3jS7QOe9BhG1NQleM0clXXO85MPSDl2lsedBvO42wdk3nuUp5bPtOE8eW8q9zsMwal9IIVKG2sln7zAg87DeNh1BOGTPsdnzkij8279OpORl0YOLfeJHxIxfBJPuw3GoY2pX2mnQgh9awihbw8lesaneE4fDYDa2wOnXl0I6z2c0B7vI9QqHNoE5an1Su+tN4oZxVetU0O8c9UR5QKr41HSl08DR7Np0kq6GOoIoRJ0mjWQHwcs4vOW40zCuvi5U6ZJFeKeRGUdq9OzGQCL24xn/zdbeKNxFeYHjWLDpO/pMXewWZc7ftKbQ6u2My9oFKnPkqj3tj6O2MdRLHt7FovbTmDP0j95a76+XtSkZ7K892w+bTuBxe0+oWxAVbosfI+fByxiWcvxVOnUwKQeLBtYDY+SvnwZOIatk1bRcW72a1dD/jjCz/0XmaSry4LB7F24jq/bfMK13Wdp9H57ACoEVsezpB/zAj/m9zz86mDwa34uv1Lik9g0YzUHczT+AXzfKEL9ns34ovNkPmv/CTW6NeWvKT/wVctxVLFQt3uU9OVzM3V7iIW6vc0nvTjw5Z983W4S+5f8QZuJvQAoH1gdr5K+LAgcxR+Tvqf73HfN+tT+k94cXrWDhUGjSX2WTN239WU5NT6JLTPWGHVoAHQaHX/N+YXFLcaytOtUOvbvQLGy2fdOnaA6FC7pz8Am7/LlhK/4cJ75gYarZ6/xSa+JhD+OMDr+x7cbGdZmBMPajOCHBau5fPJyVgdAqASBc/qzuf8ifm4+njc61ce9rL9R+BJB1XAt4cuapmPY/8kqms0dAICDjxvVBrbit/ZTWdtyIkKt4o2O9QE4/+121raexK9tJ3N/fwj1RnY1TqxKRdE5Q7jTbybXm43ArXMTbMsa38MZjyO49eYkrrcaSfiX6ym2cDgAtuWK4dm7FTc6jOV665G4NK+DTQk/s3nyr6LTFdzffxTZCXj1NAPSFEX5EUBRFC0wChiEfvQ/C8OI/SzgRUOTfkC0oijphnDRiqKYa5mdAAqbOZ4bZyAuH3bZAWqWIeV+OGkPI1EytURuPo5XmzpGNglnb6F5lqz/fO42tn4eWee0KekACGs1Kis1KIpR3LEPI4h7HIk2U8ulv05QoVUto7grtKpFyJ9HAHgccgdbJ3ucvFwBeHD6BinPkkzSfOfIZXRaXVYYZ18Po/N21d4g/WEYmY8jUDI1PNt2GKeW9Y1sdClpWZ9VdrZZydZExZF2VT/So0tOJf3OY6xyxf+y1K5eBRdnp78VB4B7jdIkPogg+VEUukwtj7acpHBr4/xMj0kg9uI9dJlao+NWjnZ41S/PvV+DAdBlasnMNXuSE4fqZUl/EEbGI30exm45imurekY2yeduojWUi+TzNymUo1wIKzUq20KgVqGyK0RmRKxZHfvqZcl4GEaG4VrF/3UY51w6RtfK3saojCWfvormmfEolyVsqpQj81EomifhoNGQtDMYh2YNjGyU1GwtYWcL6LWEgz22taqQuHGX/qRGgy4x2aKWbdU3yHwUSuaTcMjUkLjjEI7NjcugksMvYW9r5JeVjycOAXV59vvuF/tVuRyax6Fonur9St4djH1gQ4t+5SzvAEKtRtjYgFqFsLVBExVjUetV31sODasZxXfxJeqIotXLEJOjfskdtv3Uvuyc/6tRXN5lC3P32BUAytSvSHxoDEWrluJhyB3snOxxNtQ9OSnTsBIXd5wC4PTGw1RpVRuAB+dvkZqgLwMPz9/Gxdc9K0yGoV5UW6mxcbLnWVgscY+j0GZqufzXScrn8rF8q1pcMPj4xOCjoyEtD0/fINVMPehRyp8Hp24AcPfoZSq2rQtA5Va1OfvnYX1Yg19OFvy6ZPDrzMbDVDb4lRSTwONL99BpjOsQnzKFeRhym8y0DIpUKUVCRCz+VUsZfDJ/3Sz59MCCTwpg42gHgK2zHQkR+p+zSq1qcdYQ16NcvxOWfDqbD58So+KzZhTSk9N4fOcxnjnq+wat6rNv434AboTcwMHZEXdvNxPdu1fvEvEk0uR4ToI6BxC85VDWd5/qpXn2IIIEQ31+66+TlMqVh6Va1eL6xqMAhIfcxcbZAXtvvd8qKzVWtoUQahXWdoVINuRVRlJqVnhrexuUnDc7z+v28Ky6PW7rEVxa1TWyST53I7tuD7mJtaFuty1ThOTzt1DSMkCrI+nUFVzbGN//kv8GshPw6qkEnMt5QFGUBOARUMaM/Xmg/Avi3AMUFULcEkIsF0IEWLBrA2y2cM5OCHFBCHEDWAnMfoGmETa+7qSHZv/op4fGYJPjxyw3fr2bEXMgJPuASlBn/yIaX11J7KHLJJzPXl5i4+vOsxxxJ4TF4uJjHLezjxvPQrMbiAnhsTj7mlaylqj1ZiC3gi8YHbP29SAzLHv0TxMWjbWPaUPeqVUDyu79huKrpvN0wpcm560Le2NbqRSpF27mOz3/JHa+7qQ+zc7PlLBY7PKZV47FvUmPSaTuF0NotWcudT4djNrOxqJ9IT93MsKis75nhMdQyM9yufDs2YJnB88DkBkeS/i3m6l66nuqnf8RbWIKCYcvmA1n7eNBZmi2TmZYjNlr5dy6Pm/s/4YSP0znyXjTa5UfrLw90ITnKBcR0Vh5e5rYOTRvSNGtK/FbPpvIqUv06SziizbuGV5zxlDk96/xmvkxIo/8s/LxNC6D4dFYmfHLsUVDSuz4jiIrZhE++fOs496ThhD16SqUfDzlQu3taeKX2oxf9kGNKLxpFd5L5xAzQ78yURsZw7Of/qDIrrUU3bseXVIyaSfOmYR9zqu+t7TPkoziM1dHuPi4EZ+jjnhmqCP0dYf5+qVCi5okRMQRft14JiXs+iMqtqyNSq3Cs7gv7kU8cTU0cOLDY40a8gAObk6kJqRkDTo8M5M+gHpvB3EjRz0kVIKxOxYw+9x3RN8NJfJm9gRvQlgszj7G962zT666Mh/14P9j76zDo7jev33PbtyVCG7FIXhwEjwtmra4FChWpLgX90KhQKFQwwstFdytxV2CBQ/EjfgmuzvvH7sk2exuCJV8+/4493VxkZ155nzmOXPmmTk60ffDqNhK99JYNag+zvr708nLjcRcaZnzK6MAfuUm4l4YZepVws7FAffiRbB2sMVZn3c6nwyPd/wLsX3frI20ndyD8WdW0nZKTw4v1g1rc87j00sTPtm5OpKelJrtU2JE3Gt9yo1rMQ/KVinL3as58d7D252YXPEpNiIWd2/je+t1WNtYU6d5Hf7c/2f2NgdvV5Jz5U9KRDwOecqFg7crKRE5fqdExuPg7UpqVAJX1u2j/7kVDLy0ClVSGs/+uJVt12D8B/Q/t4IKnRpybqnhUB9Lb3cy88bcfBq63Lu1Ikkf2zPuPcOhfmWULo5INlY4BdTG0vfN8+NfR5YL799/FFEJ+OeReNUsWPDt+SLLcgpQGxgExADbJUnql8tkiyRJz4GJwEozybwaDlQRXWVho37oUsEwYSqbdAdcGlXBt0cAD+ZsydmolbnYYgJn/IbgVKss9hVzdSuaSjvPTWPqVAt6XzX/pCNajYbrv51+vbGJRJMPnSW01VCeDZ6L15heBvsUdjaU+GoKkXPWo83VsvI/xdRVLWBmSRYKXKuV4sGGIxxqPRV1uopKI9q/kZg5KceGVfHo1pLn8zYCoHS2x6V1PW42GMyN2v1R2Nrg1sVM/dZUUTUhlHTwHPdbDOXpoHlG16rAFFAr9egZwjoMJHLkTNyG99UdaqHEulI5krbv4fkHn6BNz8BlQNc30zeRfylHzvAkaBAvhs/GY2QfAOyb10Mdl4gqxPx8DQMK6Ffa8dO86DyA6NEzcRnWDwCFowN2zRvw/N3ehLXuhsLWBvugfEcxFkiroPeWnJFpIrk86ZmLEWbii6WNFQHDO3F42U9G+y/vOMHLyDjG7J6P9zvFCL8XhlajMTjeUNsoCSObcg0q4981gN0Lc3odZK3M50GTmNlgGG4lvbBxts83DdOXMP97+7cJ66jXuxVDds/FysEWTZbabFpG1+gvxJLoh+EcX7uLIZun0nJ4Z9ISU/LNO5OPodeEq3q9WrJvziaWNBzBvjmb6LxokNm0/ok8fIWVnTV914xm7cyvSUvJ1UNaAN2C4N+qPiEXb2cPBTKfdt4tpu9ta2c7yrSqxQ+NRvNt3RFY2llToXOjbJOzS37iO/9R3PvtDDX6tXptkuauvUODanh0bcmL+RsAyHjwnKivfqH81lmU2zyT9NtPkDX/3SExbzOiEvDPEwLUyb1BkiQnoDhgPFMIagJ38tgX17faX5MkaQjohhXJsnxCluUZ6IYPBec6pCdQGtiKbkIykiR9rz9+H3mQZfks4AF45t0nSdIgSZIuSZJ0aU/6o+ztqog4rH1zWgGsfd3JjDQeUWRfuQSVlg3mRt8lqBOMu3HVSWkknL6NW4CfQdrOudJ28nEjKdow7ZeR8Tj75rTWOHm7kRz1+hFNNYObUKFFLXaMWm20LysyDkufnCyw8PEgK9r0cBSAtIshWJXwRunqpD9ASfGvppC46wRJB8++9lwKi/SIeGyL5uSnnY8b6VGJBTs2PJ70iHjir+qKatieC7hWK2XWPjMiDiufnBYeK293siKN89C2UklKLh7Og/4L0CTqhuU4Na6BKiwadXwSslpD4v6zONQ23SmWFRlr0JJk6eOe77VKvRCCdUmfnGv1BqijYrHwzlUuvDzyHfqScfkWlsV9ULg4oY6MRR0Vg+qmrpUw9dCfWFc21QGYo2VQBr09UEeb10q/dAvLEj4oXZywrVUZh0B/yhz9Ad+lk7CrXwOfxePNHquJijHyS5OPX6orN7HQ+2XjXwv1i0i0CS9BrSH16J9Y+1U2e+w/fW/lTc9cjHDJFSOc9TEiKTLeZHxxK+mFazFPRu1fyIQ/V+Dk7caIPfNw8HSmXo8WlGmg8y/kyGXsXRyJeRwJgIu3W/bwk1ekxidj62SHQql7pDrnOT+fiiXounAw3378OWmJxnExIymNsKsP8HonZz6Nk48bydGJRj4a+OLtRvJr7u3YhxFs7LOQte2nISkkLGysGLtvIUlRCbjkSsvF242XJvyyyePXy+jXx93zO46z7L3JbJ/4NRZWlsTp807nk+HxSSZie978zUvN4KbcPnARAHs3J0o3qMzofQt4mccnZ7PXyj7bJxcfd6OyZAqFhZK+a0dz5bfTnD5whvZ93+OrA6v46sAq4qPi8MwVnzx8PIiPMn9vmaNZh2ac2HXCYFtKRDyOufLHwceN1DznmxIZj0OuYZYO3m6kRCVSvHFVksJiSI9PRqvW8ODAJXxzz8fTc++3M5RtZzi8NysiDqu8MdfEcE3biiUpueQTHg6Ynx3bAeK2H+Fu0BhC35+C5mUyqsf5zC37XyHmBIhKwL/AUcBOkqQ+AJIkKYGlwA+AweBqSZKqA9PRv7i/QpblMH2rvZ8sy2slSaogSVLuO9cPeJrnmCxgGuAvSVIlWZY/0h8flPcEJUmqCCgBoygly/I6WZbryLJc5z3bMtnbk68+xK6MDzYlPJEslRTp1JDYg5cMjrUu6k6178YR8skq0h9FZG+3dHfEwkk3HUJhY4lb02qkPXhhkLZ7KW9ci3mitFRSvX0D7h42HGpw9/BlanbRTRotXrMcquR0kmMS856+AeWbVafpkPZsGvg5WSZaEtNv3Me6lC+WxbyQLC1wfq8pyUfOG9hYlcyZzGRTpSySpSUa/YoORReOQvUwjLhvf8v3PAqb+GuPcCztjX1xTxSWSkp09OfFQfNDN3KTEfOStPA4HMvq/PZqXIWk+y/M2qdeD8WmtA9WxYsgWVrg1rExiYcvGNhY+XpQdv0kHo/6wuBBkBkeg0PNd3RzAgDHxtXJePDcpE7a9VCscl0rl/ZNScqrk+ta2VYpi2RpkX2t3gTVrXtYliiKRVEvsLDAoV1zUo+fM7CxKJ4zMc+qUjkkSwu0iUlo4hJQR8ZiWUr3Mmfr70fWQ/OTdjNu3seypC+WRb3A0gLHoGakHDPUsiyR45d1Zb1fiUnELvuBR81786hFP8LHLiTt/HUiJphbFwBUIfewKFEUC19vsLDAvk1z0k4aVl4N/KpYDsnSEm1iEuqIaKyrV0Ky0Q1tsq1fM98Jz//0vZU3vRrtG3AnT4y4kydGZOhjxPPrD/HIFV9eHRt1L4x5dYayuPEoFjceRVJkPCvfm0pKzEsu/3SStcGz+DxoEvEvYnFwcyTqwQtK1ixHenIaSSZiz4Ozt6kRpJunUi+4KbcO6eKji687H60dw5bRq4l5nBMX7d0csdHHRUtrSzzL+GDtZIeL/jyrtfc3ioP3Dl/BT+9jMb2PKa+Jg/buuoqVJEm4FvNk72cbWBo0iZuHLlGnS1MAStYsR0ZymsmY+uDsbarr/aqby6/8cNBrJsUk4uzjxrPLoXqfjGP7ncOXDXxSFcCnpOgESvtXAiDmYTgRt5/yRdBkQg5doo4+rRL5+hSS7VOd4KaEHHp9fPxw0SCiHoRz6ltdu9ruDXuyJ/OeOXiWlsG6nrGKNSuSlpxKfAEqFrmxc7Sjun81zuRpUIq6/giX0t446eP5O+39eXT4ioHNo8NXqBTcGADvmmVRJaeRFp1I8os4vGuVw0IfY4s3qkK8/tnrUsor+/gyrWqR8DDCIM3U66FYl8qJ7a4dmvAyT8y19PWg9PrJPBm13Ogl38LdOdvGpW0DEn4/9Ub5ISgcxBKh/zCyLMuSJHUGvpIkaTq6itY+YArQAGgiSdJVdJOEo4GRsiwffU2yDsBKSZJcADXwAN3QoLza6ZIkLQXGAXmXRbCVJOma/m8J6KuftFwwvzRa7k/+Dr8fpyIpFYRvO07qvef49tF1IYZvPEzpse9j6epAhUW6VSZktYZLbSZj5eVK5S8/QVIqQCER/ftZ4nIFMVmjZfdnP9Bv4yTd0ng7ThAd+oJ6PXVB9cKWo9w7fo13AvwYc/ILstJV/DL+6+zjP/xyOGX8K2Hn6siEsys5+sVOLu84QftZ/VBaWdJ/82RAN1nw96nf5Til0RI+cy2lNsxGUihI+OkwqtBnuPZoB0DC1v04tW2IS+dAZLUGOSOTsJG6lSrs6lTGtUsgGXcfU3bPlwBEfb6RlBOvf0CaY/yMhVy8eoPExCRadOrFsAG9CW7f5o3TkTVarkz5gWbbJuqWCP3xJEn3X1C2jy4/H248io2nM60OzMXS0RZZq+Wdj9uxv9kE1CnpXJm6Ef/Vw1BYWpDyLJoLn35tXkyj5dn09byzZQYolMRtP0LG/TA8e+nOO2bzQXxGd8XCxZGS84fozk+t4c6740i9GkrCvjNUOrAM1BrSQh4Ts8XMBFeNlvDP1lJm4yxQKkjYcQRV6DPcerYFIH7LAZzbNcS1SyCyWo02I5Onw3NWSinx5Tjs/ath4epExbPfE/XFVhJ2HDarFTt/NT5fz0dSKkj69RBZD5/i9KFuVZWkHXtxaNUYxw4tkdVq5AwVUePmZx8eO381RRZNRLK0QB0WSfT0pfnmX/ScNRT7di4olLzceYjMB89w7qqru7/cvg/H1o1x6thCp6XKJGL0QvPp5YdGS/zCVXitWaBbIvT3g2Q9fIrj++8BkPzzHuxaNMGhfUtQa9BmqIiZoFutKvPWXdKO/IHvtq+QNRoy7z4keadRJ6OB1j99b+VO7/iOUyZjRIUAP8bpY8TP+hih1WjZ9dkP9NfHl0v6+JIf9h5O9N8wCa0s8zIynpCjV5h6cgWZ6Sp+HJ+zfO/H309k+8R1JEUnsGfhVnqvHEm7sV15EfKEczuOA9BmZDD2rg68P1e36o1WrWFZh6k4FXGlx9KhKBQKJIWCO3vPc+qrXfTZOBGFUsGVHSeJCX1BHb2Pl7Yc5f7xa5QP8OPTk8t0S4TmioPvf/kJpfVxcOzZlRz/4meu7DhJtQ4NqNdbF6fvHLzI1Z90k07vHL9KpQA/ppxcoVv6NB+/+qwcSdDYrjwPecJ5vV+Ons6M3jUfGwdbZFmmaf92LGo1DlVKOv3WjMHO1QGtWsPRL36m2+qRuiVC9XlfV+/TRb1Pr2J7ponY/sqn8WdXckwf23+f9A1BM/qgsFCgVmXx++Rvsn2qGODHpJPLyUpXsT1XWgO+n8BPE9eTFJ3A3oXb6LVyBG3HfsiLPD6N2jUv26cm/duxpNV4fCqWoE5wU8LvPGP0vgVkyBq+X7SBi8d1vREXjl2kbmBdvv/zO1TpGSwdmzNvZ86G2XwxYTnxUfF0/KgDHwz9ADdPV9Ye/ooLxy6yXD93qVHbhlw+dQVVusqgLMoaLSemb6DTpglISgW3t58k/v4LqvXSrdJ0c/Mxnhy7RqmAGvT9Yynq9EwOj1unu2+uPeTBvgt03zcXrUZDTMhTbm3V+dpoUldcyvqAVibpRSzHJn9PrdzCGi1h09dRbvNMJKWCuO1HybgfhkcvXcyN3XwAn0+7YeHiSPF5g7PP9d67YwEos24iShcnZLWasGlfZ08g/k/xH26hLyykvzJuTfB2cMzrw0IrHEdtC68+2l3x5q3Df4UKF74sFB2AX6pNLzStssrCC+ZWFgWup/4t7B2Me4r+LdTqwuuAtbZRF5pWSpL5CdD/NFu0bz7M66+QQeG9JDiiLDStFArnvgKwK6QBBxmvm0jwD3JTk1hoWkHS31t17k1oIhdsBbV/glphvxd8TuK/RPqW6YVWaGx7zvmf+2sK0RMgEAgEAoFAIHi7KMCqav/XEXMCBAKBQCAQCASCtwzREyAQCAQCgUAgeLsQcwJET4BAIBAIBAKBQPC2IXoCBAKBQCAQCARvF2JhHNETIBAIBAKBQCAQvG2ISoBAIBAIBAKBQPCWIYYDCQQCgUAgEAjeLsTEYNETIBAIBAKBQCAQvG2IngCBWc7aWBaa1rRvmxWa1or+pwpFJ6QQv+Lb5eacQtPa6PdZoWltUkcWio4mofAmiKkL8Su09TK8Ck3rkTal0LQWWycWio4qs/AekdEZdoWmpSzEr+vaKlSForPMKqtQdADUcuF9cXlu0sVC05riXLfQtGoVmlI+iJ4A0RMgEAgEAoFAIBC8bYieAIFAIBAIBALB24UsegJET4BAIBAIBAKBQPCWIXoCBAKBQCAQCARvFbJWfCxM9AQIBAKBQCAQCARvGaInQCAQCAQCgUDwdiFWBxI9AQKBQCAQCAQCwduG6AkQCAQCgUAgELxdiNWBRE+AQCAQCAQCgUDwtiF6Av4mkiQVA1YDldFVqvYA44GGwO/AI8AOiAIWy7K8R3/cTCBFluXPc6UlATFAeVmWEyRJ8gHCgSayLP+pt4kBKgIjXh0vSZINsBtoCVwHHABP4LE+6WGAGzBHf46WwApZlr9+nX9lmlWn9YzeSEoF1348wdk1u41sWs/sQ9mAGmSlZ7Jn3NdE3nqSkz8Kif575pIcmcCO/tmuUqdfa+r0aYVWo+XBsWt6N3M4HfKYxT8dRyvLdG5Ylf5t6hvsT05XMfX7fUQmJKPWaunTsg6dGlQlMj6JaRsOEJeUiqSQCG5UnZ6Bht8mLN2sOi1n9EahVHD9xxOcM+FTy5m9KRvgR1a6ir3j1hGl92non1+gSs1A1mjRajRsaK/7em7HVcNxK+MDgI2THRlJaZxoOcUgTe+A6tScrcvLR1tPcHeVoa5jOR/qfTEY12qluLlwB/fW7sveZ+lkR92lH+NcsRjIMhdGryPu8gOj8y4I0+Yv49TpC7i5uvDb5rV/KY3cFG1eHf9Zuvy8t+0EN1Yb+uVc1oemywbhXrUUlxb/xK2vdX7Z+7jRdMUQ7DydkbUy97YeJ+Tbg2+kPWL2J/gH1iMjXcXC0YsJvWWcJ1NXTqZC9XfQZKm5c+0eSyd9gUb9+i9+jpr9Cf6B9VGlq5g/ejH3b4Ua2UxfOZmKNSqgzlJz59pdlkzUpV2ibHEmfzGBd6qWY/2i7/jx65/y1Ro9ewQNA+uTkZ7BnNGLTGrNXDmVijXeQZ2l4c61uyycuBSNWkPrzi3pPawbAOlp6SyevJwHtx+a1eoyoy+VA2qSla5iy7g1PA95YmTjVsyTvqtGYe9sT1jIEzaPXoUmS0PVVrV5d8yHaGUZrVrDr7M38ujSPbNag2YNonZAHVTpKlaMXc7DW8bn9W7f9+gwoAO+pXzpWaMHSQlJAFT1r8a0b6YRFRYFwNkDZ/hxxY9Gx9s3qY3XtMFISgWJOw4St84wrx1a+OP5aW+QtchqLVHzvib98m0kK0tKbl2MZGWJZKEk6cCfxH65xawveXFsVouiMwYiKZXE/XiI6DU7DfY7taqPz9ieoNUiazS8mPUNqZfuFDh994AaVJzbF0mp4PmWYzxZuctgv3dwI0oP7wCAJlXF7QnfkHL7Gda+7lRbNQwrTxfQanm++RjP1u/PV8stoAbvzO2HpFQQvuUYT1f+brDfK7gxJbO1Mrg34VtSbj9FYW1Jrd9norCyRFIqiN5znsdL8i/rLgF+lJ7dH5QKorce5cWqXw32e3RpQtFPOuu10nk0aR1pt58CoHSyo9zSYdhWLAGyzIPRq0m5fN+sVv+ZH1MzoA6Z6SpWjVvO41uPjGza9n2Xd/t3wKeUDx/59SQ5IRmAuq3q021sT7RaXaz/ftY33M3n+n0yexj1A+uiSlexePTnJmNRx34dCB7YmaKlitK52vvZZd3B2YHxS8fiW9KHTFUmS8Yu48m9J2a15i+aSsvWzUhLy2DksEncuH7byGb5qnnU8KuKJEk8eviYEUMnk5qaBkDDxvWYt2AKFpYWxMcl0PHd3gCUbFad5jN1sfzWjye4+JXxs7H5rN6U1j8bD41dR/StJ7iW8SFo9fBsG+cSRTi77GeufnsQz8olaDG/P0prS2SNhqNTfyDquvF1+J8gVgcSlYC/g/6l/RdgjSzLHSVJUgLrgHnAXuAPWZbf09v6Ab9JkpQuy/JRU+nJsixLknQeaADsQ1eRuKr//09JkioAsbIsx+mkQZIkK2AncFmW5Vb6bc2Bcbm0LYGnQD1Zlp9LkmQNlHqtfwqJtnP6sbXnApIi4+m/aw6hR64QG/oi26ZsQA3cSnuzptlYfGuWo+3cj/ih04zs/XX7tyX2QTjWDrbZ20o2qMw7rWqzvu1kNJlq7Nyd8FtSM3u/RqtlwfajrB35Pl4ujvRctIVm1ctR1sc922b7yWuU8XHny2GdiU9Oo9Os73m3biWUSgVjg5tRqYQXqRmZdF+4Gf9KJXMdK9F6Tl9+7LmQ5Mh4+u2aTeiRy8SF5lRCygTUwLW0N183G4tvzbK0mduPjZ1mZu/f1m0e6QkpBnn1+/BV2X8HTuuBKinNKC9rz+/Hia4LSI+Ip9X+OYQfukLS/Zy8zExI5eq0jRRtV9voWtSc05vI49c58/EKFJZKlLbW5i7ba+kU1IoewR2YMufz1xu/Bkkh0XBuXw70WEhqRDwd9s7m2aHLJObKT1ViKmc/20TJNoZ+aTVaLszeStytJ1ja29Bx/xxenLppcGx+1A+sR7HSRenZuC+Va1Vi9IJRDGs/wsjuyK9HmTdiAQDTV03h3e5B7Npk/HDLjX9gPYqVLkb3xn2oXKsSYxeMYnD74UZ2h389yhx92jNWT6V9jyB+27ibpMRkVkxfRZO2jV7rR4PA+hQvXZQPGveiSq1KTFgwmoHthxnZHfz1CDNHzANg1uppdOjxLr9u3EVEWATD3v+U5Jcp+AfUY9KisSaPB6jc3A/P0j7Mbf4pJWuW44N5A/mi0zQjuw6TenDi271c3X2WD+cNwL9rIKc3H+b+6VvcOnwZAN+KJei3ehTzW4w1qVU7oA6+pXwZ3HQQFWpWYOi8YYzraGx759JtLh69wPztC4z23b4YwuyPZpvPPIUC75nDeNZvKlmRsZTeuZzkY+fIfBCWbZJ69hopR88BYF2hFEVXTOZR28HImVk87TMZOS0DLJSU+vFzUk5dIuOa+UpNbt1icwbzsOdnZEXG8c6upbw8cgFVaI5uyunr3Dt8HgCbiqUotXoCd1uYvi7G6UtUWtifyx/OIyM8Dv+D84k5eJnUXPEi/WkMFzvNRv0yFY9AP6osHcT5dtOQ1RruzdhE8s0nKO1t8D+8gLiTNwyOzatVYWF/rn44D1V4HHUPLiD24CUD+4yn0VzpNAv1y1TcA/2ouPRjLrWbhlaVxdUus9GkqZAslNTePYu4Y9dIumxciX2Vb2Xmf0xI19lkRsRRff8i4g9dJP3+82wT1bNobnWZjuZlKi6BNSm7ZAg3350MQOk5/Uk4fpV7H3+OZGmBwtbKbBbWDKiNT2lfRjQbTPmaFRg0dyiTO403srt36Q6Xj15k1o/zDLbfPH2di/rrV7JiKcasnsAoM9evXmBdipUuSp/GH1GpVkVGLRjJ8PYjjexCLoZw7sh5lv20xGB7jxHdeRDykBkDZ1G8bHFGzhvO+G4TTWq1bNWUMmVLUa9ma2rXqcHiZTNp2+JDI7tpk+eTkpwKwOx5kxgwqCdffrEeJ2dHFi+dQdfggbx4HoGHhxsACoWCwLl9+aXnQpIj4umxezYPD18mPlc8LhVQA5dS3nzfdCzeNcsSOK8fP3acScKjCLa0mwrongkfX1jJgwOXAGgypTvnlv/CkxM3KBVQgyZTuvNz13kI/huI4UB/j0AgQ5bl7wFkWdYAo4H+6Fr/s5Fl+RowGzB+kzDkNLqXfvT/L0NXKXj1+0wuWwvgRyBUluVJ+aTpqLeN05+LSpbl1z7pfP3KEv8kisSwGLRZGm7vPsc7rQxf5N5pVZsbO/8AIPzqA2yc7HAo4qIT9XajXKAf1348bnBMrV4tOPPVLjSZagDS4pIM9t96EklxTxeKebhgaaGkTe0KnLhu2KoiAakZmciyTLoqC2d7G5QKBZ7ODlQq4QWAvY0VZbzdiE5Mzj5O4eRJwpMoXubyqXwen8q3qs2tnX/qfXqItZM99nqfCkLFd+tze9dZg21uNcuS/CSK1Gc63We/n6NonpdiVVwS8dcfoc0ybKW2cLDF078ij7aeAECbpSErTyXjTajjVw1nJ8e/fHxuPP3KkvQkimS9X49+P0eJ1oZ+ZcQlEXv9Edo8re/p0YnE6XtYslIzSAwNx87brcDajVo35ODPhwG4feUODk4OuBUxPv78sQvZf9+5dg9PH4/Xpt24TSMO/HwoJ21nB9xNpH3OIO27ePp4ApAYl8jd6/dQZ6lfq9W0TSP267VCrtzBwdnepNbZY+cNtIrotW5eCiH5ZYr++NsUyce/qq3rcPGXUwA8vfoAW0c7nDxdjOzKN6zC9X06vQs7T1GtdR0AMtNU2TZWdtbI+TSk+beuz7GdxwC4d/Ue9k72uBZxNbJ7FPKI6OfR5hPKB9vq75D5NJyssEjIUpO09xSOLRoY2MhpGdl/K2xtANlon2RhgWShzL0rX+z8yqN6EkFmWBRylpqE3X/g3Mqwt1KbW9fOmgInDjjXKkfa40jSn0YjZ2mI/O0MRdrWMbB5eek+6pe6l7zEy6FY++jKTGZ0Isk3nwC6VvvU0BdY53NfOdUqR/rjKDL0WlG/ncGjbV2zWi8vh2Kdq0FGoy8TkqUSycKC/AqFQ81ypD+JRPVMl2+xv/+JWxtDreRL99DotZIv38dKr6V0sMXJvzLRW3VtaHKWGk0+cbBuq/qc2Kl79oRevYedkz0uJsrf45BHxJgofxm5rp+1nTVyPtevUeuGHNLHojtX7uLgZG8yFj0IeUjU8yij7SXLl+Dqn1cBCHsYhncxL1w9XExqtX23Bdu3/QbA5UvXcXZ2wsvL08juVQUAwMbWJvuyBH/Qnr27D/PieQQAsbHxANSqXZ3EJ1G81Mfye7vPUTZPLC/bujZ39M/GSDPPxuKNqvDyWTTJL+IAkGUZK0ddI6C1ox2pUQkm/fqfoNUW3r//KKIS8PeoAlzOvUGW5STgGVDOhP0VdEN58uMMOZWAesBvQHH974boKgmvmACoZVn+NL8EZVmOB3YBTyVJ2iZJUk9Jkl577R293UiOiMv+nRQRj6O3q5FNUngum8h4HL10Nq1m9ObY/G1GH+RwL+1DiXoV6ffbLHptn4ZP9TIG+6MTU/B2zXlJ9XJ1JPqlYct7t+Y1eRwZT6vJX/P+vA2Mfz8AhUIysHkR95K7YdFUK+WTs9HGnuSI+OyfySZ9ciU5l0/JuXySkem6eRL99syhRvcA8lK8XgVSY1+S8MQw0Nt6u5H+IifNtIh4bL2NH0imcChZBFVcMvWWD6b1oXnU/Xzg3+oJ+Cex83ElNVd+pkXGY+9TML9y41DMA/eqJYm5an4YS148vT2ICY/J/h0TEYOnt/kXYKWFktbBLblw4mKB0o7Ok7bHa9JuE9yK88dfn7YprajwnJeQmIjY1/rRNrgV545fMNrXvlsQZ01sf4WLlxuJucr2y8h4nPO8INq7OpKelIZWo3twJUbE4+KVY1O9TV2mHF3KoO8msm2C+eFk7t7uxEbEZv+Oi4zD3dvdrL0pKtSqyJcHVjJzw0xKvFPCaL+FtzvqXBpZkbFYeBlrOLZqQJkDX1N8/SwiJi3P2aFQUHrXSt45t5WU01fJuF6AXgDA0tudrNy6EbFYmvDNuY0/FY9+RZnvP+PZ+C8LlDaAjbcbGbmuU0Z4fL4v8kV7BBB77JpxOsU9caxaipdXzA8dzKulCo/DOp/Y5NsjgLjcWgqJekcX0SRkPfEnb5CUj5a1txuZL3LyLTMiHqt8yoRX9xYkHtO9HFuX9CIrLolyy4dT/dASyn4+FEU+cdDd2524XPdwfGQc7ibKRn7Ua+PPiqNfMfn7z/gqn+vn4e2eJxbF4vEGZf3R7Uc0adcYgAp+FfAq5oWHj/GLPYCPjxfhLyKzf4eHR+Lt62XS9svV8wkJPU358mX45utNAJQtWwoXFyd+27ORIyd38mG3jrp0fb1IDs+J5SkR8Th4GZYDB29Xg3eClMh4HPKUlQodGnD395xGsJOzNtNkSncGnltB02nd+XPR9tfmh6DwEJWAv4eE6ead/La/jgtATUmS7AFLWZZTgEeSJJXDuCfgT6CBJEnvvC5RWZYHAi306Y8DvjNlJ0nSIEmSLkmSdOlhRqTRfjlPK49kwiNZlikXWJO0uJcG8wOyj7FQYONszw+dZnBs/la6fGU4hMNUi0temTO3n1ChuCeHFwxm++TeLNxxlJT0nFbKtIxMxq3bxfj3A3B43QtzXjkTTr3ye3OX2fzw7jR29F1C7T4tKV6vgoFdpQ4NuJOnF8CkA7pE8z+vV4daKHCtVooHG45wqPVU1OkqKo1oX6Bj/31M5dWbpWBhZ02LdaM4N3MzWSnpbyBt/jqZYvT8Udw4f4ObF279laTzTXvs/FFcO3+DGxduvjbtgojll4fj53/KtfM3uJ5Hq1ZDP9p3D2L1/HX5aBlvMvLrNTY3Dl5kfouxfDvoc4LGGA9DyC+h/PIwLw9vPWBAg/6MbDuC3T/sYep642FLZk7WaFPy4bM8ajuYsGFzdPMDXqHV8rjDCEKb9MG2+jtYly9ZwLMrmO7Lg+e422IYjz+er5sfUFBMPilM551ro8oU7RFA6JytBtuVdtb4fTuae9M3oMnvvjJV2M3g2qgKvj0CeTAn19wJrcyFFhM57TcU51rlsK9Y3HwCpm8sk6ZODatSpEcLns7TvbxKFkocqpUhcsNBbrQejyZdRdERnfPRMiX1ZsHpwsFzjGoxjMUfz6dbftfvDe/hvGxbvR0HZwe+PriGzh91JPTWA7PzlqQ3iHsjP5lCtQpNuH//IZ26BAFgYaGkul8Venw4mA87D2TshGGUKVvKTLpG6vlqKyyVlG1Vi9C9Ob2W1Xu34OTsLXzjP4qTs7fQesnHJs9V8L9BzAn4e4QAwbk3SJLkhK7l3lSTZk3gTh774ugm9QKslWV5rSRJD9ANKbqi334OCAKKALmbqk4BG4D9kiQ1kWU538HUsizfBG5KkrQJ3aThfiZs1qGb18APnWfIjrm6fZ183EiJSjSwT4qIx8k3l423GynRiVQMqk/5lrUp29wPC2tLrB1t6bB8KLs+XUNyRDx3D+haTMOvP9L1FFjaQJau+9XLxZHIhJwhPFEJyXg6Oxjo/n72Fv3b1EOSJEoUcaWouzOPo+KpVsqHLI2Gset3EVSvEi1qljd0MCMVR5+cngdHHzeS83RPJkfE45jLJ0e9T0D2/2lxSdw/eBkfv7KEXdBdEkmpoELbuvzw3vS82Up6RDy2RXPStPNxIz1PXpojPTye9Ih44vWt5GF7LlBp+H+jEpAWEY+9T04LpZ23G2mRBe/ulSyUtFg3ioe/nuHp/kuvte/UtwPv9dA9zO5ev4+nb05rmaePJ7FRcSaP6zu6Ny5uzkyf+IXZtDv37Uj7nvq0r92jSJ6048yk3W90b1zcXVgycIbJ/aYI7tuJDj3fBXRDe7x8i+TS8iA2Ktbkcf1H98HF3YXJAz8z2F62UhkmLxnHmN6TsicbvqJx79Y06B4IwLPrD3HJVbadvd1IylP+U+OTsXWyQ6FUoNVocfFx42W08TV9eOEuHiW9sHd1JFV/vwb1eZc23dsAEHojFI9cQ5Pcvd2Jj4o3Sscc6bleXC8fv4Ry7lCcXJ0M/FNHxmKRS8PS2wN1tHmN9Iu3sCzhg9LVCU2udLTJqaSdv4l909qoQp++9tyyImOxzK3r40FWPr6lXgjBqqQPSldHNLlimzkyIuKxyXWdbHzdUJm4rxwql6DKssFc6b6QrFzzlCQLJTW+G0PEzj+J3pd/71RGRJyBlrWvu1mtissGca37QtR55kQBqJPSSDh9G/eAGqTeDTPaD6CKiMOqaE6+Wfm4kWki3+wqlaTc0qHc7jk3WyszPA5VRBwpV3XzDeL2nKXYcMNKQNs+QbTo1hqAhzdCcff15NUj183bnfh8ykZ+3LkQgldJHxxdHbMnDnfs254gfSy6d/1enljkYTZemCItJY0lY5dm/95ydiORYTmNcP0H9qB3X12F++rVm/gW9c7e5+vrTVSE+eF0Wq2W33/ZxycjB7Btyy+Eh0cSF5dAWlo6aWnpnD1ziarVKhL+IhJH35xY7uDjRmqe+z4lMp7c7wQO3m6k5nqOlWpeg+hbT0iLzbm3Kgc34cQMXUXu/p7ztFw0sKDZ8u/zHx6mU1iInoC/x1HATpKkPgD6icFLgR8Ag8GKkiRVB6ajW0koG1mWw2RZ9tP/e9W3fhr4FHjVpHwWGAWck/NU+WVZ3gksAQ5IkuRi6iQlSXLQTxZ+hR+6icL5En79EW6lvXEu7onCUknl9v7cP2ww+onQI1eoHtwEAN+a5VAlp5MSnciJxdtZ6T+C1Y0/5dcRq3hy5ja7Pl0DwP1DlynVsDIAbqW9UVpaZFcAAKqU9OZZdCIvYl+SpdZw8PI9mlUva6Dr4+bE+bvPAIhLSuVJVALFPJyRZZlZmw5R2tud3i0Mx9ACaJNjjHx6cPiKgc2DI1eoGtxY71NZVMlppEYnYmlrjZW9DQCWttaUalqVmHs5E9pKNa5K3MNwkiONHzTx1x7hWNobe71uiY7+vDh42cjOFBkxL0kLj8OxrG5Yk1fjKgYTiv+XxFx/hFNpbxz0fpXp6M+zPPmZH00+H0jig3BuvWb1klf8tmEXA9sMYWCbIfx54DRt3m8FQOValUhNTjX5kH+3ezvqNqvD7OHz8m0J/HXD7/RvPZj+rQfzx8HTtH2/dXbaKUmpxJlI+73uQdRrXpeZn8x9o1bGnRt+o2/rj+nb+mNOHTxNO71WlVqVSDWj1b57EP7N6zLjkzkGWl6+RVi4fjazRy0g7NFzo+P+3HSIJUGTWBI0iZuHLlG3S1MAStYsR0ZyGkkxiUbHhJ69TY0g3Rj3esFNuXVIV0HzKJkz7KBYlVIoLS2yKwAA+zbuZVS7kYxqN5JzB88SGKyrfFSoWYG05DQSTFQmzOGSa65C+RrvoFBIRhWc9Jv3sSrli2UxL7C0wOndpiTrJwG/wrJEznBAm8plkSwt0CQkoXRzQuFoD4BkbYV9Qz8yTeSfKdKuh2Jd2her4l5Ilha4tm9C0uHzBjZWJXN0bauW0eu+vgIAkHT1IXZlvLEt4YlkqcS7U0Oi88QLm6Lu+H03hpufrCbtUYTBvipfDCY19AVPv97H60jWa9notbw6NST2oGGF3LqoO9W+G8vtT1aTnkvL0t0RCyfd9DeFjSVuTauS+sB8W1TKtQfYlvbBungRJEsLPDo2Jj6PllVRDyp8O57QEV+SkUsrKyaRzPBYbMr6AuDSuBpp9w2v14GN+xgf9Cnjgz7lwqHzNA/WDdksry9/iW9Q/rxzXb/SVctgYWmRXQEA+H3Dbga3GcrgNkM5feAMrfWxqFKtimZjkTnsneyxsNS1yQb1aMeN8zdJS8l5hfjum60ENOlEQJNO7N9zhK7dOwFQu04NkpKSiYqKMUqzdJmc4XOt2wUQGqpbkWf/3qP4N6yDUqnE1taGWrWrc//eQ65euYlraW+c9LG8Qnt/HuWJ5Y8OX6GS/tnoXbMsmfpn4ysqdjQcCgSQEpVAMf9KgG6+QOIT4xEGgv8doifgb6Bfzacz8JUkSdPRVar2AVPQTeZtIknSVXSThKOBkeZWBsrDaXQv/a/upitAMeAbM+exVpIkb2CXJEmtTZhIwARJkr4G0oFUTPQCGKWr0XLwsx/ovnGibjnNHSeJDX1BrZ4tdCe15SgPjl2jbIAfw04ty14i9HVc23GC95YM4uNDC9Fmqdk1di3BA3NaUSyUCiZ1DWToqp1otVo6NqhKOV8Pfjp1HYAPmtbg43b+fLbxAO/P3YAsy3zaqQmuDnZcffCcPRduU97Xgw/nbwRgRIfGNKla5lVmceizDXTdOAFJqeCG3ie/nrqXlWtbjvHw2DXKBNRg8KmlZKVnsm+cbniFnYcTwes+1WWohZLbv5/h8ckb2eddub2/0YTg3Hl5ZcoPNNs2UbdE6I8nSbr/grJ9dHn5cONRbDydaXVgLpaOtshaLe983I79zSagTknnytSN+K8ehsLSgpRn0Vz49PX5bI7xMxZy8eoNEhOTaNGpF8MG9Ca4fZu/lJas0XJ2+gbabpmApFBwf/tJEu+/oGIvXX7e3XwMW09nOu6bg6WDzq+qA9uyM2AibpWKU/79JsTfeUang7rVIi4t2sHzY9cLpH3u2HnqB9Zjy58bUWWoWDQmZ8WNhRvnsWT8MuKi4hiz4FMin0fx1e+6Mb2n9v/JxuWb80377NHz+AfW58fTm8hIz2BBrrQXb5zPovFLiYuKY+zCT4l6HsXaXSt1ae/7kx+Wb8LN05X1+9dg72CHVivzwcfB9G7e3+DB/oozR8/RMLA+P53ejCpdxdwxi7L3Ld24gAXjPyc2Ko4JC8cQ+TySdbt07Qgn9/3Bd8s30n90H5xcnRg3/1MANGoN/YOGmPTr9vGrVA7wY/rJFWSmq9g6PmdM/+DvJ7Jt4jqSohPYvXArfVeO5N2xXXke8oSzO3QTLGu0q0/dLk3QqDVkZWSyYfgKs3l46dgl6gTUYd0f63VLhI5bnr1vxg8zWTnxS+Kj4mn/UXu6DAnG1dOVLw+t5PKxS6ycuJJGQY0J6t0OjVqLKkPF4uGLjUU0WiJnraH4d3N1S4T+fIjMB89w6a5roU3ctg+nto1w7tQCWa1Gm5HJi08XAmDh6Ybv4rGgUIBCInn/H6TkM58ir+7zz76mzMaZSEoF8TuOkBEahnvPtgDEbTmAS7sGuAYHQpYarSqTp5+YOH8zyBotdyd/T60fpyApFbzYdpzUe88p1qclAM83HqHM2GAsXR2otKi/7hi1hvNtpuJSrwK+HzYl+fZT/I/qfH0w/0dij14zq3Vv8nfU/HEKKBVEbDtB6r3nFNVrvdh4hNJj38fS1YEKiwZka11sMwVrL1cqfzkMlAokhYLo388Sl18jgEbLoynfUHnbdCSlgqgfj5F+PwyvPrrHVtTGQxQf/QGWro6UWfCx/vw03GirWynn0dRveWf1KCRLSzKeRfHg01Vmpa4cu0StgNqsOvU1qnQVX43LGdM/5YfPWDNhFQnR8QT1e4+OQ7rg4unK0oNfcuX4ZdZOXIV/uwY0Cw5EnaUmU5XJF/lcv/PHLlA/sB6b/vyBjAwVS8bkrLw2f+Nclo5fRlxUPJ37d6Lr0A9w83Rj/eGvuXD8AkvHf0HJciWYuGICWo2Wp6FP+XzcMrNahw+dpGXrZly4dpj0tHRGfpKzFPW2n9bx6YhpREfFsGrNIhwc7ZEkiZBb9xg/RtdTGXr/EceO/MHJM7vQarVs2fgzd+/oeleOTd9Al026Z2PI9pPE3X9BdX0sv7H5GI+PXaNUQA0++mMp6vRMDo3LGXpoYWNFiSZVOTLZcKTxkUnfZi87qlZlcWTSt2Z9K3TedOzq/0GkNx0jJ3h7mFeyZ6EVjjHfNSssKVb0P1UoOqUzC+/e6nJzTqFpbfT77PVG/xCbKJxWI00hxkE1hdcFXc/S9ITBf4NHWuMhIv8Wi60LJw9VmYXXThadYfd6o38I5RusVPR3sVW8foWsf4JlVlmFogOQoMl4vdE/xI3k1w9P+6eY4lz39Ub/EKOfbS74ZJR/ibTlgwvtRrD79Ov/ub+mED0BAoFAIBAIBIK3CzEnQMwJEAgEAoFAIBAI3jZET4BAIBAIBAKB4O1CK4bDi54AgUAgEAgEAoHgLUP0BAgEAoFAIBAI3i5kMSdA9AQIBAKBQCAQCARvGaInQCAQCAQCgUDwdiHmBIieAIFAIBAIBAKB4G1D9AQIzGItF963LVYW0ge8ALwK59s1lFWmFo4QhfsBrz7XZhea1o06U15v9A+QSiEVCsCyENteMgvxw2SlFfaFpvUguXDyMEmhLBQdgBYVnheaVkyYY6FpuXkXThzUvCi88lfdwq3QtGq6uhealq2m0KT+E8jiOwGiJ0AgEAgEAoFAIHjbED0BAoFAIBAIBIK3CzEnQPQECAQCgUAgEAgEbxuiEiAQCAQCgUAgELxliOFAAoFAIBAIBIK3C/GxMNETIBAIBAKBQCAQvG2IngCBQCAQCAQCwduFmBgsegIEAoFAIBAIBIK3DdETIBAIBAKBQCB4uxAfCxOVgL+DJEnFgNVAZXS9KnuA8UBD4HfgEWAHRAGLZVneoz9uJpAiy/LnJtKcCvQANIAWGCzL8nlJkk4APkAGkAl8LMvyNRPHa4CbgKRPY7gsy2ckSSoF7JFlueqb+FiqWXUCZ/ZGUiq4+eMJLny128gmcFZvSgf4oU5XsX/sOqJvPQHA2smONosH4v5OMZBlDoxfT8SVB3hWLkGr+f2xsLZEq9FwZOoPWLs60GKGTufGjyc4v8ZYp8XM3pQJ8CMrXcX+ceuIyqXTdtFAPN4pBsjsH7+e8CsPqBBUj0aju+BezpdNHWYQefOxUZpFm1en/uzeSAoF97ed4OZqQ13nsj40/mIQ7lVLcWXRT9z6eh8ASmtL2u2chtLaAkmp5MneC1xb+ku+eenUvCYlZg0EpYLYbYeJXG1o79a5Kd7DugCgTc3g6eS1pN/R+eg1sD0e3VuBLJN29ylPxq5EVmWZ1SravDr+s3qjUCq4t+0EN0z41XSZzq9Li3P8svdxo+mKIdh5OiNrZe5tPU7Itwfz9Ss/ps1fxqnTF3BzdeG3zWv/UhpdZvSlckBNstJVbBm3huchT4xs3Ip50nfVKOyd7QkLecLm0avQZGmo3bERLYd0AECVpmLHtG8Iv/MMgOYDgvDvGgAyPLv3lO/Hr+aDSb2pFlCTzPRMvhu3imchxmXGo1gRBq0ajb2zA89CHvHN6JVosnRfHK7gX4Wun/VDaWFBSkISS7rOAKDVgPdo3LUFyDLh98LYOP4rukzqRZWAmmSmq9g47ivCTGi5F/NkwKpP9VqP+WH0SjRZGsr7V2bougnEPo8G4NqB8+z7cmf2cZJCYvLuhSRGxvPVgEV8OOOjf1zL1cedvss+wcnTBVkrc3bbUTxKeFFZr6O7VsY6bsU86bdqFHbODjwPecwm/bWq1qoOQWM+RJZltGoNv8zewKNL9wx8Gr97AYmR8TzuaRQ6s/EMqEHVOX2QlAqebTnOg1W7DPYX7dKIcsN1ZUKdmsHNid+SdPuZ2fRy49O8OnXm6OLFg20nuL3K8L5yKueD/7JBuFUrxfVFP3Fn7b7sfR3Pf4E6JQOtVous1nCgXf5f+baqVw+nkcNBoSR9715St2w1aWdRsQLua74iceZsVCdPoijiifOUKSjd3ZC1WtJ37yHt550mj32FQ9Na+M74GBQKErYfJmbtzwb7HVvVx2tMT9DKyGoNEXO+Ie3SbQCKLhqJU2Bd1HEvCW07PF+dvFj718X50+FISgWpu/aRsmmbSTvLShXwXL+K+OlzyDhe8K/LD5g1iNoBtVGlq1g5dgWPbj00smnX913aD+iATylf+tToSXJCEgD1WtWn+7ieyFoZjUbDd7O+4c7F2wbHdp7Rl0r62LQtn9jUZ9Uo7JzteR7yhC368l6rYyNa5IpNP+tjk4uPOz2WDdPfV1rObTvGn98foOOMvlQK8CMzPZPt49bwwoxWr1UjsXW250XIE7aNXo0mS4NnWV+6LhlMsSql2f/5dk6u35t9zJQ/v0SVko5Wq8VSreWXd02Xy+LNq9NI/y5wZ9sJruV5F3Ap60PzpYPwrFqKC0t+4vrX+0ymI/hvISoBfxFJkiTgF2CNLMsdJUlSAuuAecBe4A9Zlt/T2/oBv0mSlC7L8tF80mwAvAfUkmVZJUmSB2CVy6SnLMuXJEn6CFgCtDKRTLosy3769NoAC4Bmf8lHhUTLuX35qedCkiPi6bV7Ng8PXyYuNDzbpnRADVxLefNt07H41CxLq3n92NJxJgCBM3vz+MQNdg35EoWlEktbawCaTenO2eW/8PjEDUoH1KDZlO44+Lqxo+dCkiPj6bNrNg+OGOqUCaiBa2lv1jfT68ztx+ZOOp0WM3rz+OQNfh9qqBNz/zm/DV5B6/n9zfrnP68vB7svJC0invb7ZvPs0GVe5tJVJaZyfvomSrStbXCsRpXFgQ/no05TIVkoeffX6bw4fp2YK8YPGQAUCkrMHcz9HjPIioij0t4lJB66QEbo8xytZ1Hce38qmpepOAXUouTiYdxtPwFLbzeK9H+PW4EjkDMyKbNmPG4dmhD30zGzfjWc25cDPRaSGhFPh706vxLz+HX2s02UbGPol1aj5cLsrcTdeoKlvQ0d98/hxambBse+CZ2CWtEjuANT5ph/acuPys398Cztw9zmn1KyZjk+mDeQLzpNM7LrMKkHJ77dy9XdZ/lw3gD8uwZyevNh4sJi+LLrbNKTUqnU3I+uCwbxRadpOHu50rRfWxa0HEuWKoteq0bSZXwPipT2YUrzEZSpWZ5e8wYxv9NkI63gSb04/O0eLu4+Ta95g2jSNZATmw9h62RHzzkDWd53HvHhsTi6OwHg4uVGYL92fNZyNFmqTIauGkvH8d0pUtqbGc1HUrpmebrPG8jiTlONtDpP6sWxb/dyafcZus/7mEZdAzm1+TAADy7e4asBi0zmW+BHQUQ+eIGNgy1Vmtf8V7Q0ag07524iLOQx1vY2zDzyBXFh0cxpPopSNcvz4bwBLDNxrTpO6smJb/dxZfcZPpw3kAZdA/lz82Hunb7JzcOXAPCtWIKPVn/KvBZjso9rnssnsygkqi34iHMfzic9Io4mB+YReegyKfdfZJukPYvmTOfZZL1MpUhgDap//jF/Bk03n6YeSSFRd35fjnXTxYu2+2bz/OBlknLfVwmpXJq+ieJ54sUrjnwwD1V8ymu1UChwGj2KhDHj0MTE4L5uLRl/nkbz9KmRneOQwWRevJizTaMh+auvUN8PRbK1xf2bdaguXjI+NlcavrOH8Lj3dNSRcZT9fRlJR86jehCWbZJ6+joPDp8HwKZiKYqvmkhoy6EAJOw8StzGvRRfOvr1fuXRdRk7ithR49FEx1DkuzVk/HEG9RNjH52HDUJ1/tIbJV8roDa+pXwZ1nQw79SswOB5Q5nYcZyR3d1Ld7h09CJzt8832H7j9HUu6H0uWbEU476ayIjAodn7K+lj03x9bHp/3kCWmyjv7Sf14KQ+Nn0wbwD1uwZyZvNh4sNiWKWPTRWb+/HhgkEs7zQNrVrDrrmbeB7yBGt7G8buXoBalYVnaW8WNh9NiZrlCJ43gC87GZfZdyf14NS3+7i2+yzB8wZQr2sAZzcfIT0xhd9nbqBK6zom82pN97mkJSRTTqM0uV9SSDSe25c9+mdKlz2zeXr4Mgm5yn5GYiqnZ2yidBvTZf8/iZgTIOYE/A0CgQxZlr8HkGVZA4wG+qNr/c9G32I/G3hdM4kPECvLskp/XKwsy6bevs4CRQtwjk5AQgHsTOLtV5aEJ1G8fBaDNkvD3d3nKNva8AYv17o2ITv/BCDi6kOsneyxL+KClYMtxepV4OaPJwDQZmlQJaWh9wsrR92D3NrRDk1mFolPongZptO5s/sc5Vrl0WllqGOTW6d+BW6Y0Il/EE78owiz/nnULEvykyhS9P49+v0cJfIEsIy4JGKvP0KbpTE6Xp2mAkBhoURhaYGcTzyx9yuP6kkEmc+ikLPUxP/+Jy6t6xvYpF6+h+Zlqu7vK/ew8nHP3idZKFHYWIFSgcLWiqyoeLNann5lSXoSRXJuv1qb8Utt6Fd6dCJx+h6WrNQMEkPDsfN2M+/Ya6jjVw1nJ8e/fHzV1nW4+Iuu5e/p1QfYOtrh5OliZFe+YRWu79M9sC/sPEU1/cPuyZX7pCel6v8OxSWXLwqlEksbKxRKBVa21niX8eXsLycAeHQ1FDtHO5xNaFVsWJXL+84CcGbnCfxa1wOgfocmXDlwnvjwWACS45Kyj1EqlVhla1nhVcaXc3q/Hl8Nxc7R3qRfFRpW4cq+cwCc23mCGq3rvjbPXLzdqBpYi9M/6tobarSu869oJcUkZvcoqFIz0Gg0PLx4F4AnV0OxNaNTvmEVrul1Luw8STW9Tqb+fgKwsrM2uJ9cvN2oHFiTsz+arvi+wrVmOVIfR5L2LBo5S0P4b2fxbmP44pNwKZQs/X2WcPkBNj4FK9/ueeLF09/PUTxPvFDFJRFv4r56UywrVUTz4gWaiAhQq8k4egybxo2M7OyCu6A6eQptQmL2Nm1cPOr7oQDI6emonz5F6elhVsuuRnkyn0aQFaaLTS93n8KplWFs0qZlZP+tsLUm98VJuxCCJjH5jX20qlwR9fMXaMJ1PqYdOYZN04ZGdvYfdCb9xCk0CW/2KKvX2p/jO3Xl5f7Ve9g72eNaxNXI7nHII2L0vVy5ycjls42doc9Q8NhUrgCx6emVUJz1sSkpJjG7R0GVmkHUwxdUblmbS7/8AcCzqw+wcbTD0YzWDb3WpZ2nqKrXSolLIuzGXy+XRfI8Ux7uOkcpE8+UGDPPSsF/F9ET8NepAlzOvUGW5SRJkp4B5UzYX0E3VCg/DgGfSZJ0HzgCbJdl+aQJu7bAb2bSsJUk6Rpgg65SEfgaTbM4eruSHJ7zspkSEY+PX1kDGwdvV5Ij4rJ/J0fG4+DtilatIS0+mbZLB+FZqQRRN59wfOYmstJVHJ+1mfc3TaDZ1B5IColzX/6GV/UyOWlExONb01DH0duVpHBDHUcvV7QaDelxybT7fBBFKut0jup1XoedtyupufxLi4jHM49ufkgKifYH5uJUyou7Pxwm9qqZXgDAyseNzIjY7N+ZkXE41Cxv1t6jW0teHr8CQFZkPJFf/0b18+vRZmSSdOoaSaeumffLx5XUiFx+Rb6ZX69wKOaBe9WSxOTj17+Ni5cbibmu+8vIeJy93UiKSczeZu/qSHpSGlqNbnxnYkQ8Ll7GL3b+XQO4c+KaLp2oBI6v38PMM6vJysjk1h/XsXW0Iz6XVkJkPC7e7rzMpeXg6kh6Umq2VkJEHK56La8yPigtLBj/4yxs7G048v0+zv5yksSoeA6u38WiM2vIysjkzh83sHW0IyE8NpdWHC4m/ErLx6/Std5h6v7FvIxKYOe8TUToe5U++Kwfvy7YjLW+xdzFy+1f03qFWzFPHD1ceHAuZ7hEYmRcga6Vcy6d6m3q0n5Cdxzcnfm6/8Ls7V0+68uuBVuyfTKHjY8r6bmuYUZEHC61TIVjHcV7NCf62LV803yFrbcraXnihXutN7ivZJnAbZOQZZkHm47xYMtxs6YKD0800THZvzUxMVhWrpzHxgObJo2J/3QMzhUrmkxH6e2NZfnyZN2+Y1bLwtudrFyxKSsyDju/d4zsnFr74zWhLxbuzjztP8tsegVF4emBJjrn5VsTHYtVlUpGNrbNGhM7fCwulSq8Ufru3u7E5fIrLjION293EqILXpmo38afXhP74uzhzLx+hj4754lNiQWITS/zlPdsna4B3NXHpty4FvOkaOVSRD14YTIOJufSsssTmxIj4kxqGSHLDNo0GWSZh5uPc2ercbm093YlJc+7gNdfeKb85/iPfSdAkqS2wApACXwjy/JCEzbNgeWAJbpG47800uMVoifgryMBptp+89ueL7IspwC1gUFADLBdkqR+uUy2SJL0HJgIrDSTTLosy36yLFdEV1nYqB+6VCAkSRokSdIlSZIuhaoiTZxjHntTbskyCgslXlVLcW3TUTYFTSMrXUW9Ye0B8OvdguOzt7DOfxQnZm/Br3fL1+pgwgVZllEo9Tqbj7IhaBqZaSrq63UK4OvrdfNB1srsaj2VHXVG4lGzLC4ViuWnVmAtx4ZV8ejWkufzNgKgdLbHpXU9bjYYzI3a/VHY2uDWJb/7/u/5BWBhZ02LdaM4N3MzWSnpb3bwP4nJ4mVUCF9rU65BZfy7BrBroW5cta2TPVVb1WZWkxFMrz8UaztrXE31eBhpmS6HoGvtL1mtDCs+ms8Xfeby3oj38Srtg52TPX6t6jKpySeMqz8IKzsbXLzdjdLJq2Xqrn1lEnbrMdMaDWNeuwkc/+EAQ9bp2heqBtYiOe4lz249LlhCf0PrFdZ21gxeM5bI0Odk5q18F0Ant82NgxeZ12IM3wz6nHfHdAWgSmAtkuOSCMvtkzlMOmLa1L1RZUp0D+DOXNPj0I2TLnjapjjUcTb720zjeM8lvNOvJUXq5/NS+5p8AnAaMZzktevMTm6UbG1xmTOLpJWrkNPS8tEyX6Zzk3ToHKEth/J08Dy8xvQyn15BKUC5dPn0E16uNu/jm2LKr/w4f/AcIwKHsnDgPLqPM/TZ9On/9di0e6HhnA8rO2s+WjOa32dvRNYY+583nQKdjwlWBc9k+XtT+KbfIqr0bYmPqXL5N5+VgtejH1K+GmiHbp5pd0mSKuexcQG+AjrIslwF+ODv6oqegL9OCBCce4MkSU5AccBU02lN4E4e++LAq9k1a2VZXqsfVnQCOCFJ0k2gL/CD3qYncB1YiK6wdJEk6Xt92uGyLAflTl+W5bP6eQWeBXVKluV16OY2sKXTTNnRN+fFyMHHjZQ8rSjJkfE45hq24ujtRkpUIrIskxwRT+Q1XVbc33eB+kN1L+dVgptwbMYmAO7tOU+bJYNIiU7MScPHjZSoPDoR8Tj5uvNqZK+jt5vuGL1ORG6dAlYCUiPisc/ln52PG2l5dAtCZlIakWfuUKx5dRLvPTdtExGHlU9Ol7yVtztZkcZDemwrlaTk4uGE9p6d3cXu1LgGqrBo1PG64SWJ+8/iULsi8b+Y6iTStVDa5xriYOftRlpkwf2SLJS0WDeKh7+e4en+NxuH+0+gsHFCYaMbQvQy6jEuvjnly9nbjaQ81yg1PhlbJzsUSgVajRYXHzde5iqnvhVL0H3hYNb2W0haom48doXGVYkPi6Hmuw1o0D0QW2d7VGkZuOXScvV2IzHPsKuU+CRsneyztVx93EnUayVExpGSkExmuorMdBX3L9ymWKVSAMSGRVP33YY06d4CB2cHVGkqXH09gHt6LXcS8/iVEp+MnZFfuvPJyFUxCzlxFeXcAdi7OlK2TgWqt6xD7fcaYutoh6SQiH0W9a9opSYk07xfWzqO605GSjq3j181uFYu3u68NKGT37V6xcMLd/Ao6YW9qyNl6lSgWsvaVA7ww9LaChsHW1xWfcLV4auNjssIj8c21znY+LiTYaLsO1YqQY2lgzjfYyFZCQUYo4/uvrLLEy/S3+C+So9KBHRDhsIOXMa9Zlmiz98zaauNiUFZJCdsKz090cbGGthYVKyAywzdJE7J2Rkr//okaTSo/vwTlEpc5swi/fARVKf+yPe81BGxWOaKTZbe7qjzGW6YdiEEq5I+KF2d0CQkmbV7HdroGJRFimT/VhbxQJPHR8uK7+A2Rzf2XeHsjE2D+iRqNGScOm0yzXZ9gmjVvQ0AD26E4p7LL3dvdxLy8Ss/bl8IwbuED52GBNO0Y1OsJAXPrj/MU95fH5ucfdxIylXefSqWoOvCwazLFZtAN8R09O/zsHW0o9WoLoRdf1TAOGif695yN9AyxyublLgknhy4TBG/skTkKZepEfE45HkX+CvPyv8c/605AfWAB7IsPwKQJOlHoCOQezZ6D+AXWZafAciybDyO7Q0RPQF/naOAnSRJfSC7FrcU3Qu7QbOLJEnVgenoXtyzkWU5TN9q7yfL8lpJkipIkpR7jIgf8DTPMVnANMBfkqRKsix/pD/eoAKg162IrlspLu++ghB5/RGupb1xLu6JwlJJxfb+PDx8xcDm4eErVAluDIBPzbKoktNIjU4kLeYlyRHxuJbxAaBkoyrEhepe4VOiEijur+v2LdGoCvGPwg10KrX350EenQdHTOukxrwkKSIeNxM6ryP22iOcSnvjoNct09GfsENXXn8gYO3miJWTbuqH0sYSnyZVSXxofvJs6vVQbEr7YFW8CJKlBW4dG5N4+IKBjZWvB2XXT+LxqC9QPc5JKzM8Boea7+jmBACOjauT8cB0ZQMg5rqxX88OF8wvgCafDyTxQTi31u8v8DH/JNqMJNSJL1AnvuDmoUvU7dIUgJI1y5GRnGbQ3f6K0LO3qRGkG8dcL7gptw7pKi+uvu70XzuGTaNXE/M4Z35IQngcJWuW4/xPJ1gSNIl750K4f/4ODbo0B6BMzfKkJ6cZDAV6xb2zIdQOagBAw+DmXDukm5R57dBFytetpBv3b2NFGb/yRDx4Tnx4LGVqvsPpn44xO2g898/dJvT8bfz1fpXWa5ny697ZEGoF+QPgH9yc63q/nDyds21K1iiLJClITUjm98XbmNJgKONrDmBFrzmEHL/KT7N++Fe0AEpWK8uZHceY7D+EG4cuUk+vU6pm+XyvlZ9ep15wM27qdTxKemXbFKtSGqWlBakJyexevI3PGgxjVuMR/DBiBffP3DJZAQBIvPYQ+zLe2JbwRLJU4tupAZGHDEZtYlvUnbrfjebq8NWkPjLu7TRH3LVHOJb2xl5/X5Xs6M/zAsYLpa01FvY22X/7NKtK4l3z93DW3XsoixVD6eMNFhbYtAhEdfqMgU1s1+7EdO1GTNduqE6eJGnZcl0FAHCeOAH102ek7fjpteeWdiMU61K+WBbzQrK0wLl9U5KO5IlNJX2y/7apUhbJ0uJvVQAAMu/cxaJ40Wwf7VoGkvHHWQObqOCeRHXpQVSXHqQfP0ni5yvMVgAA9m/cx5h2oxjTbhTnD54jIFg3GvadmhVIS057o6FA3rl8LlO1LBZWFvy2didj2o3i86BJ3MoTm8zdVw/MxCYXX3c+WjuGLXliE0C3RYO598cNZvoP44ugyYQcukSdLk0AKKGPg8kmtUKorteqE9yUkDxlPy9WttZY68ulla01xZpWJd5EQ1b09Uc4l/LGUV/2y3bw58kbPFMEhqMs9P8G5TEpCoTl+v0c47mf7wCukiSdkCTp8qv3z7+D6An4i8iyLEuS1Bn4SpKk6egqVPuAKUADoIkkSVfRTRKOBkbmtzKQHgdgpb7LRw08QDc0KK92uiRJS4FxwIA8u1/NCQBdZ2RfWZY1+q7sCvrhRK8YLcuy2aeErNFydPoGgjdNQKFUcHP7SeLuv6BGL11gvb75GI+OXaN0QA0G/rGUrPRMDoxbl3380c828O6XQ1FaWpD4LDp736FJ3xIwU7d8pUaVxeFJ32Lr6cwHGyfoliLdcZK40Bf49dTpXNui0ykTUIOPTy1FnZ7J/tw6Mzbw3oqhKCwtePksmn36feXb1KHlrD7YujkS/P04om8/5ac+iw38OzdtA623TkBSKAjdfpLE+y+o0Fune2/TMWw9nWm/fw6WDrbIWi2VP27Lr80nYuflQpPlg5EUCiSFxOPd53l+5Bpm0Wh5Nn0972yZAQolcduPkHE/DM9eularmM0H8RndFQsXR0rOH6I7P7WGO++OI/VqKAn7zlDpwDJQa0gLeUzMFvPLdsoaLWenb6DtFp1f9/V+VdRft7ubdX513JfjV9WBbdkZMBG3SsUp/34T4u88o9PBeQBcWrSD58eum/ctH8bPWMjFqzdITEyiRadeDBvQm+D2bQp8/O3jV6kc4Mf0kyvITFexdXzOMqODv5/ItonrSIpOYPfCrfRdOZJ3x3blecgTzu7QjWttMzIYe1cHPpirWyFKq9awtMNUnl57wPX95xm/dwFatZYnIY/4cfZ3fDitH/NPriIzXcX347/K1hr1/RR+mLiGl9EJ/LxwE4NXjqbz2G48C3nCnzt0t3XEwxfcOnmVmQeWImtl/th+lPD7uph+ef9Zpu9dglat4XnIE36a/QPvT+vL7JNfkpmeycZcWp98P4nNE7/mZXQCvy3cwoCVn9J+bDfCQh5zZoduomPNdv407dUarUZDVkYm345YbjYPbx2/StWAWv+4Vtk6FfAPbsbzO0+Zsm8xErpxyJ+dXEFmeiZbxq/Jda0msW3i1yRFJ7Br4Rb6rRyVfa3O6XX82tWnbpemaNQ6nR+Gm/fJHLJGy60pP+C/bTKSUkHYthOk3HtOyT66IYdPNx6h/JguWLo6UG1h/+xj/mhjvFqSqbQvTd1A4FZdnHr440le3n9BeX28CN10DBtPZ9rtn4Olo+6+qjiwLbubT8TGzYGm334K6Hranvx6hogTN8yLaTQkLV+B6+dLQKEgfd9+1E+eYNtBt6Rk+q5dZg+1rFYN27ZtyHr4EPdvvwEgef16Ms+dN6OlJXzGWkpvnKVbIvSnI6hCn+HWoy0A8VsP4NS2Ia5dApHVauSMTJ6NyImjxVeMw96/GhauTlQ88z1Ry7eSsOPwa/MTjZbEpSvxWL4IFEpS9+xH/fgJdp11PblpvxovE/0mXD52idoBdVjzxzrdEqHjVmTvm/bDDFZPXElCVDzvftSeTkO64OrpyvJDX3L52GW+mriSBkENaR4ciCZLTWZGJks/WWyQ/u3jV6kU4MdUfWz6MVds+vj7iWzXx6Y9C7fSe+VI2o3tyouQJ5zLE5vezxWblnWYSuk6Fagb3JTwO08Zt083JHz/4u3EPYtm0snlZKWr2D7+62ytAd9P4KeJ60mKTmDvwm30WjmCtmM/5EXIE87rtRw9nRm1ax42DrbIskyT/u1Y0mo89q6O9FunW4FLoVTy7LczhJkol7JGy5/TN/DuZl3Zv7f9JAn3X1BZ/0y5rX+mBO+dg5X+mVJtQFu2B0783w4nfQ1yIX4nIPcoCzOYHASY57cFuiHjLQBb4KwkSedkWb7/V89LetMxcoK3h89L9Cq0wqEt8KyFv4+nunB0qkkFG2bwT3Adh0LT6nNtdqFpjaszpVB0UimkQgFY/h/tgLV8/bSnf4xWGYWTh0kK00sm/hu0KGe+Z+CfJibsr6/Y9aa4eacWis7wF/aFogNQSlF4WspCvK/MLRH6bzAkbHMhPvVNkzI5uNDecRwW7MzXX/0S8TNlWW6j/z0ZQJblBblsJgE2sizP1P/+FjiQX2Pu6/i/+TQSCAQCgUAgEAjMoZUL79/ruQiUlySptCRJVkA3IG+X3+/oRplYSJJkB9Qnz1zTN0UMBxIIBAKBQCAQCP5HyLKsliRpOHAQ3VzO72RZDpEkaYh+/1pZlu9IknQAuAFo0S0jeuvv6IpKgEAgEAgEAoFA8D9EluV96OaW5t62Ns/vJcCSf0pTVAIEAoFAIBAIBG8X/60lQv8niDkBAoFAIBAIBALBW4boCRAIBAKBQCAQvF3IhbdE6H8V0RMgEAgEAoFAIBC8ZYieAIFAIBAIBALB24WYEyB6AgQCgUAgEAgEgrcN0RMgMEuPYi8KTSv4aaFJcbBt4RT7J0dtCkUHYJM6stC0bhTSV3wBPr80v1B01LdOFIoOAPGFd62i5x4rNC2vtYMLTavL+98Xik6CJr1QdAD2PnUtNK1obVqhabmGF87XiZ0Uhfc606bwigV+FSIKTSsl1rrQtP4LyKInQPQECAQCgUAgEAgEbxuiJ0AgEAgEAoFA8HYhegJET4BAIBAIBAKBQPC2IXoCBAKBQCAQCARvF1rxnQDREyAQCAQCgUAgELxliJ4AgUAgEAgEAsHbhZgTIHoCBAKBQCAQCASCtw3REyAQCAQCgUAgeLsQPQGiElCYSJKkAW6iy/fHQG9ZlhMlSWoOjJNl+b1ctj8Ae2RZ/lmSpBOAgyzLdfT76gCfy7LcXJIkO2A9UB2QgESgrSzLKXm0vYBvgeKAJfBEluWgNzl/6/p1cf50OCiVpO3eS8qmbSbtLCtVwGPdahI+m03G8VMAuEyZgHUjf7QJicT06l8gvdGzR9AwsD4Z6RnMGb2I+7dCjWxmrpxKxRrvoM7ScOfaXRZOXIpGraF155b0HtYNgPS0dBZPXs6D2w+NjldWrYNN92FIkoLMP/aTuX+74f4K1bEbPhttrO4DT1lX/iRz92YkV09sB05AcnIDWUvWqX1kHvk1X38cmtWi6Gcfg1JB/PbDxKz52WC/U6v6eI3pCbKMrNYQPvsb0i7dBqDY4pE4BdZFHfeS+22GFyj/cjNi9if4B9YjI13FwtGLCb31wMhm6srJVKj+DposNXeu3WPppC/QqDVm0+wyoy+VA2qSla5iy7g1PA95YmTjVsyTvqtGYe9sT1jIEzaPXoUmS0Ptjo1oOaQDAKo0FTumfUP4nWcANB8QhH/XAJBB6eiKJjkGeH2wnjZ/GadOX8DN1YXfNq8tWMaY4fStRyzecRStVkvnxjXo39bfYH9yuoqp3+4mMiEJtUZLn1b16NSoOqosNf0/30qWWo1ao6VlrQoM69Akf617L1i85wJarUznuuXp37yawf4fTt1i37VHAGi0Mo+jX3J8WlcSUjOYsO1ktt2L+BSGtvSjV+PKZrVsGtbFbdwwUCpI+XU/ST/8aLDftllDXIb1A60WWaMh4fM1qK7dAsCxZzAOndqBLJP14DGxM5dAZpZ5v27cZ9Gmfbo8bF6bAe2bGeZhWgZT1vxEZNxL1FotfYMa0alpbZ5ExDBhVc59+Dw6gWHBLejVtqFZrcGzBlM3oC6qdBXLxi7j4S3je/29vu/RaUAnfEv50q1GN5ISkrL3VfOvxqAZg7CwtCApPomJH040q1UYcckUfWYOwC+gNpnpKtaOW8mTW4+MbFr3bUfb/u3xLuXDYL8+JCckFyhtgE9mDaVeYD1U6RksHrOUByZiRMe+HegysDNFS/nSpfoH2Xlo72jHpBUTKVK0CEqlkp/W/czBHYfMan0082NqBdRGla5i9bgVPDbhS9u+QbzbvwPepXzo79cr25fGnZrRaUgXADLSMlg/dQ1P7zzJPq7njP7UCKhFZnom68et5GnIY6O0PYoVYdiq0dg7O/I05BFfj/4STZYaW0c7Bn8xCveiHiiVSvav/50/fjoOgJ2THf0XDqNohRI4aeDW6LUkXtJde4+AGlSa2xeUCp5vOcbjlbsM9HyCG1FmuC7eqVNV3J7wDcm3dfGu6vLBeLaqRWZsEqebjTebZ6+wqlcPp5HDQaEkfe9eUrdsNWlnUbEC7mu+InHmbFQnT6Io4onzlCko3d2QtVrSd+8h7eed+WrZNa6Dx+QhoFSS9PN+Er/ZYbDfPrABbiP6ZD+zYheuJeNKCAAlD29Am5quiyVqDc8/HPFa3wSFjxgOVLiky7LsJ8tyVSAe+OQNji0iSVI7E9tHAVGyLFfTpzsAMPVkng0clmW5hizLlYFJb3TmCgXO40YRN3YS0T36YduyBRalSpq0cxo2CNX5iwab0/YdIH60+QdrXhoE1qd46aJ80LgXCycuZcKC0SbtDv56hG5N+9KrRX+sbKzo0ONdACLCIhj2/qf0bjWQ75ZvYtKiscYHSwpse44g7YsppEwfiGX9ABQ+JYzM1KE3SZ01hNRZQ8jcvVm3UashY/vXpE4fQOr8kVgGdDB5bDYKBUVnD+Fxv5ncb/UJLh2aYl2uuIFJyunrhLYbSWjQKJ5P+JJii3KCZsLPR3ncd2b+mWaG+oH1KFa6KD0b92XpxC8YvWCUSbsjvx6lT7OP+Kjlx1jbWPFud/N1xMrN/fAs7cPc5p/y45T1fDBvoEm7DpN6cOLbvcwNGE36yxT8uwYCEBcWw5ddZ7Oo3UQOrvyFrgsGAeDs5UrTfm1Z2n4KC9voHoiStX2B/OwU1Iq1y+YWyDY/NFotC7YdZvWID/hl5kAOXLzNw/BYA5vtx69QxseDHdP7883YHiz7+ThZag1WFkrWj+7Gjun92T79I86EPObGI/Nf3tZotSzYdY7VH7Xkl9EdOXD9MQ+jEg1s+jWtyo6RHdgxsgMj29SidmkvnO2sKeXpnL192/D3sLFUElgl/zLoNnEE0SOmEB48APu2AViWNrTPuHCFiK6DiOg+hLhZn+M+fQwASk93nLp1IrLXMCI+/BgUSuzbBOTr1/wNu/lqfB9+XTSSA2dv8vBFtGEeHjlHmaJF+Gn+cL6dMoClWw+QpVZTyseTHfOGs2PecLbNGYaNtSWBdSqZ1aoTUIeipYoysOlAvpz0JcPnma4k3750myk9phAVFmWw3d7Jnk/mfcLsAbMZ2nIo84ea/0p1ocQlE/gF1MK7tC9jmg3jm8lr6D/X9NeZ7126y/yeM4gJiza53xz1AupStHRR+jb5iC8mrmDUfNMvbCGXQpjQfRKRYYZfve7QtwNPQ58xuM1Qxn44nsHTdRUqU9QMqI1PaR9GNBvC15NX8/HcoSbt7l66w+yenxGd53pFh0Ux48MpjGs7ip+/3M7gBTmP0erNa+Fd2ocJzYfz/ZQ19J03yGTaXSf15uC3e5gYMJzUlyk069oCgBa92xL+IIzp7cayoNtndJvaF6Xej54z+nPz5FUmtxjJ6cAJpNzX39cKicoL+3Opx0L+bDIWn86NsH+nqIFe+tMYzneazemAiTxc9gtVluac14sfT3K52wKT52mEQoHT6FEkjJ9IbJ++2LQIRFnS9HPYcchgMi/meg5rNCR/9RWxvfsSP2QYdp07mT42Vxqe0z4hfPA0nrX/GMegACzLGsaLtHNXCes8lLAuw4ietowisw3vhxf9JhDWZZioAPyHEZWA/x1ngaKvtcphCTDNxHYfIPstQ5ble7Isq8zYPc9ld+MNtLGsXBH183A04RGgVpN+5Bg2TRoZ2dm/35n043+gTUg02J557QbapCQje3M0bdOI/T/rWpJCrtzBwdke9yJuRnZnj53P/vvOtbsU8fEE4OalEJJfpuiPv00RHw+jY5VlKqCNDkeOjQSNmqwLJ7Coab61MTfyy3i0z/QtZRnpaCOeIbkaa7zCzq88mU8jyAyLQs5Sk7j7FE6t6xvYaNMysv9W2FmDnNP6nXohBPXLgrfq5aZR64Yc/PkwALev3MHByQE3E3l5/tiF7L/vXLuHp4k8e0XV1nW4+Iuul+fp1QfYOtrh5OliZFe+YRWu79Ndows7T1GtdR0Anly5T3pSqv7vUFy8c85HoVRiaWOFQqkASQKt+d6I3NTxq4azk2OBbPPj1uMIihdxoZinC5YWStrUqcSJ64atvZIEqapMZFkmXZWJs70NSoUCSZKws7ECQK3RotZokSTJvFZYLMXdnSjm5qjTqlGaE3fCzNrvv/6YtjVKG20//yCCYu6O+Lo6mD3WqmoF1M/DUb/Q3cOpB09g29zwHpbTc8qgZGuDQQ+MUolkbQ1KBZKtNZqYOPN+PXxOcS93ihVxw9LCgrb+1Thx+Y6BjYREWoYKWZZJy1DhbG+LUmH4SDof8pDiRdzw9XA1q+Xf2p+jO48CcO/qPeyd7HEtYmz/KOQR0c+NX46bd2zOmf1niAmPAeBl3EuzWoURl0xRu1U9/tipa5F+cPU+dk72uJjw8WnIY2KfxxQozdw0bN2AwzuP6M736l0cnOxNxogHIQ+Jeh5ltB1Zxs7BFgBbexuSE5PN9iLWbVWPk3pfQq/ex96ML09CHhNj4nrdv3yXVH3sCL1yD3cf9+x9tVrX5fQvut6xh1dDsXO0x9lEXKrUsCoX950F4M+dJ6jVut4rR7Cx1/lhbWdDamIKWrUGGwdbKtSrzMntunImZ2lQJ6UB4FKrHGmPI0l/Go2cpSHytzN4ta1joJd46T7ql7pzTrwcio1PTt4mnLtLVmKqybzKi2WlimhevEATobuHM44ew6ax8XPYLrgLqpOnDJ7D2rh41Pd1cUxOT0f99ClKT/Plz6ZaBbKehaN+HglZalL2n8AhsIGBjZz7mWVrY/DM+v8BWZYL7d9/FVEJ+B8gSZISaAHsep1tLs4CKkmS8ja/fQdMlCTprCRJcyVJKm/m+NXAt5IkHZckaaokSb5vcs5KTw80UTkBWRMTYxRAFB4e2DRrQtpvb+KWaTy9PYgKz9GLiYjF09t8wFJaKGkb3Ipzxy8Y7WvfLYizJrZLLh5o43MemHJCLAoXE5WFspWxn7kWu0/nofA1bjmR3L1QliiH5tFds+dn6eVOVq7W5KyIOCy93I3snNr4887RNZT6bgbPJ6wwm96b4Ontkf2CAxATEfPavGwd3JILJy6atXHxciMxPOcl8GVkPM7ehi8N9q6OpCelodXo1mJOjIjHxcv4xcK/awB3TlzTpROVwPH1e5h5ZjVzLqzVdSVnpRfIz3+K6MRkvF2dsn97uToSnWgwuo5uAbV4HBFHqwmreX/2d4zv2hKFQveyr9Fq+XDO9wSOW4l/pVJUK23+VotOSsPbOaenw8vJjuiXpl8I0jPVnLn/gpZVjcvgwRtPaFfduHKQGwtPD9SRue7h6BiURYzLoG1AI3x3fkeRFfOInfW5zjYmjqRNP1F031aKHdqBnJxKxrnL5v1KSMLbzTn7dxE3J6ISDBsBurXy51F4DC1HLOL9KauY0PtdFHkqAQfO3aRtg+r5+uXh7UFMRE75jo2MxSOf8p2XomWK4uDswMLtC1mxdwWBwYFmbQsjLpnC1dud+Fz3W3xkHK4m7qW/iodRjIjFw9u4bJjjtx92UaJcCbZf2sr6w1/z1Yw1Zl983LzdicsVC+MiY3EzEQsLQmC3Vlw9cSX7t6uXm0Ha8ZFxuObxw8HVkbSk1Oy4lBCRk5dHNuzHt1wxVlz4hnkHl7Fl1nfIskyREl4kxyUx8PPhzN67hCrLBqG0swbA2tuN9FzXJiM8Hmtv89emWI8AYo5d+0v+Kjw80UTnXCdNTAwKT888Nh7YNGlM2u/mn8NKb28sy5cn6/Yd8zZe7mRF5mipI2NRFjEu6/YtGlJizzf4rJ1D9LRlOTtk8P1mPsV+WoXTB6YGMQj+C4hKQOFiK0nSNSAOcAMO67ebqybm3T6XPL0BsixfA8qg6ylwAy5KkmTUdy7L8kG93XqgInBVkiTPvHaSJA2SJOmSJEmXNkeF595jfHJ5grzzp5+Q9NXX/8wHOEy0nuZXmR4//1Ounb/B9Qs3DbbXauhH++5BrJ6/rkAaebNc8/QBKRN6kjpzCJlHf8d2+CxDc2sb7IZ9RsaPayAjzfwJmtIy4VDSwXPcbzGUp4Pm4TWml/n03gSTeWk+M0fPH8WN8ze4eeFWPmkabzJKswA25RpUxr9rALsW6sa12jrZU7VVbWY1GcH0+kNBUiBZm2/d/jcwlTN5XTkT8pgKxYtwePEnbJ/2EQu3HSYlXdcBp1Qo2DH9Iw4uHMatJxE8eGG+Zdaklpmeg1N3w/ArWQRn/cvHK7LUGk7eCaNVtVLmndIlXKATSD9+mvDg/sSMnYHL0I8AUDg6YNe8IS/e68XzNl2RbG2wD2phVspU8crr15mboVQs4cORlRPZMe8TFmzYTUqunogstZqTV+7Sul7V/P0yqV/wljelUkm5auWY0W8G03tNp/vI7hQtbaaTtjDiUsFk/9FWV9PhqeDp12lWm4e3H9K1Tg8Gtx3G8DmfYOdgZ0arYLHwdVRpUI3Ari3ZvGBD7sRfm7Yp/Ve+Vm3qx7PbjxlVbyDTg8bRe/ZAbBxsUSiVlKxahmObD/LZu+PRpKkoPaKjPkFTZ2emAtSoMsV6BHB/julx/K+lAOXAacRwkteuM/sclmxtcZkzi6SVq5DT3vCZZcKv1KNnePbeQCKGz8RtZN/s7c97jub5+8OJGDwV5+4dsKn95vfxv45WLrx//1HExODCJV2WZT9JkpyBPejmBHyJrlKQtz/UDTAYiCzL8jFJkuYA/nm2pwC/AL9IkqQFgiRJCgQ+1psEybIcLstyPLAV2CpJ0h6gKbAzT1rrgHUA4Q0DskuuJiYGpVeRbDulpyfaWMPhAJYVK+A6+zMAFM7OWDesDxoNGadOFyhzgvt2okNP3djZO9fu4uWbo+fp40FsVKzJ4/qP7oOLuwuTB35msL1spTJMXjKOMb0nGUwCzPY1IQaFW049SHL1QJuYZ4hDrhd79c0L2PQageTghJySBEoldsNmkHX+GOorf+brW1ZkLJa+Oa0olj7uZEXHm7VPvRCCdUkflK5OaEyc++vo1LcD7/XQjem/e/0+nr45fnr6eBIbZXooR9/RvXFxc2b6xC/MpmkjWfDs+kNcfHNa2Jy93UiKSjD0IT4ZWyc7FEoFWo0WFx83Xkbn2PhWLEH3hYNZ228hafqW9gqNqxIfFkNqvG7okzYzFcnCGlll2BL/b+Ll4khkrjyPSkjG08WwIvL7mZv0b+uPJEmUKOJKUQ9nHkfGGbT6O9nZUOed4pwOeUS5okb1bZ2Wkx2RuVr+o5LS8HQy/fJ0wMxQoD/vv6Cirxvujrb5+qWOjsHCO9c9XMQz3yE9qis3sSzmg8LFCZs6fqhfRKJN1A2VSTv2J9bVq5C676hpv9yciIzPGVYTHZ9EERfDoVq/n7pC//ZNdXno5U5RT1ceh8dSrWwxnV/XQ6lYygd3Z+NK4Ht93qNN9zYAhN4IxdMnJ389vD2IM1O+TREbGUtSQhKqdBWqdBW3zt+idOXSvHisG2VZ2HHpFa36tCOgWysAHt14gFuu+83N252E6ARzhxaIDn3bE9Rd10J73yhGeBAXZT4+5aXth63Z9pVu0mj4k3AiwyIpXq44967dA6BNnyBa6n15cOMB7rliobu3B/H5xEJTlKhYkiGLPmF+39k06tCElt1aIUkSj6/r0n41eM/N252EPH4kxydh52SfHZdcfdxJ1Odlkw8C2btGt8BD9NNIYsKi8S1blLjwWOIj43h0TZdy1O7zlB6hX9ggIh7bXNfGxtcNVaTxtXGoXIKqywZzqftCshL+WjzTxsSgLJJznXTPYcPyZ1GxAi4zdGVOcnbGyr8+SRoNqj//BKUSlzmzSD98BNWpP/LV0kTGYumdo2Xh7YEm2vx9lXH5FpbFdfFCm5iEJkaX75r4l6QePY1N9YpkXM6nYUnwP0H0BPwPkGX5JTASGCdJkiUQCvi+asGXJKkkUAO4ZuLwecCEVz8kSWokSZKr/m8roDLwVJbl1fpJyH6yLIdLkhSoX0kISZIcgbLAs4Kec9adu1gUK4rSxxssLLBtGUjGn2cMbKLf70F0cHeig7uTcfwkLz9fXuAKAMDODb/Rt/XH9G39MacOnqbd+60BqFKrEqlJqcSZeFC07x6Ef/O6zPhkjkHLlZdvERaun83sUQsIe/Tc6DgAzeN7KLyKInl4g9ICy3rNUV87a2AjOeXUzRSlK4Ck0FUAAJt+Y9FEPCPzUP4rLACkXQ/FqpQvlsW8kCwtcGnflKTDhkMBrEr6ZP9tW6UskqXFX6oAAPy2YRcD2wxhYJsh/HngNG3e1z2AK9eqRGpyqsmH7rvd21G3WR1mD59nshXwVZpLgiZx89Al6nZpCkDJmuXISE4jKSbR6JjQs7epEaSb+1AvuCm3Dl0CwNXXnf5rx7Bp9GpiHkdk2yeEx1GyZjks9ePqFZa2oDG/As2/QZVSPjyLTuBFbCJZag0HL92hWY1yBjY+bk6cv/sUgLikVJ5ExVPM04X45DSS9ONkMzKzOH/3KaXzGVZRpZgHz2KTeBGfrNO6/phmlYoZ2SVnZHL5cRQBlYsb7TNXOchLZsg9LIoXxcJXdw/bt2lO+knDe9iieE4lxqpiObC0RJuYhDoyGqtqlZBsdL0QNvVqkvXYfPioUqYozyLjeB4dT5ZazYFzN2lWq6KBjbe7C+dDdCvjxL1M4UlkLMVyjQ3ff/YG7cwMBdqzcQ8j2o1gRLsRnD14lhbBul6JCjUrkJqc+kYvyOcOnaNKvSoolAqsbaypULMCYaE58zIKOy694vDG/UwJGsOUoDFcOnSeJsG6kaDlar5DenJa9ovrX2XXht0MaTuMIW2HcfrgGVoFtwSgUs2KpCanvdGLeXR4DLUa+QHg4uFC8bLFiHiac18f3LiP8UGjGR80mouHztFM70v5mu+Qlpz6Rr54+How/uvJrBy9nIjH4dlpfxY0jiuHLtCoi24VqrI1y5OenMZLE3Hpztlb1A3SjW9vHNycK4d0sTg+PJbKjXSrczl5OONTxpfoZ1G8jEkkPjwW7zK6+8O9SVVS9RODX159iF0Zb2xLeCJZKvHu1JDog4ZD5WyKulPzuzHc+GQ1aY8i+Ktk3b2Hslix7OewTYtAVKcN7+HYrt2J6dqNmK7dUJ08SdKy5boKAOA8cQLqp89I2/HTa7Uybt3DsmRRLIp6gaUFDu2ak3r8nIGNZYmceGFdqRySpQXaxCQkW2skO12jhGRrjW3D2mSGPvnLfv9riJ4ApP/yhIX/a0iSlCLLskOu37uBHbIsb5IkqRGwFLBBt7rPFFmWD+vtTqBbQvSS/vdlIFm/RGgfYBy6jkIFsBeYKOe5sJIkjQc+AtR6u+9lWV6a3/nm7gkAsG5QH+dRn4BSQdqe/aRs2IJdp/YApP222+BYl6kTyThzNmeJ0FnTsK7ph8LFGW18Asnf/EDann3Z9sFPjfXHzRtF/ea6Zf/mjlnE3Rv3AVi6cQELxn9ObFQcfzw9QuTzSNJSdePGT+77g++Wb2TyknE0D2pK5AvdJDaNWkP/oCEAHGyb0wFmUa0e1t2GIikUZP55kMy9W7FsplupNevkHiwDO2LV/D3QapAzM1FtX4vm4W2U5apgP3k5mrBH2d2xql++Q30z58X+yVEbA38cm9fGV79EaMKOI0Sv3oFbz7YAxG85gOeQYFy7BCKr1WgzMomY/332EqElvhyHvX81LFydyIpNJOqLrSTsOJyd9kh1/uPmR80dQb3mdVFlqFg0Zgn39Hm5cOM8loxfRlxUHEefHCTyeRTpqbrej1P7/2Tj8s1GadWw0LXivT/7Iyo18yMzXcXW8WsJu6lb5m/w9xPZNnEdSdEJuBcvQt+VI7FzceB5yBM2jV6FJlNNt4WDqNGuHvEvdK1YWrWGpR2mAtBu9PvUfK8BWrUWr5IuaFIKNtFx/IyFXLx6g8TEJNzdXBg2oDfB7dsU6Fj1rRMGv/+4+ZAlO46i1cp0bFSNj4Ma8tPJqwB80Kwm0YnJfPbDPmJfpiAD/dv4865/Fe4/j2b6D3vRamW0skzr2hUZ/F6eiXvxhiur/HH3OUv2XEQra+lYpzwfB1Tnp/O6FtQP6lcA4PfLDzhz/wWLuhsus5meqabtwp/ZM6ELjvqKU26i5x4z+G3TqJ5uiVCFgpRdB0j6disOwbrynrJzD059u2L/XitQq5FVmSQsX5e9RKjzkD7Yt2qOrNGQee8BcbOXQVZOBc1rreGKNX9cu8fiLbolQjs1rc3HHZuz46ju/viwRT2iE5KYvm4nsYkpyLJM//ZNeU//IpmuyqTNp0vYu3QsjnaG9xFAl/e/N/g9bM4wajfXLTn5xbgvCL2ha7Gd9cMsVkxcQXxUPB0+6sD7Q97H1dOVxLhELh27xIqJunk3wYODafVhK7RaLQd/PMjv3/4OQILG+L76t+JSaQsXI63c9JsziBrNaqJKV/H1uJU8vqmrQE34YRrrJqwmMTqBNv3e5b0hnXDxdCUp7iXXjl9m/cSvjNKK1hoPAxkx9xPqNq+DKl3FkrFLua/Pw3kb5rBswhfERcXT6aOOdB36AW6ebiTEJXLh2AWWTViOu5cb45eN002SliR+XL2do7/qyp6rwvj6DZgzGL9mNclMV7F63Eoe3dQtsjD5h+msnbCahOh42vV7j45DOuPi6crLuJdcPX6ZtRNXMWTRcOq3a0CsftKwRqNlUvux2Em6uN579kCq6/Ppm/GreaLPpzHfT+W7iV+RGJ2AZ3Evhq0cjb2LA09DHvP16BWoM9W4FHHl48+H41zEFUmS2LvmV878pnuGlahciv4Lh2JhaYnVk2hujlqbPdnXo4Ufleb0RVIqeL7tOI+W/0bxPrpKVdjGI1RZNgjvd+uR/lwX72S1hrNtdPGuxtoRuDasjJWbI5kxLwld8jMvth7Pziu/Cobxwsq/Pk4jhoNCQfq+/aRu2oxtB12vRPouw3kAzpMnkXHmLKqTJ7GsVg331SvJevgw+6U0ef16Ms/lTGJPiTUcamjXtC4ek4YgKRQk/XqIhK+34dRV1yuWtH0vLgM+xLFjS128yFAR+/k3ZFwJwaKYNz5fztAlYqEkZe9xEr42XFK83O2D5ldMKCSSBrQqtBdgp28P/8/9NYWoBAjMkrcS8G9iqhLwb5G7EvBvkrcS8G/yukrAP8mrSkBh8Pkl88s1/pPkrQT8q+SpBPyb5K0E/JvkrQT8m+StBPxbmKoE/Fu8rhLwT2KqEvBvYaoS8G/wqhJQGHRPtyw0rbyVgH+TvJWAf5P/QiXg5UctC+0dx/n7I/9zf00hhgMJBAKBQCAQCARvGWJisEAgEAgEAoHg7eI/PFa/sBA9AQKBQCAQCAQCwVuG6AkQCAQCgUAgELxd/AOfNPr/HdETIBAIBAKBQCAQvGWIngCBQCAQCAQCwVuFLOYEiJ4AgUAgEAgEAoHgbUP0BAgEAoFAIBAI3i5ET4DoCRAIBAKBQCAQCN42RE+AwCxSIVYRkwvxi7faNNtC0bF3yCwUHQBNQuG1aKSiLjStwvqSr0XV5oWiA6B5fLXQtFxrnyg0LTk6rNC0YtQphaLzMqvwvqzrqCi8r7XGZRVO/gFoLApnCRY/C/dC0QHYZ1t48ba6uvA+NGttX3ixXfDfQFQCBAKBQCAQCARvF2KJUDEcSCAQCAQCgUAgeNsQPQECgUAgEAgEgrcKsUSo6AkQCAQCgUAgEAjeOkRPgEAgEAgEAoHg7ULMCRA9AQKBQCAQCAQCwduG6AkQCAQCgUAgELxViDkBoidAIBAIBAKBQCB46xA9Af+HkCQpRZZlh1y/+wF1ZFkeLklSBeBrwAWwBv6QZXnQm6RvXb8uTqOGg0JJ2p69pG7eZtLOsmIF3L9eTeKM2WScOAWA8+QJWDf0R5uQSGyf/gXSmzxvDE1aNCAjXcXUkXO4c/Oekc3Cr2ZRpUZF1Go1t67eZta4hajVGj4a1pN3g9sAoLRQUqZ8KZpUbkdSYlK+mhY16mLbR+dj5vG9qHYZ+mhRqQb24+aijY4EIPPiH6h+2Vggf2wb1cFj0hAkpZKknftJ/HaHwX67gAa4jegDWhlZoyFu4VoyroYAoHC0x3PWaKzKlQJkoqcvQ3X9Tr56o2Z/gn9gfVTpKuaPXsz9W6FGNtNXTqZijQqos9TcuXaXJRO/QKPWUKJscSZ/MYF3qpZj/aLv+PHrn4yO7T6jP9UCapKZnsl341bxLOSxkY1HsSIMWjUae2cHnoU84pvRK9Fk6T5IU8G/Cl0/64fSwoKUhCSWdJ0BQKsB79G4awuQZSxd1GTd+wO0Gk7fesTiHUfRarV0blyD/m39DbSS01VM/XY3kQlJqDVa+rSqR6dG1VFlqen/+Vay1GrUGi0ta1VgWIcm+ebd65g2fxmnTl/AzdWF3zav/Vtpnb5+n0Wb9uj8al6XAR2aGexPTstgypodRMYlotZo6RvUhE7NagOwaf+f/HLiEpIE5Yt5M3tQMNZWlma1lFXqYNNtKJJCQeYfB8g8sN1w/zvVsftkFto4XfnOuvInmXu2gIUl9hOWgoUlKJWoL/+Batem/P26+4zFv51Bq5XpXL8i/VvUNNj/w/Fr7LvyAACNVsvjqESOz+6Ds50NW07d5Jfzd5Bl6OJfkV5Nq+erNXbOSBoF+pORrmLW6AXcu3nfyGbOqulU0pf1kGt3mD/hczRqTfb+yjUq8t2eNUwZMpNje0+a1Zo2fxzNWjYiPS2DSSNncvuGcVz6fM0cqvpVRp2l5sbVED4bOw+1WoOTsyMLVnxG8VLFyFRlMnnUbELvPjSrNWzWUOoG1kWVruLzMUt5cOuBkU2Hvu3pPLAzRUv58n71D0lK0MU4O0c7Jq2YgGfRIiiVSn5e9zOHdhw2qzVh7qc0atGAjPQMZoyax10TeThv9Qwq54q388YvRq3WULthTb74YSHhzyIAOLbvJOuWfW9Wa8TsYdQPrEdGuopFo5cQasKvTv068v7AzhQtVZSO1YKz/XJwdmDC0rH4lvQlU5XJ4rFLeXLvSfZxnWf0pVJATbLSVWwbt4bnIU+M0nYr5kmfVaOwc7bnecgTtoxehSZLQ62OjWgxpAMAqjQVP0/7hvA7z7CwtmT49hlYWFuiVCq4uv88+7/4ieAZ/agcUJPMdBVbxq3huYkY6FbMk36rRmHn7MDzkMds0mtVa1WHoDEfIssyWrWGX2Zv4NGlnLIkKSTG716AW2Ik8eOnZG+3rl8X50+Hg1JJ2u69pGwy8xyuVAGPdatJ+Gw2Gcd1z2GXKROwbqR7Dsf0ev1z2KZBXVzGfgIKBam/7yN5w4+G+5s2xHnIRyBrQa0hYdlXZF6/pbtO3brg0CkIJImU3/aSsu2X1+oVOmJOgOgJeIv4EvhClmU/WZYrASvf6GiFAqcxo4gfN4mYXv2wbdkCi1IlTdo5Dh2E6sJFg83p+w4QP3ZigeWatGhAidLFCfL/gJnjFjB98QSTdnt3HqB9o650btYTaxtrgnt2BOD7r7bwfos+vN+iD8vnreHS2auvrQAgKbD9aBSpiyaRPK4fVg1boChq7KP67k2SJ39M8uSPC1wBQKHAc9onRAydxrMOH+MQFIBlmRIGJunnrvK8y1Cevz+MmOnL8Jw1Onufx6ShpJ2+RFiHgYR1GUrWo2f5yvkH1qNY6WJ0b9yHxROXMXbBKJN2h389Ss+m/ejbYiDWNta07xEEQFJiMiumrzL58g9QrXlNipT2YUrzEWycspZe80zXJ4Mn9eLwt3uYGjCC1JepNOkaCICtkx095wxk1cBFzGg9mrXDlgLg4uVGYL92zG0/kRltxoAkoSxSGo1Wy4Jth1k94gN+mTmQAxdv8zA81kBr+/ErlPHxYMf0/nwztgfLfj5OllqDlYWS9aO7sWN6f7ZP/4gzIY+58ehFvvn3OjoFtWLtsrl/Kw3QvfzO37CLryb049fFn3Lg3HUevogysNl++Bxlihbhp/kj+XbqQJZu3UeWWk1U/Eu2HjrLtjmf8MvCT9FqtRw4d8O8mKTAtsdw0lZMJeWzj7Gs1xyFTwkjM/WDm6TOHkrq7KG6CgCAOovUpROyt1tUqYuyTMV8/Vrwy2lWfxzELxM+5MDVBzyMTDCw6Rfgx46x77Nj7PuMDKpH7bI+ONvZ8CAinl/O32HzqM7sGPs+f9x+xtOYl2a1Ggb6U6J0Mbo06sH8CUuYtGCMSbv9vxzm/Sa96BbYD2sbazr1eC97n0KhYPjUIZw7cdHksa9o1rIRpcoUp1W9zkwfO49ZiyebtNu98wBtGwTzXtOu2NhY80GvTgAM+fQj7ty6T4fm3ZnwyWdMmzfWrFbdgLoULe3LR036s3ziCkbOH27SLuTSbSZ1n0xkmGG56dC3PU9DnzG0zTDGfziBQdMHYWFput2vcYsGlChTjI4NujJ33GKmLBpn0m7/L4fo3Lg7HzTvjY2NNZ17ts/ed/X8dbq17Ee3lv3yrQDUD6xH0dJF6dW4H0snLmf0gpEm7W5dvMXYbhOJDIs02N5zRHcehDxkYKvBLBi1mBGzhmXvq9TcD8/SPsxv/ik7pqzn/XkDTabdflIPTn67l/kBo0l/mUJ9fVyKD4thVdfZLGk3kUMrf+HDBbq4plZl8VWPOXzebiJLgiZRqVkNAga8i2dpb+Y0H8X2Kev5cN4Ak1odJ/XkxLf7mBvwKWkvU2mg17p3+iaL2k1gcdBEtk5YS/dFgw2Oa/5REJEP8sQphQLncaOIGzuJ6B75P4edhg1Cdd6wPKftO0D86AI+hxUKXCeMJGbUZCI/7I9d60AsShtqqS5eIarHx0T1HEz8nM9xm6Yrz5ZlS+HQKYiovp8Q2eNjbBv7Y1G8aMF0BYWKqAS8PfgAz1/9kGX55pscbFmpIprn4WjCI0CtJv3IMawbNzKyswvuTMbJP9AmJBpsz7x+AznpNS/huQho25RdP+0D4MblEBydHPAoYvxZ+D+Ons3+++bV23j5FjGyCercin2/mm8Be4WyXEW0keFooyNAoybzp4gvYAABAABJREFU7DEs6xj7+FewrlaBrGfhqJ9HglpNyv4T2Ac2MLCR0zOy/5ZsbQDdeEXJ3g6b2tVI3nlAt1OtRpucmq9e4zaNOPDz/2PvvMOjKroG/ru7m957IVTpNaF3CJ1QpClFkCpNBOm9d1RQaYoiWLCgWFCqUoL0llBC74T03jZld+/3xy5JNrsbgq9v9P2Y3/Ps8+zeOTPnnrlzz9yZOXP3IADXLl7H0cURD293E7nTh8/mf78efgMvPy8AUhJTuHHpJpo8838jH9ipEad+PArAvbDb2DvZ4+LlaiJXvXltLuzVX6OTu44S2KkxAE16tuLi/jMkGR7k0xML2oZSqcTa1hqFUoGkVCLnZHH1fjRlvV0J8HLFSqWkc8MaHL1kvLIhSZCZk4ssy6hzcnFxsEWpUCBJEva21vqq0+rQaHVIklRs/T2LhoF1cHF2+o/KALh6N5KyPh4EeLtjpVLRpWldjl4wXuGRJMhS5yDLMlnZubg42KFU6F23VqsjJzcPjVaLOjcPLzdni7qUFauhi49CTogBrYa8c6GoApuX/GRzDO1TqQKl8mnzNG/XozjKejgT4OGsv15BlTlqZkb2KfvC7tIlqDIA9+KSqVvOBztrK1RKBQ1e8uPwFdMZ1qe06dySPT8c0Ou9eA0nF0c8zPiKk4dP53+PCLuOt6GtA/Qf0Zcje0NJTkg2yVeY9l3a8NN3er906cJVnFyc8PIx1RX6x4n875cvRuDr7wNA5WqVOPWn/p67d+chZcr64+Flel8CNO/UjN93HQLgRtgNHJwdcTdzD9+NuEtsZKzJcWSwd7QDwM7BlvSUdKOVj8K06dyS33bq/cuVixE4OTuZ9bfHC/nbq2HX8fYz9bfPokWnZhz84Q8Arl+8btGuOxbsqlClPBePhwHw+O5jfAJ8cPN0BaB2p4ac+1E/6/0w7A52TvY4m/FLlZvX4tLeMwCc3XWMOp0aAvDg4i3UaXrf+vDibVx8C84rNysH0K8sK1UqKjWuzlmDrgdht7FzcjCrq0rzWoTvPW3QFUqdTo2MygOwtrdBLnQ/ufq6U7NdEKe+PWxUllXN6miK9MO2rUz7KId+vVEfMdMPh19GV8J+2LpWdfIeP0H7RK8r6/cj2LUx9hcmfZbBCFWFcuRcuY6ckwNaHTkXL2PXtmWJ9JYmsq70Pv9WxCDg/xd2kiSFP/0ASwqlrQMOS5K0T5KkyZIkuT5PwUovT7Rxcfm/dfHxKL08jWQUnp7Ytm5F1s+7/7IBT/Hx8yLmSYG+2Og4fAp12kVRqZT06NeV44dPGR23tbOhZXBTfv/tyDN1Ktw80SUWsjExHoWbp4mcskpNnFZ9isPMVSgCKpTAGlB5e6CJic//rYlNQOVtWrZD++aU3f0pfpuWEjd/LQBWAb5ok1PxWjaVgO834rX4bSQ7m2L1efl6EhdVoC8+Oh5PX1N9+TaplHTu25EzR4qfCX2Kq48HSVGJ+b+TY5Jw9TV+aHB0c0KdlolOq/eAydGJuPnoO1WfSn7Yuzgy/dvFzP91Nc366ENgUmKTOPDJblaf3Mx7Zz9B1uShS44iLiUd30IPuD5uTsSlZBjpGxBcn/vRiXScsZF+Sz5jev8OKBT6h32tTserS7fRbtp6mtaoQJ2K/iWy879NXHIqvu4u+b+93V2ITTbupAd0bMa9qDg6TFhFv9kfMmNIdxQKBT7uLgwNaUnnSWvoMGElTva2NK9TxaIuydUTXVJBm5CT41G4mj7oKSvVxGHBZuwnLkfhX2jmT1LgsGAzTu/tRHP9Itr7NyzblZqFr2t+ZCI+Lg7EpZofuKpz8zh54zEd6lYEoLKvOxfuRZOSmY06N4/j1x8RW+RaF8bL15PYqIL7Ni4qHu9ntPWQfp05deRsfv62XVux64tfLObJt8PPi5iogpnp2KhYfHwtPwirVEpefjWEPw+fBOBGxC06ddPPBNcNqoV/WV98LTxIe/h6EF/oHk6IjsfD1/R6WeKX7bspW7kc35z/mo9//4jNCz9Cls2P3Lz9vIiJMva33s/wt936debkkTP5x+o2qM13h7az4et3qVStosW8nr6exBXSlRCdUKxvKsrda/do3VX/QFk9sBq+AT75kxcuPu6kFPJLKTFJRg/yAA5uTqjTsvL9Ump0Ei4+poOQJv2DuXE0PP+3pJCYtncVSy9s4ebxyyhVyiK6Ep+pK6WIrrqdGzH30FrGfDaLr2dszj/eZ8FQdq/cYXK9lF6eaGML6k5rqR9u85/3w3pdBe1PG2uqC8CubQt8v9+G57rlJC19F4C8uw+wCaqLwsUZycYG2+ZNUPpYbk+Cfw4xCPj/hdoQ7hMoy3IgsOBpgizL24AawPdAW+C0JEkmT5KSJI2WJOm8JEnnv4qJKpxgqq2Ig3Ke9CbpH30Muv982Cthqs9SBwYwb/UMLpwO4+KZS0bH23ZqRdi5K88OBQLzNhaZ7tQ8uE3aWwNInzWKnAM/4TBl6bPLtVS2GXsyD53kcc9RxExchPuEofqsKiU2NSqT9t1vRL7yJjp1Nq4j+/8FdZbrb+qKSYSfuczlsyVbIDJfVfIzhZ6eg1KppHydSnwwfAXrXl9G97f64VPRD3tnBwI7NmJWqzeZ1mQ0klKF0ucls5PORUs/GXGfamW9+X3Nm3w3bzirvvmdDLVh9k6hYOf84RxYNZ6rD6K58yTetMB/AHOXxMSuK7eoXt6fPzbMYufyt1j5xa9kZGWTlqnmyMXr7F03jd/Xz0adk8tvhhlSs5hd/DA+Ae2jO2TMGqwPBTr8M3bjFxUS1ZG5ZBzpMwahrFANhX8Fy3aZuWKW1l6ORTwksKIPLva2AFTycWN4u0DGfryHNz/ZS1V/D5RKy12VuVWd4tr6rJVTCDt9ifCz+tCpKYvfYv3yj9CVwG89r65Fa2Zx7tRFzp8OB+DjDz7H2dWJX47sYMio/ly/chOt1vzsvNnVqmJ0FaVhmwbcu3aXgQ0HMa7LeCYsHY+9o32JdRVn1+xV07h4+hJhBn974/JNQhr2pX/7YXy7dRfrtq20mPc/tevrjd/i6OLEJwc+ovfwXty+eid/haNEfq8EMpWb1aRp/2B+XfV1gYxO5t2QWSxqNp7y9Spja64ui5TzLD95+cA5lrefwqej36XbFL1Pr9WuPumJaTy+am7169nXyeXtN0nb9Df0wyXx8YD66AliXhlO4vQFuIwdBoDmwSPSv/gWrw1r8PxwFXm374KFdv6PoivFz78UsTH4BUKW5SjgM+AzSZKuArWBC0VktgBbAKJbBuff8dq4eJTeBTNWCi8vtAmJhbNiVa0arov04w6Fiws2zZoga7Xk/HmCkjBgeF/6DdbH9F8Nv45vmQJ9Pn7exMUkmM03bupI3DxcWTxtlUla114d2PvTwRLp1yXFo/AoZKOHF7pkYxtRZ+V/1YSfQRrxNpKTM3J68YMMTWwCKt+CmRCVjyea+ESL8tkXrmJV1g+FqzOamAQ0sfHkGDZGZx48juuoV03y9B76Mj1e08f03wi/ibd/gT4vPy8SY83rGzZ5CK4errwzamGxNjwt30ZS8uDSXdz9C2Yl3XzdSYlNMpLPSErDztkBhVKBTqvDzc+DlDh9uEVyTCIZyenkqnPIVedw6+w1AmpUACDhcRwZSfr61MY/ROHsjY+rEzGFZshjk9PxKjTTDPDLySuM6NIUSZIo5+1GGU8X7sckGs36O9vb0rBqWU5E3KNymX9+ZsrH3YWYpIJ497ikVLyLhPT8EnqRET1a6+3y9aCMlxv3o+OJTkihjJcb7s76emjfsBaXbj+ie0vjDbhPkZMTULgX2Cy5eaFLMb5mZBdq31fPYatUIjk6I2cUat/qTDS3LqOq3ZDcqAfm7XJxIKbQ7H1saiZeLg5mZfeHF4QCPaV3k+r0bqLfc/Dh3jP4uBhf61eG9abXa/qY/mvhN4zCAL39vYi30NZHTRmGq4crK2bMyz9Wo151lm/Wt31Xdxeat2+KVqsldP9xAF4b8QqvDukF6EMOff19Af3Dr4+/D3Gx5geUE6a9gbuHG/Onrsg/lpmRyeyJBQu0hy/s5vHDgsmWHkN7EDKwCwA3L93Cq9A97OnnRWKRe6w4Or3aie826Td+Rz2IJuZxDGUrB3AzXL/h99Xhfejzmn4TbET4dXz9jf1tvAV/O3rqcNw8XFlWaLNqZkZBuzl+6BSzV03F1d2FFEPb7jW0J90M+41uXLqJt783EGGwy5MEC9fLHFkZWayZ+m7+71+u7GLOhzOxkRU8unQX10J+ydXXnbRY4xCvzKR07Jzt8/2Si587aXEFMn7Vy9F/1Ri2DFtFVpEVqBZDOtFsYDucPF1JehJfRJcHqUV0ZRTR5ernTmqcsQzA3bPX8Szvg4ObE5UaVqNOhwbUDA7EysYaaydbXBfOIWXxCv3Mv0/BdVJ6eaEr2g9Xr4bbkkL9cPMmoNWSfaxk/fBTtHEJRrP3Sh/TPr8wOWFXUJXxR+HijC41jczd+8jcvQ8Al/Ej0cb9OyZeBMaIlYAXBEmSukiSZGX47gt4ACXeHZl34wbKsmVQ+vmCSoVdh3bknDhpJBP/6iDiXxlI/CsDyT4aStp775d4AADw7bZd+Zt5D+8Lpecr+k6jboNaZKRnkBBn6oD6vtaTFsFNmDF2gcmMiKOTAw2bBXFk/7ES6dfevYHCtwwKL19QqrBu1o68C8Y2Si5u+d+VL1UHSXrmAAAg5+pNrMqVQVXGB1QqHLu2JfPIaSMZVdmCh1XrGpWRrFToUtLQJiajiUnAqkIAAHZNA8m7a7ox+KfPf2FEpzGM6DSGPw+coEu/TgDUrF+DjLRMEuNMHyC6DwyhcdtGLHpzWbEzf4XLXxIynbCDZ2nWpy0AlYKqoE7PIjU+xSTPzVMRNAjR731o3rct4Qf14UbhB89RpVENFEoF1rbWVAqsQvSdSJKiEqgUVBVrQwy/ws0PXVYKtSr48SgumScJKeRptBw4f5029YwfHP3cnTlz4yEAiWmZPIhNIsDLlaT0LNKy9LGr2bl5nLnxkIrPEVbx36RWpTI8ikkgMi6JPI2G/acv06Z+DSMZX08XzkTo3yCTmJrOg+gEArzd8fVw5fKdx6gN+yDORNylYjEDG+2Dmyi8yyB56tu3VaM2aC4Zh89JzgXtW1GhGkgK5Iw0JEcXsDM8xFtZo6oRhC7msWW7ynrzKCGVJ4lp+usVdoc2tUw3MKarc7hwN5rgWhWMjielqwGITk7n8OUHdC0ySPh++0+81nEkr3UcydH9f9Ktn/5NYLXr1zS0dVNf8fKgbjRr25h54xcbtfVeTfvzchP95/BvoayevTZ/AACw47PveTn4NV4Ofo0/9h2ld3+9X6rXoDYZaRlmBxyvDH6ZlsFNmTxmrpEuJ2dHrAybc18d3Ivzp8LIzCgIk/r1818Z1+VNxnV5k5MHTtGxb3sAqgdVJzM9kyQz97Al4qLiCGqhHxC6eroS8FIA0Q8LQpl2bvsxfyPvkf3H6P6qfvBRp75lf9t7UA+at23C7HELjewqvK+hVlANJEnKHwAA/Pz5bt7oPJY3Oo/lxP4TdOrXAYAa9Ws8t10Ozg75G5y7DerK6UNnGNlxDO+GzOLqwfM06tMagPJBlVGnZ5Fmxi/dOXWNeiFNAGjctzVXD57X15O/B8M/msKOyRuJvx9doNPdCVtne058eZAPes8n/kE010PDaWzQVSGoCtkWdN0+dY3AkKYGXW24YtDlWd4nXyagVkWUVioyk9P5dc03LGg2nsUt32L7Wx+QeyGMlMX6gWTe9RuoAoz74ezjxn1UXL9BxPUdSFzfgWQfCSX13fefewAAkHvtBlblyqD01+uy7xiM+pixLlVAQZ9lVa0KWFmhS9X3hwo3VwCUPt7YBbck84Dx/oZ/A2JPgFgJeJHoBHwgSdLTnTzTZVmOKS6DEVodaWs/xH3tGlAoUO/Zh+b+A+xf1r8dIuuXX4vN7rpoHtaBgShcXfD+cSfpW7ej3rPXovyxP07Sqn1z9p35AbU6m/mTCt7EsmnHWhZOWUF8bALz18wgOjKGHXs+AeCPPUf5aO1nALQPacvJ0LOos7LN6jBBp0O9/UMcZuttzD26D13kA6w76G3M/eNXrJq0wabjy6DVIufmkPlhCcOBtDoSVmzE7+MVSEoFaT8dJO/uQ5xf7QZA2s49OHZsiVPPDsgaDXJ2DrHTCmYQE1ZsxHv1TCQrFZrHMcTNf69YdacOnaFpuyZ8e+JLstXZrJzyTn7ami9WsHr6eyTGJjJ11dvERsby0W79y6KO7T3O9ve/xN3LjU/2bcbB0R6dTuaVN/oypO0IsgwzfleOXKROcH1WhG4gV53Dtumb8suftG0O22duJjUumR9WfcmY9ZPpPXUAjyIecHynfqNj9N0nXA0NY9H+95B1Mn9+d4ioW/qHygv7TjF/zzvoNFogF23UTVRKBbMGdGTcBzvR6WReblGHyv5efB+qD395pU0Qb3RrzoLte+m3eCsy8Hbvtrg52nMrMo752/eg08noZJlODarTuq7xQ+XzMn3hKs6FXSYlJY32vQYzfuQQ+vbo/NzlqJRKZg/tybg129DpZHq1aUDlAB92HtLHWr/avgmje7Vj/sc/0HfWB8jIvN2/M25ODrg5OdCxcW0GzNuAUqmgenl/+gU3tqxMpyP76w3Yv70CSVKQe+IAuqiHWLXRt8G80D2oGrTCum13ffvOy0X9ib4NSi7u2I+YDgoFSAryzoeiuXzGoiqVUsGsPi0Zt2UvOlnm5cbVqOzrzvcnrwHwSvOaABy+8oBm1QKwszF+renUzw+SmpWNSqFgdp8WONtb3gNz4tBpWrRvxk8nvyFbncOSyQWhKO9/uYZl01aTEJvIrFVTiYmM5bNf9bHXR/Ye49N1n1uuLzMc/f0EbTq04I+zP6NWZzN74uL8tE+++YC5by8lLjaBxe/MJupxDDv36X3Rwd+OsPG9T3mpakXWbFyMTqvjzs17zHnbsv84e/gsjds1Yvvxz/SvCJ26Nj9t2edLWDvjfZJik+g1/GVeGdcPdy93Pv59M2cPn2PdjPfZ8cHXTF87lY9/34wkSWxd8Vn+azaLcvyPU7Rs34zdp3eSrc5m0dsFvmf9jndZMmUV8bEJzFkzjejIWD7/bQtQ8CrQDj2CeWVob7QaDdnZucwea3lV8fThszRp14Svjn9OTnYOq6cUzOqv/GI5705fS2JsIn1G9GLAuFdx93Jn6+9bOHPkLO9OX0v5yuWY/cFMdFotD24/4p1pBb7w2pEwagQHMjf0A3LVOXw7veD1vW9sm8l3M7eQFpfMb6u+Zsj6iXSd2p8nEQ84vVO/Z6zzxL44uDnSb5n+9Zk6jZa1Pefi7O3GoPfGoVAokBQKwvacYv+Hu3hlyQgWhH5ArjqXHdMLYvrHbJvFNzM/Ji0umd2rdjBs/SS6Te1PZMQDTu/UPwwHdm1Coz6t0Wq05GXnsn3C+xbrLB+tjtS1H+Kxbg0oFWT9ZuiHexn64Z+f0Q8vnodNkL4f9vl5J+mfbifrNwv9sFZH8pr1eH24GkmpIGP3PjT3HuLQR78Cl/njb9i1a41Dt46GPiuXxDkF7dlz9SIULs7IGg3Jaz5ETre8r0fwzyE9a/ZP8OJSOBzov03H2+rSUsXxdnaloifxinWp6AEYlmz+LT7/DapZuT1b6G9i/faQUtGjqt22VPQAaO8XE7f/N5O75eNS02X1cpdS09V69M+loic1L+vZQn8TFWxLvjn2PyU+L73UdLmqzO9D+LsJVJXe6p62uFdj/c3M8o97ttDfhDav9IJDyp479J+9ou1vIKFrm1K7kJ77Qv9xe80hVgIEAoFAIBAIBC8W/+IwndJC7AkQCAQCgUAgEAheMMRKgEAgEAgEAoHgheLfvGG3tBArAQKBQCAQCAQCwQuGWAkQCAQCgUAgELxQiJUAsRIgEAgEAoFAIBC8cIiVAIFAIBAIBALBC4VYCRArAQKBQCAQCAQCwQuHWAkQCAQCgUAgELxYyP/K/+8qVcQgQGCRnIzSax4KqfRuRk1S6awBajSlt9CmKcV/PbEqzQXEpJhSUVOa/+KrrBhUaroUrg6lpouYyFJTlaXNKRU96aX4j8Eu9jalpitbmVdqupwVpWOXm6wsFT0ACVLp/UO70qb0fHtedunVoeDfgRgECAQCgUAgEAheKMSeALEnQCAQCAQCgUAgeOEQKwECgUAgEAgEghcKWSf2BIiVAIFAIBAIBAKB4AVDrAQIBAKBQCAQCF4oxJ4AsRIgEAgEAoFAIBD8o0iS1EWSpJuSJN2RJGlWMXKNJEnSSpLU7z/VKQYBAoFAIBAIBALBP4QkSUpgI9AVqAkMlCSppgW51cCBv0OvCAcSCAQCgUAgELxQyP+uPwtrDNyRZfkegCRJ3wIvA9eKyL0F7AIa/R1KxSDgfxhJknoDPwI1gAGAjSzLswulBwLfyLJcQ5KkB0A6oDUkH5NleeLz6LNr3hD3GeNBoSDjp32kbvvOOL1tM9zGDwNZRtZoSXpnEznhEajKB+C9Zl6+nKqMLymbPydtx0/F6pu5bDKt2jcnW53N/ElLuX7llonMyo2LqFWvOhqNhith11k6fRUajZZh418jpE8nvT6VkopVKtCmVghpKWlG+a0aNsZh7FtISgXZ+/ag3vm12XNRVa2Oy/ubSF+xmNzjoQC4ff4tsloNOi2yVkvqW2OKtce+ZQN85o4FhYLUH/aT9Mn3RumO7ZriOel1ZJ0OtFriVmxBfTGiQEChoPwPH6KJS+DJ2EXF6gKYvOQtmrdrQrY6m6WTV3Pr6m0TmUXr51K9XlU0eVquh99g1cz30Gq0dOrdgSHjBwCgzlKzZvb73Ll21yjvqwuHUys4iFx1Dl9M28TjiPsm5XsEeDFyw9s4uDjyKOI+2yevR5unpUrTmozbMoOEyDgAwvefYe+Hu/LzSQqJ2b+uwtpdQ+4pfT2duPmENb+dRaeT6d2oCiPa1jHStf3YVfaG3wNAq5O5H5fKkXn9Sc7MZsY3oflyT5IyGNchkMEtTSZZ8jlx6Rarv/wNnU5H77aNGNmzjVF6elY2czbvJCYxBY1Wx9CQVvRq0wCAL/cd58ej55EkqBLgy5LRfbGxtrKo61nMW7GWYyfO4u7mys9fffSXywFQVgvCpudIUCjIO/sHeUd+NE6vVAvbYbPRJeuvi+bKafL+2AmAVaseqBp3AEAX/ZCcnetBY/lPp07cj2PNoWvoZJnedcsyokllo/TtZ++y91oUAFpZx/3EDI682REXO2sW7rvEsXtxuNtbs2t4G3PFGzF7+RRat2+OWp3N3IlLuX7lponM6k2LqVWvhsFXXGPxtJVoNFocnRxYvWkxfmV8USqVbNu8g5+//c2irqWr59C+Y2vUajVvj5/DlUvXTWTeW7+UekG1kCSJe3ceMGn8XLIyC/54rF5Qbfb88Q1jhk9lz+6DFnUNX/QG9YMbkKPOYeO0D7h/9Z6JTJehIXQb0RPfCn6MCBxMenI6AC17taHX2D4AZGdl88nczTy8/sCirsJMXPImTds1IUedw8rJa8z6jvnrZ1OtXjU0eRquh9/g3Znr0Gq0Zkoz5Y3Fo2kQ3JAcdQ4fTH2fe1fvmsiEDO1Oz5E98avgz+B6g0hPNvbdletWYc0v7/Lum2s4ufcEAC+1qUvnhUNQKBWEfXuUE5t/NSm386LXqRJcjzx1Lr9M+5iYqw9Q2lgxbOd8lNYqFCol1/eeJXSd3h91mDOQqu3ro83TkPwwll+mb4F0/bn0XTiMmgYfuGPaZiLN+ED3AC+GbZiEvYsjkRH3+XLyBrR5Wup0bEjIlFeRZRmdRsuPSz7n3nl9u207MoRm/dshy+ASeYvU1asgLxcA60aNcZrwFigVqPfsIesbC31Wteq4b9xE6pLF5BwLBStr3D/4EKytkJRKskNDydy+rdjrZNu8Ee7TxoNS3+enbf/WKN2uTXNcxw8DnQ5ZqyX53c3khF8FwOm1vjj26gqyTN6d+yQsegdyS+9P6v4HKQM8LvQ7EmhSWECSpDJAb6Adf9MgQIQD/W8zEDiOfgDwDdC/SPoAoLCHCJZlOdDwea4BAAoF7rPfIvbNOTzpMwqHLsFYVSpnJJJ9JoyoV8cQ1X8sCYvexXPhFAA0DyOJ6j9W/xk4Hjk7h8zDJ4pV17J9M8pXKkv3Zq+wZNoq5q2eYVZuz48H6NlyAH3aDsbW1po+r/UEYPumHbzaYSivdhjKB8s/4sKpMJMBAAoFjm++Tdq8GSS/MRSb4PYoy5U3a7v9yDHkXThnkpQ6421Sxo965gAAhQKfBW8S+cZ87ncfg1O3tli/ZFx/mafDefDyeB72nkDMnHX4LJtklO72+svk3ntUvB4Dzdo1oWzFMrzScjCrZr7HjJWTzcod+OkPBrQeyuD2I7C2tabnoG4ARD+OZny/txnScRSfvf8ls1ZPNcpXq20Q3hV9Wdh2Il/P2cLA5aPMlt971mAOb93DwuBJZKVm0qJ/u/y0O+eusyJkBitCZhgNAADaDQ8h5s6T/N9anY6Vu0+zcXgHfpz8Mvsv3edubIpRnmGta7NzYk92TuzJxM71aVDRBxd7Gyp4ueQf/2ZCd2ytlLSrZVz3hdHqdKz4fDebZgzjpzVvs//0Je4+iTWS+e7301Qq4833Kyayde4o3vt6L3kaDbFJqXx98BTfLH2TH1e9jU6nY//pyxZ1lYReIR35aO2y/6gMACQFNr1Ho966lKx3J6IKbInkHWAipr1/HfW6KajXTckfAEjO7li17Ib6g+mo35sECgWqwJYWVWl1Mit/j2Bjv8b8OKIN+69HcTch3UhmWOOX2DmsFTuHtWJiq+o0KOuBi501AD1rB7CpX+MSmdWqfXPKVyxL16b9WDRtFQvWmPcVv+06QPcWr9KrzSBsbW3o+9rLAAwc0Y+7N+/Tp91ghvUZx4xFE7GyMj8/1q5jaypVKk/z+l2YPmkhq95baFZu4ZxVdGjZh/YtevMkMpoRbwzKT1MoFMxbPIWjh4r3gUHBDfCr6Mdbbcby8eyNvLFsnFm5G+evs+S1BcQ9Nm6jcY9jWfjqHKZ1mcQPH37HmJVvFqvvKU3bNSagYgCDWr7OOzPXMmXlJLNyv/90iMGthzGs/ShsbG3oPiikROU3CG6IXwV/xrYezcZZGxi3fLxZuevnr7Fg0Dxii9gF+jocOnsYYaEF//QtKSS6Lh3G10PXsKnDDGr1bIZnlTJG+SoH18Ojoi8b2kzlt9lb6bZsOADanDy+GLicLV3nsKXrHCq3qUuZIP2g9d6fV9ncaSYfd5lN4v0YWo7X9zE12wbiVdGXpW0n8d2cT3h1+Uizdrw86zWObt3LsuC3yUrNpJnBB948cYXVXWewJmQmX8/4iIGr9X2Ii48bbYZ15d0es1nVeRooFdi2a/fUcJwmvU3KrBkkDhuKbfv2KMub77OcRo8h91yhPisvl+Qpk0kaNZLEUSOxadwYqxqWJ0JQKHCf+RZxb80hqu9IfZ9fsUiff/Yi0f1HEz1wLImL38Vjvr7PV3p54DygFzGDxxP96hugUOLQOdiyrn8IWVd6H0mSRkuSdL7QZ3SR0zG3LCEX+f0+MFOW5ZKNtkuAGAT8jyJJkiPQAhgJDJBl+SaQIklS4ZHjq8C35vI/Lza1q6F5HIXmSQxoNGQeOIp92+ZGMrI6O/+7ws4WuWjzBWybBJEXGY02Oq5YfcGdW/Przn0AXL4YgZOzI57eHiZyxw+dyv9+Jew6Pn7eJjJde3dk30+/mxxXVauBNuoJupho0GjIOXoY62amDza2L/ch93goupTkYs+5OGzrViXvURR5kTGQpyF9byiO7ZsaychZBfUn2dtSuAJVPp44tGlM6vclCwNs3bkF+37QzzBGXLyOo4sDHt7uJnKnDp/J/349/Abefl4AXDkfQXpqhiH/Nbz9PI3y1evUkNM/HgPgftht7J0ccPZyNSm/WvNaXNx7GoDTu45Sr9OzJy9cfd2p3a4+J749lH/s6uMEyno4E+DuhJVKSed6FTl6/bHFMvZduk+XehVNjp+5E02AhxP+bo4W8169G0lZHw8CvN2xUqno0rQuRy8Yz/ZKEmSpc5BlmazsXFwc7FAq9O5Uq9WRk5uHRqtFnZuHl5vzM20ujoaBdXBxdvqPygBQlKuCLiEaOSkWtBo04cdR1SrZg7a+ACVYWYNCgWRlg5yWZFH0anQKZd3sCXC1x0qpoHN1f47eMX2Ye8q+G1F0qe6f/7tBWQ+cbUu2etKuS2t2f2/wFReu4uTsZNZX/HnoZP73K2ER+PjrfYUsg4OjPQD2DnakpqShsTCj3SWkHd9/+wsAF89fxtnFCW8fTxO5jPTM/O+2trbIhe7lkWNeY8/u30lISCzWrkYdGxO66wgAt8Nu4eDsgKu3m4ncg4j7xEea+tNbF26QmaY/j9sXb+LhZ1on5mjZuQUHDL7j2sXrOLo4mvUdpw+fzf9+PfwGXgbf8Swad2rCkV2H9ecYdhMHZwfczNh1P+IecWbsAug2vDun9p0kNTEl/1iZwJdIfhBLyuN4dHlaIn49TbWODYzyVevYgEu7/gTgSdgdbJztcfR2BSAvKwcAhUqJwkqZ73/v/XkFWat/jUxk2B2c/fR1UadTI84afOCDsNvYWfCBVZrXItzgA8/uCqWOwQfmGvQBWNvbGPWXCqUCK1trFEoFko0NusQEAKyq6/ssbbS+z8o+fBibFqZ9ln3vPmT/adpnydlq/ReVCpQqZJNnzAKsa1dDExmF5kl0fp9v17aFcXmF+nzJzhajZ1alEsnGBpQKJDsbtPHFt/f/78iyvEWW5YaFPluKiEQCZQv9DgCiisg0BL41RHb0AzZJktTrPzkvMQj436UXsF+W5VtAkiRJ9dGvBgwAkCSpKZAoy3LhddwjkiSFGz7mp4YtoPT2RBMTn/9bE5uA0tu087MPbkGZn7bivX4ZiYveNUl36NyWzH1HnqnP28+LmKiCh4bY6Pj8B1RzqFRKevTrwokjp42O29rZ0CK4Kb/vOWqSR+HhiS6+oJPRJcSj8PQ0kbFp3orsPbvN6nVZ8S6uG7Zg07VHsfaofDzJiy5UfzEJqHxMO2XHDs2psHcLAR8tIWbuuvzj3nPGEP/uVuQSvtPMy9eT2KgC2+KjE/DyNb1eT1GqlHTp25HTR86apPUYEMKpIsddfdxJjkrI/50ck4irr/GDgoObE1lpWegMHWhKdBKuPgUyFetXZe6+NUzYPhu/KgUz0q8sGMZPK79CV6hXjEvLwtfFIf+3j7M9cakFD1qFUedqOHnrCR1qm86QHbj8gK51TQcHhYlLTsXX3SX/t7e7C7FFQhEGdGzGvag4OkxYRb/ZHzJjSHcUCgU+7i4MDWlJ50lr6DBhJU72tjSvU6VYfaWF5OyOnFJwzeTURCQX0zaoLF8Nu8lrsR05H4WPvk+S05LIC/0Fh7lbcJj/GXJ2JtpblyzqisvIxtfJLv+3j5MtcRnZZmXVeVpO3o+nQ1Xfv2SXt58XMU8K+4o4fJ7pK7py/LDeV3y99XsqVa3I0ct7+Pno16yct87oob0wvn7eRD2Jyf8dHRWLn5+PWdl1G5dz+dYxKletyGdbduTn79q9A1989p3ZPIVx9/UgsdA9lhiTgLsZn1ES2g3oSNjRiyWS9fT1JC6qwFfFR8fj+Qzf0blvR84eMV0pNYeHrwcJ0QV2JcQk4uFbcrvcfTxo2rkZ+7/aZ3Tcyded1OiCB8206CScfN1MZNKiCmTSY5Jw8tHLSAqJ0XtXMO3iZu79eZUn4aYhSkGvtuHOUX27d/FxI6VQWSkxibiY8YHqIj7QpZAPrNu5EXMPrWXMZ7P4esZmAFJjkzn8yW8sPrmJZWc/RpeZSe758wAoPD3RxRXqs+LjURbtszw9sWnVCvVuM32WQoH7J5/i9dPP5F44j+a6aSjbU1RenmhiCnRp4+JRmhlc2wW3wH/XZ3h/sJyExfo+XxufSNqX31Nm79cEHNyJnJ5J9ukLFnX9U8g6qdQ+JeAcUEWSpIqSJFmjf5YzuoiyLFeUZbmCLMsVgB+A8bIs//yf1IEYBPzvMpCCWf5vC/3uJ0mSgoIQocIUDgdahxkKL1l9nRhZOMFU2ExHmXXkBE96jyRu8iJ9rGBhVCrs2zQj8/dQk3ym52FOneVZi7mrpnPhdDgXzxg/mLTp1JLwc5dNQ4EsKjH+6TD2LTK3fgw604fv1MlvkjLhDVLnzsCuZy9UtetaPD+zmDEn44+TPAgZzZMJS/Cc+Lr+HNo2RpOYQk7EnZKXbca2YqqP6SveJvzMZS6dvWJ0vH7zQHoMDGHjiiKTFiVoD8WJPL56n3ktxrO86wyObN/P2C3TAajdrj7piak8umocW2vu1CVzCoBjNx4TWN4bF3sbo+N5Gi2h1x/TsU4Fs/ksmKHXVeT3ySu3qF7enz82zGLn8rdY+cWvZGRlk5ap5sjF6+xdN43f189GnZPLb8fDTAv8JyjBNdM+uUfmitH6UKATe7AdanhLnZ0DylqNyVw5lsylI5GsbVHVtxyrb/Z6WZA9djeWwDJu+aFAz4tkpuTi2vr81TMMviIcgJbBTblx9RZt63ajb7shzF05DQdHB7N5zbU5S35p8ptzCazelts379GzT1cAlqyczbKF76Ez409KoqtYwyxQq1kd2vXvwFcrPy+R/PP63ikrJnHpzGUuF/EdFss3e71KbteoRW/w+crtJarDkvikpzKyTmZLyBzWNX2LMoEv4VXVOFSu5YSX0Wm0XPnphKGsv+YDC8tcPnCO5e2n8Onod+k2RR/Na+fsQJ2ODVncagLzmoxFsrXFtkNHywUWqTqnN98i42PzfRY6HUlvjCLhlVewql4DZYViJkRKoAtAfeQEUX1HED91Ia7j9OFVCidH7Ns250n3wUR27o9kZ4tDSHvLugTIsqwBJqB/6891YKcsyxGSJI2VJGnsf0uv2Bj8P4gkSR7oN4bUliRJBpTob88ZwAOgDdAXaPa8ZRuWqLYAPAjsmH/La2PjUfkWzK6pfDyLXd7LuXgFVVk/FK7O6AwP4HYtG5F74w66pBSzefoP70tfQ0x/RPh1fP0LZth8/LyIj0kwm2/s1BG4ebiyZPpsk7QuL5sPBQLDzL9XQfiQwtMrf9k1386q1XCavUCf7uKCdeOmZGi15J46ji5Jb7+cmkLuiT+xql4DzVXz8d+a2ASsCs1Oqnw90cRZrj/1+atYlfND6eqMXf2aOLZrimObRkjWVigc7fFbM53oGe8Y5ek7tBc9X9PH9F8Pv5Ef7gDg5edJQqz5+hsx+XVcPVyZPWqB0fGXalRi9jvTmDJkFmnJafnl20kqHl66i5u/J6DfyObm60FKrPHSc0ZSOvbO9iiUCnRaHa5+7qTG6UNIsjPU+XIRR8NQLhuJg5sTLzWsRt0ODakdHITKxhqFmwNWDXvi8/BTYgrN/MemZeHlbG/Wnv0WQoGO33pCdX93PArNUJvDx92FmKTU/N9xSal4Fwnp+SX0IiN6tEaSJMr5elDGy4370fFEJ6RQxssNd2d9uFH7hrW4dPsR3VsGFauzNJBTE5FcC2YNJRcP05CenILror1xEXqPAXsnlJXr6MOIMvX3subqaZTlq6G5aH5A7+NoS0x6QVmx6dl4Odqald1/3TgUqCQMHN6PfoP1Mf1Xw6/hW6awr/AmrtCqZWHGTR2Jm4cbi6bNzD/Wa0B3Pl3/BQCPHkTy5FEUlaqU50qY/qUcw0YN5LWhrwBw6eIV/MsUrFj4+fsQE2M5tFGn07H7p32Me2sE3+34iXpBtfjos/cAcHd3o33H1mi1Wvbv0Ye+dX49hA4D9A98dy7fwcO/4Hp5+HqSFGc5BMsc5aqXZ+zqN1kxdAkZKekW5XoPfZnur+lj+m+E38Tbv8BXefl5kRhr3lcNmzwEVw9X5o0yvzfiKSGvd6PjwM4A3Ll8G89C4YWevh4kxZbcrsp1KjNtg37fh7O7Mw2CG6LVaEmPScKlUMiTs5876UX2DaVFJ+HsXyDj5OtOepyxTE5aFg9OXady27rE39JPhNXt24qq7YOI+PU0o/euQCPJPLp0F9dCZbn6epBqxgfamfhA07DSu2ev41neBwc3J6o0q0Xi4zgykvTXK+fPP7GqXZvsP35HFx+PwrtQn+XlhbZIn2VVrRouC/S+XHJxwaZJU9K0WnJOHM+XkTMzyA0Pw6ZxY7IemG5mBtDExaPyLdCl9PZ6Zp9vFaDv820bBqJ5EoMuRe9Hsw4fx6ZuLTL3HrKY/5/gL4yp/6vIsrwX2FvkmNm3QciyPOzv0ClWAv436Qd8IctyecPSUFngPtAS/ez/OuCuLMuRxRXyPORE3ERVrgwqf19QqXDo3Jas0FNGMqqyBR25dfXKSFZW+QMAAMcuwWTutxwK9N22XfmbeQ/vP0aPV/WzZ3Xr1yI9PZMEMw/NfQb1oHnbpswct9BkNsnRyYGGzYI4cuCYWX2amzdQlglA4aO3yaZtO3JPG2/WSx46IP+T82coGevXkXvqONjYItkZHiZtbLFq0AiNBWcKkH3lFlbl/bEq4wNWKpxC2pBx2Dh0yaqcX/53m5ovIVmp0KakkbB2O/faDuFe+2FETV1F1plLJgMAgF2f/8zQTm8wtNMbHDtwgq799G9HqlW/BplpmSSaeYDoMTCEpm0bsfDNpUb15+PvzapPlrBk0koe34s0Kn9FyAwuHTxL0z6tAagYVAV1ehZp8Skm5d88FUH9EP3eh6Z923LpoH5Z29mrINymfL2XkCQFmcnp/LLmG+Y0G8e8lhPY+tb76OIfkHd+N7UCPHmUkMaTpHTyNFoOXLpPmxqmm1rTs3O5cD+W4JplTdIsDQ6KUqtSGR7FJBAZl0SeRsP+05dpU7+GkYyvpwtnIvThAomp6TyITiDA2x1fD1cu33mMOicXWZY5E3GXimVKFiv930b3+DYKTz8kN29QqlAFtkR7zTiEQ3Jyzf+uKFvFsPkhHTk5HkW5qvo9AYCycl10cZbdSy0/Fx4lZ/IkJYs8rY4DN6JoU9k0bCY9J48LkUkEm0krjm+2/UDf9kPo234Ih/Ydo+crBl/RoDYZ6RlmfUXf13rSIrgp08fON2rr0U9iaNqqIQAeXu5UeKkcjx8WbErf/uk3dGzVh46t+rBvzyFeGaAffNRvWJf0tHTizAyuKxTaQNmxSzB3but9Q5N6nWhctyON63bkt90HmDV1af4AAODAF3uZHjKZ6SGTOXfwNG366jdTVgmqSlZ6JilmHiAt4envyfSPZ7N+8vtE3y8aWmzMT5//wshOYxjZaQx/HjhBZ4PvqFmM7+g2MITGbRux+M1lz5zJ3/vFHiZ3ncjkrhM5feAUwX31G12rBlUjMz2L5Oewa3TLUYxuMZLRLUZycu8JPp63mTMHT/Pk0j3cK/riWtYLhZWSWj2acut34xCUW39cpF7fVgCUCapMTrqajLgU7N2dsDFMKqhsrKjUshYJd6IB/RuHWozrwbcj3+PMZ/vZEjKHNSEzuXzwHI0NPrBCUBWyLfjA26euEWjwgY37tuGKwQd6li9o8wG1KqK0UpGZnE5yVAIVgqpgZau/16zr10fz8CEAeTcMfZavvs+ybdeOnJPGfVbCoAEkDNR/ckJDSXt/HTknjiO5uCA5GPZCWVtj3aAhmkeWXzSRG3ETVVnjPl8detJIpmifj6HP18TEYV2nBpKtfjXWtnEQefdL9lILQekiVgL+NxkIrCpybBcwCFgAfID+XbJFOSJJ0tMdb5dlWX69xBq1OpJWbcBn80r9K0J/OUDe3Yc49esOQPoPv2HfvhWOPTqARosuO4f4GQVvNJFsbbBt2oCEZe+XSN2ff5ykVfvm7Dn9PdnqHOa/XVDWxh3vsWjKSuJjE5i3ZgbRkTF8+Zs+XOXQ3lA+XvsZAO1C2nAy9AzqLPOxyOi0ZGx8H5cV74JCQfbBvWgfPsC2m341wtI+AACFmxvOCw3npFSSc+QP8s6bxtPno9URt3QzAVuXgUJJ6q6D5N55hEt//exb6nd7cerUEueX2yNrNMg5uURPLnqJS87JQ6dp3q4J35/4ihx1DsumrM5Pe++Llayc/i4JsYnMWDWFmMgYtuzeCEDo3j/57P0vGDH5dZzdnJm24m396Wu0jAgpWJG8eiSM2sH1WRL6IbnqXL6Yvik/7c1ts/hq5sekxiXz86odjFz/Nj2mDuBxxH1O7tRvCAzq2pTWgzuh02rJy85l61vvF2uPSqlgVs8mjPvsD3SyjpcbVqGyjxvfn9GvRLzSpBoAhyMe0ayKP3ZFXsmpztVw+nY083o/e3FMpVQye2hPxq3Zhk4n06tNAyoH+LDzkH4T9avtmzC6Vzvmf/wDfWd9gIzM2/074+bkgJuTAx0b12bAvA0olQqql/enX/BzbL41w/SFqzgXdpmUlDTa9xrM+JFD6Nuj8/MXpNOR8/Mn2L2x0PCK0EPoYh+jaqovS3P6AKo6zVA16wI6LeTlkr1DP2ute3wb7ZVT2L/9HrJOh+7JPfJOW361pUqhYFaH2oz7Qf9K15frBFDZ04nvw/UPM68E6vdrHL4dQ7MKnthZG3dFs34N4/zjRFLUuXTafIhxLarQu675Nzod++MErds3Z9+ZXWSrs5k3aWl+2uYd61gwZTnxsQksWDOTqMgYvt7zKQB/7DnK5rVb+WjtZyz/cAE/Hd2BJEmsXbqRlEIrQYU5dPAY7Tu25lTYftRZ2Ux+c25+2lc7P2LqxPnExSbwweYVODk5IkkS167eZObUxcVeGnNcPHyBoOCGrD/2EbnqHDZOW5+fNnv7fD6asZHkuCS6DuvOy2N74+rlxrsHPiTsyAU+mrmBfpMG4OjmxBtL9W+d0Wp1zOox1ZK6fE4fOkOzdk345sSX5KizWTmlYMJhzRcrWD39PRJjE5m66m1iI2PZvFt/Xsf2Hufz9798ZvkXDp+nYXBDPvrzE3LUOayf9n5+2vzti9g480OSYpPoPrwHvcf2xc3LjQ8PrufC4fNsmLneYrmyVse+Bdt57YuZSEoF4TtDib/9hAav6cNQLuw4xO3D4VQODmTCsbXkqXPZPe1jABy9XXl57VgUCgWSQuLab2e4fVgfxtd1yVCU1lYM/kq/0hwZdocv5m3h2pEwagUHsSD0A3LVueyYvjn/XMZsm8U3Mz8mLS6Z3at2MGz9JLpN7U9kxANOG3xgYNcmNOrTGq1G7wO3T9DXw8PwO4TvO8OMPavQanTw8Drq3wyvOtVpSf/wfdzWGPqsfXvRPniAXQ99n6X+1XKfpfTwwHnWHP3GfoVE9tGj5J4+ZVEerY6k1evx3rhK3+fv3k/evYc49tX3+Rm7fsO+XSscuncEQ5+VMEvfJ+ZevUHWoWP47diMrNWSe/MO6T/usazrH6KEsfr/r5GeJxZP8GJROBzov03PmIzSUsXhen8t9vh5SXxoPlzlv8GwdAsDnf8CgValN7O9dm1gqeiRylYrFT0AyoqlFxqUs/rZD31/F8pqz15l+btoOLf412v+XSTlWA6h+btp6VJ6G8hjNaXnb92UxYff/V00kFyeLfQ3kSBpSk3X3Ioxzxb6m8hO/ev/Z/K8lL/4xz/+BP6wfodSe8b5N9hrDrESIBAIBAKBQCB4oRArAWJPgEAgEAgEAoFA8MIhVgIEAoFAIBAIBC8UIhperAQIBAKBQCAQCAQvHGIlQCAQCAQCgUDwQiH2BIiVAIFAIBAIBAKB4IVDDAIEAoFAIBAIBIIXDBEOJBAIBAKBQCB4oZBlEQ4kVgIEAoFAIBAIBIIXDLESILDI7wk+paarvLVNqek6dcmpVPTU8U0oFT0AjbNL71rlois1XXHLDpeKHrcGR0tFD4DC1aHUdNnMfK/UdCX1G1FquprblSsVPbJd6b1DUEnpzUo6WbmVmq7Smmmsm1161+qgbenpOnwzoNR02ehKz67ypabJMnLpdWX/WsRKgEAgEAgEAoFA8IIhVgIEAoFAIBAIBC8UOrEnQKwECAQCgUAgEAgELxpiJUAgEAgEAoFA8EIh3g4kVgIEAoFAIBAIBIIXDrESIBAIBAKBQCB4oZB1YiVArAQIBAKBQCAQCAQvGGIlQCAQCAQCgUDwQiGX3t8i/GsRg4D/ISRJmgsMArSADhgDrAb8gGwgF3hDluVwM3m1wBVAMuSfIMvyyefRH9C2Ls0WD0FSKrj5zVEubfzVKN3lJT/arB2NZ+0KnFvzPVc+3puf1vrdNyjXIRB1Qhq7Oswukb7Ri0fTILghOeocPpj6Pnev3jWR6Ta0Oz1H9sS/gj+v1RtEWnIaALWb1mHep/OIfRwLwKn9J/n2g29N8nsF16P20teRlAoe7TjCnQ27jdIdK/tT7/0xuNSpyI1V33Fv8578tIqjulBucDskSeLhV4e5/8m+Yu2xa94Q9xnjQaEg46d9pG77zji9bTPcxg8DWUbWaEl6ZxM54REAOA/ug2PvriDL5N5+QOLCd5Bz84rV12fhUGoGB5GnzmHHtM1ERjwwkXEP8GLohkk4uDjwOOIBX03egDZPS+2ODeg25VV0soxOo+WnJV9w7/xNo7yvLhxOreAgctU5fDFtE48j7puU7xHgxcgNb+Pg4sijiPtsn7webZ6WKk1rMm7LDBIi4wAI33+GvR/uws3Pg6Fr38TZyxVZJ2P7y27Sv/kJANvmjXCfNh6U+vpL2258Pe3aNMd1/DDQ6ZC1WpLf3UxO+FUAnF7ri2Mvff3l3blPwqJ3oJj6U9ZqiO2AcUgKBbl/7id3v/G1Ulati/2bi9ElxgCQd/E4ub/tAJUVDjPeA5UVKJVoLvxJzu4vi71OympB2PQcCQoFeWf/IO/Ij8bplWphO2w2umR9XWmunCbvj50AWLXqgapxBwB00Q/J2bkeNMW3C0vMW7GWYyfO4u7mys9fffSXyiiMdePGOE2YAEol6j17yPr6a7NyqmrVcN+0idQlS8gJDQVra9w/+ACsrJCUSrJDQ8ncvt0k34CFw6kTXJ9cdQ7bpm3kkZn25xngzRuF2t/WyevR5mkAqNq0JgMWDEepUpKenM67/ReisrFixndLUNmoUCqVnN93it3rdhqVOXDhCOoEB5GrzuWzaRss6h29YbJB7z0+NejtPLonTXq1AkCpVOJXuQyT648kMzUjP2//hcOpbbBr+7SNFu4rvV32Lo48jrjPZ4byqzatyfgtM/Pvq7D9Z9jz4Q8AtB/ZjZb92yPLMpE3H7F1+kZenTUk35atxdgy1mDLw4h7fGLQ1WV0T5oabFEolfhXLsMkgy12zvYMXzWOMtXKgUFf+VoV/1KdAVRrWov+C4ahVKnISE7jnf4LAeg4sjst+7fX+8xrkVx8+2N0Ofr27x1clzoG3/5wxxFubzDurxwr+1P//TG41KnA9VU7uVPIt780uivlXwsGWSbt+mOjcgvzisEH5j3DB44wtMHHhXzgU8rXfYnpPy1n64R1hO07A0Dw8K60GNAeJImYr45y89P9+fJ+betSf+kQJIWCu98c5XoRu5wq+9F07Rjc6lTg8uqd3PiooB+2cran8btv4Fo9AFmWOTNlC4kX7pic81N8gutS11CHD3Yc4ZaZOmzw/hhc61Tg2qqd3C5Uh5VHd6XCa8HIhjq8YKEOBf8sYhDwP4IkSc2A7kB9WZZzJEnyBKwNya/JsnxekqThwDtARzNFqGVZDjSU1RlYCbQpsX6FRItlQ9k7aBWZ0Un02rOEhwcvkHI7Kl8mJyWTkwu+pELnBib5b31/jIjtv9P2/TEl0tcguCH+FfwZ03o01YKqMW75eKa9PNVE7vr5a5w7dJYV3600Sbt2LoIlw5dYVqKQqLNyOKdfXYE6OpFW+5cTc/ACGbee5IvkpmRwdd7n+HVpaJTVqXoA5Qa343jXeehyNTT5ZhZxf4SReT/Ggi4F7rPfInbsTDSxCfjv2EBW6Cny7j3KF8k+E0bU0VMAWFWpiPeaeTzpPRKltwdOA3sR1WcUck4uXmvm4dAlmIzdBy2aVrNtIF4V/VjW9m3KB1XmleWjWNdrnolcz1mDOLp1D2G/nuLV5SNp2r8dJ776nVsnrnL19wsA+Fcvx7CNk1jRvqD+a7UNwruiLwvbTqRiUBUGLh/Fml5zTcrvPWswh7fu4fyvJxm4/A1a9G/Hsa9+B+DOuetsGrnaSF6r0bJr2Zc8jriPjYMt7+xdQfbpC+Q9jMR95lvEjZ+JJjYev682og49Sd79QvV39iLRoSfz689r1Xyi+o5A6eWB84BeRPUbiZyTi+eq+Th0DibzVwv1JymwGzSBzHWzkJMTcJi7Hs2lU+iiHxmJae5cQb1+gXFeTR6Z782AnGxQKnGYsQ7N1XNo792wqMum92jUWxYhpyZiN3ENmoizyHGRxvVy/zrZ25YbZ3V2x6plN7LemQiaXGwGT0MV2BLN+SPmdT2DXiEdGdS3J3OWvvuX8huhUOA0aRIp06ahjY/H/aOPyDlxAu3Dh6ZyY8aQe+5cwbHcXJKnTEFWq0GpxH39enLPniXv2rV8kdptg/Cu6Mfctm9RKagKry1/g5W95picRt9Zr/HH1t849+tJBi9/g5b92xH61UHsnO15bekbfDB0OUlRCTh5OAOgycnjvUGLycnKRqlSMuOHpVw9Gsa9sNsA1DHonWPQO3j5aFb0Mp3U6DtrML9v/Y1zv55g8PLRtOrfjqNfHeTAlt0c2KKfaKjXvgEdRnY3GgA8tWt+27eoaLBrlRm7+hjsOv/rSQbl31f69nz73HU2jlxlJO/q4067YSEs6jCZvJxcRm2YTN/pg/Cp6Mdsgy2vLx/NMjO2vDJrMAe3/sbZX08wpJAt+7fsZn8hWzoVsmXQwhFcCQ1n0/j3COrQkPbDuv7lOtNfq1G8X+Ra6W3qygKDTYs/nEZAr2Y8+u4YKCTqrRzOiVdXoo5OpO3+ZcQcvEh6Ed9+2Yxvt/V1o9KozhxqPR1ddh6NtkwsKLcQT33gorYTqRBUhQHLR/GOGR/Yy+ADLxh8YPP+7fjT4AMlhUSvWa9x7Vh4vrxf1bK0GNCe1S/PQZunYd72eTw5FEbG/VgkhUSDFcM4MmAl6ugkOu1dypMDF0m7Xciu5EwuzP+CgC6m/XCDJUOIPnqJE6M/QGGlRGlnYyKTj6EOjxvqMHj/MqKL1GFeMXX40qjO/G6ow8YW6vCfRuwJEHsC/pfwAxJkWc4BkGU5QZblqCIyp4AyJSjLGUh+HuVegS+R9iCW9Efx6PK03P3lNOU7GTuZ7MQ0Ei7dQ6fRmuSPOXOTnJQMk+OWaNqpCYd3HQbgZthNHJwdcPM2/av7exH3iDPMej0vbkGVybwfQ9ajOOQ8LVE/n8K3s7Ezy01IIzXc1CbHKmVIvnAbrToXWasj8dR1fEMaWdRlU7samsdRaJ7EgEZD5oGj2LdtbiQjq7PzvyvsbI2WKiWlEsnGBpQKJFsbNPGJxdpWu1NDzv2od7gPw+5g52SPs5eriVyV5rW4tFc/+3R21zHqdNLbn5uVky9jbW9jsmxar1NDThvKvx92G3snB7PlV2tei4t7TwNwetdR6nWyXEcAafEp+bNpOZnZ5N1/hNLbE+va1dBERqF5Ep1ff3ZtWxjlLVx/kp0tUOikC9efnQ3aYupPWbEauvgo5IQY0GrIOxeKKrC5RXkTcgznoVSBUml0GkVRlKuCLiEaOSkWtBo04cdR1Wpccl0KJVhZg0KBZGWDnJZU8rxFaBhYBxdnp7+cvzBW1aujffIEbbT+emUfPoxNixYmcvZ9+pB97Bi6lBSj47Jarf+iUoFKhVykAQZ2asTpH0MBuGdofy5m219tLhja38ldoQQZ2l+Tni0J23+GpKgEANIT0/Lz5GTpr59SpUSpUhq1/cBOjTj149FCeu3N6q3evDYX9p4y6D1KYCfTa9q4Z0vO7j5hdKxeIbvuh93GzsJ9Vb157UL3VSiBz7ivABRKBVa21iiUCqztbPCt5M/JEtpyvpAt9c3Y0qRnS84YbLF1tKNq4xr8+d0hAOq2q8/xnYdLpMdcnTXp2YqLFq6VUqnE2mCT0s4adYy+W3MLqkzG/dh83x758yl8i0xO5SakkRJ+D9lMfyUplShtrZGKlFuYup0acsbgAx88wweGWfCBbYd1JWzfGSObfCuX4X7YbfKyc9FpdcSduk7Zrvo87kEvkfEglkxDP/zol9MEFLErJzGNJDP9sMrRDq+m1bn39VEAdHla8tKyTM73Ke5BlcksUod+RXUlpJFcwjrMNlOHgn8esRLwv8NBYIEkSbeAP4DvZFkOLSLTBfjZQn47SZLCAVv0A4p2z6Pcwc+NjOiCB4zMmCS8g156niKeCw9fDxKiE/J/J8Yk4uHrQXJcyR1JtfrV+XD/epJiE/ls+Wc8umU8k2vr54Y6quBhMDs6Edf6lUtUdvqNx1Sf1R8rN0d02bl4tw8k9ZLpUvBTlN6eaGLi839rYhOwqVPdRM4+uAVuE0egcHcl7i39zL02LpHUL34gYP8O5Owc1KcvkH3qQrHn5+rjTkoh21JjknDxdSctPiX/mIObE+q0LHRaHQAp0Um4+rjnp9ft3IjuMwbg6OHClhHGM/auPu4kRxVcn+SYRFzNlJ9VTPkV61dl7r41pMYms2v5l0TfNp79dg/wwrpaZXKu3sCuaQM0MQWDPW1cPNa1TevPLrgFbhNG6utvkn5WThufSNqX31Nm79fIOTlkn7pA9mnL9Se5eqJLKrhWcnI8yoqmupSVauKwYDNyShLZP2xBF2WY5ZYUOMzfiMLLn9yju9Het7AKgH42X04pqEc5NRFFuaqmuspXw27yWuS0ZHJ/244u9jFyWhJ5ob/gMHcL5OWiuRWO9tYli7pKE4WXF7r4gjrUxcdjVbOmsYynJzYtW5I8ZQpW1YvUr0KB+5YtKMuUQf3TT2iuXzdKdvNxJ6lQ+37a/lILtT/HIu07OToxv/35VPJHqVIy7dtF2DrYcWjbHk4ZHugkhYL5v63Gq7wvR77cz/3w2/lluvp4FNGbhKuvhxm9mUZ63Qq1ewBrW2tqtwnk6wVbjY67FrErJSYRt2fcV4XtAqhUvyrz9r1DamwyPyz/gujbkaTEJvH7J7+y8uRm8rJzufrnJeyd7I10JcUk4WbGlqxCtiQV0VXYlh0GW7zK+ZCemMaId9+kbI0KOLo6cn7Pqb9cZz6V/FCqVEz/djG2Drb8sW0vp34MJSU2iQOf7Ga1waaUo1eJD70CgJ2Jb0/CrYS+PTsmmTub99D5wnq02bnEHb2SX25h/lMf6OLjRmDnxrw/cDHl643LzxN98zE9pw3AwdWR3Oxc/NsFknT5HgD2vu5kFbIrKzoJj/ol64cdy3uTk5hOk3VjcKtVjqTL97kw/0u06hyz8kX7R3V0Eu7PUYe3N++hq6EOY49eIc5MHf7TiH8MFisB/zPIspwBNABGA/HAd5IkDTMk75AkKRKYCay3UIRaluVAWZarox8sfCFJ0nPcAWZE/6ubakz1FZ0NLI67V+8wstkIJnZ5i1+3/8bcT0xDYTBnfglVZNyO4s6G3TT7bg5Nvp5FWsQjs7MhxesyVZZ15ARPeo8kbvIifXw7oHByxL5tMyK7DeFxpwEo7GxxCGlf/AmaVSc/l8zlA+dY0X4qW0e/S8iUV5/bnuJEHl+9z7wW41nedQZHtu9n7JbpRnI29jaM2TyVpPc2IWdmlfhaqY+cIKrvCOKnLsR13HDgaf0150n3wUR27o/0rPoze1cYK9M+ukPGrMFkLhlH7uGfsRu/qJCojswl40ifMQhlhWoo/CsUo+vZ9ah9co/MFaNRr5tC3ok92A6dpU+wc0BZqzGZK8eSuXQkkrUtqvoljvArfYrY5TRhAhlbtoBOZyqr05E0ahQJr7yCVY0aKCtWNE43U28mt1MxMgqlkvJ1KvHh8JW8//oyur3VD5+KfnoZnY4lIdOZ0WwMFetVxr9q2eKKNFVsVq+xTL0ODblz/qZRKJA+67PtMuu2DTKPrt5nTovxLOs6nSPb9zFuywwA7J0dqNexEXNbvcmMJqOxsbfBzdfdtBiTe/jZ7bOoLUqlkvK1K3H0q4Ms7jYdWSfTtFfrZxll8VyUhmv1wfAVrHt9Gd0N18re2YHAjo2Y1epNpjUZjcrehoC+LSyWV9JdoFYuDvh1acDBxpPYX+9N43KNTvnZ17m403hlwTB+WrUDWWecJ+buE37/6Bfe+moeEz6fQ/K1R+g0hnukJO3PAgqlArc6FbjzxR/s7zQXTVYONSf0sChfkmtviad1uL/xJPYa6rCsmToU/POIlYD/IWRZ1gJHgaOSJF0BhhqSXgMuAauAjUAfSZK2AUFAlCzLIUXKOWXYU+AFGMXSSJI0Gv1Ag8GujWntUAWAzOgkHP0KOg0HX3cy/+blvZDXu9F5YGcAbl++jaefZ36ah68HSbElD3VQZ6jzv184ch7lsnE4uznnbxwGyI5Kws7fI/+3rZ/Hcy1ZPv7mKI+/OQpA9dn9UUdbPj9tbDwqX6/83yofz2JDUnIuXkFV1g+FqzO2jQLRPIlBl5wKQOah49gE1iRz7yGjPC2HdKLZQP0Cz6NLd3EtZJuLrztpsca2ZSalY+dsj0KpQKfV4ernTqqZlZa7Z2/gWd6H9mN6UP/lFsjAw0t3cfP3BPSbhd18PUgpUn5GUjr2JuXr6yi70PWJOBqGctlIHNycyExOR6FSMvqjqZz9+U8aHT4OgCYuHpWvd34epbfXM+vPKsBQfw0N9Zeir7+sw8exqVvLpP6eIicnoHAvuFaSmxe6lCLXNrtgGV1z9Ry2SiWSozNyRkH7Qp2J5tZlVLUbkhv1wLyu1EQk14J2Lrl4mIb05BTUlfbGReg9BuydUFauow8jykwznMdplOWroblYdIGw9NHFx6PwKqhDhZcX2oQEIxmratVwWaDfUyG5uGDTpAlpWi05x4/ny8gZGeSGh2PTuDFyvXrYde8OwM1zD3Av1L7dfD1ILeIfMpLSjNq3m59HfvtLjkkkIzmNXHUOueocbp+9TkCN8sTej87Pr07L4ubpCHpNGYBnOX3be3DpbhG97qSY1etgpDelyH3VqEcLzuzW2xk8pAutBrZHQuLBpTu4+3vw9BUIrr4eZsu3L2JXipn76urRMAYuG4WDmxPVmtUi4XEcDbs1o+XADti7OJCTlW1ki7sZW9KT0rAvZIu7GVuaFLIFICkmEXV6Fq+vGA3Ak5uPKFuz/F+uM/21Ss+/VrfOXiOgRgUAEh7HkZGkb/9Re8/h3qgqkbtOoDbx7e5mQ3rM4dW6NlmP4shNTDcpt+LwjlR4LZgGCvM+MPU5fGC5ui8xcv0kABzcnKndNgidVselg+c4ufMIJ3fq9/a8OeU1sgx9S1Z0EvaF7LL3c0cdk1Iiu7Kik8iKTiIxTN+6Hv92lhrFDAKK1qHdc9Sht5k69GhUlce7Tjwjp6C0ESsB/yNIklRNkqQqhQ4FAvm77GRZzgPmAU0lSaohy/Jww8x/SJGikCSpOqAETJ6iZFneIstyQ1mWGz4dAADEX7qHc0VfnMp6obBS8tLLTXn0+8W/z0Bg7xd7mNR1IpO6TuT0gVO066t/oK0WVI2s9KznCgVyLRSbWaVeVRQKyWgAAJASfheHSr7YlfNCslLi36sZMQeLD7MpjLWnfoOaXRkP/EIaEfWT5Zct5UTcRFWuDCp/X1CpcOjclqzQU0YyqrL+BWVXr4xkZYUuJQ1NdBw2dWsg2eo3cdk1CTLaUPyU418e5J2QWbwTMosrB8/TqI9+9q18UGWy07OMlqmfcvvUNeqFNAGgcd/WXD14HgDP8j75MgG1KqC0UnHo4195J2QWK0JmcOngWZoayq8YVAW1hfJvnoqgfkhTAJr2bcslQ/nOXi75MuXrvYQkKchM1ncYQ1aPJebOEw5tLXjTRG7ETVRljetPHWpc30Xrj6f1FxOHdZ2C+rNtHGS0obgo2gc3UXiXQfL0BaUKq0Zt0FwyvlaSc8H+FEWFaiApkDPSkBxdwM5Bn2BljapGELqYxxZ16R7fRuHph+TmDUoVqsCWaK+dM5KRnFwLdJWtop9ezEpHTo7Xhw5Z6d8PoKxcF12RDcX/FHk3b6IMCEDhq79etu3akXPS+HolDBxIwoABJAwYQE5oKGnvv0/O8eNILi5Ijo56IWtrrBs0QPPoEeqffyZp1CiSRo0i/OA5mvbRr3pUMrS/VAvtr4Gh/TXv24bwg/q6DT94jiqNaujj422tqRhYmeg7T3B0d8bO2R4AKxtrarSoy/Gdh1kSMp0lIdMJO3iWZn3allBvM4Petvl6Aeyc7KnWpCbhv+uPHflyP0tCprMsZLqRXSW/r9pwyVB+4Zj0CvUqozDcV0lRCVQKqsKJ74+wLGQ6N05HcPPMdZoXsiXLgi03TkXQsJAtYUVsqdqkJmG/FxxLi08h6nYkH01Yx6KQ6agz1Ng5OfzlOit6rSoFViH6TqTBpqpY2+rbv1erWmQYNsimhN/FsZIv9gbfHvAcvl0dmYBbgyoo7UzLvb/td450mMPKkBlcPniWJgYfWKGYa3XrVARBhXzgZYMPXNBqAvNb6j9h+07z7fxP86+jo2Hzs5u/B2VDGvHwZ/29kxR+D6eKvjgY+uFyLzclsoR2ZcenkhWViNNL+hUvn1a1jDYUFyXZTB1Gl1BXVmQC7kXqsDhd/xSyLJXa59+KWAn438ERWC9JkiugAe6gn7H/4amALMtqSZLeA6YBI4vkf7onAPSLikMNKwslQtbqODn/c7rumIGkUHDzu1CSbz2hxmD9g/r1rw5j5+VCr71LsXa0Q9bpqD2qCz8EzyQvQ03whjfxb1YDW3dHBp77kIvv7eLmt5ZnLM8fPk/D4IZs+fMT/StCp72fn7Zw+yLWz/yQpNgkegzvQZ+xfXHzcuPDg+u5cPg862eup0VIS0KGdEWr0ZGTncOaCWvM2nR1znaafjMbSang8TdHybgZSfnX9a9cfPjFH9h4udDqwHJUTnagk6n0RleOtp6OJkNNw08nY+3uiC5Py5XZ28hLzbRcgVodSas24LN5pf4Vob8cIO/uQ5z66Wc203/4Dfv2rXDs0QE0WnTZOcTPWAZA7tUbZP3xJ/7fbELWasm9cZf0XXst6wKuHQmjZnAg80M/IFedw9fTC175OGbbTL6ZuYW0uGR+XfU1Q9dPpNvU/kRGPOCUYfapXtcmNOrTCq1GS152Lp9P+MCo/KtHwqgdXJ8loR+Sq87li+mb8tPe3DaLr2Z+TGpcMj+v2sHI9W/TY+oAHkfc56Rhg2BQ16a0HtwJnVZf/ta39Nf3pYbVaNq3DZHXHzJn7xp8lBqSN3xG9omzJK1ej/fGVfr6272fvHsPceyrr7+MXb9h364VDt07gkaDnJNLwqxC9XfoGH47Nuvr7+Yd0n/cg0V0OrK/3oD92yuQJAW5Jw6gi3qIVZtuAOSF7kHVoBXWbbuDVoucl4v6kxUASC7u2I+YDgoFSAryzoeiuXymWF05P3+C3RsLDa8IPYQu9jGqpvoVMc3pA6jqNEPVrAvotJCXS/aO9/RZH99Ge+UU9m+/h6zToXtyj7zTlt8Y9SymL1zFubDLpKSk0b7XYMaPHELfHp3/WmFaLekffIDbO++AQkH2vn1oHzzArmdPANS7d1vMqvTwwHn2bP1mZ4WC7CNHyD1lPAi7cuQidYKDWB66nlx1Ltunb8xPm7htNp/P/IjUuGR2rfqK0esn02vqQB5F3M/foBpz9wlXQ8NZuF9fd39+d4ioW48pU70cI96bgEKhQFJInNtzksuHLxTRW58VoRv0ryYt1O4nbZvD9pmbSY1L5odVXzJm/WR6Tx3Ao4gHHN9ZsOoU1LkxEX9eJtdMLPZVg13LDHZ9XsiuCdtm86XBrh9XfcWo9ZN5eepAHkfc54TBrvpdm9JmcCe0hvvqk7fWAfAg/A4X951m3p41aDVaHkbc55slnzFg3jBWGWz5rJAtbxtsSTFjy5+FbKlvwZYdi7Yy+v1JKK1UJDyO5cbJK3+5zqLvPuFqaBiL9r+HrJPzrxXAhX2nmL/nHXQaLbrLD3nwpb4eZK2Oy3O20/ybWfpXhH5zlPSbT6jwuj4M8MEXh7DxcqHtgWX5vv2lN7pwqPUMksPuEvXbGdoeXIGs1ZJ65UF+ucbXKoxawfVZbPCBXxaya/y2Weww+MCfCvnAyEI+sDhGb56Kg5sTWo2G83O2k5ealW/X+bnbafv1TCSlgnvfhpJ26wmVh+jtuvPlIWy9XOi8bxlWTvp+uNqoruxpOwNNhpoL876g2YbxKK1UZDyK4/Tkjy2eg6zVET5nOy2K1GFFQx3eN9RhO0MdyjqZym904XdDHT757QztDq5AV0wdCv55pOeJsxa8WHwSMLjUGsduRUppqWJ07t/zBpRnUcc34dlCfxPrUjyeLfQ3kYuZGO7/ErPc//rbbp4HtwaltyiqcHUoNV02M98rNV1J/UaUmq55D7yeLfQ3IP93Nz4ZoTS/GeW/Ql4p2lVad1b3bOtnC/1NHLTVlJquVrlWpabLRld67aJPzNf/+PT45Qo9Ss3gug9+/cftNYcIBxIIBAKBQCAQCF4wRDiQQCAQCAQCgeCFQrwiVKwECAQCgUAgEAgELxxiJUAgEAgEAoFA8ELxb35rT2khVgIEAoFAIBAIBIIXDLESIBAIBAKBQCB4oRAvxxQrAQKBQCAQCAQCwQuHWAkQCAQCgUAgELxQiLcDiZUAgUAgEAgEAoHghUOsBAgsUj6v9P4VMd0qu9R01fHNebbQ30BGmk2p6AG4p8soNV0VFaX3j7c+H40pFT1y3ONS0QNATGSpqSrNf/F1/+GzUtOlaTi9VPQk6UrHVwBUUZTOP5kDRMml5299pNLxg3/YaktFD0DjvNL7d+LqqvRS05VViv9O/G9AvB1IrAQIBAKBQCAQCAQvHGIlQCAQCAQCgUDwQiH2BIiVAIFAIBAIBAKB4IVDrAQIBAKBQCAQCF4oxN8EiJUAgUAgEAgEAoHghUMMAgQCgUAgEAgEghcMEQ4kEAgEAoFAIHihEBuDxUqAQCAQCAQCgUDwwiFWAgQCgUAgEAgELxTiz8LEIOAfQ5KkucAgQAvogDHAamAacBMIB7rIsnxbkiQr4CIwSpblM5IkaYErhYr7VpblVZIkHQX8gGwgF3hDluVwg74RwGT0G+IVwFxZln95nnP2CK5H9WVDkZQKIncc5sH63Ubpvn1bUHFCTwC0mTlcm/EpGdceYePvQZ0N47H2cgWdjsivDvPok33Po5q3lrxJ03aNyVbnsGryGm5fvWMiM3f9bKrVrYo2T8P18Ju8N2sdWk3x/yJp17wh7jPGg0JBxk/7SN32nXF622a4jR8Gsoys0ZL0ziZywiMAcB7cB8feXUGWyb39gMSF7yDn5lnU5di6Pn4LRoNCQfLOgyR89INRulOHJvhMGYysk0GrJXrpJ2Sdv4aVnydl3p2CyssNdDqSvz1A4vbdFrQUMHrxaBoENyRHncMHU9/n7tW7JjLdhnan58ie+Ffw57V6g0hLTgOgdtM6zPt0HrGPYwE4tf8k337wrVHevguHUTM4iFx1DjumbSYy4r5J+e4BXgzbMAl7F0ciI+7z5eQNaPO01OnYkJApryLLMjqNlh+XfM698zfz80kKiem/rsSqjA15t04AcOLyLVZ/uRedTkfvtg0Y2aONka70rGzmbP6emMRUNDodQ0Na0Kt1Ax5ExzNjQ8F1jYxLZnzf9gzu0txi3Z248Yg1P59Ep5Pp3aQ6I9oHGaVvPxLO3ov6NqjV6bgfm8KRJa/jYm/LjmNX+PHMdWQZ+jStzuDWdS3qAThxP441h66hk2V61y3LiCaVjXWdvcvea1F6XbKO+4kZHHmzIy521izcd4lj9+Jwt7dm1/A25oo3wrpxY5wmTAClEvWePWR9/bVZOVW1arhv2kTqkiXkhIaCtTXuH3wAVlZISiXZoaFkbt/+TH2WmLdiLcdOnMXdzZWfv/roL5fzlEELR1A3uD656ly2TlvPQzNt0TPAm7EbJuPo4sTDiHtsmfwh2jwNdk72jF43CfcyniiVSvZ/8gvHvz9iUdcbRe6re2buqxDDfeVXwZ/B9QaRbrivnlK5bhXW/PIu7765hpN7TxilvbxwKDWCA8lV5/LdtM08iXhgUr57gBeDN0zEzsWBJxEP+GbyRrR5Wrxe8qf/O2MIqFWRfe9+R+gnewBw8XNn4NrxOHm5IutkDn59gP3bfrNo4+uLRhIY3IBcdQ4fTVvPg6v3TGQ6De1KlxE98K3gx5jA10lPtvzPtr0XDqVGcBB56hy+mbaZSAs2vb5hEvYuDkRGPGCHwVfUf7kF7cfq+5WcrBx+mPcpUdcfATBgzRhqtqtPRmIayztPA6DfwmHUMvilLy34JY8AL4Yb/NLjiPt8YdD1lHJ1X2LaT8v4bML7hO87k39cUkjM+HUlRKdwaOh7+cfLtK1L4yVDkBQKbn9zlCsbfzXS5/KSHy3WjcajdgUurv6eiI/3AqC0saLLrnkobVRISiUP95wl/L0fLdYjgFObIAIWvYGkVJD47e/EbtpllO7Wqw0+4/oAoM3MJnLuZtTX9fXtNaI7HgM7gSSR+M1B4rf+WrR4I1yDA6m0dDgoFcTuOMSTDT8bpXv1aUWZCb3ydd2duYWsaw8BaHBuE9oMNbJWB1odlzrPLFaX4J9BhAP9A0iS1AzoDtSXZbku0AF4/DRdluV0YDaw0XBoGnBSluWn3kgty3Jgoc+qQsW/JstyPWAT8I5BXwAwF2hp0NcUuPxcJ62QqLFqBBcHreJEq6n49W6BQ9UyRiLqh/Gc67WEU8Ezubf2R2q9N1pvj0bLzYVfcrLVVM6EzKfs8E4meYujSbvGBFQsw2sth/LezHVMXjnJrNwfPx3i9TbDGd7hDWxsrek2MOQZNilwn/0WsW/O4UmfUTh0CcaqUjkjkewzYUS9Ooao/mNJWPQungunAKD09sBpYC+iB71JVL/RSEoFDl2Ci9Xlv3gcD4Yv5E7n8bj0aINN5bJGIpknL3En5C3udp9I5MwPKLPyLUBffzErtnKn0zju9Z2G+5BuJnmL0iC4If4V/BnTejQbZ21g3PLxZuWun7/G/EEFD/uFuXYugkldJzKp60STAUDNtoF4VfRladtJfDfnE15dPtJs+S/Peo2jW/eyLPhtslIzada/HQA3T1xhddcZrAmZydczPmLg6jFG+doODyHmzpP831qdjhWf/8qm6a/z0+qJ7D91hbtP4ozyfPfHaSqV8eb7FRPYOmck7329nzyNhgp+XuxcPoGdyyfwzdLx2NpY0a5hDYt1p9XpWPnjCTa+EcKPM15lf9gd7sYkG8kMCw5k59R+7Jzaj4khjWnwkh8u9rbciU7ixzPX+WpSb3ZO7cef1x7xMD61GF0yK3+PYGO/xvw4og37r0dxN8H4YWpY45fYOawVO4e1YmKr6jQo64GLnTUAPWsHsKlfY4vlG6FQ4DRpEikzZ5I4dCi27dqhLF/evNyYMeSeO1dwLDeX5ClTSBo1isRRo7Bp3BirmjVLptcMvUI68tHaZX85f2Hqtq2PT0U/ZrWdwPY5mxmyfLRZuVdmDeHg1t+YFTyBzNQMWvdvD0C7IV2IuvOYhV2nsnrAAvrPHYrSyvz8WIPghvhV8GdsCe6rBRbuK4VCwdDZwwgLDTNJq264r1a1ncwPcz6hr4X7qtusQRzbupfVwVNQp2bSuL/e96hTMvhl0ecc/cT4AV+n0fHrsq94p8M01veeT8fXu1KmSoDZsgOD6+Nb0Z8pbcbz6ezNjFg2xqzczfM3WPHaQuIfx5lNf0qNtoF4VfRjRdu32TnnE/otH2VWrsesQYRu3cOK4MmoUzNoYvAVSY/j2dB/Ce90ncnB9T/y6sqC63v2h1C2DF2Z//upX1rcdhLfzPmEAcX4pSNb97Ik+G3UhfwS6B/0X541iOvHLpnkCx4eQmwhv/RUvsnyofw+eA0/B8+gYq+muFTxN5LJScnkzPwvuWp4+H+KNiePA6+uYHfHuezuNJcybeviVf8ls+cMgEJB2WVjuDt0MdfbT8CtZytsqxj3BbmPY7n96hxudJ5E7IffUXbVmwDYVi2Hx8BO3OwxjRudJ+HSvhE2FfyK1VVp5SgiBi0nrPVkvHq3xK6qcZvJfhTHld4LCG83lcfrfqDyu2ON0q/2XcSlDtP/tQMAXSl+/q2IQcA/gx+QIMtyDoAsywmyLEcVFpBleSegkyRpBjAW/aDgeTgFPH3S9gbSgQxD2RmyLJtOjxSDS/3KZN2PQf0wDjlPS8zPJ/Hu0tBIJvX8LTSpmQCkXLiNjZ87ALlxKaRfeQDoZwsybz/Bxte9xLpbdGrOgR9+B+Daxes4Ojvi7m2a/8zhs/nfr4ffxMvPs9hybWpXQ/M4Cs2TGNBoyDxwFPu2xrPDsjo7/7vCzha50IuFJaUSycYGlAokWxs08YkWddnVq0rOw2jyHsci52lI/e0YTh2bGsnosszr0sQnkx2hn23UZarJufMYla9HsbY17dSEw7sOA3Az7CYOzg64ebuZyN2LuEdcZPGduDnqdGrE2R+PAfAg7DZ2Tg44e7mayFVpXovwvacBOLsrlDqdGgGQm5WTL2Ntb2NUr66+7tRsF8Spbw/nH7t6N5KyPh4EeLtjpVLRpWkdjl64bqRLQiIrOwdZlsnKzsHFwQ6lwtjFnYm4S1lvd/w9TesiX9ejOMp6OBPg4YyVSknnoMocNTNz+ZR9YXfpEqSfvb8Xl0zdcj7YWVuhUipo8JIfh69YvtWuRqdQ1s2eAFd7rJQKOlf35+gd0wfHfF03ouhSveABo0FZD5xtrSzKF8aqenW0T56gjY4GjYbsw4exadHCRM6+Tx+yjx1Dl5JidFxWq/VfVCpQqZDlv/6W7YaBdXBxdvrL+QsT1KkRJ38MBeBe2G3snRxwMdMWazSvzfm9pwA4seso9Ts9HTzJ2DrYAWBjb0tmSgY6CyuIjTs14YjhvrpVzH11v5j7qtvw7pzad5LUxBSTtFqdGnD+xz8BeBR2B1sne5zM2FK5eS0u79XPCZ3fdYzanfS+OCMxjceX75mcf3p8Sv6KQk5mNk/uROLmY96HNOjYmD936VdC7oTdwt7ZAVczNj6MuE9CZLzZMgpTu1NDzhl8xcOwO9g52Zv1FZWb1+KSwaazu45Rx2DTg4u3UKfp+5WHF2/jUqjvuHf2BpmGPgegbgn9UtXmtQgz+KUzu0KpZ/BLAG2GdeXSvjOkJxoP3l193anVLoiThfwSgGfQS6Q/iCXjUTy6PC33fzlNuc4NjGSyE9NIvHQPOc+0XWkMvlChUqKwUlHcbWUfWIWcBzHkPtL3I8m//olLJ+NJgMwLN9Aa6iQz7CZWfvrrbFslgMyLt5Czc0GrI/30VVy6NDXR8RSnoMpk348h51Eccp6G+J9P4N65kZFM+vmb+brSL9zC2q/k/brg34EYBPwzHATKSpJ0S5KkTZIkWVrHfxt9iNAyWZaTCh23kyQpvNCnv5m8XYCfDd8vAbHAfUmStkmS1ON5T9jW153sqIKH3OyopGIf5MsMCibhcLhpOWW9cKpdgdSLpuE8lvDy9SQ+qqCziY+Ox8vX8gO+UqWkU98OnD16zqIMgNLbE01MQbma2ASU3qbl2ge3oMxPW/Fev4zERe8CoI1LJPWLHwjYv4Oyv3+HLiOT7FMXLOqy8vUgL7qQrugErMx0wk6dmlHl982U37qQJzM/MC2njDe2tSqhDr9pklYYD18PEqIT8n8nxiTi8YyBQ1Gq1a/Oh/vXs+jzRZSrarxC4uLjRkqh9pASk2jUOQM4uDmhTstCp9XPg6REJ+HiUyBTt3Mj5h5ay5jPZvH1jM35x/ssGMrulTuMHjLjktPwdXfJ/+3t7kxskRCLAR2bci8qng5vrabfnA3MGNINRZFBwP7TV+jSrPjwnLjULHxdHfN/+7g4EFfoQaMw6tw8Tt54TIe6FQGo7OvOhXvRpGRmo87N4/j1R8SmZFjWlZGNr5NdgS4nW+Iyss3KqvO0nLwfT4eqvsWevyUUXl7o4gvaoC4+HqWXl7GMpyc2LVui3m0m3EyhwP3TT/H6+Wdyz59Hc/26qcw/gKuPO0lRBW09OSYRtyJt3dHNiay0zPy2mBydiKuhLR76fB9+lQNYd/ZTlh5Yy9eLP7M4wCl6XyU8533l7uNB087N2P+V+XBIFx93o/sqNSbJ5L6yd3NCXciWlOhEo/vqWbgFeFKhVkXuht8yn+7rQVKhc0iKScTtOcovSlGbUszYVNRXpBbxFU9p0j+YG0fDLepy9XEjuYhfcn2GruRCulx83KjXuRF/7vjdpOy+C4bycxG/BGDv60ZmVEH3nBmdhL2v5UmGokgKiZ4HlzPg8iaijl0hIcw0vOwp1r4e5BZq67nRiWb7kad49O9I2pGLAKhvPsKxSU2Urk5Itta4BDfAupiJMms/dxNdNsU85PsMak/K4UKrW7JMrW/nU+/AanwGd7CY759ERiq1z78VsSfgH0CW5QxJkhoArYBg4DtJkmaZEe0CRAO1ixxXy7IcaKH4HZIkOQBKoL5Bn1aSpC5AI6A9sE6SpAayLC8qmlmSpNHAaIBJTg0JsTMsTZptw+Y7SrcWNSkzKJhzPRcaHVfa2xC4dTI353+ONkNt4fTNIJkqL24WcvKKSVw+c5krZ68+d7nmpmGyjpwg68gJbOrXwXX8MGLHzkTh5Ih922ZEdhuCLj0D73fm4xDSnsy9h55pTnG60g+eIv3gKewb1cJnymAeDJmXn6awt6XcpjnELP0E3TPr7/nqrCh3r95hZLMRZGdl0yC4IXM/mceYNgXL8FIJ6s6cSGGZywfOcfnAOV5qXINuU/qzcfAyarWrT3piGo+v3qdy05rmslk8h5NXblO9nB+fzh7B47gkxqzaRv1q5XG0swUgT6Mh9OINJr3aqVjbZTPt2pILPxbxkMCKPrjY63VU8nFjeLtAxn68B3sbFVX9PVAqLc+1mLsiFnXdjSWwjFt+KNDfQpGKdZowgYwtW0BnZgFbpyNp1CgkR0dcly5FWbEi2vvPtaD4X8FcWyza1otrr7VbB/Lo2n3WDFyId3lfpn21gAVdp5Jt5h6T/sP7atSiN/h85XZ05urXwnma2mKar6TnYG1vw9DNk/lyyWeoLfiQZ923z0uJzrcEMpWb1aRp/2A+7LfQVLgYZSWpv6f29V0wjF9Wfa3fl1WI2oX8UpWmRcLgzBpo+RRNRHUyuzvNxdrZnuCtb+NaLYCUm5HmhZ/j2jg2q4NH/w7c6qsPIsi5E0ns5h+pvGMxuqxs1Ncf6OP1LfEc/a5Li1r4DGzHlZcL+qsrPeaRG5uMlacztb5bgPrOE9JO/zsmDgQFiEHAP4Qsy1rgKHBUkqQrwNDC6ZIk+QMTgcbAEUmStsqyXJI4/tfQz/yvQr+noI9BnwycBc5KkvQ7sA1YZOa8tgBbAA76DMi/47Ojk7D1L5hxsPV3J6dInDSAY81y1Fo7hosDV5GXXDADKqmU1PtsCtG7jhO3t/gZeoBeQ3vSfZA+pv/GpVt4+RfMWHr5eZEQaz70ZujkIbi6uzB/5rpn6tDGxqPyLShX5eOJtpiQnpyLV1CV9UPh6oxto0A0T2LQJeuXjDMPHccmsKbFQUBeTCJWfoV0+XmSF5dkVhYg61wE1uV8Ubo5o01OA5WSspvmkLL7KGkHTpnNE/J6NzoP7AzA7cu38Sw0y+Ph60FSrGV9RSn8gHDhyHmUy8bRd2xfWr/cBmtJwaNLd3Et1B5cfT1IjTVuDxlJ6dg526NQKtBpdbj6uZMaZ9pm7p69jmd5HxzcnKjUsBp1OjSgZnAgVjbWKJwcsarUGJ/bj4hJKliej0tKw9vVOJzkl2MXGdGjNZIkUc7HgzJebtyPSqDOS/o41uOXblO9gh8eLo4Uh4+LAzGFZu9jUzPxcnEwK7s/vCAU6Cm9m1Snd5PqAHy49ww+xejzcbQlJr2grmPTs/FytDWv67pxKNDzoouPR1Fo5l/h5YU2IcFIxqpaNVwWLABAcnHBpkkT0rRaco4fz5eRMzLIDQ/HpnFjsv6hQUC7IV1oM1A/u3j/0h3c/QvaupuvBylF2np6Uhr2zg75bdHNz4MUQ1ts+Uo79mz+CYC4hzEkPI7D76Uy3L+kX60Meb0bHQ331Z0i95Xnc95XletUZtqGGQA4uzvTILghga0CqRJYDRtJweNL94zuKxdfd9KK3FeZSenYFbLF1c+DNDP3VVEUKiVDP5rMxZ9PcG7/aaO0jq93JXhARwDuXb6De6FzcPf1ILkE5VsqL/ryvSK+wpJNBb7Cxc/dyCa/6uXov2oMW4atIqvIylrDPq3wLO/DrL2reXjpLm7P6ZfcCvmlcnUrMXz9RAAc3Zyp1TYInVZLhcAq1OnQgFoGv2TvaEerD8fx58TNZEUn4eBfMEPu4OdOVuzz1RdAbloWMSevU6ZtXYuDgNzoRKwLtXVrPw+z/Yht9fKUW/Mmd19fgjalYI9R0nd/kPTdHwD4zRhMXrTl/i43ylRXrpk+375GeV56bxzXBi1HU6jPzzXUQV5CGon7zuIYVOVfNwjQ/fWx7f8bRDjQP4AkSdUkSapS6FAg8LCI2DpghSzLkcAUYKNkdjrLFFmW84B5QFNJkmpIkuQvSVL9Z+grlrSwu9hX8sWunBeSlRLfXs2JO2Ac/mJbxoPAz6Zw5c2NZN2LNkqrtW4Mmbef8LDIxihL/Pz5bkZ1HsuozmM5vv8EnfvpO5Sa9WuQmZ5JkhnH121gVxq1aciSCctLNDOWE3ETVbkyqPx9QaXCoXNbskKNH7BVZQseuqyrV0ayskKXkoYmOg6bujWQbG0AsGsSRN69RxZ1qS/fwqaCP1YBPkhWKly6tyb9jzNGMtblCzZp2dZ6CcnKSj8AAMqsmkTO3cckbv3Zoo69X+zJ38h7+sAp2vXVb3arFlSNrPSs5+rIXQvF0VapVxWFQmLXR7uY1HUia0JmcvngORr3aQ1AhaAqZKdnkRafYlLO7VPXCAzRx5027tuGKwfPA+BZ3idfJqBWRZRWKjKT0/l1zTcsaDaexS3fYvtbH6BLjyPv3llqVSrDo5hEIuOSyNNo2H/6Cm3qVzfS5evhyhnD3onE1AwexCQQUCiWed+py3R9RigQQK2y3jxKSOVJYhp5Gi0Hwu7QppbpBtp0dQ4X7kYTXKuC0fEkw0N9dHI6hy8/oGuRQYKRLj8XHiVn8iQlizytjgM3omhT2cdELj0njwuRSQSbSSspeTdvogwIQOGrb++27dqRc/KkkUzCwIEkDBhAwoAB5ISGkvb+++QcP47k4oLkaBjMWFtj3aABmkeW2/t/m8Nf7mdhyDQWhkzj4sGzNO+jj6isFFQFdXoWqWba4o1TV2kY0gyAFn3bcvGgfg9RYlQCNVvUAcDZ0wXfSv7EPyrYl7H3iz1M7jqRyYb7KthwX1UNqkbmc95Xo1uOYnSLkYxuMZKTe0/w8bzNbJq9kcldJ7IuZDYRB8/TsE8rAMoFVSY7PYt0M7bcORVB3ZAmADTs25qIg5ZDEZ/y6urRxN6J4thWUx/8+xf7mBMyhTkhUzh/8Ayt+uo3GlcOqoo6PSt/wFRSCpd39eB5Ghl8Rfmgyqgt+Io7p65Rz2BT476tuWrwFa7+Hgz/aAo7Jm8k/n60Sb7zP/5JwsNYVpnxS5Z03Tp1jSCDX2rStw2XDboWtXqLhS31n7B9p/lu/lYuHzzP7jXfML/ZeBa2fIttb31A9Ilr/DlRH8KYEH4P54q+OJb1QmGlpOLLTXl88GKJ6snG3QlrZ3sAlLZW+LeqTerdKIvyWZduY1PRD+uy3khWKtx6tCL197NGMlb+nlTaMpuHb79Pzn3jslQeLvkyrl2akbz7mEVd6eF3sKvkh005vS6vXi1IOmg8gWddxpPqn03j9oT1ZBfq8xX2NigdbPO/u7apR9aNf85fCCwjVgL+GRyB9ZIkuQIa4A76EJwfACRJ6giUA7YCyLL8qyRJbwCvA59j2BNQqLz9siwbhRPJsqyWJOk99G8WWgK8a1hdyAbi0W82LjGyVseN2duo/+0cJKWCJ98cIfNmJAGv62fjIr/4g0pT+2Ll5kiN1SP0eTRaznSei2vjavi/2pr0aw9pekj/IqM7K74l4VC4JXVGnD58hibtGrPj+BfkZOeweso7+WmrvljOO9PXkhibyJSVbxMTGcumXz4E4Ni+43zx/leWC9bqSFq1AZ/NK/WvCP3lAHl3H+LUrzsA6T/8hn37Vjj26AAaLbrsHOJn6N9oknv1Bll//In/N5uQtVpyb9wlfVcxAxytjqhFH1Hh8yVICgXJ3/9Ozu1HuA3qCkDy1/tw7tIc197tkDVa5OxcHk9cDYB9w5q49WlH9o37vPSb3rbYd78g4+h5i+rOHz5Pw+CGbPnzE/2rDKe9n5+2cPsi1s/8kKTYJHoM70GfsX1x83Ljw4PruXD4POtnrqdFSEtChnRFq9GRk53DmglrjMq/diSMWsFBLAj9gFx1LjumF8T0j9k2i29mfkxaXDK7V+1g2PpJdJvan8iIB5zeqd9UF9i1CY36tEar0ZKXncv2Ce9THCqlktmvd2fcO5+j0+no1boBlQN82HlI3wG+2r4xo3u1Zf6WXfSdvR5Zlnm7f2fcnPQz+OqcXE5H3GH+iJeL1aPXpWBWn5aM27IXnSzzcuNqVPZ15/uT1wB4pbk+HODwlQc0qxaAnY3xxtypnx8kNSsblULB7D4tcLa3saxLoWBWh9qM++EsOp3My3UCqOzpxPfh+jH6K4H6wcfh2zE0q+CJnbWxy571axjnHyeSos6l0+ZDjGtRhd51y5noAUCrJf2DD3B75x1QKMjetw/tgwfY9dS/ftHsPgADSg8PnGfPBoUCSaEg+8gRck+ZX5EqCdMXruJc2GVSUtJo32sw40cOoW+Pzn+prMtHLlI3uD6rQzeSq85h6/SN+WmTt81l28xNpMQl8/2qrxi7fjJ9pg7kUcR9/typX7X79cPvGfnuBJbuXwuSxPerviLDwusuLxjuq48M99X6QvfV/O2L2Gi4r7oP70HvIvfVhpnrn2nL9SNhVA8OZFbo++Spc/hu+sf5aSO3zeD7mZ+QFpfMnlXfMHj9W3SZ+ipPIh5wZqd+I6+TlwuTdi/H1tEOWZZpNaIr73Scjl/1cjTs25qo64+YvHclebLMzne+IvyI6cNq+OELBAY3YN2xzeSoc/h4WsF5z9g+jy0zNpISl0znYd3oPrYXrl5urDrwPuFHLvDJzE0m5V07EkaN4EDmhn5ArjqHb6cXvBL2jW0z+W7mFtLikvlt1dcMWT+RrlP78yTiAacNNnWe2BcHN0f6LdP3KzqNlrU95wIw5MO3qNy0Jg5uTiw9tYm9674n8VEcC0M/IE+dy1eF/NK4bbP4eubHpMYl88uqHQxfP4nuU/vzOOIBp3Yab/Z9HmStjtPzPqfj1zOQFArufBdKyq0nVBtieBPal4ex83Kh+76lWDnagU5HzTe68HPbmdj7uNLy/TFICgWSQuLBr2eI/CPcsjKtjsj5W3jpy0X6V4R+d4jsW4/xGNwFgMSv9uM7aQBKNycCnr7VSavjZvepAFT8eCZKN2fI0/B4/sf5m3ot6bo351NqfTMPlArivjmM+mYkvq/rwyljvjhIuSn9sHJzotKqUfl5LnWeiZWnCzW26Ve8JJWS+B//JOVIMXb9Q+j+xbH6pYX0n7zhQfD/m8LhQP9tVlglPFvob2K7Z+nc+Blplh/+/m5ma0pNFRUV5kNj/hus2dmvVPTIcY+fLfR3EWMh3ve/QNpXJZuR/Dtw/+GzUtM1uuH0UtGTpMt5ttDfRBXF3/O2pJIQJZvffP7fwEcqHT+Y9zyB+P8hDfNK9kauv4N6Ssv/v/B3k5Vbena1iPnhH38CP+zzaqk1mnaxO/9xe80hVgIEAoFAIBAIBC8U/+a39pQWYk+AQCAQCAQCgUDwgiFWAgQCgUAgEAgELxT/5n/yLS3ESoBAIBAIBAKBQPCCIQYBAoFAIBAIBALBC4YIBxIIBAKBQCAQvFCIjcFiJUAgEAgEAoFAIHjhECsBAoFAIBAIBIIXCrExWAwCBMUwUyq9P1D6TOFZarq6R8eWip4edl6logdgjU1Kqem6k156C4h9+m0rFT3xmoxS0QOQpS29P6Bqbmfh34P/C2hK6Q+8ALacf+fZQn8DcmZKqegBSBg4tdR0lSa2XqXzqPVheECp6AGoLmeVmq5GMZb/Gf7vpppb6dVhRKlpEhSHCAcSCAQCgUAgELxQ6ErxUxIkSeoiSdJNSZLuSJI0y0z6a5IkXTZ8TkqSVO8vGV4IMQgQCAQCgUAgEAj+ISRJUgIbga5ATWCgJEk1i4jdB9rIslwXWAps+U/1inAggUAgEAgEAsELxb/s7UCNgTuyLN8DkCTpW+Bl4NpTAVmWTxaSPw38x/FbYiVAIBAIBAKBQCD4LyFJ0mhJks4X+owuIlIGKLwRM9JwzBIjgX3/6XmJlQCBQCAQCAQCwQuFrhQXAmRZ3kLx4TvmzkY2KyhJwegHAS3/0/MSgwCBQCAQCAQCgeCfIxIoW+h3ABBVVEiSpLrAp0BXWZYT/1OlYhAgEAgEAoFAIHih0P279gScA6pIklQReAIMAAYVFpAkqRzwIzBEluVbf4dSMQgQCAQCgUAgEAj+IWRZ1kiSNAE4ACiBz2RZjpAkaawh/SNgAeABbJIkCUAjy3LD/0SvGAQIBAKBQCAQCF4ozAbc/4PIsrwX2Fvk2EeFvo8CRv2dOsUgoJSQJEkLXEFf5/fRL+ekSJLUFpgmy3L3QrLbgd9kWf5BkqSjgOPT0Z4kSQ2Bd2VZbitJkj3wCVAX/aaSFKCLLMsmf38qSVJX9O+VdTDI/ibL8rTntWPGsrdp0b4Z2epsFk5azo0rpitSyzcupGa96mg0Gq6GXWP59DVoNFoaNA9i3fZVRD2KBuDw3lC2rH32P8I6tQkiYNEbSEoFid/+TuymXUbpLh0b4zftNWSdDrQ6Ihd/Sua56yW2afbyKbRq34xsdQ5zJy7l+pWbJjKrNi2mViGbFk9bhUajZfj41+jWtzMASpWSSlUqMG3MfCbOGoOzyoZz3x0hdPOvJuX1WPg61YIDyVXn8sO0j4iKeABA1TZ16b7gdRRKhdm8rd7oRsjc11gaNIas5HSUVkp6rRhFxbrlQacjdtnHZJ29gkOrBvjMG4OkVJCy8wCJW743KsexfVO83h4Csg5ZoyN2+ceoL1xDsrai/NdrkKytkFRK0vYfJ+HDHSWuS6/getRe+jqSUsGjHUe4s2G3UXqZPi2oPKEnAJrMbK7M3EratUclLh9gzOIxNApuRI46h7VT13L36l0Tme5Du9NrZC/8K/gzoN4A0pLT8tPqNK3D6IWjUVmpSEtKY+arM83qmbp0Ii3aNSVbncPiySu5aaatL90wnxr1qqHJ0xARfp0VM95Fq9Hmp9esV53PftvMnLGLOLwn1KJNs5dPoXX75qjV2Rbb4OpNi6lVrwYajYYrYddYPG0lGo0WRycHVm9ajF8ZX5RKJds27+Dnb38zyjtg4XDqBNcnV53DtmkbeRRx36R8zwBv3tjwNg4ujjyKuM/WyevR5mkAqNq0JgMWDEepUpKenM67/ReisrFixndLUNmoUCqVnNt3ip/XfWdU5qCFI6gbXJ9cdS5bp63noQW9YzdMxtHFiYcR99gy+UO0eRrsnOwZvW4S7mU8USqV7P/kF45/f8RiHVpi3oq1HDtxFnc3V37+6qNnZyiG4+fCWb35C7Q6HX26BDNqwMtG6anpGSx472MeR8diY23NkiljqFJRH+ablpHJorVbuP0gEkmCJVPHEFizqkVdNk0a4fL2BFAqyfp1DxlffmNWzqpGNTy3bCR5wRKyjxwDwHXODGxaNEWXnEL84BHPtOv/2Hvr8KiO93H7Prtxd0ODOwnuEDSE4lDcKe4Ed5dSaIsWSkuFUmip4O5eIEGCOyHutrHd8/6xIclmNxA+3zbwe5mbKxe7Z56Z58zsc8aemTmFqcvYuw4Wn40FhYK0o/tJ3f2LQTllmQrYrNxA0qoFZFw4jaJIMaz85+WEu3mQ8st3pO39PftamabV8J3XD4VSwfVfT3HOQN3bdn5/yvpUJ0OVzl/+3xB6+xk27g50XjMSK2dbZI3MtV9OcPn7w9lx6gxsTZ3+rdCoNTw8EQgLv9NJ07aZNyUXDUZSKIjYcYyQdX/qhDt2boLH6E4AaFJSeTp9Myl3nmFW2oOym3LeFG1a3JXgz38l7Fvd5zcva1YvpK1vc1JUKoYMmUhA4O18Zb9cs4iBA3pg56Bra7VqVuf8ub306jOSP/7Yn2/8/7puErxfxBGhhYdKlmUvWZarADHA6HeI65LVic/LeCBcluWqWekOATLyCkmSVAVYB/SVZbkiUAV48q4ZaNSiPsVLFaVj/R4s9l/JzBWGxxAH/zhC50a96N6sH2ZmpnTu0z47LODyDXq2HEjPlgMLNABAoaDY4uE8HrCAuy3GYN+hMWZli+mIJJ6/yb0247nfdiLP/b+m+IoxBc5T4xb1Ke5ZDL963Znvv4w5K6calNu/+xDtG/agc9M+mJqZ0rWPtvH/fsN2urXoT7cW/flyyUauXgxg0uxRjOw9kTWtplC9QwNcyuie8lW+mReOnm6sajaJP2d+S6cl2oZTUkh0WDiI7weuNBjX1t2BMo2rEhscmX2tds/mADz9ZBQvBs7CZcZQUCpxmz+Kl0Pn8rjtCGw+aYpJGd0yS74YyNP2o3naYSyhM9bgvmQ8AHJ6Bs/7z+BphzE86TAGqya1MPMqX7DCVEhUXTaIy71XcLKJPx6dG2BVTjfvKS8iuNB5IaebT+Phmj+otuqzgqWdRS2fWhQpWYShTYby9fSvGbPE8G995+odZvaeSfjLcJ3rljaWjF4ymoVDFjKy5UiWjlxqMH6D5vUo7lmULg17s3Tq50xfNsmg3ME/jtKtcV96Nh+IqZkpnXpnj+VRKBSMmTWCS6f+eWOeGrdoQAnPYrSt1435/suZm48N7tt9mE8afkqnpr0xy2WDvQZ34/H9p3Rp3peBXUYydf44jI1z5neqNPPGxdOdWc3G8tPMb+izxHCZd53eh2Nb9zHbZxwp8Uk06qG1LXMbC/os+ox1Q1cwr/Ukvhn1BQCZaRl80XsBC9tOYaHfFKo09aKUd9ns9Ko1q4GrpzvTm41h28yN9FuS90Q8Ld2n9+PI1n1M9xlDcnwSTXq0AKB5P19CHr1kXtvJrOg5lx6zBqA0fvd5q05+rdi0evE7x8uLWq1hybrv2bBkGn9vWcXBUxd4/DxYR+bbHX9ToXQJ/vhmJUumjGTFxh+yw1Zs+IGGtauz97sv2L1pBaWKv+H0P4UCW//xRE+eTkTvgZi3bIFRyRIG5WxGDSPtsq6NpRw4RMxEw4Pb963LYvgEEhdMJX7MAEwat0BRzLAuiwHDyQjI0aV59ZKEiUO1f5OHIaelknHpbHa4pJDwWzSQ7QNWsr7lVKp0qI9zWd0yLutTHQdPN75uOpm9M7bSbvEgbdpqDUcWb2d9i6l822kedfq3yo5bsn4lKrSqyUbfGWxoNY0Lm/N0mBUKPJd+xr0+i7nRbDyOHRtjXlb3+Pa0l+Hc6TqHWy0n8WrNb5RaOQKA1Mch3Go1WfvXZgoaVRoxBy+/sQjb+janbBlPKlRqxMiR01i/blm+sjVrVMPOztZA8SpYtnQWR46ceqOu/7puet98aG8Mfh+IQcD74SJvPv81L58Dsw1cd0e7gQQAWZbvy7KcZkBuKrBEluV7WXKZsixveAf9ADRt04h9uw4BcOt6ENY21ji5OOrJnTt+Mfvz7YC7uLi7vKuqbCy8ypL2LIz0F+HIGZnE7j2Lbes6OjKalNTszwoLM5AL7uTz8W3Cnt+03reb14KwtrEymKezufJ0K+AOrh76efLr3Iqb1+/w4mkwwc9DUGeoubH3IhVb19SRq9i6JgF/aBuvlwGPMLO2wNrZjmJeZYh+Hk7sywiDcdvN6cfBZbqzZi5li/D4vHYWSB0TjyYhGdvOLUh/HkLGyzDIyCRh/xmsW9TXiSfnLjNzM3I7Rl+HSUZGSEbKAvtM7b3LkPw0jJQXEcgZakL+uohbG93lirFXH5IRn6z9fO0RZu4OBUs8i3qt63F893EA7gfcx9LGEnsXez25J0FPiAiO0LverGMzLhy8QGSIdiAVHx1vUE/TNo3Y/7t2JvD29TtY21rhaMAuLpy4lP05KOAuLu7O2d97DO7KyQOniY2KfWOemvs2Yc9v2uOeb167ne9zdfZ4zntibgUEZdugLIOllQUAFpbmxMclkJnLG+HVujaX/tB6IZ4EPMTC2hJbZzu99Ms3qMK1A9r8XNh9Gu/WtQGo26ERAYcuExMSBUBidI5XJS3LVpRGSoyMjHRsxbt1bS4UQG/FBlW4ekD7fJ3ffYoa2c+3jJmlOQCmFmYkxyWhyZWvglLLqyq2NtbvHC8vt+4/oriHG8XcXTE2NqJt0/qcvHBVR+bxi2DqelcBoFTxIrwKjyQqNo6k5BSu3bpHF18fAIyNjbCxssxXl3GlCmQGh6AOCYXMTFTHTmDWuKGenGW3zqhOnkUTG6dzPT3wJpqEBD35963LqGxFNGGv0IRrdaWfPYFJHf1TDk3bdSH94mnkeMPPjlG1GqjDQtBE5gzyi3iVJuZZOLEvI1FnqLm99xLlW+nWveVb1eTGbm3dGxzwCDMbC6xc7EiKiCP09jNtfpJTiXwUgrWrtl6p3bcF5zbsQZ2u9YolR+vm1cq7DKnPQknLaqOi/z6HfRvdNirp6n3UWfVe4vUHmLjrP9+2jauS9jyc9FeRemG5ad++DT9t13o/Ll+5jq2dLW5u+u2RQqFgxfI5TJ+hPwAeM3owf/y5n4jINx8u81/XTYL3jxgEFDJZr4ZuAex5m2wuLgJpWWfD5uY7YJokSRclSVosSVJZA3FBO/N/7d3vVhcXd2fCQnI6VuGhETqdnrwYGSlp160NF07mzGxUq1mFnce3se6XVZQq7/lWnSZujqRndT4A0kOjMXY1UIG2qUfFE+spvW0Oz6esLWiWcHV3JuyVbp5c35Kn9t3acu7ERZ3rZuamNPKpx7NHz3XKKCE0BltX3Y6uras9cSEx2d/jw2KwcbPHxtWe+JBog3ErtqxBQngsYXd1l86E3n1BpVa1QKnAuKgrZlXKYFq6GJmhOWWWERaFkYEys25Vn1KHvqHYlgWETv8yJ0ChwHPPWspd+oWk8wGk3tB3/xrCzN0eVa77Tw2Nxsxdv4P+mmK9mxFxIrBAab/Gyc2JyNCcRjIqLAonN6cCxy9SqghWtlYs37mcr/Z/RfOuzQ3KObs5EZ7rd4wIicTlDXqURkr8urXh4skr2fGbtW3M7h//fus9ubg7E/YqpzNTcBvUdth/2fobpcp5curmfv469QvLZq9BzjUQtnd1ICbX7xIbFo2dm65NWtlbo0pIQaPWzlnFhkZjl2V7rqU8sLC1xP/X+czeu4L6XZpkx5MUCuYe+Jwvrm0l6NwNngQ+zA6zc3XIHji81mvvpmuHVvbWpCQkG9R7/IeDuJcpypor37Lo8Gp+WfCdTr4Km4ioWNycc+7f1dmR8GjdTmr5UiU4dk47e33r3iNCw6MIj4whOCwCezsbZq/aRPeR05m3ejMpqlTyQ+nshDo8x/7UkZEonXXtT+HkhFnTxqT89S5NyfvVJTk6oY7K0aWJjkThqKtLcnDCpF5j0g7lr8u0cQvSzxzXuWbj5kBCqG79aeNmry+Tu44Ni8HGVVfGrqgT7pVL8CpQu8zQ0dOd4nUqMPSvBQzcORuPaqV05LVtVE6a6aHRmLxhcsOlV0viTgboXXfs2Iiov84aiKFLEQ83gl/mnBz5KjiUIh5uenKjRw1i774jhIXpToZ4eLjRqaMv32z+6a26/uu6SfD+EYOAwsNckqRAIBpwAI5mXc/vich7fTF5vAGyLAcCpdB6ChyAfyRJqvh/ucncb7WLSgnLG6Z/k294oGcs9+f6pRsEXL4BwL2b9/Gr1ZUeLQby69bdrPk+fzdmjlID1wzojD98ibvNR/Nk6FI8/Pu8Pd3s5N8tT7NXTOXapQCuZ+XpNc1aNybgn1uoUlRvT89gOeZ3XcbYzASfMZ04uvo3vfBru04RHxaN559f4TprGKrrd7V7Iwwq0CXx6EWe+A7n5ahF2v0Br9FoeNphLA8b98e8WjlMyxpw1xvCwP3nZ92ODStRvJcPdxcbXnv8LrxLo6JUKilTtQzzBs5jTt859BrXiyKe+k65d7X16csmEXDpBoFXbgIwacFY1i7ZhMbQb5FXl0EbzF9+zoqpXLsUyPXLgQA08qnHvdsPaFatHV2b92PWMn8sc88y52dvOjeRv4xCqaRE1VJ8PWgZX/ZfTLux3XD1dNfKaDQs9JvC1PrD8axeliLliuVK8u1laEjmteIqTbx4cecpE+sMZZ6fP30XDsXMylxfvpCQDRhz3tsf0qMDCUnJdBsxnV/+PkyFMiUxUipRq9XcffiUHp+04reNyzE3M2Xrzjd1qN9edrYTRpOw4RsogI29mferK2+xWg4dS8oPb9BlZIRxnQaknz/1Vm36de+bZUwsTPl00wQOLfyJtCRtXa4wUmBua8m3neZxdOkvdN8w9q1p5lfv2TSogkuvFrxY8qNuEsZG2LeuTczeC4Yj5pYtwHPl7u5Kt66fsG79d3qyq79YwIyZSz+Muuk9o5GkQvv7UPlwFmf9/x+VLMtekiTZAvvQ7gn4Gu2gIO90qQMQlfuCLMsnJElaBNTLcz0J7bmxf0iSpAH8JElqDrxe+OsHBAE1Ad2eqwFyv9XO262h/OmgLnTpo93IGRR4F7dcy2Bc3V2IDIsymM6wyYOwd7Rj8ZSZ2deSk1KyP587fpEZyydj52BLXIzhJRmQNavikTNTZOLuSEZETL7yyVfuYFLcDaW9NerYRIMyPQd1pVtf7ZrF24F3cSuim6eIfPI0cvIQ7B3tWOC/XC+sbaeWHPjzCOGhETplZOPuQEKE7mxhfFgMdh4OPM/6buvmQGJ4LEYmRth6OOrFdSjhin1RZ8Yf1Oq1cXNg7L4lrO80h6TIePYv+hlH4zgASuxcRerdJ9h1aZmdjrGbE5lvKDPVP7cxLu6O0t4Gda4NtJrEZFIu38KySU3SHj7PN/5rUkNiMM91/2bujqSG6bvzrSsWp/oXw7jcezkZsXp72PX4pP8ntOml3Xz98OZDnHPNRDm5OREdXvD3pUSFRZEQm0CaKo00VRq3L9/Gs5IngQ/v031gZzr10a7pvxN4T2fJl4uHM5H56Bk6aSB2jnYsnZozRq9YvQJLNmo3Mdo52NKgRT3UajWnD50DoNegbrls8A5uRVyz42pt0PCSAK0N2jPfP2cNdqeen/DtWm2n4sWzYF69CGHEpEE0aFoXS4UJT288wiHX72Lv5kh8uK49JMUkYG5jgUKpQKPWYO/uSHyWzcSGRZMUm0C6Ko10VRoPr9ylaMUShD8NzY6vSkjh/qXbdJ7UE+fi2rxo9Trp6I3LozcxJgELG0sdvXFZz0uj7s3Zv1G7wTLieRhRLyNwL12EpzceGSyb/xpXJwfCci2fCI+MxsVBt+q2srRgsb92vbcsy/j2H0cRN2dS09JxdXagWsUyALRqXJetO/P3EqkjI1G65tif0tkZTZSu/RlXKI/9wrkAKGxtMW1QF9RqUs+cf6d8FaYuOToSpVOOLoWjM5oY3fpWWaY8Vv5ZumxsMa5Zj2S1mozL2mfHuEZd1I8f6i0VSgiLwcZdt/5MDI/TlQmNwSZ3HevmQGKEVkZhpOTTTRO49dd57h66qhPn7iGtd+fVjSfIGhkjBxsyY7R1pbaNyknTxN2R9DD9+taiYglKrRrFvb6LyMxT79k19yb51hMyogy3hSNHDGDIEO3k1tWrgRQt5pEdVqSoOyGhunufvL2qULp0Se7f1f4+Fhbm3LtzjgqVGlGzRjW2/6xdDezk5EBb3+ZkZmayZ492+eN/XTeVKluCWwF3DKYhKHyEJ6CQkWU5HhgH+EuSZAw8BDxez+BLklQCqA4EGoi+BO36frJkG0qSZJ/12QSoBDyXZXl91iZkL1mWQ9B6CmZKklQuS1YhSZLhnY552PX9H9kbeU8eOsMnn/oCULVGZZISk4iK0O8Yde7dngbN6jJj5DydGQpH5xwXaWXvikiS9MYBAEDKjYeYerpjUsxFO1vSvjHxR6/oyJiUyHGFmlcphWRilO8AAODX73dnb+Y9cfA0Hbr7AVCtZv556tqnAw196jJ1xFy9WRcra0tq1ffm5KEz3A64S/FSxShS3B2lsZLq7etz96juSqy7R6/h3aUxAMW8y5CaqCIxMo7gG49xKumGfVFnnbjh91+ypNZIVjYaz8pG40kIi2HtJ7NIiozH2MwEY3NTACwbeoNaQ+LBs5iU9MC4qCsYG2HTrgmJxy/p3INxcffsz2aVSiMZG6GOTUDpYIPCWjtTI5maYNnAi/Qnupsf8yMu8DGWpdwwL+6MZKzEo1N9wo7o5t28iCO1v5tIwJj1JD8JyyclXfb9uI+xbccytu1YLh6+SIuu2o2j5b3Lk5yYTGyE/kAjPy4duUTlOpVRKBWYmplS3rs8Lx++BOC3bX/Sp9UQ+rQawqlDZ2nXTTvwqFKjEkkJyUQbsIuOvdtRv1kdZo9aoGMXner1oGNd7d+JfadZMWN19gAAYMf3v9O1RT+6tujH8YNn6NBdu++/Ws0qb7HBekwZMUdHV+irMOo11u69cHR2oGTp4mxd9xNdW/Rjod8UAo/8Q70uTQEo5V0WVWIK8ZFxeunfvxhETT/tHEODrk0JPKLt+AQe+YeytSuiUCowMTPB06sMoY9eYeVgg7mNdr2vsakJlRpW4+yuE8zz82eenz/Xj1yhQQH03rt4m1p+2j0rDbs24/oR7fMdHRJFpYZVAbBxssWtlAeRL8L14hcWVcqX5vmrMIJDI8jIyOTg6Ys0q6+75jwhKZmMrBOVdh88Qc2qFbGytMDJwQ43Z0eeZi3juBxwm9LFi+rpeE3G3XsYFS2C0t0NjIwwb9mc1HO6s8QR3XoT0bUXEV17kXryNPGrvnznTnlh68p8eA+Fe1EULlpdJo2bk3FFN534YT2z/9IvnCb5mzXZAwAAkyYtSDt7PG/ShNx4gqOnG3bFtPVnlfb1uJ+n7r1/7DrVu2rr3qLeZUhLVJGUNQjouPIzoh694uK3B3Xi3DtyDc8GlQBw9HRDaWyUPQAASAp8hJmnO6ZZbZRjx0bEHtHdPG1SxIly307l0bivSH0SSl4cOzUm+q9zetdfs3HTD9Sq3ZpatVuzZ89h+vXpBkDdOjVIiE/QW/Jz4OBxihb3pky5epQpV4+UFBUVKmn3XpQtXz/7+u4/9jNm3MzsAQD893XTy+ev9OK/L+RC/PtQEZ6A94AsywGSJN0Aesqy/JMkSX2B7yVJMkN7us/QrMFC3ngHJEnKPQwvDWyUtP5BBbAf2G0g3k1JkiYAO7KOFZWzZN+Jc8cu0qhFffZc2kWqKpX5E3JOVlm7fRULJy0nMjyKmSv9CQ0O54d9m4Gco0Bbtveh+4DOqDMzSU1NZ8aIefmpykGtIXjOZkr/NF97ROjO46Q+eIljX+1gJPrnQ9j5NcChqw9yRiZyajrPRn9e4DydOXaBxi0acPDy76hUqcwZn7OJasP21cybtJTI8CjmrJxKaHAY2/dvAeDY/lNsWq11tbbwa8aF01dQZW2SXDpjFd/8+hXWRqZc3XWKiIevqNNH23G9sv04908GUt7HC//Ta8hQpfH7lG8A7QkVe+ZuY/CP05GUiuy4b8LSyYbBP0zHhkwyw6J55b8K1BrCFmyk2HeLtUeE/n6E9EcvsOulHezE7TiAjW9DbDu1QM7MRJOazqsJWi+DkbMDHisng0IBConEg2dJOnnlTbeQjazWcHvmNurtmIGkVPByxymS7gdTor/WK/H8x2OUndQFY3srqi4fnB3nbJtZBUof4J8T/1DbpzZbz24lTZXGGv812WELti3gq2lfERMeQ4dBHeg2ohv2zvasP7Keqyeu8tW0r3j56CXXTl1jw5ENaDQaDv96mOcP9L0c549fomGL+vx5YQepqjQWTsxZuvblTytZ7L+CqPBopi+fTFhwON/t3QjAyQNn+HbND3rpvYkzx87TpEUDDl7eTaoqldnjF2WHbdy+hrmTlhAZHsXcldMICQ7jl/3fAlob3Lh6K5tWf8eSr+fy56ntSJLE6kXrdQbXt05ep6qPN0tOryVdlc62Keuzw8Z9P4Mfpm0iPiKW3ct/ZtjaiXSa3IsXQU85t+sEAGGPX3H7dCDzDn2BrNFwdudxQh68pEiF4gz+YgwKhQJJIXFl/wVunMjpdN08eZ1qPjVYcXo96ao0tubSO/H7WXw/bQNxEbH8tvxnRqydSJcsvWd3aTt4e7/+jSGrxrDo0GqQJH5b/jNJbxjc58eUecv5J+AmcXEJtOjUl1FD+tG1fZt3TsdIqWTmmIGMmLkMtUZD5zbNKFOyGLv2aVd2fvpJK568eMWslRtRKBSULlGEBZNyTkSaMXog05evIyMzk6JurizyH56/MrWG+NVf47hmJSgVpOw7SObTZ1h00p60lvKX/tGXubFbMBtTby8Udra4/rWLxG+3kbLvgGHhwtSlUZOy+Uus56/SHhF6/ADql88w9dV6m9+0DwAAE1OMq9ciZcMXBpLWcGDuNvr9OA1JqSBg12kiH76iVlbde3X7cR6eCKSsjxfjzqwmQ5XO3/7aurd4rXJU79qY8LsvGHFA264d/3wnD0/eIGDXKTp+PoxRR5ajzsjkr8mb0DkvTa3h2axvqfDLXCSlgohfj6N68BKXfq0BiPjpCEUnfoqRvTWey7T2IGequd1WO5+nMDfBtnF1nk4t2PG1Bw4ex9e3OffvnidFpWLo0Jz5vL1//8iwEVMIDf13Bsv/dd0keP9IYpOGID+83RoWmnF8Z1LwzZ3/V/qlFc5sYnvzUm8X+pfon7UcqDB4lKh/5Nx/xUaTwmkwIjPfvizp3yJFbegAr/+GBubFC01XZiEehLf5asEH+v8X5OS4QtEDENVr8tuF/h/EzLlw7OLrwPw9K/82vhn6e7/+KxpFvfnI0H+T8vaFV4ZB4Zff+0L5ne59Cq2P0yN0+3vPryHEciCBQCAQCAQCgeAjQywHEggEAoFAIBB8VGg+yLn5wkV4AgQCgUAgEAgEgo8M4QkQCAQCgUAgEHxUaAy+5OHjQngCBAKBQCAQCASCjwzhCRAIBAKBQCAQfFSIszGFJ0AgEAgEAoFAIPjoEJ4AgUAgEAgEAsFHhTgdSHgCBAKBQCAQCASCjw7hCRDkS7qcWWi6CvPF1Sp1eqHoSS3EN6impRfeo5ygUBaarlh14byZMz4jpVD0ACQWoi7ZvPAerBhN4b0JubDe5CtZ2hWKHoDwYJtC02XvUHg2mPascPQYF44aABLUJoWmSyEV3nR1uqbw2vwPgcJroT9chCdAIBAIBAKBQCD4yBCDAIFAIBAIBAKB4CNDLAcSCAQCgUAgEHxUiCNChSdAIBAIBAKBQCD46BCeAIFAIBAIBALBR4U4IlR4AgQCgUAgEAgEgo8O4QkQCAQCgUAgEHxUiCNChSdAIBAIBAKBQCD46BCegA8MSZLWAM9lWf4y6/th4KUsy0Ozvn8BvAIGy7JcJevaZ8BK4AXaDe+VgPuAGjgErAG2AsXQvlPlmSzLfv/L/c1YMokmLRqgUqUya9wi7t66ryezYsMCKlevSGZmJrcC7rDAfxmZmWqsrC1ZsWEB7kXcUCqVfL9xO3/9us+gHptm3hSd/xkoFUTvOEr4ht064fadmuI2qgsA6uRUXs7ciOruMwCcB3+CU+/WgETUjiNEbt37Tnmcu3QKzVo2QqVKZerYeQTdvKcns+zLuVT1qoQkSTx9/JypY+eRkmz4xVad5w2goo83Gao0dvhvJDjomZ6MQ1Fn+q8bj4WtJcFBz9g+cR3qDDU1OjakxYgOAKSlpPH77G8JufsCI1Njxuych5GpMUqlAg6cI2zNDoP6rZvWoMi8oUhKJdG/HiFio25Z2rSqi/vkPqDRIKvVvFrwLclX7xa4vNybVaPWon5ICgWPdpzizjrd8rYp40691cNwqFqSGyt+4+6mA9lhHS+vITMpFY1Gg5yp5lDbuW/VN3HhWBo0r0uqKpVFE1fw4PZDPZn5a2dRoXo5MjPU3A28x/JpX6DOVNO6c0v6jeoJgCpFxcoZX/LozmODemYv9adpy4aoUlKZPm4+d27q2/qqjYuo4lWJzIxMbgYEMXfyEjIz1djYWrPsq7kUK1mU9LR0ZoxfyMN7hvUALFoxkxatmqBSqZgwaia3buiX/xdrF1HduzKSJPHk0TPGj5pFSnLOS5+qe1dh/7EdDB80mf17juSrq9e8wVT18SZdlc53/ut4EfRUT8apqAvD1k3E0taKF0FP+HbiWtQZmbQZ1oG6nRoDoFQqcS9ThIk1hpAcn2RQ12cLhlHTpxZpqjS+mvwlT27rl4HfgE/oMKQD7iU96Fu9N4mxCTrhZaqVZeXfq1g1eiUXDpzXi3/un0BWbPwRtUZDF18fhvbsqBMen5jE3C++4WVoOKYmJiycNJyynsUASEhKZv7qzTx8FowkwcLJw/GqVC7fsnsbs5eu5sz5KzjY2/HXz5v+53TyYt1UWx9KSgXRv+rXh7at6uDu3wdZowG1huAF35L8T8GfYfMGtXCYOgoUCpL+PEj89zt1w5vVx37UQJBl5Ew1MZ9vIC0wCKMSRXFZOTtbzqiIG3EbfyBh+59v1jVtFJJCQeKfB4n/TleXRbP62I8eiKyRQa0m+vMNpAUEYVyiKM65dBkXdSN2g66uUk2r0XpePySlgsBfT3Fxo37d33p+f0r7VCdDlc4+/28Iu/0sO0xSSAzet5jEsFh2DV6lE6/uMD9azurDaq/hEBqvE+boU50KiwcgKRUEbz/Bs7V7dMLdujbEc4y2Hlcnp3Fn6rck3XmBqYcjVdeNwsTZDjQagn8+wYstB/Mtu9esXr0QX9/mqFJUDBk6kcDA2/nKrlmziAH9P8XBsTwAvXp2xt9/FABJScmMHTuDm7cKZitzlk7JrhOnjZvPHQNt49Iv51ClurZtfPbkOdPGzs+3bXzfCE+A8AR8iFwAGgBIkqQAnIDKucIbANktoSRJ/YCxQFlZlqvLsuwFhAA+six7ybI8HVgIHM0KrwRM/19urHGLBpTwLEbbet2Y77+cuSunGpTbt/swnzT8lE5Ne2NmZkrXPtpGudfgbjy+/5QuzfsysMtIps4fh7GxgXGoQkGxxcN51H8Bd5uPwb5jY8zKFtMRSX8ZzoPuM7nbejxhX+2k+IrRAJiVL45T79bc+8Sfu23GY9uiNqYl3Qucx2YtG1KyVHGa1+nIrEmLWfj5DINyS2Z/wSfNetKuaQ9CXoXRb0gPg3IVm3nh7OnO0mYT2DVzC92WDDUo1356b05v3c9Sn4mo4pOo26M5ADEvI1nXYyGft53GkbV/8OmyYQBkpmWwofciVrWdxud+07FuWgML7/L6CSsUFF00nCcDFnCv5WjsOzTBNE9ZJp2/wX3fcdz3m8CLKWsptmJsQYsLSSFRe+kATvZZyb5mUynZsR42ZT10ZNJik7k65yedzn9ujnVfwsFWswo0AKjfvC7FPIvQvVFflk/7gqnLJhqUO/znMXo2GUDfFoMxMTOhQ+92AIS+DGVUtwn0azWU7778iekrJhuM37RlQ0qWKkarOp2ZM3kJC1YatoO9uw/hW78rnzTpgZmZKd37dgJgxIRB3L39gA7NejF19FxmLzGsB6B5qyaUKlWCBjV8mTJ+Hsu/mGdQbt7M5bRs1IUWDTvzKjiUwZ/1zg5TKBTMXjCJU8f1O8m5qdrMGxdPd2Y2G8uPMzfRd8kwg3Jdp/fl6NZ9zPIZS3J8Mo2z7PHw5j0s9JvCQr8p/LFyO/cv38l3AFDTpxbuJT0Y0WQY66evY+SSUQbl7l69w9zeswl/Ga4XplAoGDBjIAGnAwzGVas1LFn3PRuWTOPvLas4eOoCj58H68h8u+NvKpQuwR/frGTJlJGs2PhDdtiKDT/QsHZ19n73Bbs3raBU8SIG9RSUTn6t2LR68f8pDT2y6sPHAxZwt8UY7Dvo14eJ529yr8147redyHP/rym+Ysw7pe8wYyzho2fyqstQLH19MC5VXEck9XIAIZ8OJ6THCKLmr8Jp3iQAMp8HE9JjhPav1yjk1DSST7zBBhUKHGeOJXzUTII7G9aluhzAq+5aXZHzcnRl5NGlSU0jJZcuSSHhu2ggvw5YyTctp1K5Q32cyur+nqV9quPg6cbGppM5MGMrvosH6YTXHuxL1KMQvdu2dnfAs1FV4oOjDORJouLywVzvvZzzjSfj3rkhluV09aqeR/JPp4Vc9JnGk9V/UPkL7XMnZ6q5P+8nLjSezGW/ORQb1Fovbl58fZtTpownlSo1YuSoaaxbuyxf2Ro1qmFnq/sm6qfPXtCiZTdq1mrF0mVfsWHDyjfqe03Tlg0pUaoYLet0Ys7kxSzMp05cOns1HXx60b5ZT0KCw+ibT9so+DAQg4APj/NkDQLQdv5vA4mSJNlLkmQKVARiASRJ+hRth761LMsGaqds3IHsllGW5Zv/y401923Cnt+0sxQ3r93G2sYaJxdHPbmzxy9kf74VEISrh0uWXrC0sgDAwtKc+LgEMjPVevEtvcqS9iyM9BfhyBmZxO45i23rOjoyydfuoY5P1n4OuI+xu/Y+zMoUJfn6A+TUdFBrSLp8GzvfegXOY8u2zfhzl9Y7EXjtFja21ji7OunJJSUlZ382MzNFlg2fOFyldS3++eMMAM8DHmFubYGNs52eXJkGlblx4DIAV3afoWrrWgA8u/4AVYJW1/PrD7F1c8iOk56SBoDSSIlkbKQt4DxYeJUl7Vko6S+zynLvWWxb1dWR0aSkZn9WWJjyLqcnO3qXJvFZOEkvItFkqHn+9yWKtampI5MWnUDMjSdoDPzW70qTNg05+Lt2ljvo+l2sbC1xdHHQk7t44nL257uB93Bxdwbg1tUgErM6rUHX7+Dirv/bArTwbcqfO7WDlhvXbmNta42zq76tnz6W0wm5eT0INw9XAMqUL8XFs1cAePLoOUWKeeDorH+fAL5+zfnt178BuH71Jja21rgYsrnE3DZnpmNzQ4b3Yf+eo0RFRRvU8Rqv1rW5+Mcp7X0FPMTC2gJbA/ZYoUEVrh24CMCF3afwyvP8AdTp0Igre/Lv8NVpXZeTu08A8CDgPpY2lti72OvJPQ16QkRwhME02g36hIsHLxAfHWcw/Nb9RxT3cKOYuyvGxka0bVqfkxeu6sg8fhFMXe8qAJQqXoRX4ZFExcaRlJzCtVv36OLrA4CxsRE2Vpb55qcg1PKqiq2N9f8pjbxY5K0P9+rXh7rPsJnBuiA/TKuUJ/NlCJmvwiAzk+TDp7Bo1kBHRlblSt/czGDyZnW9yQgORR1q+Ld8rSsjt65Db9dlqDoyr+tN5stQMnPp8vAqTcyzcOJeauuiO3svUa6Vbl1UrlVNbu4+C0BIwCPMbCywcrEDwNrNgTLNvQj89aSevlZz+3Fi2Q6D9bxtjTKkPA1D9TwCOUNN2F8XcPGtpSMTf/UBmVntVdy1h5i6a+uC9Ig4Em89A7Qe7eSHrzB1M1xPvKZ9+9Zs//l3AK5cuY6dnQ1ubi56cgqFguXLZjNj5hKd65cuXSMuTuvJuHz5OkWKFGySrKVvU/7auR+AwGu3sba1KkDb+G62WNjIUuH9faiIQcAHhizLIUCmJEnF0Q4GLgKXgfpALeAmkA6UANahHQCEvSXZ9cBWSZJOSpI0S5Ikj7fIG8TF3ZmwVzmzdeGhEbhmda4MYWSkpH23tpw7cQmAX7b+Rqlynpy6uZ+/Tv3CstlrDFaqxm6OpIfkjGkyQqMxdtPvgL3GsWcrEk5eByD1/gus6lZCaWeNZGaCjU9NjD0Md/QM4eruQkiuPIaFROCWTx5XfD2fy3eOUrpsSX78dqdBGVtXB+JCcjpmcWExOh15AEt7a1QJKWjUWudkfGgMtq76DUHdHj7cOxWY/V1SSPgfWM6ia5tJPBtISuADvTjGbo5khOYuyyiDZWnbph4Vjm+g1PdzeTHla4N5MYS5mz0pITHZ31NCYzB31+/o5Yss03zHdHwPLaJMH5+3iju7OREektPwR4ZG4eyW/++rNFLi27UVl05e0Qtr39OPiwauA7i6OxMWkvNYhYeE42qgoX2NkZGSjp/6cfaEdgB8L+gBrdtpZ8+reVfGo5gbbu6G47u5uxDyKkdXaEg47u6uBmXXrF/CzQdnKFPOk+82b8+O3/aTlvz4nWEbzI2dqyMxuewxNiwGuzz2YGVvjSohOdseY0Ojsc9jjyZmJlRp6sX1g5fy1eXo5khULtuLCovG8Q3PcV4cXB2p16Y+h37Of3lERFQsbs45abo6OxIeHasjU75UCY6d+weAW/ceERoeRXhkDMFhEdjb2TB71Sa6j5zOvNWbScnVAf1QMMlTH6aHRmNsYEBq26YeFU+sp/S2OTyfsrbA6StdnMgMi8z+nhkehdJF/5my8GlIkT+34rJ2MdHzV+mFW7ZpRvJB/Q50Xl3qXLrUEVEYGehIWjRvSJG/tuK6bjFR8wzo8m1G0iFdXdZuDiSG5th2QmgM1m72ejIJuew/ISwGa1etTKt5/TixdId2GVIuyrasQWJYDBF3XxjMk5mbA6m50kwNiXljR75Ibx+iTgTqp1PMGesqJYm//ijfuAAeHm68DM7xVgS/CsXDw01PbtSoQezbf4SwsPwHZYMG9eTw4Tf/Zq9xdXchNES3bXR1M9w2Lv96HheDjlDqDW2j4MNADAI+TF57A14PAi7m+v56mj0S7R6AT9+WmCzLh4FSwBagAhAgSZLBp1eSpGGSJF2VJOlqrEq38pDQH86+aZA/Z8VUrl0K5PrlQAAa+dTj3u0HNKvWjq7N+zFrmT+WhmbeDI2a81FkVb8qTj1a8mqp1sWf+iiY8A1/UPaXBZT5eT6qO8+Q1QVf+ScZ0J1fHqeNm0/9Km149OAp7Tq1fof08iRYAJky9StRr4cPe5f/kiOjkVnlN5359Udh4VUWs3LF8yaTX+J6l+IPX+Jei1E8/Wypdn9AAZEMZrDA0TnScSEH28zmZJ/PKTewJS51DSxp0lWor+4N+qYsnUDg5ZvcuHJL53qNBl607+XH+qWb81FjSE/+iuavnM4/F69z9VIgAN989QM2dtb8fXI7/Yb24O6t+6jVhj0h76Jr4uhZeFVoxsP7T+jQpS0AC5fNYPG8L9Bo3m7nhn4uvQIswP1Ub1mLR1fv57sUCPKrLwpuHEPnf8YPy7a9MV+yAWPLe/tDenQgISmZbiOm88vfh6lQpiRGSiVqtZq7D5/S45NW/LZxOeZmpmzduUcvvfdOAevD+MOXuNt8NE+GLsXDv+DPcEErvZST53nVeQgRE+djN2qgbqCRERZN65N89PQ76zJkEyknzvOq0xAiJszHfnQ+uo68RZeBtPOrj8s09yYlOl5nfwCAkZkJDcd05Mzq3/NXYnCW17Cd2zesRJHePjxc9IvOdaWFKV5bJ3J/zg+ok968fr4g9YW7uytdu7Rj/frv802nadMGDBrYk5mzluQr8656XzN93AIaVvXl8YOntOvUqkDpvw80hfj3oSI2Bn+YvN4XUBXtcqCXwGQgAfguSyYFaAuckyQpQpbl7W9KUJblGOAX4BdJkvYBTYDdBuQ2A5sBKrvWlXsN6ka3vto1/bcD7+BWJGeG0tXdhYhcszq5GTl5CPaO9sz3n5Z9rVPPT/h27Y8AvHgWzKsXIZQqW4JbAXd04maERmOSa/be2N2RjPAY8mJeoQQlPh/No34LUcclZl+P3nmM6J3HAPCY1pf00Dcvkeg7+FN69OsMwK3AIDyKuHItK8zNw4XwfPIIoNFo2P/XET4bM4DdO/bopGerNOXFjcfYeeTM2tm5OZAQrjtTmRyTiLmNBQqlAo1ag627AwkROTLuFYrTY/lwNg9cTkqcfqcrNSGFpIu3sW5Wg9QHurNVGWFRGLvnLksng2WZfS9XgjAp4Y7S3hp1bGK+cq9JCY3BwiNn1svC3QFVWOwbYuiiCo8DtEuGXh66hqN3aSIu627A7TqgEx36aNf03w28l728DMDZ3YmocMMr4QZP7I+dox0zhuruNShdsRQzPvdnUr/pJOTahNpncHc+7dcJgFsBd3DzcANuAODq4UpEuGE7GOP/GQ6O9syZvDT7WnJSMjPGLcz+fuLaHl4+z5m9Gzi0F30GdAfgxvVbeBTJmclz93B94+ydRqNhz58HGTl2MDu3/0l178ps+u4LABwc7GnRqglqtZpD+48D4NPPl8a9WgDw7MZjHHLZo72bA3F57CEpJgFzG8tse7R3dyQuQvc3rd2+IZf3nNO7N7/+7WjVqw0Aj24+xCmX7Tm5ORLzBtvLS5mqZfBfp913ZONgQ02fWqgz1Vw+kuN9cHVyICwy5/kOj4zGxUF39tfK0oLF/iMAbafFt/84irg5k5qWjquzA9UqlgGgVeO6bN35d4Hvr7BIz1Mfmrg7khHxpmf4DibF3Qr8DKvDIzHKNaNr5OqEOjL/OjPt+i2MirmjsLNBE6d9fswb1Sb93iM0MXFv1aXMpUvp4oQ6In9dqQZ0WeSjKzEsBmv3HNu2cXcgKVxXJiE0Bptc9m/j5kBSRBwV/OpStmVNSjfzwsjUGFNrczp8OZKLG/dhV8yZoQeXZac5ZP8SAtrMIj1Su6QmNTQGs1xpmnk4kGagDrSqVJzKq4dzvddyMmJz6nHJSEn17yYRuvscEQf+MVgOI0YMYMhg7R6gq1dvUKxojjO/aBF3QkN199N4eVWmdOmS3L2jfUYtLMy5c+cclSo1AqBqlYps2rSSDh36EfOG36zP4O7ZbePNgDu4e+S0/24eLkTkU/eCtp468PcRho7uz+4d73Y4h6DwEJ6AD5PzwCdAjCzL6qwOvB3aJUEXXwvJshwJ+AJLJUlqk19ikiQ1lyTJIuuzNVAarRfhrez4/ne6tuhH1xb9OH7wDB26a2cfq9WsQlJiElEGKvCufTrQ0KceU0bM0ZkpCH0VRr3G2rWSjs4OlCxdnJfPX+nFT77xENOS7pgUc0EyNsK+Q2Pij+ou2zD2cMJzywyejf+StKe6G7mMHG2zZex86xP795k35vHn73bR3qcX7X16ceTAKTp/+gkAXjWrkpiQRKSBiq6EZ87GvBZtmvDk4VO99Fb5Tef2kavU7tJEG8e7DKrEFBIi4/TSe3TxDtX9tGv163Rtwu0j2nXNdh6ODNo0ie0T1xP5NDRb3tLBGjMb7f4KY1NjrBtVJ+1RsF66KTceYurpgUkxV21Ztm9MwtHLOjImJXLWhJpXKYVkbFSgzgNAdOATrD3dsCzmjMJYSYmO9Qg+cr1AcZXmphhZmmV/dm9ahbh7+nnY/cNfDGj9GQNaf8aZw+dp203rdalcoyLJCclEG+gQte/lR71mtZk3epGODbp6uLB8y0IWjl/Gyye6urZ/9xsdffrQ0acPxw6eonMP7QFa1WtWISkhichwfVvv3rcjjXzqMXH4LB091jZW2ZveP+3biasXA0jOtVZ227c7aNW4C60ad+Hg/uN0zzrRpkataiQmJBpsXEt65nh6Wvn68CjL5upWb02daq2oU60V+/YcZvrkRdkDAICTPx3K3swbcOQK9bs0A6CUd1lUiSnEG7DH+xeDqOlXH4AGXZsReCSnc2JubUH5upUIPKrfYTnw434mth3HxLbjuHT4Ij5dtUuiynmXJzkxhdiIgg8QhzUayrCGQxjWcAgXDpznm9kbdQYAAFXKl+b5qzCCQyPIyMjk4OmLNKuvuw48ISmZjIxMAHYfPEHNqhWxsrTAycEON2dHnr7U1h+XA25TunjRAt9fYaF9hnPVh+3160OTEjmDSPMqpZBMCv4MpwXdx6h4EYw83MDICMs2zUg5fVFHxqhYTqfTpEIZJGPj7E45gJWvD8l5lufkp8u4eBGMimTp8n13XZZtfUgysOwo5MYTHDzdsM2qiyq1r8eDo9d0ZB4eu061rtrTrTy8y5CWqCIpIo5TK3eytt5Y1jeawJ9j1/Hswh32TNhI5P2XfFlzFOsbTWB9owkkhMawtV3OAAAgIeAxFqXcMC/ujGSsxK1TAyIO6+o1K+KI13eTuDV6PSlPQnXCKq8ZTvLDVzz/xvDBCQCbNv1A7TptqF2nDXv2HqJP324A1KlTg/j4RL1Jg4MHT1C8RA3Kla9PufL1SUlRZQ8AihXzYOeuLQwaNJ6HudotQ2z/7jc6+PSmg09vjh08Race2skYr5pV8m0bi3vmPEM+rZvw+OGzN+p4nwhPgPAEfKjcQnsq0C95rlnJshwlSZLV64uyLD+VJKkDcECSpC6yLF9Gn5rAOkmSMtEO/L6VZdnwlMMbOHPsPE1aNODg5d2kqlKZPX5RdtjG7WuYO2kJkeFRzF05jZDgMH7Z/y0Ax/afYuPqrWxa/R1Lvp7Ln6e2I0kSqxetJy4mXl+RWsPLOZsp8/N87ZF4O4+T+uAlTn19AYj6+RDuE3piZGdNsSXDteWg1nC/nfYEllKbp6G0s0HOzOTl7G+yNxAXhFNHz9GsZSNO/PM3qSrtMWiv2brja2ZMXEhkeDSfr1uAlbUlkiRxN+gBc/0Nn9Bw52QAFX28mHX6K9JVafw6JefYwM++n8bOaZtJiIhl3/Jf6Ld2HG0n9+BV0DMu7dI2cm3GdcXS3opuiwcDoMlUs7rDLGxc7On9xUgUCoX2qL19Z0g4cVX/BtQagud+Q6kftWUZs+sYqQ9f4thHW5bR2w9h17Y+9l2bQ0YmmrR0no8u2GkRoC33q7N+oPkvU5GUCh7/epr4B68o20/b+Xv40wnMnG1pe3ARxtbmyBoNFYb6srfZNMwcrGiydQKgnQ179ucFQk+9ec/6heOXaNC8Lr+d/5k0VRqLJ63IDvvix2Usm7KKqPBopi6fRFhwGJv3rAfg9IGzfPfljwye2B8bexv8l2r1qjPVDPYboafn1NHzNG3ZkGNX/kKlSmXGuAXZYVt2fMWsCYuICI9iweczCHkZxq6DWgfdkX0nWf/Ft5Qu58nK9QvQqDU8uv+EmRMW6el4zfEjZ2jRqgkXAw6hSkll4uhZ2WE/79rE5HFziAiP4quNS7G2tkKSJO7cvs+0yQvyTTM/bp28TlWfGiw9vY50VRrfT9mQHTb++5lsm7aR+IhYfl/+E8PXTqTz5J68CHrGuV05gwrvNnUIOnuTdFXaG3VdO3GVWj612HR2C2mqNNb6f5kdNmfbfNZP+5qY8Bg+GdSeziO6Yu9sz9dH1nLtxFXWTSvYmnYjpZKZYwYyYuYy1BoNnds0o0zJYuzadxSATz9pxZMXr5i1ciMKhYLSJYqwYFLOiUgzRg9k+vJ1ZGRmUtTNlUX+wwukNz+mzFvOPwE3iYtLoEWnvowa0o+u7fOdnykYag3BczZT+ifd+tAxqz6M/vkQdn4NcOjqg5yRiZyazrPRn79T+jHL1+G6cZn2iNC/D5Px+DnW3bSTIYm/78OiRWOs2reETDWa1DQip+acgCSZmWJWryZRi78skK7oZetwy9KV+FeWru5Zun7bh2VLrS45Q42clkZEHl3m9WoStUhfl6zWcHjuNnr9OA2FUsGNXaeJeviKGn20XrDr24/z6EQgpX28GHVmdfYRof9XZLWGezO+p8avM5GUCl7tOEny/WCK9m8JQPCPxyg1uSvG9lZUXKGtx+VMNZfbzMKuTnk8Pm1C4p3n1Du+HIBHS38l6nhgvvoOHjyBr29z7t49hyollaGfTcoO+/vvHxkxYoqeZyA3s2ZOxNHBjrVfaz2XmZmZ1G/Q7q35PHX0HE1bNuT4lb9RqbTHJr/mdZ0YGRHNynULsLKyQpLgXtBD5k3J//QiwftHepc1moKPi8qudQvNOH4y0d/Y9F/RXfWyUPR0sPrfzxt/VwbIKW8X+pcIyrB5u9C/xDpJ31P0XxCdXrBZ03+DxIzC+63a2VUqNF2RmsLbVPvb2YVvF/oXkCztCkUPwO0aho+7/S+wdyg8GyysLsYvcflv3P+3qZ36fz/prKB8Eqe/7O6/ooSN4QMJ/gseRl5772fmrC3Wt9D6OGNf/vze82sIsRxIIBAIBAKBQCD4yBCDAIFAIBAIBAKB4CND7AkQCAQCgUAgEHxUaD7IBTqFi/AECAQCgUAgEAgEHxnCEyAQCAQCgUAg+Kj4kI/uLCyEJ0AgEAgEAoFAIPjIEJ4AgUAgEAgEAsFHhfAECE+AQCAQCAQCgUDw0SE8AQKBQCAQCASCjwrxqlwxCBC8gXRNZqHpephpVWi6nE0K54231igLRQ9ARKpFoelqUT640HTtf25fKHqsFaaFogfA1qLwdCkpvDPwyiqsC01XVK/JhaInPLjw3o5d5fqaQtOVuX9zoeky8h1UKHoe151VKHoASirNCk2XjWnh1e0lzJwKTZfgw0AMAgQCgUAgEAgEHxXiPQFiT4BAIBAIBAKBQPDRITwBAoFAIBAIBIKPCnE6kPAECAQCgUAgEAgEHx3CEyAQCAQCgUAg+KgQpwMJT4BAIBAIBAKBQPDRITwBAoFAIBAIBIKPCo3wBQhPgEAgEAgEAoFA8LEhPAHvEUmSOgN/ABWBnoCpLMszcoV7ATtkWa4oSdIzIBFQZwWfkWV5XJ70BgKfA68AY+Au0F+W5RRJkuYDSbIsr/q37n/O0ik0bdkQVUoq08bN587Ne3oyS7+cQ5XqlZAkiWdPnjNt7HxSklVvTdvNpxreC/shKRU8+eUU99bt1Qm3LuNOnTXDsa9aklvLd3F/04HsMGMbC2p/8Rm2FYqCLHNl4mairz3KV9ekRWOp37weaapUFk1czv1bD/VkFqybRYXq5cnMUHMn8C7Lp36BOlNN4zYNGT5lMBpZRp2p5st567hx5VZ2vDJNq+E3V5uP6ztPcXbjXr20/eb1p6xPdTJU6fzp/w2hQc8A6LTyM8o19yY5OoH1baZny7tWLE6HJYMxsTAjLjiS3ydsAFWSTpqOPtWpsHgAklJB8PYTPFu7R7d8uzbEc0wHANTJadyZ+i1Jd15g6uFI1XWjMHG2A42G4J9P8GLLwXzLDsCkTh1sxo0BhRLV/v0kb//FoJxRhfI4btxA3PyFpJ0+jcLFGduZM1E6OiBrNKj27iPl991v1JWX/vOH4OVTk3RVGpv81/Ls9hM9mdYD2uI7uD1uJd0Z7tWfxNjEAqU9asFIajevTZoqjVWTvuDRbX0b6jCgPZ2HdqZISQ+6VfuUhNgEACysLZj+1VSci7igVCr5ffPvHNl1NF9dg+Z/Rg2fmqSp0ljv/xVPDeTDd4Af7QZ3wK2kO4O9+mbno1GnpnQa0QWA1JRUtszayPO7z3Ti9pg3iCo+NUhXpbHNfz0vg57qpe9Y1IXP1k3AwtaKl0FP+W7iWtQZmZSrV4lRm6cRFRwBQMChy+z/+ncAWgxpR6MeLZBlmYj7waTEJVG+STXSVens9N/Iq6BnenocijrTd904zG0teRX0jB0T16POUONc2oMenw+naGVPDq7ayekt+wGwdXeg1+pRWDvbIWtkLu04Doe3ZadnWrc2thPGgFJJyt79JP20w2AZG1csj9Pm9cTOXUjqyTMA2M2cimnDemhi44jsOzjf38cQ1k29KTr/MySlguhfjxK+Qdd2bVvVwd2/D7JGA2oNwQu+Jfmfu++kIz9mL13NmfNXcLC346+fN/2f0zv/OJyVR2+ikWU6Vy/B4AbldcK3XXrAgdvalwSqNRqeRidyckI7VBmZzN5zjejkVCRJoqtXSfrUKfNGXecuX2P519+i1qjp2q41Q/t20wmPT0xizvKvefkqFFMTExZNH0fZUiUA+HHX3+zedwRJkihbqgSLp4/H1NREJ37veYOp6uNNuiqdrf7reGHA1p2KujBi3UQsba14HvSELVm27jusA/U6NQZAoVTiUaYI42sMwcTClKGrx2KbZYOhP53i/tbD2em5N6tGrUX9kBQKHu04xZ087ZVNGXfqrR6GQ9WS3FjxG3dztVcdL68hMykVjUaDnKnmUNu5byw/gKUrZ9OydVNUKSrGjpzOzRt39GS+XLcEL++qSBI8fvSMsSOnk5ycwphxQ+j6qbbuNzJSUq58acqXqkdcbLxBXaMXjKRO8zqkqVJZmU892HFAB7pk1YNdqnXPrgctrS2Y/tU0XLLqwd82/87hXUfemj9B4SE8Ae+XXsA5tAOAHUCPPOE9gdw9Kh9Zlr2y/sZhmJ1Z4ZWBdANp/is0bdmQEqWK0bJOJ+ZMXszClTMMyi2dvZoOPr1o36wnIcFh9B3y9tuRFBI1lw7kTJ+VHGo6lRKd6mNTroiOTHpsMgGzf+T+pv168b0X9SPs5A0ONp7C4RYzSHgYkq+u+s3rUsyzKN0b9mHZ1C+YumyiQblDfxyjR+P+9Gk+CFMzUzr2bgfA1bPX6dtyCP1bDWXJpBXMWDVFJx+fLBzITwNXsq7VVKp2qI9zGd18lG1WHUdPN75qNpk9M7fSfknO2zUDfj/LTwNW6t1Lp+VDObriV9b7TufO4as0HNZOV0AhUXH5YK73Xs75xpNx79wQyzzlp3oeyT+dFnLRZxpPVv9B5S+GASBnqrk/7ycuNJ7MZb85FBvUWi+uri4FNhPHEztlGlH9B2DWojnKEiUMylmPGE76P//kXFOrSdywgah+A4gZMQqLzp0Mx80HL58auHl6MKnpKL6dsZHBi4cblLt/9R5L+8wj8mVEgdOu7VObIp4eDGo8mC+nfcW4pWMMygVdvcP0XjMIexmuc73DgPY8f/iCkW1GMeXTqQybMwwjY8NzLt4+NXH3dGds0xF8M2M9ny0eaVDu3tW7LOwzl4g8uiJehjPv05n4+47n9693MnzZaJ3wKs28cfF0Z06zsfw88xv6LPnMYPpdpvfh2NZ9zPUZR3J8Eg17NM8Oe/jPXRb7TWGx35TsAYCdqwPNB/qxtP10FraZjK2bA551KrC82UR+n7mFrkuGGNTTbnpvzmw9wAqfSajik6nTwwcAVVwSf8//gVNb9unIazI17F38M5+39Gdt5zk07Ncao5JZdqJQYOs/nujJ04noPRDzli1ywnKjUGAzahhpl//RuZxy4BAxE6cZvM83olBQbPFwHg9YwN0WY7Dv0BizssV0RBLP3+Rem/HcbzuR5/5fU3yFYRv6X+jk14pNqxf/K2mpNTLLDt9gfY8G/DGsJYfuBPM4MkFHZmC9cuwa2pxdQ5szzqcyNYs7YWtuglKhYHLLqvw5vBU/DWjKzutP9OLq6FKrWbzmGzZ+Po89P67nwPEzPH72Qkdmy0+/UaGMJ39uW8vSWRNZ/vUWAMIjo9n++152blnNXz+sQ6PRcPDEWZ24VZt54+rpzoxmY/lh5ib6Lxlm8D66T+/Lka37mOEzluT4ZBpn2fqhzXuY7zeF+X5T2L1yO/cv3yE5PglNppqdi39gdssJLOk8g3IDW2JT1gPQ1vO1lw7gZJ+V7Gs2lZId62WHvSYtNpmrc37S6fzn5lj3JRxsNatAA4CWrZtSqnRJ6ni1YtL4OXy+ZoFBudkzltKsYQeaNujAq+BQhgzrC8C6r7fi06gjPo06snj+F1w4dyXfAUAdn9oU8SzCgMaDWDPtK8YvHWtQLuhqEFN7TSfsZZjO9Q4DOvD84QuGtxnJ5E+nMPwN9eD7QFOIfx8qYhDwnpAkyQpoCAwBesqyfB+IkySpbi6xT4Ff/8f0jQBLIPb/eq+GaOnblL92ajvggdduY21rhbOr/ivHk5KSsz+bmZmB/PY1eA7epUl8Fk7yi0g0GWpe/H2JIm1q6sikRScQc+MJmgy1znUjK3Oc61XgyS+nANBkqMlISMlXV5M2DTnwu3ZGJ+j6HaxsrXB0cdCTu3jicvbnOwF3cXF3BkCVkuPVMLPQzV9Rr9LEPA8n9mUk6gw1t/ZeokJr3XxUaF2TwD+0DVlwwCPMrC2wcrYD4PmVe6jidWf4ARxLefDsstbr8vjcLSq1raMTblujDClPw1A9j0DOUBP21wVcfGvpyMRffUBmvPa3ibv2EFN3bZ7TI+JIvPUMAHVyKskPX2Hqpl8erzGuWAH1q1eoQ0MhM5PU4ycwa9RQT86iaxfSTp9BExuXfU0THUPmA63XRVapyHz+HKVzwV9bX7NVHc7uPgnAo4AHWNhYYudiryf3POgpUcGRBU4XoEHr+hzdfRyAewH3sLSxwsGAXTwOekx4cLjedWSwsDIHwNzSjMS4RNSZan05oHarOpzOysfDgAdY5pOPZ0FPiQzWH8g8uHaP5ATtb/nw+n0c3R11wqu3rs2lP04D8DTgIebWlthk2VhuKjSowvUDlwC4tPs0Xq1rG7zf3CiUCozNTFAoFdgXceTu8esAvMiyZWsDeso0qMzNA9rn6eruM1RprbXNpOgEXt58giZPOSVGxmV7FNKSUwl//CrbTowrVSAzOAR1iNb+VMdOYNZY3/4su3VGdfKsjv0BpAfeRJOQf6c1Pyy8ypL2LIz0F+HIGZnE7j2LbWvd51CTkpr9WWFRsLqvoNTyqoqtjfW/ktbtkBiK2VtS1N4SY6WCNpWKcuphaL7yB4OC8a1UFABnKzMqutkBYGlqTClHayKSUvONe+vuQ4oXcaeYhxvGxsa0bdGYE+cu68g8fvaSejWrA1CqRFFehUUQFaNtxjLVGtLS0snMVKNKTcPZUfeZ9G5dmwt/nALgScBDLKwtsM3H1q8euAjAhd2nqJHntwOo26ERl/ecByA+Mi7bo5CanEr8oxAssupMx6z2KimrvXr+9yWK5dde5VMHvAtt/Vqwa8efAFz75wa2tta4ujrrySUl5m57TZEN2F+X7p/wx+/6E2mv0daDxwC4G3APKxtLg/Xgo3zrQbnA9aDg/SAGAe+PTsAhWZYfADGSJNVA6w3oCSBJUj0gWpbl3GtTTkqSFJj1Z3jKGnpIkhSIdkmQA6C//uRfwNXdhdCQnIc+LCQCVzf9ighg+dfzuBh0hFJlS/Ljtzvfmra5mwOqV9HZ31NCYzB30+8UGcKqhAtp0YnU+XI4rY8sofaqoSjNTfOVd3ZzJiIkp4MYERKJcz75AFAaKWnbrTUXT17JvtbUtxG/nvmRL35czuJJK7KvW7s6EB+Sk4+E0BhsXHXzYZNXJiwGm7fkNeLBSyq00jYyVfzqYuuuWymbuTmQmivN1JCYN3bki/T2IepEoN51s2LOWFcpSfz1/JdSKZycUUfklJ86MhKFs3MeGSfMGjci5e89eaNno3Rzw7hsWTLuFHy5hL2bIzG58hkTFo29a/75fBcc3RyJzGUXUaGROLo5viGGLn9v20OxMsXZcfUXvjm6iY3zNhlshAEc3ByJDonK/h4dFoWDa8F15aZ5z1YEnLquc83O1UGnnOLCorHPYw+W9takJKSgUWvnrGJDo7HLVZalapRj9sHPGbttJu5ltR3AuPAYjm7Zy7ILG1l5ZQsKpZK7JwOz48SHxWCbR4+FvTWqhORsPXGh0di+w29mX9SJIpVKkh6ktROlsxPq8JyBkToyUm8gqXBywqxpY1L+yt/+3hUTN0fSc/1m6aHRGBv4zWzb1KPiifWU3jaH51PW/mv6/00iElNxszHP/u5qbU5EouGOvCojkwtPwmlZQd87+CoumXvh8VT1yL/+ioiKxs0l5/dxdXYiIjJaR6Z8mZIcO6PtoN+684DQ8AjCI6NxdXZkYM9OtOw+BJ/OA7C2tKRhHW+duPaueeuEGOzzPLdW9tak5LLBmDy2DmBiZkKVpl5cO3hJLw+ORZ1xqFKCqOuPATB3syclJCY7PCU0BnP3grVXAMgyzXdMx/fQIsr08XmruLuHK6+Cc2bcQ16F4+7halD26w3LuPPoAmXLleLbb37SCTM3N6N5y8bs3XPYYFwAJzcnnXowMjQKp3eoB//atofiZYqz8+ovbDn6DRvmbcy3HnwfyIX496EiBgHvj17kzPL/mut7N0mSFOQsEcpN7uVAa/JJd6csy16AG3ALmJKPnEEkSRomSdJVSZKuxqdGvUlO71p+D/f0cQtoWNWXxw+e0q5TqwLchIFrBaw4JCMF9lVL8uiHYxxpPYtMVRoVx7bPX96ArjdVUlOXTSTg0k2ddf+nD52jZ5P+TBs8m+FTc5ZAFCTtd9UP8NfUzdTp14oRexdjYmWOOiNTV8BQ+eVTDdk3rESR3j48XKS7jl9pYYrX1oncn/MD6qQ37OEowG9lM3YMiZs2g8awU1QyN8du0QIS1q5DTsnfa6MX7/9gJ29P2+APU+D4tZrW5Mmdx/Sq1ZuRvqMYs2gUFlYW/4mu11SuX5XmPVry87If3pp+3uQN34P2vxe3nzKz4SgWt53CyW0HGbl5KgAWNpZUb1WbWY1HM7XuMBRKBeUbV8uj5/9u768xsTBlwMaJ/L3wx1x28vZ6yHbCaBI2fJOv/f1PFND24g9f4m7z0TwZuhQP/z7/nv5/EUOlb7AKAc48DMOrqCO25rrr8FPSM/H/4wpTWlbFytQ4f10Gyiiv7Q3t042ExCS6Dh7P9j/2UaFsKZRKJfGJSZw8d5nDO7dw4s9tqFJT2Xvk5FtvXN8G3/68VW9Zi0dX75OcxxNramHG6I3+XJv7M5lZ9eKbnp2CcKTjQg62mc3JPp9TbmBLXOqWf6P8u7S940bNoEq5Rjx48JhOXfx0wtq0bc6VS9fzXQqk1aV/7V068bWa1uTxncf0qNWb4b6jGLNodL71oOD98OEszvqIkCTJEWgOVJEkSQaUaKuNqcAzoCnQFaj/lnSKkTPTvwnInr6RZVmWJGkvMBZYXtB7k2V5M7AZoKxzTZ2nvc/g7vTo1xmAmwF3dGYf3DxciAjPf9Cg0Wg48PcRho7uz+4db3ZOqEJjMC+SM9tg4e6AKjyuQPevColBFRpDTIB2lublvitUHKM7COg6sBMd+3wCwN3Ae7h45Mxcu3g4E5VPPoZMGoCdox3Lp84xGB54+SZFSnhg62BLfEw8CWEx2Hrk5MPG3YHECN18xOeVcXMg8S15jXocyo/9tT+po6cb5Xy8dMJTQ2Mwy5WmmYcDaWH6q8KsKhWn8urhXO+1nIzYnMZOMlJS/btJhO4+R8SBf/Ti5UYTGYnSJaf8lM7OaKJ0y8+oQnns5mnXukq2tpjUq0uCWk3auXOgVGK3aAGqo8dIO6O7vtcQrfq3xaendiD55OYjHHLl08HNkdiI/331W/sB7fHr5QvA/RsPcM5lF07uzkSHx+QXVY/Wn7Zm5wat1yvkWShhL8MoVqYo9wMfANCmvx8ts/Lx6OYjHD1yZkcd3ZyIiSi4LoDiFUowYsVolg5YSFJcYnb6SknBsxvacnqcJWvn5khcnrwkxSRgYWOBQqlAo9Zg7+5IXNY9pOYaBN4+FUCvxUOxtLemfP3KGJsaM+Fn7fMQ/jiEEjXKZsvaujmQEK77eyTHJGJuY5mtx87dkYQC/GYKIyUDNk3k+l/nuX34H1pnLblWR0aidHXJltPan+7MsnGF8tgv1NqfwtYW0wZ1Qa0m9cz5t+rNj/TQaExy/WYm7o5kvOE3S75yB5PibijtrVEXcGN6YeFqbUZYQs5vHJ6owtnazKDsoTvB+FYuqnMtQ61h8u7L+FUuSgsDHgIdXc5OhEXk1A/hkVE4O+nOwltZWrB4xnhA2+Fs0+Mzirq7cv7KdYq4u+JgZwtAiyb1Cbx9j44dO6Mwt2X+gc95euNxnjrBQc/WE2MSsMhlgw7ujsTlscG67Rtyec85nWtKIyWjN/lz6a+zqA9ezb6eEhqDhUdOHizcHVAZqG/z43XblhadwMtD13D0Lk3E5fs6MoM/60O/AZ8CEHj9FkWKumWHeRRxJSw0//1OGo2Gv3YfYMz4oezY/kf29c5d2/HH7/v05DsMaI9fr7YAPMhTDzq7O71TPej7aWt2bNgFQMizkKx6sBj3A++/JWbh8CGv1S8shCfg/dAN+FGW5RKyLJeUZbkY8BRohHb2fw3wWJbl4DclIsvyy1yeAUNHRDSC7Lb//8z2736jg09vOvj05tjBU3Tqod2Q6lWzCokJSUQa6DwX98xpMHxaN+Hxw2dv1RMT+ARrTzcsizmjMFZSvGM9Xh2+VqB7TI2MJyUkGuvS7gC4NqpMwoNXOjK7t/1F/1ZD6d9qKKcPncOvWxsAKteoRFJCMtEGGvMOvdtRt1lt5o5aqDMTUrRkTqNXvmpZjIyNiI/Rzqy8uvEEh5Ju2BV1RmmspGr7etw7qpuP+0ev49VFexpFUe8ypCaqSIqMe2MeLR1tAO2MUNMxnfhn+3Gd8ISAx1iUcsO8uDOSsRK3Tg2IyFN+ZkUc8fpuErdGryflie7638prhpP88BXPvzG8iS03GffuoyxaFKW7GxgZYdaiOWnnL+jIRPXoRWSPnkT26Ena6dMkrP5SOwAAbKdNJfP5C1J2/fZWXQBHfzzITL9JzPSbxNUjl2ncVes+L+NdDlViil5j/i7s/WEvI31HM9J3NBcOX6RV1xYAVPCuQHJi8jt1zCNCIvBuqF2qYOdkR9HSRQl9nuPCP/zjAab4TWSK30T+OXKJpln5KOtdjpTE5HfKh5OHE1O+mcHaiV8S+jREJ/3FflMIPPIP9bo0BcDTuyyqxBQSDNjY/YtB1PCrB0C9rk25cUQ7AMy9f6Bk9TIoJAXJsYnEhERhbm3Bii6zWOw3BVmtyV7KVty7DKmJKSQa0PPoYhDV/LRbn2p1bULQkbc/25+uGEb4oxDObNW1yYy79zAqWiTb/sxbNif1nK79RXTrTUTXXkR07UXqydPEr/ry/zQAAEi58RBTT3dMirkgGRth374x8Uev6MiYlMjpqJlXKYVkYvTBDQAAKnvY8yI2iVdxyWSoNRy+E0zTsu56compGVx7EYVPrjBZllmw/zqeTtb0q1tWL05eqlQoy4vgEIJDwsjIyODg8bP4NKyrI5OQmERGRgYAu/cdoWb1ylhZWuDu6szNO/dRpaYhyzKXr92gVIliaFRxZMY8Z77fFAKOXKFBl2YAlPIuS0piCvEGbPDexSBq+Wnn2Bp0bUbAkZzJDnNrC8rVrUTAUd0JkEErRhH6KJgjW3U7ztF52qsSHesRfER3SV5+KM1NMbI0y/7s3rQKcff0m/3vtmzP3sx7YP8xPu2lnYyrWbs6CQlJhIfr73nyLFU8+3Obts15+CDnxDFrGysaNKrNwf3H9eLt+WEvI3xHMcJ3FOcPX6BV15YAVPSuQHJiyjvWg5HUaOgFaOvBYqWLEvo8//0mgsJHeALeD73Qn53fDfQG5gJfoZ3Bz8tJSZJe76q5KctyfwMyPSRJaoR2gBcMDMwVNluSpAmvv8iyrDul8w6cOnqOpi0bcvzK36hUqUwfNz87bMuOr5g1YRGREdGsXLcAKysrJAnuBT1k3pRlb01bVmu4PnMbTXdM0x4R+utpEh68onR/bafs8Y/HMXO2pdWhxRhbmyNrNJT7rC0Hm04lM0nF9Vk/Um/9KBTGRiS9iODKhG/y1XXh+CUatKjL7xe2k6pKY/HEnDX9q39azlL/z4kKj2bq8kmEBYexZe8Gbf4PnOG7NT/i064Jbbu1JjNTTZoqjTkjF2bH16g17J+7jf4/TkOhVHB912kiH76iVh9tPq5uP86Dk4GU9fFiwunV2iNCp+Tca7evR+NZryIW9tZMvriWk2t+5/qu01TtUJ86/bSzyHcP/0PAb6dplKf87s34nhq/zkRSKni14yTJ94Mp2l9bmQf/eIxSk7tibG9FxRXaYxHlTDWX28zCrk55PD5tQuKd59Q7rjXRR0t/Jep4oOECVKtJ+PIr7Fd9DgoFqgMHyXz2DPMO2iPoVHvyX4dtXLUq5r5tyHj8GMet3wKQuGUL6Zcu5xsnN4EnruHlU5M1ZzaSpkrjG/+cNddTt81m89T1xEXE0mZgOz4Z0Qk7Z3uWH/6SwJPX2DJtwxvTvnLiCnWa12bbue+0R4ROXp0dtviHhaye+iUx4TF0GtSR7iO74eDswDdHN3LlxD+smfol27/6hSmrJ/PN0Y1IksTWpd9lH5uXl+snruHtU4u1ZzaRrkpjfa58zNg2h01T1xMbEUPbgZ/QcURn7JztWXX4awJOXmPTtHV0G98TK3trPlukPR1JrdYwvf3k7DRun7xOVR9vFp9eS7oqnR+mrM8OG/P9DH6aton4iFj+WP4zQ9dOpOPkXrwMesr5XScAqNG2Hk37tkatVpORms6WsdqViM8CH3H94CVm71+JOlNNWNBzIp+FMf30l2So0tiZy5aHfD+V36ZtISEilv3Ld9B37Vh8J3/Kq6BnXN6lXdJh7WzL+D1LMLMyR5ZlGg9uy+etpuBeoTi1ujYh5O4LJh7Q1h+mWzeRdvEyqDXEr/4axzUrQakgZd9BMp8+w6KT1vuX8tebvY52C2Zj6u2Fws4W1792kfjtNlL2vX3wi1pD8JzNlP5pvvaI0J3HSX3wEse+Wk9S9M+HsPNrgENXH+SMTOTUdJ6N/vzt6RaQKfOW80/ATeLiEmjRqS+jhvSja/s2/1NaRgoF01tXZ+Sv59FooGP1EpRxtuG369qNsN1reAJw4kEI9T1dMDfJ6TYEBkez7/ZLyjrb8Om3WnsZ26wSjcu46StCeyTlzAnDGe4/H7VGQ2e/lpTxLM7Ov7XHEPfo2JYnz4OZuWQNSqWCUiWKsXC69iC8apXK06pZQz4dOgGlUkmFsqXonifPN09ep5pPDZafXke6Ko3vpuQ85xO+n8m2aRuJi4jl9+U/MXztRDpP7smLoGec3ZXTGa7Rpg5BZ2+SrkrLvla2VgUadG3Ky7vPmX/gc6xkBTeW7SLkxA1ktYars36g+S9TkZQKHv96mvgHryjbT3vi0MOfTmDmbEvbg4uy26sKQ33Z22waZg5WNNk6AdB6YJ/9eYHQUzff+HsdPXyKlq2b8s+NY6hSVIwblXMy347ftzBxzCzCwyNZt2kF1tZWSJJE0O17+E+cly3X7pNWnDpxnpSUNyz1BC5n1YM/nvueNFUan0/+IjtsyQ+LWD11DdFZ9WCPkd1xcHZg89FNXDlxhdVTv+Tnr7YzZbU/W45uAkliy9Kt+daD7wNNfuvePiKkD2mThuDDIu9yoP+SxUYVCksVa6RXbxf6F2ht8mbX+L9JI1XhnbhQrXzY24X+JSY9f4cNdv8HojRvf3fFv4WtIv+N6v82DpLJ24X+JawLcU5pkkfh2GB4sE2h6AGocj2/bV7/Ppn7NxeaLiPfQW8X+hcYXndWoegBaJFueLnUf8H4pCtvF/qX8LL1LDRdx14efu9d8Lkl+xRaH2fhs+3vPb+GEJ4AgUAgEAgEAsFHheaDPrencBB7AgQCgUAgEAgEgo8M4QkQCAQCgUAgEHxUCD+A8AQIBAKBQCAQCAQfHWIQIBAIBAKBQCD4qNAU4l9BkCTJV5Kk+5IkPZIkabqBcEmSpK+zwm9KklTjf8p4LsQgQCAQCAQCgUAgeE9IkqQE1gNtgUpAL0mSKuURawuUzfobBmz8v+oVewIEAoFAIBAIBB8VH9jpQHWAR7IsPwGQJOlXoCNwJ5dMR7QvmpWBS5Ik2UmS5C7L8v/8BjbhCRAIBAKBQCAQCP4jJEkaJknS1Vx/w/KIFAFe5voenHXtXWXeCeEJEAgEAoFAIBAI/iNkWd4MvOktfYZeJpbXVVEQmXdCDAIE+ZKmTi80XQmmhfcyvXR1ZqHoSaLw3uKrLES3ZuRL60LTFaFJKRQ90RlJhaIHIFWZUWi6rI0L543LACFyaqHpKizsHQrH/qCQ3+LbLu8k5H9H5oEthaLHlcJ7O3ZoIfacnMxsC01XTGZyoen6EPigFgNpZ/WL5fpeFAj5H2TeCbEcSCAQCAQCgUAgeH/8A5SVJMlTkiQToCewJ4/MHqB/1ilB9YD4/8t+ABCeAIFAIBAIBALBR0ZBj+4sDGRZzpQkaQxwGFAC38myHCRJ0ois8E3AAcAPeASkAIP+r3rFIEAgEAgEAoFAIHiPyLJ8AG1HP/e1Tbk+y8Dof1OnGAQIBAKBQCAQCD4qPrAjQt8LYk+AQCAQCAQCgUDwkSE8AQKBQCAQCASCjwrhBxCeAIFAIBAIBAKB4KNDeAIEAoFAIBAIBB8VH9LpQO8LMQj4AJEkSQ3cynXpV1mWl0uSZAwsAroCaWiPiJony/JBSZJsgbVAw6w454GxsizHS5JUEngKjJNleW2WjnXAVVmWt73LvS1YNh2fVo1RqVKZPHo2t2/e1ZNZ+fUCqnlVRpIknj5+xqTRs0lJVtGqrQ/+M8eg0WhQZ6pZMHMF/1wOMKinaLNq1F/QD0mp4P6OU9xYv1cn3La0O01XD8OpSkn+Wfkbt77Rbqi3dHeg2VcjMHe2BY3M3V9OErT18BvzNGXReBq1qE+qKpV5E5Zy79YDPZnF6+dSqVoFMjMzCQq4y5KpK8nMVFOzvjerty0j5IX2qN4TB06zZc02nbid5w2goo836ao0dvhv5FXQM730HYo602/deCxsLQkOesYvE9ehzlDjUtqDnp+PoGhlTw6s2smpLfuy4zQe1JZ6PZsjSZDw0wlebs45VMDBpzrlFg9EUioI2X6C52v/1tHn2rURJcZ0AECdnMr9qVtJuvMchakxNf6ej8LEGEmpIGLfZZ5+/tsby8+qSQ085n0GCgWxO48Suel3nXDrVnVxndQHNDJypprQRd+ScvUOAEVWjMOmeW0yo+N56DvmjXpeM3rBSOo0r0OaKpWVk77g0e1HejIdB3Sgy9DOFCnpQZdq3UmITQDA0tqC6V9Nw6WIC0qlkt82/87hXUcM6pm6eAINX9vF+CUG7WLJ+nlUqq61i9sBd1gyJcsuGnizZttyHbvYvPr7AuVv3MLR1GtelzRVGssmruTB7Yd6MnPWzqB89fJkZmRyN/Aeq6atQZ2Z/8vpes8bTFUfb9JV6Wz1X8eLoKd6Mk5FXRixbiKWtlY8D3rClolrUWdk4jusA/U6NQZAoVTiUaYI42sMITk+CXMbCwYtH0mR8sXRyDKbp6zj4fX7Bu+h//whePnUJF2Vxib/tTy7/URPpvWAtvgObo9bSXeGe/UnMTbxreVlWrc2thPGgFJJyt79JP20w6CcccXyOG1eT+zchaSePAOA3cypmDashyY2jsi+g9+qy7xBLRymjgKFgqQ/DxL//U7d8Gb1sR81EGStrcd8voG0wCCMShTFZeXsbDmjIm7EbfyBhO1/5qvr/ONwVh69iUaW6Vy9BIMblNcJ33bpAQduBwOg1mh4Gp3IyQntUGVkMnvPNaKTU5Ekia5eJelTp8xb85Yfs5eu5sz5KzjY2/HXz5veHuEtnH8cxsojWfnyKqmfr4sPOHD7JQBqWeZpVAInJ36CmbGSwT+eIUOtIVOjoWWFIoxqWgmFRzmMa3+CfxM1/+w8yemNe/V0tp/Xn/I+XqSr0vndfxMhWXVwuabV+GRufxRKhcG4jT9rh9+sPizyHk5KbCJKYyWdlg6lRNVSGJuboDA2Qp2eya1fT3Flg77e5gv64enjRaYqjYOTNxNxW6vX1MaCNiuH4liuKMgyh6ZsIfS6th7zHtgK7wGt0ajVPDkRyLdzF+ilO2vJZJq0bEiqKpUZYxdw55b+M/f5xkVUqV6RjIxMbgUEMc9/KZmZamxsrVny1RyKlyxKWmo6syYs4uG9x/n+Xv91+yh4v4jlQB8mKlmWvXL9Lc+6vghwB6rIslwFaA+8fn3rVuCJLMulZVkujbbT/22uNCOA8Vkvofif8GnZmJKlS9CkVjumT1zAki9mG5RbOGslvk260aZxV14FhzFwaG8Azp+5RJvGXWnbtDv+Y+ey4iv9yg1AUkg0XDyAQ/1W8rvPVEp3rIddWQ8dmbS4ZC7M/Ymb3+icpoVGreHSwl/43Wcaf3eYT+UBLfXi5qZh83oUL1WMjg16snjK58xY7m9Q7uDuI3Rp3JtPffpjamZKp97ts8MCL9+gV6tB9Go1SK+Cq9jMCydPd5Y2m8BvM7fQbclQg+l/Mr03p7fuZ5nPRFTxSdTt0RyAlLgk/py/jZO5Ov8AbuWKUq9nc77sOItVbafh1KoG5p5u2kCFRPnlgwnsvYxLjSfh2rkhluWK6MRPfR7B9U4LuOIzlWer/6DCF59pyy8tg4AuC7nSfCpXWkzDsXl1bGqWzbf8UCjwWDiCpwPn87D1aGw7NMG0TDEdkeTzN3jUdhyP2o3n1bSvKbJ8bHZY7O7jPB04P//081DHpzZFPIswoPEg1kz7ivFLxxqUC7oaxNRe0wl7GaZzvcOADjx/+ILhbUYy+dMpDJ8zDCNj/bmQRi3qU7xUUTrW78Fi/5XMXJGPXfxxhM6NetG9WT/MzEzp3CfHLgIu36Bny4H0bDmwwAOAes3rUNSzKL0b9efzaauZtGy8Qbmjfx6nb5OBDGwxFFMzUz7p7ZdvmlWbeePq6c6MZmP5YeYm+i8x/LbY7tP7cmTrPmb4jCU5PpnGWTZ4aPMe5vtNYb7fFHav3M79y3dIjte+Ybn3vMHcOh3IrBbjme47kVePXhpM28unBm6eHkxqOopvZ2xk8OLhBuXuX73H0j7ziHwZkW9+dFAosPUfT/Tk6UT0Hoh5yxYYlSxhUM5m1DDSLv+jcznlwCFiJk4rsC6HGWMJHz2TV12GYunrg3Gp4joiqZcDCPl0OCE9RhA1fxVO8yYBkPk8mJAeI7R/vUYhp6aRfOJ8vqrUGpllh2+wvkcD/hjWkkN3gnkcmaAjM7BeOXYNbc6uoc0Z51OZmsWdsDU3QalQMLllVf4c3oqfBjRl5/UnenHfhU5+rdi0evH/HD83ao3MskM3WN+zIX8Mb8WhIAP5ql+OXZ+1YNdnLRjXrDI1iztja26CiVLBlr6N2fVZC3YObcGFJ+HcDInFuG4H0o9/z5pWU6jeoQEuZXTruvLNvHD0dGNVs0n8OfNbOi3RDvYkhUSHhYP4fuBKg3Ft3R0o07gqscGR2ddq99Q+Ez/6zkRSKEhLSOH7ltOo0KEejnnaGU+f6tiXdGNrk8kcmb6VVksGZoc1n9+Pp6du8n3zqfzgO5OYR9qXvharX5EyrWvyQ5sZbGs5nat52jeAJi0aUKJUcdrU7cLcyUuZt3K6wbLe+/tB2jboRoemPTEzM6Vb304ADJ8wiHu3H9CxWW+mjZnHzMWT8/29/uv28X0jF+K/DxUxCPh/BEmSLIDP0M7upwHIshwuy/IuSZLKADXRDhJesxCoJUlS6azvkcBxYMD/eg+t/XzY/av2BXYBV29iY2ONi6uTnlxSYs6rx83MTNEebQspyars6xaW5vk+GM5epUl4Fk7ii0g0GWoe/32JEq1r6sikRicQdeMJmjyzn6qIOKKzZlsyklOJfRiCpZtDvnlq5tuYfb8dAuDW9SCsbaxwcnHUkzt/4lL256DAO7h6uOSbZm6qtK7F1T+0s47PAx5hbm2BtbOdnlyZBpW5eeAyAP/sPkOV1rUASIpO4OVN/Xy6linC84CHZKSmo1FriL1wB2e/OgDY1CiD6mk4qc8jkDPUhP91ASff2jrx468+IDNe+zvFX3uIqXtOntUpaQBIxkokIyOQ86/ALKqXJf15KBkvw5EzMonfewabVnV1ZDQpqdmfFeamOumlXAlCHff22d7XNGhdn6O7jwFwN+AeVjaWOLjo/76Pgh4THhyun4AsY2FlDoC5pRmJcYkGZ9CbtmnEvl257cLaoF2cO34x+/PtgLu4uBfMLvKjUZuGHP5d65m4c/0uVrZWOBrI36UTV7I/3w28h7O7c75pereuzYU/TgHwJOAhFtYW2BqwwQoNqnD1gDY/F3afokbrOnoydTs04vIebefVzMqccnUqcnbncQDUGZmkJKQYvIearepwdvdJAB4FPMDCxhI7F3s9uedBT4nK1el6G8aVKpAZHII6JBQyM1EdO4FZ44Z6cpbdOqM6eRZNbJzO9fTAm2gSCtZBNq1SnsyXIWS+CoPMTJIPn8KiWQMdGVmV29bNDD46ZnW9yQgORR2a/0DndkgMxewtKWpvibFSQZtKRTn1MP8Xgx4MCsa3UlEAnK3MqOhmB4ClqTGlHK2JSErNN+7bqOVVFVsb67cLFoDbITEUc8iTrwdvyNedl/hW1uZLkiQsTLQD9kyNhky1BmsPT+TEaOSkWNQZam7svUjFPG1FxdY1CfjjLAAvAx5hllUHF/MqQ/TzcGJfRhiM225OPw4u+0UnLZeyRXh8/jZuXqWJeRJKSnQCLhWLc2/vJUrn0VumdU2Cdp8DIDTgMaY2lli62GFiZU7ROuW59espADQZatKynhuvfi25vGEv6vRMAFKi9W2zRdum/L1rPwA3rt3GxtYaZwN105njF7I/3wwIwi2rbipdzpOLZ7WD4aePnlOkuDuOzobbyP+6fRS8f8Qg4MPEXJKkwFx/PYAywAtZlg21WJWAQFmWs3szWZ8Dgcq55JYDkyVJUv4vN+Xm7kLoq5yZ1bCQ8OyKJS+r1i3i2r1TlC7ryfdbcirSNu2ac+LSHrb9up4pY+cajGvpbk9SaEz29+SwGCzd9TsMb8OqqBNOVUoQEZC/q9PFzYnwkJzGOCI0Amd3/YHNa4yMlPh1a8OFkzmVXtWaVfj12DbWbl9FqXKeOvI2rg7EhURnf48Li8E2z6DE0t6a1IQUNGrtCsX40BhsXfMfuACE3n9JqToVsbCzwtjMBKeW3pgV0VbOZm4OpObSmRYSjalb/uXn0duH6BOBORcUEnWOr6Bx0BZiTt8k4br+cpvXGLk5khEalf09IywaYzf9RsKmdT3KHttIie/m8WrqV2/M25twcnMiMiSnkxgZGoWTAX358de2PRQvU5ydV39hy9Fv2DBvY/YgNTcu7s6E5bKL8NAIXN7Q0TYyUtKuWxsunLycfa1azSrsPL6Ndb+solR5z3zj5sbJzYkInfxF4uSWvz0qjZS06dqKKyf/yVfG3tWRmFz2EBMWg32eMrOytyYlITnbBmNCo7HLY4MmZiZUaerFtYNa23cu7kpidAKDV41m3v7P+WzFKEzNTQ3fg1vee4jG/i02XhCUzk6ow3N+J3VkJEpn3fJSODlh1rQxKX/t+b/pcnEiMyznt8kMj0Lpov/bWPg0pMifW3FZu5jo+av0wi3bNCP54Mk36opITMXNxjz7u6u1ORGJhjvyqoxMLjwJp2WFInphr+KSuRceT1WPd68//wsiElNxs86VLxtzIhJVBmVVGZlceKybL7VG5tMtx2m+Zj/1SrlSunhR5OT47PAEA3Wnras9cSE57Ul8WAw2bvbYuNoTn8smc8et2LIGCeGxhN19oZNW6N0XVGpVC2sPR9ISUnCtUhJrD0eSQmOwdtUtYys3exJDc9JPDIvBys0e2+LOpMQk4vvFMPodWEzrFUMxznpu7D3dKFqnPH3+nk+PXbNwq1ZKr1xc3ZwJDcmZ4AgLicD1DZMPRkZKOnT34+wJ7QD/ftBDWrfzAaCqdyU8irrl247/1+3j+0ZTiH8fKmIQ8GGSdznQzrfISxg+7UrnuizLT4ErQO//6a4kSe9SfpPE/mPmULtScx49eEL7zr7Z1w/vP0Hzeh0Y2nc8/jPyWwOur+ddvWlGFqa03Dyei/N/JiPJcCOjVfVuuqYvn0zApRsEXL4JwL1b92lXuxs9Ww7k162/s/r7pW9NXq/QCiKTh4jHIZzctIcRP89i2A8zSAx6jvx6RtugUsPYN6yMR+/mPFq0PeeiRuZKi2mc9xqJbY0yWFYoln8CBm1C/94TjlziYcuRPB++BNdJfQt8fwVQZ1BfftRqWpPHdx7To1ZvhvuOYsyi0VhYWRjQU7B8vWbGcn+uX7pBwOUbANy7eR+/Wl3p0WIgv27dzZrvlxXo/t41f5OWjufG5ZvcvHIrXxmDj1OeNA3lN68NVm9Zi0dX72cvBVIqlZSoUopTPx9hQbsppKWk0WFUF8O38D/YeMF4++9kO2E0CRu+Ac3/sSkuQBkBpJw8z6vOQ4iYOB+7UQN1A42MsGhan+Sjp9+oKr/K3BBnHobhVdQRW3PdlZ4p6Zn4/3GFKS2rYmVq/EZ9hYUh729+1ZWhfCkVErs+a8HhcW25HRJDmAHPk97zkl+7lc8zbmxmgs+YThxdrb8X6tquU8SHRdN0Rk/cvcsQcu1htpdWv1o3bC8KIyWuVUoS+NNxfvKbTYYqjTqjtMtnFEYKzGwt2d5xPqeX7KD9BgNt5DvWTXNXTOfqxQCuXQ4EYPPXP2Bja8OfJ7bTd2gP7t56QKY6n/1E/3H7KHj/iI3B/+/wCCguSZK1LMt5108EAd6SJClkWdYASJKkAKoDeXfuLgV+B84YUiJJ0jBgGIC9hQejRo2iV/+uANwMuI17EbdsWTcPV8LD8ndpazQa9v55mBFjB/LbL3/phF25eI3inkWxd7AjNiZOJyw5NAYr95zZHEs3B5LDYvPVo5cHIyWtNo/n8Z8XeHbwql74pwO7ZK/dDrpxV8d16eLuQmRYlF4cgGGTBmHvaIf/lFk595qU0widP3GJGcsnM2B0b3w7tcJKYczLG4+x88iZdbVzcyA+XDcvyTGJmNlYoFAq0Kg12Lo7EB/x9vxe3nWSy7u0M4rjJvYgNWu2KzU0GrNcOk09HEkzUH5WlYpTYfUwAnstJzM2SS88MyGF2PN3cPSpTvI9w2u9M0OjMM41M2Ts5khmeIxBWdAu/zEp4Y7S3gZ1bMGWYXQY0B6/Xm0BeHDjAc4eOTPyzu5ORL9BX158P23Njg27AAh5FkLYyzCKlSnGy38i+HRQF7r00W6WDgq8i1suu3B9k11M1trF4ikzs6/ltotzxy8yY/lk7BxsiYuJ14vfeUBHPumjXdN/L/A+Ljr5cyY6PFovDsDAif2wc7Rj9tB5+aZpKil5euMxDrnswcHNgbg8ZZYYk4CFjWW2DTq4OxKXxwbrtm/I5T3nsr/HhEUTGxbNk0DtxuXLBy7oDAJa9W+LT89WADy5+SjPPTgSWwAbfxvqyEiUrjm/k9LZGU2UbnkZVyiP/UKt11Fha4tpg7qgVpN6Jv81+QZ1hUdi5Jbz2xi5OqGONPzbAKRdv4VRMXcUdjZo4rS2bt6oNun3HqHJU+flxdXajLCEnMmL8EQVztZmBmUP3QnOXjLzmgy1hsm7L+NXuSgtDHgI3heu1uaE5Zr5D09Q4WxlblD2UNBLfCsbnoCwMTOhVnFnrt9/QtFaTXOuuzuQkMeu4sNisPNw4HnWd1s3BxLDYzEyMcI2l02+jutQwhX7os6MP6jdimfj5sDYfUtY32kOSZHx7F/0M8/3XqHBxC6Y2lgQ9yyM0q1rkpRHb2JYDNa5lllauzmQFB6HLMskhsYQFqj1UD84cIW6I7XtUWJoLA+z2qywG0+QZRl7RzvadmxF96w1/bcC7uDu4ZqdrpuHCxFhhpfQjfYfioOTHWMH5nS+k5OSmTl+Yfb341f/Jvh5SPb3/7p9zK8efB+INwYLT8D/M8iynIJ28+/Xrzf3SpLkLklSX1mWHwEBQO6durOB61lhudO5B9wBPslHz2ZZlmvJslzLytSBH7f+Stum3WnbtDuH95+ga09tJ8m7VjUSE5KICNevEEp45lTcLX2b8ujhU73rVapVxMTYWG8AABB54wk2nm5YF3NGYaykdMd6vDh6/a1l9Jqmq4YS+yiEW1sOGgzfte2P7I1Kpw6e5ZPuWk9F1RqVSUpMIipCv2Hv1PsT6jerw8yR83VmXXKvpazsVRFJoeCH9b/Qq9UgvvCbzq0jV6nVpYk2/95lSE1MITFSP8+PLt6hmp92LX3trk24fUR/8JIXK0cbAOw8HHH2q0P4n9pOTWLAYyxKuWFW3BnJWIlrpwZEHdZNz7SII1W/m8yd0etRPclZk2vsaI2RjXZmXGFmjEOTKiQ/CiE/Um4+xLSkB8ZFXZGMjbBt34SEY1d0ZExKuGd/NqtcGsnYqMADAIA9P+xlhO8oRviO4vzhC7Tq2hKAit4VSE5MISai4IOAiJBIajT0AsDOyY5ipYsS+lyb/13f/5G9kffkoTN88unb7aJz7/Y0aFaXGSPn5W8X3hWRJCnfhu/PH/5mSOvhDGk9nLOHz9OmW2sAKtWoSHJCMtEG8teulx91mtVmwejFBmcBX6c5328KAUeu0KBLMwBKeZclJTGFeAM2eO9iELX86gPQoGszAo7kLDEyt7agXN1KBBzNuZYQGUdMSDRupbQbIqs0rMarh8HZ4Ud/PMhMv0nM9JvE1SOXadxVuwShjHc5VIkpeoOM/4WMu/cwKloEpbsbGBlh3rI5qecu6MhEdOtNRNdeRHTtRerJ08Sv+vKdBwAAaUH3MSpeBCMPrS7LNs1IOX1RR8aoWM7mUJMKZZCMjbMHAABWvj4kH3rzUiCAyh72vIhN4lVcMhlqDYfvBNO0rLueXGJqBtdeROGTK0yWZRbsv46nkzX96r5hU/97oLKHPS9i8uSr3BvylSssJjmNhNR0AFIz1Fx+FoFlUjiStROSlT1KYyXV29fn7tFrOmndPXoN7y7a062KeZchNVFFYmQcwTce41TSDfuizjpxw++/ZEmtkaxsNJ6VjcaTEBbD2k9mkRQZj7GZCcbmpoTdeIJzxeJIRkpin4VToX09Hudpox4fvU7lro0AcPcuTVpiCskRcaRExpMYGoN9KW3eSjSsTPTDVwA8OnKV4g0qAdqlQQpjI2Kj4/jlu9/o3LwPnZv34fjBU3T8tB0A1WtWITEhiUgDdVO3Ph1p5FOfycNn69QR1jZWGGcdhtC9byf+uRRAclLOPr7/un38UAYAAi3CE/BhYi5JUmCu74dkWZ6OtmO/GLgjSVIqkAy8Xlg/BFgrSdIjtJ7ji1nXDLEE7aDhnThx9Cw+rZpw9toBVKpU/MfkjDm27dzAtPHziAiPYs2GJVhZWyFJcOf2A2b5a/cr+7VvRdee7cnIyCQ1NY3RQ6YY1COrNVyY8wNtt09FUii4v/M0sQ9eUbGv9mSGuz+fwNzZlk4HFmFiZY6s0VBlqC+/+0zDoWIxynZrTPTdF3Q5vASAf1bs4uWJGwZ1nTt+kUYt6vP3xZ2kqlKZPzFnxuTrnz9n4eTlRIVHM3OFP6HB4Wzb+422LLKOOmv5STO6DeiMOlNNWmoaM0bozsrePRlARR8vZp7+igxVGjum5Byx99n309g5bTMJEbHsW/4L/deOw29yD4KDnmXP8Fs72zJxz1LMrMyRZZkmg9uyopU/aUkqBm6chIW9FZpMNfdnfJe90VdWa7g/4zu8f50JSgWhO06RfD+YIv21nedXPx7Dc3I3jO2tKL9CayJyppp/2szE1NWeSl+PAqUCSaEg4u+LRL9pAKbWEDJvE54/LtAeEfrbMdIevsCht7bhiPnlEDa+DbDv0hw5MxM5NZ0XY1dmRy/2lT+W9apiZG9DhQvfE/7lL8TuOpqvussnrlCneW1+PPc9aao0Pp/8RXbYkh8WsXrqGqLDY+g0qCM9RnbHwdmBzUc3ceXEFVZP/ZKfv9rOlNX+bDm6CSSJLUu3Zh8fqmMXx7R2sefSLq1dTMixi7XbV7Fw0nIiw6OYuVJrFz/s25xtF5tXf0/L9j50H9AZdWYmqanpenaRH5eOX6Z+87rsOP8TaapUlk36PDts5Y9LWTHlC6LDo5m8fALhweFs3LMWgDMHzvHDlz8ZTPPmyetU86nB8tPrSFel8d2UDdlhE76fybZpG4mLiOX35T8xfO1EOk/uyYugZ5zddTxbrkabOgSdvUm6Kk0n7e3ztzLsy/EojY0IfxHGN/5rDd5D4IlrePnUZM2ZjaSp0nTkpm6bzeap64mLiKXNwHZ8MqITds72LD/8JYEnr7Fl2gaDaQKg1hC/+msc16wEpYKUfQfJfPoMi07amcyUv/SPbcyN3YLZmHp7obCzxfWvXSR+u42UffonsrzWFbN8Ha4bl2mPCP37MBmPn2PdTTufkvj7PixaNMaqfUvIVKNJTSNyas6pOpKZKWb1ahK1+Ms33hOAkULB9NbVGfnreTQa6Fi9BGWcbfjtunZCpXsN7drqEw9CqO/pgrlJTlMeGBzNvtsvKetsw6ffngBgbLNKNC7jpq+oAEyZt5x/Am4SF5dAi059GTWkH13bt/mf0jJSKJjexouRO86j0cg5+bqmPS62e03tGvgT90OoX8pVJ19RSanM2XsVjSyjkaF1xSI0Lu1KxpU9mLQczMRjaq7uOkXEw1fU6dMCgCvbj3P/ZCDlfbzwP72GDFUav0/R1uEatYY9c7cx+MfpSEpFdtw3Yelkw+AfpqPQyCRHxmHhYM3gEyu5tfM00Q9eUT2rjbrx8wmenAjE06c6Q89+QYYqnUP+m7PTOT73B9p9PRKlsRFxLyKyw27tPI3v58MYeHQZ6nQ1Byd9o3cPp4+dp0nLhhy58iepKak6s/rf/PIlcyYuJiI8ivmfTyckOIxfD3wHwNH9J9nwxbeULufJ8nXz0ag1PHrwlNkTFunpeM1/3T4K3j/Su6ynFXxcFHeoWmjGMceiemGpYqP6WaHoaWZa9O1C/xLtVIX3HLtYGD4B5r9gYmZ6oeiJztBfDvVfYa00vKzjv6CMceFtCE2V81lX/B+wqkhcoehJTym8eTLXiTXfLvQvYdTO8DGx/wWZB7YUip6Fc5+/XehfwlFTeIsovk01/N6N/wILpeGN/f8F10PPFXzz2n/EyJKfFlrDufHZrveeX0OI5UACgUAgEAgEAsFHhlgOJBAIBAKBQCD4qBAbg4UnQCAQCAQCgUAg+OgQngCBQCAQCAQCwUfFh/wSr8JCeAIEAoFAIBAIBIKPDOEJEAgEAoFAIBB8VBh6g/XHhvAECAQCgUAgEAgEHxnCEyAQCAQCgUAg+KgQewKEJ0AgEAgEAoFAIPjoEJ4AQb4YKQrPPIpkZBaaLqWycMa+FoU4xjZXpBWaLge35ELTZR9iXSh61EaFNydkoyi8t3IW5iyPq1R4+TJzLpzfK+1ZoagBwMh3UKHpKqy3+AIY+X1WKHqC5/gXih6AcmqLQtMVnBRVaLpqOZQuNF0fAmJPgPAECAQCgUAgEAgEHx3CEyAQCAQCgUAg+KgQewKEJ0AgEAgEAoFAIPjoEJ4AgUAgEAgEAsFHhUYWewKEJ0AgEAgEAoFAIPjIEIMAgUAgEAgEAoHgI0MsBxIIBAKBQCAQfFSIxUDCEyAQCAQCgUAgEHx0CE/AB4gkSbOA3oAa7SlWw4EVgDuQCqQDn8myHGggrhq4BUhZ8cfIsnxBkqSSwD5Zlqv8X+5t7tKpNGvZkFRVKlPGziPo5j09meVfzqOqVyUkCbMIT2UAANWuSURBVJ4+fsGUsXNJSVZRqkxJVq5dQOVqFfhi6Tq+Xf9TvnqcfKpTafEAJKWCl9tP8GTtHp1wj64NKTWmAwDq5DRuT/2WxDsvAKj65XBcWtUgPSqBs02nvDVPkxeNo2HzeqSq0lgwcRn3bz3Qk1m0bg4Vq5cnMyOToMC7LJ26CnWmOju8UvUKfLdvIzNHzOfE/tPZ18s2rYbf3P4olAqu7TzJmY179dJuN68/5Xy8yFCls9t/E6FBzwDovHIY5Zt7kxydwNo207Ll3SqVoOOSwRiZGqPJ1LBnzvdw5Y5OmnY+XnguHAxKBRG/HOfVuj91y7dLY4qM7pxVfiqeTN9Myp3nAChtLCjzxSjMKxQHWebRxPUkXdMvE0OY1quN7YQxSEoFyXsOkPTTDoNyxhXL47xlHTFzFpF68kyB0n7NoPmfUcOnJmmqNNb7f8XT20/0ZHwH+NFucAfcSroz2KsvibGJADTq1JROI7oAkJqSypZZG3l+95lBPWMXjqJu8zqkqtJYMfFzHt5+pCfTaWBHug3tTJGSRehYtSsJsQkAWNlaMfWLyXiU8CA9LZ2Vk7/g2X3DegA+WzCMmj61SFOl8dXkL3ly+7GejN+AT+gwpAPuJT3oW703iVm6XlOm2v/H3lnHR3F9D/uZ3bi7Edw9QULwBAnuULxocSnuxd2LW1toKRWguBYtbsGCOyHubrvz/rHLJpvdDaHfNuX3Mg+ffEhmzr3nypk7V869U5ol+5exbPgSLh25qHWv+8z+VPb3JiM1g+/Gr+VN0Eud+J08XRi0dgyWtla8CXrB1jFrUKg/4lfWtyJdv+mL3MiIpNgElnadCUDTAa2p17UxiCLhj9+SEpdM2QZVyExNZ9f4DQQH6ebZwdOZL9eOxsLWkuCgV+wcsxZFpoJq7erSeIjqmU5PSWf39K2EPFQ9092WDKZCo2okRSewpJn2M23s7YPFVyNBJiP95GHS9vyst4zlpcphs2Q9Sctmk3npHLJChbEaPzP7vpsHKT9/R/rB3XrDA5jXqYHDpGEIMhmJfxwl/rtfte5b+NXGfnhfRKUICgXRS9eTHhiEcVFPnJdMz06zpxux67eTsPOP3Co0XLh6k0XfbkWhVNCpVQADe3XWuh+fmMSMRd/y9l0opiYmzJ08itIligKw47f97Dl0AkEQKF2iKPMmj8bU1MSgrovPw1hy4i5KUaSDVzH61ymrdf+Hy084cv8tAApR5GVUAmfGtMbMWE7/HefJVCjJUippUq4QwxpWMKjnQ0xfsILzF6/hYG/Hvp82/u143tN71gCq+lcjPTWdzePX8lpPW+Fc2IXha8ZiaWfFq/sv2ThmNYrMLCxsLPlq6QhcirqSmZ7J1gnrCH7yRq+eQn5VqDWnN4JMxpNdZ7m3Trudty3pTr2Vg3CsVIxbi3/n/qYjAMhNjWmxZzpyUyMEuZxXh69xe/nej8rj0mUzCWjmR2pKGoMHj+fO7SCDssuWz6JX7864ufy9bsDIOcPxVbeJi8Ys0dsmTlszhbJVyqDIzOLh7ccsn7xS6135KaGU1gKklYBPDUEQagOtgWqiKFYBmgBv1bd7iqJYFVgPLDUQRaooil5quSnAwn8qbX5N6lGsRBEa+bRj6th5zF06Va/cvOnLaOXXlZYNuxLyLowvB3QDID4unjlTF7N13Y68FckEKi7qz/UeizhffxweHepiVaaQlkjK60iutJ/DBf9JPFuxl8rLB2nuBf9yjuvd8pftOo18KVLck451e7Bg4lImLxyrV+7o3pN0rt+Lbo36YmpmSvserbOTK5MxYtoQrpy9rhVGkAm0mdOPHX2X8G3TCVRuWwfnUtr5KOPnhWNxN1b6jWXf1K20nd9fcy9w93m291msk5bmk7tzevVe1rWcyqkVu2k+pbu2gExGiQVf8aDnfG43/Bqn9vUwL+OpJZL+JoL7HWdwp/FYglftpuTSIZp7xef2J/ZMILfrj+JO43GkPg3OuxBz6LUbN5rosZMJ794Pi6aNMCpWVK+c7bBBpF+9kb94c+DtXx334u6MbDiETVPW8dW8oXrlHt14yJye3xDxNlzresTbcGZ+MZXxzUez+9tfGbxwuN7wtRr5UKh4IXrV68vySasYs3CUXrn71+8zrtskwt6GaV3vObI7z4KeM7DpYBaOXsLI2cMM5qm6fw3ci3kwpMEg1k1ey9D5+mUf3njANz2mE54rT6CywT5T+hJ4LlDnXmU/b1yKuzPVbyQ7pm6k1/xBOjIAnSb34uS2Q0zzH0lyfDL1uzYCwNzGgp5zB7J24GJmBoxh47DlANi5OtCobwvmtZnEzGZjsXVzoIRPORb4fc1vU7fQef5AvXraTO7BuW2HWeA/htT4JGqp9cS8jWRt1zksbTGJE2v28sXC7HRe232OzX30PNMyGRaDvyZx9kTiR/TBpH5jZIX125xFn8FkBmY/o8p3b0kYM1D1M24QYnoamVf+0pvm93E4Th1J+LCpBHcYiGVzf4xLFNESSb0ayLsugwnpOoTImctwmqlqTzJfBxPSdYjqp/swlGnppJy+qE8LAAqFgnkrN7Fh6UwO7FjHkVPnef5KuwO65cffKVeqOH/8sIYF08aw6FvVV4DDI6PZufsgv25Zwb7ta1EqlRw9bThfCqXIwmN3WNetLnsHN+VYUDDPI7UHmH1rl+G3rxrz21eNGeVXkepFnLE1N8FELmNLr/r89lVjfh3YmEsvwrn7LsZwGX6A9i2bsnHFvL8dPidV/avhWtyd8Q2H892UjfSbp9/uu07uzbFtB5ngN4Lk+CT8ujYGoO2ITrx58JJpzceyaey39JrVX294QSbgO78PJ3ot4Q//iZRo74ttaQ8tmfS4ZK7O+FHT+X+PIj2TY18sYH/TaewPmIanXxWcq+X/q70BzfwoWaoYVSv7M3LEFFatNlx23tUqY2trk++4c1OrkQ+exQvRs14flk9ayZiFo/XK/fnHKb5s2I9+Tb7C1MyEVt1b/m2dEv8+0iDg08MdiBJFMR1AFMUoURRDcslcBgrphNTFBoj9pxLWpEVD/vjtEAC3b97DxtYaZ1cnHbmkpGTN72ZmpojqY7iio2K5G/iArKysPPXYVStFysswUl9HIGYqCN13CdfmNbRk4m48IStepSf25lPM3B0092KvPCIzLpn80LBZPQ7vPg7A/VsPsLa1wtHFUUfu0ukrmt+DAh/i4u6s+btr/06cOXKO2Cjtovb0KkX063Bi30agyFRw7+BlygdU15IpH1Cd23tVL+jgwGeYWVtg5WwHwKtrj0iNT9JJiwiYWpkDYGZjTkK4tl4r71Kkvgoj/U04YmYWUfsv4NCsppZM4o3HKNTll3jzCSbuqjzLrcyx8a1AxM+nVLoys1AkpOgpOV1MKpQjK/gdipBQyMoi5c/TmDWooyNn2aUDqWfPo4j9eNOs2dSHc3vOAPA08AmWNpbYudjryL0KeklkcITO9Sc3H5GcoMr301uPcXTXrWuAugG1ObH7TwAe3nqIpY0VDi4OOnLPgp4THqzbKS9Wuii3Lqg65G+fv8XV0xV7Jzu9unwCanFmz2lV+gIfY2ljib2ePL0MekGEnjwBtOrXmstHLxEfHadzzyugJpf3ngXgReBTLKwtsHXWTUu5OpW4eeQyAJf2nMUrwAeAWm3rc+vYVWJCogBIjM7uIMrlckzMTJDJZdgXcuLBqVsAvA58hrm1BTZ69JSqU5E7R64CcG3PeSoHqJ7tV7eekKqum9e3nmLrll3eL649Ijle95k2Kl0eZdg7lOEqm8v46zQmPvV05ExbdSTj8jnEeP02Z1SlGoqwEJSRunWpiaNSWTLfhpD1Lgyyskg+dhYLP237FlPTNL/LzM30Oh2b1/Im620oWaH66xLg3sOnFCnkTmEPN4yNjWnRuD6nL1zVknn+6i2+1asCUKKoJ+/CIoiKUeUvS6EkPT2DrCwFqWnpODvq2u577ofEUNjBEk97S4zlMppV8OTsk1CD8kcfvKV5RdWkgiAIWJioHAqylEqyFEoEgyE/TA2vytjaWP8PMWRTrakPF/acBeB54BMsbCyx1fNcVahTmWtqu7+w5wzV1HZfqHRhgi7eBSD0+TucPF2wcbLVCe/kXZLEV+EkvYlEmangxf4rFGmm3c6nRScQdecFykzdGfGslHQAZEZyZMZGfMypla1bN2XXTtXKwfXrt7G1tcHVzVlHTiaTMX/+FKZP//tzgnUD6nB890kAHtx6iJWBNvHq6Wua3x/efoyzu24f4VNBLMB/nyrSIODT4wRQWBCEJ4IgrBcEoaEemebAPgPhzQVBuC0IwiNgKzD3n0qYm7sLoe+yZzzDQsJxc3fRK7vk21lce/AnJUoXY/vWXz5Kj5mbA2kh0Zq/U0NiMHUz/BIr3MOfyNO3P0rHe5zdnAgPyX4ZR4RE4uJmuNGSG8lp2bkZl89c04T3a1GfPTv268jauNoTnyMfCaEx2Lhq58Pa1Z74kOyZs4SwGGzcdF9UOTkyewfNp/RgwqU1NJ/ak5NLtF0STN0cyHgXpfk7IzQGEzf9nV0A1+6NiTut6rCaFnUlMzqBUqtGUOXEUkouG4rM3DTP9LxH5uyEIiK7LBURUcidnXVkzBvWI/kPXbeo/ODg5kh0SHbeosOicHA1nLe8aNStKYFnb+m95+TmREQOu4gKjcIpD7vIzfMHL2jQQtUZLedVFjdPV5zddV/OAI5ujkSFZucpKiwaxzzqKzcOro74NqvNsZ+O6r1v5+pITA47jA2LwS5X/Fb21qQmJKNUqL6hGRsajb3aVl1LuGNha8WEX2Yz4+BiandUNUlx4TEc33KAxZc2sPzaFmRyOQ/OZK9ExIXFaHXkASztrUlNSNHoiQ+NwdZV99mu1dWfR2dvfzDvgqMTiqjselJGRyJz1K4nwcEJE9/6pB87kDu4BtP6jck4fypPXXIXJxRhkZq/FRFRGOmZBLFoVJdC+7bhunYeUTOX6dy3bO5H0rEzeeqKiIrGzSU7bldnJyIio7VkypYqxp/nVZ3Xew+eEBoeQXhkNK7OjvTt1p4mXQbg36EP1paW1PXxNqwrMQ03a/NsXTbmRCSm6pVNzczi0vNwmpTLnoNSKEW+2HKKRisP41vClcqFDLfVBYm9m4Nm4AoQExaNQy5bs7K3JiWH3ceERuOgfjbePHhFjRa+AJSoWgqnQs6aezmxcLMnOUcbnhIag+UH2vCcCDKBtifm0/3uekLO3yMqUNcV0BDuHq4EB2cP2ELeheLh4aYjN2TIlxw+/CfhOez3Y3F2cyIyJDt8ZGgkzh94VwZ0asK1XCvkEp8W0iDgE0MUxSSgOjAIiAR+FQShr/r2TkEQgoFJwBoDUbx3ByqHarCwQxCE/2VyRoO+aEQD0xYTR83Ct1IAz5+8pHX7gI9UpO+ifj0OdStQuIc/j+bq9wP+oKqPyBPA5IVjCbxyh9vXVDNEY2ePZM38jSiVej5Ano+49VbNByYNfHo14cjcH1laZyRH5v5Ih8W5lrn1xqk/Ups6lXDp0ZjX81X7MwQjOVaVSxC2/Th3AyagSE2n0MgOeSfoI/TafT2c+HWbQV955UtF/vOWFxVrV6ZR1yb8tHD7v6Ln53W/YGVrzZbjG+nQrz1P7z8z6Bcr6DH4vGwwNwNnfcX2hT/ot0H0V4tOXvKwVblcTtHKJVjdbwErv5xH65GdcS3ujoWNJV5NazK5/nDG1xqETC6jbP0qeedDb7Fqy5SqXQHfrv4cXJSfZ/rDz4/lwJGkbN9k2OaMjDD2qUPGxbMfUJW/eko5fZF37QcQ8fUs7If31dFl0bA2ySfO6YT7ULy5bXJgz84kJCbRqf9odu49RLnSJZDL5cQnJnHmwlWO/7qF03/8QGpaGgdPGB506JulNPTGOP80DC9PR2zNs/cXyGUCv33VmOOjWnA/JIZnEfF55q2gyE/bnpfMwQ17sbSxYt6R5TTt25LXQS81g4UPx5H/dIpKkQMB0/itxiicvEtiV9bzw4Hykf73uLm70L5jSzZu0N/WfYSyD+rKyZgFo7l79S73rt3/3/T+iygL8OdTRdoY/AkiiqICOAucFQThHtBHfasncAdYBKwDOgqC8D3gDYSIotgyVzyXBUFwAvRPQepBEIRBqAYgOFp6MnzYMLr2Vm2kvHs7CPdC2bMMbh6uec4sKJVKDu07waARX7J7l+FZuNykhcZg5pE942Lu4UB6mO4yvnWFIlReMZgb3ReRGavrNmOILn070L6nyqf/we1HuHpkr2a4eDgTGR6tN9zAsX2xc7RjwcTsDX7lq5Zj/gbV5kI7B1vqNPZFoVBw7tgFEsJisM2RDxt3BxIjtPOhksmenbJxc9Bx78mNd6cGHJ6t2ldx//BV2i/6ipxODOmh0ZgUyp6hMXF3ICNc10/XonxRSi0fyoOe88hSl19GSDTpodEkBT4FIPrQZTxH5G8QoIyIRO6SXZZyFycUUVFaMsblyuAwdwYAMltbzGrXIk6hIO28Yf/oZl+2pEm3pgA8u/sMR4/svDm6ORET8XE+yEXKFWXI4uEs6DOHpLhEzfX2fdrSqofqEXp05zEuHi6AapOdk7sTUQbsQh8pSSksGZc9C7zr8o+E5tg30PLLVjTt3kydp6c45Vgyd3JzJEZPfRmiVOVSjF87EQAbBxuq+9fAq74Xpb3KYiQIvLrzHIccdmjv5kBcrviTYhIwt7FEJpehVCixd3ckTm2rsWHRJMUmkpGaTkZqOk+uPcCzfDEAjE1NGPuTqj4jnr+jaLXSmjjt9Nhyckwi5jYWGj227g4k5Hgm3MsVoeuiwWzuu4iUuA8/02J0JHKnbJuTOTqjjNG2OXmpsliN/0Z138YW4+q+JCsUZF69oMpDtVoonj816Cr0HkV4JPIcrhZyFycUEYZtIu3WPYwKuyOzs0EZp3KhsqhXk4xHz1DGxOWpy9XZibCI7HyER0bh7JRrFtvSgnlTVH7ZoijSrOtXeLq7cvHaLQq5u+Jgp3JdadygNrfvP6JNgL9+XdbmhOWY+Q9PSMXZylyv7LGgtzSvWFjvPRszE2oUcebii3BKuei6zRQETb5sjp+6rXhx9xkOOdoKBzdHYnO1v4kxCVjksHsHd0di1c9GWlIqWyas1ciuuLBRZ48RQHJoDJY52nALdwdSPtCG6yMjIYWwSw/x9KtC3GPD+7AGDe5N336qfXY3b97F09Ndc8+jkDuhodpprFq1IiVLFuPu/bOq9FmYc+feGapW1m8POWnfpy2tNW3iE5w9su3f2d3ZYJvYZ0xv7BxsmTFp5Qd1SPy3SCsBnxiCIJQVBKF0jktewOv3f4iimAlMB3wFQSgvimI/9cy/zu4bQRDKAXIg370XURQ3i6JYQxTFGjZmTvz43W+09u9Ga/9unDxyhg5fqDrPXtUrk5iQRGR4lE4cRYtnvyQaN2vA86ev8qsegPjA51iWcMO8iDOCsRz39nUIP35TS8askCPVvhvLneHrSH5h2H9VH7//8Ac9mw6gZ9MBnD32F606qzpjlapVICkhmWg9L/Z2PVpR28+H6cNma81+tPftSrtaqp/Th86xeMoKzh1TdS7e3XmOYzE37D2dkRvLqdymNo9Oaufj4cmbeHWsD4CndynSE1NJiozLM/0JEbEU9y0PQIk6FYl+pd3oJ91+hnlxd0wLuyAYG+HUrh4xx7U34ZoUcqLstgk8HfktaTnKLzMyjoyQKMxKqja22dWrTMqT/G0Mznj4CKPChZC7u6lmPJs0Iu2vy1oy4Z16Et6xB+Ede5B65hxxy1bnOQAAOL7jCBNajmFCyzFcP3GFhp1UL6/S3mVISUzWdFbzg5OHExM2TWHNmFWEvtTearNv+wG+ajaEr5oN4eKxiwR0bgJA+WrlSU5M/qjBhqWNJUbGqjmWVj1acPfqPVKSsvdWHNlxmDEtRjGmxSiuHL+MfyfV5tgy3mVJTkzR6azkxaB6AxlUdwCD6g7g0pGLbJq+gfVT1jGmxSjmtJxA4Ilr1O7oB0AJ79KkJqYQr8fGHl8OonrL2gDU6eTH7ROqZfzbJ65TumZ5ZHIZJmYmlPAqTeizYGJCojC3tmBRx2nMaTkBpUKJndr9p6h3KVITU0jQo+fZ5QdUbVkLAJ9ODbh/QmWbdh6O9Ns4lp1j1hH5Mn/PdNbTR8jcPZG5qGzOpH4jMq9p21P8oG6an4xL50jetFIzAAAwadCY9L/ydgUCSA96jHGRQhgVUumybO5Hyjlt+zYqnL0h1KRcKQRjY80AAMCyhT9JR/N2BQKoVK40b4JDCA4JIzMzk6On/sK/bi0tmYTEJDIzMwHYc+gE1atWxMrSAndXZ+4+eExqWjqiKHL15h1KFNXfcQeo6GHPm5gk3sUlk6lQcvxBMA3LuOvIJaZlcvNNFP457sUkp5OQlgFAWqaCq68iKO74z/j0/x3+3HGM6S3HMb3lOG6euEa9Tn4AlPQuQ0piCvF6nquHl+/jo7b7ep38uXVSZfcWNhbI1c+wX7cmPL72gLQkXTepqNsvsCnuhlVhZ2TGckq08+XtCf1uhrkxdbDGxMYCALmZMe71KxH3PPcWQG02b/qROr6tqOPbikMHT9C9p2qSrmZNLxISEnUm5o4fO0PJ4j5ULF+fiuXrk5KSmq8BAKjaxIHNhjCw2RAuHLtIs86qAVaFPNrEVt1bULNhDeaMmP9RK5r/BUrEAvv5VJFWAj49rIA1giDYAVnAM1Qz85pz60RRTBUEYTkwHhiQK7y5IAi31b8LQB9RFBXqZcOyanei94wRRfH3/CbszMkL+DWpx5nrB0hLTWPiqFmae9/tWsPkMXOIDI9i6do5WFtbgiDwKOgJM8YvAMDJxZH9f+7EytoSUSnSb3BPmtXppLWRGEBUKAma8j0+v0wFuYzgXWdIehxMkS9VnbI3O/6k9LhOmNhbUWmx6sQGMUvBxWbTAPDaOBKHOhUwcbDGP3AdT5fuJvhn/S/ei6euULdxbf64tIu01HTmjMneOLXqxyXMG7+YqPBoJi8aR1hwON8d3KAqiyPn2boy7+VVpULJoW9+oM+OyaojQn87S8TTd9TsqTp94vrOUzw5c5sy/l6MPbeSjNR09k7YpAn/xbcjKO5bHgt7ayZcXsPplXu4+dtZ9k/eSsuZXyIzkpGVnsn+KVvxyqlYoeTF1K1U2DUDQS4j/JfTpD55i+uXKres8B0nKDymC8b21pRY+JW6zBXcba46hvTFtG2UWTcawdiYtDfhPPt6LflCoSRu+RqcVi0GmZzkQ0fJevkKiw5tAEj5m/sAcnLr9E28/Wuw5vxGMlLTWTc+2ytuyg8z2DhxHbERMbTo25p2Qzpg52zPsuPfEnjmJhsnraXz6G5Y2Vvz1dzBqiQrlExuM05Hz5XT16jVqBY/XdhOelo6i8dmz+ov3DGfZRNWEB0eTcf+7ek29AscnB3YdnIzV89cY9mEFRQtVYQpqyehVCh49fQNS8cvN5inm6dvUMO/Bhv/2kJ6ajprxq/S3JvxwyzWTfqWmPAYWvdrQ4chnbB3tufbE2u4efoGaycZ8grM5t6ZW1T2r8aCc2vJSE3n+wnrNfdGfz+VHyZtID4ilt2LfmTwmjF0GNeNN0GvuPCbqmMc+vwd988FMuvYckSlyF+/niLkierAsptHLzPj8FKUWQpCg14T+SqMaedWk5Gazi8Tso94/Or7Sfw6aTMJEbEcWvQzvdeMosW4rrwLesWV31TPZrNRnbC0t6LzPNUzrcxSsKKt6pnu/e1ISvlWwNLempmX12G0eysZfx4BpYKUzauwnrVMdUToqSMo3r7CtLn6qNE89gEAYGKKcdUapKw3XD8aFEqiF67FbcNCkMlI3HeczOevse6imhhJ/P0Qlk3qY9WmCWKmAjE9nYiJ2ae1CGammPtWJ2ruqg+qMjKSM/XrwQwePwuFUkmHlk0oVbwIv+5X7fvo2q4FL14HM3X+SuRyGSWKFmbOZNUJVlUqlKWpX12+GPg1crmccqVL0KVNM8O6ZDImN/Ni6K6LKJUi7aoWpZSzDb/fVB2n2aV6CQBOPw6hdglXzE2yuw1RSWnMOHgDpSiiFCGgfCEalNYdQOSXCTMXcT3wLnFxCTRu34thA3rTKY+058Wd0zfx8q/GsvPryUhNZ8v47HZs/A/T2DpxPXERsfyy8EeGrx1L5/E9eB30knO/qg4E8CjlyeAVo1AqlLx7FszWCev06hEVSq5M307AzxMRZDKe/nqOuCfvKNtbNbB//ONpzJ1taXN0LsZW5ohKJRW+as4ffpOwcLWj/qrBCDIZgkzg5cGrBP95O995PH7sDM2a+XP3/llSU1IZMmSi5t6eP75j+LDJhOWxAf1juHL6KrUa+bDzwg51m5h9QOGiHfNZqm4Txy78mrDgcNbv/xaA80cvsGPVT/9IGiT+eYRPfaQm8d9Rwsm7wIxjrbzsh4X+IWbK855p+adoZpJ/387/lZbp6QWmq2ixf+zAqQ/ydUjBzCpGKfJ3AtI/gY0sfxut/wmcZWYFpsu6AOeUZlT9uNW/v0v8K8Nn6//TeJ7I52D7HyDr+A8Fpsuo5VcFoqd/9fEFogegUZZFgekaGZv3Suk/SQ2H/B9P+r9yNvjPf2Sv4v9C56JtC6yPs/v1gf88v/qQ3IEkJCQkJCQkJCQkPjMkdyAJCQkJCQkJCYnPik/51J6CQloJkJCQkJCQkJCQkPjMkFYCJCQkJCQkJCQkPiukPbHSSoCEhISEhISEhITEZ4c0CJCQkJCQkJCQkJD4zJDcgSQkJCQkJCQkJD4rPuWPeBUU0kqAhISEhISEhISExGeGtBIgISEhISEhISHxWSEdESp9MVgiD6q41S4w43AxtikoVcRlFczXYf1MC+6Lwa/E5ALTpSjANsNGVjBfbHXAuED0ANiL8gLTVSW94OrqTzNFgelyEgtm/qrgrAKeCwX31W9XCu5LyMFiWoHo+e7msgLRAzC9xrQC03VbEVdguhwK8Gvmu17v+8+/oNumSOsCayAPvjn0n+dXH9JKgISEhISEhISExGeFKO0JkPYESEhISEhISEhISHxuSCsBEhISEhISEhISnxXS6UDSSoCEhISEhISEhITEZ4e0EiAhISEhISEhIfFZIR2MI60ESEhISEhISEhISHx2SCsBEhISEhISEhISnxXSdwKklQAJCQkJCQkJCQmJzw5pJUBCQkJCQkJCQuKzQvpOgDQI+GQQBKEDsBcoL4riI/U1H2AJUAhIBEKByaIo3hMEYRbwFRCZIxo/URTj9MTdFFgEmAAZwARRFE//nXROmjeG+o3rkJaaxozRc3l474mOzMJ1s6hYtRxZWVncC3zI3AmLyMpS0HdYT1p2DADAyEhO8dLFaFixJQlxCXp1DZ89FJ9GPqSnprFk7HKe3X+mI9OuT1s6DuxAoWIedKzShYRYVVyW1hZMXj0Jl0IuyOVyft+8m+O/ndCrZ/zc0dRt7Etaajqzvl7AYz15mrtuBhWqqPIUFPiQ+ROXosjK/kJqharl+P7wRqYOnsWpw2e10zizD+X9vchIzeDX8Rt4F/RKJ34HT2d6rR2Fua0l74JesWvMOhSZCpxLetB16WA8Kxbn6LJfObflMAC27g50XzEMa2c7RKXIkZ+PcuT7g1px9p/1Fd7+NchITWft+FW8vP9CR2/zPq1o1b8t7sXc6efVk8TYRABqNq1Ft3E9USqVKBUKvp+9lUc3HuotP4ABswdR3b866anprBm3mhf3n+vItOjTijYD2uJezIMvq/YkUV1XPk1r0X18T0SliEKh4LvZW3l4/YEmXM+Z/anqX42M1Ay2jF/D66CXOnE7ebowbO0YLG2teR30gk1jvkWRmYW5tQWDV47GsZATcrmco1v289fvZwCwsLGg/6JhFCpbBLkIuyZupFqbOpT39yYzNZ1d4zcQbKCuvlw7GgtbS4KDXrFzzFoUmQqqtatL4yFtAUhPSWf39K2EPHyDkakxI36diZGpMXK5jMdHrhN86ynNZvZGJpcR+MtZLm44qKOn2awvKe1flczUDPaP30TY/VfITY3p+9sM5CZGyIzkPDxyjXMr9wDQZGp3yjSuhiIzi9jX4eyfsBkitb8k7eJfhcpzv0SQy3i98wxP12rrtSrlQbVVg7GtXIyHi37j2YbDmnslB7WgaE9/EEUSHr7l1tebUKZnaoXvPLMvFf29yUhN58fxGwjWU1eOns70WzsaC1sr3ga9ZIe6/N5TpEpJxv8xj+9GrOL20aua64JMYOLBhcSHxXB7+580V5ffrV/OckFP+bXIUX77xm8i9P4rbNwd6LByKFbOtohKkZs/n+bq98c1YXz6BuDzZVOUCiVPT9/m7MJdlGhYhYCZvRHkMm7/cpbLenQFzPqSkmpdh9R1lTPd/Q/NIzEslt/6a3/VttagljSZ1pMVXoMhTvXF4B4z+1PZ35uM1Ay2jV/LGwP2PmTtGCxtrXgd9IItY9agyMyi+aC2+LavD4BMLsejVCFGVxuAiYUpo7+bgkfJQgA8PX+PHQN1v7DbZuaXlFW3VbvHbyREbf9lGlah9TdfIpPLuP7rGc7lKoP6X7Wi5bSezPUeTEpsInJjOe0XDMSzcnEylEp+nL2NR1eCNPK9Zw2gqn810lPT2Tx+La/1tE3OhV0YvmYslnZWvLr/ko1jVqPIzMLCxpKvlo7ApagrmemZbJ2wjuAnb3TCf4jpC1Zw/uI1HOzt2PfTxo8OD9B2Zh/K+nuRmZrBb+M3aMorJ/aezvRYOwoLddv+q7ptNxTe1t2Brjna9r07D7Hvu/1acQ6dPQSfRjVJS01n+djlPNPT3rbt04YOA9vjUcyDLlW6at6NnQd3olEHfwDkRnIKlypMV69uJMYl6c1jn1kD8fKvTkZqOhvGf8srPXUV0KclLfq3wa2YO4O8emveIx4lCzF42UiKVyzJr8t+4vDm/TphJf5bJHegT4fuwAWgG4AgCK7Ab8BUURRLi6JYDVgIlMwRZqUoil45fuIMxB0FtBFFsTLQB/jx7ySwXuPaFC1RmNa1uzBn/CKmL56oV+7w3uO0rdeNjn69MDMzoWNPVcfoh/U7+aJJH75o0ofV8zdy83KgwQGAj39NChUvRJ/6/Vg5aTWjF4zUKxd0I4iJ3ScT9jZM63rbPm15/fQNg5sNZdwXExg8YxBGxrpj3rqNfClcwpMOdbozf8ISpiwap1fPsT0n6VS/J139+2BqZkr7Hm0092QyGSOnD+HK2Ws64cr5eeFc3I1FfmPYPXULneYP0Bt/q8k9OL/tCIv9x5Ian4xPV1UjnRqXxP5Z2zm75ZCWvDJLycF5P7G0yXjWdJhB8y9b4lm6sOa+t3913It7MLLhYDZOWcegeUP16n184yFzes4g4m241vV7F+8wrvkoJrT8mvUT1jB0sf7yB6jmXx2PYh4MazCYDZPXMXi+fl2PbjxkZg9dXXcv3mFMs1GMbTGateO/ZVgOXVX8quFW3J2JfiP4fuoG+swfpDfurpN7c3zbISb5jyA5PomGXRsD0Lh3c0KevWVGi3Es7PYN3ab1Qa62g54z+3PvXCBTGo9iaYuJ2Lk54FzcnQV+X/Pb1C10nj9Qr642k3twbtthFviPITU+iVpdGwEQ8zaStV3nsLTFJE6s2csXC1VpzUrPZH2PuSxrMYmlLSdTqmEV2iz5ip/7LGF9k4lUbFsbp9KFtHSU8q+KY3E31jYcx6Ep22g1rx8AivRMdnSfz+YWU9ncYiqlGlahkHcpAF78dZ8NAZPY1HwK0S/DqDesrXbCZQJVF/bjco8lnGowAc8OdbAuo603Iy6Ju9O3a3X+Aczc7CkxsBlnm03jtN8kBLkMz/a1tWQqqG19tt9odk3dQjcDtt5uck/ObDvCHP+vSY1Ppra6/EDVYW43uQcPz9/RCeffryXhz94B0HJuX3b2WcK6JhOp1LY2zrnKr7R/VRyKu/Ftw3EczFF+SoWSE/N2sq7xRLa2n4nPl001YYvVrkC5ptXZ0HwK65tO4tLmwwgygeZz+/JLnyVsMlBXJdW6NjQcx5Ep22iu1vWemv2bE/UsRCc/1u4OFK9XmfjgKM21yn7euBZ3Z4rfSLZP3ciXBuy9y+RenNh2iCn+I0mOT6a+ugyPbT7ArJYTmNVyAnuW7OTx1QckxychKpVY2VkzrfFoxvgMpHT9ypRr5K0VZ1k/LxyLu7HMbyx/TN1K+/n9NXXSdk4/vu+7hJVNJ1C1bR1cSmWXga27A6XqVyY2OHs+qmY3VXpWN5/M4l6z6TG9L4IgAFDVvxquxd0Z33A4303ZSL95hp/pY9sOMsFP9Uz7qZ/ptiM68ebBS6Y1H8umsd/Sa1Z/veE/RPuWTdm4Yt7fCguq8nIq7sZSvzHsnbqFDgbsveXkHlzYdoSl6ra9prptNxRemaXk0LyfWN5kPGs7zKBNn9YUKV1EE19N/5oUKu5Bv/oDWD3pW0YuGKFXb9CNB0zuPoWwXO3t7k17GNZ8BMOaj+C7RT9w78o9gwMAL//quBV3Z0zDoWyZsp4B84bolXty4yHze84k8m2E1vWkuCS2z9zKoS379Ib7r1EiFtjPp4o0CPgEEATBCqgLDEA9CABGANtFUbz0Xk4UxQuiKO772PhFUQwURfH9WygIMBMEwfRj4/Fv1oCDvx0F4O6tIKxtrHBycdSRu3Dqsub3e4EPcXV30ZFp0aEpR/84aVBXnYDanNzzJwAPAx9hZWOJg4uDjtyzoOeEB4frXEcUsbAyB8Dc0ozEuEStmfv3NGxejyO/HwPg/q0HWNtY4agnTxdPX9H8HnT7Ia4ezpq/uw7oxOnD54iJitMJVzGgOjf2/gXAm8BnmFlbYO1spyNXqk5F7h5RzXre2HOeSgE1AEiKTuDt3Rcoc6U9MTJOs6KQnpzGu2fBOLhmp7tm01qc3aOa8X4a+BgLG0vsXOx19L4MekFkcITO9bSUNM3vphameS6b+gT4cmaPamHpSeBjLG0ssf+buswsTCHHsW3VAmpyce85AJ4HPsXC2hJbPeVXvk4lrh9R2d2FPWepFuCjviNiZmmuzocZyXFJKLMUmFmZU9anAud+PQWAIlNBmXqVub73PACvA59hbm2BjYG6uqOuq2t7zlNZXVevbj0hNUE18/761lNs3bLtNSNFNcsrN5JjamNBQmgMcW8jUWYqCDp4hbJNq2vpKNu0Onf2qOzmXeAzTG0ssHJRpSVTHZfMSI7MWK4prxd/3UNUqLa6BQc+w8Zd+3mx9y5F0stwUt5EIGYqCN53Gbdm2nozohKIu/0CUc+zIsjlyM1MEOQy5OYmpIbFat2vElCTa+ryexX4FHNrS73lV6ZORQKPqJ6nq3vOUTWgpuZew74tuHP0KonR8Vph7NwcqNjIm0u/nMbcxpKYV+HEvo1Ekang/gfKLzjwGWbq8kuKiCNUPUufkZxG5LMQrF1VtlqzV2MurD+AIiMLgOToBDy8ShLzKlxTVw8OXqFMLl1lmlbnrlpXSA5dANZuDpRq5MXtX87olEPTb3pzeuEurWMKvQNqcmnvWQBeBD7FwtpCr72Xq1OJG2p7v6Rl79nUaluPqwcuAuBYyJmQp2+JfBtBclwy0W/CqdCshpZ8+YDqBKrbqrc52qrCXqWIfh1O7NsIFJkK7hy8TPmA7DJoNaM3Rxf+rBWXS+lCPL94H4CE6HhSEpIpXkU1f1WtqQ8X9qjy+DzwCRY2ltjqaS8q1KnMNc0zfUaTx0KlCxN08S4Aoc/f4eTpgo2TrU74D1HDqzK2NtYfHe49FQOqczNH225uoG0vWaci99Ttxc0956mobi8MhU+MjNOsKGQkp/H22Vuc3LLb9toBvvy5R9VuPQp8hKWNFQ56yu950HPC9bS3OfFv15Cz+88ZvF+9qQ9/qevqmbqu9L1HXgW9JEqProToeF7cfaa10ifxaSENAj4N2gPHRFF8AsQIglANqAjc+kC4MYIg3Fb/6L5l9NMJCBRFMf1jE+ni7kxYSHaHOzw0Ehd3Z4PyRkZy2nRuzsUzV7Sum5mbUtffl5O53GZy4uTmRGRI9sxSZGiUVkP4Ifb9cIAipYrw642f2XJyE+tnbtB7JrCzmzNhIdmNlypPTgbjlRvJadm5GZfOXFWHd8KvRQP27NC/zGnr6kBcSLTm7/iwGK3OIYCFvTWpCcko1R24uNBobF11BzyGsPd0oljFEjy9/VhzzdHNkegc5RcTFo2ja/7LD8CnmS+rT61nyvffsH7CtwblHN0ciQ7Nns2MDovG4SPqCqBWM1/WnN7AtB9msnbCas11e1cHokOy444Ji8Y+V9xW9tak5Ci/2NBo7NXl9+f2o3iU8mT1ta3MP76CnbO/QxRFXIq4khidwMBlI5hzeCldFw3C3sNJq67i9NSVpb01qQkpGl3xoTF666pWV38enb2t+VuQCYw/soi5NzcT9SyEiMdvNfcSQmOwdtN+sVq7OZCQIy2JYTGazqogExh0ZAHjb23gxV/3eXdb1xXA+4uGPDurPZtu7m5Pao4400JjMHfPn52lhcXybMNhmt1cQ/O768lMSCXy3D0tGTtXe2K1yi8auw+UX2yO8rN1tadqs5r8tVN3cqDTN33Yt3AnoihibGZMQmi2noTQGGxylZ9NrvJLCIvBxlVbxs7TCfeKRTXl51jcnSI+5Ri4bzZ9f52OR5USWLs5kJhL14fqKiFHXTWd2ZvTC3YhKrXbntJNqpEYFkPEQ203FntXR2JyxBUTFvNBe48JjcYulw2amJlQqaEXN4+q2l47Vwdi1M+Ro6czVo62mgHje2xd7YkLidH8HR+mKlcbV3viQ7TL4H2dlW9SjYTwWMJy5SP04RsqNK2BTC7DubALxSqVxMFD1a7au2WnRZXHaBxypV9fHt+3KW8evKJGC18ASlQthVMh549ub/4JbFwdtMpFVV55t+3xodHYqPOan/D2nk6UrFiSR4HZbbuTmyOROcovKjQKRzfD7yxDmJqZUsOvBheOXjAo4+Cm2/7mrqv/y4iiWGA/nyrSIODToDvwi/r3X9R/ayEIwlVBEB4KgrA6x+Wc7kD+H1IiCEJFYDEwOA+ZQYIg3BAE4UZMSniue7ryeRn3tEUTuHnlNreuandGGgbU4/b1uwZdgf6OrtzUaFid5w+e07VGDwY3H8aIucOxsLLQo0dXUV56Ji8ax60rt7l9VTUTNW7OKNbM24BSqf+wsfzE/7/k1cTClD4bxvDDnK2kJqXmiPTvx/mea8evMLrxMJZ8tYBu43p+VNiP1XX1+BVGNhrKooHz6T6+V/YN/YWj9WdeZVypgRdvHrxktM9AZrQcT+85AzGzMkcml1O0UglO/3Scb1pNICM1Haeibh/ORz7KtVTtCvh29efgouzZUVEpsqzlZGbVHoZDMVfMbS0/kCddPe9lRKXI5pZTWek7kkJeJXEu46klVm9EO5RZCu79cTFX2j9cloYwtrXEvXl1TviM5ljV4RhZmOLZqe4H48+Prb9PQ6dv+rJ/0c86HeZKjaqRGJ3A2/u6vvGG9HyonkwsTPli49ccm/Mj6ernRmYkw9zWkq3tZ3Jywc90Wa/fBS6/z2+pRt6kRMdr7Q8AMDIzoe6IdpxfsVs3YD7sS5+9567Hqk1q8OzGY5Ljk7TCmFqYMXzDeG7vu0iWesUjr4yIoqHrIsZmJviPaM/JFb/r3L/521niw6IZfnAePb/pz7NbjzSrmflrEw3LHNywF0sbK+YdWU7Tvi15HfRS08kuUPLVNukTEfMV3sTClF4bxrBx1iZSklLyjPTvdDJ9m9Yi6PoDg65AKlUGbELi/xukjcH/MYIgOAKNgEqCIIiAHBCB7UA1YD+AKIq1BEHoDLT+m3o8gT+AL0VR1J06VCOK4mZgM0AVt9pi136d6KT26Q+6/RA3D1eNrKu7M5FhUXrjGTKuP/aOdsyZMEXnXvN2+l2B2vZpQ8vuLQB4cucJzjlcbpzdnYgOj9EJY4jmXwSwa/1vAIS8CiHsbRiFSxXm8e3HdOnbgfY9VT79D+48ws3DhffDFFWeovXG+dXYvtg72rFgwlLNtfJVy7Jg4ywA7BxsqdvYl1oNa1LRuzxWMmPe3nmBnUf2LJWtmwMJ4dpuFMkxiZjbWCKTy1AqlNi5O5IQoS2jD5mRnD4bx3Br30WuHrtM8y9b0ribauP187tPcfRwBlSbeR3cHImJyH/55eThtSBci7pjbW+t2fDV4suWNO3eDIBnd5/imGP1xNHNkdiPqKucPLgWRPEKJVh1Yi1KhYLXd5/j6OHEU/V9Bz1xJ8YkYJGj/OzdHYlTl1/9Lo04vOEPACJehxH5NgKPkoWIDokiJiya4pVL0nfBYCwszDEyMdKqKzuDdWWh0WXr7qBVV+7litB10WA2911Eip6Xa1pCCsG3nlHEp6zmmo27A4nhcVpyCaEx2ORIi7WbA4kR2jLpCSm8uvyQUn5ViHwSDECVTvUp09ibHd0X6OhODYnBPEecZu4OOi49hnBuUImUNxFkRKvqP+TIdRxqlsHYxoJiPVXzD/fvPcdeq/wcic9Vfkm5ys/e3YF4dfkVqVKCfmtGAWBlb0NFP2+UCgXFvEpTuUl1Kvp7YWxqgrmNBfY5XAPzU342OcpPZiTni41fc2/fRR4eu6EV5uGx6wC8u/MCUSmSmZKGtXuOeNwdSMqHrqSIOMq1rEXpJtUp6eeFkakxptbmtF01lMsbDmFX2JmBRxeq5D0cGX1tHWGvQnh28wkOOeJycHMg7gP27pDD3t9Tq01drh7InuGNDYvGsZAzwzeO58q+v7AzNSM38WEx2Hk48Fr9t62bA4nhsRiZGGHroV0GCRGxOBR1xd7TmdFHF2nyPfLQfNa1n0FSZDyH5/4EQLCYxvK/1tN1cm86junGi7vPNKsCqjw6Epsr/fry+P65T0tKZcuEtRrZFRc26uwz+reQmdkgM7Nm9JGFBN95oVUu+Wnbbd0dSVTnNSEs2mB4mZGc3hvHcHvfRS4eu0SbPq1p0b058P7dmF1+Tu5OxITrf2flRcO2DTl74KzO9aZftqCR+j3y4u5THHXq6u+17RKfJtJKwH9PZ2CHKIpFRVEsJopiYeAlcALoKwhCnRyyulPZ+UAQBDvgMDBFFMWLHxDX4tfv92g2854+dp42X6g66VWqVSQxMZmoCN3Gp2OPNtTx82XS0Jk6MxRW1pbUqO3NmePndcId2H6QIc2HMaT5MC4ev0TTTk0AKO9djuTElI/qxEaERFKtrhcAdk52FC7pSejrUAB+/+EPejbtT8+m/Tl79C9adlE1rpWqVSApMYloPXlq16M1vn4+TBs6SytP7Wp1pa3PF7T1+YJTh86xePIKFkxcSs+m/VnZcgpBJ25Qo6PqtI4i3qVIS0whMTJOJ/5nl4Oo0rIWADU6NSDoxM0P5vGLxYMIfxbC+W1HADi24wgTWn7NhJZfc+3EVfw6qTpnpb3LkpKYotNRyAu3ou6a34tXKoGRsZFmAABwdMcRxrYYzdgWo7l6/Ar+nVQbAcuodeV+qedXV4lKJcnMyOTrgBGMbTGaWyeuUbdjQwBKepcmNTGFeD3l9/DyfWq2VG1UrdfJj1snVJu0Y0KiqFC3MgA2Tra4l/Ag4k048ZFxxIREEXTxLt+0HM/NfRd4c+c5NTs2AKCodylSE1NI0FtXD6iqriufTg24f0LVkbTzcKTfxrHsHLOOyJehGnlLB2vMbFSPrrGpMU4l3TGztsCusDMyYzkV2/jy5KR2fT/58xZVO6nsppB3KdITU0mKiMPCwRpTdVxGpsaUqFeRqGcqXSUbVqHu0Db8MmA5WWkZOumOu/0cqxJuWBRxRjCW49m+NmH5sDOA1OAo7KuXRm5uAoBz/YokPX3Hy+9PcqbJVM40mcrdE9fxUZdfMXVd6Su/J5cf4N1S5c5Rq1ND7qrLb1b9kcysp/oJPHqFX2ds4+6JGxxYsosZtYcxs95Ivh+5mscX75GVnoldYWfkxnIqtfHlca7ye5yj/DxzlB9AuyVfEfXsHZe3HtUK8+jETYrXqQCAY3E35MZGvPjrPg7F3bBV11UFPXX19M9bVFHr8sih6+ySX1njO5J19b7mj5FreXXpAQe+3kDk47esqj6MdfW+Zl29r0kIiWa1z3BmBIwl8MQ16nT0A6CEd2lSDNj7o8tB1FDbe51OfgSeuK65Z25tQZlaFQg8mX3t5Z1nlKxWhtiwaE7tOEbVNrV5mCsfD0/exFvdVhX2LkVaYiqJkXEE33mOUzE37D1V5f0+bPjjt8yvMZQl9UazpN5oEsJiWNN6GkmR8RibmWBsrtp2VqleVWJCo5nUeBTTW47j5olr1OukymNJ7zKqPOppLx5evo+P5pn255Y6PxY2FprN/X7dmvD42gPScq6C/oso0xLIinvHanXbXj0fbfvzy0FUVrcX1XO07Q9O3jIYvvPiQUQ8C+Evddt+cPshzWbeS8cv06STapN0Oe9ypCQmE/MR7S2AhbUFVXwrc+n4ZZ17J3ccZUrLMUxpOYYbJ65SX11XpbzLkJKY/FHvkU+d/ysbgwVBcBAE4aQgCE/V/+tszBAEobAgCGfUHiNBgiCMzlfcn7Kv0ueAIAhngUWiKB7LcW0UUB7VasBiVEeERqA65WeOKIo3DBwR2l4UxVd6dEwHpoBmQhUgQBTFPHcNVXGrrWMcUxeOp65/LdJS05nx9Twe3HkEwLqdy5k1diGR4VHcCv6L0OAwktVLmKeOnGPTiu8AaNu1JXX9fZk05ButeF2MbXT0j5w3nJp+NUhPTWfpuOU8uatK/vztc1kxcSXR4TG079eOrkO74ODsQGx0HNdOX2PFxFU4ujowYcV4HF0cQBD4Zd2vnPpDtXk1LitFS8/EBWOo41+LtNQ0Zo9ZyMM7Kv/L1T8tYe64xUSFR3Pl7RnCgsM1eTpz5DxbV/6gFc/MVVO5cPKS5ohQP1OVm0aHOf0o27Aqmanp/DphE8H3VEesDfh+Ir9P2qKaVSvsQq81I7Gws+Jd0Ct+HrMORUYW1s62jD4wHzMrc0RRJD05jaVNJ+Berggjds8i5OEbRFFJhqjk56U/Engm+8U+cO5gvBqqjuFbP/5bnt9THbE69Ydv2DBxLbERMbTs25p2Qzpi52xPfHQct87cZOOktbQf0pGGnRqRlZlFRnoGP87/XnNEqEJPmzFo7hC8/VS61oxfzfO7Kl3Tf5jJuklriA2PoVW/NrQf0hF7ta6bp2+yftIaOgzthF+nRigys8hIy2D7gu81R4TayEzoPWcgVRp6k56aztYJ63h1T7WQNfb7aXw3aT1xEbE4F3Zl2JoxWNpZ8TroJZvGrCYrIws7F3u+WjYCWxd7BEHg8IY/uLRPNQAtUqEY/RcNxcjYmLi3Eewav5GW47+gXEMvMlLT+WXCRt6q6+qr7yfx66TNJETE4ljYhd5rRmnq6qcxa1FkZNF10SCqtPAh9p1qdUyZpWBF22m4lytCj+VDkclkCDIZjw9dJeTeC5p9oz528rdzXFi7n+o9VS/2mztVm/5azO1LyYZVyEzN4MD4TYTee4lLucK0WzFEHZfAg0NXOf+taqVjxLnlyE2MSY1VrUAEBz4jePw2rXpybexF5Tkqva93neXJ6v0U+1Kl99WOU5g62+J3fB5G1uagFMlKTuNUg4lkJaVSbkInCrWtjahQEH/vFYHjtqDM4VLyp5mCL+b0p3xD1VGZP03YwBt1+Q39fjI/T9pEvLr8+q0ZjaWdFW+DXrFjzBod15Rey4Zy/9QtrSNCAUr7VqDxV6258+MpmqvLL/C3c/y1dj811OV3Q11+Lef2pZS6/PaP30TIvZcUqVGG/ntmEv7wjcbt6NTSX3l65g5yYzntlg7CrUJRFJlZnJj/M8GXHlDSvypNv1EdR3rnt3NcXLufampdt9S6muWoq0PquspJEd/y+A5qpXNEKMDwC6v4rs107sepJh96zRlIJbUNfjdhvcbev/5+Kj9M2qC2dxcGq+39TdArtqjtHaBuZz8qNfRm08iV2eVWoxxTds8jMz0TBNWpY3snbsG2kGom+po6H23n9KWMuq3aPWET79T5KOvnRWt1ed/47Sxn1+nugZp4YTVr20wnJTYRO08n+m+fjCiKRIRFsXXieqLfZb+q+sz9isoNVUfJbhm/lpfqPI7/YRpbJ2Y/08PXjsVK/Uxv+HoVWRlZlKpWhsErRqFUKHn3LJitE9aRkpDMdzd1yzYvJsxcxPXAu8TFJeDoYMewAb3p1KZZvsJOrzENgHbqtj0jNZ3fJ2zindre+30/kd2TtpCobtt7rBmJuZ0VIUGv+EXdthsKX6xGWYbunkWoum1PFRV8v3g7189kD+qGzxtGDb8apKemsXzcSp6q341zt89h5cRVxITH0K5fW7oM7YKDsz1x0XFcO32dVRNV3sRNuzShhl8NFg5fpJUvB5nueSH95g6iqvo9smn8t7xQ19XEH2awZeJaYiNiada3FW2GdMDO2Z6E6HgCz9xky6R12DrbMf/gMsytLBCVImkpqUxoMpLUpFR2vd6nzzmwQPH3bFpgHeAzwSf/dn4FQVgCxIiiuEgQhMmAvSiKk3LJuAPuoijeEgTBGriJqk/4QE+U2eGkQYCEIfQNAv4t9A0C/i1yDwL+Ld4PAgqCV2Lyh4X+IfQNAv4tbGQmBaLHAeMC0QNgL8oLTFeV9IKrqz/NCu4EECexYDxZC84q4Lnw0Wc1/G1cKZjnClTuQAXBxw4C/hfeDwIKgtuKuALTpW8Q8G/xKQwC/DybFFgDeTb4z/9lEPAY1XegQtWd/bOiKJb9QJj9wFpRFA0fw4jkDiQhISEhISEhISHxqeIqimIogPp/3XPXcyAIQjHAG7ialxxIG4P/v0IQhGao3Idy8lIUxQ7/RXokJCQkJCQkJD5FlAW4qi0IwiAg55fxNqsPYnl//09A94g6+KhlJ/V3p/YAX4uiaPgIRjXSIOD/I0RRPA4c/6/TISEhISEhISEhoSLnyYsG7jcxdE8QhHBBENxzuAPp3c8pCIIxqgHATlEU9+YnXZI7kISEhISEhISExGeFWIA//yMHgD7q3/ugPjo+J4Lqow7bgIeiKK7Ib8TSIEBCQkJCQkJCQkLi02QR0FQQhKdAU/XfCILgIQjCEbVMXaA30EgQhNvqn5YfilhyB5KQkJCQkJCQkPis+F/P7y8oRFGMBhrruR4CtFT/fgG93xzPG2klQEJCQkJCQkJCQuIzQ1oJkJCQkJCQkJCQ+Kz4v7IS8G8irQRISEhISEhISEhIfGZIKwESBinIr/iaCwX3bU65kVWB6LlXgF96zBIL7mutVYwcCkxXs9SC0XPEvOBmhKKErALTdcKs4PLlk1lwX6EtJxbMV78TFAWXp2JyswLTFVqAb/4yCosC0VOQX/Gdd2N+gen6tto3BabrkCK8wHR9CogF+J2ATxVpJUBCQkJCQkJCQkLiM0NaCZCQkJCQkJCQkPiskPYESCsBEhISEhISEhISEp8d0iBAQkJCQkJCQkJC4jNDcgeSkJCQkJCQkJD4rBAldyBpJUBCQkJCQkJCQkLic0NaCZCQkJCQkJCQkPiskI4IlVYCJCQkJCQkJCQkJD47PquVAEEQ3IBVQE0gHXgFfA0YA2sAT0AAdgDzRFEUBUGYBSSJorgsRzyvgBqiKEYJgiACK0RRHKe+Nx6wAjKBLuoglYF76t+/AxyAr4BIwASYC5wCrgK1RVEMU8e1HngjiuIiPXnxA8aLothaEIS+6ni9RFG8q75/H2gtiuIrdXoT1UHlwF5griiK6R9XgjB89lB8GvmQnprGkrHLeXb/mY5Muz5t6TiwA4WKedCxShcSYhMAsLS2YPLqSbgUckEul/P75t0c/+2EQV2DZg+iun8N0lPTWT1uFc/vP9eRadWnNW0HtMWjmAc9q/bQ6KrkW5npW6cT/lb18ZPLxy7xy+pf9OoZOnsIPo1qkpaazvKxy3mmR0/bPm3oMLA9HsU86FKlq0ZP58GdaNTBHwC5kZzCpQrT1asbiXFJ/7mu4XOGUatRTdJT01kyZhlP9dVV37Z0GtiBQsUK0aFyZ40uK1srJiwfh0dRdzLSM1g6bgWvHr/SCtthZh/K+3uTmZrOrvEbCA56pRO/g6czX64djYWtJcFBr9g5Zi2KTAXV2tWl8ZC2AKSnpLN7+lZCHr7Bzt2RHiuGYeNsh6hUErvjNK+3HAXAyb8q5ef1AbmM4J2nebnmgJYu9051KTFCFWdWcjoPJm4l8cEbACqtGoxz02pkRCVwseEEveUF0GlmXyr4e5ORms7O8RsIDnqpN099147GwtaK4KCX/KjOU+WmNWg59gtEUUSZpWDvnO28uPFYE06QCUw4uJC4sBg2D1jyr+nyG9CS2l0bIYrw7vEbfpywng6Te1FRXVc7xq/nrR5djp7O9F/7NZa2VrwNeskPY9agyMz+CF3RKiWZ8Md8to1YSeDRqwD492tB3W6NQRAI3nmWB1uPa+QL+VXBZ05vBJmMp7vOcm/dQS19tiXdqbtyEI6VinFr8e8EbToCgNzUmOZ7piM3NUKQy3l9+Bq3l+81WGe2ft4Um9sfQSYjYtefhKz9QztfHRrgMbw9AMqUNF5O3kzKg1eYlfSg9MZxGjnTIq4EL/2FsK2HDOpy9K9KuXl9ENQ2+CqXDbp1qktxtQ0q1DaY9OANph6OVF47DBNnO1AqCf7pNG/Udm0Id78q1JirKr9nu87yYK12+dmUcsd3xSAcKhfjzuLfebjxiOZeu6sryUpKQ6lUImYpONZC94NTxRpWodGs3ghyGfd+Ocu19Qd1ZBrN7k1xfy+yUtM5Om4zEfdfqcrKxoJmSwbiWMYTRJFjE7YQekvVvnj3bYp3nwCUCgUvTt8maK52u1vIrwq11HbxxIBd1MthF/dz2EWLHHbxyoBdtJ3Zh7L+XmSmZvDb+A2E6GmX7D2d6bF2FBa2lrwLesWvY9ZpbF1feFt3B7quGIa1sx2iUkRmZoYyLUEnXkNMX7CC8xev4WBvx76fNuY73HuKNayCv7qu7huoK/8cdXVMXVf2JdxpvW6ERsa2iAuXVuzm1rbjNJjanZJNvFFkZhH3OoLj4zdDnK7ukXOGUauRD2mp6Swes1Tve6R933Z0Vr9H2lXupPUembh8HB5FPchIz2DJuOU675H/EumI0M9oJUAQBAH4AzgrimJJURQrAFMBV+AAsEgUxTJAVaAOMCyfUacDHQVBcMp5URTF+aIoeomi6AWkvv9dFMVv1SIr1ffaAZuAWGAxsEyd3mpAPWB5PtMRDOT1yUR/URQrAz5ACWBzPuPV4ONfk0LFC9Gnfj9WTlrN6AUj9coF3QhiYvfJhL0N07retk9bXj99w+BmQxn3xQQGzxiEkbH+cWh1/xp4FPNgcINBrJu8lqHz9VfHwxsPmNEju7OfkwfXgxjdYhSjW4wyOACo6V+TQsU96Fd/AKsnfcvIBSP0ygXdeMDk7lMIy6Vn96Y9DGs+gmHNR/Ddoh+4d+WewU55QeryaVQTz+KF+LJeP1ZMWsXohaP067oexIRuunXVY2R3ngU956umQ1g0einDZw/Vul/ezwvn4u4s8Pua36ZuofP8gXrjbzO5B+e2HWaB/xhS45Oo1bURADFvI1nbdQ5LW0zixJq9fLFwEADKLAUH5v3IoibjWNVhBkX6BWBZphDIBCos6s+NHou4UH8c7h3qqq7nIPV1JFfbz+Gi/ySer9hLxeWDNPfe/XKOm90W6k3jeyr4eeFc3I25fqP5deoWvpg/QK9cu8k9ObvtCPP8vyYlPpna6jw9vniPxS0msqTlJH6euJHuiwdrhfPr15KwZ+/+VV22rvY07NuCZW2msKjZeGQyGe0mdMeluBuz/Eaxc+pmuhmoq/aTe3F622Fm+Y8mJT6ZOmpdoBrAtJ/ckwfnb2uuuZcpTN1ujVncbioLWkzAs4k31sVdNfK15vfhZK8l7POfSPH2vtiW9tDSlx6XzNUZP2o6ee9RpGdy/IsFHGg6jQMB0yjkVwXnaiX1phmZjOILvuJRz3nc8RuNY7v6mJf21NbzNpwHnWZwr8lY3q38nRJLhgCQ9jyEe03HqX6aTUCZmk6MenCjX5dA+UX9udVjERfzsMHr7edw2X8SL3LYoJil4PHMH7lUfxxXW86g8Hu7NoAgE6i5oA9nei7hkN9EirXzxSZ3+cUmc2PGj1qd/5z82WU+R5tO0zsAEGQCTeb1YU+fJXzfeCLl2vrimCv+4v5VsS/mxrYG4zgxeRtN5/fV3Gs0qzcvz97l+0YT2d58KjHPQgAoXLs8pQKqs73ZFH5oMpkbuepWkAn4zu/DiV5L+MN/IiU+0i6OfbGA/U2nsT9gGp567KKsnxdOxd1Y6jeGvVO30MHAc9Vycg8ubDvCUv+xpMYnU7Orf57hlVlKDs37ieVNxrO2wwxk5jYgz/9X7tu3bMrGFfPyLZ8TQSbQeF4f9vZZwg+NJ1K2rS8OBurquwbjODl5G03UdRX7IpQfW0zjxxbT+KnVdLJS03l67AYAr/+6xw9NJ7Oj2VRiX4biM7yNju5ajXwoVLwQver1ZfmkVYwx8B65f/0+47pN0nmP9FS/RwY2HczC0UsYOTu/3SqJguKzGQQA/kCmKIqaYbgoireBMsBFURRPqK+lACOAyfmMNwtVh3rM30mUKIpPgRTAXh1PSUEQ/IG1wAhRFDPzGdUhoKIgCGU/oC8JGAK0FwTB4WPSWiegNif3/AnAw8BHWNlY4uCiG8WzoOeEB+v5/LgoYmFlDoC5pRmJcYkoshS6coBvQC1O7zkNwOPAx1jaWGLvYq8j9yLoBRHBER+TDS1qB/jy555TADwKfISljRUOevQ8D3pO+Af0+LdryNn95z4JXXUD6nBi90kAHt76+LoqWroIgRcCAXj7/C1unq7YO9lp7lcKqMH1vecBeB34DHNrC2yc7XTiKVWnIneOqDpW1/acp3JADQBe3XpCakKyKvytp9i6qdKWEBmnWVFIT04j6ek7zNwcsKtWipSXYaS+jkDMVBC27xKuzWto6Yq78YSseFWccTefYuaend/YK4/IjEs2WF4AlQNqck2dp1eBTzG3ttSbp9J1KnL7yBV1ns5ROaAmABkp2QtrJham5HQ3tXNzoEIjby7/cvpf1yWTyzA2M0Eml2FiboJrCQ+u5tBlYUBX2ToVCVTrurLnLFXVugD8+rYg8OhVEqOzZz/dShXiZeBTMtMyUCqUhF15RFF1nTh5lyTxVThJbyJRZip4uf8KRZpV19KXFp1A9J0XiJm6bUCWOn8yIzkyYyMMue5aeZci7VUo6W/CETOziN5/AftmPloySTceo1DbReKtJ5i4O+rEY1u/Mumvw8l4F6lfEWCrxwZdctlgfC4bNFXbYEZEHIn3XgGgSE4j+ek7TN0MN7+Oucrv9f4rFM5VfunRCcTceYHSQBuaF25eJYl9FU68Ov5HB69QMkA7/lIB1QnacwGA0MDnmNpYYulih4mVOZ4+Zbn3y1kAlJkK0hNSAPDq3YSr6w+iyMgCICVae7Y8t128MGAXUXdeoPwbdlExoDo39/4FwBt1u2Stx9ZL1qnIPXW7dHPPeSqq2yVD4RMj4zQrChnJaYhZmQiy/DtS1PCqjK2Ndb7lc+LmVZK4HHX1+OAVSuWqq5IB1Xmgp65yUqRuReLeRJD4LhqA13/dR1QoVWFuPcdajz3WDajNid3qd/6th+p3Vv7fI8VKF+VWjveIa673yH+NKIoF9vOp8jkNAioBN/Vcr5j7uiiKzwErQRBs8hn3OqCnIAi2H5so9Yz/U1EUI0RRVAJDgT3AE1EUz39EVEpgCarVjTwRRTEBeAmU/pi0Ork5ERmS/ZKMDI3CyU33hWqIfT8coEipIvx642e2nNzE+pkbDD4cjm6ORIVGaf6ODovG8SN0AZStVo5vj61h1vZZFClTRK+Mk5sjkSHZeqJCo3B0c9IrmxemZqbU8KvBhaMXDMoUvK6/X1cvHrygfot6AJT1KourpytO7s6a+7auDsSFRGv+jguL0XTk32Npb01qQgpK9YsmPjQGW1fdF0itrv48Ontb57q9pzM2lYoRd+sZpm4OpObQlxYSk2cnyrOHP5GndePMC1tX+1x5iv5gnuJy5alKs5pMO7WCwd9N5ueJGzTXO37ThwMLd2rs/d/SFR8ey+kth5h9aT3zrm0iNTEFURSJzWF3sWHR2OnRlZJLl51al62rPV7NfPhrp7brXujjt5TyKY+lnRXGZiZ4NqqKpYfKxizc7EkOidHIJofGYOGmO+A1hCATaHtiPt3urifk/D2iAnXd5gBM3BzJyFGOGaHRmLgbtguX7k2IOxOoc92xXT2i9v2VZ5rM3BxI+wgbLNTDnyg9NmhW2BnrSsWIv6XrVvEeczd7UnKUX0poDObu+S8/RJFGuybT/NhcSvX017lt7WZPYo74k0JjsHbVjt/KzZ7E0Oz8JobFYOVmj20RZ1JiEmm+fBC9j8wjYPFAjM1NAbAv7oanT1l67p9F19+m4ValhFacue0iJTQGy79hF90N2IWNqwPxOeooPiwGm1x1ZGFvTWpCco52KRobta3nJ7y9pxOCkSliVlq+0/2/YJWrrhJDY7DKZ13lpFzb2jzaf1mvjkpdG/Dy7F2d605uTkSEZE9IRYVG4fQR76znD17QQP0eKedVFjdPV5xzvEck/ns+p0GAIQQw6BgmfuCe6hdVp3oHoH+tTD9jBEF4jGofwKwccd0G7gPrPyKu9/wM+AqCUDwfsoLei4IwSBCEG4Ig3HiXFJzrnq78x4xwazSszvMHz+laoweDmw9jxNzhWFhZ5Dt5H6Pr+f1nDKjdn1HNR3Lwh0NM2zLdgJr/Tc97fJvWIuj6A4PuOZ+Grvzr2LXuV6xsrdh0fAMd+rXj6f1nWqs2+bKFfMiUql0B367+HFz0s9Z1EwtT+m0Yw6MZ21EkpRqwVv0ZcqhbAc8e/jyZ+7Pe+4YQ9Gcql4y+ZGTL3D1+nfmNx7J10DJaje0KQMVG1UiMTuDt/Ww//H9Ll7mNJZWb1mB2/RFMrzUEUwsz7PQM/nLXQ17J6fJNX/5YtBNRqR0m7Pk7Tm7cz8ifpjNi+1RiH7xBqVDkEaGe/BhAVIocCJjG7zVG4eRdEruynvoF9ZaRflGbOpVw6d6YN/N3aEdhbIR9QE1iDl7KO1EfYYP2dStQqIc/T3PZoNzCFK9tY3j83q4Nqfofy+9EuzkcbTadMz2XUqZvE1xq5Vogzkf7IOh/gJEZyXGtVIzbP57ix5bTyUxNx2eYypVEZiTDzNaSne1mcW7+Ltqs13Z51Jevj2mX3tvFb4bs4m8+V5rn4QPhTSxM6bVhDIrkqI9L+P9AfmxBX13lfMZlxnJKNq3Gk8O67m61RrRFmaXk4R8X86k7//n+ed0vWNlas+X4Rjr0a6/zHvmvUSIW2M+nyue0MTgI6GzgeoOcFwRBKIFqM3CiIAjRgHuuMNbobqFZBdwCvs9nelaKorhMEISOwA5BEEqKovh+akGp/vkoRFHMEgRhOTApLzlBEKyBYsATPXFsRr1foEnhZmLbPm1o2b0FAE/uPMHZI3sU7+zuRHR4TO4oDNL8iwB2rf8NgJBXIYS9DaNwqcI8vq3a0Njyy1Y0694MgKd3n+Lknj3j4OjmSMxH6ErN8YK9eeYG8nlDsbG3ISE2gTZ9WtOie/McecrW4+TuREx4tE58H6Jh24acPXBW53pB6mrXpw0te7QE4PGdx3rqKv+6UpJSWDouezvKzss7qFbfm0mrJmAuyHlz5zl2HtmdSzs3BxLCY7XiSI5JxNzGAplchlKhxNbdgYSIbBn3ckXoumgwm/suIiXHgEZmJKffxrHc3HcBsyPXAUgPjcE8hz4zDwfSw7T1AVhVKEKlFYO50X0RmbF5DJLUFOkXgGevRlSSoSdPjsTnylNSrjzZuTsQH6GbjufXHuJU1BVLe2tK1ChL5SbV8W5dG3NrCwSZQPSb8H9FV+naFYl+G4F3q9rU7t4YC1tL0lPSsfdwAlTPmb0BXRY6ulTPW5EqJRmwZjQAlvY2VPLzRqlQcufEdS79doZLv50BYMi4XiSHqsKkhMZg6ZE9g2rp7kBKuG7aP0RGQgphlx5SyK8KcY+Dde+HRmOSoxxN3B3JCNNtJyzKF6XEsmE86jWXrFx2YdfIm+R7L8iMis8zLWmhMZjl0wYrrhjMrVw2KBjJqfrdWEL3XCBCbdeGSAmNwSJH+Vm4O5CqR5chUsPjAJXL0NtjN3H0LknE1exN6omhMVjniN/K3YGkXLaVGBaDdQ7XKWs3B5LC4xBFkcTQGMJuq2bhnxy5Rq2hbdTxxvL0qMrnPOzOC0RRxNTBmvQY1bkUybnswuJ/tAtPvyq4+ZajTE9/mgkiwXdeYJujjmwNtkuWOdolRxLVeU8IizYYXmYkp/fGMdzed5FmQ+t+dJr/LrnryjqfdZWstgGA4n5VCb//ipQobfesCp3rU6KxN793z94v1b5PW1qp3yOP7jzGxcMFVTdJ9c6K+sj3yJJxmjNV2HX5R0Jz7RuQ+G/5nFYCTgOmgiB89f6CIAg1gadAPUEQmqivmQPfonKtATgPtFV3nFF32u+Ioqg1nBVFMQb4DdC/E8kAoijuBW4Aff5OpvTwA9AE0LvmJgiCFapVhn2iKH6w9T2w/SBDmg9jSPNhXDx+iaadmgBQ3rscyYkpxETkv2MeERJJtbpeANg52VG4pCehr0M194/sOKzZyHvl+GUadVJtTCzrXZaUxBRi9XSADGGXww+0dNUyyGSC5sSCg9sPaTbYXjp+mSadGgNQzrscKYnJxHyEHgALawuq+Fbm0nHdpdaC1LV/+0EGNxvK4GZDuXjsEgGdmwJQvlo5khOTP6quLG0sNZu2W/Zowd2r99i9eQ+Dmw1lWcvJ3D9xg5odVWPnot6lSE1MISEyTieeZ5cfULVlLQB8OjXg/glVB8HOw5F+G8eyc8w6Il+GaoXptngw4c/ecW5b9sbA+MDnWJRww7yIM4KxHLf2dYg4ru3dZ1bIEe/vxnJ3+DpSXmjHaYg335/gUuPJLGk5ibsnruOjzlMx79KkGcjT08sP8Grpq85TQ+6p8+RU1FUj41mxOHJjI5JjEzm4ZBff1B7GVO+BrOs1jwdnAtkze/u/ois2JIpi3qW58vtZlrScxJMrD3h29QG1cugyVFdPLgfhrdbl28mPu2pd39QfwYx6qp/Ao1f4ZcZW7pxQdWKtHFUek/YejhRtUYOX+1Sz6VG3X2BT3A2rws7IjOUUb+fL2xO3PlQdAJg6WGNio1ohlJsZ41G/EvHPQ/TKJt1+hllxd0wLuyAYG+HYrh6xJ7Q72CaFnCizdSLPRq0mTY9dOLavT/Q+w65170nIpw16fTeWe3pssOLKwSQ/fcfrTfo38uYk+vYLrIu7Yakuv6LtfAnOZ/nJzU0xsjTT/O7esBJxj7QHUGF3XmBf3A1bdfzl2vjy/KR2/M9P3qJiJ5Urh7t3SdITU0iOiCMlMp7E0BjsS6jmxorWrUj0U9WG92cnblCkTgVA5RokMzbSDABA1y5K/A924V6/EnHPQ3i0/U8OBExjdcspBJ24QfWO9QEo4l2KtMQUEvXY+vPLQVRWt0vVOzUg6ISqHh+cvGUwfOfFg4h4FsJf2z5cf/8kYXdeYFfcDRt1mZU1UFcV9NTVe8q103UFKtawCj5DW7NvwAqy0jI01/dtP8BXzYbwVbMhXDx2kYDO6nd+tfL/03uklfo9kpKU8lH5/zcRC/Dfp4rwKW9Y+KcRBMED1Yx9dSCN7CNCzVAdEeqO6gjNH4E5orpwBEEYjOq0IBGIAIaIovhCfS9JFEUr9e+uqHztl4iiOCuHXo2M+u9Z5Dh2VBCE6qhcecqLoqgUBOEsquM/b+SRFz+0jwitIYriCPW9UcBqoHiuI0IFVAO/P1AdEZqnU2OTws10jGPkvOHU9FMd27l03HKe3H0KwPztc1kxcSXR4TG079eOrkO74ODsQGx0HNdOX2PFxFU4ujowYcV4HF0cQBD4Zd2vnPpDtVHSXNA9aWHI3CFU86uuOiJ0/Cqe3VX50M78YRZrJn1LTHgMbfq1oeOQTtg72xMXHcfN0zdYM2kNrfq0pmXvFiiylKSnpbNt7lYe3XwEQIb2+I3h84ZRw68G6alpLB+3kqfqPM3dPoeVE1cREx5Du35t6TK0Cw5qPddOX2fVxNUANO3ShBp+NVg4XOckVx3+LV1Zou4S66h5I6jpV4O0tHSWjl2mqasFO+axfMIKosNj6NC/fXZdRcVx7cw1lk9YSYVq5Zm0eiJKhZLXT1+zbPwKkuJVs5pVjFSzUp3m9KNcQy8yUtP5ZcJG3t57AcBX30/i10mbSYiIxbGwC73XjMLCzop3Qa/4acxaFBlZdF00iCotfIh9p/JVV2YpWNF2GsVrlGXU7tmEPHyNKIpYK+HJgl+IOnUbp8ZelJ+rPp5x1xlerNpH4S9VL6i3O/6k4opBuLXyITVYFaeYpeByM9WBWVU3jsS+TgVMHKzJiIzn6dLdvPv5jKasjpirTL3LnP6Ub1iVjNQMdk7YoMnT4O8ns2vSJk2e+q4ZjYWdFcFBr/hxzBqyMrJoMqQtNTs2QJGlIDMtg/0LftI6IhSglG8FGn3Vms0DlvxrulqM6UK11rVRZCl5G/SSnZM30ml6Hyqodf04YT1v1LqGfT+ZnZM2Ea/WNWDN12pdqiNCs9QbPN/Te9kw7p+6qTkidOxvs7G0t0aRlUXQrF2EXgjSyBZqVBWf2b1UR1z+eo673x6gbG/16UY/nsbc2ZbWR+dibGUOSiWZKens85uEVWEn6q0ajCCTIcgEXh28yp1V+7TSUU7M7kjYNapG0dn9EeQyIn45Rci3e3DpHQBAxI8nKLFsGA4tfUlXb/oVsxTcbzERAJm5Cd7Xt3C79lAUibqdkwSFidbfTo29KKu2wXe7zvBy1T481TYYvONPKqwYhGsuG7zabBp2PmXxOTibxAevNW5Vz9R2/Z5IufbCvEejqlSf3QtBLuP5L+cI+vYApdXl9/TH05g529Li6FyMrc0RlUqyktM56DcJMwcrGmz7GlCtPrz64xJB32ofZRpqpDpRxn9mL2RyGfd+PcfVtQeo2ksV/52fVO1y47l9KO5XhczUDI6N30z4XZVLm3OFIjRbMhC5sRFxbyI4Nn4z6fEpyIzlNF86CJeKRVBkKDg7/2dS/nqgpdszh108NWAXbdR2ISqVZKWk84faLurnsIuXuezikbGqDWw3px9lG1YlIzWd3yds4p3a1vt9P5Hdk7aQGBGLQ2EXeqwZibmdFSFBr/hlzDrNZmZ94YvVKMvQ3bMIffgGUVTiXsYdRXIMYqZhl66cTJi5iOuBd4mLS8DRwY5hA3rTqU2zfIX9tto3FPevip+6ru6r66qKuq7u5qirYuq6Op6jrozMTBh0dTVb640lIzE7vf3PL8fIxIhU9WpVaOAzvp6s+w4bPW+k6p2fls7isct4clflQLBwx3yWTVhBdHg0Hfu3p9vQLzTvkatnrrFswgoqVCvPlNWTUCoUvHr6hqXjl2veI2eCT+p1sCtIqrjVLrAO8N2wy/95fvXxWQ0CJD4OfYOAfwt9g4B/i9yDgP8f0DcI+Ld4PwgoCJrl7x37P/N+EPD/G1kFOAPlk2nyYaF/iJyDgH+T3IOAf5Pcg4B/k9ACdAR2LKCm6f0goCCYd2N+gen6tpruEa//FoeUek71+5f4FAYBlVx9C6yBvB9+5T/Prz4+J3cgCQkJCQkJCQkJCQk+r43B/ycRBKEZqo+I5eSlKIod/ov0SEhISEhISEj8X+dT9tUvKKRBwCeOKIrHgeP/dTokJCQkJCQkJCT+/0FyB5KQkJCQkJCQkJD4zJBWAiQkJCQkJCQkJD4rlNLBONJKgISEhISEhISEhMTnhrQSICEhISEhISEh8VkhbQyWVgIkJCQkJCQkJCQkPjuklQAJCQkJCQkJCYnPCmlPgDQIkMiD71yzCkxXi7dxBabrkJtNgeg5EOtaIHoA5iVcLzBd3vaOBabLq2xogeipklVwH3OUmyoLTNfpx54FpqucUWKB6aoZdqNA9MiEgrMLG1OLAtPlZGZbYLqCk6IKRE9dx3IFogcK9iu+o27NKTBdATVHF5guiU8DaRAgISEhISEhISHxWSHtCZD2BEhISEhISEhISEh8dkgrARISEhISEhISEp8V0p4AaSVAQkJCQkJCQkJC4rNDWgmQkJCQkJCQkJD4rJD2BEgrARISEhISEhISEhKfHdJKgISEhISEhISExGeFKBbccc2fKtJKgISEhISEhISEhMRnxgdXAgRBUAD31LIPgT6iKKYIgpAkiqKVgTCrgc5AYcAEuA10EUXxnvr+RKAEsAg4JIpiJUEQ/IAzQFtRFA+q5Q4By0RRPCsIghEwB+gCJKtV/S6K4nw9+l8BiYAIxAJfiqL4Wn3PE1gHVEA1CDoETBBFMUOdhvGiKLbOFd9ZwB1IVV96JopiZwN5nwV8BUQCZuo8DRfzGHIKgtAeeCKK4gNDMp8CZrVrYj9+OMhkJO87QsL2X7Tumzesg+2QfqBUIioUxC1fT/qd+wBY9+iEZbuWgEjms5dEz14CGZl56psyfywNGtchNTWNaaPm8vDeYx2ZxetnU7FqebKysrgX+IDZ4xeSlaXAytqSxetn417IDblczvcbdrLvl0M64c3r1MBh0jAEmYzEP44S/92vWvct/GpjP7wvolIEhYLopetJDwzCuKgnzkuma+SMPd2IXb+dhJ1/aIUv2rAKDWf1RpDLCPrlLDfWH9RJQ8PZvSnm70VWajonxm0m8v4rALwHNKdidz9EUST6UTAnx29GkZ6J77jOlAyohqgUSYlO4OS4TRCvW34LFk+jSUBDUlLSGDVsMnfv6JrXqrXzqepVCUEQePH8JSOHTiE5OQWAOvV8mL9wKkbGRsREx9KuVW+tsO1m9qG8vxcZqRn8On4D74Je6cTv4OlMr7WjMLe15F3QK3aNWYciU4FzSQ+6Lh2MZ8XiHF32K+e2HNaEmXrhW9KTUlEqlTjK0okeNBgAEx8fbEaNAJmc1MOHSd75s26mAaNyZXHcsJ64WXNIP3cOmYsztlOnInd0QFQqST14iJTde/SGfY9prZrYfj0C5HJSDh4m6cddeuWMy5fFafM6Yr+ZQ9qZ8wDYTZ2IaV1flLFxRPbqn6ceAJOaPliPGAlyGamHD5Oyy0C+ypbDYd164ufMJv38OTA2wWH1t2BijCCXk3buHMk/fJ+nLne/KlSb2xtBJuP5rrM8XKttj9al3PFdMRj7ysW4u/g3Hm08kp1XGwt8ln2FXTlPRFHk6tjNRN98ZlCXdUNvPGd9hSCXEf3LScLXa5e5ffuGuA7tCIAiOY3gaRtIffgKAOf+rXHsHgCCQPSuE0Ru031ucrJyxRxaNG9ESmoqAwaMIfD2fYOyq1bOpW+frtg5lNG6XqN6VS5eOEj3nkPZu/ewgdCwYsUcmjdvRGpKKgMGjuF2HrpWrpxLny+/wMGxLADdu3Vg/PhhACQlJTNy5BTu3ntoMPyCJdNpEtCQ1JRURg41/Ax7eVdGEOD5s1eMHDqZ5OQURowaQKcv2gJgZCSnTNmSlC3hS1ysnsYCmDZ/HA2a1CUtNY0pI2fzQE97u3TDXCpVLU9mZhb3AoOYOX4BWVkKbGytmb96BkWKeZKelsG0r+fy9NFzg/nSinPZTAKa+ZGaksbgweO5czvIoOyy5bPo1bszbi6V8hX30NlD8GlUk7TUdJaPXc6z+7ppatunDR0GtsejmAddqnQlITYBgM6DO9Gogz8AciM5hUsVpqtXNxLjkgAo1rAK/uq2/f4vZ7mmp233n92b4uq2/di4zUTcf4V9CXdarxuhkbEt4sKlFbu5te04DaZ2p2QTbxSZWcS9juD4+M35yud7pi9YwfmL13Cwt2PfTxs/Kqw+rBpUw2PmVyCTEfvrSSI37ta6b920Fq5je4JSRMxSEDp3Kyk3VDZaaPEobBrVJCs6nqfNR+iL/j9HKe0JyNdKQKooil6iKFYCMoAheQkLgiADOgBvgQaiKKYBXwPrBRWFgMHAFD3Bg4FpBqKeB3gAlUVR9ALqA8Z5JMVfFMUqwFlgujptArAX2CeKYmmgDGAF6Awk9NBTXQ5ehgYAOVipTmMFoDLQ8APy7dWyny4yGfaTRhExagqhXfpj0awRRsWLaomkXbtFWPevCOs5mJg5y3CYMQ4AubMT1l07EP7lUMK6DgSZDMuARnmqq9+4DkWLF6aFb2dmjV/EN0sm6pU7tOc4ret+QfuGPTAzM6VTz3YAdO/fmeePX9KxUS/6dhzKxFmjMDbONeaVyXCcOpLwYVMJ7jAQy+b+GJcooiWSejWQd10GE9J1CJEzl+E0cywAma+DCek6RPXTfRjKtHRSTl/UCivIBPzm9WFfnyX82HgiZdr64lDaQ0ummH9V7Iq5sb3BOE5N3kaj+X0BsHS1p2q/AHa1msHOplMQ5DLKtPEF4Namw+xsNpWfW0zj5alAao3uoFMuTZo2oETJYvh4BzBu9AyWrJilt/ymT1mAf712+NVtS/DbUAYM6gmAja01S5bPpFf3odT3bc2APtpfkizn54VzcTcW+Y1h99QtdJo/QG/8rSb34Py2Iyz2H0tqfDI+XVUv1dS4JPbP2s7ZLboDM4AN3eexsuUUzQAAmQybMaOJnTCJqC/7YNa4EfKiRXUDymRYDxlMxvUcX1BWKEhcv56o3n2IGTIMiw7t9YfNEYft+NFEj5tMRI++mDdpjFEx/bpshg0i/ar215pTjhwjZswkw/HnTu/or4mbPJHovn0wa9zYcL4G5cpXZgaxY8cQM3AA0QMHYOrjg3F5w82IIBOovqAvZ3su4YjfRIq2q41N6UJaMhmxydycsYNHG3U7wdXn9Cb07B0ON5jAsSZTSHgakme+Cs8bzPM+s3nYeAT2betjVrqwtq634Tz9YiqPmo0m/NtfKbxoOABmZYrg2D2Ax23G86jZaGwb18S0mLtBVS2aN6J0qeKUq1CPoUMnsW7tQoOy1atVwc5O92u5MpmMhQumceLEWcN5Apo3b0SpUsWpUKEeQ4dNYu0aw7qqVauCna3218lfvnpD4yadqV6jKQsWrmb9+iUGwzcJaKh6hr2aMnb0DJaunK1XbvqUBfjVbUvDOm15FxzKgEG9AFj77Tb867XDv1475s1azqUL1wwOABo0rkPREkVoVqsj34xbwMwlk/XKHdx9lBZ1OtO2YTfMzEzp3Ks9AIO/7sej+09o59eDSSNmMnXeOIP5yklAMz9KlipG1cr+jBwxhVWr5xmU9a5WGVvb/H/tvaZ/TQoV96Bf/QGsnvQtIxfo74gG3XjA5O5TCHsbrnV996Y9DGs+gmHNR/Ddoh+4d+WeZgAgyAQaz+vD3j5L+KHxRMrqaduL+1fFvpgb3zUYx8nJ22iibttjX4TyY4tp/NhiGj+1mk5WajpPj6m+gP36r3v80HQyO5pNJfZlKD7D2+Q7vwDtWzZl4wrDZfhRyGR4zBnCy76zeBowHNu2DTAtpf0MJ1+8w7MWo3jWajTvJn1LoUUjNfdi95ziZd9Z/0xaJP41PtYd6C+g1Adk/IH7wAagO4AoiseAUOBLYCUwSxTFWD1h7wDxgiA0zXlREAQLVLPrI9WDCkRRTBRFcVY+0nwZeP+mawSkiaL4vToOBTAG6K/W8U9jgmo1IBZAEISvBEG4LgjCHUEQ9giCYCEIQh2gLbBUEITbgiCUFARhlCAIDwRBuCsIwi/qsLMEQdguCMIJQRBeCYLQURCEJYIg3BME4ZggCMZquW/UOu4LgrBZPfBBEISzgiAsFgThmiAITwRBqP9RGalYjqy371C8C4WsLFJOnMGiYR0tGTE1TfO7YG4GOc/glcsRTE1BLkNmZoYiMu9PyTdq3oADvx8F4O7N+1jbWOPk4qgj99epS5rf7wUG4erhokqLCJZWqiq1sDQnPi6BrCyFVljTSmXJfBtC1rswyMoi+dhZLPwM50lmboa+iQPzWt5kvQ0lKzRC67qrV0niX4WT8CYSZaaCJwevUCKgupZMiYDqPNxzAYCwwOeY2lhi4WKn0mckx8jMBEEuw9jchORw1SOTkZSqCW9sYYqo56zj5q0a8+uufQDcvHEHW1sbXF2ddeSSEpM1v5uZm2mqrFOXNhw+eJJ3waEAREXFaIWrGFCdG3v/AuBN4DPMrC2wdrbTib9UnYrcPXIVgBt7zlMpoIZKb3QCb+++QJmrTgxhXL4cinfvUISq7C/t1GnM6tXVkbPo1JH0c+dRxsZprimjY8h68hQAMTWVrNevkTs7GdZVoRxZwSEoQlS6Uv88jVl9XV2WnTuQeuYvLV0AGbfvokxIyF++ypVHEZIjX6dPY1q3nm6+OnQk7a9zKOO0m00xTW0LRkYgN8rztAsH75IkvQonWW2Pb/ZfwbOZtj2mRycQc0e3XoyszHH2LceLn88CoMxUkJmQYlCXhVdp0l+FkfEmHDEzi9iDf2Eb4KMlk3zzEYp4lf0lBz7G2F31fJuV9iT51hPEtAxQKEm8ch/b5r4GdbVp04wfd6pmKK9eu4WtnS1ubi46cjKZjMWLZjB5im4nacTw/uz94zARkdEG9ah0BbDzJ5Wua9duYWdnY1DXooXTmTJVe37pypWbxMWpOuJXr96iUKE8BjctG/PbLtXK4s3rd7C1tf7wM2ymvz3o2KU1e3cbXt1o3KIh+39T3b9z8z42ttY462lvz+dob+8GBuHmrsp7yTLFufyXaoD68tlrChVxx9HZwaC+97Ru3ZRdO/cCcP36bVU75aabR5lMxvz5U5g+3fCgKze1A3z5c88pAB4FPsLSxgoHF3sduedBzwkPjtC5nhP/dg05u/+c5m83r5LEvQonXv0sPT54hVK52vaSAdV5oG7bQ9Vtu6W6bX9PkboViXsTQeI7ld29/us+okLlNBB66znWbh8uw5zU8KqMrY31R4UxhEXV0mS8DiXzreoZjj94HpumtbRklCk534+mWu/8lGtBKOIS/5G0SPx75HsQoHbHaYHKNSgvugO7gD+A1u87p6hWA+YDzqIo/phH+HmoZ+5zUAp4I4ri37Go5sA+9e8VgZs5b4qimAC84cODm53qTvptQRCWfkB2jCAIt1ENfJ6IonhbfX2vKIo1RVGsisq1aoAoipeAA6hckrxEUXwOTAa81SsZOVdeSgKtgHbAT8AZURQro3JTaqWWWavWUQkwB3K6NhmJouiDqi5mfiAPWshdnFCER2r+zoqIRO6i25Ey96uL++7vcV41n+g5ywBQREaR+NPveBzaRaFjv6NMSiLt6k2dsDlxcXcm7F32zEx4aASu7rovB03GjOS06dyCC6evAPDztt8pUaY4Z+8eZt/Zn1k4faXOy1Hu4oQiLDtPiogojFx182TRqC6F9m3Dde08omYu07lv2dyPpGNndK5budmTGJLdeU4KjcHK1V5HJik0u+ORFBaDlZs9yeGx3Np8hP5XVjPwxlrSE1J481e220HtCV3of2U1ZdvX4cpyXdcWd3dXQt6Faf4OCQnDzcNVRw7g23ULCHp6kdKlS7B1k+rRLFmyGHZ2Nuw7tIM/z+3hi27ttMLYujoQF5Kd7viwGGxzvbAs7K1JTUhGqX6pxYVGY+uaj5eaKDLoxyl8fXA+5m1U5itzckYRkaOuIiOROWvbg8zJCbP69UjZf8Bg1HI3N4xLlybzgWEXDLmzE4rw7E6BIjJSZ9Agc3LCrGF9UvYZ1pUfZE5OKCOydSkjI5E76eoyrV+f1AN6dMlkOGzZivMf+8i4eYOsh4bzZeHmQEqOOksJjcHcXbdTpA+roi6kRydSa+Vgmp+Yj8+ygcjNTQ3Km7g5khGSPdDPCI3G2FW3U/kex65NSThzC4DUx2+wqlUBuZ01gpkJtv7VMXE3PGgr5OFG8NvsVYl3waEU8nDTkRs+rB8HD50gLEy7w+fh4Ub7ds3ZtDmv11K27NvgbF3B70Lx0KNr2LB+HDqsqysn/fp14/hx3XbjPe4errwLzvEMvwvH3dAzvH4hD55donSZ7Gf4PebmZjRqUp+DB44b1OXq5kxoSHZ7GxYSgau77uDmPUZGctp2aclfpy8D8DjoKQGtVKt8lb0r4OHpphkg5IW7hyvB6okGVR71l+eQIV9y+PCfhOdorz+Ek5sjkTlsMCo0Ckc3w3ZkCFMzU2r41eDC0Quaa7nb9kQDbXtijrY9Ud2256Rc29o82n9Zr95KXRvw8uzdj07vP4WRmyOZodnllxkWjbGb7jNsE+BL6T83UPS7mbybuLogk/g/I4pigf18quRnEGCu7tDeQNVZ3mZIUBAEE6AlKnebBOAqEAAgimIIcBrVCoFBRFH8Sx2XwZlqQRD6qTvjbwVBKGxA7IwgCBFAE+C9k62A3rlcg9dzktMdaMIHZN+7A7kAloIgdFNfryQIwl+CINwDeqIalOjjLqpBRy8gK8f1o6IoZqIaiMmBY+rr94Bi6t/9BUG4qtbRKJeOver/b+aQ10IQhEGCINwQBOHGz5Hv8s6lHsNOPXuR0M79iBr/DXZD+qritLbCvGEdQtr25F3zLxDMzbFo0STPqAWE/KjTMGPxRG5euc2tq7cBqOfvy6P7T/Cr0opOjXozbeF4LK0scynRp0NXScrpi7xrP4CIr2dhP7yv9k0jIywa1ib5xDmdcPrj1xHSDSeKmNpaUKJpNX6oO4ZtNUdibGFK2Q7Zs9GXl/7Od76jebzvElX7NtWJQshn3gBGDZ9K5bL1efLkOe07tlRnS04Vr4r0+GIwX3QYyLiJwyhRsthHxa9HJF+N4dpOs1jVeipb+y7GokN7jKtW0VtMuQvTZuQIEjduBqX+7TeCuTl2c2eTsGYtYorhWWx9ynKn2/br4SSs32RQV77RW0jaf1oPH0nSJgO6lEpivhpIVJcuGJcrj7xY8Tx06bmWz5eTTC7DvnIxnu34k2MB08hKSafCiDxcFT5Cl1Xtyjh2bULIwu0ApD8LJnzDXkrtnE2pH2eR+vCVZnZUr6p82KK7uyudO7Vm7brvdGRXLJ/NlKkLUOajLvOrq1PHVqxbZ3h/RsOGdejXtxtTpxn2RP2oZ3jYFCqVqaf1DL+nWYtGXLtyy6ArkFpZvnUBfLN4MjcuB3JT3d5u/nY7NrY2/HF6J70GduXhvSdkKT68ypefPLq5u9C+Y0s2btj+wfhyRf7BuPODb9NaBF1/oHEFUkX94edW/zssW0hmLKdk02o8OXxVR67WiLYos5Q8/OOizr0CI5/ll3DiCk+bDOX14Pm4ju1VECmT+AfJzxGhqeoObX5oDtgC99QPiQWQArxfh1Sqfz7EfFR7A953gJ8BRQRBsFa7AX0PfC8Iwn1UnWF9+KPaQPwDqg3FY4EgoFNOIUEQbFBtYH4OGJ6q+huIopgpCMIxoAHwizot7UVRvCMIQl/Az0DQVuowbYEZgiC878inq+NVCoKQKWY/kUrASBAEM2A9UEMUxbfqTcpmOeJNV/+vwEDdi6K4GdgM8KZGY80Tr4iIQp5jKdrIxRlFHkvn6YH3MPL0QGZrg2kNL7JCwlCql8FTz/yFaZUKpBz9UytM936d6dxLNeN8//YD3Aplz3q5ursQYWAWaOi4Adg72jNrfLYfdvturdm6ZgcAb14F8+5NCCVKF+VeYPbGOkV4JPIcS89yFycUEYbzlHbrHkaF3ZHZ2aCMU7l7WNSrScajZyhj4nTkk0JjsPbInvm2cncgOULbnSMpLAYr92yzs3JzICk8jsL1KpHwNpLUGNXi17NjN/CoXprHuV4Kj/ddou0P42Em9B/Yg959vgAgMPAeHoWyZ9Q8PNwIDzU8K6lUKtm/9wjDRw1g1869hISEER0dS0pKKikpqVy+dIORXw/Ey6sS9kbmvL3zAjuP7HTbujmQEK6dt+SYRMxtLJHJZSgVSuzcHUnIlX99vJdJik4g/a8LGJcvT+a9e8hdctSVszPKKG2XMqNyZbGb+Q0Agq0tJr61SFAoSL9wAeRy7ObOJvXkn6Sf/ytP/YrISOSu2bOYKl3admFcriz2c1S6ZLa2mNapBQoFaec/7qWtjIxE5pKtS+bsjCJaO1/GZcti+012vkxr+arydTF7ZlJMTiLjdiCmPj6kvHqpV1dKaAwWOerMwt2B1LC4fKUzJTSGlNAYogNVGyvfHrpG+TwGARmh0Zh4ZM+6mrg7khkRoyNnVq4oRZYM5/mXc7RcB2J+/ZOYX1Xtg/vEXmSGapf/0CF9GDBAtX/lxo3beBbO9scu5OlOSKi2f7e3VyVKlizG44eq+rGwMOfRgwuUq1CP6tWqsPOn9QA4OTnQonkjsrKyOKCeOR8ypA8D+vdQ67pDYc9sXZ6F3AnNpcvLqyIlSxbj4YMLGl0PHlygQgWVm1flSuXZuHEJbdv2JiZXu9H/q56aZ/j2rXsU8szxDBdyJewDz/C+PUcYMXqgxsUGoEOnVuzdrbv3pkf/LnRR+/TfC3ygtcrg5mG4vR0+fiAOTnaM7LtAcy05KZmpo+do/j51Yz/Br/XvGRk0uDd9+6nmxW7evIunZ7ZLlIee8qxaVVWed++fBVTleefeGapW9teJu02f1rTo3hyAJ3ee4JzDBp3cnYgJz9vdSx8N2zbk7IGzWtcSc7Xt1u4OJOVq2xLDYrDO0bZbuzmQHB6n+bu4X1XC778iJUrbdbBC5/qUaOzN793z7/r0b5AVGoVxjhU4YzdHssJ1n+H3pFwLwqSoO3J7GxSx+XOH/K+RNgb/80eEdgcGiqJYTBTFYkBxIOBj/e1FUTwB2ANV1X+noFqBWKvu6CIIghyVz31e8aSicn35UhAEB+AUYCEIwpc54lgO/KDW8Y+i9sevg2qAAWANhKpdpHrmEE1U33u/sbqwKIpngImAHarNy/nhfYc/ShAEK1QnNP0jZDx4hHHhQsg93FSz3wH+pJ6/pCVjlOPlaFy2NBgbo4xPQBEWgUml8qo9AYBpzWpkvnqjo2PX97vp1Lg3nRr35tTR87Tt0gKAKtUrkZSYRJSeDnqnnm2p6+/LhCEztGYpQt+F4Vtf5X/u6OxAsZJFePtae2UjPegxxkUKYVRIlSfL5n6knNNemjXK0bkwKVcKwdhYMwAAsGzhT9JR/Uv64XdeYFfcDZvCzsiM5ZRp48uLk7e0ZF6cvEX5TqrOgZt3SdITU0iJiCPxXTRu1UphZKYy8cJ1KxLzTJV+u2LZL+sSTasR+1y1nP7d1p/xr98e//rtOXroT7p2bw9A9RpVSUhIJDxc96VePMdG6IAW/jx9+gKAo4dP4VunBnK5HHNzM6pVr8Km9dvxr9+elS2nEHTiBjU6qhbriniXIi0xhcTIOJ34n10OokpLlR9pjU4NCDqRtxuYibkpppZmmt9NatYg68VLMh89Ru7pidxdVVdmjRuRflHb/qK6dieyazciu3Yj/dw5ElasUg0AANtJE8l6/YaU337PUz9A5sNHGHkW0ugyb9KItAvauiI69yCiU3ciOnUn7cw54pet+ugBAEDmo0fIC3kic1Pnq1Ej0i9pxxPVoxtR3VU/6efOkbBqJekXLyDY2iJYqpsGExNMqtcg643uc/WemNsvsC7uhqXaHou08yX4A/XxnrTIeFJCorEuqeqwudavSMJTwyuFKXeeYlrcHZPCLgjGRti3qU/8yWtaMsYeTpTYPIXXX68i/aV2h9HI0VYjY9e8NrEHzmvd37BxOzVqBlCjZgAHDhynd09VU1fLpxoJ8Qk6bjhHjp7Cs4g3pcr4UqqMLykpqZRTd8pLl62tub5n72FGjJqqGQAAbNy4nZo+zajp04wDB4/Rs5dKl49PNeLjE3V0HT16miJFq1GmbG3KlK1NSkqqZgBQuLAHv/62hX79RvP0qe5g7bstOzWbeY8c/pMvuqs2/VevWZWEhKQPPsPNWjTi6ZMXmr+tbayoU68mRw+f0gn383e/06FRTzo06smpo2dp94XKo7Rq9UokJiQRqae97dyzHfX8azNu8HSt9tbaxkpz8EKXXu25fiWQ5KRknfAAmzf9SB3fVtTxbcWhgyfo3lN1QlTNml6qdirX4OP4sTOULO5DxfL1qVi+PikpqXoHAAAHtx/SbOa9dPwyTTo1BqCcdzlSEpOJycckRE4srC2o4luZS8e13wthudr2sm18eZ6rbX9+8hYV1G27u7ptT46I09wv107XFahYwyr4DG3NvgEryErL+Ki0/tOk3H2KaTEPjD1dEYyNsG3TgIQ/tZ9hk6LZAziziiURjI3+zwwAJFT8Lx8LsxAEITjH3+uBZqhO/gFAFMVkQRAuAG2AX/k45gP7c/w9DZgL3BcEIRGVH/x2II8jKkAUxVBBEHahOqZzriAIHVCdVDQD1SDoCDA1R5DGufLVRf3/TkEQ3u/IjBJFMS9/ljFqVx5jVK4969XXZ6BykXqNyoXn/Q6eX4AtgiCMAroB2wRBsEW1qL5SFMU4vcuPunmNEwRhizruV8D1vEN8BAolMUvX4LJmMchlJB84SuaL11h1UvlsJ+05hHnjBli2bApZWYjpGURPmQtARtAjUk+dx23nRkSFgszHz0jK4wg+gPN/XqRB4zocvbqHtNQ0po+eq7m3YedKvhk7n8jwKL5ZMomQ4DB+PrwVgD8Pn2XDim1sXPEd87/9hj/O7kQQBFbMXUdcTK7lcIWS6IVrcduwEGQyEvcdJ/P5a6y7qPKU+PshLJvUx6pNE8RMBWJ6OhETszcVCmammPtWJ2ru/2vvPsOjKLsADD8nBRISegu9WkFEERVQpEmxIHaxF1TsBQRRsWNBRezdz47YOyqKWFBEkd6r1BCqtARIcr4f7yTZJLsBwuyEwLm5crE7Mztn2s7O24eH3QfNymbs4Dfp9fYAJDaGmSN/Zt3c5Rx2oesZado7Y1g8ZjINOx7OJb8+QWb6dkZ7XcKtmryA+d9MoPc3D5KdlcXqGf8y/T2X2Gh3+7lUalILspWNy9cwZlDhagejv/+ZLl1PYMLk0aRvTefG6/Iu8REfvszNN9xF2qrVPPvCoySXT0JEmDF9Drfd6pqKzJu7kDE//MrPv39BdnY27771EbNnzctdx6yfJnFwx5bc/vNwdqRvY+RtL+XOu+J/A/hw4CtsTFvP14+M4MJnbqB7v3NYPmMxf37g9qF89Yrc9MUQEpITUVWOv7wHj514G0mVy3Ppy64HppjYWLb9/C3bJ7gfno3Dn6Ly449BTAzp34wic/FiEnu6LhDD1pf3xB92GIndu7FjwQKqvuauk02vvML28YWL4QHIyua/YU9T9cmhEBvD1q9GkbloMeV6uZzvrZ8V3V1lpfvuouwRLYmpVJGan33AplffYOtX34RfODuLTU8Pp/LQxyEmhoxR35C1eDGJp3r79WUR7RuqVqXC7XdATAwSI2SMHcv28eHrF4O7Hv++8w06vDcQiY1h4fs/s3Hucppe5B6U5r/9IwnVK9Jt1IPEl09Es7M5qE8Pvu4wgMzN6Uy86y3aPHstsfFxbF6SxvhbXooYi6xslg1+mSZv3+u6CB35Ixlzl1L1QpdLu/adb0m56TxiK5en7oNX535mzimuV5lGLw0ktnIF2JHJ0sEv5TYgDuebUT/SvXsn5swax9b0dPr0uTV33pefv8VVfW8rlLtcXKNGjaF7907MmvUb6Vsz6HNlXqzPP3+LvjuJdecdt1C1SiWeedrlomdmZtKm7clhlx393Vi6dD2Bv6b84L7D1+Z1qDfio1e45fo7WbVqNc+++Cjlyyd73+HZ9L8lr7nXyaecyNgx49i6NT1ciFw//zCO9l3a8f2ET8nYmpEvV/+l94Yz+JYHSVu1hnsfu50Vy1J5/xtXrWr01z/x/BOv0uTARjzy7L1kZ2Uzf+4i7rr5gUih8vnu25/o1q0jU6ePJX1rOn375vUC9/Gnr3PdtbcXWfpRlAlj/qJ1p9b877fX2ZaewRP9nsyd98Cb9/PkgOGsW7WO0y7rydnXnE2V6pV5cfTzTBjzF8O9uu3turdl4i//sC19W751a1Y2Ywa/yZlvDyAmNobpI39m7dzltPDu7VPfGcOiMZNp3PFwrvj1CXakb8/X3WdcQhkaHN+c0YPyV0/r9MAlxJWJ46x3Xe9MKydF7oI3nNvueYS/Jk1lw4aNdO51IddecRFnntptt9aRKyubFfe8SKO37nNdhH74A9vmLaHK+e47vO69b6nQvS2Vz+iEZmaiGdtZckNeb1f1nupP0rGHEVe5Agf//j9WDX+P9R+MLt62RMneXFc/KGIHwUQSWh0o2nos3bzzhXzyVcqudzO3J75YH74RXzQ8+J9/6b2duaTykYHF6l9/5c4X8kF25s4T2X6JLRvcKJVj5tQNLNbBccF9h1un/h1InJhdyHzxS4Wy0eigLrxqCYW7SY2WZZuL7gnOL+2qHhxIHICu4mvN4SLd+M/9O1/IJ7Nb37TzhXxy2KIvg/tyRVCncrPAnnGWr59R4vsbzp6UBBhjjDHGGFPqZFsmuCUCiktE7iSvqlCOsCMYG2OMMcYYszexREAxeQ/79sBvjDHGGFPKFDW44v7C796BjDHGGGOMMXs5KwkwxhhjjDH7FesYx0oCjDHGGGOM2e9YSYAxxhhjjNmv2IjBVhJgjDHGGGPMfsdKAowxxhhjzH7F2gRYIsAUodoVhwYWq+9D6wOLlXJz7UDiHH/3nEDiANxRsXVgsRKzAgvF5jVlA4lTNikzkDgAOzJiA4tVNju4H7mt2+MDi3VQ5WBGQt6eHdx10SChWmCx1mVuCSzWUVWaBBKnSkww9wqAr7JWBRara4Cj+B7811OBxTJ7B6sOZIwxxhhjzH7GSgKMMcYYY8x+JduqA1lJgDHGGGOMMfsbKwkwxhhjjDH7FWsYbCUBxhhjjDHG7HesJMAYY4wxxuxXbLAwKwkwxhhjjDFmv2MlAcYYY4wxZr9ibQIsEWB2w7jFa3js59lkZyu9mtfl8taNCi3z99J1PPbzHDKzs6mUWIbXznaDWL036V8+mb4MVTijeV0uOLJBvs/V79CC9vdehMTGMHPEWCY+/2Whdbe/7yIadGpJZvo2frj1ZVZPXwxAyz7dOfS8DoCydvYyfuj3MlnbdlDtkPp0ePgy4pMS2LR0Nd/d+AI7Nqfn36eFqxj6wzSys+H0w+tzeZsD881/4895fDNjGQBZ2cqitZv46cYeVEwswz1fT+KXBalUKVeWj/t02unxq9DhCOreeyXExrB2xGhWPf9xvvmVe51AyrVnuFhbMlh6xwukz3L7WP3yU6h2fldAWDPie1a/Vvj4NDihBR3uvYiY2Bimvz+Wv8Icww73XUSjji3Zkb6N7/u9TNr0xVRuXIuTnrs+d5mK9Wvwx7CPmPTad1Q/tD6dH7qc2LLxaFYWP975BhsnL4y4j/U6tKCddx5njRjL5ALbUKlJLTo8cRXVmzdkwmMfMuWlb3Z63HKUO+4oqg3qC7GxbPxoFBte/SDf/KRObahyw8WgimZmseaRF8n4Z4Y7NqPfJHtLOmRno5lZLDvnhiJjJbRpTaV+10FMDFs+/4ZNb76ff377tlTsexloNmRmsX7Y82yfMh2A5PPOILnXSSDC5s++ZvOIT4qO1bY1VfpfC7ExbP50FBvfyB8r8YS2VLr2UrftWVmsf/wFtk12scpfcCbJvXqAKjvmL2LNvY/B9h0RY9Xs2IIWD1yMxMaw+N2fmPts/vOT3LQ2rYZfTaXDGjLzkQ+Y98LXufOaXtWDhhd0RFXZOGspE29+iextkWNV6tiSxg9cBrExrHr3R5Y/+1m++dXPOJ461/cC3PW+YODLbJ35LwCt/nqerM3paFY2ZGUzpdvAIo/hoCG30r5zW9LTM7jzxgeYNa3wQH2PPn8fzQ4/hMzMTKZNmsl9/R8mMzOL5PJJPPr8fdSqk0JsbCz/e+FdPnv/qyLj5Rj80G2c0KUd6VszGHjjvcycOrvQMg8NH0zzww9FRFi88F8G3nAvW7ekh1lbYdfddw1HdzqabekZDL31CeZPn19omdMu6ckZfU6nTsPanNHibDau3whAUvly3P7UQGrUqUFsbCwfvvwR333wfcRYtz1wE8d1bkNGegb33PwQs6fNLbTMg8/dzaEtDiYzM5MZk2YxZMBQMjOzaNXmCIa98TArlqwEYMw3P/PKk2/s0j7ecP91HNvpaDLSt/HILUOZF2Yf73xmEAe1OJCsHZnMmjyHJ25/kqzMnY9aeMm9fWjZsRXb07fxQv+nWTy98L2r6yUn0ePyU0lpWIurWl7EpvWbAKjdpA5XP34DjZo1YeTj7/D1y5/vZD+u5RhvPx695bGw+9Hr0tM4q8/p1GlYh9MOOzP3XCVXTGbAE/2o3aA227dtZ2i/J1g8Z3HYOMntj6T2PVdCTAzrR45m9Ysf5Ztf/sRjqHnrBZDt7oMrH3iVrX/PBKDOozdSoVNrMtf+x7zu14db/S6766Fh/DJuAlUqV+Kzd17co3WZkrPXVwcSkRQReV9EFojITBH5RkQODJl/i4hkiEjFkGkdROQ/EZkkIrNE5B4R6SYik72/zSIyx3v9lveZ00VEReTgkPU0FJHpIetUETk1ZP5XItLBex0vIo+IyDwRmS4iE0Skh4j8JiI9Qj5zjoh8G2Y/F4vItJBtfNqb/oWIXBSy3Csicpv3eqy3H1NEZJyIHFTEcbxeROZ7+7DbQ1NmZSuP/DSLZ3sdyccXt+PbOStZsHZzvmU2ZezgoZ9mMbxnSz6+uB2PndwCgPlrNvHJ9GW8fd6xjLywDb8sWs2/60NGrBShw4OX8MXFQ3m30wAOPO1YKh+Qf1TfBh0Pp1KjFN4+vh9jBr5Gh4cuBSAppTItLuvKyFMG816XQUhMDAf0PBaATo/14fdHRjLixEEs+O5vjux7cqF9evj7qTx3Ths+ubIT385czoI1G/Mtc+kxB/DB5R354PKO3HjCobSqV42KiWUA6HlYPZ4/p82uHcCYGOo9eDXzL76PWZ2up/Jpx5NwQL18i2xfuoq5Z9/BrK43kfrUSOo/eh0ACQfVp9r5XZl9Sn9mdbuJip1bU7ZhrXyflRih04OX8NklQ3mz8wAO6nksVQocw4YdD6dSwxT+174fP9z+Gp2GuGO4fuFK3u1xJ+/2uJP3Tr6LzPRtzP/2bwCOv6M344d/wrs97uT3Jz7m+Dt6R9xFiRGOe/ASvr54KCM7DaBpmPOYsWEL4+55mykv7/rDf87xq37Xday4+i6WnHol5U/qSHyT+vkW2Tp+EktPv4alZ1xL2l3DqHH/LfnmL790AEvPuHanCQBiYqg84EZW3zSI1HMup1zXTsQ1yp9o3fbXP6w6/0pWXXA16x54nCp39QMgvklDknudxKpLriP1/CtJPO5Y4urVKTJWlYE3kHbDHaw48wqSunckvlH+/cqY8A8rz72Klb37sva+x6k6+FYAYqtXpcJ5vUi98FpWnnMlxMSS1K1jEbGEwx++jHHnD2V0+9uoe3pbyh+Yf9t2bNjM1LvezPfwD5CQUpkmfboxptud/NhhIBIbQ91eRVz7MTE0frgPM84fwqT2t1D99ONIPDD/KL8ZS9KYdvrdTO7Uj6VPfkTTx/vmmz/9zHuZ0uW2nSYAju/clgaN6tHj2LO4t/8j3D10QNjlvvr4O05pdw69TjifhISynHnBaQD0vvwsFsxZxBmdLuTSM65hwL03Eh+/8/yxE7q0o0HjenQ5uheD+z3I/UMHhV3uobuG0bNjb07tcB4rlqVy4RXn7nTdAEd3bE2dRnW45PjLeHLgU9z0UPjrdsbfMxjQ+3ZSl6bmm97zkp78O28JV3e7hn7n3MbVg68iLsJ+tet0LPUb1+O0tufx4G2PMeiR/mGXG/Xx95xx/Pmc0/FiyiaUpdf5uT+HTP5zCr1PvIzeJ162ywmAYzodTd1GdbjguEt4YuCT3PJw+NFxf/j0Ry4+4TIu63IlZRPKcHLvk3a67pYdW5HSqBa3nHANrwx6nise7Bt2ubl/z2LIBfewemlavumbN2zmzXte5atXPtul/ajTqA4XHncpTwwczi0P3xh2uel/TaffeQMLnasLbujN/BkL6HPi1Tx801BuuO/a8IFiYqh9f18WXXov87peR8We7SnbNP/vyJZxU5jf40bmn3wTywc+TZ1H8q6b9R//yKJL793p/uyKXiedyIvDHvRlXSUlWzWwvz0hIlVEZLT3fDlaRCoXsWys9+y7SzkZe3UiQEQE+BQYq6pNVPVQ4A6gZshivYG/gNMLfPxXVT0COAq4EFijqi1VtSXwN3CB9/7ikPX8BpxXxCYtA+6MMO8BoBbQXFWbA6cC5YG+wDARSRCRJGAIcF2EdXTM2UZVzbmL3AjcLyKVRKQtcAwwPOQzF6jq4cCbwGNFbPs4oAvwbxHLRDQ99T/qVSxH3YrliI+NoduBKYxdkP+mOWrOSjo3rUGtCokAVCnnhnFftG4Lh6VUIjE+lriYGFrVrcxPIZ+NqdGADYtXsXHJarJ3ZDH3i/E07toq37obd23FrI9/A2DVpAWUrZBEuRqV3OfjYolLKIPExhCXWIYtq9YDULlxLVaMdzlzS3+ZTtMerfPv08r11KucRN1KSW6fDq3D2Hn5b8759m/WMrofmvfQ1Kp+NSoklNml45fU8gC2LU5l+5JV6I5M1n/xKxW7Hp1vmS0TZ5P1n0scbZk0h/haVQFIaFqXLf/MRTO2Q1Y2m/+cTqXux+b7bErLJmxYvIr/vGM458vxNClwDJuEHMNU7xgmeccwR712zfhvSRqblq8FXHFpmfLufJYtXy732IZTo2UTNi5exSZvGxZ8MZ6GBbYhY+1GVk9ZSPaOnefihUo47CB2LFlB5rJU2JHJ5lFjSe6U/yFUt2bkvo5JTIBi3njLNDuYHUuXk7V8JWRmsnX0TySe0DZ/rPS8WBISK65hfbZNm4Vu2wZZ2Wz7ZyqJHY6LHKv5QWQuW0GmF2vLd2NJ7NCu6FihjdliY5GyZSE2BkksS9bqtRFjVTmiKVsWrWLrkjR0RxbLPvuDWt3yn59tazayfvJCNEwuq8TGEut9z2ITy5CRGvlaKH9EUzIWpbJtSRq6I5PVn42jSrf8379Nf8/Jvd43TZxLmVpVIq6vKJ26t+eLD0cBMHXidMpXKE+1GlULLffrj7/nvp42aQY1a9cA3KlLSi4HQLmkRP7bsJHMXchl7tL9BD4b6RJLkydOp3zFZKrXLJy/snlzXoZHQsKuX5dtu7Zh9Mc/ADBr0mySKyRRpUbhYzR/xgJWLVtVeAWqlEt2393EpAQ2bdgUMfe8Q/fj+epDlzc17Z8ZlK+QHPYYjhszPvf1jMkzc49hcbXr2pbvPhoNwMx/ZpFcITnsPv45ZkLu61mT51C91s7zsVqdeDS/fjwWgPmT5lKuQhKVahR+dlo8YxFrlqUVmr5x7X8snDqfrF24V7Xr2obvP/LO1T+zSIqwH5HOVcMDGvDPb5MAWLpgKTXr1qRytUqFlit3+AFs/3clO5a635H/vvyFCicek2+Z7Hz3wbL5rretE2aQtWHTTvdnVxzV8jAqVijvy7rMTt0O/KiqBwA/eu8juQmYtasr3qsTAUBHYIeq5pY1qepkVf0VQESaAMnAXbiH+EJUdQswEWgSKYiIJAPtgCsoOhEwBfhPRE4s8PlywJXADaq6zYu7SlU/UNXpwJfAQOAe4C1VXVDkXuff/sXAy8BQ4HngelUNVwb/C9C0iPVM8tZVLGlbMqhZPiH3fc3yCazesi3fMv+u38rGjEz6fPgX57/3B1/OXAFAk2rJ/LN8PRvSt5O+I4vfFq0hdVPIg01SJTavWJf7fvPKdSSn5L9ZJ6VUZvOKtYWW2ZK6nkkvfcOl45/iionPsn3TVpb+4qpKrJ2zlEZdjwSg6SnHkFw7/005bVMGKd4DrtunRNJCtitU+o5Mfl+YRpeDaoedvzPxKVXZvmJN7vsdK9cSn1L4RzZH1fNOZONP/wCQMWcJycccSmyl8khCGSp0bEV87fw/gskpldlU8BjWrFx4mZUhxzC18HE+qGcbZn/+R+77n+97h+Pv6E2f8U/R/q7e/PboyIjb7M5R/m1ISomYYbFbYmtWZUfq6tz3malriK1R+EEgqXNb6n/1KrVefIC0u4blzVCo/epD1P3wWSqc3aPQ5/LFql6NrFV5sbJWrSa2euFYiR3akfLh/6j25BDWPfA4ADsWLKbsES2IqVgBKVuWhLbHEFuzesRYcdWrkZma9wCSlbaa2DAPX4kd21H749ep8dQQ1tznYmWtXsvGtz+kzjfvUff7D9BNW8gYPzFirIRalUkP+Q6lr1xH4i4+eGekrmfeC1/TY+IznDT1eXZsTCft52kRly9Tq0q+6337yrWULSJWzfM7s2HMpLwJqjR7fzCHf/coNS/sUuS21ahVndTleQ9Wq1amUbNWEcc8LpZTz+rBb94D7XuvfUjjAxsxdurXfDb2PR6+68ldqitcs1YNVq7Ii5u6Io2aKeHjPvL0Pfwx43saH9CQt16N/B0KVS2lGqtX5F2Hq1euoVoR94yCPnvjC+o3rc/Iv9/jldEv8fw9L0Tcrxop1Vi1Iu86TFuZVuSDdlxcLCed1Y3ff8pLFBzWqjnv//AGz7z7OI0PLFxVNJzqhfZxNdVTIseNjYul65ldmDD2r52uu0pKFdaGXIPrUtdSpWbxEpo7Uy2lGmkhx2/NyjVUK2I/ClowcyHte7jMgoNbHkRK3ZpUD3MNx6VUZcfKkN+R1PC/IxW6HssBP7xAg9fvYfmAp3ZnV/YrGuC/PXQaLqMX7/9e4RYSkbrAycCru7rivT0R0Bz3AB9Jb2AE8CtwkIgUypYQkarAscCMItbTC/hWVecC60TkyCKWfRCX6AjVFFiiqhvDLA9wH3A+0AP3MB/JTyHVgULrMjwOdAdmqOovET57KhD5V3kXichVIvK3iPz9+m/T82bswjWcpcqstI080+sInju9Fa9MWMi/67fQuEoylx7VkGs+mch1n03kwOrliYuRItdV8LfKFQoVXEYpW7EcjboeyZttb+H1o24gvlxZDjrd5aT+2P8VWlxyIud+/QBlkhLI2pGZ//NhdirSVv0yfxUt61TJrQq028KtOMIPcnKbw6h2bheWP+S+8xnzl7Hq+U844L37aPrOvaTPXOzqSudbf7jjs/ONCH0oiImPpcmJRzLv6z9zp7W4qDM/3/8urx57Ez/f/y5dH7sy7Dbv+jYUU5h1h7sot/z4O0tO6cPK6++lyo2X5E5fdsEtLDvrelZefScVe/ckoVXzImKFC1U4VvrYcaSefRlrb7ubin0vBSBz8RI2vfU+1Z8dSrWnH2HHvAWQVUROYrj9CnPM0n8ax4ozL2d1v3uodM1lAMSUT6Zch7YsP+VClnU7F0lMIOmkzkWEChdr105QfMUkanVvxbdH38Q3h19HXLmy1DuzXeQPRPi+hlOxXTNq9u7E4gffyZ027dS7mNJ1ADMvGEKty7pT4dhDIocKe11H3rTBjw5g4vjJ/PPnZACO63gss6fPpUOLkzmz00Xc+XB/kpKTIq8gJ+5u7OPtN95Hu8O6s2DuIk7udWLYZQqvv/C03WnIeNQJrVgwcwHnHnU+V3e/lusfuI5yXonHrgWLvO7bH+nHpPFTmPTnVABmT5vDya3P4rwul/L+ax8x7H8P7dpG7sYxBLjloZuY+udUpk2YHnGZvFVH8X60C7F2J9h7z71PcsXyvPLdi5x+WS/mTZ8fvtRmF4/Xxu/HM6/LNfx79RBq3nrhLm+HiZ7QZyvv76rd+HhNVV0J4P0fqQhuODAAyI4wv5DS3jD4POB0Vc0WkU+As4HnvHnHi8gk3MF4RFWLSgT0Jq+Kzfve+3/CLaiqv4oIInL8rm6kqm4RkZHA5pySggg6quqaMNNb4B5NDhaRGFUNPcHvikg6sBjYSWXnXdrWl3ElD2x94Ybcu0uN5ARWheSSr9qUQfWksvk+WyM5gUoJ8STGx5EYD0fWqczc1ZtoUDmJ05vX5fTmrk7wM+PmUTM577O6ZUO+XPrkWlUKVTvZvHIdybWrFlhmA/WOa87GpavJWOeKOBeM+puUow5gzqfjWL9gJZ9f8CgAlRql0LBzy3zrrFk+kdRNeQ30Vm1Kp3pIaUeob2cuo/uhdcPO2xU7Vq6lTEjufXytquxYta7QcokHN6DBY9cx/6L78xXbrh35A2tHuuLm2gMvZPvK/NU+Nq9cR/mCxzCtwDFMXUf5WiHHMMUdwxwNOxxO2vTFbA1pF3Homccz9p63AZj71Z90ebRPxH3csnJdofO4tYjqQ7sjK3UN8SG5rHEp1chKi1z1JWPidOLr1SKmUgWyN2wka7U71lnr/mPLj+NIaHEwGRPDP0hkpa3Jl3sfW7M6WWsix9o2aRpxdWoTU7EC2f9tZMsXo9jyhaueUvHaK8hKWx3xs5lpq4lLybufx9aoXmSVnm3/TCO+rtuvhKNakrk8lewN/wGwdcxvlG3RjC3f/Bj2s+kr1pEY8h1KrFWF9CKq9ISq0b45W5eksX2tuyZXfPMXVVsfyNKPx4VdfvuK/Nd7mVpV2R4mVrlDGtDkiWuYef4QMtfntTHa7l03O9ZsZO2oCSQfcQAbx+eVcPe+7CzOutDV6Z8+eSYpdfJqiNasVYO01PDH/Jp+V1C5amXu7Z/XzqDXeafw6jNvAbBk8TKWL1lB4wMaMG3SzEKfv+Dyszn3IlfzdOqkmdSqnRc3pXYN0laFu3072dnZfPP59/S57mI+HlG40T5Az0tO5aTerqRq7pS5VK+ddx1Wr1WNtWHuGZF0P6crI553jedXLF5B6tJU6jWtx5zJrtH0OZeewekXuDr9M6bMyle1p0atGqxODb8vV916GZWrVqL/bXk1Y7ds3pr7etyY8Qx6pB+VqlRkw7r/Cn2+1yU9OeV8V6d/dqF9rM6aVeGv/0tuuYhKVSoyeOCTEff5xIt70Om8rgAsnDqPqiHXYJWUqqxP2/XjtzO9LunJybn7MYcatWuQk9dYrVa1iPsRztbNWxna7/Hc9yP+eJuVSwtXTc1cuYb4kBKa+JSqZBZxTWydMIMyDWoRW7kCWesj5VHuv/a0rv7uCH22CkdEfgBSwsyKVAW94OdPAdJUdWJOW9VdsbeXBMwAWoWbISItgAOA0SKyGJcgCK0S9KuqHqGqrUKrE4VZT1WgE/Cqt57bgHMlbNI+1xDyn5j5QH0RKaqCXDa7kToL2b4YXDWgi4B5wDUFFslp29BLVZfu7vp3VbOUCizZsJXl/21lR1Y2381NpUOT/InRDk2qM2nFBjKzs0nfkcX01A00quJy1NZtdWmflRvTGTN/Fd0PymvYmp22hEoNU6hQrzox8bEc2PNYFo3OnwZbNPofDjnTFZfWPKIJ2zdtZWvaBjYtX0vKEU2J8+rm123XjPXzlgOQWLWC+7AIrW88jWnv5H84alarEkvWbWH5hi1un2Yu54Smhb+DmzJ2MHHpWjoeEO77uWu2TJlH2Ya1KFOvBhIfR+Wex/Pf6An5lomvXY1Grwxi8U3D2bZoRb55cVUr5i5TqXsb1n+ev0AodcpCKjfKO4YHnXosCwscw4UhxzDFO4Zb0jbkzj/4tPxVgQA2r1pPXS8Xtl67ZmxYHLnNRNqUhVRsmEJ5bxua9DyWxaPDpqV3W8b0OcQ3qENcnZoQH0dyjw5sCamKABBfP6+qVtlDmiLxcWRv2IgklkXKuWpfkliWxLat2D5vccRY22fOJr5+HWJrp0BcHOVO7Ej6L7/nWyaubl6s+IMOgPh4sv9zP7IxlSsBEFuzBokdj2PLd2Mix5oxh7h6dYjzYiV160D6zwVi1cuLVebgpi7Who1kpqZR5rBDkASXoE44+gh2LFoSMdb6yQtIbpxCufrVkfhY6vZqw8rviypozbN12RqqtDqAWK8krPrxzdjofc/C2TR5PomNa1G2vrveq/dqx7rv81fhKFOnGge/3p951z9DxsKVudNjypUlNikh93WlEw5n6+z8+zXifx9xZueLOLPzRfw46hd6elW8WrRqzuZNm1kTJoF45gU9adfxWG7rOzhf7unK5akce/xRAFStXoWGTeqz9N/w+/bu6x/Ss+P59Ox4Pj+MGkuvc11nAy1bNWfTxs2sDpMIqN8oL/OgY9f2LCji2vvizS/p2/1a+na/lnHf/c6JZ7qqUIcccTBbNm1l3W48xKatWM2R7VoCUKlaJeo1qcvKf/OO8wdvfJLbkHfsqF855ezuABx2ZLOIx7DX+afQpsPR3HHNvfmOYdXqeYn/Zi0PQWJiwiYAAD578wv6dOtLn259+e3bcXQ7y5WMHHrkIWzZtCXsPp7cuwetTziK+68fUmRJwei3RjHopFsYdNIt/P39nxx/ZgcAmh5xIFs3bWFDmj+ZEjn7cWW3vlzZrS/jvh1H17O8c1XEfkSSVCEpt9H2yef3YOqf09gakrDKsXXqPMo2rE183ZpIfBwVT23Pxh/y/46UaZD325rQrAkSH2cJgFJAVbuoavMwf58Dq0SkFoD3f+FGLK5Ke0/vOfZ9oJOIvBNmuXz29pKAMcBDInKlqr4CICKtgXK46jH3qurDOQuLyCIRaRB+VRGdhaunf3XIen4GjgPCPlSr6vci8gBQ23u/VUReA54WkatVdbt3ojqr6k5Pwk5cDcxT1bEiMhf4Q0Q+UNXI2YtREBcTw8COB3Ptp/+QrcppzerQpGoyH051h+jsFvVoXCWZtg2qcs47fxAjcHqzujSt5tJF/b+awoaMHcTFCLd3PIQKCfF5K9dsfh78Jj3fGUBMbAwzR/7MurnLaX6h63Zz+jtjWDxmMg06Hc7Fvz3BjvTt/NjPJahXTV7Agm8mcN6oB8nOymL19H+Z/t5PABx4WhsOu8TdmBeO+ptZI/M/OMfFxHB71xZcM/IPt08t6tO0egU+nLTI7dMRrl7rmLkradOoBoll8n9dbv/8b/5esoYN6dvp+tx3XHPcwZx+eITLLyubpYNfpuk79yKxMawd+SMZc5dS7UL3w7vmnW+pdfN5xFUqT70h7lLUrGzmnOx6nWn88kBiK1VAMzNZetdLuQ0qcw9hVjZjBr/JGW8PQGJjmDHyZ9bOXU4L7xhOfWcMi8ZMpmHHw7ns1yfITN/O9/3zMiXiEspQ//jm/DDo9Xzr/eH213K7Hc3ctoMfbn8t/P552/Db4Dc5+R23DXNG/sz6ucs51NuGme+MIbF6Rc78+gHKJCei2dkcdkV3RnYaWKjr1nDHb/WQ56j9ykNITAwbP/2e7fP/pYL3ELZx5NcknXgc5U/rApmZaMY2Uvu5KgmxVStT6+l7vB2NZfPXP7H1t7+LjLV+6DNUf/pRJDaGzV+MInPhvySdcQoAWz75isRO7Uk6+UQ0MxPN2M7aOx7I/Xi1R+8lpqI7V+uHPo1u2hwpEmRls+7RZ6jx3CMQE8PmL75lx8J/ST7Txdr88VeU63Q8Saec6PZr23bW3O565Ng+fTZbf/yFWu++gGZlsX3OfDZ98nXEUJqVzeQ73qDdiNuR2Bj+HTGWTXOW0+hiV4Vo0Vs/UrZ6RTp99yBx5RPRbKXpld0Z3X4A6yctYPlXf9Lp+4fIzsriv2mLWfx25MQNWdksvONVmo24C2JjSBsxhvQ5y0i52OXSpr71PfVvPYv4yuVp/Eif3M9M6TaQ+GoVOeR/rocfiYtl9Se/suGnyRFD/fLDONp3bsuoPz8mIz2Du27KOxcvvPskd986hNWr1nD30IGsWJbKe1+76rI/fD2WF4a9xovDXmfI03fz6dh3ERGGPfBcxAfYUGNH/8YJXdrx44TPSU/P4PYb782d98qIp7jz5gdYnbaWoc/eR3JyMiIwe8Y87rnt4cgrDfHnmAkc3ak1b/32P7alb+Oxfk/kzhvy5gMMG/Aka1eto9dlp3HuNWdTpXoVXh79IhPGTGDYgOG889S73DasP6+MfhFEeOWh13K7pCzotx//4LjObfj8j5FkpGdw7y151Xmefucx7u/3CGtWreWOR/uzctkq3vjyJSCvK9Aup3TgrEtOJyszi20Z2xjU955d2sfxY/7kmE5H8+5vb7EtYxuP3prXt8Ujbw3hsduGsXbVWm59+GZSl63i+c+fBuCXUb/x1vCif1onjZlIy46tGP7Li2xL38ZL/Z/OnTfgjcG8MuBZ1qetp9ulJ3Nq39OpVL0yj373FJN+msgrA5+jYvVKDPnycRKTy6HZSo/LT+W2LjeQHuZeNX7MBI7pdAzv/Pamtx95ufoPvzWEx739OOPyXpx3zTlUqV6F10a/zJ8/TeDx24bRoGl9Bj01kOysLBbPW8Jj/Z8oFAOArGxW3PMijd66z3UR+uEPbJu3hCrnu9+Rde99S4Xubal8Rqfce9OSG/JqINd7qj9Jxx5GXOUKHPz7/1g1/D3WfzB65ycqjNvueYS/Jk1lw4aNdO51IddecRFnntqtWOsqKaVonIAvgEuAR7z/C/VXq6qDgEHgerME+qvqTuuCyd5+EESkNq6qTisgA1ft5Wbge6CHqs4OWXYYsAr4E3cATomwzrHe/L+914+o6rch828EDgEeBb5S1eYhB/UUb5meuBPR0XtAL4NrL3CGt51bgLtV9Ttv+Xtx1YHy7g75t2kxsAnIqQg4FegPTACOVdVUb7lbgcNU9bLQ/Sj6KObu0wBccVMa8I2qRq7bQf7qQNH22kP+5dDsTJ/7i9e4d3fNurtwX+XR8nORhVD+SgzwltGlfDBp3bJJmTtfyCfZWUW3h/HTxBV71nvL7qjJ9sBiXRW9Qs98tmcHd100SNjtnpuLbV3mlp0v5JMKseGrWPqtVmxyIHEAUrOCO35Pxwdz/AAO/iu4RsTx1RoHdyOMICGhfmC/ZhkZS4q9v16NlQ+A+sAS4GxVXec9H7+qqicVWL4DRTwDh9rbSwJQ1RXAOWFmFep+QFVvDXk7toh1dgj3OmTa0yFvm3vTxoauU1W/IKQJoapuxz1kh+2kWlXvjbQ93vyGEWblm66qw0JeF9r2Itb/NPD0Thc0xhhjjDF7BVVdCxTq9cF7Pi40aEbB59Wi7PWJAGOMMcYYY/zkQ9edpZ4lAvYxIvIphUtJBuZUSzLGGGOMMcYSAfsYVS04crIxxhhjjAmxt7eJDcLe3kWoMcYYY4wxxmdWEmCMMcYYY/YrVhJgJQHGGGOMMcbsd6wkwBhjjDHG7FesHMBKAowxxhhjjNnv7PUjBpvSRUSuUtWXLdbeH2tf3CeLVXriWKzSFWtf3Kd9Nda+uE8mOqwkwPjtKotVamLti/tksUpPHItVumLti/u0r8baF/fJRIElAowxxhhjjNnPWCLAGGOMMcaY/YwlAozfgqwbaLFKRxyLVbpi7Yv7ZLFKTxyLVXriBB3L+MwaBhtjjDHGGLOfsZIAY4wxxhhj9jOWCDDGGGOMMWY/Y4kAY4wxxhhj9jOWCDC+EJF4ETlCRGpEYd2tRSQl5P3FIvK5iDwtIlX8jmeiR0TOLOltMMaUDna/KD1ExBoIl0KWCDDFIiIvikgz73VFYArwFjBJRHr7HO4lYLsXqz3wiBfrP6LcM4GIVBWR00WkVTTjhMSrJiIShfVeKSIHeK9FRP4nIhtFZKqIHOl3vCI8Gc2VB3m+opXwFZHhIa9vKjDvDT9jRYgfrWvwVBFpEPL+bhGZIiJfiEgjn2MNFZG+YabfIiKP+hmrwPoDvV+ExF3i8/ouLurPz1g74fv9QkTiw0yr5neckHVX8jKyWnu/ldGK005ERovIXBFZKCKLRGShzzGqRPirCpzkZywTDEsEmOI6XlVneK8vA+aq6mFAK2CAz7FiVXWd9/pc4GVV/VhVBwNN/QwkIl+JSHPvdS1gOnA58LaI3OxzrGNFZKyIfOI9TE734q0Ske5+xgJuAhZ7r3sDLYBGwK3AUz7HKoqvD5cBn6+gEr7tQ15fUmBeCx/jBH0NDgFWe3FPAS7EnasvgBd9jnUK4TMIngJO9itIkNffzjbF5/W1DvN3NPAA8LrPsYri236JSEcRWQasEJHvRaRhyOzv/YoTEq+Ml2hfjLsWXwEWi8jrIlLG73jAa8Aw4Djc+TrK+99Pq4G/gYkhf397f77XAjDRZ4kAU1zbQ16fCHwGoKqpUYgVKyJx3uvOwJiQeXFhlt8TjVR1uvf6MmC0qp4KHIP7cffTs8BDwAjcPvVR1RTcQ+DDPsfKVNUd3utTgLdUda2q/gAk+RyrKH73SRzk+Qoq4SsRXkdDkNegqupW7/UZwGuqOlFVXwWqRyFWdpiJ2fh7TIO8/ori6/dKVW/I+QNuBP4ETgDGA0GWHPq5X0OBbqpaHfdQPlpEjvXmReN7dhcQD9RT1SNUtSVQH/ebNTgK8f5T1VGqmubd29eq6lqfYywEOqhqo5C/xqraCFjlcywTAL8foMz+Y4OXm7ccaAdcAeA9rCf6HGsE8LOIrAHSgV+9WE1xVYL8tCPkdWdc7g2quklECj1U7KE4Vf0eQETuV9XxXqzZUaiNke3lVK7H7deQkHm+ni8RmUb4H28BavoZi2DPV8GE74derFSfz1eMiFTGZdLkvM4JEOtnIIK9BkVEkoGtuHP1fMi8BJ9jbRWRA1R1XoENOAB3D/FLYNefiNwaaRaQ7GcsL14ccCnQD5cIOEtV50QhTlD3izI5iXhV/UhEZgGfiMjtEeLvqTOAo0MSvjnXxbW4xJTfCYGfROQx4BNgW0jMf3yMMRyoDISrfjbUxzgmIJYIMMV1NfA0kALcHFIC0Bn42udYjwI/ArWA7zVvhLsY4AafYy0VkRuAZbgcr28BRCQRl6vjp9CHhIIPJn7/KA3GFdnGAl/k/BiKyAm43B0/neLz+ooS5PkKKuFbEVfMnvMUHvoj7vd1EeQ1OByYDGwEZqnq3wAicgSw0udYdwOjRORB3LEEVz1iEHCzj3GCvP7KFzHP1yp9InIdrgrhj0B3Vf3Xz/UXENT9YoeIpOT8VqnqDBHpDHwFNIlCvOzQBEAOVd0sItFIdBzj/X9UaDigk18BVPW5IuY9k/NaRE5U1dF+xTXRYyMGm72eiPyjqoEUQYtr5Hk/LsHxXEguaUeglao+7mOsLGAL7mEvEZdDmvPgl6Cqvj1EeMXefwPlVXV9yPQk3H1gs1+xIsSvBqxVn284AZ+vA8lL+A5X1Te86d2Arqraz69YQQnyGvTi1cHVHZ6SU13HK6GKU9WlPsdqDtwGNPcmTQceV9VpPsYI7PoLkleKkYarAx76nRVcVStf26aEie/7/UJEugCrVXVKgekVgetVdUj4TxY73hSgA+GrGv2kqof7GW9vEuRvttkzlggwxSIidxcxW1X1AR9jTVLVI/xaXzHi5+YelVYBJ6SOxfXgtA7XkPBtoBqu5OZiVf02yvFL9fkSkZnAO8D7qup3Kc1eR0QOAvqr6pVRjuP7dSEiD6nqHX6us4hYCbiOEdYDX+ISOO2BBcADqrrGx1gNiprvZ8lASd8vwmzPx6q6x12TishiXElbuESAqmrjPY0RJubJQDNCqtep6v1+x9mF7SjR32yz66w6kCmuLWGmlQP6AFVxN3O/VC+iPiyqOszHWOF8Q0CN4UTkK1UNsjpNNDwL3IGr1jIG6KGq40XkYFz7jmj/qEflfInIMxRRRUZVb/QpVG/gPFzDxTW4Y/aBqq7waf1FitY1KCItgMeB2riOBJ7BtQs4BnjC73hhROO66I671oPwFq4NQhKunv503HftOOANfKxWk/OQL67r1ma4635WlBKlJX2/KMiXh3NVbejHenaViLyI+w3uCLwKnAVMCHIbQljucilhiQBTLKqa+6MtIuVx9UcvB97H/x/0WFzDt2j3lBJJkHHrRGm9jUXki0gzVbWnj7GCbGwaTrSC/B3y+j7gnmgE8aorTAEGebmk5wLjRWQ+MEJVX4lG3BDRugZfAV4A/sA9PP8DvAdcoKoZUYoZKhrXRWyBhtv5aF7Xxn44VFWbe21QlqnqCd70b72qJ74RkQq4B8mjcO04BDhcRCYCV6jqRh/DlfT9oiBfHmBFpH6RQVR9HdsBaKuqLURkqqreJyJP4BoJGxORJQJMsYkbrfdW4ALgTeDI0PrmPkotiSLNENF+6Ao1KUrrXU0wua0QbGPTcKJyvlT1zZzXInJz6Pto8R6IxovI57iBk54l+tdjtK7BsjntKIA5ItIfuF1Vs6IUr6BoHLeDyd+IO5TiU66yZzuAqmaKSMFSIb+P4dPATOC8kLYbgutg4FnAzwHDSvp+ES1f47Y/9NpQXHe4NfC/p6+cY7dVRGoDa3FjwZSExSUU1+wmSwSYYvG6IjsD19/yYVFuWBpxYBUReVRVB/oVSETKATvU61Pfq69cVkTOUNWo56qo6uVRqiO9SVV/DjcjCgPXHC4iG/Eam4a8Bp+7gizB8xX1hxMRaY2rGnQmeQMOfRjtuFG8BhO8noByroXNQAvv4dLXrgwDvC5mBlj3ua6IPI07fjmv8d77XXrTTlUvDZ3gNdK9X0Tmhf9IsQV2v9hFvhQ/qBtDJG+lbnCygUAX3NgcfvtKRCoBj+FK2RRXmuMbr4vdx3G9KU3D3SOWF1xOVc/wM66JHmsYbIrF6z1iG5BJ+N4jKvgYay5wi6p+HTItBjdyZYqq+jayqYj8givuniduHIIJwLvAocAEVR3kY6wi60ir6pM+xspS1UI5T17PGJ+rage/YgUpyPNVIG7UGlqLyEO4KkAbcHWi31fVZSJyHNBbVa/zMVaQ1+BYIieeVFV968owqOuiqAaQItJaVf/yI463voKjR+fjZ8mUiMxX1bCjsYvIPFU9wK9YQRGRCpGqMYlI/ZzqOSLSNad6kk9xDwDuJK/ty5uaN3BjVIhIWVzvXr6OoyMiv+LapvwC9ATa2AN/6WaJALPX83JQvgXuUNVPvF4yPsINFHapnzdUEZmWk4MjIg8AVVT1Oi+3fGLB3J09jPUn+etID8DVkR7sdx1pERmNe/i5M2RaCvAd8HG0q1tFsbFpkOdrE3kPseVw3WmCzwlfEbkHV/d/roi0xJUGnAssAj7RkP64fYgV2DUYpKCuCxFZBzQpWA1SRLriRkSu50ecIuJfpaovR2G9b5LX65CGTB8MHKiqF/kds0B83+8XoQl3EflRVTuHm+djvOa4h/9muIG0RkSz6puIxAInAw0JqeXhZ8cZIjJZ3cjHOe+tK9BSzqoDmWLx2gOEUmBD6A+GX1R1sbg+nr8T1y/3RcCfqhqxx6A9CRfyuhOuaBVV3S7+j0AbZB3pU4CPRWSYqt7q5U6NAh5T1ZeiEK+gaDU2Dex8qWpRgzX5aQRwnoj0xtXrHYnLsOkYhViBXYMiMkBVh3qvz1bVD0Pm+d3VZlDXxUDcSK0nqupqABE5Hzci98k+xomkL66amN9uAF4D5ovIZNzxPALXXqRPFOIVFI37RWg1n4K/X9FogTwFWIprG3A0cHRoQ2f1rzexHF8CGbhqOn7/VuUoWKUvMfS9n1X6TDAsEWCKayKFGz2V934w+qjqYr8CiUhOTsMAXFHkaOCdnOk+33imisjjuFFhmwI5vVZU8jFGjsDqSKvqNhE5HXhfRN4H2uBGev7Urxg7Ea3GpkGer6DMBn4FTlXV+QAickuUYgV2DeK6PR3qvR5E/vYNfne1Gch1oaqviEgGMMbL/T8X92De0c97YBGi0n2OV23mbBFpgqtCJcBAVV0QjXhhRON+oRFeh3vvhyuitN5I6mqUB3EDUoFhEd77OjqxCYZVBzK+EpEzgKt8rqf/UxGz/a5LnIjr7rQW8Lp6o0uKSFtcsf/bPsYaS3B1pHNKTeJxialfcfU6c4JFe6yFqAwKFeT5CoqXWDsPaIurBvc+8Kqq+t7Th/fdCteDiXvh7zWYW3++YF36ourWFzNWoNeFiJyNa0+xBNfP/Vo/119E3LqquiwK6+2GG138owLTLwDSVHW03zHDbIOv9wsRWYZ7YBXgFvIeXgWXIRLVqlvRJiKPAj/62Z7B7PssEWB8Z/UE9z5ePfOIVPU+H2MF1th0XyYiSUAvXJuATrhueD/1udHi0cBSVV3pvb+EvN6I7lUf+7kvUCc73z2itN4zRGQaeYmoBriueLeQ107Et5xZERmuqjd7r29S1adC5r2hBXrz2cNY43ElUasLTE/BXYNtfIwVyP0iyHugF+9Lih5c0M+xWXIyD97BjbS8g+h00tEPeFK9bmNDplcFhqrqFX7FMsGwRIDxlYgkA7+FNh7yYZ1TgN+A34Fx0SxmD8kZDUdDG5P5EKs17gEs1Xt/Me4B7F/8fwCrXLDxYrQE3OA5sPNVkrw2OGcD5/qcO/8P0EVV14lIe1ypww1AS+AQVT3Lx1hZ5D0gJ5K/YXWCqsb7GCuQ68KrpvUbsB734FUw0L9+xPFiBZaIEjfgVNgETFHzihlrX22cfkJR8zVCl817EG8hLtNgWjTa5nkxXsa1b7hOVcd5067FnbPhqjo8GnFN9FgiwBRLSPWSUJVx3YY9qz6Oaur1stA25C8JlyD4HfhdVf/0MVarMJOPxd3k0lS1tY+xgnwAS8PlUv4OjMMdt7l+rb9ArII9SCwFGkapsWlg52tfJCJTVPVw7/VzwGpVvdd7n+88liZBXRdeu4O2uEHDppL3/frDz0S8F6uo6lR+JwLm4kYoziwwPR43NoJvXYQGdb8QkWa4qmBfeO+fBCp6s58tqUatIvKxqp7pw3q+w1VFi1aj4Jw4bXEDxs3AXffzgH45pYmmdLGGwaa4CvaUorhGQheq6jQ/A6nqdGA6Xi8YIlINV2f6Zlwxsm8jL6rqxJzXXk7OYKAs0FdVR/kVxxMb8qBwLvCyqn6M68Vnsp+BVLWGiBxIXkKqv4hUB8bjSleGFrmC3RNkg+cgz9e+KFZE4ryHvc7AVSHzfP19kCL6zReRi/yspx/UdaGq/b0YZYCjcN+ty4FXRGSDqh7qVywgRkQq46p75LzO+Y75PfrsJ7h9uF5Vt0Bu9bSnvXl+Cup+8QjwcMj7brjrohxwNy4XvST4Nar0SmCsiIzCjeEDRKW913TgL1ypjWAJgFLNEgGmWIqqPykiDXwuBo/FdU/XFmiHG61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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# correlation matrix\n", "\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "\n", "plt.figure(figsize=(12,10))\n", "\n", "sns.heatmap(x[columns_with_large_vif].corr(), annot=True)\n", "\n", "plt.title(\"Correlation between Features\")" ] }, { "cell_type": "code", "execution_count": 19, "id": "b369ed31", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['ODI-WKTS',\n", " 'COUNTRY_IND',\n", " 'COUNTRY_PAK',\n", " 'PLAYING ROLE_Batsman',\n", " 'COUNTRY_ENG',\n", " 'COUNTRY_NZ',\n", " 'COUNTRY_SL',\n", " 'COUNTRY_SA',\n", " 'PLAYING ROLE_Bowler',\n", " 'SIXERS',\n", " 'COUNTRY_ZIM',\n", " 'WKTS',\n", " 'PLAYING ROLE_W. Keeper',\n", " 'AGE_3',\n", " 'CAPTAINCY EXP_1',\n", " 'ODI-SR-BL',\n", " 'COUNTRY_WI',\n", " 'COUNTRY_BAN']" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "columns_to_be_removed = [\n", " 'T-RUNS', 'T-WKTS', 'RUNS-S', 'HS', 'AVE', 'RUNS-C', 'SR-B',\n", " 'AVE-BL', 'ECON', 'ODI-SR-B', 'ODI-RUNS-S', 'AGE_2', 'SR-BL'\n", "]\n", "\n", "xnewfeatures = list( set(xfeatures) - set(columns_to_be_removed) )\n", "xnewfeatures" ] }, { "cell_type": "code", "execution_count": 20, "id": "a03c4574", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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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", "
" ], "text/plain": [ " Column VIF\n", "0 ODI-WKTS 2.742889\n", "1 COUNTRY_IND 3.144668\n", "2 COUNTRY_PAK 1.334773\n", "3 PLAYING ROLE_Batsman 2.680207\n", "4 COUNTRY_ENG 1.131869\n", "5 COUNTRY_NZ 1.173418\n", "6 COUNTRY_SL 1.519752\n", "7 COUNTRY_SA 1.416657\n", "8 PLAYING ROLE_Bowler 3.060168\n", "9 SIXERS 2.397409\n", "10 COUNTRY_ZIM 1.205305\n", "11 WKTS 2.883101\n", "12 PLAYING ROLE_W. Keeper 1.900941\n", "13 AGE_3 1.779861\n", "14 CAPTAINCY EXP_1 2.458745\n", "15 ODI-SR-BL 2.822148\n", "16 COUNTRY_WI 1.194093\n", "17 COUNTRY_BAN 1.094293" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "get_vif_score(x[xnewfeatures])" ] }, { "cell_type": "code", "execution_count": 21, "id": "d36ca194", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\himan\\anaconda3\\lib\\site-packages\\statsmodels\\regression\\linear_model.py:1918: RuntimeWarning: divide by zero encountered in double_scalars\n", " return np.sqrt(eigvals[0]/eigvals[-1])\n" ] }, { "data": { "text/html": [ "\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
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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", "
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
" ], "text/plain": [ "\n", "\"\"\"\n", " Results: Ordinary least squares\n", "========================================================================================\n", "Model: OLS Adj. R-squared (uncentered): 0.746 \n", "Dependent Variable: SOLD PRICE AIC: 2953.6299 \n", "Date: 2023-04-24 21:26 BIC: 2998.5846 \n", "No. Observations: 104 Log-Likelihood: -1459.8 \n", "Df Model: 17 F-statistic: 19.01 \n", "Df Residuals: 87 Prob (F-statistic): 2.46e-22 \n", "R-squared (uncentered): 0.788 Scale: 1.0893e+11\n", "----------------------------------------------------------------------------------------\n", " Coef. Std.Err. t P>|t| [0.025 0.975] \n", "----------------------------------------------------------------------------------------\n", "ODI-WKTS 1000.2719 383.6520 2.6072 0.0107 237.7220 1762.8218\n", "COUNTRY_IND 241916.0736 90265.6898 2.6800 0.0088 62503.2409 421328.9062\n", "COUNTRY_PAK 60298.7726 141684.1935 0.4256 0.6715 -221313.9038 341911.4489\n", "PLAYING ROLE_Batsman 177457.3060 100597.5501 1.7640 0.0812 -22491.2177 377405.8296\n", "COUNTRY_ENG 678083.6712 208244.3343 3.2562 0.0016 264175.5079 1091991.8346\n", "COUNTRY_NZ 147178.4532 150707.9524 0.9766 0.3315 -152369.9212 446726.8275\n", "COUNTRY_SL -24754.1190 143387.0044 -0.1726 0.8633 -309751.3164 260243.0784\n", "COUNTRY_SA 81761.5043 118244.2325 0.6915 0.4911 -153261.7115 316784.7202\n", "PLAYING ROLE_Bowler 70814.4129 98253.6432 0.7207 0.4730 -124475.3419 266104.1678\n", "SIXERS 6898.3644 1658.7173 4.1589 0.0001 3601.4841 10195.2446\n", "COUNTRY_ZIM -138202.6253 374302.3654 -0.3692 0.7129 -882169.1065 605763.8559\n", "WKTS 1815.9081 1930.2590 0.9408 0.3494 -2020.6907 5652.5069\n", "PLAYING ROLE_W. Keeper 28857.7846 150328.1606 0.1920 0.8482 -269935.7123 327651.2816\n", "AGE_3 -56497.7482 101704.1167 -0.5555 0.5800 -258645.6927 145650.1963\n", "CAPTAINCY EXP_1 198466.1639 93102.8927 2.1317 0.0359 13414.0834 383518.2445\n", "ODI-SR-BL 806.6222 1262.5348 0.6389 0.5246 -1702.8024 3316.0468\n", "COUNTRY_WI -204026.7967 164456.0856 -1.2406 0.2181 -530901.0743 122847.4810\n", "COUNTRY_BAN 0.0000 0.0000 nan nan 0.0000 0.0000\n", "----------------------------------------------------------------------------------------\n", "Omnibus: 16.784 Durbin-Watson: 2.074 \n", "Prob(Omnibus): 0.000 Jarque-Bera (JB): 19.711\n", "Skew: 0.911 Prob(JB): 0.000 \n", "Kurtosis: 4.107 Condition No.: inf \n", "========================================================================================\n", "* The condition number is large (inf). This might indicate strong\n", "multicollinearity or other numerical problems.\n", "\"\"\"" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "xtrain = xtrain[xnewfeatures]\n", "\n", "model2 = sm.OLS(ytrain, xtrain).fit()\n", "\n", "model2.summary2()" ] }, { "cell_type": "code", "execution_count": 22, "id": "1260dd00", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\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", "
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", "
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
" ], "text/plain": [ "\n", "\"\"\"\n", " Results: Ordinary least squares\n", "================================================================================\n", "Model: OLS Adj. R-squared (uncentered): 0.690 \n", "Dependent Variable: SOLD PRICE AIC: 2963.0980 \n", "Date: 2023-04-24 21:31 BIC: 2973.6756 \n", "No. Observations: 104 Log-Likelihood: -1477.5 \n", "Df Model: 4 F-statistic: 58.80 \n", "Df Residuals: 100 Prob (F-statistic): 1.95e-25 \n", "R-squared (uncentered): 0.702 Scale: 1.3328e+11\n", "--------------------------------------------------------------------------------\n", " Coef. Std.Err. t P>|t| [0.025 0.975] \n", "--------------------------------------------------------------------------------\n", "COUNTRY_IND 412178.1281 60936.7223 6.7640 0.0000 291281.4066 533074.8497\n", "COUNTRY_ENG 737378.1579 215586.2418 3.4203 0.0009 309661.1943 1165095.1214\n", "SIXERS 7185.4933 1509.6537 4.7597 0.0000 4190.3833 10180.6033\n", "CAPTAINCY EXP_1 375372.1235 74692.9719 5.0255 0.0000 227183.3947 523560.8523\n", "--------------------------------------------------------------------------------\n", "Omnibus: 2.870 Durbin-Watson: 1.830\n", "Prob(Omnibus): 0.238 Jarque-Bera (JB): 2.549\n", "Skew: 0.383 Prob(JB): 0.280\n", "Kurtosis: 3.036 Condition No.: 176 \n", "================================================================================\n", "\n", "\"\"\"" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "significant_variables = ['COUNTRY_IND','COUNTRY_ENG','SIXERS','CAPTAINCY EXP_1']\n", "\n", "model2 = sm.OLS(ytrain, xtrain[significant_variables]).fit()\n", "\n", "model2.summary2()" ] }, { "cell_type": "markdown", "id": "56344941", "metadata": {}, "source": [ "## hypothesis" ] }, { "cell_type": "code", "execution_count": null, "id": "e4f353f4", "metadata": {}, "outputs": [], "source": [ "1. Null Hypothesis H0: it is considered ture till it is proven false.\n", " \n", "2. Alternative Hypothesis: it is the negation of the Null Hypothesis." ] }, { "cell_type": "markdown", "id": "7f705a97", "metadata": {}, "source": [ "## Select a test\n", "\n", "it is used to accept or reject the alternative hypothesis.\n", "\n", "Result: Test Statistics" ] }, { "cell_type": "markdown", "id": "72ffcc59", "metadata": {}, "source": [ "## p-value" ] }, { "cell_type": "code", "execution_count": null, "id": "1dad8cfb", "metadata": {}, "outputs": [], "source": [ "you have to convert this test statistics into p-value(0-1)" ] }, { "cell_type": "markdown", "id": "af38612c", "metadata": {}, "source": [ "## significance level" ] }, { "cell_type": "code", "execution_count": null, "id": "b3f51f20", "metadata": {}, "outputs": [], "source": [ "5% or 0.05" ] }, { "cell_type": "code", "execution_count": null, "id": "e4a4bde6", "metadata": {}, "outputs": [], "source": [ "a large p-value indicates that the results are due to luck." ] }, { "cell_type": "code", "execution_count": null, "id": "6dd95570", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.12" } }, "nbformat": 4, "nbformat_minor": 5 } ================================================ FILE: March Cohort 2023/12_15_16_17_18_Statistics/.ipynb_checkpoints/18_Hypothesis Testing-checkpoint.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "id": "c585a495", "metadata": {}, "source": [ "# Hypothesis Testing\n", "Proposed Explanation\n", "\n", "1. Null Hypothesis H0: It is considered true till it proven false.\n", "2. Alternative Hypothesis: It is the negation of the null hypothesis.\n" ] }, { "cell_type": "markdown", "id": "8c03609b", "metadata": {}, "source": [ "Select a test: and apply it -> test statistics\n", "\n", "we need to convert this test statistics to a p-value\n", "\n", "#### significance level\n", "threshold for luck -> 5%\n", "\n", "p-value < 0.05 -> we will reject the null hypothesis and accept the alternative hypothesis.\n", "\n", "#### a large p-value indicates that the results are due to luck." ] }, { "cell_type": "code", "execution_count": 2, "id": "1262f0c8", "metadata": {}, "outputs": [], "source": [ "# Lady tasting Tea\n", "\n", "#is it possible for you to tell weather the tea added before or after the milk?" ] }, { "cell_type": "markdown", "id": "4ebc5491", "metadata": {}, "source": [ "H0: the lady cannot tell if the milk was poured first.\n", " (NH possess no relationship and no difference)\n", "H1: the lady can tell if the milk was poured first." ] }, { "cell_type": "markdown", "id": "c46c9066", "metadata": {}, "source": [ "#### select a test\n", "\n", "8 cups of tea, 4 of each type\n", "\n", "Lady got all 8 correct -> test statistics\n", "\n", "8C4 -> 70 is the number of combinations\n", "\n", "p-value = 1/70 = 1.4%\n", "\n", "1.4% < 5% => Reject the H0\n", "\n", "\n", "### Errors in Hypothesis Testing\n", "\n", "Type I error\n", "When you reject the null hypothesis when it was actually true.\n", "\n", "Type II error\n", "When we don't reject the null hypothesis and accept the alternative hypothesis.\n", "\n", "\n", "### Power (Recall)\n", "power of statistical test\n", "\n", "probability of rejecting H0 when H1 is true.\n", "\n", "If the value of power is high than it is good.\n", "\n", "if it is high that means the low probability of Type-II error\n", "\n", "\n", "### alpha\n", "\n", "alpha is the probability of rejecting H0 when H0 is true.\n", "\n", "alpha = probability of commiting a Type-I error.\n", "\n", "if the test has high alpha value that is not good." ] }, { "cell_type": "markdown", "id": "0cf026df", "metadata": {}, "source": [ "# Chi-Square Test" ] }, { "cell_type": "code", "execution_count": 6, "id": "a36e5cfa", "metadata": {}, "outputs": [], "source": [ "import scipy.stats as stats" ] }, { "cell_type": "code", "execution_count": 7, "id": "08662308", "metadata": {}, "outputs": [], "source": [ "import seaborn as sns\n", "import pandas as pd\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 8, "id": "8316d566", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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total_billtipsexsmokerdaytimesize
016.991.01FemaleNoSunDinner2
110.341.66MaleNoSunDinner3
221.013.50MaleNoSunDinner3
323.683.31MaleNoSunDinner2
424.593.61FemaleNoSunDinner4
\n", "
" ], "text/plain": [ " total_bill tip sex smoker day time size\n", "0 16.99 1.01 Female No Sun Dinner 2\n", "1 10.34 1.66 Male No Sun Dinner 3\n", "2 21.01 3.50 Male No Sun Dinner 3\n", "3 23.68 3.31 Male No Sun Dinner 2\n", "4 24.59 3.61 Female No Sun Dinner 4" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dataset = sns.load_dataset('tips')\n", "\n", "dataset.head()" ] }, { "cell_type": "code", "execution_count": 9, "id": "4dd3d2da", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "smoker Yes No\n", "sex \n", "Male 60 97\n", "Female 33 54\n" ] } ], "source": [ "dataset_table = pd.crosstab(dataset['sex'], dataset['smoker'])\n", "\n", "print(dataset_table)" ] }, { "cell_type": "code", "execution_count": 13, "id": "2464bd7a", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[60, 97],\n", " [33, 54]], dtype=int64)" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "observed_values = dataset_table.values\n", "observed_values" ] }, { "cell_type": "code", "execution_count": 11, "id": "3a55191e", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(0.0,\n", " 1.0,\n", " 1,\n", " array([[59.84016393, 97.15983607],\n", " [33.15983607, 53.84016393]]))" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "val = stats.chi2_contingency(dataset_table)\n", "\n", "val" ] }, { "cell_type": "code", "execution_count": 12, "id": "5c52c6d3", "metadata": {}, "outputs": [], "source": [ "expected_values = val[3]" ] }, { "cell_type": "code", "execution_count": 14, "id": "55db1242", "metadata": {}, "outputs": [], "source": [ "from scipy.stats import chi2\n", "\n", "chi_square = sum([(o-e)**2./e for o, e in zip(observed_values, expected_values)])\n", "\n", "chi_square_stats = chi_square[0] + chi_square[1]" ] }, { "cell_type": "code", "execution_count": 15, "id": "f1f1261b", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.001934818536627623" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "chi_square_stats" ] }, { "cell_type": "code", "execution_count": 17, "id": "9ae6c359", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Degree of Freedom: 1\n" ] } ], "source": [ "no_of_rows = len(dataset_table.iloc[0:2,0])\n", "no_of_columns = len(dataset_table.iloc[0,0:2])\n", "ddof = (no_of_rows - 1) * (no_of_columns - 1)\n", "\n", "print(\"Degree of Freedom:\",ddof)" ] }, { "cell_type": "code", "execution_count": 19, "id": "5d7188ed", "metadata": {}, "outputs": [], "source": [ "alpha = 0.05" ] }, { "cell_type": "code", "execution_count": 20, "id": "6aa1816a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Critical Value: 3.841458820694124\n" ] } ], "source": [ "critical_value = chi2.ppf(q=1-alpha, df=ddof)\n", "\n", "print(\"Critical Value:\", critical_value)" ] }, { "cell_type": "code", "execution_count": 21, "id": "0dc7e08d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "p-value: 0.964915107315732\n", "Significance Level: 0.05\n", "Degree of Freedom: 1\n" ] } ], "source": [ "# p-value\n", "\n", "p_value = 1 - chi2.cdf(x=chi_square_stats, df=ddof)\n", "\n", "print(\"p-value:\", p_value)\n", "print(\"Significance Level:\", alpha)\n", "print(\"Degree of Freedom:\", ddof)" ] }, { "cell_type": "code", "execution_count": 23, "id": "cbcc9460", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accepting H0, there is no relationship between 2 categorical variables\n" ] } ], "source": [ "if chi_square_stats >= critical_value:\n", " print(\"Reject H0, there is a relationship between 2 categorical variables\")\n", "else:\n", " print(\"Accepting H0, there is no relationship between 2 categorical variables\")" ] }, { "cell_type": "code", "execution_count": 24, "id": "822ee03e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accepting H0, there is no relationship between 2 categorical variables\n" ] } ], "source": [ "if p_value <= alpha:\n", " print(\"Reject H0, there is a relationship between 2 categorical variables\")\n", "else:\n", " print(\"Accepting H0, there is no relationship between 2 categorical variables\")" ] }, { "cell_type": "markdown", "id": "d4ee4b89", "metadata": {}, "source": [ "# t-test\n", "\n", "if there is a significant difference between the means of two groups" ] }, { "cell_type": "code", "execution_count": 25, "id": "497d7ce4", "metadata": {}, "outputs": [], "source": [ "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]" ] }, { "cell_type": "code", "execution_count": 26, "id": "c1d8bb90", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "26" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(ages)" ] }, { "cell_type": "code", "execution_count": 27, "id": "adcb366f", "metadata": {}, "outputs": [], "source": [ "ages_mean = np.mean(ages)" ] }, { "cell_type": "code", "execution_count": 28, "id": "3e10989c", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "30.23076923076923" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ages_mean" ] }, { "cell_type": "code", "execution_count": 29, "id": "320ad6c6", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([60, 18, 66, 32, 17, 55, 43, 17, 16, 55])" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sample_size = 10\n", "\n", "age_sample = np.random.choice(ages, sample_size)\n", "\n", "age_sample" ] }, { "cell_type": "code", "execution_count": 30, "id": "19a1f903", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.24751746592331406\n" ] } ], "source": [ "from scipy.stats import ttest_1samp\n", "\n", "ttest, p_value = ttest_1samp(age_sample, 30)\n", "\n", "print(p_value)" ] }, { "cell_type": "code", "execution_count": 31, "id": "33a09441", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "We are accepting the Null Hypothesis\n" ] } ], "source": [ "if p_value < 0.05:\n", " print(\"We are rejecting null hypothesis\")\n", "else:\n", " print(\"We are accepting the Null Hypothesis\")" ] }, { "cell_type": "code", "execution_count": null, "id": "d11fd400", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.12" } }, "nbformat": 4, "nbformat_minor": 5 } ================================================ FILE: March Cohort 2023/12_15_16_17_18_Statistics/12_15_Statistics.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "id": "df89b0c5", "metadata": {}, "source": [ "# Statistics\n", "\n", "Data Science = Software Development + Statistics" ] }, { "cell_type": "code", "execution_count": 2, "id": "231d4e70", "metadata": {}, "outputs": [ { "ename": "IndentationError", "evalue": "unindent does not match any outer indentation level (, line 3)", "output_type": "error", "traceback": [ "\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" ] } ], "source": [ " Stats\n", " Descriptive Inferential\n", " Univariate Hypothesis Testing\n", " Bivariate Model Fitting\n", " Multivariate" ] }, { "cell_type": "code", "execution_count": 4, "id": "fa6fd519", "metadata": {}, "outputs": [ { "ename": "SyntaxError", "evalue": "invalid syntax (117687496.py, line 2)", "output_type": "error", "traceback": [ "\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" ] } ], "source": [ "- Univariate\n", "1. Measure of Frequency - count\n", "2. Central Tendency - mean, median, mode\n", " - Geometric mean } ratio or rates\n", " - Harmonic mean\n", "3. Despersion\n", " - range\n", " - inter quartile range\n", " - variance\n", " - standard deviation" ] }, { "cell_type": "code", "execution_count": 5, "id": "94473ea3", "metadata": {}, "outputs": [ { "ename": "IndentationError", "evalue": "unexpected indent (880973301.py, line 2)", "output_type": "error", "traceback": [ "\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" ] } ], "source": [ " Data\n", " Qualitative Quantitative\n", " " ] }, { "cell_type": "markdown", "id": "e7ee5068", "metadata": {}, "source": [ "# Central Tendancy" ] }, { "cell_type": "code", "execution_count": 6, "id": "acd1670e", "metadata": {}, "outputs": [], "source": [ "import statistics\n", "\n", "import numpy as np\n", "import pandas as pd\n", "\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 7, "id": "27b667c2", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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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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" ], "text/plain": [ " Rank Company Country Sales Profits Assets \\\n", "0 1 ICBC China 151.4 42.0 3473.2 \n", "1 2 China Construction Bank China 134.2 35.0 3016.6 \n", "2 3 Berkshire Hathaway United States 222.9 24.1 620.9 \n", "3 4 JPMorgan Chase United States 102.5 24.2 2513.0 \n", "4 5 Wells Fargo United States 97.6 21.9 1943.4 \n", "\n", " Market Value Sector Industry \n", "0 229.8 Financials Major Banks \n", "1 200.5 Financials Regional Banks \n", "2 409.9 Financials Investment Services \n", "3 306.6 Financials Major Banks \n", "4 274.4 Financials Major Banks " ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_csv('datasets/forbes.csv')\n", "\n", "df.head()" ] }, { "cell_type": "code", "execution_count": 8, "id": "40528971", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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RankCompanyCountrySalesProfitsAssetsMarket ValueSectorIndustry
0FalseFalseFalseFalseFalseFalseFalseFalseFalse
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1995FalseFalseFalseFalseFalseFalseFalseFalseFalse
1996FalseFalseFalseFalseFalseFalseFalseFalseTrue
1997FalseFalseFalseFalseFalseFalseFalseFalseFalse
1998FalseFalseFalseFalseFalseFalseFalseFalseFalse
1999FalseFalseFalseFalseFalseFalseFalseFalseTrue
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2000 rows × 9 columns

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" ], "text/plain": [ " Rank Company Country Sales Profits Assets Market Value Sector \\\n", "0 False False False False False False False False \n", "1 False False False False False False False False \n", "2 False False False False False False False False \n", "3 False False False False False False False False \n", "4 False False False False False False False False \n", "... ... ... ... ... ... ... ... ... \n", "1995 False False False False False False False False \n", "1996 False False False False False False False False \n", "1997 False False False False False False False False \n", "1998 False False False False False False False False \n", "1999 False False False False False False False False \n", "\n", " Industry \n", "0 False \n", "1 False \n", "2 False \n", "3 False \n", "4 False \n", "... ... \n", "1995 False \n", "1996 True \n", "1997 False \n", "1998 False \n", "1999 True \n", "\n", "[2000 rows x 9 columns]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.isnull()" ] }, { "cell_type": "code", "execution_count": 9, "id": "db69da00", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Rank 0\n", "Company 0\n", "Country 0\n", "Sales 0\n", "Profits 0\n", "Assets 0\n", "Market Value 0\n", "Sector 197\n", "Industry 491\n", "dtype: int64" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.isnull().sum()" ] }, { "cell_type": "code", "execution_count": 10, "id": "2a980414", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Rank 0\n", "Company 0\n", "Country 0\n", "Sales 0\n", "Profits 0\n", "Assets 0\n", "Market Value 0\n", "Sector 0\n", "Industry 0\n", "dtype: int64" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.dropna(inplace=True)\n", "\n", "df.isnull().sum()" ] }, { "cell_type": "code", "execution_count": 11, "id": "b2de3fb4", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(1508, 9)" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.shape" ] }, { "cell_type": "code", "execution_count": 12, "id": "9aa68486", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "26.978494031830294" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "market_value_mean = df['Market Value'].mean()\n", "\n", "market_value_mean" ] }, { "cell_type": "markdown", "id": "11d37231", "metadata": {}, "source": [ "## Mean Key Points\n", "\n", "- best represents the data\n", "- mean consider all the data points\n", "- extremly sensitive to the presence of outliers\n" ] }, { "cell_type": "code", "execution_count": 13, "id": "88065b85", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "13.1" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "market_value_median = df['Market Value'].median()\n", "\n", "market_value_median" ] }, { "cell_type": "markdown", "id": "7594c091", "metadata": {}, "source": [ "## Median Key points\n", "\n", "- It is less sensitive to outliers\n", "- 50% of the data is either side of it.\n" ] }, { "cell_type": "code", "execution_count": 14, "id": "585fdb6d", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.24" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['Market Value'].min()" ] }, { "cell_type": "code", "execution_count": 15, "id": "da70e1e2", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "752.0" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['Market Value'].max()" ] }, { "cell_type": "code", "execution_count": 16, "id": "6f4c63c0", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "751.76" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "range1 = df['Market Value'].max() - df['Market Value'].min()\n", "\n", "range1" ] }, { "cell_type": "code", "execution_count": 17, "id": "434e6e34", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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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
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" ], "text/plain": [ " Rank Sales Profits Assets Market Value\n", "count 1508.000000 1508.000000 1508.000000 1508.000000 1508.000000\n", "mean 909.135279 19.623046 1.407430 97.647016 26.978494\n", "std 554.044456 30.875809 3.244201 287.036157 47.099541\n", "min 1.000000 0.001000 -13.000000 1.300000 0.240000\n", "25% 433.750000 4.900000 0.349750 12.800000 7.300000\n", "50% 883.500000 10.300000 0.692500 25.900000 13.100000\n", "75% 1354.250000 19.900000 1.425000 60.325000 26.900000\n", "max 1999.000000 485.300000 45.200000 3473.200000 752.000000" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.describe()" ] }, { "cell_type": "code", "execution_count": 18, "id": "c4b36bb3", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Financials 474\n", "Consumer Discretionary 209\n", "Industrials 192\n", "Materials 145\n", "Consumer Staples 106\n", "Information Technology 103\n", "Utilities 86\n", "Energy 79\n", "Health Care 71\n", "Telecommunication Services 43\n", "Name: Sector, dtype: int64" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# sector column is a categorical variable\n", "\n", "df['Sector'].value_counts()" ] }, { "cell_type": "code", "execution_count": 19, "id": "b4d8b09e", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 Financials\n", "Name: Sector, dtype: object" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['Sector'].mode()" ] }, { "cell_type": "markdown", "id": "0f6db83f", "metadata": {}, "source": [ "## Mode\n", "\n", "- the value that occured most in the data\n", "- vote in an election\n", "- mode is not good for continuous data." ] }, { "cell_type": "markdown", "id": "214ca025", "metadata": {}, "source": [ "# box plot\n", "\n", "- mean\n", "- median\n", "- 25th percentile\n", "- 75th percentile\n", "- maximum values are where in the data\n", "- outliers" ] }, { "cell_type": "code", "execution_count": 20, "id": "ab0791e2", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(10,8))\n", "\n", "sns.boxplot(data=df, y = 'Market Value', showmeans=True)\n", "\n", "plt.axhline(y=market_value_mean, color='r', linestyle='-')\n", "plt.axhline(y=market_value_median, color='g', linestyle='-')\n", "\n", "plt.title('Forbes Data')\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 21, "id": "e11c59db", "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(10,8))\n", "\n", "sns.boxplot(data=df, y = 'Market Value', showmeans=True)\n", "\n", "plt.ylim(0,80)\n", "\n", "plt.axhline(y=market_value_mean, color='r', linestyle='-')\n", "plt.axhline(y=market_value_median, color='g', linestyle='-')\n", "\n", "plt.title('Forbes Data')\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "3a8efbb2", "metadata": {}, "source": [ "# mode" ] }, { "cell_type": "code", "execution_count": 22, "id": "0afbc532", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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SectorRankCompanyCountrySalesProfitsAssetsMarket ValueIndustry
0Consumer Discretionary209209209209209209209209
1Consumer Staples106106106106106106106106
2Energy7979797979797979
3Financials474474474474474474474474
4Health Care7171717171717171
5Industrials192192192192192192192192
6Information Technology103103103103103103103103
7Materials145145145145145145145145
8Telecommunication Services4343434343434343
9Utilities8686868686868686
\n", "
" ], "text/plain": [ " Sector Rank Company Country Sales Profits Assets \\\n", "0 Consumer Discretionary 209 209 209 209 209 209 \n", "1 Consumer Staples 106 106 106 106 106 106 \n", "2 Energy 79 79 79 79 79 79 \n", "3 Financials 474 474 474 474 474 474 \n", "4 Health Care 71 71 71 71 71 71 \n", "5 Industrials 192 192 192 192 192 192 \n", "6 Information Technology 103 103 103 103 103 103 \n", "7 Materials 145 145 145 145 145 145 \n", "8 Telecommunication Services 43 43 43 43 43 43 \n", "9 Utilities 86 86 86 86 86 86 \n", "\n", " Market Value Industry \n", "0 209 209 \n", "1 106 106 \n", "2 79 79 \n", "3 474 474 \n", "4 71 71 \n", "5 192 192 \n", "6 103 103 \n", "7 145 145 \n", "8 43 43 \n", "9 86 86 " ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gb = df.groupby('Sector', as_index=False).count()\n", "\n", "gb" ] }, { "cell_type": "code", "execution_count": 23, "id": "635fa133", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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SectorRank
3Financials474
0Consumer Discretionary209
5Industrials192
7Materials145
1Consumer Staples106
6Information Technology103
9Utilities86
2Energy79
4Health Care71
8Telecommunication Services43
\n", "
" ], "text/plain": [ " Sector Rank\n", "3 Financials 474\n", "0 Consumer Discretionary 209\n", "5 Industrials 192\n", "7 Materials 145\n", "1 Consumer Staples 106\n", "6 Information Technology 103\n", "9 Utilities 86\n", "2 Energy 79\n", "4 Health Care 71\n", "8 Telecommunication Services 43" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gb = gb[['Sector', 'Rank']]\n", "\n", "gb = gb.sort_values(by='Rank', ascending=False)\n", "\n", "gb" ] }, { "cell_type": "markdown", "id": "4efa0eb2", "metadata": {}, "source": [ "# pie chart\n", "\n", "- mode\n", "- category wise display values\n", "- percentage of values aquired by categories" ] }, { "cell_type": "code", "execution_count": 24, "id": "70799918", "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(10,8))\n", "\n", "plt.pie(gb['Rank'], labels=gb['Sector'], autopct='%.2f')\n", "\n", "plt.suptitle(\"Sector Vs Rank\")\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "d4812687", "metadata": {}, "source": [ "# Range" ] }, { "cell_type": "code", "execution_count": 25, "id": "fee0db23", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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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
\n", "
" ], "text/plain": [ " Rank Company Country Sales Profits Assets \\\n", "0 1 ICBC China 151.4 42.0 3473.2 \n", "1 2 China Construction Bank China 134.2 35.0 3016.6 \n", "2 3 Berkshire Hathaway United States 222.9 24.1 620.9 \n", "3 4 JPMorgan Chase United States 102.5 24.2 2513.0 \n", "4 5 Wells Fargo United States 97.6 21.9 1943.4 \n", "\n", " Market Value Sector Industry \n", "0 229.8 Financials Major Banks \n", "1 200.5 Financials Regional Banks \n", "2 409.9 Financials Investment Services \n", "3 306.6 Financials Major Banks \n", "4 274.4 Financials Major Banks " ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "code", "execution_count": 26, "id": "ad69c17b", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "45.2" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "profit_max = df['Profits'].max()\n", "\n", "profit_max" ] }, { "cell_type": "code", "execution_count": 27, "id": "4a88e4a1", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "-13.0" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "profit_min = df['Profits'].min()\n", "\n", "profit_min" ] }, { "cell_type": "code", "execution_count": 28, "id": "29b12a1f", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "58.2" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "profit_range = profit_max - profit_min\n", "\n", "profit_range" ] }, { "cell_type": "code", "execution_count": 29, "id": "356b1be1", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(10,8))\n", "\n", "sns.boxplot(data = df, y = 'Profits', showmeans=True)\n", "\n", "plt.title('Forbes Data')\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 30, "id": "8be191bb", "metadata": {}, "outputs": [ { "data": { "image/png": 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bGiUBeqOAAd2yBwxoRQEDumUPGNCKAgZ064orrhiZr7zyykZJgN4oYEC37r777mVngHFRwIBuzc7Ojsx79uxplATojQIGdGt6enpk3rhxY6MkQG8UMKBbp59++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", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(10,8))\n", "\n", "sns.boxplot(data = df, y = 'Profits', showmeans=True)\n", "\n", "plt.ylim(-3,8)\n", "\n", "plt.title('Forbes Data')\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "60b33559", "metadata": {}, "source": [ "# Range\n", "\n", "- range is sensitive to outliers" ] }, { "cell_type": "markdown", "id": "57270870", "metadata": {}, "source": [ "# Inter Quartile Range" ] }, { "cell_type": "code", "execution_count": 31, "id": "55f9d714", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.34975" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 25th percentile\n", "\n", "Q1 = np.quantile(df['Profits'], 0.25)\n", "\n", "Q1" ] }, { "cell_type": "code", "execution_count": 32, "id": "7dfaa0d9", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1.4249999999999998" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# 75th percentile\n", "\n", "Q3 = np.quantile(df['Profits'], 0.75)\n", "\n", "Q3" ] }, { "cell_type": "code", "execution_count": 33, "id": "e1aab44b", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1.0752499999999998" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "IQR = Q3 - Q1\n", "\n", "IQR" ] }, { "cell_type": "markdown", "id": "194f9cf6", "metadata": {}, "source": [ "# IQR\n", "\n", "- resistant to outliers\n", "- better than range" ] }, { "cell_type": "markdown", "id": "d30aaeac", "metadata": {}, "source": [ "# Standard Deviation and Variance" ] }, { "cell_type": "code", "execution_count": 34, "id": "94231206", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "3.244201278603241" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "profit_std = df['Profits'].std()\n", "\n", "profit_std" ] }, { "cell_type": "code", "execution_count": 36, "id": "054b2250", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1.4074303713527847" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "profit_mean = df['Profits'].mean()\n", "\n", "profit_mean" ] }, { "cell_type": "code", "execution_count": null, "id": "7e91b587", "metadata": {}, "outputs": [], "source": [ "cricket\n", "\n", "virat kohli rohit sharma\n", "40 0\n", "50 100\n", "30 10\n", "20 15\n", "45 0\n", "67 101\n", "40 200\n", "\n", "mean mean\n", "35 60\n", "\n", "5 60\n", "15 40\n", "5 50\n", "15 60\n", "25 41\n", " 140" ] }, { "cell_type": "code", "execution_count": 37, "id": "86d516fd", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "10.524841936090901" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "profit_var = df['Profits'].var()\n", "\n", "profit_var" ] }, { "cell_type": "code", "execution_count": null, "id": "f5f846b7", "metadata": {}, "outputs": [], "source": [ "appl msoft\n", "100 100\n", "500 120\n", "-200 130\n", "600 125" ] }, { "cell_type": "code", "execution_count": 38, "id": "0c7d3fa2", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "10.524841936090903" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "profit_std ** 2 # variance" ] }, { "cell_type": "markdown", "id": "27a5b442", "metadata": {}, "source": [ "# Summary Stats" ] }, { "cell_type": "code", "execution_count": 39, "id": "3d683610", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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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", "
" ], "text/plain": [ " Rank Sales Profits Assets Market Value\n", "count 1508.000000 1508.000000 1508.000000 1508.000000 1508.000000\n", "mean 909.135279 19.623046 1.407430 97.647016 26.978494\n", "std 554.044456 30.875809 3.244201 287.036157 47.099541\n", "min 1.000000 0.001000 -13.000000 1.300000 0.240000\n", "25% 433.750000 4.900000 0.349750 12.800000 7.300000\n", "50% 883.500000 10.300000 0.692500 25.900000 13.100000\n", "75% 1354.250000 19.900000 1.425000 60.325000 26.900000\n", "max 1999.000000 485.300000 45.200000 3473.200000 752.000000" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.describe()" ] }, { "cell_type": "markdown", "id": "45cddf9c", "metadata": {}, "source": [ "# Noraml Distribution" ] }, { "cell_type": "code", "execution_count": 40, "id": "eeec52ce", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([-0.21471611, 0.61754921, -0.60251843, 0.14196059, 0.60611692,\n", " 0.33409508, 0.93308273, -0.83391941, -2.49212651, -0.64909842])" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# mean = 0\n", "# std = 1\n", "\n", "normally_distributed_data = np.random.normal(loc=0, scale=1, size=100000)\n", "\n", "normally_distributed_data[0:10]" ] }, { "cell_type": "code", "execution_count": null, "id": "91a281e8", "metadata": {}, "outputs": [], "source": [ "# mean == median # normal distribution" ] }, { "cell_type": "code", "execution_count": 42, "id": "3524740c", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "-0.00010732710555090066" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sample_mean = np.mean(normally_distributed_data)\n", "\n", "sample_mean" ] }, { "cell_type": "code", "execution_count": 43, "id": "c995dc03", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "-0.0014516077341794609" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sample_median = np.median(normally_distributed_data)\n", "\n", "sample_median" ] }, { "cell_type": "code", "execution_count": 44, "id": "9dc19349", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1.0014081381099627" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sample_std = np.std(normally_distributed_data)\n", "\n", "sample_std" ] }, { "cell_type": "markdown", "id": "81360486", "metadata": {}, "source": [ "## plot normal distribution" ] }, { "cell_type": "code", "execution_count": 46, "id": "908217a4", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(12,8))\n", "\n", "sns.histplot(x = normally_distributed_data)\n", "\n", "plt.title(\"Normal Distribution\", fontsize='15')\n", "\n", "plt.axvline(x = sample_mean, color='r', linestyle='-')\n", "plt.axvline(x = sample_median, color='g', linestyle='-')\n", "\n", "plt.xlabel('Values', fontsize='15')\n", "plt.ylabel('probability', fontsize='15')\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "91fcee90", "metadata": {}, "source": [ "# Probability'\n", "\n", "an event is likely to occur\n", "\n", "favorable cases / total number of cases\n" ] }, { "cell_type": "code", "execution_count": null, "id": "04e37d20", "metadata": {}, "outputs": [], "source": [ "1/2\n", "\n", " H T\n", "0 - 0.5 , 0.5 - 1" ] }, { "cell_type": "code", "execution_count": null, "id": "ef7d552b", "metadata": {}, "outputs": [], "source": [ " 0 1 2\n", "0-0.1 0.1 - 0.2 0.2 - 0.3" ] }, { "cell_type": "markdown", "id": "7556d169", "metadata": {}, "source": [ "# Coin" ] }, { "cell_type": "code", "execution_count": 49, "id": "c3a058c9", "metadata": {}, "outputs": [], "source": [ "import random\n", "import scipy.stats" ] }, { "cell_type": "code", "execution_count": 50, "id": "977c6909", "metadata": {}, "outputs": [], "source": [ "def flip(n):\n", " \n", " result = []\n", " \n", " for i in range(1,n+1):\n", " result.append(random.choice(['Head', 'Tails']))\n", " \n", " return result" ] }, { "cell_type": "code", "execution_count": 51, "id": "0c567150", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['Head',\n", " 'Tails',\n", " 'Tails',\n", " 'Tails',\n", " 'Head',\n", " 'Head',\n", " 'Tails',\n", " 'Tails',\n", " 'Tails',\n", " 'Head']" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ "flip(10)" ] }, { "cell_type": "code", "execution_count": 52, "id": "a45cb78e", "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "f = flip(10)\n", "\n", "plt.figure(figsize=(12,8))\n", "\n", "sns.countplot(x = f)\n", "\n", "plt.xlabel('Coin Flips')\n", "plt.title('Counts')\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 53, "id": "3f563d5f", "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "f = flip(100)\n", "\n", "plt.figure(figsize=(12,8))\n", "\n", "sns.countplot(x = f)\n", "\n", "plt.xlabel('Coin Flips')\n", "plt.title('Counts')\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 54, "id": "85907d8a", "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "f = flip(100000)\n", "\n", "plt.figure(figsize=(12,8))\n", "\n", "sns.countplot(x = f)\n", "\n", "plt.xlabel('Coin Flips')\n", "plt.title('Counts')\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "1081d8ad", "metadata": {}, "source": [ "# Dice" ] }, { "cell_type": "code", "execution_count": 55, "id": "b8e72228", "metadata": {}, "outputs": [], "source": [ "def roll(n):\n", " \n", " result = []\n", " \n", " for i in range(1,n+1):\n", " result.append(random.choice([1,2,3,4,5,6]))\n", " \n", " return result" ] }, { "cell_type": "code", "execution_count": 56, "id": "acb6b988", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[4, 5, 4, 5, 3, 3, 3, 3, 4, 6]" ] }, "execution_count": 56, "metadata": {}, "output_type": "execute_result" } ], "source": [ "roll(10)" ] }, { "cell_type": "code", "execution_count": 57, "id": "ca45a0e6", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "f = roll(10)\n", "\n", "plt.figure(figsize=(12,8))\n", "\n", "sns.countplot(x = f)\n", "\n", "plt.xlabel('Dice Roll')\n", "plt.title('Counts')\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 58, "id": "d9bd67b6", "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "f = roll(100)\n", "\n", "plt.figure(figsize=(12,8))\n", "\n", "sns.countplot(x = f)\n", "\n", "plt.xlabel('Dice Roll')\n", "plt.title('Counts')\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 59, "id": "a0b6328e", "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "f = roll(100000)\n", "\n", "plt.figure(figsize=(12,8))\n", "\n", "sns.countplot(x = f)\n", "\n", "plt.xlabel('Dice Roll')\n", "plt.title('Counts')\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 60, "id": "3cf86622", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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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
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" ], "text/plain": [ " Date Open High Low Close AdjClose Volume\n", "0 31-Dec-1986 243.37 244.03 241.28 242.17 242.17 13,92,00,000\n", "1 2-Jan-1987 242.17 246.45 242.17 246.45 246.45 9,18,80,000\n", "2 5-Jan-1987 246.45 252.57 246.45 252.19 252.19 18,19,00,000\n", "3 6-Jan-1987 252.20 253.99 252.14 252.78 252.78 18,93,00,000\n", "4 7-Jan-1987 252.78 255.72 252.65 255.33 255.33 19,09,00,000" ] }, "execution_count": 60, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = pd.read_csv('datasets/SP_500_1987.csv')\n", "\n", "data.head()" ] }, { "cell_type": "code", "execution_count": 61, "id": "9d2c64dc", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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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
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" ], "text/plain": [ " Open High Low Close AdjClose\n", "count 253.000000 253.000000 253.000000 253.000000 253.000000\n", "mean 286.964466 289.328063 284.412095 286.978617 286.978617\n", "std 28.923683 28.241142 29.535528 28.889053 28.889053\n", "min 223.980000 225.770000 216.460000 223.920000 223.920000\n", "25% 267.840000 270.400000 264.310000 267.840000 267.840000\n", "50% 290.520000 292.470000 288.340000 290.520000 290.520000\n", "75% 308.940000 310.270000 307.420000 308.960000 308.960000\n", "max 336.770000 337.890000 334.460000 336.770000 336.770000" ] }, "execution_count": 61, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.describe()" ] }, { "cell_type": "code", "execution_count": 62, "id": "52bb2007", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Date object\n", "Open float64\n", "High float64\n", "Low float64\n", "Close float64\n", "AdjClose float64\n", "Volume object\n", "dtype: object" ] }, "execution_count": 62, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.dtypes" ] }, { "cell_type": "code", "execution_count": 63, "id": "3e57630e", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Date datetime64[ns]\n", "Open float64\n", "High float64\n", "Low float64\n", "Close float64\n", "AdjClose float64\n", "Volume object\n", "dtype: object" ] }, "execution_count": 63, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data['Date'] = pd.to_datetime(data['Date'])\n", "\n", "data.dtypes" ] }, { "cell_type": "code", "execution_count": 64, "id": "ad432bf2", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 1.0, 'S&P 500 1987')" ] }, "execution_count": 64, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(12,8))\n", "\n", "sns.lineplot(data=data, x = 'Date', y='AdjClose', color='red')\n", "\n", "plt.title(\"S&P 500 1987\")" ] }, { "cell_type": "code", "execution_count": 67, "id": "2cecaa85", "metadata": { "scrolled": false }, "outputs": [], "source": [ "# help(data['AdjClose'].pct_change())" ] }, { "cell_type": "code", "execution_count": 65, "id": "15c92d9e", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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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
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" ], "text/plain": [ " Date Open High Low Close AdjClose Volume Returns\n", "0 1986-12-31 243.37 244.03 241.28 242.17 242.17 13,92,00,000 NaN\n", "1 1987-01-02 242.17 246.45 242.17 246.45 246.45 9,18,80,000 0.017674\n", "2 1987-01-05 246.45 252.57 246.45 252.19 252.19 18,19,00,000 0.023291\n", "3 1987-01-06 252.20 253.99 252.14 252.78 252.78 18,93,00,000 0.002340\n", "4 1987-01-07 252.78 255.72 252.65 255.33 255.33 19,09,00,000 0.010088" ] }, "execution_count": 65, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data['Returns'] = data['AdjClose'].pct_change()\n", "\n", "data.head()" ] }, { "cell_type": "code", "execution_count": 68, "id": "2d892d6c", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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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", "
" ], "text/plain": [ " Date Open High Low Close AdjClose Volume Returns\n", "1 1987-01-02 242.17 246.45 242.17 246.45 246.45 9,18,80,000 0.017674\n", "2 1987-01-05 246.45 252.57 246.45 252.19 252.19 18,19,00,000 0.023291\n", "3 1987-01-06 252.20 253.99 252.14 252.78 252.78 18,93,00,000 0.002340\n", "4 1987-01-07 252.78 255.72 252.65 255.33 255.33 19,09,00,000 0.010088\n", "5 1987-01-08 255.36 257.28 254.97 257.28 257.28 19,45,00,000 0.007637" ] }, "execution_count": 68, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = data.dropna()\n", "data.head()" ] }, { "cell_type": "code", "execution_count": 69, "id": "c23e43a2", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(252, 8)" ] }, "execution_count": 69, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.shape" ] }, { "cell_type": "markdown", "id": "44c9342e", "metadata": {}, "source": [ "# Skewness" ] }, { "cell_type": "code", "execution_count": 70, "id": "86c6f635", "metadata": {}, "outputs": [ { "data": { "image/png": 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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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" ], "text/plain": [ " Sl.NO. PLAYER NAME AGE COUNTRY TEAM PLAYING ROLE T-RUNS T-WKTS \\\n", "0 1 Abdulla, YA 2 SA KXIP Allrounder 0 0 \n", "1 2 Abdur Razzak 2 BAN RCB Bowler 214 18 \n", "2 3 Agarkar, AB 2 IND KKR Bowler 571 58 \n", "3 4 Ashwin, R 1 IND CSK Bowler 284 31 \n", "4 5 Badrinath, S 2 IND CSK Batsman 63 0 \n", "\n", " ODI-RUNS-S ODI-SR-B ... SR-B SIXERS RUNS-C WKTS AVE-BL ECON \\\n", "0 0 0.00 ... 0.00 0 307 15 20.47 8.90 \n", "1 657 71.41 ... 0.00 0 29 0 0.00 14.50 \n", "2 1269 80.62 ... 121.01 5 1059 29 36.52 8.81 \n", "3 241 84.56 ... 76.32 0 1125 49 22.96 6.23 \n", "4 79 45.93 ... 120.71 28 0 0 0.00 0.00 \n", "\n", " SR-BL AUCTION YEAR BASE PRICE SOLD PRICE \n", "0 13.93 2009 50000 50000 \n", "1 0.00 2008 50000 50000 \n", "2 24.90 2008 200000 350000 \n", "3 22.14 2011 100000 850000 \n", "4 0.00 2011 100000 800000 \n", "\n", "[5 rows x 26 columns]" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_csv('datasets/IPL2013.csv')\n", "\n", "df.head()" ] }, { "cell_type": "code", "execution_count": 3, "id": "89f7bb2b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 130 entries, 0 to 129\n", "Data columns (total 26 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 Sl.NO. 130 non-null int64 \n", " 1 PLAYER NAME 130 non-null object \n", " 2 AGE 130 non-null int64 \n", " 3 COUNTRY 130 non-null object \n", " 4 TEAM 130 non-null object \n", " 5 PLAYING ROLE 130 non-null object \n", " 6 T-RUNS 130 non-null int64 \n", " 7 T-WKTS 130 non-null int64 \n", " 8 ODI-RUNS-S 130 non-null int64 \n", " 9 ODI-SR-B 130 non-null float64\n", " 10 ODI-WKTS 130 non-null int64 \n", " 11 ODI-SR-BL 130 non-null float64\n", " 12 CAPTAINCY EXP 130 non-null int64 \n", " 13 RUNS-S 130 non-null int64 \n", " 14 HS 130 non-null int64 \n", " 15 AVE 130 non-null float64\n", " 16 SR-B 130 non-null float64\n", " 17 SIXERS 130 non-null int64 \n", " 18 RUNS-C 130 non-null int64 \n", " 19 WKTS 130 non-null int64 \n", " 20 AVE-BL 130 non-null float64\n", " 21 ECON 130 non-null float64\n", " 22 SR-BL 130 non-null float64\n", " 23 AUCTION YEAR 130 non-null int64 \n", " 24 BASE PRICE 130 non-null int64 \n", " 25 SOLD PRICE 130 non-null int64 \n", "dtypes: float64(7), int64(15), object(4)\n", "memory usage: 26.5+ KB\n" ] } ], "source": [ "df.info()" ] }, { "cell_type": "code", "execution_count": 4, "id": "983b5a17", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['Sl.NO.', 'PLAYER NAME', 'AGE', 'COUNTRY', 'TEAM', 'PLAYING ROLE',\n", " 'T-RUNS', 'T-WKTS', 'ODI-RUNS-S', 'ODI-SR-B', 'ODI-WKTS', 'ODI-SR-BL',\n", " 'CAPTAINCY EXP', 'RUNS-S', 'HS', 'AVE', 'SR-B', 'SIXERS', 'RUNS-C',\n", " 'WKTS', 'AVE-BL', 'ECON', 'SR-BL', 'AUCTION YEAR', 'BASE PRICE',\n", " 'SOLD PRICE'],\n", " dtype='object')" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "xfeatures = df.columns\n", "xfeatures" ] }, { "cell_type": "code", "execution_count": 5, "id": "beac70ae", "metadata": {}, "outputs": [], "source": [ "xfeatures = ['AGE', 'COUNTRY', 'PLAYING ROLE',\n", " 'T-RUNS', 'T-WKTS', 'ODI-RUNS-S', 'ODI-SR-B', 'ODI-WKTS', 'ODI-SR-BL',\n", " 'CAPTAINCY EXP', 'RUNS-S', 'HS', 'AVE', 'SR-B', 'SIXERS', 'RUNS-C',\n", " 'WKTS', 'AVE-BL', 'ECON', 'SR-BL']" ] }, { "cell_type": "markdown", "id": "489a7f42", "metadata": {}, "source": [ "# Feature encoding on categorical columns" ] }, { "cell_type": "code", "execution_count": 6, "id": "9f8602c5", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array(['Allrounder', 'Bowler', 'Batsman', 'W. Keeper'], dtype=object)" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['PLAYING ROLE'].unique()" ] }, { "cell_type": "code", "execution_count": 7, "id": "4b4f0c3e", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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AllrounderBatsmanBowlerW. Keeper
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" ], "text/plain": [ " Allrounder Batsman Bowler W. Keeper\n", "0 1 0 0 0\n", "1 0 0 1 0\n", "2 0 0 1 0\n", "3 0 0 1 0\n", "4 0 1 0 0" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pd.get_dummies(df['PLAYING ROLE'])[0:5]" ] }, { "cell_type": "code", "execution_count": 8, "id": "704e7c42", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['T-RUNS', 'T-WKTS', 'ODI-RUNS-S', 'ODI-SR-B', 'ODI-WKTS', 'ODI-SR-BL',\n", " 'RUNS-S', 'HS', 'AVE', 'SR-B', 'SIXERS', 'RUNS-C', 'WKTS', 'AVE-BL',\n", " 'ECON', 'SR-BL', 'AGE_2', 'AGE_3', 'COUNTRY_BAN', 'COUNTRY_ENG',\n", " 'COUNTRY_IND', 'COUNTRY_NZ', 'COUNTRY_PAK', 'COUNTRY_SA', 'COUNTRY_SL',\n", " 'COUNTRY_WI', 'COUNTRY_ZIM', 'PLAYING ROLE_Batsman',\n", " 'PLAYING ROLE_Bowler', 'PLAYING ROLE_W. Keeper', 'CAPTAINCY EXP_1'],\n", " dtype='object')" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "categorical_features = ['AGE', 'COUNTRY', 'PLAYING ROLE', 'CAPTAINCY EXP']\n", "\n", "encoded_df = pd.get_dummies(df[xfeatures],\n", " columns = categorical_features,\n", " drop_first=True)\n", "\n", "encoded_df.columns" ] }, { "cell_type": "code", "execution_count": 9, "id": "7dea8fb2", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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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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" ], "text/plain": [ " T-RUNS T-WKTS ODI-RUNS-S ODI-SR-B ODI-WKTS ODI-SR-BL RUNS-S HS \\\n", "0 0 0 0 0.00 0 0.0 0 0 \n", "1 214 18 657 71.41 185 37.6 0 0 \n", "2 571 58 1269 80.62 288 32.9 167 39 \n", "3 284 31 241 84.56 51 36.8 58 11 \n", "4 63 0 79 45.93 0 0.0 1317 71 \n", "\n", " AVE SR-B ... COUNTRY_NZ COUNTRY_PAK COUNTRY_SA COUNTRY_SL \\\n", "0 0.00 0.00 ... 0 0 1 0 \n", "1 0.00 0.00 ... 0 0 0 0 \n", "2 18.56 121.01 ... 0 0 0 0 \n", "3 5.80 76.32 ... 0 0 0 0 \n", "4 32.93 120.71 ... 0 0 0 0 \n", "\n", " COUNTRY_WI COUNTRY_ZIM PLAYING ROLE_Batsman PLAYING ROLE_Bowler \\\n", "0 0 0 0 0 \n", "1 0 0 0 1 \n", "2 0 0 0 1 \n", "3 0 0 0 1 \n", "4 0 0 1 0 \n", "\n", " PLAYING ROLE_W. Keeper CAPTAINCY EXP_1 \n", "0 0 0 \n", "1 0 0 \n", "2 0 0 \n", "3 0 0 \n", "4 0 0 \n", "\n", "[5 rows x 31 columns]" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "xfeatures = encoded_df.columns\n", "\n", "encoded_df.head()" ] }, { "cell_type": "code", "execution_count": 10, "id": "8af55ff4", "metadata": {}, "outputs": [], "source": [ "from sklearn.model_selection import train_test_split\n", "\n", "x = sm.add_constant(encoded_df)\n", "\n", "y = df['SOLD PRICE']\n", "\n", "xtrain, xtest, ytrain, ytest = train_test_split(x, y,\n", " train_size=0.8,\n", " random_state=101)" ] }, { "cell_type": "code", "execution_count": 11, "id": "d5cbd477", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\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
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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
" ], "text/plain": [ "\n", "\"\"\"\n", " Results: Ordinary least squares\n", "========================================================================================\n", "Model: OLS Adj. R-squared: 0.367 \n", "Dependent Variable: SOLD PRICE AIC: 2952.4618 \n", "Date: 2023-04-24 21:08 BIC: 3034.4379 \n", "No. Observations: 104 Log-Likelihood: -1445.2 \n", "Df Model: 30 F-statistic: 2.989 \n", "Df Residuals: 73 Prob (F-statistic): 7.43e-05 \n", "R-squared: 0.551 Scale: 9.8071e+10\n", "----------------------------------------------------------------------------------------\n", " Coef. Std.Err. t P>|t| [0.025 0.975] \n", "----------------------------------------------------------------------------------------\n", "const 321792.1559 214961.4272 1.4970 0.1387 -106625.3506 750209.6624\n", "T-RUNS -15.3217 31.7900 -0.4820 0.6313 -78.6790 48.0356\n", "T-WKTS -92.1211 532.7482 -0.1729 0.8632 -1153.8869 969.6446\n", "ODI-RUNS-S 46.4615 32.7116 1.4203 0.1598 -18.7326 111.6555\n", "ODI-SR-B 1254.6514 1524.3920 0.8231 0.4132 -1783.4575 4292.7602\n", "ODI-WKTS 1034.2327 705.8108 1.4653 0.1471 -372.4462 2440.9116\n", "ODI-SR-BL -1951.6835 1570.2876 -1.2429 0.2179 -5081.2621 1177.8952\n", "RUNS-S 79.5617 164.1206 0.4848 0.6293 -247.5303 406.6536\n", "HS -2845.5968 2659.2674 -1.0701 0.2881 -8145.5090 2454.3155\n", "AVE 16170.0170 7116.7912 2.2721 0.0260 1986.2726 30353.7615\n", "SR-B -1412.9785 1268.6500 -1.1138 0.2690 -3941.3943 1115.4373\n", "SIXERS 2933.8502 3250.6472 0.9025 0.3697 -3544.6804 9412.3808\n", "RUNS-C 90.3731 250.6836 0.3605 0.7195 -409.2387 589.9848\n", "WKTS 1023.5083 6199.6413 0.1651 0.8693 -11332.3589 13379.3755\n", "AVE-BL 10484.2466 10614.6012 0.9877 0.3266 -10670.6230 31639.1163\n", "ECON 1582.7941 10799.9290 0.1466 0.8839 -19941.4334 23107.0217\n", "SR-BL -16112.2676 14683.6651 -1.0973 0.2761 -45376.7699 13152.2347\n", "AGE_2 -204982.5473 122684.6949 -1.6708 0.0990 -449492.7915 39527.6970\n", "AGE_3 -314011.4605 166581.1800 -1.8850 0.0634 -646007.2734 17984.3525\n", "COUNTRY_BAN -0.0000 0.0000 -0.4054 0.6864 -0.0000 0.0000\n", "COUNTRY_ENG 568937.2590 223554.3597 2.5450 0.0130 123394.0628 1014480.4552\n", "COUNTRY_IND 105574.3654 116110.3502 0.9093 0.3662 -125833.2288 336981.9597\n", "COUNTRY_NZ 120771.0634 169814.2346 0.7112 0.4792 -217668.2181 459210.3449\n", "COUNTRY_PAK -18339.1812 162409.9584 -0.1129 0.9104 -342021.7614 305343.3991\n", "COUNTRY_SA -48587.6579 135853.6982 -0.3576 0.7216 -319343.6880 222168.3722\n", "COUNTRY_SL -132932.0463 156295.0094 -0.8505 0.3978 -444427.5507 178563.4581\n", "COUNTRY_WI -292568.4951 188284.0245 -1.5539 0.1245 -667818.0146 82681.0244\n", "COUNTRY_ZIM -70610.1889 377372.9760 -0.1871 0.8521 -822713.4453 681493.0676\n", "PLAYING ROLE_Batsman 45944.5780 131655.5496 0.3490 0.7281 -216444.5538 308333.7098\n", "PLAYING ROLE_Bowler 103751.1060 110824.6388 0.9362 0.3523 -117122.0806 324624.2926\n", "PLAYING ROLE_W. Keeper -89731.6479 187093.4020 -0.4796 0.6329 -462608.2602 283144.9645\n", "CAPTAINCY EXP_1 71431.5146 116029.1349 0.6156 0.5401 -159814.2177 302677.2470\n", "----------------------------------------------------------------------------------------\n", "Omnibus: 6.983 Durbin-Watson: 1.857 \n", "Prob(Omnibus): 0.030 Jarque-Bera (JB): 6.499 \n", "Skew: 0.583 Prob(JB): 0.039 \n", "Kurtosis: 3.376 Condition No.: 12332195260360820\n", "========================================================================================\n", "* The condition number is large (1e+16). This might indicate strong\n", "multicollinearity or other numerical problems.\n", "\"\"\"" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model1 = sm.OLS(ytrain, xtrain).fit()\n", "\n", "model1.summary2()" ] }, { "cell_type": "code", "execution_count": 12, "id": "ad35c43b", "metadata": {}, "outputs": [ { "ename": "SyntaxError", "evalue": "invalid syntax (1120071531.py, line 3)", "output_type": "error", "traceback": [ "\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" ] } ], "source": [ "p-value\n", "\n", "95% 5%\n", "0.95 0.05\n", "\n", "\n", "# VIF (Variance Inflation Factor)\n", "\n", "" ] }, { "cell_type": "code", "execution_count": 13, "id": "81de17e2", "metadata": {}, "outputs": [], "source": [ "from statsmodels.stats.outliers_influence import variance_inflation_factor\n", "\n", "def get_vif_score(x):\n", " \n", " xmatrix = x.values\n", " vif = [variance_inflation_factor(xmatrix, i) for i in range(xmatrix.shape[1])]\n", " \n", " vif_factor = pd.DataFrame()\n", " vif_factor['Column'] = x.columns\n", " vif_factor['VIF'] = vif\n", " \n", " return vif_factor" ] }, { "cell_type": "code", "execution_count": 14, "id": "4368e988", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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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", "
" ], "text/plain": [ " Column VIF\n", "0 T-RUNS 12.612694\n", "1 T-WKTS 7.679284\n", "2 ODI-RUNS-S 16.426209\n", "3 ODI-SR-B 13.829376\n", "4 ODI-WKTS 9.951800\n", "5 ODI-SR-BL 4.426818\n", "6 RUNS-S 16.135407\n", "7 HS 22.781017\n", "8 AVE 25.226566\n", "9 SR-B 21.576204\n", "10 SIXERS 9.547268\n", "11 RUNS-C 38.229691\n", "12 WKTS 33.366067\n", "13 AVE-BL 100.198105\n", "14 ECON 7.650140\n", "15 SR-BL 103.723846\n", "16 AGE_2 6.996226\n", "17 AGE_3 3.855003\n", "18 COUNTRY_BAN 1.469017\n", "19 COUNTRY_ENG 1.391524\n", "20 COUNTRY_IND 4.568898\n", "21 COUNTRY_NZ 1.497856\n", "22 COUNTRY_PAK 1.796355\n", "23 COUNTRY_SA 1.886555\n", "24 COUNTRY_SL 1.984902\n", "25 COUNTRY_WI 1.531847\n", "26 COUNTRY_ZIM 1.312168\n", "27 PLAYING ROLE_Batsman 4.843136\n", "28 PLAYING ROLE_Bowler 3.795864\n", "29 PLAYING ROLE_W. Keeper 3.132044\n", "30 CAPTAINCY EXP_1 4.245128" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "vif_factor = get_vif_score(x[xfeatures])\n", "\n", "vif_factor" ] }, { "cell_type": "code", "execution_count": 15, "id": "21f1c866", "metadata": {}, "outputs": [], "source": [ "columns_with_large_vif = vif_factor[vif_factor.VIF > 4].Column" ] }, { "cell_type": "code", "execution_count": 16, "id": "f6f77c94", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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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", "
" ], "text/plain": [ " T-RUNS T-WKTS ODI-RUNS-S ODI-SR-B ODI-WKTS \\\n", "T-RUNS 1.000000 0.026285 0.892823 0.231411 0.045505 \n", "T-WKTS 0.026285 1.000000 -0.088276 0.012052 0.822940 \n", "ODI-RUNS-S 0.892823 -0.088276 1.000000 0.319264 0.056554 \n", "ODI-SR-B 0.231411 0.012052 0.319264 1.000000 0.160114 \n", "ODI-WKTS 0.045505 0.822940 0.056554 0.160114 1.000000 \n", "ODI-SR-BL 0.067700 0.060641 0.126795 0.284584 0.124361 \n", "RUNS-S 0.410143 -0.218544 0.523955 0.306338 -0.198101 \n", "HS 0.411209 -0.268432 0.495765 0.360086 -0.209109 \n", "AVE 0.374046 -0.265540 0.446280 0.340327 -0.222062 \n", "SR-B 0.114298 -0.147752 0.194111 0.375371 -0.030404 \n", "SIXERS 0.216571 -0.198036 0.376012 0.320041 -0.148722 \n", "RUNS-C -0.253083 0.297302 -0.268950 0.004855 0.327122 \n", "WKTS -0.277157 0.289735 -0.302745 -0.022502 0.302466 \n", "AVE-BL -0.298999 0.162456 -0.225109 0.018958 0.217302 \n", "ECON -0.329022 0.117530 -0.249795 -0.019067 0.230221 \n", "SR-BL -0.309105 0.205208 -0.237517 0.017522 0.259800 \n", "AGE_2 -0.266649 -0.178391 -0.242422 -0.011977 -0.125708 \n", "COUNTRY_IND -0.179278 -0.147490 -0.168224 -0.168609 -0.187669 \n", "PLAYING ROLE_Batsman 0.384422 -0.276450 0.367995 0.083485 -0.358824 \n", "CAPTAINCY EXP_1 0.690647 0.088782 0.714058 0.291373 0.077536 \n", "\n", " ODI-SR-BL RUNS-S HS AVE SR-B \\\n", "T-RUNS 0.067700 0.410143 0.411209 0.374046 0.114298 \n", "T-WKTS 0.060641 -0.218544 -0.268432 -0.265540 -0.147752 \n", "ODI-RUNS-S 0.126795 0.523955 0.495765 0.446280 0.194111 \n", "ODI-SR-B 0.284584 0.306338 0.360086 0.340327 0.375371 \n", "ODI-WKTS 0.124361 -0.198101 -0.209109 -0.222062 -0.030404 \n", "ODI-SR-BL 1.000000 -0.027772 -0.067615 -0.053766 -0.051684 \n", "RUNS-S -0.027772 1.000000 0.834631 0.767023 0.380022 \n", "HS -0.067615 0.834631 1.000000 0.876249 0.531028 \n", "AVE -0.053766 0.767023 0.876249 1.000000 0.583579 \n", "SR-B -0.051684 0.380022 0.531028 0.583579 1.000000 \n", "SIXERS -0.033616 0.866213 0.788439 0.705365 0.425394 \n", "RUNS-C 0.196887 -0.160762 -0.235327 -0.278482 -0.063179 \n", "WKTS 0.151594 -0.220959 -0.291494 -0.343958 -0.069823 \n", "AVE-BL 0.416627 -0.101818 -0.176049 -0.120430 -0.057895 \n", "ECON 0.313295 -0.179459 -0.254494 -0.202301 -0.076228 \n", "SR-BL 0.402495 -0.118567 -0.192990 -0.140809 -0.068248 \n", "AGE_2 -0.133908 -0.085762 -0.022883 0.031758 0.181181 \n", "COUNTRY_IND -0.022494 0.209292 -0.034029 -0.037809 -0.049031 \n", "PLAYING ROLE_Batsman -0.014689 0.418555 0.408826 0.432485 0.157903 \n", "CAPTAINCY EXP_1 0.103315 0.352422 0.392749 0.378271 0.176919 \n", "\n", " SIXERS RUNS-C WKTS AVE-BL ECON \\\n", "T-RUNS 0.216571 -0.253083 -0.277157 -0.298999 -0.329022 \n", "T-WKTS -0.198036 0.297302 0.289735 0.162456 0.117530 \n", "ODI-RUNS-S 0.376012 -0.268950 -0.302745 -0.225109 -0.249795 \n", "ODI-SR-B 0.320041 0.004855 -0.022502 0.018958 -0.019067 \n", "ODI-WKTS -0.148722 0.327122 0.302466 0.217302 0.230221 \n", "ODI-SR-BL -0.033616 0.196887 0.151594 0.416627 0.313295 \n", "RUNS-S 0.866213 -0.160762 -0.220959 -0.101818 -0.179459 \n", "HS 0.788439 -0.235327 -0.291494 -0.176049 -0.254494 \n", "AVE 0.705365 -0.278482 -0.343958 -0.120430 -0.202301 \n", "SR-B 0.425394 -0.063179 -0.069823 -0.057895 -0.076228 \n", "SIXERS 1.000000 -0.080295 -0.144558 -0.012210 -0.101027 \n", "RUNS-C -0.080295 1.000000 0.959210 0.409505 0.410502 \n", "WKTS -0.144558 0.959210 1.000000 0.297822 0.374875 \n", "AVE-BL -0.012210 0.409505 0.297822 1.000000 0.722758 \n", "ECON -0.101027 0.410502 0.374875 0.722758 1.000000 \n", "SR-BL -0.021412 0.471224 0.368264 0.983415 0.726480 \n", "AGE_2 -0.043516 0.025722 0.044467 -0.072386 0.004942 \n", "COUNTRY_IND 0.132099 0.266465 0.216827 0.149050 0.065552 \n", "PLAYING ROLE_Batsman 0.294896 -0.436411 -0.422958 -0.366341 -0.288471 \n", "CAPTAINCY EXP_1 0.254991 -0.277903 -0.300074 -0.183775 -0.248974 \n", "\n", " SR-BL AGE_2 COUNTRY_IND PLAYING ROLE_Batsman \\\n", "T-RUNS -0.309105 -0.266649 -0.179278 0.384422 \n", "T-WKTS 0.205208 -0.178391 -0.147490 -0.276450 \n", "ODI-RUNS-S -0.237517 -0.242422 -0.168224 0.367995 \n", "ODI-SR-B 0.017522 -0.011977 -0.168609 0.083485 \n", "ODI-WKTS 0.259800 -0.125708 -0.187669 -0.358824 \n", "ODI-SR-BL 0.402495 -0.133908 -0.022494 -0.014689 \n", "RUNS-S -0.118567 -0.085762 0.209292 0.418555 \n", "HS -0.192990 -0.022883 -0.034029 0.408826 \n", "AVE -0.140809 0.031758 -0.037809 0.432485 \n", "SR-B -0.068248 0.181181 -0.049031 0.157903 \n", "SIXERS -0.021412 -0.043516 0.132099 0.294896 \n", "RUNS-C 0.471224 0.025722 0.266465 -0.436411 \n", "WKTS 0.368264 0.044467 0.216827 -0.422958 \n", "AVE-BL 0.983415 -0.072386 0.149050 -0.366341 \n", "ECON 0.726480 0.004942 0.065552 -0.288471 \n", "SR-BL 1.000000 -0.090249 0.126109 -0.395188 \n", "AGE_2 -0.090249 1.000000 -0.002036 -0.170277 \n", "COUNTRY_IND 0.126109 -0.002036 1.000000 0.071734 \n", "PLAYING ROLE_Batsman -0.395188 -0.170277 0.071734 1.000000 \n", "CAPTAINCY EXP_1 -0.198688 -0.109261 -0.192539 0.314284 \n", "\n", " CAPTAINCY EXP_1 \n", "T-RUNS 0.690647 \n", "T-WKTS 0.088782 \n", "ODI-RUNS-S 0.714058 \n", "ODI-SR-B 0.291373 \n", "ODI-WKTS 0.077536 \n", "ODI-SR-BL 0.103315 \n", "RUNS-S 0.352422 \n", "HS 0.392749 \n", "AVE 0.378271 \n", "SR-B 0.176919 \n", "SIXERS 0.254991 \n", "RUNS-C -0.277903 \n", "WKTS -0.300074 \n", "AVE-BL -0.183775 \n", "ECON -0.248974 \n", "SR-BL -0.198688 \n", "AGE_2 -0.109261 \n", "COUNTRY_IND -0.192539 \n", "PLAYING ROLE_Batsman 0.314284 \n", "CAPTAINCY EXP_1 1.000000 " ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x[columns_with_large_vif].corr()" ] }, { "cell_type": "code", "execution_count": 17, "id": "8eb9de04", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 1.0, 'Correlation between Features')" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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UwW3scFCpSN68g4Q164zO2wU0xGXoQNDpULRa4j9bTvrFKwA49e6OQ+d2gELmnfvEzFwEGZkW8+/Yg2gWH7qBTqfQpXIRBtUpaWJz9nEsiw/dRKPT4WpXiFVv1gHg15CH/HnlCYoC3SoXoU/N4hZ1JP8eshNQgCiKEgNUB/MN+hwcURSlgxDCDggRQmxSFOVYPiTSgW5CiPmKokTnOvcV8LmiKFsM+lX+Vz/M0aVdS3p378Sk2ebceTHtZg/g5z7zSQiP5b2ts7m57zxRt7MbgGWDquFe0pevAsZQpEYZ2s8ZyMou09FpdeyZs5awKw8o5GDLkG1zuHf0ClG3n1KiQUXKt6zFN20mos3Q4OBh/Kp3rU7HvB/+5NvJQ/DxcKH3xC8IrF2J0kV8s2zW7z5GqSI+LJ3wLrEJSXT+eAHtm9TkQWgUG/efYu28kVhbqRk273ua1KxAcT8v8w6qBOUWvEvIW3NID42h9u75RO0+S8qtbB9TH0ZyvssMNM+ScW9WnXKfvc+5tpPRpWcS0m0m2pR0hJWamn/NIubABRLO3c4KK1SCNrMH8KshDwdtnc3tfeeJzpGHpQ15+E3AGPxrlKHNnIGs7jI963ydQW2IvhOKjaNd1rHiDSryRstafG/IQ3sPZ4RK0GJOf37vs4DEsFje+WsWd/eeI+Z29ntLSgZVw62EL6uajsGvRmlazh3A2s4zAGg2oy/3gy+xdehXqKzVWNvZABAwqRcnvviT+8GXKBlUjYBJvWDMEUP+qXAZO5KYkePQRkbhtWoFaUeOo3nwMFc+q3Ae9j7pp84YHU7ZsYvkPzbhOu3FjVdUKpxHjyR2lF7Lc+UK0o+a13L64H3STxtrpe7YRfLGTbhOyZ+W+8QPiRg6AU1ENP5rl5Fy6ASZ9x5lmaSdCiE0+AQA1mVL4r1oCk+7vovm4RNC3x6aFU/RPb+RfMByNSFUgkZz+rOj9wKSw2Lpsn0WD/ecIz7HdUuPT+b4tJ8p0bqWSfhbvx/m6uq9BH4xJB9+CSosGMS5t+aSFhpD/d3ziNp9jmSj8h7FmS6z0DxLxrNZdSp99j6n2k5B0Wi5Of1nEi8/QO1gS/2984k5dMkobE4aN29AsVJF6NzgbarUrMSkhWPp1850fGPnn3uYPHwmAPO/mUHXPh35fc1mAEJOXWRk3/EvdKtJ84YUL1mUtvV7ULVWZaYtGk+vtu+a2G3buJsJw/T31uIVs+nepzPr1/xJr0E9uHvzPsP7jsXNw5XtxzawfeMuMjM1eeoGtGhE8VJFaVG3C9VrVWbWoon0aNPfxG7elCUkJSUDMHHWKN55922++2r1C/0KatGEEqWL07R2e2rUrsrcz6bQuWUfE7tZkxeRlKiPf+qccQwY3JvlX67i2OGT7N15EIDyFd9g+Q+f0qx+J7NagS0aU6JUMZrV7Uz1WlWYvXgS3Vr3M7GbM+XTLF8mzx5Dv3d7suKrH3kW/4xZkxbSsm1Qnj41bt6A4qWK0qHBm1StWYkpC8fTp91gE7vtf+5m4vAZACz8Zibd+nRiw5pNrF6+ltXL1wIQ0LIxfYe8bbEDUDeoDoVLFqZ/k4FUqFGekfM+5MNOI03srp69ysn9p/hswyKj4536d+Lh7UdMHTQdF3cXfjy0iv2bDqDJXS5UKtwmfETk8PFoI6Lw/Wk5KYdPoLmfXTelnT5P6qHjAFiXKYXngqmE9RiI2ssTp7e7EvbWIJT0DDzmT8WhVTOSt+3GHFqdwoKD1/mmWy18HG3p89tJAkp5UdrDMcsmMS2TeQev83WXmvg52xGbkg7AnehE/rzyhJ971sdaLRi+6TyNS3pS3M3BrNa/htwTIPcE/JdRFCUVuAAUzmcQDfrXUo8yc84P/Rvnnsd9+e+mLye1q1fBxdnpfworrGyIfRBB3OMotJlarvx1knItjRsh5VrW4uJGfYPwScgdbJ3tcfR2JSkynjDDCHNGchpRd0Jx8nEDoM47zTm6fCvaDH1FmhxjXIFfufOIoj4eFPHxwNrKijYNaxB85qpx2hCkpKajKAopaem4ONqjVqm4/zSSqmWLYWdTCCu1mloVS3PgtOUsda5ZhpT74aQ9jETJ1BK5+ThebeoY2SScvYXmmf5HL+HcbWz9PLLOaQ2Vq7BWo7JSg2L8tnD/6qWJfRBB/OModJlarv11kjdy5eEbLWtxyZCHoTnyEMDJ150yzapzYd1BozA132nO8Rx5mBKTgG/10sQ9iODZI73Wjb9OUrqVsVaZVrW4uvEoAGEhd7FxdsDB25VCjnYUqVuOy+uCAdBlaklPSAFAURQKOek7IDZO9iRFxGXFZ12xPJonoWhDw0CjIXXfAWybNDLJZ4ceXUk9eARdXLzR8YwLl9AlmP8Bz411hfJoc2nZNDbVsu/elbRDZrQuXkLJp5ZN5XJoHoeieRoOGg3Ju4OxD2xoZKOkpmV9VtnZ5r70ANjWq0HmkzC0YZEWtbyqlybhQQSJhut2d8tJiue6bmkxCURfvIdOY/oSnfBTN0mPT8qXXy6G8p5qKO/hm4/j3aa2kc2zHOU9/txtbPzcAciIjCfx8gNAPyOWfPspNr7uFrUCWjdm24ZdAFw+fxUnZyc8vT1M7I7uP5H1+UrIdbz9vPPlS06atWnK1t/1sxKXzl2xqHVk//Gsz5dDruLjr9dSFHBwtAfA3sGOZ/EJaMzkdW5atAlg8/rtAFw4dwUnF0e8fDxN7J43mgFsbW1N6glLtGoXxMZ1+pmakLOXcHZ2wttc/Ik547dBMcSfc7bB3sEOBcu6LdoGsGnDNoMvl3F2ccqHL9laMdFxXAq5hkaTd8cpqHVT/tpguFbnr+Lk7PjCcnE55Do+ZspF264t2blpr0Wthq0asHfjPgCuh9zA0dkBd2/TMnvn6l0inkSYRqAo2BsGX+wcbEmMT0RrplwUqlQezeOnaJ/q66aUPQexD7BcXwi7XGVArUbY2IBahcrWFm1U7rHCbK6EP6Ooiz1FXOyxVqto/YYvwXeN65edN8NoXsYbP2d92t3t9QM692OTqeLrip21GiuVilpF3Dh413LdJPn3kJ2A/zBCCDegLHD4JYJ9DfQRQrjkOv45cEAIsVMIMUoI4fqKkvn3UVmREBaT9TUhLBZnXzcjE2dfdxJCc9iEx+LsY2zjWsQTv0rFeXrhLgAeJf0oVrc8gzfPZMD6KfhXLWVkHxn7DF8P16zv3h4uRMQ9M7Lp2aYR955G0GLoTHqM/ZTxA7qgUqkoU9SXczfuEZ+YTGp6BkdDrhMeE2/RRRtfd9JzpD89NCbPho1f72bEHMgxna4S1Nm/iMZXVxJ76DIJ542nmp183UnMlYdOufLQyUwePu8wtZzelwPzfkPRGf94P8/DAZtn8s76KfhVLYWTrxuJobFZNklh2fE8x9HXzSg9ieGxOPq64VLMi5TYRNp89j59d8yh1cLBWTMBB2f+QsCkXrx/8ksCpvTiyMLsZTFqL0+0Edk/ItqoKNRexg0HlacntgFNSNlsvOzkZVF7eaKNzNbSWdJq+gq0vD3RhEdlfddERKP2Nm0Q2Qc1ovCmVXgvnUPMDNPZNofWgSTvPGhy3MjGz42ksOzrlhwei4OfWx4h/ndsfd1Jy1HW0kJj8yzvhXsHEX3ggmk8Rb1wqlyCZ+dNl1Y8x9vPi/DQ7OsVERaJt6UZOfRLO9r3aM3xg6eyjlWtVZn1+1ez7NdPKVXOdMmDkdbT7EZcRFgkPi/Q6tijLUcPnATg11W/U+qNkgRf2s7m4F+ZP+XzrMZtXvj4eRMWmq0bHhqJj6953QVfTefE1T2UKluCn1auN2uTG18/b8KehueIPwJfC52kT5fN5tyNYEqXLcmP3/+adbx1+2YcOLmV1eu+ZtyH016J1qKvZnD62j5KlS3BmpXrzNpYQl8ucl6rqBeWi4492nDs4Emj47Z2NjQKqs/e7cEWw3r6ehIVmn0fR4VF4+lr2uGwxObVWylWphjrz/7K93u/Zfn0b8yWC7W3J9qIHPVFZJTZ+sIusBF+f/yI1xdziZmlry+0UdEk/vI7/tt+o/Cu39ElJZF26pzFNEUmp+HjZJv13cfJlqjkdCObh3EpJKRpGPz7GXr/eoK/rulnFUt7OnL+aRzxqRmkZmo5ej+a8MQ0/nMouoL7+48iOwH/TZoIIS4B4ej3BDyvMS39WmQdVxQlAfgJ+MjIQFF+BCoAv6N/K91JIYRN7oiEEO8LIc4KIc6u/Mn8OuiCwKQCFHnbFLK34a0VH7Nr1s+kJ+lHpVRWKuxcHFjZZTp75/3Km8s/zBXeNM7cMscv3qR8icLsWzGdDYvGMP+HTSSlpFGqiA8DOzVjyJxvGTbve94o7o+VWm3ZIWHqgKXRMtdGlfDvHcSd2WuzD+oUzjQfz/HqQ3GuWRqH8kUta2X5Zxy/mSSgKAplmtUgJeaZ0f6ArDBWKmxdHFjdZToH5v1Kt+UfmvfF5HKZFUNlpcancgku/Lyfn9tNITM1nbrDOgJQvW9zDs5ay3f1RxI8ay2tF79nFOOL/HP5eDgJy7/9+1O85jPK6KvzyOEkrigYLYCUg8d42vVdIkfNwHXYAOOTVlbYBzQgee+hF4mZ0cp3Sl8OM1KWxNwaVaRw7yBuz/7V6Lja3obqq0Zxc+oatEmW17ULs+XRsmMTF4zl/MmLhJy6CMCNSzdpV7s7bzcfwLpVG/n8x/mWtcyWQ4vmTF04nnMnL3D+1AUAGgfV58aVWwRWbU/3Zn2ZPH8sDo4vXiLxMj5+8tFMGlVpw91b92nfpeUL4zYImInfvOnYEVOpU7EZd27do2PXNlnHd28/QLP6nRj8zkjGThyRh1T+fRn/0QzqV27F3Vv36dCl1QucyK1jeiyvcjF5wTjDtbpodDygVWMunLlkcSnQ/6KVm9oBtbh77S5v1+7NkDbDGDF7OPaGGaMXYkYnNfgYYT0GEj12Gq5DB+jT6OSIXUBDQjv14WmbtxB2dti3bZFHvC+W1ioK1yMTWNqlBl93rcX3p+/xMC6ZUu6ODKhdgg/+PMfwzed4w8sJK5XZSkHyLyM7Af8yQoiuQogLhr/n8+VHFEWpClQBPhBCVDccjwFyD905AfG5jn0BvAsY/booihKqKMoPiqJ0Rr90qHLu9CiK8p2iKLUVRak9uF+eL7B7deg0OOdY+uLs505iRLyRSUJYLM7+OWx83UmM1NuorNS8teJjLm8+xvVdZ43CXN+lX6/99OI9FJ2CvXv2kiUfDxej0fvImGd4uxlPoGwJPkPzulUQQlDM15PC3u7cN4w6dmtWj/ULR/PjzOG4ONpTzNd0ROY56WEx2ORIv42/BxnhcSZ2DhWLUWHJEC71X4wmznTphSYhhbhj13APqm50PDE8FqdceZiUjzxMioynSO03KNuiFsOPfkHXpSMo0bAinb74QB9vWCw3DHkYasjDjOQ0nPyzR3Ud/dxJijT2JXd6nHz16UkMiyUxLJZww2zNrR2n8alcAoBK3Ztwe6de6+a2U/hWK50VXhsVhdone7RQ7eWFLjp7pBn0m3TdZk3De+Nv2AYF4DL2Y2ybmi7jeRHayCjU3tlaKi8vtLm1ypXDdcY0vH7/DdvAAJzHfIyNmeVJL9SKiMIqx4iulY8n2qgYi/bp5y9jVdQPlWv2/ha7xnXIuHEHXWx8nlrJYbE4+mVfNwdfd5LNlMFXQVpYLLY5ypqtvzvpZrQcKxaj0pIhXOj/KZk5yruwUlPth9GEbTxK5I4zJuHeGtiNdftWs27faqLCo/H1z75ePn7eRIWbX+bw/piBuHm48tn0r7KOJSelkJqi72Qc3X8CK2srXN2z64FeA3uwcf/PbNz/M1ERUfgW9jHSiswxk5OTD8a8i5uHGwunfZF1rEvPDlkjyo8ePOHpo1BKlTW/YbLPoDfZevBXth78lYjwKPz8s3V9/b2JjLC8lEOn07Fjyx5ad2hu0abfuz3Zeeh3dh76ncjwSPwKZ++F8vX3ISLc8vINnU7HX5t2066jaSPy9IlzFCtZBDd316xjfQe9xbaD69h2cJ3eFxMt83n4XGvb5j206WjZl+e8PbA7G/atYcO+NYZykfNaeVksF0PHDMLNw5XF0780Odems/mlQJ36d2TFruWs2LWcmIhYvPyz72MvP09iImJNwliizVutOLJTv58n9EEo4Y/DKVrGdKBHGxmN2idHfeHtlXd9EXIZqyL+qFycsa1bE01oOLr4Z6DVknrwCDZVK1oM6+1oS0SO0fuIxDS8HGxMbBoW98DO2go3u0LULOzGrahEALpWLsJvfRrww5t1cbG1pphrPjs1BYlOW3B//1FkJ+BfRlGUTYqiVDf8nc117hYwH5hgOHQY6CSEcAIQQnQDLiqKos0VLhbYgL4jgMG2jRDC2vDZF/AAzO+0K2AUTToeJX1xLeqF2lpN5Y71ubnXeJry5r7zVOveBIAiNcqQnphKkqET0HnRe0TfecqJlcZPELmx5xwlG+orOY+SvqitrUiJTcw6X6l0UR6FR/MkMoZMjYZdx0MIqF3JKA5fT1dOXdFvwI2JT+RBaCRFDGs9Y57p4wqLjmP/6Uu0bVTDoo+JIXexL+WHbTEvhLUa7y4Nid5tdLmxKexBlR/GcnX4MlLvhWUdt/ZwwspZX4GqbK1xb1qFlDvGly704j3cS/riUtQLlbWaih3rcytXHt7ed56qhjz0z5GHwYvWs7T+h3zd+GM2fbiMB8evsfXjbwC4teccJQx56G7Iw4dHruCWQ6t8x/rc3XveSOvu3vNU6t4YAL8apUlPTCE5Mp6UqGckhsXiVsoPgOKNKhFj2LycFBFH0foVACjWqBJxD7KXDGRev4FVkcKo/XzBygq7Fs1IO3rcSDOyR28iu/cisnsv0g4e4tmnX5B2OD/76Y3JvHEDdVFjrfRjxlpRb/Um6s1eRL3Zi7TgQyR89gXpR15eK/3qTayKFcbKX6/l0DqQlEMnjGysivpnfS5UvgzC2hpdjlFJxzZBJO/KeykQQNTFeziX9MXJcN1Kd67Po1zX7VWREHIX+1K+2BnKu2+XhkTuNi6PtoU9qP7DaC4P/5qUHOUdoNLnQ0i+/ZSHOZ5OlJMNP/5JzxYD6NliAAd3HabDW/oR6So1K5GUmER0pGnDqGvvjjQMrMfED6YbjdJ6eGV3jCrVqIAQgvjY7GWBv/34B92b96V7877s33mYTm+2BfRLiCxpde/TiUZB9Rk3dKqRVtjTcOo3qZ2lW6J0MR4/NF8Nr/3hdzoF9aZTUG/27Qymy9vtAaheqzKJCUlEmekEFCtZJOtzUKum3L39wGzcAD+tWkfbgDdpG/Amu7cfoHtP/UbeGrWrkpiQZLaTUbxkdsO0RZsA7ty+b3K8ctUKFLK2Ji5Hp/TnHzbQIagnHYJ6snfHQbq+1cHgSxWLvuSMs3nrvH15zvofN/JWi/681aI/B3YdpuNbhmtVsxKJiclmr1W33h1pGFifCbnKBYCjkwO1G9Tg4G7TFblb1/zF0DbDGNpmGMd2H6dld32HqEKN8iQnphAbmf9OQGRoFDUbVQfA1dOVoqWLEPYwzMQu49oNrIsWRm2oL+xbBZF62LhusiqSXV9YlysL1tboniWgDY+kUOUK+j0BgE2dmmQ+eIQlKvk68yg+hafPUsjU6th9K5zA0sbLtgJLexESGo9GpyM1U8uV8HhKuuvHHp9vEg5LSOXAnQjalPPLd35ICg75dKD/PiuAsUKIkoqiXBJCLAOOGh4HGgmYPu5Az2dAzjnZVsCXQojnXftxOZYZ/W3GTV/AmZBLxMcn0LzLOwx7ty/dO7bOd/gd01bT96cJCLWKkA2HiLr9lNp99CM/Z9fu5/aBC5QNqs5Hh5eQmZrBlrHfAlCs9htU696EiOuPGLpjHgD7F6/n9sGLhGwIpvPi9xm2ZwHaTA2bxxg/utRKrWbioG58MO87/SPQAutSpqgvG/bqK9W3Wjbk/W4tmfrNOrqPXYyiwMd9OuDmrH86wpgla3iWmIKVWsWkQd1wzmP6VtHquDXxB6qvm4xQqwj97SDJN5/g308/XR/6015KjumBtZsj5RbqL6mi0XK29UQK+bhR8avhCLUKVILILSeIydV4U7Q6dk9bTa+fJqBSq7i44RDRt59S05CH59fu586BC5QOqs4wQx5uM+RhXlzYEEyHxe/z3p4F6DI1bB2zAkWrY//UNXT/eTwqtYrL6w8Rc+sp1d5pBsDFXw5w78AFSgZVY/CRz8hMzWDX2O+y4tw/bQ3tv/oAtbUV8Y8is87t+WQVQTP0j4nVpmey95NVZI39aXU8W/IVHp8vArWKlG070dx/gH0X/VKilM2mjyjNievMKdjUqI7K1QWfzRtIXLmalG3mG5hodSQs+Qr3JYtApSJ1u0Grs0Frywu0ZkyhUHW9lvefG0hctZrU7Za1Yhcsw+eb+fpHhG7ZTebdhzj10DeSEv/Yhn3zJjh2bAEaLbq0dKLGz8kKLmxtsK1fi+g5X+SZJtCXkeNT19B27XiESsXN9YeIu/WUCobrdv2XA9h5udBlx2wKOdqh6HRUHtyGP4ImkJmUStCy4fg3qICtuyO9znzF+c82cnOd+SVIilbHjYk/UnPdJIRaxVNDeS/ST99QevLTPkqN6Y61myMVFuofo6potJxqPRnXuuXwf6spidceUn//AgDuzFtH9P4LZrWO7jtB4+YN2HpyA2mpacz4eF7WuaVrP2XW6AVERUQzadFYwp5EsGabvrw9fxRoi45BvNm/K1qNhrS0DCYOnW5WB+DwvmM0bd6Qnac2kpaaxpSRs7POfbP2c6aNnktURDTTFk0g9Ek4v25fCcC+7cF8s2QVK5b8wNyvprEpeC1CCJbM/tqow2GJ4L1HCWjRiP2nt5CamsYnH83IOvf9b18y+ePZREXGsGjZTBwdHRECbly9zfRxlpc25eTA3iMEtWzKkXM7SE1NY+yI7EfPrl6/nAkjpxMZEc3ny+fi6KSP/9qVW0weq/e/XceWdO/ZkcxMDWlp6Qx/d5xFrYN7jxLYojEHz2wlLTWN8Tl8+eG3pXwyahZREdEsXjYLJycHEIIbV28xdaz+unp6e7Bl31ocnRxQdAoDh/ShdcPukGY82npk33GaNG/I9pO/k5aaztSPs++br9d+xozR84mKiGbKovGEPQnnZ0O52L/jEN8u+QGAZu0COH7oFKkpea9nP3XgNHWb1eGnoz+SnprO4jGfZZ2bu2Y2S8Z/TkxELF0GdubtD97E3cud7/au4PSB0ywZ/wW/fLmWcUvG8v3eFSAE389blfX4UCO0OmIXL8V76UJQq0jeupPMew9x7K6vL5I2bsOueVMc2rUEjQYlPYOYifprlHH1Bqn7D+O7dgWKVkvmzTsk/bndok9WKhUTgsozbNN5dIpC50qFKe3hyO+XHgPwZtWilHJ3pGFxD9765QQqAV0rFaGMp362fey2i8SnZWKlEnwSVAFnW+s88/Bf4T+8Vr+gEC+zbk3yepEZfa/ACsfcWlMLSopPthTMMqfjrX8qEB2AEwVYwdooBbe2s3eRgpmsEgU4J5qeVHBjL3ujfV5s9Ioo/oLHXL5KJojHBaKToRScTxm6gtNK12YUmJaVqmDKu6OV7YuNXhHe1s4vNnpF/OBTcOXC813Ly4NeNfYfLP3XNwlkPDhbYG2cQiVq/+v+mkMuB5JIJBKJRCKRSF4z5HIgiUQikUgkEsnrhXxZmJwJkEgkEolEIpFIXjfkTIBEIpFIJBKJ5LVCkRuD5UyARCKRSCQSiUTyuiFnAiQSiUQikUgkrxdyT4CcCZBIJBKJRCKRSF435EyARCKRSCQSieT1Qu4JkDMBEolEIpFIJBLJ64acCZBYpCDf4jv53OwC0xpc2/Ir7V8lRexsCkQHYMqqgALTWjrocIFpdX9YMDqJmtSCEQJUouBeHFm8UMGVwUTrtALT+kHlWSA6SoG9TxRuaxwLTCvBpuDKYOECepP0Euu4AtEBsBMF94b2to/jC0xr6LyCy8MPPygwKcvotP92Cv515EyARCKRSCQSiUTymiFnAiQSiUQikUgkrxdyT4CcCZBIJBKJRCKRSF435EyARCKRSCQSieT1Qr4nQM4ESCQSiUQikUgkrxtyJkAikUgkEolE8noh9wTImQCJRCKRSCQSieR1Q3YCJBKJRCKRSCSS1wy5HKiAEUJ4APsNX30BLRBl+F4XqACsURSlusG+F7AKcFEUJVMIUQVYqyhKVSFEMDBWUZSzQogSwF5AA6RbiHsc0NtwXAcMURTlVF7pLRNQlTbT+6JSqzi/Lpij3/xlYtN2Rj/KBlUjMzWDzWO/JezKA5z93On6+Qc4ermg6BTO/XqAUz/uzgpTd0Ar6vZriU6r4/aBC/nOP4Ap85Zw+Nhp3N1c2fzLipcK+5w+0wdRLagmGakZfD92KQ+v3jex8SzizbBlo3BwceLh1Xt8O+ortJka7JzsGfL5SDwKe6JWq9n5/RaO/H4QgNbvdiDg7RYoikLMzSdc2nqcNhN7o1KrOLv+IIfN5F/76f0oF1SdzNQMNo5dQejVBwB0W/Q+5ZrVIDkmga9aT8iybzOxN+Vb1ESboSH2UQQbx31rEuexq/dZ9PtBdIpC14aVGdS6ntH5xNR0Jv+4g/C4RDQ6Hf1a1KZLg8qExyYwZc0uYhKSESpB90ZV6dOspkn8JQOq0nx6X4RaxaV1wZwy41fzGX0pFVSdzNR0do79jogrer9snO1ps3Awnm8UARR2jvue0PN3KNeuLo1GdcOjjD8/d5pO+GXTawIwataHNGxWj7TUNGaPWsitK7dNbGYsnUz5am+gydRy/cINFkz4DK1GS6uuLeg7rCcAqSmpLJr4BXeu3TWrM3HuaJo0b0BaajqTP5rN9cs3TWwWLJ9JpWrl0Wg0XAm5xsyxC9BotAwc1of23VsDoLZSU6psCZpUbEtCfIJZrQlzRtGkeUPSUtOYOnI21y/fMrGZ//WMLK3LIdeZPU6vNWBYH9p1awWAlZWakmVLEFCpnUWt92e+T62g2qSnpvPlmC+4e8XU//b9O9Dp3U74l/CnT7XeJMTp46pcvwpTVk4h4nEEACd2HWfdl+vM6uTmw1nDqd+sLmmp6SwYtYjbV+6Y2ExeOpFyVd9Am6nh+oWbfPbJ52g1L/dCH6eAGhSZ8R5CrSJm3V4ilm80Ou/Ssi5+Y/ug6HSg1fFk5kqSz1zPV9zOgfq4UauI+c00brcuAfgO6waANjmNx5O+IfX6AwC8BnXAs3crQBD92x6iVpneMznxDapKjVn6e+zer8HcWGZs71TGj7qfD8GtSgkuL9jAzRU7ss5ZO9tT57P3cClfBBSF06O+I+acaX4/p0hgVRrM1Gvd/C2Yi18ba7mU9iNgyft4Vi7BmUW/c/lbvZaDnzuBXw7FzssFdArXfz3I1VW7zUlk4RlUjYpz+iPUKh6vPcC9pVuNzvt3b0SpEZ0MeZjOlfErSbz2CIAqXwzBu2VNMqITOBLw4pc+Dp/5AXWb1SU9NY1Foz/jjpky17l/J7oN7krhEv50q/pmVll3cLLnky8n4F3YG7Vaze/f/cHuDXssahXkfTVx7miaNm9Iamqaxbpp4fKZVKpWwVBfXGPm2PloNFocnRxYuHwmfoV9UavV/PjNWjav2wZAscCqNJ2hLwfXfgvm3HLTMtp0Zl+KN6uOJjWdfaO/I8pQr1cf3IaKPQMBhZgbT9g35ju06Zl4VihG4PyBWDvYkvg4it0ffUNmUsG9nDFP5MZgORNQ0CiKEqMoSnVDI38F8Pnz74qiZACXgeJCCCdDkIbADaBGju/HcsYphCgC7AbGKIpSwVzcQC2gA1BTUZSqQAvgcV5pFSpBu9kDWNt/EV+3GE/lTg3wKlvYyKZsUDXcS/ryVcAY/pq4ivZzBgKg0+rYM2ctXzcfz8ou06nbr2VW2BINKlK+ZS2+aTOR5S0ncPy77S+Vh13atWTFkjkvFSYnVQNr4lvSj/GBI/hx0jf0n/u+Wbu3P+nL7lXbmBA0guRnSQS83RyA5n3bEHrnMVPbjmF+z2n0nNwftbUVbj7utBzQjukdxzO59ShUKhVdF7zHmgGL+LLlOKp2aohXGeP8eyOwOp4lfVkSOJrNk1bSae6grHPn/zjMmv4LTdJ15+hlvmo1nqVtPyH6fhgBwzoZndfqdMxfv5+vR3Tjz6kD2HX2JnfDYoxs1h+6QCk/DzZM7sfKj99iycZDZGq0qNUqxnQPYNP0gfw8rjfrD18wCStUghaz+/N7/0WsajGeCp3q41HW38imVFA13Er68n3AGHZPXEXLOQOyzjWf3pf7hy6xqvl4fmwziZg7oQBE3XrC5iFf8viU6Q/acxo0q0fRkoV5s/E7LJjwGePnjzJrt3vTPno27c87zQdRyLYQnXq3ByDscRjDenxM35aD+eGLn/lk4Riz4Zs0b0CxkkVpV/9NZoydz9RF483abd+4i46N3qZrQB9sbG3o3qczAD8uX0uP5v3o0bwfX8z9hrMnQiw2yhs3b0DxUkXp0OBNZo1dwJSFFrT+3E2nxj3pFvgOtraF6NZHf91XL1/LWy3681aL/nw5dwXn8tCqFVQb/xL+DGn6Pl9/sowP5g4za3f97DWm9s5ulOTk2pmrjGz7ESPbfpTvDkC9ZnUpUrIwfRr357MJnzNq/kizdvs27adfwEAGtngPG9tCtO/VLl/xZ6FSUXTOEO72n8n15iNw69QE27JFjUwSj13iRuuR3Gw7iodjv6LYwhEvFfedfjO53mwEbp1N4854HMGtNydxvdVIwr9cT7GFwwGwLVcMz96tuNFhLNdbj8SleR1sSvhZlBIqQa15AzjcZxG7AsZTvEsDnN8wrjsy4pIJmfITN1eY1p81Zvcl/OBFdjYZx+7mE0m4HZqnVqM5/dnVdxF/BI2ndOf6uOa6n9Pjkzk+7WcufbvD6LhOq+PkrF/5I2gCWzrNoFL/FiZhjVAJKi0YxJneCzjcZAz+XRvhmMuvlIdRnOwyi6NBE7iz5E+qfJZdPz9Zd4gzPedbjj8HdYPqULhkYfo3GcjnE75k5LwPzdpdPXuV8b0+IfxxuNHxTv078fD2I4a0/oAxb41jyNT3sbI2P25akPdVk+YNKV6yKG3r92DG2AVMs1A3bdu4mw6N3qJLQG9sc9RNvQb14O7N+3Rr9g4Dun3A+BkfYW1thUqlInBOf7b2W8TaZuN5o3N93HJdy+JB1XAt6cvPTcZwYMIqAucNAMDB142qA1uxvsNUfm0xEaFSUbZTfQCaLR7M8QXr+a3lRO7uPkvNoe0t+iYpeGQn4D+Goig64AzwfOi2FvA1+sY/hv/HcwTxBfYAUxRFMR5SMcYPiFYUJd2gE60oiuVfBqBw9dLEPogg7nEU2kwtV/46SbmWtYxsyrWsxcWNRwB4EnIHW2d7HL1dSYqMJ8wwQpCRnEbUnVCcfNwAqPNOc44u34o2Q/86+eQY8w0WS9SuXgUXZ6cXG1qgZqs6HPvzEAB3Q25j7+SAi5eriV2FhpU5s+MEAEc3BlOzVV3DGQVbBzsAbOxtSY5PQmcYrVSp1RSyLYRKrcLJx41noTHEPY5Em6nl0l8nqNDKOP8qtKpFyJ/6/HsccgdbJ3ucDGl5cPoGKc+STNJ158hldFpdVhhnXw+j81cehFPUy5Uinq5YW6lpXascwReNR8AEkJyWgaIopKZn4uJgi1qlwsvFkQrFfABwsC1EKV93IuMTjcL6VS9N/IMInj2OQpep5fpfJymTq1yUaVmLqxuPAhAWchdbZwccvF0p5GhHkXrluLQuGABdppb0hBQAYu+EEnsvzMTfnDRt3Yidf+hH466ev46jiwMe3u4mdicOZE9wXb9wA28/LwAun71KoiFPr56/hrefp1mdoDZN2fq7vsFz6dxVnJwd8fT2MLE7sv9E1ufLIdfw8fc2sWnXtSU7Nu216FNQ66b8tWGnXuu8Za2jRlrX8fEz1WrbtSU789Cq36oeBzYeAOBmyE0cnB1w83Yzsbt39R6RTyItxvOyNGrVkN1/6NN17fx1HJ0dcTdz3U4dOJ31+fqFm3hZuD6WsK9elvQH4WQ8ikDJ1BD31xFcsu5bPbqUtKzPKntbUJR8xe2QO+6tpnEnn7uB9lmy/nPITaz99NfRtkwRks/fQknLAK2OpFNXcG1T36KWe43SJD6IIPmR/h57tOUkhVsb32PpMQnEXryHLtN4psTK0Q6v+uW592uw3t9MLZmGe8wcXtVLk/AggkSD1t0tJymeq55Ki0kg+uK9rHruOamR8cQY6vnM5DTibofi4Gt6XZ/jWrMMKffDSX0YiZKpJWzzcXza1DayiT97C40hD+PO3cbWLzu+uJM3yIxPthh/Thq2asDejfsAuB5yA0dnB7Nl7s7Vu0Q8MW2UoyjYO+rreTsHWxLjEy3OShXkfdWsTVO2/m6oL85dwcnZyULdlN1MuBxyNatuUhRwcLQHwN7BjmfxCWg0WqrUrEj8gwgSDOXg1taTlMpVDkq1qsV1Q70eEXIXG2cH7L1dAVBZqbGyLYRQq7CyK0RyRBwAbqX8CD15A4DHh69Qpm2dv+X/q0RRtAX2919FdgL+mxwHGgohHNAv2wnGuBOQcybgJ2CZoii/vyDOPUBRIcQtIcRyIUTAixLh7OtOQo5R4ISwWJx93UxtQnPYhMfi7GNs41rEE79KxXl6QT896lHSj2J1yzN480wGrJ+Cf9VSL0rKK8XNx52Y0Ois77HhMbjlakg7ujmRkpCc1diOC4vBzUf/A7JvzU78yxThy9Mrmbt7CWtn/oCiKMRFxLLz+60sOb6CL0+vRFEUwq49zIozISwWFx/jHyFnHzeehcZm24Sb5nFe1HozkFvBF4yORcYn4euW3UnycXMiMldnomdgDe6Hx9Jy4rf0mLuGcT2CUKmEkc3TmGfceBxJlVwjlo6+biSGZac5MSwWp1xpdvJ1MyoXieGxOPm44VrMi9SYRNp++j79d8yhzcLBWNvZ5NtfL19PIkKzf0SjwqLx8rXcUFRbqWnTvSUnD542OdexZztOmDkO4OPnRfjTbJ2IsEh8DB0Jc1hZqenYoy1HD5wwOm5rZ0PjoPrs3XbQYlhvPy/CQ7MbIRFhUVmdFstabTh28KSJVqOg+uzdHmwxrIevB9Fh2WU/JjwGD1/TBkRelKtZnq92LWXGmhkUe6NYvsJ4+XoSFRqV9T0qLOqF161V9xacDj7zUmkr5OtBRo57OyMsBmsfU/9cWtenwoGvKb16Kg/HLc1X3Na54s4Mi8E6j7zz6NmShIPnAUi7+QjHehVRuzohbAvhHFQLa3/L/tv5upP6NPv+SQmLxS6f9YJjcW/SYxKp+8UQWu2ZS51PB6PO4x5z8HMjKcf9nBwei4Nf/uugLN0innhWLk5kiPnldQC2vu6k5agXUkNjscmj01C0dxBRL7lc9DmeJmUuGs+XKOubV2+lWJlirD/7K9/v/Zbl079BsdBhLMj7ytvPi/CnOeuL/NZN+vri11W/U+qNkgRf2s7m4F+ZP+VzFEXBx9ebpBy/RUlhsTjmKnMOvm4k5bh+z22Sw+MI+XYHA05+ybvnlpGRmMLjw1f0eXHzMSVb6ZeUlulQD0d/y9dbUvDITsB/k2PoG/t1gTOKotwFygghvABHRVHu5bDdB/QVQtjnFaGiKEnoZxXeR79PYL0QYkBuOyHE+0KIs0KIs/fSwnOfNq0EhYmJkU0hexveWvExu2b9TLphHaDKSoWdiwMru0xn77xfeXO5+WnafwxhNtG5TExtnvtVuWl1Hl27z8i6g5nabix9Zw3G1tEOe2cHarasw9gmw/i43ntY2VjjXtzHbBx56+TPjcDhndFptVzcbLQ6DAXTCHKrHL/2gHJFvdg7fwjrJ/ZlwYb9JKWmZ51PSctg7HdbGdcjCMdcDQhh5qKbpNlC/qnUanwql+DCL/tZ024KGSnp1BvWMW9HXxivZfNx8z7mwqlLXDx92eh4zYbV6dirHV/P+868jFkfLQtNWTiecydDOH/qotHxwFZNCDlz2eLyHLBUHC1rTV4wjnMnL5hoBbRqzIUzl/LUMnfD5qWVm7tX7vBug0F81OZD/lq9jcnfT8lfwDzuJ3OMmjeSS6cucfn0lXynTa9j5pgZnWe7T3K92XDuDZ6H/9g+rzRuAMcGVfB8uwVP560BIO3OEyKW/0nZX2dS5pcZpF57gKLNYz3yS2iZBLVS4ValBHfW7GNPq8loUtOp8GFe95i5ApgvqSys7G1o8d1ITsz4Je/13ub8siDm3qgiRXsHcWP2ry+XmOdSL3lf5aZ2QC3uXrvL27V7M6TNMEbMHo69o6Wf2YK7r/JV/+Zg6sLxhvriAgCNg+pz48otAqu2p3uzvkyePxYHRwcLv+W5tC3cxzYu9pRsVZM1DUfxQ+0Psba3oVzXRgDsH/s9Vfu35O3tsynkYIs2U2M5sQWNoiu4v/8ocmPwfwAhRFdguuHrYOAkUAdoDDwfWnwC9MR4KRDAIuAd4HchRGdFUSzeYYp+TioYCBZCXAb6A6tz2XwHfAewsut0xdkvezTD2c+dxIh4ozgTwmJx9s9h4+tOYqTeRmWl5q0VH3N58zGu7zprFOb6Lv0I39OL91B0CgjVP3qjqGydUdnqR8fjI+7i4e/J8+2k7r4exEXEGtknxiZg7+yASq1Cp9Xh5udBfKR+erPJm83Y/s0mACIfhhP1OBL/0oXxKOxF1ONIEmP1jbBbwRep9WbTrDid/dxJMMTxnGfhsbjkGBlx9nUnMcLYxhw1ujehXPOa/NB7rsk5H1cnwuOyl/BExCXi5eJoZLPlxBUGta6LEIJi3m4U9nDhfkQsVUr4kanVMub7rbSrW4HmNcqaxJ8YHotTjil6Jz93knKlOdFQLp4+t/F1JykyHhSFxLBYwgyzQrd2nH5hJ6B7/y506qNfR3r9wg2jJTdefp5ER0SbDTdoVD9cPVyZOHia0fHSFUoxcfFYRvf9JGtjHkDPgd3p8Y5+3eyVC9fxLZyt4+PnTWS4eZ0PxryLm4crM8cuMDnXtksLdmwy3Uz49sDudDes6b964Tq+/tmdRR8/L6IsaA0dMwg3D1dmjZtocq5NZ/NLgdr1a0/rXvpNyrcv3cYzxxIbD18PYnOV/bxIzdHAO3fwLOo5H+Ds5myUj8/p0r8THXrr1/TfuHgLL//s0UovPy+iI2JMwgD0H9UXV3cXpk74PN/pek5GWAyFcoywF/LzIDPSsn/Jp69RqJgvajcntHGJFu1AP/KfM25rPw8yzeSdXfniFF88nDt9Z6HNsZQuZv0+Ytbrl6f4T3iHjDDz/gOkhsViVzi7XrX3cyc1V91rMWxoLKlhscQaRuQfbztNhRGW77HksFgcc9zPDr7uJIe/uA56jrBS0/K7kdzddJwHO8/maZsWFottjt8LO3930s1oOVUsRpUlQzjbawGZcaZLIi3RqX9H2vVqC8AtkzLnScxLlPU2b7Xit+UbAAh9EEr443CKlinKzQv6PUsFeV/1GtgjR910Dd/COesLbyLDozCHvm5yY8bY7AdLdOnZgZVLfwLg0YMnPH0USqmyxYkIizQapXf0c89a0vOcpLBYHHNcP71NPEUbVybhcRRpsfryfnfnWXxrl+XmpmPE3Q1jSx/93jbXkr6UaF493/ki+eeRMwH/ARRF2ZRjc/BZRVES0W/aHUB2J+AE8DGmnQCAUUACsEqY66oDQohyQoicLbrqwENzts8JvXgPj5K+uBb1Qm2tpnLH+tzce87I5ua+81Tr3gSAIjXKkJ6Yqm/sAZ0XvUf0naecWLnTKMyNPeco2bAiAB4lfVFbW/3jPWVdWgKa+Kdo4p9yfs9pGnXTr4YqXaMsqYkpPIuKNwlz/cQV6rRrAEDj7oGc36NfOhIbGk3FRlUAcPZ0wa+UP5GPIogJjaZMjTcoZFsIALeinljb2uBWRJ9/VTs24Eau/Lux9xw1uunzr6gh/xLNpCUnZQOq0nRoR34e/CmZaRkm5ysV9+VRZDxPo5+RqdGy+9xNAqqWNrLxc3fm1A39EzdiEpJ5EBFHEU8XFEVh5s97KOnrQd/mtU3iBgi7eA+3kr64FPVCZa2mQsf63Nl73sjmzr7zVOreWK9VozTpiSkkR8aTHPWMhLBY3EvplxgVb1SJmNtPTTRysnHNZvq3eo/+rd7j8O5jtO2hfxJOpZoVSE5IJsZMI69jr3bUD6zD9OGzjUbkfPy9WfD9LGaNnM/je0+Mwqz7cWPWZt4DOw/R6U19A7ZqrUokJSYRHWnaaOvepxONguoxfug0k5E/RycHajeowcFdh03Crf9xY9Zm3gO7DtPxLX3DpWrNSiQmJpvV6ta7Iw0D6zPhg+mWtXabau34aXvWhsOTu0/QrHszAMrVKEdKYgpxkflv8Lnm2DtTttobqFTCbAcAYPOarQxuPZTBrYdydNcxWvdoCUDFmhVITkwm1sx1a9+rLXUCajNrxNyXGkl9TsrF29iU9KNQUW+EtRVuHZvwbK/xkq9CxX2zPttVLoUoZPXCDgBA8sXb2JTIEXcn07it/T0p+f1EHoz8gvT7xluurDxcsmxc2zQgbovptXpO7IV7OJX0xcFwjxXrXJ+nu89ZtM9JWtQzUkJjcCqtv8d8Glci4Zbleyzq4j2cS/riZNAq3bk+j3Ldz3kR8Olg4u6Ecvn7nS+0fRZyF4dSvtgV80JYq/Hr0pCIXH7ZFvag5g+juTj8a5JfsEcoN1vX/MXQNsMY2mYYx3Yfp2X3FgBUqFGe5MQUs2XOEpGhUdRsVB0AV09XipYuQtjD7PQU5H31249/0L15X7o378v+nYfp9KahvqhV+QV1U33GDZ1qdC+FPQ2nfhN93e7h5U6J0sV4/PApV0Ku41rCF2dDOXijU33u5yoH9/eep4KhXvepUZqMxBRSIuNJfBqDb40yWBl++4o0qkScoV6383DWBxaCOh915vIv+/nPoNMV3N9/FPG/VLSSV4MQYgaQpCjKp2bOfQ10VhSliOF7IHAQaKgoygnDsWCyHxFaCNgGXFQUZVzuuIUQtYClgCv6x4jeAd5XFMX8kCMwo3gfpWxQNdpM0z8yLGTDIY4s20LtPvqn5Jxdq7+Z280eQJmAqmSmZrBl7LeEXr5PsdpvMGjjdCKuP9KP9AP7F6/n9sGLqK3VdF78Pr4Vi6PN1LBn7q/0Xtor3/k2bvoCzoRcIj4+AQ93V4a925fuHVvnO/zg2uPoO2swVQNqkJ6azspxX/Pgsn7UbPSPk/lhwnLiI+PwKurDsKWjcHB15OHV+3w76ks0GRpcvd1479MRuHi7IYRg+zebOL5Z/4PeddTb1O3QCJ1GS9TVh1zZeYq2E/sg1CrObwgm+Ost1DXk32lD/nWcNYCyAdXITE3nz3Hf8tTwaMy3vhpBqfoVsHdzIin6Gfs/38i5DcGMDl6CupA1qYZRxschd6jie8/IxyNX7rH4j2B0Oh2dG1Tmvbb1+f2wfvnIm02rERmfxLSfdhGdkIyiKAxqVZf29SoScucJA5esp6y/J8KwR+DDTo1pUjl738bSQYcpFVSNZtPeQahVXN5wiJPLtlK9j/5H8MJa/Qa5FrP7UzKgKprUDHaO/S7rkZ/eFYvRZuFgVNZWPHsUyY6x35GekELZ1rVpMbMfdu5OpCekEHntIb17mz5FZuzckdQLrEN6ajpzRi/kxiX94zQ/+2k+88d9SnREDEce7iP8STgpyfoRtkM7jvDDFz8xcfFYAts1zVpTq9VoGdRuKIka0yUMk+ePpXGz+qSmpjF15ByuXtRvblu+dgnTR88jKiKaC0+PEvYknOQk/cbLfduDWbHkBwA6v92exs3qM27IVKN4VWb66ZPmj6VRUD3SUtOZ+vEcrhm0vl77GTNGzycqIprzT44Yae3fcYhvDVqd3m5Ho6D6TBhqPOtRvJDpuuShs4dSM7CW/lGGY7/gziX9pvHpq2ewdMJXxEbE0nFgR7oN7Y6blxvxMfGcO3CWpROW0r5/B9r1bYtWoyM9LZ1Vs1dy45w+rYm6NBOtnIyc8yF1A+uQnpbOwtGLuWm4bgt+msvicUuIiYhh/4PdhD+JIDVZ7+PhnUf56YtfTOL6XOVgUcc5qBaFp7+rf0To+v1ELPsdj3faABDzyy68P+iGe/cglEwNSloGT+ettviI0Nw/j85BtSgyIzvu8KW/42mIO/qXXRRbNALXtg3IMOwnUbQ6brbXP4HqjY3zULs6o2g0PJ31A4nHLhnFfVtjPFvn16xa9iNC1x3i+pdbKN1PX3fc/Wk/tl4utNw1B2snOxSdDk1yOjsDxqNJSsW1UnHqfKa/x5IeRXL642/JfJa9OThBbVwGizarRoMZ7yBUKm6uP8SFpVup8I7+fr7+ywHsvFzosmM2hRz1Wpkp6fwRNAH3CkXptGkaMdcfgaGeP7NwA48PZC9VK5xr+YdX8+pUnN0f1Cqe/HaQu19splg/fWP90U/7qLLkfXzb1yX1if6nSdFoOdZ6MgDVV3yIe8OKFHJ3Ij3qGbcX/8GTX/X7bZYUMm10fzhnOHUC9Y/tXDzmM25d0s//zl0zmyXjPycmIpYuAzvz9gdv4u7lTlxMPKcPnGbJ+C/w8HFn3JKx+gcPCMG6r9ezf5O+brMT1iZa/9R9dS/ddJR/yvxxNGpWn7TUNKaMnJ1VN32z9nOmjZ5LVEQ0F58eI/RJOCk56qZvlqzCy8eTuV9Nw8vHAyEEK7/6iW0bdwEwv3UPmsx4B5VaxbX1hzi7dCuVDeXgyi963wPm9Kd4oP73fv+Y74i8pK/X643uRtmO9dFptURdecj+8SvRZWioNqg1Vfrrr++9nWc5vmC9/to8/sXsgGVBknZ+a4E1gG1rdvrX/TWH7ARILDKjeJ8CKxyTz80uKCkG137x86VfBUXI/4bXv8uUVYEFprV0kOURzFfNJk3eswSvCnOdgH8Kc52AfwpznYB/ihd1Al4leXUCXiUF+fOYuxPwT5K7E/BPkrsT8E9hrhPwT2GuE/BPYa4T8E8xtJDp8s9/iv9EJ+Dc5oLrBNTq8q/7aw65HEgikUgkEolEInnNkBuDJRKJRCKRSCSvF7r/7vP7Cwo5EyCRSCQSiUQikbxmyJkAiUQikUgkEsnrxX/4+f0FhZwJkEgkEolEIpFIXjPkTIBEIpFIJBKJ5PXiP/z8/oJCzgRIJBKJRCKRSCSvGbITIJFIJBKJRCKRvGbI5UASiUQikUgkktcLuTFYdgIklvlkS68C0yqot/gCrDy7uEB0btb9qEB0AL4swLf4+hTMC0AB2N2mYKooXYpdgegAaGIL7ofnxEWnAtOq4pteYFodwiIKRCdVm1EgOgBehZwLTCtDW3A3sVpdMAsOFE3Bvd5ZbVVwb3fe5ltw5cL3Y/8C05L8N5CdAIlEIpFIJBLJ64XcGCz3BEgkEolEIpFIJK8bciZAIpFIJBKJRPJ6IWcC5EyARCKRSCQSiUTyuiFnAiQSiUQikUgkrxWKov23k/CvI2cCJBKJRCKRSCSS1ww5EyCRSCQSiUQieb2QewLkTIBEIpFIJBKJRPK6IWcCLCCEKAJ8DVRE31naBowDGgJbgHuAPRABLFIUZZsh3AwgSVGUT83EqQUuo8/3+0BfRVHihRCBwFhFUTrksF0NbFMU5Q8hRDDgqChKbcO52sCniqIECiHsge+BqoAA4oE2iqIk5dL2AVYBRQFr4IGiKO1eJk+OXbjBwtWb0el0dG1Wj3e7NDc6n5iSyqSlvxIeHYdGp6N/h0C6BNUFYO2Ow2zcfwoFhe7N6vNO+6Ym8feZPohqQTXJSM3g+7FLeXj1vomNZxFvhi0bhYOLEw+v3uPbUV+hzdRg52TPkM9H4lHYE7Vazc7vt3Dk94MAtH63AwFvt0BRFJ7cfGTIpvy9WGbKvCUcPnYadzdXNv+y4mWyywTHpjXxm/Y+qFTEbdhD9Io/jM47taiHz+h3UHQKaLWEzf6elLPXsPbzpPCno7HycgOdjrh1u4lZvdUk/pIBVWkxvS8qtYqL64I5+c1fJjYtZvSldFB1MlPT2T72OyKuPADgg6Ofk56chqLVodNqWdNxGgCdl43AvZQfALbO9qQlpLCj1WSjOAsHVqXerL4IlYpbvwVz+WtjXZfSfjT+/H08Kpfg/MLfufLtDgDUNta03TgFtY0VQq3mwfbTXPjsT4v5p65cG9tewxBCRcaRnWTsXG98vlxV7EfMQhcdDkDm+aNk/PULws0Lu8HjEc7uoOjIPLyDjH2bLOrkxqpaHez6jQCVmoyD20nf+pvx+QrVcBg7B12kXjfjzBHS//wp3/Fb166Lw9APEWoVaTu3k7rhV/PpeKM8Ll8sJ3HeTDKOHgLAbc06lNRU0GlRtFqefTgkTy2voGpUnt0PoVbxaO1B7iwzLkeOZfyp9sUQXKqU5MaC9dz7ZnvWuZKD21DsnWYIIXj4ywHuf78zTy27hrVxHz8MVCqSNu3k2Y/G18susAFuwwaAoqBotMQuXk76hasAOL/TDceubUFRyLj9gJjpi1EyMi1qTZw7mibNG5CWms7kj2Zz/fJNE5sFy2dSqVp5NBoNV0KuMXPsAjQaLQOH9aF999YAqK3UlCpbgiYV25IQn5CnfwDT5o0jsEVjUlPTGP/hdK5eumFiM/+LaVSpXhEhBPfvPmT8h9NJSU59YdwAo2d/SINm9UlPTWP2qAXcvHzbxGbmssmUr1YOTaaWaxeus2D8Z2g1Wpq0bsSQcYPQKQpajZYvpi/j4unLFrXGzR5J4+YNSEtNY/rH87hx+ZaJzZyvp1Gxqj4Pr4ZcZ+74RWg0Wmo1qMGS1fMJfRQGwIEdh/j+89UWtcbM/ohGzeqTlprOzFHzuWlGa/ayqVSoVg5NpoarF64zb/ynaDXZa7grVivPD9u+YdLQGRzYfsisztjZI2nUXK8z4+N55nW+nprLp8UmOj9uX8GkITPYvz3Yok8fzBxK3WZ1SEtN57PRn3Hnyl0Tm079O9J1cBf8S/jzZtW3SYjTl7EeQ7rTrGsQoC+DRcsU5e3qPUmMTzKJw65hbdwnDEOoVCRu2smzH4zvK/vABrgNH5D1OxKzeDnpIVexLl4Er0VTsuysi/gSt3wNCWst14XH7kWwaN9ldDroWq0Ygxq8YXR+9anb7Lj6BACtTuF+TCIHP2qLi10hpm8P4fDdcNztbdg4uJlFjX8V+cZg2QkwhxBCAH8C3yiK0lkIoQa+A+YC24EjzxvsQojqwGYhRKqiKPtfEHWqoijVDeHWAMMNceYHbyFEW0VRcv/yjgQiFEWpYoi3HGDu13IWsFdRlC8NdlXzqQuAVqdj3g9/8u3kIfh4uNB74hcE1q5E6SK+WTbrdx+jVBEflk54l9iEJDp/vID2TWryIDSKjftPsXbeSKyt1Ayb9z1NalaguJ9XVtiqgTXxLenH+MARlK5Rlv5z32dWl4km6Xj7k77sXrWNU38do//c9wl4uzkHftlN875tCL3zmC8Gz8fJ3ZkFB77i+OYjOLs703JAOya2+JjM9AyGLxuDsHFASTetXM3RpV1LenfvxKTZJn26l0Olwn/mB9zvNwVNeAylNn9O4r5TpN95nGWSfPwid/adAsCmfAmKLZ3A7ZYfoGi0hM9bRdrVu6gc7Ci99QuSjoYYhRUqQavZ/VnXZwGJ4bEM2DqL2/vOEXM7NMumVFA13Er68m3AGPxrlKb1nAH81GVG1vnfes4lNc44X7aMWJb1udmU3qQnpBidFypB/bn92d1rASlhsXTcMYtHe87xLIduenwyp6b+TLE2tYzCatMz2fXWPDQp6QgrNe03TeXpwYtEnTf98USosOvzIcmfTUCJi8Zh6jI0F06gC3tkZKa5fZnUr6Yah9VpSVv/LbpHd8DWDoepy9FcPWcS1ixChd3AkSTPG4cuJgqnuSvIPHcc3dOHxro3LpO8eNKL48uNSoXj8I95NnEMuugoXJd+S8bJY2gfPTSxs393CJnnzphE8Wz8xygJz/KhJagyfyAn35pHalgMTXbNJXzPOZJuPc0yyYhP4sqUNfi1qW0U1Kl8EYq904yjbaegy9BQ77dPiNwXQvL9cIt+uU/8kIihE9BEROO/dhkph06QeS87z9NOhRAafAIA67Il8V40hadd30Xt7YFTry6EdhuMkp6B16IpOLQJImnrHrNSTZo3oFjJorSr/yZVa1Vi6qLx9G77rond9o27+GTYdAAWrZhF9z6dWb/mT35cvpYfl68FIKBVY/oN6ZmvDkBgi0aUKFWMZnU7U71WFWYtnkj31v1N7OZO+YykpGQAJs0eTd933+bbr1a/MP4GzepRtGQR3mzUh0o1KzJ+/ije7TDMxG7Xn/uYPkL/MzJr+VQ6927Pnz9t5eyR8xzZfQyAMhVKMefbGfRs2s+sVqNm9SlWqiidG/akSs1KTFwwlv7t3zex27lxD1OGzwJg3vIZdOndkT9+2gzAhVMXGdlvwgv9atisPsVKFqFbo95UrlmRT+aPZmCHoaZaf+5l6ojZAMxZPo0uvTuw8actAKhUKkZMHsrJYNP7IadPRUsVoWvDXlSuWZGJC8YwoL1pJ3nXxr1MHa7Xmbt8Ol16d2SjwSeVSsWHU4ZyMvh0nj7VCapD4ZL+DGzyLuVrlOfDeSMY2WmUid3Vs9c4tf8UizYsMjr+x7cb+ePbjQDUa1GPboO7mO0AoFLhMelDwocY7qtfl5ESbHxfpZ4KISXnfbV4Ck+7vEvmwyeEvj00K56ie38j5cAxiz5pdQrz91xiRc+G+DjZ0Wf1IQLK+lLaM/sNxgPqlWVAvbIAHLodzi9n7uJiVwiATlWK0rNWSaZsO59n3kn+XeRyIPM0A9IURfkRQNFvIR8FDEI/+p+FoigX0DewR7ykxgmg8EvYLwammDnuB2T9iiuKclNRlHQLdk9y2F16CW2u3HlEUR8Pivh4YG1lRZuGNQg+c9XIRiBISU1HURRS0tJxcbRHrVJx/2kkVcsWw86mEFZqNbUqluZArhGpmq3qcOxP/WjO3ZDb2Ds54OLlapKOCg0rc2aHvoI7ujGYmq3qPvcIWwc7AGzsbUmOT0JnGM1RqdUUsi2ESq2ikF0h0OX/iQC1q1fBxdkp3/aWsKv2BukPw8h8HIGSqeHZtsM4taxvZKNLScv6rLKzRTFMVmii4ki7qm8Y65JTSb/zGCtfD6OwftVLE/cggmePo9Blarn210nKtjRudJdtWYsrG48CEBpyFxtnBxy8XfPtQ/n29bi29YTRMc8apUl8EEHSI73uvS0nKdbaWDctJoHoi/fQZZrmuyZFX1RVVmpU1lZZPudGXaocushQlOhw0GrIPB2MVY2G+Uq38ixW3wEASEtFF/YI4eaZr7DqMuXRhYeiiwwDrYaMEwewrt0oX2Hzg1W5CmhDn6ILDwONhvTgAxRq0NjEzrZzNzKOHkIXH/c/a7nVKEPy/XBSHkWiZGoJ3XwC39bGjf2M6ASeXbiXde88x7FsYeLO3UabmoGi1RFz4jq+7epY1LKpXA7N41A0T8NBoyF5dzD2gcbXS0k1X94BhFqNsLEBtQpha4MmKsaiVlCbpmz9XT+7dOncVZycHfH09jCxO7I/u+xeDrmGj7+3iU27ri3ZsWmvRa2ctGgbyKYN2wC4cO4yzi5OePmYlqvnHQAAW1sbFEuFPBdNWzdixx+7Abh6/hqOLo54eLub2J04cCrr87WQ63gbBldSU7JnG2ztbbF4cwGBbZqw7fddAFw+bzkPjx04mfX56gXzefgiAlo3ZrvBryvnr+Hk4oiHGa3jObVy+AXw9qDuHNxxiLhoy/dDQJvG7DD4dOX8NZyczesY+3QdH/8cOu9258D2Q8RGx+fpU4NW9dm3UT8GeCPkBg7Ojrh7u5nY3b16l4gnkXnGFdQ5gOAt5mc2bCqXIzPnfbXrxfeVuUlvu3o10DwOQxNmOS1XwuIo6uZAEVcHrNUqWlcsTPBtC51+YOf1J7SpmN2kqVXME2fbQhbt/xPodAX39x9FdgLMUwk4l/OAoigJwCOgjBn780D5/EZumFloDpiu6bDMCSBdCBGU6/gPwAQhxAkhxBwhRFkL4b8GVgkhDgohJgsh/F9Cm8jYZ/h6uGZ99/ZwISLOePSxZ5tG3HsaQYuhM+kx9lPGD+iCSqWiTFFfzt24R3xiMqnpGRwNuU54TLxRWDcfd2JCo7O+x4bH4Jaroevo5kRKQjI6rf6GiguLwc1H/6O4b81O/MsU4cvTK5m7ewlrZ/6AoijERcSy8/utLDm+gi9PryQlMQUlM39T8a8Sa18PMsOisr5rwqKx9jH9QXJq1YCye7+h+KrpPJ3wpWk8hb2xrVSK1AvGyx2cfN1IDIvN+p4YFouTr5upTWh2YyoxPBYnH72NgsLbv3zCgG2zqdYrdxGDonXLkRz9jLgHEUbH7X3dSA7N1k0Ji8XB1/THzxJCJei0Zy69Li0n9PBlokPMzAIAwtUTXWx2/ilx0ahcTRtc6tIVcZixAvuP56LyL24aj4cP6mJl0N4zXbZhDpWbJ7qY7B9KXUwUKjMdCHXZijgtWInDhAWoipTIV9wAKg9PdFE54o+OQuXpaWJj07AJadvNVxcu8z7Fddl32LTtmKeWrZ8bqTmuf1pYDLZ++btWiTce41G/AtZujqjtCuHdvDp2/qbl9zlqb0804TnKe0Q0am/TfLMPakThTavwXjqHmBn62TZtZAzPfvqDIrvWUnTvenRJyaSdOGcS9jk+fl6EP83Ow4iwSHxyNBhzY2WlpmOPthw9YNyhtbWzoXFQffZuO2gxrLGuN6FPs++H8NBIfC3oLvxqBqeu7aV02RL8tHK9WZvcePl6ERmanYeRoVF4+Vr2S22lpm2PVpw4mD1qHdCmMesO/8RnPy1gzuiFFsN6+3oSEZqdh5FhkXj5We4oW1mpadejNccPZjegq9SqzLp9q1m69lNKvVEyD79yaYVG4e1rWUtt0Hrul5evJ4Ftm2TNCljW8SI8NGe5iMI7D5/UWT6dyqHT9IU6AJ6+HkTl+P2KDovGIw+fLGFja0PtwNoc3XnUfBq9PdHmuK+0kdFYmel42jdrROHNq/BZNofo6aaz2A5tAknalXc5j0xMw9fJLuu7j5MdkYlpZm1TMzUcvxdJi3Iv1ayQ/AeQnQDzWFo0ntfx/GAnhLgAxADuwPMhJ0tDNLmPzyHXbIBhJqIU+pkCd+CMEKKCSUSKsttg9z36DkuIEMLkF0UI8b4Q4qwQ4uyqjbtyhDdNXG6nj1+8SfkShdm3YjobFo1h/g+bSEpJo1QRHwZ2asaQOd8ybN73vFHcHyu1OrewGe+VXCamNs9H1So3rc6ja/cZWXcwU9uNpe+swdg62mHv7EDNlnUY22QYH9d7Dxt7W4SNo6nWv4GZTE3cc4LbLT/g0ZA5+Ix+x+icyt6WYssnET77e3RJuTsy5vIvt4nl/Pul2yxWt5/Chv6LqdWvBUXrljOyq9CpAddzzQLoozQXp2lSLKHoFLa2msyG2h/hWaM0ruWKmDc0Vz5yOah9eIek8X1InjGUjP1bsBsx09jcxhb7YdNIW/cNpBkva7JIPnQ1D26T8GFPEj8ZTPruTTiMnp2/uC3Fnyv/HIZ+SPKqb82OJj0bNZz4Ee/xbPJ47Dp1wapyHqv88qFliaTbodxZtpUG6ydR79dPSLj6CEWTx4xaPu5ngJSDx3ja9V0iR83AddgAAFROjtgHNuBJ+748btUTlZ0tDu2am4TNkjJT9vMabZ+ycDznToZw/tRFo+OBrZoQcuZyvpYCQb5dBGDCRzNoULk1d27dp32XVn8jfst+jZ8/ipCTl4zW/R/adZSeTfsxYdAUhow3XSKVt5hl808WjCHk5EVCTuknlG9cvkn7Oj3o2WIA61b9wZIf5+Uh9XLX65P5owk5eZELp/Vao2d+yNK5K9C9YHT1pXUWjOH8yQtcMPg0ZtZHLJ3zzQt1DGIvpWWJ+i3rcfXMNfNLgV5CJ+XAMZ52eZfIj2fgNnyA8UkrK+wDGpC8x/xsQ1a8ZgqApYbO4TsRVC/snrUU6P8Miq7g/v6jyD0B5rkKdM95QAjhjH5TrbmhyhrA9Vz2RYHnOyRXKIqyAsOeACGEC/qNxsOBr9B3CnIPybkD0TkPKIpyQAgxG6if63gS+j0MfwohdEA7IUQz4D2DSTtFUUIVRYkFfgV+FUJsA5oCG3PF9R36/Q+kXdiWVQv4eLgYjd5HxjzD283FKMFbgs8wqLN+42AxX08Ke7tzPzSSKmWK0a1ZPbo1qwfAV7/twMfdBbVncdQexQCI33waD39Pnm97c/f1IC4i1ij+xNgE7J0dUKlV6LQ63Pw8iI/UTwc3ebMZ27/Rb3CKfBhO1ONI/EsXxqOwF1GPI0mM1f+wn9t1kupNyuV7T8CrIjM8Busco4RWfp5kRsZatE85c5VCxXxRuzmjjUsAKzVFl08ifmswCbtNG+OJ4bE4+WUvFXDycycxwniqPDEsFqcco7dOvu4kRcYDZP1PiUng1u5z+FUvzePT+tkGoVZRrk0dVnfItdYeSA6LxcE/W9fez52UXLr5ISMhhfDj1ykSWJX4m09MzitxUajcs/NPuHmii8+1RCRHw15z+TS273yIcHRGSUoAtRr7YdPJPHUAzXnzo2zm0MVGofLIXvKg8vBCF5dLNzWH7oVTiEEfI5ycURJf3JjURUeh8soRv6cXuhij2x6rN8rhNFG/UVvl4kKhuvVJ0mrJOHEUXaw+LcqzeDKOHcG6fAU0V8yv9EsLjTUavbf18yAtPP/X6vFvwTz+LRiA8hPfJjXMcvnVRkRhlWPU2srHE20eS3rSz1/GqqgfKldnbOtUR/M0HJ1hpjF5/1FsqlckeUf2lqueA7vT453OAFy5cB3fwtl56OPnTWS4cR4+54Mx7+Lm4crMsQtMzrXt0oIdm8zvO3jOO4Pe4u2+XQG4fOEq/oV9sqaMff29icgxSpsbnU7H9s17eG9Efzb+Zn5Wp/uALnTuo38+xPULN/DOsTTF29+L6Ajzfr07uj+uHq4sGG96jwJcOHWJwsX9cXF34VmsPl/fGtCNrn30s0dXL143Wtrj7edNlIU8fH/0QNw8XBk7LvsBAclJ2ffAsQMnmbhgDK7uLsQbtN4c0JUuBr+uXbhhrOXvRVSE+bIxePQAXD1cmTc+e+yrQrXyzP1Gv7fD1d2Fhs3ro9VqCd55xKCj9+naxRv4+nvzvKvn4+dFVLh5nfdGD8DNw5V54xbn0CnHvBUzsnQaNa+PRqvl0K4jAHTs34G2vdoAcOviLbz8s0fkPf08ibXgU14EdAogeGuwxfPaiCjUOe4rtbcn2kjLOmk57iudoXNr37gOGTfuoIuNzzMtPk52hCdmDzZFJKbi5WRr1nbXtSe0qWhhAEfyn0bOBJhnP2AvhOgHWct3PgNWA0ZDiIYNtlPRL7fJQlGUx4qiVDf8rch17hnwETBWCGEN3Ab8n4/gCyGKA9WAC2bSNhcYn0O/kRDCzfC5EPqnGT1UFOXrHPqhQohmhicJIYRwAkqjX96ULyqVLsqj8GieRMaQqdGw63gIAbUrGdn4erpy6oq+GR8Tn8iD0EiKGNawxjxLBCAsOo79py/RtlENtNEPybh5hIybRzi/5zSNugUAULpGWVITU3gWFW+SjusnrlCnXQMAGncP5Pwe/RRxbGg0FRtVAcDZ0wW/Uv5EPoogJjSaMjXeoJBhbWLFRlVAa/kpI/8UqZduYVPCH+siPghrK1w6NCVx3ykjm0LF/bI+21YqjbC21ncAgMILRpJ+9zExqzabjT/s4j3cS/riUtQLlbWaih3rc2ev8YasO/vOU7m7fr25f43SpCemkBwZj7WdDYUc9JW7tZ0NJZpWJipHQ7xE48rE3A0lMdy00Rd94R7OJX1xNOiW6lyfx3vytxHMxt2JQs76LTZqW2v8mlQm/m6oWVvt/ZuofAojPH1BbYV13UA0F4w7Q8I5ux+tKlkOhErfAQBsB4xBG/aIjD1Gfd4Xor17A5VvYVReet1CDZqRee64sa5Ltq66dHkQIl8dAADNzRuoCxdB5eMLVlbYBDYj46TxZr24/j2z/tKPHCJp6edknDgKNrYIO8N0vY0t1rXqoHlg+kSt58RfuItDKV/sinkhrNX4d2lA+B7Ly2xyU8iwIdCusAd+7eoQuum4Rdv0qzexKlYYK3+9Xw6tA0k5ZHy9rIpmLx0oVL4MwtoaXXwCmrBIbKpWQNja6PXq1TDa+Aiw7seN9Gjejx7N+3Fg5yE6val/0FnVWpVISkwi2kzDqHufTjQKqsf4odNMRk8dnRyo3aAGB3cdzjMPfvlhAx2DetExqBd7dgTT9S19w7Z6rSokJiQRZaaRXrxk0azPzVs35d5ty9do4+rN9Gs5mH4tB3No11Ha9dA/tahSzYokJSQTY2bgoFPv9tQLrMO0YbOM/CpSInt9drkqZbGytsrqAABsWP0nvVoOpFfLgQTvPEKHN/UN2io1Ledhl94daBBYl0kfzDDS8vDKHgioVL0CQqXK6gAA/L56E31avkuflu8SvOsI7Q1+Vc7yy1Src+/2NAisy5RhM420utR/m8719H8Hth1i4cQlHNp1NIfOIPq0HETwziO0M/hUuWZFkhKTLOh0oH5gXSbn8qlzvbfpVPctOtV9i/3bDrHwkyVZHQCAv9ZsY1ibEQxrM4Lju0/Qort+tqp8jfKkJCYTG/lygyH2TvZUrV+F42YGeZ6TfvUm1sUKY1XYcF+1yf999RyHtkEk7XzxkrdKfq48ik3maXwymVodu689JaCMr4ldYlom5x7HEFTW9Jzkv4+cCTCDoiiKEKIrsFwIMRV9Z2kHMAloADQRQoSg3yQcCXyUjycD5dYIEUJcBHoqivKzEOId4EchhC36p/sMNnQWcofbIYTIOdxUGvjG8EQjFfqnF5lr6dQClgkhNAa7lYqiWH60Qi6s1GomDurGB/O+Q6dT6BJYlzJFfdmwV98QeKtlQ97v1pKp36yj+9jFKAp83KcDbs76pTdjlqzhWWIKVmoVkwZ1w9nRaH81Fw+ep2pQTRYf+pr01HRWjsvuU43+cTI/TFhOfGQcGxb8wrClo+g+phcPr97n8AZ9tm/56nfe+3QEc3YtQQjBhgW/kBSXSFJcImd2nmDm9k/RabQ8vHofXVr+GmgA46Yv4EzIJeLjE2je5R2GvduX7h1b5zt8FlodoTNWUGLNLIRKRdzve0m//Qi33m0BiPt1J85tGuLatRmKRouSlsHjj/RreO1rV8StWzPSbtyn9LavAIj49CeSgs9mRa9odeyZtoa3fxqPUKu4tOEQ0befUr2P/tFsF9Ye4O6BC5QKqsaQw5+RmZrBjrHf6eP3dKb7dx8DIKzUXNtynPuHskeTK3asb7IhOKfuySlraPXreIRKxe31h4i/9ZRyffW6N38+gJ2XCx13zsba0Q5Fp6Pie23YFDgBex9XmnwxBKFSIVSC+3+d4sm+C+bzT6cjbe0y7EfNR6hUZBzdjS70IdYB+kZY5qFtWNVuSqHADvrHZWZkkPqt/okp6jKVKNSwJdrH97Caru+Pp//5A5rLeT/x47lu6uqvcJi4CFQqMoJ3onvygEIt9KONGfv+wrpeADYtO4NWi5KRTvJXL7EcSKcl6esvcJn3KahUpO3ZgfbhA2zbdwKwuA8AQOXmhvP0OfovajXpB/eRedayT4pWx5VJq6n/20SEWsXj34JJuvmE4v1aAPDwp33YeLnQZPdcrJzsQKdQ6r22BDcdhyYpldorR1HI3RFdppbLE38k81myRS20OmIXLMPnm/n6R4Ru2U3m3Yc49dBfr8Q/tmHfvAmOHVuARosuLZ2o8XpfMq7cIGXfEfx/W46i1ZJx4y6JG3dYlDq87zhNmjdk56k/SE1NY+rIOVnnlq9dwvTR84iKiGbqovGEPQln7fbvAdi3PZgVS34AoHm7QI4fOk1qivk1z+YI3nuUwBaNOXBmC2mpaUz4aEbWuVW/fcXEUbOIiohh8bKZODo5IITg+tVbTBs7P1/xH99/kobN6/HH8bWkpaYzZ1T2mv4lPy9g3tjFREfEMH7BaMKfhPP9X8v16dpxmB8+/4mg9k1p26MVGo2W9NR0pn4wy6LW0f0naNy8AVtOrCctNY0Zo7KX83z1y2JmjVlAdEQMkxaOJexJBKv/+hbIfhRoiw6B9OjfFa1GS3paOhOHTreodWz/SRo1b8Cm47+RlprOrFHZ+fHFz4uYM3Yh0RExfLJgDOFPIvjhr28AOLjjMCs/X5OvvNPrnKBR8/psPrGOtNQ0ZubQ+fKXRcweo9eZuPC5zoocOqvzrQNw+sAZ6jSrw49HfyA9NY3PxnyedW72mll8Pv4LYiNi6TywE29+8CbuXm6s2Luc0wfO8MV4/d6vRm0acu7wedJTzT3Xw4BWR8z8Zfga7qvEzYb76k3DffX7Nhxa6O8rJVOLkp5O5Pjs+0HY2mBXvxbRs794oU9WKhWftKrKB+tPoFMUOlctRhkvZ34P0Xdi36yh3/dx4FYYDUp6Y1fIuDn5yZaznH0UTXxqBq2+3s0HjcvTtZrpPq1/lf/wht2CQvwv69Ykrwc5lwP90wzp8mNBSbHy7OIXG70Cbtb9qEB0ALZpXQtMy0dTYFJ0bxPxYqNXgC6l4JzSxBbcD8+JiwW3Ua+Kr/nlI/8EHcISC0QnVZtRIDoAXoWcX2z0ishQCq68q0XBLDgoyLaMh1XB7Stb4V5w18r34xoFpmU3cFF+91L+Y6TuWV5ghcau1bB/3V9zyOVAEolEIpFIJJLXi//YxmAhRBshxE0hxB0hxCdmzrsIIf4SQlwUQlwVQgz8u1kgOwESiUQikUgkEsm/hGHv6ddAW/R7O3sJISrmMhsOXFMUpRoQCHxm2Av6PyP3BEgkEolEIpFIXi/+W3sC6gJ3FEW5ByCEWAd0Bq7lsFEAJ8MeUEcgFvhb68XkTIBEIpFIJBKJRPIPkfMdTIa/93OZFAYe5/j+xHAsJ8uACkAocBkYqSh/7yUEciZAIpFIJBKJRPJ6UYAzATnfwWSBF7+ZElqjf3R8M/RPhtwrhDiiKEr+H3mYCzkTIJFIJBKJRCKR/Hs8Qf9C2ucUQT/in5OBwJ+KnjvAfaD83xGVnQCJRCKRSCQSyevFf+vpQGeAskKIkobNvj2B3C+KeQQ0BxBC+ADlgHt/JwvkciCJRCKRSCQSieRfQlEUjRBiBLAbUAM/KIpyVQgx1HB+BTAbWC2EuIx++dAERVH+1gtaZCdAIpFIJBKJRPJ68d96OhCKouwAduQ6tiLH51Cg1avUlJ0AiUWOt/6pwLSK2NkUmFZBvcm33OmvCkQH4GqVqQWmVVqdXGBaD/bbFoiOg2PBvRlWoym4VZgF+RbfpISCu4c72nkViE4aBddIcEJdYFpJaAtMy76AVh2nmeyh/Oe4rI0vMK2tcT4FptVk2s0C06r5t19zJXkVyE6ARCKRSCQSieT14u89XfP/C+TGYIlEIpFIJBKJ5DVDzgRIJBKJRCKRSF4v/mN7Av4N5EyARCKRSCQSiUTymiE7ARKJRCKRSCQSyWuGXA4kkUgkEolEInm9kBuD5UyARCKRSCQSiUTyuiFnAiQSiUQikUgkrxdyY7DsBPwTCCGKAF8DFdHPtmwDxgENgS3APcAeiAAWKYqyzRBuBpCkKMqnZuKcDPQGtIAOGKIoyikhRDDgB6QBGcB7iqJcMBNeCzx/1bQWGKEoyvGX8cs9qBpl5wxEqFWErd3Pw6VbjM77dG9M8RGdAdAmp3Fz/EqSrj1EZWNNzS0zEYWsEGo1UdtOcn/x70ZhywZUpf20fqjUKs6uP8jhb/4y0W8/vR/lgqqTmZrBxrErCL36AIBui96nXLMaJMck8FXrCVn2bSb2pnyLmmgzNMQ+imDjuG9JS0gxitOxaU38pr0PKhVxG/YQveIPo/NOLerhM/odFJ0CWi1hs78n5ew1rP08KfzpaKy83ECnI27dbmJWb32Z7DRhyrwlHD52Gnc3Vzb/suLFAfLAN6gqNWb1RahV3Ps1mBvLjPPTqYwfdT8fgluVElxesIGbK7JfUmjtbE+dz97DpXwRUBROj/qOmHN3LGo5B9ag2MzBoFYR/dtewr/+0+i8e9em+A7rBoAuOY2HE1eQev0BAD6DO+LZqyUoCik3HvJgzFKU9EyzOo4BNSk87T1Qq4hdv5eob4yvlXPLeviM7gOKgqLREjprJSlnrwFQZNFHODergybmGbdaj3hh/tk1qo3nJ0MRajUJG3cSv2qD0Xn7oAa4f9gPdAqKVkvMghWkhVwFQOXkgNfMURQqUwJQiJy6hPSL1y1q2Teuhc/koaBS8eyPXcR+b3xvODarj+fIfig6HWi1RM77jtTzV7MNVCqK//EVmshong6dkbdfDWvjPn4YqFQkbdrJsx/XG58PbIDbsAFZeRi7eDnpF/Razu90w7FrW1AUMm4/IGb6YpQM89cKXv29lTO+gPWHOGSmjuhoqCMyUjP4I0cd8UZAVToY6pcz6w+ahG3yXnvaTe7D7BpDSIlLRG2tpsu8wfhXKYmiKEQ/CMe/QnEyU9P5bew3PDHEmxP3Il70WzYSexcHnlx9wNpRy9BmaqnZuRHNh3YCID0lnT+mrCT0+iOsbKwZsX46VjbWqNUqru88w+Pzt2k3TX/fnl8fzBEzPrab3o+yQdXITM1g09hvCTOkpcui93jDUA9+3fqTLHufCsXoNHcQhextiX8SxR8fLycpKQmArtP7UyGoBhkGv55a8KtvDr9+NfjlXdqfnouHUqRSSXZ8up7g77dl5+fAttTv2Qwh4NGZm5SsXxGVWsW5POr2N3LU7c996pqjbl+ao273rVicznMHYWVjjU6jY+vUH7l7UV9HdZ7enwqGMrA+D5/eWfYRdi4OPL36gN9GfY02U4tXaX/eXjyEIpVKsvPT9Rz6fjsALn7u9FoyDCcvVxSdwqa129j8g/Hv3wczh1K3WR3SUtP5bPRn3Lly10S3U/+OdB3cBf8S/rxZ9W0S4hIA6DGkO826BgGgtlJTtExR3q7ek8R4/XUqHlCVgBn6cnF1XTBnl5vmYcDMvpQIqo4mNZ09Y74j6ore7xrvtqFSr0AURSHmxhP2jv0ObXom9cf0oHSrmig6hZSYBPaO+RbCE43idA6sQZEZ+jo35re9RCzfaHTerUtAVt2uTU7j8aRvsup2r0Ed8OzdChBE/7aHqFWmaZb8+8jlQK8YIYQA/gQ2K4pSFngDcATmGkyOKIpSQ1GUcsBHwDIhRPMXxNkA6ADUVBSlKtACeJzDpI+iKNWA5cBiC9GkKopS3WA3EZj/Uo6pBOUWvMvF3vM41WQU3l0bYf9GYWOBh5Gc7zKD00HjuL9kI+U+ex8AXXomId1mcqbZeM40H497s+o41yprFHfHWQNZM2ARX7YcR9VODfEqYxz3G4HV8Szpy5LA0WyetJJOcwdlnTv/x2HW9F9okuQ7Ry/zVavxLG37CdH3wwgY1imXTyr8Z37Ag4HTudN6GC4dA7ApU9TIJPn4Re60+5C7HT7iyYQvKTz/QwAUjZbweau40+oD7nUfi3vf9iZhX5Yu7VqyYsmcvxUHgFAJas0bwOE+i9gVMJ7iXRrgnOtaZcQlEzLlJ26u2G4SvsbsvoQfvMjOJuPY3XwiCbdDLYupVBSbM4RbfWdxNehD3Ds3wbZsESOT9EcR3OwxmWstPyb0yw0UXzQMAGtfd7wHdeBa+7FcbTESoVbj3qmJRZ3Cs4Zyf8AMbrUcjmunpib5nXTsIrfbfsTtdiN5Mv4riiz8MOtc3B/7ud9/Rh65ZqzlNWU4YR9M4VGn93BsF4R1qWJGJqknQ3jS7QOe9BhG1NQleM0clXXO85MPSDl2lsedBvO42wdk3nuUp5bPtOE8eW8q9zsMwal9IIVKG2sln7zAg87DeNh1BOGTPsdnzkij8279OpORl0YOLfeJHxIxfBJPuw3GoY2pX2mnQgh9awihbw8lesaneE4fDYDa2wOnXl0I6z2c0B7vI9QqHNoE5an1Su+tN4oZxVetU0O8c9UR5QKr41HSl08DR7Np0kq6GOoIoRJ0mjWQHwcs4vOW40zCuvi5U6ZJFeKeRGUdq9OzGQCL24xn/zdbeKNxFeYHjWLDpO/pMXewWZc7ftKbQ6u2My9oFKnPkqj3tj6O2MdRLHt7FovbTmDP0j95a76+XtSkZ7K892w+bTuBxe0+oWxAVbosfI+fByxiWcvxVOnUwKQeLBtYDY+SvnwZOIatk1bRcW72a1dD/jjCz/0XmaSry4LB7F24jq/bfMK13Wdp9H57ACoEVsezpB/zAj/m9zz86mDwa34uv1Lik9g0YzUHczT+AXzfKEL9ns34ovNkPmv/CTW6NeWvKT/wVctxVLFQt3uU9OVzM3V7iIW6vc0nvTjw5Z983W4S+5f8QZuJvQAoH1gdr5K+LAgcxR+Tvqf73HfN+tT+k94cXrWDhUGjSX2WTN239WU5NT6JLTPWGHVoAHQaHX/N+YXFLcaytOtUOvbvQLGy2fdOnaA6FC7pz8Am7/LlhK/4cJ75gYarZ6/xSa+JhD+OMDr+x7cbGdZmBMPajOCHBau5fPJyVgdAqASBc/qzuf8ifm4+njc61ce9rL9R+BJB1XAt4cuapmPY/8kqms0dAICDjxvVBrbit/ZTWdtyIkKt4o2O9QE4/+121raexK9tJ3N/fwj1RnY1TqxKRdE5Q7jTbybXm43ArXMTbMsa38MZjyO49eYkrrcaSfiX6ym2cDgAtuWK4dm7FTc6jOV665G4NK+DTQk/s3nyr6LTFdzffxTZCXj1NAPSFEX5EUBRFC0wChiEfvQ/C8OI/SzgRUOTfkC0oijphnDRiqKYa5mdAAqbOZ4bZyAuH3bZAWqWIeV+OGkPI1EytURuPo5XmzpGNglnb6F5lqz/fO42tn4eWee0KekACGs1Kis1KIpR3LEPI4h7HIk2U8ulv05QoVUto7grtKpFyJ9HAHgccgdbJ3ucvFwBeHD6BinPkkzSfOfIZXRaXVYYZ18Po/N21d4g/WEYmY8jUDI1PNt2GKeW9Y1sdClpWZ9VdrZZydZExZF2VT/So0tOJf3OY6xyxf+y1K5eBRdnp78VB4B7jdIkPogg+VEUukwtj7acpHBr4/xMj0kg9uI9dJlao+NWjnZ41S/PvV+DAdBlasnMNXuSE4fqZUl/EEbGI30exm45imurekY2yeduojWUi+TzNymUo1wIKzUq20KgVqGyK0RmRKxZHfvqZcl4GEaG4VrF/3UY51w6RtfK3saojCWfvormmfEolyVsqpQj81EomifhoNGQtDMYh2YNjGyU1GwtYWcL6LWEgz22taqQuHGX/qRGgy4x2aKWbdU3yHwUSuaTcMjUkLjjEI7NjcugksMvYW9r5JeVjycOAXV59vvuF/tVuRyax6Fonur9St4djH1gQ4t+5SzvAEKtRtjYgFqFsLVBExVjUetV31sODasZxXfxJeqIotXLEJOjfskdtv3Uvuyc/6tRXN5lC3P32BUAytSvSHxoDEWrluJhyB3snOxxNtQ9OSnTsBIXd5wC4PTGw1RpVRuAB+dvkZqgLwMPz9/Gxdc9K0yGoV5UW6mxcbLnWVgscY+j0GZqufzXScrn8rF8q1pcMPj4xOCjoyEtD0/fINVMPehRyp8Hp24AcPfoZSq2rQtA5Va1OfvnYX1Yg19OFvy6ZPDrzMbDVDb4lRSTwONL99BpjOsQnzKFeRhym8y0DIpUKUVCRCz+VUsZfDJ/3Sz59MCCTwpg42gHgK2zHQkR+p+zSq1qcdYQ16NcvxOWfDqbD58So+KzZhTSk9N4fOcxnjnq+wat6rNv434AboTcwMHZEXdvNxPdu1fvEvEk0uR4ToI6BxC85VDWd5/qpXn2IIIEQ31+66+TlMqVh6Va1eL6xqMAhIfcxcbZAXtvvd8qKzVWtoUQahXWdoVINuRVRlJqVnhrexuUnDc7z+v28Ky6PW7rEVxa1TWyST53I7tuD7mJtaFuty1ThOTzt1DSMkCrI+nUFVzbGN//kv8GshPw6qkEnMt5QFGUBOARUMaM/Xmg/Avi3AMUFULcEkIsF0IEWLBrA2y2cM5OCHFBCHEDWAnMfoGmETa+7qSHZv/op4fGYJPjxyw3fr2bEXMgJPuASlBn/yIaX11J7KHLJJzPXl5i4+vOsxxxJ4TF4uJjHLezjxvPQrMbiAnhsTj7mlaylqj1ZiC3gi8YHbP29SAzLHv0TxMWjbWPaUPeqVUDyu79huKrpvN0wpcm560Le2NbqRSpF27mOz3/JHa+7qQ+zc7PlLBY7PKZV47FvUmPSaTuF0NotWcudT4djNrOxqJ9IT93MsKis75nhMdQyM9yufDs2YJnB88DkBkeS/i3m6l66nuqnf8RbWIKCYcvmA1n7eNBZmi2TmZYjNlr5dy6Pm/s/4YSP0znyXjTa5UfrLw90ITnKBcR0Vh5e5rYOTRvSNGtK/FbPpvIqUv06SziizbuGV5zxlDk96/xmvkxIo/8s/LxNC6D4dFYmfHLsUVDSuz4jiIrZhE++fOs496ThhD16SqUfDzlQu3taeKX2oxf9kGNKLxpFd5L5xAzQ78yURsZw7Of/qDIrrUU3bseXVIyaSfOmYR9zqu+t7TPkoziM1dHuPi4EZ+jjnhmqCP0dYf5+qVCi5okRMQRft14JiXs+iMqtqyNSq3Cs7gv7kU8cTU0cOLDY40a8gAObk6kJqRkDTo8M5M+gHpvB3EjRz0kVIKxOxYw+9x3RN8NJfJm9gRvQlgszj7G962zT666Mh/14P9j76zDo7jev33PbtyVCG7FIXhwEjwtmra4FChWpLgX90KhQKFQwwstFdytxV2CBQ/EjfgmuzvvH7sk2exuCJV8+/4493VxkZ155nzmOXPmmTk60ffDqNhK99JYNag+zvr708nLjcRcaZnzK6MAfuUm4l4YZepVws7FAffiRbB2sMVZn3c6nwyPd/wLsX3frI20ndyD8WdW0nZKTw4v1g1rc87j00sTPtm5OpKelJrtU2JE3Gt9yo1rMQ/KVinL3as58d7D252YXPEpNiIWd2/je+t1WNtYU6d5Hf7c/2f2NgdvV5Jz5U9KRDwOecqFg7crKRE5fqdExuPg7UpqVAJX1u2j/7kVDLy0ClVSGs/+uJVt12D8B/Q/t4IKnRpybqnhUB9Lb3cy88bcfBq63Lu1Ikkf2zPuPcOhfmWULo5INlY4BdTG0vfN8+NfR5YL799/FFEJ+OeReNUsWPDt+SLLcgpQGxgExADbJUnql8tkiyRJz4GJwEozybwaDlQRXWVho37oUsEwYSqbdAdcGlXBt0cAD+ZsydmolbnYYgJn/IbgVKss9hVzdSuaSjvPTWPqVAt6XzX/pCNajYbrv51+vbGJRJMPnSW01VCeDZ6L15heBvsUdjaU+GoKkXPWo83VsvI/xdRVLWBmSRYKXKuV4sGGIxxqPRV1uopKI9q/kZg5KceGVfHo1pLn8zYCoHS2x6V1PW42GMyN2v1R2Nrg1sVM/dZUUTUhlHTwHPdbDOXpoHlG16rAFFAr9egZwjoMJHLkTNyG99UdaqHEulI5krbv4fkHn6BNz8BlQNc30zeRfylHzvAkaBAvhs/GY2QfAOyb10Mdl4gqxPx8DQMK6Ffa8dO86DyA6NEzcRnWDwCFowN2zRvw/N3ehLXuhsLWBvugfEcxFkiroPeWnJFpIrk86ZmLEWbii6WNFQHDO3F42U9G+y/vOMHLyDjG7J6P9zvFCL8XhlajMTjeUNsoCSObcg0q4981gN0Lc3odZK3M50GTmNlgGG4lvbBxts83DdOXMP97+7cJ66jXuxVDds/FysEWTZbabFpG1+gvxJLoh+EcX7uLIZun0nJ4Z9ISU/LNO5OPodeEq3q9WrJvziaWNBzBvjmb6LxokNm0/ok8fIWVnTV914xm7cyvSUvJ1UNaAN2C4N+qPiEXb2cPBTKfdt4tpu9ta2c7yrSqxQ+NRvNt3RFY2llToXOjbJOzS37iO/9R3PvtDDX6tXptkuauvUODanh0bcmL+RsAyHjwnKivfqH81lmU2zyT9NtPkDX/3SExbzOiEvDPEwLUyb1BkiQnoDhgPFMIagJ38tgX17faX5MkaQjohhXJsnxCluUZ6IYPBec6pCdQGtiKbkIykiR9rz9+H3mQZfks4AF45t0nSdIgSZIuSZJ0aU/6o+ztqog4rH1zWgGsfd3JjDQeUWRfuQSVlg3mRt8lqBOMu3HVSWkknL6NW4CfQdrOudJ28nEjKdow7ZeR8Tj75rTWOHm7kRz1+hFNNYObUKFFLXaMWm20LysyDkufnCyw8PEgK9r0cBSAtIshWJXwRunqpD9ASfGvppC46wRJB8++9lwKi/SIeGyL5uSnnY8b6VGJBTs2PJ70iHjir+qKatieC7hWK2XWPjMiDiufnBYeK293siKN89C2UklKLh7Og/4L0CTqhuU4Na6BKiwadXwSslpD4v6zONQ23SmWFRlr0JJk6eOe77VKvRCCdUmfnGv1BqijYrHwzlUuvDzyHfqScfkWlsV9ULg4oY6MRR0Vg+qmrpUw9dCfWFc21QGYo2VQBr09UEeb10q/dAvLEj4oXZywrVUZh0B/yhz9Ad+lk7CrXwOfxePNHquJijHyS5OPX6orN7HQ+2XjXwv1i0i0CS9BrSH16J9Y+1U2e+w/fW/lTc9cjHDJFSOc9TEiKTLeZHxxK+mFazFPRu1fyIQ/V+Dk7caIPfNw8HSmXo8WlGmg8y/kyGXsXRyJeRwJgIu3W/bwk1ekxidj62SHQql7pDrnOT+fiiXounAw3378OWmJxnExIymNsKsP8HonZz6Nk48bydGJRj4a+OLtRvJr7u3YhxFs7LOQte2nISkkLGysGLtvIUlRCbjkSsvF242XJvyyyePXy+jXx93zO46z7L3JbJ/4NRZWlsTp807nk+HxSSZie978zUvN4KbcPnARAHs3J0o3qMzofQt4mccnZ7PXyj7bJxcfd6OyZAqFhZK+a0dz5bfTnD5whvZ93+OrA6v46sAq4qPi8MwVnzx8PIiPMn9vmaNZh2ac2HXCYFtKRDyOufLHwceN1DznmxIZj0OuYZYO3m6kRCVSvHFVksJiSI9PRqvW8ODAJXxzz8fTc++3M5RtZzi8NysiDqu8MdfEcE3biiUpueQTHg6Ynx3bAeK2H+Fu0BhC35+C5mUyqsf5zC37XyHmBIhKwL/AUcBOkqQ+AJIkKYGlwA+AweBqSZKqA9PRv7i/QpblMH2rvZ8sy2slSaogSVLuO9cPeJrnmCxgGuAvSVIlWZY/0h8flPcEJUmqCCgBoygly/I6WZbryLJc5z3bMtnbk68+xK6MDzYlPJEslRTp1JDYg5cMjrUu6k6178YR8skq0h9FZG+3dHfEwkk3HUJhY4lb02qkPXhhkLZ7KW9ci3mitFRSvX0D7h42HGpw9/BlanbRTRotXrMcquR0kmMS856+AeWbVafpkPZsGvg5WSZaEtNv3Me6lC+WxbyQLC1wfq8pyUfOG9hYlcyZzGRTpSySpSUa/YoORReOQvUwjLhvf8v3PAqb+GuPcCztjX1xTxSWSkp09OfFQfNDN3KTEfOStPA4HMvq/PZqXIWk+y/M2qdeD8WmtA9WxYsgWVrg1rExiYcvGNhY+XpQdv0kHo/6wuBBkBkeg0PNd3RzAgDHxtXJePDcpE7a9VCscl0rl/ZNScqrk+ta2VYpi2RpkX2t3gTVrXtYliiKRVEvsLDAoV1zUo+fM7CxKJ4zMc+qUjkkSwu0iUlo4hJQR8ZiWUr3Mmfr70fWQ/OTdjNu3seypC+WRb3A0gLHoGakHDPUsiyR45d1Zb1fiUnELvuBR81786hFP8LHLiTt/HUiJphbFwBUIfewKFEUC19vsLDAvk1z0k4aVl4N/KpYDsnSEm1iEuqIaKyrV0Ky0Q1tsq1fM98Jz//0vZU3vRrtG3AnT4y4kydGZOhjxPPrD/HIFV9eHRt1L4x5dYayuPEoFjceRVJkPCvfm0pKzEsu/3SStcGz+DxoEvEvYnFwcyTqwQtK1ixHenIaSSZiz4Ozt6kRpJunUi+4KbcO6eKji687H60dw5bRq4l5nBMX7d0csdHHRUtrSzzL+GDtZIeL/jyrtfc3ioP3Dl/BT+9jMb2PKa+Jg/buuoqVJEm4FvNk72cbWBo0iZuHLlGnS1MAStYsR0ZymsmY+uDsbarr/aqby6/8cNBrJsUk4uzjxrPLoXqfjGP7ncOXDXxSFcCnpOgESvtXAiDmYTgRt5/yRdBkQg5doo4+rRL5+hSS7VOd4KaEHHp9fPxw0SCiHoRz6ltdu9ruDXuyJ/OeOXiWlsG6nrGKNSuSlpxKfAEqFrmxc7Sjun81zuRpUIq6/giX0t446eP5O+39eXT4ioHNo8NXqBTcGADvmmVRJaeRFp1I8os4vGuVw0IfY4s3qkK8/tnrUsor+/gyrWqR8DDCIM3U66FYl8qJ7a4dmvAyT8y19PWg9PrJPBm13Ogl38LdOdvGpW0DEn4/9Ub5ISgcxBKh/zCyLMuSJHUGvpIkaTq6itY+YArQAGgiSdJVdJOEo4GRsiwffU2yDsBKSZJcADXwAN3QoLza6ZIkLQXGAXmXRbCVJOma/m8J6KuftFwwvzRa7k/+Dr8fpyIpFYRvO07qvef49tF1IYZvPEzpse9j6epAhUW6VSZktYZLbSZj5eVK5S8/QVIqQCER/ftZ4nIFMVmjZfdnP9Bv4yTd0ng7ThAd+oJ6PXVB9cKWo9w7fo13AvwYc/ILstJV/DL+6+zjP/xyOGX8K2Hn6siEsys5+sVOLu84QftZ/VBaWdJ/82RAN1nw96nf5Til0RI+cy2lNsxGUihI+OkwqtBnuPZoB0DC1v04tW2IS+dAZLUGOSOTsJG6lSrs6lTGtUsgGXcfU3bPlwBEfb6RlBOvf0CaY/yMhVy8eoPExCRadOrFsAG9CW7f5o3TkTVarkz5gWbbJuqWCP3xJEn3X1C2jy4/H248io2nM60OzMXS0RZZq+Wdj9uxv9kE1CnpXJm6Ef/Vw1BYWpDyLJoLn35tXkyj5dn09byzZQYolMRtP0LG/TA8e+nOO2bzQXxGd8XCxZGS84fozk+t4c6740i9GkrCvjNUOrAM1BrSQh4Ts8XMBFeNlvDP1lJm4yxQKkjYcQRV6DPcerYFIH7LAZzbNcS1SyCyWo02I5Onw3NWSinx5Tjs/ath4epExbPfE/XFVhJ2HDarFTt/NT5fz0dSKkj69RBZD5/i9KFuVZWkHXtxaNUYxw4tkdVq5AwVUePmZx8eO381RRZNRLK0QB0WSfT0pfnmX/ScNRT7di4olLzceYjMB89w7qqru7/cvg/H1o1x6thCp6XKJGL0QvPp5YdGS/zCVXitWaBbIvT3g2Q9fIrj++8BkPzzHuxaNMGhfUtQa9BmqIiZoFutKvPWXdKO/IHvtq+QNRoy7z4keadRJ6OB1j99b+VO7/iOUyZjRIUAP8bpY8TP+hih1WjZ9dkP9NfHl0v6+JIf9h5O9N8wCa0s8zIynpCjV5h6cgWZ6Sp+HJ+zfO/H309k+8R1JEUnsGfhVnqvHEm7sV15EfKEczuOA9BmZDD2rg68P1e36o1WrWFZh6k4FXGlx9KhKBQKJIWCO3vPc+qrXfTZOBGFUsGVHSeJCX1BHb2Pl7Yc5f7xa5QP8OPTk8t0S4TmioPvf/kJpfVxcOzZlRz/4meu7DhJtQ4NqNdbF6fvHLzI1Z90k07vHL9KpQA/ppxcoVv6NB+/+qwcSdDYrjwPecJ5vV+Ons6M3jUfGwdbZFmmaf92LGo1DlVKOv3WjMHO1QGtWsPRL36m2+qRuiVC9XlfV+/TRb1Pr2J7ponY/sqn8WdXckwf23+f9A1BM/qgsFCgVmXx++Rvsn2qGODHpJPLyUpXsT1XWgO+n8BPE9eTFJ3A3oXb6LVyBG3HfsiLPD6N2jUv26cm/duxpNV4fCqWoE5wU8LvPGP0vgVkyBq+X7SBi8d1vREXjl2kbmBdvv/zO1TpGSwdmzNvZ86G2XwxYTnxUfF0/KgDHwz9ADdPV9Ye/ooLxy6yXD93qVHbhlw+dQVVusqgLMoaLSemb6DTpglISgW3t58k/v4LqvXSrdJ0c/Mxnhy7RqmAGvT9Yynq9EwOj1unu2+uPeTBvgt03zcXrUZDTMhTbm3V+dpoUldcyvqAVibpRSzHJn9PrdzCGi1h09dRbvNMJKWCuO1HybgfhkcvXcyN3XwAn0+7YeHiSPF5g7PP9d67YwEos24iShcnZLWasGlfZ08g/k/xH26hLyykvzJuTfB2cMzrw0IrHEdtC68+2l3x5q3Df4UKF74sFB2AX6pNLzStssrCC+ZWFgWup/4t7B2Me4r+LdTqwuuAtbZRF5pWSpL5CdD/NFu0bz7M66+QQeG9JDiiLDStFArnvgKwK6QBBxmvm0jwD3JTk1hoWkHS31t17k1oIhdsBbV/glphvxd8TuK/RPqW6YVWaGx7zvmf+2sK0RMgEAgEAoFAIHi7KMCqav/XEXMCBAKBQCAQCASCtwzREyAQCAQCgUAgeLsQcwJET4BAIBAIBAKBQPC2IXoCBAKBQCAQCARvF2JhHNETIBAIBAKBQCAQvG2ISoBAIBAIBAKBQPCWIYYDCQQCgUAgEAjeLsTEYNETIBAIBAKBQCAQvG2IngCBWc7aWBaa1rRvmxWa1or+pwpFJ6QQv+Lb5eacQtPa6PdZoWltUkcWio4mofAmiKkL8Su09TK8Ck3rkTal0LQWWycWio4qs/AekdEZdoWmpSzEr+vaKlSForPMKqtQdADUcuF9cXlu0sVC05riXLfQtGoVmlI+iJ4A0RMgEAgEAoFAIBC8bYieAIFAIBAIBALB24UsegJET4BAIBAIBAKBQPCWIXoCBAKBQCAQCARvFbJWfCxM9AQIBAKBQCAQCARvGaInQCAQCAQCgUDwdiFWBxI9AQKBQCAQCAQCwduG6AkQCAQCgUAgELxdiNWBRE+AQCAQCAQCgUDwtiF6Av4mkiQVA1YDldFVqvYA44GGwO/AI8AOiAIWy7K8R3/cTCBFluXPc6UlATFAeVmWEyRJ8gHCgSayLP+pt4kBKgIjXh0vSZINsBtoCVwHHABP4LE+6WGAGzBHf46WwApZlr9+nX9lmlWn9YzeSEoF1348wdk1u41sWs/sQ9mAGmSlZ7Jn3NdE3nqSkz8Kif575pIcmcCO/tmuUqdfa+r0aYVWo+XBsWt6N3M4HfKYxT8dRyvLdG5Ylf5t6hvsT05XMfX7fUQmJKPWaunTsg6dGlQlMj6JaRsOEJeUiqSQCG5UnZ6Bht8mLN2sOi1n9EahVHD9xxOcM+FTy5m9KRvgR1a6ir3j1hGl92non1+gSs1A1mjRajRsaK/7em7HVcNxK+MDgI2THRlJaZxoOcUgTe+A6tScrcvLR1tPcHeVoa5jOR/qfTEY12qluLlwB/fW7sveZ+lkR92lH+NcsRjIMhdGryPu8gOj8y4I0+Yv49TpC7i5uvDb5rV/KY3cFG1eHf9Zuvy8t+0EN1Yb+uVc1oemywbhXrUUlxb/xK2vdX7Z+7jRdMUQ7DydkbUy97YeJ+Tbg2+kPWL2J/gH1iMjXcXC0YsJvWWcJ1NXTqZC9XfQZKm5c+0eSyd9gUb9+i9+jpr9Cf6B9VGlq5g/ejH3b4Ua2UxfOZmKNSqgzlJz59pdlkzUpV2ibHEmfzGBd6qWY/2i7/jx65/y1Ro9ewQNA+uTkZ7BnNGLTGrNXDmVijXeQZ2l4c61uyycuBSNWkPrzi3pPawbAOlp6SyevJwHtx+a1eoyoy+VA2qSla5iy7g1PA95YmTjVsyTvqtGYe9sT1jIEzaPXoUmS0PVVrV5d8yHaGUZrVrDr7M38ujSPbNag2YNonZAHVTpKlaMXc7DW8bn9W7f9+gwoAO+pXzpWaMHSQlJAFT1r8a0b6YRFRYFwNkDZ/hxxY9Gx9s3qY3XtMFISgWJOw4St84wrx1a+OP5aW+QtchqLVHzvib98m0kK0tKbl2MZGWJZKEk6cCfxH65xawveXFsVouiMwYiKZXE/XiI6DU7DfY7taqPz9ieoNUiazS8mPUNqZfuFDh994AaVJzbF0mp4PmWYzxZuctgv3dwI0oP7wCAJlXF7QnfkHL7Gda+7lRbNQwrTxfQanm++RjP1u/PV8stoAbvzO2HpFQQvuUYT1f+brDfK7gxJbO1Mrg34VtSbj9FYW1Jrd9norCyRFIqiN5znsdL8i/rLgF+lJ7dH5QKorce5cWqXw32e3RpQtFPOuu10nk0aR1pt58CoHSyo9zSYdhWLAGyzIPRq0m5fN+sVv+ZH1MzoA6Z6SpWjVvO41uPjGza9n2Xd/t3wKeUDx/59SQ5IRmAuq3q021sT7RaXaz/ftY33M3n+n0yexj1A+uiSlexePTnJmNRx34dCB7YmaKlitK52vvZZd3B2YHxS8fiW9KHTFUmS8Yu48m9J2a15i+aSsvWzUhLy2DksEncuH7byGb5qnnU8KuKJEk8eviYEUMnk5qaBkDDxvWYt2AKFpYWxMcl0PHd3gCUbFad5jN1sfzWjye4+JXxs7H5rN6U1j8bD41dR/StJ7iW8SFo9fBsG+cSRTi77GeufnsQz8olaDG/P0prS2SNhqNTfyDquvF1+J8gVgcSlYC/g/6l/RdgjSzLHSVJUgLrgHnAXuAPWZbf09v6Ab9JkpQuy/JRU+nJsixLknQeaADsQ1eRuKr//09JkioAsbIsx+mkQZIkK2AncFmW5Vb6bc2Bcbm0LYGnQD1Zlp9LkmQNlHqtfwqJtnP6sbXnApIi4+m/aw6hR64QG/oi26ZsQA3cSnuzptlYfGuWo+3cj/ih04zs/XX7tyX2QTjWDrbZ20o2qMw7rWqzvu1kNJlq7Nyd8FtSM3u/RqtlwfajrB35Pl4ujvRctIVm1ctR1sc922b7yWuU8XHny2GdiU9Oo9Os73m3biWUSgVjg5tRqYQXqRmZdF+4Gf9KJXMdK9F6Tl9+7LmQ5Mh4+u2aTeiRy8SF5lRCygTUwLW0N183G4tvzbK0mduPjZ1mZu/f1m0e6QkpBnn1+/BV2X8HTuuBKinNKC9rz+/Hia4LSI+Ip9X+OYQfukLS/Zy8zExI5eq0jRRtV9voWtSc05vI49c58/EKFJZKlLbW5i7ba+kU1IoewR2YMufz1xu/Bkkh0XBuXw70WEhqRDwd9s7m2aHLJObKT1ViKmc/20TJNoZ+aTVaLszeStytJ1ja29Bx/xxenLppcGx+1A+sR7HSRenZuC+Va1Vi9IJRDGs/wsjuyK9HmTdiAQDTV03h3e5B7Npk/HDLjX9gPYqVLkb3xn2oXKsSYxeMYnD74UZ2h389yhx92jNWT6V9jyB+27ibpMRkVkxfRZO2jV7rR4PA+hQvXZQPGveiSq1KTFgwmoHthxnZHfz1CDNHzANg1uppdOjxLr9u3EVEWATD3v+U5Jcp+AfUY9KisSaPB6jc3A/P0j7Mbf4pJWuW44N5A/mi0zQjuw6TenDi271c3X2WD+cNwL9rIKc3H+b+6VvcOnwZAN+KJei3ehTzW4w1qVU7oA6+pXwZ3HQQFWpWYOi8YYzraGx759JtLh69wPztC4z23b4YwuyPZpvPPIUC75nDeNZvKlmRsZTeuZzkY+fIfBCWbZJ69hopR88BYF2hFEVXTOZR28HImVk87TMZOS0DLJSU+vFzUk5dIuOa+UpNbt1icwbzsOdnZEXG8c6upbw8cgFVaI5uyunr3Dt8HgCbiqUotXoCd1uYvi7G6UtUWtifyx/OIyM8Dv+D84k5eJnUXPEi/WkMFzvNRv0yFY9AP6osHcT5dtOQ1RruzdhE8s0nKO1t8D+8gLiTNwyOzatVYWF/rn44D1V4HHUPLiD24CUD+4yn0VzpNAv1y1TcA/2ouPRjLrWbhlaVxdUus9GkqZAslNTePYu4Y9dIumxciX2Vb2Xmf0xI19lkRsRRff8i4g9dJP3+82wT1bNobnWZjuZlKi6BNSm7ZAg3350MQOk5/Uk4fpV7H3+OZGmBwtbKbBbWDKiNT2lfRjQbTPmaFRg0dyiTO403srt36Q6Xj15k1o/zDLbfPH2di/rrV7JiKcasnsAoM9evXmBdipUuSp/GH1GpVkVGLRjJ8PYjjexCLoZw7sh5lv20xGB7jxHdeRDykBkDZ1G8bHFGzhvO+G4TTWq1bNWUMmVLUa9ma2rXqcHiZTNp2+JDI7tpk+eTkpwKwOx5kxgwqCdffrEeJ2dHFi+dQdfggbx4HoGHhxsACoWCwLl9+aXnQpIj4umxezYPD18mPlc8LhVQA5dS3nzfdCzeNcsSOK8fP3acScKjCLa0mwrongkfX1jJgwOXAGgypTvnlv/CkxM3KBVQgyZTuvNz13kI/huI4UB/j0AgQ5bl7wFkWdYAo4H+6Fr/s5Fl+RowGzB+kzDkNLqXfvT/L0NXKXj1+0wuWwvgRyBUluVJ+aTpqLeN05+LSpbl1z7pfP3KEv8kisSwGLRZGm7vPsc7rQxf5N5pVZsbO/8AIPzqA2yc7HAo4qIT9XajXKAf1348bnBMrV4tOPPVLjSZagDS4pIM9t96EklxTxeKebhgaaGkTe0KnLhu2KoiAakZmciyTLoqC2d7G5QKBZ7ODlQq4QWAvY0VZbzdiE5Mzj5O4eRJwpMoXubyqXwen8q3qs2tnX/qfXqItZM99nqfCkLFd+tze9dZg21uNcuS/CSK1Gc63We/n6NonpdiVVwS8dcfoc0ybKW2cLDF078ij7aeAECbpSErTyXjTajjVw1nJ8e/fHxuPP3KkvQkimS9X49+P0eJ1oZ+ZcQlEXv9Edo8re/p0YnE6XtYslIzSAwNx87brcDajVo35ODPhwG4feUODk4OuBUxPv78sQvZf9+5dg9PH4/Xpt24TSMO/HwoJ21nB9xNpH3OIO27ePp4ApAYl8jd6/dQZ6lfq9W0TSP267VCrtzBwdnepNbZY+cNtIrotW5eCiH5ZYr++NsUyce/qq3rcPGXUwA8vfoAW0c7nDxdjOzKN6zC9X06vQs7T1GtdR0AMtNU2TZWdtbI+TSk+beuz7GdxwC4d/Ue9k72uBZxNbJ7FPKI6OfR5hPKB9vq75D5NJyssEjIUpO09xSOLRoY2MhpGdl/K2xtANlon2RhgWShzL0rX+z8yqN6EkFmWBRylpqE3X/g3Mqwt1KbW9fOmgInDjjXKkfa40jSn0YjZ2mI/O0MRdrWMbB5eek+6pe6l7zEy6FY++jKTGZ0Isk3nwC6VvvU0BdY53NfOdUqR/rjKDL0WlG/ncGjbV2zWi8vh2Kdq0FGoy8TkqUSycKC/AqFQ81ypD+JRPVMl2+xv/+JWxtDreRL99DotZIv38dKr6V0sMXJvzLRW3VtaHKWGk0+cbBuq/qc2Kl79oRevYedkz0uJsrf45BHxJgofxm5rp+1nTVyPtevUeuGHNLHojtX7uLgZG8yFj0IeUjU8yij7SXLl+Dqn1cBCHsYhncxL1w9XExqtX23Bdu3/QbA5UvXcXZ2wsvL08juVQUAwMbWJvuyBH/Qnr27D/PieQQAsbHxANSqXZ3EJ1G81Mfye7vPUTZPLC/bujZ39M/GSDPPxuKNqvDyWTTJL+IAkGUZK0ddI6C1ox2pUQkm/fqfoNUW3r//KKIS8PeoAlzOvUGW5STgGVDOhP0VdEN58uMMOZWAesBvQHH974boKgmvmACoZVn+NL8EZVmOB3YBTyVJ2iZJUk9Jkl577R293UiOiMv+nRQRj6O3q5FNUngum8h4HL10Nq1m9ObY/G1GH+RwL+1DiXoV6ffbLHptn4ZP9TIG+6MTU/B2zXlJ9XJ1JPqlYct7t+Y1eRwZT6vJX/P+vA2Mfz8AhUIysHkR95K7YdFUK+WTs9HGnuSI+OyfySZ9ciU5l0/JuXySkem6eRL99syhRvcA8lK8XgVSY1+S8MQw0Nt6u5H+IifNtIh4bL2NH0imcChZBFVcMvWWD6b1oXnU/Xzg3+oJ+Cex83ElNVd+pkXGY+9TML9y41DMA/eqJYm5an4YS148vT2ICY/J/h0TEYOnt/kXYKWFktbBLblw4mKB0o7Ok7bHa9JuE9yK88dfn7YprajwnJeQmIjY1/rRNrgV545fMNrXvlsQZ01sf4WLlxuJucr2y8h4nPO8INq7OpKelIZWo3twJUbE4+KVY1O9TV2mHF3KoO8msm2C+eFk7t7uxEbEZv+Oi4zD3dvdrL0pKtSqyJcHVjJzw0xKvFPCaL+FtzvqXBpZkbFYeBlrOLZqQJkDX1N8/SwiJi3P2aFQUHrXSt45t5WU01fJuF6AXgDA0tudrNy6EbFYmvDNuY0/FY9+RZnvP+PZ+C8LlDaAjbcbGbmuU0Z4fL4v8kV7BBB77JpxOsU9caxaipdXzA8dzKulCo/DOp/Y5NsjgLjcWgqJekcX0SRkPfEnb5CUj5a1txuZL3LyLTMiHqt8yoRX9xYkHtO9HFuX9CIrLolyy4dT/dASyn4+FEU+cdDd2524XPdwfGQc7ibKRn7Ua+PPiqNfMfn7z/gqn+vn4e2eJxbF4vEGZf3R7Uc0adcYgAp+FfAq5oWHj/GLPYCPjxfhLyKzf4eHR+Lt62XS9svV8wkJPU358mX45utNAJQtWwoXFyd+27ORIyd38mG3jrp0fb1IDs+J5SkR8Th4GZYDB29Xg3eClMh4HPKUlQodGnD395xGsJOzNtNkSncGnltB02nd+XPR9tfmh6DwEJWAv4eE6ead/La/jgtATUmS7AFLWZZTgEeSJJXDuCfgT6CBJEnvvC5RWZYHAi306Y8DvjNlJ0nSIEmSLkmSdOlhRqTRfjlPK49kwiNZlikXWJO0uJcG8wOyj7FQYONszw+dZnBs/la6fGU4hMNUi0temTO3n1ChuCeHFwxm++TeLNxxlJT0nFbKtIxMxq3bxfj3A3B43QtzXjkTTr3ye3OX2fzw7jR29F1C7T4tKV6vgoFdpQ4NuJOnF8CkA7pE8z+vV4daKHCtVooHG45wqPVU1OkqKo1oX6Bj/31M5dWbpWBhZ02LdaM4N3MzWSnpbyBt/jqZYvT8Udw4f4ObF279laTzTXvs/FFcO3+DGxduvjbtgojll4fj53/KtfM3uJ5Hq1ZDP9p3D2L1/HX5aBlvMvLrNTY3Dl5kfouxfDvoc4LGGA9DyC+h/PIwLw9vPWBAg/6MbDuC3T/sYep642FLZk7WaFPy4bM8ajuYsGFzdPMDXqHV8rjDCEKb9MG2+jtYly9ZwLMrmO7Lg+e422IYjz+er5sfUFBMPilM551ro8oU7RFA6JytBtuVdtb4fTuae9M3oMnvvjJV2M3g2qgKvj0CeTAn19wJrcyFFhM57TcU51rlsK9Y3HwCpm8sk6ZODatSpEcLns7TvbxKFkocqpUhcsNBbrQejyZdRdERnfPRMiX1ZsHpwsFzjGoxjMUfz6dbftfvDe/hvGxbvR0HZwe+PriGzh91JPTWA7PzlqQ3iHsjP5lCtQpNuH//IZ26BAFgYaGkul8Venw4mA87D2TshGGUKVvKTLpG6vlqKyyVlG1Vi9C9Ob2W1Xu34OTsLXzjP4qTs7fQesnHJs9V8L9BzAn4e4QAwbk3SJLkhK7l3lSTZk3gTh774ugm9QKslWV5rSRJD9ANKbqi334OCAKKALmbqk4BG4D9kiQ1kWU538HUsizfBG5KkrQJ3aThfiZs1qGb18APnWfIjrm6fZ183EiJSjSwT4qIx8k3l423GynRiVQMqk/5lrUp29wPC2tLrB1t6bB8KLs+XUNyRDx3D+haTMOvP9L1FFjaQJau+9XLxZHIhJwhPFEJyXg6Oxjo/n72Fv3b1EOSJEoUcaWouzOPo+KpVsqHLI2Gset3EVSvEi1qljd0MCMVR5+cngdHHzeS83RPJkfE45jLJ0e9T0D2/2lxSdw/eBkfv7KEXdBdEkmpoELbuvzw3vS82Up6RDy2RXPStPNxIz1PXpojPTye9Ih44vWt5GF7LlBp+H+jEpAWEY+9T04LpZ23G2mRBe/ulSyUtFg3ioe/nuHp/kuvte/UtwPv9dA9zO5ev4+nb05rmaePJ7FRcSaP6zu6Ny5uzkyf+IXZtDv37Uj7nvq0r92jSJ6048yk3W90b1zcXVgycIbJ/aYI7tuJDj3fBXRDe7x8i+TS8iA2Ktbkcf1H98HF3YXJAz8z2F62UhkmLxnHmN6TsicbvqJx79Y06B4IwLPrD3HJVbadvd1IylP+U+OTsXWyQ6FUoNVocfFx42W08TV9eOEuHiW9sHd1JFV/vwb1eZc23dsAEHojFI9cQ5Pcvd2Jj4o3Sscc6bleXC8fv4Ry7lCcXJ0M/FNHxmKRS8PS2wN1tHmN9Iu3sCzhg9LVCU2udLTJqaSdv4l909qoQp++9tyyImOxzK3r40FWPr6lXgjBqqQPSldHNLlimzkyIuKxyXWdbHzdUJm4rxwql6DKssFc6b6QrFzzlCQLJTW+G0PEzj+J3pd/71RGRJyBlrWvu1mtissGca37QtR55kQBqJPSSDh9G/eAGqTeDTPaD6CKiMOqaE6+Wfm4kWki3+wqlaTc0qHc7jk3WyszPA5VRBwpV3XzDeL2nKXYcMNKQNs+QbTo1hqAhzdCcff15NUj183bnfh8ykZ+3LkQgldJHxxdHbMnDnfs254gfSy6d/1enljkYTZemCItJY0lY5dm/95ydiORYTmNcP0H9qB3X12F++rVm/gW9c7e5+vrTVSE+eF0Wq2W33/ZxycjB7Btyy+Eh0cSF5dAWlo6aWnpnD1ziarVKhL+IhJH35xY7uDjRmqe+z4lMp7c7wQO3m6k5nqOlWpeg+hbT0iLzbm3Kgc34cQMXUXu/p7ztFw0sKDZ8u/zHx6mU1iInoC/x1HATpKkPgD6icFLgR8Ag8GKkiRVB6ajW0koG1mWw2RZ9tP/e9W3fhr4FHjVpHwWGAWck/NU+WVZ3gksAQ5IkuRi6iQlSXLQTxZ+hR+6icL5En79EW6lvXEu7onCUknl9v7cP2ww+onQI1eoHtwEAN+a5VAlp5MSnciJxdtZ6T+C1Y0/5dcRq3hy5ja7Pl0DwP1DlynVsDIAbqW9UVpaZFcAAKqU9OZZdCIvYl+SpdZw8PI9mlUva6Dr4+bE+bvPAIhLSuVJVALFPJyRZZlZmw5R2tud3i0Mx9ACaJNjjHx6cPiKgc2DI1eoGtxY71NZVMlppEYnYmlrjZW9DQCWttaUalqVmHs5E9pKNa5K3MNwkiONHzTx1x7hWNobe71uiY7+vDh42cjOFBkxL0kLj8OxrG5Yk1fjKgYTiv+XxFx/hFNpbxz0fpXp6M+zPPmZH00+H0jig3BuvWb1klf8tmEXA9sMYWCbIfx54DRt3m8FQOValUhNTjX5kH+3ezvqNqvD7OHz8m0J/HXD7/RvPZj+rQfzx8HTtH2/dXbaKUmpxJlI+73uQdRrXpeZn8x9o1bGnRt+o2/rj+nb+mNOHTxNO71WlVqVSDWj1b57EP7N6zLjkzkGWl6+RVi4fjazRy0g7NFzo+P+3HSIJUGTWBI0iZuHLlG3S1MAStYsR0ZyGkkxiUbHhJ69TY0g3Rj3esFNuXVIV0HzKJkz7KBYlVIoLS2yKwAA+zbuZVS7kYxqN5JzB88SGKyrfFSoWYG05DQSTFQmzOGSa65C+RrvoFBIRhWc9Jv3sSrli2UxL7C0wOndpiTrJwG/wrJEznBAm8plkSwt0CQkoXRzQuFoD4BkbYV9Qz8yTeSfKdKuh2Jd2her4l5Ilha4tm9C0uHzBjZWJXN0bauW0eu+vgIAkHT1IXZlvLEt4YlkqcS7U0Oi88QLm6Lu+H03hpufrCbtUYTBvipfDCY19AVPv97H60jWa9notbw6NST2oGGF3LqoO9W+G8vtT1aTnkvL0t0RCyfd9DeFjSVuTauS+sB8W1TKtQfYlvbBungRJEsLPDo2Jj6PllVRDyp8O57QEV+SkUsrKyaRzPBYbMr6AuDSuBpp9w2v14GN+xgf9Cnjgz7lwqHzNA/WDdksry9/iW9Q/rxzXb/SVctgYWmRXQEA+H3Dbga3GcrgNkM5feAMrfWxqFKtimZjkTnsneyxsNS1yQb1aMeN8zdJS8l5hfjum60ENOlEQJNO7N9zhK7dOwFQu04NkpKSiYqKMUqzdJmc4XOt2wUQGqpbkWf/3qP4N6yDUqnE1taGWrWrc//eQ65euYlraW+c9LG8Qnt/HuWJ5Y8OX6GS/tnoXbMsmfpn4ysqdjQcCgSQEpVAMf9KgG6+QOIT4xEGgv8doifgb6Bfzacz8JUkSdPRVar2AVPQTeZtIknSVXSThKOBkeZWBsrDaXQv/a/upitAMeAbM+exVpIkb2CXJEmtTZhIwARJkr4G0oFUTPQCGKWr0XLwsx/ovnGibjnNHSeJDX1BrZ4tdCe15SgPjl2jbIAfw04ty14i9HVc23GC95YM4uNDC9Fmqdk1di3BA3NaUSyUCiZ1DWToqp1otVo6NqhKOV8Pfjp1HYAPmtbg43b+fLbxAO/P3YAsy3zaqQmuDnZcffCcPRduU97Xgw/nbwRgRIfGNKla5lVmceizDXTdOAFJqeCG3ie/nrqXlWtbjvHw2DXKBNRg8KmlZKVnsm+cbniFnYcTwes+1WWohZLbv5/h8ckb2eddub2/0YTg3Hl5ZcoPNNs2UbdE6I8nSbr/grJ9dHn5cONRbDydaXVgLpaOtshaLe983I79zSagTknnytSN+K8ehsLSgpRn0Vz49PX5bI7xMxZy8eoNEhOTaNGpF8MG9Ca4fZu/lJas0XJ2+gbabpmApFBwf/tJEu+/oGIvXX7e3XwMW09nOu6bg6WDzq+qA9uyM2AibpWKU/79JsTfeUang7rVIi4t2sHzY9cLpH3u2HnqB9Zjy58bUWWoWDQmZ8WNhRvnsWT8MuKi4hiz4FMin0fx1e+6Mb2n9v/JxuWb80377NHz+AfW58fTm8hIz2BBrrQXb5zPovFLiYuKY+zCT4l6HsXaXSt1ae/7kx+Wb8LN05X1+9dg72CHVivzwcfB9G7e3+DB/oozR8/RMLA+P53ejCpdxdwxi7L3Ld24gAXjPyc2Ko4JC8cQ+TySdbt07Qgn9/3Bd8s30n90H5xcnRg3/1MANGoN/YOGmPTr9vGrVA7wY/rJFWSmq9g6PmdM/+DvJ7Jt4jqSohPYvXArfVeO5N2xXXke8oSzO3QTLGu0q0/dLk3QqDVkZWSyYfgKs3l46dgl6gTUYd0f63VLhI5bnr1vxg8zWTnxS+Kj4mn/UXu6DAnG1dOVLw+t5PKxS6ycuJJGQY0J6t0OjVqLKkPF4uGLjUU0WiJnraH4d3N1S4T+fIjMB89w6a5roU3ctg+nto1w7tQCWa1Gm5HJi08XAmDh6Ybv4rGgUIBCInn/H6TkM58ir+7zz76mzMaZSEoF8TuOkBEahnvPtgDEbTmAS7sGuAYHQpYarSqTp5+YOH8zyBotdyd/T60fpyApFbzYdpzUe88p1qclAM83HqHM2GAsXR2otKi/7hi1hvNtpuJSrwK+HzYl+fZT/I/qfH0w/0dij14zq3Vv8nfU/HEKKBVEbDtB6r3nFNVrvdh4hNJj38fS1YEKiwZka11sMwVrL1cqfzkMlAokhYLo388Sl18jgEbLoynfUHnbdCSlgqgfj5F+PwyvPrrHVtTGQxQf/QGWro6UWfCx/vw03GirWynn0dRveWf1KCRLSzKeRfHg01Vmpa4cu0StgNqsOvU1qnQVX43LGdM/5YfPWDNhFQnR8QT1e4+OQ7rg4unK0oNfcuX4ZdZOXIV/uwY0Cw5EnaUmU5XJF/lcv/PHLlA/sB6b/vyBjAwVS8bkrLw2f+Nclo5fRlxUPJ37d6Lr0A9w83Rj/eGvuXD8AkvHf0HJciWYuGICWo2Wp6FP+XzcMrNahw+dpGXrZly4dpj0tHRGfpKzFPW2n9bx6YhpREfFsGrNIhwc7ZEkiZBb9xg/RtdTGXr/EceO/MHJM7vQarVs2fgzd+/oeleOTd9Al026Z2PI9pPE3X9BdX0sv7H5GI+PXaNUQA0++mMp6vRMDo3LGXpoYWNFiSZVOTLZcKTxkUnfZi87qlZlcWTSt2Z9K3TedOzq/0GkNx0jJ3h7mFeyZ6EVjjHfNSssKVb0P1UoOqUzC+/e6nJzTqFpbfT77PVG/xCbKJxWI00hxkE1hdcFXc/S9ITBf4NHWuMhIv8Wi60LJw9VmYXXThadYfd6o38I5RusVPR3sVW8foWsf4JlVlmFogOQoMl4vdE/xI3k1w9P+6eY4lz39Ub/EKOfbS74ZJR/ibTlgwvtRrD79Ov/ub+mED0BAoFAIBAIBIK3CzEnQMwJEAgEAoFAIBAI3jZET4BAIBAIBAKB4O1CK4bDi54AgUAgEAgEAoHgLUP0BAgEAoFAIBAI3i5kMSdA9AQIBAKBQCAQCARvGaInQCAQCAQCgUDwdiHmBIieAIFAIBAIBAKB4G1D9AQIzGItF963LVYW0ge8ALwK59s1lFWmFo4QhfsBrz7XZhea1o06U15v9A+QSiEVCsCyENteMgvxw2SlFfaFpvUguXDyMEmhLBQdgBYVnheaVkyYY6FpuXkXThzUvCi88lfdwq3QtGq6uhealq2m0KT+E8jiOwGiJ0AgEAgEAoFAIHjbED0BAoFAIBAIBIK3CzEnQPQECAQCgUAgEAgEbxuiEiAQCAQCgUAgELxliOFAAoFAIBAIBIK3C/GxMNETIBAIBAKBQCAQvG2IngCBQCAQCAQCwduFmBgsegIEAoFAIBAIBIK3DdETIBAIBAKBQCB4uxAfCxOVgL+DJEnFgNVAZXS9KnuA8UBD4HfgEWAHRAGLZVneoz9uJpAiy/LnJtKcCvQANIAWGCzL8nlJkk4APkAGkAl8LMvyNRPHa4CbgKRPY7gsy2ckSSoF7JFlueqb+FiqWXUCZ/ZGUiq4+eMJLny128gmcFZvSgf4oU5XsX/sOqJvPQHA2smONosH4v5OMZBlDoxfT8SVB3hWLkGr+f2xsLZEq9FwZOoPWLs60GKGTufGjyc4v8ZYp8XM3pQJ8CMrXcX+ceuIyqXTdtFAPN4pBsjsH7+e8CsPqBBUj0aju+BezpdNHWYQefOxUZpFm1en/uzeSAoF97ed4OZqQ13nsj40/mIQ7lVLcWXRT9z6eh8ASmtL2u2chtLaAkmp5MneC1xb+ku+eenUvCYlZg0EpYLYbYeJXG1o79a5Kd7DugCgTc3g6eS1pN/R+eg1sD0e3VuBLJN29ylPxq5EVmWZ1SravDr+s3qjUCq4t+0EN0z41XSZzq9Li3P8svdxo+mKIdh5OiNrZe5tPU7Itwfz9Ss/ps1fxqnTF3BzdeG3zWv/UhpdZvSlckBNstJVbBm3huchT4xs3Ip50nfVKOyd7QkLecLm0avQZGmo3bERLYd0AECVpmLHtG8Iv/MMgOYDgvDvGgAyPLv3lO/Hr+aDSb2pFlCTzPRMvhu3imchxmXGo1gRBq0ajb2zA89CHvHN6JVosnRfHK7gX4Wun/VDaWFBSkISS7rOAKDVgPdo3LUFyDLh98LYOP4rukzqRZWAmmSmq9g47ivCTGi5F/NkwKpP9VqP+WH0SjRZGsr7V2bougnEPo8G4NqB8+z7cmf2cZJCYvLuhSRGxvPVgEV8OOOjf1zL1cedvss+wcnTBVkrc3bbUTxKeFFZr6O7VsY6bsU86bdqFHbODjwPecwm/bWq1qoOQWM+RJZltGoNv8zewKNL9wx8Gr97AYmR8TzuaRQ6s/EMqEHVOX2QlAqebTnOg1W7DPYX7dKIcsN1ZUKdmsHNid+SdPuZ2fRy49O8OnXm6OLFg20nuL3K8L5yKueD/7JBuFUrxfVFP3Fn7b7sfR3Pf4E6JQOtVous1nCgXf5f+baqVw+nkcNBoSR9715St2w1aWdRsQLua74iceZsVCdPoijiifOUKSjd3ZC1WtJ37yHt550mj32FQ9Na+M74GBQKErYfJmbtzwb7HVvVx2tMT9DKyGoNEXO+Ie3SbQCKLhqJU2Bd1HEvCW07PF+dvFj718X50+FISgWpu/aRsmmbSTvLShXwXL+K+OlzyDhe8K/LD5g1iNoBtVGlq1g5dgWPbj00smnX913aD+iATylf+tToSXJCEgD1WtWn+7ieyFoZjUbDd7O+4c7F2wbHdp7Rl0r62LQtn9jUZ9Uo7JzteR7yhC368l6rYyNa5IpNP+tjk4uPOz2WDdPfV1rObTvGn98foOOMvlQK8CMzPZPt49bwwoxWr1UjsXW250XIE7aNXo0mS4NnWV+6LhlMsSql2f/5dk6u35t9zJQ/v0SVko5Wq8VSreWXd02Xy+LNq9NI/y5wZ9sJruV5F3Ap60PzpYPwrFqKC0t+4vrX+0ymI/hvISoBfxFJkiTgF2CNLMsdJUlSAuuAecBe4A9Zlt/T2/oBv0mSlC7L8tF80mwAvAfUkmVZJUmSB2CVy6SnLMuXJEn6CFgCtDKRTLosy3769NoAC4Bmf8lHhUTLuX35qedCkiPi6bV7Ng8PXyYuNDzbpnRADVxLefNt07H41CxLq3n92NJxJgCBM3vz+MQNdg35EoWlEktbawCaTenO2eW/8PjEDUoH1KDZlO44+Lqxo+dCkiPj6bNrNg+OGOqUCaiBa2lv1jfT68ztx+ZOOp0WM3rz+OQNfh9qqBNz/zm/DV5B6/n9zfrnP68vB7svJC0invb7ZvPs0GVe5tJVJaZyfvomSrStbXCsRpXFgQ/no05TIVkoeffX6bw4fp2YK8YPGQAUCkrMHcz9HjPIioij0t4lJB66QEbo8xytZ1Hce38qmpepOAXUouTiYdxtPwFLbzeK9H+PW4EjkDMyKbNmPG4dmhD30zGzfjWc25cDPRaSGhFPh706vxLz+HX2s02UbGPol1aj5cLsrcTdeoKlvQ0d98/hxambBse+CZ2CWtEjuANT5ph/acuPys398Cztw9zmn1KyZjk+mDeQLzpNM7LrMKkHJ77dy9XdZ/lw3gD8uwZyevNh4sJi+LLrbNKTUqnU3I+uCwbxRadpOHu50rRfWxa0HEuWKoteq0bSZXwPipT2YUrzEZSpWZ5e8wYxv9NkI63gSb04/O0eLu4+Ta95g2jSNZATmw9h62RHzzkDWd53HvHhsTi6OwHg4uVGYL92fNZyNFmqTIauGkvH8d0pUtqbGc1HUrpmebrPG8jiTlONtDpP6sWxb/dyafcZus/7mEZdAzm1+TAADy7e4asBi0zmW+BHQUQ+eIGNgy1Vmtf8V7Q0ag07524iLOQx1vY2zDzyBXFh0cxpPopSNcvz4bwBLDNxrTpO6smJb/dxZfcZPpw3kAZdA/lz82Hunb7JzcOXAPCtWIKPVn/KvBZjso9rnssnsygkqi34iHMfzic9Io4mB+YReegyKfdfZJukPYvmTOfZZL1MpUhgDap//jF/Bk03n6YeSSFRd35fjnXTxYu2+2bz/OBlknLfVwmpXJq+ieJ54sUrjnwwD1V8ymu1UChwGj2KhDHj0MTE4L5uLRl/nkbz9KmRneOQwWRevJizTaMh+auvUN8PRbK1xf2bdaguXjI+NlcavrOH8Lj3dNSRcZT9fRlJR86jehCWbZJ6+joPDp8HwKZiKYqvmkhoy6EAJOw8StzGvRRfOvr1fuXRdRk7ithR49FEx1DkuzVk/HEG9RNjH52HDUJ1/tIbJV8roDa+pXwZ1nQw79SswOB5Q5nYcZyR3d1Ld7h09CJzt8832H7j9HUu6H0uWbEU476ayIjAodn7K+lj03x9bHp/3kCWmyjv7Sf14KQ+Nn0wbwD1uwZyZvNh4sNiWKWPTRWb+/HhgkEs7zQNrVrDrrmbeB7yBGt7G8buXoBalYVnaW8WNh9NiZrlCJ43gC87GZfZdyf14NS3+7i2+yzB8wZQr2sAZzcfIT0xhd9nbqBK6zom82pN97mkJSRTTqM0uV9SSDSe25c9+mdKlz2zeXr4Mgm5yn5GYiqnZ2yidBvTZf8/iZgTIOYE/A0CgQxZlr8HkGVZA4wG+qNr/c9G32I/G3hdM4kPECvLskp/XKwsy6bevs4CRQtwjk5AQgHsTOLtV5aEJ1G8fBaDNkvD3d3nKNva8AYv17o2ITv/BCDi6kOsneyxL+KClYMtxepV4OaPJwDQZmlQJaWh9wsrR92D3NrRDk1mFolPongZptO5s/sc5Vrl0WllqGOTW6d+BW6Y0Il/EE78owiz/nnULEvykyhS9P49+v0cJfIEsIy4JGKvP0KbpTE6Xp2mAkBhoURhaYGcTzyx9yuP6kkEmc+ikLPUxP/+Jy6t6xvYpF6+h+Zlqu7vK/ew8nHP3idZKFHYWIFSgcLWiqyoeLNann5lSXoSRXJuv1qb8Utt6Fd6dCJx+h6WrNQMEkPDsfN2M+/Ya6jjVw1nJ8e/fHzV1nW4+Iuu5e/p1QfYOtrh5OliZFe+YRWu79M9sC/sPEU1/cPuyZX7pCel6v8OxSWXLwqlEksbKxRKBVa21niX8eXsLycAeHQ1FDtHO5xNaFVsWJXL+84CcGbnCfxa1wOgfocmXDlwnvjwWACS45Kyj1EqlVhla1nhVcaXc3q/Hl8Nxc7R3qRfFRpW4cq+cwCc23mCGq3rvjbPXLzdqBpYi9M/6tobarSu869oJcUkZvcoqFIz0Gg0PLx4F4AnV0OxNaNTvmEVrul1Luw8STW9Tqb+fgKwsrM2uJ9cvN2oHFiTsz+arvi+wrVmOVIfR5L2LBo5S0P4b2fxbmP44pNwKZQs/X2WcPkBNj4FK9/ueeLF09/PUTxPvFDFJRFv4r56UywrVUTz4gWaiAhQq8k4egybxo2M7OyCu6A6eQptQmL2Nm1cPOr7oQDI6emonz5F6elhVsuuRnkyn0aQFaaLTS93n8KplWFs0qZlZP+tsLUm98VJuxCCJjH5jX20qlwR9fMXaMJ1PqYdOYZN04ZGdvYfdCb9xCk0CW/2KKvX2p/jO3Xl5f7Ve9g72eNaxNXI7nHII2L0vVy5ycjls42doc9Q8NhUrgCx6emVUJz1sSkpJjG7R0GVmkHUwxdUblmbS7/8AcCzqw+wcbTD0YzWDb3WpZ2nqKrXSolLIuzGXy+XRfI8Ux7uOkcpE8+UGDPPSsF/F9ET8NepAlzOvUGW5SRJkp4B5UzYX0E3VCg/DgGfSZJ0HzgCbJdl+aQJu7bAb2bSsJUk6Rpgg65SEfgaTbM4eruSHJ7zspkSEY+PX1kDGwdvV5Ij4rJ/J0fG4+DtilatIS0+mbZLB+FZqQRRN59wfOYmstJVHJ+1mfc3TaDZ1B5IColzX/6GV/UyOWlExONb01DH0duVpHBDHUcvV7QaDelxybT7fBBFKut0jup1XoedtyupufxLi4jHM49ufkgKifYH5uJUyou7Pxwm9qqZXgDAyseNzIjY7N+ZkXE41Cxv1t6jW0teHr8CQFZkPJFf/0b18+vRZmSSdOoaSaeumffLx5XUiFx+Rb6ZX69wKOaBe9WSxOTj17+Ni5cbibmu+8vIeJy93UiKSczeZu/qSHpSGlqNbnxnYkQ8Ll7GL3b+XQO4c+KaLp2oBI6v38PMM6vJysjk1h/XsXW0Iz6XVkJkPC7e7rzMpeXg6kh6Umq2VkJEHK56La8yPigtLBj/4yxs7G048v0+zv5yksSoeA6u38WiM2vIysjkzh83sHW0IyE8NpdWHC4m/ErLx6/Std5h6v7FvIxKYOe8TUToe5U++Kwfvy7YjLW+xdzFy+1f03qFWzFPHD1ceHAuZ7hEYmRcga6Vcy6d6m3q0n5Cdxzcnfm6/8Ls7V0+68uuBVuyfTKHjY8r6bmuYUZEHC61TIVjHcV7NCf62LV803yFrbcraXnihXutN7ivZJnAbZOQZZkHm47xYMtxs6YKD0800THZvzUxMVhWrpzHxgObJo2J/3QMzhUrmkxH6e2NZfnyZN2+Y1bLwtudrFyxKSsyDju/d4zsnFr74zWhLxbuzjztP8tsegVF4emBJjrn5VsTHYtVlUpGNrbNGhM7fCwulSq8Ufru3u7E5fIrLjION293EqILXpmo38afXhP74uzhzLx+hj4754lNiQWITS/zlPdsna4B3NXHpty4FvOkaOVSRD14YTIOJufSsssTmxIj4kxqGSHLDNo0GWSZh5uPc2ercbm093YlJc+7gNdfeKb85/iPfSdAkqS2wApACXwjy/JCEzbNgeWAJbpG47800uMVoifgryMBptp+89ueL7IspwC1gUFADLBdkqR+uUy2SJL0HJgIrDSTTLosy36yLFdEV1nYqB+6VCAkSRokSdIlSZIuhaoiTZxjHntTbskyCgslXlVLcW3TUTYFTSMrXUW9Ye0B8OvdguOzt7DOfxQnZm/Br3fL1+pgwgVZllEo9Tqbj7IhaBqZaSrq63UK4OvrdfNB1srsaj2VHXVG4lGzLC4ViuWnVmAtx4ZV8ejWkufzNgKgdLbHpXU9bjYYzI3a/VHY2uDWJb/7/u/5BWBhZ02LdaM4N3MzWSnpb3bwP4nJ4mVUCF9rU65BZfy7BrBroW5cta2TPVVb1WZWkxFMrz8UaztrXE31eBhpmS6HoGvtL1mtDCs+ms8Xfeby3oj38Srtg52TPX6t6jKpySeMqz8IKzsbXLzdjdLJq2Xqrn1lEnbrMdMaDWNeuwkc/+EAQ9bp2heqBtYiOe4lz249LlhCf0PrFdZ21gxeM5bI0Odk5q18F0Ant82NgxeZ12IM3wz6nHfHdAWgSmAtkuOSCMvtkzlMOmLa1L1RZUp0D+DOXNPj0I2TLnjapjjUcTb720zjeM8lvNOvJUXq5/NS+5p8AnAaMZzktevMTm6UbG1xmTOLpJWrkNPS8tEyX6Zzk3ToHKEth/J08Dy8xvQyn15BKUC5dPn0E16uNu/jm2LKr/w4f/AcIwKHsnDgPLqPM/TZ9On/9di0e6HhnA8rO2s+WjOa32dvRNYY+583nQKdjwlWBc9k+XtT+KbfIqr0bYmPqXL5N5+VgtejH1K+GmiHbp5pd0mSKuexcQG+AjrIslwF+ODv6oqegL9OCBCce4MkSU5AccBU02lN4E4e++LAq9k1a2VZXqsfVnQCOCFJ0k2gL/CD3qYncB1YiK6wdJEk6Xt92uGyLAflTl+W5bP6eQWeBXVKluV16OY2sKXTTNnRN+fFyMHHjZQ8rSjJkfE45hq24ujtRkpUIrIskxwRT+Q1XVbc33eB+kN1L+dVgptwbMYmAO7tOU+bJYNIiU7MScPHjZSoPDoR8Tj5uvNqZK+jt5vuGL1ORG6dAlYCUiPisc/ln52PG2l5dAtCZlIakWfuUKx5dRLvPTdtExGHlU9Ol7yVtztZkcZDemwrlaTk4uGE9p6d3cXu1LgGqrBo1PG64SWJ+8/iULsi8b+Y6iTStVDa5xriYOftRlpkwf2SLJS0WDeKh7+e4en+NxuH+0+gsHFCYaMbQvQy6jEuvjnly9nbjaQ81yg1PhlbJzsUSgVajRYXHzde5iqnvhVL0H3hYNb2W0haom48doXGVYkPi6Hmuw1o0D0QW2d7VGkZuOXScvV2IzHPsKuU+CRsneyztVx93EnUayVExpGSkExmuorMdBX3L9ymWKVSAMSGRVP33YY06d4CB2cHVGkqXH09gHt6LXcS8/iVEp+MnZFfuvPJyFUxCzlxFeXcAdi7OlK2TgWqt6xD7fcaYutoh6SQiH0W9a9opSYk07xfWzqO605GSjq3j181uFYu3u68NKGT37V6xcMLd/Ao6YW9qyNl6lSgWsvaVA7ww9LaChsHW1xWfcLV4auNjssIj8c21znY+LiTYaLsO1YqQY2lgzjfYyFZCQUYo4/uvrLLEy/S3+C+So9KBHRDhsIOXMa9Zlmiz98zaauNiUFZJCdsKz090cbGGthYVKyAywzdJE7J2Rkr//okaTSo/vwTlEpc5swi/fARVKf+yPe81BGxWOaKTZbe7qjzGW6YdiEEq5I+KF2d0CQkmbV7HdroGJRFimT/VhbxQJPHR8uK7+A2Rzf2XeHsjE2D+iRqNGScOm0yzXZ9gmjVvQ0AD26E4p7LL3dvdxLy8Ss/bl8IwbuED52GBNO0Y1OsJAXPrj/MU95fH5ucfdxIylXefSqWoOvCwazLFZtAN8R09O/zsHW0o9WoLoRdf1TAOGif695yN9AyxyublLgknhy4TBG/skTkKZepEfE45HkX+CvPyv8c/605AfWAB7IsPwKQJOlHoCOQezZ6D+AXWZafAciybDyO7Q0RPQF/naOAnSRJfSC7FrcU3Qu7QbOLJEnVgenoXtyzkWU5TN9q7yfL8lpJkipIkpR7jIgf8DTPMVnANMBfkqRKsix/pD/eoAKg162IrlspLu++ghB5/RGupb1xLu6JwlJJxfb+PDx8xcDm4eErVAluDIBPzbKoktNIjU4kLeYlyRHxuJbxAaBkoyrEhepe4VOiEijur+v2LdGoCvGPwg10KrX350EenQdHTOukxrwkKSIeNxM6ryP22iOcSnvjoNct09GfsENXXn8gYO3miJWTbuqH0sYSnyZVSXxofvJs6vVQbEr7YFW8CJKlBW4dG5N4+IKBjZWvB2XXT+LxqC9QPc5JKzM8Boea7+jmBACOjauT8cB0ZQMg5rqxX88OF8wvgCafDyTxQTi31u8v8DH/JNqMJNSJL1AnvuDmoUvU7dIUgJI1y5GRnGbQ3f6K0LO3qRGkG8dcL7gptw7pKi+uvu70XzuGTaNXE/M4Z35IQngcJWuW4/xPJ1gSNIl750K4f/4ODbo0B6BMzfKkJ6cZDAV6xb2zIdQOagBAw+DmXDukm5R57dBFytetpBv3b2NFGb/yRDx4Tnx4LGVqvsPpn44xO2g898/dJvT8bfz1fpXWa5ny697ZEGoF+QPgH9yc63q/nDyds21K1iiLJClITUjm98XbmNJgKONrDmBFrzmEHL/KT7N++Fe0AEpWK8uZHceY7D+EG4cuUk+vU6pm+XyvlZ9ep15wM27qdTxKemXbFKtSGqWlBakJyexevI3PGgxjVuMR/DBiBffP3DJZAQBIvPYQ+zLe2JbwRLJU4tupAZGHDEZtYlvUnbrfjebq8NWkPjLu7TRH3LVHOJb2xl5/X5Xs6M/zAsYLpa01FvY22X/7NKtK4l3z93DW3XsoixVD6eMNFhbYtAhEdfqMgU1s1+7EdO1GTNduqE6eJGnZcl0FAHCeOAH102ek7fjpteeWdiMU61K+WBbzQrK0wLl9U5KO5IlNJX2y/7apUhbJ0uJvVQAAMu/cxaJ40Wwf7VoGkvHHWQObqOCeRHXpQVSXHqQfP0ni5yvMVgAA9m/cx5h2oxjTbhTnD54jIFg3GvadmhVIS057o6FA3rl8LlO1LBZWFvy2didj2o3i86BJ3MoTm8zdVw/MxCYXX3c+WjuGLXliE0C3RYO598cNZvoP44ugyYQcukSdLk0AKKGPg8kmtUKorteqE9yUkDxlPy9WttZY68ulla01xZpWJd5EQ1b09Uc4l/LGUV/2y3bw58kbPFMEhqMs9P8G5TEpCoTl+v0c47mf7wCukiSdkCTp8qv3z7+D6An4i8iyLEuS1Bn4SpKk6egqVPuAKUADoIkkSVfRTRKOBkbmtzKQHgdgpb7LRw08QDc0KK92uiRJS4FxwIA8u1/NCQBdZ2RfWZY1+q7sCvrhRK8YLcuy2aeErNFydPoGgjdNQKFUcHP7SeLuv6BGL11gvb75GI+OXaN0QA0G/rGUrPRMDoxbl3380c828O6XQ1FaWpD4LDp736FJ3xIwU7d8pUaVxeFJ32Lr6cwHGyfoliLdcZK40Bf49dTpXNui0ykTUIOPTy1FnZ7J/tw6Mzbw3oqhKCwtePksmn36feXb1KHlrD7YujkS/P04om8/5ac+iw38OzdtA623TkBSKAjdfpLE+y+o0Fune2/TMWw9nWm/fw6WDrbIWi2VP27Lr80nYuflQpPlg5EUCiSFxOPd53l+5Bpm0Wh5Nn0972yZAQolcduPkHE/DM9eularmM0H8RndFQsXR0rOH6I7P7WGO++OI/VqKAn7zlDpwDJQa0gLeUzMFvPLdsoaLWenb6DtFp1f9/V+VdRft7ubdX513JfjV9WBbdkZMBG3SsUp/34T4u88o9PBeQBcWrSD58eum/ctH8bPWMjFqzdITEyiRadeDBvQm+D2bQp8/O3jV6kc4Mf0kyvITFexdXzOMqODv5/ItonrSIpOYPfCrfRdOZJ3x3blecgTzu7QjWttMzIYe1cHPpirWyFKq9awtMNUnl57wPX95xm/dwFatZYnIY/4cfZ3fDitH/NPriIzXcX347/K1hr1/RR+mLiGl9EJ/LxwE4NXjqbz2G48C3nCnzt0t3XEwxfcOnmVmQeWImtl/th+lPD7uph+ef9Zpu9dglat4XnIE36a/QPvT+vL7JNfkpmeycZcWp98P4nNE7/mZXQCvy3cwoCVn9J+bDfCQh5zZoduomPNdv407dUarUZDVkYm345YbjYPbx2/StWAWv+4Vtk6FfAPbsbzO0+Zsm8xErpxyJ+dXEFmeiZbxq/Jda0msW3i1yRFJ7Br4Rb6rRyVfa3O6XX82tWnbpemaNQ6nR+Gm/fJHLJGy60pP+C/bTKSUkHYthOk3HtOyT66IYdPNx6h/JguWLo6UG1h/+xj/mhjvFqSqbQvTd1A4FZdnHr440le3n9BeX28CN10DBtPZ9rtn4Olo+6+qjiwLbubT8TGzYGm334K6Hranvx6hogTN8yLaTQkLV+B6+dLQKEgfd9+1E+eYNtBt6Rk+q5dZg+1rFYN27ZtyHr4EPdvvwEgef16Ms+dN6OlJXzGWkpvnKVbIvSnI6hCn+HWoy0A8VsP4NS2Ia5dApHVauSMTJ6NyImjxVeMw96/GhauTlQ88z1Ry7eSsOPwa/MTjZbEpSvxWL4IFEpS9+xH/fgJdp11PblpvxovE/0mXD52idoBdVjzxzrdEqHjVmTvm/bDDFZPXElCVDzvftSeTkO64OrpyvJDX3L52GW+mriSBkENaR4ciCZLTWZGJks/WWyQ/u3jV6kU4MdUfWz6MVds+vj7iWzXx6Y9C7fSe+VI2o3tyouQJ5zLE5vezxWblnWYSuk6Fagb3JTwO08Zt083JHz/4u3EPYtm0snlZKWr2D7+62ytAd9P4KeJ60mKTmDvwm30WjmCtmM/5EXIE87rtRw9nRm1ax42DrbIskyT/u1Y0mo89q6O9FunW4FLoVTy7LczhJkol7JGy5/TN/DuZl3Zv7f9JAn3X1BZ/0y5rX+mBO+dg5X+mVJtQFu2B0783w4nfQ1yIX4nIPcoCzOYHASY57cFuiHjLQBb4KwkSedkWb7/V89LetMxcoK3h89L9Cq0wqEt8KyFv4+nunB0qkkFG2bwT3Adh0LT6nNtdqFpjaszpVB0UimkQgFY/h/tgLV8/bSnf4xWGYWTh0kK00sm/hu0KGe+Z+CfJibsr6/Y9aa4eacWis7wF/aFogNQSlF4WspCvK/MLRH6bzAkbHMhPvVNkzI5uNDecRwW7MzXX/0S8TNlWW6j/z0ZQJblBblsJgE2sizP1P/+FjiQX2Pu6/i/+TQSCAQCgUAgEAjMoZUL79/ruQiUlySptCRJVkA3IG+X3+/oRplYSJJkB9Qnz1zTN0UMBxIIBAKBQCAQCP5HyLKsliRpOHAQ3VzO72RZDpEkaYh+/1pZlu9IknQAuAFo0S0jeuvv6IpKgEAgEAgEAoFA8D9EluV96OaW5t62Ns/vJcCSf0pTVAIEAoFAIBAIBG8X/60lQv8niDkBAoFAIBAIBALBW4boCRAIBAKBQCAQvF3IhbdE6H8V0RMgEAgEAoFAIBC8ZYieAIFAIBAIBALB24WYEyB6AgQCgUAgEAgEgrcN0RMgMEuPYi8KTSv4aaFJcbBt4RT7J0dtCkUHYJM6stC0bhTSV3wBPr80v1B01LdOFIoOAPGFd62i5x4rNC2vtYMLTavL+98Xik6CJr1QdAD2PnUtNK1obVqhabmGF87XiZ0Uhfc606bwigV+FSIKTSsl1rrQtP4LyKInQPQECAQCgUAgEAgEbxuiJ0AgEAgEAoFA8HYhegJET4BAIBAIBAKBQPC2IXoCBAKBQCAQCARvF1rxnQDREyAQCAQCgUAgELxliJ4AgUAgEAgEAsHbhZgTIHoCBAKBQCAQCASCtw3REyAQCAQCgUAgeLsQPQGiElCYSJKkAW6iy/fHQG9ZlhMlSWoOjJNl+b1ctj8Ae2RZ/lmSpBOAgyzLdfT76gCfy7LcXJIkO2A9UB2QgESgrSzLKXm0vYBvgeKAJfBEluWgNzl/6/p1cf50OCiVpO3eS8qmbSbtLCtVwGPdahI+m03G8VMAuEyZgHUjf7QJicT06l8gvdGzR9AwsD4Z6RnMGb2I+7dCjWxmrpxKxRrvoM7ScOfaXRZOXIpGraF155b0HtYNgPS0dBZPXs6D2w+NjldWrYNN92FIkoLMP/aTuX+74f4K1bEbPhttrO4DT1lX/iRz92YkV09sB05AcnIDWUvWqX1kHvk1X38cmtWi6Gcfg1JB/PbDxKz52WC/U6v6eI3pCbKMrNYQPvsb0i7dBqDY4pE4BdZFHfeS+22GFyj/cjNi9if4B9YjI13FwtGLCb31wMhm6srJVKj+DposNXeu3WPppC/QqDVm0+wyoy+VA2qSla5iy7g1PA95YmTjVsyTvqtGYe9sT1jIEzaPXoUmS0Ptjo1oOaQDAKo0FTumfUP4nWcANB8QhH/XAJBB6eiKJjkGeH2wnjZ/GadOX8DN1YXfNq8tWMaY4fStRyzecRStVkvnxjXo39bfYH9yuoqp3+4mMiEJtUZLn1b16NSoOqosNf0/30qWWo1ao6VlrQoM69Akf617L1i85wJarUznuuXp37yawf4fTt1i37VHAGi0Mo+jX3J8WlcSUjOYsO1ktt2L+BSGtvSjV+PKZrVsGtbFbdwwUCpI+XU/ST/8aLDftllDXIb1A60WWaMh4fM1qK7dAsCxZzAOndqBLJP14DGxM5dAZpZ5v27cZ9Gmfbo8bF6bAe2bGeZhWgZT1vxEZNxL1FotfYMa0alpbZ5ExDBhVc59+Dw6gWHBLejVtqFZrcGzBlM3oC6qdBXLxi7j4S3je/29vu/RaUAnfEv50q1GN5ISkrL3VfOvxqAZg7CwtCApPomJH040q1UYcckUfWYOwC+gNpnpKtaOW8mTW4+MbFr3bUfb/u3xLuXDYL8+JCckFyhtgE9mDaVeYD1U6RksHrOUByZiRMe+HegysDNFS/nSpfoH2Xlo72jHpBUTKVK0CEqlkp/W/czBHYfMan0082NqBdRGla5i9bgVPDbhS9u+QbzbvwPepXzo79cr25fGnZrRaUgXADLSMlg/dQ1P7zzJPq7njP7UCKhFZnom68et5GnIY6O0PYoVYdiq0dg7O/I05BFfj/4STZYaW0c7Bn8xCveiHiiVSvav/50/fjoOgJ2THf0XDqNohRI4aeDW6LUkXtJde4+AGlSa2xeUCp5vOcbjlbsM9HyCG1FmuC7eqVNV3J7wDcm3dfGu6vLBeLaqRWZsEqebjTebZ6+wqlcPp5HDQaEkfe9eUrdsNWlnUbEC7mu+InHmbFQnT6Io4onzlCko3d2QtVrSd+8h7eed+WrZNa6Dx+QhoFSS9PN+Er/ZYbDfPrABbiP6ZD+zYheuJeNKCAAlD29Am5quiyVqDc8/HPFa3wSFjxgOVLiky7LsJ8tyVSAe+OQNji0iSVI7E9tHAVGyLFfTpzsAMPVkng0clmW5hizLlYFJb3TmCgXO40YRN3YS0T36YduyBRalSpq0cxo2CNX5iwab0/YdIH60+QdrXhoE1qd46aJ80LgXCycuZcKC0SbtDv56hG5N+9KrRX+sbKzo0ONdACLCIhj2/qf0bjWQ75ZvYtKiscYHSwpse44g7YsppEwfiGX9ABQ+JYzM1KE3SZ01hNRZQ8jcvVm3UashY/vXpE4fQOr8kVgGdDB5bDYKBUVnD+Fxv5ncb/UJLh2aYl2uuIFJyunrhLYbSWjQKJ5P+JJii3KCZsLPR3ncd2b+mWaG+oH1KFa6KD0b92XpxC8YvWCUSbsjvx6lT7OP+Kjlx1jbWPFud/N1xMrN/fAs7cPc5p/y45T1fDBvoEm7DpN6cOLbvcwNGE36yxT8uwYCEBcWw5ddZ7Oo3UQOrvyFrgsGAeDs5UrTfm1Z2n4KC9voHoiStX2B/OwU1Iq1y+YWyDY/NFotC7YdZvWID/hl5kAOXLzNw/BYA5vtx69QxseDHdP7883YHiz7+ThZag1WFkrWj+7Gjun92T79I86EPObGI/Nf3tZotSzYdY7VH7Xkl9EdOXD9MQ+jEg1s+jWtyo6RHdgxsgMj29SidmkvnO2sKeXpnL192/D3sLFUElgl/zLoNnEE0SOmEB48APu2AViWNrTPuHCFiK6DiOg+hLhZn+M+fQwASk93nLp1IrLXMCI+/BgUSuzbBOTr1/wNu/lqfB9+XTSSA2dv8vBFtGEeHjlHmaJF+Gn+cL6dMoClWw+QpVZTyseTHfOGs2PecLbNGYaNtSWBdSqZ1aoTUIeipYoysOlAvpz0JcPnma4k3750myk9phAVFmWw3d7Jnk/mfcLsAbMZ2nIo84ea/0p1ocQlE/gF1MK7tC9jmg3jm8lr6D/X9NeZ7126y/yeM4gJiza53xz1AupStHRR+jb5iC8mrmDUfNMvbCGXQpjQfRKRYYZfve7QtwNPQ58xuM1Qxn44nsHTdRUqU9QMqI1PaR9GNBvC15NX8/HcoSbt7l66w+yenxGd53pFh0Ux48MpjGs7ip+/3M7gBTmP0erNa+Fd2ocJzYfz/ZQ19J03yGTaXSf15uC3e5gYMJzUlyk069oCgBa92xL+IIzp7cayoNtndJvaF6Xej54z+nPz5FUmtxjJ6cAJpNzX39cKicoL+3Opx0L+bDIWn86NsH+nqIFe+tMYzneazemAiTxc9gtVluac14sfT3K52wKT52mEQoHT6FEkjJ9IbJ++2LQIRFnS9HPYcchgMi/meg5rNCR/9RWxvfsSP2QYdp07mT42Vxqe0z4hfPA0nrX/GMegACzLGsaLtHNXCes8lLAuw4ietowisw3vhxf9JhDWZZioAPyHEZWA/x1ngaKvtcphCTDNxHYfIPstQ5ble7Isq8zYPc9ld+MNtLGsXBH183A04RGgVpN+5Bg2TRoZ2dm/35n043+gTUg02J557QbapCQje3M0bdOI/T/rWpJCrtzBwdke9yJuRnZnj53P/vvOtbsU8fEE4OalEJJfpuiPv00RHw+jY5VlKqCNDkeOjQSNmqwLJ7Coab61MTfyy3i0z/QtZRnpaCOeIbkaa7zCzq88mU8jyAyLQs5Sk7j7FE6t6xvYaNMysv9W2FmDnNP6nXohBPXLgrfq5aZR64Yc/PkwALev3MHByQE3E3l5/tiF7L/vXLuHp4k8e0XV1nW4+Iuul+fp1QfYOtrh5OliZFe+YRWu79Ndows7T1GtdR0Anly5T3pSqv7vUFy8c85HoVRiaWOFQqkASQKt+d6I3NTxq4azk2OBbPPj1uMIihdxoZinC5YWStrUqcSJ64atvZIEqapMZFkmXZWJs70NSoUCSZKws7ECQK3RotZokSTJvFZYLMXdnSjm5qjTqlGaE3fCzNrvv/6YtjVKG20//yCCYu6O+Lo6mD3WqmoF1M/DUb/Q3cOpB09g29zwHpbTc8qgZGuDQQ+MUolkbQ1KBZKtNZqYOPN+PXxOcS93ihVxw9LCgrb+1Thx+Y6BjYREWoYKWZZJy1DhbG+LUmH4SDof8pDiRdzw9XA1q+Xf2p+jO48CcO/qPeyd7HEtYmz/KOQR0c+NX46bd2zOmf1niAmPAeBl3EuzWoURl0xRu1U9/tipa5F+cPU+dk72uJjw8WnIY2KfxxQozdw0bN2AwzuP6M736l0cnOxNxogHIQ+Jeh5ltB1Zxs7BFgBbexuSE5PN9iLWbVWPk3pfQq/ex96ML09CHhNj4nrdv3yXVH3sCL1yD3cf9+x9tVrX5fQvut6xh1dDsXO0x9lEXKrUsCoX950F4M+dJ6jVut4rR7Cx1/lhbWdDamIKWrUGGwdbKtSrzMntunImZ2lQJ6UB4FKrHGmPI0l/Go2cpSHytzN4ta1joJd46T7ql7pzTrwcio1PTt4mnLtLVmKqybzKi2WlimhevEATobuHM44ew6ax8XPYLrgLqpOnDJ7D2rh41Pd1cUxOT0f99ClKT/Plz6ZaBbKehaN+HglZalL2n8AhsIGBjZz7mWVrY/DM+v8BWZYL7d9/FVEJ+B8gSZISaAHsep1tLs4CKkmS8ja/fQdMlCTprCRJcyVJKm/m+NXAt5IkHZckaaokSb5vcs5KTw80UTkBWRMTYxRAFB4e2DRrQtpvb+KWaTy9PYgKz9GLiYjF09t8wFJaKGkb3Ipzxy8Y7WvfLYizJrZLLh5o43MemHJCLAoXE5WFspWxn7kWu0/nofA1bjmR3L1QliiH5tFds+dn6eVOVq7W5KyIOCy93I3snNr4887RNZT6bgbPJ6wwm96b4Ontkf2CAxATEfPavGwd3JILJy6atXHxciMxPOcl8GVkPM7ehi8N9q6OpCelodXo1mJOjIjHxcv4xcK/awB3TlzTpROVwPH1e5h5ZjVzLqzVdSVnpRfIz3+K6MRkvF2dsn97uToSnWgwuo5uAbV4HBFHqwmreX/2d4zv2hKFQveyr9Fq+XDO9wSOW4l/pVJUK23+VotOSsPbOaenw8vJjuiXpl8I0jPVnLn/gpZVjcvgwRtPaFfduHKQGwtPD9SRue7h6BiURYzLoG1AI3x3fkeRFfOInfW5zjYmjqRNP1F031aKHdqBnJxKxrnL5v1KSMLbzTn7dxE3J6ISDBsBurXy51F4DC1HLOL9KauY0PtdFHkqAQfO3aRtg+r5+uXh7UFMRE75jo2MxSOf8p2XomWK4uDswMLtC1mxdwWBwYFmbQsjLpnC1dud+Fz3W3xkHK4m7qW/iodRjIjFw9u4bJjjtx92UaJcCbZf2sr6w1/z1Yw1Zl983LzdicsVC+MiY3EzEQsLQmC3Vlw9cSX7t6uXm0Ha8ZFxuObxw8HVkbSk1Oy4lBCRk5dHNuzHt1wxVlz4hnkHl7Fl1nfIskyREl4kxyUx8PPhzN67hCrLBqG0swbA2tuN9FzXJiM8Hmtv89emWI8AYo5d+0v+Kjw80UTnXCdNTAwKT888Nh7YNGlM2u/mn8NKb28sy5cn6/Yd8zZe7mRF5mipI2NRFjEu6/YtGlJizzf4rJ1D9LRlOTtk8P1mPsV+WoXTB6YGMQj+C4hKQOFiK0nSNSAOcAMO67ebqybm3T6XPL0BsixfA8qg6ylwAy5KkmTUdy7L8kG93XqgInBVkiTPvHaSJA2SJOmSJEmXNkeF595jfHJ5grzzp5+Q9NXX/8wHOEy0nuZXmR4//1Ounb/B9Qs3DbbXauhH++5BrJ6/rkAaebNc8/QBKRN6kjpzCJlHf8d2+CxDc2sb7IZ9RsaPayAjzfwJmtIy4VDSwXPcbzGUp4Pm4TWml/n03gSTeWk+M0fPH8WN8ze4eeFWPmkabzJKswA25RpUxr9rALsW6sa12jrZU7VVbWY1GcH0+kNBUiBZm2/d/jcwlTN5XTkT8pgKxYtwePEnbJ/2EQu3HSYlXdcBp1Qo2DH9Iw4uHMatJxE8eGG+Zdaklpmeg1N3w/ArWQRn/cvHK7LUGk7eCaNVtVLmndIlXKATSD9+mvDg/sSMnYHL0I8AUDg6YNe8IS/e68XzNl2RbG2wD2phVspU8crr15mboVQs4cORlRPZMe8TFmzYTUqunogstZqTV+7Sul7V/P0yqV/wljelUkm5auWY0W8G03tNp/vI7hQtbaaTtjDiUsFk/9FWV9PhqeDp12lWm4e3H9K1Tg8Gtx3G8DmfYOdgZ0arYLHwdVRpUI3Ari3ZvGBD7sRfm7Yp/Ve+Vm3qx7PbjxlVbyDTg8bRe/ZAbBxsUSiVlKxahmObD/LZu+PRpKkoPaKjPkFTZ2emAtSoMsV6BHB/julx/K+lAOXAacRwkteuM/sclmxtcZkzi6SVq5DT3vCZZcKv1KNnePbeQCKGz8RtZN/s7c97jub5+8OJGDwV5+4dsKn95vfxv45WLrx//1HExODCJV2WZT9JkpyBPejmBHyJrlKQtz/UDTAYiCzL8jFJkuYA/nm2pwC/AL9IkqQFgiRJCgQ+1psEybIcLstyPLAV2CpJ0h6gKbAzT1rrgHUA4Q0DskuuJiYGpVeRbDulpyfaWMPhAJYVK+A6+zMAFM7OWDesDxoNGadOFyhzgvt2okNP3djZO9fu4uWbo+fp40FsVKzJ4/qP7oOLuwuTB35msL1spTJMXjKOMb0nGUwCzPY1IQaFW049SHL1QJuYZ4hDrhd79c0L2PQageTghJySBEoldsNmkHX+GOorf+brW1ZkLJa+Oa0olj7uZEXHm7VPvRCCdUkflK5OaEyc++vo1LcD7/XQjem/e/0+nr45fnr6eBIbZXooR9/RvXFxc2b6xC/MpmkjWfDs+kNcfHNa2Jy93UiKSjD0IT4ZWyc7FEoFWo0WFx83Xkbn2PhWLEH3hYNZ228hafqW9gqNqxIfFkNqvG7okzYzFcnCGlll2BL/b+Ll4khkrjyPSkjG08WwIvL7mZv0b+uPJEmUKOJKUQ9nHkfGGbT6O9nZUOed4pwOeUS5okb1bZ2Wkx2RuVr+o5LS8HQy/fJ0wMxQoD/vv6Cirxvujrb5+qWOjsHCO9c9XMQz3yE9qis3sSzmg8LFCZs6fqhfRKJN1A2VSTv2J9bVq5C676hpv9yciIzPGVYTHZ9EERfDoVq/n7pC//ZNdXno5U5RT1ceh8dSrWwxnV/XQ6lYygd3Z+NK4Ht93qNN9zYAhN4IxdMnJ389vD2IM1O+TREbGUtSQhKqdBWqdBW3zt+idOXSvHisG2VZ2HHpFa36tCOgWysAHt14gFuu+83N252E6ARzhxaIDn3bE9Rd10J73yhGeBAXZT4+5aXth63Z9pVu0mj4k3AiwyIpXq44967dA6BNnyBa6n15cOMB7rliobu3B/H5xEJTlKhYkiGLPmF+39k06tCElt1aIUkSj6/r0n41eM/N252EPH4kxydh52SfHZdcfdxJ1Odlkw8C2btGt8BD9NNIYsKi8S1blLjwWOIj43h0TZdy1O7zlB6hX9ggIh7bXNfGxtcNVaTxtXGoXIKqywZzqftCshL+WjzTxsSgLJJznXTPYcPyZ1GxAi4zdGVOcnbGyr8+SRoNqj//BKUSlzmzSD98BNWpP/LV0kTGYumdo2Xh7YEm2vx9lXH5FpbFdfFCm5iEJkaX75r4l6QePY1N9YpkXM6nYUnwP0H0BPwPkGX5JTASGCdJkiUQCvi+asGXJKkkUAO4ZuLwecCEVz8kSWokSZKr/m8roDLwVJbl1fpJyH6yLIdLkhSoX0kISZIcgbLAs4Kec9adu1gUK4rSxxssLLBtGUjGn2cMbKLf70F0cHeig7uTcfwkLz9fXuAKAMDODb/Rt/XH9G39MacOnqbd+60BqFKrEqlJqcSZeFC07x6Ef/O6zPhkjkHLlZdvERaun83sUQsIe/Tc6DgAzeN7KLyKInl4g9ICy3rNUV87a2AjOeXUzRSlK4Ck0FUAAJt+Y9FEPCPzUP4rLACkXQ/FqpQvlsW8kCwtcGnflKTDhkMBrEr6ZP9tW6UskqXFX6oAAPy2YRcD2wxhYJsh/HngNG3e1z2AK9eqRGpyqsmH7rvd21G3WR1mD59nshXwVZpLgiZx89Al6nZpCkDJmuXISE4jKSbR6JjQs7epEaSb+1AvuCm3Dl0CwNXXnf5rx7Bp9GpiHkdk2yeEx1GyZjks9ePqFZa2oDG/As2/QZVSPjyLTuBFbCJZag0HL92hWY1yBjY+bk6cv/sUgLikVJ5ExVPM04X45DSS9ONkMzKzOH/3KaXzGVZRpZgHz2KTeBGfrNO6/phmlYoZ2SVnZHL5cRQBlYsb7TNXOchLZsg9LIoXxcJXdw/bt2lO+knDe9iieE4lxqpiObC0RJuYhDoyGqtqlZBsdL0QNvVqkvXYfPioUqYozyLjeB4dT5ZazYFzN2lWq6KBjbe7C+dDdCvjxL1M4UlkLMVyjQ3ff/YG7cwMBdqzcQ8j2o1gRLsRnD14lhbBul6JCjUrkJqc+kYvyOcOnaNKvSoolAqsbaypULMCYaE58zIKOy694vDG/UwJGsOUoDFcOnSeJsG6kaDlar5DenJa9ovrX2XXht0MaTuMIW2HcfrgGVoFtwSgUs2KpCanvdGLeXR4DLUa+QHg4uFC8bLFiHiac18f3LiP8UGjGR80mouHztFM70v5mu+Qlpz6Rr54+How/uvJrBy9nIjH4dlpfxY0jiuHLtCoi24VqrI1y5OenMZLE3Hpztlb1A3SjW9vHNycK4d0sTg+PJbKjXSrczl5OONTxpfoZ1G8jEkkPjwW7zK6+8O9SVVS9RODX159iF0Zb2xLeCJZKvHu1JDog4ZD5WyKulPzuzHc+GQ1aY8i+Ktk3b2Hslix7OewTYtAVKcN7+HYrt2J6dqNmK7dUJ08SdKy5boKAOA8cQLqp89I2/HTa7Uybt3DsmRRLIp6gaUFDu2ak3r8nIGNZYmceGFdqRySpQXaxCQkW2skO12jhGRrjW3D2mSGPvnLfv9riJ4ApP/yhIX/a0iSlCLLskOu37uBHbIsb5IkqRGwFLBBt7rPFFmWD+vtTqBbQvSS/vdlIFm/RGgfYBy6jkIFsBeYKOe5sJIkjQc+AtR6u+9lWV6a3/nm7gkAsG5QH+dRn4BSQdqe/aRs2IJdp/YApP222+BYl6kTyThzNmeJ0FnTsK7ph8LFGW18Asnf/EDann3Z9sFPjfXHzRtF/ea6Zf/mjlnE3Rv3AVi6cQELxn9ObFQcfzw9QuTzSNJSdePGT+77g++Wb2TyknE0D2pK5AvdJDaNWkP/oCEAHGyb0wFmUa0e1t2GIikUZP55kMy9W7FsplupNevkHiwDO2LV/D3QapAzM1FtX4vm4W2U5apgP3k5mrBH2d2xql++Q30z58X+yVEbA38cm9fGV79EaMKOI0Sv3oFbz7YAxG85gOeQYFy7BCKr1WgzMomY/332EqElvhyHvX81LFydyIpNJOqLrSTsOJyd9kh1/uPmR80dQb3mdVFlqFg0Zgn39Hm5cOM8loxfRlxUHEefHCTyeRTpqbrej1P7/2Tj8s1GadWw0LXivT/7Iyo18yMzXcXW8WsJu6lb5m/w9xPZNnEdSdEJuBcvQt+VI7FzceB5yBM2jV6FJlNNt4WDqNGuHvEvdK1YWrWGpR2mAtBu9PvUfK8BWrUWr5IuaFIKNtFx/IyFXLx6g8TEJNzdXBg2oDfB7dsU6Fj1rRMGv/+4+ZAlO46i1cp0bFSNj4Ma8tPJqwB80Kwm0YnJfPbDPmJfpiAD/dv4865/Fe4/j2b6D3vRamW0skzr2hUZ/F6eiXvxhiur/HH3OUv2XEQra+lYpzwfB1Tnp/O6FtQP6lcA4PfLDzhz/wWLuhsus5meqabtwp/ZM6ELjvqKU26i5x4z+G3TqJ5uiVCFgpRdB0j6disOwbrynrJzD059u2L/XitQq5FVmSQsX5e9RKjzkD7Yt2qOrNGQee8BcbOXQVZOBc1rreGKNX9cu8fiLbolQjs1rc3HHZuz46ju/viwRT2iE5KYvm4nsYkpyLJM//ZNeU//IpmuyqTNp0vYu3QsjnaG9xFAl/e/N/g9bM4wajfXLTn5xbgvCL2ha7Gd9cMsVkxcQXxUPB0+6sD7Q97H1dOVxLhELh27xIqJunk3wYODafVhK7RaLQd/PMjv3/4OQILG+L76t+JSaQsXI63c9JsziBrNaqJKV/H1uJU8vqmrQE34YRrrJqwmMTqBNv3e5b0hnXDxdCUp7iXXjl9m/cSvjNKK1hoPAxkx9xPqNq+DKl3FkrFLua/Pw3kb5rBswhfERcXT6aOOdB36AW6ebiTEJXLh2AWWTViOu5cb45eN002SliR+XL2do7/qyp6rwvj6DZgzGL9mNclMV7F63Eoe3dQtsjD5h+msnbCahOh42vV7j45DOuPi6crLuJdcPX6ZtRNXMWTRcOq3a0CsftKwRqNlUvux2Em6uN579kCq6/Ppm/GreaLPpzHfT+W7iV+RGJ2AZ3Evhq0cjb2LA09DHvP16BWoM9W4FHHl48+H41zEFUmS2LvmV878pnuGlahciv4Lh2JhaYnVk2hujlqbPdnXo4Ufleb0RVIqeL7tOI+W/0bxPrpKVdjGI1RZNgjvd+uR/lwX72S1hrNtdPGuxtoRuDasjJWbI5kxLwld8jMvth7Pziu/Cobxwsq/Pk4jhoNCQfq+/aRu2oxtB12vRPouw3kAzpMnkXHmLKqTJ7GsVg331SvJevgw+6U0ef16Ms/lTGJPiTUcamjXtC4ek4YgKRQk/XqIhK+34dRV1yuWtH0vLgM+xLFjS128yFAR+/k3ZFwJwaKYNz5fztAlYqEkZe9xEr42XFK83O2D5ldMKCSSBrQqtBdgp28P/8/9NYWoBAjMkrcS8G9iqhLwb5G7EvBvkrcS8G/yukrAP8mrSkBh8Pkl88s1/pPkrQT8q+SpBPyb5K0E/JvkrQT8m+StBPxbmKoE/Fu8rhLwT2KqEvBvYaoS8G/wqhJQGHRPtyw0rbyVgH+TvJWAf5P/QiXg5UctC+0dx/n7I/9zf00hhgMJBAKBQCAQCARvGWJisEAgEAgEAoHg7eI/PFa/sBA9AQKBQCAQCAQCwVuG6AkQCAQCgUAgELxd/AOfNPr/HdETIBAIBAKBQCAQvGWIngCBQCAQCAQCwVuFLOYEiJ4AgUAgEAgEAoHgbUP0BAgEAoFAIBAI3i5ET4DoCRAIBAKBQCAQCN42RE+AwCxSIVYRkwvxi7faNNtC0bF3yCwUHQBNQuG1aKSiLjStwvqSr0XV5oWiA6B5fLXQtFxrnyg0LTk6rNC0YtQphaLzMqvwvqzrqCi8r7XGZRVO/gFoLApnCRY/C/dC0QHYZ1t48ba6uvA+NGttX3ixXfDfQFQCBAKBQCAQCARvF2KJUDEcSCAQCAQCgUAgeNsQPQECgUAgEAgEgrcKsUSo6AkQCAQCgUAgEAjeOkRPgEAgEAgEAoHg7ULMCRA9AQKBQCAQCAQCwduG6AkQCAQCgUAgELxViDkBoidAIBAIBAKBQCB46xA9Af+HkCQpRZZlh1y/+wF1ZFkeLklSBeBrwAWwBv6QZXnQm6RvXb8uTqOGg0JJ2p69pG7eZtLOsmIF3L9eTeKM2WScOAWA8+QJWDf0R5uQSGyf/gXSmzxvDE1aNCAjXcXUkXO4c/Oekc3Cr2ZRpUZF1Go1t67eZta4hajVGj4a1pN3g9sAoLRQUqZ8KZpUbkdSYlK+mhY16mLbR+dj5vG9qHYZ+mhRqQb24+aijY4EIPPiH6h+2Vggf2wb1cFj0hAkpZKknftJ/HaHwX67gAa4jegDWhlZoyFu4VoyroYAoHC0x3PWaKzKlQJkoqcvQ3X9Tr56o2Z/gn9gfVTpKuaPXsz9W6FGNtNXTqZijQqos9TcuXaXJRO/QKPWUKJscSZ/MYF3qpZj/aLv+PHrn4yO7T6jP9UCapKZnsl341bxLOSxkY1HsSIMWjUae2cHnoU84pvRK9Fk6T5IU8G/Cl0/64fSwoKUhCSWdJ0BQKsB79G4awuQZSxd1GTd+wO0Gk7fesTiHUfRarV0blyD/m39DbSS01VM/XY3kQlJqDVa+rSqR6dG1VFlqen/+Vay1GrUGi0ta1VgWIcm+ebd65g2fxmnTl/AzdWF3zav/Vtpnb5+n0Wb9uj8al6XAR2aGexPTstgypodRMYlotZo6RvUhE7NagOwaf+f/HLiEpIE5Yt5M3tQMNZWlma1lFXqYNNtKJJCQeYfB8g8sN1w/zvVsftkFto4XfnOuvInmXu2gIUl9hOWgoUlKJWoL/+Batem/P26+4zFv51Bq5XpXL8i/VvUNNj/w/Fr7LvyAACNVsvjqESOz+6Ds50NW07d5Jfzd5Bl6OJfkV5Nq+erNXbOSBoF+pORrmLW6AXcu3nfyGbOqulU0pf1kGt3mD/hczRqTfb+yjUq8t2eNUwZMpNje0+a1Zo2fxzNWjYiPS2DSSNncvuGcVz6fM0cqvpVRp2l5sbVED4bOw+1WoOTsyMLVnxG8VLFyFRlMnnUbELvPjSrNWzWUOoG1kWVruLzMUt5cOuBkU2Hvu3pPLAzRUv58n71D0lK0MU4O0c7Jq2YgGfRIiiVSn5e9zOHdhw2qzVh7qc0atGAjPQMZoyax10TeThv9Qwq54q388YvRq3WULthTb74YSHhzyIAOLbvJOuWfW9Wa8TsYdQPrEdGuopFo5cQasKvTv068v7AzhQtVZSO1YKz/XJwdmDC0rH4lvQlU5XJ4rFLeXLvSfZxnWf0pVJATbLSVWwbt4bnIU+M0nYr5kmfVaOwc7bnecgTtoxehSZLQ62OjWgxpAMAqjQVP0/7hvA7z7CwtmT49hlYWFuiVCq4uv88+7/4ieAZ/agcUJPMdBVbxq3huYkY6FbMk36rRmHn7MDzkMds0mtVa1WHoDEfIssyWrWGX2Zv4NGlnLIkKSTG716AW2Ik8eOnZG+3rl8X50+Hg1JJ2u69pGwy8xyuVAGPdatJ+Gw2Gcd1z2GXKROwbqR7Dsf0ev1z2KZBXVzGfgIKBam/7yN5w4+G+5s2xHnIRyBrQa0hYdlXZF6/pbtO3brg0CkIJImU3/aSsu2X1+oVOmJOgOgJeIv4EvhClmU/WZYrASvf6GiFAqcxo4gfN4mYXv2wbdkCi1IlTdo5Dh2E6sJFg83p+w4QP3ZigeWatGhAidLFCfL/gJnjFjB98QSTdnt3HqB9o650btYTaxtrgnt2BOD7r7bwfos+vN+iD8vnreHS2auvrQAgKbD9aBSpiyaRPK4fVg1boChq7KP67k2SJ39M8uSPC1wBQKHAc9onRAydxrMOH+MQFIBlmRIGJunnrvK8y1Cevz+MmOnL8Jw1Onufx6ShpJ2+RFiHgYR1GUrWo2f5yvkH1qNY6WJ0b9yHxROXMXbBKJN2h389Ss+m/ejbYiDWNta07xEEQFJiMiumrzL58g9QrXlNipT2YUrzEWycspZe80zXJ4Mn9eLwt3uYGjCC1JepNOkaCICtkx095wxk1cBFzGg9mrXDlgLg4uVGYL92zG0/kRltxoAkoSxSGo1Wy4Jth1k94gN+mTmQAxdv8zA81kBr+/ErlPHxYMf0/nwztgfLfj5OllqDlYWS9aO7sWN6f7ZP/4gzIY+58ehFvvn3OjoFtWLtsrl/Kw3QvfzO37CLryb049fFn3Lg3HUevogysNl++Bxlihbhp/kj+XbqQJZu3UeWWk1U/Eu2HjrLtjmf8MvCT9FqtRw4d8O8mKTAtsdw0lZMJeWzj7Gs1xyFTwkjM/WDm6TOHkrq7KG6CgCAOovUpROyt1tUqYuyTMV8/Vrwy2lWfxzELxM+5MDVBzyMTDCw6Rfgx46x77Nj7PuMDKpH7bI+ONvZ8CAinl/O32HzqM7sGPs+f9x+xtOYl2a1Ggb6U6J0Mbo06sH8CUuYtGCMSbv9vxzm/Sa96BbYD2sbazr1eC97n0KhYPjUIZw7cdHksa9o1rIRpcoUp1W9zkwfO49ZiyebtNu98wBtGwTzXtOu2NhY80GvTgAM+fQj7ty6T4fm3ZnwyWdMmzfWrFbdgLoULe3LR036s3ziCkbOH27SLuTSbSZ1n0xkmGG56dC3PU9DnzG0zTDGfziBQdMHYWFput2vcYsGlChTjI4NujJ33GKmLBpn0m7/L4fo3Lg7HzTvjY2NNZ17ts/ed/X8dbq17Ee3lv3yrQDUD6xH0dJF6dW4H0snLmf0gpEm7W5dvMXYbhOJDIs02N5zRHcehDxkYKvBLBi1mBGzhmXvq9TcD8/SPsxv/ik7pqzn/XkDTabdflIPTn67l/kBo0l/mUJ9fVyKD4thVdfZLGk3kUMrf+HDBbq4plZl8VWPOXzebiJLgiZRqVkNAga8i2dpb+Y0H8X2Kev5cN4Ak1odJ/XkxLf7mBvwKWkvU2mg17p3+iaL2k1gcdBEtk5YS/dFgw2Oa/5REJEP8sQphQLncaOIGzuJ6B75P4edhg1Cdd6wPKftO0D86AI+hxUKXCeMJGbUZCI/7I9d60AsShtqqS5eIarHx0T1HEz8nM9xm6Yrz5ZlS+HQKYiovp8Q2eNjbBv7Y1G8aMF0BYWKqAS8PfgAz1/9kGX55pscbFmpIprn4WjCI0CtJv3IMawbNzKyswvuTMbJP9AmJBpsz7x+AznpNS/huQho25RdP+0D4MblEBydHPAoYvxZ+D+Ons3+++bV23j5FjGyCercin2/mm8Be4WyXEW0keFooyNAoybzp4gvYAABAABJREFU7DEs6xj7+FewrlaBrGfhqJ9HglpNyv4T2Ac2MLCR0zOy/5ZsbQDdeEXJ3g6b2tVI3nlAt1OtRpucmq9e4zaNOPDz/2PvvMOjKroG/ru7m957IVTpNaF3CJ1QpClFkCpNBOm9d1RQaYoiWLCgWFCqUoL0llBC74T03jZld+/3xy5JNrsbgq9v9P2Y3/Ps8+zeOTPnnrlzz9yZOXP3IADXLl7H0cURD293E7nTh8/mf78efgMvPy8AUhJTuHHpJpo8838jH9ipEad+PArAvbDb2DvZ4+LlaiJXvXltLuzVX6OTu44S2KkxAE16tuLi/jMkGR7k0xML2oZSqcTa1hqFUoGkVCLnZHH1fjRlvV0J8HLFSqWkc8MaHL1kvLIhSZCZk4ssy6hzcnFxsEWpUCBJEva21vqq0+rQaHVIklRs/T2LhoF1cHF2+o/KALh6N5KyPh4EeLtjpVLRpWldjl4wXuGRJMhS5yDLMlnZubg42KFU6F23VqsjJzcPjVaLOjcPLzdni7qUFauhi49CTogBrYa8c6GoApuX/GRzDO1TqQKl8mnzNG/XozjKejgT4OGsv15BlTlqZkb2KfvC7tIlqDIA9+KSqVvOBztrK1RKBQ1e8uPwFdMZ1qe06dySPT8c0Ou9eA0nF0c8zPiKk4dP53+PCLuOt6GtA/Qf0Zcje0NJTkg2yVeY9l3a8NN3er906cJVnFyc8PIx1RX6x4n875cvRuDr7wNA5WqVOPWn/p67d+chZcr64+Flel8CNO/UjN93HQLgRtgNHJwdcTdzD9+NuEtsZKzJcWSwd7QDwM7BlvSUdKOVj8K06dyS33bq/cuVixE4OTuZ9bfHC/nbq2HX8fYz9bfPokWnZhz84Q8Arl+8btGuOxbsqlClPBePhwHw+O5jfAJ8cPN0BaB2p4ac+1E/6/0w7A52TvY4m/FLlZvX4tLeMwCc3XWMOp0aAvDg4i3UaXrf+vDibVx8C84rNysH0K8sK1UqKjWuzlmDrgdht7FzcjCrq0rzWoTvPW3QFUqdTo2MygOwtrdBLnQ/ufq6U7NdEKe+PWxUllXN6miK9MO2rUz7KId+vVEfMdMPh19GV8J+2LpWdfIeP0H7RK8r6/cj2LUx9hcmfZbBCFWFcuRcuY6ckwNaHTkXL2PXtmWJ9JYmsq70Pv9WxCDg/xd2kiSFP/0ASwqlrQMOS5K0T5KkyZIkuT5PwUovT7Rxcfm/dfHxKL08jWQUnp7Ytm5F1s+7/7IBT/Hx8yLmSYG+2Og4fAp12kVRqZT06NeV44dPGR23tbOhZXBTfv/tyDN1Ktw80SUWsjExHoWbp4mcskpNnFZ9isPMVSgCKpTAGlB5e6CJic//rYlNQOVtWrZD++aU3f0pfpuWEjd/LQBWAb5ok1PxWjaVgO834rX4bSQ7m2L1efl6EhdVoC8+Oh5PX1N9+TaplHTu25EzR4qfCX2Kq48HSVGJ+b+TY5Jw9TV+aHB0c0KdlolOq/eAydGJuPnoO1WfSn7Yuzgy/dvFzP91Nc366ENgUmKTOPDJblaf3Mx7Zz9B1uShS44iLiUd30IPuD5uTsSlZBjpGxBcn/vRiXScsZF+Sz5jev8OKBT6h32tTserS7fRbtp6mtaoQJ2K/iWy879NXHIqvu4u+b+93V2ITTbupAd0bMa9qDg6TFhFv9kfMmNIdxQKBT7uLgwNaUnnSWvoMGElTva2NK9TxaIuydUTXVJBm5CT41G4mj7oKSvVxGHBZuwnLkfhX2jmT1LgsGAzTu/tRHP9Itr7NyzblZqFr2t+ZCI+Lg7EpZofuKpz8zh54zEd6lYEoLKvOxfuRZOSmY06N4/j1x8RW+RaF8bL15PYqIL7Ni4qHu9ntPWQfp05deRsfv62XVux64tfLObJt8PPi5iogpnp2KhYfHwtPwirVEpefjWEPw+fBOBGxC06ddPPBNcNqoV/WV98LTxIe/h6EF/oHk6IjsfD1/R6WeKX7bspW7kc35z/mo9//4jNCz9Cls2P3Lz9vIiJMva33s/wt936debkkTP5x+o2qM13h7az4et3qVStosW8nr6exBXSlRCdUKxvKsrda/do3VX/QFk9sBq+AT75kxcuPu6kFPJLKTFJRg/yAA5uTqjTsvL9Ump0Ei4+poOQJv2DuXE0PP+3pJCYtncVSy9s4ebxyyhVyiK6Ep+pK6WIrrqdGzH30FrGfDaLr2dszj/eZ8FQdq/cYXK9lF6eaGML6k5rqR9u85/3w3pdBe1PG2uqC8CubQt8v9+G57rlJC19F4C8uw+wCaqLwsUZycYG2+ZNUPpYbk+Cfw4xCPj/hdoQ7hMoy3IgsOBpgizL24AawPdAW+C0JEkmT5KSJI2WJOm8JEnnv4qJKpxgqq2Ig3Ke9CbpH30Muv982Cthqs9SBwYwb/UMLpwO4+KZS0bH23ZqRdi5K88OBQLzNhaZ7tQ8uE3aWwNInzWKnAM/4TBl6bPLtVS2GXsyD53kcc9RxExchPuEofqsKiU2NSqT9t1vRL7yJjp1Nq4j+/8FdZbrb+qKSYSfuczlsyVbIDJfVfIzhZ6eg1KppHydSnwwfAXrXl9G97f64VPRD3tnBwI7NmJWqzeZ1mQ0klKF0ucls5PORUs/GXGfamW9+X3Nm3w3bzirvvmdDLVh9k6hYOf84RxYNZ6rD6K58yTetMB/AHOXxMSuK7eoXt6fPzbMYufyt1j5xa9kZGWTlqnmyMXr7F03jd/Xz0adk8tvhhlSs5hd/DA+Ae2jO2TMGqwPBTr8M3bjFxUS1ZG5ZBzpMwahrFANhX8Fy3aZuWKW1l6ORTwksKIPLva2AFTycWN4u0DGfryHNz/ZS1V/D5RKy12VuVWd4tr6rJVTCDt9ifCz+tCpKYvfYv3yj9CVwG89r65Fa2Zx7tRFzp8OB+DjDz7H2dWJX47sYMio/ly/chOt1vzsvNnVqmJ0FaVhmwbcu3aXgQ0HMa7LeCYsHY+9o32JdRVn1+xV07h4+hJhBn974/JNQhr2pX/7YXy7dRfrtq20mPc/tevrjd/i6OLEJwc+ovfwXty+eid/haNEfq8EMpWb1aRp/2B+XfV1gYxO5t2QWSxqNp7y9Spja64ui5TzLD95+cA5lrefwqej36XbFL1Pr9WuPumJaTy+am7169nXyeXtN0nb9Df0wyXx8YD66AliXhlO4vQFuIwdBoDmwSPSv/gWrw1r8PxwFXm374KFdv6PoivFz78UsTH4BUKW5SjgM+AzSZKuArWBC0VktgBbAKJbBuff8dq4eJTeBTNWCi8vtAmJhbNiVa0arov04w6Fiws2zZoga7Xk/HmCkjBgeF/6DdbH9F8Nv45vmQJ9Pn7exMUkmM03bupI3DxcWTxtlUla114d2PvTwRLp1yXFo/AoZKOHF7pkYxtRZ+V/1YSfQRrxNpKTM3J68YMMTWwCKt+CmRCVjyea+ESL8tkXrmJV1g+FqzOamAQ0sfHkGDZGZx48juuoV03y9B76Mj1e08f03wi/ibd/gT4vPy8SY83rGzZ5CK4errwzamGxNjwt30ZS8uDSXdz9C2Yl3XzdSYlNMpLPSErDztkBhVKBTqvDzc+DlDh9uEVyTCIZyenkqnPIVedw6+w1AmpUACDhcRwZSfr61MY/ROHsjY+rEzGFZshjk9PxKjTTDPDLySuM6NIUSZIo5+1GGU8X7sckGs36O9vb0rBqWU5E3KNymX9+ZsrH3YWYpIJ497ikVLyLhPT8EnqRET1a6+3y9aCMlxv3o+OJTkihjJcb7s76emjfsBaXbj+ie0vjDbhPkZMTULgX2Cy5eaFLMb5mZBdq31fPYatUIjk6I2cUat/qTDS3LqOq3ZDcqAfm7XJxIKbQ7H1saiZeLg5mZfeHF4QCPaV3k+r0bqLfc/Dh3jP4uBhf61eG9abXa/qY/mvhN4zCAL39vYi30NZHTRmGq4crK2bMyz9Wo151lm/Wt31Xdxeat2+KVqsldP9xAF4b8QqvDukF6EMOff19Af3Dr4+/D3Gx5geUE6a9gbuHG/Onrsg/lpmRyeyJBQu0hy/s5vHDgsmWHkN7EDKwCwA3L93Cq9A97OnnRWKRe6w4Or3aie826Td+Rz2IJuZxDGUrB3AzXL/h99Xhfejzmn4TbET4dXz9jf1tvAV/O3rqcNw8XFlWaLNqZkZBuzl+6BSzV03F1d2FFEPb7jW0J90M+41uXLqJt783EGGwy5MEC9fLHFkZWayZ+m7+71+u7GLOhzOxkRU8unQX10J+ydXXnbRY4xCvzKR07Jzt8/2Si587aXEFMn7Vy9F/1Ri2DFtFVpEVqBZDOtFsYDucPF1JehJfRJcHqUV0ZRTR5ernTmqcsQzA3bPX8Szvg4ObE5UaVqNOhwbUDA7EysYaaydbXBfOIWXxCv3Mv0/BdVJ6eaEr2g9Xr4bbkkL9cPMmoNWSfaxk/fBTtHEJRrP3Sh/TPr8wOWFXUJXxR+HijC41jczd+8jcvQ8Al/Ej0cb9OyZeBMaIlYAXBEmSukiSZGX47gt4ACXeHZl34wbKsmVQ+vmCSoVdh3bknDhpJBP/6iDiXxlI/CsDyT4aStp775d4AADw7bZd+Zt5D+8Lpecr+k6jboNaZKRnkBBn6oD6vtaTFsFNmDF2gcmMiKOTAw2bBXFk/7ES6dfevYHCtwwKL19QqrBu1o68C8Y2Si5u+d+VL1UHSXrmAAAg5+pNrMqVQVXGB1QqHLu2JfPIaSMZVdmCh1XrGpWRrFToUtLQJiajiUnAqkIAAHZNA8m7a7ox+KfPf2FEpzGM6DSGPw+coEu/TgDUrF+DjLRMEuNMHyC6DwyhcdtGLHpzWbEzf4XLXxIynbCDZ2nWpy0AlYKqoE7PIjU+xSTPzVMRNAjR731o3rct4Qf14UbhB89RpVENFEoF1rbWVAqsQvSdSJKiEqgUVBVrQwy/ws0PXVYKtSr48SgumScJKeRptBw4f5029YwfHP3cnTlz4yEAiWmZPIhNIsDLlaT0LNKy9LGr2bl5nLnxkIrPEVbx36RWpTI8ikkgMi6JPI2G/acv06Z+DSMZX08XzkTo3yCTmJrOg+gEArzd8fVw5fKdx6gN+yDORNylYjEDG+2Dmyi8yyB56tu3VaM2aC4Zh89JzgXtW1GhGkgK5Iw0JEcXsDM8xFtZo6oRhC7msWW7ynrzKCGVJ4lp+usVdoc2tUw3MKarc7hwN5rgWhWMjielqwGITk7n8OUHdC0ySPh++0+81nEkr3UcydH9f9Ktn/5NYLXr1zS0dVNf8fKgbjRr25h54xcbtfVeTfvzchP95/BvoayevTZ/AACw47PveTn4NV4Ofo0/9h2ld3+9X6rXoDYZaRlmBxyvDH6ZlsFNmTxmrpEuJ2dHrAybc18d3Ivzp8LIzCgIk/r1818Z1+VNxnV5k5MHTtGxb3sAqgdVJzM9kyQz97Al4qLiCGqhHxC6eroS8FIA0Q8LQpl2bvsxfyPvkf3H6P6qfvBRp75lf9t7UA+at23C7HELjewqvK+hVlANJEnKHwAA/Pz5bt7oPJY3Oo/lxP4TdOrXAYAa9Ws8t10Ozg75G5y7DerK6UNnGNlxDO+GzOLqwfM06tMagPJBlVGnZ5Fmxi/dOXWNeiFNAGjctzVXD57X15O/B8M/msKOyRuJvx9doNPdCVtne058eZAPes8n/kE010PDaWzQVSGoCtkWdN0+dY3AkKYGXW24YtDlWd4nXyagVkWUVioyk9P5dc03LGg2nsUt32L7Wx+QeyGMlMX6gWTe9RuoAoz74ezjxn1UXL9BxPUdSFzfgWQfCSX13fefewAAkHvtBlblyqD01+uy7xiM+pixLlVAQZ9lVa0KWFmhS9X3hwo3VwCUPt7YBbck84Dx/oZ/A2JPgFgJeJHoBHwgSdLTnTzTZVmOKS6DEVodaWs/xH3tGlAoUO/Zh+b+A+xf1r8dIuuXX4vN7rpoHtaBgShcXfD+cSfpW7ej3rPXovyxP07Sqn1z9p35AbU6m/mTCt7EsmnHWhZOWUF8bALz18wgOjKGHXs+AeCPPUf5aO1nALQPacvJ0LOos7LN6jBBp0O9/UMcZuttzD26D13kA6w76G3M/eNXrJq0wabjy6DVIufmkPlhCcOBtDoSVmzE7+MVSEoFaT8dJO/uQ5xf7QZA2s49OHZsiVPPDsgaDXJ2DrHTCmYQE1ZsxHv1TCQrFZrHMcTNf69YdacOnaFpuyZ8e+JLstXZrJzyTn7ami9WsHr6eyTGJjJ11dvERsby0W79y6KO7T3O9ve/xN3LjU/2bcbB0R6dTuaVN/oypO0IsgwzfleOXKROcH1WhG4gV53Dtumb8suftG0O22duJjUumR9WfcmY9ZPpPXUAjyIecHynfqNj9N0nXA0NY9H+95B1Mn9+d4ioW/qHygv7TjF/zzvoNFogF23UTVRKBbMGdGTcBzvR6WReblGHyv5efB+qD395pU0Qb3RrzoLte+m3eCsy8Hbvtrg52nMrMo752/eg08noZJlODarTuq7xQ+XzMn3hKs6FXSYlJY32vQYzfuQQ+vbo/NzlqJRKZg/tybg129DpZHq1aUDlAB92HtLHWr/avgmje7Vj/sc/0HfWB8jIvN2/M25ODrg5OdCxcW0GzNuAUqmgenl/+gU3tqxMpyP76w3Yv70CSVKQe+IAuqiHWLXRt8G80D2oGrTCum13ffvOy0X9ib4NSi7u2I+YDgoFSAryzoeiuXzGoiqVUsGsPi0Zt2UvOlnm5cbVqOzrzvcnrwHwSvOaABy+8oBm1QKwszF+renUzw+SmpWNSqFgdp8WONtb3gNz4tBpWrRvxk8nvyFbncOSyQWhKO9/uYZl01aTEJvIrFVTiYmM5bNf9bHXR/Ye49N1n1uuLzMc/f0EbTq04I+zP6NWZzN74uL8tE+++YC5by8lLjaBxe/MJupxDDv36X3Rwd+OsPG9T3mpakXWbFyMTqvjzs17zHnbsv84e/gsjds1Yvvxz/SvCJ26Nj9t2edLWDvjfZJik+g1/GVeGdcPdy93Pv59M2cPn2PdjPfZ8cHXTF87lY9/34wkSWxd8Vn+azaLcvyPU7Rs34zdp3eSrc5m0dsFvmf9jndZMmUV8bEJzFkzjejIWD7/bQtQ8CrQDj2CeWVob7QaDdnZucwea3lV8fThszRp14Svjn9OTnYOq6cUzOqv/GI5705fS2JsIn1G9GLAuFdx93Jn6+9bOHPkLO9OX0v5yuWY/cFMdFotD24/4p1pBb7w2pEwagQHMjf0A3LVOXw7veD1vW9sm8l3M7eQFpfMb6u+Zsj6iXSd2p8nEQ84vVO/Z6zzxL44uDnSb5n+9Zk6jZa1Pefi7O3GoPfGoVAokBQKwvacYv+Hu3hlyQgWhH5ArjqXHdMLYvrHbJvFNzM/Ji0umd2rdjBs/SS6Te1PZMQDTu/UPwwHdm1Coz6t0Wq05GXnsn3C+xbrLB+tjtS1H+Kxbg0oFWT9ZuiHexn64Z+f0Q8vnodNkL4f9vl5J+mfbifrNwv9sFZH8pr1eH24GkmpIGP3PjT3HuLQR78Cl/njb9i1a41Dt46GPiuXxDkF7dlz9SIULs7IGg3Jaz5ETre8r0fwzyE9a/ZP8OJSOBzov03H2+rSUsXxdnaloifxinWp6AEYlmz+LT7/DapZuT1b6G9i/faQUtGjqt22VPQAaO8XE7f/N5O75eNS02X1cpdS09V69M+loic1L+vZQn8TFWxLvjn2PyU+L73UdLmqzO9D+LsJVJXe6p62uFdj/c3M8o97ttDfhDav9IJDyp479J+9ou1vIKFrm1K7kJ77Qv9xe80hVgIEAoFAIBAIBC8W/+IwndJC7AkQCAQCgUAgEAheMMRKgEAgEAgEAoHgheLfvGG3tBArAQKBQCAQCAQCwQuGWAkQCAQCgUAgELxQiJUAsRIgEAgEAoFAIBC8cIiVAIFAIBAIBALBC4VYCRArAQKBQCAQCAQCwQuHWAkQCAQCgUAgELxYyP/K/+8qVcQgQGCRnIzSax4KqfRuRk1S6awBajSlt9CmKcV/PbEqzQXEpJhSUVOa/+KrrBhUaroUrg6lpouYyFJTlaXNKRU96aX4j8Eu9jalpitbmVdqupwVpWOXm6wsFT0ACVLp/UO70qb0fHtedunVoeDfgRgECAQCgUAgEAheKMSeALEnQCAQCAQCgUAgeOEQKwECgUAgEAgEghcKWSf2BIiVAIFAIBAIBAKB4AVDrAQIBAKBQCAQCF4oxJ4AsRIgEAgEAoFAIBD8o0iS1EWSpJuSJN2RJGlWMXKNJEnSSpLU7z/VKQYBAoFAIBAIBALBP4QkSUpgI9AVqAkMlCSppgW51cCBv0OvCAcSCAQCgUAgELxQyP+uPwtrDNyRZfkegCRJ3wIvA9eKyL0F7AIa/R1KxSDgfxhJknoDPwI1gAGAjSzLswulBwLfyLJcQ5KkB0A6oDUkH5NleeLz6LNr3hD3GeNBoSDjp32kbvvOOL1tM9zGDwNZRtZoSXpnEznhEajKB+C9Zl6+nKqMLymbPydtx0/F6pu5bDKt2jcnW53N/ElLuX7llonMyo2LqFWvOhqNhith11k6fRUajZZh418jpE8nvT6VkopVKtCmVghpKWlG+a0aNsZh7FtISgXZ+/ag3vm12XNRVa2Oy/ubSF+xmNzjoQC4ff4tsloNOi2yVkvqW2OKtce+ZQN85o4FhYLUH/aT9Mn3RumO7ZriOel1ZJ0OtFriVmxBfTGiQEChoPwPH6KJS+DJ2EXF6gKYvOQtmrdrQrY6m6WTV3Pr6m0TmUXr51K9XlU0eVquh99g1cz30Gq0dOrdgSHjBwCgzlKzZvb73Ll21yjvqwuHUys4iFx1Dl9M28TjiPsm5XsEeDFyw9s4uDjyKOI+2yevR5unpUrTmozbMoOEyDgAwvefYe+Hu/LzSQqJ2b+uwtpdQ+4pfT2duPmENb+dRaeT6d2oCiPa1jHStf3YVfaG3wNAq5O5H5fKkXn9Sc7MZsY3oflyT5IyGNchkMEtTSZZ8jlx6Rarv/wNnU5H77aNGNmzjVF6elY2czbvJCYxBY1Wx9CQVvRq0wCAL/cd58ej55EkqBLgy5LRfbGxtrKo61nMW7GWYyfO4u7mys9fffSXywFQVgvCpudIUCjIO/sHeUd+NE6vVAvbYbPRJeuvi+bKafL+2AmAVaseqBp3AEAX/ZCcnetBY/lPp07cj2PNoWvoZJnedcsyokllo/TtZ++y91oUAFpZx/3EDI682REXO2sW7rvEsXtxuNtbs2t4G3PFGzF7+RRat2+OWp3N3IlLuX7lponM6k2LqVWvhsFXXGPxtJVoNFocnRxYvWkxfmV8USqVbNu8g5+//c2irqWr59C+Y2vUajVvj5/DlUvXTWTeW7+UekG1kCSJe3ceMGn8XLIyC/54rF5Qbfb88Q1jhk9lz+6DFnUNX/QG9YMbkKPOYeO0D7h/9Z6JTJehIXQb0RPfCn6MCBxMenI6AC17taHX2D4AZGdl88nczTy8/sCirsJMXPImTds1IUedw8rJa8z6jvnrZ1OtXjU0eRquh9/g3Znr0Gq0Zkoz5Y3Fo2kQ3JAcdQ4fTH2fe1fvmsiEDO1Oz5E98avgz+B6g0hPNvbdletWYc0v7/Lum2s4ufcEAC+1qUvnhUNQKBWEfXuUE5t/NSm386LXqRJcjzx1Lr9M+5iYqw9Q2lgxbOd8lNYqFCol1/eeJXSd3h91mDOQqu3ro83TkPwwll+mb4F0/bn0XTiMmgYfuGPaZiLN+ED3AC+GbZiEvYsjkRH3+XLyBrR5Wup0bEjIlFeRZRmdRsuPSz7n3nl9u207MoRm/dshy+ASeYvU1asgLxcA60aNcZrwFigVqPfsIesbC31Wteq4b9xE6pLF5BwLBStr3D/4EKytkJRKskNDydy+rdjrZNu8Ee7TxoNS3+enbf/WKN2uTXNcxw8DnQ5ZqyX53c3khF8FwOm1vjj26gqyTN6d+yQsegdyS+9P6v4HKQM8LvQ7EmhSWECSpDJAb6Adf9MgQIQD/W8zEDiOfgDwDdC/SPoAoLCHCJZlOdDwea4BAAoF7rPfIvbNOTzpMwqHLsFYVSpnJJJ9JoyoV8cQ1X8sCYvexXPhFAA0DyOJ6j9W/xk4Hjk7h8zDJ4pV17J9M8pXKkv3Zq+wZNoq5q2eYVZuz48H6NlyAH3aDsbW1po+r/UEYPumHbzaYSivdhjKB8s/4sKpMJMBAAoFjm++Tdq8GSS/MRSb4PYoy5U3a7v9yDHkXThnkpQ6421Sxo965gAAhQKfBW8S+cZ87ncfg1O3tli/ZFx/mafDefDyeB72nkDMnHX4LJtklO72+svk3ntUvB4Dzdo1oWzFMrzScjCrZr7HjJWTzcod+OkPBrQeyuD2I7C2tabnoG4ARD+OZny/txnScRSfvf8ls1ZPNcpXq20Q3hV9Wdh2Il/P2cLA5aPMlt971mAOb93DwuBJZKVm0qJ/u/y0O+eusyJkBitCZhgNAADaDQ8h5s6T/N9anY6Vu0+zcXgHfpz8Mvsv3edubIpRnmGta7NzYk92TuzJxM71aVDRBxd7Gyp4ueQf/2ZCd2ytlLSrZVz3hdHqdKz4fDebZgzjpzVvs//0Je4+iTWS+e7301Qq4833Kyayde4o3vt6L3kaDbFJqXx98BTfLH2TH1e9jU6nY//pyxZ1lYReIR35aO2y/6gMACQFNr1Ho966lKx3J6IKbInkHWAipr1/HfW6KajXTckfAEjO7li17Ib6g+mo35sECgWqwJYWVWl1Mit/j2Bjv8b8OKIN+69HcTch3UhmWOOX2DmsFTuHtWJiq+o0KOuBi501AD1rB7CpX+MSmdWqfXPKVyxL16b9WDRtFQvWmPcVv+06QPcWr9KrzSBsbW3o+9rLAAwc0Y+7N+/Tp91ghvUZx4xFE7GyMj8/1q5jaypVKk/z+l2YPmkhq95baFZu4ZxVdGjZh/YtevMkMpoRbwzKT1MoFMxbPIWjh4r3gUHBDfCr6Mdbbcby8eyNvLFsnFm5G+evs+S1BcQ9Nm6jcY9jWfjqHKZ1mcQPH37HmJVvFqvvKU3bNSagYgCDWr7OOzPXMmXlJLNyv/90iMGthzGs/ShsbG3oPiikROU3CG6IXwV/xrYezcZZGxi3fLxZuevnr7Fg0Dxii9gF+jocOnsYYaEF//QtKSS6Lh3G10PXsKnDDGr1bIZnlTJG+SoH18Ojoi8b2kzlt9lb6bZsOADanDy+GLicLV3nsKXrHCq3qUuZIP2g9d6fV9ncaSYfd5lN4v0YWo7X9zE12wbiVdGXpW0n8d2cT3h1+Uizdrw86zWObt3LsuC3yUrNpJnBB948cYXVXWewJmQmX8/4iIGr9X2Ii48bbYZ15d0es1nVeRooFdi2a/fUcJwmvU3KrBkkDhuKbfv2KMub77OcRo8h91yhPisvl+Qpk0kaNZLEUSOxadwYqxqWJ0JQKHCf+RZxb80hqu9IfZ9fsUiff/Yi0f1HEz1wLImL38Vjvr7PV3p54DygFzGDxxP96hugUOLQOdiyrn8IWVd6H0mSRkuSdL7QZ3SR0zG3LCEX+f0+MFOW5ZKNtkuAGAT8jyJJkiPQAhgJDJBl+SaQIklS4ZHjq8C35vI/Lza1q6F5HIXmSQxoNGQeOIp92+ZGMrI6O/+7ws4WuWjzBWybBJEXGY02Oq5YfcGdW/Przn0AXL4YgZOzI57eHiZyxw+dyv9+Jew6Pn7eJjJde3dk30+/mxxXVauBNuoJupho0GjIOXoY62amDza2L/ch93goupTkYs+5OGzrViXvURR5kTGQpyF9byiO7ZsaychZBfUn2dtSuAJVPp44tGlM6vclCwNs3bkF+37QzzBGXLyOo4sDHt7uJnKnDp/J/349/Abefl4AXDkfQXpqhiH/Nbz9PI3y1evUkNM/HgPgftht7J0ccPZyNSm/WvNaXNx7GoDTu45Sr9OzJy9cfd2p3a4+J749lH/s6uMEyno4E+DuhJVKSed6FTl6/bHFMvZduk+XehVNjp+5E02AhxP+bo4W8169G0lZHw8CvN2xUqno0rQuRy8Yz/ZKEmSpc5BlmazsXFwc7FAq9O5Uq9WRk5uHRqtFnZuHl5vzM20ujoaBdXBxdvqPygBQlKuCLiEaOSkWtBo04cdR1SrZg7a+ACVYWYNCgWRlg5yWZFH0anQKZd3sCXC1x0qpoHN1f47eMX2Ye8q+G1F0qe6f/7tBWQ+cbUu2etKuS2t2f2/wFReu4uTsZNZX/HnoZP73K2ER+PjrfYUsg4OjPQD2DnakpqShsTCj3SWkHd9/+wsAF89fxtnFCW8fTxO5jPTM/O+2trbIhe7lkWNeY8/u30lISCzWrkYdGxO66wgAt8Nu4eDsgKu3m4ncg4j7xEea+tNbF26QmaY/j9sXb+LhZ1on5mjZuQUHDL7j2sXrOLo4mvUdpw+fzf9+PfwGXgbf8Swad2rCkV2H9ecYdhMHZwfczNh1P+IecWbsAug2vDun9p0kNTEl/1iZwJdIfhBLyuN4dHlaIn49TbWODYzyVevYgEu7/gTgSdgdbJztcfR2BSAvKwcAhUqJwkqZ73/v/XkFWat/jUxk2B2c/fR1UadTI84afOCDsNvYWfCBVZrXItzgA8/uCqWOwQfmGvQBWNvbGPWXCqUCK1trFEoFko0NusQEAKyq6/ssbbS+z8o+fBibFqZ9ln3vPmT/adpnydlq/ReVCpQqZJNnzAKsa1dDExmF5kl0fp9v17aFcXmF+nzJzhajZ1alEsnGBpQKJDsbtPHFt/f/78iyvEWW5YaFPluKiEQCZQv9DgCiisg0BL41RHb0AzZJktTrPzkvMQj436UXsF+W5VtAkiRJ9dGvBgwAkCSpKZAoy3LhddwjkiSFGz7mp4YtoPT2RBMTn/9bE5uA0tu087MPbkGZn7bivX4ZiYveNUl36NyWzH1HnqnP28+LmKiCh4bY6Pj8B1RzqFRKevTrwokjp42O29rZ0CK4Kb/vOWqSR+HhiS6+oJPRJcSj8PQ0kbFp3orsPbvN6nVZ8S6uG7Zg07VHsfaofDzJiy5UfzEJqHxMO2XHDs2psHcLAR8tIWbuuvzj3nPGEP/uVuQSvtPMy9eT2KgC2+KjE/DyNb1eT1GqlHTp25HTR86apPUYEMKpIsddfdxJjkrI/50ck4irr/GDgoObE1lpWegMHWhKdBKuPgUyFetXZe6+NUzYPhu/KgUz0q8sGMZPK79CV6hXjEvLwtfFIf+3j7M9cakFD1qFUedqOHnrCR1qm86QHbj8gK51TQcHhYlLTsXX3SX/t7e7C7FFQhEGdGzGvag4OkxYRb/ZHzJjSHcUCgU+7i4MDWlJ50lr6DBhJU72tjSvU6VYfaWF5OyOnFJwzeTURCQX0zaoLF8Nu8lrsR05H4WPvk+S05LIC/0Fh7lbcJj/GXJ2JtpblyzqisvIxtfJLv+3j5MtcRnZZmXVeVpO3o+nQ1Xfv2SXt58XMU8K+4o4fJ7pK7py/LDeV3y99XsqVa3I0ct7+Pno16yct87oob0wvn7eRD2Jyf8dHRWLn5+PWdl1G5dz+dYxKletyGdbduTn79q9A1989p3ZPIVx9/UgsdA9lhiTgLsZn1ES2g3oSNjRiyWS9fT1JC6qwFfFR8fj+Qzf0blvR84eMV0pNYeHrwcJ0QV2JcQk4uFbcrvcfTxo2rkZ+7/aZ3Tcyded1OiCB8206CScfN1MZNKiCmTSY5Jw8tHLSAqJ0XtXMO3iZu79eZUn4aYhSkGvtuHOUX27d/FxI6VQWSkxibiY8YHqIj7QpZAPrNu5EXMPrWXMZ7P4esZmAFJjkzn8yW8sPrmJZWc/RpeZSe758wAoPD3RxRXqs+LjURbtszw9sWnVCvVuM32WQoH7J5/i9dPP5F44j+a6aSjbU1RenmhiCnRp4+JRmhlc2wW3wH/XZ3h/sJyExfo+XxufSNqX31Nm79cEHNyJnJ5J9ukLFnX9U8g6qdQ+JeAcUEWSpIqSJFmjf5YzuoiyLFeUZbmCLMsVgB+A8bIs//yf1IEYBPzvMpCCWf5vC/3uJ0mSgoIQocIUDgdahxkKL1l9nRhZOMFU2ExHmXXkBE96jyRu8iJ9rGBhVCrs2zQj8/dQk3ym52FOneVZi7mrpnPhdDgXzxg/mLTp1JLwc5dNQ4EsKjH+6TD2LTK3fgw604fv1MlvkjLhDVLnzsCuZy9UtetaPD+zmDEn44+TPAgZzZMJS/Cc+Lr+HNo2RpOYQk7EnZKXbca2YqqP6SveJvzMZS6dvWJ0vH7zQHoMDGHjiiKTFiVoD8WJPL56n3ktxrO86wyObN/P2C3TAajdrj7piak8umocW2vu1CVzCoBjNx4TWN4bF3sbo+N5Gi2h1x/TsU4Fs/ksmKHXVeT3ySu3qF7enz82zGLn8rdY+cWvZGRlk5ap5sjF6+xdN43f189GnZPLb8fDTAv8JyjBNdM+uUfmitH6UKATe7AdanhLnZ0DylqNyVw5lsylI5GsbVHVtxyrb/Z6WZA9djeWwDJu+aFAz4tkpuTi2vr81TMMviIcgJbBTblx9RZt63ajb7shzF05DQdHB7N5zbU5S35p8ptzCazelts379GzT1cAlqyczbKF76Ez409KoqtYwyxQq1kd2vXvwFcrPy+R/PP63ikrJnHpzGUuF/EdFss3e71KbteoRW/w+crtJarDkvikpzKyTmZLyBzWNX2LMoEv4VXVOFSu5YSX0Wm0XPnphKGsv+YDC8tcPnCO5e2n8Onod+k2RR/Na+fsQJ2ODVncagLzmoxFsrXFtkNHywUWqTqnN98i42PzfRY6HUlvjCLhlVewql4DZYViJkRKoAtAfeQEUX1HED91Ia7j9OFVCidH7Ns250n3wUR27o9kZ4tDSHvLugTIsqwBJqB/6891YKcsyxGSJI2VJGnsf0uv2Bj8P4gkSR7oN4bUliRJBpTob88ZwAOgDdAXaPa8ZRuWqLYAPAjsmH/La2PjUfkWzK6pfDyLXd7LuXgFVVk/FK7O6AwP4HYtG5F74w66pBSzefoP70tfQ0x/RPh1fP0LZth8/LyIj0kwm2/s1BG4ebiyZPpsk7QuL5sPBQLDzL9XQfiQwtMrf9k1386q1XCavUCf7uKCdeOmZGi15J46ji5Jb7+cmkLuiT+xql4DzVXz8d+a2ASsCs1Oqnw90cRZrj/1+atYlfND6eqMXf2aOLZrimObRkjWVigc7fFbM53oGe8Y5ek7tBc9X9PH9F8Pv5Ef7gDg5edJQqz5+hsx+XVcPVyZPWqB0fGXalRi9jvTmDJkFmnJafnl20kqHl66i5u/J6DfyObm60FKrPHSc0ZSOvbO9iiUCnRaHa5+7qTG6UNIsjPU+XIRR8NQLhuJg5sTLzWsRt0ODakdHITKxhqFmwNWDXvi8/BTYgrN/MemZeHlbG/Wnv0WQoGO33pCdX93PArNUJvDx92FmKTU/N9xSal4Fwnp+SX0IiN6tEaSJMr5elDGy4370fFEJ6RQxssNd2d9uFH7hrW4dPsR3VsGFauzNJBTE5FcC2YNJRcP05CenILror1xEXqPAXsnlJXr6MOIMvX3subqaZTlq6G5aH5A7+NoS0x6QVmx6dl4Odqald1/3TgUqCQMHN6PfoP1Mf1Xw6/hW6awr/AmrtCqZWHGTR2Jm4cbi6bNzD/Wa0B3Pl3/BQCPHkTy5FEUlaqU50qY/qUcw0YN5LWhrwBw6eIV/MsUrFj4+fsQE2M5tFGn07H7p32Me2sE3+34iXpBtfjos/cAcHd3o33H1mi1Wvbv0Ye+dX49hA4D9A98dy7fwcO/4Hp5+HqSFGc5BMsc5aqXZ+zqN1kxdAkZKekW5XoPfZnur+lj+m+E38Tbv8BXefl5kRhr3lcNmzwEVw9X5o0yvzfiKSGvd6PjwM4A3Ll8G89C4YWevh4kxZbcrsp1KjNtg37fh7O7Mw2CG6LVaEmPScKlUMiTs5876UX2DaVFJ+HsXyDj5OtOepyxTE5aFg9OXady27rE39JPhNXt24qq7YOI+PU0o/euQCPJPLp0F9dCZbn6epBqxgfamfhA07DSu2ev41neBwc3J6o0q0Xi4zgykvTXK+fPP7GqXZvsP35HFx+PwrtQn+XlhbZIn2VVrRouC/S+XHJxwaZJU9K0WnJOHM+XkTMzyA0Pw6ZxY7IemG5mBtDExaPyLdCl9PZ6Zp9vFaDv820bBqJ5EoMuRe9Hsw4fx6ZuLTL3HrKY/5/gL4yp/6vIsrwX2FvkmNm3QciyPOzv0ClWAv436Qd8IctyecPSUFngPtAS/ez/OuCuLMuRxRXyPORE3ERVrgwqf19QqXDo3Jas0FNGMqqyBR25dfXKSFZW+QMAAMcuwWTutxwK9N22XfmbeQ/vP0aPV/WzZ3Xr1yI9PZMEMw/NfQb1oHnbpswct9BkNsnRyYGGzYI4cuCYWX2amzdQlglA4aO3yaZtO3JPG2/WSx46IP+T82coGevXkXvqONjYItkZHiZtbLFq0AiNBWcKkH3lFlbl/bEq4wNWKpxC2pBx2Dh0yaqcX/53m5ovIVmp0KakkbB2O/faDuFe+2FETV1F1plLJgMAgF2f/8zQTm8wtNMbHDtwgq799G9HqlW/BplpmSSaeYDoMTCEpm0bsfDNpUb15+PvzapPlrBk0koe34s0Kn9FyAwuHTxL0z6tAagYVAV1ehZp8Skm5d88FUH9EP3eh6Z923LpoH5Z29mrINymfL2XkCQFmcnp/LLmG+Y0G8e8lhPY+tb76OIfkHd+N7UCPHmUkMaTpHTyNFoOXLpPmxqmm1rTs3O5cD+W4JplTdIsDQ6KUqtSGR7FJBAZl0SeRsP+05dpU7+GkYyvpwtnIvThAomp6TyITiDA2x1fD1cu33mMOicXWZY5E3GXimVKFiv930b3+DYKTz8kN29QqlAFtkR7zTiEQ3Jyzf+uKFvFsPkhHTk5HkW5qvo9AYCycl10cZbdSy0/Fx4lZ/IkJYs8rY4DN6JoU9k0bCY9J48LkUkEm0krjm+2/UDf9kPo234Ih/Ydo+crBl/RoDYZ6RlmfUXf13rSIrgp08fON2rr0U9iaNqqIQAeXu5UeKkcjx8WbErf/uk3dGzVh46t+rBvzyFeGaAffNRvWJf0tHTizAyuKxTaQNmxSzB3but9Q5N6nWhctyON63bkt90HmDV1af4AAODAF3uZHjKZ6SGTOXfwNG366jdTVgmqSlZ6JilmHiAt4envyfSPZ7N+8vtE3y8aWmzMT5//wshOYxjZaQx/HjhBZ4PvqFmM7+g2MITGbRux+M1lz5zJ3/vFHiZ3ncjkrhM5feAUwX31G12rBlUjMz2L5Oewa3TLUYxuMZLRLUZycu8JPp63mTMHT/Pk0j3cK/riWtYLhZWSWj2acut34xCUW39cpF7fVgCUCapMTrqajLgU7N2dsDFMKqhsrKjUshYJd6IB/RuHWozrwbcj3+PMZ/vZEjKHNSEzuXzwHI0NPrBCUBWyLfjA26euEWjwgY37tuGKwQd6li9o8wG1KqK0UpGZnE5yVAIVgqpgZau/16zr10fz8CEAeTcMfZavvs+ybdeOnJPGfVbCoAEkDNR/ckJDSXt/HTknjiO5uCA5GPZCWVtj3aAhmkeWXzSRG3ETVVnjPl8detJIpmifj6HP18TEYV2nBpKtfjXWtnEQefdL9lILQekiVgL+NxkIrCpybBcwCFgAfID+XbJFOSJJ0tMdb5dlWX69xBq1OpJWbcBn80r9K0J/OUDe3Yc49esOQPoPv2HfvhWOPTqARosuO4f4GQVvNJFsbbBt2oCEZe+XSN2ff5ykVfvm7Dn9PdnqHOa/XVDWxh3vsWjKSuJjE5i3ZgbRkTF8+Zs+XOXQ3lA+XvsZAO1C2nAy9AzqLPOxyOi0ZGx8H5cV74JCQfbBvWgfPsC2m341wtI+AACFmxvOCw3npFSSc+QP8s6bxtPno9URt3QzAVuXgUJJ6q6D5N55hEt//exb6nd7cerUEueX2yNrNMg5uURPLnqJS87JQ6dp3q4J35/4ihx1DsumrM5Pe++Llayc/i4JsYnMWDWFmMgYtuzeCEDo3j/57P0vGDH5dZzdnJm24m396Wu0jAgpWJG8eiSM2sH1WRL6IbnqXL6Yvik/7c1ts/hq5sekxiXz86odjFz/Nj2mDuBxxH1O7tRvCAzq2pTWgzuh02rJy85l61vvF2uPSqlgVs8mjPvsD3SyjpcbVqGyjxvfn9GvRLzSpBoAhyMe0ayKP3ZFXsmpztVw+nY083o/e3FMpVQye2hPxq3Zhk4n06tNAyoH+LDzkH4T9avtmzC6Vzvmf/wDfWd9gIzM2/074+bkgJuTAx0b12bAvA0olQqql/enX/BzbL41w/SFqzgXdpmUlDTa9xrM+JFD6Nuj8/MXpNOR8/Mn2L2x0PCK0EPoYh+jaqovS3P6AKo6zVA16wI6LeTlkr1DP2ute3wb7ZVT2L/9HrJOh+7JPfJOW361pUqhYFaH2oz7Qf9K15frBFDZ04nvw/UPM68E6vdrHL4dQ7MKnthZG3dFs34N4/zjRFLUuXTafIhxLarQu675Nzod++MErds3Z9+ZXWSrs5k3aWl+2uYd61gwZTnxsQksWDOTqMgYvt7zKQB/7DnK5rVb+WjtZyz/cAE/Hd2BJEmsXbqRlEIrQYU5dPAY7Tu25lTYftRZ2Ux+c25+2lc7P2LqxPnExSbwweYVODk5IkkS167eZObUxcVeGnNcPHyBoOCGrD/2EbnqHDZOW5+fNnv7fD6asZHkuCS6DuvOy2N74+rlxrsHPiTsyAU+mrmBfpMG4OjmxBtL9W+d0Wp1zOox1ZK6fE4fOkOzdk345sSX5KizWTmlYMJhzRcrWD39PRJjE5m66m1iI2PZvFt/Xsf2Hufz9798ZvkXDp+nYXBDPvrzE3LUOayf9n5+2vzti9g480OSYpPoPrwHvcf2xc3LjQ8PrufC4fNsmLneYrmyVse+Bdt57YuZSEoF4TtDib/9hAav6cNQLuw4xO3D4VQODmTCsbXkqXPZPe1jABy9XXl57VgUCgWSQuLab2e4fVgfxtd1yVCU1lYM/kq/0hwZdocv5m3h2pEwagUHsSD0A3LVueyYvjn/XMZsm8U3Mz8mLS6Z3at2MGz9JLpN7U9kxANOG3xgYNcmNOrTGq1G7wO3T9DXw8PwO4TvO8OMPavQanTw8Drq3wyvOtVpSf/wfdzWGPqsfXvRPniAXQ99n6X+1XKfpfTwwHnWHP3GfoVE9tGj5J4+ZVEerY6k1evx3rhK3+fv3k/evYc49tX3+Rm7fsO+XSscuncEQ5+VMEvfJ+ZevUHWoWP47diMrNWSe/MO6T/usazrH6KEsfr/r5GeJxZP8GJROBzov03PmIzSUsXhen8t9vh5SXxoPlzlv8GwdAsDnf8CgValN7O9dm1gqeiRylYrFT0AyoqlFxqUs/rZD31/F8pqz15l+btoOLf412v+XSTlWA6h+btp6VJ6G8hjNaXnb92UxYff/V00kFyeLfQ3kSBpSk3X3Ioxzxb6m8hO/ev/Z/K8lL/4xz/+BP6wfodSe8b5N9hrDrESIBAIBAKBQCB4oRArAWJPgEAgEAgEAoFA8MIhVgIEAoFAIBAIBC8UIhperAQIBAKBQCAQCAQvHGIlQCAQCAQCgUDwQiH2BIiVAIFAIBAIBAKB4IVDDAIEAoFAIBAIBIIXDBEOJBAIBAKBQCB4oZBlEQ4kVgIEAoFAIBAIBIIXDLESILDI7wk+paarvLVNqek6dcmpVPTU8U0oFT0AjbNL71rlois1XXHLDpeKHrcGR0tFD4DC1aHUdNnMfK/UdCX1G1FquprblSsVPbJd6b1DUEnpzUo6WbmVmq7Smmmsm1161+qgbenpOnwzoNR02ehKz67ypabJMnLpdWX/WsRKgEAgEAgEAoFA8IIhVgIEAoFAIBAIBC8UOrEnQKwECAQCgUAgEAgELxpiJUAgEAgEAoFA8EIh3g4kVgIEAoFAIBAIBIIXDrESIBAIBAKBQCB4oZB1YiVArAQIBAKBQCAQCAQvGGIlQCAQCAQCgUDwQiGX3t8i/GsRg4D/ISRJmgsMArSADhgDrAb8gGwgF3hDluVwM3m1wBVAMuSfIMvyyefRH9C2Ls0WD0FSKrj5zVEubfzVKN3lJT/arB2NZ+0KnFvzPVc+3puf1vrdNyjXIRB1Qhq7Oswukb7Ri0fTILghOeocPpj6Pnev3jWR6Ta0Oz1H9sS/gj+v1RtEWnIaALWb1mHep/OIfRwLwKn9J/n2g29N8nsF16P20teRlAoe7TjCnQ27jdIdK/tT7/0xuNSpyI1V33Fv8578tIqjulBucDskSeLhV4e5/8m+Yu2xa94Q9xnjQaEg46d9pG77zji9bTPcxg8DWUbWaEl6ZxM54REAOA/ug2PvriDL5N5+QOLCd5Bz84rV12fhUGoGB5GnzmHHtM1ERjwwkXEP8GLohkk4uDjwOOIBX03egDZPS+2ODeg25VV0soxOo+WnJV9w7/xNo7yvLhxOreAgctU5fDFtE48j7puU7xHgxcgNb+Pg4sijiPtsn7webZ6WKk1rMm7LDBIi4wAI33+GvR/uws3Pg6Fr38TZyxVZJ2P7y27Sv/kJANvmjXCfNh6U+vpL2258Pe3aNMd1/DDQ6ZC1WpLf3UxO+FUAnF7ri2Mvff3l3blPwqJ3oJj6U9ZqiO2AcUgKBbl/7id3v/G1Ulati/2bi9ElxgCQd/E4ub/tAJUVDjPeA5UVKJVoLvxJzu4vi71OympB2PQcCQoFeWf/IO/Ij8bplWphO2w2umR9XWmunCbvj50AWLXqgapxBwB00Q/J2bkeNMW3C0vMW7GWYyfO4u7mys9fffSXyiiMdePGOE2YAEol6j17yPr6a7NyqmrVcN+0idQlS8gJDQVra9w/+ACsrJCUSrJDQ8ncvt0k34CFw6kTXJ9cdQ7bpm3kkZn25xngzRuF2t/WyevR5mkAqNq0JgMWDEepUpKenM67/ReisrFixndLUNmoUCqVnN93it3rdhqVOXDhCOoEB5GrzuWzaRss6h29YbJB7z0+NejtPLonTXq1AkCpVOJXuQyT648kMzUjP2//hcOpbbBr+7SNFu4rvV32Lo48jrjPZ4byqzatyfgtM/Pvq7D9Z9jz4Q8AtB/ZjZb92yPLMpE3H7F1+kZenTUk35atxdgy1mDLw4h7fGLQ1WV0T5oabFEolfhXLsMkgy12zvYMXzWOMtXKgUFf+VoV/1KdAVRrWov+C4ahVKnISE7jnf4LAeg4sjst+7fX+8xrkVx8+2N0Ofr27x1clzoG3/5wxxFubzDurxwr+1P//TG41KnA9VU7uVPIt780uivlXwsGWSbt+mOjcgvzisEH5j3DB44wtMHHhXzgU8rXfYnpPy1n64R1hO07A0Dw8K60GNAeJImYr45y89P9+fJ+betSf+kQJIWCu98c5XoRu5wq+9F07Rjc6lTg8uqd3PiooB+2cran8btv4Fo9AFmWOTNlC4kX7pic81N8gutS11CHD3Yc4ZaZOmzw/hhc61Tg2qqd3C5Uh5VHd6XCa8HIhjq8YKEOBf8sYhDwP4IkSc2A7kB9WZZzJEnyBKwNya/JsnxekqThwDtARzNFqGVZDjSU1RlYCbQpsX6FRItlQ9k7aBWZ0Un02rOEhwcvkHI7Kl8mJyWTkwu+pELnBib5b31/jIjtv9P2/TEl0tcguCH+FfwZ03o01YKqMW75eKa9PNVE7vr5a5w7dJYV3600Sbt2LoIlw5dYVqKQqLNyOKdfXYE6OpFW+5cTc/ACGbee5IvkpmRwdd7n+HVpaJTVqXoA5Qa343jXeehyNTT5ZhZxf4SReT/Ggi4F7rPfInbsTDSxCfjv2EBW6Cny7j3KF8k+E0bU0VMAWFWpiPeaeTzpPRKltwdOA3sR1WcUck4uXmvm4dAlmIzdBy2aVrNtIF4V/VjW9m3KB1XmleWjWNdrnolcz1mDOLp1D2G/nuLV5SNp2r8dJ776nVsnrnL19wsA+Fcvx7CNk1jRvqD+a7UNwruiLwvbTqRiUBUGLh/Fml5zTcrvPWswh7fu4fyvJxm4/A1a9G/Hsa9+B+DOuetsGrnaSF6r0bJr2Zc8jriPjYMt7+xdQfbpC+Q9jMR95lvEjZ+JJjYev682og49Sd79QvV39iLRoSfz689r1Xyi+o5A6eWB84BeRPUbiZyTi+eq+Th0DibzVwv1JymwGzSBzHWzkJMTcJi7Hs2lU+iiHxmJae5cQb1+gXFeTR6Z782AnGxQKnGYsQ7N1XNo792wqMum92jUWxYhpyZiN3ENmoizyHGRxvVy/zrZ25YbZ3V2x6plN7LemQiaXGwGT0MV2BLN+SPmdT2DXiEdGdS3J3OWvvuX8huhUOA0aRIp06ahjY/H/aOPyDlxAu3Dh6ZyY8aQe+5cwbHcXJKnTEFWq0GpxH39enLPniXv2rV8kdptg/Cu6Mfctm9RKagKry1/g5W95picRt9Zr/HH1t849+tJBi9/g5b92xH61UHsnO15bekbfDB0OUlRCTh5OAOgycnjvUGLycnKRqlSMuOHpVw9Gsa9sNsA1DHonWPQO3j5aFb0Mp3U6DtrML9v/Y1zv55g8PLRtOrfjqNfHeTAlt0c2KKfaKjXvgEdRnY3GgA8tWt+27eoaLBrlRm7+hjsOv/rSQbl31f69nz73HU2jlxlJO/q4067YSEs6jCZvJxcRm2YTN/pg/Cp6Mdsgy2vLx/NMjO2vDJrMAe3/sbZX08wpJAt+7fsZn8hWzoVsmXQwhFcCQ1n0/j3COrQkPbDuv7lOtNfq1G8X+Ra6W3qygKDTYs/nEZAr2Y8+u4YKCTqrRzOiVdXoo5OpO3+ZcQcvEh6Ed9+2Yxvt/V1o9KozhxqPR1ddh6NtkwsKLcQT33gorYTqRBUhQHLR/GOGR/Yy+ADLxh8YPP+7fjT4AMlhUSvWa9x7Vh4vrxf1bK0GNCe1S/PQZunYd72eTw5FEbG/VgkhUSDFcM4MmAl6ugkOu1dypMDF0m7Xciu5EwuzP+CgC6m/XCDJUOIPnqJE6M/QGGlRGlnYyKTj6EOjxvqMHj/MqKL1GFeMXX40qjO/G6ow8YW6vCfRuwJEHsC/pfwAxJkWc4BkGU5QZblqCIyp4AyJSjLGUh+HuVegS+R9iCW9Efx6PK03P3lNOU7GTuZ7MQ0Ei7dQ6fRmuSPOXOTnJQMk+OWaNqpCYd3HQbgZthNHJwdcPM2/av7exH3iDPMej0vbkGVybwfQ9ajOOQ8LVE/n8K3s7Ezy01IIzXc1CbHKmVIvnAbrToXWasj8dR1fEMaWdRlU7samsdRaJ7EgEZD5oGj2LdtbiQjq7PzvyvsbI2WKiWlEsnGBpQKJFsbNPGJxdpWu1NDzv2od7gPw+5g52SPs5eriVyV5rW4tFc/+3R21zHqdNLbn5uVky9jbW9jsmxar1NDThvKvx92G3snB7PlV2tei4t7TwNwetdR6nWyXEcAafEp+bNpOZnZ5N1/hNLbE+va1dBERqF5Ep1ff3ZtWxjlLVx/kp0tUOikC9efnQ3aYupPWbEauvgo5IQY0GrIOxeKKrC5RXkTcgznoVSBUml0GkVRlKuCLiEaOSkWtBo04cdR1Wpccl0KJVhZg0KBZGWDnJZU8rxFaBhYBxdnp7+cvzBW1aujffIEbbT+emUfPoxNixYmcvZ9+pB97Bi6lBSj47Jarf+iUoFKhVykAQZ2asTpH0MBuGdofy5m219tLhja38ldoQQZ2l+Tni0J23+GpKgEANIT0/Lz5GTpr59SpUSpUhq1/cBOjTj149FCeu3N6q3evDYX9p4y6D1KYCfTa9q4Z0vO7j5hdKxeIbvuh93GzsJ9Vb157UL3VSiBz7ivABRKBVa21iiUCqztbPCt5M/JEtpyvpAt9c3Y0qRnS84YbLF1tKNq4xr8+d0hAOq2q8/xnYdLpMdcnTXp2YqLFq6VUqnE2mCT0s4adYy+W3MLqkzG/dh83x758yl8i0xO5SakkRJ+D9lMfyUplShtrZGKlFuYup0acsbgAx88wweGWfCBbYd1JWzfGSObfCuX4X7YbfKyc9FpdcSduk7Zrvo87kEvkfEglkxDP/zol9MEFLErJzGNJDP9sMrRDq+m1bn39VEAdHla8tKyTM73Ke5BlcksUod+RXUlpJFcwjrMNlOHgn8esRLwv8NBYIEkSbeAP4DvZFkOLSLTBfjZQn47SZLCAVv0A4p2z6Pcwc+NjOiCB4zMmCS8g156niKeCw9fDxKiE/J/J8Yk4uHrQXJcyR1JtfrV+XD/epJiE/ls+Wc8umU8k2vr54Y6quBhMDs6Edf6lUtUdvqNx1Sf1R8rN0d02bl4tw8k9ZLpUvBTlN6eaGLi839rYhOwqVPdRM4+uAVuE0egcHcl7i39zL02LpHUL34gYP8O5Owc1KcvkH3qQrHn5+rjTkoh21JjknDxdSctPiX/mIObE+q0LHRaHQAp0Um4+rjnp9ft3IjuMwbg6OHClhHGM/auPu4kRxVcn+SYRFzNlJ9VTPkV61dl7r41pMYms2v5l0TfNp79dg/wwrpaZXKu3sCuaQM0MQWDPW1cPNa1TevPLrgFbhNG6utvkn5WThufSNqX31Nm79fIOTlkn7pA9mnL9Se5eqJLKrhWcnI8yoqmupSVauKwYDNyShLZP2xBF2WY5ZYUOMzfiMLLn9yju9Het7AKgH42X04pqEc5NRFFuaqmuspXw27yWuS0ZHJ/244u9jFyWhJ5ob/gMHcL5OWiuRWO9tYli7pKE4WXF7r4gjrUxcdjVbOmsYynJzYtW5I8ZQpW1YvUr0KB+5YtKMuUQf3TT2iuXzdKdvNxJ6lQ+37a/lILtT/HIu07OToxv/35VPJHqVIy7dtF2DrYcWjbHk4ZHugkhYL5v63Gq7wvR77cz/3w2/lluvp4FNGbhKuvhxm9mUZ63Qq1ewBrW2tqtwnk6wVbjY67FrErJSYRt2fcV4XtAqhUvyrz9r1DamwyPyz/gujbkaTEJvH7J7+y8uRm8rJzufrnJeyd7I10JcUk4WbGlqxCtiQV0VXYlh0GW7zK+ZCemMaId9+kbI0KOLo6cn7Pqb9cZz6V/FCqVEz/djG2Drb8sW0vp34MJSU2iQOf7Ga1waaUo1eJD70CgJ2Jb0/CrYS+PTsmmTub99D5wnq02bnEHb2SX25h/lMf6OLjRmDnxrw/cDHl643LzxN98zE9pw3AwdWR3Oxc/NsFknT5HgD2vu5kFbIrKzoJj/ol64cdy3uTk5hOk3VjcKtVjqTL97kw/0u06hyz8kX7R3V0Eu7PUYe3N++hq6EOY49eIc5MHf7TiH8MFisB/zPIspwBNABGA/HAd5IkDTMk75AkKRKYCay3UIRaluVAWZarox8sfCFJ0nPcAWZE/6ubakz1FZ0NLI67V+8wstkIJnZ5i1+3/8bcT0xDYTBnfglVZNyO4s6G3TT7bg5Nvp5FWsQjs7MhxesyVZZ15ARPeo8kbvIifXw7oHByxL5tMyK7DeFxpwEo7GxxCGlf/AmaVSc/l8zlA+dY0X4qW0e/S8iUV5/bnuJEHl+9z7wW41nedQZHtu9n7JbpRnI29jaM2TyVpPc2IWdmlfhaqY+cIKrvCOKnLsR13HDgaf0150n3wUR27o/0rPoze1cYK9M+ukPGrMFkLhlH7uGfsRu/qJCojswl40ifMQhlhWoo/CsUo+vZ9ah9co/MFaNRr5tC3ok92A6dpU+wc0BZqzGZK8eSuXQkkrUtqvoljvArfYrY5TRhAhlbtoBOZyqr05E0ahQJr7yCVY0aKCtWNE43U28mt1MxMgqlkvJ1KvHh8JW8//oyur3VD5+KfnoZnY4lIdOZ0WwMFetVxr9q2eKKNFVsVq+xTL0ODblz/qZRKJA+67PtMuu2DTKPrt5nTovxLOs6nSPb9zFuywwA7J0dqNexEXNbvcmMJqOxsbfBzdfdtBiTe/jZ7bOoLUqlkvK1K3H0q4Ms7jYdWSfTtFfrZxll8VyUhmv1wfAVrHt9Gd0N18re2YHAjo2Y1epNpjUZjcrehoC+LSyWV9JdoFYuDvh1acDBxpPYX+9N43KNTvnZ17m403hlwTB+WrUDWWecJ+buE37/6Bfe+moeEz6fQ/K1R+g0hnukJO3PAgqlArc6FbjzxR/s7zQXTVYONSf0sChfkmtviad1uL/xJPYa6rCsmToU/POIlYD/IWRZ1gJHgaOSJF0BhhqSXgMuAauAjUAfSZK2AUFAlCzLIUXKOWXYU+AFGMXSSJI0Gv1Ag8GujWntUAWAzOgkHP0KOg0HX3cy/+blvZDXu9F5YGcAbl++jaefZ36ah68HSbElD3VQZ6jzv184ch7lsnE4uznnbxwGyI5Kws7fI/+3rZ/Hcy1ZPv7mKI+/OQpA9dn9UUdbPj9tbDwqX6/83yofz2JDUnIuXkFV1g+FqzO2jQLRPIlBl5wKQOah49gE1iRz7yGjPC2HdKLZQP0Cz6NLd3EtZJuLrztpsca2ZSalY+dsj0KpQKfV4ernTqqZlZa7Z2/gWd6H9mN6UP/lFsjAw0t3cfP3BPSbhd18PUgpUn5GUjr2JuXr6yi70PWJOBqGctlIHNycyExOR6FSMvqjqZz9+U8aHT4OgCYuHpWvd34epbfXM+vPKsBQfw0N9Zeir7+sw8exqVvLpP6eIicnoHAvuFaSmxe6lCLXNrtgGV1z9Ry2SiWSozNyRkH7Qp2J5tZlVLUbkhv1wLyu1EQk14J2Lrl4mIb05BTUlfbGReg9BuydUFauow8jykwznMdplOWroblYdIGw9NHFx6PwKqhDhZcX2oQEIxmratVwWaDfUyG5uGDTpAlpWi05x4/ny8gZGeSGh2PTuDFyvXrYde8OwM1zD3Av1L7dfD1ILeIfMpLSjNq3m59HfvtLjkkkIzmNXHUOueocbp+9TkCN8sTej87Pr07L4ubpCHpNGYBnOX3be3DpbhG97qSY1etgpDelyH3VqEcLzuzW2xk8pAutBrZHQuLBpTu4+3vw9BUIrr4eZsu3L2JXipn76urRMAYuG4WDmxPVmtUi4XEcDbs1o+XADti7OJCTlW1ki7sZW9KT0rAvZIu7GVuaFLIFICkmEXV6Fq+vGA3Ak5uPKFuz/F+uM/21Ss+/VrfOXiOgRgUAEh7HkZGkb/9Re8/h3qgqkbtOoDbx7e5mQ3rM4dW6NlmP4shNTDcpt+LwjlR4LZgGCvM+MPU5fGC5ui8xcv0kABzcnKndNgidVselg+c4ufMIJ3fq9/a8OeU1sgx9S1Z0EvaF7LL3c0cdk1Iiu7Kik8iKTiIxTN+6Hv92lhrFDAKK1qHdc9Sht5k69GhUlce7Tjwjp6C0ESsB/yNIklRNkqQqhQ4FAvm77GRZzgPmAU0lSaohy/Jww8x/SJGikCSpOqAETJ6iZFneIstyQ1mWGz4dAADEX7qHc0VfnMp6obBS8tLLTXn0+8W/z0Bg7xd7mNR1IpO6TuT0gVO066t/oK0WVI2s9KznCgVyLRSbWaVeVRQKyWgAAJASfheHSr7YlfNCslLi36sZMQeLD7MpjLWnfoOaXRkP/EIaEfWT5Zct5UTcRFWuDCp/X1CpcOjclqzQU0YyqrL+BWVXr4xkZYUuJQ1NdBw2dWsg2eo3cdk1CTLaUPyU418e5J2QWbwTMosrB8/TqI9+9q18UGWy07OMlqmfcvvUNeqFNAGgcd/WXD14HgDP8j75MgG1KqC0UnHo4195J2QWK0JmcOngWZoayq8YVAW1hfJvnoqgfkhTAJr2bcslQ/nOXi75MuXrvYQkKchM1ncYQ1aPJebOEw5tLXjTRG7ETVRljetPHWpc30Xrj6f1FxOHdZ2C+rNtHGS0obgo2gc3UXiXQfL0BaUKq0Zt0FwyvlaSc8H+FEWFaiApkDPSkBxdwM5Bn2BljapGELqYxxZ16R7fRuHph+TmDUoVqsCWaK+dM5KRnFwLdJWtop9ezEpHTo7Xhw5Z6d8PoKxcF12RDcX/FHk3b6IMCEDhq79etu3akXPS+HolDBxIwoABJAwYQE5oKGnvv0/O8eNILi5Ijo56IWtrrBs0QPPoEeqffyZp1CiSRo0i/OA5mvbRr3pUMrS/VAvtr4Gh/TXv24bwg/q6DT94jiqNaujj422tqRhYmeg7T3B0d8bO2R4AKxtrarSoy/Gdh1kSMp0lIdMJO3iWZn3allBvM4Petvl6Aeyc7KnWpCbhv+uPHflyP0tCprMsZLqRXSW/r9pwyVB+4Zj0CvUqozDcV0lRCVQKqsKJ74+wLGQ6N05HcPPMdZoXsiXLgi03TkXQsJAtYUVsqdqkJmG/FxxLi08h6nYkH01Yx6KQ6agz1Ng5OfzlOit6rSoFViH6TqTBpqpY2+rbv1erWmQYNsimhN/FsZIv9gbfHvAcvl0dmYBbgyoo7UzLvb/td450mMPKkBlcPniWJgYfWKGYa3XrVARBhXzgZYMPXNBqAvNb6j9h+07z7fxP86+jo2Hzs5u/B2VDGvHwZ/29kxR+D6eKvjgY+uFyLzclsoR2ZcenkhWViNNL+hUvn1a1jDYUFyXZTB1Gl1BXVmQC7kXqsDhd/xSyLJXa59+KWAn438ERWC9JkiugAe6gn7H/4amALMtqSZLeA6YBI4vkf7onAPSLikMNKwslQtbqODn/c7rumIGkUHDzu1CSbz2hxmD9g/r1rw5j5+VCr71LsXa0Q9bpqD2qCz8EzyQvQ03whjfxb1YDW3dHBp77kIvv7eLmt5ZnLM8fPk/D4IZs+fMT/StCp72fn7Zw+yLWz/yQpNgkegzvQZ+xfXHzcuPDg+u5cPg862eup0VIS0KGdEWr0ZGTncOaCWvM2nR1znaafjMbSang8TdHybgZSfnX9a9cfPjFH9h4udDqwHJUTnagk6n0RleOtp6OJkNNw08nY+3uiC5Py5XZ28hLzbRcgVodSas24LN5pf4Vob8cIO/uQ5z66Wc203/4Dfv2rXDs0QE0WnTZOcTPWAZA7tUbZP3xJ/7fbELWasm9cZf0XXst6wKuHQmjZnAg80M/IFedw9fTC175OGbbTL6ZuYW0uGR+XfU1Q9dPpNvU/kRGPOCUYfapXtcmNOrTCq1GS152Lp9P+MCo/KtHwqgdXJ8loR+Sq87li+mb8tPe3DaLr2Z+TGpcMj+v2sHI9W/TY+oAHkfc56Rhg2BQ16a0HtwJnVZf/ta39Nf3pYbVaNq3DZHXHzJn7xp8lBqSN3xG9omzJK1ej/fGVfr6272fvHsPceyrr7+MXb9h364VDt07gkaDnJNLwqxC9XfoGH47Nuvr7+Yd0n/cg0V0OrK/3oD92yuQJAW5Jw6gi3qIVZtuAOSF7kHVoBXWbbuDVoucl4v6kxUASC7u2I+YDgoFSAryzoeiuXymWF05P3+C3RsLDa8IPYQu9jGqpvoVMc3pA6jqNEPVrAvotJCXS/aO9/RZH99Ge+UU9m+/h6zToXtyj7zTlt8Y9SymL1zFubDLpKSk0b7XYMaPHELfHp3/WmFaLekffIDbO++AQkH2vn1oHzzArmdPANS7d1vMqvTwwHn2bP1mZ4WC7CNHyD1lPAi7cuQidYKDWB66nlx1Ltunb8xPm7htNp/P/IjUuGR2rfqK0esn02vqQB5F3M/foBpz9wlXQ8NZuF9fd39+d4ioW48pU70cI96bgEKhQFJInNtzksuHLxTRW58VoRv0ryYt1O4nbZvD9pmbSY1L5odVXzJm/WR6Tx3Ao4gHHN9ZsOoU1LkxEX9eJtdMLPZVg13LDHZ9XsiuCdtm86XBrh9XfcWo9ZN5eepAHkfc54TBrvpdm9JmcCe0hvvqk7fWAfAg/A4X951m3p41aDVaHkbc55slnzFg3jBWGWz5rJAtbxtsSTFjy5+FbKlvwZYdi7Yy+v1JKK1UJDyO5cbJK3+5zqLvPuFqaBiL9r+HrJPzrxXAhX2nmL/nHXQaLbrLD3nwpb4eZK2Oy3O20/ybWfpXhH5zlPSbT6jwuj4M8MEXh7DxcqHtgWX5vv2lN7pwqPUMksPuEvXbGdoeXIGs1ZJ65UF+ucbXKoxawfVZbPCBXxaya/y2Weww+MCfCvnAyEI+sDhGb56Kg5sTWo2G83O2k5ealW/X+bnbafv1TCSlgnvfhpJ26wmVh+jtuvPlIWy9XOi8bxlWTvp+uNqoruxpOwNNhpoL876g2YbxKK1UZDyK4/Tkjy2eg6zVET5nOy2K1GFFQx3eN9RhO0MdyjqZym904XdDHT757QztDq5AV0wdCv55pOeJsxa8WHwSMLjUGsduRUppqWJ07t/zBpRnUcc34dlCfxPrUjyeLfQ3kYuZGO7/ErPc//rbbp4HtwaltyiqcHUoNV02M98rNV1J/UaUmq55D7yeLfQ3IP93Nz4ZoTS/GeW/Ql4p2lVad1b3bOtnC/1NHLTVlJquVrlWpabLRld67aJPzNf/+PT45Qo9Ss3gug9+/cftNYcIBxIIBAKBQCAQCF4wRDiQQCAQCAQCgeCFQrwiVKwECAQCgUAgEAgELxxiJUAgEAgEAoFA8ELxb35rT2khVgIEAoFAIBAIBIIXDLESIBAIBAKBQCB4oRAvxxQrAQKBQCAQCAQCwQuHWAkQCAQCgUAgELxQiLcDiZUAgUAgEAgEAoHghUOsBAgsUj6v9P4VMd0qu9R01fHNebbQ30BGmk2p6AG4p8soNV0VFaX3j7c+H40pFT1y3ONS0QNATGSpqSrNf/F1/+GzUtOlaTi9VPQk6UrHVwBUUZTOP5kDRMml5299pNLxg3/YaktFD0DjvNL7d+LqqvRS05VViv9O/G9AvB1IrAQIBAKBQCAQCAQvHGIlQCAQCAQCgUDwQiH2BIiVAIFAIBAIBAKB4IVDrAQIBAKBQCAQCF4oxN8EiJUAgUAgEAgEAoHghUMMAgQCgUAgEAgEghcMEQ4kEAgEAoFAIHihEBuDxUqAQCAQCAQCgUDwwiFWAgQCgUAgEAgELxTiz8LEIOAfQ5KkucAgQAvogDHAamAacBMIB7rIsnxbkiQr4CIwSpblM5IkaYErhYr7VpblVZIkHQX8gGwgF3hDluVwg74RwGT0G+IVwFxZln95nnP2CK5H9WVDkZQKIncc5sH63Ubpvn1bUHFCTwC0mTlcm/EpGdceYePvQZ0N47H2cgWdjsivDvPok33Po5q3lrxJ03aNyVbnsGryGm5fvWMiM3f9bKrVrYo2T8P18Ju8N2sdWk3x/yJp17wh7jPGg0JBxk/7SN32nXF622a4jR8Gsoys0ZL0ziZywiMAcB7cB8feXUGWyb39gMSF7yDn5lnU5di6Pn4LRoNCQfLOgyR89INRulOHJvhMGYysk0GrJXrpJ2Sdv4aVnydl3p2CyssNdDqSvz1A4vbdFrQUMHrxaBoENyRHncMHU9/n7tW7JjLdhnan58ie+Ffw57V6g0hLTgOgdtM6zPt0HrGPYwE4tf8k337wrVHevguHUTM4iFx1DjumbSYy4r5J+e4BXgzbMAl7F0ciI+7z5eQNaPO01OnYkJApryLLMjqNlh+XfM698zfz80kKiem/rsSqjA15t04AcOLyLVZ/uRedTkfvtg0Y2aONka70rGzmbP6emMRUNDodQ0Na0Kt1Ax5ExzNjQ8F1jYxLZnzf9gzu0txi3Z248Yg1P59Ep5Pp3aQ6I9oHGaVvPxLO3ov6NqjV6bgfm8KRJa/jYm/LjmNX+PHMdWQZ+jStzuDWdS3qAThxP441h66hk2V61y3LiCaVjXWdvcvea1F6XbKO+4kZHHmzIy521izcd4lj9+Jwt7dm1/A25oo3wrpxY5wmTAClEvWePWR9/bVZOVW1arhv2kTqkiXkhIaCtTXuH3wAVlZISiXZoaFkbt/+TH2WmLdiLcdOnMXdzZWfv/roL5fzlEELR1A3uD656ly2TlvPQzNt0TPAm7EbJuPo4sTDiHtsmfwh2jwNdk72jF43CfcyniiVSvZ/8gvHvz9iUdcbRe6re2buqxDDfeVXwZ/B9QaRbrivnlK5bhXW/PIu7765hpN7TxilvbxwKDWCA8lV5/LdtM08iXhgUr57gBeDN0zEzsWBJxEP+GbyRrR5Wrxe8qf/O2MIqFWRfe9+R+gnewBw8XNn4NrxOHm5IutkDn59gP3bfrNo4+uLRhIY3IBcdQ4fTVvPg6v3TGQ6De1KlxE98K3gx5jA10lPtvzPtr0XDqVGcBB56hy+mbaZSAs2vb5hEvYuDkRGPGCHwVfUf7kF7cfq+5WcrBx+mPcpUdcfATBgzRhqtqtPRmIayztPA6DfwmHUMvilLy34JY8AL4Yb/NLjiPt8YdD1lHJ1X2LaT8v4bML7hO87k39cUkjM+HUlRKdwaOh7+cfLtK1L4yVDkBQKbn9zlCsbfzXS5/KSHy3WjcajdgUurv6eiI/3AqC0saLLrnkobVRISiUP95wl/L0fLdYjgFObIAIWvYGkVJD47e/EbtpllO7Wqw0+4/oAoM3MJnLuZtTX9fXtNaI7HgM7gSSR+M1B4rf+WrR4I1yDA6m0dDgoFcTuOMSTDT8bpXv1aUWZCb3ydd2duYWsaw8BaHBuE9oMNbJWB1odlzrPLFaX4J9BhAP9A0iS1AzoDtSXZbku0AF4/DRdluV0YDaw0XBoGnBSluWn3kgty3Jgoc+qQsW/JstyPWAT8I5BXwAwF2hp0NcUuPxcJ62QqLFqBBcHreJEq6n49W6BQ9UyRiLqh/Gc67WEU8Ezubf2R2q9N1pvj0bLzYVfcrLVVM6EzKfs8E4meYujSbvGBFQsw2sth/LezHVMXjnJrNwfPx3i9TbDGd7hDWxsrek2MOQZNilwn/0WsW/O4UmfUTh0CcaqUjkjkewzYUS9Ooao/mNJWPQungunAKD09sBpYC+iB71JVL/RSEoFDl2Ci9Xlv3gcD4Yv5E7n8bj0aINN5bJGIpknL3En5C3udp9I5MwPKLPyLUBffzErtnKn0zju9Z2G+5BuJnmL0iC4If4V/BnTejQbZ21g3PLxZuWun7/G/EEFD/uFuXYugkldJzKp60STAUDNtoF4VfRladtJfDfnE15dPtJs+S/Peo2jW/eyLPhtslIzada/HQA3T1xhddcZrAmZydczPmLg6jFG+doODyHmzpP831qdjhWf/8qm6a/z0+qJ7D91hbtP4ozyfPfHaSqV8eb7FRPYOmck7329nzyNhgp+XuxcPoGdyyfwzdLx2NpY0a5hDYt1p9XpWPnjCTa+EcKPM15lf9gd7sYkG8kMCw5k59R+7Jzaj4khjWnwkh8u9rbciU7ixzPX+WpSb3ZO7cef1x7xMD61GF0yK3+PYGO/xvw4og37r0dxN8H4YWpY45fYOawVO4e1YmKr6jQo64GLnTUAPWsHsKlfY4vlG6FQ4DRpEikzZ5I4dCi27dqhLF/evNyYMeSeO1dwLDeX5ClTSBo1isRRo7Bp3BirmjVLptcMvUI68tHaZX85f2Hqtq2PT0U/ZrWdwPY5mxmyfLRZuVdmDeHg1t+YFTyBzNQMWvdvD0C7IV2IuvOYhV2nsnrAAvrPHYrSyvz8WIPghvhV8GdsCe6rBRbuK4VCwdDZwwgLDTNJq264r1a1ncwPcz6hr4X7qtusQRzbupfVwVNQp2bSuL/e96hTMvhl0ecc/cT4AV+n0fHrsq94p8M01veeT8fXu1KmSoDZsgOD6+Nb0Z8pbcbz6ezNjFg2xqzczfM3WPHaQuIfx5lNf0qNtoF4VfRjRdu32TnnE/otH2VWrsesQYRu3cOK4MmoUzNoYvAVSY/j2dB/Ce90ncnB9T/y6sqC63v2h1C2DF2Z//upX1rcdhLfzPmEAcX4pSNb97Ik+G3UhfwS6B/0X541iOvHLpnkCx4eQmwhv/RUvsnyofw+eA0/B8+gYq+muFTxN5LJScnkzPwvuWp4+H+KNiePA6+uYHfHuezuNJcybeviVf8ls+cMgEJB2WVjuDt0MdfbT8CtZytsqxj3BbmPY7n96hxudJ5E7IffUXbVmwDYVi2Hx8BO3OwxjRudJ+HSvhE2FfyK1VVp5SgiBi0nrPVkvHq3xK6qcZvJfhTHld4LCG83lcfrfqDyu2ON0q/2XcSlDtP/tQMAXSl+/q2IQcA/gx+QIMtyDoAsywmyLEcVFpBleSegkyRpBjAW/aDgeTgFPH3S9gbSgQxD2RmyLJtOjxSDS/3KZN2PQf0wDjlPS8zPJ/Hu0tBIJvX8LTSpmQCkXLiNjZ87ALlxKaRfeQDoZwsybz/Bxte9xLpbdGrOgR9+B+Daxes4Ojvi7m2a/8zhs/nfr4ffxMvPs9hybWpXQ/M4Cs2TGNBoyDxwFPu2xrPDsjo7/7vCzha50IuFJaUSycYGlAokWxs08YkWddnVq0rOw2jyHsci52lI/e0YTh2bGsnosszr0sQnkx2hn23UZarJufMYla9HsbY17dSEw7sOA3Az7CYOzg64ebuZyN2LuEdcZPGduDnqdGrE2R+PAfAg7DZ2Tg44e7mayFVpXovwvacBOLsrlDqdGgGQm5WTL2Ntb2NUr66+7tRsF8Spbw/nH7t6N5KyPh4EeLtjpVLRpWkdjl64bqRLQiIrOwdZlsnKzsHFwQ6lwtjFnYm4S1lvd/w9TesiX9ejOMp6OBPg4YyVSknnoMocNTNz+ZR9YXfpEqSfvb8Xl0zdcj7YWVuhUipo8JIfh69YvtWuRqdQ1s2eAFd7rJQKOlf35+gd0wfHfF03ouhSveABo0FZD5xtrSzKF8aqenW0T56gjY4GjYbsw4exadHCRM6+Tx+yjx1Dl5JidFxWq/VfVCpQqZDlv/6W7YaBdXBxdvrL+QsT1KkRJ38MBeBe2G3snRxwMdMWazSvzfm9pwA4seso9Ts9HTzJ2DrYAWBjb0tmSgY6CyuIjTs14YjhvrpVzH11v5j7qtvw7pzad5LUxBSTtFqdGnD+xz8BeBR2B1sne5zM2FK5eS0u79XPCZ3fdYzanfS+OCMxjceX75mcf3p8Sv6KQk5mNk/uROLmY96HNOjYmD936VdC7oTdwt7ZAVczNj6MuE9CZLzZMgpTu1NDzhl8xcOwO9g52Zv1FZWb1+KSwaazu45Rx2DTg4u3UKfp+5WHF2/jUqjvuHf2BpmGPgegbgn9UtXmtQgz+KUzu0KpZ/BLAG2GdeXSvjOkJxoP3l193anVLoiThfwSgGfQS6Q/iCXjUTy6PC33fzlNuc4NjGSyE9NIvHQPOc+0XWkMvlChUqKwUlHcbWUfWIWcBzHkPtL3I8m//olLJ+NJgMwLN9Aa6iQz7CZWfvrrbFslgMyLt5Czc0GrI/30VVy6NDXR8RSnoMpk348h51Eccp6G+J9P4N65kZFM+vmb+brSL9zC2q/k/brg34EYBPwzHATKSpJ0S5KkTZIkWVrHfxt9iNAyWZaTCh23kyQpvNCnv5m8XYCfDd8vAbHAfUmStkmS1ON5T9jW153sqIKH3OyopGIf5MsMCibhcLhpOWW9cKpdgdSLpuE8lvDy9SQ+qqCziY+Ox8vX8gO+UqWkU98OnD16zqIMgNLbE01MQbma2ASU3qbl2ge3oMxPW/Fev4zERe8CoI1LJPWLHwjYv4Oyv3+HLiOT7FMXLOqy8vUgL7qQrugErMx0wk6dmlHl982U37qQJzM/MC2njDe2tSqhDr9pklYYD18PEqIT8n8nxiTi8YyBQ1Gq1a/Oh/vXs+jzRZSrarxC4uLjRkqh9pASk2jUOQM4uDmhTstCp9XPg6REJ+HiUyBTt3Mj5h5ay5jPZvH1jM35x/ssGMrulTuMHjLjktPwdXfJ/+3t7kxskRCLAR2bci8qng5vrabfnA3MGNINRZFBwP7TV+jSrPjwnLjULHxdHfN/+7g4EFfoQaMw6tw8Tt54TIe6FQGo7OvOhXvRpGRmo87N4/j1R8SmZFjWlZGNr5NdgS4nW+Iyss3KqvO0nLwfT4eqvsWevyUUXl7o4gvaoC4+HqWXl7GMpyc2LVui3m0m3EyhwP3TT/H6+Wdyz59Hc/26qcw/gKuPO0lRBW09OSYRtyJt3dHNiay0zPy2mBydiKuhLR76fB9+lQNYd/ZTlh5Yy9eLP7M4wCl6XyU8533l7uNB087N2P+V+XBIFx93o/sqNSbJ5L6yd3NCXciWlOhEo/vqWbgFeFKhVkXuht8yn+7rQVKhc0iKScTtOcovSlGbUszYVNRXpBbxFU9p0j+YG0fDLepy9XEjuYhfcn2GruRCulx83KjXuRF/7vjdpOy+C4bycxG/BGDv60ZmVEH3nBmdhL2v5UmGokgKiZ4HlzPg8iaijl0hIcw0vOwp1r4e5BZq67nRiWb7kad49O9I2pGLAKhvPsKxSU2Urk5Itta4BDfAupiJMms/dxNdNsU85PsMak/K4UKrW7JMrW/nU+/AanwGd7CY759ERiq1z78VsSfgH0CW5QxJkhoArYBg4DtJkmaZEe0CRAO1ixxXy7IcaKH4HZIkOQBKoL5Bn1aSpC5AI6A9sE6SpAayLC8qmlmSpNHAaIBJTg0JsTMsTZptw+Y7SrcWNSkzKJhzPRcaHVfa2xC4dTI353+ONkNt4fTNIJkqL24WcvKKSVw+c5krZ68+d7nmpmGyjpwg68gJbOrXwXX8MGLHzkTh5Ih922ZEdhuCLj0D73fm4xDSnsy9h55pTnG60g+eIv3gKewb1cJnymAeDJmXn6awt6XcpjnELP0E3TPr7/nqrCh3r95hZLMRZGdl0yC4IXM/mceYNgXL8FIJ6s6cSGGZywfOcfnAOV5qXINuU/qzcfAyarWrT3piGo+v3qdy05rmslk8h5NXblO9nB+fzh7B47gkxqzaRv1q5XG0swUgT6Mh9OINJr3aqVjbZTPt2pILPxbxkMCKPrjY63VU8nFjeLtAxn68B3sbFVX9PVAqLc+1mLsiFnXdjSWwjFt+KNDfQpGKdZowgYwtW0BnZgFbpyNp1CgkR0dcly5FWbEi2vvPtaD4X8FcWyza1otrr7VbB/Lo2n3WDFyId3lfpn21gAVdp5Jt5h6T/sP7atSiN/h85XZ05urXwnma2mKar6TnYG1vw9DNk/lyyWeoLfiQZ923z0uJzrcEMpWb1aRp/2A+7LfQVLgYZSWpv6f29V0wjF9Wfa3fl1WI2oX8UpWmRcLgzBpo+RRNRHUyuzvNxdrZnuCtb+NaLYCUm5HmhZ/j2jg2q4NH/w7c6qsPIsi5E0ns5h+pvGMxuqxs1Ncf6OP1LfEc/a5Li1r4DGzHlZcL+qsrPeaRG5uMlacztb5bgPrOE9JO/zsmDgQFiEHAP4Qsy1rgKHBUkqQrwNDC6ZIk+QMTgcbAEUmStsqyXJI4/tfQz/yvQr+noI9BnwycBc5KkvQ7sA1YZOa8tgBbAA76DMi/47Ojk7D1L5hxsPV3J6dInDSAY81y1Fo7hosDV5GXXDADKqmU1PtsCtG7jhO3t/gZeoBeQ3vSfZA+pv/GpVt4+RfMWHr5eZEQaz70ZujkIbi6uzB/5rpn6tDGxqPyLShX5eOJtpiQnpyLV1CV9UPh6oxto0A0T2LQJeuXjDMPHccmsKbFQUBeTCJWfoV0+XmSF5dkVhYg61wE1uV8Ubo5o01OA5WSspvmkLL7KGkHTpnNE/J6NzoP7AzA7cu38Sw0y+Ph60FSrGV9RSn8gHDhyHmUy8bRd2xfWr/cBmtJwaNLd3Et1B5cfT1IjTVuDxlJ6dg526NQKtBpdbj6uZMaZ9pm7p69jmd5HxzcnKjUsBp1OjSgZnAgVjbWKJwcsarUGJ/bj4hJKliej0tKw9vVOJzkl2MXGdGjNZIkUc7HgzJebtyPSqDOS/o41uOXblO9gh8eLo4Uh4+LAzGFZu9jUzPxcnEwK7s/vCAU6Cm9m1Snd5PqAHy49ww+xejzcbQlJr2grmPTs/FytDWv67pxKNDzoouPR1Fo5l/h5YU2IcFIxqpaNVwWLABAcnHBpkkT0rRaco4fz5eRMzLIDQ/HpnFjsv6hQUC7IV1oM1A/u3j/0h3c/QvaupuvBylF2np6Uhr2zg75bdHNz4MUQ1ts+Uo79mz+CYC4hzEkPI7D76Uy3L+kX60Meb0bHQ331Z0i95Xnc95XletUZtqGGQA4uzvTILghga0CqRJYDRtJweNL94zuKxdfd9KK3FeZSenYFbLF1c+DNDP3VVEUKiVDP5rMxZ9PcG7/aaO0jq93JXhARwDuXb6De6FzcPf1ILkE5VsqL/ryvSK+wpJNBb7Cxc/dyCa/6uXov2oMW4atIqvIylrDPq3wLO/DrL2reXjpLm7P6ZfcCvmlcnUrMXz9RAAc3Zyp1TYInVZLhcAq1OnQgFoGv2TvaEerD8fx58TNZEUn4eBfMEPu4OdOVuzz1RdAbloWMSevU6ZtXYuDgNzoRKwLtXVrPw+z/Yht9fKUW/Mmd19fgjalYI9R0nd/kPTdHwD4zRhMXrTl/i43ylRXrpk+375GeV56bxzXBi1HU6jPzzXUQV5CGon7zuIYVOVfNwjQ/fWx7f8bRDjQP4AkSdUkSapS6FAg8LCI2DpghSzLkcAUYKNkdjrLFFmW84B5QFNJkmpIkuQvSVL9Z+grlrSwu9hX8sWunBeSlRLfXs2JO2Ac/mJbxoPAz6Zw5c2NZN2LNkqrtW4Mmbef8LDIxihL/Pz5bkZ1HsuozmM5vv8EnfvpO5Sa9WuQmZ5JkhnH121gVxq1aciSCctLNDOWE3ETVbkyqPx9QaXCoXNbskKNH7BVZQseuqyrV0ayskKXkoYmOg6bujWQbG0AsGsSRN69RxZ1qS/fwqaCP1YBPkhWKly6tyb9jzNGMtblCzZp2dZ6CcnKSj8AAMqsmkTO3cckbv3Zoo69X+zJ38h7+sAp2vXVb3arFlSNrPSs5+rIXQvF0VapVxWFQmLXR7uY1HUia0JmcvngORr3aQ1AhaAqZKdnkRafYlLO7VPXCAzRx5027tuGKwfPA+BZ3idfJqBWRZRWKjKT0/l1zTcsaDaexS3fYvtbH6BLjyPv3llqVSrDo5hEIuOSyNNo2H/6Cm3qVzfS5evhyhnD3onE1AwexCQQUCiWed+py3R9RigQQK2y3jxKSOVJYhp5Gi0Hwu7QppbpBtp0dQ4X7kYTXKuC0fEkw0N9dHI6hy8/oGuRQYKRLj8XHiVn8iQlizytjgM3omhT2cdELj0njwuRSQSbSSspeTdvogwIQOGrb++27dqRc/KkkUzCwIEkDBhAwoAB5ISGkvb+++QcP47k4oLkaBjMWFtj3aABmkeW2/t/m8Nf7mdhyDQWhkzj4sGzNO+jj6isFFQFdXoWqWba4o1TV2kY0gyAFn3bcvGgfg9RYlQCNVvUAcDZ0wXfSv7EPyrYl7H3iz1M7jqRyYb7KthwX1UNqkbmc95Xo1uOYnSLkYxuMZKTe0/w8bzNbJq9kcldJ7IuZDYRB8/TsE8rAMoFVSY7PYt0M7bcORVB3ZAmADTs25qIg5ZDEZ/y6urRxN6J4thWUx/8+xf7mBMyhTkhUzh/8Ayt+uo3GlcOqoo6PSt/wFRSCpd39eB5Ghl8Rfmgyqgt+Io7p65Rz2BT476tuWrwFa7+Hgz/aAo7Jm8k/n60Sb7zP/5JwsNYVpnxS5Z03Tp1jSCDX2rStw2XDboWtXqLhS31n7B9p/lu/lYuHzzP7jXfML/ZeBa2fIttb31A9Ilr/DlRH8KYEH4P54q+OJb1QmGlpOLLTXl88GKJ6snG3QlrZ3sAlLZW+LeqTerdKIvyWZduY1PRD+uy3khWKtx6tCL197NGMlb+nlTaMpuHb79Pzn3jslQeLvkyrl2akbz7mEVd6eF3sKvkh005vS6vXi1IOmg8gWddxpPqn03j9oT1ZBfq8xX2NigdbPO/u7apR9aNf85fCCwjVgL+GRyB9ZIkuQIa4A76EJwfACRJ6giUA7YCyLL8qyRJbwCvA59j2BNQqLz9siwbhRPJsqyWJOk99G8WWgK8a1hdyAbi0W82LjGyVseN2duo/+0cJKWCJ98cIfNmJAGv62fjIr/4g0pT+2Ll5kiN1SP0eTRaznSei2vjavi/2pr0aw9pekj/IqM7K74l4VC4JXVGnD58hibtGrPj+BfkZOeweso7+WmrvljOO9PXkhibyJSVbxMTGcumXz4E4Ni+43zx/leWC9bqSFq1AZ/NK/WvCP3lAHl3H+LUrzsA6T/8hn37Vjj26AAaLbrsHOJn6N9oknv1Bll//In/N5uQtVpyb9wlfVcxAxytjqhFH1Hh8yVICgXJ3/9Ozu1HuA3qCkDy1/tw7tIc197tkDVa5OxcHk9cDYB9w5q49WlH9o37vPSb3rbYd78g4+h5i+rOHz5Pw+CGbPnzE/2rDKe9n5+2cPsi1s/8kKTYJHoM70GfsX1x83Ljw4PruXD4POtnrqdFSEtChnRFq9GRk53DmglrjMq/diSMWsFBLAj9gFx1LjumF8T0j9k2i29mfkxaXDK7V+1g2PpJdJvan8iIB5zeqd9UF9i1CY36tEar0ZKXncv2Ce9THCqlktmvd2fcO5+j0+no1boBlQN82HlI3wG+2r4xo3u1Zf6WXfSdvR5Zlnm7f2fcnPQz+OqcXE5H3GH+iJeL1aPXpWBWn5aM27IXnSzzcuNqVPZ15/uT1wB4pbk+HODwlQc0qxaAnY3xxtypnx8kNSsblULB7D4tcLa3saxLoWBWh9qM++EsOp3My3UCqOzpxPfh+jH6K4H6wcfh2zE0q+CJnbWxy571axjnHyeSos6l0+ZDjGtRhd51y5noAUCrJf2DD3B75x1QKMjetw/tgwfY9dS/ftHsPgADSg8PnGfPBoUCSaEg+8gRck+ZX5EqCdMXruJc2GVSUtJo32sw40cOoW+Pzn+prMtHLlI3uD6rQzeSq85h6/SN+WmTt81l28xNpMQl8/2qrxi7fjJ9pg7kUcR9/typX7X79cPvGfnuBJbuXwuSxPerviLDwusuLxjuq48M99X6QvfV/O2L2Gi4r7oP70HvIvfVhpnrn2nL9SNhVA8OZFbo++Spc/hu+sf5aSO3zeD7mZ+QFpfMnlXfMHj9W3SZ+ipPIh5wZqd+I6+TlwuTdi/H1tEOWZZpNaIr73Scjl/1cjTs25qo64+YvHclebLMzne+IvyI6cNq+OELBAY3YN2xzeSoc/h4WsF5z9g+jy0zNpISl0znYd3oPrYXrl5urDrwPuFHLvDJzE0m5V07EkaN4EDmhn5ArjqHb6cXvBL2jW0z+W7mFtLikvlt1dcMWT+RrlP78yTiAacNNnWe2BcHN0f6LdP3KzqNlrU95wIw5MO3qNy0Jg5uTiw9tYm9674n8VEcC0M/IE+dy1eF/NK4bbP4eubHpMYl88uqHQxfP4nuU/vzOOIBp3Yab/Z9HmStjtPzPqfj1zOQFArufBdKyq0nVBtieBPal4ex83Kh+76lWDnagU5HzTe68HPbmdj7uNLy/TFICgWSQuLBr2eI/CPcsjKtjsj5W3jpy0X6V4R+d4jsW4/xGNwFgMSv9uM7aQBKNycCnr7VSavjZvepAFT8eCZKN2fI0/B4/sf5m3ot6bo351NqfTMPlArivjmM+mYkvq/rwyljvjhIuSn9sHJzotKqUfl5LnWeiZWnCzW26Ve8JJWS+B//JOVIMXb9Q+j+xbH6pYX0n7zhQfD/m8LhQP9tVlglPFvob2K7Z+nc+Blplh/+/m5ma0pNFRUV5kNj/hus2dmvVPTIcY+fLfR3EWMh3ve/QNpXJZuR/Dtw/+GzUtM1uuH0UtGTpMt5ttDfRBXF3/O2pJIQJZvffP7fwEcqHT+Y9zyB+P8hDfNK9kauv4N6Ssv/v/B3k5Vbena1iPnhH38CP+zzaqk1mnaxO/9xe80hVgIEAoFAIBAIBC8U/+a39pQWYk+AQCAQCAQCgUDwgiFWAgQCgUAgEAgELxT/5n/yLS3ESoBAIBAIBAKBQPCCIQYBAoFAIBAIBALBC4YIBxIIBAKBQCAQvFCIjcFiJUAgEAgEAoFAIHjhECsBAoFAIBAIBIIXCrExWAwCBMUwUyq9P1D6TOFZarq6R8eWip4edl6logdgjU1Kqem6k156C4h9+m0rFT3xmoxS0QOQpS29P6Bqbmfh34P/C2hK6Q+8ALacf+fZQn8DcmZKqegBSBg4tdR0lSa2XqXzqPVheECp6AGoLmeVmq5GMZb/Gf7vpppb6dVhRKlpEhSHCAcSCAQCgUAgELxQ6ErxUxIkSeoiSdJNSZLuSJI0y0z6a5IkXTZ8TkqSVO8vGV4IMQgQCAQCgUAgEAj+ISRJUgIbga5ATWCgJEk1i4jdB9rIslwXWAps+U/1inAggUAgEAgEAsELxb/s7UCNgTuyLN8DkCTpW+Bl4NpTAVmWTxaSPw38x/FbYiVAIBAIBAKBQCD4LyFJ0mhJks4X+owuIlIGKLwRM9JwzBIjgX3/6XmJlQCBQCAQCAQCwQuFrhQXAmRZ3kLx4TvmzkY2KyhJwegHAS3/0/MSgwCBQCAQCAQCgeCfIxIoW+h3ABBVVEiSpLrAp0BXWZYT/1OlYhAgEAgEAoFAIHih0P279gScA6pIklQReAIMAAYVFpAkqRzwIzBEluVbf4dSMQgQCAQCgUAgEAj+IWRZ1kiSNAE4ACiBz2RZjpAkaawh/SNgAeABbJIkCUAjy3LD/0SvGAQIBAKBQCAQCF4ozAbc/4PIsrwX2Fvk2EeFvo8CRv2dOsUgoJSQJEkLXEFf5/fRL+ekSJLUFpgmy3L3QrLbgd9kWf5BkqSjgOPT0Z4kSQ2Bd2VZbitJkj3wCVAX/aaSFKCLLMsmf38qSVJX9O+VdTDI/ibL8rTntWPGsrdp0b4Z2epsFk5azo0rpitSyzcupGa96mg0Gq6GXWP59DVoNFoaNA9i3fZVRD2KBuDw3lC2rH32P8I6tQkiYNEbSEoFid/+TuymXUbpLh0b4zftNWSdDrQ6Ihd/Sua56yW2afbyKbRq34xsdQ5zJy7l+pWbJjKrNi2mViGbFk9bhUajZfj41+jWtzMASpWSSlUqMG3MfCbOGoOzyoZz3x0hdPOvJuX1WPg61YIDyVXn8sO0j4iKeABA1TZ16b7gdRRKhdm8rd7oRsjc11gaNIas5HSUVkp6rRhFxbrlQacjdtnHZJ29gkOrBvjMG4OkVJCy8wCJW743KsexfVO83h4Csg5ZoyN2+ceoL1xDsrai/NdrkKytkFRK0vYfJ+HDHSWuS6/getRe+jqSUsGjHUe4s2G3UXqZPi2oPKEnAJrMbK7M3EratUclLh9gzOIxNApuRI46h7VT13L36l0Tme5Du9NrZC/8K/gzoN4A0pLT8tPqNK3D6IWjUVmpSEtKY+arM83qmbp0Ii3aNSVbncPiySu5aaatL90wnxr1qqHJ0xARfp0VM95Fq9Hmp9esV53PftvMnLGLOLwn1KJNs5dPoXX75qjV2Rbb4OpNi6lVrwYajYYrYddYPG0lGo0WRycHVm9ajF8ZX5RKJds27+Dnb38zyjtg4XDqBNcnV53DtmkbeRRx36R8zwBv3tjwNg4ujjyKuM/WyevR5mkAqNq0JgMWDEepUpKenM67/ReisrFixndLUNmoUCqVnNt3ip/XfWdU5qCFI6gbXJ9cdS5bp63noQW9YzdMxtHFiYcR99gy+UO0eRrsnOwZvW4S7mU8USqV7P/kF45/f8RiHVpi3oq1HDtxFnc3V37+6qNnZyiG4+fCWb35C7Q6HX26BDNqwMtG6anpGSx472MeR8diY23NkiljqFJRH+ablpHJorVbuP0gEkmCJVPHEFizqkVdNk0a4fL2BFAqyfp1DxlffmNWzqpGNTy3bCR5wRKyjxwDwHXODGxaNEWXnEL84BHPtOv/2Hvr8KiO93H7Prtxd0ODOwnuEDSE4lDcKe4Ed5dSaIsWSkuFUmip4O5eIEGCOyHutrHd8/6xIclmNxA+3zbwe5mbKxe7Z56Z58zsc8aemTmFqcvYuw4Wn40FhYK0o/tJ3f2LQTllmQrYrNxA0qoFZFw4jaJIMaz85+WEu3mQ8st3pO39PftamabV8J3XD4VSwfVfT3HOQN3bdn5/yvpUJ0OVzl/+3xB6+xk27g50XjMSK2dbZI3MtV9OcPn7w9lx6gxsTZ3+rdCoNTw8EQgLv9NJ07aZNyUXDUZSKIjYcYyQdX/qhDt2boLH6E4AaFJSeTp9Myl3nmFW2oOym3LeFG1a3JXgz38l7Fvd5zcva1YvpK1vc1JUKoYMmUhA4O18Zb9cs4iBA3pg56Bra7VqVuf8ub306jOSP/7Yn2/8/7puErxfxBGhhYdKlmUvWZarADHA6HeI65LVic/LeCBcluWqWekOATLyCkmSVAVYB/SVZbkiUAV48q4ZaNSiPsVLFaVj/R4s9l/JzBWGxxAH/zhC50a96N6sH2ZmpnTu0z47LODyDXq2HEjPlgMLNABAoaDY4uE8HrCAuy3GYN+hMWZli+mIJJ6/yb0247nfdiLP/b+m+IoxBc5T4xb1Ke5ZDL963Znvv4w5K6calNu/+xDtG/agc9M+mJqZ0rWPtvH/fsN2urXoT7cW/flyyUauXgxg0uxRjOw9kTWtplC9QwNcyuie8lW+mReOnm6sajaJP2d+S6cl2oZTUkh0WDiI7weuNBjX1t2BMo2rEhscmX2tds/mADz9ZBQvBs7CZcZQUCpxmz+Kl0Pn8rjtCGw+aYpJGd0yS74YyNP2o3naYSyhM9bgvmQ8AHJ6Bs/7z+BphzE86TAGqya1MPMqX7DCVEhUXTaIy71XcLKJPx6dG2BVTjfvKS8iuNB5IaebT+Phmj+otuqzgqWdRS2fWhQpWYShTYby9fSvGbPE8G995+odZvaeSfjLcJ3rljaWjF4ymoVDFjKy5UiWjlxqMH6D5vUo7lmULg17s3Tq50xfNsmg3ME/jtKtcV96Nh+IqZkpnXpnj+VRKBSMmTWCS6f+eWOeGrdoQAnPYrSt1435/suZm48N7tt9mE8afkqnpr0xy2WDvQZ34/H9p3Rp3peBXUYydf44jI1z5neqNPPGxdOdWc3G8tPMb+izxHCZd53eh2Nb9zHbZxwp8Uk06qG1LXMbC/os+ox1Q1cwr/Ukvhn1BQCZaRl80XsBC9tOYaHfFKo09aKUd9ns9Ko1q4GrpzvTm41h28yN9FuS90Q8Ld2n9+PI1n1M9xlDcnwSTXq0AKB5P19CHr1kXtvJrOg5lx6zBqA0fvd5q05+rdi0evE7x8uLWq1hybrv2bBkGn9vWcXBUxd4/DxYR+bbHX9ToXQJ/vhmJUumjGTFxh+yw1Zs+IGGtauz97sv2L1pBaWKv+H0P4UCW//xRE+eTkTvgZi3bIFRyRIG5WxGDSPtsq6NpRw4RMxEw4Pb963LYvgEEhdMJX7MAEwat0BRzLAuiwHDyQjI0aV59ZKEiUO1f5OHIaelknHpbHa4pJDwWzSQ7QNWsr7lVKp0qI9zWd0yLutTHQdPN75uOpm9M7bSbvEgbdpqDUcWb2d9i6l822kedfq3yo5bsn4lKrSqyUbfGWxoNY0Lm/N0mBUKPJd+xr0+i7nRbDyOHRtjXlb3+Pa0l+Hc6TqHWy0n8WrNb5RaOQKA1Mch3Go1WfvXZgoaVRoxBy+/sQjb+janbBlPKlRqxMiR01i/blm+sjVrVMPOztZA8SpYtnQWR46ceqOu/7puet98aG8Mfh+IQcD74SJvPv81L58Dsw1cd0e7gQQAWZbvy7KcZkBuKrBEluV7WXKZsixveAf9ADRt04h9uw4BcOt6ENY21ji5OOrJnTt+Mfvz7YC7uLi7vKuqbCy8ypL2LIz0F+HIGZnE7j2Lbes6OjKalNTszwoLM5AL7uTz8W3Cnt+03reb14KwtrEymKezufJ0K+AOrh76efLr3Iqb1+/w4mkwwc9DUGeoubH3IhVb19SRq9i6JgF/aBuvlwGPMLO2wNrZjmJeZYh+Hk7sywiDcdvN6cfBZbqzZi5li/D4vHYWSB0TjyYhGdvOLUh/HkLGyzDIyCRh/xmsW9TXiSfnLjNzM3I7Rl+HSUZGSEbKAvtM7b3LkPw0jJQXEcgZakL+uohbG93lirFXH5IRn6z9fO0RZu4OBUs8i3qt63F893EA7gfcx9LGEnsXez25J0FPiAiO0LverGMzLhy8QGSIdiAVHx1vUE/TNo3Y/7t2JvD29TtY21rhaMAuLpy4lP05KOAuLu7O2d97DO7KyQOniY2KfWOemvs2Yc9v2uOeb167ne9zdfZ4zntibgUEZdugLIOllQUAFpbmxMclkJnLG+HVujaX/tB6IZ4EPMTC2hJbZzu99Ms3qMK1A9r8XNh9Gu/WtQGo26ERAYcuExMSBUBidI5XJS3LVpRGSoyMjHRsxbt1bS4UQG/FBlW4ekD7fJ3ffYoa2c+3jJmlOQCmFmYkxyWhyZWvglLLqyq2NtbvHC8vt+4/oriHG8XcXTE2NqJt0/qcvHBVR+bxi2DqelcBoFTxIrwKjyQqNo6k5BSu3bpHF18fAIyNjbCxssxXl3GlCmQGh6AOCYXMTFTHTmDWuKGenGW3zqhOnkUTG6dzPT3wJpqEBD35963LqGxFNGGv0IRrdaWfPYFJHf1TDk3bdSH94mnkeMPPjlG1GqjDQtBE5gzyi3iVJuZZOLEvI1FnqLm99xLlW+nWveVb1eTGbm3dGxzwCDMbC6xc7EiKiCP09jNtfpJTiXwUgrWrtl6p3bcF5zbsQZ2u9YolR+vm1cq7DKnPQknLaqOi/z6HfRvdNirp6n3UWfVe4vUHmLjrP9+2jauS9jyc9FeRemG5ad++DT9t13o/Ll+5jq2dLW5u+u2RQqFgxfI5TJ+hPwAeM3owf/y5n4jINx8u81/XTYL3jxgEFDJZr4ZuAex5m2wuLgJpWWfD5uY7YJokSRclSVosSVJZA3FBO/N/7d3vVhcXd2fCQnI6VuGhETqdnrwYGSlp160NF07mzGxUq1mFnce3se6XVZQq7/lWnSZujqRndT4A0kOjMXY1UIG2qUfFE+spvW0Oz6esLWiWcHV3JuyVbp5c35Kn9t3acu7ERZ3rZuamNPKpx7NHz3XKKCE0BltX3Y6uras9cSEx2d/jw2KwcbPHxtWe+JBog3ErtqxBQngsYXd1l86E3n1BpVa1QKnAuKgrZlXKYFq6GJmhOWWWERaFkYEys25Vn1KHvqHYlgWETv8yJ0ChwHPPWspd+oWk8wGk3tB3/xrCzN0eVa77Tw2Nxsxdv4P+mmK9mxFxIrBAab/Gyc2JyNCcRjIqLAonN6cCxy9SqghWtlYs37mcr/Z/RfOuzQ3KObs5EZ7rd4wIicTlDXqURkr8urXh4skr2fGbtW3M7h//fus9ubg7E/YqpzNTcBvUdth/2fobpcp5curmfv469QvLZq9BzjUQtnd1ICbX7xIbFo2dm65NWtlbo0pIQaPWzlnFhkZjl2V7rqU8sLC1xP/X+czeu4L6XZpkx5MUCuYe+Jwvrm0l6NwNngQ+zA6zc3XIHji81mvvpmuHVvbWpCQkG9R7/IeDuJcpypor37Lo8Gp+WfCdTr4Km4ioWNycc+7f1dmR8GjdTmr5UiU4dk47e33r3iNCw6MIj4whOCwCezsbZq/aRPeR05m3ejMpqlTyQ+nshDo8x/7UkZEonXXtT+HkhFnTxqT89S5NyfvVJTk6oY7K0aWJjkThqKtLcnDCpF5j0g7lr8u0cQvSzxzXuWbj5kBCqG79aeNmry+Tu44Ni8HGVVfGrqgT7pVL8CpQu8zQ0dOd4nUqMPSvBQzcORuPaqV05LVtVE6a6aHRmLxhcsOlV0viTgboXXfs2Iiov84aiKFLEQ83gl/mnBz5KjiUIh5uenKjRw1i774jhIXpToZ4eLjRqaMv32z+6a26/uu6SfD+EYOAwsNckqRAIBpwAI5mXc/vich7fTF5vAGyLAcCpdB6ChyAfyRJqvh/ucncb7WLSgnLG6Z/k294oGcs9+f6pRsEXL4BwL2b9/Gr1ZUeLQby69bdrPk+fzdmjlID1wzojD98ibvNR/Nk6FI8/Pu8Pd3s5N8tT7NXTOXapQCuZ+XpNc1aNybgn1uoUlRvT89gOeZ3XcbYzASfMZ04uvo3vfBru04RHxaN559f4TprGKrrd7V7Iwwq0CXx6EWe+A7n5ahF2v0Br9FoeNphLA8b98e8WjlMyxpw1xvCwP3nZ92ODStRvJcPdxcbXnv8LrxLo6JUKilTtQzzBs5jTt859BrXiyKe+k65d7X16csmEXDpBoFXbgIwacFY1i7ZhMbQb5FXl0EbzF9+zoqpXLsUyPXLgQA08qnHvdsPaFatHV2b92PWMn8sc88y52dvOjeRv4xCqaRE1VJ8PWgZX/ZfTLux3XD1dNfKaDQs9JvC1PrD8axeliLliuVK8u1laEjmteIqTbx4cecpE+sMZZ6fP30XDsXMylxfvpCQDRhz3tsf0qMDCUnJdBsxnV/+PkyFMiUxUipRq9XcffiUHp+04reNyzE3M2Xrzjd1qN9edrYTRpOw4RsogI29mferK2+xWg4dS8oPb9BlZIRxnQaknz/1Vm36de+bZUwsTPl00wQOLfyJtCRtXa4wUmBua8m3neZxdOkvdN8w9q1p5lfv2TSogkuvFrxY8qNuEsZG2LeuTczeC4Yj5pYtwHPl7u5Kt66fsG79d3qyq79YwIyZSz+Muuk9o5GkQvv7UPlwFmf9/x+VLMtekiTZAvvQ7gn4Gu2gIO90qQMQlfuCLMsnJElaBNTLcz0J7bmxf0iSpAH8JElqDrxe+OsHBAE1Ad2eqwFyv9XO262h/OmgLnTpo93IGRR4F7dcy2Bc3V2IDIsymM6wyYOwd7Rj8ZSZ2deSk1KyP587fpEZyydj52BLXIzhJRmQNavikTNTZOLuSEZETL7yyVfuYFLcDaW9NerYRIMyPQd1pVtf7ZrF24F3cSuim6eIfPI0cvIQ7B3tWOC/XC+sbaeWHPjzCOGhETplZOPuQEKE7mxhfFgMdh4OPM/6buvmQGJ4LEYmRth6OOrFdSjhin1RZ8Yf1Oq1cXNg7L4lrO80h6TIePYv+hlH4zgASuxcRerdJ9h1aZmdjrGbE5lvKDPVP7cxLu6O0t4Gda4NtJrEZFIu38KySU3SHj7PN/5rUkNiMM91/2bujqSG6bvzrSsWp/oXw7jcezkZsXp72PX4pP8ntOml3Xz98OZDnHPNRDm5OREdXvD3pUSFRZEQm0CaKo00VRq3L9/Gs5IngQ/v031gZzr10a7pvxN4T2fJl4uHM5H56Bk6aSB2jnYsnZozRq9YvQJLNmo3Mdo52NKgRT3UajWnD50DoNegbrls8A5uRVyz42pt0PCSAK0N2jPfP2cNdqeen/DtWm2n4sWzYF69CGHEpEE0aFoXS4UJT288wiHX72Lv5kh8uK49JMUkYG5jgUKpQKPWYO/uSHyWzcSGRZMUm0C6Ko10VRoPr9ylaMUShD8NzY6vSkjh/qXbdJ7UE+fi2rxo9Trp6I3LozcxJgELG0sdvXFZz0uj7s3Zv1G7wTLieRhRLyNwL12EpzceGSyb/xpXJwfCci2fCI+MxsVBt+q2srRgsb92vbcsy/j2H0cRN2dS09JxdXagWsUyALRqXJetO/P3EqkjI1G65tif0tkZTZSu/RlXKI/9wrkAKGxtMW1QF9RqUs+cf6d8FaYuOToSpVOOLoWjM5oY3fpWWaY8Vv5ZumxsMa5Zj2S1mozL2mfHuEZd1I8f6i0VSgiLwcZdt/5MDI/TlQmNwSZ3HevmQGKEVkZhpOTTTRO49dd57h66qhPn7iGtd+fVjSfIGhkjBxsyY7R1pbaNyknTxN2R9DD9+taiYglKrRrFvb6LyMxT79k19yb51hMyogy3hSNHDGDIEO3k1tWrgRQt5pEdVqSoOyGhunufvL2qULp0Se7f1f4+Fhbm3LtzjgqVGlGzRjW2/6xdDezk5EBb3+ZkZmayZ492+eN/XTeVKluCWwF3DKYhKHyEJ6CQkWU5HhgH+EuSZAw8BDxez+BLklQCqA4EGoi+BO36frJkG0qSZJ/12QSoBDyXZXl91iZkL1mWQ9B6CmZKklQuS1YhSZLhnY552PX9H9kbeU8eOsMnn/oCULVGZZISk4iK0O8Yde7dngbN6jJj5DydGQpH5xwXaWXvikiS9MYBAEDKjYeYerpjUsxFO1vSvjHxR6/oyJiUyHGFmlcphWRilO8AAODX73dnb+Y9cfA0Hbr7AVCtZv556tqnAw196jJ1xFy9WRcra0tq1ffm5KEz3A64S/FSxShS3B2lsZLq7etz96juSqy7R6/h3aUxAMW8y5CaqCIxMo7gG49xKumGfVFnnbjh91+ypNZIVjYaz8pG40kIi2HtJ7NIiozH2MwEY3NTACwbeoNaQ+LBs5iU9MC4qCsYG2HTrgmJxy/p3INxcffsz2aVSiMZG6GOTUDpYIPCWjtTI5maYNnAi/Qnupsf8yMu8DGWpdwwL+6MZKzEo1N9wo7o5t28iCO1v5tIwJj1JD8JyyclXfb9uI+xbccytu1YLh6+SIuu2o2j5b3Lk5yYTGyE/kAjPy4duUTlOpVRKBWYmplS3rs8Lx++BOC3bX/Sp9UQ+rQawqlDZ2nXTTvwqFKjEkkJyUQbsIuOvdtRv1kdZo9aoGMXner1oGNd7d+JfadZMWN19gAAYMf3v9O1RT+6tujH8YNn6NBdu++/Ws0qb7HBekwZMUdHV+irMOo11u69cHR2oGTp4mxd9xNdW/Rjod8UAo/8Q70uTQEo5V0WVWIK8ZFxeunfvxhETT/tHEODrk0JPKLt+AQe+YeytSuiUCowMTPB06sMoY9eYeVgg7mNdr2vsakJlRpW4+yuE8zz82eenz/Xj1yhQQH03rt4m1p+2j0rDbs24/oR7fMdHRJFpYZVAbBxssWtlAeRL8L14hcWVcqX5vmrMIJDI8jIyOTg6Ys0q6+75jwhKZmMrBOVdh88Qc2qFbGytMDJwQ43Z0eeZi3juBxwm9LFi+rpeE3G3XsYFS2C0t0NjIwwb9mc1HO6s8QR3XoT0bUXEV17kXryNPGrvnznTnlh68p8eA+Fe1EULlpdJo2bk3FFN534YT2z/9IvnCb5mzXZAwAAkyYtSDt7PG/ShNx4gqOnG3bFtPVnlfb1uJ+n7r1/7DrVu2rr3qLeZUhLVJGUNQjouPIzoh694uK3B3Xi3DtyDc8GlQBw9HRDaWyUPQAASAp8hJmnO6ZZbZRjx0bEHtHdPG1SxIly307l0bivSH0SSl4cOzUm+q9zetdfs3HTD9Sq3ZpatVuzZ89h+vXpBkDdOjVIiE/QW/Jz4OBxihb3pky5epQpV4+UFBUVKmn3XpQtXz/7+u4/9jNm3MzsAQD893XTy+ev9OK/L+RC/PtQEZ6A94AsywGSJN0Aesqy/JMkSX2B7yVJMkN7us/QrMFC3ngHJEnKPQwvDWyUtP5BBbAf2G0g3k1JkiYAO7KOFZWzZN+Jc8cu0qhFffZc2kWqKpX5E3JOVlm7fRULJy0nMjyKmSv9CQ0O54d9m4Gco0Bbtveh+4DOqDMzSU1NZ8aIefmpykGtIXjOZkr/NF97ROjO46Q+eIljX+1gJPrnQ9j5NcChqw9yRiZyajrPRn9e4DydOXaBxi0acPDy76hUqcwZn7OJasP21cybtJTI8CjmrJxKaHAY2/dvAeDY/lNsWq11tbbwa8aF01dQZW2SXDpjFd/8+hXWRqZc3XWKiIevqNNH23G9sv04908GUt7HC//Ta8hQpfH7lG8A7QkVe+ZuY/CP05GUiuy4b8LSyYbBP0zHhkwyw6J55b8K1BrCFmyk2HeLtUeE/n6E9EcvsOulHezE7TiAjW9DbDu1QM7MRJOazqsJWi+DkbMDHisng0IBConEg2dJOnnlTbeQjazWcHvmNurtmIGkVPByxymS7gdTor/WK/H8x2OUndQFY3srqi4fnB3nbJtZBUof4J8T/1DbpzZbz24lTZXGGv812WELti3gq2lfERMeQ4dBHeg2ohv2zvasP7Keqyeu8tW0r3j56CXXTl1jw5ENaDQaDv96mOcP9L0c549fomGL+vx5YQepqjQWTsxZuvblTytZ7L+CqPBopi+fTFhwON/t3QjAyQNn+HbND3rpvYkzx87TpEUDDl7eTaoqldnjF2WHbdy+hrmTlhAZHsXcldMICQ7jl/3fAlob3Lh6K5tWf8eSr+fy56ntSJLE6kXrdQbXt05ep6qPN0tOryVdlc62Keuzw8Z9P4Mfpm0iPiKW3ct/ZtjaiXSa3IsXQU85t+sEAGGPX3H7dCDzDn2BrNFwdudxQh68pEiF4gz+YgwKhQJJIXFl/wVunMjpdN08eZ1qPjVYcXo96ao0tubSO/H7WXw/bQNxEbH8tvxnRqydSJcsvWd3aTt4e7/+jSGrxrDo0GqQJH5b/jNJbxjc58eUecv5J+AmcXEJtOjUl1FD+tG1fZt3TsdIqWTmmIGMmLkMtUZD5zbNKFOyGLv2aVd2fvpJK568eMWslRtRKBSULlGEBZNyTkSaMXog05evIyMzk6JurizyH56/MrWG+NVf47hmJSgVpOw7SObTZ1h00p60lvKX/tGXubFbMBtTby8Udra4/rWLxG+3kbLvgGHhwtSlUZOy+Uus56/SHhF6/ADql88w9dV6m9+0DwAAE1OMq9ciZcMXBpLWcGDuNvr9OA1JqSBg12kiH76iVlbde3X7cR6eCKSsjxfjzqwmQ5XO3/7aurd4rXJU79qY8LsvGHFA264d/3wnD0/eIGDXKTp+PoxRR5ajzsjkr8mb0DkvTa3h2axvqfDLXCSlgohfj6N68BKXfq0BiPjpCEUnfoqRvTWey7T2IGequd1WO5+nMDfBtnF1nk4t2PG1Bw4ex9e3OffvnidFpWLo0Jz5vL1//8iwEVMIDf13Bsv/dd0keP9IYpOGID+83RoWmnF8Z1LwzZ3/V/qlFc5sYnvzUm8X+pfon7UcqDB4lKh/5Nx/xUaTwmkwIjPfvizp3yJFbegAr/+GBubFC01XZiEehLf5asEH+v8X5OS4QtEDENVr8tuF/h/EzLlw7OLrwPw9K/82vhn6e7/+KxpFvfnI0H+T8vaFV4ZB4Zff+0L5ne59Cq2P0yN0+3vPryHEciCBQCAQCAQCgeAjQywHEggEAoFAIBB8VGg+yLn5wkV4AgQCgUAgEAgEgo8M4QkQCAQCgUAgEHxUaAy+5OHjQngCBAKBQCAQCASCjwzhCRAIBAKBQCAQfFSIszGFJ0AgEAgEAoFAIPjoEJ4AgUAgEAgEAsFHhTgdSHgCBAKBQCAQCASCjw7hCRDkS7qcWWi6CvPF1Sp1eqHoSS3EN6impRfeo5ygUBaarlh14byZMz4jpVD0ACQWoi7ZvPAerBhN4b0JubDe5CtZ2hWKHoDwYJtC02XvUHg2mPascPQYF44aABLUJoWmSyEV3nR1uqbw2vwPgcJroT9chCdAIBAIBAKBQCD4yBCDAIFAIBAIBAKB4CNDLAcSCAQCgUAgEHxUiCNChSdAIBAIBAKBQCD46BCeAIFAIBAIBALBR4U4IlR4AgQCgUAgEAgEgo8O4QkQCAQCgUAgEHxUiCNChSdAIBAIBAKBQCD46BCegA8MSZLWAM9lWf4y6/th4KUsy0Ozvn8BvAIGy7JcJevaZ8BK4AXaDe+VgPuAGjgErAG2AsXQvlPlmSzLfv/L/c1YMokmLRqgUqUya9wi7t66ryezYsMCKlevSGZmJrcC7rDAfxmZmWqsrC1ZsWEB7kXcUCqVfL9xO3/9us+gHptm3hSd/xkoFUTvOEr4ht064fadmuI2qgsA6uRUXs7ciOruMwCcB3+CU+/WgETUjiNEbt37Tnmcu3QKzVo2QqVKZerYeQTdvKcns+zLuVT1qoQkSTx9/JypY+eRkmz4xVad5w2goo83Gao0dvhvJDjomZ6MQ1Fn+q8bj4WtJcFBz9g+cR3qDDU1OjakxYgOAKSlpPH77G8JufsCI1Njxuych5GpMUqlAg6cI2zNDoP6rZvWoMi8oUhKJdG/HiFio25Z2rSqi/vkPqDRIKvVvFrwLclX7xa4vNybVaPWon5ICgWPdpzizjrd8rYp40691cNwqFqSGyt+4+6mA9lhHS+vITMpFY1Gg5yp5lDbuW/VN3HhWBo0r0uqKpVFE1fw4PZDPZn5a2dRoXo5MjPU3A28x/JpX6DOVNO6c0v6jeoJgCpFxcoZX/LozmODemYv9adpy4aoUlKZPm4+d27q2/qqjYuo4lWJzIxMbgYEMXfyEjIz1djYWrPsq7kUK1mU9LR0ZoxfyMN7hvUALFoxkxatmqBSqZgwaia3buiX/xdrF1HduzKSJPHk0TPGj5pFSnLOS5+qe1dh/7EdDB80mf17juSrq9e8wVT18SZdlc53/ut4EfRUT8apqAvD1k3E0taKF0FP+HbiWtQZmbQZ1oG6nRoDoFQqcS9ThIk1hpAcn2RQ12cLhlHTpxZpqjS+mvwlT27rl4HfgE/oMKQD7iU96Fu9N4mxCTrhZaqVZeXfq1g1eiUXDpzXi3/un0BWbPwRtUZDF18fhvbsqBMen5jE3C++4WVoOKYmJiycNJyynsUASEhKZv7qzTx8FowkwcLJw/GqVC7fsnsbs5eu5sz5KzjY2/HXz5v+53TyYt1UWx9KSgXRv+rXh7at6uDu3wdZowG1huAF35L8T8GfYfMGtXCYOgoUCpL+PEj89zt1w5vVx37UQJBl5Ew1MZ9vIC0wCKMSRXFZOTtbzqiIG3EbfyBh+59v1jVtFJJCQeKfB4n/TleXRbP62I8eiKyRQa0m+vMNpAUEYVyiKM65dBkXdSN2g66uUk2r0XpePySlgsBfT3Fxo37d33p+f0r7VCdDlc4+/28Iu/0sO0xSSAzet5jEsFh2DV6lE6/uMD9azurDaq/hEBqvE+boU50KiwcgKRUEbz/Bs7V7dMLdujbEc4y2Hlcnp3Fn6rck3XmBqYcjVdeNwsTZDjQagn8+wYstB/Mtu9esXr0QX9/mqFJUDBk6kcDA2/nKrlmziAH9P8XBsTwAvXp2xt9/FABJScmMHTuDm7cKZitzlk7JrhOnjZvPHQNt49Iv51ClurZtfPbkOdPGzs+3bXzfCE+A8AR8iFwAGgBIkqQAnIDKucIbANktoSRJ/YCxQFlZlqvLsuwFhAA+six7ybI8HVgIHM0KrwRM/19urHGLBpTwLEbbet2Y77+cuSunGpTbt/swnzT8lE5Ne2NmZkrXPtpGudfgbjy+/5QuzfsysMtIps4fh7GxgXGoQkGxxcN51H8Bd5uPwb5jY8zKFtMRSX8ZzoPuM7nbejxhX+2k+IrRAJiVL45T79bc+8Sfu23GY9uiNqYl3Qucx2YtG1KyVHGa1+nIrEmLWfj5DINyS2Z/wSfNetKuaQ9CXoXRb0gPg3IVm3nh7OnO0mYT2DVzC92WDDUo1356b05v3c9Sn4mo4pOo26M5ADEvI1nXYyGft53GkbV/8OmyYQBkpmWwofciVrWdxud+07FuWgML7/L6CSsUFF00nCcDFnCv5WjsOzTBNE9ZJp2/wX3fcdz3m8CLKWsptmJsQYsLSSFRe+kATvZZyb5mUynZsR42ZT10ZNJik7k65yedzn9ujnVfwsFWswo0AKjfvC7FPIvQvVFflk/7gqnLJhqUO/znMXo2GUDfFoMxMTOhQ+92AIS+DGVUtwn0azWU7778iekrJhuM37RlQ0qWKkarOp2ZM3kJC1YatoO9uw/hW78rnzTpgZmZKd37dgJgxIRB3L39gA7NejF19FxmLzGsB6B5qyaUKlWCBjV8mTJ+Hsu/mGdQbt7M5bRs1IUWDTvzKjiUwZ/1zg5TKBTMXjCJU8f1O8m5qdrMGxdPd2Y2G8uPMzfRd8kwg3Jdp/fl6NZ9zPIZS3J8Mo2z7PHw5j0s9JvCQr8p/LFyO/cv38l3AFDTpxbuJT0Y0WQY66evY+SSUQbl7l69w9zeswl/Ga4XplAoGDBjIAGnAwzGVas1LFn3PRuWTOPvLas4eOoCj58H68h8u+NvKpQuwR/frGTJlJGs2PhDdtiKDT/QsHZ19n73Bbs3raBU8SIG9RSUTn6t2LR68f8pDT2y6sPHAxZwt8UY7Dvo14eJ529yr8147redyHP/rym+Ysw7pe8wYyzho2fyqstQLH19MC5VXEck9XIAIZ8OJ6THCKLmr8Jp3iQAMp8HE9JjhPav1yjk1DSST7zBBhUKHGeOJXzUTII7G9aluhzAq+5aXZHzcnRl5NGlSU0jJZcuSSHhu2ggvw5YyTctp1K5Q32cyur+nqV9quPg6cbGppM5MGMrvosH6YTXHuxL1KMQvdu2dnfAs1FV4oOjDORJouLywVzvvZzzjSfj3rkhluV09aqeR/JPp4Vc9JnGk9V/UPkL7XMnZ6q5P+8nLjSezGW/ORQb1Fovbl58fZtTpownlSo1YuSoaaxbuyxf2Ro1qmFnq/sm6qfPXtCiZTdq1mrF0mVfsWHDyjfqe03Tlg0pUaoYLet0Ys7kxSzMp05cOns1HXx60b5ZT0KCw+ibT9so+DAQg4APj/NkDQLQdv5vA4mSJNlLkmQKVARiASRJ+hRth761LMsGaqds3IHsllGW5Zv/y401923Cnt+0sxQ3r93G2sYaJxdHPbmzxy9kf74VEISrh0uWXrC0sgDAwtKc+LgEMjPVevEtvcqS9iyM9BfhyBmZxO45i23rOjoyydfuoY5P1n4OuI+xu/Y+zMoUJfn6A+TUdFBrSLp8GzvfegXOY8u2zfhzl9Y7EXjtFja21ji7OunJJSUlZ382MzNFlg2fOFyldS3++eMMAM8DHmFubYGNs52eXJkGlblx4DIAV3afoWrrWgA8u/4AVYJW1/PrD7F1c8iOk56SBoDSSIlkbKQt4DxYeJUl7Vko6S+zynLvWWxb1dWR0aSkZn9WWJjyLqcnO3qXJvFZOEkvItFkqHn+9yWKtampI5MWnUDMjSdoDPzW70qTNg05+Lt2ljvo+l2sbC1xdHHQk7t44nL257uB93Bxdwbg1tUgErM6rUHX7+Dirv/bArTwbcqfO7WDlhvXbmNta42zq76tnz6W0wm5eT0INw9XAMqUL8XFs1cAePLoOUWKeeDorH+fAL5+zfnt178BuH71Jja21rgYsrnE3DZnpmNzQ4b3Yf+eo0RFRRvU8Rqv1rW5+Mcp7X0FPMTC2gJbA/ZYoUEVrh24CMCF3afwyvP8AdTp0Igre/Lv8NVpXZeTu08A8CDgPpY2lti72OvJPQ16QkRwhME02g36hIsHLxAfHWcw/Nb9RxT3cKOYuyvGxka0bVqfkxeu6sg8fhFMXe8qAJQqXoRX4ZFExcaRlJzCtVv36OLrA4CxsRE2Vpb55qcg1PKqiq2N9f8pjbxY5K0P9+rXh7rPsJnBuiA/TKuUJ/NlCJmvwiAzk+TDp7Bo1kBHRlblSt/czGDyZnW9yQgORR1q+Ld8rSsjt65Db9dlqDoyr+tN5stQMnPp8vAqTcyzcOJeauuiO3svUa6Vbl1UrlVNbu4+C0BIwCPMbCywcrEDwNrNgTLNvQj89aSevlZz+3Fi2Q6D9bxtjTKkPA1D9TwCOUNN2F8XcPGtpSMTf/UBmVntVdy1h5i6a+uC9Ig4Em89A7Qe7eSHrzB1M1xPvKZ9+9Zs//l3AK5cuY6dnQ1ubi56cgqFguXLZjNj5hKd65cuXSMuTuvJuHz5OkWKFGySrKVvU/7auR+AwGu3sba1KkDb+G62WNjIUuH9faiIQcAHhizLIUCmJEnF0Q4GLgKXgfpALeAmkA6UANahHQCEvSXZ9cBWSZJOSpI0S5Ikj7fIG8TF3ZmwVzmzdeGhEbhmda4MYWSkpH23tpw7cQmAX7b+Rqlynpy6uZ+/Tv3CstlrDFaqxm6OpIfkjGkyQqMxdtPvgL3GsWcrEk5eByD1/gus6lZCaWeNZGaCjU9NjD0Md/QM4eruQkiuPIaFROCWTx5XfD2fy3eOUrpsSX78dqdBGVtXB+JCcjpmcWExOh15AEt7a1QJKWjUWudkfGgMtq76DUHdHj7cOxWY/V1SSPgfWM6ia5tJPBtISuADvTjGbo5khOYuyyiDZWnbph4Vjm+g1PdzeTHla4N5MYS5mz0pITHZ31NCYzB31+/o5Yss03zHdHwPLaJMH5+3iju7OREektPwR4ZG4eyW/++rNFLi27UVl05e0Qtr39OPiwauA7i6OxMWkvNYhYeE42qgoX2NkZGSjp/6cfaEdgB8L+gBrdtpZ8+reVfGo5gbbu6G47u5uxDyKkdXaEg47u6uBmXXrF/CzQdnKFPOk+82b8+O3/aTlvz4nWEbzI2dqyMxuewxNiwGuzz2YGVvjSohOdseY0Ojsc9jjyZmJlRp6sX1g5fy1eXo5khULtuLCovG8Q3PcV4cXB2p16Y+h37Of3lERFQsbs45abo6OxIeHasjU75UCY6d+weAW/ceERoeRXhkDMFhEdjb2TB71Sa6j5zOvNWbScnVAf1QMMlTH6aHRmNsYEBq26YeFU+sp/S2OTyfsrbA6StdnMgMi8z+nhkehdJF/5my8GlIkT+34rJ2MdHzV+mFW7ZpRvJB/Q50Xl3qXLrUEVEYGehIWjRvSJG/tuK6bjFR8wzo8m1G0iFdXdZuDiSG5th2QmgM1m72ejIJuew/ISwGa1etTKt5/TixdId2GVIuyrasQWJYDBF3XxjMk5mbA6m50kwNiXljR75Ibx+iTgTqp1PMGesqJYm//ijfuAAeHm68DM7xVgS/CsXDw01PbtSoQezbf4SwsPwHZYMG9eTw4Tf/Zq9xdXchNES3bXR1M9w2Lv96HheDjlDqDW2j4MNADAI+TF57A14PAi7m+v56mj0S7R6AT9+WmCzLh4FSwBagAhAgSZLBp1eSpGGSJF2VJOlqrEq38pDQH86+aZA/Z8VUrl0K5PrlQAAa+dTj3u0HNKvWjq7N+zFrmT+WhmbeDI2a81FkVb8qTj1a8mqp1sWf+iiY8A1/UPaXBZT5eT6qO8+Q1QVf+ScZ0J1fHqeNm0/9Km149OAp7Tq1fof08iRYAJky9StRr4cPe5f/kiOjkVnlN5359Udh4VUWs3LF8yaTX+J6l+IPX+Jei1E8/Wypdn9AAZEMZrDA0TnScSEH28zmZJ/PKTewJS51DSxp0lWor+4N+qYsnUDg5ZvcuHJL53qNBl607+XH+qWb81FjSE/+iuavnM4/F69z9VIgAN989QM2dtb8fXI7/Yb24O6t+6jVhj0h76Jr4uhZeFVoxsP7T+jQpS0AC5fNYPG8L9Bo3m7nhn4uvQIswP1Ub1mLR1fv57sUCPKrLwpuHEPnf8YPy7a9MV+yAWPLe/tDenQgISmZbiOm88vfh6lQpiRGSiVqtZq7D5/S45NW/LZxOeZmpmzduUcvvfdOAevD+MOXuNt8NE+GLsXDv+DPcEErvZST53nVeQgRE+djN2qgbqCRERZN65N89PQ76zJkEyknzvOq0xAiJszHfnQ+uo68RZeBtPOrj8s09yYlOl5nfwCAkZkJDcd05Mzq3/NXYnCW17Cd2zesRJHePjxc9IvOdaWFKV5bJ3J/zg+ok968fr4g9YW7uytdu7Rj/frv802nadMGDBrYk5mzluQr8656XzN93AIaVvXl8YOntOvUqkDpvw80hfj3oSI2Bn+YvN4XUBXtcqCXwGQgAfguSyYFaAuckyQpQpbl7W9KUJblGOAX4BdJkvYBTYDdBuQ2A5sBKrvWlXsN6ka3vto1/bcD7+BWJGeG0tXdhYhcszq5GTl5CPaO9sz3n5Z9rVPPT/h27Y8AvHgWzKsXIZQqW4JbAXd04maERmOSa/be2N2RjPAY8mJeoQQlPh/No34LUcclZl+P3nmM6J3HAPCY1pf00Dcvkeg7+FN69OsMwK3AIDyKuHItK8zNw4XwfPIIoNFo2P/XET4bM4DdO/bopGerNOXFjcfYeeTM2tm5OZAQrjtTmRyTiLmNBQqlAo1ag627AwkROTLuFYrTY/lwNg9cTkqcfqcrNSGFpIu3sW5Wg9QHurNVGWFRGLvnLksng2WZfS9XgjAp4Y7S3hp1bGK+cq9JCY3BwiNn1svC3QFVWOwbYuiiCo8DtEuGXh66hqN3aSIu627A7TqgEx36aNf03w28l728DMDZ3YmocMMr4QZP7I+dox0zhuruNShdsRQzPvdnUr/pJOTahNpncHc+7dcJgFsBd3DzcANuAODq4UpEuGE7GOP/GQ6O9syZvDT7WnJSMjPGLcz+fuLaHl4+z5m9Gzi0F30GdAfgxvVbeBTJmclz93B94+ydRqNhz58HGTl2MDu3/0l178ps+u4LABwc7GnRqglqtZpD+48D4NPPl8a9WgDw7MZjHHLZo72bA3F57CEpJgFzG8tse7R3dyQuQvc3rd2+IZf3nNO7N7/+7WjVqw0Aj24+xCmX7Tm5ORLzBtvLS5mqZfBfp913ZONgQ02fWqgz1Vw+kuN9cHVyICwy5/kOj4zGxUF39tfK0oLF/iMAbafFt/84irg5k5qWjquzA9UqlgGgVeO6bN35d4Hvr7BIz1Mfmrg7khHxpmf4DibF3Qr8DKvDIzHKNaNr5OqEOjL/OjPt+i2MirmjsLNBE6d9fswb1Sb93iM0MXFv1aXMpUvp4oQ6In9dqQZ0WeSjKzEsBmv3HNu2cXcgKVxXJiE0Bptc9m/j5kBSRBwV/OpStmVNSjfzwsjUGFNrczp8OZKLG/dhV8yZoQeXZac5ZP8SAtrMIj1Su6QmNTQGs1xpmnk4kGagDrSqVJzKq4dzvddyMmJz6nHJSEn17yYRuvscEQf+MVgOI0YMYMhg7R6gq1dvUKxojjO/aBF3QkN199N4eVWmdOmS3L2jfUYtLMy5c+cclSo1AqBqlYps2rSSDh36EfOG36zP4O7ZbePNgDu4e+S0/24eLkTkU/eCtp468PcRho7uz+4d73Y4h6DwEJ6AD5PzwCdAjCzL6qwOvB3aJUEXXwvJshwJ+AJLJUlqk19ikiQ1lyTJIuuzNVAarRfhrez4/ne6tuhH1xb9OH7wDB26a2cfq9WsQlJiElEGKvCufTrQ0KceU0bM0ZkpCH0VRr3G2rWSjs4OlCxdnJfPX+nFT77xENOS7pgUc0EyNsK+Q2Pij+ou2zD2cMJzywyejf+StKe6G7mMHG2zZex86xP795k35vHn73bR3qcX7X16ceTAKTp/+gkAXjWrkpiQRKSBiq6EZ87GvBZtmvDk4VO99Fb5Tef2kavU7tJEG8e7DKrEFBIi4/TSe3TxDtX9tGv163Rtwu0j2nXNdh6ODNo0ie0T1xP5NDRb3tLBGjMb7f4KY1NjrBtVJ+1RsF66KTceYurpgUkxV21Ztm9MwtHLOjImJXLWhJpXKYVkbFSgzgNAdOATrD3dsCzmjMJYSYmO9Qg+cr1AcZXmphhZmmV/dm9ahbh7+nnY/cNfDGj9GQNaf8aZw+dp203rdalcoyLJCclEG+gQte/lR71mtZk3epGODbp6uLB8y0IWjl/Gyye6urZ/9xsdffrQ0acPxw6eonMP7QFa1WtWISkhichwfVvv3rcjjXzqMXH4LB091jZW2ZveP+3biasXA0jOtVZ227c7aNW4C60ad+Hg/uN0zzrRpkataiQmJBpsXEt65nh6Wvn68CjL5upWb02daq2oU60V+/YcZvrkRdkDAICTPx3K3swbcOQK9bs0A6CUd1lUiSnEG7DH+xeDqOlXH4AGXZsReCSnc2JubUH5upUIPKrfYTnw434mth3HxLbjuHT4Ij5dtUuiynmXJzkxhdiIgg8QhzUayrCGQxjWcAgXDpznm9kbdQYAAFXKl+b5qzCCQyPIyMjk4OmLNKuvuw48ISmZjIxMAHYfPEHNqhWxsrTAycEON2dHnr7U1h+XA25TunjRAt9fYaF9hnPVh+3160OTEjmDSPMqpZBMCv4MpwXdx6h4EYw83MDICMs2zUg5fVFHxqhYTqfTpEIZJGPj7E45gJWvD8l5lufkp8u4eBGMimTp8n13XZZtfUgysOwo5MYTHDzdsM2qiyq1r8eDo9d0ZB4eu061rtrTrTy8y5CWqCIpIo5TK3eytt5Y1jeawJ9j1/Hswh32TNhI5P2XfFlzFOsbTWB9owkkhMawtV3OAAAgIeAxFqXcMC/ujGSsxK1TAyIO6+o1K+KI13eTuDV6PSlPQnXCKq8ZTvLDVzz/xvDBCQCbNv1A7TptqF2nDXv2HqJP324A1KlTg/j4RL1Jg4MHT1C8RA3Kla9PufL1SUlRZQ8AihXzYOeuLQwaNJ6HudotQ2z/7jc6+PSmg09vjh08Race2skYr5pV8m0bi3vmPEM+rZvw+OGzN+p4nwhPgPAEfKjcQnsq0C95rlnJshwlSZLV64uyLD+VJKkDcECSpC6yLF9Gn5rAOkmSMtEO/L6VZdnwlMMbOHPsPE1aNODg5d2kqlKZPX5RdtjG7WuYO2kJkeFRzF05jZDgMH7Z/y0Ax/afYuPqrWxa/R1Lvp7Ln6e2I0kSqxetJy4mXl+RWsPLOZsp8/N87ZF4O4+T+uAlTn19AYj6+RDuE3piZGdNsSXDteWg1nC/nfYEllKbp6G0s0HOzOTl7G+yNxAXhFNHz9GsZSNO/PM3qSrtMWiv2brja2ZMXEhkeDSfr1uAlbUlkiRxN+gBc/0Nn9Bw52QAFX28mHX6K9JVafw6JefYwM++n8bOaZtJiIhl3/Jf6Ld2HG0n9+BV0DMu7dI2cm3GdcXS3opuiwcDoMlUs7rDLGxc7On9xUgUCoX2qL19Z0g4cVX/BtQagud+Q6kftWUZs+sYqQ9f4thHW5bR2w9h17Y+9l2bQ0YmmrR0no8u2GkRoC33q7N+oPkvU5GUCh7/epr4B68o20/b+Xv40wnMnG1pe3ARxtbmyBoNFYb6srfZNMwcrGiydQKgnQ179ucFQk+9ec/6heOXaNC8Lr+d/5k0VRqLJ63IDvvix2Usm7KKqPBopi6fRFhwGJv3rAfg9IGzfPfljwye2B8bexv8l2r1qjPVDPYboafn1NHzNG3ZkGNX/kKlSmXGuAXZYVt2fMWsCYuICI9iweczCHkZxq6DWgfdkX0nWf/Ft5Qu58nK9QvQqDU8uv+EmRMW6el4zfEjZ2jRqgkXAw6hSkll4uhZ2WE/79rE5HFziAiP4quNS7G2tkKSJO7cvs+0yQvyTTM/bp28TlWfGiw9vY50VRrfT9mQHTb++5lsm7aR+IhYfl/+E8PXTqTz5J68CHrGuV05gwrvNnUIOnuTdFXaG3VdO3GVWj612HR2C2mqNNb6f5kdNmfbfNZP+5qY8Bg+GdSeziO6Yu9sz9dH1nLtxFXWTSvYmnYjpZKZYwYyYuYy1BoNnds0o0zJYuzadxSATz9pxZMXr5i1ciMKhYLSJYqwYFLOiUgzRg9k+vJ1ZGRmUtTNlUX+wwukNz+mzFvOPwE3iYtLoEWnvowa0o+u7fOdnykYag3BczZT+ifd+tAxqz6M/vkQdn4NcOjqg5yRiZyazrPRn79T+jHL1+G6cZn2iNC/D5Px+DnW3bSTIYm/78OiRWOs2reETDWa1DQip+acgCSZmWJWryZRi78skK7oZetwy9KV+FeWru5Zun7bh2VLrS45Q42clkZEHl3m9WoStUhfl6zWcHjuNnr9OA2FUsGNXaeJeviKGn20XrDr24/z6EQgpX28GHVmdfYRof9XZLWGezO+p8avM5GUCl7tOEny/WCK9m8JQPCPxyg1uSvG9lZUXKGtx+VMNZfbzMKuTnk8Pm1C4p3n1Du+HIBHS38l6nhgvvoOHjyBr29z7t49hyollaGfTcoO+/vvHxkxYoqeZyA3s2ZOxNHBjrVfaz2XmZmZ1G/Q7q35PHX0HE1bNuT4lb9RqbTHJr/mdZ0YGRHNynULsLKyQpLgXtBD5k3J//QiwftHepc1moKPi8qudQvNOH4y0d/Y9F/RXfWyUPR0sPrfzxt/VwbIKW8X+pcIyrB5u9C/xDpJ31P0XxCdXrBZ03+DxIzC+63a2VUqNF2RmsLbVPvb2YVvF/oXkCztCkUPwO0aho+7/S+wdyg8GyysLsYvcflv3P+3qZ36fz/prKB8Eqe/7O6/ooSN4QMJ/gseRl5772fmrC3Wt9D6OGNf/vze82sIsRxIIBAIBAKBQCD4yBCDAIFAIBAIBAKB4CND7AkQCAQCgUAgEHxUaD7IBTqFi/AECAQCgUAgEAgEHxnCEyAQCAQCgUAg+Kj4kI/uLCyEJ0AgEAgEAoFAIPjIEJ4AgUAgEAgEAsFHhfAECE+AQCAQCAQCgUDw0SE8AQKBQCAQCASCjwrxqlwxCBC8gXRNZqHpephpVWi6nE0K54231igLRQ9ARKpFoelqUT640HTtf25fKHqsFaaFogfA1qLwdCkpvDPwyiqsC01XVK/JhaInPLjw3o5d5fqaQtOVuX9zoeky8h1UKHoe151VKHoASirNCk2XjWnh1e0lzJwKTZfgw0AMAgQCgUAgEAgEHxXiPQFiT4BAIBAIBAKBQPDRITwBAoFAIBAIBIKPCnE6kPAECAQCgUAgEAgEHx3CEyAQCAQCgUAg+KgQpwMJT4BAIBAIBAKBQPDRITwBAoFAIBAIBIKPCo3wBQhPgEAgEAgEAoFA8LEhPAHvEUmSOgN/ABWBnoCpLMszcoV7ATtkWa4oSdIzIBFQZwWfkWV5XJ70BgKfA68AY+Au0F+W5RRJkuYDSbIsr/q37n/O0ik0bdkQVUoq08bN587Ne3oyS7+cQ5XqlZAkiWdPnjNt7HxSklVvTdvNpxreC/shKRU8+eUU99bt1Qm3LuNOnTXDsa9aklvLd3F/04HsMGMbC2p/8Rm2FYqCLHNl4mairz3KV9ekRWOp37weaapUFk1czv1bD/VkFqybRYXq5cnMUHMn8C7Lp36BOlNN4zYNGT5lMBpZRp2p5st567hx5VZ2vDJNq+E3V5uP6ztPcXbjXr20/eb1p6xPdTJU6fzp/w2hQc8A6LTyM8o19yY5OoH1baZny7tWLE6HJYMxsTAjLjiS3ydsAFWSTpqOPtWpsHgAklJB8PYTPFu7R7d8uzbEc0wHANTJadyZ+i1Jd15g6uFI1XWjMHG2A42G4J9P8GLLwXzLDsCkTh1sxo0BhRLV/v0kb//FoJxRhfI4btxA3PyFpJ0+jcLFGduZM1E6OiBrNKj27iPl991v1JWX/vOH4OVTk3RVGpv81/Ls9hM9mdYD2uI7uD1uJd0Z7tWfxNjEAqU9asFIajevTZoqjVWTvuDRbX0b6jCgPZ2HdqZISQ+6VfuUhNgEACysLZj+1VSci7igVCr5ffPvHNl1NF9dg+Z/Rg2fmqSp0ljv/xVPDeTDd4Af7QZ3wK2kO4O9+mbno1GnpnQa0QWA1JRUtszayPO7z3Ti9pg3iCo+NUhXpbHNfz0vg57qpe9Y1IXP1k3AwtaKl0FP+W7iWtQZmZSrV4lRm6cRFRwBQMChy+z/+ncAWgxpR6MeLZBlmYj7waTEJVG+STXSVens9N/Iq6BnenocijrTd904zG0teRX0jB0T16POUONc2oMenw+naGVPDq7ayekt+wGwdXeg1+pRWDvbIWtkLu04Doe3ZadnWrc2thPGgFJJyt79JP20w2AZG1csj9Pm9cTOXUjqyTMA2M2cimnDemhi44jsOzjf38cQ1k29KTr/MySlguhfjxK+Qdd2bVvVwd2/D7JGA2oNwQu+Jfmfu++kIz9mL13NmfNXcLC346+fN/2f0zv/OJyVR2+ikWU6Vy/B4AbldcK3XXrAgdvalwSqNRqeRidyckI7VBmZzN5zjejkVCRJoqtXSfrUKfNGXecuX2P519+i1qjp2q41Q/t20wmPT0xizvKvefkqFFMTExZNH0fZUiUA+HHX3+zedwRJkihbqgSLp4/H1NREJ37veYOp6uNNuiqdrf7reGHA1p2KujBi3UQsba14HvSELVm27jusA/U6NQZAoVTiUaYI42sMwcTClKGrx2KbZYOhP53i/tbD2em5N6tGrUX9kBQKHu04xZ087ZVNGXfqrR6GQ9WS3FjxG3dztVcdL68hMykVjUaDnKnmUNu5byw/gKUrZ9OydVNUKSrGjpzOzRt39GS+XLcEL++qSBI8fvSMsSOnk5ycwphxQ+j6qbbuNzJSUq58acqXqkdcbLxBXaMXjKRO8zqkqVJZmU892HFAB7pk1YNdqnXPrgctrS2Y/tU0XLLqwd82/87hXUfemj9B4SE8Ae+XXsA5tAOAHUCPPOE9gdw9Kh9Zlr2y/sZhmJ1Z4ZWBdANp/is0bdmQEqWK0bJOJ+ZMXszClTMMyi2dvZoOPr1o36wnIcFh9B3y9tuRFBI1lw7kTJ+VHGo6lRKd6mNTroiOTHpsMgGzf+T+pv168b0X9SPs5A0ONp7C4RYzSHgYkq+u+s3rUsyzKN0b9mHZ1C+YumyiQblDfxyjR+P+9Gk+CFMzUzr2bgfA1bPX6dtyCP1bDWXJpBXMWDVFJx+fLBzITwNXsq7VVKp2qI9zGd18lG1WHUdPN75qNpk9M7fSfknO2zUDfj/LTwNW6t1Lp+VDObriV9b7TufO4as0HNZOV0AhUXH5YK73Xs75xpNx79wQyzzlp3oeyT+dFnLRZxpPVv9B5S+GASBnqrk/7ycuNJ7MZb85FBvUWi+uri4FNhPHEztlGlH9B2DWojnKEiUMylmPGE76P//kXFOrSdywgah+A4gZMQqLzp0Mx80HL58auHl6MKnpKL6dsZHBi4cblLt/9R5L+8wj8mVEgdOu7VObIp4eDGo8mC+nfcW4pWMMygVdvcP0XjMIexmuc73DgPY8f/iCkW1GMeXTqQybMwwjY8NzLt4+NXH3dGds0xF8M2M9ny0eaVDu3tW7LOwzl4g8uiJehjPv05n4+47n9693MnzZaJ3wKs28cfF0Z06zsfw88xv6LPnMYPpdpvfh2NZ9zPUZR3J8Eg17NM8Oe/jPXRb7TWGx35TsAYCdqwPNB/qxtP10FraZjK2bA551KrC82UR+n7mFrkuGGNTTbnpvzmw9wAqfSajik6nTwwcAVVwSf8//gVNb9unIazI17F38M5+39Gdt5zk07Ncao5JZdqJQYOs/nujJ04noPRDzli1ywnKjUGAzahhpl//RuZxy4BAxE6cZvM83olBQbPFwHg9YwN0WY7Dv0BizssV0RBLP3+Rem/HcbzuR5/5fU3yFYRv6X+jk14pNqxf/K2mpNTLLDt9gfY8G/DGsJYfuBPM4MkFHZmC9cuwa2pxdQ5szzqcyNYs7YWtuglKhYHLLqvw5vBU/DWjKzutP9OLq6FKrWbzmGzZ+Po89P67nwPEzPH72Qkdmy0+/UaGMJ39uW8vSWRNZ/vUWAMIjo9n++152blnNXz+sQ6PRcPDEWZ24VZt54+rpzoxmY/lh5ib6Lxlm8D66T+/Lka37mOEzluT4ZBpn2fqhzXuY7zeF+X5T2L1yO/cv3yE5PglNppqdi39gdssJLOk8g3IDW2JT1gPQ1vO1lw7gZJ+V7Gs2lZId62WHvSYtNpmrc37S6fzn5lj3JRxsNatAA4CWrZtSqnRJ6ni1YtL4OXy+ZoFBudkzltKsYQeaNujAq+BQhgzrC8C6r7fi06gjPo06snj+F1w4dyXfAUAdn9oU8SzCgMaDWDPtK8YvHWtQLuhqEFN7TSfsZZjO9Q4DOvD84QuGtxnJ5E+nMPwN9eD7QFOIfx8qYhDwnpAkyQpoCAwBesqyfB+IkySpbi6xT4Ff/8f0jQBLIPb/eq+GaOnblL92ajvggdduY21rhbOr/ivHk5KSsz+bmZmB/PY1eA7epUl8Fk7yi0g0GWpe/H2JIm1q6sikRScQc+MJmgy1znUjK3Oc61XgyS+nANBkqMlISMlXV5M2DTnwu3ZGJ+j6HaxsrXB0cdCTu3jicvbnOwF3cXF3BkCVkuPVMLPQzV9Rr9LEPA8n9mUk6gw1t/ZeokJr3XxUaF2TwD+0DVlwwCPMrC2wcrYD4PmVe6jidWf4ARxLefDsstbr8vjcLSq1raMTblujDClPw1A9j0DOUBP21wVcfGvpyMRffUBmvPa3ibv2EFN3bZ7TI+JIvPUMAHVyKskPX2Hqpl8erzGuWAH1q1eoQ0MhM5PU4ycwa9RQT86iaxfSTp9BExuXfU0THUPmA63XRVapyHz+HKVzwV9bX7NVHc7uPgnAo4AHWNhYYudiryf3POgpUcGRBU4XoEHr+hzdfRyAewH3sLSxwsGAXTwOekx4cLjedWSwsDIHwNzSjMS4RNSZan05oHarOpzOysfDgAdY5pOPZ0FPiQzWH8g8uHaP5ATtb/nw+n0c3R11wqu3rs2lP04D8DTgIebWlthk2VhuKjSowvUDlwC4tPs0Xq1rG7zf3CiUCozNTFAoFdgXceTu8esAvMiyZWsDeso0qMzNA9rn6eruM1RprbXNpOgEXt58giZPOSVGxmV7FNKSUwl//CrbTowrVSAzOAR1iNb+VMdOYNZY3/4su3VGdfKsjv0BpAfeRJOQf6c1Pyy8ypL2LIz0F+HIGZnE7j2LbWvd51CTkpr9WWFRsLqvoNTyqoqtjfW/ktbtkBiK2VtS1N4SY6WCNpWKcuphaL7yB4OC8a1UFABnKzMqutkBYGlqTClHayKSUvONe+vuQ4oXcaeYhxvGxsa0bdGYE+cu68g8fvaSejWrA1CqRFFehUUQFaNtxjLVGtLS0snMVKNKTcPZUfeZ9G5dmwt/nALgScBDLKwtsM3H1q8euAjAhd2nqJHntwOo26ERl/ecByA+Mi7bo5CanEr8oxAssupMx6z2KimrvXr+9yWK5dde5VMHvAtt/Vqwa8efAFz75wa2tta4ujrrySUl5m57TZEN2F+X7p/wx+/6E2mv0daDxwC4G3APKxtLg/Xgo3zrQbnA9aDg/SAGAe+PTsAhWZYfADGSJNVA6w3oCSBJUj0gWpbl3GtTTkqSFJj1Z3jKGnpIkhSIdkmQA6C//uRfwNXdhdCQnIc+LCQCVzf9ighg+dfzuBh0hFJlS/Ljtzvfmra5mwOqV9HZ31NCYzB30+8UGcKqhAtp0YnU+XI4rY8sofaqoSjNTfOVd3ZzJiIkp4MYERKJcz75AFAaKWnbrTUXT17JvtbUtxG/nvmRL35czuJJK7KvW7s6EB+Sk4+E0BhsXHXzYZNXJiwGm7fkNeLBSyq00jYyVfzqYuuuWymbuTmQmivN1JCYN3bki/T2IepEoN51s2LOWFcpSfz1/JdSKZycUUfklJ86MhKFs3MeGSfMGjci5e89eaNno3Rzw7hsWTLuFHy5hL2bIzG58hkTFo29a/75fBcc3RyJzGUXUaGROLo5viGGLn9v20OxMsXZcfUXvjm6iY3zNhlshAEc3ByJDonK/h4dFoWDa8F15aZ5z1YEnLquc83O1UGnnOLCorHPYw+W9takJKSgUWvnrGJDo7HLVZalapRj9sHPGbttJu5ltR3AuPAYjm7Zy7ILG1l5ZQsKpZK7JwOz48SHxWCbR4+FvTWqhORsPXGh0di+w29mX9SJIpVKkh6ktROlsxPq8JyBkToyUm8gqXBywqxpY1L+yt/+3hUTN0fSc/1m6aHRGBv4zWzb1KPiifWU3jaH51PW/mv6/00iElNxszHP/u5qbU5EouGOvCojkwtPwmlZQd87+CoumXvh8VT1yL/+ioiKxs0l5/dxdXYiIjJaR6Z8mZIcO6PtoN+684DQ8AjCI6NxdXZkYM9OtOw+BJ/OA7C2tKRhHW+duPaueeuEGOzzPLdW9tak5LLBmDy2DmBiZkKVpl5cO3hJLw+ORZ1xqFKCqOuPATB3syclJCY7PCU0BnP3grVXAMgyzXdMx/fQIsr08XmruLuHK6+Cc2bcQ16F4+7halD26w3LuPPoAmXLleLbb37SCTM3N6N5y8bs3XPYYFwAJzcnnXowMjQKp3eoB//atofiZYqz8+ovbDn6DRvmbcy3HnwfyIX496EiBgHvj17kzPL/mut7N0mSFOQsEcpN7uVAa/JJd6csy16AG3ALmJKPnEEkSRomSdJVSZKuxqdGvUlO71p+D/f0cQtoWNWXxw+e0q5TqwLchIFrBaw4JCMF9lVL8uiHYxxpPYtMVRoVx7bPX96ArjdVUlOXTSTg0k2ddf+nD52jZ5P+TBs8m+FTc5ZAFCTtd9UP8NfUzdTp14oRexdjYmWOOiNTV8BQ+eVTDdk3rESR3j48XKS7jl9pYYrX1oncn/MD6qQ37OEowG9lM3YMiZs2g8awU1QyN8du0QIS1q5DTsnfa6MX7/9gJ29P2+APU+D4tZrW5Mmdx/Sq1ZuRvqMYs2gUFlYW/4mu11SuX5XmPVry87If3pp+3uQN34P2vxe3nzKz4SgWt53CyW0HGbl5KgAWNpZUb1WbWY1HM7XuMBRKBeUbV8uj5/9u768xsTBlwMaJ/L3wx1x28vZ6yHbCaBI2fJOv/f1PFND24g9f4m7z0TwZuhQP/z7/nv5/EUOlb7AKAc48DMOrqCO25rrr8FPSM/H/4wpTWlbFytQ4f10Gyiiv7Q3t042ExCS6Dh7P9j/2UaFsKZRKJfGJSZw8d5nDO7dw4s9tqFJT2Xvk5FtvXN8G3/68VW9Zi0dX75OcxxNramHG6I3+XJv7M5lZ9eKbnp2CcKTjQg62mc3JPp9TbmBLXOqWf6P8u7S940bNoEq5Rjx48JhOXfx0wtq0bc6VS9fzXQqk1aV/7V068bWa1uTxncf0qNWb4b6jGLNodL71oOD98OEszvqIkCTJEWgOVJEkSQaUaKuNqcAzoCnQFaj/lnSKkTPTvwnInr6RZVmWJGkvMBZYXtB7k2V5M7AZoKxzTZ2nvc/g7vTo1xmAmwF3dGYf3DxciAjPf9Cg0Wg48PcRho7uz+4db3ZOqEJjMC+SM9tg4e6AKjyuQPevColBFRpDTIB2lublvitUHKM7COg6sBMd+3wCwN3Ae7h45Mxcu3g4E5VPPoZMGoCdox3Lp84xGB54+SZFSnhg62BLfEw8CWEx2Hrk5MPG3YHECN18xOeVcXMg8S15jXocyo/9tT+po6cb5Xy8dMJTQ2Mwy5WmmYcDaWH6q8KsKhWn8urhXO+1nIzYnMZOMlJS/btJhO4+R8SBf/Ti5UYTGYnSJaf8lM7OaKJ0y8+oQnns5mnXukq2tpjUq0uCWk3auXOgVGK3aAGqo8dIO6O7vtcQrfq3xaendiD55OYjHHLl08HNkdiI/331W/sB7fHr5QvA/RsPcM5lF07uzkSHx+QXVY/Wn7Zm5wat1yvkWShhL8MoVqYo9wMfANCmvx8ts/Lx6OYjHD1yZkcd3ZyIiSi4LoDiFUowYsVolg5YSFJcYnb6SknBsxvacnqcJWvn5khcnrwkxSRgYWOBQqlAo9Zg7+5IXNY9pOYaBN4+FUCvxUOxtLemfP3KGJsaM+Fn7fMQ/jiEEjXKZsvaujmQEK77eyTHJGJuY5mtx87dkYQC/GYKIyUDNk3k+l/nuX34H1pnLblWR0aidHXJltPan+7MsnGF8tgv1NqfwtYW0wZ1Qa0m9cz5t+rNj/TQaExy/WYm7o5kvOE3S75yB5PibijtrVEXcGN6YeFqbUZYQs5vHJ6owtnazKDsoTvB+FYuqnMtQ61h8u7L+FUuSgsDHgIdXc5OhEXk1A/hkVE4O+nOwltZWrB4xnhA2+Fs0+Mzirq7cv7KdYq4u+JgZwtAiyb1Cbx9j44dO6Mwt2X+gc95euNxnjrBQc/WE2MSsMhlgw7ujsTlscG67Rtyec85nWtKIyWjN/lz6a+zqA9ezb6eEhqDhUdOHizcHVAZqG/z43XblhadwMtD13D0Lk3E5fs6MoM/60O/AZ8CEHj9FkWKumWHeRRxJSw0//1OGo2Gv3YfYMz4oezY/kf29c5d2/HH7/v05DsMaI9fr7YAPMhTDzq7O71TPej7aWt2bNgFQMizkKx6sBj3A++/JWbh8CGv1S8shCfg/dAN+FGW5RKyLJeUZbkY8BRohHb2fw3wWJbl4DclIsvyy1yeAUNHRDSC7Lb//8z2736jg09vOvj05tjBU3Tqod2Q6lWzCokJSUQa6DwX98xpMHxaN+Hxw2dv1RMT+ARrTzcsizmjMFZSvGM9Xh2+VqB7TI2MJyUkGuvS7gC4NqpMwoNXOjK7t/1F/1ZD6d9qKKcPncOvWxsAKteoRFJCMtEGGvMOvdtRt1lt5o5aqDMTUrRkTqNXvmpZjIyNiI/Rzqy8uvEEh5Ju2BV1RmmspGr7etw7qpuP+0ev49VFexpFUe8ypCaqSIqMe2MeLR1tAO2MUNMxnfhn+3Gd8ISAx1iUcsO8uDOSsRK3Tg2IyFN+ZkUc8fpuErdGryflie7638prhpP88BXPvzG8iS03GffuoyxaFKW7GxgZYdaiOWnnL+jIRPXoRWSPnkT26Ena6dMkrP5SOwAAbKdNJfP5C1J2/fZWXQBHfzzITL9JzPSbxNUjl2ncVes+L+NdDlViil5j/i7s/WEvI31HM9J3NBcOX6RV1xYAVPCuQHJi8jt1zCNCIvBuqF2qYOdkR9HSRQl9nuPCP/zjAab4TWSK30T+OXKJpln5KOtdjpTE5HfKh5OHE1O+mcHaiV8S+jREJ/3FflMIPPIP9bo0BcDTuyyqxBQSDNjY/YtB1PCrB0C9rk25cUQ7AMy9f6Bk9TIoJAXJsYnEhERhbm3Bii6zWOw3BVmtyV7KVty7DKmJKSQa0PPoYhDV/LRbn2p1bULQkbc/25+uGEb4oxDObNW1yYy79zAqWiTb/sxbNif1nK79RXTrTUTXXkR07UXqydPEr/ry/zQAAEi58RBTT3dMirkgGRth374x8Uev6MiYlMjpqJlXKYVkYvTBDQAAKnvY8yI2iVdxyWSoNRy+E0zTsu56compGVx7EYVPrjBZllmw/zqeTtb0q1tWL05eqlQoy4vgEIJDwsjIyODg8bP4NKyrI5OQmERGRgYAu/cdoWb1ylhZWuDu6szNO/dRpaYhyzKXr92gVIliaFRxZMY8Z77fFAKOXKFBl2YAlPIuS0piCvEGbPDexSBq+Wnn2Bp0bUbAkZzJDnNrC8rVrUTAUd0JkEErRhH6KJgjW3U7ztF52qsSHesRfER3SV5+KM1NMbI0y/7s3rQKcff0m/3vtmzP3sx7YP8xPu2lnYyrWbs6CQlJhIfr73nyLFU8+3Obts15+CDnxDFrGysaNKrNwf3H9eLt+WEvI3xHMcJ3FOcPX6BV15YAVPSuQHJiyjvWg5HUaOgFaOvBYqWLEvo8//0mgsJHeALeD73Qn53fDfQG5gJfoZ3Bz8tJSZJe76q5KctyfwMyPSRJaoR2gBcMDMwVNluSpAmvv8iyrDul8w6cOnqOpi0bcvzK36hUqUwfNz87bMuOr5g1YRGREdGsXLcAKysrJAnuBT1k3pRlb01bVmu4PnMbTXdM0x4R+utpEh68onR/bafs8Y/HMXO2pdWhxRhbmyNrNJT7rC0Hm04lM0nF9Vk/Um/9KBTGRiS9iODKhG/y1XXh+CUatKjL7xe2k6pKY/HEnDX9q39azlL/z4kKj2bq8kmEBYexZe8Gbf4PnOG7NT/i064Jbbu1JjNTTZoqjTkjF2bH16g17J+7jf4/TkOhVHB912kiH76iVh9tPq5uP86Dk4GU9fFiwunV2iNCp+Tca7evR+NZryIW9tZMvriWk2t+5/qu01TtUJ86/bSzyHcP/0PAb6dplKf87s34nhq/zkRSKni14yTJ94Mp2l9bmQf/eIxSk7tibG9FxRXaYxHlTDWX28zCrk55PD5tQuKd59Q7rjXRR0t/Jep4oOECVKtJ+PIr7Fd9DgoFqgMHyXz2DPMO2iPoVHvyX4dtXLUq5r5tyHj8GMet3wKQuGUL6Zcu5xsnN4EnruHlU5M1ZzaSpkrjG/+cNddTt81m89T1xEXE0mZgOz4Z0Qk7Z3uWH/6SwJPX2DJtwxvTvnLiCnWa12bbue+0R4ROXp0dtviHhaye+iUx4TF0GtSR7iO74eDswDdHN3LlxD+smfol27/6hSmrJ/PN0Y1IksTWpd9lH5uXl+snruHtU4u1ZzaRrkpjfa58zNg2h01T1xMbEUPbgZ/QcURn7JztWXX4awJOXmPTtHV0G98TK3trPlukPR1JrdYwvf3k7DRun7xOVR9vFp9eS7oqnR+mrM8OG/P9DH6aton4iFj+WP4zQ9dOpOPkXrwMesr5XScAqNG2Hk37tkatVpORms6WsdqViM8CH3H94CVm71+JOlNNWNBzIp+FMf30l2So0tiZy5aHfD+V36ZtISEilv3Ld9B37Vh8J3/Kq6BnXN6lXdJh7WzL+D1LMLMyR5ZlGg9uy+etpuBeoTi1ujYh5O4LJh7Q1h+mWzeRdvEyqDXEr/4axzUrQakgZd9BMp8+w6KT1vuX8tebvY52C2Zj6u2Fws4W1792kfjtNlL2vX3wi1pD8JzNlP5pvvaI0J3HSX3wEse+Wk9S9M+HsPNrgENXH+SMTOTUdJ6N/vzt6RaQKfOW80/ATeLiEmjRqS+jhvSja/s2/1NaRgoF01tXZ+Sv59FooGP1EpRxtuG369qNsN1reAJw4kEI9T1dMDfJ6TYEBkez7/ZLyjrb8Om3WnsZ26wSjcu46StCeyTlzAnDGe4/H7VGQ2e/lpTxLM7Ov7XHEPfo2JYnz4OZuWQNSqWCUiWKsXC69iC8apXK06pZQz4dOgGlUkmFsqXonifPN09ep5pPDZafXke6Ko3vpuQ85xO+n8m2aRuJi4jl9+U/MXztRDpP7smLoGec3ZXTGa7Rpg5BZ2+SrkrLvla2VgUadG3Ky7vPmX/gc6xkBTeW7SLkxA1ktYars36g+S9TkZQKHv96mvgHryjbT3vi0MOfTmDmbEvbg4uy26sKQ33Z22waZg5WNNk6AdB6YJ/9eYHQUzff+HsdPXyKlq2b8s+NY6hSVIwblXMy347ftzBxzCzCwyNZt2kF1tZWSJJE0O17+E+cly3X7pNWnDpxnpSUNyz1BC5n1YM/nvueNFUan0/+IjtsyQ+LWD11DdFZ9WCPkd1xcHZg89FNXDlxhdVTv+Tnr7YzZbU/W45uAkliy9Kt+daD7wNNfuvePiKkD2mThuDDIu9yoP+SxUYVCksVa6RXbxf6F2ht8mbX+L9JI1XhnbhQrXzY24X+JSY9f4cNdv8HojRvf3fFv4WtIv+N6v82DpLJ24X+JawLcU5pkkfh2GB4sE2h6AGocj2/bV7/Ppn7NxeaLiPfQW8X+hcYXndWoegBaJFueLnUf8H4pCtvF/qX8LL1LDRdx14efu9d8Lkl+xRaH2fhs+3vPb+GEJ4AgUAgEAgEAsFHheaDPrencBB7AgQCgUAgEAgEgo8M4QkQCAQCgUAgEHxUCD+A8AQIBAKBQCAQCAQfHWIQIBAIBAKBQCD4qNAU4l9BkCTJV5Kk+5IkPZIkabqBcEmSpK+zwm9KklTjf8p4LsQgQCAQCAQCgUAgeE9IkqQE1gNtgUpAL0mSKuURawuUzfobBmz8v+oVewIEAoFAIBAIBB8VH9jpQHWAR7IsPwGQJOlXoCNwJ5dMR7QvmpWBS5Ik2UmS5C7L8v/8BjbhCRAIBAKBQCAQCP4jJEkaJknS1Vx/w/KIFAFe5voenHXtXWXeCeEJEAgEAoFAIBAI/iNkWd4MvOktfYZeJpbXVVEQmXdCDAIE+ZKmTi80XQmmhfcyvXR1ZqHoSaLw3uKrLES3ZuRL60LTFaFJKRQ90RlJhaIHIFWZUWi6rI0L543LACFyaqHpKizsHQrH/qCQ3+LbLu8k5H9H5oEthaLHlcJ7O3ZoIfacnMxsC01XTGZyoen6EPigFgNpZ/WL5fpeFAj5H2TeCbEcSCAQCAQCgUAgeH/8A5SVJMlTkiQToCewJ4/MHqB/1ilB9YD4/8t+ABCeAIFAIBAIBALBR0ZBj+4sDGRZzpQkaQxwGFAC38myHCRJ0ois8E3AAcAPeASkAIP+r3rFIEAgEAgEAoFAIHiPyLJ8AG1HP/e1Tbk+y8Dof1OnGAQIBAKBQCAQCD4qPrAjQt8LYk+AQCAQCAQCgUDwkSE8AQKBQCAQCASCjwrhBxCeAIFAIBAIBAKB4KNDeAIEAoFAIBAIBB8VH9LpQO8LMQj4AJEkSQ3cynXpV1mWl0uSZAwsAroCaWiPiJony/JBSZJsgbVAw6w454GxsizHS5JUEngKjJNleW2WjnXAVVmWt73LvS1YNh2fVo1RqVKZPHo2t2/e1ZNZ+fUCqnlVRpIknj5+xqTRs0lJVtGqrQ/+M8eg0WhQZ6pZMHMF/1wOMKinaLNq1F/QD0mp4P6OU9xYv1cn3La0O01XD8OpSkn+Wfkbt77Rbqi3dHeg2VcjMHe2BY3M3V9OErT18BvzNGXReBq1qE+qKpV5E5Zy79YDPZnF6+dSqVoFMjMzCQq4y5KpK8nMVFOzvjerty0j5IX2qN4TB06zZc02nbid5w2goo836ao0dvhv5FXQM730HYo602/deCxsLQkOesYvE9ehzlDjUtqDnp+PoGhlTw6s2smpLfuy4zQe1JZ6PZsjSZDw0wlebs45VMDBpzrlFg9EUioI2X6C52v/1tHn2rURJcZ0AECdnMr9qVtJuvMchakxNf6ej8LEGEmpIGLfZZ5+/tsby8+qSQ085n0GCgWxO48Suel3nXDrVnVxndQHNDJypprQRd+ScvUOAEVWjMOmeW0yo+N56DvmjXpeM3rBSOo0r0OaKpWVk77g0e1HejIdB3Sgy9DOFCnpQZdq3UmITQDA0tqC6V9Nw6WIC0qlkt82/87hXUcM6pm6eAINX9vF+CUG7WLJ+nlUqq61i9sBd1gyJcsuGnizZttyHbvYvPr7AuVv3MLR1GtelzRVGssmruTB7Yd6MnPWzqB89fJkZmRyN/Aeq6atQZ2Z/8vpes8bTFUfb9JV6Wz1X8eLoKd6Mk5FXRixbiKWtlY8D3rClolrUWdk4jusA/U6NQZAoVTiUaYI42sMITk+CXMbCwYtH0mR8sXRyDKbp6zj4fX7Bu+h//whePnUJF2Vxib/tTy7/URPpvWAtvgObo9bSXeGe/UnMTbxreVlWrc2thPGgFJJyt79JP20w6CcccXyOG1eT+zchaSePAOA3cypmDashyY2jsi+g9+qy7xBLRymjgKFgqQ/DxL//U7d8Gb1sR81EGStrcd8voG0wCCMShTFZeXsbDmjIm7EbfyBhO1/5qvr/ONwVh69iUaW6Vy9BIMblNcJ33bpAQduBwOg1mh4Gp3IyQntUGVkMnvPNaKTU5Ekia5eJelTp8xb85Yfs5eu5sz5KzjY2/HXz5veHuEtnH8cxsojWfnyKqmfr4sPOHD7JQBqWeZpVAInJ36CmbGSwT+eIUOtIVOjoWWFIoxqWgmFRzmMa3+CfxM1/+w8yemNe/V0tp/Xn/I+XqSr0vndfxMhWXVwuabV+GRufxRKhcG4jT9rh9+sPizyHk5KbCJKYyWdlg6lRNVSGJuboDA2Qp2eya1fT3Flg77e5gv64enjRaYqjYOTNxNxW6vX1MaCNiuH4liuKMgyh6ZsIfS6th7zHtgK7wGt0ajVPDkRyLdzF+ilO2vJZJq0bEiqKpUZYxdw55b+M/f5xkVUqV6RjIxMbgUEMc9/KZmZamxsrVny1RyKlyxKWmo6syYs4uG9x/n+Xv91+yh4v4jlQB8mKlmWvXL9Lc+6vghwB6rIslwFaA+8fn3rVuCJLMulZVkujbbT/22uNCOA8Vkvofif8GnZmJKlS9CkVjumT1zAki9mG5RbOGslvk260aZxV14FhzFwaG8Azp+5RJvGXWnbtDv+Y+ey4iv9yg1AUkg0XDyAQ/1W8rvPVEp3rIddWQ8dmbS4ZC7M/Ymb3+icpoVGreHSwl/43Wcaf3eYT+UBLfXi5qZh83oUL1WMjg16snjK58xY7m9Q7uDuI3Rp3JtPffpjamZKp97ts8MCL9+gV6tB9Go1SK+Cq9jMCydPd5Y2m8BvM7fQbclQg+l/Mr03p7fuZ5nPRFTxSdTt0RyAlLgk/py/jZO5Ov8AbuWKUq9nc77sOItVbafh1KoG5p5u2kCFRPnlgwnsvYxLjSfh2rkhluWK6MRPfR7B9U4LuOIzlWer/6DCF59pyy8tg4AuC7nSfCpXWkzDsXl1bGqWzbf8UCjwWDiCpwPn87D1aGw7NMG0TDEdkeTzN3jUdhyP2o3n1bSvKbJ8bHZY7O7jPB04P//081DHpzZFPIswoPEg1kz7ivFLxxqUC7oaxNRe0wl7GaZzvcOADjx/+ILhbUYy+dMpDJ8zDCNj/bmQRi3qU7xUUTrW78Fi/5XMXJGPXfxxhM6NetG9WT/MzEzp3CfHLgIu36Bny4H0bDmwwAOAes3rUNSzKL0b9efzaauZtGy8Qbmjfx6nb5OBDGwxFFMzUz7p7ZdvmlWbeePq6c6MZmP5YeYm+i8x/LbY7tP7cmTrPmb4jCU5PpnGWTZ4aPMe5vtNYb7fFHav3M79y3dIjte+Ybn3vMHcOh3IrBbjme47kVePXhpM28unBm6eHkxqOopvZ2xk8OLhBuXuX73H0j7ziHwZkW9+dFAosPUfT/Tk6UT0Hoh5yxYYlSxhUM5m1DDSLv+jcznlwCFiJk4rsC6HGWMJHz2TV12GYunrg3Gp4joiqZcDCPl0OCE9RhA1fxVO8yYBkPk8mJAeI7R/vUYhp6aRfOJ8vqrUGpllh2+wvkcD/hjWkkN3gnkcmaAjM7BeOXYNbc6uoc0Z51OZmsWdsDU3QalQMLllVf4c3oqfBjRl5/UnenHfhU5+rdi0evH/HD83ao3MskM3WN+zIX8Mb8WhIAP5ql+OXZ+1YNdnLRjXrDI1iztja26CiVLBlr6N2fVZC3YObcGFJ+HcDInFuG4H0o9/z5pWU6jeoQEuZXTruvLNvHD0dGNVs0n8OfNbOi3RDvYkhUSHhYP4fuBKg3Ft3R0o07gqscGR2ddq99Q+Ez/6zkRSKEhLSOH7ltOo0KEejnnaGU+f6tiXdGNrk8kcmb6VVksGZoc1n9+Pp6du8n3zqfzgO5OYR9qXvharX5EyrWvyQ5sZbGs5nat52jeAJi0aUKJUcdrU7cLcyUuZt3K6wbLe+/tB2jboRoemPTEzM6Vb304ADJ8wiHu3H9CxWW+mjZnHzMWT8/29/uv28X0jF+K/DxUxCPh/BEmSLIDP0M7upwHIshwuy/IuSZLKADXRDhJesxCoJUlS6azvkcBxYMD/eg+t/XzY/av2BXYBV29iY2ONi6uTnlxSYs6rx83MTNEebQspyars6xaW5vk+GM5epUl4Fk7ii0g0GWoe/32JEq1r6sikRicQdeMJmjyzn6qIOKKzZlsyklOJfRiCpZtDvnlq5tuYfb8dAuDW9SCsbaxwcnHUkzt/4lL256DAO7h6uOSbZm6qtK7F1T+0s47PAx5hbm2BtbOdnlyZBpW5eeAyAP/sPkOV1rUASIpO4OVN/Xy6linC84CHZKSmo1FriL1wB2e/OgDY1CiD6mk4qc8jkDPUhP91ASff2jrx468+IDNe+zvFX3uIqXtOntUpaQBIxkokIyOQ86/ALKqXJf15KBkvw5EzMonfewabVnV1ZDQpqdmfFeamOumlXAlCHff22d7XNGhdn6O7jwFwN+AeVjaWOLjo/76Pgh4THhyun4AsY2FlDoC5pRmJcYkGZ9CbtmnEvl257cLaoF2cO34x+/PtgLu4uBfMLvKjUZuGHP5d65m4c/0uVrZWOBrI36UTV7I/3w28h7O7c75pereuzYU/TgHwJOAhFtYW2BqwwQoNqnD1gDY/F3afokbrOnoydTs04vIebefVzMqccnUqcnbncQDUGZmkJKQYvIearepwdvdJAB4FPMDCxhI7F3s9uedBT4nK1el6G8aVKpAZHII6JBQyM1EdO4FZ44Z6cpbdOqM6eRZNbJzO9fTAm2gSCtZBNq1SnsyXIWS+CoPMTJIPn8KiWQMdGVmV29bNDD46ZnW9yQgORR2a/0DndkgMxewtKWpvibFSQZtKRTn1MP8Xgx4MCsa3UlEAnK3MqOhmB4ClqTGlHK2JSErNN+7bqOVVFVsb67cLFoDbITEUc8iTrwdvyNedl/hW1uZLkiQsTLQD9kyNhky1BmsPT+TEaOSkWNQZam7svUjFPG1FxdY1CfjjLAAvAx5hllUHF/MqQ/TzcGJfRhiM225OPw4u+0UnLZeyRXh8/jZuXqWJeRJKSnQCLhWLc2/vJUrn0VumdU2Cdp8DIDTgMaY2lli62GFiZU7ROuW59espADQZatKynhuvfi25vGEv6vRMAFKi9W2zRdum/L1rPwA3rt3GxtYaZwN105njF7I/3wwIwi2rbipdzpOLZ7WD4aePnlOkuDuOzobbyP+6fRS8f8Qg4MPEXJKkwFx/PYAywAtZlg21WJWAQFmWs3szWZ8Dgcq55JYDkyVJUv4vN+Xm7kLoq5yZ1bCQ8OyKJS+r1i3i2r1TlC7ryfdbcirSNu2ac+LSHrb9up4pY+cajGvpbk9SaEz29+SwGCzd9TsMb8OqqBNOVUoQEZC/q9PFzYnwkJzGOCI0Amd3/YHNa4yMlPh1a8OFkzmVXtWaVfj12DbWbl9FqXKeOvI2rg7EhURnf48Li8E2z6DE0t6a1IQUNGrtCsX40BhsXfMfuACE3n9JqToVsbCzwtjMBKeW3pgV0VbOZm4OpObSmRYSjalb/uXn0duH6BOBORcUEnWOr6Bx0BZiTt8k4br+cpvXGLk5khEalf09IywaYzf9RsKmdT3KHttIie/m8WrqV2/M25twcnMiMiSnkxgZGoWTAX358de2PRQvU5ydV39hy9Fv2DBvY/YgNTcu7s6E5bKL8NAIXN7Q0TYyUtKuWxsunLycfa1azSrsPL6Ndb+solR5z3zj5sbJzYkInfxF4uSWvz0qjZS06dqKKyf/yVfG3tWRmFz2EBMWg32eMrOytyYlITnbBmNCo7HLY4MmZiZUaerFtYNa23cu7kpidAKDV41m3v7P+WzFKEzNTQ3fg1vee4jG/i02XhCUzk6ow3N+J3VkJEpn3fJSODlh1rQxKX/t+b/pcnEiMyznt8kMj0Lpov/bWPg0pMifW3FZu5jo+av0wi3bNCP54Mk36opITMXNxjz7u6u1ORGJhjvyqoxMLjwJp2WFInphr+KSuRceT1WPd68//wsiElNxs86VLxtzIhJVBmVVGZlceKybL7VG5tMtx2m+Zj/1SrlSunhR5OT47PAEA3Wnras9cSE57Ul8WAw2bvbYuNoTn8smc8et2LIGCeGxhN19oZNW6N0XVGpVC2sPR9ISUnCtUhJrD0eSQmOwdtUtYys3exJDc9JPDIvBys0e2+LOpMQk4vvFMPodWEzrFUMxznpu7D3dKFqnPH3+nk+PXbNwq1ZKr1xc3ZwJDcmZ4AgLicD1DZMPRkZKOnT34+wJ7QD/ftBDWrfzAaCqdyU8irrl247/1+3j+0ZTiH8fKmIQ8GGSdznQzrfISxg+7UrnuizLT4ErQO//6a4kSe9SfpPE/mPmULtScx49eEL7zr7Z1w/vP0Hzeh0Y2nc8/jPyWwOur+ddvWlGFqa03Dyei/N/JiPJcCOjVfVuuqYvn0zApRsEXL4JwL1b92lXuxs9Ww7k162/s/r7pW9NXq/QCiKTh4jHIZzctIcRP89i2A8zSAx6jvx6RtugUsPYN6yMR+/mPFq0PeeiRuZKi2mc9xqJbY0yWFYoln8CBm1C/94TjlziYcuRPB++BNdJfQt8fwVQZ1BfftRqWpPHdx7To1ZvhvuOYsyi0VhYWRjQU7B8vWbGcn+uX7pBwOUbANy7eR+/Wl3p0WIgv27dzZrvlxXo/t41f5OWjufG5ZvcvHIrXxmDj1OeNA3lN68NVm9Zi0dX72cvBVIqlZSoUopTPx9hQbsppKWk0WFUF8O38D/YeMF4++9kO2E0CRu+Ac3/sSkuQBkBpJw8z6vOQ4iYOB+7UQN1A42MsGhan+Sjp9+oKr/K3BBnHobhVdQRW3PdlZ4p6Zn4/3GFKS2rYmVq/EZ9hYUh729+1ZWhfCkVErs+a8HhcW25HRJDmAHPk97zkl+7lc8zbmxmgs+YThxdrb8X6tquU8SHRdN0Rk/cvcsQcu1htpdWv1o3bC8KIyWuVUoS+NNxfvKbTYYqjTqjtMtnFEYKzGwt2d5xPqeX7KD9BgNt5DvWTXNXTOfqxQCuXQ4EYPPXP2Bja8OfJ7bTd2gP7t56QKY6n/1E/3H7KHj/iI3B/+/wCCguSZK1LMt5108EAd6SJClkWdYASJKkAKoDeXfuLgV+B84YUiJJ0jBgGIC9hQejRo2iV/+uANwMuI17EbdsWTcPV8LD8ndpazQa9v55mBFjB/LbL3/phF25eI3inkWxd7AjNiZOJyw5NAYr95zZHEs3B5LDYvPVo5cHIyWtNo/n8Z8XeHbwql74pwO7ZK/dDrpxV8d16eLuQmRYlF4cgGGTBmHvaIf/lFk595qU0widP3GJGcsnM2B0b3w7tcJKYczLG4+x88iZdbVzcyA+XDcvyTGJmNlYoFAq0Kg12Lo7EB/x9vxe3nWSy7u0M4rjJvYgNWu2KzU0GrNcOk09HEkzUH5WlYpTYfUwAnstJzM2SS88MyGF2PN3cPSpTvI9w2u9M0OjMM41M2Ts5khmeIxBWdAu/zEp4Y7S3gZ1bMGWYXQY0B6/Xm0BeHDjAc4eOTPyzu5ORL9BX158P23Njg27AAh5FkLYyzCKlSnGy38i+HRQF7r00W6WDgq8i1suu3B9k11M1trF4ikzs6/ltotzxy8yY/lk7BxsiYuJ14vfeUBHPumjXdN/L/A+Ljr5cyY6PFovDsDAif2wc7Rj9tB5+aZpKil5euMxDrnswcHNgbg8ZZYYk4CFjWW2DTq4OxKXxwbrtm/I5T3nsr/HhEUTGxbNk0DtxuXLBy7oDAJa9W+LT89WADy5+SjPPTgSWwAbfxvqyEiUrjm/k9LZGU2UbnkZVyiP/UKt11Fha4tpg7qgVpN6Jv81+QZ1hUdi5Jbz2xi5OqGONPzbAKRdv4VRMXcUdjZo4rS2bt6oNun3HqHJU+flxdXajLCEnMmL8EQVztZmBmUP3QnOXjLzmgy1hsm7L+NXuSgtDHgI3heu1uaE5Zr5D09Q4WxlblD2UNBLfCsbnoCwMTOhVnFnrt9/QtFaTXOuuzuQkMeu4sNisPNw4HnWd1s3BxLDYzEyMcI2l02+jutQwhX7os6MP6jdimfj5sDYfUtY32kOSZHx7F/0M8/3XqHBxC6Y2lgQ9yyM0q1rkpRHb2JYDNa5lllauzmQFB6HLMskhsYQFqj1UD84cIW6I7XtUWJoLA+z2qywG0+QZRl7RzvadmxF96w1/bcC7uDu4ZqdrpuHCxFhhpfQjfYfioOTHWMH5nS+k5OSmTl+Yfb341f/Jvh5SPb3/7p9zK8efB+INwYLT8D/M8iynIJ28+/Xrzf3SpLkLklSX1mWHwEBQO6durOB61lhudO5B9wBPslHz2ZZlmvJslzLytSBH7f+Stum3WnbtDuH95+ga09tJ8m7VjUSE5KICNevEEp45lTcLX2b8ujhU73rVapVxMTYWG8AABB54wk2nm5YF3NGYaykdMd6vDh6/a1l9Jqmq4YS+yiEW1sOGgzfte2P7I1Kpw6e5ZPuWk9F1RqVSUpMIipCv2Hv1PsT6jerw8yR83VmXXKvpazsVRFJoeCH9b/Qq9UgvvCbzq0jV6nVpYk2/95lSE1MITFSP8+PLt6hmp92LX3trk24fUR/8JIXK0cbAOw8HHH2q0P4n9pOTWLAYyxKuWFW3BnJWIlrpwZEHdZNz7SII1W/m8yd0etRPclZk2vsaI2RjXZmXGFmjEOTKiQ/CiE/Um4+xLSkB8ZFXZGMjbBt34SEY1d0ZExKuGd/NqtcGsnYqMADAIA9P+xlhO8oRviO4vzhC7Tq2hKAit4VSE5MISai4IOAiJBIajT0AsDOyY5ipYsS+lyb/13f/5G9kffkoTN88unb7aJz7/Y0aFaXGSPn5W8X3hWRJCnfhu/PH/5mSOvhDGk9nLOHz9OmW2sAKtWoSHJCMtEG8teulx91mtVmwejFBmcBX6c5328KAUeu0KBLMwBKeZclJTGFeAM2eO9iELX86gPQoGszAo7kLDEyt7agXN1KBBzNuZYQGUdMSDRupbQbIqs0rMarh8HZ4Ud/PMhMv0nM9JvE1SOXadxVuwShjHc5VIkpeoOM/4WMu/cwKloEpbsbGBlh3rI5qecu6MhEdOtNRNdeRHTtRerJ08Sv+vKdBwAAaUH3MSpeBCMPrS7LNs1IOX1RR8aoWM7mUJMKZZCMjbMHAABWvj4kH3rzUiCAyh72vIhN4lVcMhlqDYfvBNO0rLueXGJqBtdeROGTK0yWZRbsv46nkzX96r5hU/97oLKHPS9i8uSr3BvylSssJjmNhNR0AFIz1Fx+FoFlUjiStROSlT1KYyXV29fn7tFrOmndPXoN7y7a062KeZchNVFFYmQcwTce41TSDfuizjpxw++/ZEmtkaxsNJ6VjcaTEBbD2k9mkRQZj7GZCcbmpoTdeIJzxeJIRkpin4VToX09Hudpox4fvU7lro0AcPcuTVpiCskRcaRExpMYGoN9KW3eSjSsTPTDVwA8OnKV4g0qAdqlQQpjI2Kj4/jlu9/o3LwPnZv34fjBU3T8tB0A1WtWITEhiUgDdVO3Ph1p5FOfycNn69QR1jZWGGcdhtC9byf+uRRAclLOPr7/un38UAYAAi3CE/BhYi5JUmCu74dkWZ6OtmO/GLgjSVIqkAy8Xlg/BFgrSdIjtJ7ji1nXDLEE7aDhnThx9Cw+rZpw9toBVKpU/MfkjDm27dzAtPHziAiPYs2GJVhZWyFJcOf2A2b5a/cr+7VvRdee7cnIyCQ1NY3RQ6YY1COrNVyY8wNtt09FUii4v/M0sQ9eUbGv9mSGuz+fwNzZlk4HFmFiZY6s0VBlqC+/+0zDoWIxynZrTPTdF3Q5vASAf1bs4uWJGwZ1nTt+kUYt6vP3xZ2kqlKZPzFnxuTrnz9n4eTlRIVHM3OFP6HB4Wzb+422LLKOOmv5STO6DeiMOlNNWmoaM0bozsrePRlARR8vZp7+igxVGjum5Byx99n309g5bTMJEbHsW/4L/deOw29yD4KDnmXP8Fs72zJxz1LMrMyRZZkmg9uyopU/aUkqBm6chIW9FZpMNfdnfJe90VdWa7g/4zu8f50JSgWhO06RfD+YIv21nedXPx7Dc3I3jO2tKL9CayJyppp/2szE1NWeSl+PAqUCSaEg4u+LRL9pAKbWEDJvE54/LtAeEfrbMdIevsCht7bhiPnlEDa+DbDv0hw5MxM5NZ0XY1dmRy/2lT+W9apiZG9DhQvfE/7lL8TuOpqvussnrlCneW1+PPc9aao0Pp/8RXbYkh8WsXrqGqLDY+g0qCM9RnbHwdmBzUc3ceXEFVZP/ZKfv9rOlNX+bDm6CSSJLUu3Zh8fqmMXx7R2sefSLq1dTMixi7XbV7Fw0nIiw6OYuVJrFz/s25xtF5tXf0/L9j50H9AZdWYmqanpenaRH5eOX6Z+87rsOP8TaapUlk36PDts5Y9LWTHlC6LDo5m8fALhweFs3LMWgDMHzvHDlz8ZTPPmyetU86nB8tPrSFel8d2UDdlhE76fybZpG4mLiOX35T8xfO1EOk/uyYugZ5zddTxbrkabOgSdvUm6Kk0n7e3ztzLsy/EojY0IfxHGN/5rDd5D4IlrePnUZM2ZjaSp0nTkpm6bzeap64mLiKXNwHZ8MqITds72LD/8JYEnr7Fl2gaDaQKg1hC/+msc16wEpYKUfQfJfPoMi07amcyUv/SPbcyN3YLZmHp7obCzxfWvXSR+u42UffonsrzWFbN8Ha4bl2mPCP37MBmPn2PdTTufkvj7PixaNMaqfUvIVKNJTSNyas6pOpKZKWb1ahK1+Ms33hOAkULB9NbVGfnreTQa6Fi9BGWcbfjtunZCpXsN7drqEw9CqO/pgrlJTlMeGBzNvtsvKetsw6ffngBgbLNKNC7jpq+oAEyZt5x/Am4SF5dAi059GTWkH13bt/mf0jJSKJjexouRO86j0cg5+bqmPS62e03tGvgT90OoX8pVJ19RSanM2XsVjSyjkaF1xSI0Lu1KxpU9mLQczMRjaq7uOkXEw1fU6dMCgCvbj3P/ZCDlfbzwP72GDFUav0/R1uEatYY9c7cx+MfpSEpFdtw3Yelkw+AfpqPQyCRHxmHhYM3gEyu5tfM00Q9eUT2rjbrx8wmenAjE06c6Q89+QYYqnUP+m7PTOT73B9p9PRKlsRFxLyKyw27tPI3v58MYeHQZ6nQ1Byd9o3cPp4+dp0nLhhy58iepKak6s/rf/PIlcyYuJiI8ivmfTyckOIxfD3wHwNH9J9nwxbeULufJ8nXz0ag1PHrwlNkTFunpeM1/3T4K3j/Su6ynFXxcFHeoWmjGMceiemGpYqP6WaHoaWZa9O1C/xLtVIX3HLtYGD4B5r9gYmZ6oeiJztBfDvVfYa00vKzjv6CMceFtCE2V81lX/B+wqkhcoehJTym8eTLXiTXfLvQvYdTO8DGx/wWZB7YUip6Fc5+/XehfwlFTeIsovk01/N6N/wILpeGN/f8F10PPFXzz2n/EyJKfFlrDufHZrveeX0OI5UACgUAgEAgEAsFHhlgOJBAIBAKBQCD4qBAbg4UnQCAQCAQCgUAg+OgQngCBQCAQCAQCwUfFh/wSr8JCeAIEAoFAIBAIBIKPDOEJEAgEAoFAIBB8VBh6g/XHhvAECAQCgUAgEAgEHxnCEyAQCAQCgUAg+KgQewKEJ0AgEAgEAoFAIPjoEJ4AQb4YKQrPPIpkZBaaLqWycMa+FoU4xjZXpBWaLge35ELTZR9iXSh61EaFNydkoyi8t3IW5iyPq1R4+TJzLpzfK+1ZoagBwMh3UKHpKqy3+AIY+X1WKHqC5/gXih6AcmqLQtMVnBRVaLpqOZQuNF0fAmJPgPAECAQCgUAgEAgEHx3CEyAQCAQCgUAg+KgQewKEJ0AgEAgEAoFAIPjoEJ4AgUAgEAgEAsFHhUYWewKEJ0AgEAgEAoFAIPjIEIMAgUAgEAgEAoHgI0MsBxIIBAKBQCAQfFSIxUDCEyAQCAQCgUAgEHx0CE/AB4gkSbOA3oAa7SlWw4EVgDuQCqQDn8myHGggrhq4BUhZ8cfIsnxBkqSSwD5Zlqv8X+5t7tKpNGvZkFRVKlPGziPo5j09meVfzqOqVyUkCbMIT2UAANWuSURBVJ4+fsGUsXNJSVZRqkxJVq5dQOVqFfhi6Tq+Xf9TvnqcfKpTafEAJKWCl9tP8GTtHp1wj64NKTWmAwDq5DRuT/2WxDsvAKj65XBcWtUgPSqBs02nvDVPkxeNo2HzeqSq0lgwcRn3bz3Qk1m0bg4Vq5cnMyOToMC7LJ26CnWmOju8UvUKfLdvIzNHzOfE/tPZ18s2rYbf3P4olAqu7TzJmY179dJuN68/5Xy8yFCls9t/E6FBzwDovHIY5Zt7kxydwNo207Ll3SqVoOOSwRiZGqPJ1LBnzvdw5Y5OmnY+XnguHAxKBRG/HOfVuj91y7dLY4qM7pxVfiqeTN9Myp3nAChtLCjzxSjMKxQHWebRxPUkXdMvE0OY1quN7YQxSEoFyXsOkPTTDoNyxhXL47xlHTFzFpF68kyB0n7NoPmfUcOnJmmqNNb7f8XT20/0ZHwH+NFucAfcSroz2KsvibGJADTq1JROI7oAkJqSypZZG3l+95lBPWMXjqJu8zqkqtJYMfFzHt5+pCfTaWBHug3tTJGSRehYtSsJsQkAWNlaMfWLyXiU8CA9LZ2Vk7/g2X3DegA+WzCMmj61SFOl8dXkL3ly+7GejN+AT+gwpAPuJT3oW703iVm6XlOm2v/H3lnHR3F9D/uZ3bi7Edw9QULwBAnuULxocSnuxd2LW1toKRWguBYtbsGCOyHubrvz/rHLJpvdDaHfNuX3Mg+ffEhmzr3nypk7V869U5ol+5exbPgSLh25qHWv+8z+VPb3JiM1g+/Gr+VN0Eud+J08XRi0dgyWtla8CXrB1jFrUKg/4lfWtyJdv+mL3MiIpNgElnadCUDTAa2p17UxiCLhj9+SEpdM2QZVyExNZ9f4DQQH6ebZwdOZL9eOxsLWkuCgV+wcsxZFpoJq7erSeIjqmU5PSWf39K2EPFQ9092WDKZCo2okRSewpJn2M23s7YPFVyNBJiP95GHS9vyst4zlpcphs2Q9Sctmk3npHLJChbEaPzP7vpsHKT9/R/rB3XrDA5jXqYHDpGEIMhmJfxwl/rtfte5b+NXGfnhfRKUICgXRS9eTHhiEcVFPnJdMz06zpxux67eTsPOP3Co0XLh6k0XfbkWhVNCpVQADe3XWuh+fmMSMRd/y9l0opiYmzJ08itIligKw47f97Dl0AkEQKF2iKPMmj8bU1MSgrovPw1hy4i5KUaSDVzH61ymrdf+Hy084cv8tAApR5GVUAmfGtMbMWE7/HefJVCjJUippUq4QwxpWMKjnQ0xfsILzF6/hYG/Hvp82/u143tN71gCq+lcjPTWdzePX8lpPW+Fc2IXha8ZiaWfFq/sv2ThmNYrMLCxsLPlq6QhcirqSmZ7J1gnrCH7yRq+eQn5VqDWnN4JMxpNdZ7m3Trudty3pTr2Vg3CsVIxbi3/n/qYjAMhNjWmxZzpyUyMEuZxXh69xe/nej8rj0mUzCWjmR2pKGoMHj+fO7SCDssuWz6JX7864ufy9bsDIOcPxVbeJi8Ys0dsmTlszhbJVyqDIzOLh7ccsn7xS6135KaGU1gKklYBPDUEQagOtgWqiKFYBmgBv1bd7iqJYFVgPLDUQRaooil5quSnAwn8qbX5N6lGsRBEa+bRj6th5zF06Va/cvOnLaOXXlZYNuxLyLowvB3QDID4unjlTF7N13Y68FckEKi7qz/UeizhffxweHepiVaaQlkjK60iutJ/DBf9JPFuxl8rLB2nuBf9yjuvd8pftOo18KVLck451e7Bg4lImLxyrV+7o3pN0rt+Lbo36YmpmSvserbOTK5MxYtoQrpy9rhVGkAm0mdOPHX2X8G3TCVRuWwfnUtr5KOPnhWNxN1b6jWXf1K20nd9fcy9w93m291msk5bmk7tzevVe1rWcyqkVu2k+pbu2gExGiQVf8aDnfG43/Bqn9vUwL+OpJZL+JoL7HWdwp/FYglftpuTSIZp7xef2J/ZMILfrj+JO43GkPg3OuxBz6LUbN5rosZMJ794Pi6aNMCpWVK+c7bBBpF+9kb94c+DtXx334u6MbDiETVPW8dW8oXrlHt14yJye3xDxNlzresTbcGZ+MZXxzUez+9tfGbxwuN7wtRr5UKh4IXrV68vySasYs3CUXrn71+8zrtskwt6GaV3vObI7z4KeM7DpYBaOXsLI2cMM5qm6fw3ci3kwpMEg1k1ey9D5+mUf3njANz2mE54rT6CywT5T+hJ4LlDnXmU/b1yKuzPVbyQ7pm6k1/xBOjIAnSb34uS2Q0zzH0lyfDL1uzYCwNzGgp5zB7J24GJmBoxh47DlANi5OtCobwvmtZnEzGZjsXVzoIRPORb4fc1vU7fQef5AvXraTO7BuW2HWeA/htT4JGqp9cS8jWRt1zksbTGJE2v28sXC7HRe232OzX30PNMyGRaDvyZx9kTiR/TBpH5jZIX125xFn8FkBmY/o8p3b0kYM1D1M24QYnoamVf+0pvm93E4Th1J+LCpBHcYiGVzf4xLFNESSb0ayLsugwnpOoTImctwmqlqTzJfBxPSdYjqp/swlGnppJy+qE8LAAqFgnkrN7Fh6UwO7FjHkVPnef5KuwO65cffKVeqOH/8sIYF08aw6FvVV4DDI6PZufsgv25Zwb7ta1EqlRw9bThfCqXIwmN3WNetLnsHN+VYUDDPI7UHmH1rl+G3rxrz21eNGeVXkepFnLE1N8FELmNLr/r89lVjfh3YmEsvwrn7LsZwGX6A9i2bsnHFvL8dPidV/avhWtyd8Q2H892UjfSbp9/uu07uzbFtB5ngN4Lk+CT8ujYGoO2ITrx58JJpzceyaey39JrVX294QSbgO78PJ3ot4Q//iZRo74ttaQ8tmfS4ZK7O+FHT+X+PIj2TY18sYH/TaewPmIanXxWcq+X/q70BzfwoWaoYVSv7M3LEFFatNlx23tUqY2trk++4c1OrkQ+exQvRs14flk9ayZiFo/XK/fnHKb5s2I9+Tb7C1MyEVt1b/m2dEv8+0iDg08MdiBJFMR1AFMUoURRDcslcBgrphNTFBoj9pxLWpEVD/vjtEAC3b97DxtYaZ1cnHbmkpGTN72ZmpojqY7iio2K5G/iArKysPPXYVStFysswUl9HIGYqCN13CdfmNbRk4m48IStepSf25lPM3B0092KvPCIzLpn80LBZPQ7vPg7A/VsPsLa1wtHFUUfu0ukrmt+DAh/i4u6s+btr/06cOXKO2Cjtovb0KkX063Bi30agyFRw7+BlygdU15IpH1Cd23tVL+jgwGeYWVtg5WwHwKtrj0iNT9JJiwiYWpkDYGZjTkK4tl4r71Kkvgoj/U04YmYWUfsv4NCsppZM4o3HKNTll3jzCSbuqjzLrcyx8a1AxM+nVLoys1AkpOgpOV1MKpQjK/gdipBQyMoi5c/TmDWooyNn2aUDqWfPo4j9eNOs2dSHc3vOAPA08AmWNpbYudjryL0KeklkcITO9Sc3H5GcoMr301uPcXTXrWuAugG1ObH7TwAe3nqIpY0VDi4OOnLPgp4THqzbKS9Wuii3Lqg65G+fv8XV0xV7Jzu9unwCanFmz2lV+gIfY2ljib2ePL0MekGEnjwBtOrXmstHLxEfHadzzyugJpf3ngXgReBTLKwtsHXWTUu5OpW4eeQyAJf2nMUrwAeAWm3rc+vYVWJCogBIjM7uIMrlckzMTJDJZdgXcuLBqVsAvA58hrm1BTZ69JSqU5E7R64CcG3PeSoHqJ7tV7eekKqum9e3nmLrll3eL649Ijle95k2Kl0eZdg7lOEqm8v46zQmPvV05ExbdSTj8jnEeP02Z1SlGoqwEJSRunWpiaNSWTLfhpD1Lgyyskg+dhYLP237FlPTNL/LzM30Oh2b1/Im620oWaH66xLg3sOnFCnkTmEPN4yNjWnRuD6nL1zVknn+6i2+1asCUKKoJ+/CIoiKUeUvS6EkPT2DrCwFqWnpODvq2u577ofEUNjBEk97S4zlMppV8OTsk1CD8kcfvKV5RdWkgiAIWJioHAqylEqyFEoEgyE/TA2vytjaWP8PMWRTrakPF/acBeB54BMsbCyx1fNcVahTmWtqu7+w5wzV1HZfqHRhgi7eBSD0+TucPF2wcbLVCe/kXZLEV+EkvYlEmangxf4rFGmm3c6nRScQdecFykzdGfGslHQAZEZyZMZGfMypla1bN2XXTtXKwfXrt7G1tcHVzVlHTiaTMX/+FKZP//tzgnUD6nB890kAHtx6iJWBNvHq6Wua3x/efoyzu24f4VNBLMB/nyrSIODT4wRQWBCEJ4IgrBcEoaEemebAPgPhzQVBuC0IwiNgKzD3n0qYm7sLoe+yZzzDQsJxc3fRK7vk21lce/AnJUoXY/vWXz5Kj5mbA2kh0Zq/U0NiMHUz/BIr3MOfyNO3P0rHe5zdnAgPyX4ZR4RE4uJmuNGSG8lp2bkZl89c04T3a1GfPTv268jauNoTnyMfCaEx2Lhq58Pa1Z74kOyZs4SwGGzcdF9UOTkyewfNp/RgwqU1NJ/ak5NLtF0STN0cyHgXpfk7IzQGEzf9nV0A1+6NiTut6rCaFnUlMzqBUqtGUOXEUkouG4rM3DTP9LxH5uyEIiK7LBURUcidnXVkzBvWI/kPXbeo/ODg5kh0SHbeosOicHA1nLe8aNStKYFnb+m95+TmREQOu4gKjcIpD7vIzfMHL2jQQtUZLedVFjdPV5zddV/OAI5ujkSFZucpKiwaxzzqKzcOro74NqvNsZ+O6r1v5+pITA47jA2LwS5X/Fb21qQmJKNUqL6hGRsajb3aVl1LuGNha8WEX2Yz4+BiandUNUlx4TEc33KAxZc2sPzaFmRyOQ/OZK9ExIXFaHXkASztrUlNSNHoiQ+NwdZV99mu1dWfR2dvfzDvgqMTiqjselJGRyJz1K4nwcEJE9/6pB87kDu4BtP6jck4fypPXXIXJxRhkZq/FRFRGOmZBLFoVJdC+7bhunYeUTOX6dy3bO5H0rEzeeqKiIrGzSU7bldnJyIio7VkypYqxp/nVZ3Xew+eEBoeQXhkNK7OjvTt1p4mXQbg36EP1paW1PXxNqwrMQ03a/NsXTbmRCSm6pVNzczi0vNwmpTLnoNSKEW+2HKKRisP41vClcqFDLfVBYm9m4Nm4AoQExaNQy5bs7K3JiWH3ceERuOgfjbePHhFjRa+AJSoWgqnQs6aezmxcLMnOUcbnhIag+UH2vCcCDKBtifm0/3uekLO3yMqUNcV0BDuHq4EB2cP2ELeheLh4aYjN2TIlxw+/CfhOez3Y3F2cyIyJDt8ZGgkzh94VwZ0asK1XCvkEp8W0iDgE0MUxSSgOjAIiAR+FQShr/r2TkEQgoFJwBoDUbx3ByqHarCwQxCE/2VyRoO+aEQD0xYTR83Ct1IAz5+8pHX7gI9UpO+ifj0OdStQuIc/j+bq9wP+oKqPyBPA5IVjCbxyh9vXVDNEY2ePZM38jSiVej5Ano+49VbNByYNfHo14cjcH1laZyRH5v5Ih8W5lrn1xqk/Ups6lXDp0ZjX81X7MwQjOVaVSxC2/Th3AyagSE2n0MgOeSfoI/TafT2c+HWbQV955UtF/vOWFxVrV6ZR1yb8tHD7v6Ln53W/YGVrzZbjG+nQrz1P7z8z6Bcr6DH4vGwwNwNnfcX2hT/ot0H0V4tOXvKwVblcTtHKJVjdbwErv5xH65GdcS3ujoWNJV5NazK5/nDG1xqETC6jbP0qeedDb7Fqy5SqXQHfrv4cXJSfZ/rDz4/lwJGkbN9k2OaMjDD2qUPGxbMfUJW/eko5fZF37QcQ8fUs7If31dFl0bA2ySfO6YT7ULy5bXJgz84kJCbRqf9odu49RLnSJZDL5cQnJnHmwlWO/7qF03/8QGpaGgdPGB506JulNPTGOP80DC9PR2zNs/cXyGUCv33VmOOjWnA/JIZnEfF55q2gyE/bnpfMwQ17sbSxYt6R5TTt25LXQS81g4UPx5H/dIpKkQMB0/itxiicvEtiV9bzw4Hykf73uLm70L5jSzZu0N/WfYSyD+rKyZgFo7l79S73rt3/3/T+iygL8OdTRdoY/AkiiqICOAucFQThHtBHfasncAdYBKwDOgqC8D3gDYSIotgyVzyXBUFwAvRPQepBEIRBqAYgOFp6MnzYMLr2Vm2kvHs7CPdC2bMMbh6uec4sKJVKDu07waARX7J7l+FZuNykhcZg5pE942Lu4UB6mO4yvnWFIlReMZgb3ReRGavrNmOILn070L6nyqf/we1HuHpkr2a4eDgTGR6tN9zAsX2xc7RjwcTsDX7lq5Zj/gbV5kI7B1vqNPZFoVBw7tgFEsJisM2RDxt3BxIjtPOhksmenbJxc9Bx78mNd6cGHJ6t2ldx//BV2i/6ipxODOmh0ZgUyp6hMXF3ICNc10/XonxRSi0fyoOe88hSl19GSDTpodEkBT4FIPrQZTxH5G8QoIyIRO6SXZZyFycUUVFaMsblyuAwdwYAMltbzGrXIk6hIO28Yf/oZl+2pEm3pgA8u/sMR4/svDm6ORET8XE+yEXKFWXI4uEs6DOHpLhEzfX2fdrSqofqEXp05zEuHi6AapOdk7sTUQbsQh8pSSksGZc9C7zr8o+E5tg30PLLVjTt3kydp6c45Vgyd3JzJEZPfRmiVOVSjF87EQAbBxuq+9fAq74Xpb3KYiQIvLrzHIccdmjv5kBcrviTYhIwt7FEJpehVCixd3ckTm2rsWHRJMUmkpGaTkZqOk+uPcCzfDEAjE1NGPuTqj4jnr+jaLXSmjjt9Nhyckwi5jYWGj227g4k5Hgm3MsVoeuiwWzuu4iUuA8/02J0JHKnbJuTOTqjjNG2OXmpsliN/0Z138YW4+q+JCsUZF69oMpDtVoonj816Cr0HkV4JPIcrhZyFycUEYZtIu3WPYwKuyOzs0EZp3KhsqhXk4xHz1DGxOWpy9XZibCI7HyER0bh7JRrFtvSgnlTVH7ZoijSrOtXeLq7cvHaLQq5u+Jgp3JdadygNrfvP6JNgL9+XdbmhOWY+Q9PSMXZylyv7LGgtzSvWFjvPRszE2oUcebii3BKuei6zRQETb5sjp+6rXhx9xkOOdoKBzdHYnO1v4kxCVjksHsHd0di1c9GWlIqWyas1ciuuLBRZ48RQHJoDJY52nALdwdSPtCG6yMjIYWwSw/x9KtC3GPD+7AGDe5N336qfXY3b97F09Ndc8+jkDuhodpprFq1IiVLFuPu/bOq9FmYc+feGapW1m8POWnfpy2tNW3iE5w9su3f2d3ZYJvYZ0xv7BxsmTFp5Qd1SPy3SCsBnxiCIJQVBKF0jktewOv3f4iimAlMB3wFQSgvimI/9cy/zu4bQRDKAXIg370XURQ3i6JYQxTFGjZmTvz43W+09u9Ga/9unDxyhg5fqDrPXtUrk5iQRGR4lE4cRYtnvyQaN2vA86ev8qsegPjA51iWcMO8iDOCsRz39nUIP35TS8askCPVvhvLneHrSH5h2H9VH7//8Ac9mw6gZ9MBnD32F606qzpjlapVICkhmWg9L/Z2PVpR28+H6cNma81+tPftSrtaqp/Th86xeMoKzh1TdS7e3XmOYzE37D2dkRvLqdymNo9Oaufj4cmbeHWsD4CndynSE1NJiozLM/0JEbEU9y0PQIk6FYl+pd3oJ91+hnlxd0wLuyAYG+HUrh4xx7U34ZoUcqLstgk8HfktaTnKLzMyjoyQKMxKqja22dWrTMqT/G0Mznj4CKPChZC7u6lmPJs0Iu2vy1oy4Z16Et6xB+Ede5B65hxxy1bnOQAAOL7jCBNajmFCyzFcP3GFhp1UL6/S3mVISUzWdFbzg5OHExM2TWHNmFWEvtTearNv+wG+ajaEr5oN4eKxiwR0bgJA+WrlSU5M/qjBhqWNJUbGqjmWVj1acPfqPVKSsvdWHNlxmDEtRjGmxSiuHL+MfyfV5tgy3mVJTkzR6azkxaB6AxlUdwCD6g7g0pGLbJq+gfVT1jGmxSjmtJxA4Ilr1O7oB0AJ79KkJqYQr8fGHl8OonrL2gDU6eTH7ROqZfzbJ65TumZ5ZHIZJmYmlPAqTeizYGJCojC3tmBRx2nMaTkBpUKJndr9p6h3KVITU0jQo+fZ5QdUbVkLAJ9ODbh/QmWbdh6O9Ns4lp1j1hH5Mn/PdNbTR8jcPZG5qGzOpH4jMq9p21P8oG6an4xL50jetFIzAAAwadCY9L/ydgUCSA96jHGRQhgVUumybO5Hyjlt+zYqnL0h1KRcKQRjY80AAMCyhT9JR/N2BQKoVK40b4JDCA4JIzMzk6On/sK/bi0tmYTEJDIzMwHYc+gE1atWxMrSAndXZ+4+eExqWjqiKHL15h1KFNXfcQeo6GHPm5gk3sUlk6lQcvxBMA3LuOvIJaZlcvNNFP457sUkp5OQlgFAWqaCq68iKO74z/j0/x3+3HGM6S3HMb3lOG6euEa9Tn4AlPQuQ0piCvF6nquHl+/jo7b7ep38uXVSZfcWNhbI1c+wX7cmPL72gLQkXTepqNsvsCnuhlVhZ2TGckq08+XtCf1uhrkxdbDGxMYCALmZMe71KxH3PPcWQG02b/qROr6tqOPbikMHT9C9p2qSrmZNLxISEnUm5o4fO0PJ4j5ULF+fiuXrk5KSmq8BAKjaxIHNhjCw2RAuHLtIs86qAVaFPNrEVt1bULNhDeaMmP9RK5r/BUrEAvv5VJFWAj49rIA1giDYAVnAM1Qz85pz60RRTBUEYTkwHhiQK7y5IAi31b8LQB9RFBXqZcOyanei94wRRfH3/CbszMkL+DWpx5nrB0hLTWPiqFmae9/tWsPkMXOIDI9i6do5WFtbgiDwKOgJM8YvAMDJxZH9f+7EytoSUSnSb3BPmtXppLWRGEBUKAma8j0+v0wFuYzgXWdIehxMkS9VnbI3O/6k9LhOmNhbUWmx6sQGMUvBxWbTAPDaOBKHOhUwcbDGP3AdT5fuJvhn/S/ei6euULdxbf64tIu01HTmjMneOLXqxyXMG7+YqPBoJi8aR1hwON8d3KAqiyPn2boy7+VVpULJoW9+oM+OyaojQn87S8TTd9TsqTp94vrOUzw5c5sy/l6MPbeSjNR09k7YpAn/xbcjKO5bHgt7ayZcXsPplXu4+dtZ9k/eSsuZXyIzkpGVnsn+KVvxyqlYoeTF1K1U2DUDQS4j/JfTpD55i+uXKres8B0nKDymC8b21pRY+JW6zBXcba46hvTFtG2UWTcawdiYtDfhPPt6LflCoSRu+RqcVi0GmZzkQ0fJevkKiw5tAEj5m/sAcnLr9E28/Wuw5vxGMlLTWTc+2ytuyg8z2DhxHbERMbTo25p2Qzpg52zPsuPfEnjmJhsnraXz6G5Y2Vvz1dzBqiQrlExuM05Hz5XT16jVqBY/XdhOelo6i8dmz+ov3DGfZRNWEB0eTcf+7ek29AscnB3YdnIzV89cY9mEFRQtVYQpqyehVCh49fQNS8cvN5inm6dvUMO/Bhv/2kJ6ajprxq/S3JvxwyzWTfqWmPAYWvdrQ4chnbB3tufbE2u4efoGaycZ8grM5t6ZW1T2r8aCc2vJSE3n+wnrNfdGfz+VHyZtID4ilt2LfmTwmjF0GNeNN0GvuPCbqmMc+vwd988FMuvYckSlyF+/niLkierAsptHLzPj8FKUWQpCg14T+SqMaedWk5Gazi8Tso94/Or7Sfw6aTMJEbEcWvQzvdeMosW4rrwLesWV31TPZrNRnbC0t6LzPNUzrcxSsKKt6pnu/e1ISvlWwNLempmX12G0eysZfx4BpYKUzauwnrVMdUToqSMo3r7CtLn6qNE89gEAYGKKcdUapKw3XD8aFEqiF67FbcNCkMlI3HeczOevse6imhhJ/P0Qlk3qY9WmCWKmAjE9nYiJ2ae1CGammPtWJ2ruqg+qMjKSM/XrwQwePwuFUkmHlk0oVbwIv+5X7fvo2q4FL14HM3X+SuRyGSWKFmbOZNUJVlUqlKWpX12+GPg1crmccqVL0KVNM8O6ZDImN/Ni6K6LKJUi7aoWpZSzDb/fVB2n2aV6CQBOPw6hdglXzE2yuw1RSWnMOHgDpSiiFCGgfCEalNYdQOSXCTMXcT3wLnFxCTRu34thA3rTKY+058Wd0zfx8q/GsvPryUhNZ8v47HZs/A/T2DpxPXERsfyy8EeGrx1L5/E9eB30knO/qg4E8CjlyeAVo1AqlLx7FszWCev06hEVSq5M307AzxMRZDKe/nqOuCfvKNtbNbB//ONpzJ1taXN0LsZW5ohKJRW+as4ffpOwcLWj/qrBCDIZgkzg5cGrBP95O995PH7sDM2a+XP3/llSU1IZMmSi5t6eP75j+LDJhOWxAf1juHL6KrUa+bDzwg51m5h9QOGiHfNZqm4Txy78mrDgcNbv/xaA80cvsGPVT/9IGiT+eYRPfaQm8d9Rwsm7wIxjrbzsh4X+IWbK855p+adoZpJ/387/lZbp6QWmq2ixf+zAqQ/ydUjBzCpGKfJ3AtI/gY0sfxut/wmcZWYFpsu6AOeUZlT9uNW/v0v8K8Nn6//TeJ7I52D7HyDr+A8Fpsuo5VcFoqd/9fEFogegUZZFgekaGZv3Suk/SQ2H/B9P+r9yNvjPf2Sv4v9C56JtC6yPs/v1gf88v/qQ3IEkJCQkJCQkJCQkPjMkdyAJCQkJCQkJCYnPik/51J6CQloJkJCQkJCQkJCQkPjMkFYCJCQkJCQkJCQkPiukPbHSSoCEhISEhISEhITEZ4c0CJCQkJCQkJCQkJD4zJDcgSQkJCQkJCQkJD4rPuWPeBUU0kqAhISEhISEhISExGeGtBIgISEhISEhISHxWSEdESp9MVgiD6q41S4w43AxtikoVcRlFczXYf1MC+6Lwa/E5ALTpSjANsNGVjBfbHXAuED0ANiL8gLTVSW94OrqTzNFgelyEgtm/qrgrAKeCwX31W9XCu5LyMFiWoHo+e7msgLRAzC9xrQC03VbEVdguhwK8Gvmu17v+8+/oNumSOsCayAPvjn0n+dXH9JKgISEhISEhISExGeFKO0JkPYESEhISEhISEhISHxuSCsBEhISEhISEhISnxXS6UDSSoCEhISEhISEhITEZ4e0EiAhISEhISEhIfFZIR2MI60ESEhISEhISEhISHx2SCsBEhISEhISEhISnxXSdwKklQAJCQkJCQkJCQmJzw5pJUBCQkJCQkJCQuKzQvpOgDQI+GQQBKEDsBcoL4riI/U1H2AJUAhIBEKByaIo3hMEYRbwFRCZIxo/URTj9MTdFFgEmAAZwARRFE//nXROmjeG+o3rkJaaxozRc3l474mOzMJ1s6hYtRxZWVncC3zI3AmLyMpS0HdYT1p2DADAyEhO8dLFaFixJQlxCXp1DZ89FJ9GPqSnprFk7HKe3X+mI9OuT1s6DuxAoWIedKzShYRYVVyW1hZMXj0Jl0IuyOVyft+8m+O/ndCrZ/zc0dRt7Etaajqzvl7AYz15mrtuBhWqqPIUFPiQ+ROXosjK/kJqharl+P7wRqYOnsWpw2e10zizD+X9vchIzeDX8Rt4F/RKJ34HT2d6rR2Fua0l74JesWvMOhSZCpxLetB16WA8Kxbn6LJfObflMAC27g50XzEMa2c7RKXIkZ+PcuT7g1px9p/1Fd7+NchITWft+FW8vP9CR2/zPq1o1b8t7sXc6efVk8TYRABqNq1Ft3E9USqVKBUKvp+9lUc3HuotP4ABswdR3b866anprBm3mhf3n+vItOjTijYD2uJezIMvq/YkUV1XPk1r0X18T0SliEKh4LvZW3l4/YEmXM+Z/anqX42M1Ay2jF/D66CXOnE7ebowbO0YLG2teR30gk1jvkWRmYW5tQWDV47GsZATcrmco1v289fvZwCwsLGg/6JhFCpbBLkIuyZupFqbOpT39yYzNZ1d4zcQbKCuvlw7GgtbS4KDXrFzzFoUmQqqtatL4yFtAUhPSWf39K2EPHyDkakxI36diZGpMXK5jMdHrhN86ynNZvZGJpcR+MtZLm44qKOn2awvKe1flczUDPaP30TY/VfITY3p+9sM5CZGyIzkPDxyjXMr9wDQZGp3yjSuhiIzi9jX4eyfsBkitb8k7eJfhcpzv0SQy3i98wxP12rrtSrlQbVVg7GtXIyHi37j2YbDmnslB7WgaE9/EEUSHr7l1tebUKZnaoXvPLMvFf29yUhN58fxGwjWU1eOns70WzsaC1sr3ga9ZIe6/N5TpEpJxv8xj+9GrOL20aua64JMYOLBhcSHxXB7+580V5ffrV/OckFP+bXIUX77xm8i9P4rbNwd6LByKFbOtohKkZs/n+bq98c1YXz6BuDzZVOUCiVPT9/m7MJdlGhYhYCZvRHkMm7/cpbLenQFzPqSkmpdh9R1lTPd/Q/NIzEslt/6a3/VttagljSZ1pMVXoMhTvXF4B4z+1PZ35uM1Ay2jV/LGwP2PmTtGCxtrXgd9IItY9agyMyi+aC2+LavD4BMLsejVCFGVxuAiYUpo7+bgkfJQgA8PX+PHQN1v7DbZuaXlFW3VbvHbyREbf9lGlah9TdfIpPLuP7rGc7lKoP6X7Wi5bSezPUeTEpsInJjOe0XDMSzcnEylEp+nL2NR1eCNPK9Zw2gqn810lPT2Tx+La/1tE3OhV0YvmYslnZWvLr/ko1jVqPIzMLCxpKvlo7ApagrmemZbJ2wjuAnb3TCf4jpC1Zw/uI1HOzt2PfTxo8OD9B2Zh/K+nuRmZrBb+M3aMorJ/aezvRYOwoLddv+q7ptNxTe1t2Brjna9r07D7Hvu/1acQ6dPQSfRjVJS01n+djlPNPT3rbt04YOA9vjUcyDLlW6at6NnQd3olEHfwDkRnIKlypMV69uJMYl6c1jn1kD8fKvTkZqOhvGf8srPXUV0KclLfq3wa2YO4O8emveIx4lCzF42UiKVyzJr8t+4vDm/TphJf5bJHegT4fuwAWgG4AgCK7Ab8BUURRLi6JYDVgIlMwRZqUoil45fuIMxB0FtBFFsTLQB/jx7ySwXuPaFC1RmNa1uzBn/CKmL56oV+7w3uO0rdeNjn69MDMzoWNPVcfoh/U7+aJJH75o0ofV8zdy83KgwQGAj39NChUvRJ/6/Vg5aTWjF4zUKxd0I4iJ3ScT9jZM63rbPm15/fQNg5sNZdwXExg8YxBGxrpj3rqNfClcwpMOdbozf8ISpiwap1fPsT0n6VS/J139+2BqZkr7Hm0092QyGSOnD+HK2Ws64cr5eeFc3I1FfmPYPXULneYP0Bt/q8k9OL/tCIv9x5Ian4xPV1UjnRqXxP5Z2zm75ZCWvDJLycF5P7G0yXjWdJhB8y9b4lm6sOa+t3913It7MLLhYDZOWcegeUP16n184yFzes4g4m241vV7F+8wrvkoJrT8mvUT1jB0sf7yB6jmXx2PYh4MazCYDZPXMXi+fl2PbjxkZg9dXXcv3mFMs1GMbTGateO/ZVgOXVX8quFW3J2JfiP4fuoG+swfpDfurpN7c3zbISb5jyA5PomGXRsD0Lh3c0KevWVGi3Es7PYN3ab1Qa62g54z+3PvXCBTGo9iaYuJ2Lk54FzcnQV+X/Pb1C10nj9Qr642k3twbtthFviPITU+iVpdGwEQ8zaStV3nsLTFJE6s2csXC1VpzUrPZH2PuSxrMYmlLSdTqmEV2iz5ip/7LGF9k4lUbFsbp9KFtHSU8q+KY3E31jYcx6Ep22g1rx8AivRMdnSfz+YWU9ncYiqlGlahkHcpAF78dZ8NAZPY1HwK0S/DqDesrXbCZQJVF/bjco8lnGowAc8OdbAuo603Iy6Ju9O3a3X+Aczc7CkxsBlnm03jtN8kBLkMz/a1tWQqqG19tt9odk3dQjcDtt5uck/ObDvCHP+vSY1Ppra6/EDVYW43uQcPz9/RCeffryXhz94B0HJuX3b2WcK6JhOp1LY2zrnKr7R/VRyKu/Ftw3EczFF+SoWSE/N2sq7xRLa2n4nPl001YYvVrkC5ptXZ0HwK65tO4tLmwwgygeZz+/JLnyVsMlBXJdW6NjQcx5Ep22iu1vWemv2bE/UsRCc/1u4OFK9XmfjgKM21yn7euBZ3Z4rfSLZP3ciXBuy9y+RenNh2iCn+I0mOT6a+ugyPbT7ArJYTmNVyAnuW7OTx1QckxychKpVY2VkzrfFoxvgMpHT9ypRr5K0VZ1k/LxyLu7HMbyx/TN1K+/n9NXXSdk4/vu+7hJVNJ1C1bR1cSmWXga27A6XqVyY2OHs+qmY3VXpWN5/M4l6z6TG9L4IgAFDVvxquxd0Z33A4303ZSL95hp/pY9sOMsFP9Uz7qZ/ptiM68ebBS6Y1H8umsd/Sa1Z/veE/RPuWTdm4Yt7fCguq8nIq7sZSvzHsnbqFDgbsveXkHlzYdoSl6ra9prptNxRemaXk0LyfWN5kPGs7zKBNn9YUKV1EE19N/5oUKu5Bv/oDWD3pW0YuGKFXb9CNB0zuPoWwXO3t7k17GNZ8BMOaj+C7RT9w78o9gwMAL//quBV3Z0zDoWyZsp4B84bolXty4yHze84k8m2E1vWkuCS2z9zKoS379Ib7r1EiFtjPp4o0CPgEEATBCqgLDEA9CABGANtFUbz0Xk4UxQuiKO772PhFUQwURfH9WygIMBMEwfRj4/Fv1oCDvx0F4O6tIKxtrHBycdSRu3Dqsub3e4EPcXV30ZFp0aEpR/84aVBXnYDanNzzJwAPAx9hZWOJg4uDjtyzoOeEB4frXEcUsbAyB8Dc0ozEuEStmfv3NGxejyO/HwPg/q0HWNtY4agnTxdPX9H8HnT7Ia4ezpq/uw7oxOnD54iJitMJVzGgOjf2/gXAm8BnmFlbYO1spyNXqk5F7h5RzXre2HOeSgE1AEiKTuDt3Rcoc6U9MTJOs6KQnpzGu2fBOLhmp7tm01qc3aOa8X4a+BgLG0vsXOx19L4MekFkcITO9bSUNM3vphameS6b+gT4cmaPamHpSeBjLG0ssf+buswsTCHHsW3VAmpyce85AJ4HPsXC2hJbPeVXvk4lrh9R2d2FPWepFuCjviNiZmmuzocZyXFJKLMUmFmZU9anAud+PQWAIlNBmXqVub73PACvA59hbm2BjYG6uqOuq2t7zlNZXVevbj0hNUE18/761lNs3bLtNSNFNcsrN5JjamNBQmgMcW8jUWYqCDp4hbJNq2vpKNu0Onf2qOzmXeAzTG0ssHJRpSVTHZfMSI7MWK4prxd/3UNUqLa6BQc+w8Zd+3mx9y5F0stwUt5EIGYqCN53Gbdm2nozohKIu/0CUc+zIsjlyM1MEOQy5OYmpIbFat2vElCTa+ryexX4FHNrS73lV6ZORQKPqJ6nq3vOUTWgpuZew74tuHP0KonR8Vph7NwcqNjIm0u/nMbcxpKYV+HEvo1Ekang/gfKLzjwGWbq8kuKiCNUPUufkZxG5LMQrF1VtlqzV2MurD+AIiMLgOToBDy8ShLzKlxTVw8OXqFMLl1lmlbnrlpXSA5dANZuDpRq5MXtX87olEPTb3pzeuEurWMKvQNqcmnvWQBeBD7FwtpCr72Xq1OJG2p7v6Rl79nUaluPqwcuAuBYyJmQp2+JfBtBclwy0W/CqdCshpZ8+YDqBKrbqrc52qrCXqWIfh1O7NsIFJkK7hy8TPmA7DJoNaM3Rxf+rBWXS+lCPL94H4CE6HhSEpIpXkU1f1WtqQ8X9qjy+DzwCRY2ltjqaS8q1KnMNc0zfUaTx0KlCxN08S4Aoc/f4eTpgo2TrU74D1HDqzK2NtYfHe49FQOqczNH225uoG0vWaci99Ttxc0956mobi8MhU+MjNOsKGQkp/H22Vuc3LLb9toBvvy5R9VuPQp8hKWNFQ56yu950HPC9bS3OfFv15Cz+88ZvF+9qQ9/qevqmbqu9L1HXgW9JEqProToeF7cfaa10ifxaSENAj4N2gPHRFF8AsQIglANqAjc+kC4MYIg3Fb/6L5l9NMJCBRFMf1jE+ni7kxYSHaHOzw0Ehd3Z4PyRkZy2nRuzsUzV7Sum5mbUtffl5O53GZy4uTmRGRI9sxSZGiUVkP4Ifb9cIAipYrw642f2XJyE+tnbtB7JrCzmzNhIdmNlypPTgbjlRvJadm5GZfOXFWHd8KvRQP27NC/zGnr6kBcSLTm7/iwGK3OIYCFvTWpCcko1R24uNBobF11BzyGsPd0oljFEjy9/VhzzdHNkegc5RcTFo2ja/7LD8CnmS+rT61nyvffsH7CtwblHN0ciQ7Nns2MDovG4SPqCqBWM1/WnN7AtB9msnbCas11e1cHokOy444Ji8Y+V9xW9tak5Ci/2NBo7NXl9+f2o3iU8mT1ta3MP76CnbO/QxRFXIq4khidwMBlI5hzeCldFw3C3sNJq67i9NSVpb01qQkpGl3xoTF666pWV38enb2t+VuQCYw/soi5NzcT9SyEiMdvNfcSQmOwdtN+sVq7OZCQIy2JYTGazqogExh0ZAHjb23gxV/3eXdb1xXA+4uGPDurPZtu7m5Pao4400JjMHfPn52lhcXybMNhmt1cQ/O768lMSCXy3D0tGTtXe2K1yi8auw+UX2yO8rN1tadqs5r8tVN3cqDTN33Yt3AnoihibGZMQmi2noTQGGxylZ9NrvJLCIvBxlVbxs7TCfeKRTXl51jcnSI+5Ri4bzZ9f52OR5USWLs5kJhL14fqKiFHXTWd2ZvTC3YhKrXbntJNqpEYFkPEQ203FntXR2JyxBUTFvNBe48JjcYulw2amJlQqaEXN4+q2l47Vwdi1M+Ro6czVo62mgHje2xd7YkLidH8HR+mKlcbV3viQ7TL4H2dlW9SjYTwWMJy5SP04RsqNK2BTC7DubALxSqVxMFD1a7au2WnRZXHaBxypV9fHt+3KW8evKJGC18ASlQthVMh549ub/4JbFwdtMpFVV55t+3xodHYqPOan/D2nk6UrFiSR4HZbbuTmyOROcovKjQKRzfD7yxDmJqZUsOvBheOXjAo4+Cm2/7mrqv/y4iiWGA/nyrSIODToDvwi/r3X9R/ayEIwlVBEB4KgrA6x+Wc7kD+H1IiCEJFYDEwOA+ZQYIg3BAE4UZMSniue7ryeRn3tEUTuHnlNreuandGGgbU4/b1uwZdgf6OrtzUaFid5w+e07VGDwY3H8aIucOxsLLQo0dXUV56Ji8ax60rt7l9VTUTNW7OKNbM24BSqf+wsfzE/7/k1cTClD4bxvDDnK2kJqXmiPTvx/mea8evMLrxMJZ8tYBu43p+VNiP1XX1+BVGNhrKooHz6T6+V/YN/YWj9WdeZVypgRdvHrxktM9AZrQcT+85AzGzMkcml1O0UglO/3Scb1pNICM1Haeibh/ORz7KtVTtCvh29efgouzZUVEpsqzlZGbVHoZDMVfMbS0/kCddPe9lRKXI5pZTWek7kkJeJXEu46klVm9EO5RZCu79cTFX2j9cloYwtrXEvXl1TviM5ljV4RhZmOLZqe4H48+Prb9PQ6dv+rJ/0c86HeZKjaqRGJ3A2/u6vvGG9HyonkwsTPli49ccm/Mj6ernRmYkw9zWkq3tZ3Jywc90Wa/fBS6/z2+pRt6kRMdr7Q8AMDIzoe6IdpxfsVs3YD7sS5+9567Hqk1q8OzGY5Ljk7TCmFqYMXzDeG7vu0iWesUjr4yIoqHrIsZmJviPaM/JFb/r3L/521niw6IZfnAePb/pz7NbjzSrmflrEw3LHNywF0sbK+YdWU7Tvi15HfRS08kuUPLVNukTEfMV3sTClF4bxrBx1iZSklLyjPTvdDJ9m9Yi6PoDg65AKlUGbELi/xukjcH/MYIgOAKNgEqCIIiAHBCB7UA1YD+AKIq1BEHoDLT+m3o8gT+AL0VR1J06VCOK4mZgM0AVt9pi136d6KT26Q+6/RA3D1eNrKu7M5FhUXrjGTKuP/aOdsyZMEXnXvN2+l2B2vZpQ8vuLQB4cucJzjlcbpzdnYgOj9EJY4jmXwSwa/1vAIS8CiHsbRiFSxXm8e3HdOnbgfY9VT79D+48ws3DhffDFFWeovXG+dXYvtg72rFgwlLNtfJVy7Jg4ywA7BxsqdvYl1oNa1LRuzxWMmPe3nmBnUf2LJWtmwMJ4dpuFMkxiZjbWCKTy1AqlNi5O5IQoS2jD5mRnD4bx3Br30WuHrtM8y9b0ribauP187tPcfRwBlSbeR3cHImJyH/55eThtSBci7pjbW+t2fDV4suWNO3eDIBnd5/imGP1xNHNkdiPqKucPLgWRPEKJVh1Yi1KhYLXd5/j6OHEU/V9Bz1xJ8YkYJGj/OzdHYlTl1/9Lo04vOEPACJehxH5NgKPkoWIDokiJiya4pVL0nfBYCwszDEyMdKqKzuDdWWh0WXr7qBVV+7litB10WA2911Eip6Xa1pCCsG3nlHEp6zmmo27A4nhcVpyCaEx2ORIi7WbA4kR2jLpCSm8uvyQUn5ViHwSDECVTvUp09ibHd0X6OhODYnBPEecZu4OOi49hnBuUImUNxFkRKvqP+TIdRxqlsHYxoJiPVXzD/fvPcdeq/wcic9Vfkm5ys/e3YF4dfkVqVKCfmtGAWBlb0NFP2+UCgXFvEpTuUl1Kvp7YWxqgrmNBfY5XAPzU342OcpPZiTni41fc2/fRR4eu6EV5uGx6wC8u/MCUSmSmZKGtXuOeNwdSMqHrqSIOMq1rEXpJtUp6eeFkakxptbmtF01lMsbDmFX2JmBRxeq5D0cGX1tHWGvQnh28wkOOeJycHMg7gP27pDD3t9Tq01drh7InuGNDYvGsZAzwzeO58q+v7AzNSM38WEx2Hk48Fr9t62bA4nhsRiZGGHroV0GCRGxOBR1xd7TmdFHF2nyPfLQfNa1n0FSZDyH5/4EQLCYxvK/1tN1cm86junGi7vPNKsCqjw6Epsr/fry+P65T0tKZcuEtRrZFRc26uwz+reQmdkgM7Nm9JGFBN95oVUu+Wnbbd0dSVTnNSEs2mB4mZGc3hvHcHvfRS4eu0SbPq1p0b058P7dmF1+Tu5OxITrf2flRcO2DTl74KzO9aZftqCR+j3y4u5THHXq6u+17RKfJtJKwH9PZ2CHKIpFRVEsJopiYeAlcALoKwhCnRyyulPZ+UAQBDvgMDBFFMWLHxDX4tfv92g2854+dp42X6g66VWqVSQxMZmoCN3Gp2OPNtTx82XS0Jk6MxRW1pbUqO3NmePndcId2H6QIc2HMaT5MC4ev0TTTk0AKO9djuTElI/qxEaERFKtrhcAdk52FC7pSejrUAB+/+EPejbtT8+m/Tl79C9adlE1rpWqVSApMYloPXlq16M1vn4+TBs6SytP7Wp1pa3PF7T1+YJTh86xePIKFkxcSs+m/VnZcgpBJ25Qo6PqtI4i3qVIS0whMTJOJ/5nl4Oo0rIWADU6NSDoxM0P5vGLxYMIfxbC+W1HADi24wgTWn7NhJZfc+3EVfw6qTpnpb3LkpKYotNRyAu3ou6a34tXKoGRsZFmAABwdMcRxrYYzdgWo7l6/Ar+nVQbAcuodeV+qedXV4lKJcnMyOTrgBGMbTGaWyeuUbdjQwBKepcmNTGFeD3l9/DyfWq2VG1UrdfJj1snVJu0Y0KiqFC3MgA2Tra4l/Ag4k048ZFxxIREEXTxLt+0HM/NfRd4c+c5NTs2AKCodylSE1NI0FtXD6iqriufTg24f0LVkbTzcKTfxrHsHLOOyJehGnlLB2vMbFSPrrGpMU4l3TGztsCusDMyYzkV2/jy5KR2fT/58xZVO6nsppB3KdITU0mKiMPCwRpTdVxGpsaUqFeRqGcqXSUbVqHu0Db8MmA5WWkZOumOu/0cqxJuWBRxRjCW49m+NmH5sDOA1OAo7KuXRm5uAoBz/YokPX3Hy+9PcqbJVM40mcrdE9fxUZdfMXVd6Su/J5cf4N1S5c5Rq1ND7qrLb1b9kcysp/oJPHqFX2ds4+6JGxxYsosZtYcxs95Ivh+5mscX75GVnoldYWfkxnIqtfHlca7ye5yj/DxzlB9AuyVfEfXsHZe3HtUK8+jETYrXqQCAY3E35MZGvPjrPg7F3bBV11UFPXX19M9bVFHr8sih6+ySX1njO5J19b7mj5FreXXpAQe+3kDk47esqj6MdfW+Zl29r0kIiWa1z3BmBIwl8MQ16nT0A6CEd2lSDNj7o8tB1FDbe51OfgSeuK65Z25tQZlaFQg8mX3t5Z1nlKxWhtiwaE7tOEbVNrV5mCsfD0/exFvdVhX2LkVaYiqJkXEE33mOUzE37D1V5f0+bPjjt8yvMZQl9UazpN5oEsJiWNN6GkmR8RibmWBsrtp2VqleVWJCo5nUeBTTW47j5olr1OukymNJ7zKqPOppLx5evo+P5pn255Y6PxY2FprN/X7dmvD42gPScq6C/oso0xLIinvHanXbXj0fbfvzy0FUVrcX1XO07Q9O3jIYvvPiQUQ8C+Evddt+cPshzWbeS8cv06STapN0Oe9ypCQmE/MR7S2AhbUFVXwrc+n4ZZ17J3ccZUrLMUxpOYYbJ65SX11XpbzLkJKY/FHvkU+d/ysbgwVBcBAE4aQgCE/V/+tszBAEobAgCGfUHiNBgiCMzlfcn7Kv0ueAIAhngUWiKB7LcW0UUB7VasBiVEeERqA65WeOKIo3DBwR2l4UxVd6dEwHpoBmQhUgQBTFPHcNVXGrrWMcUxeOp65/LdJS05nx9Twe3HkEwLqdy5k1diGR4VHcCv6L0OAwktVLmKeOnGPTiu8AaNu1JXX9fZk05ButeF2MbXT0j5w3nJp+NUhPTWfpuOU8uatK/vztc1kxcSXR4TG079eOrkO74ODsQGx0HNdOX2PFxFU4ujowYcV4HF0cQBD4Zd2vnPpDtXk1LitFS8/EBWOo41+LtNQ0Zo9ZyMM7Kv/L1T8tYe64xUSFR3Pl7RnCgsM1eTpz5DxbV/6gFc/MVVO5cPKS5ohQP1OVm0aHOf0o27Aqmanp/DphE8H3VEesDfh+Ir9P2qKaVSvsQq81I7Gws+Jd0Ct+HrMORUYW1s62jD4wHzMrc0RRJD05jaVNJ+Berggjds8i5OEbRFFJhqjk56U/Engm+8U+cO5gvBqqjuFbP/5bnt9THbE69Ydv2DBxLbERMbTs25p2Qzpi52xPfHQct87cZOOktbQf0pGGnRqRlZlFRnoGP87/XnNEqEJPmzFo7hC8/VS61oxfzfO7Kl3Tf5jJuklriA2PoVW/NrQf0hF7ta6bp2+yftIaOgzthF+nRigys8hIy2D7gu81R4TayEzoPWcgVRp6k56aztYJ63h1T7WQNfb7aXw3aT1xEbE4F3Zl2JoxWNpZ8TroJZvGrCYrIws7F3u+WjYCWxd7BEHg8IY/uLRPNQAtUqEY/RcNxcjYmLi3Eewav5GW47+gXEMvMlLT+WXCRt6q6+qr7yfx66TNJETE4ljYhd5rRmnq6qcxa1FkZNF10SCqtPAh9p1qdUyZpWBF22m4lytCj+VDkclkCDIZjw9dJeTeC5p9oz528rdzXFi7n+o9VS/2mztVm/5azO1LyYZVyEzN4MD4TYTee4lLucK0WzFEHZfAg0NXOf+taqVjxLnlyE2MSY1VrUAEBz4jePw2rXpybexF5Tkqva93neXJ6v0U+1Kl99WOU5g62+J3fB5G1uagFMlKTuNUg4lkJaVSbkInCrWtjahQEH/vFYHjtqDM4VLyp5mCL+b0p3xD1VGZP03YwBt1+Q39fjI/T9pEvLr8+q0ZjaWdFW+DXrFjzBod15Rey4Zy/9QtrSNCAUr7VqDxV6258+MpmqvLL/C3c/y1dj811OV3Q11+Lef2pZS6/PaP30TIvZcUqVGG/ntmEv7wjcbt6NTSX3l65g5yYzntlg7CrUJRFJlZnJj/M8GXHlDSvypNv1EdR3rnt3NcXLufampdt9S6muWoq0PquspJEd/y+A5qpXNEKMDwC6v4rs107sepJh96zRlIJbUNfjdhvcbev/5+Kj9M2qC2dxcGq+39TdArtqjtHaBuZz8qNfRm08iV2eVWoxxTds8jMz0TBNWpY3snbsG2kGom+po6H23n9KWMuq3aPWET79T5KOvnRWt1ed/47Sxn1+nugZp4YTVr20wnJTYRO08n+m+fjCiKRIRFsXXieqLfZb+q+sz9isoNVUfJbhm/lpfqPI7/YRpbJ2Y/08PXjsVK/Uxv+HoVWRlZlKpWhsErRqFUKHn3LJitE9aRkpDMdzd1yzYvJsxcxPXAu8TFJeDoYMewAb3p1KZZvsJOrzENgHbqtj0jNZ3fJ2zindre+30/kd2TtpCobtt7rBmJuZ0VIUGv+EXdthsKX6xGWYbunkWoum1PFRV8v3g7189kD+qGzxtGDb8apKemsXzcSp6q341zt89h5cRVxITH0K5fW7oM7YKDsz1x0XFcO32dVRNV3sRNuzShhl8NFg5fpJUvB5nueSH95g6iqvo9smn8t7xQ19XEH2awZeJaYiNiada3FW2GdMDO2Z6E6HgCz9xky6R12DrbMf/gMsytLBCVImkpqUxoMpLUpFR2vd6nzzmwQPH3bFpgHeAzwSf/dn4FQVgCxIiiuEgQhMmAvSiKk3LJuAPuoijeEgTBGriJqk/4QE+U2eGkQYCEIfQNAv4t9A0C/i1yDwL+Ld4PAgqCV2Lyh4X+IfQNAv4tbGQmBaLHAeMC0QNgL8oLTFeV9IKrqz/NCu4EECexYDxZC84q4Lnw0Wc1/G1cKZjnClTuQAXBxw4C/hfeDwIKgtuKuALTpW8Q8G/xKQwC/DybFFgDeTb4z/9lEPAY1XegQtWd/bOiKJb9QJj9wFpRFA0fw4jkDiQhISEhISEhISHxqeIqimIogPp/3XPXcyAIQjHAG7ialxxIG4P/v0IQhGao3Idy8lIUxQ7/RXokJCQkJCQkJD5FlAW4qi0IwiAg55fxNqsPYnl//09A94g6+KhlJ/V3p/YAX4uiaPgIRjXSIOD/I0RRPA4c/6/TISEhISEhISEhoSLnyYsG7jcxdE8QhHBBENxzuAPp3c8pCIIxqgHATlEU9+YnXZI7kISEhISEhISExGeFWIA//yMHgD7q3/ugPjo+J4Lqow7bgIeiKK7Ib8TSIEBCQkJCQkJCQkLi02QR0FQQhKdAU/XfCILgIQjCEbVMXaA30EgQhNvqn5YfilhyB5KQkJCQkJCQkPis+F/P7y8oRFGMBhrruR4CtFT/fgG93xzPG2klQEJCQkJCQkJCQuIzQ1oJkJCQkJCQkJCQ+Kz4v7IS8G8irQRISEhISEhISEhIfGZIKwESBinIr/iaCwX3bU65kVWB6LlXgF96zBIL7mutVYwcCkxXs9SC0XPEvOBmhKKErALTdcKs4PLlk1lwX6EtJxbMV78TFAWXp2JyswLTFVqAb/4yCosC0VOQX/Gdd2N+gen6tto3BabrkCK8wHR9CogF+J2ATxVpJUBCQkJCQkJCQkLiM0NaCZCQkJCQkJCQkPiskPYESCsBEhISEhISEhISEp8d0iBAQkJCQkJCQkJC4jNDcgeSkJCQkJCQkJD4rBAldyBpJUBCQkJCQkJCQkLic0NaCZCQkJCQkJCQkPiskI4IlVYCJCQkJCQkJCQkJD47PquVAEEQ3IBVQE0gHXgFfA0YA2sAT0AAdgDzRFEUBUGYBSSJorgsRzyvgBqiKEYJgiACK0RRHKe+Nx6wAjKBLuoglYF76t+/AxyAr4BIwASYC5wCrgK1RVEMU8e1HngjiuIiPXnxA8aLothaEIS+6ni9RFG8q75/H2gtiuIrdXoT1UHlwF5griiK6R9XgjB89lB8GvmQnprGkrHLeXb/mY5Muz5t6TiwA4WKedCxShcSYhMAsLS2YPLqSbgUckEul/P75t0c/+2EQV2DZg+iun8N0lPTWT1uFc/vP9eRadWnNW0HtMWjmAc9q/bQ6KrkW5npW6cT/lb18ZPLxy7xy+pf9OoZOnsIPo1qkpaazvKxy3mmR0/bPm3oMLA9HsU86FKlq0ZP58GdaNTBHwC5kZzCpQrT1asbiXFJ/7mu4XOGUatRTdJT01kyZhlP9dVV37Z0GtiBQsUK0aFyZ40uK1srJiwfh0dRdzLSM1g6bgWvHr/SCtthZh/K+3uTmZrOrvEbCA56pRO/g6czX64djYWtJcFBr9g5Zi2KTAXV2tWl8ZC2AKSnpLN7+lZCHr7Bzt2RHiuGYeNsh6hUErvjNK+3HAXAyb8q5ef1AbmM4J2nebnmgJYu9051KTFCFWdWcjoPJm4l8cEbACqtGoxz02pkRCVwseEEveUF0GlmXyr4e5ORms7O8RsIDnqpN099147GwtaK4KCX/KjOU+WmNWg59gtEUUSZpWDvnO28uPFYE06QCUw4uJC4sBg2D1jyr+nyG9CS2l0bIYrw7vEbfpywng6Te1FRXVc7xq/nrR5djp7O9F/7NZa2VrwNeskPY9agyMz+CF3RKiWZ8Md8to1YSeDRqwD492tB3W6NQRAI3nmWB1uPa+QL+VXBZ05vBJmMp7vOcm/dQS19tiXdqbtyEI6VinFr8e8EbToCgNzUmOZ7piM3NUKQy3l9+Bq3l+81WGe2ft4Um9sfQSYjYtefhKz9QztfHRrgMbw9AMqUNF5O3kzKg1eYlfSg9MZxGjnTIq4EL/2FsK2HDOpy9K9KuXl9ENQ2+CqXDbp1qktxtQ0q1DaY9OANph6OVF47DBNnO1AqCf7pNG/Udm0Id78q1JirKr9nu87yYK12+dmUcsd3xSAcKhfjzuLfebjxiOZeu6sryUpKQ6lUImYpONZC94NTxRpWodGs3ghyGfd+Ocu19Qd1ZBrN7k1xfy+yUtM5Om4zEfdfqcrKxoJmSwbiWMYTRJFjE7YQekvVvnj3bYp3nwCUCgUvTt8maK52u1vIrwq11HbxxIBd1MthF/dz2EWLHHbxyoBdtJ3Zh7L+XmSmZvDb+A2E6GmX7D2d6bF2FBa2lrwLesWvY9ZpbF1feFt3B7quGIa1sx2iUkRmZoYyLUEnXkNMX7CC8xev4WBvx76fNuY73HuKNayCv7qu7huoK/8cdXVMXVf2JdxpvW6ERsa2iAuXVuzm1rbjNJjanZJNvFFkZhH3OoLj4zdDnK7ukXOGUauRD2mp6Swes1Tve6R933Z0Vr9H2lXupPUembh8HB5FPchIz2DJuOU675H/EumI0M9oJUAQBAH4AzgrimJJURQrAFMBV+AAsEgUxTJAVaAOMCyfUacDHQVBcMp5URTF+aIoeomi6AWkvv9dFMVv1SIr1ffaAZuAWGAxsEyd3mpAPWB5PtMRDOT1yUR/URQrAz5ACWBzPuPV4ONfk0LFC9Gnfj9WTlrN6AUj9coF3QhiYvfJhL0N07retk9bXj99w+BmQxn3xQQGzxiEkbH+cWh1/xp4FPNgcINBrJu8lqHz9VfHwxsPmNEju7OfkwfXgxjdYhSjW4wyOACo6V+TQsU96Fd/AKsnfcvIBSP0ygXdeMDk7lMIy6Vn96Y9DGs+gmHNR/Ddoh+4d+WewU55QeryaVQTz+KF+LJeP1ZMWsXohaP067oexIRuunXVY2R3ngU956umQ1g0einDZw/Vul/ezwvn4u4s8Pua36ZuofP8gXrjbzO5B+e2HWaB/xhS45Oo1bURADFvI1nbdQ5LW0zixJq9fLFwEADKLAUH5v3IoibjWNVhBkX6BWBZphDIBCos6s+NHou4UH8c7h3qqq7nIPV1JFfbz+Gi/ySer9hLxeWDNPfe/XKOm90W6k3jeyr4eeFc3I25fqP5deoWvpg/QK9cu8k9ObvtCPP8vyYlPpna6jw9vniPxS0msqTlJH6euJHuiwdrhfPr15KwZ+/+VV22rvY07NuCZW2msKjZeGQyGe0mdMeluBuz/Eaxc+pmuhmoq/aTe3F622Fm+Y8mJT6ZOmpdoBrAtJ/ckwfnb2uuuZcpTN1ujVncbioLWkzAs4k31sVdNfK15vfhZK8l7POfSPH2vtiW9tDSlx6XzNUZP2o6ee9RpGdy/IsFHGg6jQMB0yjkVwXnaiX1phmZjOILvuJRz3nc8RuNY7v6mJf21NbzNpwHnWZwr8lY3q38nRJLhgCQ9jyEe03HqX6aTUCZmk6MenCjX5dA+UX9udVjERfzsMHr7edw2X8SL3LYoJil4PHMH7lUfxxXW86g8Hu7NoAgE6i5oA9nei7hkN9EirXzxSZ3+cUmc2PGj1qd/5z82WU+R5tO0zsAEGQCTeb1YU+fJXzfeCLl2vrimCv+4v5VsS/mxrYG4zgxeRtN5/fV3Gs0qzcvz97l+0YT2d58KjHPQgAoXLs8pQKqs73ZFH5oMpkbuepWkAn4zu/DiV5L+MN/IiU+0i6OfbGA/U2nsT9gGp567KKsnxdOxd1Y6jeGvVO30MHAc9Vycg8ubDvCUv+xpMYnU7Orf57hlVlKDs37ieVNxrO2wwxk5jYgz/9X7tu3bMrGFfPyLZ8TQSbQeF4f9vZZwg+NJ1K2rS8OBurquwbjODl5G03UdRX7IpQfW0zjxxbT+KnVdLJS03l67AYAr/+6xw9NJ7Oj2VRiX4biM7yNju5ajXwoVLwQver1ZfmkVYwx8B65f/0+47pN0nmP9FS/RwY2HczC0UsYOTu/3SqJguKzGQQA/kCmKIqaYbgoireBMsBFURRPqK+lACOAyfmMNwtVh3rM30mUKIpPgRTAXh1PSUEQ/IG1wAhRFDPzGdUhoKIgCGU/oC8JGAK0FwTB4WPSWiegNif3/AnAw8BHWNlY4uCiG8WzoOeEB+v5/LgoYmFlDoC5pRmJcYkoshS6coBvQC1O7zkNwOPAx1jaWGLvYq8j9yLoBRHBER+TDS1qB/jy555TADwKfISljRUOevQ8D3pO+Af0+LdryNn95z4JXXUD6nBi90kAHt76+LoqWroIgRcCAXj7/C1unq7YO9lp7lcKqMH1vecBeB34DHNrC2yc7XTiKVWnIneOqDpW1/acp3JADQBe3XpCakKyKvytp9i6qdKWEBmnWVFIT04j6ek7zNwcsKtWipSXYaS+jkDMVBC27xKuzWto6Yq78YSseFWccTefYuaend/YK4/IjEs2WF4AlQNqck2dp1eBTzG3ttSbp9J1KnL7yBV1ns5ROaAmABkp2QtrJham5HQ3tXNzoEIjby7/cvpf1yWTyzA2M0Eml2FiboJrCQ+u5tBlYUBX2ToVCVTrurLnLFXVugD8+rYg8OhVEqOzZz/dShXiZeBTMtMyUCqUhF15RFF1nTh5lyTxVThJbyJRZip4uf8KRZpV19KXFp1A9J0XiJm6bUCWOn8yIzkyYyMMue5aeZci7VUo6W/CETOziN5/AftmPloySTceo1DbReKtJ5i4O+rEY1u/Mumvw8l4F6lfEWCrxwZdctlgfC4bNFXbYEZEHIn3XgGgSE4j+ek7TN0MN7+Oucrv9f4rFM5VfunRCcTceYHSQBuaF25eJYl9FU68Ov5HB69QMkA7/lIB1QnacwGA0MDnmNpYYulih4mVOZ4+Zbn3y1kAlJkK0hNSAPDq3YSr6w+iyMgCICVae7Y8t128MGAXUXdeoPwbdlExoDo39/4FwBt1u2Stx9ZL1qnIPXW7dHPPeSqq2yVD4RMj4zQrChnJaYhZmQiy/DtS1PCqjK2Ndb7lc+LmVZK4HHX1+OAVSuWqq5IB1Xmgp65yUqRuReLeRJD4LhqA13/dR1QoVWFuPcdajz3WDajNid3qd/6th+p3Vv7fI8VKF+VWjveIa673yH+NKIoF9vOp8jkNAioBN/Vcr5j7uiiKzwErQRBs8hn3OqCnIAi2H5so9Yz/U1EUI0RRVAJDgT3AE1EUz39EVEpgCarVjTwRRTEBeAmU/pi0Ork5ERmS/ZKMDI3CyU33hWqIfT8coEipIvx642e2nNzE+pkbDD4cjm6ORIVGaf6ODovG8SN0AZStVo5vj61h1vZZFClTRK+Mk5sjkSHZeqJCo3B0c9IrmxemZqbU8KvBhaMXDMoUvK6/X1cvHrygfot6AJT1KourpytO7s6a+7auDsSFRGv+jguL0XTk32Npb01qQgpK9YsmPjQGW1fdF0itrv48Ontb57q9pzM2lYoRd+sZpm4OpObQlxYSk2cnyrOHP5GndePMC1tX+1x5iv5gnuJy5alKs5pMO7WCwd9N5ueJGzTXO37ThwMLd2rs/d/SFR8ey+kth5h9aT3zrm0iNTEFURSJzWF3sWHR2OnRlZJLl51al62rPV7NfPhrp7brXujjt5TyKY+lnRXGZiZ4NqqKpYfKxizc7EkOidHIJofGYOGmO+A1hCATaHtiPt3urifk/D2iAnXd5gBM3BzJyFGOGaHRmLgbtguX7k2IOxOoc92xXT2i9v2VZ5rM3BxI+wgbLNTDnyg9NmhW2BnrSsWIv6XrVvEeczd7UnKUX0poDObu+S8/RJFGuybT/NhcSvX017lt7WZPYo74k0JjsHbVjt/KzZ7E0Oz8JobFYOVmj20RZ1JiEmm+fBC9j8wjYPFAjM1NAbAv7oanT1l67p9F19+m4ValhFacue0iJTQGy79hF90N2IWNqwPxOeooPiwGm1x1ZGFvTWpCco52KRobta3nJ7y9pxOCkSliVlq+0/2/YJWrrhJDY7DKZ13lpFzb2jzaf1mvjkpdG/Dy7F2d605uTkSEZE9IRYVG4fQR76znD17QQP0eKedVFjdPV5xzvEck/ns+p0GAIQQw6BgmfuCe6hdVp3oHoH+tTD9jBEF4jGofwKwccd0G7gPrPyKu9/wM+AqCUDwfsoLei4IwSBCEG4Ig3HiXFJzrnq78x4xwazSszvMHz+laoweDmw9jxNzhWFhZ5Dt5H6Pr+f1nDKjdn1HNR3Lwh0NM2zLdgJr/Tc97fJvWIuj6A4PuOZ+Grvzr2LXuV6xsrdh0fAMd+rXj6f1nWqs2+bKFfMiUql0B367+HFz0s9Z1EwtT+m0Yw6MZ21EkpRqwVv0ZcqhbAc8e/jyZ+7Pe+4YQ9Gcql4y+ZGTL3D1+nfmNx7J10DJaje0KQMVG1UiMTuDt/Ww//H9Ll7mNJZWb1mB2/RFMrzUEUwsz7PQM/nLXQ17J6fJNX/5YtBNRqR0m7Pk7Tm7cz8ifpjNi+1RiH7xBqVDkEaGe/BhAVIocCJjG7zVG4eRdEruynvoF9ZaRflGbOpVw6d6YN/N3aEdhbIR9QE1iDl7KO1EfYYP2dStQqIc/T3PZoNzCFK9tY3j83q4Nqfofy+9EuzkcbTadMz2XUqZvE1xq5Vogzkf7IOh/gJEZyXGtVIzbP57ix5bTyUxNx2eYypVEZiTDzNaSne1mcW7+Ltqs13Z51Jevj2mX3tvFb4bs4m8+V5rn4QPhTSxM6bVhDIrkqI9L+P9AfmxBX13lfMZlxnJKNq3Gk8O67m61RrRFmaXk4R8X86k7//n+ed0vWNlas+X4Rjr0a6/zHvmvUSIW2M+nyue0MTgI6GzgeoOcFwRBKIFqM3CiIAjRgHuuMNbobqFZBdwCvs9nelaKorhMEISOwA5BEEqKovh+akGp/vkoRFHMEgRhOTApLzlBEKyBYsATPXFsRr1foEnhZmLbPm1o2b0FAE/uPMHZI3sU7+zuRHR4TO4oDNL8iwB2rf8NgJBXIYS9DaNwqcI8vq3a0Njyy1Y0694MgKd3n+Lknj3j4OjmSMxH6ErN8YK9eeYG8nlDsbG3ISE2gTZ9WtOie/McecrW4+TuREx4tE58H6Jh24acPXBW53pB6mrXpw0te7QE4PGdx3rqKv+6UpJSWDouezvKzss7qFbfm0mrJmAuyHlz5zl2HtmdSzs3BxLCY7XiSI5JxNzGAplchlKhxNbdgYSIbBn3ckXoumgwm/suIiXHgEZmJKffxrHc3HcBsyPXAUgPjcE8hz4zDwfSw7T1AVhVKEKlFYO50X0RmbF5DJLUFOkXgGevRlSSoSdPjsTnylNSrjzZuTsQH6GbjufXHuJU1BVLe2tK1ChL5SbV8W5dG3NrCwSZQPSb8H9FV+naFYl+G4F3q9rU7t4YC1tL0lPSsfdwAlTPmb0BXRY6ulTPW5EqJRmwZjQAlvY2VPLzRqlQcufEdS79doZLv50BYMi4XiSHqsKkhMZg6ZE9g2rp7kBKuG7aP0RGQgphlx5SyK8KcY+Dde+HRmOSoxxN3B3JCNNtJyzKF6XEsmE86jWXrFx2YdfIm+R7L8iMis8zLWmhMZjl0wYrrhjMrVw2KBjJqfrdWEL3XCBCbdeGSAmNwSJH+Vm4O5CqR5chUsPjAJXL0NtjN3H0LknE1exN6omhMVjniN/K3YGkXLaVGBaDdQ7XKWs3B5LC4xBFkcTQGMJuq2bhnxy5Rq2hbdTxxvL0qMrnPOzOC0RRxNTBmvQY1bkUybnswuJ/tAtPvyq4+ZajTE9/mgkiwXdeYJujjmwNtkuWOdolRxLVeU8IizYYXmYkp/fGMdzed5FmQ+t+dJr/LrnryjqfdZWstgGA4n5VCb//ipQobfesCp3rU6KxN793z94v1b5PW1qp3yOP7jzGxcMFVTdJ9c6K+sj3yJJxmjNV2HX5R0Jz7RuQ+G/5nFYCTgOmgiB89f6CIAg1gadAPUEQmqivmQPfonKtATgPtFV3nFF32u+Ioqg1nBVFMQb4DdC/E8kAoijuBW4Aff5OpvTwA9AE0LvmJgiCFapVhn2iKH6w9T2w/SBDmg9jSPNhXDx+iaadmgBQ3rscyYkpxETkv2MeERJJtbpeANg52VG4pCehr0M194/sOKzZyHvl+GUadVJtTCzrXZaUxBRi9XSADGGXww+0dNUyyGSC5sSCg9sPaTbYXjp+mSadGgNQzrscKYnJxHyEHgALawuq+Fbm0nHdpdaC1LV/+0EGNxvK4GZDuXjsEgGdmwJQvlo5khOTP6quLG0sNZu2W/Zowd2r99i9eQ+Dmw1lWcvJ3D9xg5odVWPnot6lSE1MISEyTieeZ5cfULVlLQB8OjXg/glVB8HOw5F+G8eyc8w6Il+GaoXptngw4c/ecW5b9sbA+MDnWJRww7yIM4KxHLf2dYg4ru3dZ1bIEe/vxnJ3+DpSXmjHaYg335/gUuPJLGk5ibsnruOjzlMx79KkGcjT08sP8Grpq85TQ+6p8+RU1FUj41mxOHJjI5JjEzm4ZBff1B7GVO+BrOs1jwdnAtkze/u/ois2JIpi3qW58vtZlrScxJMrD3h29QG1cugyVFdPLgfhrdbl28mPu2pd39QfwYx6qp/Ao1f4ZcZW7pxQdWKtHFUek/YejhRtUYOX+1Sz6VG3X2BT3A2rws7IjOUUb+fL2xO3PlQdAJg6WGNio1ohlJsZ41G/EvHPQ/TKJt1+hllxd0wLuyAYG+HYrh6xJ7Q72CaFnCizdSLPRq0mTY9dOLavT/Q+w65170nIpw16fTeWe3pssOLKwSQ/fcfrTfo38uYk+vYLrIu7Yakuv6LtfAnOZ/nJzU0xsjTT/O7esBJxj7QHUGF3XmBf3A1bdfzl2vjy/KR2/M9P3qJiJ5Urh7t3SdITU0iOiCMlMp7E0BjsS6jmxorWrUj0U9WG92cnblCkTgVA5RokMzbSDABA1y5K/A924V6/EnHPQ3i0/U8OBExjdcspBJ24QfWO9QEo4l2KtMQUEvXY+vPLQVRWt0vVOzUg6ISqHh+cvGUwfOfFg4h4FsJf2z5cf/8kYXdeYFfcDRt1mZU1UFcV9NTVe8q103UFKtawCj5DW7NvwAqy0jI01/dtP8BXzYbwVbMhXDx2kYDO6nd+tfL/03uklfo9kpKU8lH5/zcRC/Dfp4rwKW9Y+KcRBMED1Yx9dSCN7CNCzVAdEeqO6gjNH4E5orpwBEEYjOq0IBGIAIaIovhCfS9JFEUr9e+uqHztl4iiOCuHXo2M+u9Z5Dh2VBCE6qhcecqLoqgUBOEsquM/b+SRFz+0jwitIYriCPW9UcBqoHiuI0IFVAO/P1AdEZqnU2OTws10jGPkvOHU9FMd27l03HKe3H0KwPztc1kxcSXR4TG079eOrkO74ODsQGx0HNdOX2PFxFU4ujowYcV4HF0cQBD4Zd2vnPpDtVHSXNA9aWHI3CFU86uuOiJ0/Cqe3VX50M78YRZrJn1LTHgMbfq1oeOQTtg72xMXHcfN0zdYM2kNrfq0pmXvFiiylKSnpbNt7lYe3XwEQIb2+I3h84ZRw68G6alpLB+3kqfqPM3dPoeVE1cREx5Du35t6TK0Cw5qPddOX2fVxNUANO3ShBp+NVg4XOckVx3+LV1Zou4S66h5I6jpV4O0tHSWjl2mqasFO+axfMIKosNj6NC/fXZdRcVx7cw1lk9YSYVq5Zm0eiJKhZLXT1+zbPwKkuJVs5pVjFSzUp3m9KNcQy8yUtP5ZcJG3t57AcBX30/i10mbSYiIxbGwC73XjMLCzop3Qa/4acxaFBlZdF00iCotfIh9p/JVV2YpWNF2GsVrlGXU7tmEPHyNKIpYK+HJgl+IOnUbp8ZelJ+rPp5x1xlerNpH4S9VL6i3O/6k4opBuLXyITVYFaeYpeByM9WBWVU3jsS+TgVMHKzJiIzn6dLdvPv5jKasjpirTL3LnP6Ub1iVjNQMdk7YoMnT4O8ns2vSJk2e+q4ZjYWdFcFBr/hxzBqyMrJoMqQtNTs2QJGlIDMtg/0LftI6IhSglG8FGn3Vms0DlvxrulqM6UK11rVRZCl5G/SSnZM30ml6Hyqodf04YT1v1LqGfT+ZnZM2Ea/WNWDN12pdqiNCs9QbPN/Te9kw7p+6qTkidOxvs7G0t0aRlUXQrF2EXgjSyBZqVBWf2b1UR1z+eo673x6gbG/16UY/nsbc2ZbWR+dibGUOSiWZKens85uEVWEn6q0ajCCTIcgEXh28yp1V+7TSUU7M7kjYNapG0dn9EeQyIn45Rci3e3DpHQBAxI8nKLFsGA4tfUlXb/oVsxTcbzERAJm5Cd7Xt3C79lAUibqdkwSFidbfTo29KKu2wXe7zvBy1T481TYYvONPKqwYhGsuG7zabBp2PmXxOTibxAevNW5Vz9R2/Z5IufbCvEejqlSf3QtBLuP5L+cI+vYApdXl9/TH05g529Li6FyMrc0RlUqyktM56DcJMwcrGmz7GlCtPrz64xJB32ofZRpqpDpRxn9mL2RyGfd+PcfVtQeo2ksV/52fVO1y47l9KO5XhczUDI6N30z4XZVLm3OFIjRbMhC5sRFxbyI4Nn4z6fEpyIzlNF86CJeKRVBkKDg7/2dS/nqgpdszh108NWAXbdR2ISqVZKWk84faLurnsIuXuezikbGqDWw3px9lG1YlIzWd3yds4p3a1vt9P5Hdk7aQGBGLQ2EXeqwZibmdFSFBr/hlzDrNZmZ94YvVKMvQ3bMIffgGUVTiXsYdRXIMYqZhl66cTJi5iOuBd4mLS8DRwY5hA3rTqU2zfIX9tto3FPevip+6ru6r66qKuq7u5qirYuq6Op6jrozMTBh0dTVb640lIzE7vf3PL8fIxIhU9WpVaOAzvp6s+w4bPW+k6p2fls7isct4clflQLBwx3yWTVhBdHg0Hfu3p9vQLzTvkatnrrFswgoqVCvPlNWTUCoUvHr6hqXjl2veI2eCT+p1sCtIqrjVLrAO8N2wy/95fvXxWQ0CJD4OfYOAfwt9g4B/i9yDgP8f0DcI+Ld4PwgoCJrl7x37P/N+EPD/G1kFOAPlk2nyYaF/iJyDgH+T3IOAf5Pcg4B/k9ACdAR2LKCm6f0goCCYd2N+gen6tpruEa//FoeUek71+5f4FAYBlVx9C6yBvB9+5T/Prz4+J3cgCQkJCQkJCQkJCQk+r43B/ycRBKEZqo+I5eSlKIod/ov0SEhISEhISEj8X+dT9tUvKKRBwCeOKIrHgeP/dTokJCQkJCQkJCT+/0FyB5KQkJCQkJCQkJD4zJBWAiQkJCQkJCQkJD4rlNLBONJKgISEhISEhISEhMTnhrQSICEhISEhISEh8VkhbQyWVgIkJCQkJCQkJCQkPjuklQAJCQkJCQkJCYnPCmlPgDQIkMiD71yzCkxXi7dxBabrkJtNgeg5EOtaIHoA5iVcLzBd3vaOBabLq2xogeipklVwH3OUmyoLTNfpx54FpqucUWKB6aoZdqNA9MiEgrMLG1OLAtPlZGZbYLqCk6IKRE9dx3IFogcK9iu+o27NKTBdATVHF5guiU8DaRAgISEhISEhISHxWSHtCZD2BEhISEhISEhISEh8dkgrARISEhISEhISEp8V0p4AaSVAQkJCQkJCQkJC4rNDWgmQkJCQkJCQkJD4rJD2BEgrARISEhISEhISEhKfHdJKgISEhISEhISExGeFKBbccc2fKtJKgISEhISEhISEhMRnxgdXAgRBUAD31LIPgT6iKKYIgpAkiqKVgTCrgc5AYcAEuA10EUXxnvr+RKAEsAg4JIpiJUEQ/IAzQFtRFA+q5Q4By0RRPCsIghEwB+gCJKtV/S6K4nw9+l8BiYAIxAJfiqL4Wn3PE1gHVEA1CDoETBBFMUOdhvGiKLbOFd9ZwB1IVV96JopiZwN5nwV8BUQCZuo8DRfzGHIKgtAeeCKK4gNDMp8CZrVrYj9+OMhkJO87QsL2X7Tumzesg+2QfqBUIioUxC1fT/qd+wBY9+iEZbuWgEjms5dEz14CGZl56psyfywNGtchNTWNaaPm8vDeYx2ZxetnU7FqebKysrgX+IDZ4xeSlaXAytqSxetn417IDblczvcbdrLvl0M64c3r1MBh0jAEmYzEP44S/92vWvct/GpjP7wvolIEhYLopetJDwzCuKgnzkuma+SMPd2IXb+dhJ1/aIUv2rAKDWf1RpDLCPrlLDfWH9RJQ8PZvSnm70VWajonxm0m8v4rALwHNKdidz9EUST6UTAnx29GkZ6J77jOlAyohqgUSYlO4OS4TRCvW34LFk+jSUBDUlLSGDVsMnfv6JrXqrXzqepVCUEQePH8JSOHTiE5OQWAOvV8mL9wKkbGRsREx9KuVW+tsO1m9qG8vxcZqRn8On4D74Je6cTv4OlMr7WjMLe15F3QK3aNWYciU4FzSQ+6Lh2MZ8XiHF32K+e2HNaEmXrhW9KTUlEqlTjK0okeNBgAEx8fbEaNAJmc1MOHSd75s26mAaNyZXHcsJ64WXNIP3cOmYsztlOnInd0QFQqST14iJTde/SGfY9prZrYfj0C5HJSDh4m6cddeuWMy5fFafM6Yr+ZQ9qZ8wDYTZ2IaV1flLFxRPbqn6ceAJOaPliPGAlyGamHD5Oyy0C+ypbDYd164ufMJv38OTA2wWH1t2BijCCXk3buHMk/fJ+nLne/KlSb2xtBJuP5rrM8XKttj9al3PFdMRj7ysW4u/g3Hm08kp1XGwt8ln2FXTlPRFHk6tjNRN98ZlCXdUNvPGd9hSCXEf3LScLXa5e5ffuGuA7tCIAiOY3gaRtIffgKAOf+rXHsHgCCQPSuE0Ru031ucrJyxRxaNG9ESmoqAwaMIfD2fYOyq1bOpW+frtg5lNG6XqN6VS5eOEj3nkPZu/ewgdCwYsUcmjdvRGpKKgMGjuF2HrpWrpxLny+/wMGxLADdu3Vg/PhhACQlJTNy5BTu3ntoMPyCJdNpEtCQ1JRURg41/Ax7eVdGEOD5s1eMHDqZ5OQURowaQKcv2gJgZCSnTNmSlC3hS1ysnsYCmDZ/HA2a1CUtNY0pI2fzQE97u3TDXCpVLU9mZhb3AoOYOX4BWVkKbGytmb96BkWKeZKelsG0r+fy9NFzg/nSinPZTAKa+ZGaksbgweO5czvIoOyy5bPo1bszbi6V8hX30NlD8GlUk7TUdJaPXc6z+7ppatunDR0GtsejmAddqnQlITYBgM6DO9Gogz8AciM5hUsVpqtXNxLjkgAo1rAK/uq2/f4vZ7mmp233n92b4uq2/di4zUTcf4V9CXdarxuhkbEt4sKlFbu5te04DaZ2p2QTbxSZWcS9juD4+M35yud7pi9YwfmL13Cwt2PfTxs/Kqw+rBpUw2PmVyCTEfvrSSI37ta6b920Fq5je4JSRMxSEDp3Kyk3VDZaaPEobBrVJCs6nqfNR+iL/j9HKe0JyNdKQKooil6iKFYCMoAheQkLgiADOgBvgQaiKKYBXwPrBRWFgMHAFD3Bg4FpBqKeB3gAlUVR9ALqA8Z5JMVfFMUqwFlgujptArAX2CeKYmmgDGAF6Awk9NBTXQ5ehgYAOVipTmMFoDLQ8APy7dWyny4yGfaTRhExagqhXfpj0awRRsWLaomkXbtFWPevCOs5mJg5y3CYMQ4AubMT1l07EP7lUMK6DgSZDMuARnmqq9+4DkWLF6aFb2dmjV/EN0sm6pU7tOc4ret+QfuGPTAzM6VTz3YAdO/fmeePX9KxUS/6dhzKxFmjMDbONeaVyXCcOpLwYVMJ7jAQy+b+GJcooiWSejWQd10GE9J1CJEzl+E0cywAma+DCek6RPXTfRjKtHRSTl/UCivIBPzm9WFfnyX82HgiZdr64lDaQ0ummH9V7Iq5sb3BOE5N3kaj+X0BsHS1p2q/AHa1msHOplMQ5DLKtPEF4Namw+xsNpWfW0zj5alAao3uoFMuTZo2oETJYvh4BzBu9AyWrJilt/ymT1mAf712+NVtS/DbUAYM6gmAja01S5bPpFf3odT3bc2APtpfkizn54VzcTcW+Y1h99QtdJo/QG/8rSb34Py2Iyz2H0tqfDI+XVUv1dS4JPbP2s7ZLboDM4AN3eexsuUUzQAAmQybMaOJnTCJqC/7YNa4EfKiRXUDymRYDxlMxvUcX1BWKEhcv56o3n2IGTIMiw7t9YfNEYft+NFEj5tMRI++mDdpjFEx/bpshg0i/ar215pTjhwjZswkw/HnTu/or4mbPJHovn0wa9zYcL4G5cpXZgaxY8cQM3AA0QMHYOrjg3F5w82IIBOovqAvZ3su4YjfRIq2q41N6UJaMhmxydycsYNHG3U7wdXn9Cb07B0ON5jAsSZTSHgakme+Cs8bzPM+s3nYeAT2betjVrqwtq634Tz9YiqPmo0m/NtfKbxoOABmZYrg2D2Ax23G86jZaGwb18S0mLtBVS2aN6J0qeKUq1CPoUMnsW7tQoOy1atVwc5O92u5MpmMhQumceLEWcN5Apo3b0SpUsWpUKEeQ4dNYu0aw7qqVauCna3218lfvnpD4yadqV6jKQsWrmb9+iUGwzcJaKh6hr2aMnb0DJaunK1XbvqUBfjVbUvDOm15FxzKgEG9AFj77Tb867XDv1475s1azqUL1wwOABo0rkPREkVoVqsj34xbwMwlk/XKHdx9lBZ1OtO2YTfMzEzp3Ks9AIO/7sej+09o59eDSSNmMnXeOIP5yklAMz9KlipG1cr+jBwxhVWr5xmU9a5WGVvb/H/tvaZ/TQoV96Bf/QGsnvQtIxfo74gG3XjA5O5TCHsbrnV996Y9DGs+gmHNR/Ddoh+4d+WeZgAgyAQaz+vD3j5L+KHxRMrqaduL+1fFvpgb3zUYx8nJ22iibttjX4TyY4tp/NhiGj+1mk5WajpPj6m+gP36r3v80HQyO5pNJfZlKD7D2+Q7vwDtWzZl4wrDZfhRyGR4zBnCy76zeBowHNu2DTAtpf0MJ1+8w7MWo3jWajTvJn1LoUUjNfdi95ziZd9Z/0xaJP41PtYd6C+g1Adk/IH7wAagO4AoiseAUOBLYCUwSxTFWD1h7wDxgiA0zXlREAQLVLPrI9WDCkRRTBRFcVY+0nwZeP+mawSkiaL4vToOBTAG6K/W8U9jgmo1IBZAEISvBEG4LgjCHUEQ9giCYCEIQh2gLbBUEITbgiCUFARhlCAIDwRBuCsIwi/qsLMEQdguCMIJQRBeCYLQURCEJYIg3BME4ZggCMZquW/UOu4LgrBZPfBBEISzgiAsFgThmiAITwRBqP9RGalYjqy371C8C4WsLFJOnMGiYR0tGTE1TfO7YG4GOc/glcsRTE1BLkNmZoYiMu9PyTdq3oADvx8F4O7N+1jbWOPk4qgj99epS5rf7wUG4erhokqLCJZWqiq1sDQnPi6BrCyFVljTSmXJfBtC1rswyMoi+dhZLPwM50lmboa+iQPzWt5kvQ0lKzRC67qrV0niX4WT8CYSZaaCJwevUCKgupZMiYDqPNxzAYCwwOeY2lhi4WKn0mckx8jMBEEuw9jchORw1SOTkZSqCW9sYYqo56zj5q0a8+uufQDcvHEHW1sbXF2ddeSSEpM1v5uZm2mqrFOXNhw+eJJ3waEAREXFaIWrGFCdG3v/AuBN4DPMrC2wdrbTib9UnYrcPXIVgBt7zlMpoIZKb3QCb+++QJmrTgxhXL4cinfvUISq7C/t1GnM6tXVkbPo1JH0c+dRxsZprimjY8h68hQAMTWVrNevkTs7GdZVoRxZwSEoQlS6Uv88jVl9XV2WnTuQeuYvLV0AGbfvokxIyF++ypVHEZIjX6dPY1q3nm6+OnQk7a9zKOO0m00xTW0LRkYgN8rztAsH75IkvQonWW2Pb/ZfwbOZtj2mRycQc0e3XoyszHH2LceLn88CoMxUkJmQYlCXhVdp0l+FkfEmHDEzi9iDf2Eb4KMlk3zzEYp4lf0lBz7G2F31fJuV9iT51hPEtAxQKEm8ch/b5r4GdbVp04wfd6pmKK9eu4WtnS1ubi46cjKZjMWLZjB5im4nacTw/uz94zARkdEG9ah0BbDzJ5Wua9duYWdnY1DXooXTmTJVe37pypWbxMWpOuJXr96iUKE8BjctG/PbLtXK4s3rd7C1tf7wM2ymvz3o2KU1e3cbXt1o3KIh+39T3b9z8z42ttY462lvz+dob+8GBuHmrsp7yTLFufyXaoD68tlrChVxx9HZwaC+97Ru3ZRdO/cCcP36bVU75aabR5lMxvz5U5g+3fCgKze1A3z5c88pAB4FPsLSxgoHF3sduedBzwkPjtC5nhP/dg05u/+c5m83r5LEvQonXv0sPT54hVK52vaSAdV5oG7bQ9Vtu6W6bX9PkboViXsTQeI7ld29/us+okLlNBB66znWbh8uw5zU8KqMrY31R4UxhEXV0mS8DiXzreoZjj94HpumtbRklCk534+mWu/8lGtBKOIS/5G0SPx75HsQoHbHaYHKNSgvugO7gD+A1u87p6hWA+YDzqIo/phH+HmoZ+5zUAp4I4ri37Go5sA+9e8VgZs5b4qimAC84cODm53qTvptQRCWfkB2jCAIt1ENfJ6IonhbfX2vKIo1RVGsisq1aoAoipeAA6hckrxEUXwOTAa81SsZOVdeSgKtgHbAT8AZURQro3JTaqWWWavWUQkwB3K6NhmJouiDqi5mfiAPWshdnFCER2r+zoqIRO6i25Ey96uL++7vcV41n+g5ywBQREaR+NPveBzaRaFjv6NMSiLt6k2dsDlxcXcm7F32zEx4aASu7rovB03GjOS06dyCC6evAPDztt8pUaY4Z+8eZt/Zn1k4faXOy1Hu4oQiLDtPiogojFx182TRqC6F9m3Dde08omYu07lv2dyPpGNndK5budmTGJLdeU4KjcHK1V5HJik0u+ORFBaDlZs9yeGx3Np8hP5XVjPwxlrSE1J481e220HtCV3of2U1ZdvX4cpyXdcWd3dXQt6Faf4OCQnDzcNVRw7g23ULCHp6kdKlS7B1k+rRLFmyGHZ2Nuw7tIM/z+3hi27ttMLYujoQF5Kd7viwGGxzvbAs7K1JTUhGqX6pxYVGY+uaj5eaKDLoxyl8fXA+5m1U5itzckYRkaOuIiOROWvbg8zJCbP69UjZf8Bg1HI3N4xLlybzgWEXDLmzE4rw7E6BIjJSZ9Agc3LCrGF9UvYZ1pUfZE5OKCOydSkjI5E76eoyrV+f1AN6dMlkOGzZivMf+8i4eYOsh4bzZeHmQEqOOksJjcHcXbdTpA+roi6kRydSa+Vgmp+Yj8+ygcjNTQ3Km7g5khGSPdDPCI3G2FW3U/kex65NSThzC4DUx2+wqlUBuZ01gpkJtv7VMXE3PGgr5OFG8NvsVYl3waEU8nDTkRs+rB8HD50gLEy7w+fh4Ub7ds3ZtDmv11K27NvgbF3B70Lx0KNr2LB+HDqsqysn/fp14/hx3XbjPe4errwLzvEMvwvH3dAzvH4hD55donSZ7Gf4PebmZjRqUp+DB44b1OXq5kxoSHZ7GxYSgau77uDmPUZGctp2aclfpy8D8DjoKQGtVKt8lb0r4OHpphkg5IW7hyvB6okGVR71l+eQIV9y+PCfhOdorz+Ek5sjkTlsMCo0Ckc3w3ZkCFMzU2r41eDC0Quaa7nb9kQDbXtijrY9Ud2256Rc29o82n9Zr95KXRvw8uzdj07vP4WRmyOZodnllxkWjbGb7jNsE+BL6T83UPS7mbybuLogk/g/I4pigf18quRnEGCu7tDeQNVZ3mZIUBAEE6AlKnebBOAqEAAgimIIcBrVCoFBRFH8Sx2XwZlqQRD6qTvjbwVBKGxA7IwgCBFAE+C9k62A3rlcg9dzktMdaMIHZN+7A7kAloIgdFNfryQIwl+CINwDeqIalOjjLqpBRy8gK8f1o6IoZqIaiMmBY+rr94Bi6t/9BUG4qtbRKJeOver/b+aQ10IQhEGCINwQBOHGz5Hv8s6lHsNOPXuR0M79iBr/DXZD+qritLbCvGEdQtr25F3zLxDMzbFo0STPqAWE/KjTMGPxRG5euc2tq7cBqOfvy6P7T/Cr0opOjXozbeF4LK0scynRp0NXScrpi7xrP4CIr2dhP7yv9k0jIywa1ib5xDmdcPrj1xHSDSeKmNpaUKJpNX6oO4ZtNUdibGFK2Q7Zs9GXl/7Od76jebzvElX7NtWJQshn3gBGDZ9K5bL1efLkOe07tlRnS04Vr4r0+GIwX3QYyLiJwyhRsthHxa9HJF+N4dpOs1jVeipb+y7GokN7jKtW0VtMuQvTZuQIEjduBqX+7TeCuTl2c2eTsGYtYorhWWx9ynKn2/br4SSs32RQV77RW0jaf1oPH0nSJgO6lEpivhpIVJcuGJcrj7xY8Tx06bmWz5eTTC7DvnIxnu34k2MB08hKSafCiDxcFT5Cl1Xtyjh2bULIwu0ApD8LJnzDXkrtnE2pH2eR+vCVZnZUr6p82KK7uyudO7Vm7brvdGRXLJ/NlKkLUOajLvOrq1PHVqxbZ3h/RsOGdejXtxtTpxn2RP2oZ3jYFCqVqaf1DL+nWYtGXLtyy6ArkFpZvnUBfLN4MjcuB3JT3d5u/nY7NrY2/HF6J70GduXhvSdkKT68ypefPLq5u9C+Y0s2btj+wfhyRf7BuPODb9NaBF1/oHEFUkX94edW/zssW0hmLKdk02o8OXxVR67WiLYos5Q8/OOizr0CI5/ll3DiCk+bDOX14Pm4ju1VECmT+AfJzxGhqeoObX5oDtgC99QPiQWQArxfh1Sqfz7EfFR7A953gJ8BRQRBsFa7AX0PfC8Iwn1UnWF9+KPaQPwDqg3FY4EgoFNOIUEQbFBtYH4OGJ6q+huIopgpCMIxoAHwizot7UVRvCMIQl/Az0DQVuowbYEZgiC878inq+NVCoKQKWY/kUrASBAEM2A9UEMUxbfqTcpmOeJNV/+vwEDdi6K4GdgM8KZGY80Tr4iIQp5jKdrIxRlFHkvn6YH3MPL0QGZrg2kNL7JCwlCql8FTz/yFaZUKpBz9UytM936d6dxLNeN8//YD3Aplz3q5ursQYWAWaOi4Adg72jNrfLYfdvturdm6ZgcAb14F8+5NCCVKF+VeYPbGOkV4JPIcS89yFycUEYbzlHbrHkaF3ZHZ2aCMU7l7WNSrScajZyhj4nTkk0JjsPbInvm2cncgOULbnSMpLAYr92yzs3JzICk8jsL1KpHwNpLUGNXi17NjN/CoXprHuV4Kj/ddou0P42Em9B/Yg959vgAgMPAeHoWyZ9Q8PNwIDzU8K6lUKtm/9wjDRw1g1869hISEER0dS0pKKikpqVy+dIORXw/Ey6sS9kbmvL3zAjuP7HTbujmQEK6dt+SYRMxtLJHJZSgVSuzcHUnIlX99vJdJik4g/a8LGJcvT+a9e8hdctSVszPKKG2XMqNyZbGb+Q0Agq0tJr61SFAoSL9wAeRy7ObOJvXkn6Sf/ytP/YrISOSu2bOYKl3admFcriz2c1S6ZLa2mNapBQoFaec/7qWtjIxE5pKtS+bsjCJaO1/GZcti+012vkxr+arydTF7ZlJMTiLjdiCmPj6kvHqpV1dKaAwWOerMwt2B1LC4fKUzJTSGlNAYogNVGyvfHrpG+TwGARmh0Zh4ZM+6mrg7khkRoyNnVq4oRZYM5/mXc7RcB2J+/ZOYX1Xtg/vEXmSGapf/0CF9GDBAtX/lxo3beBbO9scu5OlOSKi2f7e3VyVKlizG44eq+rGwMOfRgwuUq1CP6tWqsPOn9QA4OTnQonkjsrKyOKCeOR8ypA8D+vdQ67pDYc9sXZ6F3AnNpcvLqyIlSxbj4YMLGl0PHlygQgWVm1flSuXZuHEJbdv2JiZXu9H/q56aZ/j2rXsU8szxDBdyJewDz/C+PUcYMXqgxsUGoEOnVuzdrbv3pkf/LnRR+/TfC3ygtcrg5mG4vR0+fiAOTnaM7LtAcy05KZmpo+do/j51Yz/Br/XvGRk0uDd9+6nmxW7evIunZ7ZLlIee8qxaVVWed++fBVTleefeGapW9teJu02f1rTo3hyAJ3ee4JzDBp3cnYgJz9vdSx8N2zbk7IGzWtcSc7Xt1u4OJOVq2xLDYrDO0bZbuzmQHB6n+bu4X1XC778iJUrbdbBC5/qUaOzN793z7/r0b5AVGoVxjhU4YzdHssJ1n+H3pFwLwqSoO3J7GxSx+XOH/K+RNgb/80eEdgcGiqJYTBTFYkBxIOBj/e1FUTwB2ANV1X+noFqBWKvu6CIIghyVz31e8aSicn35UhAEB+AUYCEIwpc54lgO/KDW8Y+i9sevg2qAAWANhKpdpHrmEE1U33u/sbqwKIpngImAHarNy/nhfYc/ShAEK1QnNP0jZDx4hHHhQsg93FSz3wH+pJ6/pCVjlOPlaFy2NBgbo4xPQBEWgUml8qo9AYBpzWpkvnqjo2PX97vp1Lg3nRr35tTR87Tt0gKAKtUrkZSYRJSeDnqnnm2p6+/LhCEztGYpQt+F4Vtf5X/u6OxAsZJFePtae2UjPegxxkUKYVRIlSfL5n6knNNemjXK0bkwKVcKwdhYMwAAsGzhT9JR/Uv64XdeYFfcDZvCzsiM5ZRp48uLk7e0ZF6cvEX5TqrOgZt3SdITU0iJiCPxXTRu1UphZKYy8cJ1KxLzTJV+u2LZL+sSTasR+1y1nP7d1p/xr98e//rtOXroT7p2bw9A9RpVSUhIJDxc96VePMdG6IAW/jx9+gKAo4dP4VunBnK5HHNzM6pVr8Km9dvxr9+elS2nEHTiBjU6qhbriniXIi0xhcTIOJ34n10OokpLlR9pjU4NCDqRtxuYibkpppZmmt9NatYg68VLMh89Ru7pidxdVVdmjRuRflHb/qK6dieyazciu3Yj/dw5ElasUg0AANtJE8l6/YaU337PUz9A5sNHGHkW0ugyb9KItAvauiI69yCiU3ciOnUn7cw54pet+ugBAEDmo0fIC3kic1Pnq1Ej0i9pxxPVoxtR3VU/6efOkbBqJekXLyDY2iJYqpsGExNMqtcg643uc/WemNsvsC7uhqXaHou08yX4A/XxnrTIeFJCorEuqeqwudavSMJTwyuFKXeeYlrcHZPCLgjGRti3qU/8yWtaMsYeTpTYPIXXX68i/aV2h9HI0VYjY9e8NrEHzmvd37BxOzVqBlCjZgAHDhynd09VU1fLpxoJ8Qk6bjhHjp7Cs4g3pcr4UqqMLykpqZRTd8pLl62tub5n72FGjJqqGQAAbNy4nZo+zajp04wDB4/Rs5dKl49PNeLjE3V0HT16miJFq1GmbG3KlK1NSkqqZgBQuLAHv/62hX79RvP0qe5g7bstOzWbeY8c/pMvuqs2/VevWZWEhKQPPsPNWjTi6ZMXmr+tbayoU68mRw+f0gn383e/06FRTzo06smpo2dp94XKo7Rq9UokJiQRqae97dyzHfX8azNu8HSt9tbaxkpz8EKXXu25fiWQ5KRknfAAmzf9SB3fVtTxbcWhgyfo3lN1QlTNml6qdirX4OP4sTOULO5DxfL1qVi+PikpqXoHAAAHtx/SbOa9dPwyTTo1BqCcdzlSEpOJycckRE4srC2o4luZS8e13wthudr2sm18eZ6rbX9+8hYV1G27u7ptT46I09wv107XFahYwyr4DG3NvgEryErL+Ki0/tOk3H2KaTEPjD1dEYyNsG3TgIQ/tZ9hk6LZAziziiURjI3+zwwAJFT8Lx8LsxAEITjH3+uBZqhO/gFAFMVkQRAuAG2AX/k45gP7c/w9DZgL3BcEIRGVH/x2II8jKkAUxVBBEHahOqZzriAIHVCdVDQD1SDoCDA1R5DGufLVRf3/TkEQ3u/IjBJFMS9/ljFqVx5jVK4969XXZ6BykXqNyoXn/Q6eX4AtgiCMAroB2wRBsEW1qL5SFMU4vcuPunmNEwRhizruV8D1vEN8BAolMUvX4LJmMchlJB84SuaL11h1UvlsJ+05hHnjBli2bApZWYjpGURPmQtARtAjUk+dx23nRkSFgszHz0jK4wg+gPN/XqRB4zocvbqHtNQ0po+eq7m3YedKvhk7n8jwKL5ZMomQ4DB+PrwVgD8Pn2XDim1sXPEd87/9hj/O7kQQBFbMXUdcTK7lcIWS6IVrcduwEGQyEvcdJ/P5a6y7qPKU+PshLJvUx6pNE8RMBWJ6OhETszcVCmammPtWJ2ru/2vvPsOjKLsADD8nBRISegu9WkFEERVQpEmxIHaxF1TsBQRRsWNBRezdz47YOyqKWFBEkd6r1BCqtARIcr4f7yTZJLsBwuyEwLm5crE7Mztn2s7O24eH3QfNymbs4Dfp9fYAJDaGmSN/Zt3c5Rx2oesZado7Y1g8ZjINOx7OJb8+QWb6dkZ7XcKtmryA+d9MoPc3D5KdlcXqGf8y/T2X2Gh3+7lUalILspWNy9cwZlDhagejv/+ZLl1PYMLk0aRvTefG6/Iu8REfvszNN9xF2qrVPPvCoySXT0JEmDF9Drfd6pqKzJu7kDE//MrPv39BdnY27771EbNnzctdx6yfJnFwx5bc/vNwdqRvY+RtL+XOu+J/A/hw4CtsTFvP14+M4MJnbqB7v3NYPmMxf37g9qF89Yrc9MUQEpITUVWOv7wHj514G0mVy3Ppy64HppjYWLb9/C3bJ7gfno3Dn6Ly449BTAzp34wic/FiEnu6LhDD1pf3xB92GIndu7FjwQKqvuauk02vvML28YWL4QHIyua/YU9T9cmhEBvD1q9GkbloMeV6uZzvrZ8V3V1lpfvuouwRLYmpVJGan33AplffYOtX34RfODuLTU8Pp/LQxyEmhoxR35C1eDGJp3r79WUR7RuqVqXC7XdATAwSI2SMHcv28eHrF4O7Hv++8w06vDcQiY1h4fs/s3Hucppe5B6U5r/9IwnVK9Jt1IPEl09Es7M5qE8Pvu4wgMzN6Uy86y3aPHstsfFxbF6SxvhbXooYi6xslg1+mSZv3+u6CB35Ixlzl1L1QpdLu/adb0m56TxiK5en7oNX535mzimuV5lGLw0ktnIF2JHJ0sEv5TYgDuebUT/SvXsn5swax9b0dPr0uTV33pefv8VVfW8rlLtcXKNGjaF7907MmvUb6Vsz6HNlXqzPP3+LvjuJdecdt1C1SiWeedrlomdmZtKm7clhlx393Vi6dD2Bv6b84L7D1+Z1qDfio1e45fo7WbVqNc+++Cjlyyd73+HZ9L8lr7nXyaecyNgx49i6NT1ciFw//zCO9l3a8f2ET8nYmpEvV/+l94Yz+JYHSVu1hnsfu50Vy1J5/xtXrWr01z/x/BOv0uTARjzy7L1kZ2Uzf+4i7rr5gUih8vnu25/o1q0jU6ePJX1rOn375vUC9/Gnr3PdtbcXWfpRlAlj/qJ1p9b877fX2ZaewRP9nsyd98Cb9/PkgOGsW7WO0y7rydnXnE2V6pV5cfTzTBjzF8O9uu3turdl4i//sC19W751a1Y2Ywa/yZlvDyAmNobpI39m7dzltPDu7VPfGcOiMZNp3PFwrvj1CXakb8/X3WdcQhkaHN+c0YPyV0/r9MAlxJWJ46x3Xe9MKydF7oI3nNvueYS/Jk1lw4aNdO51IddecRFnntptt9aRKyubFfe8SKO37nNdhH74A9vmLaHK+e47vO69b6nQvS2Vz+iEZmaiGdtZckNeb1f1nupP0rGHEVe5Agf//j9WDX+P9R+MLt62RMneXFc/KGIHwUQSWh0o2nos3bzzhXzyVcqudzO3J75YH74RXzQ8+J9/6b2duaTykYHF6l9/5c4X8kF25s4T2X6JLRvcKJVj5tQNLNbBccF9h1un/h1InJhdyHzxS4Wy0eigLrxqCYW7SY2WZZuL7gnOL+2qHhxIHICu4mvN4SLd+M/9O1/IJ7Nb37TzhXxy2KIvg/tyRVCncrPAnnGWr59R4vsbzp6UBBhjjDHGGFPqZFsmuCUCiktE7iSvqlCOsCMYG2OMMcYYszexREAxeQ/79sBvjDHGGFPKFDW44v7C796BjDHGGGOMMXs5KwkwxhhjjDH7FesYx0oCjDHGGGOM2e9YSYAxxhhjjNmv2IjBVhJgjDHGGGPMfsdKAowxxhhjzH7F2gRYIsAUodoVhwYWq+9D6wOLlXJz7UDiHH/3nEDiANxRsXVgsRKzAgvF5jVlA4lTNikzkDgAOzJiA4tVNju4H7mt2+MDi3VQ5WBGQt6eHdx10SChWmCx1mVuCSzWUVWaBBKnSkww9wqAr7JWBRara4Cj+B7811OBxTJ7B6sOZIwxxhhjzH7GSgKMMcYYY8x+JduqA1lJgDHGGGOMMfsbKwkwxhhjjDH7FWsYbCUBxhhjjDHG7HesJMAYY4wxxuxXbLAwKwkwxhhjjDFmv2MlAcYYY4wxZr9ibQIsEWB2w7jFa3js59lkZyu9mtfl8taNCi3z99J1PPbzHDKzs6mUWIbXznaDWL036V8+mb4MVTijeV0uOLJBvs/V79CC9vdehMTGMHPEWCY+/2Whdbe/7yIadGpJZvo2frj1ZVZPXwxAyz7dOfS8DoCydvYyfuj3MlnbdlDtkPp0ePgy4pMS2LR0Nd/d+AI7Nqfn36eFqxj6wzSys+H0w+tzeZsD881/4895fDNjGQBZ2cqitZv46cYeVEwswz1fT+KXBalUKVeWj/t02unxq9DhCOreeyXExrB2xGhWPf9xvvmVe51AyrVnuFhbMlh6xwukz3L7WP3yU6h2fldAWDPie1a/Vvj4NDihBR3uvYiY2Bimvz+Wv8Icww73XUSjji3Zkb6N7/u9TNr0xVRuXIuTnrs+d5mK9Wvwx7CPmPTad1Q/tD6dH7qc2LLxaFYWP975BhsnL4y4j/U6tKCddx5njRjL5ALbUKlJLTo8cRXVmzdkwmMfMuWlb3Z63HKUO+4oqg3qC7GxbPxoFBte/SDf/KRObahyw8WgimZmseaRF8n4Z4Y7NqPfJHtLOmRno5lZLDvnhiJjJbRpTaV+10FMDFs+/4ZNb76ff377tlTsexloNmRmsX7Y82yfMh2A5PPOILnXSSDC5s++ZvOIT4qO1bY1VfpfC7ExbP50FBvfyB8r8YS2VLr2UrftWVmsf/wFtk12scpfcCbJvXqAKjvmL2LNvY/B9h0RY9Xs2IIWD1yMxMaw+N2fmPts/vOT3LQ2rYZfTaXDGjLzkQ+Y98LXufOaXtWDhhd0RFXZOGspE29+iextkWNV6tiSxg9cBrExrHr3R5Y/+1m++dXPOJ461/cC3PW+YODLbJ35LwCt/nqerM3paFY2ZGUzpdvAIo/hoCG30r5zW9LTM7jzxgeYNa3wQH2PPn8fzQ4/hMzMTKZNmsl9/R8mMzOL5PJJPPr8fdSqk0JsbCz/e+FdPnv/qyLj5Rj80G2c0KUd6VszGHjjvcycOrvQMg8NH0zzww9FRFi88F8G3nAvW7ekh1lbYdfddw1HdzqabekZDL31CeZPn19omdMu6ckZfU6nTsPanNHibDau3whAUvly3P7UQGrUqUFsbCwfvvwR333wfcRYtz1wE8d1bkNGegb33PwQs6fNLbTMg8/dzaEtDiYzM5MZk2YxZMBQMjOzaNXmCIa98TArlqwEYMw3P/PKk2/s0j7ecP91HNvpaDLSt/HILUOZF2Yf73xmEAe1OJCsHZnMmjyHJ25/kqzMnY9aeMm9fWjZsRXb07fxQv+nWTy98L2r6yUn0ePyU0lpWIurWl7EpvWbAKjdpA5XP34DjZo1YeTj7/D1y5/vZD+u5RhvPx695bGw+9Hr0tM4q8/p1GlYh9MOOzP3XCVXTGbAE/2o3aA227dtZ2i/J1g8Z3HYOMntj6T2PVdCTAzrR45m9Ysf5Ztf/sRjqHnrBZDt7oMrH3iVrX/PBKDOozdSoVNrMtf+x7zu14db/S6766Fh/DJuAlUqV+Kzd17co3WZkrPXVwcSkRQReV9EFojITBH5RkQODJl/i4hkiEjFkGkdROQ/EZkkIrNE5B4R6SYik72/zSIyx3v9lveZ00VEReTgkPU0FJHpIetUETk1ZP5XItLBex0vIo+IyDwRmS4iE0Skh4j8JiI9Qj5zjoh8G2Y/F4vItJBtfNqb/oWIXBSy3Csicpv3eqy3H1NEZJyIHFTEcbxeROZ7+7DbQ1NmZSuP/DSLZ3sdyccXt+PbOStZsHZzvmU2ZezgoZ9mMbxnSz6+uB2PndwCgPlrNvHJ9GW8fd6xjLywDb8sWs2/60NGrBShw4OX8MXFQ3m30wAOPO1YKh+Qf1TfBh0Pp1KjFN4+vh9jBr5Gh4cuBSAppTItLuvKyFMG816XQUhMDAf0PBaATo/14fdHRjLixEEs+O5vjux7cqF9evj7qTx3Ths+ubIT385czoI1G/Mtc+kxB/DB5R354PKO3HjCobSqV42KiWUA6HlYPZ4/p82uHcCYGOo9eDXzL76PWZ2up/Jpx5NwQL18i2xfuoq5Z9/BrK43kfrUSOo/eh0ACQfVp9r5XZl9Sn9mdbuJip1bU7ZhrXyflRih04OX8NklQ3mz8wAO6nksVQocw4YdD6dSwxT+174fP9z+Gp2GuGO4fuFK3u1xJ+/2uJP3Tr6LzPRtzP/2bwCOv6M344d/wrs97uT3Jz7m+Dt6R9xFiRGOe/ASvr54KCM7DaBpmPOYsWEL4+55mykv7/rDf87xq37Xday4+i6WnHol5U/qSHyT+vkW2Tp+EktPv4alZ1xL2l3DqHH/LfnmL790AEvPuHanCQBiYqg84EZW3zSI1HMup1zXTsQ1yp9o3fbXP6w6/0pWXXA16x54nCp39QMgvklDknudxKpLriP1/CtJPO5Y4urVKTJWlYE3kHbDHaw48wqSunckvlH+/cqY8A8rz72Klb37sva+x6k6+FYAYqtXpcJ5vUi98FpWnnMlxMSS1K1jEbGEwx++jHHnD2V0+9uoe3pbyh+Yf9t2bNjM1LvezPfwD5CQUpkmfboxptud/NhhIBIbQ91eRVz7MTE0frgPM84fwqT2t1D99ONIPDD/KL8ZS9KYdvrdTO7Uj6VPfkTTx/vmmz/9zHuZ0uW2nSYAju/clgaN6tHj2LO4t/8j3D10QNjlvvr4O05pdw69TjifhISynHnBaQD0vvwsFsxZxBmdLuTSM65hwL03Eh+/8/yxE7q0o0HjenQ5uheD+z3I/UMHhV3uobuG0bNjb07tcB4rlqVy4RXn7nTdAEd3bE2dRnW45PjLeHLgU9z0UPjrdsbfMxjQ+3ZSl6bmm97zkp78O28JV3e7hn7n3MbVg68iLsJ+tet0LPUb1+O0tufx4G2PMeiR/mGXG/Xx95xx/Pmc0/FiyiaUpdf5uT+HTP5zCr1PvIzeJ162ywmAYzodTd1GdbjguEt4YuCT3PJw+NFxf/j0Ry4+4TIu63IlZRPKcHLvk3a67pYdW5HSqBa3nHANrwx6nise7Bt2ubl/z2LIBfewemlavumbN2zmzXte5atXPtul/ajTqA4XHncpTwwczi0P3xh2uel/TaffeQMLnasLbujN/BkL6HPi1Tx801BuuO/a8IFiYqh9f18WXXov87peR8We7SnbNP/vyJZxU5jf40bmn3wTywc+TZ1H8q6b9R//yKJL793p/uyKXiedyIvDHvRlXSUlWzWwvz0hIlVEZLT3fDlaRCoXsWys9+y7SzkZe3UiQEQE+BQYq6pNVPVQ4A6gZshivYG/gNMLfPxXVT0COAq4EFijqi1VtSXwN3CB9/7ikPX8BpxXxCYtA+6MMO8BoBbQXFWbA6cC5YG+wDARSRCRJGAIcF2EdXTM2UZVzbmL3AjcLyKVRKQtcAwwPOQzF6jq4cCbwGNFbPs4oAvwbxHLRDQ99T/qVSxH3YrliI+NoduBKYxdkP+mOWrOSjo3rUGtCokAVCnnhnFftG4Lh6VUIjE+lriYGFrVrcxPIZ+NqdGADYtXsXHJarJ3ZDH3i/E07toq37obd23FrI9/A2DVpAWUrZBEuRqV3OfjYolLKIPExhCXWIYtq9YDULlxLVaMdzlzS3+ZTtMerfPv08r11KucRN1KSW6fDq3D2Hn5b8759m/WMrofmvfQ1Kp+NSoklNml45fU8gC2LU5l+5JV6I5M1n/xKxW7Hp1vmS0TZ5P1n0scbZk0h/haVQFIaFqXLf/MRTO2Q1Y2m/+cTqXux+b7bErLJmxYvIr/vGM458vxNClwDJuEHMNU7xgmeccwR712zfhvSRqblq8FXHFpmfLufJYtXy732IZTo2UTNi5exSZvGxZ8MZ6GBbYhY+1GVk9ZSPaOnefihUo47CB2LFlB5rJU2JHJ5lFjSe6U/yFUt2bkvo5JTIBi3njLNDuYHUuXk7V8JWRmsnX0TySe0DZ/rPS8WBISK65hfbZNm4Vu2wZZ2Wz7ZyqJHY6LHKv5QWQuW0GmF2vLd2NJ7NCu6FihjdliY5GyZSE2BkksS9bqtRFjVTmiKVsWrWLrkjR0RxbLPvuDWt3yn59tazayfvJCNEwuq8TGEut9z2ITy5CRGvlaKH9EUzIWpbJtSRq6I5PVn42jSrf8379Nf8/Jvd43TZxLmVpVIq6vKJ26t+eLD0cBMHXidMpXKE+1GlULLffrj7/nvp42aQY1a9cA3KlLSi4HQLmkRP7bsJHMXchl7tL9BD4b6RJLkydOp3zFZKrXLJy/snlzXoZHQsKuX5dtu7Zh9Mc/ADBr0mySKyRRpUbhYzR/xgJWLVtVeAWqlEt2393EpAQ2bdgUMfe8Q/fj+epDlzc17Z8ZlK+QHPYYjhszPvf1jMkzc49hcbXr2pbvPhoNwMx/ZpFcITnsPv45ZkLu61mT51C91s7zsVqdeDS/fjwWgPmT5lKuQhKVahR+dlo8YxFrlqUVmr5x7X8snDqfrF24V7Xr2obvP/LO1T+zSIqwH5HOVcMDGvDPb5MAWLpgKTXr1qRytUqFlit3+AFs/3clO5a635H/vvyFCicek2+Z7Hz3wbL5rretE2aQtWHTTvdnVxzV8jAqVijvy7rMTt0O/KiqBwA/eu8juQmYtasr3qsTAUBHYIeq5pY1qepkVf0VQESaAMnAXbiH+EJUdQswEWgSKYiIJAPtgCsoOhEwBfhPRE4s8PlywJXADaq6zYu7SlU/UNXpwJfAQOAe4C1VXVDkXuff/sXAy8BQ4HngelUNVwb/C9C0iPVM8tZVLGlbMqhZPiH3fc3yCazesi3fMv+u38rGjEz6fPgX57/3B1/OXAFAk2rJ/LN8PRvSt5O+I4vfFq0hdVPIg01SJTavWJf7fvPKdSSn5L9ZJ6VUZvOKtYWW2ZK6nkkvfcOl45/iionPsn3TVpb+4qpKrJ2zlEZdjwSg6SnHkFw7/005bVMGKd4DrtunRNJCtitU+o5Mfl+YRpeDaoedvzPxKVXZvmJN7vsdK9cSn1L4RzZH1fNOZONP/wCQMWcJycccSmyl8khCGSp0bEV87fw/gskpldlU8BjWrFx4mZUhxzC18HE+qGcbZn/+R+77n+97h+Pv6E2f8U/R/q7e/PboyIjb7M5R/m1ISomYYbFbYmtWZUfq6tz3malriK1R+EEgqXNb6n/1KrVefIC0u4blzVCo/epD1P3wWSqc3aPQ5/LFql6NrFV5sbJWrSa2euFYiR3akfLh/6j25BDWPfA4ADsWLKbsES2IqVgBKVuWhLbHEFuzesRYcdWrkZma9wCSlbaa2DAPX4kd21H749ep8dQQ1tznYmWtXsvGtz+kzjfvUff7D9BNW8gYPzFirIRalUkP+Q6lr1xH4i4+eGekrmfeC1/TY+IznDT1eXZsTCft52kRly9Tq0q+6337yrWULSJWzfM7s2HMpLwJqjR7fzCHf/coNS/sUuS21ahVndTleQ9Wq1amUbNWEcc8LpZTz+rBb94D7XuvfUjjAxsxdurXfDb2PR6+68ldqitcs1YNVq7Ii5u6Io2aKeHjPvL0Pfwx43saH9CQt16N/B0KVS2lGqtX5F2Hq1euoVoR94yCPnvjC+o3rc/Iv9/jldEv8fw9L0Tcrxop1Vi1Iu86TFuZVuSDdlxcLCed1Y3ff8pLFBzWqjnv//AGz7z7OI0PLFxVNJzqhfZxNdVTIseNjYul65ldmDD2r52uu0pKFdaGXIPrUtdSpWbxEpo7Uy2lGmkhx2/NyjVUK2I/ClowcyHte7jMgoNbHkRK3ZpUD3MNx6VUZcfKkN+R1PC/IxW6HssBP7xAg9fvYfmAp3ZnV/YrGuC/PXQaLqMX7/9e4RYSkbrAycCru7rivT0R0Bz3AB9Jb2AE8CtwkIgUypYQkarAscCMItbTC/hWVecC60TkyCKWfRCX6AjVFFiiqhvDLA9wH3A+0AP3MB/JTyHVgULrMjwOdAdmqOovET57KhD5V3kXichVIvK3iPz9+m/T82bswjWcpcqstI080+sInju9Fa9MWMi/67fQuEoylx7VkGs+mch1n03kwOrliYuRItdV8LfKFQoVXEYpW7EcjboeyZttb+H1o24gvlxZDjrd5aT+2P8VWlxyIud+/QBlkhLI2pGZ//NhdirSVv0yfxUt61TJrQq028KtOMIPcnKbw6h2bheWP+S+8xnzl7Hq+U844L37aPrOvaTPXOzqSudbf7jjs/ONCH0oiImPpcmJRzLv6z9zp7W4qDM/3/8urx57Ez/f/y5dH7sy7Dbv+jYUU5h1h7sot/z4O0tO6cPK6++lyo2X5E5fdsEtLDvrelZefScVe/ckoVXzImKFC1U4VvrYcaSefRlrb7ubin0vBSBz8RI2vfU+1Z8dSrWnH2HHvAWQVUROYrj9CnPM0n8ax4ozL2d1v3uodM1lAMSUT6Zch7YsP+VClnU7F0lMIOmkzkWEChdr105QfMUkanVvxbdH38Q3h19HXLmy1DuzXeQPRPi+hlOxXTNq9u7E4gffyZ027dS7mNJ1ADMvGEKty7pT4dhDIocKe11H3rTBjw5g4vjJ/PPnZACO63gss6fPpUOLkzmz00Xc+XB/kpKTIq8gJ+5u7OPtN95Hu8O6s2DuIk7udWLYZQqvv/C03WnIeNQJrVgwcwHnHnU+V3e/lusfuI5yXonHrgWLvO7bH+nHpPFTmPTnVABmT5vDya3P4rwul/L+ax8x7H8P7dpG7sYxBLjloZuY+udUpk2YHnGZvFVH8X60C7F2J9h7z71PcsXyvPLdi5x+WS/mTZ8fvtRmF4/Xxu/HM6/LNfx79RBq3nrhLm+HiZ7QZyvv76rd+HhNVV0J4P0fqQhuODAAyI4wv5DS3jD4POB0Vc0WkU+As4HnvHnHi8gk3MF4RFWLSgT0Jq+Kzfve+3/CLaiqv4oIInL8rm6kqm4RkZHA5pySggg6quqaMNNb4B5NDhaRGFUNPcHvikg6sBjYSWXnXdrWl3ElD2x94Ybcu0uN5ARWheSSr9qUQfWksvk+WyM5gUoJ8STGx5EYD0fWqczc1ZtoUDmJ05vX5fTmrk7wM+PmUTM577O6ZUO+XPrkWlUKVTvZvHIdybWrFlhmA/WOa87GpavJWOeKOBeM+puUow5gzqfjWL9gJZ9f8CgAlRql0LBzy3zrrFk+kdRNeQ30Vm1Kp3pIaUeob2cuo/uhdcPO2xU7Vq6lTEjufXytquxYta7QcokHN6DBY9cx/6L78xXbrh35A2tHuuLm2gMvZPvK/NU+Nq9cR/mCxzCtwDFMXUf5WiHHMMUdwxwNOxxO2vTFbA1pF3Homccz9p63AZj71Z90ebRPxH3csnJdofO4tYjqQ7sjK3UN8SG5rHEp1chKi1z1JWPidOLr1SKmUgWyN2wka7U71lnr/mPLj+NIaHEwGRPDP0hkpa3Jl3sfW7M6WWsix9o2aRpxdWoTU7EC2f9tZMsXo9jyhaueUvHaK8hKWx3xs5lpq4lLybufx9aoXmSVnm3/TCO+rtuvhKNakrk8lewN/wGwdcxvlG3RjC3f/Bj2s+kr1pEY8h1KrFWF9CKq9ISq0b45W5eksX2tuyZXfPMXVVsfyNKPx4VdfvuK/Nd7mVpV2R4mVrlDGtDkiWuYef4QMtfntTHa7l03O9ZsZO2oCSQfcQAbx+eVcPe+7CzOutDV6Z8+eSYpdfJqiNasVYO01PDH/Jp+V1C5amXu7Z/XzqDXeafw6jNvAbBk8TKWL1lB4wMaMG3SzEKfv+Dyszn3IlfzdOqkmdSqnRc3pXYN0laFu3072dnZfPP59/S57mI+HlG40T5Az0tO5aTerqRq7pS5VK+ddx1Wr1WNtWHuGZF0P6crI553jedXLF5B6tJU6jWtx5zJrtH0OZeewekXuDr9M6bMyle1p0atGqxODb8vV916GZWrVqL/bXk1Y7ds3pr7etyY8Qx6pB+VqlRkw7r/Cn2+1yU9OeV8V6d/dqF9rM6aVeGv/0tuuYhKVSoyeOCTEff5xIt70Om8rgAsnDqPqiHXYJWUqqxP2/XjtzO9LunJybn7MYcatWuQk9dYrVa1iPsRztbNWxna7/Hc9yP+eJuVSwtXTc1cuYb4kBKa+JSqZBZxTWydMIMyDWoRW7kCWesj5VHuv/a0rv7uCH22CkdEfgBSwsyKVAW94OdPAdJUdWJOW9VdsbeXBMwAWoWbISItgAOA0SKyGJcgCK0S9KuqHqGqrUKrE4VZT1WgE/Cqt57bgHMlbNI+1xDyn5j5QH0RKaqCXDa7kToL2b4YXDWgi4B5wDUFFslp29BLVZfu7vp3VbOUCizZsJXl/21lR1Y2381NpUOT/InRDk2qM2nFBjKzs0nfkcX01A00quJy1NZtdWmflRvTGTN/Fd0PymvYmp22hEoNU6hQrzox8bEc2PNYFo3OnwZbNPofDjnTFZfWPKIJ2zdtZWvaBjYtX0vKEU2J8+rm123XjPXzlgOQWLWC+7AIrW88jWnv5H84alarEkvWbWH5hi1un2Yu54Smhb+DmzJ2MHHpWjoeEO77uWu2TJlH2Ya1KFOvBhIfR+Wex/Pf6An5lomvXY1Grwxi8U3D2bZoRb55cVUr5i5TqXsb1n+ev0AodcpCKjfKO4YHnXosCwscw4UhxzDFO4Zb0jbkzj/4tPxVgQA2r1pPXS8Xtl67ZmxYHLnNRNqUhVRsmEJ5bxua9DyWxaPDpqV3W8b0OcQ3qENcnZoQH0dyjw5sCamKABBfP6+qVtlDmiLxcWRv2IgklkXKuWpfkliWxLat2D5vccRY22fOJr5+HWJrp0BcHOVO7Ej6L7/nWyaubl6s+IMOgPh4sv9zP7IxlSsBEFuzBokdj2PLd2Mix5oxh7h6dYjzYiV160D6zwVi1cuLVebgpi7Who1kpqZR5rBDkASXoE44+gh2LFoSMdb6yQtIbpxCufrVkfhY6vZqw8rviypozbN12RqqtDqAWK8krPrxzdjofc/C2TR5PomNa1G2vrveq/dqx7rv81fhKFOnGge/3p951z9DxsKVudNjypUlNikh93WlEw5n6+z8+zXifx9xZueLOLPzRfw46hd6elW8WrRqzuZNm1kTJoF45gU9adfxWG7rOzhf7unK5akce/xRAFStXoWGTeqz9N/w+/bu6x/Ss+P59Ox4Pj+MGkuvc11nAy1bNWfTxs2sDpMIqN8oL/OgY9f2LCji2vvizS/p2/1a+na/lnHf/c6JZ7qqUIcccTBbNm1l3W48xKatWM2R7VoCUKlaJeo1qcvKf/OO8wdvfJLbkHfsqF855ezuABx2ZLOIx7DX+afQpsPR3HHNvfmOYdXqeYn/Zi0PQWJiwiYAAD578wv6dOtLn259+e3bcXQ7y5WMHHrkIWzZtCXsPp7cuwetTziK+68fUmRJwei3RjHopFsYdNIt/P39nxx/ZgcAmh5xIFs3bWFDmj+ZEjn7cWW3vlzZrS/jvh1H17O8c1XEfkSSVCEpt9H2yef3YOqf09gakrDKsXXqPMo2rE183ZpIfBwVT23Pxh/y/46UaZD325rQrAkSH2cJgFJAVbuoavMwf58Dq0SkFoD3f+FGLK5Ke0/vOfZ9oJOIvBNmuXz29pKAMcBDInKlqr4CICKtgXK46jH3qurDOQuLyCIRaRB+VRGdhaunf3XIen4GjgPCPlSr6vci8gBQ23u/VUReA54WkatVdbt3ojqr6k5Pwk5cDcxT1bEiMhf4Q0Q+UNXI2YtREBcTw8COB3Ptp/+QrcppzerQpGoyH051h+jsFvVoXCWZtg2qcs47fxAjcHqzujSt5tJF/b+awoaMHcTFCLd3PIQKCfF5K9dsfh78Jj3fGUBMbAwzR/7MurnLaX6h63Zz+jtjWDxmMg06Hc7Fvz3BjvTt/NjPJahXTV7Agm8mcN6oB8nOymL19H+Z/t5PABx4WhsOu8TdmBeO+ptZI/M/OMfFxHB71xZcM/IPt08t6tO0egU+nLTI7dMRrl7rmLkradOoBoll8n9dbv/8b/5esoYN6dvp+tx3XHPcwZx+eITLLyubpYNfpuk79yKxMawd+SMZc5dS7UL3w7vmnW+pdfN5xFUqT70h7lLUrGzmnOx6nWn88kBiK1VAMzNZetdLuQ0qcw9hVjZjBr/JGW8PQGJjmDHyZ9bOXU4L7xhOfWcMi8ZMpmHHw7ns1yfITN/O9/3zMiXiEspQ//jm/DDo9Xzr/eH213K7Hc3ctoMfbn8t/P552/Db4Dc5+R23DXNG/sz6ucs51NuGme+MIbF6Rc78+gHKJCei2dkcdkV3RnYaWKjr1nDHb/WQ56j9ykNITAwbP/2e7fP/pYL3ELZx5NcknXgc5U/rApmZaMY2Uvu5KgmxVStT6+l7vB2NZfPXP7H1t7+LjLV+6DNUf/pRJDaGzV+MInPhvySdcQoAWz75isRO7Uk6+UQ0MxPN2M7aOx7I/Xi1R+8lpqI7V+uHPo1u2hwpEmRls+7RZ6jx3CMQE8PmL75lx8J/ST7Txdr88VeU63Q8Saec6PZr23bW3O565Ng+fTZbf/yFWu++gGZlsX3OfDZ98nXEUJqVzeQ73qDdiNuR2Bj+HTGWTXOW0+hiV4Vo0Vs/UrZ6RTp99yBx5RPRbKXpld0Z3X4A6yctYPlXf9Lp+4fIzsriv2mLWfx25MQNWdksvONVmo24C2JjSBsxhvQ5y0i52OXSpr71PfVvPYv4yuVp/Eif3M9M6TaQ+GoVOeR/rocfiYtl9Se/suGnyRFD/fLDONp3bsuoPz8mIz2Du27KOxcvvPskd986hNWr1nD30IGsWJbKe1+76rI/fD2WF4a9xovDXmfI03fz6dh3ERGGPfBcxAfYUGNH/8YJXdrx44TPSU/P4PYb782d98qIp7jz5gdYnbaWoc/eR3JyMiIwe8Y87rnt4cgrDfHnmAkc3ak1b/32P7alb+Oxfk/kzhvy5gMMG/Aka1eto9dlp3HuNWdTpXoVXh79IhPGTGDYgOG889S73DasP6+MfhFEeOWh13K7pCzotx//4LjObfj8j5FkpGdw7y151Xmefucx7u/3CGtWreWOR/uzctkq3vjyJSCvK9Aup3TgrEtOJyszi20Z2xjU955d2sfxY/7kmE5H8+5vb7EtYxuP3prXt8Ujbw3hsduGsXbVWm59+GZSl63i+c+fBuCXUb/x1vCif1onjZlIy46tGP7Li2xL38ZL/Z/OnTfgjcG8MuBZ1qetp9ulJ3Nq39OpVL0yj373FJN+msgrA5+jYvVKDPnycRKTy6HZSo/LT+W2LjeQHuZeNX7MBI7pdAzv/Pamtx95ufoPvzWEx739OOPyXpx3zTlUqV6F10a/zJ8/TeDx24bRoGl9Bj01kOysLBbPW8Jj/Z8oFAOArGxW3PMijd66z3UR+uEPbJu3hCrnu9+Rde99S4Xubal8Rqfce9OSG/JqINd7qj9Jxx5GXOUKHPz7/1g1/D3WfzB65ycqjNvueYS/Jk1lw4aNdO51IddecRFnntqtWOsqKaVonIAvgEuAR7z/C/VXq6qDgEHgerME+qvqTuuCyd5+EESkNq6qTisgA1ft5Wbge6CHqs4OWXYYsAr4E3cATomwzrHe/L+914+o6rch828EDgEeBb5S1eYhB/UUb5meuBPR0XtAL4NrL3CGt51bgLtV9Ttv+Xtx1YHy7g75t2kxsAnIqQg4FegPTACOVdVUb7lbgcNU9bLQ/Sj6KObu0wBccVMa8I2qRq7bQf7qQNH22kP+5dDsTJ/7i9e4d3fNurtwX+XR8nORhVD+SgzwltGlfDBp3bJJmTtfyCfZWUW3h/HTxBV71nvL7qjJ9sBiXRW9Qs98tmcHd100SNjtnpuLbV3mlp0v5JMKseGrWPqtVmxyIHEAUrOCO35Pxwdz/AAO/iu4RsTx1RoHdyOMICGhfmC/ZhkZS4q9v16NlQ+A+sAS4GxVXec9H7+qqicVWL4DRTwDh9rbSwJQ1RXAOWFmFep+QFVvDXk7toh1dgj3OmTa0yFvm3vTxoauU1W/IKQJoapuxz1kh+2kWlXvjbQ93vyGEWblm66qw0JeF9r2Itb/NPD0Thc0xhhjjDF7BVVdCxTq9cF7Pi40aEbB59Wi7PWJAGOMMcYYY/zkQ9edpZ4lAvYxIvIphUtJBuZUSzLGGGOMMcYSAfsYVS04crIxxhhjjAmxt7eJDcLe3kWoMcYYY4wxxmdWEmCMMcYYY/YrVhJgJQHGGGOMMcbsd6wkwBhjjDHG7FesHMBKAowxxhhjjNnv7PUjBpvSRUSuUtWXLdbeH2tf3CeLVXriWKzSFWtf3Kd9Nda+uE8mOqwkwPjtKotVamLti/tksUpPHItVumLti/u0r8baF/fJRIElAowxxhhjjNnPWCLAGGOMMcaY/YwlAozfgqwbaLFKRxyLVbpi7Yv7ZLFKTxyLVXriBB3L+MwaBhtjjDHGGLOfsZIAY4wxxhhj9jOWCDDGGGOMMWY/Y4kAY4wxxhhj9jOWCDC+EJF4ETlCRGpEYd2tRSQl5P3FIvK5iDwtIlX8jmeiR0TOLOltMMaUDna/KD1ExBoIl0KWCDDFIiIvikgz73VFYArwFjBJRHr7HO4lYLsXqz3wiBfrP6LcM4GIVBWR00WkVTTjhMSrJiIShfVeKSIHeK9FRP4nIhtFZKqIHOl3vCI8Gc2VB3m+opXwFZHhIa9vKjDvDT9jRYgfrWvwVBFpEPL+bhGZIiJfiEgjn2MNFZG+YabfIiKP+hmrwPoDvV+ExF3i8/ouLurPz1g74fv9QkTiw0yr5neckHVX8jKyWnu/ldGK005ERovIXBFZKCKLRGShzzGqRPirCpzkZywTDEsEmOI6XlVneK8vA+aq6mFAK2CAz7FiVXWd9/pc4GVV/VhVBwNN/QwkIl+JSHPvdS1gOnA58LaI3OxzrGNFZKyIfOI9TE734q0Ske5+xgJuAhZ7r3sDLYBGwK3AUz7HKoqvD5cBn6+gEr7tQ15fUmBeCx/jBH0NDgFWe3FPAS7EnasvgBd9jnUK4TMIngJO9itIkNffzjbF5/W1DvN3NPAA8LrPsYri236JSEcRWQasEJHvRaRhyOzv/YoTEq+Ml2hfjLsWXwEWi8jrIlLG73jAa8Aw4Djc+TrK+99Pq4G/gYkhf397f77XAjDRZ4kAU1zbQ16fCHwGoKqpUYgVKyJx3uvOwJiQeXFhlt8TjVR1uvf6MmC0qp4KHIP7cffTs8BDwAjcPvVR1RTcQ+DDPsfKVNUd3utTgLdUda2q/gAk+RyrKH73SRzk+Qoq4SsRXkdDkNegqupW7/UZwGuqOlFVXwWqRyFWdpiJ2fh7TIO8/ori6/dKVW/I+QNuBP4ETgDGA0GWHPq5X0OBbqpaHfdQPlpEjvXmReN7dhcQD9RT1SNUtSVQH/ebNTgK8f5T1VGqmubd29eq6lqfYywEOqhqo5C/xqraCFjlcywTAL8foMz+Y4OXm7ccaAdcAeA9rCf6HGsE8LOIrAHSgV+9WE1xVYL8tCPkdWdc7g2quklECj1U7KE4Vf0eQETuV9XxXqzZUaiNke3lVK7H7deQkHm+ni8RmUb4H28BavoZi2DPV8GE74derFSfz1eMiFTGZdLkvM4JEOtnIIK9BkVEkoGtuHP1fMi8BJ9jbRWRA1R1XoENOAB3D/FLYNefiNwaaRaQ7GcsL14ccCnQD5cIOEtV50QhTlD3izI5iXhV/UhEZgGfiMjtEeLvqTOAo0MSvjnXxbW4xJTfCYGfROQx4BNgW0jMf3yMMRyoDISrfjbUxzgmIJYIMMV1NfA0kALcHFIC0Bn42udYjwI/ArWA7zVvhLsY4AafYy0VkRuAZbgcr28BRCQRl6vjp9CHhIIPJn7/KA3GFdnGAl/k/BiKyAm43B0/neLz+ooS5PkKKuFbEVfMnvMUHvoj7vd1EeQ1OByYDGwEZqnq3wAicgSw0udYdwOjRORB3LEEVz1iEHCzj3GCvP7KFzHP1yp9InIdrgrhj0B3Vf3Xz/UXENT9YoeIpOT8VqnqDBHpDHwFNIlCvOzQBEAOVd0sItFIdBzj/X9UaDigk18BVPW5IuY9k/NaRE5U1dF+xTXRYyMGm72eiPyjqoEUQYtr5Hk/LsHxXEguaUeglao+7mOsLGAL7mEvEZdDmvPgl6Cqvj1EeMXefwPlVXV9yPQk3H1gs1+xIsSvBqxVn284AZ+vA8lL+A5X1Te86d2Arqraz69YQQnyGvTi1cHVHZ6SU13HK6GKU9WlPsdqDtwGNPcmTQceV9VpPsYI7PoLkleKkYarAx76nRVcVStf26aEie/7/UJEugCrVXVKgekVgetVdUj4TxY73hSgA+GrGv2kqof7GW9vEuRvttkzlggwxSIidxcxW1X1AR9jTVLVI/xaXzHi5+YelVYBJ6SOxfXgtA7XkPBtoBqu5OZiVf02yvFL9fkSkZnAO8D7qup3Kc1eR0QOAvqr6pVRjuP7dSEiD6nqHX6us4hYCbiOEdYDX+ISOO2BBcADqrrGx1gNiprvZ8lASd8vwmzPx6q6x12TishiXElbuESAqmrjPY0RJubJQDNCqtep6v1+x9mF7SjR32yz66w6kCmuLWGmlQP6AFVxN3O/VC+iPiyqOszHWOF8Q0CN4UTkK1UNsjpNNDwL3IGr1jIG6KGq40XkYFz7jmj/qEflfInIMxRRRUZVb/QpVG/gPFzDxTW4Y/aBqq7waf1FitY1KCItgMeB2riOBJ7BtQs4BnjC73hhROO66I671oPwFq4NQhKunv503HftOOANfKxWk/OQL67r1ma4635WlBKlJX2/KMiXh3NVbejHenaViLyI+w3uCLwKnAVMCHIbQljucilhiQBTLKqa+6MtIuVx9UcvB97H/x/0WFzDt2j3lBJJkHHrRGm9jUXki0gzVbWnj7GCbGwaTrSC/B3y+j7gnmgE8aorTAEGebmk5wLjRWQ+MEJVX4lG3BDRugZfAV4A/sA9PP8DvAdcoKoZUYoZKhrXRWyBhtv5aF7Xxn44VFWbe21QlqnqCd70b72qJ74RkQq4B8mjcO04BDhcRCYCV6jqRh/DlfT9oiBfHmBFpH6RQVR9HdsBaKuqLURkqqreJyJP4BoJGxORJQJMsYkbrfdW4ALgTeDI0PrmPkotiSLNENF+6Ao1KUrrXU0wua0QbGPTcKJyvlT1zZzXInJz6Pto8R6IxovI57iBk54l+tdjtK7BsjntKIA5ItIfuF1Vs6IUr6BoHLeDyd+IO5TiU66yZzuAqmaKSMFSIb+P4dPATOC8kLYbgutg4FnAzwHDSvp+ES1f47Y/9NpQXHe4NfC/p6+cY7dVRGoDa3FjwZSExSUU1+wmSwSYYvG6IjsD19/yYVFuWBpxYBUReVRVB/oVSETKATvU61Pfq69cVkTOUNWo56qo6uVRqiO9SVV/DjcjCgPXHC4iG/Eam4a8Bp+7gizB8xX1hxMRaY2rGnQmeQMOfRjtuFG8BhO8noByroXNQAvv4dLXrgwDvC5mBlj3ua6IPI07fjmv8d77XXrTTlUvDZ3gNdK9X0Tmhf9IsQV2v9hFvhQ/qBtDJG+lbnCygUAX3NgcfvtKRCoBj+FK2RRXmuMbr4vdx3G9KU3D3SOWF1xOVc/wM66JHmsYbIrF6z1iG5BJ+N4jKvgYay5wi6p+HTItBjdyZYqq+jayqYj8givuniduHIIJwLvAocAEVR3kY6wi60ir6pM+xspS1UI5T17PGJ+rage/YgUpyPNVIG7UGlqLyEO4KkAbcHWi31fVZSJyHNBbVa/zMVaQ1+BYIieeVFV968owqOuiqAaQItJaVf/yI463voKjR+fjZ8mUiMxX1bCjsYvIPFU9wK9YQRGRCpGqMYlI/ZzqOSLSNad6kk9xDwDuJK/ty5uaN3BjVIhIWVzvXr6OoyMiv+LapvwC9ATa2AN/6WaJALPX83JQvgXuUNVPvF4yPsINFHapnzdUEZmWk4MjIg8AVVT1Oi+3fGLB3J09jPUn+etID8DVkR7sdx1pERmNe/i5M2RaCvAd8HG0q1tFsbFpkOdrE3kPseVw3WmCzwlfEbkHV/d/roi0xJUGnAssAj7RkP64fYgV2DUYpKCuCxFZBzQpWA1SRLriRkSu50ecIuJfpaovR2G9b5LX65CGTB8MHKiqF/kds0B83+8XoQl3EflRVTuHm+djvOa4h/9muIG0RkSz6puIxAInAw0JqeXhZ8cZIjJZ3cjHOe+tK9BSzqoDmWLx2gOEUmBD6A+GX1R1sbg+nr8T1y/3RcCfqhqxx6A9CRfyuhOuaBVV3S7+j0AbZB3pU4CPRWSYqt7q5U6NAh5T1ZeiEK+gaDU2Dex8qWpRgzX5aQRwnoj0xtXrHYnLsOkYhViBXYMiMkBVh3qvz1bVD0Pm+d3VZlDXxUDcSK0nqupqABE5Hzci98k+xomkL66amN9uAF4D5ovIZNzxPALXXqRPFOIVFI37RWg1n4K/X9FogTwFWIprG3A0cHRoQ2f1rzexHF8CGbhqOn7/VuUoWKUvMfS9n1X6TDAsEWCKayKFGz2V934w+qjqYr8CiUhOTsMAXFHkaOCdnOk+33imisjjuFFhmwI5vVZU8jFGjsDqSKvqNhE5HXhfRN4H2uBGev7Urxg7Ea3GpkGer6DMBn4FTlXV+QAickuUYgV2DeK6PR3qvR5E/vYNfne1Gch1oaqviEgGMMbL/T8X92De0c97YBGi0n2OV23mbBFpgqtCJcBAVV0QjXhhRON+oRFeh3vvhyuitN5I6mqUB3EDUoFhEd77OjqxCYZVBzK+EpEzgKt8rqf/UxGz/a5LnIjr7rQW8Lp6o0uKSFtcsf/bPsYaS3B1pHNKTeJxialfcfU6c4JFe6yFqAwKFeT5CoqXWDsPaIurBvc+8Kqq+t7Th/fdCteDiXvh7zWYW3++YF36ourWFzNWoNeFiJyNa0+xBNfP/Vo/119E3LqquiwK6+2GG138owLTLwDSVHW03zHDbIOv9wsRWYZ7YBXgFvIeXgWXIRLVqlvRJiKPAj/62Z7B7PssEWB8Z/UE9z5ePfOIVPU+H2MF1th0XyYiSUAvXJuATrhueD/1udHi0cBSVV3pvb+EvN6I7lUf+7kvUCc73z2itN4zRGQaeYmoBriueLeQ107Et5xZERmuqjd7r29S1adC5r2hBXrz2cNY43ElUasLTE/BXYNtfIwVyP0iyHugF+9Lih5c0M+xWXIyD97BjbS8g+h00tEPeFK9bmNDplcFhqrqFX7FMsGwRIDxlYgkA7+FNh7yYZ1TgN+A34Fx0SxmD8kZDUdDG5P5EKs17gEs1Xt/Me4B7F/8fwCrXLDxYrQE3OA5sPNVkrw2OGcD5/qcO/8P0EVV14lIe1ypww1AS+AQVT3Lx1hZ5D0gJ5K/YXWCqsb7GCuQ68KrpvUbsB734FUw0L9+xPFiBZaIEjfgVNgETFHzihlrX22cfkJR8zVCl817EG8hLtNgWjTa5nkxXsa1b7hOVcd5067FnbPhqjo8GnFN9FgiwBRLSPWSUJVx3YY9qz6Oaur1stA25C8JlyD4HfhdVf/0MVarMJOPxd3k0lS1tY+xgnwAS8PlUv4OjMMdt7l+rb9ArII9SCwFGkapsWlg52tfJCJTVPVw7/VzwGpVvdd7n+88liZBXRdeu4O2uEHDppL3/frDz0S8F6uo6lR+JwLm4kYoziwwPR43NoJvXYQGdb8QkWa4qmBfeO+fBCp6s58tqUatIvKxqp7pw3q+w1VFi1aj4Jw4bXEDxs3AXffzgH45pYmmdLGGwaa4CvaUorhGQheq6jQ/A6nqdGA6Xi8YIlINV2f6Zlwxsm8jL6rqxJzXXk7OYKAs0FdVR/kVxxMb8qBwLvCyqn6M68Vnsp+BVLWGiBxIXkKqv4hUB8bjSleGFrmC3RNkg+cgz9e+KFZE4ryHvc7AVSHzfP19kCL6zReRi/yspx/UdaGq/b0YZYCjcN+ty4FXRGSDqh7qVywgRkQq46p75LzO+Y75PfrsJ7h9uF5Vt0Bu9bSnvXl+Cup+8QjwcMj7brjrohxwNy4XvST4Nar0SmCsiIzCjeEDRKW913TgL1ypjWAJgFLNEgGmWIqqPykiDXwuBo/FdU/XFmiHG61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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# correlation matrix\n", "\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "\n", "plt.figure(figsize=(12,10))\n", "\n", "sns.heatmap(x[columns_with_large_vif].corr(), annot=True)\n", "\n", "plt.title(\"Correlation between Features\")" ] }, { "cell_type": "code", "execution_count": 19, "id": "b369ed31", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['ODI-WKTS',\n", " 'COUNTRY_IND',\n", " 'COUNTRY_PAK',\n", " 'PLAYING ROLE_Batsman',\n", " 'COUNTRY_ENG',\n", " 'COUNTRY_NZ',\n", " 'COUNTRY_SL',\n", " 'COUNTRY_SA',\n", " 'PLAYING ROLE_Bowler',\n", " 'SIXERS',\n", " 'COUNTRY_ZIM',\n", " 'WKTS',\n", " 'PLAYING ROLE_W. Keeper',\n", " 'AGE_3',\n", " 'CAPTAINCY EXP_1',\n", " 'ODI-SR-BL',\n", " 'COUNTRY_WI',\n", " 'COUNTRY_BAN']" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "columns_to_be_removed = [\n", " 'T-RUNS', 'T-WKTS', 'RUNS-S', 'HS', 'AVE', 'RUNS-C', 'SR-B',\n", " 'AVE-BL', 'ECON', 'ODI-SR-B', 'ODI-RUNS-S', 'AGE_2', 'SR-BL'\n", "]\n", "\n", "xnewfeatures = list( set(xfeatures) - set(columns_to_be_removed) )\n", "xnewfeatures" ] }, { "cell_type": "code", "execution_count": 20, "id": "a03c4574", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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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", "
" ], "text/plain": [ " Column VIF\n", "0 ODI-WKTS 2.742889\n", "1 COUNTRY_IND 3.144668\n", "2 COUNTRY_PAK 1.334773\n", "3 PLAYING ROLE_Batsman 2.680207\n", "4 COUNTRY_ENG 1.131869\n", "5 COUNTRY_NZ 1.173418\n", "6 COUNTRY_SL 1.519752\n", "7 COUNTRY_SA 1.416657\n", "8 PLAYING ROLE_Bowler 3.060168\n", "9 SIXERS 2.397409\n", "10 COUNTRY_ZIM 1.205305\n", "11 WKTS 2.883101\n", "12 PLAYING ROLE_W. Keeper 1.900941\n", "13 AGE_3 1.779861\n", "14 CAPTAINCY EXP_1 2.458745\n", "15 ODI-SR-BL 2.822148\n", "16 COUNTRY_WI 1.194093\n", "17 COUNTRY_BAN 1.094293" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "get_vif_score(x[xnewfeatures])" ] }, { "cell_type": "code", "execution_count": 21, "id": "d36ca194", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\himan\\anaconda3\\lib\\site-packages\\statsmodels\\regression\\linear_model.py:1918: RuntimeWarning: divide by zero encountered in double_scalars\n", " return np.sqrt(eigvals[0]/eigvals[-1])\n" ] }, { "data": { "text/html": [ "\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
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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", "
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
" ], "text/plain": [ "\n", "\"\"\"\n", " Results: Ordinary least squares\n", "========================================================================================\n", "Model: OLS Adj. R-squared (uncentered): 0.746 \n", "Dependent Variable: SOLD PRICE AIC: 2953.6299 \n", "Date: 2023-04-24 21:26 BIC: 2998.5846 \n", "No. Observations: 104 Log-Likelihood: -1459.8 \n", "Df Model: 17 F-statistic: 19.01 \n", "Df Residuals: 87 Prob (F-statistic): 2.46e-22 \n", "R-squared (uncentered): 0.788 Scale: 1.0893e+11\n", "----------------------------------------------------------------------------------------\n", " Coef. Std.Err. t P>|t| [0.025 0.975] \n", "----------------------------------------------------------------------------------------\n", "ODI-WKTS 1000.2719 383.6520 2.6072 0.0107 237.7220 1762.8218\n", "COUNTRY_IND 241916.0736 90265.6898 2.6800 0.0088 62503.2409 421328.9062\n", "COUNTRY_PAK 60298.7726 141684.1935 0.4256 0.6715 -221313.9038 341911.4489\n", "PLAYING ROLE_Batsman 177457.3060 100597.5501 1.7640 0.0812 -22491.2177 377405.8296\n", "COUNTRY_ENG 678083.6712 208244.3343 3.2562 0.0016 264175.5079 1091991.8346\n", "COUNTRY_NZ 147178.4532 150707.9524 0.9766 0.3315 -152369.9212 446726.8275\n", "COUNTRY_SL -24754.1190 143387.0044 -0.1726 0.8633 -309751.3164 260243.0784\n", "COUNTRY_SA 81761.5043 118244.2325 0.6915 0.4911 -153261.7115 316784.7202\n", "PLAYING ROLE_Bowler 70814.4129 98253.6432 0.7207 0.4730 -124475.3419 266104.1678\n", "SIXERS 6898.3644 1658.7173 4.1589 0.0001 3601.4841 10195.2446\n", "COUNTRY_ZIM -138202.6253 374302.3654 -0.3692 0.7129 -882169.1065 605763.8559\n", "WKTS 1815.9081 1930.2590 0.9408 0.3494 -2020.6907 5652.5069\n", "PLAYING ROLE_W. Keeper 28857.7846 150328.1606 0.1920 0.8482 -269935.7123 327651.2816\n", "AGE_3 -56497.7482 101704.1167 -0.5555 0.5800 -258645.6927 145650.1963\n", "CAPTAINCY EXP_1 198466.1639 93102.8927 2.1317 0.0359 13414.0834 383518.2445\n", "ODI-SR-BL 806.6222 1262.5348 0.6389 0.5246 -1702.8024 3316.0468\n", "COUNTRY_WI -204026.7967 164456.0856 -1.2406 0.2181 -530901.0743 122847.4810\n", "COUNTRY_BAN 0.0000 0.0000 nan nan 0.0000 0.0000\n", "----------------------------------------------------------------------------------------\n", "Omnibus: 16.784 Durbin-Watson: 2.074 \n", "Prob(Omnibus): 0.000 Jarque-Bera (JB): 19.711\n", "Skew: 0.911 Prob(JB): 0.000 \n", "Kurtosis: 4.107 Condition No.: inf \n", "========================================================================================\n", "* The condition number is large (inf). This might indicate strong\n", "multicollinearity or other numerical problems.\n", "\"\"\"" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "xtrain = xtrain[xnewfeatures]\n", "\n", "model2 = sm.OLS(ytrain, xtrain).fit()\n", "\n", "model2.summary2()" ] }, { "cell_type": "code", "execution_count": 22, "id": "1260dd00", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\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", "
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", "
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
" ], "text/plain": [ "\n", "\"\"\"\n", " Results: Ordinary least squares\n", "================================================================================\n", "Model: OLS Adj. R-squared (uncentered): 0.690 \n", "Dependent Variable: SOLD PRICE AIC: 2963.0980 \n", "Date: 2023-04-24 21:31 BIC: 2973.6756 \n", "No. Observations: 104 Log-Likelihood: -1477.5 \n", "Df Model: 4 F-statistic: 58.80 \n", "Df Residuals: 100 Prob (F-statistic): 1.95e-25 \n", "R-squared (uncentered): 0.702 Scale: 1.3328e+11\n", "--------------------------------------------------------------------------------\n", " Coef. Std.Err. t P>|t| [0.025 0.975] \n", "--------------------------------------------------------------------------------\n", "COUNTRY_IND 412178.1281 60936.7223 6.7640 0.0000 291281.4066 533074.8497\n", "COUNTRY_ENG 737378.1579 215586.2418 3.4203 0.0009 309661.1943 1165095.1214\n", "SIXERS 7185.4933 1509.6537 4.7597 0.0000 4190.3833 10180.6033\n", "CAPTAINCY EXP_1 375372.1235 74692.9719 5.0255 0.0000 227183.3947 523560.8523\n", "--------------------------------------------------------------------------------\n", "Omnibus: 2.870 Durbin-Watson: 1.830\n", "Prob(Omnibus): 0.238 Jarque-Bera (JB): 2.549\n", "Skew: 0.383 Prob(JB): 0.280\n", "Kurtosis: 3.036 Condition No.: 176 \n", "================================================================================\n", "\n", "\"\"\"" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "significant_variables = ['COUNTRY_IND','COUNTRY_ENG','SIXERS','CAPTAINCY EXP_1']\n", "\n", "model2 = sm.OLS(ytrain, xtrain[significant_variables]).fit()\n", "\n", "model2.summary2()" ] }, { "cell_type": "markdown", "id": "56344941", "metadata": {}, "source": [ "## hypothesis" ] }, { "cell_type": "code", "execution_count": null, "id": "e4f353f4", "metadata": {}, "outputs": [], "source": [ "1. Null Hypothesis H0: it is considered ture till it is proven false.\n", " \n", "2. Alternative Hypothesis: it is the negation of the Null Hypothesis." ] }, { "cell_type": "markdown", "id": "7f705a97", "metadata": {}, "source": [ "## Select a test\n", "\n", "it is used to accept or reject the alternative hypothesis.\n", "\n", "Result: Test Statistics" ] }, { "cell_type": "markdown", "id": "72ffcc59", "metadata": {}, "source": [ "## p-value" ] }, { "cell_type": "code", "execution_count": null, "id": "1dad8cfb", "metadata": {}, "outputs": [], "source": [ "you have to convert this test statistics into p-value(0-1)" ] }, { "cell_type": "markdown", "id": "af38612c", "metadata": {}, "source": [ "## significance level" ] }, { "cell_type": "code", "execution_count": null, "id": "b3f51f20", "metadata": {}, "outputs": [], "source": [ "5% or 0.05" ] }, { "cell_type": "code", "execution_count": null, "id": "e4a4bde6", "metadata": {}, "outputs": [], "source": [ "a large p-value indicates that the results are due to luck." ] }, { "cell_type": "code", "execution_count": null, "id": "6dd95570", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.12" } }, "nbformat": 4, "nbformat_minor": 5 } ================================================ FILE: March Cohort 2023/12_15_16_17_18_Statistics/18_Hypothesis Testing.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "id": "c585a495", "metadata": {}, "source": [ "# Hypothesis Testing\n", "Proposed Explanation\n", "\n", "1. Null Hypothesis H0: It is considered true till it proven false.\n", "2. Alternative Hypothesis: It is the negation of the null hypothesis.\n" ] }, { "cell_type": "markdown", "id": "8c03609b", "metadata": {}, "source": [ "Select a test: and apply it -> test statistics\n", "\n", "we need to convert this test statistics to a p-value\n", "\n", "#### significance level\n", "threshold for luck -> 5%\n", "\n", "p-value < 0.05 -> we will reject the null hypothesis and accept the alternative hypothesis.\n", "\n", "#### a large p-value indicates that the results are due to luck." ] }, { "cell_type": "code", "execution_count": 2, "id": "1262f0c8", "metadata": {}, "outputs": [], "source": [ "# Lady tasting Tea\n", "\n", "#is it possible for you to tell weather the tea added before or after the milk?" ] }, { "cell_type": "markdown", "id": "4ebc5491", "metadata": {}, "source": [ "H0: the lady cannot tell if the milk was poured first.\n", " (NH possess no relationship and no difference)\n", "H1: the lady can tell if the milk was poured first." ] }, { "cell_type": "markdown", "id": "c46c9066", "metadata": {}, "source": [ "#### select a test\n", "\n", "8 cups of tea, 4 of each type\n", "\n", "Lady got all 8 correct -> test statistics\n", "\n", "8C4 -> 70 is the number of combinations\n", "\n", "p-value = 1/70 = 1.4%\n", "\n", "1.4% < 5% => Reject the H0\n", "\n", "\n", "### Errors in Hypothesis Testing\n", "\n", "Type I error\n", "When you reject the null hypothesis when it was actually true.\n", "\n", "Type II error\n", "When we don't reject the null hypothesis and accept the alternative hypothesis.\n", "\n", "\n", "### Power (Recall)\n", "power of statistical test\n", "\n", "probability of rejecting H0 when H1 is true.\n", "\n", "If the value of power is high than it is good.\n", "\n", "if it is high that means the low probability of Type-II error\n", "\n", "\n", "### alpha\n", "\n", "alpha is the probability of rejecting H0 when H0 is true.\n", "\n", "alpha = probability of commiting a Type-I error.\n", "\n", "if the test has high alpha value that is not good." ] }, { "cell_type": "markdown", "id": "0cf026df", "metadata": {}, "source": [ "# Chi-Square Test" ] }, { "cell_type": "code", "execution_count": 6, "id": "a36e5cfa", "metadata": {}, "outputs": [], "source": [ "import scipy.stats as stats" ] }, { "cell_type": "code", "execution_count": 7, "id": "08662308", "metadata": {}, "outputs": [], "source": [ "import seaborn as sns\n", "import pandas as pd\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 8, "id": "8316d566", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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total_billtipsexsmokerdaytimesize
016.991.01FemaleNoSunDinner2
110.341.66MaleNoSunDinner3
221.013.50MaleNoSunDinner3
323.683.31MaleNoSunDinner2
424.593.61FemaleNoSunDinner4
\n", "
" ], "text/plain": [ " total_bill tip sex smoker day time size\n", "0 16.99 1.01 Female No Sun Dinner 2\n", "1 10.34 1.66 Male No Sun Dinner 3\n", "2 21.01 3.50 Male No Sun Dinner 3\n", "3 23.68 3.31 Male No Sun Dinner 2\n", "4 24.59 3.61 Female No Sun Dinner 4" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dataset = sns.load_dataset('tips')\n", "\n", "dataset.head()" ] }, { "cell_type": "code", "execution_count": 9, "id": "4dd3d2da", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "smoker Yes No\n", "sex \n", "Male 60 97\n", "Female 33 54\n" ] } ], "source": [ "dataset_table = pd.crosstab(dataset['sex'], dataset['smoker'])\n", "\n", "print(dataset_table)" ] }, { "cell_type": "code", "execution_count": 13, "id": "2464bd7a", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[60, 97],\n", " [33, 54]], dtype=int64)" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "observed_values = dataset_table.values\n", "observed_values" ] }, { "cell_type": "code", "execution_count": 11, "id": "3a55191e", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(0.0,\n", " 1.0,\n", " 1,\n", " array([[59.84016393, 97.15983607],\n", " [33.15983607, 53.84016393]]))" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "val = stats.chi2_contingency(dataset_table)\n", "\n", "val" ] }, { "cell_type": "code", "execution_count": 12, "id": "5c52c6d3", "metadata": {}, "outputs": [], "source": [ "expected_values = val[3]" ] }, { "cell_type": "code", "execution_count": 14, "id": "55db1242", "metadata": {}, "outputs": [], "source": [ "from scipy.stats import chi2\n", "\n", "chi_square = sum([(o-e)**2./e for o, e in zip(observed_values, expected_values)])\n", "\n", "chi_square_stats = chi_square[0] + chi_square[1]" ] }, { "cell_type": "code", "execution_count": 15, "id": "f1f1261b", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.001934818536627623" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "chi_square_stats" ] }, { "cell_type": "code", "execution_count": 17, "id": "9ae6c359", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Degree of Freedom: 1\n" ] } ], "source": [ "no_of_rows = len(dataset_table.iloc[0:2,0])\n", "no_of_columns = len(dataset_table.iloc[0,0:2])\n", "ddof = (no_of_rows - 1) * (no_of_columns - 1)\n", "\n", "print(\"Degree of Freedom:\",ddof)" ] }, { "cell_type": "code", "execution_count": 19, "id": "5d7188ed", "metadata": {}, "outputs": [], "source": [ "alpha = 0.05" ] }, { "cell_type": "code", "execution_count": 20, "id": "6aa1816a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Critical Value: 3.841458820694124\n" ] } ], "source": [ "critical_value = chi2.ppf(q=1-alpha, df=ddof)\n", "\n", "print(\"Critical Value:\", critical_value)" ] }, { "cell_type": "code", "execution_count": 21, "id": "0dc7e08d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "p-value: 0.964915107315732\n", "Significance Level: 0.05\n", "Degree of Freedom: 1\n" ] } ], "source": [ "# p-value\n", "\n", "p_value = 1 - chi2.cdf(x=chi_square_stats, df=ddof)\n", "\n", "print(\"p-value:\", p_value)\n", "print(\"Significance Level:\", alpha)\n", "print(\"Degree of Freedom:\", ddof)" ] }, { "cell_type": "code", "execution_count": 23, "id": "cbcc9460", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accepting H0, there is no relationship between 2 categorical variables\n" ] } ], "source": [ "if chi_square_stats >= critical_value:\n", " print(\"Reject H0, there is a relationship between 2 categorical variables\")\n", "else:\n", " print(\"Accepting H0, there is no relationship between 2 categorical variables\")" ] }, { "cell_type": "code", "execution_count": 24, "id": "822ee03e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Accepting H0, there is no relationship between 2 categorical variables\n" ] } ], "source": [ "if p_value <= alpha:\n", " print(\"Reject H0, there is a relationship between 2 categorical variables\")\n", "else:\n", " print(\"Accepting H0, there is no relationship between 2 categorical variables\")" ] }, { "cell_type": "markdown", "id": "d4ee4b89", "metadata": {}, "source": [ "# t-test\n", "\n", "if there is a significant difference between the means of two groups" ] }, { "cell_type": "code", "execution_count": 25, "id": "497d7ce4", "metadata": {}, "outputs": [], "source": [ "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]" ] }, { "cell_type": "code", "execution_count": 26, "id": "c1d8bb90", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "26" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(ages)" ] }, { "cell_type": "code", "execution_count": 27, "id": "adcb366f", "metadata": {}, "outputs": [], "source": [ "ages_mean = np.mean(ages)" ] }, { "cell_type": "code", "execution_count": 28, "id": "3e10989c", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "30.23076923076923" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ages_mean" ] }, { "cell_type": "code", "execution_count": 29, "id": "320ad6c6", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([60, 18, 66, 32, 17, 55, 43, 17, 16, 55])" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sample_size = 10\n", "\n", "age_sample = np.random.choice(ages, sample_size)\n", "\n", "age_sample" ] }, { "cell_type": "code", "execution_count": 30, "id": "19a1f903", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.24751746592331406\n" ] } ], "source": [ "from scipy.stats import ttest_1samp\n", "\n", "ttest, p_value = ttest_1samp(age_sample, 30)\n", "\n", "print(p_value)" ] }, { "cell_type": "code", "execution_count": 31, "id": "33a09441", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "We are accepting the Null Hypothesis\n" ] } ], "source": [ "if p_value < 0.05:\n", " print(\"We are rejecting null hypothesis\")\n", "else:\n", " print(\"We are accepting the Null Hypothesis\")" ] }, { "cell_type": "code", "execution_count": null, "id": "d11fd400", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.12" } }, "nbformat": 4, "nbformat_minor": 5 } ================================================ FILE: March Cohort 2023/12_15_16_17_18_Statistics/datasets/Birthweight_reduced_kg_R.csv ================================================ ID,Length,Birthweight,Headcirc,Gestation,smoker,mage,mnocig,mheight,mppwt,fage,fedyrs,fnocig,fheight,lowbwt,mage35 1360,56,4.55,34,44,0,20,0,162,57,23,10,35,179,0,0 1016,53,4.32,36,40,0,19,0,171,62,19,12,0,183,0,0 462,58,4.1,39,41,0,35,0,172,58,31,16,25,185,0,1 1187,53,4.07,38,44,0,20,0,174,68,26,14,25,189,0,0 553,54,3.94,37,42,0,24,0,175,66,30,12,0,184,0,0 1636,51,3.93,38,38,0,29,0,165,61,31,16,0,180,0,0 820,52,3.77,34,40,0,24,0,157,50,31,16,0,173,0,0 1191,53,3.65,33,42,0,21,0,165,61,21,10,25,185,0,0 1081,54,3.63,38,38,0,18,0,172,50,20,12,7,172,0,0 822,50,3.42,35,38,0,20,0,157,48,22,14,0,179,0,0 1683,53,3.35,33,41,0,27,0,164,62,37,14,0,170,0,0 1088,51,3.27,36,40,0,24,0,168,53,29,16,0,181,0,0 1107,52,3.23,36,38,0,31,0,164,57,35,16,0,183,0,0 755,53,3.2,33,41,0,21,0,155,55,25,14,25,183,0,0 1058,53,3.15,34,40,0,29,0,167,60,30,16,25,182,0,0 321,48,3.11,33,37,0,28,0,158,54,39,10,0,171,0,0 697,48,3.03,35,39,0,27,0,162,62,27,14,0,178,0,0 808,48,2.92,33,34,0,26,0,167,64,25,12,25,175,0,0 1600,53,2.9,34,39,0,19,0,165,57,23,14,2,193,0,0 1313,43,2.65,32,33,0,24,0,149,45,26,16,0,169,1,0 792,53,3.64,38,40,1,20,2,170,59,24,12,12,185,0,0 1388,51,3.14,33,41,1,22,7,160,53,24,16,12,176,0,0 575,50,2.78,30,37,1,19,7,165,60,20,14,0,183,0,0 569,50,2.51,35,39,1,22,7,159,52,23,14,25,200,1,0 1363,48,2.37,30,37,1,20,7,163,47,20,10,35,185,1,0 300,46,2.05,32,35,1,41,7,166,57,37,14,25,173,1,1 431,48,1.92,30,33,1,20,7,161,50,20,10,35,180,1,0 1764,58,4.57,39,41,1,32,12,173,70,38,14,25,180,0,0 532,53,3.59,34,40,1,31,12,163,49,41,12,50,191,0,0 752,49,3.32,36,40,1,27,12,152,48,37,12,25,170,0,0 1023,52,3,35,38,1,30,12,165,64,38,14,50,180,0,0 57,51,3.32,38,39,1,23,17,157,48,32,12,25,169,0,0 1522,50,2.74,33,39,1,21,17,156,53,24,12,7,179,0,0 223,50,3.87,33,45,1,28,25,163,54,30,16,0,183,0,0 272,52,3.86,36,39,1,30,25,170,78,40,16,50,178,0,0 27,53,3.55,37,41,1,37,25,161,66,46,16,0,175,0,1 365,52,3.53,37,40,1,26,25,170,62,30,10,25,181,0,0 619,52,3.41,33,39,1,23,25,181,69,23,16,2,181,0,0 1369,49,3.18,34,38,1,31,25,162,57,32,16,50,194,0,0 1262,53,3.19,34,41,1,27,35,163,51,31,16,25,185,0,0 516,47,2.66,33,35,1,20,35,170,57,23,12,50,186,1,0 1272,53,2.75,32,40,1,37,50,168,61,31,16,0,173,0,1 ================================================ FILE: March Cohort 2023/12_15_16_17_18_Statistics/datasets/CarPrice_Assignment.csv ================================================ 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 1,3,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 2,3,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 3,1,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 4,2,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 5,2,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 6,2,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 7,1,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 8,1,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 9,1,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 10,0,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 11,2,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 12,0,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 13,0,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 14,0,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 15,1,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 16,0,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 17,0,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 18,0,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 19,2,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 20,1,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 21,0,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 22,1,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 23,1,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 24,1,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 25,1,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 26,1,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 27,1,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 28,1,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 29,-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 30,3,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 31,2,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 32,2,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 33,1,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 34,1,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 35,1,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 36,0,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 37,0,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 38,0,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 39,0,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 40,0,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 41,0,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 42,0,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 43,1,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 44,0,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 45,1,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 46,0,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 47,2,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 48,0,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 49,0,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 50,0,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 51,1,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 52,1,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 53,1,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 54,1,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 55,1,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 56,3,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 57,3,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 58,3,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 59,3,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 60,1,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 61,0,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 62,1,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 63,0,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 64,0,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 65,0,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 66,0,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 67,0,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 68,-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 69,-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 70,0,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 71,-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 72,-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 73,3,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 74,0,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 75,1,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 76,1,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 77,2,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 78,2,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 79,2,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 80,1,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 81,3,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 82,3,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 83,3,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 84,3,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 85,3,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 86,1,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 87,1,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 88,1,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 89,-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 90,1,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 91,1,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 92,1,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 93,1,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 94,1,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 95,1,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 96,1,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 97,1,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 98,1,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 99,2,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 100,0,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 101,0,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 102,0,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 103,0,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 104,0,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 105,3,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 106,3,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 107,1,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 108,0,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 109,0,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 110,0,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 111,0,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 112,0,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 113,0,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 114,0,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 115,0,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 116,0,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 117,0,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 118,0,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 119,1,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 120,1,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 121,1,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 122,1,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 123,1,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 124,-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 125,3,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 126,3,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 127,3,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 128,3,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 129,3,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 130,1,porsche cayenne,gas,std,two,hatchback,rwd,front,98.4,175.7,72.3,50.5,3366,dohcv,eight,203,mpfi,3.94,3.11,10,288,5750,17,28,31400.5 131,0,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 132,2,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 133,3,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 134,2,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 135,3,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 136,2,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 137,3,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 138,2,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 139,2,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 140,2,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 141,2,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 142,0,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 143,0,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 144,0,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 145,0,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 146,0,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 147,0,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 148,0,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 149,0,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 150,0,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 151,1,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 152,1,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 153,1,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 154,0,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 155,0,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 156,0,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 157,0,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 158,0,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 159,0,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 160,0,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 161,0,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 162,0,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 163,0,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 164,1,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 165,1,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 166,1,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 167,1,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 168,2,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 169,2,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 170,2,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 171,2,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 172,2,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 173,2,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 174,-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 175,-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 176,-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 177,-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 178,-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 179,3,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 180,3,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 181,-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 182,-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 183,2,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 184,2,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 185,2,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 186,2,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 187,2,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 188,2,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 189,2,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 190,3,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 191,3,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 192,0,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 193,0,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 194,0,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 195,-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 196,-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 197,-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 198,-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 199,-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 200,-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 201,-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 202,-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 203,-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 204,-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 205,-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 ================================================ FILE: March Cohort 2023/12_15_16_17_18_Statistics/datasets/Crime_R.csv ================================================ 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 45.5,135,0,12.4,69,540,965,0,6,80,22,1,564,139,26.5,135,12.5,71,564,974,0,6,82,20,1,632,142 52.3,140,0,10.9,55,535,1045,1,6,135,40,1,453,200,35.9,135,10.9,54,540,1039,1,7,138,39,1,521,210 56.6,157,1,11.2,47,512,962,0,22,97,34,0,288,276,37.1,153,11,44,529,959,0,24,98,33,0,359,256 60.3,139,1,11.9,46,480,968,0,19,135,53,0,457,249,42.7,139,11.8,41,497,983,0,20,131,50,0,510,235 64.2,126,0,12.2,106,599,989,0,40,78,25,1,593,171,46.7,125,12.2,97,602,989,0,42,79,24,1,660,162 67.6,128,0,13.5,67,624,972,0,28,77,25,1,507,206,47.9,128,13.8,60,621,983,0,28,81,24,1,571,199 70.5,130,0,14.1,63,641,984,0,14,70,21,1,486,196,50.6,153,14.1,57,641,993,0,14,71,23,1,556,176 73.2,143,0,12.9,66,537,977,0,10,114,35,1,487,166,55.9,143,13,63,549,973,0,11,119,36,1,561,168 75,141,0,12.9,56,523,968,0,4,107,37,0,489,170,61.8,153,12.9,54,538,968,0,5,110,36,1,550,126 78.1,133,0,11.4,51,599,1024,1,7,99,27,1,425,225,65.4,134,11.2,47,600,1024,1,7,97,28,1,499,215 79.8,142,1,12.9,45,533,969,0,18,94,33,0,318,250,71.4,142,13.1,44,552,969,0,19,93,36,0,378,247 82.3,123,0,12.5,97,526,948,0,113,124,50,0,572,158,75.4,134,12.4,87,529,949,0,117,125,49,0,639,146 83.1,135,0,13.6,62,595,986,0,22,77,27,0,529,190,77.3,137,13.7,61,599,993,0,23,80,28,0,591,189 84.9,121,0,13.2,118,547,964,0,25,84,29,0,689,126,78.6,132,13.3,115,538,968,0,25,82,30,0,742,127 85.6,166,1,11.4,58,521,973,0,46,72,26,0,396,237,80.6,153,11.2,54,543,983,0,47,76,25,1,568,246 88,140,0,12.9,71,632,1029,1,7,100,24,1,526,174,82.2,130,12.9,68,620,1024,1,8,104,25,1,570,182 92.3,126,0,12.7,74,602,984,0,34,102,33,1,557,195,87.5,134,12.9,67,599,982,0,33,107,34,1,621,199 94.3,130,0,13.3,128,536,934,0,51,78,34,0,627,135,92.9,127,13.3,128,530,949,0,52,79,33,0,692,140 95.3,125,0,12,90,586,964,0,97,105,43,0,617,163,94.1,134,11.9,81,571,971,0,99,106,41,0,679,162 96.8,151,1,10,58,510,950,0,33,108,41,0,394,261,96.2,161,10.1,56,515,1001,1,32,110,40,0,465,254 97.4,152,1,10.8,57,530,986,0,30,92,43,0,405,264,97.8,152,11,53,541,989,0,30,92,41,0,470,243 98.7,162,1,12.1,75,522,996,0,40,73,27,0,496,224,99.9,162,12,70,533,992,0,41,80,28,0,562,229 99.9,149,1,10.7,61,515,953,0,36,86,35,0,395,251,101.4,150,10.7,54,520,952,0,35,84,32,0,476,249 103,177,1,11,58,638,974,0,24,76,28,0,382,254,103.5,164,10.9,56,638,978,0,25,79,28,0,456,257 104.3,134,0,12.5,75,595,972,0,47,83,31,0,580,172,104.5,133,12.7,71,599,982,0,50,87,32,0,649,182 105.9,130,0,13.4,90,623,1049,1,3,113,40,0,588,160,106.4,153,13.4,91,622,1050,1,3,119,41,0,649,159 106.6,157,1,11.1,65,553,955,0,39,81,28,0,421,239,107.8,156,11.2,62,562,956,0,39,85,29,0,499,243 107.2,148,0,13.7,72,601,998,0,9,84,20,1,590,144,110.1,134,13.9,66,602,999,0,9,87,15,0,656,151 108.3,126,0,13.8,97,542,990,0,18,102,35,0,589,166,110.5,126,13.8,97,549,993,0,19,103,34,1,659,160 109.4,135,1,11.4,123,537,978,0,31,89,34,0,631,165,113.5,134,11.3,115,529,978,0,32,93,35,0,703,175 112.1,142,1,10.9,81,497,956,0,33,116,47,0,427,247,116.3,147,10.7,77,501,962,0,33,117,44,0,500,256 114.3,127,1,12.8,82,519,982,0,4,97,38,0,620,168,119.7,125,12.9,79,510,945,0,4,99,39,0,696,170 115.1,131,0,13.7,78,574,1038,1,7,142,42,1,540,176,124.5,134,13.6,73,581,1029,1,7,143,41,1,615,177 117.2,136,0,12.9,95,574,1012,1,29,111,37,1,622,162,127.8,140,13,96,581,1011,1,29,115,36,1,691,169 119.7,119,0,11.9,166,521,938,0,168,92,36,0,637,154,129.8,120,11.9,157,524,935,0,180,93,27,1,698,169 121.6,147,1,13.9,63,560,972,0,23,76,24,1,462,233,130.7,139,14,64,571,970,0,24,78,24,1,511,220 123.4,145,1,11.7,82,560,981,0,96,88,31,0,488,228,132.5,154,11.8,74,563,980,0,99,89,29,1,550,230 127.2,132,0,10.4,87,564,953,0,43,83,32,0,513,227,134.6,135,10.2,83,560,948,0,44,83,32,0,589,234 132.4,152,0,12,82,571,1018,1,10,103,28,1,537,215,137.5,151,12.1,76,567,1079,1,11,105,27,1,617,204 135.5,125,0,12.5,113,567,985,0,78,130,58,0,626,166,140.5,140,12.5,105,571,993,0,77,131,59,0,684,174 137.8,141,0,14.2,109,591,985,0,18,91,20,1,578,174,145.7,142,14.2,101,590,987,0,19,94,19,1,649,180 140.8,150,0,12,109,531,964,0,9,87,38,0,559,153,150.6,153,12,98,539,982,0,10,88,36,0,635,151 145.4,131,1,12.2,115,542,969,0,50,79,35,0,472,206,157.3,131,12.1,109,548,976,0,52,82,34,0,539,219 149.3,143,0,12.3,103,583,1012,1,13,96,36,0,557,194,162.7,142,12.2,95,612,1003,1,13,97,36,0,625,196 154.3,124,0,12.3,121,580,966,0,101,77,35,0,657,170,169.6,134,12.2,116,580,987,0,104,79,36,0,719,172 157.7,136,0,15.1,149,577,994,0,157,102,39,0,673,167,177.2,140,15.2,141,578,995,0,160,110,40,0,739,169 161.8,131,0,13.2,160,631,1071,1,3,102,41,0,674,152,178.2,132,13.2,143,632,1058,1,4,100,40,0,748,150 ================================================ FILE: March Cohort 2023/12_15_16_17_18_Statistics/datasets/IPL2013.csv ================================================ 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 1,"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 2,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 3,"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 4,"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 5,"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 6,"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 7,"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 8,"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 9,"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 10,"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 11,"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 12,"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 13,"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 14,"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 15,"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 16,"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 17,"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 18,"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 19,"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 20,"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 21,"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 22,"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 23,"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 24,"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 25,"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 26,"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 27,"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 28,"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 29,"Gilchrist, AC",3,AUS,DC+,W. Keeper,5570,0,9619,96.94,0,0,1,1775,109,27.73,140.21,86,0,0,0,0,0,2008,300000,700000 30,"Gony, MS",2,IND,CSK+,Bowler,0,0,0,0,2,39,0,54,15,9,117.39,5,999,30,33.3,8.47,23.6,2011,50000,290000 31,Harbhajan Singh,2,IND,MI,Bowler,2164,406,1190,80.51,259,46.5,1,430,49,16.54,151.41,22,1469,54,27.2,6.85,23.83,2008,250000,850000 32,"Harris, RJ",2,AUS,DC+,Bowler,199,46,48,100,44,23.4,0,115,17,10.45,107.48,3,975,44,22.16,7.71,17.27,2011,200000,325000 33,"Hayden, ML",3,AUS,CSK,Batsman,8625,0,6133,78.96,0,0,0,1107,93,36.9,137.52,44,0,0,0,0,0,2008,225000,375000 34,"Henderson, T",3,SA,RR,Allrounder,0,0,0,0,0,0,0,11,11,5.5,68.75,1,40,1,40,6.66,36,2009,100000,650000 35,"Henriques, MC",1,AUS,KKR+,Allrounder,0,0,18,60,1,90,0,49,30,16.33,108.89,1,142,3,47.33,8.82,32.33,2011,50000,50000 36,"Hodge, BJ",3,AUS,KKR+,Batsman,503,0,575,87.51,1,66,0,1006,73,31.44,121.5,28,300,17,17.65,7.89,13.41,2011,200000,425000 37,"Hopes, JR",2,AUS,KXIP,Allrounder,0,0,1326,93.71,67,47.1,0,417,71,26.06,136.27,11,548,14,39.14,9.13,25.71,2011,200000,350000 38,"Hussey, DJ",2,AUS,KKR+,Allrounder,0,0,1488,91.4,18,36.2,0,971,71,26.24,125.78,48,345,6,57.5,8.85,39,2008,100000,625000 39,"Hussey, MEK",3,AUS,CSK,Batsman,5708,7,5262,86.97,2,117,1,958,116,39.92,120.65,25,0,0,0,0,0,2008,250000,250000 40,"Jadeja, RA",1,IND,RR+,Allrounder,0,0,860,78.61,57,46.2,0,904,48,23.18,120.86,35,750,26,28.85,7.33,23.65,2011,100000,950000 41,"Jaffer, W",2,IND,RCB,Allrounder,1944,2,10,43.47,0,0,0,130,50,16.25,107.44,3,0,0,0,0,0,2008,150000,150000 42,"Jayasuriya, ST",3,SL,MI,Allrounder,6973,98,13430,91.21,323,46,1,768,114,27.43,144.36,39,390,13,30,7.96,22.62,2008,250000,975000 43,"Jayawardena, DPMD",2,SL,KXIP+,Batsman,10440,6,10596,78.08,7,83.1,1,1471,110,30.65,128.02,33,0,0,0,0,0,2008,250000,475000 44,"Kaif, M",2,IND,RR+,Batsman,624,0,2753,72.03,0,0,0,259,34,14.39,103.6,6,0,0,0,0,0,2008,125000,675000 45,"Kallis, JH",3,SA,RCB,Allrounder,12379,276,11498,72.97,270,39.3,1,1965,89,30.7,110.95,37,1713,45,38.07,7.96,28.71,2008,225000,900000 46,Kamran Akmal,2,PAK,RR,W. Keeper,2648,0,2924,84.31,0,0,0,128,53,25.6,164.1,8,0,0,0,0,0,2008,150000,150000 47,Kamran Khan,1,IND,RR+,Bowler,0,0,0,0,0,0,0,3,3,3,60,0,224,9,24.89,8.48,17.78,2009,20000,24000 48,"Karthik, KD",2,IND,DD+,W. Keeper,1000,0,1008,74.5,0,0,0,1231,69,24.14,123.84,28,0,0,0,0,0,2008,200000,525000 49,"Kartik, M",2,IND,KKR+,Bowler,88,24,126,70.78,37,51.5,0,111,21,18.5,105.71,1,1013,21,48.24,7.02,41.33,2008,200000,425000 50,"Katich, SM",3,AUS,KXIP,Batsman,4188,21,1324,68.74,0,0,0,241,75,24.1,129.57,8,0,0,0,0,0,2008,200000,200000 51,"Kohli, V",1,IND,RCB,Batsman,491,0,3590,86.31,2,137,1,1639,73,28.26,119.29,49,345,4,86.25,8.84,58.5,2011,150000,1800000 52,"Kumar, P",1,IND,RCB+,Bowler,149,27,292,88.21,77,42.1,0,243,34,10.57,114.08,14,1919,53,36.21,7.73,28.11,2011,200000,800000 53,"Kumble, A",3,IND,RCB,Bowler,2506,619,938,61.06,337,43,1,35,8,11.67,74.47,0,105,2,52.5,9.55,33,2008,250000,500000 54,"Langeveldt, CK",3,SA,KKR+,Bowler,16,16,73,58.87,100,34.8,0,8,8,4,88.89,1,187,13,14.38,7.19,12,2011,100000,140000 55,"Laxman, VVS",3,IND,DC+,Batsman,8781,2,2338,71.23,0,0,1,282,52,15.67,105.62,5,0,0,0,0,0,2008,150000,375000 56,"Lee, B",2,AUS,KXI+,Bowler,1451,310,1100,82.45,377,29.2,0,103,25,11.44,121.18,6,1009,21,48.05,7.56,38.24,2008,300000,900000 57,"Maharoof, MF",2,SL,DD,Allrounder,556,25,1042,84.44,133,33.3,0,177,39,17.7,143.9,9,520,27,19.26,7.43,15.56,2008,150000,225000 58,"Malinga, SL",2,SL,MI,Bowler,275,101,327,73.81,185,31.1,0,64,17,5.82,100,4,1381,83,16.64,6.36,15.69,2008,200000,350000 59,"Mascarenhas, AD",2,ENG,RR+,Allrounder,0,0,245,95.33,13,63.2,0,74,27,8.22,101.37,1,331,19,17.42,7.01,14.95,2011,100000,100000 60,"Mathews, AD",1,SL,KKR+,Allrounder,1219,7,1447,82.59,42,43,0,376,65,25.07,123.28,12,537,15,35.8,8.2,26.33,2011,300000,950000 61,"McCullum, BB",2,NZ,KKR+,W. Keeper,3763,0,4511,89.62,0,0,1,1233,158,28.02,123.42,48,0,0,0,0,0,2008,175000,700000 62,"McDonald, AB",2,AUS,DD+,Allrounder,107,9,0,0,0,0,0,123,33,30.75,125.51,4,244,10,24.4,8.41,17.4,2011,50000,80000 63,"McGrath, GD",3,AUS,DD,Bowler,641,563,115,48.72,381,34,0,4,4,4,80,0,357,12,29.75,6.61,27,2008,350000,350000 64,Misbah-ul-Haq,3,PAK,RCB,Batsman,2173,0,2763,75.1,0,0,1,117,47,16.71,144.44,6,0,0,0,0,0,2010,100000,100000 65,"Mishra, A",2,IND,DD+,Bowler,392,43,5,27.77,19,40.1,0,186,31,10.94,102.2,3,1530,74,20.68,7.11,17.46,2011,100000,300000 66,"Mithun, A",1,IND,RCB,Bowler,120,9,51,92.72,3,60,0,32,11,8,133.33,1,435,6,72.5,9.89,44,2011,100000,260000 67,Mohammad Asif,2,PAK,DD,Bowler,141,106,34,34,46,42.1,0,3,3,1.5,50,0,296,8,37,9.25,24,2008,225000,650000 68,"Morkel, JA",2,SA,CSK,Allrounder,58,1,782,100.25,50,41.4,0,781,71,24.41,146.25,45,1899,69,27.52,8.25,20.01,2008,225000,675000 69,"Morkel, M",2,SA,RR+,Bowler,555,139,117,75.97,94,28.5,0,60,16,10,111.11,2,884,38,23.26,7.37,18.95,2011,100000,475000 70,"Muralitharan, M",3,SL,CSK+,Bowler,1261,800,674,77.56,534,35.2,0,20,6,3.33,66.67,0,1395,57,24.47,6.49,22.63,2008,250000,600000 71,"Nannes, DP",3,AUS,DD+,Bowler,0,0,1,50,1,42,0,4,3,4,30.77,0,627,24,26.13,7.17,21.92,2011,200000,650000 72,"Nayar, AM",2,IND,MI+,Allrounder,0,0,0,0,0,0,0,563,35,19.41,123.19,19,263,7,37.57,8.74,25.86,2011,50000,800000 73,"Nehra, A",2,IND,DD+,Bowler,77,44,141,57.31,157,36.6,0,38,22,19,82.61,1,1192,48,24.83,7.57,19.73,2011,200000,850000 74,"Noffke, AA",2,AUS,RCB,Allrounder,0,0,0,0,1,54,0,9,9,9,90,0,40,1,40,10,24,2010,20000,20000 75,"Ntini, M",2,SA,CSK,Bowler,699,390,199,66.77,266,32.6,0,11,11,11,61.11,0,242,7,34.57,6.91,30,2008,200000,200000 76,"Ojha, NV",2,IND,RR+,W. Keeper,0,0,1,14.28,0,0,0,960,94,22.33,117.94,50,0,0,0,0,0,2011,100000,270000 77,"Ojha, PP",1,IND,DC+,Bowler,70,62,41,43.61,20,41.7,0,10,3,1,30.3,0,1548,69,22.43,7.16,18.8,2011,200000,500000 78,"Oram, JDP",2,NZ,CSK+,Allrounder,1780,60,2377,87.16,168,39.7,0,106,41,13.25,98.15,5,327,9,36.33,9.26,23.67,2008,200000,675000 79,Pankaj Singh,2,IND,RCB+,Bowler,0,0,3,100,0,0,0,7,4,3.5,58.33,0,468,11,42.55,9.36,27.27,2011,50000,95000 80,"Patel, MM",2,IND,RR+,Bowler,60,35,74,66.07,86,36.6,0,39,23,7.8,235.49,0,1504,70,21.49,7.39,17.47,2008,100000,275000 81,"Patel, PA",2,IND,CSK+,W. Keeper,683,0,736,76.5,0,0,0,912,57,20.27,107.29,13,0,0,0,0,0,2008,150000,325000 82,"Pathan, IK",2,IND,KXIP+,Allrounder,1105,100,1468,78.96,165,34,0,929,60,23.82,128.31,34,1975,66,29.92,7.74,23.23,2008,200000,925000 83,"Pathan, YK",2,IND,RR+,Allrounder,0,0,810,113.6,33,45.1,0,1488,100,25.66,149.25,81,1139,36,31.64,7.2,26.36,2008,100000,475000 84,"Pietersen, KP",2,ENG,RCB+,Batsman,6654,5,4184,86.76,7,57.1,1,634,103,42.27,141.2,30,215,7,30.71,7.41,24.86,2009,1350000,1550000 85,"Pollock, SM",3,SA,MI,Allrounder,3781,421,3519,86.69,393,39.9,1,147,33,18.37,132.43,8,301,11,27.36,6.54,25,2008,200000,550000 86,"Pomersbach, LA",2,AUS,KXIP+,Batsman,0,0,0,0,0,0,0,244,79,27.11,130.48,12,0,0,0,0,0,2011,20000,50000 87,"Ponting, RT",3,AUS,KKR,Batsman,13218,5,13704,80.39,3,50,1,39,20,9.75,73.58,1,0,0,0,0,0,2008,335000,400000 88,"Powar, RR",2,IND,KXIP+,Bowler,13,6,163,62.69,34,45.1,0,67,28,22.33,104.69,1,527,13,40.54,7.42,32.77,2008,150000,170000 89,"Raina, SK",1,IND,CSK,Batsman,710,13,3525,92.71,16,61.9,0,2254,98,33.64,139.39,97,678,20,33.9,7.05,28.9,2008,125000,650000 90,"Ryder, JD",2,NZ,RCB+,Allrounder,1269,5,1100,89.72,11,34.8,0,604,86,21.57,131.88,19,303,8,37.88,7.73,29.5,2009,100000,160000 91,"Saha, WP",2,IND,KKR+,W. Keeper,74,0,4,80,0,0,0,372,59,28.62,128.28,16,0,0,0,0,0,2011,100000,100000 92,"Sangakkara, KC",2,SL,KXIP+,W. Keeper,9382,0,10472,75.75,0,0,1,1567,94,27.98,124.76,27,0,0,0,0,0,2008,250000,700000 93,"Sarwan, RR",2,WI,KXIP,Batsman,5842,23,5644,75.76,16,36.3,0,73,31,18.25,97.33,1,0,0,0,0,0,2008,225000,225000 94,"Sehwag, V",2,IND,DD,Batsman,8178,40,8090,104.68,95,45.4,1,1879,119,30.31,167.32,79,226,6,37.67,10.56,21.67,2011,400000,1800000 95,Shahid Afridi,2,PAK,DC,Allrounder,1716,48,7040,113.87,344,43.4,1,81,33,10.12,176.08,6,225,9,25,7.5,20,2008,225000,675000 96,"Sharma, I",1,IND,KKR+,Bowler,432,133,47,34.05,64,33.6,0,37,9,9.25,80.43,1,1176,36,32.67,7.63,23.61,2008,150000,950000 97,"Sharma, J",2,IND,CSK,Allrounder,0,0,35,116.66,1,150,0,36,16,9,120,2,419,12,34.92,9.88,21.33,2008,100000,225000 98,"Sharma, RG",1,IND,DC+,Batsman,0,0,1961,78.85,8,59.1,0,1975,109,31.35,129.17,82,408,14,29.14,8,21.86,2008,150000,750000 99,Shoaib Akhtar,3,PAK,KKR,Bowler,544,178,394,73.23,247,31.4,0,2,2,2,28.57,0,54,5,10.8,7.71,8.4,2008,250000,425000 100,Shoaib Malik,2,PAK,DD,Allrounder,1606,21,5253,78.37,139,47.6,1,52,24,13,110.63,0,85,2,42.5,10,25.5,2008,300000,500000 101,"Silva, LPC",2,SL,DC,Batsman,537,1,1587,70.4,1,42,0,40,23,20,153.84,1,21,0,0,21,0,2008,100000,100000 102,"Singh, RP",2,IND,DC+,Bowler,116,40,104,42.97,69,37.1,0,52,10,3.47,68.42,1,1892,74,25.57,7.75,19.78,2008,200000,875000 103,"Smith, DR",2,WI,DC+,Allrounder,320,7,925,97.26,56,44.8,0,439,87,25.82,148.81,24,338,9,37.56,8.14,27.89,2009,100000,100000 104,"Smith, GC",2,SA,RR+,Batsman,8042,8,6598,81.58,18,57,1,739,91,28.42,110.63,9,0,0,0,0,0,2008,250000,250000 105,Sohail Tanvir,2,PAK,RR,Bowler,17,5,268,94.03,55,37.4,0,36,13,12,124.13,1,266,22,12.09,6.46,11.2,2008,100000,100000 106,"Sreesanth, S",2,IND,KXIP+,Bowler,281,87,44,36.36,75,33,0,33,15,11,64.71,0,1031,35,29.46,8.25,21.43,2008,200000,625000 107,"Steyn, DW",2,SA,RCB+,Bowler,770,272,142,73.57,91,33.7,0,70,13,4.67,86.42,1,1304,59,22.1,6.58,20.15,2008,150000,325000 108,"Styris, SB",3,NZ,DC+,Allrounder,1586,20,4483,79.41,137,44.6,0,131,36,18.71,98.5,3,276,8,34.5,7.67,27,2008,175000,175000 109,"Symonds, A",3,AUS,DC+,Allrounder,1462,24,5088,92.44,133,44.6,0,974,117,36.07,129.87,41,674,20,33.7,7.7,26.35,2008,250000,1350000 110,"Taibu, T",2,ZIM,KKR,W. Keeper,1546,1,3393,67.58,2,42,1,31,15,10.33,119.23,0,0,0,0,0,0,2008,125000,125000 111,"Taylor, LRPL",2,NZ,RCB+,Batsman,2742,2,3185,81.77,0,0,1,895,81,27.97,130.28,45,24,0,0,12,0,2008,400000,1000000 112,"Tendulkar, SR",3,IND,MI,Batsman,15470,45,18426,86.23,154,52.2,1,2047,100,37.91,119.22,24,58,0,0,9.67,0,2011,400000,1800000 113,"Tiwary, MK",2,IND,DD+,Batsman,0,0,165,75.68,1,60,0,969,75,31.26,113.33,22,45,1,45,11.25,24,2008,100000,675000 114,"Tiwary, SS",1,IND,MI+,Batsman,0,0,49,87.5,0,0,0,836,42,25.33,119.6,32,0,0,0,0,0,2011,100000,1600000 115,"Tyagi, S",1,IND,CSK,Bowler,0,0,1,50,3,55,0,3,3,3,0.75,0,295,6,49.17,8.55,34.83,2011,50000,240000 116,Umar Gul,2,PAK,KKR,Bowler,541,157,368,69.04,154,32.2,0,39,24,13,205.26,5,184,12,15.33,8.17,11.2,2008,150000,150000 117,"Uthappa, RV",2,IND,RCB+,Batsman,0,0,786,91.92,0,0,0,1538,69,26.98,126.17,59,0,0,0,0,0,2008,200000,800000 118,"Vaas, 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 119,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 120,"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 121,"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 122,"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 123,"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 124,"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 125,"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 126,"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 127,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 128,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 129,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 130,"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 ================================================ FILE: March Cohort 2023/12_15_16_17_18_Statistics/datasets/SP_500_1987.csv ================================================ Date,Open,High,Low,Close,AdjClose,Volume 31-Dec-1986,243.37,244.03,241.28,242.17,242.17,"13,92,00,000" 2-Jan-1987,242.17,246.45,242.17,246.45,246.45,"9,18,80,000" 5-Jan-1987,246.45,252.57,246.45,252.19,252.19,"18,19,00,000" 6-Jan-1987,252.2,253.99,252.14,252.78,252.78,"18,93,00,000" 7-Jan-1987,252.78,255.72,252.65,255.33,255.33,"19,09,00,000" 8-Jan-1987,255.36,257.28,254.97,257.28,257.28,"19,45,00,000" 9-Jan-1987,257.26,259.2,256.11,258.73,258.73,"19,30,00,000" 12-Jan-1987,258.72,261.36,257.92,260.3,260.3,"18,42,00,000" 13-Jan-1987,260.3,260.45,259.21,259.95,259.95,"17,09,00,000" 14-Jan-1987,259.95,262.72,259.62,262.64,262.64,"21,42,00,000" 15-Jan-1987,262.65,266.68,262.64,265.49,265.49,"25,31,00,000" 16-Jan-1987,265.46,267.24,264.31,266.28,266.28,"21,84,00,000" 19-Jan-1987,266.26,269.34,264,269.34,269.34,"16,28,00,000" 20-Jan-1987,269.34,271.03,267.65,269.04,269.04,"22,48,00,000" 21-Jan-1987,269.04,270.87,267.35,267.84,267.84,"18,42,00,000" 22-Jan-1987,267.84,274.05,267.32,273.91,273.91,"18,87,00,000" 23-Jan-1987,273.91,280.96,268.41,270.1,270.1,"30,24,00,000" 26-Jan-1987,270.1,270.4,267.73,269.61,269.61,"13,89,00,000" 27-Jan-1987,269.61,274.31,269.61,273.75,273.75,"19,23,00,000" 28-Jan-1987,273.75,275.71,273.03,275.4,275.4,"19,58,00,000" 29-Jan-1987,275.4,276.85,272.54,274.24,274.24,"20,53,00,000" 30-Jan-1987,274.24,274.24,271.38,274.08,274.08,"16,34,00,000" 2-Feb-1987,274.08,277.35,273.16,276.45,276.45,"17,74,00,000" 3-Feb-1987,276.45,277.83,275.84,275.99,275.99,"19,81,00,000" 4-Feb-1987,275.99,279.65,275.35,279.64,279.64,"22,24,00,000" 5-Feb-1987,279.64,282.26,278.66,281.16,281.16,"25,67,00,000" 6-Feb-1987,281.16,281.79,279.87,280.04,280.04,"18,41,00,000" 09-Feb-1987,280.04,280.04,277.24,278.16,278.16,"14,33,00,000" 10-Feb-1987,278.16,278.16,273.49,275.07,275.07,"16,83,00,000" 11-Feb-1987,275.07,277.71,274.71,277.54,277.54,"17,24,00,000" 12-Feb-1987,277.54,278.04,273.89,275.62,275.62,"20,04,00,000" 13-Feb-1987,275.62,280.91,275.01,279.7,279.7,"18,44,00,000" 17-Feb-1987,279.7,285.49,279.7,285.49,285.49,"18,78,00,000" 18-Feb-1987,285.49,287.55,282.97,285.42,285.42,"21,82,00,000" 19-Feb-1987,285.42,286.24,283.84,285.57,285.57,"18,15,00,000" 20-Feb-1987,285.57,285.98,284.31,285.48,285.48,"17,58,00,000" 23-Feb-1987,285.48,285.5,279.37,282.38,282.38,"17,05,00,000" 24-Feb-1987,282.38,283.33,281.45,282.88,282.88,"15,13,00,000" 25-Feb-1987,282.88,285.35,282.14,284,284,"18,41,00,000" 26-Feb-1987,284,284.4,280.73,282.96,282.96,"16,58,00,000" 27-Feb-1987,282.96,284.55,282.77,284.2,284.2,"14,28,00,000" 2-Mar-1987,284.17,284.83,282.3,283,283,"15,67,00,000" 3-Mar-1987,283,284.19,282.92,284.12,284.12,"14,92,00,000" 4-Mar-1987,284.12,288.62,284.12,288.62,288.62,"19,84,00,000" 5-Mar-1987,288.62,291.24,288.6,290.52,290.52,"20,54,00,000" 6-Mar-1987,290.52,290.67,288.77,290.66,290.66,"18,16,00,000" 9-Mar-1987,290.66,290.66,287.12,288.3,288.3,"16,54,00,000" 10-Mar-1987,288.3,290.87,287.89,290.86,290.86,"17,48,00,000" 11-Mar-1987,290.87,292.51,289.33,290.31,290.31,"18,69,00,000" 12-Mar-1987,290.33,291.91,289.66,291.22,291.22,"17,45,00,000" 13-Mar-1987,291.22,291.79,289.88,289.89,289.89,"15,09,00,000" 16-Mar-1987,289.88,289.89,286.64,288.23,288.23,"13,49,00,000" 17-Mar-1987,288.09,292.47,287.96,292.47,292.47,"17,73,00,000" 18-Mar-1987,292.49,294.58,290.87,292.78,292.78,"19,81,00,000" 19-Mar-1987,292.73,294.46,292.26,294.08,294.08,"16,61,00,000" 20-Mar-1987,294.08,298.17,294.08,298.17,298.17,"23,40,00,000" 23-Mar-1987,298.16,301.17,297.5,301.16,301.16,"18,91,00,000" 24-Mar-1987,301.17,301.92,300.14,301.64,301.64,"18,99,00,000" 25-Mar-1987,301.52,301.85,299.36,300.38,300.38,"17,13,00,000" 26-Mar-1987,300.39,302.72,300.38,300.93,300.93,"19,60,00,000" 27-Mar-1987,300.96,301.41,296.06,296.13,296.13,"18,44,00,000" 30-Mar-1987,296.1,296.13,286.69,289.2,289.2,"20,84,00,000" 31-Mar-1987,289.21,291.87,289.07,291.7,291.7,"17,18,00,000" 1-Apr-1987,291.59,292.38,288.34,292.38,292.38,"18,26,00,000" 2-Apr-1987,292.41,294.47,292.02,293.63,293.63,"18,30,00,000" 3-Apr-1987,293.64,301.3,292.3,300.41,300.41,"21,34,00,000" 6-Apr-1987,300.46,302.21,300.41,301.95,301.95,"17,37,00,000" 7-Apr-1987,301.94,303.65,296.67,296.69,296.69,"18,64,00,000" 8-Apr-1987,296.72,299.2,295.18,297.26,297.26,"17,98,00,000" 9-Apr-1987,297.25,297.71,291.5,292.86,292.86,"18,03,00,000" 10-April-1987,292.82,293.74,290.94,292.49,292.49,"16,95,00,000" 13-April-1987,292.48,293.36,285.62,285.62,285.62,"18,10,00,000" 14-April-1987,285.61,285.62,275.67,279.16,279.16,"26,65,00,000" 15-April-1987,279.17,285.14,279.16,284.44,284.44,"19,82,00,000" 16-April-1987,284.45,289.57,284.44,286.91,286.91,"18,96,00,000" 20-April-1987,286.91,288.36,284.55,286.09,286.09,"13,91,00,000" 21-April-1987,285.88,293.07,282.89,293.07,293.07,"19,13,00,000" 22-April-1987,293.05,293.46,286.98,287.19,287.19,"18,59,00,000" 23-April-1987,287.19,289.12,284.28,286.82,286.82,"17,39,00,000" 24-April-1987,286.81,286.82,281.18,281.52,281.52,"17,80,00,000" 27-April-1987,281.52,284.45,276.22,281.83,281.83,"22,27,00,000" 28-April-1987,281.83,285.95,281.83,282.51,282.51,"18,01,00,000" 29-April-1987,282.58,286.42,282.58,284.57,284.57,"17,36,00,000" 30-April-1987,284.58,290.08,284.57,288.36,288.36,"18,31,00,000" 1-May-1987,286.99,289.71,286.52,288.03,288.03,"16,01,00,000" 4-May-1987,288.02,289.99,286.39,289.36,289.36,"14,06,00,000" 5-May-1987,289.36,295.4,289.34,295.34,295.34,"19,23,00,000" 6-May-1987,295.35,296.19,293.6,295.47,295.47,"19,66,00,000" 7-May-1987,295.45,296.8,294.07,294.71,294.71,"21,52,00,000" 8-May-1987,294.73,296.18,291.73,293.37,293.37,"16,19,00,000" 11-May-1987,293.37,298.69,291.55,291.57,291.57,"20,37,00,000" 12-May-1987,291.57,293.3,290.18,293.3,293.3,"15,53,00,000" 13-May-1987,293.31,294.54,290.74,293.98,293.98,"17,10,00,000" 14-May-1987,293.98,295.1,292.95,294.24,294.24,"15,20,00,000" 15-May-1987,294.23,294.24,287.11,287.43,287.43,"18,08,00,000" 18-May-1987,287.43,287.43,282.57,286.65,286.65,"17,42,00,000" 19-May-1987,286.66,287.39,278.83,279.62,279.62,"17,54,00,000" 20-May-1987,279.62,280.89,277.01,278.21,278.21,"20,68,00,000" 21-May-1987,278.23,282.31,278.21,280.17,280.17,"16,48,00,000" 22-May-1987,280.17,283.33,280.17,282.16,282.16,"13,58,00,000" 26-May-1987,282.16,289.11,282.16,289.11,289.11,"15,25,00,000" 27-May-1987,289.07,290.78,288.19,288.73,288.73,"17,14,00,000" 28-May-1987,288.73,291.5,286.33,290.76,290.76,"15,38,00,000" 29-May-1987,290.77,292.87,289.7,290.1,290.1,"15,35,00,000" 1-Jun-1987,290.12,291.96,289.23,289.83,289.83,"14,93,00,000" 2-Jun-1987,289.82,290.94,286.93,288.46,288.46,"15,34,00,000" 3-Jun-1987,288.56,293.47,288.56,293.47,293.47,"16,42,00,000" 4-Jun-1987,293.46,295.09,292.76,295.09,295.09,"14,03,00,000" 5-Jun-1987,295.11,295.11,292.8,293.45,293.45,"12,91,00,000" 8-Jun-1987,293.46,297.03,291.55,296.72,296.72,"13,64,00,000" 9-Jun-1987,296.72,297.59,295.9,297.28,297.28,"16,42,00,000" 10-Jun-1987,297.28,300.81,295.66,297.47,297.47,"19,74,00,000" 11-Jun-1987,297.5,298.94,297.47,298.73,298.73,"13,89,00,000" 12-Jun-1987,298.77,302.26,298.73,301.62,301.62,"17,51,00,000" 15-Jun-1987,301.62,304.11,301.62,303.14,303.14,"15,69,00,000" 16-Jun-1987,303.12,304.86,302.6,304.76,304.76,"15,78,00,000" 17-Jun-1987,304.77,305.74,304.03,304.81,304.81,"18,47,00,000" 18-Jun-1987,304.78,306.13,303.38,305.69,305.69,"16,86,00,000" 19-Jun-1987,305.71,306.97,305.55,306.97,306.97,"22,05,00,000" 22-Jun-1987,306.98,310.2,306.97,309.65,309.65,"17,82,00,000" 23-Jun-1987,309.66,310.27,307.48,308.43,308.43,"19,42,00,000" 24-Jun-1987,308.44,308.91,306.32,306.86,306.86,"15,38,00,000" 25-Jun-1987,306.87,309.44,306.86,308.96,308.96,"17,35,00,000" 26-Jun-1987,308.94,308.96,306.36,307.16,307.16,"15,05,00,000" 29-Jun-1987,307.15,308.15,306.75,307.9,307.9,"14,25,00,000" 30-Jun-1987,307.89,308,303.01,304,304,"16,55,00,000" 1-Jul-1987,303.99,304,302.53,302.94,302.94,"15,70,00,000" 2-Jul-1987,302.96,306.34,302.94,305.63,305.63,"15,49,00,000" 6-Jul-1987,305.64,306.75,304.23,304.92,304.92,"15,50,00,000" 7-Jul-1987,304.91,308.63,304.73,307.4,307.4,"20,07,00,000" 8-Jul-1987,307.41,308.48,306.01,308.29,308.29,"20,75,00,000" 9-Jul-1987,308.3,309.56,307.42,307.52,307.52,"19,54,00,000" 10-Jul-1987,307.55,308.4,306.96,308.37,308.37,"17,21,00,000" 13-Jul-1987,308.41,308.41,305.49,307.63,307.63,"15,25,00,000" 14-Jul-1987,307.67,310.69,307.46,310.68,310.68,"18,59,00,000" 15-Jul-1987,310.67,312.08,309.07,310.42,310.42,"20,23,00,000" 16-Jul-1987,311,312.83,310.42,312.7,312.7,"21,09,00,000" 17-Jul-1987,312.71,314.59,312.38,314.59,314.59,"21,00,00,000" 20-Jul-1987,314.56,314.59,311.24,311.39,311.39,"16,81,00,000" 21-Jul-1987,311.36,312.41,307.51,308.55,308.55,"18,66,00,000" 22-Jul-1987,308.56,309.12,307.22,308.47,308.47,"17,47,00,000" 23-Jul-1987,308.5,309.63,306.1,307.81,307.81,"16,37,00,000" 24-Jul-1987,307.82,309.28,307.78,309.27,309.27,"15,84,00,000" 27-Jul-1987,309.3,310.7,308.61,310.65,310.65,"15,20,00,000" 28-Jul-1987,310.65,312.33,310.28,312.33,312.33,"17,26,00,000" 29-Jul-1987,312.34,315.65,311.73,315.65,315.65,"19,62,00,000" 30-Jul-1987,315.69,318.53,315.65,318.05,318.05,"20,80,00,000" 31-Jul-1987,318.05,318.85,317.56,318.66,318.66,"18,19,00,000" 3-Aug-1987,318.62,320.26,316.52,317.57,317.57,"20,78,00,000" 4-Aug-1987,317.59,318.25,314.51,316.23,316.23,"16,65,00,000" 5-Aug-1987,316.25,319.74,316.23,318.45,318.45,"19,27,00,000" 6-Aug-1987,318.49,322.09,317.5,322.09,322.09,"19,20,00,000" 7-Aug-1987,322.1,324.15,321.82,323,323,"21,27,00,000" 10-Aug-1987,322.98,328,322.95,328,328,"18,72,00,000" 11-Aug-1987,328.02,333.4,328,333.33,333.33,"27,81,00,000" 12-Aug-1987,333.32,334.57,331.06,332.39,332.39,"23,58,00,000" 13-Aug-1987,332.38,335.52,332.38,334.65,334.65,"21,71,00,000" 14-Aug-1987,334.63,336.08,332.63,333.99,333.99,"19,61,00,000" 17-Aug-1987,333.98,335.43,332.88,334.11,334.11,"16,61,00,000" 18-Aug-1987,334.1,334.11,326.43,329.25,329.25,"19,84,00,000" 19-Aug-1987,329.26,329.89,326.54,329.83,329.83,"18,09,00,000" 20-Aug-1987,331.49,335.19,329.83,334.84,334.84,"19,66,00,000" 21-Aug-1987,334.85,336.37,334.3,335.9,335.9,"18,96,00,000" 24-Aug-1987,335.89,335.9,331.92,333.33,333.33,"14,94,00,000" 25-Aug-1987,333.37,337.89,333.33,336.77,336.77,"21,35,00,000" 26-Aug-1987,336.77,337.39,334.46,334.57,334.57,"19,62,00,000" 27-Aug-1987,334.56,334.57,331.1,331.38,331.38,"16,36,00,000" 28-Aug-1987,331.37,331.38,327.03,327.04,327.04,"15,63,00,000" 31-Aug-1987,327.03,330.09,326.99,329.8,329.8,"16,58,00,000" 1-Sep-1987,329.81,332.18,322.83,323.4,323.4,"19,35,00,000" 2-Sep-1987,323.4,324.53,318.76,321.68,321.68,"19,99,00,000" 3-Sep-1987,321.47,324.29,317.39,320.21,320.21,"16,52,00,000" 4-Sep-1987,320.21,322.03,316.53,316.7,316.7,"12,91,00,000" 8-Sep-1987,316.68,316.7,308.56,313.56,313.56,"24,29,00,000" 9-Sep-1987,313.6,315.41,312.29,313.92,313.92,"16,49,00,000" 10-Sep-1987,313.92,317.59,313.92,317.13,317.13,"17,98,00,000" 11-Sep-1987,317.14,322.45,317.13,321.98,321.98,"17,80,00,000" 14-Sep-1987,322.02,323.81,320.4,323.08,323.08,"15,44,00,000" 15-Sep-1987,323.07,323.08,317.63,317.74,317.74,"13,62,00,000" 16-Sep-1987,317.75,319.5,314.61,314.86,314.86,"19,57,00,000" 17-Sep-1987,314.94,316.08,313.45,314.93,314.93,"15,07,00,000" 18-Sep-1987,314.98,316.99,314.86,314.86,314.86,"18,81,00,000" 21-Sep-1987,314.92,317.66,310.12,310.54,310.54,"17,01,00,000" 22-Sep-1987,310.54,319.51,308.69,319.5,319.5,"20,95,00,000" 23-Sep-1987,319.49,321.83,319.12,321.19,321.19,"22,03,00,000" 24-Sep-1987,321.09,322.01,319.12,319.72,319.72,"16,22,00,000" 25-Sep-1987,319.72,320.55,318.1,320.16,320.16,"13,80,00,000" 28-Sep-1987,320.16,325.33,320.16,323.2,323.2,"18,81,00,000" 29-Sep-1987,323.2,324.63,320.27,321.69,321.69,"17,35,00,000" 30-Sep-1987,321.69,322.53,320.16,321.83,321.83,"18,31,00,000" 1-Oct-1987,321.83,327.34,321.83,327.33,327.33,"19,32,00,000" 2-Oct-1987,327.33,328.94,327.22,328.07,328.07,"18,91,00,000" 5-Oct-1987,328.07,328.57,326.09,328.08,328.08,"15,97,00,000" 6-Oct-1987,328.08,328.08,319.17,319.22,319.22,"17,56,00,000" 7-Oct-1987,319.22,319.39,315.78,318.54,318.54,"18,63,00,000" 8-Oct-1987,318.54,319.34,312.02,314.16,314.16,"19,87,00,000" 9-Oct-1987,314.16,315.04,310.97,311.07,311.07,"15,83,00,000" 12-Oct-1987,311.07,311.07,306.76,309.39,309.39,"14,19,00,000" 13-Oct-1987,309.39,314.53,309.39,314.52,314.52,"17,29,00,000" 14-Oct-1987,314.52,314.52,304.78,305.23,305.23,"20,74,00,000" 15-Oct-1987,305.21,305.23,298.07,298.08,298.08,"26,32,00,000" 16-Oct-1987,298.08,298.92,281.52,282.7,282.7,"33,85,00,000" 19-Oct-1987,282.7,282.7,224.83,224.84,224.84,"60,43,00,000" 20-Oct-1987,225.06,245.62,216.46,236.83,236.83,"60,81,00,000" 21-Oct-1987,236.83,259.27,236.83,258.38,258.38,"44,96,00,000" 22-Oct-1987,258.24,258.38,242.99,248.25,248.25,"39,22,00,000" 23-Oct-1987,248.29,250.7,242.76,248.22,248.22,"24,56,00,000" 26-Oct-1987,248.2,248.22,227.26,227.67,227.67,"30,88,00,000" 27-Oct-1987,227.67,237.81,227.67,233.19,233.19,"26,02,00,000" 28-Oct-1987,233.19,238.58,226.26,233.28,233.28,"27,94,00,000" 29-Oct-1987,233.31,246.69,233.28,244.77,244.77,"25,81,00,000" 30-Oct-1987,244.77,254.04,244.77,251.79,251.79,"30,34,00,000" 2-Nov-1987,251.73,255.75,249.15,255.75,255.75,"17,60,00,000" 3-Nov-1987,255.75,255.75,242.78,250.82,250.82,"22,78,00,000" 4-Nov-1987,250.81,251,246.34,248.96,248.96,"20,25,00,000" 5-Nov-1987,248.93,256.09,247.72,254.48,254.48,"22,60,00,000" 6-Nov-1987,254.49,257.21,249.68,250.41,250.41,"22,82,90,000" 9-Nov-1987,250.41,250.41,243.01,243.17,243.17,"16,06,90,000" 10-Nov-1987,243.14,243.17,237.64,239,239,"18,43,10,000" 11-Nov-1987,239.01,243.86,239,241.9,241.9,"14,78,50,000" 12-Nov-1987,241.93,249.9,241.9,248.52,248.52,"20,62,80,000" 13-Nov-1987,248.54,249.42,245.64,245.64,245.64,"17,49,20,000" 16-Nov-1987,245.69,249.54,244.98,246.76,246.76,"16,43,40,000" 17-Nov-1987,246.73,246.76,240.81,243.04,243.04,"14,82,40,000" 18-Nov-1987,243.09,245.55,240.67,245.55,245.55,"15,82,70,000" 19-Nov-1987,245.54,245.55,239.7,240.05,240.05,"15,71,40,000" 20-Nov-1987,240.04,242.01,235.89,242,242,"18,91,70,000" 23-Nov-1987,242,242.99,240.5,242.99,242.99,"14,31,60,000" 24-Nov-1987,242.98,247.9,242.98,246.39,246.39,"19,95,20,000" 25-Nov-1987,246.42,246.54,244.08,244.1,244.1,"13,97,80,000" 27-Nov-1987,244.11,244.12,240.34,240.34,240.34,"8,63,60,000" 30-Nov-1987,240.27,240.34,225.75,230.3,230.3,"26,89,10,000" 1-Dec-1987,230.32,234.02,230.3,232,232,"14,98,70,000" 2-Dec-1987,232.01,234.56,230.31,233.45,233.45,"14,88,90,000" 3-Dec-1987,233.46,233.9,225.21,225.21,225.21,"20,41,60,000" 4-Dec-1987,225.2,225.77,221.24,223.92,223.92,"18,48,00,000" 7-Dec-1987,223.98,228.77,223.92,228.76,228.76,"14,66,60,000" 8-Dec-1987,228.77,234.92,228.69,234.91,234.91,"22,73,10,000" 9-Dec-1987,234.91,240.09,233.83,238.89,238.89,"23,14,30,000" 10-Dec-1987,238.89,240.05,233.4,233.57,233.57,"18,89,60,000" 11-Dec-1987,233.6,235.48,233.35,235.32,235.32,"15,16,80,000" 14-Dec-1987,235.3,242.34,235.04,242.19,242.19,"18,76,80,000" 15-Dec-1987,242.19,245.59,241.31,242.81,242.81,"21,49,70,000" 16-Dec-1987,242.81,248.11,242.8,248.08,248.08,"19,38,20,000" 17-Dec-1987,248.08,248.6,242.96,242.98,242.98,"19,17,80,000" 18-Dec-1987,243.01,249.18,243.01,249.16,249.16,"27,62,20,000" 21-Dec-1987,249.14,250.25,248.3,249.54,249.54,"16,17,90,000" 22-Dec-1987,249.56,249.97,247.01,249.95,249.95,"19,26,50,000" 23-Dec-1987,249.96,253.35,249.95,253.16,253.16,"20,31,10,000" 24-Dec-1987,253.13,253.16,251.68,252.03,252.03,"10,88,00,000" 28-Dec-1987,252.01,252.02,244.19,245.57,245.57,"13,12,20,000" 29-Dec-1987,245.58,245.88,244.28,244.59,244.59,"11,15,80,000" 30-Dec-1987,244.63,248.06,244.59,247.86,247.86,"14,92,30,000" ================================================ FILE: March Cohort 2023/12_15_16_17_18_Statistics/datasets/data_loan.csv ================================================ ID,Default,Loan_type,Gender,Age,Degree,Income,Credit_score,Loan_length,Signers,Citizenship 1,0,Car ,Female,30,HS ,114885,641,0,2,Citizen 2,0,Home,Female,43,HS ,95770,534,7,2,Citizen 3,0,Home,Male ,39,HS ,94220,558,5,2,Citizen 4,0,Home,Male ,39,College ,58946,622,7,2,Citizen 5,0,Car ,Female,42,HS ,79754,702,2,1,Citizen 6,0,Car ,Female,57,Graduate,119627,624,3,2,Non-citizen 7,0,Car ,Male ,41,HS ,78765,594,3,2,Citizen 8,0,Car ,Female,53,HS ,123167,652,2,2,Non-citizen 9,0,Car ,Female,44,HS ,116175,694,3,2,Citizen 10,0,Car ,Female,57,College ,111085,586,3,2,Citizen 11,0,Car ,Male ,50,HS ,54387,593,2,2,Citizen 12,0,Car ,Male ,39,HS ,89468,600,0,2,Citizen 13,0,Car ,Male ,52,HS ,118613,622,0,2,Citizen 14,0,Car ,Male ,37,HS ,84429,615,0,2,Citizen 15,0,Car ,Female,46,College ,113281,577,3,2,Citizen 16,0,Home,Female,45,College ,93253,580,10,1,Citizen 17,0,Car ,Male ,45,College ,89304,665,3,1,Citizen 18,0,Car ,Male ,45,College ,126260,638,1,2,Citizen 19,0,Car ,Male ,42,HS ,77616,655,3,2,Citizen 20,0,Car ,Female,34,HS ,88568,630,0,2,Citizen 21,0,Car ,Male ,47,HS ,118155,730,3,1,Citizen 22,0,Car ,Female,50,College ,132630,625,3,2,Citizen 23,0,Home,Male ,46,HS ,99880,551,8,2,Citizen 24,1,Home,Female,53,Graduate,118848,701,8,1,Citizen 25,1,Home,Female,36,College ,109327,682,8,1,Citizen 26,0,Car ,Female,41,HS ,90653,641,0,2,Citizen 27,0,Car ,Female,44,Graduate,122276,709,3,1,Non-citizen 28,0,Car ,Male ,36,College ,97064,564,2,2,Citizen 29,0,Car ,Female,41,College ,107481,632,1,2,Citizen 30,0,Car ,Female,45,College ,113738,625,2,1,Citizen 31,0,Car ,Male ,46,HS ,90207,663,3,2,Citizen 32,0,Car ,Female,55,Graduate,120783,585,3,1,Citizen 33,0,Home,Male ,41,HS ,104234,586,7,2,Citizen 34,1,Car ,Female,46,College ,106650,697,3,2,Citizen 35,0,Home,Male ,56,College ,109333,586,11,2,Citizen 36,0,Car ,Male ,41,HS ,114009,780,3,2,Citizen 37,0,Car ,Female,33,College ,73802,551,1,2,Citizen 38,0,Car ,Male ,44,HS ,93651,639,1,2,Citizen 39,1,Home,Male ,36,HS ,90710,667,5,2,Citizen 40,0,Home,Female,43,HS ,75956,592,6,2,Citizen 41,0,Home,Male ,41,HS ,80480,689,5,2,Citizen 42,0,Home,Male ,50,HS ,113200,573,6,2,Citizen 43,0,Car ,Female,51,HS ,94323,632,0,2,Citizen 44,0,Car ,Female,39,College ,122986,629,3,1,Citizen 45,0,Car ,Female,50,HS ,118155,705,0,2,Citizen 46,0,Car ,Male ,41,HS ,92730,614,1,2,Citizen 47,0,Car ,Male ,39,HS ,106330,650,3,2,Citizen 48,0,Car ,Male ,47,HS ,92742,668,1,1,Citizen 49,0,Car ,Female,40,HS ,90204,714,0,1,Citizen 50,0,Car ,Male ,57,College ,105074,678,2,2,Citizen 51,0,Car ,Female,44,HS ,67887,546,0,2,Citizen 52,0,Car ,Female,52,College ,117994,753,0,2,Citizen 53,0,Car ,Female,47,HS ,115866,669,2,2,Citizen 54,0,Car ,Male ,39,HS ,86162,457,1,2,Citizen 55,0,Car ,Male ,45,HS ,97152,534,0,2,Citizen 56,0,Home,Female,50,HS ,127891,596,8,1,Citizen 57,0,Home,Female,40,HS ,86997,571,6,2,Citizen 58,0,Car ,Female,44,HS ,94050,620,0,2,Citizen 59,0,Car ,Male ,47,College ,84924,644,2,2,Citizen 60,0,Car ,Male ,48,HS ,92904,605,3,2,Citizen 61,0,Car ,Male ,40,HS ,94239,563,3,2,Citizen 62,0,Car ,Male ,46,HS ,117596,602,0,2,Citizen 63,0,Home,Male ,49,HS ,98343,686,10,2,Citizen 64,0,Car ,Female,36,HS ,70946,637,0,2,Citizen 65,0,Home,Male ,34,College ,120250,590,6,2,Citizen 66,0,Car ,Female,37,College ,94604,535,1,2,Citizen 67,0,Home,Male ,40,HS ,86004,524,7,2,Citizen 68,0,Car ,Male ,37,HS ,111413,592,0,2,Citizen 69,0,Home,Male ,48,HS ,98027,655,5,2,Citizen 70,0,Home,Male ,36,HS ,64901,483,6,2,Citizen 71,0,Car ,Female,42,HS ,96461,591,0,2,Citizen 72,1,Car ,Male ,48,HS ,112781,680,2,2,Citizen 73,0,Car ,Female,42,HS ,105276,690,2,1,Citizen 74,0,Home,Male ,36,HS ,89483,663,12,2,Citizen 75,0,Car ,Female,41,College ,107629,650,1,2,Citizen 76,0,Car ,Male ,50,HS ,88480,582,0,2,Citizen 77,0,Car ,Female,48,HS ,63332,597,1,2,Citizen 78,0,Car ,Female,47,College ,110486,605,2,2,Citizen 79,0,Car ,Male ,36,HS ,91003,584,1,2,Citizen 80,0,Car ,Female,46,Graduate,87658,688,2,2,Citizen 81,0,Home,Male ,35,HS ,67801,514,6,2,Citizen 82,0,Car ,Female,44,HS ,107880,654,0,2,Citizen 83,0,Car ,Female,42,College ,126326,608,3,1,Citizen 84,0,Car ,Female,43,HS ,94438,687,1,2,Citizen 85,0,Home,Male ,33,HS ,95915,557,5,2,Citizen 86,0,Car ,Male ,47,College ,75815,582,2,2,Citizen 87,0,Car ,Female,44,HS ,109771,596,2,2,Citizen 88,0,Home,Male ,41,HS ,99144,621,13,2,Citizen 89,0,Car ,Female,42,Graduate,118537,623,2,2,Citizen 90,0,Car ,Female,44,HS ,132693,660,2,1,Citizen 91,0,Car ,Male ,40,HS ,108362,703,1,2,Citizen 92,0,Car ,Female,40,HS ,104512,701,0,1,Citizen 93,0,Car ,Female,39,College ,113403,657,1,2,Non-citizen 94,0,Car ,Male ,41,HS ,82365,572,0,2,Citizen 95,0,Car ,Female,36,HS ,113265,596,1,2,Citizen 96,0,Car ,Female,38,HS ,84750,598,0,2,Citizen 97,0,Car ,Male ,49,HS ,100032,584,2,2,Citizen 98,0,Car ,Male ,32,College ,94471,468,3,2,Citizen 99,0,Car ,Male ,37,HS ,61508,562,2,2,Citizen 100,0,Car ,Female,55,College ,128124,691,3,2,Citizen 101,0,Car ,Male ,48,HS ,104622,626,2,1,Citizen 102,0,Car ,Male ,41,HS ,101887,526,1,2,Citizen 103,0,Car ,Male ,37,College ,94400,637,2,2,Citizen 104,0,Car ,Female,50,College ,102471,571,1,2,Citizen 105,0,Car ,Male ,54,Graduate,118278,471,3,2,Citizen 106,0,Car ,Female,62,College ,98850,622,1,2,Citizen 107,0,Car ,Female,51,College ,121978,644,3,2,Citizen 108,1,Home,Male ,47,HS ,116594,650,7,2,Citizen 109,0,Car ,Female,47,HS ,106577,712,2,1,Citizen 110,0,Home,Male ,42,HS ,82115,578,7,2,Citizen 111,0,Home,Female,37,HS ,88622,523,5,2,Citizen 112,0,Car ,Male ,41,College ,106759,580,3,1,Citizen 113,0,Home,Female,26,College ,95496,649,5,2,Citizen 114,0,Car ,Female,46,College ,78871,583,1,2,Citizen 115,0,Home,Female,37,College ,72233,583,6,2,Citizen 116,0,Car ,Male ,44,HS ,96670,720,1,2,Citizen 117,1,Car ,Female,56,College ,137669,689,1,1,Non-citizen 118,1,Car ,Female,50,College ,114114,696,2,2,Non-citizen 119,0,Car ,Female,41,College ,86496,604,2,2,Citizen 120,0,Car ,Female,31,HS ,97583,506,0,2,Citizen 121,0,Car ,Male ,37,HS ,120005,502,2,2,Citizen 122,0,Home,Male ,33,HS ,104788,535,6,2,Citizen 123,1,Car ,Female,54,HS ,91587,742,0,2,Citizen 124,0,Home,Male ,46,College ,113556,613,10,2,Citizen 125,0,Car ,Male ,52,Graduate,101998,636,3,2,Non-citizen 126,0,Car ,Male ,40,College ,111874,529,3,2,Citizen 127,0,Car ,Female,32,HS ,122053,540,0,2,Citizen 128,0,Home,Male ,44,HS ,83691,521,5,2,Citizen 129,0,Car ,Female,38,HS ,106560,558,0,2,Citizen 130,0,Car ,Male ,41,College ,98747,596,1,2,Citizen 131,0,Car ,Male ,37,HS ,79054,632,0,2,Citizen 132,0,Home,Male ,42,HS ,70309,572,5,2,Citizen 133,0,Home,Male ,37,HS ,79672,552,3,2,Citizen 134,0,Car ,Female,41,HS ,96418,657,1,1,Citizen 135,0,Home,Male ,32,HS ,105689,525,6,2,Citizen 136,0,Home,Female,41,College ,105493,718,8,2,Citizen 137,1,Car ,Male ,48,HS ,129293,706,2,2,Citizen 138,0,Car ,Male ,38,HS ,93754,588,0,2,Citizen 139,0,Car ,Female,51,HS ,105797,580,2,2,Citizen 140,0,Car ,Male ,41,College ,112033,563,2,2,Citizen 141,0,Car ,Male ,48,College ,94165,583,2,2,Citizen 142,0,Home,Male ,30,HS ,71571,566,1,2,Citizen 143,1,Car ,Female,44,College ,104553,688,2,2,Citizen 144,0,Home,Male ,38,Graduate,107839,682,12,2,Citizen 145,0,Car ,Female,42,HS ,86878,487,2,2,Citizen 146,0,Car ,Male ,56,College ,114940,633,2,2,Citizen 147,0,Car ,Male ,47,College ,85479,653,1,2,Citizen 148,0,Home,Female,42,HS ,84707,627,6,2,Citizen 149,0,Home,Male ,47,College ,65487,504,9,2,Citizen 150,0,Car ,Female,35,HS ,95957,625,0,2,Citizen 151,0,Car ,Male ,35,HS ,121765,568,3,2,Citizen 152,0,Car ,Male ,46,College ,94219,701,1,2,Citizen 153,0,Car ,Female,41,HS ,95467,639,1,2,Non-citizen 154,0,Car ,Male ,40,HS ,81158,625,1,2,Citizen 155,0,Home,Male ,30,HS ,55276,535,5,2,Citizen 156,1,Car ,Female,39,College ,92009,738,1,2,Citizen 157,0,Car ,Female,31,HS ,99295,572,0,2,Citizen 158,0,Home,Male ,36,College ,105141,593,8,2,Citizen 159,0,Car ,Male ,46,HS ,83935,767,1,2,Citizen 160,0,Home,Male ,39,HS ,112444,537,3,2,Citizen 161,0,Car ,Male ,49,HS ,109448,601,2,2,Citizen 162,0,Car ,Female,37,HS ,88295,601,0,2,Citizen 163,0,Car ,Male ,52,HS ,101542,552,2,2,Citizen 164,0,Home,Male ,49,HS ,105387,585,12,2,Citizen 165,0,Car ,Male ,36,HS ,105836,505,1,2,Citizen 166,0,Car ,Male ,47,HS ,98693,662,2,2,Citizen 167,0,Home,Male ,45,HS ,103574,521,10,2,Citizen 168,0,Home,Male ,44,HS ,85816,605,9,2,Citizen 169,0,Car ,Male ,52,HS ,105639,822,0,2,Citizen 170,0,Home,Female,44,HS ,110446,621,6,2,Citizen 171,0,Car ,Male ,40,College ,96149,560,2,2,Citizen 172,0,Car ,Female,56,HS ,129233,659,0,2,Citizen 173,0,Car ,Male ,48,College ,142083,683,2,2,Citizen 174,0,Car ,Female,37,HS ,96187,600,1,2,Citizen 175,0,Home,Female,43,College ,115134,578,10,2,Citizen 176,0,Home,Male ,36,HS ,53709,541,7,2,Citizen 177,0,Car ,Female,55,HS ,117622,575,0,2,Non-citizen 178,0,Car ,Male ,50,College ,128119,822,3,2,Citizen 179,0,Car ,Female,41,HS ,90013,595,2,2,Citizen 180,0,Car ,Male ,31,HS ,61786,533,0,2,Citizen 181,0,Home,Female,34,College ,64024,693,5,2,Citizen 182,0,Car ,Male ,45,College ,75261,602,2,2,Citizen 183,0,Home,Female,42,College ,106206,570,4,2,Citizen 184,0,Car ,Female,43,College ,120510,595,0,2,Citizen 185,0,Home,Male ,33,HS ,116700,549,1,2,Citizen 186,0,Home,Male ,32,HS ,87202,575,3,2,Citizen 187,0,Car ,Male ,40,HS ,83125,569,0,2,Citizen 188,0,Car ,Male ,42,HS ,83844,650,0,2,Citizen 189,0,Car ,Male ,43,HS ,100112,453,2,2,Citizen 190,0,Car ,Male ,49,Graduate,126511,716,3,2,Citizen 191,0,Car ,Male ,50,HS ,106383,535,1,2,Non-citizen 192,0,Car ,Male ,46,HS ,102451,559,2,2,Citizen 193,1,Home,Female,38,Graduate,87650,641,5,2,Citizen 194,0,Home,Male ,46,HS ,105334,534,10,2,Citizen 195,0,Car ,Male ,39,HS ,101608,522,0,2,Citizen 196,0,Home,Male ,42,HS ,108430,629,13,2,Citizen 197,0,Car ,Female,48,HS ,115372,591,2,2,Citizen 198,0,Car ,Female,41,HS ,101821,655,2,2,Citizen 199,0,Car ,Male ,39,HS ,101002,629,3,2,Citizen 200,0,Car ,Female,48,College ,108378,688,0,2,Non-citizen 201,0,Car ,Male ,44,Graduate,87408,537,3,2,Citizen 202,0,Home,Male ,53,College ,112383,671,8,2,Citizen 203,1,Home,Male ,31,HS ,108061,614,9,2,Citizen 204,0,Car ,Female,39,HS ,101420,701,2,2,Citizen 205,0,Car ,Male ,35,College ,93252,517,1,2,Citizen 206,0,Car ,Male ,45,HS ,79590,629,2,2,Citizen 207,0,Car ,Female,53,HS ,106569,701,1,2,Citizen 208,0,Home,Male ,45,College ,109824,708,9,2,Non-citizen 209,0,Home,Female,36,HS ,112640,649,4,2,Citizen 210,0,Car ,Female,47,HS ,71524,644,0,2,Citizen 211,0,Car ,Female,29,HS ,45103,585,0,2,Citizen 212,0,Car ,Female,51,HS ,106947,700,1,2,Citizen 213,0,Home,Female,47,HS ,90422,598,3,2,Citizen 214,1,Car ,Male ,46,HS ,120777,658,2,2,Citizen 215,0,Home,Male ,42,HS ,113592,588,5,2,Citizen 216,0,Home,Male ,36,HS ,97782,638,6,2,Citizen 217,0,Car ,Female,43,HS ,109092,649,0,2,Citizen 218,0,Home,Male ,44,HS ,88268,608,7,1,Citizen 219,1,Car ,Male ,50,HS ,97334,626,0,2,Citizen 220,0,Car ,Female,39,HS ,95519,563,1,2,Citizen 221,0,Home,Male ,48,College ,57502,579,9,2,Citizen 222,0,Home,Male ,33,HS ,98858,616,7,2,Citizen 223,0,Car ,Male ,44,HS ,77614,614,0,2,Citizen 224,0,Car ,Male ,46,College ,89245,649,2,2,Citizen 225,0,Car ,Female,40,College ,95368,638,1,2,Citizen 226,0,Car ,Female,42,HS ,123815,684,3,2,Citizen 227,0,Home,Male ,50,HS ,106085,664,2,2,Citizen 228,0,Car ,Male ,50,HS ,118184,645,1,2,Citizen 229,0,Car ,Male ,31,HS ,75153,522,0,2,Citizen 230,0,Car ,Female,38,HS ,102509,512,3,2,Citizen 231,0,Car ,Male ,47,HS ,110604,576,1,2,Citizen 232,1,Home,Female,51,Graduate,110699,683,3,2,Citizen 233,0,Home,Female,43,HS ,72684,625,4,2,Citizen 234,0,Car ,Male ,42,HS ,81629,690,0,2,Citizen 235,0,Home,Male ,41,HS ,47859,517,1,2,Citizen 236,0,Car ,Male ,38,HS ,97805,561,0,2,Citizen 237,0,Car ,Female,48,College ,95118,696,1,1,Citizen 238,0,Home,Male ,35,HS ,85182,603,4,2,Citizen 239,0,Car ,Female,41,HS ,69221,697,0,2,Citizen 240,0,Home,Male ,38,College ,84649,578,6,2,Citizen 241,0,Car ,Female,49,College ,101001,719,1,2,Citizen 242,0,Home,Male ,39,HS ,87312,571,3,2,Citizen 243,1,Car ,Female,44,College ,100978,685,2,2,Citizen 244,0,Home,Female,45,HS ,98420,592,3,2,Citizen 245,0,Car ,Male ,35,HS ,76690,517,0,2,Citizen 246,0,Car ,Male ,49,HS ,105512,669,0,2,Citizen 247,0,Car ,Male ,38,HS ,83456,512,2,2,Citizen 248,0,Car ,Male ,49,College ,107705,695,3,2,Citizen 249,0,Car ,Female,45,HS ,102789,549,1,2,Citizen 250,0,Car ,Male ,37,College ,65698,620,1,2,Citizen 251,0,Car ,Male ,49,HS ,84677,566,0,2,Citizen 252,0,Car ,Male ,53,HS ,126542,659,3,2,Citizen 253,0,Car ,Female,46,HS ,117076,584,2,2,Citizen 254,0,Car ,Male ,53,HS ,105099,583,0,2,Citizen 255,0,Car ,Male ,51,College ,98294,558,3,2,Citizen 256,0,Car ,Male ,38,HS ,93933,637,1,2,Citizen 258,0,Home,Female,34,HS ,82406,601,3,2,Citizen 259,0,Car ,Male ,47,HS ,96505,635,1,2,Citizen 260,0,Car ,Male ,43,College ,102415,688,2,2,Citizen 261,0,Car ,Female,45,HS ,94892,643,0,2,Citizen 262,0,Car ,Female,42,HS ,80752,616,0,2,Citizen 263,0,Home,Female,34,HS ,107424,539,1,2,Citizen 264,0,Car ,Female,45,Graduate,127969,662,3,2,Citizen 265,0,Home,Female,50,HS ,107040,584,4,2,Citizen 266,0,Home,Male ,42,HS ,56255,630,6,2,Citizen 267,0,Car ,Male ,40,College ,85385,678,3,2,Citizen 268,0,Home,Male ,42,HS ,78929,579,1,2,Citizen 269,0,Car ,Female,48,HS ,101432,598,1,2,Citizen 270,0,Car ,Female,44,College ,138385,558,2,2,Citizen 271,0,Car ,Female,57,Graduate,93078,707,3,1,Citizen 272,0,Home,Male ,38,College ,95156,619,7,2,Citizen 273,0,Home,Male ,42,HS ,96701,588,8,1,Citizen 274,0,Home,Male ,44,HS ,100606,579,9,1,Citizen 275,0,Home,Male ,29,HS ,94702,464,4,2,Citizen 276,0,Car ,Male ,44,College ,116089,608,1,2,Citizen 277,0,Car ,Female,44,HS ,91502,726,0,2,Citizen 278,0,Home,Male ,41,HS ,79165,634,3,2,Citizen 279,0,Car ,Male ,40,HS ,62904,666,0,2,Citizen 280,0,Home,Male ,40,HS ,111836,616,3,2,Non-citizen 281,0,Car ,Female,37,HS ,108773,579,0,2,Citizen 282,0,Car ,Male ,44,College ,127623,642,2,2,Citizen 283,1,Car ,Male ,47,HS ,90080,698,0,2,Citizen 284,1,Home,Female,43,HS ,112638,564,6,2,Citizen 285,0,Car ,Male ,40,HS ,69589,630,0,2,Citizen 286,0,Home,Male ,30,HS ,89490,565,2,2,Citizen 287,0,Car ,Male ,35,HS ,92995,463,0,2,Citizen 288,0,Car ,Male ,44,College ,92371,624,3,2,Citizen 289,0,Car ,Male ,36,HS ,114615,684,2,2,Citizen 290,0,Home,Male ,39,HS ,103997,590,6,2,Citizen 291,0,Home,Female,38,College ,108771,592,13,2,Citizen 292,0,Home,Male ,33,HS ,92762,524,10,2,Citizen 293,0,Car ,Female,49,College ,93237,666,1,2,Citizen 294,0,Car ,Male ,37,HS ,91143,571,0,2,Citizen 295,0,Car ,Female,43,HS ,97926,510,0,2,Citizen 296,0,Home,Female,50,HS ,139022,682,10,1,Non-citizen 297,0,Car ,Male ,32,HS ,64108,629,0,2,Citizen 298,0,Home,Male ,39,HS ,91205,519,1,2,Citizen 299,0,Car ,Male ,41,HS ,98098,679,1,2,Citizen 300,0,Car ,Male ,33,HS ,73509,510,0,2,Citizen 301,0,Car ,Female,52,HS ,134028,625,2,2,Citizen 302,0,Home,Male ,31,HS ,94993,654,5,2,Citizen 303,0,Car ,Male ,37,HS ,66753,504,0,2,Citizen 304,0,Home,Female,40,HS ,97553,631,1,2,Citizen 305,0,Home,Male ,37,HS ,47752,512,7,2,Citizen 306,0,Home,Male ,38,HS ,94635,629,9,2,Citizen 307,0,Home,Female,44,HS ,104991,506,6,2,Citizen 308,0,Car ,Female,42,HS ,111666,479,0,2,Citizen 309,0,Car ,Male ,45,College ,90679,617,1,2,Citizen 310,0,Car ,Male ,36,College ,90156,659,2,2,Citizen 311,0,Home,Male ,45,HS ,111981,715,7,2,Citizen 312,0,Car ,Male ,41,College ,123681,575,1,2,Citizen 313,0,Car ,Male ,43,HS ,106828,673,0,2,Citizen 314,0,Car ,Male ,44,HS ,126459,647,3,2,Citizen 315,0,Car ,Male ,45,HS ,102523,592,0,2,Citizen 316,0,Car ,Male ,33,HS ,60002,728,1,2,Citizen 317,0,Home,Male ,37,HS ,90529,596,8,2,Citizen 318,0,Car ,Female,41,HS ,90085,616,0,2,Citizen 319,0,Home,Male ,35,HS ,94422,577,4,2,Citizen 320,0,Car ,Male ,47,HS ,107142,591,1,2,Citizen 321,0,Home,Male ,30,HS ,117138,535,6,2,Citizen 322,0,Car ,Female,35,HS ,74330,638,1,1,Citizen 323,0,Car ,Female,45,HS ,113073,599,0,2,Citizen 324,0,Home,Male ,35,HS ,73092,523,6,2,Citizen 325,0,Car ,Male ,52,HS ,109324,582,2,2,Citizen 326,0,Car ,Male ,38,College ,105928,614,2,2,Citizen 327,0,Car ,Female,37,College ,107889,597,0,2,Citizen 328,0,Home,Male ,37,HS ,93045,443,6,2,Citizen 329,0,Car ,Female,46,HS ,92836,624,0,1,Citizen 330,0,Car ,Male ,45,HS ,85088,622,1,2,Citizen 331,0,Home,Male ,34,HS ,61951,494,4,2,Citizen 332,0,Car ,Male ,49,HS ,104406,643,1,2,Citizen 333,0,Car ,Male ,42,HS ,77268,634,2,2,Citizen 334,0,Home,Female,40,College ,99521,595,7,2,Citizen 335,0,Home,Male ,47,HS ,111971,560,2,2,Citizen 336,0,Car ,Male ,42,HS ,85432,530,1,2,Citizen 337,0,Car ,Male ,38,HS ,108023,626,0,2,Citizen 338,0,Car ,Male ,43,HS ,89821,631,1,2,Citizen 339,1,Car ,Male ,56,Graduate,116286,701,2,2,Non-citizen 340,0,Car ,Female,44,HS ,86820,744,0,1,Citizen 341,0,Home,Male ,37,HS ,92839,718,8,2,Citizen 342,0,Car ,Female,43,College ,98519,637,2,2,Citizen 343,0,Car ,Male ,42,HS ,72996,553,0,2,Citizen 344,0,Car ,Female,51,College ,109867,635,3,2,Citizen 345,0,Car ,Female,51,College ,104754,627,2,2,Citizen 346,0,Car ,Male ,36,HS ,109294,509,1,2,Citizen 347,0,Car ,Male ,58,HS ,111420,633,2,2,Citizen 348,1,Car ,Female,45,College ,111484,644,2,1,Non-citizen 349,0,Home,Male ,33,College ,95112,666,6,2,Citizen 350,0,Car ,Female,42,HS ,100902,564,0,2,Citizen 351,0,Home,Female,48,Graduate,86260,668,11,2,Citizen 352,0,Home,Male ,44,HS ,76710,542,6,2,Citizen 353,0,Car ,Male ,40,College ,103332,614,1,2,Citizen 354,0,Home,Male ,47,HS ,104901,518,9,2,Citizen 355,0,Car ,Female,42,HS ,100738,653,0,2,Citizen 356,0,Home,Female,39,HS ,109824,650,7,2,Citizen 357,0,Car ,Male ,35,HS ,105831,540,1,2,Citizen 358,0,Car ,Female,45,College ,98573,656,0,2,Citizen 359,0,Car ,Female,41,College ,108167,574,2,2,Citizen 360,0,Home,Male ,38,HS ,99921,499,10,2,Citizen 361,0,Home,Male ,33,HS ,78738,433,5,2,Citizen 362,0,Home,Female,37,Graduate,106738,592,8,2,Citizen 363,0,Car ,Male ,40,HS ,95502,567,0,2,Citizen 364,0,Car ,Male ,39,HS ,91658,403,1,2,Citizen 365,0,Car ,Female,43,HS ,76332,674,0,2,Citizen 366,1,Car ,Female,51,Graduate,124592,796,3,2,Non-citizen 367,0,Car ,Male ,49,College ,84298,589,3,2,Citizen 368,0,Home,Male ,43,HS ,119009,587,8,2,Citizen 369,0,Car ,Male ,40,HS ,90963,627,2,2,Citizen 370,0,Car ,Female,40,HS ,115422,672,0,1,Citizen 371,0,Car ,Male ,27,HS ,82144,550,0,2,Citizen 372,0,Home,Male ,39,HS ,90919,527,4,2,Citizen 373,1,Car ,Female,53,College ,101185,709,3,1,Citizen 374,0,Car ,Male ,38,HS ,104208,667,0,2,Citizen 375,0,Home,Male ,44,HS ,84069,720,4,2,Citizen 376,0,Home,Male ,26,HS ,79546,546,4,2,Citizen 377,0,Car ,Male ,42,College ,81982,454,1,2,Citizen 378,0,Car ,Female,51,HS ,118404,655,2,1,Citizen 379,0,Car ,Male ,46,College ,83902,641,2,2,Citizen 380,0,Car ,Male ,53,HS ,132712,647,2,2,Citizen 381,0,Car ,Female,49,Graduate,105788,682,3,2,Citizen 382,0,Car ,Female,51,HS ,70941,634,1,2,Citizen 383,0,Home,Male ,38,HS ,100456,455,8,2,Citizen 384,0,Home,Female,37,HS ,98387,532,2,2,Citizen 385,0,Home,Male ,32,HS ,78638,501,5,2,Citizen 386,1,Car ,Male ,42,College ,133041,641,0,2,Citizen 387,0,Home,Male ,44,HS ,106028,586,4,2,Citizen 388,1,Car ,Male ,41,College ,118404,690,0,2,Citizen 389,0,Home,Female,44,HS ,73558,641,8,2,Citizen 390,0,Car ,Male ,51,HS ,121742,675,1,2,Citizen 391,0,Car ,Female,44,HS ,123021,693,1,2,Citizen 392,0,Car ,Male ,49,HS ,121560,664,3,2,Citizen 393,0,Home,Male ,44,HS ,127522,595,7,2,Citizen 394,0,Home,Female,45,HS ,79765,514,3,2,Citizen 395,0,Home,Female,35,HS ,110407,636,2,2,Citizen 396,0,Car ,Male ,40,HS ,106680,711,1,2,Citizen 397,0,Car ,Female,51,Graduate,102979,693,3,1,Non-citizen 398,0,Car ,Male ,46,HS ,123397,700,2,1,Non-citizen 399,0,Home,Male ,26,HS ,80687,502,4,2,Citizen 400,0,Car ,Female,35,College ,96089,586,1,1,Citizen 401,0,Car ,Female,44,College ,110755,749,2,2,Citizen 402,0,Car ,Female,47,Graduate,99830,675,3,2,Non-citizen 403,0,Home,Female,37,HS ,121760,687,5,2,Citizen 404,0,Home,Male ,41,HS ,99238,518,7,2,Citizen 405,0,Car ,Male ,38,HS ,98333,600,1,1,Citizen 406,0,Car ,Female,53,College ,95259,685,2,2,Citizen 407,0,Car ,Female,50,Graduate,120905,641,2,2,Citizen 408,0,Home,Male ,44,HS ,53413,505,3,2,Citizen 409,0,Home,Female,46,HS ,81750,579,7,2,Citizen 410,0,Car ,Male ,32,HS ,83209,576,0,2,Citizen 411,0,Car ,Female,29,College ,84191,588,0,2,Citizen 412,0,Car ,Female,42,HS ,108036,605,0,2,Citizen 413,1,Car ,Female,41,College ,99189,540,0,2,Non-citizen 414,0,Home,Female,44,HS ,79854,684,2,2,Citizen 415,0,Car ,Male ,52,HS ,70678,570,0,2,Citizen 416,0,Car ,Male ,49,Graduate,107097,651,2,2,Citizen 417,0,Car ,Male ,56,College ,120219,639,1,2,Citizen 418,0,Car ,Female,43,HS ,118860,539,1,2,Citizen 419,0,Car ,Male ,47,HS ,111913,683,0,2,Citizen 420,0,Home,Male ,46,HS ,72652,569,2,2,Citizen 421,0,Home,Male ,40,College ,116710,501,5,2,Citizen 422,1,Car ,Female,46,College ,115253,695,3,2,Citizen 423,0,Car ,Female,54,HS ,74634,618,0,2,Citizen 424,0,Car ,Female,56,HS ,101975,659,1,2,Citizen 425,0,Car ,Male ,35,College ,127232,583,2,2,Citizen 426,0,Home,Male ,41,HS ,84763,623,2,2,Citizen 427,0,Car ,Male ,44,HS ,140787,573,1,2,Citizen 428,0,Car ,Female,45,HS ,94827,530,0,2,Citizen 429,0,Car ,Female,40,HS ,89780,625,0,2,Citizen 430,0,Car ,Male ,38,HS ,85635,527,0,2,Citizen 431,0,Car ,Male ,39,HS ,81346,631,0,2,Citizen 432,0,Car ,Male ,39,HS ,106550,560,2,2,Citizen 433,0,Car ,Male ,49,HS ,79303,702,0,2,Citizen 434,0,Car ,Female,48,Graduate,89068,595,3,2,Citizen 435,0,Home,Female,44,College ,111674,701,10,2,Citizen 436,0,Car ,Male ,57,HS ,101010,659,3,2,Citizen 437,0,Home,Male ,40,HS ,66717,645,0,2,Citizen 438,0,Car ,Male ,39,College ,102409,625,2,2,Citizen 439,0,Car ,Male ,49,College ,83150,607,3,2,Citizen 440,0,Car ,Male ,44,HS ,93250,532,2,2,Citizen 441,0,Home,Female,30,HS ,119535,614,5,2,Citizen 442,0,Car ,Male ,36,College ,131647,596,1,2,Citizen 443,0,Car ,Male ,43,HS ,91123,613,2,2,Citizen 444,0,Car ,Female,36,HS ,106879,623,2,2,Citizen 445,0,Car ,Female,41,HS ,123004,699,0,2,Non-citizen 446,0,Car ,Female,50,College ,90253,615,3,2,Citizen 447,0,Car ,Female,41,HS ,112445,572,1,2,Citizen 448,0,Home,Female,33,HS ,87944,533,5,2,Citizen 449,0,Car ,Male ,43,College ,108624,611,2,2,Citizen 450,0,Car ,Female,55,College ,125573,597,3,2,Citizen 451,0,Car ,Female,37,HS ,72853,452,0,2,Citizen 452,0,Car ,Male ,42,HS ,122876,593,2,2,Citizen 453,0,Car ,Female,42,Graduate,76813,599,1,2,Citizen 454,0,Car ,Male ,38,College ,114388,552,0,2,Citizen 455,0,Car ,Male ,44,HS ,74075,586,3,2,Citizen 456,0,Car ,Female,40,HS ,116857,616,0,2,Citizen 457,0,Car ,Female,39,HS ,113036,626,3,2,Citizen 458,0,Home,Male ,29,HS ,100284,543,9,2,Citizen 459,0,Car ,Female,51,HS ,92992,650,2,1,Citizen 460,0,Home,Male ,33,HS ,77608,563,2,2,Citizen 461,0,Home,Male ,32,HS ,79725,462,10,2,Citizen 462,0,Car ,Male ,48,HS ,67437,642,3,2,Citizen 463,0,Car ,Female,33,HS ,93955,581,0,2,Citizen 464,0,Home,Male ,55,HS ,102176,607,13,2,Citizen 465,0,Car ,Male ,41,College ,101409,665,2,2,Citizen 466,0,Car ,Male ,38,HS ,75265,604,0,2,Citizen 467,0,Car ,Female,40,HS ,69944,582,1,2,Citizen 468,0,Car ,Male ,39,HS ,75964,559,2,2,Citizen 469,0,Car ,Female,33,HS ,71606,563,0,1,Citizen 470,0,Home,Male ,43,HS ,109159,566,8,2,Citizen 471,0,Home,Male ,42,HS ,119276,640,7,2,Citizen 472,0,Home,Female,45,HS ,80671,699,7,2,Citizen 473,1,Car ,Female,42,Graduate,102688,656,2,2,Citizen 474,0,Car ,Male ,47,HS ,97613,598,1,2,Citizen 475,0,Car ,Male ,34,College ,114805,544,1,2,Citizen 476,0,Car ,Female,48,HS ,88976,572,1,2,Citizen 477,0,Home,Male ,47,College ,88143,630,8,2,Citizen 478,0,Home,Female,36,HS ,55916,563,6,2,Citizen 479,0,Car ,Male ,45,HS ,68525,644,0,2,Citizen 480,0,Car ,Male ,30,HS ,95725,653,0,2,Citizen 481,0,Home,Male ,46,HS ,101531,578,11,2,Citizen 482,0,Car ,Male ,47,HS ,110843,607,2,2,Citizen 483,0,Car ,Male ,31,HS ,65063,477,0,2,Citizen 484,0,Car ,Female,35,HS ,89168,632,1,2,Citizen 485,0,Car ,Female,47,HS ,120479,710,2,2,Citizen 486,0,Car ,Male ,34,HS ,92419,560,1,2,Citizen 487,0,Car ,Male ,51,HS ,80782,562,0,2,Citizen 488,0,Home,Male ,45,HS ,90216,581,6,2,Citizen 489,0,Car ,Male ,38,HS ,82131,600,0,2,Citizen 490,0,Car ,Female,44,Graduate,114328,556,3,2,Citizen 491,0,Car ,Male ,48,HS ,106250,621,0,2,Citizen 492,0,Home,Female,44,College ,85907,491,6,2,Citizen 493,0,Home,Female,36,HS ,113961,659,4,2,Citizen 494,0,Car ,Male ,54,HS ,80983,591,1,2,Citizen 495,0,Car ,Male ,24,HS ,64642,652,0,2,Citizen 496,0,Home,Male ,33,HS ,97980,658,7,2,Citizen 497,0,Home,Female,40,College ,69611,609,7,2,Citizen 498,0,Home,Male ,41,HS ,93844,638,6,2,Citizen 499,0,Car ,Male ,34,HS ,88094,618,0,2,Citizen 500,0,Car ,Male ,45,College ,97515,585,3,2,Citizen 501,0,Car ,Female,47,HS ,127611,597,2,1,Citizen 502,0,Car ,Male ,33,HS ,120653,638,2,2,Citizen 503,0,Car ,Female,48,HS ,87623,584,0,2,Citizen 504,0,Car ,Male ,37,HS ,76163,578,2,2,Citizen 505,0,Car ,Male ,47,College ,93307,571,1,2,Citizen 506,0,Home,Male ,35,HS ,96804,630,5,1,Citizen 507,0,Home,Male ,47,College ,81461,775,6,2,Citizen 508,0,Car ,Female,45,College ,119436,639,0,2,Citizen 509,0,Car ,Male ,45,HS ,92921,674,1,2,Citizen 510,0,Car ,Female,43,HS ,109026,593,1,2,Citizen 511,0,Car ,Female,46,College ,103601,658,2,2,Citizen 512,0,Car ,Male ,50,College ,94578,585,3,2,Citizen 513,0,Car ,Male ,50,HS ,96042,614,2,2,Citizen 514,0,Home,Male ,35,HS ,91465,540,5,2,Citizen 515,0,Home,Male ,45,HS ,87212,684,5,2,Citizen 516,1,Car ,Male ,41,HS ,97202,785,1,2,Citizen 517,0,Car ,Female,38,HS ,95068,558,1,2,Citizen 518,0,Home,Male ,40,College ,77838,601,8,1,Citizen 519,0,Car ,Female,51,HS ,113921,677,0,2,Citizen 520,0,Car ,Male ,49,College ,87883,720,3,2,Citizen 521,0,Home,Male ,51,HS ,114891,617,8,2,Citizen 522,0,Car ,Male ,51,College ,112162,602,3,2,Citizen 523,0,Car ,Male ,42,College ,68536,654,0,2,Citizen 524,1,Car ,Male ,53,HS ,118509,691,0,2,Citizen 525,0,Home,Male ,40,HS ,70753,521,7,2,Citizen 526,0,Car ,Female,49,College ,112219,664,1,2,Non-citizen 527,0,Car ,Male ,42,College ,90458,471,2,2,Citizen 528,0,Car ,Male ,42,HS ,101512,576,3,2,Citizen 529,0,Car ,Male ,44,College ,83725,588,2,2,Citizen 530,0,Home,Male ,46,HS ,85747,662,3,2,Citizen 531,0,Car ,Male ,50,HS ,115657,631,2,2,Citizen 532,0,Car ,Female,42,HS ,112556,637,2,1,Citizen 533,1,Car ,Female,52,Graduate,123444,690,2,1,Non-citizen 534,0,Home,Female,39,HS ,122509,627,6,2,Citizen 535,0,Car ,Male ,34,HS ,70708,654,1,2,Citizen 536,1,Car ,Male ,38,College ,83865,566,0,2,Citizen 537,0,Car ,Male ,45,HS ,109564,616,1,2,Citizen 538,0,Car ,Female,51,Graduate,105208,733,3,2,Citizen 539,0,Car ,Male ,40,College ,81726,553,0,2,Citizen 540,0,Car ,Male ,33,HS ,81146,702,0,2,Citizen 541,0,Car ,Male ,42,HS ,84029,618,1,2,Citizen 542,1,Home,Female,39,HS ,121291,556,6,2,Citizen 543,0,Car ,Male ,43,College ,120473,613,2,2,Citizen 544,0,Home,Male ,36,HS ,110647,585,8,2,Citizen 545,0,Car ,Male ,43,HS ,103793,680,3,1,Citizen 546,0,Car ,Male ,47,HS ,84822,655,3,1,Citizen 547,1,Car ,Female,46,Graduate,93990,741,3,2,Non-citizen 548,0,Home,Male ,44,HS ,99915,488,3,2,Citizen 549,0,Car ,Female,35,Graduate,74995,635,0,2,Non-citizen 550,0,Car ,Female,51,HS ,117071,609,0,1,Citizen 551,0,Car ,Male ,29,HS ,121462,566,0,2,Citizen 552,0,Car ,Female,53,Graduate,104113,682,2,2,Citizen 553,0,Home,Male ,41,HS ,57556,505,3,2,Citizen 554,0,Car ,Female,41,HS ,89158,563,0,2,Citizen 555,0,Car ,Female,33,HS ,83195,481,0,2,Citizen 556,0,Car ,Female,41,HS ,90136,518,2,2,Citizen 557,0,Car ,Male ,35,HS ,94961,526,0,2,Citizen 558,0,Car ,Male ,46,HS ,97995,516,0,2,Citizen 559,0,Home,Female,40,HS ,109967,642,7,2,Citizen 560,0,Car ,Female,41,HS ,120044,746,1,2,Citizen 561,0,Car ,Male ,36,HS ,101401,581,0,2,Citizen 562,0,Car ,Male ,36,HS ,103486,620,2,2,Citizen 563,0,Home,Male ,50,HS ,96958,596,7,2,Citizen 564,0,Home,Male ,55,HS ,99347,687,7,2,Citizen 565,0,Car ,Female,45,HS ,106629,655,2,2,Citizen 566,1,Car ,Female,53,College ,107720,743,3,1,Citizen 567,0,Car ,Male ,45,College ,59721,560,0,2,Citizen 568,0,Car ,Female,47,College ,132663,724,2,1,Non-citizen 569,0,Home,Male ,46,College ,119322,648,7,2,Citizen 570,0,Car ,Female,39,College ,105644,582,2,2,Citizen 571,0,Home,Male ,41,HS ,114628,599,0,2,Citizen 572,0,Car ,Female,33,HS ,114313,635,0,2,Citizen 573,0,Home,Female,46,HS ,109345,628,11,2,Citizen 574,0,Car ,Female,53,HS ,94195,525,1,2,Citizen 575,0,Home,Female,44,HS ,114406,679,5,2,Citizen 576,0,Home,Male ,43,HS ,116973,664,8,2,Citizen 577,0,Home,Female,47,College ,122232,678,7,2,Citizen 578,1,Car ,Female,45,Graduate,123065,642,2,2,Citizen 579,0,Home,Male ,54,HS ,95154,593,9,1,Citizen 580,0,Car ,Male ,44,HS ,101525,535,0,2,Citizen 581,0,Car ,Female,45,College ,98490,717,1,2,Citizen 582,0,Car ,Male ,37,College ,97314,617,3,1,Citizen 583,0,Home,Female,48,HS ,83052,604,5,1,Citizen 584,0,Car ,Female,39,HS ,118404,630,0,1,Citizen 585,0,Car ,Male ,46,HS ,101158,632,3,2,Citizen 586,0,Home,Male ,38,HS ,91920,584,7,2,Citizen 587,0,Home,Male ,43,HS ,82312,477,1,2,Citizen 588,0,Home,Male ,35,HS ,78448,609,8,2,Citizen 589,0,Home,Male ,35,HS ,84759,615,4,2,Citizen 590,0,Home,Male ,44,HS ,99072,777,7,2,Citizen 591,0,Home,Female,42,College ,93705,587,6,2,Citizen 592,0,Car ,Male ,35,HS ,108584,630,0,2,Citizen 593,0,Car ,Female,39,Graduate,98791,634,1,2,Non-citizen 594,1,Car ,Male ,46,HS ,101439,661,0,2,Citizen 595,0,Home,Male ,43,HS ,121200,550,8,2,Citizen 596,0,Car ,Male ,26,HS ,56397,538,0,2,Citizen 597,0,Car ,Female,38,HS ,83498,694,0,1,Citizen 598,0,Car ,Female,41,HS ,98914,589,1,2,Citizen 599,0,Car ,Male ,41,HS ,105853,555,1,2,Citizen 600,0,Home,Female,48,HS ,94949,630,5,2,Citizen 601,0,Car ,Female,34,HS ,103728,656,2,1,Citizen 602,0,Car ,Female,48,College ,115287,651,2,2,Citizen 603,0,Car ,Male ,44,HS ,102137,594,1,2,Citizen 604,0,Home,Female,32,College ,95647,559,7,2,Citizen 605,0,Car ,Male ,46,HS ,75777,551,0,2,Citizen 606,0,Home,Male ,44,College ,104945,627,14,2,Citizen 607,0,Car ,Male ,29,HS ,99674,577,0,2,Citizen 608,0,Home,Male ,41,College ,83289,558,8,2,Citizen 609,0,Car ,Female,48,HS ,109876,650,2,2,Citizen 610,0,Home,Male ,32,HS ,61820,547,2,2,Citizen 611,0,Car ,Male ,47,HS ,99958,522,1,2,Citizen 612,0,Home,Female,43,HS ,93071,606,3,2,Citizen 613,0,Car ,Male ,34,HS ,91068,627,1,2,Citizen 614,0,Home,Male ,33,HS ,115094,714,9,2,Citizen 615,1,Home,Female,34,HS ,85744,703,5,2,Citizen 616,0,Car ,Female,57,Graduate,103925,676,1,2,Non-citizen 617,0,Car ,Male ,43,HS ,69818,611,0,2,Citizen 618,0,Car ,Female,53,College ,119650,589,1,2,Non-citizen 619,0,Car ,Female,44,College ,115049,569,2,2,Citizen 620,0,Car ,Female,49,College ,94946,650,1,2,Citizen 621,0,Home,Female,38,HS ,87019,637,3,2,Citizen 622,0,Car ,Male ,44,College ,92800,549,3,2,Citizen 623,0,Car ,Female,35,HS ,80559,701,0,2,Citizen 624,0,Car ,Male ,41,HS ,97224,624,1,1,Citizen 625,0,Car ,Female,47,HS ,90720,609,3,2,Citizen 626,0,Car ,Female,47,HS ,90947,664,2,1,Citizen 627,0,Car ,Male ,46,College ,87839,543,1,2,Citizen 628,0,Home,Male ,41,HS ,79163,536,6,2,Citizen 629,0,Car ,Male ,43,HS ,107491,576,0,1,Citizen 630,0,Car ,Male ,37,HS ,70410,609,0,2,Citizen 631,0,Home,Female,45,HS ,124777,723,7,2,Citizen 632,0,Car ,Female,51,HS ,92203,673,0,1,Citizen 633,0,Car ,Male ,47,HS ,85847,605,0,2,Citizen 634,0,Home,Male ,39,HS ,92451,588,5,2,Citizen 635,0,Car ,Female,43,HS ,111023,641,1,2,Citizen 636,0,Car ,Female,45,HS ,109479,615,1,2,Citizen 637,0,Home,Male ,38,HS ,75416,585,5,2,Citizen 638,0,Car ,Male ,44,Graduate,76821,568,2,2,Citizen 639,0,Home,Female,37,College ,108077,603,9,2,Citizen 640,1,Car ,Female,47,College ,89303,643,2,1,Citizen 641,0,Car ,Female,48,HS ,104973,709,1,2,Citizen 642,0,Car ,Female,45,College ,112422,613,2,2,Citizen 643,0,Car ,Male ,40,HS ,83503,564,0,2,Citizen 644,0,Car ,Male ,42,HS ,107131,608,0,2,Citizen 645,0,Home,Female,36,HS ,85450,610,4,2,Citizen 646,0,Car ,Female,42,HS ,84303,656,0,2,Citizen 647,0,Car ,Female,41,College ,96638,671,2,2,Citizen 648,0,Car ,Female,36,HS ,78946,557,0,2,Citizen 649,0,Car ,Male ,43,College ,67920,632,0,2,Citizen 650,1,Car ,Female,45,HS ,97505,618,0,2,Citizen 651,0,Home,Male ,49,HS ,89720,602,1,2,Citizen 652,0,Home,Male ,35,HS ,122706,583,6,2,Citizen 653,0,Car ,Male ,46,HS ,109763,638,0,2,Citizen 654,0,Home,Male ,32,HS ,86462,566,6,2,Citizen 655,1,Car ,Female,56,Graduate,135156,673,2,2,Citizen 656,0,Home,Female,50,HS ,99471,718,0,2,Citizen 657,0,Car ,Female,41,HS ,100161,652,1,2,Citizen 658,0,Car ,Female,41,HS ,93052,612,0,2,Citizen 659,0,Car ,Male ,43,HS ,75819,570,2,2,Citizen 660,0,Car ,Female,38,HS ,96671,695,2,2,Citizen 661,0,Home,Male ,36,HS ,101256,597,2,2,Citizen 662,1,Car ,Female,34,Graduate,113531,671,2,2,Citizen 663,0,Car ,Male ,46,HS ,84402,619,0,2,Citizen 664,0,Home,Male ,36,HS ,101790,569,6,2,Citizen 665,0,Car ,Female,46,HS ,102701,569,3,1,Citizen 666,0,Car ,Female,47,College ,140220,735,2,2,Citizen 667,0,Car ,Male ,41,HS ,102863,590,0,2,Citizen 668,0,Car ,Male ,32,HS ,87778,616,2,2,Citizen 669,0,Home,Female,46,College ,86501,629,12,1,Citizen 670,0,Home,Female,43,HS ,102367,589,8,2,Citizen 671,0,Car ,Male ,34,College ,90661,576,0,2,Citizen 672,0,Home,Female,49,HS ,97635,714,5,2,Citizen 673,0,Home,Male ,37,HS ,89572,598,2,2,Citizen 674,0,Home,Male ,42,HS ,108829,699,7,1,Citizen 675,0,Car ,Male ,41,HS ,93533,581,0,2,Citizen 676,1,Home,Male ,41,College ,105459,608,13,2,Citizen 677,0,Car ,Female,44,College ,104436,625,0,2,Citizen 678,0,Home,Male ,46,HS ,107246,654,10,2,Citizen 679,0,Car ,Male ,41,HS ,79444,587,0,2,Citizen 680,0,Car ,Female,51,College ,106328,749,2,2,Non-citizen 681,0,Car ,Male ,39,HS ,105976,661,1,1,Citizen 682,0,Home,Female,44,Graduate,93994,683,11,2,Citizen 683,0,Home,Female,38,HS ,88894,628,5,2,Citizen 684,0,Car ,Male ,35,HS ,61728,534,0,2,Citizen 685,0,Home,Male ,46,HS ,99711,662,12,1,Citizen 686,0,Car ,Female,40,College ,86159,643,0,2,Citizen 687,0,Car ,Female,42,HS ,87053,624,1,2,Citizen 688,0,Home,Male ,49,HS ,103573,604,10,2,Citizen 689,1,Car ,Male ,44,HS ,65005,713,0,2,Citizen 690,0,Home,Male ,39,HS ,85973,540,3,2,Citizen 691,0,Car ,Male ,40,HS ,64613,618,0,2,Citizen 692,0,Car ,Female,44,HS ,95595,568,0,2,Citizen 693,0,Car ,Male ,41,HS ,61649,592,0,2,Citizen 694,0,Home,Male ,46,HS ,92772,740,4,2,Citizen 695,0,Car ,Female,44,HS ,86942,568,2,2,Citizen 696,0,Car ,Female,46,HS ,63190,702,1,2,Citizen 697,0,Home,Male ,47,HS ,104779,696,7,2,Citizen 698,0,Car ,Male ,53,College ,95542,556,2,2,Citizen 699,0,Car ,Female,37,College ,88263,587,1,1,Citizen 700,1,Car ,Male ,40,HS ,85449,536,0,2,Citizen 701,0,Home,Male ,43,HS ,89154,591,4,2,Citizen 702,0,Home,Male ,40,HS ,98471,662,4,2,Citizen 703,0,Car ,Male ,49,College ,98863,643,2,2,Citizen 704,0,Home,Female,45,HS ,102348,546,5,2,Citizen 705,0,Home,Male ,38,HS ,64813,468,7,2,Citizen 706,0,Car ,Male ,37,HS ,79426,579,1,2,Citizen 707,0,Home,Male ,46,Graduate,90416,640,11,2,Citizen 708,0,Car ,Female,41,College ,84974,602,1,2,Citizen 709,0,Car ,Male ,42,College ,108786,705,2,2,Citizen 710,0,Home,Male ,42,HS ,88753,558,6,2,Citizen 711,0,Home,Male ,33,HS ,70548,535,5,2,Citizen 712,0,Car ,Male ,42,HS ,113174,644,0,2,Citizen 713,0,Home,Male ,47,HS ,84607,652,9,2,Citizen 714,0,Car ,Female,39,HS ,108313,681,2,2,Citizen 715,0,Car ,Female,41,HS ,119686,600,1,1,Citizen 716,0,Car ,Male ,37,HS ,83496,507,0,2,Citizen 717,0,Car ,Male ,30,HS ,72949,499,0,2,Citizen 718,0,Car ,Male ,48,HS ,100749,512,1,2,Citizen 719,0,Car ,Male ,34,College ,94148,625,1,1,Citizen 720,0,Car ,Female,51,College ,124110,664,1,1,Citizen 721,0,Home,Male ,48,HS ,75398,600,11,1,Citizen 722,0,Car ,Male ,38,College ,97842,557,2,2,Citizen 723,0,Car ,Female,46,College ,107163,711,1,1,Citizen 724,0,Car ,Male ,41,HS ,111052,611,1,2,Citizen 725,0,Car ,Female,38,College ,102906,627,2,1,Citizen 726,0,Car ,Female,44,College ,119756,606,2,2,Citizen 727,0,Car ,Male ,38,College ,80667,524,1,2,Citizen 728,0,Car ,Female,42,HS ,99314,659,0,2,Citizen 729,0,Car ,Male ,52,College ,106805,631,0,2,Non-citizen 730,0,Car ,Male ,33,HS ,89059,560,1,2,Citizen 731,0,Car ,Male ,43,HS ,102051,635,0,2,Citizen 732,0,Car ,Female,37,HS ,112473,497,0,2,Citizen 733,0,Car ,Female,40,HS ,110347,605,1,2,Citizen 734,0,Home,Male ,46,HS ,113547,562,8,2,Citizen 735,0,Car ,Male ,52,HS ,112583,658,3,1,Citizen 736,0,Home,Male ,30,HS ,66511,514,5,2,Citizen 737,0,Car ,Female,51,HS ,106488,650,1,2,Citizen 738,0,Car ,Male ,43,HS ,71642,561,0,2,Citizen 739,0,Car ,Male ,31,HS ,79613,624,0,2,Citizen 740,0,Car ,Female,44,HS ,97169,633,3,1,Citizen 741,0,Home,Female,34,HS ,120747,586,5,2,Citizen 742,0,Car ,Male ,46,HS ,89105,550,3,2,Citizen 743,0,Car ,Female,49,HS ,97799,658,1,2,Non-citizen 744,0,Car ,Female,43,HS ,66134,564,0,2,Citizen 745,0,Car ,Female,38,HS ,100078,644,0,2,Citizen 746,1,Car ,Female,38,HS ,127735,612,2,2,Citizen 747,1,Car ,Male ,39,College ,82112,713,3,2,Citizen 748,0,Home,Male ,37,HS ,77376,556,2,2,Citizen 749,0,Car ,Female,40,HS ,107645,660,2,2,Citizen 750,0,Car ,Male ,40,HS ,81750,496,2,2,Citizen 751,0,Home,Female,36,HS ,66473,514,5,2,Citizen 752,0,Car ,Male ,35,HS ,75283,538,1,2,Citizen 753,0,Home,Female,41,HS ,110422,693,2,2,Citizen 754,1,Car ,Male ,51,College ,101498,688,3,1,Citizen 755,0,Home,Male ,42,HS ,69180,590,2,2,Citizen 756,0,Car ,Male ,45,HS ,74423,574,2,2,Citizen 757,0,Car ,Male ,50,HS ,100783,653,1,1,Citizen 758,0,Car ,Female,46,HS ,90736,690,0,1,Citizen 759,0,Home,Male ,38,HS ,97007,563,0,2,Citizen 760,0,Home,Male ,52,HS ,108001,561,10,2,Citizen 761,0,Home,Male ,40,College ,89546,704,7,2,Citizen 762,0,Home,Male ,42,HS ,112497,554,8,2,Citizen 763,0,Car ,Male ,32,HS ,63293,586,0,2,Citizen 764,1,Car ,Female,34,HS ,81891,683,0,2,Citizen 765,0,Car ,Male ,47,College ,85524,590,1,2,Citizen 766,1,Car ,Female,42,HS ,107848,657,2,2,Citizen 767,0,Car ,Male ,48,HS ,98357,599,1,2,Citizen 768,0,Home,Male ,38,HS ,113308,600,10,2,Citizen 769,0,Car ,Male ,49,HS ,90435,778,1,2,Citizen 770,0,Car ,Female,36,HS ,95659,557,1,2,Citizen 771,0,Car ,Female,49,HS ,101074,632,0,2,Citizen 772,0,Home,Male ,28,HS ,48991,571,3,2,Citizen 773,0,Car ,Male ,36,College ,76197,496,1,2,Citizen 774,0,Car ,Female,45,HS ,86833,548,0,2,Citizen 775,0,Car ,Male ,34,HS ,61076,483,2,2,Citizen 776,0,Home,Male ,35,HS ,113555,563,4,2,Citizen 777,0,Car ,Male ,52,College ,131046,628,2,2,Citizen 778,1,Car ,Female,45,HS ,91765,606,1,2,Citizen 779,0,Car ,Female,49,HS ,127703,600,1,2,Citizen 780,0,Home,Male ,36,HS ,79281,523,8,2,Citizen 781,0,Car ,Female,48,HS ,112970,556,1,2,Citizen 782,0,Home,Male ,44,HS ,92037,553,7,2,Citizen 783,0,Car ,Female,54,HS ,109130,695,0,1,Citizen 784,0,Car ,Female,47,HS ,100167,669,0,2,Citizen 785,0,Car ,Female,47,HS ,102549,643,3,1,Citizen 786,0,Car ,Female,42,HS ,89811,712,0,1,Citizen 787,0,Car ,Male ,32,HS ,94539,618,1,2,Citizen 788,0,Car ,Male ,42,HS ,77790,556,1,2,Citizen 789,0,Home,Male ,37,HS ,75418,567,4,2,Citizen 790,0,Car ,Female,40,HS ,110722,603,0,2,Citizen 791,0,Car ,Female,49,HS ,116579,625,0,2,Citizen 792,0,Car ,Female,43,HS ,108353,628,1,1,Citizen 793,0,Car ,Female,40,HS ,95586,585,0,2,Citizen 794,0,Car ,Female,43,HS ,98705,600,0,2,Citizen 795,0,Home,Male ,38,HS ,77307,585,5,2,Citizen 796,0,Car ,Male ,35,HS ,94008,693,2,2,Citizen 797,0,Home,Female,48,HS ,115138,503,7,2,Citizen 798,0,Car ,Female,46,College ,106310,682,0,2,Citizen 799,0,Car ,Male ,32,HS ,89526,528,0,2,Citizen 800,0,Home,Male ,35,HS ,96173,651,1,2,Citizen 801,0,Home,Male ,47,HS ,87035,637,3,2,Citizen 802,0,Car ,Male ,42,HS ,98652,659,2,2,Citizen 803,0,Home,Female,43,HS ,97974,670,6,2,Citizen 804,0,Car ,Male ,42,HS ,102945,683,2,2,Citizen 805,0,Car ,Male ,41,Graduate,87601,683,2,2,Citizen 806,0,Car ,Female,37,HS ,106338,633,0,2,Citizen 807,0,Car ,Male ,40,HS ,97956,622,0,2,Citizen 808,1,Car ,Male ,62,College ,147798,771,3,1,Citizen 809,0,Home,Male ,50,HS ,102943,604,5,2,Citizen 810,0,Car ,Male ,42,HS ,113958,597,2,2,Citizen 811,0,Car ,Female,42,HS ,111679,573,2,1,Citizen 812,1,Car ,Female,43,HS ,139702,605,1,2,Citizen 813,0,Car ,Female,44,College ,106871,599,3,2,Citizen 814,0,Car ,Female,38,Graduate,81722,577,1,2,Citizen 815,0,Home,Female,42,College ,110901,552,8,2,Citizen 816,0,Car ,Male ,37,HS ,107701,608,2,2,Citizen 817,0,Car ,Female,42,HS ,106891,627,0,2,Citizen 818,0,Home,Female,56,College ,116817,754,13,1,Citizen 819,0,Car ,Male ,43,Graduate,97351,572,3,2,Citizen 820,0,Car ,Male ,35,HS ,88425,562,0,2,Citizen 821,1,Car ,Female,49,College ,114163,648,0,2,Citizen 822,0,Car ,Female,48,HS ,106237,663,1,1,Citizen 823,0,Home,Male ,44,HS ,99157,555,5,2,Citizen 824,0,Car ,Female,49,College ,95537,713,2,1,Non-citizen 825,1,Car ,Female,42,HS ,100775,613,2,2,Citizen 826,0,Car ,Female,41,College ,65252,548,1,2,Citizen 827,0,Home,Male ,39,HS ,81820,586,3,2,Citizen 828,0,Car ,Male ,52,HS ,116967,632,3,2,Citizen 829,0,Car ,Male ,38,HS ,100467,528,2,2,Citizen 830,0,Car ,Female,46,Graduate,74597,700,2,2,Citizen 831,0,Car ,Male ,39,HS ,103638,576,3,2,Citizen 832,0,Car ,Male ,49,HS ,117767,649,2,1,Citizen 833,0,Car ,Male ,43,College ,86011,630,0,2,Citizen 834,0,Car ,Female,51,HS ,91355,580,0,2,Citizen 835,0,Car ,Male ,35,HS ,110027,579,0,2,Citizen 836,0,Home,Female,39,HS ,71096,576,5,2,Citizen 837,0,Home,Female,43,College ,123215,567,10,2,Citizen 838,0,Car ,Male ,37,HS ,116645,710,2,2,Citizen 839,0,Car ,Female,48,College ,80794,553,3,2,Citizen 840,0,Car ,Female,41,College ,102818,633,0,2,Citizen 841,0,Car ,Male ,50,HS ,103719,676,2,1,Citizen 842,0,Home,Female,46,HS ,128328,606,11,2,Citizen 843,0,Car ,Female,43,College ,125766,594,2,2,Citizen 844,0,Car ,Female,45,Graduate,90558,631,3,2,Citizen 845,0,Home,Male ,30,HS ,103907,532,4,2,Citizen 846,0,Home,Male ,40,HS ,84951,619,4,2,Citizen 847,0,Home,Male ,40,HS ,109889,602,3,2,Citizen 848,0,Car ,Male ,38,HS ,93495,560,1,1,Citizen 849,1,Car ,Female,44,HS ,102875,707,0,2,Citizen 850,0,Car ,Male ,47,HS ,108857,667,1,2,Citizen 851,0,Home,Male ,45,HS ,109930,573,4,2,Citizen 852,0,Car ,Male ,51,College ,97097,605,2,2,Citizen 853,0,Car ,Male ,46,College ,150094,561,2,2,Citizen 854,0,Car ,Female,37,Graduate,85341,529,3,2,Citizen 855,0,Home,Female,31,HS ,85237,592,0,2,Citizen 856,0,Home,Male ,46,HS ,120429,650,5,2,Citizen 857,0,Home,Male ,32,HS ,84159,581,4,2,Citizen 858,0,Home,Male ,36,HS ,84172,543,0,2,Citizen 859,0,Car ,Female,42,College ,98314,658,1,2,Citizen 860,0,Car ,Female,42,College ,74910,691,0,2,Citizen 861,0,Home,Male ,41,HS ,55401,510,6,2,Citizen 862,0,Car ,Male ,44,HS ,98643,615,1,2,Citizen 863,0,Home,Male ,43,HS ,85795,584,2,2,Citizen 864,0,Home,Female,47,HS ,119765,530,6,2,Citizen 865,0,Car ,Male ,46,HS ,98234,589,2,2,Citizen 866,0,Car ,Female,37,College ,79413,563,2,2,Citizen 867,0,Home,Female,41,College ,84233,620,5,2,Citizen 868,0,Car ,Female,38,HS ,104259,618,2,2,Citizen 869,0,Car ,Male ,37,HS ,108333,523,0,2,Citizen 870,0,Home,Male ,41,HS ,86777,656,1,2,Citizen 871,0,Car ,Female,41,HS ,86638,651,2,2,Citizen 872,0,Car ,Female,47,HS ,100230,555,1,1,Non-citizen 873,0,Car ,Male ,28,HS ,86377,588,2,2,Citizen 874,0,Home,Male ,29,HS ,93095,511,6,2,Citizen 875,0,Car ,Female,46,College ,117194,628,3,2,Citizen 876,0,Car ,Female,42,HS ,96519,639,0,1,Citizen 877,0,Car ,Female,44,HS ,74997,615,1,2,Citizen 878,0,Car ,Male ,43,College ,84505,611,1,2,Citizen 879,0,Home,Male ,45,College ,84038,655,6,2,Citizen 880,1,Car ,Female,48,College ,87896,763,1,1,Citizen 881,0,Car ,Male ,43,College ,74134,626,2,2,Citizen 882,0,Home,Male ,44,HS ,83217,580,8,2,Citizen 883,0,Car ,Male ,51,HS ,116792,768,0,1,Citizen 884,0,Car ,Male ,40,HS ,91575,552,0,2,Citizen 885,0,Car ,Male ,49,HS ,103860,616,0,2,Citizen 886,0,Car ,Female,43,HS ,100386,620,2,2,Citizen 887,0,Car ,Male ,43,HS ,80689,564,2,2,Citizen 888,1,Car ,Female,36,HS ,110339,609,2,2,Citizen 889,0,Car ,Male ,40,HS ,82254,607,2,2,Citizen 890,0,Home,Female,38,HS ,106506,554,5,2,Citizen 891,0,Car ,Male ,47,HS ,90707,555,1,2,Citizen 892,0,Car ,Female,48,HS ,89779,612,0,2,Citizen 893,0,Car ,Male ,31,HS ,88428,511,2,2,Citizen 894,0,Car ,Male ,48,Graduate,111332,661,3,2,Citizen 895,0,Home,Male ,35,College ,54989,476,2,2,Citizen 896,0,Car ,Female,44,HS ,74430,690,1,2,Citizen 897,0,Car ,Female,44,HS ,102439,641,1,2,Citizen 898,0,Home,Male ,48,HS ,98443,582,9,2,Citizen 899,0,Car ,Male ,48,HS ,106882,602,2,2,Citizen 900,0,Home,Male ,33,HS ,82247,464,10,2,Citizen 901,1,Car ,Female,55,College ,112841,759,0,2,Citizen 902,0,Car ,Male ,49,HS ,104857,711,2,1,Citizen 903,0,Car ,Male ,29,College ,75558,623,2,2,Citizen 904,1,Car ,Female,35,College ,83220,620,2,2,Citizen 905,0,Car ,Male ,37,College ,89435,588,0,2,Citizen 906,0,Home,Male ,52,HS ,59505,588,2,2,Citizen 907,0,Car ,Female,46,College ,84313,525,2,2,Non-citizen 908,0,Car ,Female,41,HS ,90017,591,1,1,Non-citizen 909,0,Car ,Male ,56,College ,99733,521,2,2,Citizen 910,0,Car ,Female,43,Graduate,108116,744,1,2,Citizen 911,0,Home,Female,43,HS ,81237,577,8,2,Citizen 912,0,Car ,Female,41,HS ,110597,641,3,1,Citizen 913,0,Home,Male ,41,HS ,84825,636,1,2,Citizen 914,0,Car ,Male ,48,College ,81185,583,2,2,Citizen 915,0,Car ,Female,47,HS ,69053,545,0,2,Citizen 916,0,Car ,Male ,44,HS ,110452,678,0,2,Citizen 917,0,Car ,Male ,37,HS ,90420,596,2,2,Citizen 918,0,Home,Male ,50,HS ,88827,705,9,2,Citizen 919,0,Car ,Female,42,HS ,70323,640,0,2,Citizen 920,0,Car ,Male ,55,College ,103035,702,3,1,Citizen 921,0,Home,Male ,41,HS ,95695,542,6,2,Citizen 922,0,Car ,Female,51,College ,128301,665,3,1,Citizen 923,0,Home,Male ,39,HS ,65422,543,8,2,Citizen 924,0,Home,Male ,47,HS ,111349,520,6,2,Citizen 925,0,Car ,Male ,50,HS ,98316,615,2,2,Citizen 926,0,Car ,Male ,35,HS ,91955,501,0,2,Citizen 927,0,Home,Male ,35,HS ,63712,568,2,2,Citizen 928,0,Car ,Female,46,HS ,124000,601,1,2,Citizen 929,0,Car ,Female,36,HS ,91644,554,1,2,Citizen 930,0,Car ,Male ,51,HS ,119669,618,2,2,Citizen 931,0,Home,Male ,49,College ,84993,621,11,2,Citizen 932,0,Home,Female,46,HS ,83739,598,7,2,Citizen 933,0,Home,Male ,37,HS ,85549,520,7,2,Citizen 934,0,Car ,Male ,41,HS ,86935,595,2,2,Citizen 935,0,Car ,Female,47,College ,107596,652,3,1,Non-citizen 936,0,Car ,Male ,31,HS ,71264,590,0,2,Citizen 937,0,Car ,Male ,29,HS ,102032,506,1,2,Citizen 938,0,Home,Female,37,HS ,115304,421,7,2,Citizen 939,0,Car ,Male ,36,College ,83802,667,3,2,Citizen 940,0,Car ,Male ,55,HS ,112159,612,1,2,Citizen 941,0,Car ,Female,31,HS ,98078,478,0,2,Citizen 942,0,Car ,Male ,47,HS ,96520,592,2,2,Citizen 943,0,Car ,Female,43,Graduate,106981,695,1,2,Citizen 944,0,Home,Male ,46,HS ,65328,530,5,2,Citizen 945,0,Car ,Male ,39,HS ,98570,598,2,2,Citizen 946,0,Car ,Male ,36,HS ,96850,566,2,2,Citizen 947,1,Car ,Male ,44,College ,103305,624,3,2,Citizen 948,0,Car ,Female,44,College ,112642,651,2,1,Non-citizen 949,0,Home,Female,48,HS ,96491,612,4,2,Citizen 950,0,Car ,Female,51,HS ,126740,688,2,2,Non-citizen 951,0,Home,Male ,41,HS ,74628,510,2,2,Citizen 952,0,Car ,Female,43,HS ,91442,553,2,2,Citizen 953,0,Car ,Male ,45,HS ,76664,635,0,2,Citizen 954,0,Home,Male ,33,HS ,105360,671,5,2,Citizen 955,0,Home,Male ,44,HS ,110435,563,4,1,Citizen 956,0,Car ,Male ,52,College ,116523,512,2,2,Citizen 957,0,Car ,Male ,48,HS ,92426,670,2,2,Citizen 958,0,Car ,Male ,37,HS ,91139,618,0,2,Citizen 959,0,Car ,Male ,40,HS ,98178,604,2,2,Citizen 960,0,Car ,Male ,27,HS ,85700,661,0,2,Citizen 961,0,Car ,Male ,44,College ,98725,618,2,2,Citizen 962,0,Car ,Female,40,HS ,87841,699,2,2,Citizen 963,0,Car ,Male ,46,HS ,106169,565,2,2,Citizen 964,0,Home,Male ,41,HS ,92829,682,2,2,Citizen 965,0,Car ,Male ,25,HS ,81741,523,0,2,Citizen 966,0,Car ,Male ,36,HS ,78837,574,2,1,Citizen 967,0,Car ,Female,43,HS ,121743,517,1,2,Citizen 968,0,Home,Male ,37,HS ,90407,608,4,2,Citizen 969,0,Car ,Female,44,Graduate,121406,622,2,2,Citizen 970,0,Car ,Male ,38,HS ,83333,595,0,2,Citizen 971,0,Home,Female,45,HS ,77352,612,4,2,Citizen 972,0,Home,Male ,42,HS ,91238,645,12,1,Citizen 973,0,Car ,Female,28,HS ,95128,586,0,2,Citizen 974,0,Car ,Female,48,College ,123328,641,2,2,Citizen 975,0,Car ,Male ,37,HS ,75214,488,2,2,Citizen 976,0,Car ,Female,45,HS ,102647,630,1,2,Citizen 977,0,Car ,Female,47,HS ,102850,650,2,2,Citizen 978,0,Car ,Female,58,HS ,98552,661,2,2,Citizen 979,0,Car ,Male ,47,College ,99017,527,2,2,Citizen 980,0,Home,Female,39,Graduate,106841,635,13,2,Citizen 981,0,Car ,Female,38,HS ,92303,560,0,2,Citizen 982,0,Home,Male ,43,HS ,76301,537,3,2,Citizen 983,0,Car ,Female,45,College ,90783,667,2,2,Citizen 984,0,Home,Male ,39,HS ,76488,672,8,2,Citizen 985,0,Car ,Male ,42,HS ,85484,590,1,2,Citizen 986,0,Car ,Male ,47,HS ,77810,553,1,2,Citizen 987,0,Home,Male ,28,HS ,64063,583,6,2,Citizen 988,0,Home,Male ,39,HS ,112930,617,5,2,Citizen 989,0,Car ,Male ,42,HS ,106976,552,0,2,Citizen 990,0,Car ,Female,44,HS ,71407,664,1,2,Citizen 991,0,Car ,Female,40,HS ,95594,661,0,2,Citizen 992,0,Car ,Female,46,College ,87508,655,1,2,Citizen 993,0,Car ,Male ,44,HS ,107724,648,1,2,Citizen 994,1,Car ,Female,54,College ,91540,650,0,2,Non-citizen 995,0,Home,Female,54,HS ,88485,614,7,1,Citizen 996,0,Car ,Female,38,HS ,108412,622,0,2,Citizen 997,0,Car ,Female,52,College ,117000,602,3,2,Citizen 998,0,Home,Male ,36,HS ,79377,524,3,2,Citizen 999,0,Car ,Male ,42,HS ,104840,655,0,2,Citizen 1000,0,Home,Male ,34,HS ,74649,658,4,2,Citizen 1001,0,Home,Male ,45,HS ,103598,552,7,2,Citizen 1002,0,Car ,Female,47,HS ,77196,635,0,2,Citizen 1003,0,Car ,Male ,52,HS ,73936,600,1,2,Citizen 1004,0,Home,Male ,27,HS ,79007,560,3,2,Citizen 1005,0,Car ,Female,50,College ,111821,641,1,2,Citizen 1006,0,Car ,Male ,37,HS ,102417,590,1,2,Citizen 1007,0,Car ,Male ,34,HS ,92043,578,2,2,Citizen 1008,0,Car ,Female,44,College ,141178,612,0,2,Citizen 1009,0,Car ,Female,50,College ,136811,595,2,1,Non-citizen 1010,0,Home,Male ,31,HS ,98991,640,7,2,Citizen 1011,0,Car ,Male ,33,HS ,59393,528,0,2,Citizen 1012,0,Home,Male ,29,HS ,101886,632,2,2,Citizen 1013,0,Home,Male ,50,College ,100375,578,10,2,Citizen 1014,0,Car ,Male ,22,HS ,55649,459,0,2,Citizen 1016,0,Home,Male ,44,HS ,77047,664,5,2,Citizen 1017,0,Car ,Female,38,Graduate,135378,720,1,2,Non-citizen 1018,0,Car ,Male ,39,HS ,89861,606,0,2,Citizen 1019,0,Car ,Male ,42,HS ,112390,625,1,2,Citizen 1020,0,Home,Female,40,HS ,82273,590,6,2,Citizen 1021,0,Car ,Male ,31,HS ,110933,750,0,2,Citizen 1022,0,Home,Male ,51,HS ,107237,642,6,2,Citizen 1023,1,Car ,Female,48,HS ,97481,631,0,2,Citizen 1024,0,Car ,Male ,44,HS ,101614,678,3,2,Citizen 1025,0,Car ,Female,49,College ,131574,651,2,1,Non-citizen 1026,0,Car ,Female,36,HS ,81568,626,0,2,Citizen 1027,0,Home,Male ,35,HS ,87989,678,5,2,Citizen 1028,0,Car ,Female,49,HS ,65788,743,0,2,Non-citizen 1029,0,Car ,Male ,38,HS ,63725,574,0,2,Citizen 1030,0,Home,Male ,40,College ,101335,691,6,2,Citizen 1031,0,Home,Female,37,HS ,91958,649,8,2,Citizen 1032,0,Car ,Female,36,HS ,101202,672,0,2,Citizen 1033,0,Car ,Female,47,HS ,109354,640,3,2,Citizen 1034,0,Car ,Female,36,HS ,85825,575,0,2,Citizen 1035,0,Car ,Female,52,HS ,122941,588,0,2,Citizen 1036,0,Car ,Male ,44,College ,98837,624,1,2,Citizen 1037,0,Home,Male ,45,HS ,101268,625,7,2,Citizen 1038,0,Car ,Female,53,Graduate,142959,671,2,1,Non-citizen 1039,0,Car ,Male ,44,Graduate,93865,637,2,2,Citizen 1040,0,Home,Male ,44,HS ,73868,577,9,2,Citizen 1041,0,Home,Male ,60,HS ,109354,631,11,2,Citizen 1042,0,Home,Female,35,HS ,113976,582,8,1,Citizen 1043,0,Home,Male ,37,HS ,102125,625,3,2,Citizen 1044,0,Car ,Male ,38,College ,65554,471,2,2,Citizen 1045,1,Home,Female,42,HS ,91239,726,8,2,Citizen 1046,0,Car ,Male ,48,HS ,87479,637,2,2,Citizen 1047,0,Home,Male ,52,College ,75713,552,9,2,Citizen 1048,0,Car ,Male ,31,HS ,68898,555,0,2,Citizen 1049,0,Car ,Male ,48,College ,66514,558,0,2,Citizen 1050,1,Car ,Female,45,HS ,119518,671,0,2,Citizen 1051,0,Home,Female,38,College ,100125,604,6,1,Citizen 1052,0,Home,Male ,43,HS ,98160,592,9,2,Citizen 1053,0,Home,Female,37,College ,98135,607,8,2,Citizen 1054,0,Home,Female,40,College ,92532,682,5,2,Citizen 1055,0,Car ,Female,39,HS ,121981,568,0,2,Citizen 1056,0,Car ,Male ,42,HS ,95048,716,0,1,Citizen 1057,0,Car ,Female,57,College ,95454,648,1,2,Citizen 1058,0,Car ,Female,43,HS ,125406,633,3,2,Citizen 1059,0,Home,Male ,37,HS ,74451,661,2,2,Citizen 1060,0,Car ,Male ,49,HS ,102846,629,0,2,Citizen 1061,1,Home,Female,37,HS ,118777,633,6,2,Citizen 1063,0,Car ,Male ,38,HS ,73642,516,0,2,Citizen 1064,0,Home,Male ,40,HS ,90926,543,5,2,Citizen 1065,0,Car ,Female,49,HS ,116367,680,0,2,Citizen 1066,0,Home,Male ,33,HS ,96148,674,6,2,Citizen 1067,0,Home,Male ,34,HS ,105258,590,6,2,Citizen 1068,0,Car ,Male ,38,HS ,74672,544,0,2,Citizen 1069,0,Car ,Female,35,HS ,77939,630,0,2,Citizen 1070,0,Home,Male ,46,HS ,78055,587,5,2,Citizen 1071,0,Car ,Male ,39,HS ,125171,551,2,2,Citizen 1072,0,Car ,Female,38,HS ,64211,615,2,2,Citizen 1073,0,Home,Male ,38,HS ,102998,540,7,2,Citizen 1074,0,Home,Male ,38,HS ,62399,544,5,2,Citizen 1075,0,Car ,Male ,43,College ,91217,705,0,2,Citizen 1076,0,Home,Male ,48,HS ,113063,692,7,2,Citizen 1077,0,Home,Male ,37,HS ,105306,605,6,2,Citizen 1078,0,Home,Male ,45,HS ,93313,625,4,2,Citizen 1079,0,Home,Male ,47,HS ,99015,694,5,2,Citizen 1080,0,Home,Male ,39,HS ,93493,599,6,2,Citizen 1081,0,Home,Male ,40,HS ,106081,635,8,2,Citizen 1082,0,Car ,Male ,45,HS ,85585,590,2,2,Citizen 1083,0,Car ,Male ,45,College ,119690,593,0,2,Citizen 1084,0,Car ,Male ,43,HS ,107134,616,0,2,Citizen 1085,0,Home,Male ,42,HS ,91392,655,11,2,Citizen 1086,0,Car ,Male ,39,HS ,100892,648,1,2,Citizen 1087,0,Car ,Male ,42,HS ,107847,600,0,2,Citizen 1088,0,Car ,Male ,50,HS ,69296,637,2,2,Citizen 1089,0,Home,Male ,45,HS ,81952,581,7,2,Citizen 1090,0,Car ,Female,50,College ,98775,694,2,2,Citizen 1091,1,Car ,Female,40,College ,112136,716,2,2,Citizen 1092,0,Car ,Female,36,HS ,91909,636,0,2,Citizen 1093,0,Car ,Female,39,HS ,117820,579,2,2,Citizen 1094,0,Car ,Male ,44,HS ,83852,607,2,2,Citizen 1095,0,Car ,Female,45,HS ,105645,576,0,2,Citizen 1096,1,Home,Male ,45,College ,80338,667,3,2,Citizen 1097,0,Home,Male ,33,College ,89253,619,6,2,Citizen 1098,0,Home,Male ,43,HS ,88793,597,2,2,Citizen 1099,1,Home,Male ,48,HS ,97653,614,2,2,Citizen 1100,0,Car ,Female,53,College ,116541,716,2,1,Citizen 1101,0,Car ,Female,45,College ,85153,603,0,2,Citizen 1102,0,Home,Female,38,HS ,93469,654,6,2,Citizen 1103,1,Car ,Female,49,College ,129954,744,3,2,Citizen 1104,0,Car ,Female,45,HS ,120386,592,0,2,Non-citizen 1105,0,Home,Male ,32,HS ,55979,587,1,2,Citizen 1106,0,Car ,Female,46,HS ,110429,724,2,1,Citizen 1107,0,Car ,Male ,50,HS ,105758,626,2,1,Citizen 1108,0,Home,Male ,42,HS ,82291,516,3,2,Citizen 1109,0,Car ,Male ,42,HS ,115772,598,1,2,Citizen 1110,0,Home,Male ,37,HS ,106550,561,9,2,Citizen 1111,0,Car ,Female,44,HS ,82678,582,0,2,Citizen 1112,0,Home,Male ,36,HS ,96528,528,5,2,Citizen 1113,1,Car ,Female,50,College ,123119,646,2,1,Citizen 1114,0,Home,Male ,33,HS ,82841,467,13,2,Citizen 1115,0,Home,Male ,45,HS ,112001,552,5,2,Citizen 1116,0,Home,Male ,34,HS ,85679,572,7,2,Citizen 1117,0,Home,Male ,46,HS ,116594,675,10,1,Citizen 1118,0,Car ,Female,46,HS ,127430,609,1,2,Citizen 1119,0,Car ,Female,40,HS ,92327,557,0,2,Citizen 1120,0,Car ,Male ,38,College ,59505,527,0,2,Citizen 1121,0,Car ,Female,48,College ,111026,589,2,2,Citizen 1122,0,Car ,Female,45,College ,93763,625,3,2,Citizen 1123,0,Home,Male ,38,HS ,69758,489,0,2,Citizen 1124,0,Car ,Female,52,College ,121456,615,3,2,Citizen 1125,0,Home,Male ,47,College ,121503,608,9,2,Citizen 1126,0,Car ,Male ,45,HS ,79233,667,0,2,Citizen 1127,0,Car ,Male ,31,HS ,97882,685,2,2,Citizen 1128,0,Car ,Male ,36,HS ,102534,645,0,2,Citizen 1129,0,Car ,Female,52,College ,112908,660,0,1,Citizen 1130,0,Car ,Female,47,HS ,100588,632,2,2,Citizen 1131,0,Home,Male ,40,College ,100211,634,13,2,Citizen 1132,1,Car ,Male ,44,HS ,99806,560,0,2,Citizen 1133,0,Car ,Male ,45,College ,102694,625,1,2,Citizen 1134,1,Car ,Female,47,HS ,96529,642,2,2,Citizen 1135,0,Car ,Female,38,College ,116576,610,2,2,Citizen 1136,0,Car ,Female,45,HS ,88567,573,1,2,Citizen 1137,0,Car ,Female,33,HS ,91145,519,0,2,Citizen 1138,0,Home,Male ,44,College ,70608,645,7,2,Citizen 1139,1,Home,Female,43,HS ,98644,618,7,2,Citizen 1140,0,Car ,Female,55,College ,113409,680,2,2,Citizen 1141,0,Car ,Female,45,HS ,111282,654,0,2,Non-citizen 1142,0,Car ,Female,50,College ,72220,578,0,2,Citizen 1143,0,Car ,Female,39,HS ,139700,636,1,2,Citizen 1144,0,Car ,Male ,41,HS ,101906,538,2,2,Citizen 1145,0,Car ,Male ,46,HS ,77471,556,3,2,Citizen 1146,0,Home,Female,45,HS ,109313,572,7,2,Citizen 1147,0,Car ,Male ,36,HS ,105238,605,0,2,Citizen 1148,0,Car ,Female,48,HS ,105018,710,2,2,Citizen 1149,0,Car ,Female,47,College ,125485,626,2,1,Citizen 1150,0,Car ,Male ,44,College ,104119,577,2,2,Citizen 1151,0,Home,Female,42,HS ,93970,693,5,2,Citizen 1152,0,Home,Male ,42,HS ,77979,646,6,2,Citizen 1153,0,Car ,Female,50,HS ,113666,672,0,2,Citizen 1154,0,Home,Male ,44,College ,96173,615,9,2,Citizen 1155,0,Home,Male ,36,HS ,83498,518,4,2,Citizen 1156,0,Home,Male ,46,HS ,119246,691,7,2,Citizen 1157,0,Home,Male ,42,HS ,74800,536,5,2,Citizen 1158,0,Home,Male ,42,HS ,93466,623,2,2,Citizen 1160,0,Home,Female,47,College ,112921,689,10,2,Citizen 1161,0,Car ,Female,44,College ,106839,657,2,2,Citizen 1162,0,Car ,Female,38,HS ,82795,645,0,2,Citizen 1163,0,Home,Male ,40,HS ,92513,536,2,1,Citizen 1164,0,Car ,Male ,37,HS ,84324,665,0,2,Citizen 1165,0,Car ,Female,46,HS ,115588,653,1,2,Citizen 1166,0,Home,Male ,35,HS ,94711,598,7,2,Citizen 1167,0,Car ,Male ,42,HS ,58819,636,0,2,Citizen 1168,0,Home,Male ,47,HS ,90402,658,7,2,Citizen 1169,0,Car ,Female,35,HS ,88892,738,1,2,Citizen 1171,0,Car ,Male ,35,College ,67457,664,2,2,Citizen 1172,0,Car ,Female,39,HS ,101589,636,0,2,Citizen 1173,0,Car ,Male ,49,HS ,86160,582,2,2,Citizen 1174,0,Home,Male ,37,College ,91784,625,5,2,Citizen 1175,0,Home,Male ,36,HS ,95911,662,5,2,Citizen 1176,0,Home,Male ,48,HS ,110571,590,2,2,Citizen 1177,0,Car ,Female,37,College ,93485,642,1,2,Citizen 1178,0,Home,Male ,39,HS ,93650,693,5,2,Citizen 1179,0,Car ,Female,42,HS ,106090,644,0,2,Citizen 1180,0,Car ,Female,52,HS ,83575,725,2,1,Citizen 1181,0,Home,Male ,36,HS ,100746,613,1,2,Citizen 1182,0,Home,Male ,36,HS ,82775,502,6,2,Citizen 1183,0,Car ,Male ,42,College ,118592,594,2,2,Citizen 1184,0,Car ,Female,42,HS ,111033,607,3,2,Citizen 1185,0,Car ,Male ,53,College ,112571,676,2,2,Citizen 1186,0,Home,Male ,41,College ,90298,607,8,2,Citizen 1187,0,Car ,Male ,40,HS ,98249,571,0,2,Citizen 1188,0,Car ,Female,35,College ,98057,615,3,1,Citizen 1189,0,Car ,Female,36,HS ,114245,534,0,2,Citizen 1190,0,Car ,Female,53,HS ,94735,637,2,1,Citizen 1191,0,Car ,Male ,44,HS ,108989,612,2,2,Citizen 1192,0,Car ,Male ,31,HS ,95416,582,0,2,Citizen 1193,1,Car ,Female,41,HS ,117052,638,2,2,Citizen 1194,0,Car ,Male ,47,HS ,122781,653,2,2,Citizen 1195,0,Car ,Female,43,College ,92476,551,1,2,Citizen 1196,0,Home,Male ,35,HS ,78251,570,5,2,Citizen 1197,0,Home,Male ,44,HS ,106567,601,11,1,Citizen 1198,0,Car ,Male ,53,College ,119536,560,2,2,Citizen 1199,1,Car ,Male ,53,HS ,90420,681,1,2,Citizen 1200,1,Home,Female,57,College ,118825,710,12,1,Citizen 1201,0,Car ,Male ,45,College ,121857,638,3,2,Citizen 1202,0,Car ,Male ,50,HS ,81695,589,0,2,Citizen 1203,0,Car ,Male ,40,HS ,99874,554,1,2,Citizen 1204,0,Home,Female,30,HS ,65436,589,0,2,Citizen 1205,0,Home,Male ,46,HS ,102649,581,6,1,Citizen 1206,0,Car ,Male ,36,HS ,103601,636,3,2,Citizen 1207,1,Car ,Female,51,HS ,126963,686,2,2,Citizen 1208,0,Home,Female,38,HS ,104127,666,4,1,Citizen 1209,0,Home,Female,38,HS ,76570,708,5,2,Citizen 1210,0,Home,Male ,39,HS ,85507,535,5,2,Citizen 1211,0,Car ,Male ,42,HS ,66386,454,0,2,Citizen 1212,0,Car ,Male ,43,Graduate,102841,675,0,2,Citizen 1213,0,Car ,Male ,39,HS ,108303,645,1,2,Citizen 1214,0,Home,Female,43,College ,73032,585,7,2,Citizen 1215,0,Car ,Male ,31,HS ,86023,559,1,2,Citizen 1216,0,Home,Male ,47,College ,97255,613,12,2,Citizen 1217,0,Home,Male ,35,HS ,70638,535,2,2,Citizen 1218,0,Home,Female,41,HS ,132971,628,5,2,Citizen 1219,0,Home,Male ,50,HS ,60219,547,6,2,Citizen 1220,0,Home,Male ,41,HS ,92658,541,7,2,Citizen 1221,0,Home,Male ,44,HS ,87121,528,5,2,Citizen 1222,1,Home,Female,54,College ,105355,698,3,2,Citizen 1223,0,Home,Male ,40,College ,74887,578,5,2,Citizen 1224,0,Car ,Male ,37,College ,70524,593,0,2,Citizen 1225,0,Car ,Male ,35,HS ,61022,532,2,2,Citizen 1226,0,Car ,Male ,45,HS ,85810,562,0,2,Citizen 1227,0,Home,Male ,45,HS ,112330,692,4,2,Citizen 1228,0,Home,Male ,39,HS ,81765,658,8,1,Citizen 1229,0,Car ,Female,48,HS ,92723,605,0,2,Citizen 1230,0,Home,Male ,38,HS ,79850,569,7,2,Citizen 1231,0,Car ,Male ,43,HS ,67235,701,2,2,Citizen 1232,0,Car ,Female,50,College ,100680,599,3,1,Citizen 1233,0,Home,Male ,38,HS ,103340,560,8,2,Citizen 1234,0,Car ,Female,41,College ,106651,669,1,2,Non-citizen 1235,0,Car ,Female,44,HS ,124451,723,0,2,Citizen 1236,0,Car ,Female,34,HS ,117835,620,1,2,Citizen 1237,0,Car ,Female,40,HS ,103534,631,1,2,Citizen 1238,0,Home,Male ,24,HS ,71836,675,5,2,Citizen 1239,0,Car ,Male ,37,HS ,97335,647,1,2,Citizen 1240,1,Car ,Male ,41,Graduate,134819,655,2,2,Citizen 1241,0,Car ,Male ,37,HS ,107560,621,1,2,Citizen 1242,0,Home,Male ,39,HS ,72243,725,5,2,Citizen 1243,0,Car ,Female,38,HS ,102463,547,0,2,Citizen 1244,0,Car ,Male ,45,College ,104142,522,2,2,Citizen 1245,0,Home,Female,40,College ,115813,685,10,1,Citizen 1246,0,Home,Male ,29,HS ,48601,562,2,2,Citizen 1247,0,Home,Male ,42,HS ,100293,600,2,2,Citizen 1248,0,Car ,Male ,47,HS ,129402,684,2,2,Citizen 1249,0,Car ,Male ,41,Graduate,68241,506,1,2,Citizen 1250,0,Home,Female,39,HS ,111124,671,5,2,Citizen 1251,0,Car ,Female,54,HS ,109932,649,0,2,Citizen 1252,0,Car ,Male ,52,HS ,93400,681,2,1,Citizen 1253,0,Car ,Male ,34,HS ,81283,614,2,2,Citizen 1254,0,Car ,Male ,49,College ,122325,815,3,1,Citizen 1255,0,Car ,Male ,42,HS ,98947,574,0,2,Citizen 1256,1,Car ,Male ,48,College ,129023,718,3,2,Citizen 1257,0,Car ,Female,38,HS ,72236,625,0,2,Citizen 1258,0,Home,Male ,44,College ,100239,581,7,2,Citizen 1259,0,Home,Male ,32,HS ,89534,531,7,2,Citizen 1260,0,Home,Male ,38,College ,105069,559,8,2,Citizen 1261,0,Car ,Female,40,HS ,93418,526,1,2,Citizen 1262,0,Car ,Male ,37,HS ,94374,601,1,2,Citizen 1263,1,Car ,Female,44,College ,113661,753,3,1,Citizen 1264,0,Car ,Female,45,HS ,78848,661,2,1,Citizen 1265,0,Car ,Male ,35,HS ,83072,630,2,2,Citizen 1266,0,Car ,Male ,42,HS ,98761,631,2,2,Citizen 1267,0,Car ,Male ,49,College ,93148,653,1,2,Citizen 1268,0,Car ,Male ,39,HS ,102320,644,1,2,Citizen 1269,0,Car ,Female,49,HS ,110658,594,0,1,Citizen 1270,0,Car ,Male ,54,HS ,94028,587,1,2,Citizen 1271,0,Car ,Female,52,HS ,85075,657,1,2,Citizen 1272,0,Car ,Male ,39,HS ,76516,525,0,2,Citizen 1273,0,Home,Male ,45,HS ,78650,606,6,2,Citizen 1274,0,Home,Female,43,HS ,103136,594,7,2,Citizen 1275,0,Home,Female,35,HS ,72272,662,5,1,Citizen 1276,0,Car ,Female,47,College ,111690,604,2,2,Citizen 1277,0,Car ,Female,46,HS ,102067,505,0,2,Citizen 1278,0,Home,Male ,32,HS ,71611,586,7,2,Citizen 1279,0,Car ,Male ,29,HS ,95687,623,0,2,Citizen 1280,0,Car ,Male ,42,HS ,95740,591,3,2,Citizen 1281,1,Car ,Female,41,Graduate,76918,645,0,2,Citizen 1282,0,Home,Male ,43,HS ,100152,523,4,2,Citizen 1283,0,Home,Female,48,HS ,93852,564,8,2,Citizen 1284,0,Car ,Female,42,HS ,111015,616,1,2,Citizen 1285,0,Home,Female,44,HS ,74457,661,7,2,Citizen 1286,0,Car ,Male ,58,Graduate,104720,670,2,2,Non-citizen 1287,0,Car ,Male ,42,College ,91711,608,2,2,Non-citizen 1288,0,Car ,Male ,37,College ,81712,594,1,2,Citizen 1289,0,Car ,Male ,42,College ,103246,711,2,2,Citizen 1290,0,Home,Male ,34,HS ,71177,557,8,2,Citizen 1291,0,Home,Male ,32,HS ,81877,652,7,2,Citizen 1292,0,Car ,Female,45,HS ,91868,590,1,2,Citizen 1293,0,Car ,Male ,42,HS ,118095,783,3,1,Citizen 1294,1,Car ,Female,50,College ,123149,668,2,2,Citizen 1295,0,Car ,Male ,43,HS ,73429,592,0,2,Citizen 1296,0,Car ,Female,50,College ,133490,664,1,2,Citizen 1297,0,Car ,Female,52,College ,109154,677,0,2,Citizen 1298,0,Car ,Female,46,College ,102072,697,2,2,Citizen 1299,0,Car ,Male ,39,HS ,108771,627,3,2,Citizen 1300,0,Car ,Male ,54,Graduate,94095,747,1,2,Non-citizen 1301,0,Car ,Female,48,Graduate,103509,701,0,2,Citizen 1302,1,Car ,Female,58,Graduate,110395,707,1,2,Non-citizen 1303,0,Car ,Male ,39,College ,81210,590,1,2,Non-citizen 1304,0,Car ,Female,36,HS ,95604,581,0,2,Citizen 1305,0,Home,Male ,36,HS ,99023,580,4,2,Citizen 1306,0,Car ,Female,39,HS ,66437,631,0,2,Citizen 1307,0,Car ,Female,46,HS ,108527,617,2,1,Citizen 1308,0,Car ,Male ,31,HS ,72903,546,2,2,Citizen 1309,0,Car ,Male ,54,Graduate,91534,631,2,2,Citizen 1310,0,Car ,Male ,38,HS ,85303,579,2,2,Citizen 1311,0,Car ,Female,53,HS ,97180,702,1,2,Citizen 1312,0,Car ,Male ,42,HS ,67681,581,1,2,Citizen 1314,1,Car ,Female,47,Graduate,122899,620,2,2,Citizen 1315,1,Car ,Male ,37,College ,132393,648,0,2,Citizen 1316,0,Car ,Male ,40,HS ,94832,548,0,2,Citizen 1317,1,Car ,Female,44,College ,121667,620,2,2,Citizen 1318,0,Car ,Male ,47,HS ,119588,614,0,2,Citizen 1319,0,Car ,Male ,49,HS ,98805,644,1,2,Citizen 1320,0,Home,Male ,33,HS ,79937,541,0,2,Citizen 1321,0,Car ,Male ,42,HS ,70726,702,0,2,Citizen 1322,1,Car ,Female,42,College ,91830,521,2,2,Citizen 1323,0,Car ,Female,49,HS ,103814,765,2,2,Citizen 1324,0,Car ,Male ,45,HS ,105343,613,1,2,Citizen 1325,1,Car ,Male ,38,College ,115190,577,1,2,Citizen 1326,0,Car ,Male ,55,HS ,115171,736,2,2,Citizen 1327,0,Car ,Male ,37,College ,85113,596,2,2,Citizen 1328,0,Car ,Male ,51,HS ,113810,638,2,2,Citizen 1329,0,Car ,Female,47,HS ,95159,621,0,2,Citizen 1330,1,Car ,Male ,38,HS ,89529,654,0,2,Citizen 1331,0,Car ,Male ,46,HS ,103748,652,1,2,Citizen 1332,0,Car ,Male ,42,HS ,111654,558,3,2,Citizen 1333,0,Car ,Male ,31,HS ,73590,537,0,2,Citizen 1334,0,Home,Male ,41,HS ,97031,691,7,2,Citizen 1335,0,Home,Male ,44,College ,102112,725,6,2,Citizen 1336,0,Car ,Female,30,HS ,96754,567,0,2,Citizen 1337,0,Car ,Male ,50,College ,84917,627,2,2,Citizen 1338,1,Car ,Male ,44,College ,119778,666,1,2,Citizen 1339,0,Car ,Female,42,College ,93672,636,2,2,Citizen 1340,0,Car ,Male ,49,HS ,110257,551,1,2,Citizen 1341,0,Home,Male ,39,HS ,99049,552,3,2,Citizen 1342,1,Car ,Male ,53,College ,86532,589,1,2,Citizen 1343,0,Home,Male ,33,HS ,87421,606,3,2,Citizen 1344,0,Home,Female,49,HS ,101868,727,5,2,Citizen 1345,0,Car ,Female,35,College ,122956,501,2,2,Citizen 1346,0,Home,Male ,39,HS ,80743,585,3,2,Citizen 1347,0,Home,Female,40,HS ,102569,662,6,2,Citizen 1348,1,Car ,Female,47,College ,140368,736,0,2,Citizen 1349,0,Car ,Male ,44,HS ,108300,617,0,2,Citizen 1350,0,Car ,Female,51,Graduate,101050,681,1,2,Citizen 1351,0,Home,Male ,43,HS ,99254,545,9,2,Citizen 1352,0,Home,Male ,34,HS ,95940,520,4,2,Citizen 1353,0,Car ,Male ,44,HS ,99668,581,2,2,Citizen 1354,0,Car ,Male ,44,HS ,74273,571,2,2,Citizen 1355,0,Car ,Male ,40,College ,72785,534,2,2,Citizen 1356,0,Home,Male ,40,HS ,92779,623,3,2,Citizen 1357,0,Car ,Male ,37,HS ,111631,590,0,2,Citizen 1358,0,Car ,Male ,46,HS ,84324,586,1,2,Citizen 1359,0,Car ,Female,48,Graduate,74595,681,2,2,Citizen 1360,0,Home,Male ,36,HS ,80699,672,3,2,Citizen 1361,0,Car ,Male ,41,HS ,80962,613,2,2,Citizen 1362,0,Car ,Female,40,College ,51881,611,0,2,Citizen 1363,0,Car ,Female,53,College ,119421,615,3,2,Citizen 1364,0,Car ,Male ,30,HS ,72742,516,0,2,Citizen 1365,1,Home,Male ,38,HS ,116684,682,7,2,Citizen 1366,0,Car ,Male ,41,College ,93605,567,3,1,Citizen 1367,0,Car ,Male ,49,College ,95036,655,2,2,Citizen 1368,0,Home,Male ,37,HS ,83528,527,6,2,Citizen 1369,0,Home,Female,44,HS ,123102,631,8,2,Citizen 1370,0,Car ,Female,45,HS ,86537,688,3,2,Citizen 1371,0,Car ,Female,41,Graduate,96592,530,0,2,Non-citizen 1372,0,Car ,Female,47,College ,95819,570,0,2,Citizen 1373,0,Car ,Female,41,HS ,80582,576,1,2,Citizen 1374,1,Car ,Female,55,College ,136333,668,3,2,Citizen 1375,0,Home,Female,37,HS ,88174,699,5,2,Citizen 1376,0,Car ,Female,42,College ,149116,752,0,2,Citizen 1377,0,Home,Female,27,HS ,108001,626,4,2,Citizen 1378,1,Car ,Male ,40,HS ,101049,607,0,2,Citizen 1379,0,Car ,Male ,49,College ,112216,635,3,2,Citizen 1380,0,Car ,Female,48,College ,108506,570,2,2,Citizen 1381,0,Car ,Female,38,HS ,95433,550,1,1,Citizen 1382,1,Car ,Male ,35,HS ,108275,598,0,2,Citizen 1383,0,Car ,Female,43,College ,90908,583,0,2,Citizen 1384,0,Car ,Male ,48,College ,98321,687,2,2,Citizen 1385,0,Car ,Female,43,College ,85530,652,0,2,Citizen 1386,0,Car ,Male ,35,HS ,104126,585,2,2,Citizen 1387,0,Home,Female,44,HS ,122168,657,10,2,Citizen 1388,0,Car ,Female,41,HS ,79997,561,0,2,Citizen 1389,0,Home,Female,49,HS ,126765,615,12,1,Citizen 1390,0,Car ,Male ,44,HS ,73410,630,0,2,Citizen 1391,0,Home,Female,46,HS ,92940,524,3,2,Citizen 1392,0,Car ,Female,46,HS ,108096,725,3,1,Citizen 1393,0,Car ,Male ,54,HS ,61898,560,0,2,Citizen 1394,0,Car ,Female,46,College ,122937,651,1,1,Citizen 1395,0,Car ,Female,54,College ,110492,610,2,2,Citizen 1396,0,Car ,Male ,39,College ,85797,646,3,2,Citizen 1397,0,Car ,Male ,44,College ,81826,590,0,2,Citizen 1398,0,Car ,Female,52,HS ,82476,707,0,2,Citizen 1399,0,Car ,Female,40,College ,112338,590,2,2,Citizen 1400,0,Car ,Male ,50,HS ,99492,653,1,2,Citizen 1401,0,Home,Female,49,HS ,127681,625,9,2,Citizen 1402,0,Car ,Female,40,HS ,82284,668,1,2,Citizen 1403,0,Car ,Male ,30,HS ,105668,473,2,2,Citizen 1404,0,Car ,Male ,34,HS ,100246,615,1,2,Citizen 1405,0,Home,Male ,39,HS ,56204,607,12,2,Citizen 1406,0,Car ,Male ,37,College ,86573,552,0,2,Citizen 1407,0,Home,Female,48,HS ,99007,623,10,2,Citizen 1408,0,Car ,Male ,43,HS ,92606,669,1,2,Citizen 1409,0,Car ,Female,40,HS ,102575,722,0,2,Citizen 1410,0,Home,Male ,40,HS ,79030,687,5,1,Citizen 1411,0,Car ,Male ,20,HS ,59066,536,0,1,Citizen 1412,0,Home,Female,38,HS ,86835,629,5,2,Citizen 1413,1,Car ,Female,47,College ,104642,685,3,2,Citizen 1414,0,Home,Male ,33,College ,100385,658,7,2,Citizen 1415,0,Home,Male ,38,HS ,74705,668,5,2,Citizen 1416,0,Home,Male ,34,HS ,102108,632,2,2,Citizen 1417,0,Car ,Male ,38,HS ,90868,528,0,2,Citizen 1418,0,Car ,Male ,41,HS ,53187,508,0,2,Citizen 1419,0,Home,Male ,45,HS ,117222,622,6,2,Citizen 1420,0,Car ,Male ,36,HS ,88281,554,0,2,Citizen 1421,0,Home,Male ,45,HS ,78971,584,9,2,Citizen 1422,0,Car ,Male ,40,HS ,95799,499,0,2,Citizen 1423,0,Car ,Female,45,HS ,100986,642,1,2,Citizen 1424,0,Car ,Female,40,HS ,92813,617,1,2,Citizen 1425,0,Car ,Male ,41,College ,97520,553,1,2,Citizen 1426,0,Car ,Male ,41,HS ,70988,632,0,2,Citizen 1427,0,Car ,Female,43,College ,111384,563,1,2,Citizen 1428,0,Car ,Male ,49,HS ,80549,664,2,2,Citizen 1429,0,Car ,Male ,35,College ,69649,625,3,2,Citizen 1430,0,Home,Male ,46,HS ,83874,565,6,2,Citizen 1431,0,Home,Female,39,College ,88400,570,12,2,Citizen 1432,0,Car ,Male ,41,HS ,66530,583,1,2,Citizen 1433,0,Car ,Female,34,HS ,98385,602,1,2,Citizen 1434,0,Car ,Male ,56,College ,103480,670,3,2,Citizen 1435,0,Car ,Female,30,HS ,109920,557,1,2,Citizen 1436,0,Car ,Female,50,College ,102080,647,2,2,Citizen 1437,0,Car ,Female,39,HS ,94158,625,1,2,Citizen 1438,0,Home,Male ,38,HS ,74191,590,4,2,Citizen 1439,0,Car ,Female,56,HS ,91160,609,2,2,Citizen 1440,0,Car ,Female,48,College ,115603,716,1,1,Citizen 1441,0,Home,Male ,36,HS ,95741,561,8,2,Citizen 1442,0,Home,Female,47,College ,140500,596,8,2,Citizen 1443,0,Home,Male ,41,HS ,48845,585,4,2,Citizen 1444,0,Home,Female,41,HS ,96136,698,5,2,Citizen 1445,0,Car ,Female,48,College ,98837,630,0,2,Citizen 1446,0,Home,Male ,34,HS ,96090,653,6,2,Citizen 1447,0,Home,Female,39,College ,102706,672,9,2,Citizen 1448,0,Car ,Male ,51,HS ,99280,644,2,2,Citizen 1449,0,Home,Male ,40,HS ,97336,625,7,1,Citizen 1450,0,Home,Male ,45,HS ,89471,556,8,2,Citizen 1451,1,Car ,Male ,43,HS ,118176,662,0,2,Citizen 1452,0,Home,Male ,30,HS ,77826,504,1,2,Citizen 1453,0,Car ,Female,38,HS ,100483,565,1,2,Citizen 1454,0,Car ,Female,38,College ,99351,720,1,1,Citizen 1455,0,Home,Female,52,College ,88594,664,12,2,Citizen 1456,0,Home,Male ,32,College ,104079,620,6,2,Citizen 1457,0,Car ,Female,47,College ,125746,666,3,2,Citizen 1458,0,Car ,Female,45,HS ,83183,557,0,2,Citizen 1459,0,Home,Male ,47,HS ,91547,558,6,2,Citizen 1460,0,Car ,Male ,41,College ,78343,592,2,2,Citizen 1461,0,Home,Male ,41,HS ,96956,647,5,2,Citizen 1462,0,Home,Male ,32,Graduate,89250,590,10,2,Citizen 1463,0,Car ,Male ,48,College ,104672,547,3,2,Citizen 1464,0,Car ,Male ,39,HS ,98653,607,0,2,Citizen 1465,0,Car ,Female,49,HS ,100434,803,0,1,Citizen 1466,0,Car ,Male ,35,HS ,94903,532,0,2,Citizen 1467,1,Car ,Female,44,Graduate,109812,645,1,2,Citizen 1468,0,Home,Male ,37,College ,103659,667,11,2,Citizen 1469,0,Car ,Male ,36,HS ,77129,583,1,1,Citizen 1470,0,Car ,Female,46,College ,122099,730,3,2,Citizen 1471,0,Car ,Female,58,Graduate,103473,611,3,2,Citizen 1472,0,Home,Male ,47,College ,119675,679,9,2,Citizen 1473,0,Car ,Female,41,HS ,98479,643,0,2,Citizen 1474,0,Home,Male ,39,HS ,102854,548,12,1,Citizen 1475,0,Home,Male ,38,HS ,88140,643,5,2,Citizen 1476,0,Home,Male ,40,College ,92335,746,7,2,Citizen 1477,0,Car ,Male ,52,College ,95952,533,1,2,Citizen 1478,0,Car ,Male ,45,HS ,88037,560,0,2,Citizen 1479,0,Car ,Male ,56,HS ,129745,704,3,1,Citizen 1480,0,Home,Female,41,HS ,75871,523,5,2,Citizen 1481,0,Car ,Male ,37,College ,89146,569,1,2,Citizen 1482,0,Car ,Male ,32,HS ,95925,647,0,2,Citizen 1483,0,Car ,Male ,39,College ,71351,469,2,2,Citizen 1484,0,Car ,Male ,43,HS ,113472,658,3,1,Citizen 1485,1,Car ,Male ,28,HS ,104058,565,0,2,Citizen 1486,0,Car ,Male ,48,College ,80817,624,2,2,Citizen 1487,0,Home,Female,43,HS ,106131,630,7,2,Citizen 1488,0,Car ,Female,47,Graduate,104433,642,3,1,Non-citizen 1489,0,Car ,Male ,41,HS ,109859,695,0,2,Citizen 1490,0,Home,Female,32,HS ,87367,574,2,2,Citizen 1491,0,Car ,Female,48,HS ,88659,670,1,2,Citizen 1492,0,Home,Male ,35,HS ,73591,606,9,2,Citizen 1493,0,Home,Male ,30,HS ,64623,548,1,2,Citizen 1494,0,Home,Male ,46,HS ,92017,755,6,2,Citizen 1495,0,Car ,Female,41,HS ,76749,659,0,2,Citizen 1496,0,Car ,Female,49,HS ,102137,556,0,2,Citizen 1497,0,Home,Female,45,Graduate,72079,680,9,2,Citizen 1498,0,Car ,Female,43,College ,74082,625,2,2,Citizen 1499,0,Home,Female,38,College ,82836,636,8,2,Citizen 1500,0,Car ,Male ,42,HS ,94505,596,1,1,Citizen 1501,0,Car ,Female,46,HS ,114567,732,0,2,Non-citizen 1502,0,Home,Female,30,HS ,96044,685,7,2,Citizen 1503,0,Car ,Female,48,HS ,105119,657,1,2,Citizen 1504,0,Home,Male ,49,HS ,97621,567,8,2,Citizen 1505,0,Home,Female,38,College ,100057,636,3,2,Citizen 1506,0,Car ,Male ,43,HS ,90159,647,1,2,Citizen 1507,0,Home,Male ,50,College ,97325,650,10,2,Citizen 1508,0,Car ,Male ,43,HS ,107715,582,0,2,Citizen 1509,0,Car ,Female,46,HS ,95771,611,0,2,Citizen 1510,0,Home,Female,45,HS ,106319,548,6,2,Citizen 1511,0,Car ,Male ,42,HS ,100389,568,2,2,Citizen 1512,0,Car ,Female,29,HS ,114142,554,1,2,Citizen 1513,1,Car ,Female,46,HS ,90209,771,2,1,Citizen 1514,0,Car ,Male ,44,HS ,79919,656,0,2,Citizen 1515,0,Car ,Male ,52,HS ,110066,674,0,1,Citizen 1516,0,Car ,Male ,36,College ,90277,624,1,2,Citizen 1517,0,Car ,Male ,35,HS ,66957,610,0,2,Citizen 1518,0,Car ,Male ,32,HS ,67593,550,0,2,Citizen 1519,0,Car ,Male ,39,HS ,81073,709,0,2,Citizen 1520,0,Home,Male ,35,HS ,91087,632,6,2,Citizen 1521,0,Car ,Male ,50,HS ,133800,734,2,1,Citizen 1522,1,Car ,Male ,43,College ,96699,669,2,2,Citizen 1523,0,Car ,Female,45,Graduate,76754,807,0,2,Citizen 1524,0,Car ,Female,35,HS ,92225,530,0,2,Citizen 1525,0,Home,Male ,40,HS ,88871,592,1,2,Citizen 1526,0,Car ,Female,38,College ,89829,615,1,2,Citizen 1527,0,Car ,Female,48,College ,119714,632,1,1,Citizen 1528,1,Car ,Female,43,Graduate,82369,587,2,2,Citizen 1529,0,Car ,Female,54,College ,119961,700,3,1,Citizen 1530,0,Car ,Female,45,HS ,121139,665,0,2,Citizen 1531,0,Car ,Male ,39,HS ,105730,558,1,2,Citizen 1532,0,Home,Male ,41,College ,83603,606,8,2,Citizen 1533,0,Car ,Female,39,HS ,93060,578,0,2,Citizen 1534,0,Car ,Female,32,HS ,64940,604,0,2,Citizen 1535,0,Home,Male ,46,HS ,76618,532,8,2,Citizen 1536,0,Car ,Male ,45,College ,117169,646,2,2,Citizen 1537,0,Car ,Male ,49,College ,93156,654,2,1,Non-citizen 1538,0,Home,Female,49,HS ,96732,558,7,2,Citizen 1539,0,Home,Male ,39,HS ,78664,578,11,2,Citizen 1540,1,Car ,Male ,44,HS ,90906,718,1,2,Citizen 1541,0,Car ,Male ,38,College ,98914,710,2,2,Citizen 1542,0,Car ,Male ,35,HS ,98805,582,2,2,Citizen 1543,0,Car ,Female,48,HS ,104461,629,1,2,Citizen 1544,1,Car ,Female,55,College ,120422,770,2,2,Citizen 1545,0,Home,Male ,38,HS ,83760,609,3,2,Citizen 1546,0,Car ,Male ,39,College ,87377,498,0,2,Citizen 1547,0,Home,Male ,37,HS ,82267,486,6,2,Citizen 1548,0,Car ,Male ,38,HS ,87259,544,0,2,Citizen 1549,0,Car ,Male ,49,HS ,99188,647,0,2,Citizen 1550,0,Home,Male ,41,HS ,107508,588,9,2,Citizen 1551,0,Car ,Male ,40,HS ,95976,591,2,2,Citizen 1552,0,Home,Male ,46,HS ,108251,677,8,2,Citizen 1553,0,Home,Male ,44,HS ,90107,576,12,2,Citizen 1554,0,Car ,Female,38,HS ,124918,502,2,2,Citizen 1555,0,Home,Female,28,HS ,81821,671,4,2,Citizen 1556,0,Home,Male ,36,College ,76718,572,4,2,Citizen 1557,0,Car ,Female,48,HS ,83503,713,0,1,Citizen 1558,1,Car ,Female,41,College ,118979,672,2,2,Citizen 1559,0,Car ,Female,31,College ,112651,537,0,2,Citizen 1560,0,Car ,Male ,45,College ,118572,614,3,2,Citizen 1561,0,Car ,Female,33,College ,89101,598,1,2,Citizen 1562,0,Home,Male ,45,HS ,89332,501,6,2,Citizen 1563,0,Home,Male ,32,HS ,105637,493,7,2,Citizen 1564,0,Car ,Female,36,HS ,109389,611,0,2,Citizen 1565,1,Car ,Male ,41,Graduate,93460,661,2,2,Citizen 1566,0,Car ,Female,44,HS ,99565,640,0,2,Citizen 1567,0,Car ,Female,40,HS ,81595,571,1,2,Citizen 1568,1,Home,Male ,34,College ,111179,568,10,2,Citizen 1569,0,Car ,Female,41,HS ,89810,482,0,1,Citizen 1570,0,Home,Male ,38,HS ,81105,629,3,2,Citizen 1571,0,Car ,Female,44,College ,96835,657,2,2,Citizen 1572,0,Car ,Male ,36,HS ,80037,530,1,2,Citizen 1574,0,Car ,Male ,32,HS ,85075,499,2,2,Citizen 1575,0,Car ,Male ,36,HS ,74640,484,3,2,Citizen 1576,0,Home,Female,40,HS ,105960,610,3,2,Citizen 1577,0,Car ,Male ,44,HS ,85190,585,2,2,Citizen 1578,0,Car ,Male ,45,Graduate,105227,630,2,2,Citizen 1579,0,Home,Male ,50,HS ,78480,654,6,2,Citizen 1580,0,Car ,Female,40,HS ,73927,610,0,1,Citizen 1581,0,Home,Female,39,College ,82744,631,2,2,Citizen 1582,0,Home,Male ,48,HS ,107996,662,9,1,Citizen 1583,0,Car ,Female,45,HS ,115538,565,1,2,Citizen 1584,0,Home,Female,31,HS ,56456,485,4,2,Citizen 1585,0,Car ,Female,44,Graduate,128996,631,3,1,Citizen 1586,0,Car ,Female,51,College ,104009,671,3,2,Citizen 1587,0,Car ,Female,54,College ,105630,729,2,2,Citizen 1588,0,Car ,Male ,34,College ,87161,573,0,2,Citizen 1589,0,Car ,Female,35,HS ,123648,610,2,2,Citizen 1590,0,Home,Male ,31,HS ,101457,648,4,2,Citizen 1591,0,Car ,Male ,47,HS ,93375,642,2,2,Citizen 1592,0,Home,Male ,50,College ,105507,669,6,2,Citizen 1593,1,Car ,Female,42,Graduate,122608,625,2,2,Non-citizen 1594,0,Car ,Female,46,HS ,98037,649,1,2,Citizen 1595,0,Home,Female,43,HS ,82209,713,9,1,Citizen 1596,0,Car ,Male ,44,HS ,77005,597,0,2,Citizen 1597,1,Car ,Male ,43,HS ,99622,677,0,2,Citizen 1598,0,Car ,Female,46,HS ,96656,597,2,2,Citizen 1599,0,Home,Male ,28,HS ,94195,601,10,2,Citizen 1600,0,Car ,Male ,49,College ,82949,603,1,2,Citizen 1601,0,Car ,Male ,41,HS ,82770,692,2,2,Citizen 1602,0,Car ,Male ,38,HS ,67675,643,0,2,Citizen 1603,0,Home,Male ,35,HS ,69371,442,5,2,Citizen 1604,0,Car ,Male ,51,Graduate,95163,599,2,2,Citizen 1605,1,Car ,Male ,35,HS ,104294,573,2,2,Citizen 1606,0,Car ,Female,48,College ,109865,601,2,2,Citizen 1607,0,Home,Male ,42,HS ,82195,555,5,2,Citizen 1608,0,Car ,Male ,45,College ,82905,620,3,2,Citizen 1609,1,Car ,Male ,37,HS ,114525,670,2,2,Non-citizen 1610,0,Home,Male ,44,HS ,94067,627,6,2,Citizen 1611,0,Home,Female,29,HS ,95390,563,4,2,Citizen 1612,0,Car ,Male ,30,HS ,72457,619,2,2,Citizen 1613,0,Home,Male ,43,HS ,84328,561,7,2,Citizen 1614,0,Car ,Male ,44,College ,99815,646,1,2,Citizen 1615,0,Car ,Female,50,HS ,89066,623,1,2,Citizen 1616,0,Car ,Male ,35,HS ,92274,505,2,2,Citizen 1617,0,Car ,Male ,31,College ,94679,611,1,2,Citizen 1618,0,Home,Male ,42,HS ,75713,544,6,2,Citizen 1619,0,Car ,Female,46,Graduate,88604,660,2,1,Citizen 1620,0,Home,Male ,43,HS ,80099,595,2,2,Citizen 1621,0,Home,Male ,46,HS ,83367,583,3,2,Citizen 1622,0,Home,Male ,28,HS ,47993,540,6,2,Citizen 1623,0,Home,Male ,41,HS ,62884,550,10,2,Citizen 1624,0,Car ,Male ,37,College ,99426,613,2,2,Citizen 1625,0,Home,Male ,48,HS ,110506,648,9,2,Citizen 1626,0,Car ,Female,44,HS ,93393,621,1,2,Non-citizen 1627,0,Car ,Female,43,College ,104777,684,0,2,Non-citizen 1628,0,Car ,Female,41,HS ,93728,541,0,2,Citizen 1629,0,Home,Male ,31,HS ,108350,596,5,2,Citizen 1630,0,Home,Male ,39,HS ,86363,575,4,2,Citizen 1631,0,Car ,Male ,39,College ,106875,524,3,2,Citizen 1632,0,Car ,Female,42,HS ,102575,639,0,2,Citizen 1633,0,Car ,Male ,45,HS ,124210,569,2,2,Citizen 1634,0,Car ,Female,48,HS ,107807,598,2,2,Citizen 1635,0,Car ,Female,51,College ,89052,558,2,2,Citizen 1636,0,Car ,Female,49,College ,121354,610,2,2,Citizen 1637,0,Home,Male ,50,HS ,103021,626,5,2,Citizen 1638,0,Car ,Female,48,HS ,131116,663,2,1,Citizen 1639,0,Car ,Male ,35,HS ,84424,573,0,2,Citizen 1640,0,Car ,Male ,38,HS ,90043,478,2,2,Citizen 1641,1,Car ,Female,43,College ,91930,688,2,2,Citizen 1642,0,Home,Male ,38,College ,98967,638,7,2,Citizen 1643,0,Car ,Male ,42,HS ,77620,548,0,2,Citizen 1644,0,Car ,Male ,41,HS ,93377,621,0,2,Citizen 1645,0,Car ,Male ,52,HS ,68108,640,2,2,Citizen 1646,0,Car ,Male ,47,College ,84802,688,0,2,Citizen 1647,0,Car ,Male ,45,College ,85276,543,1,2,Citizen 1648,0,Home,Male ,47,College ,106771,582,12,2,Citizen 1649,1,Car ,Male ,43,HS ,121809,659,1,2,Citizen 1650,0,Car ,Female,53,HS ,114260,588,1,2,Citizen 1651,0,Car ,Female,38,College ,116818,635,0,2,Citizen 1652,0,Car ,Male ,49,HS ,110881,588,0,2,Citizen 1653,0,Car ,Female,43,College ,113168,632,0,2,Citizen 1654,0,Car ,Male ,29,HS ,81855,683,0,2,Citizen 1655,0,Car ,Female,48,College ,98551,632,0,2,Citizen 1656,0,Car ,Male ,41,HS ,56519,584,0,2,Citizen 1657,0,Car ,Female,31,HS ,87574,565,2,2,Citizen 1658,0,Car ,Male ,40,College ,89498,586,2,2,Citizen 1659,0,Home,Male ,49,HS ,87332,528,8,2,Citizen 1660,0,Car ,Male ,43,HS ,84456,559,0,2,Citizen 1661,0,Car ,Female,35,College ,103033,564,2,2,Citizen 1662,1,Car ,Female,50,College ,118983,727,2,2,Non-citizen 1663,1,Car ,Female,46,Graduate,111791,755,0,2,Citizen 1664,1,Car ,Female,42,College ,100525,623,1,2,Citizen 1665,0,Car ,Male ,38,HS ,73485,521,2,2,Citizen 1666,0,Home,Male ,36,HS ,108650,534,6,2,Citizen 1667,1,Home,Female,42,College ,115102,631,4,2,Citizen 1668,0,Car ,Male ,45,HS ,114505,485,1,2,Citizen 1669,0,Home,Female,41,HS ,90726,605,3,2,Citizen 1670,0,Home,Male ,31,HS ,78283,533,4,2,Citizen 1671,0,Car ,Female,47,College ,98294,608,0,2,Citizen 1672,0,Car ,Male ,51,Graduate,114126,689,2,2,Citizen 1673,0,Car ,Male ,38,HS ,97889,671,1,2,Citizen 1674,0,Car ,Female,53,College ,118144,577,2,2,Citizen 1675,0,Car ,Male ,41,College ,93172,641,3,2,Citizen 1676,0,Home,Male ,33,HS ,74023,542,4,2,Citizen 1677,0,Home,Female,35,HS ,74771,474,0,2,Citizen 1678,0,Home,Male ,43,HS ,86215,512,4,2,Citizen 1679,0,Home,Male ,46,HS ,87789,500,9,2,Citizen 1680,0,Home,Female,36,College ,97590,567,5,2,Citizen 1681,0,Home,Female,31,HS ,90975,555,4,2,Citizen 1682,0,Home,Male ,33,HS ,81043,596,1,2,Citizen 1683,0,Car ,Male ,39,College ,67291,517,0,2,Citizen 1684,0,Car ,Male ,44,HS ,96634,599,3,2,Citizen 1685,0,Car ,Female,53,HS ,78733,632,1,1,Citizen 1686,0,Car ,Female,40,HS ,78850,573,1,2,Citizen 1687,0,Car ,Male ,33,College ,100338,546,1,2,Citizen 1688,0,Car ,Male ,45,HS ,100326,665,2,2,Citizen 1689,0,Home,Male ,42,HS ,76288,592,6,2,Citizen 1690,0,Car ,Male ,46,HS ,83441,575,2,2,Citizen 1691,0,Car ,Female,40,HS ,83243,542,0,2,Citizen 1692,0,Home,Male ,44,HS ,91509,607,2,2,Citizen 1693,0,Home,Male ,35,HS ,51474,550,5,2,Citizen 1694,0,Home,Male ,29,HS ,84992,520,4,2,Citizen 1695,0,Car ,Female,42,Graduate,117096,580,2,2,Citizen 1696,0,Home,Female,37,HS ,96354,570,6,2,Citizen 1697,0,Car ,Female,35,Graduate,86397,734,0,2,Non-citizen 1698,1,Car ,Female,40,HS ,117167,599,0,2,Citizen 1699,0,Home,Male ,36,HS ,72600,630,4,2,Citizen 1700,0,Home,Male ,44,HS ,87353,682,4,2,Citizen 1701,0,Car ,Male ,50,HS ,103637,595,1,2,Citizen 1702,0,Car ,Male ,54,HS ,109762,632,2,2,Citizen 1703,0,Car ,Female,44,College ,79593,656,1,2,Citizen 1705,0,Car ,Male ,47,HS ,93565,677,2,1,Citizen 1706,0,Car ,Female,35,HS ,91910,583,1,2,Citizen 1707,0,Car ,Male ,47,College ,94717,570,2,2,Citizen 1708,0,Car ,Male ,47,College ,88681,627,2,1,Citizen 1709,0,Home,Male ,37,HS ,74640,592,5,2,Citizen 1710,0,Car ,Female,45,HS ,76359,545,2,2,Citizen 1711,0,Home,Female,43,HS ,103942,579,3,2,Citizen 1712,0,Home,Male ,49,College ,113663,603,11,2,Citizen 1713,0,Home,Female,48,HS ,100789,695,6,2,Citizen 1714,0,Home,Female,34,HS ,87558,616,4,2,Citizen 1715,0,Home,Male ,40,HS ,84428,596,5,2,Citizen 1716,0,Car ,Female,49,HS ,105644,533,0,2,Citizen 1717,0,Home,Female,57,HS ,102853,679,5,2,Citizen 1718,0,Home,Male ,49,HS ,74179,531,6,2,Citizen 1719,0,Home,Male ,39,HS ,92994,576,7,2,Citizen 1720,0,Car ,Female,51,College ,111811,712,1,1,Non-citizen 1721,0,Car ,Male ,45,HS ,105904,584,0,2,Citizen 1722,0,Car ,Female,45,College ,86517,691,1,2,Citizen 1723,0,Car ,Female,48,College ,100247,702,0,2,Citizen 1724,0,Home,Male ,40,HS ,91295,673,7,2,Citizen 1725,0,Car ,Male ,38,HS ,80159,638,2,2,Citizen 1726,0,Car ,Female,44,HS ,74338,568,1,2,Citizen 1727,0,Home,Male ,53,HS ,92428,602,6,2,Citizen 1728,0,Home,Male ,37,HS ,103558,629,7,2,Citizen 1729,1,Car ,Female,49,College ,87829,679,3,2,Citizen 1730,0,Home,Female,40,HS ,107633,564,6,2,Citizen 1731,0,Car ,Male ,45,College ,82533,573,1,2,Citizen 1732,0,Home,Male ,36,HS ,86592,538,7,2,Citizen 1733,0,Car ,Male ,47,HS ,101888,620,0,2,Citizen 1734,0,Home,Male ,58,HS ,102651,571,5,2,Citizen 1735,0,Car ,Female,33,College ,102805,713,0,1,Citizen 1736,0,Car ,Male ,38,HS ,86680,588,0,2,Citizen 1737,0,Car ,Male ,42,HS ,79449,665,0,2,Citizen 1738,0,Car ,Male ,51,HS ,103838,573,3,2,Citizen 1739,0,Car ,Female,35,College ,107172,626,0,2,Citizen 1740,0,Home,Male ,38,College ,119864,568,10,2,Citizen 1741,0,Home,Female,50,College ,107202,499,8,2,Citizen 1742,0,Home,Male ,30,HS ,84913,528,4,2,Citizen 1743,0,Home,Female,44,HS ,105181,671,11,2,Citizen 1744,1,Car ,Male ,42,HS ,70100,618,0,2,Citizen 1745,0,Home,Male ,31,HS ,99851,611,3,2,Citizen 1746,1,Car ,Male ,50,College ,135228,617,1,2,Citizen 1747,0,Car ,Female,44,Graduate,111580,697,1,2,Non-citizen 1748,0,Car ,Male ,51,College ,105432,512,2,2,Citizen 1749,1,Car ,Male ,41,HS ,97953,711,2,2,Citizen 1750,0,Home,Female,40,HS ,93427,573,5,2,Citizen 1751,0,Car ,Male ,53,HS ,96759,642,3,2,Citizen 1752,1,Car ,Male ,45,College ,151256,664,2,2,Non-citizen 1753,0,Car ,Female,37,HS ,101531,593,2,2,Citizen 1754,0,Home,Male ,34,HS ,64524,529,2,2,Citizen 1755,0,Home,Male ,47,HS ,66407,620,11,2,Citizen 1756,0,Car ,Female,51,College ,111284,752,3,1,Citizen 1757,0,Home,Female,60,College ,84774,611,10,2,Citizen 1758,1,Car ,Male ,34,HS ,77093,617,0,2,Citizen 1759,0,Car ,Male ,44,HS ,101146,635,0,2,Citizen 1760,0,Car ,Female,44,HS ,90215,676,1,2,Citizen 1761,0,Car ,Male ,41,HS ,105565,558,1,2,Citizen 1762,0,Car ,Female,47,HS ,119917,609,0,2,Citizen 1763,0,Home,Female,29,HS ,96724,631,3,2,Citizen 1764,0,Car ,Male ,36,HS ,101002,478,0,2,Citizen 1765,0,Home,Female,42,College ,99114,679,9,2,Citizen 1766,0,Car ,Female,48,HS ,96197,604,3,2,Citizen 1767,0,Car ,Female,48,HS ,96705,638,1,2,Citizen 1768,0,Home,Male ,39,HS ,99951,566,1,2,Citizen 1769,0,Car ,Male ,42,HS ,87793,643,0,2,Citizen 1770,0,Car ,Female,45,HS ,99817,641,0,2,Citizen 1771,0,Car ,Female,38,HS ,104703,610,0,2,Citizen 1772,0,Home,Male ,42,HS ,79232,658,10,2,Citizen 1773,0,Car ,Male ,47,College ,84761,682,1,2,Citizen 1774,0,Car ,Male ,43,HS ,90232,596,0,2,Citizen 1775,0,Car ,Male ,36,College ,82784,566,3,2,Citizen 1776,0,Car ,Female,53,HS ,133991,638,2,2,Citizen 1777,0,Car ,Female,42,College ,62840,568,1,2,Citizen 1778,0,Home,Male ,40,HS ,94869,675,5,2,Citizen 1779,0,Home,Female,46,College ,105734,601,5,2,Citizen 1780,0,Car ,Female,48,Graduate,106017,671,2,2,Citizen 1781,0,Car ,Male ,36,College ,93780,557,1,2,Citizen 1782,1,Car ,Female,49,Graduate,124194,684,2,2,Non-citizen 1783,0,Car ,Male ,40,HS ,94936,538,0,2,Citizen 1784,0,Car ,Female,45,College ,119962,697,1,2,Citizen 1785,0,Home,Male ,28,HS ,79889,536,0,2,Citizen 1786,0,Car ,Male ,48,HS ,94249,678,2,2,Citizen 1787,0,Car ,Female,47,HS ,70257,570,0,2,Citizen 1788,0,Car ,Male ,43,HS ,92998,593,2,2,Citizen 1789,0,Car ,Male ,46,College ,96985,651,2,1,Citizen 1790,0,Car ,Male ,43,HS ,86817,505,2,2,Citizen 1791,0,Car ,Male ,39,HS ,116109,615,0,2,Citizen 1792,0,Home,Male ,48,HS ,73439,610,4,2,Citizen 1793,0,Car ,Male ,39,HS ,75689,646,0,2,Citizen 1794,0,Home,Male ,37,HS ,71075,544,6,2,Citizen 1795,0,Car ,Female,48,College ,82144,712,2,1,Citizen 1797,0,Car ,Male ,38,HS ,116117,570,0,2,Citizen 1798,0,Car ,Male ,46,HS ,96381,694,2,2,Citizen 1799,0,Car ,Male ,37,HS ,92082,462,1,2,Citizen 1800,0,Home,Male ,44,HS ,63415,523,10,2,Citizen 1801,0,Car ,Female,46,College ,84926,539,1,2,Citizen 1802,0,Home,Male ,43,HS ,76149,583,1,2,Citizen 1803,0,Car ,Female,57,College ,148406,697,2,1,Citizen 1804,0,Home,Male ,48,HS ,105869,691,9,2,Citizen 1805,0,Home,Female,45,HS ,95250,638,4,2,Citizen 1806,0,Car ,Male ,44,HS ,120244,604,0,2,Citizen 1807,0,Car ,Female,35,College ,111488,635,1,2,Non-citizen 1808,1,Car ,Female,51,HS ,99260,679,2,2,Citizen 1809,0,Car ,Female,43,Graduate,125587,633,2,2,Citizen 1810,0,Car ,Male ,44,HS ,103917,614,0,2,Citizen 1811,1,Car ,Male ,46,HS ,113640,634,1,2,Citizen 1812,0,Car ,Male ,41,HS ,84355,601,0,2,Citizen 1813,0,Car ,Male ,40,HS ,115403,476,3,1,Citizen 1814,0,Car ,Male ,35,HS ,117315,575,2,2,Citizen 1815,0,Car ,Female,41,HS ,81854,598,2,2,Citizen 1816,0,Car ,Male ,26,HS ,87296,569,0,2,Citizen 1817,0,Car ,Female,38,HS ,91375,576,1,2,Citizen 1818,1,Car ,Female,45,HS ,104437,621,0,2,Citizen 1819,0,Car ,Female,42,College ,77153,598,2,2,Citizen 1820,0,Home,Male ,26,HS ,77848,545,1,2,Citizen 1821,0,Car ,Male ,36,HS ,92837,539,2,2,Citizen 1822,0,Car ,Male ,46,HS ,91892,674,2,2,Citizen 1823,0,Car ,Male ,43,Graduate,95967,572,3,2,Citizen 1824,0,Home,Male ,33,HS ,94166,466,5,2,Citizen 1825,0,Car ,Female,43,College ,85255,668,3,2,Citizen 1826,0,Car ,Male ,38,HS ,87960,517,1,2,Citizen 1827,0,Car ,Male ,42,HS ,90294,754,0,2,Citizen 1828,0,Home,Male ,38,HS ,99277,644,4,2,Citizen 1829,0,Car ,Female,32,HS ,74965,563,0,2,Citizen 1830,0,Car ,Male ,44,HS ,91084,546,0,2,Citizen 1831,0,Car ,Female,46,HS ,117215,674,0,2,Citizen 1832,0,Car ,Male ,36,College ,58971,524,1,2,Citizen 1833,0,Car ,Male ,46,College ,102699,629,2,2,Citizen 1834,0,Car ,Male ,39,College ,88758,655,0,2,Citizen 1835,0,Home,Male ,40,HS ,72266,621,3,1,Citizen 1836,0,Car ,Male ,46,College ,82378,573,3,2,Citizen 1837,0,Car ,Male ,40,HS ,87183,646,0,2,Citizen 1838,0,Car ,Female,46,HS ,70316,601,0,2,Citizen 1839,0,Car ,Female,49,HS ,77309,661,1,2,Citizen 1840,0,Car ,Female,42,HS ,76455,575,0,2,Citizen 1841,0,Car ,Male ,37,College ,90718,580,1,2,Citizen 1842,0,Car ,Male ,34,HS ,99355,663,2,2,Citizen 1843,0,Car ,Male ,34,HS ,83518,513,1,2,Citizen 1844,0,Car ,Male ,42,College ,110614,623,3,2,Citizen 1845,0,Car ,Male ,51,HS ,116019,525,2,2,Citizen 1846,0,Car ,Male ,52,HS ,117720,733,2,1,Citizen 1847,0,Car ,Female,47,College ,115197,684,0,2,Citizen 1848,0,Car ,Male ,42,HS ,83865,581,0,2,Citizen 1849,0,Car ,Female,47,Graduate,105696,622,0,2,Non-citizen 1850,0,Car ,Female,47,HS ,107610,624,0,2,Citizen 1851,0,Car ,Male ,49,HS ,110934,697,1,2,Citizen 1852,0,Car ,Male ,42,HS ,84947,588,0,2,Citizen 1853,0,Car ,Female,52,HS ,110049,567,3,2,Citizen 1854,0,Home,Male ,48,HS ,107662,590,5,2,Citizen 1855,0,Car ,Male ,40,HS ,84749,530,0,2,Citizen 1856,0,Car ,Male ,36,HS ,99174,581,1,2,Citizen 1857,0,Car ,Male ,47,HS ,114180,591,1,2,Citizen 1858,0,Home,Female,39,HS ,103308,591,6,2,Citizen 1859,0,Home,Male ,43,College ,93121,536,5,2,Citizen 1860,0,Car ,Male ,41,HS ,92854,603,0,2,Citizen 1861,0,Car ,Female,41,HS ,86773,575,0,2,Citizen 1862,0,Car ,Female,52,College ,102523,581,2,2,Citizen 1863,0,Home,Male ,43,HS ,94033,573,1,2,Citizen 1864,0,Home,Male ,43,HS ,87937,667,4,2,Citizen 1865,0,Car ,Female,32,HS ,98324,582,0,2,Citizen 1866,0,Car ,Female,44,HS ,128883,663,2,1,Citizen 1867,0,Home,Female,41,HS ,87043,601,1,2,Citizen 1868,0,Car ,Female,49,HS ,96596,564,1,2,Citizen 1869,0,Car ,Female,52,Graduate,104816,732,2,1,Citizen 1870,0,Car ,Male ,43,HS ,83456,546,2,2,Citizen 1871,0,Home,Female,49,College ,108097,542,6,2,Citizen 1872,0,Car ,Male ,41,HS ,107492,547,2,2,Citizen 1873,0,Car ,Male ,42,HS ,104870,482,0,2,Citizen 1874,0,Car ,Male ,44,HS ,95066,531,1,2,Citizen 1875,0,Car ,Female,38,HS ,112433,708,0,2,Citizen 1876,0,Home,Male ,37,HS ,98868,688,7,2,Citizen 1877,0,Home,Female,40,HS ,90340,645,5,2,Citizen 1879,0,Home,Male ,31,HS ,75384,559,4,2,Citizen 1880,0,Car ,Male ,36,HS ,135633,634,1,2,Citizen 1881,0,Car ,Female,49,HS ,66243,535,0,2,Citizen 1882,0,Car ,Male ,44,HS ,79150,624,0,2,Citizen 1883,0,Car ,Male ,42,HS ,96152,616,2,2,Citizen 1884,0,Car ,Female,40,College ,108721,546,0,2,Citizen 1885,0,Home,Male ,46,College ,95117,595,9,2,Citizen 1886,0,Car ,Female,50,HS ,109206,693,2,2,Citizen 1887,0,Car ,Male ,45,HS ,58189,485,0,2,Citizen 1888,0,Car ,Female,42,College ,102827,567,2,2,Citizen 1889,0,Car ,Female,42,College ,104965,587,2,2,Citizen 1890,0,Car ,Female,52,College ,98444,588,3,1,Citizen 1891,0,Car ,Male ,47,College ,93764,575,2,2,Citizen 1892,0,Car ,Male ,39,HS ,81373,644,0,2,Citizen 1893,0,Car ,Male ,47,HS ,96402,562,2,2,Citizen 1894,0,Car ,Female,43,HS ,113919,541,0,2,Citizen 1895,0,Car ,Female,56,HS ,65179,585,0,1,Citizen 1896,0,Car ,Male ,39,HS ,108898,591,2,2,Citizen 1897,0,Car ,Female,49,College ,99212,681,1,2,Citizen 1898,0,Car ,Male ,50,College ,123897,558,2,2,Citizen 1899,0,Car ,Male ,46,HS ,137353,668,0,2,Citizen 1900,0,Car ,Female,59,HS ,79888,578,3,1,Citizen 1901,0,Car ,Female,47,College ,99661,672,1,1,Citizen 1902,0,Car ,Male ,43,HS ,107596,485,0,2,Citizen 1903,0,Car ,Male ,41,HS ,104846,579,0,2,Citizen 1904,0,Home,Male ,34,HS ,97426,550,6,2,Citizen 1905,1,Home,Male ,38,HS ,113722,688,4,2,Citizen 1906,0,Car ,Male ,42,HS ,61320,567,0,2,Citizen 1907,0,Car ,Male ,50,Graduate,94601,683,0,2,Non-citizen 1908,0,Car ,Male ,44,HS ,76656,540,0,2,Citizen 1909,0,Car ,Male ,46,HS ,74701,668,0,2,Citizen 1910,0,Home,Male ,38,HS ,109773,635,6,2,Citizen 1911,0,Home,Male ,39,HS ,104538,651,7,2,Citizen 1912,0,Home,Male ,41,HS ,102680,621,8,2,Citizen 1913,0,Car ,Female,52,College ,123323,720,0,2,Citizen 1914,0,Home,Male ,33,HS ,78528,530,3,2,Citizen 1915,0,Car ,Male ,45,College ,91214,629,3,1,Citizen 1916,0,Car ,Female,41,HS ,80524,584,2,2,Citizen 1917,0,Car ,Female,48,HS ,105954,593,0,2,Citizen 1918,0,Home,Female,52,College ,92294,665,8,2,Citizen 1919,0,Car ,Female,49,College ,106616,628,3,1,Citizen 1920,0,Home,Male ,43,HS ,63398,600,2,2,Citizen 1921,0,Home,Male ,53,HS ,95627,554,5,1,Citizen 1922,0,Car ,Male ,37,HS ,68380,523,2,2,Citizen 1923,0,Car ,Male ,39,HS ,106948,604,0,1,Citizen 1924,0,Car ,Male ,41,College ,94146,700,2,2,Citizen 1925,0,Home,Male ,38,HS ,99805,565,4,2,Citizen 1926,0,Car ,Female,43,Graduate,109155,583,2,2,Citizen 1927,0,Home,Male ,37,HS ,54748,547,0,2,Citizen 1928,0,Car ,Male ,31,HS ,66020,508,0,2,Citizen 1929,0,Car ,Male ,31,HS ,82449,618,0,2,Citizen 1930,0,Home,Female,47,HS ,105250,633,10,2,Citizen 1931,0,Home,Male ,40,College ,71938,591,4,2,Citizen 1932,0,Car ,Female,48,HS ,118182,643,2,1,Citizen 1933,0,Car ,Male ,37,College ,118936,636,0,2,Non-citizen 1934,0,Home,Female,38,HS ,101441,709,5,2,Citizen 1935,0,Car ,Male ,45,Graduate,116372,751,3,2,Citizen 1936,0,Car ,Female,48,HS ,116976,689,1,2,Citizen 1937,0,Home,Female,42,College ,91311,543,9,2,Citizen 1938,0,Car ,Female,41,College ,97423,558,3,2,Citizen 1939,0,Car ,Male ,41,HS ,118393,612,2,2,Citizen 1940,1,Car ,Female,46,HS ,113656,589,1,2,Citizen 1941,0,Car ,Female,44,College ,95662,602,2,2,Citizen 1942,0,Car ,Female,45,College ,81405,711,2,1,Citizen 1943,0,Car ,Male ,30,College ,72084,451,0,2,Citizen 1944,0,Home,Male ,42,HS ,105285,712,5,2,Citizen 1945,0,Home,Female,38,Graduate,111834,599,6,2,Non-citizen 1946,0,Car ,Male ,48,HS ,114255,628,1,2,Citizen 1947,0,Car ,Male ,42,HS ,90643,569,2,2,Citizen 1948,0,Home,Male ,29,HS ,51774,539,6,2,Citizen 1949,0,Home,Female,36,College ,97286,739,2,2,Citizen 1950,0,Car ,Male ,49,HS ,104062,560,0,2,Citizen 1951,0,Car ,Female,46,HS ,100459,627,1,2,Citizen 1952,0,Home,Male ,30,HS ,78753,466,3,2,Citizen 1953,0,Car ,Male ,41,HS ,91197,519,3,2,Citizen 1954,0,Home,Male ,46,College ,103984,569,7,2,Citizen 1955,0,Car ,Male ,36,HS ,101886,603,0,2,Citizen 1956,0,Car ,Male ,43,HS ,84323,557,1,2,Citizen 1957,0,Home,Female,36,HS ,99001,702,3,2,Citizen 1958,0,Car ,Male ,49,Graduate,100156,719,3,2,Citizen 1959,0,Car ,Male ,41,HS ,98350,637,2,2,Citizen 1960,0,Home,Male ,30,HS ,88767,431,8,2,Citizen 1961,0,Car ,Female,44,Graduate,84255,576,2,2,Non-citizen 1962,0,Car ,Female,45,HS ,125380,683,2,1,Citizen 1963,1,Car ,Male ,42,HS ,109047,633,0,2,Citizen 1964,0,Car ,Male ,30,HS ,82337,522,0,2,Citizen 1965,0,Car ,Female,43,HS ,96669,589,2,2,Citizen 1966,0,Car ,Male ,37,HS ,111587,610,0,2,Citizen 1967,0,Car ,Female,45,HS ,110725,544,0,2,Citizen 1968,1,Car ,Male ,42,HS ,107627,584,1,2,Citizen 1969,0,Car ,Male ,40,HS ,92030,592,0,2,Citizen 1970,0,Home,Male ,34,HS ,102239,569,13,2,Citizen 1971,0,Home,Male ,43,Graduate,117046,681,8,2,Citizen 1972,0,Car ,Male ,43,HS ,97652,704,2,2,Citizen 1973,0,Car ,Male ,45,HS ,89432,595,0,2,Citizen 1974,0,Car ,Male ,44,College ,101880,655,2,2,Citizen 1975,0,Home,Male ,29,HS ,87259,498,1,2,Citizen 1976,0,Car ,Female,50,HS ,134043,623,0,2,Citizen 1977,1,Car ,Female,50,College ,126264,684,1,2,Citizen 1978,0,Car ,Female,49,HS ,98529,619,1,2,Non-citizen 1979,0,Car ,Male ,43,HS ,116011,663,1,2,Citizen 1980,0,Home,Male ,39,HS ,96679,563,6,2,Citizen 1981,0,Car ,Female,35,HS ,76862,536,2,2,Citizen 1982,0,Car ,Male ,40,HS ,75702,437,1,2,Citizen 1983,0,Car ,Male ,45,HS ,75268,639,2,2,Citizen 1984,0,Car ,Female,42,College ,100412,550,2,2,Citizen 1985,0,Car ,Male ,47,HS ,108510,604,2,2,Citizen 1986,0,Car ,Male ,36,College ,93979,542,1,2,Citizen 1987,0,Car ,Male ,36,HS ,125154,532,0,2,Citizen 1988,0,Home,Male ,27,HS ,72194,564,3,2,Citizen 1989,1,Home,Male ,38,Graduate,122329,736,13,2,Citizen 1990,0,Car ,Male ,48,HS ,91065,634,1,2,Citizen 1991,0,Car ,Female,48,HS ,108428,633,1,2,Citizen 1992,0,Car ,Male ,53,College ,110150,597,1,2,Citizen 1993,0,Car ,Male ,45,College ,123503,646,1,2,Citizen 1994,0,Car ,Female,40,College ,93824,643,1,2,Citizen 1995,0,Car ,Female,46,HS ,89539,623,1,2,Citizen 1996,0,Car ,Female,55,College ,115981,623,2,2,Citizen 1997,0,Car ,Female,41,College ,86550,649,2,2,Citizen 1998,0,Car ,Female,57,HS ,80870,672,0,2,Citizen 1999,1,Car ,Male ,43,Graduate,114963,661,2,2,Citizen 2000,0,Car ,Male ,41,College ,118291,606,1,2,Citizen 2001,0,Car ,Female,49,College ,116450,606,1,1,Citizen 2002,0,Car ,Male ,44,HS ,124023,736,3,1,Non-citizen 2003,0,Car ,Female,42,HS ,73544,612,2,2,Citizen 2004,0,Car ,Male ,36,HS ,83483,559,2,2,Citizen 2005,0,Car ,Male ,41,HS ,88697,572,1,2,Citizen 2006,0,Car ,Female,47,College ,105374,606,1,2,Citizen 2007,0,Car ,Female,50,College ,119950,649,3,1,Citizen 2008,0,Car ,Female,39,College ,94044,576,3,2,Citizen 2009,0,Car ,Female,49,College ,86890,647,0,2,Citizen 2010,0,Car ,Female,48,College ,116004,645,2,2,Citizen 2011,0,Car ,Male ,42,HS ,105638,733,2,2,Citizen 2012,0,Car ,Male ,51,HS ,111482,704,1,1,Citizen 2013,0,Car ,Male ,39,HS ,92273,519,0,2,Citizen 2014,0,Home,Male ,44,HS ,89949,572,4,2,Citizen 2015,0,Car ,Male ,34,HS ,98413,608,0,2,Citizen 2016,0,Car ,Male ,33,HS ,109580,643,0,2,Citizen 2017,0,Home,Female,23,HS ,89663,562,5,2,Citizen 2018,0,Car ,Male ,33,College ,90853,730,2,2,Citizen 2019,0,Home,Male ,47,HS ,95646,615,5,2,Citizen 2020,0,Car ,Male ,39,College ,94461,483,0,2,Citizen 2021,0,Car ,Female,44,College ,111027,676,2,2,Non-citizen 2022,0,Car ,Male ,47,HS ,96951,591,2,2,Citizen 2023,0,Car ,Female,30,College ,99390,677,2,2,Citizen 2024,0,Home,Male ,43,HS ,98185,642,6,2,Citizen 2025,0,Home,Male ,36,HS ,73296,626,8,2,Citizen 2026,0,Car ,Male ,46,HS ,93508,574,2,2,Citizen 2027,0,Car ,Male ,37,HS ,85455,654,1,2,Citizen 2028,0,Car ,Male ,43,Graduate,96485,542,3,2,Citizen 2029,0,Car ,Male ,44,HS ,90993,614,0,2,Citizen 2030,0,Home,Male ,41,HS ,94218,518,7,2,Citizen 2031,0,Car ,Female,30,College ,94654,596,1,2,Citizen 2032,0,Home,Female,39,HS ,117451,582,14,2,Citizen 2034,0,Car ,Male ,46,HS ,105689,590,2,2,Citizen 2035,0,Car ,Female,48,HS ,107408,656,0,2,Citizen 2036,0,Home,Male ,49,HS ,102095,671,7,2,Citizen 2037,0,Car ,Female,47,College ,106427,612,2,2,Citizen 2038,0,Car ,Female,45,HS ,125103,589,2,1,Citizen 2039,0,Car ,Male ,45,HS ,104882,555,1,2,Citizen 2040,0,Car ,Male ,38,HS ,102891,542,0,2,Citizen 2041,0,Car ,Male ,45,HS ,106479,658,2,1,Citizen 2042,0,Car ,Male ,48,College ,82874,634,1,2,Citizen 2043,0,Car ,Male ,49,College ,85080,720,1,2,Citizen 2044,1,Car ,Male ,37,College ,97907,547,1,2,Citizen 2045,0,Home,Male ,34,HS ,101090,587,10,2,Citizen 2046,0,Home,Male ,42,HS ,79110,507,10,2,Citizen 2047,0,Car ,Female,50,Graduate,95026,642,1,2,Citizen 2048,0,Car ,Male ,42,HS ,112039,592,2,2,Citizen 2049,1,Home,Female,38,HS ,80441,581,4,2,Citizen 2050,0,Home,Male ,36,HS ,96591,587,6,2,Citizen 2051,0,Car ,Female,44,HS ,75868,614,1,2,Citizen 2052,1,Car ,Male ,46,HS ,122637,683,3,2,Citizen 2053,1,Car ,Female,40,College ,92853,622,1,1,Citizen 2054,0,Car ,Male ,49,HS ,99932,625,0,2,Citizen 2055,0,Car ,Male ,42,College ,110395,631,1,2,Citizen 2056,0,Home,Male ,38,College ,74354,562,7,2,Citizen 2057,0,Home,Female,43,HS ,97450,704,0,2,Citizen 2058,0,Home,Male ,41,HS ,118385,638,4,2,Citizen 2059,0,Car ,Female,47,HS ,117891,713,0,2,Non-citizen 2060,0,Home,Female,44,HS ,102061,684,10,1,Non-citizen 2061,0,Car ,Male ,44,HS ,80951,743,0,2,Citizen 2062,0,Car ,Female,44,HS ,94199,484,0,2,Citizen 2063,0,Home,Male ,34,HS ,92983,635,5,2,Citizen 2064,0,Home,Female,43,HS ,101812,676,7,2,Citizen 2065,0,Home,Male ,35,HS ,106304,626,8,2,Citizen 2066,0,Car ,Female,45,HS ,106561,549,2,2,Citizen 2067,0,Car ,Male ,42,College ,105438,590,2,2,Citizen 2068,0,Car ,Male ,45,HS ,92335,614,0,2,Citizen 2069,0,Home,Male ,43,HS ,72346,589,3,2,Citizen 2070,0,Car ,Female,45,Graduate,102455,551,1,2,Non-citizen 2071,0,Home,Male ,34,HS ,80993,561,3,2,Citizen 2072,0,Car ,Female,46,College ,108346,656,2,2,Citizen 2073,0,Home,Male ,38,HS ,98316,570,3,2,Citizen 2074,0,Car ,Male ,40,HS ,76163,548,1,2,Citizen 2075,0,Car ,Female,42,College ,99517,660,0,2,Citizen 2076,1,Car ,Female,48,College ,131874,596,2,2,Citizen 2077,0,Car ,Male ,49,HS ,110572,645,0,2,Citizen 2078,0,Car ,Female,49,College ,89147,686,2,2,Citizen 2079,0,Home,Male ,36,HS ,61574,435,2,2,Citizen 2080,0,Car ,Male ,36,HS ,75010,587,0,2,Citizen 2081,0,Home,Male ,41,HS ,104701,491,5,2,Citizen 2082,0,Car ,Male ,49,HS ,91816,472,0,2,Citizen 2083,0,Home,Male ,31,HS ,97984,588,3,2,Citizen 2084,0,Home,Male ,42,HS ,93230,620,7,2,Citizen 2085,0,Car ,Female,43,HS ,100175,635,0,2,Citizen 2086,0,Car ,Female,58,College ,90008,676,3,2,Citizen 2087,0,Home,Male ,39,HS ,113988,591,3,2,Citizen 2088,0,Home,Female,39,College ,93373,540,9,2,Citizen 2089,0,Car ,Female,51,College ,105179,581,3,2,Citizen 2090,0,Car ,Male ,47,HS ,92653,625,0,2,Citizen 2091,0,Home,Male ,44,HS ,89844,610,14,2,Citizen 2092,0,Car ,Female,43,College ,130407,736,0,2,Citizen 2093,0,Car ,Female,53,College ,126074,631,2,2,Citizen 2094,0,Home,Male ,43,College ,106714,662,13,2,Citizen 2095,0,Car ,Male ,38,HS ,67615,611,2,2,Citizen 2096,0,Car ,Female,49,HS ,117353,606,2,2,Citizen 2097,0,Car ,Female,50,College ,75567,622,2,2,Citizen 2098,0,Car ,Male ,38,HS ,97633,621,0,2,Citizen 2099,0,Car ,Male ,30,College ,94383,572,0,2,Citizen 2100,1,Car ,Male ,43,College ,82840,527,2,2,Citizen 2101,0,Car ,Male ,36,HS ,84242,658,0,2,Citizen 2102,0,Car ,Male ,38,HS ,72736,634,0,2,Citizen 2103,0,Car ,Male ,48,HS ,72342,581,0,2,Citizen 2104,0,Car ,Male ,48,HS ,93788,647,2,2,Citizen 2105,0,Car ,Female,60,HS ,137542,674,2,1,Citizen 2106,0,Home,Male ,35,HS ,95753,733,1,2,Citizen 2107,0,Home,Female,40,HS ,101725,577,1,2,Citizen 2108,0,Home,Male ,39,College ,103283,484,7,2,Citizen 2109,0,Car ,Male ,49,College ,105402,612,3,1,Citizen 2110,0,Home,Male ,39,HS ,95779,627,4,2,Citizen 2111,0,Home,Male ,40,HS ,122225,524,6,2,Citizen 2112,0,Home,Female,44,HS ,80277,708,5,2,Citizen 2113,0,Home,Male ,33,HS ,73986,594,6,2,Citizen 2114,1,Car ,Female,40,College ,116457,637,1,2,Citizen 2115,0,Car ,Male ,44,HS ,117401,605,1,2,Citizen 2116,0,Car ,Male ,36,HS ,89574,527,0,2,Citizen 2117,0,Home,Male ,34,HS ,68400,472,5,2,Citizen 2118,0,Car ,Female,49,HS ,109886,726,1,2,Citizen 2119,0,Car ,Female,44,College ,102246,694,1,2,Citizen 2120,0,Car ,Male ,50,HS ,82350,633,3,2,Citizen 2121,0,Car ,Male ,44,HS ,93973,612,1,2,Citizen 2122,0,Car ,Male ,35,HS ,112929,576,0,2,Citizen 2123,0,Car ,Female,50,HS ,113819,610,0,2,Citizen 2124,0,Car ,Male ,42,HS ,85757,594,1,2,Citizen 2125,0,Car ,Male ,44,HS ,137820,631,1,2,Citizen 2126,0,Car ,Male ,34,HS ,83562,598,0,2,Citizen 2127,0,Car ,Male ,44,HS ,79181,674,0,2,Citizen 2128,0,Home,Male ,38,HS ,112880,585,7,2,Citizen 2129,0,Home,Male ,45,HS ,114062,572,8,2,Citizen 2130,0,Home,Female,43,College ,84280,612,10,2,Citizen 2131,0,Home,Male ,51,HS ,83576,585,8,2,Non-citizen 2132,0,Car ,Female,54,Graduate,86118,722,2,2,Citizen 2133,0,Car ,Male ,48,College ,106875,627,3,2,Citizen 2134,0,Home,Male ,32,College ,66924,588,6,2,Citizen 2135,0,Car ,Female,38,HS ,79563,576,1,2,Citizen 2136,0,Car ,Female,47,College ,131633,577,2,2,Non-citizen 2137,0,Car ,Male ,50,Graduate,112209,622,2,2,Non-citizen 2138,0,Car ,Male ,40,HS ,106178,606,0,2,Citizen 2139,0,Car ,Female,46,HS ,70112,633,0,2,Citizen 2140,0,Home,Female,47,HS ,118490,632,7,2,Citizen 2141,0,Car ,Female,38,HS ,80410,612,0,2,Citizen 2142,0,Car ,Female,41,HS ,115643,596,1,2,Citizen 2143,0,Car ,Female,35,HS ,57037,653,0,1,Citizen 2144,0,Car ,Male ,43,HS ,88719,563,3,1,Citizen 2145,0,Car ,Male ,41,HS ,87832,619,0,1,Citizen 2146,0,Home,Female,42,HS ,103818,513,2,2,Citizen 2147,0,Car ,Male ,51,College ,121405,698,2,1,Non-citizen 2148,0,Car ,Female,45,College ,111183,721,3,1,Citizen 2149,0,Home,Male ,46,HS ,105757,750,7,1,Citizen 2150,0,Car ,Female,38,HS ,119368,605,0,2,Citizen 2151,0,Home,Female,47,College ,123429,721,11,2,Citizen 2152,0,Car ,Male ,32,HS ,93180,635,1,2,Citizen 2153,0,Car ,Male ,48,Graduate,81623,590,2,2,Citizen 2154,1,Car ,Female,48,College ,113525,687,2,1,Non-citizen 2155,0,Car ,Male ,48,HS ,91582,575,2,2,Citizen 2156,0,Home,Female,39,College ,84634,675,9,2,Citizen 2157,0,Car ,Female,47,College ,77055,560,2,2,Non-citizen 2158,0,Car ,Female,42,College ,111672,644,1,2,Citizen 2159,0,Car ,Male ,51,Graduate,72540,552,3,2,Citizen 2160,0,Home,Male ,35,HS ,80938,557,0,2,Citizen 2161,0,Car ,Male ,44,HS ,104768,668,0,2,Citizen 2162,0,Car ,Male ,40,College ,101308,657,2,2,Citizen 2163,0,Home,Female,32,HS ,87119,580,0,2,Citizen 2164,0,Car ,Male ,56,HS ,126647,702,2,2,Citizen 2165,0,Car ,Male ,48,HS ,130460,707,3,2,Citizen 2166,0,Car ,Male ,45,HS ,92539,664,0,2,Citizen 2167,0,Car ,Male ,40,HS ,92114,634,2,2,Citizen 2168,0,Home,Male ,25,HS ,80766,521,2,2,Citizen 2169,0,Car ,Female,40,Graduate,88334,519,2,2,Citizen 2170,0,Car ,Male ,45,College ,70010,646,1,2,Citizen 2171,0,Car ,Female,43,HS ,80957,652,0,2,Citizen 2172,0,Car ,Male ,46,HS ,95683,674,0,2,Citizen 2173,0,Home,Male ,34,HS ,78124,583,4,2,Citizen 2174,0,Home,Female,50,Graduate,101345,615,6,2,Citizen 2175,0,Home,Male ,27,HS ,74024,610,5,2,Citizen 2176,0,Car ,Male ,31,HS ,72812,456,1,2,Citizen 2177,0,Car ,Male ,38,HS ,101407,597,2,2,Citizen 2178,0,Home,Male ,25,College ,109189,690,7,2,Citizen 2179,0,Car ,Male ,36,HS ,75655,551,2,2,Citizen 2180,0,Home,Male ,51,HS ,84474,591,8,2,Citizen 2181,0,Home,Male ,49,College ,77398,585,8,2,Citizen 2182,0,Home,Male ,42,HS ,81352,616,8,2,Citizen 2183,0,Car ,Female,47,HS ,89454,650,0,2,Citizen 2184,0,Car ,Female,42,HS ,107675,643,1,2,Citizen 2185,0,Home,Male ,49,HS ,101891,675,7,2,Citizen 2186,0,Home,Male ,29,HS ,77903,475,6,2,Citizen 2187,0,Car ,Female,44,HS ,120121,631,1,2,Citizen 2188,0,Car ,Male ,41,College ,50005,550,0,2,Citizen 2189,0,Car ,Male ,42,HS ,100845,597,0,2,Citizen 2190,0,Home,Male ,45,College ,58058,678,0,2,Citizen 2191,0,Car ,Male ,42,HS ,94761,563,0,2,Citizen 2192,0,Car ,Female,48,College ,98927,609,0,1,Citizen 2193,0,Car ,Male ,40,College ,117787,571,2,2,Citizen 2194,0,Car ,Male ,48,HS ,78518,672,1,2,Citizen 2195,0,Car ,Male ,50,College ,95046,556,1,2,Citizen 2196,0,Car ,Male ,38,College ,63133,587,0,2,Citizen 2197,0,Car ,Male ,41,College ,88738,596,3,2,Citizen 2198,1,Home,Male ,40,HS ,103613,652,6,2,Citizen 2199,0,Car ,Male ,34,College ,82750,595,0,2,Citizen 2200,0,Car ,Male ,48,HS ,127964,711,0,1,Non-citizen 2201,1,Car ,Male ,41,HS ,115877,650,0,2,Citizen 2202,0,Car ,Male ,45,HS ,111012,631,1,2,Citizen 2203,0,Home,Male ,32,HS ,82359,459,5,2,Citizen 2204,0,Car ,Female,56,HS ,88170,691,1,2,Citizen 2205,0,Car ,Female,52,HS ,66893,671,0,2,Citizen 2206,0,Home,Female,42,HS ,88512,619,3,2,Citizen 2207,0,Car ,Male ,47,HS ,108399,495,0,2,Citizen 2208,0,Car ,Female,36,HS ,96174,591,0,2,Citizen 2209,0,Car ,Male ,51,HS ,92168,543,2,2,Citizen 2210,0,Home,Male ,39,HS ,67080,498,5,2,Citizen 2211,0,Car ,Female,50,College ,116809,598,2,1,Citizen 2212,0,Car ,Male ,46,HS ,93992,564,0,2,Citizen 2213,0,Car ,Female,45,HS ,96083,589,0,2,Citizen 2214,0,Car ,Male ,43,HS ,107934,597,0,2,Citizen 2215,0,Home,Male ,39,HS ,83364,577,5,2,Citizen 2216,0,Car ,Female,44,HS ,108501,674,3,2,Citizen 2217,1,Car ,Female,54,HS ,116084,753,1,1,Citizen 2218,0,Car ,Female,47,HS ,88511,636,0,2,Citizen 2219,0,Home,Female,47,HS ,125712,634,9,1,Citizen 2220,0,Home,Male ,41,HS ,99367,658,7,2,Citizen 2221,0,Car ,Male ,40,HS ,101934,610,2,2,Citizen 2222,1,Car ,Female,46,College ,112731,771,0,2,Citizen 2223,0,Home,Male ,45,HS ,96448,669,4,2,Citizen 2224,0,Home,Female,41,HS ,86819,557,7,2,Citizen 2225,0,Car ,Female,45,HS ,111710,657,0,1,Citizen 2226,0,Car ,Female,44,College ,135851,682,3,2,Citizen 2227,0,Home,Female,45,HS ,86613,641,3,2,Citizen 2228,0,Home,Male ,35,HS ,85079,621,4,2,Citizen 2229,0,Car ,Female,57,HS ,130119,686,3,1,Non-citizen 2230,0,Car ,Male ,38,College ,107538,548,0,2,Citizen 2231,0,Home,Male ,40,HS ,124326,647,9,2,Citizen 2232,0,Car ,Female,39,College ,108902,629,3,2,Citizen 2233,0,Home,Male ,53,HS ,84456,628,9,2,Citizen 2234,0,Car ,Male ,45,HS ,70520,549,1,2,Citizen 2235,0,Car ,Male ,45,HS ,85890,556,1,2,Citizen 2236,0,Car ,Female,41,HS ,96726,645,0,2,Citizen 2237,0,Home,Male ,41,HS ,96883,606,4,2,Citizen 2238,0,Home,Male ,47,HS ,64104,607,6,2,Citizen 2239,0,Car ,Female,46,HS ,88807,557,1,2,Citizen 2240,0,Car ,Female,43,HS ,108321,657,2,1,Citizen 2241,0,Home,Male ,39,HS ,68238,662,6,2,Citizen 2242,0,Home,Female,43,College ,113097,679,11,2,Citizen 2243,0,Car ,Male ,38,College ,85125,625,0,2,Citizen 2244,0,Home,Male ,38,HS ,80815,520,0,2,Citizen 2245,0,Home,Male ,40,HS ,83071,595,5,2,Citizen 2246,0,Home,Male ,44,HS ,85313,677,4,2,Citizen 2247,0,Car ,Male ,50,HS ,141194,659,3,2,Citizen 2248,0,Car ,Female,41,College ,100333,525,0,2,Citizen 2249,0,Car ,Female,43,HS ,100260,571,1,2,Citizen 2250,1,Car ,Female,54,HS ,124041,733,3,1,Citizen 2251,0,Car ,Male ,43,HS ,102750,536,0,2,Citizen 2252,0,Car ,Male ,46,HS ,94890,622,1,2,Citizen 2253,0,Car ,Male ,40,HS ,82202,576,0,2,Citizen 2254,0,Home,Female,53,College ,109575,637,7,2,Citizen 2255,0,Home,Male ,42,HS ,102059,594,4,2,Citizen 2256,0,Car ,Female,35,HS ,89206,630,2,1,Citizen 2257,0,Car ,Male ,49,HS ,110573,695,1,1,Citizen 2258,0,Car ,Female,55,College ,92919,574,2,2,Citizen 2259,0,Car ,Male ,42,HS ,102114,639,0,2,Citizen 2260,0,Home,Male ,38,HS ,87612,563,10,2,Citizen 2261,0,Car ,Female,36,College ,74600,518,0,2,Citizen 2262,0,Car ,Male ,48,HS ,113742,694,0,2,Citizen 2263,1,Home,Female,45,College ,101127,604,6,2,Citizen 2264,0,Car ,Female,38,College ,90889,698,0,2,Citizen 2265,0,Car ,Female,42,College ,97098,663,2,2,Citizen 2266,0,Car ,Male ,50,HS ,80563,590,2,2,Citizen 2267,0,Car ,Female,40,College ,99363,674,2,1,Citizen 2268,0,Car ,Male ,37,Graduate,127284,564,3,2,Non-citizen 2269,0,Car ,Female,56,College ,99285,757,2,1,Citizen 2270,0,Car ,Female,52,HS ,116843,653,3,2,Citizen 2271,0,Car ,Male ,43,HS ,84973,563,0,2,Citizen 2272,0,Home,Male ,40,HS ,83161,537,6,2,Citizen 2273,0,Car ,Female,39,HS ,79964,582,0,2,Citizen 2274,0,Car ,Male ,38,College ,108986,641,1,2,Citizen 2275,0,Car ,Female,42,College ,92859,740,0,2,Citizen 2276,0,Car ,Female,45,HS ,76783,667,1,1,Citizen 2277,0,Car ,Female,42,HS ,100737,618,2,1,Citizen 2278,0,Car ,Male ,42,HS ,90737,703,3,2,Citizen 2279,0,Car ,Male ,40,College ,82646,558,2,2,Citizen 2280,0,Car ,Female,46,HS ,89155,640,0,2,Citizen 2281,0,Car ,Female,50,HS ,89631,683,1,2,Citizen 2282,0,Car ,Male ,33,HS ,105723,618,3,2,Citizen 2283,0,Home,Male ,38,College ,93312,602,10,2,Non-citizen 2284,1,Car ,Female,39,HS ,111760,566,0,2,Citizen 2285,0,Home,Male ,38,HS ,98325,572,6,2,Citizen 2286,0,Home,Male ,33,HS ,80389,560,3,2,Citizen 2287,0,Home,Male ,44,HS ,100386,607,7,2,Citizen 2288,0,Car ,Female,50,HS ,83569,642,2,2,Citizen 2289,0,Car ,Male ,38,College ,73692,517,0,2,Citizen 2290,0,Car ,Female,47,HS ,103733,624,0,1,Non-citizen 2291,0,Car ,Male ,36,Graduate,97728,579,2,2,Citizen 2292,0,Car ,Female,40,HS ,91103,566,0,2,Citizen 2293,0,Car ,Female,38,HS ,76898,491,0,2,Citizen 2294,0,Car ,Female,42,HS ,128550,629,3,1,Citizen 2295,0,Home,Male ,23,HS ,87837,553,6,2,Citizen 2296,0,Car ,Female,45,HS ,99654,787,0,2,Citizen 2297,0,Home,Male ,49,HS ,85240,657,4,2,Citizen 2298,0,Car ,Male ,44,College ,95839,670,1,2,Citizen 2299,0,Home,Female,39,HS ,97264,542,5,2,Citizen 2300,0,Car ,Female,46,College ,99001,629,2,2,Citizen 2301,0,Home,Male ,29,HS ,63099,584,0,2,Citizen 2302,0,Car ,Female,51,College ,112972,612,1,2,Citizen 2303,0,Home,Male ,39,HS ,82308,494,5,2,Citizen 2304,0,Car ,Female,49,College ,100183,685,3,1,Non-citizen 2305,0,Car ,Female,33,Graduate,103581,611,2,2,Citizen 2307,0,Home,Male ,41,HS ,103362,666,7,2,Citizen 2308,0,Car ,Female,51,College ,94602,654,1,2,Citizen 2309,0,Car ,Male ,46,Graduate,115596,609,3,1,Non-citizen 2310,0,Car ,Male ,46,HS ,92958,665,1,2,Citizen 2311,0,Car ,Male ,42,HS ,78563,577,1,2,Citizen 2312,0,Car ,Female,40,College ,132989,608,2,2,Citizen 2313,0,Car ,Female,49,HS ,97256,641,1,2,Citizen 2314,0,Car ,Male ,47,HS ,116197,559,0,2,Citizen 2315,1,Car ,Female,47,HS ,94003,634,0,2,Citizen 2316,0,Car ,Female,53,Graduate,134927,690,2,2,Citizen 2317,0,Car ,Male ,46,HS ,82488,622,1,2,Citizen 2318,0,Car ,Female,44,HS ,110310,559,0,2,Citizen 2319,0,Car ,Female,42,HS ,87482,629,0,2,Citizen 2320,0,Car ,Male ,44,HS ,98757,626,0,2,Citizen 2321,0,Car ,Female,47,HS ,100369,640,3,1,Citizen 2322,0,Car ,Male ,43,College ,82022,557,0,2,Citizen 2323,0,Car ,Female,48,Graduate,125065,680,3,2,Non-citizen 2324,0,Car ,Male ,47,HS ,71155,504,3,2,Citizen 2325,0,Home,Male ,44,HS ,104377,644,2,2,Citizen 2326,0,Car ,Female,43,HS ,98807,584,2,2,Citizen 2328,0,Car ,Male ,51,HS ,103840,538,2,2,Citizen 2329,0,Car ,Male ,46,College ,85831,565,2,2,Citizen 2330,0,Car ,Male ,45,HS ,82055,550,1,2,Citizen 2331,0,Car ,Male ,40,College ,95017,510,2,2,Citizen 2332,0,Car ,Male ,45,HS ,108221,500,0,2,Citizen 2333,0,Home,Male ,35,HS ,64874,537,8,2,Citizen 2334,0,Car ,Male ,42,HS ,115197,713,0,2,Citizen 2335,0,Car ,Female,43,College ,90901,516,3,2,Citizen 2336,0,Car ,Female,34,HS ,77383,596,0,2,Citizen 2337,0,Car ,Male ,44,HS ,79331,576,1,2,Citizen 2338,0,Car ,Male ,52,HS ,74923,702,1,1,Citizen 2339,0,Home,Male ,38,HS ,58017,544,4,2,Citizen 2340,0,Car ,Male ,45,HS ,84253,670,0,2,Citizen 2341,0,Car ,Male ,34,College ,81218,553,2,2,Citizen 2342,0,Car ,Male ,46,HS ,84606,664,0,2,Citizen 2343,0,Car ,Female,45,College ,113363,666,3,1,Citizen 2344,0,Car ,Male ,47,College ,113728,654,1,2,Citizen 2345,0,Home,Male ,37,HS ,117095,504,4,2,Citizen 2346,0,Car ,Female,50,HS ,104352,702,2,1,Citizen 2347,0,Car ,Female,50,College ,95885,659,3,1,Citizen 2348,0,Car ,Female,49,HS ,118582,542,0,2,Non-citizen 2349,0,Home,Female,50,College ,116150,554,11,2,Citizen 2350,0,Car ,Male ,46,College ,117192,587,2,2,Citizen 2351,0,Home,Male ,38,HS ,74121,629,5,2,Citizen 2352,0,Car ,Male ,43,HS ,112376,634,1,2,Citizen 2353,1,Car ,Female,53,College ,134348,696,3,1,Non-citizen 2354,1,Car ,Female,49,College ,136674,682,2,1,Citizen 2355,0,Car ,Female,43,HS ,96708,590,2,2,Citizen 2356,0,Car ,Female,44,HS ,101831,644,0,1,Citizen 2357,0,Home,Male ,46,HS ,122394,605,7,2,Citizen 2358,0,Home,Male ,50,College ,113623,712,8,2,Citizen 2359,0,Home,Male ,29,HS ,87534,470,1,2,Citizen 2360,0,Home,Male ,41,HS ,123121,750,5,2,Citizen 2361,0,Home,Female,44,HS ,107852,608,5,2,Citizen 2362,0,Car ,Male ,38,HS ,89852,601,0,2,Citizen 2363,0,Car ,Female,33,College ,105714,721,0,2,Citizen 2364,0,Car ,Female,36,College ,102690,590,2,2,Citizen 2365,0,Car ,Male ,37,HS ,88175,560,0,2,Citizen 2366,0,Car ,Female,45,HS ,108757,672,1,2,Non-citizen 2367,0,Car ,Male ,39,College ,122589,590,0,2,Citizen 2368,0,Home,Male ,33,HS ,71706,590,3,2,Citizen 2369,0,Car ,Male ,34,HS ,62854,499,0,2,Citizen 2370,0,Home,Female,41,HS ,112108,483,7,2,Citizen 2371,0,Car ,Male ,43,HS ,113035,700,0,2,Citizen 2372,0,Car ,Male ,43,College ,99644,658,1,2,Citizen 2373,0,Car ,Male ,49,College ,140584,620,3,2,Citizen 2374,0,Car ,Male ,36,HS ,81316,491,0,2,Citizen 2375,0,Car ,Male ,47,College ,121056,602,2,2,Citizen 2376,1,Home,Female,46,HS ,116998,624,5,2,Citizen 2377,0,Home,Male ,45,HS ,72471,607,9,1,Citizen 2378,0,Car ,Male ,41,HS ,81511,577,1,2,Citizen 2379,0,Home,Male ,36,HS ,81161,591,8,2,Citizen 2380,0,Car ,Male ,42,HS ,90492,587,2,2,Citizen 2381,0,Home,Female,35,HS ,105122,611,6,2,Citizen 2382,0,Car ,Male ,42,HS ,85079,615,0,2,Citizen 2383,0,Home,Male ,40,HS ,88214,611,6,1,Citizen 2384,0,Car ,Male ,31,HS ,110861,571,1,2,Citizen 2385,0,Car ,Female,46,College ,128397,650,3,1,Non-citizen 2386,0,Car ,Female,43,HS ,90441,573,1,1,Citizen 2387,0,Home,Male ,37,College ,77979,618,5,2,Citizen 2388,0,Car ,Female,48,HS ,127361,635,2,2,Citizen 2389,0,Home,Male ,37,HS ,62035,586,9,2,Citizen 2390,1,Car ,Female,34,Graduate,94246,615,1,2,Citizen 2391,0,Car ,Male ,43,HS ,77710,513,0,2,Citizen 2392,0,Car ,Male ,37,HS ,86301,508,0,2,Citizen 2393,0,Car ,Male ,39,HS ,113144,591,2,2,Citizen 2394,0,Car ,Female,28,HS ,115263,582,1,2,Citizen 2395,0,Home,Male ,39,HS ,81608,619,2,2,Citizen 2396,0,Home,Male ,48,HS ,102002,648,5,2,Citizen 2397,0,Car ,Male ,55,HS ,136330,668,3,1,Citizen 2398,0,Car ,Female,41,HS ,102519,608,3,2,Citizen 2399,0,Car ,Female,50,HS ,97890,676,2,2,Citizen 2400,0,Car ,Male ,44,HS ,100824,587,1,2,Citizen 2401,0,Home,Male ,31,HS ,78575,489,4,2,Citizen 2402,0,Car ,Male ,54,College ,75293,546,1,2,Citizen 2403,0,Home,Female,31,HS ,98141,547,5,2,Citizen 2404,1,Home,Male ,46,HS ,124899,675,10,2,Citizen 2405,0,Car ,Male ,49,College ,68452,558,2,2,Citizen 2406,0,Car ,Male ,35,College ,89585,611,2,2,Citizen 2407,0,Home,Male ,44,HS ,119452,589,2,2,Citizen 2408,0,Home,Male ,43,HS ,75766,551,5,2,Citizen 2409,0,Car ,Female,46,College ,126423,707,2,2,Non-citizen 2410,0,Car ,Female,46,Graduate,117971,670,1,2,Non-citizen 2411,0,Car ,Male ,38,HS ,78974,554,1,2,Citizen 2412,0,Car ,Female,39,HS ,88759,670,2,2,Citizen 2413,0,Home,Female,39,HS ,80063,639,6,2,Citizen 2414,0,Car ,Male ,46,College ,91579,712,3,1,Citizen 2415,0,Home,Female,52,HS ,112822,623,7,2,Citizen 2416,0,Car ,Female,50,Graduate,120626,770,1,2,Citizen 2417,0,Home,Male ,50,HS ,86847,624,8,2,Citizen 2418,0,Car ,Female,49,HS ,123857,624,2,1,Citizen 2419,0,Home,Female,49,HS ,89712,567,5,2,Citizen 2420,0,Car ,Male ,43,College ,91267,566,2,2,Citizen 2421,0,Car ,Female,44,HS ,74939,681,2,2,Citizen 2422,1,Car ,Female,47,College ,78962,617,0,2,Citizen 2423,0,Car ,Female,35,HS ,103146,659,0,2,Citizen 2424,0,Home,Female,32,HS ,82405,561,2,2,Citizen 2425,0,Car ,Male ,43,College ,86237,544,2,2,Citizen 2426,0,Car ,Male ,41,HS ,105386,601,0,2,Citizen 2427,0,Home,Male ,41,HS ,78163,652,5,2,Citizen 2428,0,Home,Male ,44,HS ,96672,641,8,2,Citizen 2429,1,Car ,Female,45,College ,116579,605,1,2,Citizen 2430,0,Home,Male ,41,HS ,98445,599,4,2,Citizen 2431,0,Car ,Female,50,Graduate,127261,738,3,2,Citizen 2432,0,Home,Male ,46,HS ,91912,629,6,2,Citizen 2433,0,Car ,Male ,37,HS ,101134,707,2,1,Citizen 2434,0,Car ,Female,38,HS ,109375,586,0,2,Citizen 2435,0,Car ,Female,48,College ,83515,659,0,2,Citizen 2436,0,Car ,Female,46,HS ,100416,659,1,2,Citizen 2437,0,Car ,Male ,42,College ,92485,506,1,2,Citizen 2438,0,Car ,Male ,43,HS ,86023,576,0,2,Citizen 2439,0,Home,Female,47,College ,104339,647,8,2,Citizen 2440,0,Car ,Male ,53,College ,95073,676,3,1,Citizen 2441,0,Car ,Female,34,College ,72717,618,2,2,Citizen 2442,0,Car ,Female,52,College ,106712,597,2,2,Citizen 2443,0,Car ,Female,46,College ,111842,598,2,2,Citizen 2444,0,Car ,Male ,50,College ,118988,515,1,2,Non-citizen 2445,0,Car ,Male ,41,HS ,86532,702,1,2,Citizen 2446,0,Car ,Male ,43,HS ,100232,630,1,2,Citizen 2447,0,Car ,Female,38,Graduate,88841,525,1,2,Citizen 2448,0,Home,Female,43,HS ,81931,590,6,2,Citizen 2449,0,Car ,Female,41,HS ,101867,647,0,2,Citizen 2450,0,Car ,Male ,47,College ,104242,671,2,1,Non-citizen 2451,0,Car ,Male ,47,HS ,109443,594,2,2,Citizen 2452,1,Car ,Female,54,College ,122463,693,2,2,Citizen 2453,0,Car ,Male ,43,HS ,95357,616,1,2,Citizen 2454,0,Home,Male ,28,HS ,73594,562,4,2,Citizen 2455,0,Home,Male ,42,HS ,57024,644,6,2,Citizen 2456,0,Car ,Male ,44,College ,111920,660,2,2,Citizen 2457,0,Car ,Male ,38,HS ,107195,625,0,2,Citizen 2458,0,Car ,Male ,30,HS ,67180,585,1,2,Citizen 2459,0,Car ,Female,44,College ,115461,648,0,1,Citizen 2460,0,Car ,Female,47,College ,77526,627,1,1,Non-citizen 2461,0,Car ,Female,46,College ,67967,658,0,2,Citizen 2462,0,Car ,Male ,49,HS ,113309,582,2,2,Citizen 2463,0,Home,Male ,29,HS ,65257,454,2,2,Citizen 2464,0,Home,Male ,29,HS ,76010,466,4,2,Citizen 2465,0,Car ,Male ,42,HS ,116440,558,0,2,Citizen 2466,0,Car ,Male ,43,HS ,124830,635,3,2,Citizen 2467,0,Home,Male ,43,HS ,96145,612,7,2,Citizen 2468,0,Car ,Female,46,Graduate,120584,654,3,1,Non-citizen 2469,0,Home,Female,35,HS ,89819,597,3,2,Citizen 2470,0,Home,Male ,38,HS ,93552,635,7,2,Citizen 2471,0,Car ,Male ,34,HS ,106129,642,1,2,Citizen 2472,0,Car ,Female,42,HS ,93307,662,2,2,Citizen 2473,0,Home,Female,28,HS ,73110,542,3,2,Citizen 2474,0,Car ,Female,47,HS ,74288,561,1,2,Citizen 2475,0,Car ,Female,51,HS ,120378,682,1,1,Non-citizen 2476,0,Car ,Female,43,College ,120263,692,3,1,Citizen 2477,0,Car ,Female,41,HS ,98166,528,1,2,Citizen 2478,0,Car ,Male ,45,HS ,107337,633,3,1,Citizen 2479,0,Car ,Male ,37,HS ,75168,587,1,2,Citizen 2480,0,Car ,Male ,35,HS ,68848,469,1,2,Citizen 2481,0,Car ,Male ,38,College ,80988,648,2,2,Citizen 2482,0,Home,Male ,33,HS ,78731,599,3,2,Citizen 2483,0,Home,Male ,40,College ,97335,711,6,2,Citizen 2484,0,Car ,Male ,46,HS ,71650,530,0,2,Citizen 2485,0,Car ,Female,34,College ,92663,598,2,2,Citizen 2486,0,Home,Male ,37,HS ,74825,625,3,2,Citizen 2487,0,Car ,Female,34,HS ,112788,679,3,2,Citizen 2488,0,Car ,Male ,44,HS ,81470,557,2,2,Citizen 2489,0,Car ,Male ,34,HS ,96566,625,0,2,Citizen 2490,0,Home,Female,39,College ,74885,538,7,2,Citizen 2491,0,Car ,Female,45,HS ,105407,587,2,2,Citizen 2492,0,Home,Male ,42,HS ,86305,573,4,2,Citizen 2493,0,Home,Male ,35,HS ,59535,553,7,2,Citizen 2494,0,Car ,Male ,51,HS ,108558,702,2,2,Citizen 2495,0,Home,Female,45,HS ,106380,625,5,2,Citizen 2496,0,Car ,Male ,45,HS ,85981,601,1,2,Citizen 2497,0,Car ,Male ,42,HS ,86015,743,0,2,Non-citizen 2498,0,Car ,Female,42,HS ,85865,584,0,2,Citizen 2499,0,Car ,Male ,54,HS ,121933,677,2,2,Citizen 2500,0,Home,Female,39,HS ,103224,600,6,2,Citizen 2501,0,Car ,Male ,35,College ,102455,690,2,1,Citizen 2502,0,Home,Male ,49,HS ,87873,615,7,2,Citizen 2503,0,Home,Male ,39,HS ,92474,655,5,2,Citizen 2504,0,Car ,Male ,42,College ,98204,587,1,2,Citizen 2505,0,Car ,Male ,45,HS ,104240,706,0,2,Citizen 2506,0,Car ,Male ,41,HS ,87852,514,0,2,Citizen 2507,0,Car ,Female,44,HS ,80220,614,1,2,Citizen 2508,0,Home,Male ,46,HS ,85608,567,7,2,Citizen 2509,0,Car ,Male ,37,HS ,110359,520,0,2,Citizen 2510,1,Car ,Female,44,HS ,123945,578,1,2,Citizen 2511,0,Car ,Male ,46,HS ,76120,563,0,2,Citizen 2512,0,Car ,Male ,40,College ,94692,681,2,2,Citizen 2513,0,Car ,Male ,43,HS ,103613,615,1,2,Citizen 2514,0,Car ,Female,42,College ,108977,620,0,2,Citizen 2515,0,Car ,Female,54,HS ,95616,620,0,2,Citizen 2516,0,Car ,Female,41,College ,72475,613,2,2,Citizen 2517,0,Car ,Female,38,HS ,95636,605,0,2,Citizen 2518,0,Car ,Female,39,HS ,72953,585,1,2,Citizen 2519,0,Car ,Male ,53,HS ,120335,781,1,1,Non-citizen 2520,0,Car ,Male ,43,HS ,89780,704,1,2,Citizen 2521,0,Car ,Male ,34,HS ,59190,660,0,2,Citizen 2522,0,Home,Male ,44,HS ,85079,654,4,2,Citizen 2523,0,Home,Male ,45,HS ,109624,617,4,2,Citizen 2524,0,Home,Female,49,College ,92377,760,4,2,Citizen 2525,1,Car ,Male ,45,HS ,111802,698,2,2,Citizen 2526,1,Car ,Male ,58,College ,113894,665,2,2,Non-citizen 2527,0,Car ,Female,44,HS ,56841,536,0,2,Citizen 2528,0,Car ,Male ,53,HS ,112156,596,0,2,Citizen 2529,0,Car ,Male ,37,HS ,91703,673,3,2,Citizen 2530,0,Car ,Female,48,Graduate,103015,757,3,2,Citizen 2531,0,Car ,Male ,45,HS ,106660,539,0,2,Citizen 2532,0,Home,Male ,46,HS ,102578,590,4,2,Citizen 2533,0,Car ,Male ,46,HS ,98259,542,0,2,Citizen 2534,0,Car ,Female,34,HS ,88498,582,0,2,Citizen 2535,0,Car ,Female,39,Graduate,116456,477,2,2,Citizen 2536,0,Car ,Female,48,HS ,105189,719,0,1,Citizen 2537,0,Home,Male ,42,HS ,106561,628,12,1,Citizen 2538,0,Car ,Female,44,HS ,81780,614,1,2,Citizen 2539,0,Car ,Female,38,HS ,130541,665,0,2,Citizen 2540,0,Car ,Female,40,HS ,112008,724,0,1,Citizen 2541,1,Car ,Female,50,Graduate,96023,658,1,2,Citizen 2542,1,Car ,Male ,37,College ,95134,650,1,2,Citizen 2543,0,Car ,Male ,36,Graduate,127982,568,3,2,Non-citizen 2544,0,Car ,Female,38,HS ,82564,532,1,2,Citizen 2545,0,Home,Male ,34,HS ,82396,573,8,2,Citizen 2546,0,Home,Female,51,College ,68717,603,8,2,Citizen 2547,0,Car ,Male ,49,College ,90978,722,3,2,Citizen 2548,0,Home,Female,39,College ,109053,531,7,2,Citizen 2549,0,Car ,Male ,41,HS ,120126,661,1,2,Citizen 2550,0,Car ,Female,61,College ,108540,664,3,1,Citizen 2551,0,Home,Female,36,HS ,77719,629,5,2,Citizen 2552,0,Car ,Female,54,College ,121507,693,2,1,Non-citizen 2553,0,Car ,Male ,50,College ,131533,679,3,2,Citizen 2554,0,Home,Female,42,HS ,70681,653,4,2,Citizen 2555,0,Car ,Male ,51,HS ,63827,680,2,2,Citizen 2556,0,Car ,Male ,43,HS ,81802,612,0,1,Citizen 2557,0,Home,Male ,38,HS ,85709,644,3,2,Citizen 2558,1,Car ,Female,41,College ,118115,706,1,2,Citizen 2559,0,Home,Female,42,College ,108051,617,9,2,Citizen 2560,0,Car ,Male ,39,HS ,81366,488,0,2,Citizen 2561,0,Home,Male ,53,HS ,102170,779,7,2,Citizen 2562,0,Home,Male ,43,HS ,78667,630,5,2,Citizen 2563,0,Car ,Female,42,College ,84294,611,2,2,Citizen 2564,0,Car ,Female,48,College ,107480,559,2,1,Citizen 2565,0,Car ,Female,45,College ,97090,588,0,2,Citizen 2566,0,Car ,Female,42,HS ,94293,725,0,2,Citizen 2567,0,Car ,Male ,39,HS ,98485,526,0,2,Citizen 2568,0,Car ,Male ,45,College ,109458,573,2,2,Citizen 2569,0,Car ,Female,49,HS ,115798,602,2,2,Citizen 2570,0,Car ,Female,35,College ,84203,560,0,2,Citizen 2571,0,Car ,Male ,34,HS ,92180,538,1,2,Citizen 2572,0,Home,Male ,42,HS ,130503,540,9,2,Citizen 2573,0,Car ,Male ,41,HS ,70772,482,0,2,Citizen 2574,0,Car ,Male ,47,HS ,80719,533,1,1,Citizen 2575,0,Home,Female,51,College ,102291,598,13,2,Citizen 2576,0,Home,Female,45,College ,76871,641,6,2,Citizen 2577,0,Car ,Female,50,HS ,112264,678,1,2,Non-citizen 2578,0,Car ,Male ,36,HS ,79859,540,0,2,Citizen 2579,0,Car ,Male ,34,HS ,99209,529,1,2,Citizen 2580,0,Home,Male ,31,HS ,65811,541,6,2,Citizen 2581,0,Car ,Male ,41,College ,90020,616,0,2,Non-citizen 2582,0,Car ,Female,47,College ,98411,702,1,2,Citizen 2583,0,Home,Male ,41,HS ,103778,603,7,2,Citizen 2584,0,Car ,Female,36,College ,107120,618,1,2,Citizen 2585,0,Car ,Female,50,College ,119106,707,2,1,Non-citizen 2586,0,Car ,Female,42,HS ,102102,639,1,2,Citizen 2587,0,Car ,Female,55,HS ,81877,617,1,2,Citizen 2588,0,Car ,Male ,46,College ,86886,574,1,2,Citizen 2589,0,Car ,Male ,42,HS ,129967,633,1,2,Citizen 2590,0,Car ,Male ,38,Graduate,83777,572,1,2,Citizen 2591,1,Car ,Female,42,College ,118150,613,3,2,Citizen 2592,0,Car ,Female,46,HS ,82934,588,1,2,Citizen 2593,0,Car ,Male ,39,HS ,45649,559,0,2,Citizen 2594,0,Home,Male ,44,HS ,92142,561,0,2,Citizen 2595,1,Car ,Female,40,HS ,82681,648,3,1,Citizen 2596,0,Car ,Female,43,HS ,82859,675,0,2,Citizen 2597,0,Car ,Male ,46,HS ,109801,616,0,2,Citizen 2598,0,Home,Female,51,HS ,74041,645,4,2,Citizen 2599,0,Home,Male ,41,HS ,51561,512,2,2,Citizen 2600,0,Home,Male ,35,College ,61825,533,9,2,Citizen 2601,0,Home,Female,47,HS ,107298,552,1,2,Citizen 2602,1,Car ,Female,47,College ,101675,625,1,2,Non-citizen 2603,0,Car ,Female,48,College ,89329,568,1,2,Citizen 2604,0,Car ,Female,40,College ,66755,660,0,2,Citizen 2605,0,Car ,Male ,43,HS ,84954,594,1,2,Citizen 2606,0,Car ,Female,38,College ,67975,531,2,2,Citizen 2607,0,Car ,Male ,34,HS ,106489,636,1,2,Citizen 2608,0,Car ,Male ,51,HS ,90379,542,1,2,Citizen 2609,0,Car ,Female,52,HS ,108540,778,1,2,Citizen 2610,0,Car ,Male ,37,HS ,68787,518,0,2,Citizen 2611,0,Home,Male ,39,HS ,111623,576,6,2,Citizen 2612,1,Car ,Male ,42,HS ,97723,628,0,2,Citizen 2613,0,Car ,Male ,55,Graduate,88385,610,3,2,Non-citizen 2614,0,Car ,Male ,44,College ,104452,565,2,2,Citizen 2615,0,Car ,Male ,38,Graduate,99306,562,1,2,Citizen 2616,0,Car ,Male ,40,HS ,97728,639,0,2,Citizen 2617,0,Home,Male ,31,HS ,54442,511,5,2,Citizen 2618,0,Car ,Female,53,Graduate,122538,680,2,2,Non-citizen 2619,0,Car ,Female,44,College ,81030,617,1,2,Citizen 2620,0,Car ,Female,41,HS ,98832,510,1,2,Citizen 2621,0,Home,Male ,42,HS ,114359,583,5,2,Citizen 2622,1,Car ,Female,56,College ,106979,682,1,2,Citizen 2623,0,Home,Male ,44,HS ,97098,585,5,2,Citizen 2624,0,Car ,Male ,40,HS ,94550,563,3,2,Citizen 2625,0,Car ,Male ,33,HS ,63705,426,0,2,Citizen 2626,0,Car ,Male ,47,HS ,128301,636,1,2,Citizen 2627,0,Car ,Male ,31,HS ,84542,552,0,2,Citizen 2628,0,Car ,Female,46,HS ,117024,645,0,1,Citizen 2629,0,Car ,Female,44,HS ,96203,699,2,2,Citizen 2630,0,Home,Male ,36,HS ,66685,576,6,2,Citizen 2631,0,Home,Male ,39,HS ,59552,570,3,2,Citizen 2632,0,Car ,Male ,56,College ,104106,738,2,2,Citizen 2633,0,Car ,Male ,39,HS ,103451,653,0,2,Citizen 2634,1,Car ,Female,53,Graduate,101102,703,1,2,Citizen 2635,0,Home,Female,44,HS ,111681,626,9,1,Citizen 2636,0,Car ,Male ,32,HS ,86973,565,0,2,Citizen 2637,0,Car ,Male ,25,HS ,88745,578,0,2,Citizen 2638,0,Home,Male ,40,HS ,108407,661,2,2,Citizen 2639,0,Home,Female,45,HS ,98136,573,7,2,Citizen 2640,0,Car ,Female,47,HS ,86146,646,3,2,Citizen 2641,0,Home,Female,35,HS ,77892,540,6,2,Citizen 2642,0,Home,Male ,38,HS ,89738,389,5,2,Citizen 2643,0,Home,Male ,38,HS ,68502,633,3,2,Citizen 2644,0,Car ,Female,39,HS ,85576,562,2,2,Citizen 2645,0,Home,Male ,38,College ,113460,612,8,2,Citizen 2646,0,Home,Female,39,College ,95210,556,3,2,Citizen 2647,0,Car ,Female,56,College ,84013,720,1,1,Citizen 2648,0,Car ,Male ,45,HS ,103125,623,1,2,Citizen 2649,0,Car ,Male ,45,College ,101603,631,2,2,Non-citizen 2650,0,Car ,Female,53,College ,132370,644,3,2,Citizen 2651,0,Car ,Male ,39,HS ,95938,669,0,2,Citizen 2652,0,Car ,Male ,43,HS ,86084,613,1,2,Citizen 2653,0,Car ,Male ,41,HS ,107526,536,0,2,Citizen 2654,0,Car ,Female,42,HS ,117212,600,0,2,Citizen 2655,0,Car ,Male ,42,HS ,92597,612,1,2,Citizen 2656,0,Home,Male ,35,HS ,114758,642,5,2,Citizen 2657,0,Car ,Male ,52,HS ,92322,659,2,1,Citizen 2658,0,Car ,Female,48,HS ,87859,614,0,2,Citizen 2659,1,Car ,Male ,52,HS ,105417,687,2,2,Citizen 2660,0,Car ,Female,47,College ,122785,573,2,2,Non-citizen 2661,0,Home,Male ,34,HS ,64071,525,4,2,Citizen 2662,0,Car ,Female,29,HS ,75852,552,1,2,Citizen 2663,0,Car ,Female,42,HS ,87473,439,0,2,Citizen 2664,0,Car ,Male ,39,HS ,81083,485,0,2,Citizen 2665,0,Home,Male ,39,HS ,108416,600,7,2,Citizen 2666,1,Home,Male ,39,HS ,105079,621,8,2,Citizen 2667,0,Car ,Male ,39,College ,98779,582,0,2,Citizen 2668,0,Car ,Male ,50,College ,130236,632,3,2,Citizen 2669,0,Home,Male ,29,HS ,75371,557,6,2,Citizen 2670,0,Car ,Male ,44,HS ,68495,721,2,2,Citizen 2671,0,Car ,Male ,31,HS ,89909,560,0,2,Citizen 2672,0,Home,Male ,40,HS ,86612,584,4,2,Citizen 2673,0,Home,Female,38,College ,107620,599,8,2,Citizen 2674,0,Car ,Male ,39,HS ,91647,645,1,2,Citizen 2675,0,Car ,Female,41,HS ,104147,560,0,2,Citizen 2676,0,Home,Male ,47,HS ,109206,621,2,2,Citizen 2677,0,Home,Male ,43,HS ,93948,518,6,2,Citizen 2678,0,Car ,Male ,44,College ,87300,648,2,2,Citizen 2679,0,Car ,Male ,36,College ,72416,551,1,2,Citizen 2680,0,Car ,Male ,51,College ,112142,648,2,2,Citizen 2681,0,Car ,Male ,36,HS ,91626,554,1,2,Citizen 2682,0,Car ,Male ,43,HS ,88598,604,0,2,Citizen 2683,0,Car ,Male ,47,HS ,96430,580,2,2,Citizen 2684,0,Car ,Male ,40,HS ,82106,560,0,2,Citizen 2685,0,Car ,Male ,37,HS ,114252,638,1,2,Citizen 2686,0,Car ,Male ,43,College ,115413,662,0,2,Citizen 2687,0,Car ,Male ,45,HS ,83352,551,1,2,Citizen 2688,0,Car ,Female,52,HS ,118732,569,1,2,Citizen 2689,0,Car ,Female,47,HS ,113452,562,0,2,Citizen 2690,0,Car ,Male ,43,HS ,104443,654,0,2,Citizen 2691,0,Car ,Female,41,HS ,85201,622,0,2,Citizen 2692,0,Car ,Male ,43,HS ,101932,698,0,2,Citizen 2693,0,Car ,Male ,43,College ,75889,629,1,2,Citizen 2694,0,Car ,Male ,41,HS ,94351,635,2,2,Citizen 2695,0,Car ,Female,48,Graduate,126699,700,0,2,Non-citizen 2696,0,Car ,Male ,34,College ,105239,582,0,2,Citizen 2697,0,Car ,Male ,42,HS ,86807,562,1,2,Citizen 2698,0,Car ,Female,46,HS ,68661,681,0,2,Citizen 2699,0,Car ,Female,45,HS ,83237,543,0,2,Citizen 2700,0,Car ,Female,49,College ,131640,682,2,2,Citizen 2701,0,Car ,Male ,50,HS ,108037,568,2,2,Citizen 2702,0,Car ,Male ,38,HS ,63009,606,0,2,Citizen 2703,0,Car ,Male ,43,College ,111114,545,2,2,Citizen 2704,0,Home,Male ,35,HS ,93652,609,5,2,Citizen 2705,0,Car ,Male ,36,HS ,85762,547,0,2,Citizen 2706,0,Car ,Male ,38,HS ,114928,534,2,2,Citizen 2707,0,Car ,Female,50,HS ,124584,578,2,2,Citizen 2708,0,Car ,Female,48,HS ,89105,682,1,1,Citizen 2709,0,Car ,Male ,45,HS ,41568,510,0,2,Citizen 2710,1,Car ,Female,46,HS ,88157,591,1,2,Citizen 2711,0,Car ,Male ,32,HS ,79157,628,1,2,Citizen 2712,0,Car ,Male ,42,College ,123185,626,1,2,Citizen 2713,1,Car ,Female,53,Graduate,96860,746,2,2,Non-citizen 2714,0,Home,Male ,51,HS ,100704,622,6,2,Citizen 2715,0,Car ,Female,49,College ,80013,577,3,2,Citizen 2716,0,Car ,Male ,37,HS ,94275,552,1,2,Citizen 2717,0,Home,Male ,40,College ,126728,613,12,2,Citizen 2718,0,Car ,Female,50,HS ,137984,665,3,2,Citizen 2719,0,Home,Male ,42,HS ,106235,533,8,2,Citizen 2720,0,Home,Female,45,HS ,115379,805,10,1,Citizen 2721,0,Home,Male ,39,HS ,71710,550,5,2,Citizen 2722,0,Car ,Male ,51,HS ,87879,635,2,2,Citizen 2723,0,Car ,Female,43,HS ,95199,599,1,2,Citizen 2724,1,Car ,Male ,55,College ,120898,594,1,2,Citizen 2726,0,Car ,Male ,46,HS ,123932,654,1,2,Citizen 2727,0,Car ,Male ,41,HS ,95391,588,0,2,Citizen 2728,0,Home,Male ,40,HS ,43635,581,2,2,Citizen 2729,0,Car ,Male ,51,HS ,94753,602,2,2,Citizen 2730,0,Car ,Female,48,College ,88892,620,0,1,Non-citizen 2731,0,Car ,Female,42,HS ,86824,660,1,1,Citizen 2732,0,Car ,Male ,37,HS ,90987,588,0,2,Citizen 2733,0,Car ,Male ,40,HS ,107008,633,3,2,Citizen 2734,0,Car ,Male ,41,HS ,96818,620,3,2,Citizen 2735,0,Car ,Male ,49,HS ,82365,596,3,2,Citizen 2736,0,Car ,Male ,49,HS ,80904,666,0,2,Citizen 2737,0,Home,Male ,41,College ,78637,471,8,2,Citizen 2738,0,Home,Female,41,Graduate,119065,768,10,2,Citizen 2739,0,Home,Male ,39,HS ,119698,614,8,2,Citizen 2740,0,Car ,Female,46,College ,129168,696,3,2,Citizen 2741,0,Car ,Male ,42,HS ,119100,606,2,2,Citizen 2742,0,Car ,Male ,45,HS ,87813,557,2,2,Citizen 2743,0,Car ,Male ,36,HS ,95251,641,0,2,Citizen 2744,1,Car ,Male ,49,HS ,83903,718,0,2,Citizen 2745,0,Car ,Female,53,Graduate,81774,650,2,2,Citizen 2746,0,Car ,Male ,35,College ,88074,558,1,2,Citizen 2747,1,Car ,Female,47,Graduate,109658,723,2,2,Citizen 2748,0,Car ,Male ,32,College ,86725,658,1,2,Citizen 2749,0,Car ,Female,56,HS ,137210,577,2,2,Citizen 2750,0,Car ,Male ,45,HS ,118702,702,1,1,Citizen 2751,0,Car ,Male ,50,HS ,77373,581,0,2,Citizen 2752,0,Car ,Male ,46,College ,101932,684,2,2,Citizen 2753,0,Home,Male ,51,College ,96921,669,13,2,Citizen 2754,1,Car ,Male ,46,College ,111032,692,3,2,Citizen 2755,0,Home,Male ,37,HS ,57147,514,5,2,Citizen 2756,0,Home,Male ,37,HS ,84807,582,4,2,Citizen 2757,0,Home,Male ,44,HS ,51949,531,2,2,Citizen 2758,0,Home,Male ,33,HS ,85978,548,10,2,Citizen 2759,0,Home,Female,33,HS ,58406,525,1,2,Citizen 2760,0,Car ,Male ,46,HS ,81620,606,0,2,Citizen 2761,0,Car ,Male ,36,HS ,84775,602,0,2,Citizen 2762,1,Car ,Male ,40,HS ,102023,667,2,2,Citizen 2763,0,Car ,Male ,48,College ,84142,597,1,2,Citizen 2764,0,Car ,Male ,37,Graduate,90153,626,2,2,Citizen 2765,0,Car ,Female,43,HS ,98270,511,2,2,Citizen 2766,0,Home,Male ,29,College ,75730,510,8,2,Citizen 2767,0,Car ,Female,42,HS ,101091,670,1,2,Citizen 2768,0,Car ,Female,43,HS ,113027,659,0,1,Citizen 2769,1,Car ,Female,52,HS ,107225,635,1,2,Citizen 2770,0,Home,Male ,42,HS ,92684,651,2,2,Citizen 2771,0,Car ,Female,46,HS ,86699,643,3,1,Citizen 2772,0,Car ,Female,52,HS ,126475,600,0,2,Citizen 2773,0,Car ,Female,44,College ,96768,651,2,1,Citizen 2774,0,Car ,Male ,38,HS ,76436,632,1,2,Citizen 2775,0,Home,Female,40,Graduate,103626,639,10,2,Citizen 2776,0,Home,Male ,39,College ,97377,555,4,2,Citizen 2777,0,Car ,Female,49,College ,125716,752,3,1,Citizen 2778,0,Home,Male ,44,HS ,117753,683,3,2,Citizen 2779,0,Home,Male ,38,HS ,86650,612,1,2,Citizen 2780,0,Car ,Female,39,HS ,91710,492,0,2,Citizen 2781,0,Car ,Female,47,College ,124534,654,2,2,Citizen 2782,0,Car ,Male ,42,HS ,85219,585,0,2,Citizen 2783,1,Car ,Male ,42,College ,86340,695,2,1,Citizen 2784,0,Car ,Male ,36,HS ,80826,540,0,2,Citizen 2785,0,Car ,Female,46,HS ,103610,680,1,2,Citizen 2786,0,Car ,Male ,28,HS ,114473,633,2,1,Citizen 2787,0,Car ,Female,54,College ,113089,620,3,2,Citizen 2788,0,Home,Male ,39,HS ,137571,584,5,2,Citizen 2789,0,Car ,Male ,45,HS ,111936,660,3,2,Citizen 2790,0,Car ,Female,53,HS ,100482,777,1,1,Citizen 2791,0,Car ,Female,50,College ,92861,542,2,2,Citizen 2792,0,Car ,Female,57,Graduate,87294,746,2,2,Citizen 2793,0,Car ,Male ,33,HS ,144859,553,0,2,Citizen 2794,0,Home,Female,42,HS ,117013,685,5,2,Citizen 2795,0,Car ,Male ,45,College ,66880,647,2,2,Citizen 2796,0,Car ,Female,45,College ,87547,542,2,2,Citizen 2797,0,Home,Female,38,HS ,90027,527,7,2,Citizen 2798,0,Car ,Female,35,HS ,93282,538,2,1,Citizen 2799,0,Car ,Female,44,HS ,87317,589,0,2,Citizen 2800,0,Home,Male ,45,HS ,84784,667,6,2,Citizen 2801,0,Car ,Female,46,HS ,102144,598,1,2,Citizen 2802,0,Car ,Male ,50,HS ,89973,597,0,2,Citizen 2803,0,Home,Male ,40,HS ,76837,618,6,2,Citizen 2804,0,Car ,Male ,37,HS ,93548,643,0,2,Citizen 2805,0,Home,Female,38,HS ,132080,628,5,2,Citizen 2806,0,Home,Female,44,HS ,113276,657,7,1,Citizen 2807,0,Car ,Female,39,HS ,100290,564,0,2,Citizen 2808,0,Home,Male ,38,HS ,81396,633,5,2,Citizen 2809,0,Car ,Female,36,HS ,98544,493,0,2,Citizen 2810,0,Car ,Female,43,HS ,76466,566,0,2,Citizen 2811,0,Car ,Male ,47,HS ,95405,676,0,2,Citizen 2812,0,Car ,Male ,48,HS ,91110,588,0,2,Citizen 2813,0,Home,Male ,28,HS ,62278,433,2,2,Citizen 2814,0,Car ,Female,44,College ,115638,618,3,1,Citizen 2815,0,Home,Male ,36,HS ,78086,510,9,2,Citizen 2816,0,Home,Female,43,HS ,88683,496,9,2,Citizen 2817,0,Car ,Male ,51,HS ,86799,551,2,2,Citizen 2818,0,Car ,Male ,46,College ,100428,651,2,2,Citizen 2819,0,Home,Male ,24,HS ,61086,594,7,2,Citizen 2820,0,Car ,Male ,45,HS ,104838,568,0,2,Citizen 2821,1,Car ,Male ,53,HS ,113409,637,0,2,Citizen 2822,0,Home,Female,40,HS ,91359,656,9,2,Citizen 2823,0,Home,Female,48,HS ,105852,619,2,2,Citizen 2824,0,Car ,Female,51,HS ,99551,559,2,2,Citizen 2825,0,Car ,Female,34,HS ,91110,627,0,2,Citizen 2826,0,Car ,Female,48,HS ,95776,445,1,2,Citizen 2827,0,Car ,Male ,37,College ,97905,533,1,2,Citizen 2828,0,Car ,Male ,57,HS ,112035,620,3,2,Citizen 2829,0,Car ,Male ,41,HS ,87676,522,0,2,Citizen 2830,0,Car ,Male ,37,Graduate,108015,616,3,2,Citizen 2831,0,Car ,Female,38,College ,115062,561,1,2,Citizen 2832,0,Home,Male ,24,HS ,79495,464,7,2,Citizen 2833,0,Car ,Female,39,HS ,108224,669,0,2,Citizen 2834,0,Car ,Female,41,HS ,106160,605,1,2,Citizen 2835,0,Car ,Male ,46,HS ,93438,522,0,2,Citizen 2836,0,Car ,Male ,44,HS ,57882,522,0,2,Citizen 2837,0,Car ,Female,35,College ,97906,588,2,2,Citizen 2838,0,Home,Female,33,HS ,76587,540,5,2,Citizen 2839,0,Home,Male ,37,HS ,107347,560,0,2,Citizen 2840,0,Car ,Male ,48,HS ,88247,607,3,2,Citizen 2841,0,Car ,Male ,51,HS ,103143,638,1,2,Citizen 2842,0,Car ,Female,42,College ,96531,528,1,2,Citizen 2843,0,Car ,Male ,35,HS ,99805,587,1,2,Citizen 2844,0,Home,Male ,40,College ,85754,597,8,2,Citizen 2845,0,Car ,Female,45,HS ,117309,564,1,1,Citizen 2846,0,Car ,Female,41,HS ,98008,583,0,2,Citizen 2847,0,Home,Female,43,HS ,83934,620,4,2,Citizen 2848,0,Car ,Male ,50,HS ,113979,566,1,2,Citizen 2849,0,Car ,Male ,39,HS ,90389,645,1,2,Citizen 2850,0,Home,Male ,41,HS ,81808,693,7,2,Citizen 2851,1,Car ,Female,48,HS ,120006,580,0,2,Citizen 2852,0,Home,Male ,36,HS ,89092,635,2,2,Citizen 2853,0,Car ,Male ,39,HS ,104401,712,1,2,Citizen 2854,0,Car ,Female,40,College ,83297,535,0,2,Citizen 2855,0,Car ,Male ,48,HS ,91776,576,2,2,Citizen 2856,0,Car ,Female,38,College ,102361,566,2,2,Citizen 2857,0,Home,Male ,51,HS ,109399,713,5,2,Citizen 2858,0,Home,Female,46,College ,94489,541,9,2,Citizen 2859,0,Car ,Male ,46,HS ,88507,509,2,2,Citizen 2860,0,Car ,Male ,38,HS ,69593,517,1,2,Citizen 2861,0,Car ,Female,45,HS ,113500,718,2,2,Citizen 2862,0,Home,Male ,46,College ,113373,702,13,2,Citizen 2863,0,Car ,Female,53,Graduate,73900,654,1,2,Citizen 2864,0,Home,Female,42,HS ,83586,658,3,2,Citizen 2865,0,Car ,Male ,40,HS ,78161,610,1,2,Citizen 2866,0,Car ,Male ,38,HS ,76044,552,2,2,Citizen 2867,0,Home,Male ,39,HS ,84708,658,6,2,Citizen 2868,0,Home,Male ,34,College ,85824,625,1,2,Citizen 2869,0,Car ,Female,45,College ,83492,632,2,2,Non-citizen 2870,0,Car ,Female,45,HS ,114616,533,0,2,Citizen 2871,0,Car ,Male ,47,HS ,87805,530,1,2,Citizen 2872,0,Home,Male ,36,HS ,88249,690,5,2,Citizen 2873,0,Car ,Female,44,HS ,97326,689,0,2,Citizen 2874,0,Home,Male ,48,HS ,80940,621,5,2,Citizen 2875,1,Home,Female,37,College ,80535,623,9,2,Citizen 2876,0,Car ,Female,51,HS ,111877,641,0,1,Citizen 2877,0,Car ,Male ,45,College ,110703,655,2,2,Citizen 2878,0,Car ,Female,26,HS ,87355,455,0,2,Citizen 2879,1,Car ,Female,36,HS ,115077,601,0,2,Citizen 2880,0,Car ,Female,52,HS ,112518,695,0,2,Citizen 2881,0,Car ,Female,47,College ,98502,598,1,2,Citizen 2882,0,Home,Male ,40,HS ,93260,617,3,2,Citizen 2883,0,Home,Female,39,College ,114814,667,6,2,Citizen 2884,0,Car ,Male ,35,College ,95098,554,0,2,Citizen 2885,0,Home,Male ,38,HS ,94995,522,3,2,Citizen 2886,0,Car ,Female,52,HS ,103825,685,1,2,Citizen 2887,0,Car ,Male ,55,HS ,83794,649,0,2,Citizen 2888,0,Home,Female,29,HS ,91882,514,5,2,Citizen 2889,0,Car ,Male ,27,HS ,97573,562,3,2,Citizen 2890,0,Home,Male ,34,HS ,70678,568,9,2,Citizen 2891,0,Car ,Female,43,College ,97531,612,2,2,Citizen 2892,0,Car ,Male ,40,College ,87084,590,0,2,Citizen 2893,0,Car ,Female,42,College ,72148,638,0,1,Non-citizen 2894,0,Home,Female,43,HS ,91975,653,6,1,Citizen 2895,0,Home,Male ,48,HS ,127233,648,8,2,Citizen 2896,0,Home,Male ,43,HS ,89710,594,2,2,Citizen 2897,0,Car ,Female,45,College ,82292,679,1,1,Non-citizen 2898,0,Car ,Male ,42,College ,88411,566,1,2,Citizen 2899,0,Car ,Female,42,HS ,97518,607,1,2,Citizen 2900,0,Car ,Male ,45,HS ,122284,640,2,1,Citizen 2901,1,Car ,Female,49,Graduate,103559,724,3,2,Citizen 2902,0,Car ,Female,31,Graduate,86467,558,3,2,Citizen 2903,0,Home,Male ,42,HS ,84665,551,3,2,Citizen 2904,0,Home,Male ,40,HS ,90157,615,1,2,Citizen 2905,0,Home,Male ,48,HS ,81626,530,5,2,Citizen 2906,0,Car ,Male ,34,College ,107785,592,2,2,Citizen 2907,0,Home,Male ,30,HS ,109754,605,0,2,Citizen 2908,1,Car ,Female,41,College ,100964,653,2,2,Citizen 2909,0,Car ,Male ,40,HS ,112755,593,2,2,Citizen 2910,0,Car ,Female,42,College ,112425,637,1,2,Non-citizen 2911,0,Home,Female,48,HS ,108944,621,7,2,Citizen 2912,0,Car ,Male ,42,College ,93202,581,0,2,Citizen 2913,0,Home,Male ,40,HS ,71359,666,8,2,Citizen 2914,0,Car ,Male ,37,HS ,81162,555,1,2,Citizen 2915,0,Car ,Female,51,Graduate,85372,551,3,2,Citizen 2916,0,Car ,Male ,46,HS ,88487,533,0,2,Citizen 2917,0,Car ,Male ,39,HS ,109359,530,0,2,Citizen 2918,0,Car ,Female,40,HS ,85198,524,1,2,Citizen 2919,1,Home,Male ,34,HS ,98777,668,0,2,Citizen 2920,0,Home,Female,42,HS ,108881,634,6,2,Citizen 2921,0,Car ,Male ,40,HS ,98510,537,0,2,Citizen 2922,0,Car ,Male ,39,Graduate,84705,566,2,2,Non-citizen 2923,0,Car ,Male ,39,HS ,100340,684,0,2,Citizen 2924,0,Home,Male ,36,HS ,62578,548,4,2,Citizen 2925,0,Home,Male ,40,HS ,89339,607,0,2,Citizen 2926,0,Car ,Female,38,College ,88311,594,1,2,Citizen 2927,0,Car ,Male ,49,College ,72407,553,0,2,Citizen 2928,0,Home,Male ,48,HS ,74740,672,5,2,Citizen 2929,0,Car ,Male ,40,College ,83127,566,2,2,Citizen 2930,0,Car ,Male ,41,HS ,97265,664,0,2,Citizen 2931,0,Car ,Male ,45,College ,94625,554,3,2,Citizen 2932,0,Car ,Female,40,College ,93314,603,1,2,Citizen 2933,0,Home,Female,34,HS ,118487,603,4,2,Citizen 2934,0,Car ,Male ,47,HS ,90642,536,2,2,Citizen 2935,0,Car ,Male ,49,HS ,94163,602,1,2,Citizen 2936,0,Car ,Female,49,HS ,82903,631,3,2,Citizen 2937,0,Car ,Male ,45,HS ,83084,703,1,2,Citizen 2938,0,Car ,Female,49,HS ,124176,651,2,2,Citizen 2939,0,Car ,Female,43,College ,100053,780,2,1,Citizen 2940,0,Car ,Male ,46,HS ,97764,665,3,1,Citizen 2941,0,Car ,Female,42,College ,97484,578,1,2,Citizen 2942,0,Car ,Female,42,HS ,91693,521,0,2,Citizen 2943,0,Car ,Female,42,College ,98799,594,1,2,Citizen 2944,0,Car ,Female,56,HS ,138474,574,1,2,Citizen 2945,0,Car ,Female,47,College ,97351,773,3,2,Citizen 2946,0,Car ,Male ,35,HS ,91364,705,0,2,Citizen 2947,0,Home,Male ,38,HS ,87921,482,6,2,Citizen 2948,0,Car ,Female,56,Graduate,115941,698,2,2,Non-citizen 2949,0,Home,Female,42,HS ,99837,729,1,2,Citizen 2950,0,Car ,Male ,41,HS ,93920,634,1,2,Citizen 2951,0,Car ,Female,36,HS ,92039,571,0,2,Citizen 2952,0,Car ,Female,52,College ,109902,566,1,2,Citizen 2953,0,Car ,Male ,49,Graduate,74801,542,3,2,Citizen 2954,0,Car ,Female,49,College ,109323,636,2,2,Citizen 2955,0,Car ,Male ,53,College ,120223,640,1,2,Citizen 2956,1,Car ,Female,47,College ,122606,693,2,2,Citizen 2957,0,Car ,Male ,35,HS ,106899,565,1,2,Citizen 2958,0,Car ,Male ,44,HS ,105539,568,3,2,Citizen 2959,0,Car ,Male ,46,HS ,81760,609,3,2,Citizen 2960,0,Home,Male ,35,HS ,97750,548,7,2,Citizen 2961,0,Home,Male ,34,HS ,74983,565,3,2,Citizen 2962,0,Car ,Male ,37,College ,83525,540,1,2,Citizen 2963,0,Car ,Female,42,HS ,80235,497,0,2,Citizen 2964,0,Home,Male ,45,HS ,84161,561,4,2,Citizen 2965,0,Car ,Male ,39,HS ,99138,609,1,2,Citizen 2966,0,Car ,Male ,42,HS ,91918,589,1,2,Citizen 2967,0,Home,Male ,35,HS ,55221,485,7,2,Citizen 2968,0,Car ,Male ,44,College ,80046,606,1,2,Citizen 2970,0,Car ,Male ,41,HS ,90935,591,1,2,Citizen 2971,0,Home,Male ,44,HS ,74397,594,7,2,Citizen 2972,1,Car ,Female,50,College ,115545,655,0,2,Citizen 2973,0,Car ,Female,47,College ,114910,693,0,2,Citizen 2974,0,Home,Male ,41,College ,77279,559,5,2,Citizen 2975,0,Car ,Male ,50,HS ,85505,646,1,2,Citizen 2976,0,Home,Male ,44,College ,95362,577,5,2,Citizen 2977,0,Car ,Female,51,HS ,86068,651,0,2,Citizen 2978,0,Car ,Male ,40,HS ,82218,659,1,1,Citizen 2979,0,Home,Male ,57,HS ,113915,720,5,2,Citizen 2980,0,Home,Female,37,HS ,126469,685,4,1,Citizen 2981,0,Home,Female,41,HS ,91817,589,7,2,Citizen 2982,0,Home,Male ,50,HS ,102791,625,10,2,Citizen 2983,0,Car ,Female,56,College ,93382,623,3,2,Non-citizen 2984,0,Car ,Male ,36,HS ,76429,558,2,2,Citizen 2985,0,Home,Male ,46,HS ,107366,691,7,2,Citizen 2986,0,Home,Male ,49,HS ,109913,615,7,2,Citizen 2987,0,Car ,Female,48,College ,90237,602,2,2,Citizen 2988,0,Home,Male ,35,HS ,70969,581,9,2,Citizen 2989,0,Car ,Female,52,HS ,105849,641,1,1,Citizen 2990,0,Home,Male ,32,HS ,97108,550,6,2,Citizen 2991,0,Car ,Male ,42,HS ,97834,538,0,2,Citizen 2992,1,Car ,Male ,52,HS ,120201,730,2,2,Citizen 2993,0,Car ,Female,34,HS ,109159,583,0,2,Citizen 2994,0,Car ,Male ,53,College ,120011,664,1,2,Citizen 2995,0,Home,Female,36,HS ,92746,594,7,2,Citizen 2996,0,Car ,Male ,36,HS ,97013,470,0,2,Citizen 2997,0,Car ,Female,53,HS ,111729,635,0,2,Citizen 2998,0,Car ,Male ,50,HS ,113431,669,1,2,Citizen 2999,0,Home,Male ,33,HS ,95472,561,8,2,Citizen 3000,0,Car ,Male ,42,HS ,70546,631,1,2,Citizen 3001,0,Home,Male ,30,HS ,100516,479,2,2,Citizen 3002,0,Home,Female,29,HS ,65475,608,0,2,Citizen 3003,1,Car ,Male ,48,Graduate,114881,625,0,2,Citizen 3004,0,Car ,Male ,41,HS ,98178,646,3,2,Citizen 3005,0,Car ,Female,52,College ,117044,691,2,2,Non-citizen 3006,0,Car ,Male ,44,HS ,92564,543,0,2,Citizen 3007,0,Car ,Male ,40,HS ,98047,644,2,2,Citizen 3008,0,Home,Female,48,HS ,114539,628,6,2,Citizen 3009,0,Car ,Female,43,College ,103789,665,1,2,Citizen 3010,0,Home,Male ,30,HS ,82930,412,5,2,Citizen 3012,0,Home,Male ,40,HS ,58667,655,5,2,Citizen 3013,0,Home,Male ,43,HS ,85684,569,3,2,Citizen 3014,0,Car ,Male ,50,HS ,90965,555,2,2,Citizen 3015,0,Car ,Male ,47,HS ,84944,598,1,2,Citizen 3016,0,Car ,Female,43,HS ,96689,570,3,2,Citizen 3017,0,Car ,Male ,41,HS ,84328,649,0,2,Citizen 3018,0,Car ,Male ,43,HS ,69381,534,0,2,Citizen 3019,1,Car ,Male ,40,HS ,122073,667,2,2,Citizen 3020,0,Car ,Male ,44,HS ,81332,596,2,2,Citizen 3021,0,Car ,Male ,33,HS ,73313,483,0,2,Citizen 3022,0,Car ,Male ,42,HS ,94611,559,2,2,Citizen 3023,0,Home,Male ,37,Graduate,78008,569,10,2,Citizen 3024,0,Car ,Male ,48,HS ,112569,550,0,2,Citizen 3025,1,Home,Male ,37,College ,109716,636,9,2,Citizen 3026,0,Home,Male ,32,HS ,94942,604,3,2,Citizen 3027,0,Car ,Male ,36,HS ,81947,523,0,2,Citizen 3028,1,Home,Female,38,HS ,106972,641,6,1,Citizen 3029,0,Car ,Male ,49,College ,110808,673,1,2,Citizen 3030,0,Car ,Female,33,College ,92439,684,0,2,Citizen 3031,0,Home,Male ,38,HS ,106576,578,8,2,Citizen 3032,0,Car ,Female,49,HS ,111848,529,2,2,Citizen 3033,0,Home,Male ,40,HS ,92231,541,8,2,Citizen 3034,0,Car ,Male ,35,HS ,101679,670,0,2,Citizen 3035,0,Home,Male ,31,HS ,56949,527,4,2,Citizen 3036,0,Car ,Male ,37,HS ,93308,550,1,2,Citizen 3037,0,Home,Male ,49,HS ,123456,608,8,2,Citizen 3038,0,Home,Female,50,College ,110744,666,5,2,Citizen 3039,0,Car ,Female,39,College ,74691,639,1,2,Citizen 3040,0,Car ,Male ,45,HS ,75531,578,0,2,Citizen 3041,0,Home,Male ,37,HS ,51109,577,5,2,Citizen 3042,0,Car ,Male ,37,HS ,76402,619,1,2,Citizen 3043,0,Car ,Female,36,College ,97707,631,2,2,Citizen 3044,0,Home,Female,43,College ,94622,703,9,2,Citizen 3045,0,Car ,Female,52,College ,90718,725,3,1,Citizen 3046,0,Car ,Female,44,HS ,106935,591,0,2,Citizen 3047,0,Car ,Female,46,College ,87156,531,2,2,Citizen 3048,0,Car ,Female,37,HS ,78755,577,1,2,Citizen 3049,0,Car ,Female,49,College ,69510,680,0,2,Citizen 3050,0,Home,Male ,39,HS ,106421,702,9,2,Citizen 3051,0,Home,Male ,38,HS ,95421,516,3,2,Citizen 3052,0,Car ,Male ,38,HS ,77683,520,2,2,Citizen 3053,0,Home,Male ,39,HS ,49861,542,6,2,Citizen 3054,0,Car ,Female,37,College ,68424,596,2,2,Citizen 3055,0,Home,Male ,41,HS ,106555,607,8,2,Citizen 3056,0,Car ,Female,36,HS ,112709,687,2,2,Citizen 3057,0,Car ,Female,45,HS ,65742,530,0,2,Citizen 3058,0,Home,Male ,43,HS ,96432,560,6,2,Citizen 3059,0,Car ,Female,41,HS ,87028,510,0,2,Citizen 3060,0,Car ,Female,50,College ,102053,639,0,2,Citizen 3061,0,Home,Male ,44,HS ,97070,660,4,2,Citizen 3062,0,Car ,Male ,40,HS ,104094,641,0,2,Citizen 3063,0,Home,Female,43,HS ,106819,599,12,2,Citizen 3064,0,Car ,Male ,46,College ,90401,549,2,1,Citizen 3065,0,Home,Female,44,HS ,132204,647,6,2,Citizen 3066,0,Car ,Male ,31,HS ,62664,461,0,2,Citizen 3067,0,Car ,Male ,43,HS ,82482,523,0,2,Citizen 3068,0,Home,Male ,36,HS ,82036,660,5,2,Citizen 3069,0,Home,Female,35,HS ,130780,683,5,2,Citizen 3070,0,Car ,Male ,46,HS ,117432,554,0,2,Citizen 3071,0,Home,Male ,32,HS ,58470,602,5,2,Citizen 3072,0,Car ,Female,50,HS ,129491,700,2,1,Citizen 3073,0,Car ,Female,41,HS ,86201,721,2,2,Citizen 3074,0,Car ,Female,40,HS ,89051,620,3,2,Citizen 3075,0,Car ,Male ,33,College ,81669,538,1,2,Citizen 3076,1,Home,Male ,40,College ,116914,642,8,2,Citizen 3077,0,Car ,Male ,46,HS ,67877,619,0,2,Citizen 3078,1,Car ,Female,43,College ,83454,734,2,2,Citizen 3079,0,Car ,Female,45,HS ,83438,676,0,2,Citizen 3080,0,Car ,Male ,42,HS ,105833,608,1,2,Citizen 3081,0,Home,Male ,41,HS ,87935,663,6,2,Citizen 3082,0,Car ,Female,44,College ,124225,445,2,2,Citizen 3083,0,Car ,Female,50,HS ,101921,623,0,2,Citizen 3084,0,Home,Male ,47,HS ,59029,646,6,2,Citizen 3085,0,Car ,Female,50,College ,70477,689,2,2,Citizen 3086,0,Car ,Female,41,HS ,90625,667,0,1,Non-citizen 3087,0,Home,Male ,36,HS ,77049,480,6,2,Citizen 3088,0,Car ,Male ,39,HS ,69632,648,1,2,Citizen 3089,0,Car ,Female,36,HS ,92083,704,0,1,Citizen 3090,0,Car ,Male ,42,College ,88920,649,2,2,Citizen 3091,0,Car ,Male ,42,HS ,115675,624,0,2,Citizen 3092,0,Car ,Male ,33,College ,94818,646,0,2,Citizen 3093,0,Car ,Male ,36,HS ,107262,645,1,2,Citizen 3094,0,Car ,Male ,44,College ,111088,729,3,2,Citizen 3095,0,Car ,Female,41,HS ,90785,664,0,2,Citizen 3096,0,Home,Female,43,HS ,82159,661,4,2,Citizen 3097,0,Home,Male ,44,HS ,97166,571,12,2,Citizen 3098,0,Home,Male ,33,HS ,78070,544,3,2,Citizen 3099,0,Car ,Male ,50,College ,106561,491,2,2,Citizen 3100,1,Car ,Male ,50,HS ,111757,634,1,2,Citizen 3101,0,Car ,Male ,41,HS ,94102,754,0,2,Citizen 3102,0,Home,Female,50,HS ,107459,643,4,2,Citizen 3103,0,Car ,Male ,40,HS ,78279,534,2,2,Citizen 3104,0,Car ,Male ,45,College ,96796,573,2,2,Citizen 3105,0,Car ,Male ,29,College ,105096,577,1,2,Citizen 3106,0,Car ,Female,46,HS ,99588,625,2,1,Citizen 3107,0,Car ,Female,42,HS ,110591,645,2,2,Citizen 3108,0,Car ,Male ,40,HS ,93628,611,1,2,Citizen 3110,0,Home,Male ,36,HS ,66004,513,1,2,Citizen 3111,0,Car ,Female,43,College ,98483,604,0,2,Citizen 3112,1,Car ,Female,50,Graduate,108890,720,3,2,Citizen 3113,0,Car ,Male ,38,HS ,90704,494,0,2,Citizen 3114,0,Car ,Male ,54,HS ,97345,556,1,2,Citizen 3115,0,Home,Male ,37,HS ,93687,534,7,2,Citizen 3116,1,Car ,Male ,37,HS ,126629,643,1,2,Citizen 3117,0,Car ,Female,45,College ,104170,563,1,2,Citizen 3118,1,Car ,Female,42,HS ,112700,649,2,2,Citizen 3119,0,Car ,Female,43,HS ,95612,636,0,2,Citizen 3120,0,Home,Male ,38,HS ,95508,604,3,2,Citizen 3121,0,Home,Male ,37,HS ,86564,638,6,2,Citizen 3122,0,Car ,Female,34,College ,66765,431,1,2,Citizen 3123,1,Car ,Female,38,Graduate,110166,610,1,2,Citizen 3124,1,Car ,Male ,43,College ,106866,620,0,2,Citizen 3125,0,Home,Male ,41,HS ,108911,575,10,2,Citizen 3126,0,Car ,Female,44,College ,96126,623,1,2,Citizen 3127,1,Car ,Male ,49,HS ,122119,762,2,1,Citizen 3128,0,Car ,Male ,49,HS ,95191,614,1,2,Citizen 3129,0,Home,Male ,47,HS ,77066,513,8,2,Citizen 3130,0,Car ,Female,41,College ,92875,549,0,2,Citizen 3131,0,Home,Female,33,HS ,94529,684,8,2,Citizen 3132,0,Car ,Male ,26,HS ,76374,517,2,2,Citizen 3133,0,Home,Female,45,HS ,101116,631,8,2,Citizen 3134,0,Home,Female,46,HS ,71631,608,2,2,Citizen 3135,0,Home,Male ,38,HS ,76160,586,2,2,Citizen 3136,0,Car ,Male ,46,HS ,66855,519,0,2,Citizen 3137,1,Car ,Male ,37,College ,128810,680,3,2,Citizen 3138,0,Car ,Female,50,HS ,115555,615,0,2,Citizen 3139,0,Home,Female,46,HS ,68719,622,4,2,Citizen 3140,0,Home,Male ,39,HS ,94930,631,6,2,Citizen 3141,1,Car ,Male ,41,College ,101404,633,2,1,Citizen 3142,0,Home,Female,33,HS ,46195,522,1,2,Citizen 3143,1,Car ,Female,42,HS ,94362,634,0,2,Citizen 3144,0,Home,Male ,46,HS ,86168,653,7,2,Citizen 3145,0,Home,Male ,41,College ,72746,692,12,2,Citizen 3146,0,Home,Male ,38,HS ,103870,543,4,2,Citizen 3147,0,Car ,Male ,28,HS ,76584,566,1,2,Citizen 3148,0,Car ,Male ,53,HS ,84885,693,2,1,Citizen 3149,0,Home,Male ,38,HS ,61339,585,8,2,Citizen 3150,0,Car ,Male ,46,HS ,69252,513,1,2,Citizen 3151,0,Car ,Female,47,College ,87860,579,2,2,Citizen 3152,0,Home,Male ,43,HS ,91259,594,3,2,Citizen 3153,0,Car ,Male ,51,HS ,105550,712,1,1,Citizen 3154,0,Car ,Female,52,HS ,98651,686,0,2,Citizen 3155,0,Car ,Male ,43,HS ,89129,624,0,2,Citizen 3156,0,Home,Male ,36,HS ,83205,622,7,2,Citizen 3157,0,Home,Female,44,HS ,107184,639,7,1,Citizen 3158,0,Car ,Female,42,HS ,108331,708,0,2,Citizen 3159,0,Car ,Female,43,HS ,86639,601,1,2,Citizen 3160,0,Home,Male ,45,HS ,99500,584,11,2,Citizen 3161,0,Car ,Male ,30,HS ,70797,539,1,2,Citizen 3162,0,Car ,Male ,46,Graduate,63274,666,2,2,Citizen 3163,0,Car ,Male ,46,College ,83078,539,1,2,Citizen 3164,0,Home,Male ,44,HS ,91063,645,6,2,Citizen 3165,0,Car ,Male ,49,HS ,93318,574,3,1,Citizen 3166,0,Car ,Male ,37,HS ,93219,659,2,2,Citizen 3167,0,Car ,Female,47,HS ,77511,587,0,2,Citizen 3168,1,Home,Male ,40,HS ,95361,657,5,2,Citizen 3169,0,Home,Male ,36,HS ,82554,607,1,2,Citizen 3170,0,Car ,Male ,55,HS ,83643,740,0,1,Citizen 3171,0,Car ,Male ,39,HS ,86686,698,0,2,Citizen 3172,0,Car ,Male ,46,HS ,92108,659,2,2,Citizen 3173,0,Car ,Male ,32,HS ,93738,650,0,2,Citizen 3174,0,Home,Male ,39,HS ,85753,674,0,2,Citizen 3175,0,Car ,Female,39,College ,111368,643,2,1,Non-citizen 3176,0,Home,Male ,46,College ,101307,660,9,2,Citizen 3177,0,Car ,Female,47,HS ,80668,669,1,2,Citizen 3178,0,Car ,Male ,48,HS ,112090,593,2,2,Citizen 3179,0,Home,Male ,36,HS ,78279,562,3,2,Citizen 3180,0,Car ,Female,50,HS ,107233,608,1,2,Citizen 3181,1,Home,Female,49,College ,102462,635,10,2,Citizen 3182,1,Car ,Female,49,Graduate,96922,708,2,1,Citizen 3183,0,Car ,Female,40,HS ,98253,668,0,2,Citizen 3184,1,Car ,Female,32,HS ,85800,599,0,2,Citizen 3185,0,Home,Male ,44,HS ,94599,585,6,2,Citizen 3186,0,Car ,Female,53,HS ,93125,550,0,2,Citizen 3187,0,Car ,Male ,48,College ,112591,592,1,2,Citizen 3188,0,Car ,Male ,39,HS ,67206,567,1,2,Citizen 3189,0,Car ,Male ,40,HS ,88546,599,0,2,Citizen 3190,0,Home,Male ,35,HS ,62990,617,7,2,Citizen 3191,0,Home,Male ,51,College ,102101,559,10,2,Citizen 3192,0,Car ,Male ,49,College ,96654,723,3,2,Citizen 3193,0,Car ,Female,55,College ,119282,625,1,1,Non-citizen 3194,0,Home,Female,47,HS ,70673,574,4,2,Citizen 3195,0,Car ,Male ,41,HS ,103161,556,0,2,Non-citizen 3196,0,Home,Male ,41,HS ,88528,603,8,2,Citizen 3197,0,Car ,Male ,36,College ,117556,600,1,2,Citizen 3198,0,Car ,Female,45,HS ,74650,616,0,2,Citizen 3199,0,Home,Male ,42,HS ,91799,675,3,2,Citizen 3200,0,Home,Male ,43,HS ,110969,528,7,2,Citizen 3201,0,Car ,Female,44,HS ,103086,713,0,1,Citizen 3202,0,Car ,Male ,39,HS ,100464,655,1,2,Citizen 3203,0,Car ,Male ,41,HS ,79165,532,0,2,Citizen 3204,0,Home,Male ,40,HS ,95060,571,1,2,Citizen 3205,0,Car ,Male ,48,HS ,82777,582,0,2,Citizen 3206,0,Car ,Female,41,College ,81044,661,1,2,Citizen 3207,0,Home,Female,56,Graduate,119472,621,8,2,Citizen 3208,0,Car ,Male ,44,College ,98452,556,2,2,Citizen 3209,0,Home,Male ,43,HS ,117184,606,4,2,Citizen 3210,0,Car ,Female,44,HS ,82459,607,1,2,Citizen 3211,0,Car ,Female,40,HS ,100496,656,2,2,Citizen 3212,0,Home,Male ,46,HS ,77816,685,4,2,Citizen 3213,0,Home,Male ,38,HS ,101754,681,4,2,Citizen 3214,0,Car ,Male ,41,HS ,69916,629,2,2,Citizen 3215,0,Car ,Male ,48,HS ,113139,688,1,2,Citizen 3216,0,Car ,Male ,47,HS ,110239,618,2,2,Citizen 3217,1,Car ,Female,44,HS ,98853,654,0,2,Non-citizen 3218,1,Car ,Female,39,College ,139527,551,0,2,Citizen 3219,0,Car ,Female,51,HS ,101714,677,0,2,Citizen 3220,0,Car ,Female,46,HS ,97722,620,1,2,Citizen 3221,0,Home,Male ,39,HS ,135371,651,7,2,Citizen 3222,0,Home,Female,32,College ,105446,629,6,2,Citizen 3223,0,Home,Male ,43,HS ,94946,631,9,2,Citizen 3224,0,Home,Male ,30,HS ,95809,586,5,2,Citizen 3225,0,Home,Female,40,HS ,112566,680,2,2,Citizen 3226,0,Home,Male ,42,HS ,99712,601,9,2,Citizen 3227,1,Car ,Female,62,Graduate,107305,607,1,2,Citizen 3228,0,Home,Male ,32,HS ,96354,486,3,2,Citizen 3229,0,Car ,Female,48,Graduate,99720,653,2,2,Citizen 3230,0,Car ,Female,29,HS ,76406,585,0,2,Citizen 3231,0,Car ,Female,50,HS ,87718,611,2,1,Citizen 3232,0,Car ,Female,41,HS ,99204,628,1,2,Citizen 3233,0,Home,Male ,39,HS ,86985,533,5,2,Citizen 3234,0,Home,Female,42,College ,113210,603,4,2,Citizen 3235,0,Car ,Male ,40,HS ,81146,526,2,1,Citizen 3236,0,Home,Female,36,HS ,65465,549,3,2,Citizen 3237,0,Car ,Male ,36,HS ,98659,543,1,2,Citizen 3238,0,Home,Male ,45,HS ,95962,597,10,2,Citizen 3239,0,Home,Female,43,HS ,81348,635,6,2,Citizen 3240,0,Car ,Male ,42,College ,88477,676,2,2,Citizen 3241,0,Car ,Male ,46,HS ,125881,631,0,2,Citizen 3242,0,Home,Male ,39,HS ,74349,571,2,2,Citizen 3243,1,Car ,Female,43,HS ,94403,619,0,2,Citizen 3244,0,Car ,Female,44,College ,94622,650,0,2,Citizen 3245,0,Home,Female,44,College ,98878,525,7,2,Citizen 3246,0,Car ,Male ,49,College ,87719,572,1,2,Citizen 3247,0,Car ,Female,34,HS ,108520,643,0,2,Citizen 3248,0,Home,Male ,45,HS ,98341,673,9,2,Citizen 3249,0,Car ,Male ,36,College ,92253,523,3,2,Citizen 3250,0,Car ,Female,47,HS ,88539,552,0,2,Citizen 3251,0,Car ,Female,47,HS ,118047,552,0,2,Citizen 3252,0,Car ,Male ,44,College ,84011,574,2,2,Citizen 3253,0,Car ,Male ,44,HS ,92225,615,1,2,Citizen 3254,0,Car ,Female,38,HS ,74768,581,0,2,Citizen 3255,0,Car ,Female,42,College ,118106,557,0,2,Citizen 3256,1,Home,Female,31,College ,110524,691,9,2,Citizen 3257,0,Home,Male ,44,HS ,90887,599,11,2,Citizen 3258,0,Car ,Male ,29,College ,92616,639,2,2,Citizen 3259,0,Home,Female,30,HS ,93635,540,6,2,Citizen 3260,0,Car ,Female,43,HS ,111207,647,0,2,Citizen 3261,0,Car ,Female,31,HS ,104416,536,0,2,Citizen 3262,0,Car ,Male ,33,HS ,88905,683,0,2,Citizen 3263,0,Car ,Male ,40,College ,90591,674,1,2,Citizen 3264,0,Car ,Male ,41,HS ,69769,538,1,2,Citizen 3265,0,Home,Male ,38,HS ,46246,638,3,2,Citizen 3266,0,Car ,Female,37,College ,83115,554,2,2,Citizen 3267,0,Car ,Male ,36,HS ,95798,647,1,2,Citizen 3268,0,Home,Male ,52,HS ,112931,652,12,2,Citizen 3269,0,Car ,Male ,45,HS ,95613,503,2,2,Citizen 3270,0,Car ,Female,51,College ,93078,686,1,2,Citizen 3271,0,Home,Female,35,Graduate,82975,612,8,2,Citizen 3272,0,Car ,Male ,51,HS ,93648,601,0,2,Citizen 3273,0,Car ,Female,45,HS ,120763,692,1,2,Citizen 3274,0,Car ,Male ,51,College ,95927,558,3,2,Citizen 3275,0,Car ,Male ,44,HS ,83445,648,0,2,Citizen 3276,0,Car ,Male ,43,HS ,72141,605,0,2,Citizen 3277,0,Home,Male ,39,HS ,94057,585,8,2,Citizen 3278,1,Home,Male ,37,HS ,153485,691,6,2,Citizen 3279,0,Home,Female,42,HS ,99280,570,6,2,Citizen 3280,0,Car ,Female,50,Graduate,94036,630,3,1,Non-citizen 3281,0,Car ,Female,41,College ,103351,563,0,2,Citizen 3282,0,Home,Female,41,HS ,116560,569,5,2,Citizen 3283,0,Car ,Female,44,College ,112529,578,3,2,Citizen 3284,0,Car ,Male ,44,HS ,73646,553,0,2,Citizen 3285,1,Car ,Female,48,HS ,103369,650,0,1,Non-citizen 3286,0,Car ,Male ,45,HS ,80528,653,2,1,Citizen 3287,0,Car ,Male ,30,Graduate,95262,503,0,2,Citizen 3288,0,Home,Male ,48,HS ,61304,535,4,2,Citizen 3289,0,Car ,Male ,35,HS ,88000,553,1,2,Citizen 3290,0,Car ,Female,48,Graduate,105270,631,2,1,Citizen 3291,0,Car ,Female,47,HS ,65011,662,0,2,Citizen 3292,0,Home,Female,41,College ,109859,717,8,2,Citizen 3293,0,Home,Male ,44,HS ,94496,476,6,2,Citizen 3294,0,Car ,Male ,38,College ,96910,580,1,2,Citizen 3295,0,Home,Female,45,College ,84561,608,8,2,Citizen 3296,0,Car ,Male ,46,College ,102012,522,2,2,Citizen 3297,1,Car ,Female,43,HS ,119550,611,0,2,Citizen 3298,0,Car ,Male ,36,HS ,53829,440,1,2,Citizen 3299,0,Home,Female,41,HS ,109577,658,3,2,Citizen 3300,0,Car ,Female,49,College ,107024,628,3,2,Citizen 3301,0,Home,Female,41,College ,104782,627,6,2,Citizen 3302,0,Car ,Male ,38,HS ,56664,440,0,2,Citizen 3303,0,Home,Male ,47,HS ,86058,566,6,2,Citizen 3304,0,Home,Male ,39,HS ,102830,528,5,2,Citizen 3305,0,Home,Male ,41,HS ,100474,614,8,2,Citizen 3306,0,Home,Male ,27,HS ,86671,621,3,2,Citizen 3307,0,Car ,Male ,51,College ,107359,549,2,2,Citizen 3308,0,Car ,Female,46,HS ,76293,672,1,2,Citizen 3309,0,Home,Female,34,College ,117536,572,6,2,Citizen 3310,0,Home,Male ,35,HS ,101662,571,4,2,Citizen 3311,0,Car ,Female,56,Graduate,144703,605,2,2,Citizen 3312,0,Car ,Female,40,HS ,86670,612,0,2,Citizen 3313,0,Home,Male ,47,HS ,84198,645,4,2,Citizen 3314,0,Car ,Male ,50,HS ,112980,590,1,2,Citizen 3315,1,Car ,Female,52,College ,107519,713,3,2,Citizen 3316,0,Home,Male ,43,HS ,102542,596,2,2,Citizen 3317,0,Car ,Female,42,HS ,104381,591,0,2,Citizen 3318,0,Car ,Female,56,College ,113135,716,3,2,Non-citizen 3319,0,Car ,Female,40,HS ,95495,574,0,2,Citizen 3320,0,Car ,Male ,44,College ,101618,650,3,2,Citizen 3321,0,Home,Male ,32,HS ,86521,539,1,2,Citizen 3322,0,Car ,Male ,36,College ,114529,662,0,2,Citizen 3323,0,Home,Male ,35,College ,87281,583,3,2,Citizen 3324,0,Home,Female,35,HS ,81510,583,7,2,Citizen 3325,1,Car ,Female,54,Graduate,131918,723,2,2,Non-citizen 3326,0,Car ,Male ,43,College ,94918,577,2,2,Citizen 3327,0,Home,Female,40,HS ,127092,559,4,2,Citizen 3328,0,Car ,Male ,54,HS ,108611,530,2,2,Citizen 3329,0,Car ,Male ,40,College ,96462,640,1,2,Citizen 3330,0,Home,Male ,53,HS ,119465,710,6,2,Citizen 3331,0,Home,Male ,43,College ,93290,531,8,2,Citizen 3332,0,Car ,Male ,46,College ,126705,683,2,2,Citizen 3333,0,Car ,Male ,40,HS ,82623,600,0,2,Citizen 3334,0,Home,Female,42,HS ,88446,531,1,2,Citizen 3335,0,Car ,Female,52,HS ,108182,707,0,2,Citizen 3336,0,Car ,Female,52,Graduate,115924,674,3,2,Citizen 3337,0,Car ,Male ,31,HS ,79123,584,1,2,Citizen 3338,0,Car ,Male ,43,HS ,76274,482,0,2,Citizen 3339,0,Car ,Male ,34,HS ,59790,578,0,2,Citizen 3340,0,Home,Male ,52,HS ,136464,559,10,2,Citizen 3341,1,Car ,Female,45,Graduate,75042,574,2,2,Non-citizen 3342,0,Car ,Male ,58,College ,110320,563,2,2,Citizen 3343,0,Car ,Male ,43,HS ,87706,541,0,2,Citizen 3344,0,Car ,Male ,53,HS ,91028,604,2,2,Citizen 3345,0,Home,Male ,50,HS ,101161,677,5,2,Citizen 3346,0,Car ,Male ,45,HS ,87196,702,0,2,Citizen 3347,0,Car ,Male ,53,College ,121246,583,2,2,Citizen 3348,0,Car ,Male ,39,College ,74547,534,3,2,Citizen 3349,0,Car ,Male ,44,HS ,66957,478,0,2,Citizen 3350,0,Car ,Male ,47,College ,92756,660,0,2,Citizen 3351,0,Home,Female,36,HS ,99553,549,4,2,Citizen 3352,0,Car ,Female,38,College ,81728,640,0,2,Citizen 3353,0,Car ,Female,33,HS ,76935,600,0,2,Citizen 3354,0,Car ,Female,50,College ,103079,611,3,2,Citizen 3355,0,Car ,Male ,42,HS ,94311,590,1,2,Citizen 3356,0,Home,Female,50,HS ,104906,626,8,1,Citizen 3357,0,Home,Female,40,HS ,122257,608,8,1,Citizen 3358,0,Car ,Female,48,HS ,121214,590,2,2,Citizen 3359,0,Car ,Female,48,HS ,108397,617,3,2,Citizen 3360,0,Home,Male ,46,HS ,83039,572,7,2,Citizen 3361,0,Car ,Female,43,HS ,96342,647,1,2,Citizen 3362,0,Car ,Male ,52,HS ,71495,731,1,2,Citizen 3363,0,Home,Female,30,HS ,101006,672,1,2,Citizen 3364,0,Car ,Male ,37,HS ,128348,588,2,2,Citizen 3365,0,Car ,Female,40,HS ,104985,578,0,2,Citizen 3366,0,Car ,Male ,42,HS ,98272,611,2,1,Citizen 3367,0,Car ,Male ,41,College ,95433,690,1,2,Citizen 3368,0,Car ,Female,44,College ,120063,589,3,1,Citizen 3369,0,Car ,Male ,43,College ,93145,543,2,2,Citizen 3370,0,Car ,Male ,47,College ,77161,673,2,2,Citizen 3371,0,Home,Male ,43,HS ,102018,541,6,2,Citizen 3372,0,Car ,Male ,45,HS ,118975,631,2,2,Citizen 3373,0,Home,Female,41,HS ,97026,634,1,1,Citizen 3374,0,Car ,Male ,41,HS ,91887,624,2,2,Citizen 3375,0,Home,Female,43,College ,147138,613,7,2,Citizen 3376,0,Home,Male ,33,College ,84820,641,10,2,Citizen 3377,0,Home,Female,43,HS ,70114,589,5,2,Citizen 3378,0,Car ,Male ,52,College ,103898,708,1,2,Citizen 3379,0,Car ,Male ,44,HS ,104265,556,3,2,Citizen 3380,0,Home,Female,38,HS ,81330,492,6,2,Citizen 3381,0,Car ,Female,42,College ,92151,560,0,2,Citizen 3382,0,Car ,Male ,43,HS ,85421,612,0,2,Citizen 3383,0,Car ,Female,45,College ,78728,601,2,2,Citizen 3384,0,Car ,Male ,44,College ,87225,538,0,2,Citizen 3385,0,Home,Male ,47,College ,120864,582,10,2,Citizen 3386,0,Car ,Male ,51,HS ,91656,600,1,2,Citizen 3387,0,Home,Female,35,HS ,69686,560,0,2,Citizen 3388,0,Home,Male ,38,HS ,98102,593,8,2,Citizen 3389,0,Home,Male ,48,HS ,90982,567,6,2,Citizen 3390,0,Home,Female,41,HS ,104068,539,0,2,Citizen 3391,0,Car ,Male ,41,HS ,111564,525,1,2,Citizen 3392,0,Car ,Male ,28,HS ,73260,504,1,2,Citizen 3393,1,Car ,Female,49,HS ,115630,742,0,1,Non-citizen 3394,0,Car ,Male ,39,HS ,57704,663,1,2,Citizen 3395,0,Car ,Female,41,College ,105042,619,0,2,Citizen 3396,0,Home,Male ,36,HS ,95271,667,8,2,Citizen 3397,0,Car ,Female,43,College ,94000,620,1,2,Citizen 3398,0,Car ,Female,33,College ,93076,656,0,2,Citizen 3399,0,Car ,Female,53,Graduate,76856,603,3,2,Citizen 3400,0,Home,Male ,30,HS ,46387,629,5,2,Citizen 3401,0,Car ,Female,38,HS ,79166,538,0,2,Citizen 3402,0,Car ,Female,37,HS ,76355,636,0,2,Citizen 3403,0,Car ,Male ,43,College ,95022,591,3,2,Citizen 3404,0,Home,Female,39,HS ,142057,601,3,2,Citizen 3405,0,Car ,Female,41,College ,105750,604,2,2,Citizen 3406,0,Home,Male ,44,HS ,81332,588,3,2,Citizen 3407,0,Home,Male ,36,HS ,111169,644,8,2,Citizen 3408,0,Home,Male ,40,HS ,91407,625,6,2,Citizen 3409,0,Home,Female,44,HS ,71286,540,4,2,Citizen 3410,0,Car ,Female,44,HS ,108686,741,1,1,Citizen 3411,0,Car ,Male ,41,HS ,112274,593,2,1,Citizen 3412,0,Home,Female,46,HS ,92515,694,3,2,Citizen 3413,0,Car ,Male ,33,HS ,97059,543,0,2,Citizen 3414,0,Car ,Female,49,HS ,83073,638,1,2,Citizen 3415,0,Car ,Female,45,HS ,90041,602,0,2,Citizen 3416,0,Home,Male ,47,Graduate,81733,528,4,2,Citizen 3417,0,Car ,Female,42,HS ,76933,596,2,2,Citizen 3418,0,Car ,Female,35,HS ,96130,608,1,2,Citizen 3419,0,Home,Male ,31,HS ,90130,530,2,2,Citizen 3420,0,Home,Female,40,College ,123478,586,9,2,Citizen 3421,0,Car ,Male ,38,College ,78720,603,0,2,Citizen 3422,0,Car ,Male ,39,HS ,78859,608,0,2,Citizen 3423,1,Car ,Male ,40,HS ,108992,667,0,2,Citizen 3424,0,Car ,Male ,43,HS ,98138,628,3,2,Citizen 3425,0,Home,Female,39,HS ,114144,605,0,2,Citizen 3426,0,Home,Female,44,HS ,117239,600,5,2,Citizen 3427,0,Home,Male ,41,HS ,101617,660,8,2,Citizen 3428,0,Home,Female,44,HS ,107880,698,8,1,Citizen 3429,0,Car ,Female,48,College ,109322,604,3,2,Non-citizen 3430,0,Car ,Female,42,HS ,98253,589,1,2,Citizen 3431,0,Home,Male ,45,HS ,119034,581,9,2,Citizen 3432,0,Car ,Female,50,HS ,117946,619,0,2,Citizen 3433,0,Home,Female,37,HS ,97368,651,2,2,Citizen 3434,0,Car ,Male ,36,College ,87931,548,1,2,Citizen 3435,0,Car ,Male ,42,HS ,85624,634,2,2,Citizen 3436,0,Home,Female,41,HS ,109173,614,7,1,Citizen 3437,0,Car ,Female,42,College ,122580,760,0,2,Citizen 3438,0,Car ,Male ,34,HS ,79291,573,0,2,Citizen 3439,0,Home,Male ,42,HS ,100364,723,9,2,Citizen 3440,0,Car ,Female,39,HS ,92078,681,0,1,Citizen 3441,0,Car ,Female,44,HS ,129523,590,2,2,Citizen 3442,0,Car ,Female,52,College ,125693,729,1,2,Non-citizen 3443,1,Home,Female,44,College ,92999,529,6,2,Citizen 3444,0,Home,Male ,49,HS ,97058,780,6,1,Citizen 3445,0,Car ,Male ,50,HS ,103956,580,3,1,Citizen 3446,0,Car ,Male ,54,HS ,77691,661,0,2,Citizen 3447,0,Home,Female,32,HS ,86900,630,4,2,Citizen 3448,1,Car ,Male ,44,HS ,100658,589,2,1,Citizen 3449,0,Car ,Female,42,HS ,115386,695,1,2,Citizen 3450,0,Car ,Male ,37,HS ,96389,511,1,2,Citizen 3451,0,Car ,Female,44,HS ,91009,584,0,2,Citizen 3452,0,Car ,Male ,50,HS ,96828,612,2,2,Citizen 3453,0,Car ,Male ,31,HS ,93962,589,0,2,Citizen 3454,0,Car ,Female,46,HS ,106829,604,1,1,Citizen 3455,0,Home,Male ,35,HS ,78846,622,6,2,Citizen 3456,0,Car ,Female,43,HS ,113003,592,3,2,Citizen 3457,0,Car ,Male ,49,College ,111524,627,1,1,Citizen 3458,0,Home,Male ,38,HS ,102779,545,5,2,Citizen 3459,0,Car ,Female,43,HS ,103717,688,3,1,Citizen 3460,0,Home,Female,53,College ,103686,610,13,2,Citizen 3461,0,Home,Female,43,HS ,73471,627,1,2,Citizen 3462,0,Car ,Female,43,Graduate,103180,694,1,2,Non-citizen 3463,0,Car ,Male ,44,HS ,99344,712,0,1,Citizen 3464,0,Home,Male ,43,HS ,97050,519,3,2,Citizen 3465,0,Car ,Male ,42,HS ,94650,600,0,2,Citizen 3466,1,Car ,Male ,48,HS ,103448,535,0,2,Citizen 3467,1,Car ,Female,38,HS ,112239,665,0,2,Citizen 3468,0,Car ,Male ,33,HS ,79224,627,1,2,Citizen 3469,0,Car ,Male ,32,HS ,47656,491,0,2,Citizen 3470,0,Car ,Male ,31,HS ,89989,585,0,2,Citizen 3471,0,Car ,Male ,35,College ,65773,501,1,2,Citizen 3472,0,Home,Male ,34,HS ,78680,637,2,2,Citizen 3473,0,Car ,Male ,45,HS ,103583,640,0,1,Citizen 3474,1,Car ,Female,44,HS ,100315,585,0,1,Citizen 3475,0,Car ,Male ,26,HS ,85393,586,1,2,Citizen 3476,0,Car ,Female,46,College ,122995,676,2,2,Citizen 3477,0,Car ,Male ,40,HS ,53791,566,0,2,Citizen 3478,0,Home,Female,43,HS ,94461,561,7,2,Citizen 3479,0,Home,Female,33,HS ,119700,583,6,2,Citizen 3480,0,Car ,Male ,45,College ,101132,534,3,2,Citizen 3481,0,Car ,Male ,46,HS ,75843,585,0,2,Citizen 3482,0,Home,Male ,38,HS ,104880,617,1,2,Citizen 3483,0,Car ,Male ,46,HS ,94285,629,2,2,Citizen 3484,0,Car ,Female,37,College ,104766,683,0,2,Citizen 3485,0,Car ,Male ,32,HS ,81463,594,3,2,Citizen 3486,0,Home,Male ,37,HS ,95598,574,8,2,Citizen 3487,0,Car ,Male ,35,HS ,114043,686,0,2,Citizen 3488,0,Car ,Male ,44,HS ,123902,528,0,2,Citizen 3489,0,Home,Male ,34,Graduate,93302,572,10,2,Citizen 3490,0,Car ,Female,47,HS ,123514,648,0,2,Citizen 3491,0,Car ,Male ,41,HS ,101186,579,1,2,Citizen 3492,0,Car ,Male ,43,Graduate,83214,564,3,2,Citizen 3493,0,Car ,Female,51,HS ,91676,690,2,2,Citizen 3494,0,Home,Male ,46,HS ,102146,587,4,2,Citizen 3495,0,Home,Male ,33,HS ,78730,589,0,2,Citizen 3496,0,Car ,Male ,46,HS ,84190,539,2,1,Citizen 3497,0,Car ,Female,42,HS ,97056,603,2,2,Citizen 3498,0,Home,Male ,43,HS ,110200,577,4,2,Citizen 3499,0,Car ,Female,50,College ,125891,674,2,2,Citizen 3500,0,Home,Male ,41,HS ,92081,616,6,2,Citizen 3501,0,Car ,Male ,41,HS ,95781,699,0,2,Citizen 3502,0,Home,Female,44,HS ,102151,661,7,2,Citizen 3503,0,Car ,Male ,48,HS ,81659,635,0,2,Citizen 3504,0,Home,Male ,42,HS ,90017,639,4,2,Citizen 3505,0,Car ,Male ,40,HS ,90616,566,0,2,Citizen 3506,0,Car ,Male ,41,College ,103786,631,3,2,Citizen 3507,1,Car ,Female,50,HS ,106761,596,1,2,Citizen 3508,1,Car ,Female,48,HS ,125971,703,1,2,Citizen 3509,0,Car ,Male ,43,HS ,70378,577,2,2,Citizen 3510,0,Car ,Male ,43,HS ,64185,578,1,1,Citizen 3511,0,Home,Male ,38,HS ,97946,553,8,2,Citizen 3512,0,Home,Male ,50,HS ,108498,562,6,2,Citizen 3513,0,Car ,Male ,45,HS ,93972,616,2,2,Citizen 3514,0,Home,Male ,34,HS ,79484,629,3,2,Citizen 3515,0,Car ,Male ,35,HS ,72759,601,0,2,Citizen 3516,1,Car ,Female,54,HS ,91396,702,1,2,Citizen 3517,0,Car ,Female,39,Graduate,113322,628,2,2,Non-citizen 3518,0,Car ,Female,50,College ,110400,691,2,2,Citizen 3519,0,Car ,Female,51,HS ,112211,557,3,2,Citizen 3520,0,Home,Male ,42,HS ,96670,531,6,2,Citizen 3521,0,Home,Male ,36,College ,88404,563,6,2,Citizen 3522,0,Car ,Female,49,HS ,88437,690,1,1,Citizen 3523,0,Car ,Male ,48,College ,94181,618,0,2,Citizen 3524,0,Car ,Female,51,College ,89959,653,2,2,Citizen 3525,0,Car ,Male ,38,College ,103618,550,3,2,Citizen 3526,1,Car ,Male ,46,HS ,135000,639,0,2,Citizen 3527,0,Car ,Male ,57,HS ,118578,671,2,2,Citizen 3528,0,Car ,Male ,50,HS ,103288,653,0,2,Citizen 3529,0,Home,Female,43,HS ,77492,642,6,2,Citizen 3530,0,Car ,Male ,47,HS ,103521,595,0,2,Citizen 3531,0,Car ,Female,38,HS ,89551,558,0,2,Citizen 3532,0,Home,Female,35,College ,98828,602,6,2,Citizen 3533,1,Car ,Female,38,College ,134235,700,2,2,Citizen 3534,0,Car ,Male ,31,HS ,90582,519,2,2,Citizen 3535,0,Car ,Male ,47,College ,117757,613,2,2,Citizen 3536,0,Car ,Female,46,HS ,106178,656,1,1,Citizen 3537,0,Car ,Female,40,HS ,99188,683,0,2,Citizen 3538,0,Car ,Female,41,College ,111634,629,0,2,Citizen 3539,1,Car ,Male ,34,College ,77951,559,0,2,Citizen 3540,0,Home,Female,44,HS ,104029,706,6,2,Citizen 3541,0,Car ,Male ,39,College ,79805,599,2,2,Citizen 3542,0,Home,Male ,54,HS ,101433,593,5,2,Citizen 3543,0,Car ,Female,37,HS ,87715,649,0,2,Citizen 3544,0,Car ,Male ,40,HS ,99528,609,2,2,Citizen 3545,0,Car ,Male ,39,College ,126583,590,2,2,Citizen 3546,0,Car ,Male ,36,HS ,74494,532,0,2,Citizen 3547,0,Car ,Male ,39,HS ,93218,595,0,2,Citizen 3548,0,Home,Male ,43,College ,142906,644,10,2,Citizen 3549,0,Home,Male ,45,HS ,82279,536,7,2,Citizen 3550,0,Car ,Male ,42,Graduate,110733,662,2,1,Citizen 3551,0,Home,Male ,30,HS ,55569,463,3,2,Citizen 3552,0,Car ,Female,42,College ,97375,694,0,2,Citizen 3553,0,Car ,Male ,33,College ,102198,572,0,2,Citizen 3554,0,Car ,Male ,43,College ,97538,679,2,2,Citizen 3555,0,Car ,Male ,42,HS ,99751,562,0,2,Citizen 3556,0,Car ,Female,48,HS ,88963,731,3,2,Citizen 3557,0,Home,Male ,44,HS ,106682,666,5,2,Citizen 3558,0,Car ,Male ,44,HS ,94434,604,1,2,Citizen 3559,1,Car ,Female,47,College ,139708,647,2,2,Citizen 3560,0,Home,Male ,42,HS ,82072,579,4,2,Citizen 3561,1,Home,Male ,52,College ,122986,775,13,2,Citizen 3562,0,Car ,Male ,39,HS ,69317,597,0,2,Citizen 3563,0,Home,Male ,34,HS ,103254,505,10,2,Citizen 3564,0,Car ,Female,37,HS ,92770,647,2,2,Citizen 3565,0,Car ,Male ,42,HS ,108044,632,0,2,Citizen 3566,0,Car ,Male ,39,HS ,106751,582,1,2,Citizen 3567,0,Car ,Male ,42,HS ,79500,638,1,2,Citizen 3568,0,Home,Male ,43,HS ,92359,583,7,2,Citizen 3569,0,Home,Male ,44,College ,62250,500,5,2,Citizen 3570,0,Car ,Female,40,Graduate,106862,651,3,1,Non-citizen 3571,0,Car ,Female,49,HS ,100789,547,1,2,Citizen 3572,0,Car ,Female,46,HS ,111854,596,2,2,Citizen 3573,0,Car ,Male ,44,HS ,88313,656,0,2,Citizen 3574,0,Car ,Female,45,College ,124601,577,3,2,Citizen 3575,0,Car ,Male ,35,HS ,85908,542,0,2,Citizen 3576,1,Home,Female,34,HS ,87618,773,8,2,Citizen 3577,0,Car ,Male ,30,HS ,104587,671,0,2,Citizen 3578,0,Car ,Male ,45,College ,101071,522,3,2,Citizen 3579,0,Home,Male ,44,HS ,123210,599,11,2,Citizen 3580,0,Home,Male ,44,HS ,99320,623,4,2,Citizen 3581,0,Car ,Male ,39,HS ,93183,563,1,2,Citizen 3582,0,Car ,Male ,41,HS ,96830,619,0,2,Citizen 3583,0,Car ,Male ,41,HS ,71067,583,2,2,Citizen 3584,0,Car ,Male ,61,College ,112149,717,2,2,Non-citizen 3585,0,Car ,Female,47,College ,86800,628,0,2,Non-citizen 3586,0,Home,Male ,41,HS ,110335,561,4,2,Citizen 3587,0,Home,Male ,40,HS ,100152,526,2,2,Citizen 3588,0,Car ,Male ,40,HS ,69411,563,0,2,Citizen 3589,0,Home,Male ,34,HS ,78078,550,3,2,Citizen 3590,0,Home,Male ,38,HS ,136050,585,8,2,Citizen 3591,0,Home,Female,48,HS ,122548,646,4,2,Citizen 3592,0,Car ,Female,49,College ,102784,729,2,2,Citizen 3593,0,Home,Male ,41,HS ,73374,635,2,2,Citizen 3594,0,Home,Male ,31,HS ,84459,683,10,2,Citizen 3595,0,Car ,Male ,49,HS ,92143,592,3,2,Citizen 3596,0,Car ,Male ,42,College ,83494,567,2,2,Citizen 3597,0,Home,Male ,50,HS ,98009,725,10,2,Citizen 3598,0,Car ,Male ,51,HS ,83824,498,1,2,Citizen 3599,0,Home,Male ,33,HS ,102611,529,0,2,Citizen 3600,0,Home,Male ,42,HS ,111534,669,11,1,Citizen 3601,0,Car ,Female,36,College ,144433,630,0,2,Citizen 3602,0,Car ,Male ,52,HS ,101841,564,0,2,Citizen 3603,0,Home,Male ,45,HS ,75879,547,12,2,Citizen 3604,0,Car ,Female,42,HS ,94866,549,0,2,Citizen 3605,0,Car ,Male ,38,College ,102090,634,2,2,Citizen 3606,0,Car ,Male ,45,HS ,99600,639,2,2,Citizen 3607,0,Car ,Male ,37,HS ,62616,554,1,2,Citizen 3608,0,Car ,Female,42,College ,96726,656,2,2,Citizen 3609,0,Car ,Male ,34,Graduate,82234,603,0,2,Citizen 3610,0,Home,Female,39,College ,122489,540,8,2,Citizen 3611,0,Car ,Female,47,HS ,100300,674,0,2,Non-citizen 3612,0,Home,Female,38,HS ,134099,664,6,2,Citizen 3613,0,Car ,Female,44,College ,92824,665,2,2,Citizen 3614,0,Car ,Female,50,College ,101084,625,3,2,Citizen 3615,0,Car ,Female,31,HS ,96351,605,0,2,Citizen 3616,0,Home,Male ,39,College ,103096,642,10,2,Citizen 3617,0,Car ,Female,50,HS ,97864,531,0,1,Citizen 3618,0,Home,Male ,45,HS ,101199,592,11,2,Citizen 3619,0,Home,Male ,47,Graduate,113459,618,12,2,Citizen 3620,0,Home,Female,38,College ,85501,584,6,2,Citizen 3621,0,Home,Male ,38,HS ,102410,575,1,2,Citizen 3622,0,Car ,Female,39,HS ,87841,633,1,2,Citizen 3623,0,Car ,Male ,53,Graduate,104102,674,2,2,Citizen 3624,0,Home,Female,42,HS ,95283,552,5,2,Citizen 3625,0,Car ,Male ,44,HS ,124031,538,3,2,Citizen 3626,0,Home,Male ,43,HS ,100836,454,7,2,Citizen 3627,0,Home,Male ,34,HS ,78955,600,6,2,Citizen 3628,0,Car ,Female,57,HS ,115441,734,2,2,Citizen 3629,0,Home,Male ,33,HS ,110890,586,7,2,Citizen 3630,0,Car ,Male ,41,HS ,95762,647,2,1,Citizen 3631,0,Car ,Male ,39,HS ,100601,639,1,2,Citizen 3632,0,Car ,Male ,49,HS ,82874,624,0,2,Citizen 3633,0,Car ,Male ,44,HS ,84976,509,2,2,Citizen 3634,0,Car ,Male ,39,HS ,82879,529,1,2,Citizen 3635,0,Home,Male ,41,HS ,78023,545,4,2,Citizen 3636,0,Car ,Female,53,HS ,124697,618,1,1,Citizen 3637,1,Car ,Female,38,Graduate,111310,681,0,2,Non-citizen 3638,0,Car ,Male ,37,HS ,84593,524,2,2,Citizen 3639,0,Home,Female,45,HS ,94883,570,9,2,Citizen 3640,0,Car ,Male ,38,HS ,87484,614,0,2,Citizen 3641,0,Car ,Female,52,College ,74623,629,2,2,Citizen 3642,0,Car ,Female,43,HS ,103574,581,0,2,Citizen 3643,0,Home,Female,57,HS ,79427,617,3,2,Citizen 3644,0,Home,Female,29,HS ,100888,583,6,2,Citizen 3645,0,Car ,Male ,39,HS ,86468,551,2,2,Citizen 3646,0,Car ,Male ,50,HS ,91833,583,0,2,Citizen 3647,0,Car ,Female,47,College ,111797,552,2,2,Citizen 3648,0,Home,Male ,39,HS ,61522,618,1,2,Citizen 3649,0,Car ,Male ,44,HS ,91257,654,1,2,Citizen 3650,0,Home,Male ,41,HS ,89726,585,2,2,Citizen 3651,1,Car ,Male ,48,College ,126739,548,2,2,Citizen 3652,0,Car ,Male ,40,HS ,60220,561,0,2,Citizen 3653,0,Car ,Male ,39,HS ,55687,632,1,2,Citizen 3654,0,Car ,Male ,52,College ,122286,686,2,2,Citizen 3655,0,Home,Male ,34,HS ,103394,593,4,2,Citizen 3656,0,Home,Male ,51,HS ,101124,659,7,2,Citizen 3657,0,Car ,Male ,48,HS ,91212,530,2,2,Citizen 3658,0,Car ,Female,40,College ,101593,677,2,2,Citizen 3659,0,Home,Male ,49,HS ,97755,636,9,2,Citizen 3660,0,Car ,Female,38,HS ,106527,692,2,2,Citizen 3661,0,Home,Male ,37,College ,99089,672,9,2,Citizen 3662,0,Car ,Female,34,HS ,70738,585,0,2,Citizen 3663,0,Car ,Male ,52,HS ,99079,659,1,2,Citizen 3664,0,Home,Male ,37,College ,96842,569,6,2,Citizen 3665,0,Car ,Female,47,HS ,87754,628,1,2,Citizen 3666,1,Car ,Female,38,College ,126170,578,2,2,Citizen 3667,0,Home,Male ,43,HS ,89051,527,8,2,Citizen 3668,0,Home,Male ,38,HS ,89754,611,10,2,Citizen 3669,0,Car ,Male ,42,HS ,66967,580,1,2,Citizen 3670,0,Car ,Female,43,College ,99890,625,0,2,Citizen 3671,0,Home,Male ,45,HS ,98637,574,5,2,Citizen 3672,0,Car ,Female,50,College ,111434,595,3,2,Citizen 3673,0,Car ,Female,50,HS ,74315,631,0,1,Citizen 3674,0,Car ,Female,39,HS ,85413,577,3,2,Citizen 3675,1,Car ,Female,32,HS ,69210,558,1,2,Citizen 3676,0,Car ,Male ,32,HS ,40217,546,2,2,Citizen 3677,0,Home,Female,35,HS ,93961,637,9,2,Citizen 3678,0,Car ,Male ,39,HS ,88874,600,0,2,Citizen 3679,0,Car ,Female,37,HS ,96320,655,0,2,Citizen 3680,0,Car ,Male ,54,HS ,79470,606,1,2,Citizen 3681,0,Car ,Female,54,HS ,112583,671,0,2,Citizen 3682,0,Home,Male ,44,HS ,97299,700,1,2,Citizen 3683,0,Car ,Male ,45,College ,91942,608,3,1,Citizen 3684,0,Car ,Female,39,Graduate,130305,677,3,2,Citizen 3685,0,Home,Male ,49,HS ,93993,573,12,2,Citizen 3686,0,Home,Female,36,HS ,108194,627,3,2,Citizen 3687,0,Home,Male ,52,HS ,84340,747,7,2,Citizen 3688,0,Home,Male ,41,HS ,86648,535,7,2,Citizen 3689,0,Home,Male ,27,HS ,70528,480,0,2,Citizen 3690,0,Car ,Male ,47,HS ,93918,642,1,2,Citizen 3691,0,Home,Male ,39,HS ,90040,559,7,2,Citizen 3692,0,Home,Male ,47,College ,91933,637,6,2,Citizen 3693,0,Car ,Female,46,College ,105246,662,0,2,Citizen 3694,0,Home,Male ,41,HS ,97352,558,3,2,Citizen 3695,1,Car ,Male ,41,HS ,104835,681,0,2,Citizen 3696,0,Home,Male ,29,HS ,92990,602,8,2,Citizen 3697,0,Car ,Male ,40,HS ,78345,614,0,2,Citizen 3698,0,Car ,Male ,44,HS ,104403,636,2,2,Citizen 3699,0,Car ,Female,57,College ,118959,659,1,2,Citizen 3700,0,Car ,Female,55,HS ,100709,722,2,2,Citizen 3701,0,Car ,Male ,40,HS ,95811,507,0,2,Citizen 3702,0,Car ,Female,37,HS ,94846,680,2,2,Citizen 3703,0,Car ,Male ,41,HS ,126326,692,2,2,Citizen 3704,0,Car ,Female,47,College ,87749,576,1,1,Citizen 3705,0,Home,Male ,38,HS ,108516,621,5,2,Citizen 3706,0,Car ,Male ,40,Graduate,88657,559,0,2,Citizen 3707,0,Car ,Female,45,College ,116043,667,2,2,Citizen 3708,0,Home,Male ,34,HS ,82593,569,6,2,Citizen 3709,0,Home,Male ,33,HS ,87703,665,2,2,Citizen 3710,0,Car ,Female,39,HS ,83371,657,0,2,Citizen 3711,0,Car ,Male ,45,HS ,92651,567,1,2,Citizen 3712,0,Car ,Male ,39,HS ,87277,479,0,2,Citizen 3713,0,Home,Female,50,HS ,100421,576,7,2,Citizen 3714,1,Car ,Female,48,College ,108474,695,3,1,Citizen 3715,0,Car ,Male ,37,Graduate,105644,696,1,2,Citizen 3716,0,Car ,Male ,44,College ,96250,568,0,2,Citizen 3717,0,Car ,Male ,49,HS ,99253,531,0,2,Citizen 3718,0,Car ,Female,44,College ,100085,627,2,2,Citizen 3719,0,Home,Female,52,HS ,86300,698,4,2,Citizen 3720,0,Car ,Male ,43,HS ,100195,631,0,2,Citizen 3721,0,Car ,Male ,53,College ,86828,521,1,2,Citizen 3722,0,Home,Male ,41,HS ,95891,606,5,2,Citizen 3723,0,Car ,Male ,42,HS ,103042,622,1,2,Citizen 3724,0,Car ,Male ,46,College ,67278,573,0,2,Citizen 3725,0,Car ,Male ,52,Graduate,117905,685,3,2,Citizen 3726,0,Car ,Male ,45,HS ,89395,606,3,2,Citizen 3727,0,Car ,Male ,26,HS ,75512,578,2,2,Citizen 3728,1,Car ,Male ,49,Graduate,125533,699,2,2,Citizen 3729,0,Car ,Male ,56,College ,90210,642,3,1,Citizen 3730,1,Car ,Female,57,College ,114900,698,1,2,Non-citizen 3731,0,Car ,Male ,41,HS ,71194,633,1,2,Citizen 3732,1,Car ,Male ,44,HS ,98941,784,2,2,Citizen 3733,0,Car ,Male ,41,HS ,82874,670,0,2,Citizen 3734,1,Home,Male ,44,College ,104556,686,6,2,Citizen 3735,0,Car ,Female,47,HS ,95938,650,1,1,Citizen 3736,0,Home,Male ,47,College ,85215,649,5,2,Citizen 3737,0,Home,Male ,33,HS ,53948,519,5,2,Citizen 3738,0,Car ,Male ,34,HS ,62751,525,0,2,Citizen 3740,0,Home,Female,38,HS ,83048,602,7,1,Citizen 3741,0,Car ,Male ,46,HS ,82124,567,2,2,Citizen 3742,0,Home,Male ,38,HS ,87968,576,10,2,Citizen 3743,0,Car ,Male ,44,College ,118258,570,2,2,Citizen 3744,0,Car ,Female,37,HS ,85322,727,0,2,Non-citizen 3745,0,Home,Female,36,College ,105131,694,5,2,Citizen 3746,0,Car ,Female,45,HS ,96564,698,1,2,Citizen 3747,0,Car ,Female,48,College ,103086,602,1,1,Non-citizen 3748,0,Home,Female,42,College ,93558,666,7,2,Citizen 3749,0,Car ,Male ,44,HS ,91430,496,1,2,Citizen 3750,0,Car ,Male ,34,HS ,100477,575,3,2,Citizen 3751,0,Car ,Male ,41,HS ,83776,581,2,2,Citizen 3752,0,Car ,Male ,35,HS ,89571,580,2,2,Citizen 3753,0,Car ,Male ,41,HS ,86775,573,2,2,Citizen 3754,0,Car ,Female,40,HS ,126433,545,0,2,Citizen 3755,0,Car ,Female,38,HS ,133424,720,3,2,Citizen 3756,0,Car ,Male ,46,HS ,119394,605,2,2,Citizen 3757,1,Home,Female,48,College ,81945,669,8,2,Citizen 3758,0,Car ,Female,35,College ,105057,650,2,2,Citizen 3759,1,Car ,Female,38,HS ,123956,738,2,2,Citizen 3760,0,Car ,Male ,46,HS ,115785,689,1,2,Citizen 3761,0,Car ,Male ,30,HS ,63239,528,0,2,Citizen 3762,0,Home,Male ,31,HS ,72275,653,6,2,Citizen 3763,0,Car ,Male ,28,HS ,86167,514,0,2,Citizen 3764,0,Home,Female,32,Graduate,96715,584,9,2,Citizen 3765,0,Car ,Female,53,College ,101092,682,3,2,Non-citizen 3766,0,Home,Male ,37,Graduate,99074,551,5,2,Citizen 3767,0,Home,Male ,39,HS ,64177,630,4,2,Citizen 3768,0,Home,Male ,50,HS ,72215,624,4,2,Citizen 3769,0,Car ,Female,36,HS ,90778,640,0,1,Citizen 3770,0,Home,Male ,37,HS ,105785,507,9,2,Citizen 3771,0,Car ,Male ,52,HS ,124292,654,3,2,Citizen 3772,0,Car ,Male ,43,HS ,112604,600,1,2,Citizen 3773,0,Car ,Male ,42,HS ,93396,650,0,2,Citizen 3774,0,Car ,Male ,45,College ,93560,541,1,2,Citizen 3775,0,Home,Male ,40,HS ,123798,583,3,2,Citizen 3776,0,Home,Male ,44,College ,91117,632,9,2,Citizen 3777,0,Car ,Female,44,HS ,89588,695,0,1,Citizen 3778,0,Home,Male ,44,HS ,77102,600,5,2,Citizen 3779,0,Home,Female,44,HS ,118117,556,6,2,Citizen 3780,0,Car ,Male ,50,HS ,75888,591,2,1,Citizen 3781,0,Home,Female,46,College ,107226,692,6,2,Citizen 3782,0,Car ,Female,39,Graduate,86290,666,3,1,Citizen 3783,0,Home,Female,48,HS ,108216,680,3,2,Citizen 3784,0,Car ,Male ,39,HS ,118883,593,3,1,Citizen 3785,0,Car ,Female,46,HS ,124516,598,1,1,Non-citizen 3786,0,Home,Female,49,HS ,80068,566,4,2,Citizen 3787,0,Home,Male ,41,HS ,89338,526,4,2,Citizen 3789,0,Home,Male ,33,HS ,84225,659,5,2,Citizen 3790,0,Car ,Female,47,Graduate,112143,611,2,2,Citizen 3791,0,Car ,Male ,35,HS ,112917,626,1,1,Citizen 3792,0,Car ,Female,48,College ,99671,691,1,2,Citizen 3793,0,Home,Female,53,Graduate,111387,599,8,2,Citizen 3794,0,Home,Male ,44,HS ,94203,556,10,2,Citizen 3795,0,Home,Male ,48,HS ,97284,575,5,2,Citizen 3796,0,Home,Male ,45,College ,57305,545,7,2,Citizen 3797,0,Home,Female,47,HS ,117470,567,8,2,Citizen 3798,0,Car ,Male ,43,HS ,85083,595,0,2,Citizen 3799,1,Car ,Female,50,College ,124216,629,2,2,Citizen 3800,0,Home,Male ,34,HS ,81889,557,6,2,Citizen 3801,1,Car ,Male ,48,Graduate,119365,734,3,2,Citizen 3802,0,Car ,Male ,47,HS ,95760,667,0,2,Citizen 3803,0,Car ,Female,55,HS ,125615,707,2,1,Non-citizen 3804,0,Car ,Female,45,HS ,89893,595,1,2,Citizen 3805,0,Home,Female,48,HS ,99447,585,10,1,Citizen 3806,0,Car ,Female,40,College ,89705,582,0,2,Citizen 3807,0,Home,Female,44,HS ,71416,573,0,2,Citizen 3808,0,Car ,Female,37,HS ,97149,550,0,2,Citizen 3809,0,Home,Male ,37,College ,108486,715,4,2,Citizen 3810,0,Home,Male ,37,HS ,90889,511,7,2,Citizen 3811,0,Home,Male ,42,HS ,69276,557,2,2,Citizen 3812,0,Home,Female,41,HS ,113279,642,5,2,Citizen 3813,0,Home,Male ,41,HS ,125767,612,7,2,Citizen 3814,0,Car ,Female,45,College ,87470,642,1,2,Citizen 3815,0,Car ,Female,37,HS ,82576,599,1,2,Citizen 3816,0,Car ,Male ,43,HS ,95790,636,2,2,Citizen 3817,0,Car ,Male ,49,College ,106811,501,2,2,Citizen 3818,0,Home,Female,54,HS ,98113,595,5,2,Citizen 3819,0,Car ,Male ,37,HS ,99191,614,0,2,Citizen 3820,0,Home,Male ,42,HS ,105995,632,5,2,Citizen 3821,0,Home,Male ,42,HS ,76175,551,3,2,Citizen 3822,0,Car ,Male ,37,HS ,80386,482,0,2,Citizen 3823,0,Car ,Male ,48,HS ,126703,585,1,2,Citizen 3824,0,Home,Female,43,HS ,80114,621,3,2,Citizen 3825,0,Car ,Male ,39,HS ,91593,620,2,1,Citizen 3826,0,Home,Female,49,HS ,103634,600,12,2,Citizen 3827,0,Car ,Male ,39,HS ,71529,544,0,2,Citizen 3828,0,Home,Male ,46,HS ,96482,654,4,2,Citizen 3829,0,Home,Male ,33,HS ,96412,560,11,2,Citizen 3830,0,Car ,Female,49,College ,116774,673,1,2,Citizen 3831,0,Home,Male ,38,HS ,75157,575,6,2,Citizen 3832,0,Car ,Male ,40,HS ,47046,633,0,2,Citizen 3833,0,Home,Male ,45,HS ,77073,669,10,2,Citizen 3834,0,Car ,Male ,46,HS ,103632,612,2,1,Citizen 3835,0,Car ,Female,49,College ,101513,701,2,2,Citizen 3836,0,Car ,Female,46,College ,87056,637,2,2,Citizen 3837,0,Home,Male ,44,HS ,97428,610,6,2,Citizen 3838,0,Car ,Male ,48,College ,112267,554,3,2,Citizen 3839,0,Car ,Female,43,HS ,104486,651,1,2,Citizen 3840,0,Car ,Male ,41,HS ,46979,557,0,2,Citizen 3841,0,Car ,Female,36,HS ,77397,655,2,2,Citizen 3842,0,Home,Female,44,HS ,89735,558,5,2,Citizen 3843,0,Car ,Female,49,College ,117707,727,3,1,Non-citizen 3844,0,Car ,Female,39,HS ,115423,648,0,2,Citizen 3845,0,Home,Female,39,Graduate,112118,623,7,2,Citizen 3846,0,Car ,Female,50,College ,118183,623,3,2,Citizen 3847,0,Car ,Male ,48,HS ,91450,651,0,2,Citizen 3848,0,Home,Male ,48,College ,118158,674,8,2,Citizen 3849,0,Car ,Male ,42,HS ,86134,535,1,2,Citizen 3850,0,Car ,Male ,38,HS ,72217,564,1,2,Citizen 3851,0,Home,Male ,40,HS ,102174,676,7,2,Citizen 3852,0,Car ,Male ,41,HS ,96468,591,0,2,Citizen 3853,0,Car ,Female,51,HS ,89743,628,3,2,Citizen 3854,0,Home,Male ,42,College ,95105,612,10,2,Citizen 3855,0,Home,Male ,49,HS ,122796,665,8,2,Citizen 3856,0,Car ,Male ,43,HS ,111696,527,0,2,Citizen 3857,0,Car ,Female,49,HS ,84319,657,0,2,Citizen 3858,0,Home,Male ,30,HS ,88932,513,4,2,Citizen 3859,1,Car ,Female,45,College ,137277,602,3,2,Citizen 3860,0,Home,Female,47,College ,140947,814,10,1,Non-citizen 3861,0,Car ,Male ,43,HS ,127540,622,2,2,Citizen 3862,0,Car ,Female,42,HS ,99267,568,1,2,Citizen 3863,0,Car ,Male ,46,HS ,107985,634,2,2,Citizen 3864,0,Car ,Male ,37,College ,83334,538,0,2,Citizen 3865,0,Home,Male ,38,HS ,68714,476,5,2,Citizen 3866,0,Car ,Male ,38,HS ,72820,541,2,2,Citizen 3867,0,Home,Female,48,Graduate,82821,590,7,2,Citizen 3868,0,Car ,Male ,38,HS ,103578,541,1,2,Citizen 3869,0,Car ,Male ,44,College ,105486,621,3,1,Citizen 3870,1,Car ,Female,45,Graduate,120861,720,3,2,Citizen 3871,1,Car ,Female,39,HS ,105997,625,0,2,Citizen 3872,0,Car ,Female,35,College ,133405,761,3,1,Non-citizen 3873,0,Car ,Male ,48,College ,101161,646,2,2,Citizen 3874,0,Car ,Female,40,HS ,91253,523,1,2,Citizen 3875,0,Car ,Female,48,College ,92616,484,3,2,Non-citizen 3876,0,Car ,Male ,39,HS ,52426,703,2,2,Citizen 3877,0,Car ,Female,36,HS ,112905,626,1,2,Citizen 3878,0,Car ,Male ,35,HS ,112368,615,0,2,Citizen 3879,0,Car ,Male ,47,College ,95152,634,1,2,Non-citizen 3880,0,Home,Female,38,HS ,103590,639,7,2,Citizen 3881,0,Car ,Male ,37,HS ,64410,472,0,2,Citizen 3882,0,Car ,Female,51,College ,103095,614,3,2,Citizen 3883,0,Car ,Female,46,College ,106368,660,3,1,Non-citizen 3884,0,Car ,Female,41,Graduate,108673,729,1,2,Citizen 3885,0,Home,Male ,38,College ,83167,568,9,2,Citizen 3886,0,Car ,Female,45,HS ,99383,717,1,2,Citizen 3887,0,Car ,Male ,41,HS ,82104,606,2,2,Citizen 3888,0,Car ,Female,48,College ,132547,643,3,2,Citizen 3889,0,Home,Female,23,College ,106872,494,0,2,Citizen 3890,0,Car ,Female,48,College ,125187,637,1,2,Citizen 3891,0,Car ,Female,50,Graduate,65323,646,2,2,Citizen 3892,0,Car ,Male ,44,HS ,72554,712,2,2,Citizen 3893,0,Car ,Female,46,College ,94549,596,2,2,Citizen 3894,0,Home,Male ,38,HS ,106082,632,0,2,Citizen 3895,0,Car ,Male ,48,HS ,111839,596,1,2,Citizen 3896,0,Car ,Male ,39,HS ,83731,580,0,2,Citizen 3897,0,Car ,Male ,30,HS ,91294,582,0,2,Citizen 3898,0,Home,Male ,40,HS ,76465,556,2,2,Citizen 3899,0,Home,Male ,34,HS ,91433,627,2,2,Citizen 3900,1,Car ,Female,46,College ,118529,614,0,2,Citizen 3901,0,Car ,Male ,46,HS ,100821,602,1,2,Citizen 3902,0,Home,Male ,37,HS ,84846,641,3,2,Citizen 3903,0,Car ,Male ,49,Graduate,109691,638,2,1,Citizen 3904,0,Home,Female,41,HS ,100796,605,8,2,Citizen 3905,0,Car ,Male ,41,HS ,81688,549,0,2,Citizen 3906,0,Car ,Female,35,HS ,75343,535,0,2,Citizen 3907,0,Car ,Female,53,College ,116700,794,3,1,Citizen 3908,0,Car ,Male ,41,College ,81617,600,2,2,Citizen 3909,0,Home,Male ,38,HS ,74778,630,3,2,Citizen 3910,0,Car ,Female,41,HS ,80014,696,0,1,Citizen 3911,0,Car ,Female,44,College ,116936,648,3,2,Citizen 3912,0,Home,Female,42,HS ,88953,637,8,2,Citizen 3913,0,Home,Male ,47,HS ,111007,683,4,2,Citizen 3914,0,Car ,Female,45,College ,108386,669,1,1,Citizen 3915,0,Car ,Female,45,HS ,108380,644,2,2,Citizen 3916,0,Car ,Male ,30,College ,61958,569,1,2,Citizen 3917,0,Home,Male ,46,HS ,100603,647,8,2,Citizen 3918,0,Home,Female,44,HS ,81728,545,2,2,Citizen 3919,0,Car ,Male ,45,HS ,91544,571,2,2,Citizen 3920,0,Car ,Female,39,HS ,100172,659,3,2,Citizen 3921,0,Home,Female,55,HS ,88742,498,7,2,Citizen 3922,0,Car ,Male ,40,HS ,87180,622,1,1,Citizen 3923,0,Car ,Female,39,Graduate,102633,710,2,2,Citizen 3924,0,Home,Male ,31,HS ,75548,523,5,2,Citizen 3925,0,Car ,Male ,45,College ,86333,624,1,2,Citizen 3926,0,Home,Male ,37,HS ,103363,593,5,2,Citizen 3927,0,Car ,Male ,40,Graduate,74766,621,3,2,Citizen 3928,0,Car ,Male ,43,College ,76820,589,0,2,Citizen 3929,0,Car ,Female,51,College ,101468,592,2,2,Citizen 3930,0,Car ,Male ,30,HS ,43232,538,0,2,Citizen 3931,0,Car ,Male ,45,HS ,65565,602,0,2,Citizen 3932,0,Car ,Male ,40,College ,93704,608,2,2,Citizen 3933,0,Car ,Female,59,College ,130213,626,2,2,Citizen 3934,0,Car ,Female,41,HS ,112628,669,0,2,Citizen 3935,0,Car ,Male ,44,HS ,76309,493,2,2,Citizen 3936,0,Car ,Female,44,HS ,94892,674,2,2,Citizen 3937,0,Home,Female,40,HS ,78786,591,4,2,Citizen 3938,0,Car ,Male ,43,HS ,94852,628,0,2,Citizen 3939,0,Car ,Male ,43,HS ,87832,689,1,2,Citizen 3940,0,Car ,Male ,56,College ,110967,610,1,2,Citizen 3941,0,Car ,Female,51,HS ,98527,464,0,2,Citizen 3942,0,Car ,Male ,36,HS ,69548,568,2,1,Citizen 3943,0,Car ,Male ,46,HS ,100127,625,2,2,Citizen 3944,0,Car ,Male ,32,HS ,83833,550,0,2,Non-citizen 3945,0,Home,Female,42,HS ,101818,564,8,2,Citizen 3946,0,Home,Male ,35,HS ,69739,677,5,2,Citizen 3947,0,Car ,Female,45,College ,117492,662,3,2,Citizen 3948,0,Car ,Male ,49,HS ,78322,704,2,1,Citizen 3949,0,Car ,Male ,43,College ,83948,540,0,2,Citizen 3950,0,Home,Male ,35,HS ,88590,569,0,2,Citizen 3951,0,Car ,Male ,29,HS ,97160,519,0,2,Citizen 3952,0,Home,Male ,43,College ,85325,638,7,2,Citizen 3953,0,Home,Male ,41,HS ,88851,514,4,2,Citizen 3954,0,Car ,Male ,41,HS ,107743,664,0,2,Citizen 3955,0,Car ,Male ,39,HS ,70234,559,0,2,Citizen 3956,1,Car ,Female,42,HS ,103181,594,2,2,Citizen 3957,0,Home,Male ,35,College ,61717,517,3,2,Citizen 3958,0,Car ,Male ,39,HS ,103955,664,2,2,Citizen 3959,0,Home,Male ,35,HS ,105291,603,6,2,Citizen 3960,0,Car ,Male ,40,HS ,82518,610,2,2,Citizen 3961,0,Car ,Female,47,HS ,106284,576,1,2,Citizen 3962,0,Car ,Female,45,College ,97824,632,3,2,Non-citizen 3963,0,Car ,Male ,52,HS ,102608,624,1,2,Citizen 3964,1,Car ,Male ,47,HS ,124004,695,2,2,Citizen 3965,0,Car ,Male ,43,HS ,77569,646,1,2,Citizen 3966,0,Car ,Male ,44,HS ,93975,609,0,2,Citizen 3967,0,Car ,Male ,43,HS ,106168,642,2,2,Citizen 3968,0,Home,Male ,41,HS ,88837,630,6,2,Citizen 3969,0,Home,Male ,32,College ,101453,648,6,2,Citizen 3970,0,Car ,Male ,42,College ,84780,684,2,2,Non-citizen 3971,0,Car ,Female,37,College ,123480,642,0,2,Citizen 3972,0,Car ,Male ,51,HS ,126575,586,1,2,Citizen 3973,0,Home,Male ,39,College ,76202,536,5,2,Citizen 3974,0,Car ,Male ,34,HS ,91106,574,2,2,Citizen 3975,0,Car ,Male ,39,HS ,107755,625,1,2,Citizen 3976,0,Car ,Male ,45,Graduate,109439,563,3,2,Citizen 3977,0,Car ,Male ,43,HS ,96221,506,1,2,Citizen 3978,0,Home,Male ,37,College ,46846,533,8,2,Citizen 3979,0,Home,Male ,31,HS ,76992,582,6,2,Citizen 3980,0,Car ,Male ,46,HS ,106195,707,2,2,Citizen 3981,0,Car ,Female,39,HS ,108999,662,1,2,Citizen 3982,1,Car ,Female,40,College ,125431,705,2,1,Citizen 3983,0,Car ,Female,51,HS ,105912,573,0,2,Citizen 3984,0,Home,Female,46,College ,86557,625,7,2,Citizen 3985,1,Home,Female,47,College ,98839,637,9,2,Citizen 3986,1,Car ,Female,48,College ,103292,816,1,1,Non-citizen 3987,0,Home,Male ,34,HS ,78921,586,0,2,Citizen 3988,0,Car ,Male ,33,HS ,113547,539,2,2,Citizen 3989,0,Car ,Female,41,HS ,97547,556,2,2,Citizen 3990,0,Car ,Female,51,College ,109908,655,3,1,Citizen 3991,1,Car ,Female,47,College ,113317,779,2,2,Citizen 3992,0,Home,Female,44,HS ,93316,646,7,1,Citizen 3993,0,Car ,Female,51,Graduate,89943,643,2,2,Citizen 3994,0,Car ,Male ,39,Graduate,102569,604,3,2,Citizen 3995,0,Home,Female,47,HS ,78176,653,5,2,Citizen 3996,0,Car ,Male ,42,HS ,87663,594,2,2,Citizen 3997,0,Home,Male ,31,HS ,62355,515,5,2,Citizen 3998,0,Car ,Female,54,College ,105815,638,2,2,Citizen 3999,0,Car ,Female,46,HS ,121929,529,0,2,Citizen 4000,0,Car ,Male ,45,HS ,93001,657,2,2,Citizen 4001,1,Home,Female,51,HS ,112653,727,9,1,Citizen 4002,0,Home,Male ,33,HS ,80330,509,6,2,Citizen 4003,1,Car ,Female,46,HS ,123803,687,1,2,Citizen 4004,0,Car ,Female,49,HS ,111708,613,2,2,Citizen 4005,0,Car ,Male ,40,College ,86710,601,0,2,Citizen 4006,1,Car ,Male ,38,College ,93549,554,2,2,Citizen 4007,0,Home,Female,39,HS ,84923,638,6,2,Citizen 4008,0,Car ,Male ,50,HS ,115182,681,3,2,Citizen 4009,0,Car ,Male ,45,HS ,108556,622,1,2,Citizen 4010,0,Home,Male ,40,HS ,87531,676,6,2,Citizen 4011,0,Car ,Female,41,HS ,93353,515,2,2,Citizen 4012,1,Home,Female,35,College ,76240,664,5,2,Non-citizen 4013,0,Car ,Female,39,HS ,94437,611,1,2,Citizen 4014,0,Home,Male ,38,HS ,79816,635,5,1,Citizen 4015,1,Car ,Female,46,HS ,134283,748,0,2,Citizen 4016,0,Car ,Female,43,HS ,84981,621,0,2,Citizen 4017,0,Car ,Female,52,College ,109886,660,0,2,Citizen 4018,0,Car ,Male ,52,HS ,118391,660,0,2,Citizen 4019,0,Car ,Male ,33,Graduate,92917,473,3,2,Citizen 4020,0,Car ,Male ,41,HS ,108082,575,0,2,Citizen 4021,0,Car ,Male ,33,HS ,78993,510,0,2,Citizen 4022,0,Home,Male ,44,HS ,69194,617,1,2,Citizen 4023,0,Car ,Female,52,HS ,111167,576,0,2,Citizen 4024,0,Car ,Male ,48,College ,80096,618,0,2,Citizen 4025,1,Car ,Male ,53,College ,105272,734,1,2,Citizen 4026,0,Home,Male ,33,HS ,74831,632,7,2,Citizen 4027,0,Home,Male ,42,HS ,109229,656,8,2,Citizen 4028,0,Home,Male ,50,HS ,78033,613,8,2,Citizen 4029,0,Home,Male ,45,College ,96278,574,11,2,Citizen 4030,0,Car ,Male ,48,HS ,103384,536,0,2,Citizen 4031,0,Car ,Female,38,HS ,107765,681,1,2,Citizen 4032,0,Car ,Female,35,HS ,74366,594,0,2,Citizen 4033,0,Home,Male ,42,HS ,86075,584,4,2,Citizen 4034,0,Car ,Female,48,College ,108081,617,2,2,Citizen 4035,0,Car ,Male ,56,HS ,94831,675,1,1,Citizen 4036,0,Car ,Male ,51,HS ,85545,608,1,2,Citizen 4037,0,Car ,Male ,51,College ,100328,691,2,2,Citizen 4038,1,Home,Female,52,HS ,124766,651,5,1,Non-citizen 4039,0,Home,Female,45,HS ,112533,634,7,2,Citizen 4040,0,Home,Female,46,HS ,103406,679,6,1,Citizen 4041,0,Car ,Female,49,HS ,81446,568,2,2,Citizen 4042,0,Car ,Male ,46,College ,103667,665,2,2,Citizen 4043,0,Car ,Female,52,College ,97185,606,3,2,Citizen 4044,0,Car ,Female,44,College ,100545,724,0,2,Citizen 4045,0,Car ,Female,53,HS ,115793,504,0,2,Citizen 4046,0,Home,Male ,43,HS ,80111,576,5,2,Citizen 4047,0,Home,Male ,34,HS ,112809,668,3,2,Citizen 4048,0,Car ,Female,44,Graduate,113643,561,2,2,Citizen 4049,0,Home,Male ,38,HS ,73682,606,2,2,Citizen 4050,0,Home,Female,39,HS ,114416,614,2,2,Citizen 4051,0,Car ,Male ,51,College ,118953,631,2,2,Citizen 4052,0,Car ,Female,50,College ,94940,632,2,1,Citizen 4053,0,Car ,Male ,39,HS ,87434,610,0,2,Citizen 4054,0,Car ,Female,44,College ,105999,577,2,2,Citizen 4055,0,Home,Male ,31,HS ,91724,589,1,2,Citizen 4056,0,Home,Male ,36,College ,120913,617,13,2,Citizen 4057,0,Home,Male ,37,HS ,74970,596,8,2,Citizen 4058,0,Home,Female,50,College ,100260,650,6,2,Citizen 4059,0,Home,Male ,43,HS ,74827,548,10,2,Citizen 4060,0,Car ,Female,36,College ,111422,633,1,2,Citizen 4061,0,Car ,Female,41,HS ,86104,545,0,2,Citizen 4062,0,Home,Male ,41,HS ,113113,548,9,2,Citizen 4063,0,Car ,Female,41,HS ,106656,659,0,2,Citizen 4064,1,Car ,Female,39,Graduate,123493,798,0,2,Non-citizen 4065,0,Car ,Female,37,College ,78129,644,3,2,Citizen 4066,0,Car ,Male ,41,College ,77614,669,1,2,Citizen 4067,0,Car ,Male ,42,HS ,112966,606,2,2,Citizen 4068,0,Car ,Female,41,HS ,73691,593,0,2,Citizen 4069,0,Home,Male ,37,HS ,103658,600,10,2,Citizen 4070,0,Car ,Male ,50,HS ,95095,678,1,2,Citizen 4071,0,Home,Female,46,HS ,107208,614,8,2,Citizen 4072,0,Car ,Male ,42,HS ,115419,617,2,1,Citizen 4073,0,Car ,Female,42,Graduate,126308,682,2,2,Non-citizen 4074,0,Home,Male ,36,HS ,103205,702,4,2,Citizen 4075,0,Car ,Male ,47,College ,111910,619,2,2,Citizen 4076,0,Car ,Male ,35,HS ,97153,591,0,2,Citizen 4077,0,Home,Male ,33,HS ,88818,422,3,2,Citizen 4078,0,Car ,Male ,44,HS ,110218,694,3,2,Citizen 4079,0,Car ,Male ,58,HS ,99057,639,0,1,Citizen 4080,0,Car ,Female,40,College ,94641,554,3,2,Citizen 4081,0,Home,Male ,34,HS ,110183,589,4,1,Citizen 4082,0,Car ,Male ,36,College ,98639,685,1,2,Citizen 4083,0,Car ,Male ,40,HS ,99665,623,1,2,Citizen 4084,0,Car ,Male ,49,HS ,144418,727,1,2,Citizen 4085,0,Car ,Female,41,College ,113281,658,2,2,Citizen 4086,0,Car ,Female,33,College ,90520,582,0,2,Citizen 4087,0,Car ,Female,50,College ,108316,695,2,2,Citizen 4088,0,Car ,Male ,38,HS ,117226,540,2,2,Citizen 4089,0,Car ,Male ,42,HS ,112289,607,2,2,Citizen 4090,0,Home,Male ,34,College ,93864,597,4,2,Citizen 4091,0,Home,Male ,36,College ,87684,519,3,2,Citizen 4092,0,Car ,Female,39,HS ,106004,678,0,2,Citizen 4093,0,Car ,Male ,35,HS ,75703,544,0,2,Citizen 4094,0,Car ,Male ,43,Graduate,86937,721,3,1,Citizen 4095,0,Car ,Male ,42,HS ,89144,659,0,2,Citizen 4096,0,Car ,Female,47,Graduate,77577,615,2,2,Citizen 4097,0,Car ,Male ,36,College ,73045,515,0,2,Citizen 4098,0,Home,Male ,55,HS ,98377,730,4,2,Citizen 4099,0,Home,Female,34,College ,66984,663,4,1,Citizen 4100,0,Car ,Female,37,HS ,104330,523,1,2,Citizen 4101,0,Home,Male ,33,HS ,80088,484,5,2,Citizen 4102,0,Home,Female,40,HS ,114603,671,10,2,Citizen 4103,0,Home,Male ,38,HS ,70327,570,3,2,Citizen 4104,0,Car ,Male ,39,HS ,104640,577,0,2,Citizen 4105,0,Car ,Male ,46,HS ,91178,515,1,2,Citizen 4106,0,Car ,Male ,50,HS ,102734,655,1,2,Citizen 4107,0,Home,Female,41,College ,89500,638,10,2,Citizen 4108,0,Car ,Male ,42,HS ,94175,522,1,2,Citizen 4109,0,Home,Male ,45,College ,94231,651,7,2,Citizen 4110,0,Car ,Female,44,HS ,62157,630,1,1,Citizen 4111,0,Car ,Female,51,College ,135858,758,2,2,Non-citizen 4112,0,Car ,Male ,41,HS ,64427,491,1,2,Citizen 4113,0,Car ,Male ,45,College ,100258,587,3,2,Citizen 4114,0,Car ,Female,31,College ,95625,679,0,2,Citizen 4115,0,Car ,Male ,51,Graduate,87090,531,3,2,Citizen 4116,0,Car ,Male ,45,HS ,78112,552,0,2,Citizen 4117,0,Car ,Female,49,College ,130963,637,3,2,Citizen 4118,1,Car ,Female,48,College ,119099,622,2,2,Citizen 4119,0,Home,Male ,44,HS ,109565,520,2,2,Citizen 4120,0,Home,Female,41,HS ,127588,632,4,2,Citizen 4121,0,Car ,Male ,32,HS ,101480,689,2,2,Citizen 4122,0,Car ,Female,48,HS ,105260,685,2,2,Citizen 4123,0,Car ,Male ,47,HS ,77881,458,0,2,Citizen 4124,0,Car ,Female,44,Graduate,87956,729,3,2,Citizen 4125,0,Car ,Male ,40,College ,125736,604,3,2,Citizen 4126,0,Car ,Female,43,HS ,93753,632,0,2,Citizen 4127,0,Car ,Male ,38,HS ,97391,593,0,2,Citizen 4128,0,Home,Female,44,HS ,74806,517,8,2,Citizen 4129,0,Car ,Female,51,HS ,89388,689,0,2,Citizen 4130,0,Home,Male ,38,HS ,105208,720,6,2,Citizen 4131,0,Home,Female,39,HS ,74125,558,3,2,Citizen 4132,0,Car ,Female,48,HS ,120752,627,1,1,Non-citizen 4134,0,Car ,Male ,33,HS ,77536,506,2,2,Citizen 4135,0,Home,Male ,34,HS ,114299,639,7,2,Citizen 4136,0,Car ,Male ,41,HS ,87283,558,0,2,Citizen 4137,0,Car ,Male ,41,College ,102417,708,2,2,Citizen 4138,0,Home,Female,37,College ,113605,502,9,2,Citizen 4139,0,Home,Male ,44,HS ,91088,547,7,2,Citizen 4140,0,Car ,Female,35,HS ,110532,565,0,2,Citizen 4141,0,Car ,Female,42,HS ,96913,573,2,1,Citizen 4142,0,Car ,Female,47,College ,109532,663,0,2,Citizen 4143,0,Car ,Male ,38,College ,99184,607,2,2,Citizen 4144,0,Car ,Female,33,HS ,87615,481,2,2,Citizen 4145,0,Car ,Female,41,College ,71433,603,0,2,Citizen 4146,0,Car ,Male ,48,College ,86105,548,0,2,Citizen 4147,0,Home,Male ,45,HS ,98440,605,5,2,Citizen 4148,0,Home,Male ,60,HS ,92043,709,5,2,Citizen 4149,0,Car ,Female,42,HS ,88951,568,0,2,Citizen 4150,0,Car ,Male ,43,HS ,92531,693,2,2,Citizen 4151,0,Car ,Male ,44,Graduate,97332,584,2,2,Citizen 4152,0,Car ,Male ,43,HS ,78999,488,2,2,Citizen 4153,0,Car ,Male ,47,College ,112257,675,2,2,Citizen 4154,0,Car ,Female,43,HS ,93340,659,1,2,Citizen 4155,0,Car ,Male ,42,College ,65241,488,2,2,Citizen 4156,0,Car ,Male ,54,HS ,115416,685,2,2,Citizen 4157,0,Home,Male ,44,HS ,85542,646,9,2,Citizen 4158,0,Home,Male ,39,HS ,72189,552,2,2,Citizen 4159,1,Car ,Male ,52,College ,100687,583,2,2,Citizen 4160,0,Car ,Female,40,HS ,97157,638,0,2,Citizen 4161,0,Home,Female,37,HS ,86052,533,10,2,Citizen 4162,0,Car ,Male ,48,College ,113947,564,2,2,Citizen 4163,1,Car ,Female,50,College ,125988,608,1,2,Citizen 4164,0,Car ,Female,35,HS ,111865,568,3,2,Citizen 4165,0,Car ,Male ,43,College ,94990,677,3,2,Citizen 4166,1,Car ,Female,47,College ,101642,692,2,2,Citizen 4167,0,Car ,Male ,48,HS ,77928,573,0,2,Citizen 4168,1,Home,Male ,52,HS ,113996,642,3,2,Citizen 4169,1,Car ,Female,45,Graduate,114196,664,3,2,Citizen 4170,0,Car ,Male ,43,HS ,71026,580,0,2,Citizen 4171,0,Car ,Female,46,HS ,68912,639,0,2,Citizen 4172,0,Home,Male ,31,HS ,91966,539,3,2,Citizen 4173,0,Car ,Male ,35,HS ,92721,502,0,2,Citizen 4174,1,Car ,Male ,45,College ,94922,668,1,2,Citizen 4175,0,Home,Male ,33,HS ,82603,528,1,2,Citizen 4176,0,Car ,Male ,38,HS ,69189,599,0,2,Citizen 4177,0,Home,Male ,40,HS ,54885,519,7,2,Citizen 4178,0,Car ,Male ,44,HS ,105223,624,2,2,Citizen 4179,0,Car ,Female,45,Graduate,104783,710,0,2,Citizen 4180,0,Home,Male ,40,HS ,74250,585,0,2,Citizen 4181,0,Home,Male ,46,HS ,109811,586,9,2,Citizen 4182,0,Car ,Male ,40,College ,90968,564,0,2,Citizen 4183,0,Home,Male ,38,HS ,78890,581,3,2,Citizen 4184,0,Car ,Female,57,Graduate,100398,687,3,2,Citizen 4185,0,Home,Male ,45,College ,101462,569,8,2,Citizen 4186,0,Car ,Female,45,Graduate,102743,667,3,2,Non-citizen 4187,0,Home,Female,48,HS ,126747,727,7,2,Citizen 4188,0,Car ,Female,54,HS ,117815,726,2,1,Non-citizen 4189,0,Car ,Female,50,College ,116940,769,2,1,Citizen 4190,0,Car ,Female,48,College ,113270,647,2,2,Citizen 4191,0,Home,Male ,43,College ,100380,534,8,2,Citizen 4192,0,Car ,Female,39,HS ,100498,633,0,2,Citizen 4193,0,Car ,Female,42,College ,109464,646,2,2,Citizen 4194,0,Car ,Male ,52,HS ,68124,594,2,2,Citizen 4195,0,Car ,Female,43,College ,64636,550,0,2,Citizen 4196,0,Home,Male ,43,HS ,89854,672,6,1,Citizen 4197,0,Home,Male ,44,HS ,86829,603,9,2,Citizen 4198,0,Home,Female,31,HS ,54687,564,2,2,Citizen 4199,1,Car ,Male ,36,College ,95288,580,2,2,Citizen 4200,0,Car ,Female,38,HS ,121538,518,0,2,Citizen 4201,0,Home,Male ,43,HS ,58963,599,3,2,Citizen 4202,0,Home,Male ,43,College ,109095,618,13,2,Citizen 4203,0,Car ,Male ,43,HS ,92747,515,0,2,Citizen 4204,0,Car ,Male ,50,HS ,106163,483,2,1,Citizen 4205,0,Car ,Male ,38,College ,119064,677,2,2,Citizen 4206,0,Home,Female,48,HS ,106373,609,7,2,Citizen 4207,0,Home,Male ,35,HS ,80675,569,7,2,Citizen 4208,0,Car ,Male ,47,HS ,95629,634,2,2,Citizen 4209,0,Car ,Male ,45,College ,86656,609,2,2,Citizen 4210,0,Car ,Male ,43,HS ,96256,614,2,2,Citizen 4211,0,Car ,Female,30,College ,77165,569,0,2,Citizen 4212,1,Car ,Male ,40,College ,90413,596,1,2,Citizen 4213,0,Car ,Male ,35,HS ,97350,610,2,1,Citizen 4214,0,Car ,Female,48,College ,101683,671,2,1,Citizen 4215,0,Car ,Male ,50,HS ,92432,526,2,2,Citizen 4216,0,Home,Male ,35,HS ,109580,574,11,2,Citizen 4217,0,Car ,Female,45,HS ,103176,692,0,1,Citizen 4218,0,Car ,Male ,34,HS ,81474,586,1,2,Citizen 4219,1,Car ,Female,45,HS ,107278,771,0,2,Citizen 4220,0,Home,Female,43,HS ,105519,677,4,2,Citizen 4221,0,Car ,Female,48,College ,92211,715,1,2,Citizen 4222,0,Car ,Male ,37,HS ,79221,568,2,2,Citizen 4223,0,Home,Female,50,HS ,92733,725,11,2,Citizen 4224,0,Car ,Male ,45,HS ,73079,602,0,2,Citizen 4225,0,Car ,Male ,44,College ,105427,633,1,2,Citizen 4226,0,Home,Female,38,College ,92884,631,2,2,Citizen 4227,0,Car ,Male ,42,College ,97460,575,2,2,Citizen 4228,0,Home,Female,45,HS ,91823,595,6,2,Citizen 4229,0,Car ,Male ,51,College ,89476,515,3,2,Citizen 4230,0,Home,Female,37,HS ,72996,590,5,2,Citizen 4231,1,Car ,Male ,46,College ,75736,600,0,2,Citizen 4232,0,Home,Female,29,HS ,82630,710,1,2,Citizen 4233,0,Home,Male ,40,HS ,104323,592,11,2,Citizen 4234,0,Car ,Male ,40,College ,81439,576,0,2,Citizen 4235,0,Home,Female,47,HS ,106456,569,4,2,Citizen 4236,0,Car ,Female,50,HS ,115411,771,0,2,Citizen 4237,0,Car ,Female,50,HS ,108483,691,2,2,Citizen 4238,0,Car ,Male ,46,HS ,68861,601,2,2,Citizen 4239,0,Car ,Male ,39,HS ,84244,633,0,2,Citizen 4240,0,Car ,Male ,46,College ,86142,622,0,2,Citizen 4241,1,Car ,Female,52,Graduate,139008,792,1,2,Non-citizen 4242,0,Car ,Male ,46,HS ,104185,608,0,2,Citizen 4243,1,Home,Female,46,HS ,120706,681,8,2,Citizen 4244,0,Car ,Female,47,HS ,103851,729,2,1,Citizen 4245,0,Car ,Female,43,HS ,90018,622,0,2,Citizen 4246,0,Home,Male ,32,HS ,115887,596,6,2,Citizen 4247,0,Car ,Female,48,College ,85377,691,1,2,Citizen 4248,0,Car ,Male ,26,HS ,79845,595,0,2,Citizen 4249,0,Car ,Female,30,Graduate,94619,694,2,2,Citizen 4250,0,Car ,Female,46,College ,109803,670,2,2,Citizen 4251,0,Car ,Male ,42,HS ,87394,601,0,2,Citizen 4252,0,Car ,Female,36,HS ,121274,558,0,2,Citizen 4253,0,Car ,Male ,42,HS ,68529,558,1,2,Citizen 4254,0,Car ,Male ,47,College ,102738,624,3,2,Citizen 4255,0,Car ,Male ,44,HS ,106798,478,2,2,Citizen 4256,0,Home,Female,44,HS ,87484,633,4,2,Citizen 4257,0,Home,Male ,48,HS ,85902,672,5,1,Citizen 4258,0,Car ,Male ,52,HS ,114109,688,1,2,Citizen 4259,1,Car ,Male ,57,College ,129862,662,2,2,Citizen 4260,0,Car ,Female,46,HS ,113718,653,0,2,Citizen 4261,0,Home,Male ,45,HS ,84520,587,7,2,Citizen 4262,0,Car ,Female,42,HS ,98440,566,0,2,Citizen 4263,0,Car ,Male ,35,HS ,73367,584,0,2,Citizen 4264,1,Home,Female,37,HS ,140418,703,2,2,Citizen 4265,0,Car ,Male ,56,HS ,68986,592,1,2,Citizen 4266,0,Home,Female,29,HS ,117694,544,6,2,Citizen 4267,0,Car ,Female,54,HS ,155953,652,3,1,Citizen 4268,0,Car ,Male ,46,HS ,124511,508,2,2,Citizen 4269,0,Home,Male ,39,HS ,109334,543,5,2,Citizen 4270,0,Home,Male ,43,HS ,78798,641,0,2,Citizen 4271,1,Home,Female,54,HS ,94632,729,10,1,Citizen 4272,0,Car ,Female,45,HS ,105837,668,0,2,Citizen 4273,0,Car ,Male ,37,HS ,86600,579,0,2,Citizen 4274,0,Car ,Female,43,College ,96735,550,1,2,Citizen 4275,0,Car ,Female,36,HS ,104510,565,2,2,Citizen 4276,0,Car ,Female,39,HS ,72601,574,0,2,Citizen 4277,0,Home,Male ,31,HS ,73523,645,4,2,Citizen 4278,0,Home,Male ,47,HS ,105347,665,6,2,Citizen 4279,0,Home,Male ,42,HS ,132522,741,8,2,Citizen 4280,0,Home,Female,41,HS ,101384,601,0,2,Citizen 4281,0,Home,Male ,43,HS ,76369,551,2,2,Citizen 4282,0,Car ,Male ,33,HS ,71053,579,0,2,Citizen 4283,0,Car ,Female,54,HS ,94157,643,0,1,Citizen 4284,0,Car ,Male ,48,HS ,141616,554,3,2,Citizen 4285,0,Home,Female,50,HS ,102406,676,4,2,Citizen 4286,0,Car ,Female,39,HS ,126294,684,0,2,Citizen 4287,0,Car ,Male ,47,HS ,127201,681,1,2,Citizen 4288,0,Home,Male ,43,HS ,89165,787,6,2,Citizen 4289,0,Car ,Female,32,HS ,100398,711,0,2,Citizen 4290,0,Home,Male ,43,College ,130079,559,9,2,Citizen 4291,0,Home,Male ,40,HS ,105433,606,5,2,Citizen 4292,0,Car ,Male ,42,HS ,70969,632,0,2,Citizen 4293,0,Car ,Male ,43,HS ,107030,585,2,2,Citizen 4294,0,Car ,Female,53,HS ,121814,586,3,2,Citizen 4295,0,Car ,Female,43,HS ,100217,575,2,2,Citizen 4296,0,Car ,Male ,41,College ,71636,607,2,2,Citizen 4297,0,Car ,Male ,42,HS ,61144,558,0,2,Citizen 4298,0,Car ,Female,57,HS ,85407,769,2,2,Citizen 4299,0,Car ,Female,38,College ,91555,576,0,2,Citizen 4300,0,Car ,Male ,47,HS ,112050,715,3,2,Citizen 4301,0,Car ,Female,40,HS ,107635,598,0,2,Citizen 4302,0,Car ,Female,42,HS ,81075,560,3,2,Citizen 4303,0,Car ,Female,41,HS ,76185,573,0,2,Citizen 4304,0,Home,Female,41,HS ,124591,603,5,2,Citizen 4305,0,Home,Male ,44,HS ,116536,631,11,2,Citizen 4306,0,Home,Male ,36,HS ,89547,620,6,2,Citizen 4307,0,Car ,Male ,44,College ,106059,688,1,2,Citizen 4308,0,Car ,Female,41,HS ,107135,668,2,1,Non-citizen 4309,0,Car ,Male ,42,HS ,96315,687,2,2,Citizen 4310,0,Car ,Male ,25,HS ,62105,453,0,2,Citizen 4311,0,Home,Male ,49,HS ,92534,588,7,2,Citizen 4312,0,Car ,Male ,37,HS ,102801,595,1,2,Citizen 4313,0,Home,Male ,32,HS ,76153,604,3,2,Citizen 4314,0,Car ,Female,52,HS ,89526,536,0,2,Citizen 4315,0,Home,Male ,42,HS ,102301,577,6,2,Citizen 4316,0,Car ,Female,43,HS ,107459,660,1,2,Citizen 4317,0,Home,Male ,26,HS ,87524,550,8,2,Citizen 4318,0,Home,Male ,42,HS ,137442,598,5,2,Citizen 4319,0,Car ,Female,40,College ,104028,710,2,2,Citizen 4320,1,Car ,Male ,45,College ,128576,710,1,2,Citizen 4321,0,Car ,Female,44,HS ,100396,592,1,2,Citizen 4322,0,Home,Male ,33,HS ,94745,597,1,2,Citizen 4323,0,Home,Female,53,HS ,115266,589,5,1,Citizen 4324,0,Home,Male ,43,HS ,85315,515,5,2,Citizen 4325,0,Home,Female,47,HS ,135915,650,11,2,Citizen 4326,0,Home,Female,38,HS ,79057,558,5,2,Citizen 4327,0,Home,Male ,38,HS ,96622,575,5,2,Citizen 4328,0,Home,Female,44,HS ,108746,547,8,2,Citizen 4329,0,Car ,Female,35,HS ,76218,540,0,2,Citizen 4330,0,Car ,Female,36,College ,86890,608,2,2,Citizen 4331,0,Car ,Female,48,College ,109879,659,3,1,Citizen 4332,0,Car ,Male ,44,HS ,82191,546,1,2,Citizen 4333,0,Car ,Male ,40,College ,101265,747,2,2,Citizen 4334,0,Car ,Male ,33,HS ,100170,586,1,2,Citizen 4335,0,Home,Male ,43,HS ,90942,619,7,2,Citizen 4336,0,Car ,Female,45,College ,108406,611,2,2,Citizen 4337,0,Home,Male ,34,HS ,89536,610,6,2,Citizen 4338,0,Car ,Male ,43,Graduate,99401,654,2,2,Citizen 4339,0,Home,Male ,49,HS ,112824,556,5,2,Citizen 4340,0,Car ,Female,36,College ,95384,571,0,2,Citizen 4341,0,Car ,Male ,40,HS ,107593,505,0,2,Citizen 4342,0,Car ,Female,46,College ,86351,600,0,2,Citizen 4343,1,Home,Male ,54,College ,80685,611,6,2,Citizen 4344,0,Car ,Male ,40,College ,98374,549,0,2,Citizen 4345,0,Car ,Male ,47,HS ,100781,639,0,1,Citizen 4346,0,Car ,Male ,45,HS ,99581,589,0,2,Citizen 4347,0,Car ,Male ,42,HS ,61637,513,1,2,Citizen 4348,0,Home,Male ,33,HS ,74852,560,5,2,Citizen 4349,0,Car ,Male ,38,HS ,80880,618,1,2,Citizen 4350,0,Car ,Male ,33,HS ,108926,709,1,2,Citizen 4351,0,Home,Male ,39,HS ,119078,657,5,2,Citizen 4352,0,Car ,Male ,38,College ,88484,558,1,2,Citizen 4353,0,Car ,Female,40,HS ,97092,716,0,1,Citizen 4354,0,Home,Female,46,College ,107100,677,2,2,Citizen 4355,0,Car ,Male ,37,HS ,107586,687,1,2,Citizen 4356,1,Car ,Male ,53,HS ,137569,652,2,2,Citizen 4357,0,Car ,Male ,36,HS ,60038,604,0,2,Citizen 4358,0,Car ,Male ,44,HS ,97654,570,3,2,Citizen 4359,0,Home,Male ,43,HS ,103978,696,4,2,Citizen 4360,0,Home,Female,33,HS ,83560,604,1,2,Citizen 4361,0,Home,Female,49,HS ,75149,622,5,2,Citizen 4362,0,Home,Male ,41,HS ,84918,659,5,2,Citizen 4363,0,Car ,Female,30,HS ,68454,540,0,2,Citizen 4364,0,Car ,Female,47,HS ,123862,648,1,2,Citizen 4365,0,Car ,Female,48,HS ,72965,545,0,2,Citizen 4366,0,Car ,Male ,41,HS ,81326,635,2,2,Citizen 4367,0,Car ,Female,47,HS ,82698,583,2,1,Citizen 4368,0,Car ,Male ,30,HS ,106459,530,0,2,Citizen ================================================ FILE: March Cohort 2023/12_15_16_17_18_Statistics/datasets/forbes.csv ================================================ Rank,Company,Country,Sales,Profits,Assets,Market Value,Sector,Industry 1,ICBC,China,151.4,42,3473.2,229.8,Financials,Major Banks 2,China Construction Bank,China,134.2,35,3016.6,200.5,Financials,Regional Banks 3,Berkshire Hathaway,United States,222.9,24.1,620.9,409.9,Financials,Investment Services 4,JPMorgan Chase,United States,102.5,24.2,2513,306.6,Financials,Major Banks 5,Wells Fargo,United States,97.6,21.9,1943.4,274.4,Financials,Major Banks 6,Agricultural Bank of China,China,115.7,27.8,2816,149.2,Financials,Regional Banks 7,Bank of America,United States,92.2,16.6,2196.8,231.9,Financials,Major Banks 8,Bank of China,China,113.1,24.9,2611.5,141.3,Financials,Major Banks 9,Apple,United States,217.5,45.2,331.1,752,Information Technology,Computer Hardware 10,Toyota Motor,Japan,249.9,17.1,412.5,171.9,Consumer Discretionary,Auto & Truck Manufacturers 11,AT&T,United States,163.8,13,403.8,249.3,Telecommunication Services,Telecommunications services 12,Citigroup,United States,84,14.7,1795.1,164.3,Financials,Major Banks 13,Exxon Mobil,United States,197.5,7.8,330.3,343.2,Energy, 14,General Electric,United States,119.7,10,365.2,261.2,Industrials,Conglomerates 15,Samsung Electronics,South Korea,174,19.3,217.1,254.3,Information Technology,Semiconductors 16,Ping An Insurance Group,China,106.6,9.5,801,100.8,Financials,Diversified Insurance 17,Wal-Mart Stores,United States,485.3,13.6,198.8,221.1,Consumer Discretionary,Discount Stores 18,Verizon Communications,United States,126,13.1,244.2,198.4,Telecommunication Services,Telecommunications services 19,Microsoft,United States,85.3,16.8,224.6,507.5,Information Technology,Software & Programming 20,Royal Dutch Shell,Netherlands,234.8,4.7,411.3,228.8,Energy,Oil & Gas Operations 21,Allianz,Germany,115.7,7.6,935.9,83.7,Financials,Diversified Insurance 22,China Mobile,Hong Kong,106.8,16.4,218.9,225.3,Telecommunication Services,Telecommunications services 23,BNP Paribas,France,74.7,8.4,2190.7,80.5,Financials,Major Banks 24,Alphabet,United States,89.9,19.5,167.5,579.5,Information Technology, 25,China Petroleum & Chemical,China,255.7,7,216.7,105.1,Energy, 26,Total,France,128.1,6.2,231,128.1,Energy,Oil & Gas Operations 27,AXA Group,France,132.2,6.2,925.9,60.8,Financials,Diversified Insurance 28,Daimler,Germany,169.5,9.4,256.3,76.1,Consumer Discretionary,Auto & Truck Manufacturers 29,Volkswagen Group,Germany,240.3,5.7,458.7,72.9,Consumer Discretionary,Auto & Truck Manufacturers 30,Mitsubishi UFJ Financial,Japan,49.2,8.2,2589.8,83.9,Financials,Major Banks 31,Comcast,United States,80.4,8.7,180.5,193.5,Consumer Discretionary,Broadcasting & Cable 32,Johnson & Johnson,United States,71.9,16.4,141.2,338.6,Health Care,Medical Equipment & Supplies 33,Banco Santander,Spain,48.3,6.9,1412.4,89.4,Financials,Major Banks 34,Bank of Communications,China,53,10.1,1209.2,62.2,Financials,Major Banks 35,Nestle,Switzerland,90.8,8.7,129.8,229.5,Consumer Staples,Food Processing 36,UnitedHealth Group,United States,184.9,7.2,125,160.2,Health Care,Managed Health Care 37,Nippon Telegraph & Tel,Japan,105,7.4,180.3,92.2,Telecommunication Services,Telecommunications services 38,Itaú Unibanco Holding,Brazil,61.3,6.7,419.9,79.2,Financials, 39,Softbank,Japan,82.1,8.3,213.2,78.6,Telecommunication Services,Telecommunications services 40,Gazprom,Russia,91.4,12.1,265.4,51.8,Energy,Oil & Gas Operations 41,General Motors,United States,166.4,9.4,221.7,50.8,Consumer Discretionary,Auto & Truck Manufacturers 42,China Merchants Bank,China,44.5,9.4,855.1,66.4,Financials,Major Banks 43,IBM,United States,79.9,11.9,117.5,162.4,Information Technology,Computer Services 44,Royal Bank of Canada,Canada,35.3,8.3,891.1,107.2,Financials,Major Banks 45,Japan Post Holdings,Japan,123.7,3.1,2522.1,55.1,Consumer Discretionary, 46,Procter & Gamble,United States,65.2,9.5,117,228.1,Consumer Staples,Household/Personal Care 47,Pfizer,United States,52.8,7.1,171.6,203.1,Health Care,Pharmaceuticals 48,HSBC Holdings,United Kingdom,62.1,2.5,2375,162.6,Financials,Major Banks 49,Goldman Sachs Group,United States,36.9,7.4,860.2,90.6,Financials,Investment Services 50,Siemens,Germany,88.4,6.4,133.1,109.8,Industrials,Conglomerates 51,BMW Group,Germany,104.2,7.6,210.3,57.7,Consumer Discretionary,Auto & Truck Manufacturers 52,China Life Insurance,China,82.8,2.9,388.7,98.1,Financials,Life & Health Insurance 53,ING Group,Netherlands,53.6,5.1,891.3,57.9,Financials,Life & Health Insurance 54,Intel,United States,59.4,10.3,113.3,170.4,Information Technology,Semiconductors 55,Postal Savings Bank Of China,China,48,6,1189.4,55.2,Financials, 56,Sberbank,Russia,43,8.1,415.6,63.9,Financials,Regional Banks 57,TD Bank Group,Canada,31.9,6.9,910.4,92,Financials,Major Banks 58,Cisco Systems,United States,48.6,9.8,126.2,165.1,Information Technology,Communications Equipment 59,Commonwealth Bank,Australia,30.9,7.1,703.6,109.9,Financials,Major Banks 60,Morgan Stanley,United States,37.9,6,816,78.3,Financials,Investment Services 61,Novartis,Switzerland,48.5,6.7,130.4,193.2,Health Care,Pharmaceuticals 62,Banco Bradesco,Brazil,70.2,4.3,362.4,53.5,Financials,Regional Banks 63,Industrial Bank,China,44.3,8.1,872.1,45.1,Financials,Regional Banks 64,Ford Motor,United States,151.8,4.6,238,44.7,Consumer Discretionary,Auto & Truck Manufacturers 65,Shanghai Pudong Development,China,40.1,8,842.8,50.5,Financials,Regional Banks 66,CVS Health,United States,177.5,5.3,94.5,79.8,Financials, 67,Walt Disney,United States,54.9,9,91.6,178,Consumer Discretionary,Broadcasting & Cable 68,Prudential,United Kingdom,96.9,2.6,569,53.7,Financials,Life & Health Insurance 69,Prudential Financial,United States,56.3,4.3,784,45.6,Financials,Life & Health Insurance 70,Oracle,United States,37.4,8.9,125.4,182.2,Information Technology,Software & Programming 71,China State Construction Engineering,China,140.8,4.9,201.4,43.2,Industrials, 72,Citic Pacific,Hong Kong,49.1,5.6,933.6,41.4,Materials,Iron & Steel 73,Boeing,United States,94.6,4.9,90,108.8,Industrials,Aerospace & Defense 74,Honda Motor,Japan,127.9,3.9,161.8,51.4,Consumer Discretionary,Auto & Truck Manufacturers 75,China Minsheng Banking,China,40.2,7.2,848.7,43.7,Financials,Regional Banks 76,Westpac Banking Group,Australia,27.8,5.5,642.7,86.1,Financials,Major Banks 77,Deutsche Telekom,Germany,80.9,3,156.6,80,Telecommunication Services,Telecommunications services 78,China Citic Bank,China,39.7,6.3,853.5,43.1,Financials,Regional Banks 79,Roche Holding,Switzerland,51.3,9.7,75.6,219.3,Health Care,Pharmaceuticals 80,UBS,Switzerland,38,3.4,920,60,Financials,Investment Services 81,Bank of Nova Scotia,Canada,26.7,5.5,680.3,70.5,Financials,Major Banks 82,Rosneft,Russia,74.9,2.7,193.2,62.4,Energy,Oil & Gas Operations 83,Amazon.com,United States,136,2.4,86,427,Consumer Discretionary,Internet & Catalog Retail 84,PepsiCo,United States,62.8,6.3,74.1,159.4,Consumer Staples,Beverages 85,Sumitomo Mitsui Financial,Japan,30.5,5.2,1648.8,48.1,Financials,Major Banks 86,Coca-Cola,United States,41.5,6.5,87.3,182.9,Consumer Staples,Beverages 87,United Technologies,United States,57.4,5,89.7,90.6,Industrials,Conglomerates 88,Sanofi,France,37.4,5.2,110.4,116.1,Health Care,Pharmaceuticals 89,Bayer,Germany,51.8,5,90.1,94.4,Materials,Diversified Chemicals 90,Mizuho Financial,Japan,29,6,1752.3,44.4,Financials,Major Banks 91,Zurich Insurance Group,Switzerland,67.3,3.2,364.3,40,Financials,Diversified Insurance 92,ANZ,Australia,26.5,4.2,701,68.8,Financials,Major Banks 93,BASF,Germany,63.7,4.5,83.9,91,Materials,Diversified Chemicals 94,Walgreens Boots Alliance,United States,116.1,4.2,72.5,89,Consumer Discretionary, 95,Nissan Motor,Japan,105.9,4.5,157.3,38.4,Consumer Discretionary,Auto & Truck Manufacturers 96,US Bancorp,United States,22,5.9,451,86.6,Financials,Major Banks 97,American Express,United States,34,5.4,158.9,70.1,Financials,Consumer Financial Services 98,Hon Hai Precision,Taiwan,135.2,4.6,80.4,54.4,Information Technology,Electronics 99,Enel,Italy,75.9,2.8,164.1,47.5,Utilities,Electric Utilities 100,Merck,United States,39.6,3.9,95.4,173.3,Health Care,Pharmaceuticals 101,National Australia Bank,Australia,24.2,4.3,595.3,65,Financials,Major Banks 102,PetroChina,China,214.8,1.2,344.9,204.5,Energy,Oil & Gas Operations 103,Unilever,Netherlands,58.3,5.7,59.5,143.9,Consumer Staples,Food Processing 104,Hyundai Motor,South Korea,80.7,4.7,148.1,34.2,Consumer Discretionary,Auto & Truck Manufacturers 105,BBVA-Banco Bilbao Vizcaya,Spain,27.3,3.8,771.9,49.6,Financials,Major Banks 106,Reliance Industries,India,41.8,4.3,97.9,71.2,Energy,Oil & Gas Operations 107,Charter Communications,United States,29,3.5,153.2,101.6,Consumer Discretionary,Broadcasting & Cable 108,SAIC Motor,China,112.7,4.8,85,43.5,Consumer Discretionary,Auto & Truck Manufacturers 109,Chubb,Switzerland,31.9,4.1,159.8,64,Financials,Property & Casualty Insurance 110,Telefónica,Spain,57.6,2.6,130.4,55.9,Telecommunication Services, 111,AIA Group,Hong Kong,26.5,4.2,183,77.1,Financials,Life & Health Insurance 112,Société Générale,France,28,4,1457.9,39.9,, 113,Dow Chemical,United States,48.1,4.3,79.5,77,Materials,Diversified Chemicals 114,Home Depot,United States,94.6,8,43,176.7,Consumer Discretionary,Home Improvement Retail 115,Lloyds Banking Group,United Kingdom,23.4,2.8,1010.5,55.9,Financials,Major Banks 116,Medtronic,Ireland,29.4,4,97.6,110,Health Care,Medical Equipment & Supplies 117,Kraft Heinz Company,United States,26.5,3.6,120.5,110.4,Consumer Staples, 118,Saudi Basic Industries,Saudi Arabia,35.4,4.8,84.5,80.7,Materials,Diversified Chemicals 119,Facebook,United States,27.6,9.5,65,407.3,Information Technology,Computer Services 120,Munich Re,Germany,68.8,2.9,286.9,30.5,Financials,Diversified Insurance 121,Intesa Sanpaolo,Italy,24.8,3.4,764.8,45.3,Financials,Major Banks 122,Barclays,United Kingdom,29.2,2.4,1499,45.4,Financials,Major Banks 123,Rio Tinto,United Kingdom,33.9,4.7,89.3,72.9,Materials,Diversified Metals & Mining 124,BHP Billiton,Australia,34,2.4,119.5,98.3,Materials,Diversified Metals & Mining 125,CK Hutchison,Hong Kong,33.5,4.3,130.7,47.4,Energy, 126,Anheuser-Busch InBev,Belgium,45.6,1.2,261.1,213.1,Consumer Staples,Beverages 127,Taiwan Semiconductor,Taiwan,29.4,10.4,58.5,161.7,Information Technology,Semiconductors 128,Capital One Financial,United States,27.9,3.7,357,40.3,Financials,Consumer Financial Services 129,LukOil,Russia,74.6,3.1,83.2,44.6,Energy,Oil & Gas Operations 130,Swiss Re,Switzerland,43.6,3.6,207.6,32.1,Financials,Diversified Insurance 131,Amgen,United States,22.7,7.7,77.6,120.3,Health Care,Biotechs 132,Banco do Brasil,Brazil,57.3,2.3,430.6,29,Financials,Major Banks 133,Bank of Montreal,Canada,19.7,3.8,531.1,48.6,Financials,Major Banks 134,Gilead Sciences,United States,30.3,13.5,57,87,Health Care,Biotechs 135,China Communications Construction,China,64.1,2.5,115.3,38.8,Industrials,Construction Services 136,Honeywell International,United States,39.3,4.7,54.1,94.6,Industrials,Conglomerates 137,Manulife,Canada,38.9,2.2,511.3,34.4,Consumer Discretionary, 138,Korea Electric Power,South Korea,51.9,6.1,147.2,25.5,Utilities,Electric Utilities 139,Generali Group,Italy,90.5,2.3,553,23.7,Financials,Diversified Insurance 140,Alibaba,China,21.5,5.7,70.7,264.9,Telecommunication Services, 141,KDDI,Japan,43.2,5.1,52.3,68.3,Telecommunication Services,Telecommunications services 142,China Telecom,China,53,2.7,93.9,40.4,Telecommunication Services,Telecommunications services 143,AbbVie,United States,25.6,5.9,66.1,104.5,Health Care,Pharmaceuticals 144,Tokio Marine Holdings,Japan,44.6,2.8,189.5,30.6,Financials,Property & Casualty Insurance 145,Iberdrola,Spain,32.3,3,112.5,46.4,Utilities,Electric Utilities 146,EDF,France,78.8,2.5,297.1,23.3,Utilities,Electric Utilities 147,PNC Financial Services,United States,16.1,3.9,369.4,57.9,Financials,Regional Banks 148,Tencent Holdings,China,22.8,6.2,57,277.1,Information Technology,Computer Services 149,Allergan,Ireland,15,15,129,80.1,Health Care,Pharmaceuticals 150,China Everbright Bank,China,26.1,4.6,578.5,26.8,Financials,Regional Banks 151,Credit Agricole,France,18.6,3.5,1607.7,36.5,Financials,Major Banks 152,Lockheed Martin,United States,49.9,3.8,47.8,78.3,Industrials,Aerospace & Defense 153,Time Warner,United States,29.3,3.9,66,76.2,Consumer Discretionary,Broadcasting & Cable 154,Duke Energy,United States,22.7,2.7,132.8,57.6,Utilities,Electric Utilities 155,Anthem,United States,85.2,2.5,65.1,44,, 156,Vale,Brazil,27.1,3.8,99.1,45.4,Materials,Iron & Steel 157,Nordea Bank,Sweden,15.1,4.2,649.6,45.4,Financials,Regional Banks 158,Express Scripts,United States,100.5,3.4,51.7,39.9,Health Care,Healthcare Services 159,United Parcel Service,United States,60.5,3.4,40.4,91.3,Industrials,Air Courier 160,Aetna,United States,63.2,2.3,69.1,45.1,Health Care,Managed Health Care 161,China Shenhua Energy,China,26.5,3.4,82.3,54.1,Materials,Diversified Metals & Mining 162,VINCI,France,42.6,2.8,72.9,43.6,Industrials,Construction Services 163,Bank of New York Mellon,United States,15.6,3.5,333.5,48.8,Financials,Major Banks 164,Renault,France,56.7,3.8,107.7,24.6,Consumer Discretionary,Auto & Truck Manufacturers 165,China Railway Group,China,95.4,1.9,108.6,29.6,Industrials,Construction Services 166,EADS,Netherlands,73.7,1.1,126.2,59.5,Industrials,Aerospace & Defense 167,China Railway Construction,China,93.2,2.1,109.3,26.5,Industrials,Construction Services 168,China Vanke,China,33.7,3.1,119.5,30.5,Financials,Real Estate 169,Jardine Matheson,Hong Kong,37.1,2.5,71.5,47.6,Industrials,Conglomerates 170,Qualcomm,United States,23.8,4.9,52.4,83.2,Information Technology,Semiconductors 171,Hewlett Packard Enterprise,United States,48.8,3.2,76.7,30.4,Information Technology, 172,British American Tobacco,United Kingdom,19.9,6.3,49.1,123.8,Consumer Staples,Tobacco 173,Visa,United States,16,6,63.4,206.4,Financials,Consumer Financial Services 174,China Pacific Insurance,China,40.3,1.8,144,33.5,Financials,Life & Health Insurance 175,MetLife,United States,64.7,0.8,905.2,57.2,Financials,Diversified Insurance 176,AstraZeneca,United Kingdom,23,3.7,62.5,75.2,Health Care,Pharmaceuticals 177,Altria Group,United States,19.3,14.2,45.9,138.8,Consumer Staples,Tobacco 178,SAP,Germany,24.4,4,46.7,119.7,Information Technology,Software & Programming 179,Costco Wholesale,United States,121.2,2.4,35.6,74.9,Consumer Discretionary,Discount Stores 180,Travelers,United States,27.7,3,101.7,33.6,, 181,Philip Morris International,United States,26.7,6.9,36.9,176.2,Consumer Staples,Tobacco 182,Union Pacific,United States,19.9,4.2,56.8,86.8,Industrials,Railroads 183,Delta Air Lines,United States,39.6,4.4,58.9,33,Industrials,Airline 184,Southern Company,United States,19.9,2.5,109.7,49.3,, 185,Lowe's,United States,65,3.1,34.4,71.1,Energy, 186,National Grid,United Kingdom,21.9,2.9,81.7,48,Utilities,Natural Gas Utilities 187,Legal & General Group,United Kingdom,105.5,1.7,571.2,18.2,Financials,Life & Health Insurance 188,McKesson,United States,196.5,2,57.9,30.5,Health Care,Pharmaceuticals 189,Christian Dior,France,43.7,2,68.3,42.7,Consumer Discretionary,Apparel/Accessories 190,PTT PCL,Thailand,48.7,2.6,63.4,32.4,Energy,Oil & Gas Operations 191,Dai-ichi Life Insurance,Japan,55.7,1.7,438.8,19.9,Financials,Life & Health Insurance 192,Twenty-First Century Fox,United States,28.1,3.1,49.2,57.5,, 193,Canadian Imperial Bank,Canada,14.6,3.6,393.7,34.5,Financials,Major Banks 194,FedEx,United States,57.6,1.9,46.8,52.3,Industrials,Air Courier 195,GlaxoSmithKline,United Kingdom,37.6,1.2,73,99.8,Health Care,Pharmaceuticals 196,Deutsche Post,Germany,63.4,2.9,40.6,41,Industrials,Air Courier 197,3M,United States,30.1,5.1,32.9,113.5,Industrials,Conglomerates 198,L'Oréal Group,France,28.6,3.4,37.6,107.5,Industrials, 199,Aflac,United States,22.7,2.7,129.8,29.2,Financials,Life & Health Insurance 200,Allstate,United States,36.3,1.9,110.4,29.6,Financials,Diversified Insurance 201,Hitachi,Japan,85.8,1.8,82.7,25.6,Information Technology,Electronics 202,Brookfield Asset Management,Canada,24.9,1.6,159.8,35.4,Financials,Real Estate 203,BT Group,United Kingdom,31.7,3.1,54,38.5,Telecommunication Services,Telecommunications services 204,NextEra Energy,United States,15.2,2.9,90,60.4,Utilities,Electric Utilities 205,BlackRock,United States,11.2,3.2,220.2,62.6,Financials,Investment Services 206,Continental,Germany,44.9,3.1,39.4,42.7,Consumer Discretionary,Auto & Truck Parts 207,MS&AD Insurance,Japan,47.8,2,178.3,19.2,Financials,Property & Casualty Insurance 208,Aviva,United Kingdom,74.3,0.949,511.1,25.9,Financials,Life & Health Insurance 209,Cigna,United States,39.7,1.9,60.3,39,Health Care,Managed Health Care 210,Phillips 66,United States,71.2,1.5,51.7,39.9,Energy,Oil & Gas Operations 211,Valero Energy,United States,75.7,2.3,46.8,29.4,Energy,Oil & Gas Operations 212,Enbridge,Canada,26.1,1.6,64,69.3,Energy,Oil Services & Equipment 213,Mondelēz International,United States,25.9,1.6,61.5,67.4,Consumer Discretionary, 214,People's Insurance Company,China,64.6,2.1,129.7,17.5,Financials,Property & Casualty Insurance 215,McDonald's,United States,24.6,4.7,31,106.4,Consumer Discretionary,Restaurants 216,ArcelorMittal,Luxembourg,56.8,1.8,75.1,25.1,Materials,Iron & Steel 217,East Japan Railway,Japan,26.5,2.3,66.1,34.2,Industrials,Railroads 218,Itochu,Japan,44,2.4,72,22.4,Industrials,Trading Companies 219,Japan Tobacco,Japan,19.7,3.9,40.7,66.4,Consumer Staples,Tobacco 220,EI du Pont de Nemours,United States,24.6,2.5,40,69.2,Materials,Diversified Chemicals 221,Eli Lilly,United States,21.2,2.7,38.8,94.1,Health Care, 222,Panasonic,Japan,66.6,1.9,52.7,26.2,Consumer Discretionary,Consumer Electronics 223,Sun Hung Kai Properties,Hong Kong,13.4,5,80.1,43.6,Financials,Real Estate 224,Fiat Chrysler Automobiles,United Kingdom,122.8,2,110.1,15.5,, 225,LafargeHolcim,Switzerland,27.3,1.8,68.5,35.4,Consumer Discretionary, 226,Qatar National Bank,Qatar,11.7,3.4,197.6,37.7,Financials,Regional Banks 227,Target,United States,69.5,2.7,37.4,29.4,Consumer Discretionary,Discount Stores 228,Fresenius,Germany,32.2,1.8,49,43.9,Health Care,Medical Equipment & Supplies 229,Danaher,United States,19.9,2.6,45.3,59.7,Industrials,Conglomerates 230,Danske Bank,Denmark,11.7,2.9,494.2,31.9,Financials,Major Banks 231,General Dynamics,United States,31.4,3,32.9,57,Industrials,Aerospace & Defense 232,Reynolds American,United States,12.5,6.1,51.1,90.6,Consumer Staples,Tobacco 233,Denso,Japan,41.1,2.2,43.6,33.6,Consumer Discretionary,Auto & Truck Parts 234,Bristol-Myers Squibb,United States,19.4,4.5,33.7,88,Health Care,Pharmaceuticals 235,CRRC,China,34.2,1.7,48.7,42.6,, 236,ABB,Switzerland,33.8,2,39.7,49.9,Industrials,Conglomerates 237,Exelon,United States,32.2,1.1,114.9,33.3,Utilities,Electric Utilities 238,Cathay Financial,Taiwan,29,1.5,252.2,19.8,Financials,Life & Health Insurance 239,Sun Life Financial,Canada,19.9,1.9,188.7,21.9,Financials,Life & Health Insurance 240,Repsol,Spain,38.4,1.9,68.4,23.8,Energy, 241,Kroger,United States,115.3,1.9,37.5,27.3,Consumer Staples,Food Retail 242,Abbott Laboratories,United States,20.9,1.4,55,75.7,Health Care,Pharmaceuticals 243,HCA Holdings,United States,41.5,2.9,33.8,32.6,Health Care,Healthcare Services 244,State Bank of India,India,43.7,0.667,493.3,36.5,Financials,Regional Banks 245,DBS Group,Singapore,10.3,3.1,333.5,34.4,Financials,Major Banks 246,Oil & Natural Gas,India,19.9,2.2,57.7,37.2,Energy,Oil & Gas Operations 247,American Airlines Group,United States,40.2,2.7,51.3,21.1,Financials, 248,BB&T,United States,11.3,2.4,221.2,35.6,Financials,Regional Banks 249,Nike,United States,33.8,4.1,23.2,91.2,Financials, 250,Schneider Electric,France,27.3,1.9,44.1,41.9,Industrials,Electrical Equipment 251,Thermo Fisher Scientific,United States,18.3,2,45.9,59.9,Utilities,Precision Healthcare Equipment 252,Danone,France,24.3,1.9,46.4,42,Consumer Staples,Food Processing 253,Samsung Life Insurance,South Korea,24.1,1.8,219.1,18.2,Financials,Life & Health Insurance 254,Air Liquide,France,20.1,2,46.5,45,Materials,Specialized Chemicals 255,Huaxia Bank,China,15.9,3,340.9,17.6,Financials,Regional Banks 256,Orix,Japan,22.8,2.4,95.5,19.8,Financials,Consumer Financial Services 257,Bank of Beijing,China,13.4,2.7,306.3,21.5,Financials,Regional Banks 258,HDFC Bank,India,11.4,2,110.3,57.4,Financials,Regional Banks 259,KBC Group,Belgium,11.3,2.7,290.3,27.6,Financials,Major Banks 260,Deere & Company,United States,26.7,1.5,56.3,34.8,Consumer Staples, 261,Diageo,United Kingdom,15.2,3.2,37.4,71.2,Consumer Staples,Beverages 262,Central Japan Railway,Japan,16.1,3.4,51,32.6,Industrials,Railroads 263,Liberty Global,United Kingdom,20,1.7,68.7,31.2,Consumer Discretionary,Broadcasting & Cable 264,Indian Oil,India,54.1,1.7,37.3,30,Energy,Oil & Gas Operations 265,Shinhan Financial Group,South Korea,14.2,2.4,327.6,20,Financials,Investment Services 266,Canon,Japan,31.3,1.4,44.1,41.6,Industrials,Business Products & Supplies 267,Synchrony Financial,United States,15.1,2.3,90.2,26.9,Financials, 268,Saint-Gobain,France,43.2,1.5,46.2,28,Materials,Construction Materials 269,United Continental Holdings,United States,36.6,2.3,45.4,22.3,Industrials,Airline 270,Carnival,United States,16.5,3,38.7,43.9,Consumer Discretionary,Hotels & Motels 271,HP,United States,48.7,2.5,28.2,29.4,Information Technology, 272,Accenture,Ireland,35.7,3.8,20.1,77.8,Information Technology,Computer Services 273,Country Garden Holdings,Hong Kong,23,1.7,85.1,20.3,Financials,Real Estate 274,State Street,United States,10.6,2.1,242.7,30.1,Financials,Investment Services 275,Volvo Group,Sweden,35.3,1.5,44.7,28.6,Industrials,Heavy Equipment 276,Inditex,Spain,25.7,3.5,21.2,112.1,Consumer Discretionary,Apparel/Footwear Retail 277,Seven & I Holdings,Japan,53.9,0.894,49.2,37,Consumer Staples,Food Retail 278,Mitsubishi Electric,Japan,39.1,1.9,34.1,29.9,Industrials,Electrical Equipment 279,Bridgestone,Japan,30.7,2.4,31.9,31.7,Consumer Discretionary,Auto & Truck Parts 280,PG&E,United States,17.7,1.4,70.9,34,Utilities,Electric Utilities 281,Cardinal Health,United States,127.2,1.4,35,26,Health Care,Pharmaceuticals 282,Telstra,Australia,18.6,4.1,30.2,40.7,Telecommunication Services,Telecommunications services 283,Dominion Resources,United States,11.4,2.1,71.6,48.7,Utilities,Electric Utilities 284,Nomura,Japan,14.8,1.5,369.1,21.2,Financials, 285,Raytheon,United States,24.1,2.2,30.1,44.9,Industrials,Aerospace & Defense 286,Henkel,Germany,20.7,2.3,30,52,Consumer Staples,Household/Personal Care 287,Marathon Petroleum,United States,56,1.2,44.4,26.4,Energy,Oil & Gas Operations 288,Merck,Germany,16.6,1.8,41.1,49.1,Health Care,Pharmaceuticals 289,Peugeot,France,59.8,1.9,48.2,16.9,Consumer Discretionary,Auto & Truck Manufacturers 290,Tata Motors,India,41.2,1.7,39.5,24.9,Consumer Discretionary,Auto & Truck Manufacturers 291,Archer Daniels Midland,United States,62.4,1.3,39.8,25.6,Consumer Staples,Food Processing 292,CNP Assurances,France,30.7,1.2,428.1,13.8,Financials,Diversified Insurance 293,Natixis,France,14.6,1.5,556.7,19.1,Financials,Major Banks 294,China Huarong Asset Management,China,13.5,3,203.2,16.6,Financials, 295,Imperial Tobacco Group,United Kingdom,39.2,0.896,42.5,46.5,Consumer Staples,Tobacco 296,BCE,Canada,16.4,2.3,37.4,40.5,Telecommunication Services,Telecommunications services 297,Northrop Grumman,United States,24.5,2.2,28,41.7,Industrials,Aerospace & Defense 298,LyondellBasell Industries,United Kingdom,29.2,3.8,23.4,35.8,Materials,Diversified Chemicals 299,Posco,South Korea,45.8,1.1,66,19.2,Materials,Iron & Steel 300,Telecom Italia,Italy,21,2,74.3,18.1,Telecommunication Services,Telecommunications services 301,Oversea-Chinese Banking,Singapore,8.6,2.5,283.7,28.8,Financials,Regional Banks 302,KB Financial Group,South Korea,13.5,1.8,312,17.9,Financials,Investment Services 303,Charles Schwab,United States,7.6,1.9,223.4,53.3,Financials,Investment Services 304,Orange,France,45.3,0.552,101.5,40.4,Financials, 305,Surgutneftegas,Russia,13.9,7.8,60.9,18.5,Energy,Oil & Gas Operations 306,Sompo,Japan,29.4,1.7,91.1,14.4,Financials, 307,Gas Natural Fenosa,Spain,25.6,1.5,49.7,21.7,Utilities,Natural Gas Utilities 308,WPP,United Kingdom,19.4,1.9,42.7,27.4,Consumer Discretionary,Advertising 309,Bank Of Shanghai,China,10.9,2.1,236.3,20.7,Financials, 310,ICICI Bank,India,11.9,1.6,138.9,25.1,Financials,Regional Banks 311,Poly Real Estate,China,22.4,2,68.3,16.6,Financials,Real Estate 312,Hyundai Mobis,South Korea,33,2.6,34.5,18.9,Consumer Discretionary,Auto & Truck Parts 313,DNB,Norway,8.7,2.2,308.2,25.6,Financials,Regional Banks 314,Philips,Netherlands,27.1,1.6,34.1,28.9,Industrials,Conglomerates 315,AmerisourceBergen,United States,148.3,1.3,33.9,19,Health Care,Pharmaceuticals 316,Heineken Holding,Netherlands,23,1.7,42.1,23,Consumer Staples,Beverages 317,CRH,Ireland,30.1,1.4,33.3,29,Materials,Construction Materials 318,Discover Financial Services,United States,10.5,2.4,92.8,25.3,Financials,Consumer Financial Services 319,Safran,France,18.2,2.1,32.8,32.2,Industrials,Aerospace & Defense 320,Etisalat,United Arab Emirates,14.3,2.3,33.4,42.7,Telecommunication Services,Telecommunications services 321,CaixaBank,Spain,11,1.2,367,25.7,Financials,Regional Banks 322,Eaton,Ireland,19.7,1.9,30.4,33.4,Industrials,Other Industrial Equipment 323,Fubon Financial,Taiwan,15.4,1.5,196.5,16.1,Financials,Investment Services 324,Sumitomo,Japan,38.2,1.1,67.7,16.8,Financials, 325,SunTrust Banks,United States,8.9,1.9,204.9,27.2,Financials,Regional Banks 326,Greenland Holdings Group,China,32.8,1.2,99.6,14.3,Financials, 327,China Cinda Asset Management,China,13.4,2.3,169,14.7,Financials, 328,Anglo American,United Kingdom,21.5,1.6,50.1,19.8,Materials,Diversified Metals & Mining 329,China Resources Land,Hong Kong,14.5,2.5,55.1,19.8,Financials,Real Estate 330,Royal Ahold Delhaize N.V.,Netherlands,55,0.918,38.3,26,Utilities, 331,SingTel,Singapore,11.9,2.8,33,45.2,Telecommunication Services,Telecommunications services 332,United Overseas Bank,Singapore,8.2,2.2,235.4,25.6,Financials,Regional Banks 333,Subaru,Japan,29.8,2.8,22.7,27.3,Financials, 334,Automatic Data Processing,United States,12.1,1.7,40,45.9,Consumer Discretionary, 335,Midea Group,China,23.9,2.2,24.5,31.5,Utilities, 336,Linde,Germany,18.7,1.3,39,31.3,Materials,Diversified Chemicals 337,KIA Motors,South Korea,45.4,2.4,42.1,12.6,Consumer Discretionary,Auto & Truck Manufacturers 338,Hartford Financial Services,United States,18.1,0.896,225.2,17.9,Financials,Diversified Insurance 339,Southwest Airlines,United States,20.4,2.2,24.8,33.6,Industrials,Airline 340,América Móvil,Mexico,52.2,0.463,73.6,47.3,Financials, 341,Principal Financial Group,United States,12.5,1.3,228,18,Financials,Life & Health Insurance 342,Fannie Mae,United States,108.3,12.3,3288,2.8,Financials,Investment Services 343,Ameriprise Financial,United States,11.7,1.3,139.8,19.9,Financials,Investment Services 344,Macquarie Group,Australia,10.6,1.5,147.8,21.2,Financials,Investment Services 345,Carrefour,France,87.1,0.825,51.5,16.6,Consumer Staples,Food Retail 346,Celgene,United States,11,2,28.1,97.7,Health Care,Biotechs 347,China Evergrande Group,China,32.4,0.764,194.4,14.6,Health Care, 348,Takeda Pharmaceutical,Japan,15.9,1.2,35.5,36.9,Health Care,Pharmaceuticals 349,SK Hynix,South Korea,14.8,2.5,26.7,32,Information Technology,Semiconductors 350,Saudi Telecom,Saudi Arabia,13.8,2.3,27.1,37.3,Telecommunication Services,Telecommunications services 351,Tyson Foods,United States,36.9,1.9,22.3,24.2,Consumer Staples,Food Processing 352,Sinohydro Group,China,34.6,0.862,71.2,16.5,Industrials,Construction Services 353,Reckitt Benckiser Group,United Kingdom,13.3,2.5,22.3,64.1,Consumer Staples,Household/Personal Care 354,CSX,United States,11.1,1.7,36,43.8,Industrials,Railroads 355,Freddie Mac,United States,69.8,7.8,2023.4,1.5,Financials,Investment Services 356,Lincoln National,United States,13.4,1.2,263.5,14.8,Financials,Life & Health Insurance 357,Kinder Morgan,United States,12.9,0.704,80.3,48.1,Energy,Oil Services & Equipment 358,Power Corp of Canada,Canada,35.4,0.856,311.2,10.8,Financials,Life & Health Insurance 359,BP,United Kingdom,183.8,0.115,263.3,114.7,Energy,Oil & Gas Operations 360,Chevron,United States,110.5,-0.497,260.1,206.1,Energy,Oil & Gas Operations 361,Michelin Group,France,23.1,1.8,26.7,21.6,Consumer Discretionary,Auto & Truck Parts 362,Cheung Kong Property Holdings,Hong Kong,9,2.5,51.2,26.2,Financials, 363,SK Holdings,South Korea,72.1,0.66,85.3,14.7,Energy,Oil & Gas Operations 364,Gree Electric Appliances,China,15.3,2.1,27.2,27.7,Consumer Discretionary,Household Appliances 365,BAE Systems,United Kingdom,24,1.2,29.1,25.6,Industrials,Aerospace & Defense 366,Bank Of Jiangsu,China,9.7,1.6,230,16.6,Financials, 367,Edison International,United States,11.9,1.3,51.3,26.1,Utilities,Electric Utilities 368,Kansai Electric Power,Japan,28.3,1.6,58.5,11.3,Utilities,Electric Utilities 369,New China Life Insurance,China,22,0.751,100.5,17.6,Financials,Life & Health Insurance 370,Starbucks,United States,21.7,2.9,14,84.6,Consumer Discretionary,Restaurants 371,Centrica,United Kingdom,36.6,2.3,27.1,14.8,Utilities,Natural Gas Utilities 372,Biogen,United States,10.5,3.7,22.9,58,Health Care, 373,Canadian National Railway,Canada,9.1,2.7,28,55.9,Industrials,Railroads 374,HDFC,India,8.1,1.6,64.9,36.7,Financials,Consumer Financial Services 375,Novo Nordisk,Denmark,16.6,5.6,13.8,88.2,Health Care,Pharmaceuticals 376,Swedbank,Sweden,6.3,2.3,237.1,25.1,Financials,Major Banks 377,Tata Consultancy Services,India,17.4,3.9,14.5,74.5,Information Technology,Computer Services 378,Monsanto,United States,14.5,1.9,21.4,50.9,Materials,Specialized Chemicals 379,Mastercard,United States,10.8,4.1,18.7,121.3,Information Technology, 380,Femsa,Mexico,20.9,1.1,26.5,33.1,Consumer Staples,Beverages 381,PayPal,United States,10.8,1.4,33.1,51.7,Consumer Discretionary, 382,Progressive,United States,23.5,1,34.3,22.8,Financials,Property & Casualty Insurance 383,Svenska Handelsbanken,Sweden,6.4,1.9,289.2,25.9,Financials,Major Banks 384,Mitsui Fudosan,Japan,14.9,1.2,46.7,21.1,Financials,Real Estate 385,SEB,Sweden,7.7,1.2,288.5,23.8,Financials,Major Banks 386,Bank Rakyat Indonesia,Indonesia,8.3,2,74.5,24.2,Financials,Regional Banks 387,Texas Instruments,United States,13.4,3.4,16.4,80.5,Information Technology,Semiconductors 388,Wilmar International,Singapore,41.4,0.984,37,16.1,Consumer Staples,Food Processing 389,Daiwa House Industry,Japan,31.9,1.2,29.6,19.1,Industrials,Construction Services 390,Baidu,China,10.6,1.7,26.2,59.9,Information Technology,Computer Services 391,Consolidated Edison,United States,12.1,1.2,48.3,23.8,Utilities,Electric Utilities 392,Maybank,Malaysia,7.6,1.6,164.1,20.7,Financials,Regional Banks 393,DISH Network,United States,15.1,1.4,28.2,29.1,Consumer Discretionary,Broadcasting & Cable 394,Citic Securities,China,7.6,1.6,86,27.9,Financials,Investment Services 395,American Electric,United States,16.2,0.613,66.2,33.1,Utilities,Electric Utilities 396,Suzuki Motor,Japan,28.3,1.4,25.9,19.9,Consumer Discretionary,Auto & Truck Manufacturers 397,VTB Bank,Russia,19.5,0.699,206.2,14.9,Financials,Regional Banks 398,Metallurgical Corp of China,China,32.4,0.806,54.3,15.4,Industrials,Construction Services 399,Petrobras,Brazil,81.1,-4.3,247.3,61.3,Energy,Oil & Gas Operations 400,Norfolk Southern,United States,9.9,1.7,34.9,36,Industrials,Railroads 401,TJX Cos,United States,33.2,2.3,12.9,49.1,Consumer Discretionary,Department Stores 402,Investor AB,Sweden,7.3,3.9,40.7,32.2,Financials, 403,American International Group,United States,52.4,-0.849,498.3,60.1,Financials,Diversified Insurance 404,Sempra Energy,United States,10.2,1.3,47.8,27.5,Utilities,Natural Gas Utilities 405,International Airlines,United Kingdom,25,2.2,28.9,13.7,Industrials,Airline 406,SSE,United Kingdom,37.2,1.1,27.8,18.7,Utilities,Electric Utilities 407,eBay,United States,9,7.3,23.8,36.6,Consumer Discretionary,Internet & Catalog Retail 408,NTPC,India,12.2,1.6,38.7,21.5,Utilities,Electric Utilities 409,Old Mutual,United Kingdom,22.1,0.769,208.1,11.7,Financials,Investment Services 410,Magna International,Canada,36.4,2,22.6,15.3,Consumer Discretionary,Auto & Truck Parts 411,China Yangtze Power,China,6.5,2.8,45.3,42.3,Utilities,Electric Utilities 412,NN Group,Netherlands,15.6,1.3,177.5,10.3,Energy, 413,Sumitomo Mitsui Trust,Japan,9.2,1.5,548.2,12.8,Financials,Major Banks 414,General Mills,United States,15.7,1.6,21.7,33.2,Consumer Staples,Food Processing 415,Talanx,Germany,34.7,1,158.2,8.9,Financials,Diversified Insurance 416,The Priceline Group,United States,10.7,2.1,19.8,87,Financials, 417,EXOR,Italy,152.6,0.483,193,11.9,Financials,Investment Services 418,Glencore International,Switzerland,153.7,-0.453,124.6,57.3,Materials,Diversified Metals & Mining 419,Vodafone,United Kingdom,60.8,-9.3,175.7,67,Telecommunication Services,Telecommunications services 420,Huaneng Power International,China,16.9,1.3,44.5,14.4,Utilities,Electric Utilities 421,Standard Bank Group,South Africa,8.6,1.5,142.9,15.9,Financials,Major Banks 422,Swiss Life Holding,Switzerland,19.5,0.936,197,10.2,Financials,Life & Health Insurance 423,FirstRand,South Africa,8.4,1.7,86.3,18.2,Financials,Major Banks 424,Sysco,United States,53.1,1,17.6,28,Consumer Staples,Food Retail 425,International Paper,United States,21.1,0.904,33.3,20.8,Materials,Paper & Paper Products 426,Vivendi,France,12,1.4,34.1,24.2,Telecommunication Services,Telecommunications services 427,Kimberly-Clark,United States,18.2,2.2,14.6,46.7,Consumer Staples,Household/Personal Care 428,National Commercial Bank,Saudi Arabia,6.1,2.5,117.7,20.6,Financials, 429,Erste Group Bank,Austria,9.2,1.4,220.8,13.5,Financials,Major Banks 430,Baoshan Iron & Steel,China,26.8,0.667,40,21.4,Materials,Iron & Steel 431,Eni,Italy,61.6,-1.6,135.9,58.2,Energy,Oil & Gas Operations 432,Saudi Electricity,Saudi Arabia,13.3,0.561,107.4,26.6,Utilities,Electric Utilities 433,Corning,United States,9.4,3.7,27.9,24.9,Information Technology,Communications Equipment 434,Daikin Industries,Japan,18.5,1.4,20.6,28.4,Materials,Construction Materials 435,Mitsubishi Chemical,Japan,31.8,1.4,38.1,11.1,Materials,Diversified Chemicals 436,Becton Dickinson,United States,12.4,1.3,24.3,39.2,Health Care, 437,SK Innovation,South Korea,34.1,1.4,27,13.5,Energy,Oil & Gas Operations 438,Humana,United States,54.4,0.594,25.8,30.4,Health Care,Managed Health Care 439,Emerson Electric,United States,17.2,1.5,17.5,38.3,Industrials,Electrical Equipment 440,Nippon Steel & Sumitomo Metal,Japan,41.6,0.477,54.6,20.2,Materials,Iron & Steel 441,Deutsche Bank,Germany,44.3,-1.9,1686.6,34,Financials,Major Banks 442,Toyota Industries,Japan,20,1.1,38.6,14.8,Consumer Discretionary,Auto & Truck Parts 443,Pernod Ricard,France,9.7,1.4,33.5,31.4,Consumer Staples,Beverages 444,Aon,United Kingdom,11.6,1.4,26.6,31.4,Financials,Insurance Brokers 445,IntercontinentalExchange,United States,6,1.4,82,36.2,Financials,Investment Services 446,Stryker,United States,11.3,1.6,20.4,48.8,Health Care,Medical Equipment & Supplies 447,Fujifilm Holdings,Japan,21.7,1.1,28.6,18.1,Consumer Discretionary,Consumer Electronics 448,Fosun International,China,11.1,1.5,70,13.1,Materials,Iron & Steel 449,Sony,Japan,69.2,-0.393,151.7,40.4,Consumer Discretionary,Consumer Electronics 450,Chubu Electric Power,Japan,24.2,1.5,45.6,10,Utilities,Electric Utilities 451,Las Vegas Sands,United States,11.4,1.7,20.5,44.6,Consumer Discretionary,Casinos & Gaming 452,Xcel Energy,United States,11.1,1.1,41.2,22.5,Utilities,Electric Utilities 453,Fifth Third Bancorp,United States,6.7,1.6,142.2,18.8,Financials,Regional Banks 454,Bouygues,France,35.1,0.81,36.8,14.5,Industrials,Construction Services 455,ENGIE,France,73.7,-0.459,167.9,34.8,, 456,Steinhoff International,Netherlands,14.5,1.3,27.5,21.1,Consumer Discretionary,Furniture & Fixtures 457,Wheelock,Hong Kong,7.8,2.1,67.1,16.7,, 458,Marsh & McLennan,United States,13.2,1.8,18.2,37.8,Financials,Insurance Brokers 459,CBS,United States,13.2,1.3,24.2,29.5,Consumer Discretionary,Broadcasting & Cable 460,Fujitsu,Japan,41.7,1.2,27.1,12.5,Information Technology,Computer Hardware 461,Statoil,Norway,45.7,-2.9,104.5,56.2,Energy,Oil & Gas Operations 462,Wesfarmers,Australia,50,0.439,30,37.8,Consumer Staples,Food Retail 463,Axis Bank,India,7.8,1.3,80.4,18.8,Financials,Regional Banks 464,HeidelbergCement,Germany,16.8,0.782,40.4,18.5,Materials,Construction Materials 465,Illinois Tool Works,United States,13.6,2,15.2,45.5,Industrials,Other Industrial Equipment 466,Formosa Petrochemical,Taiwan,16.9,2.3,14,33.6,Energy,Oil & Gas Operations 467,Marriott International,United States,17.1,0.78,24.1,34.9,Consumer Discretionary,Hotels & Motels 468,Adidas,Germany,21.3,1.1,16,39.6,Consumer Discretionary,Apparel/Accessories 469,CLP Holdings,Hong Kong,10.2,1.6,26.6,26.8,Utilities,Electric Utilities 470,Thomson Reuters,United States,11.2,1.1,27.9,31.4,Consumer Discretionary,Printing & Publishing 471,Kering,France,13.7,0.913,25.5,33.7,Consumer Discretionary,Apparel/Accessories 472,Colgate-Palmolive,United States,15.2,2.4,12.1,64.9,Consumer Staples,Household/Personal Care 473,Richemont,Switzerland,11.5,1.1,22.6,44.3,Consumer Discretionary,Specialty Stores 474,Shin-Etsu Chemical,Japan,11.3,1.5,21,36.2,Materials,Diversified Chemicals 475,Mapfre,Spain,23.3,0.858,67.4,10.5,Financials,Diversified Insurance 476,Poste Italiane,Italy,36.6,0.688,203.8,8.6,Consumer Discretionary, 477,Aisin Seiki,Japan,31.7,1.1,27.7,13.2,Consumer Discretionary,Auto & Truck Parts 478,Mitsubishi,Japan,57,-0.164,129.1,34.7,Utilities, 479,Mitsubishi Estate,Japan,10.3,0.869,46.3,25.4,Financials,Real Estate 480,PPL,United States,7.5,1.9,38.3,25.4,Utilities,Electric Utilities 481,Waste Management,United States,13.6,1.2,21.3,32.3,Industrials,Environmental & Waste 482,Micron Technology,United States,14.7,0.689,32.4,31.6,Information Technology,Semiconductors 483,Larsen & Toubro,India,15.7,0.814,29.8,24.5,Industrials,Construction Services 484,Aegon,Netherlands,36.6,0.501,437.4,10,Financials,Diversified Insurance 485,Teva Pharmaceutical,Israel,21.9,0.337,92.9,32.4,Health Care, 486,Compass Group,United Kingdom,27.8,1.4,13.7,31.1,Consumer Discretionary,Restaurants 487,Suncor Energy,Canada,20.2,0.328,66.1,51.7,Energy,Oil & Gas Operations 488,EDP-Energias de Portugal,Portugal,16.1,1.1,46.5,12.3,Utilities,Electric Utilities 489,Syngenta,Switzerland,12.8,1.2,19.5,40.9,Materials,Specialized Chemicals 490,Tenaga Nasional,Malaysia,10.9,1.7,30.9,17.4,Utilities,Electric Utilities 491,Credit Suisse Group,Switzerland,29.9,-2.8,806.7,30.1,Financials,Investment Services 492,Hana Financial Group,South Korea,11.1,1.1,288.3,9.7,Financials,Regional Banks 493,Sampo,Finland,6.9,1.8,39.8,26.5,Financials,Diversified Insurance 494,Bank Mandiri,Indonesia,7.5,1,77.1,21.4,Financials,Regional Banks 495,Schlumberger,United States,27.8,-1.7,78,109.2,Energy,Oil Services & Equipment 496,UniCredit Group,Italy,24.4,-13,906.6,33.3,Financials,Major Banks 497,M&T Bank,United States,5.7,1.3,123.4,23.5,Financials,Regional Banks 498,Thales,France,16.5,1,23.9,20.6,Industrials,Aerospace & Defense 499,Applied Materials,United States,11.8,2.1,15.2,42,Information Technology,Semiconductors 500,Royal Bank of Scotland,United Kingdom,21.6,-7.1,986.9,35,Financials,Major Banks 501,SK Telecom,South Korea,14.7,1.4,25.9,16.3,Telecommunication Services,Telecommunications services 502,TE Connectivity,Switzerland,12.5,2.1,17.6,26,Information Technology,Electronics 503,Caterpillar,United States,38.5,-0.67,74.7,56,Industrials,Heavy Equipment 504,China Merchants Shekou Industrial Zone Holdings,China,8.6,1.4,36.1,20.8,Financials, 505,Ecolab,United States,13,1.2,18.3,36.3,Materials,Specialized Chemicals 506,H&M,Sweden,22.7,2.2,10.9,39.9,Consumer Discretionary,Apparel/Footwear Retail 507,Omnicom Group,United States,15.5,1.1,23.2,20.1,Consumer Discretionary,Advertising 508,Chinatrust Financial,Taiwan,11,0.866,150.2,11.8,Financials,Consumer Financial Services 509,Komatsu,Japan,15.7,0.939,23,24.3,Industrials,Heavy Equipment 510,Haitong Securities,China,6.3,1.2,80.7,22.9,Financials,Investment Services 511,Grupo ACS,Spain,36.3,0.831,35.2,11.2,Industrials,Construction Services 512,Resona Holdings,Japan,6.7,1.7,410.7,11.7,Financials,Regional Banks 513,Bharti Airtel,India,14.7,0.702,34.8,21.4,Telecommunication Services,Telecommunications services 514,Deutsche Lufthansa,Germany,35,2,37.8,7.4,Industrials,Airline 515,Al Rajhi Bank,Saudi Arabia,4.5,2.2,90.5,27.6,Financials,Regional Banks 516,Swisscom,Switzerland,11.8,1.6,21.1,22.5,Telecommunication Services,Telecommunications services 517,Couche Tard,Canada,35.7,1.1,13.8,25.9,Consumer Discretionary,Specialty Stores 518,Loews,United States,13,0.654,76.6,15.9,Financials,Diversified Insurance 519,ConocoPhillips,United States,23.8,-3.6,89.8,61.3,Energy,Oil & Gas Operations 520,Baxter International,United States,10.2,4.9,15.5,28.6,Health Care,Medical Equipment & Supplies 521,Praxair,United States,10.5,1.5,19.3,33.9,Materials,Specialized Chemicals 522,Kyocera,Japan,12.9,1.1,26.5,19.9,Information Technology,Electronics 523,Garanti Bank,Turkey,9.2,1.7,88.1,10.2,Financials,Regional Banks 524,China Unicom,Hong Kong,41.3,0.102,88.9,33.8,Telecommunication Services,Telecommunications services 525,CIC Group,France,11.7,1.3,304.8,7.9,Financials,Major Banks 526,Kubota,Japan,14.7,1.2,22.9,18.5,Industrials,Heavy Equipment 527,Isbank,Turkey,11.7,1.8,118,7.9,Financials,Regional Banks 528,Astellas Pharma,Japan,12.1,1.9,15.9,28,Health Care,Pharmaceuticals 529,CNOOC,Hong Kong,22,0.96,91.8,54.8,Energy,Oil & Gas Operations 530,Simon Property Group,United States,5.4,1.8,31.1,53,Financials,Real Estate 531,ASML Holding,Netherlands,7.5,1.7,19.7,56.8,Information Technology,Semiconductors 532,British Sky Broadcasting,United Kingdom,17.1,0.847,22.9,20.6,Consumer Discretionary,Broadcasting & Cable 533,Cummins,United States,17.5,1.4,15,25.1,Industrials,Heavy Equipment 534,China Zheshang Bank,China,8.1,1.5,195,9.8,Utilities, 535,Standard Chartered,United Kingdom,19.4,-0.247,646.7,30.2,Financials,Major Banks 536,Tokyo Electric Power,Japan,50.2,1,105.5,6.1,Utilities,Electric Utilities 537,Cognizant Technology,United States,13.5,1.6,14.3,35.6,Information Technology,Computer Services 538,Viacom,United States,12.7,1.4,23.3,18,Consumer Discretionary,Broadcasting & Cable 539,Sasol,South Africa,11.8,0.993,27.6,19.8,Materials,Diversified Chemicals 540,Public Service Enterprise Group,United States,8.8,0.887,40.1,22.6,Energy, 541,Coal India,India,11,1.6,17.8,27.4,Materials,Diversified Metals & Mining 542,Novatek,Russia,8,3.8,15.8,39,Energy,Oil & Gas Operations 543,Kirin Holdings,Japan,19.1,1.1,20.1,17.4,Consumer Staples,Beverages 544,T&D Holdings,Japan,16.6,0.703,128.6,9.2,Financials,Life & Health Insurance 545,Mitsui,Japan,39.2,0.115,100,26.2,Financials, 546,Regions Financial,United States,5.8,1.2,126.7,17.4,Financials,Regional Banks 547,Ingersoll Rand,Ireland,13.5,1.5,17.4,21.4,, 548,CenturyLink,United States,17.5,0.626,47,13.4,Telecommunication Services,Telecommunications services 549,Bank of Nanjing,China,7.8,1.3,153.4,10.5,Financials,Regional Banks 550,Dongfeng Motor Group,China,18.4,2,26.6,9.4,Consumer Discretionary,Auto & Truck Manufacturers 551,Citizens Financial Group,United States,5.8,1,150.6,17.6,Financials, 552,Swire Pacific,Hong Kong,8,1.2,48.2,15.1,Industrials,Conglomerates 553,RELX Group,United Kingdom,9.3,1.6,16.5,40.9,Information Technology, 554,Samsung Fire & Marine,South Korea,17.2,0.74,55.5,10.4,Financials,Property & Casualty Insurance 555,Associated British Foods,United Kingdom,19,1.2,14.8,24.4,Consumer Staples,Food Processing 556,Standard Life,United Kingdom,25.2,0.497,228.7,9,Financials,Life & Health Insurance 557,Ally Financial,United States,10.1,1.1,163.7,9.1,Financials, 558,Møller-Maersk,Denmark,35.5,-2,62.6,35,, 559,Ecopetrol,Colombia,15.6,0.513,40.4,19.7,Energy,Oil & Gas Operations 560,National Bank of Canada,Canada,6.2,1.1,179.6,14.3,Financials,Major Banks 561,Sumitomo Electric,Japan,25.6,0.905,24.6,12.5,Consumer Discretionary,Auto & Truck Parts 562,Shire,Ireland,11.6,0.342,67,51.3,Health Care,Pharmaceuticals 563,Akzo Nobel,Netherlands,15.7,1.1,17.4,21.4,Materials,Diversified Chemicals 564,Bank Central Asia,Indonesia,4.8,1.5,50.2,32.1,Financials,Regional Banks 565,Marubeni,Japan,63.5,0.429,64.3,10.7,Industrials,Trading Companies 566,DTE Energy,United States,10.7,0.868,32,18.5,Utilities,Electric Utilities 567,Koç Holding,Turkey,23.5,1.1,25.1,10.4,Financials, 568,Unum,United States,11.1,0.931,63.6,10.6,Financials, 569,Best Buy,United States,39.4,1.2,14.2,14.9,Information Technology,Computer & Electronics Retail 570,Atlas Copco,Sweden,12.1,1.4,13.2,42.9,Industrials,Other Industrial Equipment 571,MTR,Hong Kong,5.8,1.3,33.2,33.3,Industrials,Railroads 572,Suncorp Group,Australia,10.3,0.777,70.1,12.6,Financials,Diversified Insurance 573,Emirates NBD,United Arab Emirates,5.5,1.8,122,12.5,Financials,Regional Banks 574,LG Chem,South Korea,17.8,1.1,17,17.7,Materials,Specialized Chemicals 575,QBE Insurance Group,Australia,11.6,0.84,42.3,13.3,Financials,Property & Casualty Insurance 576,Northern Trust,United States,5.2,1,123.9,19.8,Financials,Regional Banks 577,CME Group,United States,3.6,1.5,69.4,40.1,Financials,Investment Services 578,Otsuka Holding,Japan,11,0.852,22.7,25.1,Health Care,Pharmaceuticals 579,Altice,Netherlands,20.6,-1.7,89.3,30.9,Telecommunication Services, 580,American Tower,United States,5.8,0.956,30.9,52.2,Financials,Real Estate 581,Sinopharm Group,China,38.9,0.701,22.7,12.7,Health Care,Pharmaceuticals 582,Falabella,Chile,12.7,0.901,20.8,21.3,Consumer Discretionary,Department Stores 583,Bharat Petroleum,India,28.8,1.2,14.4,15.2,Energy,Oil & Gas Operations 584,Newell Brands,United States,13.3,0.527,33.8,22.3,Consumer Discretionary, 585,Akbank,Turkey,7.9,1.5,91.6,9.2,Financials,Regional Banks 586,Norilsk Nickel,Russia,8.2,2.5,16.5,24.9,Materials,Diversified Metals & Mining 587,Atlantia,Italy,6,1.2,41.5,21.1,Industrials,Other Transportation 588,Tesco,United Kingdom,78.4,0.232,61.8,19.2,Consumer Staples,Food Retail 589,MGM Resorts,United States,9.5,1.1,28.9,16.1,Consumer Discretionary,Casinos & Gaming 590,Dollar Tree,United States,20.7,0.896,16,18.3,Consumer Discretionary,Discount Stores 591,GFNorte,Mexico,6.4,1,61.9,16.2,Financials,Regional Banks 592,Broadcom,Singapore,15.6,-1.9,49.6,87.6,Information Technology,Semiconductors 593,Woori Bank,South Korea,9.5,0.909,257.2,8.2,Financials, 594,Guotai Junan Securities,China,4.9,1.5,59.2,20.3,, 595,Constellation Brands,United States,7.3,1.5,18.6,33.4,Consumer Staples,Beverages 596,Royal Caribbean Cruises,United States,8.5,1.3,22.3,20.9,Consumer Discretionary,Hotels & Motels 597,Air Products & Chemicals,United States,9.1,1.5,16,29.4,Materials,Specialized Chemicals 598,Infosys,India,10.1,2.1,11.9,34.9,Information Technology,Computer Services 599,BYD,China,15.3,0.759,20.9,18.4,Consumer Discretionary,Auto & Truck Manufacturers 600,KeyCorp,United States,5.4,0.791,137.3,18.9,Financials,Regional Banks 601,Johnson Controls International,Ireland,22.1,-0.457,48.8,38.5,, 602,Asahi Kasei,Japan,17,1,20.1,13.1,Materials,Diversified Chemicals 603,New World Development,Hong Kong,7.1,1.2,54.2,12.1,Financials,Real Estate 604,Siam Cement,Thailand,12,1.6,15.1,19.1,Materials,Specialized Chemicals 605,Toray Industries,Japan,18.6,0.836,20.7,14.2,Materials,Diversified Chemicals 606,Grupo Mexico,Mexico,8.2,1.1,22.8,23.7,Materials,Diversified Metals & Mining 607,E.ON,Germany,59.8,-9.3,70.4,16.9,Utilities,Electric Utilities 608,Dell Technologies,United States,61.9,-3.7,118.2,13.5,Information Technology, 609,Nokia,Finland,26.1,-0.749,49.2,30.3,Information Technology,Communications Equipment 610,Eletrobrás,Brazil,17.4,0.983,52.4,7.2,Utilities, 611,Legend Holding,China,44.3,0.732,46.4,7,Financials, 612,Dollar General,United States,22,1.3,11.9,19.1,Consumer Discretionary,Discount Stores 613,Banco de Sabadell,Spain,7.8,0.786,224.1,10,Financials,Regional Banks 614,Eversource Energy,United States,7.6,0.942,32.1,18.8,Energy, 615,China Taiping Insurance,Hong Kong,21.6,0.582,64.4,8.6,Financials,Diversified Insurance 616,Hyundai Heavy Industries,South Korea,33.9,0.452,40.8,11,Industrials,Heavy Equipment 617,Sekisui House,Japan,18.7,1.1,19.4,11.3,Industrials,Construction Services 618,Siam Commercial Bank,Thailand,4.9,1.3,81.3,16.1,Financials,Regional Banks 619,China Eastern Airlines,China,14.8,0.694,30.2,12.9,Industrials,Airline 620,Cemex,Mexico,13.7,0.751,29.1,13.1,Materials,Construction Materials 621,Sumitomo Realty,Japan,9,0.948,41.1,12.3,Financials,Real Estate 622,Murata Manufacturing,Japan,10.4,1.4,13.9,28.4,Information Technology,Electronics 623,WEC Energy Group,United States,7.5,0.94,31.3,19,Energy, 624,JXTG Holdings,Japan,72,-0.65,58,16.6,Financials, 625,China Fortune Land Development,China,7.6,0.977,36,15.5,, 626,Evonik,Germany,14.1,0.827,22.1,14.9,, 627,Henderson Land,Hong Kong,3.3,2.8,45.9,22.9,Financials,Real Estate 628,China Southern Airlines,China,17.2,0.77,28.8,10.4,Industrials,Airline 629,ThyssenKrupp Group,Germany,44,0.362,36.9,13.5,Industrials,Conglomerates 630,Whirlpool,United States,20.7,0.888,19.2,12.4,Consumer Discretionary,Household Appliances 631,Paccar,United States,17,0.522,20.6,23.6,Industrials,Heavy Equipment 632,Public Bank,Malaysia,4.5,1.3,84.7,17.4,Financials,Regional Banks 633,Bank of Ningbo,China,6.1,1.2,125.6,10.3,Financials,Regional Banks 634,Valeo,France,18.3,1,15.2,15.4,Consumer Discretionary,Auto & Truck Parts 635,Asahi Group Holdings,Japan,15.7,0.821,17,17.8,Consumer Staples,Beverages 636,Unibail-Rodamco,France,2.4,2.7,43,23.6,Financials,Real Estate 637,Delphi Automotive,United Kingdom,16.7,1.2,12.3,20.2,Consumer Discretionary,Auto & Truck Parts 638,Bunge,United States,42.7,0.743,19.2,10.6,Consumer Staples,Food Processing 639,Capgemini,France,13.9,1,17.4,15.7,Information Technology,Computer Services 640,Mylan,United Kingdom,11.1,0.48,34.7,21.2,Health Care,Pharmaceuticals 641,Sodexo,France,22.5,0.707,15.8,17.5,Consumer Discretionary,Business & Personal Services 642,Kasikornbank,Thailand,5.4,1.1,79.5,13.4,Financials,Major Banks 643,TELUS,Canada,9.6,0.923,20.7,19.5,Telecommunication Services,Telecommunications services 644,Formosa Chemicals,Taiwan,9.9,1.4,16.9,17.9,Materials,Specialized Chemicals 645,Kao Corp,Japan,13.4,1.2,11.5,27.3,Consumer Staples,Household/Personal Care 646,VMware,United States,7.1,1.2,16.6,37.8,Information Technology,Software & Programming 647,Kweichow Moutai,China,5,2.5,15.8,71.5,Consumer Staples,Beverages 648,SCA,Sweden,13.7,0.651,18.2,21.6,Consumer Staples,Household/Personal Care 649,Fidelity National Information,United States,9.3,0.568,26,26.3,Consumer Discretionary,Business & Personal Services 650,Aeon,Japan,75.5,0.057,76.1,12.4,Consumer Discretionary,Discount Stores 651,Centene,United States,40.6,0.558,20.2,12.2,Health Care, 652,Great Wall Motor,China,14.2,1.6,13.3,15.2,Consumer Discretionary,Auto & Truck Manufacturers 653,Telkom Indonesia,Indonesia,8.7,1.5,13.3,30.4,Telecommunication Services, 654,Molson Coors Brewing,United States,4.9,2,29.3,20.5,Consumer Staples,Beverages 655,Naspers,South Africa,5.9,0.994,18.2,75.4,Consumer Discretionary,Broadcasting & Cable 656,Stanley Black & Decker,United States,11.4,0.965,15.6,20.2,Consumer Staples,Household/Personal Care 657,Nucor,United States,16.2,0.731,15.2,19.4,Materials,Iron & Steel 658,Transneft,Russia,12.7,3.5,45.6,4.7,Energy,Oil Services & Equipment 659,JD.com,China,39,-0.576,23.1,45.7,Consumer Discretionary, 660,National Bank of Abu Dhabi,United Arab Emirates,4,1.4,114.5,16.1,Financials,Regional Banks 661,West Japan Railway,Japan,13.3,0.742,24.5,12.8,Industrials,Railroads 662,DaVita,United States,14.7,0.88,18.7,13,Health Care,Healthcare Services 663,Commerzbank,Germany,15.9,0.295,513.1,11,Financials,Major Banks 664,Fortescue Metals Group,Australia,8.2,1.9,19,14.3,Materials,Iron & Steel 665,Nan Ya Plastics,Taiwan,8.5,1.5,16.1,18.5,Materials,Diversified Chemicals 666,Solvay,Belgium,13.4,0.687,25.5,12.9,Materials,Diversified Chemicals 667,Longfor Properties,China,8.2,1.4,32.4,10.1,Financials,Real Estate 668,Rogers Communications,Canada,10.3,0.644,21.1,22.9,Telecommunication Services,Telecommunications services 669,Barrick Gold,Canada,8.6,0.649,25.3,22.3,Materials,Diversified Metals & Mining 670,China Reinsurance Group,China,13.7,0.776,29.7,9.9,, 671,CIMB Group Holdings,Malaysia,6,0.86,108.3,10.9,Financials,Regional Banks 672,Krung Thai Bank,Thailand,4.8,0.915,75.1,17,Financials,Regional Banks 673,Tatneft,Russia,8.7,1.6,17.9,14.6,Energy,Oil & Gas Operations 674,WH Group,Hong Kong,21.5,1,13.6,12.8,, 675,Kellogg,United States,13,0.694,15.1,25.4,Consumer Staples,Food Processing 676,Recruit Holdings,Japan,16.1,0.778,11.2,29,Financials, 677,Fast Retailing,Japan,16.4,0.639,12.4,31.8,Consumer Discretionary,Apparel/Footwear Retail 678,Estée Lauder,United States,11.4,1.1,11.2,30.8,, 679,Allied Irish Banks,Ireland,4,1.5,100.9,14.6,Financials,Regional Banks 680,PKN Orlen,Poland,20.2,1.3,13.3,11.2,Energy,Oil & Gas Operations 681,BAIC Motor,China,17.5,0.955,24.3,8.3,Consumer Discretionary, 682,Mazda Motor,Japan,29.5,0.837,22,8.1,Consumer Discretionary,Auto & Truck Manufacturers 683,Weyerhaeuser,United States,6.8,0.986,19.2,26.1,Financials,Real Estate 684,Woolworths,Australia,41.1,0.344,17.1,25.7,Consumer Staples,Food Retail 685,Dong Energy,Denmark,8.5,1.1,19.4,16.4,Energy, 686,Level 3 Communications,United States,8.2,0.666,24.9,21.1,Telecommunication Services,Telecommunications services 687,Franklin Resources,United States,6.5,1.7,16.2,23.6,Financials,Investment Services 688,Nidec,Japan,10.6,0.944,12.8,28.1,Industrials,Electrical Equipment 689,George Weston,Canada,36.2,0.415,28.3,10.9,Consumer Staples,Food Retail 690,Lenovo Group,Hong Kong,42.6,0.608,27.2,7.7,Information Technology,Computer Hardware 691,LG Display,South Korea,22.8,0.782,20.6,9.4,Information Technology,Electronics 692,Activision Blizzard,United States,6.6,0.911,17.5,37.3,, 693,Grupo Aval,Colombia,8.5,0.701,74.7,9.2,Financials,Investment Services 694,All Nippon Airways,Japan,16.1,0.841,19.4,10.4,Industrials,Airline 695,Reinsurance Group of America,United States,11.5,0.701,53.1,8.1,Financials,Diversified Insurance 696,Tesoro,United States,24.6,0.734,20.4,9.4,Energy,Oil & Gas Operations 697,Samsung C&T,South Korea,24.2,0.093,36.8,21.2,Industrials,Trading Companies 698,RWE Group,Germany,48.2,-6.3,85.4,10.3,Utilities,Electric Utilities 699,Guosen Securities,China,6,1,28.3,17.6,Financials, 700,TransCanada,Canada,9.6,0.176,65.7,40.6,Energy,Oil Services & Equipment 701,Adobe Systems,United States,6.1,1.3,13,64.4,Information Technology,Software & Programming 702,Teliasonera,Sweden,9.8,0.544,27.9,17,Telecommunication Services,Telecommunications services 703,Dentsu,Japan,7.7,0.769,27.1,15.5,Consumer Discretionary,Advertising 704,Deutsche Boerse,Germany,3.2,0.799,172.8,17.9,Financials,Investment Services 705,Daiwa Securities,Japan,5.2,0.948,176,9.9,Financials,Investment Services 706,GF Securities,China,4.1,1.2,51.8,18.4,Financials,Investment Services 707,Textron,United States,13.8,0.962,15.4,12.9,Industrials,Conglomerates 708,Mitsubishi Heavy Industries,Japan,36,-0.007,48.9,13.4,Industrials,Heavy Equipment 709,Republic Services,United States,9.4,0.613,20.6,21.3,Industrials,Environmental & Waste 710,Ericsson,Sweden,26,0.2,31.6,21.4,Information Technology,Communications Equipment 711,Isuzu Motors,Japan,17.4,0.957,15.1,10.8,Consumer Discretionary,Auto & Truck Manufacturers 712,Anhui Conch Cement,China,8.3,1.3,15.8,16.9,Materials,Construction Materials 713,First Data,United States,11.6,0.42,40.3,14,Information Technology, 714,Vonovia,Germany,3.9,2.5,34.3,16.8,, 715,Telenor,Norway,15.5,0.337,24,24.4,Telecommunication Services,Telecommunications services 716,Halliburton,United States,15.9,-5.8,27,43.1,Energy,Oil Services & Equipment 717,PPG,United States,15.1,0.456,15.8,27.3,Materials, 718,CGN Power,China,5.1,1.1,41.4,14.2,Energy, 719,Bankia,Spain,3.7,0.962,200.6,13.2,Financials,Regional Banks 720,Chunghwa Telecom,Taiwan,7.1,1.2,13.9,26.5,Telecommunication Services,Telecommunications services 721,Veolia Environnement,France,27,0.348,40,10.2,Utilities,Diversified Utilities 722,Occidental Petroleum,United States,10.1,-0.574,43.1,48.2,Energy,Oil & Gas Operations 723,J Sainsbury,United Kingdom,33.7,0.69,25.8,7.1,Consumer Staples,Food Retail 724,Bancolombia,Colombia,6.7,0.939,65.4,8.9,Financials,Regional Banks 725,Sherwin-Williams,United States,11.9,1.1,6.8,29.2,Consumer Discretionary,Home Improvement Retail 726,NVIDIA,United States,6.9,1.6,9.8,59.1,Information Technology,Semiconductors 727,HAL Trust,Netherlands,8.9,0.964,17.5,15.1,Financials,Investment Services 728,VF,United States,12.1,1.2,9.7,22.5,Consumer Discretionary,Apparel/Accessories 729,Yum Brands,United States,11,1.6,5.5,22.7,Consumer Discretionary,Restaurants 730,Qingdao Haier,China,17,0.718,18.1,10.6,Consumer Discretionary,Household Appliances 731,Prologis,United States,2.5,1.2,30.3,28.4,Financials,Real Estate 732,Goodyear,United States,15.2,1.3,16.5,8.9,Consumer Discretionary,Auto & Truck Parts 733,Liberty Interactive,United States,10.6,1.2,20.9,9.4,Consumer Discretionary,Internet & Catalog Retail 734,Coca-Cola European Partners,United Kingdom,10.1,0.614,19.6,17.9,, 735,Ross Stores,United States,12.9,1.1,5.5,24.7,Consumer Discretionary,Apparel/Footwear Retail 736,CNH Industrial,United Kingdom,25.3,-0.373,47.8,13.5,Materials, 737,Macy's,United States,25.8,0.619,21.2,8.9,Consumer Discretionary,Department Stores 738,XL Group,Bermuda,10.4,0.441,58.4,10.6,Financials,Diversified Insurance 739,Parker-Hannifin,United States,11.2,0.88,11.7,21.5,Industrials,Other Industrial Equipment 740,Sabanci Holding,Turkey,11.9,0.88,87.9,5.4,Industrials,Conglomerates 741,Annaly Capital Management,United States,2,1.4,87.9,11.5,Financials,Real Estate 742,Industrial Bank of Korea,South Korea,7.8,0.998,212.7,6,Financials,Regional Banks 743,Hanwha,South Korea,40.6,0.424,128.2,2.2,, 744,Essilor International,France,7.9,0.899,13.9,25.7,Health Care,Medical Equipment & Supplies 745,Kotak Mahindra Bank,India,4.8,0.683,37.4,25.1,Financials,Regional Banks 746,Kyushu Electric Power,Japan,16.8,0.977,38.1,5.1,Utilities,Electric Utilities 747,Scor,France,14.6,0.667,44.2,6.8,Financials,Diversified Insurance 748,Chongqing Changan Auto,China,10.6,1.7,15.1,10.2,Consumer Discretionary,Auto & Truck Manufacturers 749,Husky Energy,Canada,9.8,0.696,24.9,11.4,Energy,Oil & Gas Operations 750,China Energy Engineering,China,33.4,0.645,42.1,5.6,Energy, 751,Abertis,Spain,5.3,0.88,32.9,16,Industrials,Other Transportation 752,Huatai Securities,China,3.7,0.944,57.8,16.9,Financials,Investment Services 753,Luxottica Group,Italy,10.1,0.941,10.9,25.9,Consumer Discretionary,Specialty Stores 754,Teck Resources,Canada,7,0.785,26.6,13.6,Materials,Diversified Metals & Mining 755,Lotte Chemical,South Korea,11.4,1.6,13.1,11.1,Materials,Specialized Chemicals 756,Tohoku Electric Power,Japan,18.2,0.658,34.9,6.8,Utilities,Electric Utilities 757,Western Digital,United States,15.9,-0.408,29,24.5,Information Technology,Computer Storage Devices 758,National Bank of Kuwait,Kuwait,3.2,0.931,79.2,14,Financials,Regional Banks 759,Rolls-Royce Holdings,United Kingdom,20.2,-5.4,31.6,18.2,Industrials,Aerospace & Defense 760,Japan Airlines,Japan,11.9,1.3,13.9,10.6,Industrials,Airline 761,Credicorp,Peru,4.5,1,46.7,12.7,Financials,Regional Banks 762,UPM-Kymmene,Finland,10.9,0.972,14.7,12.7,Materials,Paper & Paper Products 763,Guangdong Wens Foodstuffs Group,China,8.9,1.9,6,20.9,Consumer Staples, 764,Toyota Tsusho,Japan,71,-0.14,34.8,10.6,Industrials,Trading Companies 765,Wipro,India,8.1,1.3,11.8,19.4,Information Technology,Computer Services 766,Hilton,United States,11.7,0.348,26.2,18.7,, 767,Daqin Railway,China,6.7,1,17.9,16.6,Industrials,Railroads 768,Lennar,United States,11.3,0.891,15.4,11.9,Industrials,Construction Services 769,Ferrovial,Spain,11.9,0.416,24.7,14.9,Industrials,Other Transportation 770,Anadarko Petroleum,United States,8.5,-3.1,45.6,34.9,Energy,Oil & Gas Operations 771,Scentre Group,Australia,1.9,2.2,24.6,17.6,, 772,AutoZone,United States,10.8,1.3,8.9,19.9,Consumer Discretionary,Specialty Stores 773,Agrium,Canada,13.6,0.578,17,13,Materials,Specialized Chemicals 774,Vestas Wind Systems,Denmark,11.3,1.1,10.5,17.5,Industrials,Electrical Equipment 775,China Resources Power,Hong Kong,8.5,0.993,25.8,8.8,Utilities,Electric Utilities 776,Freeport-mcmoran,United States,14.9,-4,37.3,18.2,, 777,Samba Financial Group,Saudi Arabia,2.4,1.3,61.7,11.3,Financials,Regional Banks 778,ROSSETI,Russia,13.6,1.1,34.2,3.3,Energy, 779,Schaeffler,Germany,14.8,0.95,12.5,11.1,Industrials, 780,GD Power Development,China,8.3,0.638,38.4,9.5,Utilities,Electric Utilities 781,Adecco,Switzerland,25.1,0.799,10.7,11.8,Consumer Discretionary,Business & Personal Services 782,Discovery Communications,United States,6.5,1.2,15.8,16.8,Consumer Discretionary,Broadcasting & Cable 783,Canadian Natural Resources,Canada,7.9,-0.154,44.9,37.1,Energy,Oil & Gas Operations 784,Ryanair Holdings,Ireland,7.3,1.5,11.5,19.6,Industrials,Airline 785,Huntington Bancshares,United States,3.8,0.712,99.7,14.3,Financials,Regional Banks 786,Hong Kong Exchanges,Hong Kong,1.3,0.743,31.9,30.9,Financials,Investment Services 787,Carlsberg,Denmark,9.3,0.667,18.3,14.1,Consumer Staples,Beverages 788,Porsche Automobil Holding,Germany,0.001,1.5,30,16.1,Consumer Discretionary,Auto & Truck Manufacturers 789,Kone,Finland,9.7,1.1,8.4,23.7,Industrials,Construction Services 790,CarMax,United States,15.9,0.627,16.3,10.5,Consumer Discretionary,Specialty Stores 791,Itaúsa,Brazil,1.3,2.4,18.1,23,, 792,Sime Darby,Malaysia,10.7,0.697,15.4,14.3,Industrials,Conglomerates 793,Wolseley,Switzerland,21.5,0.624,10.7,15.5,Materials,Construction Materials 794,Air France-KLM,France,27.5,0.876,24.2,2.2,Industrials,Airline 795,Willis Towers Watson,United Kingdom,7.9,0.42,30.3,17.4,, 796,Welltower,United States,4.3,0.714,28.9,26.2,, 797,ITC,India,6,1.5,7.8,51.6,Consumer Staples,Tobacco 798,KT Corp,South Korea,19.6,0.613,25.3,7.1,Telecommunication Services,Telecommunications services 799,OMV Group,Austria,21.3,-0.446,33.9,13.6,Energy,Oil & Gas Operations 800,Sumitomo Chemical,Japan,17.6,0.48,24.3,8.8,Materials,Diversified Chemicals 801,Emaar Properties,United Arab Emirates,4.3,1.4,26.2,14.6,Financials,Real Estate 802,VEON,Netherlands,8.9,2.5,21,6.8,Telecommunication Services, 803,Eiffage,France,15.8,0.525,31.1,7.3,Industrials,Construction Services 804,Bangkok Bank,Thailand,4.3,0.902,82.2,10.1,Financials,Regional Banks 805,Metro Group,Germany,64.5,0.222,30.8,10.1,Consumer Staples,Food Retail 806,CSL,Australia,6.4,1.3,8.1,43.9,Health Care,Biotechs 807,Hindustan Petroleum,India,28.5,0.752,13.7,8.5,Energy,Oil & Gas Operations 808,DR Horton,United States,12.6,0.935,11.7,12.4,Industrials,Construction Services 809,Leonardo,Italy,13.3,0.559,26.8,8.3,, 810,Shenwan Hongyuan Group,China,3.2,0.814,39.6,18.3,, 811,LG Electronics,South Korea,47.7,0.066,31.3,10.2,Consumer Discretionary,Consumer Electronics 812,Kuehne + Nagel,Switzerland,16.8,0.729,6.2,16.9,Industrials,Other Transportation 813,FirstEnergy,United States,14.4,-6.2,43.1,13.9,Utilities,Electric Utilities 814,Magnit,Russia,16,0.809,7.5,15.6,Consumer Staples,Food Retail 815,S&P Global,United States,5.7,2.1,8.7,33.6,, 816,Chongqing Rural Bank,Hong Kong,5.3,1.2,115.6,6.5,Financials,Regional Banks 817,Toshiba,Japan,47.2,-3.8,47.7,8.3,Information Technology,Electronics 818,General Growth Properties,United States,2.3,1.3,22.7,20.4,Financials,Real Estate 819,JFE Holdings,Japan,29.4,0.273,36.1,9.4,Materials,Iron & Steel 820,Netease,China,5.7,1.7,8.4,36.8,Information Technology,Computer Services 821,LabCorp,United States,9.6,0.722,14.2,14.7,Health Care,Healthcare Services 822,ATOS,France,13,0.64,14.1,12.8,Information Technology,Computer Services 823,Fanuc,Japan,4.8,1.2,13,39.6,Industrials,Other Industrial Equipment 824,SM Investments,Philippines,7.3,0.65,17.3,17.8,Consumer Discretionary,Department Stores 825,Hyundai Steel,South Korea,14.4,0.706,26.8,6.7,Materials,Iron & Steel 826,Eastman Chemical,United States,8.9,0.866,15.5,11.7,Materials,Specialized Chemicals 827,O'Reilly Automotive,United States,8.6,1,7.4,23.2,Consumer Discretionary,Specialty Stores 828,L Brands,United States,12.6,1.2,8.2,13.5,Consumer Discretionary,Apparel/Footwear Retail 829,First Republic Bank,United States,2.4,0.665,73.4,14.2,Financials,Regional Banks 830,S-Oil,South Korea,14.1,1,11.6,9.9,Energy,Oil & Gas Operations 831,EOG Resources,United States,7.5,-1.1,29.5,56.2,Energy,Oil & Gas Operations 832,Secom,Japan,8.5,0.748,13.6,16.7,Industrials,Security Systems 833,LG Corp,South Korea,8.6,0.926,16.1,10.7,Consumer Discretionary,Household Appliances 834,Abu Dhabi Commercial Bank,United Arab Emirates,3.1,1.1,70.3,9.8,Financials,Regional Banks 835,Hermès International,France,5.8,1.2,5.8,50.3,, 836,Link REIT,Hong Kong,1.2,1.8,21.8,16,Financials,Real Estate 837,TD Ameritrade Holding,United States,3.4,0.846,28.8,20.3,Financials,Investment Services 838,Sanlam,South Africa,5.8,0.655,48.8,9.5,Financials,Life & Health Insurance 839,Daiichi Sankyo,Japan,8.9,0.55,17,15.5,Health Care,Pharmaceuticals 840,Norsk Hydro,Norway,9.8,0.761,15.2,11.9,Materials,Aluminum 841,Williams,United States,7.5,-0.424,47.9,25.1,, 842,Guangzhou Automobile Group,China,7.2,0.953,11.8,19.2,, 843,American Financial Group,United States,6.4,0.649,55.7,8.2,Financials,Diversified Insurance 844,NXP Semiconductors,Netherlands,9.5,0.2,24.9,36.2,Information Technology,Semiconductors 845,China National Nuclear Power,China,4.2,0.627,41.4,17,Energy, 846,Athene Holding,Bermuda,4.1,0.805,86.7,9.8,Energy, 847,Woodside Petroleum,Australia,4.1,0.866,24.8,20.9,Energy,Oil & Gas Operations 848,Boston Scientific,United States,8.4,0.347,18.1,33.7,Health Care,Medical Equipment & Supplies 849,Raiffeisen Bank International,Austria,6.8,0.513,118,7.3,Financials,Major Banks 850,AMP,Australia,9.7,-0.256,101.4,11.4,Financials,Investment Services 851,Formosa Plastics,Taiwan,5.6,1.2,13.5,19,Materials,Specialized Chemicals 852,China Steel,Taiwan,9.1,0.497,21,12.8,Materials,Iron & Steel 853,Bank of Ireland,Ireland,4.3,0.797,129.9,8.4,Financials,Major Banks 854,China Gezhouba,China,14.9,0.511,21.8,8.5,Industrials,Construction Services 855,Bâloise Group,Switzerland,9.1,0.543,78.9,6.3,, 856,Rakuten,Japan,7.2,0.35,39.5,15.2,Consumer Discretionary,Internet & Catalog Retail 857,Ameren,United States,6.1,0.653,24.7,13.2,Utilities,Electric Utilities 858,Symantec,United States,3.8,2,16.8,19.1,Information Technology,Software & Programming 859,Yara International,Norway,11.3,0.757,14,10.7,Materials,Specialized Chemicals 860,Assa Abloy,Sweden,8.4,0.777,10.5,22.6,Industrials,Other Industrial Equipment 861,Shengjing Bank,China,5.9,1,130.3,5.2,Financials, 862,Aena,Spain,4.1,1.3,16.4,24.3,Industrials, 863,Mega Financial Holding,Taiwan,2.5,0.696,103,10.6,Financials,Investment Services 864,Shanghai International Port,China,4.7,1,16.8,20.2,Industrials,Other Transportation 865,Tokyo Gas,Japan,15,0.465,18.2,10.1,Utilities,Natural Gas Utilities 866,Mohawk Industries,United States,9,0.93,10.2,17,Consumer Discretionary,Furniture & Fixtures 867,Ooredoo Telecom,Qatar,8.9,0.602,24.9,9.1,Telecommunication Services,Telecommunications services 868,China Galaxy Securities,China,2.7,0.774,35.4,16.1,Financials, 869,Unipol Gruppo,Italy,18.4,0.365,96,2.9,Financials,Diversified Insurance 870,Quanta Computer,Taiwan,27.7,0.469,18.2,7.9,Information Technology,Computer Hardware 871,Lam Research,United States,6.4,0.999,11.5,21.1,Materials, 872,China Merchants Securities,China,2.8,0.812,35,15.1,Financials,Investment Services 873,Bank of Jinzhou,China,4.1,1.2,77.6,7.9,Financials, 874,Entergy,United States,10.8,-0.564,45.9,13.7,Utilities,Electric Utilities 875,Canadian Pacific Railway,Canada,4.7,1.2,14.5,22,Industrials,Railroads 876,JM Smucker,United States,7.4,0.67,15.8,15.1,Consumer Staples,Food Processing 877,NEC,Japan,24.5,0.603,21.3,6.2,Information Technology,Electronics 878,Kingfisher,United Kingdom,15,0.813,12.9,9,Consumer Discretionary,Home Improvement Retail 879,Lear,United States,18.6,0.975,9.9,9.2,Consumer Discretionary,Auto & Truck Parts 880,Taisei,Japan,13.4,0.915,13.7,8.4,Industrials,Construction Services 881,Kajima,Japan,16.1,0.875,15.7,6.9,Industrials,Construction Services 882,Sandvik,Sweden,9.5,0.643,11.5,18.5,Industrials,Other Industrial Equipment 883,DP World,United Arab Emirates,4.2,1,20.8,18,Industrials,Other Transportation 884,Pegatron,Taiwan,35.9,0.6,13.8,7.5,Information Technology,Electronics 885,Power Grid of India,India,3.2,0.919,27.8,15.9,Utilities,Electric Utilities 886,Obayashi,Japan,16.7,0.767,17.3,6.7,Industrials,Construction Services 887,Zimmer Biomet,United States,7.7,0.3,26.7,24.1,Industrials, 888,Devon Energy,United States,10.5,-3.3,25.9,22.3,Energy,Oil & Gas Operations 889,Hormel Foods,United States,9.5,0.89,6.4,18.1,Consumer Staples,Food Processing 890,Julius Baer Group,Switzerland,3.3,0.629,94.7,11.1,Financials,Investment Services 891,S.f. Holding,China,8.6,0.629,6.4,34.4,Financials, 892,Schindler Holding,Switzerland,9.8,0.776,8.2,19.8,Industrials,Other Industrial Equipment 893,Avalonbay Communities,United States,2,1,17.9,25.4,Financials,Real Estate 894,Cencosud,Chile,15.3,0.573,15.6,9,Consumer Staples,Food Retail 895,JBS,Brazil,48.9,0.108,31.6,8.2,Consumer Staples,Food Processing 896,Uniper,Germany,74.4,-3.6,63.2,6.1,Energy, 897,Suning Appliance,China,22.3,0.106,19.7,14.8,Consumer Discretionary,Specialty Stores 898,Yahoo!,United States,5.2,-0.214,48.1,44.4,Information Technology, 899,L3 Technologies,United States,10.5,0.71,11.9,12.9,Information Technology, 900,CEZ Group,Czech Republic,8.3,0.584,24.6,9.2,Utilities,Electric Utilities 901,PKO Bank Polski,Poland,3.8,0.729,68.4,10.5,Financials,Regional Banks 902,Alliance Data Systems,United States,7.1,0.432,25.5,14,Consumer Discretionary,Business & Personal Services 903,AerCap Holdings,Ireland,5,1,41.6,7.6,Financials, 904,Genuine Parts,United States,15.3,0.687,8.9,13.3,Consumer Discretionary,Specialty Stores 905,Fortis (Canada),Canada,5.2,0.498,36.7,13.6,Utilities,Electric Utilities 906,Infineon Technologies,Germany,7.3,0.832,10.1,22.5,Information Technology,Semiconductors 907,Klepierre,France,1.5,1.3,25.7,12.4,, 908,Randstad Holding,Netherlands,22.9,0.65,10,10.3,Consumer Discretionary,Business & Personal Services 909,Skanska,Sweden,17,0.668,12,9.3,Industrials,Construction Services 910,Raymond James Financial,United States,5.7,0.568,31.7,10.9,Financials,Investment Services 911,Gemdale,China,7.9,0.948,22.1,7.3,Financials,Real Estate 912,Invesco,United States,4.8,0.829,25.7,12.3,Financials,Investment Services 913,DSM,Netherlands,8.8,0.718,13.8,12.1,Materials,Diversified Chemicals 914,Ageas,Belgium,12.9,0.03,107.7,8,Financials,Diversified Insurance 915,Zhejiang Zheneng Electric Power,China,6,1.1,15.8,11.4,Utilities,Electric Utilities 916,Mahindra & Mahindra,India,10.7,0.491,16.4,11.8,Consumer Discretionary,Auto & Truck Manufacturers 917,Swatch Group,Switzerland,7.7,0.583,12.9,19.3,Consumer Discretionary,Apparel/Accessories 918,Huishang Bank,China,5.3,1,108.6,5.3,Financials, 919,Vornado Realty,United States,2.6,0.907,20.8,19.2,Financials,Real Estate 920,Everest Re Group,Bermuda,5.8,0.986,21.3,9.5,Financials,Diversified Insurance 921,Bank Hapoalim,Israel,4.4,0.685,116.4,7.9,Financials,Major Banks 922,Shimao Property Holdings,Hong Kong,9.1,0.781,37.7,5.6,Financials,Real Estate 923,Saudi British Bank,Saudi Arabia,2.2,1,49.6,8.8,Financials,Regional Banks 924,Bank Negara Indonesia,Indonesia,4.4,0.853,44.8,9.1,Financials,Regional Banks 925,Inner Mongolia Yili,China,9.1,0.852,5.7,16.4,Consumer Staples,Food Processing 926,Asr Nederland,Netherlands,7.5,0.729,59.4,4.3,, 927,Beiersdorf,Germany,7.5,0.784,8,24.1,Consumer Staples,Household/Personal Care 928,Sharp,Japan,18.5,-1.7,15.2,20.5,Consumer Discretionary,Consumer Electronics 929,Salesforce.com,United States,8.4,0.18,17.6,59.7,Information Technology,Software & Programming 930,Aluminum Corp of China,China,21.4,0.059,27.4,9.9,Materials,Aluminum 931,Mercantil Servicios,Venezuela,0.37,2,148.7,2.7,Financials,Regional Banks 932,Halkbank,Turkey,7,0.731,71,3.5,Financials,Regional Banks 933,Guangzhou R&F,China,8.1,1,32.6,5.4,Financials,Real Estate 934,Tesla,United States,7,-0.675,22.7,49.3,Consumer Discretionary, 935,Tokyu,Japan,10.3,0.573,18.2,9,Industrials,Railroads 936,Neste Oil,Finland,12.9,1,7.9,10,Energy,Oil & Gas Operations 937,CMS Energy,United States,6.4,0.551,21.6,12.6,Utilities,Electric Utilities 938,Conagra Brands,United States,9.6,0.608,10.5,17.2,, 939,China Shipbuilding Industry,China,8.2,-0.204,28.6,20.4,Industrials,Heavy Equipment 940,Jeronimo Martins,Portugal,16.2,0.656,6,11.2,Consumer Staples,Food Retail 941,Fairfax Financial,Canada,10.3,-0.526,43.4,11.2,Financials,Property & Casualty Insurance 942,Westfield,Australia,1.1,1.4,18.8,14,, 943,Keyence,Japan,3.7,1.3,10.4,48.3,Information Technology,Electronics 944,Banque Saudi Fransi,Saudi Arabia,2.2,0.936,54.2,8.4,Financials,Regional Banks 945,Towngas,Hong Kong,3.7,0.946,15.1,26,Energy, 946,Galaxy Entertainment,Hong Kong,6.8,0.81,8.5,23.8,Consumer Discretionary,Casinos & Gaming 947,WR Berkley,United States,7.7,0.602,23.4,8.6,Financials,Property & Casualty Insurance 948,CapitaLand,Singapore,3.9,0.862,31.7,11.4,Financials,Real Estate 949,Shimizu,Japan,14.5,0.767,14,7.2,Industrials,Construction Services 950,Snam,Italy,2.7,0.952,21.2,15,Energy, 951,VakifBank,Turkey,7,0.688,69,3.7,Financials,Regional Banks 952,Baker Hughes,United States,9.8,-2.7,19,26.1,Energy,Oil Services & Equipment 953,Onex,Canada,22.5,-0.653,42.9,7.3,Financials,Investment Services 954,Publicis Groupe,France,10.6,-0.583,26.3,15.4,Consumer Discretionary,Advertising 955,St. James's Place,United Kingdom,15.3,0.151,92.6,6.7,, 956,Hershey,United States,7.4,0.072,5.5,22.7,Consumer Staples,Food Processing 957,CenterPoint Energy,United States,7.5,0.432,22.3,12,Utilities,Natural Gas Utilities 958,HCL Technologies,India,6.3,1.1,6.4,18.7,Information Technology,Software & Programming 959,Riyad Bank,Saudi Arabia,2.6,0.891,58,8.3,Financials,Regional Banks 960,TUI,Germany,18.8,0.501,14.7,7.9,Consumer Discretionary,Business & Personal Services 961,BOE Technology Group,China,8.8,-0.036,26.3,18.4,Information Technology,Electronics 962,UC Rusal,Russia,8,1.2,14.6,7.9,Materials,Aluminum 963,Idemitsu Kosan,Japan,27.9,0.475,22.5,5.6,Energy,Oil & Gas Operations 964,WestRock,United States,14.4,0.138,22.9,12.8,, 965,Deutsche Wohnen,Germany,1.3,1.8,17.7,12,, 966,Public Storage,United States,2.6,1.4,10.1,38.8,Financials,Real Estate 967,Mebuki Financial Group,Japan,1.4,1.5,137.5,4.5,Financials, 968,Comerica,United States,2.9,0.473,73.2,12,Financials,Regional Banks 969,Voya Financial,United States,10.7,-0.428,216.9,7,Financials, 970,Arch Capital Group,Bermuda,4.3,0.693,29.4,11.5,Financials,Property & Casualty Insurance 971,Daito Trust Construction,Japan,13.5,0.749,6,10.4,Industrials,Construction Services 972,Fiserv,United States,5.5,0.93,9.7,24.9,Information Technology,Software & Programming 973,Ventas,United States,3.4,0.551,23.2,23,Financials,Real Estate 974,CBRE Group,United States,13.2,0.572,10.8,11.1,Financials,Real Estate 975,Huadian Power International,China,9.4,0.511,30.2,6.8,Utilities,Electric Utilities 976,Markel,United States,5.6,0.44,25.9,13.6,Financials,Diversified Insurance 977,Fidelity National Financial,United States,8.2,0.65,14.6,10.4,Financials,Property & Casualty Insurance 978,Inpex,Japan,7.6,-0.025,37.5,14.9,Energy,Oil & Gas Operations 979,Leucadia National,United States,10.9,0.127,45.3,9.5,Industrials,Conglomerates 980,China Coal Energy,China,8.8,0.302,34.8,10.3,Materials,Diversified Metals & Mining 981,Ultrapar Participacoes,Brazil,22.2,0.448,7.4,12.5,, 982,Bank Of Hangzhou,China,4.1,0.589,85.3,8.6,Financials, 983,Electronic Arts,United States,4.6,1.3,7.7,27.4,Information Technology, 984,Fortive,United States,6.2,0.872,8.2,20.9,Industrials, 985,CP All,Thailand,12.3,0.473,9.8,16,Consumer Discretionary,Specialty Stores 986,Amphenol,United States,6.3,0.823,8.5,21.5,Information Technology,Electronics 987,Regeneron Pharmaceuticals,United States,4.9,0.88,7,40.4,Health Care,Biotechs 988,Mediobanca,Italy,2.6,0.776,77.5,7.6,Financials,Regional Banks 989,Asahi Glass,Japan,11.8,0.437,17,9.2,Materials,Construction Materials 990,Universal Health,United States,9.8,0.702,10.3,11.9,Health Care,Healthcare Services 991,Mediatek,Taiwan,8.5,0.735,11.5,11.1,Information Technology,Semiconductors 992,Singapore Airlines,Singapore,10.8,0.524,17,8.4,Industrials,Airline 993,Banco BTG Pactual Participations,Bermuda,5.3,0.922,59.4,5.7,, 994,Suntory Beverage & Food,Japan,13,0.424,11.7,13.3,Consumer Staples, 995,Shanghai Pharmaceuticals,China,18.1,0.481,11.9,9.1,Health Care,Drug Retail 996,Netflix,United States,8.8,0.187,13.6,61.6,Consumer Discretionary,Internet & Catalog Retail 997,CGI Group,Canada,8.1,0.835,8.6,14.1,Information Technology,Computer Services 998,Gap,United States,15.5,0.676,7.6,9.6,Consumer Discretionary,Apparel/Footwear Retail 999,Hankyu Hanshin Holdings,Japan,6.9,0.719,19.8,8.2,Industrials,Railroads 1000,Dr Pepper Snapple Group,United States,6.4,0.847,9.8,17.7,Consumer Staples,Beverages 1001,Cincinnati Financial,United States,5.5,0.591,20.8,11.6,Financials,Diversified Insurance 1002,Dongbu Insurance,South Korea,12.4,0.451,36.5,4.1,Financials,Property & Casualty Insurance 1003,Power Assets Holdings,Hong Kong,0.166,0.827,16.7,18.9,Utilities,Electric Utilities 1004,Bank of East Asia,Hong Kong,3.3,0.416,98.8,11.2,Financials,Regional Banks 1005,Harris,United States,7.3,0.668,11.6,13.7,Information Technology,Communications Equipment 1006,YPF,Argentina,14.2,-1.9,26.6,9.9,Energy, 1007,Finatis,France,40.7,-2.5,46,0.292,Consumer Staples,Food Retail 1008,Nielsen,United States,6.3,0.502,15.7,14.9,, 1009,Novolipetsk Steel,Russia,7.6,0.907,10.2,11.6,Materials,Iron & Steel 1010,Kuwait Finance House,Kuwait,3.1,0.547,54,10.1,Financials,Regional Banks 1011,China Grand Automotive Services,China,18,0.421,16.3,7.4,Consumer Discretionary, 1012,EnBW-Energie Baden,Germany,21.4,-2,43.1,6.5,Utilities,Electric Utilities 1013,Dubai Islamic Bank,United Arab Emirates,2.3,0.843,47.6,7.8,Financials,Regional Banks 1014,Hikvision,China,4.5,1.1,5.5,28.9,Information Technology,Electronics 1015,Navient,United States,5,0.682,121.1,4.3,Financials, 1016,South32,Australia,6,0.747,14.1,11.6,Materials, 1017,Tokyo Electron,Japan,6.5,0.797,7.6,18.1,Information Technology,Semiconductors 1018,BDO Unibank,Philippines,2.6,0.549,46.8,10.2,Financials, 1019,Kohl's,United States,18.7,0.555,13.6,6.8,Consumer Discretionary,Department Stores 1020,MTN Group,South Africa,10.1,-0.178,19.6,16.6,Telecommunication Services,Telecommunications services 1021,WW Grainger,United States,10.1,0.601,5.7,13.5,Industrials,Electrical Equipment 1022,Severstal,Russia,5.9,1.6,6.7,12.3,Materials,Iron & Steel 1023,Sun Pharma Industries,India,4.6,1.1,9.1,24.9,Health Care,Pharmaceuticals 1024,Weichai Power,China,13.9,0.366,24.1,7,Industrials,Heavy Equipment 1025,Arrow Electronics,United States,23.8,0.523,14.2,6.3,Information Technology,Electronics 1026,Grupa PZU,Poland,6.4,0.492,29.8,8.1,Financials,Property & Casualty Insurance 1027,PTT Global Chemical,Thailand,9.9,0.726,11,9.5,Materials,Diversified Chemicals 1028,Bank Leumi,Israel,3.9,0.727,114,6.7,Financials,Major Banks 1029,China Resources Pharmaceutical Group,Hong Kong,20.2,0.364,17.3,7.3,Health Care, 1030,Geely Automobile Holdings,Hong Kong,8,0.767,9.7,12.7,, 1031,Insurance Australia Group,Australia,6.7,0.45,21.2,10.6,Financials,Property & Casualty Insurance 1032,E-Trade Financial,United States,2,0.552,49.5,9.6,Financials,Investment Services 1033,Nintendo,Japan,3.6,0.726,12.5,32.8,Consumer Discretionary,Consumer Electronics 1034,Stora Enso,Finland,10.8,0.512,13,9.5,Materials,Paper & Paper Products 1035,BBMG,China,6.9,0.407,30,8.9,Materials,Construction Materials 1036,Seagate Technology,Ireland,10.9,0.513,8.6,13.8,Information Technology,Computer Storage Devices 1037,TDK,Japan,10.7,0.603,14.4,7.7,Information Technology,Electronics 1038,Uni-President,Taiwan,12.8,0.45,11.7,10.4,Consumer Staples,Food Processing 1039,Rockwell Automation,United States,5.9,0.758,7.1,20,Industrials,Other Industrial Equipment 1040,Henry Schein,United States,11.6,0.507,6.7,13.3,Health Care,Medical Equipment & Supplies 1041,Meiji Holdings,Japan,11.4,0.531,7.6,12.7,Consumer Staples,Food Processing 1042,Intuit,United States,4.9,0.793,4.4,30.3,Information Technology,Software & Programming 1043,Lixil Group,Japan,16.8,0.33,18.5,7.7,Materials,Construction Materials 1044,London Stock Exchange,United Kingdom,2.2,0.205,697.9,14,Financials,Investment Services 1045,Amadeus IT Group,Spain,4.9,0.913,8.3,21.8,Information Technology, 1046,Arconic,United States,12.4,-0.941,20,11.6,Materials, 1047,Zoetis,United States,4.9,0.821,7.6,25.8,Health Care,Pharmaceuticals 1048,Helvetia Holding,Switzerland,9.5,0.382,54.5,5.4,Financials,Diversified Insurance 1049,Advanced Semiconductor,Taiwan,8.5,0.692,11.1,10,Information Technology,Semiconductors 1050,Yamaha Motor,Japan,13.8,0.581,11.3,8,Consumer Discretionary,Recreational Products 1051,Alleghany,United States,6.1,0.457,24.1,9.4,Financials,Property & Casualty Insurance 1052,Crown Castle International,United States,3.9,0.357,22.7,34.1,Telecommunication Services,Telecommunications services 1053,ZTE,China,15.1,-0.329,20.4,9.9,Information Technology,Communications Equipment 1054,Grupo Inbursa,Mexico,3.3,0.665,25.8,11.2,Financials,Regional Banks 1055,Inter Rao,Russia,13,0.907,9.3,7.5,Utilities,Electric Utilities 1056,Newmont Mining,United States,7.7,-0.027,21,18.2,Materials,Diversified Metals & Mining 1057,AES,United States,13.7,-1.1,36.1,7.3,Utilities,Electric Utilities 1058,Oneok,United States,8.9,0.352,16.1,11.9,Energy,Oil Services & Equipment 1059,Schroders,United Kingdom,3.1,0.661,25.9,10.9,Financials,Investment Services 1060,C.H. Robinson,United States,13.1,0.513,3.7,10.7,Consumer Discretionary, 1061,Quest Diagnostics,United States,7.5,0.641,10.1,13.4,Health Care,Healthcare Services 1062,Mitsubishi UFJ Lease,Japan,7.6,0.492,44.2,4.3,Financials,Rental & Leasing 1063,Expedia,United States,8.8,0.269,15.8,18.9,, 1064,Boston Properties,United States,2.6,0.511,18.9,20.7,Financials,Real Estate 1065,Beijing Enterprises,Hong Kong,7.2,0.803,18.7,6.6,Utilities,Natural Gas Utilities 1066,Restaurant Brands International,Canada,4.1,0.675,19.1,13.1,, 1067,Analog Devices,United States,3.6,0.914,10.2,29.2,Information Technology,Semiconductors 1068,Liberty Expedia Holdings,United States,1.6,2.3,34.6,2.6,, 1069,KPN,Netherlands,7.5,0.399,15.5,12.6,Telecommunication Services,Telecommunications services 1070,GS Holdings,South Korea,11.6,0.695,16.8,4.8,Energy,Oil & Gas Operations 1071,OTP Bank,Hungary,3.7,0.718,38.6,7.9,Financials,Regional Banks 1072,Roper Industries,United States,3.8,0.659,14.3,21.2,Industrials,Other Industrial Equipment 1073,Host Hotels & Resorts,United States,5.4,0.762,11.4,13.5,Financials,Real Estate 1074,Asustek Computer,Taiwan,14.5,0.595,11.3,7.3,Information Technology,Computer Hardware 1075,Interpublic Group,United States,7.8,0.609,12.5,9.5,Consumer Discretionary,Advertising 1076,QuintilesIMS,United States,6.9,0.115,21.2,18.7,, 1077,Tata Steel,India,16.1,-0.444,25.7,7.5,Materials,Iron & Steel 1078,Bankinter,Spain,2.2,0.542,70.9,7.6,Financials,Regional Banks 1079,Campbell Soup,United States,7.9,0.497,7.6,17.2,Consumer Staples,Food Processing 1080,Osaka Gas,Japan,10.9,0.459,14.9,8,Utilities,Natural Gas Utilities 1081,First Financial Holding,Taiwan,1.8,0.538,78.8,7.3,Financials,Investment Services 1082,Lotte Shopping,South Korea,25.5,0.134,34.7,5.7,Consumer Discretionary,Department Stores 1083,Vienna Insurance Group,Austria,10.4,0.321,51.8,3.1,Financials,Diversified Insurance 1084,SMC,Japan,4.3,0.938,10.2,19.1,Financials, 1085,Investec,United Kingdom,4.8,0.539,68.2,6.3,Financials,Investment Services 1086,Power Finance,India,4.3,0.945,37.3,6.3,Consumer Discretionary,Business & Personal Services 1087,Voestalpine,Austria,11.9,0.477,15.1,7.1,Materials,Iron & Steel 1088,Hydro One,Canada,4.9,0.559,18.9,10.8,Utilities, 1089,Whole Foods Market,United States,15.8,0.445,6.4,9.9,Consumer Staples,Food Retail 1090,Japan Exchange Group,Japan,1,0.379,346.8,7.8,Financials,Investment Services 1091,Fortum,Finland,4,0.55,23.2,12.5,Utilities,Electric Utilities 1092,Grupo Bimbo,Mexico,13.4,0.316,11.9,11.8,Consumer Staples,Food Processing 1093,Alrosa,Russia,4.7,2,7.7,11.4,Materials,Diversified Metals & Mining 1094,China National Building,China,15.3,0.157,49,3.7,Materials,Construction Materials 1095,Saputo,Canada,8.4,0.532,5.7,13.2,Consumer Staples,Food Processing 1096,Shanghai Construction,China,19.8,0.315,25.1,5.5,Industrials,Construction Services 1097,Mondi,United Kingdom,7.4,0.7,7.7,11.7,Materials,Paper & Paper Products 1098,Shin Kong Financial,Taiwan,10.9,0.149,98,2.8,Financials,Life & Health Insurance 1099,SDIC Power Holdings,China,4.4,0.658,29.8,7.7,Energy,Oil & Gas Operations 1100,Land Securities Group,United Kingdom,1.4,0.758,19.3,10.8,Financials,Real Estate 1101,China Hongqiao Group,China,7.3,0.649,19.1,6.6,Materials,Diversified Metals & Mining 1102,Clorox,United States,5.9,0.655,4.6,17.1,Consumer Staples,Household/Personal Care 1103,Cielo,Brazil,3.5,1.1,9.4,20.9,Consumer Discretionary,Business & Personal Services 1104,Assurant,United States,7.2,0.565,29.7,5.2,Financials,Diversified Insurance 1105,TCL Corp,China,16.5,0.312,21.3,6.4,Consumer Discretionary,Consumer Electronics 1106,Wuliangye Yibin,China,3.4,1,8.9,23.5,Consumer Staples,Beverages 1107,Thai Beverage,Thailand,5.3,0.757,5.2,17.1,Consumer Staples,Beverages 1108,Zions Bancorp,United States,2.5,0.455,63.4,8.4,Financials,Regional Banks 1109,National Oilwell Varco,United States,7.2,-2.4,21.1,14.7,Energy,Oil Services & Equipment 1110,Flextronics International,Singapore,23.8,0.294,12.8,8.8,Information Technology,Electronics 1111,Hainan Airlines,China,6.1,0.477,21.3,8.4,Industrials,Airline 1112,Yanzhou Coal Mining,China,15,0.308,21,6.8,Materials,Diversified Metals & Mining 1113,Shanghai Electric Group,China,11.8,0.312,25.3,6.7,Industrials,Heavy Equipment 1114,Xiamen C&D,China,19.3,0.359,20.3,4.6,Industrials,Trading Companies 1115,Lend Lease Group,Australia,11.7,0.55,13.7,6.6,Financials,Real Estate 1116,T Rowe Price,United States,4.2,1.2,6.4,16.9,Financials,Investment Services 1117,Pgnig Group,Poland,8.4,0.596,11.9,8.6,Energy,Oil & Gas Operations 1118,Galp Energia,Portugal,14.5,0.198,13.1,12.8,Energy,Oil & Gas Operations 1119,Shenzhen Overseas,China,4.3,0.775,20.4,8.9,Consumer Discretionary,Hotels & Motels 1120,Kerry Group,Ireland,6.8,0.59,7.8,14.2,Consumer Staples,Food Processing 1121,Celanese,United States,5.4,0.9,8.4,13,Materials,Diversified Chemicals 1122,Experian,Ireland,4.5,0.829,7.7,18.9,Consumer Discretionary,Business & Personal Services 1123,Legrand,France,5.6,0.695,8.6,16,Industrials,Electrical Equipment 1124,Datang International Power,China,8.8,-0.39,33.6,8.3,Utilities,Electric Utilities 1125,HCP,United States,2.5,0.627,15.8,14.9,Financials,Real Estate 1126,SES,Luxembourg,2.3,1.1,14.4,10.6,Consumer Discretionary,Broadcasting & Cable 1127,AGNC Investment,United States,1.5,0.623,56.9,6.6,Financials, 1128,Bank of Tianjin,China,4,0.683,94.6,4.3,Financials, 1129,Phoenix Group Holdings,United Kingdom,9.9,-0.136,93.2,3.5,Financials,Life & Health Insurance 1130,Harbin Bank,China,3.8,0.734,77.6,3.4,Financials,Regional Banks 1131,Alaska Air Group,United States,5.9,0.814,10,11,Industrials,Airline 1132,Cenovus Energy,Canada,9.2,-0.411,19.3,11.2,Energy,Oil & Gas Operations 1133,Apache,United States,5.3,-1.5,22.5,20,Energy,Oil & Gas Operations 1134,Delta Electronics,Taiwan,6.6,0.583,7.3,13.9,Information Technology,Electronics 1135,Electrolux Group,Sweden,14.1,0.525,9.5,8,Consumer Discretionary,Household Appliances 1136,Hyundai Marine & Fire,South Korea,10.9,0.353,29.8,2.4,Financials,Property & Casualty Insurance 1137,Korea Gas,South Korea,18.2,-0.581,33.1,3.6,Utilities,Natural Gas Utilities 1138,Keppel,Singapore,4.9,0.568,20.3,8.9,, 1139,Wm Morrison Supermarkets,United Kingdom,21.7,0.407,11.6,6.8,Consumer Staples,Food Retail 1140,AntarChile,Chile,16.7,0.327,21.9,5.3,Financials,Investment Services 1141,SGS,Switzerland,6.1,0.551,5.3,16.4,Consumer Discretionary,Business & Personal Services 1142,Hyundai Engineering,South Korea,16.2,0.423,16.5,4.9,Industrials,Construction Services 1143,Toppan Printing,Japan,13.2,0.349,16.4,7,Consumer Discretionary,Printing & Publishing 1144,Actelion,Switzerland,2.5,0.707,2.1,30.5,Health Care,Pharmaceuticals 1145,Bank of Baroda,India,7.9,-0.774,104.4,6.1,Financials,Regional Banks 1146,Canadian Tire,Canada,9.6,0.505,11.4,8.5,Consumer Discretionary,Specialty Stores 1147,Intuitive Surgical,United States,2.7,0.736,6.5,28.2,Health Care,Medical Equipment & Supplies 1148,Punjab National Bank,India,8.8,-0.564,107.7,4.9,Financials,Regional Banks 1149,Charoen Pokphand Foods,Thailand,13.2,0.417,16.3,6.3,Consumer Staples,Food Processing 1150,Renesas Electronics,Japan,5.8,0.569,7.1,16.4,Information Technology,Semiconductors 1151,JG Summit Holdings,Philippines,4.9,0.634,13.4,11.6,Financials, 1152,Ball,United States,9.1,0.225,16.2,12.6,Materials,Containers & Packaging 1153,Olympus,Japan,6.9,0.56,8.6,12.8,Consumer Discretionary,Consumer Electronics 1154,Sun Art Retail Group,Hong Kong,15.1,0.388,8.7,9.6,Consumer Discretionary,Specialty Stores 1155,BCI-Banco Credito,Chile,3,0.503,46.2,7.4,Financials,Regional Banks 1156,CDW,United States,14,0.424,6.9,9.2,Information Technology, 1157,Hesteel,China,10.8,0.153,26.8,7.4,Materials, 1158,Brown-Forman,United States,3,1,4.6,17.6,Consumer Staples,Beverages 1159,Sika,Switzerland,5.8,0.572,5,15.4,Materials,Construction Materials 1160,Hoya,Japan,4.4,0.797,5.6,18.1,Information Technology,Electronics 1161,SVB Financial Group,United States,1.6,0.383,44.8,9.5,Financials,Regional Banks 1162,Genworth Financial,United States,8.4,-0.273,104.7,2,Financials,Diversified Insurance 1163,Cerner,United States,4.8,0.637,5.6,19.7,Health Care,Healthcare Services 1164,Foot Locker,United States,7.8,0.664,3.8,9.5,Consumer Discretionary,Apparel/Footwear Retail 1165,Mitsubishi Materials,Japan,12.1,0.498,15.8,3.8,Materials,Diversified Metals & Mining 1166,Interactive Brokers Group,United States,1.4,0.084,54.7,14.3,Financials,Investment Services 1167,Monster Beverage,United States,3,0.713,4.2,26,Consumer Staples,Beverages 1168,Dover,United States,6.8,0.509,10.1,12.5,Industrials,Conglomerates 1169,Intact Financial,Canada,6.7,0.408,16.8,9.3,Financials,Property & Casualty Insurance 1170,Global Logistic Properties,Singapore,0.881,0.669,20.5,9.4,Financials,Real Estate 1171,Skyworks Solutions,United States,3.3,0.898,4.1,18.8,Utilities, 1172,Terna,Italy,2.3,0.7,17.6,10,Utilities,Electric Utilities 1173,Tokyo Century,Japan,8.8,0.378,29.7,3.5,, 1174,Autoliv,Sweden,10.1,0.567,8.2,8.5,Consumer Discretionary,Auto & Truck Parts 1175,Hindalco Industries,India,15.2,0.031,22,6.8,Materials,Aluminum 1176,Advance Auto Parts,United States,9.6,0.458,8.3,10.5,Consumer Discretionary,Specialty Stores 1177,Ayala,Philippines,4.2,0.548,18.4,10.7,Industrials,Conglomerates 1178,Paychex,United States,3.1,0.8,7.3,20.9,Consumer Discretionary,Business & Personal Services 1179,Rockwell Collins,United States,5.3,0.738,7.7,12.9,Industrials,Aerospace & Defense 1180,Oriental Land,Japan,4.3,0.699,7.1,19.6,Consumer Discretionary,Hotels & Motels 1181,Industrial Alliance Insurance,Canada,6.4,0.418,41.7,4.5,Financials,Life & Health Insurance 1182,Attijariwafa Bank,Morocco,3.3,0.485,42.4,7.5,Financials,Regional Banks 1183,American Water Works,United States,3.3,0.468,18.5,13.8,Utilities,Diversified Utilities 1184,Motorola Solutions,United States,6,0.559,8.5,13.9,Information Technology,Communications Equipment 1185,United Rentals,United States,5.8,0.566,12,10.4,Financials,Rental & Leasing 1186,Norwegian Cruise Line Holdings,United States,4.9,0.633,13,11,Financials, 1187,Harley-Davidson,United States,6,0.692,9.9,10.7,Consumer Discretionary,Recreational Products 1188,Hess,United States,4.8,-6.1,28.6,15.2,Energy,Oil & Gas Operations 1189,Masraf Al Rayan,Qatar,0.755,0.568,25.1,8.8,Financials,Regional Banks 1190,Jiangsu Yanghe Brewery,China,2.6,0.879,5.3,18.6,Consumer Staples,Beverages 1191,Grifols,Spain,4.5,0.603,10.7,16.8,Health Care,Biotechs 1192,Orient Securities,China,1.8,0.349,30.6,12.2,, 1193,Mitsubishi Motors,Japan,17.9,-2,13.3,9,Consumer Discretionary,Auto & Truck Manufacturers 1194,Origin Energy,Australia,9,-1.5,19.7,9.6,Utilities,Electric Utilities 1195,Qantas Airways,Australia,11.8,0.636,12.5,5.3,Industrials,Airline 1196,Advanced Info Service,Thailand,4.3,0.869,7.7,15,Telecommunication Services,Telecommunications services 1197,Pentair,United Kingdom,5.7,0.522,11.5,11.4,Industrials,Other Industrial Equipment 1198,Industries Qatar,Qatar,1.3,0.812,9.7,18.4,Materials,Specialized Chemicals 1199,Nitto Denko,Japan,6.8,0.529,7.5,12.4,Materials,Diversified Chemicals 1200,Arab National Bank,Saudi Arabia,2.2,0.761,45.3,5.3,Financials,Regional Banks 1201,Zijin Mining Group,China,11.7,0.276,12.8,10.4,Materials,Diversified Metals & Mining 1202,Ricoh,Japan,18.7,0.144,24.3,5.8,Industrials,Business Products & Supplies 1203,Xerox,United States,15.6,-0.477,18.1,7.2,Industrials,Business Products & Supplies 1204,Tenet Healthcare,United States,19.6,-0.192,24.7,1.7,Health Care,Healthcare Services 1205,SCANA,United States,4.2,0.594,18.7,9.3,Utilities,Electric Utilities 1206,Jiangxi Copper,China,30.3,0.12,12.6,7.7,Materials,Diversified Metals & Mining 1207,Givaudan,Switzerland,4.7,0.654,6.4,16.6,Materials,Specialized Chemicals 1208,Liberty Broadband,United States,0.031,0.917,9.6,15.7,, 1209,Chiba Bank,Japan,1.9,0.493,119.9,5.2,Financials,Major Banks 1210,Everbright Securities,China,1.9,0.454,25.6,9.9,Financials, 1211,Smith & Nephew,United Kingdom,4.7,0.801,7.3,13.4,Health Care,Medical Equipment & Supplies 1212,UCB,Belgium,4.6,0.601,10.8,14.6,Health Care,Pharmaceuticals 1213,Industrivarden,Sweden,2,1.9,10.5,9.3,Financials,Investment Services 1214,PVH,United States,8.2,0.549,11.1,8,Consumer Discretionary,Apparel/Accessories 1215,ALFA,Mexico,15.7,0.144,16.9,7.3,Industrials,Conglomerates 1216,Avnet,United States,20.8,0.392,13.3,5.7,Information Technology,Electronics 1217,Lundbergs,Sweden,2.3,1.4,13.4,8.1,Financials,Real Estate 1218,NRG Energy,United States,13.1,-0.774,30.4,5.9,Utilities,Electric Utilities 1219,Shionogi,Japan,3.1,0.852,5.5,17.4,Health Care, 1220,Csc Financial,China,2.5,0.749,26.1,6.8,Financials, 1221,New York Community,United States,1.9,0.492,49.1,6.6,Financials,Thrifts & Mortgage Finance 1222,Naver,South Korea,3.5,0.646,5.3,24,Information Technology, 1223,LARGAN Precision,Taiwan,1.5,0.705,3,20.6,, 1224,Yum China Holdings,United States,6.8,0.502,3.7,12,Financials, 1225,Banco De Venezuela,Venezuela,7,2.5,2.6,2.5,Financials, 1226,CJ Corp,South Korea,20.6,0.192,22.4,4.4,Consumer Staples,Food Processing 1227,CA,United States,4,0.781,11,13.3,Information Technology,Software & Programming 1228,Top Frontier Investment Holdings,Philippines,14.2,0.021,28.8,1.9,Financials, 1229,Qatar Islamic Bank,Qatar,1.1,0.555,38.4,6.6,Financials,Regional Banks 1230,Canara Bank,India,7.5,-0.398,85.1,2.8,Financials,Major Banks 1231,Mosaic,United States,7.2,0.298,16.8,10.1,Materials,Specialized Chemicals 1232,Shinsei Bank,Japan,3,0.517,79,4.6,Financials,Regional Banks 1233,Braskem,Brazil,13.8,-0.136,15.9,7.9,Materials,Specialized Chemicals 1234,Rural Electrification,India,3.7,0.869,32.2,5.7,Financials,Investment Services 1235,Alexion Pharmaceuticals,United States,3,0.394,13.3,26,Health Care,Biotechs 1236,Aramark,United States,14.4,0.32,10.4,9,, 1237,Bank of Zhengzhou,China,2.5,0.603,52.7,3.4,Financials, 1238,Manpower,United States,19.7,0.444,7.6,6.6,Consumer Discretionary,Business & Personal Services 1239,Yes Bank,India,2.5,0.386,25,11,Financials, 1240,Dexia,Belgium,0.393,0.417,224.4,0.373,Financials,Major Banks 1241,Masco,United States,7.4,0.485,5.1,10.7,Materials,Construction Materials 1242,Torchmark,United States,3.9,0.549,21.4,9,Financials,Life & Health Insurance 1243,Proximus,Belgium,6.4,0.579,8.6,10.4,Telecommunication Services, 1244,X5 Retail Group,Netherlands,15.8,0.339,7.9,8.7,Consumer Staples,Food Retail 1245,RSA Insurance Group,United Kingdom,9.3,0.76,23.3,7.5,Financials,Property & Casualty Insurance 1246,Air Canada,Canada,11.1,0.661,11.3,2.7,Industrials,Airline 1247,KLA-Tencor,United States,3.3,0.864,5.1,15.1,Information Technology,Semiconductors 1248,Valeant Pharmaceuticals,Canada,9.7,-2.4,43.5,3.3,Health Care,Pharmaceuticals 1249,3i Group,United Kingdom,0.644,2.4,8.4,9.1,, 1250,Bank of India,India,7,-0.948,93.1,2.3,Financials,Regional Banks 1251,Check Point Software,Israel,1.7,0.725,5.2,18,Information Technology,Software & Programming 1252,Taiwan Cooperative Financial,Taiwan,2,0.427,103.7,5.9,Financials,Consumer Financial Services 1253,Community Health Systems,United States,18.4,-1.7,21.9,1,Health Care, 1254,Huntington Ingalls Industries,United States,7.1,0.573,6.9,9.4,Industrials, 1255,PGE Polska Grupa Energetyczna,Poland,7.1,0.651,16.2,5.3,, 1256,Sekisui Chemical,Japan,9.8,0.517,8.1,7.8,Industrials,Construction Services 1257,Brenntag,Germany,11.6,0.399,7.8,8.8,Materials,Specialized Chemicals 1258,Bombardier,Canada,16.3,-1,22.8,3.6,Industrials,Aerospace & Defense 1259,Coty,United States,5.4,-0.011,21.9,12.7,Industrials, 1260,Genting,Malaysia,4.4,0.518,20.6,7.9,Consumer Discretionary,Casinos & Gaming 1261,Bank Of Guiyang,China,1.8,0.554,53.6,5.7,Financials, 1262,PotashCorp,Canada,4.5,0.335,17.3,14.2,, 1263,W&W-Wüstenrot,Germany,7.1,0.258,75.9,1.8,, 1264,Sojitz,Japan,13.9,0.317,18.1,3.1,Industrials,Trading Companies 1265,Darden Restaurants,United States,7,0.495,4.7,10.3,Consumer Discretionary,Restaurants 1266,Sunac China Holdings,China,5.5,0.367,42.2,5.9,, 1267,Unicharm,Japan,6.5,0.406,6.1,14.5,Consumer Staples,Household/Personal Care 1268,Ctrip.com International,China,2.9,-0.223,20.8,25.1,, 1269,Jyske Bank,Denmark,2.3,0.46,83.2,4.8,Financials,Regional Banks 1270,China Life Insurance (Taiwan),Taiwan,7.6,0.294,41.1,3.4,Financials,Life & Health Insurance 1271,Dai Nippon Printing,Japan,13,0.292,14.4,7.2,Consumer Discretionary,Printing & Publishing 1272,CIT Group,United States,4.4,-0.861,64.2,8.8,Financials,Consumer Financial Services 1273,IndusInd Bank,India,2.3,0.349,21.2,13.1,Financials, 1274,Banca MPS,Italy,5.9,-3.6,161.8,15,Financials,Major Banks 1275,Bed Bath & Beyond,United States,12.2,0.685,6.8,5.8,Consumer Discretionary,Specialty Stores 1276,Bank of Chongqing,China,2.7,0.529,53.7,2.7,Financials,Regional Banks 1277,Marathon Oil,United States,4.1,-2.1,31.1,13.8,Energy,Oil & Gas Operations 1278,East West Bancorp,United States,1.3,0.432,34.8,7.4,Financials,Regional Banks 1279,Nitori Holdings,Japan,4.7,0.554,4.4,15.1,Financials, 1280,Yamato Holdings,Japan,13.3,0.341,10,8.3,Industrials,Trucking 1281,Cosco Shipping,China,10.7,-1.5,17.2,7.9,, 1282,SKF Group,Sweden,8.5,0.465,9.5,8.9,Industrials,Other Industrial Equipment 1283,GAIL India,India,8.3,0.344,11,10.2,Utilities,Natural Gas Utilities 1284,PulteGroup,United States,7.6,0.598,10.2,7.4,Industrials,Construction Services 1285,Signature Bank,United States,1.4,0.396,39.2,7.6,Financials,Regional Banks 1286,AGL Energy,Australia,8.6,0.272,10.7,13.8,Utilities,Electric Utilities 1287,Latam Airlines,Chile,9,0.072,19.2,8,Industrials,Airline 1288,AutoNation,United States,21.6,0.431,10.3,4.1,Consumer Discretionary,Specialty Stores 1289,Doosan,South Korea,14.3,-0.147,23.7,1.6,Industrials,Construction Services 1290,Transocean,Switzerland,4.2,0.765,26.9,4.9,Energy,Oil Services & Equipment 1291,Hua Nan Financial,Taiwan,1.8,0.437,79,5.8,Financials,Consumer Financial Services 1292,KT&G,South Korea,3.9,1.1,8.1,11.2,Consumer Staples,Tobacco 1293,LKQ,United States,8.9,0.458,8.3,8.6,Consumer Discretionary,Auto & Truck Parts 1294,Equity Residential,United States,2.4,0.232,20.7,23.1,Financials,Real Estate 1295,Amorepacific,South Korea,4.9,0.551,4.3,14.1,, 1296,Iida Group Holdings,Japan,11.1,0.715,9.6,4.3,, 1297,Gecina,France,0.668,0.9,13,8.7,Financials,Real Estate 1298,Edwards Lifesciences,United States,3,0.57,4.5,19.9,Health Care,Medical Equipment & Supplies 1299,Medipal Holdings,Japan,28.2,0.296,13.5,3.7,Health Care,Pharmaceuticals 1300,Bunzl,United Kingdom,10,0.359,5.6,9.8,Consumer Discretionary,Business & Personal Services 1301,El Puerto de Liverpool,Mexico,5.4,0.545,7.2,11.8,Consumer Discretionary,Department Stores 1302,Vicinity Centres,Australia,1,1.1,11.7,8.6,, 1303,Delta Lloyd,Netherlands,6.5,0.256,80,2.6,Financials,Diversified Insurance 1304,Eisai,Japan,4.9,0.507,8.9,14.9,Health Care,Pharmaceuticals 1305,Marks & Spencer,United Kingdom,15,0.356,10.7,6.8,Consumer Discretionary,Department Stores 1306,Pioneer Natural Resources,United States,4,-0.557,16.5,32.2,Energy,Oil & Gas Operations 1307,Arab Bank,Jordan,2.4,0.522,47.5,5.7,Financials,Regional Banks 1308,Foncière des Régions,France,1.1,0.866,20.6,6.3,, 1309,Sino-Ocean Land Holdings,China,5.4,0.572,21.8,3.7,Financials,Real Estate 1310,Nippon Express,Japan,17.1,0.341,13.2,4.8,Industrials,Trucking 1311,Dxc Technology,United States,7.5,-0.101,8.3,21.5,Information Technology, 1312,Nordstrom,United States,14.8,0.354,7.9,7.4,Consumer Discretionary,Department Stores 1313,Pandora,Denmark,3,0.895,2.1,11.8,Consumer Staples,Household/Personal Care 1314,GKN,United Kingdom,11.9,0.327,11.1,7.6,Consumer Discretionary,Auto & Truck Parts 1315,Ajinomoto,Japan,10.1,0.258,11.4,10.9,Consumer Staples,Food Processing 1316,BNK Financial Group,South Korea,3.3,0.425,77.4,2.6,Financials, 1317,Founder Securities,China,1.2,0.387,21.9,10.1,, 1318,Vedanta Resources,United Kingdom,9.9,-1.5,30,2.8,Materials,Diversified Metals & Mining 1319,Petronas Chemicals,Malaysia,3.3,0.708,7.3,14,Materials,Specialized Chemicals 1320,Magnitogorsk Iron & Steel,Russia,5.6,1.1,6.2,7,Materials,Iron & Steel 1321,Cintas,United States,5.1,0.501,4.3,12.9,, 1322,Dassault Systemes,France,3.4,0.495,7.3,22.2,Information Technology,Software & Programming 1323,Quinenco,Chile,6.8,0.117,54.6,4.3,Industrials,Conglomerates 1324,Nippon Yusen,Japan,17.7,-2.1,17.8,3.5,Industrials,Other Transportation 1325,BRF,Brazil,9.7,-0.107,13.8,9.3,Consumer Staples, 1326,Hasbro,United States,5,0.551,5.1,12,Consumer Discretionary, 1327,Illumina,United States,2.4,0.464,4.3,25.2,Health Care,Biotechs 1328,Kawasaki Heavy Industries,Japan,14,0.317,15.4,4.9,Industrials,Heavy Equipment 1329,Digital Realty Trust,United States,2.1,0.426,12.2,17.3,, 1330,Alcoa,United States,15.1,0.02,16.7,6.2,Materials,Aluminum 1331,Chugoku Electric Power,Japan,11,0.246,25.8,4,Utilities,Electric Utilities 1332,Koito Manufacturing,Japan,7.6,0.52,5.4,8.1,Industrials, 1333,AmTrust Financial Services,United States,5.6,0.485,23.1,3.2,Financials, 1334,Juniper Networks,United States,5,0.601,9.7,10.6,Information Technology,Communications Equipment 1335,United Utilities,United Kingdom,2.4,0.608,15.9,8.5,Utilities,Diversified Utilities 1336,Red Eléctrica,Spain,2.1,0.705,11.1,10.7,Utilities, 1337,RusHydro,Russia,5.6,0.6,16.3,6,Utilities,Electric Utilities 1338,Kobe Steel,Japan,15.5,-0.407,18.9,3.2,Materials,Iron & Steel 1339,Wyndham Worldwide,United States,5.6,0.612,9.8,8.9,Consumer Discretionary,Hotels & Motels 1340,MOL Hungarian Oil,Hungary,12.5,-0.74,14.7,7.3,Energy,Oil & Gas Operations 1341,Cathay Pacific Airways,Hong Kong,11.9,-0.74,22.9,5.7,Industrials,Airline 1342,JetBlue Airways,United States,6.6,0.75,9.5,7,Industrials, 1343,Agilent Technologies,United States,4.2,0.507,7.9,17,Information Technology,Electronics 1344,Wolters Kluwer,Netherlands,4.8,0.541,9.9,11.9,Consumer Discretionary,Printing & Publishing 1345,Arkema,France,8.3,0.472,9.7,7.7,Materials,Diversified Chemicals 1346,Ezdan Holding Group,Qatar,0.538,0.498,13.7,11.5,Financials,Real Estate 1347,JSW Steel,India,7.3,0.407,12.8,7.5,Materials,Iron & Steel 1348,Electric Power Development,Japan,6.7,0.411,21.4,4.2,Utilities,Electric Utilities 1349,Hexagon,Sweden,3.5,0.635,8.3,14.7,Industrials,Other Industrial Equipment 1350,Ahli United Bank,Bahrain,1.6,0.543,31.3,5.9,Financials,Regional Banks 1351,Kerry Properties,Hong Kong,1.8,0.842,22.3,5.4,, 1352,Yuanta Financial Holding,Taiwan,3.3,0.416,67.1,5.2,Financials,Investment Services 1353,Airports of Thailand,Thailand,1.5,0.568,4.8,16.4,Industrials,Other Transportation 1354,CR Bard,United States,3.7,0.529,5.3,18,Health Care,Medical Equipment & Supplies 1355,Hanwha Chemical,South Korea,8,0.651,11.4,3.8,Materials,Diversified Chemicals 1356,Noble Group,Hong Kong,46.5,-0.012,12.4,1.8,Industrials,Conglomerates 1357,Frontier Communications,United States,8.9,-0.376,30.2,2.4,Telecommunication Services,Telecommunications services 1358,Ulta Salon Cosmetcs & Fragrance,United States,4.9,0.41,2.6,17.7,Consumer Discretionary, 1359,Crown Holdings,United States,8.3,0.496,9.6,7.4,Materials,Containers & Packaging 1360,Kintetsu,Japan,11.2,0.186,16.2,6.9,Industrials,Railroads 1361,Starwood Property Trust,United States,0.786,0.363,77.3,5.9,, 1362,Arthur J. Gallagher,United States,5.6,0.414,11.5,10.1,, 1363,Johnson Matthey,United Kingdom,15,0.328,5.6,7.3,Materials,Diversified Chemicals 1364,UGI,United States,5.8,0.481,11.3,8.6,Utilities,Natural Gas Utilities 1365,Grupo Televisa,Mexico,5.2,0.199,14.9,15.8,Consumer Discretionary,Broadcasting & Cable 1366,JB Hunt Transport,United States,6.6,0.432,3.9,10.1,Industrials,Trucking 1367,Iliad,France,5.2,0.444,8.7,13.1,Information Technology,Computer Services 1368,Next,United Kingdom,5.5,0.847,3,7.3,Consumer Discretionary,Apparel/Footwear Retail 1369,Arca Continental,Mexico,5,0.483,6.8,12.5,Consumer Staples,Beverages 1370,Uralkali,Russia,2.3,1.4,8.7,8.3,Materials,Specialized Chemicals 1371,Xilinx,United States,2.3,0.614,4.8,14.1,Information Technology,Semiconductors 1372,AAC Technologies Holdings,China,2.3,0.602,3.5,14.4,, 1373,Gudang Garam,Indonesia,5.7,0.502,4.7,9.9,Consumer Staples,Tobacco 1374,Banco Davivienda,Colombia,3.3,0.563,31.2,4.8,Financials,Regional Banks 1375,Mead Johnson Nutrition,United States,3.7,0.543,4.1,16.4,Consumer Staples,Household/Personal Care 1376,Essex Property Trust,United States,1.3,0.415,12.2,15.3,, 1377,Amcor,Australia,9.3,0.22,8.6,13.3,Materials,Containers & Packaging 1378,Financiere de l'Odet,France,11.1,0.252,22.2,5.4,Industrials,Conglomerates 1379,DCC,Ireland,15.8,0.287,6.6,7.8,Industrials,Conglomerates 1380,Geberit,Switzerland,2.9,0.557,3.5,15.2,Materials,Construction Materials 1381,Alfresa Holdings,Japan,23.5,0.306,11.3,4,Health Care,Pharmaceuticals 1382,Mitsui OSK Lines,Japan,13.6,-1.5,18.8,3.7,Industrials,Other Transportation 1383,ICA Gruppen,Sweden,12.1,0.399,8.4,6.8,Consumer Discretionary,Printing & Publishing 1384,E.Sun Financial,Taiwan,1.7,0.407,58.5,5.3,Financials,Consumer Financial Services 1385,Fluor,United States,19,0.281,9.5,7.3,Industrials,Construction Services 1386,Saudi Arabian Mining,Saudi Arabia,2.5,0.107,25.9,12.7,Materials,Diversified Metals & Mining 1387,Partners Group Holding,Switzerland,1.1,0.567,1.9,14.7,, 1388,Mashreq Bank,United Arab Emirates,2.4,0.525,33.4,3.9,Financials,Regional Banks 1389,Noble Energy,United States,3.4,-0.998,21.4,15.1,Energy,Oil & Gas Operations 1390,Caltex Australia,Australia,13.1,0.453,3.8,6,Energy,Oil & Gas Operations 1391,Bank Audi,Lebanon,4,0.446,44.3,2.8,Financials,Regional Banks 1392,LG Household & Health Care,South Korea,5.3,0.49,3.7,10.8,Health Care, 1393,Colruyt,Belgium,10.3,0.417,4.8,7.1,Consumer Staples,Food Retail 1394,Focus Media Information Technology,China,1.3,0.539,1.5,15.4,Information Technology, 1395,OMRON,Japan,7.3,0.405,5.9,9.3,Information Technology,Electronics 1396,Tenaris,Luxembourg,4.5,0.056,14,20.3,Energy,Oil Services & Equipment 1397,Old Republic International,United States,5.9,0.467,19.1,5.2,Financials,Property & Casualty Insurance 1398,Fukuoka Financial Group,Japan,2.1,0.309,156,3.7,Financials,Regional Banks 1399,Baiyin Nonferrous Group,China,8.7,0.018,6.3,13.4,, 1400,Dentsply Sirona,United States,3.7,0.43,11.7,14.3,, 1401,Tractor Supply,United States,6.8,0.437,2.8,9.1,Consumer Discretionary,Specialty Stores 1402,Grupo Carso,Mexico,5.1,0.51,5.3,10.5,Industrials,Conglomerates 1403,Materials Industry Zhongda Group,China,29.2,0.221,12.6,4.5,, 1404,Almarai,Saudi Arabia,3.9,0.537,7.7,15.1,Consumer Staples,Food Processing 1405,Open Text,Canada,2,1.1,6.9,8.7,, 1406,RHB Bank,Malaysia,2.6,0.403,52.8,4.8,Financials, 1407,Targa Resources,United States,6.7,-0.187,13,11.6,Energy, 1408,Taylor Wimpey,United Kingdom,5,0.795,5.9,7.9,Industrials, 1409,Banca Mediolanum,Italy,1.7,0.435,44.2,5.4,, 1410,Expeditors International,United States,6.1,0.431,2.8,10.1,Industrials,Air Courier 1411,Inner Mongolia Baotou Steel,Mongolia,3.8,-0.267,21.1,14.6,Materials,Iron & Steel 1412,Metro,Canada,9.7,0.43,4.3,7,, 1413,Stockland Australia,Australia,1.8,0.665,12.6,8.6,Financials,Real Estate 1414,Olam International,Singapore,14.9,0.255,16.2,3.8,Consumer Staples,Food Processing 1415,Pinnacle West,United States,3.5,0.442,16,9.4,Utilities,Electric Utilities 1416,Chang Hwa Bank,Taiwan,1.3,0.375,62.2,5.5,Financials,Regional Banks 1417,Samsung SDS,South Korea,7.1,0.4,5.7,9.4,Information Technology, 1418,Brambles,Australia,5.5,0.424,7.6,11.2,Consumer Discretionary,Business & Personal Services 1419,Verisk Analytics,United States,2,0.589,4.6,13.2,Consumer Discretionary,Business & Personal Services 1420,Sealed Air,United States,6.8,0.473,7.4,8.3,Materials,Containers & Packaging 1421,Union Bank of India,India,5.5,0.207,61.8,1.6,Financials,Regional Banks 1422,British Land,United Kingdom,0.851,0.474,17.3,8.3,Financials,Real Estate 1423,Seiko Epson,Japan,9.4,0.348,8.4,8.3,Industrials,Business Products & Supplies 1424,Barratt Developments,United Kingdom,5.6,0.771,7.6,7,, 1425,NSK,Japan,8.6,0.434,9,7.3,Industrials,Other Industrial Equipment 1426,Equifax,United States,3.2,0.489,6.7,16.3,Consumer Discretionary,Business & Personal Services 1427,Transurban Group,Australia,1.9,0.095,17.1,18.2,, 1428,Perrigo,Ireland,5.6,-1.5,17.5,9.9,Health Care,Pharmaceuticals 1429,Rite Aid,United States,32.6,0.091,11.7,4.8,Consumer Staples,Drug Retail 1430,Coloplast,Denmark,2.2,0.485,1.8,16.3,Health Care,Medical Equipment & Supplies 1431,Newcrest Mining,Australia,3.6,0.439,11.2,14,Materials,Diversified Metals & Mining 1432,China Merchants Holdings,Hong Kong,1,0.708,13.3,7.6,Industrials,Other Transportation 1433,Dassault Aviation,France,4,0.419,14.4,10.4,Industrials,Aerospace & Defense 1434,UBI Banca,Italy,4.6,-0.918,118.5,3.6,Financials,Regional Banks 1435,Bajaj Auto,India,3.5,0.578,3.1,12.8,Consumer Discretionary,Recreational Products 1436,Sabesp,Brazil,4,0.846,11.6,7.4,Utilities,Diversified Utilities 1437,Steel Dynamics,United States,7.8,0.382,6.4,8.5,Materials, 1438,Ramsay Health Care,Australia,6.6,0.358,5.9,10.5,Health Care, 1439,Equinix,United States,3.6,0.124,12.6,31.1,, 1440,Coach,United States,4.5,0.511,4.7,11.1,Consumer Discretionary,Apparel/Accessories 1441,Cosmo Energy Holdings,Japan,19.7,0.201,13.5,1.4,, 1442,ISS,Denmark,11.8,0.329,7,7.2,, 1443,Coca-Cola HBC,Switzerland,6.9,0.383,6.9,9.4,Consumer Staples,Beverages 1444,Goodman Group,Australia,1.1,0.678,9.3,10.8,, 1445,Aeroports de Paris,France,3.3,0.481,11.2,11.9,Industrials,Other Transportation 1446,E-mart,South Korea,12.7,0.324,12.8,5.5,Consumer Discretionary,Discount Stores 1447,RiseSun Real Estate Development,China,4.1,0.448,19.2,7.3,Materials, 1448,China National Chemical,China,8.4,0.368,12.5,6.5,Industrials,Construction Services 1449,Acciona,Spain,6.6,0.389,18.4,4.6,Industrials,Construction Services 1450,Fastenal,United States,4,0.5,2.7,14.5,Consumer Discretionary,Home Improvement Retail 1451,Hyundai Glovis,South Korea,13.2,0.436,6.6,4.7,Industrials,Other Transportation 1452,Hanesbrands,United States,6,0.539,6.9,7.8,Consumer Discretionary,Apparel/Accessories 1453,Penske Automotive,United States,20.1,0.343,8.9,3.8,Consumer Discretionary,Specialty Stores 1454,Shoprite Holdings,South Africa,9.4,0.344,4,7.9,Consumer Staples,Food Retail 1455,Gjensidige Forsikring,Norway,3.4,0.556,15.7,7.4,Financials,Property & Casualty Insurance 1456,McCormick,United States,4.4,0.472,4.8,12.4,Consumer Staples, 1457,Shanghai Commercial & Savings Bank,Taiwan,1.3,0.364,51.1,4.1,Financials, 1458,Shiseido,Japan,7.8,0.295,8.1,10.5,Consumer Staples,Household/Personal Care 1459,AVIC Capita,China,1.4,0.35,23,8.1,, 1460,Melco International Development,Hong Kong,3.1,1.3,13.4,2.8,, 1461,Citrix Systems,United States,3.4,0.536,6.4,13,Information Technology,Software & Programming 1462,Goldcorp,Canada,3.5,0.167,21.5,12.6,Materials,Diversified Metals & Mining 1463,Realty Income,United States,1.1,0.316,13.2,16.3,, 1464,AECOM Technology,United States,17.5,0.164,13.5,5.1,Industrials, 1465,ITV,United Kingdom,4.1,0.598,4.4,10.8,Consumer Discretionary,Broadcasting & Cable 1466,Cheniere Energy,United States,1.3,-0.61,23.7,11.3,Energy, 1467,Banco Popular Espanol,Spain,4.3,-3.9,156,3.6,Financials,Regional Banks 1468,Agile Property Holdings,China,7.1,0.341,19,3.5,Financials,Real Estate 1469,NCC Group,Sweden,6.2,0.936,2.9,2.6,Industrials,Construction Services 1470,Oji Holdings,Japan,13,0.093,16.1,4.3,Materials,Paper & Paper Products 1471,Korean Air,South Korea,10.1,-0.487,19.8,2.6,Industrials,Airline 1472,Hong Leong Financial,Malaysia,2,0.371,47.5,4.2,Financials,Investment Services 1473,Smurfit Kappa Group,Ireland,9,0.49,9.6,6,Materials,Paper & Paper Products 1474,Abu Dhabi Islamic Bank,United Arab Emirates,1.8,0.449,33.3,3.4,Financials,Regional Banks 1475,Hyosung,South Korea,10.3,0.393,11.7,3.9,Materials,Diversified Metals & Mining 1476,Aozora Bank,Japan,1.1,0.405,41.1,4.1,Financials,Major Banks 1477,Ashtead Group,United Kingdom,4,0.644,7.5,10.5,, 1478,Storebrand,Norway,5.1,0.252,60.4,2.9,Financials,Diversified Insurance 1479,Persimmon,United Kingdom,4.2,0.844,5.1,8.2,, 1480,Ingredion,United States,5.7,0.482,5.8,8.6,Consumer Staples,Food Processing 1481,KB Insurance,South Korea,8.2,0.258,23.7,1.6,, 1482,Sistema,Russia,10.8,-0.045,18.1,3.7,Telecommunication Services,Telecommunications services 1483,Taishin Financial Holdings,Taiwan,1.4,0.354,48.9,3.9,Financials,Investment Services 1484,Yamaguchi Financial,Japan,1.5,0.299,87.5,2.6,Financials,Regional Banks 1485,Mizrahi Tefahot Bank,Israel,1.9,0.33,59.9,3.9,Financials,Regional Banks 1486,Meritz Financial Group,South Korea,6.7,0.218,30.9,1.5,Financials, 1487,Kangmei Pharmaceutical,China,3.2,0.496,7.9,13.4,Health Care, 1488,IHI,Japan,14,0.245,14.7,5.1,Industrials,Heavy Equipment 1489,Scripps Networks Interactive,United States,3.4,0.674,6.4,10,Consumer Discretionary,Broadcasting & Cable 1490,Hang Lung Group,Hong Kong,1.9,0.478,24,6.1,Financials,Real Estate 1491,Waters,United States,2.2,0.522,4.7,12.6,,Precision Healthcare Equipment 1492,SEIBU,Japan,4.7,0.724,13.6,5.8,Consumer Discretionary, 1493,China Longyuan Power,China,3.4,0.516,20,6.4,Utilities,Diversified Utilities 1494,Adient,Ireland,16.6,-1.5,11.3,6.3,Energy, 1495,Blom Bank,Lebanon,2.4,0.458,29.5,2.6,Financials,Regional Banks 1496,Hokuhoku Financial Group,Japan,1.7,0.279,104.9,2,Financials,Regional Banks 1497,Jiangsu Hengrui Medicine,China,1.6,0.39,2.1,18.7,Financials, 1498,Ametek,United States,3.8,0.512,7.1,12.5,Industrials,Electrical Equipment 1499,STMicroelectronics,Switzerland,7,0.167,8,13.7,Information Technology,Semiconductors 1500,Dangote Cement,Nigeria,2.4,0.745,4.9,9.1,Materials,Construction Materials 1501,SDIC Essence,China,1.6,0.386,18.5,8.2,, 1502,Wärtsilä,Finland,5.3,0.389,5.7,10.8,, 1503,Oi,Brazil,7.5,-2,25.2,0.952,Telecommunication Services,Telecommunications services 1504,Maxim Integrated Products,United States,2.2,0.5,3.9,12.7,Information Technology,Semiconductors 1505,Chow Tai Fook Jewellery,Hong Kong,6.4,0.336,6.9,10.1,Consumer Discretionary,Apparel/Accessories 1506,Shimano,Japan,3,0.469,3.8,13.6,Consumer Discretionary,Recreational Products 1507,Bank Muscat,Oman,1.4,0.459,28.1,2.8,Financials,Regional Banks 1508,Concordia Financial Group,Japan,0.303,0.058,164.1,5.7,Financials, 1509,Thai Oil,Thailand,7.8,0.602,6.1,4.4,Energy,Oil & Gas Operations 1510,Axiata Group,Malaysia,5.2,0.122,15.7,10.2,Information Technology,Communications Equipment 1511,Turkish Airlines,Turkey,9.7,-0.015,18.5,2,Industrials,Airline 1512,Moscow Exchange,Russia,0.652,0.376,40,4.4,Financials, 1513,Adani Ports & Special Economic Zone,India,1.2,0.549,6,11.2,, 1514,News Corp,United States,8.1,-0.363,14.5,7.3,Consumer Discretionary,Broadcasting & Cable 1515,Metalurgica Gerdau,Brazil,10.8,-0.395,16.8,1.4,Materials,Iron & Steel 1516,Packaging Corp of America,United States,5.8,0.445,5.8,8.6,, 1517,Oceanwide Holdings,China,3.4,0.41,21.7,6.9,Financials, 1518,China Communications Services,China,13.3,0.383,9,4.3,Telecommunication Services,Telecommunications services 1519,Hertz Global Holdings,United States,9.1,-0.492,19.2,1.3,Financials,Rental & Leasing 1520,Fresnillo,Mexico,1.9,0.432,4.3,14.8,Materials, 1521,DSV,Denmark,10.1,0.248,5.7,9.3,Industrials,Trucking 1522,Alpha Bank,Greece,4.6,0.046,68.4,2.9,Financials,Regional Banks 1523,FleetCor Technologies,United States,1.8,0.449,9.6,13.4,Energy, 1524,IDBI Bank,India,4.8,-0.548,56.6,2.4,Financials,Regional Banks 1525,EMS-Chemie Holding,Switzerland,2,0.456,2,13.6,Materials,Specialized Chemicals 1526,Whitbread,United Kingdom,4.4,0.57,6,9.1,Consumer Discretionary,Hotels & Motels 1527,Albemarle,United States,2.9,0.509,8.2,11.7,Consumer Discretionary, 1528,Taiwan Mobile,Taiwan,3.6,0.475,4.7,12.7,Telecommunication Services,Telecommunications services 1529,NEX Group,United Kingdom,0.694,0.107,146.3,2.7,Telecommunication Services, 1530,Suruga Bank,Japan,1.3,0.371,38.5,4.4,Financials,Regional Banks 1531,Compal Electronics,Taiwan,23.8,0.252,10.8,2.8,Information Technology,Computer Hardware 1532,Metropolitan Bank & Trust,Philippines,2,0.375,37.8,5.5,Financials,Regional Banks 1533,Mitsui Chemicals,Japan,10.9,0.362,11,4.8,Materials,Specialized Chemicals 1534,TransDigm Group,United States,3.3,0.457,10,12.1,, 1535,NiSource,United States,4.5,0.324,18.7,7.7,Utilities,Natural Gas Utilities 1536,Banco BPI,Portugal,0.819,0.347,43.5,1.6,Financials,Regional Banks 1537,Taiheiyo Cement,Japan,7.4,0.616,8.9,4.1,Materials,Construction Materials 1538,Alstom,France,7.9,-1.1,16.3,6.6,Industrials,Conglomerates 1539,Liberty Media Corporation Series A Liberty Formula One,United States,5.3,0.679,3,6.8,, 1540,Lindt & Sprungli,Switzerland,4,0.426,6.4,14.6,Consumer Staples,Food Processing 1541,Continental Resources,United States,2.1,-0.4,14.4,17.3,Energy,Oil & Gas Operations 1542,Vulcan Materials,United States,3.6,0.395,8.8,16.4,Materials, 1543,MISC,Malaysia,2.3,0.623,12.5,7.4,Industrials,Other Transportation 1544,Uniqa,Austria,5.7,0.164,35.1,2.4,Financials,Diversified Insurance 1545,Companhia Brasileira de Distribuicao,Brazil,12,-0.139,13.9,5.9,, 1546,Grupo Bolivar,Colombia,3.8,0.394,33.7,1.4,, 1547,Hiroshima Bank,Japan,1.3,0.282,76.4,2.6,Financials,Regional Banks 1548,Yunnan Baiyao Group,China,3.4,0.457,3.4,13.1,Health Care,Pharmaceuticals 1549,Alinma Bank,Saudi Arabia,1.1,0.401,27.9,6,Financials,Regional Banks 1550,Ferrari,Italy,3.4,0.441,4.1,13.6,Consumer Discretionary, 1551,LG Uplus,South Korea,9.9,0.425,9.9,5.7,Telecommunication Services,Telecommunications services 1552,Ensco,United Kingdom,2.8,0.874,14.4,2.8,Energy,Oil Services & Equipment 1553,Assured Guaranty,Bermuda,1.4,0.881,14.2,4.8,Financials,Diversified Insurance 1554,Banco BPM SpA,Italy,3.2,-1.9,123.8,4.3,, 1555,EFG International,Switzerland,1.1,0.344,41.6,1.7,, 1556,Rajesh Exports,India,36.3,0.173,3.5,2.8,Materials, 1557,Wendel,France,9.2,-0.449,17.2,5.8,Financials,Investment Services 1558,Spirit AeroSystems,United States,6.8,0.469,5.4,6.8,, 1559,Terumo,Japan,4.6,0.359,9.1,13.1,Health Care,Medical Equipment & Supplies 1560,Office Depot,United States,12.3,0.38,5.5,2.5,, 1561,Shizuoka Bank,Japan,1.9,0.262,102.2,5.1,Financials,Regional Banks 1562,Mercuries & Associates,Taiwan,6.1,0.064,30.1,0.56,Consumer Discretionary,Department Stores 1563,Bank Otkritie,Russia,4.9,0.206,48.3,4.1,Financials, 1564,Foshan Haitian Flavouring and Food Company,China,1.9,0.428,1.9,14,Consumer Staples, 1565,Staples,United States,19.8,-1.4,8.3,6.4,Consumer Discretionary,Specialty Stores 1566,Zhengzhou Yutong Bus,China,5.4,0.609,5.1,6.9,, 1567,Industrial Securities,China,1.3,0.412,17.4,7.5,Financials, 1568,Pacific Exploration & Production,Canada,1.4,2.5,2.7,1.6,, 1569,Ihs Markit,United Kingdom,2.7,0.153,14,17.2,Information Technology, 1570,Bendigo & Adelaide Bank,Australia,2.2,0.309,51.4,4.2,Financials,Regional Banks 1571,US Foods,United States,22.9,0.21,8.9,6.1,Consumer Staples, 1572,CCR,Brazil,2.9,0.492,7.5,11.5,Industrials,Other Transportation 1573,Church & Dwight,United States,3.5,0.459,4.4,12.6,Consumer Staples,Household/Personal Care 1574,Yamada Denki,Japan,14.4,0.305,10.5,4.8,Information Technology,Computer & Electronics Retail 1575,BCV Group,Switzerland,1.2,0.314,43.4,5.9,Financials,Regional Banks 1576,Hankook Tire,South Korea,5.7,0.752,8,5.9,Consumer Discretionary, 1577,Martin Marietta Materials,United States,3.8,0.423,7.6,14,Materials, 1578,Intelsat,Luxembourg,2.2,0.99,12.9,0.504,, 1579,Royal Mail,United Kingdom,13.4,0.302,11,5.1,Telecommunication Services, 1580,Orkla,Norway,4.5,0.511,6.5,9.1,Industrials,Conglomerates 1581,Saipem,Italy,11,-2.3,15.1,4.5,, 1582,Aeroflot-Russian Airlines,Russia,7.4,0.559,4.9,3.2,Materials, 1583,Westlake Chemical,United States,5.1,0.397,10.9,8.7,Materials,Diversified Chemicals 1584,Canadian Utilities,Canada,2.6,0.468,14,7.9,Utilities, 1585,Suzuken,Japan,20.1,0.244,10.6,3.2,Health Care,Pharmaceuticals 1586,Concho Resources,United States,1.6,-1.5,12.5,19.1,Energy,Oil & Gas Operations 1587,Hero Motocorp,India,4.3,0.473,1.9,10,Consumer Discretionary,Recreational Products 1588,Pembina Pipeline,Canada,3.2,0.352,11.2,13,Energy,Oil & Gas Operations 1589,Union National Bank,United Arab Emirates,1.4,0.409,28.3,3.4,Financials,Regional Banks 1590,Gunma Bank,Japan,1.3,0.276,68.2,2.3,Financials,Regional Banks 1591,Novozymes,Denmark,2.1,0.453,2.8,12.2,Health Care,Biotechs 1592,Avis Budget Group,United States,8.7,0.163,17.8,2.3,Financials,Rental & Leasing 1593,Sumitomo Metal Mining,Japan,7,-0.404,13.9,7.7,Materials,Diversified Metals & Mining 1594,China International Marine,China,7.6,0.077,17.9,6,Industrials,Heavy Equipment 1595,ONO Pharmaceutical,Japan,2.2,0.444,5,12.3,Health Care, 1596,Maxis,Malaysia,2.1,0.486,4.4,10.8,Telecommunication Services,Telecommunications services 1597,BM&F Bovespa,Brazil,0.666,0.415,9.7,12.8,Financials,Investment Services 1598,CNO Financial Group,United States,4,0.358,32,3.6,Financials,Life & Health Insurance 1599,Piraeus Bank,Greece,3.4,-0.039,86,1.6,Financials,Regional Banks 1600,World Fuel Services,United States,26.9,0.127,5.4,2.5,Consumer Discretionary,Specialty Stores 1601,CJ Cheiljedang,South Korea,12.6,0.238,13.1,4,Consumer Staples,Food Processing 1602,Tiffany & Co.,United States,4,0.446,5.1,11.6,Consumer Discretionary, 1603,XPO Logistics,United States,14.6,0.063,11.7,5.3,Consumer Discretionary, 1604,BBVA Banco Continental,Peru,1.8,0.397,23.5,6.2,, 1605,Tech Data,United States,26.2,0.195,7.9,3.4,Information Technology,Electronics 1606,National Bank of Greece,Greece,3.1,0.252,82.8,2.4,Financials,Major Banks 1607,EasyJet,United Kingdom,6.6,0.606,7.2,5.1,Industrials,Airline 1608,HRG Group,United States,5.2,0.037,35.7,3.9,, 1609,Kyushu Financial Group,Japan,1.5,0.125,82.6,2.8,Financials, 1610,Boliden,Sweden,4.7,0.495,6,8.5,Materials, 1611,NVR,United States,5.8,0.425,2.6,7.7,, 1612,Alliant Energy,United States,3.3,0.382,14,9,Utilities,Electric Utilities 1613,Nishi-nippon Financial Holdings,Japan,1.3,0.223,81.4,1.7,Energy, 1614,Fortune Brands Home & Security,United States,5,0.408,5.1,9.5,, 1615,NetApp,United States,5.4,0.311,9.1,11.1,Information Technology,Computer Storage Devices 1616,Snap-on,United States,3.7,0.546,4.7,9.5,, 1617,Bank of Qingdao,China,1.6,0.316,40,3.7,Financials, 1618,Far East Horizon,Hong Kong,2.1,0.434,24,3.6,, 1619,Garmin,Switzerland,3,0.511,4.5,10,Consumer Discretionary,Consumer Electronics 1620,People's United Financial,United States,1.5,0.28,40.9,6.1,Financials,Thrifts & Mortgage Finance 1621,North Pacific Bank,Japan,1,0.174,77.1,1.4,Financials,Regional Banks 1622,Grasim Industries,India,5.3,0.461,9.1,7.7,Materials,Construction Materials 1623,Banco Comercial Portugues,Portugal,3.1,0.27,75.2,2.9,Financials,Regional Banks 1624,Moscovskiy Oblastnoi Bank,Russia,2.6,1.7,4.5,0.363,Financials, 1625,Rexel,France,14.6,0.153,11.5,5.3,Information Technology,Electronics 1626,Axis Capital Holdings,Bermuda,4,0.512,18.5,5.7,Financials,Diversified Insurance 1627,Bank of Kyoto,Japan,0.947,0.156,75,2.7,Financials,Regional Banks 1628,Hachijuni Bank,Japan,1.8,0.252,74,2.7,Financials,Regional Banks 1629,Guangdong Investment,Hong Kong,1.3,0.543,6.7,9.4,Utilities,Diversified Utilities 1630,Chugoku Bank,Japan,1.2,0.219,73.3,2.7,Financials,Regional Banks 1631,77 Bank,Japan,0.9,0.088,72.5,1.6,Financials,Regional Banks 1632,STRABAG,Austria,14,0.225,11.5,3.8,Industrials,Construction Services 1633,SBI Holdings,Japan,2.5,0.359,29.8,2.9,Financials,Investment Services 1634,Vietin Bank,Vietnam,2.7,0.304,41.7,3,Financials,Regional Banks 1635,GPT Group,Australia,0.549,0.857,8.6,7,Financials,Real Estate 1636,Shandong Gold Mining,China,7.5,0.195,4.1,9.7,Utilities, 1637,Jabil Circuit,United States,18.3,0.152,10,5.2,Information Technology,Electronics 1638,Sears Holdings,United States,22.1,-2.2,9.4,1.2,Consumer Discretionary,Department Stores 1639,Smiths Group,United Kingdom,4.3,0.576,5.8,8,Industrials,Conglomerates 1640,Eurobank Ergasias,Greece,2.2,0.254,70,1.4,, 1641,Tsinghua Tongfang,China,4.2,0.887,8.9,6.5,, 1642,BPER Banca,Italy,2.7,0.016,68.5,2.3,Financials, 1643,China Jinmao,Hong Kong,4.2,0.382,24,3.5,Financials, 1644,Vertex Pharmaceuticals,United States,1.7,-0.112,2.9,28.8,Health Care,Biotechs 1645,Dixons Carphone,United Kingdom,14.2,0.302,9.4,4.5,, 1646,Incyte,United States,1.1,0.104,1.6,28.6,Health Care, 1647,President Chain Store,Taiwan,6.7,0.305,3,8.8,Consumer Staples,Food Retail 1648,New Residential Investment,United States,1.2,0.505,18.4,5.2,Financials, 1649,Deutsche Pfandbriefbank,Germany,2.2,0.218,66.1,1.7,, 1650,MAp,Australia,1,0.421,8.9,11.6,, 1651,BorgWarner,United States,9.1,0.119,8.8,8.1,Consumer Discretionary,Auto & Truck Parts 1652,Chimera Investment,United States,0.942,0.552,16.7,3.8,Financials, 1653,ProSiebenSat1 Media,Germany,4.2,0.445,7.1,9.9,Consumer Discretionary,Broadcasting & Cable 1654,Shanghai Lujianzi,China,1.9,0.395,11.5,9.6,, 1655,Tosoh,Japan,6.4,0.564,6.5,5.6,Materials,Diversified Chemicals 1656,Dana Holding,United States,5.8,0.64,4.9,2.6,Financials, 1657,Joint Stock Commercial Bank for Foreign Trade of Vietnam,Vietnam,2.1,0.305,34.6,5.8,Financials, 1658,SL Green Realty,United States,1.7,0.261,15.9,10.8,Financials,Real Estate 1659,Beijing Shougang,China,6.3,0.06,18.3,6.3,, 1660,Sysmex,Japan,2.3,0.378,2.3,12.9,, 1661,Want Want China,China,3,0.03,3.9,9.1,Consumer Staples,Food Processing 1662,WellCare Health Plans,United States,14.2,0.241,6.2,6.3,Health Care, 1663,Catcher Technology,Taiwan,2.5,0.683,5.6,7.6,Information Technology, 1664,Central Bank of India,India,4.3,-0.167,46.4,3,Financials,Regional Banks 1665,Iyo Bank,Japan,0.987,0.194,59.6,2.1,Financials,Regional Banks 1666,Mettler-Toledo International,United States,2.5,0.384,2.2,12.5,, 1667,New Hope Liuhe,China,9.1,0.39,5.6,5,Consumer Staples,Food Processing 1668,Wistron,Taiwan,19.2,0.043,9.4,2.3,Information Technology,Computer Hardware 1669,American Equity Investment,United States,2.1,0.083,58.3,2.1,Financials,Life & Health Insurance 1670,Cnpc Capital,China,0.83,-0.039,0.289,25.7,, 1671,Michael Kors Holdings,United Kingdom,4.6,0.756,2.8,6.1,Consumer Discretionary,Apparel/Accessories 1672,Ternium,Luxembourg,6,0.596,8.3,5.1,Materials,Iron & Steel 1673,Israel Discount Bank,Israel,2.4,0.236,57.1,2.8,Financials,Regional Banks 1674,Signet Jewelers,Bermuda,6.4,0.543,6.6,4.7,Consumer Discretionary,Specialty Stores 1675,Empire,Canada,18.4,-0.618,6.7,4.1,Consumer Staples,Food Retail 1676,PacWest Bancorp,United States,1.1,0.348,22,6.3,, 1677,Techtronic Industries,Hong Kong,5.5,0.409,5.1,7.6,Utilities, 1678,Tokyu Fudosan,Japan,7.1,0.245,18.3,3.3,, 1679,Hengan International Group,China,2.9,0.501,4.9,9.2,Consumer Staples,Household/Personal Care 1680,Moody's,United States,3.6,0.267,5.3,21.6,Consumer Discretionary,Business & Personal Services 1681,Juroku Bank,Japan,1,0.103,54.7,1.2,Financials,Regional Banks 1682,AU Optronics,Taiwan,10.2,0.242,13.3,3.8,Information Technology,Electronics 1683,Commercial Bank For Investment & Development Of Vietnam,Vietnam,3.3,0.275,44.2,2.6,Financials, 1684,Shaw Communications,Canada,3.8,0.357,11.4,10.1,Consumer Discretionary,Broadcasting & Cable 1685,Haseko,Japan,7.1,0.477,5,3.1,, 1686,Bureau Veritas Registre International de Classific,France,5,0.353,6.4,9.4,, 1687,DSME,South Korea,11,-2.3,12.4,1.1,Industrials,Heavy Equipment 1688,CST Brands,United States,11.1,0.324,4.4,3.7,Consumer Discretionary,Specialty Stores 1689,Molina Healthcare,United States,17.8,0.052,7.4,2.7,Health Care, 1690,Brilliance China Automotive Holdings,Hong Kong,0.771,0.554,4.5,8.4,, 1691,Toho Bank,Japan,0.665,0.074,53.3,0.912,Financials,Regional Banks 1692,Cattolica Assicurazioni,Italy,5.5,0.084,24.8,1.4,Financials,Diversified Insurance 1693,EQT,United States,1.8,-0.453,15.5,10.9,Energy,Oil & Gas Operations 1694,Snap,United States,0.404,-0.515,1.7,24.4,Financials, 1695,Tongyang Life Insurance,South Korea,6,0.01,22.1,0.945,Utilities, 1696,Samsung Heavy Industries,South Korea,9,-0.104,14.3,3.4,Industrials,Heavy Equipment 1697,SinoPac Financial,Taiwan,1.5,0.257,52.1,3.3,Financials,Consumer Financial Services 1698,INTL FCStone,United States,17.4,0.051,6.3,0.681,, 1699,Commercial International Bank,Egypt,2.2,0.597,14.8,5,Financials,Regional Banks 1700,Cybg,United Kingdom,1.8,-0.281,51.9,3.1,Financials, 1701,Orient,Japan,2,0.27,44.8,3,Financials,Consumer Financial Services 1702,Wynn Resorts,United States,4.5,0.242,12,11.9,Consumer Discretionary,Casinos & Gaming 1703,Hopson Development Holdings,Hong Kong,2.1,0.498,17.2,2.1,Financials,Real Estate 1704,JTEKT,Japan,12.1,0.295,9.7,5.3,Consumer Discretionary,Auto & Truck Parts 1705,Nippon Paint,Japan,5.8,0.25,7.1,11.6,Materials, 1706,Calpine,United States,6.6,0.092,19.3,3.8,Utilities,Electric Utilities 1707,BayWa,Germany,17,0.034,6.8,1.1,Consumer Discretionary,Specialty Stores 1708,Migdal Insurance,Israel,4.5,0.053,34.8,1,Financials, 1709,Changjiang Securities,China,0.894,0.332,15.4,8.1,Financials, 1710,Aareal Bank,Germany,1.3,0.22,51.1,2.2,Financials,Thrifts & Mortgage Finance 1711,Kansas City Southern,United States,2.4,0.478,9,9.3,Industrials,Railroads 1712,Dufry,Switzerland,7.9,0.003,9.8,8,Utilities, 1713,Interconexion Electrica,Colombia,4,0.7,12.8,4.5,Financials, 1714,San-In Godo Bank,Japan,0.83,0.14,50.8,1.2,Financials,Regional Banks 1715,Nanto Bank,Japan,0.71,0.13,50.7,0.967,Financials,Regional Banks 1716,Li & Fung,Hong Kong,16.8,0.223,7.7,3.6,Industrials,Trading Companies 1717,Lupin,India,2.5,0.444,3.6,9.9,Health Care, 1718,Encana,Canada,3.2,-0.949,14.7,11.3,Energy,Oil & Gas Operations 1719,City Developments,Singapore,2.8,0.473,13.7,6.7,Financials,Real Estate 1720,WPG Holdings,Taiwan,16.6,0.165,5.6,2.2,Information Technology,Electronics 1721,KWG Property Holding,Hong Kong,1.3,0.52,15.9,2.3,, 1722,Huntsman,United States,9.7,0.326,9.2,5.9,Materials,Diversified Chemicals 1723,Sino Land,Hong Kong,2.2,0.439,19.3,5.2,Financials,Real Estate 1724,Bohai Financial Investment Holding,China,3.1,0.272,29.3,6.4,Financials, 1725,Tokyo TY Financial Group,Japan,0.731,0.25,48.9,0.853,Materials, 1726,Nippon Steel Trading,Japan,16.4,0.169,5.8,1.3,Materials,Diversified Metals & Mining 1727,Bank of Greece,Greece,1.9,1.2,10,0.24,Financials,Major Banks 1728,Performance Food Group,United States,16.4,0.074,3.6,2.4,, 1729,Ogaki Kyoritsu Bank,Japan,0.914,0.119,48.5,1.2,Financials,Regional Banks 1730,Hyakugo Bank,Japan,0.61,0.078,48.2,0.994,Financials,Regional Banks 1731,Daishi Bank,Japan,0.656,0.105,48,1.3,Financials,Regional Banks 1732,Senshu Ikeda Holdings,Japan,0.899,0.115,47.9,1.1,Financials,Regional Banks 1733,Supervalu,United States,16,0.103,4.5,0.996,Consumer Staples,Food Retail 1734,NASDAQ OMX Group,United States,3.7,0.109,14.2,11.5,Financials,Investment Services 1735,CPFL Energia,Brazil,5.4,0.258,13,8.4,Utilities,Electric Utilities 1736,Severn Trent,United Kingdom,2.6,0.534,11.2,7,Utilities,Diversified Utilities 1737,PBF Energy,United States,15.9,0.171,7.6,2.3,Energy,Oil & Gas Operations 1738,Crown Resorts,Australia,2.6,0.82,6.5,6.5,Consumer Discretionary,Casinos & Gaming 1739,Mirae Asset Daewoo,South Korea,1.3,0.034,47.4,5.1,, 1740,Shiga Bank,Japan,0.647,0.133,47.2,1.3,Financials,Regional Banks 1741,SK Networks,South Korea,15.9,-0.7,7.2,1.7,Industrials,Trading Companies 1742,Banco de Chile,Chile,3.8,0.131,47.1,4.5,, 1743,Showa Shell Sekiyu,Japan,15.9,0.156,8.4,3.8,Energy,Oil & Gas Operations 1744,Turkcell,Turkey,4.7,0.508,9,7.1,Telecommunication Services,Telecommunications services 1745,Syndicate Bank,India,3.9,-0.232,46.7,0.985,Financials,Regional Banks 1746,Affiliated Managers Group,United States,2.2,0.471,8.7,9.1,Financials,Investment Services 1747,Vipshop Holdings,China,8.4,0.3,3.6,6.8,, 1748,Isetan Mitsukoshi Holdings,Japan,11.5,0.203,11.2,4.2,Consumer Discretionary,Department Stores 1749,Taiwan Business Bank,Taiwan,0.934,0.161,46.5,1.6,Financials,Regional Banks 1750,CalAtlantic Group,United States,6.5,0.484,8.7,4.3,Industrials, 1751,Waste Connections,Canada,3.3,0.198,11.2,15.4,, 1752,Nürnberger,Germany,4.6,0.064,30.7,0.837,, 1753,KION Group,Germany,6.2,0.272,12.5,7.1,Materials, 1754,International Flavors & Fragrances,United States,3.1,0.404,4,10.4,Utilities, 1755,China National Materials,China,7.5,0.088,15.6,1.3,Industrials,Other Industrial Equipment 1756,Prada,Italy,3.9,0.365,5.1,11.4,Consumer Discretionary,Apparel/Accessories 1757,Indorama Ventures,Thailand,7.2,0.429,7.3,4.9,, 1758,Korea Zinc,South Korea,5,0.511,5.4,6.5,Materials, 1759,Nomura Research Institute,Japan,3.8,0.47,5.3,9,, 1760,Tingyi Holding,China,8.4,0.177,8.1,7.6,Consumer Staples,Food Processing 1761,Tenneco,United States,8.6,0.363,4.3,3.2,, 1762,Cullen/Frost Bankers,United States,1.1,0.303,30.2,5.7,, 1763,First Pacific,Hong Kong,6.8,0.103,17.2,3.3,Financials,Investment Services 1764,Logan Property Holdings,China,3.1,0.671,12.4,3.1,Financials, 1765,DGB Financial Group,South Korea,2.5,0.248,44.3,1.7,Financials,Consumer Financial Services 1766,Shanghai Fosun Pharmaceutical (group),China,2.2,0.423,6.3,9.8,Health Care, 1767,Central Puerto,Argentina,0.36,0.12,0.811,20.2,, 1768,Xishui Strong Year Ltd. Inner Mongolia,China,3.8,-0.029,44.2,2.4,, 1769,China Mengniu Dairy,Hong Kong,8.1,-0.113,7.1,7.7,Consumer Staples,Food Processing 1770,Securitas,Sweden,10.3,0.309,5.3,5.8,Consumer Discretionary,Business & Personal Services 1771,AmBank Group,Malaysia,1.7,0.306,28.8,3.3,Financials,Regional Banks 1772,Ningbo Port,China,2.4,0.378,8.4,10.8,, 1773,Penn Virginia,United States,0.132,1,0.292,0.732,, 1774,Chongqing New Century Cruise,China,0.267,0.12,1.2,20,, 1775,Jacobs Engineering,United States,10.7,0.224,7.1,6.6,Industrials,Construction Services 1776,Red Star Macalline Group,China,1.4,0.609,13.2,4.1,, 1777,Virgin Money Holdings,United Kingdom,1.4,0.189,43.3,1.7,, 1778,Mattel,United States,5.5,0.318,6.5,8.5,Consumer Staples,Household/Personal Care 1779,Credit Saison,Japan,3.1,0.345,23.8,3.3,Financials,Consumer Financial Services 1780,Mirvac Group,Australia,1.9,0.794,8.6,6.3,, 1781,Hyakujushi Bank,Japan,0.728,0.083,42.9,0.961,Financials,Regional Banks 1782,GameStop,United States,8.6,0.353,5,2.2,, 1783,Asian Paints,India,2.3,0.281,1.7,16.1,Materials, 1784,Frasers Centrepoint,Singapore,2.7,0.45,17.1,3.7,, 1785,Xiamen Xiangyu,China,14.6,0.076,5.2,1.9,, 1786,Credito Emiliano,Italy,1.6,0.146,41.7,2.2,Financials,Regional Banks 1787,Remgro,South Africa,1.9,0.494,8.5,8.4,Industrials,Conglomerates 1788,Core-Mark Holding Company,United States,14.5,0.054,1.5,1.4,, 1789,Kiyo Bank,Japan,0.665,0.11,41.7,1,Financials, 1790,Shenzhou International Group Holdings,China,2.3,0.444,3.1,9.1,, 1791,SYNNEX,United States,14.5,0.248,5.1,4.2,, 1792,Damac Properties,United Arab Emirates,1.9,1,6.7,4.7,, 1793,Aboitiz Equity Ventures,Philippines,2.5,0.487,8.1,8.4,Industrials,Conglomerates 1794,HD Supply Holdings,United States,7.4,0.202,6,8.1,Financials, 1795,RioCan Real Estate Investment Trust,Canada,0.885,0.627,10.6,6.4,, 1796,Emera,Canada,3.2,0.193,21.8,7.4,Energy, 1797,Grupo Zuliano,Venezuela,1,0.999,1.5,0.072,, 1798,InterContinental Hotels,United Kingdom,1.7,0.415,2.9,9.6,Consumer Discretionary,Hotels & Motels 1799,Impulsora del Desarrollo y el Empleo en America Latina SAB Class B,Mexico,0.847,0.997,5.3,4.9,, 1800,Accor,France,1.8,0.252,12.5,12.1,Consumer Discretionary,Hotels & Motels 1801,Liberbank,Spain,1.2,0.143,40.4,1.1,Financials,Regional Banks 1802,Bandai Namco Holdings,Japan,5.6,0.428,4.2,6.5,, 1803,Zenith Bank,Nigeria,2.1,0.499,15,1.5,Financials,Regional Banks 1804,Fullshare Holdings,Hong Kong,0.645,0.468,6.9,8.6,, 1805,Hologic,United States,2.9,0.332,7.3,11.9,, 1806,Keiyo Bank,Japan,0.627,0.125,40,1.1,Financials,Regional Banks 1807,Banque Centrale Populaire,Morocco,2.4,0.27,34.8,4.7,Financials,Regional Banks 1808,Owens Corning,United States,5.7,0.393,7.7,6.8,Industrials, 1809,Autodesk,United States,2,-0.584,4.8,18.7,Information Technology, 1810,Yonghui Superstores,China,7.4,0.187,4.2,8,Consumer Discretionary, 1811,Banca Popolare di Sondrio,Italy,1.3,0.109,39.2,1.5,Financials,Regional Banks 1812,VeriSign,United States,1.1,0.441,2.3,8.9,Information Technology,Software & Programming 1813,Antofagasta,United Kingdom,3.7,0.113,13.7,10.6,Materials,Diversified Metals & Mining 1814,Fuyao Glass Industry Group,China,2.5,0.472,4.3,8.4,Materials, 1815,Bank of Queensland,Australia,1.7,0.247,39.2,3.5,Financials,Regional Banks 1816,Angang Steel,China,8.7,0.241,12.7,5.3,Materials,Iron & Steel 1817,Kingboard Chemical Holdings,Hong Kong,4.6,0.648,9.9,4,, 1818,Wanhua Chemical Group,China,3.9,0.409,7.7,9.5,Materials,Specialized Chemicals 1819,Habib Bank,Pakistan,1.6,0.325,24.1,3.7,Financials, 1820,Umicore,Belgium,11.6,0.145,4.4,6,Materials,Diversified Metals & Mining 1821,Popular,Puerto Rico,1.9,0.217,38.7,4.2,Financials,Regional Banks 1822,Chesapeake Energy,United States,8.4,-4.2,13,5.6,Energy,Oil & Gas Operations 1823,Golden Agri-Resources,Singapore,7.2,0.397,8.3,3.3,Consumer Staples,Food Processing 1824,Far Eastern New Century,Taiwan,6.7,0.196,15.9,4.5,Materials,Diversified Chemicals 1825,Musashino Bank,Japan,0.609,0.106,38,0.952,Financials,Regional Banks 1826,JB Financial Group,South Korea,1.8,0.114,37.9,0.799,Financials, 1827,Basler Kantonalbank,Switzerland,0.732,0.124,37.9,3,Financials,Regional Banks 1828,Indian Overseas Bank,India,3.5,-0.552,37.8,1,Financials,Regional Banks 1829,Bid Corp.,South Africa,9.4,0.255,3.8,6.7,, 1830,Marine Harvest ASA,Norway,3.9,0.594,5.1,7,, 1831,Nippon Paper Industries,Japan,9,-0.166,12,2.1,Materials, 1832,TAQA,United Arab Emirates,4.4,-5.2,28.4,0.941,Utilities,Electric Utilities 1833,United Aircraft,Russia,6.2,-0.037,17.2,4.4,Industrials, 1834,Inventec,Taiwan,13.3,0.175,5.7,2.6,Information Technology,Computer Hardware 1835,Bluescope Steel,Australia,7.4,0.381,6.8,5.1,Materials, 1836,Hanwa,Japan,13.2,0.249,5.9,1.4,Materials,Diversified Metals & Mining 1837,Makita,Japan,3.7,0.4,5,9.5,Consumer Staples,Household/Personal Care 1838,China Railway Signal & Communication,China,4.4,0.46,7.2,7.3,Telecommunication Services, 1839,Shanghai Industrial Holdings,Hong Kong,2.9,0.374,19.5,3.3,Financials, 1840,Xiamen International Trade Group,China,13.1,0.151,8.6,2.2,, 1841,Hokkoku Bank,Japan,0.607,0.136,36.5,1.1,Financials,Regional Banks 1842,Dali Foods Group,China,2.7,0.473,2.4,8,Financials, 1843,Commercial Bank of Qatar,Qatar,1.7,0.077,35.8,3.4,Financials,Regional Banks 1844,Industrias Peñoles,Mexico,4.4,0.307,6.9,10.4,, 1845,Allahabad Bank,India,3.2,-0.11,35.7,0.838,Financials,Regional Banks 1846,Avery Dennison,United States,6.1,0.321,4.4,7.1,, 1847,Merlin Properties SOCIMI S.A,Spain,0.401,0.645,11.5,5.4,, 1848,Petro Rabigh,Saudi Arabia,6.7,0.01,15.5,3,Materials,Diversified Chemicals 1849,Phosagro,Russia,2.8,0.894,3.8,5.5,Materials, 1850,UCO Bank,India,2.9,-0.443,35.7,0.905,Financials,Regional Banks 1851,China Molybdenum,China,1,0.15,12.7,11,Materials, 1852,NH Investment & Securities,South Korea,1.5,0.204,35.6,3,, 1853,Japan Securities,Japan,0.212,0.029,35.6,0.514,Financials,Investment Services 1854,Corporation Bank,India,3.2,-0.077,35.5,0.98,Financials,Regional Banks 1855,Public Power,Greece,5.8,0.075,18.1,0.754,Utilities,Electric Utilities 1856,Tongling Nonferrous Metals,China,12.8,-0.115,6.1,4.9,Materials,Diversified Metals & Mining 1857,Zhongsheng Group Holdings,China,10.8,0.279,5.7,3.1,, 1858,Alawwal Bank,Saudi Arabia,1.4,0.284,28,3.4,Financials, 1859,Oriental Bank of Commerce,India,3.2,0.022,34.9,0.769,Financials,Regional Banks 1860,Paramount Res,Canada,0.186,0.88,1.5,1.4,, 1861,Toyota Boshoku,Japan,12.5,0.084,5.6,4,Consumer Discretionary,Auto & Truck Parts 1862,JC Penney,United States,12.5,0.001,9.3,1.8,Consumer Discretionary,Department Stores 1863,Spectrum Brands Holdings,United States,5.1,0.331,7,8.1,, 1864,G4S,United Kingdom,10.2,0.267,6.9,6,Industrials,Security Systems 1865,Jabal Omar Development,Saudi Arabia,0.45,0.196,6.1,16.9,, 1866,Guoco Group,Hong Kong,1.8,0.465,14.9,3.8,, 1867,Two Harbors Investment,United States,0.741,0.353,20.1,3.4,Financials,Real Estate 1868,Beijing Capital Development,China,4.3,0.266,26.1,4.9,Financials, 1869,Hellenic Petroleum,Greece,7.4,0.365,7.6,1.7,Energy,Oil & Gas Operations 1870,Enka,Turkey,3.5,0.588,7.6,6.8,Industrials,Construction Services 1871,Jilin Jiutai Rural Commercial Bank,China,1.5,0.284,27.6,2.7,Financials, 1872,Innolux,Taiwan,8.9,0.058,11.5,4.2,Information Technology,Computer Hardware 1873,Luzerner Kantonalbank,Switzerland,0.62,0.189,34.3,3.6,Financials,Regional Banks 1874,China Resources Gas Group,Hong Kong,4.2,0.424,7.7,8,Materials, 1875,First Quantum Minerals,Canada,2.7,-0.057,19.5,7.4,Materials,Diversified Metals & Mining 1876,ST Engineering,Singapore,4.8,0.351,5.8,8,Industrials,Aerospace & Defense 1877,Delek Group,Israel,1.5,0.163,34.2,2.8,Financials,Investment Services 1878,Lanxess,Germany,8.5,0.212,10.6,6.2,Materials,Diversified Chemicals 1879,Extra Space Storage,United States,0.973,0.365,7.1,9.8,, 1880,Financial Street Holdings,China,2.8,0.422,16.2,5,Financials,Real Estate 1881,Burberry Group,United Kingdom,3.6,0.372,2.9,9.6,Consumer Discretionary,Apparel/Accessories 1882,Petronas Gas,Malaysia,1.1,0.42,3.7,8.8,Utilities,Natural Gas Utilities 1883,Workday,United States,1.6,-0.0408,3.2,16.6,, 1884,GS Engineering,South Korea,9.5,-0.024,11.1,2,Industrials,Construction Services 1885,Hokkaido Electric Power,Japan,6.5,0.058,15.5,1.5,Utilities,Electric Utilities 1886,Lonza Group,Switzerland,4.2,0.306,7.1,10.9,Materials, 1887,FIBI Holdings,Israel,1.1,0.066,33.6,0.732,Financials,Regional Banks 1888,GEA Group,Germany,5,0.315,6.5,8.6,Industrials,Conglomerates 1889,Mid-america Apartment Communities,United States,1.1,0.212,11.6,11.6,, 1890,Vietnam Dairy Products,Vietnam,2.1,0.396,1.3,9.1,Consumer Staples, 1891,Sumitomo Rubber,Japan,7,0.381,7.7,4.5,Consumer Discretionary,Auto & Truck Parts 1892,Areva,France,0.011,-0.736,33.3,1.7,Utilities,Diversified Utilities 1893,Leidos,United States,7,0.24,9.1,7.6,, 1894,Reliance Steel,United States,8.6,0.304,7.4,5.8,Materials,Iron & Steel 1895,Kroton Educacional,Brazil,1.5,0.535,5.4,7.1,, 1896,Laurentian Bank,Canada,1.2,0.12,33.1,1.5,Financials,Regional Banks 1897,Babcock International Group,United Kingdom,6.1,0.433,7.6,5.5,Consumer Discretionary,Business & Personal Services 1898,Fabege AB,Sweden,0.246,0.83,5.5,2.7,, 1899,First Citizens BancShares,United States,1.5,0.226,33,3.9,, 1900,Hella Kgaa Hueck &,Germany,7.1,0.367,5.5,4.9,, 1901,BOK Financial,United States,1.5,0.23,33,5,Financials,Regional Banks 1902,Jazz Pharmaceuticals,Ireland,1.5,0.389,4.8,9.2,Health Care, 1903,Toho Holdings,Japan,11.8,0.199,5.6,1.6,Financials,Pharmaceuticals 1904,Microchip Technology,United States,3.1,0.094,7.1,16,Information Technology,Semiconductors 1905,Shanghai International Airport,China,1,0.422,3.9,8.6,Industrials, 1906,Atmos Energy,United States,3.2,0.372,10.6,8.4,Energy, 1907,Amdocs,United States,3.8,0.403,5.2,9,Information Technology,Software & Programming 1908,Kuwait Projects,Kuwait,2,0.127,32.7,2.1,Financials,Investment Services 1909,China Aviation Oil,Singapore,11.7,0.089,1.3,1,Industrials,Trading Companies 1910,Hakuhodo DY Holdings,Japan,11.4,0.259,5.7,4.4,Financials, 1911,MMI Holdings,South Africa,3.7,0.11,32.5,2.5,Financials,Life & Health Insurance 1912,Chinese Estates,Hong Kong,0.893,0.819,5.9,3,Financials,Real Estate 1913,TP ICAP,United Kingdom,1.2,0.058,32.1,3.1,, 1914,Technipfmc,United Kingdom,1,0,0.001,15.6,Energy, 1915,Murphy USA,United States,11.6,0.222,2.1,2.7,Consumer Discretionary,Specialty Stores 1916,Bank of Nagoya,Japan,0.4,0.049,32,0.669,Financials,Regional Banks 1917,Emirates Integrated Telecom,United Arab Emirates,3.5,0.477,5,7.5,Telecommunication Services,Telecommunications services 1918,St Galler Kantonalbank,Switzerland,0.585,0.148,31.7,2.3,Financials,Regional Banks 1919,Ares Capital,United States,1.1,0.474,9.2,7.5,Financials, 1920,Aroundtown Property Holdings,Germany,1.1,0.806,8.5,3.4,Financials, 1921,GOME Electrical Appliances Holding,Hong Kong,11.5,0.049,8.9,3.1,, 1922,Greentown China Holdings,China,4.5,0.226,24.4,2.1,Financials,Real Estate 1923,Jiangsu Expressway,China,1.4,0.503,5.2,7.2,Industrials, 1924,Premier,United States,1.3,0.805,2.6,4.5,Consumer Discretionary, 1925,China Agri-Industries,Hong Kong,11.5,0.183,9.3,2.7,Materials,Diversified Chemicals 1926,Pou Chen,Taiwan,8.5,0.31,9.3,4.1,Consumer Discretionary,Apparel/Accessories 1927,Aldar Properties,United Arab Emirates,1.7,0.758,9.7,4.9,Financials,Real Estate 1928,Tomony Holdings,Japan,0.605,0.234,31.3,0.809,Financials,Regional Banks 1929,Vereit,United States,1.4,-0.195,15.6,8.5,, 1930,BioMarin Pharmaceutical,United States,1.1,-0.642,4,15.2,Health Care, 1931,Steel Authority of India,India,5.9,-0.656,16.2,4.3,Materials,Iron & Steel 1932,Doha Bank,Qatar,1.1,0.289,24.8,2.2,Financials,Regional Banks 1933,Jaccs,Japan,1.1,0.084,31.1,0.703,Financials,Consumer Financial Services 1934,Indiabulls Housing Finance,India,1.7,0.408,13.7,6.3,Financials, 1935,Red Hat,United States,2.4,0.254,4.5,15.1,Information Technology, 1936,Korea Investment Holdings,South Korea,1.3,0.241,30.9,2.4,Financials, 1937,Indian Bank,India,2.8,0.115,30.8,2,Financials,Regional Banks 1938,Kesko,Finland,11.3,0.108,4.6,4.5,Consumer Staples,Food Retail 1939,Minato Bank,Japan,0.561,0.066,30.8,0.734,, 1940,Andhra Bank,India,3,0.085,30.7,0.616,Financials,Regional Banks 1941,US Steel,United States,10.3,-0.44,9.2,5.9,Materials,Iron & Steel 1942,Verbund,Austria,3.1,0.47,12.2,6.1,Utilities,Electric Utilities 1943,Saudi Investment Bank,Saudi Arabia,1,0.281,25.2,2.6,Financials,Investment Services 1944,BMCE Bank,Morocco,1.9,0.208,30.2,3.5,Financials,Regional Banks 1945,NH Foods,Japan,11.1,0.223,6.4,5.6,, 1946,Thomas Cook Group,United Kingdom,11.1,0.017,9,1.6,Consumer Discretionary,Business & Personal Services 1947,Manila Electric,Philippines,5.4,0.404,6,6.3,Utilities,Electric Utilities 1948,Arab Banking,Bahrain,1.7,0.183,30.1,1.1,Financials,Regional Banks 1949,Synovus Financial,United States,1.3,0.247,30.1,5,Financials,Regional Banks 1950,Western Union,United States,5.4,0.253,9.4,9.2,Consumer Discretionary,Business & Personal Services 1951,Pearson,United Kingdom,6.1,-3.2,12.4,6.5,Consumer Discretionary,Printing & Publishing 1952,Banca IFIS,Italy,1.2,0.761,9.2,2.1,Financials, 1953,Motherson Sumi Systems,India,6.1,0.222,3.9,8.3,, 1954,SBA Communications,United States,1.6,0.076,7.4,14.7,Telecommunication Services, 1955,Advanced Micro Devices,United States,4.3,-0.497,3.3,12.7,Utilities, 1956,Hudson's Bay,Canada,11,-0.392,9.4,1.5,, 1957,Total System Services,United States,4.2,0.318,6.4,9.7,Financials, 1958,Sinopec Oilfield Service,China,6.4,-2.4,10.7,7,Energy, 1959,Westar Energy,United States,2.6,0.346,11.5,7.7,Energy, 1960,Group 1 Automotive,United States,10.9,0.141,4.5,1.4,Consumer Discretionary, 1961,Dollarama,Canada,2.3,0.338,1.5,9.7,Consumer Discretionary, 1962,Shanghai RAAS Blood Products,China,0.326,0.231,2,14.5,, 1963,Associated Banc-Corp,United States,1.2,0.198,29.3,3.6,Financials, 1964,DVB Bank,Germany,1.4,-0.154,29.2,1.2,Financials,Regional Banks 1965,RenaissanceRe Holdings,Bermuda,1.8,0.497,12.4,5.7,Financials, 1966,Global Payments,United States,3.8,0.214,10.7,11.9,Consumer Discretionary, 1967,Commerce Bancshares,United States,1.2,0.272,25.6,5.6,Financials, 1968,ServiceNow,United States,1.4,-0.452,2,14.3,Information Technology, 1969,Samsung SDI,South Korea,4.5,-0.647,12.3,8.5,Information Technology, 1970,China Great Wall Computer,China,10.8,0.02,6,4.4,Information Technology, 1971,Tian An China Investments,Hong Kong,0.244,0.736,4.3,1.2,Financials, 1972,HollyFrontier,United States,10.6,-0.261,9.6,4.7,Energy,Oil & Gas Operations 1973,Dah Sing Financial Holdings,Hong Kong,0.868,0.244,28.8,2.6,Financials, 1974,Nanya Technology,Taiwan,1.3,0.736,4.3,4.3,Information Technology, 1975,Shanxi Taigang Stainless,China,8.5,-0.322,11,4.1,Materials,Iron & Steel 1976,First Horizon National,United States,1.4,0.227,28.7,4.3,, 1977,Brother Industries,Japan,6,0.408,5.8,5.2,Materials, 1978,Chicago Bridge & Iron,Netherlands,10.7,-0.313,7.8,3.1,Industrials,Construction Services 1979,Belle International Holdings,Hong Kong,6.3,0.384,4.7,5.4,Consumer Discretionary,Apparel/Footwear Retail 1980,F5 Networks,United States,2,0.37,2.4,8.9,, 1981,Valspar,United States,4.2,0.341,4.2,8.8,Information Technology, 1982,DKSH Holding,Switzerland,10.7,0.212,4.3,5.1,Materials, 1983,Grupo Galicia,Argentina,3.7,0.407,15.3,5.1,, 1984,Bank of Iwate,Japan,0.427,0.097,28.3,0.721,Financials,Regional Banks 1985,Konica Minolta Business Solutions,Japan,8.9,0.28,8.8,4.3,Financials, 1986,Synnex Technology Intl,Taiwan,10.6,0.151,4,1.8,Information Technology, 1987,Eurazeo,France,3.4,0.575,10.9,4.5,Financials,Investment Services 1988,Bankunited,United States,1.2,0.217,28.1,3.9,Financials, 1989,Barry Callebaut,Switzerland,6.8,0.221,5.8,7.3,Consumer Discretionary, 1990,Inchcape,United Kingdom,10.6,0.249,5.4,4.4,Consumer Discretionary,Specialty Stores 1991,Yamanashi Chuo Bank,Japan,0.522,0.062,28,0.739,Financials,Regional Banks 1992,Guangxi Guiguan Electric Power,China,2,0.643,6,6,Energy, 1993,Live Nation Entertainment,United States,8.4,-0.047,6.8,6.4,Energy, 1994,Shaanxi Coal Industry,China,3.8,-0.024,13.6,9,Materials,Diversified Metals & Mining 1995,Aurubis,Germany,10.6,0.249,4.5,3.1,Materials,Diversified Metals & Mining 1996,BEKB-BCBE,Switzerland,0.555,0.131,27.9,1.7,Financials,Regional Banks 1997,Fastighets Balder,Sweden,0.63,0.639,10.2,3.8,Materials, 1998,Akamai Technologies,United States,2.3,0.316,4.4,10.1,Information Technology,Computer Services 1999,Oita Bank,Japan,0.523,0.071,27.9,0.595,Financials,Regional Banks 2000,Tech Mahindra,India,4.2,0.469,3.6,6.7,Information Technology, ================================================ FILE: March Cohort 2023/12_15_16_17_18_Statistics/sample_submission.csv ================================================ id,cost 360336,99.615 360337,99.615 360338,99.615 360339,99.615 360340,99.615 360341,99.615 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600535,99.615 600536,99.615 600537,99.615 600538,99.615 600539,99.615 600540,99.615 600541,99.615 600542,99.615 600543,99.615 600544,99.615 600545,99.615 600546,99.615 600547,99.615 600548,99.615 600549,99.615 600550,99.615 600551,99.615 600552,99.615 600553,99.615 600554,99.615 600555,99.615 600556,99.615 600557,99.615 600558,99.615 600559,99.615 ================================================ FILE: March Cohort 2023/12_15_16_17_18_Statistics/test.csv ================================================ [File too large to display: 16.9 MB] ================================================ FILE: March Cohort 2023/12_15_16_17_18_Statistics/train.csv ================================================ [File too large to display: 27.4 MB] ================================================ FILE: March Cohort 2023/13_14_Object Orientation.ipynb ================================================ { "cells": [ { "cell_type": "code", "execution_count": 1, "id": "f956ae23", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "a = 10\n", "\n", "print(type(a))" ] }, { "cell_type": "code", "execution_count": 14, "id": "a8e19eec", "metadata": { "scrolled": false }, "outputs": [], "source": [ "# help(int)" ] }, { "cell_type": "code", "execution_count": null, "id": "02596f25", "metadata": {}, "outputs": [], "source": [ "int a = 10; # 2 bytes - 16 bits\n", "\n", "struct car{\n", " int cust_id; # 2 bytes\n", " char name[10];# 10 bytes\n", " float price; # 4 bytes\n", "}\n", "\n", "struct car c1;\n", " int a;" ] }, { "cell_type": "code", "execution_count": null, "id": "73d30a38", "metadata": {}, "outputs": [], "source": [ "bit>numbers>characters>instructions>function(program)>class>modules>package>library>framework>software" ] }, { "cell_type": "markdown", "id": "8ae18de9", "metadata": {}, "source": [ "# Object Orientation\n", "\n", "7 properties of OOP\n", "\n", "1. Class\n", " Updated version of structures (struct -- C programming)\n", " collection of variables and methods\n", " class is a blueprint.\n", "2. Object\n", " Run time or real time entity.\n", " hash code/virtual code - ID(python)\n", " \n", "3. Abstraction and Encapsulation\n", " Abstraction - showing only essential features without showing any background details.\n", " Encapsulation - wrapping up of data in a single unit.\n", " \n", "4. Inheritance\n", " Acquiring properties of one class into another.\n", " code reusability\n", " - single level\n", " - multi level\n", " - heirarchical\n", " - multiple\n", " - hybride\n", " \n", "5. Polymorphism\n", " same name multiple functionalities\n", " - method overloading\n", " - method overriding\n", " \n", "6. Dynamic Memory Allocation\n", " run time memory allocation\n", " \n", "7. Message Passing\n", " Communication between objects" ] }, { "cell_type": "code", "execution_count": 8, "id": "d69e271e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2207057668688\n" ] } ], "source": [ "a = 10\n", "\n", "print(id(a))" ] }, { "cell_type": "code", "execution_count": 10, "id": "cb5639c1", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2207057669008\n", "2207057669328\n" ] } ], "source": [ "b = 20\n", "\n", "c = a + b\n", "print(id(b))\n", "\n", "print(id(c))" ] }, { "cell_type": "code", "execution_count": 11, "id": "921fed62", "metadata": {}, "outputs": [ { "ename": "TypeError", "evalue": "can only concatenate str (not \"int\") to str", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", "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", "\u001b[1;31mTypeError\u001b[0m: can only concatenate str (not \"int\") to str" ] } ], "source": [ "s = \"A\" + 10" ] }, { "cell_type": "code", "execution_count": 12, "id": "0f26ad3e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "AAAA\n" ] } ], "source": [ "s = \"A\" * 4\n", "\n", "print(s)" ] }, { "cell_type": "code", "execution_count": 15, "id": "ba90ba2a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "def hello():\n", " return \"Hi\"\n", "\n", "print(type(hello))" ] }, { "cell_type": "code", "execution_count": 16, "id": "41a3ec56", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2207169212128\n" ] } ], "source": [ "print(id(hello))" ] }, { "cell_type": "code", "execution_count": null, "id": "06ce3374", "metadata": {}, "outputs": [], "source": [ "class win7:\n", "def start()\n", "\n", "def controlpanel():\n", " \n", "\n", "add(x, y)\n", "\n", "add(a,b,c)\n", "\n", "class win10(win7):\n", " \n", " \n", "product_id = wid10()\n", "\n", "product_id.controlpanel()" ] }, { "cell_type": "code", "execution_count": null, "id": "53b1fb7c", "metadata": {}, "outputs": [], "source": [ "add()" ] }, { "cell_type": "code", "execution_count": 17, "id": "67f2a3da", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "30" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "10 + 20" ] }, { "cell_type": "markdown", "id": "8420ca86", "metadata": {}, "source": [ "# Day 14 " ] }, { "cell_type": "code", "execution_count": 1, "id": "33e8ee69", "metadata": {}, "outputs": [], "source": [ "class Cricket:\n", " \n", " def bat():\n", " print(\"Hello from Bat\")\n", "\n" ] }, { "cell_type": "code", "execution_count": 2, "id": "aee1a50c", "metadata": {}, "outputs": [], "source": [ "c = Cricket()" ] }, { "cell_type": "code", "execution_count": 3, "id": "c2d4fa85", "metadata": {}, "outputs": [ { "ename": "TypeError", "evalue": "bat() takes 0 positional arguments but 1 was given", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", "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", "\u001b[1;31mTypeError\u001b[0m: bat() takes 0 positional arguments but 1 was given" ] } ], "source": [ "c.bat()" ] }, { "cell_type": "code", "execution_count": null, "id": "cf30bd7f", "metadata": {}, "outputs": [], "source": [ "# self --> current object" ] }, { "cell_type": "code", "execution_count": 4, "id": "e50eb7d9", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Hello from Bat\n" ] } ], "source": [ "class Cricket:\n", " \n", " def bat(self): # self = c\n", " print(\"Hello from Bat\")\n", "\n", "c = Cricket()\n", "\n", "c.bat()" ] }, { "cell_type": "code", "execution_count": 5, "id": "623c69db", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<__main__.Cricket object at 0x0000020F34DB4A00>\n" ] } ], "source": [ "print(c)" ] }, { "cell_type": "code", "execution_count": 6, "id": "242b62af", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2264334551552\n" ] } ], "source": [ "print(id(c))" ] }, { "cell_type": "code", "execution_count": 7, "id": "515da4b9", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<__main__.Cricket object at 0x0000020F36BD6A90>\n" ] } ], "source": [ "print(Cricket())" ] }, { "cell_type": "code", "execution_count": null, "id": "b06677ce", "metadata": {}, "outputs": [], "source": [ "TV blueprint - Class\n", "- smart tv\n", "- colors\n", "- usb\n", "- hdmi\n", "\n", "Actual TV - Object\n", "product_id\n", "\n", "\n", "remote - reference" ] }, { "cell_type": "code", "execution_count": 8, "id": "8d0184d8", "metadata": {}, "outputs": [], "source": [ "class Football:\n", " \n", " def goal(self, x): # self = f, x = 4\n", " self.a = x # a is class variable, x is local variable\n", " # f.a = 4\n", " \n", " def display(self):\n", " print(self.a)\n", " # f.a\n", " \n", "f = Football()" ] }, { "cell_type": "code", "execution_count": 9, "id": "1d10510d", "metadata": {}, "outputs": [], "source": [ "f.goal(4)" ] }, { "cell_type": "code", "execution_count": 10, "id": "3ddef9fb", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "4\n" ] } ], "source": [ "f.display()" ] }, { "cell_type": "code", "execution_count": 11, "id": "6216ae9a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "4\n" ] } ], "source": [ "print(f.a)" ] }, { "cell_type": "code", "execution_count": 12, "id": "63e17007", "metadata": {}, "outputs": [ { "ename": "AttributeError", "evalue": "'Football' object has no attribute 'x'", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)", "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", "\u001b[1;31mAttributeError\u001b[0m: 'Football' object has no attribute 'x'" ] } ], "source": [ "print(f.x)" ] }, { "cell_type": "code", "execution_count": 13, "id": "a84e2e93", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "print(type(f))" ] }, { "cell_type": "code", "execution_count": 14, "id": "0e68d116", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2264334513824\n" ] } ], "source": [ "print(id(f))" ] }, { "cell_type": "code", "execution_count": 15, "id": "8145572b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10\n", "\n", "2264248117840\n", "2264248117840\n" ] } ], "source": [ "a = 10\n", "\n", "print(a)\n", "\n", "print(type(a))\n", "\n", "print(id(a))\n", "\n", "print(id(10))" ] }, { "cell_type": "code", "execution_count": 17, "id": "04a1cb6b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<__main__.Test object at 0x0000020F36BD6C70>\n", "2264366148720\n", "<__main__.Test object at 0x0000020F36BD6C70>\n", "2264366148720\n", "Finish\n" ] } ], "source": [ "class Test:\n", " def add(self):\n", " print(self)\n", " print(id(self))\n", " print(\"Finish\")\n", " \n", "t = Test()\n", "print(t)\n", "print(id(t))\n", "t.add()" ] }, { "cell_type": "code", "execution_count": 18, "id": "e76165f0", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<__main__.Test object at 0x0000020F37274610>\n", "2264373085712\n", "<__main__.Test object at 0x0000020F37274610>\n", "2264373085712\n", "Finish\n" ] } ], "source": [ "t2 = Test()\n", "print(t2)\n", "print(id(t2))\n", "t2.add()" ] }, { "cell_type": "markdown", "id": "c78e8f64", "metadata": {}, "source": [ "# Constructor\n", "\n", "constructors are special member method of a class" ] }, { "cell_type": "code", "execution_count": null, "id": "18cd2b35", "metadata": {}, "outputs": [], "source": [ "__init__() -> magic method or dunders" ] }, { "cell_type": "code", "execution_count": 19, "id": "0e5bc082", "metadata": {}, "outputs": [], "source": [ "class Cat:\n", " \n", " def __init__(self):\n", " print(\"Hello from constructor\") # write code that is for initializing \n", "# coins = 0\n", "# score = 0\n", " " ] }, { "cell_type": "code", "execution_count": 20, "id": "90a7183d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Hello from constructor\n" ] } ], "source": [ "f = Cat()" ] }, { "cell_type": "code", "execution_count": 22, "id": "7c63050d", "metadata": { "scrolled": false }, "outputs": [], "source": [ "# help(str)" ] }, { "cell_type": "markdown", "id": "a89195ce", "metadata": {}, "source": [ "### paramatrized constructor" ] }, { "cell_type": "code", "execution_count": 23, "id": "870a85c9", "metadata": {}, "outputs": [], "source": [ "class Car:\n", " def __init__(self, x):\n", " self.y = x\n", " " ] }, { "cell_type": "code", "execution_count": 24, "id": "3575a84e", "metadata": {}, "outputs": [], "source": [ "f = Car(10)" ] }, { "cell_type": "code", "execution_count": 25, "id": "862c1549", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10\n" ] } ], "source": [ "print(f.y)" ] }, { "cell_type": "markdown", "id": "04a7f0d7", "metadata": {}, "source": [ "### Write a class that can calculate area of square, circle, rectangle." ] }, { "cell_type": "code", "execution_count": 26, "id": "7995dcda", "metadata": {}, "outputs": [], "source": [ "class Area:\n", " \n", " def square(self, x):\n", " return x*x\n", " \n", " def circle(self, r):\n", " return 3.14 * r * r\n", " \n", " def rectangle(self, l, b):\n", " return l * b\n", " \n" ] }, { "cell_type": "code", "execution_count": 27, "id": "c19e6fac", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "16" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ar = Area()\n", "\n", "ar.square(4)" ] }, { "cell_type": "markdown", "id": "6528d7d2", "metadata": {}, "source": [ "### Write a program to create a class to reverse a string word by word." ] }, { "cell_type": "code", "execution_count": null, "id": "87016fca", "metadata": {}, "outputs": [], "source": [ "\"this is an apple\"\n", "\n", "\"apple an is this\"" ] }, { "cell_type": "markdown", "id": "70e87437", "metadata": {}, "source": [ "# Inheritance\n", "\n", "\n", "### single level" ] }, { "cell_type": "code", "execution_count": null, "id": "4213330d", "metadata": {}, "outputs": [], "source": [ "A windows 7\n", "| |\n", "B windows 10" ] }, { "cell_type": "code", "execution_count": 28, "id": "3cbdb564", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "This is add from class A\n", "This is add from class A\n" ] } ], "source": [ "class A:\n", " def add(self):\n", " print(\"This is add from class A\")\n", " \n", "\n", "class B(A):\n", " def sub(self):\n", " print(\"Sub from class B\")\n", " \n", "x = A()\n", "y = B()\n", "\n", "x.add()\n", "y.add()" ] }, { "cell_type": "code", "execution_count": 29, "id": "694d0edf", "metadata": {}, "outputs": [ { "ename": "AttributeError", "evalue": "'A' object has no attribute 'sub'", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)", "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", "\u001b[1;31mAttributeError\u001b[0m: 'A' object has no attribute 'sub'" ] } ], "source": [ "x.sub()" ] }, { "cell_type": "code", "execution_count": 30, "id": "da9ee247", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Sub from class B\n" ] } ], "source": [ "y.sub()" ] }, { "cell_type": "markdown", "id": "61a935df", "metadata": {}, "source": [ "### multi level" ] }, { "cell_type": "code", "execution_count": null, "id": "a163bf53", "metadata": {}, "outputs": [], "source": [ "A\n", "|\n", "B\n", "|\n", "C" ] }, { "cell_type": "code", "execution_count": 31, "id": "b4a8158e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "show from class C\n", "Sub from class B\n", "This is add from class A\n" ] } ], "source": [ "class A:\n", " def add(self):\n", " print(\"This is add from class A\")\n", " \n", "class B(A):\n", " def sub(self):\n", " print(\"Sub from class B\")\n", " \n", "class C(B):\n", " def show(self):\n", " print(\"show from class C\")\n", " \n", "z = C()\n", "z.show()\n", "z.sub()\n", "z.add()" ] }, { "cell_type": "markdown", "id": "6d610afa", "metadata": {}, "source": [ "### hierarchical" ] }, { "cell_type": "code", "execution_count": null, "id": "56edc720", "metadata": {}, "outputs": [], "source": [ " A\n", "B C" ] }, { "cell_type": "code", "execution_count": 32, "id": "94cdc6e5", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "show from class C\n" ] }, { "ename": "AttributeError", "evalue": "'C' object has no attribute 'sub'", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)", "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", "\u001b[1;31mAttributeError\u001b[0m: 'C' object has no attribute 'sub'" ] } ], "source": [ "class A:\n", " def add(self):\n", " print(\"This is add from class A\")\n", " \n", "class B(A):\n", " def sub(self):\n", " print(\"Sub from class B\")\n", " \n", "class C(A):\n", " def show(self):\n", " print(\"show from class C\")\n", " \n", "z = C()\n", "z.show()\n", "z.sub()\n", "z.add()" ] }, { "cell_type": "markdown", "id": "afa65f2f", "metadata": {}, "source": [ "### multiple\n", "\n", "C3 algorithm to solve ambiguty error\n", "\n", "MRO - Method Resolution Order" ] }, { "cell_type": "code", "execution_count": null, "id": "520d2d17", "metadata": {}, "outputs": [], "source": [ "A B\n", " C" ] }, { "cell_type": "code", "execution_count": 33, "id": "41a10af7", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Add from class A\n" ] } ], "source": [ "class A:\n", " def add(self):\n", " print(\"Add from class A\")\n", " \n", "class B:\n", " def add(self):\n", " print(\"Add from class B\")\n", " \n", "class C(A,B):\n", " def show(self):\n", " print(\"show from class C\")\n", " \n", "z = C()\n", "z.add()" ] }, { "cell_type": "code", "execution_count": 34, "id": "101bcde3", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(, , , )\n" ] } ], "source": [ "print(C.__mro__)" ] }, { "cell_type": "code", "execution_count": 36, "id": "e31f4dfc", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Add from class B\n", "(, , , )\n" ] } ], "source": [ "class A:\n", " def add(self):\n", " print(\"Add from class A\")\n", " \n", "class B:\n", " def add(self):\n", " print(\"Add from class B\")\n", " \n", "class C(B,A):\n", " def show(self):\n", " print(\"show from class C\")\n", " \n", "z = C()\n", "z.add()\n", "print(C.__mro__)" ] }, { "cell_type": "markdown", "id": "9361ebca", "metadata": {}, "source": [ "### Hybrid" ] }, { "cell_type": "code", "execution_count": null, "id": "e1434f79", "metadata": {}, "outputs": [], "source": [ " A\n", "B C\n", " D" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.12" } }, "nbformat": 4, "nbformat_minor": 5 } ================================================ FILE: March Cohort 2023/19_20_Machine Learning/.ipynb_checkpoints/19_Machine Learning and Linear Regression with One variable-checkpoint.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "id": "7242d236", "metadata": {}, "source": [ "# Machine Learning" ] }, { "cell_type": "code", "execution_count": null, "id": "f822ef63", "metadata": {}, "outputs": [], "source": [ "Supervised Unsupervised Reinforcement\n", "\n", "Regression - clusturing - markov decision model\n", "- Linear Regression - dimension reduction - game theory\n", "Classification - anomaly detection\n", "- Logistic Regression" ] }, { "cell_type": "markdown", "id": "59019bc8", "metadata": {}, "source": [ "# Linear Regression with One variable" ] }, { "cell_type": "code", "execution_count": 2, "id": "a0ff1cca", "metadata": {}, "outputs": [], "source": [ "# linear - arranged in straight line\n", "# regression - a technique to determine the statistical relationship \n", "# between 2 or more variables" ] }, { "cell_type": "code", "execution_count": null, "id": "1f3259c7", "metadata": {}, "outputs": [], "source": [ " x y\n", "area(sq ft) price($)\n", "2500 800000\n", "3000 950000\n", "3200 1000000\n", "3600 1200000\n", "4000 1500000" ] }, { "cell_type": "code", "execution_count": null, "id": "38b295a5", "metadata": {}, "outputs": [], "source": [ "y = mx + c\n", "y = b1x + b0\n", "\n", "800000 = m(2500) + c\n", "950000 = m(3000) + c\n", "\n", "m = 3000\n", "c = 4000\n", "\n", "y = 3000(x) + 4000 --> principle equation\n", "y = 3000(1200) + 4000 --> prediction" ] }, { "cell_type": "code", "execution_count": 3, "id": "7d739803", "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 4, "id": "ddd8d883", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " area price\n", "0 2600 550000\n", "1 3000 565000\n", "2 3200 610000\n", "3 3600 680000\n", "4 4000 725000" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_csv(\"homeprices.csv\")\n", "df.head()" ] }, { "cell_type": "code", "execution_count": 5, "id": "f2481b31", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ], "text/plain": [ " area\n", "0 2600\n", "1 3000\n", "2 3200\n", "3 3600\n", "4 4000" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x = df[['area']]\n", "x" ] }, { "cell_type": "code", "execution_count": 7, "id": "e67faccd", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 550000\n", "1 565000\n", "2 610000\n", "3 680000\n", "4 725000\n", "Name: price, dtype: int64" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y = df.price\n", "y" ] }, { "cell_type": "code", "execution_count": 8, "id": "23e39b32", "metadata": {}, "outputs": [], "source": [ "from sklearn.linear_model import LinearRegression\n", "\n", "model = LinearRegression()" ] }, { "cell_type": "code", "execution_count": 9, "id": "e9cc482f", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "LinearRegression()" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model" ] }, { "cell_type": "code", "execution_count": 11, "id": "40d56579", "metadata": { "scrolled": false }, "outputs": [], "source": [ "# help(model)" ] }, { "cell_type": "code", "execution_count": 12, "id": "949d6f6a", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "LinearRegression()" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.fit(x,y)" ] }, { "cell_type": "code", "execution_count": 13, "id": "d1ba4c61", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([135.78767123])" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# m = slope = gradient = 135.78\n", "model.coef_" ] }, { "cell_type": "code", "execution_count": 14, "id": "3696cb1a", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "180616.43835616432" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# c = intercept = 180616.43\n", "model.intercept_" ] }, { "cell_type": "code", "execution_count": 15, "id": "6eeccf2a", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "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", " warnings.warn(\n" ] }, { "data": { "text/plain": [ "array([601558.21917808])" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.predict([[3100]])" ] }, { "cell_type": "code", "execution_count": null, "id": "a6a89a8e", "metadata": {}, "outputs": [], "source": [ "# y = mx + c\n", "y = 135.78(x) + 180616.43" ] }, { "cell_type": "code", "execution_count": null, "id": "ad003525", "metadata": {}, "outputs": [], "source": [ "x = 3100" ] }, { "cell_type": "code", "execution_count": 16, "id": "7211c8b8", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "601534.4299999999" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y = 135.78*(3100) + 180616.43\n", "y" ] }, { "cell_type": "markdown", "id": "82249914", "metadata": {}, "source": [ "# Boston House Price Data" ] }, { "cell_type": "code", "execution_count": null, "id": "3f497053", "metadata": {}, "outputs": [], "source": [ "y1 = m1x1 + m2x2 + ..... + c + e\n", "y2 = m1x1 + m2x2 + ..... + c + e" ] }, { "cell_type": "code", "execution_count": 17, "id": "a20860cc", "metadata": { "scrolled": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "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", " The Boston housing prices dataset has an ethical problem. You can refer to\n", " the documentation of this function for further details.\n", "\n", " The scikit-learn maintainers therefore strongly discourage the use of this\n", " dataset unless the purpose of the code is to study and educate about\n", " ethical issues in data science and machine learning.\n", "\n", " In this special case, you can fetch the dataset from the original\n", " source::\n", "\n", " import pandas as pd\n", " import numpy as np\n", "\n", "\n", " data_url = \"http://lib.stat.cmu.edu/datasets/boston\"\n", " raw_df = pd.read_csv(data_url, sep=\"\\s+\", skiprows=22, header=None)\n", " data = np.hstack([raw_df.values[::2, :], raw_df.values[1::2, :2]])\n", " target = raw_df.values[1::2, 2]\n", "\n", " Alternative datasets include the California housing dataset (i.e.\n", " :func:`~sklearn.datasets.fetch_california_housing`) and the Ames housing\n", " dataset. You can load the datasets as follows::\n", "\n", " from sklearn.datasets import fetch_california_housing\n", " housing = fetch_california_housing()\n", "\n", " for the California housing dataset and::\n", "\n", " from sklearn.datasets import fetch_openml\n", " housing = fetch_openml(name=\"house_prices\", as_frame=True)\n", "\n", " for the Ames housing dataset.\n", " \n", " warnings.warn(msg, category=FutureWarning)\n" ] }, { "data": { "text/plain": [ "{'data': array([[6.3200e-03, 1.8000e+01, 2.3100e+00, ..., 1.5300e+01, 3.9690e+02,\n", " 4.9800e+00],\n", " [2.7310e-02, 0.0000e+00, 7.0700e+00, ..., 1.7800e+01, 3.9690e+02,\n", " 9.1400e+00],\n", " [2.7290e-02, 0.0000e+00, 7.0700e+00, ..., 1.7800e+01, 3.9283e+02,\n", " 4.0300e+00],\n", " ...,\n", " [6.0760e-02, 0.0000e+00, 1.1930e+01, ..., 2.1000e+01, 3.9690e+02,\n", " 5.6400e+00],\n", " [1.0959e-01, 0.0000e+00, 1.1930e+01, ..., 2.1000e+01, 3.9345e+02,\n", " 6.4800e+00],\n", " [4.7410e-02, 0.0000e+00, 1.1930e+01, ..., 2.1000e+01, 3.9690e+02,\n", " 7.8800e+00]]),\n", " 'target': array([24. , 21.6, 34.7, 33.4, 36.2, 28.7, 22.9, 27.1, 16.5, 18.9, 15. ,\n", " 18.9, 21.7, 20.4, 18.2, 19.9, 23.1, 17.5, 20.2, 18.2, 13.6, 19.6,\n", " 15.2, 14.5, 15.6, 13.9, 16.6, 14.8, 18.4, 21. , 12.7, 14.5, 13.2,\n", " 13.1, 13.5, 18.9, 20. , 21. , 24.7, 30.8, 34.9, 26.6, 25.3, 24.7,\n", " 21.2, 19.3, 20. , 16.6, 14.4, 19.4, 19.7, 20.5, 25. , 23.4, 18.9,\n", " 35.4, 24.7, 31.6, 23.3, 19.6, 18.7, 16. , 22.2, 25. , 33. , 23.5,\n", " 19.4, 22. , 17.4, 20.9, 24.2, 21.7, 22.8, 23.4, 24.1, 21.4, 20. ,\n", " 20.8, 21.2, 20.3, 28. , 23.9, 24.8, 22.9, 23.9, 26.6, 22.5, 22.2,\n", " 23.6, 28.7, 22.6, 22. , 22.9, 25. , 20.6, 28.4, 21.4, 38.7, 43.8,\n", " 33.2, 27.5, 26.5, 18.6, 19.3, 20.1, 19.5, 19.5, 20.4, 19.8, 19.4,\n", " 21.7, 22.8, 18.8, 18.7, 18.5, 18.3, 21.2, 19.2, 20.4, 19.3, 22. ,\n", " 20.3, 20.5, 17.3, 18.8, 21.4, 15.7, 16.2, 18. , 14.3, 19.2, 19.6,\n", " 23. , 18.4, 15.6, 18.1, 17.4, 17.1, 13.3, 17.8, 14. , 14.4, 13.4,\n", " 15.6, 11.8, 13.8, 15.6, 14.6, 17.8, 15.4, 21.5, 19.6, 15.3, 19.4,\n", " 17. , 15.6, 13.1, 41.3, 24.3, 23.3, 27. , 50. , 50. , 50. , 22.7,\n", " 25. , 50. , 23.8, 23.8, 22.3, 17.4, 19.1, 23.1, 23.6, 22.6, 29.4,\n", " 23.2, 24.6, 29.9, 37.2, 39.8, 36.2, 37.9, 32.5, 26.4, 29.6, 50. ,\n", " 32. , 29.8, 34.9, 37. , 30.5, 36.4, 31.1, 29.1, 50. , 33.3, 30.3,\n", " 34.6, 34.9, 32.9, 24.1, 42.3, 48.5, 50. , 22.6, 24.4, 22.5, 24.4,\n", " 20. , 21.7, 19.3, 22.4, 28.1, 23.7, 25. , 23.3, 28.7, 21.5, 23. ,\n", " 26.7, 21.7, 27.5, 30.1, 44.8, 50. , 37.6, 31.6, 46.7, 31.5, 24.3,\n", " 31.7, 41.7, 48.3, 29. , 24. , 25.1, 31.5, 23.7, 23.3, 22. , 20.1,\n", " 22.2, 23.7, 17.6, 18.5, 24.3, 20.5, 24.5, 26.2, 24.4, 24.8, 29.6,\n", " 42.8, 21.9, 20.9, 44. , 50. , 36. , 30.1, 33.8, 43.1, 48.8, 31. ,\n", " 36.5, 22.8, 30.7, 50. , 43.5, 20.7, 21.1, 25.2, 24.4, 35.2, 32.4,\n", " 32. , 33.2, 33.1, 29.1, 35.1, 45.4, 35.4, 46. , 50. , 32.2, 22. ,\n", " 20.1, 23.2, 22.3, 24.8, 28.5, 37.3, 27.9, 23.9, 21.7, 28.6, 27.1,\n", " 20.3, 22.5, 29. , 24.8, 22. , 26.4, 33.1, 36.1, 28.4, 33.4, 28.2,\n", " 22.8, 20.3, 16.1, 22.1, 19.4, 21.6, 23.8, 16.2, 17.8, 19.8, 23.1,\n", " 21. , 23.8, 23.1, 20.4, 18.5, 25. , 24.6, 23. , 22.2, 19.3, 22.6,\n", " 19.8, 17.1, 19.4, 22.2, 20.7, 21.1, 19.5, 18.5, 20.6, 19. , 18.7,\n", " 32.7, 16.5, 23.9, 31.2, 17.5, 17.2, 23.1, 24.5, 26.6, 22.9, 24.1,\n", " 18.6, 30.1, 18.2, 20.6, 17.8, 21.7, 22.7, 22.6, 25. , 19.9, 20.8,\n", " 16.8, 21.9, 27.5, 21.9, 23.1, 50. , 50. , 50. , 50. , 50. , 13.8,\n", " 13.8, 15. , 13.9, 13.3, 13.1, 10.2, 10.4, 10.9, 11.3, 12.3, 8.8,\n", " 7.2, 10.5, 7.4, 10.2, 11.5, 15.1, 23.2, 9.7, 13.8, 12.7, 13.1,\n", " 12.5, 8.5, 5. , 6.3, 5.6, 7.2, 12.1, 8.3, 8.5, 5. , 11.9,\n", " 27.9, 17.2, 27.5, 15. , 17.2, 17.9, 16.3, 7. , 7.2, 7.5, 10.4,\n", " 8.8, 8.4, 16.7, 14.2, 20.8, 13.4, 11.7, 8.3, 10.2, 10.9, 11. ,\n", " 9.5, 14.5, 14.1, 16.1, 14.3, 11.7, 13.4, 9.6, 8.7, 8.4, 12.8,\n", " 10.5, 17.1, 18.4, 15.4, 10.8, 11.8, 14.9, 12.6, 14.1, 13. , 13.4,\n", " 15.2, 16.1, 17.8, 14.9, 14.1, 12.7, 13.5, 14.9, 20. , 16.4, 17.7,\n", " 19.5, 20.2, 21.4, 19.9, 19. , 19.1, 19.1, 20.1, 19.9, 19.6, 23.2,\n", " 29.8, 13.8, 13.3, 16.7, 12. , 14.6, 21.4, 23. , 23.7, 25. , 21.8,\n", " 20.6, 21.2, 19.1, 20.6, 15.2, 7. , 8.1, 13.6, 20.1, 21.8, 24.5,\n", " 23.1, 19.7, 18.3, 21.2, 17.5, 16.8, 22.4, 20.6, 23.9, 22. , 11.9]),\n", " 'feature_names': array(['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD',\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", " 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CRIMZNINDUSCHASNOXRMAGEDISRADTAXPTRATIOBLSTAT
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CRIMZNINDUSCHASNOXRMAGEDISRADTAXPTRATIOBLSTATPrice
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CRIMZNINDUSCHASNOXRMAGEDISRADTAXPTRATIOBLSTATPrice
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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
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\n", "
" ], "text/plain": [ " CRIM ZN INDUS CHAS NOX RM \\\n", "count 506.000000 506.000000 506.000000 506.000000 506.000000 506.000000 \n", "mean 3.613524 11.363636 11.136779 0.069170 0.554695 6.284634 \n", "std 8.601545 23.322453 6.860353 0.253994 0.115878 0.702617 \n", "min 0.006320 0.000000 0.460000 0.000000 0.385000 3.561000 \n", "25% 0.082045 0.000000 5.190000 0.000000 0.449000 5.885500 \n", "50% 0.256510 0.000000 9.690000 0.000000 0.538000 6.208500 \n", "75% 3.677083 12.500000 18.100000 0.000000 0.624000 6.623500 \n", "max 88.976200 100.000000 27.740000 1.000000 0.871000 8.780000 \n", "\n", " AGE DIS RAD TAX PTRATIO B \\\n", "count 506.000000 506.000000 506.000000 506.000000 506.000000 506.000000 \n", "mean 68.574901 3.795043 9.549407 408.237154 18.455534 356.674032 \n", "std 28.148861 2.105710 8.707259 168.537116 2.164946 91.294864 \n", "min 2.900000 1.129600 1.000000 187.000000 12.600000 0.320000 \n", "25% 45.025000 2.100175 4.000000 279.000000 17.400000 375.377500 \n", "50% 77.500000 3.207450 5.000000 330.000000 19.050000 391.440000 \n", "75% 94.075000 5.188425 24.000000 666.000000 20.200000 396.225000 \n", "max 100.000000 12.126500 24.000000 711.000000 22.000000 396.900000 \n", "\n", " LSTAT Price \n", "count 506.000000 506.000000 \n", "mean 12.653063 22.532806 \n", "std 7.141062 9.197104 \n", "min 1.730000 5.000000 \n", "25% 6.950000 17.025000 \n", "50% 11.360000 21.200000 \n", "75% 16.955000 25.000000 \n", "max 37.970000 50.000000 " ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.describe()" ] }, { "cell_type": "code", "execution_count": 27, "id": "f124baa6", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 506 entries, 0 to 505\n", "Data columns (total 14 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 CRIM 506 non-null float64\n", " 1 ZN 506 non-null float64\n", " 2 INDUS 506 non-null float64\n", " 3 CHAS 506 non-null float64\n", " 4 NOX 506 non-null float64\n", " 5 RM 506 non-null float64\n", " 6 AGE 506 non-null float64\n", " 7 DIS 506 non-null float64\n", " 8 RAD 506 non-null float64\n", " 9 TAX 506 non-null float64\n", " 10 PTRATIO 506 non-null float64\n", " 11 B 506 non-null float64\n", " 12 LSTAT 506 non-null float64\n", " 13 Price 506 non-null float64\n", "dtypes: float64(14)\n", "memory usage: 55.5 KB\n" ] } ], "source": [ "df.info()" ] }, { "cell_type": "code", "execution_count": 28, "id": "cf7dc760", "metadata": {}, "outputs": [], "source": [ "from sklearn.model_selection import train_test_split" ] }, { "cell_type": "code", "execution_count": 29, "id": "32764bf6", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "xtrain shape: (404, 13)\n", "xtest shape: (102, 13)\n", "ytrain shape: (404,)\n", "ytest shape: (102,)\n" ] } ], "source": [ "x = boston.data\n", "\n", "y = boston.target\n", "\n", "xtrain, xtest, ytrain, ytest = train_test_split(x, y, test_size=0.2,\n", " random_state=100)\n", "\n", "print(\"xtrain shape:\", xtrain.shape)\n", "print(\"xtest shape:\", xtest.shape)\n", "print(\"ytrain shape:\", ytrain.shape)\n", "print(\"ytest shape:\", ytest.shape)" ] }, { "cell_type": "code", "execution_count": 31, "id": "c68d3cf3", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "LinearRegression()" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.linear_model import LinearRegression\n", "\n", "model = LinearRegression()\n", "\n", "model.fit(xtrain, ytrain)" ] }, { "cell_type": "code", "execution_count": 32, "id": "a4f1e3ec", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([34.4081095 , 31.18524626, 22.31286141, 17.88613877, 20.43572131,\n", " 26.14444413, 26.21920244, 23.57978445, 22.41577853, 19.51182817,\n", " 26.86691495, 17.20411302, 20.68511041, 15.67921778, 41.69912781,\n", " 20.2946735 , 28.99258631, 19.06413492, 32.48035595, 41.13627224,\n", " 34.64732462, 16.38997909, 20.42215729, 18.05324255, 13.38743087,\n", " 12.64800748, 27.45930237, 20.30199107, 18.78954741, 20.24950994,\n", " 15.60161419, 24.38040555, 38.95651978, 24.7184131 , 31.26791961,\n", " 28.26279775, 15.84707127, 14.76661568, 16.79024244, 23.23674899,\n", " 22.85417065, 23.48976177, 14.16818173, 21.42613087, 32.38362329,\n", " 26.7881669 , 19.37574824, 15.27894103, 17.21175121, 12.91591919,\n", " 21.84063224, 20.25050371, 23.65622638, 23.9608324 , 11.94749102,\n", " 14.49718052, 24.69872363, 34.18169066, 10.30437821, 21.04686616,\n", " 17.96204214, 19.76593459, 17.45231513, 29.982971 , 20.73183476,\n", " 25.24657823, 15.81416285, 24.96705225, 22.1298931 , 20.77593563,\n", " 18.69600904, 24.2201495 , 4.37889874, 15.95687399, 28.03130587,\n", " 9.28438308, 24.76810967, 35.14238234, 11.61172029, 27.04175401,\n", " 34.84290485, 40.44603313, 13.93219791, 15.95544402, 19.26007763,\n", " 12.76037799, 20.90536105, 23.85659356, 13.17899179, 14.76828889,\n", " 32.31355353, 22.93635318, 24.63095357, 23.51981407, 19.34136578,\n", " 22.83582245, 21.72263765, 36.18508621, 18.01097012, 23.18226475,\n", " 13.77270991, 14.43864146])" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ypred = model.predict(xtest)\n", "ypred" ] }, { "cell_type": "code", "execution_count": 33, "id": "811d7567", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([34.6, 31.5, 20.6, 14.5, 16.2, 22.3, 22.8, 24.4, 19.8, 16.7, 23.2,\n", " 15.1, 21. , 18.9, 48.5, 24.1, 22.5, 16.1, 30.3, 50. , 33.8, 10.4,\n", " 20.4, 14.2, 10.5, 12.7, 24.5, 16.7, 20.1, 17.1, 18.4, 24.3, 50. ,\n", " 23.8, 30.7, 33.4, 15. , 10.9, 19.1, 21. , 22.9, 22.4, 19. , 19.3,\n", " 27. , 27.1, 19.1, 15.4, 19.1, 13.5, 20.9, 19.3, 20.5, 26.2, 12. ,\n", " 19.7, 29.6, 34.9, 8.3, 21.2, 14.5, 27.5, 19.4, 32.7, 21.1, 28.1,\n", " 17.4, 30.1, 21.4, 20.9, 10.9, 21.7, 8.8, 15.2, 26.6, 8.7, 24.7,\n", " 38.7, 6.3, 22.6, 43.8, 50. , 14.5, 11.7, 12.5, 13.6, 21.5, 19.1,\n", " 9.5, 17.2, 29.8, 22.6, 21.6, 22.2, 27.1, 17. , 22. , 50. , 7.2,\n", " 50. , 14. , 11. ])" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ytest" ] }, { "cell_type": "markdown", "id": "17b0418a", "metadata": {}, "source": [ "# Evaluation" ] }, { "cell_type": "code", "execution_count": 39, "id": "40e55958", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.7555033086871303" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.score(xtest, ytest)" ] }, { "cell_type": "code", "execution_count": null, "id": "733d9d23", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.12" } }, "nbformat": 4, "nbformat_minor": 5 } ================================================ FILE: March Cohort 2023/19_20_Machine Learning/.ipynb_checkpoints/20_Logistic Regression-checkpoint.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "id": "c71657c4", "metadata": {}, "source": [ "# Logistic Regression" ] }, { "cell_type": "code", "execution_count": null, "id": "f3aa2921", "metadata": {}, "outputs": [], "source": [ "y = mx + c\n", "\n", "s(y) = 1/ 1+ e**(-y)\n", "\n", "s(mx + c) = 1/ 1+ e**(-(mx + c)) => 0 to 1" ] }, { "cell_type": "code", "execution_count": null, "id": "6513a10e", "metadata": {}, "outputs": [], "source": [ " male female\n", " 0 - 0.5 0.5 - 1" ] }, { "cell_type": "markdown", "id": "bed78bb3", "metadata": {}, "source": [ "## Insurance Prediction using Logistic Regression" ] }, { "cell_type": "code", "execution_count": 1, "id": "9c2cd6a9", "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 2, "id": "074396bc", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " age bought_insurance\n", "0 22 0\n", "1 25 0\n", "2 47 1\n", "3 52 0\n", "4 46 1" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_csv(\"insurance_data.csv\")\n", "\n", "df.head()" ] }, { "cell_type": "code", "execution_count": 3, "id": "706150aa", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.scatter(df.age, df.bought_insurance, marker='+', color='red')" ] }, { "cell_type": "code", "execution_count": 4, "id": "30fcfbb7", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(27, 2)" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.shape" ] }, { "cell_type": "code", "execution_count": 5, "id": "edd0315f", "metadata": {}, "outputs": [], "source": [ "from sklearn.model_selection import train_test_split\n", "\n", "xtrain, xtest, ytrain, ytest = train_test_split(df[['age']],\n", " df.bought_insurance,\n", " train_size=0.9)" ] }, { "cell_type": "code", "execution_count": 6, "id": "bd2f6713", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "LogisticRegression()" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.linear_model import LogisticRegression\n", "\n", "model = LogisticRegression()\n", "\n", "model.fit(xtrain, ytrain)" ] }, { "cell_type": "code", "execution_count": 7, "id": "742d8875", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " age\n", "24 50\n", "3 52\n", "11 28" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "xtest" ] }, { "cell_type": "code", "execution_count": 8, "id": "1d225ec1", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([1, 1, 0], dtype=int64)" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ypredictions = model.predict(xtest)\n", "\n", "ypredictions" ] }, { "cell_type": "code", "execution_count": 9, "id": "58338281", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[0.10748428, 0.89251572],\n", " [0.08108033, 0.91891967],\n", " [0.78666634, 0.21333366]])" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.predict_proba(xtest)" ] }, { "cell_type": "code", "execution_count": 10, "id": "104a52c5", "metadata": {}, "outputs": [], "source": [ "from sklearn.metrics import accuracy_score" ] }, { "cell_type": "code", "execution_count": 13, "id": "ad16e9c5", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.6666666666666666" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "accuracy_score(ytest,ypredictions)" ] }, { "cell_type": "markdown", "id": "995595c0", "metadata": {}, "source": [ "# Multi Class Classification\n", "\n", "## Hand-written Digit Dataset" ] }, { "cell_type": "code", "execution_count": 14, "id": "9502f54d", "metadata": {}, "outputs": [], "source": [ "from sklearn.datasets import load_digits\n", "\n", "digits = load_digits()" ] }, { "cell_type": "code", "execution_count": 15, "id": "664ee90a", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(1797, 64)" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "digits.data.shape" ] }, { "cell_type": "code", "execution_count": 16, "id": "9f0a2e82", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([0, 1, 2, ..., 8, 9, 8])" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "digits.target" ] }, { "cell_type": "code", "execution_count": 17, "id": "5aebeeed", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 0., 0., 5., ..., 0., 0., 0.],\n", " [ 0., 0., 0., ..., 10., 0., 0.],\n", " [ 0., 0., 0., ..., 16., 9., 0.],\n", " ...,\n", " [ 0., 0., 1., ..., 6., 0., 0.],\n", " [ 0., 0., 2., ..., 12., 0., 0.],\n", " [ 0., 0., 10., ..., 12., 1., 0.]])" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "digits.data" ] }, { "cell_type": "code", "execution_count": 18, "id": "8fad6b0c", "metadata": {}, "outputs": [], "source": [ "import numpy as np" ] }, { "cell_type": "code", "execution_count": 19, "id": "42d489f4", "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(20,4))\n", "\n", "for index, (image, label) in enumerate(zip(digits.data[0:5], digits.target[0:5])):\n", " plt.subplot(1,5, index + 1)\n", " plt.imshow(np.reshape(image, (8,8)), cmap=plt.cm.gray)\n", " plt.title('%i\\n'%label, fontsize=20)" ] }, { "cell_type": "code", "execution_count": 20, "id": "a0e9ee80", "metadata": {}, "outputs": [], "source": [ "xtrain, xtest, ytrain, ytest = train_test_split(digits.data,\n", " digits.target,\n", " train_size=0.75,\n", " random_state=101)" ] }, { "cell_type": "code", "execution_count": 21, "id": "89a33411", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\himan\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", "Please also refer to the documentation for alternative solver options:\n", " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", " n_iter_i = _check_optimize_result(\n" ] }, { "data": { "text/plain": [ "LogisticRegression()" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model = LogisticRegression()\n", "model.fit(xtrain, ytrain)" ] }, { "cell_type": "code", "execution_count": 22, "id": "eb0a5220", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([7])" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.predict(xtest[0].reshape(1,-1))" ] }, { "cell_type": "code", "execution_count": 23, "id": "b61ba7da", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([7, 4, 7, 9, 1, 7, 5, 7, 1, 0])" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.predict(xtest[0:10])" ] }, { "cell_type": "code", "execution_count": 24, "id": "d4fffa9b", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(450,)" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "predictions = model.predict(xtest)\n", "\n", "predictions.shape" ] }, { "cell_type": "code", "execution_count": 25, "id": "4fcfc55f", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.9711111111111111" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.score(xtest, ytest)" ] }, { "cell_type": "code", "execution_count": 26, "id": "0abeddc9", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " precision recall f1-score support\n", "\n", " 0 0.98 1.00 0.99 44\n", " 1 0.91 1.00 0.95 52\n", " 2 1.00 0.97 0.99 36\n", " 3 1.00 0.95 0.97 41\n", " 4 1.00 0.94 0.97 53\n", " 5 0.96 1.00 0.98 51\n", " 6 1.00 0.95 0.97 39\n", " 7 1.00 0.98 0.99 46\n", " 8 0.98 0.94 0.96 49\n", " 9 0.90 0.97 0.94 39\n", "\n", " accuracy 0.97 450\n", " macro avg 0.97 0.97 0.97 450\n", "weighted avg 0.97 0.97 0.97 450\n", "\n" ] } ], "source": [ "from sklearn.metrics import classification_report\n", "\n", "print(classification_report(ytest, predictions))" ] }, { "cell_type": "code", "execution_count": null, "id": "e4d6dea7", "metadata": {}, "outputs": [], "source": [ "0 - 0.1 0.1 - 0.2 0.2 - 0.3\n", " 0 1 2" ] }, { "cell_type": "code", "execution_count": 28, "id": "3261d864", "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[44 0 0 0 0 0 0 0 0 0]\n", " [ 0 52 0 0 0 0 0 0 0 0]\n", " [ 0 1 35 0 0 0 0 0 0 0]\n", " [ 0 0 0 39 0 2 0 0 0 0]\n", " [ 0 1 0 0 50 0 0 0 0 2]\n", " [ 0 0 0 0 0 51 0 0 0 0]\n", " [ 0 1 0 0 0 0 37 0 1 0]\n", " [ 0 0 0 0 0 0 0 45 0 1]\n", " [ 0 2 0 0 0 0 0 0 46 1]\n", " [ 1 0 0 0 0 0 0 0 0 38]]\n" ] } ], "source": [ "from sklearn.metrics import confusion_matrix\n", "\n", "print(confusion_matrix(ytest, predictions))" ] }, { "cell_type": "code", "execution_count": null, "id": "776ca3c3", "metadata": {}, "outputs": [], "source": [ "gender\tage\tincome\tspending\n", "M\t25\t100000\t50000\n", "M\t30\t150000\t60000\n", "F\t32\t165000\t90000\n", "F\t21\t200000\t110000" ] }, { "cell_type": "code", "execution_count": 30, "id": "4ca7d100", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " gender age income spending\n", "0 M 24 50000 30000\n", "1 F 25 100000 50000\n", "2 M 27 1200000 600000\n", "3 F 35 1500000 750000" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_excel(\"spend.xlsx\")\n", "\n", "df" ] }, { "cell_type": "code", "execution_count": 33, "id": "c598a10e", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import seaborn as sns\n", "\n", "sns.countplot(x = 'spending', hue='gender', data=df)" ] }, { "cell_type": "code", "execution_count": 34, "id": "e2d9fe0f", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.plot(df['gender'], df['spending'], label='male')" ] }, { "cell_type": "code", "execution_count": 35, "id": "aa1d58ba", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.bar(df['gender'], df['spending'])" ] }, { "cell_type": "code", "execution_count": 38, "id": "33238bde", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.bar(df['gender'], df['spending'], label='spending')\n", "plt.bar(df['gender'], df['income'], label='income')\n", "\n", "plt.legend()" ] }, { "cell_type": "code", "execution_count": null, "id": "ffd472db", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.12" } }, "nbformat": 4, "nbformat_minor": 5 } ================================================ FILE: March Cohort 2023/19_20_Machine Learning/19_Machine Learning and Linear Regression with One variable.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "id": "7242d236", "metadata": {}, "source": [ "# Machine Learning" ] }, { "cell_type": "code", "execution_count": null, "id": "f822ef63", "metadata": {}, "outputs": [], "source": [ "Supervised Unsupervised Reinforcement\n", "\n", "Regression - clusturing - markov decision model\n", "- Linear Regression - dimension reduction - game theory\n", "Classification - anomaly detection\n", "- Logistic Regression" ] }, { "cell_type": "markdown", "id": "59019bc8", "metadata": {}, "source": [ "# Linear Regression with One variable" ] }, { "cell_type": "code", "execution_count": 2, "id": "a0ff1cca", "metadata": {}, "outputs": [], "source": [ "# linear - arranged in straight line\n", "# regression - a technique to determine the statistical relationship \n", "# between 2 or more variables" ] }, { "cell_type": "code", "execution_count": null, "id": "1f3259c7", "metadata": {}, "outputs": [], "source": [ " x y\n", "area(sq ft) price($)\n", "2500 800000\n", "3000 950000\n", "3200 1000000\n", "3600 1200000\n", "4000 1500000" ] }, { "cell_type": "code", "execution_count": null, "id": "38b295a5", "metadata": {}, "outputs": [], "source": [ "y = mx + c\n", "y = b1x + b0\n", "\n", "800000 = m(2500) + c\n", "950000 = m(3000) + c\n", "\n", "m = 3000\n", "c = 4000\n", "\n", "y = 3000(x) + 4000 --> principle equation\n", "y = 3000(1200) + 4000 --> prediction" ] }, { "cell_type": "code", "execution_count": 3, "id": "7d739803", "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 4, "id": "ddd8d883", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " area price\n", "0 2600 550000\n", "1 3000 565000\n", "2 3200 610000\n", "3 3600 680000\n", "4 4000 725000" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_csv(\"homeprices.csv\")\n", "df.head()" ] }, { "cell_type": "code", "execution_count": 5, "id": "f2481b31", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ], "text/plain": [ " area\n", "0 2600\n", "1 3000\n", "2 3200\n", "3 3600\n", "4 4000" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x = df[['area']]\n", "x" ] }, { "cell_type": "code", "execution_count": 7, "id": "e67faccd", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 550000\n", "1 565000\n", "2 610000\n", "3 680000\n", "4 725000\n", "Name: price, dtype: int64" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y = df.price\n", "y" ] }, { "cell_type": "code", "execution_count": 8, "id": "23e39b32", "metadata": {}, "outputs": [], "source": [ "from sklearn.linear_model import LinearRegression\n", "\n", "model = LinearRegression()" ] }, { "cell_type": "code", "execution_count": 9, "id": "e9cc482f", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "LinearRegression()" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model" ] }, { "cell_type": "code", "execution_count": 11, "id": "40d56579", "metadata": { "scrolled": false }, "outputs": [], "source": [ "# help(model)" ] }, { "cell_type": "code", "execution_count": 12, "id": "949d6f6a", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "LinearRegression()" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.fit(x,y)" ] }, { "cell_type": "code", "execution_count": 13, "id": "d1ba4c61", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([135.78767123])" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# m = slope = gradient = 135.78\n", "model.coef_" ] }, { "cell_type": "code", "execution_count": 14, "id": "3696cb1a", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "180616.43835616432" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# c = intercept = 180616.43\n", "model.intercept_" ] }, { "cell_type": "code", "execution_count": 15, "id": "6eeccf2a", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "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", " warnings.warn(\n" ] }, { "data": { "text/plain": [ "array([601558.21917808])" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.predict([[3100]])" ] }, { "cell_type": "code", "execution_count": null, "id": "a6a89a8e", "metadata": {}, "outputs": [], "source": [ "# y = mx + c\n", "y = 135.78(x) + 180616.43" ] }, { "cell_type": "code", "execution_count": null, "id": "ad003525", "metadata": {}, "outputs": [], "source": [ "x = 3100" ] }, { "cell_type": "code", "execution_count": 16, "id": "7211c8b8", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "601534.4299999999" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "y = 135.78*(3100) + 180616.43\n", "y" ] }, { "cell_type": "markdown", "id": "82249914", "metadata": {}, "source": [ "# Boston House Price Data" ] }, { "cell_type": "code", "execution_count": null, "id": "3f497053", "metadata": {}, "outputs": [], "source": [ "y1 = m1x1 + m2x2 + ..... + c + e\n", "y2 = m1x1 + m2x2 + ..... + c + e" ] }, { "cell_type": "code", "execution_count": 17, "id": "a20860cc", "metadata": { "scrolled": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "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", " The Boston housing prices dataset has an ethical problem. You can refer to\n", " the documentation of this function for further details.\n", "\n", " The scikit-learn maintainers therefore strongly discourage the use of this\n", " dataset unless the purpose of the code is to study and educate about\n", " ethical issues in data science and machine learning.\n", "\n", " In this special case, you can fetch the dataset from the original\n", " source::\n", "\n", " import pandas as pd\n", " import numpy as np\n", "\n", "\n", " data_url = \"http://lib.stat.cmu.edu/datasets/boston\"\n", " raw_df = pd.read_csv(data_url, sep=\"\\s+\", skiprows=22, header=None)\n", " data = np.hstack([raw_df.values[::2, :], raw_df.values[1::2, :2]])\n", " target = raw_df.values[1::2, 2]\n", "\n", " Alternative datasets include the California housing dataset (i.e.\n", " :func:`~sklearn.datasets.fetch_california_housing`) and the Ames housing\n", " dataset. You can load the datasets as follows::\n", "\n", " from sklearn.datasets import fetch_california_housing\n", " housing = fetch_california_housing()\n", "\n", " for the California housing dataset and::\n", "\n", " from sklearn.datasets import fetch_openml\n", " housing = fetch_openml(name=\"house_prices\", as_frame=True)\n", "\n", " for the Ames housing dataset.\n", " \n", " warnings.warn(msg, category=FutureWarning)\n" ] }, { "data": { "text/plain": [ "{'data': array([[6.3200e-03, 1.8000e+01, 2.3100e+00, ..., 1.5300e+01, 3.9690e+02,\n", " 4.9800e+00],\n", " [2.7310e-02, 0.0000e+00, 7.0700e+00, ..., 1.7800e+01, 3.9690e+02,\n", " 9.1400e+00],\n", " [2.7290e-02, 0.0000e+00, 7.0700e+00, ..., 1.7800e+01, 3.9283e+02,\n", " 4.0300e+00],\n", " ...,\n", " [6.0760e-02, 0.0000e+00, 1.1930e+01, ..., 2.1000e+01, 3.9690e+02,\n", " 5.6400e+00],\n", " [1.0959e-01, 0.0000e+00, 1.1930e+01, ..., 2.1000e+01, 3.9345e+02,\n", " 6.4800e+00],\n", " [4.7410e-02, 0.0000e+00, 1.1930e+01, ..., 2.1000e+01, 3.9690e+02,\n", " 7.8800e+00]]),\n", " 'target': array([24. , 21.6, 34.7, 33.4, 36.2, 28.7, 22.9, 27.1, 16.5, 18.9, 15. ,\n", " 18.9, 21.7, 20.4, 18.2, 19.9, 23.1, 17.5, 20.2, 18.2, 13.6, 19.6,\n", " 15.2, 14.5, 15.6, 13.9, 16.6, 14.8, 18.4, 21. , 12.7, 14.5, 13.2,\n", " 13.1, 13.5, 18.9, 20. , 21. , 24.7, 30.8, 34.9, 26.6, 25.3, 24.7,\n", " 21.2, 19.3, 20. , 16.6, 14.4, 19.4, 19.7, 20.5, 25. , 23.4, 18.9,\n", " 35.4, 24.7, 31.6, 23.3, 19.6, 18.7, 16. , 22.2, 25. , 33. , 23.5,\n", " 19.4, 22. , 17.4, 20.9, 24.2, 21.7, 22.8, 23.4, 24.1, 21.4, 20. ,\n", " 20.8, 21.2, 20.3, 28. , 23.9, 24.8, 22.9, 23.9, 26.6, 22.5, 22.2,\n", " 23.6, 28.7, 22.6, 22. , 22.9, 25. , 20.6, 28.4, 21.4, 38.7, 43.8,\n", " 33.2, 27.5, 26.5, 18.6, 19.3, 20.1, 19.5, 19.5, 20.4, 19.8, 19.4,\n", " 21.7, 22.8, 18.8, 18.7, 18.5, 18.3, 21.2, 19.2, 20.4, 19.3, 22. ,\n", " 20.3, 20.5, 17.3, 18.8, 21.4, 15.7, 16.2, 18. , 14.3, 19.2, 19.6,\n", " 23. , 18.4, 15.6, 18.1, 17.4, 17.1, 13.3, 17.8, 14. , 14.4, 13.4,\n", " 15.6, 11.8, 13.8, 15.6, 14.6, 17.8, 15.4, 21.5, 19.6, 15.3, 19.4,\n", " 17. , 15.6, 13.1, 41.3, 24.3, 23.3, 27. , 50. , 50. , 50. , 22.7,\n", " 25. , 50. , 23.8, 23.8, 22.3, 17.4, 19.1, 23.1, 23.6, 22.6, 29.4,\n", " 23.2, 24.6, 29.9, 37.2, 39.8, 36.2, 37.9, 32.5, 26.4, 29.6, 50. ,\n", " 32. , 29.8, 34.9, 37. , 30.5, 36.4, 31.1, 29.1, 50. , 33.3, 30.3,\n", " 34.6, 34.9, 32.9, 24.1, 42.3, 48.5, 50. , 22.6, 24.4, 22.5, 24.4,\n", " 20. , 21.7, 19.3, 22.4, 28.1, 23.7, 25. , 23.3, 28.7, 21.5, 23. ,\n", " 26.7, 21.7, 27.5, 30.1, 44.8, 50. , 37.6, 31.6, 46.7, 31.5, 24.3,\n", " 31.7, 41.7, 48.3, 29. , 24. , 25.1, 31.5, 23.7, 23.3, 22. , 20.1,\n", " 22.2, 23.7, 17.6, 18.5, 24.3, 20.5, 24.5, 26.2, 24.4, 24.8, 29.6,\n", " 42.8, 21.9, 20.9, 44. , 50. , 36. , 30.1, 33.8, 43.1, 48.8, 31. ,\n", " 36.5, 22.8, 30.7, 50. , 43.5, 20.7, 21.1, 25.2, 24.4, 35.2, 32.4,\n", " 32. , 33.2, 33.1, 29.1, 35.1, 45.4, 35.4, 46. , 50. , 32.2, 22. ,\n", " 20.1, 23.2, 22.3, 24.8, 28.5, 37.3, 27.9, 23.9, 21.7, 28.6, 27.1,\n", " 20.3, 22.5, 29. , 24.8, 22. , 26.4, 33.1, 36.1, 28.4, 33.4, 28.2,\n", " 22.8, 20.3, 16.1, 22.1, 19.4, 21.6, 23.8, 16.2, 17.8, 19.8, 23.1,\n", " 21. , 23.8, 23.1, 20.4, 18.5, 25. , 24.6, 23. , 22.2, 19.3, 22.6,\n", " 19.8, 17.1, 19.4, 22.2, 20.7, 21.1, 19.5, 18.5, 20.6, 19. , 18.7,\n", " 32.7, 16.5, 23.9, 31.2, 17.5, 17.2, 23.1, 24.5, 26.6, 22.9, 24.1,\n", " 18.6, 30.1, 18.2, 20.6, 17.8, 21.7, 22.7, 22.6, 25. , 19.9, 20.8,\n", " 16.8, 21.9, 27.5, 21.9, 23.1, 50. , 50. , 50. , 50. , 50. , 13.8,\n", " 13.8, 15. , 13.9, 13.3, 13.1, 10.2, 10.4, 10.9, 11.3, 12.3, 8.8,\n", " 7.2, 10.5, 7.4, 10.2, 11.5, 15.1, 23.2, 9.7, 13.8, 12.7, 13.1,\n", " 12.5, 8.5, 5. , 6.3, 5.6, 7.2, 12.1, 8.3, 8.5, 5. , 11.9,\n", " 27.9, 17.2, 27.5, 15. , 17.2, 17.9, 16.3, 7. , 7.2, 7.5, 10.4,\n", " 8.8, 8.4, 16.7, 14.2, 20.8, 13.4, 11.7, 8.3, 10.2, 10.9, 11. ,\n", " 9.5, 14.5, 14.1, 16.1, 14.3, 11.7, 13.4, 9.6, 8.7, 8.4, 12.8,\n", " 10.5, 17.1, 18.4, 15.4, 10.8, 11.8, 14.9, 12.6, 14.1, 13. , 13.4,\n", " 15.2, 16.1, 17.8, 14.9, 14.1, 12.7, 13.5, 14.9, 20. , 16.4, 17.7,\n", " 19.5, 20.2, 21.4, 19.9, 19. , 19.1, 19.1, 20.1, 19.9, 19.6, 23.2,\n", " 29.8, 13.8, 13.3, 16.7, 12. , 14.6, 21.4, 23. , 23.7, 25. , 21.8,\n", " 20.6, 21.2, 19.1, 20.6, 15.2, 7. , 8.1, 13.6, 20.1, 21.8, 24.5,\n", " 23.1, 19.7, 18.3, 21.2, 17.5, 16.8, 22.4, 20.6, 23.9, 22. , 11.9]),\n", " 'feature_names': array(['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD',\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", " 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CRIMZNINDUSCHASNOXRMAGEDISRADTAXPTRATIOBLSTAT
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CRIMZNINDUSCHASNOXRMAGEDISRADTAXPTRATIOBLSTATPrice
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CRIMZNINDUSCHASNOXRMAGEDISRADTAXPTRATIOBLSTATPrice
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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
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\n", "
" ], "text/plain": [ " CRIM ZN INDUS CHAS NOX RM \\\n", "count 506.000000 506.000000 506.000000 506.000000 506.000000 506.000000 \n", "mean 3.613524 11.363636 11.136779 0.069170 0.554695 6.284634 \n", "std 8.601545 23.322453 6.860353 0.253994 0.115878 0.702617 \n", "min 0.006320 0.000000 0.460000 0.000000 0.385000 3.561000 \n", "25% 0.082045 0.000000 5.190000 0.000000 0.449000 5.885500 \n", "50% 0.256510 0.000000 9.690000 0.000000 0.538000 6.208500 \n", "75% 3.677083 12.500000 18.100000 0.000000 0.624000 6.623500 \n", "max 88.976200 100.000000 27.740000 1.000000 0.871000 8.780000 \n", "\n", " AGE DIS RAD TAX PTRATIO B \\\n", "count 506.000000 506.000000 506.000000 506.000000 506.000000 506.000000 \n", "mean 68.574901 3.795043 9.549407 408.237154 18.455534 356.674032 \n", "std 28.148861 2.105710 8.707259 168.537116 2.164946 91.294864 \n", "min 2.900000 1.129600 1.000000 187.000000 12.600000 0.320000 \n", "25% 45.025000 2.100175 4.000000 279.000000 17.400000 375.377500 \n", "50% 77.500000 3.207450 5.000000 330.000000 19.050000 391.440000 \n", "75% 94.075000 5.188425 24.000000 666.000000 20.200000 396.225000 \n", "max 100.000000 12.126500 24.000000 711.000000 22.000000 396.900000 \n", "\n", " LSTAT Price \n", "count 506.000000 506.000000 \n", "mean 12.653063 22.532806 \n", "std 7.141062 9.197104 \n", "min 1.730000 5.000000 \n", "25% 6.950000 17.025000 \n", "50% 11.360000 21.200000 \n", "75% 16.955000 25.000000 \n", "max 37.970000 50.000000 " ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.describe()" ] }, { "cell_type": "code", "execution_count": 27, "id": "f124baa6", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 506 entries, 0 to 505\n", "Data columns (total 14 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 CRIM 506 non-null float64\n", " 1 ZN 506 non-null float64\n", " 2 INDUS 506 non-null float64\n", " 3 CHAS 506 non-null float64\n", " 4 NOX 506 non-null float64\n", " 5 RM 506 non-null float64\n", " 6 AGE 506 non-null float64\n", " 7 DIS 506 non-null float64\n", " 8 RAD 506 non-null float64\n", " 9 TAX 506 non-null float64\n", " 10 PTRATIO 506 non-null float64\n", " 11 B 506 non-null float64\n", " 12 LSTAT 506 non-null float64\n", " 13 Price 506 non-null float64\n", "dtypes: float64(14)\n", "memory usage: 55.5 KB\n" ] } ], "source": [ "df.info()" ] }, { "cell_type": "code", "execution_count": 28, "id": "cf7dc760", "metadata": {}, "outputs": [], "source": [ "from sklearn.model_selection import train_test_split" ] }, { "cell_type": "code", "execution_count": 29, "id": "32764bf6", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "xtrain shape: (404, 13)\n", "xtest shape: (102, 13)\n", "ytrain shape: (404,)\n", "ytest shape: (102,)\n" ] } ], "source": [ "x = boston.data\n", "\n", "y = boston.target\n", "\n", "xtrain, xtest, ytrain, ytest = train_test_split(x, y, test_size=0.2,\n", " random_state=100)\n", "\n", "print(\"xtrain shape:\", xtrain.shape)\n", "print(\"xtest shape:\", xtest.shape)\n", "print(\"ytrain shape:\", ytrain.shape)\n", "print(\"ytest shape:\", ytest.shape)" ] }, { "cell_type": "code", "execution_count": 31, "id": "c68d3cf3", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "LinearRegression()" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.linear_model import LinearRegression\n", "\n", "model = LinearRegression()\n", "\n", "model.fit(xtrain, ytrain)" ] }, { "cell_type": "code", "execution_count": 32, "id": "a4f1e3ec", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([34.4081095 , 31.18524626, 22.31286141, 17.88613877, 20.43572131,\n", " 26.14444413, 26.21920244, 23.57978445, 22.41577853, 19.51182817,\n", " 26.86691495, 17.20411302, 20.68511041, 15.67921778, 41.69912781,\n", " 20.2946735 , 28.99258631, 19.06413492, 32.48035595, 41.13627224,\n", " 34.64732462, 16.38997909, 20.42215729, 18.05324255, 13.38743087,\n", " 12.64800748, 27.45930237, 20.30199107, 18.78954741, 20.24950994,\n", " 15.60161419, 24.38040555, 38.95651978, 24.7184131 , 31.26791961,\n", " 28.26279775, 15.84707127, 14.76661568, 16.79024244, 23.23674899,\n", " 22.85417065, 23.48976177, 14.16818173, 21.42613087, 32.38362329,\n", " 26.7881669 , 19.37574824, 15.27894103, 17.21175121, 12.91591919,\n", " 21.84063224, 20.25050371, 23.65622638, 23.9608324 , 11.94749102,\n", " 14.49718052, 24.69872363, 34.18169066, 10.30437821, 21.04686616,\n", " 17.96204214, 19.76593459, 17.45231513, 29.982971 , 20.73183476,\n", " 25.24657823, 15.81416285, 24.96705225, 22.1298931 , 20.77593563,\n", " 18.69600904, 24.2201495 , 4.37889874, 15.95687399, 28.03130587,\n", " 9.28438308, 24.76810967, 35.14238234, 11.61172029, 27.04175401,\n", " 34.84290485, 40.44603313, 13.93219791, 15.95544402, 19.26007763,\n", " 12.76037799, 20.90536105, 23.85659356, 13.17899179, 14.76828889,\n", " 32.31355353, 22.93635318, 24.63095357, 23.51981407, 19.34136578,\n", " 22.83582245, 21.72263765, 36.18508621, 18.01097012, 23.18226475,\n", " 13.77270991, 14.43864146])" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ypred = model.predict(xtest)\n", "ypred" ] }, { "cell_type": "code", "execution_count": 33, "id": "811d7567", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([34.6, 31.5, 20.6, 14.5, 16.2, 22.3, 22.8, 24.4, 19.8, 16.7, 23.2,\n", " 15.1, 21. , 18.9, 48.5, 24.1, 22.5, 16.1, 30.3, 50. , 33.8, 10.4,\n", " 20.4, 14.2, 10.5, 12.7, 24.5, 16.7, 20.1, 17.1, 18.4, 24.3, 50. ,\n", " 23.8, 30.7, 33.4, 15. , 10.9, 19.1, 21. , 22.9, 22.4, 19. , 19.3,\n", " 27. , 27.1, 19.1, 15.4, 19.1, 13.5, 20.9, 19.3, 20.5, 26.2, 12. ,\n", " 19.7, 29.6, 34.9, 8.3, 21.2, 14.5, 27.5, 19.4, 32.7, 21.1, 28.1,\n", " 17.4, 30.1, 21.4, 20.9, 10.9, 21.7, 8.8, 15.2, 26.6, 8.7, 24.7,\n", " 38.7, 6.3, 22.6, 43.8, 50. , 14.5, 11.7, 12.5, 13.6, 21.5, 19.1,\n", " 9.5, 17.2, 29.8, 22.6, 21.6, 22.2, 27.1, 17. , 22. , 50. , 7.2,\n", " 50. , 14. , 11. ])" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ytest" ] }, { "cell_type": "markdown", "id": "17b0418a", "metadata": {}, "source": [ "# Evaluation" ] }, { "cell_type": "code", "execution_count": 39, "id": "40e55958", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.7555033086871303" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.score(xtest, ytest)" ] }, { "cell_type": "code", "execution_count": null, "id": "733d9d23", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.12" } }, "nbformat": 4, "nbformat_minor": 5 } ================================================ FILE: March Cohort 2023/19_20_Machine Learning/20_Logistic Regression.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "id": "c71657c4", "metadata": {}, "source": [ "# Logistic Regression" ] }, { "cell_type": "code", "execution_count": null, "id": "f3aa2921", "metadata": {}, "outputs": [], "source": [ "y = mx + c\n", "\n", "s(y) = 1/ 1+ e**(-y)\n", "\n", "s(mx + c) = 1/ 1+ e**(-(mx + c)) => 0 to 1" ] }, { "cell_type": "code", "execution_count": null, "id": "6513a10e", "metadata": {}, "outputs": [], "source": [ " male female\n", " 0 - 0.5 0.5 - 1" ] }, { "cell_type": "markdown", "id": "bed78bb3", "metadata": {}, "source": [ "## Insurance Prediction using Logistic Regression" ] }, { "cell_type": "code", "execution_count": 1, "id": "9c2cd6a9", "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 2, "id": "074396bc", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " age bought_insurance\n", "0 22 0\n", "1 25 0\n", "2 47 1\n", "3 52 0\n", "4 46 1" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_csv(\"insurance_data.csv\")\n", "\n", "df.head()" ] }, { "cell_type": "code", "execution_count": 3, "id": "706150aa", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.scatter(df.age, df.bought_insurance, marker='+', color='red')" ] }, { "cell_type": "code", "execution_count": 4, "id": "30fcfbb7", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(27, 2)" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.shape" ] }, { "cell_type": "code", "execution_count": 5, "id": "edd0315f", "metadata": {}, "outputs": [], "source": [ "from sklearn.model_selection import train_test_split\n", "\n", "xtrain, xtest, ytrain, ytest = train_test_split(df[['age']],\n", " df.bought_insurance,\n", " train_size=0.9)" ] }, { "cell_type": "code", "execution_count": 6, "id": "bd2f6713", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "LogisticRegression()" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.linear_model import LogisticRegression\n", "\n", "model = LogisticRegression()\n", "\n", "model.fit(xtrain, ytrain)" ] }, { "cell_type": "code", "execution_count": 7, "id": "742d8875", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " age\n", "24 50\n", "3 52\n", "11 28" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "xtest" ] }, { "cell_type": "code", "execution_count": 8, "id": "1d225ec1", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([1, 1, 0], dtype=int64)" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ypredictions = model.predict(xtest)\n", "\n", "ypredictions" ] }, { "cell_type": "code", "execution_count": 9, "id": "58338281", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[0.10748428, 0.89251572],\n", " [0.08108033, 0.91891967],\n", " [0.78666634, 0.21333366]])" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.predict_proba(xtest)" ] }, { "cell_type": "code", "execution_count": 10, "id": "104a52c5", "metadata": {}, "outputs": [], "source": [ "from sklearn.metrics import accuracy_score" ] }, { "cell_type": "code", "execution_count": 13, "id": "ad16e9c5", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.6666666666666666" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "accuracy_score(ytest,ypredictions)" ] }, { "cell_type": "markdown", "id": "995595c0", "metadata": {}, "source": [ "# Multi Class Classification\n", "\n", "## Hand-written Digit Dataset" ] }, { "cell_type": "code", "execution_count": 14, "id": "9502f54d", "metadata": {}, "outputs": [], "source": [ "from sklearn.datasets import load_digits\n", "\n", "digits = load_digits()" ] }, { "cell_type": "code", "execution_count": 15, "id": "664ee90a", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(1797, 64)" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "digits.data.shape" ] }, { "cell_type": "code", "execution_count": 16, "id": "9f0a2e82", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([0, 1, 2, ..., 8, 9, 8])" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "digits.target" ] }, { "cell_type": "code", "execution_count": 17, "id": "5aebeeed", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 0., 0., 5., ..., 0., 0., 0.],\n", " [ 0., 0., 0., ..., 10., 0., 0.],\n", " [ 0., 0., 0., ..., 16., 9., 0.],\n", " ...,\n", " [ 0., 0., 1., ..., 6., 0., 0.],\n", " [ 0., 0., 2., ..., 12., 0., 0.],\n", " [ 0., 0., 10., ..., 12., 1., 0.]])" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "digits.data" ] }, { "cell_type": "code", "execution_count": 18, "id": "8fad6b0c", "metadata": {}, "outputs": [], "source": [ "import numpy as np" ] }, { "cell_type": "code", "execution_count": 19, "id": "42d489f4", "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(20,4))\n", "\n", "for index, (image, label) in enumerate(zip(digits.data[0:5], digits.target[0:5])):\n", " plt.subplot(1,5, index + 1)\n", " plt.imshow(np.reshape(image, (8,8)), cmap=plt.cm.gray)\n", " plt.title('%i\\n'%label, fontsize=20)" ] }, { "cell_type": "code", "execution_count": 20, "id": "a0e9ee80", "metadata": {}, "outputs": [], "source": [ "xtrain, xtest, ytrain, ytest = train_test_split(digits.data,\n", " digits.target,\n", " train_size=0.75,\n", " random_state=101)" ] }, { "cell_type": "code", "execution_count": 21, "id": "89a33411", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\himan\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n", "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", "\n", "Increase the number of iterations (max_iter) or scale the data as shown in:\n", " https://scikit-learn.org/stable/modules/preprocessing.html\n", "Please also refer to the documentation for alternative solver options:\n", " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", " n_iter_i = _check_optimize_result(\n" ] }, { "data": { "text/plain": [ "LogisticRegression()" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model = LogisticRegression()\n", "model.fit(xtrain, ytrain)" ] }, { "cell_type": "code", "execution_count": 22, "id": "eb0a5220", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([7])" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.predict(xtest[0].reshape(1,-1))" ] }, { "cell_type": "code", "execution_count": 23, "id": "b61ba7da", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([7, 4, 7, 9, 1, 7, 5, 7, 1, 0])" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.predict(xtest[0:10])" ] }, { "cell_type": "code", "execution_count": 24, "id": "d4fffa9b", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(450,)" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "predictions = model.predict(xtest)\n", "\n", "predictions.shape" ] }, { "cell_type": "code", "execution_count": 25, "id": "4fcfc55f", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.9711111111111111" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.score(xtest, ytest)" ] }, { "cell_type": "code", "execution_count": 26, "id": "0abeddc9", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " precision recall f1-score support\n", "\n", " 0 0.98 1.00 0.99 44\n", " 1 0.91 1.00 0.95 52\n", " 2 1.00 0.97 0.99 36\n", " 3 1.00 0.95 0.97 41\n", " 4 1.00 0.94 0.97 53\n", " 5 0.96 1.00 0.98 51\n", " 6 1.00 0.95 0.97 39\n", " 7 1.00 0.98 0.99 46\n", " 8 0.98 0.94 0.96 49\n", " 9 0.90 0.97 0.94 39\n", "\n", " accuracy 0.97 450\n", " macro avg 0.97 0.97 0.97 450\n", "weighted avg 0.97 0.97 0.97 450\n", "\n" ] } ], "source": [ "from sklearn.metrics import classification_report\n", "\n", "print(classification_report(ytest, predictions))" ] }, { "cell_type": "code", "execution_count": null, "id": "e4d6dea7", "metadata": {}, "outputs": [], "source": [ "0 - 0.1 0.1 - 0.2 0.2 - 0.3\n", " 0 1 2" ] }, { "cell_type": "code", "execution_count": 28, "id": "3261d864", "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[44 0 0 0 0 0 0 0 0 0]\n", " [ 0 52 0 0 0 0 0 0 0 0]\n", " [ 0 1 35 0 0 0 0 0 0 0]\n", " [ 0 0 0 39 0 2 0 0 0 0]\n", " [ 0 1 0 0 50 0 0 0 0 2]\n", " [ 0 0 0 0 0 51 0 0 0 0]\n", " [ 0 1 0 0 0 0 37 0 1 0]\n", " [ 0 0 0 0 0 0 0 45 0 1]\n", " [ 0 2 0 0 0 0 0 0 46 1]\n", " [ 1 0 0 0 0 0 0 0 0 38]]\n" ] } ], "source": [ "from sklearn.metrics import confusion_matrix\n", "\n", "print(confusion_matrix(ytest, predictions))" ] }, { "cell_type": "code", "execution_count": null, "id": "776ca3c3", "metadata": {}, "outputs": [], "source": [ "gender\tage\tincome\tspending\n", "M\t25\t100000\t50000\n", "M\t30\t150000\t60000\n", "F\t32\t165000\t90000\n", "F\t21\t200000\t110000" ] }, { "cell_type": "code", "execution_count": 30, "id": "4ca7d100", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " gender age income spending\n", "0 M 24 50000 30000\n", "1 F 25 100000 50000\n", "2 M 27 1200000 600000\n", "3 F 35 1500000 750000" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_excel(\"spend.xlsx\")\n", "\n", "df" ] }, { "cell_type": "code", "execution_count": 33, "id": "c598a10e", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import seaborn as sns\n", "\n", "sns.countplot(x = 'spending', hue='gender', data=df)" ] }, { "cell_type": "code", "execution_count": 34, "id": "e2d9fe0f", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.plot(df['gender'], df['spending'], label='male')" ] }, { "cell_type": "code", "execution_count": 35, "id": "aa1d58ba", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.bar(df['gender'], df['spending'])" ] }, { "cell_type": "code", "execution_count": 38, "id": "33238bde", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.bar(df['gender'], df['spending'], label='spending')\n", "plt.bar(df['gender'], df['income'], label='income')\n", "\n", "plt.legend()" ] }, { "cell_type": "code", "execution_count": null, "id": "ffd472db", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.12" } }, "nbformat": 4, "nbformat_minor": 5 } ================================================ FILE: March Cohort 2023/19_20_Machine Learning/areas.csv ================================================ area 1000 1500 2300 3540 4120 4560 5490 3460 4750 2300 9000 8600 7100 ================================================ FILE: March Cohort 2023/19_20_Machine Learning/homeprices.csv ================================================ area,price 2600,550000 3000,565000 3200,610000 3600,680000 4000,725000 ================================================ FILE: March Cohort 2023/19_20_Machine Learning/insurance_data.csv ================================================ age,bought_insurance 22,0 25,0 47,1 52,0 46,1 56,1 55,0 60,1 62,1 61,1 18,0 28,0 27,0 29,0 49,1 55,1 25,1 58,1 19,0 18,0 21,0 26,0 40,1 45,1 50,1 54,1 23,0 ================================================ FILE: March Cohort 2023/1_While Loops and Logic Building.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "id": "dfbc28de", "metadata": {}, "source": [ "# While Loop" ] }, { "cell_type": "code", "execution_count": 2, "id": "c1f425e3", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Himanshu\n", "Himanshu\n" ] } ], "source": [ "print(\"Himanshu\")\n", "print(\"Himanshu\")" ] }, { "cell_type": "code", "execution_count": 4, "id": "55a46f9f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "True\n", "False\n" ] } ], "source": [ "i = 1\n", "\n", "print(i <= 5)\n", "\n", "print(i >= 5)" ] }, { "cell_type": "code", "execution_count": 5, "id": "7a204214", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Himanshu\n", "Himanshu\n", "Himanshu\n", "Himanshu\n", "Himanshu\n" ] } ], "source": [ "i = 1\n", "\n", "while i <= 5 :\n", " print(\"Himanshu\")\n", " i = i + 1 # i = 1, 2, 3, 4, 5, 6" ] }, { "cell_type": "code", "execution_count": 6, "id": "b5ef4f72", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Himanshu\n", "Himanshu\n", "Himanshu\n", "Himanshu\n", "Himanshu\n", "Himanshu\n", "Himanshu\n", "Himanshu\n", "Himanshu\n", "Himanshu\n" ] } ], "source": [ "i = 1\n", "\n", "while i <= 10 :\n", " print(\"Himanshu\")\n", " i = i + 1" ] }, { "cell_type": "code", "execution_count": 7, "id": "a3fc06bd", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1\n", "2\n", "3\n", "4\n", "5\n" ] } ], "source": [ "i = 1\n", "\n", "while i <= 5 :\n", " print(i)\n", " i = i + 1" ] }, { "cell_type": "code", "execution_count": null, "id": "2b2a54cb", "metadata": {}, "outputs": [], "source": [ "1\n", "2\n", "3\n", "4\n", "5\n", "6\n", "7\n", "8\n", "9\n", "10" ] }, { "cell_type": "code", "execution_count": 8, "id": "9b83ffab", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1\n", "2\n", "3\n", "4\n", "5\n", "6\n", "7\n", "8\n", "9\n", "10\n" ] } ], "source": [ "i = 1\n", "\n", "while i <= 10:\n", " print(i)\n", " i = i + 1" ] }, { "cell_type": "code", "execution_count": 9, "id": "9522e499", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Hello\n", "World\n" ] } ], "source": [ "print(\"Hello\")\n", "print(\"World\")" ] }, { "cell_type": "code", "execution_count": 10, "id": "dd5d35e6", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Hello\n", "World\n" ] } ], "source": [ "print(\"Hello\", end='\\n')\n", "print(\"World\")" ] }, { "cell_type": "code", "execution_count": 11, "id": "c54de4e2", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Hello+World\n" ] } ], "source": [ "print(\"Hello\", end='+')\n", "print(\"World\")" ] }, { "cell_type": "code", "execution_count": 12, "id": "54a31434", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Hello World\n" ] } ], "source": [ "print(\"Hello\", end=' ')\n", "print(\"World\")" ] }, { "cell_type": "code", "execution_count": 13, "id": "b4f03f6d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "HelloWorld\n" ] } ], "source": [ "print(\"Hello\", end='')\n", "print(\"World\")" ] }, { "cell_type": "code", "execution_count": 14, "id": "2a48a921", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Hello@World\n" ] } ], "source": [ "print(\"Hello\", end='@')\n", "print(\"World\")" ] }, { "cell_type": "code", "execution_count": 15, "id": "1357e71d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Hello@World$" ] } ], "source": [ "print(\"Hello\", end='@')\n", "print(\"World\", end='$')" ] }, { "cell_type": "code", "execution_count": null, "id": "f48834ba", "metadata": {}, "outputs": [], "source": [ "1 2 3 4 5 6 7 8 9 10" ] }, { "cell_type": "code", "execution_count": 16, "id": "1c74034c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1 2 3 4 5 6 7 8 9 10 " ] } ], "source": [ "i = 1\n", "\n", "while i <= 10:\n", " print(i, end=' ')\n", " i = i + 1" ] }, { "cell_type": "code", "execution_count": null, "id": "0649f1ee", "metadata": {}, "outputs": [], "source": [ "1 4 9 16 25 36 49 64 81 100" ] }, { "cell_type": "code", "execution_count": 17, "id": "9bb2cd28", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "30\n", "10\n" ] } ], "source": [ "a = 10\n", "\n", "print(a*3)\n", "\n", "print(a)\n", "\n", "# a = a * 3" ] }, { "cell_type": "code", "execution_count": 18, "id": "67f5a9b8", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1 4 9 16 25 36 49 64 81 100 " ] } ], "source": [ "i = 1\n", "\n", "while i <= 10:\n", " print(i*i, end=' ')\n", " i = i + 1" ] }, { "cell_type": "markdown", "id": "2ad2e158", "metadata": {}, "source": [ "**Write a program to enter 5 values from user and print them.**" ] }, { "cell_type": "code", "execution_count": 19, "id": "aeee4fe6", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Enter your number: 10\n", "10\n", "Enter your number: 20\n", "20\n", "Enter your number: 30\n", "30\n", "Enter your number: 40\n", "40\n", "Enter your number: 50\n", "50\n" ] } ], "source": [ "i = 1\n", "\n", "while i <= 5:\n", " a = input(\"Enter your number: \")\n", " \n", " print(a)\n", " \n", " i = i + 1" ] }, { "cell_type": "markdown", "id": "3773b36f", "metadata": {}, "source": [ "**Write a program to enter a number from user and print reverse of it.**" ] }, { "cell_type": "code", "execution_count": null, "id": "34362203", "metadata": {}, "outputs": [], "source": [ "153\n", "\n", "351" ] }, { "cell_type": "code", "execution_count": 20, "id": "3dcb3a2f", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "7 % 2 # return reminder" ] }, { "cell_type": "code", "execution_count": 21, "id": "42cfdbf1", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "3.5" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "7 / 2" ] }, { "cell_type": "code", "execution_count": 22, "id": "885d87d3", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "3" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "7 // 2" ] }, { "cell_type": "code", "execution_count": 24, "id": "d7cbb652", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "3" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "153 % 10" ] }, { "cell_type": "code", "execution_count": null, "id": "59f14345", "metadata": {}, "outputs": [], "source": [ " x % 10 = z\n", "153 % 10 = 3\n", "153 // 10 = 15\n", "\n", "15 % 10 = 5\n", "15 // 10 = 1\n", "\n", "1 % 10 = 1\n", "1 // 10 = 0" ] }, { "cell_type": "code", "execution_count": 26, "id": "f759b069", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Enter a number: 10\n", "10\n", "\n" ] } ], "source": [ "n = int(input(\"Enter a number: \"))\n", "\n", "print(n)\n", "\n", "print(type(n))" ] }, { "cell_type": "code", "execution_count": 28, "id": "6fc34b04", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Enter a Number: 12345\n", "54321" ] } ], "source": [ "n = int(input(\"Enter a Number: \"))\n", "\n", "while n > 0:\n", " r = n % 10\n", " print(r, end='') # 351\n", " n = n // 10 # n = 153, 15, 1" ] }, { "cell_type": "markdown", "id": "5d585434", "metadata": {}, "source": [ "**Write a program to enter a number from user and print sum of its individual digits**" ] }, { "cell_type": "code", "execution_count": null, "id": "aaffcb41", "metadata": {}, "outputs": [], "source": [ "153\n", "\n", "1 + 5 + 3\n", "\n", "9\n", "\n", "\n", "0 s\n", "3 s + 3 = 3\n", "5 s + 5 = 8\n", "1 s + 1 = 9" ] }, { "cell_type": "code", "execution_count": 30, "id": "1b2f5fbb", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Enter a Number: 2345\n", "Sum = 14\n" ] } ], "source": [ "n = int(input(\"Enter a Number: \"))\n", "s = 0\n", "\n", "while n > 0:\n", " r = n % 10\n", " s = s + r # s = 0, 3, 8, 9\n", " n = n // 10\n", "\n", "print(\"Sum =\", s)" ] }, { "cell_type": "markdown", "id": "88e40510", "metadata": {}, "source": [ "**Write a program to enter a number from user and check if it palindrome or not**" ] }, { "cell_type": "code", "execution_count": null, "id": "643c209d", "metadata": {}, "outputs": [], "source": [ "121\n", "\n", "121\n", "\n", "153 = 351 not a palindrome" ] }, { "cell_type": "code", "execution_count": 34, "id": "432cbedf", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Enter a Number: 153\n", "351\n", "702\n" ] } ], "source": [ "n = int(input(\"Enter a Number: \"))\n", "s = 0\n", "\n", "while n > 0:\n", " r = n % 10\n", " s = s * 10 + r # s = 0, 3, 35, 351\n", " n = n // 10\n", "\n", "print(s)\n", "print(s * 2)" ] }, { "cell_type": "code", "execution_count": 40, "id": "59333ae3", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Enter a Number: 121\n", "Palindrome\n" ] } ], "source": [ "n = int(input(\"Enter a Number: \"))\n", "t = n\n", "s = 0\n", "\n", "while n > 0:\n", " r = n % 10\n", " s = s * 10 + r # s = 0, 3, 35, 351\n", " n = n // 10\n", "\n", "if s == t:\n", " print(\"Palindrome\")\n", "else:\n", " print(\"Not a Palindrome\")" ] }, { "cell_type": "code", "execution_count": 38, "id": "dc812c1e", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "False" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "10 == 11" ] }, { "cell_type": "markdown", "id": "28edc5f6", "metadata": {}, "source": [ "**Write a program to enter a number from user and check if it is armstrong or not**" ] }, { "cell_type": "code", "execution_count": null, "id": "b78ef4cd", "metadata": {}, "outputs": [], "source": [ "153\n", "\n", "cube(1) + cube(5) + cube(3) = 153\n", "\n", "370\n", "\n", "371" ] }, { "cell_type": "code", "execution_count": 44, "id": "de686ea3", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Enter a Number: 371\n", "Armstrong Number\n" ] } ], "source": [ "n = int(input(\"Enter a Number: \"))\n", "t = n\n", "s = 0\n", "\n", "while n > 0:\n", " r = n % 10\n", " s = s + r*r*r\n", " n = n // 10\n", "\n", "if s == t:\n", " print(\"Armstrong Number\")\n", "else:\n", " print(\"Not an Armstrong Number\")" ] }, { "cell_type": "markdown", "id": "b4fc183f", "metadata": {}, "source": [ "**Write a program to enter a number from user and print its individual digits on seperate line.**" ] }, { "cell_type": "code", "execution_count": null, "id": "75092737", "metadata": {}, "outputs": [], "source": [ "153\n", "\n", "1\n", "5\n", "3" ] }, { "cell_type": "code", "execution_count": 45, "id": "56d6ec0d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Enter a Number: 153\n", "3\n", "5\n", "1\n" ] } ], "source": [ "n = int(input(\"Enter a Number: \"))\n", "s = 0\n", "\n", "while n > 0:\n", " r = n % 10\n", " print(r)\n", " n = n // 10\n" ] }, { "cell_type": "code", "execution_count": 47, "id": "e13e7967", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Enter a Number: 12345\n", "1\n", "2\n", "3\n", "4\n", "5\n" ] } ], "source": [ "n = int(input(\"Enter a Number: \"))\n", "s = 0\n", "\n", "while n > 0:\n", " r = n % 10\n", " s = s * 10 + r\n", " n = n // 10\n", "\n", "# s = 351\n", "\n", "while s > 0:\n", " r = s % 10\n", " print(r)\n", " s = s // 10" ] }, { "cell_type": "code", "execution_count": null, "id": "4864ff19", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.12" } }, "nbformat": 4, "nbformat_minor": 5 } ================================================ FILE: March Cohort 2023/21_EDA and Data Viz/.ipynb_checkpoints/21_EDA and Data Visualization-checkpoint.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "id": "343be340", "metadata": {}, "source": [ "# EDA" ] }, { "cell_type": "code", "execution_count": 1, "id": "6d58db2b", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 2, "id": "cb96b2f2", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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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
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" ], "text/plain": [ " Track \\\n", "0 For Those About To Rock (We Salute You) \n", "1 Put The Finger On You \n", "2 Let's Get It Up \n", "3 Inject The Venom \n", "4 Snowballed \n", "\n", " Composer Milliseconds Bytes \\\n", "0 Angus Young, Malcolm Young, Brian Johnson 343719 11170334 \n", "1 Angus Young, Malcolm Young, Brian Johnson 205662 6713451 \n", "2 Angus Young, Malcolm Young, Brian Johnson 233926 7636561 \n", "3 Angus Young, Malcolm Young, Brian Johnson 210834 6852860 \n", "4 Angus Young, Malcolm Young, Brian Johnson 203102 6599424 \n", "\n", " UnitPrice Genre Album Artist \n", "0 0.99 Rock For Those About To Rock We Salute You AC/DC \n", "1 0.99 Rock For Those About To Rock We Salute You AC/DC \n", "2 0.99 Rock For Those About To Rock We Salute You AC/DC \n", "3 0.99 Rock For Those About To Rock We Salute You AC/DC \n", "4 0.99 Rock For Those About To Rock We Salute You AC/DC " ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_csv('itunes_data.csv')\n", "df.head()" ] }, { "cell_type": "code", "execution_count": 3, "id": "c1388561", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 3503 entries, 0 to 3502\n", "Data columns (total 8 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 Track 3503 non-null object \n", " 1 Composer 2525 non-null object \n", " 2 Milliseconds 3503 non-null int64 \n", " 3 Bytes 3503 non-null int64 \n", " 4 UnitPrice 3503 non-null float64\n", " 5 Genre 3503 non-null object \n", " 6 Album 3503 non-null object \n", " 7 Artist 3503 non-null object \n", "dtypes: float64(1), int64(2), object(5)\n", "memory usage: 219.1+ KB\n" ] } ], "source": [ "df.info()" ] }, { "cell_type": "code", "execution_count": 5, "id": "a0737e50", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Track 3257\n", "Composer 852\n", "Milliseconds 3080\n", "Bytes 3501\n", "UnitPrice 2\n", "Genre 25\n", "Album 347\n", "Artist 204\n", "dtype: int64" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.nunique(axis=0)" ] }, { "cell_type": "markdown", "id": "d715d6f0", "metadata": {}, "source": [ "#### Dimensional Analysis" ] }, { "cell_type": "code", "execution_count": 6, "id": "23233e4a", "metadata": {}, "outputs": [], "source": [ "df['Minutes'] = df['Milliseconds']/(1000*60)\n", "\n", "df['MB'] = df['Bytes'] / 1000000" ] }, { "cell_type": "code", "execution_count": 7, "id": "db4ccd76", "metadata": {}, "outputs": [], "source": [ "df.drop(['Milliseconds', 'Bytes'], axis=1, inplace=True)" ] }, { "cell_type": "code", "execution_count": 8, "id": "4c65b688", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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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
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" ], "text/plain": [ " Track \\\n", "0 For Those About To Rock (We Salute You) \n", "1 Put The Finger On You \n", "2 Let's Get It Up \n", "3 Inject The Venom \n", "4 Snowballed \n", "\n", " Composer UnitPrice Genre \\\n", "0 Angus Young, Malcolm Young, Brian Johnson 0.99 Rock \n", "1 Angus Young, Malcolm Young, Brian Johnson 0.99 Rock \n", "2 Angus Young, Malcolm Young, Brian Johnson 0.99 Rock \n", "3 Angus Young, Malcolm Young, Brian Johnson 0.99 Rock \n", "4 Angus Young, Malcolm Young, Brian Johnson 0.99 Rock \n", "\n", " Album Artist Minutes MB \n", "0 For Those About To Rock We Salute You AC/DC 5.728650 11.170334 \n", "1 For Those About To Rock We Salute You AC/DC 3.427700 6.713451 \n", "2 For Those About To Rock We Salute You AC/DC 3.898767 7.636561 \n", "3 For Those About To Rock We Salute You AC/DC 3.513900 6.852860 \n", "4 For Those About To Rock We Salute You AC/DC 3.385033 6.599424 " ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "code", "execution_count": 9, "id": "d12453da", "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 11, "id": "f89c2753", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "df['Minutes'].plot.box()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 12, "id": "97e62aa5", "metadata": {}, "outputs": [], "source": [ "import seaborn as sns" ] }, { "cell_type": "code", "execution_count": 13, "id": "e6063135", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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8+fK6k3SAxR3fGZHZ+Xunvr6+HBgYqDuG6JyprY1Gg4NDQ8zqmlN3lNqNDB9lwfz6S7fTpzCWKCJ2ZWbfKeMWv2ais7EDemXnMfuc+nYex4+dndK2dDUei186yWQ7j0ajwdDQEF1z5k75O4aPHmH+JMVuaasqHVX8EfEnwGZgNvD1zNw00ectftXh5B3D6I7gEOfMPf2O4NiRI8yfP++EorfYVZeOKf6ImA38AngvsAf4IfCRzDztJHmLX53glR1Bo9Fg6NAh5s7tBuDIkcPkyAhXXXUVYNGrc5yu+OuYx/+HQCMzf5mZR4F/B66tIYfUllWrVrF582be+ta3kiMjHD40xOFDQ+TICABXX301mzdvtvTV8eoo/kuAp8cs72mOnSAi1kfEQEQMDA4OTls4aTI/+MEPxh3ftm3bNCeRpqaO4o9xxk4535SZWzKzLzP7entrvve7NMbatWvbGpc6TR3Fvwe4bMzypcAzNeSQpmTt2rVEnHj8EhEWv2aMOor/h8AVEfG6iJgDfBi4u4Yc0pTddtttEy5LnWzaiz8zh4EbgP8BdgO3Z+aj051DOhOLFi1i6dKlAFxyySUsWrSo5kRS62q5V09mfhfo/JtWSxP41re+xVe/+lVuuOGGuqNIbfG2zNIZsPQ1E1n8klQYi1+SCmPxS1JhZsTdOSNiEHiy7hzSOC4Cnq87hHQal2fmKVfAzojilzpVRAyMdxMsqZN5qkeSCmPxS1JhLH7pzGypO4DULs/xS1JhPOKXpMJY/JJUGItfkgpj8UtSYSx+SSrM/wP4RJtEsHDaWwAAAABJRU5ErkJggg==\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sns.boxenplot(y = df['Minutes'])" ] }, { "cell_type": "code", "execution_count": 14, "id": "6e7b8457", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\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", "
" ], "text/plain": [ " UnitPrice Minutes MB\n", "count 3503.000000 3503.000000 3503.000000\n", "mean 1.050805 6.559987 33.510207\n", "std 0.239006 8.916757 105.392534\n", "min 0.990000 0.017850 0.038747\n", "25% 0.990000 3.454683 6.342566\n", "50% 0.990000 4.260567 8.107896\n", "75% 0.990000 5.360750 10.266789\n", "max 1.990000 88.115883 1059.546140" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.describe()" ] }, { "cell_type": "code", "execution_count": 17, "id": "4ec7f0a5", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sns.histplot(x=df['Minutes'], kde=True) # kernel density estimation" ] }, { "cell_type": "code", "execution_count": 18, "id": "19abf450", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sns.violinplot(data=df, x='Minutes')" ] }, { "cell_type": "code", "execution_count": 19, "id": "64555428", "metadata": {}, "outputs": [], "source": [ "top_5_genres = df['Genre'].value_counts().index[:5]\n", "\n", "top_5_data = df[df['Genre'].isin(top_5_genres)]" ] }, { "cell_type": "code", "execution_count": 20, "id": "6b25e96f", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sns.violinplot(data=top_5_data, x='Minutes', y='Genre')" ] }, { "cell_type": "code", "execution_count": 21, "id": "f0044743", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.scatter(df['Minutes'], df['MB'])" ] }, { "cell_type": "code", "execution_count": 22, "id": "b71d4f0d", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'MB')" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.scatter(df['Minutes'], df['MB'])\n", "plt.xlabel('minutes')\n", "plt.ylabel('MB')" ] }, { "cell_type": "code", "execution_count": 23, "id": "45dd51d8", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sns.scatterplot(data=top_5_data, x='Minutes', y='MB', hue='Genre')" ] }, { "cell_type": "code", "execution_count": 25, "id": "faedb015", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sns.heatmap(df.corr(), annot=True)" ] }, { "cell_type": "code", "execution_count": 27, "id": "ad2d66de", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sns.heatmap(df.corr(method='spearman'), annot=True)" ] }, { "cell_type": "markdown", "id": "696def26", "metadata": {}, "source": [ "### missing values through plots" ] }, { "cell_type": "code", "execution_count": 28, "id": "d445d93b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Collecting missingno\n", " Downloading missingno-0.5.2-py3-none-any.whl (8.7 kB)\n", "Requirement already satisfied: matplotlib in c:\\users\\himan\\anaconda3\\lib\\site-packages (from missingno) (3.5.1)\n", "Requirement already satisfied: numpy in c:\\users\\himan\\anaconda3\\lib\\site-packages (from missingno) (1.21.5)\n", "Requirement already satisfied: seaborn in c:\\users\\himan\\anaconda3\\lib\\site-packages (from missingno) (0.11.2)\n", "Requirement already satisfied: scipy in c:\\users\\himan\\anaconda3\\lib\\site-packages (from missingno) (1.7.3)\n", "Requirement already satisfied: fonttools>=4.22.0 in c:\\users\\himan\\anaconda3\\lib\\site-packages (from matplotlib->missingno) (4.25.0)\n", "Requirement already satisfied: packaging>=20.0 in c:\\users\\himan\\anaconda3\\lib\\site-packages (from matplotlib->missingno) (21.3)\n", "Requirement already satisfied: pillow>=6.2.0 in c:\\users\\himan\\anaconda3\\lib\\site-packages (from matplotlib->missingno) (9.0.1)\n", "Requirement already satisfied: pyparsing>=2.2.1 in c:\\users\\himan\\anaconda3\\lib\\site-packages (from matplotlib->missingno) (3.0.4)\n", "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\users\\himan\\anaconda3\\lib\\site-packages (from matplotlib->missingno) (1.3.2)\n", "Requirement already satisfied: python-dateutil>=2.7 in c:\\users\\himan\\anaconda3\\lib\\site-packages (from matplotlib->missingno) (2.8.2)\n", "Requirement already satisfied: cycler>=0.10 in c:\\users\\himan\\anaconda3\\lib\\site-packages (from matplotlib->missingno) (0.11.0)\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", "Requirement already satisfied: pandas>=0.23 in c:\\users\\himan\\anaconda3\\lib\\site-packages (from seaborn->missingno) (1.4.2)\n", "Requirement already satisfied: pytz>=2020.1 in c:\\users\\himan\\anaconda3\\lib\\site-packages (from pandas>=0.23->seaborn->missingno) (2021.3)\n", "Installing collected packages: missingno\n", "Successfully installed missingno-0.5.2\n" ] } ], "source": [ "!pip install missingno" ] }, { "cell_type": "code", "execution_count": 29, "id": "5c722f58", "metadata": {}, "outputs": [], "source": [ "import missingno as msno" ] }, { "cell_type": "code", "execution_count": 30, "id": "8b34e69c", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "msno.matrix(df)" ] }, { "cell_type": "markdown", "id": "f086e84b", "metadata": {}, "source": [ "## EDA summary using Pandas Profiling" ] }, { "cell_type": "code", "execution_count": 32, "id": "a2454c15", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Collecting pandas_profiling\n", " Downloading pandas_profiling-3.6.6-py2.py3-none-any.whl (324 kB)\n", "Collecting ydata-profiling\n", " Downloading ydata_profiling-4.1.2-py2.py3-none-any.whl (345 kB)\n", "Collecting phik<0.13,>=0.11.1\n", " Downloading phik-0.12.3-cp39-cp39-win_amd64.whl (663 kB)\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", "Collecting imagehash==4.3.1\n", " Downloading ImageHash-4.3.1-py2.py3-none-any.whl (296 kB)\n", "Collecting multimethod<1.10,>=1.4\n", " 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Stored in directory: c:\\users\\himan\\appdata\\local\\pip\\cache\\wheels\\1d\\05\\04\\c6d7d3b66539d9e659ac6dfe81e2d0fd4c1a8316cc5a403300\n", "Successfully built htmlmin\n", "Installing collected packages: tangled-up-in-unicode, multimethod, visions, typing-extensions, imagehash, typeguard, pydantic, phik, htmlmin, ydata-profiling, pandas-profiling\n", " Attempting uninstall: typing-extensions\n", " Found existing installation: typing-extensions 4.1.1\n", " Uninstalling typing-extensions-4.1.1:\n", " Successfully uninstalled typing-extensions-4.1.1\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" ] } ], "source": [ "!pip install pandas_profiling" ] }, { "cell_type": "code", "execution_count": 33, "id": "0a63bbf8", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "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", " from pandas_profiling import ProfileReport\n" ] } ], "source": [ "from pandas_profiling import ProfileReport" ] }, { "cell_type": "code", "execution_count": 34, "id": "5f6244cc", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "8648e29203de451d91ae1293b1a9479a", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Summarize dataset: 0%| | 0/5 [00:00" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "report = ProfileReport(df)\n", "\n", "report" ] }, { "cell_type": "code", "execution_count": null, "id": "31bd522b", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.12" } }, "nbformat": 4, "nbformat_minor": 5 } ================================================ FILE: March Cohort 2023/21_EDA and Data Viz/21_EDA and Data Visualization.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "id": "343be340", "metadata": {}, "source": [ "# EDA" ] }, { "cell_type": "code", "execution_count": 1, "id": "6d58db2b", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 2, "id": "cb96b2f2", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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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
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" ], "text/plain": [ " Track \\\n", "0 For Those About To Rock (We Salute You) \n", "1 Put The Finger On You \n", "2 Let's Get It Up \n", "3 Inject The Venom \n", "4 Snowballed \n", "\n", " Composer Milliseconds Bytes \\\n", "0 Angus Young, Malcolm Young, Brian Johnson 343719 11170334 \n", "1 Angus Young, Malcolm Young, Brian Johnson 205662 6713451 \n", "2 Angus Young, Malcolm Young, Brian Johnson 233926 7636561 \n", "3 Angus Young, Malcolm Young, Brian Johnson 210834 6852860 \n", "4 Angus Young, Malcolm Young, Brian Johnson 203102 6599424 \n", "\n", " UnitPrice Genre Album Artist \n", "0 0.99 Rock For Those About To Rock We Salute You AC/DC \n", "1 0.99 Rock For Those About To Rock We Salute You AC/DC \n", "2 0.99 Rock For Those About To Rock We Salute You AC/DC \n", "3 0.99 Rock For Those About To Rock We Salute You AC/DC \n", "4 0.99 Rock For Those About To Rock We Salute You AC/DC " ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_csv('itunes_data.csv')\n", "df.head()" ] }, { "cell_type": "code", "execution_count": 3, "id": "c1388561", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 3503 entries, 0 to 3502\n", "Data columns (total 8 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 Track 3503 non-null object \n", " 1 Composer 2525 non-null object \n", " 2 Milliseconds 3503 non-null int64 \n", " 3 Bytes 3503 non-null int64 \n", " 4 UnitPrice 3503 non-null float64\n", " 5 Genre 3503 non-null object \n", " 6 Album 3503 non-null object \n", " 7 Artist 3503 non-null object \n", "dtypes: float64(1), int64(2), object(5)\n", "memory usage: 219.1+ KB\n" ] } ], "source": [ "df.info()" ] }, { "cell_type": "code", "execution_count": 5, "id": "a0737e50", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Track 3257\n", "Composer 852\n", "Milliseconds 3080\n", "Bytes 3501\n", "UnitPrice 2\n", "Genre 25\n", "Album 347\n", "Artist 204\n", "dtype: int64" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.nunique(axis=0)" ] }, { "cell_type": "markdown", "id": "d715d6f0", "metadata": {}, "source": [ "#### Dimensional Analysis" ] }, { "cell_type": "code", "execution_count": 6, "id": "23233e4a", "metadata": {}, "outputs": [], "source": [ "df['Minutes'] = df['Milliseconds']/(1000*60)\n", "\n", "df['MB'] = df['Bytes'] / 1000000" ] }, { "cell_type": "code", "execution_count": 7, "id": "db4ccd76", "metadata": {}, "outputs": [], "source": [ "df.drop(['Milliseconds', 'Bytes'], axis=1, inplace=True)" ] }, { "cell_type": "code", "execution_count": 8, "id": "4c65b688", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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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
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" ], "text/plain": [ " Track \\\n", "0 For Those About To Rock (We Salute You) \n", "1 Put The Finger On You \n", "2 Let's Get It Up \n", "3 Inject The Venom \n", "4 Snowballed \n", "\n", " Composer UnitPrice Genre \\\n", "0 Angus Young, Malcolm Young, Brian Johnson 0.99 Rock \n", "1 Angus Young, Malcolm Young, Brian Johnson 0.99 Rock \n", "2 Angus Young, Malcolm Young, Brian Johnson 0.99 Rock \n", "3 Angus Young, Malcolm Young, Brian Johnson 0.99 Rock \n", "4 Angus Young, Malcolm Young, Brian Johnson 0.99 Rock \n", "\n", " Album Artist Minutes MB \n", "0 For Those About To Rock We Salute You AC/DC 5.728650 11.170334 \n", "1 For Those About To Rock We Salute You AC/DC 3.427700 6.713451 \n", "2 For Those About To Rock We Salute You AC/DC 3.898767 7.636561 \n", "3 For Those About To Rock We Salute You AC/DC 3.513900 6.852860 \n", "4 For Those About To Rock We Salute You AC/DC 3.385033 6.599424 " ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "code", "execution_count": 9, "id": "d12453da", "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 11, "id": "f89c2753", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "df['Minutes'].plot.box()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 12, "id": "97e62aa5", "metadata": {}, "outputs": [], "source": [ "import seaborn as sns" ] }, { "cell_type": "code", "execution_count": 13, "id": "e6063135", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sns.boxenplot(y = df['Minutes'])" ] }, { "cell_type": "code", "execution_count": 14, "id": "6e7b8457", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\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", "
" ], "text/plain": [ " UnitPrice Minutes MB\n", "count 3503.000000 3503.000000 3503.000000\n", "mean 1.050805 6.559987 33.510207\n", "std 0.239006 8.916757 105.392534\n", "min 0.990000 0.017850 0.038747\n", "25% 0.990000 3.454683 6.342566\n", "50% 0.990000 4.260567 8.107896\n", "75% 0.990000 5.360750 10.266789\n", "max 1.990000 88.115883 1059.546140" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.describe()" ] }, { "cell_type": "code", "execution_count": 17, "id": "4ec7f0a5", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sns.histplot(x=df['Minutes'], kde=True) # kernel density estimation" ] }, { "cell_type": "code", "execution_count": 18, "id": "19abf450", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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hvr5+TcGVy+UYGxtb6v++sQeczWZJpVLLesBbtmyhvb39hupdjoyM8Morr3D48GFeeOGF0om3NjLtN5NruwkXu/YvbFuYInb+18QvnMLSxasaOzZ3sm1rPz09PXR2dlJfX7/UHip/3NDQQEtLC6lU6ob6eor/AhXAZSdOnOCPP/lJxmezzNz67tX3+KrMS5+nfuBHWHqCltY23vc7/4a3v/3t9PX13XAnvoJqZmaG5557jr//+yf51a9eB88j29xHrrkPl2wqtmmcI5I+j5c+T2z6DN7sOcyM3bv3cO+997B//356e2u7wJPIpQIZwFBcgP3RRx8l0/UbLPbdXtFtr0d08hT1Az+ktaWJ//ChD3HgwAFisZjfZW1ov/71r3nmmWf4h2e+x/TUhWWvmxnbd+zgne94Bw888AAdHR21L1LkCgIbwACf+9zneOrpp5nb9RCFRp9/aJwjPvoqidNH2LlzF3/+53/Gpk1XnkUh/sjlcpw9e5bh4eGlK+He9KY3sWPHDurq6vwuT2SZNc+CqIUPf/jDvPTSy7hTLzD75neD51//LTZ2nMTpI9x//wE+9alPagJ/AEWjUXp6eujpqe2ypSKVFojFeBobG3n00U9g6Uli46/7VoctpqkbPsLevXv50z/9LwpfEamqQAQwwJ133klnVxfRC0O+1ZAYfIkIBT7+8Y/rJJuIVF1gAtjMuPeee4jNjkAhd/03VFjkwhCxyZP8/u//W/r6fFqTWEQ2lMAEMMBdd92Fy+eITI/Udsf5LPWDh+jr7+fhhx+u7b5FZMMKVADv2bOHRCJJdKq2bYjY+C8hM8ujn/iEppqJSM0EKoDj8Tj79v0m8enhyxa6qfp+J0+ybfsOdu/eXbN9iogEKoAB9u/fDwszePMXarI/W5zDmznLAwfur8n+RETKAhfAd955J0DN2hDRiZMA3HfffTXZn4hIWeACuKOjgx033UR0qjbLAcYvDLB9xw7NfBCRmgtcAAPcvX8/kdmzcJ1lA9fLMrN4M+d44MCBqu5HRORKAhnA+/fvB+eITg1XdT/RyZOA2g8i4o9ABvCuXbuIxeNE5saqup/45El23HSTlioUEV8EMoAjkQhb+7fizU9WbR+WmcWbPceB+zX7QUT8EcgABtixYzuxzFTVth+9ULyHmtoPIuKXwAbwtm3bcJm5qp2Ii8yN0dzSqvaDiPgm0AEMVO2CjNj8BG/etbMq2xYRWYnABvD27dsBiCxcqPzG81mYv8DOnQpgEfFPYAO4s7OTeDxRlRNxkfQEOMctt9xS8W2LiKxUYAPY8zy2bu2vSgvCS48DKIBFxFeBDWCAHTt2VGUmRGTuPE3NzbS3t1d82yIiKxXoAK7WTIjo/AS7du7SbYdExFeBD2CASCXbEPkcNj/Jzp1qP4iIv0IRwJU8EefN6wSciARDoAO4s7OTRCJZ0RNxkbniCThNQRMRvwU6gD3Po39rP95C5Y6AI+nzpJqa6ejoqNg2RUTWItABDLBj+3ZiC5WbCRFNn2fXzlt0Ak5EfBf4AN6+fTtuMV2ZmRCF8gk4tR9ExH+BD+CLMyHW34bwdAWciARIaAK4EifiIunzgK6AE5FgCHwAd3Z2Ek8k8CqwKI+XnqSuvp7Ozs71FyYisk6BD2Azo7urG8vMrntbXmaavr4+nYATkUAIfAAD9PRsIbq4/gCOLc7Qr9vPi0hAhCKAu7u78TIz4NzaN1LI4RZmdAcMEQmMUARwV1cXLp/F1jEVzcvMACiARSQwQhHA3d3dAFgpRNfCW5gGFMAiEhyhCmBvcT0BXLyarqenpyI1iYisV7gCeB0zIWxhmlRTM6lUqlJliYisSygCuL6+nsZUal0tiEhmWjMgRCRQQhHAUJ4JsfYj4OjiDH196v+KSHCEJoB7tmwhml1jAOezuMycTsCJSKCEJoC7u7thYQZcYdXv9TKaASEiwROuAHYFbDG96veWZ0AogEUkSMIVwIC3hkuSy3OANQVNRIIkdAG8lpkQ3sI0rW2bqKurq3RZIiJrFpoA3rx5M2a2dEnxaniZafr7NQVNRIIlNAEcj8dpbdu0pqloUa2CJiIBFJoABujZ0r36y5FzGdzivPq/IhI4oQrgLVu2EF2cW9V7yifg+nQELCIBE6oA7urqwmVmoZBf8Xs0B1hEgipUAbw0E2IVU9G8hanibY1K7xURCYpQBfCWLVuA1a2K5i1M07F5M/F4vFpliYisSagCuKurC2BVU9GimWm2bd1arZJERNYsVAHc3t5OJBpd+cUYzuFlpnQCTkQCKVQB7HkenZ1dKz4Ctuw8LpdVAItIIIUqgAG2be0nWprZcD1ahEdEgix0Adzb21uc27uCW9SXA7i/v7/aZYmIrFroAri/vx9XyGEruCDDW5giFo/T3t5eg8pERFYndAFcbieUj26vxVuYLh4xe6EbpohsAKFLpvIJtZUEcHRxmq1qP4hIQIUugNva2kgm65bWeLiqQh4WpjUDQkQCK3QBbGb09fVe9wjYMrPgnAJYRAIrdAEMsHXrVmKL1z4C1hQ0EQm6UAZwb28vbmEGCrmrfk45gHUELCJBFcoAvngi7upXxHkLU6SamkmlUrUqS0RkVUIewFfvA0cy02zVfeBEJMBCGcArmQscXZzWFXAiEmihDOD6+npa29qW7naxTH4Rl0mr/ysigRbKAAbo7+u/6hFweY6wZkCISJCFN4D7+666KpoW4RGRMAhtAPf19eGyC5BbWPaa7gMnImEQ2gC+eCJu+VGwl56ks7NL94ETkUALbQCX2wvL+sCFPPGZM9x++z4fqhIRWbnQBnBXVxdeJLIsgCPTZ3D5LPfcc49PlYmIrExoAzgajdLdvYXI3Pjlz18YJJFI8ta3vtWnykREVia0AQzw4AMHiE6fwcpHwc6RmDrNnXfeQSKR8Lc4EZHrCHUAv+c97yEajREfPQqANzeOW5zj3nvv9bkyEZHrC3UAt7W18Y53PEhi4leQyxC9cArP87jrrrv8Lk1E5LpCHcAA73vf+3D5HPFzvyA+NcTu3btpamryuywRkesKfQDv2LGD39y3j8TZn2PpSbUfRCQ0Qh/AAO//3d+FXAZA089EJDSifhdQCbfffjtbt24rTU3T5cciEg43RACbGV/4wudxzvldiojIit0QAQzQ3t7udwkiIqtyQ/SARUTCSAEsIuITBbCIiE8UwCIiPlEAi4j4RAEsIuITBbCIiE8UwCIiPlEAi4j4RAEsIuITBbCIiE8UwCIiPlEAi4j4RAEsIuITBbCIiE8UwCIiPlEAi4j4RAEsIuITBbCIiE9sNTeyNLMx4FT1ylnSDozXYD9BpfFr/Br/jWWrc67jjU+uKoBrxcwOO+f2+V2HXzR+jV/j3xjjVwtCRMQnCmAREZ8ENYD/2u8CfKbxb2wa/wYRyB6wiMhGENQjYBGRG54CWETEJ4EKYDP7l2Z23Mx+ZWaf8rueajOzPjP7vpkdM7OjZvZHpefbzOwfzez10v9b/a61mswsYmY/NbMnS483zPjNrMXM/s7MflH6Pti/wcb/sdL3/s/N7BtmltxI4w9MAJtZBPgy8K+AW4GHzexWf6uquhzwcefcm4G7gP9YGvOngGedczcDz5Ye38j+CDh2yeONNP7/AfyDc24XsIfi12FDjN/MeoCPAvucc7cBEeADbJDxQ4ACGLgD+JVz7oRzbhH4G+A9PtdUVc65EefcK6WPZyj+8PVQHPfXS5/2deBf+1JgDZhZL/Au4CuXPL0hxm9mTcC/AP43gHNu0Tl3gQ0y/pIoUGdmUaAeOMMGGn+QArgHGLrk8enScxuCmW0D3gq8DHQ650agGNLAZh9Lq7a/BP4YKFzy3EYZ/w5gDPhaqQXzFTNrYIOM3zk3DHweGARGgCnn3PfYIOOHYAWwXeG5DTFHzswagW8Bjzjnpv2up1bM7CHgnHPuiN+1+CQKvA34X865twJz3MB/br9Rqbf7HmA7sAVoMLPf87eq2gpSAJ8G+i553Evxz5EbmpnFKIbv/3XOfbv09Fkz6y693g2c86u+KrsHeLeZnaTYcjpgZo+zccZ/GjjtnHu59PjvKAbyRhn/g8CAc27MOZcFvg3czcYZf6AC+CfAzWa23cziFJvxT/hcU1WZmVHs/x1zzn3xkpeeAP6g9PEfAN+tdW214Jz7tHOu1zm3jeK/93POud9j44x/FBgys52lpx4AXmODjJ9i6+EuM6sv/Sw8QPE8yEYZf7CuhDOz36LYE4wAX3XO/Zm/FVWXmd0L/Aj4Zy72QP8zxT7w3wL9FL9J3+ecm/ClyBoxs/uATzjnHjKzTWyQ8ZvZXoonIOPACeDfUTww2ijj/6/A+ynOCPop8O+BRjbK+IMUwCIiG0mQWhAiIhuKAlhExCcKYBERnyiARUR8ogAWEfGJAlh8Y2bOzB675HHUzMYuWRXt3etZFc/MHjGz+krUKlINCmDx0xxwm5nVlR6/Axguv+ice8I599l1bP8Rigu8iASSAlj8dpDiamgADwPfKL9gZn9oZl8qffx/zOyvzOzHZnbCzH6n9Px95SPm0uMvld73UYrrC3zfzL5feu2dZnbIzF4xs2+W1uDAzD5rZq+Z2atm9vmajFoEBbD472+AD5hZEthN8SrAq+kG7gUeAq55ZOyc+yuKa4nc75y738zagT8BHnTOvQ04DPwnM2sDfht4i3NuN/Df1zsgkZWK+l2AbGzOuVdLS3E+DDx9nU//f865AvCamXWucld3UVzo/8XisgPEgUPANLAAfMXMngKevOoWRCpMASxB8ATFdWHvAzZd4/Myl3xcXr40x+V/ySWv8l4D/tE59/CyF8zuoLgQzAeAjwAHVlS1yDqpBSFB8FXgvznn/nkN7z0F3GpmCTNrphikZTNAqvTxS8A9ZvYmgNIKXLeU+sDNzrmnKZ6027vGMYismo6AxXfOudMU7422lvcOmdnfAq8Cr1NcUavsr4GDZjZS6gP/IfANM0uUXv8TiiH93VIP2oCPrXEYIqum1dBERHyiFoSIiE8UwCIiPlEAi4j4RAEsIuITBbCIiE8UwCIiPlEAi4j45P8DKXD3c18dZG4AAAAASUVORK5CYII=\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sns.violinplot(data=df, x='Minutes')" ] }, { "cell_type": "code", "execution_count": 19, "id": "64555428", "metadata": {}, "outputs": [], "source": [ "top_5_genres = df['Genre'].value_counts().index[:5]\n", "\n", "top_5_data = df[df['Genre'].isin(top_5_genres)]" ] }, { "cell_type": "code", "execution_count": 20, "id": "6b25e96f", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sns.violinplot(data=top_5_data, x='Minutes', y='Genre')" ] }, { "cell_type": "code", "execution_count": 21, "id": "f0044743", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.scatter(df['Minutes'], df['MB'])" ] }, { "cell_type": "code", "execution_count": 22, "id": "b71d4f0d", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'MB')" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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QX/jDd9LZ3oaAzvY2B4VZHfCVhVVF+V3XUx18Xrui0+FgVmccFjZlXkbcbPpzWNik3dm1j0f2HOV0hWXuB4dOs2XXQYeF2TThsLAJK15J/ITBoTNjHucF/symD4eFTcidXfv42g+PjOtYL/BnNn14NpSNW1dP37iDoq21xQv8mU0jvrKwcbt75/5xHdfppTjMph2HhY3bwGD63hIAz2x8f40rMbOsuRvKxsX7XJs1N4eFpSrtXmdmzcthYam27Dp4dptTM2tOHrOwVBO5X6LT02UrquZyKGZ5cFhYqvHsNQHQOkOeLjtMV08fd/z9Pn518twugF4OxRqRu6Es1YbVS2lt0ZjHtLe1suWmq/zhl+jq6eOd93yX2x577rygKCkth2LWKHxlYalG22tihuDj776cz669Mq/S6lJXTx8bvv48Q2fGHucZz9WaWb1wWNi4eNnw8enq6eP27c9RYW1Fs4aWeVhIWgh8FXgbcAbYGhH3S5oLPAYsAn4G/KeIeDV5zybgZuA08KmI2JV13WbDDR+0ft8VHXxjb5+DwqalPMYsTgF/FhG/A1wN3CppGbAR2B0RS4DdyXOS19YBy4HrgAcljb5Zs1kGSnt49A0MEhS7lB7+4ZGze3qYTTeZh0VEHI+IHyePXwcOAJ3AGmBbctg2YG3yeA3waES8GRGHgUPAykyLNhtmy66DI4LBFxQ2neU6G0rSImAFsAe4LCKOQzFQgEuTwzqBo2Vv603azHLjvTqs2eQWFpIuBr4B3BYRr411aIW2iv+Jk3SLpG5J3f39/dUo06yi0fbqGHuCsVnjyiUsJLVSDIqHI2JH0vyypHnJ6/OAE0l7L7Cw7O0LgGOVvm9EbI2IQkQUOjo6alO8GcV7T9pazx86a2tt4T9fffnZu9jTgmPO7NYaVWdWfXnMhhLwt8CBiPh82Us7gfXA5uTrE2Xt/0vS54H5wBLg2ewqNhupNI14rCU8SrOlRrufwrOmrJEoMv4XK+m9wD8B+yhOnQX4K4rjFtuBy4EjwE0R8UrynjuAP6Y4k+q2iPh22s8pFArR3d1d/b+A2QQt3vhkxX5TAYc3X591OWZjkrQ3IgrD2zO/soiI/8PoV+jXjvKee4F7a1aUWQ2NtraW9yi3RuK1ocxqbLTxDS+6aI3Ey32Y1dh4xjfM6p3DwiwDXlvLGp27oczMLJXDwszMUjkszMwslcPCzMxSOSzMzCyVw8LMzFI5LMzMLJXDwszMUvmmvBwN38PZd/WaWb1yWOSktIdzaWvOvoFBNu3YB+DAMLO6426onFTaw3lw6DRbdh3MqSIzs9E5LHIy2h7O3tvZzOqRwyIno+1l4D0OzKweOSxy4j0OzKyReIA7J97jwMwaicOiyiYyHdZ7HJhZo3BYVJGnw06c7zUxawwes6giT4edmFK49g0MEpwL166evrxLM7NhHBZV5OmwE+NwNWsc7oaqovntbfRVCIZ6ng5b3g3UPruVCPjl4NDZLiGo3SC8w9WscTgsqmjD6qXnjVlAftNhu3r6uOPv9/Grk+f/z72z7AN/+BjLq28MnT2ub2CQDY8/DwFDZ+JsWzXHYBoxXM2alcOiiqo1HbbS//YHBodokTgdQWd7G4t+vY1n/u8rE66x/AO/UjdQuaHTMaKt1E1UjbCop3A1s7EpYuQHwnRQKBSiu7u7Kt+r2jN2St+v0v+qs9LZ3saxZGB5ogQc3nx9VerwbCiz+iJpb0QUhrf7yiLFZKfDvvvep3j59ZOZ1DgZpQ/nyQRWNbuJfK+JWWNwWKQYbcbOPf+wn9seey6foqqg9L/44d1A5VpbdN6YBbibyKxZOSzGcMUd3+JfK/Tbw/mDwY2m9IE/fIwl69lQZtY4GmbMQtJ1wP1AC/BQRGwe6/jJjFnUe9dRNXT6A9/MxtDQYxaSWoC/AT4I9AI/krQzIl6o1s+YzkExZ3Yrd31kuQPCzCatIcICWAkcioiXACQ9CqwBqhYWjRoUq/7NXH72/wbpGxg8b2qtrx7MrJoaJSw6gaNlz3uBdw8/SNItwC0Al19+eTaV5aR1hthy01UOBDPLRKOsDaUKbSMGWyJia0QUIqLQ0dGRQVnZaGudwUUXnNsoqb2t1UFhZplqlCuLXmBh2fMFwLFq/oDLfu2C3Lqi2ttaufsGjymYWf1qlLD4EbBE0mKgD1gHfLyaP2DPHR+c8CD3kksv4qnPXFPNMszM6lJDhEVEnJL0J8AuilNnvxwR+6v9c/bc8cFqf0szs2mhIcICICK+BXwr7zrMzJpRowxwm5lZjhwWZmaWymFhZmapHBZmZpaqYRYSnChJ/cDPJ/n2S4BfVLGc6cDnZCSfk5F8TkZqtHPyGxEx4q7maRsWUyGpu9Kqi83M52Qkn5ORfE5Gmi7nxN1QZmaWymFhZmapHBaVbc27gDrkczKSz8lIPicjTYtz4jELMzNL5SsLMzNL5bAwM7NUDosykq6TdFDSIUkb864nD5IWSvqepAOS9kv6dNI+V9JTkl5Mvs7Ju9asSWqR1CPpm8lznxOpXdLjkv45+TfznmY+L5JuT35vfirpEUmzpsv5cFgkJLUAfwP8e2AZ8DFJy/KtKhengD+LiN8BrgZuTc7DRmB3RCwBdifPm82ngQNlz31O4H7gOxFxBXAVxfPTlOdFUifwKaAQEW+nuJ3COqbJ+XBYnLMSOBQRL0XESeBRYE3ONWUuIo5HxI+Tx69T/OXvpHgutiWHbQPW5lJgTiQtAK4HHiprbvZz8hbg94G/BYiIkxExQHOfl5lAm6SZwGyKO3pOi/PhsDinEzha9rw3aWtakhYBK4A9wGURcRyKgQJcmmNpefgC8BfAmbK2Zj8nvwn0A3+XdM89JOkimvS8REQf8DngCHAc+GVEfJdpcj4cFueoQlvTziuWdDHwDeC2iHgt73ryJOnDwImI2Jt3LXVmJvAu4EsRsQL4FQ3axVINyVjEGmAxMB+4SNIn8q2qehwW5/QCC8ueL6B4Cdl0JLVSDIqHI2JH0vyypHnJ6/OAE3nVl4NVwA2Sfkaxe/L9kr5Gc58TKP7O9EbEnuT54xTDo1nPyweAwxHRHxFDwA7g95gm58Nhcc6PgCWSFku6gOLA1M6ca8qcJFHsgz4QEZ8ve2knsD55vB54Iuva8hIRmyJiQUQsovjv4umI+ARNfE4AIuJfgKOSliZN1wIv0Lzn5QhwtaTZye/RtRTH/KbF+fAd3GUk/QHFvukW4MsRcW++FWVP0nuBfwL2ca5//q8ojltsBy6n+EtxU0S8kkuROZJ0DfDnEfFhSb9Ok58TSe+kOOh/AfAS8EcU/xPalOdF0j3AH1KcVdgD/DfgYqbB+XBYmJlZKndDmZlZKoeFmZmlcliYmVkqh4WZmaVyWJiZWSqHhVmVSLphKqsVS7pN0uxq1mRWLZ46a1YnkjvECxHxi7xrMRvOVxZm4yBpUbJnw0PJXgUPS/qApGeSfQpWSvqkpC8mx39F0gOSvi/pJUkfTdqvKe2HkTz/YvK+T1FcT+h7kr6XvPYhST+Q9GNJX0/W60LSZkkvSPqJpM9lfzasGTkszMbvtyju3/AO4Arg48B7gT+neJf7cPOS1z8MbB7rG0fEAxTXIntfRLxP0iXAncAHIuJdQDfwGUlzgf8ALI+IdwCfrcZfzCzNzLwLMGsghyNiH4Ck/RQ3tAlJ+4BFFY7viogzwAuSLpvgz7qa4iZczxSXGeIC4AfAa8C/Ag9JehL45qjfwayKHBZm4/dm2eMzZc/PUPl3qfz40hL4pzj/in7WKD9LwFMR8bERL0grKS5Stw74E+D9qZWbTZG7ocyy9XNgmaQLJb2V4od+yevAryWPfwiskvRbAMlKpr+djFu8NSK+BdwGvDOzyq2p+crCLEMRcVTSduAnwIsUVyYt2Qp8W9LxZNzik8Ajki5MXr+TYqA8IWkWxauP27Or3pqZp86amVkqd0OZmVkqh4WZmaVyWJiZWSqHhZmZpXJYmJlZKoeFmZmlcliYmVmq/w/yQlf0tzaOSwAAAABJRU5ErkJggg==\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.scatter(df['Minutes'], df['MB'])\n", "plt.xlabel('minutes')\n", "plt.ylabel('MB')" ] }, { "cell_type": "code", "execution_count": 23, "id": "45dd51d8", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sns.scatterplot(data=top_5_data, x='Minutes', y='MB', hue='Genre')" ] }, { "cell_type": "code", "execution_count": 25, "id": "faedb015", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sns.heatmap(df.corr(), annot=True)" ] }, { "cell_type": "code", "execution_count": 27, "id": "ad2d66de", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sns.heatmap(df.corr(method='spearman'), annot=True)" ] }, { "cell_type": "markdown", "id": "696def26", "metadata": {}, "source": [ "### missing values through plots" ] }, { "cell_type": "code", "execution_count": 28, "id": "d445d93b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Collecting missingno\n", " Downloading missingno-0.5.2-py3-none-any.whl (8.7 kB)\n", "Requirement already satisfied: matplotlib in c:\\users\\himan\\anaconda3\\lib\\site-packages (from missingno) (3.5.1)\n", "Requirement already satisfied: numpy in c:\\users\\himan\\anaconda3\\lib\\site-packages (from missingno) (1.21.5)\n", "Requirement already satisfied: seaborn in c:\\users\\himan\\anaconda3\\lib\\site-packages (from missingno) (0.11.2)\n", "Requirement already satisfied: scipy in c:\\users\\himan\\anaconda3\\lib\\site-packages (from missingno) (1.7.3)\n", "Requirement already satisfied: fonttools>=4.22.0 in c:\\users\\himan\\anaconda3\\lib\\site-packages (from matplotlib->missingno) (4.25.0)\n", "Requirement already satisfied: packaging>=20.0 in c:\\users\\himan\\anaconda3\\lib\\site-packages (from matplotlib->missingno) (21.3)\n", "Requirement already satisfied: pillow>=6.2.0 in c:\\users\\himan\\anaconda3\\lib\\site-packages (from matplotlib->missingno) (9.0.1)\n", "Requirement already satisfied: pyparsing>=2.2.1 in c:\\users\\himan\\anaconda3\\lib\\site-packages (from matplotlib->missingno) (3.0.4)\n", "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\users\\himan\\anaconda3\\lib\\site-packages (from matplotlib->missingno) (1.3.2)\n", "Requirement already satisfied: python-dateutil>=2.7 in c:\\users\\himan\\anaconda3\\lib\\site-packages (from matplotlib->missingno) (2.8.2)\n", "Requirement already satisfied: cycler>=0.10 in c:\\users\\himan\\anaconda3\\lib\\site-packages (from matplotlib->missingno) (0.11.0)\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", "Requirement already satisfied: pandas>=0.23 in c:\\users\\himan\\anaconda3\\lib\\site-packages (from seaborn->missingno) (1.4.2)\n", "Requirement already satisfied: pytz>=2020.1 in c:\\users\\himan\\anaconda3\\lib\\site-packages (from pandas>=0.23->seaborn->missingno) (2021.3)\n", "Installing collected packages: missingno\n", "Successfully installed missingno-0.5.2\n" ] } ], "source": [ "!pip install missingno" ] }, { "cell_type": "code", "execution_count": 29, "id": "5c722f58", "metadata": {}, "outputs": [], "source": [ "import missingno as msno" ] }, { "cell_type": "code", "execution_count": 30, "id": "8b34e69c", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "msno.matrix(df)" ] }, { "cell_type": "markdown", "id": "f086e84b", "metadata": {}, "source": [ "## EDA summary using Pandas Profiling" ] }, { "cell_type": "code", "execution_count": 32, "id": "a2454c15", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Collecting pandas_profiling\n", " Downloading pandas_profiling-3.6.6-py2.py3-none-any.whl (324 kB)\n", "Collecting ydata-profiling\n", " Downloading ydata_profiling-4.1.2-py2.py3-none-any.whl (345 kB)\n", "Collecting phik<0.13,>=0.11.1\n", " Downloading phik-0.12.3-cp39-cp39-win_amd64.whl (663 kB)\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", "Collecting imagehash==4.3.1\n", " Downloading ImageHash-4.3.1-py2.py3-none-any.whl (296 kB)\n", "Collecting multimethod<1.10,>=1.4\n", " Downloading multimethod-1.9.1-py3-none-any.whl (10 kB)\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", "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", "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", "Collecting typeguard<2.14,>=2.13.2\n", " Downloading typeguard-2.13.3-py3-none-any.whl (17 kB)\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", "Collecting pydantic<1.11,>=1.8.1\n", " Downloading pydantic-1.10.7-cp39-cp39-win_amd64.whl (2.2 MB)\n", "Collecting htmlmin==0.1.12\n", " Downloading htmlmin-0.1.12.tar.gz (19 kB)\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", "Collecting visions[type_image_path]==0.7.5\n", " Downloading visions-0.7.5-py3-none-any.whl (102 kB)\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", "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", "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", "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", "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", "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", "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", "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", "Collecting tangled-up-in-unicode>=0.0.4\n", " Downloading tangled_up_in_unicode-0.2.0-py3-none-any.whl (4.7 MB)\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", "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", "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", "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", "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", "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", "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", "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", "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", "Collecting typing-extensions>=4.2.0\n", " Downloading typing_extensions-4.5.0-py3-none-any.whl (27 kB)\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", "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", "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", "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", "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", "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", "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", "Building wheels for collected packages: htmlmin\n", " Building wheel for htmlmin (setup.py): started\n", " Building wheel for htmlmin (setup.py): finished with status 'done'\n", " Created wheel for htmlmin: filename=htmlmin-0.1.12-py3-none-any.whl size=27096 sha256=ff0bca2339091901a96f2090fdd14cc31223f1dd8c30643f8e17e6051a73d77b\n", " Stored in directory: c:\\users\\himan\\appdata\\local\\pip\\cache\\wheels\\1d\\05\\04\\c6d7d3b66539d9e659ac6dfe81e2d0fd4c1a8316cc5a403300\n", "Successfully built htmlmin\n", "Installing collected packages: tangled-up-in-unicode, multimethod, visions, typing-extensions, imagehash, typeguard, pydantic, phik, htmlmin, ydata-profiling, pandas-profiling\n", " Attempting uninstall: typing-extensions\n", " Found existing installation: typing-extensions 4.1.1\n", " Uninstalling typing-extensions-4.1.1:\n", " Successfully uninstalled typing-extensions-4.1.1\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" ] } ], "source": [ "!pip install pandas_profiling" ] }, { "cell_type": "code", "execution_count": 33, "id": "0a63bbf8", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "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", " from pandas_profiling import ProfileReport\n" ] } ], "source": [ "from pandas_profiling import ProfileReport" ] }, { "cell_type": "code", "execution_count": 34, "id": "5f6244cc", "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "8648e29203de451d91ae1293b1a9479a", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Summarize dataset: 0%| | 0/5 [00:00" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/plain": [] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "report = ProfileReport(df)\n", "\n", "report" ] }, { "cell_type": "code", "execution_count": null, "id": "31bd522b", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.12" } }, "nbformat": 4, "nbformat_minor": 5 } ================================================ FILE: March Cohort 2023/21_EDA and Data Viz/itunes_data.csv ================================================ Track,Composer,Milliseconds,Bytes,UnitPrice,Genre,Album,Artist For 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 Put 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 Let'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 Inject The Venom,"Angus Young, Malcolm Young, Brian Johnson",210834,6852860,0.99,Rock,For Those About To Rock We Salute You,AC/DC Snowballed,"Angus Young, Malcolm Young, Brian Johnson",203102,6599424,0.99,Rock,For Those About To Rock We Salute You,AC/DC Evil Walks,"Angus Young, Malcolm Young, Brian Johnson",263497,8611245,0.99,Rock,For Those About To Rock We Salute You,AC/DC C.O.D.,"Angus Young, Malcolm Young, Brian Johnson",199836,6566314,0.99,Rock,For Those About To Rock We Salute You,AC/DC Breaking The Rules,"Angus Young, Malcolm Young, Brian Johnson",263288,8596840,0.99,Rock,For Those About To Rock We Salute You,AC/DC Night 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 Spellbound,"Angus Young, Malcolm Young, Brian Johnson",270863,8817038,0.99,Rock,For Those About To Rock We Salute You,AC/DC Balls to the Wall,,342562,5510424,0.99,Rock,Balls to the Wall,Accept Fast As a Shark,"F. Baltes, S. Kaufman, U. Dirkscneider & W. Hoffman",230619,3990994,0.99,Rock,Restless and Wild,Accept Restless and Wild,"F. Baltes, R.A. Smith-Diesel, S. Kaufman, U. Dirkscneider & W. Hoffman",252051,4331779,0.99,Rock,Restless and Wild,Accept Princess of the Dawn,Deaffy & R.A. Smith-Diesel,375418,6290521,0.99,Rock,Restless and Wild,Accept Go Down,AC/DC,331180,10847611,0.99,Rock,Let There Be Rock,AC/DC Dog Eat Dog,AC/DC,215196,7032162,0.99,Rock,Let There Be Rock,AC/DC Let There Be Rock,AC/DC,366654,12021261,0.99,Rock,Let There Be Rock,AC/DC Bad Boy Boogie,AC/DC,267728,8776140,0.99,Rock,Let There Be Rock,AC/DC Problem Child,AC/DC,325041,10617116,0.99,Rock,Let There Be Rock,AC/DC Overdose,AC/DC,369319,12066294,0.99,Rock,Let There Be Rock,AC/DC Hell Ain't A Bad Place To Be,AC/DC,254380,8331286,0.99,Rock,Let There Be Rock,AC/DC Whole Lotta Rosie,AC/DC,323761,10547154,0.99,Rock,Let There Be Rock,AC/DC Walk On Water,"Steven Tyler, Joe Perry, Jack Blades, Tommy Shaw",295680,9719579,0.99,Rock,Big Ones,Aerosmith Love In An Elevator,"Steven Tyler, Joe Perry",321828,10552051,0.99,Rock,Big Ones,Aerosmith Rag Doll,"Steven Tyler, Joe Perry, Jim Vallance, Holly Knight",264698,8675345,0.99,Rock,Big Ones,Aerosmith What It Takes,"Steven Tyler, Joe Perry, Desmond Child",310622,10144730,0.99,Rock,Big Ones,Aerosmith Dude (Looks Like A Lady),"Steven Tyler, Joe Perry, Desmond Child",264855,8679940,0.99,Rock,Big Ones,Aerosmith Janie's Got A Gun,"Steven Tyler, Tom Hamilton",330736,10869391,0.99,Rock,Big Ones,Aerosmith Cryin',"Steven Tyler, Joe Perry, Taylor Rhodes",309263,10056995,0.99,Rock,Big Ones,Aerosmith Amazing,"Steven Tyler, Richie Supa",356519,11616195,0.99,Rock,Big Ones,Aerosmith Blind Man,"Steven Tyler, Joe Perry, Taylor Rhodes",240718,7877453,0.99,Rock,Big Ones,Aerosmith Deuces Are Wild,"Steven Tyler, Jim Vallance",215875,7074167,0.99,Rock,Big Ones,Aerosmith The Other Side,"Steven Tyler, Jim Vallance",244375,7983270,0.99,Rock,Big Ones,Aerosmith Crazy,"Steven Tyler, Joe Perry, Desmond Child",316656,10402398,0.99,Rock,Big Ones,Aerosmith Eat The Rich,"Steven Tyler, Joe Perry, Jim Vallance",251036,8262039,0.99,Rock,Big Ones,Aerosmith Angel,"Steven Tyler, Desmond Child",307617,9989331,0.99,Rock,Big Ones,Aerosmith Livin' On The Edge,"Steven Tyler, Joe Perry, Mark Hudson",381231,12374569,0.99,Rock,Big Ones,Aerosmith All I Really Want,Alanis Morissette & Glenn Ballard,284891,9375567,0.99,Rock,Jagged Little Pill,Alanis Morissette You Oughta Know,Alanis Morissette & Glenn Ballard,249234,8196916,0.99,Rock,Jagged Little Pill,Alanis Morissette Perfect,Alanis Morissette & Glenn Ballard,188133,6145404,0.99,Rock,Jagged Little Pill,Alanis Morissette Hand In My Pocket,Alanis Morissette & Glenn Ballard,221570,7224246,0.99,Rock,Jagged Little Pill,Alanis Morissette Right Through You,Alanis Morissette & Glenn Ballard,176117,5793082,0.99,Rock,Jagged Little Pill,Alanis Morissette Forgiven,Alanis Morissette & Glenn Ballard,300355,9753256,0.99,Rock,Jagged Little Pill,Alanis Morissette You Learn,Alanis Morissette & Glenn Ballard,239699,7824837,0.99,Rock,Jagged Little Pill,Alanis Morissette Head Over Feet,Alanis Morissette & Glenn Ballard,267493,8758008,0.99,Rock,Jagged Little Pill,Alanis Morissette Mary Jane,Alanis Morissette & Glenn Ballard,280607,9163588,0.99,Rock,Jagged Little Pill,Alanis Morissette Ironic,Alanis Morissette & Glenn Ballard,229825,7598866,0.99,Rock,Jagged Little Pill,Alanis Morissette Not The Doctor,Alanis Morissette & Glenn Ballard,227631,7604601,0.99,Rock,Jagged Little Pill,Alanis Morissette Wake Up,Alanis Morissette & Glenn Ballard,293485,9703359,0.99,Rock,Jagged Little Pill,Alanis Morissette You Oughta Know (Alternate),Alanis Morissette & Glenn Ballard,491885,16008629,0.99,Rock,Jagged Little Pill,Alanis Morissette We Die Young,Jerry Cantrell,152084,4925362,0.99,Rock,Facelift,Alice In Chains Man In The Box,"Jerry Cantrell, Layne Staley",286641,9310272,0.99,Rock,Facelift,Alice In Chains Sea Of Sorrow,Jerry Cantrell,349831,11316328,0.99,Rock,Facelift,Alice In Chains Bleed The Freak,Jerry Cantrell,241946,7847716,0.99,Rock,Facelift,Alice In Chains I Can't Remember,"Jerry Cantrell, Layne Staley",222955,7302550,0.99,Rock,Facelift,Alice In Chains "Love, Hate, Love","Jerry Cantrell, Layne Staley",387134,12575396,0.99,Rock,Facelift,Alice In Chains It Ain't Like That,"Jerry Cantrell, Michael Starr, Sean Kinney",277577,8993793,0.99,Rock,Facelift,Alice In Chains Sunshine,Jerry Cantrell,284969,9216057,0.99,Rock,Facelift,Alice In Chains Put You Down,Jerry Cantrell,196231,6420530,0.99,Rock,Facelift,Alice In Chains Confusion,"Jerry Cantrell, Michael Starr, Layne Staley",344163,11183647,0.99,Rock,Facelift,Alice In Chains I Know Somethin (Bout You),Jerry Cantrell,261955,8497788,0.99,Rock,Facelift,Alice In Chains Real Thing,"Jerry Cantrell, Layne Staley",243879,7937731,0.99,Rock,Facelift,Alice In Chains Desafinado,,185338,5990473,0.99,Jazz,Warner 25 Anos,Antônio Carlos Jobim Garota De Ipanema,,285048,9348428,0.99,Jazz,Warner 25 Anos,Antônio Carlos Jobim Samba De Uma Nota Só (One Note Samba),,137273,4535401,0.99,Jazz,Warner 25 Anos,Antônio Carlos Jobim Por Causa De Você,,169900,5536496,0.99,Jazz,Warner 25 Anos,Antônio Carlos Jobim Ligia,,251977,8226934,0.99,Jazz,Warner 25 Anos,Antônio Carlos Jobim Fotografia,,129227,4198774,0.99,Jazz,Warner 25 Anos,Antônio Carlos Jobim Dindi (Dindi),,253178,8149148,0.99,Jazz,Warner 25 Anos,Antônio Carlos Jobim Se Todos Fossem Iguais A Você (Instrumental),,134948,4393377,0.99,Jazz,Warner 25 Anos,Antônio Carlos Jobim Falando De Amor,,219663,7121735,0.99,Jazz,Warner 25 Anos,Antônio Carlos Jobim Angela,,169508,5574957,0.99,Jazz,Warner 25 Anos,Antônio Carlos Jobim Corcovado (Quiet Nights Of Quiet Stars),,205662,6687994,0.99,Jazz,Warner 25 Anos,Antônio Carlos Jobim Outra Vez,,126511,4110053,0.99,Jazz,Warner 25 Anos,Antônio Carlos Jobim O Boto (Bôto),,366837,12089673,0.99,Jazz,Warner 25 Anos,Antônio Carlos Jobim "Canta, Canta Mais",,271856,8719426,0.99,Jazz,Warner 25 Anos,Antônio Carlos Jobim Enter Sandman,Apocalyptica,221701,7286305,0.99,Metal,Plays Metallica By Four Cellos,Apocalyptica Master Of Puppets,Apocalyptica,436453,14375310,0.99,Metal,Plays Metallica By Four Cellos,Apocalyptica Harvester Of Sorrow,Apocalyptica,374543,12372536,0.99,Metal,Plays Metallica By Four Cellos,Apocalyptica The Unforgiven,Apocalyptica,322925,10422447,0.99,Metal,Plays Metallica By Four Cellos,Apocalyptica Sad But True,Apocalyptica,288208,9405526,0.99,Metal,Plays Metallica By Four Cellos,Apocalyptica Creeping Death,Apocalyptica,308035,10110980,0.99,Metal,Plays Metallica By Four Cellos,Apocalyptica Wherever I May Roam,Apocalyptica,369345,12033110,0.99,Metal,Plays Metallica By Four Cellos,Apocalyptica Welcome Home (Sanitarium),Apocalyptica,350197,11406431,0.99,Metal,Plays Metallica By Four Cellos,Apocalyptica Cochise,Audioslave/Chris Cornell,222380,5339931,0.99,Rock,Audioslave,Audioslave Show Me How to Live,Audioslave/Chris Cornell,277890,6672176,0.99,Rock,Audioslave,Audioslave Gasoline,Audioslave/Chris Cornell,279457,6709793,0.99,Rock,Audioslave,Audioslave What You Are,Audioslave/Chris Cornell,249391,5988186,0.99,Rock,Audioslave,Audioslave Like a Stone,Audioslave/Chris Cornell,294034,7059624,0.99,Rock,Audioslave,Audioslave Set It Off,Audioslave/Chris Cornell,263262,6321091,0.99,Rock,Audioslave,Audioslave Shadow on the Sun,Audioslave/Chris Cornell,343457,8245793,0.99,Rock,Audioslave,Audioslave I am the Highway,Audioslave/Chris Cornell,334942,8041411,0.99,Rock,Audioslave,Audioslave Exploder,Audioslave/Chris Cornell,206053,4948095,0.99,Rock,Audioslave,Audioslave Hypnotize,Audioslave/Chris Cornell,206628,4961887,0.99,Rock,Audioslave,Audioslave Bring'em Back Alive,Audioslave/Chris Cornell,329534,7911634,0.99,Rock,Audioslave,Audioslave Light My Way,Audioslave/Chris Cornell,303595,7289084,0.99,Rock,Audioslave,Audioslave Getaway Car,Audioslave/Chris Cornell,299598,7193162,0.99,Rock,Audioslave,Audioslave The Last Remaining Light,Audioslave/Chris Cornell,317492,7622615,0.99,Rock,Audioslave,Audioslave Your Time Has Come,"Cornell, Commerford, Morello, Wilk",255529,8273592,0.99,Alternative & Punk,Out Of Exile,Audioslave Out Of Exile,"Cornell, Commerford, Morello, Wilk",291291,9506571,0.99,Alternative & Punk,Out Of Exile,Audioslave Be Yourself,"Cornell, Commerford, Morello, Wilk",279484,9106160,0.99,Alternative & Punk,Out Of Exile,Audioslave Doesn't Remind Me,"Cornell, Commerford, Morello, Wilk",255869,8357387,0.99,Alternative & Punk,Out Of Exile,Audioslave Drown Me Slowly,"Cornell, Commerford, Morello, Wilk",233691,7609178,0.99,Alternative & Punk,Out Of Exile,Audioslave Heaven's Dead,"Cornell, Commerford, Morello, Wilk",276688,9006158,0.99,Alternative & Punk,Out Of Exile,Audioslave The Worm,"Cornell, Commerford, Morello, Wilk",237714,7710800,0.99,Alternative & Punk,Out Of Exile,Audioslave Man Or Animal,"Cornell, Commerford, Morello, Wilk",233195,7542942,0.99,Alternative & Punk,Out Of Exile,Audioslave Yesterday To Tomorrow,"Cornell, Commerford, Morello, Wilk",273763,8944205,0.99,Alternative & Punk,Out Of Exile,Audioslave Dandelion,"Cornell, Commerford, Morello, Wilk",278125,9003592,0.99,Alternative & Punk,Out Of Exile,Audioslave #1 Zero,"Cornell, Commerford, Morello, Wilk",299102,9731988,0.99,Alternative & Punk,Out Of Exile,Audioslave The Curse,"Cornell, Commerford, Morello, Wilk",309786,10029406,0.99,Alternative & Punk,Out Of Exile,Audioslave Money,"Berry Gordy, Jr./Janie Bradford",147591,2365897,0.99,Rock And Roll,BackBeat Soundtrack,BackBeat Long Tall Sally,"Enotris Johnson/Little Richard/Robert ""Bumps"" Blackwell",106396,1707084,0.99,Rock And Roll,BackBeat Soundtrack,BackBeat Bad Boy,Larry Williams,116088,1862126,0.99,Rock And Roll,BackBeat Soundtrack,BackBeat Twist And Shout,Bert Russell/Phil Medley,161123,2582553,0.99,Rock And Roll,BackBeat Soundtrack,BackBeat Please Mr. Postman,Brian Holland/Freddie Gorman/Georgia Dobbins/Robert Bateman/William Garrett,137639,2206986,0.99,Rock And Roll,BackBeat Soundtrack,BackBeat C'Mon Everybody,Eddie Cochran/Jerry Capehart,140199,2247846,0.99,Rock And Roll,BackBeat Soundtrack,BackBeat Rock 'N' Roll Music,Chuck Berry,141923,2276788,0.99,Rock And Roll,BackBeat Soundtrack,BackBeat Slow Down,Larry Williams,163265,2616981,0.99,Rock And Roll,BackBeat Soundtrack,BackBeat Roadrunner,Bo Diddley,143595,2301989,0.99,Rock And Roll,BackBeat Soundtrack,BackBeat Carol,Chuck Berry,143830,2306019,0.99,Rock And Roll,BackBeat Soundtrack,BackBeat Good Golly Miss Molly,Little Richard,106266,1704918,0.99,Rock And Roll,BackBeat Soundtrack,BackBeat 20 Flight Rock,Ned Fairchild,107807,1299960,0.99,Rock And Roll,BackBeat Soundtrack,BackBeat Quadrant,Billy Cobham,261851,8538199,0.99,Jazz,The Best Of Billy Cobham,Billy Cobham Snoopy's search-Red baron,Billy Cobham,456071,15075616,0.99,Jazz,The Best Of Billy Cobham,Billy Cobham "Spanish moss-""A sound portrait""-Spanish moss",Billy Cobham,248084,8217867,0.99,Jazz,The Best Of Billy Cobham,Billy Cobham Moon germs,Billy Cobham,294060,9714812,0.99,Jazz,The Best Of Billy Cobham,Billy Cobham Stratus,Billy Cobham,582086,19115680,0.99,Jazz,The Best Of Billy Cobham,Billy Cobham The pleasant pheasant,Billy Cobham,318066,10630578,0.99,Jazz,The Best Of Billy Cobham,Billy Cobham Solo-Panhandler,Billy Cobham,246151,8230661,0.99,Jazz,The Best Of Billy Cobham,Billy Cobham Do what cha wanna,George Duke,274155,9018565,0.99,Jazz,The Best Of Billy Cobham,Billy Cobham Intro/ Low Down,,323683,10642901,0.99,Metal,Alcohol Fueled Brewtality Live! [Disc 1],Black Label Society 13 Years Of Grief,,246987,8137421,0.99,Metal,Alcohol Fueled Brewtality Live! [Disc 1],Black Label Society Stronger Than Death,,300747,9869647,0.99,Metal,Alcohol Fueled Brewtality Live! [Disc 1],Black Label Society All For You,,235833,7726948,0.99,Metal,Alcohol Fueled Brewtality Live! [Disc 1],Black Label Society Super Terrorizer,,319373,10513905,0.99,Metal,Alcohol Fueled Brewtality Live! [Disc 1],Black Label Society Phoney Smile Fake Hellos,,273606,9011701,0.99,Metal,Alcohol Fueled Brewtality Live! [Disc 1],Black Label Society Lost My Better Half,,284081,9355309,0.99,Metal,Alcohol Fueled Brewtality Live! [Disc 1],Black Label Society Bored To Tears,,247327,8130090,0.99,Metal,Alcohol Fueled Brewtality Live! [Disc 1],Black Label Society A.N.D.R.O.T.A.Z.,,266266,8574746,0.99,Metal,Alcohol Fueled Brewtality Live! [Disc 1],Black Label Society Born To Booze,,282122,9257358,0.99,Metal,Alcohol Fueled Brewtality Live! [Disc 1],Black Label Society World Of Trouble,,359157,11820932,0.99,Metal,Alcohol Fueled Brewtality Live! [Disc 1],Black Label Society No More Tears,,555075,18041629,0.99,Metal,Alcohol Fueled Brewtality Live! [Disc 1],Black Label Society The Begining... At Last,,365662,11965109,0.99,Metal,Alcohol Fueled Brewtality Live! [Disc 1],Black Label Society Heart Of Gold,,194873,6417460,0.99,Metal,Alcohol Fueled Brewtality Live! [Disc 2],Black Label Society Snowblind,,420022,13842549,0.99,Metal,Alcohol Fueled Brewtality Live! [Disc 2],Black Label Society Like A Bird,,276532,9115657,0.99,Metal,Alcohol Fueled Brewtality Live! [Disc 2],Black Label Society Blood In The Wall,,284368,9359475,0.99,Metal,Alcohol Fueled Brewtality Live! [Disc 2],Black Label Society The Beginning...At Last,,271960,8975814,0.99,Metal,Alcohol Fueled Brewtality Live! [Disc 2],Black Label Society Black Sabbath,,382066,12440200,0.99,Metal,Black Sabbath,Black Sabbath The Wizard,,264829,8646737,0.99,Metal,Black Sabbath,Black Sabbath Behind The Wall Of Sleep,,217573,7169049,0.99,Metal,Black Sabbath,Black Sabbath N.I.B.,,368770,12029390,0.99,Metal,Black Sabbath,Black Sabbath Evil Woman,,204930,6655170,0.99,Metal,Black Sabbath,Black Sabbath Sleeping Village,,644571,21128525,0.99,Metal,Black Sabbath,Black Sabbath Warning,,212062,6893363,0.99,Metal,Black Sabbath,Black Sabbath Wheels Of Confusion / The Straightener,"Tony Iommi, Bill Ward, Geezer Butler, Ozzy Osbourne",494524,16065830,0.99,Metal,Black Sabbath Vol. 4 (Remaster),Black Sabbath Tomorrow's Dream,"Tony Iommi, Bill Ward, Geezer Butler, Ozzy Osbourne",192496,6252071,0.99,Metal,Black Sabbath Vol. 4 (Remaster),Black Sabbath Changes,"Tony Iommi, Bill Ward, Geezer Butler, Ozzy Osbourne",286275,9175517,0.99,Metal,Black Sabbath Vol. 4 (Remaster),Black Sabbath FX,"Tony Iommi, Bill Ward, Geezer Butler, Ozzy Osbourne",103157,3331776,0.99,Metal,Black Sabbath Vol. 4 (Remaster),Black Sabbath Supernaut,"Tony Iommi, Bill Ward, Geezer Butler, Ozzy Osbourne",285779,9245971,0.99,Metal,Black Sabbath Vol. 4 (Remaster),Black Sabbath Snowblind,"Tony Iommi, Bill Ward, Geezer Butler, Ozzy Osbourne",331676,10813386,0.99,Metal,Black Sabbath Vol. 4 (Remaster),Black Sabbath Cornucopia,"Tony Iommi, Bill Ward, Geezer Butler, Ozzy Osbourne",234814,7653880,0.99,Metal,Black Sabbath Vol. 4 (Remaster),Black Sabbath Laguna Sunrise,"Tony Iommi, Bill Ward, Geezer Butler, Ozzy Osbourne",173087,5671374,0.99,Metal,Black Sabbath Vol. 4 (Remaster),Black Sabbath St. Vitus Dance,"Tony Iommi, Bill Ward, Geezer Butler, Ozzy Osbourne",149655,4884969,0.99,Metal,Black Sabbath Vol. 4 (Remaster),Black Sabbath Under 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 Smoked Pork,,47333,1549074,0.99,Alternative & Punk,Body Count,Body Count Body Count's In The House,,204251,6715413,0.99,Alternative & Punk,Body Count,Body Count Now Sports,,4884,161266,0.99,Alternative & Punk,Body Count,Body Count Body Count,,317936,10489139,0.99,Alternative & Punk,Body Count,Body Count A Statistic,,6373,211997,0.99,Alternative & Punk,Body Count,Body Count Bowels Of The Devil,,223216,7324125,0.99,Alternative & Punk,Body Count,Body Count The Real Problem,,11650,387360,0.99,Alternative & Punk,Body Count,Body Count KKK Bitch,,173008,5709631,0.99,Alternative & Punk,Body Count,Body Count D Note,,95738,3067064,0.99,Alternative & Punk,Body Count,Body Count Voodoo,,300721,9875962,0.99,Alternative & Punk,Body Count,Body Count The Winner Loses,,392254,12843821,0.99,Alternative & Punk,Body Count,Body Count There Goes The Neighborhood,,350171,11443471,0.99,Alternative & Punk,Body Count,Body Count Oprah,,6635,224313,0.99,Alternative & Punk,Body Count,Body Count Evil Dick,,239020,7828873,0.99,Alternative & Punk,Body Count,Body Count Body Count Anthem,,166426,5463690,0.99,Alternative & Punk,Body Count,Body Count Momma's Gotta Die Tonight,,371539,12122946,0.99,Alternative & Punk,Body Count,Body Count Freedom Of Speech,,281234,9337917,0.99,Alternative & Punk,Body Count,Body Count King In Crimson,Roy Z,283167,9218499,0.99,Metal,Chemical Wedding,Bruce Dickinson Chemical Wedding,Roy Z,246177,8022764,0.99,Metal,Chemical Wedding,Bruce Dickinson The Tower,Roy Z,285257,9435693,0.99,Metal,Chemical Wedding,Bruce Dickinson Killing Floor,Adrian Smith,269557,8854240,0.99,Metal,Chemical Wedding,Bruce Dickinson Book Of Thel,Eddie Casillas/Roy Z,494393,16034404,0.99,Metal,Chemical Wedding,Bruce Dickinson Gates Of Urizen,Roy Z,265351,8627004,0.99,Metal,Chemical Wedding,Bruce Dickinson Jerusalem,Roy Z,402390,13194463,0.99,Metal,Chemical Wedding,Bruce Dickinson Trupets Of Jericho,Roy Z,359131,11820908,0.99,Metal,Chemical Wedding,Bruce Dickinson Machine Men,Adrian Smith,341655,11138147,0.99,Metal,Chemical Wedding,Bruce Dickinson The Alchemist,Roy Z,509413,16545657,0.99,Metal,Chemical Wedding,Bruce Dickinson Realword,Roy Z,237531,7802095,0.99,Metal,Chemical Wedding,Bruce Dickinson First Time I Met The Blues,Eurreal Montgomery,140434,4604995,0.99,Blues,The Best Of Buddy Guy - The Millenium Collection,Buddy Guy Let Me Love You Baby,Willie Dixon,175386,5716994,0.99,Blues,The Best Of Buddy Guy - The Millenium Collection,Buddy Guy Stone Crazy,Buddy Guy,433397,14184984,0.99,Blues,The Best Of Buddy Guy - The Millenium Collection,Buddy Guy Pretty Baby,Willie Dixon,237662,7848282,0.99,Blues,The Best Of Buddy Guy - The Millenium Collection,Buddy Guy When My Left Eye Jumps,Al Perkins/Willie Dixon,235311,7685363,0.99,Blues,The Best Of Buddy Guy - The Millenium Collection,Buddy Guy Leave My Girl Alone,Buddy Guy,204721,6859518,0.99,Blues,The Best Of Buddy Guy - The Millenium Collection,Buddy Guy She Suits Me To A Tee,Buddy Guy,136803,4456321,0.99,Blues,The Best Of Buddy Guy - The Millenium Collection,Buddy Guy Keep 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 My Time After Awhile,Robert Geddins/Ron Badger/Sheldon Feinberg,182491,6022698,0.99,Blues,The Best Of Buddy Guy - The Millenium Collection,Buddy Guy Too Many Ways (Alternate),Willie Dixon,135053,4459946,0.99,Blues,The Best Of Buddy Guy - The Millenium Collection,Buddy Guy Talkin' 'Bout Women Obviously,Amos Blakemore/Buddy Guy,589531,19161377,0.99,Blues,The Best Of Buddy Guy - The Millenium Collection,Buddy Guy Jorge Da Capadócia,Jorge Ben,177397,5842196,0.99,Latin,Prenda Minha,Caetano Veloso Prenda Minha,Tradicional,99369,3225364,0.99,Latin,Prenda Minha,Caetano Veloso Meditação,Tom Jobim - Newton Mendoça,148793,4865597,0.99,Latin,Prenda Minha,Caetano Veloso Terra,Caetano Veloso,482429,15889054,0.99,Latin,Prenda Minha,Caetano Veloso Eclipse Oculto,Caetano Veloso,221936,7382703,0.99,Latin,Prenda Minha,Caetano Veloso "Texto ""Verdade Tropical""",Caetano Veloso,84088,2752161,0.99,Latin,Prenda Minha,Caetano Veloso Bem Devagar,Gilberto Gil,133172,4333651,0.99,Latin,Prenda Minha,Caetano Veloso Drão,Gilberto Gil,156264,5065932,0.99,Latin,Prenda Minha,Caetano Veloso Saudosismo,Caetano Veloso,144326,4726981,0.99,Latin,Prenda Minha,Caetano Veloso Carolina,Chico Buarque,181812,5924159,0.99,Latin,Prenda Minha,Caetano Veloso Sozinho,Peninha,190589,6253200,0.99,Latin,Prenda Minha,Caetano Veloso Esse Cara,Caetano Veloso,223111,7217126,0.99,Latin,Prenda Minha,Caetano Veloso Mel,Caetano Veloso - Waly Salomão,294765,9854062,0.99,Latin,Prenda Minha,Caetano Veloso Linha Do Equador,Caetano Veloso - Djavan,299337,10003747,0.99,Latin,Prenda Minha,Caetano Veloso Odara,Caetano Veloso,141270,4704104,0.99,Latin,Prenda Minha,Caetano Veloso A Luz De Tieta,Caetano Veloso,251742,8507446,0.99,Latin,Prenda Minha,Caetano Veloso Atrás 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 Vida Boa,Fausto Nilo - Armandinho,281730,9411272,0.99,Latin,Prenda Minha,Caetano Veloso Sozinho (Hitmakers Classic Mix),,436636,14462072,0.99,Latin,Sozinho Remix Ao Vivo,Caetano Veloso Sozinho (Hitmakers Classic Radio Edit),,195004,6455134,0.99,Latin,Sozinho Remix Ao Vivo,Caetano Veloso Sozinho (Caêdrum 'n' Bass),,328071,10975007,0.99,Latin,Sozinho Remix Ao Vivo,Caetano Veloso Carolina,,163056,5375395,0.99,Latin,Minha Historia,Chico Buarque Essa Moça Ta Diferente,,167235,5568574,0.99,Latin,Minha Historia,Chico Buarque Vai Passar,,369763,12359161,0.99,Latin,Minha Historia,Chico Buarque Samba De Orly,,162429,5431854,0.99,Latin,Minha Historia,Chico Buarque "Bye, Bye Brasil",,283402,9499590,0.99,Latin,Minha Historia,Chico Buarque Atras Da Porta,,189675,6132843,0.99,Latin,Minha Historia,Chico Buarque Tatuagem,,172120,5645703,0.99,Latin,Minha Historia,Chico Buarque O Que Será (À Flor Da Terra),,167288,5574848,0.99,Latin,Minha Historia,Chico Buarque Morena De Angola,,186801,6373932,0.99,Latin,Minha Historia,Chico Buarque Apesar De Você,,234501,7886937,0.99,Latin,Minha Historia,Chico Buarque A Banda,,132493,4349539,0.99,Latin,Minha Historia,Chico Buarque Minha Historia,,182256,6029673,0.99,Latin,Minha Historia,Chico Buarque Com Açúcar E Com Afeto,,175386,5846442,0.99,Latin,Minha Historia,Chico Buarque Brejo Da Cruz,,214099,7270749,0.99,Latin,Minha Historia,Chico Buarque Meu Caro Amigo,,260257,8778172,0.99,Latin,Minha Historia,Chico Buarque Geni E O Zepelim,,317570,10342226,0.99,Latin,Minha Historia,Chico Buarque Trocando Em Miúdos,,169717,5461468,0.99,Latin,Minha Historia,Chico Buarque Vai Trabalhar Vagabundo,,139154,4693941,0.99,Latin,Minha Historia,Chico Buarque Gota D'água,,153208,5074189,0.99,Latin,Minha Historia,Chico Buarque Construção / Deus Lhe Pague,,383059,12675305,0.99,Latin,Minha Historia,Chico Buarque Meia-Lua Inteira,,222093,7466288,0.99,Latin,Minha Historia,Chico Buarque Voce e Linda,,242938,8050268,0.99,Latin,Minha Historia,Chico Buarque Um Indio,,195944,6453213,0.99,Latin,Minha Historia,Chico Buarque Podres Poderes,,259761,8622495,0.99,Latin,Minha Historia,Chico Buarque Voce Nao Entende Nada - Cotidiano,,421982,13885612,0.99,Latin,Minha Historia,Chico Buarque O Estrangeiro,,374700,12472890,0.99,Latin,Minha Historia,Chico Buarque Menino Do Rio,,147670,4862277,0.99,Latin,Minha Historia,Chico Buarque Qualquer Coisa,,193410,6372433,0.99,Latin,Minha Historia,Chico Buarque Sampa,,185051,6151831,0.99,Latin,Minha Historia,Chico Buarque Queixa,,299676,9953962,0.99,Latin,Minha Historia,Chico Buarque O Leaozinho,,184398,6098150,0.99,Latin,Minha Historia,Chico Buarque Fora Da Ordem,,354011,11746781,0.99,Latin,Minha Historia,Chico Buarque Terra,,401319,13224055,0.99,Latin,Minha Historia,Chico Buarque "Alegria, Alegria",,169221,5497025,0.99,Latin,Minha Historia,Chico Buarque Mateus Enter,Chico Science,33149,1103013,0.99,Latin,Afrociberdelia,Chico Science & Nação Zumbi O Cidadão Do Mundo,Chico Science,200933,6724966,0.99,Latin,Afrociberdelia,Chico Science & Nação Zumbi Etnia,Chico Science,152555,5061413,0.99,Latin,Afrociberdelia,Chico Science & Nação Zumbi Quilombo Groove [Instrumental],Chico Science,151823,5042447,0.99,Latin,Afrociberdelia,Chico Science & Nação Zumbi Macô,Chico Science,249600,8253934,0.99,Latin,Afrociberdelia,Chico Science & Nação Zumbi Um Passeio No Mundo Livre,Chico Science,240091,7984291,0.99,Latin,Afrociberdelia,Chico Science & Nação Zumbi Samba Do Lado,Chico Science,227317,7541688,0.99,Latin,Afrociberdelia,Chico Science & Nação Zumbi Maracatu Atômico,Chico Science,284264,9670057,0.99,Latin,Afrociberdelia,Chico Science & Nação Zumbi O Encontro De Isaac Asimov Com Santos Dumont No Céu,Chico Science,99108,3240816,0.99,Latin,Afrociberdelia,Chico Science & Nação Zumbi Corpo De Lama,Chico Science,232672,7714954,0.99,Latin,Afrociberdelia,Chico Science & Nação Zumbi Sobremesa,Chico Science,240091,7960868,0.99,Latin,Afrociberdelia,Chico Science & Nação Zumbi Manguetown,Chico Science,194560,6475159,0.99,Latin,Afrociberdelia,Chico Science & Nação Zumbi Um Satélite Na Cabeça,Chico Science,126615,4272821,0.99,Latin,Afrociberdelia,Chico Science & Nação Zumbi Baião Ambiental [Instrumental],Chico Science,152659,5198539,0.99,Latin,Afrociberdelia,Chico Science & Nação Zumbi Sangue De Bairro,Chico Science,132231,4415557,0.99,Latin,Afrociberdelia,Chico Science & Nação Zumbi Enquanto O Mundo Explode,Chico Science,88764,2968650,0.99,Latin,Afrociberdelia,Chico Science & Nação Zumbi Interlude Zumbi,Chico Science,71627,2408550,0.99,Latin,Afrociberdelia,Chico Science & Nação Zumbi Criança De Domingo,Chico Science,208222,6984813,0.99,Latin,Afrociberdelia,Chico Science & Nação Zumbi Amor De Muito,Chico Science,175333,5881293,0.99,Latin,Afrociberdelia,Chico Science & Nação Zumbi Samidarish [Instrumental],Chico Science,272431,8911641,0.99,Latin,Afrociberdelia,Chico Science & Nação Zumbi Maracatu Atômico [Atomic Version],Chico Science,273084,9019677,0.99,Latin,Afrociberdelia,Chico Science & Nação Zumbi Maracatu Atômico [Ragga Mix],Chico Science,210155,6986421,0.99,Latin,Afrociberdelia,Chico Science & Nação Zumbi Maracatu Atômico [Trip Hop],Chico Science,221492,7380787,0.99,Latin,Afrociberdelia,Chico Science & Nação Zumbi Banditismo Por Uma Questa,,307095,10251097,0.99,Latin,Da Lama Ao Caos,Chico Science & Nação Zumbi Banditismo Por Uma Questa,,243644,8147224,0.99,Latin,Da Lama Ao Caos,Chico Science & Nação Zumbi Rios Pontes & Overdrives,,286720,9659152,0.99,Latin,Da Lama Ao Caos,Chico Science & Nação Zumbi Cidade,,216346,7241817,0.99,Latin,Da Lama Ao Caos,Chico Science & Nação Zumbi Praiera,,183640,6172781,0.99,Latin,Da Lama Ao Caos,Chico Science & Nação Zumbi Samba Makossa,,271856,9095410,0.99,Latin,Da Lama Ao Caos,Chico Science & Nação Zumbi Da Lama Ao Caos,,251559,8378065,0.99,Latin,Da Lama Ao Caos,Chico Science & Nação Zumbi Maracatu De Tiro Certeiro,,88868,2901397,0.99,Latin,Da Lama Ao Caos,Chico Science & Nação Zumbi Salustiano Song,,215405,7183969,0.99,Latin,Da Lama Ao Caos,Chico Science & Nação Zumbi Antene Se,,248372,8253618,0.99,Latin,Da Lama Ao Caos,Chico Science & Nação Zumbi Risoflora,,105586,3536938,0.99,Latin,Da Lama Ao Caos,Chico Science & Nação Zumbi Lixo Do Mangue,,193253,6534200,0.99,Latin,Da Lama Ao Caos,Chico Science & Nação Zumbi Computadores Fazem Arte,,404323,13702771,0.99,Latin,Da Lama Ao Caos,Chico Science & Nação Zumbi Girassol,Bino Farias/Da Gama/Lazão/Pedro Luis/Toni Garrido,249808,8327676,0.99,Reggae,Acústico MTV [Live],Cidade Negra A Sombra Da Maldade,Da Gama/Toni Garrido,230922,7697230,0.99,Reggae,Acústico MTV [Live],Cidade Negra Johnny B. Goode,Chuck Berry,254615,8505985,0.99,Reggae,Acústico MTV [Live],Cidade Negra Soldado Da Paz,Herbert Vianna,194220,6455080,0.99,Reggae,Acústico MTV [Live],Cidade Negra Firmamento,Bino Farias/Da Gama/Henry Lawes/Lazão/Toni Garrido/Winston Foser-Vers,222145,7402658,0.99,Reggae,Acústico MTV [Live],Cidade Negra Extra,Gilberto Gil,304352,10078050,0.99,Reggae,Acústico MTV [Live],Cidade Negra O Erê,Bernardo Vilhena/Bino Farias/Da Gama/Lazão/Toni Garrido,236382,7866924,0.99,Reggae,Acústico MTV [Live],Cidade Negra Podes Crer,Bino Farias/Da Gama/Lazão/Toni Garrido,232280,7747747,0.99,Reggae,Acústico MTV [Live],Cidade Negra A Estrada,Bino Farias/Da Gama/Lazão/Toni Garrido,248842,8275673,0.99,Reggae,Acústico MTV [Live],Cidade Negra Berlim,Da Gama/Toni Garrido,207542,6920424,0.99,Reggae,Acústico MTV [Live],Cidade Negra Já Foi,Bino Farias/Da Gama/Lazão/Toni Garrido,221544,7388466,0.99,Reggae,Acústico MTV [Live],Cidade Negra Onde Você Mora?,Marisa Monte/Nando Reis,256026,8502588,0.99,Reggae,Acústico MTV [Live],Cidade Negra Pensamento,Bino Farias/Da Gamma/Lazão/Rás Bernard,173008,5748424,0.99,Reggae,Acústico MTV [Live],Cidade Negra Conciliação,Da Gama/Lazão/Rás Bernardo,257619,8552474,0.99,Reggae,Acústico MTV [Live],Cidade Negra Realidade Virtual,Bino Farias/Da Gama/Lazão/Toni Garrido,195239,6503533,0.99,Reggae,Acústico MTV [Live],Cidade Negra Mensagem,Bino Farias/Da Gama/Lazão/Rás Bernardo,225332,7488852,0.99,Reggae,Acústico MTV [Live],Cidade Negra A Cor Do Sol,Bernardo Vilhena/Da Gama/Lazão,231392,7663348,0.99,Reggae,Acústico MTV [Live],Cidade Negra Onde Você Mora?,Marisa Monte/Nando Reis,298396,10056970,0.99,Reggae,Cidade Negra - Hits,Cidade Negra O Erê,Bernardo Vilhena/Bino/Da Gama/Lazao/Toni Garrido,206942,6950332,0.99,Reggae,Cidade Negra - Hits,Cidade Negra A Sombra Da Maldade,Da Gama/Toni Garrido,285231,9544383,0.99,Reggae,Cidade Negra - Hits,Cidade Negra A Estrada,Da Gama/Lazao/Toni Garrido,282174,9344477,0.99,Reggae,Cidade Negra - Hits,Cidade Negra Falar A Verdade,Bino/Da Gama/Ras Bernardo,244950,8189093,0.99,Reggae,Cidade Negra - Hits,Cidade Negra Firmamento,Harry Lawes/Winston Foster-Vers,225488,7507866,0.99,Reggae,Cidade Negra - Hits,Cidade Negra Pensamento,Bino/Da Gama/Ras Bernardo,192391,6399761,0.99,Reggae,Cidade Negra - Hits,Cidade Negra Realidade Virtual,Bino/Da Gamma/Lazao/Toni Garrido,240300,8069934,0.99,Reggae,Cidade Negra - Hits,Cidade Negra Doutor,Bino/Da Gama/Toni Garrido,178155,5950952,0.99,Reggae,Cidade Negra - Hits,Cidade Negra Na Frente Da TV,Bino/Da Gama/Lazao/Ras Bernardo,289750,9633659,0.99,Reggae,Cidade Negra - Hits,Cidade Negra Downtown,Cidade Negra,239725,8024386,0.99,Reggae,Cidade Negra - Hits,Cidade Negra Sábado A Noite,Lulu Santos,267363,8895073,0.99,Reggae,Cidade Negra - Hits,Cidade Negra A Cor Do Sol,Bernardo Vilhena/Da Gama/Lazao,273031,9142937,0.99,Reggae,Cidade Negra - Hits,Cidade Negra Eu Também Quero Beijar,Fausto Nilo/Moraes Moreira/Pepeu Gomes,211147,7029400,0.99,Reggae,Cidade Negra - Hits,Cidade Negra Noite Do Prazer,,311353,10309980,0.99,Latin,Na Pista,Cláudio Zoli À Francesa,,244532,8150846,0.99,Latin,Na Pista,Cláudio Zoli Cada Um Cada Um (A Namoradeira),,253492,8441034,0.99,Latin,Na Pista,Cláudio Zoli Linha Do Equador,,244715,8123466,0.99,Latin,Na Pista,Cláudio Zoli Amor Demais,,254040,8420093,0.99,Latin,Na Pista,Cláudio Zoli Férias,,264202,8731945,0.99,Latin,Na Pista,Cláudio Zoli Gostava Tanto De Você,,230452,7685326,0.99,Latin,Na Pista,Cláudio Zoli Flor Do Futuro,,275748,9205941,0.99,Latin,Na Pista,Cláudio Zoli Felicidade Urgente,,266605,8873358,0.99,Latin,Na Pista,Cláudio Zoli Livre Pra Viver,,214595,7111596,0.99,Latin,Na Pista,Cláudio Zoli "Dig-Dig, Lambe-Lambe (Ao Vivo)",Cassiano Costa/Cintia Maviane/J.F./Lucas Costa,205479,6892516,0.99,Pop,Axé Bahia 2001,Various Artists Pererê,Augusto Conceição/Chiclete Com Banana,198661,6643207,0.99,Pop,Axé Bahia 2001,Various Artists TriboTchan,Cal Adan/Paulo Levi,194194,6507950,0.99,Pop,Axé Bahia 2001,Various Artists "Tapa Aqui, Descobre Ali",Paulo Levi/W. Rangel,188630,6327391,0.99,Pop,Axé Bahia 2001,Various Artists Daniela,Jorge Cardoso/Pierre Onasis,230791,7748006,0.99,Pop,Axé Bahia 2001,Various Artists Bate Lata,Fábio Nolasco/Gal Sales/Ivan Brasil,206733,7034985,0.99,Pop,Axé Bahia 2001,Various Artists Garotas do Brasil,"Garay, Ricardo Engels/Luca Predabom/Ludwig, Carlos Henrique/Maurício Vieira",210155,6973625,0.99,Pop,Axé Bahia 2001,Various Artists Levada do Amor (Ailoviu),Luiz Wanderley/Paulo Levi,190093,6457752,0.99,Pop,Axé Bahia 2001,Various Artists Lavadeira,"Do Vale, Valverde/Gal Oliveira/Luciano Pinto",214256,7254147,0.99,Pop,Axé Bahia 2001,Various Artists Reboladeira,Cal Adan/Ferrugem/Julinho Carioca/Tríona Ní Dhomhnaill,210599,7027525,0.99,Pop,Axé Bahia 2001,Various Artists É que Nessa Encarnação Eu Nasci Manga,Lucina/Luli,196519,6568081,0.99,Pop,Axé Bahia 2001,Various Artists Reggae Tchan,"Cal Adan/Del Rey, Tension/Edu Casanova",206654,6931328,0.99,Pop,Axé Bahia 2001,Various Artists My Love,Jauperi/Zeu Góes,203493,6772813,0.99,Pop,Axé Bahia 2001,Various Artists Latinha de Cerveja,Adriano Bernandes/Edmar Neves,166687,5532564,0.99,Pop,Axé Bahia 2001,Various Artists You Shook Me,J B Lenoir/Willie Dixon,315951,10249958,0.99,Rock,BBC Sessions [Disc 1] [Live],Led Zeppelin I Can't Quit You Baby,Willie Dixon,263836,8581414,0.99,Rock,BBC Sessions [Disc 1] [Live],Led Zeppelin Communication Breakdown,Jimmy Page/John Bonham/John Paul Jones,192653,6287257,0.99,Rock,BBC Sessions [Disc 1] [Live],Led Zeppelin Dazed and Confused,Jimmy Page,401920,13035765,0.99,Rock,BBC Sessions [Disc 1] [Live],Led Zeppelin The 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 What is and Should Never Be,Jimmy Page/Robert Plant,260675,8497116,0.99,Rock,BBC Sessions [Disc 1] [Live],Led Zeppelin Communication Breakdown(2),Jimmy Page/John Bonham/John Paul Jones,161149,5261022,0.99,Rock,BBC Sessions [Disc 1] [Live],Led Zeppelin Travelling Riverside Blues,Jimmy Page/Robert Johnson/Robert Plant,312032,10232581,0.99,Rock,BBC Sessions [Disc 1] [Live],Led Zeppelin Whole 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 Somethin' Else,Bob Cochran/Sharon Sheeley,127869,4165650,0.99,Rock,BBC Sessions [Disc 1] [Live],Led Zeppelin Communication Breakdown(3),Jimmy Page/John Bonham/John Paul Jones,185260,6041133,0.99,Rock,BBC Sessions [Disc 1] [Live],Led Zeppelin I Can't Quit You Baby(2),Willie Dixon,380551,12377615,0.99,Rock,BBC Sessions [Disc 1] [Live],Led Zeppelin You Shook Me(2),J B Lenoir/Willie Dixon,619467,20138673,0.99,Rock,BBC Sessions [Disc 1] [Live],Led Zeppelin How 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 Debra Kadabra,Frank Zappa,234553,7649679,0.99,Rock,Bongo Fury,Frank Zappa & Captain Beefheart Carolina Hard-Core Ecstasy,Frank Zappa,359680,11731061,0.99,Rock,Bongo Fury,Frank Zappa & Captain Beefheart Sam With The Showing Scalp Flat Top,Don Van Vliet,171284,5572993,0.99,Rock,Bongo Fury,Frank Zappa & Captain Beefheart Poofter's Froth Wyoming Plans Ahead,Frank Zappa,183902,6007019,0.99,Rock,Bongo Fury,Frank Zappa & Captain Beefheart 200 Years Old,Frank Zappa,272561,8912465,0.99,Rock,Bongo Fury,Frank Zappa & Captain Beefheart Cucamonga,Frank Zappa,144483,4728586,0.99,Rock,Bongo Fury,Frank Zappa & Captain Beefheart Advance Romance,Frank Zappa,677694,22080051,0.99,Rock,Bongo Fury,Frank Zappa & Captain Beefheart Man With The Woman Head,Don Van Vliet,88894,2922044,0.99,Rock,Bongo Fury,Frank Zappa & Captain Beefheart Muffin Man,Frank Zappa,332878,10891682,0.99,Rock,Bongo Fury,Frank Zappa & Captain Beefheart Vai-Vai 2001,,276349,9402241,0.99,Soundtrack,Carnaval 2001,Various Artists X-9 2001,,273920,9310370,0.99,Soundtrack,Carnaval 2001,Various Artists Gavioes 2001,,282723,9616640,0.99,Soundtrack,Carnaval 2001,Various Artists Nene 2001,,284969,9694508,0.99,Soundtrack,Carnaval 2001,Various Artists Rosas De Ouro 2001,,284342,9721084,0.99,Soundtrack,Carnaval 2001,Various Artists Mocidade Alegre 2001,,282488,9599937,0.99,Soundtrack,Carnaval 2001,Various Artists Camisa Verde 2001,,283454,9633755,0.99,Soundtrack,Carnaval 2001,Various Artists Leandro De Itaquera 2001,,274808,9451845,0.99,Soundtrack,Carnaval 2001,Various Artists Tucuruvi 2001,,287921,9883335,0.99,Soundtrack,Carnaval 2001,Various Artists Aguia De Ouro 2001,,284160,9698729,0.99,Soundtrack,Carnaval 2001,Various Artists Ipiranga 2001,,248293,8522591,0.99,Soundtrack,Carnaval 2001,Various Artists Morro Da Casa Verde 2001,,284708,9718778,0.99,Soundtrack,Carnaval 2001,Various Artists Perola Negra 2001,,281626,9619196,0.99,Soundtrack,Carnaval 2001,Various Artists Sao Lucas 2001,,296254,10020122,0.99,Soundtrack,Carnaval 2001,Various Artists Guanabara,Marcos Valle,247614,8499591,0.99,Latin,Chill: Brazil (Disc 1),Marcos Valle Mas Que Nada,Jorge Ben,248398,8255254,0.99,Latin,Chill: Brazil (Disc 1),Marcos Valle Vôo Sobre o Horizonte,J.r.Bertami/Parana,225097,7528825,0.99,Latin,Chill: Brazil (Disc 1),Marcos Valle A Paz,Donato/Gilberto Gil,263183,8619173,0.99,Latin,Chill: Brazil (Disc 1),Marcos Valle Wave (Vou te Contar),Antonio Carlos Jobim,271647,9057557,0.99,Latin,Chill: Brazil (Disc 1),Marcos Valle Água de Beber,Antonio Carlos Jobim/Vinicius de Moraes,146677,4866476,0.99,Latin,Chill: Brazil (Disc 1),Marcos Valle Samba da Bençaco,Baden Powell/Vinicius de Moraes,282200,9440676,0.99,Latin,Chill: Brazil (Disc 1),Marcos Valle Pode Parar,Jorge Vercilo/Jota Maranhao,179408,6046678,0.99,Latin,Chill: Brazil (Disc 1),Marcos Valle Menino do Rio,Caetano Veloso,262713,8737489,0.99,Latin,Chill: Brazil (Disc 1),Marcos Valle Ando Meio Desligado,Caetano Veloso,195813,6547648,0.99,Latin,Chill: Brazil (Disc 1),Marcos Valle Mistério da Raça,Luiz Melodia/Ricardo Augusto,184320,6191752,0.99,Latin,Chill: Brazil (Disc 1),Marcos Valle All Star,Nando Reis,176326,5891697,0.99,Latin,Chill: Brazil (Disc 1),Marcos Valle Menina Bonita,Alexandre Brazil/Pedro Luis/Rodrigo Cabelo,237087,7938246,0.99,Latin,Chill: Brazil (Disc 1),Marcos Valle Pescador de Ilusões,Macelo Yuka/O Rappa,245524,8267067,0.99,Latin,Chill: Brazil (Disc 1),Marcos Valle À Vontade (Live Mix),Bombom/Ed Motta,180636,5972430,0.99,Latin,Chill: Brazil (Disc 1),Marcos Valle Maria Fumaça,Luiz Carlos/Oberdan,141008,4743149,0.99,Latin,Chill: Brazil (Disc 1),Marcos Valle Sambassim (dj patife remix),Alba Carvalho/Fernando Porto,213655,7243166,0.99,Latin,Chill: Brazil (Disc 1),Marcos Valle Garota De Ipanema,Vários,279536,9141343,0.99,Latin,Chill: Brazil (Disc 2),Antônio Carlos Jobim Tim Tim Por Tim Tim,Vários,213237,7143328,0.99,Latin,Chill: Brazil (Disc 2),Antônio Carlos Jobim Tarde Em Itapoã,Vários,313704,10344491,0.99,Latin,Chill: Brazil (Disc 2),Antônio Carlos Jobim Tanto Tempo,Vários,170292,5572240,0.99,Latin,Chill: Brazil (Disc 2),Antônio Carlos Jobim Eu Vim Da Bahia - Live,Vários,157988,5115428,0.99,Latin,Chill: Brazil (Disc 2),Antônio Carlos Jobim Alô Alô Marciano,Vários,238106,8013065,0.99,Latin,Chill: Brazil (Disc 2),Antônio Carlos Jobim Linha Do Horizonte,Vários,279484,9275929,0.99,Latin,Chill: Brazil (Disc 2),Antônio Carlos Jobim Only A Dream In Rio,Vários,371356,12192989,0.99,Latin,Chill: Brazil (Disc 2),Antônio Carlos Jobim Abrir A Porta,Vários,271960,8991141,0.99,Latin,Chill: Brazil (Disc 2),Antônio Carlos Jobim Alice,Vários,165982,5594341,0.99,Latin,Chill: Brazil (Disc 2),Antônio Carlos Jobim Momentos Que Marcam,Vários,280137,9313740,0.99,Latin,Chill: Brazil (Disc 2),Antônio Carlos Jobim Um Jantar Pra Dois,Vários,237714,7819755,0.99,Latin,Chill: Brazil (Disc 2),Antônio Carlos Jobim Bumbo Da Mangueira,Vários,270158,9073350,0.99,Latin,Chill: Brazil (Disc 2),Antônio Carlos Jobim Mr Funk Samba,Vários,213890,7102545,0.99,Latin,Chill: Brazil (Disc 2),Antônio Carlos Jobim Santo Antonio,Vários,162716,5492069,0.99,Latin,Chill: Brazil (Disc 2),Antônio Carlos Jobim Por Você,Vários,205557,6792493,0.99,Latin,Chill: Brazil (Disc 2),Antônio Carlos Jobim Só Tinha De Ser Com Você,Vários,389642,13085596,0.99,Latin,Chill: Brazil (Disc 2),Antônio Carlos Jobim Free Speech For The Dumb,Molaney/Morris/Roberts/Wainwright,155428,5076048,0.99,Metal,Garage Inc. (Disc 1),Metallica It's Electric,Harris/Tatler,213995,6978601,0.99,Metal,Garage Inc. (Disc 1),Metallica Sabbra Cadabra,Black Sabbath,380342,12418147,0.99,Metal,Garage Inc. (Disc 1),Metallica Turn The Page,Seger,366524,11946327,0.99,Metal,Garage Inc. (Disc 1),Metallica Die Die My Darling,Danzig,149315,4867667,0.99,Metal,Garage Inc. (Disc 1),Metallica Loverman,Cave,472764,15446975,0.99,Metal,Garage Inc. (Disc 1),Metallica Mercyful Fate,Diamond/Shermann,671712,21942829,0.99,Metal,Garage Inc. (Disc 1),Metallica Astronomy,A.Bouchard/J.Bouchard/S.Pearlman,397531,13065612,0.99,Metal,Garage Inc. (Disc 1),Metallica Whiskey In The Jar,Traditional,305005,9943129,0.99,Metal,Garage Inc. (Disc 1),Metallica Tuesday's Gone,Collins/Van Zandt,545750,17900787,0.99,Metal,Garage Inc. (Disc 1),Metallica The More I See,Molaney/Morris/Roberts/Wainwright,287973,9378873,0.99,Metal,Garage Inc. (Disc 1),Metallica A Kind Of Magic,Roger Taylor,262608,8689618,0.99,Rock,Greatest Hits II,Queen Under Pressure,Queen & David Bowie,236617,7739042,0.99,Rock,Greatest Hits II,Queen Radio GA GA,Roger Taylor,343745,11358573,0.99,Rock,Greatest Hits II,Queen I Want It All,Queen,241684,7876564,0.99,Rock,Greatest Hits II,Queen I Want To Break Free,John Deacon,259108,8552861,0.99,Rock,Greatest Hits II,Queen Innuendo,Queen,387761,12664591,0.99,Rock,Greatest Hits II,Queen It's A Hard Life,Freddie Mercury,249417,8112242,0.99,Rock,Greatest Hits II,Queen Breakthru,Queen,249234,8150479,0.99,Rock,Greatest Hits II,Queen Who Wants To Live Forever,Brian May,297691,9577577,0.99,Rock,Greatest Hits II,Queen Headlong,Queen,273057,8921404,0.99,Rock,Greatest Hits II,Queen The Miracle,Queen,294974,9671923,0.99,Rock,Greatest Hits II,Queen I'm Going Slightly Mad,Queen,248032,8192339,0.99,Rock,Greatest Hits II,Queen The Invisible Man,Queen,238994,7920353,0.99,Rock,Greatest Hits II,Queen Hammer To Fall,Brian May,220316,7255404,0.99,Rock,Greatest Hits II,Queen Friends Will Be Friends,Freddie Mercury & John Deacon,248920,8114582,0.99,Rock,Greatest Hits II,Queen The Show Must Go On,Queen,263784,8526760,0.99,Rock,Greatest Hits II,Queen One Vision,Queen,242599,7936928,0.99,Rock,Greatest Hits II,Queen Detroit Rock City,"Paul Stanley, B. Ezrin",218880,7146372,0.99,Rock,Greatest Kiss,Kiss Black Diamond,Paul Stanley,314148,10266007,0.99,Rock,Greatest Kiss,Kiss Hard Luck Woman,Paul Stanley,216032,7109267,0.99,Rock,Greatest Kiss,Kiss Sure Know Something,"Paul Stanley, Vincent Poncia",242468,7939886,0.99,Rock,Greatest Kiss,Kiss Love Gun,Paul Stanley,196257,6424915,0.99,Rock,Greatest Kiss,Kiss Deuce,Gene Simmons,185077,6097210,0.99,Rock,Greatest Kiss,Kiss Goin' Blind,"Gene Simmons, S. Coronel",216215,7045314,0.99,Rock,Greatest Kiss,Kiss Shock Me,Ace Frehley,227291,7529336,0.99,Rock,Greatest Kiss,Kiss Do You Love Me,"Paul Stanley, B. Ezrin, K. Fowley",214987,6976194,0.99,Rock,Greatest Kiss,Kiss She,"Gene Simmons, S. Coronel",248346,8229734,0.99,Rock,Greatest Kiss,Kiss I Was Made For Loving You,"Paul Stanley, Vincent Poncia, Desmond Child",271360,9018078,0.99,Rock,Greatest Kiss,Kiss Shout It Out Loud,"Paul Stanley, Gene Simmons, B. Ezrin",219742,7194424,0.99,Rock,Greatest Kiss,Kiss God Of Thunder,Paul Stanley,255791,8309077,0.99,Rock,Greatest Kiss,Kiss Calling Dr. Love,Gene Simmons,225332,7395034,0.99,Rock,Greatest Kiss,Kiss Beth,"S. Penridge, Bob Ezrin, Peter Criss",166974,5360574,0.99,Rock,Greatest Kiss,Kiss Strutter,"Paul Stanley, Gene Simmons",192496,6317021,0.99,Rock,Greatest Kiss,Kiss Rock And Roll All Nite,"Paul Stanley, Gene Simmons",173609,5735902,0.99,Rock,Greatest Kiss,Kiss Cold Gin,Ace Frehley,262243,8609783,0.99,Rock,Greatest Kiss,Kiss Plaster Caster,Gene Simmons,207333,6801116,0.99,Rock,Greatest Kiss,Kiss God Gave Rock 'n' Roll To You,"Paul Stanley, Gene Simmons, Rus Ballard, Bob Ezrin",320444,10441590,0.99,Rock,Greatest Kiss,Kiss Heart of the Night,,273737,9098263,0.99,Jazz,Heart of the Night,Spyro Gyra De La Luz,,315219,10518284,0.99,Jazz,Heart of the Night,Spyro Gyra Westwood Moon,,295627,9765802,0.99,Jazz,Heart of the Night,Spyro Gyra Midnight,,266866,8851060,0.99,Jazz,Heart of the Night,Spyro Gyra Playtime,,273580,9070880,0.99,Jazz,Heart of the Night,Spyro Gyra Surrender,,287634,9422926,0.99,Jazz,Heart of the Night,Spyro Gyra Valentino's,,296124,9848545,0.99,Jazz,Heart of the Night,Spyro Gyra Believe,,310778,10317185,0.99,Jazz,Heart of the Night,Spyro Gyra As We Sleep,,316865,10429398,0.99,Jazz,Heart of the Night,Spyro Gyra When Evening Falls,,298135,9863942,0.99,Jazz,Heart of the Night,Spyro Gyra J Squared,,288757,9480777,0.99,Jazz,Heart of the Night,Spyro Gyra Best Thing,,274259,9069394,0.99,Jazz,Heart of the Night,Spyro Gyra Maria,Billie Joe Armstrong -Words Green Day -Music,167262,5484747,0.99,Alternative & Punk,International Superhits,Green Day Poprocks And Coke,Billie Joe Armstrong -Words Green Day -Music,158354,5243078,0.99,Alternative & Punk,International Superhits,Green Day Longview,Billie Joe Armstrong -Words Green Day -Music,234083,7714939,0.99,Alternative & Punk,International Superhits,Green Day Welcome To Paradise,Billie Joe Armstrong -Words Green Day -Music,224208,7406008,0.99,Alternative & Punk,International Superhits,Green Day Basket Case,Billie Joe Armstrong -Words Green Day -Music,181629,5951736,0.99,Alternative & Punk,International Superhits,Green Day When I Come Around,Billie Joe Armstrong -Words Green Day -Music,178364,5839426,0.99,Alternative & Punk,International Superhits,Green Day She,Billie Joe Armstrong -Words Green Day -Music,134164,4425128,0.99,Alternative & Punk,International Superhits,Green Day J.A.R. (Jason Andrew Relva),Mike Dirnt -Words Green Day -Music,170997,5645755,0.99,Alternative & Punk,International Superhits,Green Day Geek Stink Breath,Billie Joe Armstrong -Words Green Day -Music,135888,4408983,0.99,Alternative & Punk,International Superhits,Green Day Brain Stew,Billie Joe Armstrong -Words Green Day -Music,193149,6305550,0.99,Alternative & Punk,International Superhits,Green Day Jaded,Billie Joe Armstrong -Words Green Day -Music,90331,2950224,0.99,Alternative & Punk,International Superhits,Green Day Walking Contradiction,Billie Joe Armstrong -Words Green Day -Music,151170,4932366,0.99,Alternative & Punk,International Superhits,Green Day Stuck With Me,Billie Joe Armstrong -Words Green Day -Music,135523,4431357,0.99,Alternative & Punk,International Superhits,Green Day Hitchin' A Ride,Billie Joe Armstrong -Words Green Day -Music,171546,5616891,0.99,Alternative & Punk,International Superhits,Green Day Good Riddance (Time Of Your Life),Billie Joe Armstrong -Words Green Day -Music,153600,5075241,0.99,Alternative & Punk,International Superhits,Green Day Redundant,Billie Joe Armstrong -Words Green Day -Music,198164,6481753,0.99,Alternative & Punk,International Superhits,Green Day Nice Guys Finish Last,Billie Joe Armstrong -Words Green Day -Music,170187,5604618,0.99,Alternative & Punk,International Superhits,Green Day Minority,Billie Joe Armstrong -Words Green Day -Music,168803,5535061,0.99,Alternative & Punk,International Superhits,Green Day Warning,Billie Joe Armstrong -Words Green Day -Music,221910,7343176,0.99,Alternative & Punk,International Superhits,Green Day Waiting,Billie Joe Armstrong -Words Green Day -Music,192757,6316430,0.99,Alternative & Punk,International Superhits,Green Day Macy's Day Parade,Billie Joe Armstrong -Words Green Day -Music,213420,7075573,0.99,Alternative & Punk,International Superhits,Green Day Into The Light,David Coverdale,76303,2452653,0.99,Rock,Into The Light,David Coverdale River Song,David Coverdale,439510,14359478,0.99,Rock,Into The Light,David Coverdale She Give Me ...,David Coverdale,252551,8385478,0.99,Rock,Into The Light,David Coverdale Don't You Cry,David Coverdale,347036,11269612,0.99,Rock,Into The Light,David Coverdale Love Is Blind,David Coverdale/Earl Slick,344999,11409720,0.99,Rock,Into The Light,David Coverdale Slave,David Coverdale/Earl Slick,291892,9425200,0.99,Rock,Into The Light,David Coverdale Cry For Love,Bossi/David Coverdale/Earl Slick,293015,9567075,0.99,Rock,Into The Light,David Coverdale Living On Love,Bossi/David Coverdale/Earl Slick,391549,12785876,0.99,Rock,Into The Light,David Coverdale Midnight Blue,David Coverdale/Earl Slick,298631,9750990,0.99,Rock,Into The Light,David Coverdale Too Many Tears,Adrian Vanderberg/David Coverdale,359497,11810238,0.99,Rock,Into The Light,David Coverdale Don't Lie To Me,David Coverdale/Earl Slick,283585,9288007,0.99,Rock,Into The Light,David Coverdale Wherever You May Go,David Coverdale,239699,7803074,0.99,Rock,Into The Light,David Coverdale Grito De Alerta,Gonzaga Jr.,202213,6539422,0.99,Latin,Meus Momentos,Gonzaguinha Não Dá Mais Pra Segurar (Explode Coração),,219768,7083012,0.99,Latin,Meus Momentos,Gonzaguinha Começaria Tudo Outra Vez,,196545,6473395,0.99,Latin,Meus Momentos,Gonzaguinha O Que É O Que É ?,,259291,8650647,0.99,Latin,Meus Momentos,Gonzaguinha Sangrando,Gonzaga Jr/Gonzaguinha,169717,5494406,0.99,Latin,Meus Momentos,Gonzaguinha "Diga Lá, Coração",,255921,8280636,0.99,Latin,Meus Momentos,Gonzaguinha Lindo Lago Do Amor,Gonzaga Jr.,249678,8353191,0.99,Latin,Meus Momentos,Gonzaguinha Eu Apenas Queria Que Voçê Soubesse,,155637,5130056,0.99,Latin,Meus Momentos,Gonzaguinha Com A Perna No Mundo,Gonzaga Jr.,227448,7747108,0.99,Latin,Meus Momentos,Gonzaguinha E Vamos À Luta,,222406,7585112,0.99,Latin,Meus Momentos,Gonzaguinha Um Homem Também Chora (Guerreiro Menino),,207229,6854219,0.99,Latin,Meus Momentos,Gonzaguinha Comportamento Geral,Gonzaga Jr,181577,5997444,0.99,Latin,Meus Momentos,Gonzaguinha Ponto De Interrogação,,180950,5946265,0.99,Latin,Meus Momentos,Gonzaguinha "Espere Por Mim, Morena",Gonzaguinha,207072,6796523,0.99,Latin,Meus Momentos,Gonzaguinha Balada Do Louco,Arnaldo Baptista - Rita Lee,241057,7852328,0.99,Alternative & Punk,Minha História,Os Mutantes Ando Meio Desligado,Arnaldo Baptista - Rita Lee - Sérgio Dias,287817,9484504,0.99,Alternative & Punk,Minha História,Os Mutantes Top Top,Os Mutantes - Arnolpho Lima Filho,146938,4875374,0.99,Alternative & Punk,Minha História,Os Mutantes Baby,Caetano Veloso,177188,5798202,0.99,Alternative & Punk,Minha História,Os Mutantes A E O Z,Mutantes,518556,16873005,0.99,Alternative & Punk,Minha História,Os Mutantes Panis Et Circenses,Caetano Veloso - Gilberto Gil,125152,4069688,0.99,Alternative & Punk,Minha História,Os Mutantes Chão De Estrelas,Orestes Barbosa-Sílvio Caldas,284813,9433620,0.99,Alternative & Punk,Minha História,Os Mutantes Vida De Cachorro,Rita Lee - Arnaldo Baptista - Sérgio Baptista,195186,6411149,0.99,Alternative & Punk,Minha História,Os Mutantes Bat Macumba,Gilberto Gil - Caetano Veloso,187794,6295223,0.99,Alternative & Punk,Minha História,Os Mutantes Desculpe Babe,Arnaldo Baptista - Rita Lee,170422,5637959,0.99,Alternative & Punk,Minha História,Os Mutantes Rita Lee,Arnaldo Baptista/Rita Lee/Sérgio Dias,189257,6270503,0.99,Alternative & Punk,Minha História,Os Mutantes "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 Banho De Lua,B. de Filippi - F. Migliaci - Versão: Fred Jorge,221831,7232123,0.99,Alternative & Punk,Minha História,Os Mutantes Meu Refrigerador Não Funciona,Arnaldo Baptista - Rita Lee - Sérgio Dias,382981,12495906,0.99,Alternative & Punk,Minha História,Os Mutantes Burn,Coverdale/Lord/Paice,453955,14775708,0.99,Rock,MK III The Final Concerts [Disc 1],Deep Purple Stormbringer,Coverdale,277133,9050022,0.99,Rock,MK III The Final Concerts [Disc 1],Deep Purple Gypsy,Coverdale/Hughes/Lord/Paice,339173,11046952,0.99,Rock,MK III The Final Concerts [Disc 1],Deep Purple Lady Double Dealer,Coverdale,233586,7608759,0.99,Rock,MK III The Final Concerts [Disc 1],Deep Purple Mistreated,Coverdale,758648,24596235,0.99,Rock,MK III The Final Concerts [Disc 1],Deep Purple Smoke On The Water,Gillan/Glover/Lord/Paice,618031,20103125,0.99,Rock,MK III The Final Concerts [Disc 1],Deep Purple You Fool No One,Coverdale/Lord/Paice,804101,26369966,0.99,Rock,MK III The Final Concerts [Disc 1],Deep Purple Custard Pie,Jimmy Page/Robert Plant,253962,8348257,0.99,Rock,Physical Graffiti [Disc 1],Led Zeppelin The Rover,Jimmy Page/Robert Plant,337084,11011286,0.99,Rock,Physical Graffiti [Disc 1],Led Zeppelin In My Time Of Dying,John Bonham/John Paul Jones,666017,21676727,0.99,Rock,Physical Graffiti [Disc 1],Led Zeppelin Houses Of The Holy,Jimmy Page/Robert Plant,242494,7972503,0.99,Rock,Physical Graffiti [Disc 1],Led Zeppelin Trampled Under Foot,John Paul Jones,336692,11154468,0.99,Rock,Physical Graffiti [Disc 1],Led Zeppelin Kashmir,John Bonham,508604,16686580,0.99,Rock,Physical Graffiti [Disc 1],Led Zeppelin Imperatriz,Guga/Marquinho Lessa/Tuninho Professor,339173,11348710,0.99,Latin,Sambas De Enredo 2001,Various Artists Beija-Flor,Caruso/Cleber/Deo/Osmar,327000,10991159,0.99,Latin,Sambas De Enredo 2001,Various Artists Viradouro,Dadinho/Gilbreto Gomes/Gustavo/P.C. Portugal/R. Mocoto,344320,11484362,0.99,Latin,Sambas De Enredo 2001,Various Artists Mocidade,"Domenil/J. Brito/Joaozinho/Rap, Marcelo Do",261720,8817757,0.99,Latin,Sambas De Enredo 2001,Various Artists Unidos Da Tijuca,"Douglas/Neves, Vicente Das/Silva, Gilmar L./Toninho Gentil/Wantuir",338834,11440689,0.99,Latin,Sambas De Enredo 2001,Various Artists Salgueiro,"Augusto/Craig Negoescu/Rocco Filho/Saara, Ze Carlos Da",305920,10294741,0.99,Latin,Sambas De Enredo 2001,Various Artists Mangueira,Bizuca/Clóvis Pê/Gilson Bernini/Marelo D'Aguia,298318,9999506,0.99,Latin,Sambas De Enredo 2001,Various Artists União Da Ilha,"Dito/Djalma Falcao/Ilha, Almir Da/Márcio André",330945,11100945,0.99,Latin,Sambas De Enredo 2001,Various Artists Grande Rio,Carlos Santos/Ciro/Claudio Russo/Zé Luiz,307252,10251428,0.99,Latin,Sambas De Enredo 2001,Various Artists Portela,Flavio Bororo/Paulo Apparicio/Wagner Alves/Zeca Sereno,319608,10712216,0.99,Latin,Sambas De Enredo 2001,Various Artists Caprichosos,Gule/Jorge 101/Lequinho/Luiz Piao,351320,11870956,0.99,Latin,Sambas De Enredo 2001,Various Artists Tradição,Adalto Magalha/Lourenco,269165,9114880,0.99,Latin,Sambas De Enredo 2001,Various Artists Império Serrano,Arlindo Cruz/Carlos Sena/Elmo Caetano/Mauricao,334942,11161196,0.99,Latin,Sambas De Enredo 2001,Various Artists Tuiuti,"Claudio Martins/David Lima/Kleber Rodrigues/Livre, Cesare Som",259657,8749492,0.99,Latin,Sambas De Enredo 2001,Various Artists (Da Le) Yaleo,Santana,353488,11769507,0.99,Rock,Supernatural,Santana Love Of My Life,Carlos Santana & Dave Matthews,347820,11634337,0.99,Rock,Supernatural,Santana Put Your Lights On,E. Shrody,285178,9394769,0.99,Rock,Supernatural,Santana Africa Bamba,"I. Toure, S. Tidiane Toure, Carlos Santana & K. Perazzo",282827,9492487,0.99,Rock,Supernatural,Santana Smooth,M. Itaal Shur & Rob Thomas,298161,9867455,0.99,Rock,Supernatural,Santana Do You Like The Way,L. Hill,354899,11741062,0.99,Rock,Supernatural,Santana Maria Maria,"W. Jean, J. Duplessis, Carlos Santana, K. Perazzo & R. Rekow",262635,8664601,0.99,Rock,Supernatural,Santana Migra,"R. Taha, Carlos Santana & T. Lindsay",329064,10963305,0.99,Rock,Supernatural,Santana Corazon Espinado,F. Olivera,276114,9206802,0.99,Rock,Supernatural,Santana Wishing It Was,"Eale-Eye Cherry, M. Simpson, J. King & M. Nishita",292832,9771348,0.99,Rock,Supernatural,Santana El Farol,Carlos Santana & KC Porter,291160,9599353,0.99,Rock,Supernatural,Santana Primavera,KC Porter & JB Eckl,378618,12504234,0.99,Rock,Supernatural,Santana The Calling,Carlos Santana & C. Thompson,747755,24703884,0.99,Rock,Supernatural,Santana Solução,,247431,8100449,0.99,Latin,The Best of Ed Motta,Ed Motta Manuel,,230269,7677671,0.99,Latin,The Best of Ed Motta,Ed Motta Entre E Ouça,,286302,9391004,0.99,Latin,The Best of Ed Motta,Ed Motta Um Contrato Com Deus,,202501,6636465,0.99,Latin,The Best of Ed Motta,Ed Motta Um Jantar Pra Dois,,244009,8021589,0.99,Latin,The Best of Ed Motta,Ed Motta Vamos Dançar,,226194,7617432,0.99,Latin,The Best of Ed Motta,Ed Motta Um Love,,181603,6095524,0.99,Latin,The Best of Ed Motta,Ed Motta Seis Da Tarde,,238445,7935898,0.99,Latin,The Best of Ed Motta,Ed Motta Baixo Rio,,198008,6521676,0.99,Latin,The Best of Ed Motta,Ed Motta Sombras Do Meu Destino,,280685,9161539,0.99,Latin,The Best of Ed Motta,Ed Motta Do You Have Other Loves?,,295235,9604273,0.99,Latin,The Best of Ed Motta,Ed Motta Agora Que O Dia Acordou,,323213,10572752,0.99,Latin,The Best of Ed Motta,Ed Motta Já!!!,,217782,7103608,0.99,Latin,The Best of Ed Motta,Ed Motta A Rua,,238027,7930264,0.99,Latin,The Best of Ed Motta,Ed Motta Now's The Time,Miles Davis,197459,6358868,0.99,Jazz,The Essential Miles Davis [Disc 1],Miles Davis Jeru,Miles Davis,193410,6222536,0.99,Jazz,The Essential Miles Davis [Disc 1],Miles Davis Compulsion,Miles Davis,345025,11254474,0.99,Jazz,The Essential Miles Davis [Disc 1],Miles Davis Tempus Fugit,Miles Davis,231784,7548434,0.99,Jazz,The Essential Miles Davis [Disc 1],Miles Davis Walkin',Miles Davis,807392,26411634,0.99,Jazz,The Essential Miles Davis [Disc 1],Miles Davis 'Round Midnight,Miles Davis,357459,11590284,0.99,Jazz,The Essential Miles Davis [Disc 1],Miles Davis Bye Bye Blackbird,Miles Davis,476003,15549224,0.99,Jazz,The Essential Miles Davis [Disc 1],Miles Davis New Rhumba,Miles Davis,277968,9018024,0.99,Jazz,The Essential Miles Davis [Disc 1],Miles Davis Generique,Miles Davis,168777,5437017,0.99,Jazz,The Essential Miles Davis [Disc 1],Miles Davis Summertime,Miles Davis,200437,6461370,0.99,Jazz,The Essential Miles Davis [Disc 1],Miles Davis So What,Miles Davis,564009,18360449,0.99,Jazz,The Essential Miles Davis [Disc 1],Miles Davis The Pan Piper,Miles Davis,233769,7593713,0.99,Jazz,The Essential Miles Davis [Disc 1],Miles Davis Someday My Prince Will Come,Miles Davis,544078,17890773,0.99,Jazz,The Essential Miles Davis [Disc 1],Miles Davis My Funny Valentine (Live),Miles Davis,907520,29416781,0.99,Jazz,The Essential Miles Davis [Disc 2],Miles Davis E.S.P.,Miles Davis,330684,11079866,0.99,Jazz,The Essential Miles Davis [Disc 2],Miles Davis Nefertiti,Miles Davis,473495,15478450,0.99,Jazz,The Essential Miles Davis [Disc 2],Miles Davis Petits Machins (Little Stuff),Miles Davis,487392,16131272,0.99,Jazz,The Essential Miles Davis [Disc 2],Miles Davis Miles Runs The Voodoo Down,Miles Davis,843964,27967919,0.99,Jazz,The Essential Miles Davis [Disc 2],Miles Davis Little Church (Live),Miles Davis,196101,6273225,0.99,Jazz,The Essential Miles Davis [Disc 2],Miles Davis Black Satin,Miles Davis,316682,10529483,0.99,Jazz,The Essential Miles Davis [Disc 2],Miles Davis Jean Pierre (Live),Miles Davis,243461,7955114,0.99,Jazz,The Essential Miles Davis [Disc 2],Miles Davis Time After Time,Miles Davis,220734,7292197,0.99,Jazz,The Essential Miles Davis [Disc 2],Miles Davis Portia,Miles Davis,378775,12520126,0.99,Jazz,The Essential Miles Davis [Disc 2],Miles Davis Space Truckin',Blackmore/Gillan/Glover/Lord/Paice,1196094,39267613,0.99,Rock,The Final Concerts (Disc 2),Deep Purple Going Down / Highway Star,Gillan/Glover/Lord/Nix - Blackmore/Paice,913658,29846063,0.99,Rock,The Final Concerts (Disc 2),Deep Purple Mistreated (Alternate Version),Blackmore/Coverdale,854700,27775442,0.99,Rock,The Final Concerts (Disc 2),Deep Purple You Fool No One (Alternate Version),Blackmore/Coverdale/Lord/Paice,763924,24887209,0.99,Rock,The Final Concerts (Disc 2),Deep Purple Jeepers Creepers,,185965,5991903,0.99,Jazz,Up An' Atom,Gene Krupa Blue Rythm Fantasy,,348212,11204006,0.99,Jazz,Up An' Atom,Gene Krupa Drum Boogie,,191555,6185636,0.99,Jazz,Up An' Atom,Gene Krupa Let Me Off Uptown,,187637,6034685,0.99,Jazz,Up An' Atom,Gene Krupa Leave Us Leap,,182726,5898810,0.99,Jazz,Up An' Atom,Gene Krupa Opus No.1,,179800,5846041,0.99,Jazz,Up An' Atom,Gene Krupa Boogie Blues,,204199,6603153,0.99,Jazz,Up An' Atom,Gene Krupa How High The Moon,,201430,6529487,0.99,Jazz,Up An' Atom,Gene Krupa Disc Jockey Jump,,193149,6260820,0.99,Jazz,Up An' Atom,Gene Krupa Up An' Atom,,179565,5822645,0.99,Jazz,Up An' Atom,Gene Krupa Bop Boogie,,189596,6093124,0.99,Jazz,Up An' Atom,Gene Krupa Lemon Drop,,194089,6287531,0.99,Jazz,Up An' Atom,Gene Krupa Coronation Drop,,176222,5899898,0.99,Jazz,Up An' Atom,Gene Krupa Overtime,,163030,5432236,0.99,Jazz,Up An' Atom,Gene Krupa Imagination,,289306,9444385,0.99,Jazz,Up An' Atom,Gene Krupa Don't Take Your Love From Me,,282331,9244238,0.99,Jazz,Up An' Atom,Gene Krupa Midget,,217025,7257663,0.99,Jazz,Up An' Atom,Gene Krupa I'm Coming Virginia,,280163,9209827,0.99,Jazz,Up An' Atom,Gene Krupa Payin' Them Dues Blues,,198556,6536918,0.99,Jazz,Up An' Atom,Gene Krupa Jungle Drums,,199627,6546063,0.99,Jazz,Up An' Atom,Gene Krupa Showcase,,201560,6697510,0.99,Jazz,Up An' Atom,Gene Krupa Swedish Schnapps,,191268,6359750,0.99,Jazz,Up An' Atom,Gene Krupa Samba Da Bênção,,409965,13490008,0.99,Bossa Nova,Vinícius De Moraes - Sem Limite,Toquinho & Vinícius Pot-Pourri N.º 4,,392437,13125975,0.99,Bossa Nova,Vinícius De Moraes - Sem Limite,Toquinho & Vinícius Onde Anda Você,,168437,5550356,0.99,Bossa Nova,Vinícius De Moraes - Sem Limite,Toquinho & Vinícius Samba Da Volta,,170631,5676090,0.99,Bossa Nova,Vinícius De Moraes - Sem Limite,Toquinho & Vinícius Canto De Ossanha,,204956,6771624,0.99,Bossa Nova,Vinícius De Moraes - Sem Limite,Toquinho & Vinícius Pot-Pourri N.º 5,,219898,7117769,0.99,Bossa Nova,Vinícius De Moraes - Sem Limite,Toquinho & Vinícius Formosa,,137482,4560873,0.99,Bossa Nova,Vinícius De Moraes - Sem Limite,Toquinho & Vinícius Como É Duro Trabalhar,,226168,7541177,0.99,Bossa Nova,Vinícius De Moraes - Sem Limite,Toquinho & Vinícius Minha Namorada,,244297,7927967,0.99,Bossa Nova,Vinícius De Moraes - Sem Limite,Toquinho & Vinícius Por Que Será,,162142,5371483,0.99,Bossa Nova,Vinícius De Moraes - Sem Limite,Toquinho & Vinícius Berimbau,,190667,6335548,0.99,Bossa Nova,Vinícius De Moraes - Sem Limite,Toquinho & Vinícius Deixa,,179826,5932799,0.99,Bossa Nova,Vinícius De Moraes - Sem Limite,Toquinho & Vinícius Pot-Pourri N.º 2,,211748,6878359,0.99,Bossa Nova,Vinícius De Moraes - Sem Limite,Toquinho & Vinícius Samba Em Prelúdio,,212636,6923473,0.99,Bossa Nova,Vinícius De Moraes - Sem Limite,Toquinho & Vinícius Carta Ao Tom 74,,162560,5382354,0.99,Bossa Nova,Vinícius De Moraes - Sem Limite,Toquinho & Vinícius Linha de Passe (João Bosco),,230948,7902328,0.99,Latin,Vozes do MPB,Various Artists Pela Luz dos Olhos Teus (Miúcha e Tom Jobim),,163970,5399626,0.99,Latin,Vozes do MPB,Various Artists Chão de Giz (Elba Ramalho),,274834,9016916,0.99,Latin,Vozes do MPB,Various Artists Marina (Dorival Caymmi),,172643,5523628,0.99,Latin,Vozes do MPB,Various Artists Aquarela (Toquinho),,259944,8480140,0.99,Latin,Vozes do MPB,Various Artists Coração do Agreste (Fafá de Belém),,258194,8380320,0.99,Latin,Vozes do MPB,Various Artists Dona (Roupa Nova),,243356,7991295,0.99,Latin,Vozes do MPB,Various Artists Começaria Tudo Outra Vez (Maria Creuza),,206994,6851151,0.99,Latin,Vozes do MPB,Various Artists Caçador de Mim (Sá & Guarabyra),,238341,7751360,0.99,Latin,Vozes do MPB,Various Artists Romaria (Renato Teixeira),,244793,8033885,0.99,Latin,Vozes do MPB,Various Artists As Rosas Não Falam (Beth Carvalho),,116767,3836641,0.99,Latin,Vozes do MPB,Various Artists Wave (Os Cariocas),,130063,4298006,0.99,Latin,Vozes do MPB,Various Artists Garota de Ipanema (Dick Farney),,174367,5767474,0.99,Latin,Vozes do MPB,Various Artists Preciso Apender a Viver Só (Maysa),,143464,4642359,0.99,Latin,Vozes do MPB,Various Artists Susie Q,Hawkins-Lewis-Broadwater,275565,9043825,0.99,Rock,"Chronicle, Vol. 1",Creedence Clearwater Revival I Put A Spell On You,Jay Hawkins,272091,8943000,0.99,Rock,"Chronicle, Vol. 1",Creedence Clearwater Revival Proud Mary,J. C. Fogerty,189022,6229590,0.99,Rock,"Chronicle, Vol. 1",Creedence Clearwater Revival Bad Moon Rising,J. C. Fogerty,140146,4609835,0.99,Rock,"Chronicle, Vol. 1",Creedence Clearwater Revival Lodi,J. C. Fogerty,191451,6260214,0.99,Rock,"Chronicle, Vol. 1",Creedence Clearwater Revival Green River,J. C. Fogerty,154279,5105874,0.99,Rock,"Chronicle, Vol. 1",Creedence Clearwater Revival Commotion,J. C. Fogerty,162899,5354252,0.99,Rock,"Chronicle, Vol. 1",Creedence Clearwater Revival Down On The Corner,J. C. Fogerty,164858,5521804,0.99,Rock,"Chronicle, Vol. 1",Creedence Clearwater Revival Fortunate Son,J. C. Fogerty,140329,4617559,0.99,Rock,"Chronicle, Vol. 1",Creedence Clearwater Revival Travelin' Band,J. C. Fogerty,129358,4270414,0.99,Rock,"Chronicle, Vol. 1",Creedence Clearwater Revival Who'll Stop The Rain,J. C. Fogerty,149394,4899579,0.99,Rock,"Chronicle, Vol. 1",Creedence Clearwater Revival Up Around The Bend,J. C. Fogerty,162429,5368701,0.99,Rock,"Chronicle, Vol. 1",Creedence Clearwater Revival Run Through The Jungle,J. C. Fogerty,186044,6156567,0.99,Rock,"Chronicle, Vol. 1",Creedence Clearwater Revival Lookin' Out My Back Door,J. C. Fogerty,152946,5034670,0.99,Rock,"Chronicle, Vol. 1",Creedence Clearwater Revival Long As I Can See The Light,J. C. Fogerty,213237,6924024,0.99,Rock,"Chronicle, Vol. 1",Creedence Clearwater Revival I Heard It Through The Grapevine,Whitfield-Strong,664894,21947845,0.99,Rock,"Chronicle, Vol. 1",Creedence Clearwater Revival Have You Ever Seen The Rain?,J. C. Fogerty,160052,5263675,0.99,Rock,"Chronicle, Vol. 1",Creedence Clearwater Revival Hey Tonight,J. C. Fogerty,162847,5343807,0.99,Rock,"Chronicle, Vol. 1",Creedence Clearwater Revival Sweet Hitch-Hiker,J. C. Fogerty,175490,5716603,0.99,Rock,"Chronicle, Vol. 1",Creedence Clearwater Revival Someday Never Comes,J. C. Fogerty,239360,7945235,0.99,Rock,"Chronicle, Vol. 1",Creedence Clearwater Revival Walking On The Water,J.C. Fogerty,281286,9302129,0.99,Rock,"Chronicle, Vol. 2",Creedence Clearwater Revival "Suzie-Q, Pt. 2",J.C. Fogerty,244114,7986637,0.99,Rock,"Chronicle, Vol. 2",Creedence Clearwater Revival Born On The Bayou,J.C. Fogerty,316630,10361866,0.99,Rock,"Chronicle, Vol. 2",Creedence Clearwater Revival Good Golly Miss Molly,J.C. Fogerty,163604,5348175,0.99,Rock,"Chronicle, Vol. 2",Creedence Clearwater Revival Tombstone Shadow,J.C. Fogerty,218880,7209080,0.99,Rock,"Chronicle, Vol. 2",Creedence Clearwater Revival Wrote A Song For Everyone,J.C. Fogerty,296385,9675875,0.99,Rock,"Chronicle, Vol. 2",Creedence Clearwater Revival Night Time Is The Right Time,J.C. Fogerty,190119,6211173,0.99,Rock,"Chronicle, Vol. 2",Creedence Clearwater Revival Cotton Fields,J.C. Fogerty,178181,5919224,0.99,Rock,"Chronicle, Vol. 2",Creedence Clearwater Revival It Came Out Of The Sky,J.C. Fogerty,176718,5807474,0.99,Rock,"Chronicle, Vol. 2",Creedence Clearwater Revival Don't Look Now,J.C. Fogerty,131918,4366455,0.99,Rock,"Chronicle, Vol. 2",Creedence Clearwater Revival The Midnight Special,J.C. Fogerty,253596,8297482,0.99,Rock,"Chronicle, Vol. 2",Creedence Clearwater Revival Before You Accuse Me,J.C. Fogerty,207804,6815126,0.99,Rock,"Chronicle, Vol. 2",Creedence Clearwater Revival My Baby Left Me,J.C. Fogerty,140460,4633440,0.99,Rock,"Chronicle, Vol. 2",Creedence Clearwater Revival Pagan Baby,J.C. Fogerty,385619,12713813,0.99,Rock,"Chronicle, Vol. 2",Creedence Clearwater Revival (Wish I Could) Hideaway,J.C. Fogerty,228466,7432978,0.99,Rock,"Chronicle, Vol. 2",Creedence Clearwater Revival It's Just A Thought,J.C. Fogerty,237374,7778319,0.99,Rock,"Chronicle, Vol. 2",Creedence Clearwater Revival Molina,J.C. Fogerty,163239,5390811,0.99,Rock,"Chronicle, Vol. 2",Creedence Clearwater Revival Born To Move,J.C. Fogerty,342804,11260814,0.99,Rock,"Chronicle, Vol. 2",Creedence Clearwater Revival Lookin' For A Reason,J.C. Fogerty,209789,6933135,0.99,Rock,"Chronicle, Vol. 2",Creedence Clearwater Revival Hello Mary Lou,J.C. Fogerty,132832,4476563,0.99,Rock,"Chronicle, Vol. 2",Creedence Clearwater Revival Gatas Extraordinárias,,212506,7095702,0.99,Latin,Cássia Eller - Coleção Sem Limite [Disc 2],Cássia Eller Brasil,,243696,7911683,0.99,Latin,Cássia Eller - Coleção Sem Limite [Disc 2],Cássia Eller Eu Sou Neguinha (Ao Vivo),,251768,8376000,0.99,Latin,Cássia Eller - Coleção Sem Limite [Disc 2],Cássia Eller Geração Coca-Cola (Ao Vivo),,228153,7573301,0.99,Latin,Cássia Eller - Coleção Sem Limite [Disc 2],Cássia Eller Lanterna Dos Afogados,,204538,6714582,0.99,Latin,Cássia Eller - Coleção Sem Limite [Disc 2],Cássia Eller Coroné Antonio Bento,,200437,6713066,0.99,Latin,Cássia Eller - Coleção Sem Limite [Disc 2],Cássia Eller "Você Passa, Eu Acho Graça (Ao Vivo)",,206733,6943576,0.99,Latin,Cássia Eller - Coleção Sem Limite [Disc 2],Cássia Eller Meu Mundo Fica Completo (Com Você),,247771,8322240,0.99,Latin,Cássia Eller - Coleção Sem Limite [Disc 2],Cássia Eller 1° De Julho,,270262,9017535,0.99,Latin,Cássia Eller - Coleção Sem Limite [Disc 2],Cássia Eller Música Urbana 2,,194899,6383472,0.99,Latin,Cássia Eller - Coleção Sem Limite [Disc 2],Cássia Eller Vida Bandida (Ao Vivo),,192626,6360785,0.99,Latin,Cássia Eller - Coleção Sem Limite [Disc 2],Cássia Eller Palavras Ao Vento,,212453,7048676,0.99,Latin,Cássia Eller - Coleção Sem Limite [Disc 2],Cássia Eller Não Sei O Que Eu Quero Da Vida,,151849,5024963,0.99,Latin,Cássia Eller - Coleção Sem Limite [Disc 2],Cássia Eller Woman Is The Nigger Of The World (Ao Vivo),,298919,9724145,0.99,Latin,Cássia Eller - Coleção Sem Limite [Disc 2],Cássia Eller Juventude Transviada (Ao Vivo),,278622,9183808,0.99,Latin,Cássia Eller - Coleção Sem Limite [Disc 2],Cássia Eller Malandragem,,247588,8165048,0.99,Latin,Cássia Eller - Sem Limite [Disc 1],Cássia Eller O Segundo Sol,,252133,8335629,0.99,Latin,Cássia Eller - Sem Limite [Disc 1],Cássia Eller Smells Like Teen Spirit (Ao Vivo),,316865,10384506,0.99,Latin,Cássia Eller - Sem Limite [Disc 1],Cássia Eller E.C.T.,,227500,7571834,0.99,Latin,Cássia Eller - Sem Limite [Disc 1],Cássia Eller Todo Amor Que Houver Nesta Vida,,227160,7420347,0.99,Latin,Cássia Eller - Sem Limite [Disc 1],Cássia Eller Metrô. Linha 743,,174654,5837495,0.99,Latin,Cássia Eller - Sem Limite [Disc 1],Cássia Eller Nós (Ao Vivo),,193828,6498661,0.99,Latin,Cássia Eller - Sem Limite [Disc 1],Cássia Eller Na Cadência Do Samba,,196075,6483952,0.99,Latin,Cássia Eller - Sem Limite [Disc 1],Cássia Eller Admirável Gado Novo,,274390,9144031,0.99,Latin,Cássia Eller - Sem Limite [Disc 1],Cássia Eller Eleanor Rigby,,189466,6303205,0.99,Latin,Cássia Eller - Sem Limite [Disc 1],Cássia Eller Socorro,,258586,8549393,0.99,Latin,Cássia Eller - Sem Limite [Disc 1],Cássia Eller Blues Da Piedade,,257123,8472964,0.99,Latin,Cássia Eller - Sem Limite [Disc 1],Cássia Eller Rubens,,211853,7026317,0.99,Latin,Cássia Eller - Sem Limite [Disc 1],Cássia Eller Não Deixe O Samba Morrer - Cassia Eller e Alcione,,268173,8936345,0.99,Latin,Cássia Eller - Sem Limite [Disc 1],Cássia Eller Mis Penas Lloraba Yo (Ao Vivo) Soy Gitano (Tangos),,188473,6195854,0.99,Latin,Cássia Eller - Sem Limite [Disc 1],Cássia Eller Comin' Home,Bolin/Coverdale/Paice,235781,7644604,0.99,Rock,Come Taste The Band,Deep Purple Lady Luck,Cook/Coverdale,168202,5501379,0.99,Rock,Come Taste The Band,Deep Purple Gettin' Tighter,Bolin/Hughes,218044,7176909,0.99,Rock,Come Taste The Band,Deep Purple Dealer,Bolin/Coverdale,230922,7591066,0.99,Rock,Come Taste The Band,Deep Purple I Need Love,Bolin/Coverdale,263836,8701064,0.99,Rock,Come Taste The Band,Deep Purple Drifter,Bolin/Coverdale,242834,8001505,0.99,Rock,Come Taste The Band,Deep Purple Love Child,Bolin/Coverdale,188160,6173806,0.99,Rock,Come Taste The Band,Deep Purple This Time Around / Owed to 'G' [Instrumental],Bolin/Hughes/Lord,370102,11995679,0.99,Rock,Come Taste The Band,Deep Purple You Keep On Moving,Coverdale/Hughes,319111,10447868,0.99,Rock,Come Taste The Band,Deep Purple Speed King,"Blackmore, Gillan, Glover, Lord, Paice",264385,8587578,0.99,Rock,Deep Purple In Rock,Deep Purple Bloodsucker,"Blackmore, Gillan, Glover, Lord, Paice",256261,8344405,0.99,Rock,Deep Purple In Rock,Deep Purple Child In Time,"Blackmore, Gillan, Glover, Lord, Paice",620460,20230089,0.99,Rock,Deep Purple In Rock,Deep Purple Flight Of The Rat,"Blackmore, Gillan, Glover, Lord, Paice",478302,15563967,0.99,Rock,Deep Purple In Rock,Deep Purple Into The Fire,"Blackmore, Gillan, Glover, Lord, Paice",210259,6849310,0.99,Rock,Deep Purple In Rock,Deep Purple Living Wreck,"Blackmore, Gillan, Glover, Lord, Paice",274886,8993056,0.99,Rock,Deep Purple In Rock,Deep Purple Hard Lovin' Man,"Blackmore, Gillan, Glover, Lord, Paice",431203,13931179,0.99,Rock,Deep Purple In Rock,Deep Purple Fireball,"Ritchie Blackmore, Ian Gillan, Roger Glover, Jon Lord, Ian Paice",204721,6714807,0.99,Rock,Fireball,Deep Purple No No No,"Ritchie Blackmore, Ian Gillan, Roger Glover, Jon Lord, Ian Paice",414902,13646606,0.99,Rock,Fireball,Deep Purple Strange Kind Of Woman,"Ritchie Blackmore, Ian Gillan, Roger Glover, Jon Lord, Ian Paice",247092,8072036,0.99,Rock,Fireball,Deep Purple Anyone's Daughter,"Ritchie Blackmore, Ian Gillan, Roger Glover, Jon Lord, Ian Paice",284682,9354480,0.99,Rock,Fireball,Deep Purple The Mule,"Ritchie Blackmore, Ian Gillan, Roger Glover, Jon Lord, Ian Paice",322063,10638390,0.99,Rock,Fireball,Deep Purple Fools,"Ritchie Blackmore, Ian Gillan, Roger Glover, Jon Lord, Ian Paice",500427,16279366,0.99,Rock,Fireball,Deep Purple No One Came,"Ritchie Blackmore, Ian Gillan, Roger Glover, Jon Lord, Ian Paice",385880,12643813,0.99,Rock,Fireball,Deep Purple Knocking 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 Bad 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 Child 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 Nobody'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 Black 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 Perfect 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 The 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 Call 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 Hush,South,213054,6944928,0.99,Rock,Knocking at Your Back Door: The Best Of Deep Purple in the 80's,Deep Purple Smoke 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 Space 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 Highway Star,Ian Gillan/Ian Paice/Jon Lord/Ritchie Blckmore/Roger Glover,368770,12012452,0.99,Rock,Machine Head,Deep Purple Maybe I'm A Leo,Ian Gillan/Ian Paice/Jon Lord/Ritchie Blckmore/Roger Glover,290455,9502646,0.99,Rock,Machine Head,Deep Purple Pictures Of Home,Ian Gillan/Ian Paice/Jon Lord/Ritchie Blckmore/Roger Glover,303777,9903835,0.99,Rock,Machine Head,Deep Purple Never Before,Ian Gillan/Ian Paice/Jon Lord/Ritchie Blckmore/Roger Glover,239830,7832790,0.99,Rock,Machine Head,Deep Purple Smoke On The Water,Ian Gillan/Ian Paice/Jon Lord/Ritchie Blckmore/Roger Glover,340871,11246496,0.99,Rock,Machine Head,Deep Purple Lazy,Ian Gillan/Ian Paice/Jon Lord/Ritchie Blckmore/Roger Glover,442096,14397671,0.99,Rock,Machine Head,Deep Purple Space Truckin',Ian Gillan/Ian Paice/Jon Lord/Ritchie Blckmore/Roger Glover,272796,8981030,0.99,Rock,Machine Head,Deep Purple Vavoom : Ted The Mechanic,"Ian Gillan, Roger Glover, Jon Lord, Steve Morse, Ian Paice",257384,8510755,0.99,Rock,Purpendicular,Deep Purple Loosen My Strings,"Ian Gillan, Roger Glover, Jon Lord, Steve Morse, Ian Paice",359680,11702232,0.99,Rock,Purpendicular,Deep Purple Soon Forgotten,"Ian Gillan, Roger Glover, Jon Lord, Steve Morse, Ian Paice",287791,9401383,0.99,Rock,Purpendicular,Deep Purple Sometimes I Feel Like Screaming,"Ian Gillan, Roger Glover, Jon Lord, Steve Morse, Ian Paice",451840,14789410,0.99,Rock,Purpendicular,Deep Purple Cascades : I'm Not Your Lover,"Ian Gillan, Roger Glover, Jon Lord, Steve Morse, Ian Paice",283689,9209693,0.99,Rock,Purpendicular,Deep Purple The Aviator,"Ian Gillan, Roger Glover, Jon Lord, Steve Morse, Ian Paice",320992,10532053,0.99,Rock,Purpendicular,Deep Purple Rosa's Cantina,"Ian Gillan, Roger Glover, Jon Lord, Steve Morse, Ian Paice",312372,10323804,0.99,Rock,Purpendicular,Deep Purple A Castle Full Of Rascals,"Ian Gillan, Roger Glover, Jon Lord, Steve Morse, Ian Paice",311693,10159566,0.99,Rock,Purpendicular,Deep Purple A Touch Away,"Ian Gillan, Roger Glover, Jon Lord, Steve Morse, Ian Paice",276323,9098561,0.99,Rock,Purpendicular,Deep Purple Hey Cisco,"Ian Gillan, Roger Glover, Jon Lord, Steve Morse, Ian Paice",354089,11600029,0.99,Rock,Purpendicular,Deep Purple Somebody Stole My Guitar,"Ian Gillan, Roger Glover, Jon Lord, Steve Morse, Ian Paice",249443,8180421,0.99,Rock,Purpendicular,Deep Purple The Purpendicular Waltz,"Ian Gillan, Roger Glover, Jon Lord, Steve Morse, Ian Paice",283924,9299131,0.99,Rock,Purpendicular,Deep Purple King Of Dreams,"Blackmore, Glover, Turner",328385,10733847,0.99,Rock,Slaves And Masters,Deep Purple The Cut Runs Deep,"Blackmore, Glover, Turner, Lord, Paice",342752,11191650,0.99,Rock,Slaves And Masters,Deep Purple Fire In The Basement,"Blackmore, Glover, Turner, Lord, Paice",283977,9267550,0.99,Rock,Slaves And Masters,Deep Purple Truth Hurts,"Blackmore, Glover, Turner",314827,10224612,0.99,Rock,Slaves And Masters,Deep Purple Breakfast In Bed,"Blackmore, Glover, Turner",317126,10323804,0.99,Rock,Slaves And Masters,Deep Purple Love Conquers All,"Blackmore, Glover, Turner",227186,7328516,0.99,Rock,Slaves And Masters,Deep Purple Fortuneteller,"Blackmore, Glover, Turner, Lord, Paice",349335,11369671,0.99,Rock,Slaves And Masters,Deep Purple Too Much Is Not Enough,"Turner, Held, Greenwood",257724,8382800,0.99,Rock,Slaves And Masters,Deep Purple Wicked Ways,"Blackmore, Glover, Turner, Lord, Paice",393691,12826582,0.99,Rock,Slaves And Masters,Deep Purple Stormbringer,D.Coverdale/R.Blackmore/Ritchie Blackmore,246413,8044864,0.99,Rock,Stormbringer,Deep Purple Love 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 Holy Man,D.Coverdale/G.Hughes/Glenn Hughes/J.Lord/John Lord,270236,8818093,0.99,Rock,Stormbringer,Deep Purple Hold On,D.Coverdal/G.Hughes/Glenn Hughes/I.Paice/Ian Paice/J.Lord/John Lord,306860,10022428,0.99,Rock,Stormbringer,Deep Purple Lady Double Dealer,D.Coverdale/R.Blackmore/Ritchie Blackmore,201482,6554330,0.99,Rock,Stormbringer,Deep Purple You 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 High 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 The 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 Soldier Of Fortune,D.Coverdale/R.Blackmore/Ritchie Blackmore,193750,6315321,0.99,Rock,Stormbringer,Deep Purple The Battle Rages On,ian paice/jon lord,356963,11626228,0.99,Rock,The Battle Rages On,Deep Purple Lick It Up,roger glover,240274,7792604,0.99,Rock,The Battle Rages On,Deep Purple Anya,jon lord/roger glover,392437,12754921,0.99,Rock,The Battle Rages On,Deep Purple Talk About Love,roger glover,247823,8072171,0.99,Rock,The Battle Rages On,Deep Purple Time To Kill,roger glover,351033,11354742,0.99,Rock,The Battle Rages On,Deep Purple Ramshackle Man,roger glover,334445,10874679,0.99,Rock,The Battle Rages On,Deep Purple A Twist In The Tail,roger glover,257462,8413103,0.99,Rock,The Battle Rages On,Deep Purple Nasty Piece Of Work,jon lord/roger glover,276662,9076997,0.99,Rock,The Battle Rages On,Deep Purple Solitaire,roger glover,282226,9157021,0.99,Rock,The Battle Rages On,Deep Purple One Man's Meat,roger glover,278804,9068960,0.99,Rock,The Battle Rages On,Deep Purple Pour Some Sugar On Me,,292519,9518842,0.99,Rock,Vault: Def Leppard's Greatest Hits,Def Leppard Photograph,,248633,8108507,0.99,Rock,Vault: Def Leppard's Greatest Hits,Def Leppard Love Bites,,346853,11305791,0.99,Rock,Vault: Def Leppard's Greatest Hits,Def Leppard Let's Get Rocked,,296019,9724150,0.99,Rock,Vault: Def Leppard's Greatest Hits,Def Leppard Two Steps Behind [Acoustic Version],,259787,8523388,0.99,Rock,Vault: Def Leppard's Greatest Hits,Def Leppard Animal,,244741,7985133,0.99,Rock,Vault: Def Leppard's Greatest Hits,Def Leppard Heaven Is,,214021,6988128,0.99,Rock,Vault: Def Leppard's Greatest Hits,Def Leppard Rocket,,247248,8092463,0.99,Rock,Vault: Def Leppard's Greatest Hits,Def Leppard When Love & Hate Collide,,257280,8364633,0.99,Rock,Vault: Def Leppard's Greatest Hits,Def Leppard Action,,220604,7130830,0.99,Rock,Vault: Def Leppard's Greatest Hits,Def Leppard Make Love Like A Man,,255660,8309725,0.99,Rock,Vault: Def Leppard's Greatest Hits,Def Leppard Armageddon It,,322455,10522352,0.99,Rock,Vault: Def Leppard's Greatest Hits,Def Leppard Have You Ever Needed Someone So Bad,,319320,10400020,0.99,Rock,Vault: Def Leppard's Greatest Hits,Def Leppard Rock Of Ages,,248424,8150318,0.99,Rock,Vault: Def Leppard's Greatest Hits,Def Leppard Hysteria,,355056,11622738,0.99,Rock,Vault: Def Leppard's Greatest Hits,Def Leppard Bringin' On The Heartbreak,,272457,8853324,0.99,Rock,Vault: Def Leppard's Greatest Hits,Def Leppard Roll Call,Jim Beard,321358,10653494,0.99,Jazz,Outbreak,Dennis Chambers Otay,"John Scofield, Robert Aries, Milton Chambers and Gary Grainger",423653,14176083,0.99,Jazz,Outbreak,Dennis Chambers Groovus Interruptus,Jim Beard,319373,10602166,0.99,Jazz,Outbreak,Dennis Chambers Paris On Mine,Jon Herington,368875,12059507,0.99,Jazz,Outbreak,Dennis Chambers In Time,Sylvester Stewart,368953,12287103,0.99,Jazz,Outbreak,Dennis Chambers Plan B,"Dean Brown, Dennis Chambers & Jim Beard",272039,9032315,0.99,Jazz,Outbreak,Dennis Chambers Outbreak,Jim Beard & Jon Herington,659226,21685807,0.99,Jazz,Outbreak,Dennis Chambers "Baltimore, DC",John Scofield,346932,11394473,0.99,Jazz,Outbreak,Dennis Chambers Talkin Loud and Saying Nothin,James Brown & Bobby Byrd,360411,11994859,0.99,Jazz,Outbreak,Dennis Chambers Pétala,,270080,8856165,0.99,Latin,Djavan Ao Vivo - Vol. 02,Djavan Meu Bem-Querer,,255608,8330047,0.99,Latin,Djavan Ao Vivo - Vol. 02,Djavan Cigano,,304692,10037362,0.99,Latin,Djavan Ao Vivo - Vol. 02,Djavan Boa Noite,,338755,11283582,0.99,Latin,Djavan Ao Vivo - Vol. 02,Djavan Fato Consumado,,211565,7018586,0.99,Latin,Djavan Ao Vivo - Vol. 02,Djavan Faltando Um Pedaço,,267728,8788760,0.99,Latin,Djavan Ao Vivo - Vol. 02,Djavan Álibi,,213237,6928434,0.99,Latin,Djavan Ao Vivo - Vol. 02,Djavan Esquinas,,280999,9096726,0.99,Latin,Djavan Ao Vivo - Vol. 02,Djavan Se...,,286432,9413777,0.99,Latin,Djavan Ao Vivo - Vol. 02,Djavan Eu Te Devoro,,311614,10312775,0.99,Latin,Djavan Ao Vivo - Vol. 02,Djavan Lilás,,274181,9049542,0.99,Latin,Djavan Ao Vivo - Vol. 02,Djavan Acelerou,,284081,9396942,0.99,Latin,Djavan Ao Vivo - Vol. 02,Djavan Um Amor Puro,,327784,10687311,0.99,Latin,Djavan Ao Vivo - Vol. 02,Djavan Samurai,Djavan,330997,10872787,0.99,Latin,Djavan Ao Vivo - Vol. 1,Djavan Nem Um Dia,Djavan,337423,11181446,0.99,Latin,Djavan Ao Vivo - Vol. 1,Djavan Oceano,Djavan,217338,7026441,0.99,Latin,Djavan Ao Vivo - Vol. 1,Djavan Açai,Djavan,270968,8893682,0.99,Latin,Djavan Ao Vivo - Vol. 1,Djavan Serrado,Djavan,295314,9842240,0.99,Latin,Djavan Ao Vivo - Vol. 1,Djavan Flor De Lis,Djavan,236355,7801108,0.99,Latin,Djavan Ao Vivo - Vol. 1,Djavan Amar É Tudo,Djavan,211617,7073899,0.99,Latin,Djavan Ao Vivo - Vol. 1,Djavan Azul,Djavan,253962,8381029,0.99,Latin,Djavan Ao Vivo - Vol. 1,Djavan Seduzir,Djavan,277524,9163253,0.99,Latin,Djavan Ao Vivo - Vol. 1,Djavan A Carta,"Djavan - Gabriel, O Pensador",347297,11493463,0.99,Latin,Djavan Ao Vivo - Vol. 1,Djavan Sina,Djavan,268173,8906539,0.99,Latin,Djavan Ao Vivo - Vol. 1,Djavan Acelerou,Djavan,284133,9391439,0.99,Latin,Djavan Ao Vivo - Vol. 1,Djavan Um Amor Puro,Djavan,327105,10664698,0.99,Latin,Djavan Ao Vivo - Vol. 1,Djavan O Bêbado e a Equilibrista,,223059,7306143,0.99,Latin,Elis Regina-Minha História,Elis Regina O Mestre-Sala dos Mares,,186226,6180414,0.99,Latin,Elis Regina-Minha História,Elis Regina Atrás da Porta,,166608,5432518,0.99,Latin,Elis Regina-Minha História,Elis Regina "Dois Pra Lá, Dois Pra Cá",,263026,8684639,0.99,Latin,Elis Regina-Minha História,Elis Regina Casa no Campo,,170788,5531841,0.99,Latin,Elis Regina-Minha História,Elis Regina Romaria,,242834,7968525,0.99,Latin,Elis Regina-Minha História,Elis Regina "Alô, Alô, Marciano",,241397,8137254,0.99,Latin,Elis Regina-Minha História,Elis Regina Me Deixas Louca,,214831,6888030,0.99,Latin,Elis Regina-Minha História,Elis Regina Fascinação,,180793,5793959,0.99,Latin,Elis Regina-Minha História,Elis Regina Saudosa Maloca,,278125,9059416,0.99,Latin,Elis Regina-Minha História,Elis Regina As Aparências Enganam,,247379,8014346,0.99,Latin,Elis Regina-Minha História,Elis Regina Madalena,,157387,5243721,0.99,Latin,Elis Regina-Minha História,Elis Regina Maria Rosa,,232803,7592504,0.99,Latin,Elis Regina-Minha História,Elis Regina Aprendendo A Jogar,,290664,9391041,0.99,Latin,Elis Regina-Minha História,Elis Regina Layla,Clapton/Gordon,430733,14115792,0.99,Blues,The Cream Of Clapton,Eric Clapton Badge,Clapton/Harrison,163552,5322942,0.99,Blues,The Cream Of Clapton,Eric Clapton I Feel Free,Bruce/Clapton,174576,5725684,0.99,Blues,The Cream Of Clapton,Eric Clapton Sunshine Of Your Love,Bruce/Clapton,252891,8225889,0.99,Blues,The Cream Of Clapton,Eric Clapton Crossroads,Clapton/Robert Johnson Arr: Eric Clapton,253335,8273540,0.99,Blues,The Cream Of Clapton,Eric Clapton Strange Brew,Clapton/Collins/Pappalardi,167810,5489787,0.99,Blues,The Cream Of Clapton,Eric Clapton White Room,Bruce/Clapton,301583,9872606,0.99,Blues,The Cream Of Clapton,Eric Clapton Bell Bottom Blues,Clapton,304744,9946681,0.99,Blues,The Cream Of Clapton,Eric Clapton Cocaine,Cale/Clapton,215928,7138399,0.99,Blues,The Cream Of Clapton,Eric Clapton I Shot The Sheriff,Marley,263862,8738973,0.99,Blues,The Cream Of Clapton,Eric Clapton After Midnight,Clapton/J. J. Cale,191320,6460941,0.99,Blues,The Cream Of Clapton,Eric Clapton Swing Low Sweet Chariot,Clapton/Trad. Arr. Clapton,208143,6896288,0.99,Blues,The Cream Of Clapton,Eric Clapton Lay Down Sally,Clapton/Levy,231732,7774207,0.99,Blues,The Cream Of Clapton,Eric Clapton Knockin On Heavens Door,Clapton/Dylan,264411,8758819,0.99,Blues,The Cream Of Clapton,Eric Clapton Wonderful Tonight,Clapton,221387,7326923,0.99,Blues,The Cream Of Clapton,Eric Clapton Let It Grow,Clapton,297064,9742568,0.99,Blues,The Cream Of Clapton,Eric Clapton Promises,Clapton/F.eldman/Linn,180401,6006154,0.99,Blues,The Cream Of Clapton,Eric Clapton I Can't Stand It,Clapton,249730,8271980,0.99,Blues,The Cream Of Clapton,Eric Clapton Signe,Eric Clapton,193515,6475042,0.99,Blues,Unplugged,Eric Clapton Before You Accuse Me,Eugene McDaniel,224339,7456807,0.99,Blues,Unplugged,Eric Clapton Hey Hey,Big Bill Broonzy,196466,6543487,0.99,Blues,Unplugged,Eric Clapton Tears In Heaven,"Eric Clapton, Will Jennings",274729,9032835,0.99,Blues,Unplugged,Eric Clapton Lonely Stranger,Eric Clapton,328724,10894406,0.99,Blues,Unplugged,Eric Clapton Nobody Knows You When You're Down & Out,Jimmy Cox,231836,7669922,0.99,Blues,Unplugged,Eric Clapton Layla,"Eric Clapton, Jim Gordon",285387,9490542,0.99,Blues,Unplugged,Eric Clapton Running On Faith,Jerry Lynn Williams,378984,12536275,0.99,Blues,Unplugged,Eric Clapton Walkin' Blues,Robert Johnson,226429,7435192,0.99,Blues,Unplugged,Eric Clapton Alberta,Traditional,222406,7412975,0.99,Blues,Unplugged,Eric Clapton San Francisco Bay Blues,Jesse Fuller,203363,6724021,0.99,Blues,Unplugged,Eric Clapton Malted Milk,Robert Johnson,216528,7096781,0.99,Blues,Unplugged,Eric Clapton Old Love,"Eric Clapton, Robert Cray",472920,15780747,0.99,Blues,Unplugged,Eric Clapton Rollin' And Tumblin',McKinley Morgenfield (Muddy Waters),251768,8407355,0.99,Blues,Unplugged,Eric Clapton A Novidade,Gilberto Gil,324780,10765600,0.99,Latin,Unplugged,Eric Clapton Tenho Sede,Gilberto Gil,261616,8708114,0.99,Latin,Unplugged,Eric Clapton Refazenda,Gilberto Gil,218305,7237784,0.99,Latin,Unplugged,Eric Clapton Realce,Gilberto Gil,264489,8847612,0.99,Latin,Unplugged,Eric Clapton Esotérico,Gilberto Gil,317779,10530533,0.99,Latin,Unplugged,Eric Clapton Drão,Gilberto Gil,301453,9931950,0.99,Latin,Unplugged,Eric Clapton A Paz,Gilberto Gil,293093,9593064,0.99,Latin,Unplugged,Eric Clapton Beira Mar,Gilberto Gil,295444,9597994,0.99,Latin,Unplugged,Eric Clapton Sampa,Gilberto Gil,225697,7469905,0.99,Latin,Unplugged,Eric Clapton Parabolicamará,Gilberto Gil,284943,9543435,0.99,Latin,Unplugged,Eric Clapton Tempo Rei,Gilberto Gil,302733,10019269,0.99,Latin,Unplugged,Eric Clapton Expresso 2222,Gilberto Gil,284760,9690577,0.99,Latin,Unplugged,Eric Clapton Aquele Abraço,Gilberto Gil,263993,8805003,0.99,Latin,Unplugged,Eric Clapton Palco,Gilberto Gil,270550,9049901,0.99,Latin,Unplugged,Eric Clapton Toda Menina Baiana,Gilberto Gil,278177,9351000,0.99,Latin,Unplugged,Eric Clapton Sítio Do Pica-Pau Amarelo,Gilberto Gil,218070,7217955,0.99,Latin,Unplugged,Eric Clapton Collision,Jon Hudson/Mike Patton,204303,6656596,0.99,Alternative & Punk,Album Of The Year,Faith No More Stripsearch,Jon Hudson/Mike Bordin/Mike Patton,270106,8861119,0.99,Alternative & Punk,Album Of The Year,Faith No More Last Cup Of Sorrow,Bill Gould/Mike Patton,251663,8221247,0.99,Alternative & Punk,Album Of The Year,Faith No More Naked In Front Of The Computer,Mike Patton,128757,4225077,0.99,Alternative & Punk,Album Of The Year,Faith No More Helpless,Bill Gould/Mike Bordin/Mike Patton,326217,10753135,0.99,Alternative & Punk,Album Of The Year,Faith No More Mouth To Mouth,Bill Gould/Jon Hudson/Mike Bordin/Mike Patton,228493,7505887,0.99,Alternative & Punk,Album Of The Year,Faith No More Ashes 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 She Loves Me Not,Bill Gould/Mike Bordin/Mike Patton,209867,6887544,0.99,Alternative & Punk,Album Of The Year,Faith No More Got That Feeling,Mike Patton,140852,4643227,0.99,Alternative & Punk,Album Of The Year,Faith No More Paths 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 Home Sick Home,Mike Patton,119040,3898976,0.99,Alternative & Punk,Album Of The Year,Faith No More Pristina,Bill Gould/Mike Patton,232698,7497361,0.99,Alternative & Punk,Album Of The Year,Faith No More Land Of Sunshine,,223921,7353567,0.99,Alternative & Punk,Angel Dust,Faith No More Caffeine,,267937,8747367,0.99,Alternative & Punk,Angel Dust,Faith No More Midlife Crisis,,263235,8628841,0.99,Alternative & Punk,Angel Dust,Faith No More RV,,223242,7288162,0.99,Alternative & Punk,Angel Dust,Faith No More Smaller And Smaller,,310831,10180103,0.99,Alternative & Punk,Angel Dust,Faith No More Everything's Ruined,,273658,9010917,0.99,Alternative & Punk,Angel Dust,Faith No More Malpractice,,241371,7900683,0.99,Alternative & Punk,Angel Dust,Faith No More Kindergarten,,270680,8853647,0.99,Alternative & Punk,Angel Dust,Faith No More Be Aggressive,,222432,7298027,0.99,Alternative & Punk,Angel Dust,Faith No More A Small Victory,,297168,9733572,0.99,Alternative & Punk,Angel Dust,Faith No More Crack Hitler,,279144,9162435,0.99,Alternative & Punk,Angel Dust,Faith No More Jizzlobber,,398341,12926140,0.99,Alternative & Punk,Angel Dust,Faith No More Midnight Cowboy,,251924,8242626,0.99,Alternative & Punk,Angel Dust,Faith No More Easy,,185835,6073008,0.99,Alternative & Punk,Angel Dust,Faith No More Get Out,"Mike Bordin, Billy Gould, Mike Patton",137482,4524972,0.99,Rock,King For A Day Fool For A Lifetime,Faith No More Ricochet,"Mike Bordin, Billy Gould, Mike Patton",269400,8808812,0.99,Rock,King For A Day Fool For A Lifetime,Faith No More Evidence,"Mike Bordin, Billy Gould, Mike Patton, Trey Spruance",293590,9626136,0.99,Rock,King For A Day Fool For A Lifetime,Faith No More The 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 Star A.D.,"Mike Bordin, Billy Gould, Mike Patton",203807,6747658,0.99,Rock,King For A Day Fool For A Lifetime,Faith No More Cuckoo 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 Caralho 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 Ugly 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 Digging The Grave,"Mike Bordin, Billy Gould, Mike Patton",185129,6109259,0.99,Rock,King For A Day Fool For A Lifetime,Faith No More Take 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 King 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 What A Day,"Mike Bordin, Billy Gould, Mike Patton",158275,5203430,0.99,Rock,King For A Day Fool For A Lifetime,Faith No More The 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 Just A Man,"Mike Bordin, Billy Gould, Mike Patton",336666,11031254,0.99,Rock,King For A Day Fool For A Lifetime,Faith No More Absolute Zero,"Mike Bordin, Billy Gould, Mike Patton",181995,5929427,0.99,Rock,King For A Day Fool For A Lifetime,Faith No More From Out Of Nowhere,Faith No More,202527,6587802,0.99,Alternative & Punk,The Real Thing,Faith No More Epic,Faith No More,294008,9631296,0.99,Alternative & Punk,The Real Thing,Faith No More Falling To Pieces,Faith No More,316055,10333123,0.99,Alternative & Punk,The Real Thing,Faith No More Surprise! You're Dead!,Faith No More,147226,4823036,0.99,Alternative & Punk,The Real Thing,Faith No More Zombie Eaters,Faith No More,360881,11835367,0.99,Alternative & Punk,The Real Thing,Faith No More The Real Thing,Faith No More,493635,16233080,0.99,Alternative & Punk,The Real Thing,Faith No More Underwater Love,Faith No More,231993,7634387,0.99,Alternative & Punk,The Real Thing,Faith No More The Morning After,Faith No More,223764,7355898,0.99,Alternative & Punk,The Real Thing,Faith No More Woodpecker From Mars,Faith No More,340532,11174250,0.99,Alternative & Punk,The Real Thing,Faith No More War Pigs,"Tony Iommi, Bill Ward, Geezer Butler, Ozzy Osbourne",464770,15267802,0.99,Alternative & Punk,The Real Thing,Faith No More Edge Of The World,Faith No More,250357,8235607,0.99,Alternative & Punk,The Real Thing,Faith No More Deixa Entrar,,33619,1095012,0.99,Latin,Deixa Entrar,Falamansa Falamansa Song,,237165,7921313,0.99,Latin,Deixa Entrar,Falamansa Xote Dos Milagres,,269557,8897778,0.99,Latin,Deixa Entrar,Falamansa Rindo À Toa,,222066,7365321,0.99,Latin,Deixa Entrar,Falamansa Confidência,,222197,7460829,0.99,Latin,Deixa Entrar,Falamansa Forró De Tóquio,,169273,5588756,0.99,Latin,Deixa Entrar,Falamansa Zeca Violeiro,,143673,4781949,0.99,Latin,Deixa Entrar,Falamansa Avisa,,355030,11844320,0.99,Latin,Deixa Entrar,Falamansa Principiando/Decolagem,,116767,3923789,0.99,Latin,Deixa Entrar,Falamansa Asas,,231915,7711669,0.99,Latin,Deixa Entrar,Falamansa Medo De Escuro,,213760,7056323,0.99,Latin,Deixa Entrar,Falamansa Oração,,271072,9003882,0.99,Latin,Deixa Entrar,Falamansa Minha Gata,,181838,6039502,0.99,Latin,Deixa Entrar,Falamansa Desaforo,,174524,5853561,0.99,Latin,Deixa Entrar,Falamansa In Your Honor,"Dave Grohl, Taylor Hawkins, Nate Mendel, Chris Shiflett",230191,7468463,0.99,Rock,In Your Honor [Disc 1],Foo Fighters No Way Back,"Dave Grohl, Taylor Hawkins, Nate Mendel, Chris Shiflett",196675,6421400,0.99,Rock,In Your Honor [Disc 1],Foo Fighters Best Of You,"Dave Grohl, Taylor Hawkins, Nate Mendel, Chris Shiflett",255712,8363467,0.99,Rock,In Your Honor [Disc 1],Foo Fighters DOA,"Dave Grohl, Taylor Hawkins, Nate Mendel, Chris Shiflett",252186,8232342,0.99,Rock,In Your Honor [Disc 1],Foo Fighters Hell,"Dave Grohl, Taylor Hawkins, Nate Mendel, Chris Shiflett",117080,3819255,0.99,Rock,In Your Honor [Disc 1],Foo Fighters The Last Song,"Dave Grohl, Taylor Hawkins, Nate Mendel, Chris Shiflett",199523,6496742,0.99,Rock,In Your Honor [Disc 1],Foo Fighters Free Me,"Dave Grohl, Taylor Hawkins, Nate Mendel, Chris Shiflett",278700,9109340,0.99,Rock,In Your Honor [Disc 1],Foo Fighters Resolve,"Dave Grohl, Taylor Hawkins, Nate Mendel, Chris Shiflett",288731,9416186,0.99,Rock,In Your Honor [Disc 1],Foo Fighters The Deepest Blues Are Black,"Dave Grohl, Taylor Hawkins, Nate Mendel, Chris Shiflett",238419,7735473,0.99,Rock,In Your Honor [Disc 1],Foo Fighters End Over End,"Dave Grohl, Taylor Hawkins, Nate Mendel, Chris Shiflett",352078,11395296,0.99,Rock,In Your Honor [Disc 1],Foo Fighters Still,"Dave Grohl, Taylor Hawkins, Nate Mendel, Chris Shiflett/FOO FIGHTERS",313182,10323157,0.99,Rock,In Your Honor [Disc 2],Foo Fighters What 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 Miracle,"Dave Grohl, Taylor Hawkins, Nate Mendel, Chris Shiflett/FOO FIGHTERS",209684,6877994,0.99,Rock,In Your Honor [Disc 2],Foo Fighters Another Round,"Dave Grohl, Taylor Hawkins, Nate Mendel, Chris Shiflett/FOO FIGHTERS",265848,8752670,0.99,Rock,In Your Honor [Disc 2],Foo Fighters Friend 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 Over And Out,"Dave Grohl, Taylor Hawkins, Nate Mendel, Chris Shiflett/FOO FIGHTERS",316264,10428382,0.99,Rock,In Your Honor [Disc 2],Foo Fighters On The Mend,"Dave Grohl, Taylor Hawkins, Nate Mendel, Chris Shiflett/FOO FIGHTERS",271908,9071997,0.99,Rock,In Your Honor [Disc 2],Foo Fighters Virginia Moon,"Dave Grohl, Taylor Hawkins, Nate Mendel, Chris Shiflett/FOO FIGHTERS",229198,7494639,0.99,Rock,In Your Honor [Disc 2],Foo Fighters Cold 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 Razor,"Dave Grohl, Taylor Hawkins, Nate Mendel, Chris Shiflett/FOO FIGHTERS",293276,9721373,0.99,Rock,In Your Honor [Disc 2],Foo Fighters All My Life,Foo Fighters,263653,8665545,0.99,Alternative & Punk,One By One,Foo Fighters Low,Foo Fighters,268120,8847196,0.99,Alternative & Punk,One By One,Foo Fighters Have It All,Foo Fighters,298057,9729292,0.99,Alternative & Punk,One By One,Foo Fighters Times Like These,Foo Fighters,266370,8624691,0.99,Alternative & Punk,One By One,Foo Fighters Disenchanted Lullaby,Foo Fighters,273528,8919111,0.99,Alternative & Punk,One By One,Foo Fighters Tired Of You,Foo Fighters,311353,10094743,0.99,Alternative & Punk,One By One,Foo Fighters Halo,Foo Fighters,306442,10026371,0.99,Alternative & Punk,One By One,Foo Fighters Lonely As You,Foo Fighters,277185,9022628,0.99,Alternative & Punk,One By One,Foo Fighters Overdrive,Foo Fighters,270550,8793187,0.99,Alternative & Punk,One By One,Foo Fighters Burn Away,Foo Fighters,298396,9678073,0.99,Alternative & Punk,One By One,Foo Fighters Come Back,Foo Fighters,469968,15371980,0.99,Alternative & Punk,One By One,Foo Fighters Doll,"Dave, Taylor, Nate, Chris",83487,2702572,0.99,Rock,The Colour And The Shape,Foo Fighters Monkey Wrench,"Dave, Taylor, Nate, Chris",231523,7527531,0.99,Rock,The Colour And The Shape,Foo Fighters "Hey, Johnny Park!","Dave, Taylor, Nate, Chris",248528,8079480,0.99,Rock,The Colour And The Shape,Foo Fighters My Poor Brain,"Dave, Taylor, Nate, Chris",213446,6973746,0.99,Rock,The Colour And The Shape,Foo Fighters Wind Up,"Dave, Taylor, Nate, Chris",152163,4950667,0.99,Rock,The Colour And The Shape,Foo Fighters Up In Arms,"Dave, Taylor, Nate, Chris",135732,4406227,0.99,Rock,The Colour And The Shape,Foo Fighters My Hero,"Dave, Taylor, Nate, Chris",260101,8472365,0.99,Rock,The Colour And The Shape,Foo Fighters See You,"Dave, Taylor, Nate, Chris",146782,4888173,0.99,Rock,The Colour And The Shape,Foo Fighters Enough Space,Dave Grohl,157387,5169280,0.99,Rock,The Colour And The Shape,Foo Fighters February Stars,"Dave, Taylor, Nate, Chris",289306,9344875,0.99,Rock,The Colour And The Shape,Foo Fighters Everlong,Dave Grohl,250749,8270816,0.99,Rock,The Colour And The Shape,Foo Fighters Walking After You,Dave Grohl,303856,9898992,0.99,Rock,The Colour And The Shape,Foo Fighters New Way Home,"Dave, Taylor, Nate, Chris",342230,11205664,0.99,Rock,The Colour And The Shape,Foo Fighters My 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 Strangers 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 "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 I 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 Something Stupid,carson c. parks,158615,5210643,0.99,Easy Listening,My Way: The Best Of Frank Sinatra [Disc 1],Frank Sinatra Moon River,henry mancini/johnny mercer,198922,6395808,0.99,Easy Listening,My Way: The Best Of Frank Sinatra [Disc 1],Frank Sinatra What 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 Summer Love,hans bradtke/heinz meier/johnny mercer,174994,5693242,0.99,Easy Listening,My Way: The Best Of Frank Sinatra [Disc 1],Frank Sinatra For 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 Love And Marriage,jimmy van heusen/sammy cahn,89730,2930596,0.99,Easy Listening,My Way: The Best Of Frank Sinatra [Disc 1],Frank Sinatra They 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 My 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 Fly Me To The Moon,bart howard,149263,4856954,0.99,Easy Listening,My Way: The Best Of Frank Sinatra [Disc 1],Frank Sinatra I'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 The 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 It Was A Very Good Year,ervin drake,266605,8554066,0.99,Easy Listening,My Way: The Best Of Frank Sinatra [Disc 1],Frank Sinatra Come 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 That's Life,dean kay thompson/kelly gordon,187010,6095727,0.99,Easy Listening,My Way: The Best Of Frank Sinatra [Disc 1],Frank Sinatra The 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 The 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 "Bad, Bad Leroy Brown",jim croce,169900,5548581,0.99,Easy Listening,My Way: The Best Of Frank Sinatra [Disc 1],Frank Sinatra Mack 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 Loves Been Good To Me,rod mckuen,203964,6645365,0.99,Easy Listening,My Way: The Best Of Frank Sinatra [Disc 1],Frank Sinatra L.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 Entrando Na Sua (Intro),,179252,5840027,0.99,Latin,Roda De Funk,Funk Como Le Gusta Nervosa,,229537,7680421,0.99,Latin,Roda De Funk,Funk Como Le Gusta Funk De Bamba (Com Fernanda Abreu),,237191,7866165,0.99,Latin,Roda De Funk,Funk Como Le Gusta Call Me At Cleo´s,,236617,7920510,0.99,Latin,Roda De Funk,Funk Como Le Gusta Olhos Coloridos (Com Sandra De Sá),,321332,10567404,0.99,Latin,Roda De Funk,Funk Como Le Gusta Zambação,,301113,10030604,0.99,Latin,Roda De Funk,Funk Como Le Gusta Funk Hum,,244453,8084475,0.99,Latin,Roda De Funk,Funk Como Le Gusta Forty Days (Com DJ Hum),,221727,7347172,0.99,Latin,Roda De Funk,Funk Como Le Gusta Balada Da Paula,Emerson Villani,322821,10603717,0.99,Latin,Roda De Funk,Funk Como Le Gusta Dujji,,324597,10833935,0.99,Latin,Roda De Funk,Funk Como Le Gusta Meu Guarda-Chuva,,248528,8216625,0.99,Latin,Roda De Funk,Funk Como Le Gusta Motéis,,213498,7041077,0.99,Latin,Roda De Funk,Funk Como Le Gusta Whistle Stop,,526132,17533664,0.99,Latin,Roda De Funk,Funk Como Le Gusta 16 Toneladas,,191634,6390885,0.99,Latin,Roda De Funk,Funk Como Le Gusta Divirta-Se (Saindo Da Sua),,74919,2439206,0.99,Latin,Roda De Funk,Funk Como Le Gusta Forty Days Instrumental,,292493,9584317,0.99,Latin,Roda De Funk,Funk Como Le Gusta Óia Eu Aqui De Novo,,219454,7469735,0.99,Soundtrack,As Canções de Eu Tu Eles,Gilberto Gil Baião Da Penha,,247928,8393047,0.99,Soundtrack,As Canções de Eu Tu Eles,Gilberto Gil Esperando Na Janela,Manuca/Raimundinho DoAcordion/Targino Godim,261041,8660617,0.99,Soundtrack,As Canções de Eu Tu Eles,Gilberto Gil Juazeiro,Humberto Teixeira/Luiz Gonzaga,222275,7349779,0.99,Soundtrack,As Canções de Eu Tu Eles,Gilberto Gil Último Pau-De-Arara,Corumbá/José Gumarães/Venancio,200437,6638563,0.99,Soundtrack,As Canções de Eu Tu Eles,Gilberto Gil Asa Branca,Humberto Teixeira/Luiz Gonzaga,217051,7387183,0.99,Soundtrack,As Canções de Eu Tu Eles,Gilberto Gil Qui Nem Jiló,Humberto Teixeira/Luiz Gonzaga,204695,6937472,0.99,Soundtrack,As Canções de Eu Tu Eles,Gilberto Gil Assum Preto,Humberto Teixeira/Luiz Gonzaga,199653,6625000,0.99,Soundtrack,As Canções de Eu Tu Eles,Gilberto Gil Pau-De-Arara,"Guio De Morais E Seus ""Parentes""/Luiz Gonzaga",191660,6340649,0.99,Soundtrack,As Canções de Eu Tu Eles,Gilberto Gil A Volta Da Asa Branca,Luiz Gonzaga/Zé Dantas,271020,9098093,0.99,Soundtrack,As Canções de Eu Tu Eles,Gilberto Gil O Amor Daqui De Casa,Gilberto Gil,148636,4888292,0.99,Soundtrack,As Canções de Eu Tu Eles,Gilberto Gil As Pegadas Do Amor,Gilberto Gil,209136,6899062,0.99,Soundtrack,As Canções de Eu Tu Eles,Gilberto Gil Lamento Sertanejo,Dominguinhos/Gilberto Gil,260963,8518290,0.99,Soundtrack,As Canções de Eu Tu Eles,Gilberto Gil Casinha Feliz,Gilberto Gil,32287,1039615,0.99,Soundtrack,As Canções de Eu Tu Eles,Gilberto Gil Introdução (Live),,154096,5227579,0.99,Latin,Quanta Gente Veio Ver (Live),Gilberto Gil Palco (Live),,238315,8026622,0.99,Latin,Quanta Gente Veio Ver (Live),Gilberto Gil Is This Love (Live),,295262,9819759,0.99,Latin,Quanta Gente Veio Ver (Live),Gilberto Gil Stir It Up (Live),,282409,9594738,0.99,Latin,Quanta Gente Veio Ver (Live),Gilberto Gil Refavela (Live),,236695,7985305,0.99,Latin,Quanta Gente Veio Ver (Live),Gilberto Gil Vendedor De Caranguejo (Live),,248842,8358128,0.99,Latin,Quanta Gente Veio Ver (Live),Gilberto Gil Quanta (Live),,357485,11774865,0.99,Latin,Quanta Gente Veio Ver (Live),Gilberto Gil Estrela (Live),,285309,9436411,0.99,Latin,Quanta Gente Veio Ver (Live),Gilberto Gil Pela Internet (Live),,263471,8804401,0.99,Latin,Quanta Gente Veio Ver (Live),Gilberto Gil Cérebro Eletrônico (Live),,231627,7805352,0.99,Latin,Quanta Gente Veio Ver (Live),Gilberto Gil Opachorô (Live),,259526,8596384,0.99,Latin,Quanta Gente Veio Ver (Live),Gilberto Gil Copacabana (Live),,289671,9673672,0.99,Latin,Quanta Gente Veio Ver (Live),Gilberto Gil A Novidade (Live),,316969,10508000,0.99,Latin,Quanta Gente Veio Ver (Live),Gilberto Gil Ghandi (Live),,222458,7481950,0.99,Latin,Quanta Gente Veio Ver (Live),Gilberto Gil De Ouro E Marfim (Live),,234971,7838453,0.99,Latin,Quanta Gente Veio Ver (Live),Gilberto Gil Doce De Carnaval (Candy All),,356101,11998470,0.99,Jazz,Quanta Gente Veio ver--Bônus De Carnaval,Gilberto Gil Lamento De Carnaval,,294530,9819276,0.99,Jazz,Quanta Gente Veio ver--Bônus De Carnaval,Gilberto Gil Pretinha,,265273,8914579,0.99,Jazz,Quanta Gente Veio ver--Bônus De Carnaval,Gilberto Gil Straight Out Of Line,Sully Erna,259213,8511877,0.99,Metal,Faceless,Godsmack Faceless,Sully Erna,216006,6992417,0.99,Metal,Faceless,Godsmack Changes,Sully Erna; Tony Rombola,260022,8455835,0.99,Metal,Faceless,Godsmack Make Me Believe,Sully Erna,248607,8075050,0.99,Metal,Faceless,Godsmack I Stand Alone,Sully Erna,246125,8017041,0.99,Metal,Faceless,Godsmack Re-Align,Sully Erna,260884,8513891,0.99,Metal,Faceless,Godsmack I Fucking Hate You,Sully Erna,247170,8059642,0.99,Metal,Faceless,Godsmack Releasing The Demons,Sully Erna,252760,8276372,0.99,Metal,Faceless,Godsmack Dead And Broken,Sully Erna,251454,8206611,0.99,Metal,Faceless,Godsmack I Am,Sully Erna,239516,7803270,0.99,Metal,Faceless,Godsmack The Awakening,Sully Erna,89547,3035251,0.99,Metal,Faceless,Godsmack Serenity,Sully Erna; Tony Rombola,274834,9172976,0.99,Metal,Faceless,Godsmack American Idiot,"Billie Joe Armstrong, Mike Dirnt, Tré Cool",174419,5705793,0.99,Alternative & Punk,American Idiot,Green Day Jesus 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 Holiday,"Billie Joe Armstrong, Mike Dirnt, Tré Cool",232724,7599602,0.99,Alternative & Punk,American Idiot,Green Day Boulevard Of Broken Dreams,"Mike Dint, Billie Joe, Tré Cool",260858,8485122,0.99,Alternative & Punk,American Idiot,Green Day Are We The Waiting,Green Day,163004,5328329,0.99,Alternative & Punk,American Idiot,Green Day St. Jimmy,Green Day,175307,5716589,0.99,Alternative & Punk,American Idiot,Green Day Give Me Novacaine,Green Day,205871,6752485,0.99,Alternative & Punk,American Idiot,Green Day She's A Rebel,Green Day,120528,3901226,0.99,Alternative & Punk,American Idiot,Green Day Extraordinary Girl,Green Day,214021,6975177,0.99,Alternative & Punk,American Idiot,Green Day Letterbomb,Green Day,246151,7980902,0.99,Alternative & Punk,American Idiot,Green Day Wake Me Up When September Ends,"Mike Dint, Billie Joe, Tré Cool",285753,9325597,0.99,Alternative & Punk,American Idiot,Green Day Homecoming / 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 Whatsername,Green Day,252316,8244843,0.99,Alternative & Punk,American Idiot,Green Day Welcome to the Jungle,,273552,4538451,0.99,Rock,Appetite for Destruction,Guns N' Roses It's So Easy,,202824,3394019,0.99,Rock,Appetite for Destruction,Guns N' Roses Nightrain,,268537,4457283,0.99,Rock,Appetite for Destruction,Guns N' Roses Out Ta Get Me,,263893,4382147,0.99,Rock,Appetite for Destruction,Guns N' Roses Mr. Brownstone,,228924,3816323,0.99,Rock,Appetite for Destruction,Guns N' Roses Paradise City,,406347,6687123,0.99,Rock,Appetite for Destruction,Guns N' Roses My Michelle,,219961,3671299,0.99,Rock,Appetite for Destruction,Guns N' Roses Think About You,,231640,3860275,0.99,Rock,Appetite for Destruction,Guns N' Roses Sweet Child O' Mine,,356424,5879347,0.99,Rock,Appetite for Destruction,Guns N' Roses You're Crazy,,197135,3301971,0.99,Rock,Appetite for Destruction,Guns N' Roses Anything Goes,,206400,3451891,0.99,Rock,Appetite for Destruction,Guns N' Roses Rocket Queen,,375349,6185539,0.99,Rock,Appetite for Destruction,Guns N' Roses Right Next Door to Hell,,182321,3175950,0.99,Rock,Use Your Illusion I,Guns N' Roses Dust N' Bones,,298374,5053742,0.99,Rock,Use Your Illusion I,Guns N' Roses Live and Let Die,,184016,3203390,0.99,Rock,Use Your Illusion I,Guns N' Roses Don't Cry (Original),,284744,4833259,0.99,Rock,Use Your Illusion I,Guns N' Roses Perfect Crime,,143637,2550030,0.99,Rock,Use Your Illusion I,Guns N' Roses You Ain't the First,,156268,2754414,0.99,Rock,Use Your Illusion I,Guns N' Roses Bad Obsession,,328282,5537678,0.99,Rock,Use Your Illusion I,Guns N' Roses Back off Bitch,,303436,5135662,0.99,Rock,Use Your Illusion I,Guns N' Roses Double Talkin' Jive,,203637,3520862,0.99,Rock,Use Your Illusion I,Guns N' Roses November Rain,,537540,8923566,0.99,Rock,Use Your Illusion I,Guns N' Roses The Garden,,322175,5438862,0.99,Rock,Use Your Illusion I,Guns N' Roses Garden of Eden,,161539,2839694,0.99,Rock,Use Your Illusion I,Guns N' Roses Don't Damn Me,,318901,5385886,0.99,Rock,Use Your Illusion I,Guns N' Roses Bad Apples,,268351,4567966,0.99,Rock,Use Your Illusion I,Guns N' Roses Dead Horse,,257600,4394014,0.99,Rock,Use Your Illusion I,Guns N' Roses Coma,,616511,10201342,0.99,Rock,Use Your Illusion I,Guns N' Roses Civil War,Duff McKagan/Slash/W. Axl Rose,461165,15046579,0.99,Metal,Use Your Illusion II,Guns N' Roses 14 Years,Izzy Stradlin'/W. Axl Rose,261355,8543664,0.99,Metal,Use Your Illusion II,Guns N' Roses Yesterdays,Billy/Del James/W. Axl Rose/West Arkeen,196205,6398489,0.99,Metal,Use Your Illusion II,Guns N' Roses Knockin' On Heaven's Door,Bob Dylan,336457,10986716,0.99,Metal,Use Your Illusion II,Guns N' Roses Get In The Ring,Duff McKagan/Slash/W. Axl Rose,341054,11134105,0.99,Metal,Use Your Illusion II,Guns N' Roses Shotgun Blues,W. Axl Rose,203206,6623916,0.99,Metal,Use Your Illusion II,Guns N' Roses Breakdown,W. Axl Rose,424960,13978284,0.99,Metal,Use Your Illusion II,Guns N' Roses Pretty Tied Up,Izzy Stradlin',287477,9408754,0.99,Metal,Use Your Illusion II,Guns N' Roses Locomotive,Slash/W. Axl Rose,522396,17236842,0.99,Metal,Use Your Illusion II,Guns N' Roses So Fine,Duff McKagan,246491,8039484,0.99,Metal,Use Your Illusion II,Guns N' Roses Estranged,W. Axl Rose,563800,18343787,0.99,Metal,Use Your Illusion II,Guns N' Roses You Could Be Mine,Izzy Stradlin'/W. Axl Rose,343875,11207355,0.99,Metal,Use Your Illusion II,Guns N' Roses Don't Cry,Izzy Stradlin'/W. Axl Rose,284238,9222458,0.99,Metal,Use Your Illusion II,Guns N' Roses My World,W. Axl Rose,84532,2764045,0.99,Metal,Use Your Illusion II,Guns N' Roses Colibri,Richard Bull,361012,12055329,0.99,Jazz,Blue Moods,Incognito Love Is The Colour,R. Carless,251585,8419165,0.99,Jazz,Blue Moods,Incognito Magnetic Ocean,Patrick Claher/Richard Bull,321123,10720741,0.99,Jazz,Blue Moods,Incognito Deep Waters,Richard Bull,396460,13075359,0.99,Jazz,Blue Moods,Incognito L'Arc En Ciel De Miles,Kevin Robinson/Richard Bull,242390,8053997,0.99,Jazz,Blue Moods,Incognito Gypsy,Kevin Robinson,330997,11083374,0.99,Jazz,Blue Moods,Incognito Journey Into Sunlight,Jean Paul Maunick,249756,8241177,0.99,Jazz,Blue Moods,Incognito Sunchild,Graham Harvey,259970,8593143,0.99,Jazz,Blue Moods,Incognito Millenium,Maxton Gig Beesley Jnr.,379167,12511939,0.99,Jazz,Blue Moods,Incognito Thinking 'Bout Tomorrow,Fayyaz Virgi/Richard Bull,355395,11865384,0.99,Jazz,Blue Moods,Incognito Jacob's Ladder,Julian Crampton,367647,12201595,0.99,Jazz,Blue Moods,Incognito She Wears Black,G Harvey/R Hope-Taylor,528666,17617944,0.99,Jazz,Blue Moods,Incognito Dark Side Of The Cog,Jean Paul Maunick,377155,12491122,0.99,Jazz,Blue Moods,Incognito Different World,,258692,4383764,0.99,Rock,A Matter of Life and Death,Iron Maiden These Colours Don't Run,,412152,6883500,0.99,Rock,A Matter of Life and Death,Iron Maiden Brighter Than a Thousand Suns,,526255,8721490,0.99,Rock,A Matter of Life and Death,Iron Maiden The Pilgrim,,307593,5172144,0.99,Rock,A Matter of Life and Death,Iron Maiden The Longest Day,,467810,7785748,0.99,Rock,A Matter of Life and Death,Iron Maiden Out of the Shadows,,336896,5647303,0.99,Rock,A Matter of Life and Death,Iron Maiden The Reincarnation of Benjamin Breeg,,442106,7367736,0.99,Rock,A Matter of Life and Death,Iron Maiden For the Greater Good of God,,564893,9367328,0.99,Rock,A Matter of Life and Death,Iron Maiden Lord of Light,,444614,7393698,0.99,Rock,A Matter of Life and Death,Iron Maiden The Legacy,,562966,9314287,0.99,Rock,A Matter of Life and Death,Iron Maiden Hallowed Be Thy Name (Live) [Non Album Bonus Track],,431262,7205816,0.99,Rock,A Matter of Life and Death,Iron Maiden The Number Of The Beast,Steve Harris,294635,4718897,0.99,Metal,A Real Dead One,Iron Maiden The Trooper,Steve Harris,235311,3766272,0.99,Metal,A Real Dead One,Iron Maiden Prowler,Steve Harris,255634,4091904,0.99,Metal,A Real Dead One,Iron Maiden Transylvania,Steve Harris,265874,4255744,0.99,Metal,A Real Dead One,Iron Maiden Remember Tomorrow,Paul Di'Anno/Steve Harris,352731,5648438,0.99,Metal,A Real Dead One,Iron Maiden Where Eagles Dare,Steve Harris,289358,4630528,0.99,Metal,A Real Dead One,Iron Maiden Sanctuary,David Murray/Paul Di'Anno/Steve Harris,293250,4694016,0.99,Metal,A Real Dead One,Iron Maiden Running Free,Paul Di'Anno/Steve Harris,228937,3663872,0.99,Metal,A Real Dead One,Iron Maiden Run To The Hilss,Steve Harris,237557,3803136,0.99,Metal,A Real Dead One,Iron Maiden 2 Minutes To Midnight,Adrian Smith/Bruce Dickinson,337423,5400576,0.99,Metal,A Real Dead One,Iron Maiden Iron Maiden,Steve Harris,324623,5195776,0.99,Metal,A Real Dead One,Iron Maiden Hallowed Be Thy Name,Steve Harris,471849,7550976,0.99,Metal,A Real Dead One,Iron Maiden Be Quick Or Be Dead,Bruce Dickinson/Janick Gers,196911,3151872,0.99,Metal,A Real Live One,Iron Maiden From Here To Eternity,Steve Harris,259866,4159488,0.99,Metal,A Real Live One,Iron Maiden Can I Play With Madness,Adrian Smith/Bruce Dickinson/Steve Harris,282488,4521984,0.99,Metal,A Real Live One,Iron Maiden Wasting Love,Bruce Dickinson/Janick Gers,347846,5566464,0.99,Metal,A Real Live One,Iron Maiden Tailgunner,Bruce Dickinson/Steve Harris,249469,3993600,0.99,Metal,A Real Live One,Iron Maiden The Evil That Men Do,Adrian Smith/Bruce Dickinson/Steve Harris,325929,5216256,0.99,Metal,A Real Live One,Iron Maiden Afraid To Shoot Strangers,Steve Harris,407980,6529024,0.99,Metal,A Real Live One,Iron Maiden Bring Your Daughter... To The Slaughter,Bruce Dickinson,317727,5085184,0.99,Metal,A Real Live One,Iron Maiden Heaven Can Wait,Steve Harris,448574,7178240,0.99,Metal,A Real Live One,Iron Maiden The Clairvoyant,Steve Harris,269871,4319232,0.99,Metal,A Real Live One,Iron Maiden Fear Of The Dark,Steve Harris,431333,6906078,0.99,Metal,A Real Live One,Iron Maiden The Wicker Man,Adrian Smith/Bruce Dickinson/Steve Harris,275539,11022464,0.99,Rock,Brave New World,Iron Maiden Ghost Of The Navigator,Bruce Dickinson/Janick Gers/Steve Harris,410070,16404608,0.99,Rock,Brave New World,Iron Maiden Brave New World,Bruce Dickinson/David Murray/Steve Harris,378984,15161472,0.99,Rock,Brave New World,Iron Maiden Blood Brothers,Steve Harris,434442,17379456,0.99,Rock,Brave New World,Iron Maiden The Mercenary,Janick Gers/Steve Harris,282488,11300992,0.99,Rock,Brave New World,Iron Maiden Dream Of Mirrors,Janick Gers/Steve Harris,561162,22448256,0.99,Rock,Brave New World,Iron Maiden The Fallen Angel,Adrian Smith/Steve Harris,240718,9629824,0.99,Rock,Brave New World,Iron Maiden The Nomad,David Murray/Steve Harris,546115,21846144,0.99,Rock,Brave New World,Iron Maiden Out Of The Silent Planet,Bruce Dickinson/Janick Gers/Steve Harris,385541,15423616,0.99,Rock,Brave New World,Iron Maiden The Thin Line Between Love & Hate,David Murray/Steve Harris,506801,20273280,0.99,Rock,Brave New World,Iron Maiden Wildest Dreams,Adrian Smith/Steve Harris,232777,9312384,0.99,Heavy Metal,Dance Of Death,Iron Maiden Rainmaker,Bruce Dickinson/David Murray/Steve Harris,228623,9146496,0.99,Heavy Metal,Dance Of Death,Iron Maiden No More Lies,Steve Harris,441782,17672320,0.99,Heavy Metal,Dance Of Death,Iron Maiden Montsegur,Bruce Dickinson/Janick Gers/Steve Harris,350484,14020736,0.99,Heavy Metal,Dance Of Death,Iron Maiden Dance Of Death,Janick Gers/Steve Harris,516649,20670727,0.99,Heavy Metal,Dance Of Death,Iron Maiden Gates Of Tomorrow,Bruce Dickinson/Janick Gers/Steve Harris,312032,12482688,0.99,Heavy Metal,Dance Of Death,Iron Maiden New Frontier,Adrian Smith/Bruce Dickinson/Nicko McBrain,304509,12181632,0.99,Heavy Metal,Dance Of Death,Iron Maiden Paschendale,Adrian Smith/Steve Harris,508107,20326528,0.99,Heavy Metal,Dance Of Death,Iron Maiden Face In The Sand,Adrian Smith/Bruce Dickinson/Steve Harris,391105,15648948,0.99,Heavy Metal,Dance Of Death,Iron Maiden Age Of Innocence,David Murray/Steve Harris,370468,14823478,0.99,Heavy Metal,Dance Of Death,Iron Maiden Journeyman,Bruce Dickinson/David Murray/Steve Harris,427023,17082496,0.99,Heavy Metal,Dance Of Death,Iron Maiden Be Quick Or Be Dead,Bruce Dickinson/Janick Gers,204512,8181888,0.99,Rock,Fear Of The Dark,Iron Maiden From Here To Eternity,Steve Harris,218357,8739038,0.99,Rock,Fear Of The Dark,Iron Maiden Afraid To Shoot Strangers,Steve Harris,416496,16664589,0.99,Rock,Fear Of The Dark,Iron Maiden Fear Is The Key,Bruce Dickinson/Janick Gers,335307,13414528,0.99,Rock,Fear Of The Dark,Iron Maiden Childhood's End,Steve Harris,280607,11225216,0.99,Rock,Fear Of The Dark,Iron Maiden Wasting Love,Bruce Dickinson/Janick Gers,350981,14041216,0.99,Rock,Fear Of The Dark,Iron Maiden The Fugitive,Steve Harris,294112,11765888,0.99,Rock,Fear Of The Dark,Iron Maiden Chains Of Misery,Bruce Dickinson/David Murray,217443,8700032,0.99,Rock,Fear Of The Dark,Iron Maiden The Apparition,Janick Gers/Steve Harris,234605,9386112,0.99,Rock,Fear Of The Dark,Iron Maiden Judas Be My Guide,Bruce Dickinson/David Murray,188786,7553152,0.99,Rock,Fear Of The Dark,Iron Maiden Weekend Warrior,Janick Gers/Steve Harris,339748,13594678,0.99,Rock,Fear Of The Dark,Iron Maiden Fear Of The Dark,Steve Harris,436976,17483789,0.99,Rock,Fear Of The Dark,Iron Maiden 01 - Prowler,Steve Harris,236173,5668992,0.99,Blues,Iron Maiden,Iron Maiden 02 - Sanctuary,David Murray/Paul Di'Anno/Steve Harris,196284,4712576,0.99,Blues,Iron Maiden,Iron Maiden 03 - Remember Tomorrow,Harris/Paul Di´Anno,328620,7889024,0.99,Blues,Iron Maiden,Iron Maiden 04 - Running Free,Harris/Paul Di´Anno,197276,4739122,0.99,Blues,Iron Maiden,Iron Maiden 05 - Phantom of the Opera,Steve Harris,428016,10276872,0.99,Blues,Iron Maiden,Iron Maiden 06 - Transylvania,Steve Harris,259343,6226048,0.99,Blues,Iron Maiden,Iron Maiden 07 - Strange World,Steve Harris,332460,7981184,0.99,Blues,Iron Maiden,Iron Maiden 08 - Charlotte the Harlot,Murray Dave,252708,6066304,0.99,Blues,Iron Maiden,Iron Maiden 09 - Iron Maiden,Steve Harris,216058,5189891,0.99,Blues,Iron Maiden,Iron Maiden The Ides Of March,Steve Harris,105926,2543744,0.99,Heavy Metal,Killers,Iron Maiden Wrathchild,Steve Harris,174471,4188288,0.99,Heavy Metal,Killers,Iron Maiden Murders In The Rue Morgue,Steve Harris,258377,6205786,0.99,Heavy Metal,Killers,Iron Maiden Another Life,Steve Harris,203049,4874368,0.99,Heavy Metal,Killers,Iron Maiden Genghis Khan,Steve Harris,187141,4493440,0.99,Heavy Metal,Killers,Iron Maiden Innocent Exile,Di´Anno/Harris,232515,5584861,0.99,Heavy Metal,Killers,Iron Maiden Killers,Steve Harris,300956,7227440,0.99,Heavy Metal,Killers,Iron Maiden Prodigal Son,Steve Harris,372349,8937600,0.99,Heavy Metal,Killers,Iron Maiden Purgatory,Steve Harris,200150,4804736,0.99,Heavy Metal,Killers,Iron Maiden Drifter,Steve Harris,288757,6934660,0.99,Heavy Metal,Killers,Iron Maiden Intro- Churchill S Speech,,48013,1154488,0.99,Heavy Metal,Live After Death,Iron Maiden Aces High,,276375,6635187,0.99,Heavy Metal,Live After Death,Iron Maiden 2 Minutes To Midnight,Smith/Dickinson,366550,8799380,0.99,Metal,Live After Death,Iron Maiden The Trooper,Harris,268878,6455255,0.99,Metal,Live After Death,Iron Maiden Revelations,Dickinson,371826,8926021,0.99,Metal,Live After Death,Iron Maiden Flight Of Icarus,Smith/Dickinson,229982,5521744,0.99,Metal,Live After Death,Iron Maiden Rime Of The Ancient Mariner,,789472,18949518,0.99,Metal,Live After Death,Iron Maiden Powerslave,,454974,10921567,0.99,Metal,Live After Death,Iron Maiden The Number Of The Beast,Harris,275121,6605094,0.99,Metal,Live After Death,Iron Maiden Hallowed Be Thy Name,Harris,451422,10836304,0.99,Metal,Live After Death,Iron Maiden Iron Maiden,Harris,261955,6289117,0.99,Metal,Live After Death,Iron Maiden Run To The Hills,Harris,231627,5561241,0.99,Metal,Live After Death,Iron Maiden Running Free,Harris/Di Anno,204617,4912986,0.99,Metal,Live After Death,Iron Maiden Wrathchild,Steve Harris,183666,4410181,0.99,Heavy Metal,Live After Death,Iron Maiden Acacia Avenue,,379872,9119118,0.99,Heavy Metal,Live After Death,Iron Maiden Children Of The Damned,Steve Harris,278177,6678446,0.99,Heavy Metal,Live After Death,Iron Maiden Die With Your Boots On,Adrian Smith/Bruce Dickinson/Steve Harris,314174,7542367,0.99,Heavy Metal,Live After Death,Iron Maiden Phantom Of The Opera,Steve Harris,441155,10589917,0.99,Heavy Metal,Live After Death,Iron Maiden Be Quick Or Be Dead,,233142,5599853,0.99,Rock,Live At Donington 1992 (Disc 1),Iron Maiden The Number Of The Beast,,294008,7060625,0.99,Rock,Live At Donington 1992 (Disc 1),Iron Maiden Wrathchild,,174106,4182963,0.99,Rock,Live At Donington 1992 (Disc 1),Iron Maiden From Here To Eternity,,284447,6831163,0.99,Rock,Live At Donington 1992 (Disc 1),Iron Maiden Can I Play With Madness,,213106,5118995,0.99,Rock,Live At Donington 1992 (Disc 1),Iron Maiden Wasting Love,,336953,8091301,0.99,Rock,Live At Donington 1992 (Disc 1),Iron Maiden Tailgunner,,247640,5947795,0.99,Rock,Live At Donington 1992 (Disc 1),Iron Maiden The Evil That Men Do,,478145,11479913,0.99,Rock,Live At Donington 1992 (Disc 1),Iron Maiden Afraid To Shoot Strangers,,412525,9905048,0.99,Rock,Live At Donington 1992 (Disc 1),Iron Maiden Fear Of The Dark,,431542,10361452,0.99,Rock,Live At Donington 1992 (Disc 1),Iron Maiden Bring Your Daughter... To The Slaughter...,,376711,9045532,0.99,Rock,Live At Donington 1992 (Disc 2),Iron Maiden The Clairvoyant,,262426,6302648,0.99,Rock,Live At Donington 1992 (Disc 2),Iron Maiden Heaven Can Wait,,440555,10577743,0.99,Rock,Live At Donington 1992 (Disc 2),Iron Maiden Run To The Hills,,235859,5665052,0.99,Rock,Live At Donington 1992 (Disc 2),Iron Maiden 2 Minutes To Midnight,Adrian Smith/Bruce Dickinson,338233,8122030,0.99,Rock,Live At Donington 1992 (Disc 2),Iron Maiden Iron Maiden,,494602,11874875,0.99,Rock,Live At Donington 1992 (Disc 2),Iron Maiden Hallowed Be Thy Name,,447791,10751410,0.99,Rock,Live At Donington 1992 (Disc 2),Iron Maiden The Trooper,,232672,5588560,0.99,Rock,Live At Donington 1992 (Disc 2),Iron Maiden Sanctuary,,318511,7648679,0.99,Rock,Live At Donington 1992 (Disc 2),Iron Maiden Running Free,,474017,11380851,0.99,Rock,Live At Donington 1992 (Disc 2),Iron Maiden Tailgunner,Bruce Dickinson/Steve Harris,255582,4089856,0.99,Metal,No Prayer For The Dying,Iron Maiden Holy Smoke,Bruce Dickinson/Steve Harris,229459,3672064,0.99,Metal,No Prayer For The Dying,Iron Maiden No Prayer For The Dying,Steve Harris,263941,4225024,0.99,Metal,No Prayer For The Dying,Iron Maiden Public Enema Number One,Bruce Dickinson/David Murray,254197,4071587,0.99,Metal,No Prayer For The Dying,Iron Maiden Fates Warning,David Murray/Steve Harris,250853,4018088,0.99,Metal,No Prayer For The Dying,Iron Maiden The Assassin,Steve Harris,258768,4141056,0.99,Metal,No Prayer For The Dying,Iron Maiden Run Silent Run Deep,Bruce Dickinson/Steve Harris,275408,4407296,0.99,Metal,No Prayer For The Dying,Iron Maiden Hooks In You,Adrian Smith/Bruce Dickinson,247510,3960832,0.99,Metal,No Prayer For The Dying,Iron Maiden Bring Your Daughter... ...To The Slaughter,Bruce Dickinson,284238,4548608,0.99,Metal,No Prayer For The Dying,Iron Maiden Mother Russia,Steve Harris,332617,5322752,0.99,Metal,No Prayer For The Dying,Iron Maiden Where Eagles Dare,Steve Harris,369554,5914624,0.99,Metal,Piece Of Mind,Iron Maiden Revelations,Bruce Dickinson,408607,6539264,0.99,Metal,Piece Of Mind,Iron Maiden Flight Of The Icarus,Adrian Smith/Bruce Dickinson,230269,3686400,0.99,Metal,Piece Of Mind,Iron Maiden Die With Your Boots On,Adrian Smith/Bruce Dickinson/Steve Harris,325694,5212160,0.99,Metal,Piece Of Mind,Iron Maiden The Trooper,Steve Harris,251454,4024320,0.99,Metal,Piece Of Mind,Iron Maiden Still Life,David Murray/Steve Harris,294347,4710400,0.99,Metal,Piece Of Mind,Iron Maiden Quest For Fire,Steve Harris,221309,3543040,0.99,Metal,Piece Of Mind,Iron Maiden Sun And Steel,Adrian Smith/Bruce Dickinson,206367,3306324,0.99,Metal,Piece Of Mind,Iron Maiden To Tame A Land,Steve Harris,445283,7129264,0.99,Metal,Piece Of Mind,Iron Maiden Aces High,Harris,269531,6472088,0.99,Metal,Powerslave,Iron Maiden 2 Minutes To Midnight,Smith/Dickinson,359810,8638809,0.99,Metal,Powerslave,Iron Maiden Losfer Words,Steve Harris,252891,6074756,0.99,Metal,Powerslave,Iron Maiden Flash of The Blade,Dickinson,242729,5828861,0.99,Metal,Powerslave,Iron Maiden Duelists,Steve Harris,366471,8800686,0.99,Metal,Powerslave,Iron Maiden Back in the Village,Dickinson/Smith,320548,7696518,0.99,Metal,Powerslave,Iron Maiden Powerslave,Dickinson,407823,9791106,0.99,Metal,Powerslave,Iron Maiden Rime of the Ancient Mariner,Harris,816509,19599577,0.99,Metal,Powerslave,Iron Maiden Intro,,115931,4638848,0.99,Metal,Rock In Rio [CD1],Iron Maiden The Wicker Man,Adrian Smith/Bruce Dickinson/Steve Harris,281782,11272320,0.99,Metal,Rock In Rio [CD1],Iron Maiden Ghost Of The Navigator,Bruce Dickinson/Janick Gers/Steve Harris,408607,16345216,0.99,Metal,Rock In Rio [CD1],Iron Maiden Brave New World,Bruce Dickinson/David Murray/Steve Harris,366785,14676148,0.99,Metal,Rock In Rio [CD1],Iron Maiden Wrathchild,Steve Harris,185808,7434368,0.99,Metal,Rock In Rio [CD1],Iron Maiden 2 Minutes To Midnight,Adrian Smith/Bruce Dickinson,386821,15474816,0.99,Metal,Rock In Rio [CD1],Iron Maiden Blood Brothers,Steve Harris,435513,17422464,0.99,Metal,Rock In Rio [CD1],Iron Maiden Sign Of The Cross,Steve Harris,649116,25966720,0.99,Metal,Rock In Rio [CD1],Iron Maiden The Mercenary,Janick Gers/Steve Harris,282697,11309184,0.99,Metal,Rock In Rio [CD1],Iron Maiden The Trooper,Steve Harris,273528,10942592,0.99,Metal,Rock In Rio [CD1],Iron Maiden Dream Of Mirrors,Janick Gers/Steve Harris,578324,23134336,0.99,Rock,Rock In Rio [CD2],Iron Maiden The Clansman,Steve Harris,559203,22370432,0.99,Rock,Rock In Rio [CD2],Iron Maiden The Evil That Men Do,Adrian Smith/Bruce Dickinson/Steve Harris,280737,11231360,0.99,Metal,Rock In Rio [CD2],Iron Maiden Fear Of The Dark,Steve Harris,460695,18430080,0.99,Rock,Rock In Rio [CD2],Iron Maiden Iron Maiden,Steve Harris,351869,14076032,0.99,Rock,Rock In Rio [CD2],Iron Maiden The Number Of The Beast,Steve Harris,300434,12022107,0.99,Rock,Rock In Rio [CD2],Iron Maiden Hallowed Be Thy Name,Steve Harris,443977,17760384,0.99,Rock,Rock In Rio [CD2],Iron Maiden Sanctuary,David Murray/Paul Di'Anno/Steve Harris,317335,12695680,0.99,Rock,Rock In Rio [CD2],Iron Maiden Run To The Hills,Steve Harris,292179,11688064,0.99,Rock,Rock In Rio [CD2],Iron Maiden Moonchild,Adrian Smith; Bruce Dickinson,340767,8179151,0.99,Metal,Seventh Son of a Seventh Son,Iron Maiden Infinite Dreams,Steve Harris,369005,8858669,0.99,Metal,Seventh Son of a Seventh Son,Iron Maiden Can I Play With Madness,Adrian Smith; Bruce Dickinson; Steve Harris,211043,5067867,0.99,Metal,Seventh Son of a Seventh Son,Iron Maiden The Evil That Men Do,Adrian Smith; Bruce Dickinson; Steve Harris,273998,6578930,0.99,Metal,Seventh Son of a Seventh Son,Iron Maiden Seventh Son of a Seventh Son,Steve Harris,593580,14249000,0.99,Metal,Seventh Son of a Seventh Son,Iron Maiden The Prophecy,Dave Murray; Steve Harris,305475,7334450,0.99,Metal,Seventh Son of a Seventh Son,Iron Maiden The Clairvoyant,Adrian Smith; Bruce Dickinson; Steve Harris,267023,6411510,0.99,Metal,Seventh Son of a Seventh Son,Iron Maiden Only the Good Die Young,Bruce Dickinson; Harris,280894,6744431,0.99,Metal,Seventh Son of a Seventh Son,Iron Maiden Caught Somewhere in Time,Steve Harris,445779,10701149,0.99,Metal,Somewhere in Time,Iron Maiden Wasted Years,Adrian Smith,307565,7384358,0.99,Metal,Somewhere in Time,Iron Maiden Sea of Madness,Adrian Smith,341995,8210695,0.99,Metal,Somewhere in Time,Iron Maiden Heaven Can Wait,Steve Harris,441417,10596431,0.99,Metal,Somewhere in Time,Iron Maiden Stranger in a Strange Land,Adrian Smith,344502,8270899,0.99,Metal,Somewhere in Time,Iron Maiden Alexander the Great,Steve Harris,515631,12377742,0.99,Metal,Somewhere in Time,Iron Maiden De Ja Vu,David Murray/Steve Harris,296176,7113035,0.99,Metal,Somewhere in Time,Iron Maiden The Loneliness of the Long Dis,Steve Harris,391314,9393598,0.99,Metal,Somewhere in Time,Iron Maiden 22 Acacia Avenue,Adrian Smith/Steve Harris,395572,5542516,0.99,Metal,The Number of The Beast,Iron Maiden Children of the Damned,Steve Harris,274364,3845631,0.99,Metal,The Number of The Beast,Iron Maiden Gangland,Adrian Smith/Clive Burr/Steve Harris,228440,3202866,0.99,Metal,The Number of The Beast,Iron Maiden Hallowed Be Thy Name,Steve Harris,428669,6006107,0.99,Metal,The Number of The Beast,Iron Maiden Invaders,Steve Harris,203180,2849181,0.99,Metal,The Number of The Beast,Iron Maiden Run to the Hills,Steve Harris,228884,3209124,0.99,Metal,The Number of The Beast,Iron Maiden The Number Of The Beast,Steve Harris,293407,11737216,0.99,Rock,The Number of The Beast,Iron Maiden The Prisoner,Adrian Smith/Steve Harris,361299,5062906,0.99,Metal,The Number of The Beast,Iron Maiden Sign Of The Cross,Steve Harris,678008,27121792,0.99,Rock,The X Factor,Iron Maiden Lord Of The Flies,Janick Gers/Steve Harris,303699,12148864,0.99,Rock,The X Factor,Iron Maiden Man On The Edge,Blaze Bayley/Janick Gers,253413,10137728,0.99,Rock,The X Factor,Iron Maiden Fortunes Of War,Steve Harris,443977,17760384,0.99,Rock,The X Factor,Iron Maiden Look For The Truth,Blaze Bayley/Janick Gers/Steve Harris,310230,12411008,0.99,Rock,The X Factor,Iron Maiden The Aftermath,Blaze Bayley/Janick Gers/Steve Harris,380786,15233152,0.99,Rock,The X Factor,Iron Maiden Judgement Of Heaven,Steve Harris,312476,12501120,0.99,Rock,The X Factor,Iron Maiden Blood On The World's Hands,Steve Harris,357799,14313600,0.99,Rock,The X Factor,Iron Maiden The Edge Of Darkness,Blaze Bayley/Janick Gers/Steve Harris,399333,15974528,0.99,Rock,The X Factor,Iron Maiden 2 A.M.,Blaze Bayley/Janick Gers/Steve Harris,337658,13511087,0.99,Rock,The X Factor,Iron Maiden The Unbeliever,Janick Gers/Steve Harris,490422,19617920,0.99,Rock,The X Factor,Iron Maiden Futureal,Blaze Bayley/Steve Harris,175777,7032960,0.99,Rock,Virtual XI,Iron Maiden The Angel And The Gambler,Steve Harris,592744,23711872,0.99,Rock,Virtual XI,Iron Maiden Lightning Strikes Twice,David Murray/Steve Harris,290377,11616384,0.99,Rock,Virtual XI,Iron Maiden The Clansman,Steve Harris,539689,21592327,0.99,Rock,Virtual XI,Iron Maiden When Two Worlds Collide,Blaze Bayley/David Murray/Steve Harris,377312,15093888,0.99,Rock,Virtual XI,Iron Maiden The Educated Fool,Steve Harris,404767,16191616,0.99,Rock,Virtual XI,Iron Maiden Don't Look To The Eyes Of A Stranger,Steve Harris,483657,19347584,0.99,Rock,Virtual XI,Iron Maiden Como Estais Amigos,Blaze Bayley/Janick Gers,330292,13213824,0.99,Rock,Virtual XI,Iron Maiden Please Please Please,James Brown/Johnny Terry,165067,5394585,0.99,R&B/Soul,Sex Machine,James Brown Think,Lowman Pauling,166739,5513208,0.99,R&B/Soul,Sex Machine,James Brown Night Train,Jimmy Forrest/Lewis C. Simpkins/Oscar Washington,212401,7027377,0.99,R&B/Soul,Sex Machine,James Brown Out Of Sight,Ted Wright,143725,4711323,0.99,R&B/Soul,Sex Machine,James Brown Papa's Got A Brand New Bag Pt.1,James Brown,127399,4174420,0.99,R&B/Soul,Sex Machine,James Brown I Got You (I Feel Good),James Brown,167392,5468472,0.99,R&B/Soul,Sex Machine,James Brown It's A Man's Man's Man's World,Betty Newsome/James Brown,168228,5541611,0.99,R&B/Soul,Sex Machine,James Brown Cold Sweat,Alfred Ellis/James Brown,172408,5643213,0.99,R&B/Soul,Sex Machine,James Brown "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 Get 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 Hey America,Addie William Jones/Nat Jones,218226,7187857,0.99,R&B/Soul,Sex Machine,James Brown Make It Funky Pt.1,Charles Bobbitt/James Brown,196231,6507782,0.99,R&B/Soul,Sex Machine,James Brown I'm A Greedy Man Pt.1,Charles Bobbitt/James Brown,217730,7251211,0.99,R&B/Soul,Sex Machine,James Brown Get On The Good Foot,Fred Wesley/James Brown/Joseph Mims,215902,7182736,0.99,R&B/Soul,Sex Machine,James Brown Get Up Offa That Thing,Deanna Brown/Deidra Jenkins/Yamma Brown,250723,8355989,0.99,R&B/Soul,Sex Machine,James Brown It's Too Funky In Here,Brad Shapiro/George Jackson/Robert Miller/Walter Shaw,239072,7973979,0.99,R&B/Soul,Sex Machine,James Brown Living In America,Charlie Midnight/Dan Hartman,282880,9432346,0.99,R&B/Soul,Sex Machine,James Brown I'm Real,Full Force/James Brown,334236,11183457,0.99,R&B/Soul,Sex Machine,James Brown Hot Pants Pt.1,Fred Wesley/James Brown,188212,6295110,0.99,R&B/Soul,Sex Machine,James Brown Soul Power (Live),James Brown,260728,8593206,0.99,R&B/Soul,Sex Machine,James Brown When You Gonna Learn (Digeridoo),"Jay Kay/Kay, Jay",230635,7655482,0.99,Rock,Emergency On Planet Earth,Jamiroquai Too Young To Die,"Smith, Toby",365818,12391660,0.99,Rock,Emergency On Planet Earth,Jamiroquai Hooked Up,"Smith, Toby",275879,9301687,0.99,Rock,Emergency On Planet Earth,Jamiroquai "If I Like It, I Do It","Gelder, Nick van",293093,9848207,0.99,Rock,Emergency On Planet Earth,Jamiroquai Music Of The Wind,"Smith, Toby",383033,12870239,0.99,Rock,Emergency On Planet Earth,Jamiroquai Emergency On Planet Earth,"Smith, Toby",245263,8117218,0.99,Rock,Emergency On Planet Earth,Jamiroquai "Whatever It Is, I Just Can't Stop","Jay Kay/Kay, Jay",247222,8249453,0.99,Rock,Emergency On Planet Earth,Jamiroquai Blow Your Mind,"Smith, Toby",512339,17089176,0.99,Rock,Emergency On Planet Earth,Jamiroquai Revolution 1993,"Smith, Toby",616829,20816872,0.99,Rock,Emergency On Planet Earth,Jamiroquai Didgin' Out,"Buchanan, Wallis",157100,5263555,0.99,Rock,Emergency On Planet Earth,Jamiroquai Canned Heat,Jay Kay,331964,11042037,0.99,R&B/Soul,Synkronized,Jamiroquai Planet Home,Jay Kay/Toby Smith,284447,9566237,0.99,R&B/Soul,Synkronized,Jamiroquai Black Capricorn Day,Jay Kay,341629,11477231,0.99,R&B/Soul,Synkronized,Jamiroquai Soul Education,Jay Kay/Toby Smith,255477,8575435,0.99,R&B/Soul,Synkronized,Jamiroquai Failling,Jay Kay/Toby Smith,225227,7503999,0.99,R&B/Soul,Synkronized,Jamiroquai Destitute Illusions,Derrick McKenzie/Jay Kay/Toby Smith,340218,11452651,0.99,R&B/Soul,Synkronized,Jamiroquai Supersonic,Jay Kay,315872,10699265,0.99,R&B/Soul,Synkronized,Jamiroquai Butterfly,Jay Kay/Toby Smith,268852,8947356,0.99,R&B/Soul,Synkronized,Jamiroquai Were Do We Go From Here,Jay Kay,313626,10504158,0.99,R&B/Soul,Synkronized,Jamiroquai King For A Day,Jay Kay/Toby Smith,221544,7335693,0.99,R&B/Soul,Synkronized,Jamiroquai Deeper Underground,Toby Smith,281808,9351277,0.99,R&B/Soul,Synkronized,Jamiroquai Just Another Story,Toby Smith,529684,17582818,0.99,Electronica/Dance,The Return Of The Space Cowboy,Jamiroquai Stillness In Time,Toby Smith,257097,8644290,0.99,Electronica/Dance,The Return Of The Space Cowboy,Jamiroquai Half The Man,Toby Smith,289854,9577679,0.99,Electronica/Dance,The Return Of The Space Cowboy,Jamiroquai Light Years,Toby Smith,354560,11796244,0.99,Electronica/Dance,The Return Of The Space Cowboy,Jamiroquai Manifest Destiny,Toby Smith,382197,12676962,0.99,Electronica/Dance,The Return Of The Space Cowboy,Jamiroquai The Kids,Toby Smith,309995,10334529,0.99,Electronica/Dance,The Return Of The Space Cowboy,Jamiroquai Mr. Moon,Stuard Zender/Toby Smith,329534,11043559,0.99,Electronica/Dance,The Return Of The Space Cowboy,Jamiroquai Scam,Stuart Zender,422321,14019705,0.99,Electronica/Dance,The Return Of The Space Cowboy,Jamiroquai Journey To Arnhemland,Toby Smith/Wallis Buchanan,322455,10843832,0.99,Electronica/Dance,The Return Of The Space Cowboy,Jamiroquai Morning Glory,J. Kay/Jay Kay,384130,12777210,0.99,Electronica/Dance,The Return Of The Space Cowboy,Jamiroquai Space Cowboy,J. Kay/Jay Kay,385697,12906520,0.99,Electronica/Dance,The Return Of The Space Cowboy,Jamiroquai Last Chance,C. Cester/C. Muncey,112352,3683130,0.99,Alternative & Punk,Get Born,JET Are You Gonna Be My Girl,C. Muncey/N. Cester,213890,6992324,0.99,Alternative & Punk,Get Born,JET Rollover D.J.,C. Cester/N. Cester,196702,6406517,0.99,Alternative & Punk,Get Born,JET Look What You've Done,N. Cester,230974,7517083,0.99,Alternative & Punk,Get Born,JET Get What You Need,C. Cester/C. Muncey/N. Cester,247719,8043765,0.99,Alternative & Punk,Get Born,JET Move On,C. Cester/N. Cester,260623,8519353,0.99,Alternative & Punk,Get Born,JET Radio Song,C. Cester/C. Muncey/N. Cester,272117,8871509,0.99,Alternative & Punk,Get Born,JET Get Me Outta Here,C. Cester/N. Cester,176274,5729098,0.99,Alternative & Punk,Get Born,JET Cold Hard Bitch,C. Cester/C. Muncey/N. Cester,243278,7929610,0.99,Alternative & Punk,Get Born,JET Come Around Again,C. Muncey/N. Cester,270497,8872405,0.99,Alternative & Punk,Get Born,JET Take It Or Leave It,C. Muncey/N. Cester,142889,4643370,0.99,Alternative & Punk,Get Born,JET Lazy Gun,C. Cester/N. Cester,282174,9186285,0.99,Alternative & Punk,Get Born,JET Timothy,C. Cester,270341,8856507,0.99,Alternative & Punk,Get Born,JET Foxy Lady,Jimi Hendrix,199340,6480896,0.99,Rock,Are You Experienced?,Jimi Hendrix Manic Depression,Jimi Hendrix,222302,7289272,0.99,Rock,Are You Experienced?,Jimi Hendrix Red House,Jimi Hendrix,224130,7285851,0.99,Rock,Are You Experienced?,Jimi Hendrix Can You See Me,Jimi Hendrix,153077,4987068,0.99,Rock,Are You Experienced?,Jimi Hendrix Love Or Confusion,Jimi Hendrix,193123,6329408,0.99,Rock,Are You Experienced?,Jimi Hendrix I Don't Live Today,Jimi Hendrix,235311,7661214,0.99,Rock,Are You Experienced?,Jimi Hendrix May This Be Love,Jimi Hendrix,191216,6240028,0.99,Rock,Are You Experienced?,Jimi Hendrix Fire,Jimi Hendrix,164989,5383075,0.99,Rock,Are You Experienced?,Jimi Hendrix Third Stone From The Sun,Jimi Hendrix,404453,13186975,0.99,Rock,Are You Experienced?,Jimi Hendrix Remember,Jimi Hendrix,168150,5509613,0.99,Rock,Are You Experienced?,Jimi Hendrix Are You Experienced?,Jimi Hendrix,254537,8292497,0.99,Rock,Are You Experienced?,Jimi Hendrix Hey Joe,Billy Roberts,210259,6870054,0.99,Rock,Are You Experienced?,Jimi Hendrix Stone Free,Jimi Hendrix,216293,7002331,0.99,Rock,Are You Experienced?,Jimi Hendrix Purple Haze,Jimi Hendrix,171572,5597056,0.99,Rock,Are You Experienced?,Jimi Hendrix 51st Anniversary,Jimi Hendrix,196388,6398044,0.99,Rock,Are You Experienced?,Jimi Hendrix The Wind Cries Mary,Jimi Hendrix,200463,6540638,0.99,Rock,Are You Experienced?,Jimi Hendrix Highway Chile,Jimi Hendrix,212453,6887949,0.99,Rock,Are You Experienced?,Jimi Hendrix Surfing with the Alien,,263707,4418504,0.99,Rock,Surfing with the Alien (Remastered),Joe Satriani Ice 9,,239721,4036215,0.99,Rock,Surfing with the Alien (Remastered),Joe Satriani Crushing Day,,314768,5232158,0.99,Rock,Surfing with the Alien (Remastered),Joe Satriani "Always With Me, Always With You",,202035,3435777,0.99,Rock,Surfing with the Alien (Remastered),Joe Satriani Satch Boogie,,193560,3300654,0.99,Rock,Surfing with the Alien (Remastered),Joe Satriani Hill of the Skull,J. Satriani,108435,1944738,0.99,Rock,Surfing with the Alien (Remastered),Joe Satriani Circles,,209071,3548553,0.99,Rock,Surfing with the Alien (Remastered),Joe Satriani Lords of Karma,J. Satriani,288227,4809279,0.99,Rock,Surfing with the Alien (Remastered),Joe Satriani Midnight,J. Satriani,102630,1851753,0.99,Rock,Surfing with the Alien (Remastered),Joe Satriani Echo,J. Satriani,337570,5595557,0.99,Rock,Surfing with the Alien (Remastered),Joe Satriani Engenho De Dentro,,310073,10211473,0.99,Latin,Jorge Ben Jor 25 Anos,Jorge Ben Alcohol,,355239,12010478,0.99,Latin,Jorge Ben Jor 25 Anos,Jorge Ben Mama Africa,,283062,9488316,0.99,Latin,Jorge Ben Jor 25 Anos,Jorge Ben Salve Simpatia,,343484,11314756,0.99,Latin,Jorge Ben Jor 25 Anos,Jorge Ben W/Brasil (Chama O Síndico),,317100,10599953,0.99,Latin,Jorge Ben Jor 25 Anos,Jorge Ben País Tropical,,452519,14946972,0.99,Latin,Jorge Ben Jor 25 Anos,Jorge Ben Os Alquimistas Estão Chegando,,367281,12304520,0.99,Latin,Jorge Ben Jor 25 Anos,Jorge Ben Charles Anjo 45,,389276,13022833,0.99,Latin,Jorge Ben Jor 25 Anos,Jorge Ben Selassiê,,326321,10724982,0.99,Latin,Jorge Ben Jor 25 Anos,Jorge Ben Menina Sarará,,191477,6393818,0.99,Latin,Jorge Ben Jor 25 Anos,Jorge Ben Que Maravilha,,338076,10996656,0.99,Latin,Jorge Ben Jor 25 Anos,Jorge Ben Santa Clara Clareou,,380081,12524725,0.99,Latin,Jorge Ben Jor 25 Anos,Jorge Ben Filho Maravilha,,227526,7498259,0.99,Latin,Jorge Ben Jor 25 Anos,Jorge Ben Taj Mahal,,289750,9502898,0.99,Latin,Jorge Ben Jor 25 Anos,Jorge Ben Rapidamente,,252238,8470107,0.99,Latin,Jota Quest-1995,Jota Quest As Dores do Mundo,Hyldon,255477,8537092,0.99,Latin,Jota Quest-1995,Jota Quest Vou Pra Ai,,300878,10053718,0.99,Latin,Jota Quest-1995,Jota Quest My Brother,,253231,8431821,0.99,Latin,Jota Quest-1995,Jota Quest Há Quanto Tempo,,270027,9004470,0.99,Latin,Jota Quest-1995,Jota Quest Vício,,269897,8887216,0.99,Latin,Jota Quest-1995,Jota Quest Encontrar Alguém,Marco Tulio Lara/Rogerio Flausino,224078,7437935,0.99,Latin,Jota Quest-1995,Jota Quest Dance Enquanto é Tempo,,229093,7583799,0.99,Latin,Jota Quest-1995,Jota Quest A Tarde,,266919,8836127,0.99,Latin,Jota Quest-1995,Jota Quest Always Be All Right,,128078,4299676,0.99,Latin,Jota Quest-1995,Jota Quest Sem Sentido,,250462,8292108,0.99,Latin,Jota Quest-1995,Jota Quest Onibusfobia,,315977,10474904,0.99,Latin,Jota Quest-1995,Jota Quest Pura Elegancia,João Suplicy,284107,9632269,0.99,World,Cafezinho,João Suplicy Choramingando,João Suplicy,190484,6400532,0.99,World,Cafezinho,João Suplicy Por Merecer,João Suplicy,230582,7764601,0.99,World,Cafezinho,João Suplicy No Futuro,João Suplicy,182308,6056200,0.99,World,Cafezinho,João Suplicy Voce Inteira,João Suplicy,241084,8077282,0.99,World,Cafezinho,João Suplicy Cuando A Noite Vai Chegando,João Suplicy,270628,9081874,0.99,World,Cafezinho,João Suplicy Naquele Dia,João Suplicy,251768,8452654,0.99,World,Cafezinho,João Suplicy Equinocio,João Suplicy,269008,8871455,0.99,World,Cafezinho,João Suplicy Papelão,João Suplicy,213263,7257390,0.99,World,Cafezinho,João Suplicy Cuando Eu For Pro Ceu,João Suplicy,118804,3948371,0.99,World,Cafezinho,João Suplicy Do Nosso Amor,João Suplicy,203415,6774566,0.99,World,Cafezinho,João Suplicy Borogodo,João Suplicy,208457,7104588,0.99,World,Cafezinho,João Suplicy Cafezinho,João Suplicy,180924,6031174,0.99,World,Cafezinho,João Suplicy Enquanto O Dia Não Vem,João Suplicy,220891,7248336,0.99,World,Cafezinho,João Suplicy The Green Manalishi,,205792,6720789,0.99,Metal,Living After Midnight,Judas Priest Living After Midnight,,213289,7056785,0.99,Metal,Living After Midnight,Judas Priest Breaking The Law (Live),,144195,4728246,0.99,Metal,Living After Midnight,Judas Priest Hot Rockin',,197328,6509179,0.99,Metal,Living After Midnight,Judas Priest Heading Out To The Highway (Live),,276427,9006022,0.99,Metal,Living After Midnight,Judas Priest The Hellion,,41900,1351993,0.99,Metal,Living After Midnight,Judas Priest Electric Eye,,222197,7231368,0.99,Metal,Living After Midnight,Judas Priest You've Got Another Thing Comin',,305162,9962558,0.99,Metal,Living After Midnight,Judas Priest Turbo Lover,,335542,11068866,0.99,Metal,Living After Midnight,Judas Priest Freewheel Burning,,265952,8713599,0.99,Metal,Living After Midnight,Judas Priest Some Heads Are Gonna Roll,,249939,8198617,0.99,Metal,Living After Midnight,Judas Priest Metal Meltdown,,290664,9390646,0.99,Metal,Living After Midnight,Judas Priest Ram It Down,,292179,9554023,0.99,Metal,Living After Midnight,Judas Priest Diamonds And Rust (Live),,219350,7163147,0.99,Metal,Living After Midnight,Judas Priest Victim Of Change (Live),,430942,14067512,0.99,Metal,Living After Midnight,Judas Priest Tyrant (Live),,282253,9190536,0.99,Metal,Living After Midnight,Judas Priest Comin' Home,"Paul Stanley, Ace Frehley",172068,5661120,0.99,Rock,Unplugged [Live],Kiss Plaster Caster,Gene Simmons,198060,6528719,0.99,Rock,Unplugged [Live],Kiss Goin' Blind,"Gene Simmons, Stephen Coronel",217652,7167523,0.99,Rock,Unplugged [Live],Kiss Do You Love Me,"Paul Stanley, Bob Ezrin, Kim Fowley",193619,6343111,0.99,Rock,Unplugged [Live],Kiss Domino,Gene Simmons,226377,7488191,0.99,Rock,Unplugged [Live],Kiss Sure Know Something,"Paul Stanley, Vincent Poncia",254354,8375190,0.99,Rock,Unplugged [Live],Kiss A World Without Heroes,"Paul Stanley, Gene Simmons, Bob Ezrin, Lewis Reed",177815,5832524,0.99,Rock,Unplugged [Live],Kiss Rock Bottom,"Paul Stanley, Ace Frehley",200594,6560818,0.99,Rock,Unplugged [Live],Kiss See You Tonight,Gene Simmons,146494,4817521,0.99,Rock,Unplugged [Live],Kiss I Still Love You,Paul Stanley,369815,12086145,0.99,Rock,Unplugged [Live],Kiss Every Time I Look At You,"Paul Stanley, Vincent Cusano",283898,9290948,0.99,Rock,Unplugged [Live],Kiss "2,000 Man","Mick Jagger, Keith Richard",312450,10292829,0.99,Rock,Unplugged [Live],Kiss Beth,"Peter Criss, Stan Penridge, Bob Ezrin",170187,5577807,0.99,Rock,Unplugged [Live],Kiss Nothin' To Lose,Gene Simmons,222354,7351460,0.99,Rock,Unplugged [Live],Kiss Rock And Roll All Nite,"Paul Stanley, Gene Simmons",259631,8549296,0.99,Rock,Unplugged [Live],Kiss Immigrant Song,Robert Plant,201247,6457766,0.99,Rock,BBC Sessions [Disc 2] [Live],Led Zeppelin Heartbreaker,John Bonham/John Paul Jones/Robert Plant,316081,10179657,0.99,Rock,BBC Sessions [Disc 2] [Live],Led Zeppelin Since I've Been Loving You,John Paul Jones/Robert Plant,416365,13471959,0.99,Rock,BBC Sessions [Disc 2] [Live],Led Zeppelin Black Dog,John Paul Jones/Robert Plant,317622,10267572,0.99,Rock,BBC Sessions [Disc 2] [Live],Led Zeppelin Dazed And Confused,Jimmy Page/Led Zeppelin,1116734,36052247,0.99,Rock,BBC Sessions [Disc 2] [Live],Led Zeppelin Stairway To Heaven,Robert Plant,529658,17050485,0.99,Rock,BBC Sessions [Disc 2] [Live],Led Zeppelin Going To California,Robert Plant,234605,7646749,0.99,Rock,BBC Sessions [Disc 2] [Live],Led Zeppelin That's The Way,Robert Plant,343431,11248455,0.99,Rock,BBC Sessions [Disc 2] [Live],Led Zeppelin Whole 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 Thank You,Robert Plant,398262,12831826,0.99,Rock,BBC Sessions [Disc 2] [Live],Led Zeppelin We're Gonna Groove,Ben E.King/James Bethea,157570,5180975,0.99,Rock,Coda,Led Zeppelin Poor Tom,Jimmy Page/Robert Plant,182491,6016220,0.99,Rock,Coda,Led Zeppelin I Can't Quit You Baby,Willie Dixon,258168,8437098,0.99,Rock,Coda,Led Zeppelin Walter's Walk,"Jimmy Page, Robert Plant",270785,8712499,0.99,Rock,Coda,Led Zeppelin Ozone Baby,"Jimmy Page, Robert Plant",215954,7079588,0.99,Rock,Coda,Led Zeppelin Darlene,"Jimmy Page, Robert Plant, John Bonham, John Paul Jones",307226,10078197,0.99,Rock,Coda,Led Zeppelin Bonzo's Montreux,John Bonham,258925,8557447,0.99,Rock,Coda,Led Zeppelin Wearing And Tearing,"Jimmy Page, Robert Plant",330004,10701590,0.99,Rock,Coda,Led Zeppelin The Song Remains The Same,Jimmy Page/Jimmy Page & Robert Plant/Robert Plant,330004,10708950,0.99,Rock,Houses Of The Holy,Led Zeppelin The Rain Song,Jimmy Page/Jimmy Page & Robert Plant/Robert Plant,459180,15029875,0.99,Rock,Houses Of The Holy,Led Zeppelin Over The Hills And Far Away,Jimmy Page/Jimmy Page & Robert Plant/Robert Plant,290089,9552829,0.99,Rock,Houses Of The Holy,Led Zeppelin The Crunge,John Bonham/John Paul Jones,197407,6460212,0.99,Rock,Houses Of The Holy,Led Zeppelin Dancing Days,Jimmy Page/Jimmy Page & Robert Plant/Robert Plant,223216,7250104,0.99,Rock,Houses Of The Holy,Led Zeppelin D'Yer Mak'er,John Bonham/John Paul Jones,262948,8645935,0.99,Rock,Houses Of The Holy,Led Zeppelin No Quarter,John Paul Jones,420493,13656517,0.99,Rock,Houses Of The Holy,Led Zeppelin The Ocean,John Bonham/John Paul Jones,271098,8846469,0.99,Rock,Houses Of The Holy,Led Zeppelin In The Evening,"Jimmy Page, Robert Plant & John Paul Jones",410566,13399734,0.99,Rock,In Through The Out Door,Led Zeppelin South Bound Saurez,John Paul Jones & Robert Plant,254406,8420427,0.99,Rock,In Through The Out Door,Led Zeppelin Fool In The Rain,"Jimmy Page, Robert Plant & John Paul Jones",372950,12371433,0.99,Rock,In Through The Out Door,Led Zeppelin Hot Dog,Jimmy Page & Robert Plant,197198,6536167,0.99,Rock,In Through The Out Door,Led Zeppelin Carouselambra,"John Paul Jones, Jimmy Page & Robert Plant",634435,20858315,0.99,Rock,In Through The Out Door,Led Zeppelin All My Love,Robert Plant & John Paul Jones,356284,11684862,0.99,Rock,In Through The Out Door,Led Zeppelin I'm Gonna Crawl,"Jimmy Page, Robert Plant & John Paul Jones",329639,10737665,0.99,Rock,In Through The Out Door,Led Zeppelin Black Dog,"Jimmy Page, Robert Plant, John Paul Jones",296672,9660588,0.99,Rock,IV,Led Zeppelin Rock & Roll,"Jimmy Page, Robert Plant, John Paul Jones, John Bonham",220917,7142127,0.99,Rock,IV,Led Zeppelin The Battle Of Evermore,"Jimmy Page, Robert Plant",351555,11525689,0.99,Rock,IV,Led Zeppelin Stairway To Heaven,"Jimmy Page, Robert Plant",481619,15706767,0.99,Rock,IV,Led Zeppelin Misty Mountain Hop,"Jimmy Page, Robert Plant, John Paul Jones",278857,9092799,0.99,Rock,IV,Led Zeppelin Four Sticks,"Jimmy Page, Robert Plant",284447,9481301,0.99,Rock,IV,Led Zeppelin Going To California,"Jimmy Page, Robert Plant",215693,7068737,0.99,Rock,IV,Led Zeppelin When The Levee Breaks,"Jimmy Page, Robert Plant, John Paul Jones, John Bonham, Memphis Minnie",427702,13912107,0.99,Rock,IV,Led Zeppelin Good Times Bad Times,Jimmy Page/John Bonham/John Paul Jones,166164,5464077,0.99,Rock,Led Zeppelin I,Led Zeppelin Babe I'm Gonna Leave You,Jimmy Page/Robert Plant,401475,13189312,0.99,Rock,Led Zeppelin I,Led Zeppelin You Shook Me,J. B. Lenoir/Willie Dixon,388179,12643067,0.99,Rock,Led Zeppelin I,Led Zeppelin Dazed and Confused,Jimmy Page,386063,12610326,0.99,Rock,Led Zeppelin I,Led Zeppelin Your Time Is Gonna Come,Jimmy Page/John Paul Jones,274860,9011653,0.99,Rock,Led Zeppelin I,Led Zeppelin Black Mountain Side,Jimmy Page,132702,4440602,0.99,Rock,Led Zeppelin I,Led Zeppelin Communication Breakdown,Jimmy Page/John Bonham/John Paul Jones,150230,4899554,0.99,Rock,Led Zeppelin I,Led Zeppelin I Can't Quit You Baby,Willie Dixon,282671,9252733,0.99,Rock,Led Zeppelin I,Led Zeppelin How Many More Times,Jimmy Page/John Bonham/John Paul Jones,508055,16541364,0.99,Rock,Led Zeppelin I,Led Zeppelin Whole Lotta Love,"Jimmy Page, Robert Plant, John Paul Jones, John Bonham",334471,11026243,0.99,Rock,Led Zeppelin II,Led Zeppelin What Is And What Should Never Be,"Jimmy Page, Robert Plant",287973,9369385,0.99,Rock,Led Zeppelin II,Led Zeppelin The Lemon Song,"Jimmy Page, Robert Plant, John Paul Jones, John Bonham",379141,12463496,0.99,Rock,Led Zeppelin II,Led Zeppelin Thank You,"Jimmy Page, Robert Plant",287791,9337392,0.99,Rock,Led Zeppelin II,Led Zeppelin Heartbreaker,"Jimmy Page, Robert Plant, John Paul Jones, John Bonham",253988,8387560,0.99,Rock,Led Zeppelin II,Led Zeppelin Living Loving Maid (She's Just A Woman),"Jimmy Page, Robert Plant",159216,5219819,0.99,Rock,Led Zeppelin II,Led Zeppelin Ramble On,"Jimmy Page, Robert Plant",275591,9199710,0.99,Rock,Led Zeppelin II,Led Zeppelin Moby Dick,"John Bonham, John Paul Jones, Jimmy Page",260728,8664210,0.99,Rock,Led Zeppelin II,Led Zeppelin Bring It On Home,"Jimmy Page, Robert Plant",259970,8494731,0.99,Rock,Led Zeppelin II,Led Zeppelin Immigrant Song,"Jimmy Page, Robert Plant",144875,4786461,0.99,Rock,Led Zeppelin III,Led Zeppelin Friends,"Jimmy Page, Robert Plant",233560,7694220,0.99,Rock,Led Zeppelin III,Led Zeppelin Celebration Day,"Jimmy Page, Robert Plant, John Paul Jones",209528,6871078,0.99,Rock,Led Zeppelin III,Led Zeppelin Since I've Been Loving You,"Jimmy Page, Robert Plant, John Paul Jones",444055,14482460,0.99,Rock,Led Zeppelin III,Led Zeppelin Out On The Tiles,"Jimmy Page, Robert Plant, John Bonham",246047,8060350,0.99,Rock,Led Zeppelin III,Led Zeppelin Gallows Pole,Traditional,296228,9757151,0.99,Rock,Led Zeppelin III,Led Zeppelin Tangerine,Jimmy Page,189675,6200893,0.99,Rock,Led Zeppelin III,Led Zeppelin That's The Way,"Jimmy Page, Robert Plant",337345,11202499,0.99,Rock,Led Zeppelin III,Led Zeppelin Bron-Y-Aur Stomp,"Jimmy Page, Robert Plant, John Paul Jones",259500,8674508,0.99,Rock,Led Zeppelin III,Led Zeppelin Hats Off To (Roy) Harper,Traditional,219376,7236640,0.99,Rock,Led Zeppelin III,Led Zeppelin In The Light,John Paul Jones/Robert Plant,526785,17033046,0.99,Rock,Physical Graffiti [Disc 2],Led Zeppelin Bron-Yr-Aur,Jimmy Page,126641,4150746,0.99,Rock,Physical Graffiti [Disc 2],Led Zeppelin Down By The Seaside,Robert Plant,316186,10371282,0.99,Rock,Physical Graffiti [Disc 2],Led Zeppelin Ten Years Gone,Robert Plant,393116,12756366,0.99,Rock,Physical Graffiti [Disc 2],Led Zeppelin Night Flight,John Paul Jones/Robert Plant,217547,7160647,0.99,Rock,Physical Graffiti [Disc 2],Led Zeppelin The Wanton Song,Robert Plant,249887,8180988,0.99,Rock,Physical Graffiti [Disc 2],Led Zeppelin Boogie With Stu,Ian Stewart/John Bonham/John Paul Jones/Mrs. Valens/Robert Plant,233273,7657086,0.99,Rock,Physical Graffiti [Disc 2],Led Zeppelin Black Country Woman,Robert Plant,273084,8951732,0.99,Rock,Physical Graffiti [Disc 2],Led Zeppelin Sick Again,Robert Plant,283036,9279263,0.99,Rock,Physical Graffiti [Disc 2],Led Zeppelin Achilles Last Stand,Jimmy Page/Robert Plant,625502,20593955,0.99,Rock,Presence,Led Zeppelin For Your Life,Jimmy Page/Robert Plant,384391,12633382,0.99,Rock,Presence,Led Zeppelin Royal Orleans,John Bonham/John Paul Jones,179591,5930027,0.99,Rock,Presence,Led Zeppelin Nobody's Fault But Mine,Jimmy Page/Robert Plant,376215,12237859,0.99,Rock,Presence,Led Zeppelin Candy Store Rock,Jimmy Page/Robert Plant,252055,8397423,0.99,Rock,Presence,Led Zeppelin Hots On For Nowhere,Jimmy Page/Robert Plant,284107,9342342,0.99,Rock,Presence,Led Zeppelin Tea For One,Jimmy Page/Robert Plant,566752,18475264,0.99,Rock,Presence,Led Zeppelin Rock & Roll,John Bonham/John Paul Jones/Robert Plant,242442,7897065,0.99,Rock,The Song Remains The Same (Disc 1),Led Zeppelin Celebration Day,John Paul Jones/Robert Plant,230034,7478487,0.99,Rock,The Song Remains The Same (Disc 1),Led Zeppelin The Song Remains The Same,Robert Plant,353358,11465033,0.99,Rock,The Song Remains The Same (Disc 1),Led Zeppelin Rain Song,Robert Plant,505808,16273705,0.99,Rock,The Song Remains The Same (Disc 1),Led Zeppelin Dazed And Confused,Jimmy Page,1612329,52490554,0.99,Rock,The Song Remains The Same (Disc 1),Led Zeppelin No Quarter,John Paul Jones/Robert Plant,749897,24399285,0.99,Rock,The Song Remains The Same (Disc 2),Led Zeppelin Stairway To Heaven,Robert Plant,657293,21354766,0.99,Rock,The Song Remains The Same (Disc 2),Led Zeppelin Moby Dick,John Bonham/John Paul Jones,766354,25345841,0.99,Rock,The Song Remains The Same (Disc 2),Led Zeppelin Whole 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 Natália,Renato Russo,235728,7640230,0.99,Latin,A TempestadeTempestade Ou O Livro Dos Dias,Legião Urbana L'Avventura,Renato Russo,278256,9165769,0.99,Latin,A TempestadeTempestade Ou O Livro Dos Dias,Legião Urbana Música De Trabalho,Renato Russo,260231,8590671,0.99,Latin,A TempestadeTempestade Ou O Livro Dos Dias,Legião Urbana Longe Do Meu Lado,Renato Russo - Marcelo Bonfá,266161,8655249,0.99,Latin,A TempestadeTempestade Ou O Livro Dos Dias,Legião Urbana A Via Láctea,Renato Russo,280084,9234879,0.99,Latin,A TempestadeTempestade Ou O Livro Dos Dias,Legião Urbana Música Ambiente,Renato Russo,247614,8234388,0.99,Latin,A TempestadeTempestade Ou O Livro Dos Dias,Legião Urbana Aloha,Renato Russo,325955,10793301,0.99,Latin,A TempestadeTempestade Ou O Livro Dos Dias,Legião Urbana Soul Parsifal,Renato Russo - Marisa Monte,295053,9853589,0.99,Latin,A TempestadeTempestade Ou O Livro Dos Dias,Legião Urbana Dezesseis,Renato Russo,323918,10573515,0.99,Latin,A TempestadeTempestade Ou O Livro Dos Dias,Legião Urbana Mil Pedaços,Renato Russo,203337,6643291,0.99,Latin,A TempestadeTempestade Ou O Livro Dos Dias,Legião Urbana Leila,Renato Russo,323056,10608239,0.99,Latin,A TempestadeTempestade Ou O Livro Dos Dias,Legião Urbana 1º De Julho,Renato Russo,290298,9619257,0.99,Latin,A TempestadeTempestade Ou O Livro Dos Dias,Legião Urbana Esperando Por Mim,Renato Russo,261668,8844133,0.99,Latin,A TempestadeTempestade Ou O Livro Dos Dias,Legião Urbana Quando Você Voltar,Renato Russo,173897,5781046,0.99,Latin,A TempestadeTempestade Ou O Livro Dos Dias,Legião Urbana O Livro Dos Dias,Renato Russo,257253,8570929,0.99,Latin,A TempestadeTempestade Ou O Livro Dos Dias,Legião Urbana Será,Dado Villa-Lobos/Marcelo Bonfá,148401,4826528,0.99,Latin,Mais Do Mesmo,Legião Urbana Ainda É Cedo,Dado Villa-Lobos/Ico Ouro-Preto/Marcelo Bonfá,236826,7796400,0.99,Latin,Mais Do Mesmo,Legião Urbana Geração Coca-Cola,Renato Russo,141453,4625731,0.99,Latin,Mais Do Mesmo,Legião Urbana Eduardo E Mônica,Renato Russo,271229,9026691,0.99,Latin,Mais Do Mesmo,Legião Urbana Tempo Perdido,Renato Russo,302158,9963914,0.99,Latin,Mais Do Mesmo,Legião Urbana Indios,Renato Russo,258168,8610226,0.99,Latin,Mais Do Mesmo,Legião Urbana Que País É Este,Renato Russo,177606,5822124,0.99,Latin,Mais Do Mesmo,Legião Urbana Faroeste Caboclo,Renato Russo,543007,18092739,0.99,Latin,Mais Do Mesmo,Legião Urbana Há Tempos,Dado Villa-Lobos/Marcelo Bonfá,197146,6432922,0.99,Latin,Mais Do Mesmo,Legião Urbana Pais E Filhos,Dado Villa-Lobos/Marcelo Bonfá,308401,10130685,0.99,Latin,Mais Do Mesmo,Legião Urbana Meninos E Meninas,Dado Villa-Lobos/Marcelo Bonfá,203781,6667802,0.99,Latin,Mais Do Mesmo,Legião Urbana Vento No Litoral,Dado Villa-Lobos/Marcelo Bonfá,366445,12063806,0.99,Latin,Mais Do Mesmo,Legião Urbana Perfeição,Dado Villa-Lobos/Marcelo Bonfá,276558,9258489,0.99,Latin,Mais Do Mesmo,Legião Urbana Giz,Dado Villa-Lobos/Marcelo Bonfá,202213,6677671,0.99,Latin,Mais Do Mesmo,Legião Urbana Dezesseis,Dado Villa-Lobos/Marcelo Bonfá,321724,10501773,0.99,Latin,Mais Do Mesmo,Legião Urbana Antes Das Seis,Dado Villa-Lobos,189231,6296531,0.99,Latin,Mais Do Mesmo,Legião Urbana Are You Gonna Go My Way,Craig Ross/Lenny Kravitz,211591,6905135,0.99,Rock,Greatest Hits,Lenny Kravitz Fly Away,Lenny Kravitz,221962,7322085,0.99,Rock,Greatest Hits,Lenny Kravitz Rock And Roll Is Dead,Lenny Kravitz,204199,6680312,0.99,Rock,Greatest Hits,Lenny Kravitz Again,Lenny Kravitz,228989,7490476,0.99,Rock,Greatest Hits,Lenny Kravitz It Ain't Over 'Til It's Over,Lenny Kravitz,242703,8078936,0.99,Rock,Greatest Hits,Lenny Kravitz Can't Get You Off My Mind,Lenny Kravitz,273815,8937150,0.99,Rock,Greatest Hits,Lenny Kravitz Mr. Cab Driver,Lenny Kravitz,230321,7668084,0.99,Rock,Greatest Hits,Lenny Kravitz American Woman,B. Cummings/G. Peterson/M.J. Kale/R. Bachman,261773,8538023,0.99,Rock,Greatest Hits,Lenny Kravitz Stand By My Woman,Henry Kirssch/Lenny Kravitz/S. Pasch A. Krizan,259683,8447611,0.99,Rock,Greatest Hits,Lenny Kravitz Always On The Run,Lenny Kravitz/Slash,232515,7593397,0.99,Rock,Greatest Hits,Lenny Kravitz Heaven Help,Gerry DeVeaux/Terry Britten,190354,6222092,0.99,Rock,Greatest Hits,Lenny Kravitz I Belong To You,Lenny Kravitz,257123,8477980,0.99,Rock,Greatest Hits,Lenny Kravitz Believe,Henry Hirsch/Lenny Kravitz,295131,9661978,0.99,Rock,Greatest Hits,Lenny Kravitz Let Love Rule,Lenny Kravitz,342648,11298085,0.99,Rock,Greatest Hits,Lenny Kravitz Black Velveteen,Lenny Kravitz,290899,9531301,0.99,Rock,Greatest Hits,Lenny Kravitz Johnny B. Goode,,243200,8092024,0.99,Reggae,Greatest Hits,Lenny Kravitz Don't Look Back,,221100,7344023,0.99,Reggae,Greatest Hits,Lenny Kravitz Jah Seh No,,276871,9134476,0.99,Reggae,Greatest Hits,Lenny Kravitz I'm The Toughest,,230191,7657594,0.99,Reggae,Greatest Hits,Lenny Kravitz Nothing But Love,,221570,7335228,0.99,Reggae,Greatest Hits,Lenny Kravitz Buk-In-Hamm Palace,,265665,8964369,0.99,Reggae,Greatest Hits,Lenny Kravitz Bush Doctor,,239751,7942299,0.99,Reggae,Greatest Hits,Lenny Kravitz Wanted Dread And Alive,,260310,8670933,0.99,Reggae,Greatest Hits,Lenny Kravitz Mystic Man,,353671,11812170,0.99,Reggae,Greatest Hits,Lenny Kravitz Coming In Hot,,213054,7109414,0.99,Reggae,Greatest Hits,Lenny Kravitz Pick Myself Up,,234684,7788255,0.99,Reggae,Greatest Hits,Lenny Kravitz Crystal Ball,,309733,10319296,0.99,Reggae,Greatest Hits,Lenny Kravitz Equal Rights Downpresser Man,,366733,12086524,0.99,Reggae,Greatest Hits,Lenny Kravitz Holding Back The Years,Mick Hucknall and Neil Moss,270053,8833220,0.99,Rock,Greatest Hits,Lenny Kravitz Money's Too Tight To Mention,John and William Valentine,268408,8861921,0.99,Rock,Greatest Hits,Lenny Kravitz The Right Thing,Mick Hucknall,262687,8624063,0.99,Rock,Greatest Hits,Lenny Kravitz It's Only Love,Jimmy and Vella Cameron,232594,7659017,0.99,Rock,Greatest Hits,Lenny Kravitz A New Flame,Mick Hucknall,237662,7822875,0.99,Rock,Greatest Hits,Lenny Kravitz You've Got It,Mick Hucknall and Lamont Dozier,235232,7712845,0.99,Rock,Greatest Hits,Lenny Kravitz If You Don't Know Me By Now,Kenny Gamble and Leon Huff,206524,6712634,0.99,Rock,Greatest Hits,Lenny Kravitz Stars,Mick Hucknall,248137,8194906,0.99,Rock,Greatest Hits,Lenny Kravitz Something Got Me Started,Mick Hucknall and Fritz McIntyre,239595,7997139,0.99,Rock,Greatest Hits,Lenny Kravitz Thrill Me,Mick Hucknall and Fritz McIntyre,303934,10034711,0.99,Rock,Greatest Hits,Lenny Kravitz Your Mirror,Mick Hucknall,240666,7893821,0.99,Rock,Greatest Hits,Lenny Kravitz For Your Babies,Mick Hucknall,256992,8408803,0.99,Rock,Greatest Hits,Lenny Kravitz So Beautiful,Mick Hucknall,298083,9837832,0.99,Rock,Greatest Hits,Lenny Kravitz Angel,Carolyn Franklin and Sonny Saunders,240561,7880256,0.99,Rock,Greatest Hits,Lenny Kravitz Fairground,Mick Hucknall,263888,8793094,0.99,Rock,Greatest Hits,Lenny Kravitz Still Of The Night,Sykes,398210,13043817,0.99,Metal,Greatest Hits,Lenny Kravitz Here I Go Again,Marsden,233874,7652473,0.99,Metal,Greatest Hits,Lenny Kravitz Is This Love,Sykes,283924,9262360,0.99,Metal,Greatest Hits,Lenny Kravitz Love Ain't No Stranger,Galley,259395,8490428,0.99,Metal,Greatest Hits,Lenny Kravitz Looking For Love,Sykes,391941,12769847,0.99,Metal,Greatest Hits,Lenny Kravitz Now You're Gone,Vandenberg,251141,8162193,0.99,Metal,Greatest Hits,Lenny Kravitz Slide It In,Coverdale,202475,6615152,0.99,Metal,Greatest Hits,Lenny Kravitz Slow An' Easy,Moody,367255,11961332,0.99,Metal,Greatest Hits,Lenny Kravitz Judgement Day,Vandenberg,317074,10326997,0.99,Metal,Greatest Hits,Lenny Kravitz You're Gonna Break My Hart Again,Sykes,250853,8176847,0.99,Metal,Greatest Hits,Lenny Kravitz The Deeper The Love,Vandenberg,262791,8606504,0.99,Metal,Greatest Hits,Lenny Kravitz Crying In The Rain,Coverdale,337005,10931921,0.99,Metal,Greatest Hits,Lenny Kravitz Fool For Your Loving,Marsden/Moody,250801,8129820,0.99,Metal,Greatest Hits,Lenny Kravitz Sweet Lady Luck,Vandenberg,273737,8919163,0.99,Metal,Greatest Hits,Lenny Kravitz Assim Caminha A Humanidade,,210755,6993763,0.99,Latin,Lulu Santos - RCA 100 Anos De Música - Álbum 01,Lulu Santos Um Pro Outro,,236382,7825215,0.99,Latin,Lulu Santos - RCA 100 Anos De Música - Álbum 01,Lulu Santos Casa,,307591,10107269,0.99,Latin,Lulu Santos - RCA 100 Anos De Música - Álbum 01,Lulu Santos Condição,,263549,8778465,0.99,Latin,Lulu Santos - RCA 100 Anos De Música - Álbum 01,Lulu Santos Satisfação,,208065,6901681,0.99,Latin,Lulu Santos - RCA 100 Anos De Música - Álbum 01,Lulu Santos Brumário,,216241,7243499,0.99,Latin,Lulu Santos - RCA 100 Anos De Música - Álbum 01,Lulu Santos Sábado À Noite,,193854,6435114,0.99,Latin,Lulu Santos - RCA 100 Anos De Música - Álbum 01,Lulu Santos A Cura,,280920,9260588,0.99,Latin,Lulu Santos - RCA 100 Anos De Música - Álbum 01,Lulu Santos Atrás Do Trio Elétrico,,149080,4917615,0.99,Latin,Lulu Santos - RCA 100 Anos De Música - Álbum 01,Lulu Santos Tudo Bem,,196101,6419139,0.99,Latin,Lulu Santos - RCA 100 Anos De Música - Álbum 01,Lulu Santos Toda Forma De Amor,,227813,7496584,0.99,Latin,Lulu Santos - RCA 100 Anos De Música - Álbum 01,Lulu Santos Sereia,,278047,9121087,0.99,Latin,Lulu Santos - RCA 100 Anos De Música - Álbum 01,Lulu Santos Se Você Pensa,,195996,6552490,0.99,Latin,Lulu Santos - RCA 100 Anos De Música - Álbum 01,Lulu Santos Lá Vem O Sol (Here Comes The Sun),,189492,6229645,0.99,Latin,Lulu Santos - RCA 100 Anos De Música - Álbum 01,Lulu Santos Honolulu,,261433,8558481,0.99,Latin,Lulu Santos - RCA 100 Anos De Música - Álbum 02,Lulu Santos Dancin´Days,,237400,7875347,0.99,Latin,Lulu Santos - RCA 100 Anos De Música - Álbum 02,Lulu Santos Aviso Aos Navegantes,,242808,8058651,0.99,Latin,Lulu Santos - RCA 100 Anos De Música - Álbum 02,Lulu Santos Hyperconectividade,,180636,5948039,0.99,Latin,Lulu Santos - RCA 100 Anos De Música - Álbum 02,Lulu Santos O Descobridor Dos Sete Mares,,225854,7475780,0.99,Latin,Lulu Santos - RCA 100 Anos De Música - Álbum 02,Lulu Santos Um Certo Alguém,,194063,6430939,0.99,Latin,Lulu Santos - RCA 100 Anos De Música - Álbum 02,Lulu Santos Fullgás,,346070,11505484,0.99,Latin,Lulu Santos - RCA 100 Anos De Música - Álbum 02,Lulu Santos Aquilo,,246073,8167819,0.99,Latin,Lulu Santos - RCA 100 Anos De Música - Álbum 02,Lulu Santos Senta A Pua,,217547,7205844,0.99,Latin,Lulu Santos - RCA 100 Anos De Música - Álbum 02,Lulu Santos Ro-Que-Se-Da-Ne,,146703,4805897,0.99,Latin,Lulu Santos - RCA 100 Anos De Música - Álbum 02,Lulu Santos Tudo Igual,,276035,9201645,0.99,Latin,Lulu Santos - RCA 100 Anos De Música - Álbum 02,Lulu Santos Fogo De Palha,,246804,8133732,0.99,Latin,Lulu Santos - RCA 100 Anos De Música - Álbum 02,Lulu Santos Assaltaram A Gramática,,261041,8698959,0.99,Latin,Lulu Santos - RCA 100 Anos De Música - Álbum 02,Lulu Santos O Último Romântico (Ao Vivo),,231993,7692697,0.99,Latin,Lulu Santos - RCA 100 Anos De Música - Álbum 02,Lulu Santos Pseudo Silk Kimono,"Kelly, Mosley, Rothery, Trewaves",134739,4334038,0.99,Rock,Misplaced Childhood,Marillion Kayleigh,"Kelly, Mosley, Rothery, Trewaves",234605,7716005,0.99,Rock,Misplaced Childhood,Marillion Lavender,"Kelly, Mosley, Rothery, Trewaves",153417,4999814,0.99,Rock,Misplaced Childhood,Marillion Bitter Suite: Brief Encounter / Lost Weekend / Blue Angel,"Kelly, Mosley, Rothery, Trewaves",356493,11791068,0.99,Rock,Misplaced Childhood,Marillion Heart Of Lothian: Wide Boy / Curtain Call,"Kelly, Mosley, Rothery, Trewaves",366053,11893723,0.99,Rock,Misplaced Childhood,Marillion Waterhole (Expresso Bongo),"Kelly, Mosley, Rothery, Trewaves",133093,4378835,0.99,Rock,Misplaced Childhood,Marillion Lords Of The Backstage,"Kelly, Mosley, Rothery, Trewaves",112875,3741319,0.99,Rock,Misplaced Childhood,Marillion Blind Curve: Vocal Under A Bloodlight / Passing Strangers / Mylo / Perimeter Walk / Threshold,"Kelly, Mosley, Rothery, Trewaves",569704,18578995,0.99,Rock,Misplaced Childhood,Marillion Childhoods End?,"Kelly, Mosley, Rothery, Trewaves",272796,9015366,0.99,Rock,Misplaced Childhood,Marillion White Feather,"Kelly, Mosley, Rothery, Trewaves",143595,4711776,0.99,Rock,Misplaced Childhood,Marillion Arrepio,Carlinhos Brown,136254,4511390,0.99,Latin,Barulhinho Bom,Marisa Monte Magamalabares,Carlinhos Brown,215875,7183757,0.99,Latin,Barulhinho Bom,Marisa Monte Chuva No Brejo,Morais,145606,4857761,0.99,Latin,Barulhinho Bom,Marisa Monte Cérebro Eletrônico,Gilberto Gil,172800,5760864,0.99,Latin,Barulhinho Bom,Marisa Monte Tempos Modernos,Lulu Santos,183066,6066234,0.99,Latin,Barulhinho Bom,Marisa Monte Maraçá,Carlinhos Brown,230008,7621482,0.99,Latin,Barulhinho Bom,Marisa Monte Blanco,Marisa Monte/poema de Octavio Paz/versão: Haroldo de Campos,45191,1454532,0.99,Latin,Barulhinho Bom,Marisa Monte Panis Et Circenses,Caetano Veloso e Gilberto Gil,192339,6318373,0.99,Latin,Barulhinho Bom,Marisa Monte De Noite Na Cama,Caetano Veloso e Gilberto Gil,209005,7012658,0.99,Latin,Barulhinho Bom,Marisa Monte Beija Eu,Caetano Veloso e Gilberto Gil,197276,6512544,0.99,Latin,Barulhinho Bom,Marisa Monte Give Me Love,Caetano Veloso e Gilberto Gil,249808,8196331,0.99,Latin,Barulhinho Bom,Marisa Monte Ainda Lembro,Caetano Veloso e Gilberto Gil,218801,7211247,0.99,Latin,Barulhinho Bom,Marisa Monte A Menina Dança,Caetano Veloso e Gilberto Gil,129410,4326918,0.99,Latin,Barulhinho Bom,Marisa Monte Dança Da Solidão,Caetano Veloso e Gilberto Gil,203520,6699368,0.99,Latin,Barulhinho Bom,Marisa Monte Ao Meu Redor,Caetano Veloso e Gilberto Gil,275591,9158834,0.99,Latin,Barulhinho Bom,Marisa Monte Bem Leve,Caetano Veloso e Gilberto Gil,159190,5246835,0.99,Latin,Barulhinho Bom,Marisa Monte Segue O Seco,Caetano Veloso e Gilberto Gil,178207,5922018,0.99,Latin,Barulhinho Bom,Marisa Monte O Xote Das Meninas,Caetano Veloso e Gilberto Gil,291866,9553228,0.99,Latin,Barulhinho Bom,Marisa Monte Wherever I Lay My Hat,,136986,4477321,0.99,R&B/Soul,Seek And Shall Find: More Of The Best (1963-1981),Marvin Gaye Get My Hands On Some Lovin',,149054,4860380,0.99,R&B/Soul,Seek And Shall Find: More Of The Best (1963-1981),Marvin Gaye No 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 You'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 When 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 You'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 Loving 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 It'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 Seek 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 Gonna 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 Gonna 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 I 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 "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 Save 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 You 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 Ego Tripping Out,Marvin Gaye,314514,10383887,0.99,R&B/Soul,Seek And Shall Find: More Of The Best (1963-1981),Marvin Gaye Praise,Marvin Gaye,235833,7839179,0.99,R&B/Soul,Seek And Shall Find: More Of The Best (1963-1981),Marvin Gaye Heavy Love Affair,Marvin Gaye,227892,7522232,0.99,R&B/Soul,Seek And Shall Find: More Of The Best (1963-1981),Marvin Gaye Down Under,,222171,7366142,0.99,Rock,The Best Of Men At Work,Men At Work Overkill,,225410,7408652,0.99,Rock,The Best Of Men At Work,Men At Work Be Good Johnny,,216320,7139814,0.99,Rock,The Best Of Men At Work,Men At Work Everything I Need,,216476,7107625,0.99,Rock,The Best Of Men At Work,Men At Work Down by the Sea,,408163,13314900,0.99,Rock,The Best Of Men At Work,Men At Work Who Can It Be Now?,,202396,6682850,0.99,Rock,The Best Of Men At Work,Men At Work It's a Mistake,,273371,8979965,0.99,Rock,The Best Of Men At Work,Men At Work Dr. Heckyll & Mr. Jive,,278465,9110403,0.99,Rock,The Best Of Men At Work,Men At Work Shakes and Ladders,,198008,6560753,0.99,Rock,The Best Of Men At Work,Men At Work No Sign of Yesterday,,362004,11829011,0.99,Rock,The Best Of Men At Work,Men At Work Enter Sandman,"James Hetfield, Lars Ulrich and Kirk Hammett",332251,10852002,0.99,Metal,Black Album,Metallica Sad But True,Ulrich,324754,10541258,0.99,Metal,Black Album,Metallica Holier Than Thou,Ulrich,227892,7462011,0.99,Metal,Black Album,Metallica The Unforgiven,"James Hetfield, Lars Ulrich and Kirk Hammett",387082,12646886,0.99,Metal,Black Album,Metallica Wherever I May Roam,Ulrich,404323,13161169,0.99,Metal,Black Album,Metallica Don't Tread On Me,Ulrich,240483,7827907,0.99,Metal,Black Album,Metallica Through The Never,"James Hetfield, Lars Ulrich and Kirk Hammett",244375,8024047,0.99,Metal,Black Album,Metallica Nothing Else Matters,Ulrich,388832,12606241,0.99,Metal,Black Album,Metallica Of Wolf And Man,"James Hetfield, Lars Ulrich and Kirk Hammett",256835,8339785,0.99,Metal,Black Album,Metallica The God That Failed,Ulrich,308610,10055959,0.99,Metal,Black Album,Metallica My Friend Of Misery,"James Hetfield, Lars Ulrich and Jason Newsted",409547,13293515,0.99,Metal,Black Album,Metallica The Struggle Within,Ulrich,234240,7654052,0.99,Metal,Black Album,Metallica Helpless,Harris/Tatler,398315,12977902,0.99,Metal,Garage Inc. (Disc 2),Metallica The Small Hours,Holocaust,403435,13215133,0.99,Metal,Garage Inc. (Disc 2),Metallica The Wait,Killing Joke,295418,9688418,0.99,Metal,Garage Inc. (Disc 2),Metallica Crash Course In Brain Surgery,Bourge/Phillips/Shelley,190406,6233729,0.99,Metal,Garage Inc. (Disc 2),Metallica Last Caress/Green Hell,Danzig,209972,6854313,0.99,Metal,Garage Inc. (Disc 2),Metallica Am I Evil?,Harris/Tatler,470256,15387219,0.99,Metal,Garage Inc. (Disc 2),Metallica Blitzkrieg,Jones/Sirotto/Smith,216685,7090018,0.99,Metal,Garage Inc. (Disc 2),Metallica Breadfan,Bourge/Phillips/Shelley,341551,11100130,0.99,Metal,Garage Inc. (Disc 2),Metallica The Prince,Harris/Tatler,265769,8624492,0.99,Metal,Garage Inc. (Disc 2),Metallica Stone Cold Crazy,Deacon/May/Mercury/Taylor,137717,4514830,0.99,Metal,Garage Inc. (Disc 2),Metallica So What,Culmer/Exalt,189152,6162894,0.99,Metal,Garage Inc. (Disc 2),Metallica Killing Time,Sweet Savage,183693,6021197,0.99,Metal,Garage Inc. (Disc 2),Metallica Overkill,Clarke/Kilmister/Tayler,245133,7971330,0.99,Metal,Garage Inc. (Disc 2),Metallica Damage Case,Clarke/Farren/Kilmister/Tayler,220212,7212997,0.99,Metal,Garage Inc. (Disc 2),Metallica Stone Dead Forever,Clarke/Kilmister/Tayler,292127,9556060,0.99,Metal,Garage Inc. (Disc 2),Metallica Too Late Too Late,Clarke/Kilmister/Tayler,192052,6276291,0.99,Metal,Garage Inc. (Disc 2),Metallica Hit The Lights,"James Hetfield, Lars Ulrich",257541,8357088,0.99,Metal,Kill 'Em All,Metallica The Four Horsemen,"James Hetfield, Lars Ulrich, Dave Mustaine",433188,14178138,0.99,Metal,Kill 'Em All,Metallica Motorbreath,James Hetfield,188395,6153933,0.99,Metal,Kill 'Em All,Metallica Jump In The Fire,"James Hetfield, Lars Ulrich, Dave Mustaine",281573,9135755,0.99,Metal,Kill 'Em All,Metallica (Anesthesia) Pulling Teeth,Cliff Burton,254955,8234710,0.99,Metal,Kill 'Em All,Metallica Whiplash,"James Hetfield, Lars Ulrich",249208,8102839,0.99,Metal,Kill 'Em All,Metallica Phantom Lord,"James Hetfield, Lars Ulrich, Dave Mustaine",302053,9817143,0.99,Metal,Kill 'Em All,Metallica No Remorse,"James Hetfield, Lars Ulrich",386795,12672166,0.99,Metal,Kill 'Em All,Metallica Seek & Destroy,"James Hetfield, Lars Ulrich",415817,13452301,0.99,Metal,Kill 'Em All,Metallica Metal Militia,"James Hetfield, Lars Ulrich, Dave Mustaine",311327,10141785,0.99,Metal,Kill 'Em All,Metallica Ain't My Bitch,"James Hetfield, Lars Ulrich",304457,9931015,0.99,Metal,Load,Metallica 2 X 4,"James Hetfield, Lars Ulrich, Kirk Hammett",328254,10732251,0.99,Metal,Load,Metallica The House Jack Built,"James Hetfield, Lars Ulrich, Kirk Hammett",398942,13005152,0.99,Metal,Load,Metallica Until It Sleeps,"James Hetfield, Lars Ulrich",269740,8837394,0.99,Metal,Load,Metallica King Nothing,"James Hetfield, Lars Ulrich, Kirk Hammett",328097,10681477,0.99,Metal,Load,Metallica Hero Of The Day,"James Hetfield, Lars Ulrich, Kirk Hammett",261982,8540298,0.99,Metal,Load,Metallica Bleeding Me,"James Hetfield, Lars Ulrich, Kirk Hammett",497998,16249420,0.99,Metal,Load,Metallica Cure,"James Hetfield, Lars Ulrich",294347,9648615,0.99,Metal,Load,Metallica Poor Twisted Me,"James Hetfield, Lars Ulrich",240065,7854349,0.99,Metal,Load,Metallica Wasted My Hate,"James Hetfield, Lars Ulrich, Kirk Hammett",237296,7762300,0.99,Metal,Load,Metallica Mama Said,"James Hetfield, Lars Ulrich",319764,10508310,0.99,Metal,Load,Metallica Thorn Within,"James Hetfield, Lars Ulrich, Kirk Hammett",351738,11486686,0.99,Metal,Load,Metallica Ronnie,"James Hetfield, Lars Ulrich",317204,10390947,0.99,Metal,Load,Metallica The Outlaw Torn,"James Hetfield, Lars Ulrich",588721,19286261,0.99,Metal,Load,Metallica Battery,J.Hetfield/L.Ulrich,312424,10229577,0.99,Metal,Master Of Puppets,Metallica Master Of Puppets,K.Hammett,515239,16893720,0.99,Metal,Master Of Puppets,Metallica The Thing That Should Not Be,K.Hammett,396199,12952368,0.99,Metal,Master Of Puppets,Metallica Welcome Home (Sanitarium),K.Hammett,387186,12679965,0.99,Metal,Master Of Puppets,Metallica Disposable Heroes,J.Hetfield/L.Ulrich,496718,16135560,0.99,Metal,Master Of Puppets,Metallica Leper Messiah,C.Burton,347428,11310434,0.99,Metal,Master Of Puppets,Metallica Orion,K.Hammett,500062,16378477,0.99,Metal,Master Of Puppets,Metallica Damage Inc.,K.Hammett,330919,10725029,0.99,Metal,Master Of Puppets,Metallica Fuel,"Hetfield, Ulrich, Hammett",269557,8876811,0.99,Metal,ReLoad,Metallica The Memory Remains,"Hetfield, Ulrich",279353,9110730,0.99,Metal,ReLoad,Metallica Devil's Dance,"Hetfield, Ulrich",318955,10414832,0.99,Metal,ReLoad,Metallica The Unforgiven II,"Hetfield, Ulrich, Hammett",395520,12886474,0.99,Metal,ReLoad,Metallica Better Than You,"Hetfield, Ulrich",322899,10549070,0.99,Metal,ReLoad,Metallica Slither,"Hetfield, Ulrich, Hammett",313103,10199789,0.99,Metal,ReLoad,Metallica Carpe Diem Baby,"Hetfield, Ulrich, Hammett",372480,12170693,0.99,Metal,ReLoad,Metallica Bad Seed,"Hetfield, Ulrich, Hammett",245394,8019586,0.99,Metal,ReLoad,Metallica Where The Wild Things Are,"Hetfield, Ulrich, Newsted",414380,13571280,0.99,Metal,ReLoad,Metallica Prince Charming,"Hetfield, Ulrich",365061,12009412,0.99,Metal,ReLoad,Metallica Low Man's Lyric,"Hetfield, Ulrich",457639,14855583,0.99,Metal,ReLoad,Metallica Attitude,"Hetfield, Ulrich",315898,10335734,0.99,Metal,ReLoad,Metallica Fixxxer,"Hetfield, Ulrich, Hammett",496065,16190041,0.99,Metal,ReLoad,Metallica Fight Fire With Fire,Metallica,285753,9420856,0.99,Metal,Ride The Lightning,Metallica Ride The Lightning,Metallica,397740,13055884,0.99,Metal,Ride The Lightning,Metallica For Whom The Bell Tolls,Metallica,311719,10159725,0.99,Metal,Ride The Lightning,Metallica Fade To Black,Metallica,414824,13531954,0.99,Metal,Ride The Lightning,Metallica Trapped Under Ice,Metallica,244532,7975942,0.99,Metal,Ride The Lightning,Metallica Escape,Metallica,264359,8652332,0.99,Metal,Ride The Lightning,Metallica Creeping Death,Metallica,396878,12955593,0.99,Metal,Ride The Lightning,Metallica The Call Of Ktulu,Metallica,534883,17486240,0.99,Metal,Ride The Lightning,Metallica Frantic,Bob Rock/James Hetfield/Kirk Hammett/Lars Ulrich,350458,11510849,0.99,Metal,St. Anger,Metallica St. Anger,Bob Rock/James Hetfield/Kirk Hammett/Lars Ulrich,441234,14363779,0.99,Metal,St. Anger,Metallica Some Kind Of Monster,Bob Rock/James Hetfield/Kirk Hammett/Lars Ulrich,505626,16557497,0.99,Metal,St. Anger,Metallica Dirty Window,Bob Rock/James Hetfield/Kirk Hammett/Lars Ulrich,324989,10670604,0.99,Metal,St. Anger,Metallica Invisible Kid,Bob Rock/James Hetfield/Kirk Hammett/Lars Ulrich,510197,16591800,0.99,Metal,St. Anger,Metallica My World,Bob Rock/James Hetfield/Kirk Hammett/Lars Ulrich,345626,11253756,0.99,Metal,St. Anger,Metallica Shoot Me Again,Bob Rock/James Hetfield/Kirk Hammett/Lars Ulrich,430210,14093551,0.99,Metal,St. Anger,Metallica Sweet Amber,Bob Rock/James Hetfield/Kirk Hammett/Lars Ulrich,327235,10616595,0.99,Metal,St. Anger,Metallica The Unnamed Feeling,Bob Rock/James Hetfield/Kirk Hammett/Lars Ulrich,429479,14014582,0.99,Metal,St. Anger,Metallica Purify,Bob Rock/James Hetfield/Kirk Hammett/Lars Ulrich,314017,10232537,0.99,Metal,St. Anger,Metallica All Within My Hands,Bob Rock/James Hetfield/Kirk Hammett/Lars Ulrich,527986,17162741,0.99,Metal,St. Anger,Metallica Blackened,"James Hetfield, Lars Ulrich & Jason Newsted",403382,13254874,0.99,Metal,...And Justice For All,Metallica ...And Justice For All,"James Hetfield, Lars Ulrich & Kirk Hammett",585769,19262088,0.99,Metal,...And Justice For All,Metallica Eye Of The Beholder,"James Hetfield, Lars Ulrich & Kirk Hammett",385828,12747894,0.99,Metal,...And Justice For All,Metallica One,James Hetfield & Lars Ulrich,446484,14695721,0.99,Metal,...And Justice For All,Metallica The Shortest Straw,James Hetfield and Lars Ulrich,395389,13013990,0.99,Metal,...And Justice For All,Metallica Harvester Of Sorrow,James Hetfield and Lars Ulrich,345547,11377339,0.99,Metal,...And Justice For All,Metallica The Frayed Ends Of Sanity,"James Hetfield, Lars Ulrich and Kirk Hammett",464039,15198986,0.99,Metal,...And Justice For All,Metallica To Live Is To Die,"James Hetfield, Lars Ulrich and Cliff Burton",588564,19243795,0.99,Metal,...And Justice For All,Metallica Dyers Eve,"James Hetfield, Lars Ulrich and Kirk Hammett",313991,10302828,0.99,Metal,...And Justice For All,Metallica Springsville,J. Carisi,207725,6776219,0.99,Jazz,Miles Ahead,Miles Davis The Maids Of Cadiz,L. Delibes,233534,7505275,0.99,Jazz,Miles Ahead,Miles Davis The Duke,Dave Brubeck,214961,6977626,0.99,Jazz,Miles Ahead,Miles Davis My Ship,"Ira Gershwin, Kurt Weill",268016,8581144,0.99,Jazz,Miles Ahead,Miles Davis Miles Ahead,"Miles Davis, Gil Evans",209893,6807707,0.99,Jazz,Miles Ahead,Miles Davis Blues For Pablo,Gil Evans,318328,10218398,0.99,Jazz,Miles Ahead,Miles Davis New Rhumba,A. Jamal,276871,8980400,0.99,Jazz,Miles Ahead,Miles Davis The Meaning Of The Blues,"R. Troup, L. Worth",168594,5395412,0.99,Jazz,Miles Ahead,Miles Davis Lament,J.J. Johnson,134191,4293394,0.99,Jazz,Miles Ahead,Miles Davis I Don't Wanna Be Kissed (By Anyone But You),"H. Spina, J. Elliott",191320,6219487,0.99,Jazz,Miles Ahead,Miles Davis Springsville (Alternate Take),J. Carisi,196388,6382079,0.99,Jazz,Miles Ahead,Miles Davis Blues For Pablo (Alternate Take),Gil Evans,212558,6900619,0.99,Jazz,Miles Ahead,Miles Davis The Meaning Of The Blues/Lament (Alternate Take),"J.J. Johnson/R. Troup, L. Worth",309786,9912387,0.99,Jazz,Miles Ahead,Miles Davis I Don't Wanna Be Kissed (By Anyone But You) (Alternate Take),"H. Spina, J. Elliott",192078,6254796,0.99,Jazz,Miles Ahead,Miles Davis Coração De Estudante,"Wagner Tiso, Milton Nascimento",238550,7797308,0.99,Latin,Milton Nascimento Ao Vivo,Milton Nascimento A Noite Do Meu Bem,Dolores Duran,220081,7125225,0.99,Latin,Milton Nascimento Ao Vivo,Milton Nascimento Paisagem Na Janela,"Lô Borges, Fernando Brant",197694,6523547,0.99,Latin,Milton Nascimento Ao Vivo,Milton Nascimento Cuitelinho,Folclore,209397,6803970,0.99,Latin,Milton Nascimento Ao Vivo,Milton Nascimento Caxangá,"Milton Nascimento, Fernando Brant",245551,8144179,0.99,Latin,Milton Nascimento Ao Vivo,Milton Nascimento Nos Bailes Da Vida,"Milton Nascimento, Fernando Brant",275748,9126170,0.99,Latin,Milton Nascimento Ao Vivo,Milton Nascimento Menestrel Das Alagoas,"Milton Nascimento, Fernando Brant",199758,6542289,0.99,Latin,Milton Nascimento Ao Vivo,Milton Nascimento Brasil,"Milton Nascimento, Fernando Brant",155428,5252560,0.99,Latin,Milton Nascimento Ao Vivo,Milton Nascimento Canção Do Novo Mundo,"Beto Guedes, Ronaldo Bastos",215353,7032626,0.99,Latin,Milton Nascimento Ao Vivo,Milton Nascimento Um Gosto De Sol,"Milton Nascimento, Ronaldo Bastos",307200,9893875,0.99,Latin,Milton Nascimento Ao Vivo,Milton Nascimento Solar,"Milton Nascimento, Fernando Brant",156212,5098288,0.99,Latin,Milton Nascimento Ao Vivo,Milton Nascimento Para Lennon E McCartney,"Lô Borges, Márcio Borges, Fernando Brant",321828,10626920,0.99,Latin,Milton Nascimento Ao Vivo,Milton Nascimento "Maria, Maria","Milton Nascimento, Fernando Brant",72463,2371543,0.99,Latin,Milton Nascimento Ao Vivo,Milton Nascimento Minas,"Milton Nascimento, Caetano Veloso",152293,4921056,0.99,Latin,Minas,Milton Nascimento "Fé Cega, Faca Amolada","Milton Nascimento, Ronaldo Bastos",278099,9258649,0.99,Latin,Minas,Milton Nascimento Beijo Partido,Toninho Horta,229564,7506969,0.99,Latin,Minas,Milton Nascimento Saudade Dos Aviões Da Panair (Conversando No Bar),"Milton Nascimento, Fernando Brant",268721,8805088,0.99,Latin,Minas,Milton Nascimento Gran Circo,"Milton Nascimento, Márcio Borges",251297,8237026,0.99,Latin,Minas,Milton Nascimento Ponta de Areia,"Milton Nascimento, Fernando Brant",272796,8874285,0.99,Latin,Minas,Milton Nascimento Trastevere,"Milton Nascimento, Ronaldo Bastos",265665,8708399,0.99,Latin,Minas,Milton Nascimento Idolatrada,"Milton Nascimento, Fernando Brant",286249,9426153,0.99,Latin,Minas,Milton Nascimento Leila (Venha Ser Feliz),Milton Nascimento,209737,6898507,0.99,Latin,Minas,Milton Nascimento Paula E Bebeto,"Milton Nascimento, Caetano Veloso",135732,4583956,0.99,Latin,Minas,Milton Nascimento Simples,Nelson Angelo,133093,4326333,0.99,Latin,Minas,Milton Nascimento Norwegian Wood,"John Lennon, Paul McCartney",413910,13520382,0.99,Latin,Minas,Milton Nascimento Caso Você Queira Saber,"Beto Guedes, Márcio Borges",205688,6787901,0.99,Latin,Minas,Milton Nascimento Ace Of Spades,Clarke/Kilmister/Taylor,169926,5523552,0.99,Metal,Ace Of Spades,Motörhead Love Me Like A Reptile,Clarke/Kilmister/Taylor,203546,6616389,0.99,Metal,Ace Of Spades,Motörhead Shoot You In The Back,Clarke/Kilmister/Taylor,160026,5175327,0.99,Metal,Ace Of Spades,Motörhead Live To Win,Clarke/Kilmister/Taylor,217626,7102182,0.99,Metal,Ace Of Spades,Motörhead Fast And Loose,Clarke/Kilmister/Taylor,203337,6643350,0.99,Metal,Ace Of Spades,Motörhead (We Are) The Road Crew,Clarke/Kilmister/Taylor,192600,6283035,0.99,Metal,Ace Of Spades,Motörhead Fire Fire,Clarke/Kilmister/Taylor,164675,5416114,0.99,Metal,Ace Of Spades,Motörhead Jailbait,Clarke/Kilmister/Taylor,213916,6983609,0.99,Metal,Ace Of Spades,Motörhead Dance,Clarke/Kilmister/Taylor,158432,5155099,0.99,Metal,Ace Of Spades,Motörhead Bite The Bullet,Clarke/Kilmister/Taylor,98115,3195536,0.99,Metal,Ace Of Spades,Motörhead The Chase Is Better Than The Catch,Clarke/Kilmister/Taylor,258403,8393310,0.99,Metal,Ace Of Spades,Motörhead The Hammer,Clarke/Kilmister/Taylor,168071,5543267,0.99,Metal,Ace Of Spades,Motörhead Dirty Love,Clarke/Kilmister/Taylor,176457,5805241,0.99,Metal,Ace Of Spades,Motörhead Please Don't Touch,Heath/Robinson,169926,5557002,0.99,Metal,Ace Of Spades,Motörhead Emergency,Dufort/Johnson/McAuliffe/Williams,180427,5828728,0.99,Metal,Ace Of Spades,Motörhead Kir Royal,Mônica Marianno,234788,7706552,0.99,World,Demorou...,Mônica Marianno O Que Vai Em Meu Coração,Mônica Marianno,255373,8366846,0.99,World,Demorou...,Mônica Marianno Aos Leões,Mônica Marianno,234684,7790574,0.99,World,Demorou...,Mônica Marianno Dois Índios,Mônica Marianno,219271,7213072,0.99,World,Demorou...,Mônica Marianno Noite Negra,Mônica Marianno,206811,6819584,0.99,World,Demorou...,Mônica Marianno Beijo do Olhar,Mônica Marianno,252682,8369029,0.99,World,Demorou...,Mônica Marianno É Fogo,Mônica Marianno,194873,6501520,0.99,World,Demorou...,Mônica Marianno Já Foi,Mônica Marianno,245681,8094872,0.99,World,Demorou...,Mônica Marianno Só Se For Pelo Cabelo,Mônica Marianno,238288,8006345,0.99,World,Demorou...,Mônica Marianno No Clima,Mônica Marianno,249495,8362040,0.99,World,Demorou...,Mônica Marianno A Moça e a Chuva,Mônica Marianno,274625,8929357,0.99,World,Demorou...,Mônica Marianno Demorou!,Mônica Marianno,39131,1287083,0.99,World,Demorou...,Mônica Marianno Bitter Pill,Mick Mars/Nikki Sixx/Tommy Lee/Vince Neil,266814,8666786,0.99,Metal,Motley Crue Greatest Hits,Mötley Crüe Enslaved,Mick Mars/Nikki Sixx/Tommy Lee,269844,8789966,0.99,Metal,Motley Crue Greatest Hits,Mötley Crüe "Girls, Girls, Girls",Mick Mars/Nikki Sixx/Tommy Lee,270288,8874814,0.99,Metal,Motley Crue Greatest Hits,Mötley Crüe Kickstart My Heart,Nikki Sixx,283559,9237736,0.99,Metal,Motley Crue Greatest Hits,Mötley Crüe Wild Side,Nikki Sixx/Tommy Lee/Vince Neil,276767,9116997,0.99,Metal,Motley Crue Greatest Hits,Mötley Crüe Glitter,Bryan Adams/Nikki Sixx/Scott Humphrey,340114,11184094,0.99,Metal,Motley Crue Greatest Hits,Mötley Crüe Dr. Feelgood,Mick Mars/Nikki Sixx,282618,9281875,0.99,Metal,Motley Crue Greatest Hits,Mötley Crüe Same Ol' Situation,Mick Mars/Nikki Sixx/Tommy Lee/Vince Neil,254511,8283958,0.99,Metal,Motley Crue Greatest Hits,Mötley Crüe Home Sweet Home,Nikki Sixx/Tommy Lee/Vince Neil,236904,7697538,0.99,Metal,Motley Crue Greatest Hits,Mötley Crüe Afraid,Nikki Sixx,248006,8077464,0.99,Metal,Motley Crue Greatest Hits,Mötley Crüe Don't Go Away Mad (Just Go Away),Mick Mars/Nikki Sixx,279980,9188156,0.99,Metal,Motley Crue Greatest Hits,Mötley Crüe Without You,Mick Mars/Nikki Sixx,268956,8738371,0.99,Metal,Motley Crue Greatest Hits,Mötley Crüe Smokin' in The Boys Room,Cub Coda/Michael Lutz,206837,6735408,0.99,Metal,Motley Crue Greatest Hits,Mötley Crüe Primal Scream,Mick Mars/Nikki Sixx/Tommy Lee/Vince Neil,286197,9421164,0.99,Metal,Motley Crue Greatest Hits,Mötley Crüe Too Fast For Love,Nikki Sixx,200829,6580542,0.99,Metal,Motley Crue Greatest Hits,Mötley Crüe Looks That Kill,Nikki Sixx,240979,7831122,0.99,Metal,Motley Crue Greatest Hits,Mötley Crüe Shout At The Devil,Nikki Sixx,221962,7281974,0.99,Metal,Motley Crue Greatest Hits,Mötley Crüe Intro,Kurt Cobain,52218,1688527,0.99,Rock,From The Muddy Banks Of The Wishkah [Live],Nirvana School,Kurt Cobain,160235,5234885,0.99,Rock,From The Muddy Banks Of The Wishkah [Live],Nirvana Drain You,Kurt Cobain,215196,7013175,0.99,Rock,From The Muddy Banks Of The Wishkah [Live],Nirvana Aneurysm,Nirvana,271516,8862545,0.99,Rock,From The Muddy Banks Of The Wishkah [Live],Nirvana Smells Like Teen Spirit,Nirvana,287190,9425215,0.99,Rock,From The Muddy Banks Of The Wishkah [Live],Nirvana Been A Son,Kurt Cobain,127555,4170369,0.99,Rock,From The Muddy Banks Of The Wishkah [Live],Nirvana Lithium,Kurt Cobain,250017,8148800,0.99,Rock,From The Muddy Banks Of The Wishkah [Live],Nirvana Sliver,Kurt Cobain,116218,3784567,0.99,Rock,From The Muddy Banks Of The Wishkah [Live],Nirvana Spank Thru,Kurt Cobain,190354,6186487,0.99,Rock,From The Muddy Banks Of The Wishkah [Live],Nirvana Scentless Apprentice,Nirvana,211200,6898177,0.99,Rock,From The Muddy Banks Of The Wishkah [Live],Nirvana Heart-Shaped Box,Kurt Cobain,281887,9210982,0.99,Rock,From The Muddy Banks Of The Wishkah [Live],Nirvana Milk It,Kurt Cobain,225724,7406945,0.99,Rock,From The Muddy Banks Of The Wishkah [Live],Nirvana Negative Creep,Kurt Cobain,163761,5354854,0.99,Rock,From The Muddy Banks Of The Wishkah [Live],Nirvana Polly,Kurt Cobain,149995,4885331,0.99,Rock,From The Muddy Banks Of The Wishkah [Live],Nirvana Breed,Kurt Cobain,208378,6759080,0.99,Rock,From The Muddy Banks Of The Wishkah [Live],Nirvana Tourette's,Kurt Cobain,115591,3753246,0.99,Rock,From The Muddy Banks Of The Wishkah [Live],Nirvana Blew,Kurt Cobain,216346,7096936,0.99,Rock,From The Muddy Banks Of The Wishkah [Live],Nirvana Smells Like Teen Spirit,Kurt Cobain,301296,9823847,0.99,Rock,Nevermind,Nirvana In Bloom,Kurt Cobain,254928,8327077,0.99,Rock,Nevermind,Nirvana Come As You Are,Kurt Cobain,219219,7123357,0.99,Rock,Nevermind,Nirvana Breed,Kurt Cobain,183928,5984812,0.99,Rock,Nevermind,Nirvana Lithium,Kurt Cobain,256992,8404745,0.99,Rock,Nevermind,Nirvana Polly,Kurt Cobain,177031,5788407,0.99,Rock,Nevermind,Nirvana Territorial Pissings,Kurt Cobain,143281,4613880,0.99,Rock,Nevermind,Nirvana Drain You,Kurt Cobain,223973,7273440,0.99,Rock,Nevermind,Nirvana Lounge Act,Kurt Cobain,156786,5093635,0.99,Rock,Nevermind,Nirvana Stay Away,Kurt Cobain,212636,6956404,0.99,Rock,Nevermind,Nirvana On A Plain,Kurt Cobain,196440,6390635,0.99,Rock,Nevermind,Nirvana Something In The Way,Kurt Cobain,230556,7472168,0.99,Rock,Nevermind,Nirvana Time,,96888,3124455,0.99,Rock,Compositores,O Terço P.S.Apareça,,209188,6842244,0.99,Rock,Compositores,O Terço Sangue Latino,,223033,7354184,0.99,Rock,Compositores,O Terço Folhas Secas,,161253,5284522,0.99,Rock,Compositores,O Terço Poeira,,267075,8784141,0.99,Rock,Compositores,O Terço Mágica,,233743,7627348,0.99,Rock,Compositores,O Terço Quem Mata A Mulher Mata O Melhor,,262791,8640121,0.99,Rock,Compositores,O Terço Mundaréu,,217521,7158975,0.99,Rock,Compositores,O Terço O Braço Da Minha Guitarra,,258351,8469531,0.99,Rock,Compositores,O Terço Deus,,284160,9188110,0.99,Rock,Compositores,O Terço Mãe Terra,,306625,9949269,0.99,Rock,Compositores,O Terço Às Vezes,,330292,10706614,0.99,Rock,Compositores,O Terço Menino De Rua,,329795,10784595,0.99,Rock,Compositores,O Terço Prazer E Fé,,214831,7031383,0.99,Rock,Compositores,O Terço Elza,,199105,6517629,0.99,Rock,Compositores,O Terço Requebra,,240744,8010811,0.99,Latin,Olodum,Olodum Nossa Gente (Avisa Là),,188212,6233201,0.99,Latin,Olodum,Olodum Olodum - Alegria Geral,,233404,7754245,0.99,Latin,Olodum,Olodum Madagáscar Olodum,,252264,8270584,0.99,Latin,Olodum,Olodum Faraó Divindade Do Egito,,228571,7523278,0.99,Latin,Olodum,Olodum Todo Amor (Asas Da Liberdade),,245133,8121434,0.99,Latin,Olodum,Olodum Denúncia,,159555,5327433,0.99,Latin,Olodum,Olodum "Olodum, A Banda Do Pelô",,146599,4900121,0.99,Latin,Olodum,Olodum Cartao Postal,,211565,7082301,0.99,Latin,Olodum,Olodum Jeito Faceiro,,217286,7233608,0.99,Latin,Olodum,Olodum Revolta Olodum,,230191,7557065,0.99,Latin,Olodum,Olodum Reggae Odoyá,,224470,7499807,0.99,Latin,Olodum,Olodum Protesto Do Olodum (Ao Vivo),,206001,6766104,0.99,Latin,Olodum,Olodum Olodum - Smile (Instrumental),,235833,7871409,0.99,Latin,Olodum,Olodum Vulcão Dub - Fui Eu,Bi Ribeira/Herbert Vianna/João Barone,287059,9495202,0.99,Latin,Acústico MTV,Os Paralamas Do Sucesso O Trem Da Juventude,Herbert Vianna,225880,7507655,0.99,Latin,Acústico MTV,Os Paralamas Do Sucesso Manguetown,Chico Science/Dengue/Lúcio Maia,162925,5382018,0.99,Latin,Acústico MTV,Os Paralamas Do Sucesso "Um Amor, Um Lugar",Herbert Vianna,184555,6090334,0.99,Latin,Acústico MTV,Os Paralamas Do Sucesso Bora-Bora,Herbert Vianna,182987,6036046,0.99,Latin,Acústico MTV,Os Paralamas Do Sucesso Vai Valer,Herbert Vianna,206524,6899778,0.99,Latin,Acústico MTV,Os Paralamas Do Sucesso I Feel Good (I Got You) - Sossego,James Brown/Tim Maia,244976,8091302,0.99,Latin,Acústico MTV,Os Paralamas Do Sucesso Uns Dias,Herbert Vianna,240796,7931552,0.99,Latin,Acústico MTV,Os Paralamas Do Sucesso Sincero 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 Meu Erro,Herbert Vianna,188577,6192791,0.99,Latin,Acústico MTV,Os Paralamas Do Sucesso Selvagem,Bi Ribeiro/Herbert Vianna/João Barone,148558,4942831,0.99,Latin,Acústico MTV,Os Paralamas Do Sucesso Brasília 5:31,Herbert Vianna,178337,5857116,0.99,Latin,Acústico MTV,Os Paralamas Do Sucesso Tendo A Lua,Herbert Vianna/Tet Tillett,198922,6568180,0.99,Latin,Acústico MTV,Os Paralamas Do Sucesso Que País É Este,Renato Russo,216685,7137865,0.99,Latin,Acústico MTV,Os Paralamas Do Sucesso Navegar Impreciso,Herbert Vianna,262870,8761283,0.99,Latin,Acústico MTV,Os Paralamas Do Sucesso Feira Moderna,Beto Guedes/Fernando Brant/L Borges,182517,6001793,0.99,Latin,Acústico MTV,Os Paralamas Do Sucesso Tequila - Lourinha Bombril (Parate Y Mira),Bahiano/Chuck Rio/Diego Blanco/Herbert Vianna,255738,8514961,0.99,Latin,Acústico MTV,Os Paralamas Do Sucesso Vamo Batê Lata,Herbert Vianna,228754,7585707,0.99,Latin,Acústico MTV,Os Paralamas Do Sucesso Life During Wartime,Chris Frantz/David Byrne/Jerry Harrison/Tina Weymouth,259186,8543439,0.99,Latin,Acústico MTV,Os Paralamas Do Sucesso Nebulosa Do Amor,Herbert Vianna,203415,6732496,0.99,Latin,Acústico MTV,Os Paralamas Do Sucesso Caleidoscópio,Herbert Vianna,256522,8484597,0.99,Latin,Acústico MTV,Os Paralamas Do Sucesso Trac Trac,Fito Paez/Herbert Vianna,231653,7638256,0.99,Latin,Arquivo II,Os Paralamas Do Sucesso Tendo A Lua,Herbert Vianna/Tetê Tillet,219585,7342776,0.99,Latin,Arquivo II,Os Paralamas Do Sucesso Mensagen De Amor (2000),Herbert Vianna,183588,6061324,0.99,Latin,Arquivo II,Os Paralamas Do Sucesso Lourinha Bombril,Bahiano/Diego Blanco/Herbert Vianna,159895,5301882,0.99,Latin,Arquivo II,Os Paralamas Do Sucesso La Bella Luna,Herbert Vianna,192653,6428598,0.99,Latin,Arquivo II,Os Paralamas Do Sucesso Busca Vida,Herbert Vianna,176431,5798663,0.99,Latin,Arquivo II,Os Paralamas Do Sucesso Uma Brasileira,Carlinhos Brown/Herbert Vianna,217573,7280574,0.99,Latin,Arquivo II,Os Paralamas Do Sucesso Luis Inacio (300 Picaretas),Herbert Vianna,198191,6576790,0.99,Latin,Arquivo II,Os Paralamas Do Sucesso Saber Amar,Herbert Vianna,202788,6723733,0.99,Latin,Arquivo II,Os Paralamas Do Sucesso Ela Disse Adeus,Herbert Vianna,226298,7608999,0.99,Latin,Arquivo II,Os Paralamas Do Sucesso O Amor Nao Sabe Esperar,Herbert Vianna,241084,8042534,0.99,Latin,Arquivo II,Os Paralamas Do Sucesso Aonde Quer Que Eu Va,Herbert Vianna/Paulo Sérgio Valle,258089,8470121,0.99,Latin,Arquivo II,Os Paralamas Do Sucesso Caleidoscópio,,211330,7000017,0.99,Latin,Arquivo Os Paralamas Do Sucesso,Os Paralamas Do Sucesso Óculos,,219271,7262419,0.99,Latin,Arquivo Os Paralamas Do Sucesso,Os Paralamas Do Sucesso Cinema Mudo,,227918,7612168,0.99,Latin,Arquivo Os Paralamas Do Sucesso,Os Paralamas Do Sucesso Alagados,,302393,10255463,0.99,Latin,Arquivo Os Paralamas Do Sucesso,Os Paralamas Do Sucesso Lanterna Dos Afogados,,190197,6264318,0.99,Latin,Arquivo Os Paralamas Do Sucesso,Os Paralamas Do Sucesso Melô Do Marinheiro,,208352,6905668,0.99,Latin,Arquivo Os Paralamas Do Sucesso,Os Paralamas Do Sucesso Vital E Sua Moto,,210207,6902878,0.99,Latin,Arquivo Os Paralamas Do Sucesso,Os Paralamas Do Sucesso O Beco,,189178,6293184,0.99,Latin,Arquivo Os Paralamas Do Sucesso,Os Paralamas Do Sucesso Meu Erro,,208431,6893533,0.99,Latin,Arquivo Os Paralamas Do Sucesso,Os Paralamas Do Sucesso Perplexo,,161175,5355013,0.99,Latin,Arquivo Os Paralamas Do Sucesso,Os Paralamas Do Sucesso Me Liga,,229590,7565912,0.99,Latin,Arquivo Os Paralamas Do Sucesso,Os Paralamas Do Sucesso Quase Um Segundo,,275644,8971355,0.99,Latin,Arquivo Os Paralamas Do Sucesso,Os Paralamas Do Sucesso Selvagem,,245890,8141084,0.99,Latin,Arquivo Os Paralamas Do Sucesso,Os Paralamas Do Sucesso Romance Ideal,,250070,8260477,0.99,Latin,Arquivo Os Paralamas Do Sucesso,Os Paralamas Do Sucesso Será Que Vai Chover?,,337057,11133830,0.99,Latin,Arquivo Os Paralamas Do Sucesso,Os Paralamas Do Sucesso SKA,,148871,4943540,0.99,Latin,Arquivo Os Paralamas Do Sucesso,Os Paralamas Do Sucesso Bark at the Moon,O. Osbourne,257252,4601224,0.99,Rock,Bark at the Moon (Remastered),Ozzy Osbourne I Don't Know,"B. Daisley, O. Osbourne & R. Rhoads",312980,5525339,0.99,Rock,Blizzard of Ozz,Ozzy Osbourne Crazy Train,"B. Daisley, O. Osbourne & R. Rhoads",295960,5255083,0.99,Rock,Blizzard of Ozz,Ozzy Osbourne Flying High Again,"L. Kerslake, O. Osbourne, R. Daisley & R. Rhoads",290851,5179599,0.99,Rock,Diary of a Madman (Remastered),Ozzy Osbourne "Mama, I'm Coming Home","L. Kilmister, O. Osbourne & Z. Wylde",251586,4302390,0.99,Rock,No More Tears (Remastered),Ozzy Osbourne No More Tears,"J. Purdell, M. Inez, O. Osbourne, R. Castillo & Z. Wylde",444358,7362964,0.99,Rock,No More Tears (Remastered),Ozzy Osbourne I Don't Know,"O. Osbourne, R. Daisley, R. Rhoads",283088,9207869,0.99,Metal,Tribute,Ozzy Osbourne Crazy Train,"O. Osbourne, R. Daisley, R. Rhoads",322716,10517408,0.99,Metal,Tribute,Ozzy Osbourne Believer,"O. Osbourne, R. Daisley, R. Rhoads",308897,10003794,0.99,Metal,Tribute,Ozzy Osbourne Mr. Crowley,"O. Osbourne, R. Daisley, R. Rhoads",344241,11184130,0.99,Metal,Tribute,Ozzy Osbourne Flying High Again,"O. Osbourne, R. Daisley, R. Rhoads, L. Kerslake",261224,8481822,0.99,Metal,Tribute,Ozzy Osbourne Relvelation (Mother Earth),"O. Osbourne, R. Daisley, R. Rhoads",349440,11367866,0.99,Metal,Tribute,Ozzy Osbourne Steal Away (The Night),"O. Osbourne, R. Daisley, R. Rhoads",485720,15945806,0.99,Metal,Tribute,Ozzy Osbourne Suicide Solution (With Guitar Solo),"O. Osbourne, R. Daisley, R. Rhoads",467069,15119938,0.99,Metal,Tribute,Ozzy Osbourne Iron Man,"A. F. Iommi, W. Ward, T. Butler, J. Osbourne",172120,5609799,0.99,Metal,Tribute,Ozzy Osbourne Children Of The Grave,"A. F. Iommi, W. Ward, T. Butler, J. Osbourne",357067,11626740,0.99,Metal,Tribute,Ozzy Osbourne Paranoid,"A. F. Iommi, W. Ward, T. Butler, J. Osbourne",176352,5729813,0.99,Metal,Tribute,Ozzy Osbourne Goodbye To Romance,"O. Osbourne, R. Daisley, R. Rhoads",334393,10841337,0.99,Metal,Tribute,Ozzy Osbourne No Bone Movies,"O. Osbourne, R. Daisley, R. Rhoads",249208,8095199,0.99,Metal,Tribute,Ozzy Osbourne Dee,R. Rhoads,261302,8555963,0.99,Metal,Tribute,Ozzy Osbourne Shining In The Light,"Jimmy Page, Robert Plant, Charlie Jones, Michael Lee",240796,7951688,0.99,Rock,Walking Into Clarksdale,Page & Plant When The World Was Young,"Jimmy Page, Robert Plant, Charlie Jones, Michael Lee",373394,12198930,0.99,Rock,Walking Into Clarksdale,Page & Plant Upon A Golden Horse,"Jimmy Page, Robert Plant, Charlie Jones, Michael Lee",232359,7594829,0.99,Rock,Walking Into Clarksdale,Page & Plant Blue Train,"Jimmy Page, Robert Plant, Charlie Jones, Michael Lee",405028,13170391,0.99,Rock,Walking Into Clarksdale,Page & Plant Please Read The Letter,"Jimmy Page, Robert Plant, Charlie Jones, Michael Lee",262112,8603372,0.99,Rock,Walking Into Clarksdale,Page & Plant Most High,"Jimmy Page, Robert Plant, Charlie Jones, Michael Lee",336535,10999203,0.99,Rock,Walking Into Clarksdale,Page & Plant Heart In Your Hand,"Jimmy Page, Robert Plant, Charlie Jones, Michael Lee",230896,7598019,0.99,Rock,Walking Into Clarksdale,Page & Plant Walking Into Clarksdale,"Jimmy Page, Robert Plant, Charlie Jones, Michael Lee",318511,10396315,0.99,Rock,Walking Into Clarksdale,Page & Plant Burning Up,"Jimmy Page, Robert Plant, Charlie Jones, Michael Lee",321619,10525136,0.99,Rock,Walking Into Clarksdale,Page & Plant When I Was A Child,"Jimmy Page, Robert Plant, Charlie Jones, Michael Lee",345626,11249456,0.99,Rock,Walking Into Clarksdale,Page & Plant House Of Love,"Jimmy Page, Robert Plant, Charlie Jones, Michael Lee",335699,10990880,0.99,Rock,Walking Into Clarksdale,Page & Plant Sons Of Freedom,"Jimmy Page, Robert Plant, Charlie Jones, Michael Lee",246465,8087944,0.99,Rock,Walking Into Clarksdale,Page & Plant United Colours,"Brian Eno, Bono, Adam Clayton, The Edge & Larry Mullen Jnr.",330266,10939131,0.99,Soundtrack,Original Soundtracks 1,Passengers Slug,"Brian Eno, Bono, Adam Clayton, The Edge & Larry Mullen Jnr.",281469,9295950,0.99,Soundtrack,Original Soundtracks 1,Passengers Your Blue Room,"Brian Eno, Bono, Adam Clayton, The Edge & Larry Mullen Jnr.",328228,10867860,0.99,Soundtrack,Original Soundtracks 1,Passengers Always Forever Now,"Brian Eno, Bono, Adam Clayton, The Edge & Larry Mullen Jnr.",383764,12727928,0.99,Soundtrack,Original Soundtracks 1,Passengers A Different Kind Of Blue,"Brian Eno, Bono, Adam Clayton, The Edge & Larry Mullen Jnr.",120816,3884133,0.99,Soundtrack,Original Soundtracks 1,Passengers Beach Sequence,"Brian Eno, Bono, Adam Clayton, The Edge & Larry Mullen Jnr.",212297,6928259,0.99,Soundtrack,Original Soundtracks 1,Passengers Miss Sarajevo,"Brian Eno, Bono, Adam Clayton, The Edge & Larry Mullen Jnr.",340767,11064884,0.99,Soundtrack,Original Soundtracks 1,Passengers Ito Okashi,"Brian Eno, Bono, Adam Clayton, The Edge & Larry Mullen Jnr.",205087,6572813,0.99,Soundtrack,Original Soundtracks 1,Passengers One Minute Warning,"Brian Eno, Bono, Adam Clayton, The Edge & Larry Mullen Jnr.",279693,9335453,0.99,Soundtrack,Original Soundtracks 1,Passengers Corpse (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 Elvis Ate America,"Brian Eno, Bono, Adam Clayton, The Edge & Larry Mullen Jnr.",180166,5851053,0.99,Soundtrack,Original Soundtracks 1,Passengers Plot 180,"Brian Eno, Bono, Adam Clayton, The Edge & Larry Mullen Jnr.",221596,7253729,0.99,Soundtrack,Original Soundtracks 1,Passengers Theme From The Swan,"Brian Eno, Bono, Adam Clayton, The Edge & Larry Mullen Jnr.",203911,6638076,0.99,Soundtrack,Original Soundtracks 1,Passengers Theme From Let's Go Native,"Brian Eno, Bono, Adam Clayton, The Edge & Larry Mullen Jnr.",186723,6179777,0.99,Soundtrack,Original Soundtracks 1,Passengers Wrathchild,Steve Harris,170396,5499390,0.99,Rock,The Beast Live,Paul D'Ianno Killers,Paul Di'Anno/Steve Harris,309995,10009697,0.99,Rock,The Beast Live,Paul D'Ianno Prowler,Steve Harris,240274,7782963,0.99,Rock,The Beast Live,Paul D'Ianno Murders In The Rue Morgue,Steve Harris,258638,8360999,0.99,Rock,The Beast Live,Paul D'Ianno Women In Uniform,Greg Macainsh,189936,6139651,0.99,Rock,The Beast Live,Paul D'Ianno Remember Tomorrow,Paul Di'Anno/Steve Harris,326426,10577976,0.99,Rock,The Beast Live,Paul D'Ianno Sanctuary,David Murray/Paul Di'Anno/Steve Harris,198844,6423543,0.99,Rock,The Beast Live,Paul D'Ianno Running Free,Paul Di'Anno/Steve Harris,199706,6483496,0.99,Rock,The Beast Live,Paul D'Ianno Phantom Of The Opera,Steve Harris,418168,13585530,0.99,Rock,The Beast Live,Paul D'Ianno Iron Maiden,Steve Harris,235232,7600077,0.99,Rock,The Beast Live,Paul D'Ianno Corduroy,Pearl Jam & Eddie Vedder,305293,9991106,0.99,Rock,Live On Two Legs [Live],Pearl Jam Given To Fly,Eddie Vedder & Mike McCready,233613,7678347,0.99,Rock,Live On Two Legs [Live],Pearl Jam "Hail, Hail",Stone Gossard & Eddie Vedder & Jeff Ament & Mike McCready,223764,7364206,0.99,Rock,Live On Two Legs [Live],Pearl Jam Daughter,Dave Abbruzzese & Jeff Ament & Stone Gossard & Mike McCready & Eddie Vedder,407484,13420697,0.99,Rock,Live On Two Legs [Live],Pearl Jam Elderly 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 Untitled,Pearl Jam,122801,3957141,0.99,Rock,Live On Two Legs [Live],Pearl Jam MFC,Eddie Vedder,148192,4817665,0.99,Rock,Live On Two Legs [Live],Pearl Jam Go,Dave Abbruzzese & Jeff Ament & Stone Gossard & Mike McCready & Eddie Vedder,161541,5290810,0.99,Rock,Live On Two Legs [Live],Pearl Jam Red Mosquito,Jeff Ament & Stone Gossard & Jack Irons & Mike McCready & Eddie Vedder,242991,7944923,0.99,Rock,Live On Two Legs [Live],Pearl Jam Even Flow,Stone Gossard & Eddie Vedder,317100,10394239,0.99,Rock,Live On Two Legs [Live],Pearl Jam Off He Goes,Eddie Vedder,343222,11245109,0.99,Rock,Live On Two Legs [Live],Pearl Jam Nothingman,Jeff Ament & Eddie Vedder,278595,9107017,0.99,Rock,Live On Two Legs [Live],Pearl Jam Do The Evolution,Eddie Vedder & Stone Gossard,225462,7377286,0.99,Rock,Live On Two Legs [Live],Pearl Jam Better Man,Eddie Vedder,246204,8019563,0.99,Rock,Live On Two Legs [Live],Pearl Jam Black,Stone Gossard & Eddie Vedder,415712,13580009,0.99,Rock,Live On Two Legs [Live],Pearl Jam F*Ckin' Up,Neil Young,377652,12360893,0.99,Rock,Live On Two Legs [Live],Pearl Jam Life Wasted,Stone Gossard,234344,7610169,0.99,Alternative & Punk,Pearl Jam,Pearl Jam World Wide Suicide,Eddie Vedder,209188,6885908,0.99,Alternative & Punk,Pearl Jam,Pearl Jam Comatose,Mike McCready & Stone Gossard,139990,4574516,0.99,Alternative & Punk,Pearl Jam,Pearl Jam Severed Hand,Eddie Vedder,270341,8817438,0.99,Alternative & Punk,Pearl Jam,Pearl Jam Marker In The Sand,Mike McCready,263235,8656578,0.99,Alternative & Punk,Pearl Jam,Pearl Jam Parachutes,Stone Gossard,216555,7074973,0.99,Alternative & Punk,Pearl Jam,Pearl Jam Unemployable,Matt Cameron & Mike McCready,184398,6066542,0.99,Alternative & Punk,Pearl Jam,Pearl Jam Big Wave,Jeff Ament,178573,5858788,0.99,Alternative & Punk,Pearl Jam,Pearl Jam Gone,Eddie Vedder,249547,8158204,0.99,Alternative & Punk,Pearl Jam,Pearl Jam Wasted Reprise,Stone Gossard,53733,1731020,0.99,Alternative & Punk,Pearl Jam,Pearl Jam Army Reserve,Jeff Ament,225567,7393771,0.99,Alternative & Punk,Pearl Jam,Pearl Jam Come Back,Eddie Vedder & Mike McCready,329743,10768701,0.99,Alternative & Punk,Pearl Jam,Pearl Jam Inside Job,Eddie Vedder & Mike McCready,428643,14006924,0.99,Alternative & Punk,Pearl Jam,Pearl Jam Can't Keep,Eddie Vedder,219428,7215713,0.99,Rock,Riot Act,Pearl Jam Save You,Eddie Vedder/Jeff Ament/Matt Cameron/Mike McCready/Stone Gossard,230112,7609110,0.99,Rock,Riot Act,Pearl Jam Love Boat Captain,Eddie Vedder,276453,9016789,0.99,Rock,Riot Act,Pearl Jam Cropduster,Matt Cameron,231888,7588928,0.99,Rock,Riot Act,Pearl Jam Ghost,Jeff Ament,195108,6383772,0.99,Rock,Riot Act,Pearl Jam I Am Mine,Eddie Vedder,215719,7086901,0.99,Rock,Riot Act,Pearl Jam Thumbing My Way,Eddie Vedder,250226,8201437,0.99,Rock,Riot Act,Pearl Jam You Are,Matt Cameron,270863,8938409,0.99,Rock,Riot Act,Pearl Jam Get Right,Matt Cameron,158589,5223345,0.99,Rock,Riot Act,Pearl Jam Green Disease,Eddie Vedder,161253,5375818,0.99,Rock,Riot Act,Pearl Jam Help Help,Jeff Ament,215092,7033002,0.99,Rock,Riot Act,Pearl Jam Bushleager,Stone Gossard,237479,7849757,0.99,Rock,Riot Act,Pearl Jam 1/2 Full,Jeff Ament,251010,8197219,0.99,Rock,Riot Act,Pearl Jam Arc,Pearl Jam,65593,2099421,0.99,Rock,Riot Act,Pearl Jam All or None,Stone Gossard,277655,9104728,0.99,Rock,Riot Act,Pearl Jam Once,Stone Gossard,231758,7561555,0.99,Rock,Ten,Pearl Jam Evenflow,Stone Gossard,293720,9622017,0.99,Rock,Ten,Pearl Jam Alive,Stone Gossard,341080,11176623,0.99,Rock,Ten,Pearl Jam Why Go,Jeff Ament,200254,6539287,0.99,Rock,Ten,Pearl Jam Black,Dave Krusen/Stone Gossard,343823,11213314,0.99,Rock,Ten,Pearl Jam Jeremy,Jeff Ament,318981,10447222,0.99,Rock,Ten,Pearl Jam Oceans,Jeff Ament/Stone Gossard,162194,5282368,0.99,Rock,Ten,Pearl Jam Porch,Eddie Vedder,210520,6877475,0.99,Rock,Ten,Pearl Jam Garden,Jeff Ament/Stone Gossard,299154,9740738,0.99,Rock,Ten,Pearl Jam Deep,Jeff Ament/Stone Gossard,258324,8432497,0.99,Rock,Ten,Pearl Jam Release,Jeff Ament/Mike McCready/Stone Gossard,546063,17802673,0.99,Rock,Ten,Pearl Jam Go,Dave Abbruzzese/Eddie Vedder/Jeff Ament/Mike McCready/Stone Gossard,193123,6351920,0.99,Rock,Vs.,Pearl Jam Animal,Dave Abbruzzese/Eddie Vedder/Jeff Ament/Mike McCready/Stone Gossard,169325,5503459,0.99,Rock,Vs.,Pearl Jam Daughter,Dave Abbruzzese/Eddie Vedder/Jeff Ament/Mike McCready/Stone Gossard,235598,7824586,0.99,Rock,Vs.,Pearl Jam Glorified G,Dave Abbruzzese/Eddie Vedder/Jeff Ament/Mike McCready/Stone Gossard,206968,6772116,0.99,Rock,Vs.,Pearl Jam Dissident,Dave Abbruzzese/Eddie Vedder/Jeff Ament/Mike McCready/Stone Gossard,215510,7034500,0.99,Rock,Vs.,Pearl Jam W.M.A.,Dave Abbruzzese/Eddie Vedder/Jeff Ament/Mike McCready/Stone Gossard,359262,12037261,0.99,Rock,Vs.,Pearl Jam Blood,Dave Abbruzzese/Eddie Vedder/Jeff Ament/Mike McCready/Stone Gossard,170631,5551478,0.99,Rock,Vs.,Pearl Jam Rearviewmirror,Dave Abbruzzese/Eddie Vedder/Jeff Ament/Mike McCready/Stone Gossard,284186,9321053,0.99,Rock,Vs.,Pearl Jam Rats,Dave Abbruzzese/Eddie Vedder/Jeff Ament/Mike McCready/Stone Gossard,255425,8341934,0.99,Rock,Vs.,Pearl Jam Elderly 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 Leash,Dave Abbruzzese/Eddie Vedder/Jeff Ament/Mike McCready/Stone Gossard,189257,6191560,0.99,Rock,Vs.,Pearl Jam Indifference,Dave Abbruzzese/Eddie Vedder/Jeff Ament/Mike McCready/Stone Gossard,302053,9756133,0.99,Rock,Vs.,Pearl Jam Speak To Me/Breathe,"Mason/Waters, Gilmour, Wright",234213,7631305,0.99,Rock,Dark Side Of The Moon,Pink Floyd On The Run,"Gilmour, Waters",214595,7206300,0.99,Rock,Dark Side Of The Moon,Pink Floyd Time,"Mason, Waters, Wright, Gilmour",425195,13955426,0.99,Rock,Dark Side Of The Moon,Pink Floyd The Great Gig In The Sky,"Wright, Waters",284055,9147563,0.99,Rock,Dark Side Of The Moon,Pink Floyd Money,Waters,391888,12930070,0.99,Rock,Dark Side Of The Moon,Pink Floyd Us And Them,"Waters, Wright",461035,15000299,0.99,Rock,Dark Side Of The Moon,Pink Floyd Any Colour You Like,"Gilmour, Mason, Wright, Waters",205740,6707989,0.99,Rock,Dark Side Of The Moon,Pink Floyd Brain Damage,Waters,230556,7497655,0.99,Rock,Dark Side Of The Moon,Pink Floyd Eclipse,Waters,125361,4065299,0.99,Rock,Dark Side Of The Moon,Pink Floyd ZeroVinteUm,,315637,10426550,0.99,Hip Hop/Rap,Os Cães Ladram Mas A Caravana Não Pára,Planet Hemp Queimando Tudo,,172591,5723677,0.99,Hip Hop/Rap,Os Cães Ladram Mas A Caravana Não Pára,Planet Hemp Hip Hop Rio,,151536,4991935,0.99,Hip Hop/Rap,Os Cães Ladram Mas A Caravana Não Pára,Planet Hemp Bossa,,29048,967098,0.99,Hip Hop/Rap,Os Cães Ladram Mas A Caravana Não Pára,Planet Hemp 100% HardCore,,165146,5407744,0.99,Hip Hop/Rap,Os Cães Ladram Mas A Caravana Não Pára,Planet Hemp Biruta,,213263,7108200,0.99,Hip Hop/Rap,Os Cães Ladram Mas A Caravana Não Pára,Planet Hemp Mão Na Cabeça,,202631,6642753,0.99,Hip Hop/Rap,Os Cães Ladram Mas A Caravana Não Pára,Planet Hemp O Bicho Tá Pregando,,171964,5683369,0.99,Hip Hop/Rap,Os Cães Ladram Mas A Caravana Não Pára,Planet Hemp Adoled (Ocean),,185103,6009946,0.99,Hip Hop/Rap,Os Cães Ladram Mas A Caravana Não Pára,Planet Hemp Seus Amigos,,100858,3304738,0.99,Hip Hop/Rap,Os Cães Ladram Mas A Caravana Não Pára,Planet Hemp Paga Pau,,197485,6529041,0.99,Hip Hop/Rap,Os Cães Ladram Mas A Caravana Não Pára,Planet Hemp Rappers Reais,,202004,6684160,0.99,Hip Hop/Rap,Os Cães Ladram Mas A Caravana Não Pára,Planet Hemp Nega Do Cabelo Duro,,121808,4116536,0.99,Hip Hop/Rap,Os Cães Ladram Mas A Caravana Não Pára,Planet Hemp Hemp Family,,205923,6806900,0.99,Hip Hop/Rap,Os Cães Ladram Mas A Caravana Não Pára,Planet Hemp Quem Me Cobrou?,,121704,3947664,0.99,Hip Hop/Rap,Os Cães Ladram Mas A Caravana Não Pára,Planet Hemp Se Liga,,410409,13559173,0.99,Hip Hop/Rap,Os Cães Ladram Mas A Caravana Não Pára,Planet Hemp Bohemian Rhapsody,"Mercury, Freddie",358948,11619868,0.99,Rock,Greatest Hits I,Queen Another One Bites The Dust,"Deacon, John",216946,7172355,0.99,Rock,Greatest Hits I,Queen Killer Queen,"Mercury, Freddie",182099,5967749,0.99,Rock,Greatest Hits I,Queen Fat Bottomed Girls,"May, Brian",204695,6630041,0.99,Rock,Greatest Hits I,Queen Bicycle Race,"Mercury, Freddie",183823,6012409,0.99,Rock,Greatest Hits I,Queen You're My Best Friend,"Deacon, John",172225,5602173,0.99,Rock,Greatest Hits I,Queen Don't Stop Me Now,"Mercury, Freddie",211826,6896666,0.99,Rock,Greatest Hits I,Queen Save Me,"May, Brian",228832,7444624,0.99,Rock,Greatest Hits I,Queen Crazy Little Thing Called Love,"Mercury, Freddie",164231,5435501,0.99,Rock,Greatest Hits I,Queen Somebody To Love,"Mercury, Freddie",297351,9650520,0.99,Rock,Greatest Hits I,Queen Now I'm Here,"May, Brian",255346,8328312,0.99,Rock,Greatest Hits I,Queen Good Old-Fashioned Lover Boy,"Mercury, Freddie",175960,5747506,0.99,Rock,Greatest Hits I,Queen Play The Game,"Mercury, Freddie",213368,6915832,0.99,Rock,Greatest Hits I,Queen Flash,"May, Brian",168489,5464986,0.99,Rock,Greatest Hits I,Queen Seven Seas Of Rhye,"Mercury, Freddie",170553,5539957,0.99,Rock,Greatest Hits I,Queen We Will Rock You,"Deacon, John/May, Brian",122880,4026955,0.99,Rock,Greatest Hits I,Queen We Are The Champions,"Mercury, Freddie",180950,5880231,0.99,Rock,Greatest Hits I,Queen We Will Rock You,May,122671,4026815,0.99,Rock,News Of The World,Queen We Are The Champions,Mercury,182883,5939794,0.99,Rock,News Of The World,Queen Sheer Heart Attack,Taylor,207386,6642685,0.99,Rock,News Of The World,Queen "All Dead, All Dead",May,190119,6144878,0.99,Rock,News Of The World,Queen Spread Your Wings,Deacon,275356,8936992,0.99,Rock,News Of The World,Queen Fight From The Inside,Taylor,184737,6078001,0.99,Rock,News Of The World,Queen "Get Down, Make Love",Mercury,231235,7509333,0.99,Rock,News Of The World,Queen Sleep On The Sidewalk,May,187428,6099840,0.99,Rock,News Of The World,Queen Who Needs You,Deacon,186958,6292969,0.99,Rock,News Of The World,Queen It's Late,May,386194,12519388,0.99,Rock,News Of The World,Queen My Melancholy Blues,Mercury,206471,6691838,0.99,Rock,News Of The World,Queen Shiny 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 Me 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 Radio Song,Bill Berry/Michael Stipe/Mike Mills/Peter Buck,255477,8421172,0.99,Alternative & Punk,Out Of Time,R.E.M. Feat. Kate Pearson Losing 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 Low,Bill Berry/Michael Stipe/Mike Mills/Peter Buck,296777,9633860,0.99,Alternative & Punk,Out Of Time,R.E.M. Feat. Kate Pearson Near 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 Endgame,Bill Berry/Michael Stipe/Mike Mills/Peter Buck,230687,7664479,0.99,Alternative & Punk,Out Of Time,R.E.M. Feat. Kate Pearson Belong,Bill Berry/Michael Stipe/Mike Mills/Peter Buck,247013,8219375,0.99,Alternative & Punk,Out Of Time,R.E.M. Feat. Kate Pearson Half 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 Texarkana,Bill Berry/Michael Stipe/Mike Mills/Peter Buck,220081,7260681,0.99,Alternative & Punk,Out Of Time,R.E.M. Feat. Kate Pearson Country Feedback,Bill Berry/Michael Stipe/Mike Mills/Peter Buck,249782,8178943,0.99,Alternative & Punk,Out Of Time,R.E.M. Feat. Kate Pearson Pop Song 89,Bill Berry-Peter Buck-Mike Mills-Michael Stipe,185730,6132218,0.99,Alternative & Punk,Green,R.E.M. Get Up,Bill Berry-Peter Buck-Mike Mills-Michael Stipe,160235,5264376,0.99,Alternative & Punk,Green,R.E.M. You Are The Everything,Bill Berry-Peter Buck-Mike Mills-Michael Stipe,226298,7373181,0.99,Alternative & Punk,Green,R.E.M. Stand,Bill Berry-Peter Buck-Mike Mills-Michael Stipe,192862,6349090,0.99,Alternative & Punk,Green,R.E.M. World Leader Pretend,Bill Berry-Peter Buck-Mike Mills-Michael Stipe,259761,8537282,0.99,Alternative & Punk,Green,R.E.M. The Wrong Child,Bill Berry-Peter Buck-Mike Mills-Michael Stipe,216633,7065060,0.99,Alternative & Punk,Green,R.E.M. Orange Crush,Bill Berry-Peter Buck-Mike Mills-Michael Stipe,231706,7742894,0.99,Alternative & Punk,Green,R.E.M. Turn You Inside-Out,Bill Berry-Peter Buck-Mike Mills-Michael Stipe,257358,8395671,0.99,Alternative & Punk,Green,R.E.M. Hairshirt,Bill Berry-Peter Buck-Mike Mills-Michael Stipe,235911,7753807,0.99,Alternative & Punk,Green,R.E.M. I Remember California,Bill Berry-Peter Buck-Mike Mills-Michael Stipe,304013,9950311,0.99,Alternative & Punk,Green,R.E.M. Untitled,Bill Berry-Peter Buck-Mike Mills-Michael Stipe,191503,6332426,0.99,Alternative & Punk,Green,R.E.M. How 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. The Wake-Up Bomb,Bill Berry-Peter Buck-Mike Mills-Michael Stipe,308532,10077337,0.99,Rock,New Adventures In Hi-Fi,R.E.M. New Test Leper,Bill Berry-Peter Buck-Mike Mills-Michael Stipe,326791,10866447,0.99,Rock,New Adventures In Hi-Fi,R.E.M. Undertow,Bill Berry-Peter Buck-Mike Mills-Michael Stipe,309498,10131005,0.99,Rock,New Adventures In Hi-Fi,R.E.M. E-Bow The Letter,Bill Berry-Peter Buck-Mike Mills-Michael Stipe,324963,10714576,0.99,Rock,New Adventures In Hi-Fi,R.E.M. Leave,Bill Berry-Peter Buck-Mike Mills-Michael Stipe,437968,14433365,0.99,Rock,New Adventures In Hi-Fi,R.E.M. Departure,Bill Berry-Peter Buck-Mike Mills-Michael Stipe,209423,6818425,0.99,Rock,New Adventures In Hi-Fi,R.E.M. Bittersweet Me,Bill Berry-Peter Buck-Mike Mills-Michael Stipe,245812,8114718,0.99,Rock,New Adventures In Hi-Fi,R.E.M. Be Mine,Bill Berry-Peter Buck-Mike Mills-Michael Stipe,333087,10790541,0.99,Rock,New Adventures In Hi-Fi,R.E.M. Binky The Doormat,Bill Berry-Peter Buck-Mike Mills-Michael Stipe,301688,9950320,0.99,Rock,New Adventures In Hi-Fi,R.E.M. Zither,Bill Berry-Peter Buck-Mike Mills-Michael Stipe,154148,5032962,0.99,Rock,New Adventures In Hi-Fi,R.E.M. "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. Low Desert,Bill Berry-Peter Buck-Mike Mills-Michael Stipe,212062,6989288,0.99,Rock,New Adventures In Hi-Fi,R.E.M. Electrolite,Bill Berry-Peter Buck-Mike Mills-Michael Stipe,245315,8051199,0.99,Rock,New Adventures In Hi-Fi,R.E.M. Carnival Of Sorts,R.E.M.,233482,7669658,0.99,Alternative & Punk,The Best Of R.E.M.: The IRS Years,R.E.M. Radio Free Aurope,R.E.M.,245315,8163490,0.99,Alternative & Punk,The Best Of R.E.M.: The IRS Years,R.E.M. Perfect Circle,R.E.M.,208509,6898067,0.99,Alternative & Punk,The Best Of R.E.M.: The IRS Years,R.E.M. Talk About The Passion,R.E.M.,203206,6725435,0.99,Alternative & Punk,The Best Of R.E.M.: The IRS Years,R.E.M. So Central Rain,R.E.M.,194768,6414550,0.99,Alternative & Punk,The Best Of R.E.M.: The IRS Years,R.E.M. Don'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. Pretty Persuasion,R.E.M.,229929,7577754,0.99,Alternative & Punk,The Best Of R.E.M.: The IRS Years,R.E.M. Green Grow The Rushes,R.E.M.,225671,7422425,0.99,Alternative & Punk,The Best Of R.E.M.: The IRS Years,R.E.M. Can'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. Driver 8,R.E.M.,204747,6779076,0.99,Alternative & Punk,The Best Of R.E.M.: The IRS Years,R.E.M. Fall On Me,R.E.M.,172016,5676811,0.99,Alternative & Punk,The Best Of R.E.M.: The IRS Years,R.E.M. I Believe,R.E.M.,227709,7542929,0.99,Alternative & Punk,The Best Of R.E.M.: The IRS Years,R.E.M. Cuyahoga,R.E.M.,260623,8591057,0.99,Alternative & Punk,The Best Of R.E.M.: The IRS Years,R.E.M. The One I Love,R.E.M.,197355,6495125,0.99,Alternative & Punk,The Best Of R.E.M.: The IRS Years,R.E.M. The Finest Worksong,R.E.M.,229276,7574856,0.99,Alternative & Punk,The Best Of R.E.M.: The IRS Years,R.E.M. It'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. Infeliz Natal,Rodolfo,138266,4503299,0.99,Alternative & Punk,Cesta Básica,Raimundos A Sua,Rodolfo,142132,4622064,0.99,Alternative & Punk,Cesta Básica,Raimundos Papeau Nuky Doe,Rodolfo,121652,3995022,0.99,Alternative & Punk,Cesta Básica,Raimundos Merry Christmas,Rodolfo,126040,4166652,0.99,Alternative & Punk,Cesta Básica,Raimundos Bodies,Rodolfo,180035,5873778,0.99,Alternative & Punk,Cesta Básica,Raimundos Puteiro Em João Pessoa,Rodolfo,195578,6395490,0.99,Alternative & Punk,Cesta Básica,Raimundos Esporrei Na Manivela,Rodolfo,293276,9618499,0.99,Alternative & Punk,Cesta Básica,Raimundos Bê-a-Bá,Rodolfo,249051,8130636,0.99,Alternative & Punk,Cesta Básica,Raimundos Cajueiro,Rodolfo,158589,5164837,0.99,Alternative & Punk,Cesta Básica,Raimundos Palhas Do Coqueiro,Rodolfo,133851,4396466,0.99,Alternative & Punk,Cesta Básica,Raimundos Maluco Beleza,,203206,6628067,0.99,Rock,Raul Seixas,Raul Seixas O Dia Em Que A Terra Parou,,261720,8586678,0.99,Rock,Raul Seixas,Raul Seixas No Fundo Do Quintal Da Escola,,177606,5836953,0.99,Rock,Raul Seixas,Raul Seixas O Segredo Do Universo,,192679,6315187,0.99,Rock,Raul Seixas,Raul Seixas As Profecias,,232515,7657732,0.99,Rock,Raul Seixas,Raul Seixas Mata Virgem,,142602,4690029,0.99,Rock,Raul Seixas,Raul Seixas Sapato 36,,196702,6507301,0.99,Rock,Raul Seixas,Raul Seixas Todo Mundo Explica,,134896,4449772,0.99,Rock,Raul Seixas,Raul Seixas Que Luz É Essa,,165067,5620058,0.99,Rock,Raul Seixas,Raul Seixas Diamante De Mendigo,,206053,6775101,0.99,Rock,Raul Seixas,Raul Seixas Negócio É,,175464,5826775,0.99,Rock,Raul Seixas,Raul Seixas "Muita Estrela, Pouca Constelação",,268068,8781021,0.99,Rock,Raul Seixas,Raul Seixas Século XXI,,244897,8040563,0.99,Rock,Raul Seixas,Raul Seixas Rock Das Aranhas (Ao Vivo) (Live),,231836,7591945,0.99,Rock,Raul Seixas,Raul Seixas The Power Of Equality,Anthony Kiedis/Chad Smith/Flea/John Frusciante,243591,8148266,0.99,Alternative & Punk,Blood Sugar Sex Magik,Red Hot Chili Peppers If 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 Breaking The Girl,Anthony Kiedis/Chad Smith/Flea/John Frusciante,295497,9805526,0.99,Alternative & Punk,Blood Sugar Sex Magik,Red Hot Chili Peppers Funky Monks,Anthony Kiedis/Chad Smith/Flea/John Frusciante,323395,10708168,0.99,Alternative & Punk,Blood Sugar Sex Magik,Red Hot Chili Peppers Suck My Kiss,Anthony Kiedis/Chad Smith/Flea/John Frusciante,217234,7129137,0.99,Alternative & Punk,Blood Sugar Sex Magik,Red Hot Chili Peppers I Could Have Lied,Anthony Kiedis/Chad Smith/Flea/John Frusciante,244506,8088244,0.99,Alternative & Punk,Blood Sugar Sex Magik,Red Hot Chili Peppers Mellowship 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 The Righteous & The Wicked,Anthony Kiedis/Chad Smith/Flea/John Frusciante,248084,8134096,0.99,Alternative & Punk,Blood Sugar Sex Magik,Red Hot Chili Peppers Give It Away,Anthony Kiedis/Chad Smith/Flea/John Frusciante,283010,9308997,0.99,Alternative & Punk,Blood Sugar Sex Magik,Red Hot Chili Peppers Blood Sugar Sex Magik,Anthony Kiedis/Chad Smith/Flea/John Frusciante,271229,8940573,0.99,Alternative & Punk,Blood Sugar Sex Magik,Red Hot Chili Peppers Under The Bridge,Anthony Kiedis/Chad Smith/Flea/John Frusciante,264359,8682716,0.99,Alternative & Punk,Blood Sugar Sex Magik,Red Hot Chili Peppers Naked In The Rain,Anthony Kiedis/Chad Smith/Flea/John Frusciante,265717,8724674,0.99,Alternative & Punk,Blood Sugar Sex Magik,Red Hot Chili Peppers Apache Rose Peacock,Anthony Kiedis/Chad Smith/Flea/John Frusciante,282226,9312588,0.99,Alternative & Punk,Blood Sugar Sex Magik,Red Hot Chili Peppers The Greeting Song,Anthony Kiedis/Chad Smith/Flea/John Frusciante,193593,6346507,0.99,Alternative & Punk,Blood Sugar Sex Magik,Red Hot Chili Peppers My Lovely Man,Anthony Kiedis/Chad Smith/Flea/John Frusciante,279118,9220114,0.99,Alternative & Punk,Blood Sugar Sex Magik,Red Hot Chili Peppers Sir Psycho Sexy,Anthony Kiedis/Chad Smith/Flea/John Frusciante,496692,16354362,0.99,Alternative & Punk,Blood Sugar Sex Magik,Red Hot Chili Peppers They're Red Hot,Robert Johnson,71941,2382220,0.99,Alternative & Punk,Blood Sugar Sex Magik,Red Hot Chili Peppers By The Way,"Anthony Kiedis, Flea, John Frusciante, and Chad Smith",218017,7197430,0.99,Rock,By The Way,Red Hot Chili Peppers Universally Speaking,"Anthony Kiedis, Flea, John Frusciante, and Chad Smith",259213,8501904,0.99,Rock,By The Way,Red Hot Chili Peppers This Is The Place,"Anthony Kiedis, Flea, John Frusciante, and Chad Smith",257906,8469765,0.99,Rock,By The Way,Red Hot Chili Peppers Dosed,"Anthony Kiedis, Flea, John Frusciante, and Chad Smith",312058,10235611,0.99,Rock,By The Way,Red Hot Chili Peppers Don't Forget Me,"Anthony Kiedis, Flea, John Frusciante, and Chad Smith",277995,9107071,0.99,Rock,By The Way,Red Hot Chili Peppers The Zephyr Song,"Anthony Kiedis, Flea, John Frusciante, and Chad Smith",232960,7690312,0.99,Rock,By The Way,Red Hot Chili Peppers Can't Stop,"Anthony Kiedis, Flea, John Frusciante, and Chad Smith",269400,8872479,0.99,Rock,By The Way,Red Hot Chili Peppers I Could Die For You,"Anthony Kiedis, Flea, John Frusciante, and Chad Smith",193906,6333311,0.99,Rock,By The Way,Red Hot Chili Peppers Midnight,"Anthony Kiedis, Flea, John Frusciante, and Chad Smith",295810,9702450,0.99,Rock,By The Way,Red Hot Chili Peppers Throw Away Your Television,"Anthony Kiedis, Flea, John Frusciante, and Chad Smith",224574,7483526,0.99,Rock,By The Way,Red Hot Chili Peppers Cabron,"Anthony Kiedis, Flea, John Frusciante, and Chad Smith",218592,7458864,0.99,Rock,By The Way,Red Hot Chili Peppers Tear,"Anthony Kiedis, Flea, John Frusciante, and Chad Smith",317413,10395500,0.99,Rock,By The Way,Red Hot Chili Peppers On Mercury,"Anthony Kiedis, Flea, John Frusciante, and Chad Smith",208509,6834762,0.99,Rock,By The Way,Red Hot Chili Peppers Minor Thing,"Anthony Kiedis, Flea, John Frusciante, and Chad Smith",217835,7148115,0.99,Rock,By The Way,Red Hot Chili Peppers Warm Tape,"Anthony Kiedis, Flea, John Frusciante, and Chad Smith",256653,8358200,0.99,Rock,By The Way,Red Hot Chili Peppers Venice Queen,"Anthony Kiedis, Flea, John Frusciante, and Chad Smith",369110,12280381,0.99,Rock,By The Way,Red Hot Chili Peppers Around The World,Anthony Kiedis/Chad Smith/Flea/John Frusciante,238837,7859167,0.99,Rock,Californication,Red Hot Chili Peppers Parallel Universe,Red Hot Chili Peppers,270654,8958519,0.99,Rock,Californication,Red Hot Chili Peppers Scar Tissue,Red Hot Chili Peppers,217469,7153744,0.99,Rock,Californication,Red Hot Chili Peppers Otherside,Red Hot Chili Peppers,255973,8357989,0.99,Rock,Californication,Red Hot Chili Peppers Get On Top,Red Hot Chili Peppers,198164,6587883,0.99,Rock,Californication,Red Hot Chili Peppers Californication,Red Hot Chili Peppers,321671,10568999,0.99,Rock,Californication,Red Hot Chili Peppers Easily,Red Hot Chili Peppers,231418,7504534,0.99,Rock,Californication,Red Hot Chili Peppers Porcelain,Anthony Kiedis/Chad Smith/Flea/John Frusciante,163787,5278793,0.99,Rock,Californication,Red Hot Chili Peppers Emit Remmus,Red Hot Chili Peppers,240300,7901717,0.99,Rock,Californication,Red Hot Chili Peppers I Like Dirt,Red Hot Chili Peppers,157727,5225917,0.99,Rock,Californication,Red Hot Chili Peppers This Velvet Glove,Red Hot Chili Peppers,225280,7480537,0.99,Rock,Californication,Red Hot Chili Peppers Savior,Anthony Kiedis/Chad Smith/Flea/John Frusciante,292493,9551885,0.99,Rock,Californication,Red Hot Chili Peppers Purple Stain,Red Hot Chili Peppers,253440,8359971,0.99,Rock,Californication,Red Hot Chili Peppers Right On Time,Red Hot Chili Peppers,112613,3722219,0.99,Rock,Californication,Red Hot Chili Peppers Road Trippin',Red Hot Chili Peppers,205635,6685831,0.99,Rock,Californication,Red Hot Chili Peppers The Spirit Of Radio,Geddy Lee And Alex Lifeson/Geddy Lee And Neil Peart/Rush,299154,9862012,0.99,Rock,Retrospective I (1974-1980),Rush The Trees,Geddy Lee And Alex Lifeson/Geddy Lee And Neil Peart/Rush,285126,9345473,0.99,Rock,Retrospective I (1974-1980),Rush Something For Nothing,Geddy Lee And Alex Lifeson/Geddy Lee And Neil Peart/Rush,240770,7898395,0.99,Rock,Retrospective I (1974-1980),Rush Freewill,Geddy Lee And Alex Lifeson/Geddy Lee And Neil Peart/Rush,324362,10694110,0.99,Rock,Retrospective I (1974-1980),Rush Xanadu,Geddy Lee And Alex Lifeson/Geddy Lee And Neil Peart/Rush,667428,21753168,0.99,Rock,Retrospective I (1974-1980),Rush Bastille Day,Geddy Lee And Alex Lifeson/Geddy Lee And Neil Peart/Rush,280528,9264769,0.99,Rock,Retrospective I (1974-1980),Rush By-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 Anthem,Geddy Lee And Alex Lifeson/Geddy Lee And Neil Peart/Rush,264515,8693343,0.99,Rock,Retrospective I (1974-1980),Rush Closer To The Heart,Geddy Lee And Alex Lifeson/Geddy Lee And Neil Peart/Rush,175412,5767005,0.99,Rock,Retrospective I (1974-1980),Rush 2112 Overture,Geddy Lee And Alex Lifeson/Geddy Lee And Neil Peart/Rush,272718,8898066,0.99,Rock,Retrospective I (1974-1980),Rush The Temples Of Syrinx,Geddy Lee And Alex Lifeson/Geddy Lee And Neil Peart/Rush,133459,4360163,0.99,Rock,Retrospective I (1974-1980),Rush La Villa Strangiato,Geddy Lee And Alex Lifeson/Geddy Lee And Neil Peart/Rush,577488,19137855,0.99,Rock,Retrospective I (1974-1980),Rush Fly By Night,Geddy Lee And Alex Lifeson/Geddy Lee And Neil Peart/Rush,202318,6683061,0.99,Rock,Retrospective I (1974-1980),Rush Finding My Way,Geddy Lee And Alex Lifeson/Geddy Lee And Neil Peart/Rush,305528,9985701,0.99,Rock,Retrospective I (1974-1980),Rush Jingo,M.Babatunde Olantunji,592953,19736495,0.99,Rock,Santana - As Years Go By,Santana El Corazon Manda,E.Weiss,713534,23519583,0.99,Rock,Santana - As Years Go By,Santana La Puesta Del Sol,E.Weiss,628062,20614621,0.99,Rock,Santana - As Years Go By,Santana Persuasion,Carlos Santana,318432,10354751,0.99,Rock,Santana - As Years Go By,Santana As The Years Go by,Albert King,233064,7566829,0.99,Rock,Santana - As Years Go By,Santana Soul Sacrifice,Carlos Santana,296437,9801120,0.99,Rock,Santana - As Years Go By,Santana Fried Neckbones And Home Fries,W.Correa,638563,20939646,0.99,Rock,Santana - As Years Go By,Santana Santana Jam,Carlos Santana,882834,29207100,0.99,Rock,Santana - As Years Go By,Santana Evil Ways,,475402,15289235,0.99,Rock,Santana Live,Santana We've Got To Get Together/Jingo,,1070027,34618222,0.99,Rock,Santana Live,Santana Rock Me,,94720,3037596,0.99,Rock,Santana Live,Santana Just Ain't Good Enough,,850259,27489067,0.99,Rock,Santana Live,Santana Funky Piano,,934791,30200730,0.99,Rock,Santana Live,Santana The Way You Do To Mer,,618344,20028702,0.99,Rock,Santana Live,Santana Água E Fogo,Chico Amaral/Edgard Scandurra/Samuel Rosa,278987,9272272,0.99,Rock,Maquinarama,Skank Três Lados,Chico Amaral/Samuel Rosa,233665,7699609,0.99,Rock,Maquinarama,Skank Ela Desapareceu,Chico Amaral/Samuel Rosa,250122,8289200,0.99,Rock,Maquinarama,Skank Balada Do Amor Inabalável,Fausto Fawcett/Samuel Rosa,240613,8025816,0.99,Rock,Maquinarama,Skank Canção Noturna,Chico Amaral/Lelo Zanettik,238628,7874774,0.99,Rock,Maquinarama,Skank Muçulmano,"Leão, Rodrigo F./Samuel Rosa",249600,8270613,0.99,Rock,Maquinarama,Skank Maquinarama,Chico Amaral/Samuel Rosa,245629,8213710,0.99,Rock,Maquinarama,Skank Rebelião,Chico Amaral/Samuel Rosa,298527,9817847,0.99,Rock,Maquinarama,Skank A Última Guerra,"Leão, Rodrigo F./Lô Borges/Samuel Rosa",314723,10480391,0.99,Rock,Maquinarama,Skank Fica,Chico Amaral/Samuel Rosa,272169,8980972,0.99,Rock,Maquinarama,Skank Ali,Nando Reis/Samuel Rosa,306390,10110351,0.99,Rock,Maquinarama,Skank Preto Damião,Chico Amaral/Samuel Rosa,264568,8697658,0.99,Rock,Maquinarama,Skank É Uma Partida De Futebol,Samuel Rosa,1071,38747,0.99,Rock,O Samba Poconé,Skank Eu Disse A Ela,Samuel Rosa,254223,8479463,0.99,Rock,O Samba Poconé,Skank Zé Trindade,Samuel Rosa,247954,8331310,0.99,Rock,O Samba Poconé,Skank Garota Nacional,Samuel Rosa,317492,10511239,0.99,Rock,O Samba Poconé,Skank Tão Seu,Samuel Rosa,243748,8133126,0.99,Rock,O Samba Poconé,Skank Sem Terra,Samuel Rosa,279353,9196411,0.99,Rock,O Samba Poconé,Skank Os Exilados,Samuel Rosa,245551,8222095,0.99,Rock,O Samba Poconé,Skank Um Dia Qualquer,Samuel Rosa,292414,9805570,0.99,Rock,O Samba Poconé,Skank Los Pretos,Samuel Rosa,239229,8025667,0.99,Rock,O Samba Poconé,Skank Sul Da América,Samuel Rosa,254928,8484871,0.99,Rock,O Samba Poconé,Skank Poconé,Samuel Rosa,318406,10771610,0.99,Rock,O Samba Poconé,Skank Lucky 13,Billy Corgan,189387,6200617,0.99,Alternative & Punk,Judas 0: B-Sides and Rarities,Smashing Pumpkins Aeroplane Flies High,Billy Corgan,473391,15408329,0.99,Alternative & Punk,Judas 0: B-Sides and Rarities,Smashing Pumpkins Because You Are,Billy Corgan,226403,7405137,0.99,Alternative & Punk,Judas 0: B-Sides and Rarities,Smashing Pumpkins Slow Dawn,Billy Corgan,192339,6269057,0.99,Alternative & Punk,Judas 0: B-Sides and Rarities,Smashing Pumpkins Believe,James Iha,192940,6320652,0.99,Alternative & Punk,Judas 0: B-Sides and Rarities,Smashing Pumpkins My Mistake,Billy Corgan,240901,7843477,0.99,Alternative & Punk,Judas 0: B-Sides and Rarities,Smashing Pumpkins Marquis In Spades,Billy Corgan,192731,6304789,0.99,Alternative & Punk,Judas 0: B-Sides and Rarities,Smashing Pumpkins Here's To The Atom Bomb,Billy Corgan,266893,8763140,0.99,Alternative & Punk,Judas 0: B-Sides and Rarities,Smashing Pumpkins Sparrow,Billy Corgan,176822,5696989,0.99,Alternative & Punk,Judas 0: B-Sides and Rarities,Smashing Pumpkins Waiting,Billy Corgan,228336,7627641,0.99,Alternative & Punk,Judas 0: B-Sides and Rarities,Smashing Pumpkins Saturnine,Billy Corgan,229877,7523502,0.99,Alternative & Punk,Judas 0: B-Sides and Rarities,Smashing Pumpkins Rock On,David Cook,366471,12133825,0.99,Alternative & Punk,Judas 0: B-Sides and Rarities,Smashing Pumpkins Set The Ray To Jerry,Billy Corgan,249364,8215184,0.99,Alternative & Punk,Judas 0: B-Sides and Rarities,Smashing Pumpkins Winterlong,Billy Corgan,299389,9670616,0.99,Alternative & Punk,Judas 0: B-Sides and Rarities,Smashing Pumpkins Soot & Stars,Billy Corgan,399986,12866557,0.99,Alternative & Punk,Judas 0: B-Sides and Rarities,Smashing Pumpkins Blissed & Gone,Billy Corgan,286302,9305998,0.99,Alternative & Punk,Judas 0: B-Sides and Rarities,Smashing Pumpkins Siva,Billy Corgan,261172,8576622,0.99,Alternative & Punk,Rotten Apples: Greatest Hits,Smashing Pumpkins Rhinocerous,Billy Corgan,353462,11526684,0.99,Alternative & Punk,Rotten Apples: Greatest Hits,Smashing Pumpkins Drown,Billy Corgan,270497,8883496,0.99,Alternative & Punk,Rotten Apples: Greatest Hits,Smashing Pumpkins Cherub Rock,Billy Corgan,299389,9786739,0.99,Alternative & Punk,Rotten Apples: Greatest Hits,Smashing Pumpkins Today,Billy Corgan,202213,6596933,0.99,Alternative & Punk,Rotten Apples: Greatest Hits,Smashing Pumpkins Disarm,Billy Corgan,198556,6508249,0.99,Alternative & Punk,Rotten Apples: Greatest Hits,Smashing Pumpkins Landslide,Stevie Nicks,190275,6187754,0.99,Alternative & Punk,Rotten Apples: Greatest Hits,Smashing Pumpkins Bullet With Butterfly Wings,Billy Corgan,257306,8431747,0.99,Alternative & Punk,Rotten Apples: Greatest Hits,Smashing Pumpkins 1979,Billy Corgan,263653,8728470,0.99,Alternative & Punk,Rotten Apples: Greatest Hits,Smashing Pumpkins Zero,Billy Corgan,161123,5267176,0.99,Alternative & Punk,Rotten Apples: Greatest Hits,Smashing Pumpkins "Tonight, Tonight",Billy Corgan,255686,8351543,0.99,Alternative & Punk,Rotten Apples: Greatest Hits,Smashing Pumpkins Eye,Billy Corgan,294530,9784201,0.99,Alternative & Punk,Rotten Apples: Greatest Hits,Smashing Pumpkins Ava Adore,Billy Corgan,261433,8590412,0.99,Alternative & Punk,Rotten Apples: Greatest Hits,Smashing Pumpkins Perfect,Billy Corgan,203023,6734636,0.99,Alternative & Punk,Rotten Apples: Greatest Hits,Smashing Pumpkins The Everlasting Gaze,Billy Corgan,242155,7844404,0.99,Alternative & Punk,Rotten Apples: Greatest Hits,Smashing Pumpkins Stand Inside Your Love,Billy Corgan,253753,8270113,0.99,Alternative & Punk,Rotten Apples: Greatest Hits,Smashing Pumpkins Real Love,Billy Corgan,250697,8025896,0.99,Alternative & Punk,Rotten Apples: Greatest Hits,Smashing Pumpkins [Untitled],Billy Corgan,231784,7689713,0.99,Alternative & Punk,Rotten Apples: Greatest Hits,Smashing Pumpkins Nothing To Say,Chris Cornell/Kim Thayil,238027,7744833,0.99,Rock,A-Sides,Soundgarden Flower,Chris Cornell/Kim Thayil,208822,6830732,0.99,Rock,A-Sides,Soundgarden Loud Love,Chris Cornell,297456,9660953,0.99,Rock,A-Sides,Soundgarden Hands All Over,Chris Cornell/Kim Thayil,362475,11893108,0.99,Rock,A-Sides,Soundgarden Get On The Snake,Chris Cornell/Kim Thayil,225123,7313744,0.99,Rock,A-Sides,Soundgarden Jesus Christ Pose,Ben Shepherd/Chris Cornell/Kim Thayil/Matt Cameron,352966,11739886,0.99,Rock,A-Sides,Soundgarden Outshined,Chris Cornell,312476,10274629,0.99,Rock,A-Sides,Soundgarden Rusty Cage,Chris Cornell,267728,8779485,0.99,Rock,A-Sides,Soundgarden Spoonman,Chris Cornell,248476,8289906,0.99,Rock,A-Sides,Soundgarden The Day I Tried To Live,Chris Cornell,321175,10507137,0.99,Rock,A-Sides,Soundgarden Black Hole Sun,Soundgarden,320365,10425229,0.99,Rock,A-Sides,Soundgarden Fell On Black Days,Chris Cornell,282331,9256082,0.99,Rock,A-Sides,Soundgarden Pretty Noose,Chris Cornell,253570,8317931,0.99,Rock,A-Sides,Soundgarden Burden In My Hand,Chris Cornell,292153,9659911,0.99,Rock,A-Sides,Soundgarden Blow Up The Outside World,Chris Cornell,347898,11379527,0.99,Rock,A-Sides,Soundgarden Ty Cobb,Ben Shepherd/Chris Cornell,188786,6233136,0.99,Rock,A-Sides,Soundgarden Bleed Together,Chris Cornell,232202,7597074,0.99,Rock,A-Sides,Soundgarden Morning Dance,Jay Beckenstein,238759,8101979,0.99,Jazz,Morning Dance,Spyro Gyra Jubilee,Jeremy Wall,275147,9151846,0.99,Jazz,Morning Dance,Spyro Gyra Rasul,Jeremy Wall,238315,7854737,0.99,Jazz,Morning Dance,Spyro Gyra Song For Lorraine,Jay Beckenstein,240091,8101723,0.99,Jazz,Morning Dance,Spyro Gyra Starburst,Jeremy Wall,291500,9768399,0.99,Jazz,Morning Dance,Spyro Gyra Heliopolis,Jay Beckenstein,338729,11365655,0.99,Jazz,Morning Dance,Spyro Gyra It Doesn't Matter,Chet Catallo,270027,9034177,0.99,Jazz,Morning Dance,Spyro Gyra Little Linda,Jeremy Wall,264019,8958743,0.99,Jazz,Morning Dance,Spyro Gyra End Of Romanticism,Rick Strauss,320078,10553155,0.99,Jazz,Morning Dance,Spyro Gyra The House Is Rockin',Doyle Bramhall/Stevie Ray Vaughan,144352,4706253,0.99,Blues,In Step,Stevie Ray Vaughan & Double Trouble Crossfire,B. Carter/C. Layton/R. Ellsworth/R. Wynans/T. Shannon,251219,8238033,0.99,Blues,In Step,Stevie Ray Vaughan & Double Trouble Tightrope,Doyle Bramhall/Stevie Ray Vaughan,281155,9254906,0.99,Blues,In Step,Stevie Ray Vaughan & Double Trouble Let Me Love You Baby,Willie Dixon,164127,5378455,0.99,Blues,In Step,Stevie Ray Vaughan & Double Trouble Leave My Girl Alone,B. Guy,256365,8438021,0.99,Blues,In Step,Stevie Ray Vaughan & Double Trouble Travis Walk,Stevie Ray Vaughan,140826,4650979,0.99,Blues,In Step,Stevie Ray Vaughan & Double Trouble Wall Of Denial,Doyle Bramhall/Stevie Ray Vaughan,336927,11085915,0.99,Blues,In Step,Stevie Ray Vaughan & Double Trouble Scratch-N-Sniff,Doyle Bramhall/Stevie Ray Vaughan,163422,5353627,0.99,Blues,In Step,Stevie Ray Vaughan & Double Trouble Love Me Darlin',C. Burnett,201586,6650869,0.99,Blues,In Step,Stevie Ray Vaughan & Double Trouble Riviera Paradise,Stevie Ray Vaughan,528692,17232776,0.99,Blues,In Step,Stevie Ray Vaughan & Double Trouble Dead And Bloated,R. DeLeo/Weiland,310386,10170433,0.99,Rock,Core,Stone Temple Pilots Sex Type Thing,D. DeLeo/Kretz/Weiland,218723,7102064,0.99,Rock,Core,Stone Temple Pilots Wicked Garden,D. DeLeo/R. DeLeo/Weiland,245368,7989505,0.99,Rock,Core,Stone Temple Pilots No Memory,Dean Deleo,80613,2660859,0.99,Rock,Core,Stone Temple Pilots Sin,R. DeLeo/Weiland,364800,12018823,0.99,Rock,Core,Stone Temple Pilots Naked Sunday,D. DeLeo/Kretz/R. DeLeo/Weiland,229720,7444201,0.99,Rock,Core,Stone Temple Pilots Creep,R. DeLeo/Weiland,333191,10894988,0.99,Rock,Core,Stone Temple Pilots Piece Of Pie,R. DeLeo/Weiland,324623,10605231,0.99,Rock,Core,Stone Temple Pilots Plush,R. DeLeo/Weiland,314017,10229848,0.99,Rock,Core,Stone Temple Pilots Wet My Bed,R. DeLeo/Weiland,96914,3198627,0.99,Rock,Core,Stone Temple Pilots Crackerman,Kretz/R. DeLeo/Weiland,194403,6317361,0.99,Rock,Core,Stone Temple Pilots Where The River Goes,D. DeLeo/Kretz/Weiland,505991,16468904,0.99,Rock,Core,Stone Temple Pilots Soldier Side - Intro,"Dolmayan, John/Malakian, Daron/Odadjian, Shavo",63764,2056079,0.99,Metal,Mezmerize,System Of A Down B.Y.O.B.,"Tankian, Serj",255555,8407935,0.99,Metal,Mezmerize,System Of A Down Revenga,"Tankian, Serj",228127,7503805,0.99,Metal,Mezmerize,System Of A Down Cigaro,"Tankian, Serj",131787,4321705,0.99,Metal,Mezmerize,System Of A Down Radio/Video,"Dolmayan, John/Malakian, Daron/Odadjian, Shavo",249312,8224917,0.99,Metal,Mezmerize,System Of A Down This Cocaine Makes Me Feel Like I'm On This Song,"Tankian, Serj",128339,4185193,0.99,Metal,Mezmerize,System Of A Down Violent Pornography,"Dolmayan, John/Malakian, Daron/Odadjian, Shavo",211435,6985960,0.99,Metal,Mezmerize,System Of A Down Question!,"Tankian, Serj",200698,6616398,0.99,Metal,Mezmerize,System Of A Down Sad Statue,"Tankian, Serj",205897,6733449,0.99,Metal,Mezmerize,System Of A Down Old School Hollywood,"Dolmayan, John/Malakian, Daron/Odadjian, Shavo",176953,5830258,0.99,Metal,Mezmerize,System Of A Down Lost in Hollywood,"Tankian, Serj",320783,10535158,0.99,Metal,Mezmerize,System Of A Down The Sun Road,"Terry Bozzio, Steve Stevens, Tony Levin",880640,29008407,0.99,Rock,[1997] Black Light Syndrome,"Terry Bozzio, Tony Levin & Steve Stevens" Dark Corners,"Terry Bozzio, Steve Stevens, Tony Levin",513541,16839223,0.99,Rock,[1997] Black Light Syndrome,"Terry Bozzio, Tony Levin & Steve Stevens" Duende,"Terry Bozzio, Steve Stevens, Tony Levin",447582,14956771,0.99,Rock,[1997] Black Light Syndrome,"Terry Bozzio, Tony Levin & Steve Stevens" Black Light Syndrome,"Terry Bozzio, Steve Stevens, Tony Levin",526471,17300835,0.99,Rock,[1997] Black Light Syndrome,"Terry Bozzio, Tony Levin & Steve Stevens" Falling in Circles,"Terry Bozzio, Steve Stevens, Tony Levin",549093,18263248,0.99,Rock,[1997] Black Light Syndrome,"Terry Bozzio, Tony Levin & Steve Stevens" Book of Hours,"Terry Bozzio, Steve Stevens, Tony Levin",583366,19464726,0.99,Rock,[1997] Black Light Syndrome,"Terry Bozzio, Tony Levin & Steve Stevens" Chaos-Control,"Terry Bozzio, Steve Stevens, Tony Levin",529841,17455568,0.99,Rock,[1997] Black Light Syndrome,"Terry Bozzio, Tony Levin & Steve Stevens" Midnight From The Inside Out,Chris Robinson/Rich Robinson,286981,9442157,0.99,Blues,Live [Disc 1],The Black Crowes Sting Me,Chris Robinson/Rich Robinson,268094,8813561,0.99,Blues,Live [Disc 1],The Black Crowes Thick & Thin,Chris Robinson/Rich Robinson,222720,7284377,0.99,Blues,Live [Disc 1],The Black Crowes Greasy Grass River,Chris Robinson/Rich Robinson,218749,7157045,0.99,Blues,Live [Disc 1],The Black Crowes Sometimes Salvation,Chris Robinson/Rich Robinson,389146,12749424,0.99,Blues,Live [Disc 1],The Black Crowes Cursed Diamonds,Chris Robinson/Rich Robinson,368300,12047978,0.99,Blues,Live [Disc 1],The Black Crowes Miracle To Me,Chris Robinson/Rich Robinson,372636,12222116,0.99,Blues,Live [Disc 1],The Black Crowes Wiser Time,Chris Robinson/Rich Robinson,459990,15161907,0.99,Blues,Live [Disc 1],The Black Crowes Girl From A Pawnshop,Chris Robinson/Rich Robinson,404688,13250848,0.99,Blues,Live [Disc 1],The Black Crowes Cosmic Fiend,Chris Robinson/Rich Robinson,308401,10115556,0.99,Blues,Live [Disc 1],The Black Crowes Black Moon Creeping,Chris Robinson/Rich Robinson,359314,11740886,0.99,Blues,Live [Disc 2],The Black Crowes High Head Blues,Chris Robinson/Rich Robinson,371879,12227998,0.99,Blues,Live [Disc 2],The Black Crowes Title Song,Chris Robinson/Rich Robinson,505521,16501316,0.99,Blues,Live [Disc 2],The Black Crowes She Talks To Angels,Chris Robinson/Rich Robinson,361978,11837342,0.99,Blues,Live [Disc 2],The Black Crowes Twice As Hard,Chris Robinson/Rich Robinson,275565,9008067,0.99,Blues,Live [Disc 2],The Black Crowes Lickin',Chris Robinson/Rich Robinson,314409,10331216,0.99,Blues,Live [Disc 2],The Black Crowes Soul Singing,Chris Robinson/Rich Robinson,233639,7672489,0.99,Blues,Live [Disc 2],The Black Crowes Hard To Handle,A.Isbell/A.Jones/O.Redding,206994,6786304,0.99,Blues,Live [Disc 2],The Black Crowes Remedy,Chris Robinson/Rich Robinson,337084,11049098,0.99,Blues,Live [Disc 2],The Black Crowes White Riot,Joe Strummer/Mick Jones,118726,3922819,0.99,Alternative & Punk,The Singles,The Clash Remote Control,Joe Strummer/Mick Jones,180297,5949647,0.99,Alternative & Punk,The Singles,The Clash Complete Control,Joe Strummer/Mick Jones,192653,6272081,0.99,Alternative & Punk,The Singles,The Clash Clash City Rockers,Joe Strummer/Mick Jones,227500,7555054,0.99,Alternative & Punk,The Singles,The Clash (White Man) In Hammersmith Palais,Joe Strummer/Mick Jones,240640,7883532,0.99,Alternative & Punk,The Singles,The Clash Tommy Gun,Joe Strummer/Mick Jones,195526,6399872,0.99,Alternative & Punk,The Singles,The Clash English Civil War,Mick Jones/Traditional arr. Joe Strummer,156708,5111226,0.99,Alternative & Punk,The Singles,The Clash I Fought The Law,Sonny Curtis,159764,5245258,0.99,Alternative & Punk,The Singles,The Clash London Calling,Joe Strummer/Mick Jones,199706,6569007,0.99,Alternative & Punk,The Singles,The Clash Train In Vain,Joe Strummer/Mick Jones,189675,6329877,0.99,Alternative & Punk,The Singles,The Clash Bankrobber,Joe Strummer/Mick Jones,272431,9067323,0.99,Alternative & Punk,The Singles,The Clash The Call Up,The Clash,324336,10746937,0.99,Alternative & Punk,The Singles,The Clash Hitsville UK,The Clash,261433,8606887,0.99,Alternative & Punk,The Singles,The Clash The Magnificent Seven,The Clash,268486,8889821,0.99,Alternative & Punk,The Singles,The Clash This Is Radio Clash,The Clash,249756,8366573,0.99,Alternative & Punk,The Singles,The Clash Know Your Rights,The Clash,217678,7195726,0.99,Alternative & Punk,The Singles,The Clash Rock The Casbah,The Clash,222145,7361500,0.99,Alternative & Punk,The Singles,The Clash Should I Stay Or Should I Go,The Clash,187219,6188688,0.99,Alternative & Punk,The Singles,The Clash War (The Process),Billy Duffy/Ian Astbury,252630,8254842,0.99,Rock,Beyond Good And Evil,The Cult The Saint,Billy Duffy/Ian Astbury,216215,7061584,0.99,Rock,Beyond Good And Evil,The Cult Rise,Billy Duffy/Ian Astbury,219088,7106195,0.99,Rock,Beyond Good And Evil,The Cult Take The Power,Billy Duffy/Ian Astbury,235755,7650012,0.99,Rock,Beyond Good And Evil,The Cult Breathe,Billy Duffy/Ian Astbury/Marti Frederiksen/Mick Jones,299781,9742361,0.99,Rock,Beyond Good And Evil,The Cult Nico,Billy Duffy/Ian Astbury,289488,9412323,0.99,Rock,Beyond Good And Evil,The Cult American Gothic,Billy Duffy/Ian Astbury,236878,7739840,0.99,Rock,Beyond Good And Evil,The Cult Ashes And Ghosts,Billy Duffy/Bob Rock/Ian Astbury,300591,9787692,0.99,Rock,Beyond Good And Evil,The Cult Shape The Sky,Billy Duffy/Ian Astbury,209789,6885647,0.99,Rock,Beyond Good And Evil,The Cult Speed Of Light,Billy Duffy/Bob Rock/Ian Astbury,262817,8563352,0.99,Rock,Beyond Good And Evil,The Cult True Believers,Billy Duffy/Ian Astbury,308009,9981359,0.99,Rock,Beyond Good And Evil,The Cult My Bridges Burn,Billy Duffy/Ian Astbury,231862,7571370,0.99,Rock,Beyond Good And Evil,The Cult She Sells Sanctuary,,253727,8368634,0.99,Rock,"Pure Cult: The Best Of The Cult (For Rockers, Ravers, Lovers & Sinners) [UK]",The Cult Fire Woman,,312790,10196995,0.99,Rock,"Pure Cult: The Best Of The Cult (For Rockers, Ravers, Lovers & Sinners) [UK]",The Cult Lil' Evil,,165825,5419655,0.99,Rock,"Pure Cult: The Best Of The Cult (For Rockers, Ravers, Lovers & Sinners) [UK]",The Cult Spirit Walker,,230060,7555897,0.99,Rock,"Pure Cult: The Best Of The Cult (For Rockers, Ravers, Lovers & Sinners) [UK]",The Cult The Witch,,258768,8725403,0.99,Rock,"Pure Cult: The Best Of The Cult (For Rockers, Ravers, Lovers & Sinners) [UK]",The Cult Revolution,,256026,8371254,0.99,Rock,"Pure Cult: The Best Of The Cult (For Rockers, Ravers, Lovers & Sinners) [UK]",The Cult Wild Hearted Son,,266893,8670550,0.99,Rock,"Pure Cult: The Best Of The Cult (For Rockers, Ravers, Lovers & Sinners) [UK]",The Cult Love Removal Machine,,257619,8412167,0.99,Rock,"Pure Cult: The Best Of The Cult (For Rockers, Ravers, Lovers & Sinners) [UK]",The Cult Rain,,236669,7788461,0.99,Rock,"Pure Cult: The Best Of The Cult (For Rockers, Ravers, Lovers & Sinners) [UK]",The Cult Edie (Ciao Baby),,241632,7846177,0.99,Rock,"Pure Cult: The Best Of The Cult (For Rockers, Ravers, Lovers & Sinners) [UK]",The Cult Heart Of Soul,,274207,8967257,0.99,Rock,"Pure Cult: The Best Of The Cult (For Rockers, Ravers, Lovers & Sinners) [UK]",The Cult Love,,326739,10729824,0.99,Rock,"Pure Cult: The Best Of The Cult (For Rockers, Ravers, Lovers & Sinners) [UK]",The Cult Wild Flower,,215536,7084321,0.99,Rock,"Pure Cult: The Best Of The Cult (For Rockers, Ravers, Lovers & Sinners) [UK]",The Cult Go West,,238158,7777749,0.99,Rock,"Pure Cult: The Best Of The Cult (For Rockers, Ravers, Lovers & Sinners) [UK]",The Cult Resurrection Joe,,255451,8532840,0.99,Rock,"Pure Cult: The Best Of The Cult (For Rockers, Ravers, Lovers & Sinners) [UK]",The Cult Sun King,,368431,12010865,0.99,Rock,"Pure Cult: The Best Of The Cult (For Rockers, Ravers, Lovers & Sinners) [UK]",The Cult Sweet Soul Sister,,212009,6889883,0.99,Rock,"Pure Cult: The Best Of The Cult (For Rockers, Ravers, Lovers & Sinners) [UK]",The Cult Earth Mofo,,282200,9204581,0.99,Rock,"Pure Cult: The Best Of The Cult (For Rockers, Ravers, Lovers & Sinners) [UK]",The Cult Break on Through,"Robby Krieger, Ray Manzarek, John Densmore, Jim Morrison",149342,4943144,0.99,Rock,The Doors,The Doors Soul Kitchen,"Robby Krieger, Ray Manzarek, John Densmore, Jim Morrison",215066,7040865,0.99,Rock,The Doors,The Doors The Crystal Ship,"Robby Krieger, Ray Manzarek, John Densmore, Jim Morrison",154853,5052658,0.99,Rock,The Doors,The Doors Twentienth Century Fox,"Robby Krieger, Ray Manzarek, John Densmore, Jim Morrison",153913,5069211,0.99,Rock,The Doors,The Doors Alabama Song,Weill-Brecht,200097,6563411,0.99,Rock,The Doors,The Doors Light My Fire,"Robby Krieger, Ray Manzarek, John Densmore, Jim Morrison",428329,13963351,0.99,Rock,The Doors,The Doors Back Door Man,"Willie Dixon, C. Burnett",214360,7035636,0.99,Rock,The Doors,The Doors I Looked At You,"Robby Krieger, Ray Manzarek, John Densmore, Jim Morrison",142080,4663988,0.99,Rock,The Doors,The Doors End Of The Night,"Robby Krieger, Ray Manzarek, John Densmore, Jim Morrison",172695,5589732,0.99,Rock,The Doors,The Doors Take It As It Comes,"Robby Krieger, Ray Manzarek, John Densmore, Jim Morrison",137168,4512656,0.99,Rock,The Doors,The Doors The End,"Robby Krieger, Ray Manzarek, John Densmore, Jim Morrison",701831,22927336,0.99,Rock,The Doors,The Doors Roxanne,G M Sumner,192992,6330159,0.99,Rock,The Police Greatest Hits,The Police Can't Stand Losing You,G M Sumner,181159,5971983,0.99,Rock,The Police Greatest Hits,The Police Message in a Bottle,G M Sumner,291474,9647829,0.99,Rock,The Police Greatest Hits,The Police Walking on the Moon,G M Sumner,302080,10019861,0.99,Rock,The Police Greatest Hits,The Police Don't Stand so Close to Me,G M Sumner,241031,7956658,0.99,Rock,The Police Greatest Hits,The Police "De Do Do Do, De Da Da Da",G M Sumner,247196,8227075,0.99,Rock,The Police Greatest Hits,The Police Every Little Thing She Does is Magic,G M Sumner,261120,8646853,0.99,Rock,The Police Greatest Hits,The Police Invisible Sun,G M Sumner,225593,7304320,0.99,Rock,The Police Greatest Hits,The Police Spirit's in the Material World,G M Sumner,181133,5986622,0.99,Rock,The Police Greatest Hits,The Police Every Breath You Take,G M Sumner,254615,8364520,0.99,Rock,The Police Greatest Hits,The Police King Of Pain,G M Sumner,300512,9880303,0.99,Rock,The Police Greatest Hits,The Police Wrapped Around Your Finger,G M Sumner,315454,10361490,0.99,Rock,The Police Greatest Hits,The Police Don't Stand So Close to Me '86,G M Sumner,293590,9636683,0.99,Rock,The Police Greatest Hits,The Police Message in a Bottle (new classic rock mix),G M Sumner,290951,9640349,0.99,Rock,The Police Greatest Hits,The Police Time Is On My Side,Jerry Ragavoy,179983,5855836,0.99,Rock,"Hot Rocks, 1964-1971 (Disc 1)",The Rolling Stones Heart Of Stone,Jagger/Richards,164493,5329538,0.99,Rock,"Hot Rocks, 1964-1971 (Disc 1)",The Rolling Stones Play With Fire,Nanker Phelge,132022,4265297,0.99,Rock,"Hot Rocks, 1964-1971 (Disc 1)",The Rolling Stones Satisfaction,Jagger/Richards,226612,7398766,0.99,Rock,"Hot Rocks, 1964-1971 (Disc 1)",The Rolling Stones As Tears Go By,Jagger/Richards/Oldham,164284,5357350,0.99,Rock,"Hot Rocks, 1964-1971 (Disc 1)",The Rolling Stones Get Off Of My Cloud,Jagger/Richards,176013,5719514,0.99,Rock,"Hot Rocks, 1964-1971 (Disc 1)",The Rolling Stones Mother's Little Helper,Jagger/Richards,167549,5422434,0.99,Rock,"Hot Rocks, 1964-1971 (Disc 1)",The Rolling Stones 19th Nervous Breakdown,Jagger/Richards,237923,7742984,0.99,Rock,"Hot Rocks, 1964-1971 (Disc 1)",The Rolling Stones Paint It Black,Jagger/Richards,226063,7442888,0.99,Rock,"Hot Rocks, 1964-1971 (Disc 1)",The Rolling Stones Under My Thumb,Jagger/Richards,221387,7371799,0.99,Rock,"Hot Rocks, 1964-1971 (Disc 1)",The Rolling Stones Ruby Tuesday,Jagger/Richards,197459,6433467,0.99,Rock,"Hot Rocks, 1964-1971 (Disc 1)",The Rolling Stones Let's Spend The Night Together,Jagger/Richards,217495,7137048,0.99,Rock,"Hot Rocks, 1964-1971 (Disc 1)",The Rolling Stones Intro,Jagger/Richards,49737,1618591,0.99,Rock,No Security,The Rolling Stones You Got Me Rocking,Jagger/Richards,205766,6734385,0.99,Rock,No Security,The Rolling Stones Gimmie Shelters,Jagger/Richards,382119,12528764,0.99,Rock,No Security,The Rolling Stones Flip The Switch,Jagger/Richards,252421,8336591,0.99,Rock,No Security,The Rolling Stones Memory Motel,Jagger/Richards,365844,11982431,0.99,Rock,No Security,The Rolling Stones Corinna,Jesse Ed Davis III/Taj Mahal,257488,8449471,0.99,Rock,No Security,The Rolling Stones Saint Of Me,Jagger/Richards,325694,10725160,0.99,Rock,No Security,The Rolling Stones Wainting On A Friend,Jagger/Richards,302497,9978046,0.99,Rock,No Security,The Rolling Stones Sister Morphine,Faithfull/Jagger/Richards,376215,12345289,0.99,Rock,No Security,The Rolling Stones Live With Me,Jagger/Richards,234893,7709006,0.99,Rock,No Security,The Rolling Stones Respectable,Jagger/Richards,215693,7099669,0.99,Rock,No Security,The Rolling Stones Thief In The Night,De Beauport/Jagger/Richards,337266,10952756,0.99,Rock,No Security,The Rolling Stones The Last Time,Jagger/Richards,287294,9498758,0.99,Rock,No Security,The Rolling Stones Out Of Control,Jagger/Richards,479242,15749289,0.99,Rock,No Security,The Rolling Stones Love Is Strong,Jagger/Richards,230896,7639774,0.99,Rock,Voodoo Lounge,The Rolling Stones You Got Me Rocking,Jagger/Richards,215928,7162159,0.99,Rock,Voodoo Lounge,The Rolling Stones Sparks Will Fly,Jagger/Richards,196466,6492847,0.99,Rock,Voodoo Lounge,The Rolling Stones The Worst,Jagger/Richards,144613,4750094,0.99,Rock,Voodoo Lounge,The Rolling Stones New Faces,Jagger/Richards,172146,5689122,0.99,Rock,Voodoo Lounge,The Rolling Stones Moon Is Up,Jagger/Richards,222119,7366316,0.99,Rock,Voodoo Lounge,The Rolling Stones Out Of Tears,Jagger/Richards,327418,10677236,0.99,Rock,Voodoo Lounge,The Rolling Stones I Go Wild,Jagger/Richards,264019,8630833,0.99,Rock,Voodoo Lounge,The Rolling Stones Brand New Car,Jagger/Richards,256052,8459344,0.99,Rock,Voodoo Lounge,The Rolling Stones Sweethearts Together,Jagger/Richards,285492,9550459,0.99,Rock,Voodoo Lounge,The Rolling Stones Suck On The Jugular,Jagger/Richards,268225,8920566,0.99,Rock,Voodoo Lounge,The Rolling Stones Blinded By Rainbows,Jagger/Richards,273946,8971343,0.99,Rock,Voodoo Lounge,The Rolling Stones Baby Break It Down,Jagger/Richards,249417,8197309,0.99,Rock,Voodoo Lounge,The Rolling Stones Thru And Thru,Jagger/Richards,375092,12175406,0.99,Rock,Voodoo Lounge,The Rolling Stones Mean Disposition,Jagger/Richards,249155,8273602,0.99,Rock,Voodoo Lounge,The Rolling Stones Walking Wounded,The Tea Party,277968,9184345,0.99,Alternative & Punk,Tangents,The Tea Party Temptation,The Tea Party,205087,6711943,0.99,Alternative & Punk,Tangents,The Tea Party The Messenger,Daniel Lanois,212062,6975437,0.99,Alternative & Punk,Tangents,The Tea Party Psychopomp,The Tea Party,315559,10295199,0.99,Alternative & Punk,Tangents,The Tea Party Sister Awake,The Tea Party,343875,11299407,0.99,Alternative & Punk,Tangents,The Tea Party The Bazaar,The Tea Party,222458,7245691,0.99,Alternative & Punk,Tangents,The Tea Party Save Me (Remix),The Tea Party,396303,13053839,0.99,Alternative & Punk,Tangents,The Tea Party Fire In The Head,The Tea Party,306337,10005675,0.99,Alternative & Punk,Tangents,The Tea Party Release,The Tea Party,244114,8014606,0.99,Alternative & Punk,Tangents,The Tea Party Heaven Coming Down,The Tea Party,241867,7846459,0.99,Alternative & Punk,Tangents,The Tea Party The River (Remix),The Tea Party,343170,11193268,0.99,Alternative & Punk,Tangents,The Tea Party Babylon,The Tea Party,169795,5568808,0.99,Alternative & Punk,Tangents,The Tea Party Waiting On A Sign,The Tea Party,261903,8558590,0.99,Alternative & Punk,Tangents,The Tea Party Life Line,The Tea Party,277786,9082773,0.99,Alternative & Punk,Tangents,The Tea Party Paint It Black,Keith Richards/Mick Jagger,214752,7101572,0.99,Alternative & Punk,Tangents,The Tea Party Temptation,The Tea Party,205244,6719465,0.99,Alternative & Punk,Transmission,The Tea Party Army Ants,The Tea Party,215405,7075838,0.99,Alternative & Punk,Transmission,The Tea Party Psychopomp,The Tea Party,317231,10351778,0.99,Alternative & Punk,Transmission,The Tea Party Gyroscope,The Tea Party,177711,5810323,0.99,Alternative & Punk,Transmission,The Tea Party Alarum,The Tea Party,298187,9712545,0.99,Alternative & Punk,Transmission,The Tea Party Release,The Tea Party,266292,8725824,0.99,Alternative & Punk,Transmission,The Tea Party Transmission,The Tea Party,317257,10351152,0.99,Alternative & Punk,Transmission,The Tea Party Babylon,The Tea Party,292466,9601786,0.99,Alternative & Punk,Transmission,The Tea Party Pulse,The Tea Party,250253,8183872,0.99,Alternative & Punk,Transmission,The Tea Party Emerald,The Tea Party,289750,9543789,0.99,Alternative & Punk,Transmission,The Tea Party Aftermath,The Tea Party,343745,11085607,0.99,Alternative & Punk,Transmission,The Tea Party I Can't Explain,Pete Townshend,125152,4082896,0.99,Rock,My Generation - The Very Best Of The Who,The Who "Anyway, Anyhow, Anywhere","Pete Townshend, Roger Daltrey",161253,5234173,0.99,Rock,My Generation - The Very Best Of The Who,The Who My Generation,John Entwistle/Pete Townshend,197825,6446634,0.99,Rock,My Generation - The Very Best Of The Who,The Who Substitute,Pete Townshend,228022,7409995,0.99,Rock,My Generation - The Very Best Of The Who,The Who I'm A Boy,Pete Townshend,157126,5120605,0.99,Rock,My Generation - The Very Best Of The Who,The Who Boris The Spider,John Entwistle,149472,4835202,0.99,Rock,My Generation - The Very Best Of The Who,The Who Happy Jack,Pete Townshend,132310,4353063,0.99,Rock,My Generation - The Very Best Of The Who,The Who Pictures Of Lily,Pete Townshend,164414,5329751,0.99,Rock,My Generation - The Very Best Of The Who,The Who I Can See For Miles,Pete Townshend,262791,8604989,0.99,Rock,My Generation - The Very Best Of The Who,The Who Magic Bus,Pete Townshend,197224,6452700,0.99,Rock,My Generation - The Very Best Of The Who,The Who Pinball Wizard,John Entwistle/Pete Townshend,181890,6055580,0.99,Rock,My Generation - The Very Best Of The Who,The Who The Seeker,Pete Townshend,204643,6736866,0.99,Rock,My Generation - The Very Best Of The Who,The Who Baba O'Riley,John Entwistle/Pete Townshend,309472,10141660,0.99,Rock,My Generation - The Very Best Of The Who,The Who Won'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 Let's See Action,Pete Townshend,243513,8078418,0.99,Rock,My Generation - The Very Best Of The Who,The Who 5.15,Pete Townshend,289619,9458549,0.99,Rock,My Generation - The Very Best Of The Who,The Who Join Together,Pete Townshend,262556,8602485,0.99,Rock,My Generation - The Very Best Of The Who,The Who Squeeze Box,Pete Townshend,161280,5256508,0.99,Rock,My Generation - The Very Best Of The Who,The Who Who Are You (Single Edit Version),John Entwistle/Pete Townshend,299232,9900469,0.99,Rock,My Generation - The Very Best Of The Who,The Who You Better You Bet,Pete Townshend,338520,11160877,0.99,Rock,My Generation - The Very Best Of The Who,The Who Primavera,Genival Cassiano/Silvio Rochael,126615,4152604,0.99,Latin,Serie Sem Limite (Disc 1),Tim Maia Chocolate,Tim Maia,194690,6411587,0.99,Latin,Serie Sem Limite (Disc 1),Tim Maia Azul Da Cor Do Mar,Tim Maia,197955,6475007,0.99,Latin,Serie Sem Limite (Disc 1),Tim Maia O Descobridor Dos Sete Mares,Gilson Mendonça/Michel,262974,8749583,0.99,Latin,Serie Sem Limite (Disc 1),Tim Maia Até Que Enfim Encontrei Você,Tim Maia,105064,3477751,0.99,Latin,Serie Sem Limite (Disc 1),Tim Maia Coroné Antonio Bento,"Do Vale, João/Luiz Wanderley",131317,4340326,0.99,Latin,Serie Sem Limite (Disc 1),Tim Maia New Love,Tim Maia,237897,7786824,0.99,Latin,Serie Sem Limite (Disc 1),Tim Maia Não Vou Ficar,Tim Maia,172068,5642919,0.99,Latin,Serie Sem Limite (Disc 1),Tim Maia Música No Ar,Tim Maia,158511,5184891,0.99,Latin,Serie Sem Limite (Disc 1),Tim Maia Salve Nossa Senhora,Carlos Imperial/Edardo Araújo,115461,3827629,0.99,Latin,Serie Sem Limite (Disc 1),Tim Maia Você Fugiu,Genival Cassiano,238367,7971147,0.99,Latin,Serie Sem Limite (Disc 1),Tim Maia Cristina Nº 2,Carlos Imperial/Tim Maia,90148,2978589,0.99,Latin,Serie Sem Limite (Disc 1),Tim Maia Compadre,Tim Maia,171389,5631446,0.99,Latin,Serie Sem Limite (Disc 1),Tim Maia Over Again,Tim Maia,200489,6612634,0.99,Latin,Serie Sem Limite (Disc 1),Tim Maia Réu Confesso,Tim Maia,217391,7189874,0.99,Latin,Serie Sem Limite (Disc 1),Tim Maia O Que Me Importa,,153155,4977852,0.99,Latin,Serie Sem Limite (Disc 2),Tim Maia Gostava Tanto De Você,,253805,8380077,0.99,Latin,Serie Sem Limite (Disc 2),Tim Maia Você,,242599,7911702,0.99,Latin,Serie Sem Limite (Disc 2),Tim Maia Não Quero Dinheiro,,152607,5031797,0.99,Latin,Serie Sem Limite (Disc 2),Tim Maia Eu Amo Você,,242782,7914628,0.99,Latin,Serie Sem Limite (Disc 2),Tim Maia A Festa Do Santo Reis,,159791,5204995,0.99,Latin,Serie Sem Limite (Disc 2),Tim Maia I Don't Know What To Do With Myself,,221387,7251478,0.99,Latin,Serie Sem Limite (Disc 2),Tim Maia Padre Cícero,,139598,4581685,0.99,Latin,Serie Sem Limite (Disc 2),Tim Maia Nosso Adeus,,206471,6793270,0.99,Latin,Serie Sem Limite (Disc 2),Tim Maia Canário Do Reino,,139337,4552858,0.99,Latin,Serie Sem Limite (Disc 2),Tim Maia Preciso Ser Amado,,174001,5618895,0.99,Latin,Serie Sem Limite (Disc 2),Tim Maia Balanço,,209737,6890327,0.99,Latin,Serie Sem Limite (Disc 2),Tim Maia Preciso Aprender A Ser Só,,162220,5213894,0.99,Latin,Serie Sem Limite (Disc 2),Tim Maia Esta É A Canção,,184450,6069933,0.99,Latin,Serie Sem Limite (Disc 2),Tim Maia Formigueiro,,252943,8455132,0.99,Latin,Serie Sem Limite (Disc 2),Tim Maia Comida,Titãs,322612,10786578,0.99,Alternative & Punk,Acústico,Titãs Go Back,Titãs,230504,7668899,0.99,Alternative & Punk,Acústico,Titãs Prá Dizer Adeus,Titãs,222484,7382048,0.99,Alternative & Punk,Acústico,Titãs Família,Titãs,218331,7267458,0.99,Alternative & Punk,Acústico,Titãs Os Cegos Do Castelo,Titãs,296829,9868187,0.99,Alternative & Punk,Acústico,Titãs O Pulso,Titãs,199131,6566998,0.99,Alternative & Punk,Acústico,Titãs Marvin,Titãs,264359,8741444,0.99,Alternative & Punk,Acústico,Titãs Nem 5 Minutos Guardados,Titãs,245995,8143797,0.99,Alternative & Punk,Acústico,Titãs Flores,Titãs,215510,7148017,0.99,Alternative & Punk,Acústico,Titãs Palavras,Titãs,158458,5285715,0.99,Alternative & Punk,Acústico,Titãs Hereditário,Titãs,151693,5020547,0.99,Alternative & Punk,Acústico,Titãs A Melhor Forma,Titãs,191503,6349938,0.99,Alternative & Punk,Acústico,Titãs Cabeça Dinossauro,Titãs,37120,1220930,0.99,Alternative & Punk,Acústico,Titãs 32 Dentes,Titãs,184946,6157904,0.99,Alternative & Punk,Acústico,Titãs Bichos Escrotos (Vinheta),Titãs,104986,3503755,0.99,Alternative & Punk,Acústico,Titãs Não Vou Lutar,Titãs,189988,6308613,0.99,Alternative & Punk,Acústico,Titãs Homem Primata (Vinheta),Titãs,34168,1124909,0.99,Alternative & Punk,Acústico,Titãs Homem Primata,Titãs,195500,6486470,0.99,Alternative & Punk,Acústico,Titãs Polícia (Vinheta),Titãs,56111,1824213,0.99,Alternative & Punk,Acústico,Titãs Querem Meu Sangue,Titãs,212401,7069773,0.99,Alternative & Punk,Acústico,Titãs Diversão,Titãs,285936,9531268,0.99,Alternative & Punk,Acústico,Titãs Televisão,Titãs,293668,9776548,0.99,Alternative & Punk,Acústico,Titãs Sonifera Ilha,Branco Mello/Carlos Barmack/Ciro Pessoa/Marcelo Fromer/Toni Belloto,170684,5678290,0.99,Alternative & Punk,Volume Dois,Titãs Lugar Nenhum,Arnaldo Antunes/Charles Gavin/Marcelo Fromer/Sérgio Britto/Toni Bellotto,195840,6472780,0.99,Alternative & Punk,Volume Dois,Titãs Sua Impossivel Chance,Nando Reis,246622,8073248,0.99,Alternative & Punk,Volume Dois,Titãs Desordem,Charles Gavin/Marcelo Fromer/Sérgio Britto,213289,7067340,0.99,Alternative & Punk,Volume Dois,Titãs Não Vou Me Adaptar,Arnaldo Antunes,221831,7304656,0.99,Alternative & Punk,Volume Dois,Titãs Domingo,Sérgio Britto/Toni Bellotto,208613,6883180,0.99,Alternative & Punk,Volume Dois,Titãs Amanhã Não Se Sabe,Sérgio Britto,189440,6243967,0.99,Alternative & Punk,Volume Dois,Titãs Caras Como Eu,Toni Bellotto,183092,5999048,0.99,Alternative & Punk,Volume Dois,Titãs Senhora E Senhor,Arnaldo Anutnes/Marcelo Fromer/Paulo Miklos,203702,6733733,0.99,Alternative & Punk,Volume Dois,Titãs Era Uma Vez,Arnaldo Anutnes/Branco Mello/Marcelo Fromer/Sergio Brotto/Toni Bellotto,224261,7453156,0.99,Alternative & Punk,Volume Dois,Titãs Miséria,"Arnaldo Antunes/Britto, SergioMiklos, Paulo",262191,8727645,0.99,Alternative & Punk,Volume Dois,Titãs Insensível,Sérgio Britto,207830,6893664,0.99,Alternative & Punk,Volume Dois,Titãs Eu E Ela,Nando Reis,276035,9138846,0.99,Alternative & Punk,Volume Dois,Titãs Toda Cor,Ciro Pressoa/Marcelo Fromer,209084,6939176,0.99,Alternative & Punk,Volume Dois,Titãs É Preciso Saber Viver,Erasmo Carlos/Roberto Carlos,251115,8271418,0.99,Alternative & Punk,Volume Dois,Titãs Senhor Delegado/Eu Não Aguento,Antonio Lopes,156656,5277983,0.99,Alternative & Punk,Volume Dois,Titãs Battlestar Galactica: The Story So Far,,2622250,490750393,1.99,Science Fiction,Battlestar Galactica: The Story So Far,Battlestar Galactica Occupation / Precipice,,5286953,1054423946,1.99,TV Shows,"Battlestar Galactica, Season 3",Battlestar Galactica "Exodus, Pt. 1",,2621708,475079441,1.99,TV Shows,"Battlestar Galactica, Season 3",Battlestar Galactica "Exodus, Pt. 2",,2618000,466820021,1.99,TV Shows,"Battlestar Galactica, Season 3",Battlestar Galactica Collaborators,,2626626,483484911,1.99,TV Shows,"Battlestar Galactica, Season 3",Battlestar Galactica Torn,,2631291,495262585,1.99,TV Shows,"Battlestar Galactica, Season 3",Battlestar Galactica A Measure of Salvation,,2563938,489715554,1.99,Science Fiction,"Battlestar Galactica, Season 3",Battlestar Galactica Hero,,2713755,506896959,1.99,Science Fiction,"Battlestar Galactica, Season 3",Battlestar Galactica Unfinished Business,,2622038,528499160,1.99,Science Fiction,"Battlestar Galactica, Season 3",Battlestar Galactica The Passage,,2623875,490375760,1.99,Science Fiction,"Battlestar Galactica, Season 3",Battlestar Galactica The Eye of Jupiter,,2618750,517909587,1.99,Science Fiction,"Battlestar Galactica, Season 3",Battlestar Galactica Rapture,,2624541,508406153,1.99,Science Fiction,"Battlestar Galactica, Season 3",Battlestar Galactica Taking a Break from All Your Worries,,2624207,492700163,1.99,Science Fiction,"Battlestar Galactica, Season 3",Battlestar Galactica The Woman King,,2626376,552893447,1.99,Science Fiction,"Battlestar Galactica, Season 3",Battlestar Galactica A Day In the Life,,2620245,462818231,1.99,Science Fiction,"Battlestar Galactica, Season 3",Battlestar Galactica Dirty Hands,,2627961,537648614,1.99,Science Fiction,"Battlestar Galactica, Season 3",Battlestar Galactica Maelstrom,,2622372,514154275,1.99,Science Fiction,"Battlestar Galactica, Season 3",Battlestar Galactica The Son Also Rises,,2621830,499258498,1.99,Science Fiction,"Battlestar Galactica, Season 3",Battlestar Galactica "Crossroads, Pt. 1",,2622622,486233524,1.99,Sci Fi & Fantasy,"Battlestar Galactica, Season 3",Battlestar Galactica "Crossroads, Pt. 2",,2869953,497335706,1.99,Sci Fi & Fantasy,"Battlestar Galactica, Season 3",Battlestar Galactica Genesis,,2611986,515671080,1.99,TV Shows,"Heroes, Season 1",Heroes Don't Look Back,,2571154,493628775,1.99,Drama,"Heroes, Season 1",Heroes One Giant Leap,,2607649,521616246,1.99,Drama,"Heroes, Season 1",Heroes Collision,,2605480,526182322,1.99,Drama,"Heroes, Season 1",Heroes Hiros,,2533575,488835454,1.99,Drama,"Heroes, Season 1",Heroes Better Halves,,2573031,549353481,1.99,Drama,"Heroes, Season 1",Heroes Nothing to Hide,,2605647,510058181,1.99,TV Shows,"Heroes, Season 1",Heroes Seven Minutes to Midnight,,2613988,515590682,1.99,Drama,"Heroes, Season 1",Heroes Homecoming,,2601351,516015339,1.99,Drama,"Heroes, Season 1",Heroes Six Months Ago,,2602852,505133869,1.99,TV Shows,"Heroes, Season 1",Heroes Fallout,,2594761,501145440,1.99,Drama,"Heroes, Season 1",Heroes The Fix,,2600266,507026323,1.99,Drama,"Heroes, Season 1",Heroes Distractions,,2590382,537111289,1.99,Drama,"Heroes, Season 1",Heroes Run!,,2602602,542936677,1.99,Drama,"Heroes, Season 1",Heroes Unexpected,,2598139,511777758,1.99,Drama,"Heroes, Season 1",Heroes Company Man,,2601226,493168135,1.99,Drama,"Heroes, Season 1",Heroes Company Man,,2601101,503786316,1.99,Drama,"Heroes, Season 1",Heroes Parasite,,2602727,487461520,1.99,Drama,"Heroes, Season 1",Heroes .07%,,2585794,541715199,1.99,Drama,"Heroes, Season 1",Heroes Five Years Gone,,2587712,530551890,1.99,Drama,"Heroes, Season 1",Heroes The Hard Part,,2601017,475996611,1.99,Drama,"Heroes, Season 1",Heroes Landslide,,2600725,518677861,1.99,Drama,"Heroes, Season 1",Heroes How to Stop an Exploding Man,,2687103,487881159,1.99,Drama,"Heroes, Season 1",Heroes A Tale of Two Cities,,2636970,513691652,1.99,TV Shows,"Lost, Season 3",Lost The Glass Ballerina,,2637458,535729216,1.99,Drama,"Lost, Season 3",Lost Further Instructions,,2563980,502041019,1.99,TV Shows,"Lost, Season 3",Lost Every Man for Himself,,2637387,513803546,1.99,Drama,"Lost, Season 3",Lost The Cost of Living,,2637500,505647192,1.99,TV Shows,"Lost, Season 3",Lost I Do,,2627791,504676825,1.99,TV Shows,"Lost, Season 3",Lost Not In Portland,,2637303,499061234,1.99,Drama,"Lost, Season 3",Lost Not In Portland,,2637345,510546847,1.99,Drama,"Lost, Season 3",Lost Flashes Before Your Eyes,,2636636,537760755,1.99,Drama,"Lost, Season 3",Lost Lost Survival Guide,,2632590,486675063,1.99,Drama,"Lost, Season 3",Lost Stranger In a Strange Land,,2636428,505056021,1.99,Drama,"Lost, Season 3",Lost Tricia Tanaka Is Dead,,2635010,548197162,1.99,Drama,"Lost, Season 3",Lost Enter 77,,2629796,517521422,1.99,Drama,"Lost, Season 3",Lost Par Avion,,2629879,517079642,1.99,Drama,"Lost, Season 3",Lost The Man from Tallahassee,,2637637,550893556,1.99,Drama,"Lost, Season 3",Lost Exposé,,2593760,511338017,1.99,Drama,"Lost, Season 3",Lost Left Behind,,2635343,538491964,1.99,Drama,"Lost, Season 3",Lost One of Us,,2638096,502387276,1.99,Drama,"Lost, Season 3",Lost Catch-22,,2561394,489773399,1.99,Drama,"Lost, Season 3",Lost D.O.C.,,2616032,518556641,1.99,Drama,"Lost, Season 3",Lost The Brig,,2617325,488919543,1.99,Drama,"Lost, Season 3",Lost The Man Behind the Curtain,,2615990,493951081,1.99,Drama,"Lost, Season 3",Lost Greatest Hits,,2617117,522102916,1.99,Drama,"Lost, Season 3",Lost Through a Looking Glass,,5088838,1059546140,1.99,Drama,"Lost, Season 3",Lost "Through the Looking Glass, Pt. 2",,2617117,550943353,1.99,Drama,"Lost, Season 3",Lost "Through the Looking Glass, Pt. 1",,2610860,493211809,1.99,Drama,"Lost, Season 3",Lost "Lost (Pilot, Part 1) [Premiere]",,2548875,217124866,1.99,TV Shows,"Lost, Season 1",Lost "Lost (Pilot, Part 2)",,2436583,204995876,1.99,TV Shows,"Lost, Season 1",Lost Tabula Rasa,,2627105,210526410,1.99,TV Shows,"Lost, Season 1",Lost Walkabout,,2587370,207748198,1.99,TV Shows,"Lost, Season 1",Lost White Rabbit,,2571965,201654606,1.99,TV Shows,"Lost, Season 1",Lost House of the Rising Sun,,2590032,210379525,1.99,TV Shows,"Lost, Season 1",Lost The Moth,,2631327,228896396,1.99,TV Shows,"Lost, Season 1",Lost Confidence Man,,2615244,223756475,1.99,TV Shows,"Lost, Season 1",Lost Solitary,,2612894,207045178,1.99,TV Shows,"Lost, Season 1",Lost Raised By Another,,2590459,223623810,1.99,TV Shows,"Lost, Season 1",Lost All the Best Cowboys Have Daddy Issues,,2555492,211743651,1.99,TV Shows,"Lost, Season 1",Lost Whatever the Case May Be,,2616410,183867185,1.99,TV Shows,"Lost, Season 1",Lost Hearts and Minds,,2619462,207607466,1.99,TV Shows,"Lost, Season 1",Lost Special,,2618530,219961967,1.99,TV Shows,"Lost, Season 1",Lost Homecoming,,2515882,210675221,1.99,TV Shows,"Lost, Season 1",Lost Outlaws,,2619887,206500939,1.99,TV Shows,"Lost, Season 1",Lost ...In Translation,,2604575,215441983,1.99,TV Shows,"Lost, Season 1",Lost Numbers,,2609772,214709143,1.99,TV Shows,"Lost, Season 1",Lost Deus Ex Machina,,2582009,214996732,1.99,TV Shows,"Lost, Season 1",Lost Do No Harm,,2618487,212039309,1.99,TV Shows,"Lost, Season 1",Lost The Greater Good,,2617784,214130273,1.99,TV Shows,"Lost, Season 1",Lost Born to Run,,2618619,213772057,1.99,TV Shows,"Lost, Season 1",Lost Exodus (Part 1),,2620747,213107744,1.99,TV Shows,"Lost, Season 1",Lost Exodus (Part 2) [Season Finale],,2605557,208667059,1.99,TV Shows,"Lost, Season 1",Lost Exodus (Part 3) [Season Finale],,2619869,197937785,1.99,TV Shows,"Lost, Season 1",Lost "Man of Science, Man of Faith (Premiere)",,2612250,543342028,1.99,TV Shows,"Lost, Season 2",Lost Adrift,,2564958,502663995,1.99,TV Shows,"Lost, Season 2",Lost Orientation,,2609083,500600434,1.99,TV Shows,"Lost, Season 2",Lost Everybody Hates Hugo,,2609192,498163145,1.99,TV Shows,"Lost, Season 2",Lost ...And Found,,2563833,500330548,1.99,TV Shows,"Lost, Season 2",Lost Abandoned,,2587041,537348711,1.99,TV Shows,"Lost, Season 2",Lost The Other 48 Days,,2610625,535256753,1.99,TV Shows,"Lost, Season 2",Lost Collision,,2564916,475656544,1.99,TV Shows,"Lost, Season 2",Lost What Kate Did,,2610250,484583988,1.99,TV Shows,"Lost, Season 2",Lost The 23rd Psalm,,2610416,487401604,1.99,TV Shows,"Lost, Season 2",Lost The Hunting Party,,2611333,520350364,1.99,Drama,"Lost, Season 2",Lost Fire + Water,,2600333,488458695,1.99,Drama,"Lost, Season 2",Lost The Long Con,,2679583,518376636,1.99,TV Shows,"Lost, Season 2",Lost One of Them,,2698791,542332389,1.99,Drama,"Lost, Season 2",Lost Maternity Leave,,2780416,555244214,1.99,Drama,"Lost, Season 2",Lost The Whole Truth,,2610125,495487014,1.99,Drama,"Lost, Season 2",Lost Lockdown,,2610250,543886056,1.99,Drama,"Lost, Season 2",Lost Dave,,2825166,574325829,1.99,TV Shows,"Lost, Season 2",Lost S.O.S.,,2639541,517979269,1.99,TV Shows,"Lost, Season 2",Lost Two for the Road,,2610958,502404558,1.99,Drama,"Lost, Season 2",Lost """?""",,2782333,528227089,1.99,TV Shows,"Lost, Season 2",Lost Three Minutes,,2763666,531556853,1.99,TV Shows,"Lost, Season 2",Lost "Live Together, Die Alone, Pt. 1",,2478041,457364940,1.99,Drama,"Lost, Season 2",Lost "Live Together, Die Alone, Pt. 2",,2656531,503619265,1.99,TV Shows,"Lost, Season 2",Lost Zoo Station,U2,276349,9056902,0.99,Rock,Achtung Baby,U2 Even Better Than The Real Thing,U2,221361,7279392,0.99,Rock,Achtung Baby,U2 One,U2,276192,9158892,0.99,Rock,Achtung Baby,U2 Until The End Of The World,U2,278700,9132485,0.99,Rock,Achtung Baby,U2 Who's Gonna Ride Your Wild Horses,U2,316551,10304369,0.99,Rock,Achtung Baby,U2 So Cruel,U2,349492,11527614,0.99,Rock,Achtung Baby,U2 The Fly,U2,268982,8825399,0.99,Rock,Achtung Baby,U2 Mysterious Ways,U2,243826,8062057,0.99,Rock,Achtung Baby,U2 Tryin' To Throw Your Arms Around The World,U2,232463,7612124,0.99,Rock,Achtung Baby,U2 Ultraviolet (Light My Way),U2,330788,10754631,0.99,Rock,Achtung Baby,U2 Acrobat,U2,270288,8824723,0.99,Rock,Achtung Baby,U2 Love Is Blindness,U2,263497,8531766,0.99,Rock,Achtung Baby,U2 Beautiful Day,"Adam Clayton, Bono, Larry Mullen, The Edge",248163,8056723,0.99,Rock,All That You Can't Leave Behind,U2 Stuck 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 Elevation,"Adam Clayton, Bono, Larry Mullen, The Edge",227552,7479414,0.99,Rock,All That You Can't Leave Behind,U2 Walk On,"Adam Clayton, Bono, Larry Mullen, The Edge",296280,9800861,0.99,Rock,All That You Can't Leave Behind,U2 Kite,"Adam Clayton, Bono, Larry Mullen, The Edge",266893,8765761,0.99,Rock,All That You Can't Leave Behind,U2 In A Little While,"Adam Clayton, Bono, Larry Mullen, The Edge",219271,7189647,0.99,Rock,All That You Can't Leave Behind,U2 Wild Honey,"Adam Clayton, Bono, Larry Mullen, The Edge",226768,7466069,0.99,Rock,All That You Can't Leave Behind,U2 Peace On Earth,"Adam Clayton, Bono, Larry Mullen, The Edge",288496,9476171,0.99,Rock,All That You Can't Leave Behind,U2 When 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 New York,"Adam Clayton, Bono, Larry Mullen, The Edge",330370,10862323,0.99,Rock,All That You Can't Leave Behind,U2 Grace,"Adam Clayton, Bono, Larry Mullen, The Edge",330657,10877148,0.99,Rock,All That You Can't Leave Behind,U2 The Three Sunrises,U2,234788,7717990,0.99,Rock,B-Sides 1980-1990,U2 Spanish Eyes,U2,196702,6392710,0.99,Rock,B-Sides 1980-1990,U2 Sweetest Thing,U2,185103,6154896,0.99,Rock,B-Sides 1980-1990,U2 Love Comes Tumbling,U2,282671,9328802,0.99,Rock,B-Sides 1980-1990,U2 Bass Trap,U2,213289,6834107,0.99,Rock,B-Sides 1980-1990,U2 Dancing Barefoot,Ivan Kral/Patti Smith,287895,9488294,0.99,Rock,B-Sides 1980-1990,U2 Everlasting Love,Buzz Cason/Mac Gayden,202631,6708932,0.99,Rock,B-Sides 1980-1990,U2 Unchained Melody,Alex North/Hy Zaret,294164,9597568,0.99,Rock,B-Sides 1980-1990,U2 Walk To The Water,U2,289253,9523336,0.99,Rock,B-Sides 1980-1990,U2 Luminous Times (Hold On To Love),Brian Eno/U2,277760,9015513,0.99,Rock,B-Sides 1980-1990,U2 Hallelujah Here She Comes,U2,242364,8027028,0.99,Rock,B-Sides 1980-1990,U2 Silver And Gold,Bono,279875,9199746,0.99,Rock,B-Sides 1980-1990,U2 Endless Deep,U2,179879,5899070,0.99,Rock,B-Sides 1980-1990,U2 A Room At The Heartbreak Hotel,U2,274546,9015416,0.99,Rock,B-Sides 1980-1990,U2 "Trash, Trampoline And The Party Girl",U2,153965,5083523,0.99,Rock,B-Sides 1980-1990,U2 Vertigo,"Adam Clayton, Bono, Larry Mullen & The Edge",194612,6329502,0.99,Rock,How To Dismantle An Atomic Bomb,U2 Miracle Drug,"Adam Clayton, Bono, Larry Mullen & The Edge",239124,7760916,0.99,Rock,How To Dismantle An Atomic Bomb,U2 Sometimes 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 Love And Peace Or Else,"Adam Clayton, Bono, Larry Mullen & The Edge",290690,9476723,0.99,Rock,How To Dismantle An Atomic Bomb,U2 City Of Blinding Lights,"Adam Clayton, Bono, Larry Mullen & The Edge",347951,11432026,0.99,Rock,How To Dismantle An Atomic Bomb,U2 All Because Of You,"Adam Clayton, Bono, Larry Mullen & The Edge",219141,7198014,0.99,Rock,How To Dismantle An Atomic Bomb,U2 A Man And A Woman,"Adam Clayton, Bono, Larry Mullen & The Edge",270132,8938285,0.99,Rock,How To Dismantle An Atomic Bomb,U2 Crumbs From Your Table,"Adam Clayton, Bono, Larry Mullen & The Edge",303568,9892349,0.99,Rock,How To Dismantle An Atomic Bomb,U2 One Step Closer,"Adam Clayton, Bono, Larry Mullen & The Edge",231680,7512912,0.99,Rock,How To Dismantle An Atomic Bomb,U2 Original Of The Species,"Adam Clayton, Bono, Larry Mullen & The Edge",281443,9230041,0.99,Rock,How To Dismantle An Atomic Bomb,U2 Yahweh,"Adam Clayton, Bono, Larry Mullen & The Edge",262034,8636998,0.99,Rock,How To Dismantle An Atomic Bomb,U2 Discotheque,"Bono, The Edge, Adam Clayton, and Larry Mullen",319582,10442206,0.99,Rock,Pop,U2 Do You Feel Loved,"Bono, The Edge, Adam Clayton, and Larry Mullen",307539,10122694,0.99,Rock,Pop,U2 Mofo,"Bono, The Edge, Adam Clayton, and Larry Mullen",349178,11583042,0.99,Rock,Pop,U2 If God Will Send His Angels,"Bono, The Edge, Adam Clayton, and Larry Mullen",322533,10563329,0.99,Rock,Pop,U2 Staring At The Sun,"Bono, The Edge, Adam Clayton, and Larry Mullen",276924,9082838,0.99,Rock,Pop,U2 Last Night On Earth,"Bono, The Edge, Adam Clayton, and Larry Mullen",285753,9401017,0.99,Rock,Pop,U2 Gone,"Bono, The Edge, Adam Clayton, and Larry Mullen",266866,8746301,0.99,Rock,Pop,U2 Miami,"Bono, The Edge, Adam Clayton, and Larry Mullen",293041,9741603,0.99,Rock,Pop,U2 The Playboy Mansion,"Bono, The Edge, Adam Clayton, and Larry Mullen",280555,9274144,0.99,Rock,Pop,U2 If You Wear That Velvet Dress,"Bono, The Edge, Adam Clayton, and Larry Mullen",315167,10227333,0.99,Rock,Pop,U2 Please,"Bono, The Edge, Adam Clayton, and Larry Mullen",302602,9909484,0.99,Rock,Pop,U2 Wake Up Dead Man,"Bono, The Edge, Adam Clayton, and Larry Mullen",292832,9515903,0.99,Rock,Pop,U2 Helter Skelter,"Lennon, John/McCartney, Paul",187350,6097636,0.99,Rock,Rattle And Hum,U2 Van Diemen's Land,"Bono/Clayton, Adam/Mullen Jr., Larry/The Edge",186044,5990280,0.99,Rock,Rattle And Hum,U2 Desire,"Bono/Clayton, Adam/Mullen Jr., Larry/The Edge",179226,5874535,0.99,Rock,Rattle And Hum,U2 Hawkmoon 269,"Bono/Clayton, Adam/Mullen Jr., Larry/The Edge",382458,12494987,0.99,Rock,Rattle And Hum,U2 All Along The Watchtower,"Dylan, Bob",264568,8623572,0.99,Rock,Rattle And Hum,U2 I 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 Freedom For My People,"Mabins, Macie/Magee, Sterling/Robinson, Bobby",38164,1249764,0.99,Rock,Rattle And Hum,U2 Silver And Gold,"Bono/Clayton, Adam/Mullen Jr., Larry/The Edge",349831,11450194,0.99,Rock,Rattle And Hum,U2 Pride (In The Name Of Love),"Bono/Clayton, Adam/Mullen Jr., Larry/The Edge",267807,8806361,0.99,Rock,Rattle And Hum,U2 Angel Of Harlem,"Bono/Clayton, Adam/Mullen Jr., Larry/The Edge",229276,7498022,0.99,Rock,Rattle And Hum,U2 Love Rescue Me,"Bono/Clayton, Adam/Dylan, Bob/Mullen Jr., Larry/The Edge",384522,12508716,0.99,Rock,Rattle And Hum,U2 When Love Comes To Town,"Bono/Clayton, Adam/Mullen Jr., Larry/The Edge",255869,8340954,0.99,Rock,Rattle And Hum,U2 Heartland,"Bono/Clayton, Adam/Mullen Jr., Larry/The Edge",303360,9867748,0.99,Rock,Rattle And Hum,U2 God Part II,"Bono/Clayton, Adam/Mullen Jr., Larry/The Edge",195604,6497570,0.99,Rock,Rattle And Hum,U2 The Star Spangled Banner,"Hendrix, Jimi",43232,1385810,0.99,Rock,Rattle And Hum,U2 Bullet The Blue Sky,"Bono/Clayton, Adam/Mullen Jr., Larry/The Edge",337005,10993607,0.99,Rock,Rattle And Hum,U2 All I Want Is You,"Bono/Clayton, Adam/Mullen Jr., Larry/The Edge",390243,12729820,0.99,Rock,Rattle And Hum,U2 Pride (In The Name Of Love),U2,230243,7549085,0.99,Rock,The Best Of 1980-1990,U2 New Year's Day,U2,258925,8491818,0.99,Rock,The Best Of 1980-1990,U2 With Or Without You,U2,299023,9765188,0.99,Rock,The Best Of 1980-1990,U2 I Still Haven't Found What I'm Looking For,U2,280764,9306737,0.99,Rock,The Best Of 1980-1990,U2 Sunday Bloody Sunday,U2,282174,9269668,0.99,Rock,The Best Of 1980-1990,U2 Bad,U2,351817,11628058,0.99,Rock,The Best Of 1980-1990,U2 Where The Streets Have No Name,U2,276218,9042305,0.99,Rock,The Best Of 1980-1990,U2 I Will Follow,U2,218253,7184825,0.99,Rock,The Best Of 1980-1990,U2 The Unforgettable Fire,U2,295183,9684664,0.99,Rock,The Best Of 1980-1990,U2 Sweetest Thing,U2 & Daragh O'Toole,183066,6071385,0.99,Rock,The Best Of 1980-1990,U2 Desire,U2,179853,5893206,0.99,Rock,The Best Of 1980-1990,U2 When Love Comes To Town,U2,258194,8479525,0.99,Rock,The Best Of 1980-1990,U2 Angel Of Harlem,U2,230217,7527339,0.99,Rock,The Best Of 1980-1990,U2 All I Want Is You,U2 & Van Dyke Parks,591986,19202252,0.99,Rock,The Best Of 1980-1990,U2 Sunday Bloody Sunday,U2,278204,9140849,0.99,Rock,War,U2 Seconds,U2,191582,6352121,0.99,Rock,War,U2 New Year's Day,U2,336274,11054732,0.99,Rock,War,U2 Like A Song...,U2,287294,9365379,0.99,Rock,War,U2 Drowning Man,U2,254458,8457066,0.99,Rock,War,U2 The Refugee,U2,221283,7374043,0.99,Rock,War,U2 Two Hearts Beat As One,U2,243487,7998323,0.99,Rock,War,U2 Red Light,U2,225854,7453704,0.99,Rock,War,U2 Surrender,U2,333505,11221406,0.99,Rock,War,U2 """40""",U2,157962,5251767,0.99,Rock,War,U2 Zooropa,U2; Bono,392359,12807979,0.99,Rock,Zooropa,U2 Babyface,U2; Bono,241998,7942573,0.99,Rock,Zooropa,U2 Numb,"U2; Edge, The",260284,8577861,0.99,Rock,Zooropa,U2 Lemon,U2; Bono,418324,13988878,0.99,Rock,Zooropa,U2 "Stay (Faraway, So Close!)",U2; Bono,298475,9785480,0.99,Rock,Zooropa,U2 Daddy's Gonna Pay For Your Crashed Car,U2; Bono,320287,10609581,0.99,Rock,Zooropa,U2 Some Days Are Better Than Others,U2; Bono,257436,8417690,0.99,Rock,Zooropa,U2 The First Time,U2; Bono,225697,7247651,0.99,Rock,Zooropa,U2 Dirty Day,"U2; Bono & Edge, The",324440,10652877,0.99,Rock,Zooropa,U2 The Wanderer,U2; Bono,283951,9258717,0.99,Rock,Zooropa,U2 Breakfast In Bed,,196179,6513325,0.99,Reggae,UB40 The Best Of - Volume Two [UK],UB40 Where Did I Go Wrong,,226742,7485054,0.99,Reggae,UB40 The Best Of - Volume Two [UK],UB40 I Would Do For You,,334524,11193602,0.99,Reggae,UB40 The Best Of - Volume Two [UK],UB40 Homely Girl,,203833,6790788,0.99,Reggae,UB40 The Best Of - Volume Two [UK],UB40 Here I Am (Come And Take Me),,242102,8106249,0.99,Reggae,UB40 The Best Of - Volume Two [UK],UB40 Kingston Town,,226951,7638236,0.99,Reggae,UB40 The Best Of - Volume Two [UK],UB40 Wear You To The Ball,,213342,7159527,0.99,Reggae,UB40 The Best Of - Volume Two [UK],UB40 (I Can't Help) Falling In Love With You,,207568,6905623,0.99,Reggae,UB40 The Best Of - Volume Two [UK],UB40 Higher Ground,,260179,8665244,0.99,Reggae,UB40 The Best Of - Volume Two [UK],UB40 Bring Me Your Cup,,341498,11346114,0.99,Reggae,UB40 The Best Of - Volume Two [UK],UB40 C'est La Vie,,270053,9031661,0.99,Reggae,UB40 The Best Of - Volume Two [UK],UB40 Reggae Music,,245106,8203931,0.99,Reggae,UB40 The Best Of - Volume Two [UK],UB40 Superstition,,319582,10728099,0.99,Reggae,UB40 The Best Of - Volume Two [UK],UB40 Until My Dying Day,,235807,7886195,0.99,Reggae,UB40 The Best Of - Volume Two [UK],UB40 Where Have All The Good Times Gone?,Ray Davies,186723,6063937,0.99,Rock,Diver Down,Van Halen Hang 'Em High,Alex Van Halen/David Lee Roth/Edward Van Halen/Michael Anthony,210259,6872314,0.99,Rock,Diver Down,Van Halen Cathedral,Alex Van Halen/David Lee Roth/Edward Van Halen/Michael Anthony,82860,2650998,0.99,Rock,Diver Down,Van Halen Secrets,Alex Van Halen/David Lee Roth/Edward Van Halen/Michael Anthony,206968,6803255,0.99,Rock,Diver Down,Van Halen Intruder,Alex Van Halen/David Lee Roth/Edward Van Halen/Michael Anthony,100153,3282142,0.99,Rock,Diver Down,Van Halen (Oh) Pretty Woman,Bill Dees/Roy Orbison,174680,5665828,0.99,Rock,Diver Down,Van Halen Dancing In The Street,Ivy Jo Hunter/Marvin Gaye/William Stevenson,225985,7461499,0.99,Rock,Diver Down,Van Halen Little Guitars (Intro),Alex Van Halen/David Lee Roth/Edward Van Halen/Michael Anthony,42240,1439530,0.99,Rock,Diver Down,Van Halen Little Guitars,Alex Van Halen/David Lee Roth/Edward Van Halen/Michael Anthony,228806,7453043,0.99,Rock,Diver Down,Van Halen Big Bad Bill (Is Sweet William Now),Jack Yellen/Milton Ager,165146,5489609,0.99,Rock,Diver Down,Van Halen The Full Bug,Alex Van Halen/David Lee Roth/Edward Van Halen/Michael Anthony,201116,6551013,0.99,Rock,Diver Down,Van Halen Happy Trails,Dale Evans,65488,2111141,0.99,Rock,Diver Down,Van Halen Eruption,"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 Ain'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 Runnin' 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 Dance 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 And 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 Unchained,"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 Jump,"Edward Van Halen, Alex Van Halen, David Lee Roth",241711,7911090,0.99,Rock,"The Best Of Van Halen, Vol. I",Van Halen Panama,"Edward Van Halen, Alex Van Halen, David Lee Roth",211853,6921784,0.99,Rock,"The Best Of Van Halen, Vol. I",Van Halen Why Can't This Be Love,Van Halen,227761,7457655,0.99,Rock,"The Best Of Van Halen, Vol. I",Van Halen Dreams,"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 When 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 Poundcake,"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 Right Now,Van Halen,321828,10503352,0.99,Rock,"The Best Of Van Halen, Vol. I",Van Halen Can't Stop Loving You,Van Halen,248502,8107896,0.99,Rock,"The Best Of Van Halen, Vol. I",Van Halen Humans 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 Can'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 Me 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 Runnin' With The Devil,"Edward Van Halen, Alex Van Halen, Michael Anthony and David Lee Roth",216032,7056863,0.99,Rock,Van Halen,Van Halen Eruption,"Edward Van Halen, Alex Van Halen, Michael Anthony and David Lee Roth",102556,3286026,0.99,Rock,Van Halen,Van Halen You Really Got Me,Ray Davies,158589,5194092,0.99,Rock,Van Halen,Van Halen Ain'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 I'm The One,"Edward Van Halen, Alex Van Halen, Michael Anthony and David Lee Roth",226507,7373922,0.99,Rock,Van Halen,Van Halen Jamie's Cryin',"Edward Van Halen, Alex Van Halen, Michael Anthony and David Lee Roth",210546,6946086,0.99,Rock,Van Halen,Van Halen Atomic Punk,"Edward Van Halen, Alex Van Halen, Michael Anthony and David Lee Roth",182073,5908861,0.99,Rock,Van Halen,Van Halen Feel Your Love Tonight,"Edward Van Halen, Alex Van Halen, Michael Anthony and David Lee Roth",222850,7293608,0.99,Rock,Van Halen,Van Halen Little Dreamer,"Edward Van Halen, Alex Van Halen, Michael Anthony and David Lee Roth",203258,6648122,0.99,Rock,Van Halen,Van Halen Ice Cream Man,John Brim,200306,6573145,0.99,Rock,Van Halen,Van Halen On Fire,"Edward Van Halen, Alex Van Halen, Michael Anthony and David Lee Roth",180636,5879235,0.99,Rock,Van Halen,Van Halen Neworld,Van Halen,105639,3495897,0.99,Rock,Van Halen III,Van Halen Without You,Van Halen,390295,12619558,0.99,Rock,Van Halen III,Van Halen One I Want,Van Halen,330788,10743970,0.99,Rock,Van Halen III,Van Halen From Afar,Van Halen,324414,10524554,0.99,Rock,Van Halen III,Van Halen Dirty Water Dog,Van Halen,327392,10709202,0.99,Rock,Van Halen III,Van Halen Once,Van Halen,462837,15378082,0.99,Rock,Van Halen III,Van Halen Fire in the Hole,Van Halen,331728,10846768,0.99,Rock,Van Halen III,Van Halen Josephina,Van Halen,342491,11161521,0.99,Rock,Van Halen III,Van Halen Year to the Day,Van Halen,514612,16621333,0.99,Rock,Van Halen III,Van Halen Primary,Van Halen,86987,2812555,0.99,Rock,Van Halen III,Van Halen Ballot or the Bullet,Van Halen,342282,11212955,0.99,Rock,Van Halen III,Van Halen How Many Say I,Van Halen,363937,11716855,0.99,Rock,Van Halen III,Van Halen Sucker Train Blues,"Dave Kushner, Duff, Matt Sorum, Scott Weiland & Slash",267859,8738780,0.99,Rock,Contraband,Velvet Revolver Do It For The Kids,"Dave Kushner, Duff, Matt Sorum, Scott Weiland & Slash",235911,7693331,0.99,Rock,Contraband,Velvet Revolver Big Machine,"Dave Kushner, Duff, Matt Sorum, Scott Weiland & Slash",265613,8673442,0.99,Rock,Contraband,Velvet Revolver Illegal I Song,"Dave Kushner, Duff, Matt Sorum, Scott Weiland & Slash",257750,8483347,0.99,Rock,Contraband,Velvet Revolver Spectacle,"Dave Kushner, Duff, Matt Sorum, Scott Weiland & Slash",221701,7252876,0.99,Rock,Contraband,Velvet Revolver Fall To Pieces,"Dave Kushner, Duff, Matt Sorum, Scott Weiland & Slash",270889,8823096,0.99,Rock,Contraband,Velvet Revolver Headspace,"Dave Kushner, Duff, Matt Sorum, Scott Weiland & Slash",223033,7237986,0.99,Rock,Contraband,Velvet Revolver Superhuman,"Dave Kushner, Duff, Matt Sorum, Scott Weiland & Slash",255921,8365328,0.99,Rock,Contraband,Velvet Revolver Set Me Free,"Dave Kushner, Duff, Matt Sorum, Scott Weiland & Slash",247954,8053388,0.99,Rock,Contraband,Velvet Revolver You Got No Right,"Dave Kushner, Duff, Matt Sorum, Scott Weiland & Slash",335412,10991094,0.99,Rock,Contraband,Velvet Revolver Slither,"Dave Kushner, Duff, Matt Sorum, Scott Weiland & Slash",248398,8118785,0.99,Rock,Contraband,Velvet Revolver Dirty Little Thing,"Dave Kushner, Duff, Keith Nelson, Matt Sorum, Scott Weiland & Slash",237844,7732982,0.99,Rock,Contraband,Velvet Revolver Loving The Alien,"Dave Kushner, Duff, Matt Sorum, Scott Weiland & Slash",348786,11412762,0.99,Rock,Contraband,Velvet Revolver Pela Luz Dos Olhos Teus,,119196,3905715,0.99,Latin,Vinicius De Moraes,Vinícius De Moraes A Bencao E Outros,,421093,14234427,0.99,Latin,Vinicius De Moraes,Vinícius De Moraes Tudo Na Mais Santa Paz,,222406,7426757,0.99,Latin,Vinicius De Moraes,Vinícius De Moraes O Velho E Aflor,,275121,9126828,0.99,Latin,Vinicius De Moraes,Vinícius De Moraes Cotidiano N 2,,55902,1805797,0.99,Latin,Vinicius De Moraes,Vinícius De Moraes Adeus,,221884,7259351,0.99,Latin,Vinicius De Moraes,Vinícius De Moraes Samba Pra Endrigo,,259265,8823551,0.99,Latin,Vinicius De Moraes,Vinícius De Moraes So Por Amor,,236591,7745764,0.99,Latin,Vinicius De Moraes,Vinícius De Moraes Meu Pranto Rolou,,181760,6003345,0.99,Latin,Vinicius De Moraes,Vinícius De Moraes Mulher Carioca,,191686,6395048,0.99,Latin,Vinicius De Moraes,Vinícius De Moraes Um Homem Chamado Alfredo,,151640,4976227,0.99,Latin,Vinicius De Moraes,Vinícius De Moraes Samba Do Jato,,220813,7357840,0.99,Latin,Vinicius De Moraes,Vinícius De Moraes "Oi, La",,167053,5562700,0.99,Latin,Vinicius De Moraes,Vinícius De Moraes "Vinicius, Poeta Do Encontro",,336431,10858776,0.99,Latin,Vinicius De Moraes,Vinícius De Moraes Soneto Da Separacao,,193880,6277511,0.99,Latin,Vinicius De Moraes,Vinícius De Moraes Faixa Amarela,Beto Gogo/Jessé Pai/Luiz Carlos/Zeca Pagodinho,240692,8082036,0.99,Latin,Ao Vivo [IMPORT],Zeca Pagodinho Posso Até Me Apaixonar,Dudu Nobre,200698,6735526,0.99,Latin,Ao Vivo [IMPORT],Zeca Pagodinho Não Sou Mais Disso,Jorge Aragão/Zeca Pagodinho,225985,7613817,0.99,Latin,Ao Vivo [IMPORT],Zeca Pagodinho Vivo Isolado Do Mundo,Alcides Dias Lopes,180035,6073995,0.99,Latin,Ao Vivo [IMPORT],Zeca Pagodinho Coração Em Desalinho,Mauro Diniz/Ratino Sigem,185208,6225948,0.99,Latin,Ao Vivo [IMPORT],Zeca Pagodinho Seu Balancê,Paulinho Rezende/Toninho Geraes,219454,7311219,0.99,Latin,Ao Vivo [IMPORT],Zeca Pagodinho Vai Adiar,Alcino Corrêa/Monarco,270393,9134882,0.99,Latin,Ao Vivo [IMPORT],Zeca Pagodinho Rugas,Augusto Garcez/Nelson Cavaquinho,140930,4703182,0.99,Latin,Ao Vivo [IMPORT],Zeca Pagodinho Feirinha 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 Sem Essa de Malandro Agulha,Aldir Blanc/Jayme Vignoli,158484,5332668,0.99,Latin,Ao Vivo [IMPORT],Zeca Pagodinho Chico Não Vai na Corimba,Dudu Nobre/Zeca Pagodinho,269374,9122188,0.99,Latin,Ao Vivo [IMPORT],Zeca Pagodinho Papel Principal,Almir Guineto/Dedé Paraiso/Luverci Ernesto,217495,7325302,0.99,Latin,Ao Vivo [IMPORT],Zeca Pagodinho Saudade Louca,Acyr Marques/Arlindo Cruz/Franco,243591,8136475,0.99,Latin,Ao Vivo [IMPORT],Zeca Pagodinho Camarão que Dorme e Onda Leva,"Acyi Marques/Arlindo Bruz/Braço, Beto Sem/Zeca Pagodinho",299102,10012231,0.99,Latin,Ao Vivo [IMPORT],Zeca Pagodinho Sapopemba e Maxambomba,Nei Lopes/Wilson Moreira,245394,8268712,0.99,Latin,Ao Vivo [IMPORT],Zeca Pagodinho Minha Fé,Murilão,206994,6981474,0.99,Latin,Ao Vivo [IMPORT],Zeca Pagodinho Lua de Ogum,Ratinho/Zeca Pagodinho,168463,5719129,0.99,Latin,Ao Vivo [IMPORT],Zeca Pagodinho Samba pras moças,Grazielle/Roque Ferreira,152816,5121366,0.99,Latin,Ao Vivo [IMPORT],Zeca Pagodinho Verdade,Carlinhos Santana/Nelson Rufino,332826,11120708,0.99,Latin,Ao Vivo [IMPORT],Zeca Pagodinho The Office: An American Workplace (Pilot),,1380833,290482361,1.99,TV Shows,"The Office, Season 1",The Office Diversity Day,,1306416,257879716,1.99,TV Shows,"The Office, Season 1",The Office Health Care,,1321791,260493577,1.99,TV Shows,"The Office, Season 1",The Office The Alliance,,1317125,266203162,1.99,TV Shows,"The Office, Season 1",The Office Basketball,,1323541,267464180,1.99,TV Shows,"The Office, Season 1",The Office Hot Girl,,1325458,267836576,1.99,TV Shows,"The Office, Season 1",The Office The Dundies,,1253541,246845576,1.99,TV Shows,"The Office, Season 2",The Office Sexual Harassment,,1294541,273069146,1.99,TV Shows,"The Office, Season 2",The Office Office Olympics,,1290458,256247623,1.99,TV Shows,"The Office, Season 2",The Office The Fire,,1288166,266856017,1.99,TV Shows,"The Office, Season 2",The Office Halloween,,1315333,249205209,1.99,TV Shows,"The Office, Season 2",The Office The Fight,,1320028,277149457,1.99,TV Shows,"The Office, Season 2",The Office The Client,,1299341,253836788,1.99,TV Shows,"The Office, Season 2",The Office Performance Review,,1292458,256143822,1.99,TV Shows,"The Office, Season 2",The Office Email Surveillance,,1328870,265101113,1.99,TV Shows,"The Office, Season 2",The Office Christmas Party,,1282115,260891300,1.99,TV Shows,"The Office, Season 2",The Office Booze Cruise,,1267958,252518021,1.99,TV Shows,"The Office, Season 2",The Office The Injury,,1275275,253912762,1.99,TV Shows,"The Office, Season 2",The Office The Secret,,1264875,253143200,1.99,TV Shows,"The Office, Season 2",The Office The Carpet,,1264375,256477011,1.99,TV Shows,"The Office, Season 2",The Office Boys and Girls,,1278333,255245729,1.99,TV Shows,"The Office, Season 2",The Office Valentine's Day,,1270375,253552710,1.99,TV Shows,"The Office, Season 2",The Office Dwight's Speech,,1278041,255001728,1.99,TV Shows,"The Office, Season 2",The Office Take Your Daughter to Work Day,,1268333,253451012,1.99,TV Shows,"The Office, Season 2",The Office Michael's Birthday,,1237791,247238398,1.99,TV Shows,"The Office, Season 2",The Office Drug Testing,,1278625,244626927,1.99,TV Shows,"The Office, Season 2",The Office Conflict Resolution,,1274583,253808658,1.99,TV Shows,"The Office, Season 2",The Office Casino Night - Season Finale,,1712791,327642458,1.99,TV Shows,"The Office, Season 2",The Office Gay Witch Hunt,,1326534,276942637,1.99,TV Shows,"The Office, Season 3",The Office The Convention,,1297213,255117055,1.99,TV Shows,"The Office, Season 3",The Office The Coup,,1276526,267205501,1.99,TV Shows,"The Office, Season 3",The Office Grief Counseling,,1282615,256912833,1.99,TV Shows,"The Office, Season 3",The Office The Initiation,,1280113,251728257,1.99,TV Shows,"The Office, Season 3",The Office Diwali,,1279904,252726644,1.99,TV Shows,"The Office, Season 3",The Office Branch Closing,,1822781,358761786,1.99,TV Shows,"The Office, Season 3",The Office The Merger,,1801926,345960631,1.99,TV Shows,"The Office, Season 3",The Office The Convict,,1273064,248863427,1.99,Comedy,"The Office, Season 3",The Office "A Benihana Christmas, Pts. 1 & 2",,2519436,515301752,1.99,Comedy,"The Office, Season 3",The Office Back from Vacation,,1271688,245378749,1.99,Comedy,"The Office, Season 3",The Office Traveling Salesmen,,1289039,250822697,1.99,Comedy,"The Office, Season 3",The Office Producer's Cut: The Return,,1700241,337219980,1.99,Comedy,"The Office, Season 3",The Office Ben Franklin,,1271938,264168080,1.99,Comedy,"The Office, Season 3",The Office Phyllis's Wedding,,1271521,258561054,1.99,Comedy,"The Office, Season 3",The Office Business School,,1302093,254402605,1.99,Comedy,"The Office, Season 3",The Office Cocktails,,1272522,259011909,1.99,Comedy,"The Office, Season 3",The Office The Negotiation,,1767851,371663719,1.99,Comedy,"The Office, Season 3",The Office Safety Training,,1271229,253054534,1.99,Comedy,"The Office, Season 3",The Office Product Recall,,1268268,251208610,1.99,Comedy,"The Office, Season 3",The Office Women's Appreciation,,1732649,338778844,1.99,Comedy,"The Office, Season 3",The Office Beach Games,,1676134,333671149,1.99,Comedy,"The Office, Season 3",The Office The Job,,2541875,501060138,1.99,Comedy,"The Office, Season 3",The Office Branch Closing,,1814855,360331351,1.99,Comedy,"The Office, Season 3",The Office The Return,,1705080,343877320,1.99,Comedy,"The Office, Season 3",The Office Your Time Is Gonna Come,"Page, Jones",310774,5126563,0.99,Rock,Un-Led-Ed,Dread Zeppelin "Battlestar Galactica, Pt. 1",,2952702,541359437,1.99,Sci Fi & Fantasy,"Battlestar Galactica (Classic), Season 1",Battlestar Galactica (Classic) "Battlestar Galactica, Pt. 2",,2956081,521387924,1.99,Sci Fi & Fantasy,"Battlestar Galactica (Classic), Season 1",Battlestar Galactica (Classic) "Battlestar Galactica, Pt. 3",,2927802,554509033,1.99,Sci Fi & Fantasy,"Battlestar Galactica (Classic), Season 1",Battlestar Galactica (Classic) "Lost Planet of the Gods, Pt. 1",,2922547,537812711,1.99,Sci Fi & Fantasy,"Battlestar Galactica (Classic), Season 1",Battlestar Galactica (Classic) "Lost Planet of the Gods, Pt. 2",,2914664,534343985,1.99,Sci Fi & Fantasy,"Battlestar Galactica (Classic), Season 1",Battlestar Galactica (Classic) The Lost Warrior,,2920045,558872190,1.99,Sci Fi & Fantasy,"Battlestar Galactica (Classic), Season 1",Battlestar Galactica (Classic) The Long Patrol,,2925008,513122217,1.99,Sci Fi & Fantasy,"Battlestar Galactica (Classic), Season 1",Battlestar Galactica (Classic) "The Gun On Ice Planet Zero, Pt. 1",,2907615,540980196,1.99,Sci Fi & Fantasy,"Battlestar Galactica (Classic), Season 1",Battlestar Galactica (Classic) "The Gun On Ice Planet Zero, Pt. 2",,2924341,546542281,1.99,Sci Fi & Fantasy,"Battlestar Galactica (Classic), Season 1",Battlestar Galactica (Classic) The Magnificent Warriors,,2924716,570152232,1.99,Sci Fi & Fantasy,"Battlestar Galactica (Classic), Season 1",Battlestar Galactica (Classic) The Young Lords,,2863571,587051735,1.99,Sci Fi & Fantasy,"Battlestar Galactica (Classic), Season 1",Battlestar Galactica (Classic) "The Living Legend, Pt. 1",,2924507,503641007,1.99,Sci Fi & Fantasy,"Battlestar Galactica (Classic), Season 1",Battlestar Galactica (Classic) "The Living Legend, Pt. 2",,2923298,515632754,1.99,Sci Fi & Fantasy,"Battlestar Galactica (Classic), Season 1",Battlestar Galactica (Classic) Fire In Space,,2926593,536784757,1.99,Sci Fi & Fantasy,"Battlestar Galactica (Classic), Season 1",Battlestar Galactica (Classic) "War of the Gods, Pt. 1",,2922630,505761343,1.99,Sci Fi & Fantasy,"Battlestar Galactica (Classic), Season 1",Battlestar Galactica (Classic) "War of the Gods, Pt. 2",,2923381,487899692,1.99,Sci Fi & Fantasy,"Battlestar Galactica (Classic), Season 1",Battlestar Galactica (Classic) The Man With Nine Lives,,2956998,577829804,1.99,Sci Fi & Fantasy,"Battlestar Galactica (Classic), Season 1",Battlestar Galactica (Classic) Murder On the Rising Star,,2935894,551759986,1.99,Sci Fi & Fantasy,"Battlestar Galactica (Classic), Season 1",Battlestar Galactica (Classic) "Greetings from Earth, Pt. 1",,2960293,536824558,1.99,Sci Fi & Fantasy,"Battlestar Galactica (Classic), Season 1",Battlestar Galactica (Classic) "Greetings from Earth, Pt. 2",,2903778,527842860,1.99,Sci Fi & Fantasy,"Battlestar Galactica (Classic), Season 1",Battlestar Galactica (Classic) Baltar's Escape,,2922088,525564224,1.99,Sci Fi & Fantasy,"Battlestar Galactica (Classic), Season 1",Battlestar Galactica (Classic) Experiment In Terra,,2923548,547982556,1.99,Sci Fi & Fantasy,"Battlestar Galactica (Classic), Season 1",Battlestar Galactica (Classic) Take the Celestra,,2927677,512381289,1.99,Sci Fi & Fantasy,"Battlestar Galactica (Classic), Season 1",Battlestar Galactica (Classic) The Hand of God,,2924007,536583079,1.99,Sci Fi & Fantasy,"Battlestar Galactica (Classic), Season 1",Battlestar Galactica (Classic) Pilot,,2484567,492670102,1.99,TV Shows,Aquaman,Aquaman Instant Karma,,193188,3150090,0.99,Pop,Instant Karma: The Amnesty International Campaign to Save Darfur,U2 #9 Dream,,278312,4506425,0.99,Pop,Instant Karma: The Amnesty International Campaign to Save Darfur,U2 Mother,,287740,4656660,0.99,Pop,Instant Karma: The Amnesty International Campaign to Save Darfur,U2 Give Peace a Chance,,274644,4448025,0.99,Pop,Instant Karma: The Amnesty International Campaign to Save Darfur,U2 Cold Turkey,,281424,4556003,0.99,Pop,Instant Karma: The Amnesty International Campaign to Save Darfur,U2 Whatever Gets You Thru the Night,,215084,3499018,0.99,Pop,Instant Karma: The Amnesty International Campaign to Save Darfur,U2 I'm Losing You,,240719,3907467,0.99,Pop,Instant Karma: The Amnesty International Campaign to Save Darfur,U2 Gimme Some Truth,,232778,3780807,0.99,Pop,Instant Karma: The Amnesty International Campaign to Save Darfur,U2 "Oh, My Love",,159473,2612788,0.99,Pop,Instant Karma: The Amnesty International Campaign to Save Darfur,U2 Imagine,,192329,3136271,0.99,Pop,Instant Karma: The Amnesty International Campaign to Save Darfur,U2 Nobody Told Me,,210348,3423395,0.99,Pop,Instant Karma: The Amnesty International Campaign to Save Darfur,U2 Jealous Guy,,239094,3881620,0.99,Pop,Instant Karma: The Amnesty International Campaign to Save Darfur,U2 Working Class Hero,,265449,4301430,0.99,Pop,Instant Karma: The Amnesty International Campaign to Save Darfur,U2 Power to the People,,213018,3466029,0.99,Pop,Instant Karma: The Amnesty International Campaign to Save Darfur,U2 Imagine,,219078,3562542,0.99,Pop,Instant Karma: The Amnesty International Campaign to Save Darfur,U2 Beautiful Boy,,227995,3704642,0.99,Pop,Instant Karma: The Amnesty International Campaign to Save Darfur,U2 Isolation,,156059,2558399,0.99,Pop,Instant Karma: The Amnesty International Campaign to Save Darfur,U2 Watching the Wheels,,198645,3237063,0.99,Pop,Instant Karma: The Amnesty International Campaign to Save Darfur,U2 Grow Old With Me,,149093,2447453,0.99,Pop,Instant Karma: The Amnesty International Campaign to Save Darfur,U2 Gimme Some Truth,,187546,3060083,0.99,Pop,Instant Karma: The Amnesty International Campaign to Save Darfur,U2 [Just Like] Starting Over,,215549,3506308,0.99,Pop,Instant Karma: The Amnesty International Campaign to Save Darfur,U2 God,,260410,4221135,0.99,Pop,Instant Karma: The Amnesty International Campaign to Save Darfur,U2 Real Love,,236911,3846658,0.99,Pop,Instant Karma: The Amnesty International Campaign to Save Darfur,U2 Sympton of the Universe,,340890,5489313,0.99,Rock,Speak of the Devil,Ozzy Osbourne Snowblind,,295960,4773171,0.99,Rock,Speak of the Devil,Ozzy Osbourne Black Sabbath,,364180,5860455,0.99,Rock,Speak of the Devil,Ozzy Osbourne Fairies Wear Boots,,392764,6315916,0.99,Rock,Speak of the Devil,Ozzy Osbourne War Pigs,,515435,8270194,0.99,Rock,Speak of the Devil,Ozzy Osbourne The Wizard,,282678,4561796,0.99,Rock,Speak of the Devil,Ozzy Osbourne N.I.B.,,335248,5399456,0.99,Rock,Speak of the Devil,Ozzy Osbourne Sweet Leaf,,354706,5709700,0.99,Rock,Speak of the Devil,Ozzy Osbourne Never Say Die,,258343,4173799,0.99,Rock,Speak of the Devil,Ozzy Osbourne "Sabbath, Bloody Sabbath",,333622,5373633,0.99,Rock,Speak of the Devil,Ozzy Osbourne Iron Man/Children of the Grave,,552308,8858616,0.99,Rock,Speak of the Devil,Ozzy Osbourne Paranoid,,189171,3071042,0.99,Rock,Speak of the Devil,Ozzy Osbourne Rock You Like a Hurricane,,255766,4300973,0.99,Rock,20th Century Masters - The Millennium Collection: The Best of Scorpions,Scorpions No One Like You,,240325,4050259,0.99,Rock,20th Century Masters - The Millennium Collection: The Best of Scorpions,Scorpions The Zoo,,332740,5550779,0.99,Rock,20th Century Masters - The Millennium Collection: The Best of Scorpions,Scorpions Loving You Sunday Morning,,339125,5654493,0.99,Rock,20th Century Masters - The Millennium Collection: The Best of Scorpions,Scorpions Still Loving You,,390674,6491444,0.99,Rock,20th Century Masters - The Millennium Collection: The Best of Scorpions,Scorpions Big City Nights,,251865,4237651,0.99,Rock,20th Century Masters - The Millennium Collection: The Best of Scorpions,Scorpions Believe in Love,,325774,5437651,0.99,Rock,20th Century Masters - The Millennium Collection: The Best of Scorpions,Scorpions Rhythm of Love,,231246,3902834,0.99,Rock,20th Century Masters - The Millennium Collection: The Best of Scorpions,Scorpions I Can't Explain,,205332,3482099,0.99,Rock,20th Century Masters - The Millennium Collection: The Best of Scorpions,Scorpions Tease Me Please Me,,287229,4811894,0.99,Rock,20th Century Masters - The Millennium Collection: The Best of Scorpions,Scorpions Wind of Change,,315325,5268002,0.99,Rock,20th Century Masters - The Millennium Collection: The Best of Scorpions,Scorpions Send Me an Angel,,273041,4581492,0.99,Rock,20th Century Masters - The Millennium Collection: The Best of Scorpions,Scorpions Jump Around,E. Schrody/L. Muggerud,217835,8715653,0.99,Hip Hop/Rap,House of Pain,House Of Pain Salutations,E. Schrody/L. Dimant,69120,2767047,0.99,Hip Hop/Rap,House of Pain,House Of Pain Put Your Head Out,E. Schrody/L. Freese/L. Muggerud,182230,7291473,0.99,Hip Hop/Rap,House of Pain,House Of Pain Top O' The Morning To Ya,E. Schrody/L. Dimant,216633,8667599,0.99,Hip Hop/Rap,House of Pain,House Of Pain Commercial 1,L. Muggerud,7941,319888,0.99,Hip Hop/Rap,House of Pain,House Of Pain House And The Rising Sun,E. Schrody/J. Vasquez/L. Dimant,219402,8778369,0.99,Hip Hop/Rap,House of Pain,House Of Pain Shamrocks And Shenanigans,E. Schrody/L. Dimant,218331,8735518,0.99,Hip Hop/Rap,House of Pain,House Of Pain House Of Pain Anthem,E. Schrody/L. Dimant,155611,6226713,0.99,Hip Hop/Rap,House of Pain,House Of Pain "Danny Boy, Danny Boy",E. Schrody/L. Muggerud,114520,4583091,0.99,Hip Hop/Rap,House of Pain,House Of Pain Guess Who's Back,E. Schrody/L. Muggerud,238393,9537994,0.99,Hip Hop/Rap,House of Pain,House Of Pain Commercial 2,L. Muggerud,21211,850698,0.99,Hip Hop/Rap,House of Pain,House Of Pain Put On Your Shit Kickers,E. Schrody/L. Muggerud,190432,7619569,0.99,Hip Hop/Rap,House of Pain,House Of Pain Come And Get Some Of This,E. Schrody/L. Muggerud/R. Medrano,170475,6821279,0.99,Hip Hop/Rap,House of Pain,House Of Pain Life Goes On,E. Schrody/R. Medrano,163030,6523458,0.99,Hip Hop/Rap,House of Pain,House Of Pain One For The Road,E. Schrody/L. Dimant/L. Muggerud,170213,6810820,0.99,Hip Hop/Rap,House of Pain,House Of Pain Feel It,E. Schrody/R. Medrano,239908,9598588,0.99,Hip Hop/Rap,House of Pain,House Of Pain All My Love,E. Schrody/L. Dimant,200620,8027065,0.99,Hip Hop/Rap,House of Pain,House Of Pain Jump Around (Pete Rock Remix),E. Schrody/L. Muggerud,236120,9447101,0.99,Hip Hop/Rap,House of Pain,House Of Pain Shamrocks 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 Instinto Colectivo,,300564,12024875,0.99,Electronica/Dance,Radio Brasil (O Som da Jovem Vanguarda) - Seleccao de Henrique Amaro,O Rappa Chapa o Coco,,143830,5755478,0.99,Electronica/Dance,Radio Brasil (O Som da Jovem Vanguarda) - Seleccao de Henrique Amaro,O Rappa Prostituta,,359000,14362307,0.99,Electronica/Dance,Radio Brasil (O Som da Jovem Vanguarda) - Seleccao de Henrique Amaro,O Rappa Eu So Queria Sumir,,269740,10791921,0.99,Electronica/Dance,Radio Brasil (O Som da Jovem Vanguarda) - Seleccao de Henrique Amaro,O Rappa Tres Reis,,304143,12168015,0.99,Electronica/Dance,Radio Brasil (O Som da Jovem Vanguarda) - Seleccao de Henrique Amaro,O Rappa Um Lugar ao Sol,,212323,8495217,0.99,Electronica/Dance,Radio Brasil (O Som da Jovem Vanguarda) - Seleccao de Henrique Amaro,O Rappa Batalha Naval,,285727,11431382,0.99,Electronica/Dance,Radio Brasil (O Som da Jovem Vanguarda) - Seleccao de Henrique Amaro,O Rappa Todo o Carnaval tem seu Fim,,237426,9499371,0.99,Electronica/Dance,Radio Brasil (O Som da Jovem Vanguarda) - Seleccao de Henrique Amaro,O Rappa O Misterio do Samba,,226142,9047970,0.99,Electronica/Dance,Radio Brasil (O Som da Jovem Vanguarda) - Seleccao de Henrique Amaro,O Rappa Armadura,,232881,9317533,0.99,Electronica/Dance,Radio Brasil (O Som da Jovem Vanguarda) - Seleccao de Henrique Amaro,O Rappa Na Ladeira,,221570,8865099,0.99,Electronica/Dance,Radio Brasil (O Som da Jovem Vanguarda) - Seleccao de Henrique Amaro,O Rappa Carimbo,,328751,13152314,0.99,Electronica/Dance,Radio Brasil (O Som da Jovem Vanguarda) - Seleccao de Henrique Amaro,O Rappa Catimbo,,254484,10181692,0.99,Electronica/Dance,Radio Brasil (O Som da Jovem Vanguarda) - Seleccao de Henrique Amaro,O Rappa Funk de Bamba,,237322,9495184,0.99,Electronica/Dance,Radio Brasil (O Som da Jovem Vanguarda) - Seleccao de Henrique Amaro,O Rappa Chega no Suingue,,221805,8874509,0.99,Electronica/Dance,Radio Brasil (O Som da Jovem Vanguarda) - Seleccao de Henrique Amaro,O Rappa Mun-Ra,,274651,10988338,0.99,Electronica/Dance,Radio Brasil (O Som da Jovem Vanguarda) - Seleccao de Henrique Amaro,O Rappa Freestyle Love,,318484,12741680,0.99,Electronica/Dance,Radio Brasil (O Som da Jovem Vanguarda) - Seleccao de Henrique Amaro,O Rappa War Pigs,,234013,8052374,0.99,Alternative,Cake: B-Sides and Rarities,Cake "Past, Present, and Future",,2492867,490796184,1.99,Drama,"LOST, Season 4",Lost The Beginning of the End,,2611903,526865050,1.99,Drama,"LOST, Season 4",Lost LOST Season 4 Trailer,,112712,20831818,1.99,Drama,"LOST, Season 4",Lost LOST In 8:15,,497163,98460675,1.99,Drama,"LOST, Season 4",Lost Confirmed Dead,,2611986,512168460,1.99,Drama,"LOST, Season 4",Lost The Economist,,2609025,516934914,1.99,Drama,"LOST, Season 4",Lost Eggtown,,2608817,501061240,1.99,TV Shows,"LOST, Season 4",Lost The Constant,,2611569,520209363,1.99,Drama,"LOST, Season 4",Lost The Other Woman,,2605021,513246663,1.99,Drama,"LOST, Season 4",Lost Ji Yeon,,2588797,506458858,1.99,TV Shows,"LOST, Season 4",Lost Meet Kevin Johnson,,2612028,504132981,1.99,TV Shows,"LOST, Season 4",Lost The Shape of Things to Come,,2591299,502284266,1.99,Drama,"LOST, Season 4",Lost Something Nice Back Home,,2612779,484711353,1.99,Drama,"LOST, Season 4",Lost Cabin Fever,,2612028,477733942,1.99,Drama,"LOST, Season 4",Lost "There's No Place Like Home, Pt. 1",,2609526,522919189,1.99,Drama,"LOST, Season 4",Lost "There's No Place Like Home, Pt. 2",,2497956,523748920,1.99,Drama,"LOST, Season 4",Lost "There's No Place Like Home, Pt. 3",,2582957,486161766,1.99,Drama,"LOST, Season 4",Lost Amanda,Luca Gusella,246503,4011615,0.99,Jazz,Quiet Songs,Aisha Duo Despertar,Andrea Dulbecco,307385,4821485,0.99,Jazz,Quiet Songs,Aisha Duo Din Din Wo (Little Child),Habib Koité,285837,4615841,0.99,World,Muso Ko,Habib Koité and Bamada I Ka Barra (Your Work),Habib Koité,300605,4855457,0.99,World,Muso Ko,Habib Koité and Bamada Distance,Karsh Kale/Vishal Vaid,327122,5327463,0.99,Electronica/Dance,Realize,Karsh Kale One Step Beyond,Karsh Kale,366085,6034098,0.99,Electronica/Dance,Realize,Karsh Kale I 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 Love Comes,"Darius ""Take One"" Minwalla/Jon Auer/Ken Stringfellow/Matt Harris",199923,3240609,0.99,Rock,Every Kind of Light,The Posies Muita Bobeira,Luciana Souza,172710,2775071,0.99,Latin,Duos II,Luciana Souza/Romero Lubambo OAM's Blues,Aaron Goldberg,266936,4292028,0.99,Jazz,Worlds,Aaron Goldberg "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 Say Hello 2 Heaven,,384497,6477217,0.99,Alternative,Temple of the Dog,Temple of the Dog Reach Down,,672773,11157785,0.99,Alternative,Temple of the Dog,Temple of the Dog Hunger Strike,,246292,4233212,0.99,Alternative,Temple of the Dog,Temple of the Dog Pushin Forward Back,,225278,3892066,0.99,Alternative,Temple of the Dog,Temple of the Dog Call Me a Dog,,304458,5177612,0.99,Alternative,Temple of the Dog,Temple of the Dog Times of Trouble,,342539,5795951,0.99,Alternative,Temple of the Dog,Temple of the Dog Wooden Jesus,,250565,4302603,0.99,Alternative,Temple of the Dog,Temple of the Dog Your Savior,,244226,4199626,0.99,Alternative,Temple of the Dog,Temple of the Dog Four Walled World,,414474,6964048,0.99,Alternative,Temple of the Dog,Temple of the Dog All Night Thing,,231803,3997982,0.99,Alternative,Temple of the Dog,Temple of the Dog No Such Thing,Chris Cornell,224837,3691272,0.99,Alternative,Carry On,Chris Cornell Poison Eye,Chris Cornell,237120,3890037,0.99,Alternative,Carry On,Chris Cornell Arms Around Your Love,Chris Cornell,214016,3516224,0.99,Alternative,Carry On,Chris Cornell Safe and Sound,Chris Cornell,256764,4207769,0.99,Alternative,Carry On,Chris Cornell She'll Never Be Your Man,Chris Cornell,204078,3355715,0.99,Alternative,Carry On,Chris Cornell Ghosts,Chris Cornell,231547,3799745,0.99,Alternative,Carry On,Chris Cornell Killing Birds,Chris Cornell,218498,3588776,0.99,Alternative,Carry On,Chris Cornell Billie Jean,Michael Jackson,281401,4606408,0.99,Alternative,Carry On,Chris Cornell Scar On the Sky,Chris Cornell,220193,3616618,0.99,Alternative,Carry On,Chris Cornell Your Soul Today,Chris Cornell,205959,3385722,0.99,Alternative,Carry On,Chris Cornell Finally Forever,Chris Cornell,217035,3565098,0.99,Alternative,Carry On,Chris Cornell Silence the Voices,Chris Cornell,267376,4379597,0.99,Alternative,Carry On,Chris Cornell Disappearing Act,Chris Cornell,273320,4476203,0.99,Alternative,Carry On,Chris Cornell You Know My Name,Chris Cornell,240255,3940651,0.99,Alternative,Carry On,Chris Cornell Revelations,,252376,4111051,0.99,Alternative,Revelations,Audioslave One and the Same,,217732,3559040,0.99,Alternative,Revelations,Audioslave Sound of a Gun,,260154,4234990,0.99,Alternative,Revelations,Audioslave Until We Fall,,230758,3766605,0.99,Alternative,Revelations,Audioslave Original Fire,,218916,3577821,0.99,Alternative,Revelations,Audioslave Broken City,,228366,3728955,0.99,Alternative,Revelations,Audioslave Somedays,,213831,3497176,0.99,Alternative,Revelations,Audioslave Shape of Things to Come,,274597,4465399,0.99,Alternative,Revelations,Audioslave Jewel of the Summertime,,233242,3806103,0.99,Alternative,Revelations,Audioslave Wide Awake,,266308,4333050,0.99,Alternative,Revelations,Audioslave Nothing Left to Say But Goodbye,,213041,3484335,0.99,Alternative,Revelations,Audioslave Moth,,298049,4838884,0.99,Alternative,Revelations,Audioslave Show Me How to Live (Live at the Quart Festival),,301974,4901540,0.99,Alternative,Revelations,Audioslave "Band Members Discuss Tracks from ""Revelations""",,294294,61118891,0.99,Alternative,Revelations,Audioslave Intoitus: Adorate Deum,Anonymous,245317,4123531,0.99,Classical,Adorate Deum: Gregorian Chant from the Proper of the Mass,Alberto Turco & Nova Schola Gregoriana "Miserere mei, Deus",Gregorio Allegri,501503,8285941,0.99,Classical,Allegri: Miserere,"Richard Marlow & The Choir of Trinity College, Cambridge" Canon and Gigue in D Major: I. Canon,Johann Pachelbel,271788,4438393,0.99,Classical,Pachelbel: Canon & Gigue,English Concert & Trevor Pinnock "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" "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" "Aria Mit 30 Veränderungen, BWV 988 ""Goldberg Variations"": Aria",Johann Sebastian Bach,120463,2081895,0.99,Classical,Bach: Goldberg Variations,Wilhelm Kempff "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 "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 Solomon 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 Fantasia 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 """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 "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" "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 Symphony 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 Ave Maria,Franz Schubert,338243,5605648,0.99,Classical,A Soprano Inspired,"Britten Sinfonia, Ivor Bolton & Lesley Garrett" "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" Die Walküre: The Ride of the Valkyries,Richard Wagner,189008,3114209,0.99,Classical,Wagner: Favourite Overtures,Sir Georg Solti & Wiener Philharmoniker "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" "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 "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 Madama Butterfly: Un Bel Dì Vedremo,Giacomo Puccini,277639,4588197,0.99,Classical,Puccini: Madama Butterfly - Highlights,"Herbert Von Karajan, Mirella Freni & Wiener Philharmoniker" "Jupiter, the Bringer of Jollity",Gustav Holst,522099,8547876,0.99,Classical,"Holst: The Planets, Op. 32 & Vaughan Williams: Fantasies",Eugene Ormandy "Turandot, Act III, Nessun dorma!",Giacomo Puccini,176911,2920890,0.99,Classical,Pavarotti's Opera Made Easy,Luciano Pavarotti "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 Carmina Burana: O Fortuna,Carl Orff,156710,2630293,0.99,Classical,Carmina Burana,Boston Symphony Orchestra & Seiji Ozawa Fanfare for the Common Man,Aaron Copland,198064,3211245,0.99,Classical,"A Copland Celebration, Vol. I",Aaron Copland & London Symphony Orchestra "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 "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 "Scheherazade, Op. 35: I. The Sea and Sindbad's Ship",Nikolai Rimsky-Korsakov,545203,8916313,0.99,Classical,Scheherazade,Chicago Symphony Orchestra & Fritz Reiner "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 "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" Cavalleria Rusticana \ Act \ Intermezzo Sinfonico,Pietro Mascagni,243436,4001276,0.99,Classical,Mascagni: Cavalleria Rusticana,James Levine "Karelia Suite, Op.11: 2. Ballade (Tempo Di Menuetto)",Jean Sibelius,406000,5908455,0.99,Classical,Sibelius: Finlandia,Berliner Philharmoniker & Hans Rosbaud "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 "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 "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" Two 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 "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 Missa Papae Marcelli: Kyrie,Giovanni Pierluigi da Palestrina,240666,4244149,0.99,Classical,Palestrina: Missa Papae Marcelli & Allegri: Miserere,Choir Of Westminster Abbey & Simon Preston Romeo et Juliette: No. 11 - Danse des Chevaliers,,275015,4519239,0.99,Classical,Prokofiev: Romeo & Juliet,Michael Tilson Thomas & San Francisco Symphony On the Beautiful Blue Danube,Johann Strauss II,526696,8610225,0.99,Classical,Strauss: Waltzes,Eugene Ormandy "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 Carmen: Overture,Georges Bizet,132932,2189002,0.99,Classical,Bizet: Carmen Highlights,"Chor der Wiener Staatsoper, Herbert Von Karajan & Wiener Philharmoniker" "Lamentations of Jeremiah, First Set \ Incipit Lamentatio",Thomas Tallis,69194,1208080,0.99,Classical,English Renaissance,The King's Singers Sing Joyfully,William Byrd,133768,2256484,0.99,Classical,English Renaissance,The King's Singers "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 "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 "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" "SCRIABIN: Prelude in B Major, Op. 11, No. 11",,101293,3819535,0.99,Classical,SCRIABIN: Vers la flamme,Christopher O'Riley "Pavan, Lachrimae Antiquae",John Dowland,253281,4211495,0.99,Classical,Armada: Music from the Courts of England and Spain,Fretwork "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 Rehab,,213240,3416878,0.99,R&B/Soul,Back to Black,Amy Winehouse You Know I'm No Good,,256946,4133694,0.99,R&B/Soul,Back to Black,Amy Winehouse Me & Mr. Jones,,151706,2449438,0.99,R&B/Soul,Back to Black,Amy Winehouse Just Friends,,191933,3098906,0.99,R&B/Soul,Back to Black,Amy Winehouse Back to Black,Mark Ronson,240320,3852953,0.99,R&B/Soul,Back to Black,Amy Winehouse Love Is a Losing Game,,154386,2509409,0.99,R&B/Soul,Back to Black,Amy Winehouse Tears Dry On Their Own,Nickolas Ashford & Valerie Simpson,185293,2996598,0.99,R&B/Soul,Back to Black,Amy Winehouse Wake Up Alone,Paul O'duffy,221413,3576773,0.99,R&B/Soul,Back to Black,Amy Winehouse Some Unholy War,,141520,2304465,0.99,R&B/Soul,Back to Black,Amy Winehouse He Can Only Hold Her,Richard Poindexter & Robert Poindexter,166680,2666531,0.99,R&B/Soul,Back to Black,Amy Winehouse You Know I'm No Good (feat. Ghostface Killah),,202320,3260658,0.99,R&B/Soul,Back to Black,Amy Winehouse Rehab (Hot Chip Remix),,418293,6670600,0.99,R&B/Soul,Back to Black,Amy Winehouse Intro / Stronger Than Me,,234200,3832165,0.99,Pop,Frank,Amy Winehouse You Sent Me Flying / Cherry,,409906,6657517,0.99,Pop,Frank,Amy Winehouse F**k Me Pumps,Salaam Remi,200253,3324343,0.99,Pop,Frank,Amy Winehouse I Heard Love Is Blind,,129666,2190831,0.99,Pop,Frank,Amy Winehouse (There Is) No Greater Love (Teo Licks),Isham Jones & Marty Symes,167933,2773507,0.99,Pop,Frank,Amy Winehouse In My Bed,Salaam Remi,315960,5211774,0.99,Pop,Frank,Amy Winehouse Take the Box,Luke Smith,199160,3281526,0.99,Pop,Frank,Amy Winehouse October Song,Matt Rowe & Stefan Skarbek,204846,3358125,0.99,Pop,Frank,Amy Winehouse What 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 Help Yourself,"Freddy James, Jimmy hogarth & Larry Stock",300884,5029266,0.99,Pop,Frank,Amy Winehouse Amy 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 Slowness,,215386,3644793,0.99,Alternative,Carried to Dust (Bonus Track Version),Calexico "Prometheus Overture, Op. 43",Ludwig van Beethoven,339567,10887931,0.99,Classical,Beethoven: Symphony No. 6 'Pastoral' Etc.,Otto Klemperer & Philharmonia Orchestra Sonata for Solo Violin: IV: Presto,Béla Bartók,299350,9785346,0.99,Classical,Bartok: Violin & Viola Concertos,Yehudi Menuhin "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 "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" "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 Adios nonino,Astor Piazzolla,289388,4781384,0.99,Classical,South American Getaway,The 12 Cellists of The Berlin Philharmonic "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 "Act IV, Symphony",Henry Purcell,364296,5987695,0.99,Classical,Purcell: The Fairy Queen,"Roger Norrington, London Classical Players" "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 "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" Symphony 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 "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 Le 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 "Metopes, Op. 29: Calypso",Karol Szymanowski,333669,5548755,0.99,Classical,"Szymanowski: Piano Works, Vol. 1",Martin Roscoe "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 "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 "Étude 1, In C Major - Preludio (Presto) - Liszt",,51780,2229617,0.99,Classical,Liszt - 12 Études D'Execution Transcendante,Michele Campanella "Erlkonig, D.328",,261849,4307907,0.99,Classical,"Great Recordings of the Century - Shubert: Schwanengesang, 4 Lieder",Gerald Moore "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" Pini Di Roma (Pinien Von Rom) \ I Pini Della Via Appia,,286741,4718950,0.99,Classical,Respighi:Pines of Rome,Eugene Ormandy "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 "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" "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 Koyaanisqatsi,Philip Glass,206005,3305164,0.99,Soundtrack,Koyaanisqatsi (Soundtrack from the Motion Picture),Philip Glass Ensemble ================================================ FILE: March Cohort 2023/22_23_Time Series Forecasting/.ipynb_checkpoints/22_Forecasting-checkpoint.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "id": "12c7b612", "metadata": {}, "source": [ "# Time series data and forecasting\n", "\n", "components\n", "- trend\n", "- seasonality\n", "- cyclical \n", "- random\n", "\n", "Models/ forecasting techniques\n", "- AR\n", "- MA\n", "- ARMA\n", "- ARIMA" ] }, { "cell_type": "code", "execution_count": null, "id": "861324fd", "metadata": {}, "outputs": [], "source": [ "time series data -> random variable\n", "- single variable (demand of the product at time t) univariate time series data\n", "- multivariate time series data -> \n", "\n", "4 components\n", "- trend\n", "- seasonal\n", "- cyclical\n", "- irregular" ] }, { "cell_type": "markdown", "id": "c3f8bc6b", "metadata": {}, "source": [ "## Moving Average\n" ] }, { "cell_type": "code", "execution_count": 2, "id": "dacb8d91", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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MonthSale QuantityPromotion ExpensesCompetition Promotion
0130026661051
1244015531450
2332052791181
3442453491300
453001940981
5643777661560
672798343981
7843036681440
8929581851121
91036233861200
\n", "
" ], "text/plain": [ " Month Sale Quantity Promotion Expenses Competition Promotion\n", "0 1 3002666 105 1\n", "1 2 4401553 145 0\n", "2 3 3205279 118 1\n", "3 4 4245349 130 0\n", "4 5 3001940 98 1\n", "5 6 4377766 156 0\n", "6 7 2798343 98 1\n", "7 8 4303668 144 0\n", "8 9 2958185 112 1\n", "9 10 3623386 120 0" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "\n", "df = pd.read_csv('wsb.csv')\n", "\n", "df.head(10)" ] }, { "cell_type": "code", "execution_count": 3, "id": "fecf8a63", "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import seaborn as sns" ] }, { "cell_type": "code", "execution_count": 4, "id": "b1c6bf16", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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rjeavm5uP45w7gNOTQZyZCuDMVAC/edGNBw6Np6/zh7vX4P/8wTbNt60XDsIYYywLd0Daqjo7FYAQoqqvlpk+pPYUYbx6Uzc6HFakBDAdiqLTYSvr91XbLV/R12JHLJmCNxhFZ3N511YppWTCiuW0m3HNQCuuGWhddPl0MIozU1JgNtjeWLH1ZMNBGGOMZaE8acyG4/AGYxV98mDlcdk3j3hSYLCtId2zyxOoQBCmcm6kQmlTMe6b5yCsDNqarNjTZMWe9W3VXgqfjmSMsWwye0jVQ13YB779HH51aqray6hpSqf8wfZGdMiBVyV6hc2GY6qL8oGFhq0rqVeYJxAFEdDaqD4YXQ04CGOMsSw8gSjWd0hbFWdrvC4sGE3gZ89P4MfHLld7KTVNmRk51N6ITjkjU4k2Fb5wvKhM2Eo6IekNRtHaYIHZyGFHprL/NIjISERHiOinWT53ExHNEdFR+e3j5V4PY4yp4QlGsbnXiWabCWemajsTpgyiPrnCTtTpbcQrtafodFjT22KVC8LUZ8IcNjOabaYVlwmrha3IWlOJmrCPAHgBQHOOzz8hhHhtBdbBGGOqCCHg9kfR6bBiY5ej5jNhypbayHQIwWgCTVYu981mbFpqT0FEsJmNcNhMZQ/C4skUgtGEqkatmfpaGlZUJkzr3MjVoqyZMCLqB3A7gK+U8/swxpieQrEk5uNJdDisGO5qwhl3oKa7cytBmBBSk1mW3ch0CEMZp+E6KtA13xdWP7w7U5/LvvIyYUW0p1jpyr0d+XkAfw0glec6e4joGBH9nIg2Z7sCEb2PiA4R0SGPZ2XOj2KM1Q5le6+z2YrhTgd88gnJWqWsF8CKa/Kpl0QyhYszYQy0ZQRhTZUIwqTfGy01YQDQL3fNr+XgXy0hRFHDu1eDsgVhRPRaAG4hxOE8V3sOwIAQYjuALwJ4MNuVhBBfFkLsEkLs6ujo0H+xjDGWIX2cvsmGjV0OAFK/sFrlCURhMRnQ3mTFycucCctmYi6CeFJgqL0hfVmHw5puIlouvnk5E6ahTxggZcKC0QT884lyLKuiAtEEookUZ8KyKGcmbC+A1xPRKIDvAbiFiL6VeQUhhF8IEZTffxiAmYjay7gmxhgrKN3d22HFxq4mAKjp4nx3QKpf29LXzJmwHNLtKTIyYZ0O26IsYjnMhtQP786ktKkY94V1X1Ol1VKPsFpTtiBMCPE3Qoh+IcQggDsB/EYI8Y7M6xBRN8ltqIlot7ye6XKtiTG1nn7Jix8fvVT4imxFcvulJw3lFF2zzYSz7totzp/yR6QgrNeJs+4gIvFktZdUc0anF3qEKTocVoRiSYSi5cs2pTNhRdSEAcBlX2WGjJcTB2G5VbxhBxHdQ0T3yB++CcAJIjoG4AsA7hQrYQOc1b0v/+48PvXQqRVRj8G08wSjMBsJrgYziKjmT0i6A1F0NduwubcZyZTA6cnazdpVy6g3jAaLMd0fDFgICrxl3JJcqAnTGISlG7ZyJmwlq0gQJoR4TGlDIYS4Twhxn/z+vwkhNgshtgshrhdCPF2J9TBWiDcYxXQoVpEeQqz2KCe5lHmRw12Omj4h6VYyYX1OAOC6sCxGp0MYaGtcNAO0Er3CZsNxmAykuW1IW6MFVpNhRbSp8JYwvHul49a1JRBCpF/lsJXFG5Du15N83H9Vci9pLLmxq6lmT0hG4kn4Iwl0NtvQ32JHs82EE5f1qQs7cmEWd375GczH6n97c9QbwmBbw6LLlKxYOUcXKY1atQ6AJyKpTcUKCMI8ASmz7NR4OGE14CCsBL84MYnr/vevMROqvQdmVjwhBKZD0oPyKc4orEpLu3sPd9buCcnMrR4iwuZep26d8x88cgn7z8/g2LhPl9urlkQyhYuz4UX1YEBlMmFah3dn6mtZGb3CPIEo2pusMBi0BaKrAQdhJTg9FUA0kUqfuqllQgg8fsaDVKo2t1Nqydx8HPGk9HPixperkycQSQ94BlDTJyTdAbmnmRxQbOlrxguTAcST+dozqnNgZAZA/fceu+yT21O0LQ7CWhssMBqozEFYXNPw7kwrJhPG3fJz4iCsBFPyCap6+CN57oIPd3/tIJ485632UmqeUr9gMhBOcRC26iSSKUyHYoueNDocVjjtZpypwROSCyc5paBxS58TsUQKL3lKW6svHMNpOeg8Pl7fQdiIfDJyYMl2pMFAaG+ypAPZcpgNx+C0F5kJc9nhDcbq/rSrkgljy6kKwojoB0R0OxFx0JZhSu4vUw/p4nH5hM3EXO2vtdo8cj3Y1WtbMOINIRyr/2aJTL2ZUAxCLD7JJZ2QbMK5GjwhOZXR3R8ANvdKY3pPXCrtBcSh0VkIAbQ3Wes+EzYmB2FDS7YjgfKPLiopE6ackKyDF/r58Mii3NQGVfcCeBuAs0T0aSK6soxrqhvpIKwOmukpa63FwuJao2TCXraxHUKAj/uvMkqRdueS7ZMNnbV5QtIdiMJkILTKdUdD7U2wm40lB04HR2dgMRrw1mv7cd4bQiAS12O5VTHiDaHBYsy6JdbRVN6u+b75mOb2FAqlV1g9vNDPJZkSyzLLbIGqIEwI8SshxNsBXA1gFMCjRPQ0Eb2LiFbtcQdlO7IemulNzklr5ZYLhSlB2I3D0ogs3pJcXXL1NFJOSJZ7zI1WyklOpejZaCBs6m0u+VDJgZEZbF/jxK7BVgClZ9aqadS7vD2FopyZsEg8iUg8VVJhPlDfmbDZcAzJlOAgLAfV24tE1AbgnQDeC+AIgH+FFJQ9WpaV1bh4MpU+QVcPr1KUTFitPYHUIm8wCqOBsKXPCYfNxCckVxlPjkzYwgzJ2tqSVEYWZdrS24yTl+eKPogTiiZw8tIcdg+1Yqvce6yetyTHpsOLZkZm6nTY4A1KgYLefGEpe6h1ZJGiu9kGo4Hq4jkmF2+QG7Xmo7Ym7IcAngDQAOB1QojXCyH+nxDiQwCayrnAWuUJRCEE0GAx1sWk+0llO1LnV3w/OjKOT//8RV1vs9q8gRhaG6VTU1f1NPMJyVVGKdJeWkg8LJ+QrLU2FW7/4pOcALC514lQLJke1aPVkQs+JFICu4fa0N5kRa/ThuN1GoQlkilcmAkvmhmZqcNhRTIlMFuGno+zRXbLV5iMBnQ32+o6E8bd8vNTmwn7ihBikxDi/wghJgCAiKwAIITYVbbV1TAls7S931UXk+7LlQn76bEJfPfgBV1vs9q8wYWTPJt6mvHiZKAsr5JZbfIEomi2mWAzGxdd3tFUmyckPYFouihfsblPKs4vtnP+wZFpGAi4ZqAFgHTisl4zYZd880ikRN4gDChPqUapQRggt6mo40xYOgjjwvys1AZh/5Dlsmf0XEi9UYKaqwdcAGp7z14IkT7GrncmzBOMYm4+viI6aiukIEzaPtjU24xwLJk+XcVWPncgis5m27LLlROStZQJi8vtNJZuRw53OmAxGorunH9gZAZb+pzpUTvb+p0Y8Ybgr8Pi/NFp6eDU0katis4yBmFzJW5HAnLD1hp+fimEM2H55Q3CiKibiK4BYCeinUR0tfx2E6StyVVLKcq/eq30SrGW/0hmQjHEkim0NVrgjyQQTegXMCnBnbLduRJ4g7H0q7ZNPVJG4YWJ2nniZeWV7zj9cJcDZ6aCNVN+sFC/tjhotJgM2NjdhJNFFNNHE0kcuejDbrkgH0B6JmU9ZsNG5WbagzlqwjrKOLpoVg7CSs2ETfojSOjQfLcaPIEoGixGNGqcnblaFMqE/R6AzwLoB/A5AP8sv/05gL8t79Jq25Q/ApOB0kWrek66f358Dud03PJQAqTN8lr1alORSol00eXk3MoIwoQQ8ASjaJcfmDd0NslNW+vvyYcVJ193742dTZibr50Tkkrg0NW8fL1bep04cXlOc8B4fHwOsUQKu4cWgrB6Ls4f8YbQaDHmDKyV0oNyZMJ889JjbamZsGRKYKpOT7Z7gtyoNZ+8QZgQ4htCiJsBvFMIcXPG2+uFED+s0Bpr0qQ/gk6HFR0Oq+6T7j/6wFH8w89O6XZ7SrZqi9zEUa8tSd98HAm5VmpqhWTCAtEEYolUejvSZjZiQ2cTn5BcJZSt+6Xbe4rhGjsh6VYatTqWb59u7nPCF47jssYXSAflUUXXZmTC2pqs6HPZ8XwdtqkYm87dngIAGq0mNFqM5QnCwnFYTYZl9YVarG2VMnj1+hi0dA4rW6zQduQ75HcHiejPl75VYH01y+2X6kb0nnSfTAmMTYcwrmMhppIJ25LOhOnzYJM56mNihWTClAA185WbdEKStyNXg1Asifl4MueTxnCNzZBMN5bNmglTOudry14dGJnBFV0OtDQuzt5s7XPi+Toc5D06Hc7aKT9TZ7OtLKOLZkOxkrJggBQMdzqs+Nb+MZ1Wpd3XnhzB42c8RX0td8vPr9B2pPKb2wTAseRtVbamUEz5I+iWi3d7XXZc0qlh68TcPOJJgcs6tr2YnIuAaKG+Sa8gLPOV40rJhE2HpO2DzCBsU08zJv0RTNfIFhQrH0+eoAaQTni5Gsw4UyuZsEAUREBb4/In+iu7m2Eg4KSGICyRTOHw6MyirUjF1n4nRqfDmJuvn+L8RDKFizPhZTMjl+poKk/DVt98vKR6MECq73vH9QN4/IxH1zIVtYQQ+Owjp/Efvz1X1Nfz8O78Cm1Hfkl+91dCiE9mvgH4dfmXV7um/JF0HYaeR4jH5JM84VhSt7YXU/4I2hqt6HZKQaNeDzbKNqfNbFgxMymzZcI29XJx/mqhbO91NC3f3gPkE5KdDpxz18bvgicg/W2bjMsfyu0WaStdS5uKUxN+hGLJrEGYkknXEtRVW7o9RYFMWIejPKOLfOHiRxZlett1a2ExGvCNp0dLX5RGnkAU4Zh0WEProa5YIgVfOM5BWB5qW1R8UeVlq8J8LAl/JJE+xt7XYoc3GNVl0r0ShAH6nbhUAkab2QiH1aRbYb7yoCVlilZGlkjJErY7FjILV6VPSNZnTQZTz6Oiu/eGrqaaOSGZr34NWCjOV0upB8uaCZODsOfrKAgb8eYe3J2pXKOLpOHdpW1HAtKLwtfv6MUPnhuveCZSafERS6Tw/Li2+16ZKsNBWG6FasL2ENFfAOhYUg/2CQDFVxrWOWXrTdmOVIasXtYhaMrsR6VXdmnSH02vtV3HV3xuv3T0eH1HE6ZWSE2YJxgDEdLDkAGgtdGC7mYbz5BcBZTsbr7AJn1CsgZOq00FIjm3TgEpizvlj6pe64GRGQy2NaArS5+01kaLXJxfP0GY0p6i4Hakw4pAJKHLC+lMs+HStyMV79o7iHAsiQeevajL7aml/AwBaai7FtyotbBCmTALpNovExbXg/kBvKm8S6tdShDWlZEJA/QZ5D02HYbTbpZvT8dMmLwVqWftgycovQrvdkpFrfXaxyaTNxhFa4Nl2fbOpl4eX7QaeIJRmI2U/hvMRpkhWQt1YW5/FF1ZTkYq0luIKrJhqZTAsznqwRTb+p31FYRNh/O2p1CUo2u+EELejiw9EwZIo6h2D7XiG8+MVnSCx8h0CCYDYai9Ec+OFBmEcSYsp0I1YY/L9V/XL6kJ+5wQ4myF1lhzJtNB2EJNGABc8pXeK2xsJoyda10wG0mXYv9oIomZUCwjE2bR73SkP4IOhxVdzTakhH79x6rJG8je0+aqHgfOuYO6v1JmtcUj3/8GQ/Z2BkBGm4oq14Ul5T59hTJhgLrxRWfdQfjCceweast5nS19ToxNh9Od4Gvd6HQIg+2521MoytGwNRRLIpESaNEpEwYA7947iPHZeTx6akq32yxkbDqENa0NuH5dGw6NzmoKAJUgrJ2DsJzU1oRZiejLRPQIEf1GeSvrymqYsmWhZJe6nTYYCCUX5wshtacYam9Et9Omy3Zkeq3yA3V7k1W3PmFSJsyGHvnnsBK65nuD0UX1YIpNPU4kUqIqp5NY5bgD+WusAKC9yVITJySnQ1GkRP6t02abGYNtDaraVBwcmQYAXJcnE5Zu2lrkOKRKG/WGcs6MzFSO0UWz8klrl12fTBgAvGpTN/pcdnz9qRHdbrOQEW8Yg20N2D3UgkA0gRcn1e8IpIOwJv1+BiuN2iDs+wCOAPg7AH+V8bYqTfkjsMtF7gBgNhrQ1WzDeInbh95gDOFYEgOtDeh12nXZjly6ddrRZIVfp9oHjz+azoQBwOQKOCHpDcbQ1rj8SU3JKHBd2MqmprGkckKy2jMklRdYHXm2IwFpG0tN0HRgZAY9Thv65fKKbOqpOD+eTGF8dj7nuKJMC9uR+r2QVAro9aoJAwCjgXD3DQM4MDJTkeatSmJgsL0xnSHVsiXpCUbhtJthNa3aEvKC1AZhCSHEvUKIg0KIw8pbWVdWwybl04aZKe4+V+lBk1KUP9DeiF6XXZcaMyU7pbSnUNLCSj+sYs3HkghEE+hwLLS+WAmji7w5RmwMtDagwWKs267VTB0pCMsf1ABS09YzU4GqnpAs1NNMsbmvGRdn5vNuIQohcHBEqgfLt3XX0mhBf0t9FOdfmpXbU6jIhLU1WmEgnTNhYTkTplNNmOKtu9bCbjbiv54ufzZMaU8x2NaIPpcdfS47nh2d1fT1XA+Wn9og7CEiej8R9RBRq/JW1pXVMKVbfqZeHbrmK+0pBlob0OuyYdIfKbkAUxk0nq4JkwOMUrckvRlH+VsbLLAYDXXfpiIcSyAcS2bdjjQYCFd2OzgTtoIlkilMh9Q9aWzscsAfSVT1hKTS4b3Q9umWXrk4P8/807HpMNyBaN6ifMW2fqfmVgXVMDKtDO4uHIQZDYTWRn17hSnDu/WsCQMAZ4MZf3B1Hx48ernsDaRHvIt/htcOtuDAyIzqFx/eIHfLL0RtEHY3pO3HpwEclt8OlWtRtW4qsNAtX9HXYseEr7SgaWwmDAMB/S0N6HFKQ1tLHaUx5Y/AYjKkT3spTzClFudnPgEYDITOZmvdb0d6A8u75WdSTkjWQn8opr+ZUAxCqDvJNdypjC+qXl3YlF/dybPNSnF+nrmPSn+wfPVgii19TlyYqf3i/DElgFCRCQOkxzK3ji8k58qUCQOkdhWxRArfPXhB99vOpCQGBuUWH9cOtcIbjKZ7hxXCmbDCVAVhQoihLG/ryr24WiSEwOTcQrd8RZ/LjkSJQdPYdAi9LjssJkNG77HSgrDJOSlgVLYYlALJUl/BLz163N1sq/vC/HSjzhxB2FU9zQhEErrO9WS1Iz2HUU0Qlm5TUb26MHcggpaGwvU2bU1W9DhtedtUHBiZQWujBes7Ck+jq5e6sNHpMJqsJtVF4Xp3zVcyYfnanRRrQ6cDNw634/79Y4iXsTWQ0p5CeT7aLQ91V1sXxkFYYWozYSCiLUT0FiK6S3kr58JqlX8+gWgitayZodIrrJQTkmPTCzPOenVqADvpX5y1S29HlpwJWxKEOW11XxOW7pafKxPWw8X5K5mWnkbtTRa0NJir2qZC6pZfuH4NUIrz82TCRqexezB/PZiifoKwEAbaGlT9nwD9u+b7wnE0WU2wmFQ/zWry7r1DmPJH8fDzE2W5fUA6Xbq2tSHdN3FDZxNaGy2qmraGogmEYkkOwgpQ9dtBRH8PaUzRFwHcDOAzAF5fxnXVrKnA4tOGiv50r7Dig6YLM2GsbZVS5z0u6fZLbVPhzmjUCkAaXWQrfXSRJxCFgZA+Sahkwup5qy7byKJMykBkbtq6Mmnp7k1EGO5y4GwVtyPdgfw9wjJt7m3GS54gwrHl82gv++ZxcWZeVT0YIG2vrWm1q2p7UU2j3pCqejBFp8MKbzCKlE6NUPWaG5nLyzd2YF17I77+1GjZvsfo9OLh50SEXQMt6e3rfLwFdhaYRG2I/iYArwAwKYR4F4DtAFT9ZInISERHiOinWT5HRPQFIjpHRMeJ6GrVK6+SpS0fFL0lBmH+SBwzoVh6773ZZobDaippO1IIIZ3kXPJKRI+u+W5/FG1NVhjlppbdThsi8ZRuQ8erQakJy9aiApAGIg+1N/IJyRVKKSVQ+8p9uLO6JyS1bPVs6XNCiOwvIJ4dzT0vMpdtfS4cv+RTff1KiydTuDg7n348VaPDYUU8KeDTaTbjbJmDMIOBcPcNgzh60YcjF9SfWFQrsz1Fpt1DrbgwE04/F+bCjVrVURuEzQshUgASRNQMwA1AbU3YRwC8kONztwEYlt/eB+BelbdZNcqW29KasEarCa4Gc9HbkReUk5EZDxo9LltJmTX/fAKReCrdQkLR3lR67YMyskihfI8Jf/3WS3nlnjb5tg+u6mnm7cgVyhOIotlmgs2srqeRckJSzy7ragkh1Z+q3Y7c0pe7c/6BkRk4rKb0oHp1t+fExZl5+MK1OSXj0uw8kirbUyj0Hl3km9dneHc+b7ymHw6rCf/19Kjut620p1g6/PxauS6sUDaM50aqozYIO0RELgD/Celk5HMADhb6IiLqB3A7gK/kuModAL4pJPsBuIioR+WaqkJ5wM024LavhDYVo0qPsIwHjV6XvaTtyMkcWTs9Rhe5A5FFr8K70w1b67cuTOoRlv9Bc1NvM8Zn59ONGNnK4Qkubz2Tz3CXckKy8nVhs+E44kmh6hABIP19tjVasm4hHhyZwa7BlnRWW41t/bVdF6a0p1gaQOSjBAu6BWHheFmK8jM1WU148641+NnxiYKZKa1GvMufkwBpa7vBYkxnUHPxBNXXWK5mak9Hvl8I4RNC3AfgVQDulrclC/k8gL8GkOv4Rh+AzJHw4/JlixDR+4joEBEd8ng8apZcNlP+CJx2c9ZXy6U0bFWOAq9tXciEldqwdWpJo1aFHtuRnkD2TJjeDwSVlKtRayalOP9FzoatOG6/tp5Gw53yDMkq1IWlW8SorAkjImzqbcaJJW0qvMEozrmDeedFZqP0HqvVIGw0RwCRjxKAe4L6PIb5wrGyZ8IA4J03DCIpBL61f0zX21USA0NLfoYmowHXqKgLU+qGWxt5ZFE+agvzX6a8AVgLKWP1sgJf81oA7gKd9bO99FpWYCGE+LIQYpcQYldHR4eaJZdNtvYUil6XHZdm54uqEbkwHUZ7kxWN8igkAOh12jATihU9YiidCXMs344MlDC6KJUS8AZji17hKNsiE3WdCYsVrF/gE5Irlyeo7Th9NU9ILsyEVZ+529LnxFl3ANHEwt/9oSLqwQCpYejaVnUzKathTGN7CiBjiLcOvcJSKYG5+bjujVqzWdvWgFdc2YXvHLigyzg6xeh0GCYDode1/Hfs2sFWnJ4K5O0V5w0urhtm2andjsycF/kxAA8B+ESBr9kL4PVENArgewBuIaJvLbnOOIA1GR/3A7isck1VMRWI5nzg62+xIxRLFrVVNTodWlZEWmqbiqm57K+WSx1dNBOOIZkSi+pRLCYD2pss9Z0JCxTOhHQ4rGhvsvAJyRVGCCG3fFAfhCknJKvRsFVLTzPFll4n4kmxKHN3YGQGNrMh3XZCi639Thyv0c75I94QBtvVt6cAgEaLEXazUZftSH8kjpQAnBXIhAHAu/cOYjoUw0+O6ff0ubQ9RaZrB1shBHBoLHc2zKPi8ZSp3458XcbbqwBsATBV4Gv+RgjRL4QYBHAngN8IId6x5Go/AXCXfEryegBzQojyNT3RgdsfyRmEKQ3timnmeWEmjLVLgrAeZ2kNWyf9UjPHpVunHSWOLsrVT6mee4VF4tIszEKvnImIi/NXoFAsifm49p5GG6s0Q3JhYoX6TJjSOT8ze3VwZAZXr20pqpfV1j4nxmfnMVviHNpykHqEqd+KBKS/7c5mfRq2+so0siiXPevbcEWXA19/alS338XR6XDOFh8717pgNlLefmHcqFWdYrvIjUMKxDQjonuI6B75w4cBnAdwDlLR//uLXE9FSGOEojm3I9MNWzVmriLxJCbmIhhoXfwLn+6aX2Rx/pQ/e9auvcRTQEsbtSq6m211ux2pZAUL1YQB0pbkmclgWTtVs8rS0qg103CnA4EqnJB0+6NwWE2wW9Sd5ASkelOH1YQTcud8fySOUxN+zVuRim012rQ1nkxhfHZ+WS2TGh1N+owuWhjeXZkgjIjwrr2DeGHCjwMqu9nno7SnGMjR4sNmNmJbvytvXRgHYeqorQn7otzP6wtE9G8AngRwTO03EUI8JoR4rfz+fXKBP+RTkR8QQqwXQmwVQtT0PMrpUBTJlCiYCdO6fXhxRp7P1b74F77LaQVRCduRObJ2Sran2BOSnhxbIV3NtrrdjlSygqqCsN5mxJIpnPeEyr0sViFuv/bMElC9E5KeQBQdKovyFQaDVJyvtKk4PDoLIbTXgyk212gQNq60p9BwMlKh1+gipddYOeZG5vL7O/vQ0mDG158aKfm23DnaU2S6drAVz4/PYT62vA5NCKG5xnK1UpsJexFStuocgGcA/HWWrcUVr1AxbGujBTazQXOvsGwnIwHAajKivcladBC2dGSRotTRRbmaWvY4bZgNx3UtDq2UhW756jJhAHBqoraefFjxij1OvzE9Q7KydWFT/oimejDFlj4nXpjwI5FM4cDIDMxGws41LUWtwWk3Y6Ct9orzR9ODu9U3alXoNbpI6Z9WidORCpvZiLdcuwaPnpoquYWOmtOlu4dakEgJHLm4vFHs3LzUQkXNi9rVzpTvk0RkBvBPAO4CMArpNGMnpPFFTxHRTiHEkXIvslYsNGrNHoQRkXRCUmPQpBwFztZYUOoVpj27FE+m4A1GF40sUiiji4p9sPEEomiymtBgWfzro/xcpvwRzfUY1bYwN7Lwg+ZQeyMsJgNOXfbjDTvLvTL9nJkK4AeHxzExF0Ffix19Ljv6Wuzol/9den+uJrmyu4W0N1nR2mjB2QpnwtyBKHascWn+us29zYjEUzjvDeHgyDS29bs0bWkutbXPiSMXfEV/fTmkH0+LyIR1OqyYm48jmkgWHIyez2xIzoSVuU/YUnvXt+NLj5/HiUtz2LuhvejbydWeItM1A60gAp4dmcUN6xd/r2K391ejQo+6/wygAcCAECIAAHLH/M8S0b0AbgUwVN4l1o6FuZG5f7GKadh6YSYMh82UtX6gz2XDi5PaH+C9wSiEQNZMGCDVPhQ7P9KdY69fOUgwOVePQZj6mjCT0YArux14YaJ6w5vV8kfieOjYZTxwaBzHLvpgMhB6XDb8/MQE4snFBbytjRYpMFOCsxY7bt/Wo3mLrh65A1GYjVRUc80NnU04665cJkzplp/vcSiXLfIW4qHRWRwfn8Mfv0zt4JPstvY58dPjE5gJxWqmH9SoNwSH1YS2ItaT2TW/v0V7Jk3hm4+DCGiucBCmNNE9Nu4rMQgLw2zM3p5C4bSbcWV3Mw6OTkMaerOAu+WrVygIew2AYZFx3EII4SeiPwXghTR2aNWY8kdBlP8Xq7/Frrl9wZg8JDXbceoepx2/fdEDIYSm49a5xispShldlKvgstspXTZZh3VhSnZP7ciaTT3NeOTUlOb7pRJSKYFnzk/j+4cu4ucnJhFNpHBFlwN/d/tVeMPOPrQ1WZGSD5lc8oUxPjuP8dl5XPLN49LsPM66A3jsjBuReArfOXABP/7g3hWfJfMEpEa9hiJ6Gm3sasKPj16u2O9CICqNIysmOF7X3gib2YD7948hkRJF14MptmZ0zn/5xur2cFSMTIcxoLE9hUK3ICwcQ7PNXPEeWa4GCwbaGnDsoq+k2xn1hrCmJXt7iky7B1vwwKFxxJMpmDOuy93y1Sv0yJoSWc67CiGSROSRRw2tGm5/BO1N1ry/mL1OO7xBqcGq2if0selQush12e257JiPJ+ELx9Gi4ZVdrkHjig6HteheV55AFJt6l8+Z66rj0UVqRhZluqqnGd979iKm/NFlEwmq5eJMGD94bhzfPzSOS755OGwmvHlXP96yaw229jkXPSkZDIRupw3dThuuGVh+W0IIPH7Gg3f917P4+I9P4rNv3l7B/0nluQPaeoRl2tglnZCs1O+CUpuqtlt+JimL24yjF30wEHDNQHH1YAols3aihoKwselQUX3PgIWDGaXWhfnClWnUms32flfBkUKFSH3WCu9mXDvUim88M4aTl/2Ltsd5O1K9QoX5p4jorqUXEtE7kHso94o16S+8BaC1TUVCPk490Jr9VVev/KCudYtTCYRyPSm0N1lKyoRle8Jy2MxosprqMhOmZmRRJiUIrYWmrc9dmMXbv7IfN37mt/jXX5/FUHsj/vXOHXj2f74S//D7W7Gt36U5K0BEuOmKTnzolmH89+FxfP/QxcJfVMdKOU6fHl9Uoc75uQ7GqKUM897U24xmW2mBQrPNjMG2BjxfI01b0+0piqgHAzIyYSWekJwNxyp6MjLTtn4nJuYi6d8TraT2FOGc7Sky7ZaHeT+7pFWFJxiFxWRAs21lZ9D1UCgI+wCADxDRY0T0z0T0WSJ6HMCHUeM9vcphyh/NWWOlUNpUqD0hedkXQSIlcv7CK13ztRbnT8k1Lq05HgiKHV0UjiUQjCZyPgF0NVvrNBMW0xSEXdktPfFWs2mrEAL37x/DW7/0DF5yh/DRV27EE399M7713utwx44+1ZnYfD7yimHsWdeGj/34RFUGVVdKSUFYuk1FZerCFg4RFJd1U+Y+7h7UNi8yl639Lk1tKpIpgc89chpff2pE9ya3F2fCSKZE0TWprY0WEJU+usgXjlesR9hS2+WM1PGLxQXG7kAU8/H87SkUnc02DLY1LOtNpnTLr7VSjVqUNwgTQlwSQlwH4FOQTkdeAPApIcRuIcSlCqyvprj9kfSQ11yUTJjathJjM/mPAhc7umhqLoJOhy1njYvyhKN1dFGhgstup61+M2EO9a9cHTZpdt6py9UJwiLxJP7qv4/jYw+ewL4N7fjln70MH3nlcEl1LNkYDYR//cMdaLKa8f5vP4dwLKHr7deCRDKF6VAUHUUGNZU+IamUGhSzHQlIW5BEwMs2Fl+4nWlrXzMu+eYxrSJ7NB9L4p5vHcYXfnMOn3zoFP7hZy8gldIvEFPa/Qy1F/d3YDYa0NpQ/C6BwjdfmeHd2WzubYaBgOPjvqK+fqHFh7pA9trBVhwam1l0P3KjVvXUji36jRDii0KILwghfl3uRdWiaCKJ6VBs2TDspbqbbTCQ+u1D5UEjVyasrdECi9GguWt+oa1TJeujtfYh/So8RzDa3WxPz6ysF/FkCr5wXHNPm009zVXZjrw4E8ab7nsa/314HB95xTC+eve1cJbxVXenw4Z/vXMHXvIE8bEHT5bt+1TLTCgGIUqrXxnubKpYptDtj8JmNsBhLW6rZ7jLgf1/8wrcdEWnLuvZ2ucCULhp60wohrd9ZT9+9cIUPvG6TXjnDYP46pMj+OsfHEdCp+kTIxoDiGz06BXmC1UvE9ZgMWFjlwPHitwiztcyKZtrh1rhC8dxzrOQCVYOurDCih1btOoof5TKCcBcTEYDupttqrcjx6ZDsJoMOYM7g9xSQOv8yEl/JG+RsNKUVOv8SHfBTJgVUwFpskC9mNbQniLTpt5mjEyHEIpWLjv0xFkPXvdvT2JsOoyv3r0LH33VxqJO9Gm1d0M7PnzLsFz4v7Lqw4oZhr3Uxi4HzrqDFZkhKY1Os5W01ZPrwE4xNvctn0m51MWZMN5079M4edmPe99+Nd65dwh//7pN+LNXSjWH7//2c7o0eR6dltpTlNIuo8NhLWkMVTyZQiCagMtevZYd2/qdOD7uK+r3ccRbuD1FJqUuLHOEkZe75avGQZhKU/78GaBMfS12jGvIhK1tbcj7RNrjtGnejnTnmBupUP5AtHbNX8iE5QjCmm1IpoSqrYlasdCoVduDxlU9zRACRfVx00oIgf947Bzu/tpBdDlseOiD+/CKq7rK/n0zfTijPux0Bf7PlaLHSa7hriYEIomKzE51B4rrll8uzTYzhtobc2bCTlyawxv+42lMh2L49nuvw61begBIhz/+7JUb8YnXbcIjp6bwrq8/i2CJL2iUodOlBKgdDqvmF6eZlG71LY3VyYQBwLZ+F2bDcYxrnN4CSImBNa2F21MoBtoa0OGwpoMwaXs/xkGYShyEqZRu+aCibqTPZVedCbswU/gUSq/LjgkNQVgwKhXP5wvClEaGWtPu7kAERkPugv9uZ3EHCappoaeNtleulTohGYjEcc+3DuMzvziN27f14kcfuKGobuClWlwfdriiGcBy0qOxpHI8/9DY8hEuepPaadRGWxTF1j5n1hOSvzvjwVu/9AysJgN+8Kd7cO3g8r5k79w7hH9563YcHJ3B2/5zP2Y01qlmGlXZWiEfZTuy2KymMrKomMa/etne7wIgNW3VasQb0rSdS0TYPdSKZ0dnIITATLj07f3VhIMwlRb6bhX+xeprsWPSHym4JbdwFDj/L3yfS7o9tXUT6fYUeYIwZXRRMZmw9iZLzsyd8j3rqThfy/DuTL1OG5x2c1lPSJ5zB3DHvz+FX73gxsdeuwlfuHNHVRundjps+MKdO3DeG8LHHjxRke23ciu15QMg1Qc2WU04cH5ar2Xl5PHX3lbP1j4nLs9FFmXAf3B4HO/+r2exprUBP3z/Ddggt/LI5g07+/Gld1yD05MBvPm+p4ualxtLpDA+Gy5qZmSmTocNsWQK/vniXmT4wnImrEqF+QBwRbcDFqMBxzXWhSnPSVpr6nYPtmJiLoLx2Xnulq8RB2EqTfnllg8qag16XXYkUyIduOXikY8CF8qE9TjtSAmorlMo1KhV0eHQProo18gihVKHVuj/Xku0jCzKRES4qsdRthOSP39+Anf821Pwz8fx7fdeh/fsG6qJI983bGjHR14xjB8euYTvHx6v9nJK5glE0WxTPy0hG5PRgF2DLcuO6ustHEsgEE0UfTKyXDI75wsh8O+/PYe/+P4xXLeuFQ/cs0dVDdorN3Xhm+/eDbc/ijff9wzOe9S1/BBCYMQbwleePI+UKK0oH1gIxovtszVbA0GYxWTAVb3NmjvnK+0pBjWeLlUynM+OznCjVo04CFPJ7ZdaPqh5Ekz3Civwam5UPhm5NkejVoVSIKn21aESABXq3t3epP0UkKfAVkhbowVmI9XVdqQ3GIXdbERjEafNNvU4cXoyoPtBhEdPTeFPv/0cNnY78NCH9uH6dfr0dNLLh24Zxg3r2/DxFVAf5tGpiHj3UCvOuYOas8tapLvl19h25GZ5a/7oRR8+/uOT+Kdfnsbv7+jF19+5W1ND2OvWteG777sekXgSb77vmZzF/nPzcfzixAT+5ofP48bP/BY3f/YxfOYXpzHY1lDyKKaOIk+OK5TtyGqdjlRs73fixKU5TY9NxZ4uvaLbAYfNhIMjMxl97DgIU4ODMJXUdMtX9Ctd8wvUhY2pPArcqzKoU0yq3DqVhnhrD8LypZkNBkKnw1ZXbSq09gjLtKm3GfPxZPpYtx4i8SQ++dBJXNHlwPfed316MHotMRoIn79zZdSHuf361FhdNyQFyku7h+tJj5Oc5eCwmbGuoxH//ttzuH//GP7k5evwubfsgMWk/SlmS58T379nD2xmI/7wy/tx4Pw0EskUDo/N4F8ePYM/+I+nsPNTj+Cebz2Hh45dxlU9zfhfd2zG4391Ex77q5uxpsCL2kKULGOxvcKU7chqB2Hb+l0IxZKqM4rAwnOS1okDRgPh2sFWHBydSf/cuEWFOjxTQKUpfwRXdOeuacikNmi6MBOG0UDpBq+59MgZLbXZpam5CBw2U8HaIa2ji5Ipoerocb01bJ3W2C0/01U9cuf8y36s72jSZT33Pf4Sxmfn8d0/vh5WU+ld78tFqQ97x1cP4O8ePIHPvWV7TWyXauUJRtOFzKXY1u+E3WzEgZEZ3La1p/SFZaFskenZYkIvO9e0YMQbknqA7R0q6bbWdTTh+/fswR999QD+6GsHYTUaEIgmYCApuPjgzRtw48YO7FjjWjQ4Wg+ZQ7yLMRuOwWQgNBXZx00v2+Ut4mPjcxjuUvfcpbSn6CliBuq1g634zYtuvDgRgMNqgt1Su49dtYSDMJXc/ihuHFY3oLbBIvWpUbMd2eeyF3wQcdjMaLaZVG9HTvojBccrAdKDjTK6SE09zEwohpQo3Km7u9lWEzMV1fIGo0W/eh7udMBsJJy4NIfXbe8teS0XZ8K497GXcPu2HuxZX1tbkNlI9WEb8S+/OoNXb+oqW/BRiDsQAYS6FjKZhBByJqz0V+1mowHXDLRgfxmL8xe2I2svy/C3r7kS79k3lD41XKpelx3fv+cG/K+fnoLVZMCNwx3Yu6Gt7DMZHVYTrCZD0b3CfPNSo9ZqvyBZ19GEJqsJxy768KZr+lV9zahXW3uKTLuHpGHwv3nRzfVgGvB2pAqhqFQMq+XVZ6+rcMPWC9MhVUNSpduzq27YOuWPFqwHAxbSxWq3JNOnyApkjbqapUxYvZyc0zq8O5PFZMDeDe24f/+YprR/Lv/4sxdgIML/fM1VJd9WpXzwlg0Yam/EvY+/VJX7PJUSeMdXDuBPvnVY89eGYknMx5O6PWlcN9SK01OBdF2Q3tyBKCxGQ9W3urJpa7LqFoApWhst+Je37sCn37gNt2/rqchQbCIqqWu+r4rDuzMZDYQtfc2axheNTmtrT5Fpa58LVpNBmi3MW5GqcRCmwkKhu/pfrD6XXVUmrFBRvkIKwtQX5qsJGBeCMHVPGIUatSp6nDaEY0kE6qBOKJkSmAnF0NFU/IPm//mDrbCYDPjQd48gmii+6/eTZ734xclJfODm9ekt7XpgNBDevW8Ix8fn8Oxo+ftkLfXrF904MxXE0Ys+zcGP3ie5dg+1QojF3cP15A5E0OHgwcjl1llSEBaHq4o9wjJt73fhhYkAYonC7Y2KbU+hsJgM6X55nAlTj4MwFZRu+WoatSr6XA24NDufMzMwF45jbj6u+he+12VTNT8ymRLyWJPCfwTprvkqH2wWRhbl/zl0yVm4yToozle2WNtLeNDocdrxmTduw8nLfvzTL04XdRvxZAqfeOgk1rY24L03rit6LdXypqv74Wow4ytPnK/4977v8ZdgNxshBDRvBbqVYdg6nTbcvsYFi8lQtiCMByNXhjS6qPgWFbWQCQOk+rlYMoUXJwuXhyjtKYodfg5ImWCAgzAtOAhTQcmEaak36WuxYz6eTJ+UWWpsRjqFslbldmSP0w5fOI5wLH92aToozW1UUxOmBB5qi/PVZg3SDVuLDMKSKYG//u9jOHKh/FmVYkcWLfXqzd24a88AvvLkCB477db89d94ehTn3EF8/LWbSupXVS12ixHvuG4Aj74wlT7mXgnPjs7g8Ngs/vL3rkCjxYgnz3k1ff3CtAR9njRsZiN2rnGVrV/YlL+2RhatVKVuR7bUyHbxtozi/EKUv9tCzcPzuZaDMM04CFNBbd+tTH1yb69cW5JKjzC1NWFK77FCdWHprJ2KIEwZXaQ2E+YJRFWdeukpMRN2ZiqABw6N458fOVPU12uhBGFtJQz8Vfzta67CFV0O/OX3j2l6Fe0ORPD5X53FTVd04BVXdZa8jmq564YBmA0GfP2pkYp9z3sfewmtjRa8bfda7B5qxdPntGXCytFY8rp1bTh5eQ7+SPYXYKVwB6I116h1Jep02DAbjqvaxlvKF47XTM1ef4sdrY0WHFfRtHXUW1x7iky7Blqxe7A1nRFjhXEQpsKUP4pGi1HTkeM+lxRc5RqgekHux6K2JmyhTUX+LclJDQGjzWxEs4bRRZ5AFB0qngCUJ4li21QcueADADx5zouXdCh2zyedCdPhSdhmNuKLb9uJQCSBv3jgGFIqmyT+35+fRjSRxMdfu6mua306HTa8fkcvvn9ovGyF6ZlOTwbwmxfdeOcNg7BbjNi7oR3nvSFNI2/cAWkShp41PNcPtSIlgMM618dFE1JmXUtZBCuOEpRPh7RlwyJx6aBHrWxHEhG29TtVjS8anS6+PYXCbjHigXv2YFeWGaEsOw7CVFBb6J5J6f2VKxM2Nh1Gp8Oqeg5gbzoTpi4IU7vedodV03akmlMvVpMRbY2WooOwoxdn4bCaYDYS7n9mrKjbUMsbKG5kUS4buxz4+Os24YmzXnzlycL1UYfHZvGD58bxnn3rsE6nPmPV9J59Q5iPJ/HtAxfK/r2+9PhLaLAYcdeeAQDA3g3tAICnNGxJSrNQrTlnoRZj59oWmI2E/SP6tqpQezCGlU55nFNagqg1N18bjVozbet34aw7ULCUpZT2FKx4/NNWYcof0fzA19Jght1szBk0aT2F0u20gQi4VGg7ci4Co4FUBxXtTdZ0IFKIcjJLja5mW9HbkUcv+rBrsAW3benBDw6Pl7UbuzcoHflvtunXMu9tu9fi1s3d+MwvTuc9Hp5MCXziJyfR1WzFh27ZoNv3r6areppx43A7vvH0aFFbOWpd8s3jJ8cu485r16azDld0OdDeZMHTL6kPftwBfXqEZbJbjNjW78KB8/rWhS10y+dMWLkV27B1Vs4AV3Nu5FLb+51ICeDEpfzF+aPTIQyVOHeTacdBmApTAXXNTzMRUd5eYWMzIdVF+YDUCLLTYcVEgUzYlD+CjiYrjCpf2WsZXVRobmSmbmdxQVggEsdZdxA71rTgrj0DCEQTePDoJc23o5YnGEV7k0XXbUAiwqffuBUdDis+/N0jCOYIIv/fsxfx/KU5/O1rripqbmWtes++IbgDUTx07HLZvodyCvO9Ny50ZjcYCHvWt+PJc17V/crKddrwuqFWnLg0p+sLCCUrw0XP5Vfs6KLZUG1mwgDkfUGotKcopSifFYeDsAKEEJjyR4saE9LX0pB1O3I+lsSUP4oBjV3ae132gm0qJv2RdIsINTpUbkeGogmEYuqbWnY7bekDDVocH5+DEMCOtS5cM9CCq3qacf8zY2VrAuoNxnSpB1vK1WDB59+6Axdmwvj4j08s+7wvHMM//fJF7B5qxet16LRfS16+sQPDnU34ypMjZbnfZkMxfO/gRbx+R++yfmp717fBE4jinFtdLWHZgrB1bUikBJ7T8YSvJ6Cc0uYgrNzaGovLhM3Ny8O77bWTCetwWNHrtOU9ITnlL709BStO2YIwIrIR0UEiOkZEJ4nok1mucxMRzRHRUfnt4+VaT7F88gkZreNQgNwNWy/MyCcjNZ5C6XXaMVHwdGQEXRqeVNqbLOnRRfmk61HUBmHNNkyHYpqblx6VT/Hs6HeBiHDXngG8OBnAobHytKvwBorvll/Ideva8KFbhvHD5y7hR0fGF33uc4+ewdx8HJ943ea6LsbPhojw3huH8MKEX9PWoFrfeGYU8/Ek7nn5+mWf01IXlkimMB2KoqMM23vXDLTAaCBdtySn/FEYaCFAYOVjMRnQ0mDW3CtsVm5J1NJYO5kwQMqG5cuEjcoHxQZLOBnJilPOTFgUwC1CiO0AdgC4lYiuz3K9J4QQO+S3T5VxPUWZkv8ItW5HAtLx4JlQDPOxxYGIMqleeybMhku+3A1gAakthJZWGmpHF7k1HuVXfl5aC1uPXJjFuo5GOOV0/h07euGwmfDNMhXoe+XtyHL50C0bcO1gC/7uRyfS9/upy358a/8Y3nH9gO5jXmrFHTv60N5k0b15aziWwDeeHsUrr+rExixDide0NmBtawOeVNGqYiYUgxDl2d5rspqwpc+JAzoW57sDEbRrKDVgpel02DRnwpS+kLWUCQOkJsJj0+Gcp5aV9hTFdstnxStbECYkyp6AWX6rj2GCGRb6bml/oFZ6ey3Nho1p7BGm6HXZEU2kMBPK/oc0H0vCH9E24zLdNb/A6CKtJ7OUQFDLCUkhBI5e9KVHXwDSMPQ3X7MGvzgxUXQH61xSKYHpUKxsmTAAMBkN+PydO2E0ED783SOIJVL4xE9Owmk3489ftbFs37fabGYj/uj6Qfz2tAfn3AHdbveBZy9iNhzPmgVT7N3QhgPnp5FI5j8YsDABojz3//VDrTh2ca5gllktaRIGF+VXSjENW33hGKwmQ8FeipW2XW7amqtVhdKeop7Gpa0UZa0JIyIjER0F4AbwqBDiQJar7ZG3LH9ORJtz3M77iOgQER3yeDzlXPIyU3PaWj5k6s0VhM2E4LSbNfeS6XFKtzeRo+A93VRWw1qVAKTQg43a4d2K7nRfM/WB0/jsPLzBGHaubVl0+R/tGUA8KfC9gxdV35Yavvk4kilR1iAMkILxT79xG46Nz+EP/3M/Do7O4K9+78qa6SVULu+4fi2sJgO++qQ+zVvjyRT+84kR7BpoyduHaO+GdgSiCRy/lL83UrlbPuweakUsmdKtLszt1/8kJ8tNGl2k/XRkLZ2MVGxROufnaNqqtKfgLGvllTUIE0IkhRA7APQD2E1EW5Zc5TkAA/KW5RcBPJjjdr4shNglhNjV0dFRziUvszCyqIhMmNIrbHZ5JkxrFgzInVlTaO0RBiw0KS20HekJRGEykOoHGGUNUxqCsCPyA8TOjEwYIHVwvnG4Hd85cKFgdkMLPRu1FvKarT34w91rcXhsFlv6mvHWa9eU/XtWW1uTFX9wdT9+8Nwl1Sdw8/nZ8Qlc8s3nzYIBwJ51bQCApwvUhXnKnAnbNdgKIv2GeXO3/MpSMmFaDpfUUrf8TM02M9Z1NOYszuf2FNVTkdORQggfgMcA3Lrkcr+yZSmEeBiAmYjaK7EmtaYCEbQ0mGE1aU8vdzmk+o1LvvCiy4s9Ctwjj0LK1XtsYbyStsJ8oPDoIrfGppbNNhMaLEZN25FHL/hgMxtwRffyWp+79gxi0h/Bo6emVN9eIcr/uZw1YZk+/tpNeOcNg/jnN+9YNa8437NvCLFECt/aX1pNnxAC9z3+EoY7m3DLlflHO7U1WXFVTzOeKlAXls7ulikId9rN2NTTrEtxfjkPEbDsOh1WRBMpBDS0GanVIAwAtucozhdCYHQ6xEX5VVLO05EdROSS37cDeCWAF5dcp5vko2FEtFtej/7HqUowOVd8HYbJaEB3s23RvMd4MoVLvnnNRfmANN/QYjIU3I7Usl6rSRpdVKhNhdaj/ESEbo0NW49enMXWPifMWTo233JlJ/pcdl0L9NPDm8u8HamwW4z4xOs3Zw0yV6oNctB0/zNjJdVGPXbagxcnA/iTl69X9UJg34Y2HB6bXXYoJpMnEEWzzVTWgenXDbXhuQuzmk8JL+UNSocIeDuycrQ2bE3Ij+21uB0JSMO83YHossfkKX8UkXgKg0XszrDSlTMT1gPgt0R0HMCzkGrCfkpE9xDRPfJ13gTgBBEdA/AFAHeKcjWEKpI7ECmqPYWiz2VftB15aXYeyZTQ1KhVQUQ5214AUsDYaDHCYdP2SqzdUbhhq6eIzuLdTpvqTFgskcKJy/5FRfmZjAbC265bi2fOT+PslD6F3tNBfUcWsezeu28I06EYHjxSfNPdex9/CT1Om+qeajdsaEcsmcKhsdxZKE+wPD3CMl23rhXRRErV7L58lKwdB2GVo3V00bf2j+GSbx6vq9G+f0rT1mNLsmHcnqK6ynk68rgQYqcQYpsQYovSfkIIcZ8Q4j75/X8TQmwWQmwXQlwvhHi6XOsp1pQ/gu4S6jD6WhYHTWNyj7BijwL3OG05u+YXM+MSkLvmFxhd5C6iqaWWTNgLE37EEqllRfmZ7rx2DSxGQ8lbWwpvUKpzc+o4vJktt2d9Gzb1NBfdvPW5C7M4ODKD9964DhaTuoes3YOtMBsp75akVOhe3u29a+UDBAfOl5bgVwKBUl4QMm20dM13+yP450fO4Mbhdty2pbvcSyvK5t5mmAy0bEuS21NUF3fMzyORTMFT4rHwPpcdk/5IuqD8gtIjrMjUb6/Lvmh7M9NkkUFYoSHeyZTATEh7JqxL7pqfShV+4k03ac2RCQOkWp/bt/XgB89dyjkKSAtvMIq2Jouuw5vZckrz1nPuIB47o/10832PvQSn3Yw7NRxmaLSasHNNS96mrZXIhLU2WnBFlwMHSizOV07pFdMqhxWno0l6LFWzHfm/H34B0UQKn7pjS802X7aZjdjY5ViWlR2ZDsFiNHB7iirhICyP6VAMKVHaq8++FjuSKYEp+Q95dDoMm9lQ9LZCr8uOqUAE8SynBKf82hq1KqRMWO4HmulgFKkimlr2OG1IyL24Cjl60YdOhxU9Bdb/R3sGEIwm8KMStrYU3mB5e4SxBa/d1ouuZiu++oS2dhXn3AE8cmoKd+8Z0Dxf84YNbThxeS5ng8pyjSxa6rp1rTg8Npv1b1YtdyACIt46r6RmuwkWo6FgEPb0S148ePQy7nn5OgzV+Jbe9jVOeTTcwgvjMW8Ya1rtq+awUK3hICyPYvpuLZVuKyHXhY1NhzHQ2lj0q6Vepw1CYNlcRiEE3EXOuGxvsiAQzT26aKFbvrbbVtaiZkvyyIVZ7FzrKvhz2bnGhS19zbj/mdGS5xJK3fL5Sa0SLCYD7r5hEE+e8+LUZb/qr/vS4+dhM0tfq9W+De0QAngmy+ikYDSBcCxZkRqr64baEI4lcaJA37J83IEoWhssWQ+tsPIgIrlXWO7Hr1gihY//+CTWtNrx/ps3VHB1xdnW78LcfDzdMByQasJ4K7J6+C86j1K65SsWGrZKv/Rj06GiivKX3t7SLcmZUAyxZKqotXYU6BXm0TiySKEEr4WK82dDMYxOh7FjTe56MAUR4a7rB3FmKljyFo83IG1Hssp4++4B2M3GnM1bUymBaCKJYDQBXziG05MBPHj0Et6yaw3aigiWt69xodFixFMvLd+SLPZ3uhi7h+S6sBJ+X93+ymTt2GKFuuZ/9ckRnHMH8YnXbS7rKVu9bFOatsp1YakUt6eoNm35/VWmmOanS2VmwlIpgQszYdx0RfENZ3tdSif6xcX5kyVk7TK75ve3LA8QtQ7vVvSoHF10VH5AyFcPlul123vxjw+/gPufGcP1cmNOrYQQ8AZjFWtPwQBngxlv2dWP+/eP4alzXsSTKcSSKSSSAvFkCokstYNGA+GPb1xX1PczGw3YPdSKp7MU57uVJswV6LvV4bBifUcjDpyfLthoNpdST2mz4nQ4rLg4E876uUu+eXzh12fxqk1deMVVXRVeWXE2djlgNRlwfHwOd+zogzvA7SmqjYOwPNz+CAwl1mHYLUa0NVpwyTcPdyCKaCKFtSWkfpXRRUvbVCinp7qKqAlbGOKdvXam2KaWbfKw4cm57Kc5FUcu+GCghVdphdgtRrxlVz++/tRo0SdC/ZEEYskUb0dW2Ptv3oBoIoWUEDAbDTAbDbCYDDAZKP2+2UgwGQwwmwxY396INUX01FPs3dCO355+AZd984sKj9M94iqUXdo91IafHruMZEoUVXvj9kezDixn5dXhsOK5sexjpz710EkICPz96zZVeFXFMxsN2NLnTJ+QHPFye4pq4yAsjyl/BB1y1/tSSG0qIul+LMU0alU0Wk1wNZiXdc0vJROmZjuymKaWRgOh02HF5Fz+wtajF33Y2OXQVHj99usG8J9PjOA7By7go0UMwl4YWcTbkZXU1WzDp9+4rWLfb+8GaQDHU+e8ePOuhdOVldyOBIDr17Xiuwcv4IUJP7b0qXuxoUilBLxBnhtZDR1NVsyEY4gnU4vq8X572o1fnpzCX/3eFVl3D2rZtn4nvnfwIhLJFMamuT1FtXFNWB6TRRa6L9XrtOPSbBgXpkvrEabocdoxsaQmbHJOOj1VzJOKUheVq/ahlKP83XKbilxSKYFjF33Yudal6XYH2xvx8o0d+O7BC0WdOlsYWcRPbCvZFV0OtDdZ8PSS4nx3IAqzkeCqUI+464akbfP9RfQLmwnHkEgJXR6LmDadzVYIIdXcKiLxJP7+xyexvqOx6K3yatre78J8PIlzniC3p6gBHITl4S5yq2sppWHr6HQIJgOl67qKvj2Xbdl25JQ/grZGa1Gnp5TRRbkyYaUUBXc325bVr2UamQ5hbj6OnSqK8pe6a88A3IEoHjmpfZ6kl7vlrwoGA2HP+nY8ec676DStR+Ms1FJ1O20YaGsoqjg/3aiVM2EV15FRL6u497GXcGEmjP91xxbVzYNrSbo4/6IPo94Qt6eosvr7Daogqd6o9Ae+PpcdkXgKRy/60N9ih6nEY+Y9Tvuy+ZFSj7Di19qRZ3SRJ1h8Z3EpE5Z7O/LoBR8AYIfGTBgA3HRFJ/pb7PjmM6Oavza9HclB2Iq3d30bPIEozrmD6csq1SMs03VDrXh2dEZV8+JM6ZFF3Ki14pTfEeU+GPWGcO/jL+H123txg7zVXW8G2xrhsJlwbHwOY9Phmu9tttJxEJZDJJ7EbDiOLh1OT/W1SKneQ2OzJRXlK3pddszNxxd1jZ/0R0vqZ9belP0ottJ/rJRMWDCaQCASz/r5oxd9aLKasL6jSfNtGw2Ed1w/gAMjM5rnSXqDURhI6mjOVjalLuzJjO757iJmoZbquqE2+MJxnNb4u+pOn07m7chKU06kegJRCCHw9z85CYvRgL+7/aoqr6x4BgNhW79TyoRNhzDA9WBVxUFYDp70mBAdgjB5vz2WSJVUlK9It6nI2JKc8pd2hF0a4r38dGQolsR8vPimlkoH/1x1YUcuzmL7GmfR6fA3Xt0PIuCh4xOavs4bjKK10cJp+FVgTWsD1rY2LJojWY1MWLpfmMa6MKWdBvcJq7z2jHrZX56cxONnPPjoqzbWfbuQbf0unLzsl9pTcCasqjgIy0EJGopp+bBUX0bRY7EzIzOlG7bKW5LRRBIzoVhJmbBco4tKfQJIN2zNckIyEk/ixYmA6v5g2XQ4rNg92IqHn9cWhHkCPLJoNdm7oQ0Hzk8jkUwhkUxhOhTVPAGiVGtaG9DnsuPgqLa6MHeRp5NZ6awmI5x2M0anw/jkQ6dwZbcDd+8ZqPaySrY9ox0Q9wirLg7Cclho1Fr6E7WrwYwGi/QAqkfqd6FrvpQJUwp3SwrCHNaso4s8JW6FKJmwbMX5Jy7NIZESqjrl53P7th6ccwc1bUnyyKLVZe+GdgSiCRy/NIeZUAyiiFmoerhuqBUHR2Y0jdxy+6N1n3mpZx0OK3743Dgm5iL4xzdsKbmmtxZs63el3+f2FNVV/79NZZIeWaTDq2UiSmfD9MiEdTmsMNDCdqQeWbv2HG0q3CX2U1K2c7NtRx5RivJLyIQBwK2bu0EE/ExDNkwKwrgebLXYI09WePqcd+F3ugpB+HXrWuENxvCSJ1j4yjJ3QJ8DQqw4nQ4rUgJ4y65+XDPQWu3l6KLHaUN7k5XbU9QADsJycPsjsJgMcDXo00dIKc5fq0NNmMloQFezDZfkXmF6ZO0WuuYvDsKKHVmksJmNaGkwZx1dpJwWLTUj0dlsw7UDrfj585Oqri+NLOJM2GrS1mTFpp5mPHVueuF3ugqBzUK/MPVbktIhAs6EVcualga4Gsz4/269stpL0Q0RYfdQC4a7mrgutso4CMtBaU9BpM8v6LY+J67qadatrqPXZU9vR07ptB0JLB9dpDS1dJbQ1LKr2YbJuexBWKlZMMVrtnbj9FRgURuCXEKxJCLxFNq50HlV2buhDYfHZnFBngVYjUzYQFsDOh1W1f3ChBBVOcnJFvzt7Vfh4Q/fWNQQ+Vr2v9+wFV9757XVXsaqx0FYDpP+iC5bkYo/e+VG/OSDe3W7vR7nQhPUKX8EVpOhpECpPUtTQuXjUptadjttyzJhbn8El3zz2Lm2tHowxa1begAAP1exJTnNPcJWpRs2tCOWTOHnJ6TfkWrUhBERrl/XhsdedOORk4Uzt/75BGKJFJ+MrCKn3bwit+xcDRaewlADOAjLwe2P6nIyUmGQBxTrpc9lx+W5CIQQmJyLoNtpKylrp4wuWrYdqcPMuh6nbdnpyCMXfQBKrwdTdDtt2DXQoqoubKFRK9eErSa7B1thNhL2n5+p6mnDP3vlMPpa7Hjf/Yfxwe88l7NJMgBMpRu18pMlYysRB2E5TOmcCdNbr8uOWCKF6VBMl6ydchR76ROCWx5iXoquZhu8wShiiYUZj0cv+mA2Ejb3Npd025lu29qDFycDOF+g6NkT4JFFq1Gj1ZQej1XNzNK6jib85IP78Bev2ohHTk7hlZ97HD86Mp71xCSPLGJsZeMgLItAJI5QLFnTJ5J65CzdZd+8NONSh6xde5Nl2XakN1h6PyVlrcroDwA4cmEWm3SskQOA27Z0AwB+fiL/No8SaPIWz+pzwwapML7ahe4WkwEfesUwfvbhfRhqb8RH/98xvPu/nk3XeSrSI4v4d5WxFYmDsCzShe46bkfqLbNX2KQ/gm4dAsb2psXzI6WmljFdMmEA0sX5yZTA8+Nzum1FKnpdduxc68LPCnTPV/6PPLJo9dknjzCqlQB8uMuB/77nBnz8tZuw//wMXv0vv8P9+8fS8yXTI4t4O5KxFYmDsCyULvHVfrWcjxKEvTARQCSe0qXAsmPJ6KJpuallqa/ClWBWKc4/6w4gFEsWNbS7kNu39uDUhB+j3lDO63iDUbgazLrW6LH6sH2NC22NlpoaWmw0EN69bwiPfPRl2LHGhY89eAJ3/ud+jHhDcPujaLQY0WQ1VXuZjLEy4GehLPTsll8uLQ1m2MyGdIG7HkFY+5LRRUo9SqlZg55mKWBUMmFKk9adJXbKz+a2rdIpyYdP5M6GeXlk0aplNhrwy4++DO+/eX21l7LMmtYG3P+e3fjMG7fhhQk/bv387/DLk5OcBWNsBeMgLIt0t/wafvAjIvS67Dh6YRaAPlunS0cXeYL61KM0202wmQ3pIOzoBR9aGsy6TA9Yqs9lx/Y1rryNW7lb/urW3mSF1VSbcxiJCG+5dg1+9ecvx8s3duCSb76mXwwyxkrDQVgWU/4IHFYTGmt8C6DXaYc/kgBQWqNWxdLRRXplwogI3c0LvcKOXvRh+xqXbo1wl7p9azeevzSHC9PhrJ/nbvms1nU12/ClP7oG33j3bnzstZuqvRzGWJlwEJaFOxCpykgTrXpdC4GXHutd6JovBV9KMKZHwNLttGHKH0EgEscZd6AsW5GK25TGrTm2JL1B3o5ktY+I8PKNHdjc66z2UhhjZcJBWBaTc5Ga3opU9DilWquWBrMu2ytLu+Z7glE47WZd2kh0N9swMRfB8+NzEAJlKcpXrGltwLZ+Jx7O0rg1Ek8iGE3UzOk4xhhjqxcHYVlM+aO6bO+VW598QlKvgHFhiLd0QtLtj+oWrHQ5bXD7owud8vtdutxuLrdt6cGx8TlcnFm8JbmQ3eOaMMYYY9VVtiCMiGxEdJCIjhHRSSL6ZJbrEBF9gYjOEdFxIrq6XOtRSxqYG6mLE0lKmwq9+pktHV2kx8giRU+zDbFkCr9+YQrrOhrhbCh+zqUar9kqNW79xZLGrV6eG8kYY6xGlDMTFgVwixBiO4AdAG4louuXXOc2AMPy2/sA3FvG9agyE4ohnhR1cSKpR64J0ytrp4wuShfmB0ofWaRQAsXnLvh0b9KazUBbIzb3Ni+bJalk+TgIY4wxVm1lC8KERBniZ5bflg5HuwPAN+Xr7gfgIqKecq1JjXS3/HrIhDntMBsJ/S123W6zvckCbzAKIQQ8Af0yYd3OhTXurEAQBgCv2dqDoxd9uJQxCiadCeOaMMYYY1VW1powIjIS0VEAbgCPCiEOLLlKH4CLGR+Py5ctvZ33EdEhIjrk8XjKtl5AqrO69+1X4+qB8p3e04vdYsQDf7IHd90wqNttSl3zo3K/sJR+mbCMoHbn2sr8bF8jN279eUY2TGlG28YjixhjjFVZWYMwIURSCLEDQD+A3US0ZclVsjWKWpotgxDiy0KIXUKIXR0dHWVY6QJngxm3be2pi9ORgBTQNNv0q6+S5kfG0luSeo1uam+ywECA1WTAFd0OXW6zkKH2RlzV07xooLc3GIXDZtJ1cDhjjDFWjIqcjhRC+AA8BuDWJZ8aB7Am4+N+AJcrsSaWXXuTFZ5AVLdGrQqT0YBOhw1b+5wVndl4+9ZuHB6bxcSctCXpDcbQwfVgjDHGakA5T0d2EJFLft8O4JUAXlxytZ8AuEs+JXk9gDkhRO6hf6zsOhxWBKMJXJwNpz/Wy1+8eiM+/Iph3W5PDWWWpHJK0sPd8hljjNWIcs7l6QHwDSIyQgr2HhBC/JSI7gEAIcR9AB4G8BoA5wCEAbyrjOthKihZolOX/QBKnxuZ6c271hS+ks7WdzThym4HHn5+Au/aOwRvMIorK7QdyhhjjOVTtiBMCHEcwM4sl9+X8b4A8IFyrYFp1+6QCtZPTfhhMRrgtJe3n1cl3LalB5//9RlM+SOY5pFFjDHGagR3zGeLKAHKCxN+dDisZRuyXUm3b+uGEMBDxy5jbj7OQRhjjLGawEEYW0SpAQtEEiuml9aGTgc2djXhW/vHAHCjVsYYY7WBgzC2SFvjQoCiZz1Ytd22pQej09JhgzaeG8kYY6wGcBDGFrGYFurA9DwZWW23b1sYxMCZMMYYY7WAgzC2jBJ8raRM2HBnE9Z3NAIA9wljjDFWEzgIY8u0y9t1KykTRkS4Y0cfbGbDivp/McYYq1/l7BPG6pSyXbfSMkbvv2k93rCzD3YLjyxijDFWfZwJY8uktyPrZH6mWiajAWtaG6q9DMYYYwwAB2Esi3QmjLftGGOMsbLh7Ui2zOu29SKWSKHXubIyYYwxxlgt4SCMLbO2rQEffdXGai+DMcYYW9F4O5IxxhhjrAo4CGOMMcYYqwIOwhhjjDHGqoCDMMYYY4yxKuAgjDHGGGOsCjgIY4wxxhirAg7CGGOMMcaqgIMwxhhjjLEqICFEtdegCRF5AIxV4Fu1A/BW4Puw0vF9VV/4/qovfH/VF76/as+AEKIj2yfqLgirFCI6JITYVe11sML4vqovfH/VF76/6gvfX/WFtyMZY4wxxqqAgzDGGGOMsSrgICy3L1d7AUw1vq/qC99f9YXvr/rC91cd4ZowxhhjjLEq4EwYY4wxxlgVcBDGGGOMMVYFHIQtQUS3EtFpIjpHRP+j2uthixHR14jITUQnMi5rJaJHieis/G9LNdfIFhDRGiL6LRG9QEQniegj8uV8n9UgIrIR0UEiOibfX5+UL+f7q0YRkZGIjhDRT+WP+b6qIxyEZSAiI4B/B3AbgE0A/pCINlV3VWyJ/wJw65LL/geAXwshhgH8Wv6Y1YYEgL8QQlwF4HoAH5D/pvg+q01RALcIIbYD2AHgViK6Hnx/1bKPAHgh42O+r+oIB2GL7QZwTghxXggRA/A9AHdUeU0sgxDidwBmllx8B4BvyO9/A8DvV3JNLDchxIQQ4jn5/QCkJ4s+8H1Wk4QkKH9olt8E+P6qSUTUD+B2AF/JuJjvqzrCQdhifQAuZnw8Ll/GaluXEGICkJ70AXRWeT0sCyIaBLATwAHwfVaz5O2towDcAB4VQvD9Vbs+D+CvAaQyLuP7qo5wELYYZbmMe3gwViIiagLwAwB/JoTwV3s9LDchRFIIsQNAP4DdRLSlyktiWRDRawG4hRCHq70WVjwOwhYbB7Am4+N+AJertBam3hQR9QCA/K+7yuthGYjIDCkA+7YQ4ofyxXyf1TghhA/AY5BqMPn+qj17AbyeiEYhlc7cQkTfAt9XdYWDsMWeBTBMRENEZAFwJ4CfVHlNrLCfALhbfv9uAD+u4lpYBiIiAF8F8IIQ4nMZn+L7rAYRUQcRueT37QBeCeBF8P1Vc4QQfyOE6BdCDEJ6rvqNEOId4PuqrnDH/CWI6DWQ9tmNAL4mhPjH6q6IZSKi7wK4CUA7gCkAfw/gQQAPAFgL4AKANwshlhbvsyogon0AngDwPBbqVv4WUl0Y32c1hoi2QSrmNkJ6kf6AEOJTRNQGvr9qFhHdBOAvhRCv5fuqvnAQxhhjjDFWBbwdyRhjjDFWBRyEMcYYY4xVAQdhjDHGGGNVwEEYY4wxxlgVcBDGGGOMMVYFHIQxxuoeEQkiuj/jYxMReYjop0XenouI3p/x8U3F3hZjjOXCQRhjbCUIAdgiNxgFgFcBuFTC7bkAvL/QlRhjrBQchDHGVoqfA7hdfv8PAXxX+QQRtRLRg0R0nIj2y01JQUSfIKKvEdFjRHSeiD4sf8mnAawnoqNE9E/yZU1E9N9E9CIRfVueBgAi+jQRnZJv+7OV+a8yxlYCU7UXwBhjOvkegI/L24bbAHwNwI3y5z4J4IgQ4veJ6BYA3wSwQ/7clQBuBuAAcJqI7gXwPwBskQdZKx3JdwLYDGme7FMA9hLRKQBvAHClEEIoI38YY0wNzoQxxlYEIcRxAIOQsmAPL/n0PgD3y9f7DYA2InLKn/uZECIqhPBCGnbcleNbHBRCjAshUgCOyt/LDyAC4CtE9AcAwrr9hxhjKx4HYYyxleQnAD6LjK1IGWW5rjKzLZpxWRK5dwiWXU8IkQCwG8APAPw+gF9oXC9jbBXjIIwxtpJ8DcCnhBDPL7n8dwDeDqS3Fr1CCH+e2wlA2p7Mi4iaADiFEA8D+DMsbHEyxlhBXBPGGFsxhBDjAP41y6c+AeDrRHQc0pbh3QVuZ5qIniKiE5AK/n+W46oOAD8mIhukbNtHi107Y2z1ISFE4WsxxhhjjDFd8XYkY4wxxlgVcBDGGGOMMVYFHIQxxhhjjFUBB2GMMcYYY1XAQRhjjDHGWBVwEMYYY4wxVgUchDHGGGOMVcH/D4s39pqTypRdAAAAAElFTkSuQmCC\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(10,4))\n", "\n", "plt.xlabel(\"Months\")\n", "plt.ylabel(\"Quantity\")\n", "\n", "plt.plot(df['Sale Quantity'])" ] }, { "cell_type": "code", "execution_count": 5, "id": "174ee2cc", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 48 entries, 0 to 47\n", "Data columns (total 4 columns):\n", " # Column Non-Null Count Dtype\n", "--- ------ -------------- -----\n", " 0 Month 48 non-null int64\n", " 1 Sale Quantity 48 non-null int64\n", " 2 Promotion Expenses 48 non-null int64\n", " 3 Competition Promotion 48 non-null int64\n", "dtypes: int64(4)\n", "memory usage: 1.6 KB\n" ] } ], "source": [ "df.info()" ] }, { "cell_type": "code", "execution_count": 6, "id": "c6f568ae", "metadata": {}, "outputs": [], "source": [ "df['moving_avg_12'] = df['Sale Quantity'].rolling(window=12).mean().shift(1)" ] }, { "cell_type": "code", "execution_count": 7, "id": "c367ee45", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(10,4))\n", "\n", "plt.xlabel('Months')\n", "plt.ylabel('Quantity')\n", "\n", "plt.plot(df['Sale Quantity'][12:])\n", "plt.plot(df['moving_avg_12'][12:], '.')\n", "\n", "plt.legend()" ] }, { "cell_type": "code", "execution_count": 10, "id": "a018f130", "metadata": {}, "outputs": [], "source": [ "# there is an increasing trend in sales quantity over the months" ] }, { "cell_type": "markdown", "id": "613775c8", "metadata": {}, "source": [ "## Forecast Accurary" ] }, { "cell_type": "code", "execution_count": null, "id": "d985e8e1", "metadata": {}, "outputs": [], "source": [ "- RMSE\n", "- MAPE" ] }, { "cell_type": "markdown", "id": "94146992", "metadata": {}, "source": [ "### Mean Absolute Percentage Error" ] }, { "cell_type": "code", "execution_count": 11, "id": "dafa7a48", "metadata": {}, "outputs": [], "source": [ "import numpy as np" ] }, { "cell_type": "code", "execution_count": 13, "id": "183268aa", "metadata": {}, "outputs": [], "source": [ "def get_MAPE(actual, predicted):\n", " ytrue, ypred = np.array(actual), np.array(predicted)\n", " \n", " return np.round(np.mean(np.abs((actual - predicted) / actual))*100, 2)" ] }, { "cell_type": "code", "execution_count": 14, "id": "43515dac", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "14.04" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "get_MAPE(df['Sale Quantity'][36:].values, df['moving_avg_12'][36:].values)" ] }, { "cell_type": "code", "execution_count": 15, "id": "2cd8e001", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "734725.8359239782" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.metrics import mean_squared_error\n", "\n", "np.sqrt(mean_squared_error(df['Sale Quantity'][36:].values,\n", " df['moving_avg_12'][36:].values))" ] }, { "cell_type": "markdown", "id": "88910119", "metadata": {}, "source": [ "## Exponential Smoothing" ] }, { "cell_type": "code", "execution_count": 16, "id": "06de766f", "metadata": {}, "outputs": [], "source": [ "df['ewm'] = df['Sale Quantity'].ewm(alpha=0.2).mean()" ] }, { "cell_type": "code", "execution_count": 18, "id": "d9ad437b", "metadata": {}, "outputs": [], "source": [ "pd.options.display.float_format = '{:.2f}'.format" ] }, { "cell_type": "code", "execution_count": 19, "id": "a1a0f4a2", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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MonthSale QuantityPromotion ExpensesCompetition Promotionmoving_avg_12ewm
3637321648312113928410.333828234.64
3738345323912803810280.003753219.93
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\n", "
" ], "text/plain": [ " Month Sale Quantity Promotion Expenses Competition Promotion \\\n", "36 37 3216483 121 1 \n", "37 38 3453239 128 0 \n", "38 39 5431651 170 0 \n", "39 40 4241851 160 0 \n", "40 41 3909887 151 1 \n", "41 42 3216438 120 1 \n", "42 43 4222005 152 0 \n", "43 44 3621034 125 0 \n", "44 45 5162201 170 0 \n", "45 46 4627177 160 0 \n", "46 47 4623945 168 0 \n", "47 48 4599368 166 0 \n", "\n", " moving_avg_12 ewm \n", "36 3928410.33 3828234.64 \n", "37 3810280.00 3753219.93 \n", "38 3783643.33 4088961.93 \n", "39 3970688.42 4119543.81 \n", "40 4066369.08 4077607.99 \n", "41 4012412.75 3905359.34 \n", "42 3962369.58 3968692.78 \n", "43 3946629.42 3899157.24 \n", "44 3940489.50 4151776.99 \n", "45 4052117.17 4246860.31 \n", "46 4130274.75 4322279.35 \n", "47 4204882.33 4377698.31 " ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[36:]" ] }, { "cell_type": "code", "execution_count": 20, "id": "875b1af2", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "11.15" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "get_MAPE(df[['Sale Quantity']][36:].values,\n", " df[['ewm']][36:].values)" ] }, { "cell_type": "code", "execution_count": 21, "id": "1630eed7", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "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" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(10,4))\n", "\n", "plt.xlabel('Months')\n", "plt.ylabel('Quantity')\n", "\n", "plt.plot(df['Sale Quantity'][12:])\n", "plt.plot(df['ewm'][12:], '.')\n", "\n", "plt.legend()" ] }, { "cell_type": "code", "execution_count": 25, "id": "0f326133", "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "from statsmodels.tsa.seasonal import seasonal_decompose\n", "\n", "ts_decompose = seasonal_decompose(np.array(df['Sale Quantity']),\n", " model='multiplicative',\n", " period = 12)\n", "\n", "ts_plot = ts_decompose.plot()\n", "\n", "df['seasonal'] = ts_decompose.seasonal\n", "df['trend'] = ts_decompose.trend" ] }, { "cell_type": "code", "execution_count": null, "id": "1a68c1b4", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.12" } }, "nbformat": 4, "nbformat_minor": 5 } ================================================ FILE: March Cohort 2023/22_23_Time Series Forecasting/.ipynb_checkpoints/23_ARIMA-checkpoint.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "id": "5e5ca069", "metadata": {}, "source": [ "# Auto Regressive Models\n", "\n", "AR and MA = ARMA = ARIMA\n", "\n", "\n", "### Auto-Regressive (AR) Models" ] }, { "cell_type": "code", "execution_count": null, "id": "b389bf58", "metadata": {}, "outputs": [], "source": [ "- auto-correlation function\n", "- partial auto-correlation function" ] }, { "cell_type": "code", "execution_count": 1, "id": "91a0231c", "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 2, "id": "8e78f456", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " Month demand\n", "0 1 457\n", "1 2 439\n", "2 3 404\n", "3 4 392\n", "4 5 403" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_csv('vimana.csv')\n", "\n", "df.head()" ] }, { "cell_type": "code", "execution_count": 3, "id": "1e17a7a3", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 37 entries, 0 to 36\n", "Data columns (total 2 columns):\n", " # Column Non-Null Count Dtype\n", "--- ------ -------------- -----\n", " 0 Month 37 non-null int64\n", " 1 demand 37 non-null int64\n", "dtypes: int64(2)\n", "memory usage: 720.0 bytes\n" ] } ], "source": [ "df.info()" ] }, { "cell_type": "code", "execution_count": 4, "id": "49e834a8", "metadata": {}, "outputs": [ { "data": { "image/png": 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pQs5e3M9/++V1vlFsLeFAYNZGedv5azXMs5bM59LzznAQsJZw01AXc5/xucft/NaNHAi6lPuMz01u57du5KahLpWnLdm6l9v5rRu1JBBIukrS05J2Srq5wfZ3S3ose/2LpEvqtj0n6XFJ2yVta0V55gL3GZ+73M5v3SZ305CkXuBzwJXAbuBhSVsi4sm63f4D+KWIeEnS1cBm4PK67VdExMG8ZZlL3JZsZu3SihrBZcDOiHg2IkrAN4CN9TtExL9ExEvZ6g+B1S047pzmUclm1i6tCASrgF1167uztMncAHy3bj2A+yQ9ImnTZJkkbZK0TdK2AwcO5CrwbOC2ZDNrl1b0Gmp0Zmo4GkvSFaSB4I11yW+IiD2SlgP3S/rXiHjohA+M2EzapMTg4GAhRnt5VLKZtUMragS7gTV166uBPRN3kvSfgS8BGyPiUC09IvZk7/uBe0ibmszMrE1aEQgeBtZJukBSP3AtsKV+B0lrgbuB90TEv9WlL5K0pLYMvAV4ogVlMjOzJuVuGoqIiqQbgXuBXuC2iNgh6QPZ9luBjwFnAn+l9O5nJSIGgXOAe7K0PuCOiPhe3jKZmVnzWjKyOCK2AlsnpN1at/x+4P0N8j0LXDIx3czM2scji83MCs6BwMys4BwIzMwKzoHAzKzgPA212Umabc+LmG3ltfZxIDA7CbPteRGzrbzWXm4askJLkuDR51/i7kd38+jzL5Ekzc1eMtueFzHbymvt5RqBFVaeq+SpnhfRjfNCzbbyWnu5RjBHneyVbpHkuUquPS+iXjc/L2K2ldfayzWCOcjtwc3Jc5U82549nLe8vtE8tzkQzEH1V7pw/JWumwGOyfMUuNrzIm66+zHGylXe9/MXdPXJMU95fWEx97lpaA7y846bk/cpcLPt2cMnW17faJ77HAjmILcHN8dPgWuOLyzmPgeCOcjPO27ebLuq74S8FxbuuND9fI9gDppt7dfW3fLcaPb9hdnBNYI5qkhXur7iPLXyNKH5/sLs4BqBzWq+4myP2oXFkgV9M+p55oFss4NrBDar+Yqzu7njwuzQkkAg6SpJT0vaKenmBtsl6bPZ9sckXdpsXrOpuEdLd3PHhdkhdyCQ1At8DrgauBi4TtLFE3a7GliXvTYBn59BXrNJ+Yqzu7mL7uygiHw31iS9Hvh4RPxKtv57ABHxx3X7fAF4MCLuzNafBjYA50+Xt5Fl510UV95y24zL+uTeIQAuXnFaw+3lasJIudpw2/OHhgE478yBGR93NuadLSKCn7w4wnAp/XeTYOG8XtYuW4jU3MmmE9/xbPxN+LfYefP7epnfd/LX73d94OcfiYjBiemtuFm8CthVt74buLyJfVY1mRcASZtIaxMsXvGqkyroZAGgGXl+wLMt72w6WUhi7bKFHBmrMlauMn9eL4vn9zYdBE62rHnzzrbfRCfzOlCfeq0IBI3+x02sZky2TzN508SIzcBmgMHBwfjmb79+JmVsyr6hUZ494LblT3xnBwAfe+tr2pY3SYKb7n6M0XKVt/6nlR73YOM68XvsxDGbybtq6ULW5ggSd32gcXorbhbvBtbUra8G9jS5TzN5bY6rdQF94eURDh4p8dl/fIZPfvcpjwcwa5NWBIKHgXWSLpDUD1wLbJmwzxbgvVnvodcBr0TE3ibz2hxX6wJau13lLqBm7ZW7aSgiKpJuBO4FeoHbImKHpA9k228FtgLXADuBYeC3psqbt0w2u3jQkVlntWRkcURsJT3Z16fdWrccwAebzWvFkue5AGaWn0cWW8fVBh3N7+tBeNCRWbt5riHruNqgIz8K0awzHAisK/T0iEvPO8P3BMw6wE1DZmYF50BgZlZwDgRmZgXnQGDHSZLg8GiFA4fH/LQvs4JwILBxnurBrJgcCGycp3owKyYHAhvnp31Zt3FTZXs4ENg4P+3LuombKtvHgcDGeaoH6yZuqmwfjyy2cZ7qwbqJZ6VtHwcCO46nerBu4Vlp28dNQ9YyvrFnreSmyvZxjcBaov7GXgR89h+f4dXLF3PL1Re5aclOipsq28eBwFpiqht7bmayk+WmyvZw05C1hMcgmOXTyabVXIFA0jJJ90t6Jns/IWxLWiPpnyQ9JWmHpN+t2/ZxSS9I2p69rslTHuscj0EwO3mdHjORt0ZwM/BARKwDHsjWJ6oAH4mIi4DXAR+UdHHd9s9ExPrs5WcXz1K+sWfdphNX2Cd7zE6Pmch7j2AjsCFbvh14ELipfoeI2AvszZYPS3oKWAU8mfPY1kV8Y8+6SSc6L+Q5ZqfHTOStEZyTnehrJ/zlU+0s6XzgtcCP6pJvlPSYpNsaNS3V5d0kaZukbQcOHMhZbDsVajf2fu3S1Vx63hkOAtYxnbjCznPMTjetThsIJH1f0hMNXhtnciBJi4FvAR+KiKEs+fPAq4D1pLWGT0+WPyI2R8RgRAyeffbZMzm0mRVMJzov5Dlmp5tWp20aiog3T7ZN0j5JKyJir6QVwP5J9ptHGgS+HhF31332vrp9vgh8ZyaFNzNrpBOjkvMcs9NNq3mbhrYA12fL1wPfnriDJAFfBp6KiD+dsG1F3eo7gCdylsfMLNcV9sne8M17Vd/JplVFnPyddElnAncBa4GfAO+MiBclrQS+FBHXSHoj8APgcaAWKm+JiK2SvkraLBTAc8Bv1+45TGVwcDC2bdt20uWezL6hUZ494H7vZnNBksSMr7BrN3yf3DtExLGTebM3mU/mmDOxaulC1p45cNL5JT0SEYMT03P1GoqIQ8CbGqTvAa7Jlv8ZaPhNRMR78hzfzGwyJzMqOe8I+dk6Etoji83MMkUdIe9AYGaW6XQ3zk5xIKizbFE/F5y1iCULPBefWRF1uhtnp/iMV2debw/nnr6Ac09fwFilyqEjJQ4dKXFkrNLpoplZG3S6G2enOBBMYn5fLyuXLmTl0oWMlqscPDLGoSMlhkvVThfNzE6h2XrDNw8HgiYsmNfL6jMGWH3GAMOlCoeOlDh4ZIzRcjJ9ZjOzLudAMEMD/X0MLOtjzbIBjoxVOHRkjENHS4w5KJjZLOVAkMPi+X0snt/HeWcu4vBoOb2ncHSMUsXP6jWz2cOBoEWWLJjHkgXzOO/MAYZG05rCi0dLlKsOCmbW3RwIWkwSpy+cx+kL53HBWcErI2UOHinxykjJNQUz60oOBKeQJJYO9LN0oB+A4VKFV0bKDI1UGBotU3Ftwcy6gANBGw309zHQ38eK0yEiOFqqZoGhzOHRCtU2PqzazKzGgaBDJI3fbF61dCERweGxCq8MlxkaLXNktILjgpm1gwNBl5DEaQvmcdqCecCxOdGHRssMl6qMVaqMVRI3J5lZyzkQdKmeHnH6wDxOH5h3XHq5mjBWSRgtV4+9lxNGK1VKlYQcj5cws4JyIJhl5vX2MK+3h8XzT/yniwjGKglj5WS8BpFEEJE++ScisneAGA8atbTI0uqXkwiS7D1qy0m42cpsDnEgmEMksWBeLwvm9QLzpt0/rzQg1AeK9L0aQbUalJO0KauaBOVqQiV7T9eDSjVxQDHrAg4EdtJ6ekRP44fPNa2aBJUsYFSqaRBpZLpHqjYTT6b6iCCtNiUTakPjtabaelaWpK5WNTEQ1mpU9ekxIWCadZNcgUDSMuCbwPmkzxx+V0S81GC/54DDQBWo1J6Z2Wx+m7t6e0RvTy8NWrrmtCRJg95oucpIucpoKWG4XGGklDbpOVhYO+X973cz8EBEfErSzdn6TZPse0VEHMyR32zOqNWm5vX2sGTB8c14SRKMVqoMl6qMlKrjwWKkVHVTmp0SeQPBRmBDtnw78CAzO5HnzW825/T0aHzwYb1aZ4CRUpXhcpWRUoWjY2mQcA3C8sgbCM6JiL0AEbFX0vJJ9gvgPkkBfCEiNs8wP5I2AZsA1q5dm7PYZrNPfWeA+kemJEkwUk5rEMPjwaHiua2sadMGAknfB85tsOmjMzjOGyJiT3aiv1/Sv0bEQzPITxY8NgMMDg76F26W6ekRi+b3sWh+HzB/PL1USRguVcYDRPru2oOdaNpAEBFvnmybpH2SVmRX8yuA/ZN8xp7sfb+ke4DLgIeApvKb2cz19/XQ39fP0oFjabXmpfpeUPU9m+rHjtSPK6nvMVUfSGo9pGrLcGzMSm25fhvHpR7/OSek1S0nkXZDHu+inBzrheVxLfnlbRraAlwPfCp7//bEHSQtAnoi4nC2/BbgE83mN7PWqTUvzTW17rnVbGxLBOl4liQdr1KupmNYStWEUiWhXK293J0X8geCTwF3SboB+AnwTgBJK4EvRcQ1wDnAPZJqx7sjIr43VX4zs5mQRK/S7sgzERHjQaIWKMrVoFxJl2uj9Of6A6ZyBYKIOAS8qUH6HuCabPlZ4JKZ5DczawdJ9PeJ/r6eKferJpFO25LN65UGiGTOTAZZsGE8ZmYz1zvepbfx9ko1YbSSMJZNBlmqpFOq1EbOJwlUkiRb777mKAcCM7Oc+np7WDzJZJCNTBYgau/J+HoaUCrV9N5HX2++KV0mLf8p+VQzM5tUbWqVbK2jZQGYumHMzMzmPAcCM7OCcyAwMys4BwIzs4JzIDAzKzgHAjOzgnMgMDMrOAcCM7OCcyAwMys4BwIzs4JzIDAzKzgHAjOzgnMgMDMrOAcCM7OCcyAwMyu4XIFA0jJJ90t6Jns/o8E+F0raXvcakvShbNvHJb1Qt+2aPOUxM7OZy1sjuBl4ICLWAQ9k68eJiKcjYn1ErAd+DhgG7qnb5TO17RGxNWd5zMxshvIGgo3A7dny7cDbp9n/TcC/R8TzOY9rZmYtkjcQnBMRewGy9+XT7H8tcOeEtBslPSbptkZNS2ZmdmpNGwgkfV/SEw1eG2dyIEn9wK8Cf1uX/HngVcB6YC/w6Snyb5K0TdK2AwcOzOTQZmY2hWkfXh8Rb55sm6R9klZExF5JK4D9U3zU1cCjEbGv7rPHlyV9EfjOFOXYDGwGGBwcjOnKbWZmzcnbNLQFuD5bvh749hT7XseEZqEseNS8A3giZ3nMzGyG8gaCTwFXSnoGuDJbR9JKSeM9gCQNZNvvnpD/f0t6XNJjwBXAh3OWx8zMZmjapqGpRMQh0p5AE9P3ANfUrQ8DZzbY7z15jm9mZvl5ZLGZWcE5EJiZFZwDgZlZwTkQmJkVnAOBmVnBORCYmRWcA4GZWcE5EJiZFZwDgZlZwTkQmJkVnAOBmVnBORCYmRWcA4GZWcE5EJiZFZwDgZlZwTkQmJkVnAOBmVnBORCYmRWcA4GZWcHlCgSS3ilph6RE0uAU+10l6WlJOyXdXJe+TNL9kp7J3s/IUx4zM5u5vDWCJ4BfAx6abAdJvcDngKuBi4HrJF2cbb4ZeCAi1gEPZOtmZtZGuQJBRDwVEU9Ps9tlwM6IeDYiSsA3gI3Zto3A7dny7cDb85THzMxmrq8Nx1gF7Kpb3w1cni2fExF7ASJir6Tlk32IpE3Apmz1iKTpAtBkzgIOnmTeIvH31Dx/V83x99ScU/k9ndcocdpAIOn7wLkNNn00Ir7dxIHVIC2ayHd8hojNwOaZ5juhMNK2iJj0foal/D01z99Vc/w9NacT39O0gSAi3pzzGLuBNXXrq4E92fI+SSuy2sAKYH/OY5mZ2Qy1o/vow8A6SRdI6geuBbZk27YA12fL1wPN1DDMzKyF8nYffYek3cDrgX+QdG+WvlLSVoCIqAA3AvcCTwF3RcSO7CM+BVwp6Rngymz9VMvdvFQQ/p6a5++qOf6emtP270kRM26uNzOzOcQji83MCs6BwMys4AoVCCab6sKOJ+k5SY9L2i5pW6fL0y0k3SZpv6Qn6tI8TUoDk3xXH5f0Qva72i7pmk6WsdMkrZH0T5Keyqbq+d0sve2/qcIEgmmmurATXRER693v+zhfAa6akOZpUhr7Cid+VwCfyX5X6yNia5vL1G0qwEci4iLgdcAHs3NS239ThQkETD3Vhdm0IuIh4MUJyZ4mpYFJviurExF7I+LRbPkwaa/KVXTgN1WkQNBoqotVHSpLtwvgPkmPZFN72OSOmyYFmHSaFAPgRkmPZU1HbkbLSDofeC3wIzrwmypSIGjJVBcF8YaIuJS0Ge2Dkn6x0wWyOeHzwKuA9cBe4NMdLU2XkLQY+BbwoYgY6kQZihQIpprqwupExJ7sfT9wD2mzmjW2L5seBU+TMrWI2BcR1YhIgC/i3xWS5pEGga9HxN1Zctt/U0UKBFNNdWEZSYskLaktA28hfe6ENeZpUppUO7ll3kHBf1eSBHwZeCoi/rRuU9t/U4UaWZx1V/szoBe4LSL+V2dL1H0k/QxpLQDSSQnv8PeUknQnsIF0muB9wB8Afw/cBawFfgK8MyIKf5N0ku9qA2mzUADPAb9dawsvIklvBH4APA4kWfItpPcJ2vqbKlQgMDOzExWpacjMzBpwIDAzKzgHAjOzgnMgMDMrOAcCM7OCcyAwMys4BwIzs4L7//k4VflxLKSuAAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "from statsmodels.graphics.tsaplots import plot_acf, plot_pacf\n", "\n", "acf_plot = plot_acf(df.demand, lags=20)" ] }, { "cell_type": "code", "execution_count": 7, "id": "05dffa14", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "pacf_plot = plot_pacf(df.demand, lags=17)" ] }, { "cell_type": "markdown", "id": "538cb705", "metadata": {}, "source": [ "1. partial auto-correlation is significant for first p lags\n", "2. there is a sign of stationary" ] }, { "cell_type": "markdown", "id": "1b96e85a", "metadata": {}, "source": [ "### building AR model" ] }, { "cell_type": "code", "execution_count": 22, "id": "9b68728d", "metadata": {}, "outputs": [], "source": [ "from statsmodels.tsa.arima.model import ARIMA" ] }, { "cell_type": "code", "execution_count": 26, "id": "3a318c0a", "metadata": {}, "outputs": [], "source": [ "arima = ARIMA(df.demand[0:30].to_numpy(),\n", " order=(1,0,0))\n", "\n", "model = arima.fit()" ] }, { "cell_type": "code", "execution_count": 27, "id": "d7c7e91f", "metadata": {}, "outputs": [ { "data": { "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", "
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", "
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", "
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)." ], "text/plain": [ "\n", "\"\"\"\n", " SARIMAX Results \n", "==============================================================================\n", "Dep. Variable: y No. Observations: 30\n", "Model: ARIMA(1, 0, 0) Log Likelihood -182.765\n", "Date: Tue, 16 May 2023 AIC 371.530\n", "Time: 21:40:39 BIC 375.734\n", "Sample: 0 HQIC 372.875\n", " - 30 \n", "Covariance Type: opg \n", "==============================================================================\n", " coef std err z P>|z| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "const 513.4426 43.815 11.718 0.000 427.567 599.318\n", "ar.L1 0.4726 0.181 2.617 0.009 0.119 0.827\n", "sigma2 1.136e+04 3299.003 3.444 0.001 4896.110 1.78e+04\n", "===================================================================================\n", "Ljung-Box (L1) (Q): 0.55 Jarque-Bera (JB): 1.72\n", "Prob(Q): 0.46 Prob(JB): 0.42\n", "Heteroskedasticity (H): 2.72 Skew: 0.59\n", "Prob(H) (two-sided): 0.13 Kurtosis: 2.96\n", "===================================================================================\n", "\n", "Warnings:\n", "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n", "\"\"\"" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.summary()" ] }, { "cell_type": "code", "execution_count": 28, "id": "a5289c6f", "metadata": {}, "outputs": [], "source": [ "# AR with lag 1 is significant variable in the model" ] }, { "cell_type": "markdown", "id": "86fea566", "metadata": {}, "source": [ "#### forecast and measure accuracy" ] }, { "cell_type": "code", "execution_count": 29, "id": "e6d3acb1", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([480.15274444, 497.71044684, 506.00789345, 509.92911382,\n", " 511.78221031, 512.65794956, 513.0718078 ])" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "forecast_31_37 = model.predict(30,36)\n", "\n", "forecast_31_37" ] }, { "cell_type": "code", "execution_count": 30, "id": "7a473526", "metadata": {}, "outputs": [], "source": [ "def get_MAPE(actual, predicted):\n", " ytrue, ypred = np.array(actual), np.array(predicted)\n", " \n", " return np.round(np.mean(np.abs((actual - predicted) / actual))*100, 2)" ] }, { "cell_type": "code", "execution_count": 31, "id": "f5df3b3a", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "19.12" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "get_MAPE(df.demand[30:], forecast_31_37)" ] }, { "cell_type": "markdown", "id": "8f8bcf16", "metadata": {}, "source": [ "### moving average (MA) " ] }, { "cell_type": "code", "execution_count": 32, "id": "59388c6b", "metadata": {}, "outputs": [ { "data": { "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", "
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", "
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", "
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)." ], "text/plain": [ "\n", "\"\"\"\n", " SARIMAX Results \n", "==============================================================================\n", "Dep. Variable: y No. Observations: 30\n", "Model: ARIMA(0, 0, 1) Log Likelihood -184.297\n", "Date: Tue, 16 May 2023 AIC 374.595\n", "Time: 21:47:29 BIC 378.799\n", "Sample: 0 HQIC 375.940\n", " - 30 \n", "Covariance Type: opg \n", "==============================================================================\n", " coef std err z P>|z| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "const 517.0213 31.024 16.665 0.000 456.215 577.827\n", "ma.L1 0.3173 0.224 1.414 0.157 -0.122 0.757\n", "sigma2 1.265e+04 4209.483 3.004 0.003 4395.561 2.09e+04\n", "===================================================================================\n", "Ljung-Box (L1) (Q): 0.41 Jarque-Bera (JB): 2.21\n", "Prob(Q): 0.52 Prob(JB): 0.33\n", "Heteroskedasticity (H): 2.25 Skew: 0.66\n", "Prob(H) (two-sided): 0.22 Kurtosis: 2.90\n", "===================================================================================\n", "\n", "Warnings:\n", "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n", "\"\"\"" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "arima = ARIMA(df.demand[0:30].to_numpy(),\n", " order=(0,0,1))\n", "\n", "ma_model = arima.fit()\n", "\n", "ma_model.summary()" ] }, { "cell_type": "code", "execution_count": 33, "id": "3803c499", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "17.78" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "forecast_31_37 = ma_model.predict(30,36)\n", "\n", "get_MAPE(df.demand[30:], forecast_31_37)" ] }, { "cell_type": "code", "execution_count": 34, "id": "878fd131", "metadata": {}, "outputs": [ { "data": { "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", "
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", "
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", "
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)." ], "text/plain": [ "\n", "\"\"\"\n", " SARIMAX Results \n", "==============================================================================\n", "Dep. Variable: y No. Observations: 30\n", "Model: ARIMA(1, 0, 1) Log Likelihood -181.846\n", "Date: Tue, 16 May 2023 AIC 371.692\n", "Time: 21:50:14 BIC 377.296\n", "Sample: 0 HQIC 373.485\n", " - 30 \n", "Covariance Type: opg \n", "==============================================================================\n", " coef std err z P>|z| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "const 508.3844 59.146 8.595 0.000 392.461 624.308\n", "ar.L1 0.7421 0.316 2.348 0.019 0.123 1.362\n", "ma.L1 -0.3395 0.539 -0.629 0.529 -1.397 0.718\n", "sigma2 1.065e+04 3040.079 3.505 0.000 4696.418 1.66e+04\n", "===================================================================================\n", "Ljung-Box (L1) (Q): 0.13 Jarque-Bera (JB): 2.46\n", "Prob(Q): 0.72 Prob(JB): 0.29\n", "Heteroskedasticity (H): 3.07 Skew: 0.67\n", "Prob(H) (two-sided): 0.09 Kurtosis: 3.38\n", "===================================================================================\n", "\n", "Warnings:\n", "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n", "\"\"\"" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "arima = ARIMA(df.demand[0:30].to_numpy(),\n", " order=(1,0,1))\n", "\n", "arma_model = arima.fit()\n", "\n", "arma_model.summary()" ] }, { "cell_type": "code", "execution_count": 35, "id": "6f5703b7", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "20.27" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "forecast_31_37 = arma_model.predict(30,36)\n", "\n", "get_MAPE(df.demand[30:], forecast_31_37)" ] }, { "cell_type": "markdown", "id": "30adbf67", "metadata": {}, "source": [ "## ARIMA\n", "time-series data is stationary - ARMA\n", "\n", "\n", " non-stationary - ARIMA" ] }, { "cell_type": "code", "execution_count": 36, "id": "aba6b783", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Datedemand
02014-10-0115
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22014-10-038
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42014-10-0513
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" ], "text/plain": [ " Date demand\n", "0 2014-10-01 15\n", "1 2014-10-02 7\n", "2 2014-10-03 8\n", "3 2014-10-04 10\n", "4 2014-10-05 13" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "store_df = pd.read_excel('store.xls')\n", "\n", "store_df.head()" ] }, { "cell_type": "code", "execution_count": 37, "id": "c1deb62f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 115 entries, 0 to 114\n", "Data columns (total 2 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 Date 115 non-null datetime64[ns]\n", " 1 demand 115 non-null int64 \n", "dtypes: datetime64[ns](1), int64(1)\n", "memory usage: 1.9 KB\n" ] } ], "source": [ "store_df.info()" ] }, { "cell_type": "code", "execution_count": 38, "id": "ecff6ab2", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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demand
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" ], "text/plain": [ " demand\n", "Date \n", "2014-10-01 15\n", "2014-10-02 7\n", "2014-10-03 8\n", "2014-10-04 10\n", "2014-10-05 13" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "store_df.set_index(pd.to_datetime(store_df.Date), inplace=True)\n", "\n", "store_df.drop('Date', axis=1, inplace=True)\n", "\n", "store_df[:5]" ] }, { "cell_type": "code", "execution_count": 39, "id": "6bd71b19", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(10,4))\n", "plt.xlabel('Date')\n", "plt.ylabel('Demand')\n", "\n", "plt.plot(store_df.demand)" ] }, { "cell_type": "code", "execution_count": 40, "id": "3aeb1f33", "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "acf_plot = plot_acf(store_df.demand, lags=20)" ] }, { "cell_type": "code", "execution_count": 41, "id": "362258a9", "metadata": {}, "outputs": [], "source": [ "from statsmodels.tsa.stattools import adfuller" ] }, { "cell_type": "code", "execution_count": 42, "id": "9c78933a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Test Statistics -1.649533\n", "p-value 0.457284\n", "Lags Used 13.000000\n", "Number of Observations Used 101.000000\n", "dtype: float64\n" ] } ], "source": [ "def adfuller_test(ts):\n", " adfuller_result = adfuller(ts, autolag=None)\n", " adfuller_out = pd.Series(adfuller_result[0:4],\n", " index=['Test Statistics',\n", " 'p-value',\n", " 'Lags Used',\n", " 'Number of Observations Used'])\n", " print(adfuller_out)\n", " \n", "adfuller_test(store_df.demand)" ] }, { "cell_type": "code", "execution_count": null, "id": "2f7b1ad8", "metadata": {}, "outputs": [], "source": [ "# p-value > 0.05 that means we cannot reject the null hypothesis, \n", "# as series is not stationary" ] }, { "cell_type": "markdown", "id": "eb42a0fa", "metadata": {}, "source": [ "#### differencing" ] }, { "cell_type": "code", "execution_count": 43, "id": "5a1055ea", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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demanddemand_diff
Date
2014-10-0115NaN
2014-10-027-8.0
2014-10-0381.0
2014-10-04102.0
2014-10-05133.0
\n", "
" ], "text/plain": [ " demand demand_diff\n", "Date \n", "2014-10-01 15 NaN\n", "2014-10-02 7 -8.0\n", "2014-10-03 8 1.0\n", "2014-10-04 10 2.0\n", "2014-10-05 13 3.0" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "store_df['demand_diff'] = store_df.demand - store_df.demand.shift(1)\n", "\n", "store_df.head()" ] }, { "cell_type": "code", "execution_count": 44, "id": "78378db1", "metadata": {}, "outputs": [], "source": [ "store_diff_df = store_df.dropna()" ] }, { "cell_type": "code", "execution_count": 45, "id": "e4c4e738", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(10,4))\n", "plt.xlabel('Date')\n", "plt.ylabel('First Order Differencing')\n", "\n", "plt.plot(store_diff_df.demand_diff)" ] }, { "cell_type": "code", "execution_count": 46, "id": "9ac6af67", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "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", " warnings.warn(\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "pacf_plot = plot_pacf(store_df.demand_diff.dropna(),\n", " lags=10)" ] }, { "cell_type": "code", "execution_count": 47, "id": "41d50dfe", "metadata": {}, "outputs": [], "source": [ "store_train = store_df[0:100]\n", "store_test = store_df[100:0]" ] }, { "cell_type": "code", "execution_count": 48, "id": "78743a66", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "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", " warn('Non-invertible starting MA parameters found.'\n" ] }, { "data": { "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", "
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
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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
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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)." ], "text/plain": [ "\n", "\"\"\"\n", " SARIMAX Results \n", "==============================================================================\n", "Dep. Variable: y No. Observations: 100\n", "Model: ARIMA(1, 1, 1) Log Likelihood -256.910\n", "Date: Tue, 16 May 2023 AIC 519.821\n", "Time: 22:15:55 BIC 527.606\n", "Sample: 0 HQIC 522.971\n", " - 100 \n", "Covariance Type: opg \n", "==============================================================================\n", " coef std err z P>|z| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "ar.L1 0.4091 0.311 1.315 0.189 -0.201 1.019\n", "ma.L1 -0.7179 0.236 -3.039 0.002 -1.181 -0.255\n", "sigma2 10.4865 1.026 10.223 0.000 8.476 12.497\n", "===================================================================================\n", "Ljung-Box (L1) (Q): 0.06 Jarque-Bera (JB): 75.18\n", "Prob(Q): 0.81 Prob(JB): 0.00\n", "Heteroskedasticity (H): 0.76 Skew: -0.79\n", "Prob(H) (two-sided): 0.44 Kurtosis: 6.97\n", "===================================================================================\n", "\n", "Warnings:\n", "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n", "\"\"\"" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "arima = ARIMA(store_train.demand.to_numpy(),\n", " order=(1,1,1))\n", "\n", "arima_model = arima.fit()\n", "\n", "arima_model.summary()" ] }, { "cell_type": "markdown", "id": "35dce0cf", "metadata": {}, "source": [ "### forecasting and accuracy" ] }, { "cell_type": "code", "execution_count": 50, "id": "bd238d6f", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([17.25076347, 16.12611976, 15.66605623, 15.47785577, 15.40086769,\n", " 15.36937379, 15.35649043, 15.35122017, 15.34906424, 15.3481823 ,\n", " 15.34782152, 15.34767394, 15.34761356, 15.34758887, 15.34757876])" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ "store_predict = arima_model.forecast(steps=15)\n", "\n", "store_predict" ] }, { "cell_type": "code", "execution_count": 51, "id": "6cf83df8", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "24.12" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ "get_MAPE(store_df.demand[100:], store_predict)" ] }, { "cell_type": "code", "execution_count": null, "id": "3d8e9051", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.12" } }, "nbformat": 4, "nbformat_minor": 5 } ================================================ FILE: March Cohort 2023/22_23_Time Series Forecasting/22_Forecasting.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "id": "12c7b612", "metadata": {}, "source": [ "# Time series data and forecasting\n", "\n", "components\n", "- trend\n", "- seasonality\n", "- cyclical \n", "- random\n", "\n", "Models/ forecasting techniques\n", "- AR\n", "- MA\n", "- ARMA\n", "- ARIMA" ] }, { "cell_type": "code", "execution_count": null, "id": "861324fd", "metadata": {}, "outputs": [], "source": [ "time series data -> random variable\n", "- single variable (demand of the product at time t) univariate time series data\n", "- multivariate time series data -> \n", "\n", "4 components\n", "- trend\n", "- seasonal\n", "- cyclical\n", "- irregular" ] }, { "cell_type": "markdown", "id": "c3f8bc6b", "metadata": {}, "source": [ "## Moving Average\n" ] }, { "cell_type": "code", "execution_count": 2, "id": "dacb8d91", "metadata": {}, "outputs": [ { "data": { "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", "
MonthSale QuantityPromotion ExpensesCompetition Promotion
0130026661051
1244015531450
2332052791181
3442453491300
453001940981
5643777661560
672798343981
7843036681440
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\n", "
" ], "text/plain": [ " Month Sale Quantity Promotion Expenses Competition Promotion\n", "0 1 3002666 105 1\n", "1 2 4401553 145 0\n", "2 3 3205279 118 1\n", "3 4 4245349 130 0\n", "4 5 3001940 98 1\n", "5 6 4377766 156 0\n", "6 7 2798343 98 1\n", "7 8 4303668 144 0\n", "8 9 2958185 112 1\n", "9 10 3623386 120 0" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import pandas as pd\n", "\n", "df = pd.read_csv('wsb.csv')\n", "\n", "df.head(10)" ] }, { "cell_type": "code", "execution_count": 3, "id": "fecf8a63", "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import seaborn as sns" ] }, { "cell_type": "code", "execution_count": 4, "id": "b1c6bf16", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(10,4))\n", "\n", "plt.xlabel(\"Months\")\n", "plt.ylabel(\"Quantity\")\n", "\n", "plt.plot(df['Sale Quantity'])" ] }, { "cell_type": "code", "execution_count": 5, "id": "174ee2cc", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 48 entries, 0 to 47\n", "Data columns (total 4 columns):\n", " # Column Non-Null Count Dtype\n", "--- ------ -------------- -----\n", " 0 Month 48 non-null int64\n", " 1 Sale Quantity 48 non-null int64\n", " 2 Promotion Expenses 48 non-null int64\n", " 3 Competition Promotion 48 non-null int64\n", "dtypes: int64(4)\n", "memory usage: 1.6 KB\n" ] } ], "source": [ "df.info()" ] }, { "cell_type": "code", "execution_count": 6, "id": "c6f568ae", "metadata": {}, "outputs": [], "source": [ "df['moving_avg_12'] = df['Sale Quantity'].rolling(window=12).mean().shift(1)" ] }, { "cell_type": "code", "execution_count": 7, "id": "c367ee45", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(10,4))\n", "\n", "plt.xlabel('Months')\n", "plt.ylabel('Quantity')\n", "\n", "plt.plot(df['Sale Quantity'][12:])\n", "plt.plot(df['moving_avg_12'][12:], '.')\n", "\n", "plt.legend()" ] }, { "cell_type": "code", "execution_count": 10, "id": "a018f130", "metadata": {}, "outputs": [], "source": [ "# there is an increasing trend in sales quantity over the months" ] }, { "cell_type": "markdown", "id": "613775c8", "metadata": {}, "source": [ "## Forecast Accurary" ] }, { "cell_type": "code", "execution_count": null, "id": "d985e8e1", "metadata": {}, "outputs": [], "source": [ "- RMSE\n", "- MAPE" ] }, { "cell_type": "markdown", "id": "94146992", "metadata": {}, "source": [ "### Mean Absolute Percentage Error" ] }, { "cell_type": "code", "execution_count": 11, "id": "dafa7a48", "metadata": {}, "outputs": [], "source": [ "import numpy as np" ] }, { "cell_type": "code", "execution_count": 13, "id": "183268aa", "metadata": {}, "outputs": [], "source": [ "def get_MAPE(actual, predicted):\n", " ytrue, ypred = np.array(actual), np.array(predicted)\n", " \n", " return np.round(np.mean(np.abs((actual - predicted) / actual))*100, 2)" ] }, { "cell_type": "code", "execution_count": 14, "id": "43515dac", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "14.04" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "get_MAPE(df['Sale Quantity'][36:].values, df['moving_avg_12'][36:].values)" ] }, { "cell_type": "code", "execution_count": 15, "id": "2cd8e001", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "734725.8359239782" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.metrics import mean_squared_error\n", "\n", "np.sqrt(mean_squared_error(df['Sale Quantity'][36:].values,\n", " df['moving_avg_12'][36:].values))" ] }, { "cell_type": "markdown", "id": "88910119", "metadata": {}, "source": [ "## Exponential Smoothing" ] }, { "cell_type": "code", "execution_count": 16, "id": "06de766f", "metadata": {}, "outputs": [], "source": [ "df['ewm'] = df['Sale Quantity'].ewm(alpha=0.2).mean()" ] }, { "cell_type": "code", "execution_count": 18, "id": "d9ad437b", "metadata": {}, "outputs": [], "source": [ "pd.options.display.float_format = '{:.2f}'.format" ] }, { "cell_type": "code", "execution_count": 19, "id": "a1a0f4a2", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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MonthSale QuantityPromotion ExpensesCompetition Promotionmoving_avg_12ewm
3637321648312113928410.333828234.64
3738345323912803810280.003753219.93
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\n", "
" ], "text/plain": [ " Month Sale Quantity Promotion Expenses Competition Promotion \\\n", "36 37 3216483 121 1 \n", "37 38 3453239 128 0 \n", "38 39 5431651 170 0 \n", "39 40 4241851 160 0 \n", "40 41 3909887 151 1 \n", "41 42 3216438 120 1 \n", "42 43 4222005 152 0 \n", "43 44 3621034 125 0 \n", "44 45 5162201 170 0 \n", "45 46 4627177 160 0 \n", "46 47 4623945 168 0 \n", "47 48 4599368 166 0 \n", "\n", " moving_avg_12 ewm \n", "36 3928410.33 3828234.64 \n", "37 3810280.00 3753219.93 \n", "38 3783643.33 4088961.93 \n", "39 3970688.42 4119543.81 \n", "40 4066369.08 4077607.99 \n", "41 4012412.75 3905359.34 \n", "42 3962369.58 3968692.78 \n", "43 3946629.42 3899157.24 \n", "44 3940489.50 4151776.99 \n", "45 4052117.17 4246860.31 \n", "46 4130274.75 4322279.35 \n", "47 4204882.33 4377698.31 " ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[36:]" ] }, { "cell_type": "code", "execution_count": 20, "id": "875b1af2", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "11.15" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "get_MAPE(df[['Sale Quantity']][36:].values,\n", " df[['ewm']][36:].values)" ] }, { "cell_type": "code", "execution_count": 21, "id": "1630eed7", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "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" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(10,4))\n", "\n", "plt.xlabel('Months')\n", "plt.ylabel('Quantity')\n", "\n", "plt.plot(df['Sale Quantity'][12:])\n", "plt.plot(df['ewm'][12:], '.')\n", "\n", "plt.legend()" ] }, { "cell_type": "code", "execution_count": 25, "id": "0f326133", "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "from statsmodels.tsa.seasonal import seasonal_decompose\n", "\n", "ts_decompose = seasonal_decompose(np.array(df['Sale Quantity']),\n", " model='multiplicative',\n", " period = 12)\n", "\n", "ts_plot = ts_decompose.plot()\n", "\n", "df['seasonal'] = ts_decompose.seasonal\n", "df['trend'] = ts_decompose.trend" ] }, { "cell_type": "code", "execution_count": null, "id": "1a68c1b4", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.12" } }, "nbformat": 4, "nbformat_minor": 5 } ================================================ FILE: March Cohort 2023/22_23_Time Series Forecasting/23_ARIMA.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "id": "5e5ca069", "metadata": {}, "source": [ "# Auto Regressive Models\n", "\n", "AR and MA = ARMA = ARIMA\n", "\n", "\n", "### Auto-Regressive (AR) Models" ] }, { "cell_type": "code", "execution_count": null, "id": "b389bf58", "metadata": {}, "outputs": [], "source": [ "- auto-correlation function\n", "- partial auto-correlation function" ] }, { "cell_type": "code", "execution_count": 1, "id": "91a0231c", "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 2, "id": "8e78f456", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " Month demand\n", "0 1 457\n", "1 2 439\n", "2 3 404\n", "3 4 392\n", "4 5 403" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_csv('vimana.csv')\n", "\n", "df.head()" ] }, { "cell_type": "code", "execution_count": 3, "id": "1e17a7a3", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 37 entries, 0 to 36\n", "Data columns (total 2 columns):\n", " # Column Non-Null Count Dtype\n", "--- ------ -------------- -----\n", " 0 Month 37 non-null int64\n", " 1 demand 37 non-null int64\n", "dtypes: int64(2)\n", "memory usage: 720.0 bytes\n" ] } ], "source": [ "df.info()" ] }, { "cell_type": "code", "execution_count": 4, "id": "49e834a8", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "from statsmodels.graphics.tsaplots import plot_acf, plot_pacf\n", "\n", "acf_plot = plot_acf(df.demand, lags=20)" ] }, { "cell_type": "code", "execution_count": 7, "id": "05dffa14", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "pacf_plot = plot_pacf(df.demand, lags=17)" ] }, { "cell_type": "markdown", "id": "538cb705", "metadata": {}, "source": [ "1. partial auto-correlation is significant for first p lags\n", "2. there is a sign of stationary" ] }, { "cell_type": "markdown", "id": "1b96e85a", "metadata": {}, "source": [ "### building AR model" ] }, { "cell_type": "code", "execution_count": 22, "id": "9b68728d", "metadata": {}, "outputs": [], "source": [ "from statsmodels.tsa.arima.model import ARIMA" ] }, { "cell_type": "code", "execution_count": 26, "id": "3a318c0a", "metadata": {}, "outputs": [], "source": [ "arima = ARIMA(df.demand[0:30].to_numpy(),\n", " order=(1,0,0))\n", "\n", "model = arima.fit()" ] }, { "cell_type": "code", "execution_count": 27, "id": "d7c7e91f", "metadata": {}, "outputs": [ { "data": { "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", "
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", "
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", "
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)." ], "text/plain": [ "\n", "\"\"\"\n", " SARIMAX Results \n", "==============================================================================\n", "Dep. Variable: y No. Observations: 30\n", "Model: ARIMA(1, 0, 0) Log Likelihood -182.765\n", "Date: Tue, 16 May 2023 AIC 371.530\n", "Time: 21:40:39 BIC 375.734\n", "Sample: 0 HQIC 372.875\n", " - 30 \n", "Covariance Type: opg \n", "==============================================================================\n", " coef std err z P>|z| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "const 513.4426 43.815 11.718 0.000 427.567 599.318\n", "ar.L1 0.4726 0.181 2.617 0.009 0.119 0.827\n", "sigma2 1.136e+04 3299.003 3.444 0.001 4896.110 1.78e+04\n", "===================================================================================\n", "Ljung-Box (L1) (Q): 0.55 Jarque-Bera (JB): 1.72\n", "Prob(Q): 0.46 Prob(JB): 0.42\n", "Heteroskedasticity (H): 2.72 Skew: 0.59\n", "Prob(H) (two-sided): 0.13 Kurtosis: 2.96\n", "===================================================================================\n", "\n", "Warnings:\n", "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n", "\"\"\"" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.summary()" ] }, { "cell_type": "code", "execution_count": 28, "id": "a5289c6f", "metadata": {}, "outputs": [], "source": [ "# AR with lag 1 is significant variable in the model" ] }, { "cell_type": "markdown", "id": "86fea566", "metadata": {}, "source": [ "#### forecast and measure accuracy" ] }, { "cell_type": "code", "execution_count": 29, "id": "e6d3acb1", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([480.15274444, 497.71044684, 506.00789345, 509.92911382,\n", " 511.78221031, 512.65794956, 513.0718078 ])" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "forecast_31_37 = model.predict(30,36)\n", "\n", "forecast_31_37" ] }, { "cell_type": "code", "execution_count": 30, "id": "7a473526", "metadata": {}, "outputs": [], "source": [ "def get_MAPE(actual, predicted):\n", " ytrue, ypred = np.array(actual), np.array(predicted)\n", " \n", " return np.round(np.mean(np.abs((actual - predicted) / actual))*100, 2)" ] }, { "cell_type": "code", "execution_count": 31, "id": "f5df3b3a", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "19.12" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "get_MAPE(df.demand[30:], forecast_31_37)" ] }, { "cell_type": "markdown", "id": "8f8bcf16", "metadata": {}, "source": [ "### moving average (MA) " ] }, { "cell_type": "code", "execution_count": 32, "id": "59388c6b", "metadata": {}, "outputs": [ { "data": { "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", "
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", "
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", "
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)." ], "text/plain": [ "\n", "\"\"\"\n", " SARIMAX Results \n", "==============================================================================\n", "Dep. Variable: y No. Observations: 30\n", "Model: ARIMA(0, 0, 1) Log Likelihood -184.297\n", "Date: Tue, 16 May 2023 AIC 374.595\n", "Time: 21:47:29 BIC 378.799\n", "Sample: 0 HQIC 375.940\n", " - 30 \n", "Covariance Type: opg \n", "==============================================================================\n", " coef std err z P>|z| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "const 517.0213 31.024 16.665 0.000 456.215 577.827\n", "ma.L1 0.3173 0.224 1.414 0.157 -0.122 0.757\n", "sigma2 1.265e+04 4209.483 3.004 0.003 4395.561 2.09e+04\n", "===================================================================================\n", "Ljung-Box (L1) (Q): 0.41 Jarque-Bera (JB): 2.21\n", "Prob(Q): 0.52 Prob(JB): 0.33\n", "Heteroskedasticity (H): 2.25 Skew: 0.66\n", "Prob(H) (two-sided): 0.22 Kurtosis: 2.90\n", "===================================================================================\n", "\n", "Warnings:\n", "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n", "\"\"\"" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "arima = ARIMA(df.demand[0:30].to_numpy(),\n", " order=(0,0,1))\n", "\n", "ma_model = arima.fit()\n", "\n", "ma_model.summary()" ] }, { "cell_type": "code", "execution_count": 33, "id": "3803c499", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "17.78" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "forecast_31_37 = ma_model.predict(30,36)\n", "\n", "get_MAPE(df.demand[30:], forecast_31_37)" ] }, { "cell_type": "code", "execution_count": 34, "id": "878fd131", "metadata": {}, "outputs": [ { "data": { "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", "
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", "
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", "
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)." ], "text/plain": [ "\n", "\"\"\"\n", " SARIMAX Results \n", "==============================================================================\n", "Dep. Variable: y No. Observations: 30\n", "Model: ARIMA(1, 0, 1) Log Likelihood -181.846\n", "Date: Tue, 16 May 2023 AIC 371.692\n", "Time: 21:50:14 BIC 377.296\n", "Sample: 0 HQIC 373.485\n", " - 30 \n", "Covariance Type: opg \n", "==============================================================================\n", " coef std err z P>|z| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "const 508.3844 59.146 8.595 0.000 392.461 624.308\n", "ar.L1 0.7421 0.316 2.348 0.019 0.123 1.362\n", "ma.L1 -0.3395 0.539 -0.629 0.529 -1.397 0.718\n", "sigma2 1.065e+04 3040.079 3.505 0.000 4696.418 1.66e+04\n", "===================================================================================\n", "Ljung-Box (L1) (Q): 0.13 Jarque-Bera (JB): 2.46\n", "Prob(Q): 0.72 Prob(JB): 0.29\n", "Heteroskedasticity (H): 3.07 Skew: 0.67\n", "Prob(H) (two-sided): 0.09 Kurtosis: 3.38\n", "===================================================================================\n", "\n", "Warnings:\n", "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n", "\"\"\"" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "arima = ARIMA(df.demand[0:30].to_numpy(),\n", " order=(1,0,1))\n", "\n", "arma_model = arima.fit()\n", "\n", "arma_model.summary()" ] }, { "cell_type": "code", "execution_count": 35, "id": "6f5703b7", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "20.27" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "forecast_31_37 = arma_model.predict(30,36)\n", "\n", "get_MAPE(df.demand[30:], forecast_31_37)" ] }, { "cell_type": "markdown", "id": "30adbf67", "metadata": {}, "source": [ "## ARIMA\n", "time-series data is stationary - ARMA\n", "\n", "\n", " non-stationary - ARIMA" ] }, { "cell_type": "code", "execution_count": 36, "id": "aba6b783", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Datedemand
02014-10-0115
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22014-10-038
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42014-10-0513
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" ], "text/plain": [ " Date demand\n", "0 2014-10-01 15\n", "1 2014-10-02 7\n", "2 2014-10-03 8\n", "3 2014-10-04 10\n", "4 2014-10-05 13" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "store_df = pd.read_excel('store.xls')\n", "\n", "store_df.head()" ] }, { "cell_type": "code", "execution_count": 37, "id": "c1deb62f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 115 entries, 0 to 114\n", "Data columns (total 2 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 Date 115 non-null datetime64[ns]\n", " 1 demand 115 non-null int64 \n", "dtypes: datetime64[ns](1), int64(1)\n", "memory usage: 1.9 KB\n" ] } ], "source": [ "store_df.info()" ] }, { "cell_type": "code", "execution_count": 38, "id": "ecff6ab2", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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demand
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" ], "text/plain": [ " demand\n", "Date \n", "2014-10-01 15\n", "2014-10-02 7\n", "2014-10-03 8\n", "2014-10-04 10\n", "2014-10-05 13" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "store_df.set_index(pd.to_datetime(store_df.Date), inplace=True)\n", "\n", "store_df.drop('Date', axis=1, inplace=True)\n", "\n", "store_df[:5]" ] }, { "cell_type": "code", "execution_count": 39, "id": "6bd71b19", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(10,4))\n", "plt.xlabel('Date')\n", "plt.ylabel('Demand')\n", "\n", "plt.plot(store_df.demand)" ] }, { "cell_type": "code", "execution_count": 40, "id": "3aeb1f33", "metadata": {}, "outputs": [ { "data": { "image/png": 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Sjg6UONrvk/9k5Z2M5qsJMwfBtCtXEl4bLHOkv8TJoYr7+3PKMxnNVxNmqY5WvImkmyQ9J2mPpDsbbH+vpCeyn3+WdFXdthclPSlpt6SdrWhPuwlguJrwzP4T7Hr5NV7oG+DEKYdAK9Qmo9VrdjJa/dVEcObVxFRKkmDXS69x/6597HrpNRJ/sYHNsNxXBJI6gS8ANwL7gMckPRgRz9Tt9iPgpyPiNUmbgW3AtXXbr4+II3nb0m4GShUOnhiif6hCEBw/NTzTTZpz8kxGy/toi8l0K/kqxNpRK7qGrgH2RMQLAJK+DmwBTgdBRPxz3f7fB9a34LhtKSI4OlDm4PGh0/3+zX9bq01UnsloeR5tMdkTuh+wZ+2oFV1D64C9dev7srLR3A58u249gIclPS5p62iVJG2VtFPSzr6+vlwNngrlSsLeVwfZ9fIxnj/U78HfaVSbjLZyyXyuvnBF039Z164mlO0+kauJyXYrjXUVYjZTWnFF0Oi/uoZ/Aku6njQI3lZX/NaI2C9pNfCIpH+JiEfPesOIbaRdSvT29rbNn9gnhoY5dHyIowNl9/nPMnmuJibbreQH7Fk7akUQ7AM21K2vB/aP3EnSjwFfAjZHxNFaeUTsz14PS3qAtKvprCBoJ0kSHOkvcfDEEAOl6vgVrG1N9tEWkz2h+wF71o5aEQSPARslXQy8AtwCvKd+B0kXAPcD74uIf60rXwx0RMTJbPkdwCda0KYpkUTw0tEBDp8ste2TPG16TPaEnvcBe3nmPXjOhI0mdxBEREXSHcBDQCdwT0Q8LelD2fa7gY8B5wL/Q2mHbCUieoE1wANZWRfwtYj4Tt42tdqxwTKD5SqVJGH/saGZbo61gTwn9MleheS548h3K9lYWjKhLCK2A9tHlN1dt/xB4IMN6r0AXDWyvB1Uqgl9/SUOHh9iaDihksz+J3xaa033E1Pz3HHku5VsLC2ZUDaXDJYrvNDXz66Xj/HikUGGhh0A1h7y3HHku5VsLH7ERJ2+kyX2HO6f6WaYNZTnjiPfrWRj8RVBnen+rl6zicgz7yFPXfBjMeY6XxGYzRJ5B6gnW9cDzXOfrwjMZpHJzqLOU3emHs5n08dBYGZj8kDz3OcgMLMx5XnUt80ODgIzG1PegWZrfw4CMxtTbaB53fKFrOrp5j/9zEYPFM8xvmvIzMY13bOobXr5isDMrOAcBGZmBeeuITObUn78dftzEJjZlPGs5NnBXUNmNmU8K3l2cBCY2ZTxrOTZwV1DZjZl8j7+2uML08NBYGZTZrLf7QweX5hOLekaknSTpOck7ZF0Z4PtkvS5bPsTkq5utq6ZzV55ZiXnHV/wdyg0L/cVgaRO4AvAjcA+4DFJD0bEM3W7bQY2Zj/XAl8Erm2yrpnNYpOdlTzW+MJ475PnaqKI3VGKnN/KJektwMcj4mez9d8CiIjfr9vnT4DvRcR92fpzwHXARePVbeScCy+PG++6Z8JtfebACQCuWLu04fbhasKp4WrDbS8dHQTgwnMXTfi4s63ubGvvbKw729o7E3VPDlV45dgp6k9REqxbvpAlC8b+G3aydSOCl189xanhKhFpnYXzOrngnIVIUxsGEUF/qcrQcJUF8zrpmd951jHnd3Uyv2vyHTnf+NBPPB4RvSPLWzFGsA7YW7e+j/Sv/vH2WddkXQAkbQW2AvSsvWRSDR0tAJoxmV/+2Vp3trV3Ntadbe2dibo98ztZOK/zrJNyz/zOcesOZXXqRUBpuDpmEPSXqqePV6tzarhKf2nseq8fI/jRkUGSCNYsXdDwZD5avZdfPcVgOf1DdDoDCFoTBI1aOfIyY7R9mqmbFkZsA7YB9Pb2xl/9ylsm0samHDoxxAt9vq3NrF1Mtptm10uv8bn/+fwZdyvN7+rgAz9x8ZjdSvfv2sffPL7vzMKAt7zhXN599fpx2/rJbz9LuZoQAX0nSyxb2Fx3VK29pw8ZUE2Cn/uxdWe0d93yhVyQI5C/8aHG5a0YLN4HbKhbXw/sb3KfZuqaWUF1dIirL1zBu69eP6Gv16zdrTS/qwPR/N1Keb6Epza4XbuamMjg9kzPt2jFFcFjwEZJFwOvALcA7xmxz4PAHZK+Ttr1czwiDkjqa6KumdmE1O5WmujVRC1ARg4yN3O7a57B7bzzLfLKHQQRUZF0B/AQ0AncExFPS/pQtv1uYDtwM7AHGAR+eay6edtkZla7mpjInUqTDRDIdzLPE0CtkPuuoZnQ29sbO3fubPn7eozAzCYr7wS4ZsZD8o4RSJqyu4bMzAovz9VErf5Er2BaxUFgZtYiM3kyz8NPHzUzKzgHgZlZwTkIzMwKzkFgZlZwDgIzs4JzEJiZFZyDwMys4BwEZmYF5yAwMys4B4GZWcE5CMzMCs5BYGZWcH7oXJ3VS+bT1SEOnhjixKnKTDfHzGxaOAjqSOLcnvmc2zOfwXKFg8eHONJfpprMvu9sMDNrloNgFIu6u3jDqh4uOCehr7/EweNDDA0n41c0M5tlHATj6OrsYO2yhaxdtpBjg2UOnhji2OAws/CL3czMGso1WCzpHEmPSHo+ez3r2xgkbZD0D5KelfS0pF+r2/ZxSa9I2p393JynPVNt+aJu3njeUjZtWM75yxfQ1dncNw+ZmbWzvHcN3QnsiIiNwI5sfaQK8BsRcTnwZuDDkq6o2/6ZiNiU/WzP2Z5psWBeJxeeu5gfv2AFl6xazOL5nTPdJDOzScsbBFuAe7Ple4F3jtwhIg5ExK5s+STwLLAu53HbQkeHWL10AT+2fjlXrlvKyp5u5IsEM5tl8gbBmog4AOkJH1g91s6SLgLeBPygrvgOSU9IuqdR11Jd3a2Sdkra2dfXl7PZrbd0wTw2rlnC1ResYP2KhXR3ORHMbHYYNwgkfVfSUw1+tkzkQJJ6gG8CH4mIE1nxF4FLgE3AAeDTo9WPiG0R0RsRvatWrZrIoadVd1cHG85ZxNUXrGDjmh6WLPB4vJm1t3HPUhHx9tG2STokaW1EHJC0Fjg8yn7zSEPgLyPi/rr3PlS3z58CfzeRxrczSazsmc/KnvkMlCoc6S9xpL9MueJbUM2sveTtGnoQuC1bvg341sgdJAn4MvBsRPzhiG1r61bfBTyVsz1tafH8rnRw+cIVXLluKWuXLXDXkZm1jbz9Fp8CviHpduBl4BcAJJ0PfCkibgbeCrwPeFLS7qzeXdkdQn8gaRMQwIvAr+RsT9tbumAeSxfM48JzF3FiqMLR/hKvDZYpVzwxwcxmRq4giIijwA0NyvcDN2fL/wQ0/PM3It6X5/izmSSWLZzHsoXziAhOnKpwZKDEawNlhqsOBTObPh7JbAOSWLZoHssWzSNWBsdPDXOkv8xrg2UqDgUzm2IOgjYjieWLulm+qJskSUPhxNAwJ4cqDJarfgCembWcg6CNdXSIFYu7WbG4G4CIYLBcpb9U4eRQhYFSGg5mZnk4CGYRSSye38Xi+V2sWZqWVaoJA6UqJ0vD9JfScPDAs5lNhINgluvq7GDZog6WLZp3umxouHo6FAZKVQbLFQ9Am9moHARz0IJ5nSyY18nKnvmny8qVhFPlKgPltDvpVDkNCA85mJmDoCC6uzro7jrzyiEiGBpOGMzCYTALB38Bj1mxOAgKTBILuztZ2N3JuXXl1SQ4NVxlaLhKqZKkr8MJpUq67i/lMZtbHAR2ls4O0TO/i575Z/96RASlSpL+ZEFRqlQZyoLCA9Vms4+DwCZE0ukxCBbOO2t7NQnKlYRyJaFUrTJcTdeHq2lZOXv1VYVZ+3AQWEt1drze3QRnB0VNfTgMV9MrjHI1oVINhqtpWSUJz6w2mwYOApsRtcHrxfPH3i8iGK4GlSRhuBIMJ6+HRRoUCcN160kE1SR8xWE2AQ4Ca2uS6O4S3XRAd/P1kiSoZqFQzZaTM5ahkiQkCaf3S+L1IEkS0uWsXhL48R42ZzkIbE7q6BAdiHmdrX3fat1Vx+uv6ZVLEpwOkzi9nIZSbb0acea+WeBEpIGU1G33lY1NFweB2QR0dojOKQiY0dSHRjWpD5gggEggeD2MgnQ76f9Oh0yQBlL6nrxeP3t/qL13+n6n98mCqP59asepvVetbn1ZnH7/6fmcLB8HgVkbk0SnmNbwmQoRzV/dFDE7oskPp0NT882GDgIzm3KSmKJz2Bwxsx9O3u8sNjOzWS5XEEg6R9Ijkp7PXleMst+Lkp6UtFvSzonWNzOzqZP3iuBOYEdEbAR2ZOujuT4iNkVE7yTrm5nZFMgbBFuAe7Ple4F3TnN9MzPLKW8QrImIAwDZ6+pR9gvgYUmPS9o6ifpI2ippp6SdfX19OZttZmY14941JOm7wHkNNn10Asd5a0Tsl7QaeETSv0TEoxOoT0RsA7YB9Pb2FvEOMzOzKTFuEETE20fbJumQpLURcUDSWuDwKO+xP3s9LOkB4BrgUaCp+mZmNnXydg09CNyWLd8GfGvkDpIWS1pSWwbeATzVbH0zM5taeYPgU8CNkp4HbszWkXS+pO3ZPmuAf5L0Q+D/An8fEd8Zq76ZmU2fXDOLI+IocEOD8v3AzdnyC8BVE6lvZmbTxzOLzcwKzkFgZlZwDgIzs4JzEJiZFZyDwMys4BwEZmYF5yAwMys4B4GZWcE5CMzMCs5BYGZWcA4CM7OCcxCYmRWcg8DMrOAcBGZmBecgMDMrOAeBmVnBOQjMzArOQWBmVnC5gkDSOZIekfR89rqiwT6XSdpd93NC0keybR+X9ErdtpvztMfMzCYu7xXBncCOiNgI7MjWzxARz0XEpojYBPw4MAg8ULfLZ2rbI2L7yPpmZja18gbBFuDebPle4J3j7H8D8G8R8VLO45qZWYvkDYI1EXEAIHtdPc7+twD3jSi7Q9ITku5p1LVkZmZTa9wgkPRdSU81+NkykQNJ6gZ+HvjruuIvApcAm4ADwKfHqL9V0k5JO/v6+iZyaDMzG0PXeDtExNtH2ybpkKS1EXFA0lrg8BhvtRnYFRGH6t779LKkPwX+box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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "acf_plot = plot_acf(store_df.demand, lags=20)" ] }, { "cell_type": "code", "execution_count": 41, "id": "362258a9", "metadata": {}, "outputs": [], "source": [ "from statsmodels.tsa.stattools import adfuller" ] }, { "cell_type": "code", "execution_count": 42, "id": "9c78933a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Test Statistics -1.649533\n", "p-value 0.457284\n", "Lags Used 13.000000\n", "Number of Observations Used 101.000000\n", "dtype: float64\n" ] } ], "source": [ "def adfuller_test(ts):\n", " adfuller_result = adfuller(ts, autolag=None)\n", " adfuller_out = pd.Series(adfuller_result[0:4],\n", " index=['Test Statistics',\n", " 'p-value',\n", " 'Lags Used',\n", " 'Number of Observations Used'])\n", " print(adfuller_out)\n", " \n", "adfuller_test(store_df.demand)" ] }, { "cell_type": "code", "execution_count": null, "id": "2f7b1ad8", "metadata": {}, "outputs": [], "source": [ "# p-value > 0.05 that means we cannot reject the null hypothesis, \n", "# as series is not stationary" ] }, { "cell_type": "markdown", "id": "eb42a0fa", "metadata": {}, "source": [ "#### differencing" ] }, { "cell_type": "code", "execution_count": 43, "id": "5a1055ea", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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demanddemand_diff
Date
2014-10-0115NaN
2014-10-027-8.0
2014-10-0381.0
2014-10-04102.0
2014-10-05133.0
\n", "
" ], "text/plain": [ " demand demand_diff\n", "Date \n", "2014-10-01 15 NaN\n", "2014-10-02 7 -8.0\n", "2014-10-03 8 1.0\n", "2014-10-04 10 2.0\n", "2014-10-05 13 3.0" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "store_df['demand_diff'] = store_df.demand - store_df.demand.shift(1)\n", "\n", "store_df.head()" ] }, { "cell_type": "code", "execution_count": 44, "id": "78378db1", "metadata": {}, "outputs": [], "source": [ "store_diff_df = store_df.dropna()" ] }, { "cell_type": "code", "execution_count": 45, "id": "e4c4e738", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(10,4))\n", "plt.xlabel('Date')\n", "plt.ylabel('First Order Differencing')\n", "\n", "plt.plot(store_diff_df.demand_diff)" ] }, { "cell_type": "code", "execution_count": 46, "id": "9ac6af67", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "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", " warnings.warn(\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "pacf_plot = plot_pacf(store_df.demand_diff.dropna(),\n", " lags=10)" ] }, { "cell_type": "code", "execution_count": 47, "id": "41d50dfe", "metadata": {}, "outputs": [], "source": [ "store_train = store_df[0:100]\n", "store_test = store_df[100:0]" ] }, { "cell_type": "code", "execution_count": 48, "id": "78743a66", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "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", " warn('Non-invertible starting MA parameters found.'\n" ] }, { "data": { "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", "
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", "
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", "
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)." ], "text/plain": [ "\n", "\"\"\"\n", " SARIMAX Results \n", "==============================================================================\n", "Dep. Variable: y No. Observations: 100\n", "Model: ARIMA(1, 1, 1) Log Likelihood -256.910\n", "Date: Tue, 16 May 2023 AIC 519.821\n", "Time: 22:15:55 BIC 527.606\n", "Sample: 0 HQIC 522.971\n", " - 100 \n", "Covariance Type: opg \n", "==============================================================================\n", " coef std err z P>|z| [0.025 0.975]\n", "------------------------------------------------------------------------------\n", "ar.L1 0.4091 0.311 1.315 0.189 -0.201 1.019\n", "ma.L1 -0.7179 0.236 -3.039 0.002 -1.181 -0.255\n", "sigma2 10.4865 1.026 10.223 0.000 8.476 12.497\n", "===================================================================================\n", "Ljung-Box (L1) (Q): 0.06 Jarque-Bera (JB): 75.18\n", "Prob(Q): 0.81 Prob(JB): 0.00\n", "Heteroskedasticity (H): 0.76 Skew: -0.79\n", "Prob(H) (two-sided): 0.44 Kurtosis: 6.97\n", "===================================================================================\n", "\n", "Warnings:\n", "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n", "\"\"\"" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "arima = ARIMA(store_train.demand.to_numpy(),\n", " order=(1,1,1))\n", "\n", "arima_model = arima.fit()\n", "\n", "arima_model.summary()" ] }, { "cell_type": "markdown", "id": "35dce0cf", "metadata": {}, "source": [ "### forecasting and accuracy" ] }, { "cell_type": "code", "execution_count": 50, "id": "bd238d6f", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([17.25076347, 16.12611976, 15.66605623, 15.47785577, 15.40086769,\n", " 15.36937379, 15.35649043, 15.35122017, 15.34906424, 15.3481823 ,\n", " 15.34782152, 15.34767394, 15.34761356, 15.34758887, 15.34757876])" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ "store_predict = arima_model.forecast(steps=15)\n", "\n", "store_predict" ] }, { "cell_type": "code", "execution_count": 51, "id": "6cf83df8", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "24.12" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ "get_MAPE(store_df.demand[100:], store_predict)" ] }, { "cell_type": "code", "execution_count": null, "id": "3d8e9051", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.12" } }, "nbformat": 4, "nbformat_minor": 5 } ================================================ FILE: March Cohort 2023/22_23_Time Series Forecasting/vimana.csv ================================================ Month,demand 1,457 2,439 3,404 4,392 5,403 6,371 7,382 8,358 9,594 10,482 11,574 12,704 13,486 14,509 15,537 16,407 17,523 18,363 19,479 20,516 21,656 22,558 23,647 24,864 25,610 26,677 27,609 28,673 29,400 30,443 31,503 32,688 33,602 34,629 35,823 36,671 37,487 ================================================ FILE: March Cohort 2023/22_23_Time Series Forecasting/wsb.csv ================================================ Month,Sale Quantity,Promotion Expenses,Competition Promotion 1,3002666,105,1 2,4401553,145,0 3,3205279,118,1 4,4245349,130,0 5,3001940,98,1 6,4377766,156,0 7,2798343,98,1 8,4303668,144,0 9,2958185,112,1 10,3623386,120,0 11,3279115,125,0 12,2843766,102,1 13,4447581,160,0 14,3675305,130,0 15,3477156,130,0 16,3720794,140,0 17,3834086,167,1 18,3888913,148,1 19,3871342,150,1 20,3679862,129,0 21,3358242,120,0 22,3361488,122,0 23,3670362,135,0 24,3123966,110,1 25,4634047,165,0 26,3772879,129,1 27,3187110,120,1 28,3093683,112,1 29,4557363,162,0 30,3816956,140,1 31,4410887,160,0 32,3694713,139,0 33,3822669,141,1 34,3689286,136,0 35,3728654,130,1 36,4732677,168,0 37,3216483,121,1 38,3453239,128,0 39,5431651,170,0 40,4241851,160,0 41,3909887,151,1 42,3216438,120,1 43,4222005,152,0 44,3621034,125,0 45,5162201,170,0 46,4627177,160,0 47,4623945,168,0 48,4599368,166,0 ================================================ FILE: March Cohort 2023/24_SQL/SQLqueriesQandA.sql ================================================ -- DROP DATABASE ORG; CREATE DATABASE ORG; SHOW DATABASES; USE ORG; -- drop table Worker; CREATE TABLE Worker ( WORKER_ID INT NOT NULL PRIMARY KEY AUTO_INCREMENT, FIRST_NAME CHAR(25), LAST_NAME CHAR(25), SALARY INT, JOINING_DATE DATETIME, DEPARTMENT CHAR(25) ); INSERT INTO Worker (WORKER_ID, FIRST_NAME, LAST_NAME, SALARY, JOINING_DATE, DEPARTMENT) VALUES (001, 'Monika', 'Arora', 100000, '14-02-20 09.00.00', 'HR'), (002, 'Niharika', 'Verma', 80000, '14-06-11 09.00.00', 'Admin'), (003, 'Vishal', 'Singhal', 300000, '14-02-20 09.00.00', 'HR'), (004, 'Amitabh', 'Singh', 500000, '14-02-20 09.00.00', 'Admin'), (005, 'Vivek', 'Bhati', 500000, '14-06-11 09.00.00', 'Admin'), (006, 'Vipul', 'Diwan', 200000, '14-06-11 09.00.00', 'Account'), (007, 'Satish', 'Kumar', 75000, '14-01-20 09.00.00', 'Account'), (008, 'Geetika', 'Chauhan', 90000, '14-04-11 09.00.00', 'Admin'); CREATE TABLE Bonus ( WORKER_REF_ID INT, BONUS_AMOUNT INT, BONUS_DATE DATETIME, FOREIGN KEY (WORKER_REF_ID) REFERENCES Worker(WORKER_ID) ON DELETE CASCADE ); INSERT INTO Bonus (WORKER_REF_ID, BONUS_AMOUNT, BONUS_DATE) VALUES (001, 5000, '16-02-20'), (002, 3000, '16-06-11'), (003, 4000, '16-02-20'), (001, 4500, '16-02-20'), (002, 3500, '16-06-11'); CREATE TABLE Title ( WORKER_REF_ID INT, WORKER_TITLE CHAR(25), AFFECTED_FROM DATETIME, FOREIGN KEY (WORKER_REF_ID) REFERENCES Worker(WORKER_ID) ON DELETE CASCADE ); INSERT INTO Title (WORKER_REF_ID, WORKER_TITLE, AFFECTED_FROM) VALUES (001, 'Manager', '2016-02-20 00:00:00'), (002, 'Executive', '2016-06-11 00:00:00'), (008, 'Executive', '2016-06-11 00:00:00'), (005, 'Manager', '2016-06-11 00:00:00'), (004, 'Asst. Manager', '2016-06-11 00:00:00'), (007, 'Executive', '2016-06-11 00:00:00'), (006, 'Lead', '2016-06-11 00:00:00'), (003, 'Lead', '2016-06-11 00:00:00'); select * from Worker; select * from bonus; select * from title; -- Q-1. Write an SQL query to fetch “FIRST_NAME” from Worker table -- using the alias name as . SELECT FIRST_NAME AS WORKER_NAME FROM Worker; -- Q-2. Write an SQL query to fetch “FIRST_NAME” from Worker table -- in upper case. SELECT upper(FIRST_NAME) FROM Worker; -- Q-3. Write an SQL query to fetch unique values of DEPARTMENT -- from Worker table. SELECT distinct DEPARTMENT FROM Worker; -- Q-4. Write an SQL query to print the first three characters -- of FIRST_NAME from Worker table. SELECT substring(FIRST_NAME, 1, 3) FROM Worker; -- INITIALS OF FIRST NAME AND LAST NAME SELECT substring(FIRST_NAME, 1,1), substring(LAST_NAME, 1,1) FROM Worker; SELECT CONCAT(substring(FIRST_NAME, 1,1), substring(LAST_NAME, 1,1)) FROM Worker; -- Q-5. Write an SQL query to find the position of the -- alphabet (‘a’) in the first name column ‘Amitabh’ from Worker -- table. SELECT instr(FIRST_NAME, 'a') FROM Worker WHERE FIRST_NAME = 'Amitabh'; -- CASE SENSITIVE/ BINARY DEPRECATED -- SELECT instr(FIRST_NAME, BINARY 'a') FROM Worker WHERE FIRST_NAME = 'Amitabh'; -- SELECT INSTR(FIRST_NAME, BINARY 'a') FROM Worker WHERE FIRST_NAME='Amitabh'; -- Q-6. Write an SQL query to print the FIRST_NAME from Worker -- table after removing white spaces from the right side. SELECT rtrim(FIRST_NAME) FROM Worker; -- Q-7. Write an SQL query to print the DEPARTMENT from Worker -- table after removing white spaces from the left side. SELECT ltrim(DEPARTMENT) FROM Worker; -- Q-8. Write an SQL query that fetches the unique values of -- DEPARTMENT from Worker table and prints its length. SELECT distinct length(DEPARTMENT) FROM Worker; -- Q-9. Write an SQL query to print the FIRST_NAME from -- Worker table after replacing ‘a’ with ‘A’. SELECT replace(FIRST_NAME, 'a', 'A') FROM Worker; -- Q-10. Write an SQL query to print the FIRST_NAME and -- LAST_NAME from Worker table into a single column -- COMPLETE_NAME. A space char should separate them. -- Q-11. Write an SQL query to print all Worker details -- from the Worker table order by FIRST_NAME Ascending. -- Q-12. Write an SQL query to print all Worker details -- from the Worker table order by FIRST_NAME Ascending -- and DEPARTMENT Descending. -- Q-13. Write an SQL query to print details for Workers -- with the first name as “Vipul” and “Satish” from Worker -- table. -- Q-14. Write an SQL query to print details of workers -- excluding first names, “Vipul” and “Satish” from Worker -- table. -- Q-15. Write an SQL query to print details of Workers -- with DEPARTMENT name as “Admin”. -- Q-16. Write an SQL query to print details of the Workers -- whose FIRST_NAME contains ‘a’. -- Q-17. Write an SQL query to print details of the Workers -- whose FIRST_NAME ends with ‘a’. -- Q-18. Write an SQL query to print details of the Workers -- whose FIRST_NAME ends with ‘h’ and contains six alphabets. -- Q-19. Write an SQL query to print details of the Workers -- whose SALARY lies between 100000 and 500000. -- Q-20. Write an SQL query to print details of the Workers -- who have joined in Feb’2014. -- Q-21. Write an SQL query to fetch the count of employees -- working in the department ‘Admin’. -- Q-22. Write an SQL query to fetch worker names with -- salaries >= 50000 and <= 100000. -- Q-23. Write an SQL query to fetch the no. of workers -- for each department in the descending order. -- Q-24. Write an SQL query to print details of the -- Workers who are also Managers. -- Q-25. Write an SQL query to fetch duplicate records -- having matching data in some fields of a table. -- Q-26. Write an SQL query to show only odd rows -- from a table. -- Q-27. Write an SQL query to show only even rows -- from a table. -- Q-28. Write an SQL query to clone a new table from -- another table. -- Q-29. Write an SQL query to fetch intersecting -- records of two tables. -- Q-30. Write an SQL query to show records from one -- table that another table does not have. -- Q-31. Write an SQL query to show the current date -- and time. -- Q-32. Write an SQL query to show the top n (say 10) -- records of a table. -- Q-33. Write an SQL query to determine the nth (say n=5) -- highest salary from a table. -- Q-34. Write an SQL query to determine the 5th highest -- salary without using TOP or limit method. -- Q-35. Write an SQL query to fetch the list of employees -- with the same salary. -- Q-36. Write an SQL query to show the second highest -- salary from a table. -- Q-37. Write an SQL query to show one row twice in -- results from a table. -- Q-38. Write an SQL query to fetch intersecting -- records of two tables. -- Q-39. Write an SQL query to fetch the first 50% -- records from a table. -- Q-40. Write an SQL query to fetch the departments -- that have less than five people in it. -- Q-41. Write an SQL query to show all departments -- along with the number of people in there. -- Q-42. Write an SQL query to show the last record -- from a table. -- Q-43. Write an SQL query to fetch the first row -- of a table. -- Q-44. Write an SQL query to fetch the last five -- records from a table. -- Q-45. Write an SQL query to print the name of employees -- having the highest salary in each department. -- Q-46. Write an SQL query to fetch three max salaries -- from a table. -- Q-47. Write an SQL query to fetch three min salaries -- from a table. -- Q-48. Write an SQL query to fetch nth max salaries -- from a table. -- Q-49. Write an SQL query to fetch departments along -- with the total salaries paid for each of them. -- Q-50. Write an SQL query to fetch the names of workers -- who earn the highest salary. ================================================ FILE: March Cohort 2023/2_List and Strings.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "id": "f18f8832", "metadata": {}, "source": [ "# List\n", "\n", "collection of different types of elements" ] }, { "cell_type": "code", "execution_count": null, "id": "4a53537e", "metadata": {}, "outputs": [], "source": [ "a = 10 # int\n", "b = 20\n", "c = 30" ] }, { "cell_type": "code", "execution_count": 1, "id": "8179aaea", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[10, 'A', 'India', 10.6]\n" ] } ], "source": [ "l = [10, \"A\", 'India', 10.6]\n", "# 0 1 2 3\n", "\n", "print(l)" ] }, { "cell_type": "code", "execution_count": 2, "id": "b073284b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10\n" ] } ], "source": [ "print(l[0])" ] }, { "cell_type": "code", "execution_count": 3, "id": "0b18a752", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10.6\n" ] } ], "source": [ "print(l[3])" ] }, { "cell_type": "code", "execution_count": 4, "id": "562832f4", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "India\n" ] } ], "source": [ "print(l[2])" ] }, { "cell_type": "code", "execution_count": 5, "id": "2ce1c1e2", "metadata": {}, "outputs": [ { "ename": "IndexError", "evalue": "list index out of range", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mIndexError\u001b[0m Traceback (most recent call last)", "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", "\u001b[1;31mIndexError\u001b[0m: list index out of range" ] } ], "source": [ "print(l[5])" ] }, { "cell_type": "code", "execution_count": 6, "id": "b56a18f7", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[10, 'A', 'India', 10.6]\n" ] } ], "source": [ "l = [10, \"A\", 'India', 10.6]\n", "# 0 1 2 3\n", "\n", "print(l)" ] }, { "cell_type": "code", "execution_count": 7, "id": "56810d39", "metadata": {}, "outputs": [], "source": [ "l[0] = 700" ] }, { "cell_type": "code", "execution_count": 8, "id": "cec85000", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[700, 'A', 'India', 10.6]\n" ] } ], "source": [ "print(l)" ] }, { "cell_type": "code", "execution_count": 9, "id": "a839bab4", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "20\n" ] } ], "source": [ "a = 10\n", "\n", "a = 20\n", "\n", "print(a)" ] }, { "cell_type": "code", "execution_count": 10, "id": "07f87123", "metadata": {}, "outputs": [ { "ename": "IndexError", "evalue": "list assignment index out of range", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mIndexError\u001b[0m Traceback (most recent call last)", "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", "\u001b[1;31mIndexError\u001b[0m: list assignment index out of range" ] } ], "source": [ "l[5] = 400" ] }, { "cell_type": "code", "execution_count": 11, "id": "d61e6a52", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[700, 10.6, 'India', 10.6]\n" ] } ], "source": [ "l[1] = l[3]\n", "#l[1] = 10.6\n", "\n", "print(l)" ] }, { "cell_type": "code", "execution_count": 12, "id": "cd0e6f72", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[10, 'A', 'India', 10.6]\n" ] } ], "source": [ "# -4 -3 -2 -1\n", "l = [10, \"A\", 'India', 10.6]\n", "# 0 1 2 3\n", "\n", "print(l)" ] }, { "cell_type": "code", "execution_count": 13, "id": "28fe4466", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10\n", "10\n" ] } ], "source": [ "print(l[0])\n", "\n", "print(l[-4])" ] }, { "cell_type": "code", "execution_count": 14, "id": "a4ddd5ef", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10.6\n" ] } ], "source": [ "print(l[-1])" ] }, { "cell_type": "code", "execution_count": 15, "id": "edb5d5de", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "India\n" ] } ], "source": [ "print(l[-2])" ] }, { "cell_type": "markdown", "id": "e57f0121", "metadata": {}, "source": [ "# List slicing" ] }, { "cell_type": "code", "execution_count": 16, "id": "536c7f69", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[11, 22, 33, 10, 24, 44, 36, 68, 99, 81, 100]\n", "\n" ] } ], "source": [ "l = [11,22,33,10,24,44,36,68,99,81,100]\n", "# 0 1 2 3 4 5 6 7 8 9 10\n", "\n", "print(l)\n", "print(type(l))" ] }, { "cell_type": "code", "execution_count": 17, "id": "c4ed3b05", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[22, 33, 10, 24, 44, 36]\n" ] } ], "source": [ "print(l[1:7]) # l[start : end]" ] }, { "cell_type": "code", "execution_count": 18, "id": "44a00013", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[11, 22, 33, 10, 24, 44, 36, 68, 99, 81, 100]\n" ] } ], "source": [ "print(l)" ] }, { "cell_type": "code", "execution_count": 19, "id": "a344e33f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[24, 44, 36, 68, 99, 81]\n" ] } ], "source": [ "print(l[4:10])" ] }, { "cell_type": "code", "execution_count": 20, "id": "07f07f98", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[99, 81]\n" ] } ], "source": [ "print(l[8:10])" ] }, { "cell_type": "code", "execution_count": 21, "id": "47353b5f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[11, 22, 33, 10, 24, 44, 36, 68, 99, 81, 100]\n" ] } ], "source": [ "l = [11,22,33,10,24,44,36,68,99,81,100]\n", "# 0 1 2 3 4 5 6 7 8 9 10\n", "\n", "print(l)" ] }, { "cell_type": "code", "execution_count": 22, "id": "3d8b979b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[11]\n" ] } ], "source": [ "print(l[0:1])" ] }, { "cell_type": "code", "execution_count": 23, "id": "94eda01d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[22, 33, 10, 24, 44, 36, 68, 99, 81, 100]\n" ] } ], "source": [ "print(l[1:])" ] }, { "cell_type": "code", "execution_count": 24, "id": "c5f21115", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[99, 81, 100]\n" ] } ], "source": [ "print(l[8:])" ] }, { "cell_type": "code", "execution_count": 25, "id": "0e399980", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[11, 22, 33, 10, 24, 44, 36, 68, 99, 81, 100]\n" ] } ], "source": [ "# -11-10 -9 -8 -7 -6 -5 -4 -3 -2 -1\n", "l = [17,22,33,101,24,44,36,68,99,81,100]\n", "# 0 1 2 3 4 5 6 7 8 9 10\n", "\n", "print(l[:])" ] }, { "cell_type": "code", "execution_count": 26, "id": "9da33ce5", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[11, 22, 33, 10, 24]\n" ] } ], "source": [ "print(l[:5])" ] }, { "cell_type": "code", "execution_count": 27, "id": "858c11cb", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[17, 22, 33, 101, 24, 44, 36]\n" ] } ], "source": [ "# -11-10 -9 -8 -7 -6 -5 -4 -3 -2 -1\n", "l = [17,22,33,101,24,44,36,68,99,81,100]\n", "# 0 1 2 3 4 5 6 7 8 9 10\n", "\n", "print(l[0:-4])" ] }, { "cell_type": "code", "execution_count": 28, "id": "9b6f435f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[33, 101, 24, 44, 36, 68, 99]\n" ] } ], "source": [ "print(l[2:-2])" ] }, { "cell_type": "code", "execution_count": 29, "id": "556aceba", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[]\n" ] } ], "source": [ "print(l[2:0])" ] }, { "cell_type": "code", "execution_count": 30, "id": "fcab2e6a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[]\n" ] } ], "source": [ "# -11-10 -9 -8 -7 -6 -5 -4 -3 -2 -1\n", "l = [17,22,33,101,24,44,36,68,99,81,100]\n", "# 0 1 2 3 4 5 6 7 8 9 10\n", "\n", "print(l[0:0])" ] }, { "cell_type": "code", "execution_count": 31, "id": "a34bdf8d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[44, 36, 68, 99, 81, 100]\n" ] } ], "source": [ "print(l[5:15])" ] }, { "cell_type": "code", "execution_count": 32, "id": "69241fb4", "metadata": {}, "outputs": [ { "ename": "IndexError", "evalue": "list index out of range", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mIndexError\u001b[0m Traceback (most recent call last)", "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", "\u001b[1;31mIndexError\u001b[0m: list index out of range" ] } ], "source": [ "print(l[15])" ] }, { "cell_type": "code", "execution_count": 33, "id": "b4f6a948", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[22, 33, 101]\n" ] } ], "source": [ "# -11-10 -9 -8 -7 -6 -5 -4 -3 -2 -1\n", "l = [17,22,33,101,24,44,36,68,99,81,100]\n", "# 0 1 2 3 4 5 6 7 8 9 10\n", "\n", "print(l[1:4])" ] }, { "cell_type": "code", "execution_count": 34, "id": "2c46b2a1", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[]\n" ] } ], "source": [ "print(l[4:1])" ] }, { "cell_type": "code", "execution_count": 35, "id": "bb318e72", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[68, 99, 81]\n" ] } ], "source": [ "print(l[-4:-1])" ] }, { "cell_type": "code", "execution_count": 36, "id": "05c49a41", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[]\n" ] } ], "source": [ "print(l[-1:-4])" ] }, { "cell_type": "code", "execution_count": 38, "id": "bd75ab98", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[17, 33, 24, 36, 99, 100]\n" ] } ], "source": [ "# -11-10 -9 -8 -7 -6 -5 -4 -3 -2 -1\n", "l = [17,22,33,101,24,44,36,68,99,81,100]\n", "# 0 1 2 3 4 5 6 7 8 9 10\n", "\n", "print(l[0:11:2]) # l[start : end : steps]" ] }, { "cell_type": "code", "execution_count": 39, "id": "ed0ebd7d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[17, 101, 36, 81]\n" ] } ], "source": [ "print(l[0:11:3])" ] }, { "cell_type": "code", "execution_count": 40, "id": "591165f5", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[17, 22, 33, 101, 24, 44, 36, 68, 99, 81, 100]\n" ] } ], "source": [ "print(l[::])" ] }, { "cell_type": "code", "execution_count": 42, "id": "9325c782", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[17, 22, 33, 101, 24, 44, 36, 68, 99, 81, 100]\n" ] } ], "source": [ "print(l[::1])" ] }, { "cell_type": "code", "execution_count": 41, "id": "7c4d1a41", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[100, 81, 99, 68, 36, 44, 24, 101, 33, 22, 17]\n" ] } ], "source": [ "print(l[::-1]) # reverse" ] }, { "cell_type": "code", "execution_count": 43, "id": "769b5e71", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[]\n" ] } ], "source": [ "# -11-10 -9 -8 -7 -6 -5 -4 -3 -2 -1\n", "l = [17,22,33,101,24,44,36,68,99,81,100]\n", "# 0 1 2 3 4 5 6 7 8 9 10\n", "\n", "print(l[4:8:-1])" ] }, { "cell_type": "code", "execution_count": 44, "id": "61f07964", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[99, 68, 36, 44]\n" ] } ], "source": [ "print(l[8:4:-1])" ] }, { "cell_type": "code", "execution_count": 45, "id": "9ccf1239", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[100, 99, 36, 24, 33, 17]\n" ] } ], "source": [ "print(l[::-2])" ] }, { "cell_type": "code", "execution_count": 46, "id": "0dee2ab9", "metadata": {}, "outputs": [ { "ename": "ValueError", "evalue": "slice step cannot be zero", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", "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", "\u001b[1;31mValueError\u001b[0m: slice step cannot be zero" ] } ], "source": [ "print(l[::0])" ] }, { "cell_type": "markdown", "id": "4f3a41ae", "metadata": {}, "source": [ "# List functions" ] }, { "cell_type": "code", "execution_count": 47, "id": "5d15c64c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "5\n" ] } ], "source": [ "l = [10,20,30,40,50]\n", "\n", "print(len(l))" ] }, { "cell_type": "code", "execution_count": 48, "id": "d0c326c5", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "4\n" ] } ], "source": [ "print(len(l) - 1)" ] }, { "cell_type": "code", "execution_count": 49, "id": "c33408b9", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "150\n" ] } ], "source": [ "print(sum(l))" ] }, { "cell_type": "code", "execution_count": 50, "id": "29a67088", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "50\n" ] } ], "source": [ "print(max(l))" ] }, { "cell_type": "code", "execution_count": 51, "id": "7ce163a3", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10\n" ] } ], "source": [ "print(min(l))" ] }, { "cell_type": "code", "execution_count": 52, "id": "2a9052cb", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "4\n" ] }, { "ename": "TypeError", "evalue": "unsupported operand type(s) for +: 'int' and 'str'", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", "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", "\u001b[1;31mTypeError\u001b[0m: unsupported operand type(s) for +: 'int' and 'str'" ] } ], "source": [ "l = [\"A\", 10, 20, 30]\n", "\n", "print(len(l))\n", "print(sum(l))" ] }, { "cell_type": "code", "execution_count": 55, "id": "e62fa754", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'ab'" ] }, "execution_count": 55, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\"a\" + \"b\"" ] }, { "cell_type": "code", "execution_count": 53, "id": "5d8a7988", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "3\n" ] } ], "source": [ "l = [\"a\", \"b\", \"c\"]\n", "\n", "print(len(l))" ] }, { "cell_type": "code", "execution_count": 54, "id": "99cb555e", "metadata": {}, "outputs": [ { "ename": "TypeError", "evalue": "unsupported operand type(s) for +: 'int' and 'str'", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", "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", "\u001b[1;31mTypeError\u001b[0m: unsupported operand type(s) for +: 'int' and 'str'" ] } ], "source": [ "print(sum(l))" ] }, { "cell_type": "code", "execution_count": 56, "id": "1eff053f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "c\n" ] } ], "source": [ "print(max(l))" ] }, { "cell_type": "code", "execution_count": 57, "id": "c3f7d556", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "a\n" ] } ], "source": [ "print(min(l))" ] }, { "cell_type": "code", "execution_count": null, "id": "8cb11d63", "metadata": {}, "outputs": [], "source": [ "# ASCII\n", "\n", "A - 65/2 - binary\n", "B - 66\n", "Z - 90\n", "\n", "space - 32\n", "\n", "a - 97\n", "z - 122" ] }, { "cell_type": "code", "execution_count": 58, "id": "eea43d51", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "False" ] }, "execution_count": 58, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\"A\" > \"a\"" ] }, { "cell_type": "code", "execution_count": 59, "id": "6ffe5a48", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 59, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\"A\" < 'a'" ] }, { "cell_type": "markdown", "id": "55fc39d8", "metadata": {}, "source": [ "# List methods" ] }, { "cell_type": "code", "execution_count": 65, "id": "d0f38ad0", "metadata": {}, "outputs": [], "source": [ "# help(print)" ] }, { "cell_type": "code", "execution_count": 64, "id": "d56335b3", "metadata": {}, "outputs": [], "source": [ "# help(sum)" ] }, { "cell_type": "code", "execution_count": 63, "id": "4832e051", "metadata": { "scrolled": false }, "outputs": [], "source": [ "# help(list)" ] }, { "cell_type": "code", "execution_count": 66, "id": "315f91a5", "metadata": {}, "outputs": [], "source": [ "sum?" ] }, { "cell_type": "code", "execution_count": 68, "id": "1e64def7", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[10, 20, 30, 40]\n" ] } ], "source": [ "l = [10,20,30]\n", "\n", "l.append(40)\n", "\n", "print(l)" ] }, { "cell_type": "code", "execution_count": 69, "id": "9b78984e", "metadata": {}, "outputs": [], "source": [ "l.append(\"India\")" ] }, { "cell_type": "code", "execution_count": 70, "id": "86227543", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[10, 20, 30, 40, 'India']\n" ] } ], "source": [ "print(l)" ] }, { "cell_type": "code", "execution_count": 71, "id": "5a90e417", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "4\n" ] } ], "source": [ "print(l.index('India'))" ] }, { "cell_type": "code", "execution_count": 72, "id": "43bf35e2", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[10, 20, 30, 40, 'India']\n" ] } ], "source": [ "print(l)" ] }, { "cell_type": "code", "execution_count": 73, "id": "df0bd6ca", "metadata": {}, "outputs": [], "source": [ "l.reverse()" ] }, { "cell_type": "code", "execution_count": 74, "id": "826857cc", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['India', 40, 30, 20, 10]\n" ] } ], "source": [ "print(l)" ] }, { "cell_type": "code", "execution_count": 75, "id": "42f82dab", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "India\n" ] } ], "source": [ "print(l[0])" ] }, { "cell_type": "code", "execution_count": 76, "id": "5fd18a26", "metadata": {}, "outputs": [], "source": [ "l.reverse() # l[::-1]" ] }, { "cell_type": "code", "execution_count": 77, "id": "acf2c947", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[10, 20, 30, 40, 'India']\n" ] } ], "source": [ "print(l)" ] }, { "cell_type": "markdown", "id": "50f6bb18", "metadata": {}, "source": [ "# Strings\n", "\n", "set of characters" ] }, { "cell_type": "code", "execution_count": 78, "id": "2282691e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "hello\n" ] } ], "source": [ "# -1\n", "s = 'hello'\n", "# 01234\n", "\n", "print(s)" ] }, { "cell_type": "code", "execution_count": 79, "id": "30758f8a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "h\n" ] } ], "source": [ "print(s[0])" ] }, { "cell_type": "code", "execution_count": 80, "id": "401c2af6", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "o\n" ] } ], "source": [ "print(s[4])" ] }, { "cell_type": "code", "execution_count": 81, "id": "3543dcff", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "l\n" ] } ], "source": [ "print(s[3])" ] }, { "cell_type": "code", "execution_count": 82, "id": "70efef69", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "o\n" ] } ], "source": [ "print(s[-1])" ] }, { "cell_type": "code", "execution_count": 83, "id": "8b776e03", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "l\n" ] } ], "source": [ "print(s[-2])" ] }, { "cell_type": "code", "execution_count": 84, "id": "7a00d157", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "print(type(s))" ] }, { "cell_type": "code", "execution_count": 85, "id": "bafe5d33", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "print(type(s[0]))" ] }, { "cell_type": "markdown", "id": "69fd97c5", "metadata": {}, "source": [ "# String slicing" ] }, { "cell_type": "code", "execution_count": 86, "id": "cd9dff16", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "commu\n" ] } ], "source": [ "s = 'communication'\n", "# 0123456789\n", "print(s[0:5])" ] }, { "cell_type": "code", "execution_count": 87, "id": "0d50213f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "noitacinummoc\n" ] } ], "source": [ "print(s[::-1])" ] }, { "cell_type": "code", "execution_count": 88, "id": "0d4f5af4", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "omm\n" ] } ], "source": [ "print(s[1:4])" ] }, { "cell_type": "code", "execution_count": 89, "id": "e4712f62", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "print(s[4:1])" ] }, { "cell_type": "code", "execution_count": 90, "id": "2d5ffb11", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "a = ''\n", "\n", "print(type(a))" ] }, { "cell_type": "code", "execution_count": 91, "id": "b1755966", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "communication\n" ] } ], "source": [ "print(s)" ] }, { "cell_type": "code", "execution_count": 92, "id": "e82d1c2b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "noitacin\n" ] } ], "source": [ "print(s[-1:-9:-1])" ] }, { "cell_type": "markdown", "id": "918d1a64", "metadata": {}, "source": [ "# String functions" ] }, { "cell_type": "code", "execution_count": 93, "id": "87ad584b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "13\n" ] } ], "source": [ "s = 'communication'\n", "\n", "print(len(s))" ] }, { "cell_type": "code", "execution_count": 94, "id": "34ab8cff", "metadata": {}, "outputs": [ { "ename": "TypeError", "evalue": "unsupported operand type(s) for +: 'int' and 'str'", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", "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", "\u001b[1;31mTypeError\u001b[0m: unsupported operand type(s) for +: 'int' and 'str'" ] } ], "source": [ "print(sum(s))" ] }, { "cell_type": "code", "execution_count": 95, "id": "7378c889", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "u\n" ] } ], "source": [ "print(max(s))" ] }, { "cell_type": "code", "execution_count": 96, "id": "de330ab1", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "a\n" ] } ], "source": [ "print(min(s))" ] }, { "cell_type": "markdown", "id": "7bcef9c5", "metadata": {}, "source": [ "# string methods" ] }, { "cell_type": "code", "execution_count": 97, "id": "b0b078e8", "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Help on class str in module builtins:\n", "\n", "class str(object)\n", " | str(object='') -> str\n", " | str(bytes_or_buffer[, encoding[, errors]]) -> str\n", " | \n", " | Create a new string object from the given object. If encoding or\n", " | errors is specified, then the object must expose a data buffer\n", " | that will be decoded using the given encoding and error handler.\n", " | Otherwise, returns the result of object.__str__() (if defined)\n", " | or repr(object).\n", " | encoding defaults to sys.getdefaultencoding().\n", " | errors defaults to 'strict'.\n", " | \n", " | Methods defined here:\n", " | \n", " | __add__(self, value, /)\n", " | Return self+value.\n", " | \n", " | __contains__(self, key, /)\n", " | Return key in self.\n", " | \n", " | __eq__(self, value, /)\n", " | Return self==value.\n", " | \n", " | __format__(self, format_spec, /)\n", " | Return a formatted version of the string as described by format_spec.\n", " | \n", " | __ge__(self, value, /)\n", " | Return self>=value.\n", " | \n", " | __getattribute__(self, name, /)\n", " | Return getattr(self, name).\n", " | \n", " | __getitem__(self, key, /)\n", " | Return self[key].\n", " | \n", " | __getnewargs__(...)\n", " | \n", " | __gt__(self, value, /)\n", " | Return self>value.\n", " | \n", " | __hash__(self, /)\n", " | Return hash(self).\n", " | \n", " | __iter__(self, /)\n", " | Implement iter(self).\n", " | \n", " | __le__(self, value, /)\n", " | Return self<=value.\n", " | \n", " | __len__(self, /)\n", " | Return len(self).\n", " | \n", " | __lt__(self, value, /)\n", " | Return self int\n", " | \n", " | Return the number of non-overlapping occurrences of substring sub in\n", " | string S[start:end]. Optional arguments start and end are\n", " | interpreted as in slice notation.\n", " | \n", " | encode(self, /, encoding='utf-8', errors='strict')\n", " | Encode the string using the codec registered for encoding.\n", " | \n", " | encoding\n", " | The encoding in which to encode the string.\n", " | errors\n", " | The error handling scheme to use for encoding errors.\n", " | The default is 'strict' meaning that encoding errors raise a\n", " | UnicodeEncodeError. Other possible values are 'ignore', 'replace' and\n", " | 'xmlcharrefreplace' as well as any other name registered with\n", " | codecs.register_error that can handle UnicodeEncodeErrors.\n", " | \n", " | endswith(...)\n", " | S.endswith(suffix[, start[, end]]) -> bool\n", " | \n", " | Return True if S ends with the specified suffix, False otherwise.\n", " | With optional start, test S beginning at that position.\n", " | With optional end, stop comparing S at that position.\n", " | suffix can also be a tuple of strings to try.\n", " | \n", " | expandtabs(self, /, tabsize=8)\n", " | Return a copy where all tab characters are expanded using spaces.\n", " | \n", " | If tabsize is not given, a tab size of 8 characters is assumed.\n", " | \n", " | find(...)\n", " | S.find(sub[, start[, end]]) -> int\n", " | \n", " | Return the lowest index in S where substring sub is found,\n", " | such that sub is contained within S[start:end]. Optional\n", " | arguments start and end are interpreted as in slice notation.\n", " | \n", " | Return -1 on failure.\n", " | \n", " | format(...)\n", " | S.format(*args, **kwargs) -> str\n", " | \n", " | Return a formatted version of S, using substitutions from args and kwargs.\n", " | The substitutions are identified by braces ('{' and '}').\n", " | \n", " | format_map(...)\n", " | S.format_map(mapping) -> str\n", " | \n", " | Return a formatted version of S, using substitutions from mapping.\n", " | The substitutions are identified by braces ('{' and '}').\n", " | \n", " | index(...)\n", " | S.index(sub[, start[, end]]) -> int\n", " | \n", " | Return the lowest index in S where substring sub is found,\n", " | such that sub is contained within S[start:end]. Optional\n", " | arguments start and end are interpreted as in slice notation.\n", " | \n", " | Raises ValueError when the substring is not found.\n", " | \n", " | isalnum(self, /)\n", " | Return True if the string is an alpha-numeric string, False otherwise.\n", " | \n", " | A string is alpha-numeric if all characters in the string are alpha-numeric and\n", " | there is at least one character in the string.\n", " | \n", " | isalpha(self, /)\n", " | Return True if the string is an alphabetic string, False otherwise.\n", " | \n", " | A string is alphabetic if all characters in the string are alphabetic and there\n", " | is at least one character in the string.\n", " | \n", " | isascii(self, /)\n", " | Return True if all characters in the string are ASCII, False otherwise.\n", " | \n", " | ASCII characters have code points in the range U+0000-U+007F.\n", " | Empty string is ASCII too.\n", " | \n", " | isdecimal(self, /)\n", " | Return True if the string is a decimal string, False otherwise.\n", " | \n", " | A string is a decimal string if all characters in the string are decimal and\n", " | there is at least one character in the string.\n", " | \n", " | isdigit(self, /)\n", " | Return True if the string is a digit string, False otherwise.\n", " | \n", " | A string is a digit string if all characters in the string are digits and there\n", " | is at least one character in the string.\n", " | \n", " | isidentifier(self, /)\n", " | Return True if the string is a valid Python identifier, False otherwise.\n", " | \n", " | Call keyword.iskeyword(s) to test whether string s is a reserved identifier,\n", " | such as \"def\" or \"class\".\n", " | \n", " | islower(self, /)\n", " | Return True if the string is a lowercase string, False otherwise.\n", " | \n", " | A string is lowercase if all cased characters in the string are lowercase and\n", " | there is at least one cased character in the string.\n", " | \n", " | isnumeric(self, /)\n", " | Return True if the string is a numeric string, False otherwise.\n", " | \n", " | A string is numeric if all characters in the string are numeric and there is at\n", " | least one character in the string.\n", " | \n", " | isprintable(self, /)\n", " | Return True if the string is printable, False otherwise.\n", " | \n", " | A string is printable if all of its characters are considered printable in\n", " | repr() or if it is empty.\n", " | \n", " | isspace(self, /)\n", " | Return True if the string is a whitespace string, False otherwise.\n", " | \n", " | A string is whitespace if all characters in the string are whitespace and there\n", " | is at least one character in the string.\n", " | \n", " | istitle(self, /)\n", " | Return True if the string is a title-cased string, False otherwise.\n", " | \n", " | In a title-cased string, upper- and title-case characters may only\n", " | follow uncased characters and lowercase characters only cased ones.\n", " | \n", " | isupper(self, /)\n", " | Return True if the string is an uppercase string, False otherwise.\n", " | \n", " | A string is uppercase if all cased characters in the string are uppercase and\n", " | there is at least one cased character in the string.\n", " | \n", " | join(self, iterable, /)\n", " | Concatenate any number of strings.\n", " | \n", " | The string whose method is called is inserted in between each given string.\n", " | The result is returned as a new string.\n", " | \n", " | Example: '.'.join(['ab', 'pq', 'rs']) -> 'ab.pq.rs'\n", " | \n", " | ljust(self, width, fillchar=' ', /)\n", " | Return a left-justified string of length width.\n", " | \n", " | Padding is done using the specified fill character (default is a space).\n", " | \n", " | lower(self, /)\n", " | Return a copy of the string converted to lowercase.\n", " | \n", " | lstrip(self, chars=None, /)\n", " | Return a copy of the string with leading whitespace removed.\n", " | \n", " | If chars is given and not None, remove characters in chars instead.\n", " | \n", " | partition(self, sep, /)\n", " | Partition the string into three parts using the given separator.\n", " | \n", " | This will search for the separator in the string. If the separator is found,\n", " | returns a 3-tuple containing the part before the separator, the separator\n", " | itself, and the part after it.\n", " | \n", " | If the separator is not found, returns a 3-tuple containing the original string\n", " | and two empty strings.\n", " | \n", " | removeprefix(self, prefix, /)\n", " | Return a str with the given prefix string removed if present.\n", " | \n", " | If the string starts with the prefix string, return string[len(prefix):].\n", " | Otherwise, return a copy of the original string.\n", " | \n", " | removesuffix(self, suffix, /)\n", " | Return a str with the given suffix string removed if present.\n", " | \n", " | If the string ends with the suffix string and that suffix is not empty,\n", " | return string[:-len(suffix)]. Otherwise, return a copy of the original\n", " | string.\n", " | \n", " | replace(self, old, new, count=-1, /)\n", " | Return a copy with all occurrences of substring old replaced by new.\n", " | \n", " | count\n", " | Maximum number of occurrences to replace.\n", " | -1 (the default value) means replace all occurrences.\n", " | \n", " | If the optional argument count is given, only the first count occurrences are\n", " | replaced.\n", " | \n", " | rfind(...)\n", " | S.rfind(sub[, start[, end]]) -> int\n", " | \n", " | Return the highest index in S where substring sub is found,\n", " | such that sub is contained within S[start:end]. Optional\n", " | arguments start and end are interpreted as in slice notation.\n", " | \n", " | Return -1 on failure.\n", " | \n", " | rindex(...)\n", " | S.rindex(sub[, start[, end]]) -> int\n", " | \n", " | Return the highest index in S where substring sub is found,\n", " | such that sub is contained within S[start:end]. Optional\n", " | arguments start and end are interpreted as in slice notation.\n", " | \n", " | Raises ValueError when the substring is not found.\n", " | \n", " | rjust(self, width, fillchar=' ', /)\n", " | Return a right-justified string of length width.\n", " | \n", " | Padding is done using the specified fill character (default is a space).\n", " | \n", " | rpartition(self, sep, /)\n", " | Partition the string into three parts using the given separator.\n", " | \n", " | This will search for the separator in the string, starting at the end. If\n", " | the separator is found, returns a 3-tuple containing the part before the\n", " | separator, the separator itself, and the part after it.\n", " | \n", " | If the separator is not found, returns a 3-tuple containing two empty strings\n", " | and the original string.\n", " | \n", " | rsplit(self, /, sep=None, maxsplit=-1)\n", " | Return a list of the words in the string, using sep as the delimiter string.\n", " | \n", " | sep\n", " | The delimiter according which to split the string.\n", " | None (the default value) means split according to any whitespace,\n", " | and discard empty strings from the result.\n", " | maxsplit\n", " | Maximum number of splits to do.\n", " | -1 (the default value) means no limit.\n", " | \n", " | Splits are done starting at the end of the string and working to the front.\n", " | \n", " | rstrip(self, chars=None, /)\n", " | Return a copy of the string with trailing whitespace removed.\n", " | \n", " | If chars is given and not None, remove characters in chars instead.\n", " | \n", " | split(self, /, sep=None, maxsplit=-1)\n", " | Return a list of the words in the string, using sep as the delimiter string.\n", " | \n", " | sep\n", " | The delimiter according which to split the string.\n", " | None (the default value) means split according to any whitespace,\n", " | and discard empty strings from the result.\n", " | maxsplit\n", " | Maximum number of splits to do.\n", " | -1 (the default value) means no limit.\n", " | \n", " | splitlines(self, /, keepends=False)\n", " | Return a list of the lines in the string, breaking at line boundaries.\n", " | \n", " | Line breaks are not included in the resulting list unless keepends is given and\n", " | true.\n", " | \n", " | startswith(...)\n", " | S.startswith(prefix[, start[, end]]) -> bool\n", " | \n", " | Return True if S starts with the specified prefix, False otherwise.\n", " | With optional start, test S beginning at that position.\n", " | With optional end, stop comparing S at that position.\n", " | prefix can also be a tuple of strings to try.\n", " | \n", " | strip(self, chars=None, /)\n", " | Return a copy of the string with leading and trailing whitespace removed.\n", " | \n", " | If chars is given and not None, remove characters in chars instead.\n", " | \n", " | swapcase(self, /)\n", " | Convert uppercase characters to lowercase and lowercase characters to uppercase.\n", " | \n", " | title(self, /)\n", " | Return a version of the string where each word is titlecased.\n", " | \n", " | More specifically, words start with uppercased characters and all remaining\n", " | cased characters have lower case.\n", " | \n", " | translate(self, table, /)\n", " | Replace each character in the string using the given translation table.\n", " | \n", " | table\n", " | Translation table, which must be a mapping of Unicode ordinals to\n", " | Unicode ordinals, strings, or None.\n", " | \n", " | The table must implement lookup/indexing via __getitem__, for instance a\n", " | dictionary or list. If this operation raises LookupError, the character is\n", " | left untouched. Characters mapped to None are deleted.\n", " | \n", " | upper(self, /)\n", " | Return a copy of the string converted to uppercase.\n", " | \n", " | zfill(self, width, /)\n", " | Pad a numeric string with zeros on the left, to fill a field of the given width.\n", " | \n", " | The string is never truncated.\n", " | \n", " | ----------------------------------------------------------------------\n", " | Static methods defined here:\n", " | \n", " | __new__(*args, **kwargs) from builtins.type\n", " | Create and return a new object. See help(type) for accurate signature.\n", " | \n", " | maketrans(...)\n", " | Return a translation table usable for str.translate().\n", " | \n", " | If there is only one argument, it must be a dictionary mapping Unicode\n", " | ordinals (integers) or characters to Unicode ordinals, strings or None.\n", " | Character keys will be then converted to ordinals.\n", " | If there are two arguments, they must be strings of equal length, and\n", " | in the resulting dictionary, each character in x will be mapped to the\n", " | character at the same position in y. If there is a third argument, it\n", " | must be a string, whose characters will be mapped to None in the result.\n", "\n" ] } ], "source": [ "help(str)" ] }, { "cell_type": "code", "execution_count": 98, "id": "a795fbdb", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "False" ] }, "execution_count": 98, "metadata": {}, "output_type": "execute_result" } ], "source": [ "'raj123' == 'RAJ123'" ] }, { "cell_type": "code", "execution_count": 100, "id": "78f9fd1d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "raj123\n" ] } ], "source": [ "s = 'RAJ123'\n", "\n", "print(s.casefold())" ] }, { "cell_type": "code", "execution_count": 101, "id": "d7d25e0d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "INDIA\n" ] } ], "source": [ "s = 'India'\n", "\n", "print(s.upper())" ] }, { "cell_type": "code", "execution_count": 102, "id": "4965c3b0", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "india\n" ] } ], "source": [ "print(s.lower())" ] }, { "cell_type": "code", "execution_count": 103, "id": "feb1f94f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['This', 'is', 'an', 'apple']\n" ] } ], "source": [ "# split\n", "\n", "s = \"This is an apple\"\n", "\n", "print(s.split(\" \"))" ] }, { "cell_type": "code", "execution_count": 105, "id": "d1231581", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['H', 'llo Us', 'r']\n" ] } ], "source": [ "s = \"Hello User\"\n", "\n", "print(s.split(\"e\"))" ] }, { "cell_type": "code", "execution_count": 106, "id": "2deaf22c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "This+is+an+apple\n" ] } ], "source": [ "l = ['This', 'is', 'an', 'apple']\n", "\n", "a = \"+\"\n", "\n", "print(a.join(l))" ] }, { "cell_type": "code", "execution_count": 107, "id": "d7bf333b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "This is an apple\n" ] } ], "source": [ "l = ['This', 'is', 'an', 'apple']\n", "\n", "a = \" \"\n", "\n", "print(a.join(l))" ] }, { "cell_type": "code", "execution_count": 108, "id": "99c38b4d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Thisisanapple\n" ] } ], "source": [ "s = \"This is an apple\"\n", "\n", "l = s.split(\" \")\n", "\n", "x = \"\".join(l)\n", "\n", "print(x)" ] }, { "cell_type": "markdown", "id": "6c209c90", "metadata": {}, "source": [ "**WAP to enter a string from user and print the biggest word from the paragraph**" ] }, { "cell_type": "code", "execution_count": null, "id": "92b19368", "metadata": {}, "outputs": [], "source": [ "text = input(\"Enter your para: \")\n", "\n" ] }, { "cell_type": "code", "execution_count": 109, "id": "78217b1a", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "False" ] }, "execution_count": 109, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\"abc\" > \"def\"" ] }, { "cell_type": "code", "execution_count": null, "id": "16d89cc5", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.12" } }, "nbformat": 4, "nbformat_minor": 5 } ================================================ FILE: March Cohort 2023/3_For Loop Dictionary Tuple and Sets.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "id": "958dd5a9", "metadata": {}, "source": [ "# Range function" ] }, { "cell_type": "code", "execution_count": 1, "id": "ecc0f5d9", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "range(0, 5)\n" ] } ], "source": [ "n = range(5)\n", "\n", "print(n)" ] }, { "cell_type": "code", "execution_count": 2, "id": "a414c992", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "print(type(n))" ] }, { "cell_type": "code", "execution_count": 3, "id": "c50d39c0", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0, 1, 2, 3, 4]\n" ] } ], "source": [ "n = list(range(5))\n", "\n", "print(n)" ] }, { "cell_type": "code", "execution_count": 4, "id": "8ff4b4f0", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[3, 4, 5, 6, 7]\n" ] } ], "source": [ "n = list(range(3,8))\n", "\n", "print(n)" ] }, { "cell_type": "code", "execution_count": 5, "id": "c82ff6db", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "True\n" ] } ], "source": [ "print(range(20) == range(0,20))" ] }, { "cell_type": "code", "execution_count": 6, "id": "14dbda38", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[5, 7, 9, 11, 13, 15, 17, 19]\n" ] } ], "source": [ "n = list(range(5,20,2))\n", "\n", "print(n)" ] }, { "cell_type": "markdown", "id": "f4b1e93a", "metadata": {}, "source": [ "# for loop" ] }, { "cell_type": "code", "execution_count": 8, "id": "a1e6f836", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "False\n" ] } ], "source": [ "l = [\"A\", 'B', 'C', 'D']\n", "\n", "print(\"Z\" in l)" ] }, { "cell_type": "code", "execution_count": 9, "id": "fe7eb160", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "A\n", "B\n", "C\n", "D\n" ] } ], "source": [ "l = [\"A\", 'B', 'C', 'D']\n", "# i\n", "\n", "# \"D\"in l\n", "for i in l:\n", " print(i)" ] }, { "cell_type": "code", "execution_count": 10, "id": "c237efad", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "A\n", "Hello\n", "B\n", "Hello\n", "C\n", "Hello\n", "D\n", "Hello\n" ] } ], "source": [ "l = [\"A\", 'B', 'C', 'D']\n", "\n", "for i in l:\n", " print(i)\n", " print(\"Hello\")" ] }, { "cell_type": "code", "execution_count": 11, "id": "6efc03f4", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "a\n", "b\n", "c\n", "d\n" ] } ], "source": [ "s = \"abcd\"\n", "# i\n", "\n", "for i in s:\n", " print(i)" ] }, { "cell_type": "code", "execution_count": 12, "id": "0d1d9074", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'AB'" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "'ab'.upper()" ] }, { "cell_type": "code", "execution_count": 13, "id": "754cc5de", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['ab', 'cd']\n" ] } ], "source": [ "x = ['ab', 'cd']\n", "# i\n", "\n", "for i in x:\n", " i.upper() # 'ab'.upper() - 'AB'\n", "\n", "print(x)" ] }, { "cell_type": "code", "execution_count": 14, "id": "8ba916b9", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "AB\n", "CD\n", "['ab', 'cd']\n" ] } ], "source": [ "x = ['ab', 'cd']\n", "# i\n", "\n", "for i in x:\n", " print(i.upper()) # 'AB', 'CD'\n", "\n", "print(x)" ] }, { "cell_type": "code", "execution_count": 15, "id": "7835c572", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "a\n", "b\n", "c\n", "d\n" ] } ], "source": [ "x = 'abcd'\n", "\n", "for i in x:\n", " print(i)\n", " x.upper()" ] }, { "cell_type": "code", "execution_count": 16, "id": "0ce5284c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "A\n", "B\n", "C\n", "D\n" ] } ], "source": [ "x = 'abcd'\n", "\n", "for i in x:\n", " print(i.upper())" ] }, { "cell_type": "code", "execution_count": 17, "id": "4f9188d4", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0\n", "1\n", "2\n", "3\n", "4\n" ] } ], "source": [ "# i\n", "# 0,1,2,3,4\n", "for i in range(5):\n", " print(i)" ] }, { "cell_type": "code", "execution_count": 18, "id": "fab18b0b", "metadata": {}, "outputs": [ { "ename": "TypeError", "evalue": "'str' object cannot be interpreted as an integer", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", "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", "\u001b[1;31mTypeError\u001b[0m: 'str' object cannot be interpreted as an integer" ] } ], "source": [ "x = 'abcd'\n", "\n", "for i in range(x):\n", " print(i)" ] }, { "cell_type": "code", "execution_count": 19, "id": "1044b515", "metadata": {}, "outputs": [ { "ename": "AttributeError", "evalue": "'int' object has no attribute 'upper'", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)", "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", "\u001b[1;31mAttributeError\u001b[0m: 'int' object has no attribute 'upper'" ] } ], "source": [ "x = 'abcd'\n", "\n", "# 0,1,2,3\n", "for i in range(len(x)):\n", " print(i.upper())" ] }, { "cell_type": "code", "execution_count": 20, "id": "9594cd6a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0\n", "1\n", "2\n", "3\n" ] } ], "source": [ "x = 'abcd'\n", "\n", "# 0,1,2,3\n", "for i in range(len(x)):\n", " print(i)" ] }, { "cell_type": "code", "execution_count": 21, "id": "0f7f2436", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2\n", "3\n", "4\n", "5\n" ] } ], "source": [ "for i in range(2,6):\n", " print(i)" ] }, { "cell_type": "code", "execution_count": 22, "id": "cab0c2b5", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "abcd\n" ] } ], "source": [ "x = 'abcd'\n", "\n", "# 0,1,2,3\n", "for i in range(len(x)):\n", " x[i].upper()\n", "\n", "print(x)" ] }, { "cell_type": "code", "execution_count": 23, "id": "e2525fbc", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "a\n", "a\n", "a\n", "a\n" ] } ], "source": [ "x = 'abcd'\n", "\n", "# 0,1,2,3\n", "for i in range(len(x)):\n", " x = 'a'\n", " print(x)" ] }, { "cell_type": "code", "execution_count": 24, "id": "d53ac18d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "abcd\n", "a\n", "a\n", "a\n" ] } ], "source": [ "x = 'abcd'\n", "\n", "# 0,1,2,3\n", "for i in range(len(x)):\n", " print(x)\n", " x = 'a'" ] }, { "cell_type": "markdown", "id": "6ab70d2a", "metadata": {}, "source": [ "# Dictionary" ] }, { "cell_type": "code", "execution_count": null, "id": "e22d4eef", "metadata": {}, "outputs": [], "source": [ "l = [1,2,3,4,54]\n", "# 0 1 2 3 4\n", "\n", "# hashing\n", "\n", "# [, , ,44 , 55,]\n", "# 45\n", "# 65\n", "\n", "55 % 10 = 5 ---> \n", "44 % 10 = 4\n", "45 % 10 = 5\n", "65 % 10 = 5" ] }, { "cell_type": "code", "execution_count": null, "id": "c47a4099", "metadata": {}, "outputs": [], "source": [ "# JSON\n", "\n", "{\n", " \"username\": \"hemansnation\",\n", " \"password\": \"12334545\"\n", "}" ] }, { "cell_type": "code", "execution_count": 25, "id": "e5b3ba3d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'ravi': 24, 'viky': 21, 'roy': 36}\n" ] } ], "source": [ "# key: value\n", "\n", "d = {\"ravi\": 24, \"viky\": 21, \"roy\": 36}\n", "\n", "print(d)" ] }, { "cell_type": "code", "execution_count": 26, "id": "c846e290", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "print(type(d))" ] }, { "cell_type": "code", "execution_count": null, "id": "ea68592a", "metadata": {}, "outputs": [], "source": [ "# unordered data structures" ] }, { "cell_type": "code", "execution_count": 27, "id": "7a710f26", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "36\n" ] } ], "source": [ "print(d['roy'])" ] }, { "cell_type": "code", "execution_count": 28, "id": "b7cd33ff", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "24\n" ] } ], "source": [ "print(d['ravi'])" ] }, { "cell_type": "code", "execution_count": 29, "id": "53e9fcdd", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[255, 0, 0]\n" ] } ], "source": [ "color = {\n", " \"red\": [255,0,0],\n", " \"green\": [0,255,0],\n", " \"blue\": [0,0,255]\n", "}\n", "print(color['red'])" ] }, { "cell_type": "code", "execution_count": 30, "id": "1ddbc289", "metadata": {}, "outputs": [ { "ename": "KeyError", "evalue": "'yellow'", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", "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", "\u001b[1;31mKeyError\u001b[0m: 'yellow'" ] } ], "source": [ "print(color['yellow'])" ] }, { "cell_type": "code", "execution_count": 31, "id": "a7eeec1c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{}\n", "\n" ] } ], "source": [ "d = {}\n", "\n", "print(d)\n", "\n", "print(type(d))" ] }, { "cell_type": "code", "execution_count": 32, "id": "c86ce0ee", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1\n" ] } ], "source": [ "d = {1:1, 2:2, 3:4}\n", "\n", "print(d[1])" ] }, { "cell_type": "code", "execution_count": 33, "id": "9f399123", "metadata": {}, "outputs": [ { "ename": "TypeError", "evalue": "unhashable type: 'list'", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", "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", "\u001b[1;31mTypeError\u001b[0m: unhashable type: 'list'" ] } ], "source": [ "d = {[1,2]:1, 2:2, 3:4}\n", "\n", "print(d[1,2])" ] }, { "cell_type": "code", "execution_count": 34, "id": "d166c563", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[100, 20]\n" ] } ], "source": [ "# mutable object - list\n", "\n", "l = [10,20]\n", "\n", "l[0] = 100\n", "\n", "print(l)" ] }, { "cell_type": "code", "execution_count": 35, "id": "a8d2e6d7", "metadata": {}, "outputs": [ { "ename": "TypeError", "evalue": "'str' object does not support item assignment", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", "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", "\u001b[1;31mTypeError\u001b[0m: 'str' object does not support item assignment" ] } ], "source": [ "s = \"India\" # immutable objects\n", "\n", "s[0] = \"A\"\n", "\n", "print(s)" ] }, { "cell_type": "code", "execution_count": null, "id": "d78cdcd4", "metadata": {}, "outputs": [], "source": [ "# int are immutable\n", "\n", "1 + 1 = 2" ] }, { "cell_type": "markdown", "id": "d3d32bf3", "metadata": {}, "source": [ "# Dictionary Functions" ] }, { "cell_type": "code", "execution_count": 36, "id": "b57d9f4a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "4\n" ] } ], "source": [ "d = { 1:1, 2:4, 3:9, 4:16}\n", "\n", "print(len(d))" ] }, { "cell_type": "code", "execution_count": 37, "id": "788f5c87", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10\n" ] } ], "source": [ "print(sum(d))" ] }, { "cell_type": "code", "execution_count": 38, "id": "63a2eb0e", "metadata": {}, "outputs": [ { "ename": "TypeError", "evalue": "unsupported operand type(s) for +: 'int' and 'str'", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", "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", "\u001b[1;31mTypeError\u001b[0m: unsupported operand type(s) for +: 'int' and 'str'" ] } ], "source": [ "a = {1:1,2:4, \"hi\":16}\n", "\n", "print(sum(a))" ] }, { "cell_type": "code", "execution_count": 39, "id": "650cf24f", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{1: 1, 2: 4, 3: 9, 4: 16}" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d" ] }, { "cell_type": "code", "execution_count": 40, "id": "d78c3139", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "4\n" ] } ], "source": [ "print(max(d))" ] }, { "cell_type": "code", "execution_count": 41, "id": "08fe0e6e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1\n" ] } ], "source": [ "print(min(d))" ] }, { "cell_type": "markdown", "id": "1a16abb5", "metadata": {}, "source": [ "# Dictionary Methods" ] }, { "cell_type": "code", "execution_count": 43, "id": "9a2f7d81", "metadata": { "scrolled": false }, "outputs": [], "source": [ "# help(dict)" ] }, { "cell_type": "code", "execution_count": 44, "id": "5d682511", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{1: 'Apple', 'orange': [2, 3, 4], '2': '3'}\n" ] } ], "source": [ "pair = { 1: \"Apple\", \"orange\": [2,3,4], \"2\": \"3\"}\n", "\n", "print(pair)" ] }, { "cell_type": "code", "execution_count": 45, "id": "1374c33c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[2, 3, 4]\n" ] } ], "source": [ "# get\n", "\n", "print(pair.get(\"orange\"))" ] }, { "cell_type": "code", "execution_count": 48, "id": "49f1f231", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[2, 3, 4]\n" ] } ], "source": [ "print(pair[\"orange\"])" ] }, { "cell_type": "code", "execution_count": 49, "id": "0538283b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "None\n" ] } ], "source": [ "print(pair.get(7))" ] }, { "cell_type": "code", "execution_count": null, "id": "6a643576", "metadata": {}, "outputs": [], "source": [ "None => NULL # nothing" ] }, { "cell_type": "code", "execution_count": 50, "id": "ab1ab099", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Apple\n" ] } ], "source": [ "print(pair.get(1, \"Not Available\"))" ] }, { "cell_type": "code", "execution_count": 51, "id": "26d164e1", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Not Available\n" ] } ], "source": [ "print(pair.get(10, \"Not Available\"))" ] }, { "cell_type": "code", "execution_count": 52, "id": "0632cda6", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{1: 1, 2: 4, 3: 9, 4: 16}" ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# clear\n", "\n", "d" ] }, { "cell_type": "code", "execution_count": 53, "id": "cb797c19", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{}\n" ] } ], "source": [ "d.clear()\n", "\n", "print(d)" ] }, { "cell_type": "code", "execution_count": 54, "id": "406f5d55", "metadata": {}, "outputs": [ { "ename": "IndexError", "evalue": "list assignment index out of range", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mIndexError\u001b[0m Traceback (most recent call last)", "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", "\u001b[1;31mIndexError\u001b[0m: list assignment index out of range" ] } ], "source": [ "l = [10,20,30]\n", "\n", "l[3] = 100\n", "\n", "print(l)" ] }, { "cell_type": "code", "execution_count": 55, "id": "45715900", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'A': 100, 'Z': 20}\n" ] } ], "source": [ "d = {\"A\": 10, \"Z\": 20}\n", "\n", "d[\"A\"] = 100\n", "\n", "print(d)" ] }, { "cell_type": "code", "execution_count": 56, "id": "3ab37c57", "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'A': 100, 'Z': 20, 'goa': 200}\n" ] } ], "source": [ "d[\"goa\"] = 200\n", "\n", "print(d)" ] }, { "cell_type": "markdown", "id": "f6f67a8d", "metadata": {}, "source": [ "# Question" ] }, { "cell_type": "code", "execution_count": null, "id": "f7c72ebe", "metadata": {}, "outputs": [], "source": [ "{1:1, 2:4, 3:9, 4:16, 5:25 ...........10:100}" ] }, { "cell_type": "markdown", "id": "2435adc7", "metadata": {}, "source": [ "# Tuple\n", "\n", "tuple are immutable" ] }, { "cell_type": "code", "execution_count": 57, "id": "b3e4ebb7", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(1, 2, 3, 4)\n", "\n" ] } ], "source": [ "t = (1,2,3,4)\n", "\n", "print(t)\n", "\n", "print(type(t))" ] }, { "cell_type": "code", "execution_count": 58, "id": "eed35b67", "metadata": {}, "outputs": [ { "ename": "TypeError", "evalue": "'tuple' object does not support item assignment", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", "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", "\u001b[1;31mTypeError\u001b[0m: 'tuple' object does not support item assignment" ] } ], "source": [ "t[0] = 100" ] }, { "cell_type": "code", "execution_count": 59, "id": "b630c971", "metadata": {}, "outputs": [ { "ename": "TypeError", "evalue": "'str' object does not support item assignment", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", "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", "\u001b[1;31mTypeError\u001b[0m: 'str' object does not support item assignment" ] } ], "source": [ "s = 'goa'\n", "\n", "s[0] = \"A\"" ] }, { "cell_type": "code", "execution_count": 60, "id": "cf15d6bb", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(5, 6, 67)\n" ] } ], "source": [ "t = (2,3,4,5,6,67)\n", "\n", "print(t[3:])" ] }, { "cell_type": "code", "execution_count": 61, "id": "05edf72b", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(2, 3, 4, 5, 6, 67)" ] }, "execution_count": 61, "metadata": {}, "output_type": "execute_result" } ], "source": [ "t" ] }, { "cell_type": "code", "execution_count": 62, "id": "0c3d2bc3", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "5\n" ] } ], "source": [ "t = (1,2,3,4,5)\n", "\n", "print(len(t))" ] }, { "cell_type": "code", "execution_count": 63, "id": "54aa0e3f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "15\n" ] } ], "source": [ "print(sum(t))" ] }, { "cell_type": "code", "execution_count": 64, "id": "f0052bd4", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "5\n" ] } ], "source": [ "print(max(t))" ] }, { "cell_type": "code", "execution_count": 65, "id": "28a92019", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1\n" ] } ], "source": [ "print(min(t))" ] }, { "cell_type": "code", "execution_count": 66, "id": "7885f892", "metadata": {}, "outputs": [], "source": [ "# -1\n", "t = (\"A\", 1,2,4)\n", "# 0 1 2 3" ] }, { "cell_type": "code", "execution_count": 67, "id": "da09ee4f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "('A', 1, 2, 4)\n" ] } ], "source": [ "print(t)" ] }, { "cell_type": "code", "execution_count": 68, "id": "b337cd33", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "A\n" ] } ], "source": [ "print(t[0])" ] }, { "cell_type": "code", "execution_count": 69, "id": "504bca81", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "4\n" ] } ], "source": [ "print(t[-1])" ] }, { "cell_type": "code", "execution_count": 70, "id": "3be05cc3", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "()\n", "\n" ] } ], "source": [ "t = ()\n", "\n", "print(t)\n", "\n", "print(type(t))" ] }, { "cell_type": "code", "execution_count": 71, "id": "915d9c00", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10\n", "\n" ] } ], "source": [ "t = (10)\n", "\n", "print(t)\n", "\n", "print(type(t))" ] }, { "cell_type": "code", "execution_count": 72, "id": "54ec18a0", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(10,)\n", "\n" ] } ], "source": [ "t = (10,)\n", "\n", "print(t)\n", "\n", "print(type(t))" ] }, { "cell_type": "markdown", "id": "2792fe85", "metadata": {}, "source": [ "# Tuple Methods" ] }, { "cell_type": "code", "execution_count": 73, "id": "5bd0d610", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2\n" ] } ], "source": [ "t = (2,3,4,5,5,3,3,3,7)\n", "\n", "print(t.count(5))" ] }, { "cell_type": "code", "execution_count": 74, "id": "79899432", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1\n" ] } ], "source": [ "print(t.count(7))" ] }, { "cell_type": "code", "execution_count": 75, "id": "4b0c56f1", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0\n" ] } ], "source": [ "print(t.count(10))" ] }, { "cell_type": "code", "execution_count": 76, "id": "b17ad603", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(2, 3, 4, 5, 5, 3, 3, 3, 7)\n" ] } ], "source": [ "# index\n", "\n", "print(t)" ] }, { "cell_type": "code", "execution_count": 77, "id": "7f10fbae", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "3\n" ] } ], "source": [ "print(t.index(5))" ] }, { "cell_type": "code", "execution_count": 78, "id": "e6811f4e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Help on built-in function index:\n", "\n", "index(value, start=0, stop=9223372036854775807, /) method of builtins.tuple instance\n", " Return first index of value.\n", " \n", " Raises ValueError if the value is not present.\n", "\n" ] } ], "source": [ "help(t.index)" ] }, { "cell_type": "code", "execution_count": 79, "id": "068bf8b3", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(2, 3, 4, 5, 5, 3, 3, 3, 7)" ] }, "execution_count": 79, "metadata": {}, "output_type": "execute_result" } ], "source": [ "t" ] }, { "cell_type": "code", "execution_count": 81, "id": "a65ca47a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "4\n" ] } ], "source": [ "print(t.index(5,4))" ] }, { "cell_type": "markdown", "id": "df156bc0", "metadata": {}, "source": [ "# Questions " ] }, { "cell_type": "code", "execution_count": null, "id": "f623b5de", "metadata": {}, "outputs": [], "source": [ "t = ('a','e','i','o','u')" ] }, { "cell_type": "markdown", "id": "1f2328fc", "metadata": {}, "source": [ "# Sets\n", "\n", "unordered and unindex\n", "\n", "sets are mutable" ] }, { "cell_type": "code", "execution_count": 82, "id": "11b203a2", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{1, 2, 3, 4, 5, 6}\n", "\n" ] } ], "source": [ "n = {1,2,3,4,5,6}\n", "\n", "print(n)\n", "\n", "print(type(n))" ] }, { "cell_type": "code", "execution_count": 83, "id": "615a8e7e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{1, 2, 3, 4, 5, 6}\n", "\n" ] } ], "source": [ "n = {1,2,3,4,5,6,6,6,6,5,5}\n", "\n", "print(n)\n", "\n", "print(type(n))" ] }, { "cell_type": "code", "execution_count": 84, "id": "d7bf4db2", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "set()\n" ] } ], "source": [ "s = set()\n", "\n", "print(s)" ] }, { "cell_type": "code", "execution_count": 86, "id": "98a00a15", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'b', 4, 'a'}\n" ] } ], "source": [ "n = set(['a','b', 4])\n", "\n", "print(n)" ] }, { "cell_type": "code", "execution_count": 87, "id": "8d03b0ed", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1\n", "2\n", "3\n", "55\n", "44\n" ] } ], "source": [ "n = {1,2,3,44,55}\n", "\n", "for i in n:\n", " print(i)" ] }, { "cell_type": "markdown", "id": "dec2b22e", "metadata": {}, "source": [ "## set operations" ] }, { "cell_type": "code", "execution_count": 88, "id": "c961559b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{1, 2, 3, 4, 5, 6, 7, 8, 9}\n" ] } ], "source": [ "# union\n", "\n", "f = {1,2,3,4,5,6}\n", "s = {4,5,6,7,8,9}\n", "\n", "print(f | s)" ] }, { "cell_type": "code", "execution_count": 89, "id": "ab540fe2", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{4, 5, 6}\n" ] } ], "source": [ "# intersection\n", "\n", "f = {1,2,3,4,5,6}\n", "s = {4,5,6,7,8,9}\n", "\n", "print(f & s)" ] }, { "cell_type": "code", "execution_count": 90, "id": "abf57b8a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{1, 2, 3}\n" ] } ], "source": [ "# difference\n", "\n", "f = {1,2,3,4,5,6}\n", "s = {4,5,6,7,8,9}\n", "\n", "print(f - s)" ] }, { "cell_type": "code", "execution_count": 91, "id": "9a9f6ea6", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{8, 9, 7}\n" ] } ], "source": [ "print(s - f)" ] }, { "cell_type": "code", "execution_count": 92, "id": "88a67b87", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{1, 2, 3, 7, 8, 9}\n" ] } ], "source": [ "# symmetry\n", "\n", "f = {1,2,3,4,5,6}\n", "s = {4,5,6,7,8,9}\n", "\n", "print(f ^ s)" ] }, { "cell_type": "markdown", "id": "2682d1f9", "metadata": {}, "source": [ "# set functions" ] }, { "cell_type": "code", "execution_count": 93, "id": "553d14f2", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "4\n", "10\n", "4\n", "1\n" ] } ], "source": [ "s = {1,2,3,4}\n", "\n", "print(len(s))\n", "print(sum(s))\n", "print(max(s))\n", "print(min(s))" ] }, { "cell_type": "markdown", "id": "a144e8bf", "metadata": {}, "source": [ "# set methods" ] }, { "cell_type": "code", "execution_count": 95, "id": "ceb0c93f", "metadata": { "scrolled": true }, "outputs": [], "source": [ "# help(set)" ] }, { "cell_type": "code", "execution_count": 96, "id": "83fb99b3", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{1, 2, 3, 4}\n", "{1, 2, 3, 4, -7}\n" ] } ], "source": [ "# add\n", "\n", "n = {1,2,3,4}\n", "\n", "print(n)\n", "\n", "n.add(-7)\n", "\n", "print(n)" ] }, { "cell_type": "code", "execution_count": 97, "id": "2e99b696", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{1, 2, 3, 4, -7}\n", "set()\n" ] } ], "source": [ "print(n)\n", "n.clear()\n", "\n", "print(n)" ] }, { "cell_type": "code", "execution_count": 98, "id": "feade89d", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "set()" ] }, "execution_count": 98, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# remove\n", "\n", "n" ] }, { "cell_type": "code", "execution_count": 99, "id": "b83f2433", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{1, 2, 4}\n" ] } ], "source": [ "n = {1,2,3,4}\n", "\n", "n.remove(3)\n", "print(n)" ] }, { "cell_type": "code", "execution_count": null, "id": "3730faed", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.12" } }, "nbformat": 4, "nbformat_minor": 5 } ================================================ FILE: March Cohort 2023/4_Functions.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "id": "a98c255e", "metadata": {}, "source": [ "# Functions" ] }, { "cell_type": "code", "execution_count": 1, "id": "18360579", "metadata": {}, "outputs": [], "source": [ "def myfunction(): # definition of function\n", " print(\"Hey\")" ] }, { "cell_type": "code", "execution_count": 2, "id": "22e39c3e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Hey\n" ] } ], "source": [ "myfunction() # calling of function" ] }, { "cell_type": "code", "execution_count": 3, "id": "c24cd8e5", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1\n", "2\n", "3\n" ] } ], "source": [ "def paint():\n", " print(1)\n", " print(2)\n", " print(3)\n", "\n", "paint()" ] }, { "cell_type": "code", "execution_count": 4, "id": "1cd655a2", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1\n", "2\n", "3\n" ] } ], "source": [ "paint()" ] }, { "cell_type": "code", "execution_count": 5, "id": "f6b4813a", "metadata": {}, "outputs": [ { "ename": "NameError", "evalue": "name 'hello' is not defined", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", "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", "\u001b[1;31mNameError\u001b[0m: name 'hello' is not defined" ] } ], "source": [ "hello()\n", "\n", "def hello():\n", " print(\"Hello\")" ] }, { "cell_type": "code", "execution_count": 6, "id": "28ae369c", "metadata": {}, "outputs": [ { "ename": "TypeError", "evalue": "indore() missing 1 required positional argument: 'a'", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", "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", "\u001b[1;31mTypeError\u001b[0m: indore() missing 1 required positional argument: 'a'" ] } ], "source": [ "def indore(a): # a = \"A\"\n", " print(a)\n", "\n", "indore()" ] }, { "cell_type": "code", "execution_count": 7, "id": "52bc36b9", "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10\n" ] } ], "source": [ "indore(10)" ] }, { "cell_type": "code", "execution_count": 8, "id": "527fe9e4", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "A\n" ] } ], "source": [ "indore(\"A\")" ] }, { "cell_type": "code", "execution_count": 9, "id": "4af5fdc0", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "right\n" ] } ], "source": [ "indore(\"right\")" ] }, { "cell_type": "code", "execution_count": 10, "id": "aa11eea7", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1\n", "One\n", "2\n", "Two\n", "right\n", "three\n" ] } ], "source": [ "indore(1)\n", "print(\"One\")\n", "indore(2)\n", "print(\"Two\")\n", "indore(\"right\")\n", "print(\"three\")" ] }, { "cell_type": "code", "execution_count": 11, "id": "84a3b11a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "9\n" ] } ], "source": [ "def add(x, y):\n", " print(x + y)\n", "\n", "add(5,4)" ] }, { "cell_type": "markdown", "id": "74774c31", "metadata": {}, "source": [ "# Return statement" ] }, { "cell_type": "code", "execution_count": 12, "id": "bd50418a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "7\n" ] } ], "source": [ "def play(x, y):\n", " if x >= y:\n", " return x\n", " else:\n", " return y\n", "\n", "print(play(4,7))\n", "# 7" ] }, { "cell_type": "code", "execution_count": 13, "id": "86b599e4", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "8\n" ] } ], "source": [ "z = play(8,5)\n", "#z = 8\n", "\n", "print(z)" ] }, { "cell_type": "code", "execution_count": 14, "id": "ad6e3060", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "9\n" ] } ], "source": [ "def sub(x, y):\n", " t = x + y\n", " return t\n", " print(\"Hi\")\n", "\n", "print(sub(4,5))" ] }, { "cell_type": "code", "execution_count": 18, "id": "8cc83a2f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1\n", "2\n", "hello\n" ] } ], "source": [ "def pre():\n", " print(1)\n", " print(2)\n", " print(\"hello\")\n", " return\n", " print(3)\n", " print(4)\n", "\n", "pre()" ] }, { "cell_type": "code", "execution_count": 19, "id": "cb329c92", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1\n", "2\n", "hello\n", "None\n" ] } ], "source": [ "print(pre())" ] }, { "cell_type": "code", "execution_count": 17, "id": "a463dbf9", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1\n", "2\n", "None\n" ] } ], "source": [ "r = pre()\n", "\n", "print(r)" ] }, { "cell_type": "code", "execution_count": null, "id": "280c0539", "metadata": {}, "outputs": [], "source": [ "None ==> NULL ==> nothing" ] }, { "cell_type": "code", "execution_count": 20, "id": "79b26fc0", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "None\n" ] } ], "source": [ "print(None)" ] }, { "cell_type": "code", "execution_count": 21, "id": "f781cf14", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Hi\n", "None\n" ] } ], "source": [ "print(print(\"Hi\"))\n", "# print(None)" ] }, { "cell_type": "markdown", "id": "d25d6edf", "metadata": {}, "source": [ "# Default Argument" ] }, { "cell_type": "code", "execution_count": 22, "id": "f6141d42", "metadata": {}, "outputs": [ { "ename": "TypeError", "evalue": "can only concatenate str (not \"int\") to str", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", "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", "\u001b[1;31mTypeError\u001b[0m: can only concatenate str (not \"int\") to str" ] } ], "source": [ "\"hi\" + 6" ] }, { "cell_type": "code", "execution_count": 23, "id": "c3edf839", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'hihihi'" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "\"hi\" * 3" ] }, { "cell_type": "code", "execution_count": 24, "id": "2456f1b9", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Hello\n" ] } ], "source": [ "def say(msg, time = 1):\n", " print(msg * time)\n", "\n", "say(\"Hello\")" ] }, { "cell_type": "code", "execution_count": 25, "id": "4b65a1da", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "ByeByeByeByeBye\n" ] } ], "source": [ "say(\"Bye\", 5)" ] }, { "cell_type": "code", "execution_count": 27, "id": "a6382071", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Hello+++Indore\n" ] } ], "source": [ "print(\"Hello\", end='+++')\n", "print(\"Indore\")" ] }, { "cell_type": "code", "execution_count": 28, "id": "d7115a71", "metadata": {}, "outputs": [], "source": [ "def said(a, b = 5, c = 10):\n", " print(a)\n", " print(b)\n", " print(c)" ] }, { "cell_type": "code", "execution_count": 29, "id": "811ff6b8", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "3\n", "7\n", "10\n" ] } ], "source": [ "said(3,7)" ] }, { "cell_type": "code", "execution_count": 30, "id": "dbd412b0", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "25\n", "5\n", "24\n" ] } ], "source": [ "said(25, c = 24)" ] }, { "cell_type": "code", "execution_count": 31, "id": "c29189f6", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "15\n", "5\n", "100\n" ] } ], "source": [ "said(c = 100, a = 15)" ] }, { "cell_type": "code", "execution_count": 32, "id": "e9d0ccbd", "metadata": {}, "outputs": [ { "ename": "SyntaxError", "evalue": "positional argument follows keyword argument (2634499495.py, line 1)", "output_type": "error", "traceback": [ "\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" ] } ], "source": [ "said(c = 19, 20, 30)" ] }, { "cell_type": "code", "execution_count": 33, "id": "25993e69", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0\n" ] } ], "source": [ "i = 0 # i is a global variable\n", "\n", "def change(i): # i is a local variable\n", " i += 1 # i = 2\n", " return 1\n", "\n", "change(1) # 1\n", "print(i)" ] }, { "cell_type": "code", "execution_count": 34, "id": "30f844d2", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "8\n", "3\n" ] } ], "source": [ "x = 3 # global\n", "\n", "def add(x): # local\n", " x += 1 # x = x + 1\n", " print(x)\n", "\n", "add(7)\n", "print(x)" ] }, { "cell_type": "code", "execution_count": 35, "id": "bdce8f2a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "11\n" ] }, { "ename": "NameError", "evalue": "name 'v' is not defined", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", "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", "\u001b[1;31mNameError\u001b[0m: name 'v' is not defined" ] } ], "source": [ "def add(v):\n", " v += 1\n", " print(v)\n", "\n", "add(10)\n", "print(v)" ] }, { "cell_type": "markdown", "id": "7578af9e", "metadata": {}, "source": [ "# Reference Function" ] }, { "cell_type": "code", "execution_count": 36, "id": "ead4ef51", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10\n" ] } ], "source": [ "a = 10\n", "\n", "b = a\n", "\n", "print(b)" ] }, { "cell_type": "code", "execution_count": 37, "id": "06f37e14", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10\n" ] } ], "source": [ "print(a)" ] }, { "cell_type": "code", "execution_count": 38, "id": "6b6bb7d6", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "print(type(add))" ] }, { "cell_type": "code", "execution_count": 39, "id": "fe8316b6", "metadata": {}, "outputs": [], "source": [ "def multiply(x, y):\n", " return x * y\n" ] }, { "cell_type": "code", "execution_count": 40, "id": "08e7fd6a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "200\n" ] } ], "source": [ "indore = multiply\n", "\n", "print(indore(10,20))\n", "# multiply(10,20)" ] }, { "cell_type": "code", "execution_count": 41, "id": "1578b9d0", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "15\n" ] } ], "source": [ "print(multiply(3,5))" ] }, { "cell_type": "markdown", "id": "9c39298b", "metadata": {}, "source": [ "# functions and an argument" ] }, { "cell_type": "code", "execution_count": 42, "id": "28047e15", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "30\n" ] } ], "source": [ "def sub(x, y):\n", " return x + y\n", "\n", "def do(red, x, y): # red = sub, x = 5, y = 10\n", " return red(red(x,y), red(x,y))\n", "# sub(sub(5,10),sub(5,10))\n", "# sub( 15 , 15 )\n", "# 30\n", "\n", "a = 5\n", "b = 10\n", "\n", "\n", "print(do(sub, a, b))\n", "# 30" ] }, { "cell_type": "code", "execution_count": 43, "id": "82b1bbd9", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "81\n" ] } ], "source": [ "def square(x):\n", " return x * x\n", "\n", "def test(blue, x):\n", " print(blue(x))\n", "\n", "test(square, 9)" ] }, { "cell_type": "code", "execution_count": 44, "id": "f438ee68", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "3\n", "1\n" ] } ], "source": [ "def green(red, a):\n", " print(red(a))\n", "\n", "green(max, [1,2,3])\n", "green(min, [1,2,3])" ] }, { "cell_type": "code", "execution_count": 45, "id": "51c59408", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0\n" ] } ], "source": [ "def printn(x):\n", " for i in range(x):\n", " print(i)\n", " return\n", "\n", "printn(10)" ] }, { "cell_type": "markdown", "id": "3e07954c", "metadata": {}, "source": [ "# doc string" ] }, { "cell_type": "code", "execution_count": 46, "id": "b6cc4e7d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Help on built-in function max in module builtins:\n", "\n", "max(...)\n", " max(iterable, *[, default=obj, key=func]) -> value\n", " max(arg1, arg2, *args, *[, key=func]) -> value\n", " \n", " With a single iterable argument, return its biggest item. The\n", " default keyword-only argument specifies an object to return if\n", " the provided iterable is empty.\n", " With two or more arguments, return the largest argument.\n", "\n" ] } ], "source": [ "help(max)" ] }, { "cell_type": "code", "execution_count": 48, "id": "6596e128", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "this \n", "is an\n", "apple.\n", "\n", "\n" ] } ], "source": [ "# this is a single line comment\n", "\n", "s = '''this \n", "is an\n", "apple.\n", "'''\n", "print(s)\n", "print(type(s))" ] }, { "cell_type": "code", "execution_count": 50, "id": "53161af0", "metadata": {}, "outputs": [], "source": [ "def function_one():\n", " '''\n", " this function print 10,\n", " also this funtion will print 5\n", " '''\n", " print(10)\n", " print(5)" ] }, { "cell_type": "code", "execution_count": 51, "id": "2156a9ff", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10\n", "5\n" ] } ], "source": [ "function_one()" ] }, { "cell_type": "code", "execution_count": 52, "id": "ad1302c3", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Help on function function_one in module __main__:\n", "\n", "function_one()\n", " this function print 10,\n", " also this funtion will print 5\n", "\n" ] } ], "source": [ "help(function_one)" ] }, { "cell_type": "code", "execution_count": 53, "id": "50e6b7cb", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", " this function print 10,\n", " also this funtion will print 5\n", " \n" ] } ], "source": [ "print(function_one.__doc__) # dunder" ] }, { "cell_type": "markdown", "id": "a1973a4e", "metadata": {}, "source": [ "# Map Function" ] }, { "cell_type": "code", "execution_count": 54, "id": "66ca8c7e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Help on class map in module builtins:\n", "\n", "class map(object)\n", " | map(func, *iterables) --> map object\n", " | \n", " | Make an iterator that computes the function using arguments from\n", " | each of the iterables. Stops when the shortest iterable is exhausted.\n", " | \n", " | Methods defined here:\n", " | \n", " | __getattribute__(self, name, /)\n", " | Return getattr(self, name).\n", " | \n", " | __iter__(self, /)\n", " | Implement iter(self).\n", " | \n", " | __next__(self, /)\n", " | Implement next(self).\n", " | \n", " | __reduce__(...)\n", " | Return state information for pickling.\n", " | \n", " | ----------------------------------------------------------------------\n", " | Static methods defined here:\n", " | \n", " | __new__(*args, **kwargs) from builtins.type\n", " | Create and return a new object. See help(type) for accurate signature.\n", "\n" ] } ], "source": [ "help(map)" ] }, { "cell_type": "code", "execution_count": 55, "id": "f473bded", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[6, 7, 8, 9, 10]\n" ] } ], "source": [ "def add(x):\n", " return x + 5\n", "\n", "n = [1,2,3,4,5]\n", "\n", "r = list(map(add, n))\n", "# 6, 7, 8, 9, 10\n", "\n", "print(r)" ] }, { "cell_type": "code", "execution_count": 56, "id": "aeb1a9de", "metadata": {}, "outputs": [ { "ename": "TypeError", "evalue": "'int' object is not iterable", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", "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", "\u001b[1;31mTypeError\u001b[0m: 'int' object is not iterable" ] } ], "source": [ "def add(x):\n", " return x + 5\n", "\n", "n = 7\n", "\n", "r = list(map(add, n))\n", "\n", "print(r)" ] }, { "cell_type": "markdown", "id": "cbaca289", "metadata": {}, "source": [ "# lambda function\n", "\n", "anonymous function" ] }, { "cell_type": "code", "execution_count": 57, "id": "fef01377", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "343\n" ] } ], "source": [ "def cube(y):\n", " return y * y * y\n", "\n", "\n", "# lambda argument: expresion\n", "\n", "g = lambda x: x * x * x\n", "\n", "print(g(7))" ] }, { "cell_type": "code", "execution_count": 59, "id": "477123f3", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "343\n" ] } ], "source": [ "print(cube(7))" ] }, { "cell_type": "markdown", "id": "5caf7279", "metadata": {}, "source": [ "# lambda with map function" ] }, { "cell_type": "code", "execution_count": 60, "id": "00915aaf", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1, 4, 9, 16, 25]\n" ] } ], "source": [ "n = [1,2,3,4,5]\n", "\n", "r = list(map(lambda y: y * y, n))\n", "# 1, 4, 9, 16, 25\n", "\n", "print(r)" ] }, { "cell_type": "markdown", "id": "0bc6d570", "metadata": {}, "source": [ "# filter function" ] }, { "cell_type": "code", "execution_count": 61, "id": "74819624", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Help on class filter in module builtins:\n", "\n", "class filter(object)\n", " | filter(function or None, iterable) --> filter object\n", " | \n", " | Return an iterator yielding those items of iterable for which function(item)\n", " | is true. If function is None, return the items that are true.\n", " | \n", " | Methods defined here:\n", " | \n", " | __getattribute__(self, name, /)\n", " | Return getattr(self, name).\n", " | \n", " | __iter__(self, /)\n", " | Implement iter(self).\n", " | \n", " | __next__(self, /)\n", " | Implement next(self).\n", " | \n", " | __reduce__(...)\n", " | Return state information for pickling.\n", " | \n", " | ----------------------------------------------------------------------\n", " | Static methods defined here:\n", " | \n", " | __new__(*args, **kwargs) from builtins.type\n", " | Create and return a new object. See help(type) for accurate signature.\n", "\n" ] } ], "source": [ "help(filter)" ] }, { "cell_type": "code", "execution_count": 62, "id": "166df9b8", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[22, 44]\n" ] } ], "source": [ "n = [11,22,33,44,55]\n", "\n", "r = list(filter(lambda x: x%2 == 0 , n))\n", "# 22, 44\n", "\n", "print(r)" ] }, { "cell_type": "code", "execution_count": null, "id": "6bb659fc", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.12" } }, "nbformat": 4, "nbformat_minor": 5 } ================================================ FILE: March Cohort 2023/5_Modules and Packages.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "id": "507b0a7e", "metadata": {}, "source": [ "# OS\n", "\n", "operating system" ] }, { "cell_type": "code", "execution_count": null, "id": "b4a5e0c7", "metadata": {}, "outputs": [], "source": [ "# hello.py\n", "# something.exe\n", "# .pdf .jpg .png\n", "\n", "# os.py" ] }, { "cell_type": "code", "execution_count": 7, "id": "c43e9e98", "metadata": {}, "outputs": [], "source": [ "import os" ] }, { "cell_type": "code", "execution_count": 3, "id": "083fa6d1", "metadata": { "scrolled": false }, "outputs": [], "source": [ "# help(os)" ] }, { "cell_type": "code", "execution_count": 8, "id": "3533b409", "metadata": {}, "outputs": [], "source": [ "# make directory\n", "\n", "os.mkdir('prime')" ] }, { "cell_type": "code", "execution_count": 5, "id": "c73636df", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'C:\\\\Users\\\\himan\\\\Documents\\\\Himanshu Ramchandani Live\\\\Data-Analyst-Roadmap'" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# get current working directory\n", "\n", "os.getcwd()" ] }, { "cell_type": "code", "execution_count": 9, "id": "8a5e48f9", "metadata": {}, "outputs": [], "source": [ "os.rmdir('prime')" ] }, { "cell_type": "markdown", "id": "deb43505", "metadata": {}, "source": [ "# sys" ] }, { "cell_type": "code", "execution_count": 13, "id": "fc53dc5b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "3\n" ] } ], "source": [ "import sys\n", "\n", "a = 5000\n", "\n", "print(sys.getrefcount(a)) # reference count of the object" ] }, { "cell_type": "code", "execution_count": 16, "id": "4973d1d1", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "432\n", "431\n" ] } ], "source": [ "a = 10\n", "\n", "b = a\n", "\n", "print(sys.getrefcount(a))\n", "\n", "del a\n", "\n", "print(sys.getrefcount(b))" ] }, { "cell_type": "code", "execution_count": 17, "id": "d26bf1fa", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "9223372036854775807" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sys.maxsize" ] }, { "cell_type": "code", "execution_count": 18, "id": "1a9e67d3", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['C:\\\\Users\\\\himan\\\\Documents\\\\Himanshu Ramchandani Live\\\\Data-Analyst-Roadmap',\n", " 'C:\\\\Users\\\\himan\\\\anaconda3\\\\python39.zip',\n", " 'C:\\\\Users\\\\himan\\\\anaconda3\\\\DLLs',\n", " 'C:\\\\Users\\\\himan\\\\anaconda3\\\\lib',\n", " 'C:\\\\Users\\\\himan\\\\anaconda3',\n", " '',\n", " 'C:\\\\Users\\\\himan\\\\anaconda3\\\\lib\\\\site-packages',\n", " 'C:\\\\Users\\\\himan\\\\anaconda3\\\\lib\\\\site-packages\\\\win32',\n", " 'C:\\\\Users\\\\himan\\\\anaconda3\\\\lib\\\\site-packages\\\\win32\\\\lib',\n", " 'C:\\\\Users\\\\himan\\\\anaconda3\\\\lib\\\\site-packages\\\\Pythonwin']" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sys.path" ] }, { "cell_type": "code", "execution_count": 19, "id": "e3a7ec79", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'3.9.12 (main, Apr 4 2022, 05:22:27) [MSC v.1916 64 bit (AMD64)]'" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sys.version" ] }, { "cell_type": "markdown", "id": "b9d075a2", "metadata": {}, "source": [ "# statistics" ] }, { "cell_type": "code", "execution_count": 21, "id": "7cab6f36", "metadata": {}, "outputs": [], "source": [ "# import statistics\n", "\n", "from statistics import *" ] }, { "cell_type": "code", "execution_count": 22, "id": "1b06511a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "35.666666666666664\n" ] } ], "source": [ "ages = [20,10,40,33,45,66]\n", "\n", "print(mean(ages))" ] }, { "cell_type": "code", "execution_count": 23, "id": "88b2b6d7", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "36.5\n" ] } ], "source": [ "print(median(ages))" ] }, { "cell_type": "code", "execution_count": 24, "id": "ea23370e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "20\n" ] } ], "source": [ "print(mode(ages))" ] }, { "cell_type": "code", "execution_count": 25, "id": "63d2eb9d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "19.684172999307506\n" ] } ], "source": [ "print(stdev(ages))" ] }, { "cell_type": "markdown", "id": "8aed4e64", "metadata": {}, "source": [ "# math" ] }, { "cell_type": "code", "execution_count": 26, "id": "bd9eaf48", "metadata": {}, "outputs": [], "source": [ "import math" ] }, { "cell_type": "code", "execution_count": 27, "id": "4cc7951a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "3.141592653589793\n" ] } ], "source": [ "print(math.pi)" ] }, { "cell_type": "code", "execution_count": 28, "id": "d11c039f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2.0\n" ] } ], "source": [ "print(math.sqrt(4))" ] }, { "cell_type": "code", "execution_count": 29, "id": "d06915ac", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "8.0\n" ] } ], "source": [ "print(math.pow(2,3))" ] }, { "cell_type": "code", "execution_count": 30, "id": "1851237f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "3\n" ] } ], "source": [ "print(math.floor(3.4))" ] }, { "cell_type": "code", "execution_count": 31, "id": "066f7ea3", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "5\n" ] } ], "source": [ "print(math.floor(5.9))" ] }, { "cell_type": "code", "execution_count": 32, "id": "ee095949", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "6\n" ] } ], "source": [ "print(math.ceil(5.9))" ] }, { "cell_type": "code", "execution_count": 34, "id": "bce4d491", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "4\n" ] } ], "source": [ "print(math.ceil(3.14))" ] }, { "cell_type": "code", "execution_count": 36, "id": "2fe20f12", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "3.141592653589793\n" ] } ], "source": [ "from math import pi, sqrt, pow, floor, ceil\n", "\n", "\n", "print(pi)" ] }, { "cell_type": "markdown", "id": "fffd6d73", "metadata": {}, "source": [ "# random" ] }, { "cell_type": "code", "execution_count": 37, "id": "3686b11a", "metadata": {}, "outputs": [], "source": [ "import random as r" ] }, { "cell_type": "code", "execution_count": 41, "id": "79f3ce91", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.36521596355895625\n" ] } ], "source": [ "print(r.random()) # 0 to 1" ] }, { "cell_type": "code", "execution_count": 48, "id": "fab41c2c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "14\n" ] } ], "source": [ "print(r.randint(5,20))" ] }, { "cell_type": "code", "execution_count": 51, "id": "9ac057b2", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.12067598483950182\n", "1.2067598483950182\n", "1\n" ] } ], "source": [ "x = r.random()\n", "print(x)\n", "\n", "x = x * 10\n", "print(x)\n", "\n", "print(math.floor(x))" ] }, { "cell_type": "code", "execution_count": null, "id": "785f6932", "metadata": {}, "outputs": [], "source": [ "s = ['a', 'b', 'c', 'd', 'e', '1','2','5']\n", "# 0 1 2 3 4" ] }, { "cell_type": "code", "execution_count": null, "id": "19c71f93", "metadata": {}, "outputs": [], "source": [ "flow of charges > electricity > bits > numbers > character > instructions > function > class > module > package > library > framework" ] }, { "cell_type": "code", "execution_count": null, "id": "daf99091", "metadata": {}, "outputs": [], "source": [ "source code ------> byte code ------> output\n", " compile run \n", " .py .pyc\n", "\n" ] }, { "cell_type": "code", "execution_count": 52, "id": "3da5a878", "metadata": {}, "outputs": [], "source": [ "def color():\n", " return \"red\"\n", "\n", "def flower():\n", " return \"color of the flower is blue\"" ] }, { "cell_type": "code", "execution_count": null, "id": "3eff71a8", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.12" } }, "nbformat": 4, "nbformat_minor": 5 } ================================================ FILE: March Cohort 2023/5_Modules-Packages/main.py ================================================ # import rose # print(rose.pie) # print(rose.color()) from statsmeme import descriptive print(descriptive.mode()) from statsmeme.inferential import models print(models()) # wrong statement # from statsmeme import inferential.models ================================================ FILE: March Cohort 2023/5_Modules-Packages/rose.py ================================================ pie = 3000 def color(): return "red" def flower(): return "color of the flower is blue" ================================================ FILE: March Cohort 2023/5_Modules-Packages/statsmeme/__init__.py ================================================ ================================================ FILE: March Cohort 2023/5_Modules-Packages/statsmeme/descriptive.py ================================================ def mean(l): m = sum(l)/len(l) return m def mode(): return "this is mode function from descriptive stats" ================================================ FILE: March Cohort 2023/5_Modules-Packages/statsmeme/inferential.py ================================================ def models(): return "models from inferetial stats" ================================================ FILE: March Cohort 2023/6_VirtualEnvironmentAndFlask/application.py ================================================ from flask import Flask, render_template import requests from bs4 import BeautifulSoup app = Flask(__name__) # __main__ url = 'https://archive.ics.uci.edu/ml/datasets.php' response = requests.get(url) status = response.status_code # print(status) content = response.content soup = BeautifulSoup(content, 'html.parser') # print(soup.body) print(soup.title) print(soup.title.get_text()) @app.route('/') def hello(): return "Hello from index route" @app.route('/home') def home(): return "

Welcome User, this is Home

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

Hello, {{ n }}

================================================ FILE: March Cohort 2023/7_NumPy/.ipynb_checkpoints/7_NumPy-checkpoint.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "id": "26f54c18", "metadata": {}, "source": [ "# NumPy\n", "\n", "Numeric Python" ] }, { "cell_type": "code", "execution_count": null, "id": "126668cf", "metadata": {}, "outputs": [], "source": [ "# !pip install numpy" ] }, { "cell_type": "code", "execution_count": 1, "id": "2bfbe2df", "metadata": {}, "outputs": [], "source": [ "import numpy as np" ] }, { "cell_type": "code", "execution_count": 2, "id": "22636407", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1 2 3]\n" ] } ], "source": [ "v = np.array([1,2,3])\n", "\n", "print(v)" ] }, { "cell_type": "code", "execution_count": 3, "id": "0cd6ad0d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[1]\n", " [2]\n", " [3]]\n" ] } ], "source": [ "v = np.array([[1],[2],[3]])\n", "\n", "print(v)" ] }, { "cell_type": "code", "execution_count": null, "id": "1acb7495", "metadata": {}, "outputs": [], "source": [ "0th dimension -> .\n", "1 D ._____.\n", "\n", "2 D .___|___.\n", "\n", "3 D .___|___.\n", " /\n", "4 D" ] }, { "cell_type": "markdown", "id": "61e93431", "metadata": {}, "source": [ "## Create a matrix" ] }, { "cell_type": "code", "execution_count": 5, "id": "d863cd16", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[1 2 3]\n", " [4 5 6]\n", " [7 8 9]]\n" ] } ], "source": [ "matrix = np.array([[1,2,3], [4,5,6], [7,8,9]])\n", "\n", "print(matrix)" ] }, { "cell_type": "markdown", "id": "c0266ec6", "metadata": {}, "source": [ "## selecting elements" ] }, { "cell_type": "code", "execution_count": 6, "id": "34cf8022", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "3\n" ] } ], "source": [ "v = np.array([1,2,3,4,5,6,7])\n", "# 0 1 2 3\n", "print(v[2])" ] }, { "cell_type": "code", "execution_count": 7, "id": "e2869c95", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([1, 2, 3, 4, 5, 6, 7])" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "v[:]" ] }, { "cell_type": "code", "execution_count": 8, "id": "9ea056c4", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([3, 4, 5])" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "v[2:5]" ] }, { "cell_type": "code", "execution_count": 9, "id": "ee5da0b4", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "7" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "v[-1]" ] }, { "cell_type": "code", "execution_count": 10, "id": "46c135df", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[1 2 3]\n", " [4 5 6]\n", " [7 8 9]]\n" ] } ], "source": [ "matrix = np.array([[1,2,3], [4,5,6], [7,8,9]])\n", "\n", "print(matrix)" ] }, { "cell_type": "code", "execution_count": 11, "id": "3d0a3a64", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[1 2 3]\n", " [4 5 6]\n", " [7 8 9]]\n" ] } ], "source": [ "m = np.array([[1,2,3], [4,5,6], [7,8,9]])\n", "\n", "print(m)" ] }, { "cell_type": "code", "execution_count": 12, "id": "a1b8f65f", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "m[0,0]" ] }, { "cell_type": "code", "execution_count": 13, "id": "93c8144a", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "5" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "m[1,1]" ] }, { "cell_type": "code", "execution_count": 14, "id": "1758d8de", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[1, 2],\n", " [4, 5],\n", " [7, 8]])" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "m[:3,:2]" ] }, { "cell_type": "code", "execution_count": 15, "id": "2782cb49", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[1, 2, 3],\n", " [4, 5, 6],\n", " [7, 8, 9]])" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "m" ] }, { "cell_type": "code", "execution_count": 16, "id": "89140708", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[2],\n", " [5],\n", " [8]])" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "m[:,1:2]" ] }, { "cell_type": "code", "execution_count": 17, "id": "6cebac94", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[5]])" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "m[1:2,1:2]" ] }, { "cell_type": "code", "execution_count": 18, "id": "04b19a8a", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[4, 5, 6]])" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "m[1:2,:]" ] }, { "cell_type": "code", "execution_count": null, "id": "b98fde70", "metadata": {}, "outputs": [], "source": [ "00 01 02\n", "10 11 12\n", "20 21 22" ] }, { "cell_type": "markdown", "id": "ab6051ab", "metadata": {}, "source": [ "## Describe a Matrix" ] }, { "cell_type": "code", "execution_count": 25, "id": "fc23b733", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 1 2 3 4]\n", " [ 4 5 6 4]\n", " [ 7 8 9 13]]\n" ] } ], "source": [ "matrix = np.array([[1,2,3,4], [4,5,6,4], [7,8,9,13]])\n", "\n", "print(matrix)" ] }, { "cell_type": "code", "execution_count": 26, "id": "5605cd43", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(3, 4)\n" ] } ], "source": [ "print(matrix.shape)" ] }, { "cell_type": "code", "execution_count": 27, "id": "f97c5f02", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "12\n" ] } ], "source": [ "print(matrix.size)" ] }, { "cell_type": "code", "execution_count": 28, "id": "d60657d9", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2\n" ] } ], "source": [ "print(matrix.ndim)" ] }, { "cell_type": "markdown", "id": "48dee353", "metadata": {}, "source": [ "## max and min values" ] }, { "cell_type": "code", "execution_count": 29, "id": "9a5bd9cf", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[1 2 3]\n", " [4 5 6]\n", " [7 8 9]]\n" ] } ], "source": [ "matrix = np.array([[1,2,3], [4,5,6], [7,8,9]])\n", "\n", "print(matrix)" ] }, { "cell_type": "code", "execution_count": 30, "id": "b7b433c7", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "9\n" ] } ], "source": [ "print(np.max(matrix))" ] }, { "cell_type": "code", "execution_count": 31, "id": "069def1a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1\n" ] } ], "source": [ "print(np.min(matrix))" ] }, { "cell_type": "code", "execution_count": 32, "id": "f6d3191c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[7 8 9]\n" ] } ], "source": [ "print(np.max(matrix, axis=0)) # column" ] }, { "cell_type": "code", "execution_count": 33, "id": "e7f42b91", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[3 6 9]\n" ] } ], "source": [ "print(np.max(matrix, axis=1)) # row" ] }, { "cell_type": "markdown", "id": "bca10442", "metadata": {}, "source": [ "## Mean, Variance and Standard Deviation" ] }, { "cell_type": "code", "execution_count": 34, "id": "807c45c9", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[1 2 3]\n", " [4 5 6]\n", " [7 8 9]]\n" ] } ], "source": [ "matrix = np.array([[1,2,3], [4,5,6], [7,8,9]])\n", "\n", "print(matrix)" ] }, { "cell_type": "code", "execution_count": 35, "id": "403da62b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "5.0\n" ] } ], "source": [ "print(np.mean(matrix)) # 45/9 = 5.0" ] }, { "cell_type": "code", "execution_count": 36, "id": "32f7f4eb", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "6.666666666666667\n" ] } ], "source": [ "print(np.var(matrix))" ] }, { "cell_type": "code", "execution_count": 37, "id": "f935ec11", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2.581988897471611\n" ] } ], "source": [ "print(np.std(matrix))" ] }, { "cell_type": "markdown", "id": "a5ff96c9", "metadata": {}, "source": [ "## Reshaping Array" ] }, { "cell_type": "code", "execution_count": 38, "id": "d61f922b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[1 2 3]\n", " [4 5 6]\n", " [7 8 9]]\n" ] } ], "source": [ "matrix = np.array([[1,2,3], [4,5,6], [7,8,9]])\n", "\n", "print(matrix)" ] }, { "cell_type": "code", "execution_count": 39, "id": "5e4cb0aa", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(3, 3)\n" ] } ], "source": [ "print(matrix.shape)" ] }, { "cell_type": "code", "execution_count": 40, "id": "c96a11df", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[1]\n", " [2]\n", " [3]\n", " [4]\n", " [5]\n", " [6]\n", " [7]\n", " [8]\n", " [9]]\n" ] } ], "source": [ "print(matrix.reshape(9,1))" ] }, { "cell_type": "code", "execution_count": 41, "id": "8ba5abef", "metadata": {}, "outputs": [ { "ename": "ValueError", "evalue": "cannot reshape array of size 9 into shape (9,2)", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", "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", "\u001b[1;31mValueError\u001b[0m: cannot reshape array of size 9 into shape (9,2)" ] } ], "source": [ "print(matrix.reshape(9,2))" ] }, { "cell_type": "code", "execution_count": 42, "id": "31fd6936", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1 2 3 4 5 6 7 8 9]\n" ] } ], "source": [ "print(matrix.reshape(9)) # 1 - d array" ] }, { "cell_type": "code", "execution_count": 43, "id": "a0fe4023", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[1 2 3 4 5 6 7 8 9]]\n" ] } ], "source": [ "print(matrix.reshape(1,-1))" ] }, { "cell_type": "code", "execution_count": 44, "id": "c8e1142a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1 2 3 4 5 6 7 8 9]\n" ] } ], "source": [ "print(matrix.flatten())" ] }, { "cell_type": "markdown", "id": "fd8bffb7", "metadata": {}, "source": [ "## Transpose of matrix" ] }, { "cell_type": "code", "execution_count": 45, "id": "9abc8c81", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[1 2 3]\n", " [4 5 6]\n", " [7 8 9]]\n" ] } ], "source": [ "matrix = np.array([[1,2,3], [4,5,6], [7,8,9]])\n", "\n", "print(matrix)" ] }, { "cell_type": "code", "execution_count": 46, "id": "e33de6cf", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[1 4 7]\n", " [2 5 8]\n", " [3 6 9]]\n" ] } ], "source": [ "print(matrix.T)" ] }, { "cell_type": "code", "execution_count": null, "id": "a87bc2c8", "metadata": {}, "outputs": [], "source": [ "720PX\n", "1080px" ] }, { "cell_type": "markdown", "id": "af80fec7", "metadata": {}, "source": [ "## Determinant and Rank" ] }, { "cell_type": "code", "execution_count": 47, "id": "9e192e55", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[1 2 3]\n", " [4 5 6]\n", " [7 8 9]]\n" ] } ], "source": [ "matrix = np.array([[1,2,3], [4,5,6], [7,8,9]])\n", "\n", "print(matrix)" ] }, { "cell_type": "code", "execution_count": 48, "id": "0d8ff2a2", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-9.51619735392994e-16\n" ] } ], "source": [ "print(np.linalg.det(matrix))" ] }, { "cell_type": "code", "execution_count": 50, "id": "3879e071", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2\n" ] } ], "source": [ "print(np.linalg.matrix_rank(matrix)) # number of non zero rows" ] }, { "cell_type": "markdown", "id": "91c3d0b9", "metadata": {}, "source": [ "# Random Values" ] }, { "cell_type": "code", "execution_count": 55, "id": "ab89dd83", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[7 4 8]\n" ] } ], "source": [ "print(np.random.randint(0,11,3))" ] }, { "cell_type": "code", "execution_count": 60, "id": "cda283bb", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[3 6 6]\n" ] } ], "source": [ "np.random.seed(5)\n", "\n", "print(np.random.randint(0,11,3))" ] }, { "cell_type": "code", "execution_count": 62, "id": "b56ac918", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 2.82374728 -1.88768311 4.64888033 3.91525025 -0.8205163 -0.98003789\n", " -0.63509625 -1.33655895 0.57844071 0.55140067]\n" ] } ], "source": [ "print(np.random.normal(1.0, 2.0, 10)) # normal distribution" ] }, { "cell_type": "code", "execution_count": null, "id": "5b17b378", "metadata": {}, "outputs": [], "source": [ "-1 and 0 and 1" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.12" } }, "nbformat": 4, "nbformat_minor": 5 } ================================================ FILE: March Cohort 2023/7_NumPy/7_NumPy.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "id": "26f54c18", "metadata": {}, "source": [ "# NumPy\n", "\n", "Numeric Python" ] }, { "cell_type": "code", "execution_count": null, "id": "126668cf", "metadata": {}, "outputs": [], "source": [ "# !pip install numpy" ] }, { "cell_type": "code", "execution_count": 1, "id": "2bfbe2df", "metadata": {}, "outputs": [], "source": [ "import numpy as np" ] }, { "cell_type": "code", "execution_count": 2, "id": "22636407", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1 2 3]\n" ] } ], "source": [ "v = np.array([1,2,3])\n", "\n", "print(v)" ] }, { "cell_type": "code", "execution_count": 3, "id": "0cd6ad0d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[1]\n", " [2]\n", " [3]]\n" ] } ], "source": [ "v = np.array([[1],[2],[3]])\n", "\n", "print(v)" ] }, { "cell_type": "code", "execution_count": null, "id": "1acb7495", "metadata": {}, "outputs": [], "source": [ "0th dimension -> .\n", "1 D ._____.\n", "\n", "2 D .___|___.\n", "\n", "3 D .___|___.\n", " /\n", "4 D" ] }, { "cell_type": "markdown", "id": "61e93431", "metadata": {}, "source": [ "## Create a matrix" ] }, { "cell_type": "code", "execution_count": 5, "id": "d863cd16", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[1 2 3]\n", " [4 5 6]\n", " [7 8 9]]\n" ] } ], "source": [ "matrix = np.array([[1,2,3], [4,5,6], [7,8,9]])\n", "\n", "print(matrix)" ] }, { "cell_type": "markdown", "id": "c0266ec6", "metadata": {}, "source": [ "## selecting elements" ] }, { "cell_type": "code", "execution_count": 6, "id": "34cf8022", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "3\n" ] } ], "source": [ "v = np.array([1,2,3,4,5,6,7])\n", "# 0 1 2 3\n", "print(v[2])" ] }, { "cell_type": "code", "execution_count": 7, "id": "e2869c95", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([1, 2, 3, 4, 5, 6, 7])" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "v[:]" ] }, { "cell_type": "code", "execution_count": 8, "id": "9ea056c4", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([3, 4, 5])" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "v[2:5]" ] }, { "cell_type": "code", "execution_count": 9, "id": "ee5da0b4", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "7" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "v[-1]" ] }, { "cell_type": "code", "execution_count": 10, "id": "46c135df", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[1 2 3]\n", " [4 5 6]\n", " [7 8 9]]\n" ] } ], "source": [ "matrix = np.array([[1,2,3], [4,5,6], [7,8,9]])\n", "\n", "print(matrix)" ] }, { "cell_type": "code", "execution_count": 11, "id": "3d0a3a64", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[1 2 3]\n", " [4 5 6]\n", " [7 8 9]]\n" ] } ], "source": [ "m = np.array([[1,2,3], [4,5,6], [7,8,9]])\n", "\n", "print(m)" ] }, { "cell_type": "code", "execution_count": 12, "id": "a1b8f65f", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "m[0,0]" ] }, { "cell_type": "code", "execution_count": 13, "id": "93c8144a", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "5" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "m[1,1]" ] }, { "cell_type": "code", "execution_count": 14, "id": "1758d8de", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[1, 2],\n", " [4, 5],\n", " [7, 8]])" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "m[:3,:2]" ] }, { "cell_type": "code", "execution_count": 15, "id": "2782cb49", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[1, 2, 3],\n", " [4, 5, 6],\n", " [7, 8, 9]])" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "m" ] }, { "cell_type": "code", "execution_count": 16, "id": "89140708", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[2],\n", " [5],\n", " [8]])" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "m[:,1:2]" ] }, { "cell_type": "code", "execution_count": 17, "id": "6cebac94", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[5]])" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "m[1:2,1:2]" ] }, { "cell_type": "code", "execution_count": 18, "id": "04b19a8a", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[4, 5, 6]])" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "m[1:2,:]" ] }, { "cell_type": "code", "execution_count": null, "id": "b98fde70", "metadata": {}, "outputs": [], "source": [ "00 01 02\n", "10 11 12\n", "20 21 22" ] }, { "cell_type": "markdown", "id": "ab6051ab", "metadata": {}, "source": [ "## Describe a Matrix" ] }, { "cell_type": "code", "execution_count": 25, "id": "fc23b733", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[ 1 2 3 4]\n", " [ 4 5 6 4]\n", " [ 7 8 9 13]]\n" ] } ], "source": [ "matrix = np.array([[1,2,3,4], [4,5,6,4], [7,8,9,13]])\n", "\n", "print(matrix)" ] }, { "cell_type": "code", "execution_count": 26, "id": "5605cd43", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(3, 4)\n" ] } ], "source": [ "print(matrix.shape)" ] }, { "cell_type": "code", "execution_count": 27, "id": "f97c5f02", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "12\n" ] } ], "source": [ "print(matrix.size)" ] }, { "cell_type": "code", "execution_count": 28, "id": "d60657d9", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2\n" ] } ], "source": [ "print(matrix.ndim)" ] }, { "cell_type": "markdown", "id": "48dee353", "metadata": {}, "source": [ "## max and min values" ] }, { "cell_type": "code", "execution_count": 29, "id": "9a5bd9cf", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[1 2 3]\n", " [4 5 6]\n", " [7 8 9]]\n" ] } ], "source": [ "matrix = np.array([[1,2,3], [4,5,6], [7,8,9]])\n", "\n", "print(matrix)" ] }, { "cell_type": "code", "execution_count": 30, "id": "b7b433c7", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "9\n" ] } ], "source": [ "print(np.max(matrix))" ] }, { "cell_type": "code", "execution_count": 31, "id": "069def1a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1\n" ] } ], "source": [ "print(np.min(matrix))" ] }, { "cell_type": "code", "execution_count": 32, "id": "f6d3191c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[7 8 9]\n" ] } ], "source": [ "print(np.max(matrix, axis=0)) # column" ] }, { "cell_type": "code", "execution_count": 33, "id": "e7f42b91", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[3 6 9]\n" ] } ], "source": [ "print(np.max(matrix, axis=1)) # row" ] }, { "cell_type": "markdown", "id": "bca10442", "metadata": {}, "source": [ "## Mean, Variance and Standard Deviation" ] }, { "cell_type": "code", "execution_count": 34, "id": "807c45c9", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[1 2 3]\n", " [4 5 6]\n", " [7 8 9]]\n" ] } ], "source": [ "matrix = np.array([[1,2,3], [4,5,6], [7,8,9]])\n", "\n", "print(matrix)" ] }, { "cell_type": "code", "execution_count": 35, "id": "403da62b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "5.0\n" ] } ], "source": [ "print(np.mean(matrix)) # 45/9 = 5.0" ] }, { "cell_type": "code", "execution_count": 36, "id": "32f7f4eb", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "6.666666666666667\n" ] } ], "source": [ "print(np.var(matrix))" ] }, { "cell_type": "code", "execution_count": 37, "id": "f935ec11", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2.581988897471611\n" ] } ], "source": [ "print(np.std(matrix))" ] }, { "cell_type": "markdown", "id": "a5ff96c9", "metadata": {}, "source": [ "## Reshaping Array" ] }, { "cell_type": "code", "execution_count": 38, "id": "d61f922b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[1 2 3]\n", " [4 5 6]\n", " [7 8 9]]\n" ] } ], "source": [ "matrix = np.array([[1,2,3], [4,5,6], [7,8,9]])\n", "\n", "print(matrix)" ] }, { "cell_type": "code", "execution_count": 39, "id": "5e4cb0aa", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(3, 3)\n" ] } ], "source": [ "print(matrix.shape)" ] }, { "cell_type": "code", "execution_count": 40, "id": "c96a11df", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[1]\n", " [2]\n", " [3]\n", " [4]\n", " [5]\n", " [6]\n", " [7]\n", " [8]\n", " [9]]\n" ] } ], "source": [ "print(matrix.reshape(9,1))" ] }, { "cell_type": "code", "execution_count": 41, "id": "8ba5abef", "metadata": {}, "outputs": [ { "ename": "ValueError", "evalue": "cannot reshape array of size 9 into shape (9,2)", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", "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", "\u001b[1;31mValueError\u001b[0m: cannot reshape array of size 9 into shape (9,2)" ] } ], "source": [ "print(matrix.reshape(9,2))" ] }, { "cell_type": "code", "execution_count": 42, "id": "31fd6936", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1 2 3 4 5 6 7 8 9]\n" ] } ], "source": [ "print(matrix.reshape(9)) # 1 - d array" ] }, { "cell_type": "code", "execution_count": 43, "id": "a0fe4023", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[1 2 3 4 5 6 7 8 9]]\n" ] } ], "source": [ "print(matrix.reshape(1,-1))" ] }, { "cell_type": "code", "execution_count": 44, "id": "c8e1142a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1 2 3 4 5 6 7 8 9]\n" ] } ], "source": [ "print(matrix.flatten())" ] }, { "cell_type": "markdown", "id": "fd8bffb7", "metadata": {}, "source": [ "## Transpose of matrix" ] }, { "cell_type": "code", "execution_count": 45, "id": "9abc8c81", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[1 2 3]\n", " [4 5 6]\n", " [7 8 9]]\n" ] } ], "source": [ "matrix = np.array([[1,2,3], [4,5,6], [7,8,9]])\n", "\n", "print(matrix)" ] }, { "cell_type": "code", "execution_count": 46, "id": "e33de6cf", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[1 4 7]\n", " [2 5 8]\n", " [3 6 9]]\n" ] } ], "source": [ "print(matrix.T)" ] }, { "cell_type": "code", "execution_count": null, "id": "a87bc2c8", "metadata": {}, "outputs": [], "source": [ "720PX\n", "1080px" ] }, { "cell_type": "markdown", "id": "af80fec7", "metadata": {}, "source": [ "## Determinant and Rank" ] }, { "cell_type": "code", "execution_count": 47, "id": "9e192e55", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[1 2 3]\n", " [4 5 6]\n", " [7 8 9]]\n" ] } ], "source": [ "matrix = np.array([[1,2,3], [4,5,6], [7,8,9]])\n", "\n", "print(matrix)" ] }, { "cell_type": "code", "execution_count": 48, "id": "0d8ff2a2", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-9.51619735392994e-16\n" ] } ], "source": [ "print(np.linalg.det(matrix))" ] }, { "cell_type": "code", "execution_count": 50, "id": "3879e071", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2\n" ] } ], "source": [ "print(np.linalg.matrix_rank(matrix)) # number of non zero rows" ] }, { "cell_type": "markdown", "id": "91c3d0b9", "metadata": {}, "source": [ "# Random Values" ] }, { "cell_type": "code", "execution_count": 55, "id": "ab89dd83", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[7 4 8]\n" ] } ], "source": [ "print(np.random.randint(0,11,3))" ] }, { "cell_type": "code", "execution_count": 60, "id": "cda283bb", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[3 6 6]\n" ] } ], "source": [ "np.random.seed(5)\n", "\n", "print(np.random.randint(0,11,3))" ] }, { "cell_type": "code", "execution_count": 62, "id": "b56ac918", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 2.82374728 -1.88768311 4.64888033 3.91525025 -0.8205163 -0.98003789\n", " -0.63509625 -1.33655895 0.57844071 0.55140067]\n" ] } ], "source": [ "print(np.random.normal(1.0, 2.0, 10)) # normal distribution" ] }, { "cell_type": "code", "execution_count": null, "id": "5b17b378", "metadata": {}, "outputs": [], "source": [ "-1 and 0 and 1" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.12" } }, "nbformat": 4, "nbformat_minor": 5 } ================================================ FILE: March Cohort 2023/8_9_Pandas/.ipynb_checkpoints/8_9_Pandas-checkpoint.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "id": "a6a858e0", "metadata": {}, "source": [ "# Pandas" ] }, { "cell_type": "code", "execution_count": 1, "id": "848d64c8", "metadata": {}, "outputs": [], "source": [ "# !pip install pandas" ] }, { "cell_type": "code", "execution_count": 2, "id": "eabb0abf", "metadata": {}, "outputs": [], "source": [ "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 3, "id": "4e949120", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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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
\n", "
" ], "text/plain": [ " EST Temperature DewPoint Humidity Sea Level PressureIn \\\n", "0 1/1/2016 38 23 52 30.03 \n", "1 1/2/2016 36 18 46 30.02 \n", "2 1/3/2016 40 21 47 29.86 \n", "3 1/4/2016 25 9 44 30.05 \n", "4 1/5/2016 20 -3 41 30.57 \n", "5 1/6/2016 33 4 35 30.50 \n", "6 1/7/2016 39 11 33 30.28 \n", "7 1/8/2016 39 29 64 30.20 \n", "8 1/9/2016 44 38 77 30.16 \n", "9 1/10/2016 50 46 71 29.59 \n", "10 1/11/2016 33 8 37 29.92 \n", "11 1/12/2016 35 15 53 29.85 \n", "12 1/13/2016 26 4 42 29.94 \n", "13 1/14/2016 30 12 47 29.95 \n", "14 1/15/2016 43 31 62 29.82 \n", "15 1/16/2016 47 37 70 29.52 \n", "16 1/17/2016 36 23 66 29.78 \n", "17 1/18/2016 25 6 53 29.83 \n", "18 1/19/2016 22 3 42 30.03 \n", "19 1/20/2016 32 15 49 30.13 \n", "20 1/21/2016 31 11 45 30.15 \n", "21 1/22/2016 26 6 41 30.21 \n", "22 1/23/2016 26 21 78 29.77 \n", "23 1/24/2016 28 11 53 29.92 \n", "24 1/25/2016 34 18 54 30.25 \n", "25 1/26/2016 43 29 56 30.03 \n", "26 1/27/2016 41 22 45 30.03 \n", "27 1/28/2016 37 20 51 29.90 \n", "28 1/29/2016 36 21 50 29.58 \n", "29 1/30/2016 34 16 46 30.01 \n", "30 1/31/2016 46 28 52 29.90 \n", "\n", " VisibilityMiles WindSpeedMPH PrecipitationIn CloudCover Events \\\n", "0 10 8.0 0 5 NaN \n", "1 10 7.0 0 3 NaN \n", "2 10 8.0 0 1 NaN \n", "3 10 9.0 0 3 NaN \n", "4 10 5.0 0 0 NaN \n", "5 10 4.0 0 0 NaN \n", "6 10 2.0 0 3 NaN \n", "7 10 4.0 0 8 NaN \n", "8 9 8.0 T 8 Rain \n", "9 4 NaN 1.8 7 Rain \n", "10 10 NaN 0 1 NaN \n", "11 10 6.0 T 4 NaN \n", "12 10 10.0 0 0 NaN \n", "13 10 5.0 T 7 NaN \n", "14 9 5.0 T 2 NaN \n", "15 8 7.0 0.24 7 Rain \n", "16 8 6.0 0.05 6 Fog-Snow \n", "17 9 12.0 T 2 Snow \n", "18 10 11.0 0 1 NaN \n", "19 10 6.0 0 2 NaN \n", "20 10 6.0 0 1 NaN \n", "21 9 NaN 0.01 3 Snow \n", "22 1 16.0 2.31 8 Fog-Snow \n", "23 8 6.0 T 3 Snow \n", "24 10 3.0 0 2 NaN \n", "25 10 7.0 0 2 NaN \n", "26 10 7.0 T 3 Rain \n", "27 10 5.0 0 1 NaN \n", "28 10 8.0 0 4 NaN \n", "29 10 7.0 0 0 NaN \n", "30 10 5.0 0 0 NaN \n", "\n", " WindDirDegrees \n", "0 281 \n", "1 275 \n", "2 277 \n", "3 345 \n", "4 333 \n", "5 259 \n", "6 293 \n", "7 79 \n", "8 76 \n", "9 109 \n", "10 289 \n", "11 235 \n", "12 284 \n", "13 266 \n", "14 101 \n", "15 340 \n", "16 345 \n", "17 293 \n", "18 293 \n", "19 302 \n", "20 312 \n", "21 34 \n", "22 42 \n", "23 327 \n", "24 286 \n", "25 244 \n", "26 311 \n", "27 234 \n", "28 298 \n", "29 257 \n", "30 241 " ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_csv('nyc_weather.csv')\n", "df" ] }, { "cell_type": "code", "execution_count": 4, "id": "a345f8d9", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "pandas.core.frame.DataFrame" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(df)" ] }, { "cell_type": "code", "execution_count": 5, "id": "debc5617", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 38\n", "1 36\n", "2 40\n", "3 25\n", "4 20\n", "5 33\n", "6 39\n", "7 39\n", "8 44\n", "9 50\n", "10 33\n", "11 35\n", "12 26\n", "13 30\n", "14 43\n", "15 47\n", "16 36\n", "17 25\n", "18 22\n", "19 32\n", "20 31\n", "21 26\n", "22 26\n", "23 28\n", "24 34\n", "25 43\n", "26 41\n", "27 37\n", "28 36\n", "29 34\n", "30 46\n", "Name: Temperature, dtype: int64" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['Temperature']" ] }, { "cell_type": "code", "execution_count": 6, "id": "02f6142e", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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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
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2737
2836
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" ], "text/plain": [ " Temperature\n", "0 38\n", "1 36\n", "2 40\n", "3 25\n", "4 20\n", "5 33\n", "6 39\n", "7 39\n", "8 44\n", "9 50\n", "10 33\n", "11 35\n", "12 26\n", "13 30\n", "14 43\n", "15 47\n", "16 36\n", "17 25\n", "18 22\n", "19 32\n", "20 31\n", "21 26\n", "22 26\n", "23 28\n", "24 34\n", "25 43\n", "26 41\n", "27 37\n", "28 36\n", "29 34\n", "30 46" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[['Temperature']]" ] }, { "cell_type": "code", "execution_count": 7, "id": "28cf94ea", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "pandas.core.series.Series" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(df['Temperature'])" ] }, { "cell_type": "code", "execution_count": 8, "id": "ef48c13e", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "38" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['Temperature'][0]" ] }, { "cell_type": "code", "execution_count": 9, "id": "147b71a5", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "numpy.int64" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(df['Temperature'][0])" ] }, { "cell_type": "code", "execution_count": 10, "id": "b20d7b6b", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "50" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['Temperature'].max()" ] }, { "cell_type": "code", "execution_count": 11, "id": "4257b3db", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "6.892857142857143" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['WindSpeedMPH'].mean()" ] }, { "cell_type": "markdown", "id": "2c0372ce", "metadata": {}, "source": [ "# create a dataframe" ] }, { "cell_type": "code", "execution_count": 12, "id": "fb886d1f", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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daytemperaturewindspeedevent
01/1/2023326Rain
11/2/2023357Sunny
21/3/2023282Snow
31/4/2023244Rain
41/5/2023322Sunny
51/6/2023317Snow
\n", "
" ], "text/plain": [ " day temperature windspeed event\n", "0 1/1/2023 32 6 Rain\n", "1 1/2/2023 35 7 Sunny\n", "2 1/3/2023 28 2 Snow\n", "3 1/4/2023 24 4 Rain\n", "4 1/5/2023 32 2 Sunny\n", "5 1/6/2023 31 7 Snow" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "weather_data = {\n", " 'day': ['1/1/2023', '1/2/2023', '1/3/2023', '1/4/2023', '1/5/2023', '1/6/2023'],\n", " 'temperature': [32,35,28,24,32,31],\n", " 'windspeed': [6,7,2,4,2,7],\n", " 'event': ['Rain', 'Sunny', 'Snow','Rain', 'Sunny', 'Snow']\n", "}\n", "\n", "# int() float() str()\n", "\n", "df = pd.DataFrame(weather_data)\n", "df" ] }, { "cell_type": "code", "execution_count": 13, "id": "439c0c87", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(6, 4)" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.shape" ] }, { "cell_type": "code", "execution_count": 14, "id": "dc7b5d56", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "24" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.size" ] }, { "cell_type": "code", "execution_count": 15, "id": "bc2f37a1", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.ndim" ] }, { "cell_type": "markdown", "id": "1338a1f1", "metadata": {}, "source": [ "## Rows and slicing" ] }, { "cell_type": "code", "execution_count": 18, "id": "420f437e", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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daytemperaturewindspeedevent
01/1/2023326Rain
11/2/2023357Sunny
21/3/2023282Snow
31/4/2023244Rain
41/5/2023322Sunny
\n", "
" ], "text/plain": [ " day temperature windspeed event\n", "0 1/1/2023 32 6 Rain\n", "1 1/2/2023 35 7 Sunny\n", "2 1/3/2023 28 2 Snow\n", "3 1/4/2023 24 4 Rain\n", "4 1/5/2023 32 2 Sunny" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "code", "execution_count": 20, "id": "b8c7073d", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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daytemperaturewindspeedevent
31/4/2023244Rain
41/5/2023322Sunny
51/6/2023317Snow
\n", "
" ], "text/plain": [ " day temperature windspeed event\n", "3 1/4/2023 24 4 Rain\n", "4 1/5/2023 32 2 Sunny\n", "5 1/6/2023 31 7 Snow" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.tail(3)" ] }, { "cell_type": "code", "execution_count": 21, "id": "0a72cefc", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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daytemperaturewindspeedevent
11/2/2023357Sunny
21/3/2023282Snow
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" ], "text/plain": [ " day temperature windspeed\n", "event \n", "Sunny 1/2/2023 35 7\n", "Sunny 1/5/2023 32 2" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.loc['Sunny']" ] }, { "cell_type": "markdown", "id": "8c16b66d", "metadata": {}, "source": [ "# Different ways to create dataframe\n", "\n", "### using excel" ] }, { "cell_type": "code", "execution_count": 51, "id": "36d8a05a", "metadata": {}, "outputs": [ { "data": { "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", "
daytemperaturewindspeedevent
02017-01-01326Rain
12017-01-02357Sunny
22017-01-03282Snow
\n", "
" ], "text/plain": [ " day temperature windspeed event\n", "0 2017-01-01 32 6 Rain\n", "1 2017-01-02 35 7 Sunny\n", "2 2017-01-03 28 2 Snow" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_excel('weather_data.xlsx', 'Sheet1')\n", "df" ] }, { "cell_type": "markdown", "id": "4e1681da", "metadata": {}, "source": [ "### using tuples and list" ] }, { "cell_type": "code", "execution_count": 52, "id": "46ba1a56", "metadata": {}, "outputs": [ { "data": { "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", "
0123
01/1/2023326Rain
11/2/2023356Sunny
21/3/2023322Snow
\n", "
" ], "text/plain": [ " 0 1 2 3\n", "0 1/1/2023 32 6 Rain\n", "1 1/2/2023 35 6 Sunny\n", "2 1/3/2023 32 2 Snow" ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" } ], "source": [ "weather_data = [\n", " ('1/1/2023', 32, 6, 'Rain'),\n", " ('1/2/2023', 35, 6, 'Sunny'),\n", " ('1/3/2023', 32, 2, 'Snow')\n", "]\n", "\n", "df = pd.DataFrame(data=weather_data)\n", "\n", "df" ] }, { "cell_type": "code", "execution_count": 53, "id": "f6e5f754", "metadata": {}, "outputs": [ { "data": { "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", "
daytemperaturewindspeedevent
01/1/2023326Rain
11/2/2023356Sunny
21/3/2023322Snow
\n", "
" ], "text/plain": [ " day temperature windspeed event\n", "0 1/1/2023 32 6 Rain\n", "1 1/2/2023 35 6 Sunny\n", "2 1/3/2023 32 2 Snow" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" } ], "source": [ "weather_data = [\n", " ('1/1/2023', 32, 6, 'Rain'),\n", " ('1/2/2023', 35, 6, 'Sunny'),\n", " ('1/3/2023', 32, 2, 'Snow')\n", "]\n", "\n", "df = pd.DataFrame(data=weather_data, columns=['day', 'temperature', 'windspeed', 'event'])\n", "\n", "df" ] }, { "cell_type": "markdown", "id": "f2ed69e8", "metadata": {}, "source": [ "### list of dictionary" ] }, { "cell_type": "code", "execution_count": 54, "id": "588425b3", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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daytemperaturewindspeedevent
01/1/2023326Rain
11/2/2023357Sunny
21/3/2023326Snow
\n", "
" ], "text/plain": [ " day temperature windspeed event\n", "0 1/1/2023 32 6 Rain\n", "1 1/2/2023 35 7 Sunny\n", "2 1/3/2023 32 6 Snow" ] }, "execution_count": 54, "metadata": {}, "output_type": "execute_result" } ], "source": [ "weather_data = [\n", " {'day': '1/1/2023', 'temperature': 32, 'windspeed': 6, 'event': 'Rain'},\n", " {'day': '1/2/2023', 'temperature': 35, 'windspeed': 7, 'event': 'Sunny'},\n", " {'day': '1/3/2023', 'temperature': 32, 'windspeed': 6, 'event': 'Snow'}\n", "]\n", "\n", "df = pd.DataFrame(data=weather_data)\n", "df" ] }, { "cell_type": "code", "execution_count": 55, "id": "2e6b7433", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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daytemperaturewindspeedevent
01/1/2023326Rain
11/2/2023357Sunny
21/3/2023326Snow
\n", "
" ], "text/plain": [ " day temperature windspeed event\n", "0 1/1/2023 32 6 Rain\n", "1 1/2/2023 35 7 Sunny\n", "2 1/3/2023 32 6 Snow" ] }, "execution_count": 55, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df" ] }, { "cell_type": "markdown", "id": "5636c651", "metadata": {}, "source": [ "### write to csv" ] }, { "cell_type": "code", "execution_count": 56, "id": "49a42525", "metadata": {}, "outputs": [], "source": [ "df.to_csv('indore.csv', index=True)" ] }, { "cell_type": "markdown", "id": "2076e420", "metadata": {}, "source": [ "# Day 9 - Pandas day 2\n", "\n", "### Handling missing values" ] }, { "cell_type": "code", "execution_count": 1, "id": "0bcbb714", "metadata": {}, "outputs": [], "source": [ "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 3, "id": "f64c5df9", "metadata": { "scrolled": false }, "outputs": [], "source": [ "# help(pd.read_csv)" ] }, { "cell_type": "code", "execution_count": 4, "id": "8b9179c5", "metadata": {}, "outputs": [ { "data": { "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", "
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", "
" ], "text/plain": [ " day temperature windspeed event\n", "0 2017-01-01 32.0 6.0 Rain\n", "1 2017-01-04 NaN 9.0 Sunny\n", "2 2017-01-05 28.0 NaN Snow\n", "3 2017-01-06 NaN 7.0 NaN\n", "4 2017-01-07 32.0 NaN Rain\n", "5 2017-01-08 NaN NaN Sunny\n", "6 2017-01-09 NaN NaN NaN\n", "7 2017-01-10 34.0 8.0 Cloudy\n", "8 2017-01-11 40.0 12.0 Sunny" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_csv('weather_data2.csv', parse_dates=['day'])\n", "df" ] }, { "cell_type": "code", "execution_count": 5, "id": "ed579b5c", "metadata": {}, "outputs": [], "source": [ "import numpy as np" ] }, { "cell_type": "code", "execution_count": 6, "id": "a7814651", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "nan" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.nan # " ] }, { "cell_type": "code", "execution_count": 7, "id": "d96c216f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "print(type(df.day[0]))" ] }, { "cell_type": "code", "execution_count": 8, "id": "f6d09c15", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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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
\n", "
" ], "text/plain": [ " temperature windspeed event\n", "day \n", "2017-01-01 32.0 6.0 Rain\n", "2017-01-04 NaN 9.0 Sunny\n", "2017-01-05 28.0 NaN Snow\n", "2017-01-06 NaN 7.0 NaN\n", "2017-01-07 32.0 NaN Rain\n", "2017-01-08 NaN NaN Sunny\n", "2017-01-09 NaN NaN NaN\n", "2017-01-10 34.0 8.0 Cloudy\n", "2017-01-11 40.0 12.0 Sunny" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.set_index('day', inplace=True)\n", "df" ] }, { "cell_type": "markdown", "id": "c32e25ab", "metadata": {}, "source": [ "## fillna\n", "\n", "Fill Not Available" ] }, { "cell_type": "code", "execution_count": 9, "id": "6b9159d9", "metadata": {}, "outputs": [ { "data": { "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", "
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", "
" ], "text/plain": [ " temperature windspeed event\n", "day \n", "2017-01-01 32.0 6.0 Rain\n", "2017-01-04 0.0 9.0 Sunny\n", "2017-01-05 28.0 0.0 Snow\n", "2017-01-06 0.0 7.0 0\n", "2017-01-07 32.0 0.0 Rain\n", "2017-01-08 0.0 0.0 Sunny\n", "2017-01-09 0.0 0.0 0\n", "2017-01-10 34.0 8.0 Cloudy\n", "2017-01-11 40.0 12.0 Sunny" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "new_df = df.fillna(0)\n", "new_df" ] }, { "cell_type": "code", "execution_count": 10, "id": "4f61e387", "metadata": {}, "outputs": [ { "data": { "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", "
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
\n", "
" ], "text/plain": [ " temperature windspeed event\n", "day \n", "2017-01-01 32.0 6.0 Rain\n", "2017-01-04 0.0 9.0 Sunny\n", "2017-01-05 28.0 0.0 Snow\n", "2017-01-06 0.0 7.0 No Event\n", "2017-01-07 32.0 0.0 Rain\n", "2017-01-08 0.0 0.0 Sunny\n", "2017-01-09 0.0 0.0 No Event\n", "2017-01-10 34.0 8.0 Cloudy\n", "2017-01-11 40.0 12.0 Sunny" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "new_df = df.fillna({\n", " 'temperature': 0,\n", " 'windspeed': 0,\n", " 'event': 'No Event'\n", "})\n", "\n", "new_df" ] }, { "cell_type": "markdown", "id": "dbddb92f", "metadata": {}, "source": [ "## forward fill" ] }, { "cell_type": "code", "execution_count": 12, "id": "629fbc00", "metadata": {}, "outputs": [ { "data": { "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", "
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", "
" ], "text/plain": [ " temperature windspeed event\n", "day \n", "2017-01-01 32.0 6.0 Rain\n", "2017-01-04 32.0 9.0 Sunny\n", "2017-01-05 28.0 9.0 Snow\n", "2017-01-06 28.0 7.0 Snow\n", "2017-01-07 32.0 7.0 Rain\n", "2017-01-08 32.0 7.0 Sunny\n", "2017-01-09 32.0 7.0 Sunny\n", "2017-01-10 34.0 8.0 Cloudy\n", "2017-01-11 40.0 12.0 Sunny" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "new_df = df.fillna(method='ffill')\n", "\n", "new_df" ] }, { "cell_type": "markdown", "id": "706c34ab", "metadata": {}, "source": [ "## backward fill" ] }, { "cell_type": "code", "execution_count": 13, "id": "31913361", "metadata": {}, "outputs": [ { "data": { "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", "
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", "
" ], "text/plain": [ " temperature windspeed event\n", "day \n", "2017-01-01 32.0 6.0 Rain\n", "2017-01-04 28.0 9.0 Sunny\n", "2017-01-05 28.0 7.0 Snow\n", "2017-01-06 32.0 7.0 Rain\n", "2017-01-07 32.0 8.0 Rain\n", "2017-01-08 34.0 8.0 Sunny\n", "2017-01-09 34.0 8.0 Cloudy\n", "2017-01-10 34.0 8.0 Cloudy\n", "2017-01-11 40.0 12.0 Sunny" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "new_df = df.fillna(method='bfill')\n", "\n", "new_df" ] }, { "cell_type": "code", "execution_count": 14, "id": "e5b32ea5", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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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
\n", "
" ], "text/plain": [ " temperature windspeed event\n", "day \n", "2017-01-01 32.0 6.0 Rain\n", "2017-01-04 NaN 9.0 Sunny\n", "2017-01-05 28.0 NaN Snow\n", "2017-01-06 NaN 7.0 NaN\n", "2017-01-07 32.0 NaN Rain\n", "2017-01-08 NaN NaN Sunny\n", "2017-01-09 NaN NaN NaN\n", "2017-01-10 34.0 8.0 Cloudy\n", "2017-01-11 40.0 12.0 Sunny" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df" ] }, { "cell_type": "markdown", "id": "13e839db", "metadata": {}, "source": [ "## Interpolate" ] }, { "cell_type": "code", "execution_count": 15, "id": "df4a0b3b", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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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
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2017-01-0429.0000009.00Sunny
2017-01-0528.0000008.00Snow
2017-01-0630.0000007.00NaN
2017-01-0732.0000007.25Rain
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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
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temperaturewindspeedevent
day
2017-01-0132.06.0Rain
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temperaturewindspeedevent
day
2017-01-0132.06.0Rain
2017-01-04NaN9.0Sunny
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temperaturewindspeedevent
day
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temperaturewindspeedevent
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2017-01-04NaN9.0Sunny
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temperaturewindspeedevent
2017-01-0132.06.0Rain
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2017-01-03NaNNaNNaN
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daytemperaturewindspeedevent
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daytemperaturewindspeedevent
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11/2/2017NaN7.0Sunny
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daytemperaturewindspeedevent
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daytemperaturewindspeedevent
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11/2/2017NaN7.0Sunny
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daytemperaturewindspeedevent
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daytemperaturewindspeedevent
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61/6/201734.05.00
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" ], "text/plain": [ " day temperature windspeed event\n", "0 1/1/2017 32.0 6.0 Sunny\n", "1 1/2/2017 NaN 7.0 Sunny\n", "2 1/3/2017 28.0 NaN Snow\n", "3 1/4/2017 NaN 7.0 0\n", "4 1/5/2017 32.0 NaN Sunny\n", "5 1/6/2017 31.0 2.0 Sunny\n", "6 1/6/2017 34.0 5.0 0" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "new_df = df.replace({\n", " -99999: np.nan,\n", " 'Rain': 'Sunny'\n", "})\n", "\n", "new_df" ] }, { "cell_type": "markdown", "id": "891181f3", "metadata": {}, "source": [ "## RegEx\n", "\n", "Regular Expression" ] }, { "cell_type": "code", "execution_count": null, "id": "32f1c97e", "metadata": {}, "outputs": [], "source": [ "y = mx + c" ] }, { "cell_type": "code", "execution_count": 30, "id": "a5f09b2b", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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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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" ], "text/plain": [ " day temperature windspeed event\n", "0 1/1/2017 32 6 Rain\n", "1 1/2/2017 -99999 7 Sunny\n", "2 1/3/2017 28 -99999 Snow\n", "3 1/4/2017 -99999 7 0\n", "4 1/5/2017 32 -99999 Rain\n", "5 1/6/2017 31 2 Sunny\n", "6 1/6/2017 34 5 0" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df" ] }, { "cell_type": "code", "execution_count": null, "id": "2cc501a3", "metadata": {}, "outputs": [], "source": [ "45km - 45 \n", "45k\n", "45kmp\n", "45kmph\n", "45kph\n", "45" ] }, { "cell_type": "code", "execution_count": 31, "id": "22efab8c", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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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
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" ], "text/plain": [ " day temperature windspeed event\n", "0 1/1/2017 32 6 R\n", "1 1/2/2017 -99999 7 S\n", "2 1/3/2017 28 -99999 S\n", "3 1/4/2017 -99999 7 0\n", "4 1/5/2017 32 -99999 R\n", "5 1/6/2017 31 2 S\n", "6 1/6/2017 34 5 0" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "new_df = df.replace({\n", " 'temperature': '[A-Za-z]',\n", " 'event': '[a-z]'\n", "}, '', regex=True)\n", "\n", "new_df" ] }, { "cell_type": "code", "execution_count": 32, "id": "102ce591", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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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", "
" ], "text/plain": [ " day temperature windspeed event speed\n", "0 01-01-2017 32C 6 Rain 45kmph\n", "1 01-02-2017 -99999 7 Sunny 40km\n", "2 01-03-2017 28F -99999 Snow 30kmp\n", "3 01-04-2017 -99999 7 0 56kmph\n", "4 01-05-2017 32C -99999 Rain 65\n", "5 01-06-2017 31c 2 Sunny 76\n", "6 01-06-2017 34f 5 0 80k" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_csv('weather_datamissing_regex.csv')\n", "\n", "df" ] }, { "cell_type": "code", "execution_count": null, "id": "65f7bcb5", "metadata": {}, "outputs": [], "source": [ "# '[!@#$%^&]'" ] }, { "cell_type": "code", "execution_count": 34, "id": "854610ff", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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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", "
" ], "text/plain": [ " day temperature windspeed event speed\n", "0 01-01-2017 32 6 Rain 45\n", "1 01-02-2017 -99999 7 Sunny 40\n", "2 01-03-2017 28 -99999 Snow 30\n", "3 01-04-2017 -99999 7 0 56\n", "4 01-05-2017 32 -99999 Rain 65\n", "5 01-06-2017 31 2 Sunny 76\n", "6 01-06-2017 34 5 0 80" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "new_df = df.replace({\n", " 'temperature': '[CcFf]',\n", " 'speed': '[kmph]'\n", "}, '', regex=True)\n", "\n", "new_df" ] }, { "cell_type": "code", "execution_count": 36, "id": "8b919c4a", "metadata": {}, "outputs": [], "source": [ "# new_df = df.replace({\n", "# 'temperature': '[-100000-0]',\n", "# 'windspeed': '[-100000-0]'\n", "# }, '', regex=True)\n", "\n", "# new_df" ] }, { "cell_type": "markdown", "id": "cfbe57f1", "metadata": {}, "source": [ "# Group By" ] }, { "cell_type": "code", "execution_count": 37, "id": "5e885561", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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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", "
" ], "text/plain": [ " day city temperature windspeed event\n", "0 1/1/2017 new york 32 6 Rain\n", "1 1/2/2017 new york 36 7 Sunny\n", "2 1/3/2017 new york 28 12 Snow\n", "3 1/4/2017 new york 33 7 Sunny\n", "4 1/1/2017 mumbai 90 5 Sunny\n", "5 1/2/2017 mumbai 85 12 Fog\n", "6 1/3/2017 mumbai 87 15 Fog\n", "7 1/4/2017 mumbai 92 5 Rain\n", "8 1/1/2017 paris 45 20 Sunny\n", "9 1/2/2017 paris 50 13 Cloudy\n", "10 1/3/2017 paris 54 8 Cloudy\n", "11 1/4/2017 paris 42 10 Cloudy" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_csv('weather_by_cities.csv')\n", "df" ] }, { "cell_type": "code", "execution_count": 38, "id": "c9952665", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gb = df.groupby('city')\n", "gb" ] }, { "cell_type": "code", "execution_count": 39, "id": "517be304", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "City: mumbai\n", "\n", "Data: \n", " day city temperature windspeed event\n", "4 1/1/2017 mumbai 90 5 Sunny\n", "5 1/2/2017 mumbai 85 12 Fog\n", "6 1/3/2017 mumbai 87 15 Fog\n", "7 1/4/2017 mumbai 92 5 Rain\n", "\n", "City: new york\n", "\n", "Data: \n", " day city temperature windspeed event\n", "0 1/1/2017 new york 32 6 Rain\n", "1 1/2/2017 new york 36 7 Sunny\n", "2 1/3/2017 new york 28 12 Snow\n", "3 1/4/2017 new york 33 7 Sunny\n", "\n", "City: paris\n", "\n", "Data: \n", " day city temperature windspeed event\n", "8 1/1/2017 paris 45 20 Sunny\n", "9 1/2/2017 paris 50 13 Cloudy\n", "10 1/3/2017 paris 54 8 Cloudy\n", "11 1/4/2017 paris 42 10 Cloudy\n" ] } ], "source": [ "for city, data in gb:\n", " print()\n", " print(\"City: \", city)\n", " print()\n", " print(\"Data: \\n\", data)" ] }, { "cell_type": "code", "execution_count": 40, "id": "f09dcab9", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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daycitytemperaturewindspeedevent
41/1/2017mumbai905Sunny
51/2/2017mumbai8512Fog
61/3/2017mumbai8715Fog
71/4/2017mumbai925Rain
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" ], "text/plain": [ " day city temperature windspeed event\n", "4 1/1/2017 mumbai 90 5 Sunny\n", "5 1/2/2017 mumbai 85 12 Fog\n", "6 1/3/2017 mumbai 87 15 Fog\n", "7 1/4/2017 mumbai 92 5 Rain" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gb.get_group('mumbai')" ] }, { "cell_type": "code", "execution_count": 41, "id": "b8c88700", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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daycitytemperaturewindspeedevent
81/1/2017paris4520Sunny
91/2/2017paris5013Cloudy
101/3/2017paris548Cloudy
111/4/2017paris4210Cloudy
\n", "
" ], "text/plain": [ " day city temperature windspeed event\n", "8 1/1/2017 paris 45 20 Sunny\n", "9 1/2/2017 paris 50 13 Cloudy\n", "10 1/3/2017 paris 54 8 Cloudy\n", "11 1/4/2017 paris 42 10 Cloudy" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gb.get_group('paris')" ] }, { "cell_type": "code", "execution_count": 42, "id": "e4883217", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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temperaturewindspeed
city
mumbai88.509.25
new york32.258.00
paris47.7512.75
\n", "
" ], "text/plain": [ " temperature windspeed\n", "city \n", "mumbai 88.50 9.25\n", "new york 32.25 8.00\n", "paris 47.75 12.75" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gb.mean()" ] }, { "cell_type": "code", "execution_count": 43, "id": "fcab2c81", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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daytemperaturewindspeedevent
city
mumbai1/4/20179215Sunny
new york1/4/20173612Sunny
paris1/4/20175420Sunny
\n", "
" ], "text/plain": [ " day temperature windspeed event\n", "city \n", "mumbai 1/4/2017 92 15 Sunny\n", "new york 1/4/2017 36 12 Sunny\n", "paris 1/4/2017 54 20 Sunny" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gb.max()" ] }, { "cell_type": "code", "execution_count": 44, "id": "0ad8cca3", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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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", "
" ], "text/plain": [ " temperature \\\n", " count mean std min 25% 50% 75% max \n", "city \n", "mumbai 4.0 88.50 3.109126 85.0 86.50 88.5 90.50 92.0 \n", "new york 4.0 32.25 3.304038 28.0 31.00 32.5 33.75 36.0 \n", "paris 4.0 47.75 5.315073 42.0 44.25 47.5 51.00 54.0 \n", "\n", " windspeed \n", " count mean std min 25% 50% 75% max \n", "city \n", "mumbai 4.0 9.25 5.057997 5.0 5.00 8.5 12.75 15.0 \n", "new york 4.0 8.00 2.708013 6.0 6.75 7.0 8.25 12.0 \n", "paris 4.0 12.75 5.251984 8.0 9.50 11.5 14.75 20.0 " ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gb.describe()" ] }, { "cell_type": "code", "execution_count": 45, "id": "559ef865", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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daytemperaturewindspeedevent
city
mumbai1/1/2017855Fog
new york1/1/2017286Rain
paris1/1/2017428Cloudy
\n", "
" ], "text/plain": [ " day temperature windspeed event\n", "city \n", "mumbai 1/1/2017 85 5 Fog\n", "new york 1/1/2017 28 6 Rain\n", "paris 1/1/2017 42 8 Cloudy" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gb.min()" ] }, { "cell_type": "code", "execution_count": 46, "id": "d36da53e", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "city\n", "mumbai 4\n", "new york 4\n", "paris 4\n", "dtype: int64" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gb.size()" ] }, { "cell_type": "code", "execution_count": 47, "id": "0bad1050", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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daytemperaturewindspeedevent
city
mumbai4444
new york4444
paris4444
\n", "
" ], "text/plain": [ " day temperature windspeed event\n", "city \n", "mumbai 4 4 4 4\n", "new york 4 4 4 4\n", "paris 4 4 4 4" ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gb.count()" ] }, { "cell_type": "markdown", "id": "b794dff7", "metadata": {}, "source": [ "## concatenation/combine" ] }, { "cell_type": "code", "execution_count": 48, "id": "7c28637d", "metadata": {}, "outputs": [ { "data": { "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", "
citytemperaturehumidity
0mumbai3280
1delhi4560
2banglore3078
\n", "
" ], "text/plain": [ " city temperature humidity\n", "0 mumbai 32 80\n", "1 delhi 45 60\n", "2 banglore 30 78" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "india_weather = pd.DataFrame({\n", " \"city\": ['mumbai', 'delhi', 'banglore'],\n", " \"temperature\": [32,45,30],\n", " \"humidity\": [80,60,78]\n", "})\n", "\n", "india_weather" ] }, { "cell_type": "code", "execution_count": 49, "id": "7388747e", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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citytemperaturehumidity
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" ], "text/plain": [ " city temperature humidity\n", "0 new york 21 68\n", "1 chicago 14 65\n", "2 orlando 35 75" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "us_weather = pd.DataFrame({\n", " \"city\": ['new york', 'chicago', 'orlando'],\n", " \"temperature\": [21,14,35],\n", " \"humidity\": [68,65,75]\n", "})\n", "\n", "us_weather" ] }, { "cell_type": "code", "execution_count": 50, "id": "aa5817cd", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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citytemperaturehumidity
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" ], "text/plain": [ " city temperature humidity\n", "0 mumbai 32 80\n", "1 delhi 45 60\n", "2 banglore 30 78\n", "0 new york 21 68\n", "1 chicago 14 65\n", "2 orlando 35 75" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.concat([india_weather, us_weather])\n", "\n", "df" ] }, { "cell_type": "code", "execution_count": 51, "id": "f483201b", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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citytemperaturehumidity
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" ], "text/plain": [ " city temperature humidity\n", "0 mumbai 32 80\n", "1 delhi 45 60\n", "2 banglore 30 78\n", "3 new york 21 68\n", "4 chicago 14 65\n", "5 orlando 35 75" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.concat([india_weather, us_weather], ignore_index=True)\n", "\n", "df" ] }, { "cell_type": "code", "execution_count": 52, "id": "bb3ef14f", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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citytemperaturehumidity
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" ], "text/plain": [ " city temperature humidity\n", "india 0 mumbai 32 80\n", " 1 delhi 45 60\n", " 2 banglore 30 78\n", "us 0 new york 21 68\n", " 1 chicago 14 65\n", " 2 orlando 35 75" ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.concat([india_weather, us_weather], keys=['india', 'us'])\n", "\n", "df" ] }, { "cell_type": "code", "execution_count": 53, "id": "0c75d41f", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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citytemperaturehumidity
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" ], "text/plain": [ " city temperature humidity\n", "0 new york 21 68\n", "1 chicago 14 65\n", "2 orlando 35 75" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.loc['us']" ] }, { "cell_type": "code", "execution_count": 54, "id": "bc932452", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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citytemperaturehumiditycitytemperaturehumidity
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" ], "text/plain": [ " city temperature humidity city temperature humidity\n", "0 mumbai 32 80 new york 21 68\n", "1 delhi 45 60 chicago 14 65\n", "2 banglore 30 78 orlando 35 75" ] }, "execution_count": 54, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.concat([india_weather, us_weather], axis=1) # column wise\n", "\n", "df" ] }, { "cell_type": "code", "execution_count": 55, "id": "eaaf6a69", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 Humid\n", "1 Dry\n", "2 Rain\n", "Name: event, dtype: object" ] }, "execution_count": 55, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s = pd.Series(['Humid', 'Dry', 'Rain'], name='event')\n", "\n", "s" ] }, { "cell_type": "code", "execution_count": 56, "id": "36063710", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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citytemperaturehumidityevent
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" ], "text/plain": [ " city temperature humidity event\n", "0 mumbai 32 80 Humid\n", "1 delhi 45 60 Dry\n", "2 banglore 30 78 Rain" ] }, "execution_count": 56, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.concat([india_weather, s], axis=1)\n", "\n", "df" ] }, { "cell_type": "code", "execution_count": null, "id": "346ab668", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.12" } }, "nbformat": 4, "nbformat_minor": 5 } ================================================ FILE: March Cohort 2023/8_9_Pandas/8_9_Pandas.ipynb ================================================ { "cells": [ { "cell_type": "markdown", "id": "a6a858e0", "metadata": {}, "source": [ "# Pandas" ] }, { "cell_type": "code", "execution_count": 1, "id": "848d64c8", "metadata": {}, "outputs": [], "source": [ "# !pip install pandas" ] }, { "cell_type": "code", "execution_count": 2, "id": "eabb0abf", "metadata": {}, "outputs": [], "source": [ "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 3, "id": "4e949120", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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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
\n", "
" ], "text/plain": [ " EST Temperature DewPoint Humidity Sea Level PressureIn \\\n", "0 1/1/2016 38 23 52 30.03 \n", "1 1/2/2016 36 18 46 30.02 \n", "2 1/3/2016 40 21 47 29.86 \n", "3 1/4/2016 25 9 44 30.05 \n", "4 1/5/2016 20 -3 41 30.57 \n", "5 1/6/2016 33 4 35 30.50 \n", "6 1/7/2016 39 11 33 30.28 \n", "7 1/8/2016 39 29 64 30.20 \n", "8 1/9/2016 44 38 77 30.16 \n", "9 1/10/2016 50 46 71 29.59 \n", "10 1/11/2016 33 8 37 29.92 \n", "11 1/12/2016 35 15 53 29.85 \n", "12 1/13/2016 26 4 42 29.94 \n", "13 1/14/2016 30 12 47 29.95 \n", "14 1/15/2016 43 31 62 29.82 \n", "15 1/16/2016 47 37 70 29.52 \n", "16 1/17/2016 36 23 66 29.78 \n", "17 1/18/2016 25 6 53 29.83 \n", "18 1/19/2016 22 3 42 30.03 \n", "19 1/20/2016 32 15 49 30.13 \n", "20 1/21/2016 31 11 45 30.15 \n", "21 1/22/2016 26 6 41 30.21 \n", "22 1/23/2016 26 21 78 29.77 \n", "23 1/24/2016 28 11 53 29.92 \n", "24 1/25/2016 34 18 54 30.25 \n", "25 1/26/2016 43 29 56 30.03 \n", "26 1/27/2016 41 22 45 30.03 \n", "27 1/28/2016 37 20 51 29.90 \n", "28 1/29/2016 36 21 50 29.58 \n", "29 1/30/2016 34 16 46 30.01 \n", "30 1/31/2016 46 28 52 29.90 \n", "\n", " VisibilityMiles WindSpeedMPH PrecipitationIn CloudCover Events \\\n", "0 10 8.0 0 5 NaN \n", "1 10 7.0 0 3 NaN \n", "2 10 8.0 0 1 NaN \n", "3 10 9.0 0 3 NaN \n", "4 10 5.0 0 0 NaN \n", "5 10 4.0 0 0 NaN \n", "6 10 2.0 0 3 NaN \n", "7 10 4.0 0 8 NaN \n", "8 9 8.0 T 8 Rain \n", "9 4 NaN 1.8 7 Rain \n", "10 10 NaN 0 1 NaN \n", "11 10 6.0 T 4 NaN \n", "12 10 10.0 0 0 NaN \n", "13 10 5.0 T 7 NaN \n", "14 9 5.0 T 2 NaN \n", "15 8 7.0 0.24 7 Rain \n", "16 8 6.0 0.05 6 Fog-Snow \n", "17 9 12.0 T 2 Snow \n", "18 10 11.0 0 1 NaN \n", "19 10 6.0 0 2 NaN \n", "20 10 6.0 0 1 NaN \n", "21 9 NaN 0.01 3 Snow \n", "22 1 16.0 2.31 8 Fog-Snow \n", "23 8 6.0 T 3 Snow \n", "24 10 3.0 0 2 NaN \n", "25 10 7.0 0 2 NaN \n", "26 10 7.0 T 3 Rain \n", "27 10 5.0 0 1 NaN \n", "28 10 8.0 0 4 NaN \n", "29 10 7.0 0 0 NaN \n", "30 10 5.0 0 0 NaN \n", "\n", " WindDirDegrees \n", "0 281 \n", "1 275 \n", "2 277 \n", "3 345 \n", "4 333 \n", "5 259 \n", "6 293 \n", "7 79 \n", "8 76 \n", "9 109 \n", "10 289 \n", "11 235 \n", "12 284 \n", "13 266 \n", "14 101 \n", "15 340 \n", "16 345 \n", "17 293 \n", "18 293 \n", "19 302 \n", "20 312 \n", "21 34 \n", "22 42 \n", "23 327 \n", "24 286 \n", "25 244 \n", "26 311 \n", "27 234 \n", "28 298 \n", "29 257 \n", "30 241 " ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_csv('nyc_weather.csv')\n", "df" ] }, { "cell_type": "code", "execution_count": 4, "id": "a345f8d9", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "pandas.core.frame.DataFrame" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(df)" ] }, { "cell_type": "code", "execution_count": 5, "id": "debc5617", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 38\n", "1 36\n", "2 40\n", "3 25\n", "4 20\n", "5 33\n", "6 39\n", "7 39\n", "8 44\n", "9 50\n", "10 33\n", "11 35\n", "12 26\n", "13 30\n", "14 43\n", "15 47\n", "16 36\n", "17 25\n", "18 22\n", "19 32\n", "20 31\n", "21 26\n", "22 26\n", "23 28\n", "24 34\n", "25 43\n", "26 41\n", "27 37\n", "28 36\n", "29 34\n", "30 46\n", "Name: Temperature, dtype: int64" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['Temperature']" ] }, { "cell_type": "code", "execution_count": 6, "id": "02f6142e", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " Temperature\n", "0 38\n", "1 36\n", "2 40\n", "3 25\n", "4 20\n", "5 33\n", "6 39\n", "7 39\n", "8 44\n", "9 50\n", "10 33\n", "11 35\n", "12 26\n", "13 30\n", "14 43\n", "15 47\n", "16 36\n", "17 25\n", "18 22\n", "19 32\n", "20 31\n", "21 26\n", "22 26\n", "23 28\n", "24 34\n", "25 43\n", "26 41\n", "27 37\n", "28 36\n", "29 34\n", "30 46" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[['Temperature']]" ] }, { "cell_type": "code", "execution_count": 7, "id": "28cf94ea", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "pandas.core.series.Series" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(df['Temperature'])" ] }, { "cell_type": "code", "execution_count": 8, "id": "ef48c13e", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "38" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['Temperature'][0]" ] }, { "cell_type": "code", "execution_count": 9, "id": "147b71a5", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "numpy.int64" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(df['Temperature'][0])" ] }, { "cell_type": "code", "execution_count": 10, "id": "b20d7b6b", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "50" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['Temperature'].max()" ] }, { "cell_type": "code", "execution_count": 11, "id": "4257b3db", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "6.892857142857143" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['WindSpeedMPH'].mean()" ] }, { "cell_type": "markdown", "id": "2c0372ce", "metadata": {}, "source": [ "# create a dataframe" ] }, { "cell_type": "code", "execution_count": 12, "id": "fb886d1f", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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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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" ], "text/plain": [ " day temperature windspeed event\n", "0 1/1/2023 32 6 Rain\n", "1 1/2/2023 35 7 Sunny\n", "2 1/3/2023 28 2 Snow\n", "3 1/4/2023 24 4 Rain\n", "4 1/5/2023 32 2 Sunny\n", "5 1/6/2023 31 7 Snow" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "weather_data = {\n", " 'day': ['1/1/2023', '1/2/2023', '1/3/2023', '1/4/2023', '1/5/2023', '1/6/2023'],\n", " 'temperature': [32,35,28,24,32,31],\n", " 'windspeed': [6,7,2,4,2,7],\n", " 'event': ['Rain', 'Sunny', 'Snow','Rain', 'Sunny', 'Snow']\n", "}\n", "\n", "# int() float() str()\n", "\n", "df = pd.DataFrame(weather_data)\n", "df" ] }, { "cell_type": "code", "execution_count": 13, "id": "439c0c87", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(6, 4)" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.shape" ] }, { "cell_type": "code", "execution_count": 14, "id": "dc7b5d56", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "24" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.size" ] }, { "cell_type": "code", "execution_count": 15, "id": "bc2f37a1", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.ndim" ] }, { "cell_type": "markdown", "id": "1338a1f1", "metadata": {}, "source": [ "## Rows and slicing" ] }, { "cell_type": "code", "execution_count": 18, "id": "420f437e", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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daytemperaturewindspeedevent
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41/5/2023322Sunny
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daytemperaturewindspeedevent
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daytemperaturewindspeedevent
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dayevent
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" ], "text/plain": [ " day event\n", "1 1/2/2023 Sunny\n", "2 1/3/2023 Snow\n", "3 1/4/2023 Rain" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[1:4][['day','event']]\n", "# df[['day','event']]" ] }, { "cell_type": "code", "execution_count": 24, "id": "7a20fcd8", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\himan\\AppData\\Local\\Temp\\ipykernel_4888\\3638179523.py:1: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n", " df[0:4][['day', 'event']][ df['windspeed'] == 7 ]\n" ] }, { "data": { "text/html": [ "
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dayevent
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" ], "text/plain": [ " day event\n", "1 1/2/2023 Sunny" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[0:4][['day', 'event']][ df['windspeed'] == 7 ]" ] }, { "cell_type": "markdown", "id": "40e92db1", "metadata": {}, "source": [ "## columns" ] }, { "cell_type": "code", "execution_count": 25, "id": "d7aee0b3", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['day', 'temperature', 'windspeed', 'event'], dtype='object')" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.columns" ] }, { "cell_type": "code", "execution_count": 26, "id": "f1de7cc8", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 1/1/2023\n", "1 1/2/2023\n", "2 1/3/2023\n", "3 1/4/2023\n", "4 1/5/2023\n", "5 1/6/2023\n", "Name: day, dtype: object" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['day']" ] }, { "cell_type": "code", "execution_count": 27, "id": "cb240d85", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 1/1/2023\n", "1 1/2/2023\n", "2 1/3/2023\n", "3 1/4/2023\n", "4 1/5/2023\n", "5 1/6/2023\n", "Name: day, dtype: object" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.day" ] }, { "cell_type": "code", "execution_count": 28, "id": "89218468", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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daytemperature
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daytemperaturewindspeedevent
11/2/2023357Sunny
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" ], "text/plain": [ " day temperature windspeed event\n", "1 1/2/2023 35 7 Sunny" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[ df['temperature'] > 32 ]" ] }, { "cell_type": "code", "execution_count": 31, "id": "3c4f76c1", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1 1/2/2023\n", "Name: day, dtype: object" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['day'][ df['temperature'] == df['temperature'].max() ]" ] }, { "cell_type": "code", "execution_count": 32, "id": "3df17c62", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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daytemperaturewindspeedevent
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daytemperaturewindspeedevent
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temperaturewindspeed
count6.0000006.000000
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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" ], "text/plain": [ " temperature windspeed\n", "count 6.000000 6.000000\n", "mean 30.333333 4.666667\n", "std 3.829708 2.338090\n", "min 24.000000 2.000000\n", "25% 28.750000 2.500000\n", "50% 31.500000 5.000000\n", "75% 32.000000 6.750000\n", "max 35.000000 7.000000" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.describe() # statistics -> 1. descriptive 2. Inferencial" ] }, { "cell_type": "markdown", "id": "36024cec", "metadata": {}, "source": [ "## Operations on DataFrame" ] }, { "cell_type": "code", "execution_count": 38, "id": "e173bf83", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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daytemperaturewindspeedevent
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temperaturewindspeedevent
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" ], "text/plain": [ " day temperature windspeed event\n", "0 1/1/2023 32 6 Rain\n", "1 1/2/2023 35 7 Sunny\n", "2 1/3/2023 28 2 Snow\n", "3 1/4/2023 24 4 Rain\n", "4 1/5/2023 32 2 Sunny\n", "5 1/6/2023 31 7 Snow" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df" ] }, { "cell_type": "code", "execution_count": 41, "id": "1d473fbf", "metadata": {}, "outputs": [], "source": [ "df.set_index('day', inplace=True)" ] }, { "cell_type": "code", "execution_count": 42, "id": "c574358f", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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temperaturewindspeedevent
day
1/1/2023326Rain
1/2/2023357Sunny
1/3/2023282Snow
1/4/2023244Rain
1/5/2023322Sunny
1/6/2023317Snow
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" ], "text/plain": [ " temperature windspeed event\n", "day \n", "1/1/2023 32 6 Rain\n", "1/2/2023 35 7 Sunny\n", "1/3/2023 28 2 Snow\n", "1/4/2023 24 4 Rain\n", "1/5/2023 32 2 Sunny\n", "1/6/2023 31 7 Snow" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df" ] }, { "cell_type": "code", "execution_count": 43, "id": "5dd5ae26", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['1/1/2023', '1/2/2023', '1/3/2023', '1/4/2023', '1/5/2023', '1/6/2023'], dtype='object', name='day')" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.index" ] }, { "cell_type": "code", "execution_count": 44, "id": "3b7212dc", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "temperature 24\n", "windspeed 4\n", "event Rain\n", "Name: 1/4/2023, dtype: object" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.loc['1/4/2023']" ] }, { "cell_type": "code", "execution_count": 45, "id": "1ce25565", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "pandas.core.series.Series" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(df.loc['1/4/2023'])" ] }, { "cell_type": "code", "execution_count": 46, "id": "fec38811", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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temperaturewindspeedevent
day
1/1/2023326Rain
1/2/2023357Sunny
1/3/2023282Snow
1/4/2023244Rain
1/5/2023322Sunny
1/6/2023317Snow
\n", "
" ], "text/plain": [ " temperature windspeed event\n", "day \n", "1/1/2023 32 6 Rain\n", "1/2/2023 35 7 Sunny\n", "1/3/2023 28 2 Snow\n", "1/4/2023 24 4 Rain\n", "1/5/2023 32 2 Sunny\n", "1/6/2023 31 7 Snow" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df" ] }, { "cell_type": "code", "execution_count": 47, "id": "c08dca3e", "metadata": {}, "outputs": [], "source": [ "df.reset_index(inplace=True)" ] }, { "cell_type": "code", "execution_count": 48, "id": "1d75ebd4", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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daytemperaturewindspeedevent
01/1/2023326Rain
11/2/2023357Sunny
21/3/2023282Snow
31/4/2023244Rain
41/5/2023322Sunny
51/6/2023317Snow
\n", "
" ], "text/plain": [ " day temperature windspeed event\n", "0 1/1/2023 32 6 Rain\n", "1 1/2/2023 35 7 Sunny\n", "2 1/3/2023 28 2 Snow\n", "3 1/4/2023 24 4 Rain\n", "4 1/5/2023 32 2 Sunny\n", "5 1/6/2023 31 7 Snow" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df" ] }, { "cell_type": "code", "execution_count": 49, "id": "6a2cd4ba", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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daytemperaturewindspeed
event
Rain1/1/2023326
Sunny1/2/2023357
Snow1/3/2023282
Rain1/4/2023244
Sunny1/5/2023322
Snow1/6/2023317
\n", "
" ], "text/plain": [ " day temperature windspeed\n", "event \n", "Rain 1/1/2023 32 6\n", "Sunny 1/2/2023 35 7\n", "Snow 1/3/2023 28 2\n", "Rain 1/4/2023 24 4\n", "Sunny 1/5/2023 32 2\n", "Snow 1/6/2023 31 7" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.set_index('event', inplace=True)\n", "df" ] }, { "cell_type": "code", "execution_count": 50, "id": "84000505", "metadata": {}, "outputs": [ { "data": { "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", "
daytemperaturewindspeed
event
Sunny1/2/2023357
Sunny1/5/2023322
\n", "
" ], "text/plain": [ " day temperature windspeed\n", "event \n", "Sunny 1/2/2023 35 7\n", "Sunny 1/5/2023 32 2" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.loc['Sunny']" ] }, { "cell_type": "markdown", "id": "8c16b66d", "metadata": {}, "source": [ "# Different ways to create dataframe\n", "\n", "### using excel" ] }, { "cell_type": "code", "execution_count": 51, "id": "36d8a05a", "metadata": {}, "outputs": [ { "data": { "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", "
daytemperaturewindspeedevent
02017-01-01326Rain
12017-01-02357Sunny
22017-01-03282Snow
\n", "
" ], "text/plain": [ " day temperature windspeed event\n", "0 2017-01-01 32 6 Rain\n", "1 2017-01-02 35 7 Sunny\n", "2 2017-01-03 28 2 Snow" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_excel('weather_data.xlsx', 'Sheet1')\n", "df" ] }, { "cell_type": "markdown", "id": "4e1681da", "metadata": {}, "source": [ "### using tuples and list" ] }, { "cell_type": "code", "execution_count": 52, "id": "46ba1a56", "metadata": {}, "outputs": [ { "data": { "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", "
0123
01/1/2023326Rain
11/2/2023356Sunny
21/3/2023322Snow
\n", "
" ], "text/plain": [ " 0 1 2 3\n", "0 1/1/2023 32 6 Rain\n", "1 1/2/2023 35 6 Sunny\n", "2 1/3/2023 32 2 Snow" ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" } ], "source": [ "weather_data = [\n", " ('1/1/2023', 32, 6, 'Rain'),\n", " ('1/2/2023', 35, 6, 'Sunny'),\n", " ('1/3/2023', 32, 2, 'Snow')\n", "]\n", "\n", "df = pd.DataFrame(data=weather_data)\n", "\n", "df" ] }, { "cell_type": "code", "execution_count": 53, "id": "f6e5f754", "metadata": {}, "outputs": [ { "data": { "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", "
daytemperaturewindspeedevent
01/1/2023326Rain
11/2/2023356Sunny
21/3/2023322Snow
\n", "
" ], "text/plain": [ " day temperature windspeed event\n", "0 1/1/2023 32 6 Rain\n", "1 1/2/2023 35 6 Sunny\n", "2 1/3/2023 32 2 Snow" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" } ], "source": [ "weather_data = [\n", " ('1/1/2023', 32, 6, 'Rain'),\n", " ('1/2/2023', 35, 6, 'Sunny'),\n", " ('1/3/2023', 32, 2, 'Snow')\n", "]\n", "\n", "df = pd.DataFrame(data=weather_data, columns=['day', 'temperature', 'windspeed', 'event'])\n", "\n", "df" ] }, { "cell_type": "markdown", "id": "f2ed69e8", "metadata": {}, "source": [ "### list of dictionary" ] }, { "cell_type": "code", "execution_count": 54, "id": "588425b3", "metadata": {}, "outputs": [ { "data": { "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", "
daytemperaturewindspeedevent
01/1/2023326Rain
11/2/2023357Sunny
21/3/2023326Snow
\n", "
" ], "text/plain": [ " day temperature windspeed event\n", "0 1/1/2023 32 6 Rain\n", "1 1/2/2023 35 7 Sunny\n", "2 1/3/2023 32 6 Snow" ] }, "execution_count": 54, "metadata": {}, "output_type": "execute_result" } ], "source": [ "weather_data = [\n", " {'day': '1/1/2023', 'temperature': 32, 'windspeed': 6, 'event': 'Rain'},\n", " {'day': '1/2/2023', 'temperature': 35, 'windspeed': 7, 'event': 'Sunny'},\n", " {'day': '1/3/2023', 'temperature': 32, 'windspeed': 6, 'event': 'Snow'}\n", "]\n", "\n", "df = pd.DataFrame(data=weather_data)\n", "df" ] }, { "cell_type": "code", "execution_count": 55, "id": "2e6b7433", "metadata": {}, "outputs": [ { "data": { "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", "
daytemperaturewindspeedevent
01/1/2023326Rain
11/2/2023357Sunny
21/3/2023326Snow
\n", "
" ], "text/plain": [ " day temperature windspeed event\n", "0 1/1/2023 32 6 Rain\n", "1 1/2/2023 35 7 Sunny\n", "2 1/3/2023 32 6 Snow" ] }, "execution_count": 55, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df" ] }, { "cell_type": "markdown", "id": "5636c651", "metadata": {}, "source": [ "### write to csv" ] }, { "cell_type": "code", "execution_count": 56, "id": "49a42525", "metadata": {}, "outputs": [], "source": [ "df.to_csv('indore.csv', index=True)" ] }, { "cell_type": "markdown", "id": "2076e420", "metadata": {}, "source": [ "# Day 9 - Pandas day 2\n", "\n", "### Handling missing values" ] }, { "cell_type": "code", "execution_count": 1, "id": "0bcbb714", "metadata": {}, "outputs": [], "source": [ "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 3, "id": "f64c5df9", "metadata": { "scrolled": false }, "outputs": [], "source": [ "# help(pd.read_csv)" ] }, { "cell_type": "code", "execution_count": 4, "id": "8b9179c5", "metadata": {}, "outputs": [ { "data": { "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", "
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", "
" ], "text/plain": [ " day temperature windspeed event\n", "0 2017-01-01 32.0 6.0 Rain\n", "1 2017-01-04 NaN 9.0 Sunny\n", "2 2017-01-05 28.0 NaN Snow\n", "3 2017-01-06 NaN 7.0 NaN\n", "4 2017-01-07 32.0 NaN Rain\n", "5 2017-01-08 NaN NaN Sunny\n", "6 2017-01-09 NaN NaN NaN\n", "7 2017-01-10 34.0 8.0 Cloudy\n", "8 2017-01-11 40.0 12.0 Sunny" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_csv('weather_data2.csv', parse_dates=['day'])\n", "df" ] }, { "cell_type": "code", "execution_count": 5, "id": "ed579b5c", "metadata": {}, "outputs": [], "source": [ "import numpy as np" ] }, { "cell_type": "code", "execution_count": 6, "id": "a7814651", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "nan" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.nan # " ] }, { "cell_type": "code", "execution_count": 7, "id": "d96c216f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "print(type(df.day[0]))" ] }, { "cell_type": "code", "execution_count": 8, "id": "f6d09c15", "metadata": {}, "outputs": [ { "data": { "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", "
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", "
" ], "text/plain": [ " temperature windspeed event\n", "day \n", "2017-01-01 32.0 6.0 Rain\n", "2017-01-04 NaN 9.0 Sunny\n", "2017-01-05 28.0 NaN Snow\n", "2017-01-06 NaN 7.0 NaN\n", "2017-01-07 32.0 NaN Rain\n", "2017-01-08 NaN NaN Sunny\n", "2017-01-09 NaN NaN NaN\n", "2017-01-10 34.0 8.0 Cloudy\n", "2017-01-11 40.0 12.0 Sunny" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.set_index('day', inplace=True)\n", "df" ] }, { "cell_type": "markdown", "id": "c32e25ab", "metadata": {}, "source": [ "## fillna\n", "\n", "Fill Not Available" ] }, { "cell_type": "code", "execution_count": 9, "id": "6b9159d9", "metadata": {}, "outputs": [ { "data": { "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", "
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", "
" ], "text/plain": [ " temperature windspeed event\n", "day \n", "2017-01-01 32.0 6.0 Rain\n", "2017-01-04 0.0 9.0 Sunny\n", "2017-01-05 28.0 0.0 Snow\n", "2017-01-06 0.0 7.0 0\n", "2017-01-07 32.0 0.0 Rain\n", "2017-01-08 0.0 0.0 Sunny\n", "2017-01-09 0.0 0.0 0\n", "2017-01-10 34.0 8.0 Cloudy\n", "2017-01-11 40.0 12.0 Sunny" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "new_df = df.fillna(0)\n", "new_df" ] }, { "cell_type": "code", "execution_count": 10, "id": "4f61e387", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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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
\n", "
" ], "text/plain": [ " temperature windspeed event\n", "day \n", "2017-01-01 32.0 6.0 Rain\n", "2017-01-04 0.0 9.0 Sunny\n", "2017-01-05 28.0 0.0 Snow\n", "2017-01-06 0.0 7.0 No Event\n", "2017-01-07 32.0 0.0 Rain\n", "2017-01-08 0.0 0.0 Sunny\n", "2017-01-09 0.0 0.0 No Event\n", "2017-01-10 34.0 8.0 Cloudy\n", "2017-01-11 40.0 12.0 Sunny" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "new_df = df.fillna({\n", " 'temperature': 0,\n", " 'windspeed': 0,\n", " 'event': 'No Event'\n", "})\n", "\n", "new_df" ] }, { "cell_type": "markdown", "id": "dbddb92f", "metadata": {}, "source": [ "## forward fill" ] }, { "cell_type": "code", "execution_count": 12, "id": "629fbc00", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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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
2017-01-1140.012.0Sunny
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" ], "text/plain": [ " temperature windspeed event\n", "day \n", "2017-01-01 32.0 6.0 Rain\n", "2017-01-04 32.0 9.0 Sunny\n", "2017-01-05 28.0 9.0 Snow\n", "2017-01-06 28.0 7.0 Snow\n", "2017-01-07 32.0 7.0 Rain\n", "2017-01-08 32.0 7.0 Sunny\n", "2017-01-09 32.0 7.0 Sunny\n", "2017-01-10 34.0 8.0 Cloudy\n", "2017-01-11 40.0 12.0 Sunny" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "new_df = df.fillna(method='ffill')\n", "\n", "new_df" ] }, { "cell_type": "markdown", "id": "706c34ab", "metadata": {}, "source": [ "## backward fill" ] }, { "cell_type": "code", "execution_count": 13, "id": "31913361", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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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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" ], "text/plain": [ " temperature windspeed event\n", "day \n", "2017-01-01 32.0 6.0 Rain\n", "2017-01-04 28.0 9.0 Sunny\n", "2017-01-05 28.0 7.0 Snow\n", "2017-01-06 32.0 7.0 Rain\n", "2017-01-07 32.0 8.0 Rain\n", "2017-01-08 34.0 8.0 Sunny\n", "2017-01-09 34.0 8.0 Cloudy\n", "2017-01-10 34.0 8.0 Cloudy\n", "2017-01-11 40.0 12.0 Sunny" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "new_df = df.fillna(method='bfill')\n", "\n", "new_df" ] }, { "cell_type": "code", "execution_count": 14, "id": "e5b32ea5", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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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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" ], "text/plain": [ " temperature windspeed event\n", "day \n", "2017-01-01 32.0 6.0 Rain\n", "2017-01-04 NaN 9.0 Sunny\n", "2017-01-05 28.0 NaN Snow\n", "2017-01-06 NaN 7.0 NaN\n", "2017-01-07 32.0 NaN Rain\n", "2017-01-08 NaN NaN Sunny\n", "2017-01-09 NaN NaN NaN\n", "2017-01-10 34.0 8.0 Cloudy\n", "2017-01-11 40.0 12.0 Sunny" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df" ] }, { "cell_type": "markdown", "id": "13e839db", "metadata": {}, "source": [ "## Interpolate" ] }, { "cell_type": "code", "execution_count": 15, "id": "df4a0b3b", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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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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" ], "text/plain": [ " temperature windspeed event\n", "day \n", "2017-01-01 32.0 6.0 Rain\n", "2017-01-04 NaN 9.0 Sunny\n", "2017-01-05 28.0 NaN Snow\n", "2017-01-06 NaN 7.0 NaN\n", "2017-01-07 32.0 NaN Rain\n", "2017-01-08 NaN NaN Sunny\n", "2017-01-09 NaN NaN NaN\n", "2017-01-10 34.0 8.0 Cloudy\n", "2017-01-11 40.0 12.0 Sunny" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df" ] }, { "cell_type": "code", "execution_count": 18, "id": "f4c780fa", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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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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" ], "text/plain": [ " temperature windspeed event\n", "2017-01-01 32.0 6.0 Rain\n", "2017-01-02 NaN NaN NaN\n", "2017-01-03 NaN NaN NaN\n", "2017-01-04 NaN 9.0 Sunny\n", "2017-01-05 28.0 NaN Snow\n", "2017-01-06 NaN 7.0 NaN\n", "2017-01-07 32.0 NaN Rain\n", "2017-01-08 NaN NaN Sunny\n", "2017-01-09 NaN NaN NaN\n", "2017-01-10 34.0 8.0 Cloudy\n", "2017-01-11 40.0 12.0 Sunny" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "date = pd.date_range('01-01-2017','01-11-2017')\n", "\n", "idx = pd.DatetimeIndex(date)\n", "\n", "df.reindex(idx)" ] }, { "cell_type": "code", "execution_count": 23, "id": "2461bb6a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n" ] } ], "source": [ "print(type(date))" ] }, { "cell_type": "markdown", "id": "cd64da01", "metadata": {}, "source": [ "# replace data" ] }, { "cell_type": "code", "execution_count": 24, "id": "bb8a1c0f", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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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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" ], "text/plain": [ " day temperature windspeed event\n", "0 1/1/2017 32 6 Rain\n", "1 1/2/2017 -99999 7 Sunny\n", "2 1/3/2017 28 -99999 Snow\n", "3 1/4/2017 -99999 7 0\n", "4 1/5/2017 32 -99999 Rain\n", "5 1/6/2017 31 2 Sunny\n", "6 1/6/2017 34 5 0" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_csv('weather_datamissing.csv')\n", "df" ] }, { "cell_type": "code", "execution_count": 25, "id": "348da8ec", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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daytemperaturewindspeedevent
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11/2/2017NaN7.0Sunny
21/3/201728.0NaNSnow
31/4/2017NaN7.00
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" ], "text/plain": [ " day temperature windspeed event\n", "0 1/1/2017 32.0 6.0 Rain\n", "1 1/2/2017 NaN 7.0 Sunny\n", "2 1/3/2017 28.0 NaN Snow\n", "3 1/4/2017 NaN 7.0 0\n", "4 1/5/2017 32.0 NaN Rain\n", "5 1/6/2017 31.0 2.0 Sunny\n", "6 1/6/2017 34.0 5.0 0" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "new_df = df.replace(-99999, value=np.nan)\n", "\n", "new_df" ] }, { "cell_type": "code", "execution_count": 26, "id": "9719843c", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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daytemperaturewindspeedevent
01/1/2017326Rain
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41/5/2017320Rain
51/6/2017312Sunny
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" ], "text/plain": [ " day temperature windspeed event\n", "0 1/1/2017 32 6 Rain\n", "1 1/2/2017 0 7 Sunny\n", "2 1/3/2017 28 0 Snow\n", "3 1/4/2017 0 7 0\n", "4 1/5/2017 32 0 Rain\n", "5 1/6/2017 31 2 Sunny\n", "6 1/6/2017 34 5 0" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "new_df = df.replace(to_replace=[-99999, -88888], value = 0)\n", "\n", "new_df" ] }, { "cell_type": "code", "execution_count": 27, "id": "e5e3aa7b", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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daytemperaturewindspeedevent
01/1/201732.06.0Rain
11/2/2017NaN7.0Sunny
21/3/201728.0NaNSnow
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" ], "text/plain": [ " day temperature windspeed event\n", "0 1/1/2017 32.0 6.0 Rain\n", "1 1/2/2017 NaN 7.0 Sunny\n", "2 1/3/2017 28.0 NaN Snow\n", "3 1/4/2017 NaN 7.0 NaN\n", "4 1/5/2017 32.0 NaN Rain\n", "5 1/6/2017 31.0 2.0 Sunny\n", "6 1/6/2017 34.0 5.0 NaN" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "new_df = df.replace({\n", " 'temperature': -99999,\n", " 'windspeed': -99999,\n", " 'event': '0'\n", "}, np.nan)\n", "\n", "new_df" ] }, { "cell_type": "code", "execution_count": 28, "id": "d920f96d", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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daytemperaturewindspeedevent
01/1/2017326Rain
11/2/2017-999997Sunny
21/3/201728-99999Snow
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daytemperaturewindspeedevent
01/1/201732.06.0Sunny
11/2/2017NaN7.0Sunny
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" ], "text/plain": [ " day temperature windspeed event\n", "0 1/1/2017 32.0 6.0 Sunny\n", "1 1/2/2017 NaN 7.0 Sunny\n", "2 1/3/2017 28.0 NaN Snow\n", "3 1/4/2017 NaN 7.0 0\n", "4 1/5/2017 32.0 NaN Sunny\n", "5 1/6/2017 31.0 2.0 Sunny\n", "6 1/6/2017 34.0 5.0 0" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "new_df = df.replace({\n", " -99999: np.nan,\n", " 'Rain': 'Sunny'\n", "})\n", "\n", "new_df" ] }, { "cell_type": "markdown", "id": "891181f3", "metadata": {}, "source": [ "## RegEx\n", "\n", "Regular Expression" ] }, { "cell_type": "code", "execution_count": null, "id": "32f1c97e", "metadata": {}, "outputs": [], "source": [ "y = mx + c" ] }, { "cell_type": "code", "execution_count": 30, "id": "a5f09b2b", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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daytemperaturewindspeedevent
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daytemperaturewindspeedevent
01/1/2017326R
11/2/2017-999997S
21/3/201728-99999S
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" ], "text/plain": [ " day temperature windspeed event\n", "0 1/1/2017 32 6 R\n", "1 1/2/2017 -99999 7 S\n", "2 1/3/2017 28 -99999 S\n", "3 1/4/2017 -99999 7 0\n", "4 1/5/2017 32 -99999 R\n", "5 1/6/2017 31 2 S\n", "6 1/6/2017 34 5 0" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "new_df = df.replace({\n", " 'temperature': '[A-Za-z]',\n", " 'event': '[a-z]'\n", "}, '', regex=True)\n", "\n", "new_df" ] }, { "cell_type": "code", "execution_count": 32, "id": "102ce591", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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daytemperaturewindspeedeventspeed
001-01-201732C6Rain45kmph
101-02-2017-999997Sunny40km
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401-05-201732C-99999Rain65
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daytemperaturewindspeedeventspeed
001-01-2017326Rain45
101-02-2017-999997Sunny40
201-03-201728-99999Snow30
301-04-2017-999997056
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501-06-2017312Sunny76
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" ], "text/plain": [ " day temperature windspeed event speed\n", "0 01-01-2017 32 6 Rain 45\n", "1 01-02-2017 -99999 7 Sunny 40\n", "2 01-03-2017 28 -99999 Snow 30\n", "3 01-04-2017 -99999 7 0 56\n", "4 01-05-2017 32 -99999 Rain 65\n", "5 01-06-2017 31 2 Sunny 76\n", "6 01-06-2017 34 5 0 80" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "new_df = df.replace({\n", " 'temperature': '[CcFf]',\n", " 'speed': '[kmph]'\n", "}, '', regex=True)\n", "\n", "new_df" ] }, { "cell_type": "code", "execution_count": 36, "id": "8b919c4a", "metadata": {}, "outputs": [], "source": [ "# new_df = df.replace({\n", "# 'temperature': '[-100000-0]',\n", "# 'windspeed': '[-100000-0]'\n", "# }, '', regex=True)\n", "\n", "# new_df" ] }, { "cell_type": "markdown", "id": "cfbe57f1", "metadata": {}, "source": [ "# Group By" ] }, { "cell_type": "code", "execution_count": 37, "id": "5e885561", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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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
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" ], "text/plain": [ " day city temperature windspeed event\n", "0 1/1/2017 new york 32 6 Rain\n", "1 1/2/2017 new york 36 7 Sunny\n", "2 1/3/2017 new york 28 12 Snow\n", "3 1/4/2017 new york 33 7 Sunny\n", "4 1/1/2017 mumbai 90 5 Sunny\n", "5 1/2/2017 mumbai 85 12 Fog\n", "6 1/3/2017 mumbai 87 15 Fog\n", "7 1/4/2017 mumbai 92 5 Rain\n", "8 1/1/2017 paris 45 20 Sunny\n", "9 1/2/2017 paris 50 13 Cloudy\n", "10 1/3/2017 paris 54 8 Cloudy\n", "11 1/4/2017 paris 42 10 Cloudy" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_csv('weather_by_cities.csv')\n", "df" ] }, { "cell_type": "code", "execution_count": 38, "id": "c9952665", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gb = df.groupby('city')\n", "gb" ] }, { "cell_type": "code", "execution_count": 39, "id": "517be304", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "City: mumbai\n", "\n", "Data: \n", " day city temperature windspeed event\n", "4 1/1/2017 mumbai 90 5 Sunny\n", "5 1/2/2017 mumbai 85 12 Fog\n", "6 1/3/2017 mumbai 87 15 Fog\n", "7 1/4/2017 mumbai 92 5 Rain\n", "\n", "City: new york\n", "\n", "Data: \n", " day city temperature windspeed event\n", "0 1/1/2017 new york 32 6 Rain\n", "1 1/2/2017 new york 36 7 Sunny\n", "2 1/3/2017 new york 28 12 Snow\n", "3 1/4/2017 new york 33 7 Sunny\n", "\n", "City: paris\n", "\n", "Data: \n", " day city temperature windspeed event\n", "8 1/1/2017 paris 45 20 Sunny\n", "9 1/2/2017 paris 50 13 Cloudy\n", "10 1/3/2017 paris 54 8 Cloudy\n", "11 1/4/2017 paris 42 10 Cloudy\n" ] } ], "source": [ "for city, data in gb:\n", " print()\n", " print(\"City: \", city)\n", " print()\n", " print(\"Data: \\n\", data)" ] }, { "cell_type": "code", "execution_count": 40, "id": "f09dcab9", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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daycitytemperaturewindspeedevent
41/1/2017mumbai905Sunny
51/2/2017mumbai8512Fog
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71/4/2017mumbai925Rain
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daycitytemperaturewindspeedevent
81/1/2017paris4520Sunny
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temperaturewindspeed
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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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daytemperaturewindspeedevent
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citytemperaturehumidity
0mumbai3280
1delhi4560
2banglore3078
\n", "
" ], "text/plain": [ " city temperature humidity\n", "0 mumbai 32 80\n", "1 delhi 45 60\n", "2 banglore 30 78" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "india_weather = pd.DataFrame({\n", " \"city\": ['mumbai', 'delhi', 'banglore'],\n", " \"temperature\": [32,45,30],\n", " \"humidity\": [80,60,78]\n", "})\n", "\n", "india_weather" ] }, { "cell_type": "code", "execution_count": 49, "id": "7388747e", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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citytemperaturehumidity
0new york2168
1chicago1465
2orlando3575
\n", "
" ], "text/plain": [ " city temperature humidity\n", "0 new york 21 68\n", "1 chicago 14 65\n", "2 orlando 35 75" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "us_weather = pd.DataFrame({\n", " \"city\": ['new york', 'chicago', 'orlando'],\n", " \"temperature\": [21,14,35],\n", " \"humidity\": [68,65,75]\n", "})\n", "\n", "us_weather" ] }, { "cell_type": "code", "execution_count": 50, "id": "aa5817cd", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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citytemperaturehumidity
0mumbai3280
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0new york2168
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2orlando3575
\n", "
" ], "text/plain": [ " city temperature humidity\n", "0 mumbai 32 80\n", "1 delhi 45 60\n", "2 banglore 30 78\n", "0 new york 21 68\n", "1 chicago 14 65\n", "2 orlando 35 75" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.concat([india_weather, us_weather])\n", "\n", "df" ] }, { "cell_type": "code", "execution_count": 51, "id": "f483201b", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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citytemperaturehumidity
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" ], "text/plain": [ " city temperature humidity\n", "0 mumbai 32 80\n", "1 delhi 45 60\n", "2 banglore 30 78\n", "3 new york 21 68\n", "4 chicago 14 65\n", "5 orlando 35 75" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.concat([india_weather, us_weather], ignore_index=True)\n", "\n", "df" ] }, { "cell_type": "code", "execution_count": 52, "id": "bb3ef14f", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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citytemperaturehumidity
india0mumbai3280
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us0new york2168
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\n", "
" ], "text/plain": [ " city temperature humidity\n", "india 0 mumbai 32 80\n", " 1 delhi 45 60\n", " 2 banglore 30 78\n", "us 0 new york 21 68\n", " 1 chicago 14 65\n", " 2 orlando 35 75" ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.concat([india_weather, us_weather], keys=['india', 'us'])\n", "\n", "df" ] }, { "cell_type": "code", "execution_count": 53, "id": "0c75d41f", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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citytemperaturehumiditycitytemperaturehumidity
0mumbai3280new york2168
1delhi4560chicago1465
2banglore3078orlando3575
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" ], "text/plain": [ " city temperature humidity city temperature humidity\n", "0 mumbai 32 80 new york 21 68\n", "1 delhi 45 60 chicago 14 65\n", "2 banglore 30 78 orlando 35 75" ] }, "execution_count": 54, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.concat([india_weather, us_weather], axis=1) # column wise\n", "\n", "df" ] }, { "cell_type": "code", "execution_count": 55, "id": "eaaf6a69", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 Humid\n", "1 Dry\n", "2 Rain\n", "Name: event, dtype: object" ] }, "execution_count": 55, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s = pd.Series(['Humid', 'Dry', 'Rain'], name='event')\n", "\n", "s" ] }, { "cell_type": "code", "execution_count": 56, "id": "36063710", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Data Cleaning with Python and Pandas: Detecting Missing Values

data cleaning with python
Data cleaning can be a tedious task.

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

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

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

data cleaning from ibm analytics

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.

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.

Click here to get the FREE Data Cleaning Cheat Sheet

Sources of Missing Values

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

  • User forgot to fill in a field.
  • Data was lost while transferring manually from a legacy database.
  • There was a programming error.
  • Users chose not to fill out a field tied to their beliefs about how the results would be used or interpreted.

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.

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.

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.

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.

Getting Started

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.

I like to start by asking the following questions:

  • What are the features?
  • What are the expected types (int, float, string, boolean)?
  • Is there obvious missing data (values that Pandas can detect)?
  • Is there other types of missing data that’s not so obvious (can’t easily detect with Pandas)?

To show you what I mean, let’s start working through the example.

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.

Here’s a quick look at the data:

real estate data

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.

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:

# Importing libraries
import pandas as pd
import numpy as np

# Read csv file into a pandas dataframe
df = pd.read_csv("property data.csv")

# Take a look at the first few rows
print df.head()

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Out:
   ST_NUM    ST_NAME OWN_OCCUPIED  NUM_BEDROOMS
0   104.0     PUTNAM            Y           3.0
1   197.0  LEXINGTON            N           3.0
2     NaN  LEXINGTON            N           3.0
3   201.0   BERKELEY          NaN           1.0
4   203.0   BERKELEY            Y           3.0

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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.

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

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

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

  • ST_NUM: Street number
  • ST_NAME: Street name
  • OWN_OCCUPIED: Is the residence owner occupied
  • NUM_BEDROOMS: Number of bedrooms

We can also answer, what are the expected types?

  • ST_NUM: float or int… some sort of numeric type
  • ST_NAME: string
  • OWN_OCCUPIED: string… Y (“Yes”) or N (“No”)
  • NUM_BEDROOMS: float or int, a numeric type

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

Standard Missing Values

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

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

standard missing values

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

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

# Looking at the ST_NUM column
print df['ST_NUM']
print df['ST_NUM'].isnull()

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# Looking at the ST_NUM column
Out:
0    104.0
1    197.0
2      NaN
3    201.0
4    203.0
5    207.0
6      NaN
7    213.0
8    215.0

Out:
0    False
1    False
2     True
3    False
4    False
5    False
6     True
7    False
8    False

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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.

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.

Non-Standard Missing Values

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

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

non-standard missing values

In this column, there’s four missing values.

  • n/a
  • NA
  • —
  • na

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.

# Looking at the NUM_BEDROOMS column
print df['NUM_BEDROOMS']
print df['NUM_BEDROOMS'].isnull()

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Out:
0      3
1      3
2    n/a
3      1
4      3
5    NaN
6      2
7     --
8     na

Out:
0    False
1    False
2    False
3    False
4    False
5     True
6    False
7    False
8    False

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Just like before, Pandas recognized the “NA” as a missing value. Unfortunately, the other types weren’t recognized.

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”.

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.

# Making a list of missing value types
missing_values = ["n/a", "na", "--"]
df = pd.read_csv("property data.csv", na_values = missing_values)

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Now let’s take another look at this column and see what happens.

# Looking at the NUM_BEDROOMS column
print df['NUM_BEDROOMS']
print df['NUM_BEDROOMS'].isnull()

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Out:
0    3.0
1    3.0
2    NaN
3    1.0
4    3.0
5    NaN
6    2.0
7    NaN
8    NaN

Out:
0    False
1    False
2     True
3    False
4    False
5     True
6    False
7     True
8     True

Copy

This time, all of the different formats were recognized as missing values.

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.

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.

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

Unexpected Missing Values

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

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.

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

unexpected missing values

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.

# Looking at the OWN_OCCUPIED column
print df['OWN_OCCUPIED']
print df['OWN_OCCUPIED'].isnull()

Copy
# Looking at the ST_NUM column
Out:
0      Y
1      N
2      N
3     12
4      Y
5      Y
6    NaN
7      Y
8      Y

Out:
0    False
1    False
2    False
3    False
4    False
5    False
6     True
7    False
8    False

Copy

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.

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.

  1. Loop through the OWN_OCCUPIED column
  2. Try and turn the entry into an integer
  3. If the entry can be changed into an integer, enter a missing value
  4. If the number can’t be an integer, we know it’s a string, so keep going

Let’s take a look at the code and then we’ll go through it in detail.

# Detecting numbers 
cnt=0
for row in df['OWN_OCCUPIED']:
    try:
        int(row)
        df.loc[cnt, 'OWN_OCCUPIED']=np.nan
    except ValueError:
        pass
    cnt+=1

Copy

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).

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

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

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

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.

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.

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

Summarizing Missing Values

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.

# Total missing values for each feature
print df.isnull().sum()

Copy
Out:
ST_NUM          2
ST_NAME         0
OWN_OCCUPIED    2
NUM_BEDROOMS    4

Copy

Other times we might want to do a quick check to see if we have any missing values at all.

# Any missing values?
print df.isnull().values.any()

Copy
Out:
True

Copy

We might also want to get a total count of missing values.

# Total number of missing values
print df.isnull().sum().sum()

Copy
Out:
8

Copy

Now that we’ve summarized the number of missing values, let’s take a look at doing some simple replacements.

Replacing

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

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

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.

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

# Replace missing values with a number
df['ST_NUM'].fillna(125, inplace=True)

Copy

More likely, you might want to do a location based imputation. Here’s how you would do that.

# Location based replacement
df.loc[2,'ST_NUM'] = 125

Copy

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

# Replace using median 
median = df['NUM_BEDROOMS'].median()
df['NUM_BEDROOMS'].fillna(median, inplace=True)

Copy

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.

Conclusion

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

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

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

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

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

Click here to get the FREE Data Cleaning Cheat Sheet

13 Comments

  1. Satish
    Satish on July 12, 2018 at 11:19 am

    Thanks for the great tutorial.

    Reply
    • DataOptimal
      DataOptimal on July 12, 2018 at 1:40 pm

      Thanks Satish, glad you enjoyed it!

      Reply
  2. Shoeb Mohammad
    Shoeb Mohammad on July 12, 2018 at 6:01 pm

    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.

    Thanks again. Keep up the great work.

    Reply
    • DataOptimal
      DataOptimal on July 12, 2018 at 6:43 pm

      Thank you for the kind words Shoeb, I’m glad it helped!

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

      Reply
  3. Ravi
    Ravi on August 9, 2018 at 4:49 pm

    Wonderful and effective explanation with an example, Great work guys @DataOptimal.

    Reply
    • DataOptimal
      DataOptimal on August 28, 2018 at 2:11 pm

      Thanks, glad you enjoyed it!

      Reply
  4. sasha
    sasha on August 28, 2018 at 4:24 am

    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 😉

    Reply
    • DataOptimal
      DataOptimal on August 28, 2018 at 2:12 pm

      I’m glad you enjoyed it Sasha! We’re working on plenty of more tutorials, so stay tuned!

      Reply
  5. KARN KUMAR
    KARN KUMAR on September 11, 2018 at 2:52 pm

    This is awesome tutorial, would be great if you could create more videos or blogs.

    Reply
    • DataOptimal
      DataOptimal on October 24, 2018 at 6:42 pm

      Thanks Karn! I’ll be posting more soon, so stay tuned!

      Reply
  6. Junior
    Junior on October 11, 2018 at 8:06 am

    This is a great resource, exactly what I was looking for! Thank you!

    Reply
    • DataOptimal
      DataOptimal on October 24, 2018 at 6:42 pm

      You’re welcome Junior, glad you enjoyed it!

      Reply
  7. Jacobs
    Jacobs on October 12, 2018 at 7:13 am

    Do you have a similar example of data cleaning with R

    Reply

Submit a Comment

Your email address will not be published. Required fields are marked *

Get Access to the top Data Science books in 2018.
Get Access Now

Data Cleaning with Python and Pandas: Detecting Missing Values

data cleaning with python
FREE Data Cleaning Cheat Sheet

Data cleaning can be a tedious task.

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

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

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

data cleaning from ibm analytics

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.

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.

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

Sources of Missing Values

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

  • User forgot to fill in a field.
  • Data was lost while transferring manually from a legacy database.
  • There was a programming error.
  • Users chose not to fill out a field tied to their beliefs about how the results would be used or interpreted.

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.

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.

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.

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.

Getting Started

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.

I like to start by asking the following questions:

  • What are the features?
  • What are the expected types (int, float, string, boolean)?
  • Is there obvious missing data (values that Pandas can detect)?
  • Is there other types of missing data that’s not so obvious (can’t easily detect with Pandas)?

To show you what I mean, let’s start working through the example.

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.

Here’s a quick look at the data:

real estate data

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.

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:

# Importing libraries
import pandas as pd
import numpy as np

# Read csv file into a pandas dataframe
df = pd.read_csv("property data.csv")

# Take a look at the first few rows
print df.head()

Copy
Out:
   ST_NUM    ST_NAME OWN_OCCUPIED  NUM_BEDROOMS
0   104.0     PUTNAM            Y           3.0
1   197.0  LEXINGTON            N           3.0
2     NaN  LEXINGTON            N           3.0
3   201.0   BERKELEY          NaN           1.0
4   203.0   BERKELEY            Y           3.0

Copy

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.

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

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

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

  • ST_NUM: Street number
  • ST_NAME: Street name
  • OWN_OCCUPIED: Is the residence owner occupied
  • NUM_BEDROOMS: Number of bedrooms

We can also answer, what are the expected types?

  • ST_NUM: float or int… some sort of numeric type
  • ST_NAME: string
  • OWN_OCCUPIED: string… Y (“Yes”) or N (“No”)
  • NUM_BEDROOMS: float or int, a numeric type

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

Standard Missing Values

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

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

standard missing values

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

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

# Looking at the ST_NUM column
print df['ST_NUM']
print df['ST_NUM'].isnull()

Copy
# Looking at the ST_NUM column
Out:
0    104.0
1    197.0
2      NaN
3    201.0
4    203.0
5    207.0
6      NaN
7    213.0
8    215.0

Out:
0    False
1    False
2     True
3    False
4    False
5    False
6     True
7    False
8    False

Copy

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.

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.

Non-Standard Missing Values

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

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

non-standard missing values

In this column, there’s four missing values.

  • n/a
  • NA
  • —
  • na

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.

# Looking at the NUM_BEDROOMS column
print df['NUM_BEDROOMS']
print df['NUM_BEDROOMS'].isnull()

Copy

Out:
0      3
1      3
2    n/a
3      1
4      3
5    NaN
6      2
7     --
8     na

Out:
0    False
1    False
2    False
3    False
4    False
5     True
6    False
7    False
8    False

Copy

Just like before, Pandas recognized the “NA” as a missing value. Unfortunately, the other types weren’t recognized.

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”.

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.

# Making a list of missing value types
missing_values = ["n/a", "na", "--"]
df = pd.read_csv("property data.csv", na_values = missing_values)

Copy

Now let’s take another look at this column and see what happens.

# Looking at the NUM_BEDROOMS column
print df['NUM_BEDROOMS']
print df['NUM_BEDROOMS'].isnull()

Copy

Out:
0    3.0
1    3.0
2    NaN
3    1.0
4    3.0
5    NaN
6    2.0
7    NaN
8    NaN

Out:
0    False
1    False
2     True
3    False
4    False
5     True
6    False
7     True
8     True

Copy

This time, all of the different formats were recognized as missing values.

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.

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.

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

Unexpected Missing Values

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

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.

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

unexpected missing values

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.

# Looking at the OWN_OCCUPIED column
print df['OWN_OCCUPIED']
print df['OWN_OCCUPIED'].isnull()

Copy
# Looking at the ST_NUM column
Out:
0      Y
1      N
2      N
3     12
4      Y
5      Y
6    NaN
7      Y
8      Y

Out:
0    False
1    False
2    False
3    False
4    False
5    False
6     True
7    False
8    False

Copy

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.

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.

  1. Loop through the OWN_OCCUPIED column
  2. Try and turn the entry into an integer
  3. If the entry can be changed into an integer, enter a missing value
  4. If the number can’t be an integer, we know it’s a string, so keep going

Let’s take a look at the code and then we’ll go through it in detail.

# Detecting numbers 
cnt=0
for row in df['OWN_OCCUPIED']:
    try:
        int(row)
        df.loc[cnt, 'OWN_OCCUPIED']=np.nan
    except ValueError:
        pass
    cnt+=1

Copy

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).

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

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

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

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.

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.

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

Summarizing Missing Values

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.

# Total missing values for each feature
print df.isnull().sum()

Copy
Out:
ST_NUM          2
ST_NAME         0
OWN_OCCUPIED    2
NUM_BEDROOMS    4

Copy

Other times we might want to do a quick check to see if we have any missing values at all.

# Any missing values?
print df.isnull().values.any()

Copy
Out:
True

Copy

We might also want to get a total count of missing values.

# Total number of missing values
print df.isnull().sum().sum()

Copy
Out:
8

Copy

Now that we’ve summarized the number of missing values, let’s take a look at doing some simple replacements.

Replacing

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

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

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.

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

# Replace missing values with a number
df['ST_NUM'].fillna(125, inplace=True)

Copy

More likely, you might want to do a location based imputation. Here’s how you would do that.

# Location based replacement
df.loc[2,'ST_NUM'] = 125

Copy

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

# Replace using median 
median = df['NUM_BEDROOMS'].median()
df['NUM_BEDROOMS'].fillna(median, inplace=True)

Copy

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.

Conclusion

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

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

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

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

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

FREE Data Cleaning Cheat Sheet

13 Comments

  1. Satish
    Satish on July 12, 2018 at 11:19 am

    Thanks for the great tutorial.

    Reply
    • DataOptimal
      DataOptimal on July 12, 2018 at 1:40 pm

      Thanks Satish, glad you enjoyed it!

      Reply
  2. Shoeb Mohammad
    Shoeb Mohammad on July 12, 2018 at 6:01 pm

    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.

    Thanks again. Keep up the great work.

    Reply
    • DataOptimal
      DataOptimal on July 12, 2018 at 6:43 pm

      Thank you for the kind words Shoeb, I’m glad it helped!

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

      Reply
  3. Ravi
    Ravi on August 9, 2018 at 4:49 pm

    Wonderful and effective explanation with an example, Great work guys @DataOptimal.

    Reply
    • DataOptimal
      DataOptimal on August 28, 2018 at 2:11 pm

      Thanks, glad you enjoyed it!

      Reply
  4. sasha
    sasha on August 28, 2018 at 4:24 am

    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 😉

    Reply
    • DataOptimal
      DataOptimal on August 28, 2018 at 2:12 pm

      I’m glad you enjoyed it Sasha! We’re working on plenty of more tutorials, so stay tuned!

      Reply
  5. KARN KUMAR
    KARN KUMAR on September 11, 2018 at 2:52 pm

    This is awesome tutorial, would be great if you could create more videos or blogs.

    Reply
    • DataOptimal
      DataOptimal on October 24, 2018 at 6:42 pm

      Thanks Karn! I’ll be posting more soon, so stay tuned!

      Reply
  6. Junior
    Junior on October 11, 2018 at 8:06 am

    This is a great resource, exactly what I was looking for! Thank you!

    Reply
    • DataOptimal
      DataOptimal on October 24, 2018 at 6:42 pm

      You’re welcome Junior, glad you enjoyed it!

      Reply
  7. Jacobs
    Jacobs on October 12, 2018 at 7:13 am

    Do you have a similar example of data cleaning with R

    Reply

Submit a Comment

Your email address will not be published. Required fields are marked *

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$this_el.data( 'container_id', $i ); $locked_containers.push( content ); if ( checkCookieValue( 'et_bloom_unlocked' + optin_id + page_id, 'true' ) ) { content.css( {'display' : 'block'} ); form.remove(); } else { content.remove(); update_stats_table( 'imp', $this_el ); } $i++; }); } $( 'body' ).on( 'click', '.et_bloom_locked_container .et_bloom_submit_subscription', function(){ var $current_container = $( this ).closest( '.et_bloom_locked_container' ), container_id = $current_container.data( 'container_id' ), page_id = $current_container.data( 'page_id' ), optin_id = typeof $current_container.data( 'current_optin_id' ) !== 'undefined' ? $current_container.data( 'current_optin_id' ) : $current_container.data( 'optin_id' ); perform_subscription( $( this ), $current_container, container_id, page_id, optin_id ); return false; }); // unlock content immediately if custom HTML form is used. $( '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() { var current_container = $( this ).closest( '.et_bloom_locked_container' ), container_id = current_container.data( 'container_id' ), page_id = current_container.data( 'page_id' ), optin_id = current_container.data( 'optin_id' ); unlock_content( current_container, container_id, page_id, optin_id ); } ); function unlock_content( current_container, container_id, locked_page_id, locked_optin_id ) { set_cookie( 365, 'et_bloom_unlocked' + locked_optin_id + locked_page_id + '=true' ); current_container.find( '.et_bloom_locked_form' ).replaceWith( $locked_containers[container_id] ); current_container.find( '.et_bloom_locked_content' ).css( { 'display' : 'block' } ); } // Move inline forms into appropriate sections in Divi theme if( $( '.et_bloom_below_post' ).length ) { if ( $( 'body' ).hasClass( 'et_pb_pagebuilder_layout' ) ) { var bottom_inline = $( '.et_bloom_below_post' ), divi_container = '
'; if ( bottom_inline.length ) { $( '.et_pb_section' ).not( '.et_pb_fullwidth_section' ).last().append( divi_container ).find( '.et_pb_row' ).last().find( '.et_pb_column' ).append( bottom_inline ); } } } function define_popup_position( $this_popup, $just_loaded, $message_space ) { var this_popup = $this_popup.find( '.et_bloom_form_container' ), popup_max_height = this_popup.hasClass( 'et_bloom_popup_container' ) ? $( window ).height() - 40 : $( window ).height() - 20, real_popup_height = 0, percentage = this_popup.parent().hasClass( 'et_bloom_flyin' ) ? 0.03 : 0.05, percentage = this_popup.hasClass( 'et_bloom_with_border' ) ? percentage + 0.03 : percentage, breakout_offset = this_popup.hasClass( 'breakout_edge' ) ? 0.95 : 1, dashed_offset = this_popup.hasClass( 'et_bloom_border_dashed' ) ? 4 : 0, form_height = this_popup.find( 'form' ).innerHeight() + $message_space, form_add = true == $just_loaded ? 5 : 0; this_popup.css( { 'max-height' : popup_max_height } ); if ( this_popup.hasClass( 'et_bloom_popup_container' ) ) { var top_position = $( window ).height() / 2 - this_popup.innerHeight() / 2; this_popup.css( { 'top' : top_position + 'px' } ); } this_popup.find( '.et_bloom_form_container_wrapper' ).css( { 'max-height' : popup_max_height - 20 } ); if ( ( 768 > $( 'body' ).outerWidth() + 15 ) || this_popup.hasClass( 'et_bloom_form_bottom' ) ) { if ( this_popup.hasClass( 'et_bloom_form_right' ) || this_popup.hasClass( 'et_bloom_form_left' ) ) { this_popup.find( '.et_bloom_form_header' ).css( { 'height' : 'auto' } ); } real_popup_height = this_popup.find( '.et_bloom_form_container_wrapper' ).height() + 30 + form_add; if ( this_popup.hasClass( 'et_bloom_form_right' ) || this_popup.hasClass( 'et_bloom_form_left' ) ) { this_popup.find( '.et_bloom_form_container_wrapper' ).css( { 'height' : real_popup_height - 30 + dashed_offset } ); } } else { real_popup_height = this_popup.find( '.et_bloom_form_container_wrapper' ).height() + $message_space; /** * Sometimes real_popup_height return 0 because the page is no loaded fully. This * is added to set the Bloom container height as 100% to avoid 0 height. Note, the * height will be set as 100% only when the real_popup_height is 0. */ var container_height = real_popup_height; if (parseInt(real_popup_height) === 0) { container_height = '100%'; } if ( this_popup.hasClass( 'et_bloom_form_right' ) || this_popup.hasClass( 'et_bloom_form_left' ) ) { this_popup.find( '.et_bloom_form_header' ).css( { 'height' : real_popup_height * breakout_offset - dashed_offset } ); this_popup.find( '.et_bloom_form_content' ).css( { 'min-height' : real_popup_height - dashed_offset } ); this_popup.find( '.et_bloom_form_container_wrapper' ).css( { 'height' : container_height } ); } } if ( real_popup_height > popup_max_height ) { this_popup.find( '.et_bloom_form_container_wrapper' ).addClass( 'et_bloom_vertical_scroll' ); } else { this_popup.find( '.et_bloom_form_container_wrapper' ).removeClass( 'et_bloom_vertical_scroll' ); } if ( $this_popup.hasClass( 'et_bloom_popup' ) ) { $( 'body' ).addClass( 'et_bloom_popup_active' ); } } $( 'body' ).on( 'click', '.et_bloom_submit_subscription:not(.et_bloom_submit_subscription_locked)', function() { perform_subscription( $( this ), '', '', '', '' ); return false; }); function perform_subscription(this_button, current_container, container_id, locked_page_id, locked_optin_id) { var this_form = this_button.parents('form'); var list_id = this_button.data('list_id'); var account_name = this_button.data('account'); var service = this_button.data('service'); var name = this_form.find('.et_bloom_subscribe_name input').val(); var last_name = undefined !== this_form.find('.et_bloom_subscribe_last input').val() ? this_form.find('.et_bloom_subscribe_last input').val() : ''; var email = this_form.find('.et_bloom_subscribe_email input').val(); var page_id = this_button.data('page_id'); var optin_id = this_button.data('optin_id'); var $popup_container = this_form.closest('.et_bloom_optin'); var is_popup = $popup_container.hasClass('et_bloom_popup') || $popup_container.hasClass('et_bloom_flyin'); var $success_action_el = this_button.closest('.et_bloom_success_action'); var success_action_details = $success_action_el.length > 0 ? $success_action_el.parent().data('success_action_details').split('|') : []; var success_action = 2 === success_action_details.length ? success_action_details[0] : ''; var success_action_url = '' !== success_action ? success_action_details[1] : ''; var ip_address = this_button.data('ip_address'); var $fields_container = this_form.find('.et_bloom_fields'); var $custom_fields = $fields_container.find('.et_bloom_custom_field input[type=text], .et_bloom_checkbox_handle, [data-field_type="radio"], textarea, select'); var custom_fields = {}; var is_valid = true; // Email Validation // Use the regex defined in the HTML5 spec for input[type=email] validation // (see https://www.w3.org/TR/2016/REC-html51-20161101/sec-forms.html#email-state-typeemail) var et_email_reg_html5 = /^[a-zA-Z0-9.!#$%&'*+\/=?^_`{|}~-]+@[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])?)*$/; this_form.find('.et_bloom_warn_field').removeClass('et_bloom_warn_field'); if (this_form.length > 0 && typeof this_form[0].reportValidity === 'function') { // Checks HTML5 validation constraints is_valid = this_form[0].reportValidity(); } $custom_fields.each( function() { var $this_el = $(this); var $this_wrapper = false; var this_id = $this_el.attr('data-id'); var field_type = typeof $this_el.data( 'field_type' ) !== 'undefined' ? $this_el.data( 'field_type' ) : 'text'; if ('checkbox' === field_type || 'radio' === field_type) { $this_wrapper = $this_el.closest('.et_bloom_custom_field'); } var this_val = $this_el.val(); var required_mark = typeof $this_el.data('required_mark') !== 'undefined' ? $this_el.data('required_mark') : 'not_required'; var original_id = typeof $this_el.data('original_id') !== 'undefined' ? $this_el.data('original_id') : ''; var unchecked = false; var default_value; // radio field properties adjustment if ('radio' === field_type) { if (0 !== $this_wrapper.find('input[type="radio"]').length) { var $firstRadio = $this_wrapper.find('input[type="radio"]:first'); required_mark = typeof $firstRadio.data('required_mark') !== 'undefined' ? $firstRadio.data('required_mark') : 'not_required'; this_val = ''; if ($this_wrapper.find('input[type="radio"]:checked')) { this_val = $this_wrapper.find('input[type="radio"]:checked').val(); } } if (!$.isEmptyObject(this_val)) { custom_fields[this_id] = this_val; } if (0 === $this_wrapper.find('input[type="radio"]:checked').length) { unchecked = true; } if (this_val) { custom_fields[this_id] = this_val; } } else if ('checkbox' === field_type) { this_val = {}; if ( 0 !== $this_wrapper.find('input[type="checkbox"]').length) { var $checkboxHandle = $this_wrapper.find('.et_bloom_checkbox_handle'); required_mark = typeof $checkboxHandle.data('required_mark') !== 'undefined' ? $checkboxHandle.data('required_mark') : 'not_required'; if ($this_wrapper.find('input[type="checkbox"]:checked').length > 0) { $this_wrapper.find('input[type="checkbox"]:checked').each(function() { var field_id = $(this).data('id'); this_val[field_id] = $(this).val(); } ); } } if (!$.isEmptyObject(this_val)) { custom_fields[this_id] = this_val; } if (0 === $this_wrapper.find('input[type="checkbox"]:checked').length) { unchecked = true; } } else if ('ontraport' === service && 'select' === field_type) { // Need to pass option ID as a value for dropdown menu in Ontraport var $selected_option = $this_el.find(':selected'); custom_fields[this_id] = $selected_option.length > 0 ? $selected_option.data('id') : this_val; } else { custom_fields[this_id] = this_val; } // add error message for the field if it is required and empty if ('required' === required_mark && ('' === this_val || true === unchecked)) { if (false === $this_wrapper) { $this_el.addClass('et_bloom_warn_field'); } else { $this_wrapper.addClass('et_bloom_warn_field'); } is_valid = false; } if ('email' === field_type) { // remove trailing/leading spaces and convert email to lowercase var processed_email = this_val.trim().toLowerCase(); var is_valid_email = et_email_reg_html5.test(processed_email); if ('' !== processed_email && !is_valid_email) { $this_el.addClass('et_bloom_warn_field'); is_valid = false; } } } ); if (! is_valid) { return; } if ('' == email) { this_form.find('.et_bloom_subscribe_email input').addClass('et_bloom_warn_field'); } else { ip_address = ip_address ? 'true' : 'false'; $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 }); $.ajax({ type: 'POST', dataType: 'json', url: bloomSettings.ajaxurl, data: { action : 'bloom_subscribe', subscribe_data_array : $subscribe_data, custom_fields: custom_fields, subscribe_nonce : bloomSettings.subscribe_nonce }, beforeSend: function(data) { this_button.addClass('et_bloom_button_text_loading'); this_button.find('.et_bloom_subscribe_loader').css({ 'display' : 'block' }); }, success: function(data) { this_button.removeClass('et_bloom_button_text_loading'); this_button.find('.et_bloom_subscribe_loader').css({ 'display' : 'none' }); if (data) { if ('' !== current_container && (data.success || 'Invalid email' !== data.error)) { unlock_content(current_container, container_id, locked_page_id, locked_optin_id); } else { if (data.error) { this_form.find('.et_bloom_error_message').remove(); this_form.prepend('

' + data.error + '

'); this_form.parent().parent().find('.et_bloom_form_header').addClass('et_bloom_with_error'); } if (data.success && '' == current_container) { set_cookie(365, 'et_bloom_subscribed_to_' + optin_id + list_id + '=true'); if ('' === success_action || '' === success_action_url) { this_form.parent().find('.et_bloom_success_message').addClass('et_bloom_animate_message'); this_form.parent().find('.et_bloom_success_container').addClass('et_bloom_animate_success'); this_form.remove(); } else { window.location = success_action_url; } // auto close popup if enabled if (is_popup && $popup_container.hasClass('et_bloom_auto_close')) { setTimeout(function() { perform_popup_closing($popup_container); },1400); } } } if (is_popup) { define_popup_position($popup_container, false, 50); } } } }); } } $( 'body' ).on( 'click', '.et_bloom_custom_html_form input[type="submit"], .et_bloom_custom_html_form button[type="submit"]', function() { var this_button = $( this ), form_container = this_button.closest( '.et_bloom_custom_html_form' ); update_stats_table( 'con', form_container ); } ); $( window ).resize( function(){ if ( $( '.et_bloom_resize' ).length ) { $( '.et_bloom_resize' ).each( function() { define_popup_position( $( this ), false, 0 ); }); } }); }); })(jQuery) ================================================ FILE: March Cohort 2023/8_9_Pandas/Data Cleaning With Pandas/Data Cleaning with Python and Pandas_ Detecting Missing Values_files/custom.js.download ================================================ (function($){ $(document).ready(function() { var all_networks_opened = 0; // fix the "on media" wrapper inside the Divi Gallery grid et_pb_fix_gallery_wrapper(); $( 'body' ).on( 'click', '.et_social_share', function() { var $this_el = $(this), social_type = $this_el.data( 'social_type' ), social_network = $this_el.data( 'social_name' ), share_link = 'media' == social_type ? $this_el.data( 'social_link' ) : $this_el.prop( 'href' ); update_stats_table( social_network, $this_el ); if ( 'like' === social_network ) { return false; } var left = ( $( window ).width()/2 ) - ( 550/2 ); var top = ( $( window ).height()/2 ) - ( 450/2 ); var new_window = window.open( share_link, '', 'scrollbars=1, height=450, width=550, left=' + left + ', top=' + top ); if ( window.focus ) { new_window.focus(); } return false; }); $( '.et_social_follow' ).click( function() { var $this_el = $(this), social_network = $this_el.data( 'social_name' ); update_stats_table( social_network, $this_el ); if ( 'like' === social_network ) { return false; } }); $( 'body' ).on( 'click', '.et_social_share_pinterest', function() { if ( $( this ).hasClass( 'et_social_pin_all' ) ) { var left = ( $( window ).width()/2 ) - ( 550/2 ), top = ( $( window ).height()/2 ) - ( 450/2 ), share_link = $( this ).attr( 'href' ), new_window = window.open( share_link, '', 'scrollbars=1, height=450, width=550, left=' + left + ', top=' + top ); if ( window.focus ) { new_window.focus(); } } else { $( '.et_social_pin_images_outer' ).fadeToggle( 400 ); } return false; }); function get_url_parameter( param_name ) { var page_url = window.location.search.substring(1); var url_variables = page_url.split('&'); for ( var i = 0; i < url_variables.length; i++ ) { var curr_param_name = url_variables[i].split( '=' ); if ( curr_param_name[0] == param_name ) { return curr_param_name[1]; } } } function update_stats_table( $social_network, $this_el ) { var action = $this_el.data( 'social_type' ), media_url = 'media' == action ? $this_el.closest( '.et_social_media_wrapper' ).find( 'img' ).attr( 'src' ) : '', post_id = $this_el.data( 'post_id' ), location = $this_el.data( 'location' ), stats_data = ''; stats_data = JSON.stringify({ 'action' : action, 'network' : $social_network, 'media_url' : media_url, 'post_id' : post_id, 'location' : location }); $.ajax({ type: 'POST', url: monarchSettings.ajaxurl, data: { action : 'add_stats_record_db', stats_data_array : stats_data, add_stats_nonce : monarchSettings.stats_nonce }, success: function( data ) { if ( true == data ){ if ( 'like' === action ){ update_single_shares( $this_el, '', post_id, $social_network, 'like' ); } if ( 'media' === action ){ update_total_media_shares( $this_el.closest( '.et_social_media_wrapper' ) ); update_single_shares( $this_el, media_url, post_id, $social_network, 'media' ); } } } }); } function append_share_counts( $current_network ) { var network = $current_network.data( 'social_name' ), min_count = $current_network.data( 'min_count' ), post_id = $current_network.data( 'post_id' ), url = monarchSettings.pageurl !== '' ? monarchSettings.pageurl : window.location.href, label_div = $current_network.find( '.et_social_network_label' ), append_to = ( 0 != ( label_div.length ) ) ? label_div : $current_network; $share_count_data = JSON.stringify({ 'network' : network, 'min_count' : min_count, 'post_id' : post_id, 'url' : url }); $.ajax({ type: 'POST', url: monarchSettings.ajaxurl, data: { action : 'get_shares_count', share_count_array : $share_count_data, get_share_counts_nonce : monarchSettings.share_counts }, beforeSend: function( data ){ append_to.append( '' ); }, success: function( data ){ $current_network.find( 'span.et_social_placeholder' ).remove(); append_to.append( data ); } }); } function append_total_shares( $current_area ) { var post_id = $current_area.data( 'post_id' ), url = monarchSettings.pageurl !== '' ? monarchSettings.pageurl : window.location.href, append_to = $current_area; $share_total_count_data = JSON.stringify({ 'post_id' : post_id, 'url' : url }); $.ajax({ type: 'POST', url: monarchSettings.ajaxurl, data: { action : 'get_total_shares', share_total_count_array : $share_total_count_data, get_total_counts_nonce : monarchSettings.total_counts }, beforeSend: function( data ){ append_to.append( '' ); }, success: function( data ){ append_to.find( 'span.et_social_placeholder' ).remove(); append_to.append( data ); } }); } function append_follow_counts( $current_area ) { var network = $current_area.data( 'network' ), min_count = $current_area.data( 'min_count' ), index = $current_area.data( 'index' ), append_to = $current_area; $follow_count_data = JSON.stringify({ 'network' : network, 'min_count' : min_count, 'index' : index }); $.ajax({ type: 'POST', url: monarchSettings.ajaxurl, data: { action : 'get_follow_counts', follow_count_array : $follow_count_data, get_follow_counts_nonce : monarchSettings.follow_counts }, beforeSend: function( data ){ append_to.append( '' ); }, success: function( data ){ $current_area.find( 'span.et_social_placeholder' ).remove(); append_to.append( data ); } }); } function append_total_follows( $current_area ) { var append_to = $current_area; $.ajax({ type: 'POST', url: monarchSettings.ajaxurl, data: { action : 'get_follow_total', get_total_counts_nonce : monarchSettings.total_counts }, beforeSend: function( data ){ append_to.append( '' ); }, success: function( data ){ append_to.find( 'span.et_social_placeholder' ).remove(); append_to.append( data ); } }); } if ( $( '.et_social_display_follow_counts' ).length ) { $( '.et_social_display_follow_counts' ).each( function(){ append_follow_counts( $( this) ); }); } if ( $( '.et_social_follow_total' ).length ) { $( '.et_social_follow_total' ).each( function(){ append_total_follows( $( this) ); }); } if ( $( '.et_social_total_share' ).length ) { $( '.et_social_total_share' ).each( function(){ append_total_shares( $( this) ); }); } if ( $( '.et_social_display_count' ).length ) { $( '.et_social_display_count' ).each( function(){ append_share_counts( $( this) ); }); } if ( $( '.et_social_media_wrapper' ).length && $( '.et_social_media_wrapper .et_social_totalcount' ).length ) { $( '.et_social_media_wrapper' ).each( function() { update_total_media_shares( $( this ) ); }); } if ( $( '.et_social_media_wrapper' ).length && $( '.et_social_media_wrapper .et_social_withcounts' ).length ) { $( '.et_social_media_wrapper .et_social_share' ).each( function() { var this_el = $( this ), media_url = this_el.closest( '.et_social_media_wrapper' ).find('img').attr('src'), post_id = this_el.data( 'post_id' ), social_network = this_el.data( 'social_name' ); update_single_shares( this_el, media_url, post_id, social_network, 'media' ); }); } function update_total_media_shares( $element ) { if ( $( '.et_social_totalcount' ).length ) { var this_el = $element, media_url = this_el.find( 'img' ).attr( 'src' ), post_id = this_el.find( '.et_social_share' ).first().data( 'post_id' ), media_data = JSON.stringify({ 'media_url' : media_url, 'post_id' : post_id }); $.ajax({ type: 'POST', url: monarchSettings.ajaxurl, data: { action : 'get_media_shares_total', media_total : media_data, get_media_shares_total_nonce : monarchSettings.media_total }, success: function( data ){ this_el.find( '.et_social_totalcount_count' ).empty().append( data ); } }); } } function update_single_shares( $this_el, $media_url, $post_id, $network, $action ) { if( $( '.et_social_withcounts' ).length ) { var media_data = JSON.stringify({ 'media_url' : $media_url, 'post_id' : $post_id, 'network' : $network, 'action' : $action }); $.ajax({ type: 'POST', url: monarchSettings.ajaxurl, data: { action : 'get_shares_single', media_single : media_data, get_media_shares_nonce : monarchSettings.media_single }, success: function( data ){ $this_el.find( '.et_social_count span' ).not('.et_social_count_label').empty().append( data ); } }); } } function setCookieExpire(days) { var ms = days*24*60*60*1000; var date = new Date(); date.setTime(date.getTime() + ms); return "; expires=" + date.toUTCString(); } function checkCookieValue(cookieName, value) { return parseCookies()[cookieName] === value; } function parseCookies() { var cookies = document.cookie.split('; '); var ret = {}; for (var i = cookies.length - 1; i >= 0; i--) { var el = cookies[i].split('='); ret[el[0]] = el[1]; } return ret; } function set_cookie( $expire ) { cookieExpire = setCookieExpire( $expire ); document.cookie = 'etSocialCookie=true' + cookieExpire; } //separate function for the setTimeout to make it work properly within the loop. function make_popup_visible( $popup, $delay ){ setTimeout( function() { $popup.addClass( 'et_social_visible et_social_animated' ); if ( $( '.et_social_resize' ).length ) { $( '.et_social_resize' ).each( function() { define_popup_position( $( this ) ); }); } }, $delay ); } function auto_popup( this_el, delay ) { var $current_popup_auto = this_el; if ( ! $current_popup_auto.hasClass( 'et_social_animated' ) ) { var $cookies_expire_auto = $current_popup_auto.data( 'cookie_duration' ) ? $current_popup_auto.data( 'cookie_duration' ) : false, $delay = delay; if ( ( false !== $cookies_expire_auto && ! checkCookieValue( 'etSocialCookie', 'true' ) ) || false == $cookies_expire_auto ) { make_popup_visible ( $current_popup_auto, $delay ); if ( false !== $cookies_expire_auto ) { set_cookie( $cookies_expire_auto ); } } } } if ( 'true' == get_url_parameter( 'et_monarch_popup' ) ) { $( '.et_social_after_comment' ).each( function() { var $current_popup = $( this ); auto_popup( $current_popup, 0 ); }); } if( $( '.et_social_auto_popup' ).length ) { $( '.et_social_auto_popup' ).each( function() { var $current_popup_auto = $( this ); auto_popup( $current_popup_auto, '' !== $current_popup_auto.data( 'delay' ) ? $current_popup_auto.data( 'delay' ) * 1000 : 0 ); }); } $( '.et_social_pinterest_window .et_social_close' ).on( 'click', function(){ $( '.et_social_pin_images_outer' ).fadeToggle( 400 ); }); $ ( 'body' ).on( 'click', '.et_social_icon_cancel', function(){ var this_el = $( this ); if ( this_el.parent().hasClass( 'et_social_flyin' ) ) { $popup = this_el.parent(); } else { $popup = this_el.parent().parent(); if ( $popup.hasClass( 'et_social_all_networks_popup' ) ) { all_networks_opened = 0; } } $popup.addClass( 'et_social_fadeout' ); setTimeout( function() { $popup.remove(); }, 800 ); return false; }); if ( $( '.et_monarch_after_order' ).length ) { $( '.et_social_after_purchase' ).each( function() { var $current_popup = $( this ); if ( ! $current_popup.hasClass( 'et_social_animated' ) ) { var $cookies_expire = $current_popup.data( 'cookie_duration' ) ? $current_popup.data( 'cookie_duration' ) : false, $delay = 0; if ( ( false !== $cookies_expire && ! checkCookieValue( 'etSocialCookie', 'true' ) ) || false == $cookies_expire ) { make_popup_visible ( $current_popup, $delay ); if ( false !== $cookies_expire ) { set_cookie( $cookies_expire ); } } } } ); } if( $( '.et_social_trigger_bottom' ).length ) { $( '.et_social_trigger_bottom' ).each(function(){ scroll_trigger( $( this ), true ); }); } if( $( '.et_social_scroll' ).length ) { $( '.et_social_scroll' ).each(function(){ scroll_trigger( $( this ), false ); }); } function scroll_trigger( this_el, is_bottom_trigger ) { var current_popup_bottom = this_el; if ( ! current_popup_bottom.hasClass( 'et_social_animated' ) ) { var cookies_expire_bottom = current_popup_bottom.data( 'cookie_duration' ) ? current_popup_bottom.data( 'cookie_duration' ) : false; if ( true == is_bottom_trigger ) { var scroll_trigger = $( '.et_social_bottom_trigger' ).length ? $( '.et_social_bottom_trigger' ).offset().top : $( document ).height() - 500; } else { var scroll_pos = this_el.data( 'scroll_pos' ) > 100 ? 100 : this_el.data( 'scroll_pos' ), scroll_trigger = 100 == scroll_pos ? $( document ).height() - 10 : $( document ).height() * scroll_pos / 100; } $( window ).scroll( function(){ if ( ( false !== cookies_expire_bottom && ! checkCookieValue( 'etSocialCookie', 'true' ) ) || false == cookies_expire_bottom ) { if( $( window ).scrollTop() + $( window ).height() > scroll_trigger ) { current_popup_bottom.addClass( 'et_social_visible et_social_animated' ); if ( $( '.et_social_resize' ).length ) { $( '.et_social_resize' ).each( function() { define_popup_position( $( this ) ); }); } if ( false !== cookies_expire_bottom ) { set_cookie( cookies_expire_bottom ); } } } }); } } if( $( '.et_social_trigger_idle' ).length ) { $( '.et_social_trigger_idle' ).each( function() { var this_el = $( this ); if ( ! this_el.hasClass( 'et_social_animated' ) ) { var $cookies_expire_idle = this_el.data( 'cookie_duration' ) ? this_el.data( 'cookie_duration' ) : false, $idle_timeout = '' !== this_el.data( 'idle_timeout' ) ? this_el.data( 'idle_timeout' ) * 1000 : 30000; if ( ( false !== $cookies_expire_idle && ! checkCookieValue( 'etSocialCookie', 'true' ) ) || false == $cookies_expire_idle ) { $( document ).idleTimer( $idle_timeout ); $( document ).on( "idle.idleTimer", function(){ make_popup_visible ( this_el, 0 ); }); if ( false !== $cookies_expire_idle ) { set_cookie( $cookies_expire_idle ); } } } }); } // open close the mobile sideabr on header click $( '.et_social_heading, .et_social_mobile_button' ).click( function(){ $this_mobile_div = $( '.et_social_mobile' ); $this_mobile_div.css( {'display' : 'block' } ); $( '.et_social_mobile_button').removeClass( 'et_social_active_button' ); if ( $this_mobile_div.hasClass( 'et_social_opened' ) ) { $this_mobile_div.find( '.et_social_networks' ).slideToggle( 600 ); $this_mobile_div.removeClass( 'et_social_opened' ).addClass( 'et_social_closed' ); $( '.et_social_mobile_overlay' ).removeClass( 'et_social_visible_overlay' ); $( '.et_social_mobile_overlay' ).fadeToggle( 600 ); } else { $this_mobile_div.removeClass( 'et_social_closed' ).addClass( 'et_social_opened' ); $this_mobile_div.find( '.et_social_networks' ).slideToggle( 600 ); $( '.et_social_mobile_overlay' ).addClass( 'et_social_visible_overlay' ).css({ 'display' : 'block' }); } }); //if close button clicked - hide the mobile sidebar from screen $( '.et_social_mobile .et_social_close' ).click( function(){ $mobile_div = $( '.et_social_mobile' ); $mobile_div.fadeToggle( 600 ); $( '.et_social_mobile_button' ).addClass( 'et_social_active_button' ); if ( $mobile_div.hasClass( 'et_social_opened' ) ) { $( '.et_social_mobile_overlay' ).fadeToggle( 600 ); $mobile_div.removeClass( 'et_social_opened' ); $mobile_div.find( '.et_social_networks' ).fadeToggle( 600 ); } }); // Move inline icons into appropriate sections in Divi theme if( $( '.et_social_inline' ).length ) { if ( $( 'body' ).hasClass( 'et_pb_pagebuilder_layout' ) ) { var top_inline = $( '.et_social_inline_top' ), bottom_inline = $( '.et_social_inline_bottom' ), divi_container = '
'; if ( top_inline.length ) { $( '.et_pb_section' ).not( '.et_pb_fullwidth_section' ).first().prepend( divi_container ).find( '.et_pb_row' ).first().find( '.et_pb_column' ).append( top_inline ); } if ( bottom_inline.length ) { $( '.et_pb_section' ).not( '.et_pb_fullwidth_section' ).last().append( divi_container ).find( '.et_pb_row' ).last().find( '.et_pb_column' ).append( bottom_inline ); } } } function define_popup_position( $this_popup ) { setTimeout( function() { // make sure all css transitions are finished to calculate the heights correctly var this_popup = $this_popup, networks_div = this_popup.find( '.et_social_networks' ), header_height = this_popup.find( '.et_social_header' ).outerHeight(), total_count_height = this_popup.find( '.et_social_totalcount' ).height(), extra_height = 0 < total_count_height ? 20 : 0; this_popup.height( this_popup.find( '.et_social_icons_container' ).innerHeight() + header_height + total_count_height + 20 + extra_height ); var popup_max_height = this_popup.hasClass( 'et_social_popup_content' ) ? $( window ).height() : $( window ).height() - 60; if ( this_popup.hasClass( 'et_social_popup_content' ) && 768 < $( window ).width() ) { popup_max_height = popup_max_height - 50; } this_popup.css( { 'max-height' : popup_max_height } ); if( this_popup.hasClass( 'et_social_popup_content' ) ) { var top_position = $( window ).height() / 2 - this_popup.innerHeight() / 2; this_popup.css( { 'top' : top_position + 'px' } ); } var networks_div_height = this_popup.height() - header_height + total_count_height - extra_height; networks_div.height( networks_div_height ); }, 400 ); } function set_mobile_sidebar_height() { setTimeout( function() { // make sure all css transitions are finished to calculate the heights correctly var mobile_div = $( '.et_social_mobile' ); if ( !mobile_div.hasClass( 'et_social_opened' ) ){ $('.et_social_mobile .et_social_networks').css({'display' : 'block'}); } if( $('.et_social_active_button').length ) { mobile_div.css({'display' : 'block'}); } var inner_contatiner_height = mobile_div.find( '.et_social_icons_container' ).innerHeight() + 45; if ( !mobile_div.hasClass( 'et_social_opened' ) ){ $('.et_social_mobile .et_social_networks').css({'display' : 'none'}); } if( $('.et_social_active_button').length ) { mobile_div.css({'display' : 'none'}); } mobile_div.find( '.et_social_networks' ).css( { 'max-height' : inner_contatiner_height, 'height' : inner_contatiner_height } ); if ( $( window ).height() < inner_contatiner_height ) { var inner_height = $( window ).height() - mobile_div.find( '.et_social_heading' ).innerHeight() + 10; mobile_div.find( '.et_social_networks' ).css({ 'height' : inner_height }); } }, 400 ); } function set_sidebar_position(){ if( $( '.et_social_sidebar_networks' ).length ){ var this_sidebar = $( '.et_social_sidebar_networks' ), top_position = $( window ).height() / 2 - this_sidebar.innerHeight() / 2; top_position = 0 > top_position ? 0 : top_position; this_sidebar.css( { 'top' : top_position + 'px' } ); } } function set_media_wrapper_size() { if ( $( '.et_social_media_wrapper' ).length ) { $( '.et_social_media_wrapper' ).each( function(){ var this_wrapper = $( this ), this_wrapper_media = this_wrapper.find( '.et_social_media' ), this_image = this_wrapper.find( 'img' ), image_position = this_image.position(), image_left_offset = typeof image_position !== 'undefined' ? image_position.left : 0, this_image_height = this_image.height(), this_image_width = this_image.width(), this_wrapper_networks_height = this_wrapper.find( '.et_social_networks' ).innerHeight(); this_wrapper.addClass( this_image.attr( 'class' ) ); this_wrapper_media.css( { 'max-height' : this_image_height } ); this_wrapper_media.css( { 'height' : this_wrapper_networks_height + 50 } ); this_wrapper_media.width( this_image_width - 80 ); // adjust the icons position based on image alignement if ( 0 !== image_left_offset ) { this_wrapper_media.css( { 'left' : image_left_offset + 20 } ); } }); // fix the media wrapper sizes in Divi Gallery Slider if needed et_pb_fix_gallery_slider_icons_size(); } } function et_pb_fix_gallery_wrapper() { var $media_wrappers_inside_gallery = $( '.et_pb_gallery_grid .et_social_media_wrapper' ); if ( 0 > $media_wrappers_inside_gallery.length ) { return; } $media_wrappers_inside_gallery.each( function() { var $this_wrapper = $( this ); var $gallery_item = $this_wrapper.closest( '.et_pb_gallery_image' ); var $gallery_overlay = $gallery_item.find( '.et_overlay' ); if ( 0 < $gallery_overlay.length ) { // move the gallery overlay inside media icons wrapper so it won't break the sharing functionality $this_wrapper.append( $gallery_overlay ); } }); } function et_pb_fix_gallery_slider_icons_size() { var $gallery_slider_icons = $( '.et_pb_gallery_fullwidth .et_social_media_wrapper' ); if ( 0 > $gallery_slider_icons.length ) { return; } var $all_galleries = $( '.et_pb_gallery_fullwidth' ); $all_galleries.each( function() { var $this_gallery = $( this ); var $this_gallery_media = $this_gallery.find( '.et_social_media_wrapper .et_social_media' ); if ( 0 > $this_gallery_media.length ) { return; } var $first_icon_wrapper = $this_gallery_media.first(); var icons_width = $first_icon_wrapper.width(); var icons_height = $first_icon_wrapper.css( 'height' ); var icons_max_height = $first_icon_wrapper.css( 'max-height' ); // use the dimensions from first slide and apply it to all the media icons in the slider $this_gallery_media.width( icons_width - 80 ); $this_gallery_media.height( icons_height ); $this_gallery_media.css( { 'max-height' : icons_max_height } ); }); } $ ( 'body' ).on( 'click', '.et_social_open_all', function() { all_networks_opened++; if ( 1 == all_networks_opened ) { var this_button = $( this ), page_id = this_button.data( 'page_id' ), permalink = this_button.data( 'permalink' ), title = this_button.data( 'title' ), media = typeof this_button.data( 'media' ) !== 'undefined' ? this_button.data( 'media' ) : '', is_popup = 'popup' == this_button.data( 'location' ) ? 'true' : 'false'; $.ajax({ type: 'POST', url: monarchSettings.ajaxurl, data: { action : 'generate_all_networks_popup', all_networks_page_id : page_id, all_networks_link : permalink, all_networks_title : title, all_networks_media : media, is_popup : is_popup, generate_all_window_nonce : monarchSettings.generate_all_window_nonce }, success: function( data ) { if ( 'false' == is_popup ) { $( 'body' ).append( data ); make_popup_visible( $( '.et_social_all_networks_popup' ), 1 ); } else { var popup_container = this_button.parent().closest( '.et_social_popup_content' ); this_button.parent().replaceWith( data ); define_popup_position( popup_container ); all_networks_opened = 0; } } }); } return false; }); set_mobile_sidebar_height(); set_sidebar_position(); $( window ).resize( function(){ if ( $( '.et_social_resize' ).length ) { $( '.et_social_resize' ).each( function() { define_popup_position( $( this ) ); }); } if ( $('.et_social_mobile') ) { set_mobile_sidebar_height(); } if( $( '.et_social_sidebar_networks' ).length ){ set_sidebar_position(); } set_media_wrapper_size(); }); $( '.et_social_hide_sidebar' ).click( function(){ $( '.et_social_hide_sidebar' ).toggleClass( 'et_social_hidden_sidebar' ); $( '.et_social_sidebar_networks' ).toggleClass( 'et_social_hidden_sidebar et_social_visible_sidebar' ); }); $( window ).load( function(){ set_media_wrapper_size(); if ( $( '.et_social_pin_images' ).length && ( $( '.et_social_all_button' ).length || $( '.et_social_pinterest' ).length ) ) { var pin_container = $( '.et_social_pin_images' ), permalink = pin_container.data( 'permalink' ), title = pin_container.data( 'title' ), post_id = pin_container.data( 'post_id' ), $i = 0; $( 'img' ).each( function(){ //do not include comment avatars into the Modal if ( ! $( this ).hasClass( 'avatar' ) ) { var this_img = $( this ).attr( 'src' ), this_alt = $( this ).attr( 'alt' ); if ( '' != this_img ) { var pin_link = 'http://www.pinterest.com/pin/create/button/?url=' + permalink + '&media=' + this_img + '&description=' + title, this_img_container = '
' + this_alt + '
'; $( '.et_social_pin_images' ).append( this_img_container ); $i++; } } }); //Append error message if no images found on page if ( 0 == $i ) { $( '.et_social_pin_images' ).append( monarchSettings.no_img_message ); } } }); }); })(jQuery) ================================================ FILE: March Cohort 2023/8_9_Pandas/Data Cleaning With Pandas/Data Cleaning with Python and Pandas_ Detecting Missing Values_files/custom.min.js.download ================================================ /*! ET et_shortcodes_frontend.js */ !function($){$.fn.et_shortcodes_switcher=function(options){options=$.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
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