[
  {
    "path": ".gitignore",
    "content": "*/.ipynb_checkpoints/*\n"
  },
  {
    "path": "README.md",
    "content": "![Python 2.7](https://img.shields.io/badge/python-2.7-blue.svg)\n![Python 3.5](https://img.shields.io/badge/python-3.5-blue.svg)\n![License](https://img.shields.io/badge/license-MIT%20License-blue.svg)\n\n# Randy Olson's data analysis and machine learning projects\n\n© 2016 - current, Randal S. Olson\n\nThis is a repository of teaching materials, code, and data for my data analysis and machine learning projects.\n\nEach repository will (usually) correspond to one of the blog posts on my [web site](http://www.randalolson.com/blog/).\n\nBe sure to check the documentation (usually in IPython Notebook format) in the directory you're interested in for the notes on the analysis, data usage terms, etc.\n\nIf you don't have the necessary software installed to run IPython Notebook, don't fret. You can use [nbviewer](http://nbviewer.ipython.org/) to view a notebook on the web.\n\nFor example, if you want to view the notebook in the `wheres-waldo-path-optimization` directory, copy the [full link](https://github.com/rhiever/Data-Analysis-and-Machine-Learning-Projects/blob/master/wheres-waldo-path-optimization/Where's%20Waldo%20path%20optimization.ipynb) to the notebook then paste it into [nbviewer](http://nbviewer.ipython.org/github/rhiever/Data-Analysis-and-Machine-Learning-Projects/blob/master/wheres-waldo-path-optimization/Where%27s%20Waldo%20path%20optimization.ipynb).\n\n## License\n\n### Instructional Material\n\nAll instructional material in this repository is made available under the [Creative Commons Attribution license](https://creativecommons.org/licenses/by/4.0/). The following is a human-readable summary of (and not a substitute for) the [full legal text of the CC BY 4.0 license](https://creativecommons.org/licenses/by/4.0/legalcode).\n\nYou are free to:\n\n* **Share**—copy and redistribute the material in any medium or format\n* **Adapt**—remix, transform, and build upon the material\n\nfor any purpose, even commercially.\n\nThe licensor cannot revoke these freedoms as long as you follow the license terms.\n\nUnder the following terms:\n\n* **Attribution**—You must give appropriate credit (mentioning that your work is derived from work that is © Randal S. Olson and, where practical, linking to http://www.randalolson.com/), provide a [link to the license](https://creativecommons.org/licenses/by/4.0/), and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.\n\n**No additional restrictions**—You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.\n\n**Notices:**\n\n* You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation.\n* No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.\n\n### Software\n\nExcept where otherwise noted, the example programs and other software provided in this repository are made available under the [OSI](http://opensource.org/)-approved [MIT license](http://opensource.org/licenses/mit-license.html).\n\nPermission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the \"Software\"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n"
  },
  {
    "path": "environment.yml",
    "content": "channels:\n  - conda-forge\n  - defaults\ndependencies:\n  - numpy\n  - pandas\n  - scikit-learn\n  - matplotlib\n  - seaborn\n  - watermark\n  - tqdm\n  - joblib\n  - tpot\n  - pip:\n    - twitter\n    - googlemaps\n"
  },
  {
    "path": "example-data-science-notebook/Example Machine Learning Notebook.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# An example machine learning notebook\\n\",\n    \"\\n\",\n    \"### Notebook by [Randal S. Olson](http://www.randalolson.com/)\\n\",\n    \"#### Supported by [Jason H. Moore](http://www.epistasis.org/)\\n\",\n    \"#### [University of Pennsylvania Institute for Bioinformatics](http://upibi.org/)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"**It is recommended to [view this notebook in nbviewer](http://nbviewer.ipython.org/github/rhiever/Data-Analysis-and-Machine-Learning-Projects/blob/master/example-data-science-notebook/Example%20Machine%20Learning%20Notebook.ipynb) for the best viewing experience.**\\n\",\n    \"\\n\",\n    \"**You can also [execute the code in this notebook on Binder](https://mybinder.org/v2/gh/rhiever/Data-Analysis-and-Machine-Learning-Projects/master?filepath=example-data-science-notebook%2FExample%20Machine%20Learning%20Notebook.ipynb) - no local installation required.**\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Table of contents\\n\",\n    \"\\n\",\n    \"1. [Introduction](#Introduction)\\n\",\n    \"\\n\",\n    \"2. [License](#License)\\n\",\n    \"\\n\",\n    \"3. [Required libraries](#Required-libraries)\\n\",\n    \"\\n\",\n    \"4. [The problem domain](#The-problem-domain)\\n\",\n    \"\\n\",\n    \"5. [Step 1: Answering the question](#Step-1:-Answering-the-question)\\n\",\n    \"\\n\",\n    \"6. [Step 2: Checking the data](#Step-2:-Checking-the-data)\\n\",\n    \"\\n\",\n    \"7. [Step 3: Tidying the data](#Step-3:-Tidying-the-data)\\n\",\n    \"\\n\",\n    \"    - [Bonus: Testing our data](#Bonus:-Testing-our-data)\\n\",\n    \"\\n\",\n    \"8. [Step 4: Exploratory analysis](#Step-4:-Exploratory-analysis)\\n\",\n    \"\\n\",\n    \"9. [Step 5: Classification](#Step-5:-Classification)\\n\",\n    \"\\n\",\n    \"    - [Cross-validation](#Cross-validation)\\n\",\n    \"\\n\",\n    \"    - [Parameter tuning](#Parameter-tuning)\\n\",\n    \"\\n\",\n    \"10. [Step 6: Reproducibility](#Step-6:-Reproducibility)\\n\",\n    \"\\n\",\n    \"11. [Conclusions](#Conclusions)\\n\",\n    \"\\n\",\n    \"12. [Further reading](#Further-reading)\\n\",\n    \"\\n\",\n    \"13. [Acknowledgements](#Acknowledgements)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Introduction\\n\",\n    \"\\n\",\n    \"[[ go back to the top ]](#Table-of-contents)\\n\",\n    \"\\n\",\n    \"In the time it took you to read this sentence, terabytes of data have been collectively generated across the world — more data than any of us could ever hope to process, much less make sense of, on the machines we're using to read this notebook.\\n\",\n    \"\\n\",\n    \"In response to this massive influx of data, the field of Data Science has come to the forefront in the past decade. Cobbled together by people from a diverse array of fields — statistics, physics, computer science, design, and many more — the field of Data Science represents our collective desire to understand and harness the abundance of data around us to build a better world.\\n\",\n    \"\\n\",\n    \"In this notebook, I'm going to go over a basic Python data analysis pipeline from start to finish to show you what a typical data science workflow looks like.\\n\",\n    \"\\n\",\n    \"In addition to providing code examples, I also hope to imbue in you a sense of good practices so you can be a more effective — and more collaborative — data scientist.\\n\",\n    \"\\n\",\n    \"I will be following along with the data analysis checklist from [The Elements of Data Analytic Style](https://leanpub.com/datastyle), which I strongly recommend reading as a free and quick guidebook to performing outstanding data analysis.\\n\",\n    \"\\n\",\n    \"**This notebook is intended to be a public resource. As such, if you see any glaring inaccuracies or if a critical topic is missing, please feel free to point it out or (preferably) submit a pull request to improve the notebook.**\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## License\\n\",\n    \"\\n\",\n    \"[[ go back to the top ]](#Table-of-contents)\\n\",\n    \"\\n\",\n    \"Please see the [repository README file](https://github.com/rhiever/Data-Analysis-and-Machine-Learning-Projects#license) for the licenses and usage terms for the instructional material and code in this notebook. In general, I have licensed this material so that it is as widely usable and shareable as possible.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Required libraries\\n\",\n    \"\\n\",\n    \"[[ go back to the top ]](#Table-of-contents)\\n\",\n    \"\\n\",\n    \"If you don't have Python on your computer, you can use the [Anaconda Python distribution](http://continuum.io/downloads) to install most of the Python packages you need. Anaconda provides a simple double-click installer for your convenience.\\n\",\n    \"\\n\",\n    \"This notebook uses several Python packages that come standard with the Anaconda Python distribution. The primary libraries that we'll be using are:\\n\",\n    \"\\n\",\n    \"* **NumPy**: Provides a fast numerical array structure and helper functions.\\n\",\n    \"* **pandas**: Provides a DataFrame structure to store data in memory and work with it easily and efficiently.\\n\",\n    \"* **scikit-learn**: The essential Machine Learning package in Python.\\n\",\n    \"* **matplotlib**: Basic plotting library in Python; most other Python plotting libraries are built on top of it.\\n\",\n    \"* **Seaborn**: Advanced statistical plotting library.\\n\",\n    \"* **watermark**: A Jupyter Notebook extension for printing timestamps, version numbers, and hardware information.\\n\",\n    \"\\n\",\n    \"To make sure you have all of the packages you need, install them with `conda`:\\n\",\n    \"\\n\",\n    \"    conda install numpy pandas scikit-learn matplotlib seaborn\\n\",\n    \"    \\n\",\n    \"    conda install -c conda-forge watermark\\n\",\n    \"\\n\",\n    \"`conda` may ask you to update some of them if you don't have the most recent version. Allow it to do so.\\n\",\n    \"\\n\",\n    \"**Note:** I will not be providing support for people trying to run this notebook outside of the Anaconda Python distribution.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## The problem domain\\n\",\n    \"\\n\",\n    \"[[ go back to the top ]](#Table-of-contents)\\n\",\n    \"\\n\",\n    \"For the purposes of this exercise, let's pretend we're working for a startup that just got funded to create a smartphone app that automatically identifies species of flowers from pictures taken on the smartphone. We're working with a moderately-sized team of data scientists and will be building part of the data analysis pipeline for this app.\\n\",\n    \"\\n\",\n    \"We've been tasked by our company's Head of Data Science to create a demo machine learning model that takes four measurements from the flowers (sepal length, sepal width, petal length, and petal width) and identifies the species based on those measurements alone.\\n\",\n    \"\\n\",\n    \"<img src=\\\"images/petal_sepal.jpg\\\" />\\n\",\n    \"\\n\",\n    \"We've been given a [data set](https://github.com/rhiever/Data-Analysis-and-Machine-Learning-Projects/raw/master/example-data-science-notebook/iris-data.csv) from our field researchers to develop the demo, which only includes measurements for three types of *Iris* flowers:\\n\",\n    \"\\n\",\n    \"### *Iris setosa*\\n\",\n    \"\\n\",\n    \"<img src=\\\"images/iris_setosa.jpg\\\" />\\n\",\n    \"\\n\",\n    \"### *Iris versicolor*\\n\",\n    \"<img src=\\\"images/iris_versicolor.jpg\\\" />\\n\",\n    \"\\n\",\n    \"### *Iris virginica*\\n\",\n    \"<img src=\\\"images/iris_virginica.jpg\\\" />\\n\",\n    \"\\n\",\n    \"The four measurements we're using currently come from hand-measurements by the field researchers, but they will be automatically measured by an image processing model in the future.\\n\",\n    \"\\n\",\n    \"**Note:** The data set we're working with is the famous [*Iris* data set](https://archive.ics.uci.edu/ml/datasets/Iris) — included with this notebook — which I have modified slightly for demonstration purposes.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Step 1: Answering the question\\n\",\n    \"\\n\",\n    \"[[ go back to the top ]](#Table-of-contents)\\n\",\n    \"\\n\",\n    \"The first step to any data analysis project is to define the question or problem we're looking to solve, and to define a measure (or set of measures) for our success at solving that task. The data analysis checklist has us answer a handful of questions to accomplish that, so let's work through those questions.\\n\",\n    \"\\n\",\n    \">Did you specify the type of data analytic question (e.g. exploration, association causality) before touching the data?\\n\",\n    \"\\n\",\n    \"We're trying to classify the species (i.e., class) of the flower based on four measurements that we're provided: sepal length, sepal width, petal length, and petal width.\\n\",\n    \"\\n\",\n    \">Did you define the metric for success before beginning?\\n\",\n    \"\\n\",\n    \"Let's do that now. Since we're performing classification, we can use [accuracy](https://en.wikipedia.org/wiki/Accuracy_and_precision) — the fraction of correctly classified flowers — to quantify how well our model is performing. Our company's Head of Data has told us that we should achieve at least 90% accuracy.\\n\",\n    \"\\n\",\n    \">Did you understand the context for the question and the scientific or business application?\\n\",\n    \"\\n\",\n    \"We're building part of a data analysis pipeline for a smartphone app that will be able to classify the species of flowers from pictures taken on the smartphone. In the future, this pipeline will be connected to another pipeline that automatically measures from pictures the traits we're using to perform this classification.\\n\",\n    \"\\n\",\n    \">Did you record the experimental design?\\n\",\n    \"\\n\",\n    \"Our company's Head of Data has told us that the field researchers are hand-measuring 50 randomly-sampled flowers of each species using a standardized methodology. The field researchers take pictures of each flower they sample from pre-defined angles so the measurements and species can be confirmed by the other field researchers at a later point. At the end of each day, the data is compiled and stored on a private company GitHub repository.\\n\",\n    \"\\n\",\n    \">Did you consider whether the question could be answered with the available data?\\n\",\n    \"\\n\",\n    \"The data set we currently have is only for three types of *Iris* flowers. The model built off of this data set will only work for those *Iris* flowers, so we will need more data to create a general flower classifier.\\n\",\n    \"\\n\",\n    \"<hr />\\n\",\n    \"\\n\",\n    \"Notice that we've spent a fair amount of time working on the problem without writing a line of code or even looking at the data.\\n\",\n    \"\\n\",\n    \"**Thinking about and documenting the problem we're working on is an important step to performing effective data analysis that often goes overlooked.** Don't skip it.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Step 2: Checking the data\\n\",\n    \"\\n\",\n    \"[[ go back to the top ]](#Table-of-contents)\\n\",\n    \"\\n\",\n    \"The next step is to look at the data we're working with. Even curated data sets from the government can have errors in them, and it's vital that we spot these errors before investing too much time in our analysis.\\n\",\n    \"\\n\",\n    \"Generally, we're looking to answer the following questions:\\n\",\n    \"\\n\",\n    \"* Is there anything wrong with the data?\\n\",\n    \"* Are there any quirks with the data?\\n\",\n    \"* Do I need to fix or remove any of the data?\\n\",\n    \"\\n\",\n    \"Let's start by reading the data into a pandas DataFrame.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>sepal_length_cm</th>\\n\",\n       \"      <th>sepal_width_cm</th>\\n\",\n       \"      <th>petal_length_cm</th>\\n\",\n       \"      <th>petal_width_cm</th>\\n\",\n       \"      <th>class</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>5.1</td>\\n\",\n       \"      <td>3.5</td>\\n\",\n       \"      <td>1.4</td>\\n\",\n       \"      <td>0.2</td>\\n\",\n       \"      <td>Iris-setosa</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>4.9</td>\\n\",\n       \"      <td>3.0</td>\\n\",\n       \"      <td>1.4</td>\\n\",\n       \"      <td>0.2</td>\\n\",\n       \"      <td>Iris-setosa</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>4.7</td>\\n\",\n       \"      <td>3.2</td>\\n\",\n       \"      <td>1.3</td>\\n\",\n       \"      <td>0.2</td>\\n\",\n       \"      <td>Iris-setosa</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>4.6</td>\\n\",\n       \"      <td>3.1</td>\\n\",\n       \"      <td>1.5</td>\\n\",\n       \"      <td>0.2</td>\\n\",\n       \"      <td>Iris-setosa</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>5.0</td>\\n\",\n       \"      <td>3.6</td>\\n\",\n       \"      <td>1.4</td>\\n\",\n       \"      <td>0.2</td>\\n\",\n       \"      <td>Iris-setosa</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   sepal_length_cm  sepal_width_cm  petal_length_cm  petal_width_cm  \\\\\\n\",\n       \"0              5.1             3.5              1.4             0.2   \\n\",\n       \"1              4.9             3.0              1.4             0.2   \\n\",\n       \"2              4.7             3.2              1.3             0.2   \\n\",\n       \"3              4.6             3.1              1.5             0.2   \\n\",\n       \"4              5.0             3.6              1.4             0.2   \\n\",\n       \"\\n\",\n       \"         class  \\n\",\n       \"0  Iris-setosa  \\n\",\n       \"1  Iris-setosa  \\n\",\n       \"2  Iris-setosa  \\n\",\n       \"3  Iris-setosa  \\n\",\n       \"4  Iris-setosa  \"\n      ]\n     },\n     \"execution_count\": 1,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"import pandas as pd\\n\",\n    \"\\n\",\n    \"iris_data = pd.read_csv('iris-data.csv')\\n\",\n    \"iris_data.head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We're in luck! The data seems to be in a usable format.\\n\",\n    \"\\n\",\n    \"The first row in the data file defines the column headers, and the headers are descriptive enough for us to understand what each column represents. The headers even give us the units that the measurements were recorded in, just in case we needed to know at a later point in the project.\\n\",\n    \"\\n\",\n    \"Each row following the first row represents an entry for a flower: four measurements and one class, which tells us the species of the flower.\\n\",\n    \"\\n\",\n    \"**One of the first things we should look for is missing data.** Thankfully, the field researchers already told us that they put a 'NA' into the spreadsheet when they were missing a measurement.\\n\",\n    \"\\n\",\n    \"We can tell pandas to automatically identify missing values if it knows our missing value marker.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"iris_data = pd.read_csv('iris-data.csv', na_values=['NA'])\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Voilà! Now pandas knows to treat rows with 'NA' as missing values.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Next, it's always a good idea to look at the distribution of our data — especially the outliers.\\n\",\n    \"\\n\",\n    \"Let's start by printing out some summary statistics about the data set.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>sepal_length_cm</th>\\n\",\n       \"      <th>sepal_width_cm</th>\\n\",\n       \"      <th>petal_length_cm</th>\\n\",\n       \"      <th>petal_width_cm</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>count</th>\\n\",\n       \"      <td>150.000000</td>\\n\",\n       \"      <td>150.000000</td>\\n\",\n       \"      <td>150.000000</td>\\n\",\n       \"      <td>145.000000</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>mean</th>\\n\",\n       \"      <td>5.644627</td>\\n\",\n       \"      <td>3.054667</td>\\n\",\n       \"      <td>3.758667</td>\\n\",\n       \"      <td>1.236552</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>std</th>\\n\",\n       \"      <td>1.312781</td>\\n\",\n       \"      <td>0.433123</td>\\n\",\n       \"      <td>1.764420</td>\\n\",\n       \"      <td>0.755058</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>min</th>\\n\",\n       \"      <td>0.055000</td>\\n\",\n       \"      <td>2.000000</td>\\n\",\n       \"      <td>1.000000</td>\\n\",\n       \"      <td>0.100000</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>25%</th>\\n\",\n       \"      <td>5.100000</td>\\n\",\n       \"      <td>2.800000</td>\\n\",\n       \"      <td>1.600000</td>\\n\",\n       \"      <td>0.400000</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>50%</th>\\n\",\n       \"      <td>5.700000</td>\\n\",\n       \"      <td>3.000000</td>\\n\",\n       \"      <td>4.350000</td>\\n\",\n       \"      <td>1.300000</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>75%</th>\\n\",\n       \"      <td>6.400000</td>\\n\",\n       \"      <td>3.300000</td>\\n\",\n       \"      <td>5.100000</td>\\n\",\n       \"      <td>1.800000</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>max</th>\\n\",\n       \"      <td>7.900000</td>\\n\",\n       \"      <td>4.400000</td>\\n\",\n       \"      <td>6.900000</td>\\n\",\n       \"      <td>2.500000</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"       sepal_length_cm  sepal_width_cm  petal_length_cm  petal_width_cm\\n\",\n       \"count       150.000000      150.000000       150.000000      145.000000\\n\",\n       \"mean          5.644627        3.054667         3.758667        1.236552\\n\",\n       \"std           1.312781        0.433123         1.764420        0.755058\\n\",\n       \"min           0.055000        2.000000         1.000000        0.100000\\n\",\n       \"25%           5.100000        2.800000         1.600000        0.400000\\n\",\n       \"50%           5.700000        3.000000         4.350000        1.300000\\n\",\n       \"75%           6.400000        3.300000         5.100000        1.800000\\n\",\n       \"max           7.900000        4.400000         6.900000        2.500000\"\n      ]\n     },\n     \"execution_count\": 3,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"iris_data.describe()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We can see several useful values from this table. For example, we see that five `petal_width_cm` entries are missing.\\n\",\n    \"\\n\",\n    \"If you ask me, though, tables like this are rarely useful unless we know that our data should fall in a particular range. It's usually better to visualize the data in some way. Visualization makes outliers and errors immediately stand out, whereas they might go unnoticed in a large table of numbers.\\n\",\n    \"\\n\",\n    \"Since we know we're going to be plotting in this section, let's set up the notebook so we can plot inside of it.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# This line tells the notebook to show plots inside of the notebook\\n\",\n    \"%matplotlib inline\\n\",\n    \"\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"import seaborn as sb\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Next, let's create a **scatterplot matrix**. Scatterplot matrices plot the distribution of each column along the diagonal, and then plot a scatterplot matrix for the combination of each variable. They make for an efficient tool to look for errors in our data.\\n\",\n    \"\\n\",\n    \"We can even have the plotting package color each entry by its class to look for trends within the classes.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"''\"\n      ]\n     },\n     \"execution_count\": 5,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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OuKsLZUx5ce8cg2F0W+NxBuGt8CRWaCTMHwmpjr1ZKf5rUkrawOC1OLLpika5FZsnVS5BnzYNdtSPoC88eNvaW/tj0B4P8c+dg7N1aiUu/0QsTZw7ER1srMWZGf92YlwkzB0JRBGY9PRoBbzjvsq9/7nJanOju7K4rn11WFxb/c7FucP5fP/srlly9RNdCs/jKxY1OxAKEZwz75J/6qfE/+edRDBnbW9d6Aolol2AA0S7Bk2YNwiUjekany3e4LPji4yrd+33xcVXCdWHizIFQVKG7pjhclpSvb5Q9WHkhynKpzoNvNAhz8qxBuNjeDxt+vUf3Y+mhkQ9hzt/m6LqNGXb9clp0a7qoqkDHLk7d7EhjZvSHM8+KSXcO5mxjlFEiXXTcAbfhXeyD1Qfx/J7nwz8aOxXDE/DAZXVFfyBa7QJHD1Qjv9CRtOtkv5G9sGnVPrgK7Bj5rYvQoYsdk2YNgs1pQcAbgsWq4PQxN7b8+VNM+PEACMGKSy7zhXwoG1ama2V5YvQTOO45jutfuT6634ieIzCm9xhdhWbZB8vwn6P+s9FzWGxKwsQRY2b0h8Wu4JUndkW3ffu+kqTXhdhWlmtv/zp6XFiAt1ftjW4bd+sAWK2K7vqhqAI2pyXhWgaA67zkGFZecth35zftzz/rvVZKCDWb0fSsRnfOJCRk/UT6UkpISAjoC/eAP4SP6ufVj9z9kkB0rv3Itr1bKzFozPlYOPz/oXdhEQ5XVcKfZAaZ6hMevLjwn9FtRf06YfT0r+nuxL2zOnyHGYhM9C8B0fypOInaSqSLTldnVywZtQTuoBtF+UWorK1Ega0Aaz5Zg42HNuKk52S0m5jUJHz+QH1ca5j11Gj4k3Tt9HtD0QH6APDp9mMo6tcJ194xCDteO4hBoy/Ar+/ZDKC+6403BJuD+SWXaVLDK5+9oquUvH/0fSwZtUQ/VfKoJfiy9ij65l8CRSgosl+Acb3Hwxv0QkI2eE0J+DRdXEbK8WvvGKTbVn3CuEtX9QmPbj+vO4jNL3ys2/b2qr0Y/+MBujXB9vztCIaO793q3yGlP1ZeiDJEfEXFoTpw2ne60TVYNE2Dp8ZgpeIOVijKuf2M7qZNvXeo4R02m8OCXStOYMOBA+jVtwC97uph2MS/de0B3Wc4eqAaHbs6E7bZHCr+71flujtxAOB1B9GxqxPus344XBbYXVb+IKO04bQ40cPZHQ8MvAeFjkJoVW4s2PoQjnmOY8nVSzCj/wzcNug2VNZWYuPBjZjS+1tw5ikI+ALwu0PRO82lUy407DopAEy9tyQ8IHrD5/h0x7Honet+I3vB5lSjY10mzByI3ZsOY8f6zzldbA5zqA7cdMmNCLndEAAKQnYM616CAkcnLB+7PHr9sCt2OAP5eHPF3piYGwUpg5j9zuwGryk2h/Fg+vgxittePWjYpSv+umA06N5VYIfFoiAYs/DxwFHnIxTQ8Ppz/0roSha/jbGf3dgvgygDRLqnzN40G8NXD8fsTbNRF6wzXiE56NEdG/CFjKcijlvVPvZuWmQ/o23vrN6HgE/TbdvwzL9gd6qYdOdgzHp6NCbdGW7Cd8cNrowMLo7fFrkTF3m/YFCLrhvz7D2bsfmFj+H3hRAMcPpLSh/+oA8LLp2N2p88hI8HD4Hn/p/jwX53o5ujC+b+fS5OeU/hmPsYth7Ziok9J2PTc/ux4u7NCPo1vL1qbzTmt716CHu3VuLaOwbhjqdGY/Kdg6EFJV779W48e89mbPnTflz27WJcUtojOnA/kg8j+W3v1kpse/UQp4vNcf6gD44aHzz3/xyfDB4Kz/0/h6PGh0DQjzxrHhShIM+ah5BfYuPKvQnXBSVkafSaEmlpjxVuKQzqtrmrfbA64q4LjsTrQmTQfawrbrg44RoQDIQQDOqvPW+u/Ahed5BTJecYUysvQojrhBDlQogqIcRZIUSNEOKsmecgykVG60XkWfNSmurS5rAk6U8fHoAvpawfZKkk7JfsDlv8NMZHD1RDsSiI7fqlWhVMnDlQN5//xJkD4XBZErZte/VgQpqN5viXEkRtLn7djMhaGFa/hpNzH4T7/W1AMAj3+9tQNW8BZn/tdnR3dkcXZxf0zOuJ6y+6CVt+/xkq95/BxcO6o8DgTvOO9Z/D5rTg2Xs2AwK6yk3kpsHIqRdhzIz+2Lnh82g+FELA5lCxY/3nuvfjdLG5IT42bQGZEJMn5z4IW1DA5wlASgmfJ5B0KmKHw6bbZnRNURRg3A8H6MrxcT8cEB1MH1u2W6wq7E4LhBCwOy2wWNWE60Jk0H3stmTXgPgZxJK15jP2s5vZ3caeAHA9gD1Spv4zQwjRCcBvAHwd4V8/P5ZScoQFUT2jRcYOnjmY0lSX/mRTEcdNTTlh5kCUTrkQ2149FN2voWmWY5VOudC4a1q+NWEgpYTEpDsHRStPQoiEO3FWW/PXpiEyk9G6GdGuNE4n3Ds/0O3v3vkBLu1WjLK8MszeFO5+s3P6Thw9UI1LSnvgsm8XozrJ9K6RqZCT/bAs6OrExt/uxac7wmNfIquSt2Q6dMpcRrG5csJvEmJS7d4TvoCCN1c2XN4btZ4YXVNUqwJFFQmD6VWr0ujA+WQTyABxg+5TXJ8sWWs+Yz+7md1t7AsA/2pKxaXekwBel1J+DcAQAPtMThdRRvMEPZg1ZBZenfIaPpzxIV6d8hoOVh/CklFLMKLnCFiEBSN6jsCSq5ck3CWz2lVMiLurNWHmQOx+54uELgODx1yg2w8wXg0ZErptg0dfEB3Yf8dTozHqe/2wd2slAv7wBSR8d9gCoQgoigK701p/J84Ki01JSF+kwhXLqNJE1NqMWj0jXWk0jweu4cN0+7uGD0OwrhYL/rEgeszhqkqUTrkQV0/rhw5dHFBUgXG36u9cj5nRHxW7jmPizIHRykisXn0LUH3Sg88+OJ6wKnlkOvT4u96cLjZzJWvti2UUm2erjyfEZOGcnySsQm9U3k+YORDCKrH15q348JYPsfXmrVg2ZlnCNSXg07B3a6VuMP3erZUI+LSE8t6IUETCfvHbkuUBvyeYUms+Yz+7iabXMxp4MyFGAPgPAH8DEL2VKqV8vIFjOgL4EEBxqpWe0tJSuWPHjhamlgatGtSk/We992ST9r97xdgm7d9K2n3EnhnxGgqF4K0NYOPK2MGVA2DPt8Cn+RqdbUzTtOidqMg8/M/e8zdo2rksF10rwnduhi/VIhDwhqID5yNz8lsQhP9MDZy9usFz9AQc53WDpyaAgC8U3c9qVxMmBQCMZ0iDhC59FqsCb12w0UkGslRWxGy20KSG4auHIyjP3ZG2CAt2ztgJSInQqSp8+cADcO/8AK7hw1D02GNQCjtj+Aul0WPmj5iPbxfdkBDPoYCG/M726ArkPk8AmhqEw+qAtyagG+g87tYBkJDI7+yIrmgeOzVyO87Ox3g1WYOtfbGTsRjE5nUXTcF/DHgAlfffH43J3r//PVbcvdmwvK8+4dGV7T6LB/f97b7oeR8d9SgKHYVQlXOVgcgkMKmU981lNHX/xJkD4ci3IhjQElptmhD77R6v1HJmt6n9EkAtAAcAWyP7RhQDOAHgd0KIIQB2ApgjpawzOW1EGSvo16KDK4HIYl97MenOQchzhpvzG1oVOdzaEb6o2J3WpF3JYpvabQ4Lav21ePGzF3FN0WR0RBHOKKfwxmevYUb/GaiynsV5oiuqrGfR3d8FoaCGzS98HLfisgabQ3+xTXZRtjut0fTVBerwwqEXcM0tkzG1cCgOV1XihUO/x/QB05GnNLz6M5GZkq3hUuOvweg/jcaswXfgx08vh8WVj5C7DisPvIix6jjdMVd0H2W4WN/YW/uj9oxPt1L5N390CZR8P5wd7NFuNH5vCLs3HdZ18Snq10mXXyN3rgGwu0yGi21RARBt7Vs+drmunPcEPQmxecxzHKedEuqSBbi0WzG+PFEBnzeQ8lT2l/2oSHfeeVvmYdmYZci35Uf3C/q1lMr7lmhofTJb/RpgsXHO2M8tZv+VC6WUE5uRhmEAZksp3xdCPAngZwB+HruTEOJ2ALcDQO/enOeb0pvZ8drQoPvmiHQzib+rpVoEfJ5gdLFIl82FQ9WHgKJzxx6qPgSH1YGFux6JVkBWfPO56OBK4NwK4ZPvGqw7b6oXZafFiRUfrsBTu56KbrMIC24bfFuzPm9TaJqEOxCCy6bC7Q/BZQ1PR5vtmhuz2fx9aVKDEALPT3getYFa5NvyUVlTiQJ7AWyqDRP6TMBTHz6N949tC09Dm5eHFbufxWdnK7DoikVY+O5ClB8rR+/CImw4kDhtuCPPitee2a3LN3/73aeYfNdgCGtMZcSuYuBVRaj85AxXEa/X0jI2nePWaXGiu7M71k1dF12rZeWelQndt5wWJ5aNWYaQDKGDrQNq/DVQhYqQDGFeTPm8fMzylKey71EwVLet/Fg5XFZXQhqTlfex14+WLjZsVCHn+l8EmF95eUsIMVFK+WYTjjkC4IiU8v3612sRrrzoSCmfA/AcEG4ibnFKiVqR2fGadNC9NxhtsWhS+oRE0O7DZT8qQo+CoThWfQKKUxp21Vow7BfY+Nze6Jouc3/0IPxBv27NAKsltcGVRhMPGM1mY3RH0WjgqNk0TeJUnR9la8qx/VAVRvQpxLJpJeiSZ0ubHzatpTkxm83fV6SVcO3+tbiu+LpoRaSkRwl+eeUvsebjNSgrKQMAbDy0MZoXSnqUYMPBDQAQXiiwUzECSRZxTXVQckN3oXNVS8rYdI9bX8iHsmFlWPCPBdGYW3zlYvhCPl1ZKaWEJ+jBvC3zdN28Ojs668pnp8UJqeonSlGUxIlSevUtwLHqE7ptJT1K4A64dS0vDcVt7HpdkUlbWlKBiZWsKxnXdMk9ZncevxvA60IIT6pTJUspvwLwhRDi0vpN4wDsNTldRBkt2aD7VGffkprUTYvsD/px79/uxcRXxmPI6iGY+Mp4yKAwXg/GrZ9X/2+/+xRKyAKHHxAScPiRdIB9/Mw1kR93sSKVklhOixNLrm58MoIm0TTAVwvI+mctcQCsOxBC2ZpyvFdxCkFN4r2KUyhbUw53C9aX0TSJWl8Qmqx/1rLj3oumSdT5g0m/r8TP3fj3n04irYTjeo/DwncXYvtX2zHxwvFYNHQ+erp64LbiH+A8Vy88UvJz/PeoJdEfiZG43XhoIx7Z9ghOe08DVg3f/NElCfnX70mWbxLjzWiQM6UmPhbdfuN87g+GEmK0PfKvJjXdhA/bv9qOBf9YkDBo3xP0YN3+l7Fo6HzsnL4Di4bOx7r9LyMUDMLh1cLls1cDNA2qquomSgkpwYSYHPfj/nA5Hbpy99FRjyaUu8nK+/j1ujau/AhBv6a79sgWfH8Bfyhh4gGu6ZKbTG15kVJ2aOahswG8KISwAagA8CPzUkWU+RRFgbODVXfnzGpXUxocmexuVQ9nD91+Dqct6fSsMx8bBbvLAp87iE/e/wpWu4rDP7zr3CDlZYndEibMHAiLVZ++yI+7+DEvCesICAWFjsKEu4dGkxGkRNMA9wlg7Uzg8HtA78uBG1cCrm7hRQvquWwqth+q0h26/VAVXM3snpPud3ibK/K5CvNsht+X06roPvecsX1xz2UFwMsNf//txWgSiUgrYXFBMcqPlWNyn2ux4NIyVM1dgI/r477X4sWofvVVjL7pJiiKHUB4hfNfjf6VrhuPRbFAdZ5r6fR7g9FuMGNm9Mc7q8+NeRkzoz+s9rb9TrK5K45RHnzxtssS4rZnRxvs/lO6MkLeuBK1Smfc8cIHbZp/k7VQO1QH6gJ10Th1WZz4fs8pqJq7AJ/Ux+StTy2DWl2DI7GTSCxdChR2hqLGjBGx2BDKC2HirAFwOu3weHyQlhCcljwsG7MMLqsL7oAbTotTN1gfOHczLZVuaFaHCp87fBNLC0kEvEFYm1n5TrWlkrKf2YtU/psQoiDmdSchxHcaO05KuUtKWSqlHCyl/I6U8rSZ6SLKBvFTDKc6q0uyu1Vlg+7V7efzBJK2nrz+7B6suHszXn92D/oO746gL6hbBK2ybDZs/rOY8J2umLX8m5jwna5w//G3kF59i0pspWTnjJ1YPnZ5wgw6sfvGrgjd7IoLAARoC73WAAAgAElEQVTc4R8lh7YAWjD8vHZmeHsMtz+EEX0KddtG9CmEu5l39lqjJScdRD7XgeO1Sb+v2M/9b4M6Qbzc+PffHiLdw2Zvmo3hq4dj9qbZqPJWRVsJK6orUNKjBLO/djuq5i3Qxf3RBQvQYfwEVD7wAKTbA1/Ih7P+s7hv830Yvno47tt8H876z8Kv+ZFvz0fn/AJ4awJ4fcW/8Ow9f8PZk17s33ZUN8X4/m1HEfC1XatU5ObGa8/sxoq7N+O1Z3bDU+Nv0R3ydGKUBw+fcifE7YMTLoSIKyPE2pnweWraPP8atVDfMeQOnPae1sWp5nYnxKRVAyofeEBfPtfHZ8J5Qh7cs+UuDFs9DPdsuQuekAeKUJBvy48+x1dcAP3NtFlPj8akOwfBYlcNu6H5PfrrRyCgIRRs3veXbPpkTqGfe8y+vfMLKWW0WiylPAPgFyafg4jqpbIWQLK7VT07ddd1D4BFS3k9GE3q75q5d34AS2EhDk++Bh8PHIjDk6/ByaefgepKHOjZkkpJs7tw2Fzhu6mxDr8X3h7DZVWxbFoJLi/uAosicHlxFyybVgKXNfECnkpazG7JSReRz/X0Owfw6A2DcXlxF3xn6HnY/MBovHjbZQCA1TNH4o17r8bUIefhvG5dU/r+20OydVwEBBZfuRhvH34bv7zylzivW7HhgpT2i8Pblbw8hLQQeuT1wPyR8zGhzwRddx9FKFBCFt2NhG2vHMSl3+iFLX/aj2fv2Ywtf9qPAVcVwWptu5aXbO+KY5QHn3hrP56/ZTh2LZyAikcmY9fCCeha2NkwRrt07qzb1Bb516jb7Pf7fx9zt+jjVHXlJcSk0qGDYZwqefqxgg2tX5SKhPW6rInrdSW7foSaGVpcz4gizB6wb1Tict46olaQ6loAyVbf9nkDePjyh1GUX4TK2krYrDb8+cCfMfWOf4PTZYPH7YfDacWO9Z/rzhue5Ux/sXANH4bAkSMJ2zSPB2qeOQPsW9QFy+8Od1U6tOXctt6Xh7fbzw1EVRSBLnk2PH9raYOzEKWalkhLznsVp6LbIi0T+fbMLRojn+uVD78EACy9aTBsFgVla3ZFv49HbxiMN/51FA9MvBRnzpxBYQrff3tI2kXH4sCy95ahbFgZXFYXgnW1cA0fFr6jXc81fBh8n1WEY72uDmXvzonmxUVXLAJwbjA/kHgj4dMdxwAB3ZTIVqsCxdJ2lZds74pjlAevGdgDbn8Ic146F68vzBgA1SBGT53WdwRpi/ybrNtsfJxWnf4yISa1mhrDONXq6qB2ONezP9XJU1JOs6rAmW/FpDsHn5ttzK6kdP1IFSeuoAizS8gdQojHhRAXCyGKhRC/QnjdFiIyWap3zkJKIGFg5viZA7Bq/+8w5X+mYOjqoZjyP1NwpOYI3vziTVy2thSD/zAYl60tbXAgvuuykYDFAtdlI3He0qXQOubrtnVd8p/wW5t/UQmFNNR4A9CkRI03AHcDg8MbZXWFx1j0GQUolvDzjSuhWZy6c4RCGhRFIN9ugSLqnw0ujKl2B2tKS04mif1cr+05ikBIomzNLt33Me/l3bjm670w7+XdsLvyIW/Qf//yxpXhv0s7SzaJhDvgxnHPcdQF6nDf5vvw8w8Wo/DRxboY77V4MWre2ojzli5FrUXT5cWF7y7Eg5c9iDuG3BHNk0bdXtzVPrjP+uE564fNrrZpxSVZmrKpK45RHrzqkm6Y85I+Xp/5x1eGMdqhQ0ddC83ztwxvk/wb30JtFKeaw46uS/5TF5NSVXHe0qUJ5XPQrp+V0hP04I4hd2Dd1HXYNWMX1k1dp4vVZqVZVWB3WupbYywI+LSUJ6RIFSeuIAAQKS5qn9qbCZGH8Pos4+s3vQngl2YvOJltq+m2l0GrBjVp/1nvPdmk/e9eMbZJ+7eSdi/ZWiteG1r5O34V5oe2PIQ7BtyF3oVFOFxViQs6n4fSF0t1x065aAoeGPGAriXnqdFPQXrVhIH4qlPi7Nnj6FpYhJNVlehY0B2+kA++2uroNnt+AfJsebAoTb9DGQppOFXn190ZffG2y9DvoQ0IxnTPsigC+385CYpIbBlJWMMBMjzGwuYC/G5oFidO1gV053jy5qHokmeD2sjUnpqULUtLwxfcjIjZ2M8FIOH7+M7Q87D4O4PgtKnw+EMQ0BDy1SG/QwFOnT4NmzMf+XZro991a0vWgtnZ3hmnfafR2dEZpavDeWVyn2sx9+tzUNj5PGh1dRAuF0J1tQjYVSx6fzHWH1wffV+LsGDHjB2o8lRFVyhPNnmG1aHCYm2fO8gmTD+b9vGakAdtKn7yp124c3Rf9O2ejwPHa/HrzQfw+HeHQIkpI0KqE6fciWVEocsGSxtXMo3i9DcTf4OHtjyIuy+difPqF6Tccmo7vn3xVFh9IVjyOyBYWwOPDciLG78S0kKo8lYlTLMciVVT0hzS4KkNJFw/zJw+uRnaPV6p5cyebawOBmu0RAghlkspZ5t5TqJclepaKJ6gB8c8x/Ct9ZOj29b/2/qEY497jiPPmqfrquCwOAAL9F0BbAqgAHkFXQAhkFfQBSGEsOaTNRjXexy6AKhWfXj7kzWYMWBGsyov7kAoemcUgG6QbWNdsBrs0hXpomTPR503kHCOOS/twnO3DEeHRi6sTekOFmnJAZDRXcXixX6uGm9A931MHXIeHrjmUtz2hx3Rv8F/3zQYAnYMfjC8BsrlxV1S+q5bW0Mz2xU6CuEOuKN55bVDr+O1Q6/j7qF3Y3r/6XApAn6nBYpQcNxzXPe+JT1KUHGmAo9seyS8CKuSl5bdXtIxTWaLz4NuXxAPXHMpfvqX3br49AY0uGLKCHdDZUQbV16M4jRcth/HpNevj+43oucIjO8zAWdCZ1CMfBwKHcfbn7yN6f2n69Zq8Ya8mLdlnm7B4Hlb5kVj1ZQ0G3Ula+HClUSA+d3GGnNlG5+PKGuluhaK0X4F9gLDY22KLeE88V0BFFVJ6NLgtDjx7IfP4vpXrsfQ1UNx/SvX49kPn212/+k8u8VwkO2yaUPjumANTejCkWqXLqNzbD9UhTyDCkb84HynRcnK7mDN5bKp0YH7FkXgJxP64ad/2a37G/z0L7tR4DwXX8m+6/bQ0CQSqqIm5JUbLrkhWsHJs+bBrtoT9ll0xSI8v+f5hHEE6djtJR3TZKaEyTWkNIxPLa4nSlPKiLZgVO4mK8cf2fYISl8oxSPbHsENl9wAh8Whm9zF7DEvSdNscP0gaqn0uHIQUZOluhZKsv0A6FtZVAdO+043OgGAkVRbgVJV5wsmtGwcO+uDy2bBI9cPwgWFLnxR5YbN4EKY6gxfRucY0acQdb4gOjjO9Q9P1pJT6LI2OrA/V3gCGv63/AiW3DgYRZ3DsWX4N4gZBG70XaeT2G463Z3d8fDlD+P8Ducbrn0RyWPLxiyD0+pExZkKLCtfhg0HN2BEzxHNzgfUcqmu8xKOT/1PolTLiPaSrGyXUurWanFYHDjjO6Mr258Y84SpZTZRW2LlhSiDRe7AAWjwgiMk4PADQj33LBT9sXWBuugEAACiEwAsH7u80YtZqotPJhPfJ10VAsu/PxS13lC0opLvUFHrC2L00s3R4y4v7oLnby3VdcdK2qXLF4RE+G5qnS8Ip0XFkzcPTejP3lBLDoBoS07sebOpO1hzuKwqpl12Iep8Qfzg+fexYvrwpD/6DvxyEo6c9qCTywqnJfw3NbsCKDUNmscDxemMPosmLoYZOyEGAKw/uB4jeo7A8rHLjde+EApcVheqvFV4ZNsjKD9WnrQ1lNqOUf5N1gW1zhceAxhbRvx6+jCccQei5VBrxm1jjOJaUQyuAQLRLmL5tnzDsv2P+/6IJaOWYO6W5pXZRO2pra+4uXlbkqgBRqt7p7r2SSrHSk1DqKoKlffff27F5cceg1pYqPtB15JuBIpQ0NleGLcys8vwc8RXVJwWBbW+IE7X/0A4WePD+Z2dOO2WmL9uT7RisfSmIejRwY5dCyego9OKs54A/m9XJVw2/Q8Jh6okVkqmDUVQk7gzZqXsyMDb524ZHv2x4rKqCQPIs3WtFrNE/p6FeVY4rApWzxwJTyCEF2+7DIdPufHEW/tx7KwPj94wGL/behDfHdEbr+yqxK1X9MFpj183vbIZq5c3FO9SQJdfHKoD3pDXMP80Jz+k2hpKbcco/x46VZu0UnLkjAcumwWnav3o3sEOf0jTlUNPThuKOn8Qs2LKkkhLrCeomVahCWkheIKec+Wp6oA8fabRctyIUSw/++GzuG3QbYxVykhtHaVNm66KKMslW93baLHJ5h6reTzhC17sisv33w/No58SM9mUsalMnalpElV1Adz2+3+h30Ov47bf/wtVdYGEhRsjXThuW7UD/R7agNtW7UCNL4g6fwjz1+3BpQs2YP66Pahy+/Hyzi90fdJ3HzmNKrcfd77wAfo9tAF3vvABJn29F/yBkO793IEQXtp2GA9PHYhPFk/Cw1MHwmVVcecLH+jeb85Lu+AJhtDBYYUiBDo4jGe+irTkxIoMzs91kb/nb7dUoPK0F7/behBfnvHi9j/sRL+Hwn/Lh6b0x9KbBmPpm5/g8bc+xX1/2oXvf+NCnHYHEqZXNmP18obi3Si/rN672jD/NDc/tGQRVjKfUf4t7dMF7rgyx+0PwVdfUYlsC2oSc+JidM6aXXD7Q7pta97/HKfc+nLtVJ0/9UV040RmAit7pwzDVw9H2Ttl0DzulMpxI8li2RvyMlYpI5kaqUKIfkKI54UQbwohNkUekf+XUv7ezPNRy9Ts+68mPch8LVnluKFjYwdmKk4n1O490fu1N/C1jz5C79fegNq9JxRn4wP7U+1GkGyQvD8Y0q2j4jXY74w7gPv//GHCD4Rrvt5Ld47LL+6asDbDnJd2IVD/78i2PLsFyzYdwDVP/B0XP/garnni73C1YOBttq7VYobI3z2ynkvkWR8Hu1DrC0UXtNx+qAqFeTZcUOhqlRYtxek0XmHc6UzIL/O2zMO43uMM815z84PUJPzeIKSsf27mD1gyh8uq4vlbhuvWaoFEQplz/58/hJRIKEuMYrRHgUO37Zqv90qo5LSkIu4JeqIzgUViU3XlJY3rxrSkbAcY05R+zO429hcAKwA8D4C3JYka0ZKuWg0d++9v/nu0H/Nzo55Cp7kLsHH1pzh64DP06luA8XMXQPP5oTrPXYRb0uXFqGvGtV/vgbPeoK771rJpQ9Gjo123X7IfsX2761de7+i0plQBOXC8NqE/+1lPoNkDbxVFoEuejYPzDUT+7n275+ueY8X/LcPjj0I4WetLebrpptA8HuMVxj0ew/xSXFCsex3Je83JDyasmUImk1LC7Q8lrBnV2IQSgHFZEonfWMnivrkVcZfVlRCrX544mDSu1byGxyS2pGxnTFM6MruNMCil/LWUcpuUcmfkYfI5iLJGS7pqJTv2SM0R3R27s0E/3lr9KSr3n4GmSVTuP4O3Vn+KkEj8gdjcLi91viDKxvbFG/dejc/+czLeuPdq3DDs/ISWkrI1u3Dv+H66Y7+ocht2y6r1BXWtHbX1M//E71fjDeq2vfGvo3gybkpliyLw5M36bU/ePBROS2o/LiLrRCii/pkXbQDnuuREfuRFnmON6FOIL6rc0e/9v28aDCGAzi5rSlNfN5XidKLoscd0K4wXPfYY/FZhmF8qqit0r2PzXlPzQ8AfwpsrP9LltTdXfoQAuxi2m9g1oyLl0Kn6inOsEX0KcarWpyvDKk7UJJQbkfiN3VaXpGxqbtfSyNpCsdYffQvnLV2aENeptLwAzS/bGdOUjkxpeRFCRHLtq0KIuwD8DwBf5P+llFWGBxLluJbM0pXs2KXbl+r265TfEUcPVOu2HT1QDavdvG5PTouKm0f2brSVZfuhKvTu4sLlxV2i+0V+xOoHbg+FKqCbFtkihOHsYGr9D4nItmmXXWg4jbHDouoG5zstapuvkp1tIl3q1rz/OR69YTA++vKM4WQJFiHwyeJJ4Vnj7BZYBeCwW1DrCzY69XVTCUWBWliI8595Rj/bGCQWXbEIC99dqFtR/OVPX4ZFWEyZbclqV1s9r1HTGHX9+uX6fYZljlVV8PArH+nKl3y7fnr2DnYLHBZFV75E1n2Kn069uRVxh8WBR0c9inlb5kVj9fp+N0DYOyfGdRNn0WsqxjSlI7O6je0EIHFuNrGfxvyfBFCccAQRtag53+hYo5W+j1WfQK++Bajcfya6rVffAgR8Idgc5hQBnmAoYSXqsjW78Mj1g/C/u76M7hfpqhX7Y0AC6OzUd8tSBPCbv1dEx734ghp+s6UCt19dnDA7mBDCsEtXfv0P4UgXJEUR0VWx02GNhmwQ6VL346uK4bQp6JzXDb/behAPTx2Ivt3zceB4LV56/zBuuaIPAKAwz4bf/+MgfnxVMSA1zKqfRCHCaOrr5hCKEu1KE3n2Burw14q/Yv7I+SguKEZFdQV2Hd+F6f2n4/bBt5sy21LAF2r1vEZNk2zNKKfNgl9PHxaduVCrn40wtgyb89IuPH9LKbp2sEMIoGsHe9Lyxcyupb6QD7uO78Ljox9HR1tHnPWfxfaj23FF0RXIi4vr1saYpnRkSpVdSnmRlLIYQP/6f0cfAAaYcQ6ibNWS2YnijzVa6TvP6cTEmQNR1K8TFEWgqF8nTJw5EFYTp/pNNrA10soS31XLFwzP6OQLavjDu4fgCYZ03bIcVjVh0P2yTQdgs6qNzg5GrSt+tXIAgACm/2Yb8uwWVJys0+1fcbIOHRxWnKr1Yd0HR7Bs0wG47GqbT0HttDhxY78bdSuPl/QogcvqMm22JatNbfW8Rk3jtKqGXUZ3HDqFY2d9kDJcmUk2ps5lV1PqMmpm11KnxYmSHiX4yeafYPjq4fjJ5p+gpEdJu6zBwpimdGR2tfldAMNS2EYZaOzmu5u0/9Oznm7S/nevGNuk/SlRspYcYROYfNdgWO0qAr4QrDbV1MGWyVaiPlXr093dPHiyFhLQdc149IbBCT9Y3b5kC02GkB9zt89o9Wwz1gohY8m+78K88A8/ty+EB665FD/9y+7o///3TYPhDYTw2p6jePjVvbi8uEt4wLNAqwzYT6Yt1mARioCzg61V8xo1jTeoYefnVbpyyGlR0Ld7B12cPnnzUJSN7YvH3/o0eqxRmdMW0mm9IMY0pSNTcoIQoqcQYjgApxCiRAgxrP4xGoDLjHMQUaL4u+CaJg1bcoQiYHNYIET9s8kXHpfR3c1pQ6EIoVuX5eJuHXBv3ODZeS/vThjYqijAf980OGGgbHz37mRTNLd0rRAylvT79odQNrYvJCR++hf9VMk//ctunPUEsHj9Pt3fsT2moG6LNVhaO69R07isKkr7dNGVQ34tMU7nvLQLP7zyIl08PvbdIQllTltJp/WCGNOUbsy6nXANgB8COB/A4zHbawA8aNI5iChGOrU6qKqSsFq906LigbUf6sY/5Ke43orDqmLpG5/ojl36xid4/HtDdfu1ddejXNfQ933LFX0aXBcjMmA/POBZ5RTU1CYM4yxJHOc79IPzrYqAnZN6EKUdUyovUspVAFYJIW6QUr5sxnsSUcNi74IDiN4FN2PAc1NpmsRpT0BXkXpy2lAUd83DNU/8Pbrfez8bm1J3MLc/hGNnfbpjLy/uktClKDJVb1t1Pcp1yb5vX0BDrS+IM27j9XRqvUHk2S3okm9Dnu3ceIDIOAEA/HtRq4mPsxqvcZweq/Zi9NLN0W2XF3fBc7cMRwcHKzBE6cTsHHmhEOIncY+ZQoihjR9KRE2RTq0ORt2J5qxJ7IbR0WlNqTtYql2K2qPrUS5L9n1rEvjpX3bj8Y378egNgxMGR//jwAlU1fl1FRei9qIKYVgOaVK/crxRqzARtT+zc2Vp/ePV+tdTAGwHMEsI8Rcp5RKTz0eUs9Kp1aGhbhi69RBsKh76n8a7g6XapYhdj9pWsu8bIvz3DmrhH3+Rv6/HH4LDqmBUv+78u1DacNhULDUohx77rr4cYisuUXoyO0d2ATBMSlkLAEKIXwBYC+BqhNeCYeWFyCSRu+BmLYzWEqlUpPLrFyVMpTsYkHqXInY9altG33dsN5xXPvwSr3z4ZbTLjUVVomtiEKWDuiTlUJ0vqFvwlq24ROnJ7Ct9bwD+mNcBABdKKT1CCJ/J5yLKaenU6pBqRSqdKlxkHpdVxZPThmJOzIrlT04byr8rpaXI7IhzXtqlmyrZZVXTojwlooaZXXn5I4B/CiH+r/71twCsEULkAdjb0IFCCBXADgCVUsrrTE4XUVZKl1YHdvPKbaqqoEvcbHMuq8pFRCktqaqCLnnG8ZpfP7sYW3GJ0pepuVNK+R9CiA0ArgQgAMySUu6o/+8fNHL4HAD7AHQ0M01E1DbYzSu3qaqCDvWVlQ4OazunhqhhjFeizNUat8XKAfwFwDoAx4UQvRs7QAhxPsKD+3/TCukhIiIiIqIsYOptTyHEbAC/AHAMQAjh1hcJYHAjhz4BYC6ADmamh4iIiIiIsofZfTbmALhUSnmq0T3rCSGuA3BcSrlTCDG6gf1uB3A7APTu3WhjDrWC785vWrjMeq+VEpIBGK+UaRizlEkYr0S5y+xuY18AqG7iMVcCmCqEOATgJQBjhRAvxO8kpXxOSlkqpSzt1q1by1NK1IoYr5RpGLOUSRivRLnL7JaXCgCbhRDrAUSnRpZSPp7sACnlfADzAaC+5eUBKeV0k9NFREREREQZzuzKy+H6h63+QUREREREZAqzp0r+fwAghMiTUtY14/jNADabmSYiIiIiIsoOpo55EUJcLoTYi/B6LRBCDBFCPGPmOYiIiIiIKDeZPWD/CQDXADgFAFLKDwFcbfI5iIiIiIgoB5m+SKWU8ou4TSGzz0FERERERLnH7AH7XwghrgAghRA2AGWo70JGRERERETUEma3vMwCcDeAIgBHAAytf01ERERERNQiZs82dhLAD8x8TyIiIiIiIsCkyosQYjkAmez/pZRlZpyHiIiIiIhyl1ktLztMeh8iIiIiIiJDplRepJSrUtlPCLFcSjnbjHMSEREREVFuMX2q5EZc2cbnIyIiIiKiLNHWlRciIiIiIqJmYeWFiIiIiIgyQltXXkQbn4+IiIiIiLJEW1denmzj8xERERERUZYwa52XV9HwOi9T659/b8b5iIiIiIgo95i1zstSk96HiIiIiIjIkFnrvPzNjPchIiIiIiJKxqyWFwCAEOISAI8AGADAEdkupSw28zxERERERJR7zB6w/zsAvwYQBDAGwB8ArDb5HERERERElIPMrrw4pZRvAxBSys+llA8DGGvyOYiIiIiIKAeZ2m0MgFcIoQD4VAhxD4BKAN1NPgcREREREeUgs1te7gXgAlAGYDiAGQBuNfkcRERERESUg0xteZFSbgeA+taXMilljZnvT0REREREucvUlhchRKkQYg+A3QD2CCE+FEIMN/McRERERESUm8we8/JbAHdJKbcAgBDiKoRnIBts8nmIiIiIiCjHmD3mpSZScQEAKeVWAA12HRNCXCCEeEcIsU8I8ZEQYo7JaSIiIiIioixgdsvLNiHEswDWAJAAvgdgsxBiGABIKT8wOCYI4H4p5QdCiA4AdgohNkop95qcNiIiIiIiymBmV16G1j//Im77FQhXZhLWfJFSHgVwtP7fNUKIfQCKALDyQkREREREUWbPNjamJccLIfoAKAHwvsH/3Q7gdgDo3bt3S05D1OoYr5RpGLOUSRivRLnL7NnGegghVgohNtS/HiCEmJnisfkAXgZwr5TybPz/Symfk1KWSilLu3XrZmayiUzHeKVMw5ilTMJ4JcpdZg/Y/z2ANwCcV/96P8ILVzZICGFFuOLyopRynclpIiIiIiKiLGB25aWrlPLPADQAkFIGAYQaOkAIIQCsBLBPSvm4yekhIiIiIqIsYXblpU4I0QXhwfkQQnwDQHUjx1wJYAaAsUKIXfWPySani4iIiIiIMpzZs439BMArAC4WQvwDQDcANzZ0QP1aMMLkdBARERERUZYxu+XlYgCTEJ4a+Q0An8L8ChIREREREeUgsysvP6+fKawzgPEAngPwa5PPQUREREREOcjsyktkcP4UACuklP8HwGbyOYiIiIiIKAeZXXmpFEI8C+C7AF4TQthb4RxERERERJSDzK5YfBfhsS7XSinPACgE8FOTz0FERERERDnI1MH0Uko3gHUxr48COGrmOYiIiIiIKDexSxcREREREWUETmNMrWbF5XOadsCsJ5u0+90rxjbt/YmIiIgoo7HlhYiIiIiIMgIrL0RERERElBHYbSyL9PnZ+vZOAhERERFRq2HLCxERERERZQRWXoiIiIiIKCOw8kJERERERBmBlRciIiIiIsoIrLwQEREREVFGYOWFiIiIiIgyAisvRERERESUEVh5ISIiIiKijMDKCxERERERZQRWXoiIiIiIKCOw8kJERERERBnB0t4JaC2DVg1q8jF7bt3TCikhIiIiIiIzsOWFiEyhSQ11gTrdMxEZY36hTMFYpXSTFpUXIcS1QohPhBAHhBA/a+/0EGWykBZCrb8WmtRQ669FSAsZ7teSC1L8sSEthCpvFWZvmo3hq4dj9qbZqPJW8SJHac0ojiOvI3mnpXnD6FhNaswv1CCjctzsSgRjlTJVu1dehBAqgKcBTAIwAMA0IcSA9k0VGdlz8HCTHtT2IpWIsnfKMHz1cJS9U4Yqb1VCBaYlFySjY097T2Pt/rXY/tV2BGUQ27/ajrl/nwtP0NNaH5WoReLjePXe1brXZe+U4Wjd0ej25uYNo2M9QQ/m/n0u8wsZMirHT3tPo8ZfY1olgrFKmazdKy8ARgI4IKWskFL6AbwE4NvtnCaijOQJejBvyzzdhWbelnkJF5qWXJAMj90yF+N7j9ftV36sHE6L09TPR2SW+Dge13tcQt5Z+Ny4Q7oAACAASURBVO5CjOs9rmV5w+BYp8WJ8mPlum3MLxRhVI7P3TIX1b5q0yoRjFXKZOlQeSkC8EXM6yP124ioiVxWl+GFxmV16ba15IKU7NiLOl2k21bSo4R35yhtxcdxcUGxYVxHtrckb8Qf6wl6UNKjRLeN+YUikpXjRflFCduaW4lgrFImS4fKizDYJhN2EuJ2IcQOIcSOEydOtEGyKF4f7x+b9Mhl7RWv7oDb8ELjDrh121pyQUp2bF2gDiN6joBFWDCi5wgsuXoJ785lkFwrY+PjuKK6wjCuI9tbkjeM7mYvuXoJ80sLZHO8JivHK2srE7a1pOWFsUqZKh0qL0cAXBDz+nwAX8bvJKV8TkpZKqUs7datW5sljqg52itenRYnHh31qO5C8+ioRxMuNC25ICU7Ns+Sh+Vjl2PnjJ1YPnY5Ch2FUEQ6FDGUilwrY+Pj+O3DbyfknUVXLMLbh99ucd6IP1YRCgodhcwvLZDN8WpUji8ZtQQF9gLTKhGMVcpkQsqERo62TYAQFgD7AYwDUAlgO4DvSyk/SnZMaWmp3LFjR4Pvm4vrvPT52fr2ToJOh/5Nmzhu1ntPNmn/u1eMTWU3o5a9NpVKvJoppIXgCXrgsrrgDrjhtDihKmrCfprU4Al64LQ4o8+pXpBaciw1Kuditr3Ex7FDdcAb8sJpcUbzTuQ180ZSjNdWYFSOCyFMja0cjFUgDeKVWq7dF6mUUgaFEPcAeAOACuC3DVVciKhhqqIi35YPANFnI4pQkGfNA4Doc6pacixRujCK4zwl/BzJO5HXLXlPoqZKVo6bGVuMVcpU7V55AQAp5WsAXmvvdBARERERUfrK+vZBIiIiIiLKDqy8EBERERFRRmDlhYiIiIiIMkK7zzbWHEKIEwA+b2S3rgBOtkFymorparqWpO2klPJaMxPTVCnGK5Def4Om4OdomUyKWbOle+yke/qAtk9jOsZrJvydUpUtnyVdPke7xyu1XEZWXlIhhNghpSxt73TEY7qaLp3TZqZs+Zz8HNRc6f6dp3v6gMxIY2vLpu8gWz5LtnwOSg/sNkZERERERBmBlRciIiIiIsoI2Vx5ea69E5AE09V06Zw2M2XL5+TnoOZK9+883dMHZEYaW1s2fQfZ8lmy5XNQGsjaMS9ERERERJRdsrnlhYiIiIiIsggrL0RERERElBFYeSEiIiIioozAygsREREREWUEVl6IiIiIiCgjsPJCREREREQZgZUXIiIiIiLKCKy8EBERERFRRmDlhYiIiIiIMgIrL0RERERElBFYeSEiIiIioozAygsREREREWUEVl6IiIiIiCgjsPJCREREREQZgZUXIiIiIiLKCBlZebn22mslAD74SOXR7hivfDTx0e4Ys3w04dHuGK98NOFBWaBNKi9CCFUIUS6E+KvB//1QCHFCCLGr/vHvjb3fyZMnWyehRK2A8UqZhjFLmYTxSpRbLG10njkA9gHomOT//ySlvKeN0kJERERERBmo1VtehBDnA5gC4DetfS4iIiIiIspebdFt7AkAcwFoDexzgxBitxBirRDigjZIExERERERZZhWrbwIIa4DcFxKubOB3V4F0EdKORjAWwBWJXmv24UQO4QQO06cONEKqSUyD+OVMg1jljIJ45Uod7V2y8uVAKYKIQ4BeAnAWCHEC7E7SClPSSl99S+fBzDc6I2klM9JKUullKXdunVrzTQTtRjjlTINY5YyCeOVKHe1auVFSjlfSnm+lLIPgJsBbJJSTo/dRwjRK+blVIQH9lMa0jSJWl8Qmqx/1jjrIBEZY3lB2YBxTJR+2mq2MR0hxCIAO6SUrwAoE0JMBRAEUAXgh+2RJmqYpkmcqvOjbE05th+qwog+hVg2rQRd8mxQFNHeySOiNMLygrIB45goPbXZIpVSys1Syuvq/72wvuISaZ0ZKKUcIqUcI6X8uK3SRKlzB0IoW1OO9ypOIahJvFdxCmVryuEOhNo7aUSUZlheUDZgHBOlpzarvFBmc9lUbD9Updu2/VAVXDa1nVJEROmK5QVlA8YxUXpi5YVS4vaHMKJPoW7biD6FcPt5B4qI9FheUDZgHBOlJ1ZeKCUuq4pl00pweXEXWBSBy4u7YNm0ErisvANFRHosLygbMI6J0lO7DNinzKMoAl3ybHj+1lK4bCrc/hBcVpWDFokoAcsLygaMY6L0xMoLpUxRBPLt4ZCJPBMRGWF5QdmAcUyUfthtjIiIiIiIMgIrL0RERERElBFYeSEiIiIioozAygs1SNMkan1BaLL+WZPtnSQiSmMsMygTME6JMhdHn1FSmiZxqs6PsjXl2H6oCiP6FGLZtBJ0ybNxthUiSsAygzIB45Qos7HlhZJyB0IoW1OO9ypOIahJvFdxCmVryuEOcIEuIkrEMoMyAeOUKLOx8kJJuWwqth+q0m3bfqgKLhsX6CKiRCwzKBMwTokyGysvlJTbH8KIPoW6bSP6FMLt590pIkrEMoMyAeOUKLOx8kJR8QMYnRYFy6aV4PLiLrAoApcXd8GyaSVwWXl3iogSuayqQZkxFIoAB0RTu+G1jSi7cMA+AUg+gLHQZcXzt5bCZVPh9ofgsqoc0EhEhhRFoEueDc/fUgqXXcXhU278cv0+HDvr44Boahe8thFlH7a8EIDkAxg9QQ35dgsUIcLPLNyJqAGKIgAB/OD59zF66Wb8764vOSCa2g2vbUTZh5UXAsABjERkHpYnlC4Yi0TZh5UXAsABjERkHpYnlC4Yi0TZp00qL0IIVQhRLoT4q8H/2YUQfxJCHBBCvC+E6NMWaSI944G2HMBIRE3H8oTSBWORKPu01YD9OQD2Aeho8H8zAZyWUvYVQtwM4FEA32ujdFG96EDbFAYwapqEOxDiQEciSloepFqeELWmZLEIALW+IOOTKAO1esuLEOJ8AFMA/CbJLt8GsKr+32sBjBNCsARpB4oiGh3AGJm55bZVO9DvoQ24bdUO/H/2zjw+qvLe/5/nLLMlIZAAEqOIFNmiNKyKFYtYV5TrrbYXW9B6+amABTcKtYuXS2+vhQu1ghW1l966FXsr7ZW2imgVwZbKIimyKGKKSmQPhGTWszy/PyZnmDNzzsyZZCYzk/m+X6+8JvOc58w8ST75PueZcz6fc8IfoRhUgihBUtUDJ/WEILqCRC0CoHmMIIqYrrhs7GcA5gPQbbbXAvgMADjnKoAWANVdMC6iA9glt1CKEEGUHlQPiGKEdEsQxU1OFy+MsRsAHOWcb0/VzaIt6eMPxthdjLFtjLFtx44dy9oYicyg5BZnkF6JYqMjmqV6QOSLztRY0i1BFDe5PvPyJQBTGGMHALwIYBJj7PmEPgcBnAsAjDEJQCWA5oQ+4Jw/zTkfwzkf06dPn9yOmrCFklucQXolio2OaJbqAZEvOlNjSbcEUdzkdPHCOX+Ic34O53wAgKkA3uScT0vothbA7e3f39Lehy48LQA0TUdrSIHOOVpDCjRNp+QWgiBixNeDm+rPxoZ5E/HCnRcDHEn+AV3naAur0Hn7I/kLiDyRyTxmNQ8SBJFfuiptzARjbBGAbZzztQBWAXiOMbYf0TMuU/MxJsKMpuk44Y/g3hcbsPVAM8YOqMJjU+tRXeaiFCGCIACcSXJa9a0x8IdVzF19pl4sv3UkqstcEAQWM/bPXb3DcjtBdCVO0/BSzYOiSLfJI4h80WX/fZzzDZzzG9q/f7h94QLOeYhz/jXO+SDO+TjOeWNXjYmwJ6BouPfFBpOh8d4XGxBQNEoRIggihiAw6ByYu7rB1gBNBmmi0HAyj6WaBwmCyB/00QFhSZlbsjQ0lrnzcrKOIIgCJp0BmgzSRDFC8yBBFCaOFy+MsZ6MsbmMsZ8yxpYbX7kcHJE//GHV0tDoD6t5GhFBEIVKOgM0GaSJYoTmQYIoTDI58/IKgAEA3gewPe6L6Ib4ZBGPTa03GRofm1pPxnyCIJJIZ4CmoA+iGKF5kCAKk0zOfXo45w/kbCREXlFVHUFVQ5lbgj+swiuJqC5z4enbRsfaymQBghoABB8QCQCyDxC65spDrnMoEQ2yW4Qa0cA5ILtFKGENsksEI98NQeQNKwO0VxIQUDR4ZQGBiIbqMhkv3FYH5i4DD/sBlwwAaAurFP7RBcTX0MS6mWpbd0XXOQKKZtIewIGIP6ZR5ipDlc88D3olkcz6GeBUW6WoQaLjZLJ4eY4xdieAPwIIG42c86R7shDFharqaA4kJ6pU+Vyo8EQPMCpcIhA4Brw0A/h0M9B/PHDLKsDXJ+cLGK5zBFsjWL9qN3yVblxy00C8+cxeHNrfgppBlbh6Rh28FS4qdASRRwwDNBD9xPqEP4LV736Cm0aeg5d3HMSMUeXwrb0L+HQzWP/x4LesQqvQC3c//x4lkOWY+BqaWDcB2G7rrjXVKv3uqWkjUaGdAlsz44xGb16FgNgTdz9PKXkdIZXu4rXltB9BGDCnt1RhjN0D4McATgEwduKc84E5GpstY8aM4du2bevqt+22tIYU3PXsdmxuPBFrGz+wGk/fNjq2eEG4DVg9FTiw6cyOAyYAt74IuMtzOr5ISMUrT+xE075TmPrDcdj0m31o2ncqtr12cE9cP3sEXB7LtXjeK1+p6PXnM9/MqP89T07K0UiKnqLXbFtYxZ3PbMPCKXVYuHY3/uufBuKcV/81qX4cu/EZjP2vv8Waxg+sxi9uHxNbBBHZIb6GGhh1E4DtNpuamkjR6dXQZ/yct/U7l6DPH24njWaRVLqL15bTflki73olOk8mqngAwCDO+fFcDYbID44SVVy+6BmXeD7dHG3PMbJbxKH9LQCAXjVlse8NDu1vgeyma5AJolAw0sUG9S3H1gPNOLvPOMv6Ud2rl6mJEshyQ3wNNYivm6VWU63S76p79SKNZpl0usu0H0EYZHK9z24AgVwNhMgfjhJVIoHopWLx9B8fbc8xSlhDzaBKAMDJQ/7Y9wY1gyqhhCm1iCAKBSNdbP/RNowdUIXPjx23rB8nTp40NVECWW6Ir6EGRt1Mta27YpV+d+LkSdJolnGqrVLUINE5Mlm8aAAaGGNPUVRy98IrmRNVHvjKBXhqetSg2BZWoes8as6/ZVX0UjFBij7esiranmNkl4irZ9ShdnBPbF/3CSbdPgy1g3tCEBhqB/fE1TPqINMnYQRRMPhkEb+4bTT6VXrwwp0Xw+2tQOSmX5jqB79lFdzeCkog6wLia2hi3Uy1rbvik0U8OW0UNsybiI//83psmDcRZWUV4Deb5zh+8yq4veUJGqW0Mac41VYpapDoHJl4Xm63auecP5PVETmgVDwEXYWucwQiKlSdo8Ij4YQ/gntXNyQbFMEBJRC9VKx40sbyfn1rqeiVPC9Zo+g1q2l6tI7EhYA8NW0kyoUImLsMrL1+6GBJiU9khM4NOUwby/sfLFO9Rg37Ycw1zXP1qPbJgBI4k4gn+9Aa1nAyoODcKh8+aw6gl09GhUcmnTqkANPG6A/XDcjE8/ISgBDnXAMAxpgIwJ2TURFdSkDRcGe7Yf+1+y7HwrW7Y0bGzY0nMHf1jjMGRcOcn2OTfiJMYDHjnhznxcmBmY8giE4SUDTc+2KDqY7c/fyOaAgIE2L1QwBixmcyQOeW+BqaWDdTbeuOBBQNc1c3JMxzDdF5zlMBAGCeCrSFVcx8/r2kMBsy7DvHqbZKTYNE58jkY/M/A/DGPfcCeCO7wyHyQbx50TDYxkMGRYIgMsFRCAhB5Akrw77VPOe0H0EQXUsmixcP57zNeNL+fe4ND0TOiTcvGgbbeMigSBBEJjgKASGIPGFl2Lea55z2Iwiia8lk8eJnjI0ynjDGRgMIZn9IRFfjk0Usv3Ukxg+sxsoN+/FfXxtBJlqCIDqMTzaHgIwfWI3HppLRmSgM4ue8VPOc034EQXQtmZzDvw/Abxljn7c/rwHwL9kfEpELdJ1bGmON9qoyGU/fFk0YCykafnHbGPjcXWei7UKzHkEQncCulsRvD6o6qspcsZriD6vwySJEsWsCPkoZqqXpEQSGXl7ZpE+vlDzPCQJDdZkLv7h9DIVKFDCk+dLD8eKFc76VMTYUwBBE0xo+4JwrxnbG2FWc89dzMEaik0STVSKYu3qHKUGsyiejOaAktVeXuWLFuStMiVznCLZGsH7Vbhza34KaQZW4ekYdvBUuKkAEUUDY1RKjZqTbTuQWqqXO0DQdzQFzGt5jU+tRXeZKWmALAqNQiQKGNF+aZPQxGOdc4Zzv4py/H79waWdxFsdFZJFossoObG48AVXnsQSxVO1diRLRsH7VbjTtOwVd52jadwrrV+2GQtcVE0RBka5mFEpNKVWoljojPg3P0Om9LzaQTosQ0nxpks1z+LTELVDsElPsEoG6OklFdos4tL/F1HZofwtkN11XTBCFRLr0JUpnyi9US51BaXjdB9J8aZLNxUvS3S4ZYx7G2BbG2N8ZY7sZY/9u0edbjLFjjLGG9q//l8UxEbBPTLFLBOrqJBUlrKFmUKWprWZQJZQwfXJCEIVEuvQlSmfKL1RLnUFpeN0H0nxpkuuPGcIAJnHO2xhjMoB3GGOvcs7/ltDvN5zzb+d4LCWLkZiSeB26Txbxi9tGQ9U5enhltIVVuAQGlyyiLaTA54rKI9d3wJZdIq6eUZd0zapMn9YSREGRqpZomg7OOV6482K0haIG/baIigqPhEBEg845GZ5zDNVSZyTOfaeDCiSBwSOJaA0pHQqZSBdkQeQG0nxpks3Fy4HEBs45B2DcG0Zu/0o6Q0PkFrvEFN5+MJFoWnx122dYt+sInpw2ChFNx9zVDTk13zKBwVvhwvWzR1BaCEEUMKlqyQl/sgG66VQA1WUeLFizkwz8XQDVUmdwjqS5b+W0UWgORnDv6vQm/kQoqCJ/kOZLk4wuG2OMXcoY+wZj7Dbjy9jGOf+qzT4iY6wBwFEAr3PO37XodjNjbCdj7CXG2LkZ/QSEI4zEFIG1PwrM1rT4T/W12Nx4AicDCuaubugS8y0TGFweCYy1P1LhIYiCJJNa8oU+FViwZicZ+LsQqqXpCarJej0VUHDv6o6Z+CmoIr+Q5ksPx4sXxthzAJYCuAzA2PavMen245xrnPN6AOcAGMcYuzChyx8ADOCcjwDwBoBnbN7/LsbYNsbYtmPHjjkdNpECO9NiD68MADi3ykfm2w5CeiWKjc5o1q6WlHsKIxSE6H5kW692850TEz8FVRBE15LJmZcxAL7EOZ/NOZ/T/jXX6c6c81MANgC4NqH9BOc83P70FwBG2+z/NOd8DOd8TJ8+fTIYNmGHnWnxdDCagv1Zc4DMtx2E9EoUG53RrF0taQsVRigI0f3Itl7t5jsnJn4KqiCIriWTxcsuAP0yeXHGWB/GWM/2770AvgLgg4Q+NXFPpwDYm8l7EM7QdY62sNpumlXRFlLhlUU8NrUe4wdWQxIYxg+sxmNT6+GRBLz7vSvRt8KNx241bzfMubHX5Tr8ih861xFSQoiEVHDOEQmpUMIquE4WJ4Lo7visasmt9XCJDMuTakg9BBatSfF1qS2sQu8G9SK+JhqPHYHr3FRPdU03Pafa2nG8koiV00Zhw7yJ+Pg/r8eGeRPRu9yVNN89NrUe3nYTv845WkMKNC3572kEWaSaK4uFbOk3GyT+D3CdW7YRpUfa86GMsT8garKvALCHMbYF0RQxAADnfEqK3WsAPMMYExFdKP0v5/yPjLFFALZxztcCmMsYmwJABdAM4Fsd/WEIa+LNhGf1cGPeNUPwnd/uxFk93Pj3f6rDk9NHo8Ij4XRQwcsNTVi36wiWff2LiGg6Xnz3UyycUodBfctj6SuGAVHnOppDzZi/cT7O8p6F7474Ad765Z5Y4sek24fB5dbh9sl0DSpBdGNEUUB1mQtP3zYaZW4JbaHoguSOX23DWT3ceOSrF6F/tQ8n2sIAB/57YyNuv3RAlwSCdCXxNXHHkR0YedZILLl8Cao8VRCY888Kre4aftWMOux5pwnb/vQJ3UU8C0Q0HQ/97v0z5vxb69HTLWHltFGxBDKPJKA5kBxEkWjitwuyKDYdZ0u/2cDqf+DqGXUQRIZ1T+8ytdH/QenBooFgKTow9uVU2znnb2d1RA4YM2YM37ZtW1e/bdHSFlZx5zPbsLnxBF6773IsXLvb9P3CKXWxNoPxA6vxyFcvwsSlG0xtT982GhWeqCfGr/gx58052Hp4K/4w+RU0PHsMTftOxfrXDu6JidOGwtfDBZcnbzf/yntFKxW9/nzmmxn1v+fJSTkaSdFTtJptDSm469ntWDltFGY9/15STTHaF06pg1sS8NDv3k/q84vbx6C8SG8WGF8TDcb2G4sVk1agTC5z/DqRkIpXntiZVE8n/MtgvPijLbHn188ekc/aalB0ejV0mqi9p28bjYsWro+1NTx8laWO4+fB7kS29JsN7P4HJk4bihce/pupLcP/g7zrleg8af/axuKEMbaYc74gfhtjbDGALl+8EJkRbyYc1Lc86fv4NoOtB5pxbpUvqS3evOiVvNhxZAcAoH9VLV7dv9/U/9D+FvTo7QWjUkEQJYFhhO7hlW3DQIyaY7Ql9ilmk3N8TTTYcWQHvJI3o9exu2t4r5oy03O6i3jHsAuYSDTn2+nYiYm/GMmWfrOB3f9Aj97epDb6Pyg9MjkPeJVF23XZGgiRO+LNhPuPtiV9H99mMHZAFT5rDiS1xZsXg2oQI88aCQD4tLnJ8i63p48H6U63BFEiGEbo00HFNgzEqDndMRAkviYajDxrJIJqMKPXsbtr+MlDftNzqq0dwy5gItGcb6djJyb+YiRb+s0Gdv8Dp48Hk9ro/6D0cHLZ2CwAswEMBPBx3KYKAH/hnE/L3fCsKZXLcLJFKs/LvGuG4HfbD+Lm0edi3m//Hruud9nXvwifS8Ss59+zvdbX2vPyYYLnRcy35yXv531KRa+ZXjaWKSV0mVnRalbTdJzwR7D9k2aMPq8qySuw/ZNm1J3dE/+34yB5XlJQZJ6XvA8gU72qqm7pZenhkXDHr7bF2n5x22jLGzk7uXFlMVIinpe865XoPE4WL5UAegF4BMB34za1cs6brffKLaVyMJhNdJ0joGjwuUSEFA26DvjcIiKKBkXnKHNLUUO+S0QwokNggEsUEFS1M9tkMalg61xHUA3CK3kRUSMQVAmyR4x9EiLJAgRRgK7pUCI6XB4RkZAG2RVt7wLyXqhKRa+0eMkaRa1ZVdXNdaPdwOyVRQTba5BhaGaMxepSsZqcE4mviUE1CI/oQUgLxZ57Ja+jA0GucygRLXbXcEkWoCo6JJcAJZxcSxP7d+FdxvP+B+uIXhN16pXE2A1X4/XIeXTujJ8Hu6NuDTRdQ1ANwif7EFAC8EpeiEJ2L8tyqlWrfgCS2jjnmRxfdI8/VInj5MJNEcBpAPckbmCMVeVrAUNkhnFXbADwuaKPus7RGo7eGdj4VGnxzSPwfzsO4taLz4OnTIyZEu3MiQITYkY+l+hCMKDglZU7TZ8WestkBP0KXk/4FNFbLnfVAoYgiC5A1zlOBhVTTVl+60hU+WQ0B5Lbq8tcsbpUrCb9ROJrolfydviTbOOu4QBijxKAYKtFLS2TEfIrSZ9SF8iZmYLDTqfWemSoaJ+nKjyy6UqG7nLG0EDnOk6GT+b0zIvdGRUrrVr9D8R/7/JI0DUdwTY6vig1nPxltwPY1v54DMA+AB+1f789d0Mjck1AiS5cNjeegKpzbG48gQVrduKaC2swd/UOBJTMriNVwhpeX7UbTftOQdc5mvadwuurdkNRdOv2SP7y4wmCyD5WNcWoJXbt3ZmgGsT8jfOx9fBWqFzF1sNbMX/j/A57CJSITS1VdKxPaF+/ajeUIvYP5ZLO6LE7aznberVCiWhZ1art/wQdX3Rr0i5eOOfnc84HAngNwI2c896c82oANwD4Xa4HSOSO+BQyg/j0sUxTf1weyTIdxOWxTg1xeSghhCC6E3Y1xS7dqZiTxZyQ7fSmVLXUqp1SmKyx06kTPXZm30KnK9LG7FLEOqpVOr4oTTI5pzaWc/6K8YRz/iqAlPeAIQqb+BQyg/j0sUxTfyIh1TIdJBKyTg2JhIr/kyqCIM5gV1Ps0p2KOVnMCdlOb0pVS63aKYXJGjudOtFjZ/YtdLoibcwuRayjWqXji9IkrWE/1pGx1wBsAvA8AA5gGoDLOefX5G541pSKAbqz2Jn0DRNtWNHhj6imtJ+lX/si1mz/DLdefF7Ka3gTTaleyQvwM9dj+yrduPSrX0BZTzc0RUM4qJmuSb16Rh1ktwjRJUCN6ICoA5qQC7Np3i9CLhW9kmE/axSVZo0645UFy5qy/NZ6lLkkeFwiPj0RwM/e2Icjp8PdxieQSHxtDGth+BU/5m+cj77evphdPxvnVJxjMu7bGfOtaqHd9f0utwjJLSIcUOHyiDh9IgSPT+qqpMe8/wEzrbFR30o4SafVZe60eixWz4vlnA0khUs49bzoug4lrMHlkRAJqZDdIgQh+fNwK32H2pz5s3RVh6LEGfFlAYKUMI7MPS+F+0ciHJPJ4qUKwL8BuLy9aSOAf8+HYb9UDgY7g108crwxf/fnpzD+C71xKqDg3CofPmsOoJdPhiwJ8Ej26Smp4hTBATWiQw1rpuJ07d0XggkCXB4RLceC2PKHfyDQEsYV04fh2GenUfOFnkmLmyyZTfNeqEpFr7R4yRpFo1mjzqx+9xPcNPKcaNjHuP7wR7RYTenpk/HsXw9g+Zv7zyxm3FLKGlOsWNXGR7/8KHTo0LiWXDPdVQi1njmQGzP5PAy/rNa2FnKdQwmp0Dng9kmIBFVwnZuiY6+YPgz7thxC3WW1XWXYz/sfsSNRyW0R1TT39fTJKHdJkKT0F6TEfzBYDGljlrqc+CgUXUnSZC93r7TpeLquWwdHVMimBYydOd9TLtsu0GPvoerWQT9lsuUChtLGSgvHi5dColQOq4nh8QAAIABJREFUBjtDW1jFnc9sw+bGE3jtvsuxcO1ubG48Eds+fmA1Vk4bhVnPv5fU/ovbx6RM/vErfsx5cw62Ht4aaxvbbyxWTFqBMrkM4aCCV1e+j6Z9p2Lbawf3xLV3X4R1Tzlvv372CFPCSAfJe6EqFb3S4iVrFI1mjTqzcEodFq7dHXtMrCkLp9Thmp9tjD1PV2OKFbva+NOJP8UDGx5Ial95+VOmWjn1h+Ow6Tf7bGthJKTilSd2pu0/4V8GY9Nv9mWrhqajaPRq0BpScNez25N0+vRto22TNYsZK13+6Z//hIWbF9rO46mwm+Ovm3UR3N4zv79EvRr9nOgyHFTx6srkfa+bNQJub6c0nXe9Ep0nrQIYYz/jnN/HGPsDopeLmeCcT8nJyIhOEW8qNAz48Ww90IweXrlDxsN0pj47477bl1k7mU0JorAx6oxRY+xqzaC+5abn3cHcbIVdbezh6mHZnlgre9WUpayFiWZnu/5GO9VQa+wCJMq64YIasNZlbXlth8359uE85t9fZ8z5ZMQnUuHEsP9c++NSAMssvogCJN5UaBjw4xk7oAqng0qHjIfpTH12xv1wILN2MpsSRGFj1BmjxtjVmv1H20zPu4O52Qq72ng6ctqyPbFWnjzkT1kLE83Odv2Ndqqh1tgFSPjDap5GlFusdNnU1tRhc759OI/599cZcz4Z8YlUOIlKNu7lIgLYyjl/O/4rt8MjOopPFrH81pHRy8M27Md/fW0Exg+shiQwjB9YjcU3j8Dmj4/jsan1pvblt46ET05/5mXJ5Uswtt9YSEzC2H5jseTyJbFPbGS3iKtm1KF2cE8IAkPt4J6YdPswCAy4OqH9iunDcPDD5qT+V8+oi91NlyCIwsSoM6/tOoTFN4+IPcbXlMem1uO1XYcyqjHFilVtXDxhMbYe2opFly5KqpmyWzTVxMaGoylroexK3/+K6cPQ2HCUamgKvJKYNPc9NrUeXql7/r6sdFnprkw5j6fCao6/qj2Ex9QvQa+ZzO2yLFi/h0w3niQyM+w/C+ASACcQTR3bBOAdzvnJ3A3PmlLxEHQWU9pYRIPGOcrcEvxhFSJjcEkCgoqGMreEQFiDwACPQ+OhVXJJvKkv0UAnioCm8XaDXpyxzi1ACesQZUBXQWljRQx5XrJGUWk2Pm0sEDljYjZqjfHcK4sIKlFzs9gN73wdXxMDSgBeyYuQFoJbdCOkhuCTfbF2o19IC8EreM1mY1mAquqQXIK5VroEcz93NKkxPp0svj2LNTQdBa9XTdMRUOI0KYvgHAiqZ9q8kujIrF+saLqGoBo06ZCDm7TpkTyQBGeXzjk1ySemjRkLl8Q2K61apY0xgTnaNwV51yvReRxf4Mk5vw0AGGNnA7gFwM8BnJ3JaxBdiyAwlLsl6DqHP6KZksfeP3gKo8+rwr0vnomKfGxqPdySACf/2wITYqa+RHMf17kpCjExQccqUceINmSMdYXBlCCILGHUGQCo8EQPXrwScLw1nFRftn/SjDEDqgs+VjZT7BIYe7l7JUXPLp6wGGs+WoOn/v4UZn5xJqYN+FZSupinXM4ohczVftBoGJldnu57EJ4pmqbjhD+SpEWfS8Rdz243tVWXubrtwjpRh49OfBSKpmD+JrM2qzxVEIXUZ0YS5/hUCaFMODOnuzySbQJZ4r5c5wj5k99DEJkpXS+LyaREEZHJmZdpACYAuAjAcQDvANjEOd+cu+FZUyqfZGcLq+Qxu6SxbKStpEvEsUvIyUKKiBV5r2jFqtdcn0nJFDrz0nV0VrN2aU5G3eluaWN2KWPLr1iOuW/NTWp/aNxD+Orar+IPk19Bw7PHLGthfNJSuhSyPFPQek2lxfpFr5vaKG0sqtlyV7nVy8ToTIqY033t+k2cNhQvPPy3jN83jrzrleg8mVS9nwH4GMCTAN7inB/IyYiIrGOVPGaXNJaNtJV0iTh2CTmUIkIQ3QO7NCej7nS3tDG7lDGf7LNsH1g5EADQv6oWr+7fb9pu1MJMUsgIe1JpMbGN0saimk1HZ1LEnO5r169Hb29SG/0flB6Oz49yznsD+FcAHgA/ZoxtYYw9l2ofxpinvd/fGWO7GWP/btHHzRj7DWNsP2PsXcbYgAx/BiINVsljdklj2UhbSZeIY5eQQykiBNE9sEtzMupOd0sbs0sZCygBy/bGlkYAwKfNTba1MJMUMsKeVFpMbKO0sahm09GZFDGn+9r1O308mNRG/welRyaXjfUA8CUAX0b08rHeAP7GOb89xT4MQBnnvI0xJiN6qdm9nPO/xfWZDWAE53wmY2wqgH/mnP9LqrGku6QhnZm8VIg37LeFVPzqL/9A43E/fnjDcOico7rcjU9PBPCzN/bhyOkwHptajyqfK2ZajP89hrUwoHO4FA7B64UeCkNlUvuN086YRAFAkgWE2hTsfqcJA0f2RVW/MkRCKpSIBl8PNwItYUiyYLpu9aoZdXB7RYiy2YRnmP3izasZmvTyfoqYLhvLDnTZWNfhRLOJdxn3SgKCqh4z7pe5paT6EvW8VKG6zF2UnpdYTRQ90AMBiL4yaAE/mNeLNtWPlnALzi4/O2qMlnwxg/TB1oP4y8G/YGLNJPTr2RfhoAKXW0JEUQEVCAVUVPb2IhI+E2LCOMcrT76f0vMiucUzRn1X1Lgfb+BPVSutjNQd9A3k/Q+ZSq92npdyt4SjrWGcW+XDZ80B9PTJKHdJCGl6TNM+hwE2+YTrOvRgMDovtz9yBtMxkEf0IKgGoXENFa4KtEZaIQsyAmrA7NOaEPVpaQo36YIzbj6mEr1QQxp0Drh9EsIBFQIDZI+UpKFEnRnHB4leFk+5bNKtJAuIBFWEAip69Pbi9PEgPL7ombFOel4K+w9KOCKTc6TvxH09zjk/mG4HHl0ZGQH/cvtX4mrpnwAsbP/+JQCPM8YYd7qqSsDOOFnlqSqpBYyuc5zwRzB39Y4zBfvWelR5XWgORnDv6jOFfPmt9fDKIjZ9dCx2YAHGY7/Hvt6+mD/mO3C3hnBw/vcg9u2HnvN/gDee2xsrIFdMH4Z9Ww5hyCU1cLlFeMrkpMl20u3D8Mb/7EGgJYyv/Gsdrp81AnJ7igjXdfzx8Z3JptX2RdDgcTV4K+79yKRHEPkjsb7MnTQIU8f1x4tbPsVNI8/BgjU7TfXF55LgFgUMq6mMmcuLDWNuWfPhS/hGv8loXvADBLa/B9/oUTh76VKwChde+ccruGHgDfhj4x9xw8Ab8PBfH8aOIzsSjPl7YjXz2GenUfOFntjw/AdJtXT4ZbW44Z4REF0iwgEVLreIa+++CG6fBCWkQdd0vPLETtMHQKdPBNGj2mtr7DdwapruLvhcIlZOG4UeXhmngwo8koDTYRUP/e79mE5XThuFk8EI5prmxpEFHS7BdR1aczOaHnwwpsXaZcsQKJdx39v3pzTnL5mwBB7Jg4XjF6K2vBZNbU3wil6E2tQk/ajuMO57+77Yvk9e8SQUhSeF7kgu7khnkixg4rShsUWJIDKoYS22WK8ZVInrZ14ETeOm/42rZtTBWybj+tkjcpFMShQRmVw2NoJzPptz/murhQtjbIXVfowxkTHWAOAogNc55+8mdKkF8Fn7e6gAWgBUOx1XIkE1iPkb52Pr4a1QuYqth7di/sb5jm681J0IKNF0sc2NJ6DqHJsbT+De1Q0IqhruXd1gap+7ugFNp0KY9cIOzF3dgICimX6PMy6aATXgx/H530Pg3S3oNesevPHcR2jadwq6ztG07xTeem4vBtb3xZvP7EUooEJRdLy+arepz5vP7MXoa89D075TeOOXu9HaHMLLj+4A1znWPbXL1Hf9qt1QIjrWr9qNgfV98dZzey2206ligsgHifXlmgtrcO+LDbjmwhosWLMzqb4cPBnEoB+8iolLN2Dm8+8hoBTf/65REyfXXBlduLy7BVBVBN7dgs/nzUOorQVX9r8SD//14dijMQ9dU3t9Uj1867m9OGdIlWX7wPq+eH3Vbmg68PKjO7DqwU1Y+e0NWPXgpmjNRPTT5/j9Xl+1G1U15UmvZ1UrlYiG9Q76dQcCioY7n92O+kWvY+BDr6B+0esIqXrSPHgqoGBu0ty4o6C1qgeD0YVLnBabHnwQobYW0zFQS7gF8zclHBdtmo/mUDMm/34y6p+rx+TfT8bpQJulfvzBoGlfrglJ/V5ftRuKopvGZ6ezQJuCFx7+G1bOfgsvPPw3rHt6F3QOUz+dw/Y9XB4plkxKC5fSJJsfgX3JqpFzrnHO6wGcA2AcY+zChC5Wyks668IYu4sxto0xtu3YsWO2g7AzTjq58VJ3It6kb2AYEq3aB/Utj33vc4mm3+PAyoHoXVWLwPb3oq993jmWRjrDVNqjtzfJcBrfJ7G/2yfZGvjj+yVuL2STnlO9EkShkIlmE+uLEQRiPMYTX1+M58Vo2Ddq4tl9BsZqoUFg+3voXVWLgZUDY4b8+Hmof1WtZQ2zq31GzbOro5m2OzVDF3JNTcSpXq3mPKvAmnOrfJbaLWStCl6vrRbjsTPn15ab+51V2cdSF2dV9jG1pdJfPJmY7t0+84VAqY4LCKLLzt9zzk8B2ADg2oRNBwGcCwCMMQlAJYDmhD7gnD/NOR/DOR/Tp0+fxM0x7IyTJXfmJc6kb2AYEq3a9x9ti30fiGim32NjSyOONzfBN3pU9LU/OWhppDNMpaePB5MMp/F9EvuHA2pK02oxmlWd6pUgCoVMNJtYX4wgEOMxnvj6YjwvRsO+URM/P9YYq4UGvtGjcLy5CY0tjTFDfvw8ZGfMt6t9Rs2zq6OZtjs1QxdyTU3EqV6t5jyrwJrPmgOW2i1krerBoK0W47Ez5ze1mfsdaTlmqYsjLebFYSr9xZOJ6T4cMIclpDouIIicLl4YY30YYz3bv/cC+AqADxK6rQVgmP5vAfBmR/0uQPTTsSWXL8HYfmMhMQlj+43FksuXlN6ZF1nE8ltHYvzAakgCw/iB1Vh+60h4JRGPTa03tf/X10Zg5Yb97X3q4ZNF0+9x1furIPnK0HvJf8J38TicXPlzfGX6Bagd3BOCwFA7uCeumD4MjQ1HMen2YfD4JMiygKtm1Jn6TLp9GLav+8TU/+oZdRAYMOn2Yaa+V8+og+wScPWMOjQ2HMUV06220ycwBJEPEuvLa7sO4bGp9Xht1yEsvnmEqb4Y7fF1yCcX3/+uURP/dOjPqFr8H/BdPA6QJPguHoezly6Fp7wSf/70z1h06aLYozEPvdb0SlI9vGL6MBz8sNmyvbHhKK6aUWdZR1O1Nx9qS2q3qpWyS8TVDvp1B3xy8pwnCSypradPxvJb65PmzELWquD1onbZMpMWa5ctg6e80nQMVOmuxJIJCcdFE5ag0mXu5/N6LPVT5vWa+jFRt9VlPHY68/ikpH0FBlObwGD9Hq7i9MwR2cVx2ljaF2JsB+d8ZELbCADPABARXSj9L+d8EWNsEYBtnPO1jDEPgOcAjET0jMtUznljqvdKl4Sj6Vos5SWgBOCVvGnvGNsdSUwDMpJTNE1HQImmAfnDKkTG4HGJCISjfQ1zYmLamKAzSGEFQlkZ9FAIKpOT0sYYADBAconQFA2aFj3FHAlpEBggGck47f2NyVJVNHCOM8k57duN5BxKG+s8uU4Pm7Thnoz6vznx5xn1p7SxriNbaWP+sAqvJBZ8gpPThMpYP8ENHgjGaiF0HYLPBy3gB7xehLRQ7LVi85DohRrRTTXTqGVKREto1yG7BAiiAF3ToUT0WB2NpYpJQvv1/6nTxuzSx0olbQxA0pznk0VwDgRVs04FgVnOmYWMVdqYputgwXZ9+v3gXi8gACE1FNOjR/IASG4TISbpIiltTPICGsz6kwUIUvL/jJXOuM6T9mUCS+7HeZL2hc4HfhT2H5RwRDbvyPRYYgPnfCeii5LE9ofjvg8B+Fq2BqFzHSfDJ0s+bQwABIHF7mIdfzdrURRQ0V4A4u8mXJ5wh1qBCSiTy6BzHYoagdjShsPzv2dKNeHuXmjFKazZ+RKmn3cLdNWNN577KJYOMvGOwfjVvhdw4xduxPL3luNo8Gj078GqTHfEld1SNJnktH0CjtsbHXMB3FGaIEoeq/pSHqsr5vpS3n5QU16ANwHMJKFSYAJ8ojeW8CT17Ys+992HQ9/7nil57IWmNXhy51MYedZILJ6wGGs+WoOn/v5U0msbNe3Mo2R6DgCCKMDlZva1kbHYfkaSm/Eou8SUqWJGLe3uNdVuzquQLOZBizmzkGGCALEs6iUVy8qgqipw8iQOzpt3Zq5euhRaZQXmvjXXnECmK5a6T9QFA0OZHH2PMrkMuqYj6FeS0sa85XLS4sJKZ0xgcEtmzcdvj3/fxP8NggAcXDbGGPsDY2yt3ZfRj3P+q5yO1CGUNpZ9gmoQobaWWNpYfKqJHgjEEnjg15JSyDb8zz5cU3s9fvCXH2DGRTNS/j1KKQGHIIjCINM5Iz7hqfrOu6ILl4Tksck1V8Zea8GmBbiy/5Wdmo86WhupppYeLBhE07x55rl63jxIYSU5gayDx0pKJDlN9PX2hFCC6AqcLGWX5nwUWYTSxrKPV/LCW1WLDy1STURfWSyBh3GGQ/v3mPoc2t+CKVX1sQQewP7v0R0ScEqZrz+U2SdjMzfnaCAEkQGZzhnxCU/uL1gnjw3pM9D0WkbtS/fadnS0NlJNLT2EsjJLTQrtZ2cM7BLInGjTadoYQeSKtGdeOOdvp/rqikFmAqWNZZ+gGjSljRn4Ro+CFvDHEniCh6yTSj5tbool8AD2f4/ukIBDEERxkemcEZ/wFP7YOnns82NnbJvxtS/da9vR0dpINbX00P1+S03qfr+pzS6BzIk2naaNEUSucGzYZ4xdAOARAMMBeIx2zvlA251yRCpzXibXLxPO0LmOiBIB08SYqVRgHJI7eqdnTVTgllzQAyHoohtgZuN9JKRCEcL48ZYfY0DlAHxj2DdQLpcnGWONu/HufqcJA0f2Ra9+ZVBCKmS32BmTXt7NeaVi2H9y/L0Z9Z+5OckmlxIy7HcdhaLZrsDJnBEz6ose6IEgRJ8PymefoXXTJlRccUWS5+WXCZ4XAOjl7gU1osPtkc2hJBbG+8R6x3UOJaRC59H7X4QDKgQGyBY36Us0SOuqjnVP77L0vNjtk6F5n/SaRxLDJtzMDfj90E+dgnzOOVAOHoTQsyd0nweHg0dQW16LprYmVHmqoOs6/MEgzqrsgyMtx1Dm9aLMVWYKnLAKr9A1HZGgilBARY/eXpw+HoTHJ8HlkaCqelrTPWPJ5nwAjjSYhaCJvOuV6DyZLF7eAfBvAB4FcCOAO9r3/7fcDc+adIXKaXIM4Qxd0xFsSzbn7d9+FP9oOIYv33EBXjv0J9zY/yZoYY43n9kb63fF9GHYt+UQhl9WC0+ZhJORk5i/KcVBgsV7WU22GZD3QlUoE2uhLV4y5f3b38/p6xcQpNkuJtWcYSxu1nz4Er7RbzKaF/zAFFrCysvBVRVie9qY4hJxLHQctRW18Ct+cM6x5dBWXNrrMry+ao+pNh777DRqvtAzrfHZrgYn9jM+AEo06MseEZLL+mDPbp8Mai7pNU9YLbx/9uVH4W2N4PM4w/7ZS5ciWOHCfW/fH9fvZxDD7iRNae4w7nv7vpQf/lpp5tq7LoSu8SQdCSIzLZ6t+tnta7XI7qRWgQLQK9F5Mjmi93LO/4zoguUTzvlCAAX5UaiRkhX/SHQcO3PekIv7oWnfKbz9Px9hynn/DCWg481n9pr6vfXcXgys74vXV+2GquiYvym1QVBVkt+LDKYEQeSSVHOGYeifXHNldOGSEFoCTYNUXg4mCAi7Bdzz1rcx+feTUf9sPY74j+D+DffjS32jC5fE2njOkCpHxmenBmk7gz7nAGPR1KfEgzwy9RcvVmETobaW6MIlIUQi1NZi6ucPBi015Q8G05r4rTQTCqiWOgoF1LT97PZN1CBplTDI5Kg+xBgTAHzEGPs2Y+yfAfTN0biIAsLOnOf2SbHvvT4XevT2WvbrVVPWbuaT0hoEyWBKEEQhYRj6z+5jbc4XvN6kvgYDKwdGa5zXbVtDnRifnRqkO1I/qeYWL1ZhE72rai112ruq1tR2VmUfy7/7WZV9TG1WJn4rzdjN/z16e9P2s9s3UYOkVcIgk8XLfQB8AOYCGA1gOoDbczEoorCwM+eFA2rs+2AggtPHg5b9Th7yt5v51LQGQTKYEgRRSBiG/s+PWZvz9WAwqa9BY0tjtMYFw7Y11Inx2alBuiP1k2pu8WIVNmEXrnO8ucnUdqTFOmDnSMsxU5uVid9KM3bz/+njwbT97PZN1CBplTBw7HmJ7cBYDwCcc96amyGlp1Svb801dtd9J15vPWbyeRhxxblweSS0HA9C9gn4w6f/hxvOnQI9wmw9L6KXo01rS2mMzdI1rfHk/frWQtEreV6KBtJsgaDpGoJqED7ZhxOBEyhrU3Bs/kMmz4taWQaX5I7WygQPwt1fvBs3X3AzGo7+PcnzcvWMOjCRgQFJxmfJJUCUzabnznheUtVP8rwUJk68uzrX0RppRUu4JWbEr/ZUwdUSzIvnBUjWMgDyvBBZJxPD/hgA/wOgor2pBcC/cs6352hstnTHQpVvUiXuAEAoEoKgRdNEgq3Wk6iqq4DWfmfnuLQxJaSBAZA8IsCQtiBnIU0knrwXqkLRKy1eigbSbAGg6RqaQ81YsGmByQztUwWIvjIcaz6IR/c8gSPBo6YDvPiDzoASgEfyIKSG4BW9UCN69EOfY0Fs+cM/0KufF8Mvq02qp3veacK2P31iOjizSm2ySmHsSP2ktLHCwmlqqlW/Ryc+CsaBUFsLelfV4nhzEzzllRAEEc2h5tgip9JdiXKpPKbJSHuyp9M5OhxQTAsVX7kMpd2zajo2KJOT0vQobYzoLJksXnYCuIdzvqn9+WUAnuCcj8jh+CzpboWqEPArfsx5cw62Ht4aaxvbbyxWTFoBALFt796yDeufihrmDGoH98R1s0aAMeCVJ3YmbZvwL4Ox6Tf7cP3sEXB5MruRYRbIe6EqFL3S4qVoIM0WAG2RNsx9a25STfzpxJ/igQ0PWNbKMrnM6qViREKqqUZO/eE4bPrNPsua+eKPtsSe56l2OoX0mmVSzcfxGrPq96d//hMWbl6YtO/C8Qsx+feTU76eUxJ1DADfXHQJNjz/geWxgdtbUNrNu16JzpOJolqNhQsAcM7fYYzl7dIxIruku8u0sc3rc6U0jqYy7JOpjiCIYsEn+yxrYg9Xjw7fmTzRcGzUxniMmhn/nGpnaZFuPk7Vr7a81nLf2vLapDYnmrUiE8N+YqgEQWSDTAz7WxhjTzHGJjLGvswYewLABsbYKMbYqLR7EwVNqrtMx28LBiK2xlE7M51h2CdTHUEQxUJACVjWxNOR0x2+M3lijTRqYzxGzYx/TrWztEg1H6fr19TWZLlvU1tTUpsTzVqRiWE/MVSCILJBJouXegCDEb1R5UIAwwBcCmAZgKVZHxnRpXglL5ZcvgRj+42FxCSM7TcWSy5fAq/kNW1b+8nvcdWMOtQO7glBYKgd3BNXzaiDJDPILhFXJ2y7YvowNDYcjd4ozUWfwBAEURx4JS8WT1hsqomLJyzG1kNbsejSRZa1Mh2JNbKx4ahlPW1sOBp7TrWz9Eg1H6frV+mutNy30l3ZIc1aYTXXe3ySpZZlme6zR2SfjNPGCoFU17c6SeggrLH63QHRT3c8oieWuqOoCqCKMQOeKAO6ipiBjjFAcp0x7KsRvbOm+86Y9PJ+fWuhXI9NnpeigTTbRaSbL4y0MWO7T/aZTPg+2ZdUK9PNPYm1TJIEs6FZFqCqelKty3KQSUoyfC/Saw6IT7oLKAF4JS9EIXkRq+pqTIuGNgUmJM/lPHrGJN6cLwgdPzay0gjXeZKWBSm775EFzeddr0Tncex5YYydBeA/AZzNOb+OMTYcwHjO+aqcjS5DnCZ0ENYYd5cGgDK5zDbJRNGVWNvML87EtAHfMiWMXD2jDpIsxkx6Lk/nfvc5iE8mCKLEcTJfiIIIn+xL2c+uVtrNPUxgMfO98ehuP8CL1cz258b2rqyBVG/zj851nAyfTKsnTddwMnTSlIi3eMJiVHmqTHN5Lv6mVjpmAkvSckchHRKpyOSo8lcAXgNwdvvzfYjeuLJgCKpBzN84H1sPb4XKVWw9vBXzN87v8HWdpY7V77Ml3GJqu6b2ery+Kpo+puscTftOYf2q3VAi2bvOVYloWJ/j9yAIorRwOl846ZfruacrayDV2/yTiTYXbFpg6rdg04Lkm0oW4d+0GMdMdB2ZLF56c87/F4AOAJxzFUBBqchpQgfhDCdJJv2rai0TRrKZjmOVbEIJPARBdIbOJDol9sv13NOVNZDqbf5xqie7RDyf7DO1FePftBjHTHQdmZzX8zPGqgFwAGCMXYLojSoLBiN5Iz7f3EjU6EiWealj9fs0kkyMtk+bm1AzqNKU7W6k42TrvgRGskku34NIZtKGezLq/+T4zP4WrXt/klF/gsgmTucLJ/1yPfd0ZQ2kept/nOrJSMRL7BdQAih3lcfaivFvWoxjJrqOTM68PABgLYAvMMb+AuBZAHNS7cAYO5cx9hZjbC9jbDdjLMnR2x693MIYa2j/ejijnyAOpwkdRBSuc0RCKjiPPmrBELiuQ/P7wXU96ff57fpvo6/cD6uuXoX1U97ADeffgNeaXjEljIy7cQCum3kRZLeIcFCBruuW78V150ERVskmlMBDEIQd8XXMeEzEyXyhcx0CE9L2i3+tG86/AeunvIFVV6+CrLmTap3OdfgVP3RNRzgYrYnhoApdSx6jgV0NZAwdqqmpoHqbf+y06RHcUNtawXUdalsrPKLHMhHPI3miGmvXmuQSLP+mkksw9dO5tQat5m+nc3pH537ZJeLauy7ENxddgllPXIFvLroE1951IenlMWJFAAAgAElEQVSQAJBB2hhj7GuIel7OBXAzgIsB/JBz/l6KfWoA1HDO32OMVQDYDuAmzvmeuD4TAczjnN/gdNCUNtZ5rMxwX5l+AU4t+Q9oRw+jdtkyCFW90Kq0oSXcgtqyWgTbFLyxao/JPCf4OFyiC2pEh+QSEGxVTOb9q2bUwVsuI9SmdMp4R2ljnSfTtLFMz7x8/aHcnnk58JPJ6Tt1D0iznYDrOrTmZjQ9+CAC29+Db/Qo1C5bBrGqCiwhXSnVfGGY8F/a9xJuueAWBNQAastr0dTWhEp3JSpcFaa5Rec6ImoEeoDZ1jrjNRuONGB8rwmWtVIQreerxBqoqzrWPb0rJ2ZmShvLP4na9Ahu6M0n8fm8eTFdn710KVhVL4Q0c9rYqfCpZLO/uyqa/Gmk3LkENIfTh0xYHStce9eF0DWedk7vjOk+h4b9vOuV6DyZHNX/kHN+GkAvAF8B8DSAlal24JwfMhY3nPNWAHsB1Kbap7MY6S/xj0QyVma4N577CL1m3YPAu1vQ9OCD0AMB3L/hfkz+/WR8dvJzvLFqT5J5TtAkCIIAl0eCEtaSzPuvr9oNJaJ32nhnJJsw1v5IaSMEQVigB4PRhcu7WwBVPVPPgsnm+VTzhWGavrL/lZi/aT4m/34y6p+rx+TfT8b9G+5PMkULTICgSSlrnfGal/aZYFsr7YivgQCw7uldOTMzU73NP4na5MFgdOESp+vP580DgkGUu8ohMAHlrnKEtbC12V8Lmv6mQc1ZKIDVsUIooDqa0ztjuifDPpGKTI7sDcVMBvAk5/xlAC6nOzPGBgAYCeBdi83jGWN/Z4y9yhirs9n/LsbYNsbYtmPHjmUwbMIKOzOc77xzAACB7e9B9JXFzIBOjPkuj2TZx+UpPeMd6ZUoNrqLZgWvF4Ht5gsCAtvfg+DN7PJhwzQ9sHKgYzN+OpOx8Zpen8u2VjqBzMzdR69OEX1llroWfWZPVTaDKABrrfXo7XWkv87olDROpCKTxUsTY+wpAF8H8ApjzO10f8ZYOYA1AO5rP3sTz3sAzuOcfxHACgD/Z/UanPOnOedjOOdj+vTpk8GwCSsMM1w8NYMqEfjkIADAN3oUtIAfI88aCeCMMT+xvxI+8ylIJKRa9omErN8rft/uBumVKDa6i2b1YBC+0aNMbb7RoyzPvKTCME03tjTG6qCBYZ5OxK6uGrXOeM1gIGJbK52Q7n1Kge6iV6doAb+lrrWA39RmaCweK7067WeltdPHg4701xmdksaJVGSyePk6op6XaznnpwBUAfhOup0YYzKiC5cXOOe/S9zOOT/NOW9r//4VADJjrHcG4yI6gJUp8yvTL8DJlT+H7+JxUc+LzxczDT615wl8+Y4LUpo4ZbdoMu/XDu6Jq2bUQbYxC5LxjiCIbCN4vahdtgy+i8cBknSmnnXgzMuSy5fgz5/+GYsuXeQoCCad2d14zb8e22RbK51ApvrSQ/D5cPbSpSZdn710KQSfORbZaXCR035WWvP4JEf664xOSeNEKhwb9jv04tGLc58B0Mw5t7yhJWOsH4AjnHPOGBsH4CVEz8TYDiyVOU/TNQTVYMy85pW8EAUSuxW6rsdiByMhFRI0CG4X9GAQgtcLJggm02BYCUPU5ZQmzsTXlN0iBEHojOG+s+T9Yu1CMZOSYb9oIM12Eq7rsTpmV8+sAl3itxvzR1ANwiN5EFKjpuh0QTDpal3sPQQvlIgOl0dEJKRBdgm2Zv2OvE8XQnrNAVbHMuAcPBiE6CuDFvCDeb0QxeS66zS4yGk/K60BcKS/zug0RxrPu16JzpNrN/uXAEwHMCkuCvl6xthMxtjM9j63ANjFGPs7gOUApqZauKRC0zU0h5ox9625GP3caMx9ay6aQ83QdDrNmIjOdTSHmzFr490Y9dwozNp4N06iDZwBYllZLJUn3jTodXnTmjgFQYDbK4MxBrdXhtD+OmQAJQiiq2CCEKtjxqOR9DXnzTkY/dxozHlzDppDzbF42MTtc9+ai0P+Q3h+7/M4FT4Fn+xzFASTrtbFXkMU4PZK7bVSymjh4uR9iOLF6ljmZOgk2lQ/7vrLvRj1/Gjc9Zd7cTJyyjLe2GlwkdN+Vlpzqr/O6JQ0TtiR0zMvucLuU5a2SBvmvjXXdMOmsf3GYvkVy003bCIAv+LHnDfnJP2uVkxa0d1u6Jn3alconwruHToso/6ZnknJFDrzYgtpNgekq3l22x8a9xAe2fJId6yN2YL0mmXsjmUWjl+Iyb+fbGojXWZM3vVKdJ5ulSPsk32W6Rk+2WezR+niNGmEIAiiO5Cu5tltN9LGqDYSXYXdsUxteW1SG+mSKEW61eIloAQs0zMCSiBPIypcnCaNEARBdAfS1Ty77UbaGNVGoquwO5ZpamtKaiNdEqVIbq8L6WK8kheLJyzGgk0LYneMXTxhccl/MqHrHAFFg88lIhDR4JPFWNLIS/tewlf6fwXn9zwffsUPj+gx7+vQ0NfR/kTXUWiXgeWaTAMK7nlyUo5GUhpY1RmhgK5R90pePDrxUbSEW1BbXoumtiZUuitNZ16WXL7EdMfxRZcuwh8b/4glly+BwMyG/8S6RrWv+CkUDXslLx798qNoicRp1VUJsOilYoY+7VLvKLiI6O50q8WLKIio8lRh+RXL6Z+2HV3nOOGPYO7qHdh6oBljB1Rh+a0jUV3mQi93L9xywS2Yv2m+qRhWeapiE3VzqNk0mcdvT3qvDPsTBNE9SFVnCmkBo+gKFm5eaKpPBgITUOWpwopJK0xpY9OHT4eqq7jnz/fY1jWqfcVPIWmYMQaFJ2u1l6tXTJ92C2TD7J/4IW6Vp6qkj4WI7kW3q6qiIKLcVQ6BCSh3lZf8P2tA0TB39Q5sbjwBVefY3HgCc1fvQEDRENJCmL9pPrYe3gqVq9h6eCvmb5xvuoxi/kb77Ylk2p8giO5BqjpTKDipT/GpS/Hzx/0b7k+5H9W+4qeQNGynp5AWSpsOFlSDWLBpgWnfBZsWkBaJbkW3OvNCJONzidh6oNnUtvVAM3wuEUDHDKx2l+FRCACRSzK9DIzoOlLXmcKgo/XJyX5U+4qfQtJwZ/REwUVEKdDtzrwQZgIRDWMHVJnaxg6oQiCiddjAmurMC4UAEETpkarOFAodrU9O9qPaV/wUkoY7oycKLiJKAVq8dHN8sojlt47E+IHVkASG8QOrsfzWkSbT/th+YyExCWP7jTUZANNtTyTT/gRBdA9S1ZlCoaP1ycl+VPuKn0LScGf0ZAQXxe9LwUVEd6Nb3aSSsCZVgkq6hJy023UdSliDyyMhElIBWYfK1UJK3Mm7W7hQ9HrRMxfl9PULLW3sgOcbme2wsCU3A8mcotRsoSQ1paKjiWBO0pviX9voE9JCtu/BdQ4lokF2i1DCGmSXWKx3EM/7oLNVYwtJw51JDHO6b+L8LbtFCEK3/0w773olOg95XkoAQWAod0f/1MZjbFu78Q+A5V16U23XdR3BVgWvr9qNQ/tbUDOoEl++4wIsef8/cSR4hD55JIgSIlWdKRTS1TsrdK7jZPhk2iQxgQnwSl5HqWNc5wi2RrA+rnZePaMO3gpXsS5gugWFomGnmrPDCC4CEHtMeg+L+fuqGXXwVsilsIBJYvv27X0lSfpvABeCrkoqFHQAu1RV/X+jR48+Gr+hMGcYoihQwhpeX7UbTftOAQCa9p3C2//zEe6+bTZu/NP1mL9xPlZMWuH4IIEgCKLQiE9+AhBLfrKqbU77KhEN6xNq5/pVu3H97BFweWhaLnUy0VxHsZq/X1+1G9fNughub+kdu0uS9N/9+vUb1qdPn5OCIBTfJUndEF3X2bFjx4YfPnz4vwFMid9GVZLoMC6PhEP7zZfaHNrfgilV9QAobSfX5PoysGJnQOjXGfU/kJthEEVOJslPTvvKbtGydsruwvEIEfmjK9Lr7ObvEl48X0gLl8JCEATep0+flsOHD1+YtC0fAyK6B5GQippBlaa2mkGV+LS5CQCl7RAEUfxkkvzktK8S1ixrpxIunHQ2In90RXqd3fwdCalZe48iQ6CFS+HR/jdJWqvQ4oXoMLJbxFUz6lA7uCcEgaF2cE98+Y4L8NSeJyhthyCIbkEmyU9O+8ouEVcn1M6rZ9RBLqD74hD5oyvS66zm76tm1NHZP6IoKNnzg0TnEQQB3goZ1826KJZWwiUNP57w40JJGiMIgugUAhNQ5anCikkr0qaUOe3LBAZvhQvXzx7RHdLGiCyTieY6/B4W83eJpI11Cx544IGzy8vLtUWLFh3J91jyAS1eiE4hCELM3Of2ygBkAM6TfAiCIAqdTFLKnPZlAov5C0rYZ0DY0JFkvIzfI2n+JojigJbYBEEQBEEQBFGgPP7449WDBw8ePmTIkOE33XTT+fHbli1b1vvCCy8cNmTIkOHXXHPNF1pbWwUA+OUvf9nrggsuqBsyZMjwMWPGDAGAbdu2eS666KJhQ4cOHT548ODh77//vjsfP09nocULQRAEQRAEQRQg27Zt8yxdurTm7bff3vfhhx/ueeqppz6N3/7Nb37z5K5du/Z++OGHe4YMGRJcvnx5bwD4yU9+UrN+/fp9H3744Z5169btB4AVK1b0mT179pEPPvhgz86dO/eef/75kXz8TJ0lp4sXxti5jLG3GGN7GWO7GWP3WvRhjLHljLH9jLGdjLFRuRwTYUbnOvyK3/RIEARRClD9IwoV0iZh8Nprr/W48cYbT9bU1KgAcNZZZ5liCbdv3+4dPXr0kMGDBw9fs2ZN9e7duz0AMGbMmLZvfvObA5YtW9ZbVaMpcuPHj/cvW7as5vvf/36/jz76yFVeXl6UCWu5PvOiAniQcz4MwCUA7mGMDU/ocx2AC9q/7gKwMsdjItrRuY7mUDPmvDkHo58bjTlvzkFzqJmKJEEQ3R6qf0ShQtok4uGcgzFmu8i46667zn/88cc/3bdv354FCxZ8Hg6HBQD49a9//el//Md/fP7ZZ5+56uvr6w4fPizOnDmz+eWXX97v9Xr16667bvDatWsruu4nyR45dQlyzg8BONT+fStjbC+AWgB74rr9E4BnOeccwN8YYz0ZYzXt+xI5pCvu4ks45+cz38xsh/G5GQdBlAJU/4hChbRJxHPttdeevuWWWwZ973vfO9KvXz/tyJEjpjzrQCAg9O/fXwmHw+zFF1+sqqmpUQBg9+7d7kmTJvknTZrkf+2113o2Nja6mpubtWHDhoXr6uqONjY2uhsaGrxTpkxpzc9P1nG6LOKEMTYAwEgA7yZsqgXwWdzzg+1tpsULY+wuRM/MoH///rkaZknRFXfxLVVIr0SxUWqapfpX3HRnvZI2iXjGjBkTevDBBw9NmDBhqCAI/MILLwycd955Ma/Kd7/73c/HjRs3rLa2NjJs2LBAW1ubCAD333//OQcOHHBzztlll112+pJLLgl+//vf7/fb3/62WpIk3qdPH+WRRx75PH8/WcfpksULY6wcwBoA93HOTyduttgl6fQY5/xpAE8DwJgxY4ryGr1Cw7iLr/HpDnDmLr706U7n6IhenxyfZAkjiC6j1Gos1b/ipjvrlbRJJDJnzpwTc+bMOWG1bcGCBccWLFhwLLF9/fr1Hye2PfLII4cfeeSRw7kYY1eS87QxxpiM6MLlBc757yy6HARwbtzzcwAU5Uqw2OiKu/gSBEEUIlT/iEKFtEkQqcnpmRfGGAOwCsBezvlPbbqtBfBtxtiLAC4G0EJ+l66hK+7iSxAEUYhQ/SMKFdImQaQm15eNfQnAdADvM8Ya2tu+B6A/AHDOnwTwCoDrAewHEABwR47HRMTRFXfxJYhiYMB3/5TT1z/wk8k5fX0ic6j+EYUKaZMg7Ml12tg7sPa0xPfhAO7J5TgIgiAIgiAIgih+6BwkQRAEQRAEQRBFQZdFJRMEkV1a9/4ko/4Vw76bo5EQBEEQBEF0DXTmhSAIgiAIgiDyiM/nG2m3beTIkUNz9b7f/e53++XqtXMFLV4IgiAIgiAIosBQVRUAsGPHjg9y9R7Lly+vydVr54qivGxs+/btxxljn6Tp1hvA8a4YT4bQuDKnM2Nbxzm/NpuDyRSHegUy/jlv6OiQckRsPIWspUzI6s/BFjvuWkyazTaFrp1CHx/Q9WMsRL0Ww9/JKd3lZymUnyMretV1XhVQtFqfS3QFIlrEJ4tNgsCaszHAP/7xjxU/+tGPavr27avs2bPH9/HHH+/2+XwjA4HAjk8++US++eabB7a1tYmaprEVK1Z8cu2117bF779t2zbPHXfccb6iKEzXdaxZs+bjiy66KPzEE09UrVy58ixFUdioUaP8zz777Cdz586tDYfDwtChQ4cPHjw4uHbt2n8sXLjwrBdeeKE3AEyfPv3Yww8/fPT06dPClClTBh46dMil6zqbP3/+53feeefJefPm1axbt65nOBwWxowZ0/bCCy98Igi5Py9SlIsXznmfdH0YY9s452O6YjyZQOPKnEIemxOc6BUo/p/TgH6O4sepZrNNof/OC318QHGMMdsk6rU7/Q66y8/SXX4OILpwOeEPnzd3dYOw9UAzxg6oci2/tf686jI3srWA2blzZ9mOHTt2Dx06NBLf/stf/rLqyiuvbFm8ePFhVVXR2tqatFJYsWJFn9mzZx+ZNWtWcygUYqqq4r333vO89NJLVdu2bfvA7XbzadOm9X/yySern3jiiaZf/epXfT/44IM9ALBp0ybfr3/96+rt27fv5Zxj9OjRw6688srWjz76yN2vXz9lw4YN+wHgxIkTIgB85zvfObp06dJDAHDTTTed/+KLL1Z+4xvfaMnG7yAVdNkYQRAEQRAEQTggoGi1c1c3CJsbT0DVOTY3nsDc1Q1CQNFqs/UeI0aM8CcuXADgkksu8a9evbr3Aw88cPaWLVu8vXr10hP7jB8/3r9s2bKa73//+/0++ugjV3l5OV+3bl3Frl27fF/84heHDR06dPg777zTo7Gx0Z2474YNG8qvv/76Uz169NArKyv1yZMnn3zrrbcqRo0aFdy0aVOPWbNm1a5bt668urpaA4BXX321YsSIEUMHDx48/K9//WvFrl27vNn6HaSCFi8EQRAEQRAE4QCfS3RtPWA+wbL1QDN8LtGVtffw+ZIWJQBw3XXXtW3cuPHD2trayLe+9a3zH3/88epnn32259ChQ4cPHTp0+MaNG30zZ85sfvnll/d7vV79uuuuG7x27doK/v/Zu/P4qKr7f/yvc2efJGRhCRBklZAFEgIBAQ3rB8oibtCistnSsokUwR+USvlQSqHwAbUBEVSsCui3Vagiq6hEcANDAmELQSAsAcISCElmn3t+fyQzzJ25k0zITDIJ7+fjwSPkzr333Mw9c87cs7wP5+zXv/71rdzc3JO5ubkn8/Pzj7/22mtX3M9fvvSip6SkJHNWVtbJLl26GF999dWYV155pYXBYGBz5sxps3Xr1rN5eXknx40bd9NkMtXKc0VDfnh5u64vwAu6ruoL5mvzp4byd9LfQe5XsL/nwX59QP24xkBrSO9BQ/lbGsrfAYPFbunRNkqyrUfbKBgsdo+eEn/Ly8tTx8TEWOfMmXNz3LhxN7OysvQTJky443go6du3r+HkyZPq+Ph484IFC64PGTLkzpEjR3RDhw69u3379siCggIlABQWFiry8vLUAKBUKrnZbGYAMHDgwNKdO3dGlJSUCHfv3hV27twZOWDAgJL8/HxVWFiYOH369KJZs2YVHjlyRG8wGAQAaN68ua24uFj44osvIgP99zvUyzkvvuCcB+UHha6r+oL52vypofyd9HeQ+xXs73mwXx9QP64x0BrSe9BQ/paG8ncAgF6lKEh/rqvrnBekP9dV1KsUBYFOe8+ePWHp6enNlUol1+v19s2bN59332fjxo1Rn3zySWOlUsmbNm1qXbZs2ZXo6Gj7ggULCgYNGhQriiJUKhVPT0+/GBsbaxk7duyN+Pj4hM6dOxu2bdt2/vnnn7/VrVu3eKB8wv6jjz5q3LJlS6P58+e3EgQBSqWSr1279kKTJk3sY8eOvZGQkJDYqlUrS3Jyclmg/34H5q2LiBBCCCGEkIbu6NGj+cnJyT5HQwtktDEidfTo0SbJycltXbc12J4XQgghhBBC/E0QWFGoRlkEAKEa+ipd2xrynBdCCCGEEEJIA0IPL4QQQgghhJB6gR5eCCGEEEIIIfUCPbwQQgghhBBC6gV6eCGEEEIIIYTUC/TwQgghhBBCSB3S6/Up3l5LSUmJC1S6f/rTn5oH6tyBQg8vhBBCCCGEBBmbzQYAyM7Ozg1UGunp6S0Cde5AoYcXQgghhBBCfCWKUTCXdAEXu8Nc0gWiGOWvU2/fvj3skUceiR05cmS7Tp06JQL3emUuXLigSk1N7RQXF5fQsWPHxN27d4e6H5+Zmant0qVLfFxcXEJsbGzCsWPHNACwdu3aKMf2559/vo3NZsP06dNjzGazEBcXl/DEE0+0A4BFixZFd+zYMbFjx46JixcvbgYAd+/eFfr37/9wp06dEjp27Jj4zjvvRALA9OnTYzp06JAYGxubMHny5FYA8NFHH4UnJSXFxcfHJ/Tp0yf20qVLfl8Ih1bWIYQQQgghxBeiGAXDjTb4dJKAiz8CrXurMXpDG+ibAoJQ5I8kcnJyQrKzs0/ExcVZXLe/9957UYMGDSpevnz5NZvNhpKSEo9OiNWrVzedPn164bRp04pMJhOz2WzIysrSfvrpp1GZmZm5Go2Gjxs3rvW6desar127tuD9999vlpubexIADhw4oP/oo48aHz58+BTnHN27d48fNGhQyZkzZzTNmze3ZmRk/AIAt27dUhQWFip27twZee7cueOCIODmzZsKABg8eHDps88+mysIAl577bUmixcvbv7OO+9c9sf74kAPL4QQQgghhPjCWhaDTycJyD9Q/nv+AeDTSQKe+zgGmjC/PLwkJSWVuT+4AECvXr3KpkyZ0tZqtQqjR4++3adPH6P7Pr179y5buXJli8uXL6ufffbZ2126dDHv3r077Pjx4/rk5OR4ADCZTEKzZs1s7sdmZGSEDh8+/E6jRo1EABgxYsTtffv2hT3xxBPFr7766kPTpk2LefLJJ4uHDh1aarVaodFoxGeffbbNiBEjiseMGVMMAOfPn1c/9dRTrW7cuKGyWCzCQw89ZPbHe+KKho0RQgghhBDiC3WIGhd/lG67+GP5dj/R6/Wi3PZhw4aV7t+//3RMTIzlhRdeaLdmzZrGH374YURcXFxCXFxcwv79+/VTp04t+vzzz3/R6XTisGHDYrdt2xbGOWe//vWvb+Xm5p7Mzc09mZ+ff/y111674n5+zrns9SQlJZmzsrJOdunSxfjqq6/GvPLKKy1UKhWOHDlyatSoUXc+++yziP79+3cEgBkzZrSePn369by8vJNr1qy5YDab/f6sQQ8vhBBCCCGE+MJSZkHr3tJtrXuXbw+wvLw8dUxMjHXOnDk3x40bdzMrK0s/YcKEO46Hkr59+xpOnjypjo+PNy9YsOD6kCFD7hw5ckQ3dOjQu9u3b48sKChQAkBhYaEiLy9PDQBKpZKbzWYGAAMHDizduXNnRElJiXD37l1h586dkQMGDCjJz89XhYWFidOnTy+aNWtW4ZEjR/TFxcVCUVGRYsyYMcXr1q27dOrUKT0AlJSUKFq3bm0FgPfff79xIN4HGjZGCCGEEEKIL1QhBRi9wXXOCzB6gwhVSEGgk96zZ09Yenp6c6VSyfV6vX3z5s3n3ffZuHFj1CeffNJYqVTypk2bWpctW3YlOjravmDBgoJBgwbFiqIIlUrF09PTL8bGxlrGjh17Iz4+PqFz586Gbdu2nX/++edvdevWLR4Axo8ff+PRRx81btmypdH8+fNbCYIApVLJ165de+HOnTuKxx9//GHHg8+SJUsuAcCrr7565bnnnusQHR1tSU1NLbt48aLG3+8D89ZFFMyGDh3Kd+/eXdeXQeoHVtcXQPmVVBPlWVKfUH4l9Ylsfj169Gh+cnLyTZ/PIopRsJbFQB2ihqXMAlVIgb8m6xOpo0ePNklOTm7ruq1e9rzcvOl7/iKkrlF+JfUN5VlSn1B+JbVOEIqck/M1YXV8MQ8emvNCCCGEEEIIqRfo4YUQQgghhBBSLwTFwwtjrBNj7IjLv7uMsVl1fV2EEEIIIYSQ4BEUc14456cBdAUAxpgCQAGA/9bpRRFCCCGEEEKCSlD0vLgZBOAs5/xCXV8IIYQQQgghJHgE48PLswA+ruuLIMGFixwWkw2cV/wU61+I72BD7ykhhJBg9aDVUXq9PsXbaykpKXG1eS1y+vXr9/DNmzcV1T1u9uzZLRcuXBjtz2sJimFjDowxNYAnAMyXeW0ygMkA0Lp161q+MlKXuMhhLLHgyw0ncPWXYrR4OBxDJiVCF6YGE+p8iQFZwZ5f6+N7SgIr2PMsIa4ovzZsVEeVs9lsUCqVyM7Ozq2N9KxWK1Qqlexr33777S91fQ0OwdbzMgxAFue80P0FzvnbnPNUznlq06ZN6+DSSF2xWuz4csMJFOTdgShyFOTdwZcbTsBqsdf1pXkV7Pm1Pr6nJLCCPc8S4orya8MW7HWUyMWoMmtZF5GL3St+Rvnr3Nu3bw975JFHYkeOHNmuU6dOicC9XpkLFy6oUlNTO8XFxSV07Ngxcffu3aHuxyclJcVlZmZqHb/37Nmz04EDB/R3794Vfv3rX7ft3LlzfHx8fMKmTZsiACA9Pb3xsGHD2g8cOPDhtLS0WG9pxMTEdLl69aoSANasWdM4NjY2oVOnTglPPfVUOwDIy8tT9+7dOzY2Njahd+/esWfOnFG7X9sPP/ygS05OjouNjU0YPHhwhxs3bigc1zhjxoyYHj16dFqyZEmVvTTB9vDyHGjIGHGj0ihw9ZdiybarvxRDpal27yWpQO8pIYSQYBXMdZTIxagiU1Gbl755SZCntmoAACAASURBVN19Y3e89M1L6iJTURt/PsDk5OSE/N///V/B2bNnT7huf++996IGDRpUnJube/LUqVMnHnnkEYP7saNGjSravHlzFFD+sHP9+nVVWlqa4c9//nOLAQMG3D1+/PipAwcOnF6wYEGru3fvCgCQlZUV+vHHH5//6aef8qpKIzMzU7ty5coW3377bd7p06dPrl+//iIATJ06tfXzzz9/Ky8v7+SYMWNuTZs27SH3a3vhhRfaLV269HJeXt7JxMRE47x581o6Xrtz547i559/Pv3Xv/7VowPDXdA8vDDG9AAGA9ha19dCPNXl2FOr2Y4WD4dLtrV4OBxWc3C0wNRH1XlPG8q444bydxBCSH3ha7nrvl8w1/tGmzFm7v65ws/XfoaN2/DztZ8xd/9cwWgzxvgrjaSkpLK4uDiL+/ZevXqVffzxx01mz57d8tChQ7rIyEjRfZ8JEybc3rZtWyQAfPjhh5EjR468DQAZGRmNXn/99RZxcXEJjz32WCez2cx++eUXNQCkpaXdjY6OtvuSxp49exqNHDnydosWLWwA4DguOzs7ZPLkyUUAMG3atKLDhw9LeoVu3bqlKCkpUYwYMaIUAP7whz/c+umnn5z7PPfcc0W+vj9B8/DCOTdwzhtzzour3pvUJsfY051rc7DuxQzsXJsDY4ml1r78KVUCBk9KRExsBASBISY2AoMnJUKpCprsW++o1AoMcXtPh0xKhEotbdWq63vvLw3l7yCEkPrC13JXbj/RJvpUR9UFnVKnzi7MlmzLLsyGTqnzGCZ1v/R6vcdDCQAMGzasdP/+/adjYmIsL7zwQrs1a9Y0/vDDDyPi4uIS4uLiEvbv369v166dNSIiwnbw4EHd1q1bo8aPH18EAJxzfPrpp7/k5uaezM3NPXn16tVj3bp1M7mnJ5eG6zVwzsEY83vlGRYWJvs3y6Fvf6RKdTn2lIscVouIk98VIG1MLKas6Y+0MbE4+V0BbFaf8zlxwwQGXZgaw6cnYeqb/TF8epLsRMjq3Ptg7tkI9vHThBDS0FRW7kp6WWT22/32cai0iirrqLpgtBktKdHSwGAp0Skw2owePSX+lpeXp46JibHOmTPn5rhx425mZWXpJ0yYcMfxQNK3b18DAIwePbpo6dKlzUtKShQ9e/Y0AsCAAQPurlq1KloUy787ff/99zpf03B9fejQoXe3bdsWde3aNQUAFBYWKgAgJSWl7N13340EgPXr10elpqaWuh7XuHFje6NGjeyOOTQbNmxo3Lt3b8k+vgqqaGMkONXV2FNHa4w2TI3MHRdw6It852uCwJA6vF1A02/omMCg1pYXAY6f7ny998EeGSaYx08TQkhDVFm5+/nr2c664smXU2T3U6oVYKy8/vBWR9UFnVJXsKLvijZz988VsguzkRKdghV9V4g6pa4g0Gnv2bMnLD09vblSqeR6vd6+efPm83L7jRs37vZf/vKX1n/84x+vOLb94x//uDJ58uTWcXFxCZxz1qpVK/O+ffs8IohVlUZqaqppzpw5V9PS0uIEQeCdO3c2bNmyJf+tt966OHHixLb//Oc/mzdu3Nj24Ycf5ruf+1//+tf5adOmtZk5c6bQunVr88cff+yxjy8Y58HTOuqr1NRUnpmZWdeX8cCwmGzYuTYHBXl3nNtiYiMwfHpSQAsUR7ppY2Jx4N9595t+nX9zrs/51dd7X1d5xFfBfn1uKM+S+oTyK5HlrdztPy4Omxf+5Nw2dnEvZGzKra3yWTa/Hj16ND85OfmmrycRuRhltBljdEqd2mgzWnRKXYHABJ/nbBDfHT16tElycnJb1200bIxUydf5EX5Pt6LV5vCuCxgwPj4ox742dL7e+2Dv2airPEwIIQ8qb+XuoS+knQWHvjhf78pngQlFIaqQYwITDlf8pAeXWhR0TY4k+LjOj1BpFLCa7VCpFQEfDuSINnImszxqXtqYWES2CIHVbIdaE/j0ie/33nGvXFvOHJFhgqFno67yMCGEPKjkyl3GAEOxWbKfodjsnN9C5TPxBfW8EJ845kcwVvGzFgoV11abs1nXceDfeTCVWKBSC7Ba7EE5Mbwh8uXeU88GIYSQqiiUgmxdoVQpav07Bqm/6r5JlBAv5FptlCoBplJr0E4Mf1AFe89GsAcUIISQhsZbuasNVQVtXUHqB+p5IUHFPdwuAElrjM0qUsjbICAXFrkueud8RaGSCSHEf3wJje+t3LVZxaCtK0j9QD0vJGj40joe7BPDHwT1sReD8g0hhPiHr3UAlbskUKjnhQQNX1rHHRPDXTkmhpPaUR97MSjfEEKIf/haB1C5W7dmzZrV8rPPPgur7nHbt28PGzBgwMOBuCZ/oYcXEjR8aaWhieF1rz62plG+IYQQ//C1DqByN/BEUYTdLv8w+MYbb1x56qmnSgJ9DVarNdBJeKBhYyRo+BJuN9gnhj8Igj0sshzKN4QQ4h++1gENudzlohglGgwxgl6vFg0Gi6DXFzDh/td6mTZtWkybNm0sf/rTn24AwOzZs1uGhYXZRVHEf//73yiLxcJGjBhx5/XXX79y+vRp9bBhwzr26dOn5PDhw6Gff/75L/Pnz2+Zk5MTwhjjY8eOvfm///u/10eNGtX28ccfL/7tb397+9tvv9XPmjWrtcFgENRqNd+/f/9pjUbDJ0yY0CYnJ0evUCiwYsWKSyNHjpQ87BQWFirGjh3b9uLFixqdTie+/fbbFx555BHj7NmzW169elV18eJFdVRUlO2LL9wW7wmw4PymQR44jsl+T76cgrJiM0QbR2iUFlaTDUqVAC5yWC328gLQUlEAVkz2I4Elee8rIr4NmZToMd5ZqRJgMdlqvZJyvz5v6ToCCgCgfEMIIffJ0aPiax1wv+Wur2W7r/v5CxfFKPutojYFr8wRDIezoO/eTR2zclUbReMo3O8DzLhx44pmzZrV2vHw8vnnn0e+/PLL177//vvQnJycU5xz/M///M/Du3btCm3fvr0lPz9f+8477+Rv2rTp4oEDB/RXr15VnTlz5gQA3Lx5U9K1ZTKZ2NixYzts3rz5bL9+/QxFRUVCaGiouGTJkmgAyMvLO5mdna0dPnx4x7Nnzx53PXbu3Lktk5OTDV999dXZbdu2hU2cOLFdbm7uSQDIycnRHzx4MDc0NLTW16sIihqcMRYB4F0AnQFwAL/jnP9Yt1dFaovr5D99uAa9nmqPbz485SwUh07uDNHO69UE8YbC11CXdRXCuj4GDyCEkPqsNpYx8LVsr4s6QDQYYgpemSMYDh4CABgOHkLBK3OEVm++GaMIDb2vh5dHH33UeOvWLWV+fr7q6tWryvDwcHtOTo5u//79jRISEhIAwGAwCLm5udr27dtbWrRoYRk0aFAZAMTFxZkvXbqkmThx4kMjR44sfvrpp++6njsnJ0fbrFkza79+/QwAEBUVJQLADz/8EPrSSy9dB4CUlBRTy5YtLceOHdO6Hnvo0KGwLVu2/AIATzzxRMnkyZOVt27dUgDA0KFD79TFgwsQPHNe/glgN+c8DkAygFN1fD3kPvkSPtGd1WLHie8KkDYmFv3HdsI3H5ySTAQ0GWz1boJ4Q+FrqMvqhLCWyyP3k28quz7KG4QQUnv8vYyBz0EB6qAOEPR6teFwlmSb4XAWBL1eXZPzjhw58vamTZsiN2/eHDVq1KgizjlmzZp1NTc392Rubu7JixcvHn/55ZdvAoBerxcdxzVt2tR+/PjxkwMGDChZu3Zts2effbat63k552CMeVSqnFddz8rt4zhXSEiI6PFiLanzhxfGWCMAfQFsAADOuYVzfqfyo0gwcrSA7Fybg3UvZmDn2hwYSyxVfhFVqgXE9myBA//Og1LtORGwURNdvZsg3lD4PDHTx/285RGzwVrtfFOddAkhhPiHXDnu77LY33WPP4kGg0XfvZtkm757N4gGg6Um5x0/fnzRli1borZv3x45bty428OGDbu7cePGJsXFxQIAnD9/XlVQUOAxYurq1atKu92OF1544c6SJUsKjh07pnd9PTk52VRYWKj+9ttv9QBw+/ZtwWq14rHHHivdtGlTFADk5ORorl69qk5KSjK5HturV6+Sf/3rX42B8ihkkZGRNkfPTV0KhmFj7QHcAPAvxlgygMMA/sg5L6vbyyLV5doCAsDZAjJ8epJzrKvc2FSrWcS+jeW9LbevlnlMBLx701jvJog3FL5OzPR5P5detsgWIbh9tQwnvitAxx7NK803Nb0+Qggh/iFX1xff8L2e9mWOitVsR+qINmjftZmzrjh35LrH+Swm+TrAYrJDowtMHSDo9QUxK1e5znlBzMpVoqDXF9TkvKmpqaaysjIhOjra0qZNG2ubNm2sJ06c0Pbo0SMOKO9t2bx583mlUilp2cvPz1dNmjSprSiKDAAWL1582fV1rVbLN2/efHbmzJmtTSaToNVqxf379+fNnTv3+vjx49vExsYmKBQKrF+/Pl+n00nOvXz58ivPP/9829jY2ASdTie+//77tTox3xvmS7dRQC+AsVQAPwF4lHN+kDH2TwB3Oed/cdtvMoDJANC6devuFy5cqP2LJZXinGPdixkQXVrMBYFh6pv9wRjzPn8iTIX1M76FKHJ0TI3GI0+2x76NfpvzUicTHxpKfvV1PLFoF2EstWKvy36DJyVCF6qCoLjXwSuKIkpumSX3d8D4eIRFafDWixnO/VzzjT+ur56hPEvqE8qvDxi5ur5jj2g8Nvphv81RqXGd0lgDQZAdXCSbX48ePZqfnJx80+f3wM/Rxoh3R48ebZKcnNzWdVswPLw0B/AT57xtxe9pAP7EOR/h7ZjU1FSemZlZS1dIfGUx2bBzbY6zBaRjajR6PtEO4U11sFnsEEVg11s5khaSmNgIDJ3SBccyLjlbWEpvmyAIDCERGmerDID7jSZS599ggyG/1iQaiy/HWkw23LpSiqgWoVBrFbCY7Ci6WorGLUMlrWRmo81rHtgw54Bkmy89LzX924JUnV98MORZUm9Qfm1AfC3vXet6oLzMHvFiEjiHX44FILufe71gMdlw9OuLHj00yYNae6s//PLwQmqP3MNLnY+r4JxfY4xdYox14pyfBjAIwMm6vi5Sfa7hE51Rwz445fx/aKRWdmyqRqdEwmMxHi0sXOSSwofC3N6fmvZO+BLqUqkS0KixDrveypHcQ6VK2vKl1sqPT9bolYiJjZBcn68LmVEIZEIIqTlf6wqlSsDgSYkedbZCKTh7RbyVxXJzVPThGlhNdkm6T76c4vNCmImPxXhcMy2E2bAFS03/EoDNjDE1gHMAflvH10Pug2v4ROBeq8mzf+mJ6xfuQqNXYcqa/rh9tQyHd13AmczC8rGpZjv2uo2f3euY86Co85gS9Z4vc5FqnoYoew+HTUuCRnfvHnqbo2Iz2zFsWpKz10alEup77wkhhNQrvtYVNquIk25zF09+V1De21FFnS03l0UTorzvOTRMYNCGqqT1h5rqj4YuKB5eOOdHAKTW9XWQmnO0gnPOna0mkdF6aENVklb5AePjEdlCh9ieLaBSCxQxKoBqIxqLtx4VtVaahlyL3dDJnZ0PsJWNbSaEEBI41YnwlbnjAg59ke/cJggMqcPbVZmGUiV4jLQYMikR+nCNZL8Lx2/J9u649+ZzkdfJGmOkbtE3AxIQjhZ2ALBa77XKO+Kw79t4CkkDW+Pg5+dw+5rBua+Do4WF1JzrvXDw9/vriPjinobFJE3DtcVuypr+SBsTCyYwj/yxd8MJWC11Ho2REEIeGL7WFTWpU2wy3we+3HACPUdKH3zadG7sUVec/K4ANqu0XqC1vh5MQdHzQuo31wl+NosdXARUWgWGTUuCSiOAMea1Vf5MZiEAYMD4eEm0EBqz6j+uc5Fc31+lSoDFZKtyortoF2G1iNIuecakkzq9jIFWuaWhVAseLXbT3xrgNX/4cn2EEEIq58tEfG91hXtd7Ot+clQaBfThGjz7l57OYWOHd19AoyZajF3cC42a6HD3phGNmmhle3e6D20Ls9HmrI+89frTyI2Gze8PL4yxCAATALR1PT/nfKa/0yJ1z3WCn+skfdfhYSqNIDt21Wy0AQDOZBYisoXOOWaVvqj6l+tcJOdDhErwqavdW7hKlUrAznXHpMeGqDzmrZjKpGkMnpSI1BFtJBWS2WCTj9NvtGGXexo0FIAQQqrF14n4cnWFt7pYUDD0HxfnfNgQFL6VyzaL3eN7wpDfJ8JYYkXGptxK64rBv0/wqI+GTe3SYNb60uv1KQaDIVvutZSUlLjs7Ozcmpx/8+bN4SdOnNAtXbr0WnWO8yXtMWPGtJk7d25h9+7dTZXt5y+BGDa2E+UPLsdQvuCk4x9pgFy7bLsPbYNvPihfbLJDt2ZIGxOLsMZaKNUKDJwYj5jYCAgCQ0xsBAZOjAe3c+fviY/FQK1RgLHyOTP0BdW/HHORHO+vzSr61NXuOhHfdUiXyOFxrChKu/Pl0ti74QSSBjwkyQsCAwZPSpRsGzwpETn7LtFQAEIIqaHqDK1yryvk6mKrxY7LebehDVGBMUAbosLlvNs+lc+cw/k9wXEtFpNdtp5xryseim/ssV/Ovkse9UdDGrlhs5U38tb0wQUAxo4dWyz34GK1Wis9zpe0//3vf1+orQcXIDDDxrSc89kBOC8JQq4T/CJbhODqL8UeC01OWdMfP20+LYlM8tNn5zD4dwmY+mZ/6mmpA75OzKwstLGrdl2bwmyUTrr3FupSrVN6tOwpNVzaa6MpH15W1fURQgipnL+DtihVAlp0iMDu9cckPSXuk+l9vZZGTXS+1RUyx2buuIDuw9r61FvkT6LIo2wWe4xKo1BbzXaLUq0oEATml0Uqt2/fHva3v/2tRbNmzawnT57Unz179oSjV+bChQuqUaNGtS8tLVXY7Xa2evXqC0OHDi11PT4pKSnuvffey09NTTUBQM+ePTutWrXqUnZ2ti4zMzPkww8/vDhq1Ki2kZGRtmPHjumTkpIMf/3rX6+NHj263Z07d5Rdu3Y1ZGRkNDp8+PCpFi1a2Bxpb9++PWzx4sUto6KirKdPn9Z16dLF8Nlnn50XBAE9e/bstHLlykt9+/Y1fPrpp40WLlwYY7fbWVRUlO3HH3/M27dvn3727NmtTSaToNVqxffff/98cnKy+X7fo0A8vGxkjP0BwHYAzgvjnNPKow2Qa+jb0tsmPLfoEYQ30aH4phH6RhqIIsftq2UwFJvx//52yHlcTGwEzAYb1Dol6nid1AeSt5DF7l3tjon4HkP+DDbJ+To90hy71x+ThLr0OhzMZIdGJ12XhYE5QyprdEpYTDaPcJrnjlyvl0MBCCGkLvla3vt8Ph9D48uxmDxDJRvumuVD6LuPBPD6d4gedUogiSKPMpVY2ny54YRQ8fCmHjIpsY02TA1/PcDk5OSEZGdnn4iLi7O4bn/vvfeiBg0aVLx8+fJrNpsNJSUlHm/4qFGjijZv3hyVmpp65cKFC6rr16+r0tLSDNnZ2TrX/c6ePav9/vvv85RKJSZMmNC6X79+JcuWLbv26aefNvr444+byF3XqVOndEeOHDnXtm1ba/fu3eP27t0b+qtf/cr58HTlyhXljBkz2mZkZOTGxcVZCgsLFQCQnJxsOnToUK5KpcJnn30WNnfu3FZ79uw5e7/vTyCGjVkA/B+AH3FvyBgtfdtAOSbu9RzZFgwMGZtysW5GBjI25eKRJ9ujY2o0Du+6gAHj4z2GBR3LuIT1MzKw660cGEss4CI9xdQWx32rqqtdpRZkh3QJDJJtGr3So0VMrVHIHqtSV13sOMJpHvh3HtbPyMCBf+ch4bEYn1r2CCGE3ONree8rX0Pjy1+LZ9nOGMPQyZ0l1zd0cmdYTXbsXJuDdS9mYOfaHNjMdo/9fK1T/Mlmscd8ueGE4DYMT7BZ7DH+SiMpKanM/cEFAHr16lX28ccfN5k9e3bLQ4cO6SIjIz3Cck6YMOH2tm3bIgHgww8/jBw5cuRtuTSeeeaZ20pl+cPeoUOHQidOnFgEAKNHj77bqFEj2TGAXbp0KevQoYNVoVAgMTHRcPbsWbXr6xkZGSE9e/YscVx7dHS0HQCKiooUw4cP79CxY8fEuXPnPpSXl6et1hviJhCPqLMBPMw5vxmAc5Mg45jglzSwNXa9lSNpidm38RTSxsTi//3tECJb6DB0Shdo9EqYDTYcy7jknIgXiEUTSeW8TcwE4BHhSye3ABhjkmPlemgsZrvsQmZJA1uDMbHSLn7XcJoALVxKCCH3qzoT8X3hrUfeYrKDMVSahrdem+HTkiTXBwC735Z+p/iyYj/3+qi21wNTaRRqL8Pw1F4OqTa9Xi+7VsCwYcNK9+/ff3rLli3hL7zwQruZM2cWNmrUyL506dKWAPD222/n9+3b1xAREWE7ePCgbuvWrVHr16+/IHeu0NBQZxrcxyEwGo3GuaNCoYDNZpPcYM45GPPMV/PmzYvp169fyd69e8+ePn1aPXDgwE4+JehFIL4pngBgCMB5SZAqn+An3xIT2SIEMbER6NSrBcA5zGVWqPUqms8QBBwTM4HyrvbKItI4uuQdPx3HOH6KdtEjVLJGr/S6kNnnr2dXGvWmNhbWJISQB4V7eV8Tjh559yiUjHPsfKvyCJFeQxtrFc4vve4LXXvbz7U+qk1Ws93S4uFwtcwwPEugG2Dz8vLU7dq1s8yZM+dmWVmZkJWVpX/vvfcuTZgw4Y7rfqNHjy5aunRp85KSEkXPnj2NVZ23Z8+epRs3boz6+9//fm3r1q2N7t69e1+V7YABA8rmzJnTJjc3V+0YNhYdHW2/e/euolWrVhYAWL9+veyQtOoIxOOqHcARxth6xli6418A0iFBpLJFq558OQUanRKCUoAmRAWruXzMq9y+pO7UZLEvQSE4QyVPfbM/hk1Lgt0inyeKbxirjnJWCwtrEkIIqT5BITh75B3lvUanwM51xzzKdpvVDovJBs45LCYbbF7qBfey3deFj+uCUq0oGDIpUXQbhicq1YqCQKe9Z8+esISEhMT4+PiEzz//PHLu3LmFcvuNGzfu9o4dO6KefPJJn+bg/OMf/7jyzTffNEpISIjfsWNHeNOmTa0RERHVfrNbtmxpS09Pz3/66acf7tSpU8LTTz/dHgDmzZt3bdGiRa26desWZ7fX/B4yX7uKfD4hYxPltnPOP/BXGqmpqTwzk6bRBBO5VvvBkxJx8rsCZO64gBYPh2PgxHj89Nk5GIrNHq8FcA2POg9hVl/yK+cc617MgOgy90gQGKa+2V+2G9hVddaD+e7TX3Dm53vlrVwavq5L0EDV+R9YX/IsCQqUX4ls/dGxRzQeG/2wRzkuKBh2v338vtYY04WqajpMTDa/Hj16ND85Odnn6Q6BjDZWF4xGI1MqlVylUuGrr74KmTFjRpvc3NyTdX1dAHD06NEmycnJbV23BaJ/61MAJs65HQAYYwoAmgCkQ4KI+5hauXkt33xwbw6MIzJJ6vB2FCo5SNQkIk1l0WdcxzEzBhiKpdER5dLw9xhtQgghgSNXf/Qc2c7Zmw9I57dWVbZbLaLXOZNVRTSrDYLAitRaZRFQOxHOAu2XX35R/+Y3v+kgiiJUKhVfv359fl1fU2UC8Y5/DeB/ADhCp+kAfAmgTwDSInVAtImw2crneak0ClhNdig1Aox2I3RqHawmu3O+gyvHHBjH/32JTEJqj0qtwNDJnWEy2JyrJmv1SiiVAsxGW6UTJNVaBfThGjz7l57Oiubw7gtQaxWS4QAKpYAhkxI9WuLkot74c4w2IYQQKS5yWC12yUMEZxxGmxE6pc75U2CeDwvuxypVgkf9Ed5Ufv0WlUY6v0WOWqvwOmfSPagMNWrVXJcuXcynTp0Kip4WXwRqkUpnzGfOeSljTF/VQYyxfAAlKJ8zY+Ocpwbg2kgNiTYRFpMNFrMd33xwyvkFdNDv4nHwzo/oHZmGvRtOIO3ZWNlW/NtXy5z/L75hxMeLDj5oQ4KCFuccdjtHxqZcSTe9xWSTdPHLdd1bzXb0eqq9JE8MnBgPq7k81KXrg4pSJaD/uDhnBSco6J4TQkht8jY016YxY9a3s5BdmI2U6BSs6LsCUdooyQOMt2MFBZPUH8Omdrn/3nwvIwEsRht2ras8KMB9EkVRZIIg0JoNQUQURQbAI/JaIPreyhhj3Ry/MMa6A6gy0kGFAZzzrjV9cBG5iDJrmeQnqTkuclitIkwGG7754JRkYt7X751CWnS/e9280XoM/p00rvzAifE4vPuC8/+Htp2v9sTwBxkXuWTiY3XWxRHtIszG8mPNRhtEu+dnwnXol+O+7N1wAiaDzWOb1eJ2PIdHnvjmg1OwWeweEzgNpVZsXvgT3pq+D5sX/oTdbx+ne08IIbXIW4CWMqMRP1/7GTZuw8/Xfsbc/XNhtBkl36m8HeteV+Tsu+Sx1tegFxJQxRRKAABjwJDfJ2Ls4l6YtnYAxi7uhSG/T4RVpk7xU/1x/MaNG+EVX5ZJEBBFkd24cSMcwHH31wLR8zILwCeMsSsVv7cAMCYA6cgSuYgiUxHm7p9bacsBqR5HS4uukRoqjXxXsFqrRGzPFti3sbz1PXVEG+faLiW3TAADBv8uAVazHRmbT+NMZqHkeAqD611NJrD7OvHRWwjLRk10Htvch/ypvByrDVV7bJM7H917QgipPd7C0UeHd5Vsyy7Mhk6pw++//L3zO9WGIRt8qisyd1xA92FtPXraFT4sNqxQCTCV2iQ9OYMmJiAkwrNO8Uf9YbPZfn/t2rV3r1271hmBadgn1ScCOG6z2X7v/oLfH1445z8zxuIAdEJ5VIdczrnV8TpjbDDnfK/coQC+ZIxxAOs552/fT/pGmxFz98/Fz9d+BgBny8HqgasRogq5n1MSoDzcodkOpdkOw12L1wWq9m085dx+6It8FJy+g6FTumDjgh8RVDQpAAAAIABJREFUExuB4dOTAPg2aZvc49rSBVRvYc/KJtO7Tnz0tvDY3ZvSjlPHvXaNsW8x2Xw+Vm4b3XtCCKk93sp7s8kq2S8lOgWXSy5LvlNdu3Pd5/LeUGyB3VreU2+3ijjz8zWfJt1bzSK+/uCkpN76+oOTGDqli0ca/qg/unfvfh3AEzU6Cak1AXm65JxbOefHOefHXB9cKiz3ctijnPNuAIYBeJEx1tf1RcbYZMZYJmMs88aNG17T1il1yC7MlmxztByQ+8NFDqvJjoxNucjYfBpqrQIDJ8Z7dAWr1IJsa4xGp3TEQYdKrYCyYoEr1+MHT0qEUt1wGjt8za++qsmijd56VDx6T1Ty90WrV3psU7m1nKnUCp+Pdd/mbcI+qV3+zrOEBBLl15pRaQQMGC+txweMj4dGq0KP5j2gZEr0aN4DK/quwNojayXHni8961N5P2RSIpjAcODfeVg/IwMH/p2H2J4toNJUXdd7q7c0VH8QBGCdlyoTZCybc55SxT6LAJRyzlfKvV5ZTPcyaxle+uYlZysBAPRo3oN6XmrAYrJh59ocZwtIx9Ro9BndAWqtEiqNAhZjeeQPi8mO3euPSVpjYmIjMGxaEhiDM5KJwWqATqmD2WSFVqvGxaIC7CnYiXEJ4wJxj+p8/Ko/1iBwvwcAnD1ZVbU4mY027HrL89jynhdp78nRry+ifddmzohh545cR+d+rcAYg0avhNlgw+XTRWid0FiSrtloxaVTRWjVKUqyX6uESFwrLUTrqBjnfX4h8QXAKkCtVTqjxghCw3lw9YMGkWfJA4Pyaz0gclESRUxpUyPnm0se5X3SwIdgU1qc+wlMwItfvyj5TnXo15koyL3jUd63TmgMzuGMTMkYZOst97pHTmV1HoCaRBur8/xKaq4uxml4PC0xxkIACJzzkor/DwGw+H5OrlPqsKLvCo85L9Tzcv/cW/3PZBbibNZ1TFnTD8a79+ZhpI5og8GTEiVzKwZNTIBKI0AQBNn5SIv7LMbak6uxN38v/pD0hzr8K4ObSq3wOcSw57GCx30ZPCkRKreeLpXGMzRlxx7R4HaOPRvuRRsbMD7eo5dMpVGg6UONsHv9Mcl+apUSI3cMd+43ot0IlFhLaE5aA9D2TzuqtX/+P0YE6EoIIZWRq3vf6PcGOvZphox/5TnL7LQXOkClUUAjlDcihqhCIHLR4zuVRquSLe+VasHZEKXRKcE596nXX05ldZ7jYYWGGj+4guXORwP4b0XcbyWAjzjnu+/nRAITEKWNwuqBq6uMU0584y1kodlkk8zDcHzpHTqlCzQ6JYpvGsEU5certIDBakCkNhLze87HO8fewa7zu7Dwh4WY33M+bhpvwmgzUu+YFzVZtNEoGvHj7R8xZEoadHo1jAYLfrhxAL31vRGiuPd++7rI2L6NpzBsWhJsStdWPI1kvpNjv6FTO2PrE1vRPrw9zhWfQ4QmwuucNAD0mSWEkGpw71GRKzvl5gLP+nYWXu/3OoZMTYBOp4HRaMb3179D79BeYIxJzuf+ncpqFmXLe/d5lDVZ+JgWKiaVqYtvB/nuGzjn5zjnyRX/Ejnnf69JAgITEKIKkfwk98/RAuI6zjTthQ7QaJUerSqZOy5Ao1di73sn8fGigwgJ10ChZigyFWHmvplI3ZiKZYeWYWbKTAxrNwzZhdloH9EeSx5dAptoo7DWlXAs2shYxU8fC3GdUoeu0V0x47tp6LaxG2Z8Nw1do7t69EbK3Wdvi4yptQq89M1L6L6xO1765iXv45N1Kiw7tAypm8rvO2MMzXTNJPs55qS5nq/IVER5gRBCKuHoUamq7JSbC9xM1wxWbsWMA9PL64UD09G1WTIAeJwPgOQ7lc/zKGXqlOrMUbnfOo80fAHpeWGM9QHQ1vX8nPMPK34+E4g0SeC4t4BYTDa8n/cefiUOl21VKbllwpnMQsTERqCkrAyCGh6tPq49LiWWEryR9QZuGm/S3KQA8LU3Uq6ly1sUMbPJ6lP0mWt3rnv0sizqvQg7zt8bciQXzYYiBBJCSOV8ja5qsBqQEp0imbcyvet0j2PnHZiHRb0XVXk+b5HK3KNQUu8JCRS/d0kwxjYCWAngMQA9Kv7VaNFJUvdcW0BUWgVGxY7CnoKdHq0qAyfG46fPzzl7Z5Zl/R16lV42Alz7iPZY3Gcxlh5cil3nd1FUuADytTfSvaWrPDJcgltUmQSPOS/px96QbWFLP/aGZL/swmy0CmtVZTQbyguEEFK5yqKrui4qqVPqsCJtBXY8vQNHxh/Bjqd3ICY0RvbYmNAY2fO5UnmJGOo+jxKg3hMSGIHoeUkFkMBrO4wZqTWOlvxxCeOgYMCvpiZCo1Xh5t0i2BVmDP5dAiwmG/5yaAHaR7RDqbXUo9UnJToFpZZSpGenY9f5Xc5tNO8luJhEE364/SOGTH1MMi66E4uV7NcmvA1sGjMe+W0MosO7orD4BmwaM9qEt5HslxKdguuG65jfc75zHgznHNeN1z32o7xACCHeGW1G+brVWopZ+2Y5J9ivHrgaNm7Doh8XObctT1uOKclT8OaRNyXHFpQWSNKQK4sFhQBdqArDpiU5I4up1IJk0WNCAikQDy/HATQHcDUA5/aJLxPYSMX6LRa71+5c9/dRYAI0Co3z/zqlDgabATMPzPQITf3GgDfQPqIdnot7Dh+d+giL+yzGwh8W3oswlbYCAHDTeBNKpqSocD6oSb6u6l57IzABXZp2xowD0yWVnkahcbbeFZQWIFIbiT/u+6NsPjh07ZAksphWoYXFbgEAaBQarxECtQqts9WQPseEECIlW3amrcBHpz6SDP0qsZTgz9/92WOImHv5vDxtOXRKnaRsD9eEQ6fQOcPaO+oPQSE4J+dXFfaYEH/zW45jjH2B8jDIYQBOMsYOAXAuo845r5WVS+VCAlIoVk9c5DCWWDzCEOrC1GACk30flzy6BOlZ6bhuvI4ljy5BI3Ujr0PCQlQhGNVxFEJUIVh/dD3OFZ9ztrafLz6PSG0kGGMUFc5HNcnXVd3ryqgFNZRMiUW9F0keVG4Ybkha8d4d8q5sPghVhUrusVahxW3zbcmxK/quQKQmUnY/+hwTQog8ufmMWoUW64+ul+zXVNfUa/nsWrbrlDqY7CZJ+fxGvzdgKrHeV/1BSKD481vASgCrACwC8BSApRW/O/7VCtcJbDZuc044M9qMtXUJ9YLVYneGwBVFjoK8O/hywwlYLXYA997HJrom+M/I/+Cdwe/AJtows1t5L8uC7xfAzu24XHIZKdHSNUdTolNgsplgtBlhspkwJXkKZqbMRIeIDjDZTQjXhMMiWigqXDV4y9dmu1kytlkuQldl97qqY402I+YemIsR/x2Brhu7YsR/R6DMWoZXv39Vci0llhLZfGCwGiTbLKJF9u8w2U2SvGCym+hzTAgh1WS2mzEleQq2PrEVR8YfwdYntuKG8YZs+Xy55LKkbHc0kLmWu2VGY6XfFQipC377tsg5/5Zz/i2A4Y7/u27zVzpVqWwCG7nHfeFJoDzUoUpTHsJQp9Shma4ZZqbMdIa6XfTjIqgVameI4zB1GNYeWYvFfRZLJmAveXQJ/vbj37Dox0Uw2U14rtNzWPjDQmfoRatohcVuoVC41eAt1GWZtazKMJmV3euqjpXrWQtVh3psC1GFeOSDxX0We4RALrOWeQ2VXNXfS59jQgi5Ry5UssFmwHOdnpOEqNcpyifsVxUoRW4Sf3R400q/KxBSFwLR1D1YZtuwAKQjyzGBzZVjwhm5x7F4lCvH4lFA+fs4vet0LPxhoUfr9x+6/AEp0SkosZTguvE60rPTMb/nfBwel4l1fV5Hc300ZnSahKbaxpi7fy6KLcWScyz4fgGKzcV0T6pBLl+7hrqsrHfC2712hDGu7Fi5dAtKCjA1aQp2Dd2Ko+OPYNfQrbhtvI3t57Zjfs/5yByXifk952P7ue0w2AweaUzvOl1yPrnPJ32OCSGkct565C12CxZ3La+TF3edj/+X+zE4uKR81iq0HoFSCkoLPMrdwuIblX5XIKQu+O3hhTE2jTF2DEAnxliOy7/zAHL8lU5VHBPY3FsYqMVWqqrFo3RKHVqFtfIa4njJo0ugYAqs6LsCN4038d6xDRCLbuPqjJk4ndQV9rlLsKDTTETrmsmGXowJjaF7Ug1y+drb/fFl8UlvYYzlekDc022qbYLfxYyCfe4S572OMABjOv5G0tr3ePvHPc7nLVSyL+nS55gQQu6R66GO1jVDpJFJyufnm49AlCYSz2x7Bl03dsUz257Btl+2YXnackkZG64O9yh3Q3S6Gi00SUgg+DNExEcAdgFYBuBPLttLOOdFfkynUr4uyPegq2rxKIEJKLOWyYZhNFqNiNRGQqPQQK/SY/XA1dBagMvTp8Nw8BAAlP+ctwAvr/qbbOjFgtICNNY1plC4PpLL197CZLqHtZS716LChkJjoSQN2ZCYMumqzHYUvPKK5F5feeUVRLyxXBICefu57RjebrhsGlV9PulzTOq7N6d+U639X1w3MEBXQhoquTrg5YTpKJgzx6Mu1qxJlxz71aWv8EzsM0gfkA69Sg+D1QCtUguBCR7lLlMzWmiSBBV/znkp5pznA3gRQInLPzDGVP5Kxxc0Edw3VS0e5dr6PaLdCHz3m+/wTp9/IkSph9pog81mce4r6HQwHM6SHG84nIWmUa2gV+o95sSEa8KpFb2a3PN1dXon3O+1WqmWPdYRnth1Er97ugp9iOy9bhTeDOF2DRiAcLsGz3d6DuGacNnr8+XzSZ9jQgjxTqfU4Y1+r+PrETtwdPwRfD1iB5pGtZItn1UhYZKyeHnaciiF8joBABhjEJggW+7SQpMk2AQiOHcWgIcA3AbAAEQAuMoYuw7gD5zzwwFIkwSAo/X7zUFvgtvtUN014vIrr8BwOAv67t3QcuVK/OfGXnx56Susf/QN6Lt3c7b2AIC+ezcU3b6CFSfSsaj3IrQKayVZL4a+jNZMTXonvIXY9CU8sd1gkL3XYlkZjHP+gtMV+SNy5UoIUZHUe0IIIQHAOKAvteK2S7krrlkjXz4bDB69LHfMdygcPamXApFDd6M84lgTznljlE/W/w+A6QDWVnokCToCEyByESqzHXc++QTRry5A3NEjiH51Ae588gl+/dBINNE1wXu/fISWK1dC/0hPQKmE/pGeaLJiKVaeWIObxpvQq/QA4OwxoMLRP2rSO+F+rLfwxBabGfayMnBRhL2sDEypRIulSyX3usXSpRCNxvIK02ZzDiXjRiP1nhBCSDW59n57C2UvGo33hohVlLui0YiWr72GDnt2I+7EcXTYsxstX3sNXBAQqg6FwMp/mu1mCkdP6q1A9Lykcs6nOn7hnH/JGFvKOZ/NGNN4O4gxpgCQCaCAc/54AK6L3CedUgeEcISPHImrCxY4e15aLFkCptdjZspMrMleA0VSFFqtXQtBp4NoNMKqFvD3tKXU4l5PeJv8qSwuw+U5c5z3PWbVKpTs24foVxdA06E9zGfP4cYbb6DlP/4hOdZwOAsKPc1pIoSQ6vB1UWK54dqKqCjYr1/H1YUL742SWLYMQkSEZD8KR0/qs0B8myxijM1jjLWp+DcXwO2Kh5PKFvb4I4BTAbgeUgN20Q6D1QBuMJQ/uLi08FxdsADcYMCpW6ewoNcCgDGY1ABngCIkBFqVjlrc/UiuJc6X1jlfjzVYDR5hMiWTPyvue8GcOQhLS8P5J55AbmJnnH/iCdiuX4f18mXJsfru3WA3lAXs/SCEkIbI18W2RaMR+u7dJNt4WRmuzJ8v7QWfPx/cIF0wmMLRk/osEN8onwfQCsBnAD4H0LpimwLAb+QOYIy1AjACwLsBuB5yn+yiHUWmImw6tQlCiPwkbSEkBF2bdcWmU5sqXeyQ1IzcYmRFpiKUWEqqXGhS7tgSS4nHNju34++P/l0aFtnb5M+HHpIMG4tZtQq2RnrJtqjlSyDo9bX5NhFCSL3na68I02k9hmsLYWFe62r3NCgcPamv/P7wwjm/yTl/iXOewjnvyjmfwTm/wTm3cM5/8XLYGwDmovKeGRJg7i3xRpsR8w7Mw5tH3kTR7SseLTz67t1QcOMs5h2Yh0GtB9G42QDy1hJXbC72aaFJ92OLzcUe217OeBlapRav9X8Nh8cfxmv9X4PdUCZ73+0GA1qtXYu4nKNotXYtbOEh2HhhCxQrFqBTzhEoVizAR9d2wGg31ebbRAgh9Z5cL3hKdApMNpO0jrab8F6BtNy9W3zdS5ldJjkWgDNoy+Hxh7F64GqarE/qDb/PeWGMxQJ4BUBb1/NzzmWD2DPGHgdwnXN+mDHWv5LzTgYwGQBat27txysmgPwY23eHvOts/Vlx/J9YsHwJMO/enJeo5UuwJDfduXClA42b9X9+9dYSJ7cAqNyCj+7HxoTGyJ4vXBOO33/5e2ceeKPf62i6YhluzJ0vue+CXgdBKF+kTBESAsZFjOo02mOM9oOeD+oTKmMrV911W0hgNeT8qlPqsLjPYiz8YeG98jRtBQw2g0cdvS5nPdYcfdN57OPtRuBvq1aVD/eVlNl6vLT3Dx5zaBzretGaa6Q+CcSE/U8ArEP5EDC7D/s/CuAJxthwAFoAjRhjmzjn41x34py/DeBtAEhNTeXeTiZy0TlBnCaK+861dR4Afr72My6XXHYugLUzfzcA4G9r0qEMCcWVG+ewJDcdO/N3o0fzHiixlDjPJbfY4YPG1/zqK28LUsotAGq2myFy0fkZ4Jx7HFtQWiB7vssllyV5YNa3L2P5Y8vLW/aatseVG+fw0dUdGNd4PEKEyhezpM9e/eLvPEtIIDXk/Gqym7D93HbJor82bsNnZz6TbCu1lHqU44XG67it4x5l9rORz0nK9rn752L1wNUPdD1N6q9AfLOwcc7f4pwf4pwfdvzztjPnfD7nvBXnvC2AZwF84/7g4itv8wJo/kXV5Frn1x5ZKxkTe8N0C0Y1UGi4joVHluHLC1+hR/MeWNxnMUJVoTRuNoC8jU92XwTy9f6vo8xaVuVcFr1SL3u+tUek0cyzC7PRWN8YC48sQ/dNqVh4ZBlGdRote39pUUlCCKk5nVKH0bGjsezQMqRuSsWyQ8vQVNcUj7d/XLItRBWCxX0WS8rxvz/6d6iVakmZ/UzsKOw4u0OSBo2QIPVZIHpevmCMTQfwXwBmx0bOeVEA0pKQ6z2g1gXfyLXsXzdeR4gqBOkD0qFT6XDuzjmUWcuw7ew2SevP9nPbMS5+HA6PP0wt7gHirWcDgGQbALyc8bLkM/ByxstYnrZccs8+PfMpJiZOlBzLwHDdeF2Sbkp0CgxWA/WokAfewIwXq7X/N/3frHonQmTIlfdGmxELf1goKdsLSguw8/xOSdn++dnPMTFxomRBSqWgxFeXvpKkQSMkSH0WiIeXiRU//z+XbRxAe5l9JTjnGQAy7jdhnVKHZrpm2PrEVucHecOxDdS64ANHy777nAWNQgMogCJTEZYdWoZmumaY2W0mFny/QLKfXqV3triTwHB9f13fZ9dtIhdlPwONdY0x78A8j3vreAgJUYXALtqxPG25ZL/lacuhU+qgqJjfQveXEEJqn7fREa/0eEW23haU5WV7qDoUIhdl63f6bkTqK78/vHDO2/n7nL4y280eX6yXPLoEZruZPqRVqGrOgutrZruZWuKDVGWfgarumUJQIEobJWmxc31wIYQQEniyi1SmrcCU5Cl488i9Hj3H6Iiqynaak0gaGr/nXMaYnjG2gDH2dsXvHSsiigWcyEUs+H6BJPzrgu8X0JwXH/k6Z8ExGZzmNgSfyj4DvtxbhaBAqDoUAhMQqg71+uDi6+KYhBBCqkc2NP6BuXg+/nmPeYoahcajbJcrn2lOImlIAjFs7F8ADgPoU/H7ZZRHINsegLQkfF3YiVSPbCtQRZhFKgCDS218Big/EEJI4Hgrx0NVoVX2nlD5TB4EgcjJHTjnKwBYAYBzbgTAApCOB8ekc1eOSWlEni8t6N4WSKT31T/82YtRnc/A/aZL+YEQQgKnsnK8qt4TKp/JgyAQDy8WxpgO5ZP0wRjrAJeoY4GkVWixPG25pFt1edpyaBXa2ki+3vE1tDT1aAWOv8N7ewup7H6vapIu5QdCCAkcjUIj+11Go9BUeSyVz+RBEIiHl/8FsBvAQ4yxzQC+BjA3AOl4MNlN2HJmC+b3nI/McZmY33M+tpzZApPdVBvJ1zu+ttBQj1bg+LuVzHVi5uHxh7F64GrZ4QI1SZfyAyGEBI7J5uW7jK3q7zJUPpMHQSCije1ljGUB6IXy4WJ/5Jzf9Hc6cnRKHdYfXS+JxqFkSkxOmlwbydc7vrbQeAujTC05NReIVjJvIZX9lS7lB0IICRy9Sn/f32WofCYPAr89vDDGurltulrxszVjrDXnPMtfaXkjt9AiLcTkndFmxJTkKRjUepBzTZCvL37t8X5RmMXAqWmeFbnovB+uC1e6b5PrebnfdCk/EEJI4BisBtm62WA1IFQdWumxVD6TB4E/e15WVfIaBzDQj2nJohYH34lcBOccozqO8liUUG6OkC+t+aT6apJnvUWVUQkqvJzxcqWRZmr6WaH8QAghgaFVauXrZqVv83epfCYNnd8eXjjnA3zZjzE2mHO+11/puqIWB98ZbUYUmYqw6MdFztb3n6/9jHkH5mH1wNUIEajAqw01ybOu81YAOOetLOq9yGPb6oGrqTeNEELqAZPNhHkH5nnUzekD0qvseSHkQRCIdV6qshxAQB5eAGpx8JVOqUNMaAxFJQkC95tnvc1biQmN8dgmd0/ps0KI734zv3rV5dQfA3QhpMHTq/SyZbtepa+jKyIkuNRFM2utrPlCKme0GVFQWkBRSeoxb1FlCkoLPLbRPSWEkPrBYDXIlu0Gq6GOroiQ4FIXDy+8DtIkbnRKHcI14Vjy6JIq1wQhwcnbmi7hmnC6p4QQUk/plDrZdV6oHCekXF0MG/PAGNMC2A9Ag/Jr+pRz/r91e1UNm8AEhKnDoFaoad5DPeVt3goAuqeEEFJPKQQForRRSB+QDr1KD4PVAJ1SB4WgqOtLIyQo1MXDS77MNjOAgZzzUsaYCsB3jLFdnPOfavfSHiwCE5xfdrkoQmsBmAKwG8og6HRgAn3hDXbe5q24b+OiCNFohKDTOX/S/SWEkOCkEBTOyfmOn1SOE1LOn+u8PFPZ65zzrRU/PfbjnHMApRW/qir+0fCyWsJFEfaiIhTMmQPD4Szou3dDzKpVUERFUcHYAND9JYSQ+o3KcULu8WeOH1nJv8erOpgxpmCMHQFwHcBezvlBP14bqYRoNJYXiAcPATYbDAcPoWDOHIhGmuTdEND9JYSQ+o3KcULu8ec6L7+t4fF2AF0ZYxEA/ssY68w5P+54nTE2GcBkAGjdunWNrpVICTodDIezJNsMh7Mg6Ghy4P0KpvxK95f4IpjyLCFVedDyK5XjhNwTkL5GxtgIxthcxthCxz9fj+Wc3wGQAWCo2/a3OeepnPPUpk2b+vmKH2yi0Qh9926Sbfru3ahFpwaCKb/S/SW+CKY8S0hVHrT8SuU4Iff4fcI+Y2wdAD2AAQDeBTAawKEqjmkKwMo5v8MY0wH4H5QvZklqgaDTIWbVKo+xtNSi0zDQ/SUk+L059ZtqH/PiuoEBuBISjKgcJ+SeQEQb68M5T2KM5XDO/8oYWwVgaxXHtADwAWNMgfLeoP9wzrcH4NqIDCYIUERFodXatRTFpAGi+0sIIfUbleOE3BOIhxdHH6aBMdYSwC0A7So7gHOeAyClsn1IYDFBgCKkPKyu4ydpOOj+EkJI/UblOCHlAvHwsr1i0v3/AchCecjjdwOQDiGEEEIIIeQBEoiHlxWcczOALYyx7QC0AEwBSIcQQgghhBDyAAnEYMkfHf/hnJs558Wu2wghhBBCCCHkfvit54Ux1hxADAAdYywFAKt4qRHKo48RQgghhBBCyH3z57CxXwF4AUArAK+5bL8L4M9+TIcQQgghhBDyAPLbwwvn/AOUhzsexTnf4q/zEkIIIYQQQggQmDkv3zPGNjDGdgEAYyyBMTYpAOkQQgghhBBCHiCBeHj5F4A9AFpW/J4HYFYA0iGEEEIIIYQ8QAIRKrkJ5/w/jLH5AMA5tzHG7AFIhxBCCAlK63r/sVr7T/3xnwG6EkIIaVgC0fNSxhhrjPLFKcEY6wWgOADpEEIIIYQQQh4ggeh5mQ1gG4D2jLHvATQFMDoA6RBCCCGEEEIeIIF4eDkJ4L8ADABKAHyG8nkvhBBCCCGEEHLfAjFs7EMAcQCWAlgNoCOAjQFIhxBCCCGEEPIACUTPSyfOebLL7/sYY0e97cwYewjlDzzNAYgA3uac08xFQgghhBBCiEQgHl6yGWO9OOc/AQBj7BEA31eyvw3AHM55FmMsDMBhxthezvnJAFwbIYSQurAovJr7U5wXQgghngLx8PIIgAmMsYsVv7cGcIoxdgwA55wnue7MOb8K4GrF/0sYY6cAxKB87gwhhBBCCCGEAAjMw8vQ+z2QMdYWQAqAg/66GOJJFDn+f/buPEyK6t4b+PdU9T4DA8MmjiJBgyKCMzKoRHEBcSMxi+ZG3rjEEFcU8cZATHxyeY03CUSCYtwlN4oJ3veK8ZrgEgIYiTEKyogikSAhyIhsA8zSa3Wd949e6Oqqnq6e7p5e5vt5nnl6urqq+nTV75yZ6qrfr/yRKHwuFf5wFD6nCgCmaYoiStxS6sus4jSfmCz0+oioctkdDzhuEJWfgh+8SCn/1ZPlhBC1AFYAmCOlbLd4/QYANwDAiBEj8mpjX6brEge6wpi9fCPW72jDxJH1ePSq0xCO6pi9vCU5bcmMJgyqcXGQ7iHGa36s4jSfmCz0+qpRsWN2ZPC3Oc2/o+AtqD4P3bQmp/lnPTqlSC3pffnEq93xgOMGUXkqRrWxnAkZP7ewAAAgAElEQVQhnIgduPxGSvm81TxSysellM1SyuYhQ4b0bgOriD8SxezlG/Hm9gPQdIk3tx/AQX8Es5e3GKbNXr4R/ki01M2tWIzX/FjFaT4xWej1VSPGLFWSfOLV7njAcYOoPJX84EUIIQAsBbBFSvmLUren2vlcKtbvaDNMO7beZ5q2fkcbfC61N5tGlGQVp/nEZKHXR0SVy+54wHGDqDyV/OAFwFkArgYwRQjREv+5tNSNqlb+cBQTR9Ybpn3S5jdNmziyHv4wv12i0rCK03xistDrI6LKZXc84LhBVJ5KfvAipfyLlFJIKcdLKRvjPy+Vul3VyudUsWRGEyaNGgSHIjBp1CAM9DmxZEajYdqSGY3wqAo6ghHoUsYeo1Eg1AlIPfao66X+OFQldF2iM6RBl7FHr0OxjMlEcYlc+ZwqHrvqNKz/3pnY/pNLsP57Z+Kxq06D16EY3lfXZYE/GRGVm9jfQavxRTH8jfM5rcchjhtEpVWMamNUxhRFYFCNC09c25ysnpIYiH/6tXE4tt6HT9r8cKkKOsMabn7mXazf0Ybbp5yAW8+oA1bMBHa+CYyYBFyxFPANAZSSHwNTBbNOim2E16maYrKnFEj00w+i/+9j8TtkxCTIK5aiIzQQN8ZjnMm4RH2HS1UM40uNU4Hw7wOeO/I3TlyxFDXOgYb5vE4Vbf4wC9wQlRD/6+xjdF0iqEWB+BdFCmJJibUeBwbVuiAAhDQdT/11Bw75I8lExa+OGwCxYiawYx2ga7HH52YCEX8pPw6VkfSzJ7oubU0LalZJsS3oCkcR0mJn9xIxGdSiPfvGM+KHeM4Yv+K5mQgFOpiMS9TH+CNRHOgKxf7mCWBQrQtOPWg5RmihLsM41BWOWha46fHYREQ545mXPkTXY5d/dYQ0fO9/NmFYfzfuvOhEPP/OLnyl6RjMW7Ep+U3SgsvH4+gBnuSyRw8ZHPs2KtXONwGXr5c/BZUj+yW4G+FSFdxkONvRiGH93Yb1DevvhiKA+S9uPjLflY3oCmk9+sZTunwQFvE7eOBAwyQm4xJVP4+qoNbtxA1Pv5McS357/emWf+P69a/Djf/9dtbxqqdjExHljgcvfYSuS3SFNRz0R3DX8+/jze0H8NZdUzHnv1sw/7KxmLdiE97cfgAAMKSfG7qUUBSBlh9NQ63bgWiwE8qISbFvpRJGTALCfsBdW6JPReUitaQogGQJ7i27D+ORq05Df68T7YEI3vx4P8YMrzPMN3t5C376tXF4oeXT5PrmXDAaG3a0GZaNxA+EjMtuxBPXNqPWbRzKolEd/kgUNW4HukIaahCEsIjfaKjTsFwiGTd9ffncqI43uSMqLU3TEdCOjAcCwLNv78T8y8bihKG12La3Ex3th9HfYozoaD9smG/5Wzsx54LRpvHK7thERPljr+oDEt+K19e44Kt3YP2ONsz/0skY0t+N9TvacMLQ2mQ5yMtOPRp3Xnii6SzMh5924KLLl8YuHYtfDywvXwrp9PHaQ7IsKXrMAC98LjWZNzVxZD0euLIR9T6XYb71O9owYpAPk0YNSs53zEDzsr+5/gxbZUujUR0HusK4/dkj34I+dlUTPF95Aq4Xrk/Gr/+yx+Fx1xred8mMJlNRgHxuVMeb3JWvcU+NK3UTqBdomo42v3E8eOY7p5uuNnjwykbIK5bGLh1L/I27YinCuhfzX2wx/D1sGOgxjBsjBvF2A0S9iQcvfYA/EsWGHQdw1glDUOtxYN3c89Hf6wQAvPH9KegMavjo3kuwbW8nat0q7vyfTYZvkOat2IQnrmnG/nAEA7/xWyjuWny6bz9+99YhXDd5EGrdPHzp6/zhKGZPOQEXnTI8+Q1lKKrj9meN30be/mwLnry2Ga/OOSc536sf7EYwHMXj10xIfjMa1szL7jwQK+mdmAbEzpR0hTQIIZJnNqSUpmVvfGYjllx5KkKX/ApHDxkci993D+FbZ0cNxSuszopYnVWavXwjln6rGbpEj5blN7JEvSOgRU3jQVcoihc27jKcUfnt2ztx/Tmfg+PffguHtxZaoBNRhw+3/XqD6e/h49dMMIwb/lDUcmyyOotLRPljr+oDPA4FE46rx43Ljlzf+/Ovj8dz7+zB1DHDMOfZlm6v512/ow1el4rrn96AJTMa8eMX3sOL730KhyIwa+rnS/SpqJx4VAVXnj7C8O1mpmvD/WHNkMvyyFWnoSucfr24edn7/7QVS2Y0GuZbcPl4+Fwqrnry7axnaAb18+Cbz76H9Ts2GJZV49XyMv2TYXVWye417rzJHVFp1bgdpj5Y41Yt8zxdDhVX/frDrH8Pa9wOKCLWz2vdDui6xJIZTaYzrD0t7U5E3eNX5n1AIHLkm6dEdZTv/c8mfLmxAd+Ln2VJrfI054LRhuUnjqzHtr2dyddnnX9Ccjpv1kWA8dvN7mIp9drwxHyH/BGL6j3mZfe0h+BzOTD/srH46N5LMP+ysXhh4y50BDXDsu2BiOWN5TqCmmnZQCT7vYqsblRn9TmsKpXxJndEpdUV0kx90B+OJvM8E/133opN6ApFbf097ApphmmptyDY+p+X4Ilrm3lpKFER8eClikWjOjqCEctvntbvaEN/r9NyeiL/IHFTrvuvbMTxQ2rw6pxzMKy/GycMrY3frIvfLJWaVSniUsgUY+mxZHVt+LH11teLpy/7wJWNcKsKhvV3Q4jY2Y9rvzDSdMbkf1ta8cCVjaZl1Xj1shPvfhnzX9yMGWccZ+tmc1Y3drV7jbvVsuw3RL3H61BN40Gm8arW4zBNsxqHvA5z/1UUgdr4GZlat4MHLkRFxMvGqlRq0vL8y8ZaXo+b+IbaKofg0asnoJ/HgY6ghqfe+CeWrNmWvNwsFInnCbBqUkmVUzJ44ttN0zXfIc2Qy+K3mO+TNutclgOdIUO1sbauEAAYLk174MpG6DB+C/rKB3vwjeZjDe/rc6oQQphuztrmj2TdflY3drV7jbvVsuw3RL0nGNXResiPx66egFqPA51BLeN41R6IGJZNjGGp45BDEey/RCXGMy9Vyp9yqdhDa7dhweXjDd8e/fzr4/G/La34+dfN09sDEdy07B10BjXctOwd/OJP/zBcbqbL+LfTArwZVwmlJoOX+iaLqhCWsaQIgX4ep+Exfb5aj4oHZjSmnZ1ohFNVcPMz72L0D1/Gzc+8iyH9PKZL025/tiW5TOqZDZdDNbyvqiqmb0YDmm69/cLmm82lL+tz2T+jwm9kiUrH51RxzMAa3LjsHYz+4cu4cdk7cCrC8uxs+ljy86+PB4RA4z2rMOqul9B4zypc//Q7vJEtUYnxzEuVSj0t/uJ7sXr08y8bi88Pq0XrwQDqfS5cPWkkdh8KYOEV43H0AC+27e3Efa9+hF98oxFPXDMBXpf1qXWf24FvPvFWyb/t7+vKKRnc41Jx3+8+MlTvScRStvnu/cMWLPq3U01nJ6SUhrMnmS71qHE7enRmI+P2c6tZ45tnVIgqg1VfdTlVvLzhE8MZlf9tacU3zzgu6xjGghtEpceDlyqVflr8xfc+xb6OEOZfNhbzX9yM+ZeNxbD+bkNZZACYNGpQsvRspst5dh7ws/RrGUgkg5dDeU5/OIo97SFcdP/ryWmTRg0ytaUrpGWcr58nVr77yPwC/dTYyeF+Hic6gpkvczQva6/N+cR34oxKru9LVCoP3bQmp/lnPTqlSC0pra6Qhlc+2IP/ePHD5LRJowbh3NFDTWOT5aVkLIFMVFK8bKxK+ZzmJMUFl4/Hqx/sxs+/Ph6PvLYNtW6H6XKyRPlYn1PFQJ/TdInPkhmNuP9PWw3vxW+iSqOcksG9DsXyMgyvwzjE+FxqxpjLxiqmH7iyscef13r7Mb6JqkkiN/D6pzZg9A9fxvVPbbBM4n9gRiMG+JxZLyVjwQ2i0iuLrw6EEL8C8EUAe6WUp5S6PdVAVRUMqnEZk5ZdKr599igoAlj0b6fCHzbfqOuFjbvw7cmjUOt2oJ/HCadDwRPXNMPnVrHncBBhTcee9pDhvfhNVGmU06VLAU3Hs2/vNMTSs2/vjMWSeuQAJhDRu4m57r9LsYzpeCJ+Zzy+c9kGVttPEWB8E1URqxvFdoY1UxL/x/s6MOao/lkLffDyUKLSK5e/xr8G8EsAT5e4HVVFVRX0UxXoukQwouOGp98xXMdf73NixhnHZbyxlqII+FyxENGlxOSFa3HpuOFYcPl4w829+E1U6ZTLpUs+l4ola7bhF3/6R3KaQxG4Ne0mpj6n2m3MZZOIaSB2KVm+FdfStx9vNkdUXaxy21o+OYiTh9cZbtwcO8uiwOM6Mr4klMMYS0RHlEVPlFK+LoQYWep2VCurb54S1/Hb/eY+kR+Qmvx/wtBa+MMaalysoNTX2c2/KfTZou5iuyf/aJTT2Swiyp/V2DRyUG3mM8UOXk1PVO4qppcKIW4QQmwQQmzYt29fqZtTUbqrSmW3jGtqfsBL7+/G/Bc3o60rzAOXDPpavOaSf1PI0sHFqLjWV0sb97WYpcpmN14z3WR2yZptuOj+13H8D17CRfe/jiVrtjG3jahClMWZFzuklI8DeBwAmpubeWORHBSiKhW/kc5NX4vXUsVHOVVcq3SVHrNbThqT2wJ3MT4qmd14zecms0RUnirmzAv1XKGqUvXVb6TJnlLERzlVXCOi8pTPTWaJqPzwK4Y+gGdNqFoxtokoVxw3iCpbWRy8CCGWAzgPwGAhxC4A/yGlXFraVlWXcqlKRVRojG0CgH/jZWCUA44bRJWrLHqslHJGqdtARERERETljTkvRERERERUEXjwQkREREREFYEHL0REREREVBF48EJERERERBVBSFlx9yKDEGIfgH9lmW0wgP290JxcsV25y6dt+6WUFxeyMbmyGa9Aee+DXPBz5KeSYrbQyj12yr19QO+3sRzjtRL2k13V8lnK5XOUPF4pfxV58GKHEGKDlLK51O1Ix3blrpzbVkjV8jn5Oainyn2bl3v7gMpoY7FV0zaols9SLZ+DygMvGyMiIiIioorAgxciIiIiIqoI1Xzw8nipG5AB25W7cm5bIVXL5+TnoJ4q921e7u0DKqONxVZN26BaPku1fA4qA1Wb80JERERERNWlms+8EBERERFRFeHBCxERERERVQQevBARERERUUXgwQsREREREVUEHrwQEREREVFF4MELERERERFVBB68EBERERFRReDBCxERERERVQQevBARERERUUXgwQsREREREVUEHrwQEREREVFF4MELERERERFVBB68EBERERFRReDBCxERERERVQQevBARERERUUWoyIOXiy++WALgD3/s/JQc45U/Of6UHGOWPzn8lBzjlT85/FAVqMiDl/3795e6CUS2MV6p0jBmqZIwXon6loo8eCEiIiIior6HBy9ERERERFQRePBCREREREQVgQcvRERERERUEYp68CKEOFYIsVYIsUUIsVkIcbvFPOcJIQ4LIVriPz8qZpuIiIiIiKgyOYq8fg3Ad6WU7woh+gF4RwixSkr5Ydp866SUXyxyW6gXSF0iEo7C6VYRCUXhdKkQisg4Pdty1DfoUR2RsA6XR0U4GIXTpUBR7X23wtihviTbGOtwKYiEYn2J/YGIqlFRD16klLsB7I7/3iGE2AKgAUD6wQtVAalLBDrC+OPSzdi97TCGn1CHC2eOhafWiWBnxDTd28+V/KNrtVzidapuelRHoDOCVSn7f9rMsfDWOrMewDB2qC/JNsZu/ksrRp8+HGuXbWF/IKKq1Ws5L0KIkQCaALxl8fIkIcR7QoiXhRBje6tNVFiRcBR/XLoZrVsPQdclWrcewh+XbkYkrGeYHs2yXLTEn4h6QySsY1Xa/l8Vj5vsyzJ2qO/INsaOahyKtcu2sD8QUVXrlYMXIUQtgBUA5kgp29NefhfAcVLKUwE8COCFDOu4QQixQQixYd++fcVtMPWI061i97bDhmm7tx2Gy2M93elWu10u8XolYrzalyk+XJ7s+78aY6dUGLPlL9sYO3B4TZ/pD4xXor6r6AcvQggnYgcuv5FSPp/+upSyXUrZGf/9JQBOIcRgi/kel1I2SymbhwwZUuxmUw9EQlEMP6HOMG34CXUIB62nR0LRbpdLvF6JGK/2ZYqPcDD7/q/G2CkVxmz5yzbGHtzd1Wf6A+OVqO8qdrUxAWApgC1Syl9kmOeo+HwQQpweb9OBYraL8iN1iXBQg5TxR10CAJwuFRffcAq+ec+ZuPnh8/HNe87ExTecAqdLwYUzx6Jh9AAoikDD6AG4cOZYOF1qcrnuXqfSyrS/C7Ws06VgWtr+nzZzLJxOxcayjB2qbHb7lx6NXUZpHe+xMXZ7y16cf/UY9gciqmrFrjZ2FoCrAbwvhGiJT/sBgBEAIKV8FMAVAG4WQmgAAgCulFLa/++IelV3CdIAoEclXnvm74bXAEBRBc676iT0H+xF+/4AFPVI8qhQBLz9XLj0lvGsGFVm8kmIt7usoirw1jpxyc3jj1QbcyoIdmUu8pDA2KFKZrePpBa18NW5cd5VJ6FuiBeRYKzqWKIfnDp1BBwuJdmX2B+IqBqJSjxOaG5ulhs2bCh1M/qkcFDDSw9vQuvWQ8lpDaMH4NJbxgOA5WuX3DweLz9ivYzLU+zjZ5T8r3Ylx2t3+zvbvivVslWAMdtH2I3zUECzHEMvuXk83N6S9wfGK1WSkscr5a/kox5VlmwJ0j1J2KfylU9CfKmWJaoUduM8n6IWRETVptdKJVN16C5BuqcJ+1S+8kmIL9WyRJXCbpznU9SCiKja8OCFctJdgnTm17pP2KfylU9CfKmWJaoUduM8Y1ELF/+EE1Hfw5wXypnUJSLhqClBWuoy9jyeKAoADqcCLaLD4VAQiehHErLdCiIh/cijS+n2bup6VEcknLJ8lvlTlPz61nKIV6t9BsByP9pZ1mo+q30Ue4+0aRLGWHAqUBzmfZnHPq90jNk+ILVfJeI7Pd4TzyOhqHkMdcXH1vgYm+if6c97krBvt8/HMV6pkpQ8Xil/zHmhnAlFJJNJE49WVXOmXDsGqkPB7o8PYfjxA7Aq5bXzrx6DrW/vxujTh2Pr27tx8tkN8NY6Lf85Ta20k1h+2syxGecno0wVjRRV4JXHP8haRcxqf6fLtI/UtPe4+IZTEI1K876scRoOYKQuEezMXm2MqBJZ9clpM8fiw7+0YsPKf6F5+nE4+ewGQz+5cOZYeGqdCLQfWc5qvtT19KTf5FNhkIioN/A/PyqISDiKPy7djNath6DrEq1bD2HNU1sQCUVxzIn1WJX22tplWzCqcWjycdXSzYiE9Qzr1k3Ldzc/GVntmz8u3YygXzNNi4R7dg19pn2U/h5Bv2a9LyN62vqs29zT9hGVE6v4XrV0M0Y1DoWuy+SYaI5/3bCc1Xyp6+lJv2HfI6JyxzMvVBCZqub0H+yFENZVyAYOrzE8Zqqcw0o7+elu36RP62k1r0z7KP09+g/22tqXrDZG1SxTfA8cXgMAyTEx/fX0fpZpvsR6Es9z6Tfse0RU7njmhQoiU9Wc9v0BhPya5WsHd3cZHjNVzmGlnfx0t2/Sp/W0mlemfZT+Hu37A7b2JauNUTXLFN8Hd3cBQHJMTH89vZ9lmi+xnsTzXPoN+x4RlTsevFBBWFXNmXLtGHh8DkgpTZVyzr96DLa37E0+dlc5h5V28pOpopHH57BVzUvqEuGgBinjj7q5yEemfZT+Hh6fw3pfOpW09RW+2pidz0HUG6zie9rMsdjesheKIpJjYnr8Kwrw5Tua8M17zsTnJw6znC91PXb7TWrfEAKs9EdEZY3Vxqhg0ivUhIMa/rriY/gPh3Dhd06B6hBweR05VxuTukQkqEGXgNvnQMivQRGA0+Owk0Ba8gzTcojXnlYbs5u8m2kfOdyqqfKR1KWtamM5VjzK+vkrKAm55A0qh5itdrlWG9M13VRgQ5cSH7+7D8edMgh1Q7w9qjZm1TcuvuEUKA6F1caoGpU8Xil/PHihggsHNbz08Ca0bj2UnNYwegDOu+ok+Pq7MlasynV9l94y3s66Sj5QVXK82t32ee6joiv39qVhzJJBpvid/I3RePbHbyef9ySeC9A3GK9USUoer5S/svurTZUvW/J+odbHBNLis7vty30flXv7iLqTLcE/8bwn8cy+QUSVhkkDVHDdJYj3JOmTCaSlY3fbl/s+Kvf2EXUnW4J/4jnHVyLqC3jwQgVnlYx6wXUnw9ffBadbRSigQY/qCAWOJE9HQtaJ1FJnAmkp2U2cd7pUXHrTOMxcNBm3PHI+Zi6ajEtvGgeHU7GVJF/sZPpiFAAgKqbUPgEJXHzDKab4ddc48PmJw5LPhYDtvpNYv8Oi2Ab7BhGVM142RkWhqALnXXUS+g/2on1/AA6ngvdW78x49+gp147B3174KJbcH0+kBpBMJPXVuXHeVSclk1LzSd6m3KTvS0U1b/dEEv6qtKTfYGfEVrJ/sZPphSLg7efCpbeML0gBAKJisuoT02aOxQXfPhm+/m607w9AAtjyxqc4+4oToEuJvzy3zTB+5pKk3zz9OFxy0zi4vA72DSIqezx4oYKLhKN45fEPLJNL3/79DsNdoQGgdeshrHlqSzL59I9LN+PSW8YDQPJOzwDwj/V7komk/MPaOzLty/Rk3sSBS+o+Dfo1vPbM3w3TEvvWsGzKHb27my9fQhHJ9ZVhkj5RklWfWLV0MyZ/YzSe+v5fARwZU/8Yn/6P9XsAwFbfSV//27/fgdaPDpVrAQsiIgOOUlRwPb17dOL11GRRJpKWlt1k3vQ7fwNA/8Heqkj2J+ptdhP0E2Npron77HNEVMmY80IF19O7RydeTySLMpG09Ozug/Q7fwNA+/5AVST7E/U2uwn6ibE018R99jkiqmRFPXgRQhwrhFgrhNgihNgshLjdYh4hhFgihNgmhNgkhDitmG2i4uvJ3aOnXDsG77zyL0OyKJOsS892wr7TnPTr8TlsJ/tzPxMdkW0MbRg9AOdfPSY5lqZOt9N32OeIqJIV9SaVQojhAIZLKd8VQvQD8A6Ar0gpP0yZ51IAtwG4FMAZAB6QUp7R3Xp5Q6rep0d1w92fVRVQHZmTOqUuoUWikDJ2iUIkGIVQAIfL+u7RQsReS08W7emd4VOUPDmmHOI1n7vV65qOSCTlzt9OBYrD/L2H1XxCEbb2n9W0Quc15bMNelnJG1UOMVutUuMwHIzC6VYQCemmMdHhVKBF9CPzpbyeWE4L64b5uovr9PhPXU4Lp4zVufcNxitVkpLHK+WvqDkvUsrdAHbHf+8QQmwB0ADgw5TZvgzgaRk7ivqbEGKAEGJ4fFkqA3pUR6AzYqgkNW3mWDidEk6Pw/oPpZQIB6OmZVSHArc3FnZub+wf4NQE0fRk0fQk696oTFVt8tlmelRHoMu87721TijqkQMYqUsEu6wri9ndf8VMpmfcUDmwisPzrx6DfZ+0Y/jxAwz9LBmfQpjGzMRzlyc+hqrmsTTb+yarOkogEoyybxBRxei1nBchxEgATQDeSnupAcAnKc93xadRmYiEj1SS0nWZrHyjy9i35bksEwnrebblSJWcxHr/uHRzxnZQftvM7n60+x6l2n+MGyoHVnG4dtkWHHNivamfFTI+u4t/9g0iqjS9cvAihKgFsALAHClle/rLFouYrmUTQtwghNgghNiwb9++YjSTMrCqJLV722G4fY6M1WkyLePy5HdNdaVUySmneM1nm9ndj3bfo1T7r1LippTKKWarVaY4dPscRY3P7uK/UvsG45Wo78rp4EUIMVAIMV4IcVrix8YyTsQOXH4jpXzeYpZdAI5NeX4MgE/TZ5JSPi6lbJZSNg8ZMiSXZlOerCpJDT+hDiG/lrE6TaZlwsH8vs2rlCo55RSv+Wwzu/vR7nuUav9VStyUUjnFbLXKFIchv1bU+Owu/iu1bzBeifou2wcvQogfA9gEYAmARfGf+7IsIwAsBbBFSvmLDLO9COCaeNWxMwEcZr5LeXG6jJWkTv/SSFxy0zi4fQ5AxvIiTMtYVJ+aNnMsnK7sISd1iXBQi+fNaJD6kRNxrJKTu1y2ma7pCAVi2z4U0OB0Krj4hlPwzXvOxM0Pn49v3nMmLr7hFNN+tF2VrET7j3FD5SBTFTGpS8v4FAKG8c8Oq/Gzu/hn3yCiSmO72pgQ4iMA46SUYdsrF+JsAOsAvA8g8R/uDwCMAAAp5aPxA5xfArgYgB/AdVLKbsuGsLJI70tUG3O6FQQ6uk/gTiSHbv5LK0Y1DcXAo2oQCWpwulTLKlWp7CRW51g1quQZp+UQr3a2ma5ZJ+erqsArj3+QNZnX7n4pVdUvVhuzrxxitlqlx6EWiuWc+OrcOP1Ln0PdEC+6DoXw1+c/hv9wKKfk+W4T85G5ol+efYPxSpWk5PFK+cvl4GUFgJullHuL26TsOFCVTiig4eVHNqF166HktIbRA3DJzeOTFXDCQQ0vPWye59JbxmetJJXPshmUfKCqlHjNtG/Pu+ok/OZHfzNMy2N/UHaM2T4iU5+b/I3RePbHbyef2+1vRRg/7WC8UiUpebxS/nIZzX4KYKMQ4gMAocREKeVlBW8VlS07Cdz5JIBWavJoNci0b/sP9pqmcX8Q5S9Tnxs4vMbw3G5/4/hJRH1BLgn7TwFYAOBnOJLzsqgYjaLyZSeBO58E0EpNHq0GmfZt+/6AaRr3B1H+MvW5g7u7DM/t9jeOn0TUF+Ry8LJfSrlESrlWSvnnxE/RWkZlKT153yoRP58EUCaPlk6mIgsen4P7g6gIMo2n21v29qi/cfwkor4gl5yXXyB2udiLMF429m5xmpYZr28tLT2qQ4vE6i843SrCwSicbgVa/MaFDpeCaESHrscui4iEorGLTAXgcFknhKYnjCbWXYDE6pJf31oO8Wo3IVfXdEQiOlye+H51KoCI3awyOS1+oGqaJmFaVijC9NfGiIwAACAASURBVL4SEpFQFC6PA+GgBqdbhaL02v1yKwFjtgolip4k+4dbQSSkw+lUTP1G0/Rkn3E40l53KdAiOhzO2GNyvvhzhyu23sTYmz5fEQpVVE68zq/LPo9h/sPZ56FKU/J4pfzlkvPSFH88M2WaBDClcM2hiiBilyekVqU6/+ox2Pr2bpx45nC4NBWRsI7Vv/4w+fqUa8fA4VSw9pmPTBV0rCrknH/1GLy3eifGnt3Abw3zZKeCW2K+YFfEMN/FN5wCPSpNyyppFci+OGs8QsGoqVKZ06ngpUff73bZaTPHwtvPyQMYqlp6VEegM2IaM/d90o7hxw8wTE/2TSHgcCqm5abNHIv2AwH0H+RNTm+efhxOPrvBtB5PrRPBzkjWvk9EVEls/7cgpTzf4ocHLn1Q4sCldesh6LpE69ZDWLtsC0Y1DsWap7YAQmD1rz80vL7mqS0IB6OYcPFxaN16CH9cuhmRcOwMSyQcKxVqtb7U+ahnrLav1Xa1mi/o1yyXDfo1w7SoDlNMrFq6GbpE1mVXLd3Ma/KpqkXCuuWYecyJ9abpxrHRvNyqpZtRP7zWMH1U49AM69Ft9X0iokpi+8yLEOInABZKKQ/Fnw8E8F0p5d3FahyVJ5fHkbFCzu5th+H2Wb+eWrUqtQJOpgo5ifWxUk5+7FYgspqv/2CvrQpkmaomuX0O0zSr6mUsu0zVrLv+0V3f7K66Y+r0xFiZbb709fc1I4O/zWn+HcVpBhHlKZfrNC5JHLgAgJTyIIBLC98kKnfhoJaxQs7wE+oQ8lu/3r4/kKyik1oBJ1OFnMT6+K18fuxWILKar31/wFYFskxVk0J+LeuysWp1xvmIqkl3/aO7vtlddcfU6YmxMtt86esnIqpEuRy8qEIId+KJEMILwN3N/CWjSx1dkS7DI5lJXSIc1CBl/FG3V7zB6VZNFXLOv3oMtrfsxZRrxwBSYuq3Tja8PuXaMXB5VLzzyr9MFXCsKuQk1sdKOfmzW4HIaj6Pz2G5bHoFMlWBZdUkRSDrstNmju2z3wRT9bEaV62qip1/9Rjs+qjNNN04NlpXI2vb3WmYvr1lb4b1KKw+RkRVJ5dqY3MBXAbgvxBL1P82gBellAuL1zxr3VUW0aWOtmAb5r4+Fxv3bETTsCYsPGch6j31UAQTghPsJnFnout6SsUo62pjUgeciWpj8VXaqTaWur4CVMYpeVZqqSs3SV0i5I8g6NfQf7AX7fsD8PgccPucpm1rVZUMgLlimJSmamNaVIOMKslpQtXhcDhi+5HVxnLR52O2UnU3rpr6jFtBKBjBxgPv4HO1x+OoAUMtx0ZTlbIs1cbSq4rZrTSYh4qJ15HfX5nTenf8bHpPm0Tlq+TxSvmzfaG5lHKhEGITgAsQ2/k/llK+WrSW9VBAC2Du63Ox/rP1AID1n63H3Nfn4sEpD6LGWZNl6b4jNTkbQDKR89JbxtvKP1AUBW5v7B9Otzc2v8tz5B9QxX3k9/T1Wa1fKCI53Wp91HORcBSvPP5Bcl8DsbMhVvs6dT+kvpY+TUAY9n9XpAu3rb0t2e8AYOJRE2P9zlPTzbLOgn5WolLKNq4m4l5zhHDzKuv+4lKMf6cU1WKsVRXjo8dh+RzI3KeJiCpVTiOZlPIVAK9YvSaEeFNKOakgrcqD1+HFxj0bDdM27tkIr8ObYYm+yW4SN1W+3tjX7HdE9vsa+wsRUc8V8msYTwHX1WMBLYCmYU2Gb7SahjUhoAV45iVFIjk79dv4RCInv52rLr2xr9nviOz3NfYX6g0P3bQmp/lnPcq7X1BlKOR1OfaSZ4rM6/Bi4TkLMfGoiXAIByYeNRELz1nIb7TS2E3ipsrXG/ua/Y7Ifl9jfyEi6rmq+4pdEQrqPfV4cMqD8Dq8CGgBeB1eJuunEYqAt58Ll94yPq9EzqgeRUALwOf0wR/xw+PwIBQN5bzteyGptM/Kd1/rUk/uy0z7VBEK6t31eOScx4yJ+Db7ndV7AMj6vkTlRCgCnn5OXHLzuGQ/cLgU+KN+eIUxjhN/pzyqJzmGcswkIsqukP8JlM2oqQgFNc4awyOZJRI5hYg/9uDApS3YhtlrZ2PCsgmYvXY2DgYP4q+tf8WEZRNw25rb0BZsy1qqOlGh56WHN+HRWa/hpYc3IdARtl26mbLr6b5OVO+7bc1t3e5TqUsEOyJ4+ZH38eis1/DyI+8j2BGxtQ+t3qMj3GHrfYnKiS51tIXacPPrN+K0Zafh5tdvRFuoDcs+XGaKY0Uo8Dq8OBg6mBxDOWYSEWVXyP/qry7guqgCBLQA5q2bh/WfrYcmNaz/bD3mrZuHicMnJp/PfX0uAlqg2/WkVujRdZms0BMJ80ZqpZZava+7fZrPPrR6j8Ohw7bel6icWMXyvHXzMHXEVMs4ttu/0nHMJKK+zPbBixDia0KIfwghDgsh2oUQHUKI9sTrUsoPitNEKlc+p8+yYk5/V3/D82zXcbPyWfmyWxUpn31o9R4NtQ2sxkQVJ1N/GVU3yvA8Ecc9rTrGMZOI+rJczrwsBHCZlLJOStlfStlPStm/uwWEEL8SQuwVQlge2AghzosfDLXEf36US+OptPwRP5qGNRmmNQ1rQnu43fA867eI8Qo9qRIVeqi0ElWRUlnt03z2odV7tHa22npfonKSqb9sP7zd8Dz1zEtP4pxjJhH1Zbkk7O+RUm7Jcf2/BvBLAE93M886KeUXc1xvt+wkGFN26dtREQrcqhsBLQCP6oFDcWDxuYvhDwQxrG4I9hzeh/6+Wjh0FzZdswnBYBi6oiUTUnWpJ9flUT0IRoPwOrzQVQ0Xzhxruis1K58Z6bre4zvT97RPeB1e3H/u/egKBJL7uMbrhUf1oCvSdWR9Li+mzRyLVSn7cNrMsVCdAp3hzmRBB6/DCyGEoS0e1YOF5yzE3NfnYuOejWga1oQ6d51pGqsxUbnI1J8SVcRS43bxeYsR0AJouboFrZ2tqPfUQ0oJXdfh0Fx4ctqTCAUjcHucyf6V9cxLvKoZx0wi6ouyHrwIIb4W/3WDEOK/AbwAIJR4XUr5fKZlpZSvCyFG5tnGnCSSf9P/6an31PMAJgdW2/Hes+7FkneXYG9gLxZMXoCWve/hjAFfwFv/9TF2b/sQzdOPw8lnD8DLS99P+Qf2ZLy5928YNfBzuPuNu5PrWjB5AVb8YwUee+8xNA1rwv3n3p935bNqpus6Ah0R08GBt58z6wFMXn1CAmrIjbf+axt2b/sw+b5d6MKcP88x/IMm3AJnXNeAYXWN2HN4H+DRcDDUgbnr5hr2u9fhxey1sw1tGegeaKoQCIBVA6nsdNefdKnDKZyYP2k+Gmob0BZsQyQawQ/+8gNDH2jZ+x5O6z8R2988gNGnD8faZVsMByHCJbotgVOoapFU3aa8NivHJXL9fpqoNOz8J/Cl+E9/AH4AF6ZMK8QZk0lCiPeEEC8LIcbmu7KeJkCSkdV2vPuNuzFz3MxkEupZQ8/Gml/9PZk0OqpxKFalJZGuWvohmgdPxN1v3N1tEuucP89BRA31uPJZtYuEohbbdrPty7J62icyvW9XIGBKsJ/z5zm48MULcOqyU3HhixfgQOgA5q4zJy9HZdTUlmA0aKoQyKqBVI66609BLYg7/nwHpv9uOhqXNeJQ6JBlHzhr6NlY9+uPMapxKNYu29KjxPt8q0USEVWqrGdepJTXAYAQ4iwp5Ruprwkhzsrz/d8FcJyUslMIcSliZ3U+bzWjEOIGADcAwIgRIzKusKcJkGSULfF0456N8HrdhqTRgcNrLJNI3R5nTkms1cBuvNrl8jgst23qXbszyadPZHrfYXWNhmlWCfaZku77ufr1qC1UXIWO2WqVrT+lvjaqbpT1vPGxM9OYycT77BivRH1XLl9lPmhzmm1SynYpZWf895cAOIUQgzPM+7iUsllK2TxkyJCM6+xpAiQZZUs8bRrWhEAgZEgaPbi7yzKJNBSM5JTEWg3sxqtd4aBmuW3DQS3rsvn0iUzvu+fwPsM0qwT7TEn3HeGOHrWFiqvQMVutuutP6UVMth/ebj1vfOzMNGYy8T47xitR35X14EUIMUkI8V0AQ4QQ/57yMx9AXl8PCSGOEkKI+O+nx9tzIJ91JhImJx41EQ7hwMSjJjLR1yZd6uiKdCVvoJa+He89614sfX8pJh41EQsmL8Abe/+Cyd86Hqd/aSSu/NHpqB9eg0tuGofTvzQSiiLQMHoAps08GTu6tmPB5AWmfTLAPQDTPzc99nzyQnhUT6k3QdlyulVMmzkWDaMHpGzbsbZLEVv2CdWLcFCDlDL2qEtDDHRFuuBwKZbvW+P1GtZX567D4nMXY+VXV6Ll6has/OpK1Hvqze87eSFUobJ/UkVI7w+JoiNW/cmtuuFQHMmxbvrnpqOfqx+evPBJrPzqyiNj3TkLEVFCOO+60djeshfnXz3G0L8yJd5LXZr6KxFRXySk7H4AFEKcC+A8ADcBeDTlpQ4Av5dS/qObZZfHlx0MYA+A/wDgBAAp5aNCiFsB3AxAAxAA8O9Syr9ma3Rzc7PcsGFDxtdZbSx3Vkmoi89bDIfiyFhtLBgNwi3cCHVFLRPJI6EohFOiPdKO57Y+hy+O+iKOrj0auzp24eGWh7E3sDf2hzwawf9+/L+4YvQVxSisUPILwbPFq10FrTamehHsiJiqFWnukDkRXwpTtTFFUdAWbENDbQNaO1sx2DsY/ojfkJy/cPJC+Jw+7A/sT85X566DKlTDsnXuOvRz9WMfPaJqYraSdZeYD8DQn9yqGweDBzFv3TxccOwFuOyEyxCMBk3LSimxaMMi7A3sxf3n3g+39MYT7nW4PJkT76UuEegIm/qrt5+rHHJdSt4Au/E68vsrc1rvjp9N72mTysKWk8bkNP+Yv/eJhP2SxyvlL+vBS3JGIY6TUv6ryO2xhX9YC68r0oXb1tyG9Z+tT06beNREPDjlQdQ4azIuFwpE8PIj76N166HktIbRA3DJzePg9joN633+sufx07d/anqPu06/C1978Wu23q8HSj5QlWO8hoMaXnp4k2m/nXFdAy588YLktJVfXYn5b8437bP5k+Zj+u+O/GH/y5V/wR2v3WGab/F5i3H2s2d3u2yR9nslY8yWgVzGxM5wJ2avnZ2cN9tY1926rGTqr5feMt5W3luRVUy88uClezx4oUphp1Ty7wHI+O+m16WUlxW+WdTbeprUnS2RPHW9mZJXDUUAePlQr8h0h247ifgb92xEQ22DYVo/Vz/byfnpy3K/UznKZUz0OX22EvV7WqQkU39lYj8R9UV2rtO4D8AiAP9E7NKuJ+I/nQA+KF7TqDf1NKk7WyJ56nozJa8aigAwcbtXZLpDt51E/KZhTWjtbDVM6wh32E7OT1+W+53KUS5jot1E/Z4WKcnUX5nYT0R9UdaDFynln6WUfwbQJKX8hpTy9/Gf/wPg7GzLU2XoaaGDWEL3yWkJ3SfD4VJM6136/lLce9a9GYsAMHG79yTu0J2eKGyViG8VF3XuOsM0VaimogwLJi+wTM5PX5b7ncpRLmOi1+E1xP/qnast+8Pqnat7FPeZ+qtVYj9Rwr/d5cjph6hS5JLzsgXAdCnl9vjzzwF4SUqZ20WVBcDrsYvDqtCBlDL5u6ZF4AhFoNTUQO/qgvB5oagOaFEN0bCEy+NAJKDB4VYhgwGEnQIuhxthPYyoHoXP6UMoGkr+nl4EoEiFFUp+fWsx4zWqRxHQAvA5ffBH/PA6vFAV8z80VvtWSIFIOGq4Q7cO3bQ+KSUQCED11SDq7wK8XkACIhBIxoL0egEFCGrB5LIehweKUEzvC4AFNbpX1TFbjjIVeUlMd6vuWGw7vND9fqi+xBjogx4KQeg6FJ8PUX8XFJ8Pfi0Aj8Nj6g+J5z2Je6lLU38tg2R9oILita/lvIx7alxO879/7ftFaklZKXm8Uv5y+Y/hDgCvCSFeE0K8BmAtgDlFaRWVRPodzaWUaAu24Zktz6Ar3AnlcAd23Xor/j7+VOy69VbobQcR1TQcCh/Crz9aisDe3dh983fw0amnYtctt0A/eBCHggfx1Oan0B5uxw/W/QCzVs9CMBoEANQ4a5J/wHkH9dxF9Sjagm2YvXY2JiybgNlrZ6Mt2IaobryUJFE16bY1t2HCsgm4bc1taAu2QQppuEO3FBIHQwcN6+sKd0K2HUTrrNh+b511K2TbQaCryxALOHgQ4UjQsOyhUCy5ODWmFKGY4oz7nUopU/9IlIxPVBL7zYfPoGvvp8m+sOvWWxHdswcyEMCuWbOS/aNr76f4zYfP4GDwIJ7Z8oyhP/icvh7HvVCEob+WyYELEVGvsz16SilfAfB5ALfHf06UUr5arIZR6QW0AOatm4epI6bCGwY+vfNO+N96G9A0+N96G6133gkEApj7+lxMHz4V++beZXh9/9wfIOr3Y+qIqbj7jbsxc9xMrP9sPea+Ppc5DgWQ2D/rP1sPTWpY/9l6zFs3z7RtA1psH6XOZ7UPrOYLdh427fdP77wT+qFDplhwhqJZ34Oo3GTrH0EtiHnr5mH68Klom3e3sS/cdRdkZ6dhWtu8uzF9+NTk2Mn+QERUWHaqjU2RUq4RQnwt7aXjhRCQUj5fpLZRiSUq6IyqGwUFAv533jW87n/nXSg1Ndi4ZyOOHjIKH1m8fmx9AwaBVcWKIb3CERDbtj6nzzDNbtUkq/kG1zdY7lfnMceYpjlqzZXFuJ+p3GXrH4l+lmmMs+oLJw4ZlVd1MSIiyszOmZdz449fsvj5YpHaRWUgUUFn++Ht0Do74JtwmuF134TToHd1oWlYEz7dt93y9f1trcnKO6wqVljpFY6A2Lb1R/yGaXarJlnNt7+t1XK/RnbtMk3TOs2Vxbifqdxl6x+JfpZpjLPqC5/u255XdTEiIsrMTrWx/4g/Xmfx8+3iN5FKxevwYvG5izHAPQARt4qj77sPvjNOBxwO+M44HQ333QfN7cQvp/wS7toBaFi0yPD64IU/gerzYfXO1awqVgTpFY4SFY2szqhYVU1yq250hjuhSx2d4U64VbdpPk9tnWm/H33ffVAGDDDFQtTjxOrpK/He1S1YPX0l7j93Mfczlb3uqorpUoeqqFh83mLLMe7on/4UorbW2BcWLYK7dgAWn7sYq3euxvTPTcfKr67Ekxc+CSCWY0NERD2XS7WxjwH8DcA6AK9LKT8sZsO609cq4ZRKIpF17utzMe3YC/D1EZdBRKNQ+vWD3tGBiAI89fFyfG305WjZ24I1O1fj++PmoH/dUOiBI9XGgtFgb1QVy6TkWa3lWG0skYQ8b908bNyzEU3DmrBg8gLUOGuwP7AfDbUNaO1sRZ27Dg6hoqu9DYPrG7C/rRWe2jr4HDXmamOHDqH1u9+F/5134ZtwGhoWLYJaXw+hMCE/R1Uds+XIqtoYALQF2/Dc1ufw5eO/jB++8UMM8w49MsZ1dEDU1CDa1gbF64VSU4N9bbuw+MOHsSewFwvPWYhaZy06I52Y+/rcZD9beM5C1Hvqq6lQRcXEK6uNdY/VxqhS5DJ6ngzgMQCDANwnhNguhPhdcZpF5SA1kXXyoIlonTULW884E38/eSy2nnEm9tw6O5mYOnH4RPzhnytx9ovTMHPVdxB0AV6XD6qisqpYEamKilpXLRShoNZVa3ngApgrySWSkNOT/SN6BNN/Nx2Nyxox/XfTccdrd+BAsA1TV07HqcsaMXXldMz58x0IyRDUfv0gFCX2GArFDlxSk/i/+13oAV4mQ+XPqgJeYvybOmIqfvjGD7H+s/X4wz9XoiPcgZ3XXYetZ5yJj04Zh23nnItdt96KfW27MHXldPzhnyuTCfqartkqlkFERPbl8l9kFEAk/qgD2ANgbzEaReUhNZH16CGjLBP2j44npvZ39U9OZ2Jq+cuU7N/PZU66b6htME1L37+K12td0MHLOKDKlBj/RtWNMvSVTGPh4HpzP8nUzzg+EhH1XC4HL+0A7gfwTwDXSiknSSlvLE6zqBykJrJmSlZNJKa2h9uT05mYWv4yJft3hM1J962draZp6ftXDwSsCzrwzAtVqMT4lyg4ktBdcZJUieIZdoplEBGRfbkcvMwA8DqAWwA8K4T4v0KIqcVpFpWD1ETWhz5aisELf2JITK1fcC9W7l6NBZMXYP3u9aZkVypfmZL9VaGaEpfr3HWWycypFK/XlMzcsGgRz7xQxUqMf6t3rsY9X7gn2QdW7l5tLl6yaBE8tdb9JFMxACIi6hnbCfvJBYQ4CcAlAOYAGCql7PVRuLvkPKvES+ZYZCd1HXogAMXrTT4KRTFsz1AkCFdYh+LzIervguLzwa8F4HF4EIqGynWblzw5r7eTny2Tj6WE7vdD9dUk950ETMn+QgjLxGU7fSpTDFHO+lzMFlq+sZjoQx7VY4h9n9MXe656IANBw/qlsO4nfeBvUsXEKxP2u8eEfaoUtkdQIcSKeMWxBwDUALgGwMBiNawnEtWxbltzGyYsm4Db1tyGtmAbS1NmIXUd0bY27LrlFvx9/KnYdcstiLa1Qeo6FKHA6/CiLdiGWWtvRfPzkzFz1XdwSA0BQqDWVQuH4jAlu1JpWPWBzlAHogfa0DrrVvx9/KlonXUrogfaIABTsr9V4rLVNCtCUaDW1BgeiXpbd+OZHal9qPmZZsxeOxu7u3bjmS3PoC3YFjv4UFRTrGfqJ3b7DxER2ZPLKPozAKOllBdJKe+VUv5ZShlMvCiEmFb45uUmtToWK7vYpwcC3VaK4natHFb7Kth5GJ/eeadh/356553Q/f7sKySqMNnGs2ys+tCP/vojTB0xleMeEVEZcNidUUq5PsssCwCsyq85+UmtjpXAyi7ZZasUxe1aOaz21eD6BnxksX9VX01vNo2oV+Rb+S7TeJeoOsZxjyjmoZvW5DT/rEenFKkl1NcU8vy16TpCIcSvhBB7hRAfWC4Qs0QIsU0IsUkIcZrVfHalVsdKYGWX7LJViuJ2rRxW+2p/W6vl/o36u3qzaUS9It/Kd5nGu0TVMY57RESlZfvMiw1Wmf+/BvBLAE9nWOYSAJ+P/5wB4JH4Y494HV4sPm8xDocOG+4Qzm/KrOlSRzgahnQKNCxaZLo7euqZl4XnLDTeJXryQnhUD7oiXVCEArfqrtZk1KKzm9Cb6S7gqdM8qsfUB2o89Rh4332xS8fi+/fo++6D8HrRFenqUXI+UblKVL6zGs+ietRQpMLj8CCoBQ3PAeDJC5/Ero5deLjlYewN7MU9X7gHf9j+Byw8ZyEEhKEvsn8QEfWuQh68mEgpXxdCjOxmli8DeFrGSp79TQgxQAgxXEq5u4fvh0g0gvlvzjf8ky2lZH2JNLrU4Y/4EdACmLduHoZ5h+KORT/GiPpjoPv9UHy+ZMK1IhTUe+rx4JQH4XV40RnpxG+3/BaPvfcYmoY14d6z7sWSd5dgb2AvFp6zEPWeev5BtymRHGw4MLTYhlbzLT5vMSJ6xLSsUzhNfcBdPxAND/0yWW1MeL04GD5kXlZx4o7X7ui2LUTlTCgK1Pp6HPPww4ZqYDok2oJtmLduXjK+F0xegBX/WIHH3nsMN556Iy7//OWG1xeesxA+hw9u1Y2vnPAVQAK3rrmV/YOIqIQKOeLu6MEyDQA+SXm+Kz6tRwJaAHPXpSWWr2OCpZWAFkBURjFv3Tys/2w9/vDPlZi6cjpmrvoOAi6YKkUlKuUEtADmrJ2Dh1oeSm7ju9+4GzPHzWQifw/YLYZgNd/h0GHLZQ+HD5v6QDAahKO2H4SiwFHbD0E9ZL1s6DALM1DFs6p8l/iiJjW+562bh6kjpkKTGqaOmGp6fe7rc6FLHdevuh4d4Q7z3xf2DyKiXpf1zIsQ4mvdvS6lfD7+2O18mVZvtcoM7bgBwA0AMGLECMuV+Zw+y0RLn9PXg6ZVt8QlQrlur+6SWRO/8zI9e/EK2C+GYDVfQ22D5bINtQ2maen7NNP7Wi3L/dk32I3ZSpXp70Ni7Eok5Ke/nlgu0+vsH6VR7fFKRJnZOfPypW5+vpjn++8CcGzK82MAfGo1o5TycSlls5SyeciQIZYr80f8lomW/ghLwqYLaAF0hDty3l7dJbMmfuc3kfbiFbBfDMFqvtbOVstlWztbTdPS92mm97Valvuzb7Abs5Uq09+HxNiVSMhPfz2xXKbX2T9Ko9rjlYgyy3rwIqW8rpufb+f5/i8CuCZedexMAId7mu8CxL5NXjB5ASYeNREO4cDEoyZiweQF/GbMgtfhhSrUnLdXInk/dZl7z7oXS99fiolHTcTCcxZye+fAantabUOr+ercdZbL1rnqsu7TTO9b567L2haiSpTp78PqnavhEA6s3rna9Hoi/heesxCrd67GPV+4h/2DiKjERCxX3ubMQkwHMBaAJzFNSnlPN/MvB3AegMEA9gD4DwDO+HKPCiEEYtXILgbgB3CdlHJDtnY0NzfLDRusZ0uvJuN1eKEqqs1P2Lckqo1pupbT9kqvelXm1cZKXqqhu3gFClttzOvwQkppqw/YXV+Z7c++oOxjtlLZqTYWioZM8Z/oKx7Vk1ye/SOpYuJ15PdX5rTeHT+b3tMmlYVxT43Laf73r30/p/kr9D4vJY9Xyp/tamNCiEcB+ACcD+BJAFcAeLu7ZaSUM7K8LgHMstsGO1RFRa2rFgCSj2RNEUqyNChgf3slkvcBJB/Tfyf7Mm1Pu/OZpgnY6gO210dUJaz+PqQ/OpTYn8XU+E/tK4n52D+IiEojl6+MviClvAbAQSnl/wUwCcZ8FSIiIiIioqLJ5eAlkZXoF0IcDSAC4HOFbxIREREREZFZLjep/IMQYgCAnwN4F7GSxk8WpVVERERERERpcjl4WSilDAFYIYT4A2JJ+8HiNIuIiIiIiMgol8vG3kz8IqUMSSkPp04jIiIiIiIqpqxnXoQQRwFoAOAVQjThSJm5/ohVHyMiIiIiKG2k1QAAH4FJREFUIio6O5eNXQTgWwCOAfCLlOntAH5QhDYRERERERGZZD14kVI+BeApIcTlUsoVvdAmIiIiIiIik1xyXt4QQiwVQrwMAEKIk4UQM4vULiIiIiIiIoNcDl7+C8CrAI6OP98KYE7BW0RERERERGQhl1LJg6WU/08IcRcASCk1IUS0SO0iIiIioj7qoZvW5DT/rEenFKklVG5yOfPSJYQYhNjNKSGEOBPA4aK0ioiIiIiIKE0uZ17+HcCLAEYJId4AMATAFUVpFRERERERUZpcDl4+BPA7AH4AHQBeQCzvhYiIiIiIqOhyuWzsaQAnAfgJgAcBfB7AsmI0inqPrkt0hjToMv6oy1I3iSgjxmvl4T4jIqJCyuXMy4lSylNTnq8VQrxX6AZR79F1iQNdYcxevhHrd7Rh4sh6LJnRhEE1LiiKKHXziAwYr5WH+4yIiAotlzMvG+NJ+gAAIcQZAN4ofJOot/gjUcxevhFvbj8ATZd4c/sBzF6+Ef4Ii8hR+WG8Vh7uMyIiKrRczrycAeAaIcTO+PMRALYIId4HIKWU4wveOioqn0vF+h1thmnrd7TB51JL1CKizBivlYf7jIiICi2Xg5eLi9YKKgl/OIqJI+vx5vYDyWkTR9bDH46i1p1LaBAVH+O18nCfERFRodm+bExK+a/ufjItJ4S4WAjxkRBimxDi+xavf0sIsU8I0RL/+U5PPwzlxudUsWRGEyaNGgSHIjBp1CAsmdEIRYBJtdSr7CR1W8drE3xOfotfrjjGEBFRoRX1qy8hhArgIQDTAOwCsF4I8aKU8sO0Wf9bSnlrMdtCZooiMKjGhSeuaYbPrWLnAT/+c+UW7GkPMamWeo3dpO5kvF7bDJ9LhT8chc+pMkbLGMcYIiIqtGKftz8dwDYp5XYAEEI8C+DLiN0zhsqAoghAAN984i3DpR2zl2/EE9c289IOKrrUpG4AyaRuq/hTFJGcxtisDBxjiIiokHKpNtYTDQA+SXm+Kz4t3eVCiE1CiOeEEMdarUgIcYMQYoMQYsO+ffuK0dY+i0m1hcd4tY/xVx6KGbPcx1RoHGOJ+q5iH7xYXQ+QfqHz7wGMjFcr+xOAp6xWJKV8XErZLKVsHjJkSIGb2bclkmpTJZJqqWcYr/Yx/spDMWOW+5gKjWMsUd9V7IOXXQBSz6QcA+DT1BmklAeklKH40ycATChymygNE6GplBh/1Y/7mIiICqXYFxuvB/B5IcTnALQCuBLA/0mdQQgxXEq5O/70MgBbitwmSqHrEv5IFPU1Tjx+zQTUuB3oCmnwOlToukRXWDNMC0Z1JktTQWVKxJdSoiN4JP58ThWqau/7lkRcp64PgGmalLH5evIedlm1pRr7Tfrn9KgKAlp824a15BiTvk86Q1rVbxsiIiqcoh68SCk1IcStAF4FoAL4lZRysxDiHgAbpJQvApgthLgMgAagDcC3itkmOiJR5Wn5W//CV5qOwbwVm5LVnh6Y0QiXquDmZ949Mu3KRrzzrzbctrwlY0Uoop5IT8SPRnUc6Arj9mdbDPE3qMaV9eDCqnrZo1edhnBUx+zlLSkVzRrhtIhxO+9hl91KapUu/XM+OKMRE46rN+y/BZePxwsbd+ErTcfghY27cO0XRlrsk+rbNkREVFjFvmwMUsqXpJSjpZTHSyn/Mz7tR/EDF0gp75JSjpVSniqlPF9K+fdit4liElWeLjplOOat2IQ3tx+Apku8uf0Abl/egkP+iHHasy2YdPzg5PPZyzfCH+E161R4/kgUtz/bYoo/O/GWWr0ssexBfwSzlxvXNztDjBcypq3aUo39Jv1zTjp+sGn/zVuxKTnWXHTK8Az7pPq2DRERFRZrVPZhiQpAJwyttawEdGy9zzStv9dpeM5qQVQMNW6HZUzW2Cira1XZ6th6n+0Yt/MedvWVKlvpn7O/12n5uRNjzQlDa5PT0ueptm1DRDEP3bSm1E2gKlH0My9UvhIVgLbt7bSsBPRJm980rT0QMTxntSAqhq6QZhmTXSEt67JWla0+afPbjnE772FXX6mylf452wMRy8+dGGu27e3MuE+qbdsQEVFh8cxLH6DrMpYM61bhD0XhUABNB3xuFY9dPQFRKbFkRqPh2vNEzsukUYNS8gaa4NL92P6TS3Dg4EG4vf1YLajEyikZvJBt8TlVPHBloynnxetQ0RGMdJtgn6hslZpnMtDnxGNXNSEU6MSggQPj8VsLHcIQ4w9c2VjQmLZqSzVW2fI5VTxxzQRoukQ/jwP+cBS/uf4M7Dzgx/1/2oo97SE8eGUjXDKA315/OjraD8PhrjGNO9W4bYiIqLB48FLlYom0oeQ/CLOnnIArTx9h+Kfw518fj9Vb9uCnXxuHEYN86AhqUAXgcajJCmShiAZPuA3iuZnAzjcxZMQkyCuWQmAIrG/nQ8VWTsnghW6LEAI+l4pHrjoN/b1OtAci8DgUtPmzJ/FbVi9zCAj/fvT/fUr8Xr4UunewocpeoauNZaqkVm0J6VLGviB59u2dpuIfS2Y0op/bAXf4QHL86B8fPzw1Q6p+21DlGvn9lTnNv+Nn04vUkpgtJ43JbYG7+C8eVSdeNlblYom0R5JiLzpluCmR9nv/swlnjhqM8+57Dd984i18djiI659+BwEtin4eJxQh4JWh2D8eO9YBugbsWBd7HvFnbwQVRTklgxe6Lf5IFNc//Q4a71mFUXe9hMZ7ViGo6baT+BPVyxQRr2IW8UOsSIvfFTMhNH8yxvt5nAUvk2zVlmr85zxRYMGq+Mfs5S1QNb/l+KFE/FW/bYiIqLB4WF7l0hNpMyXnpybQJuYxJC67fMDON40r3/lmbDqVRDklgxe6LVbry5QEbifBXrhrLONXuGt61D4yShRYyDS+OLy1HD+I0ox7alxO8/+/IrWDqNLwzEuVS0+kzZScv21vp+F3U+Jy2A+MmGRc+YhJselUEuWUDF7otlitL1MSuJ0EexnqsoxfGerqUfvIKFFgIdP4ogU6OX4QEVFB8OClysUShhsxadQgOBSBVz/YjQeuPPJ80qhB+PnXx+OR17Zh0qhBWHD5+OQ8hsRZpw+4YikwcjKgOGKPVyyNTaeSSCSDp+7LUiU8F7otVutzKMIUu7YT7F01kJcb41devhRw8cxLISQKLLz6wW4suHy8YR8tuHw8Vn/cYdr+HD+IiKgnhJSy1G3IWXNzs9ywYUOpm1H2olEd/kgUNW4HguEodCnhczvgD2lQhIDHpSIYjiIq5ZGEZZeKUEQ3TFPj84YiGjwyGPtHMNQFuGrgj+jlnmRb8oYVM17LqdqYpukIaNFk3HgdKoRAMgYTCfFSwjSfogjT59B1aZoPMC9r9R5CmNcHqcdyX9zx+HX6oKjmAx+727SI276sYzb9c3tUBQEtCq9DNTymjin+cBQ1LgUIp2x/Vw0A834q47GErJV8h9kdY3NNwM9Vrgn7uV42Vmw3vflAqZuQl1mPTrEzW8njlfLHnJcqFY3qONAVq8w0rL8bd150Ir73P5sMFca27ejAycPrDNWb7vv6qXA7BW77rbEa2X2/+wh72kOxb7plFNc/vbnkFa7oSDI4gORjKWiabq4EFi+3ffMz7yanLb22GZ0hzTDfI1ed9v/bu/MwOeo6j+Pvz/TMJJkchhy4SBJBLg/UQGI0BtmogQUv1hWX8KgQ12dZVAR2F+9ns8C6D2b1UUFcMAhyaVCubAQFwqPZAHLkICEEUGIMJsJKSEIOkkwyk+/+Ub8eavqY6Tm6q6r7+3qefqa6qrrqW1Xfquk6ft9mf+eBgpK5k2kp+Gyp6V0+ezJtrTnOuXFFr9NrzTVx7s2FedvULW8rrZqWpkpvtVS43PnqhX9+eTeHjm5jxXNbmfL6Md2277yPvY2Fj2/ib4+bwMLHX2D2tEnsaoexwzvYV7Sd6n8dOuecGxh/bKxO5av/PLx+C5+deSRfvPWJogpjx08aU1S96aJbV7Nrb2fRuJ+deWRXdad8/6QrXLn02NPRWVwJbMEqXt69v1u/jgNWNN7Lu/d3q4iXr1BV+NlS0yuVj+Wmt63gs6XyttKqaWmq9FZLhcudr154xPiRXHDLKqYfMa5o+3759ie6qpDlxx8/cijbSm6n+l+HzjnnBsbvvNSpfPUfKF9hbMTQ5pL9J45pK+oXr0Y2alhL0fAkKly59IjnW16pXCqVcxPHtFWch6X6FeZjX6ZXmLeVVk1LU6W3WipXvTC/XctVhMuPF69k2NZaOmfqfR0655wbGL/zUqfy1X+gfIWxXXs7SvbfuHV3Ub94NbIde/YXDU+iwpVLj3i+5ZXKpVI5t3Hr7orzsFS/wnzsy/QK87bSqmlpqvRWS+WqF+a3a7mKcPnx4pUMy22nel+HzjnnBsZPXupUvvrP9DeM5aol6/jWx99WVGHs5d3tXH5m9+pN3/742xkxNFe2Gtnlsyd3q/6UZIUrlx7DmnPFlcDOnMzBI4ewau5JrL/sA6yaexJDcsUVw0a3tXSriBfl1GRGt7UUTa+oX4l8LDe9gwo+Wypvy1VNG9bcxK72Dg6Ysau9g2HNTamp9FZLhesnX5nwD5t3cvnsyTz8h5eKtm++gmG8kuHmnXs5qOR2qv916JxzbmC82lgdK6w2Fq8g1toktu/t4JbH/sTfHHsIRx08glf2RZWB9uzrZM/+TsaOGNKtMtkr7R08+Oxm7l37Fy6cdTSTxraxuz2qFJTiBraJB9YI+drZeYA9+zvpOGCMGtbCjj37GdrcxI693RvnXz57MmPaWpOpNgb9qiI2rLmJrbv3FzXOH9PWwp6OA41ZbWxfJ21Dcvxpy242bNnF5IkHMaK1Oao21pJjz/7u1cZeae9k+JDo2DK0OcfezgN92iYu1RLfYF5tLBlpq07m1cYah7d5qWO5XBMjc9HNtbZQiWpXewfn3LiCqz55fFfD2u/c/yxAdJfmk8cz+dLFXe+vOXsqbUOauz738PotACxc9XzXcP+y4Xbv7+yWHwCr5p7UlWNAVwP7+WdNYeTQqJ1K/i9QVDWtqUmMbG4qGq9kv1xxv1JV2CqpzFZYwW1Xe0dXI/X8cpy/4HGuOXtqKiq91VpTk0DwiWse7ba9p79hLPPPmsKnrn2MH35qStnhzc1NjGh+9aZ/I65D55xz/eePjTWYfIPbcg1r442f441nG7WBsqtMqQb75XJseMa+pHruFyu3TvJ5UK4YSNa2vXPOufTxk5cGk29wW65hbbzxc7zxbKM2UHaVKdVgv1yOvdLeUcvQBsxzv1i5dZLPg3LFQLK27Z1zzqVP1U9eJJ0i6XeS1kn6SonhQyT9LAx/VNJh1Y6pkeUb3JZqWJtvcFuq8Wy5hszeuNZB9wIR8Qb0pXIsaznjuV+sp3VyxZnHdTXgL9z2+XZLzjnnXH9V9R6+pBzwA+AkYBOwTNIiM3sqNtpngG1mdqSk2cA84IxqxtXImprE2OGtvOfogxna3MT8s6Z0a/z8nqMP5vf/eWpR49n85645e6o3rnVFcrkmxg5v7Z5PLTmGteS69WtryZHLZeuGr+d+sZ7WydjhrQxrzTE0V3x8aW7O1rZ3zjmXPtV+AHkasM7M1gNIugU4DYifvJwGXBy6bwOulCTLYhm0jIg3SC5s6JxvSFuq8WxhQ2bn4uIFInprTJ81nvvFyq2TbseXEsUVnHPOuYGo9mWwQ4GNsfebQr+S45hZB7AdGFvluJxzzjnnnHMZU+2Tl1LPVRTeUalkHCSdI2m5pOWbN28elOCcqxbPV5c1nrMuSzxfnWtc1X7+YRMwMfZ+AvB8mXE2SWoGXgNsLRgHM5sPzIfoB6mqEq1zg8Tz1WWN56zLkjTma19/BHPkm6oUSI1cPf2CPo2fth+1dNlV7Tsvy4CjJB0uqRWYDSwqGGcRcHboPh34tbd3cc4555xzzhWq6p0XM+uQdB5wL5ADrjOztZIuBZab2SLgWuAmSeuI7rjMrmZMzjnnnHPOuWxSFm9ySNoMPNfLaOOAl2oQTl95XH03kNheMrNTBjOYvqowXyHd26AvfDkGJks5O9jSnjtpjw9qH2Ma8zUL26lS9bIsaVmOxPPVDVwmT14qIWm5mU1NOo5CHlffpTm2wVQvy+nL4for7es87fFBNmKstnpaB/WyLPWyHC4d/BfDnHPOOeecc5ngJy/OOeecc865TKjnk5f5SQdQhsfVd2mObTDVy3L6crj+Svs6T3t8kI0Yq62e1kG9LEu9LIdLgbpt8+Kcc84555yrL/V858U555xzzjlXRzJ98iLpFEm/k7RO0ldKDB8i6Wdh+KOSDqtRXBMl/UbS05LWSir6GVpJMyVtl7QqvObWKLYNktaEeS4vMVySrgjr7AlJx9cormNi62KVpB2SLiwYJ5F1Vk2V5EpWSBoq6TFJq8OyXJJ0TP0lKSfpcUl3JR1LvcvSPpD2vJA0WtJtkp4J63N60jHVWm/fC7JC0nWSXpT0ZNKx9FeW9m2XLVX9kcpqkpQDfgCcBGwClklaZGZPxUb7DLDNzI6UNBuYB5xRg/A6gH81s5WSRgIrJC0uiA3gATP7UA3iKfReMytXb/1U4KjweidwVfhbVWb2O2AydG3bPwN3lhg1qXVWLZXmSha0A+8zs12SWoAHJf3KzB5JOrB+uAB4GhiVdCANIEv7QNrz4nLgHjM7XVIr0JZ0QLVU4feCrLgeuBK4MeE4BiJL+7bLkCzfeZkGrDOz9Wa2D7gFOK1gnNOAG0L3bcD7JanagZnZC2a2MnTvJPpnd2i15ztITgNutMgjwGhJh9Q4hvcDfzCzJH4kr6YynivdhJzZFd62hFfmGtVJmgB8EPhR0rE0gqzsA2nPC0mjgBOBawHMbJ+ZvZxsVDVXyfeCTDCzpcDWpOMYiKzs2y57snzyciiwMfZ+E8U7Rdc4ZtYBbAfG1iS6IDyqdhzwaInB08MjNr+S9JYahWTAfZJWSDqnxPBK1mu1zQYWlBmWxDqriV5yJRPCYzWrgBeBxWaWxWX5HvAl4EDSgTSalO8Dac+LNwCbgR+HR9t+JGl40kHVWBr+f7kSUr5vu4zJ8slLqTsohVd5KxmnaiSNAG4HLjSzHQWDVwKvN7O3A98HFtYorBlmdjzR42Gfl3RiwfCk11kr8BHg1hKDk1pnVddLrmSGmXWa2WRgAjBN0rFJx9QXkj4EvGhmK5KOpdGkeR/ISF40A8cDV5nZccArQGbbfPRTov+/XGlp3rddNmX55GUTMDH2fgLwfLlxJDUDr6FGt2HDM/+3Az8xszsKh5vZjvwjNmb2S6BF0rhqx2Vmz4e/LxK1KZlWMEol67WaTgVWmtlfCgcktc6qrbdcyaLwuMoS4JSEQ+mrGcBHJG0geuTkfZJuTjak+peBfSALebEJ2BS723kb0clMI0n6/5crkIF922VQlk9elgFHSTo8XK2fDSwqGGcRcHboPh34tdXgh21Cu5prgafN7DtlxvmrfPsbSdOItsWWKsc1PDSaIzxOcDJQWMlkEXBWVHRM7wK2m9kL1YyrwJmUeWQsiXVWbZXkSlZIGi9pdOgeBswCnkk2qr4xs6+a2QQzO4zomPJrM/tkwmHVtSzsA1nICzP7P2CjpGNCr/cDjdYwupLvBa5GsrBvu2zKbLUxM+uQdB5wL5ADrjOztZIuBZab2SKineYmSeuI7rjMrlF4M4BPAWvC8/8AXwMmhdivJjqZ+qykDmAPMLsGJ1avBe4M3/+bgZ+a2T2Szo3F9UvgA8A6YDfw6SrH1EVSG1GVmH+K9YvHlsQ6q7aSuRLuLGXNIcANoeJPE/BzM0tlSVmXKvW0DyTtC8BPwhf39dTw+J0G5b4XJBxWv0haAMwExknaBPy7mV2bbFR95vu2qwpl/7ufc84555xzrhFk+bEx55xzzjnnXAPxkxfnnHPOOedcJvjJi3POOeeccy4T/OTFOeecc845lwl+8uKcc84555zLBD95cc4555xzzmWCn7wkTNJMSWV/C0PSHElXVmG+cyS9LvZ+Qz38Wr2rnd5yt4LPT5V0RZlhGySNkzRa0ucGa56ufhQew3oY73pJp/cwfImkqYMcm+etK2uwcreCz18qaVaJ/l35GLrfPVjzdK4W/OSlcc0Bej14OlctZrbczM7vZbTRwOd6Gcc1pjmk9xjmeet6Moca5K6ZzTWz+3sZbSbw7l7GcS5V/OSlApKGS7pb0mpJT0o6Q9IUSf8raYWkeyUdEsZdIul7kn4bxp0W+k8L/R4Pf4/pRxzjJd0uaVl4zQj9L5Z0XZj3eknnxz7zb5KekbRY0gJJF4WrKlOJfol5laRhYfQvSFopaY2kN/YQxwhJPw7jPSHpY6H/Lknzwjq5PyxzPqaP9HV53cAlmbshP0YrskXSWaH/TZJmFVz9GyvpvjCPHwIKk/kmcETI02+FfiMk3Rby+ieSVDz3rhjeEWJeLekxSSPDVc+Fkn4h6Y+SzpP0L2Hej0ga07+17QZC0mFhm94Qjiu3SWorla+ljmGS5obj4pOS5veUFz3EcLKkh8Nx8FZJI0L/DZIuKTw+hmPy4tD/h5KeU3QH2/O2gSSRu+G4fEfoPk3SHkmtkoZKWh/6d91FkXRKiPFB4O/ycQPnAv8cYnlPmPyJIf/Wq5e7MJK+FPaJ1ZK+GfotkfRdSUslPR3y+Q5Jz0r6Rn/WsXPdmJm/enkBHwOuib1/DfBbYHx4fwZwXehekh8XOBF4MnSPAppD9yzg9tA9E7irh3nPAa4M3T8FTgjdk4CnQ/fFIZ4hwDhgC9BCdIBcBQwDRgLPAhfF4pwam88G4Auh+3PAj3qIaR7wvdj7g8JfA04N3XcC94U43g6sSno7NuIr4dy9GvggcCywLDbtZ4ER8c8DVwBzQ/cHQy6NAw7LxxGb53ZgAtHFl4fz+0SJ+bcC64F3xJcj7FPrwj4xPkzv3DDOd4ELk95ujfgK29qAGeH9dcAXe8nX+DFsTKz7JuDDoft64PQe5ruE6Fg5DlgKDA/9vxzLyQ2UOD4CVwJfDd2neN425iuJ3A058cfQ/W2iY+wM4K+BBfHPA0OBjcBRRBeGfs6rx96LCd8LYp+5NeTpm4F1PSz3qWEZ2+LLEZZvXui+AHgeOIToO8omYGzS28xf2X414yqxBvi2pHnAXcA2oi9ki8MFkhzwQmz8BQBmtlTSKEmjif7h3CDpKKKDXEs/4pgFvDl2UWaUpJGh+24zawfaJb0IvBY4AfgfM9sDIOkXvUz/jvB3BeHKTA9xzM6/MbNtoXMfcE/oXgO0m9l+SWuIDu6u9pLM3QeIToKeA64CzpF0KLDVzHYVXFw8kZBzZna3pG2FE4t5zMw2AUhaRZRbD5YY7xjgBTNbFqa7I3wG4DdmthPYKWk7kN831gBvq3D53ODbaGYPhe6bga/Rc77GvVfSl4A2YAywlle3ayXeRfRl7aEwr1aik4y8UsfHE4CPApjZPZ63Da2muWtmHZLWSXoTMA34DtFxNEd07I17I9GJzrMAkm4Gzulh8gvN7ADwlKTX9jDeLODHZrY7xLQ1NmxR+LsGWGtmL4R5rwcmEl1kda5f/OSlAmb2e0lTgA8AlwGLiXbG6eU+UuL9fxD94/louFW7pB+hNAHT8ycjeeHA2B7r1Um0bfv62ER+GvnPlyOKlxFgv5nl+x/IT8/MDkjyXEtAwrm7FPg80V3CrxN9yTud4n+s5eZdTqlcL6VcnhZO40Ds/YEepueqr3B77aTnfAVA0lDgv4muZm+UdDHR1ea+ELDYzM4sM7zU8bEvx1jP2/qWRO4+QHT3Yz9wP9FdkxxwUQXx9SSeZz3leCW5Gs/T/HvPVTcg3ualAoqqguw2s5uJbs++ExgvaXoY3iLpLbGPnBH6nwBsN7PtRI/r/DkMn9PPUO4DzovFNbmX8R8EPhyegR1B9DhO3k6iK+qDEcdB/ZyOq7Ikc9fMNhI9QnOUma0nyseLKH3yshT4RJj3qUA+pwaSp88Ar5P0jjDdkX4SnXqT8rkJnAk8Qvl8jedG/sveS+FY159qSY8AMyQdGebVJunoXj7zIPD3YfyT8bxtZEnk7lLgQuBhM9sMjCW6y7K2YLxngMMlHRGLL2+g3wX+QVIbgLzdlasRP3mpzFuBx8Kt/q8Dc4kOMPMkrSZqVxKv1rFN0m+Jnvn/TOj3X8Blkh4iujLSH+cDUxU1CHyKqKFdWeGxg0XAaqJHHpYTPScN0RWaq9W9wX6lvgEcpKhx4WrgvX38vKudpHP3UeD3ofsB4FBKPypzCVEj0ZXAycCfAMxsC9FjPE/q1YbPFTGzfUQnY98Py7qYvl+Nd7X1NHC2pCeIHp/5PuXz9XrCMYzoyu41RI+oLCR6/r9Pwpe/OcCCMP9HiL4I9uQS4OSQt6cSPRa00/O2ISWRu48SPSK+NLx/Angi9gQEAGa2l+gxsbsVNdh/Ljb4F8BH1b3BfkXM7B6i7xjLw7KUuuPj3KBTQY67AZK0hKjx2/KkY4GoMlhoX9BGdIA7x8xWJh2XS5+05a5rLOGRxLvM7NiEQ6mYpCFAZ2h/MB24ysx6uyPu6kwWc9e5LPNb0fVvvqQ3E125u8FPXJxzbtBMAn4uqYmoYMk/JhyPc87VPb/zkhKSPk1UUjDuITP7fBLxQDpjcumThjyRdCdweEHvL5vZvbWKwaVbGnMkjTG59Ek6TyS9laiEc1y7mb2zFvN3rpCfvDjnnHPOOecywRvsO+ecc8455zLBT16cc84555xzmeAnL84555xzzrlM8JMX55xzzjnnXCb4yYtzzjnnnHMuE/4fzz0owZW/NOYAAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<Figure size 823.5x720 with 20 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# We have to temporarily drop the rows with 'NA' values\\n\",\n    \"# because the Seaborn plotting function does not know\\n\",\n    \"# what to do with them\\n\",\n    \"sb.pairplot(iris_data.dropna(), hue='class')\\n\",\n    \";\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"From the scatterplot matrix, we can already see some issues with the data set:\\n\",\n    \"\\n\",\n    \"1. There are five classes when there should only be three, meaning there were some coding errors.\\n\",\n    \"\\n\",\n    \"2. There are some clear outliers in the measurements that may be erroneous: one `sepal_width_cm` entry for `Iris-setosa` falls well outside its normal range, and several `sepal_length_cm` entries for `Iris-versicolor` are near-zero for some reason.\\n\",\n    \"\\n\",\n    \"3. We had to drop those rows with missing values.\\n\",\n    \"\\n\",\n    \"In all of these cases, we need to figure out what to do with the erroneous data. Which takes us to the next step...\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Step 3: Tidying the data\\n\",\n    \"\\n\",\n    \"[[ go back to the top ]](#Table-of-contents)\\n\",\n    \"\\n\",\n    \"Now that we've identified several errors in the data set, we need to fix them before we proceed with the analysis.\\n\",\n    \"\\n\",\n    \"Let's walk through the issues one-by-one.\\n\",\n    \"\\n\",\n    \">There are five classes when there should only be three, meaning there were some coding errors.\\n\",\n    \"\\n\",\n    \"After talking with the field researchers, it sounds like one of them forgot to add `Iris-` before their `Iris-versicolor` entries. The other extraneous class, `Iris-setossa`, was simply a typo that they forgot to fix.\\n\",\n    \"\\n\",\n    \"Let's use the DataFrame to fix these errors.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array(['Iris-setosa', 'Iris-versicolor', 'Iris-virginica'], dtype=object)\"\n      ]\n     },\n     \"execution_count\": 6,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"iris_data.loc[iris_data['class'] == 'versicolor', 'class'] = 'Iris-versicolor'\\n\",\n    \"iris_data.loc[iris_data['class'] == 'Iris-setossa', 'class'] = 'Iris-setosa'\\n\",\n    \"\\n\",\n    \"iris_data['class'].unique()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Much better! Now we only have three class types. Imagine how embarrassing it would've been to create a model that used the wrong classes.\\n\",\n    \"\\n\",\n    \">There are some clear outliers in the measurements that may be erroneous: one `sepal_width_cm` entry for `Iris-setosa` falls well outside its normal range, and several `sepal_length_cm` entries for `Iris-versicolor` are near-zero for some reason.\\n\",\n    \"\\n\",\n    \"Fixing outliers can be tricky business. It's rarely clear whether the outlier was caused by measurement error, recording the data in improper units, or if the outlier is a real anomaly. For that reason, we should be judicious when working with outliers: if we decide to exclude any data, we need to make sure to document what data we excluded and provide solid reasoning for excluding that data. (i.e., \\\"This data didn't fit my hypothesis\\\" will not stand peer review.)\\n\",\n    \"\\n\",\n    \"In the case of the one anomalous entry for `Iris-setosa`, let's say our field researchers know that it's impossible for `Iris-setosa` to have a sepal width below 2.5 cm. Clearly this entry was made in error, and we're better off just scrapping the entry than spending hours finding out what happened.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"''\"\n      ]\n     },\n     \"execution_count\": 7,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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     \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"# This line drops any 'Iris-setosa' rows with a separal width less than 2.5 cm\\n\",\n    \"iris_data = iris_data.loc[(iris_data['class'] != 'Iris-setosa') | (iris_data['sepal_width_cm'] >= 2.5)]\\n\",\n    \"iris_data.loc[iris_data['class'] == 'Iris-setosa', 'sepal_width_cm'].hist()\\n\",\n    \";\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Excellent! Now all of our `Iris-setosa` rows have a sepal width greater than 2.5.\\n\",\n    \"\\n\",\n    \"The next data issue to address is the several near-zero sepal lengths for the `Iris-versicolor` rows. Let's take a look at those rows.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>sepal_length_cm</th>\\n\",\n       \"      <th>sepal_width_cm</th>\\n\",\n       \"      <th>petal_length_cm</th>\\n\",\n       \"      <th>petal_width_cm</th>\\n\",\n       \"      <th>class</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>77</th>\\n\",\n       \"      <td>0.067</td>\\n\",\n       \"      <td>3.0</td>\\n\",\n       \"      <td>5.0</td>\\n\",\n       \"      <td>1.7</td>\\n\",\n       \"      <td>Iris-versicolor</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>78</th>\\n\",\n       \"      <td>0.060</td>\\n\",\n       \"      <td>2.9</td>\\n\",\n       \"      <td>4.5</td>\\n\",\n       \"      <td>1.5</td>\\n\",\n       \"      <td>Iris-versicolor</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>79</th>\\n\",\n       \"      <td>0.057</td>\\n\",\n       \"      <td>2.6</td>\\n\",\n       \"      <td>3.5</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>Iris-versicolor</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>80</th>\\n\",\n       \"      <td>0.055</td>\\n\",\n       \"      <td>2.4</td>\\n\",\n       \"      <td>3.8</td>\\n\",\n       \"      <td>1.1</td>\\n\",\n       \"      <td>Iris-versicolor</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>81</th>\\n\",\n       \"      <td>0.055</td>\\n\",\n       \"      <td>2.4</td>\\n\",\n       \"      <td>3.7</td>\\n\",\n       \"      <td>1.0</td>\\n\",\n       \"      <td>Iris-versicolor</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"    sepal_length_cm  sepal_width_cm  petal_length_cm  petal_width_cm  \\\\\\n\",\n       \"77            0.067             3.0              5.0             1.7   \\n\",\n       \"78            0.060             2.9              4.5             1.5   \\n\",\n       \"79            0.057             2.6              3.5             1.0   \\n\",\n       \"80            0.055             2.4              3.8             1.1   \\n\",\n       \"81            0.055             2.4              3.7             1.0   \\n\",\n       \"\\n\",\n       \"              class  \\n\",\n       \"77  Iris-versicolor  \\n\",\n       \"78  Iris-versicolor  \\n\",\n       \"79  Iris-versicolor  \\n\",\n       \"80  Iris-versicolor  \\n\",\n       \"81  Iris-versicolor  \"\n      ]\n     },\n     \"execution_count\": 8,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"iris_data.loc[(iris_data['class'] == 'Iris-versicolor') &\\n\",\n    \"              (iris_data['sepal_length_cm'] < 1.0)]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"How about that? All of these near-zero `sepal_length_cm` entries seem to be off by two orders of magnitude, as if they had been recorded in meters instead of centimeters.\\n\",\n    \"\\n\",\n    \"After some brief correspondence with the field researchers, we find that one of them forgot to convert those measurements to centimeters. Let's do that for them.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"''\"\n      ]\n     },\n     \"execution_count\": 9,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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     \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"iris_data.loc[(iris_data['class'] == 'Iris-versicolor') &\\n\",\n    \"              (iris_data['sepal_length_cm'] < 1.0),\\n\",\n    \"              'sepal_length_cm'] *= 100.0\\n\",\n    \"\\n\",\n    \"iris_data.loc[iris_data['class'] == 'Iris-versicolor', 'sepal_length_cm'].hist()\\n\",\n    \";\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Phew! Good thing we fixed those outliers. They could've really thrown our analysis off.\\n\",\n    \"\\n\",\n    \">We had to drop those rows with missing values.\\n\",\n    \"\\n\",\n    \"Let's take a look at the rows with missing values:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>sepal_length_cm</th>\\n\",\n       \"      <th>sepal_width_cm</th>\\n\",\n       \"      <th>petal_length_cm</th>\\n\",\n       \"      <th>petal_width_cm</th>\\n\",\n       \"      <th>class</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>7</th>\\n\",\n       \"      <td>5.0</td>\\n\",\n       \"      <td>3.4</td>\\n\",\n       \"      <td>1.5</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>Iris-setosa</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>8</th>\\n\",\n       \"      <td>4.4</td>\\n\",\n       \"      <td>2.9</td>\\n\",\n       \"      <td>1.4</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>Iris-setosa</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>9</th>\\n\",\n       \"      <td>4.9</td>\\n\",\n       \"      <td>3.1</td>\\n\",\n       \"      <td>1.5</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>Iris-setosa</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>10</th>\\n\",\n       \"      <td>5.4</td>\\n\",\n       \"      <td>3.7</td>\\n\",\n       \"      <td>1.5</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>Iris-setosa</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>11</th>\\n\",\n       \"      <td>4.8</td>\\n\",\n       \"      <td>3.4</td>\\n\",\n       \"      <td>1.6</td>\\n\",\n       \"      <td>NaN</td>\\n\",\n       \"      <td>Iris-setosa</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"    sepal_length_cm  sepal_width_cm  petal_length_cm  petal_width_cm  \\\\\\n\",\n       \"7               5.0             3.4              1.5             NaN   \\n\",\n       \"8               4.4             2.9              1.4             NaN   \\n\",\n       \"9               4.9             3.1              1.5             NaN   \\n\",\n       \"10              5.4             3.7              1.5             NaN   \\n\",\n       \"11              4.8             3.4              1.6             NaN   \\n\",\n       \"\\n\",\n       \"          class  \\n\",\n       \"7   Iris-setosa  \\n\",\n       \"8   Iris-setosa  \\n\",\n       \"9   Iris-setosa  \\n\",\n       \"10  Iris-setosa  \\n\",\n       \"11  Iris-setosa  \"\n      ]\n     },\n     \"execution_count\": 10,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"iris_data.loc[(iris_data['sepal_length_cm'].isnull()) |\\n\",\n    \"              (iris_data['sepal_width_cm'].isnull()) |\\n\",\n    \"              (iris_data['petal_length_cm'].isnull()) |\\n\",\n    \"              (iris_data['petal_width_cm'].isnull())]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"It's not ideal that we had to drop those rows, especially considering they're all `Iris-setosa` entries. Since it seems like the missing data is systematic — all of the missing values are in the same column for the same *Iris* type — this error could potentially bias our analysis.\\n\",\n    \"\\n\",\n    \"One way to deal with missing data is **mean imputation**: If we know that the values for a measurement fall in a certain range, we can fill in empty values with the average of that measurement.\\n\",\n    \"\\n\",\n    \"Let's see if we can do that here.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"''\"\n      ]\n     },\n     \"execution_count\": 11,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"iris_data.loc[iris_data['class'] == 'Iris-setosa', 'petal_width_cm'].hist()\\n\",\n    \";\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Most of the petal widths for `Iris-setosa` fall within the 0.2-0.3 range, so let's fill in these entries with the average measured petal width.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>sepal_length_cm</th>\\n\",\n       \"      <th>sepal_width_cm</th>\\n\",\n       \"      <th>petal_length_cm</th>\\n\",\n       \"      <th>petal_width_cm</th>\\n\",\n       \"      <th>class</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>7</th>\\n\",\n       \"      <td>5.0</td>\\n\",\n       \"      <td>3.4</td>\\n\",\n       \"      <td>1.5</td>\\n\",\n       \"      <td>0.25</td>\\n\",\n       \"      <td>Iris-setosa</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>8</th>\\n\",\n       \"      <td>4.4</td>\\n\",\n       \"      <td>2.9</td>\\n\",\n       \"      <td>1.4</td>\\n\",\n       \"      <td>0.25</td>\\n\",\n       \"      <td>Iris-setosa</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>9</th>\\n\",\n       \"      <td>4.9</td>\\n\",\n       \"      <td>3.1</td>\\n\",\n       \"      <td>1.5</td>\\n\",\n       \"      <td>0.25</td>\\n\",\n       \"      <td>Iris-setosa</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>10</th>\\n\",\n       \"      <td>5.4</td>\\n\",\n       \"      <td>3.7</td>\\n\",\n       \"      <td>1.5</td>\\n\",\n       \"      <td>0.25</td>\\n\",\n       \"      <td>Iris-setosa</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>11</th>\\n\",\n       \"      <td>4.8</td>\\n\",\n       \"      <td>3.4</td>\\n\",\n       \"      <td>1.6</td>\\n\",\n       \"      <td>0.25</td>\\n\",\n       \"      <td>Iris-setosa</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"    sepal_length_cm  sepal_width_cm  petal_length_cm  petal_width_cm  \\\\\\n\",\n       \"7               5.0             3.4              1.5            0.25   \\n\",\n       \"8               4.4             2.9              1.4            0.25   \\n\",\n       \"9               4.9             3.1              1.5            0.25   \\n\",\n       \"10              5.4             3.7              1.5            0.25   \\n\",\n       \"11              4.8             3.4              1.6            0.25   \\n\",\n       \"\\n\",\n       \"          class  \\n\",\n       \"7   Iris-setosa  \\n\",\n       \"8   Iris-setosa  \\n\",\n       \"9   Iris-setosa  \\n\",\n       \"10  Iris-setosa  \\n\",\n       \"11  Iris-setosa  \"\n      ]\n     },\n     \"execution_count\": 12,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"average_petal_width = iris_data.loc[iris_data['class'] == 'Iris-setosa', 'petal_width_cm'].mean()\\n\",\n    \"\\n\",\n    \"iris_data.loc[(iris_data['class'] == 'Iris-setosa') &\\n\",\n    \"              (iris_data['petal_width_cm'].isnull()),\\n\",\n    \"              'petal_width_cm'] = average_petal_width\\n\",\n    \"\\n\",\n    \"iris_data.loc[(iris_data['class'] == 'Iris-setosa') &\\n\",\n    \"              (iris_data['petal_width_cm'] == average_petal_width)]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<style scoped>\\n\",\n       \"    .dataframe tbody tr th:only-of-type {\\n\",\n       \"        vertical-align: middle;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe tbody tr th {\\n\",\n       \"        vertical-align: top;\\n\",\n       \"    }\\n\",\n       \"\\n\",\n       \"    .dataframe thead th {\\n\",\n       \"        text-align: right;\\n\",\n       \"    }\\n\",\n       \"</style>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>sepal_length_cm</th>\\n\",\n       \"      <th>sepal_width_cm</th>\\n\",\n       \"      <th>petal_length_cm</th>\\n\",\n       \"      <th>petal_width_cm</th>\\n\",\n       \"      <th>class</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"Empty DataFrame\\n\",\n       \"Columns: [sepal_length_cm, sepal_width_cm, petal_length_cm, petal_width_cm, class]\\n\",\n       \"Index: []\"\n      ]\n     },\n     \"execution_count\": 13,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"iris_data.loc[(iris_data['sepal_length_cm'].isnull()) |\\n\",\n    \"              (iris_data['sepal_width_cm'].isnull()) |\\n\",\n    \"              (iris_data['petal_length_cm'].isnull()) |\\n\",\n    \"              (iris_data['petal_width_cm'].isnull())]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Great! Now we've recovered those rows and no longer have missing data in our data set.\\n\",\n    \"\\n\",\n    \"**Note:** If you don't feel comfortable imputing your data, you can drop all rows with missing data with the `dropna()` call:\\n\",\n    \"\\n\",\n    \"    iris_data.dropna(inplace=True)\\n\",\n    \"\\n\",\n    \"After all this hard work, we don't want to repeat this process every time we work with the data set. Let's save the tidied data file *as a separate file* and work directly with that data file from now on.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"iris_data.to_csv('iris-data-clean.csv', index=False)\\n\",\n    \"\\n\",\n    \"iris_data_clean = pd.read_csv('iris-data-clean.csv')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now, let's take a look at the scatterplot matrix now that we've tidied the data.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"''\"\n      ]\n     },\n     \"execution_count\": 15,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 823.5x720 with 20 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"sb.pairplot(iris_data_clean, hue='class')\\n\",\n    \";\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Of course, I purposely inserted numerous errors into this data set to demonstrate some of the many possible scenarios you may face while tidying your data.\\n\",\n    \"\\n\",\n    \"The general takeaways here should be:\\n\",\n    \"\\n\",\n    \"* Make sure your data is encoded properly\\n\",\n    \"\\n\",\n    \"* Make sure your data falls within the expected range, and use domain knowledge whenever possible to define that expected range\\n\",\n    \"\\n\",\n    \"* Deal with missing data in one way or another: replace it if you can or drop it\\n\",\n    \"\\n\",\n    \"* Never tidy your data manually because that is not easily reproducible\\n\",\n    \"\\n\",\n    \"* Use code as a record of how you tidied your data\\n\",\n    \"\\n\",\n    \"* Plot everything you can about the data at this stage of the analysis so you can *visually* confirm everything looks correct\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Bonus: Testing our data\\n\",\n    \"\\n\",\n    \"[[ go back to the top ]](#Table-of-contents)\\n\",\n    \"\\n\",\n    \"At SciPy 2015, I was exposed to a great idea: We should test our data. Just how we use unit tests to verify our expectations from code, we can similarly set up unit tests to verify our expectations about a data set.\\n\",\n    \"\\n\",\n    \"We can quickly test our data using `assert` statements: We assert that something must be true, and if it is, then nothing happens and the notebook continues running. However, if our assertion is wrong, then the notebook stops running and brings it to our attention. For example,\\n\",\n    \"\\n\",\n    \"```Python\\n\",\n    \"assert 1 == 2\\n\",\n    \"```\\n\",\n    \"\\n\",\n    \"will raise an `AssertionError` and stop execution of the notebook because the assertion failed.\\n\",\n    \"\\n\",\n    \"Let's test a few things that we know about our data set now.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 16,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# We know that we should only have three classes\\n\",\n    \"assert len(iris_data_clean['class'].unique()) == 3\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 17,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# We know that sepal lengths for 'Iris-versicolor' should never be below 2.5 cm\\n\",\n    \"assert iris_data_clean.loc[iris_data_clean['class'] == 'Iris-versicolor', 'sepal_length_cm'].min() >= 2.5\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# We know that our data set should have no missing measurements\\n\",\n    \"assert len(iris_data_clean.loc[(iris_data_clean['sepal_length_cm'].isnull()) |\\n\",\n    \"                               (iris_data_clean['sepal_width_cm'].isnull()) |\\n\",\n    \"                               (iris_data_clean['petal_length_cm'].isnull()) |\\n\",\n    \"                               (iris_data_clean['petal_width_cm'].isnull())]) == 0\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"And so on. If any of these expectations are violated, then our analysis immediately stops and we have to return to the tidying stage.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Step 4: Exploratory analysis\\n\",\n    \"\\n\",\n    \"[[ go back to the top ]](#Table-of-contents)\\n\",\n    \"\\n\",\n    \"Now after spending entirely too much time tidying our data, we can start analyzing it!\\n\",\n    \"\\n\",\n    \"Exploratory analysis is the step where we start delving deeper into the data set beyond the outliers and errors. We'll be looking to answer questions such as:\\n\",\n    \"\\n\",\n    \"* How is my data distributed?\\n\",\n    \"\\n\",\n    \"* Are there any correlations in my data?\\n\",\n    \"\\n\",\n    \"* Are there any confounding factors that explain these correlations?\\n\",\n    \"\\n\",\n    \"This is the stage where we plot all the data in as many ways as possible. Create many charts, but don't bother making them pretty — these charts are for internal use.\\n\",\n    \"\\n\",\n    \"Let's return to that scatterplot matrix that we used earlier.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 19,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"''\"\n      ]\n     },\n     \"execution_count\": 19,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 720x720 with 20 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"sb.pairplot(iris_data_clean)\\n\",\n    \";\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Our data is normally distributed for the most part, which is great news if we plan on using any modeling methods that assume the data is normally distributed.\\n\",\n    \"\\n\",\n    \"There's something strange going on with the petal measurements. Maybe it's something to do with the different `Iris` types. Let's color code the data by the class again to see if that clears things up.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 20,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"''\"\n      ]\n     },\n     \"execution_count\": 20,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 823.5x720 with 20 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"sb.pairplot(iris_data_clean, hue='class')\\n\",\n    \";\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Sure enough, the strange distribution of the petal measurements exist because of the different species. This is actually great news for our classification task since it means that the petal measurements will make it easy to distinguish between `Iris-setosa` and the other `Iris` types.\\n\",\n    \"\\n\",\n    \"Distinguishing `Iris-versicolor` and `Iris-virginica` will prove more difficult given how much their measurements overlap.\\n\",\n    \"\\n\",\n    \"There are also correlations between petal length and petal width, as well as sepal length and sepal width. The field biologists assure us that this is to be expected: Longer flower petals also tend to be wider, and the same applies for sepals.\\n\",\n    \"\\n\",\n    \"We can also make **violin plots** of the data to compare the measurement distributions of the classes. Violin plots contain the same information as [box plots](https://en.wikipedia.org/wiki/Box_plot), but also scales the box according to the density of the data.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 21,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 720x720 with 4 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.figure(figsize=(10, 10))\\n\",\n    \"\\n\",\n    \"for column_index, column in enumerate(iris_data_clean.columns):\\n\",\n    \"    if column == 'class':\\n\",\n    \"        continue\\n\",\n    \"    plt.subplot(2, 2, column_index + 1)\\n\",\n    \"    sb.violinplot(x='class', y=column, data=iris_data_clean)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Enough flirting with the data. Let's get to modeling.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Step 5: Classification\\n\",\n    \"\\n\",\n    \"[[ go back to the top ]](#Table-of-contents)\\n\",\n    \"\\n\",\n    \"Wow, all this work and we *still* haven't modeled the data!\\n\",\n    \"\\n\",\n    \"As tiresome as it can be, tidying and exploring our data is a vital component to any data analysis. If we had jumped straight to the modeling step, we would have created a faulty classification model.\\n\",\n    \"\\n\",\n    \"Remember: **Bad data leads to bad models.** Always check your data first.\\n\",\n    \"\\n\",\n    \"<hr />\\n\",\n    \"\\n\",\n    \"Assured that our data is now as clean as we can make it — and armed with some cursory knowledge of the distributions and relationships in our data set — it's time to make the next big step in our analysis: Splitting the data into training and testing sets.\\n\",\n    \"\\n\",\n    \"A **training set** is a random subset of the data that we use to train our models.\\n\",\n    \"\\n\",\n    \"A **testing set** is a random subset of the data (mutually exclusive from the training set) that we use to validate our models on unforseen data.\\n\",\n    \"\\n\",\n    \"Especially in sparse data sets like ours, it's easy for models to **overfit** the data: The model will learn the training set so well that it won't be able to handle most of the cases it's never seen before. This is why it's important for us to build the model with the training set, but score it with the testing set.\\n\",\n    \"\\n\",\n    \"Note that once we split the data into a training and testing set, we should treat the testing set like it no longer exists: We cannot use any information from the testing set to build our model or else we're cheating.\\n\",\n    \"\\n\",\n    \"Let's set up our data first.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 22,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"array([[ 5.1,  3.5,  1.4,  0.2],\\n\",\n       \"       [ 4.9,  3. ,  1.4,  0.2],\\n\",\n       \"       [ 4.7,  3.2,  1.3,  0.2],\\n\",\n       \"       [ 4.6,  3.1,  1.5,  0.2],\\n\",\n       \"       [ 5. ,  3.6,  1.4,  0.2]])\"\n      ]\n     },\n     \"execution_count\": 22,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"iris_data_clean = pd.read_csv('iris-data-clean.csv')\\n\",\n    \"\\n\",\n    \"# We're using all four measurements as inputs\\n\",\n    \"# Note that scikit-learn expects each entry to be a list of values, e.g.,\\n\",\n    \"# [ [val1, val2, val3],\\n\",\n    \"#   [val1, val2, val3],\\n\",\n    \"#   ... ]\\n\",\n    \"# such that our input data set is represented as a list of lists\\n\",\n    \"\\n\",\n    \"# We can extract the data in this format from pandas like this:\\n\",\n    \"all_inputs = iris_data_clean[['sepal_length_cm', 'sepal_width_cm',\\n\",\n    \"                             'petal_length_cm', 'petal_width_cm']].values\\n\",\n    \"\\n\",\n    \"# Similarly, we can extract the class labels\\n\",\n    \"all_labels = iris_data_clean['class'].values\\n\",\n    \"\\n\",\n    \"# Make sure that you don't mix up the order of the entries\\n\",\n    \"# all_inputs[5] inputs should correspond to the class in all_labels[5]\\n\",\n    \"\\n\",\n    \"# Here's what a subset of our inputs looks like:\\n\",\n    \"all_inputs[:5]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now our data is ready to be split.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 23,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from sklearn.model_selection import train_test_split\\n\",\n    \"\\n\",\n    \"(training_inputs,\\n\",\n    \" testing_inputs,\\n\",\n    \" training_classes,\\n\",\n    \" testing_classes) = train_test_split(all_inputs, all_labels, test_size=0.25, random_state=1)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"With our data split, we can start fitting models to our data. Our company's Head of Data is all about decision tree classifiers, so let's start with one of those.\\n\",\n    \"\\n\",\n    \"Decision tree classifiers are incredibly simple in theory. In their simplest form, decision tree classifiers ask a series of Yes/No questions about the data — each time getting closer to finding out the class of each entry — until they either classify the data set perfectly or simply can't differentiate a set of entries. Think of it like a game of [Twenty Questions](https://en.wikipedia.org/wiki/Twenty_Questions), except the computer is *much*, *much* better at it.\\n\",\n    \"\\n\",\n    \"Here's an example decision tree classifier:\\n\",\n    \"\\n\",\n    \"<img src=\\\"iris_dtc.png\\\" />\\n\",\n    \"\\n\",\n    \"Notice how the classifier asks Yes/No questions about the data — whether a certain feature is <= 1.75, for example — so it can differentiate the records. This is the essence of every decision tree.\\n\",\n    \"\\n\",\n    \"The nice part about decision tree classifiers is that they are **scale-invariant**, i.e., the scale of the features does not affect their performance, unlike many Machine Learning models. In other words, it doesn't matter if our features range from 0 to 1 or 0 to 1,000; decision tree classifiers will work with them just the same.\\n\",\n    \"\\n\",\n    \"There are several [parameters](http://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html) that we can tune for decision tree classifiers, but for now let's use a basic decision tree classifier.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 24,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"0.97368421052631582\"\n      ]\n     },\n     \"execution_count\": 24,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"from sklearn.tree import DecisionTreeClassifier\\n\",\n    \"\\n\",\n    \"# Create the classifier\\n\",\n    \"decision_tree_classifier = DecisionTreeClassifier()\\n\",\n    \"\\n\",\n    \"# Train the classifier on the training set\\n\",\n    \"decision_tree_classifier.fit(training_inputs, training_classes)\\n\",\n    \"\\n\",\n    \"# Validate the classifier on the testing set using classification accuracy\\n\",\n    \"decision_tree_classifier.score(testing_inputs, testing_classes)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Heck yeah! Our model achieves 97% classification accuracy without much effort.\\n\",\n    \"\\n\",\n    \"However, there's a catch: Depending on how our training and testing set was sampled, our model can achieve anywhere from 80% to 100% accuracy:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 25,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"''\"\n      ]\n     },\n     \"execution_count\": 25,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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U1Hk+vzSEO91hM/rmc9QWLKV0EWcPoAsY5fO+Cz+sWGfdjwJO0zxO0vrXASa19OvA4R7iQe6zXyehiz6+39mvbL0yA1/H9F2Of4Ni9GLuaOucO1sXoAuBXgVNnXdMq6jwdeFlr/wnw/tY+Ffhy+/1d29rHXJ2rrPG4eW2O1TLP4S/GXs73X4z93LSey5k/EFN8gC8DvsjoCO69re/9wFvHxrwPuO6Q7X4CeKj9Aj4EbJ11Laupk9GFrX9u9dwPvHls2/e27R4DLp11LdOok9G53kda/xeAn591Laus820t4L4IfOhg8LV1v8Hoovpe4JpZ1zLpGo/D1+bHGJ06/B9GR+lbgXcA72jrw+iPNH2p1bNpWs+ln4yVpM71eo5ektQY9JLUOYNekjpn0EtS5wx6SeqcQS9JnTPoJalzBr0kde7/AC40cbqbQG9XAAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"model_accuracies = []\\n\",\n    \"\\n\",\n    \"for repetition in range(1000):\\n\",\n    \"    (training_inputs,\\n\",\n    \"     testing_inputs,\\n\",\n    \"     training_classes,\\n\",\n    \"     testing_classes) = train_test_split(all_inputs, all_labels, test_size=0.25)\\n\",\n    \"    \\n\",\n    \"    decision_tree_classifier = DecisionTreeClassifier()\\n\",\n    \"    decision_tree_classifier.fit(training_inputs, training_classes)\\n\",\n    \"    classifier_accuracy = decision_tree_classifier.score(testing_inputs, testing_classes)\\n\",\n    \"    model_accuracies.append(classifier_accuracy)\\n\",\n    \"    \\n\",\n    \"plt.hist(model_accuracies)\\n\",\n    \";\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"It's obviously a problem that our model performs quite differently depending on the subset of the data it's trained on. This phenomenon is known as **overfitting**: The model is learning to classify the training set so well that it doesn't generalize and perform well on data it hasn't seen before.\\n\",\n    \"\\n\",\n    \"### Cross-validation\\n\",\n    \"\\n\",\n    \"[[ go back to the top ]](#Table-of-contents)\\n\",\n    \"\\n\",\n    \"This problem is the main reason that most data scientists perform ***k*-fold cross-validation** on their models: Split the original data set into *k* subsets, use one of the subsets as the testing set, and the rest of the subsets are used as the training set. This process is then repeated *k* times such that each subset is used as the testing set exactly once.\\n\",\n    \"\\n\",\n    \"10-fold cross-validation is the most common choice, so let's use that here. Performing 10-fold cross-validation on our data set looks something like this:\\n\",\n    \"\\n\",\n    \"(each square is an entry in our data set)\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 26,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAA3IAAABpCAYAAAB/LhEkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAADPlJREFUeJzt3X+wbWVdx/H3x3vBgHKuwMWMy5WLgxUxgdwrA4pGKopF0G8gnIhsiIYGdGIMoqnsxzSNP6imsEgQGg1r+BU2ojLoCE2C3APyQxAkJLiAwAVESgeFvv2x1o3DZe9zDtyzz97PPu/XzJmz1tpr7/Xd873Pc9f3PGs9K1WFJEmSJKkdLxl3AJIkSZKkF8ZCTpIkSZIaYyEnSZIkSY2xkJMkSZKkxljISZIkSVJjLOQkSZIkqTEWcpIkSZLUGAs5SZIkSWqMhZwkSZIkNWakhVySw5PckeSuJKeP8liSJEmStFykqkbzwckK4E7gMGATcD1wbFXdNsd7RhPMEOvXr1/Kw0laIjMzM0t6PPsSaXmxj5E0SjMzM5uravV8+42ykDsY+KOqenu/fgZAVf35HO9Z0kJuVN9d0nglWdLj2ZdIy4t9jKRRSjJTVRvm22+Ul1buDtw3a31Tv+05kpyYZGOSjSOMRZIkSZKmxsoRfvagP1c9709KVXUOcA4s/YicJEmSJLVolCNym4A9Zq2vAR4Y4fEkSZIkaVkY5Yjc9cDeSdYB9wPHAL8y1xvWr1/Pxo1Ld4Wl17hL02mp25p9ibS82MdImgQjK+Sq6ukkvw18BlgBnFdVXxnV8SRJkiRpuRjliBxV9SngU6M8hiRJkiQtNyN9ILgkSZIkafFZyEmSJElSY0Z6aeWk82ZlSYvBvkTSKNnHSBrEETlJkiRJaoyFnCRJkiQ1xkJOkiRJkhpjISdJkiRJjbGQkyRJkqTGLOtZK5eas05JWgzT3peA/Yk0TtPex9i/aFo4IidJkiRJjbGQkyRJkqTGzHlpZZKd53q9qh5b3HAkSZIkSfOZ7x65GaCAAGuBx/vlVcC9wLqRRidJkiRJep45L62sqnVVtRfwGeBnqmrXqtoFOAK4ZCkClCRJkiQ910JnrXxdVZ20ZaWqrkjyJyOKSYvEWackLYZxtDX7E2n5mPbzFbCP0WgstJDbnOT3gY/RXWr5TuDRkUUlSZIkSRpqobNWHgusBi4FLgN267dJkiRJkpbYgkbk+tkpTx1xLJIkSZKkBZjv8QOfpLuUcqCqOnLRI5IkSZIkzWm+EbkPbMuHJ7kHeBJ4Bni6qjZsy+dpsnmzsqTFMu39iX2JND5O4KRpMWchV1Vf2LKcZHvgNf3qHVX1vQUe4yeravOLjE+SJEmStJUF3SOX5FDgAuAeugeC75Hk+Kq6enShSZIkSZIGWejjBz4IvK2q7gBI8hrgQmD9PO8r4LNJCvj7qjrnRUcqSZIkSQIWXshtt6WIA6iqO5Nst4D3vaGqHkiyG3Blkq9uPYqX5ETgRIC1a9cuNG5JkiRJWrYW+hy5jUnOTXJo//MPwMx8b6qqB/rfD9M9g+7AAfucU1UbqmrD6tWrX0jskiRJkrQsLXRE7reAk4FT6O6Ruxo4e643JNkJeElVPdkvvw34422IVXoOZ52StFicJVPSKNnHaBTme47c2qq6t6qeAj7U/yzUK4BL+39IK4F/qqpPv+hIJUmSJEnA/CNylwEHACS5uKp+YaEfXFV3A/ttQ2ySJEmSpAHmu0du9rjsXqMMRJIkSZK0MPMVcjVkWZIkSZI0JvNdWrlfkm/Rjczt0C/Tr1dVvWyk0UmSJEmSnmfOQq6qVixVIFILnHVK0mKwL5E0SvYxy8NCnyMnSZIkSZoQFnKSJEmS1BgLOUmSJElqjIWcJEmSJDXGQk6SJEmSGjPf4wckjZGzTklaDPYlkkbJPmY8HJGTJEmSpMZYyEmSJElSYyzkJEmSJKkxFnKSJEmS1BgnO5H0/6blZuVJvSlZWi6mpS8Zxj5GGq9p72MWyhE5SZIkSWqMhZwkSZIkNcZCTpIkSZIaYyEnSZIkSY2xkJMkSZKkxmSSZl5K8gjwX8CuwOYxh6NtYw7bZv7aZv7aZw7bZv7aZv7a13oOX1VVq+fbaaIKuS2SbKyqDeOOQy+eOWyb+Wub+WufOWyb+Wub+Wvfcsmhl1ZKkiRJUmMs5CRJkiSpMZNayJ0z7gC0zcxh28xf28xf+8xh28xf28xf+5ZFDifyHjlJkiRJ0nCTOiInSZIkSRpi4gq5JIcnuSPJXUlOH3c8mluSPZJ8PsntSb6S5NR++85Jrkzytf73y8cdq4ZLsiLJjUn+rV9fl+S6Pn//nGT7cceo4ZKsSnJRkq/2bfFg22A7kryn7z9vTXJhku+zDU62JOcleTjJrbO2DWxz6fx1f15zc5IDxhe5YGj+3t/3oTcnuTTJqlmvndHn744kbx9P1JptUA5nvXZakkqya78+tW1wogq5JCuAvwXeAewDHJtkn/FGpXk8DfxOVf0ocBBwcp+z04Grqmpv4Kp+XZPrVOD2Wet/AZzV5+9x4F1jiUoL9VfAp6vqR4D96HJpG2xAkt2BU4ANVbUvsAI4BtvgpDsfOHyrbcPa3DuAvfufE4EPL1GMGu58np+/K4F9q+rHgTuBMwD6c5pjgB/r33N2f76q8Tqf5+eQJHsAhwH3zto8tW1wogo54EDgrqq6u6q+C3wCOGrMMWkOVfVgVd3QLz9JdwK5O13eLuh3uwD42fFEqPkkWQP8NPCRfj3Am4GL+l3M3wRL8jLgTcC5AFX13ar6JrbBlqwEdkiyEtgReBDb4ESrqquBx7baPKzNHQX8Y3WuBVYleeXSRKpBBuWvqj5bVU/3q9cCa/rlo4BPVNVTVfV14C6681WN0ZA2CHAW8F5g9iQgU9sGJ62Q2x24b9b6pn6bGpBkT+C1wHXAK6rqQeiKPWC38UWmefwlXaf3v/36LsA3Z/2HZjucbHsBjwAf7S+P/UiSnbANNqGq7gc+QPfX4weBJ4AZbIMtGtbmPLdpz68DV/TL5q8RSY4E7q+qm7Z6aWpzOGmFXAZsc1rNBiT5fuBi4N1V9a1xx6OFSXIE8HBVzczePGBX2+HkWgkcAHy4ql4L/A9eRtmM/j6qo4B1wA8BO9FdBrQ122C77FMbkuRMuttGPr5l04DdzN+ESbIjcCbwB4NeHrBtKnI4aYXcJmCPWetrgAfGFIsWKMl2dEXcx6vqkn7zQ1uGrfvfD48rPs3pDcCRSe6hu5T5zXQjdKv6y7zAdjjpNgGbquq6fv0iusLONtiGtwJfr6pHqup7wCXA67ENtmhYm/PcphFJjgeOAI6rZ5/PZf7a8Gq6P4jd1J/TrAFuSPKDTHEOJ62Qux7Yu5+ta3u6m0svH3NMmkN/P9W5wO1V9aFZL10OHN8vHw/861LHpvlV1RlVtaaq9qRrb5+rquOAzwO/2O9m/iZYVX0DuC/JD/eb3gLchm2wFfcCByXZse9Pt+TPNtieYW3ucuBX+5nzDgKe2HIJpiZHksOB3wWOrKpvz3rpcuCYJC9Nso5uwowvjSNGDVdVt1TVblW1Z39Oswk4oP8/cmrb4MQ9EDzJT9GNCKwAzquqPxtzSJpDkkOAa4BbePYeq9+ju0/uX4C1dCcqv1RVg25K1YRIcihwWlUdkWQvuhG6nYEbgXdW1VPjjE/DJdmfbrKa7YG7gRPo/lBnG2xAkvcBR9NdznUj8Bt092/YBidUkguBQ4FdgYeAPwQuY0Cb6wv0v6GbYe/bwAlVtXEccaszJH9nAC8FHu13u7aqTur3P5Puvrmn6W4huWLrz9TSGpTDqjp31uv30M0GvHma2+DEFXKSJEmSpLlN2qWVkiRJkqR5WMhJkiRJUmMs5CRJkiSpMRZykiRJktQYCzlJkiRJaoyFnCRpqiR5JsmXk9ya5JNJVo3oOL+Z5NeS7J/k70ZxDEmShrGQkyRNm+9U1f5VtS/wGHDyiI7zRrrnaP5E/1uSpCVjISdJmmZfpHu4Num8vx+puyXJ0f32s5Mc2S9fmuS8fvldSf506w9M8p4kXwZ+DrgYeB9wpqNykqSlZCEnSZpKSVYAbwEu7zf9PLA/sB/wVuD9SV4JXE03ugZd0bdPv3wIA0baquos4DDgqqraH/haVe1TVSeN6rtIkrQ1CzlJ0rTZoR8xexTYGbiy334IcGFVPVNVDwFfAF5HV6y9Mck+wG3AQ32BdzDwH0OOcQBwU5IfAB4f3VeRJGkwCzlJ0rT5Tj9S9ipge569Ry6Ddq6q+4GXA4fTjc5dA/wy8N9V9eTsfZPs1heJHwWOA24E9usnV3n1KL6MJEmDWMhJkqZSVT0BnAKclmQ7uiLt6CQrkqwG3gR8qd/9i8C7ebaQO43Bl1U+3BeJNwAHAh8DTugnV/nPUX8nSZK2sJCTJE2tqroRuAk4BrgUuLlf/xzw3qr6Rr/rNcDKqrqLrkjbmSEzUfb33u1SVZuB1wP/PtIvIUnSAKmqcccgSZIkSXoBHJGTJEmSpMZYyEmSJElSYyzkJEmSJKkxFnKSJEmS1BgLOUmSJElqjIWcJEmSJDXGQk6SJEmSGmMhJ0mSJEmN+T/R/lzZK9lJ+wAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<Figure size 1080x1080 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"import numpy as np\\n\",\n    \"from sklearn.model_selection import StratifiedKFold\\n\",\n    \"\\n\",\n    \"def plot_cv(cv, features, labels):\\n\",\n    \"    masks = []\\n\",\n    \"    for train, test in cv.split(features, labels):\\n\",\n    \"        mask = np.zeros(len(labels), dtype=bool)\\n\",\n    \"        mask[test] = 1\\n\",\n    \"        masks.append(mask)\\n\",\n    \"    \\n\",\n    \"    plt.figure(figsize=(15, 15))\\n\",\n    \"    plt.imshow(masks, interpolation='none', cmap='gray_r')\\n\",\n    \"    plt.ylabel('Fold')\\n\",\n    \"    plt.xlabel('Row #')\\n\",\n    \"\\n\",\n    \"plot_cv(StratifiedKFold(n_splits=10), all_inputs, all_labels)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"You'll notice that we used **Stratified *k*-fold cross-validation** in the code above. Stratified *k*-fold keeps the class proportions the same across all of the folds, which is vital for maintaining a representative subset of our data set. (e.g., so we don't have 100% `Iris setosa` entries in one of the folds.)\\n\",\n    \"\\n\",\n    \"We can perform 10-fold cross-validation on our model with the following code:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 27,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"''\"\n      ]\n     },\n     \"execution_count\": 27,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAAAW4AAAEICAYAAAB/Dx7IAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAEzdJREFUeJzt3X+UZHV95vH3wwyg4iAqHVRgaImiDllFnRATRQnxNytGTDSiBiJn57DJJrqb6OL+dD2aI666G48mcWIMiQE90dUkiggsOhIMGAFlAoy/QBAEA4iIQ3ZV8LN/3O+sRdM9XT3dVdNffL/OuWdu1b1171N3qp6+9a2q7lQVkqR+7LG7A0iSlsbilqTOWNyS1BmLW5I6Y3FLUmcsbknqjMUtSZ2xuAVAki1JvpNk792d5SdFkhOSXJfkziR/neQhO1n3BUmuSLI9yd8n2TBn+aFJPp7ke0luTfLWyd8D7S4Wt0gyCxwFFHDchPaxdhLb3V2SrFnm7Q8H3gO8EjgA+GfgDxdY99HAGcApwH7Ax4C/3XFMk+wFnAd8CngYcBDwl8vJp9XN4hbArwMXA6cDJ+64MslTknxrtKSSvCjJ1ja/R5JTk1yd5NtJ/mrHWWOS2SSV5OQk32AoFZJ8qG3zu0kuaAW2Y9sPTfKxJHck+XySNyW5cGT5Y5Ocl+S2JF9O8pKF7lCSk5Jc085Av57k5SPL/lWSbW3ZVUme1K5/XHvlcXuSK5McN3Kb05P8UZJPJLkT+MUkeyd5W5JvJPmnJH+c5P5jHvOXAx+rqguqajvwn4Hjk6ybZ93nAH9XVRdW1V3AacCBwDPa8pOAG6vqHVV1Z1X936raOmYOdcjiFgzFfUabnpPkAICquhi4EzhmZN0TgDPb/O8Av8xQII8AvgO8e862nwE8jqF8AM4GHg38FHBZ2+cO7277exjDD5DRHyL7MJxVntlu+zLgD0eLf8667wSeV1XrgF8AvtiW/Srwhnaf92V4hfHtJHsynMme27b/28AZSR4z576/GVgHXMhQoIcBRwCPYijT/zKS4/YkT5ubrzkcuHzHhaq6GvhB29697lKb5l7+mXb5KcC1Sc5uwyRbkvyLBfar+4KqcvoJnoCnAT8E9m+XvwT825HlbwLe1+bXMRTrIe3yNuCXRtZ9eNvWWmCWYejl0J3se7+2zoOANe22j5mz7wvb/EsZzjpHb/8e4L/Os919gNuBFwP3n7PsHODV89zmKOBbwB4j130AeEObPx34i5Flacfip0eu+3ng62Me9/OBU+Zc903g6HnWfWzb19HAXgxn5z8CXt+Wn9uO3fPa8tcC1wB77e7Hl9NkJs+4dSJwblXd2i6fyciZbrt8fHvT8njgsqq6ri07BPhoO7O8naHI72YYs93h+h0zSdYkeUsbWrkDuLYt2h+YYSj86+e7bdvXz+3YV9vfyxnOzu+hqu5kKPpTgJuSnJXksW3xwcDV8xyHRwDXV9WPRq67juEser48M8ADgEtH8nyyXT+O7Qxn/KP2Bb43z/35EsP/ybuAmxiO11XADW2V/8PwA+7sqvoB8DbgoQyvdHQfdJ96w0hL08ZjXwKsSfKtdvXewH5JnlBVl1fVVUmuYzibGx0mgaHIXlVVn51n27NtdvTXT54AvBB4JkNpP4hheCXALcBdDG+sfaWtf/CcfX2mqp41zn2rqnOAc9p9fBPwJwxn1dcDPz3PTW4EDk6yx0h5rx/JMve+3MpQmIdX1TfHyTTHlcATdlxIcijDsf/KfCtX1YeBD7d19wNeBXy+Ld4KPHUXMqhTnnH/ZPtlhjPkDQzjtEcwnKX9HcMY8A5nMoxnPx340Mj1fwy8OckhAElmkrxwJ/tbB3wf+DbD2erv71hQVXcDHwHekOQB7Qx5NMPHgcOSvDLJnm362ST3OqtMckCS49pY9/cZzm7vbovfC/xekidn8KiW/3MMwxGva9s+GngB8MH57kgr9z8B/keSn2r7PTDJc+Zbfx5nAC9IclTL+UbgI1V1rzPutu0nt1csMwxDRB9rZ+IwfILkKUme2d5Ifg3DD5ZtY2ZRb3b3WI3T7psYXtq/fZ7rX8Iw3ru2XV7PMKZ61pz19gD+HfBlhpf4VwO/35bNMpyhrh1Z/4HA37R1r2Mo5gIe1ZbPAGcBdzCcTZ4GnD9y+8e05bcwlP+ngCPmyf9w4DPAdxnGurcAG0aWn9IybweuAJ7Yrj985HZXAS8auc3pwJvm7Od+DD98rmmZtwG/M7J8O3DUTo7/CcA3GH5g/A3wkJFlZwP/YeTyhe243cZQ3PvM2dbxwNdaji0MrwR2+2PMaTJT2n+6tOokOQ14WFWduOjK0k8Qh0q0arTPaT++DWEcCZwMfHR355JWG9+c1GqyjuEjeI8AbgbezjCEIGmEQyWS1BmHSiSpMxMZKtl///1rdnZ2EpuWpPukSy+99NaqGusLXBMp7tnZWS655JJJbFqS7pPaF93G4lCJJHXG4pakzljcktQZi1uSOmNxS1JnLG5J6sxYHwdMci3Dbya7G7irqjZOMpQkaWFL+Rz3L9aP/0qKJGk3cahEkjoz7hl3AecmKeA9VbV57gpJNgGbANavX79yCSVpiWZPPWu37Pfatxw7lf2Me8b91Kp6EsPfHfytJE+fu0JVba6qjVW1cWZm3L+XKklaqrGKu6pubP/ezPCL7Y+cZChJ0sIWLe4k+yRZt2MeeDbD3+mTJO0G44xxHwB8NMmO9c+sqk9ONJUkaUGLFndVXQM8YQpZJElj8OOAktQZi1uSOmNxS1JnLG5J6ozFLUmdsbglqTMWtyR1xuKWpM5Y3JLUGYtbkjpjcUtSZyxuSeqMxS1JnbG4JakzFrckdcbilqTOWNyS1BmLW5I6Y3FLUmcsbknqjMUtSZ2xuCWpMxa3JHXG4pakzljcktQZi1uSOmNxS1JnLG5J6ozFLUmdsbglqTMWtyR1xuKWpM6MXdxJ1iT5QpKPTzKQJGnnlnLG/Wpg26SCSJLGM1ZxJzkIOBZ472TjSJIWs3bM9f4n8Dpg3UIrJNkEbAJYv3798pNJEzB76lm7Zb/XvuXY3bJf3Tctesad5F8CN1fVpTtbr6o2V9XGqto4MzOzYgElSfc0zlDJU4HjklwLfBA4JslfTjSVJGlBixZ3Vb2+qg6qqlng14BPVdUrJp5MkjQvP8ctSZ0Z981JAKpqC7BlIkkkSWPxjFuSOmNxS1JnLG5J6ozFLUmdsbglqTMWtyR1xuKWpM5Y3JLUGYtbkjpjcUtSZyxuSeqMxS1JnbG4JakzFrckdcbilqTOWNyS1BmLW5I6Y3FLUmcsbknqjMUtSZ2xuCWpMxa3JHXG4pakzljcktQZi1uSOmNxS1JnLG5J6ozFLUmdsbglqTMWtyR1xuKWpM5Y3JLUmUWLO8n9kvxDksuTXJnkv00jmCRpfmvHWOf7wDFVtT3JnsCFSc6uqosnnE2SNI9Fi7uqCtjeLu7ZpppkKEnSwsYa406yJskXgZuB86rqc5ONJUlayFjFXVV3V9URwEHAkUl+Zu46STYluSTJJbfccstK55QkNUv6VElV3Q5sAZ47z7LNVbWxqjbOzMysUDxJ0lzjfKpkJsl+bf7+wDOBL006mCRpfuN8quThwJ8nWcNQ9H9VVR+fbCxJ0kLG+VTJVuCJU8giSRqD35yUpM5Y3JLUGYtbkjpjcUtSZyxuSeqMxS1JnbG4JakzFrckdcbilqTOWNyS1BmLW5I6Y3FLUmcsbknqjMUtSZ2xuCWpMxa3JHXG4pakzljcktQZi1uSOmNxS1JnLG5J6ozFLUmdsbglqTMWtyR1xuKWpM5Y3JLUGYtbkjpjcUtSZyxuSeqMxS1JnbG4JakzFrckdcbilqTOLFrcSQ5O8ukk25JcmeTV0wgmSZrf2jHWuQv43aq6LMk64NIk51XVVRPOJkmax6Jn3FV1U1Vd1ua/B2wDDpx0MEnS/JY0xp1kFngi8Ll5lm1KckmSS2655ZaVSSdJupexizvJA4H/Bbymqu6Yu7yqNlfVxqraODMzs5IZJUkjxiruJHsylPYZVfWRyUaSJO3MOJ8qCfCnwLaqesfkI0mSdmacM+6nAq8EjknyxTY9f8K5JEkLWPTjgFV1IZApZJEkjcFvTkpSZyxuSeqMxS1JnbG4JakzFrckdcbilqTOWNyS1BmLW5I6Y3FLUmcsbknqjMUtSZ2xuCWpMxa3JHXG4pakzljcktQZi1uSOmNxS1JnLG5J6ozFLUmdsbglqTMWtyR1xuKWpM5Y3JLUGYtbkjpjcUtSZyxuSeqMxS1JnbG4JakzFrckdcbilqTOWNyS1BmLW5I6s2hxJ3lfkpuTXDGNQJKknRvnjPt04LkTziFJGtOixV1VFwC3TSGLJGkMa1dqQ0k2AZsA1q9fv8vbmT31rJWKtCTXvuXY3bJfSVqqFXtzsqo2V9XGqto4MzOzUpuVJM3hp0okqTMWtyR1ZpyPA34AuAh4TJIbkpw8+ViSpIUs+uZkVb1sGkEkSeNxqESSOmNxS1JnLG5J6ozFLUmdsbglqTMWtyR1xuKWpM5Y3JLUGYtbkjpjcUtSZyxuSeqMxS1JnbG4JakzFrckdcbilqTOWNyS1BmLW5I6Y3FLUmcsbknqjMUtSZ2xuCWpMxa3JHXG4pakzljcktQZi1uSOmNxS1JnLG5J6ozFLUmdsbglqTMWtyR1xuKWpM5Y3JLUGYtbkjozVnEneW6SLyf5WpJTJx1KkrSwRYs7yRrg3cDzgA3Ay5JsmHQwSdL8xjnjPhL4WlVdU1U/AD4IvHCysSRJC1k7xjoHAtePXL4B+Lm5KyXZBGxqF7+f5Irlx5uenMb+wK27O8cuMPf07HLmnLbCSZamx2MNHeZeZo8cMu6K4xR35rmu7nVF1WZgM0CSS6pq47ghVoMeM4O5p6nHzGDuaZpW5nGGSm4ADh65fBBw42TiSJIWM05xfx54dJJHJtkL+DXgbycbS5K0kEWHSqrqriT/BjgHWAO8r6quXORmm1ci3JT1mBnMPU09ZgZzT9NUMqfqXsPVkqRVzG9OSlJnLG5J6sySinuxr74nWZ/k00m+kGRrkuePLHt8kouSXJnkH5PcbyXuwCRzJ9kzyZ+3vNuSvH4VZT4kyfkt75YkB40sOzHJV9t04rQyLyd3kiNGHh9bk7y0h9wjy/dN8s0k7+ohc3vMn9se11clme0k91vbY2Rbkncmme/jypPI/L4kNy/0/ZQM3tnu09YkTxpZtvLPx6oaa2J4Y/Jq4FBgL+ByYMOcdTYD/7rNbwCubfNrga3AE9rlhwJrxt33cqZl5j4B+GCbfwBwLTC7SjJ/CDixzR8DvL/NPwS4pv374Db/4FV0rBfKfRjw6Db/COAmYL/Vnntk+R8AZwLv6iEzsAV4Vpt/IPCA1Z4b+AXgs20ba4CLgKOnlPvpwJOAKxZY/nzgbIbvvTwF+Fy7fiLPx6WccY/z1fcC9m3zD+LHn/d+NrC1qi4HqKpvV9XdS9j3ciwndwH7JFkL3B/4AXDH5COPlXkDcH6b//TI8ucA51XVbVX1HeA84LlTyAzLyF1VX6mqr7b5G4GbgZmppF7e8SbJk4EDgHOnkHWHXc6c4XcNra2q8wCqantV/fN0Yi/rWBdwP4bC3xvYE/iniScGquoC4LadrPJC4C9qcDGwX5KHM6Hn41KKe76vvh84Z503AK9IcgPwCeC32/WHAZXknCSXJXndLubdFcvJ/WHgToazv28Ab6uqnf3nrZRxMl8OvLjNvwhYl+ShY952UpaT+/9LciTDk/PqCeWca5dzJ9kDeDvw2omnvKflHOvDgNuTfKQND/73DL9Mbhp2OXdVXcRQ5De16Zyq2jbhvONa6H5N5Pm4lOIe56vvLwNOr6qDGF46vL89sNcCTwNe3v59UZJf2oW8u2I5uY8E7mZ46f5I4HeTHDrJsM04mX8PeEaSLwDPAL4J3DXmbSdlObmHDQxnKe8HfqOqfjSpoHMsJ/dvAp+oquuZruVkXgsc1Zb/LMOwxUkTS3pPu5w7yaOAxzF8e/tA4JgkT59k2CVY6H5N5Pk4zu8q2WGcr76fTHsZUFUXZXgDcv92289U1a0AST7BMF50PpO3nNwnAJ+sqh8CNyf5LLCRYZxqt2ZuwwnHAyR5IPDiqvpue9Vw9Jzbbplk2BG7nLtd3hc4C/hP7eXmtCzneP88cFSS32QYK94ryfaqmvTvrV/uY+QLVXVNW/bXDOOyfzrhzMvNvQm4uKq2t2Vnt9wXTCH3Yha6X5N5Pi5hcH4tQ2E9kh+/qXD4nHXOBk5q849rwcMwKH8Zwxt8a4H/DRw7pTcVlpP73wN/1ub3Aa4CHr9KMu8P7NHm3wy8sX78ZsjX2zF/cJt/yCo61gvl3ovhB/lrppF1pXLPWeckpvfm5HKO9Zq2/ky7/GfAb3WQ+6WtO9YyjG+fD7xgio+TWRZ+c/JY7vnm5D+06yfyfFxq8OcDX2EYe/yP7bo3Ase1+Q0M7/peDnwRePbIbV8BXAlcAbx1Wgd7ObkZzqA+1HJfBbx2FWX+FeCrbZ33AnuP3PZVwNfa9Bur7FjPm7s9Pn7Yjv+O6YjVnnvONk5iSsW9Ao+RZzF80usfgdOBvVZ7boYfOO8BtrXn4zummPkDDOPqP2Q4iz4ZOAU4pS0Pwx+cubod040jt13x56NfeZekzvjNSUnqjMUtSZ2xuCWpMxa3JHXG4pakzljcktQZi1uSOvP/ANCoyZRRxeHrAAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"from sklearn.model_selection import cross_val_score\\n\",\n    \"\\n\",\n    \"decision_tree_classifier = DecisionTreeClassifier()\\n\",\n    \"\\n\",\n    \"# cross_val_score returns a list of the scores, which we can visualize\\n\",\n    \"# to get a reasonable estimate of our classifier's performance\\n\",\n    \"cv_scores = cross_val_score(decision_tree_classifier, all_inputs, all_labels, cv=10)\\n\",\n    \"plt.hist(cv_scores)\\n\",\n    \"plt.title('Average score: {}'.format(np.mean(cv_scores)))\\n\",\n    \";\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now we have a much more consistent rating of our classifier's general classification accuracy.\\n\",\n    \"\\n\",\n    \"### Parameter tuning\\n\",\n    \"\\n\",\n    \"[[ go back to the top ]](#Table-of-contents)\\n\",\n    \"\\n\",\n    \"Every Machine Learning model comes with a variety of parameters to tune, and these parameters can be vitally important to the performance of our classifier. For example, if we severely limit the depth of our decision tree classifier:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 28,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"''\"\n      ]\n     },\n     \"execution_count\": 28,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"decision_tree_classifier = DecisionTreeClassifier(max_depth=1)\\n\",\n    \"\\n\",\n    \"cv_scores = cross_val_score(decision_tree_classifier, all_inputs, all_labels, cv=10)\\n\",\n    \"plt.hist(cv_scores)\\n\",\n    \"plt.title('Average score: {}'.format(np.mean(cv_scores)))\\n\",\n    \";\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"the classification accuracy falls tremendously.\\n\",\n    \"\\n\",\n    \"Therefore, we need to find a systematic method to discover the best parameters for our model and data set.\\n\",\n    \"\\n\",\n    \"The most common method for model parameter tuning is **Grid Search**. The idea behind Grid Search is simple: explore a range of parameters and find the best-performing parameter combination. Focus your search on the best range of parameters, then repeat this process several times until the best parameters are discovered.\\n\",\n    \"\\n\",\n    \"Let's tune our decision tree classifier. We'll stick to only two parameters for now, but it's possible to simultaneously explore dozens of parameters if we want.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 29,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Best score: 0.959731543624161\\n\",\n      \"Best parameters: {'max_depth': 3, 'max_features': 3}\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from sklearn.model_selection import GridSearchCV\\n\",\n    \"\\n\",\n    \"decision_tree_classifier = DecisionTreeClassifier()\\n\",\n    \"\\n\",\n    \"parameter_grid = {'max_depth': [1, 2, 3, 4, 5],\\n\",\n    \"                  'max_features': [1, 2, 3, 4]}\\n\",\n    \"\\n\",\n    \"cross_validation = StratifiedKFold(n_splits=10)\\n\",\n    \"\\n\",\n    \"grid_search = GridSearchCV(decision_tree_classifier,\\n\",\n    \"                           param_grid=parameter_grid,\\n\",\n    \"                           cv=cross_validation)\\n\",\n    \"\\n\",\n    \"grid_search.fit(all_inputs, all_labels)\\n\",\n    \"print('Best score: {}'.format(grid_search.best_score_))\\n\",\n    \"print('Best parameters: {}'.format(grid_search.best_params_))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now let's visualize the grid search to see how the parameters interact.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 30,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"''\"\n      ]\n     },\n     \"execution_count\": 30,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 2 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"grid_visualization = grid_search.cv_results_['mean_test_score']\\n\",\n    \"grid_visualization.shape = (5, 4)\\n\",\n    \"sb.heatmap(grid_visualization, cmap='Blues', annot=True)\\n\",\n    \"plt.xticks(np.arange(4) + 0.5, grid_search.param_grid['max_features'])\\n\",\n    \"plt.yticks(np.arange(5) + 0.5, grid_search.param_grid['max_depth'])\\n\",\n    \"plt.xlabel('max_features')\\n\",\n    \"plt.ylabel('max_depth')\\n\",\n    \";\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now we have a better sense of the parameter space: We know that we need a `max_depth` of at least 2 to allow the decision tree to make more than a one-off decision.\\n\",\n    \"\\n\",\n    \"`max_features` doesn't really seem to make a big difference here as long as we have 2 of them, which makes sense since our data set has only 4 features and is relatively easy to classify. (Remember, one of our data set's classes was easily separable from the rest based on a single feature.)\\n\",\n    \"\\n\",\n    \"Let's go ahead and use a broad grid search to find the best settings for a handful of parameters.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 31,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Best score: 0.9664429530201343\\n\",\n      \"Best parameters: {'criterion': 'entropy', 'max_depth': 3, 'max_features': 3, 'splitter': 'best'}\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"decision_tree_classifier = DecisionTreeClassifier()\\n\",\n    \"\\n\",\n    \"parameter_grid = {'criterion': ['gini', 'entropy'],\\n\",\n    \"                  'splitter': ['best', 'random'],\\n\",\n    \"                  'max_depth': [1, 2, 3, 4, 5],\\n\",\n    \"                  'max_features': [1, 2, 3, 4]}\\n\",\n    \"\\n\",\n    \"cross_validation = StratifiedKFold(n_splits=10)\\n\",\n    \"\\n\",\n    \"grid_search = GridSearchCV(decision_tree_classifier,\\n\",\n    \"                           param_grid=parameter_grid,\\n\",\n    \"                           cv=cross_validation)\\n\",\n    \"\\n\",\n    \"grid_search.fit(all_inputs, all_labels)\\n\",\n    \"print('Best score: {}'.format(grid_search.best_score_))\\n\",\n    \"print('Best parameters: {}'.format(grid_search.best_params_))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now we can take the best classifier from the Grid Search and use that:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 32,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"DecisionTreeClassifier(class_weight=None, criterion='entropy', max_depth=3,\\n\",\n       \"            max_features=3, max_leaf_nodes=None, min_impurity_decrease=0.0,\\n\",\n       \"            min_impurity_split=None, min_samples_leaf=1,\\n\",\n       \"            min_samples_split=2, min_weight_fraction_leaf=0.0,\\n\",\n       \"            presort=False, random_state=None, splitter='best')\"\n      ]\n     },\n     \"execution_count\": 32,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"decision_tree_classifier = grid_search.best_estimator_\\n\",\n    \"decision_tree_classifier\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"We can even visualize the decision tree with [GraphViz](http://www.graphviz.org/) to see how it's making the classifications:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 33,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import sklearn.tree as tree\\n\",\n    \"from sklearn.externals.six import StringIO\\n\",\n    \"\\n\",\n    \"with open('iris_dtc.dot', 'w') as out_file:\\n\",\n    \"    out_file = tree.export_graphviz(decision_tree_classifier, out_file=out_file)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"<img src=\\\"iris_dtc.png\\\" />\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"(This classifier may look familiar from earlier in the notebook.)\\n\",\n    \"\\n\",\n    \"Alright! We finally have our demo classifier. Let's create some visuals of its performance so we have something to show our company's Head of Data.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 34,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"''\"\n      ]\n     },\n     \"execution_count\": 34,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"dt_scores = cross_val_score(decision_tree_classifier, all_inputs, all_labels, cv=10)\\n\",\n    \"\\n\",\n    \"sb.boxplot(dt_scores)\\n\",\n    \"sb.stripplot(dt_scores, jitter=True, color='black')\\n\",\n    \";\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Hmmm... that's a little boring by itself though. How about we compare another classifier to see how they perform?\\n\",\n    \"\\n\",\n    \"We already know from previous projects that Random Forest classifiers usually work better than individual decision trees. A common problem that decision trees face is that they're prone to overfitting: They complexify to the point that they classify the training set near-perfectly, but fail to generalize to data they have not seen before.\\n\",\n    \"\\n\",\n    \"**Random Forest classifiers** work around that limitation by creating a whole bunch of decision trees (hence \\\"forest\\\") — each trained on random subsets of training samples (drawn with replacement) and features (drawn without replacement) — and have the decision trees work together to make a more accurate classification.\\n\",\n    \"\\n\",\n    \"Let that be a lesson for us: **Even in Machine Learning, we get better results when we work together!**\\n\",\n    \"\\n\",\n    \"Let's see if a Random Forest classifier works better here.\\n\",\n    \"\\n\",\n    \"The great part about scikit-learn is that the training, testing, parameter tuning, etc. process is the same for all models, so we only need to plug in the new classifier.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 35,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Best score: 0.9664429530201343\\n\",\n      \"Best parameters: {'criterion': 'gini', 'max_features': 1, 'n_estimators': 25}\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\\n\",\n       \"            max_depth=None, max_features=1, max_leaf_nodes=None,\\n\",\n       \"            min_impurity_decrease=0.0, min_impurity_split=None,\\n\",\n       \"            min_samples_leaf=1, min_samples_split=2,\\n\",\n       \"            min_weight_fraction_leaf=0.0, n_estimators=25, n_jobs=1,\\n\",\n       \"            oob_score=False, random_state=None, verbose=0,\\n\",\n       \"            warm_start=False)\"\n      ]\n     },\n     \"execution_count\": 35,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"from sklearn.ensemble import RandomForestClassifier\\n\",\n    \"\\n\",\n    \"random_forest_classifier = RandomForestClassifier()\\n\",\n    \"\\n\",\n    \"parameter_grid = {'n_estimators': [10, 25, 50, 100],\\n\",\n    \"                  'criterion': ['gini', 'entropy'],\\n\",\n    \"                  'max_features': [1, 2, 3, 4]}\\n\",\n    \"\\n\",\n    \"cross_validation = StratifiedKFold(n_splits=10)\\n\",\n    \"\\n\",\n    \"grid_search = GridSearchCV(random_forest_classifier,\\n\",\n    \"                           param_grid=parameter_grid,\\n\",\n    \"                           cv=cross_validation)\\n\",\n    \"\\n\",\n    \"grid_search.fit(all_inputs, all_labels)\\n\",\n    \"print('Best score: {}'.format(grid_search.best_score_))\\n\",\n    \"print('Best parameters: {}'.format(grid_search.best_params_))\\n\",\n    \"\\n\",\n    \"grid_search.best_estimator_\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now we can compare their performance:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 36,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"''\"\n      ]\n     },\n     \"execution_count\": 36,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"random_forest_classifier = grid_search.best_estimator_\\n\",\n    \"\\n\",\n    \"rf_df = pd.DataFrame({'accuracy': cross_val_score(random_forest_classifier, all_inputs, all_labels, cv=10),\\n\",\n    \"                       'classifier': ['Random Forest'] * 10})\\n\",\n    \"dt_df = pd.DataFrame({'accuracy': cross_val_score(decision_tree_classifier, all_inputs, all_labels, cv=10),\\n\",\n    \"                      'classifier': ['Decision Tree'] * 10})\\n\",\n    \"both_df = rf_df.append(dt_df)\\n\",\n    \"\\n\",\n    \"sb.boxplot(x='classifier', y='accuracy', data=both_df)\\n\",\n    \"sb.stripplot(x='classifier', y='accuracy', data=both_df, jitter=True, color='black')\\n\",\n    \";\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"How about that? They both seem to perform about the same on this data set. This is probably because of the limitations of our data set: We have only 4 features to make the classification, and Random Forest classifiers excel when there's hundreds of possible features to look at. In other words, there wasn't much room for improvement with this data set.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Step 6: Reproducibility\\n\",\n    \"\\n\",\n    \"[[ go back to the top ]](#Table-of-contents)\\n\",\n    \"\\n\",\n    \"Ensuring that our work is reproducible is the last and — arguably — most important step in any analysis. **As a rule, we shouldn't place much weight on a discovery that can't be reproduced**. As such, if our analysis isn't reproducible, we might as well not have done it.\\n\",\n    \"\\n\",\n    \"Notebooks like this one go a long way toward making our work reproducible. Since we documented every step as we moved along, we have a written record of what we did and why we did it — both in text and code.\\n\",\n    \"\\n\",\n    \"Beyond recording what we did, we should also document what software and hardware we used to perform our analysis. This typically goes at the top of our notebooks so our readers know what tools to use.\\n\",\n    \"\\n\",\n    \"[Sebastian Raschka](http://sebastianraschka.com/) created a handy [notebook tool](https://github.com/rasbt/watermark) for this:\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 38,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Randal S. Olson Thu Jul 12 2018 \\n\",\n      \"\\n\",\n      \"CPython 3.6.6\\n\",\n      \"IPython 6.4.0\\n\",\n      \"\\n\",\n      \"numpy 1.12.1\\n\",\n      \"pandas 0.23.1\\n\",\n      \"sklearn 0.19.1\\n\",\n      \"matplotlib 2.2.2\\n\",\n      \"seaborn 0.8.1\\n\",\n      \"\\n\",\n      \"compiler   : GCC 4.2.1 Compatible Clang 4.0.1 (tags/RELEASE_401/final)\\n\",\n      \"system     : Darwin\\n\",\n      \"release    : 17.6.0\\n\",\n      \"machine    : x86_64\\n\",\n      \"processor  : i386\\n\",\n      \"CPU cores  : 8\\n\",\n      \"interpreter: 64bit\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%watermark -a 'Randal S. Olson' -nmv --packages numpy,pandas,sklearn,matplotlib,seaborn\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Finally, let's extract the core of our work from Steps 1-5 and turn it into a single pipeline.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 39,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[ 7.2  3.6  6.1  2.5]\\t-->\\tIris-virginica\\t(Actual: Iris-virginica)\\n\",\n      \"[ 7.4  2.8  6.1  1.9]\\t-->\\tIris-virginica\\t(Actual: Iris-virginica)\\n\",\n      \"[ 5.8  2.7  5.1  1.9]\\t-->\\tIris-virginica\\t(Actual: Iris-virginica)\\n\",\n      \"[ 6.1  2.6  5.6  1.4]\\t-->\\tIris-virginica\\t(Actual: Iris-virginica)\\n\",\n      \"[ 6.9  3.1  5.4  2.1]\\t-->\\tIris-virginica\\t(Actual: Iris-virginica)\\n\",\n      \"[ 4.9  3.   1.4  0.2]\\t-->\\tIris-setosa\\t(Actual: Iris-setosa)\\n\",\n      \"[ 5.1  3.7  1.5  0.4]\\t-->\\tIris-setosa\\t(Actual: Iris-setosa)\\n\",\n      \"[ 5.   3.2  1.2  0.2]\\t-->\\tIris-setosa\\t(Actual: Iris-setosa)\\n\",\n      \"[ 5.8  2.6  4.   1.2]\\t-->\\tIris-versicolor\\t(Actual: Iris-versicolor)\\n\",\n      \"[ 5.5  2.6  4.4  1.2]\\t-->\\tIris-versicolor\\t(Actual: Iris-versicolor)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": \"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\\n\",\n      \"text/plain\": [\n       \"<Figure size 432x288 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"%matplotlib inline\\n\",\n    \"import pandas as pd\\n\",\n    \"import seaborn as sb\\n\",\n    \"from sklearn.ensemble import RandomForestClassifier\\n\",\n    \"from sklearn.model_selection import train_test_split, cross_val_score\\n\",\n    \"\\n\",\n    \"# We can jump directly to working with the clean data because we saved our cleaned data set\\n\",\n    \"iris_data_clean = pd.read_csv('iris-data-clean.csv')\\n\",\n    \"\\n\",\n    \"# Testing our data: Our analysis will stop here if any of these assertions are wrong\\n\",\n    \"\\n\",\n    \"# We know that we should only have three classes\\n\",\n    \"assert len(iris_data_clean['class'].unique()) == 3\\n\",\n    \"\\n\",\n    \"# We know that sepal lengths for 'Iris-versicolor' should never be below 2.5 cm\\n\",\n    \"assert iris_data_clean.loc[iris_data_clean['class'] == 'Iris-versicolor', 'sepal_length_cm'].min() >= 2.5\\n\",\n    \"\\n\",\n    \"# We know that our data set should have no missing measurements\\n\",\n    \"assert len(iris_data_clean.loc[(iris_data_clean['sepal_length_cm'].isnull()) |\\n\",\n    \"                               (iris_data_clean['sepal_width_cm'].isnull()) |\\n\",\n    \"                               (iris_data_clean['petal_length_cm'].isnull()) |\\n\",\n    \"                               (iris_data_clean['petal_width_cm'].isnull())]) == 0\\n\",\n    \"\\n\",\n    \"all_inputs = iris_data_clean[['sepal_length_cm', 'sepal_width_cm',\\n\",\n    \"                             'petal_length_cm', 'petal_width_cm']].values\\n\",\n    \"\\n\",\n    \"all_labels = iris_data_clean['class'].values\\n\",\n    \"\\n\",\n    \"# This is the classifier that came out of Grid Search\\n\",\n    \"random_forest_classifier = RandomForestClassifier(criterion='gini', max_features=3, n_estimators=50)\\n\",\n    \"\\n\",\n    \"# All that's left to do now is plot the cross-validation scores\\n\",\n    \"rf_classifier_scores = cross_val_score(random_forest_classifier, all_inputs, all_labels, cv=10)\\n\",\n    \"sb.boxplot(rf_classifier_scores)\\n\",\n    \"sb.stripplot(rf_classifier_scores, jitter=True, color='black')\\n\",\n    \"\\n\",\n    \"# ...and show some of the predictions from the classifier\\n\",\n    \"(training_inputs,\\n\",\n    \" testing_inputs,\\n\",\n    \" training_classes,\\n\",\n    \" testing_classes) = train_test_split(all_inputs, all_labels, test_size=0.25)\\n\",\n    \"\\n\",\n    \"random_forest_classifier.fit(training_inputs, training_classes)\\n\",\n    \"\\n\",\n    \"for input_features, prediction, actual in zip(testing_inputs[:10],\\n\",\n    \"                                              random_forest_classifier.predict(testing_inputs[:10]),\\n\",\n    \"                                              testing_classes[:10]):\\n\",\n    \"    print('{}\\\\t-->\\\\t{}\\\\t(Actual: {})'.format(input_features, prediction, actual))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"There we have it: We have a complete and reproducible Machine Learning pipeline to demo to our company's Head of Data. We've met the success criteria that we set from the beginning (>90% accuracy), and our pipeline is flexible enough to handle new inputs or flowers when that data set is ready. Not bad for our first week on the job!\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Conclusions\\n\",\n    \"\\n\",\n    \"[[ go back to the top ]](#Table-of-contents)\\n\",\n    \"\\n\",\n    \"I hope you found this example notebook useful for your own work and learned at least one new trick by reading through it.\\n\",\n    \"\\n\",\n    \"If you've spotted any errors or would like to contribute to this notebook, please don't hestitate to get in touch. I can be reached in the following ways:\\n\",\n    \"\\n\",\n    \"* [Email](http://www.randalolson.com/contact/) me\\n\",\n    \"\\n\",\n    \"* [Tweet](https://twitter.com/randal_olson/) at me\\n\",\n    \"\\n\",\n    \"* [Submit an issue](https://github.com/rhiever/Data-Analysis-and-Machine-Learning-Projects/issues) on GitHub\\n\",\n    \"\\n\",\n    \"* Fork the [notebook repository](https://github.com/rhiever/Data-Analysis-and-Machine-Learning-Projects/), make the fix/addition yourself, then send over a pull request\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Further reading\\n\",\n    \"\\n\",\n    \"[[ go back to the top ]](#Table-of-contents)\\n\",\n    \"\\n\",\n    \"This notebook covers a broad variety of topics but skips over many of the specifics. If you're looking to dive deeper into a particular topic, here's some recommended reading.\\n\",\n    \"\\n\",\n    \"**Data Science**: William Chen compiled a [list of free books](http://www.wzchen.com/data-science-books/) for newcomers to Data Science, ranging from the basics of R & Python to Machine Learning to interviews and advice from prominent data scientists.\\n\",\n    \"\\n\",\n    \"**Machine Learning**: /r/MachineLearning has a useful [Wiki page](https://www.reddit.com/r/MachineLearning/wiki/index) containing links to online courses, books, data sets, etc. for Machine Learning. There's also a [curated list](https://github.com/josephmisiti/awesome-machine-learning) of Machine Learning frameworks, libraries, and software sorted by language.\\n\",\n    \"\\n\",\n    \"**Unit testing**: Dive Into Python 3 has a [great walkthrough](https://www.diveinto.org/python3/unit-testing.html) of unit testing in Python, how it works, and how it should be used\\n\",\n    \"\\n\",\n    \"**pandas** has [several tutorials](http://pandas.pydata.org/pandas-docs/stable/tutorials.html) covering its myriad features.\\n\",\n    \"\\n\",\n    \"**scikit-learn** has a [bunch of tutorials](http://scikit-learn.org/stable/tutorial/index.html) for those looking to learn Machine Learning in Python. Andreas Mueller's [scikit-learn workshop materials](https://github.com/amueller/scipy_2015_sklearn_tutorial) are top-notch and freely available.\\n\",\n    \"\\n\",\n    \"**matplotlib** has many [books, videos, and tutorials](http://matplotlib.org/resources/index.html) to teach plotting in Python.\\n\",\n    \"\\n\",\n    \"**Seaborn** has a [basic tutorial](http://stanford.edu/~mwaskom/software/seaborn/tutorial.html) covering most of the statistical plotting features.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Acknowledgements\\n\",\n    \"\\n\",\n    \"[[ go back to the top ]](#Table-of-contents)\\n\",\n    \"\\n\",\n    \"Many thanks to [Andreas Mueller](http://amueller.github.io/) for some of his [examples](https://github.com/amueller/scipy_2015_sklearn_tutorial) in the Machine Learning section. I drew inspiration from several of his excellent examples.\\n\",\n    \"\\n\",\n    \"The photo of a flower with annotations of the petal and sepal was taken by [Eric Guinther](https://commons.wikimedia.org/wiki/File:Petal-sepal.jpg).\\n\",\n    \"\\n\",\n    \"The photos of the various *Iris* flower types were taken by [Ken Walker](http://www.signa.org/index.pl?Display+Iris-setosa+2) and [Barry Glick](http://www.signa.org/index.pl?Display+Iris-virginica+3).\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.6.6\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n"
  },
  {
    "path": "example-data-science-notebook/iris-data-clean.csv",
    "content": "sepal_length_cm,sepal_width_cm,petal_length_cm,petal_width_cm,class\n5.1,3.5,1.4,0.2,Iris-setosa\n4.9,3.0,1.4,0.2,Iris-setosa\n4.7,3.2,1.3,0.2,Iris-setosa\n4.6,3.1,1.5,0.2,Iris-setosa\n5.0,3.6,1.4,0.2,Iris-setosa\n5.4,3.9,1.7,0.4,Iris-setosa\n4.6,3.4,1.4,0.3,Iris-setosa\n5.0,3.4,1.5,0.24999999999999997,Iris-setosa\n4.4,2.9,1.4,0.24999999999999997,Iris-setosa\n4.9,3.1,1.5,0.24999999999999997,Iris-setosa\n5.4,3.7,1.5,0.24999999999999997,Iris-setosa\n4.8,3.4,1.6,0.24999999999999997,Iris-setosa\n4.8,3.0,1.4,0.1,Iris-setosa\n5.7,3.0,1.1,0.1,Iris-setosa\n5.8,4.0,1.2,0.2,Iris-setosa\n5.7,4.4,1.5,0.4,Iris-setosa\n5.4,3.9,1.3,0.4,Iris-setosa\n5.1,3.5,1.4,0.3,Iris-setosa\n5.7,3.8,1.7,0.3,Iris-setosa\n5.1,3.8,1.5,0.3,Iris-setosa\n5.4,3.4,1.7,0.2,Iris-setosa\n5.1,3.7,1.5,0.4,Iris-setosa\n4.6,3.6,1.0,0.2,Iris-setosa\n5.1,3.3,1.7,0.5,Iris-setosa\n4.8,3.4,1.9,0.2,Iris-setosa\n5.0,3.0,1.6,0.2,Iris-setosa\n5.0,3.4,1.6,0.4,Iris-setosa\n5.2,3.5,1.5,0.2,Iris-setosa\n5.2,3.4,1.4,0.2,Iris-setosa\n4.7,3.2,1.6,0.2,Iris-setosa\n4.8,3.1,1.6,0.2,Iris-setosa\n5.4,3.4,1.5,0.4,Iris-setosa\n5.2,4.1,1.5,0.1,Iris-setosa\n5.5,4.2,1.4,0.2,Iris-setosa\n4.9,3.1,1.5,0.1,Iris-setosa\n5.0,3.2,1.2,0.2,Iris-setosa\n5.5,3.5,1.3,0.2,Iris-setosa\n4.9,3.1,1.5,0.1,Iris-setosa\n4.4,3.0,1.3,0.2,Iris-setosa\n5.1,3.4,1.5,0.2,Iris-setosa\n5.0,3.5,1.3,0.3,Iris-setosa\n4.4,3.2,1.3,0.2,Iris-setosa\n5.0,3.5,1.6,0.6,Iris-setosa\n5.1,3.8,1.9,0.4,Iris-setosa\n4.8,3.0,1.4,0.3,Iris-setosa\n5.1,3.8,1.6,0.2,Iris-setosa\n4.6,3.2,1.4,0.2,Iris-setosa\n5.3,3.7,1.5,0.2,Iris-setosa\n5.0,3.3,1.4,0.2,Iris-setosa\n7.0,3.2,4.7,1.4,Iris-versicolor\n6.4,3.2,4.5,1.5,Iris-versicolor\n6.9,3.1,4.9,1.5,Iris-versicolor\n5.5,2.3,4.0,1.3,Iris-versicolor\n6.5,2.8,4.6,1.5,Iris-versicolor\n5.7,2.8,4.5,1.3,Iris-versicolor\n6.3,3.3,4.7,1.6,Iris-versicolor\n4.9,2.4,3.3,1.0,Iris-versicolor\n6.6,2.9,4.6,1.3,Iris-versicolor\n5.2,2.7,3.9,1.4,Iris-versicolor\n5.0,2.0,3.5,1.0,Iris-versicolor\n5.9,3.0,4.2,1.5,Iris-versicolor\n6.0,2.2,4.0,1.0,Iris-versicolor\n6.1,2.9,4.7,1.4,Iris-versicolor\n5.6,2.9,3.6,1.3,Iris-versicolor\n6.7,3.1,4.4,1.4,Iris-versicolor\n5.6,3.0,4.5,1.5,Iris-versicolor\n5.8,2.7,4.1,1.0,Iris-versicolor\n6.2,2.2,4.5,1.5,Iris-versicolor\n5.6,2.5,3.9,1.1,Iris-versicolor\n5.9,3.2,4.8,1.8,Iris-versicolor\n6.1,2.8,4.0,1.3,Iris-versicolor\n6.3,2.5,4.9,1.5,Iris-versicolor\n6.1,2.8,4.7,1.2,Iris-versicolor\n6.4,2.9,4.3,1.3,Iris-versicolor\n6.6,3.0,4.4,1.4,Iris-versicolor\n6.8,2.8,4.8,1.4,Iris-versicolor\n6.7,3.0,5.0,1.7,Iris-versicolor\n6.0,2.9,4.5,1.5,Iris-versicolor\n5.7,2.6,3.5,1.0,Iris-versicolor\n5.5,2.4,3.8,1.1,Iris-versicolor\n5.5,2.4,3.7,1.0,Iris-versicolor\n5.8,2.7,3.9,1.2,Iris-versicolor\n6.0,2.8,5.1,1.6,Iris-versicolor\n5.4,3.0,4.5,1.5,Iris-versicolor\n6.0,3.4,4.5,1.6,Iris-versicolor\n6.7,3.1,4.7,1.5,Iris-versicolor\n6.3,2.3,4.4,1.3,Iris-versicolor\n5.6,3.0,4.1,1.3,Iris-versicolor\n5.5,2.5,4.0,1.3,Iris-versicolor\n5.5,2.6,4.4,1.2,Iris-versicolor\n6.1,3.0,4.6,1.4,Iris-versicolor\n5.8,2.6,4.0,1.2,Iris-versicolor\n5.0,2.3,3.3,1.0,Iris-versicolor\n5.6,2.7,4.2,1.3,Iris-versicolor\n5.7,3.0,4.2,1.2,Iris-versicolor\n5.7,2.9,4.2,1.3,Iris-versicolor\n6.2,2.9,4.3,1.3,Iris-versicolor\n5.1,2.5,3.0,1.1,Iris-versicolor\n5.7,2.8,4.1,1.3,Iris-versicolor\n6.3,3.3,6.0,2.5,Iris-virginica\n5.8,2.7,5.1,1.9,Iris-virginica\n7.1,3.0,5.9,2.1,Iris-virginica\n6.3,2.9,5.6,1.8,Iris-virginica\n6.5,3.0,5.8,2.2,Iris-virginica\n7.6,3.0,6.6,2.1,Iris-virginica\n4.9,2.5,4.5,1.7,Iris-virginica\n7.3,2.9,6.3,1.8,Iris-virginica\n6.7,2.5,5.8,1.8,Iris-virginica\n7.2,3.6,6.1,2.5,Iris-virginica\n6.5,3.2,5.1,2.0,Iris-virginica\n6.4,2.7,5.3,1.9,Iris-virginica\n6.8,3.0,5.5,2.1,Iris-virginica\n5.7,2.5,5.0,2.0,Iris-virginica\n5.8,2.8,5.1,2.4,Iris-virginica\n6.4,3.2,5.3,2.3,Iris-virginica\n6.5,3.0,5.5,1.8,Iris-virginica\n7.7,3.8,6.7,2.2,Iris-virginica\n7.7,2.6,6.9,2.3,Iris-virginica\n6.0,2.2,5.0,1.5,Iris-virginica\n6.9,3.2,5.7,2.3,Iris-virginica\n5.6,2.8,4.9,2.0,Iris-virginica\n5.6,2.8,6.7,2.0,Iris-virginica\n6.3,2.7,4.9,1.8,Iris-virginica\n6.7,3.3,5.7,2.1,Iris-virginica\n7.2,3.2,6.0,1.8,Iris-virginica\n6.2,2.8,4.8,1.8,Iris-virginica\n6.1,3.0,4.9,1.8,Iris-virginica\n6.4,2.8,5.6,2.1,Iris-virginica\n7.2,3.0,5.8,1.6,Iris-virginica\n7.4,2.8,6.1,1.9,Iris-virginica\n7.9,3.8,6.4,2.0,Iris-virginica\n6.4,2.8,5.6,2.2,Iris-virginica\n6.3,2.8,5.1,1.5,Iris-virginica\n6.1,2.6,5.6,1.4,Iris-virginica\n7.7,3.0,6.1,2.3,Iris-virginica\n6.3,3.4,5.6,2.4,Iris-virginica\n6.4,3.1,5.5,1.8,Iris-virginica\n6.0,3.0,4.8,1.8,Iris-virginica\n6.9,3.1,5.4,2.1,Iris-virginica\n6.7,3.1,5.6,2.4,Iris-virginica\n6.9,3.1,5.1,2.3,Iris-virginica\n5.8,2.7,5.1,1.9,Iris-virginica\n6.8,3.2,5.9,2.3,Iris-virginica\n6.7,3.3,5.7,2.5,Iris-virginica\n6.7,3.0,5.2,2.3,Iris-virginica\n6.3,2.5,5.0,2.3,Iris-virginica\n6.5,3.0,5.2,2.0,Iris-virginica\n6.2,3.4,5.4,2.3,Iris-virginica\n5.9,3.0,5.1,1.8,Iris-virginica\n"
  },
  {
    "path": "example-data-science-notebook/iris-data.csv",
    "content": "sepal_length_cm,sepal_width_cm,petal_length_cm,petal_width_cm,class\r5.1,3.5,1.4,0.2,Iris-setosa\r4.9,3,1.4,0.2,Iris-setosa\r4.7,3.2,1.3,0.2,Iris-setosa\r4.6,3.1,1.5,0.2,Iris-setosa\r5,3.6,1.4,0.2,Iris-setosa\r5.4,3.9,1.7,0.4,Iris-setosa\r4.6,3.4,1.4,0.3,Iris-setosa\r5,3.4,1.5,NA,Iris-setosa\r4.4,2.9,1.4,NA,Iris-setosa\r4.9,3.1,1.5,NA,Iris-setosa\r5.4,3.7,1.5,NA,Iris-setosa\r4.8,3.4,1.6,NA,Iris-setosa\r4.8,3,1.4,0.1,Iris-setosa\r5.7,3,1.1,0.1,Iris-setosa\r5.8,4,1.2,0.2,Iris-setosa\r5.7,4.4,1.5,0.4,Iris-setosa\r5.4,3.9,1.3,0.4,Iris-setosa\r5.1,3.5,1.4,0.3,Iris-setosa\r5.7,3.8,1.7,0.3,Iris-setossa\r5.1,3.8,1.5,0.3,Iris-setosa\r5.4,3.4,1.7,0.2,Iris-setosa\r5.1,3.7,1.5,0.4,Iris-setosa\r4.6,3.6,1,0.2,Iris-setosa\r5.1,3.3,1.7,0.5,Iris-setosa\r4.8,3.4,1.9,0.2,Iris-setosa\r5,3,1.6,0.2,Iris-setosa\r5,3.4,1.6,0.4,Iris-setosa\r5.2,3.5,1.5,0.2,Iris-setosa\r5.2,3.4,1.4,0.2,Iris-setosa\r4.7,3.2,1.6,0.2,Iris-setosa\r4.8,3.1,1.6,0.2,Iris-setosa\r5.4,3.4,1.5,0.4,Iris-setosa\r5.2,4.1,1.5,0.1,Iris-setosa\r5.5,4.2,1.4,0.2,Iris-setosa\r4.9,3.1,1.5,0.1,Iris-setosa\r5,3.2,1.2,0.2,Iris-setosa\r5.5,3.5,1.3,0.2,Iris-setosa\r4.9,3.1,1.5,0.1,Iris-setosa\r4.4,3,1.3,0.2,Iris-setosa\r5.1,3.4,1.5,0.2,Iris-setosa\r5,3.5,1.3,0.3,Iris-setosa\r4.5,2.3,1.3,0.3,Iris-setosa\r4.4,3.2,1.3,0.2,Iris-setosa\r5,3.5,1.6,0.6,Iris-setosa\r5.1,3.8,1.9,0.4,Iris-setosa\r4.8,3,1.4,0.3,Iris-setosa\r5.1,3.8,1.6,0.2,Iris-setosa\r4.6,3.2,1.4,0.2,Iris-setosa\r5.3,3.7,1.5,0.2,Iris-setosa\r5,3.3,1.4,0.2,Iris-setosa\r7,3.2,4.7,1.4,Iris-versicolor\r6.4,3.2,4.5,1.5,Iris-versicolor\r6.9,3.1,4.9,1.5,Iris-versicolor\r5.5,2.3,4,1.3,Iris-versicolor\r6.5,2.8,4.6,1.5,Iris-versicolor\r5.7,2.8,4.5,1.3,Iris-versicolor\r6.3,3.3,4.7,1.6,Iris-versicolor\r4.9,2.4,3.3,1,Iris-versicolor\r6.6,2.9,4.6,1.3,Iris-versicolor\r5.2,2.7,3.9,1.4,Iris-versicolor\r5,2,3.5,1,Iris-versicolor\r5.9,3,4.2,1.5,Iris-versicolor\r6,2.2,4,1,Iris-versicolor\r6.1,2.9,4.7,1.4,Iris-versicolor\r5.6,2.9,3.6,1.3,Iris-versicolor\r6.7,3.1,4.4,1.4,Iris-versicolor\r5.6,3,4.5,1.5,Iris-versicolor\r5.8,2.7,4.1,1,Iris-versicolor\r6.2,2.2,4.5,1.5,Iris-versicolor\r5.6,2.5,3.9,1.1,Iris-versicolor\r5.9,3.2,4.8,1.8,Iris-versicolor\r6.1,2.8,4,1.3,Iris-versicolor\r6.3,2.5,4.9,1.5,Iris-versicolor\r6.1,2.8,4.7,1.2,Iris-versicolor\r6.4,2.9,4.3,1.3,Iris-versicolor\r6.6,3,4.4,1.4,Iris-versicolor\r6.8,2.8,4.8,1.4,Iris-versicolor\r0.067,3,5,1.7,Iris-versicolor\r0.06,2.9,4.5,1.5,Iris-versicolor\r0.057,2.6,3.5,1,Iris-versicolor\r0.055,2.4,3.8,1.1,Iris-versicolor\r0.055,2.4,3.7,1,Iris-versicolor\r5.8,2.7,3.9,1.2,Iris-versicolor\r6,2.8,5.1,1.6,Iris-versicolor\r5.4,3,4.5,1.5,Iris-versicolor\r6,3.4,4.5,1.6,Iris-versicolor\r6.7,3.1,4.7,1.5,Iris-versicolor\r6.3,2.3,4.4,1.3,Iris-versicolor\r5.6,3,4.1,1.3,Iris-versicolor\r5.5,2.5,4,1.3,Iris-versicolor\r5.5,2.6,4.4,1.2,Iris-versicolor\r6.1,3,4.6,1.4,Iris-versicolor\r5.8,2.6,4,1.2,Iris-versicolor\r5,2.3,3.3,1,Iris-versicolor\r5.6,2.7,4.2,1.3,Iris-versicolor\r5.7,3,4.2,1.2,versicolor\r5.7,2.9,4.2,1.3,versicolor\r6.2,2.9,4.3,1.3,versicolor\r5.1,2.5,3,1.1,versicolor\r5.7,2.8,4.1,1.3,versicolor\r6.3,3.3,6,2.5,Iris-virginica\r5.8,2.7,5.1,1.9,Iris-virginica\r7.1,3,5.9,2.1,Iris-virginica\r6.3,2.9,5.6,1.8,Iris-virginica\r6.5,3,5.8,2.2,Iris-virginica\r7.6,3,6.6,2.1,Iris-virginica\r4.9,2.5,4.5,1.7,Iris-virginica\r7.3,2.9,6.3,1.8,Iris-virginica\r6.7,2.5,5.8,1.8,Iris-virginica\r7.2,3.6,6.1,2.5,Iris-virginica\r6.5,3.2,5.1,2,Iris-virginica\r6.4,2.7,5.3,1.9,Iris-virginica\r6.8,3,5.5,2.1,Iris-virginica\r5.7,2.5,5,2,Iris-virginica\r5.8,2.8,5.1,2.4,Iris-virginica\r6.4,3.2,5.3,2.3,Iris-virginica\r6.5,3,5.5,1.8,Iris-virginica\r7.7,3.8,6.7,2.2,Iris-virginica\r7.7,2.6,6.9,2.3,Iris-virginica\r6,2.2,5,1.5,Iris-virginica\r6.9,3.2,5.7,2.3,Iris-virginica\r5.6,2.8,4.9,2,Iris-virginica\r5.6,2.8,6.7,2,Iris-virginica\r6.3,2.7,4.9,1.8,Iris-virginica\r6.7,3.3,5.7,2.1,Iris-virginica\r7.2,3.2,6,1.8,Iris-virginica\r6.2,2.8,4.8,1.8,Iris-virginica\r6.1,3,4.9,1.8,Iris-virginica\r6.4,2.8,5.6,2.1,Iris-virginica\r7.2,3,5.8,1.6,Iris-virginica\r7.4,2.8,6.1,1.9,Iris-virginica\r7.9,3.8,6.4,2,Iris-virginica\r6.4,2.8,5.6,2.2,Iris-virginica\r6.3,2.8,5.1,1.5,Iris-virginica\r6.1,2.6,5.6,1.4,Iris-virginica\r7.7,3,6.1,2.3,Iris-virginica\r6.3,3.4,5.6,2.4,Iris-virginica\r6.4,3.1,5.5,1.8,Iris-virginica\r6,3,4.8,1.8,Iris-virginica\r6.9,3.1,5.4,2.1,Iris-virginica\r6.7,3.1,5.6,2.4,Iris-virginica\r6.9,3.1,5.1,2.3,Iris-virginica\r5.8,2.7,5.1,1.9,Iris-virginica\r6.8,3.2,5.9,2.3,Iris-virginica\r6.7,3.3,5.7,2.5,Iris-virginica\r6.7,3,5.2,2.3,Iris-virginica\r6.3,2.5,5,2.3,Iris-virginica\r6.5,3,5.2,2,Iris-virginica\r6.2,3.4,5.4,2.3,Iris-virginica\r5.9,3,5.1,1.8,Iris-virginica"
  },
  {
    "path": "example-data-science-notebook/iris_dtc.dot",
    "content": "digraph Tree {\nnode [shape=box] ;\n0 [label=\"X[2] <= 2.45\\nentropy = 1.585\\nsamples = 149\\nvalue = [49, 50, 50]\"] ;\n1 [label=\"entropy = 0.0\\nsamples = 49\\nvalue = [49, 0, 0]\"] ;\n0 -> 1 [labeldistance=2.5, labelangle=45, headlabel=\"True\"] ;\n2 [label=\"X[3] <= 1.75\\nentropy = 1.0\\nsamples = 100\\nvalue = [0, 50, 50]\"] ;\n0 -> 2 [labeldistance=2.5, labelangle=-45, headlabel=\"False\"] ;\n3 [label=\"X[3] <= 1.35\\nentropy = 0.445\\nsamples = 54\\nvalue = [0, 49, 5]\"] ;\n2 -> 3 ;\n4 [label=\"entropy = 0.0\\nsamples = 28\\nvalue = [0, 28, 0]\"] ;\n3 -> 4 ;\n5 [label=\"entropy = 0.706\\nsamples = 26\\nvalue = [0, 21, 5]\"] ;\n3 -> 5 ;\n6 [label=\"X[2] <= 4.85\\nentropy = 0.151\\nsamples = 46\\nvalue = [0, 1, 45]\"] ;\n2 -> 6 ;\n7 [label=\"entropy = 0.918\\nsamples = 3\\nvalue = [0, 1, 2]\"] ;\n6 -> 7 ;\n8 [label=\"entropy = 0.0\\nsamples = 43\\nvalue = [0, 0, 43]\"] ;\n6 -> 8 ;\n}"
  },
  {
    "path": "follower-factory/Twitter Follower Factory.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Twitter Follower Factory analysis script\\n\",\n    \"\\n\",\n    \"This Python script was inspired by the New York Times' [Follower Factory article](https://www.nytimes.com/interactive/2018/01/27/technology/social-media-bots.html), which showed that Twitter accounts can be analyzed to discover whether someone has purchased fake followers for that account. This script enables you to analyze any Twitter account and generate a similar scatter plot visualization of their followers. You can typically tell when someone purchased fake followers for an account when there are solid lines of followers that were created around the same time, as shown in the New York Times article.\\n\",\n    \"\\n\",\n    \"**Note that you can [execute the code in this notebook on Binder](https://mybinder.org/v2/gh/rhiever/Data-Analysis-and-Machine-Learning-Projects/master?filepath=follower-factory%2FTwitter%2520Follower%2520Factory.ipynb) - no local installation required.**\\n\",\n    \"\\n\",\n    \"## Dependencies\\n\",\n    \"\\n\",\n    \"You will need to install Python's [python-twitter](https://github.com/sixohsix/twitter/) library:\\n\",\n    \"\\n\",\n    \"    pip install twitter\\n\",\n    \"\\n\",\n    \"You will also need to create an app account on https://dev.twitter.com/apps\\n\",\n    \"\\n\",\n    \"1. Sign in with your Twitter account\\n\",\n    \"2. Create a new app account\\n\",\n    \"3. Modify the settings for that app account to allow read & write\\n\",\n    \"4. Generate a new OAuth token with those permissions\\n\",\n    \"\\n\",\n    \"Following these steps will create 4 tokens that you will need to place in the script, as discussed below.\\n\",\n    \"\\n\",\n    \"## Usage\\n\",\n    \"\\n\",\n    \"Before you can run this script, you need to fill in the following 5 variables in the file:\\n\",\n    \"\\n\",\n    \"```Python\\n\",\n    \"USER_TO_ANALYZE = ''\\n\",\n    \"OAUTH_TOKEN = ''\\n\",\n    \"OAUTH_SECRET = ''\\n\",\n    \"CONSUMER_KEY = ''\\n\",\n    \"CONSUMER_SECRET = ''\\n\",\n    \"```\\n\",\n    \"\\n\",\n    \"`USER_TO_ANALYZE` is straightforward: If you want to analyze my Twitter account, you would enter `randal_olson` in between the two single quotes.\\n\",\n    \"\\n\",\n    \"Once you've created the Twitter app account as described under the Dependencies section, you should be able to find the OAUTH and CONSUMER keys on the \\\"Key and Access Tokens\\\" section, as shown in the image below.\\n\",\n    \"\\n\",\n    \"<img src='images/twitter-app-example.png' />\\n\",\n    \"\\n\",\n    \"Once you've filled out those 5 variables, the script below will handle the rest.\\n\",\n    \"\\n\",\n    \"If you're analyzing an account with 100,000s or more of followers, running the script may take a while as the script follows the Twitter API's usage restrictions. A progress bar will keep you updated on the progress of the analysis.\\n\",\n    \"\\n\",\n    \"Once the script finishes, it will save an image to the directory that you ran the script in. That image will contain the scatter plot visualization for the account you targeted.\\n\",\n    \"\\n\",\n    \"## Security concerns\\n\",\n    \"\\n\",\n    \"Although you should be fine with this notebook, you should beware of placing private API keys and security-related information into scripts like this one. If you're particularly paranoid about the security of your Twitter account, you are welcome to review the code below and all of the open source libraries that it relies upon.\\n\",\n    \"\\n\",\n    \"If it looks like anything fishy is going on with the API keys, please don't hesitate to [file an issue on this repository](https://github.com/rhiever/Data-Analysis-and-Machine-Learning-Projects/issues/new) and raise your concerns.\\n\",\n    \"\\n\",\n    \"## Questions and Comments\\n\",\n    \"\\n\",\n    \"If you have any questions or comments, please [file an issue on this repository](https://github.com/rhiever/Data-Analysis-and-Machine-Learning-Projects/issues/new) and I'll get back to you as soon as I can. If you'd like to submit a pull request to improve this script in any way, please file an issue first to discuss your change(s). I'm generally open to accepting pull requests on this repository.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"%matplotlib inline\\n\",\n    \"from __future__ import print_function\\n\",\n    \"import time\\n\",\n    \"from datetime import datetime\\n\",\n    \"import os\\n\",\n    \"\\n\",\n    \"from twitter import Twitter, OAuth, TwitterHTTPError\\n\",\n    \"from tqdm import tqdm_notebook as tqdm\\n\",\n    \"import pandas as pd\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"\\n\",\n    \"USER_TO_ANALYZE = ''\\n\",\n    \"OAUTH_TOKEN = ''\\n\",\n    \"OAUTH_SECRET = ''\\n\",\n    \"CONSUMER_KEY = ''\\n\",\n    \"CONSUMER_SECRET = ''\\n\",\n    \"\\n\",\n    \"twitter_connection = Twitter(auth=OAuth(OAUTH_TOKEN, OAUTH_SECRET, CONSUMER_KEY, CONSUMER_SECRET))\\n\",\n    \"\\n\",\n    \"pbar = tqdm()\\n\",\n    \"pbar.write('Collecting list of Twitter followers for @{}'.format(USER_TO_ANALYZE))\\n\",\n    \"\\n\",\n    \"rl_status = twitter_connection.application.rate_limit_status()\\n\",\n    \"if rl_status['resources']['followers']['/followers/ids']['remaining'] <= 0:\\n\",\n    \"    sleep_until = rl_status['resources']['followers']['/followers/ids']['reset']\\n\",\n    \"    sleep_for = int(sleep_until - time.time()) + 10 # wait a little extra time just in case\\n\",\n    \"    if sleep_for > 0:\\n\",\n    \"        pbar.write('Sleeping for {} seconds...'.format(sleep_for))\\n\",\n    \"        time.sleep(sleep_for)\\n\",\n    \"        pbar.write('Awake!')\\n\",\n    \"\\n\",\n    \"followers_status = twitter_connection.followers.ids(screen_name=USER_TO_ANALYZE)\\n\",\n    \"followers = followers_status['ids']\\n\",\n    \"next_cursor = followers_status['next_cursor']\\n\",\n    \"pbar.update(len(followers))\\n\",\n    \"\\n\",\n    \"while next_cursor != 0:\\n\",\n    \"    rl_status = twitter_connection.application.rate_limit_status()\\n\",\n    \"    if rl_status['resources']['followers']['/followers/ids']['remaining'] <= 0:\\n\",\n    \"        sleep_until = rl_status['resources']['followers']['/followers/ids']['reset']\\n\",\n    \"        sleep_for = int(sleep_until - time.time()) + 10 # wait a little extra time just in case\\n\",\n    \"        if sleep_for > 0:\\n\",\n    \"            pbar.write('Sleeping for {} seconds...'.format(sleep_for))\\n\",\n    \"            time.sleep(sleep_for)\\n\",\n    \"            pbar.write('Awake!')\\n\",\n    \"\\n\",\n    \"    followers_status = twitter_connection.followers.ids(screen_name=USER_TO_ANALYZE, cursor=next_cursor)\\n\",\n    \"    # Prevent duplicate Twitter user IDs\\n\",\n    \"    more_followers = [follower for follower in followers_status['ids'] if follower not in followers]\\n\",\n    \"    followers += more_followers\\n\",\n    \"    next_cursor = followers_status['next_cursor']\\n\",\n    \"\\n\",\n    \"    pbar.update(len(more_followers))\\n\",\n    \"\\n\",\n    \"pbar.close()\\n\",\n    \"\\n\",\n    \"pbar = tqdm(total=len(followers))\\n\",\n    \"pbar.write('Collecting join dates of Twitter followers for @{}'.format(USER_TO_ANALYZE))\\n\",\n    \"followers_created = list()\\n\",\n    \"\\n\",\n    \"rl_status = twitter_connection.application.rate_limit_status()\\n\",\n    \"remaining_calls = rl_status['resources']['users']['/users/lookup']['remaining']\\n\",\n    \"\\n\",\n    \"for base_index in range(0, len(followers), 100):\\n\",\n    \"    if remaining_calls == 50:\\n\",\n    \"        # Update the remaining calls count just in case\\n\",\n    \"        rl_status = twitter_connection.application.rate_limit_status()\\n\",\n    \"        remaining_calls = rl_status['resources']['users']['/users/lookup']['remaining']\\n\",\n    \"\\n\",\n    \"    if remaining_calls <= 0:\\n\",\n    \"        sleep_until = rl_status['resources']['users']['/users/lookup']['reset']\\n\",\n    \"        sleep_for = int(sleep_until - time.time()) + 10 # wait a little extra time just in case\\n\",\n    \"        if sleep_for > 0:\\n\",\n    \"            pbar.write('Sleeping for {} seconds...'.format(sleep_for))\\n\",\n    \"            time.sleep(sleep_for)\\n\",\n    \"            pbar.write('Awake!')\\n\",\n    \"            rl_status = twitter_connection.application.rate_limit_status()\\n\",\n    \"            remaining_calls = rl_status['resources']['users']['/users/lookup']['remaining']\\n\",\n    \"\\n\",\n    \"    remaining_calls -= 1\\n\",\n    \"\\n\",\n    \"    # 100 users per request\\n\",\n    \"    user_info = twitter_connection.users.lookup(user_id=list(followers[base_index:base_index + 100]))\\n\",\n    \"    followers_created += [x['created_at'] for x in user_info]\\n\",\n    \"\\n\",\n    \"    pbar.update(len(followers[base_index:base_index + 100]))\\n\",\n    \"\\n\",\n    \"pbar.close()\\n\",\n    \"print('Creating Follower Factory visualization for @{}'.format(USER_TO_ANALYZE))\\n\",\n    \"\\n\",\n    \"days_since_2006 = [(x.year - 2006) * 365 + x.dayofyear for x in pd.to_datetime(followers_created)]\\n\",\n    \"\\n\",\n    \"mpl_style_url = 'https://gist.githubusercontent.com/rhiever/d0a7332fe0beebfdc3d5/raw/1b807615235ff6f4c919b5b70b01a609619e1e9c/tableau10.mplstyle'\\n\",\n    \"alpha = 0.1 * min(9, 80000. / len(days_since_2006))\\n\",\n    \"with plt.style.context(mpl_style_url):\\n\",\n    \"    plt.figure(figsize=(9, 12))\\n\",\n    \"    plt.scatter(x=range(len(days_since_2006)), y=days_since_2006[::-1], s=2, alpha=alpha)\\n\",\n    \"    plt.yticks(range(0, 365 * (datetime.today().year + 1 - 2006), 365), range(2006, datetime.today().year + 1))\\n\",\n    \"    plt.xlabel('Follower count for @{}'.format(USER_TO_ANALYZE))\\n\",\n    \"    plt.ylabel('Date follower joined Twitter')\\n\",\n    \"    plt.savefig('{}-follower-factory.png'.format(USER_TO_ANALYZE))\\n\",\n    \"\\n\",\n    \"print('Follower Factory visualization saved to {}'.format(os.getcwd()))\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.6.5\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "optimal-road-trip/Computing the optimal road trip across the U.S..ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Computing the optimal road trip across the U.S.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"This notebook provides the methodology and code used in the blog post, [Computing the optimal road trip across the U.S.](http://www.randalolson.com/2015/03/08/computing-the-optimal-road-trip-across-the-u-s/)\\n\",\n    \"\\n\",\n    \"### Notebook by [Randal S. Olson](http://www.randalolson.com)\\n\",\n    \"\\n\",\n    \"Please see the [repository README file](https://github.com/rhiever/Data-Analysis-and-Machine-Learning-Projects#license) for the licenses and usage terms for the instructional material and code in this notebook. In general, I have licensed this material so that it is as widely useable and shareable as possible.\\n\",\n    \"\\n\",\n    \"The code in this notebook is also available as a single Python script [here](https://github.com/rhiever/Data-Analysis-and-Machine-Learning-Projects/blob/master/optimal-road-trip/OptimalRoadTripHtmlSaveAndDisplay.py) courtesy of [Andrew Liesinger](https://github.com/AndrewLiesinger).\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Required Python libraries\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"If you don't have Python on your computer, you can use the [Anaconda Python distribution](https://www.anaconda.com/download/) to install most of the Python packages you need. Anaconda provides a simple double-click installer for your convenience.\\n\",\n    \"\\n\",\n    \"This code uses base Python libraries except for `googlemaps` and `pandas` packages. You can install these packages using `pip` by typing the following commands into your command line:\\n\",\n    \"\\n\",\n    \"> pip install pandas\\n\",\n    \"\\n\",\n    \"> pip install googlemaps\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Construct a list of road trip waypoints\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The first step is to decide where you want to stop on your road trip.\\n\",\n    \"\\n\",\n    \"Make sure you look all of the locations up on [Google Maps](http://maps.google.com) first so you have the correct address, city, state, etc. If the text you use to look up the location doesn't work on Google Maps, then it won't work here either.\\n\",\n    \"\\n\",\n    \"Add all of your waypoints to the list below. Make sure they're formatted the same way as in the example below.\\n\",\n    \"\\n\",\n    \"*Technical note: Due to daily usage limitations of the Google Maps API, you can only have a maximum of 70 waypoints. You will have to pay Google for an increased API limit if you want to add more waypoints.*\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"all_waypoints = [\\\"USS Alabama, Battleship Parkway, Mobile, AL\\\",\\n\",\n    \"                 \\\"Grand Canyon National Park, Arizona\\\",\\n\",\n    \"                 \\\"Toltec Mounds, Scott, AR\\\",\\n\",\n    \"                 \\\"San Andreas Fault, San Benito County, CA\\\",\\n\",\n    \"                 \\\"Cable Car Museum, 94108, 1201 Mason St, San Francisco, CA 94108\\\",\\n\",\n    \"                 \\\"Pikes Peak, Colorado\\\",\\n\",\n    \"                 \\\"The Mark Twain House & Museum, Farmington Avenue, Hartford, CT\\\",\\n\",\n    \"                 \\\"New Castle Historic District, Delaware\\\",\\n\",\n    \"                 \\\"White House, Pennsylvania Avenue Northwest, Washington, DC\\\",\\n\",\n    \"                 \\\"Cape Canaveral, FL\\\",\\n\",\n    \"                 \\\"Okefenokee Swamp Park, Okefenokee Swamp Park Road, Waycross, GA\\\",\\n\",\n    \"                 \\\"Craters of the Moon National Monument & Preserve, Arco, ID\\\",\\n\",\n    \"                 \\\"Lincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL\\\",\\n\",\n    \"                 \\\"West Baden Springs Hotel, West Baden Avenue, West Baden Springs, IN\\\",\\n\",\n    \"                 \\\"Terrace Hill, Grand Avenue, Des Moines, IA\\\",\\n\",\n    \"                 \\\"C. W. Parker Carousel Museum, South Esplanade Street, Leavenworth, KS\\\",\\n\",\n    \"                 \\\"Mammoth Cave National Park, Mammoth Cave Pkwy, Mammoth Cave, KY\\\",\\n\",\n    \"                 \\\"French Quarter, New Orleans, LA\\\",\\n\",\n    \"                 \\\"Acadia National Park, Maine\\\",\\n\",\n    \"                 \\\"Maryland State House, 100 State Cir, Annapolis, MD 21401\\\",\\n\",\n    \"                 \\\"USS Constitution, Boston, MA\\\",\\n\",\n    \"                 \\\"Olympia Entertainment, Woodward Avenue, Detroit, MI\\\",\\n\",\n    \"                 \\\"Fort Snelling, Tower Avenue, Saint Paul, MN\\\",\\n\",\n    \"                 \\\"Vicksburg National Military Park, Clay Street, Vicksburg, MS\\\",\\n\",\n    \"                 \\\"Gateway Arch, Washington Avenue, St Louis, MO\\\",\\n\",\n    \"                 \\\"Glacier National Park, West Glacier, MT\\\",\\n\",\n    \"                 \\\"Ashfall Fossil Bed, Royal, NE\\\",\\n\",\n    \"                 \\\"Hoover Dam, NV\\\",\\n\",\n    \"                 \\\"Omni Mount Washington Resort, Mount Washington Hotel Road, Bretton Woods, NH\\\",\\n\",\n    \"                 \\\"Congress Hall, Congress Place, Cape May, NJ 08204\\\",\\n\",\n    \"                 \\\"Carlsbad Caverns National Park, Carlsbad, NM\\\",\\n\",\n    \"                 \\\"Statue of Liberty, Liberty Island, NYC, NY\\\",\\n\",\n    \"                 \\\"Wright Brothers National Memorial Visitor Center, Manteo, NC\\\",\\n\",\n    \"                 \\\"Fort Union Trading Post National Historic Site, Williston, North Dakota 1804, ND\\\",\\n\",\n    \"                 \\\"Spring Grove Cemetery, Spring Grove Avenue, Cincinnati, OH\\\",\\n\",\n    \"                 \\\"Chickasaw National Recreation Area, 1008 W 2nd St, Sulphur, OK 73086\\\",\\n\",\n    \"                 \\\"Columbia River Gorge National Scenic Area, Oregon\\\",\\n\",\n    \"                 \\\"Liberty Bell, 6th Street, Philadelphia, PA\\\",\\n\",\n    \"                 \\\"The Breakers, Ochre Point Avenue, Newport, RI\\\",\\n\",\n    \"                 \\\"Fort Sumter National Monument, Sullivan's Island, SC\\\",\\n\",\n    \"                 \\\"Mount Rushmore National Memorial, South Dakota 244, Keystone, SD\\\",\\n\",\n    \"                 \\\"Graceland, Elvis Presley Boulevard, Memphis, TN\\\",\\n\",\n    \"                 \\\"The Alamo, Alamo Plaza, San Antonio, TX\\\",\\n\",\n    \"                 \\\"Bryce Canyon National Park, Hwy 63, Bryce, UT\\\",\\n\",\n    \"                 \\\"Shelburne Farms, Harbor Road, Shelburne, VT\\\",\\n\",\n    \"                 \\\"Mount Vernon, Fairfax County, Virginia\\\",\\n\",\n    \"                 \\\"Hanford Site, Benton County, WA\\\",\\n\",\n    \"                 \\\"Lost World Caverns, Lewisburg, WV\\\",\\n\",\n    \"                 \\\"Taliesin, County Road C, Spring Green, Wisconsin\\\",\\n\",\n    \"                 \\\"Yellowstone National Park, WY 82190\\\"]\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Next you'll have to register this script with the Google Maps API so they know who's hitting their servers with hundreds of Google Maps routing requests.\\n\",\n    \"\\n\",\n    \"1) Enable the Google Maps Distance Matrix API on your Google account. Google explains how to do that [here](https://github.com/googlemaps/google-maps-services-python#api-keys).\\n\",\n    \"\\n\",\n    \"2) Copy and paste the API key they had you create into the code below.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import googlemaps\\n\",\n    \"\\n\",\n    \"gmaps = googlemaps.Client(key=\\\"PASTE YOUR API KEY HERE\\\")\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now we're going to query the Google Maps API for the shortest route between all of the waypoints.\\n\",\n    \"\\n\",\n    \"This is equivalent to doing Google Maps directions lookups on the Google Maps site, except now we're performing hundreds of lookups automatically using code.\\n\",\n    \"\\n\",\n    \"If you get an error on this part, that most likely means one of the waypoints you entered couldn't be found on Google Maps. Another possible reason for an error here is if it's not possible to drive between the points, e.g., finding the driving directions between Hawaii and Florida will return an error until we invent flying cars.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Gather the distance traveled on the shortest route between all waypoints\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from itertools import combinations\\n\",\n    \"\\n\",\n    \"waypoint_distances = {}\\n\",\n    \"waypoint_durations = {}\\n\",\n    \"\\n\",\n    \"for (waypoint1, waypoint2) in combinations(all_waypoints, 2):\\n\",\n    \"    try:\\n\",\n    \"        route = gmaps.distance_matrix(origins=[waypoint1],\\n\",\n    \"                                      destinations=[waypoint2],\\n\",\n    \"                                      mode=\\\"driving\\\", # Change this to \\\"walking\\\" for walking directions,\\n\",\n    \"                                                      # \\\"bicycling\\\" for biking directions, etc.\\n\",\n    \"                                      language=\\\"English\\\",\\n\",\n    \"                                      units=\\\"metric\\\")\\n\",\n    \"\\n\",\n    \"        # \\\"distance\\\" is in meters\\n\",\n    \"        distance = route[\\\"rows\\\"][0][\\\"elements\\\"][0][\\\"distance\\\"][\\\"value\\\"]\\n\",\n    \"\\n\",\n    \"        # \\\"duration\\\" is in seconds\\n\",\n    \"        duration = route[\\\"rows\\\"][0][\\\"elements\\\"][0][\\\"duration\\\"][\\\"value\\\"]\\n\",\n    \"\\n\",\n    \"        waypoint_distances[frozenset([waypoint1, waypoint2])] = distance\\n\",\n    \"        waypoint_durations[frozenset([waypoint1, waypoint2])] = duration\\n\",\n    \"    \\n\",\n    \"    except Exception as e:\\n\",\n    \"        print(\\\"Error with finding the route between %s and %s.\\\" % (waypoint1, waypoint2))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now that we have the routes between all of our waypoints, let's save them to a text file so we don't have to bother Google about them again.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"with open(\\\"my-waypoints-dist-dur.tsv\\\", \\\"w\\\") as out_file:\\n\",\n    \"    out_file.write(\\\"\\\\t\\\".join([\\\"waypoint1\\\",\\n\",\n    \"                              \\\"waypoint2\\\",\\n\",\n    \"                              \\\"distance_m\\\",\\n\",\n    \"                              \\\"duration_s\\\"]))\\n\",\n    \"    \\n\",\n    \"    for (waypoint1, waypoint2) in waypoint_distances.keys():\\n\",\n    \"        out_file.write(\\\"\\\\n\\\" +\\n\",\n    \"                       \\\"\\\\t\\\".join([waypoint1,\\n\",\n    \"                                  waypoint2,\\n\",\n    \"                                  str(waypoint_distances[frozenset([waypoint1, waypoint2])]),\\n\",\n    \"                                  str(waypoint_durations[frozenset([waypoint1, waypoint2])])]))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Use a genetic algorithm to optimize the order to visit the waypoints in\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Instead of exhaustively looking at every possible solution, genetic algorithms start with a handful of random solutions and continually tinkers with these solutions — always trying something slightly different from the current solutions and keeping the best ones — until they can’t find a better solution any more.\\n\",\n    \"\\n\",\n    \"Below, all you need to do is make sure that the file name above matches the file name below (both currently `my-waypoints-dist-dur.tsv`) and run the code. The code will read in your route information and use a genetic algorithm to discover an optimized driving route.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import pandas as pd\\n\",\n    \"import numpy as np\\n\",\n    \"\\n\",\n    \"waypoint_distances = {}\\n\",\n    \"waypoint_durations = {}\\n\",\n    \"all_waypoints = set()\\n\",\n    \"\\n\",\n    \"waypoint_data = pd.read_csv(\\\"my-waypoints-dist-dur.tsv\\\", sep=\\\"\\\\t\\\")\\n\",\n    \"\\n\",\n    \"for i, row in waypoint_data.iterrows():\\n\",\n    \"    waypoint_distances[frozenset([row.waypoint1, row.waypoint2])] = row.distance_m\\n\",\n    \"    waypoint_durations[frozenset([row.waypoint1, row.waypoint2])] = row.duration_s\\n\",\n    \"    all_waypoints.update([row.waypoint1, row.waypoint2])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 15,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import random\\n\",\n    \"\\n\",\n    \"def compute_fitness(solution):\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"        This function returns the total distance traveled on the current road trip.\\n\",\n    \"        \\n\",\n    \"        The genetic algorithm will favor road trips that have shorter\\n\",\n    \"        total distances traveled.\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    \\n\",\n    \"    solution_fitness = 0.0\\n\",\n    \"    \\n\",\n    \"    for index in range(len(solution)):\\n\",\n    \"        waypoint1 = solution[index - 1]\\n\",\n    \"        waypoint2 = solution[index]\\n\",\n    \"        solution_fitness += waypoint_distances[frozenset([waypoint1, waypoint2])]\\n\",\n    \"        \\n\",\n    \"    return solution_fitness\\n\",\n    \"\\n\",\n    \"def generate_random_agent():\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"        Creates a random road trip from the waypoints.\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    \\n\",\n    \"    new_random_agent = list(all_waypoints)\\n\",\n    \"    random.shuffle(new_random_agent)\\n\",\n    \"    return tuple(new_random_agent)\\n\",\n    \"\\n\",\n    \"def mutate_agent(agent_genome, max_mutations=3):\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"        Applies 1 - `max_mutations` point mutations to the given road trip.\\n\",\n    \"        \\n\",\n    \"        A point mutation swaps the order of two waypoints in the road trip.\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    \\n\",\n    \"    agent_genome = list(agent_genome)\\n\",\n    \"    num_mutations = random.randint(1, max_mutations)\\n\",\n    \"    \\n\",\n    \"    for mutation in range(num_mutations):\\n\",\n    \"        swap_index1 = random.randint(0, len(agent_genome) - 1)\\n\",\n    \"        swap_index2 = swap_index1\\n\",\n    \"\\n\",\n    \"        while swap_index1 == swap_index2:\\n\",\n    \"            swap_index2 = random.randint(0, len(agent_genome) - 1)\\n\",\n    \"\\n\",\n    \"        agent_genome[swap_index1], agent_genome[swap_index2] = agent_genome[swap_index2], agent_genome[swap_index1]\\n\",\n    \"            \\n\",\n    \"    return tuple(agent_genome)\\n\",\n    \"\\n\",\n    \"def shuffle_mutation(agent_genome):\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"        Applies a single shuffle mutation to the given road trip.\\n\",\n    \"        \\n\",\n    \"        A shuffle mutation takes a random sub-section of the road trip\\n\",\n    \"        and moves it to another location in the road trip.\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    \\n\",\n    \"    agent_genome = list(agent_genome)\\n\",\n    \"    \\n\",\n    \"    start_index = random.randint(0, len(agent_genome) - 1)\\n\",\n    \"    length = random.randint(2, 20)\\n\",\n    \"    \\n\",\n    \"    genome_subset = agent_genome[start_index:start_index + length]\\n\",\n    \"    agent_genome = agent_genome[:start_index] + agent_genome[start_index + length:]\\n\",\n    \"    \\n\",\n    \"    insert_index = random.randint(0, len(agent_genome) + len(genome_subset) - 1)\\n\",\n    \"    agent_genome = agent_genome[:insert_index] + genome_subset + agent_genome[insert_index:]\\n\",\n    \"    \\n\",\n    \"    return tuple(agent_genome)\\n\",\n    \"\\n\",\n    \"def generate_random_population(pop_size):\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"        Generates a list with `pop_size` number of random road trips.\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    \\n\",\n    \"    random_population = []\\n\",\n    \"    for agent in range(pop_size):\\n\",\n    \"        random_population.append(generate_random_agent())\\n\",\n    \"    return random_population\\n\",\n    \"    \\n\",\n    \"def run_genetic_algorithm(generations=5000, population_size=100):\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"        The core of the Genetic Algorithm.\\n\",\n    \"        \\n\",\n    \"        `generations` and `population_size` must be a multiple of 10.\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    \\n\",\n    \"    population_subset_size = int(population_size / 10.)\\n\",\n    \"    generations_10pct = int(generations / 10.)\\n\",\n    \"    \\n\",\n    \"    # Create a random population of `population_size` number of solutions.\\n\",\n    \"    population = generate_random_population(population_size)\\n\",\n    \"\\n\",\n    \"    # For `generations` number of repetitions...\\n\",\n    \"    for generation in range(generations):\\n\",\n    \"        \\n\",\n    \"        # Compute the fitness of the entire current population\\n\",\n    \"        population_fitness = {}\\n\",\n    \"\\n\",\n    \"        for agent_genome in population:\\n\",\n    \"            if agent_genome in population_fitness:\\n\",\n    \"                continue\\n\",\n    \"\\n\",\n    \"            population_fitness[agent_genome] = compute_fitness(agent_genome)\\n\",\n    \"\\n\",\n    \"        # Take the top 10% shortest road trips and produce offspring each from them\\n\",\n    \"        new_population = []\\n\",\n    \"        for rank, agent_genome in enumerate(sorted(population_fitness,\\n\",\n    \"                                                   key=population_fitness.get)[:population_subset_size]):\\n\",\n    \"            \\n\",\n    \"            if (generation % generations_10pct == 0 or generation == generations - 1) and rank == 0:\\n\",\n    \"                print(\\\"Generation %d best: %d | Unique genomes: %d\\\" % (generation,\\n\",\n    \"                                                                       population_fitness[agent_genome],\\n\",\n    \"                                                                       len(population_fitness)))\\n\",\n    \"                print(agent_genome)\\n\",\n    \"                print(\\\"\\\")\\n\",\n    \"\\n\",\n    \"            # Create 1 exact copy of each of the top road trips\\n\",\n    \"            new_population.append(agent_genome)\\n\",\n    \"\\n\",\n    \"            # Create 2 offspring with 1-3 point mutations\\n\",\n    \"            for offspring in range(2):\\n\",\n    \"                new_population.append(mutate_agent(agent_genome, 3))\\n\",\n    \"                \\n\",\n    \"            # Create 7 offspring with a single shuffle mutation\\n\",\n    \"            for offspring in range(7):\\n\",\n    \"                new_population.append(shuffle_mutation(agent_genome))\\n\",\n    \"\\n\",\n    \"        # Replace the old population with the new population of offspring \\n\",\n    \"        for i in range(len(population))[::-1]:\\n\",\n    \"            del population[i]\\n\",\n    \"\\n\",\n    \"        population = new_population\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Try running the genetic algorithm a few times to see the different solutions it comes up with. It should take about a minute to finish running.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 18,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Generation 0 best: 90432537 | Unique genomes: 100\\n\",\n      \"('Mount Rushmore National Memorial, South Dakota 244, Keystone, SD', 'Olympia Entertainment, Woodward Avenue, Detroit, MI', 'Terrace Hill, Grand Avenue, Des Moines, IA', 'Statue of Liberty, Liberty Island, NYC, NY', 'Maryland State House, 100 State Cir, Annapolis, MD 21401', 'Shelburne Farms, Harbor Road, Shelburne, VT', 'Fort Union Trading Post National Historic Site, Williston, North Dakota 1804, ND', 'Gateway Arch, Washington Avenue, St Louis, MO', 'USS Alabama, Battleship Parkway, Mobile, AL', \\\"Fort Sumter National Monument, Sullivan's Island, SC\\\", 'Glacier National Park, West Glacier, MT', 'The Alamo, Alamo Plaza, San Antonio, TX', 'Hanford Site, Benton County, WA', 'Columbia River Gorge National Scenic Area, Oregon', 'White House, Pennsylvania Avenue Northwest, Washington, DC', 'Mammoth Cave National Park, Mammoth Cave Pkwy, Mammoth Cave, KY', 'Mount Vernon, Fairfax County, Virginia', 'Lost World Caverns, Lewisburg, WV', 'Graceland, Elvis Presley Boulevard, Memphis, TN', 'Taliesin, County Road C, Spring Green, Wisconsin', 'Wright Brothers National Memorial Visitor Center, Manteo, NC', 'The Breakers, Ochre Point Avenue, Newport, RI', 'Hoover Dam, NV', 'The Mark Twain House & Museum, Farmington Avenue, Hartford, CT', 'New Castle Historic District, Delaware', 'USS Constitution, Boston, MA', 'West Baden Springs Hotel, West Baden Avenue, West Baden Springs, IN', 'Fort Snelling, Tower Avenue, Saint Paul, MN', 'Chickasaw National Recreation Area, 1008 W 2nd St, Sulphur, OK 73086', 'Vicksburg National Military Park, Clay Street, Vicksburg, MS', 'Acadia National Park, Maine', 'Bryce Canyon National Park, Hwy 63, Bryce, UT', 'San Andreas Fault, San Benito County, CA', 'Ashfall Fossil Bed, Royal, NE', 'French Quarter, New Orleans, LA', 'C. W. Parker Carousel Museum, South Esplanade Street, Leavenworth, KS', 'Liberty Bell, 6th Street, Philadelphia, PA', 'Omni Mount Washington Resort, Mount Washington Hotel Road, Bretton Woods, NH', 'Carlsbad Caverns National Park, Carlsbad, NM', 'Pikes Peak, Colorado', 'Toltec Mounds, Scott, AR', 'Grand Canyon National Park, Arizona', 'Cable Car Museum, 94108, 1201 Mason St, San Francisco, CA 94108', 'Yellowstone National Park, WY 82190', 'Cape Canaveral, FL', 'Congress Hall, Congress Place, Cape May, NJ 08204', 'Okefenokee Swamp Park, Okefenokee Swamp Park Road, Waycross, GA', 'Spring Grove Cemetery, Spring Grove Avenue, Cincinnati, OH', 'Lincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL', 'Craters of the Moon National Monument & Preserve, Arco, ID')\\n\",\n      \"\\n\",\n      \"Generation 1000 best: 22613362 | Unique genomes: 95\\n\",\n      \"('Terrace Hill, Grand Avenue, Des Moines, IA', 'C. W. Parker Carousel Museum, South Esplanade Street, Leavenworth, KS', 'Ashfall Fossil Bed, Royal, NE', 'Fort Union Trading Post National Historic Site, Williston, North Dakota 1804, ND', 'Mount Rushmore National Memorial, South Dakota 244, Keystone, SD', 'Yellowstone National Park, WY 82190', 'Craters of the Moon National Monument & Preserve, Arco, ID', 'Glacier National Park, West Glacier, MT', 'Hanford Site, Benton County, WA', 'Columbia River Gorge National Scenic Area, Oregon', 'Cable Car Museum, 94108, 1201 Mason St, San Francisco, CA 94108', 'San Andreas Fault, San Benito County, CA', 'Hoover Dam, NV', 'Grand Canyon National Park, Arizona', 'Bryce Canyon National Park, Hwy 63, Bryce, UT', 'Pikes Peak, Colorado', 'Carlsbad Caverns National Park, Carlsbad, NM', 'The Alamo, Alamo Plaza, San Antonio, TX', 'Chickasaw National Recreation Area, 1008 W 2nd St, Sulphur, OK 73086', 'Toltec Mounds, Scott, AR', 'Graceland, Elvis Presley Boulevard, Memphis, TN', 'Vicksburg National Military Park, Clay Street, Vicksburg, MS', 'French Quarter, New Orleans, LA', 'USS Alabama, Battleship Parkway, Mobile, AL', 'Cape Canaveral, FL', 'Okefenokee Swamp Park, Okefenokee Swamp Park Road, Waycross, GA', \\\"Fort Sumter National Monument, Sullivan's Island, SC\\\", 'Wright Brothers National Memorial Visitor Center, Manteo, NC', 'Congress Hall, Congress Place, Cape May, NJ 08204', 'Statue of Liberty, Liberty Island, NYC, NY', 'Shelburne Farms, Harbor Road, Shelburne, VT', 'Omni Mount Washington Resort, Mount Washington Hotel Road, Bretton Woods, NH', 'Acadia National Park, Maine', 'USS Constitution, Boston, MA', 'The Breakers, Ochre Point Avenue, Newport, RI', 'The Mark Twain House & Museum, Farmington Avenue, Hartford, CT', 'Liberty Bell, 6th Street, Philadelphia, PA', 'New Castle Historic District, Delaware', 'Maryland State House, 100 State Cir, Annapolis, MD 21401', 'White House, Pennsylvania Avenue Northwest, Washington, DC', 'Mount Vernon, Fairfax County, Virginia', 'Lost World Caverns, Lewisburg, WV', 'Olympia Entertainment, Woodward Avenue, Detroit, MI', 'Spring Grove Cemetery, Spring Grove Avenue, Cincinnati, OH', 'Mammoth Cave National Park, Mammoth Cave Pkwy, Mammoth Cave, KY', 'West Baden Springs Hotel, West Baden Avenue, West Baden Springs, IN', 'Gateway Arch, Washington Avenue, St Louis, MO', 'Lincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL', 'Taliesin, County Road C, Spring Green, Wisconsin', 'Fort Snelling, Tower Avenue, Saint Paul, MN')\\n\",\n      \"\\n\",\n      \"Generation 2000 best: 22324940 | Unique genomes: 98\\n\",\n      \"('Graceland, Elvis Presley Boulevard, Memphis, TN', 'Vicksburg National Military Park, Clay Street, Vicksburg, MS', 'French Quarter, New Orleans, LA', 'USS Alabama, Battleship Parkway, Mobile, AL', 'Cape Canaveral, FL', 'Okefenokee Swamp Park, Okefenokee Swamp Park Road, Waycross, GA', \\\"Fort Sumter National Monument, Sullivan's Island, SC\\\", 'Wright Brothers National Memorial Visitor Center, Manteo, NC', 'Congress Hall, Congress Place, Cape May, NJ 08204', 'Shelburne Farms, Harbor Road, Shelburne, VT', 'Omni Mount Washington Resort, Mount Washington Hotel Road, Bretton Woods, NH', 'Acadia National Park, Maine', 'USS Constitution, Boston, MA', 'The Breakers, Ochre Point Avenue, Newport, RI', 'The Mark Twain House & Museum, Farmington Avenue, Hartford, CT', 'Statue of Liberty, Liberty Island, NYC, NY', 'Liberty Bell, 6th Street, Philadelphia, PA', 'New Castle Historic District, Delaware', 'Maryland State House, 100 State Cir, Annapolis, MD 21401', 'White House, Pennsylvania Avenue Northwest, Washington, DC', 'Mount Vernon, Fairfax County, Virginia', 'Lost World Caverns, Lewisburg, WV', 'Olympia Entertainment, Woodward Avenue, Detroit, MI', 'Spring Grove Cemetery, Spring Grove Avenue, Cincinnati, OH', 'Mammoth Cave National Park, Mammoth Cave Pkwy, Mammoth Cave, KY', 'West Baden Springs Hotel, West Baden Avenue, West Baden Springs, IN', 'Gateway Arch, Washington Avenue, St Louis, MO', 'Lincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL', 'Taliesin, County Road C, Spring Green, Wisconsin', 'Fort Snelling, Tower Avenue, Saint Paul, MN', 'Terrace Hill, Grand Avenue, Des Moines, IA', 'C. W. Parker Carousel Museum, South Esplanade Street, Leavenworth, KS', 'Ashfall Fossil Bed, Royal, NE', 'Mount Rushmore National Memorial, South Dakota 244, Keystone, SD', 'Fort Union Trading Post National Historic Site, Williston, North Dakota 1804, ND', 'Glacier National Park, West Glacier, MT', 'Yellowstone National Park, WY 82190', 'Craters of the Moon National Monument & Preserve, Arco, ID', 'Hanford Site, Benton County, WA', 'Columbia River Gorge National Scenic Area, Oregon', 'Cable Car Museum, 94108, 1201 Mason St, San Francisco, CA 94108', 'San Andreas Fault, San Benito County, CA', 'Hoover Dam, NV', 'Grand Canyon National Park, Arizona', 'Bryce Canyon National Park, Hwy 63, Bryce, UT', 'Pikes Peak, Colorado', 'Carlsbad Caverns National Park, Carlsbad, NM', 'The Alamo, Alamo Plaza, San Antonio, TX', 'Chickasaw National Recreation Area, 1008 W 2nd St, Sulphur, OK 73086', 'Toltec Mounds, Scott, AR')\\n\",\n      \"\\n\",\n      \"Generation 3000 best: 22324940 | Unique genomes: 95\\n\",\n      \"('Graceland, Elvis Presley Boulevard, Memphis, TN', 'Vicksburg National Military Park, Clay Street, Vicksburg, MS', 'French Quarter, New Orleans, LA', 'USS Alabama, Battleship Parkway, Mobile, AL', 'Cape Canaveral, FL', 'Okefenokee Swamp Park, Okefenokee Swamp Park Road, Waycross, GA', \\\"Fort Sumter National Monument, Sullivan's Island, SC\\\", 'Wright Brothers National Memorial Visitor Center, Manteo, NC', 'Congress Hall, Congress Place, Cape May, NJ 08204', 'Shelburne Farms, Harbor Road, Shelburne, VT', 'Omni Mount Washington Resort, Mount Washington Hotel Road, Bretton Woods, NH', 'Acadia National Park, Maine', 'USS Constitution, Boston, MA', 'The Breakers, Ochre Point Avenue, Newport, RI', 'The Mark Twain House & Museum, Farmington Avenue, Hartford, CT', 'Statue of Liberty, Liberty Island, NYC, NY', 'Liberty Bell, 6th Street, Philadelphia, PA', 'New Castle Historic District, Delaware', 'Maryland State House, 100 State Cir, Annapolis, MD 21401', 'White House, Pennsylvania Avenue Northwest, Washington, DC', 'Mount Vernon, Fairfax County, Virginia', 'Lost World Caverns, Lewisburg, WV', 'Olympia Entertainment, Woodward Avenue, Detroit, MI', 'Spring Grove Cemetery, Spring Grove Avenue, Cincinnati, OH', 'Mammoth Cave National Park, Mammoth Cave Pkwy, Mammoth Cave, KY', 'West Baden Springs Hotel, West Baden Avenue, West Baden Springs, IN', 'Gateway Arch, Washington Avenue, St Louis, MO', 'Lincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL', 'Taliesin, County Road C, Spring Green, Wisconsin', 'Fort Snelling, Tower Avenue, Saint Paul, MN', 'Terrace Hill, Grand Avenue, Des Moines, IA', 'C. W. Parker Carousel Museum, South Esplanade Street, Leavenworth, KS', 'Ashfall Fossil Bed, Royal, NE', 'Mount Rushmore National Memorial, South Dakota 244, Keystone, SD', 'Fort Union Trading Post National Historic Site, Williston, North Dakota 1804, ND', 'Glacier National Park, West Glacier, MT', 'Yellowstone National Park, WY 82190', 'Craters of the Moon National Monument & Preserve, Arco, ID', 'Hanford Site, Benton County, WA', 'Columbia River Gorge National Scenic Area, Oregon', 'Cable Car Museum, 94108, 1201 Mason St, San Francisco, CA 94108', 'San Andreas Fault, San Benito County, CA', 'Hoover Dam, NV', 'Grand Canyon National Park, Arizona', 'Bryce Canyon National Park, Hwy 63, Bryce, UT', 'Pikes Peak, Colorado', 'Carlsbad Caverns National Park, Carlsbad, NM', 'The Alamo, Alamo Plaza, San Antonio, TX', 'Chickasaw National Recreation Area, 1008 W 2nd St, Sulphur, OK 73086', 'Toltec Mounds, Scott, AR')\\n\",\n      \"\\n\",\n      \"Generation 4000 best: 22324940 | Unique genomes: 97\\n\",\n      \"('Graceland, Elvis Presley Boulevard, Memphis, TN', 'Vicksburg National Military Park, Clay Street, Vicksburg, MS', 'French Quarter, New Orleans, LA', 'USS Alabama, Battleship Parkway, Mobile, AL', 'Cape Canaveral, FL', 'Okefenokee Swamp Park, Okefenokee Swamp Park Road, Waycross, GA', \\\"Fort Sumter National Monument, Sullivan's Island, SC\\\", 'Wright Brothers National Memorial Visitor Center, Manteo, NC', 'Congress Hall, Congress Place, Cape May, NJ 08204', 'Shelburne Farms, Harbor Road, Shelburne, VT', 'Omni Mount Washington Resort, Mount Washington Hotel Road, Bretton Woods, NH', 'Acadia National Park, Maine', 'USS Constitution, Boston, MA', 'The Breakers, Ochre Point Avenue, Newport, RI', 'The Mark Twain House & Museum, Farmington Avenue, Hartford, CT', 'Statue of Liberty, Liberty Island, NYC, NY', 'Liberty Bell, 6th Street, Philadelphia, PA', 'New Castle Historic District, Delaware', 'Maryland State House, 100 State Cir, Annapolis, MD 21401', 'White House, Pennsylvania Avenue Northwest, Washington, DC', 'Mount Vernon, Fairfax County, Virginia', 'Lost World Caverns, Lewisburg, WV', 'Olympia Entertainment, Woodward Avenue, Detroit, MI', 'Spring Grove Cemetery, Spring Grove Avenue, Cincinnati, OH', 'Mammoth Cave National Park, Mammoth Cave Pkwy, Mammoth Cave, KY', 'West Baden Springs Hotel, West Baden Avenue, West Baden Springs, IN', 'Gateway Arch, Washington Avenue, St Louis, MO', 'Lincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL', 'Taliesin, County Road C, Spring Green, Wisconsin', 'Fort Snelling, Tower Avenue, Saint Paul, MN', 'Terrace Hill, Grand Avenue, Des Moines, IA', 'C. W. Parker Carousel Museum, South Esplanade Street, Leavenworth, KS', 'Ashfall Fossil Bed, Royal, NE', 'Mount Rushmore National Memorial, South Dakota 244, Keystone, SD', 'Fort Union Trading Post National Historic Site, Williston, North Dakota 1804, ND', 'Glacier National Park, West Glacier, MT', 'Yellowstone National Park, WY 82190', 'Craters of the Moon National Monument & Preserve, Arco, ID', 'Hanford Site, Benton County, WA', 'Columbia River Gorge National Scenic Area, Oregon', 'Cable Car Museum, 94108, 1201 Mason St, San Francisco, CA 94108', 'San Andreas Fault, San Benito County, CA', 'Hoover Dam, NV', 'Grand Canyon National Park, Arizona', 'Bryce Canyon National Park, Hwy 63, Bryce, UT', 'Pikes Peak, Colorado', 'Carlsbad Caverns National Park, Carlsbad, NM', 'The Alamo, Alamo Plaza, San Antonio, TX', 'Chickasaw National Recreation Area, 1008 W 2nd St, Sulphur, OK 73086', 'Toltec Mounds, Scott, AR')\\n\",\n      \"\\n\",\n      \"Generation 4999 best: 22324940 | Unique genomes: 99\\n\",\n      \"('Graceland, Elvis Presley Boulevard, Memphis, TN', 'Vicksburg National Military Park, Clay Street, Vicksburg, MS', 'French Quarter, New Orleans, LA', 'USS Alabama, Battleship Parkway, Mobile, AL', 'Cape Canaveral, FL', 'Okefenokee Swamp Park, Okefenokee Swamp Park Road, Waycross, GA', \\\"Fort Sumter National Monument, Sullivan's Island, SC\\\", 'Wright Brothers National Memorial Visitor Center, Manteo, NC', 'Congress Hall, Congress Place, Cape May, NJ 08204', 'Shelburne Farms, Harbor Road, Shelburne, VT', 'Omni Mount Washington Resort, Mount Washington Hotel Road, Bretton Woods, NH', 'Acadia National Park, Maine', 'USS Constitution, Boston, MA', 'The Breakers, Ochre Point Avenue, Newport, RI', 'The Mark Twain House & Museum, Farmington Avenue, Hartford, CT', 'Statue of Liberty, Liberty Island, NYC, NY', 'Liberty Bell, 6th Street, Philadelphia, PA', 'New Castle Historic District, Delaware', 'Maryland State House, 100 State Cir, Annapolis, MD 21401', 'White House, Pennsylvania Avenue Northwest, Washington, DC', 'Mount Vernon, Fairfax County, Virginia', 'Lost World Caverns, Lewisburg, WV', 'Olympia Entertainment, Woodward Avenue, Detroit, MI', 'Spring Grove Cemetery, Spring Grove Avenue, Cincinnati, OH', 'Mammoth Cave National Park, Mammoth Cave Pkwy, Mammoth Cave, KY', 'West Baden Springs Hotel, West Baden Avenue, West Baden Springs, IN', 'Gateway Arch, Washington Avenue, St Louis, MO', 'Lincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL', 'Taliesin, County Road C, Spring Green, Wisconsin', 'Fort Snelling, Tower Avenue, Saint Paul, MN', 'Terrace Hill, Grand Avenue, Des Moines, IA', 'C. W. Parker Carousel Museum, South Esplanade Street, Leavenworth, KS', 'Ashfall Fossil Bed, Royal, NE', 'Mount Rushmore National Memorial, South Dakota 244, Keystone, SD', 'Fort Union Trading Post National Historic Site, Williston, North Dakota 1804, ND', 'Glacier National Park, West Glacier, MT', 'Yellowstone National Park, WY 82190', 'Craters of the Moon National Monument & Preserve, Arco, ID', 'Hanford Site, Benton County, WA', 'Columbia River Gorge National Scenic Area, Oregon', 'Cable Car Museum, 94108, 1201 Mason St, San Francisco, CA 94108', 'San Andreas Fault, San Benito County, CA', 'Hoover Dam, NV', 'Grand Canyon National Park, Arizona', 'Bryce Canyon National Park, Hwy 63, Bryce, UT', 'Pikes Peak, Colorado', 'Carlsbad Caverns National Park, Carlsbad, NM', 'The Alamo, Alamo Plaza, San Antonio, TX', 'Chickasaw National Recreation Area, 1008 W 2nd St, Sulphur, OK 73086', 'Toltec Mounds, Scott, AR')\\n\",\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"run_genetic_algorithm(generations=5000, population_size=100)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Visualize your road trip on a Google map\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now that we have an ordered list of the waypoints, we should put them on a Google map so we can see the trip from a high level and make any extra adjustments.\\n\",\n    \"\\n\",\n    \"There's no easy way to make this visualization in Python, but the Google Maps team provides a nice JavaScript library for visualizing routes on a Google Map.\\n\",\n    \"\\n\",\n    \"Here's an example map with the route between 50 waypoints visualized: [link](http://rhiever.github.io/optimal-roadtrip-usa/major-landmarks.html)\\n\",\n    \"\\n\",\n    \"The tricky part here is that the JavaScript library only plots routes with a maximum of 10 waypoints. If we want to plot a route with >10 waypoints, we need to call the route plotting function multiple times.\\n\",\n    \"\\n\",\n    \"Thanks to some optimizations by [Nicholas Clarke](https://github.com/nicholasgodfreyclarke) to my original map, this is a simple operation:\\n\",\n    \"\\n\",\n    \"1) Copy the final route generated by the genetic algorithm above.\\n\",\n    \"\\n\",\n    \"2) Place brackets (`[` & `]`) around the route, e.g.,\\n\",\n    \"\\n\",\n    \"    ['Graceland, Elvis Presley Boulevard, Memphis, TN', 'Vicksburg National Military Park, Clay Street, Vicksburg, MS', 'French Quarter, New Orleans, LA', 'USS Alabama, Battleship Parkway, Mobile, AL', 'Cape Canaveral, FL', 'Okefenokee Swamp Park, Okefenokee Swamp Park Road, Waycross, GA', \\\"Fort Sumter National Monument, Sullivan's Island, SC\\\", 'Wright Brothers National Memorial Visitor Center, Manteo, NC', 'Congress Hall, Congress Place, Cape May, NJ 08204', 'Shelburne Farms, Harbor Road, Shelburne, VT', 'Omni Mount Washington Resort, Mount Washington Hotel Road, Bretton Woods, NH', 'Acadia National Park, Maine', 'USS Constitution, Boston, MA', 'The Breakers, Ochre Point Avenue, Newport, RI', 'The Mark Twain House & Museum, Farmington Avenue, Hartford, CT', 'Statue of Liberty, Liberty Island, NYC, NY', 'Liberty Bell, 6th Street, Philadelphia, PA', 'New Castle Historic District, Delaware', 'Maryland State House, 100 State Cir, Annapolis, MD 21401', 'White House, Pennsylvania Avenue Northwest, Washington, DC', 'Mount Vernon, Fairfax County, Virginia', 'Lost World Caverns, Lewisburg, WV', 'Olympia Entertainment, Woodward Avenue, Detroit, MI', 'Spring Grove Cemetery, Spring Grove Avenue, Cincinnati, OH', 'Mammoth Cave National Park, Mammoth Cave Pkwy, Mammoth Cave, KY', 'West Baden Springs Hotel, West Baden Avenue, West Baden Springs, IN', 'Gateway Arch, Washington Avenue, St Louis, MO', 'Lincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL', 'Taliesin, County Road C, Spring Green, Wisconsin', 'Fort Snelling, Tower Avenue, Saint Paul, MN', 'Terrace Hill, Grand Avenue, Des Moines, IA', 'C. W. Parker Carousel Museum, South Esplanade Street, Leavenworth, KS', 'Ashfall Fossil Bed, Royal, NE', 'Mount Rushmore National Memorial, South Dakota 244, Keystone, SD', 'Fort Union Trading Post National Historic Site, Williston, North Dakota 1804, ND', 'Glacier National Park, West Glacier, MT', 'Yellowstone National Park, WY 82190', 'Craters of the Moon National Monument & Preserve, Arco, ID', 'Hanford Site, Benton County, WA', 'Columbia River Gorge National Scenic Area, Oregon', 'Cable Car Museum, 94108, 1201 Mason St, San Francisco, CA 94108', 'San Andreas Fault, San Benito County, CA', 'Hoover Dam, NV', 'Grand Canyon National Park, Arizona', 'Bryce Canyon National Park, Hwy 63, Bryce, UT', 'Pikes Peak, Colorado', 'Carlsbad Caverns National Park, Carlsbad, NM', 'The Alamo, Alamo Plaza, San Antonio, TX', 'Chickasaw National Recreation Area, 1008 W 2nd St, Sulphur, OK 73086', 'Toltec Mounds, Scott, AR']\\n\",\n    \"\\n\",\n    \"3) Paste the final route with brackets into [line 93](https://github.com/rhiever/optimal-roadtrip-usa/blob/gh-pages/major-landmarks.html#L93) of my road trip map code. It should look like this:\\n\",\n    \"\\n\",\n    \"    optimal_route = [ ... ]\\n\",\n    \"    \\n\",\n    \"where `...` is your optimized road trip.\\n\",\n    \"\\n\",\n    \"That's all it takes! Now you have your own optimized road trip ready to show off to the world.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Some technical notes\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"As I mentioned in the [original article](http://www.randalolson.com/2015/03/08/computing-the-optimal-road-trip-across-the-u-s/), by the end of 5,000 generations, the genetic algorithm will very likely find a *good* but probably not the *absolute best* solution to the optimal routing problem. It is in the nature of genetic algorithms that we never know if we found the absolute best solution.\\n\",\n    \"\\n\",\n    \"However, there exist some brilliant analytical solutions to the optimal routing problem such as the [Concorde TSP solver](http://en.wikipedia.org/wiki/Concorde_TSP_Solver). If you're interested in learning more about Concorde and how it's possible to find a perfect solution to the routing problem, I advise you check out [Bill Cook's article](http://www.math.uwaterloo.ca/tsp/usa50/index.html) on the topic.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### If you have any questions\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Please feel free to:\\n\",\n    \"\\n\",\n    \"* [Email me](http://www.randalolson.com/contact/),\\n\",\n    \"\\n\",\n    \"* [Tweet](https://twitter.com/randal_olson) at me, or\\n\",\n    \"\\n\",\n    \"* comment on the [blog post](http://www.randalolson.com/2015/03/08/computing-the-optimal-road-trip-across-the-u-s/)\\n\",\n    \"\\n\",\n    \"I'm usually pretty good about getting back to people within a day or two.\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.6.5\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n"
  },
  {
    "path": "optimal-road-trip/OptimalRoadTripHtmlSaveAndDisplay.py",
    "content": "\"\"\"\nRandy Olson's Shortest Route Program modified By Andrew Liesinger to:\n    1: Detect waypoints file at runtime - if found use it, otherwise look up distances via google calls (and then save to waypoint file)\n    2: Dynamically create and open an HTML file showing the route when a shorter route is found\n    3: Make it easier to tinker with the Generation / Population parameters\n\"\"\"\nfrom __future__ import print_function\nfrom itertools import combinations\nimport googlemaps\nimport pandas as pd\nimport numpy as np\nimport os.path\nimport random\nimport webbrowser\n\nGOOGLE_MAPS_API_KEY = \"Your Key Here\"\nwaypoints_file = \"my-waypoints-dist-dur.tsv\"\n\n#This is the general filename - as shorter routes are discovered the Population fitness score will be inserted into the filename\n#so that interim results are saved for comparision.  The actual filenames using the default below will be:\n#Output_<Population Fitness Score>.html \noutput_file = 'Output.html'\n\n#parameters for the Genetic algoritim\nthisRunGenerations=5000\nthisRunPopulation_size=100\n\n\nall_waypoints = [\"USS Alabama, Battleship Parkway, Mobile, AL\",\n                 \"Grand Canyon National Park, Arizona\",\n                 \"Toltec Mounds, Scott, AR\",\n                 \"San Andreas Fault, San Benito County, CA\",\n                 \"Cable Car Museum, 94108, 1201 Mason St, San Francisco, CA 94108\",\n                 \"Pikes Peak, Colorado\",\n                 \"The Mark Twain House & Museum, Farmington Avenue, Hartford, CT\",\n                 \"New Castle Historic District, Delaware\",\n                 \"White House, Pennsylvania Avenue Northwest, Washington, DC\",\n                 \"Cape Canaveral, FL\",\n                 \"Okefenokee Swamp Park, Okefenokee Swamp Park Road, Waycross, GA\",\n                 \"Craters of the Moon National Monument & Preserve, Arco, ID\",\n                 \"Lincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL\",\n                 \"West Baden Springs Hotel, West Baden Avenue, West Baden Springs, IN\",\n                 \"Terrace Hill, Grand Avenue, Des Moines, IA\",\n                 \"C. W. Parker Carousel Museum, South Esplanade Street, Leavenworth, KS\",\n                 \"Mammoth Cave National Park, Mammoth Cave Pkwy, Mammoth Cave, KY\",\n                 \"French Quarter, New Orleans, LA\",\n                 \"Acadia National Park, Maine\",\n                 \"Maryland State House, 100 State Cir, Annapolis, MD 21401\",\n                 \"USS Constitution, Boston, MA\",\n                 \"Olympia Entertainment, Woodward Avenue, Detroit, MI\",\n                 \"Fort Snelling, Tower Avenue, Saint Paul, MN\",\n                 \"Vicksburg National Military Park, Clay Street, Vicksburg, MS\",\n                 \"Gateway Arch, Washington Avenue, St Louis, MO\",\n                 \"Glacier National Park, West Glacier, MT\",\n                 \"Ashfall Fossil Bed, Royal, NE\",\n                 \"Hoover Dam, NV\",\n                 \"Omni Mount Washington Resort, Mount Washington Hotel Road, Bretton Woods, NH\",\n                 \"Congress Hall, Congress Place, Cape May, NJ 08204\",\n                 \"Carlsbad Caverns National Park, Carlsbad, NM\",\n                 \"Statue of Liberty, Liberty Island, NYC, NY\",\n                 \"Wright Brothers National Memorial Visitor Center, Manteo, NC\",\n                 \"Fort Union Trading Post National Historic Site, Williston, North Dakota 1804, ND\",\n                 \"Spring Grove Cemetery, Spring Grove Avenue, Cincinnati, OH\",\n                 \"Chickasaw National Recreation Area, 1008 W 2nd St, Sulphur, OK 73086\",\n                 \"Columbia River Gorge National Scenic Area, Oregon\",\n                 \"Liberty Bell, 6th Street, Philadelphia, PA\",\n                 \"The Breakers, Ochre Point Avenue, Newport, RI\",\n                 \"Fort Sumter National Monument, Sullivan's Island, SC\",\n                 \"Mount Rushmore National Memorial, South Dakota 244, Keystone, SD\",\n                 \"Graceland, Elvis Presley Boulevard, Memphis, TN\",\n                 \"The Alamo, Alamo Plaza, San Antonio, TX\",\n                 \"Bryce Canyon National Park, Hwy 63, Bryce, UT\",\n                 \"Shelburne Farms, Harbor Road, Shelburne, VT\",\n                 \"Mount Vernon, Fairfax County, Virginia\",\n                 \"Hanford Site, Benton County, WA\",\n                 \"Lost World Caverns, Lewisburg, WV\",\n                 \"Taliesin, County Road C, Spring Green, Wisconsin\",\n                 \"Yellowstone National Park, WY 82190\"]\n\ndef CreateOptimalRouteHtmlFile(optimal_route, distance, display=True):\n    optimal_route = list(optimal_route)\n    optimal_route += [optimal_route[0]]\n\n    Page_1 = \"\"\"\n    <!DOCTYPE html>\n    <html lang=\"en\">\n      <head>\n        <meta charset=\"utf-8\">\n        <meta name=\"viewport\" content=\"initial-scale=1.0, user-scalable=no\">\n        <meta name=\"description\" content=\"Randy Olson uses machine learning to find the optimal road trip across the U.S.\">\n        <meta name=\"author\" content=\"Randal S. Olson\">\n        \n        <title>The optimal road trip across the U.S. according to machine learning</title>\n        <style>\n          html, body, #map-canvas {\n            height: 100%;\n            margin: 0px;\n            padding: 0px\n          }\n          #panel {\n            position: absolute;\n            top: 5px;\n            left: 50%;\n            margin-left: -180px;\n            z-index: 5;\n            background-color: #fff;\n            padding: 10px;\n            border: 1px solid #999;\n          }\n        </style>\n        <script src=\"https://maps.googleapis.com/maps/api/js?v=3.exp&signed_in=true\"></script>\n        <script>\n            var routes_list = []\n            var markerOptions = {icon: \"http://maps.gstatic.com/mapfiles/markers2/marker.png\"};\n            var directionsDisplayOptions = {preserveViewport: true,\n                                            markerOptions: markerOptions};\n            var directionsService = new google.maps.DirectionsService();\n            var map;\n\n            function initialize() {\n              var center = new google.maps.LatLng(39, -96);\n              var mapOptions = {\n                zoom: 5,\n                center: center\n              };\n              map = new google.maps.Map(document.getElementById('map-canvas'), mapOptions);\n              for (i=0; i<routes_list.length; i++) {\n                routes_list[i].setMap(map); \n              }\n            }\n\n            function calcRoute(start, end, routes) {\n              \n              var directionsDisplay = new google.maps.DirectionsRenderer(directionsDisplayOptions);\n\n              var waypts = [];\n              for (var i = 0; i < routes.length; i++) {\n                waypts.push({\n                  location:routes[i],\n                  stopover:true});\n                }\n              \n              var request = {\n                  origin: start,\n                  destination: end,\n                  waypoints: waypts,\n                  optimizeWaypoints: false,\n                  travelMode: google.maps.TravelMode.DRIVING\n              };\n\n              directionsService.route(request, function(response, status) {\n                if (status == google.maps.DirectionsStatus.OK) {\n                    directionsDisplay.setDirections(response);      \n                }\n              });\n\n              routes_list.push(directionsDisplay);\n            }\n\n            function createRoutes(route) {\n                // Google's free map API is limited to 10 waypoints so need to break into batches\n                route.push(route[0]);\n                var subset = 0;\n                while (subset < route.length) {\n                    var waypointSubset = route.slice(subset, subset + 10);\n\n                    var startPoint = waypointSubset[0];\n                    var midPoints = waypointSubset.slice(1, waypointSubset.length - 1);\n                    var endPoint = waypointSubset[waypointSubset.length - 1];\n\n                    calcRoute(startPoint, endPoint, midPoints);\n\n                    subset += 9;\n                }\n            }\n    \"\"\"\n    Page_2 = \"\"\"\n            \n            createRoutes(optimal_route);\n\n            google.maps.event.addDomListener(window, 'load', initialize);\n\n        </script>\n      </head>\n      <body>\n        <div id=\"map-canvas\"></div>\n      </body>\n    </html>\n    \"\"\"\n\n    localoutput_file = output_file.replace('.html', '_' + str(distance) + '.html')\n    with open(localoutput_file, 'w') as fs:\n        fs.write(Page_1)\n        fs.write(\"\\t\\t\\toptimal_route = {0}\".format(str(optimal_route)))\n        fs.write(Page_2)\n\n    if display:\n        webbrowser.open_new_tab(localoutput_file)\n\n\ndef compute_fitness(solution):\n    \"\"\"\n        This function returns the total distance traveled on the current road trip.\n        \n        The genetic algorithm will favor road trips that have shorter\n        total distances traveled.\n    \"\"\"\n    \n    solution_fitness = 0.0\n    \n    for index in range(len(solution)):\n        waypoint1 = solution[index - 1]\n        waypoint2 = solution[index]\n        solution_fitness += waypoint_distances[frozenset([waypoint1, waypoint2])]\n        \n    return solution_fitness\n\ndef generate_random_agent():\n    \"\"\"\n        Creates a random road trip from the waypoints.\n    \"\"\"\n    \n    new_random_agent = list(all_waypoints)\n    random.shuffle(new_random_agent)\n    return tuple(new_random_agent)\n\ndef mutate_agent(agent_genome, max_mutations=3):\n    \"\"\"\n        Applies 1 - `max_mutations` point mutations to the given road trip.\n        \n        A point mutation swaps the order of two waypoints in the road trip.\n    \"\"\"\n    \n    agent_genome = list(agent_genome)\n    num_mutations = random.randint(1, max_mutations)\n    \n    for mutation in range(num_mutations):\n        swap_index1 = random.randint(0, len(agent_genome) - 1)\n        swap_index2 = swap_index1\n\n        while swap_index1 == swap_index2:\n            swap_index2 = random.randint(0, len(agent_genome) - 1)\n\n        agent_genome[swap_index1], agent_genome[swap_index2] = agent_genome[swap_index2], agent_genome[swap_index1]\n            \n    return tuple(agent_genome)\n\ndef shuffle_mutation(agent_genome):\n    \"\"\"\n        Applies a single shuffle mutation to the given road trip.\n        \n        A shuffle mutation takes a random sub-section of the road trip\n        and moves it to another location in the road trip.\n    \"\"\"\n    \n    agent_genome = list(agent_genome)\n    \n    start_index = random.randint(0, len(agent_genome) - 1)\n    length = random.randint(2, 20)\n    \n    genome_subset = agent_genome[start_index:start_index + length]\n    agent_genome = agent_genome[:start_index] + agent_genome[start_index + length:]\n    \n    insert_index = random.randint(0, len(agent_genome) + len(genome_subset) - 1)\n    agent_genome = agent_genome[:insert_index] + genome_subset + agent_genome[insert_index:]\n    \n    return tuple(agent_genome)\n\ndef generate_random_population(pop_size):\n    \"\"\"\n        Generates a list with `pop_size` number of random road trips.\n    \"\"\"\n    \n    random_population = []\n    for agent in range(pop_size):\n        random_population.append(generate_random_agent())\n    return random_population\n    \ndef run_genetic_algorithm(generations=5000, population_size=100):\n    \"\"\"\n        The core of the Genetic Algorithm.\n        \n        `generations` and `population_size` must be a multiple of 10.\n    \"\"\"\n    \n    current_best_distance = -1\n    population_subset_size = int(population_size / 10.)\n    generations_10pct = int(generations / 10.)\n    \n    # Create a random population of `population_size` number of solutions.\n    population = generate_random_population(population_size)\n\n    # For `generations` number of repetitions...\n    for generation in range(generations):\n        \n        # Compute the fitness of the entire current population\n        population_fitness = {}\n\n        for agent_genome in population:\n            if agent_genome in population_fitness:\n                continue\n\n            population_fitness[agent_genome] = compute_fitness(agent_genome)\n\n        # Take the top 10% shortest road trips and produce offspring each from them\n        new_population = []\n        for rank, agent_genome in enumerate(sorted(population_fitness,\n                                                   key=population_fitness.get)[:population_subset_size]):\n            if (generation % generations_10pct == 0 or generation == generations - 1) and rank == 0:\n                current_best_genome = agent_genome\n                print(\"Generation %d best: %d | Unique genomes: %d\" % (generation,\n                                                                       population_fitness[agent_genome],\n                                                                       len(population_fitness)))\n                print(agent_genome)                \n                print(\"\")\n\n                # If this is the first route found, or it is shorter than the best route we know,\n                # create a html output and display it\n                if population_fitness[agent_genome] < current_best_distance or current_best_distance < 0:\n                    current_best_distance = population_fitness[agent_genome]\n                    CreateOptimalRouteHtmlFile(agent_genome, current_best_distance, False)\n                    \n\n            # Create 1 exact copy of each of the top road trips\n            new_population.append(agent_genome)\n\n            # Create 2 offspring with 1-3 point mutations\n            for offspring in range(2):\n                new_population.append(mutate_agent(agent_genome, 3))\n                \n            # Create 7 offspring with a single shuffle mutation\n            for offspring in range(7):\n                new_population.append(shuffle_mutation(agent_genome))\n\n        # Replace the old population with the new population of offspring \n        for i in range(len(population))[::-1]:\n            del population[i]\n\n        population = new_population\n    return current_best_genome\n\n\nif __name__ == '__main__':\n    # If this file exists, read the data stored in it - if not then collect data by asking google\n    print(\"Begin finding shortest route\")\n    file_path = waypoints_file\n    if os.path.exists(file_path):\n        print(\"Waypoints exist\")\n        #file exists used saved results\n        waypoint_distances = {}\n        waypoint_durations = {}\n        all_waypoints = set()\n\n        waypoint_data = pd.read_csv(file_path, sep=\"\\t\")\n\n        for i, row in waypoint_data.iterrows():\n            waypoint_distances[frozenset([row.waypoint1, row.waypoint2])] = row.distance_m\n            waypoint_durations[frozenset([row.waypoint1, row.waypoint2])] = row.duration_s\n            all_waypoints.update([row.waypoint1, row.waypoint2])\n\n    else:\n        # File does not exist - compute results       \n        print(\"Collecting Waypoints\")\n        waypoint_distances = {}\n        waypoint_durations = {}\n\n\n        gmaps = googlemaps.Client(GOOGLE_MAPS_API_KEY)\n        for (waypoint1, waypoint2) in combinations(all_waypoints, 2):\n            try:\n                route = gmaps.distance_matrix(origins=[waypoint1],\n                                              destinations=[waypoint2],\n                                              mode=\"driving\", # Change to \"walking\" for walking directions,\n                                                              # \"bicycling\" for biking directions, etc.\n                                              language=\"English\",\n                                              units=\"metric\")\n\n                # \"distance\" is in meters\n                distance = route[\"rows\"][0][\"elements\"][0][\"distance\"][\"value\"]\n\n                # \"duration\" is in seconds\n                duration = route[\"rows\"][0][\"elements\"][0][\"duration\"][\"value\"]\n\n                waypoint_distances[frozenset([waypoint1, waypoint2])] = distance\n                waypoint_durations[frozenset([waypoint1, waypoint2])] = duration\n        \n            except Exception as e:\n                print(\"Error with finding the route between %s and %s.\" % (waypoint1, waypoint2))\n        \n        print(\"Saving Waypoints\")\n        with open(waypoints_file, \"w\") as out_file:\n            out_file.write(\"\\t\".join([\"waypoint1\",\n                                      \"waypoint2\",\n                                      \"distance_m\",\n                                      \"duration_s\"]))\n        \n            for (waypoint1, waypoint2) in waypoint_distances.keys():\n                out_file.write(\"\\n\" +\n                               \"\\t\".join([waypoint1,\n                                          waypoint2,\n                                          str(waypoint_distances[frozenset([waypoint1, waypoint2])]),\n                                          str(waypoint_durations[frozenset([waypoint1, waypoint2])])]))\n\n    print(\"Search for optimal route\")\n    optimal_route = run_genetic_algorithm(generations=thisRunGenerations, population_size=thisRunPopulation_size)\n\n    # This is probably redundant now that the files are created in run_genetic_algorithm,\n    # but leaving it active to ensure  the final result is not lost\n    CreateOptimalRouteHtmlFile(optimal_route, 1, True)\n"
  },
  {
    "path": "optimal-road-trip/my-waypoints-dist-dur.tsv",
    "content": "waypoint1\twaypoint2\tdistance_m\tduration_s\nTaliesin, County Road C, Spring Green, Wisconsin\tGlacier National Park, West Glacier, MT\t2218019\t74837\nFrench Quarter, New Orleans, LA\tFort Snelling, Tower Avenue, Saint Paul, MN\t1916146\t63591\nMount Rushmore National Memorial, South Dakota 244, Keystone, SD\tThe Breakers, Ochre Point Avenue, Newport, RI\t3057999\t101536\nFort Union Trading Post National Historic Site, Williston, North Dakota 1804, ND\tCable Car Museum, 94108, 1201 Mason St, San Francisco, CA 94108\t2347381\t78156\nC. W. Parker Carousel Museum, South Esplanade Street, Leavenworth, KS\tGateway Arch, Washington Avenue, St Louis, MO\t455098\t15430\nMaryland State House, 100 State Cir, Annapolis, MD 21401\tGraceland, Elvis Presley Boulevard, Memphis, TN\t1476772\t48116\nChickasaw National Recreation Area, 1008 W 2nd St, Sulphur, OK 73086\tCarlsbad Caverns National Park, Carlsbad, NM\t874048\t32551\nLiberty Bell, 6th Street, Philadelphia, PA\tCape Canaveral, FL\t1618906\t52203\nLiberty Bell, 6th Street, Philadelphia, PA\tThe Breakers, Ochre Point Avenue, Newport, RI\t441382\t16915\nSan Andreas Fault, San Benito County, CA\tFrench Quarter, New Orleans, LA\t3481242\t111261\nUSS Alabama, Battleship Parkway, Mobile, AL\tFort Union Trading Post National Historic Site, Williston, North Dakota 1804, ND\t2975383\t98743\nGraceland, Elvis Presley Boulevard, Memphis, TN\tYellowstone National Park, WY 82190\t2487165\t87170\nShelburne Farms, Harbor Road, Shelburne, VT\tSpring Grove Cemetery, Spring Grove Avenue, Cincinnati, OH\t1280422\t46354\nThe Mark Twain House & Museum, Farmington Avenue, Hartford, CT\tOkefenokee Swamp Park, Okefenokee Swamp Park Road, Waycross, GA\t1667022\t56534\nLiberty Bell, 6th Street, Philadelphia, PA\tTerrace Hill, Grand Avenue, Des Moines, IA\t1737865\t57267\nOmni Mount Washington Resort, Mount Washington Hotel Road, Bretton Woods, NH\tStatue of Liberty, Liberty Island, NYC, NY\t577249\t21127\nChickasaw National Recreation Area, 1008 W 2nd St, Sulphur, OK 73086\tAshfall Fossil Bed, Royal, NE\t976321\t34111\nLiberty Bell, 6th Street, Philadelphia, PA\tBryce Canyon National Park, Hwy 63, Bryce, UT\t3687679\t120631\nUSS Alabama, Battleship Parkway, Mobile, AL\tCongress Hall, Congress Place, Cape May, NJ 08204\t1862015\t62154\nMaryland State House, 100 State Cir, Annapolis, MD 21401\tLincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL\t1297516\t44127\nTaliesin, County Road C, Spring Green, Wisconsin\tThe Breakers, Ochre Point Avenue, Newport, RI\t1855961\t64286\nToltec Mounds, Scott, AR\tFort Union Trading Post National Historic Site, Williston, North Dakota 1804, ND\t2344430\t75072\nMount Vernon, Fairfax County, Virginia\tCongress Hall, Congress Place, Cape May, NJ 08204\t324587\t13291\nAcadia National Park, Maine\tWest Baden Springs Hotel, West Baden Avenue, West Baden Springs, IN\t2101168\t71928\nNew Castle Historic District, Delaware\tOkefenokee Swamp Park, Okefenokee Swamp Park Road, Waycross, GA\t1264487\t42249\nHanford Site, Benton County, WA\tColumbia River Gorge National Scenic Area, Oregon\t202197\t7339\nGraceland, Elvis Presley Boulevard, Memphis, TN\tThe Breakers, Ochre Point Avenue, Newport, RI\t2057815\t69018\nHanford Site, Benton County, WA\tThe Mark Twain House & Museum, Farmington Avenue, Hartford, CT\t4548872\t147740\nHoover Dam, NV\tWest Baden Springs Hotel, West Baden Avenue, West Baden Springs, IN\t2925806\t92712\nLiberty Bell, 6th Street, Philadelphia, PA\tThe Alamo, Alamo Plaza, San Antonio, TX\t2800673\t90492\nFort Snelling, Tower Avenue, Saint Paul, MN\tCable Car Museum, 94108, 1201 Mason St, San Francisco, CA 94108\t3156193\t102831\nMount Vernon, Fairfax County, Virginia\tFort Union Trading Post National Historic Site, Williston, North Dakota 1804, ND\t2868913\t94308\nChickasaw National Recreation Area, 1008 W 2nd St, Sulphur, OK 73086\tThe Breakers, Ochre Point Avenue, Newport, RI\t2700531\t92033\nMount Rushmore National Memorial, South Dakota 244, Keystone, SD\tToltec Mounds, Scott, AR\t1872657\t59896\nMount Vernon, Fairfax County, Virginia\tSpring Grove Cemetery, Spring Grove Avenue, Cincinnati, OH\t829832\t28982\nCarlsbad Caverns National Park, Carlsbad, NM\tCraters of the Moon National Monument & Preserve, Arco, ID\t1875346\t66294\nSpring Grove Cemetery, Spring Grove Avenue, Cincinnati, OH\tCable Car Museum, 94108, 1201 Mason St, San Francisco, CA 94108\t3794883\t121470\nBryce Canyon National Park, Hwy 63, Bryce, UT\tAshfall Fossil Bed, Royal, NE\t1693953\t58524\nLost World Caverns, Lewisburg, WV\tStatue of Liberty, Liberty Island, NYC, NY\t770142\t25955\nCraters of the Moon National Monument & Preserve, Arco, ID\tCongress Hall, Congress Place, Cape May, NJ 08204\t3853634\t125539\nHanford Site, Benton County, WA\tNew Castle Historic District, Delaware\t4363313\t142580\nSan Andreas Fault, San Benito County, CA\tMammoth Cave National Park, Mammoth Cave Pkwy, Mammoth Cave, KY\t3667715\t117791\nToltec Mounds, Scott, AR\tGlacier National Park, West Glacier, MT\t3041488\t98106\nUSS Alabama, Battleship Parkway, Mobile, AL\tMaryland State House, 100 State Cir, Annapolis, MD 21401\t1613845\t52212\nLost World Caverns, Lewisburg, WV\tFort Sumter National Monument, Sullivan's Island, SC\t690404\t24395\nToltec Mounds, Scott, AR\tShelburne Farms, Harbor Road, Shelburne, VT\t2274624\t78824\nMaryland State House, 100 State Cir, Annapolis, MD 21401\tNew Castle Historic District, Delaware\t100318\t4328\nToltec Mounds, Scott, AR\tAshfall Fossil Bed, Royal, NE\t1276957\t43608\nMount Vernon, Fairfax County, Virginia\tOlympia Entertainment, Woodward Avenue, Detroit, MI\t869309\t29349\nFort Union Trading Post National Historic Site, Williston, North Dakota 1804, ND\tColumbia River Gorge National Scenic Area, Oregon\t1747344\t56909\nUSS Alabama, Battleship Parkway, Mobile, AL\tGraceland, Elvis Presley Boulevard, Memphis, TN\t582585\t21366\nChickasaw National Recreation Area, 1008 W 2nd St, Sulphur, OK 73086\tFort Sumter National Monument, Sullivan's Island, SC\t1885895\t62411\nLost World Caverns, Lewisburg, WV\tFrench Quarter, New Orleans, LA\t1441808\t46167\nUSS Alabama, Battleship Parkway, Mobile, AL\tOlympia Entertainment, Woodward Avenue, Detroit, MI\t1585831\t51531\nMount Vernon, Fairfax County, Virginia\tAcadia National Park, Maine\t1159086\t41873\nC. W. Parker Carousel Museum, South Esplanade Street, Leavenworth, KS\tThe Breakers, Ochre Point Avenue, Newport, RI\t2264052\t77724\nAcadia National Park, Maine\tLincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL\t2293420\t77193\nHoover Dam, NV\tMammoth Cave National Park, Mammoth Cave Pkwy, Mammoth Cave, KY\t2972025\t94061\nHoover Dam, NV\tAshfall Fossil Bed, Royal, NE\t2042375\t68608\nFort Sumter National Monument, Sullivan's Island, SC\tFrench Quarter, New Orleans, LA\t1223403\t39567\nFort Union Trading Post National Historic Site, Williston, North Dakota 1804, ND\tYellowstone National Park, WY 82190\t1152968\t43043\nFort Union Trading Post National Historic Site, Williston, North Dakota 1804, ND\tThe Mark Twain House & Museum, Farmington Avenue, Hartford, CT\t3143831\t103818\nWright Brothers National Memorial Visitor Center, Manteo, NC\tGateway Arch, Washington Avenue, St Louis, MO\t1619422\t53683\nMammoth Cave National Park, Mammoth Cave Pkwy, Mammoth Cave, KY\tLincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL\t553214\t19824\nLost World Caverns, Lewisburg, WV\tCraters of the Moon National Monument & Preserve, Arco, ID\t3375573\t109217\nStatue of Liberty, Liberty Island, NYC, NY\tColumbia River Gorge National Scenic Area, Oregon\t4503584\t145711\nTerrace Hill, Grand Avenue, Des Moines, IA\tYellowstone National Park, WY 82190\t1736070\t60164\nHanford Site, Benton County, WA\tTaliesin, County Road C, Spring Green, Wisconsin\t2865645\t92549\nThe Alamo, Alamo Plaza, San Antonio, TX\tAshfall Fossil Bed, Royal, NE\t1582862\t53486\nVicksburg National Military Park, Clay Street, Vicksburg, MS\tStatue of Liberty, Liberty Island, NYC, NY\t1993400\t64754\nAshfall Fossil Bed, Royal, NE\tLincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL\t957962\t33654\nSan Andreas Fault, San Benito County, CA\tThe Alamo, Alamo Plaza, San Antonio, TX\t2614335\t83174\nC. W. Parker Carousel Museum, South Esplanade Street, Leavenworth, KS\tWright Brothers National Memorial Visitor Center, Manteo, NC\t2070353\t68518\nOmni Mount Washington Resort, Mount Washington Hotel Road, Bretton Woods, NH\tAcadia National Park, Maine\t370453\t16119\nLost World Caverns, Lewisburg, WV\tOlympia Entertainment, Woodward Avenue, Detroit, MI\t751740\t27138\nToltec Mounds, Scott, AR\tMount Vernon, Fairfax County, Virginia\t1628719\t53196\nCape Canaveral, FL\tLincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL\t1787806\t57333\nHanford Site, Benton County, WA\tCraters of the Moon National Monument & Preserve, Arco, ID\t804532\t27912\nAshfall Fossil Bed, Royal, NE\tWest Baden Springs Hotel, West Baden Avenue, West Baden Springs, IN\t1348425\t46160\nShelburne Farms, Harbor Road, Shelburne, VT\tOlympia Entertainment, Woodward Avenue, Detroit, MI\t1075881\t37603\nMount Rushmore National Memorial, South Dakota 244, Keystone, SD\tStatue of Liberty, Liberty Island, NYC, NY\t2772422\t91153\nLost World Caverns, Lewisburg, WV\tGlacier National Park, West Glacier, MT\t3442230\t115082\nMammoth Cave National Park, Mammoth Cave Pkwy, Mammoth Cave, KY\tCraters of the Moon National Monument & Preserve, Arco, ID\t2830185\t93012\nHoover Dam, NV\tShelburne Farms, Harbor Road, Shelburne, VT\t4286375\t141744\nMaryland State House, 100 State Cir, Annapolis, MD 21401\tTaliesin, County Road C, Spring Green, Wisconsin\t1461552\t50064\nLiberty Bell, 6th Street, Philadelphia, PA\tCraters of the Moon National Monument & Preserve, Arco, ID\t3705218\t120341\nLost World Caverns, Lewisburg, WV\tFort Snelling, Tower Avenue, Saint Paul, MN\t1626905\t54854\nTerrace Hill, Grand Avenue, Des Moines, IA\tStatue of Liberty, Liberty Island, NYC, NY\t1787020\t59289\nHanford Site, Benton County, WA\tAshfall Fossil Bed, Royal, NE\t2197917\t74421\nSpring Grove Cemetery, Spring Grove Avenue, Cincinnati, OH\tWhite House, Pennsylvania Avenue Northwest, Washington, DC\t809619\t28300\nTaliesin, County Road C, Spring Green, Wisconsin\tColumbia River Gorge National Scenic Area, Oregon\t3013796\t97923\nThe Mark Twain House & Museum, Farmington Avenue, Hartford, CT\tStatue of Liberty, Liberty Island, NYC, NY\t192047\t7989\nToltec Mounds, Scott, AR\tFrench Quarter, New Orleans, LA\t670161\t24369\nUSS Constitution, Boston, MA\tAcadia National Park, Maine\t443026\t16367\nMammoth Cave National Park, Mammoth Cave Pkwy, Mammoth Cave, KY\tColumbia River Gorge National Scenic Area, Oregon\t3571513\t116405\nSan Andreas Fault, San Benito County, CA\tAshfall Fossil Bed, Royal, NE\t2748296\t91875\nToltec Mounds, Scott, AR\tOmni Mount Washington Resort, Mount Washington Hotel Road, Bretton Woods, NH\t2503709\t84931\nChickasaw National Recreation Area, 1008 W 2nd St, Sulphur, OK 73086\tBryce Canyon National Park, Hwy 63, Bryce, UT\t1828713\t63879\nWright Brothers National Memorial Visitor Center, Manteo, NC\tBryce Canyon National Park, Hwy 63, Bryce, UT\t3890985\t126573\nBryce Canyon National Park, Hwy 63, Bryce, UT\tWest Baden Springs Hotel, West Baden Avenue, West Baden Springs, IN\t2651338\t85582\nToltec Mounds, Scott, AR\tVicksburg National Military Park, Clay Street, Vicksburg, MS\t342172\t13312\nFort Union Trading Post National Historic Site, Williston, North Dakota 1804, ND\tWest Baden Springs Hotel, West Baden Avenue, West Baden Springs, IN\t2168326\t72590\nToltec Mounds, Scott, AR\tChickasaw National Recreation Area, 1008 W 2nd St, Sulphur, OK 73086\t612075\t20648\nMount Vernon, Fairfax County, Virginia\tShelburne Farms, Harbor Road, Shelburne, VT\t838747\t31297\nCape Canaveral, FL\tCongress Hall, Congress Place, Cape May, NJ 08204\t1697361\t55802\nMaryland State House, 100 State Cir, Annapolis, MD 21401\tBryce Canyon National Park, Hwy 63, Bryce, UT\t3616879\t118534\nMaryland State House, 100 State Cir, Annapolis, MD 21401\tStatue of Liberty, Liberty Island, NYC, NY\t346519\t12361\nMaryland State House, 100 State Cir, Annapolis, MD 21401\tAcadia National Park, Maine\t1120643\t39839\nLiberty Bell, 6th Street, Philadelphia, PA\tCarlsbad Caverns National Park, Carlsbad, NM\t3152378\t105074\nThe Mark Twain House & Museum, Farmington Avenue, Hartford, CT\tColumbia River Gorge National Scenic Area, Oregon\t4663563\t150807\nToltec Mounds, Scott, AR\tCape Canaveral, FL\t1567778\t51965\nWhite House, Pennsylvania Avenue Northwest, Washington, DC\tThe Alamo, Alamo Plaza, San Antonio, TX\t2579805\t82868\nMammoth Cave National Park, Mammoth Cave Pkwy, Mammoth Cave, KY\tBryce Canyon National Park, Hwy 63, Bryce, UT\t2718623\t88932\nNew Castle Historic District, Delaware\tVicksburg National Military Park, Clay Street, Vicksburg, MS\t1794302\t58751\nMaryland State House, 100 State Cir, Annapolis, MD 21401\tFort Sumter National Monument, Sullivan's Island, SC\t911048\t29592\nPikes Peak, Colorado\tOlympia Entertainment, Woodward Avenue, Detroit, MI\t2190092\t71624\nToltec Mounds, Scott, AR\tStatue of Liberty, Liberty Island, NYC, NY\t1983218\t65145\nOlympia Entertainment, Woodward Avenue, Detroit, MI\tFrench Quarter, New Orleans, LA\t1716724\t55770\nHoover Dam, NV\tGateway Arch, Washington Avenue, St Louis, MO\t2550203\t80416\nHanford Site, Benton County, WA\tWhite House, Pennsylvania Avenue Northwest, Washington, DC\t4241452\t137764\nMount Vernon, Fairfax County, Virginia\tVicksburg National Military Park, Clay Street, Vicksburg, MS\t1638902\t52805\nOkefenokee Swamp Park, Okefenokee Swamp Park Road, Waycross, GA\tFort Snelling, Tower Avenue, Saint Paul, MN\t2146870\t73185\nLost World Caverns, Lewisburg, WV\tThe Alamo, Alamo Plaza, San Antonio, TX\t2277257\t72943\nChickasaw National Recreation Area, 1008 W 2nd St, Sulphur, OK 73086\tThe Alamo, Alamo Plaza, San Antonio, TX\t652795\t22214\nWright Brothers National Memorial Visitor Center, Manteo, NC\tNew Castle Historic District, Delaware\t422162\t17408\nFort Sumter National Monument, Sullivan's Island, SC\tWest Baden Springs Hotel, West Baden Avenue, West Baden Springs, IN\t1080930\t36445\nThe Mark Twain House & Museum, Farmington Avenue, Hartford, CT\tFort Sumter National Monument, Sullivan's Island, SC\t1407416\t47697\nNew Castle Historic District, Delaware\tCable Car Museum, 94108, 1201 Mason St, San Francisco, CA 94108\t4651287\t150314\nLost World Caverns, Lewisburg, WV\tWhite House, Pennsylvania Avenue Northwest, Washington, DC\t416487\t13963\nPikes Peak, Colorado\tFort Sumter National Monument, Sullivan's Island, SC\t2763349\t91159\nSan Andreas Fault, San Benito County, CA\tPikes Peak, Colorado\t2028101\t70650\nHanford Site, Benton County, WA\tFort Snelling, Tower Avenue, Saint Paul, MN\t2476769\t78773\nHanford Site, Benton County, WA\tSpring Grove Cemetery, Spring Grove Avenue, Cincinnati, OH\t3537439\t115348\nMaryland State House, 100 State Cir, Annapolis, MD 21401\tColumbia River Gorge National Scenic Area, Oregon\t4394752\t141872\nUSS Alabama, Battleship Parkway, Mobile, AL\tMount Vernon, Fairfax County, Virginia\t1538446\t49634\nTaliesin, County Road C, Spring Green, Wisconsin\tBryce Canyon National Park, Hwy 63, Bryce, UT\t2408644\t77823\nC. W. Parker Carousel Museum, South Esplanade Street, Leavenworth, KS\tOlympia Entertainment, Woodward Avenue, Detroit, MI\t1245101\t40896\nMount Rushmore National Memorial, South Dakota 244, Keystone, SD\tGrand Canyon National Park, Arizona\t1651973\t56937\nOlympia Entertainment, Woodward Avenue, Detroit, MI\tWest Baden Springs Hotel, West Baden Avenue, West Baden Springs, IN\t625402\t22359\nCraters of the Moon National Monument & Preserve, Arco, ID\tGateway Arch, Washington Avenue, St Louis, MO\t2387912\t77468\nMaryland State House, 100 State Cir, Annapolis, MD 21401\tGateway Arch, Washington Avenue, St Louis, MO\t1357038\t46244\nCongress Hall, Congress Place, Cape May, NJ 08204\tStatue of Liberty, Liberty Island, NYC, NY\t254433\t9322\nGraceland, Elvis Presley Boulevard, Memphis, TN\tColumbia River Gorge National Scenic Area, Oregon\t3466308\t113158\nTerrace Hill, Grand Avenue, Des Moines, IA\tLost World Caverns, Lewisburg, WV\t1444979\t48126\nMount Vernon, Fairfax County, Virginia\tSan Andreas Fault, San Benito County, CA\t4600219\t148058\nWright Brothers National Memorial Visitor Center, Manteo, NC\tLincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL\t1641490\t55760\nGlacier National Park, West Glacier, MT\tGrand Canyon National Park, Arizona\t1671390\t57638\nMount Rushmore National Memorial, South Dakota 244, Keystone, SD\tLiberty Bell, 6th Street, Philadelphia, PA\t2722541\t89251\nC. W. Parker Carousel Museum, South Esplanade Street, Leavenworth, KS\tGlacier National Park, West Glacier, MT\t2204537\t75486\nYellowstone National Park, WY 82190\tAshfall Fossil Bed, Royal, NE\t1235995\t47419\nCape Canaveral, FL\tOlympia Entertainment, Woodward Avenue, Detroit, MI\t1869664\t62291\nAshfall Fossil Bed, Royal, NE\tGrand Canyon National Park, Arizona\t1845322\t64321\nPikes Peak, Colorado\tWest Baden Springs Hotel, West Baden Avenue, West Baden Springs, IN\t1765180\t59041\nMount Rushmore National Memorial, South Dakota 244, Keystone, SD\tAcadia National Park, Maine\t3512958\t116635\nCarlsbad Caverns National Park, Carlsbad, NM\tPikes Peak, Colorado\t922977\t35011\nFort Union Trading Post National Historic Site, Williston, North Dakota 1804, ND\tOkefenokee Swamp Park, Okefenokee Swamp Park Road, Waycross, GA\t3254574\t107170\nHoover Dam, NV\tOlympia Entertainment, Woodward Avenue, Detroit, MI\t3287993\t104894\nMammoth Cave National Park, Mammoth Cave Pkwy, Mammoth Cave, KY\tWest Baden Springs Hotel, West Baden Avenue, West Baden Springs, IN\t199119\t8955\nWright Brothers National Memorial Visitor Center, Manteo, NC\tMammoth Cave National Park, Mammoth Cave Pkwy, Mammoth Cave, KY\t1179764\t41494\nPikes Peak, Colorado\tCongress Hall, Congress Place, Cape May, NJ 08204\t2949957\t99167\nUSS Constitution, Boston, MA\tLincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL\t1863224\t61974\nFort Sumter National Monument, Sullivan's Island, SC\tOkefenokee Swamp Park, Okefenokee Swamp Park Road, Waycross, GA\t363482\t13635\nToltec Mounds, Scott, AR\tNew Castle Historic District, Delaware\t1783543\t59257\nVicksburg National Military Park, Clay Street, Vicksburg, MS\tColumbia River Gorge National Scenic Area, Oregon\t3726654\t119687\nMount Vernon, Fairfax County, Virginia\tStatue of Liberty, Liberty Island, NYC, NY\t386403\t14398\nMount Vernon, Fairfax County, Virginia\tGraceland, Elvis Presley Boulevard, Memphis, TN\t1414540\t45977\nStatue of Liberty, Liberty Island, NYC, NY\tFort Snelling, Tower Avenue, Saint Paul, MN\t1934123\t64833\nTaliesin, County Road C, Spring Green, Wisconsin\tCable Car Museum, 94108, 1201 Mason St, San Francisco, CA 94108\t3320261\t105749\nYellowstone National Park, WY 82190\tFort Snelling, Tower Avenue, Saint Paul, MN\t1822887\t63288\nWhite House, Pennsylvania Avenue Northwest, Washington, DC\tOkefenokee Swamp Park, Okefenokee Swamp Park Road, Waycross, GA\t1114274\t36642\nGraceland, Elvis Presley Boulevard, Memphis, TN\tBryce Canyon National Park, Hwy 63, Bryce, UT\t2504346\t83235\nShelburne Farms, Harbor Road, Shelburne, VT\tNew Castle Historic District, Delaware\t751134\t27927\nWright Brothers National Memorial Visitor Center, Manteo, NC\tAcadia National Park, Maine\t1492775\t55252\nTerrace Hill, Grand Avenue, Des Moines, IA\tFort Snelling, Tower Avenue, Saint Paul, MN\t394558\t12643\nPikes Peak, Colorado\tAshfall Fossil Bed, Royal, NE\t948929\t35456\nMount Vernon, Fairfax County, Virginia\tCape Canaveral, FL\t1373791\t43281\nCape Canaveral, FL\tCable Car Museum, 94108, 1201 Mason St, San Francisco, CA 94108\t4607140\t147726\nAcadia National Park, Maine\tAshfall Fossil Bed, Royal, NE\t2988611\t101499\nUSS Constitution, Boston, MA\tVicksburg National Military Park, Clay Street, Vicksburg, MS\t2347308\t77140\nFort Union Trading Post National Historic Site, Williston, North Dakota 1804, ND\tGateway Arch, Washington Avenue, St Louis, MO\t1957477\t64076\nThe Breakers, Ochre Point Avenue, Newport, RI\tAcadia National Park, Maine\t561691\t21320\nLost World Caverns, Lewisburg, WV\tPikes Peak, Colorado\t2378452\t78521\nWhite House, Pennsylvania Avenue Northwest, Washington, DC\tFort Sumter National Monument, Sullivan's Island, SC\t854668\t27805\nStatue of Liberty, Liberty Island, NYC, NY\tGrand Canyon National Park, Arizona\t3838968\t124490\nUSS Alabama, Battleship Parkway, Mobile, AL\tUSS Constitution, Boston, MA\t2267282\t75766\nThe Alamo, Alamo Plaza, San Antonio, TX\tWest Baden Springs Hotel, West Baden Avenue, West Baden Springs, IN\t1800120\t58441\nNew Castle Historic District, Delaware\tOlympia Entertainment, Woodward Avenue, Detroit, MI\t969661\t33480\nTerrace Hill, Grand Avenue, Des Moines, IA\tFort Sumter National Monument, Sullivan's Island, SC\t1936485\t62907\nBryce Canyon National Park, Hwy 63, Bryce, UT\tLincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL\t2375844\t76706\nLiberty Bell, 6th Street, Philadelphia, PA\tFort Union Trading Post National Historic Site, Williston, North Dakota 1804, ND\t2943788\t96463\nMount Rushmore National Memorial, South Dakota 244, Keystone, SD\tGraceland, Elvis Presley Boulevard, Memphis, TN\t1905830\t63373\nMount Vernon, Fairfax County, Virginia\tUSS Constitution, Boston, MA\t729479\t26780\nOmni Mount Washington Resort, Mount Washington Hotel Road, Bretton Woods, NH\tCable Car Museum, 94108, 1201 Mason St, San Francisco, CA 94108\t5079239\t165660\nMount Rushmore National Memorial, South Dakota 244, Keystone, SD\tGateway Arch, Washington Avenue, St Louis, MO\t1568984\t50208\nLiberty Bell, 6th Street, Philadelphia, PA\tTaliesin, County Road C, Spring Green, Wisconsin\t1520502\t52001\nFrench Quarter, New Orleans, LA\tCable Car Museum, 94108, 1201 Mason St, San Francisco, CA 94108\t3655745\t115357\nGlacier National Park, West Glacier, MT\tLincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL\t2627806\t88466\nStatue of Liberty, Liberty Island, NYC, NY\tFort Sumter National Monument, Sullivan's Island, SC\t1222041\t40861\nUSS Alabama, Battleship Parkway, Mobile, AL\tThe Mark Twain House & Museum, Farmington Avenue, Hartford, CT\t2109747\t70257\nMount Vernon, Fairfax County, Virginia\tFort Snelling, Tower Avenue, Saint Paul, MN\t1810094\t60656\nChickasaw National Recreation Area, 1008 W 2nd St, Sulphur, OK 73086\tGateway Arch, Washington Avenue, St Louis, MO\t879834\t29469\nChickasaw National Recreation Area, 1008 W 2nd St, Sulphur, OK 73086\tCraters of the Moon National Monument & Preserve, Arco, ID\t2302132\t74104\nCape Canaveral, FL\tAshfall Fossil Bed, Royal, NE\t2651598\t86966\nTerrace Hill, Grand Avenue, Des Moines, IA\tGraceland, Elvis Presley Boulevard, Memphis, TN\t1008442\t33550\nCraters of the Moon National Monument & Preserve, Arco, ID\tVicksburg National Military Park, Clay Street, Vicksburg, MS\t2984500\t96748\nShelburne Farms, Harbor Road, Shelburne, VT\tCape Canaveral, FL\t2212383\t74063\nGraceland, Elvis Presley Boulevard, Memphis, TN\tWright Brothers National Memorial Visitor Center, Manteo, NC\t1513958\t50106\nAcadia National Park, Maine\tFort Snelling, Tower Avenue, Saint Paul, MN\t2666567\t90398\nNew Castle Historic District, Delaware\tFrench Quarter, New Orleans, LA\t1900188\t62217\nPikes Peak, Colorado\tCable Car Museum, 94108, 1201 Mason St, San Francisco, CA 94108\t2110257\t72715\nThe Breakers, Ochre Point Avenue, Newport, RI\tCongress Hall, Congress Place, Cape May, NJ 08204\t545003\t20300\nLiberty Bell, 6th Street, Philadelphia, PA\tShelburne Farms, Harbor Road, Shelburne, VT\t603814\t22908\nSan Andreas Fault, San Benito County, CA\tBryce Canyon National Park, Hwy 63, Bryce, UT\t1153959\t39401\nCarlsbad Caverns National Park, Carlsbad, NM\tFort Sumter National Monument, Sullivan's Island, SC\t2529332\t84159\nToltec Mounds, Scott, AR\tCable Car Museum, 94108, 1201 Mason St, San Francisco, CA 94108\t3179769\t101423\nMount Vernon, Fairfax County, Virginia\tOkefenokee Swamp Park, Okefenokee Swamp Park Road, Waycross, GA\t1094658\t35633\nColumbia River Gorge National Scenic Area, Oregon\tGlacier National Park, West Glacier, MT\t844416\t29826\nGraceland, Elvis Presley Boulevard, Memphis, TN\tLincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL\t622132\t20233\nTerrace Hill, Grand Avenue, Des Moines, IA\tCape Canaveral, FL\t2229372\t72962\nTaliesin, County Road C, Spring Green, Wisconsin\tWhite House, Pennsylvania Avenue Northwest, Washington, DC\t1422941\t49023\nSpring Grove Cemetery, Spring Grove Avenue, Cincinnati, OH\tColumbia River Gorge National Scenic Area, Oregon\t3653599\t116971\nOlympia Entertainment, Woodward Avenue, Detroit, MI\tLincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL\t725616\t23888\nHanford Site, Benton County, WA\tVicksburg National Military Park, Clay Street, Vicksburg, MS\t3685723\t118499\nGraceland, Elvis Presley Boulevard, Memphis, TN\tMammoth Cave National Park, Mammoth Cave Pkwy, Mammoth Cave, KY\t482758\t15622\nChickasaw National Recreation Area, 1008 W 2nd St, Sulphur, OK 73086\tHanford Site, Benton County, WA\t3007742\t95841\nFort Union Trading Post National Historic Site, Williston, North Dakota 1804, ND\tGrand Canyon National Park, Arizona\t1966377\t66850\nWright Brothers National Memorial Visitor Center, Manteo, NC\tGlacier National Park, West Glacier, MT\t4052231\t135932\nFort Sumter National Monument, Sullivan's Island, SC\tColumbia River Gorge National Scenic Area, Oregon\t4507989\t144600\nGraceland, Elvis Presley Boulevard, Memphis, TN\tAcadia National Park, Maine\t2553219\t85419\nYellowstone National Park, WY 82190\tBryce Canyon National Park, Hwy 63, Bryce, UT\t962304\t36167\nThe Breakers, Ochre Point Avenue, Newport, RI\tNew Castle Historic District, Delaware\t588702\t21933\nTerrace Hill, Grand Avenue, Des Moines, IA\tBryce Canyon National Park, Hwy 63, Bryce, UT\t1980517\t63788\nCarlsbad Caverns National Park, Carlsbad, NM\tWest Baden Springs Hotel, West Baden Avenue, West Baden Springs, IN\t2081182\t70149\nMount Rushmore National Memorial, South Dakota 244, Keystone, SD\tCongress Hall, Congress Place, Cape May, NJ 08204\t2857920\t94583\nSpring Grove Cemetery, Spring Grove Avenue, Cincinnati, OH\tFort Sumter National Monument, Sullivan's Island, SC\t1007099\t33020\nTerrace Hill, Grand Avenue, Des Moines, IA\tCongress Hall, Congress Place, Cape May, NJ 08204\t1886282\t62465\nUSS Constitution, Boston, MA\tBryce Canyon National Park, Hwy 63, Bryce, UT\t4079429\t133189\nHoover Dam, NV\tVicksburg National Military Park, Clay Street, Vicksburg, MS\t2492392\t79680\nChickasaw National Recreation Area, 1008 W 2nd St, Sulphur, OK 73086\tOkefenokee Swamp Park, Okefenokee Swamp Park Road, Waycross, GA\t1717447\t60333\nLiberty Bell, 6th Street, Philadelphia, PA\tMount Vernon, Fairfax County, Virginia\t242317\t9386\nMaryland State House, 100 State Cir, Annapolis, MD 21401\tCarlsbad Caverns National Park, Carlsbad, NM\t2992105\t99733\nMount Rushmore National Memorial, South Dakota 244, Keystone, SD\tFort Union Trading Post National Historic Site, Williston, North Dakota 1804, ND\t581412\t21547\nThe Breakers, Ochre Point Avenue, Newport, RI\tVicksburg National Military Park, Clay Street, Vicksburg, MS\t2284060\t75766\nC. W. Parker Carousel Museum, South Esplanade Street, Leavenworth, KS\tThe Mark Twain House & Museum, Farmington Avenue, Hartford, CT\t2170327\t72448\nThe Mark Twain House & Museum, Farmington Avenue, Hartford, CT\tYellowstone National Park, WY 82190\t3684032\t124272\nCongress Hall, Congress Place, Cape May, NJ 08204\tFort Snelling, Tower Avenue, Saint Paul, MN\t2033385\t68009\nSpring Grove Cemetery, Spring Grove Avenue, Cincinnati, OH\tLost World Caverns, Lewisburg, WV\t508306\t18411\nChickasaw National Recreation Area, 1008 W 2nd St, Sulphur, OK 73086\tCable Car Museum, 94108, 1201 Mason St, San Francisco, CA 94108\t2701523\t87464\nLost World Caverns, Lewisburg, WV\tCable Car Museum, 94108, 1201 Mason St, San Francisco, CA 94108\t4297208\t137228\nWhite House, Pennsylvania Avenue Northwest, Washington, DC\tNew Castle Historic District, Delaware\t145742\t6395\nSpring Grove Cemetery, Spring Grove Avenue, Cincinnati, OH\tPikes Peak, Colorado\t1931715\t64442\nSan Andreas Fault, San Benito County, CA\tWest Baden Springs Hotel, West Baden Avenue, West Baden Springs, IN\t3619999\t116303\nPikes Peak, Colorado\tGateway Arch, Washington Avenue, St Louis, MO\t1390792\t46772\nThe Mark Twain House & Museum, Farmington Avenue, Hartford, CT\tGateway Arch, Washington Avenue, St Louis, MO\t1734279\t58145\nCape Canaveral, FL\tStatue of Liberty, Liberty Island, NYC, NY\t1759380\t57091\nThe Mark Twain House & Museum, Farmington Avenue, Hartford, CT\tUSS Constitution, Boston, MA\t167183\t6166\nNew Castle Historic District, Delaware\tGrand Canyon National Park, Arizona\t3741677\t122144\nSpring Grove Cemetery, Spring Grove Avenue, Cincinnati, OH\tMammoth Cave National Park, Mammoth Cave Pkwy, Mammoth Cave, KY\t310125\t10608\nChickasaw National Recreation Area, 1008 W 2nd St, Sulphur, OK 73086\tWhite House, Pennsylvania Avenue Northwest, Washington, DC\t2202900\t71934\nSpring Grove Cemetery, Spring Grove Avenue, Cincinnati, OH\tTaliesin, County Road C, Spring Green, Wisconsin\t770373\t26747\nGraceland, Elvis Presley Boulevard, Memphis, TN\tGateway Arch, Washington Avenue, St Louis, MO\t469145\t15247\nTaliesin, County Road C, Spring Green, Wisconsin\tStatue of Liberty, Liberty Island, NYC, NY\t1570383\t53903\nCarlsbad Caverns National Park, Carlsbad, NM\tCongress Hall, Congress Place, Cape May, NJ 08204\t3232567\t109057\nMammoth Cave National Park, Mammoth Cave Pkwy, Mammoth Cave, KY\tFort Snelling, Tower Avenue, Saint Paul, MN\t1277557\t42826\nTerrace Hill, Grand Avenue, Des Moines, IA\tGrand Canyon National Park, Arizona\t2126579\t69490\nTaliesin, County Road C, Spring Green, Wisconsin\tFort Snelling, Tower Avenue, Saint Paul, MN\t402693\t14609\nCraters of the Moon National Monument & Preserve, Arco, ID\tStatue of Liberty, Liberty Island, NYC, NY\t3754373\t122363\nMount Vernon, Fairfax County, Virginia\tCable Car Museum, 94108, 1201 Mason St, San Francisco, CA 94108\t4551979\t146197\nTaliesin, County Road C, Spring Green, Wisconsin\tAcadia National Park, Maine\t2310920\t79386\nShelburne Farms, Harbor Road, Shelburne, VT\tLost World Caverns, Lewisburg, WV\t1199298\t41645\nVicksburg National Military Park, Clay Street, Vicksburg, MS\tBryce Canyon National Park, Hwy 63, Bryce, UT\t2490228\t83656\nMaryland State House, 100 State Cir, Annapolis, MD 21401\tMammoth Cave National Park, Mammoth Cave Pkwy, Mammoth Cave, KY\t1105058\t36238\nFort Union Trading Post National Historic Site, Williston, North Dakota 1804, ND\tGraceland, Elvis Presley Boulevard, Memphis, TN\t2399554\t77775\nShelburne Farms, Harbor Road, Shelburne, VT\tHanford Site, Benton County, WA\t4564119\t151892\nSpring Grove Cemetery, Spring Grove Avenue, Cincinnati, OH\tWright Brothers National Memorial Visitor Center, Manteo, NC\t1136995\t39795\nGraceland, Elvis Presley Boulevard, Memphis, TN\tAshfall Fossil Bed, Royal, NE\t1310105\t46863\nVicksburg National Military Park, Clay Street, Vicksburg, MS\tFort Snelling, Tower Avenue, Saint Paul, MN\t1667139\t56039\nLiberty Bell, 6th Street, Philadelphia, PA\tWright Brothers National Memorial Visitor Center, Manteo, NC\t582053\t22681\nLiberty Bell, 6th Street, Philadelphia, PA\tWest Baden Springs Hotel, West Baden Avenue, West Baden Springs, IN\t1171045\t40708\nPikes Peak, Colorado\tBryce Canyon National Park, Hwy 63, Bryce, UT\t998488\t37341\nTerrace Hill, Grand Avenue, Des Moines, IA\tMammoth Cave National Park, Mammoth Cave Pkwy, Mammoth Cave, KY\t1011679\t35365\nMount Rushmore National Memorial, South Dakota 244, Keystone, SD\tTaliesin, County Road C, Spring Green, Wisconsin\t1208996\t40460\nVicksburg National Military Park, Clay Street, Vicksburg, MS\tGlacier National Park, West Glacier, MT\t3369066\t109995\nTerrace Hill, Grand Avenue, Des Moines, IA\tAcadia National Park, Maine\t2529755\t84694\nOmni Mount Washington Resort, Mount Washington Hotel Road, Bretton Woods, NH\tCape Canaveral, FL\t2330047\t77079\nUSS Alabama, Battleship Parkway, Mobile, AL\tAshfall Fossil Bed, Royal, NE\t1889577\t67851\nGraceland, Elvis Presley Boulevard, Memphis, TN\tCongress Hall, Congress Place, Cape May, NJ 08204\t1717234\t57440\nThe Alamo, Alamo Plaza, San Antonio, TX\tBryce Canyon National Park, Hwy 63, Bryce, UT\t2013900\t69315\nThe Mark Twain House & Museum, Farmington Avenue, Hartford, CT\tAcadia National Park, Maine\t597379\t21385\nHoover Dam, NV\tTerrace Hill, Grand Avenue, Des Moines, IA\t2328938\t73873\nSan Andreas Fault, San Benito County, CA\tLincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL\t3397300\t109030\nToltec Mounds, Scott, AR\tWhite House, Pennsylvania Avenue Northwest, Washington, DC\t1627313\t53061\nUSS Alabama, Battleship Parkway, Mobile, AL\tGlacier National Park, West Glacier, MT\t3545115\t123166\nShelburne Farms, Harbor Road, Shelburne, VT\tFort Sumter National Monument, Sullivan's Island, SC\t1672509\t57554\nMammoth Cave National Park, Mammoth Cave Pkwy, Mammoth Cave, KY\tFort Sumter National Monument, Sullivan's Island, SC\t922894\t31784\nLiberty Bell, 6th Street, Philadelphia, PA\tThe Mark Twain House & Museum, Farmington Avenue, Hartford, CT\t337338\t12880\nCarlsbad Caverns National Park, Carlsbad, NM\tStatue of Liberty, Liberty Island, NYC, NY\t3284729\t109318\nGraceland, Elvis Presley Boulevard, Memphis, TN\tCable Car Museum, 94108, 1201 Mason St, San Francisco, CA 94108\t3375034\t106909\nGlacier National Park, West Glacier, MT\tCable Car Museum, 94108, 1201 Mason St, San Francisco, CA 94108\t1852892\t68182\nCape Canaveral, FL\tOkefenokee Swamp Park, Okefenokee Swamp Park Road, Waycross, GA\t377410\t13220\nThe Alamo, Alamo Plaza, San Antonio, TX\tGateway Arch, Washington Avenue, St Louis, MO\t1454861\t48938\nC. W. Parker Carousel Museum, South Esplanade Street, Leavenworth, KS\tMount Vernon, Fairfax County, Virginia\t1776793\t60250\nChickasaw National Recreation Area, 1008 W 2nd St, Sulphur, OK 73086\tPikes Peak, Colorado\t1138992\t41032\nWhite House, Pennsylvania Avenue Northwest, Washington, DC\tFrench Quarter, New Orleans, LA\t1743678\t56192\nThe Breakers, Ochre Point Avenue, Newport, RI\tYellowstone National Park, WY 82190\t3809631\t129558\nSpring Grove Cemetery, Spring Grove Avenue, Cincinnati, OH\tThe Breakers, Ochre Point Avenue, Newport, RI\t1317092\t46456\nCarlsbad Caverns National Park, Carlsbad, NM\tLincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL\t1858566\t62840\nMaryland State House, 100 State Cir, Annapolis, MD 21401\tSpring Grove Cemetery, Spring Grove Avenue, Cincinnati, OH\t848229\t29340\nYellowstone National Park, WY 82190\tThe Alamo, Alamo Plaza, San Antonio, TX\t2356077\t82050\nChickasaw National Recreation Area, 1008 W 2nd St, Sulphur, OK 73086\tSan Andreas Fault, San Benito County, CA\t2527020\t83368\nNew Castle Historic District, Delaware\tGateway Arch, Washington Avenue, St Louis, MO\t1440290\t50020\nFrench Quarter, New Orleans, LA\tGrand Canyon National Park, Arizona\t2486793\t79674\nStatue of Liberty, Liberty Island, NYC, NY\tLincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL\t1477357\t50369\nHoover Dam, NV\tOkefenokee Swamp Park, Okefenokee Swamp Park Road, Waycross, GA\t3441584\t113235\nMaryland State House, 100 State Cir, Annapolis, MD 21401\tChickasaw National Recreation Area, 1008 W 2nd St, Sulphur, OK 73086\t2265577\t74139\nHanford Site, Benton County, WA\tLincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL\t3072833\t99361\nFort Sumter National Monument, Sullivan's Island, SC\tCable Car Museum, 94108, 1201 Mason St, San Francisco, CA 94108\t4455913\t142569\nTerrace Hill, Grand Avenue, Des Moines, IA\tGateway Arch, Washington Avenue, St Louis, MO\t567564\t19773\nChickasaw National Recreation Area, 1008 W 2nd St, Sulphur, OK 73086\tLost World Caverns, Lewisburg, WV\t1869023\t61109\nPikes Peak, Colorado\tThe Alamo, Alamo Plaza, San Antonio, TX\t1435151\t49274\nGraceland, Elvis Presley Boulevard, Memphis, TN\tVicksburg National Military Park, Clay Street, Vicksburg, MS\t382413\t12329\nLiberty Bell, 6th Street, Philadelphia, PA\tHanford Site, Benton County, WA\t4339013\t140742\nUSS Alabama, Battleship Parkway, Mobile, AL\tFort Sumter National Monument, Sullivan's Island, SC\t1002499\t32623\nLiberty Bell, 6th Street, Philadelphia, PA\tColumbia River Gorge National Scenic Area, Oregon\t4452738\t143085\nC. W. Parker Carousel Museum, South Esplanade Street, Leavenworth, KS\tChickasaw National Recreation Area, 1008 W 2nd St, Sulphur, OK 73086\t702803\t22713\nThe Alamo, Alamo Plaza, San Antonio, TX\tColumbia River Gorge National Scenic Area, Oregon\t3174721\t106519\nC. W. Parker Carousel Museum, South Esplanade Street, Leavenworth, KS\tWest Baden Springs Hotel, West Baden Avenue, West Baden Springs, IN\t827545\t27595\nWright Brothers National Memorial Visitor Center, Manteo, NC\tColumbia River Gorge National Scenic Area, Oregon\t4743875\t154046\nColumbia River Gorge National Scenic Area, Oregon\tGrand Canyon National Park, Arizona\t1682141\t56226\nTerrace Hill, Grand Avenue, Des Moines, IA\tAshfall Fossil Bed, Royal, NE\t460092\t17005\nChickasaw National Recreation Area, 1008 W 2nd St, Sulphur, OK 73086\tShelburne Farms, Harbor Road, Shelburne, VT\t2658016\t91689\nOkefenokee Swamp Park, Okefenokee Swamp Park Road, Waycross, GA\tCable Car Museum, 94108, 1201 Mason St, San Francisco, CA 94108\t4384469\t140971\nCraters of the Moon National Monument & Preserve, Arco, ID\tAcadia National Park, Maine\t4497107\t147768\nWhite House, Pennsylvania Avenue Northwest, Washington, DC\tFort Snelling, Tower Avenue, Saint Paul, MN\t1778589\t60035\nGateway Arch, Washington Avenue, St Louis, MO\tAcadia National Park, Maine\t2328971\t79163\nToltec Mounds, Scott, AR\tGateway Arch, Washington Avenue, St Louis, MO\t568157\t20503\nWright Brothers National Memorial Visitor Center, Manteo, NC\tYellowstone National Park, WY 82190\t3764344\t127510\nSan Andreas Fault, San Benito County, CA\tThe Breakers, Ochre Point Avenue, Newport, RI\t5103126\t166192\nGateway Arch, Washington Avenue, St Louis, MO\tLincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL\t158520\t5593\nMammoth Cave National Park, Mammoth Cave Pkwy, Mammoth Cave, KY\tYellowstone National Park, WY 82190\t2591982\t89869\nShelburne Farms, Harbor Road, Shelburne, VT\tOmni Mount Washington Resort, Mount Washington Hotel Road, Bretton Woods, NH\t192042\t8293\nMount Rushmore National Memorial, South Dakota 244, Keystone, SD\tFort Snelling, Tower Avenue, Saint Paul, MN\t956302\t31450\nLiberty Bell, 6th Street, Philadelphia, PA\tFort Sumter National Monument, Sullivan's Island, SC\t1077955\t35849\nLiberty Bell, 6th Street, Philadelphia, PA\tGateway Arch, Washington Avenue, St Louis, MO\t1425131\t48171\nYellowstone National Park, WY 82190\tAcadia National Park, Maine\t4264590\t144658\nNew Castle Historic District, Delaware\tColumbia River Gorge National Scenic Area, Oregon\t4478003\t145647\nCongress Hall, Congress Place, Cape May, NJ 08204\tWest Baden Springs Hotel, West Baden Avenue, West Baden Springs, IN\t1311091\t45188\nCarlsbad Caverns National Park, Carlsbad, NM\tLost World Caverns, Lewisburg, WV\t2627620\t87418\nTerrace Hill, Grand Avenue, Des Moines, IA\tCarlsbad Caverns National Park, Carlsbad, NM\t1775662\t59430\nFort Union Trading Post National Historic Site, Williston, North Dakota 1804, ND\tCape Canaveral, FL\t3635791\t117123\nMount Rushmore National Memorial, South Dakota 244, Keystone, SD\tFrench Quarter, New Orleans, LA\t2529104\t82743\nVicksburg National Military Park, Clay Street, Vicksburg, MS\tAshfall Fossil Bed, Royal, NE\t1604535\t55497\nThe Breakers, Ochre Point Avenue, Newport, RI\tWhite House, Pennsylvania Avenue Northwest, Washington, DC\t653295\t24507\nFort Union Trading Post National Historic Site, Williston, North Dakota 1804, ND\tLincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL\t1901493\t61580\nHoover Dam, NV\tCraters of the Moon National Monument & Preserve, Arco, ID\t1149083\t35910\nToltec Mounds, Scott, AR\tGrand Canyon National Park, Arizona\t2072393\t65425\nYellowstone National Park, WY 82190\tOkefenokee Swamp Park, Okefenokee Swamp Park Road, Waycross, GA\t3446653\t117372\nC. W. Parker Carousel Museum, South Esplanade Street, Leavenworth, KS\tToltec Mounds, Scott, AR\t756195\t24900\nUSS Alabama, Battleship Parkway, Mobile, AL\tVicksburg National Military Park, Clay Street, Vicksburg, MS\t370688\t13387\nLiberty Bell, 6th Street, Philadelphia, PA\tMammoth Cave National Park, Mammoth Cave Pkwy, Mammoth Cave, KY\t1233782\t40742\nMount Rushmore National Memorial, South Dakota 244, Keystone, SD\tVicksburg National Military Park, Clay Street, Vicksburg, MS\t2200235\t71786\nHoover Dam, NV\tFort Union Trading Post National Historic Site, Williston, North Dakota 1804, ND\t2084706\t67807\nUSS Alabama, Battleship Parkway, Mobile, AL\tWright Brothers National Memorial Visitor Center, Manteo, NC\t1489642\t47392\nUSS Alabama, Battleship Parkway, Mobile, AL\tGrand Canyon National Park, Arizona\t2615332\t85785\nHoover Dam, NV\tWright Brothers National Memorial Visitor Center, Manteo, NC\t4004005\t128209\nGraceland, Elvis Presley Boulevard, Memphis, TN\tFort Snelling, Tower Avenue, Saint Paul, MN\t1288825\t43921\nChickasaw National Recreation Area, 1008 W 2nd St, Sulphur, OK 73086\tLincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL\t1032820\t34455\nFrench Quarter, New Orleans, LA\tBryce Canyon National Park, Hwy 63, Bryce, UT\t2723632\t91296\nCraters of the Moon National Monument & Preserve, Arco, ID\tColumbia River Gorge National Scenic Area, Oregon\t845463\t29100\nCarlsbad Caverns National Park, Carlsbad, NM\tTaliesin, County Road C, Spring Green, Wisconsin\t2204052\t73510\nOlympia Entertainment, Woodward Avenue, Detroit, MI\tAshfall Fossil Bed, Royal, NE\t1419146\t48026\nThe Alamo, Alamo Plaza, San Antonio, TX\tCable Car Museum, 94108, 1201 Mason St, San Francisco, CA 94108\t2788838\t87269\nMaryland State House, 100 State Cir, Annapolis, MD 21401\tWest Baden Springs Hotel, West Baden Avenue, West Baden Springs, IN\t1103913\t37085\nWhite House, Pennsylvania Avenue Northwest, Washington, DC\tGrand Canyon National Park, Arizona\t3663661\t116717\nYellowstone National Park, WY 82190\tGateway Arch, Washington Avenue, St Louis, MO\t2149556\t74278\nCraters of the Moon National Monument & Preserve, Arco, ID\tAshfall Fossil Bed, Royal, NE\t1480088\t56115\nFort Union Trading Post National Historic Site, Williston, North Dakota 1804, ND\tNew Castle Historic District, Delaware\t2958272\t98658\nGraceland, Elvis Presley Boulevard, Memphis, TN\tOlympia Entertainment, Woodward Avenue, Detroit, MI\t1204474\t40007\nMaryland State House, 100 State Cir, Annapolis, MD 21401\tOmni Mount Washington Resort, Mount Washington Hotel Road, Bretton Woods, NH\t917186\t32349\nLiberty Bell, 6th Street, Philadelphia, PA\tLincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL\t1369812\t46284\nTaliesin, County Road C, Spring Green, Wisconsin\tUSS Constitution, Boston, MA\t1881312\t64293\nCape Canaveral, FL\tVicksburg National Military Park, Clay Street, Vicksburg, MS\t1250556\t40658\nMount Rushmore National Memorial, South Dakota 244, Keystone, SD\tTerrace Hill, Grand Avenue, Des Moines, IA\t1038320\t32938\nToltec Mounds, Scott, AR\tThe Mark Twain House & Museum, Farmington Avenue, Hartford, CT\t2177479\t71850\nBryce Canyon National Park, Hwy 63, Bryce, UT\tColumbia River Gorge National Scenic Area, Oregon\t1502684\t48423\nC. W. Parker Carousel Museum, South Esplanade Street, Leavenworth, KS\tVicksburg National Military Park, Clay Street, Vicksburg, MS\t1085667\t36886\nHoover Dam, NV\tYellowstone National Park, WY 82190\t1260922\t45106\nWhite House, Pennsylvania Avenue Northwest, Washington, DC\tYellowstone National Park, WY 82190\t3376611\t114295\nLost World Caverns, Lewisburg, WV\tAcadia National Park, Maine\t1555610\t53471\nLiberty Bell, 6th Street, Philadelphia, PA\tUSS Constitution, Boston, MA\t495038\t18425\nTerrace Hill, Grand Avenue, Des Moines, IA\tUSS Constitution, Boston, MA\t2099559\t69476\nFort Union Trading Post National Historic Site, Williston, North Dakota 1804, ND\tWhite House, Pennsylvania Avenue Northwest, Washington, DC\t2836410\t93842\nGraceland, Elvis Presley Boulevard, Memphis, TN\tTaliesin, County Road C, Spring Green, Wisconsin\t1107487\t36315\nStatue of Liberty, Liberty Island, NYC, NY\tAcadia National Park, Maine\t779355\t28628\nOlympia Entertainment, Woodward Avenue, Detroit, MI\tBryce Canyon National Park, Hwy 63, Bryce, UT\t2939571\t94810\nC. W. Parker Carousel Museum, South Esplanade Street, Leavenworth, KS\tPikes Peak, Colorado\t989094\t33954\nCape Canaveral, FL\tFort Sumter National Monument, Sullivan's Island, SC\t642615\t21283\nLost World Caverns, Lewisburg, WV\tYellowstone National Park, WY 82190\t3138421\t106104\nHanford Site, Benton County, WA\tSan Andreas Fault, San Benito County, CA\t1398524\t51329\nFrench Quarter, New Orleans, LA\tColumbia River Gorge National Scenic Area, Oregon\t3922705\t128082\nMount Rushmore National Memorial, South Dakota 244, Keystone, SD\tOkefenokee Swamp Park, Okefenokee Swamp Park Road, Waycross, GA\t2866081\t93302\nCarlsbad Caverns National Park, Carlsbad, NM\tOmni Mount Washington Resort, Mount Washington Hotel Road, Bretton Woods, NH\t3711294\t126359\nOmni Mount Washington Resort, Mount Washington Hotel Road, Bretton Woods, NH\tCongress Hall, Congress Place, Cape May, NJ 08204\t825912\t29227\nGraceland, Elvis Presley Boulevard, Memphis, TN\tStatue of Liberty, Liberty Island, NYC, NY\t1767750\t57903\nMount Vernon, Fairfax County, Virginia\tYellowstone National Park, WY 82190\t3398494\t115127\nStatue of Liberty, Liberty Island, NYC, NY\tFrench Quarter, New Orleans, LA\t2098917\t68067\nCarlsbad Caverns National Park, Carlsbad, NM\tBryce Canyon National Park, Hwy 63, Bryce, UT\t1371003\t50784\nHoover Dam, NV\tBryce Canyon National Park, Hwy 63, Bryce, UT\t470908\t16041\nCarlsbad Caverns National Park, Carlsbad, NM\tAcadia National Park, Maine\t4029017\t136409\nUSS Alabama, Battleship Parkway, Mobile, AL\tYellowstone National Park, WY 82190\t3112156\t106169\nLost World Caverns, Lewisburg, WV\tColumbia River Gorge National Scenic Area, Oregon\t4123092\t131961\nCarlsbad Caverns National Park, Carlsbad, NM\tCape Canaveral, FL\t2624095\t87210\nLiberty Bell, 6th Street, Philadelphia, PA\tCongress Hall, Congress Place, Cape May, NJ 08204\t135319\t5538\nToltec Mounds, Scott, AR\tAcadia National Park, Maine\t2768590\t92702\nFort Sumter National Monument, Sullivan's Island, SC\tGateway Arch, Washington Avenue, St Louis, MO\t1376724\t44982\nFort Sumter National Monument, Sullivan's Island, SC\tGrand Canyon National Park, Arizona\t3346258\t107267\nHoover Dam, NV\tColumbia River Gorge National Scenic Area, Oregon\t1586117\t53673\nNew Castle Historic District, Delaware\tFort Sumter National Monument, Sullivan's Island, SC\t1004882\t33412\nLost World Caverns, Lewisburg, WV\tGateway Arch, Washington Avenue, St Louis, MO\t991827\t32343\nCraters of the Moon National Monument & Preserve, Arco, ID\tGrand Canyon National Park, Arizona\t1092664\t37411\nWright Brothers National Memorial Visitor Center, Manteo, NC\tCraters of the Moon National Monument & Preserve, Arco, ID\t4003167\t130557\nPikes Peak, Colorado\tFort Snelling, Tower Avenue, Saint Paul, MN\t1621098\t52897\nTerrace Hill, Grand Avenue, Des Moines, IA\tCraters of the Moon National Monument & Preserve, Arco, ID\t1969397\t63464\nVicksburg National Military Park, Clay Street, Vicksburg, MS\tYellowstone National Park, WY 82190\t2747124\t93151\nChickasaw National Recreation Area, 1008 W 2nd St, Sulphur, OK 73086\tUSS Constitution, Boston, MA\t2773664\t92931\nLost World Caverns, Lewisburg, WV\tOmni Mount Washington Resort, Mount Washington Hotel Road, Bretton Woods, NH\t1353504\t45969\nTaliesin, County Road C, Spring Green, Wisconsin\tGateway Arch, Washington Avenue, St Louis, MO\t640177\t21609\nShelburne Farms, Harbor Road, Shelburne, VT\tFort Snelling, Tower Avenue, Saint Paul, MN\t2108269\t74013\nMount Rushmore National Memorial, South Dakota 244, Keystone, SD\tWest Baden Springs Hotel, West Baden Avenue, West Baden Springs, IN\t1944125\t62449\nC. W. Parker Carousel Museum, South Esplanade Street, Leavenworth, KS\tLost World Caverns, Lewisburg, WV\t1442759\t47179\nCarlsbad Caverns National Park, Carlsbad, NM\tOlympia Entertainment, Woodward Avenue, Detroit, MI\t2557663\t85968\nWright Brothers National Memorial Visitor Center, Manteo, NC\tOlympia Entertainment, Woodward Avenue, Detroit, MI\t1296121\t44397\nMaryland State House, 100 State Cir, Annapolis, MD 21401\tVicksburg National Military Park, Clay Street, Vicksburg, MS\t1700468\t54930\nMount Vernon, Fairfax County, Virginia\tThe Mark Twain House & Museum, Farmington Avenue, Hartford, CT\t571779\t21234\nVicksburg National Military Park, Clay Street, Vicksburg, MS\tGateway Arch, Washington Avenue, St Louis, MO\t847491\t27193\nThe Mark Twain House & Museum, Farmington Avenue, Hartford, CT\tCable Car Museum, 94108, 1201 Mason St, San Francisco, CA 94108\t4838236\t155507\nFort Union Trading Post National Historic Site, Williston, North Dakota 1804, ND\tSan Andreas Fault, San Benito County, CA\t2485901\t83726\nHoover Dam, NV\tFrench Quarter, New Orleans, LA\t2725797\t87321\nLincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL\tGrand Canyon National Park, Arizona\t2462148\t77824\nHanford Site, Benton County, WA\tOmni Mount Washington Resort, Mount Washington Hotel Road, Bretton Woods, NH\t4790512\t157993\nC. W. Parker Carousel Museum, South Esplanade Street, Leavenworth, KS\tLincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL\t524146\t17801\nMount Vernon, Fairfax County, Virginia\tOmni Mount Washington Resort, Mount Washington Hotel Road, Bretton Woods, NH\t956980\t34372\nMaryland State House, 100 State Cir, Annapolis, MD 21401\tLost World Caverns, Lewisburg, WV\t479163\t16168\nWright Brothers National Memorial Visitor Center, Manteo, NC\tAshfall Fossil Bed, Royal, NE\t2531422\t86090\nLost World Caverns, Lewisburg, WV\tCape Canaveral, FL\t1230767\t40942\nFort Union Trading Post National Historic Site, Williston, North Dakota 1804, ND\tWright Brothers National Memorial Visitor Center, Manteo, NC\t3283326\t109628\nC. W. Parker Carousel Museum, South Esplanade Street, Leavenworth, KS\tMammoth Cave National Park, Mammoth Cave Pkwy, Mammoth Cave, KY\t897371\t30973\nGraceland, Elvis Presley Boulevard, Memphis, TN\tCarlsbad Caverns National Park, Carlsbad, NM\t1530542\t52124\nMammoth Cave National Park, Mammoth Cave Pkwy, Mammoth Cave, KY\tGrand Canyon National Park, Arizona\t2732563\t86585\nHanford Site, Benton County, WA\tGrand Canyon National Park, Arizona\t1641209\t55038\nLost World Caverns, Lewisburg, WV\tWright Brothers National Memorial Visitor Center, Manteo, NC\t630856\t21882\nThe Breakers, Ochre Point Avenue, Newport, RI\tFort Sumter National Monument, Sullivan's Island, SC\t1509644\t51864\nMaryland State House, 100 State Cir, Annapolis, MD 21401\tAshfall Fossil Bed, Royal, NE\t2139243\t72176\nLost World Caverns, Lewisburg, WV\tBryce Canyon National Park, Hwy 63, Bryce, UT\t3263699\t104829\nUSS Alabama, Battleship Parkway, Mobile, AL\tLiberty Bell, 6th Street, Philadelphia, PA\t1783560\t58556\nFort Union Trading Post National Historic Site, Williston, North Dakota 1804, ND\tAshfall Fossil Bed, Royal, NE\t1048083\t38446\nFort Sumter National Monument, Sullivan's Island, SC\tGlacier National Park, West Glacier, MT\t3936065\t130151\nUSS Constitution, Boston, MA\tFort Snelling, Tower Avenue, Saint Paul, MN\t2236960\t75305\nSpring Grove Cemetery, Spring Grove Avenue, Cincinnati, OH\tVicksburg National Military Park, Clay Street, Vicksburg, MS\t1166316\t37905\nNew Castle Historic District, Delaware\tAshfall Fossil Bed, Royal, NE\t2222494\t75952\nFort Union Trading Post National Historic Site, Williston, North Dakota 1804, ND\tGlacier National Park, West Glacier, MT\t788472\t29371\nThe Mark Twain House & Museum, Farmington Avenue, Hartford, CT\tAshfall Fossil Bed, Royal, NE\t2408053\t81112\nUSS Alabama, Battleship Parkway, Mobile, AL\tAcadia National Park, Maine\t2697479\t90984\nSan Andreas Fault, San Benito County, CA\tTaliesin, County Road C, Spring Green, Wisconsin\t3458781\t111318\nC. W. Parker Carousel Museum, South Esplanade Street, Leavenworth, KS\tOkefenokee Swamp Park, Okefenokee Swamp Park Road, Waycross, GA\t1749501\t58448\nOkefenokee Swamp Park, Okefenokee Swamp Park Road, Waycross, GA\tGrand Canyon National Park, Arizona\t3274814\t105668\nMount Rushmore National Memorial, South Dakota 244, Keystone, SD\tUSS Constitution, Boston, MA\t3083351\t101542\nToltec Mounds, Scott, AR\tYellowstone National Park, WY 82190\t2419546\t81262\nCongress Hall, Congress Place, Cape May, NJ 08204\tGateway Arch, Washington Avenue, St Louis, MO\t1573547\t53369\nMaryland State House, 100 State Cir, Annapolis, MD 21401\tSan Andreas Fault, San Benito County, CA\t4594279\t149454\nSan Andreas Fault, San Benito County, CA\tStatue of Liberty, Liberty Island, NYC, NY\t4774120\t155696\nPikes Peak, Colorado\tYellowstone National Park, WY 82190\t1010111\t40861\nMount Rushmore National Memorial, South Dakota 244, Keystone, SD\tMount Vernon, Fairfax County, Virginia\t2656789\t87096\nStatue of Liberty, Liberty Island, NYC, NY\tOkefenokee Swamp Park, Okefenokee Swamp Park Road, Waycross, GA\t1480247\t49443\nSan Andreas Fault, San Benito County, CA\tCraters of the Moon National Monument & Preserve, Arco, ID\t1361109\t46565\nThe Alamo, Alamo Plaza, San Antonio, TX\tGrand Canyon National Park, Arizona\t1780052\t58087\nWright Brothers National Memorial Visitor Center, Manteo, NC\tThe Alamo, Alamo Plaza, San Antonio, TX\t2548833\t81099\nColumbia River Gorge National Scenic Area, Oregon\tAshfall Fossil Bed, Royal, NE\t2359209\t79778\nBryce Canyon National Park, Hwy 63, Bryce, UT\tGlacier National Park, West Glacier, MT\t1475714\t47994\nThe Alamo, Alamo Plaza, San Antonio, TX\tFrench Quarter, New Orleans, LA\t875710\t28421\nCraters of the Moon National Monument & Preserve, Arco, ID\tPikes Peak, Colorado\t1310714\t47209\nSan Andreas Fault, San Benito County, CA\tOlympia Entertainment, Woodward Avenue, Detroit, MI\t3989459\t128044\nGraceland, Elvis Presley Boulevard, Memphis, TN\tCraters of the Moon National Monument & Preserve, Arco, ID\t2718789\t90414\nFort Sumter National Monument, Sullivan's Island, SC\tBryce Canyon National Park, Hwy 63, Bryce, UT\t3649551\t117677\nFort Union Trading Post National Historic Site, Williston, North Dakota 1804, ND\tBryce Canyon National Park, Hwy 63, Bryce, UT\t1801093\t58970\nToltec Mounds, Scott, AR\tThe Breakers, Ochre Point Avenue, Newport, RI\t2273878\t76157\nThe Breakers, Ochre Point Avenue, Newport, RI\tCraters of the Moon National Monument & Preserve, Arco, ID\t4042971\t132612\nOmni Mount Washington Resort, Mount Washington Hotel Road, Bretton Woods, NH\tVicksburg National Military Park, Clay Street, Vicksburg, MS\t2575996\t84758\nLiberty Bell, 6th Street, Philadelphia, PA\tOmni Mount Washington Resort, Mount Washington Hotel Road, Bretton Woods, NH\t722539\t26017\nCarlsbad Caverns National Park, Carlsbad, NM\tGateway Arch, Washington Avenue, St Louis, MO\t1705580\t57853\nYellowstone National Park, WY 82190\tOlympia Entertainment, Woodward Avenue, Detroit, MI\t2695125\t91185\nOmni Mount Washington Resort, Mount Washington Hotel Road, Bretton Woods, NH\tWest Baden Springs Hotel, West Baden Avenue, West Baden Springs, IN\t1761665\t61810\nTerrace Hill, Grand Avenue, Des Moines, IA\tWright Brothers National Memorial Visitor Center, Manteo, NC\t2072573\t69466\nVicksburg National Military Park, Clay Street, Vicksburg, MS\tFrench Quarter, New Orleans, LA\t331934\t11302\nOmni Mount Washington Resort, Mount Washington Hotel Road, Bretton Woods, NH\tCraters of the Moon National Monument & Preserve, Arco, ID\t4157603\t137649\nShelburne Farms, Harbor Road, Shelburne, VT\tAshfall Fossil Bed, Royal, NE\t2420015\t85093\nC. W. Parker Carousel Museum, South Esplanade Street, Leavenworth, KS\tCape Canaveral, FL\t2130718\t68401\nHoover Dam, NV\tNew Castle Historic District, Delaware\t3984959\t129829\nCape Canaveral, FL\tYellowstone National Park, WY 82190\t3827870\t127325\nCape Canaveral, FL\tGrand Canyon National Park, Arizona\t3497485\t112423\nGraceland, Elvis Presley Boulevard, Memphis, TN\tGlacier National Park, West Glacier, MT\t2982190\t101527\nMount Rushmore National Memorial, South Dakota 244, Keystone, SD\tLincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL\t1553662\t49942\nHanford Site, Benton County, WA\tLost World Caverns, Lewisburg, WV\t4081860\t130602\nOlympia Entertainment, Woodward Avenue, Detroit, MI\tGlacier National Park, West Glacier, MT\t2895149\t97734\nOlympia Entertainment, Woodward Avenue, Detroit, MI\tAcadia National Park, Maine\t1587595\t54638\nShelburne Farms, Harbor Road, Shelburne, VT\tThe Alamo, Alamo Plaza, San Antonio, TX\t3222592\t109018\nHoover Dam, NV\tFort Snelling, Tower Avenue, Saint Paul, MN\t2719740\t86231\nCape Canaveral, FL\tGateway Arch, Washington Avenue, St Louis, MO\t1684309\t53709\nC. W. Parker Carousel Museum, South Esplanade Street, Leavenworth, KS\tTaliesin, County Road C, Spring Green, Wisconsin\t760692\t25343\nFort Union Trading Post National Historic Site, Williston, North Dakota 1804, ND\tHanford Site, Benton County, WA\t1586051\t51552\nShelburne Farms, Harbor Road, Shelburne, VT\tWhite House, Pennsylvania Avenue Northwest, Washington, DC\t815727\t30501\nToltec Mounds, Scott, AR\tFort Sumter National Monument, Sullivan's Island, SC\t1309910\t43611\nShelburne Farms, Harbor Road, Shelburne, VT\tSan Andreas Fault, San Benito County, CA\t4988674\t165123\nCraters of the Moon National Monument & Preserve, Arco, ID\tNew Castle Historic District, Delaware\t3799076\t125459\nShelburne Farms, Harbor Road, Shelburne, VT\tColumbia River Gorge National Scenic Area, Oregon\t4676038\t154286\nFrench Quarter, New Orleans, LA\tAshfall Fossil Bed, Royal, NE\t1916780\t65306\nThe Breakers, Ochre Point Avenue, Newport, RI\tOlympia Entertainment, Woodward Avenue, Detroit, MI\t1201574\t41180\nChickasaw National Recreation Area, 1008 W 2nd St, Sulphur, OK 73086\tMammoth Cave National Park, Mammoth Cave Pkwy, Mammoth Cave, KY\t1271563\t41645\nMount Vernon, Fairfax County, Virginia\tAshfall Fossil Bed, Royal, NE\t2121840\t71736\nTaliesin, County Road C, Spring Green, Wisconsin\tVicksburg National Military Park, Clay Street, Vicksburg, MS\t1485999\t48318\nAshfall Fossil Bed, Royal, NE\tCable Car Museum, 94108, 1201 Mason St, San Francisco, CA 94108\t2609776\t86305\nThe Mark Twain House & Museum, Farmington Avenue, Hartford, CT\tCongress Hall, Congress Place, Cape May, NJ 08204\t440711\t16089\nThe Breakers, Ochre Point Avenue, Newport, RI\tGrand Canyon National Park, Arizona\t4129628\t135501\nShelburne Farms, Harbor Road, Shelburne, VT\tGlacier National Park, West Glacier, MT\t3923595\t134241\nCarlsbad Caverns National Park, Carlsbad, NM\tVicksburg National Military Park, Clay Street, Vicksburg, MS\t1374729\t46713\nPikes Peak, Colorado\tGrand Canyon National Park, Arizona\t973994\t38556\nWright Brothers National Memorial Visitor Center, Manteo, NC\tPikes Peak, Colorado\t3006046\t99860\nCraters of the Moon National Monument & Preserve, Arco, ID\tLincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL\t2367353\t77196\nThe Mark Twain House & Museum, Farmington Avenue, Hartford, CT\tWright Brothers National Memorial Visitor Center, Manteo, NC\t905467\t34613\nUSS Alabama, Battleship Parkway, Mobile, AL\tOmni Mount Washington Resort, Mount Washington Hotel Road, Bretton Woods, NH\t2494702\t83431\nHanford Site, Benton County, WA\tFort Sumter National Monument, Sullivan's Island, SC\t4461509\t143963\nLiberty Bell, 6th Street, Philadelphia, PA\tOkefenokee Swamp Park, Okefenokee Swamp Park Road, Waycross, GA\t1339772\t44555\nThe Breakers, Ochre Point Avenue, Newport, RI\tMammoth Cave National Park, Mammoth Cave Pkwy, Mammoth Cave, KY\t1632118\t55615\nThe Breakers, Ochre Point Avenue, Newport, RI\tThe Alamo, Alamo Plaza, San Antonio, TX\t3223524\t105975\nWright Brothers National Memorial Visitor Center, Manteo, NC\tCongress Hall, Congress Place, Cape May, NJ 08204\t448155\t23747\nShelburne Farms, Harbor Road, Shelburne, VT\tCarlsbad Caverns National Park, Carlsbad, NM\t3483464\t120037\nCape Canaveral, FL\tBryce Canyon National Park, Hwy 63, Bryce, UT\t3739594\t124152\nPikes Peak, Colorado\tVicksburg National Military Park, Clay Street, Vicksburg, MS\t1787044\t60791\nSan Andreas Fault, San Benito County, CA\tLost World Caverns, Lewisburg, WV\t4233272\t135783\nC. W. Parker Carousel Museum, South Esplanade Street, Leavenworth, KS\tNew Castle Historic District, Delaware\t1876337\t64324\nTerrace Hill, Grand Avenue, Des Moines, IA\tSan Andreas Fault, San Benito County, CA\t3029552\t97044\nToltec Mounds, Scott, AR\tFort Snelling, Tower Avenue, Saint Paul, MN\t1399583\t44781\nCraters of the Moon National Monument & Preserve, Arco, ID\tCable Car Museum, 94108, 1201 Mason St, San Francisco, CA 94108\t1222590\t40995\nThe Breakers, Ochre Point Avenue, Newport, RI\tGlacier National Park, West Glacier, MT\t4038048\t135310\nThe Mark Twain House & Museum, Farmington Avenue, Hartford, CT\tNew Castle Historic District, Delaware\t484456\t17754\nCape Canaveral, FL\tAcadia National Park, Maine\t2532824\t84632\nC. W. Parker Carousel Museum, South Esplanade Street, Leavenworth, KS\tStatue of Liberty, Liberty Island, NYC, NY\t1973392\t66712\nMount Rushmore National Memorial, South Dakota 244, Keystone, SD\tCraters of the Moon National Monument & Preserve, Arco, ID\t1100123\t41870\nC. W. Parker Carousel Museum, South Esplanade Street, Leavenworth, KS\tTerrace Hill, Grand Avenue, Des Moines, IA\t333113\t11215\nSan Andreas Fault, San Benito County, CA\tWright Brothers National Memorial Visitor Center, Manteo, NC\t4699698\t152179\nChickasaw National Recreation Area, 1008 W 2nd St, Sulphur, OK 73086\tGraceland, Elvis Presley Boulevard, Memphis, TN\t805016\t26751\nCraters of the Moon National Monument & Preserve, Arco, ID\tUSS Constitution, Boston, MA\t4066911\t132549\nChickasaw National Recreation Area, 1008 W 2nd St, Sulphur, OK 73086\tOlympia Entertainment, Woodward Avenue, Detroit, MI\t1731916\t57583\nYellowstone National Park, WY 82190\tLincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL\t2134234\t74013\nGlacier National Park, West Glacier, MT\tWest Baden Springs Hotel, West Baden Avenue, West Baden Springs, IN\t2894639\t99476\nMount Rushmore National Memorial, South Dakota 244, Keystone, SD\tSpring Grove Cemetery, Spring Grove Avenue, Cincinnati, OH\t1954380\t62884\nCongress Hall, Congress Place, Cape May, NJ 08204\tFort Sumter National Monument, Sullivan's Island, SC\t1160225\t39754\nShelburne Farms, Harbor Road, Shelburne, VT\tGrand Canyon National Park, Arizona\t4087112\t135156\nHanford Site, Benton County, WA\tUSS Constitution, Boston, MA\t4699823\t153033\nGraceland, Elvis Presley Boulevard, Memphis, TN\tHanford Site, Benton County, WA\t3425764\t112518\nUSS Constitution, Boston, MA\tAshfall Fossil Bed, Royal, NE\t2559004\t86405\nFort Union Trading Post National Historic Site, Williston, North Dakota 1804, ND\tShelburne Farms, Harbor Road, Shelburne, VT\t3167088\t107665\nUSS Alabama, Battleship Parkway, Mobile, AL\tStatue of Liberty, Liberty Island, NYC, NY\t1924035\t63443\nTaliesin, County Road C, Spring Green, Wisconsin\tThe Alamo, Alamo Plaza, San Antonio, TX\t2046331\t65654\nThe Breakers, Ochre Point Avenue, Newport, RI\tFrench Quarter, New Orleans, LA\t2389577\t79078\nShelburne Farms, Harbor Road, Shelburne, VT\tFrench Quarter, New Orleans, LA\t2527076\t83700\nSpring Grove Cemetery, Spring Grove Avenue, Cincinnati, OH\tSan Andreas Fault, San Benito County, CA\t3796749\t122084\nTerrace Hill, Grand Avenue, Des Moines, IA\tThe Breakers, Ochre Point Avenue, Newport, RI\t2075619\t69538\nGraceland, Elvis Presley Boulevard, Memphis, TN\tNew Castle Historic District, Delaware\t1570606\t51937\nSan Andreas Fault, San Benito County, CA\tGlacier National Park, West Glacier, MT\t2190913\t74514\nTerrace Hill, Grand Avenue, Des Moines, IA\tShelburne Farms, Harbor Road, Shelburne, VT\t1961166\t68468\nGateway Arch, Washington Avenue, St Louis, MO\tOkefenokee Swamp Park, Okefenokee Swamp Park Road, Waycross, GA\t1303092\t43755\nLiberty Bell, 6th Street, Philadelphia, PA\tStatue of Liberty, Liberty Island, NYC, NY\t151962\t6043\nCarlsbad Caverns National Park, Carlsbad, NM\tUSS Constitution, Boston, MA\t3599410\t121316\nSan Andreas Fault, San Benito County, CA\tFort Snelling, Tower Avenue, Saint Paul, MN\t3294713\t108401\nWright Brothers National Memorial Visitor Center, Manteo, NC\tWhite House, Pennsylvania Avenue Northwest, Washington, DC\t462796\t16916\nLincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL\tCable Car Museum, 94108, 1201 Mason St, San Francisco, CA 94108\t3288988\t105207\nToltec Mounds, Scott, AR\tGraceland, Elvis Presley Boulevard, Memphis, TN\t229031\t7951\nStatue of Liberty, Liberty Island, NYC, NY\tGateway Arch, Washington Avenue, St Louis, MO\t1532676\t52256\nGraceland, Elvis Presley Boulevard, Memphis, TN\tThe Mark Twain House & Museum, Farmington Avenue, Hartford, CT\t1963583\t64635\nMount Vernon, Fairfax County, Virginia\tGrand Canyon National Park, Arizona\t3665067\t116851\nWright Brothers National Memorial Visitor Center, Manteo, NC\tCable Car Museum, 94108, 1201 Mason St, San Francisco, CA 94108\t4874201\t156275\nUSS Constitution, Boston, MA\tFrench Quarter, New Orleans, LA\t2454093\t80405\nWhite House, Pennsylvania Avenue Northwest, Washington, DC\tStatue of Liberty, Liberty Island, NYC, NY\t362725\t13529\nAshfall Fossil Bed, Royal, NE\tGlacier National Park, West Glacier, MT\t1691765\t60397\nTerrace Hill, Grand Avenue, Des Moines, IA\tCable Car Museum, 94108, 1201 Mason St, San Francisco, CA 94108\t2891033\t91475\nCarlsbad Caverns National Park, Carlsbad, NM\tGlacier National Park, West Glacier, MT\t2483417\t83906\nChickasaw National Recreation Area, 1008 W 2nd St, Sulphur, OK 73086\tNew Castle Historic District, Delaware\t2359410\t77959\nHoover Dam, NV\tStatue of Liberty, Liberty Island, NYC, NY\t4078427\t131726\nShelburne Farms, Harbor Road, Shelburne, VT\tBryce Canyon National Park, Hwy 63, Bryce, UT\t3938537\t131628\nC. W. Parker Carousel Museum, South Esplanade Street, Leavenworth, KS\tFrench Quarter, New Orleans, LA\t1413656\t47943\nUSS Alabama, Battleship Parkway, Mobile, AL\tTerrace Hill, Grand Avenue, Des Moines, IA\t1568964\t54581\nHanford Site, Benton County, WA\tOkefenokee Swamp Park, Okefenokee Swamp Park Road, Waycross, GA\t4385252\t142720\nFrench Quarter, New Orleans, LA\tWest Baden Springs Hotel, West Baden Avenue, West Baden Springs, IN\t1196556\t38645\nHoover Dam, NV\tLincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL\t2703190\t85402\nThe Mark Twain House & Museum, Farmington Avenue, Hartford, CT\tFrench Quarter, New Orleans, LA\t2293165\t74916\nHanford Site, Benton County, WA\tCape Canaveral, FL\t4766469\t152673\nMount Rushmore National Memorial, South Dakota 244, Keystone, SD\tWhite House, Pennsylvania Avenue Northwest, Washington, DC\t2624980\t86273\nOmni Mount Washington Resort, Mount Washington Hotel Road, Bretton Woods, NH\tOlympia Entertainment, Woodward Avenue, Detroit, MI\t1220596\t42219\nCarlsbad Caverns National Park, Carlsbad, NM\tOkefenokee Swamp Park, Okefenokee Swamp Park Road, Waycross, GA\t2281669\t80307\nVicksburg National Military Park, Clay Street, Vicksburg, MS\tGrand Canyon National Park, Arizona\t2254207\t71839\nUSS Constitution, Boston, MA\tOlympia Entertainment, Woodward Avenue, Detroit, MI\t1157988\t39544\nChickasaw National Recreation Area, 1008 W 2nd St, Sulphur, OK 73086\tWright Brothers National Memorial Visitor Center, Manteo, NC\t2302762\t76129\nChickasaw National Recreation Area, 1008 W 2nd St, Sulphur, OK 73086\tGrand Canyon National Park, Arizona\t1591868\t52162\nMount Vernon, Fairfax County, Virginia\tFort Sumter National Monument, Sullivan's Island, SC\t833917\t26773\nStatue of Liberty, Liberty Island, NYC, NY\tThe Alamo, Alamo Plaza, San Antonio, TX\t2933460\t94860\nHanford Site, Benton County, WA\tAcadia National Park, Maine\t5129430\t168127\nUSS Alabama, Battleship Parkway, Mobile, AL\tThe Alamo, Alamo Plaza, San Antonio, TX\t1069098\t34228\nColumbia River Gorge National Scenic Area, Oregon\tWest Baden Springs Hotel, West Baden Avenue, West Baden Springs, IN\t3504228\t113055\nSpring Grove Cemetery, Spring Grove Avenue, Cincinnati, OH\tOmni Mount Washington Resort, Mount Washington Hotel Road, Bretton Woods, NH\t1508650\t52479\nLiberty Bell, 6th Street, Philadelphia, PA\tLost World Caverns, Lewisburg, WV\t637355\t21587\nUSS Alabama, Battleship Parkway, Mobile, AL\tBryce Canyon National Park, Hwy 63, Bryce, UT\t2859726\t96881\nC. W. Parker Carousel Museum, South Esplanade Street, Leavenworth, KS\tSpring Grove Cemetery, Spring Grove Avenue, Cincinnati, OH\t996021\t33100\nSpring Grove Cemetery, Spring Grove Avenue, Cincinnati, OH\tYellowstone National Park, WY 82190\t2674069\t90435\nHoover Dam, NV\tSan Andreas Fault, San Benito County, CA\t763736\t27404\nGateway Arch, Washington Avenue, St Louis, MO\tGlacier National Park, West Glacier, MT\t2628822\t89235\nMount Vernon, Fairfax County, Virginia\tBryce Canyon National Park, Hwy 63, Bryce, UT\t3597733\t117900\nOmni Mount Washington Resort, Mount Washington Hotel Road, Bretton Woods, NH\tAshfall Fossil Bed, Royal, NE\t2649100\t91200\nBryce Canyon National Park, Hwy 63, Bryce, UT\tCable Car Museum, 94108, 1201 Mason St, San Francisco, CA 94108\t1328462\t43497\nSan Andreas Fault, San Benito County, CA\tOkefenokee Swamp Park, Okefenokee Swamp Park Road, Waycross, GA\t4209966\t136875\nChickasaw National Recreation Area, 1008 W 2nd St, Sulphur, OK 73086\tTaliesin, County Road C, Spring Green, Wisconsin\t1440063\t46409\nChickasaw National Recreation Area, 1008 W 2nd St, Sulphur, OK 73086\tFort Snelling, Tower Avenue, Saint Paul, MN\t1402360\t44596\nHoover Dam, NV\tUSS Constitution, Boston, MA\t4427850\t143274\nLiberty Bell, 6th Street, Philadelphia, PA\tWhite House, Pennsylvania Avenue Northwest, Washington, DC\t222250\t8641\nGateway Arch, Washington Avenue, St Louis, MO\tFort Snelling, Tower Avenue, Saint Paul, MN\t847947\t30144\nOmni Mount Washington Resort, Mount Washington Hotel Road, Bretton Woods, NH\tGlacier National Park, West Glacier, MT\t4152680\t140348\nFort Snelling, Tower Avenue, Saint Paul, MN\tGrand Canyon National Park, Arizona\t2517491\t81762\nSpring Grove Cemetery, Spring Grove Avenue, Cincinnati, OH\tGateway Arch, Washington Avenue, St Louis, MO\t555304\t18644\nCongress Hall, Congress Place, Cape May, NJ 08204\tGrand Canyon National Park, Arizona\t3879839\t125602\nVicksburg National Military Park, Clay Street, Vicksburg, MS\tWest Baden Springs Hotel, West Baden Avenue, West Baden Springs, IN\t1049651\t34167\nC. W. Parker Carousel Museum, South Esplanade Street, Leavenworth, KS\tCongress Hall, Congress Place, Cape May, NJ 08204\t2014264\t67825\nStatue of Liberty, Liberty Island, NYC, NY\tBryce Canyon National Park, Hwy 63, Bryce, UT\t3768500\t122799\nThe Mark Twain House & Museum, Farmington Avenue, Hartford, CT\tMammoth Cave National Park, Mammoth Cave Pkwy, Mammoth Cave, KY\t1533836\t51383\nTaliesin, County Road C, Spring Green, Wisconsin\tYellowstone National Park, WY 82190\t2075224\t72149\nShelburne Farms, Harbor Road, Shelburne, VT\tThe Breakers, Ochre Point Avenue, Newport, RI\t470924\t17258\nFort Union Trading Post National Historic Site, Williston, North Dakota 1804, ND\tFrench Quarter, New Orleans, LA\t3023850\t97576\nMammoth Cave National Park, Mammoth Cave Pkwy, Mammoth Cave, KY\tNew Castle Historic District, Delaware\t1187971\t40241\nSan Andreas Fault, San Benito County, CA\tAcadia National Park, Maine\t5557263\t181349\nToltec Mounds, Scott, AR\tTerrace Hill, Grand Avenue, Des Moines, IA\t1007426\t32538\nToltec Mounds, Scott, AR\tWright Brothers National Memorial Visitor Center, Manteo, NC\t1728198\t57317\nToltec Mounds, Scott, AR\tWest Baden Springs Hotel, West Baden Avenue, West Baden Springs, IN\t849822\t27914\nPikes Peak, Colorado\tGlacier National Park, West Glacier, MT\t1632053\t57705\nUSS Constitution, Boston, MA\tThe Alamo, Alamo Plaza, San Antonio, TX\t3289321\t107282\nTerrace Hill, Grand Avenue, Des Moines, IA\tLincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL\t545245\t17997\nSpring Grove Cemetery, Spring Grove Avenue, Cincinnati, OH\tOkefenokee Swamp Park, Okefenokee Swamp Park Road, Waycross, GA\t1160395\t39050\nHanford Site, Benton County, WA\tCarlsbad Caverns National Park, Carlsbad, NM\t2485831\t86610\nUSS Alabama, Battleship Parkway, Mobile, AL\tGateway Arch, Washington Avenue, St Louis, MO\t1043023\t36000\nColumbia River Gorge National Scenic Area, Oregon\tCable Car Museum, 94108, 1201 Mason St, San Francisco, CA 94108\t1044669\t35450\nNew Castle Historic District, Delaware\tUSS Constitution, Boston, MA\t641379\t23278\nFort Union Trading Post National Historic Site, Williston, North Dakota 1804, ND\tOmni Mount Washington Resort, Mount Washington Hotel Road, Bretton Woods, NH\t3385471\t114071\nMount Rushmore National Memorial, South Dakota 244, Keystone, SD\tPikes Peak, Colorado\t754485\t28424\nOlympia Entertainment, Woodward Avenue, Detroit, MI\tCable Car Museum, 94108, 1201 Mason St, San Francisco, CA 94108\t3850939\t122474\nCraters of the Moon National Monument & Preserve, Arco, ID\tThe Alamo, Alamo Plaza, San Antonio, TX\t2521886\t85067\nMount Rushmore National Memorial, South Dakota 244, Keystone, SD\tThe Mark Twain House & Museum, Farmington Avenue, Hartford, CT\t2932400\t96249\nMount Rushmore National Memorial, South Dakota 244, Keystone, SD\tShelburne Farms, Harbor Road, Shelburne, VT\t2954965\t100452\nMammoth Cave National Park, Mammoth Cave Pkwy, Mammoth Cave, KY\tUSS Constitution, Boston, MA\t1696634\t56942\nGraceland, Elvis Presley Boulevard, Memphis, TN\tThe Alamo, Alamo Plaza, San Antonio, TX\t1179943\t38509\nToltec Mounds, Scott, AR\tMammoth Cave National Park, Mammoth Cave Pkwy, Mammoth Cave, KY\t696215\t22929\nUSS Alabama, Battleship Parkway, Mobile, AL\tWest Baden Springs Hotel, West Baden Avenue, West Baden Springs, IN\t1064711\t34283\nCarlsbad Caverns National Park, Carlsbad, NM\tFort Snelling, Tower Avenue, Saint Paul, MN\t2166013\t71753\nHanford Site, Benton County, WA\tBryce Canyon National Park, Hwy 63, Bryce, UT\t1460920\t47057\nToltec Mounds, Scott, AR\tTaliesin, County Road C, Spring Green, Wisconsin\t1206666\t41628\nMount Vernon, Fairfax County, Virginia\tThe Breakers, Ochre Point Avenue, Newport, RI\t674006\t25401\nThe Breakers, Ochre Point Avenue, Newport, RI\tOkefenokee Swamp Park, Okefenokee Swamp Park Road, Waycross, GA\t1770817\t60421\nMaryland State House, 100 State Cir, Annapolis, MD 21401\tTerrace Hill, Grand Avenue, Des Moines, IA\t1679749\t55332\nC. W. Parker Carousel Museum, South Esplanade Street, Leavenworth, KS\tWhite House, Pennsylvania Avenue Northwest, Washington, DC\t1754476\t59507\nGateway Arch, Washington Avenue, St Louis, MO\tGrand Canyon National Park, Arizona\t2307961\t72717\nMaryland State House, 100 State Cir, Annapolis, MD 21401\tGrand Canyon National Park, Arizona\t3659127\t118247\nWhite House, Pennsylvania Avenue Northwest, Washington, DC\tWest Baden Springs Hotel, West Baden Avenue, West Baden Springs, IN\t1065777\t36173\nAcadia National Park, Maine\tOkefenokee Swamp Park, Okefenokee Swamp Park Road, Waycross, GA\t2253691\t76984\nShelburne Farms, Harbor Road, Shelburne, VT\tStatue of Liberty, Liberty Island, NYC, NY\t477387\t18879\nChickasaw National Recreation Area, 1008 W 2nd St, Sulphur, OK 73086\tCongress Hall, Congress Place, Cape May, NJ 08204\t2437518\t82535\nSpring Grove Cemetery, Spring Grove Avenue, Cincinnati, OH\tCape Canaveral, FL\t1541612\t49003\nLiberty Bell, 6th Street, Philadelphia, PA\tGlacier National Park, West Glacier, MT\t3700295\t123040\nHoover Dam, NV\tHanford Site, Benton County, WA\t1545185\t52485\nC. W. Parker Carousel Museum, South Esplanade Street, Leavenworth, KS\tMount Rushmore National Memorial, South Dakota 244, Keystone, SD\t1144699\t36459\nStatue of Liberty, Liberty Island, NYC, NY\tYellowstone National Park, WY 82190\t3524053\t119175\nLiberty Bell, 6th Street, Philadelphia, PA\tSpring Grove Cemetery, Spring Grove Avenue, Cincinnati, OH\t918887\t31359\nHoover Dam, NV\tCongress Hall, Congress Place, Cape May, NJ 08204\t4119298\t132839\nYellowstone National Park, WY 82190\tGrand Canyon National Park, Arizona\t1142593\t44149\nWhite House, Pennsylvania Avenue Northwest, Washington, DC\tGateway Arch, Washington Avenue, St Louis, MO\t1318428\t45204\nLiberty Bell, 6th Street, Philadelphia, PA\tOlympia Entertainment, Woodward Avenue, Detroit, MI\t944184\t31505\nThe Alamo, Alamo Plaza, San Antonio, TX\tFort Snelling, Tower Avenue, Saint Paul, MN\t2008901\t63971\nCongress Hall, Congress Place, Cape May, NJ 08204\tAshfall Fossil Bed, Royal, NE\t2345131\t79089\nThe Alamo, Alamo Plaza, San Antonio, TX\tLincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL\t1607847\t53924\nSan Andreas Fault, San Benito County, CA\tCongress Hall, Congress Place, Cape May, NJ 08204\t4814991\t156809\nGateway Arch, Washington Avenue, St Louis, MO\tBryce Canyon National Park, Hwy 63, Bryce, UT\t2276197\t73341\nUSS Alabama, Battleship Parkway, Mobile, AL\tHoover Dam, NV\t2861891\t92906\nCarlsbad Caverns National Park, Carlsbad, NM\tNew Castle Historic District, Delaware\t3085939\t103553\nCarlsbad Caverns National Park, Carlsbad, NM\tThe Alamo, Alamo Plaza, San Antonio, TX\t758015\t26423\nCarlsbad Caverns National Park, Carlsbad, NM\tThe Mark Twain House & Museum, Farmington Avenue, Hartford, CT\t3434325\t115391\nOmni Mount Washington Resort, Mount Washington Hotel Road, Bretton Woods, NH\tUSS Constitution, Boston, MA\t251490\t9163\nThe Mark Twain House & Museum, Farmington Avenue, Hartford, CT\tThe Breakers, Ochre Point Avenue, Newport, RI\t145604\t6359\nShelburne Farms, Harbor Road, Shelburne, VT\tGateway Arch, Washington Avenue, St Louis, MO\t1780820\t62923\nHoover Dam, NV\tPikes Peak, Colorado\t1346909\t47426\nC. W. Parker Carousel Museum, South Esplanade Street, Leavenworth, KS\tHanford Site, Benton County, WA\t2663870\t85877\nC. W. Parker Carousel Museum, South Esplanade Street, Leavenworth, KS\tHoover Dam, NV\t2223982\t70608\nMount Rushmore National Memorial, South Dakota 244, Keystone, SD\tBryce Canyon National Park, Hwy 63, Bryce, UT\t1417261\t48534\nMount Rushmore National Memorial, South Dakota 244, Keystone, SD\tHoover Dam, NV\t1715316\t56972\nLiberty Bell, 6th Street, Philadelphia, PA\tFrench Quarter, New Orleans, LA\t1966566\t63823\nGraceland, Elvis Presley Boulevard, Memphis, TN\tFort Sumter National Monument, Sullivan's Island, SC\t1084382\t36047\nSan Andreas Fault, San Benito County, CA\tCape Canaveral, FL\t4432637\t143630\nCarlsbad Caverns National Park, Carlsbad, NM\tColumbia River Gorge National Scenic Area, Oregon\t2526763\t87797\nMammoth Cave National Park, Mammoth Cave Pkwy, Mammoth Cave, KY\tFrench Quarter, New Orleans, LA\t995629\t31618\nOlympia Entertainment, Woodward Avenue, Detroit, MI\tOkefenokee Swamp Park, Okefenokee Swamp Park Road, Waycross, GA\t1575841\t53083\nWhite House, Pennsylvania Avenue Northwest, Washington, DC\tCongress Hall, Congress Place, Cape May, NJ 08204\t300705\t12240\nUSS Alabama, Battleship Parkway, Mobile, AL\tHanford Site, Benton County, WA\t4050755\t131517\nGraceland, Elvis Presley Boulevard, Memphis, TN\tOkefenokee Swamp Park, Okefenokee Swamp Park Road, Waycross, GA\t937381\t34311\nHoover Dam, NV\tOmni Mount Washington Resort, Mount Washington Hotel Road, Bretton Woods, NH\t4515460\t147852\nMount Vernon, Fairfax County, Virginia\tLincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL\t1280757\t43906\nWhite House, Pennsylvania Avenue Northwest, Washington, DC\tBryce Canyon National Park, Hwy 63, Bryce, UT\t3578269\t117494\nAcadia National Park, Maine\tFrench Quarter, New Orleans, LA\t2884290\t95624\nLiberty Bell, 6th Street, Philadelphia, PA\tYellowstone National Park, WY 82190\t3474172\t117273\nChickasaw National Recreation Area, 1008 W 2nd St, Sulphur, OK 73086\tStatue of Liberty, Liberty Island, NYC, NY\t2406284\t81083\nTerrace Hill, Grand Avenue, Des Moines, IA\tColumbia River Gorge National Scenic Area, Oregon\t2715601\t86700\nCraters of the Moon National Monument & Preserve, Arco, ID\tFort Sumter National Monument, Sullivan's Island, SC\t3760470\t121856\nPikes Peak, Colorado\tColumbia River Gorge National Scenic Area, Oregon\t1989641\t67397\nUSS Constitution, Boston, MA\tFort Sumter National Monument, Sullivan's Island, SC\t1565117\t53243\nFort Union Trading Post National Historic Site, Williston, North Dakota 1804, ND\tVicksburg National Military Park, Clay Street, Vicksburg, MS\t2672008\t86961\nShelburne Farms, Harbor Road, Shelburne, VT\tOkefenokee Swamp Park, Okefenokee Swamp Park Road, Waycross, GA\t1933249\t66415\nShelburne Farms, Harbor Road, Shelburne, VT\tCongress Hall, Congress Place, Cape May, NJ 08204\t705691\t26152\nOmni Mount Washington Resort, Mount Washington Hotel Road, Bretton Woods, NH\tBryce Canyon National Park, Hwy 63, Bryce, UT\t4170118\t138148\nVicksburg National Military Park, Clay Street, Vicksburg, MS\tCable Car Museum, 94108, 1201 Mason St, San Francisco, CA 94108\t3363863\t107141\nSan Andreas Fault, San Benito County, CA\tColumbia River Gorge National Scenic Area, Oregon\t1228419\t42635\nHoover Dam, NV\tCape Canaveral, FL\t3741759\t120177\nVicksburg National Military Park, Clay Street, Vicksburg, MS\tCongress Hall, Congress Place, Cape May, NJ 08204\t1939504\t64109\nTerrace Hill, Grand Avenue, Des Moines, IA\tFort Union Trading Post National Historic Site, Williston, North Dakota 1804, ND\t1452974\t46585\nMount Rushmore National Memorial, South Dakota 244, Keystone, SD\tUSS Alabama, Battleship Parkway, Mobile, AL\t2485277\t84139\nFort Union Trading Post National Historic Site, Williston, North Dakota 1804, ND\tLost World Caverns, Lewisburg, WV\t2685723\t88506\nCraters of the Moon National Monument & Preserve, Arco, ID\tYellowstone National Park, WY 82190\t413219\t17526\nC. W. Parker Carousel Museum, South Esplanade Street, Leavenworth, KS\tCraters of the Moon National Monument & Preserve, Arco, ID\t1957641\t63753\nThe Breakers, Ochre Point Avenue, Newport, RI\tPikes Peak, Colorado\t3199746\t109066\nMaryland State House, 100 State Cir, Annapolis, MD 21401\tOlympia Entertainment, Woodward Avenue, Detroit, MI\t886410\t29704\nToltec Mounds, Scott, AR\tHoover Dam, NV\t2312537\t73064\nCraters of the Moon National Monument & Preserve, Arco, ID\tWest Baden Springs Hotel, West Baden Avenue, West Baden Springs, IN\t2762301\t89737\nCarlsbad Caverns National Park, Carlsbad, NM\tAshfall Fossil Bed, Royal, NE\t1584981\t59676\nShelburne Farms, Harbor Road, Shelburne, VT\tAcadia National Park, Maine\t544911\t23436\nOkefenokee Swamp Park, Okefenokee Swamp Park Road, Waycross, GA\tBryce Canyon National Park, Hwy 63, Bryce, UT\t3573294\t116435\nUSS Alabama, Battleship Parkway, Mobile, AL\tCable Car Museum, 94108, 1201 Mason St, San Francisco, CA 94108\t3851355\t120882\nGateway Arch, Washington Avenue, St Louis, MO\tOlympia Entertainment, Woodward Avenue, Detroit, MI\t857616\t28721\nUSS Constitution, Boston, MA\tGateway Arch, Washington Avenue, St Louis, MO\t1899364\t64069\nMaryland State House, 100 State Cir, Annapolis, MD 21401\tCraters of the Moon National Monument & Preserve, Arco, ID\t3647102\t118406\nSan Andreas Fault, San Benito County, CA\tFort Sumter National Monument, Sullivan's Island, SC\t4281410\t138473\nWright Brothers National Memorial Visitor Center, Manteo, NC\tGrand Canyon National Park, Arizona\t3764546\t120973\nWhite House, Pennsylvania Avenue Northwest, Washington, DC\tVicksburg National Military Park, Clay Street, Vicksburg, MS\t1637791\t52725\nThe Mark Twain House & Museum, Farmington Avenue, Hartford, CT\tPikes Peak, Colorado\t3109610\t103980\nFort Union Trading Post National Historic Site, Williston, North Dakota 1804, ND\tThe Alamo, Alamo Plaza, San Antonio, TX\t2558847\t84752\nChickasaw National Recreation Area, 1008 W 2nd St, Sulphur, OK 73086\tColumbia River Gorge National Scenic Area, Oregon\t3048674\t97029\nMammoth Cave National Park, Mammoth Cave Pkwy, Mammoth Cave, KY\tWhite House, Pennsylvania Avenue Northwest, Washington, DC\t1066109\t35425\nUSS Alabama, Battleship Parkway, Mobile, AL\tCarlsbad Caverns National Park, Carlsbad, NM\t1744227\t59939\nMammoth Cave National Park, Mammoth Cave Pkwy, Mammoth Cave, KY\tThe Alamo, Alamo Plaza, San Antonio, TX\t1645458\t53310\nUSS Alabama, Battleship Parkway, Mobile, AL\tThe Breakers, Ochre Point Avenue, Newport, RI\t2214605\t74421\nHoover Dam, NV\tThe Alamo, Alamo Plaza, San Antonio, TX\t2012743\t63275\nCarlsbad Caverns National Park, Carlsbad, NM\tFrench Quarter, New Orleans, LA\t1626516\t54348\nToltec Mounds, Scott, AR\tSan Andreas Fault, San Benito County, CA\t3004953\t97302\nSpring Grove Cemetery, Spring Grove Avenue, Cincinnati, OH\tCraters of the Moon National Monument & Preserve, Arco, ID\t2873247\t93460\nCarlsbad Caverns National Park, Carlsbad, NM\tCable Car Museum, 94108, 1201 Mason St, San Francisco, CA 94108\t2122087\t70306\nGateway Arch, Washington Avenue, St Louis, MO\tColumbia River Gorge National Scenic Area, Oregon\t3129086\t100814\nOmni Mount Washington Resort, Mount Washington Hotel Road, Bretton Woods, NH\tNew Castle Historic District, Delaware\t868799\t30943\nHanford Site, Benton County, WA\tWest Baden Springs Hotel, West Baden Avenue, West Baden Springs, IN\t3463296\t111867\nCongress Hall, Congress Place, Cape May, NJ 08204\tCable Car Museum, 94108, 1201 Mason St, San Francisco, CA 94108\t4775270\t153549\nMaryland State House, 100 State Cir, Annapolis, MD 21401\tShelburne Farms, Harbor Road, Shelburne, VT\t799522\t29333\nTaliesin, County Road C, Spring Green, Wisconsin\tGrand Canyon National Park, Arizona\t2555807\t83764\nUSS Constitution, Boston, MA\tWest Baden Springs Hotel, West Baden Avenue, West Baden Springs, IN\t1670972\t56710\nFort Sumter National Monument, Sullivan's Island, SC\tLincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL\t1483499\t48668\nTaliesin, County Road C, Spring Green, Wisconsin\tPikes Peak, Colorado\t1659414\t54899\nMount Rushmore National Memorial, South Dakota 244, Keystone, SD\tHanford Site, Benton County, WA\t1661609\t56332\nLiberty Bell, 6th Street, Philadelphia, PA\tNew Castle Historic District, Delaware\t162565\t6109\nToltec Mounds, Scott, AR\tMaryland State House, 100 State Cir, Annapolis, MD 21401\t1690140\t55321\nSan Andreas Fault, San Benito County, CA\tWhite House, Pennsylvania Avenue Northwest, Washington, DC\t4598813\t147923\nCongress Hall, Congress Place, Cape May, NJ 08204\tOlympia Entertainment, Woodward Avenue, Detroit, MI\t1092600\t36702\nTerrace Hill, Grand Avenue, Des Moines, IA\tOkefenokee Swamp Park, Okefenokee Swamp Park Road, Waycross, GA\t1848155\t63009\nUSS Alabama, Battleship Parkway, Mobile, AL\tFort Snelling, Tower Avenue, Saint Paul, MN\t1867680\t64758\nWright Brothers National Memorial Visitor Center, Manteo, NC\tFort Snelling, Tower Avenue, Saint Paul, MN\t2236906\t75704\nFort Union Trading Post National Historic Site, Williston, North Dakota 1804, ND\tMammoth Cave National Park, Mammoth Cave Pkwy, Mammoth Cave, KY\t2335379\t76633\nOkefenokee Swamp Park, Okefenokee Swamp Park Road, Waycross, GA\tWest Baden Springs Hotel, West Baden Avenue, West Baden Springs, IN\t1147157\t39324\nNew Castle Historic District, Delaware\tAcadia National Park, Maine\t1071576\t38496\nHoover Dam, NV\tMount Vernon, Fairfax County, Virginia\t3904526\t124088\nTaliesin, County Road C, Spring Green, Wisconsin\tMammoth Cave National Park, Mammoth Cave Pkwy, Mammoth Cave, KY\t921910\t31814\nC. W. Parker Carousel Museum, South Esplanade Street, Leavenworth, KS\tUSS Constitution, Boston, MA\t2359929\t78386\nThe Mark Twain House & Museum, Farmington Avenue, Hartford, CT\tGrand Canyon National Park, Arizona\t4039492\t130415\nOkefenokee Swamp Park, Okefenokee Swamp Park Road, Waycross, GA\tLincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL\t1406589\t47379\nSan Andreas Fault, San Benito County, CA\tVicksburg National Military Park, Clay Street, Vicksburg, MS\t3189360\t103045\nVicksburg National Military Park, Clay Street, Vicksburg, MS\tFort Sumter National Monument, Sullivan's Island, SC\t1155865\t37713\nTerrace Hill, Grand Avenue, Des Moines, IA\tPikes Peak, Colorado\t1230185\t40625\nStatue of Liberty, Liberty Island, NYC, NY\tWest Baden Springs Hotel, West Baden Avenue, West Baden Springs, IN\t1278590\t44793\nFort Sumter National Monument, Sullivan's Island, SC\tFort Snelling, Tower Avenue, Saint Paul, MN\t2120739\t69923\nGateway Arch, Washington Avenue, St Louis, MO\tCable Car Museum, 94108, 1201 Mason St, San Francisco, CA 94108\t3309548\t105479\nMaryland State House, 100 State Cir, Annapolis, MD 21401\tCable Car Museum, 94108, 1201 Mason St, San Francisco, CA 94108\t4568737\t146417\nMaryland State House, 100 State Cir, Annapolis, MD 21401\tMount Vernon, Fairfax County, Virginia\t75403\t3165\nMount Vernon, Fairfax County, Virginia\tHanford Site, Benton County, WA\t4263334\t138596\nNew Castle Historic District, Delaware\tThe Alamo, Alamo Plaza, San Antonio, TX\t2736315\t88893\nFort Union Trading Post National Historic Site, Williston, North Dakota 1804, ND\tOlympia Entertainment, Woodward Avenue, Detroit, MI\t2168837\t70848\nWright Brothers National Memorial Visitor Center, Manteo, NC\tFort Sumter National Monument, Sullivan's Island, SC\t715393\t23430\nUSS Constitution, Boston, MA\tGrand Canyon National Park, Arizona\t4225504\t136164\nTaliesin, County Road C, Spring Green, Wisconsin\tCape Canaveral, FL\t2250465\t72627\nFrench Quarter, New Orleans, LA\tLincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL\t1245550\t40104\nMount Vernon, Fairfax County, Virginia\tLost World Caverns, Lewisburg, WV\t420070\t14282\nOmni Mount Washington Resort, Mount Washington Hotel Road, Bretton Woods, NH\tGrand Canyon National Park, Arizona\t4316197\t141264\nNew Castle Historic District, Delaware\tPikes Peak, Colorado\t2811795\t95709\nLost World Caverns, Lewisburg, WV\tUSS Constitution, Boston, MA\t1126003\t38377\nTerrace Hill, Grand Avenue, Des Moines, IA\tOmni Mount Washington Resort, Mount Washington Hotel Road, Bretton Woods, NH\t2190251\t74576\nUSS Alabama, Battleship Parkway, Mobile, AL\tPikes Peak, Colorado\t2157571\t74324\nUSS Alabama, Battleship Parkway, Mobile, AL\tFrench Quarter, New Orleans, LA\t232384\t7544\nCraters of the Moon National Monument & Preserve, Arco, ID\tWhite House, Pennsylvania Avenue Northwest, Washington, DC\t3677215\t120643\nGraceland, Elvis Presley Boulevard, Memphis, TN\tLost World Caverns, Lewisburg, WV\t1111929\t36026\nCarlsbad Caverns National Park, Carlsbad, NM\tThe Breakers, Ochre Point Avenue, Newport, RI\t3575389\t120329\nSpring Grove Cemetery, Spring Grove Avenue, Cincinnati, OH\tLincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL\t509622\t16928\nMount Vernon, Fairfax County, Virginia\tPikes Peak, Colorado\t2712486\t91592\nCongress Hall, Congress Place, Cape May, NJ 08204\tOkefenokee Swamp Park, Okefenokee Swamp Park Road, Waycross, GA\t1418227\t48154\nGraceland, Elvis Presley Boulevard, Memphis, TN\tGrand Canyon National Park, Arizona\t2265379\t71607\nUSS Alabama, Battleship Parkway, Mobile, AL\tSan Andreas Fault, San Benito County, CA\t3676539\t116761\nC. W. Parker Carousel Museum, South Esplanade Street, Leavenworth, KS\tFort Union Trading Post National Historic Site, Williston, North Dakota 1804, ND\t1616472\t51634\nLost World Caverns, Lewisburg, WV\tCongress Hall, Congress Place, Cape May, NJ 08204\t719625\t25492\nCape Canaveral, FL\tWest Baden Springs Hotel, West Baden Avenue, West Baden Springs, IN\t1528374\t49277\nWright Brothers National Memorial Visitor Center, Manteo, NC\tCape Canaveral, FL\t1253439\t39835\nNew Castle Historic District, Delaware\tCongress Hall, Congress Place, Cape May, NJ 08204\t84730\t8914\nMount Rushmore National Memorial, South Dakota 244, Keystone, SD\tGlacier National Park, West Glacier, MT\t1154489\t41962\nGraceland, Elvis Presley Boulevard, Memphis, TN\tUSS Constitution, Boston, MA\t2123612\t70326\nC. W. Parker Carousel Museum, South Esplanade Street, Leavenworth, KS\tShelburne Farms, Harbor Road, Shelburne, VT\t2221537\t77379\nNew Castle Historic District, Delaware\tBryce Canyon National Park, Hwy 63, Bryce, UT\t3700130\t122310\nVicksburg National Military Park, Clay Street, Vicksburg, MS\tOkefenokee Swamp Park, Okefenokee Swamp Park Road, Waycross, GA\t908130\t33755\nThe Mark Twain House & Museum, Farmington Avenue, Hartford, CT\tBryce Canyon National Park, Hwy 63, Bryce, UT\t3928478\t127896\nMaryland State House, 100 State Cir, Annapolis, MD 21401\tCape Canaveral, FL\t1449191\t45859\nWhite House, Pennsylvania Avenue Northwest, Washington, DC\tAshfall Fossil Bed, Royal, NE\t2100633\t71136\nOmni Mount Washington Resort, Mount Washington Hotel Road, Bretton Woods, NH\tFort Snelling, Tower Avenue, Saint Paul, MN\t2337354\t80120\nOlympia Entertainment, Woodward Avenue, Detroit, MI\tColumbia River Gorge National Scenic Area, Oregon\t3674656\t117721\nTaliesin, County Road C, Spring Green, Wisconsin\tWright Brothers National Memorial Visitor Center, Manteo, NC\t1869857\t64810\nHanford Site, Benton County, WA\tYellowstone National Park, WY 82190\t1214950\t42730\nAcadia National Park, Maine\tCable Car Museum, 94108, 1201 Mason St, San Francisco, CA 94108\t5418743\t175779\nCraters of the Moon National Monument & Preserve, Arco, ID\tOkefenokee Swamp Park, Okefenokee Swamp Park Road, Waycross, GA\t3747256\t123720\nWest Baden Springs Hotel, West Baden Avenue, West Baden Springs, IN\tLincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL\t354930\t15229\nCape Canaveral, FL\tNew Castle Historic District, Delaware\t1544583\t49962\nSpring Grove Cemetery, Spring Grove Avenue, Cincinnati, OH\tThe Alamo, Alamo Plaza, San Antonio, TX\t1949778\t63675\nCongress Hall, Congress Place, Cape May, NJ 08204\tColumbia River Gorge National Scenic Area, Oregon\t4589082\t149141\nUSS Alabama, Battleship Parkway, Mobile, AL\tShelburne Farms, Harbor Road, Shelburne, VT\t2387575\t79031\nThe Alamo, Alamo Plaza, San Antonio, TX\tGlacier National Park, West Glacier, MT\t2995591\t98170\nStatue of Liberty, Liberty Island, NYC, NY\tAshfall Fossil Bed, Royal, NE\t2245869\t75913\nMammoth Cave National Park, Mammoth Cave Pkwy, Mammoth Cave, KY\tCable Car Museum, 94108, 1201 Mason St, San Francisco, CA 94108\t3751821\t121023\nLost World Caverns, Lewisburg, WV\tThe Breakers, Ochre Point Avenue, Newport, RI\t1060206\t37070\nShelburne Farms, Harbor Road, Shelburne, VT\tPikes Peak, Colorado\t3189307\t108703\nChickasaw National Recreation Area, 1008 W 2nd St, Sulphur, OK 73086\tSpring Grove Cemetery, Spring Grove Avenue, Cincinnati, OH\t1432857\t47614\nMaryland State House, 100 State Cir, Annapolis, MD 21401\tCongress Hall, Congress Place, Cape May, NJ 08204\t284500\t11072\nMount Vernon, Fairfax County, Virginia\tCraters of the Moon National Monument & Preserve, Arco, ID\t3630343\t118186\nLiberty Bell, 6th Street, Philadelphia, PA\tCable Car Museum, 94108, 1201 Mason St, San Francisco, CA 94108\t4626854\t148352\nToltec Mounds, Scott, AR\tOlympia Entertainment, Woodward Avenue, Detroit, MI\t1402648\t46020\nLost World Caverns, Lewisburg, WV\tAshfall Fossil Bed, Royal, NE\t1903828\t64750\nCongress Hall, Congress Place, Cape May, NJ 08204\tFrench Quarter, New Orleans, LA\t2045021\t67422\nTaliesin, County Road C, Spring Green, Wisconsin\tNew Castle Historic District, Delaware\t1544803\t53839\nFort Union Trading Post National Historic Site, Williston, North Dakota 1804, ND\tFort Snelling, Tower Avenue, Saint Paul, MN\t1071728\t34851\nHanford Site, Benton County, WA\tGlacier National Park, West Glacier, MT\t683123\t24469\nHanford Site, Benton County, WA\tCongress Hall, Congress Place, Cape May, NJ 08204\t4474392\t146074\nNew Castle Historic District, Delaware\tWest Baden Springs Hotel, West Baden Avenue, West Baden Springs, IN\t1187638\t40989\nGraceland, Elvis Presley Boulevard, Memphis, TN\tPikes Peak, Colorado\t1792648\t59301\nOkefenokee Swamp Park, Okefenokee Swamp Park Road, Waycross, GA\tAshfall Fossil Bed, Royal, NE\t2270381\t77013\nSan Andreas Fault, San Benito County, CA\tUSS Constitution, Boston, MA\t5127066\t166130\nChickasaw National Recreation Area, 1008 W 2nd St, Sulphur, OK 73086\tMount Vernon, Fairfax County, Virginia\t2204704\t71995\nSan Andreas Fault, San Benito County, CA\tOmni Mount Washington Resort, Mount Washington Hotel Road, Bretton Woods, NH\t5217759\t171230\nMaryland State House, 100 State Cir, Annapolis, MD 21401\tPikes Peak, Colorado\t2729245\t91812\nMammoth Cave National Park, Mammoth Cave Pkwy, Mammoth Cave, KY\tOkefenokee Swamp Park, Okefenokee Swamp Park Road, Waycross, GA\t947062\t32347\nC. W. Parker Carousel Museum, South Esplanade Street, Leavenworth, KS\tSan Andreas Fault, San Benito County, CA\t2905229\t93739\nSan Andreas Fault, San Benito County, CA\tYellowstone National Park, WY 82190\t1747331\t61356\nWright Brothers National Memorial Visitor Center, Manteo, NC\tVicksburg National Military Park, Clay Street, Vicksburg, MS\t1643008\t52482\nThe Mark Twain House & Museum, Farmington Avenue, Hartford, CT\tCraters of the Moon National Monument & Preserve, Arco, ID\t3984636\t130620\nGraceland, Elvis Presley Boulevard, Memphis, TN\tWest Baden Springs Hotel, West Baden Avenue, West Baden Springs, IN\t671337\t22049\nFort Union Trading Post National Historic Site, Williston, North Dakota 1804, ND\tFort Sumter National Monument, Sullivan's Island, SC\t3179558\t103575\nOmni Mount Washington Resort, Mount Washington Hotel Road, Bretton Woods, NH\tPikes Peak, Colorado\t3418392\t114811\nGraceland, Elvis Presley Boulevard, Memphis, TN\tOmni Mount Washington Resort, Mount Washington Hotel Road, Bretton Woods, NH\t2291550\t77952\nChickasaw National Recreation Area, 1008 W 2nd St, Sulphur, OK 73086\tVicksburg National Military Park, Clay Street, Vicksburg, MS\t810507\t26740\nMount Vernon, Fairfax County, Virginia\tNew Castle Historic District, Delaware\t169236\t6985\nPikes Peak, Colorado\tFrench Quarter, New Orleans, LA\t2019630\t68626\nMount Vernon, Fairfax County, Virginia\tThe Alamo, Alamo Plaza, San Antonio, TX\t2579868\t82894\nAshfall Fossil Bed, Royal, NE\tFort Snelling, Tower Avenue, Saint Paul, MN\t607553\t22708\nNew Castle Historic District, Delaware\tFort Snelling, Tower Avenue, Saint Paul, MN\t1900450\t64852\nMount Vernon, Fairfax County, Virginia\tCarlsbad Caverns National Park, Carlsbad, NM\t2930231\t97369\nUSS Alabama, Battleship Parkway, Mobile, AL\tNew Castle Historic District, Delaware\t1707248\t56147\nCongress Hall, Congress Place, Cape May, NJ 08204\tLincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL\t1518228\t51482\nFort Union Trading Post National Historic Site, Williston, North Dakota 1804, ND\tStatue of Liberty, Liberty Island, NYC, NY\t2983852\t98722\nHanford Site, Benton County, WA\tMammoth Cave National Park, Mammoth Cave Pkwy, Mammoth Cave, KY\t3530581\t115217\nSpring Grove Cemetery, Spring Grove Avenue, Cincinnati, OH\tAshfall Fossil Bed, Royal, NE\t1399554\t47258\nThe Breakers, Ochre Point Avenue, Newport, RI\tUSS Constitution, Boston, MA\t120154\t5338\nMaryland State House, 100 State Cir, Annapolis, MD 21401\tLiberty Bell, 6th Street, Philadelphia, PA\t206045\t7473\nLost World Caverns, Lewisburg, WV\tVicksburg National Military Park, Clay Street, Vicksburg, MS\t1336291\t42854\nGraceland, Elvis Presley Boulevard, Memphis, TN\tCape Canaveral, FL\t1341318\t44354\nTaliesin, County Road C, Spring Green, Wisconsin\tCraters of the Moon National Monument & Preserve, Arco, ID\t2398626\t77738\nHoover Dam, NV\tFort Sumter National Monument, Sullivan's Island, SC\t3585717\t114503\nYellowstone National Park, WY 82190\tGlacier National Park, West Glacier, MT\t905478\t36382\nShelburne Farms, Harbor Road, Shelburne, VT\tWest Baden Springs Hotel, West Baden Avenue, West Baden Springs, IN\t1532580\t55702\nMaryland State House, 100 State Cir, Annapolis, MD 21401\tHanford Site, Benton County, WA\t4280062\t138804\nSpring Grove Cemetery, Spring Grove Avenue, Cincinnati, OH\tGrand Canyon National Park, Arizona\t2861597\t90877\nChickasaw National Recreation Area, 1008 W 2nd St, Sulphur, OK 73086\tCape Canaveral, FL\t2059873\t67236\nWhite House, Pennsylvania Avenue Northwest, Washington, DC\tAcadia National Park, Maine\t1136168\t41070\nToltec Mounds, Scott, AR\tThe Alamo, Alamo Plaza, San Antonio, TX\t974447\t31908\nWhite House, Pennsylvania Avenue Northwest, Washington, DC\tLincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL\t1259332\t43180\nFort Sumter National Monument, Sullivan's Island, SC\tYellowstone National Park, WY 82190\t3522910\t118615\nSan Andreas Fault, San Benito County, CA\tGateway Arch, Washington Avenue, St Louis, MO\t3243113\t103924\nCraters of the Moon National Monument & Preserve, Arco, ID\tFrench Quarter, New Orleans, LA\t3174475\t105249\nToltec Mounds, Scott, AR\tUSS Constitution, Boston, MA\t2338394\t77484\nPikes Peak, Colorado\tStatue of Liberty, Liberty Island, NYC, NY\t2909086\t98054\nCape Canaveral, FL\tFrench Quarter, New Orleans, LA\t1112328\t34804\nNew Castle Historic District, Delaware\tLincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL\t1381193\t47996\nStatue of Liberty, Liberty Island, NYC, NY\tCable Car Museum, 94108, 1201 Mason St, San Francisco, CA 94108\t4676008\t150374\nHoover Dam, NV\tLost World Caverns, Lewisburg, WV\t3537579\t111813\nMaryland State House, 100 State Cir, Annapolis, MD 21401\tOkefenokee Swamp Park, Okefenokee Swamp Park Road, Waycross, GA\t1170057\t38211\nFort Union Trading Post National Historic Site, Williston, North Dakota 1804, ND\tThe Breakers, Ochre Point Avenue, Newport, RI\t3281541\t108734\nUSS Alabama, Battleship Parkway, Mobile, AL\tTaliesin, County Road C, Spring Green, Wisconsin\t1681531\t57125\nShelburne Farms, Harbor Road, Shelburne, VT\tUSS Constitution, Boston, MA\t352908\t12461\nFort Union Trading Post National Historic Site, Williston, North Dakota 1804, ND\tAcadia National Park, Maine\t3724389\t124204\nSpring Grove Cemetery, Spring Grove Avenue, Cincinnati, OH\tAcadia National Park, Maine\t1826373\t62529\nThe Breakers, Ochre Point Avenue, Newport, RI\tGateway Arch, Washington Avenue, St Louis, MO\t1823335\t63268\nMount Rushmore National Memorial, South Dakota 244, Keystone, SD\tMammoth Cave National Park, Mammoth Cave Pkwy, Mammoth Cave, KY\t2011410\t65799\nTaliesin, County Road C, Spring Green, Wisconsin\tFrench Quarter, New Orleans, LA\t1729998\t55959\nFort Sumter National Monument, Sullivan's Island, SC\tAcadia National Park, Maine\t1994724\t68336\nThe Mark Twain House & Museum, Farmington Avenue, Hartford, CT\tLincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL\t1675183\t56120\nGateway Arch, Washington Avenue, St Louis, MO\tAshfall Fossil Bed, Royal, NE\t973284\t33919\nMount Vernon, Fairfax County, Virginia\tWest Baden Springs Hotel, West Baden Avenue, West Baden Springs, IN\t1078689\t36453\nMaryland State House, 100 State Cir, Annapolis, MD 21401\tThe Alamo, Alamo Plaza, San Antonio, TX\t2642482\t85073\nWest Baden Springs Hotel, West Baden Avenue, West Baden Springs, IN\tCable Car Museum, 94108, 1201 Mason St, San Francisco, CA 94108\t3683936\t117748\nC. W. Parker Carousel Museum, South Esplanade Street, Leavenworth, KS\tCable Car Museum, 94108, 1201 Mason St, San Francisco, CA 94108\t2879277\t91764\nUSS Alabama, Battleship Parkway, Mobile, AL\tCraters of the Moon National Monument & Preserve, Arco, ID\t3412760\t112517\nHoover Dam, NV\tSpring Grove Cemetery, Spring Grove Avenue, Cincinnati, OH\t3101055\t98114\nThe Mark Twain House & Museum, Farmington Avenue, Hartford, CT\tWhite House, Pennsylvania Avenue Northwest, Washington, DC\t550976\t20506\nTerrace Hill, Grand Avenue, Des Moines, IA\tFrench Quarter, New Orleans, LA\t1631859\t53421\nMount Rushmore National Memorial, South Dakota 244, Keystone, SD\tFort Sumter National Monument, Sullivan's Island, SC\t2942338\t94545\nWright Brothers National Memorial Visitor Center, Manteo, NC\tUSS Constitution, Boston, MA\t1063168\t40159\nFort Sumter National Monument, Sullivan's Island, SC\tThe Alamo, Alamo Plaza, San Antonio, TX\t2061745\t66283\nUSS Constitution, Boston, MA\tCable Car Museum, 94108, 1201 Mason St, San Francisco, CA 94108\t4988547\t160560\nWright Brothers National Memorial Visitor Center, Manteo, NC\tFrench Quarter, New Orleans, LA\t1710546\t54335\nCape Canaveral, FL\tFort Snelling, Tower Avenue, Saint Paul, MN\t2528087\t83138\nMount Vernon, Fairfax County, Virginia\tTaliesin, County Road C, Spring Green, Wisconsin\t1444824\t49855\nTaliesin, County Road C, Spring Green, Wisconsin\tLincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL\t488221\t16708\nToltec Mounds, Scott, AR\tBryce Canyon National Park, Hwy 63, Bryce, UT\t2310372\t77039\nWhite House, Pennsylvania Avenue Northwest, Washington, DC\tColumbia River Gorge National Scenic Area, Oregon\t4356142\t140831\nMammoth Cave National Park, Mammoth Cave Pkwy, Mammoth Cave, KY\tCape Canaveral, FL\t1328279\t42300\nThe Mark Twain House & Museum, Farmington Avenue, Hartford, CT\tThe Alamo, Alamo Plaza, San Antonio, TX\t3129292\t101592\nFort Union Trading Post National Historic Site, Williston, North Dakota 1804, ND\tPikes Peak, Colorado\t1210842\t43490\nChickasaw National Recreation Area, 1008 W 2nd St, Sulphur, OK 73086\tWest Baden Springs Hotel, West Baden Avenue, West Baden Springs, IN\t1255436\t41765\nChickasaw National Recreation Area, 1008 W 2nd St, Sulphur, OK 73086\tYellowstone National Park, WY 82190\t2069144\t70493\nTerrace Hill, Grand Avenue, Des Moines, IA\tOlympia Entertainment, Woodward Avenue, Detroit, MI\t961951\t31389\nCape Canaveral, FL\tGlacier National Park, West Glacier, MT\t4307136\t142282\nMammoth Cave National Park, Mammoth Cave Pkwy, Mammoth Cave, KY\tVicksburg National Military Park, Clay Street, Vicksburg, MS\t861072\t27740\nFort Union Trading Post National Historic Site, Williston, North Dakota 1804, ND\tCarlsbad Caverns National Park, Carlsbad, NM\t2045404\t70443\nOmni Mount Washington Resort, Mount Washington Hotel Road, Bretton Woods, NH\tMammoth Cave National Park, Mammoth Cave Pkwy, Mammoth Cave, KY\t1811850\t62418\nC. W. Parker Carousel Museum, South Esplanade Street, Leavenworth, KS\tLiberty Bell, 6th Street, Philadelphia, PA\t1865848\t62627\nVicksburg National Military Park, Clay Street, Vicksburg, MS\tAcadia National Park, Maine\t2776915\t92233\nMount Rushmore National Memorial, South Dakota 244, Keystone, SD\tCape Canaveral, FL\t3247298\t103255\nTerrace Hill, Grand Avenue, Des Moines, IA\tTaliesin, County Road C, Spring Green, Wisconsin\t431273\t14664\nMaryland State House, 100 State Cir, Annapolis, MD 21401\tGlacier National Park, West Glacier, MT\t3601333\t121768\nUSS Alabama, Battleship Parkway, Mobile, AL\tOkefenokee Swamp Park, Okefenokee Swamp Park Road, Waycross, GA\t625397\t21721\nThe Breakers, Ochre Point Avenue, Newport, RI\tWright Brothers National Memorial Visitor Center, Manteo, NC\t1008190\t38506\nLiberty Bell, 6th Street, Philadelphia, PA\tVicksburg National Military Park, Clay Street, Vicksburg, MS\t1861049\t60510\nAcadia National Park, Maine\tGrand Canyon National Park, Arizona\t4655701\t151382\nOmni Mount Washington Resort, Mount Washington Hotel Road, Bretton Woods, NH\tLincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL\t1953916\t67074\nFort Snelling, Tower Avenue, Saint Paul, MN\tWest Baden Springs Hotel, West Baden Avenue, West Baden Springs, IN\t1110505\t38784\nHanford Site, Benton County, WA\tPikes Peak, Colorado\t1948710\t66209\nHoover Dam, NV\tGraceland, Elvis Presley Boulevard, Memphis, TN\t2504838\t78843\nNew Castle Historic District, Delaware\tStatue of Liberty, Liberty Island, NYC, NY\t298132\t10955\nToltec Mounds, Scott, AR\tCraters of the Moon National Monument & Preserve, Arco, ID\t2720150\t87610\nHoover Dam, NV\tGrand Canyon National Park, Arizona\t390505\t12874\nYellowstone National Park, WY 82190\tFrench Quarter, New Orleans, LA\t2943174\t101546\nStatue of Liberty, Liberty Island, NYC, NY\tOlympia Entertainment, Woodward Avenue, Detroit, MI\t993338\t33526\nFort Sumter National Monument, Sullivan's Island, SC\tAshfall Fossil Bed, Royal, NE\t2351786\t78304\nSpring Grove Cemetery, Spring Grove Avenue, Cincinnati, OH\tNew Castle Historic District, Delaware\t931480\t33116\nMount Vernon, Fairfax County, Virginia\tMammoth Cave National Park, Mammoth Cave Pkwy, Mammoth Cave, KY\t1079835\t35606\nHanford Site, Benton County, WA\tThe Alamo, Alamo Plaza, San Antonio, TX\t3128728\t105141\nSpring Grove Cemetery, Spring Grove Avenue, Cincinnati, OH\tThe Mark Twain House & Museum, Farmington Avenue, Hartford, CT\t1231682\t41511\nShelburne Farms, Harbor Road, Shelburne, VT\tCable Car Museum, 94108, 1201 Mason St, San Francisco, CA 94108\t4850154\t159553\nFrench Quarter, New Orleans, LA\tGlacier National Park, West Glacier, MT\t3565117\t118390\nMount Rushmore National Memorial, South Dakota 244, Keystone, SD\tLost World Caverns, Lewisburg, WV\t2460677\t80618\nUSS Alabama, Battleship Parkway, Mobile, AL\tMammoth Cave National Park, Mammoth Cave Pkwy, Mammoth Cave, KY\t864616\t27305\nPikes Peak, Colorado\tLincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL\t1489713\t50155\nChickasaw National Recreation Area, 1008 W 2nd St, Sulphur, OK 73086\tFort Union Trading Post National Historic Site, Williston, North Dakota 1804, ND\t1986021\t69191\nUSS Alabama, Battleship Parkway, Mobile, AL\tWhite House, Pennsylvania Avenue Northwest, Washington, DC\t1557762\t50504\nToltec Mounds, Scott, AR\tLincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL\t722344\t25610\nCraters of the Moon National Monument & Preserve, Arco, ID\tBryce Canyon National Park, Hwy 63, Bryce, UT\t849848\t26970\nMount Rushmore National Memorial, South Dakota 244, Keystone, SD\tCable Car Museum, 94108, 1201 Mason St, San Francisco, CA 94108\t2207410\t72609\nAcadia National Park, Maine\tGlacier National Park, West Glacier, MT\t4450701\t151090\nToltec Mounds, Scott, AR\tLiberty Bell, 6th Street, Philadelphia, PA\t1850866\t60901\nLiberty Bell, 6th Street, Philadelphia, PA\tGraceland, Elvis Presley Boulevard, Memphis, TN\t1634964\t53535\nTerrace Hill, Grand Avenue, Des Moines, IA\tNew Castle Historic District, Delaware\t1764053\t59412\nMount Rushmore National Memorial, South Dakota 244, Keystone, SD\tNew Castle Historic District, Delaware\t2746841\t91089\nThe Breakers, Ochre Point Avenue, Newport, RI\tCape Canaveral, FL\t2049951\t68069\nTerrace Hill, Grand Avenue, Des Moines, IA\tVicksburg National Military Park, Clay Street, Vicksburg, MS\t1340289\t44533\nCarlsbad Caverns National Park, Carlsbad, NM\tYellowstone National Park, WY 82190\t1844673\t67814\nOmni Mount Washington Resort, Mount Washington Hotel Road, Bretton Woods, NH\tYellowstone National Park, WY 82190\t3925672\t134524\nMammoth Cave National Park, Mammoth Cave Pkwy, Mammoth Cave, KY\tAshfall Fossil Bed, Royal, NE\t1415710\t49510\nUSS Constitution, Boston, MA\tOkefenokee Swamp Park, Okefenokee Swamp Park Road, Waycross, GA\t1823494\t61766\nShelburne Farms, Harbor Road, Shelburne, VT\tMammoth Cave National Park, Mammoth Cave Pkwy, Mammoth Cave, KY\t1582765\t56311\nMaryland State House, 100 State Cir, Annapolis, MD 21401\tFort Snelling, Tower Avenue, Saint Paul, MN\t1817199\t61076\nTaliesin, County Road C, Spring Green, Wisconsin\tAshfall Fossil Bed, Royal, NE\t751773\t27471\nGateway Arch, Washington Avenue, St Louis, MO\tWest Baden Springs Hotel, West Baden Avenue, West Baden Springs, IN\t381136\t12903\nCraters of the Moon National Monument & Preserve, Arco, ID\tOlympia Entertainment, Woodward Avenue, Detroit, MI\t2929304\t94463\nMammoth Cave National Park, Mammoth Cave Pkwy, Mammoth Cave, KY\tGlacier National Park, West Glacier, MT\t3061692\t103519\nThe Mark Twain House & Museum, Farmington Avenue, Hartford, CT\tSan Andreas Fault, San Benito County, CA\t4976755\t161077\nUSS Constitution, Boston, MA\tColumbia River Gorge National Scenic Area, Oregon\t4814513\t156100\nMaryland State House, 100 State Cir, Annapolis, MD 21401\tUSS Constitution, Boston, MA\t689767\t24684\nOmni Mount Washington Resort, Mount Washington Hotel Road, Bretton Woods, NH\tGateway Arch, Washington Avenue, St Louis, MO\t2009904\t69030\nUSS Constitution, Boston, MA\tYellowstone National Park, WY 82190\t3834982\t129564\nC. W. Parker Carousel Museum, South Esplanade Street, Leavenworth, KS\tYellowstone National Park, WY 82190\t1725271\t60529\nWright Brothers National Memorial Visitor Center, Manteo, NC\tOkefenokee Swamp Park, Okefenokee Swamp Park Road, Waycross, GA\t974305\t32187\nYellowstone National Park, WY 82190\tWest Baden Springs Hotel, West Baden Avenue, West Baden Springs, IN\t2524697\t86519\nWright Brothers National Memorial Visitor Center, Manteo, NC\tStatue of Liberty, Liberty Island, NYC, NY\t720092\t27777\nCraters of the Moon National Monument & Preserve, Arco, ID\tFort Snelling, Tower Avenue, Saint Paul, MN\t2074907\t66319\nSpring Grove Cemetery, Spring Grove Avenue, Cincinnati, OH\tOlympia Entertainment, Woodward Avenue, Detroit, MI\t418716\t14381\nVicksburg National Military Park, Clay Street, Vicksburg, MS\tLincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL\t1000446\t32351\nGraceland, Elvis Presley Boulevard, Memphis, TN\tSpring Grove Cemetery, Spring Grove Avenue, Cincinnati, OH\t787078\t25987\nChickasaw National Recreation Area, 1008 W 2nd St, Sulphur, OK 73086\tGlacier National Park, West Glacier, MT\t2707726\t86377\nLost World Caverns, Lewisburg, WV\tLincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL\t1013896\t34420\nThe Breakers, Ochre Point Avenue, Newport, RI\tFort Snelling, Tower Avenue, Saint Paul, MN\t2222722\t75082\nCongress Hall, Congress Place, Cape May, NJ 08204\tBryce Canyon National Park, Hwy 63, Bryce, UT\t3823059\t125963\nOlympia Entertainment, Woodward Avenue, Detroit, MI\tFort Snelling, Tower Avenue, Saint Paul, MN\t1111015\t37041\nThe Alamo, Alamo Plaza, San Antonio, TX\tOkefenokee Swamp Park, Okefenokee Swamp Park Road, Waycross, GA\t1693831\t55844\nMaryland State House, 100 State Cir, Annapolis, MD 21401\tYellowstone National Park, WY 82190\t3415222\t115336\nHoover Dam, NV\tThe Mark Twain House & Museum, Farmington Avenue, Hartford, CT\t4276900\t137981\nLiberty Bell, 6th Street, Philadelphia, PA\tFort Snelling, Tower Avenue, Saint Paul, MN\t1884969\t62812\nMount Rushmore National Memorial, South Dakota 244, Keystone, SD\tThe Alamo, Alamo Plaza, San Antonio, TX\t1939427\t67437\nAcadia National Park, Maine\tColumbia River Gorge National Scenic Area, Oregon\t5244120\t171194\nThe Breakers, Ochre Point Avenue, Newport, RI\tCable Car Museum, 94108, 1201 Mason St, San Francisco, CA 94108\t4964607\t160622\nLost World Caverns, Lewisburg, WV\tMammoth Cave National Park, Mammoth Cave Pkwy, Mammoth Cave, KY\t666822\t22049\nUSS Alabama, Battleship Parkway, Mobile, AL\tSpring Grove Cemetery, Spring Grove Avenue, Cincinnati, OH\t1170385\t37497\nC. W. Parker Carousel Museum, South Esplanade Street, Leavenworth, KS\tMaryland State House, 100 State Cir, Annapolis, MD 21401\t1793552\t60470\nBryce Canyon National Park, Hwy 63, Bryce, UT\tGrand Canyon National Park, Arizona\t232427\t10298\nSpring Grove Cemetery, Spring Grove Avenue, Cincinnati, OH\tFrench Quarter, New Orleans, LA\t1301278\t41736\nC. W. Parker Carousel Museum, South Esplanade Street, Leavenworth, KS\tBryce Canyon National Park, Hwy 63, Bryce, UT\t1875561\t60523\nToltec Mounds, Scott, AR\tLost World Caverns, Lewisburg, WV\t1326109\t43245\nColumbia River Gorge National Scenic Area, Oregon\tFort Snelling, Tower Avenue, Saint Paul, MN\t2638062\t84130\nThe Alamo, Alamo Plaza, San Antonio, TX\tAcadia National Park, Maine\t3718928\t122376\nMount Vernon, Fairfax County, Virginia\tColumbia River Gorge National Scenic Area, Oregon\t4377863\t140930\nFort Union Trading Post National Historic Site, Williston, North Dakota 1804, ND\tUSS Constitution, Boston, MA\t3294781\t109111\nOkefenokee Swamp Park, Okefenokee Swamp Park Road, Waycross, GA\tColumbia River Gorge National Scenic Area, Oregon\t4426183\t143908\nMount Vernon, Fairfax County, Virginia\tGlacier National Park, West Glacier, MT\t3625420\t120884\nMount Rushmore National Memorial, South Dakota 244, Keystone, SD\tWright Brothers National Memorial Visitor Center, Manteo, NC\t3071895\t102059\nCape Canaveral, FL\tPikes Peak, Colorado\t3065349\t99886\nHoover Dam, NV\tTaliesin, County Road C, Spring Green, Wisconsin\t2756482\t87940\nC. W. Parker Carousel Museum, South Esplanade Street, Leavenworth, KS\tAcadia National Park, Maine\t2790125\t93605\nYellowstone National Park, WY 82190\tCable Car Museum, 94108, 1201 Mason St, San Francisco, CA 94108\t1608811\t55787\nHoover Dam, NV\tAcadia National Park, Maine\t4857457\t158367\nFort Union Trading Post National Historic Site, Williston, North Dakota 1804, ND\tCraters of the Moon National Monument & Preserve, Arco, ID\t1183571\t39177\nMammoth Cave National Park, Mammoth Cave Pkwy, Mammoth Cave, KY\tAcadia National Park, Maine\t2126830\t72160\nThe Breakers, Ochre Point Avenue, Newport, RI\tOmni Mount Washington Resort, Mount Washington Hotel Road, Bretton Woods, NH\t370511\t14011\nOmni Mount Washington Resort, Mount Washington Hotel Road, Bretton Woods, NH\tThe Alamo, Alamo Plaza, San Antonio, TX\t3516822\t114874\nThe Breakers, Ochre Point Avenue, Newport, RI\tAshfall Fossil Bed, Royal, NE\t2534468\t86162\nC. W. Parker Carousel Museum, South Esplanade Street, Leavenworth, KS\tUSS Alabama, Battleship Parkway, Mobile, AL\t1368815\t49143\nC. W. Parker Carousel Museum, South Esplanade Street, Leavenworth, KS\tThe Alamo, Alamo Plaza, San Antonio, TX\t1309345\t42088\nToltec Mounds, Scott, AR\tSpring Grove Cemetery, Spring Grove Avenue, Cincinnati, OH\t1001984\t33121\nFort Union Trading Post National Historic Site, Williston, North Dakota 1804, ND\tSpring Grove Cemetery, Spring Grove Avenue, Cincinnati, OH\t2183781\t71315\nNew Castle Historic District, Delaware\tYellowstone National Park, WY 82190\t3498473\t119111\nMount Rushmore National Memorial, South Dakota 244, Keystone, SD\tSan Andreas Fault, San Benito County, CA\t2345929\t78179\nThe Mark Twain House & Museum, Farmington Avenue, Hartford, CT\tLost World Caverns, Lewisburg, WV\t965974\t32687\nOmni Mount Washington Resort, Mount Washington Hotel Road, Bretton Woods, NH\tOkefenokee Swamp Park, Okefenokee Swamp Park Road, Waycross, GA\t2050914\t69431\nToltec Mounds, Scott, AR\tCongress Hall, Congress Place, Cape May, NJ 08204\t1929321\t64500\nMount Rushmore National Memorial, South Dakota 244, Keystone, SD\tChickasaw National Recreation Area, 1008 W 2nd St, Sulphur, OK 73086\t1523445\t51973\nWhite House, Pennsylvania Avenue Northwest, Washington, DC\tOlympia Entertainment, Woodward Avenue, Detroit, MI\t847800\t28664\nThe Breakers, Ochre Point Avenue, Newport, RI\tWest Baden Springs Hotel, West Baden Avenue, West Baden Springs, IN\t1569250\t55804\nToltec Mounds, Scott, AR\tCarlsbad Caverns National Park, Carlsbad, NM\t1327080\t45836\nSan Andreas Fault, San Benito County, CA\tCable Car Museum, 94108, 1201 Mason St, San Francisco, CA 94108\t212841\t10052\nChickasaw National Recreation Area, 1008 W 2nd St, Sulphur, OK 73086\tOmni Mount Washington Resort, Mount Washington Hotel Road, Bretton Woods, NH\t2885548\t97975\nTaliesin, County Road C, Spring Green, Wisconsin\tOlympia Entertainment, Woodward Avenue, Detroit, MI\t755368\t26029\nC. W. Parker Carousel Museum, South Esplanade Street, Leavenworth, KS\tColumbia River Gorge National Scenic Area, Oregon\t2704802\t87065\nUSS Alabama, Battleship Parkway, Mobile, AL\tColumbia River Gorge National Scenic Area, Oregon\t4091687\t132705\nOlympia Entertainment, Woodward Avenue, Detroit, MI\tGrand Canyon National Park, Arizona\t3086485\t100490\nGraceland, Elvis Presley Boulevard, Memphis, TN\tWhite House, Pennsylvania Avenue Northwest, Washington, DC\t1414096\t45911\nWright Brothers National Memorial Visitor Center, Manteo, NC\tWest Baden Springs Hotel, West Baden Avenue, West Baden Springs, IN\t1293271\t44236\nWest Baden Springs Hotel, West Baden Avenue, West Baden Springs, IN\tGrand Canyon National Park, Arizona\t2684847\t85097\nMount Vernon, Fairfax County, Virginia\tGateway Arch, Washington Avenue, St Louis, MO\t1336076\t45794\nMount Rushmore National Memorial, South Dakota 244, Keystone, SD\tOlympia Entertainment, Woodward Avenue, Detroit, MI\t1957406\t63279\nC. W. Parker Carousel Museum, South Esplanade Street, Leavenworth, KS\tGrand Canyon National Park, Arizona\t1903087\t63470\nToltec Mounds, Scott, AR\tPikes Peak, Colorado\t1600474\t53363\nCape Canaveral, FL\tColumbia River Gorge National Scenic Area, Oregon\t4807400\t153861\nCarlsbad Caverns National Park, Carlsbad, NM\tWright Brothers National Memorial Visitor Center, Manteo, NC\t3016475\t98927\nMount Rushmore National Memorial, South Dakota 244, Keystone, SD\tAshfall Fossil Bed, Royal, NE\t614858\t21957\nGraceland, Elvis Presley Boulevard, Memphis, TN\tShelburne Farms, Harbor Road, Shelburne, VT\t2060445\t71605\nTerrace Hill, Grand Avenue, Des Moines, IA\tGlacier National Park, West Glacier, MT\t2098158\t71965\nSpring Grove Cemetery, Spring Grove Avenue, Cincinnati, OH\tUSS Constitution, Boston, MA\t1396766\t47436\nWhite House, Pennsylvania Avenue Northwest, Washington, DC\tGlacier National Park, West Glacier, MT\t3562723\t120728\nUSS Alabama, Battleship Parkway, Mobile, AL\tToltec Mounds, Scott, AR\t703118\t26278\nUSS Constitution, Boston, MA\tGlacier National Park, West Glacier, MT\t4021094\t135997\nCape Canaveral, FL\tThe Alamo, Alamo Plaza, San Antonio, TX\t1949042\t61487\nTaliesin, County Road C, Spring Green, Wisconsin\tWest Baden Springs Hotel, West Baden Avenue, West Baden Springs, IN\t754858\t27771\nHoover Dam, NV\tCable Car Museum, 94108, 1201 Mason St, San Francisco, CA 94108\t938239\t31500\nThe Mark Twain House & Museum, Farmington Avenue, Hartford, CT\tTaliesin, County Road C, Spring Green, Wisconsin\t1730362\t59000\nHanford Site, Benton County, WA\tCable Car Museum, 94108, 1201 Mason St, San Francisco, CA 94108\t1214774\t44145\nShelburne Farms, Harbor Road, Shelburne, VT\tVicksburg National Military Park, Clay Street, Vicksburg, MS\t2421559\t80387\nHoover Dam, NV\tChickasaw National Recreation Area, 1008 W 2nd St, Sulphur, OK 73086\t1831327\t59398\nTaliesin, County Road C, Spring Green, Wisconsin\tOmni Mount Washington Resort, Mount Washington Hotel Road, Bretton Woods, NH\t1972002\t69252\nFort Union Trading Post National Historic Site, Williston, North Dakota 1804, ND\tTaliesin, County Road C, Spring Green, Wisconsin\t1461512\t48261\nHanford Site, Benton County, WA\tGateway Arch, Washington Avenue, St Louis, MO\t3088155\t99626\nShelburne Farms, Harbor Road, Shelburne, VT\tYellowstone National Park, WY 82190\t3699278\t128423\nMount Rushmore National Memorial, South Dakota 244, Keystone, SD\tYellowstone National Park, WY 82190\t856780\t33369\nThe Mark Twain House & Museum, Farmington Avenue, Hartford, CT\tCape Canaveral, FL\t1947118\t64246\nMaryland State House, 100 State Cir, Annapolis, MD 21401\tWright Brothers National Memorial Visitor Center, Manteo, NC\t519176\t18703\nMount Rushmore National Memorial, South Dakota 244, Keystone, SD\tCarlsbad Caverns National Park, Carlsbad, NM\t1589047\t55377\nOmni Mount Washington Resort, Mount Washington Hotel Road, Bretton Woods, NH\tFort Sumter National Monument, Sullivan's Island, SC\t1792618\t60835\nMammoth Cave National Park, Mammoth Cave Pkwy, Mammoth Cave, KY\tGateway Arch, Washington Avenue, St Louis, MO\t448421\t16253\nCongress Hall, Congress Place, Cape May, NJ 08204\tAcadia National Park, Maine\t1028018\t36728\nThe Mark Twain House & Museum, Farmington Avenue, Hartford, CT\tWest Baden Springs Hotel, West Baden Avenue, West Baden Springs, IN\t1484285\t50922\nOkefenokee Swamp Park, Okefenokee Swamp Park Road, Waycross, GA\tGlacier National Park, West Glacier, MT\t3925919\t132329\nThe Mark Twain House & Museum, Farmington Avenue, Hartford, CT\tOlympia Entertainment, Woodward Avenue, Detroit, MI\t1065861\t36337\nSpring Grove Cemetery, Spring Grove Avenue, Cincinnati, OH\tCarlsbad Caverns National Park, Carlsbad, NM\t2257948\t75758\nMount Vernon, Fairfax County, Virginia\tTerrace Hill, Grand Avenue, Des Moines, IA\t1662991\t55112\nMaryland State House, 100 State Cir, Annapolis, MD 21401\tThe Breakers, Ochre Point Avenue, Newport, RI\t637090\t23339\nHoover Dam, NV\tLiberty Bell, 6th Street, Philadelphia, PA\t3970882\t127641\nVicksburg National Military Park, Clay Street, Vicksburg, MS\tOlympia Entertainment, Woodward Avenue, Detroit, MI\t1582788\t52125\nMaryland State House, 100 State Cir, Annapolis, MD 21401\tThe Mark Twain House & Museum, Farmington Avenue, Hartford, CT\t533335\t19200\nC. W. Parker Carousel Museum, South Esplanade Street, Leavenworth, KS\tFort Sumter National Monument, Sullivan's Island, SC\t1827656\t59817\nUSS Alabama, Battleship Parkway, Mobile, AL\tChickasaw National Recreation Area, 1008 W 2nd St, Sulphur, OK 73086\t1180006\t39965\nTerrace Hill, Grand Avenue, Des Moines, IA\tWhite House, Pennsylvania Avenue Northwest, Washington, DC\t1642191\t54596\nC. W. Parker Carousel Museum, South Esplanade Street, Leavenworth, KS\tOmni Mount Washington Resort, Mount Washington Hotel Road, Bretton Woods, NH\t2450621\t83486\nUSS Constitution, Boston, MA\tStatue of Liberty, Liberty Island, NYC, NY\t349747\t13535\nGlacier National Park, West Glacier, MT\tFort Snelling, Tower Avenue, Saint Paul, MN\t1798041\t61737\nWhite House, Pennsylvania Avenue Northwest, Washington, DC\tUSS Constitution, Boston, MA\t705972\t25852\nThe Breakers, Ochre Point Avenue, Newport, RI\tBryce Canyon National Park, Hwy 63, Bryce, UT\t4054078\t133182\nLost World Caverns, Lewisburg, WV\tGrand Canyon National Park, Arizona\t3298120\t104576\nMount Vernon, Fairfax County, Virginia\tFrench Quarter, New Orleans, LA\t1744419\t56118\nSpring Grove Cemetery, Spring Grove Avenue, Cincinnati, OH\tGlacier National Park, West Glacier, MT\t2940288\t97891\nHoover Dam, NV\tThe Breakers, Ochre Point Avenue, Newport, RI\t4369087\t142738\nC. W. Parker Carousel Museum, South Esplanade Street, Leavenworth, KS\tGraceland, Elvis Presley Boulevard, Memphis, TN\t787287\t28159\nFort Snelling, Tower Avenue, Saint Paul, MN\tLincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL\t793789\t27595\nCarlsbad Caverns National Park, Carlsbad, NM\tWhite House, Pennsylvania Avenue Northwest, Washington, DC\t2929429\t97527\nChickasaw National Recreation Area, 1008 W 2nd St, Sulphur, OK 73086\tTerrace Hill, Grand Avenue, Des Moines, IA\t1012008\t32273\nHanford Site, Benton County, WA\tFrench Quarter, New Orleans, LA\t3881773\t126894\nColumbia River Gorge National Scenic Area, Oregon\tLincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL\t3113765\t100548\nShelburne Farms, Harbor Road, Shelburne, VT\tWright Brothers National Memorial Visitor Center, Manteo, NC\t1170622\t44500\nC. W. Parker Carousel Museum, South Esplanade Street, Leavenworth, KS\tAshfall Fossil Bed, Royal, NE\t548999\t20170\nMount Vernon, Fairfax County, Virginia\tWright Brothers National Memorial Visitor Center, Manteo, NC\t441414\t16045\nTerrace Hill, Grand Avenue, Des Moines, IA\tHanford Site, Benton County, WA\t2621379\t85403\nTerrace Hill, Grand Avenue, Des Moines, IA\tSpring Grove Cemetery, Spring Grove Avenue, Cincinnati, OH\t940705\t30634\nOmni Mount Washington Resort, Mount Washington Hotel Road, Bretton Woods, NH\tFrench Quarter, New Orleans, LA\t2681513\t88071\nMount Rushmore National Memorial, South Dakota 244, Keystone, SD\tOmni Mount Washington Resort, Mount Washington Hotel Road, Bretton Woods, NH\t3174040\t106502\nCraters of the Moon National Monument & Preserve, Arco, ID\tCape Canaveral, FL\t4128473\t133673\nHanford Site, Benton County, WA\tWright Brothers National Memorial Visitor Center, Manteo, NC\t4702943\t152858\nC. W. Parker Carousel Museum, South Esplanade Street, Leavenworth, KS\tFort Snelling, Tower Avenue, Saint Paul, MN\t723916\t23333\nLiberty Bell, 6th Street, Philadelphia, PA\tSan Andreas Fault, San Benito County, CA\t4666575\t151611\nCongress Hall, Congress Place, Cape May, NJ 08204\tYellowstone National Park, WY 82190\t3609552\t122605\nCarlsbad Caverns National Park, Carlsbad, NM\tGrand Canyon National Park, Arizona\t1133513\t39315\nUSS Alabama, Battleship Parkway, Mobile, AL\tCape Canaveral, FL\t880086\t27420\nC. W. Parker Carousel Museum, South Esplanade Street, Leavenworth, KS\tCarlsbad Caverns National Park, Carlsbad, NM\t1466457\t49870\nLost World Caverns, Lewisburg, WV\tWest Baden Springs Hotel, West Baden Avenue, West Baden Springs, IN\t665677\t22896\nShelburne Farms, Harbor Road, Shelburne, VT\tThe Mark Twain House & Museum, Farmington Avenue, Hartford, CT\t387386\t13408\nLost World Caverns, Lewisburg, WV\tOkefenokee Swamp Park, Okefenokee Swamp Park Road, Waycross, GA\t951633\t33294\nMaryland State House, 100 State Cir, Annapolis, MD 21401\tHoover Dam, NV\t3901708\t126053\nVicksburg National Military Park, Clay Street, Vicksburg, MS\tThe Alamo, Alamo Plaza, San Antonio, TX\t931061\t31591\nYellowstone National Park, WY 82190\tColumbia River Gorge National Scenic Area, Oregon\t1256183\t44089\nShelburne Farms, Harbor Road, Shelburne, VT\tLincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL\t1724831\t60967\nToltec Mounds, Scott, AR\tColumbia River Gorge National Scenic Area, Oregon\t3399077\t107798\nCraters of the Moon National Monument & Preserve, Arco, ID\tGlacier National Park, West Glacier, MT\t794318\t29136\nHanford Site, Benton County, WA\tThe Breakers, Ochre Point Avenue, Newport, RI\t4674471\t153027\nHoover Dam, NV\tCarlsbad Caverns National Park, Carlsbad, NM\t1372972\t46551\nTerrace Hill, Grand Avenue, Des Moines, IA\tThe Mark Twain House & Museum, Farmington Avenue, Hartford, CT\t1949612\t64573\nMount Rushmore National Memorial, South Dakota 244, Keystone, SD\tColumbia River Gorge National Scenic Area, Oregon\t1808374\t61862\nChickasaw National Recreation Area, 1008 W 2nd St, Sulphur, OK 73086\tThe Mark Twain House & Museum, Farmington Avenue, Hartford, CT\t2608579\t87006\nSan Andreas Fault, San Benito County, CA\tGrand Canyon National Park, Arizona\t1082921\t37113\nThe Mark Twain House & Museum, Farmington Avenue, Hartford, CT\tVicksburg National Military Park, Clay Street, Vicksburg, MS\t2187279\t71449\nCarlsbad Caverns National Park, Carlsbad, NM\tSan Andreas Fault, San Benito County, CA\t1947584\t66210\nWhite House, Pennsylvania Avenue Northwest, Washington, DC\tPikes Peak, Colorado\t2685136\t90854\nLiberty Bell, 6th Street, Philadelphia, PA\tAcadia National Park, Maine\t924645\t33519\nToltec Mounds, Scott, AR\tHanford Site, Benton County, WA\t3358145\t106610\nMaryland State House, 100 State Cir, Annapolis, MD 21401\tWhite House, Pennsylvania Avenue Northwest, Washington, DC\t51172\t2473\nBryce Canyon National Park, Hwy 63, Bryce, UT\tFort Snelling, Tower Avenue, Saint Paul, MN\t2371319\t76146\nThe Alamo, Alamo Plaza, San Antonio, TX\tOlympia Entertainment, Woodward Avenue, Detroit, MI\t2353130\t76673\nMount Rushmore National Memorial, South Dakota 244, Keystone, SD\tMaryland State House, 100 State Cir, Annapolis, MD 21401\t2663590\t87313\nMammoth Cave National Park, Mammoth Cave Pkwy, Mammoth Cave, KY\tStatue of Liberty, Liberty Island, NYC, NY\t1341458\t44603\nTaliesin, County Road C, Spring Green, Wisconsin\tFort Sumter National Monument, Sullivan's Island, SC\t1766175\t59104\nOmni Mount Washington Resort, Mount Washington Hotel Road, Bretton Woods, NH\tWhite House, Pennsylvania Avenue Northwest, Washington, DC\t933392\t33517\nTerrace Hill, Grand Avenue, Des Moines, IA\tWest Baden Springs Hotel, West Baden Avenue, West Baden Springs, IN\t878386\t30913\nSan Andreas Fault, San Benito County, CA\tNew Castle Historic District, Delaware\t4676829\t153351\nThe Breakers, Ochre Point Avenue, Newport, RI\tStatue of Liberty, Liberty Island, NYC, NY\t294397\t11884\nLiberty Bell, 6th Street, Philadelphia, PA\tAshfall Fossil Bed, Royal, NE\t2196715\t73892\nToltec Mounds, Scott, AR\tOkefenokee Swamp Park, Okefenokee Swamp Park Road, Waycross, GA\t1238466\t42013\nThe Breakers, Ochre Point Avenue, Newport, RI\tColumbia River Gorge National Scenic Area, Oregon\t4790491\t155356\nMount Vernon, Fairfax County, Virginia\tWhite House, Pennsylvania Avenue Northwest, Washington, DC\t23912\t1729\nPikes Peak, Colorado\tOkefenokee Swamp Park, Okefenokee Swamp Park Road, Waycross, GA\t2684344\t89867\nMaryland State House, 100 State Cir, Annapolis, MD 21401\tFort Union Trading Post National Historic Site, Williston, North Dakota 1804, ND\t2875021\t94882\nSpring Grove Cemetery, Spring Grove Avenue, Cincinnati, OH\tStatue of Liberty, Liberty Island, NYC, NY\t1023174\t35317\nSpring Grove Cemetery, Spring Grove Avenue, Cincinnati, OH\tWest Baden Springs Hotel, West Baden Avenue, West Baden Springs, IN\t259939\t10000\nLiberty Bell, 6th Street, Philadelphia, PA\tGrand Canyon National Park, Arizona\t3731423\t120405\nChickasaw National Recreation Area, 1008 W 2nd St, Sulphur, OK 73086\tAcadia National Park, Maine\t3203271\t108025\nCape Canaveral, FL\tWhite House, Pennsylvania Avenue Northwest, Washington, DC\t1394370\t44355\nShelburne Farms, Harbor Road, Shelburne, VT\tCraters of the Moon National Monument & Preserve, Arco, ID\t3928519\t131542\nTaliesin, County Road C, Spring Green, Wisconsin\tLost World Caverns, Lewisburg, WV\t1273680\t44029\nHanford Site, Benton County, WA\tOlympia Entertainment, Woodward Avenue, Detroit, MI\t3573878\t114770\nWright Brothers National Memorial Visitor Center, Manteo, NC\tOmni Mount Washington Resort, Mount Washington Hotel Road, Bretton Woods, NH\t1290669\t47751\nMammoth Cave National Park, Mammoth Cave Pkwy, Mammoth Cave, KY\tOlympia Entertainment, Woodward Avenue, Detroit, MI\t725571\t24642\nSpring Grove Cemetery, Spring Grove Avenue, Cincinnati, OH\tFort Snelling, Tower Avenue, Saint Paul, MN\t1124962\t37663\nFrench Quarter, New Orleans, LA\tOkefenokee Swamp Park, Okefenokee Swamp Park Road, Waycross, GA\t857117\t29161\nCape Canaveral, FL\tUSS Constitution, Boston, MA\t2102628\t69414\nThe Breakers, Ochre Point Avenue, Newport, RI\tLincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL\t1768017\t61380\nThe Mark Twain House & Museum, Farmington Avenue, Hartford, CT\tFort Snelling, Tower Avenue, Saint Paul, MN\t2086009\t70012\nTaliesin, County Road C, Spring Green, Wisconsin\tOkefenokee Swamp Park, Okefenokee Swamp Park Road, Waycross, GA\t1869248\t62674\nPikes Peak, Colorado\tAcadia National Park, Maine\t3757896\t124929\nNew Castle Historic District, Delaware\tGlacier National Park, West Glacier, MT\t3684584\t125544\nGraceland, Elvis Presley Boulevard, Memphis, TN\tFrench Quarter, New Orleans, LA\t626412\t19970\nHoover Dam, NV\tGlacier National Park, West Glacier, MT\t1774950\t56934\nStatue of Liberty, Liberty Island, NYC, NY\tGlacier National Park, West Glacier, MT\t3749449\t125061\nHoover Dam, NV\tWhite House, Pennsylvania Avenue Northwest, Washington, DC\t3903362\t124350\nUSS Alabama, Battleship Parkway, Mobile, AL\tLincoln Home National Historic Site Visitor Center, 426 South 7th Street, Springfield, IL\t1197210\t41107\nChickasaw National Recreation Area, 1008 W 2nd St, Sulphur, OK 73086\tLiberty Bell, 6th Street, Philadelphia, PA\t2302327\t76937\nMammoth Cave National Park, Mammoth Cave Pkwy, Mammoth Cave, KY\tPikes Peak, Colorado\t1833065\t62315\nHanford Site, Benton County, WA\tStatue of Liberty, Liberty Island, NYC, NY\t4388894\t142644\nShelburne Farms, Harbor Road, Shelburne, VT\tTaliesin, County Road C, Spring Green, Wisconsin\t1745609\t63151\nLiberty Bell, 6th Street, Philadelphia, PA\tPikes Peak, Colorado\t2801541\t93969\nTerrace Hill, Grand Avenue, Des Moines, IA\tThe Alamo, Alamo Plaza, San Antonio, TX\t1618550\t51648\nUSS Constitution, Boston, MA\tCongress Hall, Congress Place, Cape May, NJ 08204\t598411\t21635\nThe Mark Twain House & Museum, Farmington Avenue, Hartford, CT\tGlacier National Park, West Glacier, MT\t3870144\t130704\nTaliesin, County Road C, Spring Green, Wisconsin\tCongress Hall, Congress Place, Cape May, NJ 08204\t1655882\t57333\nCable Car Museum, 94108, 1201 Mason St, San Francisco, CA 94108\tGrand Canyon National Park, Arizona\t1257737\t41233\nCongress Hall, Congress Place, Cape May, NJ 08204\tThe Alamo, Alamo Plaza, San Antonio, TX\t2882943\t94397\nChickasaw National Recreation Area, 1008 W 2nd St, Sulphur, OK 73086\tFrench Quarter, New Orleans, LA\t1043911\t34380\nCarlsbad Caverns National Park, Carlsbad, NM\tMammoth Cave National Park, Mammoth Cave Pkwy, Mammoth Cave, KY\t1998091\t67238\nAcadia National Park, Maine\tBryce Canyon National Park, Hwy 63, Bryce, UT\t4509036\t148282\nMaryland State House, 100 State Cir, Annapolis, MD 21401\tFrench Quarter, New Orleans, LA\t1805839\t58242\nGateway Arch, Washington Avenue, St Louis, MO\tFrench Quarter, New Orleans, LA\t1091490\t34833\nMammoth Cave National Park, Mammoth Cave Pkwy, Mammoth Cave, KY\tCongress Hall, Congress Place, Cape May, NJ 08204\t1312237\t44340\nFort Sumter National Monument, Sullivan's Island, SC\tOlympia Entertainment, Woodward Avenue, Detroit, MI\t1329533\t45974\nGraceland, Elvis Presley Boulevard, Memphis, TN\tSan Andreas Fault, San Benito County, CA\t3200531\t102813\nSpring Grove Cemetery, Spring Grove Avenue, Cincinnati, OH\tCongress Hall, Congress Place, Cape May, NJ 08204\t1054409\t36769\nWhite House, Pennsylvania Avenue Northwest, Washington, DC\tCable Car Museum, 94108, 1201 Mason St, San Francisco, CA 94108\t4524629\t145459\nLost World Caverns, Lewisburg, WV\tNew Castle Historic District, Delaware\t572997\t19988\nPikes Peak, Colorado\tUSS Constitution, Boston, MA\t3327699\t109711\nCongress Hall, Congress Place, Cape May, NJ 08204\tGlacier National Park, West Glacier, MT\t3848711\t128237\nThe Mark Twain House & Museum, Farmington Avenue, Hartford, CT\tOmni Mount Washington Resort, Mount Washington Hotel Road, Bretton Woods, NH\t391642\t13719\nSpring Grove Cemetery, Spring Grove Avenue, Cincinnati, OH\tBryce Canyon National Park, Hwy 63, Bryce, UT\t2818397\t91042\nUSS Alabama, Battleship Parkway, Mobile, AL\tLost World Caverns, Lewisburg, WV\t1302307\t41498\nOmni Mount Washington Resort, Mount Washington Hotel Road, Bretton Woods, NH\tColumbia River Gorge National Scenic Area, Oregon\t4905203\t161060\nFort Union Trading Post National Historic Site, Williston, North Dakota 1804, ND\tCongress Hall, Congress Place, Cape May, NJ 08204\t3069351\t102152"
  },
  {
    "path": "pareto-optimized-road-trip/my-waypoints-dist-dur.tsv",
    "content": "waypoint1\twaypoint2\tdistance_m\tduration_s\n2 E Main St, Madison, WI 53703\tWyoming State Capitol, Cheyenne, WY 82001\t1484756\t13.459166666666667\nL St & 10th St, Sacramento, CA 95814\tVirginia State Capitol, Richmond, VA 23219\t4496033\t40.22388888888889\nMichigan State Capitol, Lansing, MI 48933\tNorth Dakota State Capitol, Bismarck, ND 58501\t1702226\t15.2425\nMichigan State Capitol, Lansing, MI 48933\tOklahoma State Capitol, Oklahoma City, OK 73105\t1577431\t14.1075\nNew Jersey State House, Trenton, NJ 08608\tSouth Carolina State House, 1100 Gervais Street, Columbia, SC 29201\t1050714\t9.756944444444445\nMaryland State House, 100 State Cir, Annapolis, MD 21401\tWest Virginia State Capitol, Charleston, WV 25317\t624275\t5.811111111111111\nConnecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106\tMissouri State Capitol, Jefferson City, MO 65101\t1933062\t17.850277777777777\n402 S Monroe St, Tallahassee, FL 32301\tVermont State House, 115 State Street, Montpelier, VT 05633\t2270947\t20.70388888888889\nArizona State Capitol, 1700 W Washington St, Phoenix, AZ 85007\tVirginia State Capitol, Richmond, VA 23219\t3620386\t32.61944444444445\nAlabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130\tOregon State Capitol, 900 Court St NE, Salem, OR 97301\t4310134\t38.85166666666667\nNevada State Capitol, Carson City, NV 89701\tNew York State Capitol, State St. and Washington Ave, Albany, NY 12224\t4417580\t39.431666666666665\nLegislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901\tMissouri State Capitol, Jefferson City, MO 65101\t1654361\t15.761111111111111\nMaryland State House, 100 State Cir, Annapolis, MD 21401\tNevada State Capitol, Carson City, NV 89701\t4238501\t38.34916666666667\nOhio State Capitol, 1 Capitol Square, Columbus, OH 43215\tWashington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504\t3965649\t35.45805555555555\nArizona State Capitol, 1700 W Washington St, Phoenix, AZ 85007\tMichigan State Capitol, Lansing, MI 48933\t3125553\t28.08\n300 SW 10th Ave, Topeka, KS 66612\tVirginia State Capitol, Richmond, VA 23219\t1817890\t16.451666666666668\nLegislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901\tPennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120\t207363\t2.6302777777777777\nIowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319\tOklahoma State Capitol, Oklahoma City, OK 73105\t873294\t7.731944444444444\nNew Jersey State House, Trenton, NJ 08608\tVermont State House, 115 State Street, Montpelier, VT 05633\t609928\t5.969722222222222\nMinnesota State Capitol, St Paul, MN 55155\tNorth Dakota State Capitol, Bismarck, ND 58501\t701087\t6.226111111111111\n700 W Jefferson St, Boise, ID 83720\tRhode Island State House, 82 Smith Street, Providence, RI 02903\t4262023\t38.382222222222225\nAlabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130\tPennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120\t1410057\t12.826666666666666\nMaryland State House, 100 State Cir, Annapolis, MD 21401\tMichigan State Capitol, Lansing, MI 48933\t984795\t9.165\nNew Jersey State House, Trenton, NJ 08608\tWest Virginia State Capitol, Charleston, WV 25317\t775909\t7.272222222222222\nNew York State Capitol, State St. and Washington Ave, Albany, NY 12224\tWyoming State Capitol, Cheyenne, WY 82001\t2836858\t25.635833333333334\nMontana State Capitol, 1301 E 6th Ave, Helena, MT 59601\tVermont State House, 115 State Street, Montpelier, VT 05633\t3858914\t35.9\nOklahoma State Capitol, Oklahoma City, OK 73105\tSouth Carolina State House, 1100 Gervais Street, Columbia, SC 29201\t1708829\t15.338333333333333\nLouisiana State Capitol, Baton Rouge, LA 70802\tOregon State Capitol, 900 Court St NE, Salem, OR 97301\t4032674\t36.91388888888889\nMinnesota State Capitol, St Paul, MN 55155\tOhio State Capitol, 1 Capitol Square, Columbus, OH 43215\t1215946\t11.09361111111111\nArkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201\tSouth Carolina State House, 1100 Gervais Street, Columbia, SC 29201\t1177643\t10.660277777777777\nArizona State Capitol, 1700 W Washington St, Phoenix, AZ 85007\tIndiana State Capitol, Indianapolis, IN 46204\t2734360\t24.705277777777777\nAlabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130\tState House, 107 North Main Street, Concord, NH 03303\t2090541\t19.531666666666666\nLegislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901\t500 E Capitol Ave, Pierre, SD 57501\t2545226\t23.341944444444444\nIowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319\tWyoming State Capitol, Cheyenne, WY 82001\t1012033\t9.015\nArkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201\t200 E Colfax Ave, Denver, CO 80203\t1556144\t13.736944444444445\nL St & 10th St, Sacramento, CA 95814\tNevada State Capitol, Carson City, NV 89701\t209218\t2.620277777777778\nL St & 10th St, Sacramento, CA 95814\tIndiana State Capitol, Indianapolis, IN 46204\t3518613\t31.3025\nNew Mexico State Capitol, Santa Fe, NM 87501\t500 E Capitol Ave, Pierre, SD 57501\t1398667\t13.206666666666667\nLegislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901\t300 SW 10th Ave, Topeka, KS 66612\t1939553\t18.27861111111111\nKentucky State Capitol Building, 700 Capitol Avenue, Frankfort, KY 40601\tNebraska State Capitol, 1445 K Street, Lincoln, NE 68509\t1209098\t11.276388888888889\nMissouri State Capitol, Jefferson City, MO 65101\tRhode Island State House, 82 Smith Street, Providence, RI 02903\t2036038\t19.129722222222224\nLouisiana State Capitol, Baton Rouge, LA 70802\t2 E Main St, Madison, WI 53703\t1648573\t14.640555555555556\n402 S Monroe St, Tallahassee, FL 32301\tNew York State Capitol, State St. and Washington Ave, Albany, NY 12224\t1991155\t17.946944444444444\n700 W Jefferson St, Boise, ID 83720\tMassachusetts State House, Boston, MA 02108\t4283333\t38.500277777777775\nArizona State Capitol, 1700 W Washington St, Phoenix, AZ 85007\tNorth Carolina State Capitol, Raleigh, NC 27601\t3499526\t31.790555555555557\nLegislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901\tMassachusetts State House, Boston, MA 02108\t611507\t6.089722222222222\nArkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201\tLouisiana State Capitol, Baton Rouge, LA 70802\t552559\t6.1066666666666665\nIndiana State Capitol, Indianapolis, IN 46204\tWyoming State Capitol, Cheyenne, WY 82001\t1769573\t15.93138888888889\n700 W Jefferson St, Boise, ID 83720\tNorth Dakota State Capitol, Bismarck, ND 58501\t1653552\t14.968055555555555\nL St & 10th St, Sacramento, CA 95814\tNew Jersey State House, Trenton, NJ 08608\t4526276\t40.489444444444445\nWashington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504\tWest Virginia State Capitol, Charleston, WV 25317\t4182133\t37.61138888888889\nGeorgia State Capitol, Atlanta, GA 30334\tNevada State Capitol, Carson City, NV 89701\t3827390\t34.82694444444444\nArizona State Capitol, 1700 W Washington St, Phoenix, AZ 85007\tOhio State Capitol, 1 Capitol Square, Columbus, OH 43215\t3016589\t27.24\n200 E Colfax Ave, Denver, CO 80203\tUtah State Capitol, Salt Lake City, UT 84103\t841152\t7.91\nSouth Carolina State House, 1100 Gervais Street, Columbia, SC 29201\tUtah State Capitol, Salt Lake City, UT 84103\t3334079\t30.226666666666667\nOhio State Capitol, 1 Capitol Square, Columbus, OH 43215\tWest Virginia State Capitol, Charleston, WV 25317\t265168\t2.6933333333333334\n200 E Colfax Ave, Denver, CO 80203\tVermont State House, 115 State Street, Montpelier, VT 05633\t3117244\t28.791666666666668\nArkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201\tConnecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106\t2180753\t20.04\nNebraska State Capitol, 1445 K Street, Lincoln, NE 68509\tVermont State House, 115 State Street, Montpelier, VT 05633\t2344034\t22.141111111111112\nMontana State Capitol, 1301 E 6th Ave, Helena, MT 59601\tUtah State Capitol, Salt Lake City, UT 84103\t774035\t6.633333333333334\n300 SW 10th Ave, Topeka, KS 66612\tMaine State House, Augusta, ME 04330\t2649936\t24.170833333333334\nMichigan State Capitol, Lansing, MI 48933\tWest Virginia State Capitol, Charleston, WV 25317\t679517\t6.6338888888888885\nNew Mexico State Capitol, Santa Fe, NM 87501\tPennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120\t2920958\t26.529444444444444\n400-498 N West St, Jackson, MS 39201\t2 E Main St, Madison, WI 53703\t1365734\t12.152222222222223\nState House, 107 North Main Street, Concord, NH 03303\tRhode Island State House, 82 Smith Street, Providence, RI 02903\t198603\t1.9908333333333332\nMassachusetts State House, Boston, MA 02108\tOklahoma State Capitol, Oklahoma City, OK 73105\t2710778\t24.665277777777778\nIllinois State Capitol, Springfield, IL 62756\tPennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120\t1213082\t11.370833333333334\n200 E Colfax Ave, Denver, CO 80203\tWest Virginia State Capitol, Charleston, WV 25317\t2179520\t19.378888888888888\nMaine State House, Augusta, ME 04330\tNorth Dakota State Capitol, Bismarck, ND 58501\t3181562\t28.85638888888889\n400-498 N West St, Jackson, MS 39201\tNorth Carolina State Capitol, Raleigh, NC 27601\t1266179\t11.560555555555556\nIllinois State Capitol, Springfield, IL 62756\tWyoming State Capitol, Cheyenne, WY 82001\t1414419\t12.964166666666667\nIowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319\tNevada State Capitol, Carson City, NV 89701\t2565429\t23.017222222222223\nOhio State Capitol, 1 Capitol Square, Columbus, OH 43215\tSouth Carolina State House, 1100 Gervais Street, Columbia, SC 29201\t832772\t7.963055555555556\nMaryland State House, 100 State Cir, Annapolis, MD 21401\tTexas Capitol, 1100 Congress Avenue, Austin, TX 78701\t2513292\t22.845833333333335\nConnecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106\tLouisiana State Capitol, Baton Rouge, LA 70802\t2386413\t21.665555555555557\nAlabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130\tNorth Dakota State Capitol, Bismarck, ND 58501\t2555394\t22.523611111111112\nMontana State Capitol, 1301 E 6th Ave, Helena, MT 59601\tNorth Carolina State Capitol, Raleigh, NC 27601\t3628835\t33.34361111111111\nMaine State House, Augusta, ME 04330\tUtah State Capitol, Salt Lake City, UT 84103\t4056973\t36.73361111111111\nGeorgia State Capitol, Atlanta, GA 30334\t2 E Main St, Madison, WI 53703\t1396771\t12.522777777777778\nVermont State House, 115 State Street, Montpelier, VT 05633\t2 E Main St, Madison, WI 53703\t1777622\t17.235833333333332\nLegislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901\tOregon State Capitol, 900 Court St NE, Salem, OR 97301\t4722133\t43.030277777777776\nAlabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130\tNevada State Capitol, Carson City, NV 89701\t3735753\t34.01277777777778\nMassachusetts State House, Boston, MA 02108\tOhio State Capitol, 1 Capitol Square, Columbus, OH 43215\t1246212\t11.47888888888889\nArkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201\t300 SW 10th Ave, Topeka, KS 66612\t780685\t7.1033333333333335\nLouisiana State Capitol, Baton Rouge, LA 70802\tNevada State Capitol, Carson City, NV 89701\t3319161\t30.426666666666666\nArizona State Capitol, 1700 W Washington St, Phoenix, AZ 85007\tMaryland State House, 100 State Cir, Annapolis, MD 21401\t3696277\t33.754444444444445\nNevada State Capitol, Carson City, NV 89701\tWashington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504\t1086169\t11.026111111111112\nKentucky State Capitol Building, 700 Capitol Avenue, Frankfort, KY 40601\tTennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243\t334062\t3.2086111111111113\n402 S Monroe St, Tallahassee, FL 32301\tWest Virginia State Capitol, Charleston, WV 25317\t1133547\t11.511111111111111\nLegislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901\tLouisiana State Capitol, Baton Rouge, LA 70802\t1998869\t18.3225\nKentucky State Capitol Building, 700 Capitol Avenue, Frankfort, KY 40601\tWashington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504\t3894102\t35.33111111111111\n700 W Jefferson St, Boise, ID 83720\tNorth Carolina State Capitol, Raleigh, NC 27601\t3930023\t35.38611111111111\nArizona State Capitol, 1700 W Washington St, Phoenix, AZ 85007\tVermont State House, 115 State Street, Montpelier, VT 05633\t4207835\t38.82194444444445\n200 E Colfax Ave, Denver, CO 80203\t500 E Capitol Ave, Pierre, SD 57501\t844954\t8.028333333333334\n402 S Monroe St, Tallahassee, FL 32301\tKentucky State Capitol Building, 700 Capitol Avenue, Frankfort, KY 40601\t1078970\t10.289722222222222\nArkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201\tMissouri State Capitol, Jefferson City, MO 65101\t554812\t5.858888888888889\nNew Jersey State House, Trenton, NJ 08608\tOklahoma State Capitol, Oklahoma City, OK 73105\t2251698\t20.604166666666668\nIowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319\tNorth Carolina State Capitol, Raleigh, NC 27601\t1779142\t16.510277777777777\nIllinois State Capitol, Springfield, IL 62756\tNebraska State Capitol, 1445 K Street, Lincoln, NE 68509\t697787\t6.743888888888889\nNew York State Capitol, State St. and Washington Ave, Albany, NY 12224\tNorth Dakota State Capitol, Bismarck, ND 58501\t2668685\t24.16583333333333\n300 SW 10th Ave, Topeka, KS 66612\tOregon State Capitol, 900 Court St NE, Salem, OR 97301\t2925507\t26.209444444444443\nGeorgia State Capitol, Atlanta, GA 30334\tRhode Island State House, 82 Smith Street, Providence, RI 02903\t1687443\t15.93\nLouisiana State Capitol, Baton Rouge, LA 70802\tNew York State Capitol, State St. and Washington Ave, Albany, NY 12224\t2390708\t21.575277777777778\nNevada State Capitol, Carson City, NV 89701\tUtah State Capitol, Salt Lake City, UT 84103\t880839\t7.743611111111111\n402 S Monroe St, Tallahassee, FL 32301\tSouth Carolina State House, 1100 Gervais Street, Columbia, SC 29201\t570645\t6.329722222222222\nNorth Carolina State Capitol, Raleigh, NC 27601\tNorth Dakota State Capitol, Bismarck, ND 58501\t2649871\t24.302222222222223\n200 E Colfax Ave, Denver, CO 80203\tNorth Carolina State Capitol, Raleigh, NC 27601\t2688370\t24.213333333333335\n500 E Capitol Ave, Pierre, SD 57501\tWest Virginia State Capitol, Charleston, WV 25317\t2059330\t18.691388888888888\nNebraska State Capitol, 1445 K Street, Lincoln, NE 68509\tState House, 107 North Main Street, Concord, NH 03303\t2364261\t22.52111111111111\nLouisiana State Capitol, Baton Rouge, LA 70802\tOhio State Capitol, 1 Capitol Square, Columbus, OH 43215\t1553207\t14.011944444444444\n400-498 N West St, Jackson, MS 39201\tNevada State Capitol, Carson City, NV 89701\t3284019\t30.118333333333332\nAlabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130\tIllinois State Capitol, Springfield, IL 62756\t1053119\t9.519444444444444\nMissouri State Capitol, Jefferson City, MO 65101\tVirginia State Capitol, Richmond, VA 23219\t1532730\t13.918333333333333\nLegislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901\tUtah State Capitol, Salt Lake City, UT 84103\t3490982\t31.87527777777778\nAlabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130\tOhio State Capitol, 1 Capitol Square, Columbus, OH 43215\t1059373\t9.633333333333333\nAlabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130\t300 SW 10th Ave, Topeka, KS 66612\t1442611\t12.900833333333333\nPennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120\tWyoming State Capitol, Cheyenne, WY 82001\t2580236\t23.32777777777778\nNebraska State Capitol, 1445 K Street, Lincoln, NE 68509\tSouth Carolina State House, 1100 Gervais Street, Columbia, SC 29201\t1909649\t17.545833333333334\nIndiana State Capitol, Indianapolis, IN 46204\tIowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319\t758123\t7.004444444444444\nConnecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106\tMassachusetts State House, Boston, MA 02108\t163033\t1.6980555555555557\n300 SW 10th Ave, Topeka, KS 66612\tRhode Island State House, 82 Smith Street, Providence, RI 02903\t2321198\t21.662777777777777\nArizona State Capitol, 1700 W Washington St, Phoenix, AZ 85007\tIllinois State Capitol, Springfield, IL 62756\t2502392\t22.606944444444444\nNevada State Capitol, Carson City, NV 89701\t2 E Main St, Madison, WI 53703\t3065319\t27.260277777777777\nArizona State Capitol, 1700 W Washington St, Phoenix, AZ 85007\t400-498 N West St, Jackson, MS 39201\t2366978\t20.807222222222222\nIndiana State Capitol, Indianapolis, IN 46204\tRhode Island State House, 82 Smith Street, Providence, RI 02903\t1446931\t13.763333333333334\nMaryland State House, 100 State Cir, Annapolis, MD 21401\t500 E Capitol Ave, Pierre, SD 57501\t2446586\t22.220277777777778\nArizona State Capitol, 1700 W Washington St, Phoenix, AZ 85007\tMassachusetts State House, Boston, MA 02108\t4261483\t38.669444444444444\n500 E Capitol Ave, Pierre, SD 57501\tVermont State House, 115 State Street, Montpelier, VT 05633\t2804324\t26.13222222222222\nVermont State House, 115 State Street, Montpelier, VT 05633\tWashington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504\t4928200\t45.08638888888889\nIllinois State Capitol, Springfield, IL 62756\tUtah State Capitol, Salt Lake City, UT 84103\t2121657\t19.371388888888887\nMontana State Capitol, 1301 E 6th Ave, Helena, MT 59601\t2 E Main St, Madison, WI 53703\t2099306\t19.149166666666666\nNorth Dakota State Capitol, Bismarck, ND 58501\tRhode Island State House, 82 Smith Street, Providence, RI 02903\t2907302\t26.524444444444445\n700 W Jefferson St, Boise, ID 83720\tWest Virginia State Capitol, Charleston, WV 25317\t3421174\t30.551666666666666\n300 SW 10th Ave, Topeka, KS 66612\tWyoming State Capitol, Cheyenne, WY 82001\t1016304\t8.745555555555555\nGeorgia State Capitol, Atlanta, GA 30334\tMaryland State House, 100 State Cir, Annapolis, MD 21401\t1082137\t9.9925\nSouth Carolina State House, 1100 Gervais Street, Columbia, SC 29201\tTexas Capitol, 1100 Congress Avenue, Austin, TX 78701\t1870907\t16.675833333333333\nNorth Carolina State Capitol, Raleigh, NC 27601\t500 E Capitol Ave, Pierre, SD 57501\t2566101\t23.543055555555554\nMaine State House, Augusta, ME 04330\tWyoming State Capitol, Cheyenne, WY 82001\t3349736\t30.32638888888889\nAlabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130\tMontana State Capitol, 1301 E 6th Ave, Helena, MT 59601\t3364264\t30.154166666666665\nL St & 10th St, Sacramento, CA 95814\tState House, 107 North Main Street, Concord, NH 03303\t4819339\t44.10638888888889\nArkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201\tNevada State Capitol, Carson City, NV 89701\t3008237\t27.350833333333334\nNebraska State Capitol, 1445 K Street, Lincoln, NE 68509\tPennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120\t1875100\t17.163611111111113\nL St & 10th St, Sacramento, CA 95814\tMaryland State House, 100 State Cir, Annapolis, MD 21401\t4434054\t39.71194444444444\nLouisiana State Capitol, Baton Rouge, LA 70802\tVermont State House, 115 State Street, Montpelier, VT 05633\t2643406\t24.621944444444445\n700 W Jefferson St, Boise, ID 83720\tMichigan State Capitol, Lansing, MI 48933\t3051430\t27.051388888888887\nMichigan State Capitol, Lansing, MI 48933\t500 E Capitol Ave, Pierre, SD 57501\t1631881\t14.58611111111111\nAlabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130\tMaryland State House, 100 State Cir, Annapolis, MD 21401\t1340229\t12.249444444444444\nTennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243\tUtah State Capitol, Salt Lake City, UT 84103\t2629654\t23.787777777777777\nMaryland State House, 100 State Cir, Annapolis, MD 21401\tNew Jersey State House, Trenton, NJ 08608\t262813\t2.633611111111111\nNevada State Capitol, Carson City, NV 89701\tState House, 107 North Main Street, Concord, NH 03303\t4648498\t42.49944444444444\nConnecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106\tNevada State Capitol, Carson City, NV 89701\t4507409\t40.7575\nMaine State House, Augusta, ME 04330\tWest Virginia State Capitol, Charleston, WV 25317\t1458292\t13.589166666666667\n200 E Colfax Ave, Denver, CO 80203\tNorth Dakota State Capitol, Bismarck, ND 58501\t1115101\t10.553333333333333\nNevada State Capitol, Carson City, NV 89701\tOregon State Capitol, 900 Court St NE, Salem, OR 97301\t832440\t8.6625\nConnecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106\tOregon State Capitol, 900 Court St NE, Salem, OR 97301\t4892402\t44.316944444444445\nMontana State Capitol, 1301 E 6th Ave, Helena, MT 59601\tNew Mexico State Capitol, Santa Fe, NM 87501\t1899972\t16.586111111111112\nNew Jersey State House, Trenton, NJ 08608\tOregon State Capitol, 900 Court St NE, Salem, OR 97301\t4712435\t42.61805555555556\nIllinois State Capitol, Springfield, IL 62756\tMissouri State Capitol, Jefferson City, MO 65101\t335367\t3.391111111111111\nNew York State Capitol, State St. and Washington Ave, Albany, NY 12224\tTexas Capitol, 1100 Congress Avenue, Austin, TX 78701\t2969380\t26.964444444444446\n402 S Monroe St, Tallahassee, FL 32301\tIndiana State Capitol, Indianapolis, IN 46204\t1253894\t11.976666666666667\nMaine State House, Augusta, ME 04330\tMinnesota State Capitol, St Paul, MN 55155\t2483764\t22.751666666666665\nMaryland State House, 100 State Cir, Annapolis, MD 21401\tMassachusetts State House, Boston, MA 02108\t686203\t6.848611111111111\n400-498 N West St, Jackson, MS 39201\tNew York State Capitol, State St. and Washington Ave, Albany, NY 12224\t2126538\t19.275555555555556\nNebraska State Capitol, 1445 K Street, Lincoln, NE 68509\t2 E Main St, Madison, WI 53703\t781082\t7.281944444444444\n200 E Colfax Ave, Denver, CO 80203\tConnecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106\t3023952\t27.3175\nMichigan State Capitol, Lansing, MI 48933\tMissouri State Capitol, Jefferson City, MO 65101\t960368\t8.884722222222223\nGeorgia State Capitol, Atlanta, GA 30334\tMinnesota State Capitol, St Paul, MN 55155\t1803693\t16.06972222222222\n300 SW 10th Ave, Topeka, KS 66612\tNew Mexico State Capitol, Santa Fe, NM 87501\t1113558\t10.723333333333333\nLegislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901\tMinnesota State Capitol, St Paul, MN 55155\t1917773\t17.89361111111111\nGeorgia State Capitol, Atlanta, GA 30334\tMassachusetts State House, Boston, MA 02108\t1737475\t16.463055555555556\nNew Mexico State Capitol, Santa Fe, NM 87501\tTennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243\t1948400\t17.435833333333335\nIowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319\tMaine State House, Augusta, ME 04330\t2338330\t21.378055555555555\nLegislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901\tOklahoma State Capitol, Oklahoma City, OK 73105\t2249555\t20.97888888888889\nArkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201\tNebraska State Capitol, 1445 K Street, Lincoln, NE 68509\t993281\t9.273055555555555\n200 E Colfax Ave, Denver, CO 80203\tRhode Island State House, 82 Smith Street, Providence, RI 02903\t3149582\t28.52111111111111\nL St & 10th St, Sacramento, CA 95814\tPennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120\t4330179\t38.74888888888889\n402 S Monroe St, Tallahassee, FL 32301\tMontana State Capitol, 1301 E 6th Ave, Helena, MT 59601\t3703198\t33.8\n400-498 N West St, Jackson, MS 39201\tNebraska State Capitol, 1445 K Street, Lincoln, NE 68509\t1371664\t13.203611111111112\nLouisiana State Capitol, Baton Rouge, LA 70802\tMassachusetts State House, Boston, MA 02108\t2546224\t23.336111111111112\nL St & 10th St, Sacramento, CA 95814\tNorth Carolina State Capitol, Raleigh, NC 27601\t4499451\t40.45944444444444\nMichigan State Capitol, Lansing, MI 48933\tMinnesota State Capitol, St Paul, MN 55155\t1004428\t9.137777777777778\nOklahoma State Capitol, Oklahoma City, OK 73105\tTennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243\t1092426\t9.8425\nConnecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106\tKentucky State Capitol Building, 700 Capitol Avenue, Frankfort, KY 40601\t1369160\t12.65638888888889\nAlabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130\tMassachusetts State House, Boston, MA 02108\t1995567\t18.72\n300 SW 10th Ave, Topeka, KS 66612\tNorth Carolina State Capitol, Raleigh, NC 27601\t1821308\t16.68722222222222\n200 E Colfax Ave, Denver, CO 80203\tOregon State Capitol, 900 Court St NE, Salem, OR 97301\t2072303\t19.064722222222223\nKentucky State Capitol Building, 700 Capitol Avenue, Frankfort, KY 40601\tPennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120\t905401\t8.324722222222222\nMaine State House, Augusta, ME 04330\tTexas Capitol, 1100 Congress Avenue, Austin, TX 78701\t3405003\t31.066388888888888\n400-498 N West St, Jackson, MS 39201\tVermont State House, 115 State Street, Montpelier, VT 05633\t2379235\t22.322222222222223\nNebraska State Capitol, 1445 K Street, Lincoln, NE 68509\tOregon State Capitol, 900 Court St NE, Salem, OR 97301\t2651879\t23.929444444444446\n200 E Colfax Ave, Denver, CO 80203\tOhio State Capitol, 1 Capitol Square, Columbus, OH 43215\t2024609\t18.102777777777778\nAlabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130\tVirginia State Capitol, Richmond, VA 23219\t1111807\t10.0725\nVermont State House, 115 State Street, Montpelier, VT 05633\tWest Virginia State Capitol, Charleston, WV 25317\t1307877\t12.747777777777777\n200 E Colfax Ave, Denver, CO 80203\tSouth Carolina State House, 1100 Gervais Street, Columbia, SC 29201\t2564693\t22.948333333333334\nSouth Carolina State House, 1100 Gervais Street, Columbia, SC 29201\tWest Virginia State Capitol, Charleston, WV 25317\t569053\t5.368888888888889\nAlabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130\t400-498 N West St, Jackson, MS 39201\t399927\t4.009444444444444\nLouisiana State Capitol, Baton Rouge, LA 70802\tMinnesota State Capitol, St Paul, MN 55155\t1900670\t17.549166666666668\nKentucky State Capitol Building, 700 Capitol Avenue, Frankfort, KY 40601\tVermont State House, 115 State Street, Montpelier, VT 05633\t1492754\t14.696666666666667\nNew Mexico State Capitol, Santa Fe, NM 87501\t2 E Main St, Madison, WI 53703\t2012902\t18.82138888888889\nNew York State Capitol, State St. and Washington Ave, Albany, NY 12224\tPennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120\t473386\t4.486944444444444\nState House, 107 North Main Street, Concord, NH 03303\t500 E Capitol Ave, Pierre, SD 57501\t2843784\t26.504166666666666\nAlabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130\tMinnesota State Capitol, St Paul, MN 55155\t1857595\t16.41888888888889\nMaryland State House, 100 State Cir, Annapolis, MD 21401\tNebraska State Capitol, 1445 K Street, Lincoln, NE 68509\t1980198\t18.176388888888887\nIndiana State Capitol, Indianapolis, IN 46204\tKentucky State Capitol Building, 700 Capitol Avenue, Frankfort, KY 40601\t267015\t2.671388888888889\nMaine State House, Augusta, ME 04330\tMissouri State Capitol, Jefferson City, MO 65101\t2364114\t21.656388888888888\n200 E Colfax Ave, Denver, CO 80203\tTennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243\t1857910\t16.49972222222222\nLegislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901\tNorth Dakota State Capitol, Bismarck, ND 58501\t2615571\t23.998333333333335\n300 SW 10th Ave, Topeka, KS 66612\tOklahoma State Capitol, Oklahoma City, OK 73105\t473488\t4.159722222222222\nArizona State Capitol, 1700 W Washington St, Phoenix, AZ 85007\tMinnesota State Capitol, St Paul, MN 55155\t2649346\t24.74138888888889\nRhode Island State House, 82 Smith Street, Providence, RI 02903\tTennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243\t1729221\t15.988611111111112\nNorth Carolina State Capitol, Raleigh, NC 27601\tUtah State Capitol, Salt Lake City, UT 84103\t3456831\t31.470833333333335\n402 S Monroe St, Tallahassee, FL 32301\tGeorgia State Capitol, Atlanta, GA 30334\t420539\t4.3533333333333335\n200 E Colfax Ave, Denver, CO 80203\tNebraska State Capitol, 1445 K Street, Lincoln, NE 68509\t779919\t6.818888888888889\nKentucky State Capitol Building, 700 Capitol Avenue, Frankfort, KY 40601\tNew Mexico State Capitol, Santa Fe, NM 87501\t2160641\t19.406111111111112\nPennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120\tSouth Carolina State House, 1100 Gervais Street, Columbia, SC 29201\t917431\t8.471944444444444\nNebraska State Capitol, 1445 K Street, Lincoln, NE 68509\tWyoming State Capitol, Cheyenne, WY 82001\t713490\t6.3675\n700 W Jefferson St, Boise, ID 83720\tMontana State Capitol, 1301 E 6th Ave, Helena, MT 59601\t782531\t7.341388888888889\nLegislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901\tNevada State Capitol, Carson City, NV 89701\t4337141\t39.470555555555556\nIndiana State Capitol, Indianapolis, IN 46204\tNorth Dakota State Capitol, Bismarck, ND 58501\t1640234\t14.748055555555556\nMassachusetts State House, Boston, MA 02108\tWyoming State Capitol, Cheyenne, WY 82001\t3101206\t28.11722222222222\nMaine State House, Augusta, ME 04330\tOklahoma State Capitol, Oklahoma City, OK 73105\t2959307\t26.874444444444446\nNebraska State Capitol, 1445 K Street, Lincoln, NE 68509\tUtah State Capitol, Salt Lake City, UT 84103\t1420727\t12.774444444444445\nIndiana State Capitol, Indianapolis, IN 46204\tMontana State Capitol, 1301 E 6th Ave, Helena, MT 59601\t2624064\t23.901944444444446\nIllinois State Capitol, Springfield, IL 62756\t500 E Capitol Ave, Pierre, SD 57501\t1322600\t11.902777777777779\n300 SW 10th Ave, Topeka, KS 66612\tMontana State Capitol, 1301 E 6th Ave, Helena, MT 59601\t1992901\t18.201944444444443\nMichigan State Capitol, Lansing, MI 48933\t2 E Main St, Madison, WI 53703\t597506\t5.591111111111111\nMinnesota State Capitol, St Paul, MN 55155\tPennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120\t1708022\t15.726944444444445\n400-498 N West St, Jackson, MS 39201\tOhio State Capitol, 1 Capitol Square, Columbus, OH 43215\t1270368\t11.52361111111111\nL St & 10th St, Sacramento, CA 95814\tNew Mexico State Capitol, Santa Fe, NM 87501\t1836427\t16.634166666666665\nIowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319\tOregon State Capitol, 900 Court St NE, Salem, OR 97301\t2950421\t26.576666666666668\nMissouri State Capitol, Jefferson City, MO 65101\tSouth Carolina State House, 1100 Gervais Street, Columbia, SC 29201\t1412471\t12.889166666666666\nKentucky State Capitol Building, 700 Capitol Avenue, Frankfort, KY 40601\tOhio State Capitol, 1 Capitol Square, Columbus, OH 43215\t299648\t3.1172222222222223\n300 SW 10th Ave, Topeka, KS 66612\tSouth Carolina State House, 1100 Gervais Street, Columbia, SC 29201\t1697631\t15.422222222222222\nSouth Carolina State House, 1100 Gervais Street, Columbia, SC 29201\t500 E Capitol Ave, Pierre, SD 57501\t2535022\t22.758055555555554\n402 S Monroe St, Tallahassee, FL 32301\tVirginia State Capitol, Richmond, VA 23219\t1225602\t10.708333333333334\nKentucky State Capitol Building, 700 Capitol Avenue, Frankfort, KY 40601\tTexas Capitol, 1100 Congress Avenue, Austin, TX 78701\t1711975\t15.538055555555555\nMaryland State House, 100 State Cir, Annapolis, MD 21401\t400-498 N West St, Jackson, MS 39201\t1637909\t14.934722222222222\nKentucky State Capitol Building, 700 Capitol Avenue, Frankfort, KY 40601\tMissouri State Capitol, Jefferson City, MO 65101\t711404\t6.59\nArkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201\tTennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243\t561239\t5.164444444444444\nGeorgia State Capitol, Atlanta, GA 30334\tWest Virginia State Capitol, Charleston, WV 25317\t804715\t7.476944444444444\nLouisiana State Capitol, Baton Rouge, LA 70802\tNew Mexico State Capitol, Santa Fe, NM 87501\t1713974\t15.636388888888888\nMinnesota State Capitol, St Paul, MN 55155\tState House, 107 North Main Street, Concord, NH 03303\t2197183\t21.084444444444443\nConnecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106\tSouth Carolina State House, 1100 Gervais Street, Columbia, SC 29201\t1326907\t12.3975\n300 SW 10th Ave, Topeka, KS 66612\t2 E Main St, Madison, WI 53703\t885264\t8.190833333333334\nIowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319\tRhode Island State House, 82 Smith Street, Providence, RI 02903\t2069524\t19.095277777777778\nMaine State House, Augusta, ME 04330\tState House, 107 North Main Street, Concord, NH 03303\t276259\t2.623611111111111\nIowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319\t300 SW 10th Ave, Topeka, KS 66612\t412567\t3.7730555555555556\nIllinois State Capitol, Springfield, IL 62756\tNew Jersey State House, Trenton, NJ 08608\t1409179\t13.11138888888889\nTexas Capitol, 1100 Congress Avenue, Austin, TX 78701\tUtah State Capitol, Salt Lake City, UT 84103\t2078970\t20.267222222222223\nAlabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130\tUtah State Capitol, Salt Lake City, UT 84103\t3078983\t27.696666666666665\nMontana State Capitol, 1301 E 6th Ave, Helena, MT 59601\tSouth Carolina State House, 1100 Gervais Street, Columbia, SC 29201\t3618995\t32.575\nAlabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130\t2 E Main St, Madison, WI 53703\t1450674\t12.872222222222222\nLouisiana State Capitol, Baton Rouge, LA 70802\tTennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243\t950242\t8.470555555555556\nKentucky State Capitol Building, 700 Capitol Avenue, Frankfort, KY 40601\t400-498 N West St, Jackson, MS 39201\t998879\t9.043611111111112\nConnecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106\tMaine State House, Augusta, ME 04330\t410528\t3.8625\nMaine State House, Augusta, ME 04330\tRhode Island State House, 82 Smith Street, Providence, RI 02903\t348608\t3.3652777777777776\nOhio State Capitol, 1 Capitol Square, Columbus, OH 43215\tTexas Capitol, 1100 Congress Avenue, Austin, TX 78701\t1987338\t18.030555555555555\nTennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243\t2 E Main St, Madison, WI 53703\t1001345\t8.963055555555556\nWashington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504\tWyoming State Capitol, Cheyenne, WY 82001\t2041131\t18.416944444444443\nKentucky State Capitol Building, 700 Capitol Avenue, Frankfort, KY 40601\tVirginia State Capitol, Richmond, VA 23219\t827652\t7.616388888888889\nLouisiana State Capitol, Baton Rouge, LA 70802\tNorth Carolina State Capitol, Raleigh, NC 27601\t1500710\t13.439722222222223\nOklahoma State Capitol, Oklahoma City, OK 73105\tRhode Island State House, 82 Smith Street, Providence, RI 02903\t2630826\t24.329722222222223\nMassachusetts State House, Boston, MA 02108\tVirginia State Capitol, Richmond, VA 23219\t888608\t8.704166666666667\nMaryland State House, 100 State Cir, Annapolis, MD 21401\tRhode Island State House, 82 Smith Street, Providence, RI 02903\t636172\t6.315555555555555\n402 S Monroe St, Tallahassee, FL 32301\tOklahoma State Capitol, Oklahoma City, OK 73105\t1613562\t15.234444444444444\nArizona State Capitol, 1700 W Washington St, Phoenix, AZ 85007\t2 E Main St, Madison, WI 53703\t2729787\t25.70916666666667\nL St & 10th St, Sacramento, CA 95814\tOregon State Capitol, 900 Court St NE, Salem, OR 97301\t861505\t8.210833333333333\nKentucky State Capitol Building, 700 Capitol Avenue, Frankfort, KY 40601\tMinnesota State Capitol, St Paul, MN 55155\t1205997\t11.106111111111112\nIowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319\tMaryland State House, 100 State Cir, Annapolis, MD 21401\t1672128\t15.351666666666667\nLouisiana State Capitol, Baton Rouge, LA 70802\tMichigan State Capitol, Lansing, MI 48933\t1767813\t15.742222222222223\n700 W Jefferson St, Boise, ID 83720\tNew Jersey State House, Trenton, NJ 08608\t3956849\t35.416111111111114\nKentucky State Capitol Building, 700 Capitol Avenue, Frankfort, KY 40601\tRhode Island State House, 82 Smith Street, Providence, RI 02903\t1475019\t13.7275\n402 S Monroe St, Tallahassee, FL 32301\tUtah State Capitol, Salt Lake City, UT 84103\t3417917\t31.342777777777776\nArizona State Capitol, 1700 W Washington St, Phoenix, AZ 85007\tSouth Carolina State House, 1100 Gervais Street, Columbia, SC 29201\t3317609\t29.146944444444443\nMinnesota State Capitol, St Paul, MN 55155\tWyoming State Capitol, Cheyenne, WY 82001\t1404993\t12.471111111111112\nSouth Carolina State House, 1100 Gervais Street, Columbia, SC 29201\tVermont State House, 115 State Street, Montpelier, VT 05633\t1641637\t15.272222222222222\nGeorgia State Capitol, Atlanta, GA 30334\tNew Mexico State Capitol, Santa Fe, NM 87501\t2222203\t20.036666666666665\nConnecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106\tMichigan State Capitol, Lansing, MI 48933\t1163752\t10.827222222222222\nIndiana State Capitol, Indianapolis, IN 46204\tOhio State Capitol, 1 Capitol Square, Columbus, OH 43215\t283280\t2.6772222222222224\nNew Mexico State Capitol, Santa Fe, NM 87501\tVermont State House, 115 State Street, Montpelier, VT 05633\t3522278\t32.493611111111115\nOklahoma State Capitol, Oklahoma City, OK 73105\tOregon State Capitol, 900 Court St NE, Salem, OR 97301\t3141325\t28.153333333333332\nKentucky State Capitol Building, 700 Capitol Avenue, Frankfort, KY 40601\tMontana State Capitol, 1301 E 6th Ave, Helena, MT 59601\t2895779\t26.269444444444446\nArizona State Capitol, 1700 W Washington St, Phoenix, AZ 85007\tNew Jersey State House, Trenton, NJ 08608\t3802612\t34.59861111111111\nLegislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901\tNew Jersey State House, Trenton, NJ 08608\t188117\t1.8747222222222222\nIndiana State Capitol, Indianapolis, IN 46204\tNorth Carolina State Capitol, Raleigh, NC 27601\t1014212\t9.731944444444444\nMissouri State Capitol, Jefferson City, MO 65101\tWashington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504\t3273298\t29.595555555555556\nArkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201\tTexas Capitol, 1100 Congress Avenue, Austin, TX 78701\t826451\t7.4655555555555555\n700 W Jefferson St, Boise, ID 83720\tIndiana State Capitol, Indianapolis, IN 46204\t2949186\t26.229166666666668\n200 E Colfax Ave, Denver, CO 80203\tMissouri State Capitol, Jefferson City, MO 65101\t1219182\t10.786388888888888\nL St & 10th St, Sacramento, CA 95814\tKentucky State Capitol Building, 700 Capitol Avenue, Frankfort, KY 40601\t3675274\t32.87861111111111\n300 SW 10th Ave, Topeka, KS 66612\tKentucky State Capitol Building, 700 Capitol Avenue, Frankfort, KY 40601\t997131\t9.106666666666667\n200 E Colfax Ave, Denver, CO 80203\tMontana State Capitol, 1301 E 6th Ave, Helena, MT 59601\t1272393\t11.210833333333333\nState House, 107 North Main Street, Concord, NH 03303\tOhio State Capitol, 1 Capitol Square, Columbus, OH 43215\t1227308\t11.992222222222223\nIowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319\t400-498 N West St, Jackson, MS 39201\t1325240\t12.133333333333333\nConnecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106\tOhio State Capitol, 1 Capitol Square, Columbus, OH 43215\t1063689\t9.881944444444445\nLegislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901\t2 E Main St, Madison, WI 53703\t1510851\t14.346666666666666\n200 E Colfax Ave, Denver, CO 80203\tVirginia State Capitol, Richmond, VA 23219\t2684952\t23.977777777777778\n400-498 N West St, Jackson, MS 39201\tNew Mexico State Capitol, Santa Fe, NM 87501\t1678832\t15.328055555555556\nArizona State Capitol, 1700 W Washington St, Phoenix, AZ 85007\tOregon State Capitol, 900 Court St NE, Salem, OR 97301\t2072368\t19.018333333333334\nArkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201\tKentucky State Capitol Building, 700 Capitol Avenue, Frankfort, KY 40601\t891342\t8.227777777777778\nLouisiana State Capitol, Baton Rouge, LA 70802\t500 E Capitol Ave, Pierre, SD 57501\t2163883\t20.43027777777778\nMissouri State Capitol, Jefferson City, MO 65101\tOklahoma State Capitol, Oklahoma City, OK 73105\t671161\t6.1819444444444445\n300 SW 10th Ave, Topeka, KS 66612\tNew York State Capitol, State St. and Washington Ave, Albany, NY 12224\t2138101\t19.470833333333335\n402 S Monroe St, Tallahassee, FL 32301\tOregon State Capitol, 900 Court St NE, Salem, OR 97301\t4649068\t42.4975\nMichigan State Capitol, Lansing, MI 48933\tOregon State Capitol, 900 Court St NE, Salem, OR 97301\t3808788\t34.27444444444444\n700 W Jefferson St, Boise, ID 83720\tOklahoma State Capitol, Oklahoma City, OK 73105\t2381578\t20.9625\nMissouri State Capitol, Jefferson City, MO 65101\tOregon State Capitol, 900 Court St NE, Salem, OR 97301\t3220845\t29.23138888888889\nAlabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130\tVermont State House, 115 State Street, Montpelier, VT 05633\t2157152\t20.068055555555556\nMinnesota State Capitol, St Paul, MN 55155\tRhode Island State House, 82 Smith Street, Providence, RI 02903\t2209294\t20.433888888888887\nArkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201\tIndiana State Capitol, Indianapolis, IN 46204\t939579\t8.519166666666667\nMichigan State Capitol, Lansing, MI 48933\tOhio State Capitol, 1 Capitol Square, Columbus, OH 43215\t409545\t4.043333333333333\nMassachusetts State House, Boston, MA 02108\tWest Virginia State Capitol, Charleston, WV 25317\t1209763\t11.38\nMissouri State Capitol, Jefferson City, MO 65101\tNew Jersey State House, Trenton, NJ 08608\t1658410\t15.402222222222223\nNorth Carolina State Capitol, Raleigh, NC 27601\tWashington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504\t4695729\t42.50138888888889\n700 W Jefferson St, Boise, ID 83720\t2 E Main St, Madison, WI 53703\t2666733\t23.79361111111111\nIowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319\tVirginia State Capitol, Richmond, VA 23219\t1775724\t16.274722222222223\nNew Mexico State Capitol, Santa Fe, NM 87501\tNew York State Capitol, State St. and Washington Ave, Albany, NY 12224\t3311587\t29.805555555555557\n700 W Jefferson St, Boise, ID 83720\tMaine State House, Augusta, ME 04330\t4530828\t40.665\n700 W Jefferson St, Boise, ID 83720\tNew Mexico State Capitol, Santa Fe, NM 87501\t1550056\t14.786944444444444\nLegislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901\tMaine State House, Augusta, ME 04330\t859002\t8.254166666666666\nNebraska State Capitol, 1445 K Street, Lincoln, NE 68509\tNorth Dakota State Capitol, Bismarck, ND 58501\t1067013\t9.260277777777778\nMassachusetts State House, Boston, MA 02108\tState House, 107 North Main Street, Concord, NH 03303\t122084\t1.2683333333333333\n200 E Colfax Ave, Denver, CO 80203\tWyoming State Capitol, Cheyenne, WY 82001\t163100\t1.6005555555555555\nMichigan State Capitol, Lansing, MI 48933\tTennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243\t865309\t7.960277777777778\nMassachusetts State House, Boston, MA 02108\tSouth Carolina State House, 1100 Gervais Street, Columbia, SC 29201\t1483905\t13.944166666666666\nNew Mexico State Capitol, Santa Fe, NM 87501\tWest Virginia State Capitol, Charleston, WV 25317\t2475742\t22.078055555555554\nConnecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106\tNew Mexico State Capitol, Santa Fe, NM 87501\t3392652\t30.780277777777776\nMassachusetts State House, Boston, MA 02108\tNebraska State Capitol, 1445 K Street, Lincoln, NE 68509\t2396299\t21.946944444444444\nGeorgia State Capitol, Atlanta, GA 30334\tMontana State Capitol, 1301 E 6th Ave, Helena, MT 59601\t3310362\t29.805\n200 E Colfax Ave, Denver, CO 80203\t400-498 N West St, Jackson, MS 39201\t1929525\t17.800833333333333\nMinnesota State Capitol, St Paul, MN 55155\tOklahoma State Capitol, Oklahoma City, OK 73105\t1265804\t11.178055555555556\nMinnesota State Capitol, St Paul, MN 55155\tTennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243\t1404656\t12.47888888888889\nNevada State Capitol, Carson City, NV 89701\tTennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243\t3498150\t31.13888888888889\nRhode Island State House, 82 Smith Street, Providence, RI 02903\t2 E Main St, Madison, WI 53703\t1807993\t16.895555555555557\nNorth Dakota State Capitol, Bismarck, ND 58501\tTexas Capitol, 1100 Congress Avenue, Austin, TX 78701\t2117504\t20.38722222222222\nIllinois State Capitol, Springfield, IL 62756\tMaine State House, Augusta, ME 04330\t2107868\t19.36722222222222\nNew Mexico State Capitol, Santa Fe, NM 87501\tSouth Carolina State House, 1100 Gervais Street, Columbia, SC 29201\t2564803\t22.93166666666667\nArkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201\tWest Virginia State Capitol, Charleston, WV 25317\t1187217\t10.837222222222222\nMaine State House, Augusta, ME 04330\tPennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120\t876031\t8.362777777777778\nMinnesota State Capitol, St Paul, MN 55155\tUtah State Capitol, Salt Lake City, UT 84103\t2018129\t18.496111111111112\nOregon State Capitol, 900 Court St NE, Salem, OR 97301\tTexas Capitol, 1100 Congress Avenue, Austin, TX 78701\t3379043\t31.99888888888889\n700 W Jefferson St, Boise, ID 83720\tUtah State Capitol, Salt Lake City, UT 84103\t544207\t4.828333333333333\nConnecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106\tNew Jersey State House, Trenton, NJ 08608\t292803\t3.0347222222222223\nNevada State Capitol, Carson City, NV 89701\tNew Jersey State House, Trenton, NJ 08608\t4355435\t38.8825\nNorth Carolina State Capitol, Raleigh, NC 27601\tOklahoma State Capitol, Oklahoma City, OK 73105\t1956768\t17.853055555555557\nNorth Carolina State Capitol, Raleigh, NC 27601\tRhode Island State House, 82 Smith Street, Providence, RI 02903\t1107472\t10.61138888888889\nRhode Island State House, 82 Smith Street, Providence, RI 02903\t500 E Capitol Ave, Pierre, SD 57501\t2842367\t25.890555555555554\n700 W Jefferson St, Boise, ID 83720\tOregon State Capitol, 900 Court St NE, Salem, OR 97301\t765315\t7.364444444444445\nAlabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130\tLegislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901\t1438743\t13.418055555555556\nLegislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901\tWest Virginia State Capitol, Charleston, WV 25317\t722915\t6.932777777777778\nConnecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106\tWyoming State Capitol, Cheyenne, WY 82001\t2954014\t26.755\nIndiana State Capitol, Indianapolis, IN 46204\tOregon State Capitol, 900 Court St NE, Salem, OR 97301\t3707961\t33.49333333333333\nNew York State Capitol, State St. and Washington Ave, Albany, NY 12224\tWest Virginia State Capitol, Charleston, WV 25317\t1055647\t9.713055555555556\nIllinois State Capitol, Springfield, IL 62756\tOregon State Capitol, 900 Court St NE, Salem, OR 97301\t3352808\t30.52611111111111\nLouisiana State Capitol, Baton Rouge, LA 70802\tNew Jersey State House, Trenton, NJ 08608\t2125065\t19.19333333333333\nArizona State Capitol, 1700 W Washington St, Phoenix, AZ 85007\tOklahoma State Capitol, Oklahoma City, OK 73105\t1551801\t14.149722222222222\nGeorgia State Capitol, Atlanta, GA 30334\tWashington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504\t4308684\t38.86666666666667\nMontana State Capitol, 1301 E 6th Ave, Helena, MT 59601\tWyoming State Capitol, Cheyenne, WY 82001\t1112354\t9.662222222222223\n200 E Colfax Ave, Denver, CO 80203\tWashington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504\t2168898\t19.80527777777778\nLouisiana State Capitol, Baton Rouge, LA 70802\tPennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120\t1926794\t17.361666666666668\n402 S Monroe St, Tallahassee, FL 32301\tMaine State House, Augusta, ME 04330\t2356857\t21.520555555555557\n400-498 N West St, Jackson, MS 39201\tMontana State Capitol, 1301 E 6th Ave, Helena, MT 59601\t3080815\t28.288055555555555\nArkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201\t400-498 N West St, Jackson, MS 39201\t410924\t4.3116666666666665\nOklahoma State Capitol, Oklahoma City, OK 73105\t2 E Main St, Madison, WI 53703\t1345956\t12.126388888888888\nArkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201\tMichigan State Capitol, Lansing, MI 48933\t1344347\t12.05\n400-498 N West St, Jackson, MS 39201\tNorth Dakota State Capitol, Bismarck, ND 58501\t2327096\t21.09027777777778\nAlabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130\tArizona State Capitol, 1700 W Washington St, Phoenix, AZ 85007\t2758173\t24.61138888888889\nArizona State Capitol, 1700 W Washington St, Phoenix, AZ 85007\tMaine State House, Augusta, ME 04330\t4508978\t40.833888888888886\nAlabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130\tIndiana State Capitol, Indianapolis, IN 46204\t914961\t8.330833333333333\n200 E Colfax Ave, Denver, CO 80203\tMassachusetts State House, Boston, MA 02108\t3170892\t28.639166666666668\nNorth Dakota State Capitol, Bismarck, ND 58501\tVermont State House, 115 State Street, Montpelier, VT 05633\t2874965\t26.795\nMassachusetts State House, Boston, MA 02108\tRhode Island State House, 82 Smith Street, Providence, RI 02903\t80922\t0.9569444444444445\nOregon State Capitol, 900 Court St NE, Salem, OR 97301\tVirginia State Capitol, Richmond, VA 23219\t4686106\t42.285555555555554\nGeorgia State Capitol, Atlanta, GA 30334\tOregon State Capitol, 900 Court St NE, Salem, OR 97301\t4256232\t38.5025\nIndiana State Capitol, Indianapolis, IN 46204\tState House, 107 North Main Street, Concord, NH 03303\t1494753\t14.639166666666666\nArizona State Capitol, 1700 W Washington St, Phoenix, AZ 85007\tPennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120\t3606514\t32.85805555555555\nArizona State Capitol, 1700 W Washington St, Phoenix, AZ 85007\tWyoming State Capitol, Cheyenne, WY 82001\t1475490\t13.96\nIllinois State Capitol, Springfield, IL 62756\tOhio State Capitol, 1 Capitol Square, Columbus, OH 43215\t623156\t5.752777777777778\n500 E Capitol Ave, Pierre, SD 57501\t2 E Main St, Madison, WI 53703\t1044717\t9.381388888888889\nMinnesota State Capitol, St Paul, MN 55155\tNebraska State Capitol, 1445 K Street, Lincoln, NE 68509\t700086\t6.300833333333333\nRhode Island State House, 82 Smith Street, Providence, RI 02903\tWashington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504\t4958571\t44.74611111111111\nGeorgia State Capitol, Atlanta, GA 30334\tVermont State House, 115 State Street, Montpelier, VT 05633\t1899060\t17.810833333333335\nMassachusetts State House, Boston, MA 02108\tPennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120\t627502\t6.153611111111111\nMaine State House, Augusta, ME 04330\tMichigan State Capitol, Lansing, MI 48933\t1501673\t13.915\n300 SW 10th Ave, Topeka, KS 66612\tWest Virginia State Capitol, Charleston, WV 25317\t1312458\t11.852777777777778\nPennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120\tTennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243\t1158389\t10.587777777777777\nMichigan State Capitol, Lansing, MI 48933\tNorth Carolina State Capitol, Raleigh, NC 27601\t1188366\t11.468333333333334\n300 SW 10th Ave, Topeka, KS 66612\tState House, 107 North Main Street, Concord, NH 03303\t2369020\t22.538611111111113\nGeorgia State Capitol, Atlanta, GA 30334\tNebraska State Capitol, 1445 K Street, Lincoln, NE 68509\t1601210\t14.720277777777778\n300 SW 10th Ave, Topeka, KS 66612\tLouisiana State Capitol, Baton Rouge, LA 70802\t1280134\t12.777222222222223\nKentucky State Capitol Building, 700 Capitol Avenue, Frankfort, KY 40601\tNevada State Capitol, Carson City, NV 89701\t3479126\t31.49888888888889\nGeorgia State Capitol, Atlanta, GA 30334\tSouth Carolina State House, 1100 Gervais Street, Columbia, SC 29201\t344713\t3.0719444444444446\nLouisiana State Capitol, Baton Rouge, LA 70802\tState House, 107 North Main Street, Concord, NH 03303\t2641198\t24.147777777777776\nOhio State Capitol, 1 Capitol Square, Columbus, OH 43215\tVermont State House, 115 State Street, Montpelier, VT 05633\t1193740\t11.68138888888889\n402 S Monroe St, Tallahassee, FL 32301\tTennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243\t790850\t7.7125\nNew Jersey State House, Trenton, NJ 08608\tOhio State Capitol, 1 Capitol Square, Columbus, OH 43215\t785681\t7.418333333333333\nMassachusetts State House, Boston, MA 02108\tMontana State Capitol, 1301 E 6th Ave, Helena, MT 59601\t3916863\t35.80138888888889\n200 E Colfax Ave, Denver, CO 80203\tKentucky State Capitol Building, 700 Capitol Avenue, Frankfort, KY 40601\t1864193\t16.6325\nL St & 10th St, Sacramento, CA 95814\tMassachusetts State House, Boston, MA 02108\t4852761\t43.57388888888889\nArizona State Capitol, 1700 W Washington St, Phoenix, AZ 85007\t500 E Capitol Ave, Pierre, SD 57501\t2159926\t20.214166666666667\nArkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201\tMaryland State House, 100 State Cir, Annapolis, MD 21401\t1692816\t15.496944444444445\nAlabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130\tMichigan State Capitol, Lansing, MI 48933\t1314610\t11.880555555555556\nMaine State House, Augusta, ME 04330\tOregon State Capitol, 900 Court St NE, Salem, OR 97301\t5288124\t47.888333333333335\nConnecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106\tNorth Carolina State Capitol, Raleigh, NC 27601\t1000236\t9.589444444444444\nLegislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901\tMontana State Capitol, 1301 E 6th Ave, Helena, MT 59601\t3599401\t33.15222222222222\n200 E Colfax Ave, Denver, CO 80203\tMaryland State House, 100 State Cir, Annapolis, MD 21401\t2704297\t24.6175\nLouisiana State Capitol, Baton Rouge, LA 70802\tOklahoma State Capitol, Oklahoma City, OK 73105\t1015864\t9.211666666666666\nLegislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901\tTexas Capitol, 1100 Congress Avenue, Austin, TX 78701\t2611932\t23.9675\nMichigan State Capitol, Lansing, MI 48933\tNew Mexico State Capitol, Santa Fe, NM 87501\t2352926\t21.808333333333334\nNew Mexico State Capitol, Santa Fe, NM 87501\tOregon State Capitol, 900 Court St NE, Salem, OR 97301\t2308293\t21.89527777777778\nGeorgia State Capitol, Atlanta, GA 30334\tNew York State Capitol, State St. and Washington Ave, Albany, NY 12224\t1615880\t14.783333333333333\nMaine State House, Augusta, ME 04330\tTennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243\t2025749\t18.705277777777777\nLegislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901\tWashington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504\t4661429\t42.1975\nIowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319\tMissouri State Capitol, Jefferson City, MO 65101\t427240\t4.4225\nMontana State Capitol, 1301 E 6th Ave, Helena, MT 59601\tState House, 107 North Main Street, Concord, NH 03303\t3879141\t36.28\nLouisiana State Capitol, Baton Rouge, LA 70802\tUtah State Capitol, Salt Lake City, UT 84103\t2687549\t25.205277777777777\nNew York State Capitol, State St. and Washington Ave, Albany, NY 12224\tUtah State Capitol, Salt Lake City, UT 84103\t3544096\t32.043055555555554\nNebraska State Capitol, 1445 K Street, Lincoln, NE 68509\tTexas Capitol, 1100 Congress Avenue, Austin, TX 78701\t1319361\t11.98638888888889\n700 W Jefferson St, Boise, ID 83720\tMaryland State House, 100 State Cir, Annapolis, MD 21401\t3864627\t34.63861111111111\nArkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201\tVirginia State Capitol, Richmond, VA 23219\t1547668\t14.019444444444444\nArizona State Capitol, 1700 W Washington St, Phoenix, AZ 85007\tGeorgia State Capitol, Atlanta, GA 30334\t2974348\t26.195833333333333\nMissouri State Capitol, Jefferson City, MO 65101\tTennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243\t705688\t6.440555555555555\nArizona State Capitol, 1700 W Washington St, Phoenix, AZ 85007\tWest Virginia State Capitol, Charleston, WV 25317\t3161299\t28.406388888888888\nL St & 10th St, Sacramento, CA 95814\t500 E Capitol Ave, Pierre, SD 57501\t2363213\t21.469722222222224\nMinnesota State Capitol, St Paul, MN 55155\tNew Jersey State House, Trenton, NJ 08608\t1904120\t17.467777777777776\n300 SW 10th Ave, Topeka, KS 66612\tNew Jersey State House, Trenton, NJ 08608\t1943570\t17.935555555555556\nState House, 107 North Main Street, Concord, NH 03303\tOregon State Capitol, 900 Court St NE, Salem, OR 97301\t5020691\t46.1925\nLegislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901\t402 S Monroe St, Tallahassee, FL 32301\t1549915\t14.0425\nIndiana State Capitol, Indianapolis, IN 46204\tSouth Carolina State House, 1100 Gervais Street, Columbia, SC 29201\t1001496\t9.178055555555556\nIllinois State Capitol, Springfield, IL 62756\t2 E Main St, Madison, WI 53703\t425309\t3.9405555555555556\nNorth Carolina State Capitol, Raleigh, NC 27601\tSouth Carolina State House, 1100 Gervais Street, Columbia, SC 29201\t363112\t3.348611111111111\nNevada State Capitol, Carson City, NV 89701\tPennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120\t4159337\t37.14194444444445\nLegislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901\tIowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319\t1772295\t16.54138888888889\nNew Mexico State Capitol, Santa Fe, NM 87501\tWyoming State Capitol, Cheyenne, WY 82001\t792157\t7.053333333333334\nGeorgia State Capitol, Atlanta, GA 30334\t700 W Jefferson St, Boise, ID 83720\t3498032\t31.39\nMissouri State Capitol, Jefferson City, MO 65101\t500 E Capitol Ave, Pierre, SD 57501\t1190637\t10.608055555555556\nMassachusetts State House, Boston, MA 02108\tNorth Carolina State Capitol, Raleigh, NC 27601\t1157235\t11.136111111111111\nLegislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901\t400-498 N West St, Jackson, MS 39201\t1736549\t16.05638888888889\nIllinois State Capitol, Springfield, IL 62756\tNorth Dakota State Capitol, Bismarck, ND 58501\t1530029\t13.591944444444444\nNevada State Capitol, Carson City, NV 89701\tVermont State House, 115 State Street, Montpelier, VT 05633\t4628271\t42.11944444444445\n402 S Monroe St, Tallahassee, FL 32301\tNebraska State Capitol, 1445 K Street, Lincoln, NE 68509\t1994047\t18.71527777777778\nLegislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901\tRhode Island State House, 82 Smith Street, Providence, RI 02903\t561475\t5.556666666666667\nAlabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130\tTexas Capitol, 1100 Congress Avenue, Austin, TX 78701\t1278074\t11.519166666666667\nLegislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901\tWyoming State Capitol, Cheyenne, WY 82001\t2783745\t25.468333333333334\n200 E Colfax Ave, Denver, CO 80203\tNevada State Capitol, Carson City, NV 89701\t1595181\t15.340833333333334\nIowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319\tState House, 107 North Main Street, Concord, NH 03303\t2057414\t19.745833333333334\nNorth Carolina State Capitol, Raleigh, NC 27601\tWest Virginia State Capitol, Charleston, WV 25317\t509177\t4.908055555555555\nMaine State House, Augusta, ME 04330\tMassachusetts State House, Boston, MA 02108\t260531\t2.618888888888889\nGeorgia State Capitol, Atlanta, GA 30334\tMichigan State Capitol, Lansing, MI 48933\t1252890\t11.448333333333334\nConnecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106\tGeorgia State Capitol, Atlanta, GA 30334\t1585527\t14.938055555555556\nIndiana State Capitol, Indianapolis, IN 46204\t500 E Capitol Ave, Pierre, SD 57501\t1545420\t13.984722222222222\nMichigan State Capitol, Lansing, MI 48933\tVirginia State Capitol, Richmond, VA 23219\t1107187\t10.313055555555556\nIllinois State Capitol, Springfield, IL 62756\tNevada State Capitol, Carson City, NV 89701\t2967815\t26.966666666666665\nL St & 10th St, Sacramento, CA 95814\tMinnesota State Capitol, St Paul, MN 55155\t3086551\t27.525277777777777\nMontana State Capitol, 1301 E 6th Ave, Helena, MT 59601\tNorth Dakota State Capitol, Bismarck, ND 58501\t987502\t9.234166666666667\n700 W Jefferson St, Boise, ID 83720\tNew York State Capitol, State St. and Washington Ave, Albany, NY 12224\t4018994\t35.965\nConnecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106\t2 E Main St, Madison, WI 53703\t1681120\t15.633611111111112\nOregon State Capitol, 900 Court St NE, Salem, OR 97301\tUtah State Capitol, Salt Lake City, UT 84103\t1303708\t11.963611111111112\nMichigan State Capitol, Lansing, MI 48933\tUtah State Capitol, Salt Lake City, UT 84103\t2577637\t23.119722222222222\nTexas Capitol, 1100 Congress Avenue, Austin, TX 78701\t2 E Main St, Madison, WI 53703\t1902526\t17.63888888888889\nMontana State Capitol, 1301 E 6th Ave, Helena, MT 59601\tNebraska State Capitol, 1445 K Street, Lincoln, NE 68509\t1827872\t15.870833333333334\nState House, 107 North Main Street, Concord, NH 03303\t2 E Main St, Madison, WI 53703\t1809409\t17.50888888888889\nNorth Dakota State Capitol, Bismarck, ND 58501\tUtah State Capitol, Salt Lake City, UT 84103\t1494479\t14.052777777777777\n300 SW 10th Ave, Topeka, KS 66612\tMinnesota State Capitol, St Paul, MN 55155\t804823\t7.222777777777778\nMaryland State House, 100 State Cir, Annapolis, MD 21401\tOklahoma State Capitol, Oklahoma City, OK 73105\t2150915\t19.857222222222223\nLegislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901\tVirginia State Capitol, Richmond, VA 23219\t329515\t3.4183333333333334\nMissouri State Capitol, Jefferson City, MO 65101\tPennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120\t1462312\t13.661666666666667\nLegislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901\tGeorgia State Capitol, Atlanta, GA 30334\t1183433\t11.198888888888888\nMaryland State House, 100 State Cir, Annapolis, MD 21401\tWyoming State Capitol, Cheyenne, WY 82001\t2685105\t24.346666666666668\n400-498 N West St, Jackson, MS 39201\tUtah State Capitol, Salt Lake City, UT 84103\t2652406\t24.896944444444443\nGeorgia State Capitol, Atlanta, GA 30334\tLouisiana State Capitol, Baton Rouge, LA 70802\t847414\t7.3630555555555555\nMissouri State Capitol, Jefferson City, MO 65101\tVermont State House, 115 State Street, Montpelier, VT 05633\t2063633\t19.625555555555554\nGeorgia State Capitol, Atlanta, GA 30334\tUtah State Capitol, Salt Lake City, UT 84103\t3025080\t27.3475\nMinnesota State Capitol, St Paul, MN 55155\tSouth Carolina State House, 1100 Gervais Street, Columbia, SC 29201\t1937523\t17.61861111111111\nIndiana State Capitol, Indianapolis, IN 46204\t300 SW 10th Ave, Topeka, KS 66612\t875152\t8.04888888888889\nIndiana State Capitol, Indianapolis, IN 46204\tLouisiana State Capitol, Baton Rouge, LA 70802\t1361626\t12.204722222222221\nOregon State Capitol, 900 Court St NE, Salem, OR 97301\tVermont State House, 115 State Street, Montpelier, VT 05633\t4989186\t45.78805555555556\n200 E Colfax Ave, Denver, CO 80203\t300 SW 10th Ave, Topeka, KS 66612\t868171\t7.5925\nIowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319\tNew Jersey State House, Trenton, NJ 08608\t1764351\t16.129166666666666\nGeorgia State Capitol, Atlanta, GA 30334\t300 SW 10th Ave, Topeka, KS 66612\t1388708\t12.551666666666666\nIndiana State Capitol, Indianapolis, IN 46204\tVermont State House, 115 State Street, Montpelier, VT 05633\t1474526\t14.259166666666667\nLouisiana State Capitol, Baton Rouge, LA 70802\tWest Virginia State Capitol, Charleston, WV 25317\t1551493\t14.017222222222221\nNew Jersey State House, Trenton, NJ 08608\tTexas Capitol, 1100 Congress Avenue, Austin, TX 78701\t2735269\t24.786666666666665\nAlabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130\tIowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319\t1502867\t13.77138888888889\n402 S Monroe St, Tallahassee, FL 32301\tLouisiana State Capitol, Baton Rouge, LA 70802\t713508\t6.373333333333333\nIllinois State Capitol, Springfield, IL 62756\tNorth Carolina State Capitol, Raleigh, NC 27601\t1354089\t12.807222222222222\nConnecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106\t402 S Monroe St, Tallahassee, FL 32301\t1952010\t17.781666666666666\n200 E Colfax Ave, Denver, CO 80203\tIowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319\t1079887\t9.487222222222222\n200 E Colfax Ave, Denver, CO 80203\tMinnesota State Capitol, St Paul, MN 55155\t1472312\t12.963055555555556\nMaine State House, Augusta, ME 04330\tMaryland State House, 100 State Cir, Annapolis, MD 21401\t936447\t8.990833333333333\nMinnesota State Capitol, St Paul, MN 55155\tNorth Carolina State Capitol, Raleigh, NC 27601\t1946878\t18.148333333333333\nMassachusetts State House, Boston, MA 02108\tMichigan State Capitol, Lansing, MI 48933\t1253144\t11.705833333333333\nMassachusetts State House, Boston, MA 02108\t2 E Main St, Madison, WI 53703\t1828313\t16.995833333333334\nSouth Carolina State House, 1100 Gervais Street, Columbia, SC 29201\t2 E Main St, Madison, WI 53703\t1530438\t14.185\nKentucky State Capitol Building, 700 Capitol Avenue, Frankfort, KY 40601\tState House, 107 North Main Street, Concord, NH 03303\t1512981\t15.07638888888889\nMissouri State Capitol, Jefferson City, MO 65101\tNew York State Capitol, State St. and Washington Ave, Albany, NY 12224\t1852941\t16.93777777777778\nL St & 10th St, Sacramento, CA 95814\t300 SW 10th Ave, Topeka, KS 66612\t2735540\t24.093055555555555\nGeorgia State Capitol, Atlanta, GA 30334\tWyoming State Capitol, Cheyenne, WY 82001\t2317843\t20.940555555555555\nNevada State Capitol, Carson City, NV 89701\tOklahoma State Capitol, Oklahoma City, OK 73105\t2470617\t22.605555555555554\nMissouri State Capitol, Jefferson City, MO 65101\tNew Mexico State Capitol, Santa Fe, NM 87501\t1436727\t13.885277777777778\nArkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201\tOhio State Capitol, 1 Capitol Square, Columbus, OH 43215\t1165605\t10.690833333333334\nGeorgia State Capitol, Atlanta, GA 30334\tNorth Carolina State Capitol, Raleigh, NC 27601\t652791\t6.0777777777777775\nConnecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106\tWashington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504\t4831698\t43.48416666666667\nL St & 10th St, Sacramento, CA 95814\tNew York State Capitol, State St. and Washington Ave, Albany, NY 12224\t4588421\t41.03861111111111\nNew Mexico State Capitol, Santa Fe, NM 87501\tRhode Island State House, 82 Smith Street, Providence, RI 02903\t3494683\t31.9975\nMaine State House, Augusta, ME 04330\t400-498 N West St, Jackson, MS 39201\t2529620\t23.155555555555555\nKentucky State Capitol Building, 700 Capitol Avenue, Frankfort, KY 40601\tOregon State Capitol, 900 Court St NE, Salem, OR 97301\t3864119\t35.05833333333333\nArizona State Capitol, 1700 W Washington St, Phoenix, AZ 85007\tMissouri State Capitol, Jefferson City, MO 65101\t2219599\t20.138055555555557\n700 W Jefferson St, Boise, ID 83720\tVirginia State Capitol, Richmond, VA 23219\t3926605\t35.15027777777778\nGeorgia State Capitol, Atlanta, GA 30334\tVirginia State Capitol, Richmond, VA 23219\t853715\t7.815277777777778\n300 SW 10th Ave, Topeka, KS 66612\tOhio State Capitol, 1 Capitol Square, Columbus, OH 43215\t1157547\t10.576666666666666\nIowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319\tUtah State Capitol, Salt Lake City, UT 84103\t1719270\t15.421944444444444\nRhode Island State House, 82 Smith Street, Providence, RI 02903\tSouth Carolina State House, 1100 Gervais Street, Columbia, SC 29201\t1433934\t13.457222222222223\nMinnesota State Capitol, St Paul, MN 55155\tNew Mexico State Capitol, Santa Fe, NM 87501\t1931932\t17.900555555555556\n402 S Monroe St, Tallahassee, FL 32301\tState House, 107 North Main Street, Concord, NH 03303\t2204336\t20.1675\nIndiana State Capitol, Indianapolis, IN 46204\tVirginia State Capitol, Richmond, VA 23219\t1010794\t9.49611111111111\nMassachusetts State House, Boston, MA 02108\tMissouri State Capitol, Jefferson City, MO 65101\t2115585\t19.447222222222223\nPennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120\t500 E Capitol Ave, Pierre, SD 57501\t2341717\t21.20138888888889\nNew York State Capitol, State St. and Washington Ave, Albany, NY 12224\t2 E Main St, Madison, WI 53703\t1563965\t14.514444444444445\nMinnesota State Capitol, St Paul, MN 55155\tNevada State Capitol, Carson City, NV 89701\t2864288\t26.09138888888889\nNew Jersey State House, Trenton, NJ 08608\tRhode Island State House, 82 Smith Street, Providence, RI 02903\t398312\t4.088888888888889\nVermont State House, 115 State Street, Montpelier, VT 05633\tWyoming State Capitol, Cheyenne, WY 82001\t3050515\t28.357222222222223\n200 E Colfax Ave, Denver, CO 80203\tTexas Capitol, 1100 Congress Avenue, Austin, TX 78701\t1478566\t13.878333333333334\nIndiana State Capitol, Indianapolis, IN 46204\tMassachusetts State House, Boston, MA 02108\t1528174\t14.106666666666667\nMissouri State Capitol, Jefferson City, MO 65101\tNorth Carolina State Capitol, Raleigh, NC 27601\t1536148\t14.154166666666667\nMaine State House, Augusta, ME 04330\tOhio State Capitol, 1 Capitol Square, Columbus, OH 43215\t1494741\t13.688055555555556\nMassachusetts State House, Boston, MA 02108\tOregon State Capitol, 900 Court St NE, Salem, OR 97301\t5039595\t45.67916666666667\nState House, 107 North Main Street, Concord, NH 03303\tNew York State Capitol, State St. and Washington Ave, Albany, NY 12224\t257015\t3.1944444444444446\nArizona State Capitol, 1700 W Washington St, Phoenix, AZ 85007\tRhode Island State House, 82 Smith Street, Providence, RI 02903\t4180240\t38.32611111111111\n700 W Jefferson St, Boise, ID 83720\tTennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243\t3099564\t27.6725\nNew York State Capitol, State St. and Washington Ave, Albany, NY 12224\tOklahoma State Capitol, Oklahoma City, OK 73105\t2446430\t22.183611111111112\nL St & 10th St, Sacramento, CA 95814\tConnecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106\t4705821\t42.25222222222222\nIllinois State Capitol, Springfield, IL 62756\tLouisiana State Capitol, Baton Rouge, LA 70802\t1226097\t10.945555555555556\nNew Jersey State House, Trenton, NJ 08608\tNorth Dakota State Capitol, Bismarck, ND 58501\t2605873\t23.586111111111112\nLouisiana State Capitol, Baton Rouge, LA 70802\tWyoming State Capitol, Cheyenne, WY 82001\t2123471\t19.44972222222222\n402 S Monroe St, Tallahassee, FL 32301\t700 W Jefferson St, Boise, ID 83720\t3890868\t35.385\nIllinois State Capitol, Springfield, IL 62756\tVermont State House, 115 State Street, Montpelier, VT 05633\t1806724\t17.35527777777778\nPennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120\tTexas Capitol, 1100 Congress Avenue, Austin, TX 78701\t2537644\t22.948888888888888\nGeorgia State Capitol, Atlanta, GA 30334\tMissouri State Capitol, Jefferson City, MO 65101\t1103516\t10.033888888888889\nMaryland State House, 100 State Cir, Annapolis, MD 21401\t2 E Main St, Madison, WI 53703\t1412212\t13.225277777777778\nRhode Island State House, 82 Smith Street, Providence, RI 02903\tWyoming State Capitol, Cheyenne, WY 82001\t3080887\t28.016944444444444\nArizona State Capitol, 1700 W Washington St, Phoenix, AZ 85007\tWashington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504\t2326097\t21.381944444444443\nOhio State Capitol, 1 Capitol Square, Columbus, OH 43215\tUtah State Capitol, Salt Lake City, UT 84103\t2767412\t24.85333333333333\nLegislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901\tNebraska State Capitol, 1445 K Street, Lincoln, NE 68509\t2078837\t19.298055555555557\nConnecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106\t500 E Capitol Ave, Pierre, SD 57501\t2715494\t24.628611111111113\nMontana State Capitol, 1301 E 6th Ave, Helena, MT 59601\tVirginia State Capitol, Richmond, VA 23219\t3617475\t33.053333333333335\nMinnesota State Capitol, St Paul, MN 55155\t2 E Main St, Madison, WI 53703\t417349\t3.953888888888889\nMaryland State House, 100 State Cir, Annapolis, MD 21401\tNorth Dakota State Capitol, Bismarck, ND 58501\t2516932\t22.876666666666665\nArkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201\tIllinois State Capitol, Springfield, IL 62756\t715903\t7.0344444444444445\n700 W Jefferson St, Boise, ID 83720\tLouisiana State Capitol, Baton Rouge, LA 70802\t3230224\t29.836666666666666\nIllinois State Capitol, Springfield, IL 62756\tTennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243\t601548\t5.588333333333333\nL St & 10th St, Sacramento, CA 95814\tMontana State Capitol, 1301 E 6th Ave, Helena, MT 59601\t1630833\t14.535833333333333\nLegislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901\tVermont State House, 115 State Street, Montpelier, VT 05633\t773092\t7.4375\nIllinois State Capitol, Springfield, IL 62756\tSouth Carolina State House, 1100 Gervais Street, Columbia, SC 29201\t1308331\t12.036944444444444\nArizona State Capitol, 1700 W Washington St, Phoenix, AZ 85007\t200 E Colfax Ave, Denver, CO 80203\t1319137\t12.647222222222222\nArkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201\tState House, 107 North Main Street, Concord, NH 03303\t2432697\t22.41861111111111\nTexas Capitol, 1100 Congress Avenue, Austin, TX 78701\tWashington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504\t3473890\t32.7875\n500 E Capitol Ave, Pierre, SD 57501\tTennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243\t1828065\t16.27888888888889\n300 SW 10th Ave, Topeka, KS 66612\tUtah State Capitol, Salt Lake City, UT 84103\t1694356\t15.054722222222223\nIndiana State Capitol, Indianapolis, IN 46204\tMaine State House, Augusta, ME 04330\t1775669\t16.27111111111111\n402 S Monroe St, Tallahassee, FL 32301\tNorth Carolina State Capitol, Raleigh, NC 27601\t992856\t8.732222222222223\nNebraska State Capitol, 1445 K Street, Lincoln, NE 68509\tRhode Island State House, 82 Smith Street, Providence, RI 02903\t2376371\t21.870555555555555\nNorth Carolina State Capitol, Raleigh, NC 27601\tTennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243\t867080\t8.14\nTennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243\tWest Virginia State Capitol, Charleston, WV 25317\t631657\t5.870555555555556\nArizona State Capitol, 1700 W Washington St, Phoenix, AZ 85007\tLegislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901\t3794792\t34.92333333333333\nIowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319\tTexas Capitol, 1100 Congress Avenue, Austin, TX 78701\t1496159\t13.354166666666666\nLegislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901\tNew York State Capitol, State St. and Washington Ave, Albany, NY 12224\t493300\t4.680555555555555\nAlabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130\tMaine State House, Augusta, ME 04330\t2243062\t20.884722222222223\nConnecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106\tNew York State Capitol, State St. and Washington Ave, Albany, NY 12224\t182763\t1.7986111111111112\nL St & 10th St, Sacramento, CA 95814\t402 S Monroe St, Tallahassee, FL 32301\t4246543\t37.35444444444445\nConnecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106\tPennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120\t470503\t4.606944444444444\nNorth Carolina State Capitol, Raleigh, NC 27601\tVirginia State Capitol, Richmond, VA 23219\t273743\t2.4966666666666666\n400-498 N West St, Jackson, MS 39201\tWashington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504\t4113442\t37.221944444444446\nTennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243\tWashington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504\t3913258\t35.306666666666665\nNew York State Capitol, State St. and Washington Ave, Albany, NY 12224\tNorth Carolina State Capitol, Raleigh, NC 27601\t1038859\t9.794444444444444\nNevada State Capitol, Carson City, NV 89701\tWest Virginia State Capitol, Charleston, WV 25317\t3819760\t34.018055555555556\n300 SW 10th Ave, Topeka, KS 66612\tNebraska State Capitol, 1445 K Street, Lincoln, NE 68509\t269993\t2.882222222222222\nMissouri State Capitol, Jefferson City, MO 65101\t2 E Main St, Madison, WI 53703\t697472\t6.839722222222222\nMinnesota State Capitol, St Paul, MN 55155\tOregon State Capitol, 900 Court St NE, Salem, OR 97301\t2871909\t25.724444444444444\nNebraska State Capitol, 1445 K Street, Lincoln, NE 68509\tVirginia State Capitol, Richmond, VA 23219\t2029907\t18.575277777777778\nIllinois State Capitol, Springfield, IL 62756\tNew York State Capitol, State St. and Washington Ave, Albany, NY 12224\t1596033\t14.6675\n200 E Colfax Ave, Denver, CO 80203\tGeorgia State Capitol, Atlanta, GA 30334\t2253063\t20.075\nGeorgia State Capitol, Atlanta, GA 30334\tIllinois State Capitol, Springfield, IL 62756\t999216\t9.17\n200 E Colfax Ave, Denver, CO 80203\tIllinois State Capitol, Springfield, IL 62756\t1465413\t13.14\nNew Mexico State Capitol, Santa Fe, NM 87501\tWashington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504\t2404888\t22.635833333333334\nConnecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106\tMaryland State House, 100 State Cir, Annapolis, MD 21401\t530919\t5.235\nPennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120\tVirginia State Capitol, Richmond, VA 23219\t354652\t3.5616666666666665\nNevada State Capitol, Carson City, NV 89701\tTexas Capitol, 1100 Congress Avenue, Austin, TX 78701\t2710776\t25.432222222222222\nL St & 10th St, Sacramento, CA 95814\tWashington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504\t1115233\t10.574444444444444\nKentucky State Capitol Building, 700 Capitol Avenue, Frankfort, KY 40601\tNew Jersey State House, Trenton, NJ 08608\t1090072\t10.113888888888889\n402 S Monroe St, Tallahassee, FL 32301\tMaryland State House, 100 State Cir, Annapolis, MD 21401\t1454024\t12.885277777777778\nMissouri State Capitol, Jefferson City, MO 65101\tNevada State Capitol, Carson City, NV 89701\t2835852\t25.671944444444446\nArizona State Capitol, 1700 W Washington St, Phoenix, AZ 85007\tL St & 10th St, Sacramento, CA 95814\t1212739\t10.910555555555556\nMaryland State House, 100 State Cir, Annapolis, MD 21401\tNorth Carolina State Capitol, Raleigh, NC 27601\t499503\t4.728611111111111\nKentucky State Capitol Building, 700 Capitol Avenue, Frankfort, KY 40601\tLouisiana State Capitol, Baton Rouge, LA 70802\t1282198\t11.503333333333334\nArkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201\tMinnesota State Capitol, St Paul, MN 55155\t1381467\t12.380555555555556\n402 S Monroe St, Tallahassee, FL 32301\tIllinois State Capitol, Springfield, IL 62756\t1392053\t13.165277777777778\nConnecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106\tNorth Dakota State Capitol, Bismarck, ND 58501\t2785840\t25.285\nArizona State Capitol, 1700 W Washington St, Phoenix, AZ 85007\tLouisiana State Capitol, Baton Rouge, LA 70802\t2327122\t20.377777777777776\nLegislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901\tState House, 107 North Main Street, Concord, NH 03303\t706481\t6.901111111111111\n700 W Jefferson St, Boise, ID 83720\t500 E Capitol Ave, Pierre, SD 57501\t1700787\t16.372222222222224\nConnecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106\tUtah State Capitol, Salt Lake City, UT 84103\t3661251\t33.16222222222222\nMissouri State Capitol, Jefferson City, MO 65101\tOhio State Capitol, 1 Capitol Square, Columbus, OH 43215\t872387\t8.043611111111112\nPennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120\tRhode Island State House, 82 Smith Street, Providence, RI 02903\t578364\t5.651666666666666\nGeorgia State Capitol, Atlanta, GA 30334\tMaine State House, Augusta, ME 04330\t1984970\t18.6275\nIowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319\tMontana State Capitol, 1301 E 6th Ave, Helena, MT 59601\t1894156\t17.088055555555556\nIowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319\tNorth Dakota State Capitol, Bismarck, ND 58501\t1074658\t9.473333333333333\n402 S Monroe St, Tallahassee, FL 32301\t2 E Main St, Madison, WI 53703\t1789607\t16.518055555555556\nUtah State Capitol, Salt Lake City, UT 84103\t2 E Main St, Madison, WI 53703\t2194529\t19.756666666666668\nMaryland State House, 100 State Cir, Annapolis, MD 21401\tNew Mexico State Capitol, Santa Fe, NM 87501\t3015312\t27.569444444444443\nL St & 10th St, Sacramento, CA 95814\tIllinois State Capitol, Springfield, IL 62756\t3161763\t28.34111111111111\nMaryland State House, 100 State Cir, Annapolis, MD 21401\tNew York State Capitol, State St. and Washington Ave, Albany, NY 12224\t567996\t5.439444444444445\nL St & 10th St, Sacramento, CA 95814\tOhio State Capitol, 1 Capitol Square, Columbus, OH 43215\t3810136\t33.81611111111111\nIllinois State Capitol, Springfield, IL 62756\tMontana State Capitol, 1301 E 6th Ave, Helena, MT 59601\t2406938\t21.828611111111112\nConnecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106\tMinnesota State Capitol, St Paul, MN 55155\t2088042\t19.18027777777778\nAlabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130\tConnecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106\t1838597\t17.153055555555557\nNevada State Capitol, Carson City, NV 89701\tVirginia State Capitol, Richmond, VA 23219\t4325191\t38.61694444444444\nMichigan State Capitol, Lansing, MI 48933\tVermont State House, 115 State Street, Montpelier, VT 05633\t1228009\t11.509444444444444\n300 SW 10th Ave, Topeka, KS 66612\tTennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243\t990848\t8.973888888888888\nNebraska State Capitol, 1445 K Street, Lincoln, NE 68509\tNevada State Capitol, Carson City, NV 89701\t2266886\t20.369722222222222\nL St & 10th St, Sacramento, CA 95814\tIowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319\t2761755\t24.421666666666667\nLouisiana State Capitol, Baton Rouge, LA 70802\tVirginia State Capitol, Richmond, VA 23219\t1701633\t15.176944444444445\nMinnesota State Capitol, St Paul, MN 55155\tVirginia State Capitol, Richmond, VA 23219\t1935517\t17.858055555555556\n700 W Jefferson St, Boise, ID 83720\tPennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120\t3760751\t33.675555555555555\nMissouri State Capitol, Jefferson City, MO 65101\tWest Virginia State Capitol, Charleston, WV 25317\t1027298\t9.319722222222222\nPennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120\tWashington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504\t4457921\t40.05694444444445\nConnecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106\tVirginia State Capitol, Richmond, VA 23219\t731609\t7.1575\nLouisiana State Capitol, Baton Rouge, LA 70802\tSouth Carolina State House, 1100 Gervais Street, Columbia, SC 29201\t1183500\t10.326944444444445\nMaryland State House, 100 State Cir, Annapolis, MD 21401\tMissouri State Capitol, Jefferson City, MO 65101\t1555721\t14.639444444444445\nMaryland State House, 100 State Cir, Annapolis, MD 21401\tVermont State House, 115 State Street, Montpelier, VT 05633\t847788\t8.196388888888889\nNorth Carolina State Capitol, Raleigh, NC 27601\tPennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120\t625512\t5.963055555555556\nAlabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130\tNew Jersey State House, Trenton, NJ 08608\t1572177\t14.505277777777778\nNew Jersey State House, Trenton, NJ 08608\tUtah State Capitol, Salt Lake City, UT 84103\t3481284\t31.463055555555556\nIndiana State Capitol, Indianapolis, IN 46204\tNew Jersey State House, Trenton, NJ 08608\t1069303\t10.036111111111111\nLegislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901\tMichigan State Capitol, Lansing, MI 48933\t1083435\t10.286666666666667\n700 W Jefferson St, Boise, ID 83720\tState House, 107 North Main Street, Concord, NH 03303\t4249912\t39.033055555555556\nMassachusetts State House, Boston, MA 02108\tNew Jersey State House, Trenton, NJ 08608\t449801\t4.581388888888889\nArizona State Capitol, 1700 W Washington St, Phoenix, AZ 85007\tKentucky State Capitol Building, 700 Capitol Avenue, Frankfort, KY 40601\t2845972\t25.66027777777778\n402 S Monroe St, Tallahassee, FL 32301\tWashington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504\t4701521\t42.861666666666665\nIllinois State Capitol, Springfield, IL 62756\tWest Virginia State Capitol, Charleston, WV 25317\t845239\t7.972777777777778\nMaryland State House, 100 State Cir, Annapolis, MD 21401\tPennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120\t179018\t1.898611111111111\nLouisiana State Capitol, Baton Rouge, LA 70802\tMaryland State House, 100 State Cir, Annapolis, MD 21401\t1901317\t17.226111111111113\nIllinois State Capitol, Springfield, IL 62756\tWashington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504\t3405261\t30.89027777777778\nMinnesota State Capitol, St Paul, MN 55155\tNew York State Capitol, State St. and Washington Ave, Albany, NY 12224\t1966265\t18.016666666666666\n400-498 N West St, Jackson, MS 39201\tTexas Capitol, 1100 Congress Avenue, Austin, TX 78701\t887636\t8.437777777777777\nGeorgia State Capitol, Atlanta, GA 30334\tState House, 107 North Main Street, Concord, NH 03303\t1832449\t17.274444444444445\nMaine State House, Augusta, ME 04330\t2 E Main St, Madison, WI 53703\t2076842\t19.205\nAlabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130\tMissouri State Capitol, Jefferson City, MO 65101\t1157419\t10.383055555555556\nAlabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130\tLouisiana State Capitol, Baton Rouge, LA 70802\t591309\t5.184444444444445\n200 E Colfax Ave, Denver, CO 80203\tState House, 107 North Main Street, Concord, NH 03303\t3137471\t29.171666666666667\n300 SW 10th Ave, Topeka, KS 66612\tNorth Dakota State Capitol, Bismarck, ND 58501\t1239183\t11.00388888888889\nL St & 10th St, Sacramento, CA 95814\tMissouri State Capitol, Jefferson City, MO 65101\t3030263\t27.0325\nIowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319\tSouth Carolina State House, 1100 Gervais Street, Columbia, SC 29201\t1760444\t15.849444444444444\nArizona State Capitol, 1700 W Washington St, Phoenix, AZ 85007\tTennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243\t2633957\t23.764166666666668\n700 W Jefferson St, Boise, ID 83720\t300 SW 10th Ave, Topeka, KS 66612\t2166113\t19.01972222222222\nLegislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901\tTennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243\t1232677\t11.606111111111112\nState House, 107 North Main Street, Concord, NH 03303\tNew Mexico State Capitol, Santa Fe, NM 87501\t3556272\t32.89055555555556\nOklahoma State Capitol, Oklahoma City, OK 73105\t500 E Capitol Ave, Pierre, SD 57501\t1174982\t11.439166666666667\nNorth Dakota State Capitol, Bismarck, ND 58501\tOregon State Capitol, 900 Court St NE, Salem, OR 97301\t2174863\t19.649722222222223\nVirginia State Capitol, Richmond, VA 23219\tWest Virginia State Capitol, Charleston, WV 25317\t507154\t4.714166666666666\nNew Mexico State Capitol, Santa Fe, NM 87501\tOklahoma State Capitol, Oklahoma City, OK 73105\t866244\t7.8213888888888885\nGeorgia State Capitol, Atlanta, GA 30334\tPennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120\t1151965\t10.569722222222222\nMassachusetts State House, Boston, MA 02108\tNew Mexico State Capitol, Santa Fe, NM 87501\t3575175\t32.37722222222222\n200 E Colfax Ave, Denver, CO 80203\tPennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120\t2614534\t23.720833333333335\nArkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201\tVermont State House, 115 State Street, Montpelier, VT 05633\t2358711\t22.27027777777778\n402 S Monroe St, Tallahassee, FL 32301\t400-498 N West St, Jackson, MS 39201\t694704\t6.720555555555555\nConnecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106\tTennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243\t1620221\t14.949444444444444\nKentucky State Capitol Building, 700 Capitol Avenue, Frankfort, KY 40601\tWest Virginia State Capitol, Charleston, WV 25317\t322220\t3.0175\nState House, 107 North Main Street, Concord, NH 03303\tWashington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504\t4959988\t45.359722222222224\n402 S Monroe St, Tallahassee, FL 32301\tNorth Dakota State Capitol, Bismarck, ND 58501\t2894327\t26.169444444444444\nRhode Island State House, 82 Smith Street, Providence, RI 02903\tUtah State Capitol, Salt Lake City, UT 84103\t3788124\t34.424166666666665\nIllinois State Capitol, Springfield, IL 62756\t400-498 N West St, Jackson, MS 39201\t945911\t8.484166666666667\nArkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201\tMassachusetts State House, Boston, MA 02108\t2337722\t21.607222222222223\nArkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201\tNorth Dakota State Capitol, Bismarck, ND 58501\t1948713\t17.15972222222222\nMichigan State Capitol, Lansing, MI 48933\tMontana State Capitol, 1301 E 6th Ave, Helena, MT 59601\t2686056\t24.39666666666667\nLegislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901\tMaryland State House, 100 State Cir, Annapolis, MD 21401\t105428\t1.3294444444444444\nArkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201\tMontana State Capitol, 1301 E 6th Ave, Helena, MT 59601\t2702432\t24.357777777777777\nMichigan State Capitol, Lansing, MI 48933\tState House, 107 North Main Street, Concord, NH 03303\t1218614\t12.2075\nState House, 107 North Main Street, Concord, NH 03303\tNorth Carolina State Capitol, Raleigh, NC 27601\t1250547\t11.9375\nIowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319\tWashington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504\t2892479\t26.149722222222223\nOklahoma State Capitol, Oklahoma City, OK 73105\tUtah State Capitol, Salt Lake City, UT 84103\t1910174\t16.99861111111111\n300 SW 10th Ave, Topeka, KS 66612\tMassachusetts State House, Boston, MA 02108\t2402441\t22.00611111111111\n400-498 N West St, Jackson, MS 39201\tRhode Island State House, 82 Smith Street, Providence, RI 02903\t2232241\t20.464444444444446\nArizona State Capitol, 1700 W Washington St, Phoenix, AZ 85007\tState House, 107 North Main Street, Concord, NH 03303\t4228062\t39.20194444444444\nKentucky State Capitol Building, 700 Capitol Avenue, Frankfort, KY 40601\tMaine State House, Augusta, ME 04330\t1772326\t16.46388888888889\nArizona State Capitol, 1700 W Washington St, Phoenix, AZ 85007\tArkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201\t2088428\t18.80666666666667\nIowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319\tNebraska State Capitol, 1445 K Street, Lincoln, NE 68509\t307125\t2.8447222222222224\nNew York State Capitol, State St. and Washington Ave, Albany, NY 12224\tVermont State House, 115 State Street, Montpelier, VT 05633\t254720\t3.1572222222222224\nGeorgia State Capitol, Atlanta, GA 30334\t500 E Capitol Ave, Pierre, SD 57501\t2226023\t19.879166666666666\n400-498 N West St, Jackson, MS 39201\tState House, 107 North Main Street, Concord, NH 03303\t2377027\t21.84777777777778\nGeorgia State Capitol, Atlanta, GA 30334\tOhio State Capitol, 1 Capitol Square, Columbus, OH 43215\t913039\t8.318055555555556\nMontana State Capitol, 1301 E 6th Ave, Helena, MT 59601\tRhode Island State House, 82 Smith Street, Providence, RI 02903\t3891251\t35.629444444444445\nGeorgia State Capitol, Atlanta, GA 30334\tOklahoma State Capitol, Oklahoma City, OK 73105\t1366266\t12.402777777777779\nMassachusetts State House, Boston, MA 02108\tTexas Capitol, 1100 Congress Avenue, Austin, TX 78701\t3156474\t28.857222222222223\nAlabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130\t200 E Colfax Ave, Denver, CO 80203\t2283660\t20.398611111111112\nNew York State Capitol, State St. and Washington Ave, Albany, NY 12224\tTennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243\t1591468\t14.635\n300 SW 10th Ave, Topeka, KS 66612\t500 E Capitol Ave, Pierre, SD 57501\t908563\t8.276111111111112\nL St & 10th St, Sacramento, CA 95814\tTexas Capitol, 1100 Congress Avenue, Austin, TX 78701\t2834432\t25.199166666666667\nIowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319\tVermont State House, 115 State Street, Montpelier, VT 05633\t2037187\t19.36611111111111\nOklahoma State Capitol, Oklahoma City, OK 73105\tWest Virginia State Capitol, Charleston, WV 25317\t1611885\t14.410277777777777\nAlabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130\tNew York State Capitol, State St. and Washington Ave, Albany, NY 12224\t1873972\t17.040555555555557\nIndiana State Capitol, Indianapolis, IN 46204\tWashington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504\t3686093\t32.94722222222222\nMontana State Capitol, 1301 E 6th Ave, Helena, MT 59601\tTexas Capitol, 1100 Congress Avenue, Austin, TX 78701\t2750503\t24.789166666666667\nNevada State Capitol, Carson City, NV 89701\tNorth Dakota State Capitol, Bismarck, ND 58501\t2234268\t20.314166666666665\nMaine State House, Augusta, ME 04330\tVirginia State Capitol, Richmond, VA 23219\t1137137\t10.913333333333334\nIowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319\tLouisiana State Capitol, Baton Rouge, LA 70802\t1605426\t14.594722222222222\nSouth Carolina State House, 1100 Gervais Street, Columbia, SC 29201\tTennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243\t711401\t6.652777777777778\nArizona State Capitol, 1700 W Washington St, Phoenix, AZ 85007\tIowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319\t2255678\t21.27111111111111\nIowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319\tPennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120\t1568253\t14.388333333333334\nKentucky State Capitol Building, 700 Capitol Avenue, Frankfort, KY 40601\tNorth Dakota State Capitol, Bismarck, ND 58501\t1903795\t17.210833333333333\nWashington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504\t2 E Main St, Madison, WI 53703\t3161453\t28.25111111111111\nVirginia State Capitol, Richmond, VA 23219\t2 E Main St, Madison, WI 53703\t1533610\t14.348055555555556\n500 E Capitol Ave, Pierre, SD 57501\tWashington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504\t2125997\t19.844722222222224\nNorth Carolina State Capitol, Raleigh, NC 27601\tTexas Capitol, 1100 Congress Avenue, Austin, TX 78701\t2187972\t19.727777777777778\nOklahoma State Capitol, Oklahoma City, OK 73105\tTexas Capitol, 1100 Congress Avenue, Austin, TX 78701\t625486\t5.748888888888889\nIndiana State Capitol, Indianapolis, IN 46204\tNebraska State Capitol, 1445 K Street, Lincoln, NE 68509\t1064665\t9.761111111111111\nIllinois State Capitol, Springfield, IL 62756\tNew Mexico State Capitol, Santa Fe, NM 87501\t1727925\t16.314444444444444\n500 E Capitol Ave, Pierre, SD 57501\tTexas Capitol, 1100 Congress Avenue, Austin, TX 78701\t1798030\t17.05472222222222\n700 W Jefferson St, Boise, ID 83720\tIllinois State Capitol, Springfield, IL 62756\t2592335\t23.267777777777777\nMaryland State House, 100 State Cir, Annapolis, MD 21401\tMontana State Capitol, 1301 E 6th Ave, Helena, MT 59601\t3500762\t32.03055555555556\nState House, 107 North Main Street, Concord, NH 03303\tTennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243\t1870532\t17.2975\nNew York State Capitol, State St. and Washington Ave, Albany, NY 12224\tWashington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504\t4714543\t42.365\nMichigan State Capitol, Lansing, MI 48933\tPennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120\t879693\t8.182222222222222\nSouth Carolina State House, 1100 Gervais Street, Columbia, SC 29201\tVirginia State Capitol, Richmond, VA 23219\t596292\t5.276666666666666\nLegislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901\tKentucky State Capitol Building, 700 Capitol Avenue, Frankfort, KY 40601\t1039311\t9.755833333333333\n300 SW 10th Ave, Topeka, KS 66612\tPennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120\t1747472\t16.19472222222222\nNebraska State Capitol, 1445 K Street, Lincoln, NE 68509\tNew Jersey State House, Trenton, NJ 08608\t2071197\t18.904444444444444\nNew York State Capitol, State St. and Washington Ave, Albany, NY 12224\t500 E Capitol Ave, Pierre, SD 57501\t2598339\t23.509444444444444\nMassachusetts State House, Boston, MA 02108\tUtah State Capitol, Salt Lake City, UT 84103\t3808444\t34.52444444444444\nMissouri State Capitol, Jefferson City, MO 65101\tState House, 107 North Main Street, Concord, NH 03303\t2083860\t20.005555555555556\nIndiana State Capitol, Indianapolis, IN 46204\t400-498 N West St, Jackson, MS 39201\t1081440\t9.743055555555555\nNevada State Capitol, Carson City, NV 89701\tWyoming State Capitol, Cheyenne, WY 82001\t1584035\t14.022777777777778\nArizona State Capitol, 1700 W Washington St, Phoenix, AZ 85007\tTexas Capitol, 1100 Congress Avenue, Austin, TX 78701\t1622824\t14.474444444444444\nMissouri State Capitol, Jefferson City, MO 65101\tMontana State Capitol, 1301 E 6th Ave, Helena, MT 59601\t2274975\t20.53388888888889\nIllinois State Capitol, Springfield, IL 62756\tMichigan State Capitol, Lansing, MI 48933\t628469\t5.753611111111111\nLouisiana State Capitol, Baton Rouge, LA 70802\tNorth Dakota State Capitol, Bismarck, ND 58501\t2494502\t23.158333333333335\n300 SW 10th Ave, Topeka, KS 66612\tVermont State House, 115 State Street, Montpelier, VT 05633\t2348793\t22.15888888888889\nAlabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130\tNorth Carolina State Capitol, Raleigh, NC 27601\t910884\t8.335\nNew Jersey State House, Trenton, NJ 08608\tTennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243\t1356014\t12.425555555555556\nL St & 10th St, Sacramento, CA 95814\tVermont State House, 115 State Street, Montpelier, VT 05633\t4799113\t43.72638888888889\nNew Jersey State House, Trenton, NJ 08608\tWashington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504\t4651731\t41.78527777777778\nNew Jersey State House, Trenton, NJ 08608\tNew Mexico State Capitol, Santa Fe, NM 87501\t3116096\t28.316388888888888\n402 S Monroe St, Tallahassee, FL 32301\t300 SW 10th Ave, Topeka, KS 66612\t1781545\t16.546666666666667\nL St & 10th St, Sacramento, CA 95814\tWest Virginia State Capitol, Charleston, WV 25317\t3990601\t35.625\nGeorgia State Capitol, Atlanta, GA 30334\tNew Jersey State House, Trenton, NJ 08608\t1314085\t12.248055555555556\nMaine State House, Augusta, ME 04330\t500 E Capitol Ave, Pierre, SD 57501\t3111216\t28.2\nArizona State Capitol, 1700 W Washington St, Phoenix, AZ 85007\t700 W Jefferson St, Boise, ID 83720\t1480533\t13.941944444444445\nNorth Dakota State Capitol, Bismarck, ND 58501\tWyoming State Capitol, Cheyenne, WY 82001\t960446\t9.149166666666666\nKentucky State Capitol Building, 700 Capitol Avenue, Frankfort, KY 40601\tOklahoma State Capitol, Oklahoma City, OK 73105\t1296244\t11.693888888888889\nConnecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106\t400-498 N West St, Jackson, MS 39201\t2124093\t19.399722222222223\nIowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319\tMassachusetts State House, Boston, MA 02108\t2090835\t19.213333333333335\nMontana State Capitol, 1301 E 6th Ave, Helena, MT 59601\tOklahoma State Capitol, Oklahoma City, OK 73105\t2340718\t20.198055555555555\nState House, 107 North Main Street, Concord, NH 03303\tNew Jersey State House, Trenton, NJ 08608\t543114\t5.3825\nArkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201\t2 E Main St, Madison, WI 53703\t1135904\t10.694166666666666\nMaryland State House, 100 State Cir, Annapolis, MD 21401\tTennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243\t1134038\t10.484444444444444\n200 E Colfax Ave, Denver, CO 80203\tLouisiana State Capitol, Baton Rouge, LA 70802\t1966426\t18.144166666666667\nL St & 10th St, Sacramento, CA 95814\t200 E Colfax Ave, Denver, CO 80203\t1881205\t16.975555555555555\nOhio State Capitol, 1 Capitol Square, Columbus, OH 43215\tPennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120\t591688\t5.702222222222222\nMaryland State House, 100 State Cir, Annapolis, MD 21401\tWashington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504\t4562790\t41.075833333333335\nArkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201\tUtah State Capitol, Salt Lake City, UT 84103\t2380471\t21.142222222222223\nKentucky State Capitol Building, 700 Capitol Avenue, Frankfort, KY 40601\tNorth Carolina State Capitol, Raleigh, NC 27601\t831070\t7.851944444444444\n300 SW 10th Ave, Topeka, KS 66612\tMichigan State Capitol, Lansing, MI 48933\t1223735\t11.0925\nConnecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106\tIowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319\t1942564\t17.828055555555554\nNorth Dakota State Capitol, Bismarck, ND 58501\tTennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243\t2102664\t18.569444444444443\nMontana State Capitol, 1301 E 6th Ave, Helena, MT 59601\tNew Jersey State House, Trenton, NJ 08608\t3586077\t32.66305555555556\nMaine State House, Augusta, ME 04330\tNew York State Capitol, State St. and Washington Ave, Albany, NY 12224\t520684\t4.886388888888889\nOregon State Capitol, 900 Court St NE, Salem, OR 97301\tTennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243\t3859065\t34.80777777777778\nOregon State Capitol, 900 Court St NE, Salem, OR 97301\tPennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120\t4520252\t40.81055555555555\nArkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201\tL St & 10th St, Sacramento, CA 95814\t3151067\t28.136666666666667\nRhode Island State House, 82 Smith Street, Providence, RI 02903\tVirginia State Capitol, Richmond, VA 23219\t838637\t8.217222222222222\nGeorgia State Capitol, Atlanta, GA 30334\tIowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319\t1448964\t13.422222222222222\nSouth Carolina State House, 1100 Gervais Street, Columbia, SC 29201\tWyoming State Capitol, Cheyenne, WY 82001\t2626841\t23.81972222222222\nL St & 10th St, Sacramento, CA 95814\tLegislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901\t4532569\t40.880833333333335\n300 SW 10th Ave, Topeka, KS 66612\tWashington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504\t3022102\t26.950277777777778\nArkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201\tGeorgia State Capitol, Atlanta, GA 30334\t834382\t7.7091666666666665\nGeorgia State Capitol, Atlanta, GA 30334\tTennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243\t402403\t3.7736111111111112\n300 SW 10th Ave, Topeka, KS 66612\tTexas Capitol, 1100 Congress Avenue, Austin, TX 78701\t1096352\t9.781944444444445\nNorth Carolina State Capitol, Raleigh, NC 27601\t2 E Main St, Madison, WI 53703\t1545151\t14.650833333333333\nArizona State Capitol, 1700 W Washington St, Phoenix, AZ 85007\t300 SW 10th Ave, Topeka, KS 66612\t1849376\t17.73027777777778\nL St & 10th St, Sacramento, CA 95814\t400-498 N West St, Jackson, MS 39201\t3427740\t30.878611111111113\nPennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120\tWest Virginia State Capitol, Charleston, WV 25317\t590933\t5.471666666666667\nAlabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130\tWashington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504\t4362587\t39.215833333333336\n402 S Monroe St, Tallahassee, FL 32301\tPennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120\t1577371\t14.174444444444445\nMichigan State Capitol, Lansing, MI 48933\tNebraska State Capitol, 1445 K Street, Lincoln, NE 68509\t1165493\t10.542222222222222\n700 W Jefferson St, Boise, ID 83720\tTexas Capitol, 1100 Congress Avenue, Austin, TX 78701\t2619542\t24.863888888888887\nState House, 107 North Main Street, Concord, NH 03303\tPennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120\t720814\t6.955\nMaine State House, Augusta, ME 04330\tNevada State Capitol, Carson City, NV 89701\t4903131\t44.32888888888889\nMichigan State Capitol, Lansing, MI 48933\tSouth Carolina State House, 1100 Gervais Street, Columbia, SC 29201\t1247121\t11.903611111111111\nLouisiana State Capitol, Baton Rouge, LA 70802\tTexas Capitol, 1100 Congress Avenue, Austin, TX 78701\t692703\t6.487777777777778\n300 SW 10th Ave, Topeka, KS 66612\tMaryland State House, 100 State Cir, Annapolis, MD 21401\t1837235\t17.09138888888889\nConnecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106\tVermont State House, 115 State Street, Montpelier, VT 05633\t321861\t3.0213888888888887\nOhio State Capitol, 1 Capitol Square, Columbus, OH 43215\tTennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243\t609426\t5.7011111111111115\n402 S Monroe St, Tallahassee, FL 32301\tNew Mexico State Capitol, Santa Fe, NM 87501\t2364174\t21.85611111111111\nOregon State Capitol, 900 Court St NE, Salem, OR 97301\t500 E Capitol Ave, Pierre, SD 57501\t2250703\t21.171944444444446\nRhode Island State House, 82 Smith Street, Providence, RI 02903\tTexas Capitol, 1100 Congress Avenue, Austin, TX 78701\t3108475\t28.349722222222223\nNew Mexico State Capitol, Santa Fe, NM 87501\tNorth Dakota State Capitol, Bismarck, ND 58501\t1744158\t16.00611111111111\nL St & 10th St, Sacramento, CA 95814\tWyoming State Capitol, Cheyenne, WY 82001\t1754877\t15.629722222222222\nAlabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130\tKentucky State Capitol Building, 700 Capitol Avenue, Frankfort, KY 40601\t785111\t7.170277777777778\nConnecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106\tNebraska State Capitol, 1445 K Street, Lincoln, NE 68509\t2249106\t20.584722222222222\nOklahoma State Capitol, Oklahoma City, OK 73105\tVirginia State Capitol, Richmond, VA 23219\t2078855\t18.6975\nMaine State House, Augusta, ME 04330\tWashington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504\t5227420\t47.05555555555556\nRhode Island State House, 82 Smith Street, Providence, RI 02903\tWest Virginia State Capitol, Charleston, WV 25317\t1161764\t10.8725\nNevada State Capitol, Carson City, NV 89701\tOhio State Capitol, 1 Capitol Square, Columbus, OH 43215\t3639294\t32.20916666666667\nL St & 10th St, Sacramento, CA 95814\tRhode Island State House, 82 Smith Street, Providence, RI 02903\t4831450\t43.455555555555556\nArkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201\tOklahoma State Capitol, Oklahoma City, OK 73105\t547112\t4.926666666666667\nMissouri State Capitol, Jefferson City, MO 65101\tWyoming State Capitol, Cheyenne, WY 82001\t1282456\t11.669444444444444\nOklahoma State Capitol, Oklahoma City, OK 73105\tPennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120\t2057100\t18.861666666666668\nMaine State House, Augusta, ME 04330\tMontana State Capitol, 1301 E 6th Ave, Helena, MT 59601\t4165392\t38.010555555555555\n200 E Colfax Ave, Denver, CO 80203\t700 W Jefferson St, Boise, ID 83720\t1314104\t11.952222222222222\nMinnesota State Capitol, St Paul, MN 55155\t500 E Capitol Ave, Pierre, SD 57501\t637790\t6.874722222222222\nMassachusetts State House, Boston, MA 02108\tNew York State Capitol, State St. and Washington Ave, Albany, NY 12224\t272155\t2.6772222222222224\n700 W Jefferson St, Boise, ID 83720\tVermont State House, 115 State Street, Montpelier, VT 05633\t4229685\t38.653055555555554\nConnecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106\t700 W Jefferson St, Boise, ID 83720\t4134203\t37.20444444444445\n500 E Capitol Ave, Pierre, SD 57501\tUtah State Capitol, Salt Lake City, UT 84103\t1321211\t12.447777777777778\n700 W Jefferson St, Boise, ID 83720\tMinnesota State Capitol, St Paul, MN 55155\t2349986\t21.033333333333335\n700 W Jefferson St, Boise, ID 83720\tIowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319\t2192328\t19.348333333333333\nLegislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901\tOhio State Capitol, 1 Capitol Square, Columbus, OH 43215\t783537\t7.793333333333333\nNebraska State Capitol, 1445 K Street, Lincoln, NE 68509\tNew Mexico State Capitol, Santa Fe, NM 87501\t1234237\t12.029722222222222\nIowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319\tTennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243\t1051234\t9.8125\nIllinois State Capitol, Springfield, IL 62756\tMassachusetts State House, Boston, MA 02108\t1860373\t17.20277777777778\nL St & 10th St, Sacramento, CA 95814\tNorth Dakota State Capitol, Bismarck, ND 58501\t2405110\t21.92111111111111\nUtah State Capitol, Salt Lake City, UT 84103\tVirginia State Capitol, Richmond, VA 23219\t3454401\t31.113333333333333\nNorth Dakota State Capitol, Bismarck, ND 58501\tOhio State Capitol, 1 Capitol Square, Columbus, OH 43215\t1913954\t17.184166666666666\nConnecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106\tLegislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901\t455898\t4.509444444444444\n500 E Capitol Ave, Pierre, SD 57501\tWyoming State Capitol, Cheyenne, WY 82001\t683179\t6.741666666666666\nIowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319\tOhio State Capitol, 1 Capitol Square, Columbus, OH 43215\t1048210\t9.455833333333333\nIndiana State Capitol, Indianapolis, IN 46204\tMichigan State Capitol, Lansing, MI 48933\t410164\t3.794722222222222\nIndiana State Capitol, Indianapolis, IN 46204\tWest Virginia State Capitol, Charleston, WV 25317\t505363\t4.8975\nNew York State Capitol, State St. and Washington Ave, Albany, NY 12224\tOregon State Capitol, 900 Court St NE, Salem, OR 97301\t4775247\t43.19777777777778\n400-498 N West St, Jackson, MS 39201\tVirginia State Capitol, Richmond, VA 23219\t1467102\t13.298055555555555\nOregon State Capitol, 900 Court St NE, Salem, OR 97301\tWest Virginia State Capitol, Charleston, WV 25317\t4180675\t37.68666666666667\nIllinois State Capitol, Springfield, IL 62756\tMinnesota State Capitol, St Paul, MN 55155\t832231\t7.4875\nIllinois State Capitol, Springfield, IL 62756\tIowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319\t541419\t4.966944444444445\nAlabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130\tGeorgia State Capitol, Atlanta, GA 30334\t259130\t2.347777777777778\nMaine State House, Augusta, ME 04330\tVermont State House, 115 State Street, Montpelier, VT 05633\t289475\t3.777222222222222\nNew York State Capitol, State St. and Washington Ave, Albany, NY 12224\tOhio State Capitol, 1 Capitol Square, Columbus, OH 43215\t981864\t8.9975\nIndiana State Capitol, Indianapolis, IN 46204\tNew Mexico State Capitol, Santa Fe, NM 87501\t2049550\t18.461388888888887\n402 S Monroe St, Tallahassee, FL 32301\tMichigan State Capitol, Lansing, MI 48933\t1653544\t15.526666666666667\nNevada State Capitol, Carson City, NV 89701\tSouth Carolina State House, 1100 Gervais Street, Columbia, SC 29201\t4204933\t37.5875\nL St & 10th St, Sacramento, CA 95814\t2 E Main St, Madison, WI 53703\t3236161\t28.86722222222222\nNorth Dakota State Capitol, Bismarck, ND 58501\tWashington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504\t2049899\t18.350555555555555\nArkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201\tIowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319\t987798\t8.910277777777777\n400-498 N West St, Jackson, MS 39201\tWyoming State Capitol, Cheyenne, WY 82001\t2107645\t19.017222222222223\nNew Jersey State House, Trenton, NJ 08608\tPennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120\t201751\t1.9908333333333332\nAlabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130\tNew Mexico State Capitol, Santa Fe, NM 87501\t2130566\t19.22222222222222\n200 E Colfax Ave, Denver, CO 80203\tNew York State Capitol, State St. and Washington Ave, Albany, NY 12224\t2906553\t26.10388888888889\nMaryland State House, 100 State Cir, Annapolis, MD 21401\tOregon State Capitol, 900 Court St NE, Salem, OR 97301\t4623494\t41.908611111111114\nNew Mexico State Capitol, Santa Fe, NM 87501\tTexas Capitol, 1100 Congress Avenue, Austin, TX 78701\t1106403\t10.647777777777778\nIndiana State Capitol, Indianapolis, IN 46204\tNevada State Capitol, Carson City, NV 89701\t3322968\t29.933611111111112\nWest Virginia State Capitol, Charleston, WV 25317\t2 E Main St, Madison, WI 53703\t1036808\t9.813333333333333\nL St & 10th St, Sacramento, CA 95814\tTennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243\t3668991\t32.74611111111111\nNorth Dakota State Capitol, Bismarck, ND 58501\tSouth Carolina State House, 1100 Gervais Street, Columbia, SC 29201\t2635531\t23.70916666666667\n700 W Jefferson St, Boise, ID 83720\tNevada State Capitol, Carson City, NV 89701\t704765\t6.965\nMinnesota State Capitol, St Paul, MN 55155\tMontana State Capitol, 1301 E 6th Ave, Helena, MT 59601\t1684917\t15.38\nArizona State Capitol, 1700 W Washington St, Phoenix, AZ 85007\tNorth Dakota State Capitol, Bismarck, ND 58501\t2376757\t22.615833333333335\nLegislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901\tIllinois State Capitol, Springfield, IL 62756\t1404632\t13.471388888888889\nOklahoma State Capitol, Oklahoma City, OK 73105\tVermont State House, 115 State Street, Montpelier, VT 05633\t2658420\t24.825555555555557\n200 E Colfax Ave, Denver, CO 80203\tNew Jersey State House, Trenton, NJ 08608\t2810632\t25.461666666666666\nNorth Carolina State Capitol, Raleigh, NC 27601\tWyoming State Capitol, Cheyenne, WY 82001\t2749593\t25.06388888888889\nTexas Capitol, 1100 Congress Avenue, Austin, TX 78701\tVermont State House, 115 State Street, Montpelier, VT 05633\t3178782\t29.566111111111113\nArkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201\tPennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120\t1718292\t15.6325\nMaryland State House, 100 State Cir, Annapolis, MD 21401\tOhio State Capitol, 1 Capitol Square, Columbus, OH 43215\t684897\t6.671666666666667\nNorth Dakota State Capitol, Bismarck, ND 58501\tPennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120\t2406031\t21.8175\nIllinois State Capitol, Springfield, IL 62756\t300 SW 10th Ave, Topeka, KS 66612\t601069\t5.632777777777778\nOhio State Capitol, 1 Capitol Square, Columbus, OH 43215\tVirginia State Capitol, Richmond, VA 23219\t770599\t7.291944444444445\nMichigan State Capitol, Lansing, MI 48933\tWyoming State Capitol, Cheyenne, WY 82001\t1870400\t16.7125\nAlabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130\t402 S Monroe St, Tallahassee, FL 32301\t339009\t3.7333333333333334\n402 S Monroe St, Tallahassee, FL 32301\tNew Jersey State House, Trenton, NJ 08608\t1685972\t15.141111111111112\nMassachusetts State House, Boston, MA 02108\tWashington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504\t4978891\t44.84638888888889\nTennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243\tVermont State House, 115 State Street, Montpelier, VT 05633\t1803150\t17.30361111111111\nAlabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130\tWyoming State Capitol, Cheyenne, WY 82001\t2371746\t21.289722222222224\nIowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319\tMinnesota State Capitol, St Paul, MN 55155\t393856\t3.536388888888889\nTexas Capitol, 1100 Congress Avenue, Austin, TX 78701\tWest Virginia State Capitol, Charleston, WV 25317\t2007288\t18.133055555555554\nMassachusetts State House, Boston, MA 02108\tNevada State Capitol, Carson City, NV 89701\t4654602\t42.11972222222222\nIndiana State Capitol, Indianapolis, IN 46204\tTennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243\t463791\t4.415555555555556\nTexas Capitol, 1100 Congress Avenue, Austin, TX 78701\tWyoming State Capitol, Cheyenne, WY 82001\t1637527\t15.243333333333334\nMichigan State Capitol, Lansing, MI 48933\tRhode Island State House, 82 Smith Street, Providence, RI 02903\t1242182\t11.545277777777779\n402 S Monroe St, Tallahassee, FL 32301\tIowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319\t1841801\t17.4175\nIllinois State Capitol, Springfield, IL 62756\tMaryland State House, 100 State Cir, Annapolis, MD 21401\t1302845\t12.267222222222221\nArkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201\tWashington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504\t3708217\t33.0375\nKentucky State Capitol Building, 700 Capitol Avenue, Frankfort, KY 40601\tUtah State Capitol, Salt Lake City, UT 84103\t2632968\t23.90361111111111\nIowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319\tKentucky State Capitol Building, 700 Capitol Avenue, Frankfort, KY 40601\t1025967\t9.347222222222221\n400-498 N West St, Jackson, MS 39201\tMissouri State Capitol, Jefferson City, MO 65101\t874641\t8.697777777777778\nMichigan State Capitol, Lansing, MI 48933\tNew Jersey State House, Trenton, NJ 08608\t1075790\t9.922777777777778\nMaine State House, Augusta, ME 04330\tNorth Carolina State Capitol, Raleigh, NC 27601\t1405764\t13.345277777777778\nMichigan State Capitol, Lansing, MI 48933\t400-498 N West St, Jackson, MS 39201\t1486311\t13.251111111111111\nMaryland State House, 100 State Cir, Annapolis, MD 21401\tUtah State Capitol, Salt Lake City, UT 84103\t3392343\t30.753611111111113\nLouisiana State Capitol, Baton Rouge, LA 70802\tMissouri State Capitol, Jefferson City, MO 65101\t1157480\t11.186111111111112\nConnecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106\t300 SW 10th Ave, Topeka, KS 66612\t2218253\t20.36777777777778\nLouisiana State Capitol, Baton Rouge, LA 70802\tRhode Island State House, 82 Smith Street, Providence, RI 02903\t2496412\t22.764166666666668\nUtah State Capitol, Salt Lake City, UT 84103\tWyoming State Capitol, Cheyenne, WY 82001\t713245\t6.519166666666667\nArizona State Capitol, 1700 W Washington St, Phoenix, AZ 85007\tConnecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106\t4078845\t37.13194444444444\nNebraska State Capitol, 1445 K Street, Lincoln, NE 68509\tOklahoma State Capitol, Oklahoma City, OK 73105\t696496\t6.364444444444445\nMassachusetts State House, Boston, MA 02108\tMinnesota State Capitol, St Paul, MN 55155\t2235235\t20.5425\nNorth Dakota State Capitol, Bismarck, ND 58501\t500 E Capitol Ave, Pierre, SD 57501\t336454\t3.381388888888889\nMontana State Capitol, 1301 E 6th Ave, Helena, MT 59601\t500 E Capitol Ave, Pierre, SD 57501\t1127755\t10.738055555555556\nIllinois State Capitol, Springfield, IL 62756\tTexas Capitol, 1100 Congress Avenue, Austin, TX 78701\t1482225\t14.017222222222221\n402 S Monroe St, Tallahassee, FL 32301\tMassachusetts State House, Boston, MA 02108\t2109362\t19.355833333333333\nOregon State Capitol, 900 Court St NE, Salem, OR 97301\tSouth Carolina State House, 1100 Gervais Street, Columbia, SC 29201\t4565848\t41.25611111111111\n402 S Monroe St, Tallahassee, FL 32301\tNevada State Capitol, Carson City, NV 89701\t3969361\t36.64666666666667\nL St & 10th St, Sacramento, CA 95814\tSouth Carolina State House, 1100 Gervais Street, Columbia, SC 29201\t4312359\t38.533055555555556\n200 E Colfax Ave, Denver, CO 80203\tMaine State House, Augusta, ME 04330\t3418388\t30.80361111111111\nIndiana State Capitol, Indianapolis, IN 46204\tOklahoma State Capitol, Oklahoma City, OK 73105\t1185153\t10.749166666666667\nLegislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901\tNew Mexico State Capitol, Santa Fe, NM 87501\t3113952\t28.691111111111113\nLouisiana State Capitol, Baton Rouge, LA 70802\tMaine State House, Augusta, ME 04330\t2793719\t25.500555555555554\nMaine State House, Augusta, ME 04330\tNew Mexico State Capitol, Santa Fe, NM 87501\t3823704\t34.58638888888889\nState House, 107 North Main Street, Concord, NH 03303\tVirginia State Capitol, Richmond, VA 23219\t981920\t9.505555555555556\n402 S Monroe St, Tallahassee, FL 32301\tMinnesota State Capitol, St Paul, MN 55155\t2196529\t20.065\nMinnesota State Capitol, St Paul, MN 55155\tWest Virginia State Capitol, Charleston, WV 25317\t1438029\t13.313888888888888\nNebraska State Capitol, 1445 K Street, Lincoln, NE 68509\tWashington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504\t2748473\t24.67\nUtah State Capitol, Salt Lake City, UT 84103\tWashington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504\t1399410\t12.69138888888889\nState House, 107 North Main Street, Concord, NH 03303\tWyoming State Capitol, Cheyenne, WY 82001\t3082303\t28.630555555555556\nMissouri State Capitol, Jefferson City, MO 65101\tNorth Dakota State Capitol, Bismarck, ND 58501\t1521256\t13.33611111111111\nL St & 10th St, Sacramento, CA 95814\tGeorgia State Capitol, Atlanta, GA 30334\t3969099\t35.581944444444446\n700 W Jefferson St, Boise, ID 83720\tOhio State Capitol, 1 Capitol Square, Columbus, OH 43215\t3240708\t28.74277777777778\nIllinois State Capitol, Springfield, IL 62756\tVirginia State Capitol, Richmond, VA 23219\t1350670\t12.571666666666667\nOhio State Capitol, 1 Capitol Square, Columbus, OH 43215\t500 E Capitol Ave, Pierre, SD 57501\t1836021\t16.49972222222222\nTennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243\tWyoming State Capitol, Cheyenne, WY 82001\t1922417\t17.38083333333333\nAlabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130\tRhode Island State House, 82 Smith Street, Providence, RI 02903\t1945535\t18.18722222222222\nNew York State Capitol, State St. and Washington Ave, Albany, NY 12224\tVirginia State Capitol, Richmond, VA 23219\t770231\t7.3625\nMissouri State Capitol, Jefferson City, MO 65101\tNebraska State Capitol, 1445 K Street, Lincoln, NE 68509\t565824\t5.4494444444444445\nConnecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106\tIndiana State Capitol, Indianapolis, IN 46204\t1344056\t12.471111111111112\nAlabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130\tOklahoma State Capitol, Oklahoma City, OK 73105\t1274629\t11.588611111111112\nMontana State Capitol, 1301 E 6th Ave, Helena, MT 59601\tPennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120\t3389980\t30.9225\nAlabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130\t700 W Jefferson St, Boise, ID 83720\t3551935\t31.739166666666666\nNorth Carolina State Capitol, Raleigh, NC 27601\tOhio State Capitol, 1 Capitol Square, Columbus, OH 43215\t771396\t7.4944444444444445\nIllinois State Capitol, Springfield, IL 62756\tKentucky State Capitol Building, 700 Capitol Avenue, Frankfort, KY 40601\t600913\t5.644166666666667\nIndiana State Capitol, Indianapolis, IN 46204\tMissouri State Capitol, Jefferson City, MO 65101\t589960\t5.5311111111111115\n402 S Monroe St, Tallahassee, FL 32301\tRhode Island State House, 82 Smith Street, Providence, RI 02903\t2059331\t18.823055555555555\nMinnesota State Capitol, St Paul, MN 55155\tWashington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504\t2746945\t24.42527777777778\nArizona State Capitol, 1700 W Washington St, Phoenix, AZ 85007\tNew Mexico State Capitol, Santa Fe, NM 87501\t774427\t7.361111111111111\nNew Mexico State Capitol, Santa Fe, NM 87501\tNorth Carolina State Capitol, Raleigh, NC 27601\t2813969\t25.462222222222223\nNebraska State Capitol, 1445 K Street, Lincoln, NE 68509\t500 E Capitol Ave, Pierre, SD 57501\t660935\t6.571111111111111\nNebraska State Capitol, 1445 K Street, Lincoln, NE 68509\tOhio State Capitol, 1 Capitol Square, Columbus, OH 43215\t1316090\t12.180277777777778\nConnecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106\tTexas Capitol, 1100 Congress Avenue, Austin, TX 78701\t2999476\t27.310555555555556\nL St & 10th St, Sacramento, CA 95814\tMichigan State Capitol, Lansing, MI 48933\t3620857\t32.124722222222225\nLegislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901\t700 W Jefferson St, Boise, ID 83720\t3963934\t35.91777777777778\nMontana State Capitol, 1301 E 6th Ave, Helena, MT 59601\tWest Virginia State Capitol, Charleston, WV 25317\t3119986\t28.509166666666665\nAlabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130\tL St & 10th St, Sacramento, CA 95814\t3878584\t34.79833333333333\nMontana State Capitol, 1301 E 6th Ave, Helena, MT 59601\tOregon State Capitol, 900 Court St NE, Salem, OR 97301\t1138460\t10.850833333333334\nLegislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901\tNorth Carolina State Capitol, Raleigh, NC 27601\t598142\t5.8502777777777775\nRhode Island State House, 82 Smith Street, Providence, RI 02903\tVermont State House, 115 State Street, Montpelier, VT 05633\t367228\t3.4922222222222223\nMaryland State House, 100 State Cir, Annapolis, MD 21401\tMinnesota State Capitol, St Paul, MN 55155\t1819133\t16.771944444444443\n402 S Monroe St, Tallahassee, FL 32301\t500 E Capitol Ave, Pierre, SD 57501\t2618860\t23.874166666666667\nOhio State Capitol, 1 Capitol Square, Columbus, OH 43215\tRhode Island State House, 82 Smith Street, Providence, RI 02903\t1165413\t11.170277777777779\nKentucky State Capitol Building, 700 Capitol Avenue, Frankfort, KY 40601\tSouth Carolina State House, 1100 Gervais Street, Columbia, SC 29201\t737764\t6.850555555555555\nAlabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130\tArkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201\t743518\t6.871666666666667\nIndiana State Capitol, Indianapolis, IN 46204\tMinnesota State Capitol, St Paul, MN 55155\t942436\t8.643333333333333\nUtah State Capitol, Salt Lake City, UT 84103\tWest Virginia State Capitol, Charleston, WV 25317\t2948970\t26.514444444444443\n400-498 N West St, Jackson, MS 39201\tNew Jersey State House, Trenton, NJ 08608\t1860894\t16.893333333333334\nMaryland State House, 100 State Cir, Annapolis, MD 21401\tState House, 107 North Main Street, Concord, NH 03303\t781177\t7.66\nUtah State Capitol, Salt Lake City, UT 84103\tVermont State House, 115 State Street, Montpelier, VT 05633\t3757481\t34.615833333333335\nMissouri State Capitol, Jefferson City, MO 65101\tTexas Capitol, 1100 Congress Avenue, Austin, TX 78701\t1200610\t11.5825\nPennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120\tUtah State Capitol, Salt Lake City, UT 84103\t3287474\t29.73472222222222\nMontana State Capitol, 1301 E 6th Ave, Helena, MT 59601\tWashington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504\t1013497\t9.551666666666666\nLouisiana State Capitol, Baton Rouge, LA 70802\tWashington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504\t4129269\t37.654444444444444\n400-498 N West St, Jackson, MS 39201\tSouth Carolina State House, 1100 Gervais Street, Columbia, SC 29201\t957477\t8.536666666666667\n700 W Jefferson St, Boise, ID 83720\tWashington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504\t861910\t8.105\nMinnesota State Capitol, St Paul, MN 55155\t400-498 N West St, Jackson, MS 39201\t1616982\t15.023333333333333\nL St & 10th St, Sacramento, CA 95814\tNebraska State Capitol, 1445 K Street, Lincoln, NE 68509\t2461787\t21.753611111111113\nArizona State Capitol, 1700 W Washington St, Phoenix, AZ 85007\tUtah State Capitol, Salt Lake City, UT 84103\t1062245\t10.053888888888888\nNew Jersey State House, Trenton, NJ 08608\t2 E Main St, Madison, WI 53703\t1501153\t13.934722222222222\nArkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201\t402 S Monroe St, Tallahassee, FL 32301\t1080808\t10.475833333333334\nNebraska State Capitol, 1445 K Street, Lincoln, NE 68509\tNew York State Capitol, State St. and Washington Ave, Albany, NY 12224\t2133342\t19.453333333333333\nArkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201\tNew Mexico State Capitol, Santa Fe, NM 87501\t1403049\t12.560555555555556\nArkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201\tMaine State House, Augusta, ME 04330\t2585218\t23.77166666666667\n700 W Jefferson St, Boise, ID 83720\tKentucky State Capitol Building, 700 Capitol Avenue, Frankfort, KY 40601\t3105847\t27.80527777777778\nNorth Dakota State Capitol, Bismarck, ND 58501\tVirginia State Capitol, Richmond, VA 23219\t2633526\t23.948611111111113\nKentucky State Capitol Building, 700 Capitol Avenue, Frankfort, KY 40601\tMaryland State House, 100 State Cir, Annapolis, MD 21401\t934431\t8.569722222222222\n400-498 N West St, Jackson, MS 39201\tWest Virginia State Capitol, Charleston, WV 25317\t1287322\t11.7175\nNebraska State Capitol, 1445 K Street, Lincoln, NE 68509\tWest Virginia State Capitol, Charleston, WV 25317\t1524476\t13.97638888888889\nState House, 107 North Main Street, Concord, NH 03303\tWest Virginia State Capitol, Charleston, WV 25317\t1303076\t12.18138888888889\nMassachusetts State House, Boston, MA 02108\t500 E Capitol Ave, Pierre, SD 57501\t2862687\t25.990833333333335\nNevada State Capitol, Carson City, NV 89701\t500 E Capitol Ave, Pierre, SD 57501\t2192372\t19.86277777777778\nIowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319\tNew York State Capitol, State St. and Washington Ave, Albany, NY 12224\t1826496\t16.678055555555556\nMaryland State House, 100 State Cir, Annapolis, MD 21401\tVirginia State Capitol, Richmond, VA 23219\t230875\t2.296666666666667\nConnecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106\tMontana State Capitol, 1301 E 6th Ave, Helena, MT 59601\t3769670\t34.43888888888889\nNorth Carolina State Capitol, Raleigh, NC 27601\tVermont State House, 115 State Street, Montpelier, VT 05633\t1319088\t12.4925\nLouisiana State Capitol, Baton Rouge, LA 70802\t400-498 N West St, Jackson, MS 39201\t283870\t2.6305555555555555\nNew Mexico State Capitol, Santa Fe, NM 87501\tOhio State Capitol, 1 Capitol Square, Columbus, OH 43215\t2331032\t20.911666666666665\nNew Jersey State House, Trenton, NJ 08608\t500 E Capitol Ave, Pierre, SD 57501\t2535528\t22.92972222222222\n700 W Jefferson St, Boise, ID 83720\t400-498 N West St, Jackson, MS 39201\t3256677\t29.30611111111111\nIndiana State Capitol, Indianapolis, IN 46204\tNew York State Capitol, State St. and Washington Ave, Albany, NY 12224\t1263835\t11.571388888888889\nAlabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130\tSouth Carolina State House, 1100 Gervais Street, Columbia, SC 29201\t593674\t5.222222222222222\nNorth Dakota State Capitol, Bismarck, ND 58501\t2 E Main St, Madison, WI 53703\t1115357\t10.044166666666667\n402 S Monroe St, Tallahassee, FL 32301\tMissouri State Capitol, Jefferson City, MO 65101\t1496353\t14.028888888888888\nArkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201\tNorth Carolina State Capitol, Raleigh, NC 27601\t1426809\t13.190555555555555\nIndiana State Capitol, Indianapolis, IN 46204\tPennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120\t873206\t8.295277777777779\nKentucky State Capitol Building, 700 Capitol Avenue, Frankfort, KY 40601\tMassachusetts State House, Boston, MA 02108\t1524831\t14.299444444444445\nAlabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130\tNebraska State Capitol, 1445 K Street, Lincoln, NE 68509\t1655113\t15.069444444444445\nGeorgia State Capitol, Atlanta, GA 30334\tNorth Dakota State Capitol, Bismarck, ND 58501\t2501491\t22.174444444444443\n700 W Jefferson St, Boise, ID 83720\tWyoming State Capitol, Cheyenne, WY 82001\t1185449\t10.55638888888889\n300 SW 10th Ave, Topeka, KS 66612\t400-498 N West St, Jackson, MS 39201\t1158756\t11.0025\nKentucky State Capitol Building, 700 Capitol Avenue, Frankfort, KY 40601\t2 E Main St, Madison, WI 53703\t799075\t7.559166666666667\nLouisiana State Capitol, Baton Rouge, LA 70802\tNebraska State Capitol, 1445 K Street, Lincoln, NE 68509\t1539070\t15.27138888888889\nNevada State Capitol, Carson City, NV 89701\tNew Mexico State Capitol, Santa Fe, NM 87501\t1693243\t15.817222222222222\nMontana State Capitol, 1301 E 6th Ave, Helena, MT 59601\tNew York State Capitol, State St. and Washington Ave, Albany, NY 12224\t3648222\t33.21222222222222\n200 E Colfax Ave, Denver, CO 80203\tNew Mexico State Capitol, Santa Fe, NM 87501\t628035\t5.675277777777778\nMichigan State Capitol, Lansing, MI 48933\tWashington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504\t3748085\t33.44166666666667\nOhio State Capitol, 1 Capitol Square, Columbus, OH 43215\tOklahoma State Capitol, Oklahoma City, OK 73105\t1468075\t13.293333333333333\n400-498 N West St, Jackson, MS 39201\t500 E Capitol Ave, Pierre, SD 57501\t1996477\t18.362222222222222\nLegislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901\tIndiana State Capitol, Indianapolis, IN 46204\t1065356\t10.381944444444445\nMaine State House, Augusta, ME 04330\tNew Jersey State House, Trenton, NJ 08608\t698330\t6.790555555555556\nL St & 10th St, Sacramento, CA 95814\t700 W Jefferson St, Boise, ID 83720\t894846\t8.514444444444445\n200 E Colfax Ave, Denver, CO 80203\t2 E Main St, Madison, WI 53703\t1554292\t13.9325\nAlabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130\tWest Virginia State Capitol, Charleston, WV 25317\t1044556\t9.495\nTexas Capitol, 1100 Congress Avenue, Austin, TX 78701\tVirginia State Capitol, Richmond, VA 23219\t2367739\t21.315277777777776\nIndiana State Capitol, Indianapolis, IN 46204\tMaryland State House, 100 State Cir, Annapolis, MD 21401\t962969\t9.191944444444445\nSouth Carolina State House, 1100 Gervais Street, Columbia, SC 29201\tWashington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504\t4617683\t41.74583333333333\n400-498 N West St, Jackson, MS 39201\tTennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243\t666003\t5.996944444444445\nVirginia State Capitol, Richmond, VA 23219\tWyoming State Capitol, Cheyenne, WY 82001\t2747570\t24.87\nMaine State House, Augusta, ME 04330\tSouth Carolina State House, 1100 Gervais Street, Columbia, SC 29201\t1732435\t16.153333333333332\nMassachusetts State House, Boston, MA 02108\tTennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243\t1777219\t16.496111111111112\n200 E Colfax Ave, Denver, CO 80203\tOklahoma State Capitol, Oklahoma City, OK 73105\t1083636\t9.535277777777777\nGeorgia State Capitol, Atlanta, GA 30334\t400-498 N West St, Jackson, MS 39201\t614336\t5.541388888888889\nNew Jersey State House, Trenton, NJ 08608\tVirginia State Capitol, Richmond, VA 23219\t455416\t4.516944444444444\nMassachusetts State House, Boston, MA 02108\tVermont State House, 115 State Street, Montpelier, VT 05633\t289370\t2.7591666666666668\nArkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201\t700 W Jefferson St, Boise, ID 83720\t2853423\t25.184444444444445\nOhio State Capitol, 1 Capitol Square, Columbus, OH 43215\t2 E Main St, Madison, WI 53703\t815071\t7.6075\nNevada State Capitol, Carson City, NV 89701\tNorth Carolina State Capitol, Raleigh, NC 27601\t4328609\t38.8525\nNorth Dakota State Capitol, Bismarck, ND 58501\tWest Virginia State Capitol, Charleston, WV 25317\t2136037\t19.404444444444444\nNew Jersey State House, Trenton, NJ 08608\tNorth Carolina State Capitol, Raleigh, NC 27601\t724043\t6.948888888888889\nIndiana State Capitol, Indianapolis, IN 46204\t2 E Main St, Madison, WI 53703\t535514\t5.096388888888889\n500 E Capitol Ave, Pierre, SD 57501\tVirginia State Capitol, Richmond, VA 23219\t2562885\t23.285555555555554\nState House, 107 North Main Street, Concord, NH 03303\tSouth Carolina State House, 1100 Gervais Street, Columbia, SC 29201\t1577218\t14.745555555555555\nNew Jersey State House, Trenton, NJ 08608\tWyoming State Capitol, Cheyenne, WY 82001\t2774047\t25.05611111111111\n400-498 N West St, Jackson, MS 39201\tPennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120\t1662623\t15.061666666666667\nMinnesota State Capitol, St Paul, MN 55155\tTexas Capitol, 1100 Congress Avenue, Austin, TX 78701\t1888668\t16.8\nGeorgia State Capitol, Atlanta, GA 30334\tIndiana State Capitol, Indianapolis, IN 46204\t861058\t7.981388888888889\n700 W Jefferson St, Boise, ID 83720\tMissouri State Capitol, Jefferson City, MO 65101\t2460836\t21.95916666666667\nNorth Dakota State Capitol, Bismarck, ND 58501\tOklahoma State Capitol, Oklahoma City, OK 73105\t1494640\t14.765277777777778\n200 E Colfax Ave, Denver, CO 80203\tIndiana State Capitol, Indianapolis, IN 46204\t1742380\t15.568333333333333\nMissouri State Capitol, Jefferson City, MO 65101\tUtah State Capitol, Salt Lake City, UT 84103\t1989694\t18.076666666666668\nArkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201\t500 E Capitol Ave, Pierre, SD 57501\t1618094\t14.431944444444444\nMontana State Capitol, 1301 E 6th Ave, Helena, MT 59601\tNevada State Capitol, Carson City, NV 89701\t1439435\t12.973055555555556\nMontana State Capitol, 1301 E 6th Ave, Helena, MT 59601\tTennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243\t2912212\t26.12638888888889\nIndiana State Capitol, Indianapolis, IN 46204\tTexas Capitol, 1100 Congress Avenue, Austin, TX 78701\t1760623\t15.893611111111111\n300 SW 10th Ave, Topeka, KS 66612\tMissouri State Capitol, Jefferson City, MO 65101\t352120\t3.2605555555555554\nOhio State Capitol, 1 Capitol Square, Columbus, OH 43215\tWyoming State Capitol, Cheyenne, WY 82001\t2060174\t18.44611111111111\nIowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319\t500 E Capitol Ave, Pierre, SD 57501\t809818\t7.162222222222222\nAlabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130\tTennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243\t451917\t4.066666666666666\nGeorgia State Capitol, Atlanta, GA 30334\tTexas Capitol, 1100 Congress Avenue, Austin, TX 78701\t1534179\t13.697777777777778\nNew York State Capitol, State St. and Washington Ave, Albany, NY 12224\tSouth Carolina State House, 1100 Gervais Street, Columbia, SC 29201\t1365529\t12.602222222222222\nConnecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106\tRhode Island State House, 82 Smith Street, Providence, RI 02903\t140938\t1.5366666666666666\nIowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319\t2 E Main St, Madison, WI 53703\t474235\t4.506666666666667\nOklahoma State Capitol, Oklahoma City, OK 73105\tWyoming State Capitol, Cheyenne, WY 82001\t1232122\t10.689444444444444\nConnecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106\tOklahoma State Capitol, Oklahoma City, OK 73105\t2528255\t23.06833333333333\nOhio State Capitol, 1 Capitol Square, Columbus, OH 43215\tOregon State Capitol, 900 Court St NE, Salem, OR 97301\t3998563\t36.00805555555556\nArkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201\tWyoming State Capitol, Cheyenne, WY 82001\t1702419\t14.832777777777778\nVermont State House, 115 State Street, Montpelier, VT 05633\tVirginia State Capitol, Richmond, VA 23219\t1050640\t10.086944444444445\n400-498 N West St, Jackson, MS 39201\tOklahoma State Capitol, Oklahoma City, OK 73105\t980722\t8.903333333333334\nIllinois State Capitol, Springfield, IL 62756\tIndiana State Capitol, Indianapolis, IN 46204\t331633\t3.2391666666666667\nConnecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106\tIllinois State Capitol, Springfield, IL 62756\t1683332\t15.560555555555556\nArizona State Capitol, 1700 W Washington St, Phoenix, AZ 85007\tMontana State Capitol, 1301 E 6th Ave, Helena, MT 59601\t1832197\t16.561944444444446\nState House, 107 North Main Street, Concord, NH 03303\tUtah State Capitol, Salt Lake City, UT 84103\t3789540\t35.0375\nArizona State Capitol, 1700 W Washington St, Phoenix, AZ 85007\tNebraska State Capitol, 1445 K Street, Lincoln, NE 68509\t1994652\t18.935\nKentucky State Capitol Building, 700 Capitol Avenue, Frankfort, KY 40601\tWyoming State Capitol, Cheyenne, WY 82001\t1925731\t17.496388888888887\n200 E Colfax Ave, Denver, CO 80203\tLegislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901\t2802811\t25.78611111111111\nMaine State House, Augusta, ME 04330\tNebraska State Capitol, 1445 K Street, Lincoln, NE 68509\t2644828\t24.156111111111112\nArkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201\tNew Jersey State House, Trenton, NJ 08608\t1916564\t17.464166666666667\nMinnesota State Capitol, St Paul, MN 55155\tMissouri State Capitol, Jefferson City, MO 65101\t816553\t7.780833333333334\nOregon State Capitol, 900 Court St NE, Salem, OR 97301\tWashington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504\t257602\t2.6061111111111113\nIllinois State Capitol, Springfield, IL 62756\tState House, 107 North Main Street, Concord, NH 03303\t1826951\t17.735277777777778\nNebraska State Capitol, 1445 K Street, Lincoln, NE 68509\tNorth Carolina State Capitol, Raleigh, NC 27601\t2033325\t18.810833333333335\nArkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201\tRhode Island State House, 82 Smith Street, Providence, RI 02903\t2287910\t21.03527777777778\n200 E Colfax Ave, Denver, CO 80203\t402 S Monroe St, Tallahassee, FL 32301\t2618682\t23.936944444444446\nArkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201\tLegislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901\t1791330\t16.66583333333333\nKentucky State Capitol Building, 700 Capitol Avenue, Frankfort, KY 40601\tNew York State Capitol, State St. and Washington Ave, Albany, NY 12224\t1282062\t12.008611111111112\nNew Mexico State Capitol, Santa Fe, NM 87501\tVirginia State Capitol, Richmond, VA 23219\t2934829\t26.29083333333333\nMinnesota State Capitol, St Paul, MN 55155\tVermont State House, 115 State Street, Montpelier, VT 05633\t2176956\t20.704722222222223\nIowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319\tWest Virginia State Capitol, Charleston, WV 25317\t1270293\t11.675833333333333\nNebraska State Capitol, 1445 K Street, Lincoln, NE 68509\tTennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243\t1202866\t11.0975\nMontana State Capitol, 1301 E 6th Ave, Helena, MT 59601\tOhio State Capitol, 1 Capitol Square, Columbus, OH 43215\t2897903\t26.289166666666667\n200 E Colfax Ave, Denver, CO 80203\tMichigan State Capitol, Lansing, MI 48933\t1938989\t17.19\nTennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243\tTexas Capitol, 1100 Congress Avenue, Austin, TX 78701\t1381842\t12.518888888888888\nIowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319\tMichigan State Capitol, Lansing, MI 48933\t858932\t7.764444444444444\nNew York State Capitol, State St. and Washington Ave, Albany, NY 12224\tRhode Island State House, 82 Smith Street, Providence, RI 02903\t261924\t2.602222222222222\nMassachusetts State House, Boston, MA 02108\t400-498 N West St, Jackson, MS 39201\t2281091\t20.94638888888889\nArizona State Capitol, 1700 W Washington St, Phoenix, AZ 85007\tNew York State Capitol, State St. and Washington Ave, Albany, NY 12224\t3997144\t36.134166666666665\nArkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201\tOregon State Capitol, 900 Court St NE, Salem, OR 97301\t3611622\t32.29694444444444\nOklahoma State Capitol, Oklahoma City, OK 73105\tWashington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504\t3237920\t28.89388888888889\nMichigan State Capitol, Lansing, MI 48933\tNew York State Capitol, State St. and Washington Ave, Albany, NY 12224\t987696\t9.139722222222222\nKentucky State Capitol Building, 700 Capitol Avenue, Frankfort, KY 40601\t500 E Capitol Ave, Pierre, SD 57501\t1811441\t16.343611111111112\nState House, 107 North Main Street, Concord, NH 03303\tNorth Dakota State Capitol, Bismarck, ND 58501\t2914129\t27.160555555555554\nL St & 10th St, Sacramento, CA 95814\tLouisiana State Capitol, Baton Rouge, LA 70802\t3538731\t31.1025\nArizona State Capitol, 1700 W Washington St, Phoenix, AZ 85007\t402 S Monroe St, Tallahassee, FL 32301\t3034934\t26.629444444444445\nState House, 107 North Main Street, Concord, NH 03303\tVermont State House, 115 State Street, Montpelier, VT 05633\t179010\t1.801111111111111\nL St & 10th St, Sacramento, CA 95814\tOklahoma State Capitol, Oklahoma City, OK 73105\t2613800\t23.4225\nOregon State Capitol, 900 Court St NE, Salem, OR 97301\tWyoming State Capitol, Cheyenne, WY 82001\t1944950\t17.691388888888888\n402 S Monroe St, Tallahassee, FL 32301\tOhio State Capitol, 1 Capitol Square, Columbus, OH 43215\t1332540\t12.580833333333333\nNevada State Capitol, Carson City, NV 89701\tRhode Island State House, 82 Smith Street, Providence, RI 02903\t4660609\t41.84861111111111\nIndiana State Capitol, Indianapolis, IN 46204\tUtah State Capitol, Salt Lake City, UT 84103\t2476810\t22.338333333333335\nTennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243\tVirginia State Capitol, Richmond, VA 23219\t988289\t9.033333333333333\nConnecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106\tWest Virginia State Capitol, Charleston, WV 25317\t1052764\t9.833333333333334\nArkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201\tNew York State Capitol, State St. and Washington Ave, Albany, NY 12224\t2148020\t19.5825\nPennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120\t2 E Main St, Madison, WI 53703\t1307343\t12.206388888888888\nMassachusetts State House, Boston, MA 02108\tNorth Dakota State Capitol, Bismarck, ND 58501\t2933033\t26.647222222222222\nWest Virginia State Capitol, Charleston, WV 25317\tWyoming State Capitol, Cheyenne, WY 82001\t2241251\t20.226388888888888\n402 S Monroe St, Tallahassee, FL 32301\tTexas Capitol, 1100 Congress Avenue, Austin, TX 78701\t1400273\t12.708055555555555\nMichigan State Capitol, Lansing, MI 48933\tTexas Capitol, 1100 Congress Avenue, Austin, TX 78701\t2165494\t19.401666666666667\nL St & 10th St, Sacramento, CA 95814\tMaine State House, Augusta, ME 04330\t5100256\t45.73833333333334\n700 W Jefferson St, Boise, ID 83720\tSouth Carolina State House, 1100 Gervais Street, Columbia, SC 29201\t3806347\t34.12111111111111\nNorth Carolina State Capitol, Raleigh, NC 27601\tOregon State Capitol, 900 Court St NE, Salem, OR 97301\t4687982\t42.62555555555556\nOregon State Capitol, 900 Court St NE, Salem, OR 97301\t2 E Main St, Madison, WI 53703\t3286034\t29.595555555555556\nConnecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106\tState House, 107 North Main Street, Concord, NH 03303\t258007\t2.5094444444444446\n700 W Jefferson St, Boise, ID 83720\tNebraska State Capitol, 1445 K Street, Lincoln, NE 68509\t1892359\t16.68\nIllinois State Capitol, Springfield, IL 62756\tOklahoma State Capitol, Oklahoma City, OK 73105\t952777\t8.616666666666667\n400-498 N West St, Jackson, MS 39201\tOregon State Capitol, 900 Court St NE, Salem, OR 97301\t4016847\t36.48111111111111\nLouisiana State Capitol, Baton Rouge, LA 70802\tMontana State Capitol, 1301 E 6th Ave, Helena, MT 59601\t3232764\t29.059722222222224\nState House, 107 North Main Street, Concord, NH 03303\tOklahoma State Capitol, Oklahoma City, OK 73105\t2691874\t25.178333333333335\nNew Jersey State House, Trenton, NJ 08608\tNew York State Capitol, State St. and Washington Ave, Albany, NY 12224\t330136\t3.2127777777777777\n300 SW 10th Ave, Topeka, KS 66612\tNevada State Capitol, Carson City, NV 89701\t2462119\t22.726666666666667\nKentucky State Capitol Building, 700 Capitol Avenue, Frankfort, KY 40601\tMichigan State Capitol, Lansing, MI 48933\t663503\t6.138055555555556\nOregon State Capitol, 900 Court St NE, Salem, OR 97301\tRhode Island State House, 82 Smith Street, Providence, RI 02903\t5021523\t45.5175\nMaryland State House, 100 State Cir, Annapolis, MD 21401\tSouth Carolina State House, 1100 Gervais Street, Columbia, SC 29201\t826173\t7.536666666666667\nVirginia State Capitol, Richmond, VA 23219\tWashington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504\t4684188\t42.19861111111111\nMichigan State Capitol, Lansing, MI 48933\tNevada State Capitol, Carson City, NV 89701\t3423796\t30.715\nNew Mexico State Capitol, Santa Fe, NM 87501\tUtah State Capitol, Salt Lake City, UT 84103\t1009968\t10.115833333333333\nAlabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130\t500 E Capitol Ave, Pierre, SD 57501\t2279926\t20.22833333333333\nState House, 107 North Main Street, Concord, NH 03303\tTexas Capitol, 1100 Congress Avenue, Austin, TX 78701\t3249787\t29.65861111111111\nIowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319\tNew Mexico State Capitol, Santa Fe, NM 87501\t1539423\t14.454444444444444\nPennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120\tVermont State House, 115 State Street, Montpelier, VT 05633\t725358\t7.509444444444444\n402 S Monroe St, Tallahassee, FL 32301\tWyoming State Capitol, Cheyenne, WY 82001\t2710679\t24.935555555555556\nGeorgia State Capitol, Atlanta, GA 30334\tKentucky State Capitol Building, 700 Capitol Avenue, Frankfort, KY 40601\t659468\t6.027222222222222\nArizona State Capitol, 1700 W Washington St, Phoenix, AZ 85007\tNevada State Capitol, Carson City, NV 89701\t1149381\t11.2775\nL St & 10th St, Sacramento, CA 95814\tUtah State Capitol, Salt Lake City, UT 84103\t1051681\t9.350555555555555\nLegislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901\tSouth Carolina State House, 1100 Gervais Street, Columbia, SC 29201\t924813\t8.658333333333333\nIllinois State Capitol, Springfield, IL 62756\tRhode Island State House, 82 Smith Street, Providence, RI 02903\t1786807\t16.83888888888889"
  },
  {
    "path": "pareto-optimized-road-trip/optimized-state-capitols-trip.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Computing optimal road trips on a limited budget\\n\",\n    \"\\n\",\n    \"This notebook provides the methodology and code used in the blog post, [Computing optimal road trips on a limited budget](http://www.randalolson.com/2016/06/05/computing-optimal-road-trips-on-a-limited-budget/).\\n\",\n    \"\\n\",\n    \"### Notebook by [Randal S. Olson](http://www.randalolson.com)\\n\",\n    \"\\n\",\n    \"Please see the [repository README file](https://github.com/rhiever/Data-Analysis-and-Machine-Learning-Projects#license) for the licenses and usage terms for the instructional material and code in this notebook. In general, I have licensed this material so that it is as widely useable and shareable as possible.\\n\",\n    \"\\n\",\n    \"### Required Python libraries\\n\",\n    \"\\n\",\n    \"If you don't have Python on your computer, you can use the [Anaconda Python distribution](http://continuum.io/downloads) to install most of the Python packages you need. Anaconda provides a simple double-click installer for your convenience.\\n\",\n    \"\\n\",\n    \"This code uses base Python libraries except for the `googlemaps`, `pandas`, `deap`, and `tqdm` packages. You can install these packages using `pip` by typing the following commands into your command line:\\n\",\n    \"\\n\",\n    \"> pip install googlemaps pandas deap tqdm\\n\",\n    \"\\n\",\n    \"### Construct a list of road trip waypoints\\n\",\n    \"\\n\",\n    \"The first step is to decide where you want to stop on your road trip.\\n\",\n    \"\\n\",\n    \"Make sure you look all of the locations up on [Google Maps](http://maps.google.com) first so you have the correct address, city, state, etc. If the text you use to look up the location doesn't work on Google Maps, then it won't work here either.\\n\",\n    \"\\n\",\n    \"Add all of your waypoints to the list below. Make sure they're formatted the same way as in the example below.\\n\",\n    \"\\n\",\n    \"*Technical note: Due to daily usage limitations of the Google Maps API, you can only have a maximum of 70 waypoints. You will have to pay Google for an increased API limit if you want to add more waypoints.*\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"# https://en.wikipedia.org/wiki/List_of_state_capitols_in_the_United_States\\n\",\n    \"\\n\",\n    \"all_waypoints = ['Alabama State Capitol, 600 Dexter Avenue, Montgomery, AL 36130',\\n\",\n    \"                 #'Alaska State Capitol, Juneau, AK',\\n\",\n    \"                 'Arizona State Capitol, 1700 W Washington St, Phoenix, AZ 85007',\\n\",\n    \"                 'Arkansas State Capitol, 500 Woodlane Street, Little Rock, AR 72201',\\n\",\n    \"                 'L St & 10th St, Sacramento, CA 95814',\\n\",\n    \"                 '200 E Colfax Ave, Denver, CO 80203',\\n\",\n    \"                 'Connecticut State Capitol, 210 Capitol Ave, Hartford, CT 06106',\\n\",\n    \"                 'Legislative Hall: The State Capitol, Legislative Avenue, Dover, DE 19901',\\n\",\n    \"                 '402 S Monroe St, Tallahassee, FL 32301',\\n\",\n    \"                 'Georgia State Capitol, Atlanta, GA 30334',\\n\",\n    \"                 #'Hawaii State Capitol, 415 S Beretania St, Honolulu, HI 96813'\\n\",\n    \"                 '700 W Jefferson St, Boise, ID 83720',\\n\",\n    \"                 'Illinois State Capitol, Springfield, IL 62756',\\n\",\n    \"                 'Indiana State Capitol, Indianapolis, IN 46204',\\n\",\n    \"                 'Iowa State Capitol, 1007 E Grand Ave, Des Moines, IA 50319',\\n\",\n    \"                 '300 SW 10th Ave, Topeka, KS 66612',\\n\",\n    \"                 'Kentucky State Capitol Building, 700 Capitol Avenue, Frankfort, KY 40601',\\n\",\n    \"                 'Louisiana State Capitol, Baton Rouge, LA 70802',\\n\",\n    \"                 'Maine State House, Augusta, ME 04330',\\n\",\n    \"                 'Maryland State House, 100 State Cir, Annapolis, MD 21401',\\n\",\n    \"                 'Massachusetts State House, Boston, MA 02108',\\n\",\n    \"                 'Michigan State Capitol, Lansing, MI 48933',\\n\",\n    \"                 'Minnesota State Capitol, St Paul, MN 55155',\\n\",\n    \"                 '400-498 N West St, Jackson, MS 39201',\\n\",\n    \"                 'Missouri State Capitol, Jefferson City, MO 65101',\\n\",\n    \"                 'Montana State Capitol, 1301 E 6th Ave, Helena, MT 59601',\\n\",\n    \"                 'Nebraska State Capitol, 1445 K Street, Lincoln, NE 68509',\\n\",\n    \"                 'Nevada State Capitol, Carson City, NV 89701',\\n\",\n    \"                 'State House, 107 North Main Street, Concord, NH 03303',\\n\",\n    \"                 'New Jersey State House, Trenton, NJ 08608',\\n\",\n    \"                 'New Mexico State Capitol, Santa Fe, NM 87501',\\n\",\n    \"                 'New York State Capitol, State St. and Washington Ave, Albany, NY 12224',\\n\",\n    \"                 'North Carolina State Capitol, Raleigh, NC 27601',\\n\",\n    \"                 'North Dakota State Capitol, Bismarck, ND 58501',\\n\",\n    \"                 'Ohio State Capitol, 1 Capitol Square, Columbus, OH 43215',\\n\",\n    \"                 'Oklahoma State Capitol, Oklahoma City, OK 73105',\\n\",\n    \"                 'Oregon State Capitol, 900 Court St NE, Salem, OR 97301',\\n\",\n    \"                 'Pennsylvania State Capitol Building, North 3rd Street, Harrisburg, PA 17120',\\n\",\n    \"                 'Rhode Island State House, 82 Smith Street, Providence, RI 02903',\\n\",\n    \"                 'South Carolina State House, 1100 Gervais Street, Columbia, SC 29201',\\n\",\n    \"                 '500 E Capitol Ave, Pierre, SD 57501',\\n\",\n    \"                 'Tennessee State Capitol, 600 Charlotte Avenue, Nashville, TN 37243',\\n\",\n    \"                 'Texas Capitol, 1100 Congress Avenue, Austin, TX 78701',\\n\",\n    \"                 'Utah State Capitol, Salt Lake City, UT 84103',\\n\",\n    \"                 'Vermont State House, 115 State Street, Montpelier, VT 05633',\\n\",\n    \"                 'Virginia State Capitol, Richmond, VA 23219',\\n\",\n    \"                 'Washington State Capitol Bldg, 416 Sid Snyder Ave SW, Olympia, WA 98504',\\n\",\n    \"                 'West Virginia State Capitol, Charleston, WV 25317',\\n\",\n    \"                 '2 E Main St, Madison, WI 53703',\\n\",\n    \"                 'Wyoming State Capitol, Cheyenne, WY 82001']\\n\",\n    \"\\n\",\n    \"len(all_waypoints)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Next you'll have to register this script with the Google Maps API so they know who's hitting their servers with hundreds of Google Maps routing requests.\\n\",\n    \"\\n\",\n    \"1) Enable the Google Maps Distance Matrix API on your Google account. Google explains how to do that [here](https://github.com/googlemaps/google-maps-services-python#api-keys).\\n\",\n    \"\\n\",\n    \"2) Copy and paste the API key they had you create into the code below.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import googlemaps\\n\",\n    \"\\n\",\n    \"gmaps = googlemaps.Client(key='ENTER YOUR GOOGLE MAPS KEY HERE')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now we're going to query the Google Maps API for the shortest route between all of the waypoints.\\n\",\n    \"\\n\",\n    \"This is equivalent to doing Google Maps directions lookups on the Google Maps site, except now we're performing hundreds of lookups automatically using code.\\n\",\n    \"\\n\",\n    \"If you get an error on this part, that most likely means one of the waypoints you entered couldn't be found on Google Maps. Another possible reason for an error here is if it's not possible to drive between the points, e.g., finding the driving directions between Hawaii and Florida will return an error until we invent flying cars.\\n\",\n    \"\\n\",\n    \"### Gather the distance traveled on the shortest route between all waypoints\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"from itertools import combinations\\n\",\n    \"\\n\",\n    \"waypoint_distances = {}\\n\",\n    \"waypoint_durations = {}\\n\",\n    \"\\n\",\n    \"for (waypoint1, waypoint2) in combinations(all_waypoints, 2):\\n\",\n    \"    try:\\n\",\n    \"        route = gmaps.distance_matrix(origins=[waypoint1],\\n\",\n    \"                                      destinations=[waypoint2],\\n\",\n    \"                                      mode='driving', # Change this to 'walking' for walking directions,\\n\",\n    \"                                                      # 'bicycling' for biking directions, etc.\\n\",\n    \"                                      language='English',\\n\",\n    \"                                      units='metric')\\n\",\n    \"\\n\",\n    \"        # 'distance' is in meters\\n\",\n    \"        distance = route['rows'][0]['elements'][0]['distance']['value']\\n\",\n    \"\\n\",\n    \"        # 'duration' is in seconds\\n\",\n    \"        duration = route['rows'][0]['elements'][0]['duration']['value']\\n\",\n    \"\\n\",\n    \"        waypoint_distances[frozenset([waypoint1, waypoint2])] = distance\\n\",\n    \"        waypoint_durations[frozenset([waypoint1, waypoint2])] = duration\\n\",\n    \"    \\n\",\n    \"    except Exception as e:\\n\",\n    \"        print('Error with finding the route between {} and {}.'.format(waypoint1, waypoint2))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now that we have the routes between all of our waypoints, let's save them to a text file so we don't have to bother Google about them again.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"with open('my-waypoints-dist-dur.tsv', 'w') as out_file:\\n\",\n    \"    out_file.write('\\\\t'.join(['waypoint1',\\n\",\n    \"                              'waypoint2',\\n\",\n    \"                              'distance_m',\\n\",\n    \"                              'duration_s']))\\n\",\n    \"    \\n\",\n    \"    for (waypoint1, waypoint2) in waypoint_distances.keys():\\n\",\n    \"        out_file.write('\\\\n' +\\n\",\n    \"                       '\\\\t'.join([waypoint1,\\n\",\n    \"                                  waypoint2,\\n\",\n    \"                                  str(waypoint_distances[frozenset([waypoint1, waypoint2])]),\\n\",\n    \"                                  str(waypoint_durations[frozenset([waypoint1, waypoint2])])]))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Use a genetic algorithm to optimize the order to visit the waypoints in\\n\",\n    \"\\n\",\n    \"Instead of exhaustively looking at every possible solution, genetic algorithms start with a handful of random solutions and continually tinkers with these solutions — always trying something slightly different from the current solutions and keeping the best ones — until they can’t find a better solution any more.\\n\",\n    \"\\n\",\n    \"Below, all you need to do is make sure that the file name above matches the file name below (both currently `my-waypoints-dist-dur.tsv`) and run the code. The code will read in your route information and use a genetic algorithm to discover an optimized driving route.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"import pandas as pd\\n\",\n    \"import numpy as np\\n\",\n    \"\\n\",\n    \"waypoint_distances = {}\\n\",\n    \"waypoint_durations = {}\\n\",\n    \"all_waypoints = set()\\n\",\n    \"\\n\",\n    \"waypoint_data = pd.read_csv('my-waypoints-dist-dur.tsv', sep='\\\\t')\\n\",\n    \"\\n\",\n    \"for i, row in waypoint_data.iterrows():\\n\",\n    \"    # Distance = meters\\n\",\n    \"    waypoint_distances[frozenset([row.waypoint1, row.waypoint2])] = row.distance_m\\n\",\n    \"    \\n\",\n    \"    # Duration = hours\\n\",\n    \"    waypoint_durations[frozenset([row.waypoint1, row.waypoint2])] = row.duration_s / (60. * 60.)\\n\",\n    \"    all_waypoints.update([row.waypoint1, row.waypoint2])\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"import random\\n\",\n    \"import numpy as np\\n\",\n    \"import copy\\n\",\n    \"from tqdm import tqdm\\n\",\n    \"\\n\",\n    \"from deap import algorithms\\n\",\n    \"from deap import base\\n\",\n    \"from deap import creator\\n\",\n    \"from deap import tools\\n\",\n    \"\\n\",\n    \"creator.create('FitnessMulti', base.Fitness, weights=(1.0, -1.0))\\n\",\n    \"creator.create('Individual', list, fitness=creator.FitnessMulti)\\n\",\n    \"\\n\",\n    \"toolbox = base.Toolbox()\\n\",\n    \"toolbox.register('waypoints', random.sample, all_waypoints, random.randint(2, 20))\\n\",\n    \"toolbox.register('individual', tools.initIterate, creator.Individual, toolbox.waypoints)\\n\",\n    \"toolbox.register('population', tools.initRepeat, list, toolbox.individual)\\n\",\n    \"\\n\",\n    \"def eval_capitol_trip(individual):\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"        This function returns the total distance traveled on the current road trip\\n\",\n    \"        as well as the number of waypoints visited in the trip.\\n\",\n    \"        \\n\",\n    \"        The genetic algorithm will favor road trips that have shorter\\n\",\n    \"        total distances traveled and more waypoints visited.\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    trip_length = 0.\\n\",\n    \"    individual = list(individual)\\n\",\n    \"    \\n\",\n    \"    # Adding the starting point to the end of the trip forces it to be a round-trip\\n\",\n    \"    individual += [individual[0]]\\n\",\n    \"    \\n\",\n    \"    for index in range(1, len(individual)):\\n\",\n    \"        waypoint1 = individual[index - 1]\\n\",\n    \"        waypoint2 = individual[index]\\n\",\n    \"        trip_length += waypoint_distances[frozenset([waypoint1, waypoint2])]\\n\",\n    \"        \\n\",\n    \"    return len(set(individual)), trip_length\\n\",\n    \"\\n\",\n    \"def pareto_selection_operator(individuals, k):\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"        This function chooses what road trips get copied into the next generation.\\n\",\n    \"        \\n\",\n    \"        The genetic algorithm will favor road trips that have shorter\\n\",\n    \"        total distances traveled and more waypoints visited.\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    return tools.selNSGA2(individuals, int(k / 5.)) * 5\\n\",\n    \"\\n\",\n    \"def mutation_operator(individual):\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"        This function applies a random change to one road trip:\\n\",\n    \"        \\n\",\n    \"            - Insert: Adds one new waypoint to the road trip\\n\",\n    \"            - Delete: Removes one waypoint from the road trip\\n\",\n    \"            - Point: Replaces one waypoint with another different one\\n\",\n    \"            - Swap: Swaps the places of two waypoints in the road trip\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    possible_mutations = ['swap']\\n\",\n    \"    \\n\",\n    \"    if len(individual) < len(all_waypoints):\\n\",\n    \"        possible_mutations.append('insert')\\n\",\n    \"        possible_mutations.append('point')\\n\",\n    \"    if len(individual) > 2:\\n\",\n    \"        possible_mutations.append('delete')\\n\",\n    \"    \\n\",\n    \"    mutation_type = random.sample(possible_mutations, 1)[0]\\n\",\n    \"    \\n\",\n    \"    # Insert mutation\\n\",\n    \"    if mutation_type == 'insert':\\n\",\n    \"        waypoint_to_add = individual[0]\\n\",\n    \"        while waypoint_to_add in individual:\\n\",\n    \"            waypoint_to_add = random.sample(all_waypoints, 1)[0]\\n\",\n    \"            \\n\",\n    \"        index_to_insert = random.randint(0, len(individual) - 1)\\n\",\n    \"        individual.insert(index_to_insert, waypoint_to_add)\\n\",\n    \"    \\n\",\n    \"    # Delete mutation\\n\",\n    \"    elif mutation_type == 'delete':\\n\",\n    \"        index_to_delete = random.randint(0, len(individual) - 1)\\n\",\n    \"        del individual[index_to_delete]\\n\",\n    \"    \\n\",\n    \"    # Point mutation\\n\",\n    \"    elif mutation_type == 'point':\\n\",\n    \"        waypoint_to_add = individual[0]\\n\",\n    \"        while waypoint_to_add in individual:\\n\",\n    \"            waypoint_to_add = random.sample(all_waypoints, 1)[0]\\n\",\n    \"        \\n\",\n    \"        index_to_replace = random.randint(0, len(individual) - 1)\\n\",\n    \"        individual[index_to_replace] = waypoint_to_add\\n\",\n    \"        \\n\",\n    \"    # Swap mutation\\n\",\n    \"    elif mutation_type == 'swap':\\n\",\n    \"        index1 = random.randint(0, len(individual) - 1)\\n\",\n    \"        index2 = index1\\n\",\n    \"        while index2 == index1:\\n\",\n    \"            index2 = random.randint(0, len(individual) - 1)\\n\",\n    \"            \\n\",\n    \"        individual[index1], individual[index2] = individual[index2], individual[index1]\\n\",\n    \"    \\n\",\n    \"    return individual,\\n\",\n    \"\\n\",\n    \"\\n\",\n    \"toolbox.register('evaluate', eval_capitol_trip)\\n\",\n    \"toolbox.register('mutate', mutation_operator)\\n\",\n    \"toolbox.register('select', pareto_selection_operator)\\n\",\n    \"\\n\",\n    \"def pareto_eq(ind1, ind2):\\n\",\n    \"    return np.all(ind1.fitness.values == ind2.fitness.values)\\n\",\n    \"\\n\",\n    \"pop = toolbox.population(n=1000)\\n\",\n    \"hof = tools.ParetoFront(similar=pareto_eq)\\n\",\n    \"stats = tools.Statistics(lambda ind: (int(ind.fitness.values[0]), round(ind.fitness.values[1], 2)))\\n\",\n    \"stats.register('Minimum', np.min, axis=0)\\n\",\n    \"stats.register('Maximum', np.max, axis=0)\\n\",\n    \"# This stores a copy of the Pareto front for every generation of the genetic algorithm\\n\",\n    \"stats.register('ParetoFront', lambda x: copy.deepcopy(hof))\\n\",\n    \"# This is a hack to make the tqdm progress bar work\\n\",\n    \"stats.register('Progress', lambda x: pbar.update())\\n\",\n    \"\\n\",\n    \"# How many iterations of the genetic algorithm to run\\n\",\n    \"# The more iterations you allow it to run, the better the solutions it will find\\n\",\n    \"total_gens = 5000\\n\",\n    \"\\n\",\n    \"pbar = tqdm(total=total_gens)\\n\",\n    \"pop, log = algorithms.eaSimple(pop, toolbox, cxpb=0., mutpb=1.0, ngen=total_gens, \\n\",\n    \"                               stats=stats, halloffame=hof, verbose=False)\\n\",\n    \"pbar.close()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Animated road trip map\\n\",\n    \"\\n\",\n    \"Now that we've optimized the road trip, let's visualize it!\\n\",\n    \"\\n\",\n    \"The function below will take the results of the genetic algorithm and generate an animated map showing the Pareto optimized road trips.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def create_animated_road_trip_map(optimized_routes):\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"        This function takes a list of optimized road trips and generates\\n\",\n    \"        an animated map of them using the Google Maps API.\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    \\n\",\n    \"    # This line makes the road trips round trips\\n\",\n    \"    optimized_routes = [list(route) + [route[0]] for route in optimized_routes]\\n\",\n    \"\\n\",\n    \"    Page_1 = \\\"\\\"\\\"\\n\",\n    \"    <!DOCTYPE html>\\n\",\n    \"    <html lang=\\\"en\\\">\\n\",\n    \"      <head>\\n\",\n    \"        <meta charset=\\\"utf-8\\\">\\n\",\n    \"        <meta name=\\\"viewport\\\" content=\\\"initial-scale=1.0, user-scalable=no\\\">\\n\",\n    \"        <meta name=\\\"description\\\" content=\\\"Randy Olson uses machine learning to find the optimal road trip across the U.S.\\\">\\n\",\n    \"        <meta name=\\\"author\\\" content=\\\"Randal S. Olson\\\">\\n\",\n    \"        \\n\",\n    \"        <title>An optimized road trip across the U.S. according to machine learning</title>\\n\",\n    \"        <style>\\n\",\n    \"          html, body, #map-canvas {\\n\",\n    \"              height: 100%;\\n\",\n    \"              margin: 0px;\\n\",\n    \"              padding: 0px\\n\",\n    \"          }\\n\",\n    \"          #panel {\\n\",\n    \"              position: absolute;\\n\",\n    \"              top: 5px;\\n\",\n    \"              left: 50%;\\n\",\n    \"              margin-left: -180px;\\n\",\n    \"              z-index: 5;\\n\",\n    \"              background-color: #fff;\\n\",\n    \"              padding: 10px;\\n\",\n    \"              border: 1px solid #999;\\n\",\n    \"          }\\n\",\n    \"        </style>\\n\",\n    \"        <script src=\\\"https://maps.googleapis.com/maps/api/js?v=3\\\"></script>\\n\",\n    \"        <script>\\n\",\n    \"            var routesList = [];\\n\",\n    \"            var markerOptions = {icon: \\\"http://maps.gstatic.com/mapfiles/markers2/marker.png\\\"};\\n\",\n    \"            var directionsDisplayOptions = {preserveViewport: true,\\n\",\n    \"                                            markerOptions: markerOptions};\\n\",\n    \"            var directionsService = new google.maps.DirectionsService();\\n\",\n    \"            var map;\\n\",\n    \"            var mapNum = 0;\\n\",\n    \"            var numRoutesRendered = 0;\\n\",\n    \"            var numRoutes = 0;\\n\",\n    \"            \\n\",\n    \"            function initialize() {\\n\",\n    \"                var center = new google.maps.LatLng(39, -96);\\n\",\n    \"                var mapOptions = {\\n\",\n    \"                    zoom: 5,\\n\",\n    \"                    center: center\\n\",\n    \"                };\\n\",\n    \"                map = new google.maps.Map(document.getElementById(\\\"map-canvas\\\"), mapOptions);\\n\",\n    \"                for (var i = 0; i < routesList.length; i++) {\\n\",\n    \"                    routesList[i].setMap(map); \\n\",\n    \"                }\\n\",\n    \"            }\\n\",\n    \"            function calcRoute(start, end, routes) {\\n\",\n    \"                var directionsDisplay = new google.maps.DirectionsRenderer(directionsDisplayOptions);\\n\",\n    \"                var waypts = [];\\n\",\n    \"                for (var i = 0; i < routes.length; i++) {\\n\",\n    \"                    waypts.push({\\n\",\n    \"                        location:routes[i],\\n\",\n    \"                        stopover:true});\\n\",\n    \"                    }\\n\",\n    \"\\n\",\n    \"                var request = {\\n\",\n    \"                    origin: start,\\n\",\n    \"                    destination: end,\\n\",\n    \"                    waypoints: waypts,\\n\",\n    \"                    optimizeWaypoints: false,\\n\",\n    \"                    travelMode: google.maps.TravelMode.DRIVING\\n\",\n    \"                };\\n\",\n    \"                directionsService.route(request, function(response, status) {\\n\",\n    \"                    if (status == google.maps.DirectionsStatus.OK) {\\n\",\n    \"                        directionsDisplay.setDirections(response);\\n\",\n    \"                        directionsDisplay.setMap(map);\\n\",\n    \"                        numRoutesRendered += 1;\\n\",\n    \"                        \\n\",\n    \"                        if (numRoutesRendered == numRoutes) {\\n\",\n    \"                            mapNum += 1;\\n\",\n    \"                            if (mapNum < 47) {\\n\",\n    \"                                setTimeout(function() {\\n\",\n    \"                                    return createRoutes(allRoutes[mapNum]);\\n\",\n    \"                                }, 5000);\\n\",\n    \"                            }\\n\",\n    \"                        }\\n\",\n    \"                    }\\n\",\n    \"                });\\n\",\n    \"                \\n\",\n    \"                routesList.push(directionsDisplay);\\n\",\n    \"            }\\n\",\n    \"            function createRoutes(route) {\\n\",\n    \"                // Clear the existing routes (if any)\\n\",\n    \"                for (var i = 0; i < routesList.length; i++) {\\n\",\n    \"                    routesList[i].setMap(null);\\n\",\n    \"                }\\n\",\n    \"                routesList = [];\\n\",\n    \"                numRoutes = Math.floor((route.length - 1) / 9 + 1);\\n\",\n    \"                numRoutesRendered = 0;\\n\",\n    \"            \\n\",\n    \"                // Google's free map API is limited to 10 waypoints so need to break into batches\\n\",\n    \"                var subset = 0;\\n\",\n    \"                while (subset < route.length) {\\n\",\n    \"                    var waypointSubset = route.slice(subset, subset + 10);\\n\",\n    \"                    var startPoint = waypointSubset[0];\\n\",\n    \"                    var midPoints = waypointSubset.slice(1, waypointSubset.length - 1);\\n\",\n    \"                    var endPoint = waypointSubset[waypointSubset.length - 1];\\n\",\n    \"                    calcRoute(startPoint, endPoint, midPoints);\\n\",\n    \"                    subset += 9;\\n\",\n    \"                }\\n\",\n    \"            }\\n\",\n    \"            \\n\",\n    \"            allRoutes = [];\\n\",\n    \"            \\\"\\\"\\\"\\n\",\n    \"    Page_2 = \\\"\\\"\\\"\\n\",\n    \"            createRoutes(allRoutes[mapNum]);\\n\",\n    \"            google.maps.event.addDomListener(window, \\\"load\\\", initialize);\\n\",\n    \"        </script>\\n\",\n    \"      </head>\\n\",\n    \"      <body>\\n\",\n    \"        <div id=\\\"map-canvas\\\"></div>\\n\",\n    \"      </body>\\n\",\n    \"    </html>\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"\\n\",\n    \"    with open('us-state-capitols-animated-map.html', 'w') as output_file:\\n\",\n    \"        output_file.write(Page_1)\\n\",\n    \"        for route in optimized_routes:\\n\",\n    \"            output_file.write('allRoutes.push({});'.format(str(route)))\\n\",\n    \"        output_file.write(Page_2)\\n\",\n    \"\\n\",\n    \"create_animated_road_trip_map(reversed(hof))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"!open us-state-capitols-animated-map.html\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Individual road trip maps\\n\",\n    \"\\n\",\n    \"We can also visualize single trips at a time instead of\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def create_individual_road_trip_maps(optimized_routes):\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"        This function takes a list of optimized road trips and generates\\n\",\n    \"        individual maps of them using the Google Maps API.\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    \\n\",\n    \"    # This line makes the road trips round trips\\n\",\n    \"    optimized_routes = [list(route) + [route[0]] for route in optimized_routes]\\n\",\n    \"\\n\",\n    \"    for route_num, route in enumerate(optimized_routes):\\n\",\n    \"        Page_1 = \\\"\\\"\\\"\\n\",\n    \"        <!DOCTYPE html>\\n\",\n    \"        <html lang=\\\"en\\\">\\n\",\n    \"          <head>\\n\",\n    \"            <meta charset=\\\"utf-8\\\">\\n\",\n    \"            <meta name=\\\"viewport\\\" content=\\\"initial-scale=1.0, user-scalable=no\\\">\\n\",\n    \"            <meta name=\\\"description\\\" content=\\\"Randy Olson uses machine learning to find the optimal road trip across the U.S.\\\">\\n\",\n    \"            <meta name=\\\"author\\\" content=\\\"Randal S. Olson\\\">\\n\",\n    \"\\n\",\n    \"            <title>An optimized road trip across the U.S. according to machine learning</title>\\n\",\n    \"            <style>\\n\",\n    \"              html, body, #map-canvas {\\n\",\n    \"                  height: 100%;\\n\",\n    \"                  margin: 0px;\\n\",\n    \"                  padding: 0px\\n\",\n    \"              }\\n\",\n    \"              #panel {\\n\",\n    \"                  position: absolute;\\n\",\n    \"                  top: 5px;\\n\",\n    \"                  left: 50%;\\n\",\n    \"                  margin-left: -180px;\\n\",\n    \"                  z-index: 5;\\n\",\n    \"                  background-color: #fff;\\n\",\n    \"                  padding: 10px;\\n\",\n    \"                  border: 1px solid #999;\\n\",\n    \"              }\\n\",\n    \"            </style>\\n\",\n    \"            <script src=\\\"https://maps.googleapis.com/maps/api/js?v=3\\\"></script>\\n\",\n    \"            <script>\\n\",\n    \"                var routesList = [];\\n\",\n    \"                var markerOptions = {icon: \\\"http://maps.gstatic.com/mapfiles/markers2/marker.png\\\"};\\n\",\n    \"                var directionsDisplayOptions = {preserveViewport: true,\\n\",\n    \"                                                markerOptions: markerOptions};\\n\",\n    \"                var directionsService = new google.maps.DirectionsService();\\n\",\n    \"                var map;\\n\",\n    \"\\n\",\n    \"                function initialize() {\\n\",\n    \"                    var center = new google.maps.LatLng(39, -96);\\n\",\n    \"                    var mapOptions = {\\n\",\n    \"                        zoom: 5,\\n\",\n    \"                        center: center\\n\",\n    \"                    };\\n\",\n    \"                    map = new google.maps.Map(document.getElementById(\\\"map-canvas\\\"), mapOptions);\\n\",\n    \"                    for (var i = 0; i < routesList.length; i++) {\\n\",\n    \"                        routesList[i].setMap(map); \\n\",\n    \"                    }\\n\",\n    \"                }\\n\",\n    \"                function calcRoute(start, end, routes) {\\n\",\n    \"                    var directionsDisplay = new google.maps.DirectionsRenderer(directionsDisplayOptions);\\n\",\n    \"                    var waypts = [];\\n\",\n    \"                    for (var i = 0; i < routes.length; i++) {\\n\",\n    \"                        waypts.push({\\n\",\n    \"                            location:routes[i],\\n\",\n    \"                            stopover:true});\\n\",\n    \"                        }\\n\",\n    \"\\n\",\n    \"                    var request = {\\n\",\n    \"                        origin: start,\\n\",\n    \"                        destination: end,\\n\",\n    \"                        waypoints: waypts,\\n\",\n    \"                        optimizeWaypoints: false,\\n\",\n    \"                        travelMode: google.maps.TravelMode.DRIVING\\n\",\n    \"                    };\\n\",\n    \"                    directionsService.route(request, function(response, status) {\\n\",\n    \"                        if (status == google.maps.DirectionsStatus.OK) {\\n\",\n    \"                            directionsDisplay.setDirections(response);\\n\",\n    \"                            directionsDisplay.setMap(map);\\n\",\n    \"                        }\\n\",\n    \"                    });\\n\",\n    \"\\n\",\n    \"                    routesList.push(directionsDisplay);\\n\",\n    \"                }\\n\",\n    \"                function createRoutes(route) {\\n\",\n    \"                    // Google's free map API is limited to 10 waypoints so need to break into batches\\n\",\n    \"                    var subset = 0;\\n\",\n    \"                    while (subset < route.length) {\\n\",\n    \"                        var waypointSubset = route.slice(subset, subset + 10);\\n\",\n    \"                        var startPoint = waypointSubset[0];\\n\",\n    \"                        var midPoints = waypointSubset.slice(1, waypointSubset.length - 1);\\n\",\n    \"                        var endPoint = waypointSubset[waypointSubset.length - 1];\\n\",\n    \"                        calcRoute(startPoint, endPoint, midPoints);\\n\",\n    \"                        subset += 9;\\n\",\n    \"                    }\\n\",\n    \"                }\\n\",\n    \"\\n\",\n    \"                \\\"\\\"\\\"\\n\",\n    \"        Page_2 = \\\"\\\"\\\"\\n\",\n    \"                createRoutes(optimized_route);\\n\",\n    \"                google.maps.event.addDomListener(window, \\\"load\\\", initialize);\\n\",\n    \"            </script>\\n\",\n    \"          </head>\\n\",\n    \"          <body>\\n\",\n    \"            <div id=\\\"map-canvas\\\"></div>\\n\",\n    \"          </body>\\n\",\n    \"        </html>\\n\",\n    \"        \\\"\\\"\\\"\\n\",\n    \"\\n\",\n    \"        with open('optimized-us-capitol-trip-{}-states.html'.format(route_num + 2), 'w') as output_file:\\n\",\n    \"            output_file.write(Page_1)\\n\",\n    \"            output_file.write('optimized_route = {};'.format(str(route)))\\n\",\n    \"            output_file.write(Page_2)\\n\",\n    \"\\n\",\n    \"create_individual_road_trip_maps(reversed(hof))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"!open optimized-us-capitol-trip-48-states.html\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Some technical notes\\n\",\n    \"\\n\",\n    \"As I mentioned in the [original article](http://www.randalolson.com/2015/03/08/computing-the-optimal-road-trip-across-the-u-s/), by the end of 5,000 generations, the genetic algorithm will very likely find a *good* but probably not the *absolute best* solution to the optimal routing problem. It is in the nature of genetic algorithms that we never know if we found the absolute best solution.\\n\",\n    \"\\n\",\n    \"However, there exist some brilliant analytical solutions to the optimal routing problem such as the [Concorde TSP solver](http://en.wikipedia.org/wiki/Concorde_TSP_Solver). If you're interested in learning more about Concorde and how it's possible to find a perfect solution to the routing problem, I advise you check out [Nathan Brixius' article](https://nathanbrixius.wordpress.com/2016/06/16/finding-optimal-state-capitol-tours-on-the-cloud-with-neos/) on the topic.\\n\",\n    \"\\n\",\n    \"### If you have any questions\\n\",\n    \"\\n\",\n    \"Please feel free to:\\n\",\n    \"\\n\",\n    \"* [Email me](http://www.randalolson.com/contact/),\\n\",\n    \"\\n\",\n    \"* [Tweet](https://twitter.com/randal_olson) at me, or\\n\",\n    \"\\n\",\n    \"* comment on the [blog post](http://www.randalolson.com/2016/06/05/computing-optimal-road-trips-on-a-limited-budget/)\\n\",\n    \"\\n\",\n    \"I'm usually pretty good about getting back to people within a day or two.\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.6.5\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n"
  },
  {
    "path": "pct-bachelors-degrees-conferred-women-usa/Percentage of Bachelor’s degrees conferred to women in the US, by major.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Percentage of Bachelor’s degrees conferred to women in the U.S., by major\\n\",\n    \"\\n\",\n    \"This notebook provides the methodology and code used in the blog post, [Percentage of Bachelor’s degrees conferred to women in the U.S., by major (1970-2014)](http://www.randalolson.com/2014/06/14/percentage-of-bachelors-degrees-conferred-to-women-by-major-1970-2012/).\\n\",\n    \"\\n\",\n    \"### Notebook by [Randal S. Olson](http://www.randalolson.com)\\n\",\n    \"\\n\",\n    \"Please see the [repository README file](https://github.com/rhiever/Data-Analysis-and-Machine-Learning-Projects#license) for the licenses and usage terms for the instructional material and code in this notebook. In general, I have licensed this material so that it is as widely useable and shareable as possible.\\n\",\n    \"\\n\",\n    \"### Required Python libraries\\n\",\n    \"\\n\",\n    \"If you don't have Python on your computer, you can use the [Anaconda Python distribution](http://continuum.io/downloads) to install most of the Python packages you need. Anaconda provides a simple double-click installer for your convenience.\\n\",\n    \"\\n\",\n    \"This code uses base Python libraries except for the `BeautifulSoup`, `pandas`, and `matplotlib` packages. You can install these packages using `pip` by typing the following command into your command line:\\n\",\n    \"\\n\",\n    \"> pip install beautifulsoup4 pandas matplotlib \\n\",\n    \"\\n\",\n    \"If you're on a Mac, Linux, or Unix machine, you may need to type `sudo` before the command to install the package with administrator privileges.\\n\",\n    \"\\n\",\n    \"### Scraping the NCES database\\n\",\n    \"\\n\",\n    \"To acquire the data used in the blog post, we need to scrape the [NCES database](http://nces.ed.gov/programs/digest/current_tables.asp). The NCES database is available for download as Excel files, but we didn't want to deal with a bunch of Excel files. Instead, let's scrape the NCES database web pages directly using [BeautifulSoup](https://www.crummy.com/software/BeautifulSoup/).\\n\",\n    \"\\n\",\n    \"Running the code below will create a file called `gender_degree_data.tsv` that contains all data about the gender breakdowns of various degree majors in the US.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": false,\n    \"scrolled\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"from bs4 import BeautifulSoup\\n\",\n    \"import requests\\n\",\n    \"\\n\",\n    \"with open('gender_degree_data.tsv', 'w') as out_file:\\n\",\n    \"\\n\",\n    \"    out_file.write('\\\\t'.join(['Year', 'Degree_Major',\\n\",\n    \"                          'Total_Bachelors',\\n\",\n    \"                          'Percent_change_Bachelors',\\n\",\n    \"                          'Male_Bachelors', 'Female_Bachelors', 'Female_percent_Bachelors',\\n\",\n    \"                          'Total_Masters', 'Male_Masters', 'Female_Masters',\\n\",\n    \"                          'Total_Doctorates', 'Male_Doctorates', 'Female_Doctorates']) + '\\\\n')\\n\",\n    \"\\n\",\n    \"    table_list_response = requests.get('http://nces.ed.gov/programs/digest/current_tables.asp')\\n\",\n    \"    table_list_response = BeautifulSoup(table_list_response.text, 'lxml')\\n\",\n    \"\\n\",\n    \"    for link in table_list_response.find_all('a', href=True):\\n\",\n    \"        # We only want the tables that stratify the data by degree and gender, which are in table group 325\\n\",\n    \"        if 'dt15_325' in link['href'] and int(link.text.split('.')[1]) % 5 == 0:\\n\",\n    \"            url = 'http://nces.ed.gov/programs/digest/{}'.format(link['href'])\\n\",\n    \"            url_response = requests.get(url)\\n\",\n    \"            url_response = BeautifulSoup(url_response.text, 'lxml')\\n\",\n    \"            degree_major = url_response.find('title').text.split('Degrees in')[1].split('conferred')[0].strip()\\n\",\n    \"\\n\",\n    \"            all_trs = url_response.find_all('tr')\\n\",\n    \"            for tr in all_trs:\\n\",\n    \"                # We only want to parse entries that correspond to a certain year\\n\",\n    \"                year_header = tr.find('th')\\n\",\n    \"                if year_header is None:\\n\",\n    \"                    continue\\n\",\n    \"\\n\",\n    \"                # Stop parsing after all of the years are listed\\n\",\n    \"                if 'Percent change' in year_header.text:\\n\",\n    \"                    break\\n\",\n    \"\\n\",\n    \"                # Years always have a dash (-) in them\\n\",\n    \"                if '-' not in year_header.text:\\n\",\n    \"                    continue\\n\",\n    \"\\n\",\n    \"                year = str(int(year_header.text.split('-')[0]) + 1)\\n\",\n    \"                year_vals = [x.text.replace(',', '').replace('†', '0').replace('#', '0') for x in tr.find_all('td')]\\n\",\n    \"\\n\",\n    \"                out_text = '\\\\t'.join([year, degree_major] + year_vals) + '\\\\n'\\n\",\n    \"                out_file.write(out_text)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Visualizing the gender breakdowns for the various degree programs\\n\",\n    \"\\n\",\n    \"Next, let's use [matplotlib](http://matplotlib.org) to visualize the trends in the gender breakdowns.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/Users/randal_olson/anaconda/lib/python3.5/site-packages/matplotlib/__init__.py:872: UserWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.\\n\",\n      \"  warnings.warn(self.msg_depr % (key, alt_key))\\n\",\n      \"/Users/randal_olson/anaconda/lib/python3.5/site-packages/matplotlib/__init__.py:872: UserWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.\\n\",\n      \"  warnings.warn(self.msg_depr % (key, alt_key))\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"''\"\n      ]\n     },\n     \"execution_count\": 2,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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Hbv3m32excsWNCudD08PMhgMAjLzJo1i8aOHSt8Dg0NpZdeeqmtzaQ3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\\nIiKiBoMBChERERERNRj/D6kSljdXPIoEAAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x116eb8f98>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"%matplotlib inline\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"import pandas as pd\\n\",\n    \"\\n\",\n    \"# This is my custom style that does most of the plot formatting\\n\",\n    \"plt.style.use('https://gist.githubusercontent.com/rhiever/a4fb39bfab4b33af0018/raw/b25b4ba478c2e163dd54fd5600e80ca7453459da/tableau20.mplstyle')\\n\",\n    \"\\n\",\n    \"degree_gender_data = pd.read_csv('gender_degree_data.tsv', sep='\\\\t')\\n\",\n    \"degree_gender_data = degree_gender_data[degree_gender_data['Year'] >= 1970]\\n\",\n    \"degree_gender_data.set_index('Year', inplace=True)\\n\",\n    \"\\n\",\n    \"# Create a list of the degree majors ranked by their last value in the time series\\n\",\n    \"# We'll use this list to determine what colors the degree majors are assigned\\n\",\n    \"degree_major_order = degree_gender_data.groupby('Degree_Major')['Female_percent_Bachelors'].last()\\n\",\n    \"degree_major_order = degree_major_order.sort_values(ascending=False).index.values\\n\",\n    \"degree_major_order_dict = dict(zip(degree_major_order, range(len(degree_major_order))))\\n\",\n    \"\\n\",\n    \"degree_gender_data['Degree_Major_Order'] = degree_gender_data[\\n\",\n    \"    'Degree_Major'].apply(lambda major: degree_major_order_dict[major])\\n\",\n    \"\\n\",\n    \"degree_gender_data.groupby('Degree_Major_Order')['Female_percent_Bachelors'].plot(figsize=(10, 12))\\n\",\n    \"\\n\",\n    \"plt.xlabel('')\\n\",\n    \"plt.yticks(range(0, 91, 10), ['{}%'.format(x) for x in range(0, 91, 10)])\\n\",\n    \"\\n\",\n    \"plt.xlim(1969, 2014)\\n\",\n    \"plt.ylim(-1, 90)\\n\",\n    \"\\n\",\n    \"plt.title('Percentage of Bachelor\\\\'s degrees conferred to women'\\n\",\n    \"          'in the U.S.A., by major (1970-2014)\\\\n', fontsize=14)\\n\",\n    \"plt.grid(False, axis='x')\\n\",\n    \"\\n\",\n    \"degree_major_pcts = dict(degree_gender_data.groupby(\\n\",\n    \"        'Degree_Major')['Female_percent_Bachelors'].last().iteritems())\\n\",\n    \"\\n\",\n    \"degree_major_color_map = [x['color'] for x in plt.rcParams['axes.prop_cycle']]\\n\",\n    \"\\n\",\n    \"# We use this dictionary to rename the degree majors to shorter names\\n\",\n    \"degree_major_name_map = {\\n\",\n    \"    'the social sciences and history': 'Social Sciences and History',\\n\",\n    \"    'the health professions and related programs': 'Health Professions',\\n\",\n    \"    'visual and performing arts': 'Art and Performance',\\n\",\n    \"    'foreign languages and literatures': 'Foreign Languages',\\n\",\n    \"    'engineering and engineering technologies': 'Engineering',\\n\",\n    \"    'the biological and biomedical sciences': 'Biology',\\n\",\n    \"    'mathematics and statistics': 'Math and Statistics',\\n\",\n    \"    'agriculture and natural resources': 'Agriculture',\\n\",\n    \"    'the physical sciences and science technologies': 'Physical Sciences',\\n\",\n    \"    'communication, journalism, and related '\\n\",\n    \"    'programs and in communications technologies': 'Communications\\\\nand Journalism',\\n\",\n    \"    'public administration and social services': 'Public Administration',\\n\",\n    \"    'psychology': 'Psychology',\\n\",\n    \"    'English language and literature/letters': 'English',\\n\",\n    \"    'computer and information sciences': 'Computer Science',\\n\",\n    \"    'education': 'Education',\\n\",\n    \"    'business': 'Business',\\n\",\n    \"    'architecture and related services': 'Architecture',\\n\",\n    \"}\\n\",\n    \"\\n\",\n    \"# We use these offsets to prevent the degree major labels from overlapping\\n\",\n    \"degree_major_offset_map = {\\n\",\n    \"    'foreign languages and literatures': 1.0,\\n\",\n    \"    'English language and literature/letters': -0.5,\\n\",\n    \"    'agriculture and natural resources': 0.5,\\n\",\n    \"    'business': -0.5,\\n\",\n    \"    'architecture and related services': 0.75,\\n\",\n    \"    'mathematics and statistics': -0.75,\\n\",\n    \"    'engineering and engineering technologies': 0.75,\\n\",\n    \"    'computer and information sciences': -0.75,\\n\",\n    \"}\\n\",\n    \"\\n\",\n    \"# Draw the degree major labels at the end of the time series lines\\n\",\n    \"for degree_major in degree_major_pcts:\\n\",\n    \"    plt.text(2014.5, degree_major_pcts[degree_major] - 0.5 + degree_major_offset_map.get(degree_major, 0),\\n\",\n    \"             degree_major_name_map[degree_major],\\n\",\n    \"             color=degree_major_color_map[degree_major_order_dict[degree_major]])\\n\",\n    \"\\n\",\n    \"plt.text(1967, -9,\\n\",\n    \"         '\\\\nData source: nces.ed.gov/programs/digest/current_tables.asp (Tables 325.*)\\\\n'\\n\",\n    \"         'Author: Randy Olson (randalolson.com / @randal_olson)',\\n\",\n    \"         ha='left', fontsize=10)\\n\",\n    \"\\n\",\n    \"plt.savefig('pct-bachelors-degrees-women-usa-1970-2014.png')\\n\",\n    \";\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.5.1\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "pct-bachelors-degrees-conferred-women-usa/gender_degree_data.tsv",
    "content": "Year\tDegree_Major\tTotal_Bachelors\tPercent_change_Bachelors\tMale_Bachelors\tFemale_Bachelors\tFemale_percent_Bachelors\tTotal_Masters\tMale_Masters\tFemale_Masters\tTotal_Doctorates\tMale_Doctorates\tFemale_Doctorates\n1971\tagriculture and natural resources\t12672\t0\t12136\t536\t4.2\t2457\t2313\t144\t1086\t1055\t31\n1972\tagriculture and natural resources\t13516\t6.7\t12779\t737\t5.5\t2680\t2490\t190\t971\t945\t26\n1973\tagriculture and natural resources\t14756\t9.2\t13661\t1095\t7.4\t2807\t2588\t219\t1059\t1031\t28\n1974\tagriculture and natural resources\t16253\t10.1\t14684\t1569\t9.7\t2928\t2640\t288\t930\t897\t33\n1975\tagriculture and natural resources\t17528\t7.8\t15061\t2467\t14.1\t3067\t2703\t364\t991\t958\t33\n1976\tagriculture and natural resources\t19402\t10.7\t15845\t3557\t18.3\t3340\t2862\t478\t928\t867\t61\n1977\tagriculture and natural resources\t21467\t10.6\t16690\t4777\t22.3\t3724\t3177\t547\t893\t831\t62\n1978\tagriculture and natural resources\t22650\t5.5\t17069\t5581\t24.6\t4023\t3268\t755\t971\t909\t62\n1979\tagriculture and natural resources\t23134\t2.1\t16854\t6280\t27.1\t3994\t3187\t807\t950\t877\t73\n1980\tagriculture and natural resources\t22802\t-1.4\t16045\t6757\t29.6\t3976\t3082\t894\t991\t879\t112\n1981\tagriculture and natural resources\t21886\t-4.0\t15154\t6732\t30.8\t4003\t3061\t942\t1067\t940\t127\n1982\tagriculture and natural resources\t21029\t-3.9\t14443\t6586\t31.3\t4163\t3114\t1049\t1079\t925\t154\n1983\tagriculture and natural resources\t20909\t-0.6\t14085\t6824\t32.6\t4254\t3129\t1125\t1149\t1004\t145\n1984\tagriculture and natural resources\t19317\t-7.6\t13206\t6111\t31.6\t4178\t2989\t1189\t1172\t1001\t171\n1985\tagriculture and natural resources\t18107\t-6.3\t12477\t5630\t31.1\t3928\t2846\t1082\t1213\t1036\t177\n1986\tagriculture and natural resources\t16823\t-7.1\t11544\t5279\t31.4\t3801\t2701\t1100\t1158\t966\t192\n1987\tagriculture and natural resources\t14991\t-10.9\t10314\t4677\t31.2\t3522\t2460\t1062\t1049\t871\t178\n1988\tagriculture and natural resources\t14222\t-5.1\t9744\t4478\t31.5\t3479\t2427\t1052\t1142\t926\t216\n1989\tagriculture and natural resources\t13492\t-5.1\t9298\t4194\t31.1\t3245\t2231\t1014\t1183\t950\t233\n1990\tagriculture and natural resources\t12900\t-4.4\t8822\t4078\t31.6\t3382\t2239\t1143\t1295\t1038\t257\n1991\tagriculture and natural resources\t13124\t1.7\t8832\t4292\t32.7\t3295\t2160\t1135\t1185\t953\t232\n1992\tagriculture and natural resources\t15113\t15.2\t9867\t5246\t34.7\t3730\t2409\t1321\t1205\t955\t250\n1993\tagriculture and natural resources\t16769\t11.0\t11079\t5690\t33.9\t3959\t2474\t1485\t1159\t869\t290\n1994\tagriculture and natural resources\t18056\t7.7\t11746\t6310\t34.9\t4110\t2512\t1598\t1262\t969\t293\n1995\tagriculture and natural resources\t19832\t9.8\t12686\t7146\t36.0\t4234\t2541\t1693\t1256\t955\t301\n1996\tagriculture and natural resources\t21425\t8.0\t13531\t7894\t36.8\t4551\t2642\t1909\t1259\t926\t333\n1997\tagriculture and natural resources\t22597\t5.5\t13791\t8806\t39.0\t4505\t2601\t1904\t1202\t875\t327\n1998\tagriculture and natural resources\t23276\t3.0\t13806\t9470\t40.7\t4464\t2545\t1919\t1290\t924\t366\n1999\tagriculture and natural resources\t24179\t3.9\t14045\t10134\t41.9\t4376\t2360\t2016\t1249\t869\t380\n2000\tagriculture and natural resources\t24238\t0.2\t13843\t10395\t42.9\t4360\t2356\t2004\t1168\t803\t365\n2001\tagriculture and natural resources\t23370\t-3.6\t12840\t10530\t45.1\t4272\t2251\t2021\t1127\t741\t386\n2002\tagriculture and natural resources\t23331\t-0.2\t12630\t10701\t45.9\t4503\t2340\t2163\t1148\t760\t388\n2003\tagriculture and natural resources\t23348\t0.1\t12343\t11005\t47.1\t4492\t2232\t2260\t1229\t790\t439\n2004\tagriculture and natural resources\t22835\t-2.2\t11889\t10946\t47.9\t4783\t2306\t2477\t1185\t758\t427\n2005\tagriculture and natural resources\t23002\t0.7\t11987\t11015\t47.9\t4746\t2288\t2458\t1173\t763\t410\n2006\tagriculture and natural resources\t23053\t0.2\t12063\t10990\t47.7\t4640\t2280\t2360\t1194\t710\t484\n2007\tagriculture and natural resources\t23133\t0.3\t12309\t10824\t46.8\t4623\t2174\t2449\t1272\t768\t504\n2008\tagriculture and natural resources\t24113\t4.2\t12634\t11479\t47.6\t4684\t2180\t2504\t1257\t742\t515\n2009\tagriculture and natural resources\t24982\t3.6\t13096\t11886\t47.6\t4878\t2328\t2550\t1328\t741\t587\n2010\tagriculture and natural resources\t26343\t5.4\t13524\t12819\t48.7\t5215\t2512\t2703\t1149\t626\t523\n2011\tagriculture and natural resources\t28630\t8.7\t14678\t13952\t48.7\t5766\t2746\t3020\t1246\t675\t571\n2012\tagriculture and natural resources\t30972\t8.2\t15485\t15487\t50.0\t6390\t3026\t3364\t1333\t721\t612\n2013\tagriculture and natural resources\t33592\t8.5\t16618\t16974\t50.5\t6336\t2912\t3424\t1411\t767\t644\n2014\tagriculture and natural resources\t35116\t4.5\t17249\t17867\t50.9\t6544\t2966\t3578\t1407\t739\t668\n1950\tarchitecture and related services\t2563\t0\t2441\t122\t4.8\t166\t159\t7\t1\t1\t0\n1960\tarchitecture and related services\t1801\t0\t1744\t57\t3.2\t319\t305\t14\t17\t17\t0\n1968\tarchitecture and related services\t3057\t0\t2931\t126\t4.1\t1021\t953\t68\t15\t15\t0\n1970\tarchitecture and related services\t4105\t0\t3888\t217\t5.3\t1427\t1260\t167\t35\t33\t2\n1971\tarchitecture and related services\t5570\t35.7\t4906\t664\t11.9\t1705\t1469\t236\t36\t33\t3\n1972\tarchitecture and related services\t6440\t15.6\t5667\t773\t12.0\t1899\t1626\t273\t50\t43\t7\n1973\tarchitecture and related services\t6962\t8.1\t6042\t920\t13.2\t2307\t1943\t364\t58\t54\t4\n1974\tarchitecture and related services\t7822\t12.4\t6665\t1157\t14.8\t2702\t2208\t494\t69\t65\t4\n1975\tarchitecture and related services\t8226\t5.2\t6791\t1435\t17.4\t2938\t2343\t595\t69\t58\t11\n1976\tarchitecture and related services\t9146\t11.2\t7396\t1750\t19.1\t3215\t2545\t670\t82\t69\t13\n1977\tarchitecture and related services\t9222\t0.8\t7249\t1973\t21.4\t3213\t2489\t724\t73\t62\t11\n1978\tarchitecture and related services\t9250\t0.3\t7054\t2196\t23.7\t3115\t2304\t811\t73\t57\t16\n1979\tarchitecture and related services\t9273\t0.2\t6876\t2397\t25.8\t3113\t2226\t887\t96\t74\t22\n1980\tarchitecture and related services\t9132\t-1.5\t6596\t2536\t27.8\t3139\t2245\t894\t79\t66\t13\n1981\tarchitecture and related services\t9455\t3.5\t6800\t2655\t28.1\t3153\t2234\t919\t93\t73\t20\n1982\tarchitecture and related services\t9728\t2.9\t6825\t2903\t29.8\t3327\t2242\t1085\t80\t58\t22\n1983\tarchitecture and related services\t9823\t1.0\t6403\t3420\t34.8\t3357\t2224\t1133\t97\t74\t23\n1984\tarchitecture and related services\t9186\t-6.5\t5895\t3291\t35.8\t3223\t2197\t1026\t84\t62\t22\n1985\tarchitecture and related services\t9325\t1.5\t6019\t3306\t35.5\t3275\t2148\t1127\t89\t66\t23\n1986\tarchitecture and related services\t9119\t-2.2\t5824\t3295\t36.1\t3260\t2129\t1131\t73\t56\t17\n1987\tarchitecture and related services\t8950\t-1.9\t5617\t3333\t37.2\t3163\t2086\t1077\t92\t66\t26\n1988\tarchitecture and related services\t8603\t-3.9\t5271\t3332\t38.7\t3159\t2042\t1117\t98\t66\t32\n1989\tarchitecture and related services\t9150\t6.4\t5545\t3605\t39.4\t3383\t2192\t1191\t86\t63\t23\n1990\tarchitecture and related services\t9364\t2.3\t5703\t3661\t39.1\t3499\t2228\t1271\t103\t73\t30\n1991\tarchitecture and related services\t9781\t4.5\t5788\t3993\t40.8\t3490\t2244\t1246\t135\t101\t34\n1992\tarchitecture and related services\t8753\t-10.5\t5805\t2948\t33.7\t3640\t2271\t1369\t132\t93\t39\n1993\tarchitecture and related services\t9167\t4.7\t5940\t3227\t35.2\t3808\t2376\t1432\t148\t105\t43\n1994\tarchitecture and related services\t8975\t-2.1\t5764\t3211\t35.8\t3943\t2428\t1515\t161\t111\t50\n1995\tarchitecture and related services\t8756\t-2.4\t5741\t3015\t34.4\t3923\t2310\t1613\t141\t95\t46\n1996\tarchitecture and related services\t8352\t-4.6\t5340\t3012\t36.1\t3993\t2361\t1632\t141\t96\t45\n1997\tarchitecture and related services\t7944\t-4.9\t5090\t2854\t35.9\t4034\t2336\t1698\t135\t93\t42\n1998\tarchitecture and related services\t7652\t-3.7\t4966\t2686\t35.1\t4347\t2537\t1810\t131\t80\t51\n1999\tarchitecture and related services\t8245\t7.7\t5145\t3100\t37.6\t4235\t2435\t1800\t119\t76\t43\n2000\tarchitecture and related services\t8462\t2.6\t5193\t3269\t38.6\t4268\t2508\t1760\t129\t85\t44\n2001\tarchitecture and related services\t8480\t0.2\t5086\t3394\t40.0\t4302\t2515\t1787\t153\t83\t70\n2002\tarchitecture and related services\t8808\t3.9\t5224\t3584\t40.7\t4566\t2606\t1960\t183\t117\t66\n2003\tarchitecture and related services\t9056\t2.8\t5331\t3725\t41.1\t4925\t2832\t2093\t152\t83\t69\n2004\tarchitecture and related services\t8838\t-2.4\t5059\t3779\t42.8\t5424\t3049\t2375\t173\t94\t79\n2005\tarchitecture and related services\t9237\t4.5\t5222\t4015\t43.5\t5674\t3180\t2494\t179\t110\t69\n2006\tarchitecture and related services\t9515\t3.0\t5414\t4101\t43.1\t5743\t3165\t2578\t201\t108\t93\n2007\tarchitecture and related services\t9717\t2.1\t5393\t4324\t44.5\t5951\t3304\t2647\t178\t104\t74\n2008\tarchitecture and related services\t9805\t0.9\t5579\t4226\t43.1\t6065\t3252\t2813\t199\t103\t96\n2009\tarchitecture and related services\t10119\t3.2\t5797\t4322\t42.7\t6587\t3657\t2930\t212\t113\t99\n2010\tarchitecture and related services\t10051\t-0.7\t5694\t4357\t43.3\t7280\t4012\t3268\t210\t116\t94\n2011\tarchitecture and related services\t9831\t-2.2\t5698\t4133\t42.0\t7788\t4265\t3523\t205\t110\t95\n2012\tarchitecture and related services\t9727\t-1.1\t5566\t4161\t42.8\t8448\t4504\t3944\t255\t147\t108\n2013\tarchitecture and related services\t9757\t0.3\t5581\t4176\t42.8\t8095\t4261\t3834\t247\t134\t113\n2014\tarchitecture and related services\t9144\t-6.3\t5173\t3971\t43.4\t8048\t4122\t3926\t247\t134\t113\n1952\tthe biological and biomedical sciences\t11094\t0\t8212\t2882\t26.0\t2307\t1908\t399\t764\t680\t84\n1954\tthe biological and biomedical sciences\t9279\t0\t6710\t2569\t27.7\t1610\t1287\t323\t1077\t977\t100\n1956\tthe biological and biomedical sciences\t12423\t0\t9515\t2908\t23.4\t1759\t1379\t380\t1025\t908\t117\n1958\tthe biological and biomedical sciences\t14308\t0\t11159\t3149\t22.0\t1852\t1448\t404\t1125\t987\t138\n1960\tthe biological and biomedical sciences\t15576\t0\t11654\t3922\t25.2\t2154\t1668\t486\t1205\t1086\t119\n1962\tthe biological and biomedical sciences\t16915\t0\t12136\t4779\t28.3\t2642\t1982\t660\t1338\t1179\t159\n1964\tthe biological and biomedical sciences\t22723\t0\t16321\t6402\t28.2\t3296\t2348\t948\t1625\t1432\t193\n1966\tthe biological and biomedical sciences\t26916\t0\t19368\t7548\t28.0\t4232\t3085\t1147\t2097\t1792\t305\n1968\tthe biological and biomedical sciences\t31826\t0\t22986\t8840\t27.8\t5506\t3959\t1547\t2784\t2345\t439\n1970\tthe biological and biomedical sciences\t34034\t0\t23919\t10115\t29.7\t5800\t3975\t1825\t3289\t2820\t469\n1971\tthe biological and biomedical sciences\t35705\t4.9\t25319\t10386\t29.1\t5625\t3782\t1843\t3603\t3018\t585\n1972\tthe biological and biomedical sciences\t37269\t4.4\t26314\t10955\t29.4\t5989\t4056\t1933\t3587\t2981\t606\n1973\tthe biological and biomedical sciences\t42207\t13.2\t29625\t12582\t29.8\t6156\t4317\t1839\t3583\t2892\t691\n1974\tthe biological and biomedical sciences\t48244\t14.3\t33217\t15027\t31.1\t6408\t4512\t1896\t3358\t2684\t674\n1975\tthe biological and biomedical sciences\t51609\t7.0\t34580\t17029\t33.0\t6429\t4554\t1875\t3334\t2612\t722\n1976\tthe biological and biomedical sciences\t54154\t4.9\t35498\t18656\t34.4\t6457\t4466\t1991\t3347\t2631\t716\n1977\tthe biological and biomedical sciences\t53464\t-1.3\t34178\t19286\t36.1\t6953\t4670\t2283\t3335\t2627\t708\n1978\tthe biological and biomedical sciences\t51360\t-3.9\t31673\t19687\t38.3\t6651\t4353\t2298\t3255\t2481\t774\n1979\tthe biological and biomedical sciences\t48713\t-5.2\t29173\t19540\t40.1\t6638\t4198\t2440\t3459\t2593\t866\n1980\tthe biological and biomedical sciences\t46254\t-5.0\t26797\t19457\t42.1\t6339\t4042\t2297\t3568\t2651\t917\n1981\tthe biological and biomedical sciences\t43078\t-6.9\t24124\t18954\t44.0\t5766\t3602\t2164\t3640\t2620\t1020\n1982\tthe biological and biomedical sciences\t41501\t-3.7\t22722\t18779\t45.2\t5679\t3384\t2295\t3662\t2611\t1051\n1983\tthe biological and biomedical sciences\t39924\t-3.8\t21572\t18352\t46.0\t5711\t3298\t2413\t3386\t2306\t1080\n1984\tthe biological and biomedical sciences\t38593\t-3.3\t20565\t18028\t46.7\t5489\t3123\t2366\t3496\t2416\t1080\n1985\tthe biological and biomedical sciences\t38354\t-0.6\t20071\t18283\t47.7\t5109\t2775\t2334\t3465\t2335\t1130\n1986\tthe biological and biomedical sciences\t38395\t0.1\t20000\t18395\t47.9\t5064\t2733\t2331\t3405\t2273\t1132\n1987\tthe biological and biomedical sciences\t38074\t-0.8\t19684\t18390\t48.3\t4995\t2646\t2349\t3469\t2268\t1201\n1988\tthe biological and biomedical sciences\t36688\t-3.6\t18267\t18421\t50.2\t4871\t2530\t2341\t3688\t2389\t1299\n1989\tthe biological and biomedical sciences\t36068\t-1.7\t17998\t18070\t50.1\t5034\t2598\t2436\t3617\t2299\t1318\n1990\tthe biological and biomedical sciences\t37304\t3.4\t18363\t18941\t50.8\t4941\t2509\t2432\t3922\t2478\t1444\n1991\tthe biological and biomedical sciences\t39482\t5.8\t19418\t20064\t50.8\t4834\t2417\t2417\t4152\t2618\t1534\n1992\tthe biological and biomedical sciences\t42892\t8.6\t20816\t22076\t51.5\t4862\t2437\t2425\t4442\t2749\t1693\n1993\tthe biological and biomedical sciences\t47009\t9.6\t22870\t24139\t51.3\t5026\t2540\t2486\t4749\t2866\t1883\n1994\tthe biological and biomedical sciences\t51296\t9.1\t25071\t26225\t51.1\t5462\t2681\t2781\t4891\t2910\t1981\n1995\tthe biological and biomedical sciences\t55983\t9.1\t26734\t29249\t52.2\t5873\t2920\t2953\t5069\t3012\t2057\n1996\tthe biological and biomedical sciences\t61014\t9.0\t28921\t32093\t52.6\t6593\t3212\t3381\t5250\t3062\t2188\n1997\tthe biological and biomedical sciences\t63973\t4.8\t29562\t34411\t53.8\t6986\t3419\t3567\t5313\t3014\t2299\n1998\tthe biological and biomedical sciences\t65917\t3.0\t29663\t36254\t55.0\t6848\t3336\t3512\t5474\t3123\t2351\n1999\tthe biological and biomedical sciences\t65310\t-0.9\t28507\t36803\t56.4\t6966\t3279\t3687\t5250\t3010\t2240\n2000\tthe biological and biomedical sciences\t63630\t-2.6\t26579\t37051\t58.2\t6850\t3171\t3679\t5463\t3068\t2395\n2001\tthe biological and biomedical sciences\t60576\t-4.8\t24600\t35976\t59.4\t7017\t3075\t3942\t5225\t2923\t2302\n2002\tthe biological and biomedical sciences\t60309\t-0.4\t23694\t36615\t60.7\t7011\t3033\t3978\t5104\t2836\t2268\n2003\tthe biological and biomedical sciences\t61294\t1.6\t23356\t37938\t61.9\t7050\t3015\t4035\t5268\t2866\t2402\n2004\tthe biological and biomedical sciences\t62624\t2.2\t23691\t38933\t62.2\t7732\t3271\t4461\t5538\t2975\t2563\n2005\tthe biological and biomedical sciences\t65915\t5.3\t25104\t40811\t61.9\t8284\t3361\t4923\t5935\t3025\t2910\n2006\tthe biological and biomedical sciences\t70607\t7.1\t27183\t43424\t61.5\t8781\t3709\t5072\t6162\t3138\t3024\n2007\tthe biological and biomedical sciences\t76832\t8.8\t30600\t46232\t60.2\t8898\t3639\t5259\t6764\t3440\t3324\n2008\tthe biological and biomedical sciences\t79829\t3.9\t32401\t47428\t59.4\t9689\t4094\t5595\t7400\t3645\t3755\n2009\tthe biological and biomedical sciences\t82828\t3.8\t33707\t49121\t59.3\t10018\t4250\t5768\t7499\t3549\t3950\n2010\tthe biological and biomedical sciences\t86391\t4.3\t35866\t50525\t58.5\t10730\t4612\t6118\t7672\t3603\t4069\n2011\tthe biological and biomedical sciences\t89984\t4.2\t36888\t53096\t59.0\t11324\t4869\t6455\t7693\t3648\t4045\n2012\tthe biological and biomedical sciences\t95850\t6.5\t39542\t56308\t58.7\t12419\t5378\t7041\t7935\t3708\t4227\n2013\tthe biological and biomedical sciences\t100397\t4.7\t41556\t58841\t58.6\t13300\t5783\t7517\t7939\t3689\t4250\n2014\tthe biological and biomedical sciences\t104633\t4.2\t43427\t61206\t58.5\t13960\t6072\t7888\t8302\t3884\t4418\n1956\tbusiness\t42813\t0\t38706\t4107\t9.6\t3280\t3118\t162\t129\t127\t2\n1958\tbusiness\t51991\t0\t48063\t3928\t7.6\t4223\t4072\t151\t110\t105\t5\n1960\tbusiness\t51076\t0\t47262\t3814\t7.5\t4643\t4476\t167\t135\t133\t2\n1962\tbusiness\t49017\t0\t45184\t3833\t7.8\t7691\t7484\t207\t226\t221\t5\n1964\tbusiness\t55474\t0\t51056\t4418\t8.0\t9251\t9008\t243\t275\t268\t7\n1966\tbusiness\t62721\t0\t57516\t5205\t8.3\t12959\t12628\t331\t387\t370\t17\n1968\tbusiness\t79074\t0\t72126\t6948\t8.8\t17795\t17186\t609\t441\t427\t14\n1970\tbusiness\t105580\t0\t96346\t9234\t8.7\t21561\t20792\t769\t620\t610\t10\n1971\tbusiness\t115396\t9.3\t104936\t10460\t9.1\t26490\t25458\t1032\t774\t753\t21\n1972\tbusiness\t121917\t5.7\t110331\t11586\t9.5\t30509\t29317\t1192\t876\t857\t19\n1973\tbusiness\t126717\t3.9\t113337\t13380\t10.6\t31208\t29689\t1519\t917\t864\t53\n1974\tbusiness\t132304\t4.4\t115363\t16941\t12.8\t32691\t30557\t2134\t922\t873\t49\n1975\tbusiness\t133639\t1.0\t111983\t21656\t16.2\t36315\t33274\t3041\t939\t900\t39\n1976\tbusiness\t143171\t7.1\t114986\t28185\t19.7\t42592\t37654\t4938\t906\t856\t50\n1977\tbusiness\t152010\t6.2\t116394\t35616\t23.4\t46505\t39852\t6653\t839\t785\t54\n1978\tbusiness\t160775\t5.8\t117103\t43672\t27.2\t48347\t40224\t8123\t834\t760\t74\n1979\tbusiness\t172392\t7.2\t119765\t52627\t30.5\t50397\t40766\t9631\t852\t752\t100\n1980\tbusiness\t186264\t8.0\t123639\t62625\t33.6\t55008\t42744\t12264\t767\t650\t117\n1981\tbusiness\t200521\t7.7\t126798\t73723\t36.8\t57888\t43411\t14477\t808\t686\t122\n1982\tbusiness\t215190\t7.3\t130693\t84497\t39.3\t61251\t44230\t17021\t826\t676\t150\n1983\tbusiness\t226442\t5.2\t131451\t94991\t41.9\t64741\t45987\t18754\t770\t638\t132\n1984\tbusiness\t229013\t1.1\t129296\t99717\t43.5\t66129\t46167\t19962\t926\t727\t199\n1985\tbusiness\t232282\t1.4\t127467\t104815\t45.1\t66981\t46199\t20782\t827\t685\t142\n1986\tbusiness\t236700\t1.9\t128415\t108285\t45.7\t66676\t45927\t20749\t923\t720\t203\n1987\tbusiness\t240346\t1.5\t128506\t111840\t46.5\t67093\t44913\t22180\t1062\t808\t254\n1988\tbusiness\t242859\t1.0\t129467\t113392\t46.7\t69230\t45980\t23250\t1063\t810\t253\n1989\tbusiness\t246262\t1.4\t131098\t115164\t46.8\t73065\t48540\t24525\t1100\t800\t300\n1990\tbusiness\t248568\t0.9\t132284\t116284\t46.8\t76676\t50585\t26091\t1093\t818\t275\n1991\tbusiness\t249165\t0.2\t131557\t117608\t47.2\t78255\t50883\t27372\t1185\t876\t309\n1992\tbusiness\t256298\t2.9\t135263\t121035\t47.2\t84517\t54609\t29908\t1242\t953\t289\n1993\tbusiness\t256473\t0.1\t135368\t121105\t47.2\t89425\t57504\t31921\t1346\t969\t377\n1994\tbusiness\t246265\t-4.0\t128946\t117319\t47.6\t93285\t59223\t34062\t1364\t980\t384\n1995\tbusiness\t233895\t-5.0\t121663\t112232\t48.0\t93540\t58931\t34609\t1391\t1011\t380\n1996\tbusiness\t226623\t-3.1\t116545\t110078\t48.6\t93554\t58400\t35154\t1366\t972\t394\n1997\tbusiness\t225934\t-0.3\t116023\t109911\t48.6\t97204\t59333\t37871\t1336\t947\t389\n1998\tbusiness\t232079\t2.7\t119379\t112700\t48.6\t101652\t62357\t39295\t1290\t885\t405\n1999\tbusiness\t239924\t3.4\t121741\t118183\t49.3\t106830\t64271\t42559\t1216\t848\t368\n2000\tbusiness\t256070\t6.7\t128521\t127549\t49.8\t111532\t67078\t44454\t1194\t812\t382\n2001\tbusiness\t263515\t2.9\t132275\t131240\t49.8\t115602\t68471\t47131\t1180\t783\t397\n2002\tbusiness\t278217\t5.6\t138343\t139874\t50.3\t119725\t70463\t49262\t1156\t746\t410\n2003\tbusiness\t293391\t5.5\t145075\t148316\t50.6\t127685\t75239\t52446\t1252\t820\t432\n2004\tbusiness\t307149\t4.7\t152513\t154636\t50.3\t139347\t80858\t58489\t1481\t960\t521\n2005\tbusiness\t311574\t1.4\t155940\t155634\t50.0\t142617\t82151\t60466\t1498\t901\t597\n2006\tbusiness\t318042\t2.1\t159683\t158359\t49.8\t146406\t83550\t62856\t1711\t1049\t662\n2007\tbusiness\t327531\t3.0\t166350\t161181\t49.2\t150211\t84115\t66096\t2029\t1188\t841\n2008\tbusiness\t335254\t2.4\t170978\t164276\t49.0\t155637\t86258\t69379\t2084\t1250\t834\n2009\tbusiness\t348056\t3.8\t177924\t170132\t48.9\t168404\t91991\t76413\t2123\t1302\t821\n2010\tbusiness\t358119\t2.9\t183272\t174847\t48.8\t177748\t96742\t81006\t2249\t1338\t911\n2011\tbusiness\t365133\t2.0\t187116\t178017\t48.8\t187178\t101440\t85738\t2286\t1357\t929\n2012\tbusiness\t367235\t0.6\t190180\t177055\t48.2\t191606\t103250\t88356\t2538\t1461\t1077\n2013\tbusiness\t360887\t-1.7\t187843\t173044\t47.9\t188617\t101599\t87018\t2828\t1605\t1223\n2014\tbusiness\t358079\t-0.8\t188418\t169661\t47.4\t189328\t101043\t88285\t3039\t1722\t1317\n1971\tcommunication, journalism, and related programs and in communications technologies\t10802\t0\t6989\t3813\t35.3\t1856\t1214\t642\t145\t126\t19\n1972\tcommunication, journalism, and related programs and in communications technologies\t12340\t14.2\t7964\t4376\t35.5\t2200\t1443\t757\t111\t96\t15\n1973\tcommunication, journalism, and related programs and in communications technologies\t14317\t16.0\t9074\t5243\t36.6\t2406\t1546\t860\t139\t114\t25\n1974\tcommunication, journalism, and related programs and in communications technologies\t17096\t19.4\t10536\t6560\t38.4\t2640\t1668\t972\t175\t146\t29\n1975\tcommunication, journalism, and related programs and in communications technologies\t19248\t12.6\t11455\t7793\t40.5\t2794\t1618\t1176\t165\t119\t46\n1976\tcommunication, journalism, and related programs and in communications technologies\t21282\t10.6\t12458\t8824\t41.5\t3126\t1818\t1308\t204\t154\t50\n1977\tcommunication, journalism, and related programs and in communications technologies\t23214\t9.1\t12932\t10282\t44.3\t3091\t1719\t1372\t171\t130\t41\n1978\tcommunication, journalism, and related programs and in communications technologies\t25400\t9.4\t13480\t11920\t46.9\t3296\t1673\t1623\t191\t138\t53\n1979\tcommunication, journalism, and related programs and in communications technologies\t26457\t4.2\t13266\t13191\t49.9\t2882\t1483\t1399\t192\t138\t54\n1980\tcommunication, journalism, and related programs and in communications technologies\t28616\t8.2\t13656\t14960\t52.3\t3082\t1527\t1555\t193\t121\t72\n1981\tcommunication, journalism, and related programs and in communications technologies\t31282\t9.3\t14179\t17103\t54.7\t3105\t1448\t1657\t182\t107\t75\n1982\tcommunication, journalism, and related programs and in communications technologies\t34222\t9.4\t14917\t19305\t56.4\t3327\t1578\t1749\t200\t136\t64\n1983\tcommunication, journalism, and related programs and in communications technologies\t38647\t12.9\t16213\t22434\t58.0\t3600\t1660\t1940\t208\t123\t85\n1984\tcommunication, journalism, and related programs and in communications technologies\t40203\t4.0\t16662\t23541\t58.6\t3620\t1578\t2042\t216\t129\t87\n1985\tcommunication, journalism, and related programs and in communications technologies\t42102\t4.7\t17233\t24869\t59.1\t3657\t1574\t2083\t232\t141\t91\n1986\tcommunication, journalism, and related programs and in communications technologies\t43145\t2.5\t17681\t25464\t59.0\t3808\t1603\t2205\t218\t116\t102\n1987\tcommunication, journalism, and related programs and in communications technologies\t45521\t5.5\t18201\t27320\t60.0\t3881\t1584\t2297\t275\t158\t117\n1988\tcommunication, journalism, and related programs and in communications technologies\t46916\t3.1\t18672\t28244\t60.2\t3916\t1568\t2348\t233\t133\t100\n1989\tcommunication, journalism, and related programs and in communications technologies\t48889\t4.2\t19357\t29532\t60.4\t4249\t1734\t2515\t248\t137\t111\n1990\tcommunication, journalism, and related programs and in communications technologies\t51572\t5.5\t20374\t31198\t60.5\t4353\t1705\t2648\t272\t145\t127\n1991\tcommunication, journalism, and related programs and in communications technologies\t53047\t2.9\t20806\t32241\t60.8\t4327\t1711\t2616\t272\t150\t122\n1992\tcommunication, journalism, and related programs and in communications technologies\t55144\t4.0\t21601\t33543\t60.8\t4463\t1692\t2771\t255\t132\t123\n1993\tcommunication, journalism, and related programs and in communications technologies\t54907\t-0.4\t22154\t32753\t59.7\t5179\t1969\t3210\t301\t146\t155\n1994\tcommunication, journalism, and related programs and in communications technologies\t52033\t-5.2\t21484\t30549\t58.7\t5388\t2088\t3300\t345\t174\t171\n1995\tcommunication, journalism, and related programs and in communications technologies\t48969\t-5.9\t20501\t28468\t58.1\t5559\t2086\t3473\t321\t162\t159\n1996\tcommunication, journalism, and related programs and in communications technologies\t48173\t-1.6\t19868\t28305\t58.8\t5561\t2153\t3408\t345\t190\t155\n1997\tcommunication, journalism, and related programs and in communications technologies\t47894\t-0.6\t19771\t28123\t58.7\t5552\t1989\t3563\t300\t155\t145\n1998\tcommunication, journalism, and related programs and in communications technologies\t50263\t4.9\t20103\t30160\t60.0\t6097\t2369\t3728\t359\t171\t188\n1999\tcommunication, journalism, and related programs and in communications technologies\t52397\t4.2\t20943\t31454\t60.0\t5582\t2001\t3581\t348\t182\t166\n2000\tcommunication, journalism, and related programs and in communications technologies\t57058\t8.9\t22152\t34906\t61.2\t5525\t2030\t3495\t357\t168\t189\n2001\tcommunication, journalism, and related programs and in communications technologies\t59191\t3.7\t22542\t36649\t61.9\t5645\t1964\t3681\t370\t190\t180\n2002\tcommunication, journalism, and related programs and in communications technologies\t64036\t8.2\t23692\t40344\t63.0\t5980\t2169\t3811\t383\t168\t215\n2003\tcommunication, journalism, and related programs and in communications technologies\t69828\t9.0\t25338\t44490\t63.7\t6495\t2301\t4194\t398\t179\t219\n2004\tcommunication, journalism, and related programs and in communications technologies\t73002\t4.5\t25813\t47189\t64.6\t6900\t2329\t4571\t426\t186\t240\n2005\tcommunication, journalism, and related programs and in communications technologies\t75238\t3.1\t26926\t48312\t64.2\t7195\t2535\t4660\t468\t195\t273\n2006\tcommunication, journalism, and related programs and in communications technologies\t76936\t2.3\t28142\t48794\t63.4\t7745\t2611\t5134\t464\t207\t257\n2007\tcommunication, journalism, and related programs and in communications technologies\t78420\t1.9\t29009\t49411\t63.0\t7272\t2485\t4787\t480\t188\t292\n2008\tcommunication, journalism, and related programs and in communications technologies\t81048\t3.4\t30384\t50664\t62.5\t7546\t2580\t4966\t496\t209\t287\n2009\tcommunication, journalism, and related programs and in communications technologies\t83084\t2.5\t31207\t51877\t62.4\t7517\t2446\t5071\t535\t225\t310\n2010\tcommunication, journalism, and related programs and in communications technologies\t86062\t3.6\t32048\t54014\t62.8\t8093\t2656\t5437\t573\t225\t348\n2011\tcommunication, journalism, and related programs and in communications technologies\t88089\t2.4\t33018\t55071\t62.5\t8804\t2819\t5985\t578\t207\t371\n2012\tcommunication, journalism, and related programs and in communications technologies\t88754\t0.8\t33525\t55229\t62.2\t9502\t3068\t6434\t567\t242\t325\n2013\tcommunication, journalism, and related programs and in communications technologies\t89805\t1.2\t33670\t56135\t62.5\t9337\t3021\t6316\t612\t246\t366\n2014\tcommunication, journalism, and related programs and in communications technologies\t92591\t3.1\t34370\t58221\t62.9\t9928\t3129\t6799\t614\t267\t347\n1971\tcomputer and information sciences\t2388\t0\t2064\t324\t13.6\t1588\t1424\t164\t128\t125\t3\n1972\tcomputer and information sciences\t3402\t42.5\t2941\t461\t13.6\t1977\t1752\t225\t167\t155\t12\n1973\tcomputer and information sciences\t4304\t26.5\t3664\t640\t14.9\t2113\t1888\t225\t196\t181\t15\n1974\tcomputer and information sciences\t4756\t10.5\t3976\t780\t16.4\t2276\t1983\t293\t198\t189\t9\n1975\tcomputer and information sciences\t5033\t5.8\t4080\t953\t18.9\t2299\t1961\t338\t213\t199\t14\n1976\tcomputer and information sciences\t5652\t12.3\t4534\t1118\t19.8\t2603\t2226\t377\t244\t221\t23\n1977\tcomputer and information sciences\t6407\t13.4\t4876\t1531\t23.9\t2798\t2332\t466\t216\t197\t19\n1978\tcomputer and information sciences\t7201\t12.4\t5349\t1852\t25.7\t3038\t2471\t567\t196\t181\t15\n1979\tcomputer and information sciences\t8719\t21.1\t6272\t2447\t28.1\t3055\t2480\t575\t236\t206\t30\n1980\tcomputer and information sciences\t11154\t27.9\t7782\t3372\t30.2\t3647\t2883\t764\t240\t213\t27\n1981\tcomputer and information sciences\t15121\t35.6\t10202\t4919\t32.5\t4218\t3247\t971\t252\t227\t25\n1982\tcomputer and information sciences\t20267\t34.0\t13218\t7049\t34.8\t4935\t3625\t1310\t251\t230\t21\n1983\tcomputer and information sciences\t24565\t21.2\t15641\t8924\t36.3\t5321\t3813\t1508\t262\t228\t34\n1984\tcomputer and information sciences\t32439\t32.1\t20416\t12023\t37.1\t6190\t4379\t1811\t251\t225\t26\n1985\tcomputer and information sciences\t39121\t20.6\t24737\t14384\t36.8\t7101\t5064\t2037\t248\t223\t25\n1986\tcomputer and information sciences\t42337\t8.2\t27208\t15129\t35.7\t8070\t5658\t2412\t344\t299\t45\n1987\tcomputer and information sciences\t39767\t-6.1\t25962\t13805\t34.7\t8481\t5985\t2496\t374\t322\t52\n1988\tcomputer and information sciences\t34651\t-12.9\t23414\t11237\t32.4\t9197\t6726\t2471\t428\t380\t48\n1989\tcomputer and information sciences\t30560\t-11.8\t21143\t9417\t30.8\t9414\t6775\t2639\t551\t466\t85\n1990\tcomputer and information sciences\t27347\t-10.5\t19159\t8188\t29.9\t9677\t6960\t2717\t627\t534\t93\n1991\tcomputer and information sciences\t25159\t-8.0\t17771\t7388\t29.4\t9324\t6563\t2761\t676\t584\t92\n1992\tcomputer and information sciences\t24821\t-1.3\t17685\t7136\t28.7\t9655\t6980\t2675\t772\t669\t103\n1993\tcomputer and information sciences\t24519\t-1.2\t17606\t6913\t28.2\t10353\t7557\t2796\t805\t689\t116\n1994\tcomputer and information sciences\t24527\t0\t17528\t6999\t28.5\t10568\t7836\t2732\t810\t685\t125\n1995\tcomputer and information sciences\t24737\t0.9\t17684\t7053\t28.5\t10595\t7805\t2790\t887\t726\t161\n1996\tcomputer and information sciences\t24506\t-0.9\t17757\t6749\t27.5\t10579\t7729\t2850\t869\t743\t126\n1997\tcomputer and information sciences\t25422\t3.7\t18527\t6895\t27.1\t10513\t7526\t2987\t857\t721\t136\n1998\tcomputer and information sciences\t27829\t9.5\t20372\t7457\t26.8\t11765\t8343\t3422\t858\t718\t140\n1999\tcomputer and information sciences\t30552\t9.8\t22289\t8263\t27.0\t12843\t8866\t3977\t806\t656\t150\n2000\tcomputer and information sciences\t37788\t23.7\t27185\t10603\t28.1\t14990\t9978\t5012\t779\t648\t131\n2001\tcomputer and information sciences\t44142\t16.8\t31923\t12219\t27.7\t16911\t11195\t5716\t768\t632\t136\n2002\tcomputer and information sciences\t50365\t14.1\t36462\t13903\t27.6\t17173\t11447\t5726\t752\t581\t171\n2003\tcomputer and information sciences\t57433\t14.0\t41950\t15483\t27.0\t19509\t13267\t6242\t816\t648\t168\n2004\tcomputer and information sciences\t59488\t3.6\t44585\t14903\t25.1\t20143\t13868\t6275\t909\t709\t200\n2005\tcomputer and information sciences\t54111\t-9.0\t42125\t11986\t22.2\t18416\t13136\t5280\t1119\t905\t214\n2006\tcomputer and information sciences\t47480\t-12.3\t37705\t9775\t20.6\t17055\t12470\t4585\t1416\t1109\t307\n2007\tcomputer and information sciences\t42170\t-11.2\t34342\t7828\t18.6\t16232\t11985\t4247\t1595\t1267\t328\n2008\tcomputer and information sciences\t38476\t-8.8\t31694\t6782\t17.6\t17087\t12513\t4574\t1698\t1323\t375\n2009\tcomputer and information sciences\t37992\t-1.3\t31213\t6779\t17.8\t17907\t13063\t4844\t1580\t1226\t354\n2010\tcomputer and information sciences\t39593\t4.2\t32414\t7179\t18.1\t17955\t13019\t4936\t1599\t1250\t349\n2011\tcomputer and information sciences\t43066\t8.8\t35477\t7589\t17.6\t19516\t14010\t5506\t1588\t1267\t321\n2012\tcomputer and information sciences\t47406\t10.1\t38796\t8610\t18.2\t20925\t15132\t5793\t1698\t1332\t366\n2013\tcomputer and information sciences\t50961\t7.5\t41874\t9087\t17.8\t22782\t16539\t6243\t1834\t1480\t354\n2014\tcomputer and information sciences\t55367\t8.6\t45393\t9974\t18.0\t24532\t17484\t7048\t1982\t1566\t416\n1950\teducation\t61472\t0\t31398\t30074\t48.9\t20069\t12025\t8044\t953\t797\t156\n1960\teducation\t89002\t0\t25556\t63446\t71.3\t33433\t18057\t15376\t1591\t1279\t312\n1968\teducation\t133965\t0\t31926\t102039\t76.2\t63399\t30672\t32727\t4078\t3250\t828\n1970\teducation\t163964\t0\t40420\t123544\t75.3\t78020\t34832\t43188\t5588\t4479\t1109\n1971\teducation\t176307\t7.5\t44896\t131411\t74.5\t87666\t38365\t49301\t6041\t4771\t1270\n1972\teducation\t190880\t8.3\t49344\t141536\t74.1\t96668\t41141\t55527\t6648\t5104\t1544\n1973\teducation\t193984\t1.6\t51300\t142684\t73.6\t103777\t43298\t60479\t6857\t5191\t1666\n1974\teducation\t184907\t-4.7\t48997\t135910\t73.5\t110402\t44112\t66290\t6757\t4974\t1783\n1975\teducation\t166758\t-9.8\t44463\t122295\t73.3\t117841\t44430\t73411\t6975\t4856\t2119\n1976\teducation\t154437\t-7.4\t42004\t112433\t72.8\t126061\t44831\t81230\t7202\t4826\t2376\n1977\teducation\t143234\t-7.3\t39867\t103367\t72.2\t124267\t42308\t81959\t7338\t4832\t2506\n1978\teducation\t135821\t-5.2\t37410\t98411\t72.5\t116916\t37662\t79254\t7018\t4281\t2737\n1979\teducation\t125873\t-7.3\t33743\t92130\t73.2\t109866\t34410\t75456\t7170\t4174\t2996\n1980\teducation\t118038\t-6.2\t30901\t87137\t73.8\t101819\t30300\t71519\t7314\t4100\t3214\n1981\teducation\t108074\t-8.4\t27039\t81035\t75.0\t96713\t27548\t69165\t7279\t3843\t3436\n1982\teducation\t100932\t-6.6\t24380\t76552\t75.8\t91601\t25339\t66262\t6999\t3612\t3387\n1983\teducation\t97908\t-3.0\t23651\t74257\t75.8\t83254\t22824\t60430\t7063\t3550\t3513\n1984\teducation\t92310\t-5.7\t22200\t70110\t76.0\t75700\t21164\t54536\t6914\t3448\t3466\n1985\teducation\t88078\t-4.6\t21254\t66824\t75.9\t74667\t20539\t54128\t6614\t3174\t3440\n1986\teducation\t87147\t-1.1\t20982\t66165\t75.9\t74816\t20302\t54514\t6610\t3088\t3522\n1987\teducation\t86788\t-0.4\t20705\t66083\t76.1\t72619\t18955\t53664\t5905\t2745\t3160\n1988\teducation\t90928\t4.8\t20947\t69981\t77.0\t75270\t18777\t56493\t5568\t2530\t3038\n1989\teducation\t96740\t6.4\t21643\t75097\t77.6\t79793\t19616\t60177\t5884\t2522\t3362\n1990\teducation\t105112\t8.7\t23007\t82105\t78.1\t84890\t20469\t64421\t6503\t2776\t3727\n1991\teducation\t110807\t5.4\t23417\t87390\t78.9\t87352\t20448\t66904\t6189\t2614\t3575\n1992\teducation\t107836\t-2.7\t22655\t85181\t79.0\t91225\t20897\t70328\t6423\t2652\t3771\n1993\teducation\t107578\t-0.2\t23199\t84379\t78.4\t94497\t21857\t72640\t6581\t2712\t3869\n1994\teducation\t107440\t-0.1\t24424\t83016\t77.3\t97427\t22656\t74771\t6450\t2555\t3895\n1995\teducation\t105929\t-1.4\t25619\t80310\t75.8\t99835\t23511\t76324\t6475\t2490\t3985\n1996\teducation\t105384\t-0.5\t26214\t79170\t75.1\t104936\t24955\t79981\t6246\t2404\t3842\n1997\teducation\t105116\t-0.3\t26242\t78874\t75.0\t108720\t25518\t83202\t6297\t2367\t3930\n1998\teducation\t105833\t0.7\t26285\t79548\t75.2\t113374\t26814\t86560\t6261\t2334\t3927\n1999\teducation\t107372\t1.5\t26321\t81051\t75.5\t118226\t28077\t90149\t6471\t2297\t4174\n2000\teducation\t108034\t0.6\t26103\t81931\t75.8\t123045\t29081\t93964\t6409\t2295\t4114\n2001\teducation\t105458\t-2.4\t24580\t80878\t76.7\t127829\t29997\t97832\t6284\t2237\t4047\n2002\teducation\t106295\t0.8\t24049\t82246\t77.4\t135189\t31907\t103282\t6549\t2211\t4338\n2003\teducation\t105845\t-0.4\t22604\t83241\t78.6\t147883\t34033\t113850\t6832\t2314\t4518\n2004\teducation\t106278\t0.4\t22802\t83476\t78.5\t162345\t37843\t124502\t7088\t2403\t4685\n2005\teducation\t105451\t-0.8\t22513\t82938\t78.7\t167490\t38863\t128627\t7681\t2557\t5124\n2006\teducation\t107238\t1.7\t22448\t84790\t79.1\t174620\t40700\t133920\t7584\t2664\t4920\n2007\teducation\t105641\t-1.5\t22516\t83125\t78.7\t176572\t40164\t136408\t8261\t2681\t5580\n2008\teducation\t102582\t-2.9\t21828\t80754\t78.7\t175880\t40055\t135825\t8491\t2773\t5718\n2009\teducation\t101716\t-0.8\t21163\t80553\t79.2\t178538\t40312\t138226\t9028\t2956\t6072\n2010\teducation\t101287\t-0.4\t20739\t80548\t79.5\t182165\t41284\t140881\t9237\t3023\t6214\n2011\teducation\t104008\t2.7\t21206\t82802\t79.6\t185127\t42043\t143084\t9642\t3070\t6572\n2012\teducation\t105656\t1.6\t21714\t83942\t79.4\t179047\t41364\t137683\t10118\t3262\t6856\n2013\teducation\t104698\t-0.9\t21824\t82874\t79.2\t164652\t37816\t126836\t10572\t3418\t7154\n2014\teducation\t98854\t-5.6\t20353\t78501\t79.4\t154636\t35984\t118652\t10920\t3464\t7456\n1950\tengineering and engineering technologies\t52246\t0\t52071\t175\t0.3\t4496\t4481\t15\t417\t416\t1\n1960\tengineering and engineering technologies\t37679\t0\t37537\t142\t0.4\t7159\t7133\t26\t786\t783\t3\n1970\tengineering and engineering technologies\t44479\t0\t44149\t330\t0.7\t15593\t15421\t172\t3681\t3657\t24\n1971\tengineering and engineering technologies\t50182\t12.8\t49775\t407\t0.8\t16947\t16734\t213\t3688\t3663\t25\n1972\tengineering and engineering technologies\t51258\t2.1\t50726\t532\t1.0\t17299\t17009\t290\t3708\t3685\t23\n1973\tengineering and engineering technologies\t51384\t0.2\t50766\t618\t1.2\t16988\t16694\t294\t3513\t3459\t54\n1974\tengineering and engineering technologies\t50412\t-1.9\t49611\t801\t1.6\t15851\t15470\t381\t3374\t3318\t56\n1975\tengineering and engineering technologies\t47131\t-6.5\t46105\t1026\t2.2\t15837\t15426\t411\t3181\t3113\t68\n1976\tengineering and engineering technologies\t46676\t-1.0\t45184\t1492\t3.2\t16800\t16174\t626\t2874\t2805\t69\n1977\tengineering and engineering technologies\t49482\t6.0\t47238\t2244\t4.5\t16659\t15891\t768\t2622\t2547\t75\n1978\tengineering and engineering technologies\t56150\t13.5\t52353\t3797\t6.8\t16887\t15940\t947\t2483\t2424\t59\n1979\tengineering and engineering technologies\t62898\t12.0\t57603\t5295\t8.4\t16012\t14971\t1041\t2545\t2459\t86\n1980\tengineering and engineering technologies\t69387\t10.3\t62877\t6510\t9.4\t16765\t15535\t1230\t2546\t2447\t99\n1981\tengineering and engineering technologies\t75355\t8.6\t67573\t7782\t10.3\t17216\t15761\t1455\t2608\t2499\t109\n1982\tengineering and engineering technologies\t80632\t7.0\t71305\t9327\t11.6\t18475\t16747\t1728\t2676\t2532\t144\n1983\tengineering and engineering technologies\t89811\t11.4\t78673\t11138\t12.4\t19949\t18038\t1911\t2871\t2742\t129\n1984\tengineering and engineering technologies\t95295\t6.1\t82841\t12454\t13.1\t21197\t18916\t2281\t3032\t2864\t168\n1985\tengineering and engineering technologies\t97099\t1.9\t83991\t13108\t13.5\t22124\t19688\t2436\t3269\t3055\t214\n1986\tengineering and engineering technologies\t97122\t0\t84050\t13072\t13.5\t22146\t19545\t2601\t3456\t3220\t236\n1987\tengineering and engineering technologies\t93560\t-3.7\t80543\t13017\t13.9\t23101\t20137\t2964\t3854\t3585\t269\n1988\tengineering and engineering technologies\t89406\t-4.4\t76886\t12520\t14.0\t23839\t20815\t3024\t4237\t3941\t296\n1989\tengineering and engineering technologies\t85982\t-3.8\t74020\t11962\t13.9\t25066\t21731\t3335\t4572\t4160\t412\n1990\tengineering and engineering technologies\t82480\t-4.1\t70859\t11621\t14.1\t25294\t21753\t3541\t5030\t4576\t454\n1991\tengineering and engineering technologies\t79751\t-3.3\t68482\t11269\t14.1\t25450\t21780\t3670\t5330\t4834\t496\n1992\tengineering and engineering technologies\t78036\t-2.2\t67086\t10950\t14.0\t26373\t22397\t3976\t5499\t4967\t532\n1993\tengineering and engineering technologies\t78619\t0.7\t67214\t11405\t14.5\t29103\t24721\t4382\t5870\t5300\t570\n1994\tengineering and engineering technologies\t78580\t0\t66867\t11713\t14.9\t30102\t25394\t4708\t5954\t5288\t666\n1995\tengineering and engineering technologies\t78483\t-0.1\t66157\t12326\t15.7\t29949\t25028\t4921\t6108\t5378\t730\n1996\tengineering and engineering technologies\t77997\t-0.6\t65362\t12635\t16.2\t28843\t23840\t5003\t6354\t5559\t795\n1997\tengineering and engineering technologies\t75659\t-3.0\t62994\t12665\t16.7\t27016\t22047\t4969\t6166\t5408\t758\n1998\tengineering and engineering technologies\t74557\t-1.5\t61880\t12677\t17.0\t27244\t21800\t5444\t5966\t5230\t736\n1999\tengineering and engineering technologies\t72796\t-2.4\t59859\t12937\t17.8\t26689\t21348\t5341\t5413\t4643\t770\n2000\tengineering and engineering technologies\t73323\t0.7\t59668\t13655\t18.6\t26648\t21047\t5601\t5367\t4539\t828\n2001\tengineering and engineering technologies\t72869\t-0.6\t59489\t13380\t18.4\t27187\t21341\t5846\t5547\t4630\t917\n2002\tengineering and engineering technologies\t74588\t2.4\t60417\t14171\t19.0\t26987\t21212\t5775\t5181\t4285\t896\n2003\tengineering and engineering technologies\t77231\t3.5\t62821\t14410\t18.7\t30583\t24097\t6486\t5252\t4353\t899\n2004\tengineering and engineering technologies\t78079\t1.1\t63401\t14678\t18.8\t35053\t27561\t7492\t5859\t4821\t1038\n2005\tengineering and engineering technologies\t79544\t1.9\t65033\t14511\t18.2\t34988\t27049\t7939\t6467\t5263\t1204\n2006\tengineering and engineering technologies\t81406\t2.3\t66866\t14540\t17.9\t33389\t25568\t7821\t7318\t5848\t1470\n2007\tengineering and engineering technologies\t81854\t0.6\t68081\t13773\t16.8\t31989\t24746\t7243\t7928\t6285\t1643\n2008\tengineering and engineering technologies\t83608\t2.1\t69540\t14068\t16.8\t34430\t26461\t7969\t7977\t6263\t1714\n2009\tengineering and engineering technologies\t84404\t1.0\t70504\t13900\t16.5\t38008\t29458\t8550\t7803\t6123\t1680\n2010\tengineering and engineering technologies\t88735\t5.1\t73838\t14897\t16.8\t39391\t30554\t8837\t7773\t5986\t1787\n2011\tengineering and engineering technologies\t93097\t4.9\t77080\t16017\t17.2\t43179\t33372\t9807\t8425\t6548\t1877\n2012\tengineering and engineering technologies\t98654\t6.0\t81364\t17290\t17.5\t45116\t34712\t10404\t8856\t6838\t2018\n2013\tengineering and engineering technologies\t102997\t4.4\t84645\t18352\t17.8\t45328\t34496\t10832\t9467\t7305\t2162\n2014\tengineering and engineering technologies\t108969\t5.8\t88938\t20031\t18.4\t47326\t35777\t11549\t10117\t7820\t2297\n1950\tEnglish language and literature/letters\t17240\t0\t8221\t9019\t52.3\t2259\t1320\t939\t230\t181\t49\n1960\tEnglish language and literature/letters\t20128\t0\t7580\t12548\t62.3\t2931\t1458\t1473\t397\t314\t83\n1968\tEnglish language and literature/letters\t47977\t0\t15700\t32277\t67.3\t7916\t3434\t4482\t977\t717\t260\n1970\tEnglish language and literature/letters\t56410\t0\t18650\t37760\t66.9\t8517\t3326\t5191\t1213\t837\t376\n1971\tEnglish language and literature/letters\t63914\t13.3\t22005\t41909\t65.6\t10441\t4126\t6315\t1554\t1107\t447\n1972\tEnglish language and literature/letters\t63707\t-0.3\t22580\t41127\t64.6\t10412\t4066\t6346\t1734\t1173\t561\n1973\tEnglish language and literature/letters\t60607\t-4.9\t22022\t38585\t63.7\t10035\t3988\t6047\t1817\t1189\t628\n1974\tEnglish language and literature/letters\t54190\t-10.6\t20082\t34108\t62.9\t9573\t3824\t5749\t1755\t1142\t613\n1975\tEnglish language and literature/letters\t47062\t-13.2\t17689\t29373\t62.4\t9178\t3463\t5715\t1595\t974\t621\n1976\tEnglish language and literature/letters\t41452\t-11.9\t15898\t25554\t61.6\t8599\t3290\t5309\t1514\t895\t619\n1977\tEnglish language and literature/letters\t37343\t-9.9\t14135\t23208\t62.1\t7824\t2907\t4917\t1373\t768\t605\n1978\tEnglish language and literature/letters\t34799\t-6.8\t12972\t21827\t62.7\t7444\t2623\t4821\t1272\t698\t574\n1979\tEnglish language and literature/letters\t33218\t-4.5\t12085\t21133\t63.6\t6503\t2307\t4196\t1186\t639\t547\n1980\tEnglish language and literature/letters\t32187\t-3.1\t11237\t20950\t65.1\t6026\t2181\t3845\t1196\t635\t561\n1981\tEnglish language and literature/letters\t31922\t-0.8\t11082\t20840\t65.3\t5742\t2026\t3716\t1040\t497\t543\n1982\tEnglish language and literature/letters\t33078\t3.6\t11300\t21778\t65.8\t5593\t1916\t3677\t986\t467\t519\n1983\tEnglish language and literature/letters\t31327\t-5.3\t10699\t20628\t65.8\t4866\t1653\t3213\t877\t419\t458\n1984\tEnglish language and literature/letters\t32296\t3.1\t11007\t21289\t65.9\t4814\t1681\t3133\t899\t413\t486\n1985\tEnglish language and literature/letters\t32686\t1.2\t11195\t21491\t65.7\t4987\t1723\t3264\t915\t414\t501\n1986\tEnglish language and literature/letters\t34083\t4.3\t11657\t22426\t65.8\t5335\t1811\t3524\t895\t390\t505\n1987\tEnglish language and literature/letters\t35667\t4.6\t12133\t23534\t66.0\t5298\t1819\t3479\t853\t367\t486\n1988\tEnglish language and literature/letters\t38106\t6.8\t12687\t25419\t66.7\t5366\t1796\t3570\t858\t380\t478\n1989\tEnglish language and literature/letters\t41786\t9.7\t13729\t28057\t67.1\t5716\t1930\t3786\t929\t405\t524\n1990\tEnglish language and literature/letters\t46803\t12.0\t15437\t31366\t67.0\t6317\t2125\t4192\t986\t444\t542\n1991\tEnglish language and literature/letters\t51064\t9.1\t16891\t34173\t66.9\t6784\t2203\t4581\t1056\t469\t587\n1992\tEnglish language and literature/letters\t54250\t6.2\t18314\t35936\t66.2\t7215\t2441\t4774\t1142\t484\t658\n1993\tEnglish language and literature/letters\t55289\t1.9\t19007\t36282\t65.6\t7537\t2570\t4967\t1201\t495\t706\n1994\tEnglish language and literature/letters\t53150\t-3.9\t18214\t34936\t65.7\t7611\t2620\t4991\t1205\t512\t693\n1995\tEnglish language and literature/letters\t51170\t-3.7\t17581\t33589\t65.6\t7612\t2672\t4940\t1393\t589\t804\n1996\tEnglish language and literature/letters\t49928\t-2.4\t17007\t32921\t65.9\t7657\t2727\t4930\t1395\t535\t860\n1997\tEnglish language and literature/letters\t48641\t-2.6\t16325\t32316\t66.4\t7487\t2650\t4837\t1431\t610\t821\n1998\tEnglish language and literature/letters\t49016\t0.8\t16280\t32736\t66.8\t7587\t2568\t5019\t1489\t611\t878\n1999\tEnglish language and literature/letters\t49877\t1.8\t16332\t33545\t67.3\t7326\t2452\t4874\t1412\t554\t858\n2000\tEnglish language and literature/letters\t50106\t0.5\t16124\t33982\t67.8\t7022\t2315\t4707\t1470\t611\t859\n2001\tEnglish language and literature/letters\t50569\t0.9\t15997\t34572\t68.4\t6763\t2160\t4603\t1330\t533\t797\n2002\tEnglish language and literature/letters\t52375\t3.6\t16457\t35918\t68.6\t7097\t2270\t4827\t1291\t532\t759\n2003\tEnglish language and literature/letters\t53699\t2.5\t16738\t36961\t68.8\t7428\t2433\t4995\t1246\t492\t754\n2004\tEnglish language and literature/letters\t53984\t0.5\t16792\t37192\t68.9\t7956\t2459\t5497\t1207\t479\t728\n2005\tEnglish language and literature/letters\t54379\t0.7\t17154\t37225\t68.5\t8468\t2615\t5853\t1212\t494\t718\n2006\tEnglish language and literature/letters\t55096\t1.3\t17316\t37780\t68.6\t8845\t2860\t5985\t1254\t510\t744\n2007\tEnglish language and literature/letters\t55122\t0\t17475\t37647\t68.3\t8742\t2867\t5875\t1178\t478\t700\n2008\tEnglish language and literature/letters\t55038\t0\t17681\t37357\t67.9\t9161\t3027\t6134\t1262\t453\t809\n2009\tEnglish language and literature/letters\t55465\t0.8\t17973\t37492\t67.6\t9262\t3000\t6262\t1271\t464\t807\n2010\tEnglish language and literature/letters\t53229\t-4.0\t17050\t36179\t68.0\t9202\t3006\t6196\t1334\t523\t811\n2011\tEnglish language and literature/letters\t52754\t-0.9\t16917\t35837\t67.9\t9475\t3137\t6338\t1344\t529\t815\n2012\tEnglish language and literature/letters\t53765\t1.9\t16976\t36789\t68.4\t9938\t3403\t6535\t1427\t548\t879\n2013\tEnglish language and literature/letters\t52401\t-2.5\t16508\t35893\t68.5\t9755\t3213\t6542\t1377\t554\t823\n2014\tEnglish language and literature/letters\t50404\t-3.8\t15809\t34595\t68.6\t9292\t3113\t6179\t1393\t557\t836\n1960\tforeign languages and literatures\t5462\t0\t2090\t3372\t61.7\t1125\t590\t535\t229\t166\t63\n1968\tforeign languages and literatures\t19254\t0\t5253\t14001\t72.7\t4849\t2068\t2781\t707\t503\t204\n1970\tforeign languages and literatures\t21109\t0\t5613\t15496\t73.4\t5137\t1917\t3220\t869\t579\t290\n1971\tforeign languages and literatures\t20988\t-0.6\t5508\t15480\t73.8\t5480\t1961\t3519\t1084\t714\t370\n1972\tforeign languages and literatures\t19890\t-5.2\t5196\t14694\t73.9\t5283\t1919\t3364\t1134\t742\t392\n1973\tforeign languages and literatures\t20170\t1.4\t5119\t15051\t74.6\t5068\t1909\t3159\t1347\t829\t518\n1974\tforeign languages and literatures\t20197\t0.1\t5063\t15134\t74.9\t4851\t1788\t3063\t1248\t703\t545\n1975\tforeign languages and literatures\t19103\t-5.4\t4723\t14380\t75.3\t4721\t1672\t3049\t1220\t665\t555\n1976\tforeign languages and literatures\t17068\t-10.7\t4270\t12798\t75.0\t4432\t1581\t2851\t1245\t643\t602\n1977\tforeign languages and literatures\t15496\t-9.2\t3965\t11531\t74.4\t4056\t1365\t2691\t1103\t574\t529\n1978\tforeign languages and literatures\t14334\t-7.5\t3684\t10650\t74.3\t3624\t1194\t2430\t1002\t490\t512\n1979\tforeign languages and literatures\t13211\t-7.8\t3391\t9820\t74.3\t3248\t1092\t2156\t960\t471\t489\n1980\tforeign languages and literatures\t12480\t-5.5\t3226\t9254\t74.2\t3067\t1026\t2041\t857\t412\t445\n1981\tforeign languages and literatures\t11638\t-6.7\t3013\t8625\t74.1\t2934\t1039\t1895\t931\t460\t471\n1982\tforeign languages and literatures\t11175\t-4.0\t2919\t8256\t73.9\t2892\t997\t1895\t869\t407\t462\n1983\tforeign languages and literatures\t11170\t0\t3048\t8122\t72.7\t2706\t1001\t1705\t790\t362\t428\n1984\tforeign languages and literatures\t10985\t-1.7\t3098\t7887\t71.8\t2814\t984\t1830\t779\t353\t426\n1985\tforeign languages and literatures\t11436\t4.1\t3186\t8250\t72.1\t2708\t932\t1776\t761\t342\t419\n1986\tforeign languages and literatures\t11550\t1.0\t3374\t8176\t70.8\t2690\t878\t1812\t768\t338\t430\n1987\tforeign languages and literatures\t11706\t1.4\t3374\t8332\t71.2\t2574\t847\t1727\t769\t332\t437\n1988\tforeign languages and literatures\t11515\t-1.6\t3223\t8292\t72.0\t2680\t931\t1749\t725\t330\t395\n1989\tforeign languages and literatures\t12403\t7.7\t3432\t8971\t72.3\t2837\t955\t1882\t727\t317\t410\n1990\tforeign languages and literatures\t13133\t5.9\t3625\t9508\t72.4\t3018\t987\t2031\t816\t348\t468\n1991\tforeign languages and literatures\t13937\t6.1\t4008\t9929\t71.2\t3049\t1018\t2031\t889\t396\t493\n1992\tforeign languages and literatures\t14634\t5.0\t4225\t10409\t71.1\t3229\t1074\t2155\t984\t434\t550\n1993\tforeign languages and literatures\t15305\t4.6\t4435\t10870\t71.0\t3513\t1182\t2331\t977\t417\t560\n1994\tforeign languages and literatures\t15242\t-0.4\t4573\t10669\t70.0\t3612\t1199\t2413\t1033\t418\t615\n1995\tforeign languages and literatures\t14558\t-4.5\t4496\t10062\t69.1\t3439\t1124\t2315\t1081\t479\t602\n1996\tforeign languages and literatures\t14832\t1.9\t4514\t10318\t69.6\t3443\t1141\t2302\t1020\t446\t574\n1997\tforeign languages and literatures\t14487\t-2.3\t4388\t10099\t69.7\t3361\t1104\t2257\t1064\t450\t614\n1998\tforeign languages and literatures\t15279\t5.5\t4585\t10694\t70.0\t3181\t1033\t2148\t1118\t473\t645\n1999\tforeign languages and literatures\t15835\t3.6\t4738\t11097\t70.1\t3109\t976\t2133\t1049\t443\t606\n2000\tforeign languages and literatures\t15886\t0.3\t4616\t11270\t70.9\t3037\t944\t2093\t1086\t446\t640\n2001\tforeign languages and literatures\t16128\t1.5\t4695\t11433\t70.9\t3035\t969\t2066\t1078\t420\t658\n2002\tforeign languages and literatures\t16258\t0.8\t4685\t11573\t71.2\t3075\t958\t2117\t1003\t418\t585\n2003\tforeign languages and literatures\t16912\t4.0\t4996\t11916\t70.5\t3049\t874\t2175\t1042\t424\t618\n2004\tforeign languages and literatures\t17754\t5.0\t5215\t12539\t70.6\t3124\t957\t2167\t1031\t410\t621\n2005\tforeign languages and literatures\t18386\t3.6\t5370\t13016\t70.8\t3407\t1056\t2351\t1027\t410\t617\n2006\tforeign languages and literatures\t19410\t5.6\t5842\t13568\t69.9\t3539\t1049\t2490\t1074\t436\t638\n2007\tforeign languages and literatures\t20275\t4.5\t6173\t14102\t69.6\t3443\t1058\t2385\t1059\t437\t622\n2008\tforeign languages and literatures\t20977\t3.5\t6254\t14723\t70.2\t3565\t1128\t2437\t1078\t431\t647\n2009\tforeign languages and literatures\t21169\t0.9\t6305\t14864\t70.2\t3592\t1211\t2381\t1111\t426\t685\n2010\tforeign languages and literatures\t21507\t1.6\t6607\t14900\t69.3\t3756\t1254\t2502\t1091\t446\t645\n2011\tforeign languages and literatures\t21705\t0.9\t6719\t14986\t69.0\t3727\t1256\t2471\t1158\t477\t681\n2012\tforeign languages and literatures\t21756\t0.2\t6629\t15127\t69.5\t3827\t1280\t2547\t1231\t497\t734\n2013\tforeign languages and literatures\t21647\t-0.5\t6842\t14805\t68.4\t3708\t1235\t2473\t1304\t531\t773\n2014\tforeign languages and literatures\t20335\t-6.1\t6266\t14069\t69.2\t3482\t1222\t2260\t1231\t498\t733\n1971\tthe health professions and related programs\t25223\t0\t5785\t19438\t77.1\t5330\t2165\t3165\t15988\t14863\t1125\n1972\tthe health professions and related programs\t28611\t13.4\t7005\t21606\t75.5\t6811\t2749\t4062\t16538\t15373\t1165\n1973\tthe health professions and related programs\t33562\t17.3\t7752\t25810\t76.9\t7978\t3189\t4789\t18215\t16870\t1345\n1974\tthe health professions and related programs\t41421\t23.4\t9347\t32074\t77.4\t9232\t3444\t5788\t20094\t18287\t1807\n1975\tthe health professions and related programs\t49002\t18.3\t10844\t38158\t77.9\t10277\t3686\t6591\t22191\t19808\t2383\n1976\tthe health professions and related programs\t53885\t10.0\t11386\t42499\t78.9\t12164\t3837\t8327\t25267\t21980\t3287\n1977\tthe health professions and related programs\t57222\t6.2\t11896\t45326\t79.2\t12627\t3865\t8762\t24972\t21022\t3950\n1978\tthe health professions and related programs\t59445\t3.9\t11600\t47845\t80.5\t14027\t3972\t10055\t26516\t21622\t4894\n1979\tthe health professions and related programs\t62095\t4.5\t11214\t50881\t81.9\t15110\t4155\t10955\t27766\t22194\t5572\n1980\tthe health professions and related programs\t63848\t2.8\t11330\t52518\t82.3\t15374\t4060\t11314\t28190\t22157\t6033\n1981\tthe health professions and related programs\t63665\t-0.3\t10531\t53134\t83.5\t16176\t4024\t12152\t29595\t22792\t6803\n1982\tthe health professions and related programs\t63660\t0\t10110\t53550\t84.1\t16212\t3743\t12469\t30096\t22968\t7128\n1983\tthe health professions and related programs\t65642\t3.1\t10247\t55395\t84.4\t16941\t4138\t12803\t30800\t22920\t7880\n1984\tthe health professions and related programs\t65305\t-0.5\t10068\t55237\t84.6\t17351\t4124\t13227\t31655\t22851\t8804\n1985\tthe health professions and related programs\t65331\t0\t9741\t55590\t85.1\t17442\t4046\t13396\t31493\t22045\t9448\n1986\tthe health professions and related programs\t65309\t0\t9629\t55680\t85.3\t18603\t4355\t14248\t31922\t22069\t9853\n1987\tthe health professions and related programs\t63963\t-2.1\t9137\t54826\t85.7\t18442\t3818\t14624\t29500\t19686\t9814\n1988\tthe health professions and related programs\t61614\t-3.7\t8955\t52659\t85.5\t18774\t4004\t14770\t30060\t19853\t10207\n1989\tthe health professions and related programs\t59850\t-2.9\t8878\t50972\t85.2\t19493\t4197\t15296\t30546\t19893\t10653\n1990\tthe health professions and related programs\t58983\t-1.4\t9075\t49908\t84.6\t20406\t4486\t15920\t30101\t19118\t10983\n1991\tthe health professions and related programs\t59875\t1.5\t9619\t50256\t83.9\t21354\t4423\t16931\t29842\t18492\t11350\n1992\tthe health professions and related programs\t62779\t4.9\t10330\t52449\t83.5\t23671\t4794\t18877\t31479\t19362\t12117\n1993\tthe health professions and related programs\t68434\t9.0\t11605\t56829\t83.0\t26190\t5249\t20941\t31089\t18446\t12643\n1994\tthe health professions and related programs\t75890\t10.9\t13377\t62513\t82.4\t28442\t5813\t22629\t30959\t17988\t12971\n1995\tthe health professions and related programs\t81596\t7.5\t14812\t66784\t81.8\t31770\t6718\t25052\t32124\t18463\t13661\n1996\tthe health professions and related programs\t86087\t5.5\t15942\t70145\t81.5\t33920\t7017\t26903\t32678\t18495\t14183\n1997\tthe health professions and related programs\t87997\t2.2\t16440\t71557\t81.3\t36162\t7536\t28626\t34971\t19619\t15352\n1998\tthe health professions and related programs\t86843\t-1.3\t15700\t71143\t81.9\t39567\t8644\t30923\t35369\t19370\t15999\n1999\tthe health professions and related programs\t84989\t-2.1\t15191\t69798\t82.1\t40628\t9152\t31476\t35939\t19673\t16266\n2000\tthe health professions and related programs\t80863\t-4.9\t13342\t67521\t83.5\t42593\t9500\t33093\t37829\t19984\t17845\n2001\tthe health professions and related programs\t75933\t-6.1\t12514\t63419\t83.5\t43623\t9711\t33912\t39019\t20260\t18759\n2002\tthe health professions and related programs\t72887\t-4.0\t10869\t62018\t85.1\t43560\t9588\t33972\t39435\t19760\t19675\n2003\tthe health professions and related programs\t71261\t-2.2\t10096\t61165\t85.8\t42748\t9280\t33468\t39799\t19493\t20306\n2004\tthe health professions and related programs\t73934\t3.8\t10017\t63917\t86.5\t44939\t9670\t35269\t41861\t19587\t22274\n2005\tthe health professions and related programs\t80685\t9.1\t10858\t69827\t86.5\t46703\t9816\t36887\t44201\t19697\t24504\n2006\tthe health professions and related programs\t91973\t14.0\t12914\t79059\t86.0\t51380\t10630\t40750\t45677\t19640\t26037\n2007\tthe health professions and related programs\t101810\t10.7\t14325\t87485\t85.9\t54531\t10636\t43895\t48943\t20522\t28421\n2008\tthe health professions and related programs\t111478\t9.5\t16286\t95192\t85.4\t58120\t11010\t47110\t51675\t21616\t30059\n2009\tthe health professions and related programs\t120420\t8.0\t17776\t102644\t85.2\t62642\t11848\t50794\t54846\t22678\t32168\n2010\tthe health professions and related programs\t129623\t7.6\t19309\t110314\t85.1\t69112\t12874\t56238\t57750\t23946\t33804\n2011\tthe health professions and related programs\t143463\t10.7\t21540\t121923\t85.0\t75571\t14034\t61537\t60221\t25386\t34835\n2012\tthe health professions and related programs\t163675\t14.1\t24905\t138770\t84.8\t84355\t15675\t68680\t62097\t26074\t36023\n2013\tthe health professions and related programs\t181149\t10.7\t28208\t152941\t84.4\t90933\t16747\t74186\t64192\t26852\t37340\n2014\tthe health professions and related programs\t198770\t9.7\t30931\t167839\t84.4\t97403\t17749\t79654\t67448\t28084\t39364\n1950\tmathematics and statistics\t6382\t0\t4942\t1440\t22.6\t974\t784\t190\t160\t151\t9\n1960\tmathematics and statistics\t11399\t0\t8293\t3106\t27.2\t1757\t1422\t335\t303\t285\t18\n1968\tmathematics and statistics\t23513\t0\t14782\t8731\t37.1\t5527\t4199\t1328\t947\t895\t52\n1970\tmathematics and statistics\t27442\t0\t17177\t10265\t37.4\t5636\t3966\t1670\t1236\t1140\t96\n1971\tmathematics and statistics\t24801\t-9.6\t15369\t9432\t38.0\t5191\t3673\t1518\t1199\t1106\t93\n1972\tmathematics and statistics\t23713\t-4.4\t14454\t9259\t39.0\t5198\t3655\t1543\t1128\t1039\t89\n1973\tmathematics and statistics\t23067\t-2.7\t13796\t9271\t40.2\t5028\t3525\t1503\t1068\t966\t102\n1974\tmathematics and statistics\t21635\t-6.2\t12791\t8844\t40.9\t4834\t3337\t1497\t1031\t931\t100\n1975\tmathematics and statistics\t18181\t-16.0\t10586\t7595\t41.8\t4327\t2905\t1422\t975\t865\t110\n1976\tmathematics and statistics\t15984\t-12.1\t9475\t6509\t40.7\t3857\t2547\t1310\t856\t762\t94\n1977\tmathematics and statistics\t14196\t-11.2\t8303\t5893\t41.5\t3695\t2396\t1299\t823\t714\t109\n1978\tmathematics and statistics\t12569\t-11.5\t7398\t5171\t41.1\t3373\t2228\t1145\t805\t681\t124\n1979\tmathematics and statistics\t11806\t-6.1\t6899\t4907\t41.6\t3036\t1985\t1051\t730\t608\t122\n1980\tmathematics and statistics\t11378\t-3.6\t6562\t4816\t42.3\t2860\t1828\t1032\t724\t624\t100\n1981\tmathematics and statistics\t11078\t-2.6\t6342\t4736\t42.8\t2567\t1692\t875\t728\t614\t114\n1982\tmathematics and statistics\t11599\t4.7\t6593\t5006\t43.2\t2727\t1821\t906\t681\t587\t94\n1983\tmathematics and statistics\t12294\t6.0\t6888\t5406\t44.0\t2810\t1838\t972\t697\t581\t116\n1984\tmathematics and statistics\t13087\t6.5\t7290\t5797\t44.3\t2723\t1773\t950\t695\t569\t126\n1985\tmathematics and statistics\t15009\t14.7\t8080\t6929\t46.2\t2859\t1858\t1001\t699\t590\t109\n1986\tmathematics and statistics\t16122\t7.4\t8623\t7499\t46.5\t3131\t2028\t1103\t742\t618\t124\n1987\tmathematics and statistics\t16257\t0.8\t8673\t7584\t46.7\t3283\t1995\t1288\t723\t598\t125\n1988\tmathematics and statistics\t15712\t-3.4\t8408\t7304\t46.5\t3413\t2052\t1361\t750\t625\t125\n1989\tmathematics and statistics\t15017\t-4.4\t8081\t6936\t46.2\t3405\t2061\t1344\t866\t700\t166\n1990\tmathematics and statistics\t14276\t-4.9\t7674\t6602\t46.2\t3624\t2172\t1452\t917\t754\t163\n1991\tmathematics and statistics\t14393\t0.8\t7580\t6813\t47.3\t3549\t2096\t1453\t978\t790\t188\n1992\tmathematics and statistics\t14468\t0.5\t7668\t6800\t47.0\t3558\t2151\t1407\t1048\t825\t223\n1993\tmathematics and statistics\t14384\t-0.6\t7566\t6818\t47.4\t3644\t2151\t1493\t1138\t867\t271\n1994\tmathematics and statistics\t14171\t-1.5\t7594\t6577\t46.4\t3682\t2237\t1445\t1125\t880\t245\n1995\tmathematics and statistics\t13494\t-4.8\t7154\t6340\t47.0\t3820\t2289\t1531\t1181\t919\t262\n1996\tmathematics and statistics\t12713\t-5.8\t6847\t5866\t46.1\t3651\t2178\t1473\t1158\t919\t239\n1997\tmathematics and statistics\t12401\t-2.5\t6649\t5752\t46.4\t3504\t2055\t1449\t1134\t861\t273\n1998\tmathematics and statistics\t11795\t-4.9\t6247\t5548\t47.0\t3409\t1985\t1424\t1215\t903\t312\n1999\tmathematics and statistics\t12011\t1.8\t6206\t5805\t48.3\t3304\t1912\t1392\t1107\t812\t295\n2000\tmathematics and statistics\t11418\t-4.9\t5955\t5463\t47.8\t3208\t1749\t1459\t1075\t803\t272\n2001\tmathematics and statistics\t11171\t-2.2\t5791\t5380\t48.2\t3209\t1857\t1352\t997\t715\t282\n2002\tmathematics and statistics\t11950\t7.0\t6333\t5617\t47.0\t3350\t1913\t1437\t923\t658\t265\n2003\tmathematics and statistics\t12505\t4.6\t6784\t5721\t45.7\t3620\t1996\t1624\t1007\t734\t273\n2004\tmathematics and statistics\t13327\t6.6\t7203\t6124\t46.0\t4191\t2302\t1889\t1060\t762\t298\n2005\tmathematics and statistics\t14351\t7.7\t7937\t6414\t44.7\t4477\t2525\t1952\t1176\t841\t335\n2006\tmathematics and statistics\t14770\t2.9\t8115\t6655\t45.1\t4730\t2712\t2018\t1293\t911\t382\n2007\tmathematics and statistics\t14954\t1.2\t8360\t6594\t44.1\t4884\t2859\t2025\t1351\t949\t402\n2008\tmathematics and statistics\t15192\t1.6\t8490\t6702\t44.1\t4980\t2860\t2120\t1360\t938\t422\n2009\tmathematics and statistics\t15507\t2.1\t8801\t6706\t43.2\t5211\t3064\t2147\t1535\t1059\t476\n2010\tmathematics and statistics\t16029\t3.4\t9087\t6942\t43.3\t5639\t3378\t2261\t1596\t1118\t478\n2011\tmathematics and statistics\t17182\t7.2\t9782\t7400\t43.1\t5866\t3459\t2407\t1586\t1132\t454\n2012\tmathematics and statistics\t18841\t9.7\t10722\t8119\t43.1\t6246\t3695\t2551\t1669\t1198\t471\n2013\tmathematics and statistics\t20449\t8.5\t11599\t8850\t43.3\t6957\t4178\t2779\t1823\t1292\t531\n2014\tmathematics and statistics\t20980\t2.6\t11967\t9013\t43.0\t7273\t4256\t3017\t1863\t1325\t538\n1960\tthe physical sciences and science technologies\t16007\t0\t14013\t1994\t12.5\t3376\t3049\t327\t1838\t1776\t62\n1968\tthe physical sciences and science technologies\t19380\t0\t16739\t2641\t13.6\t5499\t4869\t630\t3593\t3405\t188\n1970\tthe physical sciences and science technologies\t21439\t0\t18522\t2917\t13.6\t5908\t5069\t839\t4271\t4038\t233\n1971\tthe physical sciences and science technologies\t21410\t-0.1\t18457\t2953\t13.8\t6336\t5495\t841\t4324\t4082\t242\n1972\tthe physical sciences and science technologies\t20743\t-3.1\t17661\t3082\t14.9\t6268\t5390\t878\t4075\t3805\t270\n1973\tthe physical sciences and science technologies\t20692\t-0.2\t17622\t3070\t14.8\t6230\t5388\t842\t3961\t3698\t263\n1974\tthe physical sciences and science technologies\t21170\t2.3\t17669\t3501\t16.5\t6019\t5157\t862\t3558\t3312\t246\n1975\tthe physical sciences and science technologies\t20770\t-1.9\t16986\t3784\t18.2\t5782\t4949\t833\t3577\t3284\t293\n1976\tthe physical sciences and science technologies\t21458\t3.3\t17349\t4109\t19.1\t5428\t4622\t806\t3388\t3097\t291\n1977\tthe physical sciences and science technologies\t22482\t4.8\t17985\t4497\t20.0\t5281\t4411\t870\t3295\t2981\t314\n1978\tthe physical sciences and science technologies\t22975\t2.2\t18083\t4892\t21.3\t5507\t4583\t924\t3073\t2763\t310\n1979\tthe physical sciences and science technologies\t23197\t1.0\t17976\t5221\t22.5\t5418\t4438\t980\t3061\t2717\t344\n1980\tthe physical sciences and science technologies\t23407\t0.9\t17861\t5546\t23.7\t5167\t4210\t957\t3044\t2669\t375\n1981\tthe physical sciences and science technologies\t23936\t2.3\t18052\t5884\t24.6\t5246\t4172\t1074\t3105\t2733\t372\n1982\tthe physical sciences and science technologies\t24045\t0.5\t17861\t6184\t25.7\t5446\t4274\t1172\t3246\t2804\t442\n1983\tthe physical sciences and science technologies\t23374\t-2.8\t16988\t6386\t27.3\t5250\t4131\t1119\t3214\t2767\t447\n1984\tthe physical sciences and science technologies\t23645\t1.2\t17112\t6533\t27.6\t5541\t4249\t1292\t3269\t2789\t480\n1985\tthe physical sciences and science technologies\t23694\t0.2\t17065\t6629\t28.0\t5752\t4425\t1327\t3349\t2808\t541\n1986\tthe physical sciences and science technologies\t21711\t-8.4\t15750\t5961\t27.5\t5860\t4443\t1417\t3521\t2946\t575\n1987\tthe physical sciences and science technologies\t20060\t-7.6\t14365\t5695\t28.4\t5586\t4193\t1393\t3629\t3004\t625\n1988\tthe physical sciences and science technologies\t17797\t-11.3\t12385\t5412\t30.4\t5696\t4300\t1396\t3758\t3085\t673\n1989\tthe physical sciences and science technologies\t17179\t-3.5\t12071\t5108\t29.7\t5691\t4180\t1511\t3795\t3046\t749\n1990\tthe physical sciences and science technologies\t16056\t-6.5\t11026\t5030\t31.3\t5410\t3996\t1414\t4116\t3328\t788\n1991\tthe physical sciences and science technologies\t16334\t1.7\t11170\t5164\t31.6\t5281\t3823\t1458\t4248\t3417\t831\n1992\tthe physical sciences and science technologies\t16970\t3.9\t11443\t5527\t32.6\t5397\t3935\t1462\t4378\t3433\t945\n1993\tthe physical sciences and science technologies\t17577\t3.6\t11853\t5724\t32.6\t5392\t3840\t1552\t4372\t3426\t946\n1994\tthe physical sciences and science technologies\t18474\t5.1\t12271\t6203\t33.6\t5718\t4069\t1649\t4652\t3657\t995\n1995\tthe physical sciences and science technologies\t19247\t4.2\t12556\t6691\t34.8\t5798\t4058\t1740\t4486\t3443\t1043\n1996\tthe physical sciences and science technologies\t19716\t2.4\t12634\t7082\t35.9\t5910\t4031\t1879\t4589\t3543\t1046\n1997\tthe physical sciences and science technologies\t19594\t-0.6\t12285\t7309\t37.3\t5616\t3799\t1817\t4501\t3479\t1022\n1998\tthe physical sciences and science technologies\t19454\t-0.7\t11999\t7455\t38.3\t5411\t3484\t1927\t4592\t3451\t1141\n1999\tthe physical sciences and science technologies\t18448\t-5.2\t11119\t7329\t39.7\t5241\t3454\t1787\t4229\t3206\t1023\n2000\tthe physical sciences and science technologies\t18427\t-0.1\t11019\t7408\t40.2\t4888\t3167\t1721\t4017\t3002\t1015\n2001\tthe physical sciences and science technologies\t18025\t-2.2\t10628\t7397\t41.0\t5134\t3276\t1858\t3968\t2914\t1054\n2002\tthe physical sciences and science technologies\t17890\t-0.7\t10349\t7541\t42.2\t5082\t3186\t1896\t3824\t2766\t1058\n2003\tthe physical sciences and science technologies\t18038\t0.8\t10625\t7413\t41.1\t5196\t3284\t1912\t3939\t2854\t1085\n2004\tthe physical sciences and science technologies\t18131\t0.5\t10577\t7554\t41.7\t5714\t3470\t2244\t3937\t2855\t1082\n2005\tthe physical sciences and science technologies\t19104\t5.4\t11065\t8039\t42.1\t5823\t3569\t2254\t4248\t3071\t1177\n2006\tthe physical sciences and science technologies\t20522\t7.4\t11978\t8544\t41.6\t6063\t3666\t2397\t4642\t3258\t1384\n2007\tthe physical sciences and science technologies\t21291\t3.7\t12604\t8687\t40.8\t6012\t3675\t2337\t5041\t3454\t1587\n2008\tthe physical sciences and science technologies\t22179\t4.2\t13143\t9036\t40.7\t6061\t3762\t2299\t4994\t3513\t1481\n2009\tthe physical sciences and science technologies\t22691\t2.3\t13465\t9226\t40.7\t5862\t3576\t2286\t5237\t3554\t1683\n2010\tthe physical sciences and science technologies\t23381\t3.0\t13866\t9515\t40.7\t6066\t3654\t2412\t5065\t3406\t1659\n2011\tthe physical sciences and science technologies\t24705\t5.7\t14778\t9927\t40.2\t6386\t3907\t2479\t5295\t3608\t1687\n2012\tthe physical sciences and science technologies\t26664\t7.9\t15972\t10692\t40.1\t6911\t4299\t2612\t5370\t3609\t1761\n2013\tthe physical sciences and science technologies\t28053\t5.2\t17142\t10911\t38.9\t7014\t4377\t2637\t5514\t3646\t1868\n2014\tthe physical sciences and science technologies\t29304\t4.5\t17802\t11502\t39.3\t6984\t4312\t2672\t5806\t3873\t1933\n1950\tpsychology\t9569\t0\t6055\t3514\t36.7\t1316\t948\t368\t283\t241\t42\n1960\tpsychology\t8061\t0\t4773\t3288\t40.8\t1406\t981\t425\t641\t544\t97\n1968\tpsychology\t23819\t0\t13792\t10027\t42.1\t3479\t2321\t1158\t1268\t982\t286\n1970\tpsychology\t33679\t0\t19077\t14602\t43.4\t5158\t2975\t2183\t1962\t1505\t457\n1971\tpsychology\t38187\t13.4\t21227\t16960\t44.4\t5717\t3395\t2322\t2144\t1629\t515\n1972\tpsychology\t43433\t13.7\t23352\t20081\t46.2\t6764\t3934\t2830\t2277\t1694\t583\n1973\tpsychology\t47940\t10.4\t25117\t22823\t47.6\t7619\t4325\t3294\t2550\t1797\t753\n1974\tpsychology\t52139\t8.8\t25868\t26271\t50.4\t8796\t4983\t3813\t2872\t1987\t885\n1975\tpsychology\t51245\t-1.7\t24284\t26961\t52.6\t9394\t5035\t4359\t2913\t1979\t934\n1976\tpsychology\t50278\t-1.9\t22898\t27380\t54.5\t10167\t5136\t5031\t3157\t2115\t1042\n1977\tpsychology\t47861\t-4.8\t20627\t27234\t56.9\t10859\t5293\t5566\t3386\t2127\t1259\n1978\tpsychology\t44879\t-6.2\t18422\t26457\t59.0\t10282\t4670\t5612\t3164\t1974\t1190\n1979\tpsychology\t42697\t-4.9\t16540\t26157\t61.3\t10132\t4405\t5727\t3228\t1895\t1333\n1980\tpsychology\t42093\t-1.4\t15440\t26653\t63.3\t9938\t4096\t5842\t3395\t1921\t1474\n1981\tpsychology\t41068\t-2.4\t14332\t26736\t65.1\t10223\t4066\t6157\t3576\t2002\t1574\n1982\tpsychology\t41212\t0.4\t13645\t27567\t66.9\t9947\t3823\t6124\t3461\t1856\t1605\n1983\tpsychology\t40460\t-1.8\t13131\t27329\t67.5\t9981\t3647\t6334\t3602\t1838\t1764\n1984\tpsychology\t39955\t-1.2\t12812\t27143\t67.9\t9525\t3400\t6125\t3535\t1774\t1761\n1985\tpsychology\t39900\t-0.1\t12706\t27194\t68.2\t9891\t3452\t6439\t3447\t1739\t1708\n1986\tpsychology\t40628\t1.8\t12605\t28023\t69.0\t9845\t3347\t6498\t3593\t1724\t1869\n1987\tpsychology\t43152\t6.2\t13395\t29757\t69.0\t11000\t3516\t7484\t4062\t1801\t2261\n1988\tpsychology\t45371\t5.1\t13579\t31792\t70.1\t10488\t3256\t7232\t3973\t1783\t2190\n1989\tpsychology\t49083\t8.2\t14265\t34818\t70.9\t11329\t3465\t7864\t4143\t1773\t2370\n1990\tpsychology\t53952\t9.9\t15336\t38616\t71.6\t10730\t3377\t7353\t3811\t1566\t2245\n1991\tpsychology\t58655\t8.7\t16067\t42588\t72.6\t11349\t3329\t8020\t3932\t1520\t2412\n1992\tpsychology\t63683\t8.6\t17062\t46621\t73.2\t11659\t3335\t8324\t3814\t1490\t2324\n1993\tpsychology\t66931\t5.1\t17942\t48989\t73.2\t12518\t3380\t9138\t4100\t1570\t2530\n1994\tpsychology\t69419\t3.7\t18668\t50751\t73.1\t13723\t3763\t9960\t4021\t1497\t2524\n1995\tpsychology\t72233\t4.1\t19570\t52663\t72.9\t15378\t4210\t11168\t4252\t1562\t2690\n1996\tpsychology\t73416\t1.6\t19836\t53580\t73.0\t15152\t4090\t11062\t4141\t1380\t2761\n1997\tpsychology\t74308\t1.2\t19408\t54900\t73.9\t15769\t4155\t11614\t4507\t1495\t3012\n1998\tpsychology\t74107\t-0.3\t18976\t55131\t74.4\t15142\t3978\t11164\t4541\t1470\t3071\n1999\tpsychology\t73747\t-0.5\t18376\t55371\t75.1\t15560\t3959\t11601\t4678\t1528\t3150\n2000\tpsychology\t74194\t0.6\t17451\t56743\t76.5\t15740\t3821\t11919\t4731\t1529\t3202\n2001\tpsychology\t73645\t-0.7\t16585\t57060\t77.5\t16539\t3892\t12647\t5091\t1598\t3493\n2002\tpsychology\t76775\t4.3\t17284\t59491\t77.5\t16357\t3814\t12543\t4759\t1503\t3256\n2003\tpsychology\t78650\t2.4\t17514\t61136\t77.7\t17161\t3839\t13322\t4835\t1483\t3352\n2004\tpsychology\t82098\t4.4\t18193\t63905\t77.8\t17898\t3789\t14109\t4827\t1496\t3331\n2005\tpsychology\t85614\t4.3\t19000\t66614\t77.8\t18830\t3900\t14930\t5106\t1466\t3640\n2006\tpsychology\t88134\t2.9\t19865\t68269\t77.5\t19770\t4079\t15691\t4921\t1347\t3574\n2007\tpsychology\t90039\t2.2\t20343\t69696\t77.4\t21037\t4265\t16772\t5153\t1382\t3771\n2008\tpsychology\t92587\t2.8\t21202\t71385\t77.1\t21431\t4356\t17075\t5296\t1440\t3856\n2009\tpsychology\t94273\t1.8\t21490\t72783\t77.2\t23415\t4789\t18626\t5477\t1478\t3999\n2010\tpsychology\t97215\t3.1\t22262\t74953\t77.1\t23763\t4799\t18964\t5540\t1478\t4062\n2011\tpsychology\t100906\t3.8\t23230\t77676\t77.0\t25062\t5127\t19935\t5851\t1481\t4370\n2012\tpsychology\t109099\t8.1\t25420\t83679\t76.7\t27052\t5482\t21570\t5936\t1525\t4411\n2013\tpsychology\t114446\t4.9\t26814\t87632\t76.6\t27787\t5715\t22072\t6326\t1628\t4698\n2014\tpsychology\t117298\t2.5\t27304\t89994\t76.7\t27966\t5731\t22235\t6634\t1680\t4954\n1971\tpublic administration and social services\t5466\t0\t1726\t3740\t68.4\t7785\t3893\t3892\t174\t132\t42\n1972\tpublic administration and social services\t7508\t37.4\t2588\t4920\t65.5\t8756\t4537\t4219\t193\t150\t43\n1973\tpublic administration and social services\t10690\t42.4\t3998\t6692\t62.6\t10068\t5271\t4797\t198\t160\t38\n1974\tpublic administration and social services\t11966\t11.9\t4266\t7700\t64.3\t11415\t6028\t5387\t201\t154\t47\n1975\tpublic administration and social services\t13661\t14.2\t4630\t9031\t66.1\t13617\t7200\t6417\t257\t192\t65\n1976\tpublic administration and social services\t15440\t13.0\t5706\t9734\t63.0\t15209\t7969\t7240\t292\t192\t100\n1977\tpublic administration and social services\t16136\t4.5\t5544\t10592\t65.6\t17026\t8810\t8216\t292\t197\t95\n1978\tpublic administration and social services\t16607\t2.9\t5096\t11511\t69.3\t17337\t8513\t8824\t357\t237\t120\n1979\tpublic administration and social services\t17328\t4.3\t4938\t12390\t71.5\t17306\t8051\t9255\t315\t215\t100\n1980\tpublic administration and social services\t16644\t-3.9\t4451\t12193\t73.3\t17560\t7866\t9694\t342\t216\t126\n1981\tpublic administration and social services\t16707\t0.4\t4248\t12459\t74.6\t17803\t7460\t10343\t362\t212\t150\n1982\tpublic administration and social services\t16495\t-1.3\t4176\t12319\t74.7\t17416\t6975\t10441\t372\t205\t167\n1983\tpublic administration and social services\t14414\t-12.6\t3343\t11071\t76.8\t16046\t5961\t10085\t347\t184\t163\n1984\tpublic administration and social services\t12570\t-12.8\t2998\t9572\t76.1\t15060\t5634\t9426\t420\t230\t190\n1985\tpublic administration and social services\t11754\t-6.5\t2829\t8925\t75.9\t15575\t5573\t10002\t431\t213\t218\n1986\tpublic administration and social services\t11887\t1.1\t2966\t8921\t75.0\t15692\t5594\t10098\t382\t171\t211\n1987\tpublic administration and social services\t12328\t3.7\t2993\t9335\t75.7\t16432\t5673\t10759\t398\t216\t182\n1988\tpublic administration and social services\t12385\t0.5\t2923\t9462\t76.4\t16424\t5631\t10793\t470\t238\t232\n1989\tpublic administration and social services\t13162\t6.3\t3214\t9948\t75.6\t17020\t5615\t11405\t428\t210\t218\n1990\tpublic administration and social services\t13908\t5.7\t3334\t10574\t76.0\t17399\t5634\t11765\t508\t235\t273\n1991\tpublic administration and social services\t14350\t3.2\t3215\t11135\t77.6\t17905\t5679\t12226\t430\t190\t240\n1992\tpublic administration and social services\t15987\t11.4\t3479\t12508\t78.2\t19243\t5769\t13474\t432\t204\t228\n1993\tpublic administration and social services\t16775\t4.9\t3801\t12974\t77.3\t20634\t6105\t14529\t459\t215\t244\n1994\tpublic administration and social services\t17815\t6.2\t3919\t13896\t78.0\t21833\t6406\t15427\t519\t238\t281\n1995\tpublic administration and social services\t18586\t4.3\t3935\t14651\t78.8\t23501\t6870\t16631\t556\t274\t282\n1996\tpublic administration and social services\t19849\t6.8\t4205\t15644\t78.8\t24229\t6927\t17302\t499\t220\t279\n1997\tpublic administration and social services\t20649\t4.0\t4177\t16472\t79.8\t24781\t6957\t17824\t518\t243\t275\n1998\tpublic administration and social services\t20408\t-1.2\t3881\t16527\t81.0\t25144\t7025\t18119\t499\t223\t276\n1999\tpublic administration and social services\t20323\t-0.4\t3799\t16524\t81.3\t25038\t6621\t18417\t534\t240\t294\n2000\tpublic administration and social services\t20185\t-0.7\t3816\t16369\t81.1\t25594\t6808\t18786\t537\t227\t310\n2001\tpublic administration and social services\t19447\t-3.7\t3670\t15777\t81.1\t25268\t6544\t18724\t574\t263\t311\n2002\tpublic administration and social services\t19392\t-0.3\t3706\t15686\t80.9\t25448\t6505\t18943\t571\t250\t321\n2003\tpublic administration and social services\t19900\t2.6\t3726\t16174\t81.3\t25903\t6391\t19512\t599\t265\t334\n2004\tpublic administration and social services\t20552\t3.3\t3793\t16759\t81.5\t28250\t7001\t21249\t649\t275\t374\n2005\tpublic administration and social services\t21769\t5.9\t4209\t17560\t80.7\t29552\t7370\t22182\t673\t272\t401\n2006\tpublic administration and social services\t21986\t1.0\t4126\t17860\t81.2\t30510\t7572\t22938\t704\t285\t419\n2007\tpublic administration and social services\t23147\t5.3\t4354\t18793\t81.2\t31131\t7758\t23373\t726\t253\t473\n2008\tpublic administration and social services\t23493\t1.5\t4202\t19291\t82.1\t33029\t8140\t24889\t760\t269\t491\n2009\tpublic administration and social services\t23852\t1.5\t4373\t19479\t81.7\t33934\t8346\t25588\t812\t306\t506\n2010\tpublic administration and social services\t25421\t6.6\t4578\t20843\t82.0\t35740\t8868\t26872\t838\t323\t515\n2011\tpublic administration and social services\t26799\t5.4\t4913\t21886\t81.7\t38614\t9791\t28823\t851\t327\t524\n2012\tpublic administration and social services\t29695\t10.8\t5386\t24309\t81.9\t41737\t10494\t31243\t890\t343\t547\n2013\tpublic administration and social services\t31950\t7.6\t5664\t26286\t82.3\t43591\t10864\t32727\t979\t351\t628\n2014\tpublic administration and social services\t33483\t4.8\t5917\t27566\t82.3\t44490\t10833\t33657\t1047\t346\t701\n1971\tthe social sciences and history\t155324\t0\t98173\t57151\t36.8\t16539\t11833\t4706\t3660\t3153\t507\n1972\tthe social sciences and history\t158060\t1.8\t100895\t57165\t36.2\t17445\t12540\t4905\t4081\t3483\t598\n1973\tthe social sciences and history\t155970\t-1.3\t99735\t56235\t36.1\t17477\t12605\t4872\t4234\t3573\t661\n1974\tthe social sciences and history\t150320\t-3.6\t95650\t54670\t36.4\t17293\t12321\t4972\t4124\t3383\t741\n1975\tthe social sciences and history\t135190\t-10.1\t84826\t50364\t37.3\t16977\t11875\t5102\t4212\t3334\t878\n1976\tthe social sciences and history\t126396\t-6.5\t78691\t47705\t37.7\t15953\t10918\t5035\t4157\t3262\t895\n1977\tthe social sciences and history\t117040\t-7.4\t71128\t45912\t39.2\t15533\t10413\t5120\t3802\t2957\t845\n1978\tthe social sciences and history\t112952\t-3.5\t67217\t45735\t40.5\t14718\t9845\t4873\t3594\t2722\t872\n1979\tthe social sciences and history\t108059\t-4.3\t62852\t45207\t41.8\t12963\t8395\t4568\t3371\t2501\t870\n1980\tthe social sciences and history\t103662\t-4.1\t58511\t45151\t43.6\t12176\t7794\t4382\t3230\t2357\t873\n1981\tthe social sciences and history\t100513\t-3.0\t56131\t44382\t44.2\t11945\t7457\t4488\t3122\t2274\t848\n1982\tthe social sciences and history\t99705\t-0.8\t55196\t44509\t44.6\t12002\t7468\t4534\t3061\t2237\t824\n1983\tthe social sciences and history\t95228\t-4.5\t52771\t42457\t44.6\t11205\t6974\t4231\t2931\t2042\t889\n1984\tthe social sciences and history\t93323\t-2.0\t52154\t41169\t44.1\t10577\t6551\t4026\t2911\t2030\t881\n1985\tthe social sciences and history\t91570\t-1.9\t51226\t40344\t44.1\t10503\t6475\t4028\t2851\t1933\t918\n1986\tthe social sciences and history\t93840\t2.5\t52724\t41116\t43.8\t10564\t6419\t4145\t2955\t1970\t985\n1987\tthe social sciences and history\t96342\t2.7\t53949\t42393\t44.0\t10506\t6373\t4133\t2916\t2026\t890\n1988\tthe social sciences and history\t100460\t4.3\t56377\t44083\t43.9\t10412\t6310\t4102\t2781\t1849\t932\n1989\tthe social sciences and history\t108151\t7.7\t60121\t48030\t44.4\t11023\t6599\t4424\t2885\t1949\t936\n1990\tthe social sciences and history\t118083\t9.2\t65887\t52196\t44.2\t11634\t6898\t4736\t3010\t2019\t991\n1991\tthe social sciences and history\t125107\t5.9\t68701\t56406\t45.1\t12233\t7016\t5217\t3012\t1956\t1056\n1992\tthe social sciences and history\t133974\t7.1\t73001\t60973\t45.5\t12702\t7237\t5465\t3218\t2126\t1092\n1993\tthe social sciences and history\t135703\t1.3\t73589\t62114\t45.8\t13471\t7671\t5800\t3460\t2203\t1257\n1994\tthe social sciences and history\t133680\t-1.5\t72006\t61674\t46.1\t14561\t8152\t6409\t3627\t2317\t1310\n1995\tthe social sciences and history\t128154\t-4.1\t68139\t60015\t46.8\t14845\t8207\t6638\t3725\t2319\t1406\n1996\tthe social sciences and history\t126479\t-1.3\t65872\t60607\t47.9\t15012\t8093\t6919\t3760\t2339\t1421\n1997\tthe social sciences and history\t124891\t-1.3\t64115\t60776\t48.7\t14787\t7830\t6957\t3989\t2479\t1510\n1998\tthe social sciences and history\t125040\t0.1\t63537\t61503\t49.2\t14938\t7960\t6978\t4127\t2445\t1682\n1999\tthe social sciences and history\t124815\t-0.2\t61843\t62972\t50.5\t14396\t7440\t6956\t3873\t2290\t1583\n2000\tthe social sciences and history\t127101\t1.8\t62062\t65039\t51.2\t14066\t7024\t7042\t4095\t2407\t1688\n2001\tthe social sciences and history\t128036\t0.7\t61749\t66287\t51.8\t13791\t6816\t6975\t3930\t2302\t1628\n2002\tthe social sciences and history\t132874\t3.8\t64170\t68704\t51.7\t14112\t6941\t7171\t3902\t2219\t1683\n2003\tthe social sciences and history\t143256\t7.8\t69517\t73739\t51.5\t14630\t7202\t7428\t3850\t2196\t1654\n2004\tthe social sciences and history\t150357\t5.0\t73834\t76523\t50.9\t16110\t7810\t8300\t3811\t2188\t1623\n2005\tthe social sciences and history\t156892\t4.3\t77702\t79190\t50.5\t16952\t8256\t8696\t3819\t2184\t1635\n2006\tthe social sciences and history\t161485\t2.9\t80799\t80686\t50.0\t17369\t8415\t8954\t3914\t2218\t1696\n2007\tthe social sciences and history\t164183\t1.7\t82417\t81766\t49.8\t17665\t8577\t9088\t3844\t2110\t1734\n2008\tthe social sciences and history\t167363\t1.9\t84868\t82495\t49.3\t18495\t9349\t9146\t4059\t2194\t1865\n2009\tthe social sciences and history\t168517\t0.7\t85202\t83315\t49.4\t19241\t9605\t9636\t4234\t2353\t1881\n2010\tthe social sciences and history\t172782\t2.5\t87404\t85378\t49.4\t20234\t9967\t10267\t4238\t2292\t1946\n2011\tthe social sciences and history\t177169\t2.5\t89809\t87360\t49.3\t21085\t10578\t10507\t4390\t2331\t2059\n2012\tthe social sciences and history\t178534\t0.8\t90628\t87906\t49.2\t21891\t10987\t10904\t4597\t2464\t2133\n2013\tthe social sciences and history\t177767\t-0.4\t90143\t87624\t49.3\t21591\t10811\t10780\t4610\t2461\t2149\n2014\tthe social sciences and history\t173096\t-2.6\t88233\t84863\t49.0\t21475\t10750\t10725\t4724\t2494\t2230\n1971\tvisual and performing arts\t30394\t0\t12256\t18138\t59.7\t6675\t3510\t3165\t621\t483\t138\n1972\tvisual and performing arts\t33831\t11.3\t13580\t20251\t59.9\t7537\t4049\t3488\t572\t428\t144\n1973\tvisual and performing arts\t36017\t6.5\t14267\t21750\t60.4\t7254\t4005\t3249\t616\t449\t167\n1974\tvisual and performing arts\t39730\t10.3\t15821\t23909\t60.2\t8001\t4325\t3676\t585\t440\t145\n1975\tvisual and performing arts\t40782\t2.6\t15532\t25250\t61.9\t8362\t4448\t3914\t649\t446\t203\n1976\tvisual and performing arts\t42138\t3.3\t16491\t25647\t60.9\t8817\t4507\t4310\t620\t447\t173\n1977\tvisual and performing arts\t41793\t-0.8\t16166\t25627\t61.3\t8636\t4211\t4425\t662\t447\t215\n1978\tvisual and performing arts\t40951\t-2.0\t15572\t25379\t62.0\t9036\t4327\t4709\t708\t448\t260\n1979\tvisual and performing arts\t40969\t0\t15380\t25589\t62.5\t8524\t3933\t4591\t700\t454\t246\n1980\tvisual and performing arts\t40892\t-0.2\t15065\t25827\t63.2\t8708\t4067\t4641\t655\t413\t242\n1981\tvisual and performing arts\t40479\t-1.0\t14798\t25681\t63.4\t8629\t4056\t4573\t654\t396\t258\n1982\tvisual and performing arts\t40422\t-0.1\t14819\t25603\t63.3\t8746\t3866\t4880\t670\t380\t290\n1983\tvisual and performing arts\t39804\t-1.5\t14695\t25109\t63.1\t8763\t4013\t4750\t692\t404\t288\n1984\tvisual and performing arts\t40131\t0.8\t15089\t25042\t62.4\t8526\t3897\t4629\t730\t406\t324\n1985\tvisual and performing arts\t38285\t-4.6\t14518\t23767\t62.1\t8720\t3896\t4824\t696\t407\t289\n1986\tvisual and performing arts\t37241\t-2.7\t14236\t23005\t61.8\t8420\t3775\t4645\t722\t396\t326\n1987\tvisual and performing arts\t36873\t-1.0\t13980\t22893\t62.1\t8508\t3756\t4752\t793\t447\t346\n1988\tvisual and performing arts\t37150\t0.8\t14225\t22925\t61.7\t7939\t3442\t4497\t727\t424\t303\n1989\tvisual and performing arts\t38420\t3.4\t14698\t23722\t61.7\t8267\t3611\t4656\t753\t446\t307\n1990\tvisual and performing arts\t39934\t3.9\t15189\t24745\t62.0\t8481\t3706\t4775\t849\t472\t377\n1991\tvisual and performing arts\t42186\t5.6\t15761\t26425\t62.6\t8657\t3830\t4827\t838\t466\t372\n1992\tvisual and performing arts\t46522\t10.3\t17616\t28906\t62.1\t9353\t4078\t5275\t906\t504\t402\n1993\tvisual and performing arts\t47761\t2.7\t18610\t29151\t61.0\t9440\t4099\t5341\t882\t478\t404\n1994\tvisual and performing arts\t49053\t2.7\t19538\t29515\t60.2\t9925\t4229\t5696\t1054\t585\t469\n1995\tvisual and performing arts\t48690\t-0.7\t19781\t28909\t59.4\t10277\t4374\t5903\t1080\t545\t535\n1996\tvisual and performing arts\t49296\t1.2\t20126\t29170\t59.2\t10280\t4361\t5919\t1067\t524\t543\n1997\tvisual and performing arts\t50083\t1.6\t20729\t29354\t58.6\t10627\t4470\t6157\t1060\t525\t535\n1998\tvisual and performing arts\t52077\t4.0\t21483\t30594\t58.7\t11145\t4596\t6549\t1163\t566\t597\n1999\tvisual and performing arts\t54446\t4.5\t22270\t32176\t59.1\t10762\t4544\t6218\t1117\t567\t550\n2000\tvisual and performing arts\t58791\t8.0\t24003\t34788\t59.2\t10918\t4672\t6246\t1127\t537\t590\n2001\tvisual and performing arts\t61148\t4.0\t24967\t36181\t59.2\t11404\t4788\t6616\t1167\t568\t599\n2002\tvisual and performing arts\t66773\t9.2\t27130\t39643\t59.4\t11595\t4912\t6683\t1114\t490\t624\n2003\tvisual and performing arts\t71482\t7.1\t27922\t43560\t60.9\t11982\t4975\t7007\t1293\t613\t680\n2004\tvisual and performing arts\t77181\t8.0\t30037\t47144\t61.1\t12906\t5531\t7375\t1282\t572\t710\n2005\tvisual and performing arts\t80955\t4.9\t31355\t49600\t61.3\t13183\t5646\t7537\t1278\t594\t684\n2006\tvisual and performing arts\t83297\t2.9\t32117\t51180\t61.4\t13530\t5801\t7729\t1383\t639\t744\n2007\tvisual and performing arts\t85186\t2.3\t32729\t52457\t61.6\t13767\t5910\t7857\t1364\t625\t739\n2008\tvisual and performing arts\t87703\t3.0\t33862\t53841\t61.4\t14164\t5998\t8166\t1453\t675\t778\n2009\tvisual and performing arts\t89143\t1.6\t35055\t54088\t60.7\t14918\t6325\t8593\t1569\t726\t843\n2010\tvisual and performing arts\t91798\t3.0\t35768\t56030\t61.0\t15562\t6531\t9031\t1599\t700\t899\n2011\tvisual and performing arts\t93939\t2.3\t36342\t57597\t61.3\t16277\t6881\t9396\t1646\t770\t876\n2012\tvisual and performing arts\t95806\t2.0\t37164\t58642\t61.2\t17307\t7320\t9987\t1728\t790\t938\n2013\tvisual and performing arts\t97799\t2.1\t38063\t59736\t61.1\t17869\t7610\t10259\t1814\t850\t964\n2014\tvisual and performing arts\t97246\t-0.6\t38081\t59165\t60.8\t17863\t7711\t10152\t1778\t869\t909\n"
  },
  {
    "path": "python-baseball-simulator/Simulating baseball in Python.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Simulating baseball in Python\\n\",\n    \"\\n\",\n    \"This notebook provides the methodology and code used in the blog post, [How much does batting order matter in Major League Baseball? A simulation approach](http://www.randalolson.com/2018/07/04/does-batting-order-matter-in-major-league-baseball-a-simulation-approach).\\n\",\n    \"\\n\",\n    \"### Notebook by [Randal S. Olson](http://www.randalolson.com)\\n\",\n    \"\\n\",\n    \"Please see the [repository README file](https://github.com/rhiever/Data-Analysis-and-Machine-Learning-Projects#license) for the licenses and usage terms for the instructional material and code in this notebook. In general, I have licensed this material so that it is as widely useable and shareable as possible.\\n\",\n    \"\\n\",\n    \"### Required Python libraries\\n\",\n    \"\\n\",\n    \"If you don't have Python on your computer, you can use the [Anaconda Python distribution](https://www.anaconda.com/download/) to install most of the Python packages you need. Anaconda provides a simple double-click installer for your convenience.\\n\",\n    \"\\n\",\n    \"This code uses base Python libraries except for `seaborn`, `tqdm`, and `joblib` packages. You can install these packages using `pip` by typing the following commands into your command line:\\n\",\n    \"\\n\",\n    \"> pip install seaborn tqdm joblib\\n\",\n    \"\\n\",\n    \"### Using the baseball simulator\\n\",\n    \"\\n\",\n    \"Below is the Python code used to simulate baseball in my blog post, run the simulations, and generate the data visualizations shown in my blog post. When I get more time, I plan to clean up and comment this code better than it currently is.\\n\",\n    \"\\n\",\n    \"For the data visualizations, you will need to place [this tableau10.mplstyle](https://gist.github.com/rhiever/d0a7332fe0beebfdc3d5) in your `~/.matplotlib/stylelib/` directory for the visualizations to show up as they do in my blog post. Otherwise, you will have to use [other matplotlib styles](https://matplotlib.org/users/style_sheets.html).\\n\",\n    \"\\n\",\n    \"If you have any comments or questions about this project, I prefer that you [file an issue](https://github.com/rhiever/Data-Analysis-and-Machine-Learning-Projects/issues/new) on this GitHub repository. If you don't feel comfortable with GitHub, feel free to [contact me by email](http://www.randalolson.com/contact/).\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 160,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"%matplotlib inline\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"import seaborn as sb\\n\",\n    \"import numpy as np\\n\",\n    \"from tqdm import tqdm_notebook as tqdm\\n\",\n    \"from joblib import Parallel, delayed\\n\",\n    \"import time\\n\",\n    \"\\n\",\n    \"# ParallelExecutor code taken and modified from https://gist.github.com/MInner/12f9cf961059aed1a60e72c5531a697f\\n\",\n    \"def text_progessbar(seq, total=None):\\n\",\n    \"    step = 1\\n\",\n    \"    tick = time.time()\\n\",\n    \"    while True:\\n\",\n    \"        time_diff = time.time() - tick\\n\",\n    \"        avg_speed = time_diff / step\\n\",\n    \"        total_str = 'of {}'.format(total if total else '')\\n\",\n    \"        print('step', step, '{}'.format(round(time_diff, 2)), 'avg: {} iter/sec'.format(round(avg_speed)), total_str)\\n\",\n    \"        step += 1\\n\",\n    \"        yield next(seq)\\n\",\n    \"\\n\",\n    \"all_bar_funcs = {\\n\",\n    \"    'tqdm': lambda args: lambda x: tqdm(x, **args),\\n\",\n    \"    'txt': lambda args: lambda x: text_progessbar(x, **args),\\n\",\n    \"    'False': lambda args: iter,\\n\",\n    \"    'None': lambda args: iter,\\n\",\n    \"}\\n\",\n    \"\\n\",\n    \"def ParallelExecutor(use_bar='tqdm', **joblib_args):\\n\",\n    \"    def aprun(bar=use_bar, **tq_args):\\n\",\n    \"        def tmp(op_iter):\\n\",\n    \"            if str(bar) in all_bar_funcs.keys():\\n\",\n    \"                bar_func = all_bar_funcs[str(bar)](tq_args)\\n\",\n    \"            else:\\n\",\n    \"                raise ValueError('Value {} not supported as bar type'.format(bar))\\n\",\n    \"            return Parallel(**joblib_args)(bar_func(op_iter))\\n\",\n    \"        return tmp\\n\",\n    \"    return aprun\\n\",\n    \"\\n\",\n    \"def simulate_game(batters, return_stats=False):\\n\",\n    \"    '''Simulates the batting side of a Major League Baseball game\\n\",\n    \"    \\n\",\n    \"    This is a simplified simulation of a baseball game, where each batter performs randomly\\n\",\n    \"    according to their corresponding batting average. This simulation incorporates\\n\",\n    \"    different types of hits, such as singles, doubles, triples, and home runs, and uses\\n\",\n    \"    2017-2018 Major League averages for the probabilities of those hit types occurring.\\n\",\n    \"    This simulation leaves out other aspects of the game, such as individual-level hit type\\n\",\n    \"    tendencies, double plays, stolen bases, errors, and so forth.\\n\",\n    \"\\n\",\n    \"    Parameters\\n\",\n    \"    ----------\\n\",\n    \"    batters: list\\n\",\n    \"        A list of batting averages for 9 batters in the desired batting order\\n\",\n    \"        Example input: [0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25]\\n\",\n    \"\\n\",\n    \"    Returns\\n\",\n    \"    ----------\\n\",\n    \"    runs_scored: int\\n\",\n    \"        The number of runs scored by the batters in one simulated game\\n\",\n    \"    batting_stats: dict\\n\",\n    \"        Dictionary containing batting statistics for each batter\\n\",\n    \"    '''\\n\",\n    \"    runs_scored = 0\\n\",\n    \"    batter_num = 0\\n\",\n    \"\\n\",\n    \"    # NOTE: Earned Bases is the total number of bases that the batter advanced themselves\\n\",\n    \"    #       AND their teammates through batting\\n\",\n    \"    batting_stats = {}\\n\",\n    \"    for batter in range(len(batters)):\\n\",\n    \"        batting_stats[batter] = {\\n\",\n    \"            'At Bat': 0, 'Single': 0, 'Double': 0, 'Triple': 0, 'Home Run': 0, 'Out': 0,\\n\",\n    \"            'RBI': 0, 'Earned Bases': 0, 'Players On Base': 0, 'Bases Loaded': 0, 'Grand Slam': 0\\n\",\n    \"        }\\n\",\n    \"\\n\",\n    \"    # Assume the game lasts for only 9 innings (no extra innings)\\n\",\n    \"    for inning in range(9):\\n\",\n    \"        bases = [\\n\",\n    \"            False, # First base, index 0\\n\",\n    \"            False, # Second base, index 1\\n\",\n    \"            False  # Third base, index 2\\n\",\n    \"        ]\\n\",\n    \"        batters_out = 0\\n\",\n    \"\\n\",\n    \"        while batters_out < 3:\\n\",\n    \"            batting_stats[batter_num]['At Bat'] += 1\\n\",\n    \"            if bases[2] and bases[1] and bases[0]:\\n\",\n    \"                batting_stats[batter_num]['Bases Loaded'] += 1\\n\",\n    \"\\n\",\n    \"            if bases[2]:\\n\",\n    \"                batting_stats[batter_num]['Players On Base'] += 1\\n\",\n    \"            if bases[1]:\\n\",\n    \"                batting_stats[batter_num]['Players On Base'] += 1\\n\",\n    \"            if bases[0]:\\n\",\n    \"                batting_stats[batter_num]['Players On Base'] += 1\\n\",\n    \"\\n\",\n    \"            if np.random.random() < batters[batter_num]:\\n\",\n    \"                # Batting estimates from MLB.com statistics in 2017/2018 seasons:\\n\",\n    \"                #     Single base hit: 64% of hits\\n\",\n    \"                #     Double base hit: 20% of hits\\n\",\n    \"                #     Triple base hit: 2% of hits\\n\",\n    \"                #     Home run: 14% of hits\\n\",\n    \"                hit_type = np.random.choice(['Single', 'Double', 'Triple', 'Home Run'], p=[.64, .2, .02, .14])\\n\",\n    \"\\n\",\n    \"                if hit_type == 'Single':\\n\",\n    \"                    batting_stats[batter_num]['Single'] += 1\\n\",\n    \"\\n\",\n    \"                    # All base runners advance 1 base\\n\",\n    \"                    if bases[2]:\\n\",\n    \"                        runs_scored += 1\\n\",\n    \"                        batting_stats[batter_num]['RBI'] += 1\\n\",\n    \"                        batting_stats[batter_num]['Earned Bases'] += 1\\n\",\n    \"                        bases[2] = False\\n\",\n    \"                    if bases[1]:\\n\",\n    \"                        bases[2] = True\\n\",\n    \"                        bases[1] = False\\n\",\n    \"                        batting_stats[batter_num]['Earned Bases'] += 1\\n\",\n    \"                    if bases[0]:\\n\",\n    \"                        bases[1] = True\\n\",\n    \"                        batting_stats[batter_num]['Earned Bases'] += 1\\n\",\n    \"\\n\",\n    \"                    bases[0] = True\\n\",\n    \"                    batting_stats[batter_num]['Earned Bases'] += 1\\n\",\n    \"\\n\",\n    \"                elif hit_type == 'Double':\\n\",\n    \"                    batting_stats[batter_num]['Double'] += 1\\n\",\n    \"\\n\",\n    \"                    # All base runners advance 2 bases\\n\",\n    \"                    if bases[2]:\\n\",\n    \"                        runs_scored += 1\\n\",\n    \"                        batting_stats[batter_num]['RBI'] += 1\\n\",\n    \"                        batting_stats[batter_num]['Earned Bases'] += 1\\n\",\n    \"                        bases[2] = False\\n\",\n    \"                    if bases[1]:\\n\",\n    \"                        runs_scored += 1\\n\",\n    \"                        batting_stats[batter_num]['RBI'] += 1\\n\",\n    \"                        batting_stats[batter_num]['Earned Bases'] += 2\\n\",\n    \"                        bases[1] = False\\n\",\n    \"                    if bases[0]:\\n\",\n    \"                        bases[2] = True\\n\",\n    \"                        batting_stats[batter_num]['Earned Bases'] += 2\\n\",\n    \"                        bases[0] = False\\n\",\n    \"\\n\",\n    \"                    bases[1] = True\\n\",\n    \"                    batting_stats[batter_num]['Earned Bases'] += 2\\n\",\n    \"\\n\",\n    \"                elif hit_type == 'Triple':\\n\",\n    \"                    batting_stats[batter_num]['Triple'] += 1\\n\",\n    \"\\n\",\n    \"                    # All base runners advance 3 bases\\n\",\n    \"                    if bases[2]:\\n\",\n    \"                        runs_scored += 1\\n\",\n    \"                        batting_stats[batter_num]['RBI'] += 1\\n\",\n    \"                        batting_stats[batter_num]['Earned Bases'] += 1\\n\",\n    \"                        bases[2] = False\\n\",\n    \"                    if bases[1]:\\n\",\n    \"                        runs_scored += 1\\n\",\n    \"                        batting_stats[batter_num]['RBI'] += 1\\n\",\n    \"                        batting_stats[batter_num]['Earned Bases'] += 2\\n\",\n    \"                        bases[1] = False\\n\",\n    \"                    if bases[0]:\\n\",\n    \"                        runs_scored += 1\\n\",\n    \"                        batting_stats[batter_num]['RBI'] += 1\\n\",\n    \"                        batting_stats[batter_num]['Earned Bases'] += 3\\n\",\n    \"                        bases[0] = False\\n\",\n    \"\\n\",\n    \"                    bases[2] = True\\n\",\n    \"                    batting_stats[batter_num]['Earned Bases'] += 3\\n\",\n    \"\\n\",\n    \"                elif hit_type == 'Home Run':\\n\",\n    \"                    batting_stats[batter_num]['Home Run'] += 1\\n\",\n    \"\\n\",\n    \"                    # Check if a Grand Slam was scored\\n\",\n    \"                    if bases[0] and bases[1] and bases[2]:\\n\",\n    \"                        batting_stats[batter_num]['Grand Slam'] += 1\\n\",\n    \"\\n\",\n    \"                    # All base runners and the hitter score a run\\n\",\n    \"                    if bases[2]:\\n\",\n    \"                        runs_scored += 1\\n\",\n    \"                        batting_stats[batter_num]['RBI'] += 1\\n\",\n    \"                        batting_stats[batter_num]['Earned Bases'] += 1\\n\",\n    \"                        bases[2] = False\\n\",\n    \"                    if bases[1]:\\n\",\n    \"                        runs_scored += 1\\n\",\n    \"                        batting_stats[batter_num]['RBI'] += 1\\n\",\n    \"                        batting_stats[batter_num]['Earned Bases'] += 2\\n\",\n    \"                        bases[1] = False\\n\",\n    \"                    if bases[0]:\\n\",\n    \"                        runs_scored += 1\\n\",\n    \"                        batting_stats[batter_num]['RBI'] += 1\\n\",\n    \"                        batting_stats[batter_num]['Earned Bases'] += 3\\n\",\n    \"                        bases[0] = False\\n\",\n    \"\\n\",\n    \"                    runs_scored += 1\\n\",\n    \"                    batting_stats[batter_num]['RBI'] += 1\\n\",\n    \"                    batting_stats[batter_num]['Earned Bases'] += 4\\n\",\n    \"\\n\",\n    \"            else:\\n\",\n    \"                # Batter struck out, flew out, or grounded out\\n\",\n    \"                batters_out += 1\\n\",\n    \"                batting_stats[batter_num]['Out'] += 1\\n\",\n    \"\\n\",\n    \"            batter_num = (batter_num + 1) % len(batters)\\n\",\n    \"\\n\",\n    \"    return runs_scored, batting_stats\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"a48d044db65b42529e0cff1ac86648fa\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"HBox(children=(IntProgress(value=0, max=6), HTML(value='')))\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"designated_hitter_spot_scores = {}\\n\",\n    \"num_simulated_games = 1000000\\n\",\n    \"for team_avg in tqdm([0.1, 0.15, 0.2, 0.25, 0.3, 0.35]):\\n\",\n    \"    for designated_hitter_spot in range(9):\\n\",\n    \"        batters = [team_avg] * 9\\n\",\n    \"        batters[designated_hitter_spot] = 0.35\\n\",\n    \"        aprun = ParallelExecutor(n_jobs=-1, use_bar=False)\\n\",\n    \"        designated_hitter_spot_scores[(team_avg, designated_hitter_spot)] = [runs_scored for runs_scored, _ in aprun(total=num_simulated_games)(delayed(simulate_game)(batters) for _ in range(num_simulated_games))]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 64,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"''\"\n      ]\n     },\n     \"execution_count\": 64,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 648x648 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"dh_batting_avgs = []\\n\",\n    \"for team_avg in reversed([0.1, 0.15, 0.2, 0.25, 0.3, 0.35]):\\n\",\n    \"    dh_spot_avgs = []\\n\",\n    \"    for designated_hitter_spot in range(9):\\n\",\n    \"        dh_spot_avgs.append(np.mean(designated_hitter_spot_scores[(team_avg, designated_hitter_spot)]))\\n\",\n    \"    dh_spot_avgs = np.array(dh_spot_avgs) / np.mean(dh_spot_avgs)\\n\",\n    \"    dh_batting_avgs.append(dh_spot_avgs)\\n\",\n    \"\\n\",\n    \"plt.figure(figsize=(9, 9))\\n\",\n    \"sb.heatmap(dh_batting_avgs, cmap='PuOr', center=1., annot=True, fmt='.3f', cbar=False)\\n\",\n    \"plt.xticks([x + 0.5 for x in range(9)], [str(x) for x in range(1, 10)], fontsize=12)\\n\",\n    \"plt.xlabel('DH Batting Position (BA=0.35)', fontsize=14)\\n\",\n    \"plt.yticks([y + 0.5 for y in range(6)], reversed([0.1, 0.15, 0.2, 0.25, 0.3, 0.35]), fontsize=12, va='center')\\n\",\n    \"plt.ylabel('Team Batting Average (BA)', fontsize=14)\\n\",\n    \"plt.title('Batting order matters when one player is much\\\\nbetter than their teammates\\\\n\\\\n', fontsize=20)\\n\",\n    \"plt.text(4.5, -0.1, 'Measured: Relative runs scored based on the DH batting position\\\\n>1 means more runs scored, <1 means fewer runs scored', fontsize=12, ha='center')\\n\",\n    \"plt.text(-0.7, 6.8, 'Data source: League averages & custom baseball simulations\\\\nAuthor: Randal S. Olson (randalolson.com / @randal_olson)', fontsize=10, ha='left')\\n\",\n    \"plt.savefig('mlb-batting-order-dh.png', bbox_inches='tight')\\n\",\n    \";\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"sig diff: team avg=0.1, dh pos=1 vs. dh pos=3 [p=1.993213882431106e-34]\\n\",\n      \"sig diff: team avg=0.1, dh pos=1 vs. dh pos=4 [p=1.812699459453188e-33]\\n\",\n      \"sig diff: team avg=0.1, dh pos=1 vs. dh pos=5 [p=4.841408611226961e-105]\\n\",\n      \"sig diff: team avg=0.1, dh pos=1 vs. dh pos=6 [p=2.1989383913301317e-231]\\n\",\n      \"sig diff: team avg=0.1, dh pos=1 vs. dh pos=7 [p=9.216188457618672e-207]\\n\",\n      \"sig diff: team avg=0.1, dh pos=1 vs. dh pos=8 [p=3.667389122454236e-218]\\n\",\n      \"sig diff: team avg=0.1, dh pos=1 vs. dh pos=9 [p=3.4321976744637874e-290]\\n\",\n      \"sig diff: team avg=0.1, dh pos=2 vs. dh pos=3 [p=8.683361888508758e-21]\\n\",\n      \"sig diff: team avg=0.1, dh pos=2 vs. dh pos=4 [p=4.275214713813377e-20]\\n\",\n      \"sig diff: team avg=0.1, dh pos=2 vs. dh pos=5 [p=2.307786280302862e-79]\\n\",\n      \"sig diff: team avg=0.1, dh pos=2 vs. dh pos=6 [p=6.56752502068817e-192]\\n\",\n      \"sig diff: team avg=0.1, dh pos=2 vs. dh pos=7 [p=1.2862878707847538e-169]\\n\",\n      \"sig diff: team avg=0.1, dh pos=2 vs. dh pos=8 [p=6.758757959239213e-180]\\n\",\n      \"sig diff: team avg=0.1, dh pos=2 vs. dh pos=9 [p=1.7225195850226612e-245]\\n\",\n      \"sig diff: team avg=0.1, dh pos=3 vs. dh pos=5 [p=1.8386948714134494e-21]\\n\",\n      \"sig diff: team avg=0.1, dh pos=3 vs. dh pos=6 [p=1.6438493001481296e-90]\\n\",\n      \"sig diff: team avg=0.1, dh pos=3 vs. dh pos=7 [p=1.8420283773808343e-75]\\n\",\n      \"sig diff: team avg=0.1, dh pos=3 vs. dh pos=8 [p=2.922048694446345e-82]\\n\",\n      \"sig diff: team avg=0.1, dh pos=3 vs. dh pos=9 [p=8.729968564474104e-128]\\n\",\n      \"sig diff: team avg=0.1, dh pos=4 vs. dh pos=5 [p=7.157137876264341e-22]\\n\",\n      \"sig diff: team avg=0.1, dh pos=4 vs. dh pos=6 [p=4.583023030013289e-91]\\n\",\n      \"sig diff: team avg=0.1, dh pos=4 vs. dh pos=7 [p=4.8140938524114977e-76]\\n\",\n      \"sig diff: team avg=0.1, dh pos=4 vs. dh pos=8 [p=7.650725116291236e-83]\\n\",\n      \"sig diff: team avg=0.1, dh pos=4 vs. dh pos=9 [p=2.4714887218395744e-128]\\n\",\n      \"sig diff: team avg=0.1, dh pos=5 vs. dh pos=6 [p=2.198816819980043e-26]\\n\",\n      \"sig diff: team avg=0.1, dh pos=5 vs. dh pos=7 [p=1.0330372720533307e-18]\\n\",\n      \"sig diff: team avg=0.1, dh pos=5 vs. dh pos=8 [p=4.768588761976132e-22]\\n\",\n      \"sig diff: team avg=0.1, dh pos=5 vs. dh pos=9 [p=2.1756207162931975e-47]\\n\",\n      \"sig diff: team avg=0.1, dh pos=7 vs. dh pos=9 [p=1.8820378340149334e-08]\\n\",\n      \"sig diff: team avg=0.1, dh pos=8 vs. dh pos=9 [p=1.532389060082728e-06]\\n\",\n      \"sig diff: team avg=0.15, dh pos=1 vs. dh pos=3 [p=3.6557297985692933e-06]\\n\",\n      \"sig diff: team avg=0.15, dh pos=1 vs. dh pos=5 [p=1.4410639275113839e-18]\\n\",\n      \"sig diff: team avg=0.15, dh pos=1 vs. dh pos=6 [p=2.813016210280305e-69]\\n\",\n      \"sig diff: team avg=0.15, dh pos=1 vs. dh pos=7 [p=2.1148242931535942e-89]\\n\",\n      \"sig diff: team avg=0.15, dh pos=1 vs. dh pos=8 [p=3.304295742713236e-120]\\n\",\n      \"sig diff: team avg=0.15, dh pos=1 vs. dh pos=9 [p=2.137620551842133e-178]\\n\",\n      \"sig diff: team avg=0.15, dh pos=2 vs. dh pos=3 [p=9.408419520887769e-10]\\n\",\n      \"sig diff: team avg=0.15, dh pos=2 vs. dh pos=5 [p=9.070076759034137e-25]\\n\",\n      \"sig diff: team avg=0.15, dh pos=2 vs. dh pos=6 [p=4.013501898130506e-81]\\n\",\n      \"sig diff: team avg=0.15, dh pos=2 vs. dh pos=7 [p=7.944112339150383e-103]\\n\",\n      \"sig diff: team avg=0.15, dh pos=2 vs. dh pos=8 [p=8.693675681304036e-136]\\n\",\n      \"sig diff: team avg=0.15, dh pos=2 vs. dh pos=9 [p=2.4995069467551577e-197]\\n\",\n      \"sig diff: team avg=0.15, dh pos=3 vs. dh pos=6 [p=2.1216224794592715e-38]\\n\",\n      \"sig diff: team avg=0.15, dh pos=3 vs. dh pos=7 [p=1.3510161214298604e-53]\\n\",\n      \"sig diff: team avg=0.15, dh pos=3 vs. dh pos=8 [p=9.038350203657884e-78]\\n\",\n      \"sig diff: team avg=0.15, dh pos=3 vs. dh pos=9 [p=1.6883407647869588e-125]\\n\",\n      \"sig diff: team avg=0.15, dh pos=4 vs. dh pos=5 [p=9.32637132490468e-16]\\n\",\n      \"sig diff: team avg=0.15, dh pos=4 vs. dh pos=6 [p=2.516207750135432e-63]\\n\",\n      \"sig diff: team avg=0.15, dh pos=4 vs. dh pos=7 [p=1.513573427948481e-82]\\n\",\n      \"sig diff: team avg=0.15, dh pos=4 vs. dh pos=8 [p=4.0313322280118733e-112]\\n\",\n      \"sig diff: team avg=0.15, dh pos=4 vs. dh pos=9 [p=2.808888831509607e-168]\\n\",\n      \"sig diff: team avg=0.15, dh pos=5 vs. dh pos=6 [p=2.2369635603850538e-18]\\n\",\n      \"sig diff: team avg=0.15, dh pos=5 vs. dh pos=7 [p=4.441855163805096e-29]\\n\",\n      \"sig diff: team avg=0.15, dh pos=5 vs. dh pos=8 [p=3.653180674858543e-47]\\n\",\n      \"sig diff: team avg=0.15, dh pos=5 vs. dh pos=9 [p=3.159014991272495e-85]\\n\",\n      \"sig diff: team avg=0.15, dh pos=6 vs. dh pos=8 [p=1.504718632116115e-08]\\n\",\n      \"sig diff: team avg=0.15, dh pos=6 vs. dh pos=9 [p=3.424427465554104e-27]\\n\",\n      \"sig diff: team avg=0.15, dh pos=7 vs. dh pos=9 [p=7.239966785374588e-17]\\n\",\n      \"sig diff: team avg=0.15, dh pos=8 vs. dh pos=9 [p=2.632610106337814e-07]\\n\",\n      \"sig diff: team avg=0.2, dh pos=1 vs. dh pos=3 [p=4.975863769272197e-06]\\n\",\n      \"sig diff: team avg=0.2, dh pos=1 vs. dh pos=6 [p=2.65483444031005e-28]\\n\",\n      \"sig diff: team avg=0.2, dh pos=1 vs. dh pos=7 [p=1.8135853092597406e-47]\\n\",\n      \"sig diff: team avg=0.2, dh pos=1 vs. dh pos=8 [p=1.150233689898291e-57]\\n\",\n      \"sig diff: team avg=0.2, dh pos=1 vs. dh pos=9 [p=7.223424302190309e-92]\\n\",\n      \"sig diff: team avg=0.2, dh pos=2 vs. dh pos=6 [p=1.0411706910861108e-19]\\n\",\n      \"sig diff: team avg=0.2, dh pos=2 vs. dh pos=7 [p=5.1747610796208696e-36]\\n\",\n      \"sig diff: team avg=0.2, dh pos=2 vs. dh pos=8 [p=6.258135807210273e-45]\\n\",\n      \"sig diff: team avg=0.2, dh pos=2 vs. dh pos=9 [p=1.5616448083207673e-75]\\n\",\n      \"sig diff: team avg=0.2, dh pos=3 vs. dh pos=6 [p=9.158553336775526e-11]\\n\",\n      \"sig diff: team avg=0.2, dh pos=3 vs. dh pos=7 [p=3.301865172490029e-23]\\n\",\n      \"sig diff: team avg=0.2, dh pos=3 vs. dh pos=8 [p=2.1319152939525667e-30]\\n\",\n      \"sig diff: team avg=0.2, dh pos=3 vs. dh pos=9 [p=4.16454555014728e-56]\\n\",\n      \"sig diff: team avg=0.2, dh pos=4 vs. dh pos=6 [p=7.091753492774976e-27]\\n\",\n      \"sig diff: team avg=0.2, dh pos=4 vs. dh pos=7 [p=1.3363453468048125e-45]\\n\",\n      \"sig diff: team avg=0.2, dh pos=4 vs. dh pos=8 [p=1.3048721867324426e-55]\\n\",\n      \"sig diff: team avg=0.2, dh pos=4 vs. dh pos=9 [p=2.650185348750165e-89]\\n\",\n      \"sig diff: team avg=0.2, dh pos=5 vs. dh pos=6 [p=1.4343465725467364e-11]\\n\",\n      \"sig diff: team avg=0.2, dh pos=5 vs. dh pos=7 [p=2.2522684790558515e-24]\\n\",\n      \"sig diff: team avg=0.2, dh pos=5 vs. dh pos=8 [p=1.0047752339155035e-31]\\n\",\n      \"sig diff: team avg=0.2, dh pos=5 vs. dh pos=9 [p=7.322521942273125e-58]\\n\",\n      \"sig diff: team avg=0.2, dh pos=6 vs. dh pos=8 [p=6.891044968687213e-07]\\n\",\n      \"sig diff: team avg=0.2, dh pos=6 vs. dh pos=9 [p=1.8501164693310642e-20]\\n\",\n      \"sig diff: team avg=0.2, dh pos=7 vs. dh pos=9 [p=5.55812122235324e-09]\\n\",\n      \"sig diff: team avg=0.25, dh pos=1 vs. dh pos=6 [p=1.3831070831091773e-08]\\n\",\n      \"sig diff: team avg=0.25, dh pos=1 vs. dh pos=7 [p=3.31894013383353e-18]\\n\",\n      \"sig diff: team avg=0.25, dh pos=1 vs. dh pos=8 [p=1.7330019936918686e-26]\\n\",\n      \"sig diff: team avg=0.25, dh pos=1 vs. dh pos=9 [p=2.313896122609819e-28]\\n\",\n      \"sig diff: team avg=0.25, dh pos=2 vs. dh pos=7 [p=2.165570151778953e-08]\\n\",\n      \"sig diff: team avg=0.25, dh pos=2 vs. dh pos=8 [p=4.208549321541679e-14]\\n\",\n      \"sig diff: team avg=0.25, dh pos=2 vs. dh pos=9 [p=1.871896887582137e-15]\\n\",\n      \"sig diff: team avg=0.25, dh pos=3 vs. dh pos=7 [p=3.2875112864625612e-06]\\n\",\n      \"sig diff: team avg=0.25, dh pos=3 vs. dh pos=8 [p=3.877797203112571e-11]\\n\",\n      \"sig diff: team avg=0.25, dh pos=3 vs. dh pos=9 [p=2.481145930009218e-12]\\n\",\n      \"sig diff: team avg=0.25, dh pos=4 vs. dh pos=7 [p=1.1449304045033806e-12]\\n\",\n      \"sig diff: team avg=0.25, dh pos=4 vs. dh pos=8 [p=1.226266128442354e-19]\\n\",\n      \"sig diff: team avg=0.25, dh pos=4 vs. dh pos=9 [p=3.0013494742914022e-21]\\n\",\n      \"sig diff: team avg=0.25, dh pos=5 vs. dh pos=7 [p=3.756596993806079e-07]\\n\",\n      \"sig diff: team avg=0.25, dh pos=5 vs. dh pos=8 [p=1.9972127491978356e-12]\\n\",\n      \"sig diff: team avg=0.25, dh pos=5 vs. dh pos=9 [p=1.0843693706190971e-13]\\n\",\n      \"sig diff: team avg=0.25, dh pos=6 vs. dh pos=8 [p=6.095539057877671e-07]\\n\",\n      \"sig diff: team avg=0.25, dh pos=6 vs. dh pos=9 [p=7.246783482517967e-08]\\n\",\n      \"sig diff: team avg=0.3, dh pos=1 vs. dh pos=7 [p=3.590625549191536e-06]\\n\",\n      \"sig diff: team avg=0.3, dh pos=1 vs. dh pos=8 [p=4.0136192312912695e-11]\\n\",\n      \"sig diff: team avg=0.3, dh pos=1 vs. dh pos=9 [p=1.7985941375427346e-10]\\n\",\n      \"sig diff: team avg=0.3, dh pos=2 vs. dh pos=8 [p=7.586368508725617e-10]\\n\",\n      \"sig diff: team avg=0.3, dh pos=2 vs. dh pos=9 [p=3.0993959675870173e-09]\\n\",\n      \"sig diff: team avg=0.3, dh pos=3 vs. dh pos=8 [p=7.627671059317768e-08]\\n\",\n      \"sig diff: team avg=0.3, dh pos=3 vs. dh pos=9 [p=2.632896126595547e-07]\\n\",\n      \"sig diff: team avg=0.3, dh pos=4 vs. dh pos=7 [p=5.161961870945482e-07]\\n\",\n      \"sig diff: team avg=0.3, dh pos=4 vs. dh pos=8 [p=2.699565677716188e-12]\\n\",\n      \"sig diff: team avg=0.3, dh pos=4 vs. dh pos=9 [p=1.3251648748904703e-11]\\n\",\n      \"sig diff: team avg=0.3, dh pos=5 vs. dh pos=8 [p=7.733403379191462e-07]\\n\",\n      \"sig diff: team avg=0.3, dh pos=5 vs. dh pos=9 [p=2.435910255012577e-06]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from scipy.stats import ranksums\\n\",\n    \"from itertools import product\\n\",\n    \"\\n\",\n    \"for team_avg1, designated_hitter_spot1, team_avg2, designated_hitter_spot2 in product([0.1, 0.15, 0.2, 0.25, 0.3, 0.35], range(9), [0.1, 0.15, 0.2, 0.25, 0.3, 0.35], range(9)):\\n\",\n    \"    if team_avg1 != team_avg2:\\n\",\n    \"        continue\\n\",\n    \"    if designated_hitter_spot1 > designated_hitter_spot2:\\n\",\n    \"        continue\\n\",\n    \"    if team_avg1 == team_avg2 and designated_hitter_spot1 == designated_hitter_spot2:\\n\",\n    \"        continue\\n\",\n    \"    statistic, pval = ranksums(designated_hitter_spot_scores[(team_avg1, designated_hitter_spot1)], designated_hitter_spot_scores[(team_avg2, designated_hitter_spot2)])\\n\",\n    \"    if pval < 1e-5:\\n\",\n    \"        print('sig diff: team avg={}, dh pos={} vs. dh pos={} [p={}]'.format(team_avg1, designated_hitter_spot1 + 1, designated_hitter_spot2 + 1, pval))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"7090098ae5344608824b5159a98774c9\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"HBox(children=(IntProgress(value=0, max=6), HTML(value='')))\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"pitcher_spot_scores = {}\\n\",\n    \"num_simulated_games = 1000000\\n\",\n    \"for team_avg in tqdm([0.1, 0.15, 0.2, 0.25, 0.3, 0.35]):\\n\",\n    \"    for pitcher_spot in range(9):\\n\",\n    \"        batters = [team_avg] * 9\\n\",\n    \"        batters[pitcher_spot] = 0.1\\n\",\n    \"        aprun = ParallelExecutor(n_jobs=-1, use_bar=False)\\n\",\n    \"        pitcher_spot_scores[(team_avg, pitcher_spot)] = [runs_scored for runs_scored, _ in aprun(total=num_simulated_games)(delayed(simulate_game)(batters) for _ in range(num_simulated_games))]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 65,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"''\"\n      ]\n     },\n     \"execution_count\": 65,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 648x648 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"p_batting_avgs = []\\n\",\n    \"for team_avg in reversed([0.1, 0.15, 0.2, 0.25, 0.3, 0.35]):\\n\",\n    \"    p_spot_avgs = []\\n\",\n    \"    for pitcher_spot in range(9):\\n\",\n    \"        p_spot_avgs.append(np.mean(pitcher_spot_scores[(team_avg, pitcher_spot)]))\\n\",\n    \"    p_spot_avgs = np.array(p_spot_avgs) / np.mean(p_spot_avgs)\\n\",\n    \"    p_batting_avgs.append(p_spot_avgs)\\n\",\n    \"\\n\",\n    \"plt.figure(figsize=(9, 9))\\n\",\n    \"sb.heatmap(p_batting_avgs, cmap='PuOr', center=1., annot=True, fmt='.3f', cbar=False)\\n\",\n    \"plt.xticks([x + 0.5 for x in range(9)], [str(x) for x in range(1, 10)], fontsize=12)\\n\",\n    \"plt.xlabel('Pitcher Batting Position (BA=0.1)', fontsize=14)\\n\",\n    \"plt.yticks([y + 0.5 for y in range(6)], reversed([0.1, 0.15, 0.2, 0.25, 0.3, 0.35]), fontsize=12, va='center')\\n\",\n    \"plt.ylabel('Team Batting Average (BA)', fontsize=14)\\n\",\n    \"plt.title('Batting order matters when one player is much\\\\nworse than their teammates\\\\n\\\\n', fontsize=20)\\n\",\n    \"plt.text(4.5, -0.1, 'Measured: Relative runs scored based on the Pitcher batting position\\\\n>1 means more runs scored, <1 means fewer runs scored', fontsize=12, ha='center')\\n\",\n    \"plt.text(-0.7, 6.8, 'Data source: League averages & custom baseball simulations\\\\nAuthor: Randal S. Olson (randalolson.com / @randal_olson)', fontsize=10, ha='left')\\n\",\n    \"plt.savefig('mlb-batting-order-pitcher.png', bbox_inches='tight')\\n\",\n    \";\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"sig diff: team avg=0.15, pitcher pos=1 vs. pitcher pos=5 [p=3.8851625783791845e-06]\\n\",\n      \"sig diff: team avg=0.15, pitcher pos=1 vs. pitcher pos=6 [p=1.6567387490416848e-09]\\n\",\n      \"sig diff: team avg=0.15, pitcher pos=1 vs. pitcher pos=7 [p=3.521864556215601e-13]\\n\",\n      \"sig diff: team avg=0.15, pitcher pos=1 vs. pitcher pos=8 [p=2.2218326239175095e-14]\\n\",\n      \"sig diff: team avg=0.15, pitcher pos=1 vs. pitcher pos=9 [p=7.785499313865129e-19]\\n\",\n      \"sig diff: team avg=0.15, pitcher pos=2 vs. pitcher pos=6 [p=2.0124828886376083e-06]\\n\",\n      \"sig diff: team avg=0.15, pitcher pos=2 vs. pitcher pos=7 [p=2.0015496834873097e-09]\\n\",\n      \"sig diff: team avg=0.15, pitcher pos=2 vs. pitcher pos=8 [p=1.995996488992889e-10]\\n\",\n      \"sig diff: team avg=0.15, pitcher pos=2 vs. pitcher pos=9 [p=3.2618519915831955e-14]\\n\",\n      \"sig diff: team avg=0.15, pitcher pos=3 vs. pitcher pos=7 [p=1.0796284981609147e-06]\\n\",\n      \"sig diff: team avg=0.15, pitcher pos=3 vs. pitcher pos=8 [p=1.6098095825185125e-07]\\n\",\n      \"sig diff: team avg=0.15, pitcher pos=3 vs. pitcher pos=9 [p=1.0132397815959589e-10]\\n\",\n      \"sig diff: team avg=0.15, pitcher pos=4 vs. pitcher pos=6 [p=7.396386504689107e-07]\\n\",\n      \"sig diff: team avg=0.15, pitcher pos=4 vs. pitcher pos=7 [p=5.706121239186367e-10]\\n\",\n      \"sig diff: team avg=0.15, pitcher pos=4 vs. pitcher pos=8 [p=5.3022601322627294e-11]\\n\",\n      \"sig diff: team avg=0.15, pitcher pos=4 vs. pitcher pos=9 [p=6.745193973135644e-15]\\n\",\n      \"sig diff: team avg=0.2, pitcher pos=1 vs. pitcher pos=6 [p=3.736793590540399e-21]\\n\",\n      \"sig diff: team avg=0.2, pitcher pos=1 vs. pitcher pos=7 [p=2.524976319110312e-18]\\n\",\n      \"sig diff: team avg=0.2, pitcher pos=1 vs. pitcher pos=8 [p=6.746491316929479e-35]\\n\",\n      \"sig diff: team avg=0.2, pitcher pos=1 vs. pitcher pos=9 [p=6.403222171714512e-45]\\n\",\n      \"sig diff: team avg=0.2, pitcher pos=2 vs. pitcher pos=6 [p=2.745377273205046e-20]\\n\",\n      \"sig diff: team avg=0.2, pitcher pos=2 vs. pitcher pos=7 [p=1.592850476032973e-17]\\n\",\n      \"sig diff: team avg=0.2, pitcher pos=2 vs. pitcher pos=8 [p=9.402831597164582e-34]\\n\",\n      \"sig diff: team avg=0.2, pitcher pos=2 vs. pitcher pos=9 [p=1.3256041606779473e-43]\\n\",\n      \"sig diff: team avg=0.2, pitcher pos=3 vs. pitcher pos=6 [p=3.052850586857199e-19]\\n\",\n      \"sig diff: team avg=0.2, pitcher pos=3 vs. pitcher pos=7 [p=1.4702483430295165e-16]\\n\",\n      \"sig diff: team avg=0.2, pitcher pos=3 vs. pitcher pos=8 [p=2.2604947523265107e-32]\\n\",\n      \"sig diff: team avg=0.2, pitcher pos=3 vs. pitcher pos=9 [p=5.097574771619022e-42]\\n\",\n      \"sig diff: team avg=0.2, pitcher pos=4 vs. pitcher pos=6 [p=3.0892533399419725e-19]\\n\",\n      \"sig diff: team avg=0.2, pitcher pos=4 vs. pitcher pos=7 [p=1.5077781232512707e-16]\\n\",\n      \"sig diff: team avg=0.2, pitcher pos=4 vs. pitcher pos=8 [p=2.1263766982882966e-32]\\n\",\n      \"sig diff: team avg=0.2, pitcher pos=4 vs. pitcher pos=9 [p=4.537418406485668e-42]\\n\",\n      \"sig diff: team avg=0.2, pitcher pos=5 vs. pitcher pos=6 [p=1.8903311286109317e-12]\\n\",\n      \"sig diff: team avg=0.2, pitcher pos=5 vs. pitcher pos=7 [p=2.4052778093024993e-10]\\n\",\n      \"sig diff: team avg=0.2, pitcher pos=5 vs. pitcher pos=8 [p=2.987994710899591e-23]\\n\",\n      \"sig diff: team avg=0.2, pitcher pos=5 vs. pitcher pos=9 [p=1.6689098012995852e-31]\\n\",\n      \"sig diff: team avg=0.2, pitcher pos=6 vs. pitcher pos=9 [p=3.2940442949580448e-06]\\n\",\n      \"sig diff: team avg=0.2, pitcher pos=7 vs. pitcher pos=9 [p=8.444971822549267e-08]\\n\",\n      \"sig diff: team avg=0.25, pitcher pos=1 vs. pitcher pos=6 [p=1.263607488343873e-23]\\n\",\n      \"sig diff: team avg=0.25, pitcher pos=1 vs. pitcher pos=7 [p=4.516058690648763e-49]\\n\",\n      \"sig diff: team avg=0.25, pitcher pos=1 vs. pitcher pos=8 [p=1.868709972805515e-46]\\n\",\n      \"sig diff: team avg=0.25, pitcher pos=1 vs. pitcher pos=9 [p=9.124875977854823e-82]\\n\",\n      \"sig diff: team avg=0.25, pitcher pos=2 vs. pitcher pos=6 [p=9.121582635686449e-20]\\n\",\n      \"sig diff: team avg=0.25, pitcher pos=2 vs. pitcher pos=7 [p=2.658571220387695e-43]\\n\",\n      \"sig diff: team avg=0.25, pitcher pos=2 vs. pitcher pos=8 [p=7.532218740635377e-41]\\n\",\n      \"sig diff: team avg=0.25, pitcher pos=2 vs. pitcher pos=9 [p=3.792046725058025e-74]\\n\",\n      \"sig diff: team avg=0.25, pitcher pos=3 vs. pitcher pos=6 [p=8.383966192001412e-09]\\n\",\n      \"sig diff: team avg=0.25, pitcher pos=3 vs. pitcher pos=7 [p=1.4330997662660238e-25]\\n\",\n      \"sig diff: team avg=0.25, pitcher pos=3 vs. pitcher pos=8 [p=1.0181059424978879e-23]\\n\",\n      \"sig diff: team avg=0.25, pitcher pos=3 vs. pitcher pos=9 [p=5.150512722206992e-50]\\n\",\n      \"sig diff: team avg=0.25, pitcher pos=4 vs. pitcher pos=6 [p=2.4556456675838104e-22]\\n\",\n      \"sig diff: team avg=0.25, pitcher pos=4 vs. pitcher pos=7 [p=3.8858858801144415e-47]\\n\",\n      \"sig diff: team avg=0.25, pitcher pos=4 vs. pitcher pos=8 [p=1.405260832926898e-44]\\n\",\n      \"sig diff: team avg=0.25, pitcher pos=4 vs. pitcher pos=9 [p=3.7388299589130853e-79]\\n\",\n      \"sig diff: team avg=0.25, pitcher pos=5 vs. pitcher pos=6 [p=1.0041981665787318e-16]\\n\",\n      \"sig diff: team avg=0.25, pitcher pos=5 vs. pitcher pos=7 [p=1.1186354391340145e-38]\\n\",\n      \"sig diff: team avg=0.25, pitcher pos=5 vs. pitcher pos=8 [p=2.2474110418558506e-36]\\n\",\n      \"sig diff: team avg=0.25, pitcher pos=5 vs. pitcher pos=9 [p=4.689603775373094e-68]\\n\",\n      \"sig diff: team avg=0.25, pitcher pos=6 vs. pitcher pos=7 [p=2.6120598419790005e-06]\\n\",\n      \"sig diff: team avg=0.25, pitcher pos=6 vs. pitcher pos=9 [p=6.531623482124316e-20]\\n\",\n      \"sig diff: team avg=0.25, pitcher pos=7 vs. pitcher pos=9 [p=8.779138829657236e-06]\\n\",\n      \"sig diff: team avg=0.25, pitcher pos=8 vs. pitcher pos=9 [p=1.194392607523925e-06]\\n\",\n      \"sig diff: team avg=0.3, pitcher pos=1 vs. pitcher pos=2 [p=8.353757123256407e-08]\\n\",\n      \"sig diff: team avg=0.3, pitcher pos=1 vs. pitcher pos=3 [p=1.6153351773311131e-22]\\n\",\n      \"sig diff: team avg=0.3, pitcher pos=1 vs. pitcher pos=4 [p=2.6074603335403655e-06]\\n\",\n      \"sig diff: team avg=0.3, pitcher pos=1 vs. pitcher pos=5 [p=6.0048940289536404e-15]\\n\",\n      \"sig diff: team avg=0.3, pitcher pos=1 vs. pitcher pos=6 [p=1.7188273321673662e-52]\\n\",\n      \"sig diff: team avg=0.3, pitcher pos=1 vs. pitcher pos=7 [p=6.956087996395689e-82]\\n\",\n      \"sig diff: team avg=0.3, pitcher pos=1 vs. pitcher pos=8 [p=3.4107621024146694e-109]\\n\",\n      \"sig diff: team avg=0.3, pitcher pos=1 vs. pitcher pos=9 [p=4.971738054338718e-173]\\n\",\n      \"sig diff: team avg=0.3, pitcher pos=2 vs. pitcher pos=3 [p=9.959224425730982e-06]\\n\",\n      \"sig diff: team avg=0.3, pitcher pos=2 vs. pitcher pos=6 [p=4.3000326686945074e-23]\\n\",\n      \"sig diff: team avg=0.3, pitcher pos=2 vs. pitcher pos=7 [p=2.0450594381375726e-43]\\n\",\n      \"sig diff: team avg=0.3, pitcher pos=2 vs. pitcher pos=8 [p=1.025315766940147e-63]\\n\",\n      \"sig diff: team avg=0.3, pitcher pos=2 vs. pitcher pos=9 [p=6.1794625367623775e-114]\\n\",\n      \"sig diff: team avg=0.3, pitcher pos=3 vs. pitcher pos=4 [p=4.4498404478852373e-07]\\n\",\n      \"sig diff: team avg=0.3, pitcher pos=3 vs. pitcher pos=6 [p=4.320334730364867e-08]\\n\",\n      \"sig diff: team avg=0.3, pitcher pos=3 vs. pitcher pos=7 [p=6.30824649646141e-21]\\n\",\n      \"sig diff: team avg=0.3, pitcher pos=3 vs. pitcher pos=8 [p=2.0209047768680628e-35]\\n\",\n      \"sig diff: team avg=0.3, pitcher pos=3 vs. pitcher pos=9 [p=2.6572390367461014e-74]\\n\",\n      \"sig diff: team avg=0.3, pitcher pos=4 vs. pitcher pos=6 [p=6.026023750905654e-26]\\n\",\n      \"sig diff: team avg=0.3, pitcher pos=4 vs. pitcher pos=7 [p=2.881407355644203e-47]\\n\",\n      \"sig diff: team avg=0.3, pitcher pos=4 vs. pitcher pos=8 [p=2.215982775474632e-68]\\n\",\n      \"sig diff: team avg=0.3, pitcher pos=4 vs. pitcher pos=9 [p=4.880235566606631e-120]\\n\",\n      \"sig diff: team avg=0.3, pitcher pos=5 vs. pitcher pos=6 [p=1.1352532168880723e-13]\\n\",\n      \"sig diff: team avg=0.3, pitcher pos=5 vs. pitcher pos=7 [p=9.404064042439993e-30]\\n\",\n      \"sig diff: team avg=0.3, pitcher pos=5 vs. pitcher pos=8 [p=8.688559264804496e-47]\\n\",\n      \"sig diff: team avg=0.3, pitcher pos=5 vs. pitcher pos=9 [p=1.391575735005446e-90]\\n\",\n      \"sig diff: team avg=0.3, pitcher pos=6 vs. pitcher pos=8 [p=3.578976447437263e-12]\\n\",\n      \"sig diff: team avg=0.3, pitcher pos=6 vs. pitcher pos=9 [p=2.2812745629023015e-37]\\n\",\n      \"sig diff: team avg=0.3, pitcher pos=7 vs. pitcher pos=9 [p=8.020784190788164e-19]\\n\",\n      \"sig diff: team avg=0.3, pitcher pos=8 vs. pitcher pos=9 [p=6.172240291460689e-09]\\n\",\n      \"sig diff: team avg=0.35, pitcher pos=1 vs. pitcher pos=2 [p=3.813780983491385e-15]\\n\",\n      \"sig diff: team avg=0.35, pitcher pos=1 vs. pitcher pos=3 [p=1.3830330447057497e-36]\\n\",\n      \"sig diff: team avg=0.35, pitcher pos=1 vs. pitcher pos=4 [p=2.5780721611446785e-27]\\n\",\n      \"sig diff: team avg=0.35, pitcher pos=1 vs. pitcher pos=5 [p=1.1216694099222578e-27]\\n\",\n      \"sig diff: team avg=0.35, pitcher pos=1 vs. pitcher pos=6 [p=3.872729624040197e-90]\\n\",\n      \"sig diff: team avg=0.35, pitcher pos=1 vs. pitcher pos=7 [p=2.626956916522825e-176]\\n\",\n      \"sig diff: team avg=0.35, pitcher pos=1 vs. pitcher pos=8 [p=9.715274614129921e-230]\\n\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"sig diff: team avg=0.35, pitcher pos=1 vs. pitcher pos=9 [p=3.220267321795051e-281]\\n\",\n      \"sig diff: team avg=0.35, pitcher pos=2 vs. pitcher pos=3 [p=1.6139670212100365e-06]\\n\",\n      \"sig diff: team avg=0.35, pitcher pos=2 vs. pitcher pos=6 [p=8.299306585791126e-35]\\n\",\n      \"sig diff: team avg=0.35, pitcher pos=2 vs. pitcher pos=7 [p=1.8586225058566005e-93]\\n\",\n      \"sig diff: team avg=0.35, pitcher pos=2 vs. pitcher pos=8 [p=2.603919487805058e-133]\\n\",\n      \"sig diff: team avg=0.35, pitcher pos=2 vs. pitcher pos=9 [p=4.022886588831997e-173]\\n\",\n      \"sig diff: team avg=0.35, pitcher pos=3 vs. pitcher pos=6 [p=6.614034101392958e-14]\\n\",\n      \"sig diff: team avg=0.35, pitcher pos=3 vs. pitcher pos=7 [p=1.7637445840810704e-55]\\n\",\n      \"sig diff: team avg=0.35, pitcher pos=3 vs. pitcher pos=8 [p=7.739828991638169e-87]\\n\",\n      \"sig diff: team avg=0.35, pitcher pos=3 vs. pitcher pos=9 [p=2.1719000592863943e-119]\\n\",\n      \"sig diff: team avg=0.35, pitcher pos=4 vs. pitcher pos=6 [p=1.2942527134733691e-20]\\n\",\n      \"sig diff: team avg=0.35, pitcher pos=4 vs. pitcher pos=7 [p=1.2835707549227605e-68]\\n\",\n      \"sig diff: team avg=0.35, pitcher pos=4 vs. pitcher pos=8 [p=3.6300281863188245e-103]\\n\",\n      \"sig diff: team avg=0.35, pitcher pos=4 vs. pitcher pos=9 [p=1.8965909045113902e-138]\\n\",\n      \"sig diff: team avg=0.35, pitcher pos=5 vs. pitcher pos=6 [p=2.730228869895967e-20]\\n\",\n      \"sig diff: team avg=0.35, pitcher pos=5 vs. pitcher pos=7 [p=5.1291556746202074e-68]\\n\",\n      \"sig diff: team avg=0.35, pitcher pos=5 vs. pitcher pos=8 [p=2.0013757721704826e-102]\\n\",\n      \"sig diff: team avg=0.35, pitcher pos=5 vs. pitcher pos=9 [p=1.3596282572447242e-137]\\n\",\n      \"sig diff: team avg=0.35, pitcher pos=6 vs. pitcher pos=7 [p=2.1711619378971672e-16]\\n\",\n      \"sig diff: team avg=0.35, pitcher pos=6 vs. pitcher pos=8 [p=1.0883133428675732e-34]\\n\",\n      \"sig diff: team avg=0.35, pitcher pos=6 vs. pitcher pos=9 [p=4.59793707847946e-56]\\n\",\n      \"sig diff: team avg=0.35, pitcher pos=7 vs. pitcher pos=9 [p=3.626139910490934e-14]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from scipy.stats import ranksums\\n\",\n    \"from itertools import product\\n\",\n    \"\\n\",\n    \"for team_avg1, pitcher_spot1, team_avg2, pitcher_spot2 in product([0.1, 0.15, 0.2, 0.25, 0.3, 0.35], range(9), [0.1, 0.15, 0.2, 0.25, 0.3, 0.35], range(9)):\\n\",\n    \"    if team_avg1 != team_avg2:\\n\",\n    \"        continue\\n\",\n    \"    if pitcher_spot1 > pitcher_spot2:\\n\",\n    \"        continue\\n\",\n    \"    if team_avg1 == team_avg2 and pitcher_spot1 == pitcher_spot2:\\n\",\n    \"        continue\\n\",\n    \"    statistic, pval = ranksums(pitcher_spot_scores[(team_avg1, pitcher_spot1)], pitcher_spot_scores[(team_avg2, pitcher_spot2)])\\n\",\n    \"    if pval < 1e-5:\\n\",\n    \"        print('sig diff: team avg={}, pitcher pos={} vs. pitcher pos={} [p={}]'.format(team_avg1, pitcher_spot1 + 1, pitcher_spot2 + 1, pval))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"application/vnd.jupyter.widget-view+json\": {\n       \"model_id\": \"96d6c555d55e45c59744678efa8bf796\",\n       \"version_major\": 2,\n       \"version_minor\": 0\n      },\n      \"text/plain\": [\n       \"HBox(children=(IntProgress(value=0, max=6), HTML(value='')))\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"hitter_spot_scores = {}\\n\",\n    \"num_simulated_games = 1000000\\n\",\n    \"for hitter_ba in tqdm([0.1, 0.15, 0.2, 0.25, 0.3, 0.35]):\\n\",\n    \"    for hitter_spot in range(9):\\n\",\n    \"        batters = [0.25] * 9\\n\",\n    \"        batters[hitter_spot] = hitter_ba\\n\",\n    \"        aprun = ParallelExecutor(n_jobs=-1, use_bar=False)\\n\",\n    \"        hitter_spot_scores[(hitter_ba, hitter_spot)] = [runs_scored for runs_scored, _ in aprun(total=num_simulated_games)(delayed(simulate_game)(batters) for _ in range(num_simulated_games))]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 66,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"''\"\n      ]\n     },\n     \"execution_count\": 66,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 648x648 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"hitter_batting_avgs = []\\n\",\n    \"for hitter_ba in reversed([0.1, 0.15, 0.2, 0.25, 0.3, 0.35]):\\n\",\n    \"    hitter_spot_avgs = []\\n\",\n    \"    for hitter_spot in range(9):\\n\",\n    \"        hitter_spot_avgs.append(np.mean(hitter_spot_scores[(hitter_ba, hitter_spot)]))\\n\",\n    \"    hitter_spot_avgs = np.array(hitter_spot_avgs) / np.mean(hitter_spot_avgs)\\n\",\n    \"    hitter_batting_avgs.append(hitter_spot_avgs)\\n\",\n    \"\\n\",\n    \"plt.figure(figsize=(9, 9))\\n\",\n    \"sb.heatmap(hitter_batting_avgs, cmap='PuOr', center=1., annot=True, fmt='.3f', cbar=False)\\n\",\n    \"plt.xticks([x + 0.5 for x in range(9)], [str(x) for x in range(1, 10)], fontsize=12)\\n\",\n    \"plt.xlabel('Hitter Batting Position (Team BA=0.25)', fontsize=14)\\n\",\n    \"plt.yticks([y + 0.5 for y in range(6)], reversed([0.1, 0.15, 0.2, 0.25, 0.3, 0.35]), fontsize=12, va='center')\\n\",\n    \"plt.ylabel('Hitter Batting Average (BA)', fontsize=14)\\n\",\n    \"plt.title('Exceptional batters should lead the batting line-up,\\\\npoor batters should conclude the line-up\\\\n\\\\n', fontsize=20)\\n\",\n    \"plt.text(4.5, -0.1, 'Measured: Relative runs scored based on the Hitter batting position & BA\\\\n>1 means more runs scored, <1 means fewer runs scored', fontsize=12, ha='center')\\n\",\n    \"plt.text(-0.7, 6.8, 'Data source: League averages & custom baseball simulations\\\\nAuthor: Randal S. Olson (randalolson.com / @randal_olson)', fontsize=10, ha='left')\\n\",\n    \"plt.savefig('mlb-batting-order-varying-hitter.png', bbox_inches='tight')\\n\",\n    \";\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"sig diff: batter avg=0.1, batter pos=1 vs. batter pos=6 [p=2.3356714160801088e-24]\\n\",\n      \"sig diff: batter avg=0.1, batter pos=1 vs. batter pos=7 [p=1.9333496033653533e-42]\\n\",\n      \"sig diff: batter avg=0.1, batter pos=1 vs. batter pos=8 [p=6.10815448231743e-53]\\n\",\n      \"sig diff: batter avg=0.1, batter pos=1 vs. batter pos=9 [p=1.575713429661131e-97]\\n\",\n      \"sig diff: batter avg=0.1, batter pos=2 vs. batter pos=6 [p=2.0215081818800176e-15]\\n\",\n      \"sig diff: batter avg=0.1, batter pos=2 vs. batter pos=7 [p=4.017891266089287e-30]\\n\",\n      \"sig diff: batter avg=0.1, batter pos=2 vs. batter pos=8 [p=5.341388537747882e-39]\\n\",\n      \"sig diff: batter avg=0.1, batter pos=2 vs. batter pos=9 [p=5.060705124564318e-78]\\n\",\n      \"sig diff: batter avg=0.1, batter pos=3 vs. batter pos=6 [p=1.9653904111195653e-11]\\n\",\n      \"sig diff: batter avg=0.1, batter pos=3 vs. batter pos=7 [p=2.7407260526466543e-24]\\n\",\n      \"sig diff: batter avg=0.1, batter pos=3 vs. batter pos=8 [p=2.816306052006508e-32]\\n\",\n      \"sig diff: batter avg=0.1, batter pos=3 vs. batter pos=9 [p=3.0275119028564315e-68]\\n\",\n      \"sig diff: batter avg=0.1, batter pos=4 vs. batter pos=6 [p=5.661422005059482e-20]\\n\",\n      \"sig diff: batter avg=0.1, batter pos=4 vs. batter pos=7 [p=1.6915555936193104e-36]\\n\",\n      \"sig diff: batter avg=0.1, batter pos=4 vs. batter pos=8 [p=2.9356014190839377e-46]\\n\",\n      \"sig diff: batter avg=0.1, batter pos=4 vs. batter pos=9 [p=2.9250009360182003e-88]\\n\",\n      \"sig diff: batter avg=0.1, batter pos=5 vs. batter pos=6 [p=1.4549070522486695e-13]\\n\",\n      \"sig diff: batter avg=0.1, batter pos=5 vs. batter pos=7 [p=1.828373272957227e-27]\\n\",\n      \"sig diff: batter avg=0.1, batter pos=5 vs. batter pos=8 [p=5.822777629859619e-36]\\n\",\n      \"sig diff: batter avg=0.1, batter pos=5 vs. batter pos=9 [p=1.0934504454814966e-73]\\n\",\n      \"sig diff: batter avg=0.1, batter pos=6 vs. batter pos=8 [p=2.9773549237136997e-07]\\n\",\n      \"sig diff: batter avg=0.1, batter pos=6 vs. batter pos=9 [p=4.8361947840322014e-27]\\n\",\n      \"sig diff: batter avg=0.1, batter pos=7 vs. batter pos=9 [p=2.7297346879156197e-13]\\n\",\n      \"sig diff: batter avg=0.1, batter pos=8 vs. batter pos=9 [p=1.5922744179739228e-08]\\n\",\n      \"sig diff: batter avg=0.15, batter pos=1 vs. batter pos=6 [p=4.366179130152077e-06]\\n\",\n      \"sig diff: batter avg=0.15, batter pos=1 vs. batter pos=7 [p=1.934107684620965e-12]\\n\",\n      \"sig diff: batter avg=0.15, batter pos=1 vs. batter pos=8 [p=7.78624198027531e-20]\\n\",\n      \"sig diff: batter avg=0.15, batter pos=1 vs. batter pos=9 [p=1.3890943563058296e-38]\\n\",\n      \"sig diff: batter avg=0.15, batter pos=2 vs. batter pos=6 [p=4.209619788129184e-08]\\n\",\n      \"sig diff: batter avg=0.15, batter pos=2 vs. batter pos=7 [p=2.28957523663897e-15]\\n\",\n      \"sig diff: batter avg=0.15, batter pos=2 vs. batter pos=8 [p=1.5958028806387556e-23]\\n\",\n      \"sig diff: batter avg=0.15, batter pos=2 vs. batter pos=9 [p=1.0540870392388548e-43]\\n\",\n      \"sig diff: batter avg=0.15, batter pos=3 vs. batter pos=7 [p=5.888897525539422e-07]\\n\",\n      \"sig diff: batter avg=0.15, batter pos=3 vs. batter pos=8 [p=1.5522832890635303e-12]\\n\",\n      \"sig diff: batter avg=0.15, batter pos=3 vs. batter pos=9 [p=7.454010617532868e-28]\\n\",\n      \"sig diff: batter avg=0.15, batter pos=4 vs. batter pos=6 [p=1.5887629310380087e-08]\\n\",\n      \"sig diff: batter avg=0.15, batter pos=4 vs. batter pos=7 [p=5.738765364593026e-16]\\n\",\n      \"sig diff: batter avg=0.15, batter pos=4 vs. batter pos=8 [p=2.814964152068496e-24]\\n\",\n      \"sig diff: batter avg=0.15, batter pos=4 vs. batter pos=9 [p=9.416275387342214e-45]\\n\",\n      \"sig diff: batter avg=0.15, batter pos=5 vs. batter pos=7 [p=5.789746845829661e-07]\\n\",\n      \"sig diff: batter avg=0.15, batter pos=5 vs. batter pos=8 [p=1.5306117655840685e-12]\\n\",\n      \"sig diff: batter avg=0.15, batter pos=5 vs. batter pos=9 [p=7.237481516249173e-28]\\n\",\n      \"sig diff: batter avg=0.15, batter pos=6 vs. batter pos=8 [p=5.940356459903494e-06]\\n\",\n      \"sig diff: batter avg=0.15, batter pos=6 vs. batter pos=9 [p=4.351690466180415e-17]\\n\",\n      \"sig diff: batter avg=0.15, batter pos=7 vs. batter pos=9 [p=2.610584051321233e-09]\\n\",\n      \"sig diff: batter avg=0.2, batter pos=1 vs. batter pos=9 [p=3.76689328874521e-08]\\n\",\n      \"sig diff: batter avg=0.2, batter pos=2 vs. batter pos=8 [p=5.205859940162342e-06]\\n\",\n      \"sig diff: batter avg=0.2, batter pos=2 vs. batter pos=9 [p=1.2174761546450417e-10]\\n\",\n      \"sig diff: batter avg=0.2, batter pos=3 vs. batter pos=9 [p=7.448777734010058e-07]\\n\",\n      \"sig diff: batter avg=0.2, batter pos=4 vs. batter pos=9 [p=4.803472139153211e-08]\\n\",\n      \"sig diff: batter avg=0.2, batter pos=5 vs. batter pos=7 [p=2.4459594614199587e-06]\\n\",\n      \"sig diff: batter avg=0.2, batter pos=5 vs. batter pos=8 [p=7.626491206096117e-07]\\n\",\n      \"sig diff: batter avg=0.2, batter pos=5 vs. batter pos=9 [p=8.845036615126957e-12]\\n\",\n      \"sig diff: batter avg=0.3, batter pos=1 vs. batter pos=7 [p=4.087726662547383e-09]\\n\",\n      \"sig diff: batter avg=0.3, batter pos=1 vs. batter pos=8 [p=1.3205738706976753e-09]\\n\",\n      \"sig diff: batter avg=0.3, batter pos=1 vs. batter pos=9 [p=1.0290332194299952e-15]\\n\",\n      \"sig diff: batter avg=0.3, batter pos=2 vs. batter pos=7 [p=9.480606656615423e-07]\\n\",\n      \"sig diff: batter avg=0.3, batter pos=2 vs. batter pos=8 [p=3.633806148920251e-07]\\n\",\n      \"sig diff: batter avg=0.3, batter pos=2 vs. batter pos=9 [p=1.8419919798882447e-12]\\n\",\n      \"sig diff: batter avg=0.3, batter pos=3 vs. batter pos=9 [p=2.3167418309385654e-10]\\n\",\n      \"sig diff: batter avg=0.3, batter pos=4 vs. batter pos=7 [p=7.55298575627675e-06]\\n\",\n      \"sig diff: batter avg=0.3, batter pos=4 vs. batter pos=8 [p=3.1216536518845004e-06]\\n\",\n      \"sig diff: batter avg=0.3, batter pos=4 vs. batter pos=9 [p=3.5434170853752895e-11]\\n\",\n      \"sig diff: batter avg=0.3, batter pos=5 vs. batter pos=8 [p=5.911151637054599e-06]\\n\",\n      \"sig diff: batter avg=0.3, batter pos=5 vs. batter pos=9 [p=8.765394985228765e-11]\\n\",\n      \"sig diff: batter avg=0.35, batter pos=1 vs. batter pos=6 [p=1.7269273863841656e-12]\\n\",\n      \"sig diff: batter avg=0.35, batter pos=1 vs. batter pos=7 [p=1.0342126228400915e-19]\\n\",\n      \"sig diff: batter avg=0.35, batter pos=1 vs. batter pos=8 [p=2.8883476180027313e-26]\\n\",\n      \"sig diff: batter avg=0.35, batter pos=1 vs. batter pos=9 [p=1.0533809552536307e-44]\\n\",\n      \"sig diff: batter avg=0.35, batter pos=2 vs. batter pos=7 [p=6.177648266053254e-08]\\n\",\n      \"sig diff: batter avg=0.35, batter pos=2 vs. batter pos=8 [p=3.9592132643227364e-12]\\n\",\n      \"sig diff: batter avg=0.35, batter pos=2 vs. batter pos=9 [p=3.60635063988907e-25]\\n\",\n      \"sig diff: batter avg=0.35, batter pos=3 vs. batter pos=7 [p=2.092547777615177e-06]\\n\",\n      \"sig diff: batter avg=0.35, batter pos=3 vs. batter pos=8 [p=3.601328161213642e-10]\\n\",\n      \"sig diff: batter avg=0.35, batter pos=3 vs. batter pos=9 [p=3.1411817736944074e-22]\\n\",\n      \"sig diff: batter avg=0.35, batter pos=4 vs. batter pos=6 [p=3.8732703941556835e-10]\\n\",\n      \"sig diff: batter avg=0.35, batter pos=4 vs. batter pos=7 [p=1.1262407382894222e-16]\\n\",\n      \"sig diff: batter avg=0.35, batter pos=4 vs. batter pos=8 [p=1.0286309709980345e-22]\\n\",\n      \"sig diff: batter avg=0.35, batter pos=4 vs. batter pos=9 [p=5.3593257566914e-40]\\n\",\n      \"sig diff: batter avg=0.35, batter pos=5 vs. batter pos=7 [p=4.938191614284398e-10]\\n\",\n      \"sig diff: batter avg=0.35, batter pos=5 vs. batter pos=8 [p=9.606821714815804e-15]\\n\",\n      \"sig diff: batter avg=0.35, batter pos=5 vs. batter pos=9 [p=5.513759189984902e-29]\\n\",\n      \"sig diff: batter avg=0.35, batter pos=6 vs. batter pos=9 [p=2.7471407448075344e-12]\\n\",\n      \"sig diff: batter avg=0.35, batter pos=7 vs. batter pos=9 [p=7.161732780696792e-07]\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"from scipy.stats import ranksums\\n\",\n    \"from itertools import product\\n\",\n    \"\\n\",\n    \"for hitter_avg1, hitter_spot1, hitter_avg2, hitter_spot2 in product([0.1, 0.15, 0.2, 0.25, 0.3, 0.35], range(9), [0.1, 0.15, 0.2, 0.25, 0.3, 0.35], range(9)):\\n\",\n    \"    if hitter_avg1 != hitter_avg2:\\n\",\n    \"        continue\\n\",\n    \"    if hitter_spot1 > hitter_spot2:\\n\",\n    \"        continue\\n\",\n    \"    if hitter_avg1 == hitter_avg2 and hitter_spot1 == hitter_spot2:\\n\",\n    \"        continue\\n\",\n    \"    statistic, pval = ranksums(hitter_spot_scores[(hitter_avg1, hitter_spot1)], hitter_spot_scores[(hitter_avg2, hitter_spot2)])\\n\",\n    \"    if pval < 1e-5:\\n\",\n    \"        print('sig diff: batter avg={}, batter pos={} vs. batter pos={} [p={}]'.format(hitter_avg1, hitter_spot1 + 1, hitter_spot2 + 1, pval))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 218,\n   \"metadata\": {\n    \"scrolled\": true\n   },\n   \"outputs\": [],\n   \"source\": [\n    \"batters = [0.25] * 9\\n\",\n    \"num_simulated_games = 1000000\\n\",\n    \"aprun = ParallelExecutor(n_jobs=-1, use_bar=False)\\n\",\n    \"average_team_stats = [game_stats for _, game_stats in aprun(total=num_simulated_games)(delayed(simulate_game)(batters) for _ in range(num_simulated_games))]\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 219,\n   \"metadata\": {\n    \"scrolled\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"''\"\n      ]\n     },\n     \"execution_count\": 219,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 864x504 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"batting_stat = 'At Bat'\\n\",\n    \"hitter_spot_avgs = []\\n\",\n    \"for hitter_spot in range(9):\\n\",\n    \"    hitter_spot_avgs.append(np.mean([game_stats[hitter_spot][batting_stat] for game_stats in average_team_stats]))\\n\",\n    \"\\n\",\n    \"with plt.style.context('tableau10'):\\n\",\n    \"    plt.figure()\\n\",\n    \"    plt.bar(range(len(hitter_spot_avgs)), hitter_spot_avgs, color='#9467BD')\\n\",\n    \"    batting_stat += 's per Game'\\n\",\n    \"    plt.ylabel(batting_stat)\\n\",\n    \"    plt.xticks(range(9), [str(x) for x in range(1, 10)])\\n\",\n    \"    plt.xlabel('Batting Position')\\n\",\n    \"    plt.title('Earlier batters have more At Bats on average')\\n\",\n    \"    plt.text(-1.3, -0.75, 'Data source: League averages & custom baseball simulations\\\\nAuthor: Randal S. Olson (randalolson.com / @randal_olson)', fontsize=10, ha='left')\\n\",\n    \"    plt.savefig('mlb-batting-order-stats-{}.png'.format(batting_stat.replace(' ', '-')), bbox_inches='tight')\\n\",\n    \";\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 220,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[4.419502,\\n\",\n       \" 4.31038,\\n\",\n       \" 4.212098,\\n\",\n       \" 4.118346,\\n\",\n       \" 4.0211,\\n\",\n       \" 3.913748,\\n\",\n       \" 3.79499,\\n\",\n       \" 3.667696,\\n\",\n       \" 3.53981]\"\n      ]\n     },\n     \"execution_count\": 220,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"batting_stat = 'At Bat'\\n\",\n    \"hitter_spot_avgs = []\\n\",\n    \"for hitter_spot in range(9):\\n\",\n    \"    hitter_spot_avgs.append(np.mean([game_stats[hitter_spot][batting_stat] for game_stats in average_team_stats]))\\n\",\n    \"hitter_spot_avgs\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 221,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"''\"\n      ]\n     },\n     \"execution_count\": 221,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 864x504 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"batting_stat = 'Players On Base'\\n\",\n    \"hitter_spot_avgs = []\\n\",\n    \"for hitter_spot in range(9):\\n\",\n    \"    hitter_spot_avgs.append(np.mean([game_stats[hitter_spot][batting_stat] for game_stats in average_team_stats]))\\n\",\n    \"\\n\",\n    \"with plt.style.context('tableau10'):\\n\",\n    \"    plt.figure()\\n\",\n    \"    plt.bar(range(len(hitter_spot_avgs)), hitter_spot_avgs, color='#9467BD')\\n\",\n    \"    batting_stat += ' per Game'\\n\",\n    \"    plt.ylabel(batting_stat)\\n\",\n    \"    plt.xticks(range(9), [str(x) for x in range(1, 10)])\\n\",\n    \"    plt.xlabel('Batting Position')\\n\",\n    \"    plt.title('Middle batters tend to have more players on base when batting')\\n\",\n    \"    plt.text(-1.5, -0.4, 'Data source: League averages & custom baseball simulations\\\\nAuthor: Randal S. Olson (randalolson.com / @randal_olson)', fontsize=10, ha='left')\\n\",\n    \"    plt.savefig('mlb-batting-order-stats-{}.png'.format(batting_stat.replace(' ', '-')), bbox_inches='tight')\\n\",\n    \";\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 222,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"''\"\n      ]\n     },\n     \"execution_count\": 222,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 864x504 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"batting_stat = 'RBI'\\n\",\n    \"hitter_spot_avgs = []\\n\",\n    \"for hitter_spot in range(9):\\n\",\n    \"    hitter_spot_avgs.append(np.mean([game_stats[hitter_spot][batting_stat] for game_stats in average_team_stats]))\\n\",\n    \"\\n\",\n    \"with plt.style.context('tableau10'):\\n\",\n    \"    plt.figure()\\n\",\n    \"    plt.bar(range(len(hitter_spot_avgs)), hitter_spot_avgs, color='#9467BD')\\n\",\n    \"    batting_stat += ' per Game'\\n\",\n    \"    plt.ylabel(batting_stat)\\n\",\n    \"    plt.xticks(range(9), [str(x) for x in range(1, 10)])\\n\",\n    \"    plt.xlabel('Batting Position')\\n\",\n    \"    plt.title('Middle batters tend to contribute more RBI')\\n\",\n    \"    plt.text(-1.6, -0.06, 'Data source: League averages & custom baseball simulations\\\\nAuthor: Randal S. Olson (randalolson.com / @randal_olson)', fontsize=10, ha='left')\\n\",\n    \"    plt.savefig('mlb-batting-order-stats-{}.png'.format(batting_stat.replace(' ', '-')), bbox_inches='tight')\\n\",\n    \";\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 225,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[0.300596,\\n\",\n       \" 0.305068,\\n\",\n       \" 0.311784,\\n\",\n       \" 0.321964,\\n\",\n       \" 0.317941,\\n\",\n       \" 0.299361,\\n\",\n       \" 0.292952,\\n\",\n       \" 0.287004,\\n\",\n       \" 0.27537]\"\n      ]\n     },\n     \"execution_count\": 225,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"batting_stat = 'RBI'\\n\",\n    \"hitter_spot_avgs = []\\n\",\n    \"for hitter_spot in range(9):\\n\",\n    \"    hitter_spot_avgs.append(np.mean([game_stats[hitter_spot][batting_stat] for game_stats in average_team_stats]))\\n\",\n    \"hitter_spot_avgs\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 223,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"''\"\n      ]\n     },\n     \"execution_count\": 223,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 864x504 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"batting_stat = 'Bases Loaded'\\n\",\n    \"hitter_spot_avgs = []\\n\",\n    \"for hitter_spot in range(9):\\n\",\n    \"    hitter_spot_avgs.append(np.mean([game_stats[hitter_spot][batting_stat] for game_stats in average_team_stats]))\\n\",\n    \"\\n\",\n    \"with plt.style.context('tableau10'):\\n\",\n    \"    plt.figure()\\n\",\n    \"    plt.bar(range(len(hitter_spot_avgs)), hitter_spot_avgs, color='#9467BD')\\n\",\n    \"    batting_stat += ' per Game'\\n\",\n    \"    plt.ylabel(batting_stat)\\n\",\n    \"    plt.xticks(range(9), [str(x) for x in range(1, 10)])\\n\",\n    \"    plt.xlabel('Batting Position')\\n\",\n    \"    plt.title('The 6th batter is most likely to face a Bases Loaded situation')\\n\",\n    \"    plt.text(-1.6, -0.013, 'Data source: League averages & custom baseball simulations\\\\nAuthor: Randal S. Olson (randalolson.com / @randal_olson)', fontsize=10, ha='left')\\n\",\n    \"    plt.savefig('mlb-batting-order-stats-{}.png'.format(batting_stat.replace(' ', '-')), bbox_inches='tight')\\n\",\n    \";\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 224,\n   \"metadata\": {\n    \"scrolled\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"''\"\n      ]\n     },\n     \"execution_count\": 224,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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\\n\",\n      \"text/plain\": [\n       \"<Figure size 864x504 with 1 Axes>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"batting_stat = 'Grand Slam'\\n\",\n    \"hitter_spot_avgs = []\\n\",\n    \"for hitter_spot in range(9):\\n\",\n    \"    hitter_spot_avgs.append(np.mean([game_stats[hitter_spot][batting_stat] for game_stats in average_team_stats]))\\n\",\n    \"\\n\",\n    \"with plt.style.context('tableau10'):\\n\",\n    \"    plt.figure()\\n\",\n    \"    plt.bar(range(len(hitter_spot_avgs)), hitter_spot_avgs, color='#9467BD')\\n\",\n    \"    batting_stat += 's per Game'\\n\",\n    \"    plt.ylabel(batting_stat)\\n\",\n    \"    plt.xticks(range(9), [str(x) for x in range(1, 10)])\\n\",\n    \"    plt.xlabel('Batting Position')\\n\",\n    \"    plt.title('The 6th batter is most likely to hit a Grand Slam')\\n\",\n    \"    plt.text(-1.85, -0.0005, 'Data source: League averages & custom baseball simulations\\\\nAuthor: Randal S. Olson (randalolson.com / @randal_olson)', fontsize=10, ha='left')\\n\",\n    \"    plt.savefig('mlb-batting-order-stats-{}.png'.format(batting_stat.replace(' ', '-')), bbox_inches='tight')\\n\",\n    \";\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 226,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"[0.001948,\\n\",\n       \" 0.001837,\\n\",\n       \" 0.001915,\\n\",\n       \" 0.001929,\\n\",\n       \" 0.002284,\\n\",\n       \" 0.002683,\\n\",\n       \" 0.002059,\\n\",\n       \" 0.001988,\\n\",\n       \" 0.002107]\"\n      ]\n     },\n     \"execution_count\": 226,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"batting_stat = 'Grand Slam'\\n\",\n    \"hitter_spot_avgs = []\\n\",\n    \"for hitter_spot in range(9):\\n\",\n    \"    hitter_spot_avgs.append(np.mean([game_stats[hitter_spot][batting_stat] for game_stats in average_team_stats]))\\n\",\n    \"hitter_spot_avgs\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.6.6\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 2\n}\n"
  },
  {
    "path": "rethinking-population-pyramid/Rethinking the population pyramid.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Rethinking the population pyramid\\n\",\n    \"\\n\",\n    \"This notebook provides the methodology and code used in the blog post, [Rethinking the population pyramid](http://www.randalolson.com/2015/07/14/rethinking-the-population-pyramid/).\\n\",\n    \"\\n\",\n    \"### Notebook by [Randal S. Olson](http://www.randalolson.com/)\\n\",\n    \"\\n\",\n    \"Please see the [repository README file](https://github.com/rhiever/Data-Analysis-and-Machine-Learning-Projects#license) for the licenses and usage terms for the instructional material and code in this notebook. In general, I have licensed this material so that it is widely useable and shareable as possible.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>Age_Range</th>\\n\",\n       \"      <th>Total_Pop_2010</th>\\n\",\n       \"      <th>Males_2010</th>\\n\",\n       \"      <th>Females_2010</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>&lt;5</td>\\n\",\n       \"      <td>20201362</td>\\n\",\n       \"      <td>10319427</td>\\n\",\n       \"      <td>9881935</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>5-9</td>\\n\",\n       \"      <td>20348657</td>\\n\",\n       \"      <td>10389638</td>\\n\",\n       \"      <td>9959019</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>10-14</td>\\n\",\n       \"      <td>20677194</td>\\n\",\n       \"      <td>10579862</td>\\n\",\n       \"      <td>10097332</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>15-19</td>\\n\",\n       \"      <td>22040343</td>\\n\",\n       \"      <td>11303666</td>\\n\",\n       \"      <td>10736677</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>20-24</td>\\n\",\n       \"      <td>21585999</td>\\n\",\n       \"      <td>11014176</td>\\n\",\n       \"      <td>10571823</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"  Age_Range  Total_Pop_2010  Males_2010  Females_2010\\n\",\n       \"0        <5        20201362    10319427       9881935\\n\",\n       \"1       5-9        20348657    10389638       9959019\\n\",\n       \"2     10-14        20677194    10579862      10097332\\n\",\n       \"3     15-19        22040343    11303666      10736677\\n\",\n       \"4     20-24        21585999    11014176      10571823\"\n      ]\n     },\n     \"execution_count\": 1,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"%matplotlib inline\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"import pandas as pd\\n\",\n    \"import numpy as np\\n\",\n    \"\\n\",\n    \"# This is a custom matplotlib style that I use for most of my charts\\n\",\n    \"plt.style.use('https://gist.githubusercontent.com/rhiever/d0a7332fe0beebfdc3d5/raw/205e477cf231330fe2f265070f7c37982fd3130c/tableau10.mplstyle')\\n\",\n    \"\\n\",\n    \"age_gender_data = pd.read_csv('http://www.randalolson.com/wp-content/uploads/us-age-gender-breakdown.csv')\\n\",\n    \"age_gender_data.head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#Problems with the population pyramid\\n\",\n    \"\\n\",\n    \"###1) Violates the [standard expectation](http://mathbench.umd.edu/modules/visualization_graph/page02.htm) of having the causal variable on the x-axis.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"''\"\n      ]\n     },\n     \"execution_count\": 2,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      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+0+5TbQ7vMEsZHly5czYcIETj/9dA444AC+9a1vccYZZ/CnP/2pYUxx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\\nEV3FBSIREREREREBAP4fFr3zea97x3YAAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x109fd0710>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"fig = plt.figure(figsize=(15, 7))\\n\",\n    \"ax1 = fig.add_subplot(111)\\n\",\n    \"\\n\",\n    \"for (i, row) in age_gender_data.iterrows():\\n\",\n    \"    plt.bar([i, i], [row['Females_2010'], -row['Males_2010']],\\n\",\n    \"            color=['#CC6699', '#008AB8'], width=0.8, align='center', edgecolor='none')\\n\",\n    \"    \\n\",\n    \"plt.xlim(-0.6, 20.6)\\n\",\n    \"plt.ylim(-12.1e6, 12.1e6)\\n\",\n    \"#plt.grid(False, axis='x')\\n\",\n    \"plt.xticks(np.arange(0, 21), age_gender_data['Age_Range'], fontsize=11)\\n\",\n    \"plt.yticks(np.arange(-12e6, 13e6, 2e6),\\n\",\n    \"           ['{}m'.format(int(abs(x) / 1e6)) if x != 0 else 0 for x in np.arange(-12e6, 13e6, 2e6)])\\n\",\n    \"plt.xlabel('Age group')\\n\",\n    \"plt.ylabel('Number of people (millions)')\\n\",\n    \"\\n\",\n    \"plt.savefig('pop_pyramid_rotated.pdf')\\n\",\n    \";\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"###2) Doesn't allow direct comparisons between the two categories.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"collapsed\": false,\n    \"scrolled\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"''\"\n      ]\n     },\n     \"execution_count\": 3,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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lpAAAAAAEBoGGoQs7OzZbVatWnTJknSE088oVtvvVWS1LFjR7322msa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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x10b26cda0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"fig = plt.figure(figsize=(15, 7))\\n\",\n    \"ax1 = fig.add_subplot(111)\\n\",\n    \"\\n\",\n    \"for (i, row) in age_gender_data.iterrows():\\n\",\n    \"    plt.bar([i - 0.2, i + 0.2], [row['Females_2010'], row['Males_2010']],\\n\",\n    \"            color=['#CC6699', '#008AB8'], width=0.4, align='center', edgecolor='none')\\n\",\n    \"    \\n\",\n    \"plt.xlim(-0.6, 20.6)\\n\",\n    \"plt.xticks(range(0, 21), age_gender_data['Age_Range'], fontsize=11)\\n\",\n    \"plt.grid(False, axis='x')\\n\",\n    \"plt.yticks(np.arange(0, 13e6, 1e6),\\n\",\n    \"           ['{}m'.format(int(x / 1e6)) if x > 0 else 0 for x in np.arange(0, 13e6, 1e6)])\\n\",\n    \"plt.xlabel('Age group')\\n\",\n    \"plt.ylabel('Number of people (millions)')\\n\",\n    \"\\n\",\n    \"plt.savefig('pop_pyramid_grouped.pdf')\\n\",\n    \";\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"###3) Relative trends between the categories can be masked by displaying absolute values.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"''\"\n      ]\n     },\n     \"execution_count\": 4,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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KXMqEB+8sknGjt2rB577DH5+Px+0tLHx0ePPfaYxo4dq08//dSjIQEA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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x10d38b2b0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"age_gender_data['Male_Pct'] = age_gender_data['Males_2010'] / age_gender_data['Total_Pop_2010']\\n\",\n    \"age_gender_data['Female_Pct'] = age_gender_data['Females_2010'] / age_gender_data['Total_Pop_2010']\\n\",\n    \"\\n\",\n    \"plt.figure(figsize=(15, 7))\\n\",\n    \"\\n\",\n    \"for (i, row) in age_gender_data.iterrows():\\n\",\n    \"    plt.bar([i], [row['Male_Pct']],\\n\",\n    \"            color=['#008AB8'], width=0.9, align='center', edgecolor='none')\\n\",\n    \"    plt.bar([i], [row['Female_Pct']], bottom=[row['Male_Pct']],\\n\",\n    \"            color=['#CC6699'], width=0.9, align='center', edgecolor='none')\\n\",\n    \"    \\n\",\n    \"plt.xlim(-0.6, 20.6)\\n\",\n    \"plt.ylim(0, 1)\\n\",\n    \"plt.xticks(range(0, 21), age_gender_data['Age_Range'], fontsize=11)\\n\",\n    \"plt.grid(False)\\n\",\n    \"plt.yticks(np.arange(0, 1.01, 0.25),\\n\",\n    \"           ['{}%'.format(int(x * 100)) for x in np.arange(0, 1.01, 0.25)])\\n\",\n    \"plt.xlabel('Age group')\\n\",\n    \"plt.ylabel('Percentage of age group')\\n\",\n    \"\\n\",\n    \"plt.plot([-0.425, 20.425], [0.5, 0.5], lw=2, color='black')\\n\",\n    \"plt.plot([-0.425, 20.425], [0.25, 0.25], lw=2, color='black')\\n\",\n    \"\\n\",\n    \"plt.savefig('pop_pyramid_stacked.pdf')\\n\",\n    \";\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": null,\n   \"metadata\": {\n    \"collapsed\": true\n   },\n   \"outputs\": [],\n   \"source\": []\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.5.1\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "revisiting-vaccine-visualizations/Revisiting the vaccine visualizations.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Revisiting the vaccine visualizations\\n\",\n    \"\\n\",\n    \"This notebook provides the methodology and code used in the blog post, [Revisiting the vaccine visualizations]( http://www.randalolson.com/2016/03/04/revisiting-the-vaccine-visualizations/).\\n\",\n    \"\\n\",\n    \"### Notebook by [Randal S. Olson](http://www.randalolson.com/)\\n\",\n    \"\\n\",\n    \"Please see the [repository README file](https://github.com/rhiever/Data-Analysis-and-Machine-Learning-Projects#license) for the licenses and usage terms for the instructional material and code in this notebook. In general, I have licensed this material so that it is widely useable and shareable as possible.\\n\",\n    \"\\n\",\n    \"### Related resources\\n\",\n    \"\\n\",\n    \"Here is the original WSJ article that inspired this rework: http://graphics.wsj.com/infectious-diseases-and-vaccines/\\n\",\n    \"\\n\",\n    \"Here is an article showing how you can remake the charts in R: http://www.r-bloggers.com/recreating-the-vaccination-heatmaps-in-r/\\n\",\n    \"\\n\",\n    \"### How to download the data\\n\",\n    \"\\n\",\n    \"This notebook assumes that you have downloaded the Project Tycho data and placed it in a directory called `data` in the directory you ran the notebook. To download the data (instructions from [here](http://www.r-bloggers.com/recreating-the-vaccination-heatmaps-in-r/)):\\n\",\n    \"\\n\",\n    \"1) Register and login to [\\\"Project Tycho\\\"](https://www.tycho.pitt.edu/)\\n\",\n    \"\\n\",\n    \"2) Go to level 1 data, then Search and retrieve data\\n\",\n    \"\\n\",\n    \"3) Now change a couple of options: geographic level := state; disease outcome := incidence\\n\",\n    \"\\n\",\n    \"4) Add all states (highlight all at once with Ctrl+A (or Cmd+A on Macs)\\n\",\n    \"\\n\",\n    \"5) Hit submit and scroll down to Click here to download results to excel\\n\",\n    \"\\n\",\n    \"6) Open in excel and export to CSV\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Import all of the libraries we'll be using\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Vendor:  Continuum Analytics, Inc.\\n\",\n      \"Package: mkl\\n\",\n      \"Message: trial mode expires in 29 days\\n\",\n      \"/Users/randal_olson/anaconda/lib/python3.5/site-packages/matplotlib/__init__.py:872: UserWarning: axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.\\n\",\n      \"  warnings.warn(self.msg_depr % (key, alt_key))\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"%matplotlib inline\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"import seaborn as sb\\n\",\n    \"import pandas as pd\\n\",\n    \"import numpy as np\\n\",\n    \"\\n\",\n    \"sb.set_style('white')\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Take a quick look at the data\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\\"Weekly Polio Incidence, 1928-1969\\\"\\r\\n\",\n      \"\\\"Data provided by Project Tycho, Data Version 1.0.0, released 28 November 2013.\\\"\\r\\n\",\n      \"YEAR,WEEK,ALABAMA,ALASKA,ARIZONA,ARKANSAS,CALIFORNIA,COLORADO,CONNECTICUT,DELAWARE,DISTRICT OF COLUMBIA,FLORIDA,GEORGIA,HAWAII,IDAHO,ILLINOIS,INDIANA,IOWA,KANSAS,KENTUCKY,LOUISIANA,MAINE,MARYLAND,MASSACHUSETTS,MICHIGAN,MINNESOTA,MISSISSIPPI,MISSOURI,MONTANA,NEBRASKA,NEVADA,NEW HAMPSHIRE,NEW JERSEY,NEW MEXICO,NEW YORK,NORTH CAROLINA,NORTH DAKOTA,OHIO,OKLAHOMA,OREGON,PENNSYLVANIA,RHODE ISLAND,SOUTH CAROLINA,SOUTH DAKOTA,TENNESSEE,TEXAS,UTAH,VERMONT,VIRGINIA,WASHINGTON,WEST VIRGINIA,WISCONSIN,WYOMING\\r\\n\",\n      \"1928,1,0.00,-,0.00,0.00,0.17,0.39,0.00,0.00,-,0.00,0.03,-,0.00,0.03,0.03,0.08,0.00,0.00,0.00,0.00,0.06,0.14,0.04,0.00,0.00,0.03,0.18,0.07,-,-,0.08,0.00,0.08,0.00,-,0.02,0.00,0.64,0.00,0.00,0.06,0.00,0.04,0.05,0.00,0.00,-,0.26,0.06,0.03,0.00\\r\\n\",\n      \"1928,2,0.00,-,0.00,0.00,0.15,0.20,0.00,0.00,-,0.00,0.00,-,0.00,0.01,0.03,-,0.22,0.00,0.05,0.13,0.06,0.14,0.04,0.04,0.00,0.06,0.00,0.07,-,-,0.03,0.00,0.05,0.03,0.45,-,0.04,0.43,0.03,0.00,0.06,0.00,0.04,0.04,0.00,0.00,-,0.39,0.24,0.03,0.00\\r\\n\",\n      \"1928,3,0.04,-,0.00,0.00,0.11,0.00,0.06,0.00,-,0.00,-,-,0.00,0.03,0.03,-,0.00,0.00,0.00,0.00,0.00,0.07,0.02,0.00,0.00,0.03,0.18,0.00,-,-,0.00,0.00,0.03,0.00,0.00,0.06,0.00,1.07,0.02,0.00,0.35,0.00,0.00,0.00,0.00,0.00,-,0.13,0.12,0.03,0.00\\r\\n\",\n      \"1928,4,0.00,-,0.24,0.11,0.07,0.20,0.06,0.00,0.00,0.00,0.00,-,0.00,0.05,0.12,0.00,0.00,0.00,0.00,0.00,0.00,0.02,0.02,0.00,0.00,0.06,0.00,0.00,-,0.00,0.03,0.00,0.06,0.00,0.15,0.00,0.09,0.53,0.02,0.00,0.23,0.00,0.04,0.05,0.00,0.00,-,0.06,0.12,0.00,0.00\\r\\n\",\n      \"1928,5,0.00,-,0.24,0.00,0.32,0.00,0.13,0.00,0.00,0.00,0.00,-,0.00,0.04,0.00,0.04,0.00,0.00,0.00,0.38,0.12,0.02,0.04,0.00,0.00,0.00,0.00,0.15,-,0.00,0.03,0.48,0.07,0.00,0.00,0.03,0.00,0.32,0.00,0.00,0.17,0.15,0.00,0.05,0.00,0.00,-,0.13,0.06,0.03,0.00\\r\\n\",\n      \"1928,6,0.00,-,0.00,0.00,0.22,0.10,0.00,0.00,0.00,0.00,0.00,-,-,0.03,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.05,0.06,0.00,0.00,0.00,0.00,0.07,-,0.00,0.00,0.00,0.03,0.00,0.00,0.05,0.04,0.21,0.04,0.00,0.06,0.29,0.04,0.00,0.20,0.00,0.04,0.06,0.00,0.14,0.00\\r\\n\",\n      \"1928,7,0.08,-,0.00,0.00,0.13,0.00,0.00,0.00,0.00,0.00,0.00,-,0.22,0.01,0.00,0.00,0.00,0.00,0.00,0.00,0.00,0.09,0.00,0.00,0.00,0.00,0.00,0.00,-,0.21,0.03,0.24,0.02,0.03,0.15,0.05,0.04,0.32,0.01,0.00,0.00,0.00,0.00,0.00,0.40,0.00,-,0.00,0.00,0.07,0.00\\r\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"!head data/POLIO_Incidence_1928-1969_20160304121200.csv\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Visualize the Polio data as a heatmap\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"''\"\n      ]\n     },\n     \"execution_count\": 4,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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xMTdX+YH1ejRg0uXLhAcnKyLg0F4MSJEzx48EB3/vVVsWJFDA0NuXnz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\\nVSIAAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x1153c2160>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"polio_data = pd.read_csv('data/POLIO_Incidence_1928-1969_20160304121200.csv', skiprows=2, na_values='-')\\n\",\n    \"\\n\",\n    \"polio_years = list(polio_data['YEAR'].unique())\\n\",\n    \"polio_states = polio_data.drop(['YEAR', 'WEEK'], axis=1).columns.values\\n\",\n    \"polio_states = [state.title() for state in polio_states]\\n\",\n    \"\\n\",\n    \"polio_data.drop(['WEEK'], axis=1, inplace=True)\\n\",\n    \"polio_data = polio_data.groupby('YEAR').sum()\\n\",\n    \"polio_data = polio_data.transpose().values\\n\",\n    \"\\n\",\n    \"plt.figure(figsize=(12, 12))\\n\",\n    \"sb.heatmap(polio_data, cmap='Reds', robust=True,\\n\",\n    \"           xticklabels=[year if year % 5 == 0 or year == max(polio_years) else '' for year in polio_years],\\n\",\n    \"           yticklabels=polio_states)\\n\",\n    \"plt.plot([polio_years.index(1955) + 0.425, polio_years.index(1955) + 0.425],\\n\",\n    \"         [0, 51],\\n\",\n    \"         color='black', lw=1.5)\\n\",\n    \"\\n\",\n    \"cax = plt.gcf().axes[-1]\\n\",\n    \"cax.set_yticklabels([10, 20, 30, 40, '50+'])\\n\",\n    \"cax.tick_params(labelsize=12)\\n\",\n    \"\\n\",\n    \"plt.xticks(fontsize=12)\\n\",\n    \"plt.yticks(fontsize=12)\\n\",\n    \"\\n\",\n    \"plt.text(0, 51.5, 'Polio cases in the United States', fontsize=14)\\n\",\n    \"plt.text(polio_years.index(1955) + 0.15, 51.5, 'Vaccine introduced', fontsize=12, weight='bold')\\n\",\n    \"plt.text(-8, -3, 'Data source: Project Tycho (tycho.pitt.edu) '\\n\",\n    \"         '| Author: Randy Olson (randalolson.com / @randal_olson)',\\n\",\n    \"         fontsize=10)\\n\",\n    \"\\n\",\n    \"plt.savefig('polio-cases-heatmap-sequential-colormap.png', bbox_inches='tight')\\n\",\n    \";\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Same process for the Measles data\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"''\"\n      ]\n     },\n     \"execution_count\": 5,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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fn1119ZtWoV1tbWKIpCmzZtAHB0dGT27Nns3LmTX3/9lXv37uk7e9u3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\\nhBBCAP8HFMuSQ3mVUoYAAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x1153ce3c8>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"measles_data = pd.read_csv('data/MEASLES_Incidence_1928-2003_20160304120254.csv', skiprows=2, na_values='-')\\n\",\n    \"\\n\",\n    \"measles_years = list(measles_data['YEAR'].unique())\\n\",\n    \"measles_states = measles_data.drop(['YEAR', 'WEEK'], axis=1).columns.values\\n\",\n    \"measles_states = [state.title() for state in measles_states]\\n\",\n    \"\\n\",\n    \"measles_data.drop(['WEEK'], axis=1, inplace=True)\\n\",\n    \"measles_data = measles_data.groupby('YEAR').sum()\\n\",\n    \"measles_data = measles_data.transpose().values\\n\",\n    \"\\n\",\n    \"plt.figure(figsize=(12, 12))\\n\",\n    \"sb.heatmap(measles_data, cmap='Reds', robust=True,\\n\",\n    \"           xticklabels=[year if year % 10 == 0 else '' for year in measles_years],\\n\",\n    \"           yticklabels=measles_states)\\n\",\n    \"plt.plot([polio_years.index(1963) + 0.425, polio_years.index(1963) + 0.425],\\n\",\n    \"         [0, 51],\\n\",\n    \"         color='black', lw=1.5)\\n\",\n    \"\\n\",\n    \"cax = plt.gcf().axes[-1]\\n\",\n    \"cax.set_yticklabels([0, 200, 400, 600, 800, '1,000+'])\\n\",\n    \"cax.tick_params(labelsize=12)\\n\",\n    \"\\n\",\n    \"plt.xticks(fontsize=12)\\n\",\n    \"plt.yticks(fontsize=12)\\n\",\n    \"\\n\",\n    \"plt.text(0, 51.5, 'Measles cases in the United States', fontsize=14)\\n\",\n    \"plt.text(polio_years.index(1963) + 0.05, 51.5, 'Vaccine introduced', fontsize=12, weight='bold')\\n\",\n    \"plt.text(-14.5, -3, 'Data source: Project Tycho (tycho.pitt.edu) '\\n\",\n    \"         '| Author: Randy Olson (randalolson.com / @randal_olson)',\\n\",\n    \"         fontsize=10)\\n\",\n    \"\\n\",\n    \"plt.savefig('measles-cases-heatmap-sequential-colormap.png', bbox_inches='tight')\\n\",\n    \";\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Visualize the Polio data as a line chart\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"''\"\n      ]\n     },\n     \"execution_count\": 6,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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0blFixYgMOHD6NFixaIi4vzWbv22muveT6Ua9euhdVqRVpaGjp37oxR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\\nIpGcoEhDTiKRSCQSieQE5f8B5ef9lNJTc+UAAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x118d3c6a0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"polio_data = pd.read_csv('data/POLIO_Incidence_1928-1969_20160304121200.csv', skiprows=2, na_values='-').fillna(0.)\\n\",\n    \"\\n\",\n    \"polio_data.drop(['WEEK'], axis=1, inplace=True)\\n\",\n    \"polio_data = polio_data.groupby('YEAR').sum()\\n\",\n    \"\\n\",\n    \"polio_data.plot(color='#3F5D7D', alpha=0.25, lw=0.5, legend=False, figsize=(10, 7))\\n\",\n    \"plt.plot([1955, 1955], [0, 160], color='black', lw=1.5)\\n\",\n    \"polio_data.transpose().median().plot(color='#3F5D7D', lw=2)\\n\",\n    \"plt.yticks(fontsize=12)\\n\",\n    \"plt.xticks(fontsize=12)\\n\",\n    \"plt.xlabel('')\\n\",\n    \"plt.ylabel('# cases per 100,000 people', fontsize=14)\\n\",\n    \"\\n\",\n    \"plt.text(1928.5, 163, 'Polio cases in the United States', fontsize=14)\\n\",\n    \"plt.text(1954.75, 163, 'Vaccine introduced', fontsize=12, weight='bold')\\n\",\n    \"plt.text(1924, -17, 'Data source: Project Tycho (tycho.pitt.edu) '\\n\",\n    \"         '| Author: Randy Olson (randalolson.com / @randal_olson)',\\n\",\n    \"         fontsize=10)\\n\",\n    \"\\n\",\n    \"plt.savefig('polio-cases-line-chart-raw.png', bbox_inches='tight')\\n\",\n    \";\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Summarize Polio data with median + 95% CI\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"/Users/randal_olson/anaconda/lib/python3.5/site-packages/numpy/lib/function_base.py:3151: RuntimeWarning: Invalid value encountered in median for 2 results\\n\",\n      \"  RuntimeWarning)\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"''\"\n      ]\n     },\n     \"execution_count\": 7,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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4cOxdy5czFo0CCcP38eHTt2xKlTp+olpFu3bkVhYSGefPJJrFmzBsOH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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x117862978>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"polio_data = pd.read_csv('data/POLIO_Incidence_1928-1969_20160304121200.csv', skiprows=2, na_values='-').fillna(0.)\\n\",\n    \"\\n\",\n    \"polio_years = list(polio_data['YEAR'].unique())\\n\",\n    \"polio_states = polio_data.drop(['YEAR', 'WEEK'], axis=1).columns.values\\n\",\n    \"polio_states = [state.title() for state in polio_states]\\n\",\n    \"\\n\",\n    \"polio_data.drop(['WEEK'], axis=1, inplace=True)\\n\",\n    \"polio_data = polio_data.groupby('YEAR').sum()\\n\",\n    \"polio_data = pd.rolling_mean(polio_data, 3)\\n\",\n    \"polio_data = polio_data.transpose().values\\n\",\n    \"\\n\",\n    \"plt.figure(figsize=(10, 7))\\n\",\n    \"sb.tsplot(data=polio_data, ci=95, estimator=np.median, color='#3F5D7D')\\n\",\n    \"plt.plot([polio_years.index(1955), polio_years.index(1955)], [0, 35], color='black', lw=1.5)\\n\",\n    \"\\n\",\n    \"plt.yticks(fontsize=12)\\n\",\n    \"plt.xticks(range(len(polio_years)), [year if year % 5 == 0 else '' for year in polio_years], fontsize=12)\\n\",\n    \"plt.ylabel('# cases per 100,000 people', fontsize=14)\\n\",\n    \"\\n\",\n    \"plt.text(0.3, 35.5, 'Polio cases in the United States', fontsize=14)\\n\",\n    \"plt.text(polio_years.index(1955) - 0.3, 35.5, 'Vaccine introduced', fontsize=12, weight='bold')\\n\",\n    \"plt.text(-3.5, -3.5, 'Data source: Project Tycho (tycho.pitt.edu) '\\n\",\n    \"         '| Author: Randy Olson (randalolson.com / @randal_olson)',\\n\",\n    \"         fontsize=10)\\n\",\n    \"\\n\",\n    \"plt.savefig('polio-cases-line-chart-statistics.png', bbox_inches='tight')\\n\",\n    \";\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Display the Polio data as small multiples\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"''\"\n      ]\n     },\n     \"execution_count\": 11,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": \"iVBORw0KGgoAAAANSUhEUgAABC4AAAL5CAYAAAB/xGULAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\\nCCGEEEJv/T8pAcFD2Oa7YAAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x11be582b0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"polio_data = pd.read_csv('data/POLIO_Incidence_1928-1969_20160304121200.csv', skiprows=2, na_values='-').fillna(0.)\\n\",\n    \"\\n\",\n    \"polio_data.drop(['WEEK'], axis=1, inplace=True)\\n\",\n    \"polio_data = polio_data.groupby('YEAR').sum()\\n\",\n    \"\\n\",\n    \"plt.figure(figsize=(15, 10))\\n\",\n    \"\\n\",\n    \"column_count = 0\\n\",\n    \"for column in polio_data.columns.values:\\n\",\n    \"    if 'DISTRICT' in column:\\n\",\n    \"        column_num = 53\\n\",\n    \"        state_name = 'Washington D.C.'\\n\",\n    \"    else:\\n\",\n    \"        column_count += 1\\n\",\n    \"        column_num = column_count\\n\",\n    \"        state_name = column.title()\\n\",\n    \"    \\n\",\n    \"    plt.subplot(11, 5, column_num)\\n\",\n    \"    polio_data[column].fillna(0.).plot(color='black', lw=0.5)\\n\",\n    \"    plt.plot([1955, 1955], [0, plt.ylim()[1]], color='black', lw=1)\\n\",\n    \"    \\n\",\n    \"    if column_num >= 46 and column_num != 48:\\n\",\n    \"        plt.xticks(range(1930, 1971, 10))\\n\",\n    \"    else:\\n\",\n    \"        plt.xticks([])\\n\",\n    \"    \\n\",\n    \"    plt.yticks([])\\n\",\n    \"    plt.xlabel('')\\n\",\n    \"    plt.title(state_name, fontsize=11)\\n\",\n    \"    \\n\",\n    \"plt.tight_layout()\\n\",\n    \"plt.text(1732.5, -220, 'Data source: Project Tycho (tycho.pitt.edu)\\\\n'\\n\",\n    \"         'Author: Randy Olson (randalolson.com / @randal_olson)',\\n\",\n    \"         fontsize=10)\\n\",\n    \"\\n\",\n    \"plt.text(1853, 1850, 'Polio cases per 100,000 residents across the United States\\\\n'\\n\",\n    \"         '(vertical line = Polio vaccine introduced)',\\n\",\n    \"         ha='center', fontsize=18)\\n\",\n    \"\\n\",\n    \"plt.savefig('polio-cases-small-multiples.png', bbox_inches='tight')\\n\",\n    \";\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.5.1\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "tpot-demo/TPOT - A Python tool for automating data science.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# TPOT: A Python tool for automating data science\\n\",\n    \"\\n\",\n    \"### Notebook by [Randal S. Olson](http://www.randalolson.com/)\\n\",\n    \"\\n\",\n    \"This notebook is a demo for the [TPOT](https://github.com/rhiever/tpot) data science automation tool under development at the [Penn Institute for Biomedical Informatics](http://upibi.org/).\\n\",\n    \"\\n\",\n    \"Below are code samples demonstrating why designing machine learning pipelines is difficult, and how TPOT automates that process.\\n\",\n    \"\\n\",\n    \"## License\\n\",\n    \"\\n\",\n    \"Please see the [repository README file](https://github.com/rhiever/Data-Analysis-and-Machine-Learning-Projects#license) for the licenses and usage terms for the instructional material and code in this notebook. In general, I have licensed this material so that it is as widely usable and shareable as possible.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Why automate data science?\\n\",\n    \"\\n\",\n    \"### Model hyperparameter tuning is important\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 1,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>class</th>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <th>5</th>\\n\",\n       \"      <th>6</th>\\n\",\n       \"      <th>7</th>\\n\",\n       \"      <th>8</th>\\n\",\n       \"      <th>9</th>\\n\",\n       \"      <th>...</th>\\n\",\n       \"      <th>775</th>\\n\",\n       \"      <th>776</th>\\n\",\n       \"      <th>777</th>\\n\",\n       \"      <th>778</th>\\n\",\n       \"      <th>779</th>\\n\",\n       \"      <th>780</th>\\n\",\n       \"      <th>781</th>\\n\",\n       \"      <th>782</th>\\n\",\n       \"      <th>783</th>\\n\",\n       \"      <th>784</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>7</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>2</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>1</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>4</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"<p>5 rows × 785 columns</p>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"   class  1  2  3  4  5  6  7  8  9 ...   775  776  777  778  779  780  781  \\\\\\n\",\n       \"0      7  0  0  0  0  0  0  0  0  0 ...     0    0    0    0    0    0    0   \\n\",\n       \"1      2  0  0  0  0  0  0  0  0  0 ...     0    0    0    0    0    0    0   \\n\",\n       \"2      1  0  0  0  0  0  0  0  0  0 ...     0    0    0    0    0    0    0   \\n\",\n       \"3      0  0  0  0  0  0  0  0  0  0 ...     0    0    0    0    0    0    0   \\n\",\n       \"4      4  0  0  0  0  0  0  0  0  0 ...     0    0    0    0    0    0    0   \\n\",\n       \"\\n\",\n       \"   782  783  784  \\n\",\n       \"0    0    0    0  \\n\",\n       \"1    0    0    0  \\n\",\n       \"2    0    0    0  \\n\",\n       \"3    0    0    0  \\n\",\n       \"4    0    0    0  \\n\",\n       \"\\n\",\n       \"[5 rows x 785 columns]\"\n      ]\n     },\n     \"execution_count\": 1,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"import pandas as pd\\n\",\n    \"import numpy as np\\n\",\n    \"from sklearn.ensemble import RandomForestClassifier\\n\",\n    \"from sklearn.cross_validation import cross_val_score\\n\",\n    \"\\n\",\n    \"mnist_data = pd.read_csv('https://raw.githubusercontent.com/rhiever/Data-Analysis-and-Machine-Learning-Projects/master/tpot-demo/mnist.csv.gz', sep='\\\\t', compression='gzip')\\n\",\n    \"mnist_data.head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"''\"\n      ]\n     },\n     \"execution_count\": 2,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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f+M8vLsX5pl63WRmWOU2uFd+yAluXdTMSjyr74r+uxTl3x1yo21olDH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\\nYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x11cde0358>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"%matplotlib inline\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"import seaborn as sb\\n\",\n    \"\\n\",\n    \"plt.figure(figsize=(8, 8))\\n\",\n    \"\\n\",\n    \"for record_num in range(1, 65):\\n\",\n    \"    plt.subplot(8, 8, record_num)\\n\",\n    \"    \\n\",\n    \"    digit_features = mnist_data.iloc[record_num].drop('class').values\\n\",\n    \"    sb.heatmap(digit_features.reshape((28, 28)),\\n\",\n    \"               cmap='Greys',\\n\",\n    \"               square=True, cbar=False,\\n\",\n    \"               xticklabels=[], yticklabels=[])\\n\",\n    \"\\n\",\n    \"plt.tight_layout()\\n\",\n    \";\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[ 0.9420414   0.9620217   0.94888635  0.94000857  0.95071429  0.94427775\\n\",\n      \"  0.94955702  0.94240389  0.93467696  0.95554603]\\n\",\n      \"0.947013394966\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"cv_scores = cross_val_score(RandomForestClassifier(n_estimators=10, n_jobs=-1),\\n\",\n    \"                            X=mnist_data.drop('class', axis=1).values,\\n\",\n    \"                            y=mnist_data.loc[:, 'class'].values,\\n\",\n    \"                            cv=10)\\n\",\n    \"\\n\",\n    \"print(cv_scores)\\n\",\n    \"print(np.mean(cv_scores))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[ 0.96445396  0.97829812  0.97015991  0.96629053  0.96828571  0.96170882\\n\",\n      \"  0.97084881  0.96698585  0.96483705  0.97641509]\\n\",\n      \"0.968828385431\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"cv_scores = cross_val_score(RandomForestClassifier(n_estimators=100, n_jobs=-1),\\n\",\n    \"                            X=mnist_data.drop('class', axis=1).values,\\n\",\n    \"                            y=mnist_data.loc[:, 'class'].values,\\n\",\n    \"                            cv=10)\\n\",\n    \"\\n\",\n    \"print(cv_scores)\\n\",\n    \"print(np.mean(cv_scores))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Model selection is important\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>X1</th>\\n\",\n       \"      <th>X2</th>\\n\",\n       \"      <th>X3</th>\\n\",\n       \"      <th>X4</th>\\n\",\n       \"      <th>X5</th>\\n\",\n       \"      <th>X6</th>\\n\",\n       \"      <th>X7</th>\\n\",\n       \"      <th>X8</th>\\n\",\n       \"      <th>X9</th>\\n\",\n       \"      <th>X10</th>\\n\",\n       \"      <th>...</th>\\n\",\n       \"      <th>X92</th>\\n\",\n       \"      <th>X93</th>\\n\",\n       \"      <th>X94</th>\\n\",\n       \"      <th>X95</th>\\n\",\n       \"      <th>X96</th>\\n\",\n       \"      <th>X97</th>\\n\",\n       \"      <th>X98</th>\\n\",\n       \"      <th>X99</th>\\n\",\n       \"      <th>X100</th>\\n\",\n       \"      <th>class</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>1317.265789</td>\\n\",\n       \"      <td>1315.220951</td>\\n\",\n       \"      <td>1312.770581</td>\\n\",\n       \"      <td>1309.834252</td>\\n\",\n       \"      <td>1306.315588</td>\\n\",\n       \"      <td>1302.099102</td>\\n\",\n       \"      <td>1297.046401</td>\\n\",\n       \"      <td>1290.991646</td>\\n\",\n       \"      <td>1283.736109</td>\\n\",\n       \"      <td>1275.041652</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>1327.575109</td>\\n\",\n       \"      <td>1327.575350</td>\\n\",\n       \"      <td>1327.575552</td>\\n\",\n       \"      <td>1327.575719</td>\\n\",\n       \"      <td>1327.575859</td>\\n\",\n       \"      <td>1327.575976</td>\\n\",\n       \"      <td>1327.576074</td>\\n\",\n       \"      <td>1327.576155</td>\\n\",\n       \"      <td>1327.576223</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>7329.967624</td>\\n\",\n       \"      <td>7379.907443</td>\\n\",\n       \"      <td>7441.799231</td>\\n\",\n       \"      <td>7518.503422</td>\\n\",\n       \"      <td>7613.565031</td>\\n\",\n       \"      <td>7731.377492</td>\\n\",\n       \"      <td>7877.385707</td>\\n\",\n       \"      <td>8058.337694</td>\\n\",\n       \"      <td>8282.596458</td>\\n\",\n       \"      <td>8560.526497</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>7121.300474</td>\\n\",\n       \"      <td>7121.300438</td>\\n\",\n       \"      <td>7121.300410</td>\\n\",\n       \"      <td>7121.300387</td>\\n\",\n       \"      <td>7121.300368</td>\\n\",\n       \"      <td>7121.300353</td>\\n\",\n       \"      <td>7121.300341</td>\\n\",\n       \"      <td>7121.300331</td>\\n\",\n       \"      <td>7121.300323</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>809.421410</td>\\n\",\n       \"      <td>809.780119</td>\\n\",\n       \"      <td>810.207191</td>\\n\",\n       \"      <td>810.715653</td>\\n\",\n       \"      <td>811.321016</td>\\n\",\n       \"      <td>812.041748</td>\\n\",\n       \"      <td>812.899834</td>\\n\",\n       \"      <td>813.921452</td>\\n\",\n       \"      <td>815.137768</td>\\n\",\n       \"      <td>816.585886</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>807.545134</td>\\n\",\n       \"      <td>807.544181</td>\\n\",\n       \"      <td>807.543381</td>\\n\",\n       \"      <td>807.542709</td>\\n\",\n       \"      <td>807.542144</td>\\n\",\n       \"      <td>807.541670</td>\\n\",\n       \"      <td>807.541272</td>\\n\",\n       \"      <td>807.540937</td>\\n\",\n       \"      <td>807.540656</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>45334.208880</td>\\n\",\n       \"      <td>45334.213560</td>\\n\",\n       \"      <td>45334.219060</td>\\n\",\n       \"      <td>45334.225500</td>\\n\",\n       \"      <td>45334.233050</td>\\n\",\n       \"      <td>45334.241910</td>\\n\",\n       \"      <td>45334.252300</td>\\n\",\n       \"      <td>45334.264480</td>\\n\",\n       \"      <td>45334.278760</td>\\n\",\n       \"      <td>45334.295520</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>47550.921710</td>\\n\",\n       \"      <td>47224.457710</td>\\n\",\n       \"      <td>46946.072760</td>\\n\",\n       \"      <td>46708.686150</td>\\n\",\n       \"      <td>46506.259970</td>\\n\",\n       \"      <td>46333.645520</td>\\n\",\n       \"      <td>46186.452370</td>\\n\",\n       \"      <td>46060.936670</td>\\n\",\n       \"      <td>45953.905930</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>1.810359</td>\\n\",\n       \"      <td>1.810359</td>\\n\",\n       \"      <td>1.810359</td>\\n\",\n       \"      <td>1.810359</td>\\n\",\n       \"      <td>1.810359</td>\\n\",\n       \"      <td>1.810359</td>\\n\",\n       \"      <td>1.810359</td>\\n\",\n       \"      <td>1.810359</td>\\n\",\n       \"      <td>1.810359</td>\\n\",\n       \"      <td>1.810359</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>1.790275</td>\\n\",\n       \"      <td>1.794794</td>\\n\",\n       \"      <td>1.798296</td>\\n\",\n       \"      <td>1.801010</td>\\n\",\n       \"      <td>1.803114</td>\\n\",\n       \"      <td>1.804744</td>\\n\",\n       \"      <td>1.806008</td>\\n\",\n       \"      <td>1.806987</td>\\n\",\n       \"      <td>1.807746</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"<p>5 rows × 101 columns</p>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"             X1            X2            X3            X4            X5  \\\\\\n\",\n       \"0   1317.265789   1315.220951   1312.770581   1309.834252   1306.315588   \\n\",\n       \"1   7329.967624   7379.907443   7441.799231   7518.503422   7613.565031   \\n\",\n       \"2    809.421410    809.780119    810.207191    810.715653    811.321016   \\n\",\n       \"3  45334.208880  45334.213560  45334.219060  45334.225500  45334.233050   \\n\",\n       \"4      1.810359      1.810359      1.810359      1.810359      1.810359   \\n\",\n       \"\\n\",\n       \"             X6            X7            X8            X9           X10  \\\\\\n\",\n       \"0   1302.099102   1297.046401   1290.991646   1283.736109   1275.041652   \\n\",\n       \"1   7731.377492   7877.385707   8058.337694   8282.596458   8560.526497   \\n\",\n       \"2    812.041748    812.899834    813.921452    815.137768    816.585886   \\n\",\n       \"3  45334.241910  45334.252300  45334.264480  45334.278760  45334.295520   \\n\",\n       \"4      1.810359      1.810359      1.810359      1.810359      1.810359   \\n\",\n       \"\\n\",\n       \"   ...             X92           X93           X94           X95  \\\\\\n\",\n       \"0  ...     1327.575109   1327.575350   1327.575552   1327.575719   \\n\",\n       \"1  ...     7121.300474   7121.300438   7121.300410   7121.300387   \\n\",\n       \"2  ...      807.545134    807.544181    807.543381    807.542709   \\n\",\n       \"3  ...    47550.921710  47224.457710  46946.072760  46708.686150   \\n\",\n       \"4  ...        1.790275      1.794794      1.798296      1.801010   \\n\",\n       \"\\n\",\n       \"            X96           X97           X98           X99          X100  class  \\n\",\n       \"0   1327.575859   1327.575976   1327.576074   1327.576155   1327.576223      0  \\n\",\n       \"1   7121.300368   7121.300353   7121.300341   7121.300331   7121.300323      1  \\n\",\n       \"2    807.542144    807.541670    807.541272    807.540937    807.540656      1  \\n\",\n       \"3  46506.259970  46333.645520  46186.452370  46060.936670  45953.905930      1  \\n\",\n       \"4      1.803114      1.804744      1.806008      1.806987      1.807746      0  \\n\",\n       \"\\n\",\n       \"[5 rows x 101 columns]\"\n      ]\n     },\n     \"execution_count\": 5,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"import pandas as pd\\n\",\n    \"import numpy as np\\n\",\n    \"from sklearn.ensemble import RandomForestClassifier\\n\",\n    \"from sklearn.linear_model import LogisticRegression\\n\",\n    \"from sklearn.cross_validation import cross_val_score\\n\",\n    \"\\n\",\n    \"hill_valley_data = pd.read_csv('https://raw.githubusercontent.com/rhiever/Data-Analysis-and-Machine-Learning-Projects/master/tpot-demo/Hill_Valley_without_noise.csv.gz', sep='\\\\t', compression='gzip')\\n\",\n    \"hill_valley_data.head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"''\"\n      ]\n     },\n     \"execution_count\": 6,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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TE6+xJM/MUL+D001URoqonSUBmmvipMfWWY6rKQelsii4AC6gI5jstw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\\nRSFMZ162AAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x11d1bc240>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"%matplotlib inline\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"import seaborn as sb\\n\",\n    \"\\n\",\n    \"with plt.style.context('seaborn-notebook'):\\n\",\n    \"    plt.figure(figsize=(6, 6))\\n\",\n    \"    for record_num in range(1, 11):\\n\",\n    \"        plt.subplot(10, 1, record_num)\\n\",\n    \"        hv_record_features = hill_valley_data.loc[record_num].drop('class').values\\n\",\n    \"        plt.plot(hv_record_features)\\n\",\n    \"        plt.yticks([])\\n\",\n    \"        plt.xticks([])\\n\",\n    \"\\n\",\n    \"    plt.tight_layout()\\n\",\n    \";\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 7,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[ 0.6147541   0.63934426  0.62809917  0.60330579  0.6446281   0.5785124\\n\",\n      \"  0.66942149  0.55371901  0.49586777  0.66115702]\\n\",\n      \"0.608880910446\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"cv_scores = cross_val_score(RandomForestClassifier(n_estimators=100, n_jobs=-1),\\n\",\n    \"                            X=hill_valley_data.drop('class', axis=1).values,\\n\",\n    \"                            y=hill_valley_data.loc[:, 'class'].values,\\n\",\n    \"                            cv=10)\\n\",\n    \"\\n\",\n    \"print(cv_scores)\\n\",\n    \"print(np.mean(cv_scores))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 8,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[ 1.          1.          0.99173554  1.          1.          1.          1.\\n\",\n      \"  0.99173554  1.          1.        ]\\n\",\n      \"0.998347107438\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"cv_scores = cross_val_score(LogisticRegression(),\\n\",\n    \"                            X=hill_valley_data.drop('class', axis=1).values,\\n\",\n    \"                            y=hill_valley_data.loc[:, 'class'].values,\\n\",\n    \"                            cv=10)\\n\",\n    \"\\n\",\n    \"print(cv_scores)\\n\",\n    \"print(np.mean(cv_scores))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Feature preprocessing is important\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 9,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>X1</th>\\n\",\n       \"      <th>X2</th>\\n\",\n       \"      <th>X3</th>\\n\",\n       \"      <th>X4</th>\\n\",\n       \"      <th>X5</th>\\n\",\n       \"      <th>X6</th>\\n\",\n       \"      <th>X7</th>\\n\",\n       \"      <th>X8</th>\\n\",\n       \"      <th>X9</th>\\n\",\n       \"      <th>X10</th>\\n\",\n       \"      <th>...</th>\\n\",\n       \"      <th>X92</th>\\n\",\n       \"      <th>X93</th>\\n\",\n       \"      <th>X94</th>\\n\",\n       \"      <th>X95</th>\\n\",\n       \"      <th>X96</th>\\n\",\n       \"      <th>X97</th>\\n\",\n       \"      <th>X98</th>\\n\",\n       \"      <th>X99</th>\\n\",\n       \"      <th>X100</th>\\n\",\n       \"      <th>class</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>39.02</td>\\n\",\n       \"      <td>36.49</td>\\n\",\n       \"      <td>38.20</td>\\n\",\n       \"      <td>38.85</td>\\n\",\n       \"      <td>39.38</td>\\n\",\n       \"      <td>39.74</td>\\n\",\n       \"      <td>37.02</td>\\n\",\n       \"      <td>39.53</td>\\n\",\n       \"      <td>38.81</td>\\n\",\n       \"      <td>38.79</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>36.62</td>\\n\",\n       \"      <td>36.92</td>\\n\",\n       \"      <td>38.80</td>\\n\",\n       \"      <td>38.52</td>\\n\",\n       \"      <td>38.07</td>\\n\",\n       \"      <td>36.73</td>\\n\",\n       \"      <td>39.46</td>\\n\",\n       \"      <td>37.50</td>\\n\",\n       \"      <td>39.10</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>1.83</td>\\n\",\n       \"      <td>1.71</td>\\n\",\n       \"      <td>1.77</td>\\n\",\n       \"      <td>1.77</td>\\n\",\n       \"      <td>1.68</td>\\n\",\n       \"      <td>1.78</td>\\n\",\n       \"      <td>1.80</td>\\n\",\n       \"      <td>1.70</td>\\n\",\n       \"      <td>1.75</td>\\n\",\n       \"      <td>1.78</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>1.80</td>\\n\",\n       \"      <td>1.79</td>\\n\",\n       \"      <td>1.77</td>\\n\",\n       \"      <td>1.74</td>\\n\",\n       \"      <td>1.74</td>\\n\",\n       \"      <td>1.80</td>\\n\",\n       \"      <td>1.78</td>\\n\",\n       \"      <td>1.75</td>\\n\",\n       \"      <td>1.69</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>68177.69</td>\\n\",\n       \"      <td>66138.42</td>\\n\",\n       \"      <td>72981.88</td>\\n\",\n       \"      <td>74304.33</td>\\n\",\n       \"      <td>67549.66</td>\\n\",\n       \"      <td>69367.34</td>\\n\",\n       \"      <td>69169.41</td>\\n\",\n       \"      <td>73268.61</td>\\n\",\n       \"      <td>74465.84</td>\\n\",\n       \"      <td>72503.37</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>73438.88</td>\\n\",\n       \"      <td>71053.35</td>\\n\",\n       \"      <td>71112.62</td>\\n\",\n       \"      <td>74916.48</td>\\n\",\n       \"      <td>72571.58</td>\\n\",\n       \"      <td>66348.97</td>\\n\",\n       \"      <td>71063.72</td>\\n\",\n       \"      <td>67404.27</td>\\n\",\n       \"      <td>74920.24</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>44889.06</td>\\n\",\n       \"      <td>39191.86</td>\\n\",\n       \"      <td>40728.46</td>\\n\",\n       \"      <td>38576.36</td>\\n\",\n       \"      <td>45876.06</td>\\n\",\n       \"      <td>47034.00</td>\\n\",\n       \"      <td>46611.43</td>\\n\",\n       \"      <td>37668.32</td>\\n\",\n       \"      <td>40980.89</td>\\n\",\n       \"      <td>38466.15</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>42625.67</td>\\n\",\n       \"      <td>40684.20</td>\\n\",\n       \"      <td>46960.73</td>\\n\",\n       \"      <td>44546.80</td>\\n\",\n       \"      <td>45410.53</td>\\n\",\n       \"      <td>47139.44</td>\\n\",\n       \"      <td>43095.68</td>\\n\",\n       \"      <td>40888.34</td>\\n\",\n       \"      <td>39615.19</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>5.70</td>\\n\",\n       \"      <td>5.40</td>\\n\",\n       \"      <td>5.28</td>\\n\",\n       \"      <td>5.38</td>\\n\",\n       \"      <td>5.27</td>\\n\",\n       \"      <td>5.61</td>\\n\",\n       \"      <td>6.00</td>\\n\",\n       \"      <td>5.38</td>\\n\",\n       \"      <td>5.34</td>\\n\",\n       \"      <td>5.87</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>5.17</td>\\n\",\n       \"      <td>5.67</td>\\n\",\n       \"      <td>5.60</td>\\n\",\n       \"      <td>5.94</td>\\n\",\n       \"      <td>5.73</td>\\n\",\n       \"      <td>5.22</td>\\n\",\n       \"      <td>5.30</td>\\n\",\n       \"      <td>5.73</td>\\n\",\n       \"      <td>5.91</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"<p>5 rows × 101 columns</p>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"         X1        X2        X3        X4        X5        X6        X7  \\\\\\n\",\n       \"0     39.02     36.49     38.20     38.85     39.38     39.74     37.02   \\n\",\n       \"1      1.83      1.71      1.77      1.77      1.68      1.78      1.80   \\n\",\n       \"2  68177.69  66138.42  72981.88  74304.33  67549.66  69367.34  69169.41   \\n\",\n       \"3  44889.06  39191.86  40728.46  38576.36  45876.06  47034.00  46611.43   \\n\",\n       \"4      5.70      5.40      5.28      5.38      5.27      5.61      6.00   \\n\",\n       \"\\n\",\n       \"         X8        X9       X10  ...         X92       X93       X94  \\\\\\n\",\n       \"0     39.53     38.81     38.79  ...       36.62     36.92     38.80   \\n\",\n       \"1      1.70      1.75      1.78  ...        1.80      1.79      1.77   \\n\",\n       \"2  73268.61  74465.84  72503.37  ...    73438.88  71053.35  71112.62   \\n\",\n       \"3  37668.32  40980.89  38466.15  ...    42625.67  40684.20  46960.73   \\n\",\n       \"4      5.38      5.34      5.87  ...        5.17      5.67      5.60   \\n\",\n       \"\\n\",\n       \"        X95       X96       X97       X98       X99      X100  class  \\n\",\n       \"0     38.52     38.07     36.73     39.46     37.50     39.10      0  \\n\",\n       \"1      1.74      1.74      1.80      1.78      1.75      1.69      1  \\n\",\n       \"2  74916.48  72571.58  66348.97  71063.72  67404.27  74920.24      1  \\n\",\n       \"3  44546.80  45410.53  47139.44  43095.68  40888.34  39615.19      0  \\n\",\n       \"4      5.94      5.73      5.22      5.30      5.73      5.91      0  \\n\",\n       \"\\n\",\n       \"[5 rows x 101 columns]\"\n      ]\n     },\n     \"execution_count\": 9,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"import pandas as pd\\n\",\n    \"import numpy as np\\n\",\n    \"from sklearn.ensemble import RandomForestClassifier\\n\",\n    \"from sklearn.decomposition import PCA\\n\",\n    \"from sklearn.pipeline import make_pipeline\\n\",\n    \"from sklearn.cross_validation import cross_val_score\\n\",\n    \"\\n\",\n    \"hill_valley_noisy_data = pd.read_csv('https://raw.githubusercontent.com/rhiever/Data-Analysis-and-Machine-Learning-Projects/master/tpot-demo/Hill_Valley_with_noise.csv.gz', sep='\\\\t', compression='gzip')\\n\",\n    \"hill_valley_noisy_data.head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 10,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"''\"\n      ]\n     },\n     \"execution_count\": 10,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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c1ft9YilUrYcudirlqVQUFaBLnJYXQN2ajvMhIVpibtU+0G5Y0jPPR6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\\nLBENlIiIiIjIsuT/AwYuWcOKMBlBAAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x11ce6ecf8>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"%matplotlib inline\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"import seaborn as sb\\n\",\n    \"\\n\",\n    \"with plt.style.context('seaborn-notebook'):\\n\",\n    \"    plt.figure(figsize=(6, 6))\\n\",\n    \"    for record_num in range(1, 11):\\n\",\n    \"        plt.subplot(10, 1, record_num)\\n\",\n    \"        hv_noisy_record_features = hill_valley_noisy_data.loc[record_num].drop('class').values\\n\",\n    \"        plt.plot(hv_noisy_record_features)\\n\",\n    \"        plt.yticks([])\\n\",\n    \"        plt.xticks([])\\n\",\n    \"\\n\",\n    \"    plt.tight_layout()\\n\",\n    \";\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 11,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[ 0.53278689  0.53278689  0.55737705  0.59016393  0.62295082  0.55737705\\n\",\n      \"  0.58333333  0.55833333  0.6         0.56666667]\\n\",\n      \"0.570177595628\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"cv_scores = cross_val_score(RandomForestClassifier(n_estimators=100, n_jobs=-1),\\n\",\n    \"                            X=hill_valley_noisy_data.drop('class', axis=1).values,\\n\",\n    \"                            y=hill_valley_noisy_data.loc[:, 'class'].values,\\n\",\n    \"                            cv=10)\\n\",\n    \"\\n\",\n    \"print(cv_scores)\\n\",\n    \"print(np.mean(cv_scores))\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 12,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"[ 0.95901639  0.98360656  0.87704918  0.98360656  0.92622951  0.93442623\\n\",\n      \"  0.90833333  0.925       0.95833333  0.96666667]\\n\",\n      \"0.942226775956\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"cv_scores = cross_val_score(make_pipeline(PCA(n_components=10), RandomForestClassifier(n_estimators=100, n_jobs=-1)),\\n\",\n    \"                            X=hill_valley_noisy_data.drop('class', axis=1).values,\\n\",\n    \"                            y=hill_valley_noisy_data.loc[:, 'class'].values,\\n\",\n    \"                            cv=10)\\n\",\n    \"\\n\",\n    \"print(cv_scores)\\n\",\n    \"print(np.mean(cv_scores))\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Automating data science with TPOT\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 13,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div>\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>X1</th>\\n\",\n       \"      <th>X2</th>\\n\",\n       \"      <th>X3</th>\\n\",\n       \"      <th>X4</th>\\n\",\n       \"      <th>X5</th>\\n\",\n       \"      <th>X6</th>\\n\",\n       \"      <th>X7</th>\\n\",\n       \"      <th>X8</th>\\n\",\n       \"      <th>X9</th>\\n\",\n       \"      <th>X10</th>\\n\",\n       \"      <th>...</th>\\n\",\n       \"      <th>X92</th>\\n\",\n       \"      <th>X93</th>\\n\",\n       \"      <th>X94</th>\\n\",\n       \"      <th>X95</th>\\n\",\n       \"      <th>X96</th>\\n\",\n       \"      <th>X97</th>\\n\",\n       \"      <th>X98</th>\\n\",\n       \"      <th>X99</th>\\n\",\n       \"      <th>X100</th>\\n\",\n       \"      <th>class</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>0</th>\\n\",\n       \"      <td>39.02</td>\\n\",\n       \"      <td>36.49</td>\\n\",\n       \"      <td>38.20</td>\\n\",\n       \"      <td>38.85</td>\\n\",\n       \"      <td>39.38</td>\\n\",\n       \"      <td>39.74</td>\\n\",\n       \"      <td>37.02</td>\\n\",\n       \"      <td>39.53</td>\\n\",\n       \"      <td>38.81</td>\\n\",\n       \"      <td>38.79</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>36.62</td>\\n\",\n       \"      <td>36.92</td>\\n\",\n       \"      <td>38.80</td>\\n\",\n       \"      <td>38.52</td>\\n\",\n       \"      <td>38.07</td>\\n\",\n       \"      <td>36.73</td>\\n\",\n       \"      <td>39.46</td>\\n\",\n       \"      <td>37.50</td>\\n\",\n       \"      <td>39.10</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>1</th>\\n\",\n       \"      <td>1.83</td>\\n\",\n       \"      <td>1.71</td>\\n\",\n       \"      <td>1.77</td>\\n\",\n       \"      <td>1.77</td>\\n\",\n       \"      <td>1.68</td>\\n\",\n       \"      <td>1.78</td>\\n\",\n       \"      <td>1.80</td>\\n\",\n       \"      <td>1.70</td>\\n\",\n       \"      <td>1.75</td>\\n\",\n       \"      <td>1.78</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>1.80</td>\\n\",\n       \"      <td>1.79</td>\\n\",\n       \"      <td>1.77</td>\\n\",\n       \"      <td>1.74</td>\\n\",\n       \"      <td>1.74</td>\\n\",\n       \"      <td>1.80</td>\\n\",\n       \"      <td>1.78</td>\\n\",\n       \"      <td>1.75</td>\\n\",\n       \"      <td>1.69</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>2</th>\\n\",\n       \"      <td>68177.69</td>\\n\",\n       \"      <td>66138.42</td>\\n\",\n       \"      <td>72981.88</td>\\n\",\n       \"      <td>74304.33</td>\\n\",\n       \"      <td>67549.66</td>\\n\",\n       \"      <td>69367.34</td>\\n\",\n       \"      <td>69169.41</td>\\n\",\n       \"      <td>73268.61</td>\\n\",\n       \"      <td>74465.84</td>\\n\",\n       \"      <td>72503.37</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>73438.88</td>\\n\",\n       \"      <td>71053.35</td>\\n\",\n       \"      <td>71112.62</td>\\n\",\n       \"      <td>74916.48</td>\\n\",\n       \"      <td>72571.58</td>\\n\",\n       \"      <td>66348.97</td>\\n\",\n       \"      <td>71063.72</td>\\n\",\n       \"      <td>67404.27</td>\\n\",\n       \"      <td>74920.24</td>\\n\",\n       \"      <td>1</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>3</th>\\n\",\n       \"      <td>44889.06</td>\\n\",\n       \"      <td>39191.86</td>\\n\",\n       \"      <td>40728.46</td>\\n\",\n       \"      <td>38576.36</td>\\n\",\n       \"      <td>45876.06</td>\\n\",\n       \"      <td>47034.00</td>\\n\",\n       \"      <td>46611.43</td>\\n\",\n       \"      <td>37668.32</td>\\n\",\n       \"      <td>40980.89</td>\\n\",\n       \"      <td>38466.15</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>42625.67</td>\\n\",\n       \"      <td>40684.20</td>\\n\",\n       \"      <td>46960.73</td>\\n\",\n       \"      <td>44546.80</td>\\n\",\n       \"      <td>45410.53</td>\\n\",\n       \"      <td>47139.44</td>\\n\",\n       \"      <td>43095.68</td>\\n\",\n       \"      <td>40888.34</td>\\n\",\n       \"      <td>39615.19</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>4</th>\\n\",\n       \"      <td>5.70</td>\\n\",\n       \"      <td>5.40</td>\\n\",\n       \"      <td>5.28</td>\\n\",\n       \"      <td>5.38</td>\\n\",\n       \"      <td>5.27</td>\\n\",\n       \"      <td>5.61</td>\\n\",\n       \"      <td>6.00</td>\\n\",\n       \"      <td>5.38</td>\\n\",\n       \"      <td>5.34</td>\\n\",\n       \"      <td>5.87</td>\\n\",\n       \"      <td>...</td>\\n\",\n       \"      <td>5.17</td>\\n\",\n       \"      <td>5.67</td>\\n\",\n       \"      <td>5.60</td>\\n\",\n       \"      <td>5.94</td>\\n\",\n       \"      <td>5.73</td>\\n\",\n       \"      <td>5.22</td>\\n\",\n       \"      <td>5.30</td>\\n\",\n       \"      <td>5.73</td>\\n\",\n       \"      <td>5.91</td>\\n\",\n       \"      <td>0</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"<p>5 rows × 101 columns</p>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"         X1        X2        X3        X4        X5        X6        X7  \\\\\\n\",\n       \"0     39.02     36.49     38.20     38.85     39.38     39.74     37.02   \\n\",\n       \"1      1.83      1.71      1.77      1.77      1.68      1.78      1.80   \\n\",\n       \"2  68177.69  66138.42  72981.88  74304.33  67549.66  69367.34  69169.41   \\n\",\n       \"3  44889.06  39191.86  40728.46  38576.36  45876.06  47034.00  46611.43   \\n\",\n       \"4      5.70      5.40      5.28      5.38      5.27      5.61      6.00   \\n\",\n       \"\\n\",\n       \"         X8        X9       X10  ...         X92       X93       X94  \\\\\\n\",\n       \"0     39.53     38.81     38.79  ...       36.62     36.92     38.80   \\n\",\n       \"1      1.70      1.75      1.78  ...        1.80      1.79      1.77   \\n\",\n       \"2  73268.61  74465.84  72503.37  ...    73438.88  71053.35  71112.62   \\n\",\n       \"3  37668.32  40980.89  38466.15  ...    42625.67  40684.20  46960.73   \\n\",\n       \"4      5.38      5.34      5.87  ...        5.17      5.67      5.60   \\n\",\n       \"\\n\",\n       \"        X95       X96       X97       X98       X99      X100  class  \\n\",\n       \"0     38.52     38.07     36.73     39.46     37.50     39.10      0  \\n\",\n       \"1      1.74      1.74      1.80      1.78      1.75      1.69      1  \\n\",\n       \"2  74916.48  72571.58  66348.97  71063.72  67404.27  74920.24      1  \\n\",\n       \"3  44546.80  45410.53  47139.44  43095.68  40888.34  39615.19      0  \\n\",\n       \"4      5.94      5.73      5.22      5.30      5.73      5.91      0  \\n\",\n       \"\\n\",\n       \"[5 rows x 101 columns]\"\n      ]\n     },\n     \"execution_count\": 13,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"import pandas as pd\\n\",\n    \"from sklearn.cross_validation import train_test_split\\n\",\n    \"from tpot import TPOTClassifier\\n\",\n    \"\\n\",\n    \"hill_valley_noisy_data = pd.read_csv('https://raw.githubusercontent.com/rhiever/Data-Analysis-and-Machine-Learning-Projects/master/tpot-demo/Hill_Valley_with_noise.csv.gz', sep='\\\\t', compression='gzip')\\n\",\n    \"hill_valley_noisy_data.head()\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 14,\n   \"metadata\": {\n    \"collapsed\": false\n   },\n   \"outputs\": [\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"GP Progress:   9%|▉         | 100/1100 [00:00<36:47,  2.21s/pipeline]\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Generation 1 - Current best internal CV score: 0.9472727662812096\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"GP Progress:  18%|█▊        | 201/1100 [07:15<50:37,  3.38s/pipeline]  \"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Generation 2 - Current best internal CV score: 0.9495601844270386\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"GP Progress:  27%|██▋       | 301/1100 [08:12<04:03,  3.29pipeline/s]\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Generation 3 - Current best internal CV score: 0.9495601844270386\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"GP Progress:  36%|███▋      | 401/1100 [09:28<05:49,  2.00pipeline/s]\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Generation 4 - Current best internal CV score: 0.9495601844270386\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"GP Progress:  46%|████▌     | 501/1100 [12:45<21:05,  2.11s/pipeline]\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Generation 5 - Current best internal CV score: 0.9495601844270386\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"GP Progress:  54%|█████▍    | 596/1100 [00:00<01:19,  6.31pipeline/s]\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Generation 6 - Current best internal CV score: 0.950678752659701\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"GP Progress:  63%|██████▎   | 695/1100 [00:00<02:43,  2.47pipeline/s]\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Generation 7 - Current best internal CV score: 0.9529158891250255\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"GP Progress:  73%|███████▎  | 801/1100 [16:28<01:18,  3.83pipeline/s]\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Generation 8 - Current best internal CV score: 0.9529158891250255\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"GP Progress:  80%|████████  | 884/1100 [00:00<02:46,  1.30pipeline/s]\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Generation 9 - Current best internal CV score: 0.9529158891250255\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"GP Progress:  91%|█████████ | 1001/1100 [19:09<00:31,  3.17pipeline/s]\"\n     ]\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Generation 10 - Current best internal CV score: 0.9529158891250255\\n\"\n     ]\n    },\n    {\n     \"name\": \"stderr\",\n     \"output_type\": \"stream\",\n     \"text\": []\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"\\n\",\n      \"Best pipeline: LogisticRegression(input_matrix, 0.57999999999999996, 25, False)\\n\",\n      \"0.983631993007\\n\"\n     ]\n    }\n   ],\n   \"source\": [\n    \"X = hill_valley_noisy_data.drop('class', axis=1).values\\n\",\n    \"y = hill_valley_noisy_data.loc[:, 'class'].values\\n\",\n    \"\\n\",\n    \"X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.75, test_size=0.25)\\n\",\n    \"\\n\",\n    \"my_tpot = TPOTClassifier(generations=10, verbosity=2)\\n\",\n    \"my_tpot.fit(X_train, y_train)\\n\",\n    \"print(my_tpot.score(X_test, y_test))\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"anaconda-cloud\": {},\n  \"kernelspec\": {\n   \"display_name\": \"Python [default]\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.5.2\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 0\n}\n"
  },
  {
    "path": "traveling-salesman-portrait/traveling-salesman-portrait.py",
    "content": "'''\nThis is a script written by Randal S. Olson (randalolson.com) for the Traveling Salesman Portrait project.\n\nMore information on the project can be found on my blog:\n\nhttp://www.randalolson.com/2018/04/11/traveling-salesman-portrait-in-python/\n\nPlease check my project repository for information on how this script can be used and shared:\n\nhttps://github.com/rhiever/Data-Analysis-and-Machine-Learning-Projects\n'''\n\nimport os\nimport math\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport urllib.request\nfrom PIL import Image\nfrom tsp_solver.greedy_numpy import solve_tsp\nfrom scipy.spatial.distance import pdist, squareform\n\nimage_url = 'http://www.randalolson.com/wp-content/uploads/Frankenstein.jpg'\nimage_path = 'Frankenstein.jpg'\n\nif not os.path.exists(image_path):\n    urllib.request.urlretrieve(image_url, image_path)\n\noriginal_image = Image.open(image_path)\nbw_image = original_image.convert('1', dither=Image.NONE)\n\nbw_image_array = np.array(bw_image, dtype=np.int)\nblack_indices = np.argwhere(bw_image_array == 0)\nchosen_black_indices = black_indices[np.random.choice(black_indices.shape[0], replace=False, size=10000)]\n\ndistances = pdist(chosen_black_indices)\ndistance_matrix = squareform(distances)\n\noptimized_path = solve_tsp(distance_matrix)\n\noptimized_path_points = [chosen_black_indices[x] for x in optimized_path]\n\nplt.figure(figsize=(8, 10), dpi=100)\nplt.plot([x[1] for x in optimized_path_points], [x[0] for x in optimized_path_points], color='black', lw=1)\nplt.xlim(0, 600)\nplt.ylim(0, 800)\nplt.gca().invert_yaxis()\nplt.xticks([])\nplt.yticks([])\nplt.savefig('traveling-salesman-portrait.png', bbox_inches='tight')\n"
  },
  {
    "path": "us-weather-history/KCLT.csv",
    "content": "date,actual_mean_temp,actual_min_temp,actual_max_temp,average_min_temp,average_max_temp,record_min_temp,record_max_temp,record_min_temp_year,record_max_temp_year,actual_precipitation,average_precipitation,record_precipitation\n2014-7-1,81,70,91,67,89,56,104,1919,2012,0.00,0.10,5.91\n2014-7-2,85,74,95,68,89,56,101,2008,1931,0.00,0.10,1.53\n2014-7-3,82,71,93,68,89,56,99,2010,1931,0.14,0.11,2.50\n2014-7-4,75,64,86,68,89,55,99,1933,1955,0.00,0.10,2.63\n2014-7-5,72,60,84,68,89,57,100,1967,1954,0.00,0.10,1.65\n2014-7-6,74,61,87,68,89,57,99,1964,1948,0.00,0.10,1.95\n2014-7-7,79,67,91,68,89,55,100,1972,1954,0.00,0.11,2.37\n2014-7-8,83,72,94,68,89,58,101,1892,2010,0.00,0.11,1.87\n2014-7-9,80,71,89,68,89,57,101,1891,1986,0.15,0.12,3.71\n2014-7-10,78,71,85,68,89,53,101,1961,1926,0.0,0.11,2.45\n2014-7-11,78,68,87,68,89,55,100,1961,1986,0.00,0.12,3.10\n2014-7-12,79,67,91,68,89,59,102,1953,1930,0.00,0.11,1.95\n2014-7-13,81,69,92,68,89,56,100,1918,1986,0.00,0.12,2.09\n2014-7-14,85,74,96,68,89,59,102,2007,1954,0.00,0.12,2.15\n2014-7-15,81,67,94,68,89,58,100,2001,1893,0.87,0.12,4.61\n2014-7-16,76,66,85,68,89,58,100,2001,1887,0.00,0.12,1.47\n2014-7-17,74,63,84,68,89,62,100,1886,1986,0.00,0.12,4.14\n2014-7-18,77,70,83,68,89,62,102,1896,1887,0.0,0.12,1.82\n2014-7-19,70,66,74,68,89,60,103,2009,1986,0.17,0.12,2.30\n2014-7-20,74,65,83,68,89,60,101,1910,1986,0.0,0.12,1.25\n2014-7-21,74,70,77,68,89,57,103,1890,1986,1.30,0.12,3.83\n2014-7-22,79,72,85,68,89,58,103,2001,1926,0.0,0.12,2.56\n2014-7-23,80,69,90,68,89,56,101,2007,1987,0.00,0.13,6.88\n2014-7-24,80,71,89,68,89,59,100,1947,1952,0.06,0.12,2.83\n2014-7-25,77,68,86,68,89,62,101,1947,2010,0.00,0.13,2.34\n2014-7-26,78,66,89,68,89,60,100,1904,2005,0.00,0.13,2.84\n2014-7-27,80,70,90,68,89,57,103,1920,1940,0.18,0.13,2.46\n2014-7-28,79,68,90,68,89,54,102,1962,1952,0.06,0.13,2.98\n2014-7-29,73,62,83,68,89,59,103,1920,1952,0.00,0.14,2.04\n2014-7-30,74,63,85,68,89,58,101,1914,2011,0.00,0.14,4.24\n2014-7-31,72,65,78,68,89,56,100,1914,1999,1.45,0.14,1.45\n2014-8-1,68,65,70,68,89,58,98,1966,1957,2.39,0.14,2.39\n2014-8-2,69,65,72,68,89,59,98,1895,1942,0.00,0.14,2.00\n2014-8-3,77,67,86,68,89,60,98,1998,2011,0.00,0.15,4.51\n2014-8-4,78,69,87,68,89,58,98,1912,1935,0.00,0.15,2.59\n2014-8-5,77,65,88,68,89,55,100,1912,1954,0.00,0.15,3.01\n2014-8-6,78,68,88,68,88,57,102,1912,1956,0.17,0.14,3.19\n2014-8-7,78,67,88,68,88,50,100,2004,1963,0.00,0.13,1.53\n2014-8-8,77,73,81,68,88,55,102,2004,2007,0.0,0.13,1.37\n2014-8-9,76,68,84,68,88,56,104,2002,2007,0.00,0.13,1.26\n2014-8-10,70,67,73,68,88,56,104,1879,2007,0.01,0.13,2.26\n2014-8-11,78,71,85,68,88,58,98,1879,1956,0.58,0.13,3.04\n2014-8-12,81,72,89,68,88,57,100,1890,1925,0.31,0.13,2.25\n2014-8-13,75,65,84,68,88,57,102,1979,1956,0.00,0.14,1.27\n2014-8-14,71,59,83,68,88,57,98,1967,1925,0.00,0.14,2.00\n2014-8-15,73,60,86,68,88,55,99,1964,2007,0.00,0.14,3.17\n2014-8-16,77,68,86,68,88,57,102,1924,1954,0.00,0.14,2.51\n2014-8-17,78,67,89,67,88,55,97,1979,1911,0.00,0.14,2.92\n2014-8-18,81,71,90,67,88,59,101,1976,1988,0.0,0.13,1.76\n2014-8-19,80,70,90,67,87,59,100,1981,1988,0.0,0.14,1.96\n2014-8-20,79,67,91,67,87,56,103,1918,1925,0.00,0.14,1.84\n2014-8-21,80,67,93,67,87,56,103,1998,1983,0.00,0.13,2.22\n2014-8-22,83,70,95,67,87,57,101,1886,1983,0.00,0.15,2.81\n2014-8-23,82,73,91,67,87,53,103,1930,1983,0.08,0.13,2.74\n2014-8-24,74,66,81,67,87,55,97,1888,1968,0.14,0.14,2.35\n2014-8-25,72,60,83,66,87,56,98,1952,1943,0.00,0.14,3.16\n2014-8-26,72,60,84,66,87,55,100,1942,1954,0.00,0.14,5.36\n2014-8-27,72,57,87,66,86,55,101,1952,1954,0.00,0.13,4.39\n2014-8-28,77,61,92,66,86,55,98,1905,1924,0.00,0.13,4.64\n2014-8-29,79,69,88,66,86,54,100,1986,1948,0.00,0.13,1.42\n2014-8-30,79,68,90,66,86,53,101,1965,1932,0.00,0.12,2.54\n2014-8-31,83,74,91,65,86,53,99,1946,1957,0.06,0.12,3.40\n2014-9-1,82,71,93,65,85,51,99,1999,1957,0.00,0.11,2.93\n2014-9-2,82,69,94,65,85,53,99,1967,1932,0.0,0.12,1.20\n2014-9-3,81,68,93,65,85,52,98,1967,1925,0.63,0.11,2.67\n2014-9-4,77,69,85,64,85,54,100,1952,1925,0.82,0.11,1.15\n2014-9-5,79,69,89,64,84,54,101,1967,1954,0.01,0.11,2.46\n2014-9-6,80,70,90,64,84,53,104,1924,1954,0.00,0.11,3.47\n2014-9-7,79,70,88,64,84,53,100,1924,1925,0.09,0.11,2.38\n2014-9-8,70,67,73,63,84,54,99,1988,1925,0.0,0.11,1.96\n2014-9-9,72,66,77,63,83,46,101,1998,1939,0.00,0.11,1.92\n2014-9-10,72,65,79,63,83,47,98,1998,1939,0.00,0.10,3.64\n2014-9-11,77,64,89,62,83,49,96,1956,1925,0.09,0.10,1.80\n2014-9-12,81,72,90,62,83,45,95,1917,1925,0.08,0.09,1.76\n2014-9-13,76,66,86,62,82,49,94,1917,1962,1.56,0.11,4.12\n2014-9-14,66,62,69,61,82,47,97,1985,1897,0.0,0.10,3.23\n2014-9-15,76,65,86,61,81,47,96,2000,1956,0.37,0.11,4.06\n2014-9-16,76,65,86,61,81,47,96,2000,1956,0.37,0.11,4.06\n2014-9-17,69,63,74,60,81,46,96,1984,1954,0.00,0.11,3.14\n2014-9-18,71,62,80,60,81,48,99,2001,1896,0.00,0.11,4.84\n2014-9-19,73,67,79,59,80,46,97,1984,1954,0.02,0.10,2.72\n2014-9-20,71,59,82,59,80,45,98,1929,1925,0.00,0.11,2.82\n2014-9-21,71,57,85,59,80,43,97,1918,1895,0.00,0.11,2.74\n2014-9-22,69,61,76,58,79,43,98,1918,1895,0.00,0.10,2.92\n2014-9-23,59,53,65,58,79,39,97,1999,1931,0.02,0.11,1.69\n2014-9-24,59,53,64,57,79,43,95,1999,1895,0.19,0.10,3.89\n2014-9-25,66,61,70,57,78,40,94,1887,2010,0.01,0.11,1.31\n2014-9-26,67,62,71,57,78,42,94,1950,1961,0.0,0.11,2.54\n2014-9-27,69,59,78,56,78,43,93,1940,1900,0.00,0.11,1.94\n2014-9-28,68,60,76,56,77,41,93,1947,1954,0.00,0.12,3.38\n2014-9-29,67,64,70,55,77,40,93,1942,1941,0.09,0.11,3.59\n2014-9-30,71,59,83,55,77,38,91,1888,1926,0.00,0.12,3.65\n2014-10-1,70,56,84,54,76,39,92,1899,1954,0.00,0.12,3.80\n2014-10-2,72,59,85,54,76,36,92,1899,1954,0.00,0.11,1.60\n2014-10-3,70,61,79,54,76,36,91,1974,1954,0.03,0.12,2.15\n2014-10-4,55,43,67,53,75,33,95,1974,1954,0.00,0.12,4.21\n2014-10-5,52,38,65,53,75,38,97,1974,1954,0.00,0.12,1.16\n2014-10-6,62,47,76,52,75,38,98,1935,1954,0.00,0.12,1.98\n2014-10-7,67,53,80,52,74,34,91,1935,1951,0.00,0.11,2.70\n2014-10-8,72,58,86,52,74,34,93,1889,1941,0.00,0.12,3.82\n2014-10-9,66,52,80,51,74,31,92,2000,2007,0.00,0.12,3.80\n2014-10-10,72,60,84,51,74,30,89,2000,1939,0.00,0.11,4.18\n2014-10-11,78,68,87,51,73,29,90,2000,1939,0.08,0.12,2.58\n2014-10-12,63,58,68,50,73,31,87,1906,1919,0.02,0.12,2.82\n2014-10-13,66,59,72,50,73,35,91,1964,1954,0.04,0.12,1.93\n2014-10-14,73,64,82,49,72,32,87,2006,1940,0.45,0.11,2.27\n2014-10-15,66,58,73,49,72,30,88,2006,1985,0.24,0.11,1.83\n2014-10-16,61,50,71,49,72,35,87,1939,1925,0.0,0.11,4.76\n2014-10-17,59,44,74,48,71,35,85,2001,1962,0.00,0.11,1.90\n2014-10-18,65,53,76,48,71,31,87,2001,1938,0.00,0.12,2.34\n2014-10-19,57,45,69,48,71,30,87,2009,1938,0.00,0.10,1.20\n2014-10-20,59,46,71,47,71,30,86,1972,1926,0.0,0.11,2.33\n2014-10-21,61,47,75,47,70,30,88,1952,1943,0.00,0.11,1.31\n2014-10-22,56,44,68,47,70,29,85,1952,1939,0.00,0.10,4.52\n2014-10-23,55,40,70,46,70,31,88,1998,1941,0.00,0.11,1.72\n2014-10-24,54,39,69,46,70,28,88,2006,1939,0.00,0.10,0.85\n2014-10-25,57,39,74,46,69,27,84,2006,1931,0.00,0.09,2.65\n2014-10-26,66,48,83,45,69,27,86,2013,1940,0.00,0.10,1.85\n2014-10-27,62,43,80,45,69,24,86,1962,1939,0.00,0.09,2.03\n2014-10-28,68,52,84,45,68,26,87,2001,1940,0.00,0.10,1.23\n2014-10-29,69,58,79,44,68,27,82,1976,1984,0.00,0.10,1.65\n2014-10-30,57,45,68,44,68,26,84,1952,1950,0.00,0.10,2.51\n2014-10-31,50,39,61,44,68,29,85,2008,1950,0.49,0.10,1.68\n2014-11-1,47,40,53,43,67,29,84,1954,1950,0.28,0.10,1.14\n2014-11-2,44,30,57,43,67,27,85,1954,1961,0.00,0.10,1.82\n2014-11-3,45,24,65,43,67,24,84,1954,1961,0.00,0.10,1.69\n2014-11-4,53,39,67,43,66,22,83,1966,1961,0.00,0.10,1.97\n2014-11-5,60,49,70,42,66,24,81,2006,2003,0.00,0.10,0.97\n2014-11-6,60,48,71,42,66,26,82,1976,2003,0.04,0.10,1.34\n2014-11-7,50,39,61,42,65,26,78,1998,1888,0.00,0.10,1.71\n2014-11-8,45,31,58,41,65,25,78,1967,1934,0.00,0.11,2.79\n2014-11-9,51,38,63,41,65,24,82,1976,2005,0.00,0.10,2.76\n2014-11-10,51,32,69,41,64,28,83,1973,2006,0.00,0.10,1.43\n2014-11-11,57,39,74,41,64,24,78,1973,2006,0.00,0.09,1.82\n2014-11-12,59,41,77,40,64,24,81,1894,1989,0.00,0.10,1.54\n2014-11-13,53,46,60,40,63,21,81,2013,1931,0.0,0.10,1.93\n2014-11-14,37,28,46,40,63,21,80,2013,1879,0.01,0.11,1.23\n2014-11-15,35,25,45,39,63,20,82,1969,1993,0.00,0.10,1.32\n2014-11-16,41,29,53,39,62,22,82,1969,1958,0.00,0.10,2.26\n2014-11-17,47,36,57,39,62,20,84,1883,1958,0.63,0.11,1.90\n2014-11-18,30,20,39,38,62,20,78,2014,1942,0.00,0.10,1.93\n2014-11-19,29,14,44,38,61,14,78,2014,1942,0.00,0.11,2.04\n2014-11-20,47,34,59,38,61,18,79,1951,1942,0.00,0.11,1.36\n2014-11-21,44,30,58,38,61,19,78,1914,1942,0.00,0.11,3.26\n2014-11-22,39,22,55,37,60,13,76,2008,2011,0.00,0.11,1.68\n2014-11-23,54,42,65,37,60,18,78,1880,1900,1.72,0.11,1.72\n2014-11-24,67,59,74,37,59,16,75,1970,1958,0.00,0.11,1.68\n2014-11-25,58,49,66,37,59,13,76,1970,1921,0.22,0.10,2.42\n2014-11-26,46,40,52,36,59,11,75,1950,2001,0.89,0.11,2.36\n2014-11-27,43,34,52,36,58,18,77,1903,2001,0.00,0.11,1.54\n2014-11-28,36,25,47,36,58,19,77,2013,1990,0.00,0.11,2.93\n2014-11-29,44,31,56,35,58,20,76,1955,2001,0.00,0.12,1.37\n2014-11-30,54,39,68,35,57,15,76,1929,1991,0.00,0.11,2.09\n2014-12-1,57,41,73,35,57,15,76,1964,1946,0.00,0.11,1.21\n2014-12-2,49,42,55,35,57,20,76,1924,1991,0.00,0.11,1.85\n2014-12-3,53,42,64,34,56,20,75,1960,1998,0.0,0.12,2.96\n2014-12-4,56,49,63,34,56,18,76,2000,1956,0.0,0.11,1.93\n2014-12-5,47,42,51,34,56,18,75,1969,1998,0.04,0.11,2.06\n2014-12-6,47,42,52,34,55,18,78,1895,1998,0.12,0.10,2.37\n2014-12-7,48,39,56,33,55,12,76,2010,1998,0.00,0.10,1.56\n2014-12-8,40,36,43,33,55,10,76,1882,1998,0.14,0.11,1.15\n2014-12-9,46,37,55,33,54,14,78,2006,1943,0.0,0.10,1.78\n2014-12-10,43,32,53,33,54,13,80,1917,2007,0.00,0.10,1.91\n2014-12-11,37,23,51,33,54,13,79,1957,2007,0.00,0.11,1.69\n2014-12-12,41,22,60,32,54,7,79,1962,2007,0.00,0.11,1.57\n2014-12-13,46,25,66,32,53,2,77,1962,2007,0.00,0.10,1.78\n2014-12-14,46,29,63,32,53,12,73,1962,1948,0.00,0.10,2.38\n2014-12-15,43,26,60,32,53,10,73,2010,1948,0.00,0.10,2.46\n2014-12-16,48,37,58,32,53,12,75,1958,1971,0.09,0.10,1.58\n2014-12-17,46,31,60,32,52,14,73,1901,1956,0.00,0.10,1.30\n2014-12-18,43,30,56,31,52,11,75,1953,1984,0.00,0.10,1.74\n2014-12-19,43,30,56,31,52,11,76,1963,1984,0.00,0.11,0.85\n2014-12-20,44,40,48,31,52,12,79,1884,1931,0.03,0.10,2.38\n2014-12-21,47,42,51,31,52,10,73,1901,2013,0.0,0.10,2.19\n2014-12-22,42,38,46,31,51,11,72,1960,1998,0.26,0.11,2.27\n2014-12-23,42,37,47,31,51,8,73,1989,1990,0.29,0.11,2.12\n2014-12-24,53,45,60,31,51,6,71,1983,1982,0.91,0.11,1.47\n2014-12-25,45,32,57,30,51,4,77,1983,1955,0.00,0.10,2.32\n2014-12-26,43,27,59,30,51,6,76,1983,1889,0.00,0.11,1.72\n2014-12-27,48,32,64,30,51,15,72,1970,1971,0.00,0.10,1.25\n2014-12-28,55,49,60,30,51,10,77,1925,1971,0.04,0.11,1.67\n2014-12-29,51,44,58,30,51,2,74,1894,1984,0.59,0.10,1.82\n2014-12-30,39,32,45,30,50,-5,76,1880,1984,0.06,0.10,1.21\n2014-12-31,37,27,46,30,50,0,70,1890,1996,0.00,0.10,2.15\n2015-1-1,40,26,53,30,50,10,74,1928,1985,0.00,0.11,1.54\n2015-1-2,47,42,52,30,50,6,78,1928,1952,0.12,0.10,2.10\n2015-1-3,50,47,52,30,50,8,74,1887,2004,0.25,0.11,1.93\n2015-1-4,56,47,65,30,50,8,75,1887,1950,0.26,0.11,1.32\n2015-1-5,45,36,54,30,50,10,72,1920,1950,0.00,0.12,1.77\n2015-1-6,41,24,57,30,50,5,73,1884,1950,0.00,0.11,3.45\n2015-1-7,29,16,41,29,50,6,77,2014,1890,0.00,0.12,1.76\n2015-1-8,18,8,28,29,50,8,74,1970,1946,0.00,0.11,1.47\n2015-1-9,34,22,45,29,50,4,72,1970,2008,0.00,0.11,1.91\n2015-1-10,30,19,41,29,50,4,76,1970,1949,0.00,0.12,2.35\n2015-1-11,31,15,46,29,50,0,78,1886,1949,0.08,0.12,1.78\n2015-1-12,41,37,45,29,50,-1,75,1886,1890,0.98,0.11,2.22\n2015-1-13,41,35,47,29,50,3,75,1895,1960,0.04,0.12,1.68\n2015-1-14,35,30,40,29,50,7,77,1912,1907,0.0,0.11,1.45\n2015-1-15,34,26,42,29,50,12,77,1994,1907,0.0,0.11,0.96\n2015-1-16,43,30,56,29,50,5,74,1893,1974,0.00,0.11,1.69\n2015-1-17,41,24,57,29,51,5,75,1977,1943,0.00,0.11,2.38\n2015-1-18,49,37,61,29,51,10,71,2003,1937,0.02,0.11,2.51\n2015-1-19,49,33,64,30,51,6,69,1994,1929,0.00,0.11,2.28\n2015-1-20,53,39,67,30,51,-2,71,1985,1951,0.00,0.11,1.98\n2015-1-21,50,36,64,30,51,-5,73,1985,1935,0.00,0.11,2.28\n2015-1-22,41,25,57,30,51,5,74,1970,1937,0.00,0.11,1.79\n2015-1-23,41,36,45,30,51,9,75,2003,1927,0.88,0.11,1.48\n2015-1-24,43,33,53,30,51,7,74,1963,1967,0.16,0.10,1.79\n2015-1-25,43,28,57,30,51,11,76,1963,1950,0.00,0.11,2.27\n2015-1-26,45,37,53,30,51,8,75,1940,1950,0.01,0.10,1.43\n2015-1-27,39,29,48,30,52,6,75,1940,1890,0.00,0.10,1.51\n2015-1-28,36,23,49,30,52,5,79,1986,1944,0.00,0.11,1.27\n2015-1-29,38,25,50,30,52,8,79,1897,2002,0.0,0.10,1.21\n2015-1-30,38,26,49,30,52,4,78,1966,2002,0.00,0.11,1.13\n2015-1-31,35,19,51,30,52,4,77,1966,1975,0.00,0.11,1.75\n2015-2-1,47,36,57,31,52,10,76,1900,1950,0.01,0.11,1.78\n2015-2-2,42,26,57,31,52,10,80,1917,1989,0.39,0.11,2.30\n2015-2-3,34,22,46,31,53,8,81,1917,1989,0.00,0.12,1.25\n2015-2-4,41,28,53,31,53,11,76,1886,1927,0.00,0.11,1.93\n2015-2-5,40,27,52,31,53,6,78,1886,1890,0.00,0.11,2.00\n2015-2-6,32,18,45,31,53,13,79,1977,1927,0.00,0.10,2.91\n2015-2-7,47,28,65,31,53,10,74,1895,1999,0.00,0.11,1.65\n2015-2-8,59,47,70,31,54,1,74,1895,2009,0.00,0.10,1.12\n2015-2-9,56,51,61,32,54,6,77,1895,1994,0.87,0.11,1.81\n2015-2-10,45,37,53,32,54,8,76,1934,1938,0.13,0.10,2.22\n2015-2-11,42,28,55,32,54,12,75,1885,1965,0.00,0.11,1.19\n2015-2-12,42,26,57,32,54,11,73,1899,1999,0.00,0.11,1.65\n2015-2-13,31,21,40,32,55,2,74,1899,1951,0.00,0.12,1.88\n2015-2-14,40,23,56,33,55,-5,77,1899,1949,0.00,0.11,1.53\n2015-2-15,25,14,36,33,55,12,80,1943,1989,0.00,0.13,1.65\n2015-2-16,29,24,33,33,55,11,76,1905,1976,0.51,0.12,2.28\n2015-2-17,29,23,34,33,55,7,79,1958,1927,0.15,0.13,1.47\n2015-2-18,32,21,42,33,56,5,78,1958,1891,0.0,0.12,1.89\n2015-2-19,19,12,26,34,56,8,76,1958,1939,0.00,0.13,2.08\n2015-2-20,19,7,31,34,56,7,77,2015,2014,0.00,0.13,1.82\n2015-2-21,34,25,42,34,56,6,75,1896,2011,0.0,0.13,1.44\n2015-2-22,47,36,57,34,57,12,74,1963,1990,0.18,0.13,1.53\n2015-2-23,44,35,52,34,57,19,76,1963,1962,0.02,0.13,1.72\n2015-2-24,32,28,35,35,57,16,80,1967,1982,0.07,0.13,1.90\n2015-2-25,36,29,42,35,57,7,82,1967,1930,0.48,0.13,1.78\n2015-2-26,37,31,42,35,58,7,81,1967,1977,0.21,0.12,1.82\n2015-2-27,38,26,50,35,58,7,82,1963,2011,0.00,0.13,1.70\n2015-2-28,37,29,44,36,58,14,78,2002,2011,0.00,0.13,2.09\n2015-3-1,36,31,40,36,59,15,82,1980,1918,0.16,0.13,2.18\n2015-3-2,55,39,70,36,59,14,80,1980,1887,0.0,0.14,1.59\n2015-3-3,46,40,51,36,59,4,84,1980,1976,0.06,0.12,3.04\n2015-3-4,56,42,69,36,59,15,82,1943,1976,0.0,0.13,1.70\n2015-3-5,52,36,68,37,60,12,84,1960,1955,0.54,0.14,1.65\n2015-3-6,35,25,44,37,60,10,78,1960,1956,0.00,0.13,2.46\n2015-3-7,42,23,61,37,60,14,85,1899,1974,0.00,0.14,1.38\n2015-3-8,52,32,71,37,61,16,84,1920,1974,0.00,0.14,1.87\n2015-3-9,58,51,65,38,61,16,83,1996,1974,0.01,0.14,1.78\n2015-3-10,66,53,78,38,61,17,82,1932,1974,0.0,0.13,1.35\n2015-3-11,71,58,83,38,62,22,83,1969,1990,0.15,0.14,1.04\n2015-3-12,61,51,70,38,62,19,86,1969,1990,0.00,0.14,2.17\n2015-3-13,50,42,58,39,62,18,86,1960,1990,0.19,0.13,2.31\n2015-3-14,53,41,64,39,62,16,83,1926,2012,0.35,0.14,1.81\n2015-3-15,63,49,76,39,63,16,86,1993,1967,0.00,0.14,4.24\n2015-3-16,62,43,81,39,63,18,83,1970,1945,0.00,0.13,1.84\n2015-3-17,63,41,84,40,63,23,90,1900,1945,0.00,0.13,2.00\n2015-3-18,54,47,61,40,64,21,85,1967,2011,0.00,0.13,1.52\n2015-3-19,49,42,55,40,64,21,86,1902,1945,0.21,0.13,2.65\n2015-3-20,50,42,58,40,64,23,85,1923,1948,0.0,0.12,2.11\n2015-3-21,56,40,71,41,65,18,87,1965,1935,0.00,0.12,1.79\n2015-3-22,62,54,70,41,65,24,89,1965,1907,0.01,0.12,0.88\n2015-3-23,58,51,65,41,65,20,91,1888,1907,0.0,0.11,2.00\n2015-3-24,59,45,72,41,66,24,88,1940,1929,0.00,0.12,1.62\n2015-3-25,62,54,69,42,66,22,90,1940,1929,0.01,0.12,1.89\n2015-3-26,65,56,74,42,66,22,86,1972,1929,0.00,0.13,2.86\n2015-3-27,54,43,65,42,67,19,85,1955,1950,0.24,0.12,1.22\n2015-3-28,43,34,51,42,67,25,90,1982,1907,0.00,0.13,1.95\n2015-3-29,42,29,55,42,67,26,89,2013,1907,0.00,0.12,2.20\n2015-3-30,55,39,70,43,67,24,86,1964,1998,0.26,0.13,2.36\n2015-3-31,56,35,77,43,68,21,86,1964,1986,0.00,0.12,2.21\n2015-4-1,61,49,73,43,68,24,86,1923,1986,0.00,0.11,1.26\n2015-4-2,66,58,73,43,68,28,86,1881,1978,0.04,0.12,2.99\n2015-4-3,70,58,82,44,69,28,90,1972,1946,0.0,0.10,1.18\n2015-4-4,58,45,71,44,69,29,87,1936,1999,0.0,0.11,1.58\n2015-4-5,52,36,67,44,69,26,88,1891,1942,0.00,0.10,1.86\n2015-4-6,64,51,77,44,70,28,90,1944,1929,0.0,0.10,3.84\n2015-4-7,65,57,72,45,70,25,89,2007,1929,0.51,0.11,1.74\n2015-4-8,73,63,82,45,70,21,88,2007,1929,0.26,0.10,1.33\n2015-4-9,77,65,88,45,70,25,89,1972,1893,0.02,0.10,2.07\n2015-4-10,75,67,82,45,71,28,90,1985,2001,0.0,0.11,1.98\n2015-4-11,67,56,78,46,71,28,88,1961,1930,0.00,0.10,3.20\n2015-4-12,62,50,74,46,71,29,92,1973,1930,0.00,0.09,1.77\n2015-4-13,66,59,73,46,71,28,90,1940,1930,0.11,0.10,1.75\n2015-4-14,73,65,80,46,72,27,89,1950,1941,0.16,0.09,1.65\n2015-4-15,58,50,65,47,72,28,89,1907,1936,1.26,0.09,1.73\n2015-4-16,51,47,54,47,72,29,89,1962,1896,0.40,0.10,1.60\n2015-4-17,62,54,69,47,72,30,94,1949,1896,0.03,0.10,1.51\n2015-4-18,70,60,79,48,73,32,93,2001,1896,0.04,0.10,1.70\n2015-4-19,67,64,69,48,73,30,89,1983,1955,2.65,0.10,2.65\n2015-4-20,67,60,74,48,73,28,91,1983,1896,0.47,0.11,1.47\n2015-4-21,58,45,71,48,73,30,90,1983,1963,0.00,0.10,2.39\n2015-4-22,60,43,77,49,74,33,91,1978,1957,0.00,0.11,2.28\n2015-4-23,62,49,74,49,74,35,91,1986,1960,0.00,0.11,1.58\n2015-4-24,54,38,70,49,74,36,96,1893,1925,0.00,0.10,2.33\n2015-4-25,56,51,61,49,74,31,93,2005,1925,0.19,0.10,1.78\n2015-4-26,53,48,58,50,75,34,92,1910,1915,0.0,0.10,2.34\n2015-4-27,55,39,71,50,75,37,90,1972,1981,0.00,0.10,1.35\n2015-4-28,54,38,69,50,75,36,90,1967,1943,0.00,0.10,2.23\n2015-4-29,55,45,64,51,75,33,91,1973,1888,0.0,0.09,2.19\n2015-4-30,63,49,76,51,75,37,91,2008,1962,0.0,0.09,2.34\n2015-5-1,57,47,67,51,76,37,92,1963,1962,0.00,0.09,1.84\n2015-5-2,61,45,76,52,76,32,91,1963,1959,0.00,0.10,1.36\n2015-5-3,63,46,80,52,76,39,94,1981,1902,0.00,0.10,3.67\n2015-5-4,69,55,83,52,76,34,92,1971,1959,0.00,0.10,1.62\n2015-5-5,69,56,82,52,76,36,91,1940,1955,0.00,0.10,2.40\n2015-5-6,69,53,84,53,77,39,93,1891,1950,0.00,0.09,1.60\n2015-5-7,70,58,81,53,77,38,95,1891,1940,0.00,0.10,2.58\n2015-5-8,71,57,85,53,77,35,96,1989,1940,0.00,0.10,2.41\n2015-5-9,72,60,84,54,77,39,94,1984,1889,0.0,0.10,1.35\n2015-5-10,74,65,82,54,78,38,94,1906,1889,0.00,0.10,0.86\n2015-5-11,76,61,90,54,78,39,95,1977,1916,0.00,0.10,1.12\n2015-5-12,77,66,88,55,78,39,94,1969,1889,0.0,0.10,1.92\n2015-5-13,73,60,85,55,78,38,93,1960,1881,0.00,0.10,2.46\n2015-5-14,67,58,75,55,78,41,95,1917,1956,0.00,0.10,1.34\n2015-5-15,73,63,82,56,79,40,94,1888,1962,0.00,0.10,2.11\n2015-5-16,75,62,87,56,79,42,94,1978,1941,0.00,0.10,1.79\n2015-5-17,75,63,86,56,79,43,93,1973,1896,0.00,0.09,1.09\n2015-5-18,78,67,89,56,79,34,94,1973,1960,0.00,0.10,4.85\n2015-5-19,77,65,89,57,80,40,95,2009,1962,0.0,0.10,2.40\n2015-5-20,76,61,91,57,80,40,95,2002,1964,0.00,0.10,3.81\n2015-5-21,75,64,86,57,80,41,97,1894,1941,0.00,0.10,2.41\n2015-5-22,64,52,76,58,80,42,98,2002,1941,0.00,0.10,3.50\n2015-5-23,65,47,82,58,81,37,98,2002,1941,0.00,0.10,3.16\n2015-5-24,68,54,82,58,81,43,95,1931,1939,0.00,0.11,1.03\n2015-5-25,73,59,86,59,81,41,95,2013,1953,0.00,0.11,2.24\n2015-5-26,78,67,88,59,81,45,96,1979,1995,0.32,0.11,3.03\n2015-5-27,75,65,84,59,82,41,96,1961,1916,0.0,0.11,1.55\n2015-5-28,78,67,88,60,82,42,97,1961,1941,0.00,0.12,2.70\n2015-5-29,78,67,88,60,82,48,98,1894,1941,0.00,0.12,1.18\n2015-5-30,76,65,87,60,82,47,95,1894,1918,0.00,0.11,2.52\n2015-5-31,78,67,89,61,83,42,97,1984,1953,0.00,0.12,1.77\n2015-6-1,77,66,88,61,83,45,98,1889,1918,0.0,0.13,2.87\n2015-6-2,73,64,81,61,83,46,99,1966,1951,0.76,0.13,2.28\n2015-6-3,67,64,69,61,83,51,100,1966,1895,0.02,0.13,3.78\n2015-6-4,69,61,77,62,84,50,98,1988,1911,0.0,0.13,2.12\n2015-6-5,72,59,84,62,84,50,102,1954,1943,0.00,0.13,2.45\n2015-6-6,76,62,90,62,84,48,98,1976,1943,0.00,0.13,2.76\n2015-6-7,78,70,86,63,84,45,97,2000,1899,0.17,0.14,2.22\n2015-6-8,79,69,88,63,85,47,99,1977,1899,0.01,0.13,1.98\n2015-6-9,79,67,90,63,85,50,99,1912,2008,0.0,0.13,3.26\n2015-6-10,79,69,89,63,85,46,99,1977,1947,0.0,0.12,2.18\n2015-6-11,79,68,90,64,85,47,100,1913,1914,0.00,0.14,2.85\n2015-6-12,81,71,90,64,85,45,98,1972,1956,0.0,0.13,2.73\n2015-6-13,83,72,94,64,86,49,97,1903,1958,0.00,0.13,3.77\n2015-6-14,83,68,97,64,86,53,99,1903,1958,0.00,0.13,3.16\n2015-6-15,84,68,99,65,86,51,99,1933,2015,0.00,0.14,1.16\n2015-6-16,84,69,99,65,86,52,99,1961,2015,0.00,0.13,3.32\n2015-6-17,86,73,99,65,87,53,100,1917,1943,0.00,0.12,2.76\n2015-6-18,87,74,100,65,87,52,100,1961,2015,0.0,0.13,1.86\n2015-6-19,84,72,96,66,87,54,102,1965,1944,0.0,0.13,1.50\n2015-6-20,83,71,95,66,87,54,102,1879,1887,0.01,0.12,1.67\n2015-6-21,84,71,97,66,87,54,99,2003,1964,0.00,0.13,1.98\n2015-6-22,83,65,100,66,87,53,100,2003,2015,0.00,0.12,1.31\n2015-6-23,87,73,100,66,88,53,100,1947,2015,0.00,0.11,1.82\n2015-6-24,88,75,100,66,88,55,102,1972,1930,0.00,0.12,1.25\n2015-6-25,86,74,98,67,88,53,102,1889,1914,0.00,0.11,2.76\n2015-6-26,85,70,100,67,88,55,102,1979,1952,1.21,0.11,1.98\n2015-6-27,82,71,92,67,88,56,103,2012,1954,0.55,0.11,1.86\n2015-6-28,76,66,85,67,88,53,101,1968,1959,0.00,0.11,1.83\n2015-6-29,73,59,87,67,88,54,104,1974,2012,0.00,0.11,2.39\n2015-6-30,83,71,94,67,89,54,104,1962,2012,0.00,0.11,2.75\n"
  },
  {
    "path": "us-weather-history/KCQT.csv",
    "content": "date,actual_mean_temp,actual_min_temp,actual_max_temp,average_min_temp,average_max_temp,record_min_temp,record_max_temp,record_min_temp_year,record_max_temp_year,actual_precipitation,average_precipitation,record_precipitation\n2014-7-1,70,63,77,63,82,52,107,1886,1985,0.00,0.00,0.09\n2014-7-2,70,62,77,63,82,51,102,1887,1985,0.00,0.00,0.00\n2014-7-3,72,63,81,63,82,52,100,1902,1985,0.00,0.00,0.00\n2014-7-4,72,62,82,63,82,53,103,1900,1907,0.00,0.00,0.07\n2014-7-5,75,64,86,63,82,50,100,1886,1907,0.00,0.00,0.02\n2014-7-6,73,64,81,63,82,51,94,1894,1992,0.00,0.00,0.03\n2014-7-7,77,64,89,63,83,51,96,1894,1954,0.00,0.00,0.02\n2014-7-8,77,68,86,63,83,51,95,1894,1886,0.00,0.00,0.13\n2014-7-9,75,67,82,63,83,52,100,1901,1985,0.00,0.00,0.05\n2014-7-10,76,66,85,63,83,55,103,1903,1959,0.00,0.00,0.00\n2014-7-11,73,65,80,64,83,52,97,1878,1959,0.00,0.00,0.04\n2014-7-12,74,65,82,64,83,49,98,1888,1953,0.00,0.00,0.05\n2014-7-13,75,67,82,64,83,51,98,1888,1990,0.00,0.00,0.02\n2014-7-14,74,68,80,64,83,52,98,1908,1984,0.00,0.00,0.24\n2014-7-15,76,69,82,64,83,50,98,1893,1886,0.00,0.00,0.00\n2014-7-16,74,66,81,64,83,52,96,1894,1930,0.00,0.01,0.02\n2014-7-17,73,65,80,64,83,52,98,1894,1998,0.00,0.00,0.01\n2014-7-18,72,64,79,64,84,53,97,1884,1936,0.00,0.00,0.36\n2014-7-19,71,66,75,64,84,52,97,1884,1960,0.0,0.00,0.02\n2014-7-20,73,66,80,64,84,52,100,1892,1960,0.00,0.00,0.00\n2014-7-21,74,66,81,64,84,53,96,1903,1960,0.00,0.00,0.00\n2014-7-22,75,65,85,64,84,52,101,1880,2006,0.00,0.00,0.10\n2014-7-23,77,65,88,64,84,53,97,1903,1890,0.00,0.00,0.08\n2014-7-24,79,65,92,64,84,52,103,1882,1891,0.00,0.00,0.00\n2014-7-25,79,69,88,64,84,52,109,1903,1891,0.00,0.00,0.04\n2014-7-26,77,68,86,64,84,52,100,1886,1891,0.00,0.00,0.04\n2014-7-27,76,67,84,64,84,52,102,1903,1972,0.0,0.00,0.00\n2014-7-28,78,68,88,64,84,52,101,1883,1995,0.0,0.00,0.01\n2014-7-29,78,68,88,64,84,53,100,1909,1995,0.00,0.00,0.00\n2014-7-30,79,68,90,64,84,54,100,1894,1980,0.00,0.00,0.02\n2014-7-31,77,67,87,64,84,52,100,1885,1972,0.00,0.00,0.01\n2014-8-1,76,66,85,64,84,54,99,1888,1959,0.00,0.00,0.01\n2014-8-2,78,65,90,64,84,51,98,1888,1997,0.02,0.00,0.02\n2014-8-3,79,72,85,64,84,52,100,1913,1918,0.02,0.00,0.02\n2014-8-4,76,65,87,64,84,52,94,1912,1971,0.00,0.00,0.05\n2014-8-5,73,63,82,64,84,52,100,1887,1997,0.00,0.00,0.09\n2014-8-6,70,63,77,64,84,50,105,1883,1983,0.00,0.01,0.02\n2014-8-7,73,64,81,64,84,54,99,1894,1884,0.00,0.00,0.11\n2014-8-8,74,65,82,64,84,54,100,1916,1881,0.00,0.00,0.02\n2014-8-9,75,67,82,64,84,53,100,1889,1998,0.00,0.00,0.19\n2014-8-10,76,66,85,64,84,54,99,1897,1882,0.00,0.00,0.04\n2014-8-11,75,65,85,64,84,53,98,1894,1879,0.00,0.00,0.02\n2014-8-12,75,66,84,64,84,52,104,1894,1994,0.00,0.00,0.32\n2014-8-13,73,65,81,64,84,54,102,1892,1994,0.00,0.00,0.13\n2014-8-14,74,65,83,64,84,54,97,1887,1885,0.00,0.01,0.01\n2014-8-15,76,63,89,64,84,52,98,1894,1994,0.00,0.00,0.40\n2014-8-16,78,66,90,64,84,52,98,1883,1890,0.00,0.00,0.19\n2014-8-17,78,66,89,64,85,52,104,1887,1885,0.00,0.00,2.06\n2014-8-18,76,65,86,64,85,52,102,1887,1885,0.00,0.00,0.44\n2014-8-19,76,69,82,64,85,54,106,1916,1885,0.00,0.00,0.14\n2014-8-20,76,67,84,64,85,51,97,1900,1986,0.00,0.00,0.03\n2014-8-21,73,65,80,64,85,53,96,1899,1982,0.00,0.00,0.00\n2014-8-22,74,66,81,64,85,52,102,1901,1972,0.00,0.01,0.04\n2014-8-23,76,66,85,64,85,54,100,1905,1968,0.00,0.00,0.00\n2014-8-24,75,67,83,64,85,53,96,1900,1931,0.00,0.00,0.05\n2014-8-25,72,64,80,64,85,52,102,1909,1985,0.00,0.00,0.01\n2014-8-26,75,65,84,64,85,53,98,1885,1981,0.00,0.00,0.10\n2014-8-27,79,66,91,64,85,53,103,1902,1981,0.00,0.00,0.96\n2014-8-28,80,68,91,64,85,52,99,1902,1995,0.00,0.01,0.10\n2014-8-29,78,65,90,64,85,51,102,1901,1884,0.00,0.00,0.08\n2014-8-30,77,65,89,64,85,49,101,1901,1998,0.00,0.00,0.21\n2014-8-31,78,69,87,64,85,52,103,1895,1967,0.00,0.00,0.61\n2014-9-1,78,70,85,64,85,49,110,1901,1955,0.00,0.00,0.41\n2014-9-2,76,68,84,64,85,51,108,1881,1955,0.00,0.00,0.02\n2014-9-3,75,67,83,64,85,51,103,1892,1988,0.00,0.00,0.05\n2014-9-4,73,66,80,64,85,52,110,1892,1988,0.00,0.01,0.30\n2014-9-5,75,66,83,64,85,50,105,1898,1984,0.00,0.00,0.32\n2014-9-6,76,65,86,64,84,48,102,1898,1955,0.00,0.00,0.17\n2014-9-7,80,68,92,64,84,50,103,1884,1949,0.00,0.00,0.00\n2014-9-8,80,71,88,64,84,47,103,1901,1984,0.01,0.00,0.19\n2014-9-9,80,68,91,64,84,48,102,1884,1956,0.00,0.01,0.01\n2014-9-10,76,64,87,64,84,52,103,1893,1983,0.00,0.00,1.74\n2014-9-11,78,67,89,64,84,48,101,1901,1983,0.00,0.01,0.33\n2014-9-12,80,68,91,64,84,48,102,1884,1971,0.00,0.01,0.13\n2014-9-13,82,69,94,64,84,48,106,1884,1971,0.00,0.00,0.07\n2014-9-14,85,71,98,63,84,48,100,1898,2012,0.00,0.01,0.04\n2014-9-15,84,73,94,63,83,49,103,1895,2012,0.00,0.01,0.03\n2014-9-16,90,76,103,63,83,48,103,1884,1909,0.00,0.01,0.07\n2014-9-17,84,72,96,63,83,46,108,1884,1913,0.00,0.01,0.42\n2014-9-18,74,67,81,63,83,48,103,1884,1939,0.00,0.01,1.39\n2014-9-19,75,67,82,63,83,49,104,1907,1939,0.00,0.01,0.27\n2014-9-20,72,66,78,63,83,49,107,1900,1939,0.00,0.01,0.29\n2014-9-21,72,65,79,63,82,47,108,1880,1885,0.00,0.01,0.15\n2014-9-22,72,63,80,63,82,48,104,1895,1883,0.00,0.02,0.40\n2014-9-23,74,63,84,62,82,44,104,1880,1978,0.00,0.01,0.28\n2014-9-24,75,62,88,62,82,45,106,1880,1978,0.00,0.01,1.95\n2014-9-25,76,67,85,62,82,48,107,1879,1978,0.00,0.01,3.96\n2014-9-26,74,65,83,62,82,48,109,1893,1963,0.00,0.01,0.24\n2014-9-27,70,63,77,62,81,46,113,1901,2010,0.00,0.02,0.68\n2014-9-28,72,64,79,62,81,49,106,1901,1963,0.00,0.01,0.74\n2014-9-29,70,61,78,62,81,44,102,1880,1999,0.00,0.01,0.49\n2014-9-30,75,64,85,62,81,48,105,1886,1906,0.00,0.02,1.58\n2014-10-1,73,64,82,61,81,49,100,1901,1991,0.00,0.01,0.74\n2014-10-2,81,64,98,61,81,44,101,1884,1995,0.00,0.02,0.53\n2014-10-3,83,67,98,61,80,44,108,1884,1987,0.00,0.02,0.37\n2014-10-4,81,65,97,61,80,44,108,1884,1987,0.00,0.01,0.24\n2014-10-5,80,65,95,61,80,46,101,1884,1971,0.00,0.02,1.15\n2014-10-6,78,64,92,61,80,44,102,1881,1971,0.00,0.02,0.30\n2014-10-7,79,64,93,60,80,43,98,1884,1971,0.00,0.02,0.17\n2014-10-8,75,63,87,60,80,43,100,1881,1994,0.00,0.02,0.42\n2014-10-9,74,67,80,60,79,46,102,1879,1988,0.00,0.01,0.44\n2014-10-10,72,64,79,60,79,46,107,1890,1991,0.00,0.02,0.71\n2014-10-11,73,64,81,60,79,42,100,1879,1971,0.00,0.02,0.34\n2014-10-12,74,67,81,60,79,43,99,1886,1950,0.00,0.02,0.43\n2014-10-13,74,65,82,59,79,42,104,1886,1950,0.00,0.02,1.09\n2014-10-14,71,66,76,59,79,44,103,1881,1961,0.00,0.02,1.75\n2014-10-15,71,63,78,59,79,43,102,1881,1961,0.00,0.01,0.27\n2014-10-16,69,61,77,59,79,44,104,1881,1958,0.00,0.02,0.35\n2014-10-17,69,62,76,59,78,44,102,1881,1958,0.00,0.03,1.72\n2014-10-18,70,61,78,58,78,44,97,1881,1940,0.00,0.02,0.48\n2014-10-19,70,62,77,58,78,43,99,1881,1940,0.00,0.02,0.79\n2014-10-20,71,63,79,58,78,43,96,1886,1964,0.00,0.02,3.16\n2014-10-21,71,64,78,58,78,45,100,1882,1929,0.00,0.02,0.53\n2014-10-22,72,60,84,58,78,40,100,1892,1965,0.00,0.02,0.77\n2014-10-23,73,60,85,57,78,41,98,1892,1965,0.00,0.03,1.87\n2014-10-24,74,59,88,57,78,43,99,1886,1909,0.00,0.02,0.55\n2014-10-25,71,61,80,57,77,43,96,1886,1983,0.00,0.03,1.04\n2014-10-26,70,61,78,57,77,45,95,1897,1879,0.00,0.02,1.74\n2014-10-27,70,62,77,57,77,41,99,1886,1890,0.00,0.03,1.37\n2014-10-28,69,59,78,56,77,45,94,1903,1931,0.00,0.03,0.56\n2014-10-29,72,59,85,56,77,43,94,1878,1931,0.00,0.03,0.84\n2014-10-30,71,60,81,56,77,41,99,1971,1939,0.00,0.03,1.02\n2014-10-31,66,60,71,56,76,42,96,1886,1918,0.25,0.03,1.64\n2014-11-1,61,55,67,56,76,38,100,1886,1966,0.18,0.03,0.44\n2014-11-2,61,51,70,55,76,36,99,1886,1997,0.00,0.03,0.35\n2014-11-3,65,52,77,55,76,41,97,1886,2010,0.00,0.02,0.35\n2014-11-4,64,51,77,55,76,44,96,1923,2010,0.00,0.03,0.42\n2014-11-5,69,53,85,55,75,41,94,1881,1898,0.00,0.03,0.56\n2014-11-6,76,61,90,54,75,40,95,1879,2006,0.00,0.02,1.07\n2014-11-7,75,60,89,54,75,41,97,1890,2006,0.00,0.03,3.85\n2014-11-8,74,59,89,54,75,40,93,1888,1956,0.00,0.03,1.82\n2014-11-9,68,58,77,54,75,43,94,1886,1996,0.00,0.03,3.41\n2014-11-10,66,62,70,53,74,42,93,1880,1990,0.00,0.02,1.95\n2014-11-11,66,62,69,53,74,39,94,1881,1900,0.00,0.03,1.55\n2014-11-12,64,58,70,53,74,38,93,1978,1974,0.00,0.04,1.88\n2014-11-13,63,55,70,53,74,35,96,1880,1933,0.0,0.03,0.99\n2014-11-14,67,60,73,52,73,36,93,1880,1933,0.00,0.03,1.33\n2014-11-15,65,58,72,52,73,40,93,1968,2008,0.00,0.03,1.64\n2014-11-16,65,55,74,52,73,35,93,1880,1924,0.00,0.04,2.01\n2014-11-17,65,53,76,52,73,36,91,1886,1945,0.00,0.04,1.58\n2014-11-18,65,52,77,51,72,34,94,1881,1895,0.00,0.03,1.50\n2014-11-19,64,53,74,51,72,34,92,1886,2006,0.00,0.04,3.61\n2014-11-20,62,53,70,51,72,37,91,1906,2006,0.0,0.03,1.38\n2014-11-21,64,55,73,51,71,39,93,1905,1924,0.00,0.04,2.97\n2014-11-22,63,52,73,50,71,36,89,1882,1950,0.00,0.04,3.51\n2014-11-23,69,57,80,50,71,39,91,2003,1933,0.00,0.05,0.91\n2014-11-24,67,53,80,50,71,36,94,1895,1933,0.00,0.04,2.03\n2014-11-25,66,52,80,50,70,36,92,1879,1977,0.00,0.04,0.78\n2014-11-26,69,53,85,50,70,40,90,1880,1903,0.00,0.04,1.28\n2014-11-27,74,58,89,49,70,37,90,1897,1903,0.00,0.05,0.99\n2014-11-28,67,53,81,49,70,36,90,1906,1924,0.00,0.04,3.51\n2014-11-29,63,51,74,49,69,37,87,1878,1995,0.00,0.04,2.43\n2014-11-30,63,60,66,49,69,37,88,1906,1964,0.30,0.05,1.96\n2014-12-1,63,57,69,49,69,38,85,1880,1972,0.00,0.05,1.40\n2014-12-2,62,58,66,49,69,36,88,1884,1893,1.21,0.05,1.21\n2014-12-3,63,59,67,48,69,36,89,1897,1958,0.31,0.05,1.59\n2014-12-4,64,57,71,48,69,37,91,1909,1979,0.00,0.05,1.59\n2014-12-5,63,57,69,48,68,38,89,1891,1979,0.00,0.06,1.81\n2014-12-6,63,55,71,48,68,36,88,1897,1938,0.00,0.06,1.01\n2014-12-7,65,54,76,48,68,32,88,1978,1940,0.00,0.06,2.78\n2014-12-8,67,55,78,48,68,30,92,1978,1938,0.00,0.07,2.29\n2014-12-9,65,54,76,48,68,32,85,1951,1973,0.00,0.06,1.70\n2014-12-10,63,53,73,48,68,36,87,1951,1950,0.00,0.06,1.58\n2014-12-11,64,60,67,48,68,34,86,1898,1895,0.01,0.07,1.65\n2014-12-12,59,52,65,47,68,34,86,1878,1895,1.60,0.06,4.30\n2014-12-13,57,48,66,47,67,31,83,1901,1952,0.00,0.07,1.67\n2014-12-14,57,47,66,47,67,30,85,1878,1886,0.00,0.07,0.96\n2014-12-15,56,47,65,47,67,36,85,1901,1998,0.0,0.07,2.26\n2014-12-16,57,54,60,47,67,37,89,1897,1980,0.41,0.07,1.96\n2014-12-17,58,51,65,47,67,34,85,1895,1998,0.15,0.08,1.97\n2014-12-18,55,48,62,47,67,35,86,1885,1917,0.00,0.08,2.18\n2014-12-19,58,49,66,47,67,32,86,1897,1917,0.00,0.08,2.80\n2014-12-20,57,50,64,47,67,35,83,1887,1985,0.00,0.08,4.19\n2014-12-21,59,50,68,47,67,30,85,1897,1917,0.00,0.08,1.57\n2014-12-22,63,50,75,47,67,33,80,1990,1960,0.00,0.09,2.63\n2014-12-23,67,51,83,47,67,33,86,1905,1989,0.00,0.08,1.84\n2014-12-24,64,53,75,47,67,33,87,1879,1985,0.00,0.09,2.64\n2014-12-25,58,50,65,47,67,30,85,1879,1980,0.00,0.09,3.01\n2014-12-26,53,42,63,47,68,35,85,1987,1980,0.00,0.09,1.20\n2014-12-27,51,39,63,47,68,36,85,1916,2013,0.00,0.10,1.81\n2014-12-28,53,43,63,47,68,36,87,1954,1919,0.00,0.10,5.55\n2014-12-29,52,41,63,47,68,35,89,1882,1897,0.00,0.11,3.84\n2014-12-30,52,47,56,47,68,37,89,1945,1980,0.19,0.10,1.85\n2014-12-31,49,41,57,47,68,36,86,1882,1897,0.00,0.10,4.86\n2015-1-1,48,37,59,47,68,35,84,1884,1898,0.00,0.11,3.12\n2015-1-2,50,39,61,47,68,32,85,1886,1994,0.00,0.10,1.57\n2015-1-3,52,40,63,47,68,33,87,1949,1902,0.00,0.11,2.27\n2015-1-4,56,42,69,47,68,28,87,1949,1902,0.00,0.10,3.24\n2015-1-5,66,50,81,48,68,31,85,1949,1902,0.00,0.10,2.11\n2015-1-6,71,57,84,48,68,30,85,1913,2006,0.00,0.10,2.30\n2015-1-7,69,53,85,48,68,28,87,1913,1996,0.00,0.10,3.52\n2015-1-8,63,51,75,48,68,32,90,1888,1923,0.00,0.10,1.19\n2015-1-9,64,56,72,48,68,31,88,1888,1923,0.00,0.10,2.58\n2015-1-10,58,52,63,48,68,31,87,1888,1986,0.48,0.11,3.30\n2015-1-11,60,56,63,48,68,32,89,1949,1996,0.50,0.10,1.57\n2015-1-12,60,53,66,48,68,33,88,1949,2009,0.00,0.11,1.40\n2015-1-13,61,54,68,48,68,34,86,1950,1986,0.00,0.10,1.45\n2015-1-14,60,47,73,48,68,34,88,2013,1975,0.00,0.10,1.56\n2015-1-15,64,51,76,48,68,32,85,1888,2009,0.00,0.11,2.13\n2015-1-16,64,50,77,48,68,31,86,1888,1976,0.00,0.10,2.74\n2015-1-17,64,50,78,48,68,34,90,1888,1971,0.00,0.11,2.74\n2015-1-18,64,53,75,48,68,37,95,1892,1971,0.00,0.10,2.30\n2015-1-19,61,50,72,48,68,32,92,1889,1971,0.00,0.09,2.26\n2015-1-20,58,53,63,48,68,30,85,1883,2009,0.00,0.10,3.30\n2015-1-21,63,54,71,48,68,32,81,1937,1912,0.00,0.09,2.98\n2015-1-22,61,47,75,48,68,34,84,1937,1994,0.00,0.09,3.69\n2015-1-23,63,50,75,48,68,33,84,1937,1968,0.00,0.10,1.05\n2015-1-24,66,49,82,48,68,36,85,1937,1992,0.00,0.09,2.04\n2015-1-25,66,48,83,48,68,36,86,1898,1986,0.00,0.09,3.43\n2015-1-26,69,57,80,48,68,31,86,1898,1987,0.08,0.10,5.71\n2015-1-27,66,57,74,48,68,32,87,1898,1986,0.00,0.09,2.28\n2015-1-28,66,56,75,48,68,35,90,1957,1971,0.00,0.10,2.81\n2015-1-29,68,60,75,48,68,30,88,1880,1971,0.0,0.10,1.96\n2015-1-30,65,59,70,48,68,35,85,1880,1992,0.03,0.11,2.19\n2015-1-31,64,56,71,48,68,32,91,1880,2003,0.00,0.11,3.03\n2015-2-1,63,49,76,48,68,34,88,1880,1995,0.00,0.12,2.26\n2015-2-2,65,51,78,48,68,34,94,1901,1995,0.00,0.11,2.84\n2015-2-3,62,51,72,49,68,30,94,1883,1995,0.00,0.12,1.84\n2015-2-4,64,56,72,49,68,32,91,1883,1995,0.00,0.12,2.55\n2015-2-5,68,53,82,49,68,33,89,1899,2007,0.00,0.13,2.30\n2015-2-6,65,54,75,49,68,28,86,1883,1987,0.00,0.13,2.39\n2015-2-7,62,54,69,49,68,32,89,1883,1996,0.02,0.13,1.60\n2015-2-8,68,59,77,49,68,36,85,1899,1996,0.00,0.13,2.43\n2015-2-9,66,58,73,49,68,33,85,1891,2006,0.00,0.14,1.92\n2015-2-10,66,53,78,49,68,37,88,1890,1988,0.00,0.13,2.55\n2015-2-11,70,55,85,49,68,35,88,1894,1971,0.00,0.13,2.12\n2015-2-12,71,56,85,49,68,35,91,1949,1971,0.00,0.14,2.45\n2015-2-13,73,57,88,49,69,35,90,1903,1985,0.00,0.14,2.71\n2015-2-14,71,56,86,49,69,32,90,1903,1985,0.00,0.14,2.50\n2015-2-15,67,54,80,49,69,33,88,1903,1985,0.00,0.14,2.78\n2015-2-16,63,56,70,49,69,35,88,1903,1896,0.00,0.15,3.03\n2015-2-17,64,57,71,50,69,33,89,1889,1890,0.00,0.16,2.18\n2015-2-18,65,55,75,50,69,32,89,1882,1981,0.00,0.15,4.26\n2015-2-19,65,57,72,50,69,34,91,1880,1995,0.00,0.15,2.32\n2015-2-20,63,56,70,50,69,34,95,1882,1995,0.00,0.15,3.60\n2015-2-21,63,57,69,50,69,35,88,1897,2002,0.00,0.15,2.08\n2015-2-22,59,53,64,50,69,35,90,1897,2002,0.70,0.15,2.19\n2015-2-23,58,50,65,50,69,35,90,1894,1954,0.0,0.14,2.11\n2015-2-24,60,46,74,50,69,35,94,1890,1986,0.00,0.13,4.80\n2015-2-25,62,51,72,50,69,40,92,1962,1921,0.00,0.14,2.33\n2015-2-26,61,51,70,50,69,37,87,1894,2002,0.00,0.13,1.08\n2015-2-27,61,55,66,50,69,38,86,1894,1992,0.00,0.13,2.17\n2015-2-28,59,52,65,50,69,39,88,1962,1901,0.11,0.12,2.85\n2015-3-1,58,50,65,50,69,36,88,1894,1967,0.66,0.11,3.42\n2015-3-2,56,49,63,50,69,35,87,1896,1994,0.21,0.11,5.88\n2015-3-3,58,48,67,50,69,38,85,1894,1931,0.00,0.12,3.12\n2015-3-4,60,48,72,51,70,35,94,1976,1972,0.00,0.10,2.54\n2015-3-5,64,51,77,51,70,35,87,1896,1899,0.00,0.11,1.98\n2015-3-6,66,49,83,51,70,39,90,1882,1899,0.00,0.11,0.88\n2015-3-7,70,54,86,51,70,40,89,1893,1914,0.00,0.10,2.52\n2015-3-8,66,53,78,51,70,35,93,1882,2004,0.00,0.09,1.71\n2015-3-9,65,57,73,51,70,31,90,1893,1934,0.00,0.10,2.67\n2015-3-10,68,55,80,51,70,38,96,1893,1916,0.00,0.09,2.22\n2015-3-11,67,60,73,51,70,41,93,1969,1916,0.0,0.09,1.45\n2015-3-12,71,56,86,51,70,36,93,1893,2007,0.00,0.09,1.44\n2015-3-13,76,61,90,51,70,40,90,1907,2015,0.00,0.09,2.23\n2015-3-14,78,62,93,51,70,39,93,1895,2015,0.00,0.08,1.74\n2015-3-15,79,66,92,51,70,37,92,1880,2015,0.00,0.08,4.10\n2015-3-16,78,66,90,51,70,36,91,1880,1914,0.00,0.08,2.53\n2015-3-17,73,63,82,51,70,37,94,1881,1914,0.00,0.06,1.28\n2015-3-18,69,62,75,51,70,38,87,1898,1997,0.0,0.07,1.03\n2015-3-19,68,60,76,51,70,37,97,1886,1997,0.00,0.07,1.45\n2015-3-20,69,59,78,51,70,39,93,1898,1997,0.00,0.06,2.42\n2015-3-21,69,62,75,51,71,35,93,1894,1931,0.00,0.06,1.34\n2015-3-22,66,59,72,51,71,37,88,1894,1986,0.00,0.06,1.11\n2015-3-23,65,57,73,51,71,36,88,1898,1926,0.00,0.06,2.08\n2015-3-24,67,58,75,51,71,37,94,1898,1988,0.00,0.06,1.39\n2015-3-25,72,60,84,51,71,37,96,1902,1988,0.00,0.06,2.19\n2015-3-26,77,61,92,51,71,39,98,1907,1988,0.00,0.06,1.53\n2015-3-27,77,62,91,52,71,37,91,1884,1986,0.00,0.05,1.98\n2015-3-28,71,61,81,52,71,37,90,1892,1879,0.00,0.06,1.62\n2015-3-29,67,56,78,52,71,41,99,1880,1879,0.00,0.05,1.04\n2015-3-30,68,57,78,52,71,36,89,1892,2003,0.00,0.05,1.27\n2015-3-31,65,57,73,52,71,40,92,1897,2011,0.00,0.05,1.65\n2015-4-1,66,58,74,52,71,40,93,1897,1985,0.00,0.04,1.01\n2015-4-2,65,54,75,52,71,39,96,1906,1985,0.00,0.05,1.43\n2015-4-3,72,57,86,52,71,40,94,1892,1961,0.00,0.04,1.14\n2015-4-4,72,59,84,52,72,43,100,1892,1989,0.00,0.04,1.44\n2015-4-5,64,57,71,52,72,42,105,1921,1989,0.00,0.04,2.74\n2015-4-6,60,53,67,52,72,40,106,1975,1989,0.00,0.04,0.84\n2015-4-7,59,51,67,52,72,39,100,1975,1989,0.13,0.04,1.21\n2015-4-8,60,49,70,53,72,36,92,1901,1989,0.00,0.04,2.19\n2015-4-9,65,53,76,53,72,40,94,1900,1890,0.00,0.03,1.71\n2015-4-10,63,54,71,53,72,41,96,1927,1904,0.00,0.04,0.63\n2015-4-11,63,53,72,53,72,40,90,1896,1940,0.00,0.03,1.97\n2015-4-12,67,57,77,53,72,42,97,1887,1888,0.00,0.03,1.90\n2015-4-13,67,58,75,53,73,41,99,1893,1898,0.00,0.02,0.49\n2015-4-14,66,58,73,53,73,39,95,1883,1964,0.00,0.03,0.83\n2015-4-15,69,57,80,53,73,43,99,1917,1966,0.00,0.03,0.81\n2015-4-16,68,54,81,54,73,42,91,1917,2011,0.00,0.03,2.10\n2015-4-17,69,56,81,54,73,40,90,1896,1999,0.00,0.03,1.24\n2015-4-18,69,56,81,54,73,40,91,1882,1939,0.00,0.03,1.27\n2015-4-19,65,55,75,54,73,40,94,1880,2009,0.00,0.02,0.86\n2015-4-20,63,55,70,54,73,38,100,1896,2009,0.00,0.03,1.28\n2015-4-21,63,58,67,54,73,38,95,1896,2009,0.00,0.03,0.89\n2015-4-22,63,57,69,54,73,44,99,1882,1910,0.00,0.02,0.96\n2015-4-23,62,55,69,55,74,42,100,1896,1910,0.00,0.02,0.98\n2015-4-24,62,57,67,55,74,44,93,1900,2001,0.00,0.03,0.41\n2015-4-25,61,56,66,55,74,42,94,1882,1898,0.0,0.02,0.50\n2015-4-26,66,54,77,55,74,42,99,1894,2004,0.00,0.02,1.62\n2015-4-27,70,57,82,55,74,41,102,1900,2004,0.00,0.02,0.82\n2015-4-28,77,61,92,55,74,41,96,1900,2008,0.00,0.03,1.02\n2015-4-29,75,60,89,55,74,44,98,1901,1981,0.00,0.02,0.89\n2015-4-30,73,60,86,56,74,44,96,1915,1996,0.00,0.02,0.96\n2015-5-1,71,57,84,56,74,41,92,1915,2014,0.00,0.02,0.76\n2015-5-2,70,59,80,56,74,44,98,1915,2004,0.00,0.02,0.54\n2015-5-3,66,58,74,56,74,42,101,1883,2004,0.00,0.01,1.76\n2015-5-4,63,56,69,56,74,44,93,1899,1987,0.00,0.01,0.66\n2015-5-5,63,58,67,56,74,46,101,1920,1990,0.00,0.01,0.90\n2015-5-6,63,58,67,56,74,43,97,1892,1990,0.00,0.02,1.53\n2015-5-7,60,54,65,56,74,42,97,1892,1941,0.03,0.01,1.00\n2015-5-8,57,50,63,56,74,43,97,1879,1984,0.18,0.01,2.02\n2015-5-9,62,55,69,57,74,44,99,1879,1923,0.00,0.01,0.75\n2015-5-10,63,54,72,57,74,43,95,1922,1934,0.00,0.00,0.15\n2015-5-11,67,57,76,57,74,43,93,1890,1996,0.00,0.01,0.29\n2015-5-12,65,57,72,57,74,44,94,1887,1979,0.00,0.01,1.18\n2015-5-13,64,58,69,57,74,43,98,1880,1979,0.00,0.01,0.84\n2015-5-14,61,53,68,57,74,45,99,1882,2014,0.69,0.01,0.97\n2015-5-15,61,55,66,57,74,42,102,1883,2014,0.03,0.01,1.46\n2015-5-16,64,56,71,57,74,40,102,1883,1967,0.00,0.00,0.04\n2015-5-17,65,58,72,57,74,42,96,1880,1892,0.00,0.01,0.18\n2015-5-18,66,60,71,57,75,45,99,1880,1892,0.0,0.01,0.26\n2015-5-19,65,56,73,57,75,45,95,1893,1892,0.00,0.00,0.35\n2015-5-20,65,57,72,58,75,45,99,1879,1942,0.00,0.01,0.84\n2015-5-21,64,57,71,58,75,43,100,1902,1883,0.00,0.01,1.03\n2015-5-22,63,57,69,58,75,44,94,1879,1967,0.00,0.00,0.67\n2015-5-23,64,57,71,58,75,41,92,1881,1932,0.00,0.01,0.10\n2015-5-24,64,58,70,58,75,45,95,1888,1896,0.00,0.00,0.48\n2015-5-25,64,57,71,58,75,47,103,1916,1896,0.00,0.01,0.42\n2015-5-26,64,59,69,58,75,46,96,1917,1896,0.00,0.01,0.21\n2015-5-27,65,58,72,58,75,47,97,1879,1880,0.00,0.00,0.57\n2015-5-28,66,57,75,58,75,48,101,1895,1973,0.00,0.01,1.14\n2015-5-29,68,58,77,58,75,46,99,1881,1978,0.00,0.00,0.09\n2015-5-30,68,59,77,58,75,48,95,1878,1972,0.00,0.01,0.14\n2015-5-31,66,59,73,58,75,47,98,1908,1909,0.00,0.00,0.09\n2015-6-1,67,59,75,58,75,47,100,1908,1879,0.00,0.01,0.57\n2015-6-2,68,60,76,59,76,47,104,1895,1879,0.00,0.00,0.58\n2015-6-3,68,61,74,59,76,49,99,1908,1879,0.0,0.01,0.49\n2015-6-4,66,61,70,59,76,47,100,1885,1895,0.0,0.00,0.33\n2015-6-5,67,60,73,59,76,50,98,1999,1890,0.0,0.01,0.76\n2015-6-6,65,58,72,59,76,47,102,1887,1890,0.00,0.00,0.02\n2015-6-7,69,60,77,59,76,48,105,1887,1890,0.00,0.01,0.10\n2015-6-8,73,60,85,59,76,49,99,1894,1890,0.00,0.00,0.02\n2015-6-9,74,62,86,59,77,50,94,1879,1979,0.01,0.01,0.32\n2015-6-10,68,62,73,60,77,46,102,1892,1979,0.00,0.00,0.22\n2015-6-11,69,62,75,60,77,48,105,1892,1979,0.00,0.01,0.11\n2015-6-12,68,63,72,60,77,47,100,1894,1979,0.00,0.00,0.63\n2015-6-13,68,63,72,60,77,46,99,1894,1896,0.00,0.00,0.53\n2015-6-14,70,63,77,60,78,49,100,1894,1917,0.00,0.01,0.01\n2015-6-15,70,62,78,60,78,48,102,1892,1981,0.00,0.00,0.22\n2015-6-16,70,61,79,60,78,49,105,1895,1981,0.00,0.00,0.38\n2015-6-17,70,62,78,61,78,49,105,1885,1917,0.00,0.01,0.02\n2015-6-18,72,62,81,61,79,47,104,1885,1957,0.00,0.00,0.11\n2015-6-19,70,58,82,61,79,48,101,1892,1973,0.00,0.00,0.01\n2015-6-20,74,63,85,61,79,48,106,1881,1973,0.00,0.00,0.05\n2015-6-21,73,64,82,61,79,50,105,1882,1973,0.00,0.00,0.07\n2015-6-22,73,62,83,61,80,51,97,1885,1949,0.00,0.00,0.05\n2015-6-23,74,63,84,61,80,49,97,1893,1976,0.00,0.00,0.03\n2015-6-24,73,63,83,62,80,50,102,1882,1976,0.00,0.00,0.01\n2015-6-25,72,62,81,62,80,49,98,1893,1883,0.00,0.01,0.00\n2015-6-26,69,60,78,62,80,48,112,1894,1990,0.00,0.00,0.19\n2015-6-27,70,62,77,62,81,49,109,1894,1990,0.0,0.00,0.39\n2015-6-28,77,67,86,62,81,52,98,1887,1980,0.0,0.00,0.00\n2015-6-29,80,68,91,62,81,50,98,1893,1996,0.00,0.00,0.03\n2015-6-30,78,67,89,62,81,49,100,1894,1985,0.0,0.00,0.01\n"
  },
  {
    "path": "us-weather-history/KHOU.csv",
    "content": "date,actual_mean_temp,actual_min_temp,actual_max_temp,average_min_temp,average_max_temp,record_min_temp,record_max_temp,record_min_temp_year,record_max_temp_year,actual_precipitation,average_precipitation,record_precipitation\n2014-7-1,84,74,94,75,93,66,102,1985,1980,0.00,0.16,1.40\n2014-7-2,84,74,94,75,93,66,104,1924,1894,0.00,0.16,5.43\n2014-7-3,84,73,94,75,93,66,100,1977,1980,0.0,0.16,1.29\n2014-7-4,82,72,91,75,93,66,101,1910,2009,0.48,0.15,3.49\n2014-7-5,82,73,90,75,93,66,102,1924,2009,0.31,0.16,3.04\n2014-7-6,81,72,89,75,93,63,101,1970,2005,0.01,0.15,1.73\n2014-7-7,84,74,93,75,93,62,100,1972,1980,0.08,0.15,2.32\n2014-7-8,83,74,92,75,93,68,104,1892,1980,0.00,0.15,1.49\n2014-7-9,85,74,95,75,93,64,102,1905,1980,0.00,0.14,2.39\n2014-7-10,83,73,93,75,93,65,101,1893,1980,0.0,0.14,1.75\n2014-7-11,83,72,94,75,93,64,103,1905,1980,0.0,0.13,3.08\n2014-7-12,84,74,94,75,93,65,104,1891,1980,0.00,0.12,5.19\n2014-7-13,87,77,96,75,94,66,102,1895,1980,0.0,0.13,5.40\n2014-7-14,86,76,96,75,94,62,103,1990,1980,0.00,0.12,2.94\n2014-7-15,82,75,89,75,94,64,103,1967,2000,0.06,0.12,4.32\n2014-7-16,82,73,90,75,94,66,104,1895,1980,0.00,0.12,2.04\n2014-7-17,83,76,89,75,94,67,104,1974,1980,0.39,0.10,2.23\n2014-7-18,81,75,87,75,94,67,102,1974,1980,0.04,0.11,2.85\n2014-7-19,79,72,85,75,94,68,102,1974,1980,0.00,0.11,1.51\n2014-7-20,83,73,93,75,94,69,102,1970,2000,0.01,0.11,2.08\n2014-7-21,87,77,96,75,94,65,101,1989,2000,0.00,0.10,1.09\n2014-7-22,87,77,96,75,94,67,101,1988,2000,0.00,0.10,2.78\n2014-7-23,86,76,95,75,94,64,101,1970,1980,0.07,0.11,2.36\n2014-7-24,84,73,94,75,94,68,101,1972,1954,0.01,0.10,2.09\n2014-7-25,85,74,95,75,94,65,102,1894,1964,0.00,0.10,3.74\n2014-7-26,87,78,95,75,94,68,105,1972,1954,0.00,0.11,2.35\n2014-7-27,87,77,97,75,95,66,101,1902,1995,0.00,0.10,5.22\n2014-7-28,88,77,99,75,95,66,103,1891,1995,0.00,0.10,2.52\n2014-7-29,83,74,92,75,95,64,101,1994,1907,0.03,0.10,4.70\n2014-7-30,83,72,94,75,95,68,102,1994,1914,0.00,0.09,2.11\n2014-7-31,84,73,94,75,95,67,103,1936,1998,3.91,0.09,3.91\n2014-8-1,80,73,87,75,95,67,103,1984,1998,0.01,0.10,2.10\n2014-8-2,77,71,82,75,95,68,104,1891,1998,0.00,0.10,1.65\n2014-8-3,81,72,89,75,95,66,103,1949,1998,0.00,0.10,1.77\n2014-8-4,83,73,92,75,95,63,102,1976,1951,0.0,0.11,2.94\n2014-8-5,83,74,92,75,95,64,103,1976,1964,0.0,0.10,2.81\n2014-8-6,85,75,94,75,95,64,101,1891,1947,0.0,0.11,1.20\n2014-8-7,87,79,95,75,95,63,104,1891,2003,0.00,0.11,2.61\n2014-8-8,87,77,97,75,95,68,104,1990,1962,0.10,0.10,1.66\n2014-8-9,86,77,94,75,95,62,106,1989,1962,0.00,0.11,1.83\n2014-8-10,86,78,94,75,95,62,104,1989,1962,0.00,0.11,2.88\n2014-8-11,86,75,97,75,95,65,102,1989,2011,0.25,0.10,2.72\n2014-8-12,87,76,97,75,95,64,105,1989,1962,0.00,0.11,1.41\n2014-8-13,84,73,94,75,95,64,104,2004,1962,0.00,0.11,1.82\n2014-8-14,83,73,93,75,95,65,102,2004,2007,0.02,0.11,2.80\n2014-8-15,86,75,96,75,95,61,101,2004,1994,0.00,0.11,2.74\n2014-8-16,89,80,98,75,95,63,102,2004,1925,0.00,0.12,3.95\n2014-8-17,88,78,97,75,95,60,104,1992,1909,0.0,0.12,3.57\n2014-8-18,86,76,96,75,95,62,108,1992,1909,0.00,0.13,6.69\n2014-8-19,85,76,94,75,95,69,107,2000,1909,0.0,0.13,1.85\n2014-8-20,85,74,96,75,95,65,107,1976,1909,0.01,0.13,1.80\n2014-8-21,87,76,98,75,94,63,106,1940,1909,0.00,0.13,2.00\n2014-8-22,87,75,98,75,94,64,106,1976,1980,0.00,0.13,3.25\n2014-8-23,87,75,98,75,94,64,107,1973,1980,0.00,0.13,2.46\n2014-8-24,86,74,98,75,94,54,105,1891,1980,0.00,0.14,2.48\n2014-8-25,87,76,97,75,94,55,103,1891,1924,0.00,0.13,1.90\n2014-8-26,86,74,97,74,94,57,104,1891,2011,0.00,0.14,1.66\n2014-8-27,85,75,94,74,94,59,109,1891,2011,0.00,0.14,8.32\n2014-8-28,83,76,90,74,94,63,107,1992,2011,0.00,0.14,3.88\n2014-8-29,83,75,91,74,93,61,107,1992,2011,0.02,0.15,2.89\n2014-8-30,84,75,92,74,93,62,104,1986,2000,0.0,0.15,2.56\n2014-8-31,79,74,84,74,93,62,107,1893,2000,0.83,0.16,5.98\n2014-9-1,84,74,93,74,93,63,107,1915,2000,0.36,0.15,2.22\n2014-9-2,85,76,94,73,93,60,104,1893,2000,0.00,0.14,1.81\n2014-9-3,84,75,93,73,93,60,105,1987,2000,0.00,0.14,3.20\n2014-9-4,85,76,93,73,92,59,109,1974,2000,0.00,0.14,2.75\n2014-9-5,84,75,93,73,92,55,108,1891,2000,0.03,0.14,2.07\n2014-9-6,85,75,95,73,92,56,99,1974,1909,0.16,0.14,2.90\n2014-9-7,84,73,95,72,92,59,98,1891,1963,0.42,0.15,4.55\n2014-9-8,82,72,92,72,92,58,100,1988,1907,0.00,0.15,0.87\n2014-9-9,85,74,95,72,91,59,100,1988,1909,0.00,0.15,4.22\n2014-9-10,87,78,95,72,91,59,99,1988,1993,0.00,0.16,1.82\n2014-9-11,87,76,97,71,91,56,99,1907,1893,0.00,0.15,4.81\n2014-9-12,82,75,89,71,91,56,101,1907,1909,0.02,0.15,2.25\n2014-9-13,72,67,77,71,90,56,102,1907,2011,0.01,0.15,7.73\n2014-9-14,71,65,77,70,90,55,100,1940,2011,0.00,0.15,4.11\n2014-9-15,80,68,92,70,90,55,98,1892,1980,0.0,0.14,3.23\n2014-9-16,83,75,91,70,90,53,98,1979,1995,0.12,0.13,1.72\n2014-9-17,82,75,89,70,89,55,100,2000,1995,0.25,0.13,1.85\n2014-9-18,77,73,81,69,89,54,99,2000,1927,2.31,0.13,2.53\n2014-9-19,79,73,84,69,89,51,97,1981,1995,0.20,0.12,6.92\n2014-9-20,81,73,89,69,89,54,99,1901,1999,0.00,0.13,7.66\n2014-9-21,82,73,91,68,88,53,100,1983,2005,0.00,0.13,3.07\n2014-9-22,83,72,93,68,88,49,100,1983,2005,0.0,0.12,3.14\n2014-9-23,74,64,84,68,88,49,96,1999,1993,0.00,0.13,2.37\n2014-9-24,75,64,85,67,88,48,95,1975,2013,0.00,0.13,2.22\n2014-9-25,74,68,80,67,87,50,99,1994,2005,0.0,0.13,3.05\n2014-9-26,77,68,86,67,87,49,99,2000,2005,0.00,0.13,2.86\n2014-9-27,79,71,86,66,87,45,100,1942,2005,0.0,0.13,3.46\n2014-9-28,81,72,89,66,87,48,98,1909,1998,0.00,0.13,1.25\n2014-9-29,80,70,89,66,86,47,96,1908,2011,0.00,0.12,2.51\n2014-9-30,80,71,89,65,86,47,95,1890,1998,0.0,0.13,2.55\n2014-10-1,83,72,93,65,86,44,99,1984,1900,0.09,0.15,4.62\n2014-10-2,82,71,93,65,86,47,97,1984,1938,0.78,0.16,2.25\n2014-10-3,74,63,84,65,85,45,95,1975,1994,0.32,0.17,3.24\n2014-10-4,68,59,77,64,85,43,95,1975,2002,0.00,0.17,3.60\n2014-10-5,69,53,85,64,85,41,94,1975,2006,0.00,0.17,2.90\n2014-10-6,75,65,84,64,85,44,94,1975,1928,0.69,0.17,4.42\n2014-10-7,78,65,90,63,84,46,95,1889,1956,0.00,0.17,5.76\n2014-10-8,80,69,90,63,84,42,96,1952,1962,0.06,0.17,2.07\n2014-10-9,79,68,90,63,84,40,93,1976,1962,0.00,0.17,3.60\n2014-10-10,83,75,91,63,84,43,95,1908,1962,0.03,0.17,3.50\n2014-10-11,76,66,85,62,83,44,94,1990,1962,0.02,0.18,3.02\n2014-10-12,76,65,87,62,83,42,96,1977,1991,0.35,0.18,3.37\n2014-10-13,72,59,85,62,83,36,94,1977,1991,0.61,0.18,2.55\n2014-10-14,67,54,79,61,83,40,92,1977,1954,0.00,0.19,4.91\n2014-10-15,68,51,84,61,82,40,92,1978,1947,0.00,0.20,4.87\n2014-10-16,72,57,87,61,82,41,92,1893,1989,0.00,0.21,5.17\n2014-10-17,73,60,86,61,82,40,93,1977,1895,0.00,0.20,4.31\n2014-10-18,75,63,86,60,82,39,96,1978,1895,0.50,0.21,4.04\n2014-10-19,72,62,81,60,81,36,94,1989,2004,0.00,0.21,1.23\n2014-10-20,71,59,83,60,81,32,93,1989,2004,0.00,0.20,2.54\n2014-10-21,69,55,82,60,81,33,93,1976,2004,0.00,0.21,2.40\n2014-10-22,72,61,82,59,80,41,91,1898,2004,0.00,0.20,3.70\n2014-10-23,69,58,80,59,80,40,91,1996,1988,0.00,0.20,3.79\n2014-10-24,68,54,82,59,80,39,92,1908,1931,0.00,0.19,2.13\n2014-10-25,70,55,85,59,80,39,91,1980,1950,0.00,0.20,9.25\n2014-10-26,71,56,85,58,79,39,92,1892,2010,0.00,0.20,2.43\n2014-10-27,72,58,86,58,79,36,94,1898,2010,0.00,0.18,2.72\n2014-10-28,73,60,86,58,79,36,90,1898,1991,0.00,0.18,8.04\n2014-10-29,73,61,84,57,78,39,90,1910,1991,0.00,0.18,2.45\n2014-10-30,67,55,79,57,78,33,89,1917,2004,0.00,0.17,5.30\n2014-10-31,67,55,79,57,78,29,88,1993,2004,0.00,0.16,3.84\n2014-11-1,55,45,65,57,77,35,89,1993,2010,0.00,0.15,6.25\n2014-11-2,57,41,72,56,77,34,89,1991,1978,0.00,0.14,4.58\n2014-11-3,67,53,80,56,77,30,87,1999,1992,0.00,0.15,3.09\n2014-11-4,72,62,82,56,76,28,89,1991,1988,0.00,0.16,3.20\n2014-11-5,69,64,74,55,76,31,88,1991,1963,1.12,0.16,5.22\n2014-11-6,62,58,66,55,76,36,88,1993,1963,0.17,0.15,1.67\n2014-11-7,57,49,65,55,75,29,88,1993,2002,0.00,0.15,1.46\n2014-11-8,63,50,75,55,75,34,89,1991,1989,0.0,0.15,1.47\n2014-11-9,60,47,72,54,75,29,88,1991,1969,0.00,0.15,3.08\n2014-11-10,63,45,80,54,74,30,88,1977,1988,0.00,0.15,2.85\n2014-11-11,60,49,71,54,74,32,87,1977,1978,0.01,0.16,1.88\n2014-11-12,51,43,58,53,74,32,85,1987,2003,0.0,0.15,4.68\n2014-11-13,41,36,45,53,73,29,86,1907,2005,0.20,0.14,4.53\n2014-11-14,41,34,47,53,73,28,89,1969,1921,0.00,0.15,1.40\n2014-11-15,48,43,53,52,73,25,88,1969,1978,0.30,0.14,2.80\n2014-11-16,50,42,57,52,72,25,85,1970,1948,0.65,0.14,3.03\n2014-11-17,44,36,52,52,72,31,85,1997,2013,0.00,0.14,5.19\n2014-11-18,44,32,55,51,72,28,84,1959,1973,0.00,0.14,3.36\n2014-11-19,50,30,69,51,71,30,85,2014,1985,0.00,0.14,2.10\n2014-11-20,61,46,76,51,71,27,84,1969,1977,0.04,0.13,1.85\n2014-11-21,64,61,66,50,71,29,84,1937,1999,0.57,0.14,1.54\n2014-11-22,63,58,68,50,70,30,87,1975,1973,0.11,0.13,3.09\n2014-11-23,71,61,80,50,70,24,85,1975,1973,0.23,0.14,3.81\n2014-11-24,56,45,66,49,70,26,84,1970,1910,0.00,0.13,3.35\n2014-11-25,54,41,66,49,69,29,86,1950,1967,0.00,0.14,1.70\n2014-11-26,58,43,73,49,69,27,86,1975,1967,0.00,0.14,3.17\n2014-11-27,55,43,67,49,69,26,84,1975,1989,0.00,0.14,3.12\n2014-11-28,55,39,71,48,68,26,83,1976,1977,0.00,0.14,1.82\n2014-11-29,69,60,78,48,68,22,83,1976,2006,0.00,0.15,3.47\n2014-11-30,71,61,80,48,68,19,82,1976,1950,0.00,0.15,1.41\n2014-12-1,57,45,69,47,67,26,83,1979,2012,0.03,0.15,5.80\n2014-12-2,50,44,56,47,67,28,84,1979,2012,0.00,0.13,3.28\n2014-12-3,59,52,66,47,67,25,85,1929,1995,0.0,0.14,2.50\n2014-12-4,63,53,72,47,67,27,83,1897,2012,0.0,0.13,1.96\n2014-12-5,72,65,79,46,66,26,83,2009,1998,0.07,0.13,2.02\n2014-12-6,65,58,71,46,66,20,82,1950,1951,0.0,0.13,3.08\n2014-12-7,57,54,59,46,66,24,84,1950,1998,0.00,0.13,4.95\n2014-12-8,57,46,67,46,66,25,83,1927,2007,0.00,0.13,1.95\n2014-12-9,57,43,70,45,65,25,82,1917,2007,0.00,0.13,1.69\n2014-12-10,56,42,69,45,65,20,83,1978,1939,0.00,0.12,4.41\n2014-12-11,64,56,72,45,65,26,83,1957,2007,0.02,0.12,2.85\n2014-12-12,66,57,75,45,65,25,82,1989,1948,0.0,0.13,2.57\n2014-12-13,64,54,74,45,65,19,82,1989,1921,0.00,0.12,2.85\n2014-12-14,67,58,76,45,64,25,83,1985,1995,0.0,0.12,5.64\n2014-12-15,70,59,80,44,64,21,82,1901,1995,0.0,0.12,2.19\n2014-12-16,57,48,66,44,64,19,82,1989,1971,0.00,0.12,3.05\n2014-12-17,54,44,64,44,64,20,80,1932,1984,0.0,0.12,2.28\n2014-12-18,64,57,70,44,64,24,82,1996,1980,0.80,0.11,2.14\n2014-12-19,56,52,59,44,64,20,81,1929,1978,3.06,0.12,3.06\n2014-12-20,52,50,54,44,63,21,81,1996,1951,0.00,0.12,2.02\n2014-12-21,55,48,61,44,63,21,82,1973,2010,0.00,0.12,3.41\n2014-12-22,64,55,72,44,63,13,81,1989,1981,0.0,0.11,2.21\n2014-12-23,54,48,60,44,63,7,80,1989,1996,0.69,0.13,1.01\n2014-12-24,46,37,55,43,63,11,82,1989,1948,0.00,0.11,2.04\n2014-12-25,50,34,66,43,63,11,82,1983,1964,0.00,0.12,2.09\n2014-12-26,61,51,71,43,63,18,78,1983,2005,0.04,0.11,2.06\n2014-12-27,60,46,74,43,63,19,81,1892,1971,0.22,0.11,1.38\n2014-12-28,44,41,46,43,63,15,80,1894,1996,0.67,0.11,2.70\n2014-12-29,44,39,48,43,63,15,81,1894,1984,0.00,0.10,2.15\n2014-12-30,46,38,53,43,63,19,83,1983,1974,0.00,0.10,1.36\n2014-12-31,42,37,46,43,63,18,82,1983,1974,0.00,0.10,3.35\n2015-1-1,44,41,47,43,63,18,81,1928,2006,0.15,0.10,3.12\n2015-1-2,51,47,54,43,63,16,81,1928,2000,0.63,0.10,2.26\n2015-1-3,47,41,53,43,62,16,83,1911,1989,0.63,0.10,3.44\n2015-1-4,46,36,56,43,62,18,79,1911,1997,0.00,0.11,2.23\n2015-1-5,43,31,54,43,62,22,80,1972,1998,0.00,0.11,1.39\n2015-1-6,50,34,65,43,62,22,80,1972,1989,0.00,0.11,3.70\n2015-1-7,51,39,62,43,62,19,79,1970,1989,0.00,0.10,2.01\n2015-1-8,35,28,42,43,62,19,79,1996,1957,0.01,0.11,5.89\n2015-1-9,39,37,40,43,63,19,80,1976,1974,0.06,0.10,2.49\n2015-1-10,39,37,41,43,63,18,82,1977,1963,0.08,0.11,1.43\n2015-1-11,44,39,49,43,63,12,81,1982,1995,0.66,0.10,2.78\n2015-1-12,46,43,49,43,63,11,81,1918,2000,0.0,0.11,2.27\n2015-1-13,45,43,46,43,63,17,81,1912,2000,0.05,0.10,1.51\n2015-1-14,42,39,45,43,63,19,82,1982,1971,0.0,0.11,1.45\n2015-1-15,47,39,54,43,63,21,79,1892,1996,0.0,0.11,2.82\n2015-1-16,47,33,61,43,63,22,81,1972,1936,0.00,0.11,2.60\n2015-1-17,53,37,69,43,63,15,80,1930,1974,0.00,0.11,2.39\n2015-1-18,57,43,71,43,63,5,81,1930,2000,0.00,0.12,2.83\n2015-1-19,53,34,72,43,63,10,81,1940,2000,0.00,0.11,2.00\n2015-1-20,61,45,76,43,63,20,81,1985,2012,0.00,0.11,1.83\n2015-1-21,62,57,67,43,63,16,81,1985,1911,0.0,0.11,2.05\n2015-1-22,52,44,60,43,63,15,80,1930,1972,0.87,0.12,2.52\n2015-1-23,42,39,44,43,63,14,83,1940,1972,0.03,0.11,2.62\n2015-1-24,49,35,62,43,63,18,82,1963,1971,0.00,0.11,1.94\n2015-1-25,55,40,70,44,63,19,81,1894,1971,0.00,0.11,2.83\n2015-1-26,56,44,67,44,64,23,84,1943,1975,0.00,0.11,2.58\n2015-1-27,64,47,81,44,64,19,83,1897,1975,0.00,0.11,2.86\n2015-1-28,61,43,79,44,64,19,82,1897,1975,0.00,0.12,0.75\n2015-1-29,62,49,75,44,64,22,82,1897,1975,0.00,0.11,1.93\n2015-1-30,55,45,65,44,64,18,84,1949,1911,0.00,0.12,1.64\n2015-1-31,53,42,63,44,64,14,82,1949,1911,0.0,0.11,2.12\n2015-2-1,63,48,77,44,64,15,82,1951,1989,0.01,0.12,3.69\n2015-2-2,43,36,50,44,64,14,83,1951,1911,0.00,0.11,1.70\n2015-2-3,44,41,46,44,64,23,80,1951,2008,0.17,0.10,1.82\n2015-2-4,52,42,61,45,65,22,81,1996,1957,0.16,0.10,2.19\n2015-2-5,48,41,55,45,65,23,83,1996,1957,0.0,0.10,1.80\n2015-2-6,49,41,56,45,65,24,81,1989,1969,0.00,0.11,1.09\n2015-2-7,53,37,69,45,65,11,80,1895,1957,0.00,0.11,1.46\n2015-2-8,66,54,77,45,65,10,82,1895,1932,0.00,0.11,1.84\n2015-2-9,69,54,83,45,65,15,86,1933,1962,0.00,0.11,2.00\n2015-2-10,59,43,74,46,65,22,85,1973,1957,0.00,0.12,2.22\n2015-2-11,60,44,75,46,66,20,82,1981,1999,0.00,0.11,1.88\n2015-2-12,56,46,66,46,66,6,84,1899,1922,0.00,0.12,2.70\n2015-2-13,53,39,66,46,66,6,83,1899,1962,0.00,0.11,2.99\n2015-2-14,61,43,78,46,66,10,83,1899,1911,0.00,0.11,1.90\n2015-2-15,61,51,71,47,66,18,83,1905,2000,0.02,0.13,2.00\n2015-2-16,53,37,68,47,66,13,82,1895,1927,0.15,0.12,2.05\n2015-2-17,44,34,54,47,67,16,86,1895,1907,0.0,0.12,0.87\n2015-2-18,49,33,64,47,67,19,87,1936,1986,0.00,0.13,2.09\n2015-2-19,51,37,64,47,67,20,88,1900,1986,0.00,0.12,1.30\n2015-2-20,61,52,69,48,67,25,91,1908,1986,0.01,0.12,2.60\n2015-2-21,70,65,75,48,67,24,89,1978,1996,0.0,0.12,1.98\n2015-2-22,63,45,80,48,68,21,90,1978,1996,0.0,0.12,1.96\n2015-2-23,40,34,46,48,68,27,88,1976,1996,0.02,0.11,1.60\n2015-2-24,38,33,43,48,68,26,87,1914,1927,0.0,0.12,1.38\n2015-2-25,42,33,50,49,68,26,85,1974,1918,0.10,0.10,2.10\n2015-2-26,43,31,55,49,69,26,86,1974,1986,0.00,0.12,1.51\n2015-2-27,42,37,46,49,69,22,86,2002,1954,0.00,0.11,1.85\n2015-2-28,47,37,57,49,69,28,90,2002,1940,0.02,0.12,1.44\n2015-3-1,54,49,59,50,69,28,84,1980,1975,0.22,0.11,3.32\n2015-3-2,54,48,60,50,70,22,91,1980,1909,0.01,0.12,1.55\n2015-3-3,68,56,80,50,70,21,86,1943,1893,0.00,0.11,1.03\n2015-3-4,63,43,83,50,70,22,86,2002,1893,0.0,0.12,4.56\n2015-3-5,44,36,51,50,70,25,84,1989,1998,0.15,0.12,2.20\n2015-3-6,44,30,57,51,71,25,86,1989,1991,0.00,0.12,1.44\n2015-3-7,48,35,60,51,71,28,90,1989,1911,0.0,0.12,1.58\n2015-3-8,49,45,53,51,71,27,88,1971,1911,1.49,0.11,2.05\n2015-3-9,58,50,66,51,71,27,88,1932,2000,1.96,0.11,1.96\n2015-3-10,57,53,60,51,72,27,89,1932,1980,0.00,0.11,3.23\n2015-3-11,58,53,62,52,72,27,87,1948,1911,0.12,0.11,2.81\n2015-3-12,59,55,62,52,72,24,89,1948,1904,0.33,0.11,4.28\n2015-3-13,61,55,66,52,72,27,88,1932,1942,0.34,0.10,1.96\n2015-3-14,64,51,77,52,73,29,88,1993,2008,0.00,0.11,1.82\n2015-3-15,64,55,73,52,73,32,89,1999,2008,0.00,0.11,2.06\n2015-3-16,67,57,77,53,73,34,87,1913,1908,0.00,0.11,2.83\n2015-3-17,70,58,82,53,73,30,88,1900,1908,0.00,0.10,2.57\n2015-3-18,74,65,82,53,74,27,90,1892,2013,0.11,0.10,1.60\n2015-3-19,73,66,79,53,74,27,89,1923,1908,0.49,0.09,4.20\n2015-3-20,74,66,82,53,74,24,90,1923,1907,0.02,0.11,7.47\n2015-3-21,65,64,66,54,74,30,87,1996,1907,1.01,0.10,1.38\n2015-3-22,68,60,76,54,75,32,89,1970,1916,0.00,0.12,2.01\n2015-3-23,68,55,80,54,75,35,89,1989,1929,0.00,0.11,2.27\n2015-3-24,67,53,80,54,75,32,87,1897,1907,0.00,0.11,1.46\n2015-3-25,68,56,80,54,75,36,93,2006,1928,0.00,0.12,7.36\n2015-3-26,62,54,69,55,75,30,94,1955,1928,0.09,0.12,2.23\n2015-3-27,62,44,80,55,76,31,89,1955,1935,0.00,0.11,3.00\n2015-3-28,65,47,82,55,76,33,89,1931,1972,0.00,0.11,1.99\n2015-3-29,70,57,82,55,76,35,90,1894,1974,0.00,0.10,1.75\n2015-3-30,72,61,83,55,76,33,96,1987,1946,0.00,0.11,2.90\n2015-3-31,74,64,83,56,77,30,88,1987,1946,0.00,0.10,1.44\n2015-4-1,74,69,79,56,77,39,89,1987,1936,0.01,0.10,2.26\n2015-4-2,77,69,84,56,77,38,89,1996,1939,0.00,0.10,4.75\n2015-4-3,77,71,82,56,77,31,87,1987,2006,0.0,0.10,3.09\n2015-4-4,67,61,73,56,77,34,88,1987,1897,0.00,0.10,3.35\n2015-4-5,62,57,67,57,78,38,91,1973,1982,0.02,0.10,2.61\n2015-4-6,75,67,82,57,78,37,88,1970,1909,0.0,0.10,1.90\n2015-4-7,79,70,87,57,78,32,91,1971,1907,0.00,0.10,2.28\n2015-4-8,78,71,84,57,78,35,92,1971,1986,0.00,0.10,3.26\n2015-4-9,78,70,86,58,78,36,92,1973,1963,0.00,0.10,2.68\n2015-4-10,68,61,74,58,79,31,92,1973,1963,1.74,0.11,2.57\n2015-4-11,70,63,77,58,79,38,89,1989,1934,0.13,0.10,1.67\n2015-4-12,74,68,80,58,79,41,89,1900,1981,0.14,0.10,4.04\n2015-4-13,79,71,86,59,79,39,92,1940,1996,0.32,0.10,6.43\n2015-4-14,70,64,76,59,79,39,89,1980,1996,0.12,0.11,3.52\n2015-4-15,71,60,81,59,80,38,90,1928,1944,0.0,0.10,3.05\n2015-4-16,74,65,82,59,80,38,92,1983,1987,1.66,0.11,4.70\n2015-4-17,74,64,83,60,80,38,92,1999,2006,1.10,0.11,1.90\n2015-4-18,72,66,78,60,80,42,94,1999,1987,0.09,0.11,8.16\n2015-4-19,72,58,86,60,80,40,92,1901,1987,0.54,0.11,2.77\n2015-4-20,67,59,74,61,81,39,93,2013,1987,0.00,0.11,2.70\n2015-4-21,68,56,79,61,81,42,90,1978,1987,0.00,0.12,3.76\n2015-4-22,76,67,84,61,81,43,90,1993,2009,0.00,0.12,1.50\n2015-4-23,79,69,88,61,81,44,91,1998,1999,0.00,0.11,2.40\n2015-4-24,78,71,85,62,81,46,92,1995,1894,0.0,0.12,2.24\n2015-4-25,78,67,88,62,82,41,90,1910,1987,0.21,0.13,2.50\n2015-4-26,76,67,85,62,82,46,91,1978,1988,0.0,0.12,3.52\n2015-4-27,72,66,78,63,82,47,93,1978,1933,0.02,0.13,2.72\n2015-4-28,64,56,71,63,82,47,95,1978,1987,0.0,0.12,3.76\n2015-4-29,65,52,78,63,82,46,92,2008,1987,0.00,0.13,2.86\n2015-4-30,68,53,82,64,83,42,92,1908,1981,0.00,0.14,3.20\n2015-5-1,71,57,84,64,83,45,94,1908,1947,0.00,0.15,4.11\n2015-5-2,70,57,83,64,83,45,96,1976,1964,0.00,0.15,3.48\n2015-5-3,70,57,83,64,83,47,93,1978,1890,0.00,0.16,5.10\n2015-5-4,73,61,84,65,84,42,94,2013,1890,0.00,0.16,1.72\n2015-5-5,73,63,82,65,84,49,94,1970,1906,0.13,0.17,3.60\n2015-5-6,78,73,83,65,84,49,94,2013,1906,0.41,0.17,5.00\n2015-5-7,80,74,85,65,84,47,94,1891,1998,0.0,0.17,2.61\n2015-5-8,81,77,85,66,85,45,96,1892,1967,0.0,0.17,2.34\n2015-5-9,82,76,87,66,85,46,94,1984,1998,0.0,0.17,2.42\n2015-5-10,82,77,87,66,85,51,94,1984,1927,0.03,0.17,5.89\n2015-5-11,75,69,80,67,85,47,94,1981,1890,0.28,0.17,2.16\n2015-5-12,74,69,79,67,85,50,92,1895,1967,0.56,0.16,5.27\n2015-5-13,75,68,81,67,86,50,94,1895,1907,1.55,0.16,3.44\n2015-5-14,80,72,87,67,86,47,93,1971,1995,0.05,0.16,2.44\n2015-5-15,79,73,84,68,86,49,92,1890,2003,0.15,0.18,4.64\n2015-5-16,81,73,89,68,86,47,93,1973,2003,0.00,0.17,2.80\n2015-5-17,78,71,85,68,87,52,95,1970,1894,0.98,0.17,4.20\n2015-5-18,80,73,86,68,87,50,95,1976,1890,0.24,0.15,6.16\n2015-5-19,80,73,86,68,87,50,95,1976,2003,0.02,0.16,6.87\n2015-5-20,81,73,89,69,87,48,95,1894,2008,0.31,0.16,4.17\n2015-5-21,77,71,82,69,88,53,96,1892,2005,1.08,0.15,5.45\n2015-5-22,72,65,79,69,88,48,96,1892,2005,0.00,0.16,3.10\n2015-5-23,79,73,85,69,88,45,95,1892,1996,0.72,0.16,3.99\n2015-5-24,75,69,81,70,88,54,95,1892,1955,0.56,0.16,3.96\n2015-5-25,77,66,88,70,88,56,95,1979,1927,4.34,0.16,4.34\n2015-5-26,76,66,85,70,89,57,98,1979,2011,0.77,0.17,2.91\n2015-5-27,76,65,86,70,89,50,98,1901,1928,1.41,0.17,3.99\n2015-5-28,80,70,90,70,89,54,98,1901,1906,0.00,0.17,2.83\n2015-5-29,80,70,89,71,89,58,99,1984,1996,0.01,0.17,4.33\n2015-5-30,80,69,91,71,89,53,97,1984,1998,0.57,0.17,5.01\n2015-5-31,78,69,87,71,90,50,98,1889,1998,0.00,0.17,3.45\n2015-6-1,78,66,89,71,90,53,98,1984,1998,0.00,0.18,1.32\n2015-6-2,79,68,90,71,90,55,100,1892,2011,0.00,0.17,4.03\n2015-6-3,80,69,91,72,90,52,98,1970,1960,0.00,0.18,3.27\n2015-6-4,80,69,90,72,90,59,98,1970,2011,0.00,0.17,3.72\n2015-6-5,82,72,92,72,90,56,105,1970,2011,0.00,0.18,5.33\n2015-6-6,83,72,93,72,91,57,105,1970,2011,0.00,0.18,2.66\n2015-6-7,83,72,93,72,91,58,99,1970,1903,0.00,0.18,2.29\n2015-6-8,83,72,93,72,91,60,99,1977,1980,0.00,0.19,8.13\n2015-6-9,84,74,93,73,91,56,97,1996,1956,0.00,0.19,2.92\n2015-6-10,84,74,94,73,91,57,100,1955,1902,0.00,0.19,2.92\n2015-6-11,84,73,94,73,91,61,101,1976,1902,0.01,0.21,5.63\n2015-6-12,83,74,92,73,91,55,101,1979,1978,0.0,0.22,3.05\n2015-6-13,80,74,86,73,91,57,101,1979,1911,0.35,0.21,3.58\n2015-6-14,81,74,88,73,91,55,102,1903,1998,0.52,0.21,3.81\n2015-6-15,83,75,90,73,92,55,100,1903,2011,0.14,0.21,5.68\n2015-6-16,78,75,80,74,92,57,100,1989,1963,2.26,0.21,2.71\n2015-6-17,80,76,84,74,92,62,100,1989,1902,0.70,0.21,1.51\n2015-6-18,81,76,86,74,92,64,103,1969,1918,0.0,0.22,3.29\n2015-6-19,83,76,90,74,92,64,101,1912,1934,0.00,0.22,4.43\n2015-6-20,84,77,90,74,92,62,101,1976,1902,0.20,0.21,1.99\n2015-6-21,86,78,93,74,92,59,100,1976,1998,0.00,0.21,2.04\n2015-6-22,84,76,92,74,92,66,101,1912,1933,0.00,0.22,3.13\n2015-6-23,84,76,92,74,92,60,101,1904,2009,0.00,0.20,2.22\n2015-6-24,84,74,93,74,92,61,104,1904,2009,0.00,0.21,4.47\n2015-6-25,85,75,94,75,92,60,102,1974,2009,0.00,0.20,2.82\n2015-6-26,86,77,94,75,92,59,105,1974,2012,0.13,0.19,10.34\n2015-6-27,83,73,93,75,92,57,102,1974,1980,2.38,0.19,2.50\n2015-6-28,82,73,90,75,93,59,102,1974,2009,2.24,0.19,2.98\n2015-6-29,84,76,92,75,93,59,107,1974,2013,0.00,0.19,2.50\n2015-6-30,82,72,92,75,93,61,103,1985,1980,2.26,0.19,2.26\n"
  },
  {
    "path": "us-weather-history/KIND.csv",
    "content": "date,actual_mean_temp,actual_min_temp,actual_max_temp,average_min_temp,average_max_temp,record_min_temp,record_max_temp,record_min_temp_year,record_max_temp_year,actual_precipitation,average_precipitation,record_precipitation\n2014-7-1,76,66,86,66,85,48,97,1885,1970,0.13,0.16,5.09\n2014-7-2,71,63,79,66,85,50,99,1904,1970,0.00,0.16,4.50\n2014-7-3,67,60,73,66,85,49,100,1968,1911,0.0,0.15,1.89\n2014-7-4,67,54,79,66,85,48,103,1968,1911,0.00,0.15,2.40\n2014-7-5,69,59,79,66,85,49,103,1972,2012,0.00,0.16,3.27\n2014-7-6,74,64,83,66,85,48,105,2001,2012,0.03,0.15,1.64\n2014-7-7,74,68,79,66,85,54,105,1894,2012,0.0,0.16,4.43\n2014-7-8,74,67,81,66,85,51,104,1984,1936,0.32,0.16,2.60\n2014-7-9,72,62,81,66,85,52,103,1952,1936,0.00,0.16,2.49\n2014-7-10,71,61,80,66,85,50,105,1963,1936,0.00,0.16,1.52\n2014-7-11,71,60,81,66,85,52,104,1945,1936,0.00,0.16,2.49\n2014-7-12,74,67,80,66,85,53,103,1975,1936,0.07,0.16,2.70\n2014-7-13,78,72,84,66,85,49,103,1976,1936,0.00,0.17,2.78\n2014-7-14,75,65,85,66,85,48,106,1975,1936,0.39,0.16,2.96\n2014-7-15,64,56,71,66,85,51,103,1967,1988,0.00,0.17,2.05\n2014-7-16,63,53,72,66,85,49,98,1945,1988,0.00,0.15,2.32\n2014-7-17,65,53,77,66,85,51,101,1976,2012,0.00,0.15,3.71\n2014-7-18,68,57,78,66,85,51,100,1976,1954,0.00,0.15,2.61\n2014-7-19,68,58,77,66,85,50,98,1984,1930,0.00,0.14,1.31\n2014-7-20,71,60,81,66,85,49,103,1947,1934,0.00,0.13,4.00\n2014-7-21,74,62,85,66,85,46,106,1944,1934,0.00,0.14,1.89\n2014-7-22,78,67,88,66,85,49,106,1947,1901,0.00,0.14,1.82\n2014-7-23,69,62,76,66,85,46,102,1947,1934,1.61,0.14,1.61\n2014-7-24,67,58,76,66,85,52,105,1947,1934,0.00,0.14,1.98\n2014-7-25,65,56,74,66,85,51,105,1911,1934,0.0,0.13,2.61\n2014-7-26,75,67,82,66,85,51,98,1911,1941,0.10,0.13,2.41\n2014-7-27,76,66,85,66,85,51,100,1962,1916,0.22,0.12,1.86\n2014-7-28,65,57,73,66,85,51,101,2013,1930,0.00,0.12,2.95\n2014-7-29,64,52,75,66,85,51,100,1881,1941,0.21,0.13,0.80\n2014-7-30,68,57,78,66,84,51,103,1965,1940,0.02,0.13,1.63\n2014-7-31,70,59,80,66,84,49,98,1895,1913,0.00,0.12,2.84\n2014-8-1,72,61,82,65,84,53,97,1971,1887,0.01,0.11,2.86\n2014-8-2,73,63,82,65,84,52,98,1927,1888,0.0,0.11,2.53\n2014-8-3,73,61,84,65,84,50,98,1965,1897,0.00,0.11,1.72\n2014-8-4,74,62,85,65,84,49,99,1912,1887,0.0,0.10,3.81\n2014-8-5,73,65,81,65,84,51,103,1974,1918,0.00,0.10,1.65\n2014-8-6,71,63,79,65,84,50,99,1994,1918,0.00,0.11,4.46\n2014-8-7,71,64,78,65,84,49,98,1989,1930,0.00,0.10,1.08\n2014-8-8,70,65,74,65,84,48,98,1989,2012,0.03,0.09,1.51\n2014-8-9,76,68,83,65,84,50,99,1884,1930,0.0,0.10,2.36\n2014-8-10,77,70,84,65,84,50,99,1972,1911,0.13,0.10,1.25\n2014-8-11,75,68,82,65,84,52,99,1967,1941,0.10,0.09,2.07\n2014-8-12,65,58,71,65,84,47,101,2004,1881,0.0,0.10,1.93\n2014-8-13,67,55,79,65,84,48,98,1964,1936,0.00,0.09,3.08\n2014-8-14,70,59,81,65,84,48,95,1964,1871,0.0,0.09,2.72\n2014-8-15,63,49,77,65,84,45,96,1979,2010,0.00,0.10,2.26\n2014-8-16,67,59,75,65,84,50,102,1979,1988,0.09,0.10,1.43\n2014-8-17,73,65,81,65,84,52,99,1963,1988,0.03,0.11,3.18\n2014-8-18,76,65,86,65,84,48,98,1963,1936,0.00,0.11,1.61\n2014-8-19,76,65,86,64,84,50,100,1896,1936,0.50,0.10,1.65\n2014-8-20,74,64,83,64,84,50,99,1897,1983,0.0,0.11,2.22\n2014-8-21,78,70,86,64,84,47,100,1950,1936,0.16,0.10,1.23\n2014-8-22,80,73,86,64,84,46,101,1950,1936,0.02,0.10,1.90\n2014-8-23,79,70,88,64,84,46,96,1890,1936,0.81,0.10,2.07\n2014-8-24,78,70,86,64,84,49,98,1927,1936,0.00,0.09,1.75\n2014-8-25,81,71,90,64,84,49,96,1887,1948,0.78,0.10,1.41\n2014-8-26,81,69,93,63,84,48,97,1945,1948,0.26,0.11,1.90\n2014-8-27,79,70,88,63,84,48,97,1968,1948,0.00,0.10,2.60\n2014-8-28,75,66,84,63,83,43,96,1986,1953,0.00,0.10,2.44\n2014-8-29,78,68,87,63,83,41,97,1965,1953,0.0,0.10,1.38\n2014-8-30,75,71,78,63,83,42,95,1986,1953,0.02,0.10,2.16\n2014-8-31,76,69,83,62,83,44,97,1915,1951,0.01,0.10,2.43\n2014-9-1,77,71,82,62,83,44,100,1987,1953,0.00,0.10,7.20\n2014-9-2,72,65,79,62,82,45,100,1885,1953,1.00,0.10,2.10\n2014-9-3,74,64,83,61,82,45,100,1952,2011,0.00,0.10,2.63\n2014-9-4,77,66,87,61,82,46,98,1952,1954,0.00,0.09,6.80\n2014-9-5,80,70,90,61,82,45,100,1974,1954,0.02,0.10,1.54\n2014-9-6,67,60,73,60,81,44,99,1988,1954,0.0,0.10,1.19\n2014-9-7,65,54,76,60,81,42,98,1988,1939,0.00,0.11,2.71\n2014-9-8,68,56,79,60,81,38,97,1986,1939,0.00,0.10,2.61\n2014-9-9,69,57,81,59,80,41,98,1883,1939,0.00,0.10,2.34\n2014-9-10,75,67,82,59,80,40,96,1969,2013,0.90,0.11,2.60\n2014-9-11,64,56,71,58,80,39,95,1917,1897,0.0,0.11,1.78\n2014-9-12,59,53,64,58,79,40,96,1955,1897,0.0,0.11,1.08\n2014-9-13,55,47,63,58,79,39,99,1975,1939,0.0,0.11,1.80\n2014-9-14,57,46,68,57,79,37,100,1964,1939,0.00,0.11,2.93\n2014-9-15,57,49,65,57,78,42,100,1974,1939,0.54,0.11,2.21\n2014-9-16,59,50,67,56,78,39,96,1984,1939,0.01,0.09,2.53\n2014-9-17,56,45,67,56,77,40,93,1984,1955,0.00,0.10,1.07\n2014-9-18,61,49,72,55,77,39,95,1959,1953,0.00,0.09,1.64\n2014-9-19,63,53,73,55,76,38,93,1956,1940,0.00,0.11,2.29\n2014-9-20,67,54,80,54,76,36,94,1991,1940,0.02,0.10,2.20\n2014-9-21,65,56,74,54,76,35,96,1875,2010,0.04,0.11,1.61\n2014-9-22,57,47,66,53,75,37,93,1999,1895,0.00,0.11,1.22\n2014-9-23,60,47,72,53,75,34,94,1974,2010,0.00,0.11,2.11\n2014-9-24,62,47,76,53,74,34,92,1887,2007,0.00,0.10,1.29\n2014-9-25,66,54,78,52,74,35,92,1950,1891,0.00,0.11,2.17\n2014-9-26,69,55,82,52,73,37,91,2001,1891,0.00,0.10,1.62\n2014-9-27,69,56,81,51,73,35,93,1991,1998,0.00,0.11,2.59\n2014-9-28,71,58,83,51,72,33,91,1942,1939,0.00,0.11,1.41\n2014-9-29,68,54,82,50,72,34,97,1970,1953,0.00,0.10,2.45\n2014-9-30,62,52,71,50,72,30,89,1899,1971,0.00,0.11,1.88\n2014-10-1,63,49,76,50,71,32,89,1946,1897,0.00,0.10,2.74\n2014-10-2,71,60,81,49,71,29,89,1974,1953,0.14,0.11,1.39\n2014-10-3,59,44,73,49,70,27,90,1974,1954,0.28,0.11,2.61\n2014-10-4,45,40,49,48,70,31,90,1987,1951,0.00,0.11,1.55\n2014-10-5,50,40,59,48,69,31,87,1968,1922,0.13,0.10,2.93\n2014-10-6,55,42,67,48,69,30,89,1980,2007,0.02,0.11,1.45\n2014-10-7,54,46,62,47,69,27,90,1952,2007,0.33,0.10,1.65\n2014-10-8,56,43,68,47,68,28,91,1987,2007,0.00,0.10,1.02\n2014-10-9,51,45,56,47,68,30,88,1989,1939,0.02,0.11,1.25\n2014-10-10,52,49,54,46,67,27,88,1964,2010,0.01,0.10,3.88\n2014-10-11,50,42,57,46,67,26,86,1906,2010,0.00,0.10,1.56\n2014-10-12,54,45,62,46,67,26,86,1988,1879,0.30,0.10,3.01\n2014-10-13,67,58,76,45,66,24,86,1988,1975,0.57,0.10,0.92\n2014-10-14,61,55,67,45,66,24,86,1979,1899,0.56,0.10,1.71\n2014-10-15,55,53,56,45,66,27,87,1876,1897,0.18,0.10,1.14\n2014-10-16,56,51,61,45,65,26,85,1991,1897,0.00,0.10,1.65\n2014-10-17,59,50,68,44,65,26,86,1948,1950,0.00,0.10,1.45\n2014-10-18,50,46,54,44,65,22,84,1948,1953,0.09,0.10,2.15\n2014-10-19,50,43,56,44,64,25,85,1992,1953,0.00,0.10,1.33\n2014-10-20,57,46,68,44,64,23,86,1972,1953,0.01,0.10,1.33\n2014-10-21,51,44,57,43,64,23,86,1952,1953,0.02,0.09,1.43\n2014-10-22,48,38,57,43,63,23,85,1976,1947,0.00,0.10,1.84\n2014-10-23,49,37,60,43,63,24,86,1969,1963,0.00,0.09,1.57\n2014-10-24,53,40,66,42,62,20,81,1981,1963,0.00,0.09,1.52\n2014-10-25,63,50,76,42,62,25,82,1887,1963,0.00,0.09,1.54\n2014-10-26,55,42,68,42,62,20,82,1962,1963,0.00,0.10,2.45\n2014-10-27,66,51,80,42,61,24,83,1976,1940,0.00,0.10,1.71\n2014-10-28,58,46,70,41,61,20,82,1976,1927,0.31,0.10,1.72\n2014-10-29,47,39,54,41,61,22,81,1952,1922,0.00,0.10,1.16\n2014-10-30,44,34,53,41,60,22,82,1895,1950,0.0,0.11,1.30\n2014-10-31,40,33,47,41,60,24,82,1988,1950,0.20,0.10,2.01\n2014-11-1,36,29,43,40,59,24,81,1949,1950,0.00,0.12,1.95\n2014-11-2,37,24,50,40,59,16,78,1951,1961,0.00,0.11,3.86\n2014-11-3,54,42,66,40,59,11,77,1951,1987,0.00,0.12,0.65\n2014-11-4,51,44,57,39,58,16,78,1951,2003,0.30,0.11,1.60\n2014-11-5,47,40,54,39,58,15,76,1991,1975,0.00,0.12,1.92\n2014-11-6,43,39,47,39,57,20,77,1982,1975,0.09,0.12,1.20\n2014-11-7,40,33,46,38,57,16,76,1971,1915,0.00,0.11,3.51\n2014-11-8,42,36,48,38,56,13,76,1991,1999,0.0,0.12,1.23\n2014-11-9,40,29,51,38,56,17,76,1991,1999,0.00,0.11,1.15\n2014-11-10,50,38,62,37,55,17,75,1957,1949,0.00,0.11,1.54\n2014-11-11,48,34,61,37,55,18,76,1995,1927,0.06,0.12,1.63\n2014-11-12,31,27,34,37,54,14,75,1911,1949,0.00,0.12,1.64\n2014-11-13,24,20,27,36,54,10,77,1986,1955,0.0,0.13,2.02\n2014-11-14,25,17,32,36,53,11,76,1986,1971,0.0,0.14,4.15\n2014-11-15,26,17,34,36,53,12,74,1933,1971,0.0,0.13,2.55\n2014-11-16,32,28,35,35,52,10,74,1883,1952,0.22,0.13,1.68\n2014-11-17,21,13,29,35,52,4,77,1959,1958,0.0,0.13,2.48\n2014-11-18,14,9,19,34,51,5,73,1880,1941,0.0,0.14,4.30\n2014-11-19,26,14,37,34,51,-3,76,1880,1930,0.0,0.13,2.74\n2014-11-20,24,17,30,33,50,8,75,1914,1942,0.0,0.13,1.73\n2014-11-21,23,12,33,33,49,2,73,1880,1990,0.00,0.14,3.71\n2014-11-22,43,30,56,33,49,-5,74,1880,1900,0.02,0.13,1.68\n2014-11-23,50,44,55,32,48,1,73,1880,1999,1.91,0.13,1.91\n2014-11-24,46,36,55,32,48,-1,65,1950,1973,0.18,0.13,1.23\n2014-11-25,31,26,36,31,47,-2,69,1950,1908,0.00,0.12,2.19\n2014-11-26,30,26,34,31,47,3,70,1898,1984,0.00,0.12,1.16\n2014-11-27,28,24,31,31,46,1,70,1930,1990,0.0,0.12,2.03\n2014-11-28,27,19,34,30,46,2,69,1930,1909,0.0,0.12,1.82\n2014-11-29,44,30,57,30,45,-1,70,1872,1927,0.00,0.12,1.00\n2014-11-30,52,39,64,29,45,-2,68,1958,1927,0.02,0.12,1.81\n2014-12-1,33,26,39,29,44,4,70,1886,1970,0.02,0.10,1.39\n2014-12-2,30,26,33,29,44,-3,74,1886,1982,0.01,0.11,1.84\n2014-12-3,36,28,43,28,43,-2,69,1929,1982,0.01,0.10,3.46\n2014-12-4,32,29,35,28,43,5,70,1991,1998,0.06,0.10,0.98\n2014-12-5,37,34,40,27,42,8,70,1886,2001,0.98,0.11,1.14\n2014-12-6,35,30,40,27,42,-1,73,1977,1998,0.26,0.10,1.05\n2014-12-7,33,26,39,27,41,-7,65,1882,1951,0.00,0.10,1.15\n2014-12-8,36,31,41,26,41,-10,66,1882,1966,0.06,0.11,1.58\n2014-12-9,36,32,39,26,41,-15,65,1876,1952,0.01,0.11,1.39\n2014-12-10,33,30,35,26,40,-8,66,1917,1971,0.0,0.10,1.37\n2014-12-11,31,25,37,25,40,-9,66,1977,1931,0.00,0.10,1.87\n2014-12-12,29,22,35,25,40,-7,63,1962,1927,0.00,0.10,1.06\n2014-12-13,40,34,46,25,39,-4,65,1962,1975,0.00,0.10,1.73\n2014-12-14,45,41,48,25,39,-4,68,1901,1984,0.0,0.09,2.12\n2014-12-15,43,41,44,24,39,-10,69,1951,1948,0.01,0.10,1.45\n2014-12-16,39,31,46,24,38,-15,65,1951,1948,0.19,0.10,0.81\n2014-12-17,27,23,31,24,38,-10,64,1989,1984,0.0,0.10,1.64\n2014-12-18,25,22,27,24,38,-10,65,1884,1877,0.0,0.09,2.05\n2014-12-19,24,19,29,23,38,-12,67,1884,1877,0.00,0.10,1.49\n2014-12-20,29,25,32,23,37,-12,66,1963,1949,0.00,0.10,0.82\n2014-12-21,28,20,36,23,37,-21,67,1989,1967,0.00,0.11,2.57\n2014-12-22,37,27,46,23,37,-23,61,1989,1949,0.04,0.11,1.62\n2014-12-23,50,45,55,23,37,-15,64,1989,1933,0.17,0.11,1.71\n2014-12-24,40,34,45,22,37,-17,68,1983,1889,0.52,0.10,1.54\n2014-12-25,36,31,41,22,36,-15,64,1983,1893,0.01,0.11,1.36\n2014-12-26,41,30,52,22,36,-5,67,1983,1875,0.00,0.11,1.22\n2014-12-27,40,33,46,22,36,-7,68,1925,2008,0.20,0.10,1.95\n2014-12-28,28,22,33,22,36,-13,65,1924,1984,0.0,0.10,2.02\n2014-12-29,29,20,37,22,36,-13,66,1880,1889,0.00,0.10,1.36\n2014-12-30,23,15,31,21,36,-14,67,1983,1965,0.00,0.10,1.94\n2014-12-31,16,9,23,21,36,-9,68,1967,1951,0.00,0.10,1.62\n2015-1-1,26,18,34,21,36,-12,69,1968,1876,0.00,0.10,3.81\n2015-1-2,32,25,39,21,36,-12,65,1887,2006,0.00,0.10,1.58\n2015-1-3,43,33,52,21,36,-22,65,1879,1950,0.79,0.10,2.82\n2015-1-4,28,12,44,21,36,-15,66,1879,1998,0.01,0.10,2.44\n2015-1-5,9,4,14,21,35,-25,61,1884,1955,0.11,0.10,2.70\n2015-1-6,14,9,19,21,35,-20,63,1884,2008,0.08,0.09,1.48\n2015-1-7,3,-6,12,21,35,-17,68,1970,2008,0.0,0.09,1.58\n2015-1-8,7,-7,20,21,35,-13,63,1970,1965,0.0,0.10,1.41\n2015-1-9,11,1,21,21,35,-18,66,1875,1880,0.0,0.09,1.15\n2015-1-10,11,-3,24,20,35,-16,62,1982,1975,0.00,0.09,1.29\n2015-1-11,28,21,34,20,35,-16,70,1977,1890,0.43,0.08,1.16\n2015-1-12,25,19,31,20,35,-19,69,1918,1890,0.19,0.09,1.28\n2015-1-13,16,8,23,20,35,-10,64,1997,1950,0.0,0.09,1.47\n2015-1-14,13,5,21,20,35,-7,65,1957,1928,0.0,0.08,1.77\n2015-1-15,25,13,36,20,35,-17,66,1972,1932,0.00,0.08,1.68\n2015-1-16,32,21,42,20,35,-20,66,1972,1990,0.00,0.08,1.42\n2015-1-17,39,27,51,20,35,-20,67,1977,1952,0.0,0.09,1.11\n2015-1-18,36,28,44,20,35,-22,64,1994,1929,0.01,0.08,1.96\n2015-1-19,35,23,47,20,35,-27,69,1994,1907,0.00,0.08,0.94\n2015-1-20,40,30,50,20,36,-22,70,1985,1906,0.0,0.08,1.26\n2015-1-21,37,30,43,20,36,-21,68,1984,1906,0.0,0.08,2.25\n2015-1-22,31,28,33,20,36,-17,64,1936,1964,0.0,0.07,1.87\n2015-1-23,29,24,34,20,36,-18,67,1936,1909,0.00,0.08,0.90\n2015-1-24,30,20,39,20,36,-16,70,1963,1967,0.00,0.08,1.06\n2015-1-25,29,20,37,20,36,-14,71,1897,1950,0.04,0.08,0.92\n2015-1-26,22,17,26,20,36,-12,67,1897,1944,0.0,0.08,1.41\n2015-1-27,25,20,29,21,36,-10,66,1936,1916,0.01,0.08,1.14\n2015-1-28,25,15,34,21,36,-18,65,1963,2002,0.00,0.08,2.00\n2015-1-29,38,30,45,21,36,-13,66,1873,2002,0.0,0.08,1.50\n2015-1-30,26,20,31,21,36,-11,65,1966,2013,0.01,0.08,1.47\n2015-1-31,26,16,36,21,37,-11,66,2004,1917,0.13,0.08,1.26\n2015-2-1,30,27,33,21,37,-11,63,1951,1911,0.29,0.08,1.21\n2015-2-2,20,13,27,21,37,-19,59,1951,1911,0.01,0.09,1.24\n2015-2-3,26,16,36,21,37,-11,62,1996,1992,0.00,0.08,2.50\n2015-2-4,29,15,42,21,37,-12,69,1996,1890,0.07,0.08,1.68\n2015-2-5,15,5,24,22,37,-16,66,1979,1927,0.00,0.09,1.83\n2015-2-6,25,12,37,22,38,-12,64,1977,1925,0.00,0.07,1.14\n2015-2-7,42,28,55,22,38,-11,66,1977,1925,0.00,0.08,1.53\n2015-2-8,47,36,57,22,38,-14,68,1895,1937,0.0,0.08,0.78\n2015-2-9,31,26,36,22,38,-18,66,1899,1943,0.0,0.08,2.04\n2015-2-10,27,18,36,23,39,-21,73,1982,1932,0.00,0.09,1.56\n2015-2-11,32,25,39,23,39,-9,75,1885,1999,0.0,0.08,1.87\n2015-2-12,20,13,27,23,39,-10,66,1899,1984,0.0,0.08,2.67\n2015-2-13,20,9,31,23,39,-17,67,1899,1938,0.00,0.08,1.48\n2015-2-14,18,6,30,24,40,-14,65,1905,1954,0.0,0.09,1.37\n2015-2-15,12,4,20,24,40,-8,72,1875,1954,0.00,0.08,1.50\n2015-2-16,11,6,16,24,40,-7,72,1943,1883,0.14,0.08,1.58\n2015-2-17,14,1,26,24,41,-9,69,1903,1911,0.01,0.08,0.91\n2015-2-18,9,0,17,25,41,-11,66,1936,1961,0.07,0.08,1.58\n2015-2-19,2,-5,9,25,41,-8,69,1979,1930,0.00,0.08,1.21\n2015-2-20,6,-6,18,25,42,-8,67,1885,1983,0.0,0.08,3.02\n2015-2-21,26,16,35,26,42,-4,68,1963,1930,0.47,0.08,1.53\n2015-2-22,22,15,29,26,43,-9,70,1963,1922,0.0,0.08,2.09\n2015-2-23,11,3,19,26,43,-1,71,1889,2000,0.01,0.09,1.10\n2015-2-24,10,-6,25,26,43,-6,71,2015,2000,0.00,0.08,1.05\n2015-2-25,24,14,33,27,44,-7,76,1900,2000,0.00,0.09,2.35\n2015-2-26,16,7,25,27,44,-6,68,1963,1998,0.04,0.09,2.49\n2015-2-27,10,2,18,27,45,-7,73,1963,1996,0.00,0.09,1.11\n2015-2-28,16,7,25,28,45,2,68,1993,1895,0.23,0.09,1.17\n2015-3-1,27,22,31,28,45,-4,71,1960,1976,0.53,0.10,1.90\n2015-3-2,24,14,34,28,46,-7,74,1980,1992,0.00,0.09,1.56\n2015-3-3,33,26,40,29,46,-5,77,1943,1992,0.28,0.09,2.01\n2015-3-4,27,19,35,29,47,2,78,1873,1983,0.07,0.10,3.00\n2015-3-5,16,8,23,29,47,-1,75,1978,1956,0.00,0.10,3.06\n2015-3-6,16,2,30,30,47,-6,75,1960,1973,0.00,0.11,1.21\n2015-3-7,32,21,42,30,48,-1,78,1943,1974,0.00,0.11,1.45\n2015-3-8,38,27,49,30,48,-6,80,1943,1974,0.00,0.10,2.28\n2015-3-9,40,31,48,31,49,-1,72,1984,1878,0.00,0.11,1.61\n2015-3-10,45,40,49,31,49,8,74,1934,2009,0.40,0.11,2.54\n2015-3-11,49,34,64,31,50,3,77,1948,1990,0.00,0.10,2.01\n2015-3-12,46,31,60,32,50,-5,76,1948,1990,0.00,0.11,1.97\n2015-3-13,48,42,53,32,51,3,80,1960,2007,0.51,0.11,1.51\n2015-3-14,51,43,59,32,51,10,81,1891,2012,0.43,0.11,3.02\n2015-3-15,49,33,65,33,51,6,80,1975,2012,0.00,0.12,3.06\n2015-3-16,61,47,74,33,52,3,79,1900,1945,0.00,0.11,1.36\n2015-3-17,47,33,60,33,52,2,77,1900,2012,0.00,0.12,1.35\n2015-3-18,37,27,46,34,53,7,80,1941,2012,0.00,0.13,2.03\n2015-3-19,45,40,50,34,53,7,82,1923,2012,0.02,0.12,1.90\n2015-3-20,44,39,48,34,54,3,83,1885,2012,0.03,0.12,2.13\n2015-3-21,49,34,63,34,54,11,84,1876,2012,0.00,0.12,1.06\n2015-3-22,44,36,52,35,54,9,82,1888,1907,0.00,0.13,2.36\n2015-3-23,36,30,41,35,55,12,80,1885,1939,0.03,0.12,1.48\n2015-3-24,35,26,44,35,55,2,84,1974,1910,0.04,0.13,2.76\n2015-3-25,48,42,54,36,56,5,81,1974,2007,0.63,0.13,4.70\n2015-3-26,40,34,45,36,56,8,80,1955,1907,0.50,0.12,1.98\n2015-3-27,28,21,35,36,56,11,84,1955,1910,0.0,0.13,2.27\n2015-3-28,29,17,40,37,57,16,82,1934,1910,0.00,0.12,1.75\n2015-3-29,38,24,51,37,57,16,82,1887,1895,0.06,0.13,1.46\n2015-3-30,49,36,61,37,58,17,82,1923,1986,0.00,0.13,1.56\n2015-3-31,54,44,63,38,58,13,85,1923,1981,0.00,0.13,1.56\n2015-4-1,53,37,69,38,58,20,82,1923,2010,0.00,0.13,2.85\n2015-4-2,62,52,72,38,59,20,81,1961,2010,0.29,0.12,2.08\n2015-4-3,50,39,61,39,59,21,84,1879,2012,0.11,0.11,1.92\n2015-4-4,43,30,55,39,59,23,80,1975,1929,0.00,0.12,1.48\n2015-4-5,49,34,64,39,60,20,84,1995,1988,0.00,0.12,1.87\n2015-4-6,55,47,62,40,60,22,83,1979,2010,0.0,0.12,1.90\n2015-4-7,61,56,66,40,61,19,83,1982,2001,0.32,0.12,2.08\n2015-4-8,64,55,72,40,61,18,83,1972,2001,0.74,0.13,2.15\n2015-4-9,71,61,80,41,61,18,84,1997,2001,0.24,0.13,1.26\n2015-4-10,56,42,69,41,62,23,84,1989,1930,0.04,0.13,1.97\n2015-4-11,50,38,62,41,62,24,89,1973,1930,0.00,0.12,1.72\n2015-4-12,57,43,70,42,62,20,86,1940,1930,0.00,0.12,0.80\n2015-4-13,64,56,71,42,63,18,86,1950,1941,0.04,0.12,1.13\n2015-4-14,58,51,64,42,63,24,85,1943,1883,0.00,0.13,1.55\n2015-4-15,58,46,69,43,63,24,84,1928,2002,0.07,0.12,1.42\n2015-4-16,61,53,68,43,64,21,87,1875,1896,0.20,0.12,2.08\n2015-4-17,61,46,76,43,64,19,87,1875,1896,0.00,0.13,1.90\n2015-4-18,64,51,76,44,64,19,87,1875,1977,0.00,0.13,2.29\n2015-4-19,62,56,67,44,65,23,85,1983,2002,0.93,0.13,3.06\n2015-4-20,52,45,58,44,65,23,85,1897,1915,0.30,0.12,2.66\n2015-4-21,52,41,62,45,65,28,84,1907,1985,0.00,0.13,1.85\n2015-4-22,44,37,50,45,66,27,85,1936,1960,0.10,0.13,2.34\n2015-4-23,45,32,57,45,66,26,88,1986,1925,0.00,0.13,1.46\n2015-4-24,47,32,62,46,66,28,90,1910,1925,0.0,0.13,1.65\n2015-4-25,50,44,56,46,67,27,87,1919,1915,0.62,0.14,2.56\n2015-4-26,49,39,58,46,67,31,87,1976,1915,0.00,0.13,1.48\n2015-4-27,47,35,58,47,67,30,84,1976,1962,0.00,0.13,1.56\n2015-4-28,51,39,63,47,68,28,86,1976,1894,0.00,0.14,1.71\n2015-4-29,54,41,67,47,68,31,86,1976,1942,0.00,0.14,1.71\n2015-4-30,53,44,61,48,68,31,90,1948,1942,0.0,0.14,1.66\n2015-5-1,55,39,71,48,68,29,89,1963,1942,0.00,0.16,1.76\n2015-5-2,62,49,75,48,69,31,87,1961,1959,0.00,0.15,2.93\n2015-5-3,66,53,78,49,69,29,86,2005,1949,0.0,0.16,1.56\n2015-5-4,67,58,76,49,69,30,88,1976,1949,0.13,0.16,1.47\n2015-5-5,73,62,83,49,70,35,92,1957,1952,0.00,0.16,1.79\n2015-5-6,74,61,86,49,70,31,89,1968,1949,0.00,0.16,2.56\n2015-5-7,75,64,86,50,70,31,87,1974,1926,0.00,0.16,2.10\n2015-5-8,74,63,85,50,70,31,90,1976,1896,0.0,0.16,2.34\n2015-5-9,73,66,80,50,71,29,93,1947,1895,0.05,0.16,1.08\n2015-5-10,74,64,83,51,71,28,92,1966,1896,0.05,0.16,1.92\n2015-5-11,70,61,78,51,71,31,89,1945,1896,0.06,0.17,1.95\n2015-5-12,57,50,64,51,72,33,88,1976,1881,0.00,0.17,2.15\n2015-5-13,55,43,67,52,72,35,88,1878,1956,0.00,0.18,1.96\n2015-5-14,60,49,70,52,72,33,88,1895,1881,0.0,0.17,1.84\n2015-5-15,71,60,82,52,72,35,88,1973,2001,0.12,0.16,1.97\n2015-5-16,68,64,71,53,73,32,90,1997,1900,0.53,0.17,1.60\n2015-5-17,72,64,79,53,73,36,90,1973,1962,0.00,0.17,2.30\n2015-5-18,71,62,79,53,73,35,90,1973,1962,0.0,0.17,2.36\n2015-5-19,57,48,65,54,74,32,90,2002,1977,0.00,0.17,1.36\n2015-5-20,48,43,52,54,74,38,90,1894,1977,0.0,0.16,1.68\n2015-5-21,56,47,64,54,74,36,92,2002,1941,0.00,0.18,2.69\n2015-5-22,60,44,76,55,75,35,93,1883,1941,0.00,0.16,2.16\n2015-5-23,67,55,79,55,75,37,92,1963,1939,0.00,0.16,3.13\n2015-5-24,72,59,85,55,75,37,90,1925,1965,0.00,0.17,1.55\n2015-5-25,73,67,79,56,76,34,93,1925,1965,0.01,0.16,1.35\n2015-5-26,72,64,80,56,76,35,94,1961,1911,0.07,0.16,2.33\n2015-5-27,73,63,82,56,76,31,94,1961,1911,0.07,0.15,1.62\n2015-5-28,72,60,84,57,76,39,96,1907,1911,0.00,0.16,1.50\n2015-5-29,75,65,84,57,77,36,93,1965,1895,0.0,0.16,2.19\n2015-5-30,75,66,84,57,77,37,94,1947,1895,1.28,0.15,3.80\n2015-5-31,61,55,67,58,77,36,96,1889,1895,0.08,0.16,2.61\n2015-6-1,58,52,64,58,78,39,96,2003,1934,0.0,0.14,1.95\n2015-6-2,60,49,70,58,78,39,100,1956,1934,0.00,0.15,3.00\n2015-6-3,61,51,71,59,78,40,100,1929,1895,0.00,0.14,2.89\n2015-6-4,70,57,82,59,79,43,95,1946,2011,0.00,0.14,2.87\n2015-6-5,75,63,87,59,79,40,98,1945,1925,0.00,0.15,1.51\n2015-6-6,69,61,76,60,79,39,96,1894,1933,0.00,0.15,2.62\n2015-6-7,75,62,88,60,80,41,98,1894,1933,0.00,0.14,3.80\n2015-6-8,70,60,79,60,80,44,95,1913,1933,1.90,0.15,2.14\n2015-6-9,70,58,82,60,80,41,94,1885,1914,0.00,0.14,2.11\n2015-6-10,78,65,90,61,81,44,96,1988,1933,0.00,0.14,2.21\n2015-6-11,80,69,90,61,81,41,96,1980,1933,0.00,0.14,2.90\n2015-6-12,81,72,89,61,81,42,96,1903,1894,0.21,0.13,3.04\n2015-6-13,80,71,88,62,82,45,95,1903,1894,0.00,0.14,2.20\n2015-6-14,78,70,86,62,82,45,94,1955,1954,0.13,0.14,2.12\n2015-6-15,79,70,88,62,82,45,94,1917,1952,0.54,0.13,1.82\n2015-6-16,78,71,84,63,82,41,96,1917,1913,0.50,0.13,1.98\n2015-6-17,76,72,79,63,83,46,95,1980,1913,0.75,0.14,1.73\n2015-6-18,77,68,86,63,83,45,96,1960,1962,0.67,0.14,2.05\n2015-6-19,73,68,78,63,83,48,97,1980,1953,0.67,0.14,1.75\n2015-6-20,75,70,79,64,83,44,100,1980,1953,0.0,0.14,2.03\n2015-6-21,75,65,84,64,84,41,98,1992,1988,0.27,0.14,1.95\n2015-6-22,78,69,86,64,84,37,99,1992,1988,0.00,0.15,1.92\n2015-6-23,76,68,83,64,84,44,96,1918,1930,0.0,0.14,2.81\n2015-6-24,71,62,80,64,84,49,98,1887,1933,0.0,0.15,0.92\n2015-6-25,75,66,83,65,84,46,102,1972,1988,0.85,0.15,3.73\n2015-6-26,71,66,76,65,85,49,102,1979,1954,1.57,0.14,3.09\n2015-6-27,66,60,72,65,85,48,100,1926,1934,0.0,0.14,2.79\n2015-6-28,67,55,78,65,85,50,104,1950,2012,0.09,0.15,1.65\n2015-6-29,70,63,77,65,85,48,103,1923,2012,0.21,0.14,1.63\n2015-6-30,73,64,82,65,85,46,97,1943,1933,0.0,0.14,1.50\n"
  },
  {
    "path": "us-weather-history/KJAX.csv",
    "content": "date,actual_mean_temp,actual_min_temp,actual_max_temp,average_min_temp,average_max_temp,record_min_temp,record_max_temp,record_min_temp_year,record_max_temp_year,actual_precipitation,average_precipitation,record_precipitation\n2014-7-1,82,72,91,72,91,65,100,1981,1990,0.00,0.21,7.26\n2014-7-2,81,73,89,72,91,65,100,2000,1902,0.00,0.21,2.30\n2014-7-3,84,71,96,72,92,63,99,2000,1970,0.41,0.22,4.87\n2014-7-4,81,70,91,72,92,65,99,2000,1954,0.00,0.21,1.40\n2014-7-5,82,73,90,72,92,66,100,2000,1877,0.01,0.21,1.85\n2014-7-6,79,70,88,72,92,66,100,1986,1969,0.00,0.21,3.49\n2014-7-7,81,70,92,72,92,65,101,1988,1902,0.0,0.21,2.07\n2014-7-8,82,72,91,72,92,61,100,1972,1879,0.00,0.21,3.78\n2014-7-9,83,71,94,72,92,63,101,2006,1941,0.00,0.22,2.07\n2014-7-10,82,73,91,72,92,65,102,2000,1879,0.31,0.21,1.45\n2014-7-11,81,72,89,73,92,65,104,1963,1879,0.27,0.21,3.14\n2014-7-12,76,72,79,73,92,66,102,1890,1879,0.38,0.20,3.97\n2014-7-13,80,71,88,73,92,66,102,1974,1980,0.02,0.21,2.35\n2014-7-14,83,73,92,73,92,63,102,1974,1951,0.00,0.20,2.11\n2014-7-15,82,72,91,73,92,62,102,1974,1981,0.0,0.20,3.16\n2014-7-16,81,72,89,73,92,67,102,2001,1981,0.62,0.20,3.66\n2014-7-17,79,69,89,73,92,64,103,1980,1981,0.00,0.21,2.20\n2014-7-18,79,71,87,73,92,68,100,2011,1887,0.71,0.21,1.10\n2014-7-19,82,74,89,73,92,65,101,2011,2000,0.00,0.22,2.17\n2014-7-20,83,73,92,73,92,68,103,2006,2000,0.0,0.22,3.17\n2014-7-21,81,73,88,73,92,66,102,1929,1942,0.00,0.23,1.80\n2014-7-22,81,72,89,73,92,67,101,1965,1942,0.00,0.22,2.50\n2014-7-23,83,72,93,73,92,64,99,2007,1952,0.00,0.22,3.19\n2014-7-24,83,72,93,73,92,68,100,1977,1952,0.00,0.23,2.32\n2014-7-25,84,74,94,73,92,64,101,1977,1875,0.02,0.22,2.14\n2014-7-26,83,73,92,73,92,67,102,2000,1926,0.0,0.22,2.94\n2014-7-27,86,76,95,73,92,67,100,1978,1872,0.00,0.20,3.01\n2014-7-28,87,78,96,73,92,67,104,1920,1872,0.00,0.20,4.46\n2014-7-29,85,75,94,73,92,69,100,2000,1875,0.00,0.20,2.88\n2014-7-30,81,70,92,73,92,68,102,1894,2010,0.00,0.20,1.74\n2014-7-31,81,69,93,73,92,69,102,1914,1999,0.00,0.21,2.03\n2014-8-1,84,74,93,73,92,68,102,1966,1999,0.00,0.21,3.73\n2014-8-2,82,73,91,73,92,68,101,1997,1999,0.59,0.20,3.38\n2014-8-3,81,73,89,73,92,69,99,1974,1957,0.70,0.21,2.72\n2014-8-4,80,75,85,73,92,68,99,2001,2011,0.01,0.19,4.02\n2014-8-5,83,74,92,73,92,69,99,1989,2011,0.00,0.20,3.23\n2014-8-6,84,74,94,73,92,67,99,1969,1956,0.00,0.19,3.35\n2014-8-7,85,74,95,73,92,66,100,1950,1956,0.00,0.20,3.30\n2014-8-8,84,74,94,73,92,64,98,2002,2007,0.0,0.19,2.66\n2014-8-9,84,73,95,73,92,63,101,2002,1987,0.00,0.20,2.04\n2014-8-10,83,74,92,73,92,66,99,1918,1987,0.0,0.21,1.92\n2014-8-11,82,72,92,73,91,68,98,2002,2011,0.70,0.21,1.46\n2014-8-12,82,75,89,73,91,65,100,1989,1874,0.01,0.20,3.15\n2014-8-13,80,74,86,73,91,64,99,1890,1954,0.08,0.21,2.34\n2014-8-14,82,73,91,73,91,66,97,1890,1938,0.04,0.21,3.62\n2014-8-15,81,73,89,73,91,67,99,1983,1956,0.00,0.21,3.06\n2014-8-16,83,73,93,73,91,67,97,1954,1957,0.00,0.20,3.83\n2014-8-17,83,73,93,73,91,67,99,1993,1954,0.25,0.20,1.40\n2014-8-18,84,74,93,73,91,67,98,1945,1954,0.02,0.21,3.26\n2014-8-19,84,73,94,73,91,64,99,1889,1900,0.0,0.21,3.22\n2014-8-20,84,73,95,73,91,68,101,1960,1900,0.00,0.22,4.44\n2014-8-21,85,73,97,73,91,66,101,1961,1901,0.00,0.22,3.83\n2014-8-22,86,73,99,73,90,64,100,1930,1900,0.00,0.22,6.80\n2014-8-23,87,75,98,73,90,65,98,1886,1966,0.0,0.24,2.51\n2014-8-24,82,73,91,73,90,66,99,1996,1987,0.0,0.25,4.46\n2014-8-25,80,74,86,72,90,65,98,1997,1968,0.49,0.25,3.63\n2014-8-26,78,70,85,72,90,64,98,1997,1872,0.14,0.26,3.74\n2014-8-27,77,66,88,72,90,64,99,1984,2011,0.00,0.26,3.42\n2014-8-28,77,64,90,72,90,64,97,2014,1959,0.00,0.25,7.82\n2014-8-29,80,67,92,72,90,63,99,1984,1990,0.00,0.26,2.73\n2014-8-30,82,72,91,72,90,64,98,1896,1948,0.00,0.25,3.88\n2014-8-31,82,71,93,72,89,64,98,1999,1970,0.00,0.26,2.93\n2014-9-1,84,73,94,72,89,62,98,1999,1912,0.00,0.28,6.40\n2014-9-2,83,73,92,72,89,57,98,1999,1970,0.10,0.29,4.13\n2014-9-3,81,70,91,72,89,64,99,1972,1912,0.00,0.29,1.81\n2014-9-4,79,70,88,72,89,66,97,1972,1951,0.0,0.29,4.08\n2014-9-5,79,70,88,72,89,63,98,1997,1999,1.27,0.29,5.56\n2014-9-6,80,73,86,71,89,61,98,1997,1875,0.58,0.29,5.92\n2014-9-7,76,73,78,71,88,63,96,1997,1951,1.31,0.29,3.85\n2014-9-8,80,71,88,71,88,61,98,2011,1990,1.42,0.28,3.11\n2014-9-9,79,71,86,71,88,62,97,2011,1990,0.55,0.27,6.10\n2014-9-10,80,71,88,71,88,62,97,1956,1945,0.0,0.27,6.01\n2014-9-11,80,70,90,71,88,62,96,1998,1954,0.00,0.27,4.12\n2014-9-12,81,71,90,71,88,61,96,1917,1991,0.00,0.28,5.43\n2014-9-13,82,72,91,70,88,61,97,1917,1991,0.00,0.28,2.96\n2014-9-14,82,72,91,70,87,62,96,1956,1991,0.00,0.29,1.90\n2014-9-15,82,72,92,70,87,62,96,1996,1972,0.12,0.29,3.85\n2014-9-16,77,72,82,70,87,57,97,2001,1990,0.74,0.28,6.61\n2014-9-17,80,71,88,70,87,60,96,2001,1896,0.00,0.28,3.82\n2014-9-18,81,69,92,69,87,58,95,1981,1972,0.00,0.28,3.02\n2014-9-19,77,70,84,69,86,48,96,1981,1942,0.37,0.29,2.84\n2014-9-20,73,68,78,69,86,48,95,1981,1925,0.23,0.28,4.68\n2014-9-21,76,64,87,69,86,49,97,1897,1990,0.00,0.26,5.27\n2014-9-22,76,62,89,68,86,49,96,1897,1990,0.01,0.26,4.24\n2014-9-23,75,69,81,68,86,53,95,1897,1925,0.05,0.26,5.10\n2014-9-24,68,63,72,68,85,53,94,1990,1925,0.00,0.26,4.46\n2014-9-25,71,63,78,68,85,51,94,1990,1961,0.00,0.26,4.04\n2014-9-26,74,66,82,67,85,55,96,1940,1961,0.00,0.25,9.82\n2014-9-27,75,71,79,67,85,54,94,1940,1970,0.00,0.26,5.26\n2014-9-28,80,73,86,67,85,56,96,1909,1900,0.00,0.24,4.80\n2014-9-29,76,72,80,66,84,53,92,1993,1936,3.05,0.24,4.73\n2014-9-30,74,70,77,66,84,50,94,1967,1959,0.00,0.24,3.25\n2014-10-1,76,70,82,66,84,48,94,1920,1958,0.00,0.21,5.14\n2014-10-2,78,70,85,65,84,43,93,1876,1977,0.0,0.20,3.57\n2014-10-3,78,68,88,65,83,45,94,1876,1911,0.61,0.20,7.83\n2014-10-4,68,56,80,65,83,47,94,2011,1990,0.15,0.19,2.24\n2014-10-5,60,47,73,64,83,46,94,1987,1951,0.00,0.18,4.51\n2014-10-6,64,50,78,64,83,49,95,1979,1951,0.00,0.17,3.48\n2014-10-7,72,60,84,64,82,47,95,2010,1951,0.00,0.18,6.14\n2014-10-8,76,63,89,63,82,44,95,1987,1949,0.00,0.17,3.91\n2014-10-9,76,64,88,63,82,46,94,2000,1941,0.00,0.17,1.80\n2014-10-10,76,65,87,63,82,46,93,2000,2009,0.00,0.16,3.56\n2014-10-11,75,62,88,62,82,45,91,1906,1959,0.00,0.16,4.30\n2014-10-12,74,62,86,62,81,46,92,1971,2009,0.00,0.15,2.31\n2014-10-13,76,66,86,62,81,43,89,1977,1986,0.03,0.14,1.82\n2014-10-14,79,70,88,61,81,40,91,1977,1912,1.04,0.12,1.99\n2014-10-15,69,59,78,61,81,40,90,1977,1990,0.04,0.13,2.24\n2014-10-16,66,53,78,61,80,39,90,1978,1925,0.00,0.12,6.47\n2014-10-17,66,52,80,60,80,37,91,1977,1989,0.00,0.11,3.86\n2014-10-18,69,54,84,60,80,36,91,1977,1980,0.00,0.11,7.47\n2014-10-19,67,57,76,59,80,43,91,1981,1980,0.00,0.10,9.21\n2014-10-20,69,56,82,59,80,40,89,1981,2006,0.00,0.09,3.87\n2014-10-21,71,59,83,59,79,36,89,1989,1993,0.00,0.09,3.18\n2014-10-22,63,52,74,58,79,39,92,1976,2006,0.00,0.09,5.26\n2014-10-23,61,49,73,58,79,40,88,1976,2007,0.00,0.08,3.60\n2014-10-24,61,46,75,58,79,37,87,2006,1949,0.00,0.09,4.69\n2014-10-25,62,47,77,57,78,38,89,2006,2010,0.00,0.08,4.76\n2014-10-26,65,47,83,57,78,38,89,1980,2010,0.00,0.07,1.79\n2014-10-27,71,54,87,57,78,40,90,1926,2010,0.00,0.07,2.64\n2014-10-28,70,55,84,56,78,37,89,2008,2010,0.00,0.08,1.93\n2014-10-29,72,58,85,56,78,33,87,2008,1984,0.00,0.07,1.76\n2014-10-30,70,59,81,56,77,37,86,1910,1951,0.00,0.08,3.97\n2014-10-31,63,50,75,55,77,38,86,1993,1896,0.00,0.07,2.84\n2014-11-1,50,42,57,55,77,33,88,1993,1961,0.00,0.07,2.75\n2014-11-2,50,37,62,55,77,35,87,1993,1961,0.00,0.08,1.37\n2014-11-3,52,36,68,54,76,30,86,1954,2004,0.00,0.07,2.50\n2014-11-4,61,47,74,54,76,31,87,1966,1992,0.00,0.08,2.68\n2014-11-5,67,56,78,54,76,35,87,1976,2003,0.0,0.07,1.51\n2014-11-6,69,56,82,53,76,32,86,1976,2003,0.00,0.08,1.92\n2014-11-7,55,43,67,53,76,36,87,1976,1986,0.00,0.07,1.64\n2014-11-8,56,41,70,53,75,30,85,1976,1986,0.0,0.08,1.86\n2014-11-9,62,54,70,53,75,25,88,1976,1986,0.0,0.08,1.28\n2014-11-10,59,52,65,52,75,33,84,1976,1948,0.00,0.07,2.29\n2014-11-11,66,52,80,52,75,35,87,1977,1986,0.00,0.07,2.79\n2014-11-12,61,45,77,52,74,31,87,2011,1986,0.00,0.07,1.30\n2014-11-13,64,55,73,52,74,31,86,1987,1889,0.00,0.07,0.86\n2014-11-14,53,44,61,51,74,31,84,1984,2008,0.00,0.07,2.21\n2014-11-15,50,40,60,51,74,29,85,1969,1958,0.00,0.07,1.50\n2014-11-16,62,47,77,51,73,25,85,1940,2011,0.00,0.07,2.10\n2014-11-17,67,52,81,50,73,29,85,1970,1957,0.95,0.06,1.28\n2014-11-18,43,33,52,50,73,32,85,2008,1938,0.0,0.07,1.26\n2014-11-19,40,27,53,50,73,27,84,2014,1948,0.00,0.07,1.21\n2014-11-20,45,24,65,50,73,24,86,2014,1988,0.00,0.06,2.17\n2014-11-21,51,33,68,49,72,26,84,1937,1962,0.00,0.06,1.16\n2014-11-22,62,51,72,49,72,25,84,2000,1948,0.0,0.06,2.08\n2014-11-23,73,68,78,49,72,28,84,2000,1992,1.11,0.06,2.01\n2014-11-24,78,70,86,49,72,25,86,1970,2014,0.00,0.06,1.21\n2014-11-25,66,61,71,48,71,21,85,1970,1992,1.69,0.07,1.69\n2014-11-26,52,41,63,48,71,27,84,1950,1946,0.21,0.06,1.48\n2014-11-27,51,40,61,48,71,30,83,1903,1973,0.00,0.07,0.94\n2014-11-28,44,35,53,48,70,26,85,1903,1948,0.00,0.08,3.61\n2014-11-29,51,34,68,47,70,30,85,1959,1978,0.00,0.08,2.24\n2014-11-30,58,41,74,47,70,26,84,1959,1982,0.00,0.08,1.81\n2014-12-1,63,49,77,47,70,28,84,1979,1981,0.00,0.08,3.43\n2014-12-2,66,52,79,47,69,25,84,1876,1991,0.00,0.09,1.12\n2014-12-3,66,56,76,47,69,24,83,1876,1991,0.00,0.09,2.70\n2014-12-4,65,54,76,46,69,25,84,1876,1994,0.00,0.09,3.81\n2014-12-5,63,60,65,46,69,25,83,1876,2013,0.00,0.09,1.28\n2014-12-6,70,60,79,46,69,26,83,1876,2013,0.00,0.09,1.38\n2014-12-7,59,54,63,46,68,21,83,1937,2013,0.01,0.09,2.03\n2014-12-8,53,49,56,45,68,24,83,2010,1978,0.00,0.08,1.80\n2014-12-9,50,40,60,45,68,28,84,1984,2004,0.00,0.09,3.29\n2014-12-10,48,36,59,45,68,25,82,1974,1961,0.00,0.08,2.86\n2014-12-11,47,34,59,45,67,23,83,1981,1961,0.00,0.09,1.34\n2014-12-12,50,35,64,45,67,22,83,1962,1961,0.00,0.09,2.72\n2014-12-13,50,31,68,44,67,12,83,1962,1956,0.00,0.09,2.86\n2014-12-14,51,31,71,44,67,20,82,2010,1956,0.00,0.08,2.07\n2014-12-15,52,34,70,44,67,21,84,2010,2009,0.00,0.09,1.59\n2014-12-16,55,34,76,44,66,26,83,1968,1956,0.00,0.09,1.86\n2014-12-17,53,40,66,44,66,26,81,1943,1961,0.00,0.09,3.96\n2014-12-18,52,39,64,43,66,25,81,1988,1961,0.00,0.10,1.54\n2014-12-19,56,41,70,43,66,23,83,1975,1967,0.0,0.10,2.14\n2014-12-20,60,51,69,43,66,19,82,1981,1990,0.0,0.10,2.86\n2014-12-21,57,56,58,43,66,20,83,1901,2013,0.16,0.10,4.69\n2014-12-22,63,56,70,43,65,25,84,1973,2013,2.59,0.10,2.89\n2014-12-23,68,62,74,43,65,24,84,1989,1956,0.74,0.09,1.41\n2014-12-24,72,64,79,42,65,18,81,1983,1981,0.22,0.10,1.65\n2014-12-25,53,42,64,42,65,11,81,1983,1984,0.00,0.09,2.72\n2014-12-26,53,38,67,42,65,13,83,1983,1987,0.00,0.10,1.59\n2014-12-27,62,49,74,42,65,23,81,1935,2008,0.00,0.08,1.63\n2014-12-28,68,54,82,42,65,20,83,2010,1971,0.00,0.09,2.61\n2014-12-29,69,58,79,42,65,14,82,1894,1990,0.02,0.08,2.06\n2014-12-30,64,56,71,42,65,19,83,1909,1990,0.0,0.09,1.12\n2014-12-31,53,49,57,42,65,19,82,1917,1973,0.00,0.09,1.83\n2015-1-1,55,48,61,41,65,21,81,1918,1967,0.0,0.09,1.20\n2015-1-2,53,48,57,42,65,21,82,1928,1988,0.00,0.09,1.83\n2015-1-3,71,60,81,41,64,19,81,1979,2015,0.0,0.09,1.97\n2015-1-4,72,65,79,41,64,22,82,1887,1973,0.27,0.09,1.74\n2015-1-5,56,45,66,41,64,23,81,1999,1972,0.00,0.09,2.40\n2015-1-6,56,40,71,41,64,21,83,1999,2007,0.00,0.09,1.97\n2015-1-7,52,39,65,41,64,21,82,1924,2007,0.00,0.10,2.96\n2015-1-8,34,27,40,41,64,21,82,1970,2005,0.0,0.09,3.09\n2015-1-9,45,35,55,41,64,19,83,1970,1974,0.00,0.11,2.10\n2015-1-10,47,38,56,41,64,21,82,1970,1957,0.00,0.10,2.11\n2015-1-11,56,48,63,41,64,17,83,1982,1972,0.07,0.11,2.69\n2015-1-12,62,57,66,41,64,15,82,1886,1949,1.07,0.11,2.68\n2015-1-13,58,52,64,41,64,13,82,1981,1972,0.0,0.11,2.23\n2015-1-14,50,47,53,41,65,23,81,1964,1888,0.00,0.11,2.35\n2015-1-15,45,42,48,41,65,22,83,1979,1989,0.19,0.11,0.89\n2015-1-16,49,38,60,41,65,22,84,1927,1947,0.0,0.10,1.67\n2015-1-17,51,35,66,41,65,21,84,1977,1943,0.00,0.11,2.43\n2015-1-18,56,42,69,41,65,21,83,1977,1937,0.00,0.11,0.62\n2015-1-19,51,33,68,41,65,19,83,1977,1937,0.00,0.11,2.90\n2015-1-20,54,36,71,41,65,19,83,1985,1990,0.00,0.11,1.61\n2015-1-21,62,48,75,41,65,7,83,1985,1921,0.0,0.12,1.71\n2015-1-22,59,45,72,42,65,16,82,1985,1999,0.00,0.12,1.21\n2015-1-23,60,52,67,42,65,25,82,1960,1974,1.34,0.11,2.33\n2015-1-24,55,40,69,42,65,19,83,2003,1937,0.12,0.12,2.18\n2015-1-25,49,35,63,42,65,20,83,2003,1990,0.00,0.12,2.64\n2015-1-26,52,44,60,42,65,17,82,1905,1950,0.02,0.12,2.31\n2015-1-27,50,36,63,42,66,17,84,1940,1962,0.00,0.11,1.11\n2015-1-28,45,34,55,42,66,16,83,1986,1957,0.00,0.12,2.06\n2015-1-29,49,31,67,42,66,22,84,1897,1957,0.00,0.11,1.63\n2015-1-30,55,42,67,42,66,20,85,1966,2013,0.00,0.12,1.14\n2015-1-31,48,36,60,42,66,20,84,1966,1982,0.00,0.11,3.43\n2015-2-1,56,38,74,43,66,24,83,1977,1957,0.00,0.11,2.82\n2015-2-2,55,41,68,43,66,23,84,1979,1990,0.32,0.11,3.68\n2015-2-3,44,31,56,43,66,16,86,1917,1990,0.00,0.11,4.93\n2015-2-4,51,45,56,43,67,23,84,1981,1957,0.42,0.11,0.99\n2015-2-5,53,42,64,43,67,19,87,1996,1957,0.87,0.10,2.00\n2015-2-6,47,37,57,43,67,23,85,2009,1989,0.00,0.11,3.06\n2015-2-7,50,34,66,43,67,25,80,1988,1904,0.00,0.10,1.89\n2015-2-8,57,40,73,44,67,14,84,1895,1957,0.00,0.11,2.49\n2015-2-9,55,42,68,44,67,19,88,1895,1937,0.13,0.11,1.64\n2015-2-10,53,45,60,44,67,26,84,1979,1957,0.00,0.10,1.22\n2015-2-11,52,40,64,44,68,24,84,1971,1939,0.00,0.11,1.80\n2015-2-12,56,41,70,44,68,24,83,2012,1965,0.00,0.11,1.64\n2015-2-13,42,32,51,44,68,10,85,1899,1959,0.00,0.10,1.86\n2015-2-14,46,29,62,45,68,16,86,1899,1959,0.00,0.11,2.66\n2015-2-15,53,42,63,45,68,23,83,1943,2001,0.00,0.11,2.65\n2015-2-16,59,43,74,45,68,25,86,1991,1990,0.00,0.12,2.46\n2015-2-17,56,45,66,45,69,23,83,2007,1956,0.62,0.12,2.44\n2015-2-18,46,35,57,45,69,18,85,1900,1956,0.00,0.12,2.07\n2015-2-19,35,27,42,45,69,24,86,1958,1891,0.00,0.12,2.66\n2015-2-20,36,24,47,46,69,24,86,2015,1961,0.00,0.12,3.19\n2015-2-21,50,30,70,46,69,25,85,1958,1997,0.00,0.12,1.77\n2015-2-22,65,50,79,46,70,29,85,1958,1961,0.00,0.13,2.53\n2015-2-23,65,53,76,46,70,25,85,1978,1962,0.00,0.12,1.59\n2015-2-24,51,49,53,46,70,27,88,1989,1962,0.32,0.12,1.47\n2015-2-25,56,47,65,47,70,24,85,1989,2001,0.0,0.13,3.20\n2015-2-26,57,47,67,47,70,22,88,1967,1962,0.62,0.12,1.19\n2015-2-27,49,44,53,47,70,25,86,1974,1962,0.00,0.12,3.28\n2015-2-28,51,48,54,47,71,25,87,2002,1962,0.02,0.12,1.98\n2015-3-1,56,49,63,47,71,28,87,2002,1997,0.20,0.14,4.36\n2015-3-2,67,53,81,47,71,27,86,1890,1997,0.00,0.13,3.30\n2015-3-3,69,57,81,48,71,23,88,1980,2012,0.00,0.15,2.38\n2015-3-4,74,62,85,48,71,24,86,1980,1953,0.00,0.14,1.31\n2015-3-5,72,58,85,48,72,28,87,1971,1985,0.15,0.14,3.00\n2015-3-6,55,45,64,48,72,28,88,1960,1961,0.0,0.15,1.60\n2015-3-7,50,39,61,48,72,29,87,1899,1956,0.00,0.14,2.71\n2015-3-8,54,38,70,48,72,26,88,1899,1945,0.00,0.13,0.93\n2015-3-9,61,45,77,49,72,27,88,1996,1974,0.00,0.13,4.87\n2015-3-10,69,52,86,49,73,26,91,1932,1974,0.00,0.13,1.69\n2015-3-11,75,64,86,49,73,31,89,1934,1967,0.0,0.13,2.66\n2015-3-12,65,54,76,49,73,32,91,2011,1967,0.00,0.13,1.41\n2015-3-13,72,64,80,49,73,32,88,1998,1982,0.01,0.14,1.74\n2015-3-14,77,66,87,49,73,28,89,1926,1956,0.0,0.12,1.40\n2015-3-15,72,61,83,50,74,27,90,1993,1967,0.00,0.13,2.63\n2015-3-16,68,51,84,50,74,30,89,1988,1945,0.00,0.13,2.35\n2015-3-17,71,54,88,50,74,30,88,1890,2002,0.00,0.12,2.63\n2015-3-18,67,59,74,50,74,32,89,1971,1963,0.00,0.13,3.02\n2015-3-19,69,59,78,50,74,29,89,1892,1955,0.01,0.12,1.51\n2015-3-20,70,61,79,50,75,34,89,1923,1945,0.00,0.12,3.04\n2015-3-21,73,64,82,51,75,30,90,1971,1907,0.53,0.12,0.95\n2015-3-22,70,62,77,51,75,31,90,1876,1935,0.08,0.11,1.77\n2015-3-23,63,59,67,51,75,33,91,1986,1935,0.47,0.11,2.34\n2015-3-24,62,57,66,51,75,35,91,1968,1907,0.03,0.11,3.31\n2015-3-25,67,59,75,51,75,34,90,2008,1954,0.00,0.12,2.34\n2015-3-26,68,58,77,51,76,32,88,1979,1949,0.53,0.12,1.65\n2015-3-27,64,51,76,51,76,32,89,1894,1991,0.28,0.13,3.91\n2015-3-28,53,42,63,52,76,35,89,2013,1994,0.00,0.12,7.12\n2015-3-29,51,39,62,52,76,34,89,1955,1991,0.00,0.12,1.92\n2015-3-30,59,40,78,52,76,36,89,1964,1961,0.00,0.12,1.43\n2015-3-31,68,55,81,52,76,32,88,1964,1954,0.00,0.12,4.31\n2015-4-1,71,58,84,52,77,34,89,1987,1974,0.05,0.11,2.24\n2015-4-2,68,54,81,52,77,36,90,1987,2012,0.00,0.10,3.24\n2015-4-3,73,59,87,52,77,39,89,1962,2012,0.00,0.11,7.35\n2015-4-4,71,59,83,53,77,38,90,1992,1974,0.12,0.10,1.24\n2015-4-5,68,60,75,53,77,35,89,1975,1880,0.00,0.10,2.46\n2015-4-6,70,60,80,53,77,34,90,1891,1947,0.00,0.09,1.80\n2015-4-7,73,60,86,53,78,38,92,1974,1967,0.46,0.10,1.82\n2015-4-8,75,63,86,53,78,31,91,2007,1978,0.00,0.10,1.92\n2015-4-9,74,62,85,53,78,37,92,2000,2011,0.00,0.10,1.75\n2015-4-10,77,65,89,54,78,37,92,2000,2011,0.00,0.10,3.83\n2015-4-11,74,65,82,54,78,37,91,1996,2011,0.00,0.09,2.80\n2015-4-12,75,69,81,54,79,41,92,1989,1965,0.48,0.10,1.70\n2015-4-13,76,69,83,54,79,35,92,1940,2001,0.22,0.09,1.68\n2015-4-14,78,67,88,54,79,40,90,1907,1922,0.0,0.10,2.42\n2015-4-15,76,67,85,54,79,39,90,2004,1972,0.11,0.08,2.08\n2015-4-16,67,64,70,55,79,40,90,2008,1972,0.0,0.09,0.91\n2015-4-17,70,62,78,55,79,38,93,1950,1967,0.00,0.09,1.00\n2015-4-18,75,63,87,55,80,37,94,2001,1967,0.27,0.08,2.60\n2015-4-19,77,68,86,55,80,36,92,2001,1968,0.00,0.09,4.53\n2015-4-20,70,63,77,55,80,40,92,1983,1968,0.04,0.09,2.46\n2015-4-21,72,62,81,56,80,38,95,1983,1968,0.00,0.08,2.29\n2015-4-22,70,54,85,56,80,42,93,1950,1968,0.00,0.08,1.21\n2015-4-23,74,60,88,56,81,37,92,1993,1970,0.00,0.08,2.40\n2015-4-24,68,61,75,56,81,39,93,1986,1958,0.00,0.07,1.04\n2015-4-25,70,60,79,57,81,40,92,2005,1958,0.91,0.07,1.89\n2015-4-26,80,70,90,57,81,40,92,1974,2011,0.00,0.07,0.86\n2015-4-27,71,65,76,57,82,42,94,1977,1986,0.00,0.07,1.81\n2015-4-28,68,65,71,57,82,43,94,1977,1991,0.01,0.07,2.65\n2015-4-29,71,62,79,58,82,43,93,1973,1991,0.00,0.07,1.50\n2015-4-30,68,57,79,58,82,42,93,1874,1991,0.00,0.07,2.39\n2015-5-1,66,53,78,58,82,47,96,2000,1990,0.00,0.06,1.99\n2015-5-2,63,49,77,59,83,47,95,1941,1990,0.00,0.06,2.28\n2015-5-3,66,51,81,59,83,47,94,1981,2002,0.00,0.06,2.17\n2015-5-4,67,54,80,59,83,45,93,1971,2006,0.00,0.06,4.21\n2015-5-5,70,59,80,59,83,46,94,1976,1898,0.00,0.07,0.95\n2015-5-6,71,61,80,60,84,45,96,1973,2012,0.0,0.07,1.64\n2015-5-7,73,59,86,60,84,49,94,1992,1977,0.0,0.07,3.79\n2015-5-8,76,64,87,60,84,45,96,1992,1955,0.0,0.07,1.29\n2015-5-9,76,62,89,61,84,48,96,1928,1962,0.00,0.07,1.24\n2015-5-10,78,63,92,61,84,49,93,1984,1899,0.00,0.07,1.18\n2015-5-11,80,67,92,61,85,47,95,1986,1973,0.00,0.07,2.42\n2015-5-12,80,67,92,62,85,47,97,1981,1967,0.00,0.07,1.90\n2015-5-13,80,67,92,62,85,47,100,1941,1967,0.00,0.06,8.18\n2015-5-14,76,67,85,62,85,47,99,2013,1967,0.00,0.07,2.02\n2015-5-15,76,70,81,62,85,52,97,2002,1967,0.00,0.07,2.18\n2015-5-16,71,60,82,63,86,52,96,1951,1995,0.00,0.06,0.63\n2015-5-17,75,63,86,63,86,46,96,1973,1995,0.00,0.07,1.36\n2015-5-18,76,65,87,63,86,46,96,2011,1899,0.00,0.07,4.94\n2015-5-19,81,69,93,64,86,51,97,1977,1960,0.14,0.07,2.14\n2015-5-20,80,67,92,64,86,46,99,1894,1962,0.00,0.07,5.07\n2015-5-21,82,69,94,64,87,50,99,2002,1938,0.00,0.09,2.61\n2015-5-22,75,64,86,65,87,47,99,1981,1938,0.00,0.09,7.59\n2015-5-23,73,63,83,65,87,48,97,1993,1953,0.00,0.09,2.87\n2015-5-24,77,70,84,65,87,52,98,2001,1953,0.0,0.10,4.09\n2015-5-25,79,70,87,66,87,55,99,1892,1953,0.0,0.10,2.74\n2015-5-26,80,71,88,66,87,47,99,1979,1953,0.00,0.10,2.40\n2015-5-27,78,69,86,66,88,49,98,1979,1962,0.00,0.11,5.40\n2015-5-28,73,62,84,66,88,52,99,1988,1967,0.00,0.10,4.47\n2015-5-29,74,62,85,67,88,53,97,1988,1967,0.00,0.11,2.83\n2015-5-30,73,65,81,67,88,51,97,1971,1945,0.94,0.12,1.82\n2015-5-31,77,67,86,67,88,50,98,1984,1945,0.0,0.13,3.38\n2015-6-1,78,66,89,67,88,47,99,1984,1945,0.72,0.16,2.80\n2015-6-2,75,64,85,68,88,53,100,1984,1985,0.03,0.17,3.13\n2015-6-3,78,68,88,68,89,57,100,1966,1985,0.24,0.17,5.31\n2015-6-4,77,67,86,68,89,61,100,1966,1985,0.0,0.17,5.85\n2015-6-5,77,67,86,68,89,59,100,1972,1943,0.02,0.17,3.31\n2015-6-6,78,66,89,69,89,60,99,1971,1985,0.00,0.18,5.92\n2015-6-7,77,66,87,69,89,59,100,2006,1993,0.00,0.19,4.72\n2015-6-8,79,68,89,69,89,58,99,1998,1993,0.00,0.19,3.42\n2015-6-9,82,73,91,69,89,58,101,1987,1872,0.00,0.20,2.36\n2015-6-10,78,70,86,69,89,58,100,1913,1954,0.92,0.20,5.11\n2015-6-11,78,67,89,70,90,57,99,1960,2010,0.00,0.21,3.44\n2015-6-12,80,68,92,70,90,54,99,1913,1998,0.0,0.22,3.38\n2015-6-13,82,71,93,70,90,57,100,1979,1977,0.00,0.23,6.07\n2015-6-14,81,68,94,70,90,57,101,1972,1998,0.00,0.23,4.15\n2015-6-15,81,68,94,70,90,57,102,1995,1981,0.00,0.23,3.60\n2015-6-16,83,69,96,70,90,62,102,2002,1981,0.00,0.24,2.45\n2015-6-17,85,72,97,70,90,61,100,1961,1944,0.00,0.24,2.49\n2015-6-18,85,72,97,71,90,59,100,2012,1950,0.00,0.24,2.78\n2015-6-19,86,73,98,71,90,60,103,1899,1998,0.71,0.24,4.37\n2015-6-20,86,74,97,71,90,60,100,1899,1990,0.0,0.23,2.15\n2015-6-21,86,76,96,71,91,60,99,1965,1950,0.06,0.23,2.85\n2015-6-22,84,70,97,71,91,62,99,1965,1970,1.70,0.24,2.11\n2015-6-23,81,71,91,71,91,63,99,1879,1981,0.25,0.23,2.27\n2015-6-24,85,74,95,71,91,63,101,2003,1914,0.00,0.23,4.60\n2015-6-25,81,73,89,71,91,64,99,1889,1950,1.16,0.23,7.36\n2015-6-26,82,73,90,72,91,63,101,1966,1952,0.02,0.23,5.20\n2015-6-27,84,75,92,72,91,65,102,1936,1950,0.0,0.24,1.39\n2015-6-28,82,73,91,72,91,63,101,2012,1954,0.23,0.23,1.37\n2015-6-29,79,71,86,72,91,63,100,1981,1998,0.23,0.24,5.90\n2015-6-30,79,69,89,72,91,66,100,1974,1990,0.03,0.24,5.91\n"
  },
  {
    "path": "us-weather-history/KMDW.csv",
    "content": "date,actual_mean_temp,actual_min_temp,actual_max_temp,average_min_temp,average_max_temp,record_min_temp,record_max_temp,record_min_temp_year,record_max_temp_year,actual_precipitation,average_precipitation,record_precipitation\n2014-7-1,76,68,84,67,84,49,103,1982,1956,0.04,0.11,1.23\n2014-7-2,66,58,74,67,84,48,99,1930,1970,0.12,0.10,2.50\n2014-7-3,66,57,75,67,84,50,102,1940,1949,0.05,0.11,1.38\n2014-7-4,70,60,79,67,84,49,101,1940,2012,0.00,0.11,0.94\n2014-7-5,71,63,79,67,84,46,103,1972,2012,0.01,0.13,1.18\n2014-7-6,78,66,89,67,84,49,105,1983,2012,0.00,0.12,3.93\n2014-7-7,81,72,89,67,85,48,102,1983,1936,0.48,0.13,1.30\n2014-7-8,77,70,84,68,85,49,106,1934,1936,0.48,0.13,2.41\n2014-7-9,73,64,81,68,85,51,100,1941,1936,0.00,0.12,2.43\n2014-7-10,71,63,78,68,85,51,106,1941,1936,0.00,0.13,0.98\n2014-7-11,73,63,83,68,85,51,107,1978,1936,0.0,0.13,6.16\n2014-7-12,76,70,81,68,85,55,100,1975,1936,3.31,0.13,6.16\n2014-7-13,79,71,86,68,85,50,106,1940,1995,0.04,0.12,1.49\n2014-7-14,73,64,81,68,85,52,104,1930,1936,0.37,0.13,1.52\n2014-7-15,64,57,71,68,85,49,103,1930,1988,0.05,0.13,1.36\n2014-7-16,65,56,73,68,85,49,100,1945,1931,0.00,0.13,2.48\n2014-7-17,68,58,78,68,85,51,100,1940,1942,0.00,0.12,5.72\n2014-7-18,73,64,81,68,84,53,101,1979,1930,0.00,0.13,2.36\n2014-7-19,71,62,80,68,84,55,104,1947,1930,0.00,0.13,3.69\n2014-7-20,75,66,84,68,84,50,103,1941,1934,0.00,0.13,3.03\n2014-7-21,78,68,87,68,84,51,108,1970,1934,0.00,0.14,1.43\n2014-7-22,82,72,91,68,84,49,104,1947,1934,0.19,0.14,1.82\n2014-7-23,70,64,76,68,84,51,109,1947,1934,0.00,0.14,2.31\n2014-7-24,68,60,75,68,84,55,107,2000,1934,0.00,0.14,4.77\n2014-7-25,69,60,77,68,84,58,105,2013,1934,0.0,0.13,1.83\n2014-7-26,78,70,85,68,84,53,102,1962,1936,0.00,0.14,1.77\n2014-7-27,78,66,89,68,84,51,100,1937,1955,0.04,0.14,4.71\n2014-7-28,68,62,73,68,84,53,100,1981,1983,0.00,0.14,1.25\n2014-7-29,71,60,81,68,84,50,101,1981,1941,0.00,0.14,1.47\n2014-7-30,71,61,80,68,84,51,104,1936,1999,0.00,0.14,2.43\n2014-7-31,74,63,85,67,83,50,99,1936,2006,0.00,0.15,1.20\n2014-8-1,75,67,83,67,83,52,100,1947,1988,0.21,0.15,2.55\n2014-8-2,74,63,84,67,83,56,102,1965,1991,0.00,0.14,3.58\n2014-8-3,77,66,87,67,83,52,101,1950,1930,0.00,0.13,1.95\n2014-8-4,78,68,88,67,83,52,100,1929,1947,1.79,0.14,2.17\n2014-8-5,74,68,80,67,83,52,100,1974,1947,0.07,0.13,1.11\n2014-8-6,73,65,81,67,83,50,101,1948,1947,0.00,0.13,2.17\n2014-8-7,75,69,81,67,83,53,96,1976,1984,0.00,0.14,1.67\n2014-8-8,76,70,82,67,83,52,103,1976,1934,0.00,0.13,2.35\n2014-8-9,74,68,80,67,83,51,104,1972,1934,0.00,0.13,2.13\n2014-8-10,77,71,82,67,83,49,99,1972,1944,0.00,0.13,2.70\n2014-8-11,76,68,84,67,83,50,100,1982,1941,1.16,0.12,2.36\n2014-8-12,67,63,70,67,83,50,100,1930,1995,0.16,0.14,1.18\n2014-8-13,71,59,83,67,83,52,101,1967,1936,0.00,0.13,1.09\n2014-8-14,67,61,73,67,82,48,99,1964,1944,0.00,0.14,1.79\n2014-8-15,67,55,79,67,82,48,98,1929,1944,0.00,0.14,2.07\n2014-8-16,73,64,81,67,82,49,101,1976,1988,0.00,0.13,3.79\n2014-8-17,69,66,72,67,82,48,99,1976,1988,0.00,0.13,2.60\n2014-8-18,76,67,85,66,82,49,99,1963,1936,0.00,0.13,1.80\n2014-8-19,80,73,86,66,82,49,99,1933,1983,0.13,0.12,1.39\n2014-8-20,78,68,87,66,82,47,98,1950,1947,0.00,0.12,1.47\n2014-8-21,77,71,83,66,82,46,99,1950,1955,1.29,0.13,1.58\n2014-8-22,81,74,87,66,82,51,103,1928,1936,3.17,0.12,3.17\n2014-8-23,79,71,87,66,82,52,97,1940,1947,0.50,0.11,2.49\n2014-8-24,81,73,89,65,82,48,100,1942,1947,0.00,0.13,1.20\n2014-8-25,83,73,92,65,81,47,95,1934,1991,0.78,0.11,1.88\n2014-8-26,79,72,86,65,81,48,97,1982,1973,0.00,0.12,3.66\n2014-8-27,76,70,81,65,81,46,97,1982,1973,0.00,0.13,1.57\n2014-8-28,73,69,77,65,81,46,97,1982,1955,0.02,0.12,1.57\n2014-8-29,81,73,88,64,81,43,98,1965,1953,0.00,0.12,2.02\n2014-8-30,80,74,86,64,81,47,96,1946,1953,0.0,0.12,1.43\n2014-8-31,79,71,87,64,80,46,99,1935,1953,0.00,0.13,1.62\n2014-9-1,80,74,85,63,80,45,101,1949,1953,0.0,0.12,2.92\n2014-9-2,77,69,85,63,80,44,101,1946,1953,0.00,0.12,1.37\n2014-9-3,76,67,85,63,80,47,97,1974,1953,0.00,0.11,1.92\n2014-9-4,80,73,87,62,79,43,95,1938,1960,0.22,0.12,3.05\n2014-9-5,78,67,89,62,79,48,97,1988,1954,0.43,0.11,1.89\n2014-9-6,69,62,76,62,79,46,98,1984,1990,0.00,0.12,0.83\n2014-9-7,69,58,79,61,79,46,101,1986,1939,0.00,0.12,3.51\n2014-9-8,70,60,79,61,78,45,98,1986,1933,0.00,0.11,1.37\n2014-9-9,74,66,81,61,78,47,95,1975,1983,0.01,0.11,3.25\n2014-9-10,70,58,82,60,78,44,96,1943,2013,1.61,0.11,1.61\n2014-9-11,55,52,58,60,77,44,95,1943,1952,0.00,0.11,4.16\n2014-9-12,52,46,57,59,77,44,96,1955,1952,0.10,0.11,2.43\n2014-9-13,51,42,60,59,77,40,99,1953,1939,0.01,0.11,3.62\n2014-9-14,57,46,67,58,76,42,101,1949,1939,0.00,0.11,2.19\n2014-9-15,58,54,62,58,76,41,102,1949,1939,0.0,0.11,1.07\n2014-9-16,58,47,69,57,75,39,93,1984,1931,0.00,0.11,1.70\n2014-9-17,61,49,72,57,75,39,93,1937,1955,0.00,0.10,1.20\n2014-9-18,63,53,72,57,75,39,94,1929,1955,0.00,0.11,1.95\n2014-9-19,67,57,76,56,74,37,93,1929,1955,0.00,0.12,0.94\n2014-9-20,72,63,80,56,74,38,92,1956,1931,0.12,0.12,1.12\n2014-9-21,61,52,70,55,73,40,92,1930,1970,0.0,0.11,1.67\n2014-9-22,59,48,69,55,73,34,93,1974,1937,0.00,0.11,1.09\n2014-9-23,63,50,76,54,73,35,93,1974,1937,0.00,0.11,2.09\n2014-9-24,66,54,77,54,72,30,91,1928,2007,0.00,0.11,1.85\n2014-9-25,68,58,78,53,72,30,91,1942,1986,0.00,0.12,2.00\n2014-9-26,70,59,81,53,71,30,93,1928,1998,0.00,0.10,2.72\n2014-9-27,70,61,79,52,71,31,91,1942,1971,0.00,0.10,1.43\n2014-9-28,69,58,79,52,70,29,92,1942,1953,0.00,0.10,3.65\n2014-9-29,71,57,84,52,70,33,99,1942,1953,0.0,0.10,1.13\n2014-9-30,56,51,61,51,69,34,92,1984,1971,0.0,0.09,0.76\n2014-10-1,63,52,73,51,69,32,92,1974,1971,0.0,0.10,0.73\n2014-10-2,71,66,75,50,68,28,91,1974,1971,0.71,0.09,3.80\n2014-10-3,57,46,68,50,68,29,91,1974,1954,1.05,0.10,3.95\n2014-10-4,43,37,49,50,68,34,90,1989,1951,0.05,0.11,1.89\n2014-10-5,49,42,56,49,67,31,89,1980,1997,0.0,0.10,2.01\n2014-10-6,56,48,63,49,67,28,94,1952,1963,0.01,0.10,1.57\n2014-10-7,59,49,69,48,66,31,88,2000,2007,0.00,0.10,1.26\n2014-10-8,58,48,67,48,66,30,88,1952,2007,0.00,0.09,1.32\n2014-10-9,57,50,64,48,66,32,87,1964,2010,0.00,0.11,2.27\n2014-10-10,52,44,59,48,65,27,87,1964,1938,0.00,0.10,3.94\n2014-10-11,50,40,60,47,65,29,88,2009,1928,0.00,0.10,1.47\n2014-10-12,55,45,64,47,64,30,86,1987,2008,0.01,0.10,1.40\n2014-10-13,65,59,71,46,64,30,88,1987,1975,0.44,0.10,2.43\n2014-10-14,61,54,67,46,64,28,89,1937,1975,1.18,0.11,1.18\n2014-10-15,56,53,58,46,63,29,88,1937,1947,0.07,0.10,1.66\n2014-10-16,56,53,58,46,63,30,87,1944,1963,0.0,0.10,0.81\n2014-10-17,56,49,63,45,62,26,86,1948,1950,0.01,0.10,1.48\n2014-10-18,46,39,52,45,62,20,87,1948,1950,0.02,0.11,3.18\n2014-10-19,47,36,57,45,62,24,84,1930,1971,0.00,0.10,1.41\n2014-10-20,58,50,66,44,61,20,86,1930,1953,0.00,0.10,0.86\n2014-10-21,52,49,54,44,61,26,87,1952,1953,0.00,0.11,2.24\n2014-10-22,50,43,56,44,60,24,87,1930,1953,0.00,0.11,1.77\n2014-10-23,52,41,62,44,60,25,85,1981,1963,0.0,0.11,1.48\n2014-10-24,61,54,68,43,60,20,83,1981,1963,0.00,0.10,1.38\n2014-10-25,64,52,75,43,59,25,79,1962,1989,0.00,0.11,1.87\n2014-10-26,55,44,66,43,59,22,84,1942,1963,0.00,0.11,1.49\n2014-10-27,67,54,79,42,58,24,82,1942,1940,0.02,0.11,1.56\n2014-10-28,57,48,66,42,58,25,81,1976,1999,0.03,0.12,0.65\n2014-10-29,45,40,49,42,58,22,79,1980,1999,0.00,0.11,1.02\n2014-10-30,45,37,53,41,57,25,85,1988,1950,0.0,0.12,1.24\n2014-10-31,42,34,49,41,57,27,84,1996,1950,0.04,0.11,2.63\n2014-11-1,39,33,44,41,56,22,81,1930,1950,0.0,0.12,2.45\n2014-11-2,41,29,53,40,56,14,78,1951,1974,0.00,0.11,1.20\n2014-11-3,55,45,64,40,55,11,76,1951,1987,0.00,0.11,1.72\n2014-11-4,53,45,61,40,55,15,74,1951,1981,0.02,0.12,1.83\n2014-11-5,51,39,63,39,54,11,79,1951,1978,0.0,0.12,1.26\n2014-11-6,46,40,52,39,54,19,75,1991,1975,0.0,0.11,1.05\n2014-11-7,41,37,45,38,53,16,74,1991,1945,0.00,0.12,1.17\n2014-11-8,44,39,49,38,53,14,74,1991,1999,0.0,0.11,1.17\n2014-11-9,45,36,53,38,52,16,75,1991,1999,0.00,0.11,2.69\n2014-11-10,55,44,65,37,52,17,71,1933,1999,0.00,0.11,3.49\n2014-11-11,47,33,61,37,51,15,74,1950,1964,0.23,0.11,1.30\n2014-11-12,30,27,33,36,51,10,70,1986,1949,0.0,0.12,0.70\n2014-11-13,29,26,31,36,50,7,74,1986,1989,0.0,0.12,1.09\n2014-11-14,27,24,29,35,50,12,78,1940,1971,0.0,0.12,1.60\n2014-11-15,27,21,33,35,49,8,72,1933,1990,0.0,0.12,0.89\n2014-11-16,29,25,33,34,48,5,73,1933,1952,0.0,0.12,1.29\n2014-11-17,19,13,25,34,48,3,74,1959,1975,0.0,0.11,1.26\n2014-11-18,17,12,21,34,47,12,72,2014,1999,0.0,0.12,2.23\n2014-11-19,26,19,33,33,47,17,77,1951,1930,0.0,0.11,0.74\n2014-11-20,24,17,30,33,46,14,69,1964,1930,0.00,0.11,1.23\n2014-11-21,25,13,36,32,46,7,66,1964,1990,0.0,0.12,1.35\n2014-11-22,44,34,54,32,45,2,67,1929,2010,0.05,0.11,1.30\n2014-11-23,52,50,54,31,45,-1,70,1950,1931,0.62,0.12,1.98\n2014-11-24,39,27,51,31,44,-2,69,1950,1966,0.35,0.12,0.75\n2014-11-25,26,22,30,30,43,0,61,1950,2006,0.0,0.10,0.76\n2014-11-26,27,22,32,30,43,2,68,1930,1990,0.0,0.11,1.20\n2014-11-27,25,19,30,29,42,0,70,1930,1990,0.0,0.11,3.34\n2014-11-28,28,20,36,29,42,-3,69,1930,1998,0.0,0.11,1.56\n2014-11-29,44,33,54,28,41,-1,69,1929,1998,0.00,0.11,1.31\n2014-11-30,43,29,56,28,41,-1,66,1947,1962,0.0,0.11,0.60\n2014-12-1,25,21,29,28,40,5,68,1930,1970,0.0,0.10,1.04\n2014-12-2,28,22,33,27,40,-1,69,1942,1982,0.0,0.10,2.78\n2014-12-3,33,26,39,27,40,-8,72,1940,2012,0.00,0.10,0.68\n2014-12-4,31,25,37,26,39,3,68,1991,1998,0.00,0.09,1.19\n2014-12-5,38,35,41,26,39,7,68,2005,2001,0.00,0.10,0.95\n2014-12-6,39,34,43,26,38,0,65,1972,1951,0.00,0.09,1.38\n2014-12-7,35,30,39,25,38,-2,61,1936,1946,0.00,0.09,1.26\n2014-12-8,37,35,39,25,37,0,64,1958,1946,0.10,0.09,0.77\n2014-12-9,37,33,40,24,37,-8,61,1958,1946,0.02,0.09,1.31\n2014-12-10,34,31,36,24,37,-8,60,1978,1971,0.0,0.10,2.18\n2014-12-11,30,26,34,24,36,-5,61,1972,1949,0.00,0.09,1.73\n2014-12-12,32,25,39,24,36,-7,62,1962,1991,0.0,0.08,1.76\n2014-12-13,43,37,49,23,36,-3,67,2000,1975,0.00,0.08,1.24\n2014-12-14,50,47,53,23,35,-4,66,1985,1975,0.0,0.08,1.00\n2014-12-15,46,43,48,23,35,-8,64,1989,,0.12,0.08,1.14\n2014-12-16,40,32,47,22,35,-12,63,1951,1984,0.02,0.08,0.47\n2014-12-17,29,26,32,22,35,-7,60,1951,1977,0.0,0.08,0.44\n2014-12-18,28,25,31,22,34,-9,59,1983,1939,0.0,0.07,1.06\n2014-12-19,32,29,34,22,34,-12,56,1983,1988,0.0,0.08,0.80\n2014-12-20,34,32,35,21,34,-9,59,1963,1988,0.0,0.08,1.12\n2014-12-21,36,34,38,21,34,-13,62,1989,1967,0.00,0.07,1.81\n2014-12-22,38,31,44,21,33,-12,57,1989,1957,0.28,0.08,0.98\n2014-12-23,46,42,50,21,33,-17,60,1983,1982,0.12,0.08,1.12\n2014-12-24,41,38,43,21,33,-20,59,1983,1982,0.12,0.07,2.61\n2014-12-25,40,35,44,20,33,-13,64,1983,1982,0.00,0.08,1.80\n2014-12-26,45,38,51,20,33,-9,55,1933,1971,0.00,0.07,0.80\n2014-12-27,41,32,50,20,33,-12,61,1933,1982,0.0,0.07,2.05\n2014-12-28,31,26,36,20,32,-3,67,1961,1984,0.00,0.08,1.13\n2014-12-29,30,24,35,20,32,-8,64,1976,1984,0.00,0.07,0.78\n2014-12-30,19,13,24,19,32,-8,60,1983,2002,0.00,0.07,1.69\n2014-12-31,13,6,20,19,32,-10,60,1967,1962,0.00,0.08,1.41\n2015-1-1,25,17,32,19,32,-10,52,1969,2000,0.00,0.08,1.33\n2015-1-2,28,20,36,19,32,-13,61,1979,2000,0.00,0.08,1.22\n2015-1-3,34,31,37,19,32,-12,60,1979,1950,0.55,0.07,0.79\n2015-1-4,21,5,36,19,32,-8,64,1999,1997,0.10,0.08,1.59\n2015-1-5,5,-1,10,19,32,-15,56,1999,1998,0.11,0.08,0.80\n2015-1-6,9,5,12,19,32,-15,60,2014,2008,0.0,0.07,0.75\n2015-1-7,2,-4,8,18,32,-14,65,1942,2008,0.00,0.08,0.69\n2015-1-8,7,-7,20,18,32,-12,63,1942,1965,0.05,0.07,1.48\n2015-1-9,10,3,16,18,31,-17,57,1982,1939,0.0,0.08,0.76\n2015-1-10,13,0,26,18,31,-22,60,1982,1975,0.00,0.07,2.29\n2015-1-11,30,25,34,18,31,-11,54,1977,2013,0.12,0.07,0.34\n2015-1-12,25,19,30,18,31,-9,62,1974,2005,0.03,0.07,2.76\n2015-1-13,18,13,23,18,31,-10,56,1929,1932,0.0,0.07,1.37\n2015-1-14,13,5,21,18,31,-14,56,1979,1952,0.00,0.07,1.00\n2015-1-15,27,17,36,18,31,-19,60,1979,1949,0.00,0.06,0.39\n2015-1-16,29,22,35,18,31,-19,56,1977,1990,0.00,0.07,0.43\n2015-1-17,37,30,44,18,31,-17,58,1982,1952,0.00,0.07,0.68\n2015-1-18,37,31,43,18,31,-20,61,1994,1996,0.00,0.06,1.39\n2015-1-19,35,27,43,18,31,-21,57,1985,1933,0.0,0.07,1.09\n2015-1-20,37,33,40,18,31,-25,53,1985,1954,0.01,0.06,0.99\n2015-1-21,34,31,36,18,31,-17,57,1984,1934,0.0,0.06,0.96\n2015-1-22,32,30,34,18,31,-17,58,1936,1964,0.00,0.06,1.33\n2015-1-23,32,30,34,18,31,-19,59,1936,1967,0.00,0.06,0.73\n2015-1-24,39,32,45,18,31,-16,65,1936,1967,0.00,0.05,0.73\n2015-1-25,32,26,38,18,32,-10,67,1936,1950,0.06,0.06,1.30\n2015-1-26,25,21,29,18,32,-10,62,1936,1944,0.04,0.06,1.66\n2015-1-27,29,24,33,18,32,-10,61,1955,2002,0.0,0.06,0.74\n2015-1-28,30,24,36,18,32,-13,55,1977,1970,0.03,0.05,0.52\n2015-1-29,35,31,39,18,32,-16,61,1966,2013,0.0,0.06,0.99\n2015-1-30,28,23,32,18,32,-15,55,1966,1988,0.0,0.05,1.25\n2015-1-31,31,23,39,19,32,-9,67,1996,1989,0.13,0.06,0.70\n2015-2-1,27,21,33,19,32,-9,56,1951,1968,0.12,0.05,0.95\n2015-2-2,16,9,22,19,32,-15,51,1951,1992,0.0,0.06,1.22\n2015-2-3,19,8,30,19,33,-17,60,1996,1992,0.05,0.06,0.43\n2015-2-4,21,12,30,19,33,-12,54,1996,1993,0.0,0.05,0.69\n2015-2-5,10,3,17,19,33,-17,56,1979,1946,0.00,0.06,0.68\n2015-2-6,24,15,32,20,33,-12,56,1982,1938,0.00,0.05,1.96\n2015-2-7,35,25,44,20,33,-5,59,1972,2009,0.00,0.06,0.67\n2015-2-8,37,31,42,20,34,-12,61,1933,1990,0.0,0.07,0.81\n2015-2-9,29,26,31,20,34,-20,57,1933,1999,0.0,0.06,0.77\n2015-2-10,29,25,33,20,34,-12,62,1982,2009,0.0,0.06,0.98\n2015-2-11,30,21,38,21,35,-8,71,1955,1999,0.0,0.07,0.82\n2015-2-12,16,11,21,21,35,-10,61,1981,1984,0.0,0.07,0.50\n2015-2-13,18,7,29,21,35,-5,63,1988,1938,0.0,0.07,0.78\n2015-2-14,17,4,29,21,35,-5,62,1936,1954,0.0,0.06,0.94\n2015-2-15,9,0,17,22,36,-6,69,1936,1954,0.0,0.07,0.78\n2015-2-16,14,8,19,22,36,-7,53,1958,1981,0.00,0.07,0.75\n2015-2-17,17,11,22,22,36,-10,58,1936,2011,0.0,0.07,0.84\n2015-2-18,6,0,11,23,37,-16,62,1936,1961,0.0,0.07,0.84\n2015-2-19,1,-5,7,23,37,-8,65,1936,1930,0.00,0.07,0.78\n2015-2-20,10,-1,20,23,37,-9,65,1929,1983,0.0,0.08,1.35\n2015-2-21,26,19,33,23,38,-6,66,1963,1930,0.00,0.07,3.25\n2015-2-22,19,7,30,24,38,-7,63,1963,1984,0.00,0.08,1.40\n2015-2-23,8,0,15,24,39,0,66,1939,2000,0.00,0.09,0.90\n2015-2-24,19,6,32,24,39,-5,64,1967,1930,0.0,0.08,1.36\n2015-2-25,22,16,28,25,39,-5,73,1967,2000,0.06,0.09,0.62\n2015-2-26,15,7,22,25,40,-1,64,1963,2000,0.01,0.08,1.76\n2015-2-27,9,0,17,25,40,-6,75,1934,1976,0.00,0.09,0.79\n2015-2-28,12,1,23,26,40,1,59,2015,1965,0.0,0.08,0.77\n2015-3-1,23,18,27,26,41,-5,71,1962,1992,0.01,0.09,1.09\n2015-3-2,25,16,34,26,41,-4,71,1913,1974,0.00,0.08,1.02\n2015-3-3,32,27,37,27,42,-6,80,1873,1974,0.10,0.09,1.21\n2015-3-4,24,14,33,27,42,-4,74,2002,1983,0.00,0.08,1.42\n2015-3-5,14,8,19,27,42,0,76,1978,1983,0.00,0.09,0.99\n2015-3-6,17,6,27,28,43,2,71,1960,1983,0.00,0.08,0.49\n2015-3-7,36,26,46,28,43,-2,79,1943,2000,0.00,0.09,1.27\n2015-3-8,37,31,42,28,44,-7,79,1943,2000,0.0,0.09,2.02\n2015-3-9,40,28,52,29,44,3,69,1932,1974,0.00,0.08,1.41\n2015-3-10,46,38,54,29,44,5,70,2003,1955,0.00,0.08,1.12\n2015-3-11,45,36,54,29,45,3,76,1979,1990,0.00,0.08,0.92\n2015-3-12,46,33,58,30,45,1,81,1948,1990,0.00,0.08,1.30\n2015-3-13,53,41,65,30,46,11,74,1948,2007,0.00,0.08,1.02\n2015-3-14,51,40,62,30,46,8,81,1993,2012,0.00,0.09,1.62\n2015-3-15,49,34,64,31,46,12,81,1979,2012,0.00,0.08,1.66\n2015-3-16,59,44,74,31,47,5,81,1941,2012,0.00,0.09,1.12\n2015-3-17,38,31,44,31,47,0,80,1941,2012,0.00,0.08,1.42\n2015-3-18,40,30,50,32,48,3,81,1941,2012,0.00,0.09,1.04\n2015-3-19,46,41,50,32,48,11,79,1965,2012,0.00,0.09,2.50\n2015-3-20,46,39,52,32,48,8,85,1965,2012,0.00,0.09,0.78\n2015-3-21,43,35,50,33,49,12,86,1951,2012,0.00,0.09,0.93\n2015-3-22,34,30,37,33,49,6,84,1940,2012,0.0,0.09,1.15\n2015-3-23,31,30,32,33,50,6,79,1940,1939,0.15,0.09,0.93\n2015-3-24,33,25,40,34,50,5,79,1974,1939,0.04,0.08,1.26\n2015-3-25,41,38,43,34,50,10,78,1940,2007,0.13,0.09,1.95\n2015-3-26,40,34,46,34,51,11,79,1955,2007,0.0,0.09,1.40\n2015-3-27,30,25,34,35,51,13,82,1965,1945,0.01,0.09,1.06\n2015-3-28,29,21,37,35,52,9,78,1934,1968,0.00,0.10,1.26\n2015-3-29,40,28,51,35,52,17,86,1969,1986,0.04,0.09,1.33\n2015-3-30,51,37,64,36,52,12,83,1969,1986,0.00,0.10,1.36\n2015-3-31,49,41,57,36,53,12,85,,1986,0.00,0.11,2.50\n2015-4-1,58,41,74,36,53,24,82,1954,2010,0.00,0.11,1.58\n2015-4-2,59,50,68,37,54,22,82,1961,1963,0.27,0.12,1.67\n2015-4-3,45,33,57,39,54,19,81,1954,1956,0.0,0.11,1.08\n2015-4-4,46,31,61,38,55,16,80,1975,1929,0.00,0.11,1.90\n2015-4-5,58,47,69,38,55,18,85,1979,1988,0.00,0.11,2.58\n2015-4-6,53,41,64,38,55,16,84,1982,1991,0.07,0.12,0.94\n2015-4-7,45,40,49,39,56,10,83,1982,2001,0.04,0.12,0.78\n2015-4-8,49,43,54,39,56,20,82,1972,2001,0.08,0.12,1.38\n2015-4-9,60,46,74,39,57,21,78,1972,1952,0.95,0.12,1.58\n2015-4-10,54,42,65,40,57,22,91,1989,1930,0.0,0.13,1.15\n2015-4-11,52,39,64,40,57,25,91,1973,1930,0.00,0.12,2.26\n2015-4-12,59,47,71,41,58,21,86,1957,1977,0.00,0.12,1.16\n2015-4-13,61,53,69,41,58,21,83,1950,1941,0.14,0.12,1.84\n2015-4-14,57,46,67,41,59,23,85,1943,2003,0.00,0.12,1.21\n2015-4-15,56,44,67,42,59,21,89,1928,2002,0.00,0.12,1.26\n2015-4-16,56,47,64,42,60,26,88,1962,2002,0.34,0.13,1.33\n2015-4-17,65,49,80,42,60,29,88,1983,1976,0.00,0.12,1.87\n2015-4-18,59,50,68,43,60,25,89,1983,2002,0.00,0.13,3.83\n2015-4-19,60,51,69,43,61,24,86,1983,1985,0.42,0.12,1.52\n2015-4-20,50,43,56,43,61,27,87,1983,1987,0.0,0.12,1.27\n2015-4-21,50,41,58,44,62,28,86,1943,1985,0.00,0.13,1.08\n2015-4-22,43,37,49,44,62,25,91,1936,1980,0.0,0.12,1.50\n2015-4-23,45,34,56,44,62,28,88,1936,1960,0.00,0.12,0.83\n2015-4-24,48,35,60,45,63,29,89,1930,1990,0.29,0.12,2.05\n2015-4-25,45,40,49,45,63,27,89,1934,1990,0.34,0.12,1.62\n2015-4-26,48,39,57,45,64,29,91,1930,1986,0.00,0.13,0.84\n2015-4-27,50,43,56,46,64,28,89,1928,1986,0.00,0.13,2.13\n2015-4-28,49,38,60,46,64,30,84,1950,1951,0.00,0.13,1.63\n2015-4-29,53,42,64,46,65,31,89,1958,1942,0.0,0.13,1.62\n2015-4-30,47,42,52,47,65,31,92,1931,1942,0.00,0.12,2.15\n2015-5-1,53,40,65,47,65,30,90,1943,1951,0.00,0.12,2.05\n2015-5-2,66,53,79,47,66,30,91,1978,1959,0.00,0.12,1.26\n2015-5-3,71,60,82,48,66,32,92,1971,1955,0.20,0.13,0.76\n2015-5-4,66,57,74,48,66,37,82,2005,1999,0.0,0.12,0.93\n2015-5-5,56,51,60,48,67,34,94,1954,1949,0.79,0.13,0.83\n2015-5-6,67,51,82,49,67,34,93,1989,1934,0.00,0.12,1.71\n2015-5-7,72,57,87,49,67,32,89,1974,1936,0.00,0.12,2.01\n2015-5-8,75,66,84,49,68,33,93,1977,2014,0.28,0.12,1.09\n2015-5-9,58,49,67,49,68,31,96,1983,1934,0.38,0.13,2.33\n2015-5-10,53,47,58,50,68,29,91,1966,2011,0.16,0.12,2.84\n2015-5-11,61,50,71,50,69,32,89,1981,1987,0.14,0.13,3.30\n2015-5-12,53,47,58,50,69,28,92,1981,1956,0.0,0.13,1.92\n2015-5-13,49,45,53,51,69,30,90,1938,1982,0.00,0.14,1.46\n2015-5-14,58,46,69,51,70,37,92,1973,1982,0.13,0.13,1.95\n2015-5-15,70,60,79,51,70,34,91,1937,2001,0.03,0.13,0.85\n2015-5-16,75,67,82,52,70,35,94,1940,1962,0.0,0.13,1.38\n2015-5-17,77,69,84,52,71,36,94,1973,1962,0.0,0.13,1.40\n2015-5-18,66,51,81,52,71,34,93,2002,1996,0.00,0.15,0.79\n2015-5-19,49,44,54,52,71,37,95,1937,1934,0.00,0.14,1.54\n2015-5-20,47,43,51,53,72,36,95,1981,1977,0.02,0.14,2.54\n2015-5-21,59,47,70,53,72,34,94,2002,1934,0.0,0.14,1.79\n2015-5-22,59,50,68,53,72,40,93,1929,1977,0.00,0.14,1.11\n2015-5-23,68,53,83,54,72,35,92,1931,1977,0.00,0.14,1.41\n2015-5-24,69,62,76,54,73,36,93,1935,1950,0.44,0.14,1.31\n2015-5-25,75,68,81,54,73,35,90,1934,1950,0.06,0.14,2.65\n2015-5-26,73,69,77,55,73,34,93,1934,1967,0.42,0.14,1.49\n2015-5-27,71,64,77,55,74,35,97,1961,2012,0.06,0.13,1.49\n2015-5-28,72,60,84,55,74,40,94,1930,2012,0.00,0.15,2.20\n2015-5-29,78,71,84,56,74,36,95,1930,1942,0.18,0.14,0.83\n2015-5-30,59,45,73,56,75,36,95,1930,1942,0.79,0.14,1.60\n2015-5-31,51,44,57,57,75,41,102,1928,1934,0.05,0.15,2.97\n2015-6-1,55,48,62,57,75,39,107,2003,1934,0.00,0.15,1.88\n2015-6-2,58,45,71,57,76,44,92,1946,1944,0.00,0.14,2.35\n2015-6-3,67,55,79,58,76,37,98,1945,1934,0.00,0.15,1.15\n2015-6-4,72,62,82,58,76,35,97,1945,1977,0.00,0.15,1.00\n2015-6-5,65,56,73,58,77,37,98,1945,1934,0.00,0.15,0.75\n2015-6-6,64,54,73,59,77,44,97,1998,1971,0.00,0.15,2.73\n2015-6-7,72,60,83,59,77,45,101,1944,1933,0.44,0.15,3.11\n2015-6-8,75,63,86,60,78,45,97,1978,1985,0.10,0.15,2.07\n2015-6-9,74,61,87,60,78,42,96,1977,1968,0.00,0.14,2.58\n2015-6-10,83,71,94,60,78,41,101,1928,1933,0.09,0.14,2.97\n2015-6-11,72,65,79,61,79,41,100,1972,1933,0.20,0.14,2.08\n2015-6-12,66,58,74,61,79,48,97,1985,1956,0.17,0.14,1.33\n2015-6-13,73,58,88,62,79,44,95,1942,1990,0.19,0.15,4.64\n2015-6-14,78,70,85,62,80,40,99,1933,1987,0.98,0.14,2.58\n2015-6-15,77,71,83,62,80,45,95,1933,1994,0.24,0.13,1.93\n2015-6-16,67,60,74,63,80,44,95,1947,1952,0.0,0.14,1.78\n2015-6-17,65,58,72,63,81,42,96,1980,1987,0.33,0.13,3.16\n2015-6-18,71,59,82,63,81,48,98,1930,1954,0.48,0.13,1.12\n2015-6-19,63,57,68,64,81,44,102,1936,1953,0.00,0.14,1.79\n2015-6-20,70,57,82,64,82,45,104,1980,1953,0.22,0.13,2.56\n2015-6-21,79,70,87,64,82,45,101,1940,1988,0.0,0.13,1.53\n2015-6-22,79,72,85,65,82,46,98,1963,1988,0.04,0.14,1.25\n2015-6-23,74,67,81,65,82,46,99,1972,1930,0.00,0.12,2.20\n2015-6-24,73,66,79,65,83,48,99,1936,1937,0.00,0.13,2.31\n2015-6-25,74,65,83,65,83,46,104,1986,1988,0.16,0.12,4.58\n2015-6-26,66,61,70,66,83,52,98,1945,1936,0.70,0.12,2.57\n2015-6-27,68,59,77,66,83,50,102,1981,1933,0.0,0.12,2.88\n2015-6-28,70,58,81,66,83,49,104,1938,1934,0.0,0.12,1.66\n2015-6-29,71,66,75,66,84,50,97,1943,1954,0.03,0.11,1.91\n2015-6-30,71,61,80,66,84,45,101,1943,1931,0.00,0.11,2.59\n"
  },
  {
    "path": "us-weather-history/KNYC.csv",
    "content": "date,actual_mean_temp,actual_min_temp,actual_max_temp,average_min_temp,average_max_temp,record_min_temp,record_max_temp,record_min_temp_year,record_max_temp_year,actual_precipitation,average_precipitation,record_precipitation\n2014-7-1,81,72,89,68,83,52,100,1943,1901,0.00,0.12,2.17\n2014-7-2,82,72,91,68,83,56,100,2001,1966,0.96,0.13,1.79\n2014-7-3,78,69,87,68,83,54,103,1933,1966,1.78,0.12,2.80\n2014-7-4,70,65,74,68,84,55,102,1986,1949,0.14,0.13,1.76\n2014-7-5,72,63,81,68,84,53,101,1979,1999,0.00,0.12,3.07\n2014-7-6,75,66,84,68,84,54,103,1979,2010,0.00,0.13,1.97\n2014-7-7,81,72,90,68,84,56,100,1914,2010,0.04,0.13,3.13\n2014-7-8,81,71,91,69,84,56,100,1894,1993,0.39,0.14,1.80\n2014-7-9,80,71,88,69,84,54,106,1963,1936,0.09,0.14,1.09\n2014-7-10,78,72,83,69,84,55,102,1890,1993,0.00,0.15,1.79\n2014-7-11,79,71,86,69,84,57,98,1898,1988,0.00,0.15,1.94\n2014-7-12,78,71,85,69,84,57,99,1926,1966,0.00,0.15,2.68\n2014-7-13,78,72,83,69,84,54,101,1888,1966,0.03,0.17,3.16\n2014-7-14,78,72,84,69,84,58,100,1926,1954,0.46,0.16,1.60\n2014-7-15,79,72,86,69,84,57,102,1930,1995,1.30,0.15,2.33\n2014-7-16,75,68,81,69,84,56,99,1946,1980,0.0,0.16,1.38\n2014-7-17,74,67,81,69,84,57,100,1892,1953,0.00,0.15,3.13\n2014-7-18,73,64,81,69,84,57,101,1925,1953,0.00,0.15,1.76\n2014-7-19,72,68,76,69,84,57,102,1924,1977,0.00,0.16,1.82\n2014-7-20,72,66,78,69,84,55,101,1890,1980,0.0,0.15,1.97\n2014-7-21,76,67,85,69,84,55,104,1890,1977,0.00,0.15,2.26\n2014-7-22,79,71,86,69,84,55,104,1873,2011,0.00,0.14,1.86\n2014-7-23,80,72,88,69,84,58,100,1890,2011,0.19,0.15,2.41\n2014-7-24,75,70,80,69,84,56,97,1893,1999,0.00,0.15,3.75\n2014-7-25,74,66,82,69,84,57,97,1953,1999,0.00,0.15,1.64\n2014-7-26,75,69,81,69,84,55,98,1920,1940,0.00,0.16,3.80\n2014-7-27,78,71,85,69,84,55,98,1920,1963,0.02,0.16,2.65\n2014-7-28,75,68,82,69,84,57,97,1903,1999,0.19,0.17,3.11\n2014-7-29,70,64,76,69,84,59,99,1914,1949,0.00,0.17,3.47\n2014-7-30,72,63,80,69,84,57,98,1956,1988,0.00,0.17,3.56\n2014-7-31,75,68,82,69,84,57,102,1914,1933,0.00,0.17,2.29\n2014-8-1,78,71,84,69,84,59,100,1964,1933,0.00,0.16,2.85\n2014-8-2,69,63,74,69,84,58,100,1924,1955,0.41,0.16,2.49\n2014-8-3,71,66,76,69,84,55,97,1927,2005,0.07,0.17,2.71\n2014-8-4,77,70,84,69,84,56,100,1886,1944,0.00,0.16,3.25\n2014-8-5,81,71,90,69,84,56,101,1951,1944,0.00,0.16,1.44\n2014-8-6,77,70,83,69,84,57,97,1994,1955,0.00,0.16,3.31\n2014-8-7,75,66,83,69,84,57,104,1994,1918,0.00,0.15,2.18\n2014-8-8,74,65,83,69,84,54,99,1903,2001,0.00,0.15,2.60\n2014-8-9,77,66,87,69,84,57,103,1989,2001,0.00,0.15,4.10\n2014-8-10,78,68,88,69,83,55,98,1879,1949,0.00,0.14,4.64\n2014-8-11,79,71,87,69,83,56,102,1962,1944,0.00,0.14,2.39\n2014-8-12,75,70,79,68,83,55,97,1889,1944,0.19,0.14,3.62\n2014-8-13,75,68,82,68,83,55,99,1930,2005,0.53,0.14,2.70\n2014-8-14,70,63,77,68,83,54,99,1964,1988,0.00,0.15,5.81\n2014-8-15,67,61,73,68,83,54,97,1964,1988,0.00,0.14,1.52\n2014-8-16,71,63,78,68,83,55,96,1880,1944,0.00,0.15,4.80\n2014-8-17,74,66,82,68,83,56,95,1979,1944,0.01,0.14,2.86\n2014-8-18,72,63,81,68,83,55,94,1915,2002,0.00,0.14,3.95\n2014-8-19,73,63,83,68,83,55,94,1924,2002,0.00,0.15,2.53\n2014-8-20,77,70,84,68,82,55,97,1949,1955,0.00,0.14,3.63\n2014-8-21,74,65,83,68,82,53,96,1922,1955,0.35,0.15,4.19\n2014-8-22,72,65,79,67,82,52,95,1895,1916,0.06,0.14,1.85\n2014-8-23,72,67,77,67,82,51,92,1923,1916,0.01,0.14,3.03\n2014-8-24,72,64,80,67,82,52,94,1890,1972,0.00,0.14,3.61\n2014-8-25,76,64,88,67,82,52,95,1940,1948,0.00,0.14,1.86\n2014-8-26,80,70,89,67,81,53,103,1887,1948,0.00,0.12,3.24\n2014-8-27,80,70,90,67,81,50,101,1885,1948,0.00,0.13,4.16\n2014-8-28,74,66,82,66,81,50,100,1885,1948,0.00,0.13,3.99\n2014-8-29,71,61,80,66,81,50,99,1986,1953,0.00,0.12,2.68\n2014-8-30,73,65,80,66,81,50,98,1965,1973,0.00,0.12,2.30\n2014-8-31,82,73,90,66,80,50,100,1976,1953,0.62,0.12,3.76\n2014-9-1,82,75,88,66,80,51,97,1872,1953,0.00,0.13,3.84\n2014-9-2,85,77,92,65,80,51,102,1886,1953,0.00,0.13,2.12\n2014-9-3,79,72,86,65,80,50,99,1893,1929,0.00,0.13,3.32\n2014-9-4,78,69,87,65,79,47,97,1883,1929,0.00,0.14,3.48\n2014-9-5,80,72,87,64,79,51,94,1963,1985,0.00,0.13,2.45\n2014-9-6,79,67,91,64,79,48,97,1924,1881,0.11,0.13,3.26\n2014-9-7,73,65,81,64,78,46,101,1888,1881,0.00,0.14,2.07\n2014-9-8,70,65,75,64,78,54,93,1955,1919,0.00,0.13,4.86\n2014-9-9,68,63,73,63,78,48,94,1883,1915,0.00,0.13,0.86\n2014-9-10,72,63,80,63,77,43,97,1883,1983,0.00,0.13,1.38\n2014-9-11,76,69,83,63,77,43,99,1917,1983,0.00,0.13,2.90\n2014-9-12,70,62,78,62,77,46,94,1917,1961,0.00,0.14,2.35\n2014-9-13,64,58,69,62,76,46,94,1963,1952,0.26,0.14,3.94\n2014-9-14,62,53,71,62,76,46,93,1975,1931,0.00,0.14,3.82\n2014-9-15,63,55,71,61,76,45,92,1913,1927,0.00,0.15,4.16\n2014-9-16,64,58,70,61,75,47,93,1966,1915,0.37,0.15,5.02\n2014-9-17,64,55,73,61,75,45,93,1986,1991,0.00,0.15,3.37\n2014-9-18,67,57,76,60,75,44,91,1990,1891,0.00,0.15,3.92\n2014-9-19,60,54,66,60,74,44,94,1929,1983,0.00,0.15,4.30\n2014-9-20,66,57,75,59,74,44,93,1993,1983,0.00,0.15,2.32\n2014-9-21,71,67,75,59,73,40,95,1871,1895,0.15,0.15,5.54\n2014-9-22,63,55,71,59,73,41,95,1904,1914,0.0,0.15,2.34\n2014-9-23,62,52,71,58,73,41,97,1947,1895,0.00,0.14,8.28\n2014-9-24,65,58,71,58,72,40,89,1963,1959,0.00,0.15,2.26\n2014-9-25,61,57,64,57,72,40,90,1887,1970,0.32,0.14,2.36\n2014-9-26,68,58,77,57,71,42,91,1940,1970,0.00,0.15,2.34\n2014-9-27,72,60,83,57,71,41,90,1957,1933,0.00,0.15,3.13\n2014-9-28,74,64,84,56,70,41,88,1947,1881,0.00,0.16,3.84\n2014-9-29,73,67,79,56,70,42,88,1942,1945,0.00,0.16,2.18\n2014-9-30,67,62,71,55,70,39,89,1912,1986,0.0,0.16,2.64\n2014-10-1,63,61,65,55,69,36,88,1947,1927,0.02,0.15,4.98\n2014-10-2,66,61,70,55,69,39,90,1886,1927,0.00,0.14,2.16\n2014-10-3,64,56,71,54,68,38,87,1974,1919,0.00,0.14,1.55\n2014-10-4,61,52,69,54,68,37,88,1888,1941,1.18,0.13,4.05\n2014-10-5,54,46,61,54,68,35,94,1881,1941,0.00,0.15,1.99\n2014-10-6,60,50,69,53,67,36,90,1881,1941,0.00,0.14,2.39\n2014-10-7,67,63,71,53,67,39,88,1999,1944,0.06,0.15,4.09\n2014-10-8,68,62,73,52,67,37,87,1988,2007,0.04,0.14,4.30\n2014-10-9,62,55,68,52,66,37,86,2000,1916,0.00,0.15,7.33\n2014-10-10,58,52,64,52,66,35,91,1888,1939,0.0,0.16,2.16\n2014-10-11,55,50,60,51,65,34,85,1964,1949,0.33,0.15,3.06\n2014-10-12,56,48,63,51,65,35,86,1876,1954,0.00,0.14,4.26\n2014-10-13,59,52,65,51,65,33,87,1875,1954,0.05,0.14,2.75\n2014-10-14,70,63,76,50,64,37,84,1988,1920,0.00,0.15,1.76\n2014-10-15,73,69,77,50,64,32,84,1876,1956,0.69,0.14,1.70\n2014-10-16,66,61,71,50,64,34,87,1876,1897,1.11,0.14,2.15\n2014-10-17,65,59,71,50,63,33,90,1886,1938,0.00,0.14,2.28\n2014-10-18,63,56,70,49,63,35,82,1974,1928,0.00,0.14,2.45\n2014-10-19,50,44,56,49,63,30,83,1940,1963,0.00,0.14,4.35\n2014-10-20,51,42,60,49,62,31,80,1974,1969,0.00,0.14,2.78\n2014-10-21,61,55,67,48,62,32,84,1974,1920,0.11,0.14,2.17\n2014-10-22,54,50,58,48,62,30,88,1940,1979,1.51,0.15,1.51\n2014-10-23,52,50,53,48,61,32,85,1969,1947,0.61,0.14,2.97\n2014-10-24,57,51,63,48,61,31,79,1969,2001,0.00,0.14,2.51\n2014-10-25,59,50,67,47,61,29,79,1879,1963,0.01,0.15,3.30\n2014-10-26,58,53,63,47,61,30,78,1879,1964,0.0,0.13,3.40\n2014-10-27,56,48,63,47,60,28,82,1936,1963,0.00,0.14,1.88\n2014-10-28,63,53,72,47,60,29,83,1976,1919,0.00,0.13,2.49\n2014-10-29,62,51,72,46,60,31,78,1925,1971,0.0,0.14,3.67\n2014-10-30,53,47,59,46,59,31,82,1925,1961,0.00,0.13,1.64\n2014-10-31,50,45,55,46,59,29,81,1925,1946,0.05,0.14,2.41\n2014-11-1,45,42,47,46,59,30,84,1885,1950,0.35,0.14,1.69\n2014-11-2,45,41,48,45,58,30,83,1887,1950,0.00,0.14,1.70\n2014-11-3,50,39,61,45,58,28,79,1875,2003,0.00,0.13,2.60\n2014-11-4,61,53,68,45,58,25,78,1879,1975,0.00,0.13,1.44\n2014-11-5,60,56,64,45,57,23,78,1879,1961,0.00,0.13,1.94\n2014-11-6,53,48,57,44,57,27,74,1879,1948,0.37,0.14,1.47\n2014-11-7,47,40,53,44,57,29,78,1930,1938,0.00,0.13,2.96\n2014-11-8,42,36,48,44,57,29,76,1886,1975,0.00,0.12,7.40\n2014-11-9,52,46,57,44,56,24,75,1976,1975,0.00,0.13,3.65\n2014-11-10,53,44,61,43,56,27,73,1914,1985,0.00,0.13,1.70\n2014-11-11,57,49,64,43,56,28,74,1933,1949,0.00,0.12,1.41\n2014-11-12,56,47,65,43,55,26,76,1926,1879,0.00,0.12,2.39\n2014-11-13,42,36,48,43,55,22,73,1873,1931,0.20,0.13,2.06\n2014-11-14,39,35,42,42,55,20,72,1905,1993,0.06,0.13,2.23\n2014-11-15,38,33,42,42,54,20,80,1967,1993,0.00,0.13,2.43\n2014-11-16,40,35,45,42,54,17,72,1933,1928,0.03,0.13,2.39\n2014-11-17,46,40,52,41,53,19,71,1933,1953,1.54,0.14,1.54\n2014-11-18,35,24,45,41,53,18,73,1936,1928,0.00,0.14,1.24\n2014-11-19,29,22,36,41,53,18,72,1936,1921,0.00,0.14,1.95\n2014-11-20,38,31,45,40,52,20,77,1873,1985,0.00,0.13,3.37\n2014-11-21,33,28,37,40,52,16,74,1879,1900,0.00,0.14,1.33\n2014-11-22,36,28,44,40,52,13,72,1880,1931,0.00,0.14,2.03\n2014-11-23,50,43,57,40,51,14,72,1880,1931,0.00,0.14,1.84\n2014-11-24,61,53,69,39,51,14,73,1880,1979,0.70,0.14,1.95\n2014-11-25,60,51,68,39,50,19,73,1938,1979,0.00,0.13,1.36\n2014-11-26,43,34,51,39,50,16,67,1938,1946,1.24,0.14,1.91\n2014-11-27,36,34,38,38,50,12,72,1932,1896,0.02,0.13,2.15\n2014-11-28,33,29,37,38,49,15,70,1930,2011,0.00,0.14,2.14\n2014-11-29,36,27,45,38,49,15,69,1872,1990,0.00,0.14,2.01\n2014-11-30,50,45,55,37,49,7,70,1875,1991,0.00,0.15,1.11\n2014-12-1,54,42,65,37,48,9,70,1875,2006,0.09,0.14,1.72\n2014-12-2,39,35,43,37,48,12,66,1875,1970,0.08,0.14,2.16\n2014-12-3,44,41,46,36,47,9,69,1976,1998,0.06,0.15,1.63\n2014-12-4,41,37,45,36,47,10,74,1882,1998,0.00,0.14,1.84\n2014-12-5,39,34,44,35,47,11,70,1926,2001,0.51,0.14,1.28\n2014-12-6,45,39,50,35,46,11,71,1871,2001,1.22,0.13,1.60\n2014-12-7,36,30,42,35,46,10,75,1926,1998,0.04,0.14,1.98\n2014-12-8,31,24,37,34,45,10,65,1882,1927,0.00,0.14,1.54\n2014-12-9,39,36,42,34,45,7,66,1876,1966,2.54,0.13,2.54\n2014-12-10,36,32,40,34,45,3,70,1876,1946,0.08,0.14,1.62\n2014-12-11,35,31,38,33,44,6,64,1880,1879,0.01,0.14,2.41\n2014-12-12,35,32,38,33,44,5,68,1988,1931,0.00,0.13,1.60\n2014-12-13,39,34,44,33,44,8,64,1960,1923,0.00,0.13,3.03\n2014-12-14,42,38,46,32,43,12,67,1976,1881,0.00,0.13,2.22\n2014-12-15,43,37,48,32,43,6,67,1874,2008,0.00,0.13,1.34\n2014-12-16,44,38,49,32,43,7,63,1876,1971,0.20,0.12,2.25\n2014-12-17,48,42,54,32,42,1,62,1919,2000,0.02,0.13,2.28\n2014-12-18,40,37,42,31,42,-1,63,1919,1984,0.00,0.12,1.30\n2014-12-19,35,31,38,31,42,-1,58,1884,1931,0.00,0.12,1.58\n2014-12-20,32,30,33,31,42,-4,60,1942,2002,0.00,0.12,1.82\n2014-12-21,34,31,36,30,41,-1,65,1942,2013,0.0,0.13,2.49\n2014-12-22,40,35,44,30,41,2,71,1872,2013,0.04,0.12,2.18\n2014-12-23,45,43,46,30,41,-1,66,1883,1990,0.16,0.13,1.61\n2014-12-24,51,44,58,30,41,6,63,1983,1996,0.80,0.13,1.42\n2014-12-25,53,44,62,29,40,-1,64,1980,1982,0.09,0.13,1.30\n2014-12-26,45,40,50,29,40,3,63,1914,1982,0.00,0.12,1.66\n2014-12-27,50,44,55,29,40,6,63,1872,1949,0.00,0.12,2.14\n2014-12-28,49,43,54,29,40,8,65,1917,1982,0.10,0.12,1.35\n2014-12-29,39,34,44,28,39,-6,70,1917,1984,0.00,0.11,2.52\n2014-12-30,31,28,34,28,39,-13,65,1917,1984,0.00,0.12,1.69\n2014-12-31,30,27,32,28,39,-7,63,1917,1965,0.00,0.11,2.31\n2015-1-1,33,27,39,28,39,-4,62,1918,1966,0.00,0.12,2.05\n2015-1-2,39,35,42,28,39,2,68,1918,1876,0.00,0.12,1.92\n2015-1-3,38,33,42,28,39,-4,64,1879,2000,0.71,0.12,2.42\n2015-1-4,49,41,56,27,39,-3,66,1918,1950,0.30,0.12,2.73\n2015-1-5,35,21,49,27,38,-4,64,1904,1993,0.00,0.12,1.50\n2015-1-6,21,19,22,27,38,-2,72,1896,2007,0.05,0.13,1.65\n2015-1-7,16,9,23,27,38,4,64,2014,1907,0.00,0.12,2.67\n2015-1-8,15,8,21,27,38,2,65,1968,1998,0.00,0.11,1.25\n2015-1-9,26,19,33,27,38,-1,64,1968,1937,0.07,0.13,1.42\n2015-1-10,20,16,23,27,38,-6,60,1875,1876,0.00,0.12,1.72\n2015-1-11,28,18,37,27,38,3,63,1968,1975,0.00,0.12,1.46\n2015-1-12,37,35,39,27,38,2,64,1981,1890,0.36,0.13,2.35\n2015-1-13,27,17,36,27,38,-3,68,1914,1932,0.00,0.12,1.44\n2015-1-14,24,16,32,27,38,-5,70,1914,1932,0.00,0.12,2.06\n2015-1-15,30,25,35,27,38,0,67,1957,1932,0.00,0.13,1.27\n2015-1-16,32,20,43,27,38,1,58,2004,1995,0.00,0.12,1.44\n2015-1-17,25,17,32,27,38,-2,63,1977,1990,0.00,0.12,1.36\n2015-1-18,37,31,42,27,38,0,66,1982,1990,2.10,0.11,2.10\n2015-1-19,39,36,42,27,38,-3,64,1875,1951,0.00,0.12,2.39\n2015-1-20,36,32,40,27,38,0,61,1994,2006,0.00,0.11,1.41\n2015-1-21,31,25,36,27,38,-2,63,1985,2006,0.00,0.12,3.45\n2015-1-22,36,31,40,27,38,0,61,1888,1959,0.00,0.12,1.70\n2015-1-23,34,28,39,27,38,-3,62,1936,1906,0.00,0.11,2.55\n2015-1-24,36,33,39,27,38,-6,68,1882,1967,0.72,0.11,2.18\n2015-1-25,37,31,42,27,38,2,60,1945,1967,0.00,0.11,1.80\n2015-1-26,27,22,31,27,39,3,72,1927,1950,0.48,0.11,2.19\n2015-1-27,25,20,30,27,39,-1,69,1927,1916,0.36,0.11,1.94\n2015-1-28,25,16,34,27,39,-2,66,1925,1916,0.00,0.11,1.87\n2015-1-29,28,19,36,27,39,0,69,1873,2002,0.02,0.11,1.03\n2015-1-30,29,19,38,27,39,2,64,1873,2006,0.06,0.11,1.19\n2015-1-31,20,13,26,27,39,-1,63,1920,1947,0.00,0.12,1.51\n2015-2-1,28,20,36,27,39,-2,67,1920,1989,0.03,0.11,2.12\n2015-2-2,24,14,34,27,39,-3,59,1881,1988,1.02,0.11,2.98\n2015-2-3,20,13,26,27,39,0,64,1955,1991,0.00,0.11,1.55\n2015-2-4,34,24,43,28,40,0,68,1918,1991,0.00,0.10,2.10\n2015-2-5,28,14,42,28,40,-6,70,1918,1991,0.00,0.11,1.43\n2015-2-6,20,12,27,28,40,-4,68,1895,2008,0.00,0.11,2.74\n2015-2-7,33,25,40,28,40,1,54,1910,1938,0.02,0.11,2.96\n2015-2-8,33,29,37,28,40,-7,61,1934,1965,0.00,0.11,1.15\n2015-2-9,27,25,29,28,40,-15,63,1934,1990,0.09,0.10,1.74\n2015-2-10,33,26,40,28,41,-6,61,1899,2001,0.01,0.11,2.63\n2015-2-11,28,22,34,28,41,-2,65,1899,1960,0.00,0.10,2.74\n2015-2-12,28,16,40,28,41,-3,62,1914,1999,0.0,0.11,1.66\n2015-2-13,15,8,21,29,41,-1,64,1914,1951,0.00,0.10,2.42\n2015-2-14,24,16,32,29,41,2,63,1916,1946,0.02,0.11,1.59\n2015-2-15,15,4,25,29,42,-8,73,1943,1949,0.0,0.10,1.73\n2015-2-16,12,3,21,29,42,1,71,1888,1954,0.00,0.12,1.40\n2015-2-17,21,14,27,29,42,-5,67,1896,1976,0.14,0.11,1.49\n2015-2-18,26,19,33,29,42,0,68,1979,1981,0.0,0.11,1.50\n2015-2-19,18,8,27,30,42,1,66,1936,1997,0.00,0.11,2.15\n2015-2-20,11,2,19,30,43,2,69,2015,1939,0.00,0.11,3.07\n2015-2-21,23,13,32,30,43,4,68,1896,1930,0.61,0.11,1.86\n2015-2-22,38,32,43,30,43,8,69,1963,1997,0.10,0.11,2.39\n2015-2-23,23,8,38,30,43,5,70,1889,1985,0.00,0.12,1.38\n2015-2-24,14,4,24,30,44,-4,75,1873,1985,0.00,0.12,1.69\n2015-2-25,29,20,37,31,44,1,75,1914,1930,0.00,0.11,2.11\n2015-2-26,27,21,32,31,44,7,65,1990,1890,0.00,0.12,1.87\n2015-2-27,24,18,30,31,44,5,72,1900,1997,0.00,0.12,1.56\n2015-2-28,21,13,29,31,45,5,67,1934,1976,0.00,0.12,1.21\n2015-3-1,28,24,31,31,45,5,73,1884,1972,0.52,0.13,2.95\n2015-3-2,33,27,39,32,45,9,72,1891,1972,0.00,0.12,2.41\n2015-3-3,30,22,37,32,45,11,65,2003,1991,0.67,0.12,2.25\n2015-3-4,40,35,45,32,46,7,70,1943,1974,0.25,0.13,1.65\n2015-3-5,30,19,40,32,46,3,72,1872,1880,0.76,0.13,1.81\n2015-3-6,20,12,27,32,46,6,68,1872,1935,0.00,0.13,2.63\n2015-3-7,28,18,38,33,47,7,74,1890,1946,0.00,0.13,1.87\n2015-3-8,43,37,49,33,47,8,76,1883,1987,0.00,0.14,1.78\n2015-3-9,47,40,54,33,47,11,69,1996,2000,0.01,0.13,1.82\n2015-3-10,46,39,53,33,48,12,74,1929,2006,0.46,0.13,1.62\n2015-3-11,52,44,59,34,48,14,73,1960,1977,0.01,0.13,2.94\n2015-3-12,42,36,47,34,48,8,71,1888,1890,0.00,0.13,2.33\n2015-3-13,37,31,43,34,49,6,85,1888,1990,0.00,0.12,3.86\n2015-3-14,46,40,51,35,49,15,75,1896,1946,0.81,0.14,1.02\n2015-3-15,40,36,44,35,49,14,77,1993,1990,0.00,0.14,1.81\n2015-3-16,44,35,52,35,50,13,82,1911,1990,0.00,0.14,2.03\n2015-3-17,46,34,57,35,50,9,75,1916,1945,0.02,0.15,1.42\n2015-3-18,34,29,39,36,50,7,77,1916,1989,0.00,0.14,3.10\n2015-3-19,36,29,43,36,51,8,76,1967,1918,0.00,0.15,2.19\n2015-3-20,34,29,38,36,51,11,83,1885,1945,0.40,0.15,1.93\n2015-3-21,38,29,47,37,51,10,84,1885,1921,0.0,0.15,2.37\n2015-3-22,36,28,43,37,52,12,78,1885,2012,0.00,0.15,3.44\n2015-3-23,31,23,38,37,52,12,76,1875,1923,0.00,0.16,1.60\n2015-3-24,36,26,45,37,52,12,76,1888,1988,0.00,0.15,2.05\n2015-3-25,42,34,49,38,53,13,79,1878,1963,0.05,0.15,4.25\n2015-3-26,52,42,62,38,53,20,76,1960,1922,0.32,0.15,1.42\n2015-3-27,43,39,46,38,54,20,83,1894,1998,0.27,0.16,1.79\n2015-3-28,33,26,40,39,54,13,84,1923,1945,0.0,0.15,2.98\n2015-3-29,36,25,46,39,54,10,86,1923,1945,0.00,0.15,2.03\n2015-3-30,45,36,54,39,55,16,82,1887,1998,0.0,0.16,2.45\n2015-3-31,41,35,47,40,55,14,86,1923,1998,0.17,0.15,2.20\n2015-4-1,42,32,51,40,56,12,83,1923,1917,0.00,0.16,1.89\n2015-4-2,54,41,67,40,56,22,81,1919,1967,0.00,0.16,1.93\n2015-4-3,62,59,64,41,56,24,81,1954,1981,0.04,0.16,1.90\n2015-4-4,51,42,60,41,57,21,80,1874,1892,0.01,0.16,1.99\n2015-4-5,52,42,61,41,57,20,80,1874,1928,0.00,0.16,2.76\n2015-4-6,53,42,63,42,58,21,79,1982,1947,0.00,0.15,2.52\n2015-4-7,52,42,62,42,58,21,92,1982,2010,0.15,0.16,1.35\n2015-4-8,41,37,45,42,58,25,90,1982,1991,0.00,0.15,1.93\n2015-4-9,40,37,43,43,59,25,86,1977,1991,0.00,0.16,3.42\n2015-4-10,48,39,56,43,59,28,86,1997,1922,0.17,0.17,4.31\n2015-4-11,51,44,57,43,60,24,84,1909,1955,0.00,0.16,1.10\n2015-4-12,55,43,66,44,60,22,90,1874,1977,0.00,0.16,2.12\n2015-4-13,59,50,68,44,60,25,88,1874,1977,0.00,0.16,1.26\n2015-4-14,60,55,65,44,61,26,85,1950,1941,0.01,0.16,2.72\n2015-4-15,62,51,72,45,61,28,87,1943,1941,0.00,0.15,7.57\n2015-4-16,58,52,64,45,62,29,92,1928,2002,0.00,0.14,3.29\n2015-4-17,63,55,71,45,62,28,96,1875,2002,0.08,0.15,1.29\n2015-4-18,70,59,80,46,62,25,96,1875,1976,0.00,0.14,2.19\n2015-4-19,56,48,64,46,63,21,92,1875,1976,0.00,0.15,1.96\n2015-4-20,52,46,57,46,63,24,90,1897,1927,1.37,0.14,1.96\n2015-4-21,59,52,65,47,63,26,87,1875,1923,0.20,0.14,2.28\n2015-4-22,59,48,69,47,64,33,86,1947,2001,0.05,0.14,2.45\n2015-4-23,47,41,52,47,64,32,86,1930,2007,0.00,0.14,2.34\n2015-4-24,46,39,52,48,64,31,87,1930,2001,0.00,0.14,2.17\n2015-4-25,50,38,62,48,65,29,91,1919,1915,0.00,0.14,1.68\n2015-4-26,55,46,64,48,65,31,92,1919,2009,0.00,0.14,1.88\n2015-4-27,56,49,62,48,65,36,92,1932,1915,0.00,0.14,2.04\n2015-4-28,61,50,71,49,66,34,90,1934,1990,0.00,0.15,2.74\n2015-4-29,64,50,78,49,66,32,89,1874,1974,0.00,0.14,0.91\n2015-4-30,58,48,67,49,66,30,91,1875,1942,0.00,0.13,4.97\n2015-5-1,56,49,63,50,67,35,87,1880,2001,0.00,0.12,2.48\n2015-5-2,61,48,74,50,67,37,90,1903,2001,0.00,0.12,1.10\n2015-5-3,66,51,80,50,67,36,90,1874,2001,0.00,0.13,1.66\n2015-5-4,71,57,85,50,68,38,92,1917,2001,0.00,0.13,2.02\n2015-5-5,76,66,85,51,68,34,90,1891,1980,0.00,0.13,1.55\n2015-5-6,66,59,73,51,68,32,92,1891,1986,0.0,0.13,1.46\n2015-5-7,68,56,80,51,68,37,93,1891,2000,0.00,0.14,3.82\n2015-5-8,69,56,82,52,69,37,91,1947,2000,0.00,0.12,3.02\n2015-5-9,64,57,70,52,69,35,94,1947,1979,0.00,0.13,1.42\n2015-5-10,72,61,83,52,69,36,94,1966,1979,0.00,0.13,2.10\n2015-5-11,77,70,84,52,70,36,92,1913,1993,0.0,0.12,1.67\n2015-5-12,75,64,86,53,70,40,93,1907,1881,0.00,0.13,1.84\n2015-5-13,62,53,70,53,70,39,89,1895,1956,0.00,0.12,1.66\n2015-5-14,62,50,73,53,70,40,88,1878,1900,0.00,0.12,3.38\n2015-5-15,65,55,75,54,71,42,90,1947,1900,0.00,0.13,1.16\n2015-5-16,66,57,75,54,71,42,90,1878,1951,0.30,0.13,2.66\n2015-5-17,74,64,83,54,71,39,92,1956,1974,0.02,0.13,1.05\n2015-5-18,68,60,75,55,71,41,90,1973,1936,0.00,0.13,2.18\n2015-5-19,70,59,80,55,72,38,99,1976,1962,0.0,0.13,2.02\n2015-5-20,61,54,67,55,72,43,96,2002,1996,0.00,0.14,2.03\n2015-5-21,57,52,62,55,72,40,93,1907,1996,0.00,0.14,1.94\n2015-5-22,65,55,74,56,72,44,96,1990,1941,0.00,0.15,1.25\n2015-5-23,60,49,70,56,73,43,94,1963,1964,0.00,0.15,2.70\n2015-5-24,69,56,81,56,73,39,93,1963,1975,0.00,0.15,2.07\n2015-5-25,75,64,85,57,73,41,95,1925,1880,0.00,0.15,0.86\n2015-5-26,78,67,88,57,74,42,95,1967,1880,0.00,0.15,1.28\n2015-5-27,78,70,85,57,74,41,96,1961,1880,0.08,0.14,2.62\n2015-5-28,78,70,85,58,74,43,94,1961,1959,0.00,0.14,1.16\n2015-5-29,75,65,85,58,74,43,97,1902,1969,0.00,0.16,3.99\n2015-5-30,76,67,85,58,75,42,97,1884,1987,0.00,0.15,2.19\n2015-5-31,72,57,87,59,75,46,96,1938,1939,1.46,0.15,3.13\n2015-6-1,55,51,58,59,75,44,96,1945,1895,0.72,0.16,2.60\n2015-6-2,53,50,55,59,76,48,96,1946,1895,0.37,0.16,2.79\n2015-6-3,61,52,70,60,76,45,95,1929,1895,0.00,0.16,3.01\n2015-6-4,60,54,65,60,76,48,99,1926,1925,0.00,0.16,2.75\n2015-6-5,63,55,70,60,76,47,99,1945,1925,0.13,0.16,2.80\n2015-6-6,68,60,76,61,77,47,98,1945,1925,0.19,0.16,2.62\n2015-6-7,65,55,74,61,77,47,96,1897,1925,0.00,0.16,4.16\n2015-6-8,71,63,79,61,77,47,95,1932,1933,0.06,0.16,1.47\n2015-6-9,76,68,83,62,78,47,97,1980,1933,0.01,0.16,2.55\n2015-6-10,74,65,82,62,78,49,96,1972,2008,0.00,0.16,2.07\n2015-6-11,81,72,89,62,78,46,95,1972,1973,0.00,0.16,1.14\n2015-6-12,81,73,88,63,78,48,93,1979,1973,0.00,0.16,2.18\n2015-6-13,79,72,86,63,79,51,96,1953,1961,0.00,0.16,1.71\n2015-6-14,77,66,88,63,79,49,99,1875,1956,0.36,0.16,2.54\n2015-6-15,74,64,83,64,79,48,96,1933,1994,0.57,0.15,1.13\n2015-6-16,72,65,79,64,80,52,97,1927,1891,0.27,0.15,1.31\n2015-6-17,74,66,82,64,80,51,96,1926,1957,0.0,0.16,1.82\n2015-6-18,68,64,71,65,80,48,95,1950,1929,0.00,0.14,2.30\n2015-6-19,78,68,87,65,80,52,98,1920,1994,0.00,0.14,2.16\n2015-6-20,70,64,75,65,81,49,98,1914,1923,0.0,0.13,1.39\n2015-6-21,80,71,88,65,81,49,97,1897,1988,0.64,0.14,1.70\n2015-6-22,79,70,87,66,81,52,98,1940,1988,0.00,0.13,1.96\n2015-6-23,83,75,90,66,81,49,96,1918,1888,0.02,0.13,1.75\n2015-6-24,76,68,84,66,82,52,96,1932,1888,0.00,0.13,1.46\n2015-6-25,74,65,83,66,82,50,99,1873,1952,0.00,0.14,1.28\n2015-6-26,75,69,81,67,82,56,100,1979,1952,0.00,0.13,4.29\n2015-6-27,65,58,71,67,82,55,101,1940,1966,1.12,0.12,2.11\n2015-6-28,68,62,73,67,83,54,96,1995,1991,0.29,0.13,1.69\n2015-6-29,70,63,76,67,83,52,101,1919,1934,0.00,0.12,2.57\n2015-6-30,75,68,82,67,83,53,99,1919,1964,0.00,0.13,3.07\n"
  },
  {
    "path": "us-weather-history/KPHL.csv",
    "content": "date,actual_mean_temp,actual_min_temp,actual_max_temp,average_min_temp,average_max_temp,record_min_temp,record_max_temp,record_min_temp_year,record_max_temp_year,actual_precipitation,average_precipitation,record_precipitation\n2014-7-1,83,72,93,68,86,52,102,1988,1901,0.00,0.13,1.04\n2014-7-2,86,75,96,68,86,52,103,1965,1901,0.21,0.13,1.73\n2014-7-3,83,74,92,68,87,54,104,1957,1966,0.09,0.12,3.66\n2014-7-4,73,68,78,68,87,52,103,1986,1966,0.04,0.13,2.08\n2014-7-5,74,64,83,69,87,55,100,1963,1999,0.00,0.13,4.38\n2014-7-6,75,64,86,69,87,52,102,1960,2010,0.00,0.13,1.36\n2014-7-7,84,74,93,69,87,55,103,1968,2010,0.00,0.13,2.97\n2014-7-8,84,72,95,69,87,53,100,1960,1993,0.17,0.14,2.28\n2014-7-9,80,71,89,69,87,53,103,1963,1936,0.0,0.15,1.79\n2014-7-10,81,74,87,69,87,53,104,1963,1936,0.0,0.14,2.99\n2014-7-11,80,70,90,69,87,55,100,1963,1988,0.0,0.14,1.92\n2014-7-12,80,70,89,69,87,55,97,1999,1936,0.00,0.15,4.68\n2014-7-13,82,72,91,69,87,57,98,1998,1994,0.05,0.14,2.80\n2014-7-14,82,74,90,69,87,52,101,1940,1954,1.18,0.15,2.89\n2014-7-15,82,73,91,70,87,56,103,1940,1995,0.81,0.15,2.96\n2014-7-16,78,72,83,70,87,55,102,1946,1988,0.0,0.15,1.89\n2014-7-17,75,65,84,70,87,56,102,1946,1988,0.00,0.15,2.06\n2014-7-18,75,65,84,70,87,57,100,1976,2012,0.00,0.15,3.45\n2014-7-19,75,68,81,70,87,58,100,1962,1930,0.0,0.14,1.93\n2014-7-20,76,69,82,70,87,57,99,1890,1930,0.0,0.14,2.32\n2014-7-21,76,66,86,70,87,51,103,1966,1930,0.00,0.14,3.45\n2014-7-22,79,70,88,70,87,56,103,1944,2011,0.00,0.14,2.98\n2014-7-23,83,72,93,70,87,58,101,1950,2011,0.05,0.14,3.05\n2014-7-24,77,70,83,70,87,57,98,1985,2011,0.0,0.14,1.45\n2014-7-25,74,63,84,70,87,56,96,1953,1999,0.00,0.14,1.35\n2014-7-26,75,69,81,70,87,56,101,1920,1892,0.22,0.15,1.97\n2014-7-27,81,70,91,70,87,53,100,1962,1940,1.25,0.15,2.16\n2014-7-28,77,69,84,70,87,53,100,1962,1941,0.23,0.14,8.02\n2014-7-29,70,64,76,70,87,59,98,1914,2002,0.00,0.14,3.53\n2014-7-30,71,62,80,69,87,58,98,1981,1954,0.00,0.14,3.36\n2014-7-31,76,65,86,69,87,57,102,1895,1954,0.00,0.14,3.00\n2014-8-1,77,70,83,69,87,56,99,1964,2002,0.19,0.13,2.01\n2014-8-2,73,68,77,69,87,58,99,1947,1955,0.36,0.12,2.86\n2014-8-3,72,67,77,69,87,56,98,1959,1975,0.23,0.12,5.63\n2014-8-4,78,71,84,69,87,52,98,1965,1995,0.00,0.12,2.74\n2014-8-5,80,69,90,69,87,52,98,1951,1930,0.00,0.12,1.60\n2014-8-6,76,69,82,69,86,53,103,1957,1918,0.0,0.11,2.74\n2014-8-7,75,66,84,69,86,54,106,1964,1918,0.00,0.12,3.70\n2014-8-8,73,63,82,69,86,55,100,1989,2001,0.00,0.12,4.40\n2014-8-9,77,67,86,69,86,54,101,1964,2001,0.00,0.12,2.36\n2014-8-10,78,67,88,69,86,54,98,1964,1949,0.00,0.12,2.24\n2014-8-11,76,66,85,69,86,55,101,1972,1900,0.00,0.11,2.05\n2014-8-12,74,68,79,69,86,53,99,1962,1900,1.08,0.11,3.29\n2014-8-13,77,69,85,69,86,51,99,1941,2002,0.0,0.11,5.21\n2014-8-14,71,62,80,68,86,50,99,1964,2002,0.25,0.12,4.84\n2014-8-15,69,60,77,68,86,50,98,1964,1988,0.00,0.11,1.54\n2014-8-16,71,61,81,68,86,53,98,1979,1997,0.00,0.12,2.25\n2014-8-17,75,65,84,68,85,50,96,1979,1995,0.0,0.11,2.76\n2014-8-18,73,63,83,68,85,52,97,1961,1995,0.00,0.11,2.54\n2014-8-19,77,66,87,68,85,51,94,1963,2002,0.00,0.12,3.73\n2014-8-20,79,71,86,68,85,52,99,1949,1983,0.0,0.12,1.87\n2014-8-21,76,68,83,68,85,54,96,1949,1916,0.00,0.11,1.77\n2014-8-22,74,68,79,67,85,52,97,1940,1916,0.08,0.12,2.73\n2014-8-23,69,64,74,67,85,53,94,1952,1916,0.25,0.12,3.74\n2014-8-24,74,64,83,67,84,50,93,1957,1972,0.00,0.10,1.70\n2014-8-25,75,63,86,67,84,47,97,1963,1968,0.00,0.11,3.03\n2014-8-26,80,69,90,67,84,51,100,1963,1948,0.00,0.10,2.52\n2014-8-27,78,67,89,66,84,51,98,1963,1948,0.00,0.11,4.77\n2014-8-28,74,66,81,66,84,49,99,1986,1948,0.00,0.09,2.33\n2014-8-29,71,61,81,66,84,44,98,1986,1991,0.00,0.10,1.88\n2014-8-30,71,60,81,66,83,47,100,1986,1953,0.00,0.10,2.32\n2014-8-31,83,72,93,66,83,45,101,1965,1953,1.11,0.10,3.41\n2014-9-1,82,74,89,65,83,49,97,1963,1953,0.01,0.10,1.80\n2014-9-2,84,74,93,65,83,47,100,1963,1953,0.18,0.12,1.89\n2014-9-3,78,71,85,65,82,51,100,1958,1953,0.0,0.12,3.37\n2014-9-4,78,68,87,65,82,52,93,1997,1973,0.00,0.12,3.25\n2014-9-5,81,72,89,64,82,53,92,1994,1961,0.00,0.12,2.45\n2014-9-6,83,72,93,64,81,48,95,1962,1983,0.01,0.11,2.19\n2014-9-7,75,66,83,64,81,47,102,1984,1881,0.00,0.12,1.22\n2014-9-8,71,67,75,63,81,45,96,1962,1939,0.00,0.12,3.65\n2014-9-9,72,67,76,63,81,49,94,1956,1884,0.00,0.12,1.59\n2014-9-10,75,67,82,63,80,45,97,1956,1983,0.00,0.11,1.59\n2014-9-11,76,67,84,62,80,43,98,1917,1983,0.00,0.13,2.10\n2014-9-12,71,63,78,62,80,45,95,1940,1931,0.00,0.12,4.60\n2014-9-13,66,59,72,62,79,44,95,1958,1952,0.36,0.13,2.80\n2014-9-14,61,53,69,61,79,44,92,1975,1995,0.00,0.13,4.69\n2014-9-15,63,53,72,61,78,45,95,1895,1927,0.00,0.13,2.74\n2014-9-16,67,59,74,60,78,45,94,1984,1991,0.20,0.13,6.63\n2014-9-17,66,56,75,60,78,44,94,1959,1991,0.00,0.13,3.48\n2014-9-18,65,56,74,60,77,42,90,1959,1891,0.00,0.14,1.91\n2014-9-19,65,58,72,59,77,43,92,1959,1983,0.00,0.13,1.95\n2014-9-20,68,56,79,59,77,45,90,1956,1983,0.00,0.13,3.04\n2014-9-21,72,65,79,58,76,38,97,1956,1895,0.0,0.13,2.44\n2014-9-22,65,57,73,58,76,39,97,1962,1895,0.00,0.13,4.65\n2014-9-23,61,50,72,58,75,40,97,1983,1895,0.00,0.12,3.72\n2014-9-24,64,55,73,57,75,40,95,1963,1970,0.04,0.14,2.08\n2014-9-25,62,59,64,57,75,35,92,1963,1970,0.89,0.13,1.66\n2014-9-26,67,55,78,56,74,38,92,1963,1970,0.00,0.13,2.79\n2014-9-27,72,59,84,56,74,36,91,1947,1998,0.00,0.14,1.85\n2014-9-28,73,60,86,55,73,37,91,1957,1886,0.00,0.13,3.22\n2014-9-29,70,64,76,55,73,37,89,1942,1921,0.0,0.14,1.87\n2014-9-30,71,63,79,54,72,38,88,1942,1881,0.00,0.13,2.41\n2014-10-1,68,62,73,54,72,34,89,1947,1927,0.0,0.11,3.00\n2014-10-2,68,62,73,54,72,39,87,1966,2002,0.00,0.11,1.52\n2014-10-3,66,59,72,53,71,33,90,1974,1919,0.06,0.12,1.11\n2014-10-4,60,50,70,53,71,34,88,1974,1941,0.17,0.10,2.73\n2014-10-5,52,43,61,52,70,31,96,1961,1941,0.00,0.12,1.71\n2014-10-6,60,47,72,52,70,32,95,1965,1941,0.00,0.11,1.48\n2014-10-7,67,63,71,52,70,37,93,1954,1941,0.38,0.10,1.60\n2014-10-8,66,56,75,51,69,32,89,1964,2007,0.0,0.11,5.53\n2014-10-9,61,51,70,51,69,35,86,1978,1939,0.00,0.11,2.17\n2014-10-10,57,53,61,50,69,33,90,1979,1939,0.0,0.11,2.10\n2014-10-11,54,50,57,50,68,28,86,1964,1949,0.48,0.10,2.04\n2014-10-12,55,46,64,50,68,28,88,1964,1954,0.00,0.11,1.62\n2014-10-13,61,56,66,49,68,32,87,1988,1954,0.02,0.10,0.90\n2014-10-14,72,65,79,49,67,30,88,1988,1975,0.00,0.11,1.66\n2014-10-15,72,67,77,49,67,32,87,1876,1975,0.66,0.10,1.04\n2014-10-16,66,60,72,48,67,31,88,1876,1897,0.24,0.10,1.60\n2014-10-17,65,57,73,48,66,34,89,1961,1938,0.00,0.10,1.87\n2014-10-18,63,54,72,48,66,32,85,1982,1908,0.00,0.10,1.11\n2014-10-19,51,45,56,47,66,29,80,1976,1947,0.00,0.09,3.59\n2014-10-20,51,39,63,47,65,27,80,1974,1938,0.00,0.10,2.72\n2014-10-21,61,52,70,47,65,28,86,1952,1947,0.0,0.10,2.21\n2014-10-22,55,52,58,46,65,26,83,1940,1920,0.44,0.10,2.71\n2014-10-23,54,51,57,46,64,31,87,1969,1947,0.05,0.10,1.92\n2014-10-24,62,53,70,46,64,25,83,1969,2001,0.00,0.09,2.26\n2014-10-25,58,46,70,45,64,25,76,1962,1993,0.00,0.10,3.82\n2014-10-26,59,50,67,45,63,29,78,1960,1920,0.00,0.09,2.11\n2014-10-27,57,46,67,45,63,29,80,1936,1963,0.00,0.09,1.45\n2014-10-28,63,49,76,44,63,28,85,1976,1919,0.00,0.10,2.03\n2014-10-29,60,50,70,44,62,30,79,1965,1918,0.03,0.10,2.68\n2014-10-30,53,45,60,44,62,27,81,1962,1946,0.00,0.10,1.77\n2014-10-31,47,40,54,44,62,26,82,1966,1946,0.0,0.10,1.32\n2014-11-1,49,45,52,43,61,30,84,1968,1950,0.92,0.10,1.81\n2014-11-2,46,42,49,43,61,27,81,1976,1950,0.0,0.10,1.65\n2014-11-3,48,36,60,43,61,28,80,1951,1990,0.0,0.10,1.58\n2014-11-4,60,49,71,43,60,26,81,1951,1974,0.00,0.09,1.36\n2014-11-5,61,52,69,42,60,26,80,1879,1961,0.01,0.10,1.48\n2014-11-6,53,49,56,42,60,26,79,1962,1948,0.52,0.09,1.41\n2014-11-7,46,38,53,42,59,20,76,1962,1938,0.0,0.10,3.99\n2014-11-8,42,34,50,41,59,25,78,1960,1975,0.00,0.09,3.07\n2014-11-9,50,42,57,41,59,23,78,1976,1975,0.00,0.09,1.57\n2014-11-10,50,37,63,41,58,23,73,1961,1999,0.00,0.09,1.46\n2014-11-11,57,43,70,41,58,21,74,1961,1949,0.00,0.09,1.03\n2014-11-12,58,46,70,40,58,26,75,1960,1879,0.0,0.08,1.90\n2014-11-13,41,35,47,40,57,24,74,1996,1902,0.16,0.09,1.83\n2014-11-14,39,35,43,40,57,19,76,1986,1955,0.0,0.09,2.64\n2014-11-15,38,32,44,39,56,19,81,1942,1993,0.00,0.10,2.00\n2014-11-16,39,33,45,39,56,20,76,1933,2005,0.09,0.09,1.46\n2014-11-17,46,40,51,39,56,21,72,1933,1987,1.05,0.10,1.58\n2014-11-18,34,24,44,39,55,20,75,1936,1921,0.00,0.10,1.07\n2014-11-19,28,20,36,38,55,20,75,1936,1921,0.00,0.11,2.13\n2014-11-20,41,32,50,38,54,21,76,1984,1985,0.00,0.10,2.59\n2014-11-21,32,27,37,38,54,19,74,1879,1931,0.00,0.10,2.30\n2014-11-22,35,23,46,37,54,14,73,1880,1883,0.0,0.11,1.66\n2014-11-23,48,38,57,37,53,10,72,1880,1931,0.0,0.10,1.80\n2014-11-24,64,56,72,37,53,16,72,1880,2014,0.37,0.11,2.22\n2014-11-25,58,47,68,36,52,20,73,2013,1973,0.00,0.11,3.46\n2014-11-26,42,34,50,36,52,18,70,1938,1999,0.95,0.10,1.69\n2014-11-27,36,30,42,36,52,16,73,1932,1896,0.0,0.11,1.95\n2014-11-28,34,28,39,35,51,17,71,1930,1990,0.00,0.11,2.78\n2014-11-29,36,26,45,35,51,15,70,1955,1927,0.00,0.12,1.68\n2014-11-30,51,42,59,35,50,8,73,1875,1933,0.00,0.12,0.96\n2014-12-1,54,42,65,34,50,11,72,1875,2001,0.05,0.12,1.38\n2014-12-2,41,36,45,34,49,11,66,1875,1970,0.20,0.13,1.48\n2014-12-3,44,40,48,34,49,9,68,1976,1950,0.11,0.12,1.60\n2014-12-4,39,32,45,34,49,12,73,1966,1998,0.00,0.13,1.69\n2014-12-5,41,36,46,33,48,13,71,1886,2001,0.07,0.12,1.52\n2014-12-6,46,42,50,33,48,15,72,1901,2001,1.02,0.13,1.16\n2014-12-7,40,32,48,33,48,15,73,1926,1998,0.0,0.12,3.71\n2014-12-8,32,27,37,32,47,14,67,1882,1980,0.00,0.12,1.22\n2014-12-9,41,37,45,32,47,6,70,1876,1966,0.66,0.13,2.13\n2014-12-10,40,36,43,32,46,4,71,1876,1966,0.0,0.13,1.45\n2014-12-11,36,33,38,31,46,8,65,1880,1971,0.04,0.12,2.72\n2014-12-12,38,34,42,31,46,8,65,1988,1931,0.0,0.13,1.45\n2014-12-13,39,35,42,31,45,8,65,1960,1923,0.00,0.12,2.90\n2014-12-14,42,37,47,30,45,10,69,1960,1881,0.00,0.12,2.02\n2014-12-15,42,36,48,30,45,7,66,1960,1971,0.00,0.11,1.13\n2014-12-16,43,32,53,30,44,9,68,1876,1971,0.12,0.12,1.54\n2014-12-17,46,39,53,30,44,7,65,1919,2000,0.00,0.11,2.23\n2014-12-18,38,34,41,29,44,4,70,1919,2006,0.00,0.11,1.72\n2014-12-19,38,33,42,29,44,4,65,1884,1929,0.00,0.11,1.70\n2014-12-20,34,33,35,29,43,1,64,1942,1957,0.00,0.11,1.32\n2014-12-21,35,29,40,29,43,1,67,1942,2013,0.00,0.11,1.40\n2014-12-22,36,27,45,28,43,6,68,1989,2013,0.02,0.11,1.77\n2014-12-23,48,44,52,28,43,7,66,1989,1990,0.11,0.12,1.62\n2014-12-24,56,47,64,28,42,3,64,1983,1990,0.82,0.11,1.74\n2014-12-25,52,39,65,28,42,1,68,1980,1964,0.03,0.11,1.11\n2014-12-26,42,34,50,28,42,3,68,1983,1964,0.00,0.11,1.90\n2014-12-27,45,32,57,27,42,8,64,1989,1949,0.00,0.10,1.55\n2014-12-28,48,39,56,27,42,9,66,1950,2008,0.02,0.10,1.45\n2014-12-29,43,37,48,27,41,-1,72,1917,1984,0.00,0.10,2.80\n2014-12-30,33,28,38,27,41,-5,65,1880,1990,0.00,0.09,2.30\n2014-12-31,29,25,33,27,41,0,66,1880,1884,0.00,0.09,1.09\n2015-1-1,35,28,42,27,41,4,64,1881,2005,0.00,0.09,1.60\n2015-1-2,37,29,44,26,41,7,67,1968,1876,0.00,0.10,1.68\n2015-1-3,36,28,44,26,41,-3,63,1879,2000,0.72,0.09,1.50\n2015-1-4,50,42,58,26,41,2,68,1918,1950,0.37,0.10,1.51\n2015-1-5,37,24,50,26,40,-2,66,1904,1997,0.00,0.10,2.24\n2015-1-6,20,16,24,26,40,4,73,1896,2007,0.12,0.09,2.00\n2015-1-7,19,13,24,26,40,4,65,2014,1950,0.0,0.10,2.49\n2015-1-8,15,9,21,26,40,2,69,1970,1998,0.00,0.09,1.20\n2015-1-9,29,21,36,26,40,1,69,1970,1930,0.01,0.10,1.54\n2015-1-10,21,16,25,26,40,-5,63,1875,1950,0.00,0.10,1.28\n2015-1-11,28,15,40,26,40,1,66,1982,1975,0.00,0.10,1.29\n2015-1-12,36,33,38,25,40,1,72,1981,1890,0.59,0.10,2.19\n2015-1-13,30,21,38,25,40,0,70,1981,1932,0.00,0.10,1.46\n2015-1-14,26,20,32,25,40,0,73,1914,1932,0.00,0.10,2.27\n2015-1-15,33,29,36,25,40,2,71,1964,1932,0.00,0.10,1.23\n2015-1-16,34,25,43,25,40,0,62,1977,2007,0.00,0.10,1.09\n2015-1-17,26,18,33,25,40,-7,63,1982,1913,0.00,0.10,1.66\n2015-1-18,34,24,43,25,40,-4,64,1982,1990,1.84,0.10,1.84\n2015-1-19,38,30,45,25,40,-5,65,1994,1951,0.00,0.09,1.40\n2015-1-20,38,29,46,25,40,-3,65,1984,1951,0.00,0.10,1.89\n2015-1-21,31,27,34,25,40,-6,62,1985,1959,0.06,0.10,1.32\n2015-1-22,36,30,41,25,40,-7,70,1984,1906,0.03,0.09,1.03\n2015-1-23,34,25,42,25,40,-2,71,1936,1906,0.16,0.10,2.32\n2015-1-24,36,33,39,25,40,0,69,1882,1950,0.55,0.10,1.61\n2015-1-25,40,33,46,25,40,-2,66,1963,1967,0.0,0.10,1.83\n2015-1-26,31,27,34,26,40,4,74,1961,1950,0.0,0.09,1.57\n2015-1-27,28,21,34,26,41,4,69,1927,1916,0.07,0.10,1.64\n2015-1-28,28,20,35,26,41,3,67,1961,1916,0.00,0.09,1.63\n2015-1-29,28,21,34,26,41,-5,72,1963,2002,0.0,0.11,1.23\n2015-1-30,31,20,42,26,41,8,70,1948,1947,0.0,0.10,1.79\n2015-1-31,22,14,29,26,41,3,65,1948,1947,0.00,0.10,1.30\n2015-2-1,30,21,39,26,41,2,70,1920,2002,0.06,0.10,2.92\n2015-2-2,32,23,41,26,41,-4,61,1961,1988,0.93,0.10,1.49\n2015-2-3,25,20,30,26,41,4,62,1881,1991,0.00,0.09,1.64\n2015-2-4,35,23,47,26,42,2,69,1886,1991,0.00,0.10,1.34\n2015-2-5,29,17,41,26,42,-2,69,1918,1991,0.0,0.10,1.62\n2015-2-6,23,14,32,26,42,-3,68,1895,2008,0.00,0.10,3.86\n2015-2-7,33,21,44,27,42,5,67,1910,2008,0.00,0.09,1.35\n2015-2-8,42,33,51,27,42,-2,63,1934,1925,0.00,0.09,1.15\n2015-2-9,33,28,37,27,42,-11,64,1934,1990,0.03,0.10,1.17\n2015-2-10,35,28,42,27,43,-6,65,1899,1925,0.0,0.09,1.10\n2015-2-11,33,26,39,27,43,-6,69,1899,2009,0.00,0.09,1.85\n2015-2-12,31,20,41,27,43,2,70,1979,1999,0.00,0.09,1.35\n2015-2-13,17,10,24,27,43,3,66,1983,1951,0.00,0.09,2.04\n2015-2-14,25,18,32,28,44,2,67,1979,1990,0.04,0.09,1.26\n2015-2-15,15,9,21,28,44,3,74,1943,1949,0.0,0.10,1.09\n2015-2-16,10,3,17,28,44,2,73,1888,1954,0.02,0.09,1.84\n2015-2-17,21,13,28,28,44,-2,68,1896,1976,0.24,0.09,1.10\n2015-2-18,22,11,32,28,44,0,69,1979,1991,0.0,0.10,1.35\n2015-2-19,16,8,23,28,45,3,68,1903,1948,0.00,0.09,1.65\n2015-2-20,10,2,18,29,45,1,70,1979,1939,0.00,0.09,2.40\n2015-2-21,22,9,34,29,45,6,72,1968,1930,0.76,0.09,1.44\n2015-2-22,40,33,47,29,46,7,68,1963,1974,0.28,0.10,1.88\n2015-2-23,25,13,37,29,46,6,75,1963,1874,0.0,0.10,1.65\n2015-2-24,16,7,25,29,46,2,74,1889,1985,0.00,0.09,1.84\n2015-2-25,27,15,38,30,46,4,79,1894,1930,0.00,0.10,0.90\n2015-2-26,29,26,32,30,47,10,69,1970,1890,0.0,0.10,2.16\n2015-2-27,27,22,31,30,47,6,74,1900,1997,0.0,0.09,1.55\n2015-2-28,24,16,32,30,47,9,68,1934,1976,0.00,0.10,1.40\n2015-3-1,27,21,32,30,48,9,76,1980,1972,0.52,0.10,1.46\n2015-3-2,35,29,40,31,48,8,77,1886,1972,0.00,0.11,1.22\n2015-3-3,30,25,34,31,48,10,73,1886,1991,0.61,0.10,2.20\n2015-3-4,38,33,43,31,49,7,74,1943,1974,0.49,0.11,1.61\n2015-3-5,30,20,40,31,49,5,79,1872,1976,0.78,0.10,1.40\n2015-3-6,19,11,26,32,49,10,71,1978,1935,0.00,0.11,2.06\n2015-3-7,26,11,41,32,50,9,74,1960,1974,0.00,0.12,1.71\n2015-3-8,38,26,50,32,50,12,80,1986,2000,0.00,0.12,1.51\n2015-3-9,45,32,57,32,50,12,73,1960,2000,0.00,0.11,0.94\n2015-3-10,46,32,59,33,51,7,76,1984,2006,0.71,0.12,1.43\n2015-3-11,53,44,61,33,51,12,72,1960,1977,0.0,0.11,1.69\n2015-3-12,45,37,52,33,51,12,83,1900,1990,0.00,0.11,2.64\n2015-3-13,40,30,49,33,52,8,84,1888,1990,0.08,0.12,2.57\n2015-3-14,45,38,51,34,52,12,85,1888,1990,0.95,0.12,1.56\n2015-3-15,44,40,48,34,52,11,81,1993,1990,0.00,0.13,2.79\n2015-3-16,44,33,55,34,53,16,82,1916,1945,0.0,0.13,1.72\n2015-3-17,53,37,68,35,53,10,86,1885,1945,0.0,0.13,1.04\n2015-3-18,39,32,45,35,53,10,78,1916,1989,0.00,0.13,1.13\n2015-3-19,37,28,46,35,54,9,78,1876,1918,0.00,0.13,2.27\n2015-3-20,35,32,37,35,54,8,83,1885,1945,0.70,0.13,1.76\n2015-3-21,42,31,53,36,54,6,85,1885,1948,0.00,0.14,2.24\n2015-3-22,41,34,48,36,55,11,80,1885,1948,0.00,0.13,1.90\n2015-3-23,35,27,43,36,55,14,80,1958,2012,0.00,0.12,1.36\n2015-3-24,36,31,40,37,56,15,76,1896,1988,0.00,0.14,1.24\n2015-3-25,38,28,47,37,56,16,82,1878,1910,0.05,0.13,2.72\n2015-3-26,53,39,67,37,56,21,80,1960,1921,0.34,0.13,1.62\n2015-3-27,44,39,48,38,57,21,83,1894,1921,0.20,0.14,1.78\n2015-3-28,34,27,41,38,57,18,83,1923,1945,0.00,0.13,1.98\n2015-3-29,36,26,46,38,57,14,87,1923,1945,0.00,0.13,1.72\n2015-3-30,46,34,57,39,58,18,86,1887,1998,0.03,0.13,1.59\n2015-3-31,48,37,59,39,58,21,81,1923,1998,0.06,0.13,1.57\n2015-4-1,45,34,56,39,59,14,81,1923,1978,0.00,0.13,1.48\n2015-4-2,52,37,67,40,59,25,84,1964,1967,0.00,0.14,2.76\n2015-4-3,60,57,63,40,59,27,87,1894,1963,0.12,0.13,2.28\n2015-4-4,53,42,63,40,60,25,80,1943,1892,0.0,0.13,1.26\n2015-4-5,53,42,64,41,60,22,81,1981,1942,0.00,0.13,1.23\n2015-4-6,57,39,75,41,60,24,87,1982,1942,0.00,0.12,1.76\n2015-4-7,57,44,69,41,61,19,90,1982,1929,0.24,0.12,1.16\n2015-4-8,42,39,44,42,61,23,90,1982,1929,0.03,0.12,2.67\n2015-4-9,41,39,43,42,62,26,84,1977,1959,0.02,0.12,2.07\n2015-4-10,49,41,57,42,62,22,89,1985,2013,0.07,0.13,1.78\n2015-4-11,55,47,63,43,62,28,84,1966,1887,0.00,0.12,1.03\n2015-4-12,53,38,67,43,63,18,92,1874,1977,0.00,0.12,1.83\n2015-4-13,60,44,75,43,63,26,89,1940,1977,0.00,0.12,1.88\n2015-4-14,58,52,63,44,63,24,91,1950,1941,0.12,0.12,2.28\n2015-4-15,59,48,70,44,64,27,88,1943,1941,0.00,0.12,4.19\n2015-4-16,61,50,71,44,64,27,90,1943,2002,0.00,0.11,3.11\n2015-4-17,65,55,74,45,65,27,95,1962,2002,0.15,0.12,1.16\n2015-4-18,68,54,81,45,65,25,94,1875,1976,0.00,0.11,2.16\n2015-4-19,58,49,67,45,65,20,91,1875,1896,0.0,0.11,1.44\n2015-4-20,63,50,76,46,66,27,92,1897,1941,2.01,0.12,2.01\n2015-4-21,59,51,66,46,66,27,89,1875,1976,0.82,0.11,2.11\n2015-4-22,60,47,73,46,66,30,90,1875,1985,0.0,0.11,1.87\n2015-4-23,48,43,52,47,67,33,91,1982,1960,0.0,0.12,1.55\n2015-4-24,48,39,57,47,67,33,86,1930,1960,0.00,0.11,1.80\n2015-4-25,48,37,58,47,67,33,92,1956,1960,0.00,0.11,2.11\n2015-4-26,55,45,64,48,68,35,93,1967,2009,0.00,0.11,2.42\n2015-4-27,54,48,60,48,68,36,93,1967,1915,0.00,0.12,1.59\n2015-4-28,61,51,71,48,68,34,90,1946,1990,0.00,0.11,2.19\n2015-4-29,64,51,77,49,69,29,90,1874,1974,0.00,0.12,0.85\n2015-4-30,61,50,72,49,69,34,91,1874,1888,0.00,0.10,4.42\n2015-5-1,57,49,64,49,69,34,90,1978,1942,0.00,0.10,1.77\n2015-5-2,61,47,74,50,70,35,89,1960,2010,0.00,0.11,2.49\n2015-5-3,65,49,81,50,70,35,90,1966,2001,0.00,0.11,1.36\n2015-5-4,70,54,85,50,70,35,91,1957,2001,0.00,0.11,1.88\n2015-5-5,74,62,85,51,71,33,90,1966,1928,0.00,0.11,1.80\n2015-5-6,67,57,77,51,71,36,90,1891,1986,0.0,0.12,2.00\n2015-5-7,69,57,81,51,71,35,94,1970,1930,0.00,0.11,2.69\n2015-5-8,69,55,83,51,71,37,93,1947,1936,0.00,0.12,1.42\n2015-5-9,70,60,80,52,72,33,93,1947,1936,0.03,0.11,1.57\n2015-5-10,76,67,84,52,72,33,93,1966,1896,0.0,0.12,1.35\n2015-5-11,77,69,84,52,72,28,93,1966,1896,0.0,0.12,1.82\n2015-5-12,77,66,88,53,73,37,91,1962,1881,0.00,0.11,1.58\n2015-5-13,63,54,72,53,73,37,92,1967,1881,0.00,0.12,1.40\n2015-5-14,61,50,72,53,73,40,92,1996,1956,0.00,0.12,1.48\n2015-5-15,65,54,76,54,74,39,93,1966,1900,0.00,0.12,1.40\n2015-5-16,73,59,86,54,74,40,90,1959,1951,0.33,0.13,2.03\n2015-5-17,76,68,84,54,74,37,92,1956,1974,0.01,0.12,1.33\n2015-5-18,74,61,87,55,74,41,94,1973,1962,0.03,0.12,1.47\n2015-5-19,72,61,83,55,75,41,96,1882,1962,0.01,0.12,2.36\n2015-5-20,67,58,75,55,75,43,94,2002,1962,0.00,0.13,1.64\n2015-5-21,55,51,58,56,75,41,95,2002,1934,0.18,0.13,2.94\n2015-5-22,65,53,77,56,76,43,96,1895,1941,0.00,0.13,1.67\n2015-5-23,61,51,71,56,76,41,96,1961,1925,0.00,0.13,1.77\n2015-5-24,67,52,82,57,76,40,90,1963,1964,0.00,0.12,2.49\n2015-5-25,75,63,87,57,77,40,96,1963,1991,0.00,0.12,1.77\n2015-5-26,77,65,89,57,77,43,96,1961,1880,0.00,0.13,1.70\n2015-5-27,80,71,89,58,77,42,95,1961,1991,0.60,0.12,3.16\n2015-5-28,80,70,89,58,77,41,94,1961,1941,0.0,0.12,2.28\n2015-5-29,77,66,87,58,78,42,95,1884,1991,0.00,0.12,2.25\n2015-5-30,78,68,87,59,78,41,97,1961,1991,0.00,0.13,1.74\n2015-5-31,81,70,92,59,78,42,97,1965,1991,0.00,0.13,1.65\n2015-6-1,67,58,76,59,79,44,97,1967,1895,1.96,0.12,1.96\n2015-6-2,56,53,58,60,79,46,98,1907,1925,0.30,0.12,2.96\n2015-6-3,63,55,70,60,79,48,97,1986,1919,0.01,0.12,1.60\n2015-6-4,64,57,70,60,80,47,98,1977,1925,0.0,0.12,1.96\n2015-6-5,66,56,76,61,80,48,100,1990,1925,0.13,0.13,2.31\n2015-6-6,73,64,81,61,80,45,100,1945,1925,0.05,0.12,1.95\n2015-6-7,68,58,77,61,80,47,98,1958,1925,0.00,0.13,3.50\n2015-6-8,75,64,86,62,81,44,97,1977,2011,0.88,0.12,2.00\n2015-6-9,78,69,86,62,81,44,99,1957,2011,0.02,0.12,2.03\n2015-6-10,76,65,86,62,81,47,98,1988,2008,0.00,0.12,2.59\n2015-6-11,81,70,92,63,82,44,95,1972,1986,0.00,0.11,2.04\n2015-6-12,85,75,95,63,82,48,95,1979,1947,0.00,0.11,3.05\n2015-6-13,83,77,88,63,82,50,95,1979,1956,0.00,0.11,2.21\n2015-6-14,81,72,90,64,83,48,96,1965,1988,0.09,0.12,1.88\n2015-6-15,82,74,89,64,83,47,100,1884,1994,0.0,0.11,2.27\n2015-6-16,83,74,91,64,83,49,100,1963,1991,0.0,0.11,3.30\n2015-6-17,78,69,86,64,83,44,98,1964,1957,0.0,0.10,2.41\n2015-6-18,70,65,74,65,84,48,96,1950,1957,0.65,0.11,1.93\n2015-6-19,79,70,87,65,84,53,100,1946,1994,0.27,0.10,1.64\n2015-6-20,79,71,86,65,84,51,98,1956,1931,0.48,0.11,1.64\n2015-6-21,82,73,91,66,84,48,99,1968,1923,0.30,0.11,1.62\n2015-6-22,82,73,91,66,85,48,100,1963,1988,0.00,0.11,2.47\n2015-6-23,83,70,95,66,85,47,97,1963,1888,0.55,0.11,2.12\n2015-6-24,77,69,85,66,85,52,99,1947,1923,0.00,0.11,2.61\n2015-6-25,76,68,84,67,85,52,98,1979,1952,0.13,0.11,2.55\n2015-6-26,76,68,84,67,85,51,100,1960,1952,0.0,0.12,2.77\n2015-6-27,67,63,71,67,86,50,97,1965,1963,1.34,0.10,3.27\n2015-6-28,73,66,79,67,86,54,97,1961,1963,0.22,0.11,2.48\n2015-6-29,72,63,81,68,86,52,102,1888,1934,0.00,0.12,4.62\n2015-6-30,77,67,86,68,86,52,100,1988,1964,1.50,0.12,1.56\n"
  },
  {
    "path": "us-weather-history/KPHX.csv",
    "content": "date,actual_mean_temp,actual_min_temp,actual_max_temp,average_min_temp,average_max_temp,record_min_temp,record_max_temp,record_min_temp_year,record_max_temp_year,actual_precipitation,average_precipitation,record_precipitation\n2014-7-1,98,86,109,82,107,65,115,1927,1990,0.00,0.02,2.68\n2014-7-2,98,86,109,82,107,65,118,1911,2011,0.00,0.01,2.81\n2014-7-3,94,79,108,82,107,64,117,1916,1907,0.0,0.02,0.22\n2014-7-4,90,81,98,83,107,63,118,1912,1989,0.00,0.02,0.22\n2014-7-5,94,84,103,83,107,63,116,1912,1983,0.01,0.02,0.18\n2014-7-6,95,84,105,83,107,65,116,1902,1942,0.00,0.02,0.60\n2014-7-7,98,88,108,83,107,66,115,1915,1905,0.00,0.02,0.75\n2014-7-8,96,86,105,83,107,67,115,1955,1985,0.0,0.03,0.53\n2014-7-9,92,83,101,83,107,66,116,1926,1958,0.0,0.02,0.44\n2014-7-10,96,87,105,83,107,67,115,1926,1958,0.00,0.03,0.64\n2014-7-11,98,88,107,84,107,68,118,1944,1958,0.00,0.03,0.48\n2014-7-12,99,88,110,84,107,69,115,1944,2005,0.00,0.03,1.02\n2014-7-13,95,80,110,84,107,66,114,1944,1989,0.03,0.03,1.30\n2014-7-14,92,81,103,84,106,68,116,1962,2003,0.02,0.04,1.24\n2014-7-15,95,86,103,84,106,66,117,1944,1998,0.0,0.04,0.72\n2014-7-16,96,84,107,84,106,68,118,1907,1925,0.00,0.04,1.48\n2014-7-17,96,86,105,84,106,65,116,1908,2005,0.00,0.04,1.31\n2014-7-18,94,83,105,84,106,68,115,1946,1989,0.00,0.04,1.04\n2014-7-19,96,88,104,84,106,68,116,1913,1989,0.0,0.04,0.80\n2014-7-20,96,85,106,84,106,67,114,1900,1978,0.00,0.04,1.70\n2014-7-21,96,85,107,84,106,65,118,1924,2006,0.00,0.05,1.54\n2014-7-22,101,89,113,84,106,66,116,1924,2006,0.00,0.04,1.15\n2014-7-23,104,94,114,84,106,69,114,1913,2006,0.00,0.04,0.41\n2014-7-24,105,93,116,84,106,68,116,1913,2014,0.0,0.04,1.66\n2014-7-25,98,87,109,84,106,67,115,1897,1943,0.0,0.05,1.10\n2014-7-26,96,83,108,84,105,65,116,1952,1995,0.0,0.04,1.38\n2014-7-27,94,83,104,84,105,65,118,1913,1995,0.0,0.04,1.23\n2014-7-28,96,85,107,84,105,66,121,1913,1995,0.00,0.05,1.90\n2014-7-29,101,92,110,84,105,68,115,1913,1995,0.00,0.04,2.05\n2014-7-30,102,92,111,84,105,66,115,1913,1934,0.00,0.04,1.16\n2014-7-31,98,87,109,84,105,70,115,1921,1972,0.0,0.04,1.33\n2014-8-1,90,81,99,84,105,68,116,1950,1972,0.0,0.03,1.48\n2014-8-2,90,78,102,84,105,70,113,1946,1918,0.0,0.04,1.09\n2014-8-3,92,80,103,84,105,66,114,1956,1975,0.0,0.04,2.12\n2014-8-4,92,79,105,84,105,68,116,1956,1975,0.00,0.03,0.78\n2014-8-5,94,83,105,84,105,69,114,1965,1969,0.00,0.04,1.16\n2014-8-6,96,84,108,84,105,68,114,1949,1995,0.00,0.03,2.01\n2014-8-7,94,82,105,83,105,66,112,1928,1905,0.00,0.04,0.87\n2014-8-8,95,85,105,83,105,67,116,1903,2012,0.00,0.03,0.58\n2014-8-9,94,84,104,83,105,70,114,1930,2012,0.0,0.03,0.45\n2014-8-10,94,84,104,83,105,68,116,1949,2003,0.00,0.04,0.32\n2014-8-11,98,87,109,83,105,65,113,1949,1962,0.00,0.03,0.54\n2014-8-12,88,77,98,83,105,64,115,1949,2012,0.44,0.03,0.84\n2014-8-13,84,73,95,83,105,69,115,1949,2012,0.11,0.04,0.30\n2014-8-14,93,83,102,83,105,69,113,1925,1988,0.00,0.03,1.20\n2014-8-15,94,83,104,83,105,66,112,1968,1992,0.00,0.03,1.84\n2014-8-16,97,87,106,83,105,64,113,1918,1992,0.00,0.03,1.32\n2014-8-17,99,88,109,83,104,64,114,1949,2013,0.0,0.03,0.89\n2014-8-18,91,83,98,83,104,62,112,1918,2011,0.0,0.03,1.73\n2014-8-19,83,74,91,83,104,63,113,1918,1986,0.43,0.03,0.78\n2014-8-20,87,75,98,83,104,58,112,1917,1982,0.00,0.04,1.59\n2014-8-21,81,71,90,82,104,62,110,1916,2007,0.19,0.03,0.72\n2014-8-22,83,72,93,82,104,65,113,1917,2011,0.00,0.03,0.50\n2014-8-23,87,76,97,82,104,61,114,1968,2011,0.00,0.03,0.80\n2014-8-24,91,79,103,82,104,61,115,1965,1985,0.00,0.04,1.16\n2014-8-25,92,83,101,82,104,65,113,1965,2011,0.00,0.03,0.90\n2014-8-26,90,80,100,82,104,65,117,1928,2011,0.00,0.03,0.65\n2014-8-27,93,83,103,82,104,64,113,1920,1981,0.00,0.03,2.43\n2014-8-28,95,83,106,82,104,64,113,1920,1998,0.00,0.03,0.94\n2014-8-29,96,83,108,82,104,64,113,1920,1981,0.00,0.03,0.68\n2014-8-30,97,83,111,81,104,64,113,1920,2011,0.00,0.03,0.34\n2014-8-31,96,83,109,81,104,62,113,1962,1950,0.00,0.02,0.61\n2014-9-1,95,81,108,81,104,63,116,1962,1950,0.00,0.03,1.21\n2014-9-2,96,82,109,81,103,64,112,1964,1982,0.00,0.02,1.36\n2014-9-3,96,84,108,81,103,64,112,1964,1983,0.00,0.03,0.35\n2014-9-4,95,83,107,80,103,60,112,1921,1948,0.0,0.02,2.91\n2014-9-5,91,80,101,80,103,61,113,1912,1945,0.00,0.03,2.43\n2014-9-6,96,86,105,80,103,63,111,1921,1986,0.00,0.02,0.43\n2014-9-7,94,84,104,80,102,61,111,1895,1979,0.00,0.03,0.82\n2014-9-8,81,71,90,80,102,62,110,1912,1979,3.29,0.02,3.29\n2014-9-9,86,79,92,79,102,61,110,1920,1974,0.0,0.03,0.66\n2014-9-10,88,79,97,79,102,59,111,1912,1990,0.00,0.02,1.01\n2014-9-11,90,79,100,79,102,58,112,1912,1990,0.00,0.02,1.08\n2014-9-12,91,78,104,78,101,58,110,1920,1971,0.00,0.02,1.90\n2014-9-13,92,83,100,78,101,55,109,1952,2000,0.00,0.02,1.25\n2014-9-14,92,81,102,78,101,58,112,1915,2000,0.00,0.02,1.50\n2014-9-15,96,87,105,77,100,53,110,1915,2000,0.00,0.02,0.07\n2014-9-16,86,80,92,77,100,56,109,1906,1928,0.01,0.03,1.33\n2014-9-17,84,78,89,77,100,56,109,1906,1962,0.14,0.01,1.19\n2014-9-18,86,76,96,76,99,58,109,1950,2010,0.00,0.02,1.46\n2014-9-19,90,82,98,76,99,54,111,1965,2010,0.00,0.02,1.06\n2014-9-20,90,81,99,76,99,47,107,1965,2010,0.00,0.02,0.49\n2014-9-21,90,80,99,75,98,47,107,1965,2003,0.00,0.02,0.63\n2014-9-22,91,80,101,75,98,47,109,1895,1989,0.00,0.02,0.41\n2014-9-23,90,77,102,75,98,54,108,1965,1982,0.00,0.02,0.72\n2014-9-24,91,77,104,74,97,55,108,1968,2002,0.00,0.02,0.68\n2014-9-25,92,81,103,74,97,49,108,1913,1979,0.00,0.01,0.42\n2014-9-26,92,82,101,73,96,50,108,1900,1989,0.00,0.02,2.62\n2014-9-27,82,69,95,73,96,51,107,1920,2009,1.64,0.02,1.64\n2014-9-28,79,71,87,72,96,50,108,1923,1992,0.02,0.02,1.31\n2014-9-29,78,67,89,72,95,52,107,1923,2003,0.00,0.02,1.66\n2014-9-30,78,66,89,72,95,49,107,1965,1980,0.00,0.02,0.31\n2014-10-1,79,67,91,71,94,49,107,1927,1980,0.00,0.02,0.73\n2014-10-2,81,69,92,71,94,49,107,1927,1980,0.00,0.02,0.60\n2014-10-3,83,67,99,70,94,49,105,1907,1988,0.00,0.02,1.29\n2014-10-4,84,69,99,70,93,46,103,1908,1993,0.00,0.02,0.91\n2014-10-5,83,69,96,69,93,46,104,1908,1987,0.00,0.02,0.60\n2014-10-6,82,70,93,69,92,46,105,1913,1917,0.00,0.02,1.17\n2014-10-7,81,72,90,69,92,44,104,1913,1991,0.00,0.02,0.30\n2014-10-8,76,69,82,68,92,47,104,1913,1950,0.05,0.02,1.04\n2014-10-9,77,70,84,68,91,48,103,1949,1996,0.0,0.02,0.62\n2014-10-10,79,69,89,67,91,44,105,1961,1991,0.00,0.01,0.56\n2014-10-11,81,70,92,67,91,45,102,1920,1991,0.00,0.02,0.31\n2014-10-12,82,70,94,66,90,44,103,1924,1950,0.00,0.02,0.32\n2014-10-13,78,65,90,66,90,48,101,1969,1989,0.00,0.02,0.16\n2014-10-14,80,65,94,66,89,43,100,1920,1973,0.00,0.02,2.32\n2014-10-15,80,69,90,65,89,41,103,1899,1991,0.00,0.02,0.72\n2014-10-16,80,67,92,65,89,40,101,1899,1991,0.00,0.01,0.32\n2014-10-17,80,71,89,64,88,42,102,1938,2009,0.0,0.02,0.28\n2014-10-18,79,68,90,64,88,43,103,1966,2003,0.00,0.02,0.75\n2014-10-19,82,69,94,64,87,40,101,1917,2003,0.08,0.02,1.69\n2014-10-20,78,66,90,63,87,40,103,1949,2003,0.00,0.02,0.41\n2014-10-21,80,69,91,63,87,39,103,1949,2003,0.00,0.02,0.40\n2014-10-22,80,67,93,62,86,37,102,1906,2003,0.00,0.02,0.38\n2014-10-23,81,67,94,62,86,37,100,1906,2003,0.00,0.01,0.52\n2014-10-24,82,68,96,62,85,41,96,1920,1959,0.00,0.02,0.22\n2014-10-25,83,70,96,61,85,40,96,1956,1990,0.00,0.02,0.36\n2014-10-26,80,71,89,61,85,42,98,1920,2001,0.00,0.02,0.33\n2014-10-27,80,71,88,60,84,39,98,1918,2001,0.00,0.02,1.12\n2014-10-28,77,65,88,60,84,41,97,1908,2007,0.00,0.01,0.92\n2014-10-29,78,64,91,60,83,40,95,1971,2007,0.00,0.02,0.48\n2014-10-30,79,64,94,59,83,34,94,1971,1990,0.00,0.02,1.00\n2014-10-31,81,67,95,59,83,36,96,1900,1988,0.00,0.02,1.05\n2014-11-1,76,69,83,58,82,37,96,1971,1924,0.00,0.02,1.25\n2014-11-2,67,60,73,58,82,34,96,1956,1924,0.00,0.02,0.44\n2014-11-3,64,53,75,58,81,36,96,1956,2009,0.00,0.02,0.31\n2014-11-4,67,53,81,57,81,32,95,1956,2001,0.00,0.02,0.32\n2014-11-5,73,60,85,57,80,32,93,1922,2007,0.00,0.02,0.63\n2014-11-6,73,60,86,56,80,34,94,1922,2007,0.00,0.02,0.50\n2014-11-7,72,59,84,56,79,34,92,1947,2007,0.00,0.02,0.39\n2014-11-8,74,58,90,56,79,32,91,1897,1906,0.00,0.02,0.36\n2014-11-9,74,61,87,55,79,31,88,1897,1973,0.00,0.02,0.55\n2014-11-10,72,59,84,55,78,33,91,1948,2009,0.00,0.02,2.24\n2014-11-11,71,58,84,54,78,35,91,1935,1934,0.00,0.01,1.41\n2014-11-12,70,60,80,54,77,33,93,1898,1999,0.00,0.02,1.02\n2014-11-13,71,61,80,54,77,31,91,1938,1999,0.00,0.02,0.96\n2014-11-14,67,61,73,53,76,28,91,1916,1999,0.00,0.02,1.58\n2014-11-15,67,57,77,53,76,33,90,1925,1999,0.00,0.02,0.58\n2014-11-16,62,53,71,52,75,32,89,1916,1999,0.00,0.02,0.94\n2014-11-17,59,47,71,52,75,32,87,1964,2008,0.00,0.01,0.78\n2014-11-18,61,47,74,52,74,31,88,1958,2008,0.00,0.02,0.65\n2014-11-19,62,50,74,51,74,30,88,1921,1897,0.00,0.02,0.65\n2014-11-20,61,48,74,51,73,31,89,1956,2006,0.00,0.03,1.16\n2014-11-21,63,53,72,51,73,30,88,1948,1924,0.00,0.02,0.60\n2014-11-22,59,46,72,50,72,30,89,1953,1950,0.00,0.02,1.60\n2014-11-23,61,48,74,50,72,27,87,1931,1950,0.00,0.03,0.98\n2014-11-24,57,42,71,49,72,28,88,1931,1950,0.00,0.03,0.54\n2014-11-25,58,46,69,49,71,32,88,1938,1950,0.00,0.03,0.64\n2014-11-26,59,44,74,49,71,32,88,1966,1950,0.00,0.02,1.02\n2014-11-27,67,47,87,48,70,31,87,1966,2014,0.00,0.03,1.29\n2014-11-28,68,52,84,48,70,32,86,1896,1901,0.00,0.03,0.51\n2014-11-29,66,51,80,48,70,29,85,1919,1999,0.00,0.03,0.72\n2014-11-30,67,53,80,47,69,30,84,1911,1949,0.00,0.02,1.23\n2014-12-1,62,51,73,47,69,31,83,1918,1949,0.00,0.03,0.40\n2014-12-2,62,54,70,47,68,30,81,1913,1940,0.03,0.02,0.41\n2014-12-3,68,60,76,47,68,30,81,1934,1940,0.01,0.03,0.69\n2014-12-4,64,60,68,46,68,28,83,1909,1979,0.61,0.03,0.91\n2014-12-5,65,57,72,46,68,25,82,1909,1965,0.00,0.03,0.67\n2014-12-6,64,57,71,46,67,27,83,1960,1939,0.00,0.03,0.50\n2014-12-7,64,51,77,46,67,27,83,1948,1939,0.00,0.02,1.08\n2014-12-8,66,55,76,45,67,24,84,1916,1939,0.00,0.03,1.06\n2014-12-9,66,54,78,45,67,24,84,1916,1981,0.00,0.03,0.77\n2014-12-10,66,54,77,45,66,29,87,1967,1950,0.00,0.03,1.12\n2014-12-11,66,56,76,45,66,26,81,1916,1950,0.00,0.03,0.87\n2014-12-12,68,57,78,45,66,28,79,1971,2010,0.0,0.03,0.72\n2014-12-13,59,51,66,45,66,26,82,1911,2010,0.08,0.03,1.04\n2014-12-14,55,46,63,45,66,24,78,1901,2010,0.00,0.03,1.43\n2014-12-15,55,46,64,44,66,26,79,1916,1952,0.00,0.03,0.86\n2014-12-16,57,49,65,44,65,28,86,1931,1980,0.00,0.03,0.26\n2014-12-17,56,51,61,44,65,28,82,1927,2013,0.07,0.03,1.35\n2014-12-18,56,50,62,44,65,29,79,1933,1950,0.00,0.03,1.02\n2014-12-19,55,45,64,44,65,25,79,1968,1917,0.00,0.03,0.98\n2014-12-20,56,45,66,44,65,24,78,1897,1954,0.00,0.02,0.28\n2014-12-21,56,47,65,44,65,27,77,1897,1985,0.00,0.03,0.52\n2014-12-22,57,47,66,44,65,23,79,1897,1917,0.00,0.03,0.96\n2014-12-23,58,47,68,44,65,26,79,1990,1950,0.00,0.02,0.83\n2014-12-24,55,43,66,44,65,25,81,1953,1955,0.00,0.03,0.93\n2014-12-25,55,45,64,44,65,27,78,1953,1950,0.00,0.03,0.63\n2014-12-26,48,38,57,44,65,22,78,1911,1980,0.00,0.02,0.56\n2014-12-27,48,35,60,44,65,24,82,1911,1980,0.00,0.03,1.05\n2014-12-28,48,36,60,44,65,24,78,1954,1917,0.00,0.03,0.79\n2014-12-29,49,36,61,44,65,26,85,1966,1980,0.00,0.03,0.47\n2014-12-30,50,37,63,44,65,23,81,1895,1980,0.00,0.03,1.50\n2014-12-31,43,36,50,44,66,22,79,1900,1897,0.11,0.03,0.80\n2015-1-1,41,35,46,44,66,24,81,1919,1981,0.00,0.03,0.22\n2015-1-2,43,31,54,45,66,23,81,1919,1981,0.00,0.03,0.99\n2015-1-3,44,34,54,45,66,25,79,1950,1956,0.00,0.03,0.82\n2015-1-4,50,35,65,45,66,23,83,1950,1927,0.00,0.03,0.65\n2015-1-5,57,40,74,45,66,23,80,1950,1927,0.00,0.03,0.31\n2015-1-6,62,44,79,45,66,17,81,1913,2006,0.00,0.03,0.76\n2015-1-7,64,47,81,45,66,16,81,1913,2015,0.00,0.03,0.67\n2015-1-8,63,54,71,45,66,19,81,1913,2002,0.00,0.03,1.22\n2015-1-9,63,51,75,45,66,24,84,1964,1923,0.00,0.03,1.18\n2015-1-10,60,54,65,45,67,25,84,1964,1990,0.0,0.03,1.51\n2015-1-11,63,54,72,45,67,20,83,1964,1953,0.01,0.03,1.74\n2015-1-12,62,56,67,45,67,22,80,1962,1996,0.02,0.03,1.04\n2015-1-13,57,51,63,45,67,20,79,1963,1904,0.11,0.03,0.81\n2015-1-14,57,46,67,45,67,22,81,1963,2000,0.00,0.04,0.77\n2015-1-15,60,47,72,46,67,23,81,1964,2000,0.00,0.03,0.69\n2015-1-16,62,49,74,46,67,22,83,1964,1976,0.00,0.03,1.05\n2015-1-17,60,47,73,46,67,26,83,1964,1976,0.00,0.03,1.07\n2015-1-18,62,48,75,46,68,27,80,1915,1971,0.00,0.03,0.51\n2015-1-19,63,50,75,46,68,27,88,1915,1971,0.00,0.03,0.87\n2015-1-20,62,48,75,46,68,22,84,1963,1950,0.00,0.03,0.77\n2015-1-21,64,53,74,46,68,24,81,1922,2014,0.00,0.02,1.33\n2015-1-22,57,48,65,46,68,21,81,1937,2013,0.00,0.03,0.35\n2015-1-23,54,43,64,46,68,24,81,1937,2013,0.00,0.03,0.25\n2015-1-24,64,50,77,46,68,24,80,1937,1951,0.00,0.02,0.51\n2015-1-25,63,48,78,46,68,23,83,1898,1951,0.00,0.03,0.71\n2015-1-26,62,53,71,46,68,26,81,1950,1987,0.01,0.03,1.18\n2015-1-27,64,53,74,47,68,26,82,1950,2003,0.00,0.03,0.87\n2015-1-28,64,52,75,47,69,30,83,1937,1971,0.00,0.02,0.57\n2015-1-29,65,58,71,47,69,27,83,1948,1935,0.24,0.03,1.45\n2015-1-30,58,55,60,47,69,28,82,1970,1935,0.35,0.03,1.19\n2015-1-31,57,53,60,47,69,27,86,1949,2003,0.07,0.03,0.59\n2015-2-1,60,50,69,47,69,28,83,1985,2003,0.00,0.03,1.13\n2015-2-2,61,48,73,47,69,29,82,1922,1925,0.00,0.03,0.35\n2015-2-3,63,50,76,47,69,28,86,1922,1925,0.00,0.03,1.06\n2015-2-4,65,52,77,47,69,27,85,1955,1963,0.00,0.02,0.89\n2015-2-5,67,52,82,47,69,28,87,1964,1963,0.00,0.03,0.82\n2015-2-6,69,54,84,48,69,28,87,1955,1963,0.00,0.03,0.92\n2015-2-7,67,53,81,48,70,24,89,1899,1963,0.00,0.03,0.74\n2015-2-8,69,55,82,48,70,24,86,1933,1970,0.00,0.03,0.83\n2015-2-9,69,55,83,48,70,28,84,1933,1987,0.00,0.03,0.88\n2015-2-10,70,57,83,48,70,28,87,1939,1951,0.00,0.03,0.81\n2015-2-11,68,54,81,48,70,26,83,1933,1951,0.00,0.03,0.77\n2015-2-12,68,58,78,48,70,28,84,1965,1971,0.00,0.02,0.74\n2015-2-13,68,55,80,48,70,27,88,1948,1957,0.00,0.03,1.42\n2015-2-14,69,54,83,49,71,28,85,1966,1957,0.00,0.04,0.92\n2015-2-15,71,63,79,49,71,26,86,1949,2014,0.00,0.03,0.79\n2015-2-16,67,55,79,49,71,28,84,1964,1977,0.00,0.03,0.68\n2015-2-17,66,55,77,49,71,26,88,1910,2014,0.00,0.04,0.57\n2015-2-18,65,50,79,49,71,29,86,1964,1977,0.00,0.03,0.72\n2015-2-19,66,52,80,49,71,29,88,1955,1977,0.00,0.04,0.93\n2015-2-20,67,55,79,50,72,27,87,1955,1977,0.00,0.04,0.55\n2015-2-21,67,54,79,50,72,29,86,1955,1982,0.00,0.04,0.73\n2015-2-22,64,56,72,50,72,26,87,1955,1982,0.00,0.04,0.45\n2015-2-23,65,59,70,50,72,29,89,1955,1989,0.01,0.03,0.94\n2015-2-24,59,51,67,50,72,29,91,1964,1904,0.0,0.04,1.46\n2015-2-25,60,47,72,50,72,29,92,1960,1921,0.00,0.04,0.81\n2015-2-26,64,51,77,51,73,33,91,1919,1986,0.00,0.04,0.62\n2015-2-27,64,50,78,51,73,28,92,1964,1986,0.00,0.03,0.57\n2015-2-28,65,60,70,51,73,30,89,1962,1986,0.0,0.04,0.75\n2015-3-1,69,61,77,51,73,31,89,1964,1986,0.00,0.04,0.94\n2015-3-2,59,52,66,51,74,30,90,1971,1921,0.23,0.04,0.74\n2015-3-3,56,47,65,51,74,28,91,1966,1921,0.07,0.04,1.98\n2015-3-4,59,47,70,52,74,25,88,1966,1972,0.00,0.04,1.16\n2015-3-5,61,46,75,52,74,31,93,1966,1972,0.00,0.03,0.97\n2015-3-6,67,51,83,52,74,34,92,1967,1972,0.00,0.04,1.53\n2015-3-7,69,55,83,52,75,34,91,1931,1972,0.00,0.04,0.71\n2015-3-8,69,55,83,52,75,32,92,1969,1989,0.00,0.03,0.84\n2015-3-9,70,56,84,52,75,29,94,1964,1972,0.00,0.04,0.98\n2015-3-10,73,58,87,53,75,34,95,1964,1972,0.00,0.04,0.52\n2015-3-11,73,59,87,53,76,33,94,1899,1900,0.00,0.03,1.40\n2015-3-12,77,68,86,53,76,34,94,1909,1900,0.00,0.04,1.10\n2015-3-13,74,60,88,53,76,32,92,1954,1972,0.00,0.04,0.51\n2015-3-14,76,63,88,53,76,33,95,1969,2013,0.00,0.03,1.07\n2015-3-15,76,63,89,53,77,31,92,1962,2007,0.00,0.04,0.79\n2015-3-16,78,65,90,54,77,34,99,1917,2007,0.00,0.04,1.14\n2015-3-17,78,64,91,54,77,31,99,1917,2007,0.00,0.03,0.54\n2015-3-18,72,62,81,54,77,36,94,1954,2007,0.02,0.03,0.25\n2015-3-19,65,60,70,54,78,34,92,1898,2004,0.01,0.04,0.54\n2015-3-20,71,61,81,54,78,35,95,1898,2004,0.00,0.03,0.86\n2015-3-21,73,60,85,54,78,35,97,1955,2004,0.00,0.02,0.27\n2015-3-22,75,60,89,55,78,32,94,1897,1990,0.00,0.03,0.81\n2015-3-23,75,61,88,55,79,31,93,1897,1990,0.00,0.03,0.52\n2015-3-24,73,59,86,55,79,36,94,1949,1990,0.00,0.03,0.40\n2015-3-25,75,62,88,55,79,34,93,1929,1990,0.00,0.02,0.64\n2015-3-26,77,64,89,55,79,35,100,1913,1988,0.00,0.03,1.19\n2015-3-27,79,63,95,55,80,33,98,1898,1986,0.00,0.02,0.65\n2015-3-28,80,64,95,56,80,34,95,1898,1986,0.00,0.02,0.91\n2015-3-29,81,65,97,56,80,36,97,1907,2015,0.00,0.02,0.42\n2015-3-30,83,70,96,56,81,38,97,1898,2004,0.00,0.02,0.14\n2015-3-31,82,68,95,56,81,32,95,1897,2015,0.00,0.02,0.27\n2015-4-1,79,67,91,56,81,37,100,1904,2011,0.00,0.02,0.46\n2015-4-2,76,64,87,57,81,37,98,1917,1943,0.00,0.01,0.68\n2015-4-3,73,60,86,57,82,38,97,1906,1943,0.00,0.02,0.69\n2015-4-4,76,62,89,57,82,39,98,1955,1961,0.00,0.02,0.43\n2015-4-5,75,61,89,57,82,37,98,1921,1989,0.00,0.01,1.30\n2015-4-6,74,61,86,57,82,38,102,1909,1989,0.00,0.02,0.56\n2015-4-7,71,58,84,58,83,38,104,1922,1989,0.00,0.02,0.19\n2015-4-8,70,61,78,58,83,39,104,1929,1989,0.00,0.01,0.99\n2015-4-9,69,54,83,58,83,40,102,1953,1989,0.00,0.02,0.36\n2015-4-10,72,57,87,58,84,35,100,1922,1989,0.00,0.01,0.83\n2015-4-11,73,60,86,59,84,39,98,1979,1989,0.00,0.01,1.05\n2015-4-12,76,65,86,59,84,40,99,1967,1936,0.00,0.01,0.27\n2015-4-13,77,63,90,59,84,39,99,1927,1962,0.00,0.01,0.16\n2015-4-14,81,67,94,60,85,40,103,1945,1925,0.00,0.01,0.38\n2015-4-15,75,65,84,60,85,40,101,1970,1962,0.00,0.00,0.30\n2015-4-16,66,56,75,60,85,40,101,1924,1984,0.00,0.01,0.67\n2015-4-17,69,55,82,61,86,38,100,1924,1987,0.00,0.01,0.45\n2015-4-18,75,59,90,61,86,38,100,1896,1962,0.00,0.00,0.21\n2015-4-19,77,62,92,61,86,37,103,1968,1989,0.00,0.01,0.82\n2015-4-20,79,65,93,61,87,38,105,1933,1989,0.00,0.01,0.41\n2015-4-21,78,65,91,62,87,42,103,1967,1989,0.00,0.00,0.44\n2015-4-22,77,65,89,62,87,39,105,1904,2012,0.00,0.01,0.90\n2015-4-23,73,62,83,62,87,40,103,1923,2012,0.00,0.00,0.57\n2015-4-24,70,62,78,63,88,41,99,1923,1987,0.02,0.00,0.17\n2015-4-25,69,56,81,63,88,41,102,1964,1898,0.02,0.01,0.33\n2015-4-26,65,54,75,63,88,42,101,1964,1992,0.14,0.00,0.20\n2015-4-27,72,57,87,64,89,45,104,1963,1992,0.00,0.01,0.34\n2015-4-28,81,68,93,64,89,45,104,1896,1992,0.00,0.00,0.65\n2015-4-29,84,71,96,64,89,43,105,1970,1992,0.00,0.01,0.22\n2015-4-30,85,70,99,65,90,39,102,1967,1943,0.00,0.00,0.17\n2015-5-1,86,70,101,65,90,42,103,1915,1985,0.00,0.00,0.27\n2015-5-2,85,72,98,65,90,40,107,1967,1947,0.00,0.01,0.47\n2015-5-3,86,75,96,66,91,39,109,1899,1947,0.00,0.00,0.09\n2015-5-4,75,65,85,66,91,45,106,1899,1947,0.24,0.01,0.91\n2015-5-5,77,67,86,66,91,45,105,1964,1989,0.00,0.00,0.46\n2015-5-6,80,69,90,67,92,44,105,1964,1947,0.00,0.00,0.12\n2015-5-7,76,65,86,67,92,45,108,1896,1989,0.00,0.01,0.15\n2015-5-8,68,61,75,67,92,40,110,1965,1989,0.0,0.00,0.12\n2015-5-9,67,57,77,68,93,45,108,1950,1934,0.00,0.00,0.46\n2015-5-10,75,61,89,68,93,46,111,1930,1934,0.00,0.01,0.78\n2015-5-11,82,67,96,68,93,43,110,1905,1934,0.00,0.00,0.17\n2015-5-12,81,70,91,68,94,48,109,1905,1996,0.00,0.01,0.22\n2015-5-13,80,69,91,69,94,48,108,1962,1978,0.00,0.00,0.08\n2015-5-14,79,70,87,69,94,50,107,1907,1927,0.00,0.00,0.21\n2015-5-15,67,57,76,69,95,50,107,1968,1937,0.93,0.01,0.93\n2015-5-16,66,57,75,70,95,49,106,1955,1997,0.0,0.00,0.08\n2015-5-17,75,64,86,70,95,49,108,1943,1970,0.00,0.01,0.09\n2015-5-18,78,65,90,70,95,44,107,1903,1970,0.00,0.00,0.06\n2015-5-19,76,65,87,70,96,48,110,1903,2008,0.00,0.00,0.23\n2015-5-20,80,67,93,71,96,49,108,1902,2008,0.00,0.01,0.73\n2015-5-21,82,72,91,71,96,48,109,1902,2005,0.00,0.00,0.42\n2015-5-22,74,66,82,71,97,48,109,1962,2000,0.00,0.00,0.28\n2015-5-23,74,64,83,71,97,49,109,1965,2001,0.00,0.01,0.12\n2015-5-24,76,66,85,72,97,52,109,1944,1983,0.00,0.00,0.34\n2015-5-25,80,67,92,72,98,48,108,1965,2001,0.00,0.00,0.05\n2015-5-26,84,71,96,72,98,48,112,1916,1951,0.00,0.01,0.14\n2015-5-27,85,73,97,72,98,48,111,1917,1951,0.00,0.00,0.05\n2015-5-28,85,71,98,73,99,53,113,1929,1983,0.00,0.00,0.27\n2015-5-29,88,73,103,73,99,50,112,1918,1910,0.00,0.00,0.08\n2015-5-30,90,74,105,73,99,51,114,1918,1910,0.00,0.01,0.01\n2015-5-31,93,78,107,73,100,54,109,1918,2001,0.00,0.00,0.10\n2015-6-1,92,78,105,74,100,50,111,1917,2012,0.00,0.00,0.02\n2015-6-2,90,76,104,74,100,53,110,1917,1977,0.00,0.00,0.14\n2015-6-3,88,74,102,74,101,51,112,1965,2006,0.00,0.00,0.41\n2015-6-4,85,73,97,75,101,49,113,1908,1990,0.00,0.00,0.16\n2015-6-5,81,72,90,75,101,55,112,1932,1990,0.16,0.00,0.16\n2015-6-6,85,73,97,75,102,58,110,1967,2002,0.03,0.00,0.11\n2015-6-7,86,71,100,75,102,56,115,1963,1985,0.00,0.00,0.06\n2015-6-8,92,78,105,76,102,55,115,1950,1985,0.00,0.00,0.15\n2015-6-9,89,83,95,76,103,54,114,1907,1985,0.0,0.00,0.07\n2015-6-10,88,80,95,76,103,55,111,1965,1978,0.0,0.00,0.26\n2015-6-11,91,82,100,76,103,55,114,1913,1918,0.00,0.00,0.02\n2015-6-12,91,78,104,77,103,59,112,1913,1974,0.00,0.00,0.92\n2015-6-13,93,80,105,77,104,59,114,1917,1936,0.00,0.00,0.04\n2015-6-14,95,83,107,77,104,56,115,1922,1974,0.00,0.00,0.02\n2015-6-15,99,86,112,78,104,54,115,1907,1974,0.00,0.00,0.08\n2015-6-16,99,85,112,78,104,54,115,1907,1974,0.00,0.00,0.01\n2015-6-17,100,86,114,78,105,54,114,1965,1896,0.00,0.00,0.19\n2015-6-18,101,86,115,78,105,54,115,1921,1989,0.00,0.01,0.25\n2015-6-19,100,85,114,79,105,61,115,1965,1968,0.00,0.00,0.19\n2015-6-20,96,80,112,79,105,61,115,1964,1968,0.00,0.00,0.15\n2015-6-21,99,88,109,79,105,57,115,1923,1968,0.00,0.00,0.27\n2015-6-22,97,85,109,80,106,56,116,1965,1988,0.00,0.00,1.37\n2015-6-23,99,87,111,80,106,61,116,1923,1974,0.00,0.00,0.84\n2015-6-24,100,87,112,80,106,62,118,1945,1929,0.00,0.00,0.26\n2015-6-25,100,90,110,80,106,60,120,1965,1990,0.00,0.00,0.34\n2015-6-26,98,89,107,81,106,62,122,1963,1990,0.00,0.00,0.07\n2015-6-27,99,90,108,81,106,55,118,1965,1990,0.01,0.00,0.04\n2015-6-28,99,87,110,81,106,59,118,1965,1990,0.00,0.01,0.26\n2015-6-29,98,86,110,81,107,59,119,1913,2013,0.05,0.00,0.09\n2015-6-30,96,85,107,82,107,64,115,1913,1950,0.00,0.00,0.21\n"
  },
  {
    "path": "us-weather-history/KSAF.csv",
    "content": "date,actual_mean_temp,actual_min_temp,actual_max_temp,average_min_temp,average_max_temp,record_min_temp,record_max_temp,record_min_temp_year,record_max_temp_year,actual_precipitation,average_precipitation,record_precipitation\n2014-7-1,73,57,86,61,95,51,97,2004,1999,0.02\n2014-7-2,70,57,82,62,91,51,96,2004,2012,0.17\n2014-7-3,70,57,84,51,88,51,98,1980,2003,0.03\n2014-7-4,68,52,84,52,92,51,99,2005,2003,0.00\n2014-7-5,74,61,87,57,94,48,94,2004,1980,0.00\n2014-7-6,74,57,91,59,98,53,98,2004,1980,0.00\n2014-7-7,74,57,91,56,94,55,96,1997,2003,0.35\n2014-7-8,72,57,86,55,92,51,96,2005,2005,0.26\n2014-7-9,70,59,84,57,93,52,98,2015,2003,0.08\n2014-7-10,74,62,87,58,94,52,94,2008,1980,0.00\n2014-7-11,74,62,90,55,96,48,96,1999,1980,0.01\n2014-7-12,71,55,89,58,96,53,96,2002,1980,0.00\n2014-7-13,70,57,82,53,91,53,99,1980,2003,0.14\n2014-7-14,73,60,87,54,88,54,98,1980,2003,0.05\n2014-7-15,66,57,75,54,90,51,97,2002,2010,0.22\n2014-7-16,68,57,81,60,95,51,96,2005,2007,0.21\n2014-7-17,70,57,84,61,96,51,98,2013,2006,0.00\n2014-7-18,74,60,89,56,97,52,97,1998,1980,0.00\n2014-7-19,76,63,90,57,98,53,100,2000,2010,0.00\n2014-7-20,74,57,95,57,94,54,99,1999,2010,0.13\n2014-7-21,78,64,93,58,91,52,95,1999,2005,0.01\n2014-7-22,78,62,93,54,91,51,95,2000,1996,0.06\n2014-7-23,72,60,84,53,89,53,93,1980,2003,0.43\n2014-7-24,74,61,89,52,88,52,96,1980,2003,0.00\n2014-7-25,77,61,93,59,90,55,96,2004,2003,0.00\n2014-7-26,80,64,96,59,96,51,96,2004,1980,0.00\n2014-7-27,74,60,89,60,95,53,95,2004,1980,1.14\n2014-7-28,70,59,81,61,96,52,96,2004,1980,0.01\n2014-7-29,68,60,75,54,92,52,93,2004,2001,0.12\n2014-7-30,72,60,84,57,92,50,93,2004,2008,0.00\n2014-7-31,66,57,78,62,93,54,95,2003,2012,0.02\n2014-8-1,64,55,73,52,95,52,95,1980,1980,0.27\n2014-8-2,66,59,73,65,96,54,96,1998,1980,0.02\n2014-8-3,66,55,78,57,91,54,95,1998,2008,0.00\n2014-8-4,68,60,78,57,89,55,93,1998,2003,1.26\n2014-8-5,68,55,82,61,91,53,95,1998,2003,0.00\n2014-8-6,70,55,86,63,92,51,95,1998,2015,0.00\n2014-8-7,68,57,80,53,92,53,95,1980,2011,0.00\n2014-8-8,67,50,84,59,84,50,93,2014,2003,0.00\n2014-8-9,70,55,84,58,93,52,93,1999,1980,0.00\n2014-8-10,68,53,84,55,84,51,93,2000,2011,0.07\n2014-8-11,66,52,80,58,85,51,96,2004,2012,0.00\n2014-8-12,69,57,82,53,84,53,96,1980,2002,0.00\n2014-8-13,71,63,80,56,76,51,95,1999,1996,0.01\n2014-8-14,70,57,82,59,86,51,96,1998,1996,0.09\n2014-8-16,72,55,89,51,82,51,93,1980,2002,0.00\n2014-8-17,71,55,87,48,83,48,93,1980,2002,0.00\n2014-8-18,72,57,87,53,88,46,93,2008,2015,0.00\n2014-8-19,70,57,87,57,90,53,95,1997,2007,0.00\n2014-8-20,72,61,84,50,82,50,95,1980,2007,0.00\n2014-8-21,70,55,86,45,84,45,96,1980,2007,0.00\n2014-8-22,62,55,68,47,90,47,95,1980,2007,0.10\n2014-8-23,63,48,79,52,89,48,93,2014,2010,0.00\n2014-8-24,66,50,84,54,92,48,92,2002,1980,0.00\n2014-8-25,70,55,87,52,87,45,93,2004,2007,0.00\n2014-8-26,66,55,79,56,84,46,93,2001,2002,0.22\n2014-8-27,64,55,77,48,83,48,93,1980,2011,0.16\n2014-8-28,62,48,78,47,84,46,91,2006,1997,0.00\n2014-8-29,65,50,80,47,86,46,91,2005,1997,0.00\n2014-8-30,68,53,86,48,85,48,95,1980,2011,0.00\n2014-8-31,72,55,89,53,88,48,95,2002,2011,0.00\n2014-9-1,70,51,89,46,85,45,91,1980,2013,0.00\n2014-9-2,73,57,89,46,86,42,93,1980,2012,0.00\n2014-9-3,72,55,90,47,88,44,90,1980,2011,0.00\n2014-9-5,68,57,80,48,89,44,91,1980,2010,0.00\n2014-9-6,65,55,77,52,84,48,90,1978,2010,0.00\n2014-9-7,66,51,82,52,81,46,89,2003,2000,0.00\n2014-9-8,70,57,82,48,81,39,87,2001,2007,0.00\n2014-9-10,68,57,80,53,77,41,89,2001,2015,0.00\n2014-9-12,56,46,66,45,78,43,88,1978,2004,0.00\n2014-9-14,63,46,82,46,82,39,91,2005,1997,0.00\n2014-9-15,68,53,84,48,81,37,89,2005,2010,0.00\n2014-9-16,66,57,77,48,83,42,90,2002,2000,0.00\n2014-9-17,60,55,66,45,84,37,89,2006,2010,0.00\n2014-9-18,66,50,84,42,78,34,91,2006,2010,0.00\n2014-9-19,69,57,82,40,83,30,89,2006,2010,0.00\n2014-9-20,71,54,89,42,78,39,89,1978,2014,0.00\n2014-9-21,68,55,82,47,76,39,88,2006,2010,0.00\n2014-9-22,68,60,75,42,76,37,84,1980,2012,0.04\n2014-9-23,70,59,81,42,75,36,84,2006,1998,0.00\n2014-9-24,66,50,82,44,78,36,86,2006,1998,0.00\n2014-9-25,66,52,81,43,71,28,86,2000,1998,0.00\n2014-9-26,63,48,79,49,69,33,87,2000,1999,0.00\n2014-9-27,64,53,77,48,72,32,88,1996,2010,0.00\n2014-9-29,58,46,73,39,79,30,90,1999,2010,0.02\n2014-9-30,56,42,72,40,83,37,84,1999,1978,0.00\n2014-10-1,56,42,73,39,84,37,89,1980,2010,0.00\n2014-10-2,52,39,68,42,84,37,87,2001,1980,0.00\n2014-10-3,57,41,73,37,79,34,84,1980,2012,0.00\n2014-10-4,58,37,78,34,77,33,80,1980,2007,0.00\n2014-10-5,58,42,75,35,81,33,83,1980,1980,0.00\n2014-10-7,60,46,77,33,79,33,81,1978,1980,0.00\n2014-10-8,59,48,71,34,79,30,79,2012,1978,0.00\n2014-10-10,51,48,54,35,77,32,81,2001,1999,0.26\n2014-10-11,56,46,66,33,77,26,82,2001,2015,0.00\n2014-10-12,52,39,66,33,77,28,81,1997,2007,0.01\n2014-10-13,48,37,60,37,76,23,82,1997,2015,0.00\n2014-10-14,50,32,68,29,66,27,81,1978,2015,0.00\n2014-10-15,54,37,73,33,73,32,80,1997,2015,0.00\n2014-10-16,58,42,75,33,69,30,80,1997,2011,0.00\n2014-10-17,57,43,73,28,68,24,78,1980,1978,0.00\n2014-10-18,56,48,64,28,66,21,82,1980,2003,0.01\n2014-10-19,54,39,70,27,65,17,80,1980,2003,0.00\n2014-10-20,56,42,73,25,72,21,81,1980,2003,0.08\n2014-10-21,54,46,63,28,69,21,81,1996,2003,0.00\n2014-10-22,58,46,70,30,59,5,80,1996,2003,0.00\n2014-10-23,56,41,72,30,64,21,79,1996,2003,0.00\n2014-10-25,60,42,79,29,57,19,79,1980,2014,0.00\n2014-10-26,56,41,73,28,61,21,77,1997,2007,0.00\n2014-10-27,57,45,69,32,55,21,70,2012,2001,0.00\n2014-10-28,51,39,64,28,54,26,72,2012,2008,0.00\n2014-10-29,52,35,69,25,54,22,73,1980,2001,0.00\n2014-10-30,54,37,71,24,65,21,73,1980,2010,0.00\n2014-10-31,52,39,64,32,65,26,104,1980,1996,0.00\n2014-11-1,52,39,66,34,63,23,73,1977,2008,0.00\n2014-11-3,42,32,53,34,64,18,69,2011,2010,0.00\n2014-11-5,45,28,62,29,68,24,72,2003,1977,0.00\n2014-11-6,46,30,61,35,67,19,72,2008,1977,0.00\n2014-11-7,47,28,66,34,68,19,71,2000,1977,0.00\n2014-11-8,44,28,60,31,52,15,71,2000,2007,0.00\n2014-11-9,48,34,61,20,58,9,69,1977,2006,0.00\n2014-11-10,52,37,66,21,52,10,70,1977,2005,0.00\n2014-11-11,43,27,59,29,54,16,70,2012,1999,0.00\n2014-11-12,23,15,30,28,52,15,70,2014,1999,0.00\n2014-11-14,40,28,53,27,58,15,71,1997,1999,0.00\n2014-11-15,44,34,55,27,54,9,66,1997,1999,0.00\n2014-11-16,32,19,46,25,48,12,66,1997,1999,0.03\n2014-11-17,24,12,37,24,53,12,68,2014,2008,0.00\n2014-11-19,34,19,48,23,58,17,69,2000,2007,0.00\n2014-11-20,38,23,53,26,57,20,66,1978,1996,0.00\n2014-11-21,38,26,51,26,51,19,62,1998,1996,0.00\n2014-11-22,40,26,55,31,49,17,66,1999,2006,0.00\n2014-11-23,36,24,44,24,54,12,63,1999,1998,0.00\n2014-11-24,26,12,39,29,54,12,62,2014,1998,0.00\n2014-11-25,26,10,43,29,51,10,60,2014,1998,0.00\n2014-11-26,42,30,53,24,59,10,63,2000,1998,0.00\n2014-11-27,44,28,59,25,50,10,64,2001,1998,0.00\n2014-11-28,42,26,57,21,46,10,62,2001,1999,0.00\n2014-11-29,44,28,60,20,48,6,60,2004,2014,0.00\n2014-11-30,40,21,60,24,47,1,63,2004,2003,0.00\n2014-12-3,40,28,53,24,48,14,59,2004,2012,0.00\n2014-12-4,41,36,46,20,54,12,62,2006,1977,0.61\n2014-12-5,42,33,52,20,54,0,57,2011,2012,0.00\n2014-12-6,42,32,51,14,52,-2,57,2011,2012,0.03\n2014-12-8,42,30,55,18,55,-2,61,2005,1977,0.00\n2014-12-9,42,32,52,16,58,6,59,2012,1977,0.00\n2014-12-10,42,28,55,18,57,-2,60,2012,1977,0.00\n2014-12-11,44,30,57,20,55,3,57,1997,1993,0.00\n2014-12-12,44,32,55,18,48,3,57,1997,1977,0.00\n2014-12-13,42,30,55,18,47,10,57,2001,2010,0.00\n2014-12-14,34,25,43,18,49,9,59,1999,2010,0.02\n2014-12-15,32,23,42,16,51,9,59,1999,2010,0.00\n2014-12-16,32,25,39,10,35,10,54,1993,1998,0.00\n2014-12-17,30,26,34,13,40,7,53,2005,1998,0.17\n2014-12-18,29,19,39,14,39,1,53,1996,1998,0.00\n2014-12-19,30,21,39,10,45,10,51,1993,2003,0.00\n2014-12-20,30,21,39,16,41,10,54,1999,1998,0.00\n2014-12-21,38,32,45,11,35,9,54,2012,2005,0.00\n2014-12-22,41,28,52,17,34,3,55,1999,2005,0.00\n2014-12-23,26,18,35,7,30,5,60,2004,2005,0.00\n2014-12-25,35,25,45,9,43,3,55,1997,2005,0.00\n2014-12-26,20,10,30,13,44,0,55,1997,2005,0.00\n2014-12-27,16,3,30,18,41,1,51,1997,1999,0.00\n2014-12-28,23,12,34,22,50,6,54,2007,2005,0.00\n2014-12-29,23,9,37,15,48,5,57,2012,2005,0.00\n2014-12-30,16,10,21,11,48,10,57,2014,1998,0.00\n2014-12-31,16,8,25,17,49,0,57,2010,1996,0.00\n2015-1-10,36,24,46,26,53,10,57,2000,2005,0.00\n2015-1-11,36,24,48,32,50,8,54,2011,2000,0.00\n2015-1-12,32,25,39,29,43,3,57,2013,2000,0.00\n2015-1-13,30,26,33,22,48,-9,55,1999,2000,0.06\n2015-1-14,29,24,34,19,47,3,55,2013,2003,0.05\n2015-1-15,34,21,46,22,47,1,57,2013,2000,0.00\n2015-1-16,34,23,46,32,36,3,61,2007,2000,0.00\n2015-1-17,39,23,53,28,44,1,64,2008,2000,0.00\n2015-1-18,34,19,50,28,40,6,57,2001,1999,0.00\n2015-1-20,40,30,51,21,29,-4,61,2012,2005,0.00\n2015-1-21,32,27,36,21,31,10,57,2006,2000,0.01\n2015-1-22,21,12,30,22,38,3,55,2007,2003,0.00\n2015-1-24,31,18,44,10,48,8,57,2014,2013,0.00\n2015-1-25,38,23,52,7,30,7,55,1978,1999,0.00\n2015-1-26,43,26,60,11,38,11,60,1978,2015,0.00\n2015-1-28,46,36,57,23,47,10,62,2014,2003,0.00\n2015-1-29,42,37,48,23,51,9,57,2000,2011,0.00\n2015-1-30,32,28,37,23,53,10,59,2013,2016,0.11\n2015-4-19,49,36,63,33,56,22,75,1973,1954,0.00\n2015-5-1,62,46,81,37,66,28,81,2004,2015,0.00\n2015-5-2,64,48,81,29,67,1,81,2001,2006,0.00\n2015-5-3,59,43,75,36,69,21,84,2011,2000,0.00\n2015-5-4,60,48,73,29,72,29,87,1980,2000,0.04\n2015-5-6,56,46,70,36,75,25,84,1999,2000,0.00\n2015-5-7,56,41,72,41,76,30,84,2007,2000,0.00\n2015-5-9,48,37,55,32,67,27,84,1964,2004,0.00\n2015-5-10,47,30,64,40,73,30,88,2003,2000,0.00\n2015-5-11,54,37,71,38,76,28,82,2003,2000,0.00\n2015-5-12,54,46,63,36,73,32,84,2005,2006,0.00\n2015-5-13,56,45,69,34,74,29,84,1980,2003,0.02\n2015-5-14,57,45,69,42,77,27,85,2014,1978,0.00\n2015-5-15,50,42,57,41,79,28,89,2014,1978,0.00\n2015-5-17,52,37,66,38,71,37,87,1978,2003,0.00\n2015-5-18,57,42,73,36,67,35,84,1978,1998,0.00\n2015-5-19,56,46,66,38,74,36,89,1978,2006,0.00\n2015-5-20,56,42,72,40,81,30,90,2011,2005,0.00\n2015-5-21,50,45,57,45,69,35,90,2003,2005,0.00\n2015-5-22,55,41,70,44,76,32,91,2001,2005,0.00\n2015-5-23,55,44,68,43,82,36,95,2008,2000,0.00\n2015-5-24,50,39,64,39,80,37,93,1998,2000,0.00\n2015-5-25,53,42,66,37,79,33,90,1980,2006,0.00\n2015-5-26,55,37,75,38,80,37,90,2015,2001,0.00\n2015-5-27,62,48,77,42,81,39,88,1998,2001,0.00\n2015-5-28,64,48,80,35,79,33,91,1980,2000,0.00\n2015-5-29,62,46,80,40,80,37,95,1980,2000,0.00\n2015-5-31,66,52,81,39,85,34,95,1980,2002,0.00\n2015-6-1,70,55,87,47,87,41,93,1980,2002,0.00\n2015-6-2,68,50,86,44,84,35,91,1980,1998,0.00\n2015-6-3,67,48,86,39,79,33,91,1980,2006,0.00\n2015-6-5,70,61,80,44,86,36,96,1980,2006,0.00\n2015-6-6,72,57,84,42,83,39,93,1999,2006,0.00\n2015-6-7,68,53,84,42,82,37,91,2007,2004,0.00\n2015-6-8,70,53,88,48,87,39,93,2007,2013,0.00\n2015-6-9,71,60,84,55,85,39,91,1998,2001,0.00\n2015-6-10,70,60,82,49,86,41,98,1999,2013,0.00\n2015-6-12,65,48,82,50,90,43,97,1998,2013,0.00\n2015-6-14,66,51,81,45,91,39,95,2001,2006,0.00\n2015-6-15,66,51,82,45,92,39,97,1980,2008,0.00\n2015-6-16,68,55,82,49,90,42,93,1997,2002,0.00\n2015-6-17,68,51,86,55,91,46,96,2005,2008,0.00\n2015-6-18,72,55,90,50,90,39,95,1998,2002,0.00\n2015-6-20,74,55,93,44,89,42,97,1980,2012,0.00\n2015-6-21,80,63,97,52,91,43,97,2003,2015,0.00\n2015-6-22,78,60,99,46,90,45,99,2003,2015,0.00\n2015-6-23,75,60,91,47,92,43,96,1980,2011,0.00\n2015-6-24,75,61,89,53,95,47,99,1980,2012,0.00\n2015-6-25,76,62,91,54,94,48,96,2003,2007,0.00\n2015-6-26,73,55,91,49,95,48,97,1978,1980,0.00\n2015-6-27,74,61,84,56,94,48,102,2006,2013,0.00\n2015-6-28,72,55,91,54,91,46,98,2010,1998,0.00\n2015-6-29,72,57,90,58,92,50,99,2005,1998,0.00\n2015-6-30,72,55,91,53,84,50,99,1978,2012,0.00\n"
  },
  {
    "path": "us-weather-history/KSEA.csv",
    "content": "date,actual_mean_temp,actual_min_temp,actual_max_temp,average_min_temp,average_max_temp,record_min_temp,record_max_temp,record_min_temp_year,record_max_temp_year,actual_precipitation,average_precipitation,record_precipitation\n2014-7-1,77,60,94,54,73,45,94,1948,2014,0.00,0.03,0.75\n2014-7-2,70,58,81,54,73,43,93,1954,2015,0.00,0.03,0.50\n2014-7-3,64,57,71,54,74,44,92,1954,2015,0.00,0.03,0.47\n2014-7-4,66,57,75,54,74,43,92,1949,2015,0.00,0.03,0.57\n2014-7-5,66,56,76,55,74,47,91,1951,1958,0.00,0.03,0.64\n2014-7-6,72,59,84,55,74,45,94,1949,1960,0.00,0.03,0.44\n2014-7-7,73,64,81,55,74,47,90,1955,2010,0.00,0.03,0.58\n2014-7-8,73,60,86,55,75,47,95,1950,2010,0.00,0.03,0.80\n2014-7-9,69,57,80,55,75,48,93,1960,2010,0.00,0.02,0.81\n2014-7-10,70,55,84,55,75,46,90,1960,1945,0.00,0.03,0.38\n2014-7-11,74,59,88,55,75,49,98,1960,2007,0.00,0.03,0.25\n2014-7-12,76,62,90,55,75,47,97,1954,1961,0.00,0.02,0.64\n2014-7-13,72,59,85,56,76,50,93,2010,1961,0.0,0.02,0.85\n2014-7-14,71,59,82,56,76,50,91,1982,1996,0.00,0.03,0.74\n2014-7-15,73,57,88,56,76,49,92,1982,1958,0.00,0.02,0.11\n2014-7-16,73,58,88,56,76,50,98,1968,1979,0.00,0.02,0.65\n2014-7-17,69,57,80,56,76,48,96,1950,1979,0.00,0.02,0.35\n2014-7-18,64,53,75,56,76,47,93,1952,1995,0.00,0.02,0.29\n2014-7-19,69,59,78,56,77,48,95,1962,1988,0.00,0.02,0.45\n2014-7-20,63,58,67,56,77,50,100,1953,1994,0.00,0.02,0.60\n2014-7-21,66,56,75,56,77,48,97,1951,2006,0.00,0.01,0.29\n2014-7-22,63,56,70,56,77,49,96,1973,2006,0.01,0.02,0.33\n2014-7-23,61,56,66,56,77,47,99,1955,1991,0.76,0.02,0.76\n2014-7-24,62,55,69,56,77,43,95,1953,2004,0.0,0.02,0.28\n2014-7-25,64,54,73,56,77,47,94,1954,1988,0.00,0.02,0.40\n2014-7-26,68,56,79,56,77,49,92,1952,1971,0.00,0.02,0.73\n2014-7-27,71,59,83,57,77,46,95,1956,1958,0.00,0.01,0.30\n2014-7-28,73,59,87,57,77,47,97,1953,1958,0.00,0.02,0.22\n2014-7-29,73,60,86,57,77,45,103,1954,2009,0.00,0.02,0.17\n2014-7-30,72,58,85,57,77,48,96,1951,2009,0.00,0.01,0.39\n2014-7-31,76,64,87,57,77,47,93,1954,1959,0.00,0.02,0.26\n2014-8-1,72,59,84,57,77,50,92,1954,1949,0.0,0.02,0.52\n2014-8-2,73,60,85,57,77,46,89,1953,1977,0.02,0.02,0.76\n2014-8-3,74,58,89,57,77,46,90,1956,1988,0.00,0.01,0.47\n2014-8-4,76,61,91,57,77,44,95,1950,1993,0.00,0.02,0.61\n2014-8-5,67,57,77,57,77,49,93,1950,2012,0.00,0.02,0.36\n2014-8-6,69,59,79,57,77,50,91,1974,1972,0.00,0.02,0.57\n2014-8-7,67,56,78,56,77,48,95,1948,1972,0.00,0.02,0.95\n2014-8-8,67,56,78,56,77,47,98,1974,1960,0.00,0.02,0.13\n2014-8-9,71,60,81,56,77,47,99,1950,1960,0.00,0.03,0.61\n2014-8-10,72,57,87,56,77,46,98,1954,1981,0.00,0.02,0.28\n2014-8-11,80,64,96,56,77,46,96,1954,2014,0.02,0.03,0.32\n2014-8-12,72,63,81,56,77,49,96,1949,1977,0.50,0.02,0.56\n2014-8-13,67,59,74,56,77,44,92,1955,2002,0.85,0.03,0.85\n2014-8-14,67,63,70,56,77,44,95,1955,2010,0.0,0.03,1.60\n2014-8-15,69,62,76,56,77,47,96,1955,2010,0.04,0.03,1.20\n2014-8-16,69,60,78,56,77,50,98,1986,1967,0.00,0.03,0.41\n2014-8-17,71,59,82,56,76,45,96,1951,1977,0.00,0.03,0.93\n2014-8-18,73,60,85,56,76,47,88,1951,1991,0.00,0.03,1.63\n2014-8-19,71,60,81,56,76,48,88,1959,1967,0.00,0.03,0.88\n2014-8-20,64,57,71,56,76,47,87,1952,1966,0.00,0.03,0.70\n2014-8-21,61,52,70,56,76,46,88,1973,1966,0.00,0.03,0.70\n2014-8-22,66,56,75,56,76,45,86,1960,1956,0.00,0.03,1.41\n2014-8-23,70,57,82,56,76,46,91,1955,1988,0.00,0.04,1.49\n2014-8-24,67,56,77,55,76,45,88,1951,1982,0.00,0.03,1.00\n2014-8-25,71,58,84,55,75,45,87,1954,1982,0.00,0.03,0.59\n2014-8-26,74,60,88,55,75,46,91,1957,1986,0.00,0.04,0.67\n2014-8-27,73,61,84,55,75,44,88,1952,1986,0.00,0.04,0.76\n2014-8-28,66,58,74,55,75,46,95,1952,1967,0.00,0.03,0.47\n2014-8-29,66,59,73,55,75,44,91,1951,1974,0.0,0.04,0.87\n2014-8-30,62,59,64,55,75,45,88,1950,1987,0.33,0.04,0.37\n2014-8-31,64,57,70,55,74,46,92,1950,1987,0.05,0.04,0.56\n2014-9-1,65,55,74,55,74,46,91,1973,1987,0.00,0.04,0.55\n2014-9-2,63,57,68,55,74,41,98,1960,1988,0.12,0.03,0.71\n2014-9-3,62,55,69,54,74,45,92,1956,1988,0.00,0.04,0.46\n2014-9-4,64,52,75,54,74,42,87,1952,1963,0.00,0.05,0.66\n2014-9-5,70,57,82,54,74,44,88,1950,1973,0.00,0.04,1.09\n2014-9-6,75,59,90,54,73,45,90,1948,2014,0.00,0.04,0.93\n2014-9-7,70,56,83,54,73,45,94,1960,1981,0.00,0.04,0.26\n2014-9-8,63,56,70,54,73,43,85,1950,1993,0.0,0.05,0.60\n2014-9-9,64,56,71,53,73,40,89,1952,1963,0.0,0.04,0.61\n2014-9-10,63,54,72,53,72,39,85,1952,2007,0.00,0.04,0.64\n2014-9-11,66,55,76,53,72,40,93,1952,2013,0.00,0.05,0.58\n2014-9-12,66,55,76,53,72,44,85,1949,1961,0.00,0.04,0.60\n2014-9-13,67,50,83,53,72,39,87,1952,1999,0.00,0.05,0.51\n2014-9-14,70,53,86,53,71,46,93,1955,1979,0.00,0.04,0.73\n2014-9-15,71,54,87,52,71,42,93,1952,1967,0.00,0.05,0.69\n2014-9-16,65,57,72,52,71,44,91,1952,1967,0.0,0.05,0.76\n2014-9-17,66,58,73,52,70,42,85,1965,1991,0.02,0.04,1.49\n2014-9-18,63,59,67,52,70,41,85,1955,1991,0.01,0.05,0.78\n2014-9-19,68,61,75,52,70,40,82,1957,1999,0.00,0.05,0.79\n2014-9-20,67,58,76,51,69,40,88,1949,1974,0.00,0.06,1.05\n2014-9-21,67,55,79,51,69,39,88,1955,1952,0.00,0.05,0.94\n2014-9-22,66,59,72,51,69,37,92,1955,1990,0.01,0.06,1.65\n2014-9-23,62,58,66,51,68,40,83,1948,1957,0.72,0.05,0.72\n2014-9-24,62,58,66,50,68,38,89,1948,1974,0.80,0.06,0.99\n2014-9-25,65,58,71,50,68,36,85,1972,1991,0.17,0.06,0.63\n2014-9-26,63,57,68,50,67,39,78,1972,1967,0.35,0.07,1.51\n2014-9-27,61,53,69,50,67,35,89,1972,1967,0.00,0.06,1.04\n2014-9-28,60,54,66,50,67,38,84,1950,2003,0.00,0.07,1.71\n2014-9-29,57,52,62,49,66,37,81,1954,1992,0.03,0.06,0.92\n2014-9-30,61,54,67,49,66,36,82,1950,1992,0.0,0.07,1.65\n2014-10-1,59,52,65,49,65,35,89,1950,1987,0.00,0.07,0.56\n2014-10-2,59,50,67,49,65,35,79,1950,1993,0.00,0.07,0.80\n2014-10-3,60,48,72,48,65,39,80,1949,1993,0.00,0.07,0.67\n2014-10-4,63,54,71,48,64,38,76,1957,1966,0.00,0.07,0.92\n2014-10-5,64,53,75,48,64,38,82,1974,1980,0.00,0.08,1.26\n2014-10-6,67,56,78,48,63,37,78,1955,1952,0.00,0.08,2.72\n2014-10-7,62,57,66,48,63,37,75,1983,1951,0.00,0.09,0.71\n2014-10-8,62,55,69,47,63,39,80,1949,1951,0.00,0.08,1.16\n2014-10-9,58,52,63,47,62,35,79,1960,1996,0.0,0.09,1.27\n2014-10-10,58,50,65,47,62,40,77,1972,1987,0.01,0.09,1.02\n2014-10-11,59,53,65,47,61,37,83,1969,1991,0.29,0.09,0.89\n2014-10-12,59,53,64,47,61,40,72,2002,2006,0.0,0.09,0.70\n2014-10-13,60,50,70,46,61,35,75,1954,1961,0.30,0.10,0.66\n2014-10-14,58,53,62,46,60,36,80,1949,1961,0.28,0.11,0.77\n2014-10-15,57,53,61,46,60,35,77,1952,1991,0.34,0.10,0.93\n2014-10-16,61,52,69,46,60,35,70,1980,1974,0.0,0.11,0.78\n2014-10-17,58,53,62,46,59,34,74,1949,1989,0.13,0.11,1.24\n2014-10-18,62,57,67,45,59,32,70,1949,1974,0.59,0.11,1.19\n2014-10-19,64,55,72,45,58,28,72,1949,2014,0.00,0.12,1.05\n2014-10-20,58,54,61,45,58,29,69,1949,1987,0.46,0.12,5.02\n2014-10-21,57,53,61,45,58,32,72,1949,1965,0.04,0.13,1.29\n2014-10-22,57,53,60,45,57,34,70,1950,1965,1.26,0.12,1.26\n2014-10-23,53,47,58,45,57,32,73,1953,1965,0.37,0.14,1.24\n2014-10-24,53,48,58,44,57,33,72,1954,1965,0.16,0.14,1.10\n2014-10-25,55,47,62,44,57,31,67,1954,1965,0.24,0.14,1.16\n2014-10-26,51,46,55,44,56,29,65,1954,1998,0.06,0.15,2.13\n2014-10-27,52,44,60,44,56,31,67,1970,1968,0.03,0.15,1.23\n2014-10-28,54,49,59,44,56,30,66,1971,1962,0.50,0.16,1.53\n2014-10-29,58,53,62,43,55,31,65,1970,1986,0.02,0.16,1.24\n2014-10-30,56,52,60,43,55,30,66,1991,1967,1.00,0.17,1.36\n2014-10-31,51,47,55,43,55,30,71,2003,1949,0.67,0.17,1.04\n2014-11-1,49,45,52,43,55,30,67,2003,1981,0.00,0.19,1.56\n2014-11-2,51,45,56,43,54,31,72,1952,1970,0.07,0.19,2.18\n2014-11-3,55,52,57,43,54,29,74,1953,2010,0.43,0.20,1.37\n2014-11-4,55,51,58,42,54,30,74,1957,1949,0.16,0.20,1.91\n2014-11-5,55,51,59,42,53,31,65,2003,1960,0.19,0.20,1.01\n2014-11-6,57,51,62,42,53,30,65,2003,1976,0.16,0.21,3.29\n2014-11-7,52,45,58,42,53,31,62,1952,2008,0.00,0.20,1.93\n2014-11-8,47,39,55,42,53,27,64,1948,1952,0.00,0.22,0.85\n2014-11-9,51,46,56,41,52,27,64,1948,1997,0.20,0.21,2.95\n2014-11-10,47,42,52,41,52,30,63,1948,1997,0.00,0.22,1.08\n2014-11-11,40,34,46,41,52,15,65,1955,1990,0.00,0.23,1.48\n2014-11-12,38,32,44,41,52,13,60,1955,1953,0.00,0.23,1.52\n2014-11-13,39,33,45,41,51,14,62,1955,1997,0.00,0.23,1.68\n2014-11-14,37,28,45,40,51,9,65,1955,1995,0.00,0.23,2.61\n2014-11-15,38,29,47,40,51,6,62,1955,2001,0.00,0.23,1.09\n2014-11-16,39,28,49,40,51,21,68,1955,1965,0.00,0.23,1.08\n2014-11-17,40,28,51,40,51,23,61,1955,1995,0.00,0.23,1.06\n2014-11-18,38,31,45,40,50,29,59,2000,1965,0.00,0.23,2.06\n2014-11-19,44,36,52,39,50,25,60,1977,2008,0.0,0.24,2.13\n2014-11-20,47,42,52,39,50,25,64,1985,1987,0.14,0.23,3.41\n2014-11-21,50,47,52,39,50,21,62,1977,1954,0.60,0.23,1.26\n2014-11-22,47,44,49,39,49,14,58,1985,1954,0.02,0.22,1.76\n2014-11-23,49,42,55,38,49,10,58,1985,1959,0.47,0.22,2.49\n2014-11-24,47,40,53,38,49,14,59,2010,1974,0.05,0.23,2.93\n2014-11-25,53,49,57,38,49,18,59,1985,1949,0.72,0.22,3.04\n2014-11-26,57,54,59,38,48,25,61,1952,1949,0.01,0.22,1.34\n2014-11-27,56,53,58,38,48,19,58,1985,2013,0.13,0.22,1.30\n2014-11-28,47,38,55,37,48,17,59,1952,1999,1.35,0.21,1.55\n2014-11-29,32,24,40,37,48,18,59,2006,1999,0.14,0.23,1.08\n2014-11-30,30,23,37,37,48,21,59,1985,2012,0.00,0.22,1.50\n2014-12-1,33,26,40,37,47,20,57,1985,1958,0.00,0.20,1.52\n2014-12-2,34,26,42,37,47,25,57,1985,1958,0.00,0.20,1.75\n2014-12-3,41,32,50,36,47,27,60,1994,1965,0.00,0.20,3.77\n2014-12-4,43,39,47,36,47,21,57,1994,1989,0.03,0.19,1.41\n2014-12-5,50,44,55,36,47,23,55,1956,1954,0.12,0.19,1.67\n2014-12-6,50,46,53,36,46,19,57,1956,1965,0.29,0.18,1.39\n2014-12-7,51,43,58,36,46,18,58,1972,2014,0.00,0.19,0.88\n2014-12-8,53,48,58,36,46,13,59,1972,1957,0.36,0.18,1.61\n2014-12-9,56,51,61,36,46,18,61,2009,2014,0.39,0.17,1.78\n2014-12-10,58,50,66,36,46,16,66,2009,2014,0.51,0.17,1.55\n2014-12-11,53,47,58,35,46,21,58,2009,2014,0.27,0.17,1.42\n2014-12-12,49,45,52,35,46,22,60,1972,1960,0.0,0.16,2.19\n2014-12-13,45,39,50,35,46,19,59,1972,1952,0.0,0.17,1.63\n2014-12-14,45,35,55,35,45,22,59,2008,2002,0.00,0.17,1.67\n2014-12-15,49,44,54,35,45,19,63,2008,1980,0.00,0.16,1.60\n2014-12-16,49,47,50,35,45,10,57,1964,1974,0.0,0.17,1.32\n2014-12-17,46,43,48,35,45,11,57,1964,1994,0.11,0.16,1.61\n2014-12-18,47,44,49,35,45,18,54,1964,1958,0.51,0.16,1.23\n2014-12-19,49,45,52,35,45,17,56,1990,1994,0.12,0.17,1.19\n2014-12-20,50,44,55,35,45,14,57,1990,1973,0.77,0.16,1.93\n2014-12-21,53,50,55,35,45,12,56,1990,2005,0.00,0.17,0.99\n2014-12-22,47,43,51,35,45,14,57,1983,2005,0.00,0.17,1.28\n2014-12-23,48,41,54,35,45,9,58,1983,1950,0.81,0.17,1.14\n2014-12-24,42,39,45,35,45,16,62,1948,2005,0.21,0.17,0.75\n2014-12-25,42,37,46,35,45,24,60,1995,1980,0.00,0.17,1.06\n2014-12-26,39,35,42,36,45,22,62,1948,1980,0.00,0.17,2.14\n2014-12-27,45,40,49,36,45,20,58,1968,1994,0.13,0.17,1.53\n2014-12-28,41,37,44,36,45,12,56,1990,1980,0.16,0.16,0.89\n2014-12-29,38,33,43,36,45,8,54,1968,1949,0.00,0.16,1.92\n2014-12-30,33,28,38,36,45,6,56,1968,1958,0.00,0.16,1.06\n2014-12-31,33,27,38,36,46,13,56,1968,1963,0.00,0.16,1.30\n2015-1-1,34,26,42,36,46,10,58,1952,2006,0.00,0.18,1.37\n2015-1-2,37,32,42,36,46,6,56,1950,2003,0.06,0.18,1.77\n2015-1-3,38,35,41,36,46,6,58,1950,1984,0.0,0.19,1.01\n2015-1-4,45,38,51,36,46,14,61,1950,1984,0.40,0.19,1.56\n2015-1-5,52,49,54,36,46,15,55,1982,2006,0.32,0.18,1.33\n2015-1-6,49,43,54,37,46,14,57,1982,2003,0.00,0.19,1.22\n2015-1-7,44,42,46,37,46,19,59,1973,2002,0.00,0.19,2.29\n2015-1-8,41,35,46,37,46,18,54,1949,1953,0.00,0.20,1.13\n2015-1-9,44,38,50,37,46,13,56,1949,2003,0.01,0.19,2.83\n2015-1-10,45,43,46,37,47,12,59,1949,1981,0.23,0.20,0.94\n2015-1-11,47,45,49,37,47,12,59,1963,1987,0.06,0.19,1.26\n2015-1-12,46,40,52,37,47,13,59,1963,1986,0.0,0.19,1.30\n2015-1-13,43,37,49,37,47,11,56,1950,1986,0.00,0.18,1.63\n2015-1-14,38,33,43,37,47,8,59,1950,1968,0.00,0.19,1.61\n2015-1-15,40,34,46,37,47,15,58,1950,1974,0.38,0.19,1.68\n2015-1-16,48,42,53,37,47,13,55,1950,2011,0.0,0.20,1.78\n2015-1-17,47,38,56,37,47,15,56,1950,2015,1.03,0.19,2.39\n2015-1-18,51,45,57,37,48,9,60,1950,2005,0.84,0.19,2.98\n2015-1-19,47,43,50,37,48,18,62,1954,2005,0.02,0.18,2.26\n2015-1-20,44,38,50,37,48,16,64,1954,1981,0.00,0.19,1.66\n2015-1-21,38,31,45,37,48,13,62,1962,1981,0.00,0.17,1.64\n2015-1-22,46,43,49,37,48,19,60,1962,1981,0.03,0.17,1.29\n2015-1-23,51,47,54,37,48,16,56,1949,2001,0.23,0.17,1.36\n2015-1-24,55,52,58,37,48,10,58,1950,1995,0.02,0.16,1.36\n2015-1-25,54,45,63,37,48,7,63,1950,2015,0.00,0.17,0.93\n2015-1-26,52,43,61,37,48,9,61,1957,2015,0.00,0.16,1.03\n2015-1-27,50,47,52,37,48,11,58,1957,1952,0.03,0.16,1.61\n2015-1-28,48,41,54,37,48,7,57,1950,1976,0.0,0.16,1.23\n2015-1-29,46,38,54,37,49,6,60,1950,1992,0.00,0.16,1.63\n2015-1-30,41,34,47,37,49,7,59,1950,1992,0.00,0.15,1.00\n2015-1-31,42,38,45,37,49,0,61,1950,1960,0.00,0.16,1.93\n2015-2-1,45,40,49,37,49,1,63,1950,1962,0.06,0.13,1.34\n2015-2-2,47,41,52,37,49,8,64,1950,1962,0.29,0.13,1.32\n2015-2-3,46,42,50,37,49,8,63,1950,1992,0.05,0.14,1.29\n2015-2-4,46,40,51,37,49,7,63,1989,1993,0.33,0.13,0.91\n2015-2-5,52,47,56,37,49,14,62,1989,1987,1.03,0.14,1.03\n2015-2-6,54,50,58,37,49,18,64,1989,1993,0.68,0.13,1.54\n2015-2-7,52,49,54,37,49,20,66,1989,1963,0.93,0.14,2.58\n2015-2-8,53,47,59,37,49,22,68,1994,1963,0.14,0.13,3.06\n2015-2-9,52,47,56,37,49,26,62,1989,1968,0.24,0.13,2.98\n2015-2-10,51,47,55,37,49,24,65,1982,1963,0.01,0.13,0.96\n2015-2-11,49,42,55,37,50,23,62,1948,1963,0.0,0.13,0.81\n2015-2-12,56,49,62,37,50,21,66,1949,1963,0.04,0.12,1.23\n2015-2-13,52,44,60,37,50,18,60,1949,2015,0.00,0.13,1.40\n2015-2-14,51,44,58,37,50,22,63,1995,1991,0.01,0.11,0.74\n2015-2-15,47,39,54,37,50,13,65,1956,1996,0.00,0.12,0.94\n2015-2-16,51,42,59,37,50,13,59,1956,2015,0.00,0.12,1.83\n2015-2-17,51,40,61,37,50,23,61,1990,2015,0.00,0.12,0.68\n2015-2-18,47,40,54,37,50,20,62,1990,1977,0.00,0.11,1.63\n2015-2-19,49,47,51,37,50,22,67,1986,1977,0.18,0.12,1.76\n2015-2-20,49,45,52,37,50,25,62,1955,1977,0.03,0.12,0.74\n2015-2-21,48,42,54,37,51,25,67,1957,1973,0.00,0.12,0.97\n2015-2-22,46,38,53,37,51,30,64,2005,1966,0.00,0.12,0.71\n2015-2-23,44,33,55,37,51,27,61,2011,1983,0.00,0.11,1.16\n2015-2-24,44,36,52,37,51,24,62,2011,1949,0.00,0.12,1.55\n2015-2-25,47,44,50,37,51,20,64,2011,1992,0.16,0.13,0.98\n2015-2-26,50,46,53,37,51,18,67,1962,1992,0.37,0.12,1.19\n2015-2-27,47,44,50,37,51,20,70,1962,1968,0.72,0.13,2.23\n2015-2-28,46,38,54,38,51,24,68,1971,1968,0.00,0.12,1.46\n2015-3-1,43,34,52,38,52,23,62,1971,1968,0.00,0.13,0.84\n2015-3-2,46,40,52,38,52,22,64,1989,1990,0.00,0.14,1.16\n2015-3-3,42,32,51,38,52,20,69,1989,1965,0.00,0.12,2.08\n2015-3-4,43,31,55,38,52,11,68,1955,1965,0.00,0.13,0.92\n2015-3-5,47,37,56,38,52,13,66,1955,1965,0.00,0.13,2.70\n2015-3-6,49,38,59,38,52,26,68,1951,2007,0.00,0.12,1.18\n2015-3-7,51,39,62,38,52,26,66,1951,2001,0.00,0.12,0.67\n2015-3-8,51,39,63,38,53,28,67,1951,2005,0.00,0.12,1.27\n2015-3-9,49,40,58,39,53,28,67,1951,1995,0.00,0.12,1.47\n2015-3-10,49,41,56,39,53,20,68,1956,1979,0.03,0.12,0.87\n2015-3-11,53,48,58,39,53,23,65,1956,2005,0.10,0.12,1.01\n2015-3-12,57,49,64,39,53,25,68,1956,1998,0.00,0.13,0.99\n2015-3-13,55,46,63,39,53,28,70,1967,1979,0.08,0.12,0.70\n2015-3-14,53,49,57,39,53,26,64,1953,1996,0.67,0.13,0.76\n2015-3-15,47,43,51,39,54,30,65,1955,2010,2.20,0.12,2.20\n2015-3-16,50,43,57,39,54,29,66,1952,2007,0.04,0.12,1.09\n2015-3-17,48,40,56,40,54,27,65,1951,1972,0.03,0.12,1.05\n2015-3-18,53,45,60,40,54,29,70,1954,1996,0.00,0.12,1.86\n2015-3-19,54,47,60,40,54,27,63,1965,1951,0.0,0.11,1.27\n2015-3-20,53,48,57,40,54,26,69,1955,1968,0.16,0.12,0.76\n2015-3-21,52,47,56,40,55,29,69,1952,1992,0.15,0.12,2.04\n2015-3-22,48,43,53,40,55,28,68,1952,1979,0.04,0.13,1.81\n2015-3-23,47,42,52,40,55,28,68,1954,1979,0.32,0.12,0.94\n2015-3-24,49,43,55,40,55,30,68,1965,2010,0.30,0.11,0.98\n2015-3-25,52,45,58,40,55,31,64,1954,1969,0.20,0.12,0.62\n2015-3-26,60,50,69,40,55,32,70,1975,1969,0.00,0.11,1.51\n2015-3-27,57,48,65,41,55,26,71,1975,1994,0.04,0.11,0.64\n2015-3-28,55,49,60,41,56,26,73,1954,1994,0.00,0.11,0.87\n2015-3-29,54,48,60,41,56,24,78,1954,2004,0.00,0.11,1.08\n2015-3-30,58,51,64,41,56,27,72,1954,1995,0.07,0.11,0.65\n2015-3-31,49,43,55,41,56,30,75,1949,1987,0.04,0.11,0.57\n2015-4-1,49,42,55,41,56,29,82,1953,1987,0.20,0.11,1.13\n2015-4-2,49,42,56,41,56,30,76,1963,1992,0.00,0.11,0.92\n2015-4-3,47,41,52,41,56,32,71,1948,2004,0.06,0.11,1.46\n2015-4-4,47,39,55,41,57,31,75,1948,1966,0.00,0.11,2.64\n2015-4-5,50,37,62,41,57,29,73,1975,1966,0.00,0.10,0.79\n2015-4-6,51,44,57,41,57,31,78,1956,2007,0.04,0.10,0.63\n2015-4-7,51,44,58,41,57,29,78,1952,1996,0.02,0.10,1.54\n2015-4-8,53,43,63,41,57,29,70,1952,2012,0.00,0.10,0.83\n2015-4-9,53,43,63,41,57,34,73,1982,1959,0.00,0.10,0.96\n2015-4-10,52,46,57,42,57,30,74,1953,2004,0.43,0.09,0.78\n2015-4-11,48,42,53,42,58,35,80,1991,2004,0.0,0.10,1.04\n2015-4-12,49,42,56,42,58,33,79,1968,2008,0.00,0.09,0.83\n2015-4-13,46,39,53,42,58,33,75,1968,1980,0.55,0.09,1.45\n2015-4-14,45,37,53,42,58,33,83,1955,1984,0.13,0.10,0.52\n2015-4-15,48,38,57,42,58,33,74,1967,1999,0.00,0.09,0.75\n2015-4-16,52,39,64,42,59,34,81,1960,1999,0.00,0.09,0.95\n2015-4-17,55,43,66,42,59,33,74,1964,1983,0.00,0.09,0.73\n2015-4-18,57,47,66,42,59,34,77,1950,1962,0.00,0.09,0.40\n2015-4-19,59,47,70,42,59,33,74,1961,1950,0.00,0.09,1.85\n2015-4-20,60,46,73,43,59,30,74,1961,2009,0.00,0.08,0.45\n2015-4-21,54,44,63,43,59,29,76,1951,1982,0.22,0.09,0.87\n2015-4-22,51,41,60,43,60,35,78,1950,1977,0.00,0.08,1.28\n2015-4-23,49,44,54,43,60,31,72,1954,1977,0.12,0.08,1.71\n2015-4-24,49,43,54,43,60,30,76,1950,1977,0.13,0.08,0.70\n2015-4-25,49,42,56,43,60,34,77,1948,1992,0.05,0.07,0.52\n2015-4-26,50,40,60,44,61,32,78,1948,1987,0.0,0.08,0.37\n2015-4-27,64,51,77,44,61,31,81,1948,1987,0.01,0.08,0.70\n2015-4-28,54,48,60,44,61,31,76,1955,1968,0.07,0.07,0.44\n2015-4-29,53,45,61,44,61,31,80,1952,1976,0.00,0.07,1.06\n2015-4-30,55,46,63,44,61,32,85,1954,1976,0.00,0.07,0.87\n2015-5-1,57,48,65,44,62,28,85,1954,2014,0.00,0.07,0.81\n2015-5-2,56,46,65,45,62,33,74,1952,1966,0.00,0.06,0.47\n2015-5-3,58,46,69,45,62,35,77,1950,1992,0.00,0.07,1.31\n2015-5-4,54,45,63,45,62,33,85,1962,1992,0.00,0.06,0.66\n2015-5-5,52,45,58,45,63,36,86,1965,1953,0.24,0.07,0.59\n2015-5-6,54,45,62,45,63,36,87,1965,2013,0.00,0.07,0.89\n2015-5-7,56,43,69,46,63,33,81,1962,1987,0.00,0.06,0.66\n2015-5-8,61,47,75,46,63,36,84,1999,1969,0.00,0.06,0.54\n2015-5-9,65,49,80,46,63,38,81,1999,1976,0.00,0.06,0.74\n2015-5-10,60,52,67,46,64,38,80,1999,1993,0.0,0.07,0.61\n2015-5-11,54,50,57,46,64,35,87,1953,1971,0.0,0.06,0.47\n2015-5-12,56,51,60,47,64,38,81,1964,1959,0.17,0.06,0.71\n2015-5-13,52,50,54,47,64,37,84,1956,1973,0.16,0.06,0.44\n2015-5-14,57,49,64,47,65,36,87,1964,1973,0.01,0.06,0.97\n2015-5-15,59,49,68,47,65,38,85,1964,2007,0.00,0.06,0.81\n2015-5-16,56,52,60,47,65,40,84,2003,1985,0.00,0.06,0.64\n2015-5-17,59,51,67,48,65,39,90,1966,2008,0.00,0.06,0.72\n2015-5-18,66,54,78,48,65,37,89,1950,1958,0.00,0.06,0.72\n2015-5-19,62,53,71,48,65,38,88,1975,1963,0.00,0.06,0.70\n2015-5-20,63,51,74,48,66,40,92,1960,1963,0.00,0.06,0.56\n2015-5-21,66,53,78,48,66,39,93,1960,1963,0.00,0.06,0.55\n2015-5-22,58,53,62,49,66,37,86,1960,1969,0.00,0.06,0.67\n2015-5-23,57,53,61,49,66,37,90,1950,1969,0.0,0.06,0.70\n2015-5-24,58,52,64,49,66,37,89,1950,1992,0.00,0.06,0.65\n2015-5-25,56,52,60,49,66,37,84,1964,2001,0.00,0.06,0.41\n2015-5-26,62,53,71,49,67,40,89,1952,2005,0.00,0.07,0.61\n2015-5-27,65,53,76,49,67,37,89,1966,2005,0.00,0.06,0.83\n2015-5-28,68,54,82,50,67,38,92,1954,1983,0.00,0.07,0.88\n2015-5-29,67,55,79,50,67,35,85,1951,1973,0.00,0.06,1.83\n2015-5-30,62,50,73,50,67,36,87,1951,1956,0.00,0.07,1.06\n2015-5-31,65,53,77,50,67,35,90,1953,1964,0.00,0.06,0.97\n2015-6-1,57,53,61,50,67,40,94,1951,1970,0.18,0.07,1.75\n2015-6-2,60,55,64,50,68,40,94,1951,1970,0.02,0.06,0.48\n2015-6-3,61,53,68,50,68,42,89,1962,2009,0.00,0.06,1.08\n2015-6-4,63,53,73,50,68,41,91,1962,2009,0.00,0.07,0.83\n2015-6-5,68,55,80,51,68,41,92,1953,1978,0.00,0.06,0.63\n2015-6-6,71,56,85,51,68,42,90,1962,2003,0.00,0.06,1.54\n2015-6-7,74,60,88,51,68,44,90,2002,2003,0.00,0.06,0.81\n2015-6-8,73,58,87,51,69,42,87,2002,1948,0.00,0.07,0.81\n2015-6-9,71,58,84,51,69,44,96,1971,1955,0.00,0.06,0.72\n2015-6-10,65,52,78,51,69,44,83,2008,1982,0.00,0.06,0.88\n2015-6-11,64,52,76,51,69,40,84,1952,1989,0.00,0.06,1.16\n2015-6-12,61,53,68,51,69,38,85,1952,1999,0.00,0.06,0.60\n2015-6-13,62,49,75,52,69,42,94,1952,2002,0.00,0.05,0.37\n2015-6-14,68,53,82,52,70,43,86,1955,1988,0.00,0.06,0.39\n2015-6-15,74,61,86,52,70,43,88,1949,1963,0.00,0.05,0.37\n2015-6-16,63,52,73,52,70,42,88,1954,1961,0.00,0.06,0.52\n2015-6-17,65,52,77,52,70,43,91,1954,1969,0.00,0.05,0.51\n2015-6-18,67,57,76,52,70,43,94,1954,1982,0.0,0.05,0.41\n2015-6-19,66,56,75,52,70,43,92,1951,1982,0.02,0.05,0.33\n2015-6-20,66,55,77,53,71,43,88,1960,2004,0.00,0.05,1.14\n2015-6-21,68,57,78,53,71,45,89,1960,1992,0.00,0.05,1.00\n2015-6-22,66,55,77,53,71,45,92,1952,1992,0.00,0.04,0.62\n2015-6-23,66,53,79,53,71,40,92,1952,1992,0.00,0.04,0.58\n2015-6-24,70,61,78,53,72,45,92,1952,1992,0.0,0.05,1.00\n2015-6-25,74,60,87,53,72,45,88,1949,2006,0.00,0.04,0.39\n2015-6-26,77,64,89,53,72,46,90,1949,2006,0.00,0.03,0.32\n2015-6-27,78,63,92,53,72,45,92,1949,2015,0.00,0.04,0.61\n2015-6-28,74,65,83,54,72,45,91,1949,1995,0.01,0.04,0.79\n2015-6-29,74,63,84,54,73,45,93,1949,1987,0.0,0.03,0.64\n2015-6-30,73,59,87,54,73,43,96,1949,1995,0.00,0.04,0.84\n"
  },
  {
    "path": "us-weather-history/README.md",
    "content": "### U.S. weather history visualization\n\nThe raw data and code behind the story [What 12 Months Of Record-Setting Temperatures Looks Like Across The U.S.](http://fivethirtyeight.com/features/what-12-months-of-record-setting-temperatures-looks-like-across-the-u-s/)\n\n#### Code\n\nCode file | Description\n---|---------\n`wunderground_scraper.py` | Downloades weather data web pages from Weather Underground\n`wunderground_parser.py` | Parses the weather data from Weather Underground into a flat CSV file\n`visualize_weather.py` | Creates the visualization of the weather data\n\n#### Data\n\nColumn | Description\n---|---------\n`date` | The date of the weather record, formatted YYYY-M-D\n`actual_mean_temp` | The measured average temperature for that day\n`actual_min_temp` | The measured minimum temperature for that day\n`actual_max_temp` | The measured maximum temperature for that day\n`average_min_temp` | The average minimum temperature on that day since 1880\n`average_max_temp` | The average maximum temperature on that day since 1880\n`record_min_temp` | The lowest ever temperature on that day since 1880\n`record_max_temp` | The highest ever temperature on that day since 1880\n`record_min_temp_year` | The year that the lowest ever temperature occurred\n`record_max_temp_year` | The year that the highest ever temperature occurred\n`actual_precipitation` | The measured amount of rain or snow for that day\n`average_precipitation` | The average amount of rain or snow on that day since 1880\n`record_precipitation` | The highest amount of rain or snow on that day since 1880\n\nSource: [Weather Underground](http://wunderground.com)\n"
  },
  {
    "path": "us-weather-history/visualize_weather.py",
    "content": "import matplotlib.pyplot as plt\nimport pandas as pd\nfrom datetime import datetime\n\n'''\nThis is an example to generate the Philadelphia, PA weather chart.\n\nIf you want to make the chart for another city, you will have to modify\nthis code slightly to read that city's data in, change the title, and\nlikely change the y-axis of the chart to fit your city's temperature range.\n\nI also use a custom matplotlib style as the basis for these charts, which you\ncan find here: https://gist.githubusercontent.com/rhiever/d0a7332fe0beebfdc3d5/raw/223d70799b48131d5ce2723cd5784f39d7a3a653/tableau10.mplstyle\n'''\n\nweather_data = pd.read_csv('KPHL.csv', parse_dates=['date'])\nprint(weather_data.describe())\n\n# Generate a bunch of histograms of the data to make sure that all of the data\n# is in an expected range.\nwith plt.style.context('https://gist.githubusercontent.com/rhiever/d0a7332fe0beebfdc3d5/raw/223d70799b48131d5ce2723cd5784f39d7a3a653/tableau10.mplstyle'):\n    for column in weather_data.columns:\n        if column in ['date']:\n            continue\n        plt.figure()\n        plt.hist(weather_data[column].values)\n        plt.title(column)\n        plt.savefig('{}.png'.format(column))\n\n    # Make sure we're only plotting temperatures for July 2014 - June 2015\n    weather_data_subset = weather_data[weather_data['date'] >= datetime(year=2014, month=7, day=1)]\n    weather_data_subset = weather_data_subset[weather_data_subset['date'] < datetime(year=2015, month=7, day=1)].copy()\n    weather_data_subset['day_order'] = range(len(weather_data_subset))\n\n    day_order = weather_data_subset['day_order']\n    record_max_temps = weather_data_subset['record_max_temp'].values\n    record_min_temps = weather_data_subset['record_min_temp'].values\n    average_max_temps = weather_data_subset['average_max_temp'].values\n    average_min_temps = weather_data_subset['average_min_temp'].values\n    actual_max_temps = weather_data_subset['actual_max_temp'].values\n    actual_min_temps = weather_data_subset['actual_min_temp'].values\n\n    fig, ax1 = plt.subplots(figsize=(15, 7))\n\n    # Create the bars showing all-time record highs and lows\n    plt.bar(day_order, record_max_temps - record_min_temps, bottom=record_min_temps,\n            edgecolor='none', color='#C3BBA4', width=1)\n\n    # Create the bars showing average highs and lows\n    plt.bar(day_order, average_max_temps - average_min_temps, bottom=average_min_temps,\n            edgecolor='none', color='#9A9180', width=1)\n\n    # Create the bars showing this year's highs and lows\n    plt.bar(day_order, actual_max_temps - actual_min_temps, bottom=actual_min_temps,\n            edgecolor='black', linewidth=0.5, color='#5A3B49', width=1)\n\n    new_max_records = weather_data_subset[weather_data_subset.record_max_temp <= weather_data_subset.actual_max_temp]\n    new_min_records = weather_data_subset[weather_data_subset.record_min_temp >= weather_data_subset.actual_min_temp]\n\n    # Create the dots marking record highs and lows for the year\n    plt.scatter(new_max_records['day_order'].values + 0.5,\n                new_max_records['actual_max_temp'].values + 1.25,\n                s=15, zorder=10, color='#d62728', alpha=0.75, linewidth=0)\n\n    plt.scatter(new_min_records['day_order'].values + 0.5,\n                new_min_records['actual_min_temp'].values - 1.25,\n                s=15, zorder=10, color='#1f77b4', alpha=0.75, linewidth=0)\n\n    plt.ylim(-15, 111)\n    plt.xlim(-5, 370)\n\n    plt.yticks(range(-10, 111, 10), [r'{}$^\\circ$'.format(x)\n                                     for x in range(-10, 111, 10)], fontsize=10)\n    plt.ylabel(r'Temperature ($^\\circ$F)', fontsize=12)\n\n    month_beginning_df = weather_data_subset[weather_data_subset['date'].apply(lambda x: True if x.day == 1 else False)]\n    month_beginning_indeces = list(month_beginning_df['day_order'].values)\n    month_beginning_names = list(month_beginning_df['date'].apply(lambda x: x.strftime(\"%B\")).values)\n    month_beginning_names[0] += '\\n\\'14'\n    month_beginning_names[6] += '\\n\\'15'\n\n    # Add the last month label manually\n    month_beginning_indeces += [weather_data_subset['day_order'].values[-1]]\n    month_beginning_names += ['July']\n\n    plt.xticks(month_beginning_indeces,\n               month_beginning_names,\n               fontsize=10)\n\n    ax2 = ax1.twiny()\n    plt.xticks(month_beginning_indeces,\n               month_beginning_names,\n               fontsize=10)\n\n    plt.xlim(-5, 370)\n    plt.grid(False)\n\n    ax3 = ax1.twinx()\n    plt.yticks(range(-10, 111, 10), [r'{}$^\\circ$'.format(x)\n                                     for x in range(-10, 111, 10)], fontsize=10)\n    plt.ylim(-15, 111)\n    plt.grid(False)\n\n    plt.title('Philadelphia, PA\\'s weather, July 2014 - June 2015\\n\\n', fontsize=20)\n\n    plt.savefig('philadelphia-weather-july14-june15.png')\n"
  },
  {
    "path": "us-weather-history/wunderground_parser.py",
    "content": "from datetime import datetime, timedelta\nfrom bs4 import BeautifulSoup\nfrom urllib.request import urlopen\n\n\ndef parse_station(station):\n    '''\n    This function parses the web pages downloaded from wunderground.com\n    into a flat CSV file for the station you provide it.\np\n    Make sure to run the wunderground scraper first so you have the web\n    pages downloaded.\n    '''\n\n    # Scrape between July 1, 2014 and July 1, 2015\n    # You can change the dates here if you prefer to parse a different range\n    current_date = datetime(year=2014, month=7, day=1)\n    end_date = datetime(year=2015, month=7, day=1)\n\n    with open('{}.csv'.format(station), 'w') as out_file:\n        out_file.write('date,actual_mean_temp,actual_min_temp,actual_max_temp,'\n                       'average_min_temp,average_max_temp,'\n                       'record_min_temp,record_max_temp,'\n                       'record_min_temp_year,record_max_temp_year,'\n                       'actual_precipitation,average_precipitation,'\n                       'record_precipitation\\n')\n\n        while current_date != end_date:\n            try_again = False\n            with open('{}/{}-{}-{}.html'.format(station,\n                                                current_date.year,\n                                                current_date.month,\n                                                current_date.day)) as in_file:\n                soup = BeautifulSoup(in_file.read(), 'html.parser')\n\n                weather_data_rows = soup.find(id='historyTable').find_all('tr')\n                weather_data = []\n                for i in range(len(weather_data_rows)):\n                    soup1 = weather_data_rows[i]\n                    weather_data.append(soup1.find_all('span', class_='wx-value'))\n                    weather_data = [x for x in weather_data if x != []]\n                \n                if len(weather_data[4]) < 2:\n                    weather_data[4].append(None)\n                    weather_data[4].append(None)\n\n\n                weather_data_units = soup.find(id='historyTable').find_all('td')\n\n\n                try:\n                    actual_mean_temp = weather_data[0][0].text\n                    actual_max_temp = weather_data[1][0].text\n                    average_max_temp = weather_data[1][1].text\n                    if weather_data[1][2]:\n                        record_max_temp = weather_data[1][2].text\n                    actual_min_temp = weather_data[2][0].text\n                    average_min_temp = weather_data[2][1].text\n                    record_min_temp = weather_data[2][2].text\n                    record_max_temp_year = weather_data_units[\n                        9].text.split('(')[-1].strip(')')\n                    record_min_temp_year = weather_data_units[\n                        13].text.split('(')[-1].strip(')')\n\n                    actual_precipitation = weather_data[4][0].text\n                    if actual_precipitation == 'T':\n                        actual_precipitation = '0.0' \n\n                    #Test whether station collects average or record precipitation data\n                    if weather_data[4][1]:\n                        average_precipitation = weather_data[4][1].text\n                    else: \n                        average_precipitation = None\n                    if weather_data[4][2]:\n                        record_precipitation = weather_data[4][2].text\n                    else: \n                        record_precipitation = None\n\n                    # Verify that the parsed data is valid\n                    if (record_max_temp_year == '-1' or record_min_temp_year == '-1' or\n                            int(record_max_temp) < max(int(actual_max_temp), int(average_max_temp)) or\n                            int(record_min_temp) > min(int(actual_min_temp), int(average_min_temp)) or\n                            ((record_precipitation is not None or average_precipitation is not None) and \n                            (float(actual_precipitation) > float(record_precipitation) or\n                            float(average_precipitation) > float(record_precipitation)))):\n                        raise Exception\n\n                    out_file.write('{}-{}-{},'.format(current_date.year, current_date.month, current_date.day))\n                    out_file.write(','.join([actual_mean_temp, actual_min_temp, actual_max_temp,\n                                             average_min_temp, average_max_temp,\n                                             record_min_temp, record_max_temp,\n                                             record_min_temp_year, record_max_temp_year,\n                                             actual_precipitation,]))\n                    if average_precipitation:\n                        out_file.write(',{}'.format(average_precipitation))\n                    if record_precipitation:\n                        out_file.write(',{}'.format(record_precipitation))\n\n                    out_file.write('\\n')\n                    current_date += timedelta(days=1)\n                    \n                except:\n                    # If the web page is formatted improperly, signal that the page may need\n                    # to be downloaded again.\n                    try_again = True\n\n            # If the web page needs to be downloaded again, re-download it from\n            # wunderground.com\n\n            # If the parser gets stuck on a certain date, you may need to investigate\n            # the page to find out what is going on. Sometimes data is missing, in\n            # which case the parser will get stuck. You can manually put in the data\n            # yourself in that case, or just tell the parser to skip this day.\n            if try_again:\n                print('Error with date {}'.format(current_date))\n\n                lookup_URL = 'http://www.wunderground.com/history/airport/{}/{}/{}/{}/DailyHistory.html'\n                formatted_lookup_URL = lookup_URL.format(station,\n                                                         current_date.year,\n                                                         current_date.month,\n                                                         current_date.day)\n                html = urlopen(formatted_lookup_URL).read().decode('utf-8')\n\n                out_file_name = '{}/{}-{}-{}.html'.format(station,\n                                                          current_date.year,\n                                                          current_date.month,\n                                                          current_date.day)\n\n                with open(out_file_name, 'w') as out_file:\n                    out_file.write(html)\n\n\n# Parse the stations used in this article\nfor station in ['KCLT', 'KCQT', 'KHOU', 'KIND', 'KJAX',\n                'KMDW', 'KNYC', 'KPHL', 'KPHX', 'KSEA', 'KSAF']:\n    parse_station(station)\n"
  },
  {
    "path": "us-weather-history/wunderground_scraper.py",
    "content": "# coding: utf-8\n\nfrom datetime import datetime, timedelta\nfrom urllib.request import urlopen\nimport os\n\n\ndef scrape_station(station):\n    '''\n    This function scrapes the weather data web pages from wunderground.com\n    for the station you provide it.\n\n    You can look up your city's weather station by performing a search for\n    it on wunderground.com then clicking on the \"History\" section.\n    The 4-letter name of the station will appear on that page.\n    '''\n\n    # Scrape between July 1, 2014 and July 1, 2015\n    # You can change the dates here if you prefer to scrape a different range\n    current_date = datetime(year=2014, month=7, day=1)\n    end_date = datetime(year=2015, month=7, day=1)\n\n    # Make sure a directory exists for the station web pages\n    os.mkdir(station)\n\n    # Use .format(station, YYYY, M, D)\n    lookup_URL = 'http://www.wunderground.com/history/airport/{}/{}/{}/{}/DailyHistory.html'\n\n    while current_date != end_date:\n\n        if current_date.day == 1:\n            print(current_date)\n\n        formatted_lookup_URL = lookup_URL.format(station,\n                                                 current_date.year,\n                                                 current_date.month,\n                                                 current_date.day)\n        html = urlopen(formatted_lookup_URL).read().decode('utf-8')\n\n        out_file_name = '{}/{}-{}-{}.html'.format(station, current_date.year,\n                                                  current_date.month,\n                                                  current_date.day)\n\n        with open(out_file_name, 'w') as out_file:\n            out_file.write(html)\n\n        current_date += timedelta(days=1)\n\n\n# Scrape the stations used in this article\nfor station in ['KCLT', 'KCQT', 'KHOU', 'KIND', 'KJAX',\n                'KMDW', 'KNYC', 'KPHL', 'KPHX', 'KSEA']:\n    scrape_station(station)\n"
  },
  {
    "path": "wheres-waldo-path-optimization/Where's Waldo path optimization.ipynb",
    "content": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Computing the optimal path for finding Waldo\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"This notebook provides the methodology and code used in the blog post, [Here’s Waldo: Computing the optimal search strategy for finding Waldo](http://www.randalolson.com/2015/02/03/heres-waldo-computing-the-optimal-search-strategy-for-finding-waldo/).\\n\",\n    \"\\n\",\n    \"### Notebook by [Randal S. Olson](http://www.randalolson.com/)\\n\",\n    \"\\n\",\n    \"Please see the [repository README file](https://github.com/rhiever/Data-Analysis-and-Machine-Learning-Projects#license) for the licenses and usage terms for the instructional material and code in this notebook. In general, I have licensed this material so that it is widely useable and shareable as possible.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Read the data into a pandas DataFrame\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Since we already have all 68 locations of Waldo from [Slate](http://www.slate.com/content/dam/slate/articles/arts/culturebox/2013/11/131111_heresWaldo920_1.jpg.CROP.original-original.jpg), we can jump right into analyzing them. \"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/html\": [\n       \"<div style=\\\"max-height:1000px;max-width:1500px;overflow:auto;\\\">\\n\",\n       \"<table border=\\\"1\\\" class=\\\"dataframe\\\">\\n\",\n       \"  <thead>\\n\",\n       \"    <tr style=\\\"text-align: right;\\\">\\n\",\n       \"      <th></th>\\n\",\n       \"      <th>Book</th>\\n\",\n       \"      <th>Page</th>\\n\",\n       \"      <th>X</th>\\n\",\n       \"      <th>Y</th>\\n\",\n       \"    </tr>\\n\",\n       \"  </thead>\\n\",\n       \"  <tbody>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>count</th>\\n\",\n       \"      <td> 68.000000</td>\\n\",\n       \"      <td> 68.000000</td>\\n\",\n       \"      <td> 68.000000</td>\\n\",\n       \"      <td> 68.000000</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>mean</th>\\n\",\n       \"      <td>  3.514706</td>\\n\",\n       \"      <td>  6.058824</td>\\n\",\n       \"      <td>  6.700776</td>\\n\",\n       \"      <td>  3.875306</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>std</th>\\n\",\n       \"      <td>  1.856756</td>\\n\",\n       \"      <td>  3.411492</td>\\n\",\n       \"      <td>  3.703276</td>\\n\",\n       \"      <td>  1.941349</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>min</th>\\n\",\n       \"      <td>  1.000000</td>\\n\",\n       \"      <td>  1.000000</td>\\n\",\n       \"      <td>  0.625000</td>\\n\",\n       \"      <td>  0.333333</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>25%</th>\\n\",\n       \"      <td>  2.000000</td>\\n\",\n       \"      <td>  3.000000</td>\\n\",\n       \"      <td>  3.513889</td>\\n\",\n       \"      <td>  2.250000</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>50%</th>\\n\",\n       \"      <td>  3.000000</td>\\n\",\n       \"      <td>  6.000000</td>\\n\",\n       \"      <td>  6.694444</td>\\n\",\n       \"      <td>  3.927083</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>75%</th>\\n\",\n       \"      <td>  5.000000</td>\\n\",\n       \"      <td>  9.000000</td>\\n\",\n       \"      <td> 10.062500</td>\\n\",\n       \"      <td>  5.291667</td>\\n\",\n       \"    </tr>\\n\",\n       \"    <tr>\\n\",\n       \"      <th>max</th>\\n\",\n       \"      <td>  7.000000</td>\\n\",\n       \"      <td> 12.000000</td>\\n\",\n       \"      <td> 12.444444</td>\\n\",\n       \"      <td>  7.708333</td>\\n\",\n       \"    </tr>\\n\",\n       \"  </tbody>\\n\",\n       \"</table>\\n\",\n       \"</div>\"\n      ],\n      \"text/plain\": [\n       \"            Book       Page          X          Y\\n\",\n       \"count  68.000000  68.000000  68.000000  68.000000\\n\",\n       \"mean    3.514706   6.058824   6.700776   3.875306\\n\",\n       \"std     1.856756   3.411492   3.703276   1.941349\\n\",\n       \"min     1.000000   1.000000   0.625000   0.333333\\n\",\n       \"25%     2.000000   3.000000   3.513889   2.250000\\n\",\n       \"50%     3.000000   6.000000   6.694444   3.927083\\n\",\n       \"75%     5.000000   9.000000  10.062500   5.291667\\n\",\n       \"max     7.000000  12.000000  12.444444   7.708333\"\n      ]\n     },\n     \"execution_count\": 2,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    }\n   ],\n   \"source\": [\n    \"from __future__ import print_function\\n\",\n    \"%matplotlib inline\\n\",\n    \"import matplotlib.pyplot as plt\\n\",\n    \"import numpy as np\\n\",\n    \"import pandas as pd\\n\",\n    \"import seaborn as sb\\n\",\n    \"import random\\n\",\n    \"import math\\n\",\n    \"\\n\",\n    \"sb.set_style(\\\"white\\\")\\n\",\n    \"plt.style.use(\\\"tableau10\\\")\\n\",\n    \"\\n\",\n    \"wheres_waldo_locations = pd.read_csv(\\\"wheres-waldo-locations.csv\\\")\\n\",\n    \"wheres_waldo_locations.describe()\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Plot the dots according to the book they came from\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"The first basic visualization that we can make is to plot all of the points according to the book that they came from.\\n\",\n    \"\\n\",\n    \"The dashed line in the center represents the crease of the book, as \\\"Where's Waldo\\\" illustrations always stretched over two pages.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 2,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"''\"\n      ]\n     },\n     \"execution_count\": 2,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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fGXq+6EHmP7B0DNJUkSzWYzkiSpuhUAFuCYcgAAKMhKNQAAFCRUAwBA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\\nZl+gygAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x10b411050>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"plt.figure(figsize=(12.75, 8))\\n\",\n    \"plt.plot([6.375, 6.375], [0, 8], \\\"--\\\", color=\\\"black\\\", alpha=0.4, lw=1.25)\\n\",\n    \"\\n\",\n    \"for book, group in wheres_waldo_locations.groupby(\\\"Book\\\"):\\n\",\n    \"    plt.plot(group.X, group.Y, \\\"o\\\", label=\\\"Book %d\\\" % (book))\\n\",\n    \"\\n\",\n    \"plt.xlim(0, 12.75)\\n\",\n    \"plt.ylim(0, 8)\\n\",\n    \"plt.xticks([])\\n\",\n    \"plt.yticks([])\\n\",\n    \"plt.legend(loc=\\\"upper center\\\", ncol=7, frameon=True, fancybox=True, bbox_to_anchor=(0.5, 1.1))\\n\",\n    \";\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Kernel Density Estimation\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Next, we can use the [Seaborn library](http://stanford.edu/~mwaskom/software/seaborn/examples/index.html) to perform a [kernel density estimation](http://en.wikipedia.org/wiki/Kernel_density_estimation) (KDE) of the points.\\n\",\n    \"\\n\",\n    \"A KDE will show us Waldo's hot spots, i.e., where he's most likely to appear.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 3,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"data\": {\n      \"text/plain\": [\n       \"''\"\n      ]\n     },\n     \"execution_count\": 3,\n     \"metadata\": {},\n     \"output_type\": \"execute_result\"\n    },\n    {\n     \"data\": {\n      \"image/png\": 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rqYrBKp+hCqZxGqzSKF6nx2IrNvn8j0u2PZ2TV1iFVjW9QTs2V4nbRq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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x10b3bb1d0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"sb.kdeplot(wheres_waldo_locations.X, wheres_waldo_locations.Y, shade=True, cmap=\\\"Blues\\\")\\n\",\n    \"plt.plot([6.375, 6.375], [0, 8], \\\"--\\\", color=\\\"black\\\", alpha=0.4, lw=1.25)\\n\",\n    \"plt.xlim(0, 12.75)\\n\",\n    \"plt.ylim(0, 8)\\n\",\n    \"plt.xlabel(\\\"\\\")\\n\",\n    \"plt.ylabel(\\\"\\\")\\n\",\n    \"plt.xticks([])\\n\",\n    \"plt.yticks([])\\n\",\n    \";\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Genetic Algorithm\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Now on to the real fun! I decided to approach this problem as a [traveling salesman problem](http://en.wikipedia.org/wiki/Travelling_salesman_problem): We need to check every possible location that Waldo could be at while taking as little time as possible. That means we need to cover as much ground as possible without any backtracking.\\n\",\n    \"\\n\",\n    \"In computer terms, that means we’re making a list of all 68 points that Waldo could be at, then sorting them based on the order that we’re going to visit them. We can use a [genetic algorithm](http://en.wikipedia.org/wiki/Genetic_algorithm) (GA) to try out hundreds of possible arrangements and continually build upon the best ones. Note that because GAs are stochastic, the end result will not always be the same each time you run it.\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Reorganize the DataFrame Xs and Ys into a lookup table\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Constantly looking up values in a DataFrame is quite slow, so it's better to use a dictionary.\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 4,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"waldo_location_map = {}\\n\",\n    \"\\n\",\n    \"for i, record in wheres_waldo_locations.iterrows():\\n\",\n    \"    key = \\\"B%dP%d\\\" % (record.Book, record.Page)\\n\",\n    \"    waldo_location_map[key] = (record.X, record.Y)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"#### Basic functions for the Genetic Algorithm\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 5,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n    \"def calculate_distance(x1, y1, x2, y2):\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"        Returns the Euclidean distance between points (x1, y1) and (x2, y2)\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    return math.sqrt( (x2 - x1)**2 + (y2 - y1)**2 )\\n\",\n    \"\\n\",\n    \"def compute_fitness(solution):\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"        Computes the distance that the Waldo-seeking solution covers.\\n\",\n    \"        \\n\",\n    \"        Lower distance is better, so the GA should try to minimize this function.\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    solution_fitness = 0.0\\n\",\n    \"    \\n\",\n    \"    for index in range(1, len(solution)):\\n\",\n    \"        w1 = solution[index]\\n\",\n    \"        w2 = solution[index - 1]\\n\",\n    \"        solution_fitness += calculate_distance(waldo_location_map[w1][0], waldo_location_map[w1][1],\\n\",\n    \"                                               waldo_location_map[w2][0], waldo_location_map[w2][1])\\n\",\n    \"        \\n\",\n    \"    return solution_fitness\\n\",\n    \"\\n\",\n    \"def generate_random_agent():\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"        Creates a random Waldo-seeking path.\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    new_random_agent = waldo_location_map.keys()\\n\",\n    \"    random.shuffle(new_random_agent)\\n\",\n    \"    return tuple(new_random_agent)\\n\",\n    \"\\n\",\n    \"def mutate_agent(agent_genome, max_mutations=3):\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"        Applies 1 - `max_mutations` point mutations to the given Waldo-seeking path.\\n\",\n    \"        \\n\",\n    \"        A point mutation swaps the order of two locations in the Waldo-seeking path.\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    agent_genome = list(agent_genome)\\n\",\n    \"    num_mutations = random.randint(1, max_mutations)\\n\",\n    \"    \\n\",\n    \"    for mutation in range(num_mutations):\\n\",\n    \"        swap_index1 = random.randint(0, len(agent_genome) - 1)\\n\",\n    \"        swap_index2 = swap_index1\\n\",\n    \"\\n\",\n    \"        while swap_index1 == swap_index2:\\n\",\n    \"            swap_index2 = random.randint(0, len(agent_genome) - 1)\\n\",\n    \"\\n\",\n    \"        agent_genome[swap_index1], agent_genome[swap_index2] = agent_genome[swap_index2], agent_genome[swap_index1]\\n\",\n    \"            \\n\",\n    \"    return tuple(agent_genome)\\n\",\n    \"\\n\",\n    \"def shuffle_mutation(agent_genome):\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"        Applies a single shuffle mutation to the given Waldo-seeking path.\\n\",\n    \"        \\n\",\n    \"        A shuffle mutation takes a random sub-section of the path and moves it to\\n\",\n    \"        another location in the path.\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    agent_genome = list(agent_genome)\\n\",\n    \"    \\n\",\n    \"    start_index = random.randint(0, len(agent_genome) - 1)\\n\",\n    \"    length = random.randint(2, 20)\\n\",\n    \"    \\n\",\n    \"    genome_subset = agent_genome[start_index:start_index + length]\\n\",\n    \"    agent_genome = agent_genome[:start_index] + agent_genome[start_index + length:]\\n\",\n    \"    \\n\",\n    \"    insert_index = random.randint(0, len(agent_genome) + len(genome_subset) - 1)\\n\",\n    \"    agent_genome = agent_genome[:insert_index] + genome_subset + agent_genome[insert_index:]\\n\",\n    \"    \\n\",\n    \"    return tuple(agent_genome)\\n\",\n    \"\\n\",\n    \"def generate_random_population(pop_size):\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"        Generates a list with `pop_size` number of random Waldo-seeking paths.\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    random_population = []\\n\",\n    \"    for agent in range(pop_size):\\n\",\n    \"        random_population.append(generate_random_agent())\\n\",\n    \"    return random_population\\n\",\n    \"\\n\",\n    \"def plot_trajectory(agent_genome):\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"        Create a visualization of the given Waldo-seeking path.\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    agent_xs = []\\n\",\n    \"    agent_ys = []\\n\",\n    \"    agent_fitness = compute_fitness(agent_genome)\\n\",\n    \"\\n\",\n    \"    for waldo_loc in agent_genome:\\n\",\n    \"        agent_xs.append(waldo_location_map[waldo_loc][0])\\n\",\n    \"        agent_ys.append(waldo_location_map[waldo_loc][1])\\n\",\n    \"\\n\",\n    \"    plt.figure()\\n\",\n    \"    plt.title(\\\"Fitness: %f\\\" % (agent_fitness))\\n\",\n    \"    plt.plot(agent_xs[:18], agent_ys[:18], \\\"-o\\\", markersize=7)\\n\",\n    \"    plt.plot(agent_xs[17:35], agent_ys[17:35], \\\"-o\\\", markersize=7)\\n\",\n    \"    plt.plot(agent_xs[34:52], agent_ys[34:52], \\\"-o\\\", markersize=7)\\n\",\n    \"    plt.plot(agent_xs[51:], agent_ys[51:], \\\"-o\\\", markersize=7)\\n\",\n    \"    plt.plot(agent_xs[0], agent_ys[0], \\\"^\\\", color=\\\"#1f77b4\\\", markersize=15)\\n\",\n    \"    plt.plot(agent_xs[-1], agent_ys[-1], \\\"v\\\", color=\\\"#d62728\\\", markersize=15)\\n\",\n    \"    plt.grid(False)\\n\",\n    \"    plt.xticks([])\\n\",\n    \"    plt.yticks([])\\n\",\n    \"    plt.show()\\n\",\n    \"    \\n\",\n    \"def run_genetic_algorithm(generations=10000, population_size=100):\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"        The core of the Genetic Algorithm.\\n\",\n    \"        \\n\",\n    \"        `generations` and `population_size` must be a multiple of 10.\\n\",\n    \"    \\\"\\\"\\\"\\n\",\n    \"    \\n\",\n    \"    population_subset_size = int(population_size / 10.)\\n\",\n    \"    generations_10pct = int(generations / 10.)\\n\",\n    \"    \\n\",\n    \"    # Create a random population of `population_size` number of solutions.\\n\",\n    \"    population = generate_random_population(population_size)\\n\",\n    \"\\n\",\n    \"    # For `generations` number of repetitions...\\n\",\n    \"    for generation in range(int(generations)):\\n\",\n    \"        \\n\",\n    \"        # Compute the fitness of the entire current population\\n\",\n    \"        population_fitness = {}\\n\",\n    \"\\n\",\n    \"        for agent_genome in population:\\n\",\n    \"            if agent_genome in population_fitness:\\n\",\n    \"                continue\\n\",\n    \"\\n\",\n    \"            population_fitness[agent_genome] = compute_fitness(agent_genome)\\n\",\n    \"\\n\",\n    \"        # Take the top 10% shortest paths and produce offspring from each of them\\n\",\n    \"        new_population = []\\n\",\n    \"        for rank, agent_genome in enumerate(sorted(population_fitness,\\n\",\n    \"                                                   key=population_fitness.get)[:population_subset_size]):\\n\",\n    \"\\n\",\n    \"            if (generation % generations_10pct == 0 or generation == (generations - 1)) and rank == 0:\\n\",\n    \"                print(\\\"Generation %d best: %f\\\" % (generation, population_fitness[agent_genome]))\\n\",\n    \"                print(agent_genome)\\n\",\n    \"                plot_trajectory(agent_genome)\\n\",\n    \"\\n\",\n    \"            # Create 1 exact copy of each top path\\n\",\n    \"            new_population.append(agent_genome)\\n\",\n    \"\\n\",\n    \"            # Create 4 offspring with 1-3 mutations\\n\",\n    \"            for offspring in range(4):\\n\",\n    \"                new_population.append(mutate_agent(agent_genome, 3))\\n\",\n    \"                \\n\",\n    \"            # Create 5 offspring with a single shuffle mutation\\n\",\n    \"            for offspring in range(5):\\n\",\n    \"                new_population.append(shuffle_mutation(agent_genome))\\n\",\n    \"\\n\",\n    \"        # Replace the old population with the new population of offspring\\n\",\n    \"        for i in range(len(population))[::-1]:\\n\",\n    \"            del population[i]\\n\",\n    \"\\n\",\n    \"        population = new_population\"\n   ]\n  },\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 6,\n   \"metadata\": {},\n   \"outputs\": [\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Generation 0 best: 283.706779\\n\",\n      \"('B3P8', 'B2P3', 'B4P3', 'B1P4', 'B4P8', 'B2P12', 'B2P1', 'B5P7', 'B4P11', 'B3P7', 'B4P6', 'B5P5', 'B5P6', 'B2P8', 'B7P7', 'B5P2', 'B4P9', 'B4P4', 'B5P3', 'B2P11', 'B4P7', 'B5P9', 'B7P6', 'B2P5', 'B6P2', 'B2P6', 'B1P10', 'B1P3', 'B3P2', 'B3P3', 'B3P6', 'B4P10', 'B3P9', 'B5P4', 'B6P11', 'B4P2', 'B1P2', 'B6P12', 'B4P1', 'B6P8', 'B6P5', 'B6P1', 'B1P11', 'B3P11', 'B1P9', 'B1P12', 'B2P2', 'B2P9', 'B3P4', 'B2P7', 'B5P1', 'B7P2', 'B2P4', 'B2P10', 'B1P8', 'B4P5', 'B5P10', 'B3P10', 'B1P5', 'B5P8', 'B3P5', 'B3P1', 'B5P11', 'B7P1', 'B1P7', 'B1P1', 'B7P3', 'B1P6')\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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v7Tp08D/yZ+SqWSrVu3smrVqhIT0uPHjwMUW5L0YYomGpX0qP5uRf1Q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\\ns1IdtGfVmC0AAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x10b396d90>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Generation 1000 best: 73.095626\\n\",\n      \"('B5P1', 'B7P2', 'B6P8', 'B2P1', 'B1P5', 'B3P4', 'B2P2', 'B2P8', 'B3P11', 'B4P7', 'B2P9', 'B2P7', 'B5P7', 'B1P8', 'B1P3', 'B1P9', 'B4P5', 'B4P1', 'B5P2', 'B4P9', 'B4P6', 'B1P12', 'B4P11', 'B6P12', 'B5P11', 'B7P7', 'B6P1', 'B1P6', 'B1P10', 'B1P11', 'B7P1', 'B5P3', 'B2P3', 'B3P5', 'B5P6', 'B3P8', 'B3P7', 'B1P2', 'B3P1', 'B7P3', 'B3P3', 'B1P1', 'B6P2', 'B2P10', 'B5P9', 'B4P8', 'B4P10', 'B2P12', 'B5P5', 'B3P9', 'B3P2', 'B2P11', 'B1P7', 'B5P8', 'B3P10', 'B5P4', 'B7P6', 'B3P6', 'B1P4', 'B4P3', 'B2P6', 'B2P5', 'B6P5', 'B4P2', 'B2P4', 'B6P11', 'B4P4', 'B5P10')\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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pzhxo0beX6wiYmJQafT5bqGEKLskAlWQogCKY5lVk09Pvft22euS7zX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\\nEEIIIYRwWf8PxlsR5HLDkJQAAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x10bac48d0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Generation 2000 best: 64.163757\\n\",\n      \"('B5P1', 'B6P8', 'B7P2', 'B2P1', 'B2P8', 'B2P2', 'B3P11', 'B4P7', 'B2P9', 'B2P7', 'B5P7', 'B5P2', 'B1P8', 'B1P3', 'B1P9', 'B4P1', 'B4P5', 'B4P9', 'B4P6', 'B1P12', 'B3P4', 'B1P5', 'B4P11', 'B6P12', 'B1P2', 'B7P7', 'B6P1', 'B3P1', 'B7P3', 'B1P6', 'B2P3', 'B5P3', 'B7P1', 'B1P11', 'B1P10', 'B3P5', 'B5P6', 'B3P8', 'B3P7', 'B5P11', 'B4P10', 'B4P8', 'B5P9', 'B2P10', 'B6P2', 'B1P1', 'B3P3', 'B2P12', 'B5P5', 'B3P9', 'B1P4', 'B3P6', 'B3P10', 'B5P4', 'B7P6', 'B2P4', 'B4P3', 'B2P6', 'B2P5', 'B6P5', 'B4P2', 'B6P11', 'B3P2', 'B4P4', 'B2P11', 'B5P8', 'B1P7', 'B5P10')\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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rWyVc+ePVEUhWXLlt01M0RGRgZTpkzh2rVr1K1blwYNGtx1nszMTC5d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\\nfVvqKFTsAAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x10bd83450>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Generation 3000 best: 61.888180\\n\",\n      \"('B5P1', 'B7P2', 'B6P8', 'B2P1', 'B2P8', 'B2P2', 'B3P11', 'B4P7', 'B2P9', 'B2P7', 'B4P9', 'B5P2', 'B1P8', 'B1P3', 'B1P9', 'B4P1', 'B4P5', 'B5P7', 'B4P6', 'B1P12', 'B3P4', 'B1P5', 'B4P11', 'B6P12', 'B3P1', 'B6P1', 'B7P3', 'B1P6', 'B2P3', 'B5P3', 'B7P1', 'B1P11', 'B1P10', 'B3P5', 'B5P6', 'B3P8', 'B3P7', 'B1P2', 'B7P7', 'B5P11', 'B4P10', 'B4P8', 'B5P9', 'B2P10', 'B6P2', 'B1P1', 'B3P3', 'B2P12', 'B5P5', 'B3P9', 'B1P4', 'B3P6', 'B7P6', 'B3P10', 'B5P4', 'B2P4', 'B4P3', 'B2P6', 'B2P5', 'B6P5', 'B4P2', 'B6P11', 'B3P2', 'B4P4', 'B2P11', 'B5P8', 'B1P7', 'B5P10')\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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8+ePXF0dGTWrFkcOnSoaP+BAweYPXs2zs7ORe9TCGGbZBqAEKJEgoKC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\\n1v8D3rvPZbpqHfkAAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x10bf6c450>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Generation 4000 best: 61.579230\\n\",\n      \"('B5P1', 'B7P2', 'B6P8', 'B2P1', 'B2P8', 'B2P2', 'B3P11', 'B4P7', 'B2P9', 'B2P7', 'B4P9', 'B5P7', 'B5P2', 'B1P8', 'B1P9', 'B4P1', 'B4P5', 'B1P3', 'B4P6', 'B1P12', 'B3P4', 'B1P5', 'B4P11', 'B6P12', 'B3P1', 'B6P1', 'B7P3', 'B1P6', 'B2P3', 'B5P3', 'B7P1', 'B1P11', 'B1P10', 'B3P5', 'B5P6', 'B3P8', 'B3P7', 'B1P2', 'B7P7', 'B5P11', 'B4P10', 'B4P8', 'B5P9', 'B2P10', 'B6P2', 'B1P1', 'B3P3', 'B2P12', 'B5P5', 'B3P9', 'B1P4', 'B3P6', 'B7P6', 'B3P10', 'B5P4', 'B2P4', 'B4P3', 'B2P6', 'B2P5', 'B6P5', 'B4P2', 'B6P11', 'B3P2', 'B4P4', 'B2P11', 'B5P8', 'B1P7', 'B5P10')\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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+6G63OP3azbgBDCNkixKoS4LY6OjqiqSnJy8h2/RpUqVWjdujUxMTG8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\\nfAAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x10ca6f8d0>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Generation 5000 best: 60.948624\\n\",\n      \"('B5P1', 'B7P2', 'B6P8', 'B2P1', 'B2P8', 'B2P2', 'B3P11', 'B4P7', 'B2P9', 'B2P7', 'B4P9', 'B1P8', 'B5P2', 'B5P7', 'B4P5', 'B4P1', 'B1P9', 'B1P3', 'B4P6', 'B1P12', 'B3P4', 'B1P5', 'B4P11', 'B6P12', 'B1P2', 'B3P1', 'B6P1', 'B7P3', 'B1P6', 'B2P3', 'B5P3', 'B7P1', 'B1P11', 'B1P10', 'B3P5', 'B5P6', 'B3P8', 'B3P7', 'B7P7', 'B5P11', 'B4P10', 'B4P8', 'B5P9', 'B2P10', 'B6P2', 'B1P1', 'B3P3', 'B2P12', 'B5P5', 'B3P9', 'B1P4', 'B3P6', 'B7P6', 'B3P10', 'B5P4', 'B2P4', 'B4P3', 'B2P6', 'B2P5', 'B6P5', 'B4P2', 'B6P11', 'B3P2', 'B4P4', 'B2P11', 'B5P8', 'B1P7', 'B5P10')\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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HfffQfA/fffD8DixYsLp0H8/Y/BYEBRFE6ePMmJEycKR0Dr1auHqqps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\\nAAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x10cd45d50>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Generation 6000 best: 60.948624\\n\",\n      \"('B5P1', 'B7P2', 'B6P8', 'B2P1', 'B2P8', 'B2P2', 'B3P11', 'B4P7', 'B2P9', 'B2P7', 'B4P9', 'B1P8', 'B5P2', 'B5P7', 'B4P5', 'B4P1', 'B1P9', 'B1P3', 'B4P6', 'B1P12', 'B3P4', 'B1P5', 'B4P11', 'B6P12', 'B1P2', 'B3P1', 'B6P1', 'B7P3', 'B1P6', 'B2P3', 'B5P3', 'B7P1', 'B1P11', 'B1P10', 'B3P5', 'B5P6', 'B3P8', 'B3P7', 'B7P7', 'B5P11', 'B4P10', 'B4P8', 'B5P9', 'B2P10', 'B6P2', 'B1P1', 'B3P3', 'B2P12', 'B5P5', 'B3P9', 'B1P4', 'B3P6', 'B7P6', 'B3P10', 'B5P4', 'B2P4', 'B4P3', 'B2P6', 'B2P5', 'B6P5', 'B4P2', 'B6P11', 'B3P2', 'B4P4', 'B2P11', 'B5P8', 'B1P7', 'B5P10')\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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HfffQfA/fffD8DixYsLp0H8/Y/BYEBRFE6ePMmJEycKR0Dr1auHqqps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\\nAAAAAElFTkSuQmCC\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x10cdef210>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Generation 7000 best: 60.934271\\n\",\n      \"('B5P1', 'B7P2', 'B6P8', 'B2P1', 'B2P8', 'B2P2', 'B3P11', 'B4P7', 'B2P9', 'B4P9', 'B2P7', 'B5P7', 'B5P2', 'B1P8', 'B4P5', 'B4P1', 'B1P9', 'B1P3', 'B4P6', 'B1P12', 'B3P4', 'B1P5', 'B4P11', 'B6P12', 'B1P2', 'B3P1', 'B6P1', 'B7P3', 'B1P6', 'B2P3', 'B5P3', 'B7P1', 'B1P11', 'B1P10', 'B3P5', 'B5P6', 'B3P8', 'B3P7', 'B7P7', 'B5P11', 'B4P10', 'B4P8', 'B5P9', 'B2P10', 'B6P2', 'B1P1', 'B3P3', 'B2P12', 'B5P5', 'B3P9', 'B1P4', 'B3P6', 'B7P6', 'B3P10', 'B5P4', 'B2P4', 'B4P3', 'B2P6', 'B2P5', 'B6P5', 'B4P2', 'B6P11', 'B3P2', 'B4P4', 'B2P11', 'B5P8', 'B1P7', 'B5P10')\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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kmu9ZxxcXF88sknnDx5kqCgIEC9rH/+/HmWLVvG/v37c73WihUrgH9m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\\nCM2SYlUIIYQQQmiWFKtCCCGEEEKz/h/xvKjigNo4ygAAAABJRU5ErkJggg==\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x10cdef550>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Generation 8000 best: 60.772526\\n\",\n      \"('B5P1', 'B7P2', 'B6P8', 'B2P1', 'B2P8', 'B2P2', 'B3P11', 'B4P7', 'B2P9', 'B4P9', 'B2P7', 'B5P7', 'B5P2', 'B1P8', 'B4P5', 'B4P1', 'B1P9', 'B1P3', 'B4P6', 'B1P12', 'B3P4', 'B1P5', 'B4P11', 'B6P12', 'B1P2', 'B3P1', 'B6P1', 'B7P3', 'B1P6', 'B2P3', 'B5P3', 'B7P1', 'B1P11', 'B1P10', 'B3P5', 'B5P6', 'B3P8', 'B3P7', 'B7P7', 'B5P11', 'B4P10', 'B4P8', 'B5P9', 'B2P10', 'B6P2', 'B1P1', 'B3P3', 'B2P12', 'B5P5', 'B3P9', 'B1P4', 'B3P6', 'B7P6', 'B5P4', 'B4P3', 'B2P6', 'B2P5', 'B6P5', 'B4P2', 'B6P11', 'B2P4', 'B3P10', 'B3P2', 'B4P4', 'B2P11', 'B5P8', 'B1P7', 'B5P10')\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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yxdurTEc4qi8MsvvwB/fw1A3ap2xIgRpKam0qVLF7799tvrFqqF2rZt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\\ngg==\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x10bf24e10>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Generation 9000 best: 60.709435\\n\",\n      \"('B5P1', 'B7P2', 'B6P8', 'B2P1', 'B2P8', 'B2P2', 'B3P11', 'B2P9', 'B4P7', 'B4P9', 'B2P7', 'B5P7', 'B5P2', 'B1P8', 'B4P5', 'B4P1', 'B1P9', 'B1P3', 'B4P6', 'B1P12', 'B3P4', 'B1P5', 'B4P11', 'B6P12', 'B1P2', 'B3P1', 'B6P1', 'B7P3', 'B1P6', 'B2P3', 'B5P3', 'B7P1', 'B1P11', 'B1P10', 'B3P5', 'B5P6', 'B3P8', 'B3P7', 'B7P7', 'B5P11', 'B4P10', 'B4P8', 'B5P9', 'B2P10', 'B6P2', 'B1P1', 'B3P3', 'B2P12', 'B5P5', 'B3P9', 'B1P4', 'B3P6', 'B4P3', 'B2P6', 'B2P5', 'B6P5', 'B4P2', 'B6P11', 'B2P4', 'B7P6', 'B5P4', 'B3P10', 'B3P2', 'B4P4', 'B2P11', 'B5P8', 'B1P7', 'B5P10')\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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+VeA7BlyxYGDRrEsmXLCly/bt060tLSaNCgAR4eHjRp0oTTp09z6tSp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\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x10b4fce10>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    },\n    {\n     \"name\": \"stdout\",\n     \"output_type\": \"stream\",\n     \"text\": [\n      \"Generation 9999 best: 60.324768\\n\",\n      \"('B5P1', 'B7P2', 'B6P8', 'B2P1', 'B2P8', 'B2P2', 'B3P11', 'B4P7', 'B2P9', 'B4P9', 'B2P7', 'B5P7', 'B5P2', 'B1P8', 'B4P5', 'B4P1', 'B1P9', 'B1P3', 'B4P6', 'B1P12', 'B3P4', 'B1P5', 'B4P11', 'B6P12', 'B1P2', 'B3P1', 'B6P1', 'B7P3', 'B2P3', 'B1P6', 'B5P3', 'B7P1', 'B1P11', 'B1P10', 'B3P5', 'B5P6', 'B3P8', 'B3P7', 'B7P7', 'B5P11', 'B4P10', 'B4P8', 'B5P9', 'B2P10', 'B6P2', 'B1P1', 'B3P3', 'B2P12', 'B5P5', 'B3P9', 'B1P4', 'B3P6', 'B4P3', 'B2P6', 'B2P5', 'B6P5', 'B4P2', 'B6P11', 'B2P4', 'B5P4', 'B7P6', 'B3P10', 'B3P2', 'B4P4', 'B2P11', 'B5P8', 'B1P7', 'B5P10')\\n\"\n     ]\n    },\n    {\n     \"data\": {\n      \"image/png\": 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tVgMKCqKpmZmXe9RghRtMkCKyHEPcmPbVazeqju3r07ex7j7Xbv3s1T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\\nEMJuSbEqhBBCCCHslhSrQgghhBDCbv0/Qet1IahPNOkAAAAASUVORK5CYII=\\n\",\n      \"text/plain\": [\n       \"<matplotlib.figure.Figure at 0x10d0b3750>\"\n      ]\n     },\n     \"metadata\": {},\n     \"output_type\": \"display_data\"\n    }\n   ],\n   \"source\": [\n    \"run_genetic_algorithm(generations=10000, population_size=100)\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"### Some notes on the methodology for the nerdy folk\"\n   ]\n  },\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"Several people have pointed out that this method is very likely [overfit](http://en.wikipedia.org/wiki/Overfitting), i.e., that the path is trained so much on the 68 points that it likely wouldn't work well on a new 8th book. While this criticism is possibly true, it also misses the purpose of this project.\\n\",\n    \"\\n\",\n    \"**The purpose of this project is to find an optimal search path for the first 7 'Where's Waldo?' books only.**\\n\",\n    \"\\n\",\n    \"If the goal were to produce an optimal search path for any future books, it would become necessary to divide the 68 points into a training and testing set so some form of cross-validation could be performed. However, because the data is already quite sparse (68 points), this would likely prove to be a fruitless endeavor anyway.\\n\",\n    \"\\n\",\n    \"That said, please feel free to continue working on this data set and discover better methods to find Waldo efficiently. I've provided the data set along with this notebook if you would like to work on it with your own methods.\\n\",\n    \"\\n\",\n    \"If you find a better solution, please [contact me](http://www.randalolson.com/contact/) so I can take a look.\"\n   ]\n  }\n ],\n \"metadata\": {\n  \"kernelspec\": {\n   \"display_name\": \"Python 3\",\n   \"language\": \"python\",\n   \"name\": \"python3\"\n  },\n  \"language_info\": {\n   \"codemirror_mode\": {\n    \"name\": \"ipython\",\n    \"version\": 3\n   },\n   \"file_extension\": \".py\",\n   \"mimetype\": \"text/x-python\",\n   \"name\": \"python\",\n   \"nbconvert_exporter\": \"python\",\n   \"pygments_lexer\": \"ipython3\",\n   \"version\": \"3.6.5\"\n  }\n },\n \"nbformat\": 4,\n \"nbformat_minor\": 1\n}\n"
  },
  {
    "path": "wheres-waldo-path-optimization/wheres-waldo-locations.csv",
    "content": "Book,Page,X,Y\r5,10,0.625,7.708333333\r7,1,4.944444444,6.569444444\r1,11,5.430555556,6.402777778\r1,10,5.902777778,6.083333333\r2,3,5.430555556,5.444444444\r5,3,4.791666667,5.444444444\r1,6,5.305555556,5.125\r1,7,3.666666667,5.291666667\r7,3,6.069444444,4.638888889\r5,8,3.513888889,4.319444444\r2,11,2.388888889,4.486111111\r4,4,1.763888889,4.333333333\r3,2,2.083333333,3.847222222\r6,11,1.125,2.569444444\r4,2,0.805555556,2.25\r2,4,1.444444444,2.097222222\r6,5,0.638888889,1.777777778\r2,5,0.958333333,1.277777778\r2,6,0.958333333,0.972222222\r4,3,2.083333333,0.805555556\r3,10,2.388888889,2.729166667\r5,4,2.166666667,2.25\r7,6,2.486111111,2.25\r3,9,3.347222222,2.569444444\r1,4,3.513888889,2.083333333\r3,6,3.041666667,1.916666667\r5,5,4.305555556,2.888888889\r2,12,5.263888889,2.729166667\r3,3,5.75,2.25\r1,1,5.597222222,1.930555556\r6,2,4.625,1.277777778\r2,10,5.263888889,0.819444444\r5,9,5.416666667,0.333333333\r5,6,7.666666667,7.361111111\r3,5,6.861111111,6.736111111\r3,8,8.458333333,6.722222222\r2,1,10.375,7.194444444\r5,1,12.44444444,7.694444444\r2,8,10.375,6.430555556\r6,8,11.16666667,6.402777778\r7,2,11.33333333,6.083333333\r2,2,9.888888889,5.777777778\r1,5,9.25,5.291666667\r3,4,9.958333333,4.791666667\r4,11,9.097222222,4.652777778\r3,7,7.944444444,5.305555556\r1,2,7.763888889,5\r6,12,8,4.791666667\r3,1,7.333333333,5.138888889\r6,1,6.694444444,4.972222222\r7,7,7.166666667,4.5\r5,11,7.486111111,4.006944444\r3,11,11.01388889,4.819444444\r4,7,11.80555556,4.652777778\r2,9,12.27777778,4.805555556\r1,12,9.416666667,3.694444444\r4,6,9.25,3.222222222\r4,9,11.48611111,3.541666667\r2,7,11.95833333,3.541666667\r4,10,7.986111111,2.430555556\r4,8,6.694444444,1.625\r5,7,12.125,2.909722222\r5,2,11.51388889,2.756944444\r1,8,11.30555556,2.555555556\r1,3,10.72222222,1.930555556\r1,9,11.16666667,1.791666667\r4,5,12.44444444,1.625\r4,1,12.125,1.291666667"
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]