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   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Wrangling DHS Border Crossing Data\n",
      "\n",
      "- **Author:** [Chris Albon](http://www.chrisalbon.com/), [@ChrisAlbon](https://twitter.com/chrisalbon)\n",
      "- **Date:** -\n",
      "- **Repo:** [Python 3 code snippets for data science](https://github.com/chrisalbon/code_py)\n",
      "- **Note:**"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd\n",
      "import os\n",
      "%matplotlib inline\n",
      "import matplotlib.pyplot as plt\n",
      "import numpy as np"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cur_dir = os.path.dirname(os.path.realpath('__file__'))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = pd.read_csv(cur_dir + '/data/cross_raw_data/' + 'bc_full_crossing_data.csv')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
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        "  <thead>\n",
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        "      <th></th>\n",
        "      <th>Port Name</th>\n",
        "      <th>Year</th>\n",
        "      <th>Month</th>\n",
        "      <th>Trucks</th>\n",
        "      <th>Loaded Truck Containers</th>\n",
        "      <th>Empty Truck Containers</th>\n",
        "      <th>Trains</th>\n",
        "      <th>Loaded Rail Containers</th>\n",
        "      <th>Empty Rail Containers</th>\n",
        "      <th>Train Passengers</th>\n",
        "      <th>Buses</th>\n",
        "      <th>Bus Passengers</th>\n",
        "      <th>Personal Vehicles</th>\n",
        "      <th>Personal Vehicle Passengers</th>\n",
        "      <th>Pedestrians</th>\n",
        "      <th>City</th>\n",
        "      <th>State</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> AZ: Douglas                                   ...</td>\n",
        "      <td> 1995</td>\n",
        "      <td>  01 - JAN</td>\n",
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        "      <td> 379047</td>\n",
        "      <td> 40735</td>\n",
        "      <td> Douglas                                       ...</td>\n",
        "      <td> AZ</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> AZ: Douglas                                   ...</td>\n",
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        "      <td>  02 - FEB</td>\n",
        "      <td> 2849</td>\n",
        "      <td> 0</td>\n",
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        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
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        "      <td> 337926</td>\n",
        "      <td> 40463</td>\n",
        "      <td> Douglas                                       ...</td>\n",
        "      <td> AZ</td>\n",
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        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> AZ: Douglas                                   ...</td>\n",
        "      <td> 1995</td>\n",
        "      <td>  03 - MAR</td>\n",
        "      <td> 3421</td>\n",
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        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
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        "      <td> 270</td>\n",
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        "      <td> 139060</td>\n",
        "      <td> 319838</td>\n",
        "      <td> 55370</td>\n",
        "      <td> Douglas                                       ...</td>\n",
        "      <td> AZ</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> AZ: Douglas                                   ...</td>\n",
        "      <td> 1995</td>\n",
        "      <td>  04 - APR</td>\n",
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        "      <td> 331575</td>\n",
        "      <td> 46279</td>\n",
        "      <td> Douglas                                       ...</td>\n",
        "      <td> AZ</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> AZ: Douglas                                   ...</td>\n",
        "      <td> 1995</td>\n",
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        "      <td> 3560</td>\n",
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        "      <td> 355573</td>\n",
        "      <td> 50160</td>\n",
        "      <td> Douglas                                       ...</td>\n",
        "      <td> AZ</td>\n",
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        "  </tbody>\n",
        "</table>\n",
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       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "                                           Port Name  Year      Month  Trucks  \\\n",
        "0  AZ: Douglas                                   ...  1995   01 - JAN    2890   \n",
        "1  AZ: Douglas                                   ...  1995   02 - FEB    2849   \n",
        "2  AZ: Douglas                                   ...  1995   03 - MAR    3421   \n",
        "3  AZ: Douglas                                   ...  1995   04 - APR    3676   \n",
        "4  AZ: Douglas                                   ...  1995   05 - MAY    3560   \n",
        "\n",
        "   Loaded Truck Containers  Empty Truck Containers  Trains  \\\n",
        "0                        0                       0       0   \n",
        "1                        0                       0       0   \n",
        "2                        0                       0       0   \n",
        "3                        0                       0       0   \n",
        "4                        0                       0       0   \n",
        "\n",
        "   Loaded Rail Containers  Empty Rail Containers  Train Passengers  Buses  \\\n",
        "0                       0                      0                 0    270   \n",
        "1                       0                      0                 0    270   \n",
        "2                       0                      0                 0    270   \n",
        "3                       0                      0                 0    270   \n",
        "4                       0                      0                 0    270   \n",
        "\n",
        "   Bus Passengers  Personal Vehicles  Personal Vehicle Passengers  \\\n",
        "0             270             164803                       379047   \n",
        "1             270             146925                       337926   \n",
        "2             270             139060                       319838   \n",
        "3             270             144163                       331575   \n",
        "4             270             154597                       355573   \n",
        "\n",
        "   Pedestrians                                               City State  \n",
        "0        40735  Douglas                                       ...    AZ  \n",
        "1        40463  Douglas                                       ...    AZ  \n",
        "2        55370  Douglas                                       ...    AZ  \n",
        "3        46279  Douglas                                       ...    AZ  \n",
        "4        50160  Douglas                                       ...    AZ  "
       ]
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     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df['Port Name'][1]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "'AZ: Douglas                                                 '"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_douglas = df[df['Port Name'] == 'AZ: Douglas                                                 '][:]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_douglas.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Port Name</th>\n",
        "      <th>Year</th>\n",
        "      <th>Month</th>\n",
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        "      <th>Loaded Truck Containers</th>\n",
        "      <th>Empty Truck Containers</th>\n",
        "      <th>Trains</th>\n",
        "      <th>Loaded Rail Containers</th>\n",
        "      <th>Empty Rail Containers</th>\n",
        "      <th>Train Passengers</th>\n",
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        "      <th>Bus Passengers</th>\n",
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        "      <th>Personal Vehicle Passengers</th>\n",
        "      <th>Pedestrians</th>\n",
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        "      <th>0</th>\n",
        "      <td> AZ: Douglas                                   ...</td>\n",
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        "      <td> Douglas                                       ...</td>\n",
        "      <td> AZ</td>\n",
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        "      <th>1</th>\n",
        "      <td> AZ: Douglas                                   ...</td>\n",
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        "      <td> 40463</td>\n",
        "      <td> Douglas                                       ...</td>\n",
        "      <td> AZ</td>\n",
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        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> AZ: Douglas                                   ...</td>\n",
        "      <td> 1995</td>\n",
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        "      <td> 3421</td>\n",
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        "      <td> 139060</td>\n",
        "      <td> 319838</td>\n",
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        "      <td> Douglas                                       ...</td>\n",
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        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> AZ: Douglas                                   ...</td>\n",
        "      <td> 1995</td>\n",
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        "      <td> Douglas                                       ...</td>\n",
        "      <td> AZ</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> AZ: Douglas                                   ...</td>\n",
        "      <td> 1995</td>\n",
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        "      <td> 3560</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
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        "      <td> 50160</td>\n",
        "      <td> Douglas                                       ...</td>\n",
        "      <td> AZ</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "                                           Port Name  Year      Month  Trucks  \\\n",
        "0  AZ: Douglas                                   ...  1995   01 - JAN    2890   \n",
        "1  AZ: Douglas                                   ...  1995   02 - FEB    2849   \n",
        "2  AZ: Douglas                                   ...  1995   03 - MAR    3421   \n",
        "3  AZ: Douglas                                   ...  1995   04 - APR    3676   \n",
        "4  AZ: Douglas                                   ...  1995   05 - MAY    3560   \n",
        "\n",
        "   Loaded Truck Containers  Empty Truck Containers  Trains  \\\n",
        "0                        0                       0       0   \n",
        "1                        0                       0       0   \n",
        "2                        0                       0       0   \n",
        "3                        0                       0       0   \n",
        "4                        0                       0       0   \n",
        "\n",
        "   Loaded Rail Containers  Empty Rail Containers  Train Passengers  Buses  \\\n",
        "0                       0                      0                 0    270   \n",
        "1                       0                      0                 0    270   \n",
        "2                       0                      0                 0    270   \n",
        "3                       0                      0                 0    270   \n",
        "4                       0                      0                 0    270   \n",
        "\n",
        "   Bus Passengers  Personal Vehicles  Personal Vehicle Passengers  \\\n",
        "0             270             164803                       379047   \n",
        "1             270             146925                       337926   \n",
        "2             270             139060                       319838   \n",
        "3             270             144163                       331575   \n",
        "4             270             154597                       355573   \n",
        "\n",
        "   Pedestrians                                               City State  \n",
        "0        40735  Douglas                                       ...    AZ  \n",
        "1        40463  Douglas                                       ...    AZ  \n",
        "2        55370  Douglas                                       ...    AZ  \n",
        "3        46279  Douglas                                       ...    AZ  \n",
        "4        50160  Douglas                                       ...    AZ  "
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     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_douglas['Total Crossers'] = df_douglas['Train Passengers'] + df_douglas['Bus Passengers'] + df_douglas['Personal Vehicle Passengers'] + df_douglas['Pedestrians']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_douglas_annual_sum = df_douglas.groupby(df_douglas['Year']).sum()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_douglas_annual_sum.head()"
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     "language": "python",
     "metadata": {},
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        "      <th></th>\n",
        "      <th>Trucks</th>\n",
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        "      <th>Year</th>\n",
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        "      <th></th>\n",
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        "      <th></th>\n",
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        "      <th>1995</th>\n",
        "      <td> 36272</td>\n",
        "      <td>     0</td>\n",
        "      <td>     0</td>\n",
        "      <td> 0</td>\n",
        "      <td>  0</td>\n",
        "      <td>  0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 3249</td>\n",
        "      <td> 3249</td>\n",
        "      <td> 1827277</td>\n",
        "      <td> 4202735</td>\n",
        "      <td> 567030</td>\n",
        "      <td> 4773014</td>\n",
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        "    <tr>\n",
        "      <th>1996</th>\n",
        "      <td> 38089</td>\n",
        "      <td>  8703</td>\n",
        "      <td> 13811</td>\n",
        "      <td> 0</td>\n",
        "      <td>  0</td>\n",
        "      <td>  0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 3353</td>\n",
        "      <td> 3433</td>\n",
        "      <td> 1915119</td>\n",
        "      <td> 4404773</td>\n",
        "      <td> 547742</td>\n",
        "      <td> 4955948</td>\n",
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        "    <tr>\n",
        "      <th>1997</th>\n",
        "      <td> 35718</td>\n",
        "      <td> 10186</td>\n",
        "      <td> 13119</td>\n",
        "      <td> 0</td>\n",
        "      <td>  0</td>\n",
        "      <td>  0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 3651</td>\n",
        "      <td> 3651</td>\n",
        "      <td> 1991904</td>\n",
        "      <td> 4803469</td>\n",
        "      <td> 599082</td>\n",
        "      <td> 5406202</td>\n",
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        "    <tr>\n",
        "      <th>1998</th>\n",
        "      <td> 35656</td>\n",
        "      <td> 14952</td>\n",
        "      <td> 14106</td>\n",
        "      <td> 0</td>\n",
        "      <td> 49</td>\n",
        "      <td> 57</td>\n",
        "      <td> 0</td>\n",
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        "      <td> 641181</td>\n",
        "      <td> 6221919</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1999</th>\n",
        "      <td> 32568</td>\n",
        "      <td> 14745</td>\n",
        "      <td> 11720</td>\n",
        "      <td> 0</td>\n",
        "      <td>  0</td>\n",
        "      <td>  0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 3650</td>\n",
        "      <td> 3650</td>\n",
        "      <td> 2150092</td>\n",
        "      <td> 5912753</td>\n",
        "      <td> 704973</td>\n",
        "      <td> 6621376</td>\n",
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        "</table>\n",
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       "metadata": {},
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        "      Trucks  Loaded Truck Containers  Empty Truck Containers  Trains  \\\n",
        "Year                                                                    \n",
        "1995   36272                        0                       0       0   \n",
        "1996   38089                     8703                   13811       0   \n",
        "1997   35718                    10186                   13119       0   \n",
        "1998   35656                    14952                   14106       0   \n",
        "1999   32568                    14745                   11720       0   \n",
        "\n",
        "      Loaded Rail Containers  Empty Rail Containers  Train Passengers  Buses  \\\n",
        "Year                                                                           \n",
        "1995                       0                      0                 0   3249   \n",
        "1996                       0                      0                 0   3353   \n",
        "1997                       0                      0                 0   3651   \n",
        "1998                      49                     57                 0   3650   \n",
        "1999                       0                      0                 0   3650   \n",
        "\n",
        "      Bus Passengers  Personal Vehicles  Personal Vehicle Passengers  \\\n",
        "Year                                                                   \n",
        "1995            3249            1827277                      4202735   \n",
        "1996            3433            1915119                      4404773   \n",
        "1997            3651            1991904                      4803469   \n",
        "1998            3650            2028032                      5577088   \n",
        "1999            3650            2150092                      5912753   \n",
        "\n",
        "      Pedestrians  Total Crossers  \n",
        "Year                               \n",
        "1995       567030         4773014  \n",
        "1996       547742         4955948  \n",
        "1997       599082         5406202  \n",
        "1998       641181         6221919  \n",
        "1999       704973         6621376  "
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_douglas_annual_sum['Bus Passengers'].plot(kind='area', grid=False)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x104a176d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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y/yGVggD2BR6o2eYu4Ki8nkcz3YD9gCOy5deS5lkOBS4GPpWNfxr4RrY8G3gI\nmAgcDKxix7fFZcCx2fKtwJy8n18Rb3V+zf1eH5/XfArwV8BHgG/t8lgt+z7Pbc8/In4JvLDL8Kxs\nHOAXwP/Klg8lvfGJiOeAjZLeWrPdq2ay7dUiYl1EPJQtbwEeI51PcTKwIFttAXBqtnwKsCgitkfE\nalIiOk7S/sAeEbEsW+/qmm2sRr1e85qH9Ht9CCN9zSNia0TcA/yp9nFa/X1elJO8+vxeUt+ZvX8H\nHJQt/w44WdIESYcAR9fcB7Ag+xr82QbG2tQkHUz65nUfMCMiOrO7OoEZ2fJM0gl3ffpOvtt1fC0+\nKW9IY3jNZ9b87Pf6CAzzNe+z6wToAbTw+7xoyf9DwLmSHiB9XevOxr9H+k94ALgE+BXQm933wYh4\nM6mM9HZJZzU25OYj6bXADcD5EfFi7X2Rvt/6KIA6q9Nr7vf6CPh9PrhCJf+IeDwi3hMRbyX193ky\nG++NiI9HxJERcSqwF/BEdt8z2b9bgB+Q+gTZACRNJH0gFkbEjdlwp6T9svv3B9Zn42vZ+RvWgaQ/\nwmuz5drxteMZdzOrw2u+FvxeH4kRvuYDaen3eaGSv6R9s38rwGeBK7Ofd5f0mmz53cD2iFiRlYH2\nycYnAicBD/f74IYkAd8FHo2Ib9bcdTNwdrZ8NnBjzfjc7GirQ4BZwLKIWAdslnRc9phn1WxjNer1\nmvu9PnyjeM1f2bT2h4h4llZ+n+c10wwsIp3N201q6vYh4DzSzPzjwNdq1j0YWAE8CtwBHJSNv4ZU\nCvod8AipJKS8Z9GLegOOB6qko0kezG5zgGmkCfYnstd3r5ptPkOadFwBvKdm/GhS8lkFXJb3cyvq\nrV6vud/r4/6arwY2AC9m+egvs/GWfZ/7JC8zsxIqVNnHzMwaw8nfzKyEnPzNzErIyd/MrISc/M3M\nSsjJ38yshJz8zQag5JeS5tSM/Z2kn+cZl1k9+Dh/s0FIehNwPak52ETgt6QTr/4wisdqi4ieOodo\nNipO/mZDkHQRsJV0lu0W4PXAm0l/DOZHxM1Z98irs3UAPhYRv5bUDnwZ6CKdNfoXjY3erH9O/mZD\nkDSFtMffDfwU+H1EXJtdCeo+0reCAKoR8SdJs4AfRMQxWfL/KfCmiHgqn2dg9mqFuIC7WZFFxFZJ\ni0l7/acDJ0n6f9ndk0ldONcBl0t6C6nd+Kyah1jmxG9F4+RvNjzV7CbgtIhYWXunpPnAsxFxlqQJ\nwLaau18N6M5kAAAAe0lEQVRqWJRmw+SjfcxG5nZS91kAJB2ZLe5J2vsH+N/AhAbHZTYiTv5mwxek\nyduJkpZLegT4YnbfFcDZkh4C/oJUIqrdzqxQPOFrZlZC3vM3MyshJ38zsxJy8jczKyEnfzOzEnLy\nNzMrISd/M7MScvI3MyshJ38zsxL6/x2Si905i7+/AAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10890cbd0>"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_douglas_annual_sum['Personal Vehicle Passengers'].plot(kind='area', grid=False)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x108951810>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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awO7d6Syir341/RU/mJJEd6R0O/Tf/M2+9y2V0sV0A/3PKMHv/R5897sDe1yzevOQVJN6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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10895e750>"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_douglas_annual_sum['Pedestrians'].plot(kind='area', grid=False)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "<matplotlib.axes._subplots.AxesSubplot at 0x10893d590>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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vdx/h7ucC/YHVcd3r8d/3gPsIc1NJDmbWm/A/0T3u/mAs3mBmB8X1BwMbY3kz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\nGCIikooCQ0REUlFgiIhIKv8fivA9NXikxBwAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x108959ed0>"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Create a figure with a single subplot\n",
      "f, ax = plt.subplots(1, figsize=(10,5))\n",
      "\n",
      "# Set bar width at 1\n",
      "bar_width = 1\n",
      "\n",
      "# positions of the left bar-boundaries\n",
      "bar_l = [i for i in range(len(df_douglas_annual_sum['Bus Passengers']))] \n",
      "\n",
      "# positions of the x-axis ticks (center of the bars as bar labels)\n",
      "tick_pos = [i+(bar_width/2) for i in bar_l] \n",
      "\n",
      "# Create the total score for each participant\n",
      "totals = [i+j+k for i,j,k in zip(df_douglas_annual_sum['Bus Passengers'], \n",
      "                                 df_douglas_annual_sum['Personal Vehicle Passengers'], \n",
      "                                 df_douglas_annual_sum['Pedestrians'])]\n",
      "\n",
      "# Create the percentage of the total score the pre_score value for each participant was\n",
      "pre_rel = [i / j * 100 for  i,j in zip(df_douglas_annual_sum['Bus Passengers'], totals)]\n",
      "\n",
      "# Create the percentage of the total score the mid_score value for each participant was\n",
      "mid_rel = [i / j * 100 for  i,j in zip(df_douglas_annual_sum['Personal Vehicle Passengers'], totals)]\n",
      "\n",
      "# Create the percentage of the total score the post_score value for each participant was\n",
      "post_rel = [i / j * 100 for  i,j in zip(df_douglas_annual_sum['Pedestrians'], totals)]\n",
      "\n",
      "# Create a bar chart in position bar_1\n",
      "ax.bar(bar_l, \n",
      "       # using pre_rel data\n",
      "       pre_rel, \n",
      "       # labeled \n",
      "       label='Bus Passengers', \n",
      "       # with alpha\n",
      "       alpha=0.9, \n",
      "       # with color\n",
      "       color='#019600',\n",
      "       # with bar width\n",
      "       width=bar_width,\n",
      "       # with border color\n",
      "       edgecolor='white'\n",
      "       )\n",
      "\n",
      "# Create a bar chart in position bar_1\n",
      "ax.bar(bar_l, \n",
      "       # using mid_rel data\n",
      "       mid_rel, \n",
      "       # with pre_rel\n",
      "       bottom=pre_rel, \n",
      "       # labeled \n",
      "       label='Personal Vehicle Passengers', \n",
      "       # with alpha\n",
      "       alpha=0.9, \n",
      "       # with color\n",
      "       color='#3C5F5A', \n",
      "       # with bar width\n",
      "       width=bar_width,\n",
      "       # with border color\n",
      "       edgecolor='white'\n",
      "       )\n",
      "\n",
      "# Create a bar chart in position bar_1\n",
      "ax.bar(bar_l, \n",
      "       # using post_rel data\n",
      "       post_rel, \n",
      "       # with pre_rel and mid_rel on bottom\n",
      "       bottom=[i+j for i,j in zip(pre_rel, mid_rel)], \n",
      "       # labeled \n",
      "       label='Pedestrians',\n",
      "       # with alpha\n",
      "       alpha=0.9, \n",
      "       # with color\n",
      "       color='#219AD8', \n",
      "       # with bar width\n",
      "       width=bar_width,\n",
      "       # with border color\n",
      "       edgecolor='white'\n",
      "       )\n",
      "\n",
      "# Set the ticks to be first names\n",
      "plt.xticks(tick_pos, df_douglas_annual_sum.index)\n",
      "ax.set_ylabel(\"Percent Of Total\")\n",
      "ax.set_xlabel(\"Year\")\n",
      "\n",
      "# Let the borders of the graphic\n",
      "plt.xlim([min(tick_pos)-bar_width, max(tick_pos)+bar_width])\n",
      "plt.ylim(-10, 110)\n",
      "\n",
      "# rotate axis labels\n",
      "plt.setp(plt.gca().get_xticklabels(), rotation=45, horizontalalignment='right')\n",
      "\n",
      "# shot plot\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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5pCRJklZqNgfh3xIRLwXmR8SuwAnAl7ttliRJ0vCaTQ/Y64E9gQeAjwH3ASd22ShJkqRh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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x108cc8c10>"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Create a figure with a single subplot\n",
      "f, ax = plt.subplots(1, figsize=(10,5))\n",
      "\n",
      "# Create a scatterplot of,\n",
      "            # x axis: Latency, and \n",
      "plt.bar(df_douglas_annual_sum.index, \n",
      "            # y axis: Download, with\n",
      "            df_douglas_annual_sum['Personal Vehicle Passengers'], \n",
      "            # the color assigned as blue\n",
      "            color='b')\n",
      "\n",
      "plt.bar(df_douglas_annual_sum.index, \n",
      "            # y axis: Download, with\n",
      "            df_douglas_annual_sum['Pedestrians'], \n",
      "            # the color assigned as blue\n",
      "            color='r')\n",
      "\n",
      "plt.bar(df_douglas_annual_sum.index, \n",
      "            # y axis: Download, with\n",
      "            df_douglas_annual_sum['Bus Passengers'], \n",
      "            # the color assigned as blue\n",
      "            color='g')\n",
      "\n",
      "# Chart title\n",
      "plt.title('Types Of Border Crossers')\n",
      "\n",
      "# y label\n",
      "plt.ylabel('Total People')\n",
      "\n",
      "# x label\n",
      "plt.xlabel('Year')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 15,
       "text": [
        "<matplotlib.text.Text at 0x108e53250>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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e1bPr5iRf7n/TJEmSNEgTLVeztv18IsnLRhOTvBTY1tdWSZIkaeAmGkoevYHx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\nfCdwTpLbgecB32/pH6X7pbs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       "text": [
        "<matplotlib.figure.Figure at 0x108c97d50>"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.columns"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 16,
       "text": [
        "Index(['Port Name', 'Year', 'Month', 'Trucks', 'Loaded Truck Containers', 'Empty Truck Containers', 'Trains', 'Loaded Rail Containers', 'Empty Rail Containers', 'Train Passengers', 'Buses', 'Bus Passengers', 'Personal Vehicles', 'Personal Vehicle Passengers', 'Pedestrians', 'City', 'State'], dtype='object')"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_crossing = df.groupby('Port Name').sum()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_crossing.sort('Trains', ascending=False).head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Year</th>\n",
        "      <th>Trucks</th>\n",
        "      <th>Loaded Truck Containers</th>\n",
        "      <th>Empty Truck Containers</th>\n",
        "      <th>Trains</th>\n",
        "      <th>Loaded Rail Containers</th>\n",
        "      <th>Empty Rail Containers</th>\n",
        "      <th>Train Passengers</th>\n",
        "      <th>Buses</th>\n",
        "      <th>Bus Passengers</th>\n",
        "      <th>Personal Vehicles</th>\n",
        "      <th>Personal Vehicle Passengers</th>\n",
        "      <th>Pedestrians</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Port Name</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>TX: Laredo                                                  </th>\n",
        "      <td> 456912</td>\n",
        "      <td> 27331291</td>\n",
        "      <td> 16555501</td>\n",
        "      <td> 9484814</td>\n",
        "      <td> 62322</td>\n",
        "      <td> 3060584</td>\n",
        "      <td> 2141567</td>\n",
        "      <td>     0</td>\n",
        "      <td> 670433</td>\n",
        "      <td> 13725607</td>\n",
        "      <td> 117969118</td>\n",
        "      <td> 273240177</td>\n",
        "      <td>  82942171</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>TX: Eagle Pass                                              </th>\n",
        "      <td> 456912</td>\n",
        "      <td>  1774624</td>\n",
        "      <td>  1054386</td>\n",
        "      <td>  676904</td>\n",
        "      <td> 31437</td>\n",
        "      <td>  803534</td>\n",
        "      <td> 1365715</td>\n",
        "      <td> 83048</td>\n",
        "      <td>  42050</td>\n",
        "      <td>   340218</td>\n",
        "      <td>  57603495</td>\n",
        "      <td> 136747990</td>\n",
        "      <td>  12947972</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>TX: El Paso                                                 </th>\n",
        "      <td> 456912</td>\n",
        "      <td> 13050522</td>\n",
        "      <td>  6504160</td>\n",
        "      <td> 5548367</td>\n",
        "      <td> 23793</td>\n",
        "      <td>  660056</td>\n",
        "      <td>  842385</td>\n",
        "      <td> 49831</td>\n",
        "      <td> 311136</td>\n",
        "      <td>  5624071</td>\n",
        "      <td> 261185949</td>\n",
        "      <td> 567649753</td>\n",
        "      <td> 128299884</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>TX: Brownsville                                             </th>\n",
        "      <td> 456912</td>\n",
        "      <td>  4503798</td>\n",
        "      <td>  2249740</td>\n",
        "      <td> 2031792</td>\n",
        "      <td> 14299</td>\n",
        "      <td>  155970</td>\n",
        "      <td> 1243865</td>\n",
        "      <td>  2592</td>\n",
        "      <td> 180687</td>\n",
        "      <td>  1591315</td>\n",
        "      <td> 119779453</td>\n",
        "      <td> 263794440</td>\n",
        "      <td>  55892176</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>AZ: Nogales                                                 </th>\n",
        "      <td> 456912</td>\n",
        "      <td>  5076159</td>\n",
        "      <td>  3812053</td>\n",
        "      <td> 1044586</td>\n",
        "      <td> 11647</td>\n",
        "      <td>  539083</td>\n",
        "      <td>  357639</td>\n",
        "      <td> 35902</td>\n",
        "      <td> 136006</td>\n",
        "      <td>  2464601</td>\n",
        "      <td>  65971957</td>\n",
        "      <td> 164483990</td>\n",
        "      <td>  97174209</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 18,
       "text": [
        "                                                                Year  \\\n",
        "Port Name                                                              \n",
        "TX: Laredo                                                    456912   \n",
        "TX: Eagle Pass                                                456912   \n",
        "TX: El Paso                                                   456912   \n",
        "TX: Brownsville                                               456912   \n",
        "AZ: Nogales                                                   456912   \n",
        "\n",
        "                                                                Trucks  \\\n",
        "Port Name                                                                \n",
        "TX: Laredo                                                    27331291   \n",
        "TX: Eagle Pass                                                 1774624   \n",
        "TX: El Paso                                                   13050522   \n",
        "TX: Brownsville                                                4503798   \n",
        "AZ: Nogales                                                    5076159   \n",
        "\n",
        "                                                              Loaded Truck Containers  \\\n",
        "Port Name                                                                               \n",
        "TX: Laredo                                                                   16555501   \n",
        "TX: Eagle Pass                                                                1054386   \n",
        "TX: El Paso                                                                   6504160   \n",
        "TX: Brownsville                                                               2249740   \n",
        "AZ: Nogales                                                                   3812053   \n",
        "\n",
        "                                                              Empty Truck Containers  \\\n",
        "Port Name                                                                              \n",
        "TX: Laredo                                                                   9484814   \n",
        "TX: Eagle Pass                                                                676904   \n",
        "TX: El Paso                                                                  5548367   \n",
        "TX: Brownsville                                                              2031792   \n",
        "AZ: Nogales                                                                  1044586   \n",
        "\n",
        "                                                              Trains  \\\n",
        "Port Name                                                              \n",
        "TX: Laredo                                                     62322   \n",
        "TX: Eagle Pass                                                 31437   \n",
        "TX: El Paso                                                    23793   \n",
        "TX: Brownsville                                                14299   \n",
        "AZ: Nogales                                                    11647   \n",
        "\n",
        "                                                              Loaded Rail Containers  \\\n",
        "Port Name                                                                              \n",
        "TX: Laredo                                                                   3060584   \n",
        "TX: Eagle Pass                                                                803534   \n",
        "TX: El Paso                                                                   660056   \n",
        "TX: Brownsville                                                               155970   \n",
        "AZ: Nogales                                                                   539083   \n",
        "\n",
        "                                                              Empty Rail Containers  \\\n",
        "Port Name                                                                             \n",
        "TX: Laredo                                                                  2141567   \n",
        "TX: Eagle Pass                                                              1365715   \n",
        "TX: El Paso                                                                  842385   \n",
        "TX: Brownsville                                                             1243865   \n",
        "AZ: Nogales                                                                  357639   \n",
        "\n",
        "                                                              Train Passengers  \\\n",
        "Port Name                                                                        \n",
        "TX: Laredo                                                                   0   \n",
        "TX: Eagle Pass                                                           83048   \n",
        "TX: El Paso                                                              49831   \n",
        "TX: Brownsville                                                           2592   \n",
        "AZ: Nogales                                                              35902   \n",
        "\n",
        "                                                               Buses  \\\n",
        "Port Name                                                              \n",
        "TX: Laredo                                                    670433   \n",
        "TX: Eagle Pass                                                 42050   \n",
        "TX: El Paso                                                   311136   \n",
        "TX: Brownsville                                               180687   \n",
        "AZ: Nogales                                                   136006   \n",
        "\n",
        "                                                              Bus Passengers  \\\n",
        "Port Name                                                                      \n",
        "TX: Laredo                                                          13725607   \n",
        "TX: Eagle Pass                                                        340218   \n",
        "TX: El Paso                                                          5624071   \n",
        "TX: Brownsville                                                      1591315   \n",
        "AZ: Nogales                                                          2464601   \n",
        "\n",
        "                                                              Personal Vehicles  \\\n",
        "Port Name                                                                         \n",
        "TX: Laredo                                                            117969118   \n",
        "TX: Eagle Pass                                                         57603495   \n",
        "TX: El Paso                                                           261185949   \n",
        "TX: Brownsville                                                       119779453   \n",
        "AZ: Nogales                                                            65971957   \n",
        "\n",
        "                                                              Personal Vehicle Passengers  \\\n",
        "Port Name                                                                                   \n",
        "TX: Laredo                                                                      273240177   \n",
        "TX: Eagle Pass                                                                  136747990   \n",
        "TX: El Paso                                                                     567649753   \n",
        "TX: Brownsville                                                                 263794440   \n",
        "AZ: Nogales                                                                     164483990   \n",
        "\n",
        "                                                              Pedestrians  \n",
        "Port Name                                                                  \n",
        "TX: Laredo                                                       82942171  \n",
        "TX: Eagle Pass                                                   12947972  \n",
        "TX: El Paso                                                     128299884  \n",
        "TX: Brownsville                                                  55892176  \n",
        "AZ: Nogales                                                      97174209  "
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df_crossing['container_total'] = df_crossing['Loaded Truck Containers'] + df_crossing['Empty Truck Containers']\n",
      "\n",
      "df_crossing = df_crossing.sort(columns='container_total')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# input data, specifically the second and \n",
      "# third rows, skipping the first column\n",
      "x1 = df_crossing['Loaded Truck Containers']\n",
      "x2 = df_crossing['Empty Truck Containers']\n",
      "\n",
      "# Create the bar labels\n",
      "bar_labels = df_crossing.index\n",
      "\n",
      "# Create a figure\n",
      "fig = plt.figure(figsize=(10,8))\n",
      "\n",
      "# Set the y position\n",
      "y_pos = np.arange(len(x1))\n",
      "y_pos = [x for x in y_pos]\n",
      "plt.yticks(y_pos, bar_labels, fontsize=10)\n",
      "\n",
      "# Create a horizontal bar in the position y_pos\n",
      "plt.barh(y_pos, \n",
      "         # using x1 data\n",
      "         x1, \n",
      "         # that is centered\n",
      "         align='center', \n",
      "         # with alpha 0.4\n",
      "         alpha=0.4, \n",
      "         # and color green\n",
      "         color='#263F13')\n",
      "\n",
      "# Create a horizontal bar in the position y_pos\n",
      "plt.barh(y_pos, \n",
      "         # using NEGATIVE x2 data\n",
      "         -x2,\n",
      "         # that is centered\n",
      "         align='center', \n",
      "         # with alpha 0.4\n",
      "         alpha=0.4, \n",
      "         # and color green\n",
      "         color='#77A61D')\n",
      "\n",
      "# annotation and labels\n",
      "plt.xlabel('Empty Containers: Light Green. Full Containers: Dark Green')\n",
      "t = plt.title('Comparison of Empty And Full Truck Containers Per Border Crossing')\n",
      "plt.ylim([-1,len(x1)+0.1])\n",
      "plt.grid()\n",
      "\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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7PebeLtYv4ERJXwCWA0ek9SdIOjgtbwmMAB4G3i/pZ8CNwE2SNgI2iog5ad9L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dkVcq5J0ysWkV0FBuBbwNHpeL5CdoWhSnGUyguAVcpO9j2B7P34WjruncjmhNcb1/9RIZ8Vjr0/\ncEWaCjUP+EWaSrCHpIsq1Ic1n+HFwAzgduDf03P1xJwLM94i+0/RSZK+UPbk2+m5byo7YXcO2cnN\npc5W+bFX+9z8GBio7KTyxcCPqry+Uoxm7eKbK5mZkV2BguxqJAfUUbcf2dzrgyPi6e6OrRmknA2M\niHfSCO0MYMfwpfTMzOriOe1mZplaI92rpekTt5KdnOkOe/0GAXdLGkg2TeA4d9jNzOrnkXYzMzMz\ns17Oc9rNzMzMzHo5d9rNzMzMzHo5d9rNzMzMzHo5d9rNzMzMzHo5d9rNzMzMzHq5/wev/GLXCvqk\nIgAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x109072610>"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 20
    }
   ],
   "metadata": {}
  }
 ]
}