Extracting Altitude of NFL Stadiums.ipynb

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   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!date"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Mon Jan 20 17:00:20 EST 2014\r\n"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Extracting Altitude for NFL Stadiums"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Recently I did some [analysis](http://nbviewer.ipython.org/github/jgbos/iPython-Notebooks/blob/master/Cold%20Weather%20FG.ipynb) on the likelihood of making NFL Field Goal over distance and temperature.  I would lke to add alitude as a feature, but unfortunately the dataset I am using has the Stadium name, but not the city the game was played in.  So took the stadium names and Google's [geocoding API](https://developers.google.com/maps/documentation/geocoding/) to get the location of the stadium and Google's [elevation API](https://developers.google.com/maps/documentation/elevation/) to get the stadium's elevation.  The following is an overview of extracting the elevation for each stadium."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd\n",
      "import pandas.io.sql as psql\n",
      "import mysql.connector as sql\n",
      "\n",
      "import urllib2\n",
      "import urllib\n",
      "import json\n",
      "import time"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 11
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "I use the NFL data set from [Armchair Analysis](http://armchairanalysis.com/). One of the tables includes the stadium each game was played in. Tge data us stored in a MySQL database, so lets extract the unique stadium names into a pandas DataFrame to get elevation using *pandas.io.sql*."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pickle\n",
      "with open('sql.config','rb') as fp:\n",
      "    config = pickle.load(fp)\n",
      "\n",
      "con = sql.connect(**config)\n",
      "df = psql.frame_query('''\n",
      "    SELECT DISTINCT(STAD) \n",
      "    FROM games;''', con)\n",
      "\n",
      "con.close()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now lets call Google's API for both location and elevation and save them into our pandas DataFrame object.  We will not get a result for every entry, but luckily we will get them for most.  Also, Google has rate limiting, so I had to use *time.sleep* value between calls to ensure Google does not deny my request."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": true,
     "input": [
      "main_url = \"http://maps.googleapis.com/maps/api/geocode/json?\"\n",
      "\n",
      "ELEVATION_BASE_URL = 'http://maps.googleapis.com/maps/api/elevation/json'\n",
      "LOCATION_BASE_URL = 'http://maps.googleapis.com/maps/api/geocode/json'\n",
      "\n",
      "def getAlt(d):    \n",
      "    loc_args = {'address': d, 'sensor': 'false'}\n",
      "    url = LOCATION_BASE_URL + '?' + urllib.urlencode(loc_args)\n",
      "    page = urllib2.urlopen(url)\n",
      "    data = json.load(page)\n",
      "    time.sleep(0.1)\n",
      "    if 'results' in data and len(data['results'])>0:\n",
      "        ll = data['results'][0]['geometry']['location']\n",
      "        loc = ','.join(str(x) for x in [ll['lat'],ll['lng']])\n",
      "        el_args = {'locations': loc, 'sensor': 'false'}\n",
      "        url = ELEVATION_BASE_URL + '?' + urllib.urlencode(el_args)\n",
      "        page = urllib2.urlopen(url)\n",
      "        el_data = json.load(page)\n",
      "        if 'results' in el_data:   \n",
      "            return ll['lat'], ll['lng'], el_data['results'][0]['elevation']\n",
      "        else:\n",
      "            return None, None, None\n",
      "    return None, None, None\n",
      "\n",
      "df['lat'], df['lng'], df['alt'] = zip(*df.STAD.map(getAlt))\n",
      "df.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>STAD</th>\n",
        "      <th>lat</th>\n",
        "      <th>lng</th>\n",
        "      <th>alt</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>             Georgia Dome</td>\n",
        "      <td> 33.75769</td>\n",
        "      <td>-84.40083</td>\n",
        "      <td> 303.1637</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> Cleveland Browns Stadium</td>\n",
        "      <td> 41.50648</td>\n",
        "      <td>-81.70004</td>\n",
        "      <td> 183.6383</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>            Texas Stadium</td>\n",
        "      <td> 32.84107</td>\n",
        "      <td> -96.9109</td>\n",
        "      <td> 133.4713</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>            Lambeau Field</td>\n",
        "      <td> 44.50136</td>\n",
        "      <td>-88.06075</td>\n",
        "      <td> 190.2768</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>        Arrowhead Stadium</td>\n",
        "      <td> 39.04894</td>\n",
        "      <td>-94.48392</td>\n",
        "      <td> 256.7705</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "                       STAD       lat       lng       alt\n",
        "0              Georgia Dome  33.75769 -84.40083  303.1637\n",
        "1  Cleveland Browns Stadium  41.50648 -81.70004  183.6383\n",
        "2             Texas Stadium  32.84107  -96.9109  133.4713\n",
        "3             Lambeau Field  44.50136 -88.06075  190.2768\n",
        "4         Arrowhead Stadium  39.04894 -94.48392  256.7705"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "As I mentioned above, there will be some stadium names that do not get a result from Google's API.  Here is the list of stadiums without a location and elevation."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df[df.lat.isnull()].head(12)"
     ],
     "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>STAD</th>\n",
        "      <th>lat</th>\n",
        "      <th>lng</th>\n",
        "      <th>alt</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>10</th>\n",
        "      <td>            Network Associates Coliseum</td>\n",
        "      <td> None</td>\n",
        "      <td> None</td>\n",
        "      <td> None</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>14</th>\n",
        "      <td>                       Trans World Dome</td>\n",
        "      <td> None</td>\n",
        "      <td> None</td>\n",
        "      <td> None</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>15</th>\n",
        "      <td>                         PSINet Stadium</td>\n",
        "      <td> None</td>\n",
        "      <td> None</td>\n",
        "      <td> None</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>19</th>\n",
        "      <td>                               RCA Dome</td>\n",
        "      <td> None</td>\n",
        "      <td> None</td>\n",
        "      <td> None</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>39</th>\n",
        "      <td>                       Seahawks Stadium</td>\n",
        "      <td> None</td>\n",
        "      <td> None</td>\n",
        "      <td> None</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>44</th>\n",
        "      <td>                            Qwest Field</td>\n",
        "      <td> None</td>\n",
        "      <td> None</td>\n",
        "      <td> None</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>47</th>\n",
        "      <td>                        McAfee Coliseum</td>\n",
        "      <td> None</td>\n",
        "      <td> None</td>\n",
        "      <td> None</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>48</th>\n",
        "      <td>                         Azteca Stadium</td>\n",
        "      <td> None</td>\n",
        "      <td> None</td>\n",
        "      <td> None</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>60</th>\n",
        "      <td> Mall of America Field at HHH Metrodome</td>\n",
        "      <td> None</td>\n",
        "      <td> None</td>\n",
        "      <td> None</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>62</th>\n",
        "      <td>                New Meadowlands Stadium</td>\n",
        "      <td> None</td>\n",
        "      <td> None</td>\n",
        "      <td> None</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>68</th>\n",
        "      <td>                Mercedes-Benz Superdome</td>\n",
        "      <td> None</td>\n",
        "      <td> None</td>\n",
        "      <td> None</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>69</th>\n",
        "      <td>                     Snapdragon Stadium</td>\n",
        "      <td> None</td>\n",
        "      <td> None</td>\n",
        "      <td> None</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "                                      STAD   lat   lng   alt\n",
        "10             Network Associates Coliseum  None  None  None\n",
        "14                        Trans World Dome  None  None  None\n",
        "15                          PSINet Stadium  None  None  None\n",
        "19                                RCA Dome  None  None  None\n",
        "39                        Seahawks Stadium  None  None  None\n",
        "44                             Qwest Field  None  None  None\n",
        "47                         McAfee Coliseum  None  None  None\n",
        "48                          Azteca Stadium  None  None  None\n",
        "60  Mall of America Field at HHH Metrodome  None  None  None\n",
        "62                 New Meadowlands Stadium  None  None  None\n",
        "68                 Mercedes-Benz Superdome  None  None  None\n",
        "69                      Snapdragon Stadium  None  None  None"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Since there are only 12, I will manually enter the rest by searching for the right city and finding the elevation.  I will ignore putting in latitude and longitude for now, those columns are actually not needed for my analsysis."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.ix[df.STAD.str.contains('Network Associates Coliseum'),'alt'] = 13.11\n",
      "df.ix[df.STAD.str.contains('Trans World Dome'),'alt'] = 142.03\n",
      "df.ix[df.STAD.str.contains('PSINet'),'alt'] = 3.05\n",
      "df.ix[df.STAD.str.contains('RCA'),'alt'] = 216.34\n",
      "df.ix[df.STAD.str.contains('Seahawks'),'alt'] = 5.31\n",
      "df.ix[df.STAD.str.contains('Qwest'),'alt'] = 5.31\n",
      "df.ix[df.STAD.str.contains('McAfee'),'alt'] = 10.74\n",
      "df.ix[df.STAD.str.contains('Azteca'),'alt'] = 2268.13\n",
      "df.ix[df.STAD.str.contains('Mall of America'),'alt'] = 253.42\n",
      "df.ix[df.STAD.str.contains('New Meadowlands'),'alt'] = 0.86\n",
      "df.ix[df.STAD.str.contains('Mercedes-Benz'),'alt'] = 0.0006\n",
      "df.ix[df.STAD.str.contains('Snapdragon'),'alt'] = 22.89\n",
      "\n",
      "\n",
      "df[df.lat.isnull()].head(12)"
     ],
     "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>STAD</th>\n",
        "      <th>lat</th>\n",
        "      <th>lng</th>\n",
        "      <th>alt</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>10</th>\n",
        "      <td>            Network Associates Coliseum</td>\n",
        "      <td> None</td>\n",
        "      <td> None</td>\n",
        "      <td>   13.11</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>14</th>\n",
        "      <td>                       Trans World Dome</td>\n",
        "      <td> None</td>\n",
        "      <td> None</td>\n",
        "      <td>  142.03</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>15</th>\n",
        "      <td>                         PSINet Stadium</td>\n",
        "      <td> None</td>\n",
        "      <td> None</td>\n",
        "      <td>    3.05</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>19</th>\n",
        "      <td>                               RCA Dome</td>\n",
        "      <td> None</td>\n",
        "      <td> None</td>\n",
        "      <td>  216.34</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>39</th>\n",
        "      <td>                       Seahawks Stadium</td>\n",
        "      <td> None</td>\n",
        "      <td> None</td>\n",
        "      <td>    5.31</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>44</th>\n",
        "      <td>                            Qwest Field</td>\n",
        "      <td> None</td>\n",
        "      <td> None</td>\n",
        "      <td>    5.31</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>47</th>\n",
        "      <td>                        McAfee Coliseum</td>\n",
        "      <td> None</td>\n",
        "      <td> None</td>\n",
        "      <td>   10.74</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>48</th>\n",
        "      <td>                         Azteca Stadium</td>\n",
        "      <td> None</td>\n",
        "      <td> None</td>\n",
        "      <td> 2268.13</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>60</th>\n",
        "      <td> Mall of America Field at HHH Metrodome</td>\n",
        "      <td> None</td>\n",
        "      <td> None</td>\n",
        "      <td>  253.42</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>62</th>\n",
        "      <td>                New Meadowlands Stadium</td>\n",
        "      <td> None</td>\n",
        "      <td> None</td>\n",
        "      <td>    0.86</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>68</th>\n",
        "      <td>                Mercedes-Benz Superdome</td>\n",
        "      <td> None</td>\n",
        "      <td> None</td>\n",
        "      <td>  0.0006</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>69</th>\n",
        "      <td>                     Snapdragon Stadium</td>\n",
        "      <td> None</td>\n",
        "      <td> None</td>\n",
        "      <td>   22.89</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 15,
       "text": [
        "                                      STAD   lat   lng      alt\n",
        "10             Network Associates Coliseum  None  None    13.11\n",
        "14                        Trans World Dome  None  None   142.03\n",
        "15                          PSINet Stadium  None  None     3.05\n",
        "19                                RCA Dome  None  None   216.34\n",
        "39                        Seahawks Stadium  None  None     5.31\n",
        "44                             Qwest Field  None  None     5.31\n",
        "47                         McAfee Coliseum  None  None    10.74\n",
        "48                          Azteca Stadium  None  None  2268.13\n",
        "60  Mall of America Field at HHH Metrodome  None  None   253.42\n",
        "62                 New Meadowlands Stadium  None  None     0.86\n",
        "68                 Mercedes-Benz Superdome  None  None   0.0006\n",
        "69                      Snapdragon Stadium  None  None    22.89"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now lets save the results to a CSV file to avoid runing this code whenever we need the data."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.to_csv('stadium_with_altitude.csv')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 16
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Lastly, here is a histogram of the elevation.  Denver and Mexico City are the outlier stadiums, and the reason there is a count greater than one for Denver is that there are multiple stadium names in the dataset for the Broncos (as expected). This is true for a number of teams."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline\n",
      "import matplotlib.pyplot as plt\n",
      "\n",
      "df.alt.hist(figsize=(12,8), bins=20)\n",
      "plt.xlabel('Elevation (m)')\n",
      "plt.ylabel('Count')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 17,
       "text": [
        "<matplotlib.text.Text at 0x10a839a50>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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DADQXHWsAACgYgzWloRtHilyQIhekyAV5M1gDAEAGdKxr6FgDADQXHWsAACgYgzWloRtH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\nGTBYAwBABgzWAACQAYM1AABk4P8HPZd7sd14s+gAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10a82c6d0>"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print 'Denver elevation: %.2f (m)' % df[df.STAD.str.contains('Mile')].alt[:1]\n",
      "print 'Mexico City elevation: %.2f (m)' % df[df.STAD.str.contains('Azteca')].alt[:1]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Denver elevation: 1583.44 (m)\n",
        "Mexico City elevation: 2268.13 (m)\n"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from IPython.core.display import HTML\n",
      "def css_styling():\n",
      "    styles = open(\"style.css\", \"r\").read()\n",
      "    return HTML(styles)\n",
      "css_styling()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<style>\n",
        ".cell {\n",
        "   width: inherit !important;\n",
        "   background-color: #f3f3f3 !important;\n",
        "   margin-left: 120px !important;\n",
        "}\n",
        "\n",
        "/* block fixes margin on input boxes */\n",
        "/* in firefox */\n",
        ".input {\n",
        "width: 50em !important;\n",
        "display: block !important;\n",
        "}\n",
        "\n",
        ".input_area { \n",
        "    background-color: white !important; \n",
        "}\n",
        "\n",
        ".output_area pre {\n",
        "    font-family: \"Source Code Pro\", source-code-pro, Consolas, monospace;\n",
        "    border: 0px;\n",
        "}\n",
        "\n",
        "div.output_text {\n",
        "    font-family: \"Source Code Pro\", source-code-pro, Consolas, monospace; \n",
        "}\n",
        "\n",
        "div.text_cell {\n",
        "    width: 35em;\n",
        "    text-align: left;\n",
        "}\n",
        "\n",
        "div.prompt {\n",
        "    width: 0px;\n",
        "    visibility: hidden !important;\n",
        "}\n",
        "\n",
        ".code_cell {\n",
        "    background-color: #f3f3f3;\n",
        "}\n",
        "\n",
        ".highlight {\n",
        "    background-color: #ffffff;\n",
        "}\n",
        "\n",
        "\n",
        "div.input_prompt {\n",
        "    visibility: hidden;\n",
        "    width: 0 !important;\n",
        "}\n",
        "\n",
        "div.text_cell_render {\n",
        "    font-family: \"Trebuchet MS\", Helvetica, sans-serif !important;\n",
        "    font-size: 14pt !important;\n",
        "    line-height: 145% !important;\n",
        "    width: 35em !important;\n",
        "    text-align: left !important;\n",
        "    background-color: #f3f3f3 !important;\n",
        "}\n",
        "\n",
        "div.text_cell_render h1 {\n",
        "    display: block;\n",
        "    font-size: 28pt;\n",
        "    color: #3B3B3B;\n",
        "    margin-bottom: 0em;\n",
        "    margin-top: 0.5em;\n",
        "\n",
        "}\n",
        "\n",
        ".rendered_html li {\n",
        "    margin-bottom: .25em;\n",
        "    color: #3B3B3B;;\n",
        "}\n",
        "\n",
        "div.text_cell_render h2:before {\n",
        "    content: \\\\\\2FFA\\;\n",
        "    margin-right: 0.5em;\n",
        "    font-size: .5em;\n",
        "    vertical-align: baseline;\n",
        "    border-top: 1px;\n",
        "\n",
        "}\n",
        "\n",
        ".hiterm {\n",
        "    font-weight: 500;\n",
        "    color: #DC143C;\n",
        "}\n",
        "\n",
        ".text_cell_render h2 {\n",
        "    font-size: 20pt;\n",
        "    margin-bottom: 0em;\n",
        "    margin-top: 0.5em;\n",
        "    display: block;\n",
        "    color: #3B3B3B;\n",
        "}\n",
        "\n",
        ".MathJax_Display {\n",
        "    /*text-align: center !important;*/\n",
        "    margin-left: 2em !important;\n",
        "    margin-top: .5em !important;\n",
        "    margin-bottom: .5em !important;\n",
        "}\n",
        "\n",
        ".text_cell_render h3 {\n",
        "    font-size: 14pt;\n",
        "    font-weight: 600;\n",
        "    font-style: italic;\n",
        "    margin-bottom: -0.5em;\n",
        "    margin-top: -0.25em;\n",
        "    color: #3B3B3B;\n",
        "    text-indent: 2em;\n",
        "}\n",
        "\n",
        ".text_cell_render h5 {\n",
        "    font-weight: 300;\n",
        "    font-size: 14pt;\n",
        "    color: #4057A1;\n",
        "    font-style: italic;\n",
        "    margin-bottom: .5em;\n",
        "    margin-top: 0.5em;\n",
        "    display: block;\n",
        "}\n",
        "\n",
        ".CodeMirror {\n",
        "    font-family: \"Source Code Pro\", source-code-pro, Consolas, monospace !important;\n",
        "    font-size: 10pt;\n",
        "    background:  #fffffe; /* #f0f8fb #e2eef9*/\n",
        "    border: 0px;\n",
        "    width: 50em !important;\n",
        "\n",
        "}\n",
        "\n",
        ".rendered_html {\n",
        "    margin-left: 20px;\n",
        "\n",
        "}\n",
        "\n",
        ".rendered_html code {\n",
        "    font-family: \"Source Code Pro\", source-code-pro, Consolas, monospace;\n",
        "    font-size: 85%;\n",
        "}\n",
        "\n",
        "pre code kbd samp { font-family: \"Source Code Pro\", source-code-pro, Consolas, monospace; }\n",
        "\n",
        ".rendered_html p {\n",
        "    text-align: left;\n",
        "    color: #3B3B3B;\n",
        "    margin-bottom: .5em;\n",
        "\n",
        "}\n",
        "\n",
        ".rendered_html p+p { \n",
        "  text-indent: 1em;\n",
        "  margin-top: 0;\n",
        "}\n",
        ".rendered_html ol {\n",
        "    list-style: decimal;\n",
        "    /*margin: 1em 2em;*/\n",
        "}\n",
        "\n",
        ".rendered_html ol ol {\n",
        "    list-style: decimal;\n",
        "}\n",
        "\n",
        ".rendered_html ol ol ol {\n",
        "    list-style: decimal;\n",
        "}\n",
        "\n",
        "body{background-color:#f3f3f3;}\n",
        "\n",
        ".rendered_html p.hangpar {\n",
        "    text-indent: 0;\n",
        "}\n",
        "</style>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 20,
       "text": [
        "<IPython.core.display.HTML at 0x10a00a7d0>"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}