aflaxman/2014_03_10d_iraq_mortality_talk_example.ipynb

## 2014_03_10d_iraq_mortality_talk_example.ipynb
{
 "metadata": {
  "name": "",
  "signature": "sha256:b7696af77060f52d1ae73edf25d9198c91fa27334e04fe255ae81248642ba79e"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!date"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Mon Mar 10 16:08:52 PDT 2014\r\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np, pandas as pd\n",
      "%matplotlib inline\n",
      "import matplotlib.pyplot as plt, seaborn"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = pd.read_csv('http://ghdx.healthmetricsandevaluation.org/sites/default/files/record-attached-files/IHME_IRAQ_MORTALITY_STUDY_2001_2011_HH_DEATHS.CSV')\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>cluster</th>\n",
        "      <th>hh</th>\n",
        "      <th>gov</th>\n",
        "      <th>sex</th>\n",
        "      <th>mod</th>\n",
        "      <th>yod</th>\n",
        "      <th>cod</th>\n",
        "      <th>death_cert</th>\n",
        "      <th>war_death</th>\n",
        "      <th>war_cod</th>\n",
        "      <th>respo</th>\n",
        "      <th>distance</th>\n",
        "      <th>year_hh_formed</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 1</td>\n",
        "      <td>  6</td>\n",
        "      <td> 18</td>\n",
        "      <td> F</td>\n",
        "      <td>  7</td>\n",
        "      <td> 2007</td>\n",
        "      <td>        cardiovascular</td>\n",
        "      <td> death cert not available</td>\n",
        "      <td> N</td>\n",
        "      <td>     NaN</td>\n",
        "      <td>              NaN</td>\n",
        "      <td>       NaN</td>\n",
        "      <td> 1980</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 1</td>\n",
        "      <td> 10</td>\n",
        "      <td> 18</td>\n",
        "      <td> M</td>\n",
        "      <td>  3</td>\n",
        "      <td> 2002</td>\n",
        "      <td> injury (not war) &gt;=18</td>\n",
        "      <td>   able to see death cert</td>\n",
        "      <td> N</td>\n",
        "      <td>     NaN</td>\n",
        "      <td>              NaN</td>\n",
        "      <td>       NaN</td>\n",
        "      <td> 1989</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 1</td>\n",
        "      <td> 10</td>\n",
        "      <td> 18</td>\n",
        "      <td> M</td>\n",
        "      <td>  4</td>\n",
        "      <td> 2006</td>\n",
        "      <td> injury (not war) &gt;=18</td>\n",
        "      <td>   able to see death cert</td>\n",
        "      <td> N</td>\n",
        "      <td>     NaN</td>\n",
        "      <td>              NaN</td>\n",
        "      <td>       NaN</td>\n",
        "      <td> 1989</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 1</td>\n",
        "      <td> 15</td>\n",
        "      <td> 18</td>\n",
        "      <td> F</td>\n",
        "      <td> 10</td>\n",
        "      <td> 2003</td>\n",
        "      <td>      injury (war) &lt;18</td>\n",
        "      <td>   able to see death cert</td>\n",
        "      <td> Y</td>\n",
        "      <td> gunshot</td>\n",
        "      <td> coalition forces</td>\n",
        "      <td> Yes &lt;1 KM</td>\n",
        "      <td> 1976</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 1</td>\n",
        "      <td> 16</td>\n",
        "      <td> 18</td>\n",
        "      <td> F</td>\n",
        "      <td>  5</td>\n",
        "      <td> 2009</td>\n",
        "      <td>  injury (not war) &lt;18</td>\n",
        "      <td>   able to see death cert</td>\n",
        "      <td> N</td>\n",
        "      <td>     NaN</td>\n",
        "      <td>              NaN</td>\n",
        "      <td>       NaN</td>\n",
        "      <td> 1983</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 13 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "   cluster  hh  gov sex  mod   yod                    cod  \\\n",
        "0        1   6   18   F    7  2007         cardiovascular   \n",
        "1        1  10   18   M    3  2002  injury (not war) >=18   \n",
        "2        1  10   18   M    4  2006  injury (not war) >=18   \n",
        "3        1  15   18   F   10  2003       injury (war) <18   \n",
        "4        1  16   18   F    5  2009   injury (not war) <18   \n",
        "\n",
        "                 death_cert war_death  war_cod             respo   distance  \\\n",
        "0  death cert not available         N      NaN               NaN        NaN   \n",
        "1    able to see death cert         N      NaN               NaN        NaN   \n",
        "2    able to see death cert         N      NaN               NaN        NaN   \n",
        "3    able to see death cert         Y  gunshot  coalition forces  Yes <1 KM   \n",
        "4    able to see death cert         N      NaN               NaN        NaN   \n",
        "\n",
        "   year_hh_formed  \n",
        "0            1980  \n",
        "1            1989  \n",
        "2            1989  \n",
        "3            1976  \n",
        "4            1983  \n",
        "\n",
        "[5 rows x 13 columns]"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s = df.death_cert.value_counts() / float(len(df.index))\n",
      "s *= 100\n",
      "\n",
      "s.order().plot(kind='barh', fontsize=24)\n",
      "plt.xticks(fontsize=18)\n",
      "plt.xlabel('Percent of Deaths in HH Survey', fontsize=24)\n",
      "plt.axis(xmax=100);"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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y92FSUpLq1KlTYlv783zy5ElT+3Dp0iX98MMPMgyj1Jruv/9+LV26tMjf7XUtXLhQH374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       "text": [
        "<matplotlib.figure.Figure at 0x5070510>"
       ]
      }
     ],
     "prompt_number": 5
    }
   ],
   "metadata": {}
  }
 ]
}