{
 "metadata": {
  "name": "",
  "signature": "sha256:fd65daf14f9dfc883408c40ce1cc3c8702a60457a2232d1a9f7959e6a9710d2e"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# This is my notebook for exploring data about economic inequality in Cambodia.\n",
      "\n",
      "%matplotlib inline\n",
      "import pandas as pd\n",
      "import matplotlib as mp\n",
      "import matplotlib.pyplot as plt\n",
      "import qgrid\n",
      "from pylab import *\n",
      "import seaborn as sb\n",
      "\n",
      "# Hey, good news! We can remotely access the World Bank's World Development Indicators Database\n",
      "# directly from pandas!\n",
      "\n",
      "from pandas.io import wb"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# First, search the database for all poverty-related indicator names and store them.\n",
      "# I didn't use qgrid because it wouldn't display the id column correctly for some reason. It'd look nicer if it worked, though.\n",
      "\n",
      "pov = wb.search('pov.*%').iloc[:,:2]\n",
      "pov"
     ],
     "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>id</th>\n",
        "      <th>name</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>5453 </th>\n",
        "      <td>         IN.POV.HCR.EST.RURL</td>\n",
        "      <td>                 Poverty HCR Estimates (%) - Rural</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5454 </th>\n",
        "      <td>         IN.POV.HCR.EST.TOTL</td>\n",
        "      <td>                 Poverty HCR Estimates (%) - Total</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5455 </th>\n",
        "      <td>         IN.POV.HCR.EST.URBN</td>\n",
        "      <td>                 Poverty HCR Estimates (%) - Urban</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7529 </th>\n",
        "      <td>                SI.POV.25DAY</td>\n",
        "      <td> Poverty headcount ratio at $2.5 a day (PPP) (%...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7530 </th>\n",
        "      <td>                 SI.POV.2DAY</td>\n",
        "      <td> Poverty headcount ratio at $2 a day (PPP) (% o...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7531 </th>\n",
        "      <td>                 SI.POV.4DAY</td>\n",
        "      <td> Poverty headcount ratio at $4 a day (PPP) (% o...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7532 </th>\n",
        "      <td>                 SI.POV.5DAY</td>\n",
        "      <td> Poverty headcount ratio at $5 a day (PPP) (% o...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7533 </th>\n",
        "      <td>                 SI.POV.DDAY</td>\n",
        "      <td> Poverty headcount ratio at $1.25 a day (PPP) (...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7534 </th>\n",
        "      <td>                 SI.POV.GAP2</td>\n",
        "      <td>                 Poverty gap at $2 a day (PPP) (%)</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7535 </th>\n",
        "      <td>                SI.POV.GAP25</td>\n",
        "      <td>               Poverty gap at $2.5 a day (PPP) (%)</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7536 </th>\n",
        "      <td>                 SI.POV.GAP4</td>\n",
        "      <td>                 Poverty gap at $4 a day (PPP) (%)</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7537 </th>\n",
        "      <td>                 SI.POV.GAP5</td>\n",
        "      <td>                 Poverty gap at $5 a day (PPP) (%)</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7538 </th>\n",
        "      <td>                 SI.POV.GAPS</td>\n",
        "      <td>              Poverty gap at $1.25 a day (PPP) (%)</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7540 </th>\n",
        "      <td>                 SI.POV.NAGP</td>\n",
        "      <td>         Poverty gap at national poverty lines (%)</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7541 </th>\n",
        "      <td>                 SI.POV.NAHC</td>\n",
        "      <td> Poverty headcount ratio at national poverty li...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7547 </th>\n",
        "      <td>                 SI.POV.RUGP</td>\n",
        "      <td>   Rural poverty gap at national poverty lines (%)</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7548 </th>\n",
        "      <td>                 SI.POV.RUHC</td>\n",
        "      <td> Rural poverty headcount ratio at national pove...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7549 </th>\n",
        "      <td>                 SI.POV.URGP</td>\n",
        "      <td>   Urban poverty gap at national poverty lines (%)</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7550 </th>\n",
        "      <td>                 SI.POV.URHC</td>\n",
        "      <td> Urban poverty headcount ratio at national pove...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10162</th>\n",
        "      <td>           ccx_povchi_40_fem</td>\n",
        "      <td> Poverty headcount of children (below bottom 40...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10163</th>\n",
        "      <td>           ccx_povchi_40_mal</td>\n",
        "      <td> Poverty headcount of children (below bottom 40...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10164</th>\n",
        "      <td>           ccx_povchi_40_rur</td>\n",
        "      <td> Poverty headcount of children (below bottom 40...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10165</th>\n",
        "      <td>           ccx_povchi_40_tot</td>\n",
        "      <td>  Poverty headcount of children (below bottom 40%)</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10166</th>\n",
        "      <td>           ccx_povchi_40_urb</td>\n",
        "      <td> Poverty headcount of children (below bottom 40...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10167</th>\n",
        "      <td>           ccx_poveld_40_fem</td>\n",
        "      <td> Poverty headcount of the elderly (below bottom...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10168</th>\n",
        "      <td>           ccx_poveld_40_mal</td>\n",
        "      <td> Poverty headcount of the elderly (below bottom...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10169</th>\n",
        "      <td>           ccx_poveld_40_rur</td>\n",
        "      <td> Poverty headcount of the elderly (below bottom...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10170</th>\n",
        "      <td>           ccx_poveld_40_tot</td>\n",
        "      <td> Poverty headcount of the elderly (below bottom...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10171</th>\n",
        "      <td>           ccx_poveld_40_urb</td>\n",
        "      <td> Poverty headcount of the elderly (below bottom...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10172</th>\n",
        "      <td>           ccx_povwka_40_fem</td>\n",
        "      <td> Poverty headcount of working age adults (below...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12761</th>\n",
        "      <td> per_si_allsi_p1_ep_preT_tot</td>\n",
        "      <td> Poverty Gap reduction (%) -  All Social Insura...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12762</th>\n",
        "      <td>      per_si_allsi_p1_ep_tot</td>\n",
        "      <td> Poverty Gap reduction (%) -  All Social Insura...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12763</th>\n",
        "      <td>    per_si_allsi_p1_preT_tot</td>\n",
        "      <td> Poverty Gap reduction (%) -  All Social Insura...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12764</th>\n",
        "      <td>         per_si_allsi_p1_rur</td>\n",
        "      <td> Poverty Gap reduction (%) -  All Social Insura...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12765</th>\n",
        "      <td>         per_si_allsi_p1_tot</td>\n",
        "      <td> Poverty Gap reduction (%) -  All Social Insura...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12766</th>\n",
        "      <td>         per_si_allsi_p1_urb</td>\n",
        "      <td> Poverty Gap reduction (%) -  All Social Insura...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12901</th>\n",
        "      <td>    per_si_oa_p0_ep_preT_tot</td>\n",
        "      <td> Poverty Headcount reduction (%) -  Old Age Con...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12902</th>\n",
        "      <td>         per_si_oa_p0_ep_tot</td>\n",
        "      <td> Poverty Headcount reduction (%) -  Old Age Con...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12903</th>\n",
        "      <td>       per_si_oa_p0_preT_tot</td>\n",
        "      <td> Poverty Headcount reduction (%) -  Old Age Con...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12904</th>\n",
        "      <td>            per_si_oa_p0_rur</td>\n",
        "      <td> Poverty Headcount reduction (%) -  Old Age Con...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12905</th>\n",
        "      <td>            per_si_oa_p0_tot</td>\n",
        "      <td> Poverty Headcount reduction (%) -  Old Age Con...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12906</th>\n",
        "      <td>            per_si_oa_p0_urb</td>\n",
        "      <td> Poverty Headcount reduction (%) -  Old Age Con...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12907</th>\n",
        "      <td>    per_si_oa_p1_ep_preT_tot</td>\n",
        "      <td> Poverty Gap reduction (%) -  Old Age Contribut...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12908</th>\n",
        "      <td>         per_si_oa_p1_ep_tot</td>\n",
        "      <td> Poverty Gap reduction (%) -  Old Age Contribut...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12909</th>\n",
        "      <td>       per_si_oa_p1_preT_tot</td>\n",
        "      <td> Poverty Gap reduction (%) -  Old Age Contribut...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12910</th>\n",
        "      <td>            per_si_oa_p1_rur</td>\n",
        "      <td> Poverty Gap reduction (%) -  Old Age Contribut...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12911</th>\n",
        "      <td>            per_si_oa_p1_tot</td>\n",
        "      <td> Poverty Gap reduction (%) -  Old Age Contribut...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12912</th>\n",
        "      <td>            per_si_oa_p1_urb</td>\n",
        "      <td> Poverty Gap reduction (%) -  Old Age Contribut...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13047</th>\n",
        "      <td>    per_si_ss_p0_ep_preT_tot</td>\n",
        "      <td> Poverty Headcount reduction (%) -  Other Socia...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13048</th>\n",
        "      <td>         per_si_ss_p0_ep_tot</td>\n",
        "      <td> Poverty Headcount reduction (%) -  Other Socia...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13049</th>\n",
        "      <td>       per_si_ss_p0_preT_tot</td>\n",
        "      <td> Poverty Headcount reduction (%) -  Other Socia...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13050</th>\n",
        "      <td>            per_si_ss_p0_rur</td>\n",
        "      <td> Poverty Headcount reduction (%) -  Other Socia...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13051</th>\n",
        "      <td>            per_si_ss_p0_tot</td>\n",
        "      <td> Poverty Headcount reduction (%) -  Other Socia...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13052</th>\n",
        "      <td>            per_si_ss_p0_urb</td>\n",
        "      <td> Poverty Headcount reduction (%) -  Other Socia...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13053</th>\n",
        "      <td>    per_si_ss_p1_ep_preT_tot</td>\n",
        "      <td> Poverty Gap reduction (%) -  Other Social Insu...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13054</th>\n",
        "      <td>         per_si_ss_p1_ep_tot</td>\n",
        "      <td> Poverty Gap reduction (%) -  Other Social Insu...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13055</th>\n",
        "      <td>       per_si_ss_p1_preT_tot</td>\n",
        "      <td> Poverty Gap reduction (%) -  Other Social Insu...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13056</th>\n",
        "      <td>            per_si_ss_p1_rur</td>\n",
        "      <td> Poverty Gap reduction (%) -  Other Social Insu...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13057</th>\n",
        "      <td>            per_si_ss_p1_tot</td>\n",
        "      <td> Poverty Gap reduction (%) -  Other Social Insu...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13058</th>\n",
        "      <td>            per_si_ss_p1_urb</td>\n",
        "      <td> Poverty Gap reduction (%) -  Other Social Insu...</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>231 rows \u00d7 2 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 2,
       "text": [
        "                                id  \\\n",
        "5453           IN.POV.HCR.EST.RURL   \n",
        "5454           IN.POV.HCR.EST.TOTL   \n",
        "5455           IN.POV.HCR.EST.URBN   \n",
        "7529                  SI.POV.25DAY   \n",
        "7530                   SI.POV.2DAY   \n",
        "7531                   SI.POV.4DAY   \n",
        "7532                   SI.POV.5DAY   \n",
        "7533                   SI.POV.DDAY   \n",
        "7534                   SI.POV.GAP2   \n",
        "7535                  SI.POV.GAP25   \n",
        "7536                   SI.POV.GAP4   \n",
        "7537                   SI.POV.GAP5   \n",
        "7538                   SI.POV.GAPS   \n",
        "7540                   SI.POV.NAGP   \n",
        "7541                   SI.POV.NAHC   \n",
        "7547                   SI.POV.RUGP   \n",
        "7548                   SI.POV.RUHC   \n",
        "7549                   SI.POV.URGP   \n",
        "7550                   SI.POV.URHC   \n",
        "10162            ccx_povchi_40_fem   \n",
        "10163            ccx_povchi_40_mal   \n",
        "10164            ccx_povchi_40_rur   \n",
        "10165            ccx_povchi_40_tot   \n",
        "10166            ccx_povchi_40_urb   \n",
        "10167            ccx_poveld_40_fem   \n",
        "10168            ccx_poveld_40_mal   \n",
        "10169            ccx_poveld_40_rur   \n",
        "10170            ccx_poveld_40_tot   \n",
        "10171            ccx_poveld_40_urb   \n",
        "10172            ccx_povwka_40_fem   \n",
        "...                            ...   \n",
        "12761  per_si_allsi_p1_ep_preT_tot   \n",
        "12762       per_si_allsi_p1_ep_tot   \n",
        "12763     per_si_allsi_p1_preT_tot   \n",
        "12764          per_si_allsi_p1_rur   \n",
        "12765          per_si_allsi_p1_tot   \n",
        "12766          per_si_allsi_p1_urb   \n",
        "12901     per_si_oa_p0_ep_preT_tot   \n",
        "12902          per_si_oa_p0_ep_tot   \n",
        "12903        per_si_oa_p0_preT_tot   \n",
        "12904             per_si_oa_p0_rur   \n",
        "12905             per_si_oa_p0_tot   \n",
        "12906             per_si_oa_p0_urb   \n",
        "12907     per_si_oa_p1_ep_preT_tot   \n",
        "12908          per_si_oa_p1_ep_tot   \n",
        "12909        per_si_oa_p1_preT_tot   \n",
        "12910             per_si_oa_p1_rur   \n",
        "12911             per_si_oa_p1_tot   \n",
        "12912             per_si_oa_p1_urb   \n",
        "13047     per_si_ss_p0_ep_preT_tot   \n",
        "13048          per_si_ss_p0_ep_tot   \n",
        "13049        per_si_ss_p0_preT_tot   \n",
        "13050             per_si_ss_p0_rur   \n",
        "13051             per_si_ss_p0_tot   \n",
        "13052             per_si_ss_p0_urb   \n",
        "13053     per_si_ss_p1_ep_preT_tot   \n",
        "13054          per_si_ss_p1_ep_tot   \n",
        "13055        per_si_ss_p1_preT_tot   \n",
        "13056             per_si_ss_p1_rur   \n",
        "13057             per_si_ss_p1_tot   \n",
        "13058             per_si_ss_p1_urb   \n",
        "\n",
        "                                                    name  \n",
        "5453                   Poverty HCR Estimates (%) - Rural  \n",
        "5454                   Poverty HCR Estimates (%) - Total  \n",
        "5455                   Poverty HCR Estimates (%) - Urban  \n",
        "7529   Poverty headcount ratio at $2.5 a day (PPP) (%...  \n",
        "7530   Poverty headcount ratio at $2 a day (PPP) (% o...  \n",
        "7531   Poverty headcount ratio at $4 a day (PPP) (% o...  \n",
        "7532   Poverty headcount ratio at $5 a day (PPP) (% o...  \n",
        "7533   Poverty headcount ratio at $1.25 a day (PPP) (...  \n",
        "7534                   Poverty gap at $2 a day (PPP) (%)  \n",
        "7535                 Poverty gap at $2.5 a day (PPP) (%)  \n",
        "7536                   Poverty gap at $4 a day (PPP) (%)  \n",
        "7537                   Poverty gap at $5 a day (PPP) (%)  \n",
        "7538                Poverty gap at $1.25 a day (PPP) (%)  \n",
        "7540           Poverty gap at national poverty lines (%)  \n",
        "7541   Poverty headcount ratio at national poverty li...  \n",
        "7547     Rural poverty gap at national poverty lines (%)  \n",
        "7548   Rural poverty headcount ratio at national pove...  \n",
        "7549     Urban poverty gap at national poverty lines (%)  \n",
        "7550   Urban poverty headcount ratio at national pove...  \n",
        "10162  Poverty headcount of children (below bottom 40...  \n",
        "10163  Poverty headcount of children (below bottom 40...  \n",
        "10164  Poverty headcount of children (below bottom 40...  \n",
        "10165   Poverty headcount of children (below bottom 40%)  \n",
        "10166  Poverty headcount of children (below bottom 40...  \n",
        "10167  Poverty headcount of the elderly (below bottom...  \n",
        "10168  Poverty headcount of the elderly (below bottom...  \n",
        "10169  Poverty headcount of the elderly (below bottom...  \n",
        "10170  Poverty headcount of the elderly (below bottom...  \n",
        "10171  Poverty headcount of the elderly (below bottom...  \n",
        "10172  Poverty headcount of working age adults (below...  \n",
        "...                                                  ...  \n",
        "12761  Poverty Gap reduction (%) -  All Social Insura...  \n",
        "12762  Poverty Gap reduction (%) -  All Social Insura...  \n",
        "12763  Poverty Gap reduction (%) -  All Social Insura...  \n",
        "12764  Poverty Gap reduction (%) -  All Social Insura...  \n",
        "12765  Poverty Gap reduction (%) -  All Social Insura...  \n",
        "12766  Poverty Gap reduction (%) -  All Social Insura...  \n",
        "12901  Poverty Headcount reduction (%) -  Old Age Con...  \n",
        "12902  Poverty Headcount reduction (%) -  Old Age Con...  \n",
        "12903  Poverty Headcount reduction (%) -  Old Age Con...  \n",
        "12904  Poverty Headcount reduction (%) -  Old Age Con...  \n",
        "12905  Poverty Headcount reduction (%) -  Old Age Con...  \n",
        "12906  Poverty Headcount reduction (%) -  Old Age Con...  \n",
        "12907  Poverty Gap reduction (%) -  Old Age Contribut...  \n",
        "12908  Poverty Gap reduction (%) -  Old Age Contribut...  \n",
        "12909  Poverty Gap reduction (%) -  Old Age Contribut...  \n",
        "12910  Poverty Gap reduction (%) -  Old Age Contribut...  \n",
        "12911  Poverty Gap reduction (%) -  Old Age Contribut...  \n",
        "12912  Poverty Gap reduction (%) -  Old Age Contribut...  \n",
        "13047  Poverty Headcount reduction (%) -  Other Socia...  \n",
        "13048  Poverty Headcount reduction (%) -  Other Socia...  \n",
        "13049  Poverty Headcount reduction (%) -  Other Socia...  \n",
        "13050  Poverty Headcount reduction (%) -  Other Socia...  \n",
        "13051  Poverty Headcount reduction (%) -  Other Socia...  \n",
        "13052  Poverty Headcount reduction (%) -  Other Socia...  \n",
        "13053  Poverty Gap reduction (%) -  Other Social Insu...  \n",
        "13054  Poverty Gap reduction (%) -  Other Social Insu...  \n",
        "13055  Poverty Gap reduction (%) -  Other Social Insu...  \n",
        "13056  Poverty Gap reduction (%) -  Other Social Insu...  \n",
        "13057  Poverty Gap reduction (%) -  Other Social Insu...  \n",
        "13058  Poverty Gap reduction (%) -  Other Social Insu...  \n",
        "\n",
        "[231 rows x 2 columns]"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Strip the English labels from the id's and store them in a separate table\n",
      "\n",
      "povnames = pov.loc[7529:7550, 'name']\n",
      "povnames = povnames.tolist()\n",
      "\n",
      "# Keep only the id's in the original pov table\n",
      "\n",
      "pov = pov.loc[7529:7550, 'id']\n",
      "pov = pov.tolist()\n",
      "\n",
      "# Take a look\n",
      "\n",
      "povnames"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "[u'Poverty headcount ratio at $2.5 a day (PPP) (% of population)',\n",
        " u'Poverty headcount ratio at $2 a day (PPP) (% of population)',\n",
        " u'Poverty headcount ratio at $4 a day (PPP) (% of population)',\n",
        " u'Poverty headcount ratio at $5 a day (PPP) (% of population)',\n",
        " u'Poverty headcount ratio at $1.25 a day (PPP) (% of population)',\n",
        " u'Poverty gap at $2 a day (PPP) (%)',\n",
        " u'Poverty gap at $2.5 a day (PPP) (%)',\n",
        " u'Poverty gap at $4 a day (PPP) (%)',\n",
        " u'Poverty gap at $5 a day (PPP) (%)',\n",
        " u'Poverty gap at $1.25 a day (PPP) (%)',\n",
        " u'Poverty gap at national poverty lines (%)',\n",
        " u'Poverty headcount ratio at national poverty lines (% of population)',\n",
        " u'Rural poverty gap at national poverty lines (%)',\n",
        " u'Rural poverty headcount ratio at national poverty lines (% of rural population)',\n",
        " u'Urban poverty gap at national poverty lines (%)',\n",
        " u'Urban poverty headcount ratio at national poverty lines (% of urban population)']"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pov"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "[u'SI.POV.25DAY',\n",
        " u'SI.POV.2DAY',\n",
        " u'SI.POV.4DAY',\n",
        " u'SI.POV.5DAY',\n",
        " u'SI.POV.DDAY',\n",
        " u'SI.POV.GAP2',\n",
        " u'SI.POV.GAP25',\n",
        " u'SI.POV.GAP4',\n",
        " u'SI.POV.GAP5',\n",
        " u'SI.POV.GAPS',\n",
        " u'SI.POV.NAGP',\n",
        " u'SI.POV.NAHC',\n",
        " u'SI.POV.RUGP',\n",
        " u'SI.POV.RUHC',\n",
        " u'SI.POV.URGP',\n",
        " u'SI.POV.URHC']"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Create a dictionary of the names and id's\n",
      "povdict = dict(zip(pov, povnames))\n",
      "povdict"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "{u'SI.POV.25DAY': u'Poverty headcount ratio at $2.5 a day (PPP) (% of population)',\n",
        " u'SI.POV.2DAY': u'Poverty headcount ratio at $2 a day (PPP) (% of population)',\n",
        " u'SI.POV.4DAY': u'Poverty headcount ratio at $4 a day (PPP) (% of population)',\n",
        " u'SI.POV.5DAY': u'Poverty headcount ratio at $5 a day (PPP) (% of population)',\n",
        " u'SI.POV.DDAY': u'Poverty headcount ratio at $1.25 a day (PPP) (% of population)',\n",
        " u'SI.POV.GAP2': u'Poverty gap at $2 a day (PPP) (%)',\n",
        " u'SI.POV.GAP25': u'Poverty gap at $2.5 a day (PPP) (%)',\n",
        " u'SI.POV.GAP4': u'Poverty gap at $4 a day (PPP) (%)',\n",
        " u'SI.POV.GAP5': u'Poverty gap at $5 a day (PPP) (%)',\n",
        " u'SI.POV.GAPS': u'Poverty gap at $1.25 a day (PPP) (%)',\n",
        " u'SI.POV.NAGP': u'Poverty gap at national poverty lines (%)',\n",
        " u'SI.POV.NAHC': u'Poverty headcount ratio at national poverty lines (% of population)',\n",
        " u'SI.POV.RUGP': u'Rural poverty gap at national poverty lines (%)',\n",
        " u'SI.POV.RUHC': u'Rural poverty headcount ratio at national poverty lines (% of rural population)',\n",
        " u'SI.POV.URGP': u'Urban poverty gap at national poverty lines (%)',\n",
        " u'SI.POV.URHC': u'Urban poverty headcount ratio at national poverty lines (% of urban population)'}"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Now, look for all income related indicators and store them\n",
      "\n",
      "inc = wb.search('income.*share.*%').iloc[:,:2]\n",
      "inc"
     ],
     "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>id</th>\n",
        "      <th>name</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>7522</th>\n",
        "      <td> SI.DST.02ND.20</td>\n",
        "      <td>  Income share held by second 20%</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7523</th>\n",
        "      <td> SI.DST.03RD.20</td>\n",
        "      <td>   Income share held by third 20%</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7524</th>\n",
        "      <td> SI.DST.04TH.20</td>\n",
        "      <td>  Income share held by fourth 20%</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7525</th>\n",
        "      <td> SI.DST.05TH.20</td>\n",
        "      <td> Income share held by highest 20%</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7526</th>\n",
        "      <td> SI.DST.10TH.10</td>\n",
        "      <td> Income share held by highest 10%</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7527</th>\n",
        "      <td> SI.DST.FRST.10</td>\n",
        "      <td>  Income share held by lowest 10%</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7528</th>\n",
        "      <td> SI.DST.FRST.20</td>\n",
        "      <td>  Income share held by lowest 20%</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "                  id                              name\n",
        "7522  SI.DST.02ND.20   Income share held by second 20%\n",
        "7523  SI.DST.03RD.20    Income share held by third 20%\n",
        "7524  SI.DST.04TH.20   Income share held by fourth 20%\n",
        "7525  SI.DST.05TH.20  Income share held by highest 20%\n",
        "7526  SI.DST.10TH.10  Income share held by highest 10%\n",
        "7527  SI.DST.FRST.10   Income share held by lowest 10%\n",
        "7528  SI.DST.FRST.20   Income share held by lowest 20%"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Repeat what was done with the poverty indicators\n",
      "\n",
      "incnames = inc.loc[:, 'name']\n",
      "incnames = incnames.tolist()\n",
      "inc = inc.loc[:,'id']\n",
      "inc = inc.tolist()\n",
      "incnames"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "[u'Income share held by second 20%',\n",
        " u'Income share held by third 20%',\n",
        " u'Income share held by fourth 20%',\n",
        " u'Income share held by highest 20%',\n",
        " u'Income share held by highest 10%',\n",
        " u'Income share held by lowest 10%',\n",
        " u'Income share held by lowest 20%']"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "inc"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "[u'SI.DST.02ND.20',\n",
        " u'SI.DST.03RD.20',\n",
        " u'SI.DST.04TH.20',\n",
        " u'SI.DST.05TH.20',\n",
        " u'SI.DST.10TH.10',\n",
        " u'SI.DST.FRST.10',\n",
        " u'SI.DST.FRST.20']"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Create another dictionary for income\n",
      "\n",
      "incdict = dict(zip(inc, incnames))\n",
      "incdict"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "{u'SI.DST.02ND.20': u'Income share held by second 20%',\n",
        " u'SI.DST.03RD.20': u'Income share held by third 20%',\n",
        " u'SI.DST.04TH.20': u'Income share held by fourth 20%',\n",
        " u'SI.DST.05TH.20': u'Income share held by highest 20%',\n",
        " u'SI.DST.10TH.10': u'Income share held by highest 10%',\n",
        " u'SI.DST.FRST.10': u'Income share held by lowest 10%',\n",
        " u'SI.DST.FRST.20': u'Income share held by lowest 20%'}"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Create master list of all of the data we want to download:\n",
      "\n",
      "idx = pov + inc\n",
      "idx"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "[u'SI.POV.25DAY',\n",
        " u'SI.POV.2DAY',\n",
        " u'SI.POV.4DAY',\n",
        " u'SI.POV.5DAY',\n",
        " u'SI.POV.DDAY',\n",
        " u'SI.POV.GAP2',\n",
        " u'SI.POV.GAP25',\n",
        " u'SI.POV.GAP4',\n",
        " u'SI.POV.GAP5',\n",
        " u'SI.POV.GAPS',\n",
        " u'SI.POV.NAGP',\n",
        " u'SI.POV.NAHC',\n",
        " u'SI.POV.RUGP',\n",
        " u'SI.POV.RUHC',\n",
        " u'SI.POV.URGP',\n",
        " u'SI.POV.URHC',\n",
        " u'SI.DST.02ND.20',\n",
        " u'SI.DST.03RD.20',\n",
        " u'SI.DST.04TH.20',\n",
        " u'SI.DST.05TH.20',\n",
        " u'SI.DST.10TH.10',\n",
        " u'SI.DST.FRST.10',\n",
        " u'SI.DST.FRST.20']"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Download data and store it as a DataFrame\n",
      "\n",
      "khm = wb.download(indicator=idx, country='KHM', start=2004, end=2012)\n",
      "khm"
     ],
     "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></th>\n",
        "      <th>SI.POV.25DAY</th>\n",
        "      <th>SI.POV.2DAY</th>\n",
        "      <th>SI.POV.4DAY</th>\n",
        "      <th>SI.POV.5DAY</th>\n",
        "      <th>SI.POV.DDAY</th>\n",
        "      <th>SI.POV.GAP2</th>\n",
        "      <th>SI.POV.GAP25</th>\n",
        "      <th>SI.POV.GAP4</th>\n",
        "      <th>SI.POV.GAP5</th>\n",
        "      <th>SI.POV.GAPS</th>\n",
        "      <th>...</th>\n",
        "      <th>SI.POV.RUHC</th>\n",
        "      <th>SI.POV.URGP</th>\n",
        "      <th>SI.POV.URHC</th>\n",
        "      <th>SI.DST.02ND.20</th>\n",
        "      <th>SI.DST.03RD.20</th>\n",
        "      <th>SI.DST.04TH.20</th>\n",
        "      <th>SI.DST.05TH.20</th>\n",
        "      <th>SI.DST.10TH.10</th>\n",
        "      <th>SI.DST.FRST.10</th>\n",
        "      <th>SI.DST.FRST.20</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>country</th>\n",
        "      <th>year</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",
        "      <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 rowspan=\"9\" valign=\"top\">Cambodia</th>\n",
        "      <th>2012</th>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>  NaN</td>\n",
        "      <td>...</td>\n",
        "      <td> 20.8</td>\n",
        "      <td>NaN</td>\n",
        "      <td>  6.4</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>  NaN</td>\n",
        "      <td>  NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011</th>\n",
        "      <td> 59.00</td>\n",
        "      <td> 41.26</td>\n",
        "      <td> 85.70</td>\n",
        "      <td> 91.83</td>\n",
        "      <td> 10.05</td>\n",
        "      <td> 10.25</td>\n",
        "      <td> 18.29</td>\n",
        "      <td> 39.60</td>\n",
        "      <td> 49.51</td>\n",
        "      <td> 1.43</td>\n",
        "      <td>...</td>\n",
        "      <td> 23.6</td>\n",
        "      <td>NaN</td>\n",
        "      <td>  8.7</td>\n",
        "      <td> 12.46</td>\n",
        "      <td> 16.11</td>\n",
        "      <td> 21.24</td>\n",
        "      <td> 41.20</td>\n",
        "      <td> 26.91</td>\n",
        "      <td> 4.04</td>\n",
        "      <td> 8.99</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010</th>\n",
        "      <td> 57.59</td>\n",
        "      <td> 40.88</td>\n",
        "      <td> 84.06</td>\n",
        "      <td> 90.63</td>\n",
        "      <td> 11.25</td>\n",
        "      <td> 10.59</td>\n",
        "      <td> 18.37</td>\n",
        "      <td> 39.00</td>\n",
        "      <td> 48.74</td>\n",
        "      <td> 1.70</td>\n",
        "      <td>...</td>\n",
        "      <td> 25.3</td>\n",
        "      <td>NaN</td>\n",
        "      <td>  8.5</td>\n",
        "      <td> 12.02</td>\n",
        "      <td> 15.80</td>\n",
        "      <td> 21.18</td>\n",
        "      <td> 42.49</td>\n",
        "      <td> 28.01</td>\n",
        "      <td> 3.80</td>\n",
        "      <td> 8.51</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2009</th>\n",
        "      <td> 56.25</td>\n",
        "      <td> 40.74</td>\n",
        "      <td> 82.20</td>\n",
        "      <td> 89.25</td>\n",
        "      <td> 12.93</td>\n",
        "      <td> 11.21</td>\n",
        "      <td> 18.71</td>\n",
        "      <td> 38.50</td>\n",
        "      <td> 48.02</td>\n",
        "      <td> 2.08</td>\n",
        "      <td>...</td>\n",
        "      <td> 27.5</td>\n",
        "      <td>NaN</td>\n",
        "      <td>  8.0</td>\n",
        "      <td> 11.66</td>\n",
        "      <td> 15.68</td>\n",
        "      <td> 21.48</td>\n",
        "      <td> 43.15</td>\n",
        "      <td> 28.20</td>\n",
        "      <td> 3.55</td>\n",
        "      <td> 8.03</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2008</th>\n",
        "      <td> 65.90</td>\n",
        "      <td> 51.05</td>\n",
        "      <td> 87.33</td>\n",
        "      <td> 92.48</td>\n",
        "      <td> 20.89</td>\n",
        "      <td> 16.27</td>\n",
        "      <td> 24.78</td>\n",
        "      <td> 45.05</td>\n",
        "      <td> 54.08</td>\n",
        "      <td> 4.39</td>\n",
        "      <td>...</td>\n",
        "      <td> 38.5</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 15.1</td>\n",
        "      <td> 11.60</td>\n",
        "      <td> 15.67</td>\n",
        "      <td> 21.43</td>\n",
        "      <td> 43.45</td>\n",
        "      <td> 28.57</td>\n",
        "      <td> 3.44</td>\n",
        "      <td> 7.85</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2007</th>\n",
        "      <td> 71.05</td>\n",
        "      <td> 59.39</td>\n",
        "      <td> 87.65</td>\n",
        "      <td> 92.07</td>\n",
        "      <td> 30.82</td>\n",
        "      <td> 21.92</td>\n",
        "      <td> 30.64</td>\n",
        "      <td> 49.51</td>\n",
        "      <td> 57.63</td>\n",
        "      <td> 7.24</td>\n",
        "      <td>...</td>\n",
        "      <td> 51.4</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 18.3</td>\n",
        "      <td> 10.05</td>\n",
        "      <td> 13.95</td>\n",
        "      <td> 20.16</td>\n",
        "      <td> 48.89</td>\n",
        "      <td> 33.99</td>\n",
        "      <td> 3.12</td>\n",
        "      <td> 6.95</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2006</th>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>  NaN</td>\n",
        "      <td>...</td>\n",
        "      <td>  NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>  NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>  NaN</td>\n",
        "      <td>  NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2005</th>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>  NaN</td>\n",
        "      <td>...</td>\n",
        "      <td>  NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>  NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>  NaN</td>\n",
        "      <td>  NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2004</th>\n",
        "      <td> 76.54</td>\n",
        "      <td> 64.43</td>\n",
        "      <td> 91.55</td>\n",
        "      <td> 94.95</td>\n",
        "      <td> 32.77</td>\n",
        "      <td> 23.64</td>\n",
        "      <td> 33.09</td>\n",
        "      <td> 52.85</td>\n",
        "      <td> 60.96</td>\n",
        "      <td> 7.79</td>\n",
        "      <td>...</td>\n",
        "      <td> 54.2</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 28.5</td>\n",
        "      <td> 11.43</td>\n",
        "      <td> 15.37</td>\n",
        "      <td> 21.22</td>\n",
        "      <td> 44.00</td>\n",
        "      <td> 29.09</td>\n",
        "      <td> 3.56</td>\n",
        "      <td> 7.98</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>9 rows \u00d7 23 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "               SI.POV.25DAY  SI.POV.2DAY  SI.POV.4DAY  SI.POV.5DAY  \\\n",
        "country  year                                                        \n",
        "Cambodia 2012           NaN          NaN          NaN          NaN   \n",
        "         2011         59.00        41.26        85.70        91.83   \n",
        "         2010         57.59        40.88        84.06        90.63   \n",
        "         2009         56.25        40.74        82.20        89.25   \n",
        "         2008         65.90        51.05        87.33        92.48   \n",
        "         2007         71.05        59.39        87.65        92.07   \n",
        "         2006           NaN          NaN          NaN          NaN   \n",
        "         2005           NaN          NaN          NaN          NaN   \n",
        "         2004         76.54        64.43        91.55        94.95   \n",
        "\n",
        "               SI.POV.DDAY  SI.POV.GAP2  SI.POV.GAP25  SI.POV.GAP4  \\\n",
        "country  year                                                        \n",
        "Cambodia 2012          NaN          NaN           NaN          NaN   \n",
        "         2011        10.05        10.25         18.29        39.60   \n",
        "         2010        11.25        10.59         18.37        39.00   \n",
        "         2009        12.93        11.21         18.71        38.50   \n",
        "         2008        20.89        16.27         24.78        45.05   \n",
        "         2007        30.82        21.92         30.64        49.51   \n",
        "         2006          NaN          NaN           NaN          NaN   \n",
        "         2005          NaN          NaN           NaN          NaN   \n",
        "         2004        32.77        23.64         33.09        52.85   \n",
        "\n",
        "               SI.POV.GAP5  SI.POV.GAPS       ...        SI.POV.RUHC  \\\n",
        "country  year                                 ...                      \n",
        "Cambodia 2012          NaN          NaN       ...               20.8   \n",
        "         2011        49.51         1.43       ...               23.6   \n",
        "         2010        48.74         1.70       ...               25.3   \n",
        "         2009        48.02         2.08       ...               27.5   \n",
        "         2008        54.08         4.39       ...               38.5   \n",
        "         2007        57.63         7.24       ...               51.4   \n",
        "         2006          NaN          NaN       ...                NaN   \n",
        "         2005          NaN          NaN       ...                NaN   \n",
        "         2004        60.96         7.79       ...               54.2   \n",
        "\n",
        "               SI.POV.URGP  SI.POV.URHC  SI.DST.02ND.20  SI.DST.03RD.20  \\\n",
        "country  year                                                             \n",
        "Cambodia 2012          NaN          6.4             NaN             NaN   \n",
        "         2011          NaN          8.7           12.46           16.11   \n",
        "         2010          NaN          8.5           12.02           15.80   \n",
        "         2009          NaN          8.0           11.66           15.68   \n",
        "         2008          NaN         15.1           11.60           15.67   \n",
        "         2007          NaN         18.3           10.05           13.95   \n",
        "         2006          NaN          NaN             NaN             NaN   \n",
        "         2005          NaN          NaN             NaN             NaN   \n",
        "         2004          NaN         28.5           11.43           15.37   \n",
        "\n",
        "               SI.DST.04TH.20  SI.DST.05TH.20  SI.DST.10TH.10  SI.DST.FRST.10  \\\n",
        "country  year                                                                   \n",
        "Cambodia 2012             NaN             NaN             NaN             NaN   \n",
        "         2011           21.24           41.20           26.91            4.04   \n",
        "         2010           21.18           42.49           28.01            3.80   \n",
        "         2009           21.48           43.15           28.20            3.55   \n",
        "         2008           21.43           43.45           28.57            3.44   \n",
        "         2007           20.16           48.89           33.99            3.12   \n",
        "         2006             NaN             NaN             NaN             NaN   \n",
        "         2005             NaN             NaN             NaN             NaN   \n",
        "         2004           21.22           44.00           29.09            3.56   \n",
        "\n",
        "               SI.DST.FRST.20  \n",
        "country  year                  \n",
        "Cambodia 2012             NaN  \n",
        "         2011            8.99  \n",
        "         2010            8.51  \n",
        "         2009            8.03  \n",
        "         2008            7.85  \n",
        "         2007            6.95  \n",
        "         2006             NaN  \n",
        "         2005             NaN  \n",
        "         2004            7.98  \n",
        "\n",
        "[9 rows x 23 columns]"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Reverse the order of the DataFrame so the years are ascending, drop Cambodia index, drop categories with all NA's\n",
      "\n",
      "khm.index = khm.index.droplevel(0)\n",
      "khm = khm.iloc[::-1]\n",
      "khm = khm.dropna(axis=1, how='all')\n",
      "qgrid.show_grid(khm, remote_js=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
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[{\"year\":\"2004\",\"SI.POV.25DAY\":76.54,\"SI.POV.2DAY\":64.43,\"SI.POV.4DAY\":91.55,\"SI.POV.5DAY\":94.95,\"SI.POV.DDAY\":32.77,\"SI.POV.GAP2\":23.64,\"SI.POV.GAP25\":33.09,\"SI.POV.GAP4\":52.85,\"SI.POV.GAP5\":60.96,\"SI.POV.GAPS\":7.79,\"SI.POV.NAGP\":14.7,\"SI.POV.NAHC\":50.2,\"SI.POV.RUHC\":54.2,\"SI.POV.URHC\":28.5,\"SI.DST.02ND.20\":11.43,\"SI.DST.03RD.20\":15.37,\"SI.DST.04TH.20\":21.22,\"SI.DST.05TH.20\":44.0,\"SI.DST.10TH.10\":29.09,\"SI.DST.FRST.10\":3.56,\"SI.DST.FRST.20\":7.98},{\"year\":\"2005\",\"SI.POV.25DAY\":null,\"SI.POV.2DAY\":null,\"SI.POV.4DAY\":null,\"SI.POV.5DAY\":null,\"SI.POV.DDAY\":null,\"SI.POV.GAP2\":null,\"SI.POV.GAP25\":null,\"SI.POV.GAP4\":null,\"SI.POV.GAP5\":null,\"SI.POV.GAPS\":null,\"SI.POV.NAGP\":null,\"SI.POV.NAHC\":null,\"SI.POV.RUHC\":null,\"SI.POV.URHC\":null,\"SI.DST.02ND.20\":null,\"SI.DST.03RD.20\":null,\"SI.DST.04TH.20\":null,\"SI.DST.05TH.20\":null,\"SI.DST.10TH.10\":null,\"SI.DST.FRST.10\":null,\"SI.DST.FRST.20\":null},{\"year\":\"2006\",\"SI.POV.25DAY\":null,\"SI.POV.2DAY\":null,\"SI.POV.4DAY\":null,\"SI.POV.5DAY\":null,\"SI.POV.DDAY\":null,\"SI.POV.GAP2\":null,\"SI.POV.GAP25\":null,\"SI.POV.GAP4\":null,\"SI.POV.GAP5\":null,\"SI.POV.GAPS\":null,\"SI.POV.NAGP\":null,\"SI.POV.NAHC\":null,\"SI.POV.RUHC\":null,\"SI.POV.URHC\":null,\"SI.DST.02ND.20\":null,\"SI.DST.03RD.20\":null,\"SI.DST.04TH.20\":null,\"SI.DST.05TH.20\":null,\"SI.DST.10TH.10\":null,\"SI.DST.FRST.10\":null,\"SI.DST.FRST.20\":null},{\"year\":\"2007\",\"SI.POV.25DAY\":71.05,\"SI.POV.2DAY\":59.39,\"SI.POV.4DAY\":87.65,\"SI.POV.5DAY\":92.07,\"SI.POV.DDAY\":30.82,\"SI.POV.GAP2\":21.92,\"SI.POV.GAP25\":30.64,\"SI.POV.GAP4\":49.51,\"SI.POV.GAP5\":57.63,\"SI.POV.GAPS\":7.24,\"SI.POV.NAGP\":13.2,\"SI.POV.NAHC\":45.0,\"SI.POV.RUHC\":51.4,\"SI.POV.URHC\":18.3,\"SI.DST.02ND.20\":10.05,\"SI.DST.03RD.20\":13.95,\"SI.DST.04TH.20\":20.16,\"SI.DST.05TH.20\":48.89,\"SI.DST.10TH.10\":33.99,\"SI.DST.FRST.10\":3.12,\"SI.DST.FRST.20\":6.95},{\"year\":\"2008\",\"SI.POV.25DAY\":65.9,\"SI.POV.2DAY\":51.05,\"SI.POV.4DAY\":87.33,\"SI.POV.5DAY\":92.48,\"SI.POV.DDAY\":20.89,\"SI.POV.GAP2\":16.27,\"SI.POV.GAP25\":24.78,\"SI.POV.GAP4\":45.05,\"SI.POV.GAP5\":54.08,\"SI.POV.GAPS\":4.39,\"SI.POV.NAGP\":8.7,\"SI.POV.NAHC\":34.0,\"SI.POV.RUHC\":38.5,\"SI.POV.URHC\":15.1,\"SI.DST.02ND.20\":11.6,\"SI.DST.03RD.20\":15.67,\"SI.DST.04TH.20\":21.43,\"SI.DST.05TH.20\":43.45,\"SI.DST.10TH.10\":28.57,\"SI.DST.FRST.10\":3.44,\"SI.DST.FRST.20\":7.85},{\"year\":\"2009\",\"SI.POV.25DAY\":56.25,\"SI.POV.2DAY\":40.74,\"SI.POV.4DAY\":82.2,\"SI.POV.5DAY\":89.25,\"SI.POV.DDAY\":12.93,\"SI.POV.GAP2\":11.21,\"SI.POV.GAP25\":18.71,\"SI.POV.GAP4\":38.5,\"SI.POV.GAP5\":48.02,\"SI.POV.GAPS\":2.08,\"SI.POV.NAGP\":5.3,\"SI.POV.NAHC\":23.9,\"SI.POV.RUHC\":27.5,\"SI.POV.URHC\":8.0,\"SI.DST.02ND.20\":11.66,\"SI.DST.03RD.20\":15.68,\"SI.DST.04TH.20\":21.48,\"SI.DST.05TH.20\":43.15,\"SI.DST.10TH.10\":28.2,\"SI.DST.FRST.10\":3.55,\"SI.DST.FRST.20\":8.03},{\"year\":\"2010\",\"SI.POV.25DAY\":57.59,\"SI.POV.2DAY\":40.88,\"SI.POV.4DAY\":84.06,\"SI.POV.5DAY\":90.63,\"SI.POV.DDAY\":11.25,\"SI.POV.GAP2\":10.59,\"SI.POV.GAP25\":18.37,\"SI.POV.GAP4\":39.0,\"SI.POV.GAP5\":48.74,\"SI.POV.GAPS\":1.7,\"SI.POV.NAGP\":4.7,\"SI.POV.NAHC\":22.1,\"SI.POV.RUHC\":25.3,\"SI.POV.URHC\":8.5,\"SI.DST.02ND.20\":12.02,\"SI.DST.03RD.20\":15.8,\"SI.DST.04TH.20\":21.18,\"SI.DST.05TH.20\":42.49,\"SI.DST.10TH.10\":28.01,\"SI.DST.FRST.10\":3.8,\"SI.DST.FRST.20\":8.51},{\"year\":\"2011\",\"SI.POV.25DAY\":59.0,\"SI.POV.2DAY\":41.26,\"SI.POV.4DAY\":85.7,\"SI.POV.5DAY\":91.83,\"SI.POV.DDAY\":10.05,\"SI.POV.GAP2\":10.25,\"SI.POV.GAP25\":18.29,\"SI.POV.GAP4\":39.6,\"SI.POV.GAP5\":49.51,\"SI.POV.GAPS\":1.43,\"SI.POV.NAGP\":4.2,\"SI.POV.NAHC\":20.5,\"SI.POV.RUHC\":23.6,\"SI.POV.URHC\":8.7,\"SI.DST.02ND.20\":12.46,\"SI.DST.03RD.20\":16.11,\"SI.DST.04TH.20\":21.24,\"SI.DST.05TH.20\":41.2,\"SI.DST.10TH.10\":26.91,\"SI.DST.FRST.10\":4.04,\"SI.DST.FRST.20\":8.99},{\"year\":\"2012\",\"SI.POV.25DAY\":null,\"SI.POV.2DAY\":null,\"SI.POV.4DAY\":null,\"SI.POV.5DAY\":null,\"SI.POV.DDAY\":null,\"SI.POV.GAP2\":null,\"SI.POV.GAP25\":null,\"SI.POV.GAP4\":null,\"SI.POV.GAP5\":null,\"SI.POV.GAPS\":null,\"SI.POV.NAGP\":3.1,\"SI.POV.NAHC\":17.7,\"SI.POV.RUHC\":20.8,\"SI.POV.URHC\":6.4,\"SI.DST.02ND.20\":null,\"SI.DST.03RD.20\":null,\"SI.DST.04TH.20\":null,\"SI.DST.05TH.20\":null,\"SI.DST.10TH.10\":null,\"SI.DST.FRST.10\":null,\"SI.DST.FRST.20\":null}], [{\"field\": \"year\"}, {\"field\": \"SI.POV.25DAY\", \"type\": \"Float\"}, {\"field\": \"SI.POV.2DAY\", \"type\": \"Float\"}, {\"field\": \"SI.POV.4DAY\", \"type\": \"Float\"}, {\"field\": \"SI.POV.5DAY\", \"type\": \"Float\"}, {\"field\": \"SI.POV.DDAY\", \"type\": \"Float\"}, {\"field\": \"SI.POV.GAP2\", \"type\": \"Float\"}, {\"field\": \"SI.POV.GAP25\", \"type\": \"Float\"}, {\"field\": \"SI.POV.GAP4\", \"type\": \"Float\"}, {\"field\": \"SI.POV.GAP5\", \"type\": \"Float\"}, {\"field\": \"SI.POV.GAPS\", \"type\": \"Float\"}, {\"field\": \"SI.POV.NAGP\", \"type\": \"Float\"}, {\"field\": \"SI.POV.NAHC\", \"type\": \"Float\"}, {\"field\": \"SI.POV.RUHC\", \"type\": \"Float\"}, {\"field\": \"SI.POV.URHC\", \"type\": \"Float\"}, {\"field\": \"SI.DST.02ND.20\", \"type\": \"Float\"}, {\"field\": \"SI.DST.03RD.20\", \"type\": \"Float\"}, {\"field\": \"SI.DST.04TH.20\", \"type\": \"Float\"}, {\"field\": \"SI.DST.05TH.20\", \"type\": \"Float\"}, {\"field\": \"SI.DST.10TH.10\", \"type\": \"Float\"}, {\"field\": \"SI.DST.FRST.10\", \"type\": \"Float\"}, {\"field\": \"SI.DST.FRST.20\", \"type\": \"Float\"}]);\n",
        "            grid.initialize_slick_grid();\n",
        "        });\n",
        "    });\n",
        "});"
       ],
       "metadata": {},
       "output_type": "display_data"
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Let's look at the data of percent of total income earned from the highest 10% next to that of\n",
      "# the lowest 10%\n",
      "\n",
      "incframe = khm[['SI.DST.10TH.10', 'SI.DST.FRST.10']]\n",
      "incframe = incframe[0:8] # No data for 2012, so let's omit it\n",
      "incframe.columns = [incdict[incframe.columns.tolist()[0]], incdict[incframe.columns.tolist()[1]]]\n",
      "incframe"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
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        "<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>Income share held by highest 10%</th>\n",
        "      <th>Income share held by lowest 10%</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>year</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2004</th>\n",
        "      <td> 29.09</td>\n",
        "      <td> 3.56</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2005</th>\n",
        "      <td>   NaN</td>\n",
        "      <td>  NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2006</th>\n",
        "      <td>   NaN</td>\n",
        "      <td>  NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2007</th>\n",
        "      <td> 33.99</td>\n",
        "      <td> 3.12</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2008</th>\n",
        "      <td> 28.57</td>\n",
        "      <td> 3.44</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2009</th>\n",
        "      <td> 28.20</td>\n",
        "      <td> 3.55</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010</th>\n",
        "      <td> 28.01</td>\n",
        "      <td> 3.80</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011</th>\n",
        "      <td> 26.91</td>\n",
        "      <td> 4.04</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "      Income share held by highest 10%  Income share held by lowest 10%\n",
        "year                                                                   \n",
        "2004                             29.09                             3.56\n",
        "2005                               NaN                              NaN\n",
        "2006                               NaN                              NaN\n",
        "2007                             33.99                             3.12\n",
        "2008                             28.57                             3.44\n",
        "2009                             28.20                             3.55\n",
        "2010                             28.01                             3.80\n",
        "2011                             26.91                             4.04"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "incframe.columns = ['Highest 10% of Income Earners', 'Lowest 10% of Income Earners']\n",
      "\n",
      "plt.figure()\n",
      "incframe.plot(title = 'Income Share in Cambodia', style = 'o-')\n",
      "plt.xlabel('Year')\n",
      "plt.ylabel('Share of National Income (%)')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "<matplotlib.text.Text at 0x10d85eb10>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<matplotlib.figure.Figure at 0x10d9f4910>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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u35LR/Xuyf7sMZi/5jdF/m83Pa+P3HKhIY6PiLiLlapGewl8v7sof/q8jv23Z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\nJGlgLO6SJA2MxV2SpIGxuEuSNDB/A4CKIc+44962AAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10d9c67d0>"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "incframe1 = khm[['SI.DST.FRST.20', 'SI.DST.02ND.20', 'SI.DST.03RD.20', 'SI.DST.04TH.20', 'SI.DST.05TH.20'\n",
      "                 ]]\n",
      "\n",
      "# Loop to look up ids in dictionary to rename the columns\n",
      "newcolumns = range(0,len(incframe1.columns))\n",
      "for i in range(0, len(incframe1.columns)):\n",
      "    newcolumns[i] = incdict[incframe1.columns.tolist()[i]]\n",
      "incframe1.columns = newcolumns\n",
      "\n",
      "incframe1 = incframe1[0:8] #Omit 2012, no data\n",
      "incframe1"
     ],
     "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>Income share held by lowest 20%</th>\n",
        "      <th>Income share held by second 20%</th>\n",
        "      <th>Income share held by third 20%</th>\n",
        "      <th>Income share held by fourth 20%</th>\n",
        "      <th>Income share held by highest 20%</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>year</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>2004</th>\n",
        "      <td> 7.98</td>\n",
        "      <td> 11.43</td>\n",
        "      <td> 15.37</td>\n",
        "      <td> 21.22</td>\n",
        "      <td> 44.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2005</th>\n",
        "      <td>  NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2006</th>\n",
        "      <td>  NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2007</th>\n",
        "      <td> 6.95</td>\n",
        "      <td> 10.05</td>\n",
        "      <td> 13.95</td>\n",
        "      <td> 20.16</td>\n",
        "      <td> 48.89</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2008</th>\n",
        "      <td> 7.85</td>\n",
        "      <td> 11.60</td>\n",
        "      <td> 15.67</td>\n",
        "      <td> 21.43</td>\n",
        "      <td> 43.45</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2009</th>\n",
        "      <td> 8.03</td>\n",
        "      <td> 11.66</td>\n",
        "      <td> 15.68</td>\n",
        "      <td> 21.48</td>\n",
        "      <td> 43.15</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010</th>\n",
        "      <td> 8.51</td>\n",
        "      <td> 12.02</td>\n",
        "      <td> 15.80</td>\n",
        "      <td> 21.18</td>\n",
        "      <td> 42.49</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011</th>\n",
        "      <td> 8.99</td>\n",
        "      <td> 12.46</td>\n",
        "      <td> 16.11</td>\n",
        "      <td> 21.24</td>\n",
        "      <td> 41.20</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 15,
       "text": [
        "      Income share held by lowest 20%  Income share held by second 20%  \\\n",
        "year                                                                     \n",
        "2004                             7.98                            11.43   \n",
        "2005                              NaN                              NaN   \n",
        "2006                              NaN                              NaN   \n",
        "2007                             6.95                            10.05   \n",
        "2008                             7.85                            11.60   \n",
        "2009                             8.03                            11.66   \n",
        "2010                             8.51                            12.02   \n",
        "2011                             8.99                            12.46   \n",
        "\n",
        "      Income share held by third 20%  Income share held by fourth 20%  \\\n",
        "year                                                                    \n",
        "2004                           15.37                            21.22   \n",
        "2005                             NaN                              NaN   \n",
        "2006                             NaN                              NaN   \n",
        "2007                           13.95                            20.16   \n",
        "2008                           15.67                            21.43   \n",
        "2009                           15.68                            21.48   \n",
        "2010                           15.80                            21.18   \n",
        "2011                           16.11                            21.24   \n",
        "\n",
        "      Income share held by highest 20%  \n",
        "year                                    \n",
        "2004                             44.00  \n",
        "2005                               NaN  \n",
        "2006                               NaN  \n",
        "2007                             48.89  \n",
        "2008                             43.45  \n",
        "2009                             43.15  \n",
        "2010                             42.49  \n",
        "2011                             41.20  "
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Change column names\n",
      "incframe1.columns = ['Lowest 20%', 'Second Lowest 20%', 'Middle 20%', 'Second Highest 20%', 'Highest 20%']\n",
      "\n",
      "plt.figure()\n",
      "incframe1.plot(title = 'Income Share in Cambodia', style = 'o-')\n",
      "plt.xlabel('Year')\n",
      "plt.ylabel('Share of National Income (%)')\n",
      "plt.ylim([0, 50])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 16,
       "text": [
        "(0, 50)"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<matplotlib.figure.Figure at 0x10d9f4850>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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l5jVRLTVQAzm4aSa404Hib8mdS9bviwvcBQsM+QsWFFPYMM0C2/POkbfsziUz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\nyRiFu4iISMYo3EVERDJG4S4iIpIx/x8wzQla8Act2wAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10ceaf650>"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Stacked bar graph of above dataframe\n",
      "\n",
      "plt.figure()\n",
      "incframe1.plot(title = 'Income Share in Cambodia', kind = 'bar', stacked = 'True')\n",
      "plt.xlabel('Year')\n",
      "plt.ylabel('Share of National Income (%)')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 17,
       "text": [
        "<matplotlib.text.Text at 0x10d816c90>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<matplotlib.figure.Figure at 0x10d967250>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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/9dTTKFeuHPv37wfQDG0iIrlEtEXunIsHxgCNgCygC5AJjPc/Xwb0NLOjmsll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\nA64cHvQAAAAESURBVE239jler0FBAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x109db01d0>"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# How does this compare to the US?\n",
      "\n",
      "usa = wb.download(indicator=['SI.DST.10TH.10', 'SI.DST.FRST.10', 'SI.DST.FRST.20', 'SI.DST.02ND.20', \n",
      "                             'SI.DST.03RD.20', 'SI.DST.04TH.20', 'SI.DST.05TH.20'], \n",
      "                  country='USA', start=2004, end=2011)\n",
      "\n",
      "usa.index = usa.index.droplevel(0)\n",
      "usa = usa.iloc[::-1]\n",
      "qgrid.show_grid(usa, remote_js=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<script type=\"text/javascript\">\n",
        "if ($(\"#dg-css\").length == 0){\n",
        "    $(\"head\").append([\n",
        "        \"<link href='https://cdn.rawgit.com/quantopian/qgrid/6988858671b6f80c7d1987e07ebff3dc810d20ce/qgrid/qgridjs//lib/slick.grid.css' rel='stylesheet'>\",\n",
        "        \"<link href='https://cdn.rawgit.com/quantopian/qgrid/6988858671b6f80c7d1987e07ebff3dc810d20ce/qgrid/qgridjs//lib/slick-default-theme.css' rel='stylesheet'>\",\n",
        "        \"<link href='http://cdnjs.cloudflare.com/ajax/libs/jqueryui/1.10.4/css/jquery-ui.min.css' rel='stylesheet'>\",\n",
        "        \"<link id='dg-css' href='https://cdn.rawgit.com/quantopian/qgrid/6988858671b6f80c7d1987e07ebff3dc810d20ce/qgrid/qgridjs//qgrid.css' rel='stylesheet'>\"\n",
        "    ]);\n",
        "}\n",
        "</script>\n",
        "<div class='q-grid-container'>\n",
        "<div id='7a7d81f8-b428-45ae-b7fd-bf32030c5975' class='q-grid'></div>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "display_data"
      },
      {
       "javascript": [
        "var path_dictionary = {\n",
        "    jquery_drag: \"https://cdn.rawgit.com/quantopian/qgrid/6988858671b6f80c7d1987e07ebff3dc810d20ce/qgrid/qgridjs//lib/jquery.event.drag-2.2\",\n",
        "    slick_core: \"https://cdn.rawgit.com/quantopian/qgrid/6988858671b6f80c7d1987e07ebff3dc810d20ce/qgrid/qgridjs//lib/slick.core.2.2\",\n",
        "    slick_data_view: \"https://cdn.rawgit.com/quantopian/qgrid/6988858671b6f80c7d1987e07ebff3dc810d20ce/qgrid/qgridjs//lib/slick.dataview.2.2\",\n",
        "    slick_grid: \"https://cdn.rawgit.com/quantopian/qgrid/6988858671b6f80c7d1987e07ebff3dc810d20ce/qgrid/qgridjs//lib/slick.grid.2.2\",\n",
        "    data_grid: \"https://cdn.rawgit.com/quantopian/qgrid/6988858671b6f80c7d1987e07ebff3dc810d20ce/qgrid/qgridjs//qgrid\",\n",
        "    date_filter: \"https://cdn.rawgit.com/quantopian/qgrid/6988858671b6f80c7d1987e07ebff3dc810d20ce/qgrid/qgridjs//qgrid.datefilter\",\n",
        "    slider_filter: \"https://cdn.rawgit.com/quantopian/qgrid/6988858671b6f80c7d1987e07ebff3dc810d20ce/qgrid/qgridjs//qgrid.sliderfilter\",\n",
        "    filter_base:  \"https://cdn.rawgit.com/quantopian/qgrid/6988858671b6f80c7d1987e07ebff3dc810d20ce/qgrid/qgridjs//qgrid.filterbase\",\n",
        "    handlebars: \"https://cdnjs.cloudflare.com/ajax/libs/handlebars.js/2.0.0/handlebars.min\"\n",
        "};\n",
        "\n",
        "var existing_config = require.s.contexts._.config;\n",
        "if (!existing_config.paths['underscore']){\n",
        "    path_dictionary['underscore'] = \"https://cdnjs.cloudflare.com/ajax/libs/underscore.js/1.7.0/underscore-min\";\n",
        "}\n",
        "\n",
        "if (!existing_config.paths['moment']){\n",
        "    path_dictionary['moment'] = \"https://cdnjs.cloudflare.com/ajax/libs/moment.js/2.8.3/moment.min\";\n",
        "}\n",
        "\n",
        "if (!existing_config.paths['jqueryui']){\n",
        "    path_dictionary['jqueryui'] = \"https://ajax.googleapis.com/ajax/libs/jqueryui/1.11.1/jquery-ui.min\";\n",
        "}\n",
        "\n",
        "require.config({\n",
        "    paths: path_dictionary\n",
        "});\n",
        "\n",
        "if (typeof jQuery === 'function') {\n",
        "    define('jquery', function() { return jQuery; });\n",
        "}\n",
        "\n",
        "require([\n",
        "    'jquery',\n",
        "    'jquery_drag',\n",
        "    'slick_core',\n",
        "    'slick_data_view'\n",
        "],\n",
        "function($){\n",
        "    $('#7a7d81f8-b428-45ae-b7fd-bf32030c5975').closest('.rendered_html').removeClass('rendered_html');\n",
        "    require(['slick_grid'], function(){\n",
        "        require([\"data_grid\"], function(dgrid){\n",
        "            var grid = new dgrid.QGrid('#7a7d81f8-b428-45ae-b7fd-bf32030c5975', [{\"year\":\"2004\",\"SI.DST.10TH.10\":30.14,\"SI.DST.FRST.10\":1.63,\"SI.DST.FRST.20\":5.11,\"SI.DST.02ND.20\":10.65,\"SI.DST.03RD.20\":15.68,\"SI.DST.04TH.20\":22.57,\"SI.DST.05TH.20\":45.99},{\"year\":\"2005\",\"SI.DST.10TH.10\":null,\"SI.DST.FRST.10\":null,\"SI.DST.FRST.20\":null,\"SI.DST.02ND.20\":null,\"SI.DST.03RD.20\":null,\"SI.DST.04TH.20\":null,\"SI.DST.05TH.20\":null},{\"year\":\"2006\",\"SI.DST.10TH.10\":null,\"SI.DST.FRST.10\":null,\"SI.DST.FRST.20\":null,\"SI.DST.02ND.20\":null,\"SI.DST.03RD.20\":null,\"SI.DST.04TH.20\":null,\"SI.DST.05TH.20\":null},{\"year\":\"2007\",\"SI.DST.10TH.10\":30.55,\"SI.DST.FRST.10\":1.24,\"SI.DST.FRST.20\":4.63,\"SI.DST.02ND.20\":10.48,\"SI.DST.03RD.20\":15.62,\"SI.DST.04TH.20\":22.62,\"SI.DST.05TH.20\":46.65},{\"year\":\"2008\",\"SI.DST.10TH.10\":null,\"SI.DST.FRST.10\":null,\"SI.DST.FRST.20\":null,\"SI.DST.02ND.20\":null,\"SI.DST.03RD.20\":null,\"SI.DST.04TH.20\":null,\"SI.DST.05TH.20\":null},{\"year\":\"2009\",\"SI.DST.10TH.10\":null,\"SI.DST.FRST.10\":null,\"SI.DST.FRST.20\":null,\"SI.DST.02ND.20\":null,\"SI.DST.03RD.20\":null,\"SI.DST.04TH.20\":null,\"SI.DST.05TH.20\":null},{\"year\":\"2010\",\"SI.DST.10TH.10\":29.61,\"SI.DST.FRST.10\":1.38,\"SI.DST.FRST.20\":4.7,\"SI.DST.02ND.20\":10.4,\"SI.DST.03RD.20\":15.77,\"SI.DST.04TH.20\":23.1,\"SI.DST.05TH.20\":46.03},{\"year\":\"2011\",\"SI.DST.10TH.10\":null,\"SI.DST.FRST.10\":null,\"SI.DST.FRST.20\":null,\"SI.DST.02ND.20\":null,\"SI.DST.03RD.20\":null,\"SI.DST.04TH.20\":null,\"SI.DST.05TH.20\":null}], [{\"field\": \"year\"}, {\"field\": \"SI.DST.10TH.10\", \"type\": \"Float\"}, {\"field\": \"SI.DST.FRST.10\", \"type\": \"Float\"}, {\"field\": \"SI.DST.FRST.20\", \"type\": \"Float\"}, {\"field\": \"SI.DST.02ND.20\", \"type\": \"Float\"}, {\"field\": \"SI.DST.03RD.20\", \"type\": \"Float\"}, {\"field\": \"SI.DST.04TH.20\", \"type\": \"Float\"}, {\"field\": \"SI.DST.05TH.20\", \"type\": \"Float\"}]);\n",
        "            grid.initialize_slick_grid();\n",
        "        });\n",
        "    });\n",
        "});"
       ],
       "metadata": {},
       "output_type": "display_data"
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# So there isn't nearly as much data for the US during this time period, but it's still worth looking at.\n",
      "\n",
      "usainc = usa[['SI.DST.10TH.10', 'SI.DST.FRST.10']]\n",
      "\n",
      "usainc.columns = ['Highest 10% of Income Earners', 'Lowest 10% of Income Earners']\n",
      "\n",
      "plt.figure()\n",
      "usainc.plot(title = 'Income Share in the USA', style = 'o-')\n",
      "plt.xlabel('Year')\n",
      "plt.ylabel('Share of National Income (%)')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 19,
       "text": [
        "<matplotlib.text.Text at 0x10aed8710>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<matplotlib.figure.Figure at 0x10d9dd210>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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IiEiKUXEXERFJMSruIiIiKUbFXUREJMWouIuIiKQYFXcREZEUo+IuIiKSYmIu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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10d9b0290>"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "usainc1 = usa[['SI.DST.FRST.20', 'SI.DST.02ND.20', 'SI.DST.03RD.20', 'SI.DST.04TH.20', 'SI.DST.05TH.20']]\n",
      "\n",
      "usainc1.columns = ['Lowest 20%', 'Second Lowest 20%', 'Middle 20%', 'Second Highest 20%', 'Highest 20%']\n",
      "\n",
      "plt.figure()\n",
      "usainc1.plot(title = 'Income Share in the USA', style = 'o-')\n",
      "plt.xlabel('Year')\n",
      "plt.ylabel('Share of National Income (%)')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 20,
       "text": [
        "<matplotlib.text.Text at 0x10d876950>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<matplotlib.figure.Figure at 0x10d9ff550>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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fvo0RI7zx21NSUqhWrTqJiT9nTAcbGxtLx45dePPN1zNmkouMjKRBg4Zs27YN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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10d835310>"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.figure()\n",
      "usainc1.plot(title = 'Income Share in the USA', kind = 'bar', stacked = 'True')\n",
      "plt.xlabel('Year')\n",
      "plt.ylabel('Share of National Income (%)')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 21,
       "text": [
        "<matplotlib.text.Text at 0x10d69ed50>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<matplotlib.figure.Figure at 0x10d926610>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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OYOrUyey/f1dSU1PJzc0FqJEV2qLaInfOxQHDgQ5AIXA5UAC8Fno8D7jGzPy1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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10d4a2790>"
       ]
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# So... we aren't much better (perhaps worse). Let's look at the Gross National Income per capita for each\n",
      "# country to get a better look at the differences\n",
      "\n",
      "khmgni = wb.download(indicator=['NY.GNP.PCAP.CD'], country='KHM', start=2004, end=2011)\n",
      "khmgni.index = khmgni.index.droplevel(0)\n",
      "khmgni = khmgni.iloc[::-1]\n",
      "\n",
      "qgrid.show_grid(khmgni, remote_js=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<script type=\"text/javascript\">\n",
        "if ($(\"#dg-css\").length == 0){\n",
        "    $(\"head\").append([\n",
        "        \"<link href='https://cdn.rawgit.com/quantopian/qgrid/6988858671b6f80c7d1987e07ebff3dc810d20ce/qgrid/qgridjs//lib/slick.grid.css' rel='stylesheet'>\",\n",
        "        \"<link href='https://cdn.rawgit.com/quantopian/qgrid/6988858671b6f80c7d1987e07ebff3dc810d20ce/qgrid/qgridjs//lib/slick-default-theme.css' rel='stylesheet'>\",\n",
        "        \"<link href='http://cdnjs.cloudflare.com/ajax/libs/jqueryui/1.10.4/css/jquery-ui.min.css' rel='stylesheet'>\",\n",
        "        \"<link id='dg-css' href='https://cdn.rawgit.com/quantopian/qgrid/6988858671b6f80c7d1987e07ebff3dc810d20ce/qgrid/qgridjs//qgrid.css' rel='stylesheet'>\"\n",
        "    ]);\n",
        "}\n",
        "</script>\n",
        "<div class='q-grid-container'>\n",
        "<div id='0c4de6e7-0d5d-42bf-82b9-99a0da77ca86' class='q-grid'></div>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "display_data"
      },
      {
       "javascript": [
        "var path_dictionary = {\n",
        "    jquery_drag: \"https://cdn.rawgit.com/quantopian/qgrid/6988858671b6f80c7d1987e07ebff3dc810d20ce/qgrid/qgridjs//lib/jquery.event.drag-2.2\",\n",
        "    slick_core: \"https://cdn.rawgit.com/quantopian/qgrid/6988858671b6f80c7d1987e07ebff3dc810d20ce/qgrid/qgridjs//lib/slick.core.2.2\",\n",
        "    slick_data_view: \"https://cdn.rawgit.com/quantopian/qgrid/6988858671b6f80c7d1987e07ebff3dc810d20ce/qgrid/qgridjs//lib/slick.dataview.2.2\",\n",
        "    slick_grid: \"https://cdn.rawgit.com/quantopian/qgrid/6988858671b6f80c7d1987e07ebff3dc810d20ce/qgrid/qgridjs//lib/slick.grid.2.2\",\n",
        "    data_grid: \"https://cdn.rawgit.com/quantopian/qgrid/6988858671b6f80c7d1987e07ebff3dc810d20ce/qgrid/qgridjs//qgrid\",\n",
        "    date_filter: \"https://cdn.rawgit.com/quantopian/qgrid/6988858671b6f80c7d1987e07ebff3dc810d20ce/qgrid/qgridjs//qgrid.datefilter\",\n",
        "    slider_filter: \"https://cdn.rawgit.com/quantopian/qgrid/6988858671b6f80c7d1987e07ebff3dc810d20ce/qgrid/qgridjs//qgrid.sliderfilter\",\n",
        "    filter_base:  \"https://cdn.rawgit.com/quantopian/qgrid/6988858671b6f80c7d1987e07ebff3dc810d20ce/qgrid/qgridjs//qgrid.filterbase\",\n",
        "    handlebars: \"https://cdnjs.cloudflare.com/ajax/libs/handlebars.js/2.0.0/handlebars.min\"\n",
        "};\n",
        "\n",
        "var existing_config = require.s.contexts._.config;\n",
        "if (!existing_config.paths['underscore']){\n",
        "    path_dictionary['underscore'] = \"https://cdnjs.cloudflare.com/ajax/libs/underscore.js/1.7.0/underscore-min\";\n",
        "}\n",
        "\n",
        "if (!existing_config.paths['moment']){\n",
        "    path_dictionary['moment'] = \"https://cdnjs.cloudflare.com/ajax/libs/moment.js/2.8.3/moment.min\";\n",
        "}\n",
        "\n",
        "if (!existing_config.paths['jqueryui']){\n",
        "    path_dictionary['jqueryui'] = \"https://ajax.googleapis.com/ajax/libs/jqueryui/1.11.1/jquery-ui.min\";\n",
        "}\n",
        "\n",
        "require.config({\n",
        "    paths: path_dictionary\n",
        "});\n",
        "\n",
        "if (typeof jQuery === 'function') {\n",
        "    define('jquery', function() { return jQuery; });\n",
        "}\n",
        "\n",
        "require([\n",
        "    'jquery',\n",
        "    'jquery_drag',\n",
        "    'slick_core',\n",
        "    'slick_data_view'\n",
        "],\n",
        "function($){\n",
        "    $('#0c4de6e7-0d5d-42bf-82b9-99a0da77ca86').closest('.rendered_html').removeClass('rendered_html');\n",
        "    require(['slick_grid'], function(){\n",
        "        require([\"data_grid\"], function(dgrid){\n",
        "            var grid = new dgrid.QGrid('#0c4de6e7-0d5d-42bf-82b9-99a0da77ca86', [{\"year\":\"2004\",\"NY.GNP.PCAP.CD\":400},{\"year\":\"2005\",\"NY.GNP.PCAP.CD\":460},{\"year\":\"2006\",\"NY.GNP.PCAP.CD\":510},{\"year\":\"2007\",\"NY.GNP.PCAP.CD\":580},{\"year\":\"2008\",\"NY.GNP.PCAP.CD\":660},{\"year\":\"2009\",\"NY.GNP.PCAP.CD\":690},{\"year\":\"2010\",\"NY.GNP.PCAP.CD\":740},{\"year\":\"2011\",\"NY.GNP.PCAP.CD\":810}], [{\"field\": \"year\"}, {\"field\": \"NY.GNP.PCAP.CD\", \"type\": \"Integer\"}]);\n",
        "            grid.initialize_slick_grid();\n",
        "        });\n",
        "    });\n",
        "});"
       ],
       "metadata": {},
       "output_type": "display_data"
      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.figure()\n",
      "khmgni.plot(title = 'Gross National Income per Capita in Cambodia', style = 'o-')\n",
      "plt.xlabel('Year')\n",
      "plt.ylabel('Income ($)')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 28,
       "text": [
        "<matplotlib.text.Text at 0x10a84bfd0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<matplotlib.figure.Figure at 0x10d067850>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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GQqEvIiJ1UlJaxrsbD7Dig1Tyj5eSENeMmy7tyeghHYmM0Fe8NCQKfREROSNl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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10ab135d0>"
       ]
      }
     ],
     "prompt_number": 28
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "usagni = wb.download(indicator=['NY.GNP.PCAP.CD'], country='USA', start=2004, end=2011)\n",
      "usagni.index = usagni.index.droplevel(0)\n",
      "usagni = usagni.iloc[::-1]\n",
      "\n",
      "qgrid.show_grid(usagni, remote_js=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<script type=\"text/javascript\">\n",
        "if ($(\"#dg-css\").length == 0){\n",
        "    $(\"head\").append([\n",
        "        \"<link href='https://cdn.rawgit.com/quantopian/qgrid/6988858671b6f80c7d1987e07ebff3dc810d20ce/qgrid/qgridjs//lib/slick.grid.css' rel='stylesheet'>\",\n",
        "        \"<link href='https://cdn.rawgit.com/quantopian/qgrid/6988858671b6f80c7d1987e07ebff3dc810d20ce/qgrid/qgridjs//lib/slick-default-theme.css' rel='stylesheet'>\",\n",
        "        \"<link href='http://cdnjs.cloudflare.com/ajax/libs/jqueryui/1.10.4/css/jquery-ui.min.css' rel='stylesheet'>\",\n",
        "        \"<link id='dg-css' href='https://cdn.rawgit.com/quantopian/qgrid/6988858671b6f80c7d1987e07ebff3dc810d20ce/qgrid/qgridjs//qgrid.css' rel='stylesheet'>\"\n",
        "    ]);\n",
        "}\n",
        "</script>\n",
        "<div class='q-grid-container'>\n",
        "<div id='7eb96902-51f5-458d-8240-e06d98418d34' class='q-grid'></div>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "display_data"
      },
      {
       "javascript": [
        "var path_dictionary = {\n",
        "    jquery_drag: \"https://cdn.rawgit.com/quantopian/qgrid/6988858671b6f80c7d1987e07ebff3dc810d20ce/qgrid/qgridjs//lib/jquery.event.drag-2.2\",\n",
        "    slick_core: \"https://cdn.rawgit.com/quantopian/qgrid/6988858671b6f80c7d1987e07ebff3dc810d20ce/qgrid/qgridjs//lib/slick.core.2.2\",\n",
        "    slick_data_view: \"https://cdn.rawgit.com/quantopian/qgrid/6988858671b6f80c7d1987e07ebff3dc810d20ce/qgrid/qgridjs//lib/slick.dataview.2.2\",\n",
        "    slick_grid: \"https://cdn.rawgit.com/quantopian/qgrid/6988858671b6f80c7d1987e07ebff3dc810d20ce/qgrid/qgridjs//lib/slick.grid.2.2\",\n",
        "    data_grid: \"https://cdn.rawgit.com/quantopian/qgrid/6988858671b6f80c7d1987e07ebff3dc810d20ce/qgrid/qgridjs//qgrid\",\n",
        "    date_filter: \"https://cdn.rawgit.com/quantopian/qgrid/6988858671b6f80c7d1987e07ebff3dc810d20ce/qgrid/qgridjs//qgrid.datefilter\",\n",
        "    slider_filter: \"https://cdn.rawgit.com/quantopian/qgrid/6988858671b6f80c7d1987e07ebff3dc810d20ce/qgrid/qgridjs//qgrid.sliderfilter\",\n",
        "    filter_base:  \"https://cdn.rawgit.com/quantopian/qgrid/6988858671b6f80c7d1987e07ebff3dc810d20ce/qgrid/qgridjs//qgrid.filterbase\",\n",
        "    handlebars: \"https://cdnjs.cloudflare.com/ajax/libs/handlebars.js/2.0.0/handlebars.min\"\n",
        "};\n",
        "\n",
        "var existing_config = require.s.contexts._.config;\n",
        "if (!existing_config.paths['underscore']){\n",
        "    path_dictionary['underscore'] = \"https://cdnjs.cloudflare.com/ajax/libs/underscore.js/1.7.0/underscore-min\";\n",
        "}\n",
        "\n",
        "if (!existing_config.paths['moment']){\n",
        "    path_dictionary['moment'] = \"https://cdnjs.cloudflare.com/ajax/libs/moment.js/2.8.3/moment.min\";\n",
        "}\n",
        "\n",
        "if (!existing_config.paths['jqueryui']){\n",
        "    path_dictionary['jqueryui'] = \"https://ajax.googleapis.com/ajax/libs/jqueryui/1.11.1/jquery-ui.min\";\n",
        "}\n",
        "\n",
        "require.config({\n",
        "    paths: path_dictionary\n",
        "});\n",
        "\n",
        "if (typeof jQuery === 'function') {\n",
        "    define('jquery', function() { return jQuery; });\n",
        "}\n",
        "\n",
        "require([\n",
        "    'jquery',\n",
        "    'jquery_drag',\n",
        "    'slick_core',\n",
        "    'slick_data_view'\n",
        "],\n",
        "function($){\n",
        "    $('#7eb96902-51f5-458d-8240-e06d98418d34').closest('.rendered_html').removeClass('rendered_html');\n",
        "    require(['slick_grid'], function(){\n",
        "        require([\"data_grid\"], function(dgrid){\n",
        "            var grid = new dgrid.QGrid('#7eb96902-51f5-458d-8240-e06d98418d34', [{\"year\":\"2004\",\"NY.GNP.PCAP.CD\":43650},{\"year\":\"2005\",\"NY.GNP.PCAP.CD\":46220},{\"year\":\"2006\",\"NY.GNP.PCAP.CD\":47340},{\"year\":\"2007\",\"NY.GNP.PCAP.CD\":48690},{\"year\":\"2008\",\"NY.GNP.PCAP.CD\":49680},{\"year\":\"2009\",\"NY.GNP.PCAP.CD\":48300},{\"year\":\"2010\",\"NY.GNP.PCAP.CD\":49110},{\"year\":\"2011\",\"NY.GNP.PCAP.CD\":50350}], [{\"field\": \"year\"}, {\"field\": \"NY.GNP.PCAP.CD\", \"type\": \"Integer\"}]);\n",
        "            grid.initialize_slick_grid();\n",
        "        });\n",
        "    });\n",
        "});"
       ],
       "metadata": {},
       "output_type": "display_data"
      }
     ],
     "prompt_number": 29
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.figure()\n",
      "usagni.plot(title = 'Gross National Income per Capita in the USA', style = 'o-')\n",
      "plt.xlabel('Year')\n",
      "plt.ylabel('Income ($)')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 30,
       "text": [
        "<matplotlib.text.Text at 0x109ca6810>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<matplotlib.figure.Figure at 0x109d0b310>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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KqXKXm5fPkp8Ps2jtQXLyPLSIjuDOQa1oUq9ijANoXDecpyd04415W1m34zjH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\nPv4jZOsIZjIAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x109d13dd0>"
       ]
      }
     ],
     "prompt_number": 30
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Explore how close people are to the poverty boundary and how it is changing over time\n",
      "\n",
      "povline = khm[['SI.POV.25DAY', 'SI.POV.2DAY', 'SI.POV.4DAY', 'SI.POV.5DAY', 'SI.POV.DDAY', 'SI.POV.NAHC']]\n",
      "newcolumns = range(0,len(povline.columns))\n",
      "for i in range(0, len(povline.columns)):\n",
      "    newcolumns[i] = povdict[povline.columns.tolist()[i]]\n",
      "povline.columns = newcolumns\n",
      "\n",
      "povline"
     ],
     "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>Poverty headcount ratio at $2.5 a day (PPP) (% of population)</th>\n",
        "      <th>Poverty headcount ratio at $2 a day (PPP) (% of population)</th>\n",
        "      <th>Poverty headcount ratio at $4 a day (PPP) (% of population)</th>\n",
        "      <th>Poverty headcount ratio at $5 a day (PPP) (% of population)</th>\n",
        "      <th>Poverty headcount ratio at $1.25 a day (PPP) (% of population)</th>\n",
        "      <th>Poverty headcount ratio at national poverty lines (% of population)</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>year</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>2004</th>\n",
        "      <td> 76.54</td>\n",
        "      <td> 64.43</td>\n",
        "      <td> 91.55</td>\n",
        "      <td> 94.95</td>\n",
        "      <td> 32.77</td>\n",
        "      <td> 50.2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2005</th>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>  NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2006</th>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>  NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2007</th>\n",
        "      <td> 71.05</td>\n",
        "      <td> 59.39</td>\n",
        "      <td> 87.65</td>\n",
        "      <td> 92.07</td>\n",
        "      <td> 30.82</td>\n",
        "      <td> 45.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2008</th>\n",
        "      <td> 65.90</td>\n",
        "      <td> 51.05</td>\n",
        "      <td> 87.33</td>\n",
        "      <td> 92.48</td>\n",
        "      <td> 20.89</td>\n",
        "      <td> 34.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2009</th>\n",
        "      <td> 56.25</td>\n",
        "      <td> 40.74</td>\n",
        "      <td> 82.20</td>\n",
        "      <td> 89.25</td>\n",
        "      <td> 12.93</td>\n",
        "      <td> 23.9</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2010</th>\n",
        "      <td> 57.59</td>\n",
        "      <td> 40.88</td>\n",
        "      <td> 84.06</td>\n",
        "      <td> 90.63</td>\n",
        "      <td> 11.25</td>\n",
        "      <td> 22.1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2011</th>\n",
        "      <td> 59.00</td>\n",
        "      <td> 41.26</td>\n",
        "      <td> 85.70</td>\n",
        "      <td> 91.83</td>\n",
        "      <td> 10.05</td>\n",
        "      <td> 20.5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012</th>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td>   NaN</td>\n",
        "      <td> 17.7</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 24,
       "text": [
        "      Poverty headcount ratio at $2.5 a day (PPP) (% of population)  \\\n",
        "year                                                                  \n",
        "2004                                              76.54               \n",
        "2005                                                NaN               \n",
        "2006                                                NaN               \n",
        "2007                                              71.05               \n",
        "2008                                              65.90               \n",
        "2009                                              56.25               \n",
        "2010                                              57.59               \n",
        "2011                                              59.00               \n",
        "2012                                                NaN               \n",
        "\n",
        "      Poverty headcount ratio at $2 a day (PPP) (% of population)  \\\n",
        "year                                                                \n",
        "2004                                              64.43             \n",
        "2005                                                NaN             \n",
        "2006                                                NaN             \n",
        "2007                                              59.39             \n",
        "2008                                              51.05             \n",
        "2009                                              40.74             \n",
        "2010                                              40.88             \n",
        "2011                                              41.26             \n",
        "2012                                                NaN             \n",
        "\n",
        "      Poverty headcount ratio at $4 a day (PPP) (% of population)  \\\n",
        "year                                                                \n",
        "2004                                              91.55             \n",
        "2005                                                NaN             \n",
        "2006                                                NaN             \n",
        "2007                                              87.65             \n",
        "2008                                              87.33             \n",
        "2009                                              82.20             \n",
        "2010                                              84.06             \n",
        "2011                                              85.70             \n",
        "2012                                                NaN             \n",
        "\n",
        "      Poverty headcount ratio at $5 a day (PPP) (% of population)  \\\n",
        "year                                                                \n",
        "2004                                              94.95             \n",
        "2005                                                NaN             \n",
        "2006                                                NaN             \n",
        "2007                                              92.07             \n",
        "2008                                              92.48             \n",
        "2009                                              89.25             \n",
        "2010                                              90.63             \n",
        "2011                                              91.83             \n",
        "2012                                                NaN             \n",
        "\n",
        "      Poverty headcount ratio at $1.25 a day (PPP) (% of population)  \\\n",
        "year                                                                   \n",
        "2004                                              32.77                \n",
        "2005                                                NaN                \n",
        "2006                                                NaN                \n",
        "2007                                              30.82                \n",
        "2008                                              20.89                \n",
        "2009                                              12.93                \n",
        "2010                                              11.25                \n",
        "2011                                              10.05                \n",
        "2012                                                NaN                \n",
        "\n",
        "      Poverty headcount ratio at national poverty lines (% of population)  \n",
        "year                                                                       \n",
        "2004                                               50.2                    \n",
        "2005                                                NaN                    \n",
        "2006                                                NaN                    \n",
        "2007                                               45.0                    \n",
        "2008                                               34.0                    \n",
        "2009                                               23.9                    \n",
        "2010                                               22.1                    \n",
        "2011                                               20.5                    \n",
        "2012                                               17.7                    "
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Change titles of columns for plotting, then plot\n",
      "povline.columns = ['<$2.50 a day', '<$2 a day', '<$4 a day', '<$5 a day', '<$1.25 a day', '<National poverty lines']\n",
      "\n",
      "plt.figure()\n",
      "povline.plot(title='Poverty Headcount Ratio at Different Incomes in Cambodia', style = 'o-')\n",
      "plt.xlabel('Year')\n",
      "plt.ylabel('% of population')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 31,
       "text": [
        "<matplotlib.text.Text at 0x10dfbee10>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<matplotlib.figure.Figure at 0x109cc6c90>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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+TEbd+wCmz9fDkQnRfeTQVAiRkAzDIEWNa3K9umVZGFlZDLr5x5LsRdyRhC+E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\nBufIMkY08gAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10d011f90>"
       ]
      }
     ],
     "prompt_number": 31
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# The World Bank does not have most of this data for the USA, so I will find US Census data later. "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 26
    }
   ],
   "metadata": {}
  }
 ]
}