World Economic Forum Comparison.ipynb

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 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd\n",
      "import numpy\n",
      "import json\n",
      "from collections import defaultdict\n",
      "import scipy.stats\n",
      "import math\n",
      "import pywikibot\n",
      "from matplotlib.pylab import style\n",
      "style.use('fivethirtyeight')\n",
      "\n",
      "%pylab inline\n",
      "java_min_int = -2147483648"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "VERBOSE:pywiki:Starting 1 threads...\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "allrecs = pd.DataFrame.from_dict(json.load(open('helpers/world_cultures_shortcut.json','r')))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Tranforming QIDs into English labels.\n",
      "enwp = pywikibot.Site('en','wikipedia')\n",
      "wikidata = enwp.data_repository()\n",
      "\n",
      "retrieved = dict()\n",
      "\n",
      "def english_label(qid):\n",
      "    \n",
      "    if type(qid) is float:\n",
      "        if math.isnan(qid):\n",
      "            return None\n",
      "    #first see if we've done it\n",
      "    try:\n",
      "        return retrieved[qid]\n",
      "    except KeyError:\n",
      "        try:\n",
      "            page = pywikibot.ItemPage(wikidata, qid)\n",
      "            data = page.get()\n",
      "            lab = data['labels']['en']\n",
      "            retrieved[qid] = lab\n",
      "            return lab\n",
      "        except (KeyError, pywikibot.exceptions.NoPage):\n",
      "            retrieved[qid] = qid\n",
      "            return qid\n",
      "#Tranforming QIDs into English labels.\n",
      "enwp = pywikibot.Site('en','wikipedia')\n",
      "wikidata = enwp.data_repository()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "allrecs['citname'] = allrecs['citizenship'].apply(english_label)\n",
      "allrecs['countryname'] = allrecs['country'].apply(english_label)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "VERBOSE:pywiki:Found 1 wikidata:wikidata processes running, including this one.\n"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "wikidatanames = set(allrecs['citname']).union(set(allrecs['countryname']))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def normname(name):\n",
      "    name = name.replace('*','')\n",
      "    try:\n",
      "        return {'Iran, Islamic Rep.': 'Iran',\n",
      "                        'Korea, Rep.':'South Korea',\n",
      "                        'Brunei Darussalam': 'Brunei',\n",
      "                        'United States':'United States of America',\n",
      "                        'Slovak Republic':'Slovakia',\n",
      "                        'China':\"People's Republic of China\",\n",
      "                        'People\u2019s Republic of China':\"People's Republic of China\",\n",
      "                         'Kyrgyz Republic': 'Kyrgyzstan',\n",
      "                         'Russian Federation': 'Russia',\n",
      "                         'Macedonia, FYR': 'Republic of Macedonia',\n",
      "                         'Lao PDR':'Laos',\n",
      "                         'Bahamas':'The Bahamas',\n",
      "                         u'C\\xf4te d\\u2019Ivoire':u\"C\\xf4te d'Ivoire\",\n",
      "                         'C\u00f4te d\u2019Ivoire':u\"C\\xf4te d'Ivoire\",\n",
      "                         'Plu. St.. of Bolivia':'Bolivia',\n",
      "                         'Viet Nam':'Vietnam',\n",
      "                         'Myanmar':'Burma',\n",
      "                         'Former Yugoslav Republic of Macedonia':'Macedonia',\n",
      "                         'Lao People\u2019s Democratic Republic':'Laos',\n",
      "                         'Bolivarian Republic of Venezuela':'Venezuela',\n",
      "                        'Republic of Moldova':'Moldova',\n",
      "                         'Central African Rep.':'Central African Republic',\n",
      "                        'Syrian Arab Republic':'Syria',\n",
      "                        'Republic of Tanzania':'Tanzania',\n",
      "                         'Palestine, State of':'Palestine',\n",
      "                         'Moldova (Republic of)':'Moldova',\n",
      "                        'Sao Tome and Principe': u'Sao Tom\\xe9 and Pr\\xedncipe',\n",
      "                        \"Lao People's Democratic Republic\":'Laos',\n",
      "                        'Venezuela (Bolivarian Republic of)':'Venezuela',\n",
      "                        'The former Yugoslav Republic of Macedonia':'Macedonia',\n",
      "                        'Iran (Islamic Republic of)':'Iran',\n",
      "                        'Congo (Democratic Republic of the)': u'Democratic Republic of the Congo',\n",
      "                        'Congo':u'Republic of the Congo',\n",
      "                        'Tanzania (United Republic of)':'Tanzania',\n",
      "                        'Hong Kong, China (SAR)':\"People's Republic of China\",\n",
      "                        'Russian Federation':'Russia',\n",
      "                        'Korea (Republic of)':'South Korea',\n",
      "                        'Bolivia (Plurinational State of)':'Bolivia'}[name]\n",
      "    except KeyError:\n",
      "        return name"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "wef = pd.io.html.read_html('http://reports.weforum.org/global-gender-gap-report-2014/rankings/')[0]\n",
      "wef['Economy'] = wef['Economy'].apply(normname)\n",
      "wefnames = set(wef['Economy'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "geidirty = pd.io.html.read_html('http://www.socialwatch.org/node/14367')[2]\n",
      "gei = geidirty.iloc[3:,6:8]\n",
      "gei.columns = ['Economy', 'Score']\n",
      "gei = gei.dropna()\n",
      "gei[\"Rank\"] = gei['Score'].rank(ascending=False).apply(lambda x: int(x))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def country_sigi_extract(text_line):\n",
      "    '''put the first strings together as name and the first float as the sigi value'''\n",
      "    economy = ''\n",
      "    sigi_val = float()\n",
      "    for w in text_line.split(' '):\n",
      "        try:\n",
      "            sigi_val  = float(w)\n",
      "            break\n",
      "        except ValueError:\n",
      "            if economy:\n",
      "                economy += ' '  + w\n",
      "            else:\n",
      "                economy = w\n",
      "    return economy, sigi_val\n",
      "\n",
      "ec_sigi = dict([country_sigi_extract(text_line) for text_line in  sigipdftext.split('\\n')] )\n",
      "\n",
      "sigi = pd.DataFrame.from_dict(ec_sigi, orient='index')\n",
      "sigi['Economy'] = sigi.index\n",
      "sigi['Economy'] = sigi['Economy'].apply(normname)\n",
      "sigi['Score'] = 1-sigi[0]\n",
      "sigi[\"Rank\"] = sigi['Score'].rank(ascending=False).apply(lambda x: int(x))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "gdidirty = pd.DataFrame.from_csv('helpers/foreign_indexes/Table_5__Gender-related_development_index.csv')\n",
      "nar = gdidirty.iloc[1:,:3]\n",
      "nar.columns = ['Economy', 'Score', 'Rank']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "gdi = nar[(nar['Score'] != '..') & (nar['Rank'] != '\u2014') ]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "gdi['Score']  = gdi['Score'].apply(lambda x: float(x))\n",
      "gdi['Rank']  = gdi['Rank'].apply(lambda x: int(x))\n",
      "gdi.sort('Score')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "WARNING: -c:1: SettingWithCopyWarning: \n",
        "A value is trying to be set on a copy of a slice from a DataFrame.\n",
        "Try using .loc[row_indexer,col_indexer] = value instead\n",
        "\n",
        "See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
        "\n",
        "WARNING:py.warnings:-c:1: SettingWithCopyWarning: \n",
        "A value is trying to be set on a copy of a slice from a DataFrame.\n",
        "Try using .loc[row_indexer,col_indexer] = value instead\n",
        "\n",
        "See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
        "\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "WARNING: -c:2: SettingWithCopyWarning: \n",
        "A value is trying to be set on a copy of a slice from a DataFrame.\n",
        "Try using .loc[row_indexer,col_indexer] = value instead\n",
        "\n",
        "See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
        "\n",
        "WARNING:py.warnings:-c:2: SettingWithCopyWarning: \n",
        "A value is trying to be set on a copy of a slice from a DataFrame.\n",
        "Try using .loc[row_indexer,col_indexer] = value instead\n",
        "\n",
        "See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
        "\n"
       ]
      },
      {
       "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>Economy</th>\n",
        "      <th>Score</th>\n",
        "      <th>Rank</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>HDI ranks</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>169</th>\n",
        "      <td>                        Afghanistan</td>\n",
        "      <td> 0.602</td>\n",
        "      <td> 148</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>187</th>\n",
        "      <td>                              Niger</td>\n",
        "      <td> 0.714</td>\n",
        "      <td> 147</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>154</th>\n",
        "      <td>                              Yemen</td>\n",
        "      <td> 0.738</td>\n",
        "      <td> 146</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>146</th>\n",
        "      <td>                           Pakistan</td>\n",
        "      <td> 0.750</td>\n",
        "      <td> 145</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>184</th>\n",
        "      <td>                               Chad</td>\n",
        "      <td> 0.762</td>\n",
        "      <td> 144</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>176</th>\n",
        "      <td>                               Mali</td>\n",
        "      <td> 0.771</td>\n",
        "      <td> 143</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>185</th>\n",
        "      <td>           Central African Republic</td>\n",
        "      <td> 0.776</td>\n",
        "      <td> 142</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>179</th>\n",
        "      <td>                             Guinea</td>\n",
        "      <td> 0.785</td>\n",
        "      <td> 141</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>175</th>\n",
        "      <td>                            Liberia</td>\n",
        "      <td> 0.786</td>\n",
        "      <td> 140</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>183</th>\n",
        "      <td>                       Sierra Leone</td>\n",
        "      <td> 0.799</td>\n",
        "      <td> 139</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>161</th>\n",
        "      <td>                         Mauritania</td>\n",
        "      <td> 0.801</td>\n",
        "      <td> 138</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>120</th>\n",
        "      <td>                               Iraq</td>\n",
        "      <td> 0.802</td>\n",
        "      <td> 137</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>166</th>\n",
        "      <td>                               Togo</td>\n",
        "      <td> 0.803</td>\n",
        "      <td> 136</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>165</th>\n",
        "      <td>                              Benin</td>\n",
        "      <td> 0.822</td>\n",
        "      <td> 134</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>186</th>\n",
        "      <td> Congo (Democratic Republic of the)</td>\n",
        "      <td> 0.822</td>\n",
        "      <td> 134</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>129</th>\n",
        "      <td>                            Morocco</td>\n",
        "      <td> 0.828</td>\n",
        "      <td> 132</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>135</th>\n",
        "      <td>                              India</td>\n",
        "      <td> 0.828</td>\n",
        "      <td> 132</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>152</th>\n",
        "      <td>                            Nigeria</td>\n",
        "      <td> 0.839</td>\n",
        "      <td> 131</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>77 </th>\n",
        "      <td>                             Jordan</td>\n",
        "      <td> 0.842</td>\n",
        "      <td> 130</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>93 </th>\n",
        "      <td>                            Algeria</td>\n",
        "      <td> 0.843</td>\n",
        "      <td> 129</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>75 </th>\n",
        "      <td>         Iran (Islamic Republic of)</td>\n",
        "      <td> 0.847</td>\n",
        "      <td> 128</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>118</th>\n",
        "      <td>               Syrian Arab Republic</td>\n",
        "      <td> 0.851</td>\n",
        "      <td> 127</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>173</th>\n",
        "      <td>                           Ethiopia</td>\n",
        "      <td> 0.853</td>\n",
        "      <td> 126</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>110</th>\n",
        "      <td>                              Egypt</td>\n",
        "      <td> 0.855</td>\n",
        "      <td> 125</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>163</th>\n",
        "      <td>                            Senegal</td>\n",
        "      <td> 0.864</td>\n",
        "      <td> 124</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>152</th>\n",
        "      <td>                           Cameroon</td>\n",
        "      <td> 0.872</td>\n",
        "      <td> 123</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>128</th>\n",
        "      <td>                        Timor-Leste</td>\n",
        "      <td> 0.875</td>\n",
        "      <td> 122</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>148</th>\n",
        "      <td>                          Swaziland</td>\n",
        "      <td> 0.877</td>\n",
        "      <td> 121</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>178</th>\n",
        "      <td>                         Mozambique</td>\n",
        "      <td> 0.879</td>\n",
        "      <td> 120</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>138</th>\n",
        "      <td>                              Ghana</td>\n",
        "      <td> 0.884</td>\n",
        "      <td> 118</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>117</th>\n",
        "      <td>                        Philippines</td>\n",
        "      <td> 0.989</td>\n",
        "      <td>  17</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10 </th>\n",
        "      <td>                            Denmark</td>\n",
        "      <td> 0.989</td>\n",
        "      <td>  17</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>20 </th>\n",
        "      <td>                             France</td>\n",
        "      <td> 0.989</td>\n",
        "      <td>  17</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>96 </th>\n",
        "      <td>                            Jamaica</td>\n",
        "      <td> 0.989</td>\n",
        "      <td>  17</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>89 </th>\n",
        "      <td>                           Thailand</td>\n",
        "      <td> 0.990</td>\n",
        "      <td>  14</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>114</th>\n",
        "      <td>              Moldova (Republic of)</td>\n",
        "      <td> 0.990</td>\n",
        "      <td>  14</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>14 </th>\n",
        "      <td>                     United Kingdom</td>\n",
        "      <td> 0.993</td>\n",
        "      <td>  13</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>87 </th>\n",
        "      <td>                            Armenia</td>\n",
        "      <td> 0.994</td>\n",
        "      <td>   8</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>64 </th>\n",
        "      <td>                Trinidad and Tobago</td>\n",
        "      <td> 0.994</td>\n",
        "      <td>   8</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>58 </th>\n",
        "      <td>                           Bulgaria</td>\n",
        "      <td> 0.994</td>\n",
        "      <td>   8</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5  </th>\n",
        "      <td>                      United States</td>\n",
        "      <td> 0.995</td>\n",
        "      <td>   7</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1  </th>\n",
        "      <td>                             Norway</td>\n",
        "      <td> 0.997</td>\n",
        "      <td>   5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>43 </th>\n",
        "      <td>                            Hungary</td>\n",
        "      <td> 0.998</td>\n",
        "      <td>   4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>67 </th>\n",
        "      <td> Venezuela (Bolivarian Republic of)</td>\n",
        "      <td> 0.999</td>\n",
        "      <td>   2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>37 </th>\n",
        "      <td>                           Slovakia</td>\n",
        "      <td> 1.000</td>\n",
        "      <td>   1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>49 </th>\n",
        "      <td>                          Argentina</td>\n",
        "      <td> 1.001</td>\n",
        "      <td>   2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12 </th>\n",
        "      <td>                             Sweden</td>\n",
        "      <td> 1.004</td>\n",
        "      <td>   6</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>24 </th>\n",
        "      <td>                            Finland</td>\n",
        "      <td> 1.006</td>\n",
        "      <td>   8</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25 </th>\n",
        "      <td>                           Slovenia</td>\n",
        "      <td> 1.006</td>\n",
        "      <td>   8</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>35 </th>\n",
        "      <td>                             Poland</td>\n",
        "      <td> 1.010</td>\n",
        "      <td>  14</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>83 </th>\n",
        "      <td>                            Ukraine</td>\n",
        "      <td> 1.012</td>\n",
        "      <td>  21</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50 </th>\n",
        "      <td>                            Uruguay</td>\n",
        "      <td> 1.015</td>\n",
        "      <td>  25</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>70 </th>\n",
        "      <td>                         Kazakhstan</td>\n",
        "      <td> 1.015</td>\n",
        "      <td>  25</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>53 </th>\n",
        "      <td>                            Belarus</td>\n",
        "      <td> 1.021</td>\n",
        "      <td>  32</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>59 </th>\n",
        "      <td>                           Barbados</td>\n",
        "      <td> 1.021</td>\n",
        "      <td>  32</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>103</th>\n",
        "      <td>                           Mongolia</td>\n",
        "      <td> 1.021</td>\n",
        "      <td>  32</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>48 </th>\n",
        "      <td>                             Latvia</td>\n",
        "      <td> 1.033</td>\n",
        "      <td>  52</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>35 </th>\n",
        "      <td>                          Lithuania</td>\n",
        "      <td> 1.036</td>\n",
        "      <td>  58</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>57 </th>\n",
        "      <td>                 Russian Federation</td>\n",
        "      <td> 1.038</td>\n",
        "      <td>  61</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>33 </th>\n",
        "      <td>                            Estonia</td>\n",
        "      <td> 1.042</td>\n",
        "      <td>  70</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>148 rows \u00d7 3 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 15,
       "text": [
        "                                      Economy  Score  Rank\n",
        "HDI ranks                                                 \n",
        "169                               Afghanistan  0.602   148\n",
        "187                                     Niger  0.714   147\n",
        "154                                     Yemen  0.738   146\n",
        "146                                  Pakistan  0.750   145\n",
        "184                                      Chad  0.762   144\n",
        "176                                      Mali  0.771   143\n",
        "185                  Central African Republic  0.776   142\n",
        "179                                    Guinea  0.785   141\n",
        "175                                   Liberia  0.786   140\n",
        "183                              Sierra Leone  0.799   139\n",
        "161                                Mauritania  0.801   138\n",
        "120                                      Iraq  0.802   137\n",
        "166                                      Togo  0.803   136\n",
        "165                                     Benin  0.822   134\n",
        "186        Congo (Democratic Republic of the)  0.822   134\n",
        "129                                   Morocco  0.828   132\n",
        "135                                     India  0.828   132\n",
        "152                                   Nigeria  0.839   131\n",
        "77                                     Jordan  0.842   130\n",
        "93                                    Algeria  0.843   129\n",
        "75                 Iran (Islamic Republic of)  0.847   128\n",
        "118                      Syrian Arab Republic  0.851   127\n",
        "173                                  Ethiopia  0.853   126\n",
        "110                                     Egypt  0.855   125\n",
        "163                                   Senegal  0.864   124\n",
        "152                                  Cameroon  0.872   123\n",
        "128                               Timor-Leste  0.875   122\n",
        "148                                 Swaziland  0.877   121\n",
        "178                                Mozambique  0.879   120\n",
        "138                                     Ghana  0.884   118\n",
        "...                                       ...    ...   ...\n",
        "117                               Philippines  0.989    17\n",
        "10                                    Denmark  0.989    17\n",
        "20                                     France  0.989    17\n",
        "96                                    Jamaica  0.989    17\n",
        "89                                   Thailand  0.990    14\n",
        "114                     Moldova (Republic of)  0.990    14\n",
        "14                             United Kingdom  0.993    13\n",
        "87                                    Armenia  0.994     8\n",
        "64                        Trinidad and Tobago  0.994     8\n",
        "58                                   Bulgaria  0.994     8\n",
        "5                               United States  0.995     7\n",
        "1                                      Norway  0.997     5\n",
        "43                                    Hungary  0.998     4\n",
        "67         Venezuela (Bolivarian Republic of)  0.999     2\n",
        "37                                   Slovakia  1.000     1\n",
        "49                                  Argentina  1.001     2\n",
        "12                                     Sweden  1.004     6\n",
        "24                                    Finland  1.006     8\n",
        "25                                   Slovenia  1.006     8\n",
        "35                                     Poland  1.010    14\n",
        "83                                    Ukraine  1.012    21\n",
        "50                                    Uruguay  1.015    25\n",
        "70                                 Kazakhstan  1.015    25\n",
        "53                                    Belarus  1.021    32\n",
        "59                                   Barbados  1.021    32\n",
        "103                                  Mongolia  1.021    32\n",
        "48                                     Latvia  1.033    52\n",
        "35                                  Lithuania  1.036    58\n",
        "57                         Russian Federation  1.038    61\n",
        "33                                    Estonia  1.042    70\n",
        "\n",
        "[148 rows x 3 columns]"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "geinames = set(gei['Economy'].apply(normname))\n",
      "print geinames\n",
      "unknown = gdinames.difference(wikidatanames)\n",
      "for uk in unknown:\n",
      "    print uk"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "set([u'Canada', u'Turkmenistan', u'Lithuania', u'Cambodia', u'Ethiopia', u'Sri Lanka', u'Swaziland', u'Argentina', u'Bolivia', u'Cameroon', u'Burkina Faso', u'Ghana', u'Saudi Arabia', u'Japan', u'Cape Verde', u'Slovenia', u'Guatemala', u'Bosnia and Herzegovina', u'Jordan', u'Congo, Rep.', u'Spain', u'Liberia', u'Netherlands', u'Pakistan', u'Oman', u'Tanzania', \"People's Republic of China\", u'Gabon', u'New Zealand', u'Yemen', u'Jamaica', u'Albania', u'United Arab Emirates', u'India', u'Azerbaijan', u'Lesotho', u'Kenya', 'South Korea', u'Tajikistan', u'Turkey', u'Afghanistan', u'Czech Republic', u'Eritrea', u'Mongolia', u'France', u'Rwanda', u'Slovakia', u'Congo, DR', u'Peru', u'Malawi', u'Benin', u'Singapore', u'United States of America', u'Togo', u'Armenia', u'Dominican Republic', u'Ukraine', u'Bahrain', u'Indonesia', u'Finland', u'Mauritius', u'Sweden', u'Belarus', u'Mali', 'Russia', u'Bulgaria', u'Romania', u'Angola', u'Portugal', u'South Africa', u'Nicaragua', u'Qatar', u'Malaysia', u'Austria', 'Vietnam', u'Mozambique', u'Uganda', u'Hungary', u'Niger', u'Brazil', u'Kuwait', u'Panama', u'Costa Rica', u'Luxembourg', u'Ireland', u'Ecuador', u'Bangladesh', 'Brunei', u'Australia', u'Iran', u'Algeria', u'El Salvador', u'Chile', u'Belgium', u'Thailand', u'Haiti', u'Belize', u'Sierra Leone', u'Georgia', u'Denmark', u'Poland', u'Moldova', u'Morocco', u'Namibia', u'Guinea-Bissau', u'Switzerland', u'Chad', u'Estonia', u'Uruguay', u'Equatorial Guinea', u'Lebanon', u'Uzbekistan', u'Djibouti', u'Colombia', u'Burundi', u'Cyprus', u'Madagascar', u'Italy', u'Bhutan', u'Sudan', u'Nepal', u'Malta', u'Maldives', u'Venezuela', u'Israel', u'Iceland', u'Zambia', u'Senegal', u'Papua New Guinea', u'Zimbabwe', u'Germany', u'Gambia', u'Kazakhstan', u'Philippines', u'Mauritania', u'Kyrgyzstan', u'Trinidad and Tobago', u'Latvia', u'Guyana', u'Syria', u\"C\\xf4te d'Ivoire\", u'Honduras', u'Mexico', u'Egypt', u'Lao, PDR', u'Cuba', u'Serbia', u'Comoros', u'United Kingdom', u'Greece', u'Paraguay', u'Croatia', u'Botswana'])\n"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "gdinames = set(gdi['Economy'].apply(normname))\n",
      "unknown = gdinames.difference(wikidatanames)\n",
      "for uk in unknown:\n",
      "    print uk"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "siginames = set(sigi['Economy']) \n",
      "unknown = siginames.difference(wikidatanames)\n",
      "for uk in unknown:\n",
      "    print uk"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def calibrate_rank_corr(foreign_index, short=False):\n",
      "    '''takes a foreign index and finds the window for which wigi most correlates with it'''\n",
      "    corr_df = pd.DataFrame(columns=['start_year', 'bios_count', 'spearman', 'spearman_p', 'mannwhitneyu', 'mannwhitneyu_p', 'ranksum', 'ranksum_p'])\n",
      "\n",
      "    if not short:\n",
      "        some_modern_history = range(1000, 1800, 100) + range(1800, 1980, 10)\n",
      "    else:\n",
      "        some_modern_history = [1900]\n",
      "    for start_year in some_modern_history:\n",
      "        modrecs = allrecs[(allrecs['dob'] >= start_year) &(allrecs['dob'] < 1990)]\n",
      "        cdf = modrecs[['country','citizenship','gender']]\n",
      "\n",
      "        def combine_economy(row):\n",
      "            cit = row['citizenship']\n",
      "            cunt = row['country']\n",
      "            return cit if cit else cunt\n",
      "        cdf['Economy_qid'] = cdf.apply(combine_economy,axis=1)\n",
      "        edf = cdf[cdf['Economy_qid'].apply(lambda x: x is not None)]\n",
      "        bios_count = len(edf)\n",
      "\n",
      "        edf['Economy'] = edf['Economy_qid'].apply(english_label)\n",
      "\n",
      "\n",
      "        country_perc = defaultdict(dict)\n",
      "        country_groups= edf.groupby(by='Economy')\n",
      "\n",
      "        for country, group in country_groups:\n",
      "            nonmale = group[group['gender'] != 'Q6581097']['gender'].count()\n",
      "            total = group['gender'].count()\n",
      "            nm_perc = nonmale / float(total)\n",
      "            country_perc[country]['Economy'] = country #for later on joining\n",
      "            country_perc[country]['Score'] = nm_perc #for later on joining\n",
      "            country_perc[country]['total']= total\n",
      "\n",
      "        wdf = pd.DataFrame.from_dict(country_perc, orient='index')\n",
      "\n",
      "        wefnames = set(foreign_index['Economy'])\n",
      "\n",
      "        wdf_matching = wdf[wdf['Economy'].apply(lambda x: x in wefnames)]\n",
      "        wdf_matching['Rank'] = wdf_matching['Score'].rank(ascending=False).apply(lambda x: int(x))\n",
      "\n",
      "        rank_compare = foreign_index.join(wdf_matching, on='Economy', how='left', rsuffix='-Wikidata')[['Economy','Rank','Rank-Wikidata','Score','Score-Wikidata']]\n",
      "        rank_compare['diff'] = rank_compare['Rank'] - rank_compare['Rank-Wikidata']\n",
      "\n",
      "        spearman_results = scipy.stats.spearmanr(rank_compare[['Rank','Rank-Wikidata']])\n",
      "        spearman = spearman_results[0]\n",
      "        spearman_p = spearman_results[1]\n",
      "\n",
      "        def scale_col(col):\n",
      "            num = col - min(col)\n",
      "            denom = max(col) - min(col)\n",
      "            return num / denom\n",
      "\n",
      "        rank_compare['Score_norm'] = scale_col(rank_compare['Score'])\n",
      "        rank_compare['Score_wikidata_norm'] = scale_col(rank_compare['Score-Wikidata'])\n",
      "\n",
      "        mannwhitneyu, mannwhitneyu_p = scipy.stats.mannwhitneyu(rank_compare['Score_norm'],rank_compare['Score_wikidata_norm'])\n",
      "        ranksum, ranksum_p = scipy.stats.ranksums(rank_compare['Score_norm'],rank_compare['Score_wikidata_norm'])\n",
      "\n",
      "        corr_df = corr_df.append(dict(start_year=start_year,\n",
      "                                                                bios_count=bios_count,\n",
      "                                                                spearman=spearman,\n",
      "                                                                spearman_p = spearman_p,\n",
      "                                                                mannwhitneyu = mannwhitneyu,\n",
      "                                                                mannwhitneyu_p = mannwhitneyu_p,\n",
      "                                                                ranksum = ranksum,\n",
      "                                                                ranksum_p = ranksum_p), ignore_index=True)\n",
      "        \n",
      "    return corr_df #todo just return the max spearman"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sigi_corr_df = calibrate_rank_corr(sigi, short=False)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "WARNING: -c:17: SettingWithCopyWarning: \n",
        "A value is trying to be set on a copy of a slice from a DataFrame.\n",
        "Try using .loc[row_indexer,col_indexer] = value instead\n",
        "\n",
        "See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
        "\n",
        "WARNING:py.warnings:-c:17: SettingWithCopyWarning: \n",
        "A value is trying to be set on a copy of a slice from a DataFrame.\n",
        "Try using .loc[row_indexer,col_indexer] = value instead\n",
        "\n",
        "See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
        "\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "WARNING: -c:21: SettingWithCopyWarning: \n",
        "A value is trying to be set on a copy of a slice from a DataFrame.\n",
        "Try using .loc[row_indexer,col_indexer] = value instead\n",
        "\n",
        "See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
        "\n",
        "WARNING:py.warnings:-c:21: SettingWithCopyWarning: \n",
        "A value is trying to be set on a copy of a slice from a DataFrame.\n",
        "Try using .loc[row_indexer,col_indexer] = value instead\n",
        "\n",
        "See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
        "\n"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "WARNING: -c:40: SettingWithCopyWarning: \n",
        "A value is trying to be set on a copy of a slice from a DataFrame.\n",
        "Try using .loc[row_indexer,col_indexer] = value instead\n",
        "\n",
        "See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
        "\n",
        "WARNING:py.warnings:-c:40: SettingWithCopyWarning: \n",
        "A value is trying to be set on a copy of a slice from a DataFrame.\n",
        "Try using .loc[row_indexer,col_indexer] = value instead\n",
        "\n",
        "See the the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
        "\n"
       ]
      },
      {
       "ename": "KeyboardInterrupt",
       "evalue": "",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
        "\u001b[1;32m<ipython-input-22-662364be4d25>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0msigi_corr_df\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcalibrate_rank_corr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msigi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mshort\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
        "\u001b[1;32m<ipython-input-21-4fa05c099ae1>\u001b[0m in \u001b[0;36mcalibrate_rank_corr\u001b[1;34m(foreign_index, short)\u001b[0m\n\u001b[0;32m     15\u001b[0m             \u001b[0mcunt\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrow\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'country'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     16\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mcit\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mcit\u001b[0m \u001b[1;32melse\u001b[0m \u001b[0mcunt\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 17\u001b[1;33m         \u001b[0mcdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Economy_qid'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcombine_economy\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     18\u001b[0m         \u001b[0medf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mcdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Economy_qid'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mx\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     19\u001b[0m         \u001b[0mbios_count\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0medf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/usr/local/lib/python2.7/dist-packages/pandas/core/frame.pyc\u001b[0m in \u001b[0;36mapply\u001b[1;34m(self, func, axis, broadcast, raw, reduce, args, **kwds)\u001b[0m\n\u001b[0;32m   3594\u001b[0m                     \u001b[1;32mif\u001b[0m \u001b[0mreduce\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3595\u001b[0m                         \u001b[0mreduce\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3596\u001b[1;33m                     \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_apply_standard\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreduce\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mreduce\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3597\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3598\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_apply_broadcast\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/usr/local/lib/python2.7/dist-packages/pandas/core/frame.pyc\u001b[0m in \u001b[0;36m_apply_standard\u001b[1;34m(self, func, axis, ignore_failures, reduce)\u001b[0m\n\u001b[0;32m   3646\u001b[0m                 \u001b[0mlabels\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_agg_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3647\u001b[0m                 result = lib.reduce(values, func, axis=axis, dummy=dummy,\n\u001b[1;32m-> 3648\u001b[1;33m                                     labels=labels)\n\u001b[0m\u001b[0;32m   3649\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mSeries\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3650\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/usr/local/lib/python2.7/dist-packages/pandas/lib.so\u001b[0m in \u001b[0;36mpandas.lib.reduce (pandas/lib.c:40234)\u001b[1;34m()\u001b[0m\n",
        "\u001b[1;32m/usr/local/lib/python2.7/dist-packages/pandas/lib.so\u001b[0m in \u001b[0;36mpandas.lib.Reducer.get_result (pandas/lib.c:30025)\u001b[1;34m()\u001b[0m\n",
        "\u001b[1;32m<ipython-input-21-4fa05c099ae1>\u001b[0m in \u001b[0;36mcombine_economy\u001b[1;34m(row)\u001b[0m\n\u001b[0;32m     12\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     13\u001b[0m         \u001b[1;32mdef\u001b[0m \u001b[0mcombine_economy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrow\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 14\u001b[1;33m             \u001b[0mcit\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrow\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'citizenship'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     15\u001b[0m             \u001b[0mcunt\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrow\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'country'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     16\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mcit\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mcit\u001b[0m \u001b[1;32melse\u001b[0m \u001b[0mcunt\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/usr/local/lib/python2.7/dist-packages/pandas/core/series.pyc\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m    509\u001b[0m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_value\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    510\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 511\u001b[1;33m             \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0misscalar\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    512\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0mis_list_like\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mand\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mSeries\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    513\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;32m/usr/local/lib/python2.7/dist-packages/numpy/core/numeric.pyc\u001b[0m in \u001b[0;36misscalar\u001b[1;34m(num)\u001b[0m\n\u001b[0;32m   1932\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1933\u001b[0m     \"\"\"\n\u001b[1;32m-> 1934\u001b[1;33m     \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnum\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgeneric\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1935\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1936\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "wef_corr_df = calibrate_rank_corr(wef, short=False)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 27
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "gdi_corr_df = calibrate_rank_corr(gdi, short=False)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "gei_corr_df = calibrate_rank_corr(gei, short=False)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "gei_corr_df"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sigi_corr_df"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "gdi_corr_df"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "wef_corr_df"
     ],
     "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>start_year</th>\n",
        "      <th>bios_count</th>\n",
        "      <th>spearman</th>\n",
        "      <th>spearman_p</th>\n",
        "      <th>mannwhitneyu</th>\n",
        "      <th>mannwhitneyu_p</th>\n",
        "      <th>ranksum</th>\n",
        "      <th>ranksum_p</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0 </th>\n",
        "      <td> 1000</td>\n",
        "      <td> 887006</td>\n",
        "      <td> 0.263995</td>\n",
        "      <td> 0.001501</td>\n",
        "      <td> 6195.0</td>\n",
        "      <td> 9.762673e-09</td>\n",
        "      <td> 5.616870</td>\n",
        "      <td> 1.944474e-08</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1 </th>\n",
        "      <td> 1100</td>\n",
        "      <td> 886514</td>\n",
        "      <td> 0.265131</td>\n",
        "      <td> 0.001429</td>\n",
        "      <td> 6193.0</td>\n",
        "      <td> 9.600785e-09</td>\n",
        "      <td> 5.619760</td>\n",
        "      <td> 1.912226e-08</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2 </th>\n",
        "      <td> 1200</td>\n",
        "      <td> 885697</td>\n",
        "      <td> 0.265446</td>\n",
        "      <td> 0.001410</td>\n",
        "      <td> 6187.0</td>\n",
        "      <td> 9.130603e-09</td>\n",
        "      <td> 5.628431</td>\n",
        "      <td> 1.818567e-08</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3 </th>\n",
        "      <td> 1300</td>\n",
        "      <td> 884571</td>\n",
        "      <td> 0.265739</td>\n",
        "      <td> 0.001392</td>\n",
        "      <td> 6200.0</td>\n",
        "      <td> 1.017908e-08</td>\n",
        "      <td> 5.609645</td>\n",
        "      <td> 2.027420e-08</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4 </th>\n",
        "      <td> 1400</td>\n",
        "      <td> 883044</td>\n",
        "      <td> 0.263195</td>\n",
        "      <td> 0.001553</td>\n",
        "      <td> 6197.0</td>\n",
        "      <td> 9.927211e-09</td>\n",
        "      <td> 5.613980</td>\n",
        "      <td> 1.977249e-08</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5 </th>\n",
        "      <td> 1500</td>\n",
        "      <td> 879276</td>\n",
        "      <td> 0.262088</td>\n",
        "      <td> 0.001628</td>\n",
        "      <td> 6206.0</td>\n",
        "      <td> 1.070155e-08</td>\n",
        "      <td> 5.600975</td>\n",
        "      <td> 2.131498e-08</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6 </th>\n",
        "      <td> 1600</td>\n",
        "      <td> 870495</td>\n",
        "      <td> 0.265848</td>\n",
        "      <td> 0.001385</td>\n",
        "      <td> 6230.0</td>\n",
        "      <td> 1.306420e-08</td>\n",
        "      <td> 5.566294</td>\n",
        "      <td> 2.602143e-08</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7 </th>\n",
        "      <td> 1700</td>\n",
        "      <td> 857099</td>\n",
        "      <td> 0.265899</td>\n",
        "      <td> 0.001382</td>\n",
        "      <td> 6252.0</td>\n",
        "      <td> 1.566935e-08</td>\n",
        "      <td> 5.534503</td>\n",
        "      <td> 3.121122e-08</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8 </th>\n",
        "      <td> 1800</td>\n",
        "      <td> 815661</td>\n",
        "      <td> 0.270712</td>\n",
        "      <td> 0.001120</td>\n",
        "      <td> 6421.0</td>\n",
        "      <td> 6.130025e-08</td>\n",
        "      <td> 5.290291</td>\n",
        "      <td> 1.221218e-07</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9 </th>\n",
        "      <td> 1810</td>\n",
        "      <td> 805811</td>\n",
        "      <td> 0.276275</td>\n",
        "      <td> 0.000874</td>\n",
        "      <td> 6483.0</td>\n",
        "      <td> 9.965155e-08</td>\n",
        "      <td> 5.200699</td>\n",
        "      <td> 1.985407e-07</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10</th>\n",
        "      <td> 1820</td>\n",
        "      <td> 794371</td>\n",
        "      <td> 0.277217</td>\n",
        "      <td> 0.000838</td>\n",
        "      <td> 6564.0</td>\n",
        "      <td> 1.858238e-07</td>\n",
        "      <td> 5.083650</td>\n",
        "      <td> 3.702490e-07</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11</th>\n",
        "      <td> 1830</td>\n",
        "      <td> 781921</td>\n",
        "      <td> 0.276718</td>\n",
        "      <td> 0.000857</td>\n",
        "      <td> 6621.0</td>\n",
        "      <td> 2.858085e-07</td>\n",
        "      <td> 5.001283</td>\n",
        "      <td> 5.694998e-07</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12</th>\n",
        "      <td> 1840</td>\n",
        "      <td> 768581</td>\n",
        "      <td> 0.282408</td>\n",
        "      <td> 0.000661</td>\n",
        "      <td> 6689.0</td>\n",
        "      <td> 4.735930e-07</td>\n",
        "      <td> 4.903020</td>\n",
        "      <td> 9.437414e-07</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13</th>\n",
        "      <td> 1850</td>\n",
        "      <td> 753693</td>\n",
        "      <td> 0.290256</td>\n",
        "      <td> 0.000458</td>\n",
        "      <td> 6753.0</td>\n",
        "      <td> 7.553225e-07</td>\n",
        "      <td> 4.810538</td>\n",
        "      <td> 1.505246e-06</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>14</th>\n",
        "      <td> 1860</td>\n",
        "      <td> 736868</td>\n",
        "      <td> 0.295216</td>\n",
        "      <td> 0.000362</td>\n",
        "      <td> 6834.0</td>\n",
        "      <td> 1.347622e-06</td>\n",
        "      <td> 4.693490</td>\n",
        "      <td> 2.685835e-06</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>15</th>\n",
        "      <td> 1870</td>\n",
        "      <td> 716540</td>\n",
        "      <td> 0.292414</td>\n",
        "      <td> 0.000414</td>\n",
        "      <td> 6940.0</td>\n",
        "      <td> 2.818059e-06</td>\n",
        "      <td> 4.540315</td>\n",
        "      <td> 5.617012e-06</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>16</th>\n",
        "      <td> 1880</td>\n",
        "      <td> 691692</td>\n",
        "      <td> 0.298912</td>\n",
        "      <td> 0.000302</td>\n",
        "      <td> 7110.0</td>\n",
        "      <td> 8.776426e-06</td>\n",
        "      <td> 4.294659</td>\n",
        "      <td> 1.749623e-05</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>17</th>\n",
        "      <td> 1890</td>\n",
        "      <td> 660609</td>\n",
        "      <td> 0.302099</td>\n",
        "      <td> 0.000258</td>\n",
        "      <td> 7285.0</td>\n",
        "      <td> 2.660525e-05</td>\n",
        "      <td> 4.041777</td>\n",
        "      <td> 5.304774e-05</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>18</th>\n",
        "      <td> 1900</td>\n",
        "      <td> 623915</td>\n",
        "      <td> 0.305469</td>\n",
        "      <td> 0.000218</td>\n",
        "      <td> 7331.0</td>\n",
        "      <td> 3.525074e-05</td>\n",
        "      <td> 3.975305</td>\n",
        "      <td> 7.028916e-05</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>19</th>\n",
        "      <td> 1910</td>\n",
        "      <td> 579592</td>\n",
        "      <td> 0.312051</td>\n",
        "      <td> 0.000157</td>\n",
        "      <td> 7367.0</td>\n",
        "      <td> 4.380497e-05</td>\n",
        "      <td> 3.923283</td>\n",
        "      <td> 8.735029e-05</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>20</th>\n",
        "      <td> 1920</td>\n",
        "      <td> 534223</td>\n",
        "      <td> 0.292366</td>\n",
        "      <td> 0.000415</td>\n",
        "      <td> 7516.5</td>\n",
        "      <td> 1.050574e-04</td>\n",
        "      <td> 3.707250</td>\n",
        "      <td> 2.095221e-04</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>21</th>\n",
        "      <td> 1930</td>\n",
        "      <td> 472919</td>\n",
        "      <td> 0.292359</td>\n",
        "      <td> 0.000415</td>\n",
        "      <td> 7953.0</td>\n",
        "      <td> 1.049781e-03</td>\n",
        "      <td> 3.076490</td>\n",
        "      <td> 2.094533e-03</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>22</th>\n",
        "      <td> 1940</td>\n",
        "      <td> 410994</td>\n",
        "      <td> 0.273103</td>\n",
        "      <td> 0.001008</td>\n",
        "      <td> 7938.0</td>\n",
        "      <td> 9.759586e-04</td>\n",
        "      <td> 3.098166</td>\n",
        "      <td> 1.947226e-03</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>23</th>\n",
        "      <td> 1950</td>\n",
        "      <td> 331071</td>\n",
        "      <td> 0.257789</td>\n",
        "      <td> 0.001953</td>\n",
        "      <td> 7283.0</td>\n",
        "      <td> 2.627829e-05</td>\n",
        "      <td> 4.044667</td>\n",
        "      <td> 5.239760e-05</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>24</th>\n",
        "      <td> 1960</td>\n",
        "      <td> 248180</td>\n",
        "      <td> 0.183820</td>\n",
        "      <td> 0.028538</td>\n",
        "      <td> 7737.0</td>\n",
        "      <td> 3.521279e-04</td>\n",
        "      <td> 3.388619</td>\n",
        "      <td> 7.024563e-04</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25</th>\n",
        "      <td> 1970</td>\n",
        "      <td> 165324</td>\n",
        "      <td> 0.138381</td>\n",
        "      <td> 0.100519</td>\n",
        "      <td> 6806.0</td>\n",
        "      <td> 1.104553e-06</td>\n",
        "      <td> 4.733951</td>\n",
        "      <td> 2.201911e-06</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 29,
       "text": [
        "    start_year  bios_count  spearman  spearman_p  mannwhitneyu  \\\n",
        "0         1000      887006  0.263995    0.001501        6195.0   \n",
        "1         1100      886514  0.265131    0.001429        6193.0   \n",
        "2         1200      885697  0.265446    0.001410        6187.0   \n",
        "3         1300      884571  0.265739    0.001392        6200.0   \n",
        "4         1400      883044  0.263195    0.001553        6197.0   \n",
        "5         1500      879276  0.262088    0.001628        6206.0   \n",
        "6         1600      870495  0.265848    0.001385        6230.0   \n",
        "7         1700      857099  0.265899    0.001382        6252.0   \n",
        "8         1800      815661  0.270712    0.001120        6421.0   \n",
        "9         1810      805811  0.276275    0.000874        6483.0   \n",
        "10        1820      794371  0.277217    0.000838        6564.0   \n",
        "11        1830      781921  0.276718    0.000857        6621.0   \n",
        "12        1840      768581  0.282408    0.000661        6689.0   \n",
        "13        1850      753693  0.290256    0.000458        6753.0   \n",
        "14        1860      736868  0.295216    0.000362        6834.0   \n",
        "15        1870      716540  0.292414    0.000414        6940.0   \n",
        "16        1880      691692  0.298912    0.000302        7110.0   \n",
        "17        1890      660609  0.302099    0.000258        7285.0   \n",
        "18        1900      623915  0.305469    0.000218        7331.0   \n",
        "19        1910      579592  0.312051    0.000157        7367.0   \n",
        "20        1920      534223  0.292366    0.000415        7516.5   \n",
        "21        1930      472919  0.292359    0.000415        7953.0   \n",
        "22        1940      410994  0.273103    0.001008        7938.0   \n",
        "23        1950      331071  0.257789    0.001953        7283.0   \n",
        "24        1960      248180  0.183820    0.028538        7737.0   \n",
        "25        1970      165324  0.138381    0.100519        6806.0   \n",
        "\n",
        "    mannwhitneyu_p   ranksum     ranksum_p  \n",
        "0     9.762673e-09  5.616870  1.944474e-08  \n",
        "1     9.600785e-09  5.619760  1.912226e-08  \n",
        "2     9.130603e-09  5.628431  1.818567e-08  \n",
        "3     1.017908e-08  5.609645  2.027420e-08  \n",
        "4     9.927211e-09  5.613980  1.977249e-08  \n",
        "5     1.070155e-08  5.600975  2.131498e-08  \n",
        "6     1.306420e-08  5.566294  2.602143e-08  \n",
        "7     1.566935e-08  5.534503  3.121122e-08  \n",
        "8     6.130025e-08  5.290291  1.221218e-07  \n",
        "9     9.965155e-08  5.200699  1.985407e-07  \n",
        "10    1.858238e-07  5.083650  3.702490e-07  \n",
        "11    2.858085e-07  5.001283  5.694998e-07  \n",
        "12    4.735930e-07  4.903020  9.437414e-07  \n",
        "13    7.553225e-07  4.810538  1.505246e-06  \n",
        "14    1.347622e-06  4.693490  2.685835e-06  \n",
        "15    2.818059e-06  4.540315  5.617012e-06  \n",
        "16    8.776426e-06  4.294659  1.749623e-05  \n",
        "17    2.660525e-05  4.041777  5.304774e-05  \n",
        "18    3.525074e-05  3.975305  7.028916e-05  \n",
        "19    4.380497e-05  3.923283  8.735029e-05  \n",
        "20    1.050574e-04  3.707250  2.095221e-04  \n",
        "21    1.049781e-03  3.076490  2.094533e-03  \n",
        "22    9.759586e-04  3.098166  1.947226e-03  \n",
        "23    2.627829e-05  4.044667  5.239760e-05  \n",
        "24    3.521279e-04  3.388619  7.024563e-04  \n",
        "25    1.104553e-06  4.733951  2.201911e-06  "
       ]
      }
     ],
     "prompt_number": 29
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for df in [gdi_corr_df, sigi_corr_df, wef_corr_df]:\n",
      "    df.plot(x='start_year',y=['spearman', 'spearman_p'])\n",
      "    plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "wef_corr_df.to_pickle('opensym/wefdf')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 87
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "wef_corr_df = pd.read_pickle('opensym/wefdf')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig, ax = plt.subplots(1, 1, figsize=(6,4))\n",
      "wef_corr_df.plot(x='start_year',y=['spearman', 'spearman_p'],ax=ax)\n",
      "ax.set_ylabel('Correlation coefficient')\n",
      "ax.set_xlabel('Start Year')\n",
      "ax.legend((r'Spearman $\\rho$','Significance $p$'),loc=3)\n",
      "fig.suptitle('WIGI-GGGI Rank Correlation by Start Year', size=24)\n",
      "fig.subplots_adjust(top=0.88)\n",
      "fig.savefig('opensym/spearman_evolution_gggi.png')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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X97dE9zeJMc0ossZupKY7uJJcLudBQUE8ODiYT58+XVhmx44deutzcXHh9vb2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\nPAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f774e977910>"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "corr_df.plot(x='start_year',y=['mannwhitneyu', 'mannwhitneyu_p'], secondary_y='mannwhitneyu_p')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "corr_df.plot(x='start_year',y=['ranksum', 'ranksum_p'], secondary_y='ranksum_p')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "wdf_matching.sort('Score',ascending=False).head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "modrecs = allrecs[(allrecs['dob'] >=1890) &(allrecs['dob'] < 1990)]\n",
      "cdf = modrecs[['country','citizenship','gender']]\n",
      "\n",
      "def combine_economy(row):\n",
      "    cit = row['citizenship']\n",
      "    cunt = row['country']\n",
      "    return cit if cit else cunt\n",
      "cdf['Economy_qid'] = cdf.apply(combine_economy,axis=1)\n",
      "edf = cdf[cdf['Economy_qid'].apply(lambda x: x is not None)]\n",
      "bios_count = len(edf)\n",
      "\n",
      "edf['Economy'] = edf['Economy_qid'].apply(english_label)\n",
      "\n",
      "\n",
      "country_perc = defaultdict(dict)\n",
      "country_groups= edf.groupby(by='Economy')\n",
      "\n",
      "for country, group in country_groups:\n",
      "    nonmale = group[group['gender'] != 'Q6581097']['gender'].count()\n",
      "    total = group['gender'].count()\n",
      "    nm_perc = nonmale / float(total)\n",
      "    country_perc[country]['Economy'] = country #for later on joining\n",
      "    country_perc[country]['Score'] = nm_perc #for later on joining\n",
      "    country_perc[country]['total']= total\n",
      "\n",
      "wdf = pd.DataFrame.from_dict(country_perc, orient='index')\n",
      "\n",
      "wdf_matching = wdf[wdf['Economy'].apply(lambda x: x in wefnames)]\n",
      "wdf_matching['Rank'] = wdf_matching['Score'].rank(ascending=False).apply(lambda x: int(x))\n",
      "\n",
      "rank_compare = wef.join(wdf_matching, on='Economy', how='left', rsuffix='_wikidata')[['Economy','Rank','Rank_wikidata','Score','Score_wikidata']]\n",
      "rank_compare['diff'] = rank_compare['Rank'] - rank_compare['Rank_wikidata']\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pd.DataFrame.to_html(formatters=)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print rank_compare.columns"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "rank_compare.columns = ['Country', 'WEF Rank', 'Wikipedia Rank','WEF  Score','Wikipedia Score','Rank Difference']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "rank_compare.sort('WEF Rank').head(10).to_html(index=False,formatters={'Wikipedia Score':lambda x: '{:0.4f}'.format(float(x))})"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "rank_compare.sort('Wikipedia Rank').to_csv('helpers/foreign_indexes/WIGI_comparison.csv',encoding = 'utf-8', index=False, formatters={'Wikipedia Score':lambda x: '{:0.4f}'.format(float(x))})"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "wdfc = wdf[wdf['total'] > 30]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "wdfc['Rank'] = wdfc['Score'].rank(ascending=False).apply(lambda x: int(x))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "UNDP's Gender-related Development Index (GDI) and the Gender Empowerment Measure (GEM), introduced only in 1995. More recently, three new measures were developed: the Gender Equity Index (GEI) introduced by Social Watch in 2005, the Global Gender Gap Index (GGGI) developed by the World Economic Forum in 2006, and the Social Institutions and Gender Index (SIGI) of the OECD Development Centre from 2007.\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fiveway = wdfc[['Economy','Score','Rank']]\n",
      "fiveway.index = fiveway['Economy']\n",
      "for findex, ftext in zip([sigi,gdi,gei,wef], ['SIGI', 'GDI', 'GEI', 'GGGI']):\n",
      "    findex.index = findex['Economy']\n",
      "    fiveway = fiveway.join(findex[['Score','Rank']], how='outer', on = \"Economy\", rsuffix=\" {}\".format(ftext))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fiveway.columns = fiveway.columns[:1] + ['Score WIGI','Rank WIGI'] + fiveway.columns[2:]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fiveway.sort('Rank').to_csv('helpers/foreign_indexes/WIGI_comparison.csv',encoding = 'utf-8', index=False, formatters={'Wikipedia Score':lambda x: '{:0.4f}'.format(float(x))})"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Quite uncorrellated. That means that the data is not good, or  that the world economic forum methods have little to do with the percentage of women born in those countries recorded semantically on a historic level. And /rho is high"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#not clean data bad pdf copy-paste BUT I think that the first string and then SIGI number have copied over \n",
      "sigipdftext = '''Belgium 0.0016 0.0038 very low 0.0316 very low 0.0824 low 0.0000 very low 0.0000 very low\n",
      "France 0.0034 0.1002 low 0.0000 very low 0.0828 low 0.0000 very low 0.0000 very low\n",
      "Slovenia 0.0037 0.0031 very low 0.0891 very low 0.1023 low 0.0000 very low 0.0000 very low\n",
      "Spain 0.0049 0.0856 low 0.0622 very low 0.1144 low 0.0000 very low 0.0000 very low\n",
      "Serbia 0.0097 0.1094 low 0.1171 very low 0.1504 medium 0.0000 very low 0.0000 very low\n",
      "Argentina 0.0107 0.0809 low 0.0148 very low 0.0691 very low 0.2048 low 0.0000 very low\n",
      "Italy 0.0116 0.0025 very low 0.1029 very low 0.0966 low 0.0000 very low 0.1951 low\n",
      "Cuba 0.0208 0.2420 medium 0.0871 very low 0.0000 very low 0.0000 very low 0.1951 low\n",
      "Trinidad and Tobago 0.0236 0.2504 medium 0.1306 very low 0.0000 very low 0.0000 very low 0.1951 low\n",
      "Czech Republic 0.0283 0.0013 very low 0.0956 very low 0.0855 low 0.0000 very low 0.3539 medium\n",
      "Bosnia and Herzegovina 0.0333 0.2437 medium 0.0672 very low 0.1497 medium 0.2048 low 0.1951 low\n",
      "Belarus 0.0336 0.0251 very low 0.3544 medium 0.0599 very low 0.0000 very low 0.1951 low\n",
      "Mongolia 0.0345 0.0226 very low 0.2584 medium 0.1582 medium 0.2048 low 0.1951 low\n",
      "Dominican Republic 0.0367 0.3691 medium 0.0958 very low 0.0118 very low 0.0000 very low 0.1951 low\n",
      "Panama 0.0375 0.2344 low 0.0148 very low 0.0855 low 0.0000 very low 0.3539 medium\n",
      "Bolivarian Republic of Venezuela 0.0389 0.2456 medium 0.0941 very low 0.0071 very low 0.0000 very low 0.3539 medium\n",
      "Ecuador 0.0422 0.1374 low 0.3737 medium 0.1037 low 0.2048 low 0.0000 very low\n",
      "Lithuania 0.0424 0.0013 very low 0.2795 medium 0.0931 low 0.0000 very low 0.3539 medium\n",
      "Bulgaria 0.0449 0.1504 low 0.3926 medium 0.0988 low 0.0000 very low 0.1951 low\n",
      "Brazil 0.0458 0.2316 low 0.1226 very low 0.0364 very low 0.1837 low 0.3539 medium\n",
      "Cambodia 0.0477 0.0684 low 0.2601 medium 0.0000 very low 0.2028 low 0.3539 medium\n",
      "El Salvador 0.0490 0.1066 low 0.2675 medium 0.1049 low 0.3885 medium 0.0000 very low\n",
      "Costa Rica 0.0506 0.2513 medium 0.1544 low 0.0121 very low 0.4076 medium 0.0000 very low\n",
      "Latvia 0.0511 0.0044 very low 0.3466 medium 0.1008 low 0.0000 very low 0.3539 medium\n",
      "Plu. St.. of Bolivia 0.0579 0.3676 medium 0.3207 medium 0.0987 low 0.2048 low 0.0000 very low\n",
      "Paraguay 0.0580 0.2880 medium 0.0440 very low 0.0291 very low 0.4076 medium 0.1951 low\n",
      "South Africa 0.0599 0.0213 very low 0.2164 low 0.2196 medium 0.4076 medium 0.1951 low\n",
      "Republic of Moldova 0.0664 0.3418 medium 0.2189 low 0.0000 very low 0.2048 low 0.3539 medium\n",
      "Romania 0.0686 0.1134 low 0.1700 low 0.0994 low 0.0000 very low 0.5399 high\n",
      "Azerbaijan 0.2403 0.1301 low 0.2057 low 0.8587 very high 0.1837 low 0.6093 high\n",
      "Armenia 0.2428 0.1910 low 0.1853 low 0.9880 very high 0.2048 low 0.3539 medium\n",
      "Ethiopia 0.2450 0.2820 medium 0.8662 very high 0.0878 low 0.5913 high 0.1951 low\n",
      "Albania 0.2476 0.1822 low 0.2596 medium 0.8767 very high 0.4076 medium 0.4505 medium\n",
      "Ukraine 0.0750 0.0414 very low 0.1517 low 0.2430 high 0.0000 very low 0.5399 high \n",
      "Peru 0.0826 0.4053 medium 0.2096 low 0.0284 very low 0.4076 medium 0.1951 low \n",
      "Colombia 0.0862 0.1748 low 0.1567 low 0.0663 very low 0.0000 very low 0.6093 high United\n",
      "Republic of Tanzania 0.2504 0.7166 very high 0.5415 high 0.1746 medium 0.5913 high 0.2554 low\n",
      "Lesotho 0.0876 0.4266 high 0.4112 medium 0.2116 medium 0.2048 low 0.0000 very low \n",
      "C\u00f4te d\u2019Ivoire 0.2537 0.4955 high 0.5895 high 0.1858 medium 0.5913 high 0.5399 high\n",
      "Madagascar 0.1002 0.4889 high 0.3079 medium 0.0000 very low 0.2048 low 0.3539 medium\n",
      "Turkey 0.1032 0.1585 low 0.1913 low 0.4036 high 0.0000 very low 0.5399 high \n",
      "Timor-Leste 0.2550 0.3882 medium 0.5421 high 0.2271 medium 0.5913 high 0.6552 high\n",
      "Iraq 0.2631 0.7035 very high 0.3347 medium 0.3834 high 0.5913 high 0.4601 medium\n",
      "Morocco 0.1052 0.4610 high 0.3159 medium 0.1574 medium 0.3885 medium 0.1951 low \n",
      "India 0.2650 0.6440 very high 0.3772 medium 0.5415 very high 0.5913 high 0.3539 medium\n",
      "Thailand 0.1056 0.3770 medium 0.2935 medium 0.1533 medium 0.3885 medium 0.3539 medium \n",
      "Benin 0.2780 0.2763 medium 0.4432 high 0.3677 high 0.5913 high 0.7953 very high\n",
      "Honduras 0.1074 0.3891 medium 0.1044 very low 0.1443 medium 0.3885 medium 0.4505 medium \n",
      "Cameroon 0.2803 0.5024 high 0.5333 high 0.2066 medium 0.7869 very high 0.4505 medium high \n",
      "Burkina Faso 0.2819 0.5419 high 0.7257 very high 0.1910 medium 0.5913 high 0.4505 medium\n",
      "Lebanon 0.2897 0.6143 very high 0.2488 medium 0.1639 medium 0.5913 high 0.7953 very high\n",
      "Namibia 0.1173 0.1709 low 0.3522 medium 0.0668 very low 0.5913 high 0.2812 low \n",
      "Kazakhstan 0.1196 0.0282 very low 0.2176 low 0.1126 low 0.4076 medium 0.6093 high \n",
      "Myanmar 0.2935 0.4963 high 0.4891 high 0.0000 very low 0.5913 high 0.7953 \n",
      "Ghana 0.2988 0.3946 medium 0.5491 high 0.3136 high 0.8044 very high 0.5399 high\n",
      "Pakistan 0.3013 0.6908 very high 0.4127 medium 0.6998 very high 0.4076 medium 0.4505 medium\n",
      "People\u2019s Republic of China 0.1310 0.2885 medium\n",
      "Guatemala 0.1318 0.3953 medium\n",
      "Rwanda 0.1339 0.2618 medium 0.1246 very low 0.5578 very high 0.4076 0.3213 medium 0.4082 medium 0.2566 high 0.2048 0.1392 medium 0.5913\n",
      "Former Yugoslav Republic of Macedonia 0.1345 0.1803 low 0.3911 \n",
      "Jamaica 0.1350 0.0031 very low 0.2046 low \n",
      "Mozambique 0.1375 0.4181 high 0.3793 medium \n",
      "Zimbabwe 0.1392 0.5700 very high 0.3435 medium 0.2951\n",
      "Tajikistan 0.1393 0.3182 medium 0.4138 medium 0.5075 medium 0.5666 medium 0.2812 low low 0.5399 high high 0.2554 low very high 0.4076 medium 0.0271 very low 0.0000 0.0000 very low 0.4076 high very high \n",
      "Jordan 0.3119 0.5274 high 0.3150 medium 0.6790 very high 0.5913 high 0.6093 high\n",
      "Guinea 0.3206 0.5413 high 0.9515 very high 0.2253 medium 0.3885 medium 0.4505 medium\n",
      "Afghanistan 0.3224 0.7316 very high 0.5473 high 0.4644 very high 0.5913 high 0.4601 medium\n",
      "Nepal 0.3229 0.1813 low 0.4083 medium 1.0000 very high 0.5913 high 0.2554 low\n",
      "Central African Rep. 0.3285 0.5327 high 0.6135 high 0.0071 very low 0.5913 high 0.7953 very high\n",
      "Bangladesh 0.3900 0.9730 very high 0.3323 medium 0.5831 very high 0.5913 high 0.4505 medium 0.2028 low 0.3539 medium \n",
      "Nigeria 0.3911 0.6723 very high 0.4766 high 0.2494 high 0.7626 very high 0.7953 very high\n",
      "Mauritania 0.3954 0.7556 very high 0.9939 very high 0.1746 medium 0.5913 high 0.1951 low\n",
      "Gabon 0.4022 0.6457 very high 0.5308 high 0.1746 medium 0.7869 very high 0.8140 very high\n",
      "Syrian Arab Republic 0.4162 0.6914 very high 0.2598 medium 0.4312 high 0.5913 high 1.0000 very high\n",
      "Lao People\u2019s Democratic Republic 0.1445 0.2606 medium 0.5321 high 0.0506 very low 0.4076 medium 0.4505 medium\n",
      "Haiti 0.1466 0.5613 very high 0.5010 high 0.0000 very low 0.2048 low 0.3539 medium\n",
      "Uzbekistan 0.1475 0.2477 medium 0.2966 medium 0.1884 medium 0.5913 high 0.4505 medium\n",
      "Indonesia 0.1532 0.5612 very high 0.2511 medium 0.3891 high 0.1837 low 0.4505 medium\n",
      "Nicaragua 0.1595 0.6303 very high 0.1868 low 0.1082 low 0.3885 medium 0.4505 medium\n",
      "Kyrgyzstan 0.1598 0.1879 low 0.3771 medium 0.2624 high 0.5913 high 0.4505 medium\n",
      "Burundi 0.1662 0.5602 very high 0.5055 high 0.1746 medium 0.4076 medium 0.2554 low\n",
      "Angola 0.1719 0.4599 high 0.5041 high 0.0791 low 0.5913 high 0.1951 low\n",
      "Philippines 0.1765 0.4929 high 0.2597 medium 0.1392 medium 0.5913 high 0.4505 medium\n",
      "Togo 0.1860 0.3696 medium 0.5488 high 0.1326 medium 0.5913 high 0.3539 medium\n",
      "Viet Nam 0.1865 0.3374 medium 0.1857 low 0.4967 very high 0.4076 medium 0.6093 high\n",
      "Sri Lanka 0.1894 0.4203 high 0.2681 medium 0.1483 medium 0.6207 high 0.5399 high\n",
      "Democratic Republic of the Congo 0.4276 0.5169 high 0.5338 high 0.0691 very low 0.9582 very high 0.8140 very high\n",
      "Egypt 0.4280 0.6665 very high 0.7373 very high 0.3741 high 0.5913 high 0.8140 very high\n",
      "Niger 0.4415 1.0000 very high 0.4059 medium 0.1746 medium 0.5913 high 0.8140 very high\n",
      "Zambia 0.4489 0.5149 high 0.5624 high 0.1746 medium 1.0000 very high 0.7953 very high\n",
      "Somalia 0.4594 0.5958 very high 0.9905 very high 0.0891 low 0.7626 very high 0.6093 high\n",
      "Chad 0.4665 0.9705 very high 0.8185 very high 0.0014 very low 0.5913 high 0.6093 high\n",
      "Mali 0.5164 0.8309 very high 1.0000 very high 0.3048 high 0.4076 medium 0.7953 very high very high\n",
      "Gambia 0.5240 0.5131 high 0.8509 very high 0.0000 very low 1.0000 very high 0.7953 \n",
      "Sudan 0.5550 0.8382 very high 0.9781 very high 0.1426 medium 0.8163 very high 0.6552 high very high 0.3414 high 0.5913 high 1.0000 very high'''"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "modrecs = allrecs[(allrecs['dob'] >= 1900) &(allrecs['dob'] < 1990)]\n",
      "cdf = modrecs[['country','citizenship','gender']]\n",
      "\n",
      "def combine_economy(row):\n",
      "    cit = row['citizenship']\n",
      "    cunt = row['country']\n",
      "    return cit if cit else cunt\n",
      "cdf['Economy_qid'] = cdf.apply(combine_economy,axis=1)\n",
      "edf = cdf[cdf['Economy_qid'].apply(lambda x: x is not None)]\n",
      "bios_count = len(edf)\n",
      "\n",
      "edf['Economy'] = edf['Economy_qid'].apply(english_label)\n",
      "\n",
      "\n",
      "country_perc = defaultdict(dict)\n",
      "country_groups= edf.groupby(by='Economy')\n",
      "\n",
      "for country, group in country_groups:\n",
      "    nonmale = group[group['gender'] != 'Q6581097']['gender'].count()\n",
      "    total = group['gender'].count()\n",
      "    nm_perc = nonmale / float(total)\n",
      "    country_perc[country]['Economy'] = country #for later on joining\n",
      "    country_perc[country]['Score'] = nm_perc #for later on joining\n",
      "    country_perc[country]['total']= total\n",
      "\n",
      "wdf = pd.DataFrame.from_dict(country_perc, orient='index')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "wdf[wdf['total']>100].tail(100)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "wdf.ix[\"People's Republic of China\"]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#magnus' special format\n",
      "nonzero = wdf[(wdf['Score'] != 0.0) & (wdf['total']> 100)]\n",
      "magnusformt = zip(nonzero['Economy'],nonzero['Score'])\n",
      "json.dump(magnusformt, open('Magnus Gender analysis/wigi_gender.json','w'))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "!less Magnus\\ Gender\\ analysis/wigi_gender.json"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}