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  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd\n",
      "import numpy\n",
      "from collections import defaultdict\n",
      "from matplotlib.pylab import style\n",
      "import json\n",
      "style.use('fivethirtyeight')\n",
      "%pylab inline\n",
      "java_min_int = -2147483648"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "allrecs = pd.read_csv('snapshot_data/2014-10-13/gender-index-data-2014-10-13.csv',na_values=[java_min_int])\n",
      "def split_column(q_str):\n",
      "    if type(q_str) is float:\n",
      "        if numpy.isnan(q_str):\n",
      "            return q_str \n",
      "    if type(q_str) is str:\n",
      "        qs = q_str.split('|')\n",
      "        return qs[:-1] #cos the format will always end with a |\n",
      "\n",
      "for col in ['gender','site_links']:\n",
      "    allrecs[col] = allrecs[col].apply(split_column)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "allrecs.head(5)"
     ],
     "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>qid</th>\n",
        "      <th>dob</th>\n",
        "      <th>dod</th>\n",
        "      <th>gender</th>\n",
        "      <th>ethnic_group</th>\n",
        "      <th>citizenship</th>\n",
        "      <th>place_of_birth</th>\n",
        "      <th>site_links</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>  Q23</td>\n",
        "      <td> 1732</td>\n",
        "      <td> 1799</td>\n",
        "      <td> [Q6581097]</td>\n",
        "      <td> NaN</td>\n",
        "      <td>      Q30|</td>\n",
        "      <td> Q494413|</td>\n",
        "      <td> [zhwiki, kywiki, euwiki, plwiki, bswiki, angwi...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>  Q42</td>\n",
        "      <td> 1952</td>\n",
        "      <td> 2001</td>\n",
        "      <td> [Q6581097]</td>\n",
        "      <td> NaN</td>\n",
        "      <td>     Q145|</td>\n",
        "      <td>    Q350|</td>\n",
        "      <td> [zhwiki, jvwiki, euwiki, plwiki, bswiki, eswik...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> Q207</td>\n",
        "      <td> 1946</td>\n",
        "      <td>  NaN</td>\n",
        "      <td> [Q6581097]</td>\n",
        "      <td> NaN</td>\n",
        "      <td>      Q30|</td>\n",
        "      <td>  Q49145|</td>\n",
        "      <td> [uzwiki, eswiki, kowikiquote, huwiki, liwikiqu...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> Q297</td>\n",
        "      <td>  NaN</td>\n",
        "      <td> 1660</td>\n",
        "      <td> [Q6581097]</td>\n",
        "      <td> NaN</td>\n",
        "      <td>      Q29|</td>\n",
        "      <td>   Q8717|</td>\n",
        "      <td> [zhwiki, kywiki, plwiki, euwiki, bswiki, uzwik...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> Q326</td>\n",
        "      <td> 1942</td>\n",
        "      <td>  NaN</td>\n",
        "      <td> [Q6581097]</td>\n",
        "      <td> NaN</td>\n",
        "      <td> Q298|Q39|</td>\n",
        "      <td>   Q2887|</td>\n",
        "      <td> [zhwiki, plwiki, euwiki, kowiki, frwiki, eswik...</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "    qid   dob   dod      gender ethnic_group citizenship place_of_birth  \\\n",
        "0   Q23  1732  1799  [Q6581097]          NaN        Q30|       Q494413|   \n",
        "1   Q42  1952  2001  [Q6581097]          NaN       Q145|          Q350|   \n",
        "2  Q207  1946   NaN  [Q6581097]          NaN        Q30|        Q49145|   \n",
        "3  Q297   NaN  1660  [Q6581097]          NaN        Q29|         Q8717|   \n",
        "4  Q326  1942   NaN  [Q6581097]          NaN   Q298|Q39|         Q2887|   \n",
        "\n",
        "                                          site_links  \n",
        "0  [zhwiki, kywiki, euwiki, plwiki, bswiki, angwi...  \n",
        "1  [zhwiki, jvwiki, euwiki, plwiki, bswiki, eswik...  \n",
        "2  [uzwiki, eswiki, kowikiquote, huwiki, liwikiqu...  \n",
        "3  [zhwiki, kywiki, plwiki, euwiki, bswiki, uzwik...  \n",
        "4  [zhwiki, plwiki, euwiki, kowiki, frwiki, eswik...  "
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "lang_culture_map = pd.DataFrame.from_csv('helpers/aggregation_maps/lang_culture.csv')\n",
      "\n",
      "def agg_culture(wikiname_list):\n",
      "    cultures = set()\n",
      "    if isinstance(wikiname_list, list):\n",
      "        for wikiname in wikiname_list:\n",
      "            parts = wikiname.split('wiki')\n",
      "            if parts[1] == '': #this was a pedia\n",
      "                lang_code = parts[0]\n",
      "                try:\n",
      "                    culture = lang_culture_map.ix[lang_code]['culture']\n",
      "                    cultures.add(culture)\n",
      "                except KeyError:\n",
      "                    continue\n",
      "    return list(cultures)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "len(allrecs)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "2561999"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "allrecs['cultures'] = allrecs['site_links'].apply(agg_culture)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "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-6-e25fe682b4b1>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mallrecs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'cultures'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mallrecs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'site_links'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0magg_culture\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
        "\u001b[1;32m/usr/local/lib/python2.7/dist-packages/pandas/core/series.pyc\u001b[0m in \u001b[0;36mapply\u001b[1;34m(self, func, convert_dtype, args, **kwds)\u001b[0m\n\u001b[0;32m   2056\u001b[0m             \u001b[0mvalues\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmap_infer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTimestamp\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2057\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2058\u001b[1;33m         \u001b[0mmapped\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmap_infer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mconvert\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mconvert_dtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2059\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmapped\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmapped\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\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   2060\u001b[0m             \u001b[1;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mframe\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mDataFrame\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.map_infer (pandas/lib.c:57158)\u001b[1;34m()\u001b[0m\n",
        "\u001b[1;32m<ipython-input-4-ad100d1b5480>\u001b[0m in \u001b[0;36magg_culture\u001b[1;34m(wikiname_list)\u001b[0m\n\u001b[0;32m      9\u001b[0m                 \u001b[0mlang_code\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mparts\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     10\u001b[0m                 \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 11\u001b[1;33m                     \u001b[0mculture\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlang_culture_map\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mix\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mlang_code\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'culture'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     12\u001b[0m                     \u001b[0mcultures\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mculture\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     13\u001b[0m                 \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\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/indexing.pyc\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m     70\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_tuple\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     71\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 72\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     73\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     74\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_get_label\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\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/indexing.pyc\u001b[0m in \u001b[0;36m_getitem_axis\u001b[1;34m(self, key, axis)\u001b[0m\n\u001b[0;32m    926\u001b[0m                     \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\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    927\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 928\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_label\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\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[0m\u001b[0;32m    929\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    930\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_getitem_iterable\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[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\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/indexing.pyc\u001b[0m in \u001b[0;36m_get_label\u001b[1;34m(self, label, axis)\u001b[0m\n\u001b[0;32m     86\u001b[0m             \u001b[1;32mraise\u001b[0m \u001b[0mIndexingError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'no slices here, handle elsewhere'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     87\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 88\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_xs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\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[0m\u001b[0;32m     89\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     90\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_get_loc\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[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\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/generic.pyc\u001b[0m in \u001b[0;36mxs\u001b[1;34m(self, key, axis, level, copy, drop_level)\u001b[0m\n\u001b[0;32m   1469\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1470\u001b[0m             result = Series(new_values, index=self.columns,\n\u001b[1;32m-> 1471\u001b[1;33m                             name=self.index[loc])\n\u001b[0m\u001b[0;32m   1472\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1473\u001b[0m         \u001b[1;32melse\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/series.pyc\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, data, index, dtype, name, copy, fastpath)\u001b[0m\n\u001b[0;32m    216\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    217\u001b[0m         \u001b[0mobject\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__setattr__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'name'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 218\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_set_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfastpath\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    219\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    220\u001b[0m     \u001b[1;33m@\u001b[0m\u001b[0mclassmethod\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_set_axis\u001b[1;34m(self, axis, labels, fastpath)\u001b[0m\n\u001b[0;32m    259\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_set_subtyp\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mis_all_dates\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    260\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 261\u001b[1;33m         \u001b[0mobject\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__setattr__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'_index'\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[0m\u001b[0;32m    262\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mfastpath\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    263\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maxis\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[1;31mKeyboardInterrupt\u001b[0m: "
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def dofd():\n",
      "    return defaultdict(int)\n",
      "\n",
      "culture_gender_dict = defaultdict(dofd)\n",
      "\n",
      "for row in allrecs.iterrows():\n",
      "    colbit = row[1]\n",
      "    gender_list = colbit[3]\n",
      "    if isinstance(gender_list, list):\n",
      "        gender = gender_list[0]\n",
      "    else:\n",
      "        gender = None\n",
      "    cultures = colbit[8]\n",
      "    for culture in cultures:\n",
      "        culture_gender_dict[culture][gender] += 1"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "lang_cultures = pd.DataFrame.from_dict(culture_gender_dict, orient='index')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "lang_cultures.to_json('helpers/lang_cultures.json')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "lang_cultures = pd.DataFrame.from_dict(json.load(open('helpers/lang_cultures.json','r')))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 37
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "lang_cultures = lang_cultures.drop('Northern Sotho Wikipedia',axis=0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 51
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pywikibot\n",
      "#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",
      "    if qid:\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:\n",
      "                retrieved[qid] = qid\n",
      "                return qid\n",
      "            except:\n",
      "                return qid\n",
      "    return qid"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 55
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "lang_cultures.columns = map(english_label, lang_cultures.columns)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "VERBOSE:pywiki:Found 1 wikidata:wikidata processes running, including this one.\n"
       ]
      }
     ],
     "prompt_number": 56
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "lang_cultures['human_total'] = lang_cultures.sum(axis=1)\n",
      "lang_cultures['gendered_total'] = lang_cultures['human_total'] - lang_cultures['null']\n",
      "lang_cultures['nonbin_total'] = lang_cultures['gendered_total'] - lang_cultures['female'] - lang_cultures['male']\n",
      "lang_cultures['fem_per'] = lang_cultures['female'] / lang_cultures['gendered_total']\n",
      "lang_cultures['nonbin_per'] = lang_cultures['nonbin_total'] / lang_cultures['gendered_total']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 57
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "lang_cultures.fillna(0,inplace=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 58
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "lang_cultures"
     ],
     "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>transgender female</th>\n",
        "      <th>intersex</th>\n",
        "      <th>fa'afafine</th>\n",
        "      <th>transgender male</th>\n",
        "      <th>female animal</th>\n",
        "      <th>male animal</th>\n",
        "      <th>woman</th>\n",
        "      <th>genderqueer</th>\n",
        "      <th>female</th>\n",
        "      <th>male</th>\n",
        "      <th>kathoey</th>\n",
        "      <th>null</th>\n",
        "      <th>human_total</th>\n",
        "      <th>gendered_total</th>\n",
        "      <th>nonbin_total</th>\n",
        "      <th>fem_per</th>\n",
        "      <th>nonbin_per</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Africa</th>\n",
        "      <td>  0</td>\n",
        "      <td>  0</td>\n",
        "      <td> 0</td>\n",
        "      <td>  0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>   1888</td>\n",
        "      <td>   12824</td>\n",
        "      <td> 0</td>\n",
        "      <td>    306</td>\n",
        "      <td>   15018</td>\n",
        "      <td>   14712</td>\n",
        "      <td>   0</td>\n",
        "      <td> 0.128331</td>\n",
        "      <td> 0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Catholic european</th>\n",
        "      <td> 39</td>\n",
        "      <td>  3</td>\n",
        "      <td> 1</td>\n",
        "      <td>  7</td>\n",
        "      <td> 0</td>\n",
        "      <td> 6</td>\n",
        "      <td> 0</td>\n",
        "      <td> 3</td>\n",
        "      <td> 122048</td>\n",
        "      <td>  712655</td>\n",
        "      <td> 0</td>\n",
        "      <td>  33480</td>\n",
        "      <td>  868242</td>\n",
        "      <td>  834762</td>\n",
        "      <td>  59</td>\n",
        "      <td> 0.146207</td>\n",
        "      <td> 0.000071</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Confucian</th>\n",
        "      <td> 24</td>\n",
        "      <td>  2</td>\n",
        "      <td> 1</td>\n",
        "      <td>  1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 2</td>\n",
        "      <td>  47355</td>\n",
        "      <td>  133508</td>\n",
        "      <td> 1</td>\n",
        "      <td> 163710</td>\n",
        "      <td>  344604</td>\n",
        "      <td>  180894</td>\n",
        "      <td>  31</td>\n",
        "      <td> 0.261783</td>\n",
        "      <td> 0.000171</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Constructed</th>\n",
        "      <td>  6</td>\n",
        "      <td>  1</td>\n",
        "      <td> 0</td>\n",
        "      <td>  1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td>   4321</td>\n",
        "      <td>   27085</td>\n",
        "      <td> 1</td>\n",
        "      <td>   2252</td>\n",
        "      <td>   33667</td>\n",
        "      <td>   31415</td>\n",
        "      <td>   9</td>\n",
        "      <td> 0.137546</td>\n",
        "      <td> 0.000286</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>English-speaking</th>\n",
        "      <td> 99</td>\n",
        "      <td> 17</td>\n",
        "      <td> 1</td>\n",
        "      <td> 19</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 7</td>\n",
        "      <td> 186455</td>\n",
        "      <td> 1016500</td>\n",
        "      <td> 1</td>\n",
        "      <td>   3259</td>\n",
        "      <td> 1206358</td>\n",
        "      <td> 1203099</td>\n",
        "      <td> 144</td>\n",
        "      <td> 0.154979</td>\n",
        "      <td> 0.000120</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Islamic</th>\n",
        "      <td> 29</td>\n",
        "      <td>  1</td>\n",
        "      <td> 0</td>\n",
        "      <td>  2</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td>  20931</td>\n",
        "      <td>  100099</td>\n",
        "      <td> 1</td>\n",
        "      <td>  19529</td>\n",
        "      <td>  140593</td>\n",
        "      <td>  121064</td>\n",
        "      <td>  34</td>\n",
        "      <td> 0.172892</td>\n",
        "      <td> 0.000281</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Latin America</th>\n",
        "      <td> 25</td>\n",
        "      <td>  3</td>\n",
        "      <td> 1</td>\n",
        "      <td>  4</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td>  38195</td>\n",
        "      <td>  202735</td>\n",
        "      <td> 0</td>\n",
        "      <td>   1979</td>\n",
        "      <td>  242943</td>\n",
        "      <td>  240964</td>\n",
        "      <td>  34</td>\n",
        "      <td> 0.158509</td>\n",
        "      <td> 0.000141</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Orthodox</th>\n",
        "      <td> 23</td>\n",
        "      <td>  1</td>\n",
        "      <td> 0</td>\n",
        "      <td>  1</td>\n",
        "      <td> 1</td>\n",
        "      <td> 2</td>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td>  53843</td>\n",
        "      <td>  324884</td>\n",
        "      <td> 1</td>\n",
        "      <td>  15316</td>\n",
        "      <td>  394074</td>\n",
        "      <td>  378758</td>\n",
        "      <td>  31</td>\n",
        "      <td> 0.142157</td>\n",
        "      <td> 0.000082</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Protestant European</th>\n",
        "      <td> 26</td>\n",
        "      <td>  2</td>\n",
        "      <td> 1</td>\n",
        "      <td>  7</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 3</td>\n",
        "      <td> 132250</td>\n",
        "      <td>  683435</td>\n",
        "      <td> 1</td>\n",
        "      <td>  17611</td>\n",
        "      <td>  833336</td>\n",
        "      <td>  815725</td>\n",
        "      <td>  40</td>\n",
        "      <td> 0.162126</td>\n",
        "      <td> 0.000049</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>South Asia</th>\n",
        "      <td> 13</td>\n",
        "      <td>  1</td>\n",
        "      <td> 0</td>\n",
        "      <td>  2</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1</td>\n",
        "      <td>  17000</td>\n",
        "      <td>   68817</td>\n",
        "      <td> 1</td>\n",
        "      <td>  15180</td>\n",
        "      <td>  101015</td>\n",
        "      <td>   85835</td>\n",
        "      <td>  18</td>\n",
        "      <td> 0.198054</td>\n",
        "      <td> 0.000210</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 59,
       "text": [
        "                     transgender female  intersex  fa'afafine  \\\n",
        "Africa                                0         0           0   \n",
        "Catholic european                    39         3           1   \n",
        "Confucian                            24         2           1   \n",
        "Constructed                           6         1           0   \n",
        "English-speaking                     99        17           1   \n",
        "Islamic                              29         1           0   \n",
        "Latin America                        25         3           1   \n",
        "Orthodox                             23         1           0   \n",
        "Protestant European                  26         2           1   \n",
        "South Asia                           13         1           0   \n",
        "\n",
        "                     transgender male  female animal  male animal  woman  \\\n",
        "Africa                              0              0            0      0   \n",
        "Catholic european                   7              0            6      0   \n",
        "Confucian                           1              0            0      0   \n",
        "Constructed                         1              0            0      0   \n",
        "English-speaking                   19              0            0      0   \n",
        "Islamic                             2              0            0      0   \n",
        "Latin America                       4              0            0      0   \n",
        "Orthodox                            1              1            2      1   \n",
        "Protestant European                 7              0            0      0   \n",
        "South Asia                          2              0            0      0   \n",
        "\n",
        "                     genderqueer  female     male  kathoey    null  \\\n",
        "Africa                         0    1888    12824        0     306   \n",
        "Catholic european              3  122048   712655        0   33480   \n",
        "Confucian                      2   47355   133508        1  163710   \n",
        "Constructed                    0    4321    27085        1    2252   \n",
        "English-speaking               7  186455  1016500        1    3259   \n",
        "Islamic                        1   20931   100099        1   19529   \n",
        "Latin America                  1   38195   202735        0    1979   \n",
        "Orthodox                       1   53843   324884        1   15316   \n",
        "Protestant European            3  132250   683435        1   17611   \n",
        "South Asia                     1   17000    68817        1   15180   \n",
        "\n",
        "                     human_total  gendered_total  nonbin_total   fem_per  \\\n",
        "Africa                     15018           14712             0  0.128331   \n",
        "Catholic european         868242          834762            59  0.146207   \n",
        "Confucian                 344604          180894            31  0.261783   \n",
        "Constructed                33667           31415             9  0.137546   \n",
        "English-speaking         1206358         1203099           144  0.154979   \n",
        "Islamic                   140593          121064            34  0.172892   \n",
        "Latin America             242943          240964            34  0.158509   \n",
        "Orthodox                  394074          378758            31  0.142157   \n",
        "Protestant European       833336          815725            40  0.162126   \n",
        "South Asia                101015           85835            18  0.198054   \n",
        "\n",
        "                     nonbin_per  \n",
        "Africa                 0.000000  \n",
        "Catholic european      0.000071  \n",
        "Confucian              0.000171  \n",
        "Constructed            0.000286  \n",
        "English-speaking       0.000120  \n",
        "Islamic                0.000281  \n",
        "Latin America          0.000141  \n",
        "Orthodox               0.000082  \n",
        "Protestant European    0.000049  \n",
        "South Asia             0.000210  "
       ]
      }
     ],
     "prompt_number": 59
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig, ax = plt.subplots(1,1,figsize=(8,8))\n",
      "lang_cultures[['gendered_total','fem_per']].plot(kind='scatter', x='gendered_total', y='fem_per', logx=True, ax=ax, c='#74bc3a')\n",
      "\n",
      "ax.yaxis.set_major_formatter(matplotlib.ticker.FuncFormatter(lambda x, y: '{:.0%}'.format(x )))\n",
      "ax.xaxis.set_major_formatter(matplotlib.ticker.FuncFormatter(lambda x, y: '{:,.0f}'.format(x )))\n",
      "\n",
      "ax.set_xlim(min(lang_cultures['gendered_total']) * 0.6, max(lang_cultures['gendered_total']) *3)\n",
      "ax.set_ylim(min(lang_cultures['fem_per']) * 0.85, max(lang_cultures['fem_per']) *1.15)\n",
      "\n",
      "for label, x, y in zip(lang_cultures.index, lang_cultures['gendered_total'], lang_cultures['fem_per']):\n",
      "    plt.annotate(\n",
      "        label, \n",
      "        xy = (x, y), xytext = (5,2),\n",
      "        textcoords = 'offset points', ha = 'left', va = 'bottom')\n",
      "        #bbox = dict(boxstyle = 'round,pad=0.5', fc = 'yellow', alpha = 0.5),\n",
      "        #arrowprops = dict(arrowstyle = '->', connectionstyle = 'arc3,rad=0'))\n",
      "\n",
      "plt.annotate(\"\", xy=(10000,0.5), xytext=(0,0)) \n",
      "plt.title('Female % vs. Total Biographies \\nLanguage Aggregated By Culture', fontsize=24)\n",
      "plt.xlabel('Number of Biographies Recorded')\n",
      "plt.ylabel('Composition of Biographies Which Are Female')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 88,
       "text": [
        "<matplotlib.text.Text at 0x7feccbfe6a50>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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UVJReu7S0NKxbtw59+vRBjRo1UKxYMTg4OKBs2bLo1asXjh8/brAsZV337t0L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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fecd002f0d0>"
       ]
      }
     ],
     "prompt_number": 88
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}