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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": 1
    },
    {
     "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": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "VERBOSE:pywiki:Starting 1 threads...\n"
       ]
      }
     ],
     "prompt_number": 42
    },
    {
     "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": [
      "for row in allrecs.iterrows():\n",
      "    print(row[1][3])\n",
      "    break"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "['Q6581097']\n"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def makedict():\n",
      "    return defaultdict(int)\n",
      "\n",
      "single = defaultdict(makedict)\n",
      "many = defaultdict(makedict)\n",
      "\n",
      "\n",
      "for index, row in allrecs.iterrows():\n",
      "    try:\n",
      "        gender = row[3][0]\n",
      "    except:\n",
      "        gender = None\n",
      "    wikiname_list = row[7]\n",
      "    if isinstance(wikiname_list, list):\n",
      "        if len(wikiname_list) == 1:\n",
      "            single[wikiname_list[0]][gender] += 1\n",
      "        else:\n",
      "            for wikiname in wikiname_list:\n",
      "                many[wikiname][gender] += 1"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 32
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def makedf(indict):\n",
      "    df = pd.DataFrame.from_dict(indict, orient='index')\n",
      "    df.columns = map(english_label, df.columns)\n",
      "    df['human_total'] = df.sum(axis=1)\n",
      "    df['gendered_total'] = df['human_total'] - df[float('nan')]\n",
      "    df['nonbin_total'] = df['gendered_total'] - df['female'] - df['male']\n",
      "    df['fem_per'] = df['female'] / df['gendered_total']\n",
      "    df['nonbin_per'] = df['nonbin_total'] / df['gendered_total']\n",
      "    return df"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 59
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sdf = makedf(single)\n",
      "mdf = makedf(many)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 60
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "tdf = sdf.join(mdf, how='inner', lsuffix='_single', rsuffix='_many')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 61
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "tdf.fillna(0, inplace=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 62
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "tdf.columns"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 65,
       "text": [
        "Index([u'male_single', u'female_single', u'None_single', u'male animal_single', u'intersex_single', u'transgender female_single', u'woman', u'transgender male_single', u'genderqueer_single', u'female animal', u'human_total_single', u'gendered_total_single', u'nonbin_total_single', u'fem_per_single', u'nonbin_per_single', u'intersex_many', u'kathoey', u'transgender male_many', u'genderqueer_many', u'transgender female_many', u'None_many', u'male_many', u'female_many', u'fa'afafine', u'male animal_many', u'human_total_many', u'gendered_total_many', u'nonbin_total_many', u'fem_per_many', u'nonbin_per_many'], dtype='object')"
       ]
      }
     ],
     "prompt_number": 65
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "tdf['fem_per_diff'] = tdf['fem_per_single'] - tdf['fem_per_many']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 68
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "tdf[tdf['gendered_total_single']>2500].sort('fem_per_diff',ascending=False)[['fem_per_diff','fem_per_single','female_single','gendered_total_single','fem_per_many']]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
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        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>fem_per_diff</th>\n",
        "      <th>fem_per_single</th>\n",
        "      <th>female_single</th>\n",
        "      <th>gendered_total_single</th>\n",
        "      <th>fem_per_many</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>zhwiki</th>\n",
        "      <td> 0.501899</td>\n",
        "      <td> 0.699730</td>\n",
        "      <td>  4409</td>\n",
        "      <td>   6301</td>\n",
        "      <td> 0.197831</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>jawiki</th>\n",
        "      <td> 0.413549</td>\n",
        "      <td> 0.603585</td>\n",
        "      <td> 13434</td>\n",
        "      <td>  22257</td>\n",
        "      <td> 0.190037</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>hewiki</th>\n",
        "      <td> 0.060952</td>\n",
        "      <td> 0.216555</td>\n",
        "      <td>  2386</td>\n",
        "      <td>  11018</td>\n",
        "      <td> 0.155602</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>dawiki</th>\n",
        "      <td> 0.036127</td>\n",
        "      <td> 0.181383</td>\n",
        "      <td>  2233</td>\n",
        "      <td>  12311</td>\n",
        "      <td> 0.145256</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>nowiki</th>\n",
        "      <td> 0.029112</td>\n",
        "      <td> 0.227961</td>\n",
        "      <td>  4277</td>\n",
        "      <td>  18762</td>\n",
        "      <td> 0.198849</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>svwiki</th>\n",
        "      <td> 0.020537</td>\n",
        "      <td> 0.213389</td>\n",
        "      <td>  8772</td>\n",
        "      <td>  41108</td>\n",
        "      <td> 0.192852</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>ukwiki</th>\n",
        "      <td> 0.001318</td>\n",
        "      <td> 0.137714</td>\n",
        "      <td>  2953</td>\n",
        "      <td>  21443</td>\n",
        "      <td> 0.136396</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>fiwiki</th>\n",
        "      <td>-0.000251</td>\n",
        "      <td> 0.167975</td>\n",
        "      <td>  3145</td>\n",
        "      <td>  18723</td>\n",
        "      <td> 0.168226</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>dewiki</th>\n",
        "      <td>-0.000691</td>\n",
        "      <td> 0.149034</td>\n",
        "      <td> 30335</td>\n",
        "      <td> 203544</td>\n",
        "      <td> 0.149725</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>cawiki</th>\n",
        "      <td>-0.000866</td>\n",
        "      <td> 0.117262</td>\n",
        "      <td>   841</td>\n",
        "      <td>   7172</td>\n",
        "      <td> 0.118128</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>plwiki</th>\n",
        "      <td>-0.008691</td>\n",
        "      <td> 0.149597</td>\n",
        "      <td> 10353</td>\n",
        "      <td>  69206</td>\n",
        "      <td> 0.158288</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>slwiki</th>\n",
        "      <td>-0.012184</td>\n",
        "      <td> 0.065586</td>\n",
        "      <td>   259</td>\n",
        "      <td>   3949</td>\n",
        "      <td> 0.077770</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>frwiki</th>\n",
        "      <td>-0.012200</td>\n",
        "      <td> 0.142818</td>\n",
        "      <td> 14247</td>\n",
        "      <td>  99756</td>\n",
        "      <td> 0.155018</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>nlwiki</th>\n",
        "      <td>-0.012330</td>\n",
        "      <td> 0.148959</td>\n",
        "      <td>  5482</td>\n",
        "      <td>  36802</td>\n",
        "      <td> 0.161290</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>enwiki</th>\n",
        "      <td>-0.015220</td>\n",
        "      <td> 0.146669</td>\n",
        "      <td> 80262</td>\n",
        "      <td> 547233</td>\n",
        "      <td> 0.161889</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>itwiki</th>\n",
        "      <td>-0.018435</td>\n",
        "      <td> 0.129315</td>\n",
        "      <td>  7102</td>\n",
        "      <td>  54920</td>\n",
        "      <td> 0.147751</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>eswiki</th>\n",
        "      <td>-0.021227</td>\n",
        "      <td> 0.143191</td>\n",
        "      <td>  7851</td>\n",
        "      <td>  54829</td>\n",
        "      <td> 0.164418</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>ptwiki</th>\n",
        "      <td>-0.021357</td>\n",
        "      <td> 0.143572</td>\n",
        "      <td>  4678</td>\n",
        "      <td>  32583</td>\n",
        "      <td> 0.164929</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>etwiki</th>\n",
        "      <td>-0.022251</td>\n",
        "      <td> 0.127290</td>\n",
        "      <td>  1042</td>\n",
        "      <td>   8186</td>\n",
        "      <td> 0.149541</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>cswiki</th>\n",
        "      <td>-0.024574</td>\n",
        "      <td> 0.136172</td>\n",
        "      <td>  2453</td>\n",
        "      <td>  18014</td>\n",
        "      <td> 0.160746</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>skwiki</th>\n",
        "      <td>-0.025449</td>\n",
        "      <td> 0.109329</td>\n",
        "      <td>   375</td>\n",
        "      <td>   3430</td>\n",
        "      <td> 0.134779</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>huwiki</th>\n",
        "      <td>-0.025591</td>\n",
        "      <td> 0.126126</td>\n",
        "      <td>   532</td>\n",
        "      <td>   4218</td>\n",
        "      <td> 0.151717</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>ltwiki</th>\n",
        "      <td>-0.025745</td>\n",
        "      <td> 0.111248</td>\n",
        "      <td>   453</td>\n",
        "      <td>   4072</td>\n",
        "      <td> 0.136993</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>ruwiki</th>\n",
        "      <td>-0.044208</td>\n",
        "      <td> 0.105331</td>\n",
        "      <td>  8083</td>\n",
        "      <td>  76739</td>\n",
        "      <td> 0.149539</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>elwiki</th>\n",
        "      <td>-0.047472</td>\n",
        "      <td> 0.075263</td>\n",
        "      <td>   408</td>\n",
        "      <td>   5421</td>\n",
        "      <td> 0.122735</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>rowiki</th>\n",
        "      <td>-0.056509</td>\n",
        "      <td> 0.120667</td>\n",
        "      <td>   362</td>\n",
        "      <td>   3000</td>\n",
        "      <td> 0.177175</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>fawiki</th>\n",
        "      <td>-0.057135</td>\n",
        "      <td> 0.149071</td>\n",
        "      <td>   377</td>\n",
        "      <td>   2529</td>\n",
        "      <td> 0.206206</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>srwiki</th>\n",
        "      <td>-0.061763</td>\n",
        "      <td> 0.121543</td>\n",
        "      <td>   378</td>\n",
        "      <td>   3110</td>\n",
        "      <td> 0.183307</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>trwiki</th>\n",
        "      <td>-0.089649</td>\n",
        "      <td> 0.073070</td>\n",
        "      <td>   353</td>\n",
        "      <td>   4831</td>\n",
        "      <td> 0.162719</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 85,
       "text": [
        "        fem_per_diff  fem_per_single  female_single  gendered_total_single  \\\n",
        "zhwiki      0.501899        0.699730           4409                   6301   \n",
        "jawiki      0.413549        0.603585          13434                  22257   \n",
        "hewiki      0.060952        0.216555           2386                  11018   \n",
        "dawiki      0.036127        0.181383           2233                  12311   \n",
        "nowiki      0.029112        0.227961           4277                  18762   \n",
        "svwiki      0.020537        0.213389           8772                  41108   \n",
        "ukwiki      0.001318        0.137714           2953                  21443   \n",
        "fiwiki     -0.000251        0.167975           3145                  18723   \n",
        "dewiki     -0.000691        0.149034          30335                 203544   \n",
        "cawiki     -0.000866        0.117262            841                   7172   \n",
        "plwiki     -0.008691        0.149597          10353                  69206   \n",
        "slwiki     -0.012184        0.065586            259                   3949   \n",
        "frwiki     -0.012200        0.142818          14247                  99756   \n",
        "nlwiki     -0.012330        0.148959           5482                  36802   \n",
        "enwiki     -0.015220        0.146669          80262                 547233   \n",
        "itwiki     -0.018435        0.129315           7102                  54920   \n",
        "eswiki     -0.021227        0.143191           7851                  54829   \n",
        "ptwiki     -0.021357        0.143572           4678                  32583   \n",
        "etwiki     -0.022251        0.127290           1042                   8186   \n",
        "cswiki     -0.024574        0.136172           2453                  18014   \n",
        "skwiki     -0.025449        0.109329            375                   3430   \n",
        "huwiki     -0.025591        0.126126            532                   4218   \n",
        "ltwiki     -0.025745        0.111248            453                   4072   \n",
        "ruwiki     -0.044208        0.105331           8083                  76739   \n",
        "elwiki     -0.047472        0.075263            408                   5421   \n",
        "rowiki     -0.056509        0.120667            362                   3000   \n",
        "fawiki     -0.057135        0.149071            377                   2529   \n",
        "srwiki     -0.061763        0.121543            378                   3110   \n",
        "trwiki     -0.089649        0.073070            353                   4831   \n",
        "\n",
        "        fem_per_many  \n",
        "zhwiki      0.197831  \n",
        "jawiki      0.190037  \n",
        "hewiki      0.155602  \n",
        "dawiki      0.145256  \n",
        "nowiki      0.198849  \n",
        "svwiki      0.192852  \n",
        "ukwiki      0.136396  \n",
        "fiwiki      0.168226  \n",
        "dewiki      0.149725  \n",
        "cawiki      0.118128  \n",
        "plwiki      0.158288  \n",
        "slwiki      0.077770  \n",
        "frwiki      0.155018  \n",
        "nlwiki      0.161290  \n",
        "enwiki      0.161889  \n",
        "itwiki      0.147751  \n",
        "eswiki      0.164418  \n",
        "ptwiki      0.164929  \n",
        "etwiki      0.149541  \n",
        "cswiki      0.160746  \n",
        "skwiki      0.134779  \n",
        "huwiki      0.151717  \n",
        "ltwiki      0.136993  \n",
        "ruwiki      0.149539  \n",
        "elwiki      0.122735  \n",
        "rowiki      0.177175  \n",
        "fawiki      0.206206  \n",
        "srwiki      0.183307  \n",
        "trwiki      0.162719  "
       ]
      }
     ],
     "prompt_number": 85
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig, ax = plt.subplots(1,1,figsize=(8,8))\n",
      "tdf[tdf['gendered_total_single']>2500][['gendered_total_single','fem_per_diff']].plot(kind='scatter', x='gendered_total_single', y='fem_per_diff', 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(tdf['gendered_total_single']) * 0.6, max(tdf['gendered_total_single']) *3)\n",
      "#ax.set_ylim(min(tdf['fem_per_diff']) * 0.85, max(tdf['fem_per_diff']) *1.15)\n",
      "\n",
      "for label, x, y in zip(tdf.index, tdf['gendered_total_single'], tdf['fem_per_diff']):\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('Differnce in Female Ratio, of \"Language-Unique\" Articles vs Others \\n by Language-Unique Articles\"', fontsize=24)\n",
      "plt.xlabel('Number of Biographies Recorded')\n",
      "plt.ylabel('Composition of Biographies Which Are Female')\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 94,
       "text": [
        "<matplotlib.text.Text at 0x7f83e125f9d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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OWrVqJRu2c+fOCA4OxqVLlxAbG4vNmzfDzs4ODx48kO2Hqo3Y3PjVq1dFz0/y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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f83e0b3eb10>"
       ]
      }
     ],
     "prompt_number": 94
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}