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   "cells": [
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Sex Ratios and Wikidata Part III"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Introduction"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "I recently got back from the [Mediawiki Hackathon in Z\u00fcrich](http://www.mediawiki.org/wiki/Z%C3%BCrich_Hackathon_2014), where I was once again energized and inspired by the Wikidata Dev Team and Community. In chatting, they reminded me of analyses I ran in March 2013, [Sex Ratios and Wikidata Parts I and II](http://hangingtogether.org/?p=2877), about the state of a controversial Wikidata Property - [P21](https://www.wikidata.org/wiki/Property:P21) a.k.a. \"sex or gender\". They suggested it was about time to reinvestigate. \n",
      "\n",
      "Since Part I and II a lot has happenend: the property has been renamed (from \"sex\" to \"sex or gender\"), its database constraints have been changed (from 3 accepted values to [13](https://www.wikidata.org/wiki/Property_talk:P21)), and of course Wikidata has continued to proliferate (now about 400 million triples). \n",
      "\n",
      "Therefore a few questions are begged:\n",
      "\n",
      "2. What are the currently used values of 'sex or gender', and their ratios in each language?\n",
      "1. How does May 2014 data compare to a year ago?\n",
      "1. What are the most represented neither 'male' nor 'female' 'sex or gender's?\n",
      "    2. And which languages use them most?\n",
      "4. Per used sex value, what are the average number of accompanying porperties?\n",
      "\n",
      "That last question is not like the others, but comes from exploring the new [Wikidata Toolkit](https://www.mediawiki.org/wiki/Wikidata_Toolkit), a library for parsing Wikidata dumps. Trying to stretch the imagination of what can be done with Wikidata data is a new hobby of mine, and I am giving a talk about it at Wikiconference USA, titled [\"Answering Big Questions with Wikidata\"](https://meta.wikimedia.org/wiki/WikiConference_USA#Schedule_of_sessions). For now the Wikidata Toolkit is at version 0.1.0, which is still not entirely feature-complete, but works perfectly to extract complete, daily-fresh data. For my own convenience I subset the data in [java and then json export](https://github.com/notconfusing/wikidataSex/tree/master/ratio_analysis),  allowing me to munge it in Python with the Pandas library, which is exactly what you see here.\n",
      "\n",
      "The biggest change in my opinion is the broadening (but still not broad enough in my opinion) of the \"accepted values\" of the property. Let's see what wikidatians are using these days. Below are the english titles of the QIDs and how many different wikidata-linked-wikis are connected to an item utilising that value."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "{english_label(qid): language_count for qid, language_count in used_sexes_count.iteritems()}"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "VERBOSE:pywiki:Found 1 wikidata:wikidata processes running, including this one.\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 53,
       "text": [
        "{u'Female': 89,\n",
        " u'female': 367,\n",
        " u'female animal': 55,\n",
        " u'genderqueer': 23,\n",
        " u'intersex': 51,\n",
        " u'kathoey': 10,\n",
        " u'male': 395,\n",
        " u'male animal': 66,\n",
        " u'man': 3,\n",
        " u'sodium': 1,\n",
        " u'transgender female': 63,\n",
        " u'transgender male': 24}"
       ]
      }
     ],
     "prompt_number": 53
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "So without delving into the _validity_ of the classifications used, which I'll adress later, we see 12 classifications in active duty. Compare this to the begrudging trinary we had a year ago, or [Facebook's announcement to use about 50 classifcations](https://duckduckgo.com/?q=facebook+50+gender+options&t=canonical). We can also see that there are two heavily used classifications - by the metric of number of wikis using them - called 'male' (395 wikis) and 'female' (367 wikis). Why the difference in the number of wikis? We must clarify what we mean by use. We are talking about a Wikipedia, or a Wikisource, or a Wikivoyage instance that has a article that is linked to a Wikidata item which has a P21 \"sex or gender\" property. So that means that there are 28 or more Wikimedia wikis which have an article which Wikidata claims is about a 'male', but have no articles about 'female's. But there are also a lot of tiny wikis out there, which might explain this discrepancy. \n",
      "\n",
      "Let's restrict our data set only to those Wikis which have 1,000 or more articles that are linked to a Wikidata item with a P21 property. There are 42 such wikis as of May 2014. Now fe plot, the ratios or composition of the values of this property in each of those 42 wikis."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "show_by_lang_plot()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
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5ePEimjdvbnDbZs2aGezPysunpqbiq6++goeHh/bv0KFDBicjV16+slu5yMhIrFu3DgCw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\nHQ6oAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fec377f0ad0>"
       ]
      }
     ],
     "prompt_number": 131
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "As is visually evident, only the 'male' and 'female' categories are large enough to appear in the plot (later on we investigate this numerically). Therefore the chart is ordered by the 'female' percentage which ranges from 8.83% - Slovenian Wikipedia, to 19.97% - Serbian Wikipedia. English Wikipedia, the largest Wikipedia by article count comes in at 14.21% which is in the lowest quarter. Wikimedia commons, the only non-Wikipedia represented here performs _relatively_ well at 18.86%.\n",
      "\n",
      "These percentages are still systematically low, and tell a story that we've long known about representational bias. But what about the momentum? What difference are our efforts at uncovering and addressing systemic bias producing?  "
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Comparing May 2013 to March 2014"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "These next tables inspect how a language's composition changed in the previous year. We consider all languages that had at least 1,000 P21 associated properties in _both_ years. I'll disclaim that the differences come from the a Wikipedia's content changing, but also from Wikidata becoming more connected to those different wikis. It's not possible at the moment to disentangle these two causes. Another complicating factor, that we will investigate later on, is the growth of the neither-male-nor-female entries which could account for this drop - but (spoiler) they don't.\n",
      "\n",
      "In each table there is the percentage female from May 2013, from March 2014, and the _'change%'_ that this represents, \"year-over-year\" (even though its about 14 months). \n",
      "\n",
      "We sort by _'change%'_, and first look at the largest losses in 'female' percentage."
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Top Losers"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "diffdf.sort(columns='change%', ascending=True)[['female_may2013','female_march2014','change%']].head(10)"
     ],
     "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>female_may2013</th>\n",
        "      <th>female_march2014</th>\n",
        "      <th>change%</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>lang</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>enwiki</th>\n",
        "      <td> 0.1845</td>\n",
        "      <td> 0.142132</td>\n",
        "      <td> -22.96</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>gawiki</th>\n",
        "      <td> 0.1456</td>\n",
        "      <td> 0.118133</td>\n",
        "      <td> -18.86</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>afwiki</th>\n",
        "      <td> 0.1406</td>\n",
        "      <td> 0.115850</td>\n",
        "      <td> -17.60</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>cswiki</th>\n",
        "      <td> 0.1705</td>\n",
        "      <td> 0.141063</td>\n",
        "      <td> -17.27</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>frwiki</th>\n",
        "      <td> 0.1658</td>\n",
        "      <td> 0.141045</td>\n",
        "      <td> -14.93</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>zhwiki</th>\n",
        "      <td> 0.2062</td>\n",
        "      <td> 0.178885</td>\n",
        "      <td> -13.25</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>itwiki</th>\n",
        "      <td> 0.1667</td>\n",
        "      <td> 0.144760</td>\n",
        "      <td> -13.16</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>hywiki</th>\n",
        "      <td> 0.1633</td>\n",
        "      <td> 0.141859</td>\n",
        "      <td> -13.13</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>ruwiki</th>\n",
        "      <td> 0.1627</td>\n",
        "      <td> 0.142226</td>\n",
        "      <td> -12.58</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>htwiki</th>\n",
        "      <td> 0.0531</td>\n",
        "      <td> 0.047382</td>\n",
        "      <td> -10.77</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>10 rows \u00d7 3 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 17,
       "text": [
        "        female_may2013  female_march2014 change%\n",
        "lang                                            \n",
        "enwiki          0.1845          0.142132  -22.96\n",
        "gawiki          0.1456          0.118133  -18.86\n",
        "afwiki          0.1406          0.115850  -17.60\n",
        "cswiki          0.1705          0.141063  -17.27\n",
        "frwiki          0.1658          0.141045  -14.93\n",
        "zhwiki          0.2062          0.178885  -13.25\n",
        "itwiki          0.1667          0.144760  -13.16\n",
        "hywiki          0.1633          0.141859  -13.13\n",
        "ruwiki          0.1627          0.142226  -12.58\n",
        "htwiki          0.0531          0.047382  -10.77\n",
        "\n",
        "[10 rows x 3 columns]"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "English Wikipedia fell the most in the percentage of it's P21 properties being marked 'female'. The reprsentation of articles marked 'female', compared to all others, dropped by about 4% in absolute terms, which is -23% year-on-year. \n",
      "\n",
      "Also of note, the Hatian Wikipedia, the previous worst at 5.3% slid to retain the dubious title at 4.7%.\n",
      "\n",
      "What about the other end of the chart?"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Top Winners"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "diffdf.sort(columns='change%', ascending=False)[['female_may2013','female_march2014','change%']].head(10)"
     ],
     "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>female_may2013</th>\n",
        "      <th>female_march2014</th>\n",
        "      <th>change%</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>lang</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>urwiki</th>\n",
        "      <td> 0.1319</td>\n",
        "      <td> 0.486671</td>\n",
        "      <td> 268.97</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>ocwiki</th>\n",
        "      <td> 0.1261</td>\n",
        "      <td> 0.159599</td>\n",
        "      <td>  26.57</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>mlwiki</th>\n",
        "      <td> 0.1636</td>\n",
        "      <td> 0.202758</td>\n",
        "      <td>  23.93</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>bnwiki</th>\n",
        "      <td> 0.1313</td>\n",
        "      <td> 0.161183</td>\n",
        "      <td>  22.76</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>mznwiki</th>\n",
        "      <td> 0.1041</td>\n",
        "      <td> 0.125305</td>\n",
        "      <td>  20.37</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>arzwiki</th>\n",
        "      <td> 0.2392</td>\n",
        "      <td> 0.287158</td>\n",
        "      <td>  20.05</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>ltwiki</th>\n",
        "      <td> 0.1190</td>\n",
        "      <td> 0.142340</td>\n",
        "      <td>  19.61</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>arwiki</th>\n",
        "      <td> 0.1293</td>\n",
        "      <td> 0.153516</td>\n",
        "      <td>  18.73</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>warwiki</th>\n",
        "      <td> 0.1003</td>\n",
        "      <td> 0.116598</td>\n",
        "      <td>  16.25</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>tlwiki</th>\n",
        "      <td> 0.2943</td>\n",
        "      <td> 0.340477</td>\n",
        "      <td>  15.69</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>10 rows \u00d7 3 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 18,
       "text": [
        "         female_may2013  female_march2014 change%\n",
        "lang                                             \n",
        "urwiki           0.1319          0.486671  268.97\n",
        "ocwiki           0.1261          0.159599   26.57\n",
        "mlwiki           0.1636          0.202758   23.93\n",
        "bnwiki           0.1313          0.161183   22.76\n",
        "mznwiki          0.1041          0.125305   20.37\n",
        "arzwiki          0.2392          0.287158   20.05\n",
        "ltwiki           0.1190          0.142340   19.61\n",
        "arwiki           0.1293          0.153516   18.73\n",
        "warwiki          0.1003          0.116598   16.25\n",
        "tlwiki           0.2943          0.340477   15.69\n",
        "\n",
        "[10 rows x 3 columns]"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Urdu Wikipedia gained a massive 268% year-on-year increase in their 'female' ratio. Now 49% of their P21-tagged articles have label 'female'. Does anyone closer to this community know if there were any tagging efforts?\n",
      "\n",
      "Tagalog Wiki, previous best, continued to increase to 34% of their P21-tagged articles having label 'female'.\n",
      "\n",
      "There has been a lot of movement in sex-ratios of different languages. As stated earlier this is also due in some part to the maturing of wikidata clusters. Next year we will be able to see if there is a deceleration in these ratios moving.\n",
      "\n",
      "We now move on to invesitage the second confounding factor, the increase in accepted \"sex or gender\" values."
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Non 'male' or 'female' values."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "As we saw earlier, there are now 12 properties that are being used to describe P21. In May 2013 the only non-male-female term was intersex, and P21 said that you __should __ be one of male, female, or intersex. I was quite angered by this prescriptiveness, but with help from online discussions, and with thanks to the [gendergap mailing list](https://lists.wikimedia.org/mailman/listinfo/gendergap) some of those policies have changed. This now is the \"sex or gender\" property, rather than just \"sex\" which I consider a mixed result - quite literally. I am pleased that there is one instance of this value that is \"sodium\", because I support this prorpety allowing any value. To be clear, what an \"accepted\" value means, is that periodically a check is run on the database, and non-accepted values are compiled into a list for user-attention. So robot won't fight you if you use an unaccepted value, but the fodder is there for a human combatant.  \n",
      "\n",
      "Two more of the new values mention \"animal\" because in [Czech and Finnish, there are seperate words to describe sexes of non-human animals](https://www.wikidata.org/wiki/Property_talk:P21#Values_not_making_sense). And of course Wikipedia has articles on famuos animals too. \n",
      "\n",
      "Some others like 'Female' (a 1933 film), and 'man' are probably due to human tagging errors.\n",
      "\n",
      "Below are the wiki's which have the highest number of non 'male' or 'female' values as represented by the new column at the end \"non_MF%\". As you can see none of them exceed two-tenths of 1%. So the upper analysis of year-on-year-change could at most be influenced by error range of \u00b10.2%."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": true,
     "input": [
      "top_non_MF.sort('non_MF%', ascending=False)"
     ],
     "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>wiki</th>\n",
        "      <th>non_MF%</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>male animal</th>\n",
        "      <td>         yiwiki</td>\n",
        "      <td> 0.181159</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>transgender female</th>\n",
        "      <td>         urwiki</td>\n",
        "      <td> 0.092994</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Female</th>\n",
        "      <td>         mgwiki</td>\n",
        "      <td> 0.080321</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>genderqueer</th>\n",
        "      <td> zh_min_nanwiki</td>\n",
        "      <td> 0.066445</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>intersex</th>\n",
        "      <td>        ckbwiki</td>\n",
        "      <td> 0.058754</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>transgender male</th>\n",
        "      <td>        arzwiki</td>\n",
        "      <td> 0.042105</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>female animal</th>\n",
        "      <td>         hywiki</td>\n",
        "      <td> 0.038812</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>kathoey</th>\n",
        "      <td>         thwiki</td>\n",
        "      <td> 0.012922</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>man</th>\n",
        "      <td>         jawiki</td>\n",
        "      <td> 0.001523</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>sodium</th>\n",
        "      <td>         eswiki</td>\n",
        "      <td> 0.000990</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>10 rows \u00d7 2 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 130,
       "text": [
        "                              wiki   non_MF%\n",
        "male animal                 yiwiki  0.181159\n",
        "transgender female          urwiki  0.092994\n",
        "Female                      mgwiki  0.080321\n",
        "genderqueer         zh_min_nanwiki  0.066445\n",
        "intersex                   ckbwiki  0.058754\n",
        "transgender male           arzwiki  0.042105\n",
        "female animal               hywiki  0.038812\n",
        "kathoey                     thwiki  0.012922\n",
        "man                         jawiki  0.001523\n",
        "sodium                      eswiki  0.000990\n",
        "\n",
        "[10 rows x 2 columns]"
       ]
      }
     ],
     "prompt_number": 130
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Intriguingly, Urdu which tops the charts in year-on-year 'female' increase also the leader in higest ratio of \"transgender female\" at nearly one-tenth of one percent, or about 1 in a 1000. This lends credence to the idea that some Urdu Wikipedians have been busy."
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Accompanying Data Richness"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Markus Kr\u00f6tzsch, instigator of the Wikidata Toolkit, on which this research rests, talks about the complexity of Wikidata data. Convincingly he discusses why full unstructured access to the data is important - creative queries. Both star and tree shaped queries should be possible and at the users discretion.\n",
      "\n",
      "One less trivial query I wanted to cook up was - \"on items with a P21 value what are the properties by per item, by P21 value?\" Framed in English, are wikidata items about males data-richer?"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sex_props_df.sort('props_per_item')"
     ],
     "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>item_count</th>\n",
        "      <th>total_props</th>\n",
        "      <th>props_per_item</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>sodium</th>\n",
        "      <td>      1</td>\n",
        "      <td>       4</td>\n",
        "      <td>  4.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>man</th>\n",
        "      <td>      2</td>\n",
        "      <td>      10</td>\n",
        "      <td>  5.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>female</th>\n",
        "      <td> 122288</td>\n",
        "      <td>  738962</td>\n",
        "      <td>  6.042801</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>male</th>\n",
        "      <td> 768646</td>\n",
        "      <td> 4816357</td>\n",
        "      <td>  6.266028</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>male animal</th>\n",
        "      <td>     55</td>\n",
        "      <td>     385</td>\n",
        "      <td>  7.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>female animal</th>\n",
        "      <td>      6</td>\n",
        "      <td>      44</td>\n",
        "      <td>  7.333333</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>genderqueer</th>\n",
        "      <td>      8</td>\n",
        "      <td>      63</td>\n",
        "      <td>  7.875000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>transgender female</th>\n",
        "      <td>     41</td>\n",
        "      <td>     398</td>\n",
        "      <td>  9.707317</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>transgender male</th>\n",
        "      <td>      4</td>\n",
        "      <td>      43</td>\n",
        "      <td> 10.750000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>intersex</th>\n",
        "      <td>      8</td>\n",
        "      <td>      88</td>\n",
        "      <td> 11.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>kathoey</th>\n",
        "      <td>      1</td>\n",
        "      <td>      11</td>\n",
        "      <td> 11.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Female</th>\n",
        "      <td>      1</td>\n",
        "      <td>      20</td>\n",
        "      <td> 20.000000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>12 rows \u00d7 3 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "                    item_count  total_props  props_per_item\n",
        "sodium                       1            4        4.000000\n",
        "man                          2           10        5.000000\n",
        "female                  122288       738962        6.042801\n",
        "male                    768646      4816357        6.266028\n",
        "male animal                 55          385        7.000000\n",
        "female animal                6           44        7.333333\n",
        "genderqueer                  8           63        7.875000\n",
        "transgender female          41          398        9.707317\n",
        "transgender male             4           43       10.750000\n",
        "intersex                     8           88       11.000000\n",
        "kathoey                      1           11       11.000000\n",
        "Female                       1           20       20.000000\n",
        "\n",
        "[12 rows x 3 columns]"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The above chart displays the properties per item, for all P21 values that occur 5 or more times. The results are telling, on avereage items with the 'male' property have 6.27 properties, and those with 'female' 6.04. It's also worth mentioning that 'transgender female' averages 9.71 properties per item.\n",
      "\n",
      "#### Update\n",
      "\n",
      "After Denny Vrande\u010di\u0107's comment on Facebook, I have new opinions about the interpretation of this chart.\n",
      "\n",
      ">Denny Vrande\u010di\u0107 In your conclusions you write that underrepresented genders are less semantically described, but just before that you shore that female and male are the genders with the last statements per item. I think your conclusions as currently written are not correct. \n",
      "\n",
      "My initial thinking was that 'male' and 'female' appear as properties on the order of 10^5 times, where as the others are all less 10^2. So I was going to claim, that only these props_per_item statistics were significant. But then as I was thinking about the subject more, part of my thesis is that theses\"marginal\" P21 are essential to describing the world non-prescriptively. Therefore discounting them because of size would be antithetical. Therefore following my own logic, it is true both 'male' and 'female' are semantically less described then most other P21 values, using a mean average. However as Denny later went on to say,\n",
      "\n",
      "> On the other hand, I think that might be an artifact of some processes that aim to add gender to instances of human. This would drive the average down.\n",
      "\n",
      "So there are no large conclusions to draw yet.\n",
      "\n",
      "###Update 2\n",
      "\n",
      "I was [altered from MHBeale on twitter](https://twitter.com/mhbeals/status/476037706750128129) that I neglected to include information on exactly how many P21 statements there actually are. On June 9th using Magnus' tools [Andrew Gray pulled up](https://twitter.com/generalising/status/476056392391221248) _2080k \"people\" know to WD of whom 1893k \"gendered\"_.\n",
      "\n"
     ]
    },
    {
     "cell_type": "heading",
     "level": 3,
     "metadata": {},
     "source": [
      "Conclusions"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Without becoming too political, the biases that exist in Wikipedia's representation of the world are systemic, and appreciable. We can see from our year-on-year calculations that there is movement in this dataset - albeit not always for the better. The representation of non-male-female items I suspect is lower than what a sample from the wolrd would indicate, but I don't have any statistical reference, and would welcome suggestions on datasets with which to compare. Lastly we showed that not only in representation, but also in attention given to each item, 'female' 'sex or genders' are less semantically-described.\n",
      "\n",
      "Questions and criticism greatly recieved,\n",
      "\n",
      "[\u203dnotconfusing](https://twitter.com/notconfusing)"
     ]
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Start of Supporting Code"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import json\n",
      "from collections import defaultdict\n",
      "import pandas as pd\n",
      "import pywikibot\n",
      "import decimal\n",
      "NOPLACES = decimal.Decimal(10) ** 0\n",
      "TWOPLACES = decimal.Decimal(10) ** -2\n",
      "%pylab inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "norm_sex[sexdf['total']>1000].sort(columns='non_MF', ascending=False).head(10)"
     ],
     "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>female animal</th>\n",
        "      <th>intersex</th>\n",
        "      <th>kathoey</th>\n",
        "      <th>Female</th>\n",
        "      <th>transgender female</th>\n",
        "      <th>male animal</th>\n",
        "      <th>male</th>\n",
        "      <th>female</th>\n",
        "      <th>transgender male</th>\n",
        "      <th>genderqueer</th>\n",
        "      <th>man</th>\n",
        "      <th>sodium</th>\n",
        "      <th>non_MF</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>zh_min_nanwiki</th>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000664</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000664</td>\n",
        "      <td> 0.787375</td>\n",
        "      <td> 0.210631</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000664</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0.001993</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>yiwiki</th>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.001812</td>\n",
        "      <td> 0.897645</td>\n",
        "      <td> 0.100543</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0.001812</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>cywiki</th>\n",
        "      <td> 0.000371</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000186</td>\n",
        "      <td> 0.000371</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.820375</td>\n",
        "      <td> 0.178326</td>\n",
        "      <td> 0.000186</td>\n",
        "      <td> 0.000186</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0.001299</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>ckbwiki</th>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000588</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000588</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.893067</td>\n",
        "      <td> 0.105758</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0.001175</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>thwiki</th>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000129</td>\n",
        "      <td> 0.000129</td>\n",
        "      <td> 0.000388</td>\n",
        "      <td> 0.000388</td>\n",
        "      <td> 0.788345</td>\n",
        "      <td> 0.210492</td>\n",
        "      <td> 0.000129</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0.001163</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>mswiki</th>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000223</td>\n",
        "      <td> 0.000223</td>\n",
        "      <td> 0.000446</td>\n",
        "      <td> 0.802679</td>\n",
        "      <td> 0.196205</td>\n",
        "      <td> 0.000223</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0.001116</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>ruwikiquote</th>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000552</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.909492</td>\n",
        "      <td> 0.089404</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000552</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0.001104</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>mlwiki</th>\n",
        "      <td> 0.000270</td>\n",
        "      <td> 0.000270</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000270</td>\n",
        "      <td> 0.000270</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.796161</td>\n",
        "      <td> 0.202758</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0.001081</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>eowiki</th>\n",
        "      <td> 0.000125</td>\n",
        "      <td> 0.000187</td>\n",
        "      <td> 0.000062</td>\n",
        "      <td> 0.000062</td>\n",
        "      <td> 0.000374</td>\n",
        "      <td> 0.000249</td>\n",
        "      <td> 0.851300</td>\n",
        "      <td> 0.147640</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0.001060</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>kowiki</th>\n",
        "      <td> 0.000075</td>\n",
        "      <td> 0.000075</td>\n",
        "      <td> 0.000038</td>\n",
        "      <td> 0.000038</td>\n",
        "      <td> 0.000491</td>\n",
        "      <td> 0.000113</td>\n",
        "      <td> 0.801608</td>\n",
        "      <td> 0.197373</td>\n",
        "      <td> 0.000075</td>\n",
        "      <td> 0.000113</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0</td>\n",
        "      <td> 0.001019</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>10 rows \u00d7 13 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 20,
       "text": [
        "                female animal  intersex   kathoey    Female  \\\n",
        "zh_min_nanwiki       0.000000  0.000000  0.000000  0.000664   \n",
        "yiwiki               0.000000  0.000000  0.000000  0.000000   \n",
        "cywiki               0.000371  0.000000  0.000000  0.000186   \n",
        "ckbwiki              0.000000  0.000588  0.000000  0.000000   \n",
        "thwiki               0.000000  0.000000  0.000129  0.000129   \n",
        "mswiki               0.000000  0.000000  0.000000  0.000223   \n",
        "ruwikiquote          0.000000  0.000000  0.000000  0.000552   \n",
        "mlwiki               0.000270  0.000270  0.000000  0.000270   \n",
        "eowiki               0.000125  0.000187  0.000062  0.000062   \n",
        "kowiki               0.000075  0.000075  0.000038  0.000038   \n",
        "\n",
        "                transgender female  male animal      male    female  \\\n",
        "zh_min_nanwiki            0.000000     0.000664  0.787375  0.210631   \n",
        "yiwiki                    0.000000     0.001812  0.897645  0.100543   \n",
        "cywiki                    0.000371     0.000000  0.820375  0.178326   \n",
        "ckbwiki                   0.000588     0.000000  0.893067  0.105758   \n",
        "thwiki                    0.000388     0.000388  0.788345  0.210492   \n",
        "mswiki                    0.000223     0.000446  0.802679  0.196205   \n",
        "ruwikiquote               0.000000     0.000000  0.909492  0.089404   \n",
        "mlwiki                    0.000270     0.000000  0.796161  0.202758   \n",
        "eowiki                    0.000374     0.000249  0.851300  0.147640   \n",
        "kowiki                    0.000491     0.000113  0.801608  0.197373   \n",
        "\n",
        "                transgender male  genderqueer  man  sodium    non_MF  \n",
        "zh_min_nanwiki          0.000000     0.000664    0       0  0.001993  \n",
        "yiwiki                  0.000000     0.000000    0       0  0.001812  \n",
        "cywiki                  0.000186     0.000186    0       0  0.001299  \n",
        "ckbwiki                 0.000000     0.000000    0       0  0.001175  \n",
        "thwiki                  0.000129     0.000000    0       0  0.001163  \n",
        "mswiki                  0.000223     0.000000    0       0  0.001116  \n",
        "ruwikiquote             0.000000     0.000552    0       0  0.001104  \n",
        "mlwiki                  0.000000     0.000000    0       0  0.001081  \n",
        "eowiki                  0.000000     0.000000    0       0  0.001060  \n",
        "kowiki                  0.000075     0.000113    0       0  0.001019  \n",
        "\n",
        "[10 rows x 13 columns]"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "jsonfile = open('lang_sex.json','r')\n",
      "bigdict = json.load(jsonfile)\n",
      "lang_sex = defaultdict(dict)\n",
      "for keystring, count in bigdict.iteritems():\n",
      "    lang, sex = keystring.split('--')\n",
      "    lang_sex[lang][sex] = count\n",
      "    \n",
      "used_sexes = defaultdict(list)\n",
      "for lang, sex_dict in lang_sex.iteritems():\n",
      "    for sex in sex_dict.iterkeys():\n",
      "        used_sexes[sex].append(lang)\n",
      "        \n",
      "used_sexes_count = {sex: len(lang_list) for sex, lang_list in used_sexes.iteritems()}"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sexdf = pd.DataFrame.from_dict(lang_sex, orient='index')\n",
      "sexdf = sexdf.fillna(value=0)\n",
      "#sexdf.plot(kind='bar', stacked=True, figsize=(10,10))\n",
      "#Norm_sex is not \"normal\" sex, but rather the Sex-data normed into percentages.\n",
      "norm_sex = sexdf.apply(lambda col: col / float(col.sum()), axis=1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#Tranforming QIDs into English labels.\n",
      "enwp = pywikibot.Site('en','wikipedia')\n",
      "wikidata = enwp.data_repository()\n",
      "\n",
      "def english_label(qid):\n",
      "    page = pywikibot.ItemPage(wikidata, qid)\n",
      "    data = page.get()\n",
      "    return data['labels']['en']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sex_qs = [str(q) for q in norm_sex.columns]\n",
      "sex_labels = [english_label(sex_q) for sex_q in sex_qs]\n",
      "\n",
      "norm_sex.columns = sex_labels"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "NameError",
       "evalue": "name 'norm_sex' is not defined",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
        "\u001b[1;32m<ipython-input-7-42dc746c5cd3>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      8\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'labels'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'en'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      9\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 10\u001b[1;33m \u001b[0msex_qs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mq\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mq\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mnorm_sex\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     11\u001b[0m \u001b[0msex_labels\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0menglish_label\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msex_q\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0msex_q\u001b[0m \u001b[1;32min\u001b[0m \u001b[0msex_qs\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     12\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
        "\u001b[1;31mNameError\u001b[0m: name 'norm_sex' is not defined"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#norm_sex.index = [label.replace('wiki','') for label in norm_sex.index]\n",
      "#comparing by total between two different dataframes requires \n",
      "#that norm_sex has not had any rows modified since it was created from sexdf\n",
      "sexdf['total'] = sexdf.sum(axis=1)\n",
      "fs1000 = norm_sex[sexdf['total']>10000].sort('female', ascending=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def show_by_lang_plot():\n",
      "    fsplot = fs1000.plot(kind='bar', stacked=True, legend=True, figsize=(13,8), alpha=0.9, ylim=(0,1),\n",
      "                         title= '''Comoposition of Wikidata Prorerty:P21 \"Sex or Gender\" by Language \n",
      "    (Languages with over 1,000 associated P21)''',\n",
      "                         colormap='Set1')\n",
      "\n",
      "    plt.yticks(linspace(0, 1, num=11), [str(decimal.Decimal(x * 100).quantize(NOPLACES))+'%' for x in arange(0,1.1,0.1)])\n",
      "    \n",
      "    ticklocs, langs = plt.xticks()\n",
      "    langstrs = [str(decimal.Decimal(norm_sex.loc[lang.get_text()]['female']* 100).quantize(TWOPLACES))+'%  '+ lang.get_text() for lang in langs]\n",
      "    plt.xticks(ticklocs, langstrs)\n",
      "    plt.xlabel('Language-Wiki percentage \"female\"')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 11
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "For your edification, the full data, and not just the 'female' percentages."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fs1000"
     ],
     "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>female animal</th>\n",
        "      <th>intersex</th>\n",
        "      <th>kathoey</th>\n",
        "      <th>Female</th>\n",
        "      <th>transgender female</th>\n",
        "      <th>male animal</th>\n",
        "      <th>male</th>\n",
        "      <th>female</th>\n",
        "      <th>transgender male</th>\n",
        "      <th>genderqueer</th>\n",
        "      <th>man</th>\n",
        "      <th>sodium</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>slwiki</th>\n",
        "      <td> 0.000074</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000074</td>\n",
        "      <td> 0.000074</td>\n",
        "      <td> 0.000074</td>\n",
        "      <td> 0.911398</td>\n",
        "      <td> 0.088307</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.00000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>lawiki</th>\n",
        "      <td> 0.000060</td>\n",
        "      <td> 0.000060</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000060</td>\n",
        "      <td> 0.000180</td>\n",
        "      <td> 0.000120</td>\n",
        "      <td> 0.889302</td>\n",
        "      <td> 0.110219</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.00000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>bewiki</th>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000099</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000099</td>\n",
        "      <td> 0.876528</td>\n",
        "      <td> 0.123273</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.00000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>cawiki</th>\n",
        "      <td> 0.000026</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000026</td>\n",
        "      <td> 0.000103</td>\n",
        "      <td> 0.000129</td>\n",
        "      <td> 0.870905</td>\n",
        "      <td> 0.128786</td>\n",
        "      <td> 0.000026</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.00000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>elwiki</th>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000068</td>\n",
        "      <td> 0.000068</td>\n",
        "      <td> 0.000068</td>\n",
        "      <td> 0.869061</td>\n",
        "      <td> 0.130734</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.00000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>euwiki</th>\n",
        "      <td> 0.000080</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000080</td>\n",
        "      <td> 0.000080</td>\n",
        "      <td> 0.000239</td>\n",
        "      <td> 0.865686</td>\n",
        "      <td> 0.133837</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.00000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>skwiki</th>\n",
        "      <td> 0.000078</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000078</td>\n",
        "      <td> 0.000078</td>\n",
        "      <td> 0.000078</td>\n",
        "      <td> 0.864363</td>\n",
        "      <td> 0.135245</td>\n",
        "      <td> 0.000078</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.00000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>frwiki</th>\n",
        "      <td> 0.000020</td>\n",
        "      <td> 0.000025</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000005</td>\n",
        "      <td> 0.000107</td>\n",
        "      <td> 0.000097</td>\n",
        "      <td> 0.858680</td>\n",
        "      <td> 0.141045</td>\n",
        "      <td> 0.000005</td>\n",
        "      <td> 0.000015</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.00000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>cswiki</th>\n",
        "      <td> 0.000048</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000024</td>\n",
        "      <td> 0.000096</td>\n",
        "      <td> 0.000096</td>\n",
        "      <td> 0.858648</td>\n",
        "      <td> 0.141063</td>\n",
        "      <td> 0.000024</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.00000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>enwiki</th>\n",
        "      <td> 0.000009</td>\n",
        "      <td> 0.000012</td>\n",
        "      <td> 0.000002</td>\n",
        "      <td> 0.000002</td>\n",
        "      <td> 0.000069</td>\n",
        "      <td> 0.000052</td>\n",
        "      <td> 0.857699</td>\n",
        "      <td> 0.142132</td>\n",
        "      <td> 0.000007</td>\n",
        "      <td> 0.000014</td>\n",
        "      <td> 0.000002</td>\n",
        "      <td> 0.00000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>ruwiki</th>\n",
        "      <td> 0.000038</td>\n",
        "      <td> 0.000019</td>\n",
        "      <td> 0.000010</td>\n",
        "      <td> 0.000010</td>\n",
        "      <td> 0.000077</td>\n",
        "      <td> 0.000077</td>\n",
        "      <td> 0.857515</td>\n",
        "      <td> 0.142226</td>\n",
        "      <td> 0.000019</td>\n",
        "      <td> 0.000010</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.00000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>dawiki</th>\n",
        "      <td> 0.000036</td>\n",
        "      <td> 0.000073</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000036</td>\n",
        "      <td> 0.000109</td>\n",
        "      <td> 0.000073</td>\n",
        "      <td> 0.856768</td>\n",
        "      <td> 0.142904</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.00000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>ukwiki</th>\n",
        "      <td> 0.000062</td>\n",
        "      <td> 0.000031</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000031</td>\n",
        "      <td> 0.000062</td>\n",
        "      <td> 0.000031</td>\n",
        "      <td> 0.855573</td>\n",
        "      <td> 0.144178</td>\n",
        "      <td> 0.000031</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.00000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>dewiki</th>\n",
        "      <td> 0.000013</td>\n",
        "      <td> 0.000006</td>\n",
        "      <td> 0.000003</td>\n",
        "      <td> 0.000003</td>\n",
        "      <td> 0.000034</td>\n",
        "      <td> 0.000047</td>\n",
        "      <td> 0.855591</td>\n",
        "      <td> 0.144277</td>\n",
        "      <td> 0.000013</td>\n",
        "      <td> 0.000013</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.00000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>itwiki</th>\n",
        "      <td> 0.000006</td>\n",
        "      <td> 0.000028</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000006</td>\n",
        "      <td> 0.000090</td>\n",
        "      <td> 0.000107</td>\n",
        "      <td> 0.854986</td>\n",
        "      <td> 0.144760</td>\n",
        "      <td> 0.000011</td>\n",
        "      <td> 0.000006</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.00000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>eowiki</th>\n",
        "      <td> 0.000125</td>\n",
        "      <td> 0.000187</td>\n",
        "      <td> 0.000062</td>\n",
        "      <td> 0.000062</td>\n",
        "      <td> 0.000374</td>\n",
        "      <td> 0.000249</td>\n",
        "      <td> 0.851300</td>\n",
        "      <td> 0.147640</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.00000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>glwiki</th>\n",
        "      <td> 0.000082</td>\n",
        "      <td> 0.000082</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000082</td>\n",
        "      <td> 0.000329</td>\n",
        "      <td> 0.000164</td>\n",
        "      <td> 0.851602</td>\n",
        "      <td> 0.147658</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.00000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>etwiki</th>\n",
        "      <td> 0.000079</td>\n",
        "      <td> 0.000079</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000079</td>\n",
        "      <td> 0.000079</td>\n",
        "      <td> 0.000237</td>\n",
        "      <td> 0.846676</td>\n",
        "      <td> 0.152771</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.00000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>arwiki</th>\n",
        "      <td> 0.000040</td>\n",
        "      <td> 0.000040</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000040</td>\n",
        "      <td> 0.000079</td>\n",
        "      <td> 0.000079</td>\n",
        "      <td> 0.846166</td>\n",
        "      <td> 0.153516</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000040</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.00000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>idwiki</th>\n",
        "      <td> 0.000208</td>\n",
        "      <td> 0.000052</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000052</td>\n",
        "      <td> 0.000104</td>\n",
        "      <td> 0.000156</td>\n",
        "      <td> 0.843220</td>\n",
        "      <td> 0.156156</td>\n",
        "      <td> 0.000052</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.00000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>hrwiki</th>\n",
        "      <td> 0.000078</td>\n",
        "      <td> 0.000078</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000078</td>\n",
        "      <td> 0.000078</td>\n",
        "      <td> 0.000156</td>\n",
        "      <td> 0.842670</td>\n",
        "      <td> 0.156863</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.00000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>eswiki</th>\n",
        "      <td> 0.000030</td>\n",
        "      <td> 0.000040</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000010</td>\n",
        "      <td> 0.000109</td>\n",
        "      <td> 0.000079</td>\n",
        "      <td> 0.841094</td>\n",
        "      <td> 0.158589</td>\n",
        "      <td> 0.000020</td>\n",
        "      <td> 0.000020</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.00001</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>ptwiki</th>\n",
        "      <td> 0.000043</td>\n",
        "      <td> 0.000043</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000014</td>\n",
        "      <td> 0.000199</td>\n",
        "      <td> 0.000114</td>\n",
        "      <td> 0.840760</td>\n",
        "      <td> 0.158785</td>\n",
        "      <td> 0.000014</td>\n",
        "      <td> 0.000028</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.00000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>bgwiki</th>\n",
        "      <td> 0.000091</td>\n",
        "      <td> 0.000045</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000045</td>\n",
        "      <td> 0.000091</td>\n",
        "      <td> 0.000181</td>\n",
        "      <td> 0.839242</td>\n",
        "      <td> 0.160305</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.00000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>huwiki</th>\n",
        "      <td> 0.000120</td>\n",
        "      <td> 0.000040</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000040</td>\n",
        "      <td> 0.000200</td>\n",
        "      <td> 0.000200</td>\n",
        "      <td> 0.839014</td>\n",
        "      <td> 0.160386</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.00000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>plwiki</th>\n",
        "      <td> 0.000031</td>\n",
        "      <td> 0.000021</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000010</td>\n",
        "      <td> 0.000063</td>\n",
        "      <td> 0.000094</td>\n",
        "      <td> 0.839206</td>\n",
        "      <td> 0.160575</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.00000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>nlwiki</th>\n",
        "      <td> 0.000041</td>\n",
        "      <td> 0.000027</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000014</td>\n",
        "      <td> 0.000082</td>\n",
        "      <td> 0.000123</td>\n",
        "      <td> 0.838302</td>\n",
        "      <td> 0.161343</td>\n",
        "      <td> 0.000014</td>\n",
        "      <td> 0.000041</td>\n",
        "      <td> 0.000014</td>\n",
        "      <td> 0.00000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>hewiki</th>\n",
        "      <td> 0.000085</td>\n",
        "      <td> 0.000042</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000042</td>\n",
        "      <td> 0.000297</td>\n",
        "      <td> 0.000127</td>\n",
        "      <td> 0.836914</td>\n",
        "      <td> 0.162449</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000042</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.00000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>trwiki</th>\n",
        "      <td> 0.000076</td>\n",
        "      <td> 0.000038</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000038</td>\n",
        "      <td> 0.000114</td>\n",
        "      <td> 0.000191</td>\n",
        "      <td> 0.833810</td>\n",
        "      <td> 0.165656</td>\n",
        "      <td> 0.000038</td>\n",
        "      <td> 0.000038</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.00000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>fiwiki</th>\n",
        "      <td> 0.000060</td>\n",
        "      <td> 0.000060</td>\n",
        "      <td> 0.000020</td>\n",
        "      <td> 0.000020</td>\n",
        "      <td> 0.000099</td>\n",
        "      <td> 0.000079</td>\n",
        "      <td> 0.824523</td>\n",
        "      <td> 0.175100</td>\n",
        "      <td> 0.000040</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.00000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>jawiki</th>\n",
        "      <td> 0.000030</td>\n",
        "      <td> 0.000030</td>\n",
        "      <td> 0.000015</td>\n",
        "      <td> 0.000015</td>\n",
        "      <td> 0.000167</td>\n",
        "      <td> 0.000107</td>\n",
        "      <td> 0.823550</td>\n",
        "      <td> 0.176039</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000030</td>\n",
        "      <td> 0.000015</td>\n",
        "      <td> 0.00000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>zhwiki</th>\n",
        "      <td> 0.000087</td>\n",
        "      <td> 0.000029</td>\n",
        "      <td> 0.000029</td>\n",
        "      <td> 0.000029</td>\n",
        "      <td> 0.000261</td>\n",
        "      <td> 0.000145</td>\n",
        "      <td> 0.820476</td>\n",
        "      <td> 0.178885</td>\n",
        "      <td> 0.000029</td>\n",
        "      <td> 0.000029</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.00000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>nowiki</th>\n",
        "      <td> 0.000020</td>\n",
        "      <td> 0.000020</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000020</td>\n",
        "      <td> 0.000082</td>\n",
        "      <td> 0.000082</td>\n",
        "      <td> 0.819593</td>\n",
        "      <td> 0.180183</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.00000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>shwiki</th>\n",
        "      <td> 0.000073</td>\n",
        "      <td> 0.000073</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000073</td>\n",
        "      <td> 0.000367</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.817768</td>\n",
        "      <td> 0.181571</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000073</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.00000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>fawiki</th>\n",
        "      <td> 0.000066</td>\n",
        "      <td> 0.000033</td>\n",
        "      <td> 0.000033</td>\n",
        "      <td> 0.000033</td>\n",
        "      <td> 0.000332</td>\n",
        "      <td> 0.000033</td>\n",
        "      <td> 0.816748</td>\n",
        "      <td> 0.182721</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.00000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>rowiki</th>\n",
        "      <td> 0.000096</td>\n",
        "      <td> 0.000048</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000048</td>\n",
        "      <td> 0.000048</td>\n",
        "      <td> 0.000096</td>\n",
        "      <td> 0.816821</td>\n",
        "      <td> 0.182844</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.00000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>simplewiki</th>\n",
        "      <td> 0.000051</td>\n",
        "      <td> 0.000051</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000051</td>\n",
        "      <td> 0.000306</td>\n",
        "      <td> 0.000102</td>\n",
        "      <td> 0.815151</td>\n",
        "      <td> 0.184084</td>\n",
        "      <td> 0.000051</td>\n",
        "      <td> 0.000153</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.00000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>viwiki</th>\n",
        "      <td> 0.000093</td>\n",
        "      <td> 0.000093</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000093</td>\n",
        "      <td> 0.000093</td>\n",
        "      <td> 0.000186</td>\n",
        "      <td> 0.813103</td>\n",
        "      <td> 0.186247</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000093</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.00000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>svwiki</th>\n",
        "      <td> 0.000042</td>\n",
        "      <td> 0.000042</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000014</td>\n",
        "      <td> 0.000111</td>\n",
        "      <td> 0.000056</td>\n",
        "      <td> 0.811433</td>\n",
        "      <td> 0.188275</td>\n",
        "      <td> 0.000014</td>\n",
        "      <td> 0.000014</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.00000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>commonswiki</th>\n",
        "      <td> 0.000045</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000045</td>\n",
        "      <td> 0.000089</td>\n",
        "      <td> 0.000268</td>\n",
        "      <td> 0.810849</td>\n",
        "      <td> 0.188614</td>\n",
        "      <td> 0.000045</td>\n",
        "      <td> 0.000045</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.00000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>kowiki</th>\n",
        "      <td> 0.000075</td>\n",
        "      <td> 0.000075</td>\n",
        "      <td> 0.000038</td>\n",
        "      <td> 0.000038</td>\n",
        "      <td> 0.000491</td>\n",
        "      <td> 0.000113</td>\n",
        "      <td> 0.801608</td>\n",
        "      <td> 0.197373</td>\n",
        "      <td> 0.000075</td>\n",
        "      <td> 0.000113</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.00000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>srwiki</th>\n",
        "      <td> 0.000074</td>\n",
        "      <td> 0.000074</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000074</td>\n",
        "      <td> 0.000074</td>\n",
        "      <td> 0.000223</td>\n",
        "      <td> 0.799718</td>\n",
        "      <td> 0.199688</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.000074</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 0.00000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>42 rows \u00d7 12 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 15,
       "text": [
        "             female animal  intersex   kathoey    Female  transgender female  \\\n",
        "slwiki            0.000074  0.000000  0.000000  0.000074            0.000074   \n",
        "lawiki            0.000060  0.000060  0.000000  0.000060            0.000180   \n",
        "bewiki            0.000000  0.000000  0.000000  0.000099            0.000000   \n",
        "cawiki            0.000026  0.000000  0.000000  0.000026            0.000103   \n",
        "elwiki            0.000000  0.000000  0.000000  0.000068            0.000068   \n",
        "euwiki            0.000080  0.000000  0.000000  0.000080            0.000080   \n",
        "skwiki            0.000078  0.000000  0.000000  0.000078            0.000078   \n",
        "frwiki            0.000020  0.000025  0.000000  0.000005            0.000107   \n",
        "cswiki            0.000048  0.000000  0.000000  0.000024            0.000096   \n",
        "enwiki            0.000009  0.000012  0.000002  0.000002            0.000069   \n",
        "ruwiki            0.000038  0.000019  0.000010  0.000010            0.000077   \n",
        "dawiki            0.000036  0.000073  0.000000  0.000036            0.000109   \n",
        "ukwiki            0.000062  0.000031  0.000000  0.000031            0.000062   \n",
        "dewiki            0.000013  0.000006  0.000003  0.000003            0.000034   \n",
        "itwiki            0.000006  0.000028  0.000000  0.000006            0.000090   \n",
        "eowiki            0.000125  0.000187  0.000062  0.000062            0.000374   \n",
        "glwiki            0.000082  0.000082  0.000000  0.000082            0.000329   \n",
        "etwiki            0.000079  0.000079  0.000000  0.000079            0.000079   \n",
        "arwiki            0.000040  0.000040  0.000000  0.000040            0.000079   \n",
        "idwiki            0.000208  0.000052  0.000000  0.000052            0.000104   \n",
        "hrwiki            0.000078  0.000078  0.000000  0.000078            0.000078   \n",
        "eswiki            0.000030  0.000040  0.000000  0.000010            0.000109   \n",
        "ptwiki            0.000043  0.000043  0.000000  0.000014            0.000199   \n",
        "bgwiki            0.000091  0.000045  0.000000  0.000045            0.000091   \n",
        "huwiki            0.000120  0.000040  0.000000  0.000040            0.000200   \n",
        "plwiki            0.000031  0.000021  0.000000  0.000010            0.000063   \n",
        "nlwiki            0.000041  0.000027  0.000000  0.000014            0.000082   \n",
        "hewiki            0.000085  0.000042  0.000000  0.000042            0.000297   \n",
        "trwiki            0.000076  0.000038  0.000000  0.000038            0.000114   \n",
        "fiwiki            0.000060  0.000060  0.000020  0.000020            0.000099   \n",
        "jawiki            0.000030  0.000030  0.000015  0.000015            0.000167   \n",
        "zhwiki            0.000087  0.000029  0.000029  0.000029            0.000261   \n",
        "nowiki            0.000020  0.000020  0.000000  0.000020            0.000082   \n",
        "shwiki            0.000073  0.000073  0.000000  0.000073            0.000367   \n",
        "fawiki            0.000066  0.000033  0.000033  0.000033            0.000332   \n",
        "rowiki            0.000096  0.000048  0.000000  0.000048            0.000048   \n",
        "simplewiki        0.000051  0.000051  0.000000  0.000051            0.000306   \n",
        "viwiki            0.000093  0.000093  0.000000  0.000093            0.000093   \n",
        "svwiki            0.000042  0.000042  0.000000  0.000014            0.000111   \n",
        "commonswiki       0.000045  0.000000  0.000000  0.000045            0.000089   \n",
        "kowiki            0.000075  0.000075  0.000038  0.000038            0.000491   \n",
        "srwiki            0.000074  0.000074  0.000000  0.000074            0.000074   \n",
        "\n",
        "             male animal      male    female  transgender male  genderqueer  \\\n",
        "slwiki          0.000074  0.911398  0.088307          0.000000     0.000000   \n",
        "lawiki          0.000120  0.889302  0.110219          0.000000     0.000000   \n",
        "bewiki          0.000099  0.876528  0.123273          0.000000     0.000000   \n",
        "cawiki          0.000129  0.870905  0.128786          0.000026     0.000000   \n",
        "elwiki          0.000068  0.869061  0.130734          0.000000     0.000000   \n",
        "euwiki          0.000239  0.865686  0.133837          0.000000     0.000000   \n",
        "skwiki          0.000078  0.864363  0.135245          0.000078     0.000000   \n",
        "frwiki          0.000097  0.858680  0.141045          0.000005     0.000015   \n",
        "cswiki          0.000096  0.858648  0.141063          0.000024     0.000000   \n",
        "enwiki          0.000052  0.857699  0.142132          0.000007     0.000014   \n",
        "ruwiki          0.000077  0.857515  0.142226          0.000019     0.000010   \n",
        "dawiki          0.000073  0.856768  0.142904          0.000000     0.000000   \n",
        "ukwiki          0.000031  0.855573  0.144178          0.000031     0.000000   \n",
        "dewiki          0.000047  0.855591  0.144277          0.000013     0.000013   \n",
        "itwiki          0.000107  0.854986  0.144760          0.000011     0.000006   \n",
        "eowiki          0.000249  0.851300  0.147640          0.000000     0.000000   \n",
        "glwiki          0.000164  0.851602  0.147658          0.000000     0.000000   \n",
        "etwiki          0.000237  0.846676  0.152771          0.000000     0.000000   \n",
        "arwiki          0.000079  0.846166  0.153516          0.000000     0.000040   \n",
        "idwiki          0.000156  0.843220  0.156156          0.000052     0.000000   \n",
        "hrwiki          0.000156  0.842670  0.156863          0.000000     0.000000   \n",
        "eswiki          0.000079  0.841094  0.158589          0.000020     0.000020   \n",
        "ptwiki          0.000114  0.840760  0.158785          0.000014     0.000028   \n",
        "bgwiki          0.000181  0.839242  0.160305          0.000000     0.000000   \n",
        "huwiki          0.000200  0.839014  0.160386          0.000000     0.000000   \n",
        "plwiki          0.000094  0.839206  0.160575          0.000000     0.000000   \n",
        "nlwiki          0.000123  0.838302  0.161343          0.000014     0.000041   \n",
        "hewiki          0.000127  0.836914  0.162449          0.000000     0.000042   \n",
        "trwiki          0.000191  0.833810  0.165656          0.000038     0.000038   \n",
        "fiwiki          0.000079  0.824523  0.175100          0.000040     0.000000   \n",
        "jawiki          0.000107  0.823550  0.176039          0.000000     0.000030   \n",
        "zhwiki          0.000145  0.820476  0.178885          0.000029     0.000029   \n",
        "nowiki          0.000082  0.819593  0.180183          0.000000     0.000000   \n",
        "shwiki          0.000000  0.817768  0.181571          0.000000     0.000073   \n",
        "fawiki          0.000033  0.816748  0.182721          0.000000     0.000000   \n",
        "rowiki          0.000096  0.816821  0.182844          0.000000     0.000000   \n",
        "simplewiki      0.000102  0.815151  0.184084          0.000051     0.000153   \n",
        "viwiki          0.000186  0.813103  0.186247          0.000000     0.000093   \n",
        "svwiki          0.000056  0.811433  0.188275          0.000014     0.000014   \n",
        "commonswiki     0.000268  0.810849  0.188614          0.000045     0.000045   \n",
        "kowiki          0.000113  0.801608  0.197373          0.000075     0.000113   \n",
        "srwiki          0.000223  0.799718  0.199688          0.000000     0.000074   \n",
        "\n",
        "                  man   sodium  \n",
        "slwiki       0.000000  0.00000  \n",
        "lawiki       0.000000  0.00000  \n",
        "bewiki       0.000000  0.00000  \n",
        "cawiki       0.000000  0.00000  \n",
        "elwiki       0.000000  0.00000  \n",
        "euwiki       0.000000  0.00000  \n",
        "skwiki       0.000000  0.00000  \n",
        "frwiki       0.000000  0.00000  \n",
        "cswiki       0.000000  0.00000  \n",
        "enwiki       0.000002  0.00000  \n",
        "ruwiki       0.000000  0.00000  \n",
        "dawiki       0.000000  0.00000  \n",
        "ukwiki       0.000000  0.00000  \n",
        "dewiki       0.000000  0.00000  \n",
        "itwiki       0.000000  0.00000  \n",
        "eowiki       0.000000  0.00000  \n",
        "glwiki       0.000000  0.00000  \n",
        "etwiki       0.000000  0.00000  \n",
        "arwiki       0.000000  0.00000  \n",
        "idwiki       0.000000  0.00000  \n",
        "hrwiki       0.000000  0.00000  \n",
        "eswiki       0.000000  0.00001  \n",
        "ptwiki       0.000000  0.00000  \n",
        "bgwiki       0.000000  0.00000  \n",
        "huwiki       0.000000  0.00000  \n",
        "plwiki       0.000000  0.00000  \n",
        "nlwiki       0.000014  0.00000  \n",
        "hewiki       0.000000  0.00000  \n",
        "trwiki       0.000000  0.00000  \n",
        "fiwiki       0.000000  0.00000  \n",
        "jawiki       0.000015  0.00000  \n",
        "zhwiki       0.000000  0.00000  \n",
        "nowiki       0.000000  0.00000  \n",
        "shwiki       0.000000  0.00000  \n",
        "fawiki       0.000000  0.00000  \n",
        "rowiki       0.000000  0.00000  \n",
        "simplewiki   0.000000  0.00000  \n",
        "viwiki       0.000000  0.00000  \n",
        "svwiki       0.000000  0.00000  \n",
        "commonswiki  0.000000  0.00000  \n",
        "kowiki       0.000000  0.00000  \n",
        "srwiki       0.000000  0.00000  \n",
        "\n",
        "[42 rows x 12 columns]"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "maydf = pd.read_table('may2013.csv',sep=',', index_col=0)\n",
      "maydf['female'] = maydf['perc'] / 100.0\n",
      "diffdf = maydf.join(other=norm_sex,how='inner',lsuffix='_may2013', rsuffix='_march2014')\n",
      "diffdf['change%'] = (diffdf['female_march2014'] - diffdf['female_may2013']) / diffdf['female_may2013']\n",
      "diffdf['change%'] = diffdf['change%'].apply(lambda x: decimal.Decimal(x * 100).quantize(TWOPLACES) )"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "non_MF_cols = [col for col in norm_sex.columns if col not in ['male','female']]\n",
      "norm_sex['non_MF'] = norm_sex[non_MF_cols].sum(axis=1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "top_non_MF_dict = dict()\n",
      "for s in non_MF_cols:\n",
      "    t = norm_sex[sexdf['total']>1000].sort(columns=s, ascending=False)[s].head(1)\n",
      "    top_non_MF_dict[s] = {'wiki':t.index[0],'non_MF%':t[0]*100}\n",
      "top_non_MF = pd.DataFrame.from_dict(data=top_non_MF_dict, orient='index')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 128
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "jsonfile = open('sex_propcount.json','r')\n",
      "sex_props_json = json.load(jsonfile)\n",
      "sex_props = defaultdict(dict)\n",
      "for keystring, count in sex_props_json.iteritems():\n",
      "    sex, prop = keystring.split('_')\n",
      "    sex_props[sex][prop] = count\n",
      "    \n",
      "sex_props_df = pd.DataFrame.from_dict(sex_props, orient='index')\n",
      "\n",
      "sex_qs = [str(q) for q in sex_props_df.index]\n",
      "sex_labels = [english_label(sex_q) for sex_q in sex_qs]\n",
      "\n",
      "sex_props_df.columns = ['item_count', 'total_props']\n",
      "\n",
      "sex_props_df.index = sex_labels\n",
      "\n",
      "sex_props_df['props_per_item'] = sex_props_df['total_props'] / sex_props_df['item_count']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "VERBOSE:pywiki:Found 1 wikidata:wikidata processes running, including this one.\n"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}