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  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Looking at French word frequency with Lexique\n",
      "\n",
      "[Lexique 3](http://lexique.org/) is a French word database from the Universit\u00e9 de Savoie. It includes:\n",
      "\n",
      "- Inflected word forms.\n",
      "- Lexemes.\n",
      "- Frequency data for movie subtitles and books.\n",
      "\n",
      "It's a great database and a ton of fun. In this notebook, we use a copy of Lexique that has been loaded into the SQLite 3 database. But since we don't want to get into the messy details of the Python, we build the database using a `Makefile`, and we keep all of our Python utility functions in an external file named `lexique.py`. If you want to see all those details, or customize this analysis, [check out this notebook on GitHub](https://github.com/emk/lexique-experiments).\n",
      "\n",
      "First, let's get everything loaded."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%matplotlib inline\n",
      "%run lexique.py"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "First, let's take a look at what data we have available. First, we have the raw data from Lexique, which includes inflected forms in the `ortho` column:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sql(\"SELECT * FROM lexique WHERE lemme = 'avoir' LIMIT 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>ortho</th>\n",
        "      <th>phon</th>\n",
        "      <th>lemme</th>\n",
        "      <th>cgram</th>\n",
        "      <th>genre</th>\n",
        "      <th>nombre</th>\n",
        "      <th>freqlemfilms2</th>\n",
        "      <th>freqlemlivres</th>\n",
        "      <th>freqfilms2</th>\n",
        "      <th>freqlivres</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>   a</td>\n",
        "      <td> a</td>\n",
        "      <td> avoir</td>\n",
        "      <td> AUX</td>\n",
        "      <td> </td>\n",
        "      <td> </td>\n",
        "      <td> 18559.22</td>\n",
        "      <td> 12800.81</td>\n",
        "      <td> 6350.91</td>\n",
        "      <td> 2926.69</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>   a</td>\n",
        "      <td> a</td>\n",
        "      <td> avoir</td>\n",
        "      <td> VER</td>\n",
        "      <td> </td>\n",
        "      <td> </td>\n",
        "      <td> 13572.40</td>\n",
        "      <td>  6426.49</td>\n",
        "      <td> 5498.34</td>\n",
        "      <td> 1669.39</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>  ai</td>\n",
        "      <td> E</td>\n",
        "      <td> avoir</td>\n",
        "      <td> AUX</td>\n",
        "      <td> </td>\n",
        "      <td> </td>\n",
        "      <td> 18559.22</td>\n",
        "      <td> 12800.81</td>\n",
        "      <td> 4902.10</td>\n",
        "      <td> 2119.12</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>  ai</td>\n",
        "      <td> E</td>\n",
        "      <td> avoir</td>\n",
        "      <td> VER</td>\n",
        "      <td> </td>\n",
        "      <td> </td>\n",
        "      <td> 13572.40</td>\n",
        "      <td>  6426.49</td>\n",
        "      <td> 2475.78</td>\n",
        "      <td>  619.05</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> aie</td>\n",
        "      <td> E</td>\n",
        "      <td> avoir</td>\n",
        "      <td> AUX</td>\n",
        "      <td> </td>\n",
        "      <td> </td>\n",
        "      <td> 18559.22</td>\n",
        "      <td> 12800.81</td>\n",
        "      <td>   31.75</td>\n",
        "      <td>   21.69</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 2,
       "text": [
        "  ortho phon  lemme cgram genre nombre  freqlemfilms2  freqlemlivres  \\\n",
        "0     a    a  avoir   AUX                    18559.22       12800.81   \n",
        "1     a    a  avoir   VER                    13572.40        6426.49   \n",
        "2    ai    E  avoir   AUX                    18559.22       12800.81   \n",
        "3    ai    E  avoir   VER                    13572.40        6426.49   \n",
        "4   aie    E  avoir   AUX                    18559.22       12800.81   \n",
        "\n",
        "   freqfilms2  freqlivres  \n",
        "0     6350.91     2926.69  \n",
        "1     5498.34     1669.39  \n",
        "2     4902.10     2119.12  \n",
        "3     2475.78      619.05  \n",
        "4       31.75       21.69  "
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We have another table, `lemme`, which sums over all the orthographies associated with a given lemma. The `lemme` column, however, is still not unique: If a given word can be either a noun or a verb, it will appear twice. And so on."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sql(\"SELECT * FROM lemme ORDER BY freqlivres DESC LIMIT 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>lemme</th>\n",
        "      <th>cgram</th>\n",
        "      <th>genre</th>\n",
        "      <th>nombre</th>\n",
        "      <th>freqfilms2</th>\n",
        "      <th>freqlivres</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> de</td>\n",
        "      <td>     PRE</td>\n",
        "      <td>  </td>\n",
        "      <td>  </td>\n",
        "      <td> 25220.86</td>\n",
        "      <td> 38928.92</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> la</td>\n",
        "      <td> ART:def</td>\n",
        "      <td> f</td>\n",
        "      <td> s</td>\n",
        "      <td> 14946.48</td>\n",
        "      <td> 23633.92</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> et</td>\n",
        "      <td>     CON</td>\n",
        "      <td>  </td>\n",
        "      <td>  </td>\n",
        "      <td> 12909.08</td>\n",
        "      <td> 20879.73</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>  \u00e0</td>\n",
        "      <td>     PRE</td>\n",
        "      <td>  </td>\n",
        "      <td>  </td>\n",
        "      <td> 12190.40</td>\n",
        "      <td> 19209.05</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> le</td>\n",
        "      <td> ART:def</td>\n",
        "      <td> m</td>\n",
        "      <td> s</td>\n",
        "      <td> 13652.76</td>\n",
        "      <td> 18310.95</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "  lemme    cgram genre nombre  freqfilms2  freqlivres\n",
        "0    de      PRE                 25220.86    38928.92\n",
        "1    la  ART:def     f      s    14946.48    23633.92\n",
        "2    et      CON                 12909.08    20879.73\n",
        "3     \u00e0      PRE                 12190.40    19209.05\n",
        "4    le  ART:def     m      s    13652.76    18310.95"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "And finally, we collapse all the `cgram`, `genre` and `nombre` values associated with a given value of `lemme`, to give us unique lemmas with frequency data:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sql(\"SELECT * FROM lemme_simple ORDER BY freqlivres DESC LIMIT 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>lemme</th>\n",
        "      <th>freqfilms2</th>\n",
        "      <th>freqlivres</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>   de</td>\n",
        "      <td> 25220.96</td>\n",
        "      <td> 38928.92</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>   la</td>\n",
        "      <td> 16028.08</td>\n",
        "      <td> 24877.30</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> \u00eatre</td>\n",
        "      <td> 40411.41</td>\n",
        "      <td> 21709.87</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>   et</td>\n",
        "      <td> 12909.08</td>\n",
        "      <td> 20879.73</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>   le</td>\n",
        "      <td> 16953.50</td>\n",
        "      <td> 20735.14</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "  lemme  freqfilms2  freqlivres\n",
        "0    de    25220.96    38928.92\n",
        "1    la    16028.08    24877.30\n",
        "2  \u00eatre    40411.41    21709.87\n",
        "3    et    12909.08    20879.73\n",
        "4    le    16953.50    20735.14"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Note that we have two sets of frequency data: `freqfilms2`, which is based on a corpus of film subtitles, and `freqlivres`, which is based on a corpus of books. There are some important differences. For example, French films use the _pass\u00e9 compos\u00e9_ far more often than books, which raises the frequencies of the auxiliary verbs _\u00eatre_ and _avoir_:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sql(\"SELECT * FROM lemme_simple ORDER BY freqfilms2 DESC LIMIT 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>lemme</th>\n",
        "      <th>freqfilms2</th>\n",
        "      <th>freqlivres</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>  \u00eatre</td>\n",
        "      <td> 40411.41</td>\n",
        "      <td> 21709.87</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> avoir</td>\n",
        "      <td> 32134.77</td>\n",
        "      <td> 19230.64</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>    je</td>\n",
        "      <td> 25988.48</td>\n",
        "      <td> 10862.77</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>    de</td>\n",
        "      <td> 25220.96</td>\n",
        "      <td> 38928.92</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>    ne</td>\n",
        "      <td> 22297.51</td>\n",
        "      <td> 13852.97</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "   lemme  freqfilms2  freqlivres\n",
        "0   \u00eatre    40411.41    21709.87\n",
        "1  avoir    32134.77    19230.64\n",
        "2     je    25988.48    10862.77\n",
        "3     de    25220.96    38928.92\n",
        "4     ne    22297.51    13852.97"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Parts of speech\n",
      "\n",
      "Using the film dataset, let's take a look at the parts of speech:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cgram_freq = sql(\"\"\"\n",
      "SELECT cgram, SUM(freqfilms2) AS freqfilms2, SUM(freqlivres) AS freqlivres\n",
      "  FROM lemme GROUP BY cgram\n",
      "\"\"\", index_col='cgram')\n",
      "cgram_freq"
     ],
     "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>freqfilms2</th>\n",
        "      <th>freqlivres</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>cgram</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th></th>\n",
        "      <td>      1.20</td>\n",
        "      <td>      0.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>ADJ</th>\n",
        "      <td>  42939.77</td>\n",
        "      <td>  56548.13</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>ADJ:dem</th>\n",
        "      <td>   6363.94</td>\n",
        "      <td>   6802.23</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>ADJ:ind</th>\n",
        "      <td>   2999.34</td>\n",
        "      <td>   3737.10</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>ADJ:int</th>\n",
        "      <td>   1273.62</td>\n",
        "      <td>    582.91</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>ADJ:num</th>\n",
        "      <td>   2525.93</td>\n",
        "      <td>   4680.43</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>ADJ:pos</th>\n",
        "      <td>  19106.03</td>\n",
        "      <td>  20005.62</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>ADV</th>\n",
        "      <td>  97693.38</td>\n",
        "      <td>  69747.44</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>ART:def</th>\n",
        "      <td>  54495.63</td>\n",
        "      <td>  83470.15</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>ART:ind</th>\n",
        "      <td>  26051.19</td>\n",
        "      <td>  33763.58</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>AUX</th>\n",
        "      <td>  26633.45</td>\n",
        "      <td>  19302.64</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>CON</th>\n",
        "      <td>  29730.47</td>\n",
        "      <td>  38189.17</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>LIA</th>\n",
        "      <td>      0.00</td>\n",
        "      <td>    412.57</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>NOM</th>\n",
        "      <td> 144894.66</td>\n",
        "      <td> 186537.81</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>ONO</th>\n",
        "      <td>   6291.15</td>\n",
        "      <td>   1501.06</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>PRE</th>\n",
        "      <td>  77439.28</td>\n",
        "      <td> 118274.42</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>PRO:dem</th>\n",
        "      <td>  15700.13</td>\n",
        "      <td>   7549.99</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>PRO:ind</th>\n",
        "      <td>   7538.14</td>\n",
        "      <td>   5716.50</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>PRO:int</th>\n",
        "      <td>   1612.64</td>\n",
        "      <td>    736.37</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>PRO:per</th>\n",
        "      <td> 133651.20</td>\n",
        "      <td>  90995.84</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>PRO:pos</th>\n",
        "      <td>    334.17</td>\n",
        "      <td>    322.16</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>PRO:rel</th>\n",
        "      <td>  11547.03</td>\n",
        "      <td>  14483.19</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>VER</th>\n",
        "      <td> 198390.74</td>\n",
        "      <td> 150350.57</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "         freqfilms2  freqlivres\n",
        "cgram                          \n",
        "               1.20        0.00\n",
        "ADJ        42939.77    56548.13\n",
        "ADJ:dem     6363.94     6802.23\n",
        "ADJ:ind     2999.34     3737.10\n",
        "ADJ:int     1273.62      582.91\n",
        "ADJ:num     2525.93     4680.43\n",
        "ADJ:pos    19106.03    20005.62\n",
        "ADV        97693.38    69747.44\n",
        "ART:def    54495.63    83470.15\n",
        "ART:ind    26051.19    33763.58\n",
        "AUX        26633.45    19302.64\n",
        "CON        29730.47    38189.17\n",
        "LIA            0.00      412.57\n",
        "NOM       144894.66   186537.81\n",
        "ONO         6291.15     1501.06\n",
        "PRE        77439.28   118274.42\n",
        "PRO:dem    15700.13     7549.99\n",
        "PRO:ind     7538.14     5716.50\n",
        "PRO:int     1612.64      736.37\n",
        "PRO:per   133651.20    90995.84\n",
        "PRO:pos      334.17      322.16\n",
        "PRO:rel    11547.03    14483.19\n",
        "VER       198390.74   150350.57"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cgram_freq_summary = cgram_freq.groupby(lambda x: x[0:3]).sum()\n",
      "plt.figure(figsize=(7,7))\n",
      "plt.subplot(aspect=True)\n",
      "plt.pie(cgram_freq_summary.freqfilms2, labels=cgram_freq_summary.index.values, colors=colors)\n",
      "plt.title(\"Parts of speech\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "<matplotlib.text.Text at 0x41c2050>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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QR8lNc/3cD+9We+WbDXjfIOHAzcPws6+fJ8YFv+FMfbv9tJA0qj9gr6f8qb1WrwUXmylx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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x41a2110>"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Text coverage\n",
      "\n",
      "How many words do we need to know to understand 98% of the individual words which appear on a given page?"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "coverage = sql(\"\"\"\n",
      "SELECT lemme, freqfilms2 FROM lemme_simple\n",
      "  ORDER BY freqfilms2 DESC\"\"\")\n",
      "coverage.index += 1\n",
      "coverage['film_coverage'] = \\\n",
      "  100*coverage['freqfilms2'].cumsum() / coverage['freqfilms2'].sum()\n",
      "del coverage['lemme']\n",
      "del coverage['freqfilms2']\n",
      "coverage[0:5]"
     ],
     "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>film_coverage</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>  4.454456</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>  7.996598</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 10.861248</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 13.641296</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td> 16.099099</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "   film_coverage\n",
        "1       4.454456\n",
        "2       7.996598\n",
        "3      10.861248\n",
        "4      13.641296\n",
        "5      16.099099"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "book_coverage = sql(\"\"\"\n",
      "SELECT lemme, freqlivres FROM lemme_simple\n",
      "  ORDER BY freqlivres DESC\"\"\")\n",
      "book_coverage.index += 1\n",
      "coverage['book_coverage'] = \\\n",
      "  100*book_coverage['freqlivres'].cumsum() / book_coverage['freqlivres'].sum()\n",
      "coverage[0:5]"
     ],
     "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>film_coverage</th>\n",
        "      <th>book_coverage</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>  4.454456</td>\n",
        "      <td>  4.260534</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>  7.996598</td>\n",
        "      <td>  6.983203</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 10.861248</td>\n",
        "      <td>  9.359217</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 13.641296</td>\n",
        "      <td> 11.644377</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td> 16.099099</td>\n",
        "      <td> 13.913712</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "   film_coverage  book_coverage\n",
        "1       4.454456       4.260534\n",
        "2       7.996598       6.983203\n",
        "3      10.861248       9.359217\n",
        "4      13.641296      11.644377\n",
        "5      16.099099      13.913712"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot(coverage.index.values, coverage.film_coverage, label=\"Film Coverage\")\n",
      "plt.plot(coverage.index.values, coverage.book_coverage, label=\"Book Coverage\")\n",
      "plt.legend(loc = 'lower right')\n",
      "plt.title('Text Coverage')\n",
      "plt.xlabel('Vocabulary size')\n",
      "plt.ylabel('% coverage')\n",
      "plt.xlim((0,10000))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "(0, 10000)"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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7cCLAhcA85/o87AOLjYQaVV+JiAQCfwWQR4DbqD8sSgq2ugzna5NT54bWKoCI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\nERHx1v8HqiIXWxPbqKUAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x50f6850>"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Or, in table form, here's how many words you need to know to get a given percentage of coverage:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "coverage.loc[[250, 500, 1000, 2000, 4000, 8000, 16000], :]"
     ],
     "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>film_coverage</th>\n",
        "      <th>book_coverage</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>250  </th>\n",
        "      <td> 76.164757</td>\n",
        "      <td> 68.074024</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>500  </th>\n",
        "      <td> 82.792428</td>\n",
        "      <td> 74.990087</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1000 </th>\n",
        "      <td> 88.386602</td>\n",
        "      <td> 81.450089</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2000 </th>\n",
        "      <td> 93.004247</td>\n",
        "      <td> 87.535363</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4000 </th>\n",
        "      <td> 96.410076</td>\n",
        "      <td> 92.756703</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8000 </th>\n",
        "      <td> 98.554939</td>\n",
        "      <td> 96.606475</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>16000</th>\n",
        "      <td> 99.666146</td>\n",
        "      <td> 99.017399</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "       film_coverage  book_coverage\n",
        "250        76.164757      68.074024\n",
        "500        82.792428      74.990087\n",
        "1000       88.386602      81.450089\n",
        "2000       93.004247      87.535363\n",
        "4000       96.410076      92.756703\n",
        "8000       98.554939      96.606475\n",
        "16000      99.666146      99.017399"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Text coverage by part of speech\n",
      "\n",
      "We want to get a feel for how many nouns, verbs, etc., are required in a well-balanced vocabulary. This requires grouping words by part of speech, sorting them by frequency, and graphing the cumulative text coverge for a given number of words. This takes a fair bit of work to set up.\n",
      "\n",
      "First, we need to do quite a bit of data munging:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Merge related cgrams, sum frequency over (cgram, lemme) groups,\n",
      "# and sort by (cgram,freqfilms2).\n",
      "cgram_lemme_freq = sql(\"\"\"\n",
      "SELECT cgram, SUM(freqfilms2) AS freqfilms2\n",
      "  FROM (SELECT CASE WHEN cgram='AUX' THEN 'VER'\n",
      "                    ELSE SUBSTR(cgram, 1, 3)\n",
      "                    END AS cgram,\n",
      "               lemme, freqfilms2\n",
      "          FROM lemme)\n",
      "  GROUP BY cgram, lemme\n",
      "  ORDER BY cgram, freqfilms2 DESC\n",
      "\"\"\")\n",
      "\n",
      "# Convert freqfilms2 to a cumulative percentage over each cgram group.\n",
      "cgram_col = cgram_lemme_freq['cgram']\n",
      "normalized_freq = cgram_lemme_freq.groupby(cgram_col).transform(lambda x: x/x.sum())\n",
      "cumulative_freq = normalized_freq.groupby(cgram_col).cumsum()\n",
      "cgram_lemme_freq['freqfilms2'] = 100.0*cumulative_freq\n",
      "\n",
      "# Sequentially number the rows within each cgram group so we can see the\n",
      "# vocabulary size corresponding to each cumulative percentage.\n",
      "cgram_lemme_freq['rang'] = cgram_lemme_freq.groupby(cgram_col).cumcount()+1\n",
      "\n",
      "# Index by cgram group, and vocabulary size within the group. Uncomment the\n",
      "# last line to view the data.\n",
      "cgram_lemme_freq.set_index(['cgram', 'rang'], inplace = True)\n",
      "#cgram_lemme_freq"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now that we have the data, we can plot it using two different graphs: One for the \"large\" parts of speech (nouns, etc.), and one for the parts of speech which are either closed classes, or at least very small."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def plot_cgrams(labels):\n",
      "    for key in labels.keys():\n",
      "        cgram_group = cgram_lemme_freq.loc[key]\n",
      "        plt.plot(cgram_group.index.values, cgram_group.freqfilms2, label=labels[key])\n",
      "    plt.legend(loc = 'lower right')\n",
      "    plt.title('Text Coverage by Part of Speech (films)')\n",
      "    plt.xlabel('Words known by part of speech')\n",
      "    plt.ylabel('% coverage')\n",
      "    plt.ylim((0,100))\n",
      "    plt.axhline(y=90, color='k', ls='dashed')\n",
      "\n",
      "plt.figure(figsize=(12,4))\n",
      "    \n",
      "plt.subplot(121)\n",
      "small_cgram_labels = {\n",
      "    'PRO': 'Pronouns',\n",
      "    'ADV': 'Adverbs',\n",
      "    'PRE': 'Prepositions',\n",
      "    'CON': 'Conjuctions',\n",
      "    'ART': 'Articles'\n",
      "}\n",
      "plot_cgrams(small_cgram_labels)\n",
      "plt.xlim((0,150))\n",
      "\n",
      "plt.subplot(122)\n",
      "large_cgram_labels = {\n",
      "    'NOM': 'Nouns',\n",
      "    'VER': 'Verbs',\n",
      "    'ADJ': 'Adjectives'\n",
      "}\n",
      "plot_cgrams(large_cgram_labels)\n",
      "plt.xlim((0,10000))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "(0, 10000)"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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cPaBB+zf0+DXuf+SI8eWAAPsdv5798/ONxiVLjMbx443G8HCj8corjcZrr7Xj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\n/wAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x4c71450>"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "It would be nice to have this as a table, too, so we can figure out\u2014for example\u2014how many nouns we need to get 75% coverage. Once again, this will require a fair bit of data munging."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Only include the parts of speech used in our graph.\n",
      "cgram_labels = small_cgram_labels.copy()\n",
      "cgram_labels.update(large_cgram_labels)\n",
      "interesting = cgram_lemme_freq.loc[cgram_labels.keys()]\n",
      "\n",
      "# We'll use this to build a list of columns in our final table.\n",
      "columns = []\n",
      "\n",
      "# Calculate minimum number words for a given percentage of coverage.\n",
      "for threshold in [75,90,95,98,99,99.5]:\n",
      "    # Discard all the rows below our threshold.\n",
      "    over_threshold = interesting[interesting['freqfilms2'] >= threshold]\n",
      "    \n",
      "    # Take the first row that remains.\n",
      "    over_threshold.reset_index(inplace=True)\n",
      "    over_threshold.set_index('cgram', inplace=True)\n",
      "    first_over = over_threshold.groupby(level=0).first()\n",
      "    \n",
      "    # Keep only a single column named after our threashold.\n",
      "    del first_over['freqfilms2']\n",
      "    first_over.rename(columns={'rang': '%r%%' % threshold}, inplace=True)\n",
      "    columns.append(first_over)\n",
      "\n",
      "# Join all the columns together.\n",
      "table = columns[0].join(columns[1:])\n",
      "\n",
      "# Clean up the table a bit and add a total\n",
      "table.index.names = ['Part of speech']\n",
      "table.index = table.index.map(lambda i: cgram_labels[i])\n",
      "table.loc['TOTAL'] = table.sum()\n",
      "table"
     ],
     "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>75%</th>\n",
        "      <th>90%</th>\n",
        "      <th>95%</th>\n",
        "      <th>98%</th>\n",
        "      <th>99%</th>\n",
        "      <th>99.5%</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Adjectives</th>\n",
        "      <td>  136</td>\n",
        "      <td>  620</td>\n",
        "      <td> 1367</td>\n",
        "      <td>  2686</td>\n",
        "      <td>  3742</td>\n",
        "      <td>  4736</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Adverbs</th>\n",
        "      <td>   17</td>\n",
        "      <td>   42</td>\n",
        "      <td>   69</td>\n",
        "      <td>   118</td>\n",
        "      <td>   182</td>\n",
        "      <td>   277</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Articles</th>\n",
        "      <td>    6</td>\n",
        "      <td>    8</td>\n",
        "      <td>    9</td>\n",
        "      <td>     9</td>\n",
        "      <td>    10</td>\n",
        "      <td>    10</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Conjuctions</th>\n",
        "      <td>    5</td>\n",
        "      <td>    9</td>\n",
        "      <td>   11</td>\n",
        "      <td>    14</td>\n",
        "      <td>    15</td>\n",
        "      <td>    17</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Nouns</th>\n",
        "      <td> 1137</td>\n",
        "      <td> 3115</td>\n",
        "      <td> 5215</td>\n",
        "      <td>  8454</td>\n",
        "      <td> 10956</td>\n",
        "      <td> 13347</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Prepositions</th>\n",
        "      <td>    6</td>\n",
        "      <td>    9</td>\n",
        "      <td>   14</td>\n",
        "      <td>    21</td>\n",
        "      <td>    26</td>\n",
        "      <td>    30</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Pronouns</th>\n",
        "      <td>   16</td>\n",
        "      <td>   24</td>\n",
        "      <td>   29</td>\n",
        "      <td>    40</td>\n",
        "      <td>    50</td>\n",
        "      <td>    65</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Verbs</th>\n",
        "      <td>   63</td>\n",
        "      <td>  290</td>\n",
        "      <td>  583</td>\n",
        "      <td>  1108</td>\n",
        "      <td>  1601</td>\n",
        "      <td>  2149</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>TOTAL</th>\n",
        "      <td> 1386</td>\n",
        "      <td> 4117</td>\n",
        "      <td> 7297</td>\n",
        "      <td> 12450</td>\n",
        "      <td> 16582</td>\n",
        "      <td> 20631</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "               75%   90%   95%    98%    99%  99.5%\n",
        "Adjectives     136   620  1367   2686   3742   4736\n",
        "Adverbs         17    42    69    118    182    277\n",
        "Articles         6     8     9      9     10     10\n",
        "Conjuctions      5     9    11     14     15     17\n",
        "Nouns         1137  3115  5215   8454  10956  13347\n",
        "Prepositions     6     9    14     21     26     30\n",
        "Pronouns        16    24    29     40     50     65\n",
        "Verbs           63   290   583   1108   1601   2149\n",
        "TOTAL         1386  4117  7297  12450  16582  20631"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Verb groups\n",
      "\n",
      "We divide verbs into the three standard groups, _-er_, _-ir_ and _-re_. We split _aller_ into its own group, because it's the only irregular _-er_ verb. For now, we treat the auxiliary versions of _\u00eatre_ and _avoir_ in the _pass\u00e9 compos\u00e9_ as being ordinary verbs."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "verbs = sql(\"SELECT * FROM verbe ORDER BY freqfilms2 DESC\")\n",
      "verbs[0:15]"
     ],
     "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>lemme</th>\n",
        "      <th>groupe</th>\n",
        "      <th>prototype</th>\n",
        "      <th>conjugaison</th>\n",
        "      <th>aux</th>\n",
        "      <th>freqfilms2</th>\n",
        "      <th>freqlivres</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0 </th>\n",
        "      <td>    \u00eatre</td>\n",
        "      <td>    re</td>\n",
        "      <td>         \u00eatre</td>\n",
        "      <td>    \u00eatre</td>\n",
        "      <td> avoir</td>\n",
        "      <td> 40310.72</td>\n",
        "      <td> 21587.31</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1 </th>\n",
        "      <td>   avoir</td>\n",
        "      <td>    ir</td>\n",
        "      <td>        avoir</td>\n",
        "      <td>   avoir</td>\n",
        "      <td> avoir</td>\n",
        "      <td> 32131.64</td>\n",
        "      <td> 19227.33</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2 </th>\n",
        "      <td>   aller</td>\n",
        "      <td> aller</td>\n",
        "      <td>        aller</td>\n",
        "      <td>   aller</td>\n",
        "      <td>  \u00eatre</td>\n",
        "      <td>  9992.78</td>\n",
        "      <td>  2854.92</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3 </th>\n",
        "      <td>   faire</td>\n",
        "      <td>    re</td>\n",
        "      <td>      .*faire</td>\n",
        "      <td>   faire</td>\n",
        "      <td> avoir</td>\n",
        "      <td>  8813.52</td>\n",
        "      <td>  5328.99</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4 </th>\n",
        "      <td>    dire</td>\n",
        "      <td>    re</td>\n",
        "      <td>  dire|redire</td>\n",
        "      <td>    dire</td>\n",
        "      <td> avoir</td>\n",
        "      <td>  5946.18</td>\n",
        "      <td>  4832.51</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5 </th>\n",
        "      <td> pouvoir</td>\n",
        "      <td>    ir</td>\n",
        "      <td>      pouvoir</td>\n",
        "      <td> pouvoir</td>\n",
        "      <td> avoir</td>\n",
        "      <td>  5524.46</td>\n",
        "      <td>  2659.75</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6 </th>\n",
        "      <td> vouloir</td>\n",
        "      <td>    ir</td>\n",
        "      <td>    .*vouloir</td>\n",
        "      <td> vouloir</td>\n",
        "      <td> avoir</td>\n",
        "      <td>  5249.31</td>\n",
        "      <td>  1640.16</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7 </th>\n",
        "      <td>  savoir</td>\n",
        "      <td>    ir</td>\n",
        "      <td>     .*savoir</td>\n",
        "      <td>  savoir</td>\n",
        "      <td> avoir</td>\n",
        "      <td>  4516.72</td>\n",
        "      <td>  2003.59</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8 </th>\n",
        "      <td>    voir</td>\n",
        "      <td>    ir</td>\n",
        "      <td> .*voir|.*oir</td>\n",
        "      <td>    voir</td>\n",
        "      <td> avoir</td>\n",
        "      <td>  4119.47</td>\n",
        "      <td>  2401.76</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9 </th>\n",
        "      <td>  devoir</td>\n",
        "      <td>    ir</td>\n",
        "      <td>     .*devoir</td>\n",
        "      <td>  devoir</td>\n",
        "      <td> avoir</td>\n",
        "      <td>  3232.59</td>\n",
        "      <td>  1318.20</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10</th>\n",
        "      <td>   venir</td>\n",
        "      <td>    ir</td>\n",
        "      <td>      .*venir</td>\n",
        "      <td>   venir</td>\n",
        "      <td>  \u00eatre</td>\n",
        "      <td>  2763.82</td>\n",
        "      <td>  1514.53</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11</th>\n",
        "      <td>  suivre</td>\n",
        "      <td>    re</td>\n",
        "      <td>     .*suivre</td>\n",
        "      <td>  suivre</td>\n",
        "      <td> avoir</td>\n",
        "      <td>  2090.55</td>\n",
        "      <td>   949.13</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12</th>\n",
        "      <td>  parler</td>\n",
        "      <td>    er</td>\n",
        "      <td>         .*er</td>\n",
        "      <td>     -er</td>\n",
        "      <td> avoir</td>\n",
        "      <td>  1970.53</td>\n",
        "      <td>  1086.02</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13</th>\n",
        "      <td> prendre</td>\n",
        "      <td>    re</td>\n",
        "      <td>    .*prendre</td>\n",
        "      <td> prendre</td>\n",
        "      <td> avoir</td>\n",
        "      <td>  1913.84</td>\n",
        "      <td>  1466.44</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>14</th>\n",
        "      <td>  croire</td>\n",
        "      <td>    re</td>\n",
        "      <td>     .*croire</td>\n",
        "      <td>  croire</td>\n",
        "      <td> avoir</td>\n",
        "      <td>  1712.02</td>\n",
        "      <td>   947.25</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 15,
       "text": [
        "      lemme groupe     prototype conjugaison    aux  freqfilms2  freqlivres\n",
        "0      \u00eatre     re          \u00eatre        \u00eatre  avoir    40310.72    21587.31\n",
        "1     avoir     ir         avoir       avoir  avoir    32131.64    19227.33\n",
        "2     aller  aller         aller       aller   \u00eatre     9992.78     2854.92\n",
        "3     faire     re       .*faire       faire  avoir     8813.52     5328.99\n",
        "4      dire     re   dire|redire        dire  avoir     5946.18     4832.51\n",
        "5   pouvoir     ir       pouvoir     pouvoir  avoir     5524.46     2659.75\n",
        "6   vouloir     ir     .*vouloir     vouloir  avoir     5249.31     1640.16\n",
        "7    savoir     ir      .*savoir      savoir  avoir     4516.72     2003.59\n",
        "8      voir     ir  .*voir|.*oir        voir  avoir     4119.47     2401.76\n",
        "9    devoir     ir      .*devoir      devoir  avoir     3232.59     1318.20\n",
        "10    venir     ir       .*venir       venir   \u00eatre     2763.82     1514.53\n",
        "11   suivre     re      .*suivre      suivre  avoir     2090.55      949.13\n",
        "12   parler     er          .*er         -er  avoir     1970.53     1086.02\n",
        "13  prendre     re     .*prendre     prendre  avoir     1913.84     1466.44\n",
        "14   croire     re      .*croire      croire  avoir     1712.02      947.25"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "As we can see, all three groups have roughly equal text coverage, but there are actually far more _-er_ verbs than all the others combined. This suggests that a small number of _-ir_ and _-re_ verbs are disproportionately common."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.figure(figsize=(8,8))\n",
      "\n",
      "plt.subplot(121, aspect=True)\n",
      "group_freq = verbs.groupby(verbs['groupe']).sum()\n",
      "plt.pie(group_freq.freqfilms2, labels=group_freq.index.values, colors=colors)\n",
      "plt.title(\"Verb group frequency\")\n",
      "\n",
      "plt.subplot(122, aspect=True)\n",
      "group_size = verbs.groupby(verbs['groupe']).count()\n",
      "plt.pie(group_size.lemme, labels=group_size.index.values, colors=colors)\n",
      "plt.title(\"Verb group size (words)\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 16,
       "text": [
        "<matplotlib.text.Text at 0x4eb8b10>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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wPbG63f6b+vQ6K93uOBqb35fFBef+gj8tc7OnXCVXO1zZTTiiku+gFoyorxex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\nTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x5439710>"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Extract the columns we need, and get rid of 'aller'.\n",
      "group_freq = verbs[['groupe', 'lemme', 'freqfilms2']].copy()\n",
      "group_freq = group_freq[group_freq['groupe'] != 'aller']\n",
      "\n",
      "# Calculate coverage percentages for frequency ranks in each group.\n",
      "groupe_col = group_freq['groupe']\n",
      "normalized_freq = group_freq.groupby(groupe_col).transform(lambda x: x/x.sum())\n",
      "cumulative_freq = 100.0*normalized_freq.groupby(groupe_col).cumsum()\n",
      "group_freq['freqfilms2'] = cumulative_freq\n",
      "group_freq['rang'] = group_freq.groupby(groupe_col).cumcount()+1\n",
      "group_freq.set_index(['groupe', 'rang'], inplace=True)\n",
      "group_freq[0:10]"
     ],
     "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></th>\n",
        "      <th>lemme</th>\n",
        "      <th>freqfilms2</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>groupe</th>\n",
        "      <th>rang</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>re</th>\n",
        "      <th>1</th>\n",
        "      <td>    \u00eatre</td>\n",
        "      <td> 53.707239</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>ir</th>\n",
        "      <th>1</th>\n",
        "      <td>   avoir</td>\n",
        "      <td> 44.826275</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">re</th>\n",
        "      <th>2</th>\n",
        "      <td>   faire</td>\n",
        "      <td> 65.449768</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>    dire</td>\n",
        "      <td> 73.372051</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"6\" valign=\"top\">ir</th>\n",
        "      <th>2</th>\n",
        "      <td> pouvoir</td>\n",
        "      <td> 52.533350</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> vouloir</td>\n",
        "      <td> 59.856569</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>  savoir</td>\n",
        "      <td> 66.157764</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td>    voir</td>\n",
        "      <td> 71.904763</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
        "      <td>  devoir</td>\n",
        "      <td> 76.414491</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
        "      <td>   venir</td>\n",
        "      <td> 80.270247</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 17,
       "text": [
        "               lemme  freqfilms2\n",
        "groupe rang                     \n",
        "re     1        \u00eatre   53.707239\n",
        "ir     1       avoir   44.826275\n",
        "re     2       faire   65.449768\n",
        "       3        dire   73.372051\n",
        "ir     2     pouvoir   52.533350\n",
        "       3     vouloir   59.856569\n",
        "       4      savoir   66.157764\n",
        "       5        voir   71.904763\n",
        "       6      devoir   76.414491\n",
        "       7       venir   80.270247"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Sigh. My database is in French, and my libraries are in English.\n",
      "# There's no way to avoid coding in franglais, I fear.\n",
      "for group in ['er', 'ir', 're']:\n",
      "    g = group_freq.loc[group]\n",
      "    plt.plot(g.index.values, g.freqfilms2, label=group)\n",
      "plt.title('Verb Coverage by Group')\n",
      "plt.legend(loc = 'lower right')\n",
      "plt.xlabel('Verbs known in group')\n",
      "plt.ylabel('% coverage')\n",
      "plt.xlim((1,100))\n",
      "plt.ylim((0,100))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 18,
       "text": [
        "(0, 100)"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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7qGOOk58PcXEcyjtE1//XlUOPHCImSi1SIo3J4cOWEL79NjitWgUdOlgSCJ16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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x4ebc9d0>"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "If we take the first 40 _-ir_ and _-re_ verbs, we get better than 96% coverage. Even the first 20 in each group will give us better than 92% coverage. Here's a list for people who want to master all the high-frequency irregular verbs."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def html_for_group(groupe):\n",
      "    lst = ', '.join(group_freq.loc[groupe].loc[1:40]['lemme'].tolist())\n",
      "    return '<p><i>-%s</i> verbs: %s.</p>' % (groupe, lst)\n",
      "HTML(\"<p><i>-er</i> verbs: aller.</p>\" + html_for_group('ir') + html_for_group('re'))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<p><i>-er</i> verbs: aller.</p><p><i>-ir</i> verbs: avoir, pouvoir, vouloir, savoir, voir, devoir, venir, falloir, partir, mourir, sortir, revenir, finir, sentir, tenir, devenir, ouvrir, dormir, asseoir, souvenir, servir, valoir, agir, recevoir, mentir, offrir, choisir, revoir, courir, r\u00e9ussir, pr\u00e9venir, d\u00e9couvrir, maintenir, r\u00e9fl\u00e9chir, souffrir, couvrir, obtenir, appartenir, ressentir, pr\u00e9voir.</p><p><i>-re</i> verbs: \u00eatre, faire, dire, suivre, prendre, croire, attendre, mettre, conna\u00eetre, comprendre, entendre, plaire, perdre, vivre, rendre, foutre, apprendre, boire, \u00e9crire, lire, r\u00e9pondre, descendre, suffire, vendre, battre, promettre, permettre, conduire, dispara\u00eetre, taire, remettre, reconna\u00eetre, rire, reprendre, d\u00e9truire, para\u00eetre, craindre, na\u00eetre, rejoindre, d\u00e9fendre.</p>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 19,
       "text": [
        "<IPython.core.display.HTML at 0x59c01d0>"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Verb groups, in horrifying detail\n",
      "\n",
      "Of course, not all _-er_ verbs are completely regular, and there are patterns among the other verb groups. Fortunately, there's a nice XML file of [French verb conjugation rules](http://sourceforge.net/projects/fvcr/) that we can use to examine these hidden details. Combining that with quite a bit of custom code, we can assign a \"conjugator\" to each verb prototype, and verify that the generated forms match the XML data. This gives us a much shorter list of key forms."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "verbs2 = sql(\"\"\"\n",
      "SELECT conjugaison.nom AS conjugaison, lemme, freqfilms2, resume\n",
      "  FROM verbe\n",
      "  LEFT OUTER JOIN conjugaison\n",
      "    ON verbe.conjugaison = conjugaison.nom\n",
      "  ORDER BY freqfilms2 DESC\n",
      "\"\"\")\n",
      "verbs2['freqfilms2'] = 100 * verbs2['freqfilms2'] / verbs2['freqfilms2'].sum()\n",
      "def summarize_conjugator(grp):\n",
      "    return pd.Series(dict(exemples=', '.join(grp.lemme[0:5]),\n",
      "                          compte=grp.lemme.count(),\n",
      "                          freqfilms2=grp.freqfilms2.sum(),\n",
      "                          resume=grp.resume.iloc[0]))\n",
      "conjugators = verbs2.groupby('conjugaison').apply(summarize_conjugator).sort('freqfilms2', ascending=False)\n",
      "conjugators.reset_index(inplace=True)\n",
      "conjugators.index.names = ['rang']\n",
      "conjugators.reset_index(inplace=True)\n",
      "conjugators['rang'] = conjugators['rang'] + 1\n",
      "conjugators['freqfilms2'] = conjugators['freqfilms2'].cumsum()\n",
      "conjugators.set_index('rang', inplace=True)\n",
      "save_tsv('conjugators.tsv', conjugators)\n",
      "conjugators"
     ],
     "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>conjugaison</th>\n",
        "      <th>compte</th>\n",
        "      <th>exemples</th>\n",
        "      <th>freqfilms2</th>\n",
        "      <th>resume</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>rang</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>1 </th>\n",
        "      <td>         -er</td>\n",
        "      <td> 5187</td>\n",
        "      <td>            parler, aimer, passer, penser, trouver</td>\n",
        "      <td> 27.057864</td>\n",
        "      <td> (p.p.) parl\u00e9, je parle, tu parles, il parle, n...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2 </th>\n",
        "      <td>        \u00eatre</td>\n",
        "      <td>    1</td>\n",
        "      <td>                                              \u00eatre</td>\n",
        "      <td> 44.971814</td>\n",
        "      <td>                                       (irregular)</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3 </th>\n",
        "      <td>       avoir</td>\n",
        "      <td>    1</td>\n",
        "      <td>                                             avoir</td>\n",
        "      <td> 59.251008</td>\n",
        "      <td>                                       (irregular)</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4 </th>\n",
        "      <td>       aller</td>\n",
        "      <td>    1</td>\n",
        "      <td>                                             aller</td>\n",
        "      <td> 63.691766</td>\n",
        "      <td>                                       (irregular)</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5 </th>\n",
        "      <td>       faire</td>\n",
        "      <td>    5</td>\n",
        "      <td>  faire, refaire, satisfaire, d\u00e9faire, contrefaire</td>\n",
        "      <td> 67.651207</td>\n",
        "      <td>                                       (irregular)</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6 </th>\n",
        "      <td>        dire</td>\n",
        "      <td>    2</td>\n",
        "      <td>                                      dire, redire</td>\n",
        "      <td> 70.297491</td>\n",
        "      <td>                Like interdire, except: vous dites</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7 </th>\n",
        "      <td>     pouvoir</td>\n",
        "      <td>    1</td>\n",
        "      <td>                                           pouvoir</td>\n",
        "      <td> 72.752543</td>\n",
        "      <td>                                       (irregular)</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8 </th>\n",
        "      <td>       venir</td>\n",
        "      <td>   26</td>\n",
        "      <td>          venir, revenir, tenir, devenir, souvenir</td>\n",
        "      <td> 75.130407</td>\n",
        "      <td> Like -ir, except: (p.p.) venu, tu viens, nous ...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9 </th>\n",
        "      <td>     vouloir</td>\n",
        "      <td>    2</td>\n",
        "      <td>                                vouloir, revouloir</td>\n",
        "      <td> 77.463383</td>\n",
        "      <td>                                       (irregular)</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10</th>\n",
        "      <td>      savoir</td>\n",
        "      <td>    3</td>\n",
        "      <td>                      savoir, non-savoir, assavoir</td>\n",
        "      <td> 79.470603</td>\n",
        "      <td>                                       (irregular)</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11</th>\n",
        "      <td>         -re</td>\n",
        "      <td>   50</td>\n",
        "      <td>      attendre, entendre, perdre, rendre, r\u00e9pondre</td>\n",
        "      <td> 81.428246</td>\n",
        "      <td> (p.p.) attendu, tu attends, nous attendons, il...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12</th>\n",
        "      <td>        voir</td>\n",
        "      <td>    5</td>\n",
        "      <td>        voir, revoir, entrevoir, ravoir, comparoir</td>\n",
        "      <td> 83.332903</td>\n",
        "      <td> Like -ir, except: (p.p.) vu, nous voyons, ils ...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13</th>\n",
        "      <td>      partir</td>\n",
        "      <td>   18</td>\n",
        "      <td>            partir, sortir, sentir, dormir, servir</td>\n",
        "      <td> 84.950418</td>\n",
        "      <td>                         Like -ir, except: tu pars</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>14</th>\n",
        "      <td>     prendre</td>\n",
        "      <td>   12</td>\n",
        "      <td> prendre, comprendre, apprendre, reprendre, sur...</td>\n",
        "      <td> 86.441360</td>\n",
        "      <td> Like -re, except: (p.p.) pris, nous prenons, i...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>15</th>\n",
        "      <td>      devoir</td>\n",
        "      <td>    2</td>\n",
        "      <td>                                  devoir, redevoir</td>\n",
        "      <td> 87.877921</td>\n",
        "      <td>                                       (irregular)</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>16</th>\n",
        "      <td> -ir (-iss-)</td>\n",
        "      <td>  304</td>\n",
        "      <td>          finir, agir, choisir, r\u00e9ussir, r\u00e9fl\u00e9chir</td>\n",
        "      <td> 89.052373</td>\n",
        "      <td> (p.p.) fini, tu finis, nous finissons, ils fin...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>17</th>\n",
        "      <td>      suivre</td>\n",
        "      <td>    2</td>\n",
        "      <td>                                suivre, poursuivre</td>\n",
        "      <td> 90.010207</td>\n",
        "      <td>           Like -re, except: (p.p.) suivi, tu suis</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>18</th>\n",
        "      <td>     esp\u00e9rer</td>\n",
        "      <td>  199</td>\n",
        "      <td>   esp\u00e9rer, inqui\u00e9ter, pr\u00e9f\u00e9rer, prot\u00e9ger, r\u00e9p\u00e9ter</td>\n",
        "      <td> 90.856970</td>\n",
        "      <td>        Like -er, except: tu esp\u00e8res, ils esp\u00e8rent</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>19</th>\n",
        "      <td>     acheter</td>\n",
        "      <td>   64</td>\n",
        "      <td>        acheter, emmener, amener, ramener, enlever</td>\n",
        "      <td> 91.628962</td>\n",
        "      <td> Like -er, except: tu ach\u00e8tes, ils ach\u00e8tent, il...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>20</th>\n",
        "      <td>      croire</td>\n",
        "      <td>    1</td>\n",
        "      <td>                                            croire</td>\n",
        "      <td> 92.389778</td>\n",
        "      <td> Like -re, except: (p.p.) cru, nous croyons, (p...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>21</th>\n",
        "      <td>     appeler</td>\n",
        "      <td>  112</td>\n",
        "      <td>       appeler, rappeler, jeter, rejeter, projeter</td>\n",
        "      <td> 93.148066</td>\n",
        "      <td> Like -er, except: tu appelles, ils appellent, ...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>22</th>\n",
        "      <td>      mettre</td>\n",
        "      <td>   15</td>\n",
        "      <td>  mettre, promettre, permettre, remettre, admettre</td>\n",
        "      <td> 93.891337</td>\n",
        "      <td>    Like battre, except: (p.p.) mis, (p.s.) il mit</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>23</th>\n",
        "      <td>     falloir</td>\n",
        "      <td>    1</td>\n",
        "      <td>                                           falloir</td>\n",
        "      <td> 94.626262</td>\n",
        "      <td>                                       (irregular)</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>24</th>\n",
        "      <td>   conna\u00eetre</td>\n",
        "      <td>   10</td>\n",
        "      <td> conna\u00eetre, dispara\u00eetre, reconna\u00eetre, para\u00eetre,...</td>\n",
        "      <td> 95.267398</td>\n",
        "      <td> Like -re, except: (p.p.) connu, je connais, tu...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25</th>\n",
        "      <td>     essayer</td>\n",
        "      <td>   30</td>\n",
        "      <td>          essayer, payer, effrayer, balayer, rayer</td>\n",
        "      <td> 95.784982</td>\n",
        "      <td> Like ennuyer, except: tu essaies/tu essayes, i...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>26</th>\n",
        "      <td>      ouvrir</td>\n",
        "      <td>    9</td>\n",
        "      <td>      ouvrir, offrir, d\u00e9couvrir, souffrir, couvrir</td>\n",
        "      <td> 96.201333</td>\n",
        "      <td> Like -ir, except: (p.p.) ouvert, j'ouvre, tu o...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>27</th>\n",
        "      <td>      mourir</td>\n",
        "      <td>    1</td>\n",
        "      <td>                                            mourir</td>\n",
        "      <td> 96.608587</td>\n",
        "      <td> Like -ir, except: (p.p.) mort, tu meurs, ils m...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>28</th>\n",
        "      <td>      plaire</td>\n",
        "      <td>    3</td>\n",
        "      <td>                       plaire, d\u00e9plaire, complaire</td>\n",
        "      <td> 96.884291</td>\n",
        "      <td>             Like taire, except: il pla\u00eet/il plait</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>29</th>\n",
        "      <td>       vivre</td>\n",
        "      <td>    3</td>\n",
        "      <td>                          vivre, survivre, revivre</td>\n",
        "      <td> 97.143889</td>\n",
        "      <td> Like suivre, except: (p.p.) v\u00e9cu, (p.s.) il v\u00e9cut</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>30</th>\n",
        "      <td>    conduire</td>\n",
        "      <td>   24</td>\n",
        "      <td> conduire, d\u00e9truire, construire, produire, r\u00e9duire</td>\n",
        "      <td> 97.396542</td>\n",
        "      <td>       Like interdire, except: (p.s.) il conduisit</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>34</th>\n",
        "      <td>      \u00e9crire</td>\n",
        "      <td>   12</td>\n",
        "      <td>    \u00e9crire, d\u00e9crire, inscrire, prescrire, r\u00e9\u00e9crire</td>\n",
        "      <td> 98.157882</td>\n",
        "      <td> Like -re, except: (p.p.) \u00e9crit, nous \u00e9crivons,...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>35</th>\n",
        "      <td>       boire</td>\n",
        "      <td>    2</td>\n",
        "      <td>                                    boire, reboire</td>\n",
        "      <td> 98.308728</td>\n",
        "      <td> Like -re, except: (p.p.) bu, nous buvons, ils ...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>36</th>\n",
        "      <td>     asseoir</td>\n",
        "      <td>    2</td>\n",
        "      <td>                                 asseoir, rasseoir</td>\n",
        "      <td> 98.453020</td>\n",
        "      <td> Like -ir, except: (p.p.) assis, tu assieds/tu ...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>37</th>\n",
        "      <td>        lire</td>\n",
        "      <td>    4</td>\n",
        "      <td>                      lire, \u00e9lire, relire, r\u00e9\u00e9lire</td>\n",
        "      <td> 98.586161</td>\n",
        "      <td>  Like interdire, except: (p.p.) lu, (p.s.) il lut</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>38</th>\n",
        "      <td>      battre</td>\n",
        "      <td>    9</td>\n",
        "      <td>    battre, abattre, combattre, d\u00e9battre, rabattre</td>\n",
        "      <td> 98.718538</td>\n",
        "      <td>                         Like -re, except: tu bats</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>39</th>\n",
        "      <td>    recevoir</td>\n",
        "      <td>    9</td>\n",
        "      <td> recevoir, apercevoir, d\u00e9cevoir, concevoir, per...</td>\n",
        "      <td> 98.845991</td>\n",
        "      <td> Like -ir, except: (p.p.) re\u00e7u, tu re\u00e7ois, nous...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>40</th>\n",
        "      <td>     ennuyer</td>\n",
        "      <td>   52</td>\n",
        "      <td>       ennuyer, nettoyer, appuyer, noyer, employer</td>\n",
        "      <td> 98.964911</td>\n",
        "      <td> Like -er, except: tu ennuies, ils ennuient, il...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>41</th>\n",
        "      <td>      valoir</td>\n",
        "      <td>    2</td>\n",
        "      <td>                                valoir, \u00e9quivaloir</td>\n",
        "      <td> 99.070811</td>\n",
        "      <td>                                       (irregular)</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>42</th>\n",
        "      <td>     suffire</td>\n",
        "      <td>    1</td>\n",
        "      <td>                                           suffire</td>\n",
        "      <td> 99.173942</td>\n",
        "      <td>              Like interdire, except: (p.p.) suffi</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>43</th>\n",
        "      <td>        rire</td>\n",
        "      <td>    2</td>\n",
        "      <td>                                     rire, sourire</td>\n",
        "      <td> 99.260244</td>\n",
        "      <td> Like -re, except: (p.p.) ri, nous rions, ils r...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>44</th>\n",
        "      <td>      courir</td>\n",
        "      <td>    9</td>\n",
        "      <td>   courir, parcourir, secourir, accourir, recourir</td>\n",
        "      <td> 99.339511</td>\n",
        "      <td> Like -ir, except: (p.p.) couru, tu cours, il c...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>45</th>\n",
        "      <td>       taire</td>\n",
        "      <td>    1</td>\n",
        "      <td>                                             taire</td>\n",
        "      <td> 99.408153</td>\n",
        "      <td> Like -re, except: (p.p.) tu, nous taisons, ils...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>46</th>\n",
        "      <td>        fuir</td>\n",
        "      <td>    2</td>\n",
        "      <td>                                      fuir, enfuir</td>\n",
        "      <td> 99.471239</td>\n",
        "      <td> Like -ir, except: tu fuis, nous fuyons, ils fu...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>47</th>\n",
        "      <td>      na\u00eetre</td>\n",
        "      <td>    1</td>\n",
        "      <td>                                            na\u00eetre</td>\n",
        "      <td> 99.522834</td>\n",
        "      <td> Like conna\u00eetre, except: (p.p.) n\u00e9, (p.s.) il n...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>48</th>\n",
        "      <td>      ficher</td>\n",
        "      <td>    1</td>\n",
        "      <td>                                            ficher</td>\n",
        "      <td> 99.566318</td>\n",
        "      <td>              Like -er, except: (p.p.) fich\u00e9/fichu</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>49</th>\n",
        "      <td>  convaincre</td>\n",
        "      <td>    3</td>\n",
        "      <td>                 convaincre, vaincre, reconvaincre</td>\n",
        "      <td> 99.603891</td>\n",
        "      <td> Like -re, except: je convaincs, tu convaincs, ...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50</th>\n",
        "      <td>   interdire</td>\n",
        "      <td>    6</td>\n",
        "      <td>     interdire, pr\u00e9dire, contredire, m\u00e9dire, adire</td>\n",
        "      <td> 99.639585</td>\n",
        "      <td> Like -re, except: (p.p.) interdit, nous interd...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>51</th>\n",
        "      <td>     pr\u00e9voir</td>\n",
        "      <td>    1</td>\n",
        "      <td>                                           pr\u00e9voir</td>\n",
        "      <td> 99.674342</td>\n",
        "      <td>                    Like voir, except: il pr\u00e9voira</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>52</th>\n",
        "      <td>    pleuvoir</td>\n",
        "      <td>    1</td>\n",
        "      <td>                                          pleuvoir</td>\n",
        "      <td> 99.704196</td>\n",
        "      <td>                                       (defective)</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>53</th>\n",
        "      <td>    parfaire</td>\n",
        "      <td>    1</td>\n",
        "      <td>                                          parfaire</td>\n",
        "      <td> 99.730887</td>\n",
        "      <td>                                       (defective)</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>54</th>\n",
        "      <td>        ha\u00efr</td>\n",
        "      <td>    1</td>\n",
        "      <td>                                              ha\u00efr</td>\n",
        "      <td> 99.755520</td>\n",
        "      <td> Like -ir (-iss-), except: tu hais, (p.s.) il h...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>55</th>\n",
        "      <td>  accueillir</td>\n",
        "      <td>    3</td>\n",
        "      <td>                  accueillir, cueillir, recueillir</td>\n",
        "      <td> 99.779695</td>\n",
        "      <td> Like -ir, except: j'accueille, tu accueilles, ...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>56</th>\n",
        "      <td>       b\u00e9nir</td>\n",
        "      <td>    1</td>\n",
        "      <td>                                             b\u00e9nir</td>\n",
        "      <td> 99.801364</td>\n",
        "      <td>       Like -ir (-iss-), except: (p.p.) b\u00e9ni/b\u00e9nit</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>57</th>\n",
        "      <td>     faillir</td>\n",
        "      <td>    1</td>\n",
        "      <td>                                           faillir</td>\n",
        "      <td> 99.821006</td>\n",
        "      <td>                                       (irregular)</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>58</th>\n",
        "      <td>    r\u00e9soudre</td>\n",
        "      <td>    1</td>\n",
        "      <td>                                          r\u00e9soudre</td>\n",
        "      <td> 99.839200</td>\n",
        "      <td> Like -re, except: (p.p.) r\u00e9solu, tu r\u00e9sous, no...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>59</th>\n",
        "      <td>    conclure</td>\n",
        "      <td>    2</td>\n",
        "      <td>                                 conclure, exclure</td>\n",
        "      <td> 99.856353</td>\n",
        "      <td> Like -re, except: (p.p.) conclu, (p.s.) il con...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>60</th>\n",
        "      <td>   conqu\u00e9rir</td>\n",
        "      <td>    6</td>\n",
        "      <td> conqu\u00e9rir, acqu\u00e9rir, requ\u00e9rir, reconqu\u00e9rir, en...</td>\n",
        "      <td> 99.870845</td>\n",
        "      <td> Like -ir, except: (p.p.) conquis, tu conquiers...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>61</th>\n",
        "      <td>   distraire</td>\n",
        "      <td>   10</td>\n",
        "      <td> distraire, extraire, traire, soustraire, rentr...</td>\n",
        "      <td> 99.885199</td>\n",
        "      <td>                                       (defective)</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>62</th>\n",
        "      <td>    pourvoir</td>\n",
        "      <td>    1</td>\n",
        "      <td>                                          pourvoir</td>\n",
        "      <td> 99.894749</td>\n",
        "      <td>           Like pr\u00e9voir, except: (p.s.) il pourvut</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>63</th>\n",
        "      <td>       douer</td>\n",
        "      <td>    1</td>\n",
        "      <td>                                             douer</td>\n",
        "      <td> 99.903770</td>\n",
        "      <td>                                       (defective)</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>63 rows \u00d7 5 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 20,
       "text": [
        "      conjugaison  compte                                           exemples  \\\n",
        "rang                                                                           \n",
        "1             -er    5187             parler, aimer, passer, penser, trouver   \n",
        "2            \u00eatre       1                                               \u00eatre   \n",
        "3           avoir       1                                              avoir   \n",
        "4           aller       1                                              aller   \n",
        "5           faire       5   faire, refaire, satisfaire, d\u00e9faire, contrefaire   \n",
        "6            dire       2                                       dire, redire   \n",
        "7         pouvoir       1                                            pouvoir   \n",
        "8           venir      26           venir, revenir, tenir, devenir, souvenir   \n",
        "9         vouloir       2                                 vouloir, revouloir   \n",
        "10         savoir       3                       savoir, non-savoir, assavoir   \n",
        "11            -re      50       attendre, entendre, perdre, rendre, r\u00e9pondre   \n",
        "12           voir       5         voir, revoir, entrevoir, ravoir, comparoir   \n",
        "13         partir      18             partir, sortir, sentir, dormir, servir   \n",
        "14        prendre      12  prendre, comprendre, apprendre, reprendre, sur...   \n",
        "15         devoir       2                                   devoir, redevoir   \n",
        "16    -ir (-iss-)     304           finir, agir, choisir, r\u00e9ussir, r\u00e9fl\u00e9chir   \n",
        "17         suivre       2                                 suivre, poursuivre   \n",
        "18        esp\u00e9rer     199    esp\u00e9rer, inqui\u00e9ter, pr\u00e9f\u00e9rer, prot\u00e9ger, r\u00e9p\u00e9ter   \n",
        "19        acheter      64         acheter, emmener, amener, ramener, enlever   \n",
        "20         croire       1                                             croire   \n",
        "21        appeler     112        appeler, rappeler, jeter, rejeter, projeter   \n",
        "22         mettre      15   mettre, promettre, permettre, remettre, admettre   \n",
        "23        falloir       1                                            falloir   \n",
        "24      conna\u00eetre      10  conna\u00eetre, dispara\u00eetre, reconna\u00eetre, para\u00eetre,...   \n",
        "25        essayer      30           essayer, payer, effrayer, balayer, rayer   \n",
        "26         ouvrir       9       ouvrir, offrir, d\u00e9couvrir, souffrir, couvrir   \n",
        "27         mourir       1                                             mourir   \n",
        "28         plaire       3                        plaire, d\u00e9plaire, complaire   \n",
        "29          vivre       3                           vivre, survivre, revivre   \n",
        "30       conduire      24  conduire, d\u00e9truire, construire, produire, r\u00e9duire   \n",
        "...           ...     ...                                                ...   \n",
        "34         \u00e9crire      12     \u00e9crire, d\u00e9crire, inscrire, prescrire, r\u00e9\u00e9crire   \n",
        "35          boire       2                                     boire, reboire   \n",
        "36        asseoir       2                                  asseoir, rasseoir   \n",
        "37           lire       4                       lire, \u00e9lire, relire, r\u00e9\u00e9lire   \n",
        "38         battre       9     battre, abattre, combattre, d\u00e9battre, rabattre   \n",
        "39       recevoir       9  recevoir, apercevoir, d\u00e9cevoir, concevoir, per...   \n",
        "40        ennuyer      52        ennuyer, nettoyer, appuyer, noyer, employer   \n",
        "41         valoir       2                                 valoir, \u00e9quivaloir   \n",
        "42        suffire       1                                            suffire   \n",
        "43           rire       2                                      rire, sourire   \n",
        "44         courir       9    courir, parcourir, secourir, accourir, recourir   \n",
        "45          taire       1                                              taire   \n",
        "46           fuir       2                                       fuir, enfuir   \n",
        "47         na\u00eetre       1                                             na\u00eetre   \n",
        "48         ficher       1                                             ficher   \n",
        "49     convaincre       3                  convaincre, vaincre, reconvaincre   \n",
        "50      interdire       6      interdire, pr\u00e9dire, contredire, m\u00e9dire, adire   \n",
        "51        pr\u00e9voir       1                                            pr\u00e9voir   \n",
        "52       pleuvoir       1                                           pleuvoir   \n",
        "53       parfaire       1                                           parfaire   \n",
        "54           ha\u00efr       1                                               ha\u00efr   \n",
        "55     accueillir       3                   accueillir, cueillir, recueillir   \n",
        "56          b\u00e9nir       1                                              b\u00e9nir   \n",
        "57        faillir       1                                            faillir   \n",
        "58       r\u00e9soudre       1                                           r\u00e9soudre   \n",
        "59       conclure       2                                  conclure, exclure   \n",
        "60      conqu\u00e9rir       6  conqu\u00e9rir, acqu\u00e9rir, requ\u00e9rir, reconqu\u00e9rir, en...   \n",
        "61      distraire      10  distraire, extraire, traire, soustraire, rentr...   \n",
        "62       pourvoir       1                                           pourvoir   \n",
        "63          douer       1                                              douer   \n",
        "\n",
        "      freqfilms2                                             resume  \n",
        "rang                                                                 \n",
        "1      27.057864  (p.p.) parl\u00e9, je parle, tu parles, il parle, n...  \n",
        "2      44.971814                                        (irregular)  \n",
        "3      59.251008                                        (irregular)  \n",
        "4      63.691766                                        (irregular)  \n",
        "5      67.651207                                        (irregular)  \n",
        "6      70.297491                 Like interdire, except: vous dites  \n",
        "7      72.752543                                        (irregular)  \n",
        "8      75.130407  Like -ir, except: (p.p.) venu, tu viens, nous ...  \n",
        "9      77.463383                                        (irregular)  \n",
        "10     79.470603                                        (irregular)  \n",
        "11     81.428246  (p.p.) attendu, tu attends, nous attendons, il...  \n",
        "12     83.332903  Like -ir, except: (p.p.) vu, nous voyons, ils ...  \n",
        "13     84.950418                          Like -ir, except: tu pars  \n",
        "14     86.441360  Like -re, except: (p.p.) pris, nous prenons, i...  \n",
        "15     87.877921                                        (irregular)  \n",
        "16     89.052373  (p.p.) fini, tu finis, nous finissons, ils fin...  \n",
        "17     90.010207            Like -re, except: (p.p.) suivi, tu suis  \n",
        "18     90.856970         Like -er, except: tu esp\u00e8res, ils esp\u00e8rent  \n",
        "19     91.628962  Like -er, except: tu ach\u00e8tes, ils ach\u00e8tent, il...  \n",
        "20     92.389778  Like -re, except: (p.p.) cru, nous croyons, (p...  \n",
        "21     93.148066  Like -er, except: tu appelles, ils appellent, ...  \n",
        "22     93.891337     Like battre, except: (p.p.) mis, (p.s.) il mit  \n",
        "23     94.626262                                        (irregular)  \n",
        "24     95.267398  Like -re, except: (p.p.) connu, je connais, tu...  \n",
        "25     95.784982  Like ennuyer, except: tu essaies/tu essayes, i...  \n",
        "26     96.201333  Like -ir, except: (p.p.) ouvert, j'ouvre, tu o...  \n",
        "27     96.608587  Like -ir, except: (p.p.) mort, tu meurs, ils m...  \n",
        "28     96.884291              Like taire, except: il pla\u00eet/il plait  \n",
        "29     97.143889  Like suivre, except: (p.p.) v\u00e9cu, (p.s.) il v\u00e9cut  \n",
        "30     97.396542        Like interdire, except: (p.s.) il conduisit  \n",
        "...          ...                                                ...  \n",
        "34     98.157882  Like -re, except: (p.p.) \u00e9crit, nous \u00e9crivons,...  \n",
        "35     98.308728  Like -re, except: (p.p.) bu, nous buvons, ils ...  \n",
        "36     98.453020  Like -ir, except: (p.p.) assis, tu assieds/tu ...  \n",
        "37     98.586161   Like interdire, except: (p.p.) lu, (p.s.) il lut  \n",
        "38     98.718538                          Like -re, except: tu bats  \n",
        "39     98.845991  Like -ir, except: (p.p.) re\u00e7u, tu re\u00e7ois, nous...  \n",
        "40     98.964911  Like -er, except: tu ennuies, ils ennuient, il...  \n",
        "41     99.070811                                        (irregular)  \n",
        "42     99.173942               Like interdire, except: (p.p.) suffi  \n",
        "43     99.260244  Like -re, except: (p.p.) ri, nous rions, ils r...  \n",
        "44     99.339511  Like -ir, except: (p.p.) couru, tu cours, il c...  \n",
        "45     99.408153  Like -re, except: (p.p.) tu, nous taisons, ils...  \n",
        "46     99.471239  Like -ir, except: tu fuis, nous fuyons, ils fu...  \n",
        "47     99.522834  Like conna\u00eetre, except: (p.p.) n\u00e9, (p.s.) il n...  \n",
        "48     99.566318               Like -er, except: (p.p.) fich\u00e9/fichu  \n",
        "49     99.603891  Like -re, except: je convaincs, tu convaincs, ...  \n",
        "50     99.639585  Like -re, except: (p.p.) interdit, nous interd...  \n",
        "51     99.674342                     Like voir, except: il pr\u00e9voira  \n",
        "52     99.704196                                        (defective)  \n",
        "53     99.730887                                        (defective)  \n",
        "54     99.755520  Like -ir (-iss-), except: tu hais, (p.s.) il h...  \n",
        "55     99.779695  Like -ir, except: j'accueille, tu accueilles, ...  \n",
        "56     99.801364        Like -ir (-iss-), except: (p.p.) b\u00e9ni/b\u00e9nit  \n",
        "57     99.821006                                        (irregular)  \n",
        "58     99.839200  Like -re, except: (p.p.) r\u00e9solu, tu r\u00e9sous, no...  \n",
        "59     99.856353  Like -re, except: (p.p.) conclu, (p.s.) il con...  \n",
        "60     99.870845  Like -ir, except: (p.p.) conquis, tu conquiers...  \n",
        "61     99.885199                                        (defective)  \n",
        "62     99.894749            Like pr\u00e9voir, except: (p.s.) il pourvut  \n",
        "63     99.903770                                        (defective)  \n",
        "\n",
        "[63 rows x 5 columns]"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot(conjugators.index.values, conjugators.freqfilms2)\n",
      "plt.title('Verb Coverage by Conjugator')\n",
      "#plt.legend(loc = 'lower right')\n",
      "plt.xlabel('Verb conjugations known')\n",
      "plt.ylabel('% coverage')\n",
      "plt.ylim((0,100))\n",
      "plt.xlim((1,60))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 21,
       "text": [
        "(1, 60)"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x58c29d0>"
       ]
      }
     ],
     "prompt_number": 21
    }
   ],
   "metadata": {}
  }
 ]
}