{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Gender roles and pronouns in some 19th century novels by women<a name=\"top\"></a>\n",
    "\n",
    "This is essentially a replication of the text analysis by Julia Silge on [Gender roles with text mining and _n_-grams](https://juliasilge.com/blog/gender-pronouns/), which in turn was an attempt at a similar study to that contained in [Understanding Gender and Character Agency in the 19th Century Novel](http://culturalanalytics.org/2016/12/understanding-gender-and-character-agency-in-the-19th-century-novel/) by Matthew Jockers and Gabi Kirilloff. \n",
    "\n",
    "The idea is to get an insight into gender roles and activity in novels by looking at the verbs which are associated with men and women. The Jockers and Kirilloff study used the Stanford CoreNLP engine for detailed parsing of the text; Silge used simple word bigram analysis to find words that follow from gendered pronouns.\n",
    "\n",
    "This notebook does the same analysis as Silge, but using the tools available to [TM351](http://www.open.ac.uk/courses/qualifications/details/tm351) students.\n",
    "\n",
    "The books were downloaded from [Project Gutenberg](http://onlinebooks.library.upenn.edu/webbin/gutbook/author?name=Austen%2C%20Jane%2C%201775-1817)\n",
    "\n",
    "## Table of contents\n",
    "\n",
    "1. [Read the books and get the bigrams](#readbooks)\n",
    "1. [Find the most skewed gendered bigrams](#skewedbigrams)\n",
    "1. [Odds ratio: a quantification of the skew](#oddsratio)\n",
    "1. [Plot the most skewed words](#plotting)\n",
    "\n",
    "### Other authors\n",
    "* [George Eliot](#eliot)\n",
    "* [Charlotte Brontë](#bronte)\n",
    "* [Oscar Wilde](#wilde)\n",
    "* [Charles Dickens](#dickens)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "import re\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read the books and get the bigrams<a name=\"readbooks\"></a>\n",
    "\n",
    "First, define the books and the files they're in. This assumes you've already downloaded the books and stored them in the same directory as this notebook.\n",
    "\n",
    "In the books I used, I removed the Gutenberg-specific introduction and licence text at the start and end of the files. \n",
    "\n",
    "* [Back to top](#top)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": true,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "austen_books_filenames = {\n",
    "    'Persuasion': '105.txt',\n",
    "    'Northanger Abbey': '121.txt',\n",
    "    'Pride and Prejudice': '1342.txt',\n",
    "    'Mansfield Park': '141.txt',\n",
    "    'Emma': '158-0.txt',\n",
    "    'Sense and Sensibility': '161.txt'\n",
    "}\n",
    "\n",
    "eliot_books_filenames = {\n",
    "    'Middlemarch': 'pg145.txt',\n",
    "    'Silas Marner': 'pg550.txt',\n",
    "    'The Mill on the Floss': '6688-0.txt'\n",
    "}\n",
    "\n",
    "bronte_books_filenames = {'Jane Eyre': 'pg1260.txt'}\n",
    "\n",
    "wilde_books_filenames = {'The Picture of Dorian Grey': '174.txt'}\n",
    "\n",
    "dickens_books_filenames = {'David Copperfield': '766-0.txt',\n",
    "                           'Great Expectations': '1400-0.txt',\n",
    "                           'The Mystery of Edwin Drood': '564-0.txt',\n",
    "                           'Hard Times': '786-0.txt'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "austen_books = {title: open(austen_books_filenames[title],  \n",
    "                            encoding='utf-8', errors='replace').read().lower()\n",
    "                for title in austen_books_filenames}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Split a book into words, dropping punctuation and excessive whitespace.\n",
    "\n",
    "The stopwords are words to be dropped from the text. We'll use that feature later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true,
    "run_control": {
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "token_split_re = re.compile(r'\\W')\n",
    "\n",
    "def tokens(text, stopwords=None):\n",
    "    if stopwords is None: stopwords = []\n",
    "    return [token.strip('_') # underscore is used to signify italic, but we don't want that in this analysis\n",
    "            for token in re.split(token_split_re, text) \n",
    "            if token\n",
    "            if token not in stopwords]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "run_control": {
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['emma',\n",
       " 'by',\n",
       " 'jane',\n",
       " 'austen',\n",
       " 'volume',\n",
       " 'i',\n",
       " 'chapter',\n",
       " 'i',\n",
       " 'emma',\n",
       " 'woodhouse',\n",
       " 'handsome',\n",
       " 'clever',\n",
       " 'and',\n",
       " 'rich',\n",
       " 'with',\n",
       " 'a',\n",
       " 'comfortable',\n",
       " 'home',\n",
       " 'and',\n",
       " 'happy',\n",
       " 'disposition',\n",
       " 'seemed',\n",
       " 'to',\n",
       " 'unite',\n",
       " 'some',\n",
       " 'of',\n",
       " 'the',\n",
       " 'best',\n",
       " 'blessings',\n",
       " 'of']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokens(austen_books['Emma'])[:30]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How many words are there in Jane Austen's books?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "729460"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "austen_books_all_tokens = [token \n",
    "                           for book in austen_books \n",
    "                           for token in tokens(austen_books[book])]\n",
    "len(austen_books_all_tokens)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now find all the bigrams (ordered pairs of words)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "def bigrams(tokens):\n",
    "    return [(tokens[i-1], tokens[i]) for i in range(1, len(tokens))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "run_control": {
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('emma', 'by'),\n",
       " ('by', 'jane'),\n",
       " ('jane', 'austen'),\n",
       " ('austen', 'volume'),\n",
       " ('volume', 'i'),\n",
       " ('i', 'chapter'),\n",
       " ('chapter', 'i'),\n",
       " ('i', 'emma'),\n",
       " ('emma', 'woodhouse'),\n",
       " ('woodhouse', 'handsome'),\n",
       " ('handsome', 'clever'),\n",
       " ('clever', 'and'),\n",
       " ('and', 'rich'),\n",
       " ('rich', 'with'),\n",
       " ('with', 'a'),\n",
       " ('a', 'comfortable'),\n",
       " ('comfortable', 'home'),\n",
       " ('home', 'and'),\n",
       " ('and', 'happy'),\n",
       " ('happy', 'disposition'),\n",
       " ('disposition', 'seemed'),\n",
       " ('seemed', 'to'),\n",
       " ('to', 'unite'),\n",
       " ('unite', 'some'),\n",
       " ('some', 'of'),\n",
       " ('of', 'the'),\n",
       " ('the', 'best'),\n",
       " ('best', 'blessings'),\n",
       " ('blessings', 'of'),\n",
       " ('of', 'existence')]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bigrams(tokens(austen_books['Emma']))[:30]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Find the most skewed gendered bigrams<a name=\"skewedbigrams\"></a>\n",
    "Gendered bigrams are those with 'he' or 'she' in the first position. I use a Pandas Series to store the gendered bigrams, then apply `value_counts()` to count how many of each there are.\n",
    "\n",
    "* [Back to top](#top)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(she, had)      334\n",
       "(she, was)      331\n",
       "(he, had)       242\n",
       "(he, was)       222\n",
       "(she, could)    172\n",
       "(she, is)       124\n",
       "(he, is)         99\n",
       "(she, would)     97\n",
       "(he, could)      96\n",
       "(he, would)      85\n",
       "dtype: int64"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gendered_bigrams_books = {book: \n",
    "                    pd.Series(bigram\n",
    "                              for bigram in bigrams(tokens(austen_books[book]))\n",
    "                              if bigram[0] == 'he' or bigram[0] == 'she').value_counts()\n",
    "                   for book in austen_books}\n",
    "gendered_bigrams_books['Emma'].head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Wrap that in a function for easy reuse later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "def gendered_bigrams(tokens): \n",
    "    return pd.Series(bigram\n",
    "                     for bigram in bigrams(tokens)\n",
    "                     if bigram[0] == 'he' or bigram[0] == 'she').value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Austen's most common gendered bigrams, across all books."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(she, had)      1478\n",
       "(she, was)      1391\n",
       "(he, had)       1030\n",
       "(he, was)        895\n",
       "(she, could)     825\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gendered_bigrams_austen = gendered_bigrams(austen_books_all_tokens)\n",
    "\n",
    "gendered_bigrams_austen.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Count the number of occurrences of each gendered bigram, separated by gender. Store the results in a Pandas DataFrame. Again, wrap the processing in a function.\n",
    "\n",
    "I do this by converting the index of tuples into a multiindex, then using `unstack` to convert to a DataFrame.\n",
    "\n",
    "The `lower_limit` will limit the frame to only those rows with at least `lower_limit` bigrams, of either gender."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "def gender_counts(bigrams, lower_limit=0):\n",
    "    bigrams.index = pd.MultiIndex.from_tuples(bigrams.index)\n",
    "    bigrams.sort_index(inplace=True)\n",
    "    gcounts = bigrams.unstack(level=0)\n",
    "    gcounts.fillna(value=0, inplace=True)\n",
    "    return gcounts[gcounts.sum(axis=1) > lower_limit]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "run_control": {
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>he</th>\n",
       "      <th>she</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>able</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>abominates</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>absented</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>absolutely</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            he  she\n",
       "a            5    7\n",
       "able         0    1\n",
       "abominates   1    0\n",
       "absented     0    1\n",
       "absolutely   3    1"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gender_counts_austen = gender_counts(gendered_bigrams_austen) \n",
    "gender_counts_austen.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These are the \"useful\" bigrams, those where the second word appears at least ten times."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "run_control": {
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>he</th>\n",
       "      <th>she</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>acknowledged</th>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>added</th>\n",
       "      <td>31</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>always</th>\n",
       "      <td>11</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>and</th>\n",
       "      <td>42</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              he  she\n",
       "a              5    7\n",
       "acknowledged   6    9\n",
       "added         31   51\n",
       "always        11   16\n",
       "and           42   43"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "useful_gender_counts_austen = gender_counts(gendered_bigrams_austen, lower_limit=10) \n",
    "useful_gender_counts_austen.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "328"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "useful_gender_counts_austen.size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Odds ratio: a quantification of the skew<a name=\"oddsratio\"></a>\n",
    "Now find the odds ratio, which is the ratio of probabilities of each word being preceeded by 'she' vs the probabilty of it being preceeded by 'he'. \n",
    "\n",
    "To keep the numbers in a sensible range, take the log of the ratio.\n",
    "\n",
    "Because not every work appears for both genders, we apply some 'smoothing' to avoid things blowing up. The smoothing in the original blog post was to add one to the number of occurrences of each genered bigram, and add one to the total number of bigrams for that gender. A slightly less bad version is to assume we've seen each possible bigram some small number of times (e.g. 0.1) and adust all the scores accordingly.\n",
    "\n",
    "* [Back to top](#top)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true,
    "run_control": {
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def find_ratios(gcounts, smoothing_add=0.1, smoothing_scale=None):\n",
    "    if smoothing_scale is None:\n",
    "        smoothing_scale = smoothing_add * gcounts.size\n",
    "\n",
    "    gender_ratio = pd.DataFrame(\n",
    "        {'she': (gcounts['she'] + smoothing_add) / (gcounts['she'].sum() + smoothing_scale),\n",
    "         'he': (gcounts['he'] + smoothing_add) / (gcounts['he'].sum() + smoothing_scale)}\n",
    "        )\n",
    "    gender_ratio['logratio'] = (gender_ratio.she / gender_ratio.he).apply(np.log2)\n",
    "    gender_ratio['abslogratio'] = gender_ratio.logratio.abs()\n",
    "    return gender_ratio"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is Silge's method for finding the odds ratios. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>he</th>\n",
       "      <th>she</th>\n",
       "      <th>logratio</th>\n",
       "      <th>abslogratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>have</th>\n",
       "      <td>0.003133</td>\n",
       "      <td>0.003154</td>\n",
       "      <td>0.009427</td>\n",
       "      <td>0.009427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>always</th>\n",
       "      <td>0.001880</td>\n",
       "      <td>0.001915</td>\n",
       "      <td>0.026500</td>\n",
       "      <td>0.026500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>remained</th>\n",
       "      <td>0.001097</td>\n",
       "      <td>0.001126</td>\n",
       "      <td>0.038573</td>\n",
       "      <td>0.038573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>acknowledged</th>\n",
       "      <td>0.001097</td>\n",
       "      <td>0.001126</td>\n",
       "      <td>0.038573</td>\n",
       "      <td>0.038573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>had</th>\n",
       "      <td>0.161523</td>\n",
       "      <td>0.166592</td>\n",
       "      <td>0.044578</td>\n",
       "      <td>0.044578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>paused</th>\n",
       "      <td>0.001410</td>\n",
       "      <td>0.001464</td>\n",
       "      <td>0.054515</td>\n",
       "      <td>0.054515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>would</th>\n",
       "      <td>0.042143</td>\n",
       "      <td>0.043816</td>\n",
       "      <td>0.056164</td>\n",
       "      <td>0.056164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loves</th>\n",
       "      <td>0.000940</td>\n",
       "      <td>0.000901</td>\n",
       "      <td>-0.060963</td>\n",
       "      <td>0.060963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>0.000940</td>\n",
       "      <td>0.000901</td>\n",
       "      <td>-0.060963</td>\n",
       "      <td>0.060963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>too</th>\n",
       "      <td>0.000940</td>\n",
       "      <td>0.000901</td>\n",
       "      <td>-0.060963</td>\n",
       "      <td>0.060963</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    he       she  logratio  abslogratio\n",
       "have          0.003133  0.003154  0.009427     0.009427\n",
       "always        0.001880  0.001915  0.026500     0.026500\n",
       "remained      0.001097  0.001126  0.038573     0.038573\n",
       "acknowledged  0.001097  0.001126  0.038573     0.038573\n",
       "had           0.161523  0.166592  0.044578     0.044578\n",
       "paused        0.001410  0.001464  0.054515     0.054515\n",
       "would         0.042143  0.043816  0.056164     0.056164\n",
       "loves         0.000940  0.000901 -0.060963     0.060963\n",
       "a             0.000940  0.000901 -0.060963     0.060963\n",
       "too           0.000940  0.000901 -0.060963     0.060963"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gender_ratio_austen = find_ratios(useful_gender_counts_austen, smoothing_add=1, smoothing_scale=1)\n",
    "gender_ratio_austen.sort_values('abslogratio').head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is using the smoothing method I prefer. Note that it gives slightly different results in some cases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>he</th>\n",
       "      <th>she</th>\n",
       "      <th>logratio</th>\n",
       "      <th>abslogratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>0.000795</td>\n",
       "      <td>0.000797</td>\n",
       "      <td>0.003333</td>\n",
       "      <td>0.003333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>when</th>\n",
       "      <td>0.000795</td>\n",
       "      <td>0.000797</td>\n",
       "      <td>0.003333</td>\n",
       "      <td>0.003333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>too</th>\n",
       "      <td>0.000795</td>\n",
       "      <td>0.000797</td>\n",
       "      <td>0.003333</td>\n",
       "      <td>0.003333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loves</th>\n",
       "      <td>0.000795</td>\n",
       "      <td>0.000797</td>\n",
       "      <td>0.003333</td>\n",
       "      <td>0.003333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>have</th>\n",
       "      <td>0.002977</td>\n",
       "      <td>0.003042</td>\n",
       "      <td>0.030731</td>\n",
       "      <td>0.030731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>had</th>\n",
       "      <td>0.160582</td>\n",
       "      <td>0.165896</td>\n",
       "      <td>0.046971</td>\n",
       "      <td>0.046971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>would</th>\n",
       "      <td>0.041794</td>\n",
       "      <td>0.043559</td>\n",
       "      <td>0.059668</td>\n",
       "      <td>0.059668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>always</th>\n",
       "      <td>0.001730</td>\n",
       "      <td>0.001807</td>\n",
       "      <td>0.062512</td>\n",
       "      <td>0.062512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wrote</th>\n",
       "      <td>0.002042</td>\n",
       "      <td>0.001919</td>\n",
       "      <td>-0.089559</td>\n",
       "      <td>0.089559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>then</th>\n",
       "      <td>0.003913</td>\n",
       "      <td>0.004164</td>\n",
       "      <td>0.089743</td>\n",
       "      <td>0.089743</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              he       she  logratio  abslogratio\n",
       "a       0.000795  0.000797  0.003333     0.003333\n",
       "when    0.000795  0.000797  0.003333     0.003333\n",
       "too     0.000795  0.000797  0.003333     0.003333\n",
       "loves   0.000795  0.000797  0.003333     0.003333\n",
       "have    0.002977  0.003042  0.030731     0.030731\n",
       "had     0.160582  0.165896  0.046971     0.046971\n",
       "would   0.041794  0.043559  0.059668     0.059668\n",
       "always  0.001730  0.001807  0.062512     0.062512\n",
       "wrote   0.002042  0.001919 -0.089559     0.089559\n",
       "then    0.003913  0.004164  0.089743     0.089743"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gender_ratio_austen = find_ratios(useful_gender_counts_austen)\n",
    "gender_ratio_austen.sort_values('abslogratio').head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These are the words with the greatest gender skew."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "run_control": {
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>he</th>\n",
       "      <th>she</th>\n",
       "      <th>logratio</th>\n",
       "      <th>abslogratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>remembered</th>\n",
       "      <td>0.000016</td>\n",
       "      <td>0.001807</td>\n",
       "      <td>6.856928</td>\n",
       "      <td>6.856928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>read</th>\n",
       "      <td>0.000327</td>\n",
       "      <td>0.002593</td>\n",
       "      <td>2.985443</td>\n",
       "      <td>2.985443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>resolved</th>\n",
       "      <td>0.000327</td>\n",
       "      <td>0.001583</td>\n",
       "      <td>2.273245</td>\n",
       "      <td>2.273245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>longed</th>\n",
       "      <td>0.000327</td>\n",
       "      <td>0.001470</td>\n",
       "      <td>2.167117</td>\n",
       "      <td>2.167117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stopped</th>\n",
       "      <td>0.002042</td>\n",
       "      <td>0.000460</td>\n",
       "      <td>-2.149860</td>\n",
       "      <td>2.149860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>takes</th>\n",
       "      <td>0.001419</td>\n",
       "      <td>0.000348</td>\n",
       "      <td>-2.027587</td>\n",
       "      <td>2.027587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>felt</th>\n",
       "      <td>0.005784</td>\n",
       "      <td>0.021336</td>\n",
       "      <td>1.883279</td>\n",
       "      <td>1.883279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>heard</th>\n",
       "      <td>0.001574</td>\n",
       "      <td>0.005511</td>\n",
       "      <td>1.807379</td>\n",
       "      <td>1.807379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>replied</th>\n",
       "      <td>0.009681</td>\n",
       "      <td>0.002817</td>\n",
       "      <td>-1.780895</td>\n",
       "      <td>1.780895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>married</th>\n",
       "      <td>0.001574</td>\n",
       "      <td>0.000460</td>\n",
       "      <td>-1.774648</td>\n",
       "      <td>1.774648</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  he       she  logratio  abslogratio\n",
       "remembered  0.000016  0.001807  6.856928     6.856928\n",
       "read        0.000327  0.002593  2.985443     2.985443\n",
       "resolved    0.000327  0.001583  2.273245     2.273245\n",
       "longed      0.000327  0.001470  2.167117     2.167117\n",
       "stopped     0.002042  0.000460 -2.149860     2.149860\n",
       "takes       0.001419  0.000348 -2.027587     2.027587\n",
       "felt        0.005784  0.021336  1.883279     1.883279\n",
       "heard       0.001574  0.005511  1.807379     1.807379\n",
       "replied     0.009681  0.002817 -1.780895     1.780895\n",
       "married     0.001574  0.000460 -1.774648     1.774648"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gender_ratio_austen.sort_values('abslogratio', ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot the most skewed words<a name=\"plotting\"></a>\n",
    "\n",
    "Extract the words with the greatest skew, put them in a new DataFrame, and give each one a number so we can get back to it in the plotting.\n",
    "\n",
    "The window says how many from each end of the list of gendered words, such as the 15 most female and the 15 most male.\n",
    "\n",
    "If we want to exclude words from plotting, pass in a list of stopwords.\n",
    "\n",
    "* [Back to top](#top)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true,
    "run_control": {
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def extract_plot_items(gratios_in, window=15, stopwords=None):\n",
    "    if stopwords:\n",
    "        gratios = gratios_in.drop(stopwords)\n",
    "    else:\n",
    "        gratios = gratios_in\n",
    "    plot_items = gratios.sort_values('logratio', ascending=False).head(window).append(\n",
    "        gratios.sort_values('logratio').head(window)).sort_values('logratio', ascending=False)\n",
    "    plot_items['index_pos'] = list(reversed(range(len(plot_items))))\n",
    "    return plot_items"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_items_austen = extract_plot_items(gender_ratio_austen)\n",
    "len(plot_items_austen)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "run_control": {
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>he</th>\n",
       "      <th>she</th>\n",
       "      <th>logratio</th>\n",
       "      <th>abslogratio</th>\n",
       "      <th>index_pos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>remembered</th>\n",
       "      <td>0.000016</td>\n",
       "      <td>0.001807</td>\n",
       "      <td>6.856928</td>\n",
       "      <td>6.856928</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>read</th>\n",
       "      <td>0.000327</td>\n",
       "      <td>0.002593</td>\n",
       "      <td>2.985443</td>\n",
       "      <td>2.985443</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>resolved</th>\n",
       "      <td>0.000327</td>\n",
       "      <td>0.001583</td>\n",
       "      <td>2.273245</td>\n",
       "      <td>2.273245</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>longed</th>\n",
       "      <td>0.000327</td>\n",
       "      <td>0.001470</td>\n",
       "      <td>2.167117</td>\n",
       "      <td>2.167117</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>felt</th>\n",
       "      <td>0.005784</td>\n",
       "      <td>0.021336</td>\n",
       "      <td>1.883279</td>\n",
       "      <td>1.883279</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>heard</th>\n",
       "      <td>0.001574</td>\n",
       "      <td>0.005511</td>\n",
       "      <td>1.807379</td>\n",
       "      <td>1.807379</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>instantly</th>\n",
       "      <td>0.000483</td>\n",
       "      <td>0.001583</td>\n",
       "      <td>1.711366</td>\n",
       "      <td>1.711366</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>expected</th>\n",
       "      <td>0.000483</td>\n",
       "      <td>0.001583</td>\n",
       "      <td>1.711366</td>\n",
       "      <td>1.711366</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dared</th>\n",
       "      <td>0.000795</td>\n",
       "      <td>0.002593</td>\n",
       "      <td>1.705335</td>\n",
       "      <td>1.705335</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feared</th>\n",
       "      <td>0.000795</td>\n",
       "      <td>0.002480</td>\n",
       "      <td>1.641488</td>\n",
       "      <td>1.641488</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ran</th>\n",
       "      <td>0.000327</td>\n",
       "      <td>0.001021</td>\n",
       "      <td>1.641488</td>\n",
       "      <td>1.641488</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>caught</th>\n",
       "      <td>0.000327</td>\n",
       "      <td>0.001021</td>\n",
       "      <td>1.641488</td>\n",
       "      <td>1.641488</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cried</th>\n",
       "      <td>0.001730</td>\n",
       "      <td>0.005174</td>\n",
       "      <td>1.580218</td>\n",
       "      <td>1.580218</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>received</th>\n",
       "      <td>0.000795</td>\n",
       "      <td>0.002256</td>\n",
       "      <td>1.504638</td>\n",
       "      <td>1.504638</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hardly</th>\n",
       "      <td>0.000795</td>\n",
       "      <td>0.002031</td>\n",
       "      <td>1.353432</td>\n",
       "      <td>1.353432</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>can</th>\n",
       "      <td>0.006251</td>\n",
       "      <td>0.002705</td>\n",
       "      <td>-1.208558</td>\n",
       "      <td>1.208558</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>scarcely</th>\n",
       "      <td>0.001107</td>\n",
       "      <td>0.000460</td>\n",
       "      <td>-1.266184</td>\n",
       "      <td>1.266184</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>expressed</th>\n",
       "      <td>0.001419</td>\n",
       "      <td>0.000572</td>\n",
       "      <td>-1.309358</td>\n",
       "      <td>1.309358</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wants</th>\n",
       "      <td>0.001419</td>\n",
       "      <td>0.000572</td>\n",
       "      <td>-1.309358</td>\n",
       "      <td>1.309358</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>came</th>\n",
       "      <td>0.010928</td>\n",
       "      <td>0.004388</td>\n",
       "      <td>-1.316235</td>\n",
       "      <td>1.316235</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>certainly</th>\n",
       "      <td>0.003757</td>\n",
       "      <td>0.001470</td>\n",
       "      <td>-1.353455</td>\n",
       "      <td>1.353455</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>shook</th>\n",
       "      <td>0.001263</td>\n",
       "      <td>0.000460</td>\n",
       "      <td>-1.456287</td>\n",
       "      <td>1.456287</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>knows</th>\n",
       "      <td>0.003757</td>\n",
       "      <td>0.001246</td>\n",
       "      <td>-1.592462</td>\n",
       "      <td>1.592462</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stopt</th>\n",
       "      <td>0.001419</td>\n",
       "      <td>0.000460</td>\n",
       "      <td>-1.624231</td>\n",
       "      <td>1.624231</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>seemed</th>\n",
       "      <td>0.007654</td>\n",
       "      <td>0.002480</td>\n",
       "      <td>-1.625665</td>\n",
       "      <td>1.625665</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>comes</th>\n",
       "      <td>0.003445</td>\n",
       "      <td>0.001021</td>\n",
       "      <td>-1.754097</td>\n",
       "      <td>1.754097</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>married</th>\n",
       "      <td>0.001574</td>\n",
       "      <td>0.000460</td>\n",
       "      <td>-1.774648</td>\n",
       "      <td>1.774648</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>replied</th>\n",
       "      <td>0.009681</td>\n",
       "      <td>0.002817</td>\n",
       "      <td>-1.780895</td>\n",
       "      <td>1.780895</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>takes</th>\n",
       "      <td>0.001419</td>\n",
       "      <td>0.000348</td>\n",
       "      <td>-2.027587</td>\n",
       "      <td>2.027587</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stopped</th>\n",
       "      <td>0.002042</td>\n",
       "      <td>0.000460</td>\n",
       "      <td>-2.149860</td>\n",
       "      <td>2.149860</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  he       she  logratio  abslogratio  index_pos\n",
       "remembered  0.000016  0.001807  6.856928     6.856928         29\n",
       "read        0.000327  0.002593  2.985443     2.985443         28\n",
       "resolved    0.000327  0.001583  2.273245     2.273245         27\n",
       "longed      0.000327  0.001470  2.167117     2.167117         26\n",
       "felt        0.005784  0.021336  1.883279     1.883279         25\n",
       "heard       0.001574  0.005511  1.807379     1.807379         24\n",
       "instantly   0.000483  0.001583  1.711366     1.711366         23\n",
       "expected    0.000483  0.001583  1.711366     1.711366         22\n",
       "dared       0.000795  0.002593  1.705335     1.705335         21\n",
       "feared      0.000795  0.002480  1.641488     1.641488         20\n",
       "ran         0.000327  0.001021  1.641488     1.641488         19\n",
       "caught      0.000327  0.001021  1.641488     1.641488         18\n",
       "cried       0.001730  0.005174  1.580218     1.580218         17\n",
       "received    0.000795  0.002256  1.504638     1.504638         16\n",
       "hardly      0.000795  0.002031  1.353432     1.353432         15\n",
       "can         0.006251  0.002705 -1.208558     1.208558         14\n",
       "scarcely    0.001107  0.000460 -1.266184     1.266184         13\n",
       "expressed   0.001419  0.000572 -1.309358     1.309358         12\n",
       "wants       0.001419  0.000572 -1.309358     1.309358         11\n",
       "came        0.010928  0.004388 -1.316235     1.316235         10\n",
       "certainly   0.003757  0.001470 -1.353455     1.353455          9\n",
       "shook       0.001263  0.000460 -1.456287     1.456287          8\n",
       "knows       0.003757  0.001246 -1.592462     1.592462          7\n",
       "stopt       0.001419  0.000460 -1.624231     1.624231          6\n",
       "seemed      0.007654  0.002480 -1.625665     1.625665          5\n",
       "comes       0.003445  0.001021 -1.754097     1.754097          4\n",
       "married     0.001574  0.000460 -1.774648     1.774648          3\n",
       "replied     0.009681  0.002817 -1.780895     1.780895          2\n",
       "takes       0.001419  0.000348 -2.027587     2.027587          1\n",
       "stopped     0.002042  0.000460 -2.149860     2.149860          0"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_items_austen"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A basic plot, just to see that we're doing something sensible."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xadb4780c>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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XSjHqhtk+Mte5BTgDWIvaMvALgVmkVdbPgENIfkvb2l4gaWVgJLAPMMD2yCpjiT2lTPFk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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xadb5518c>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_items_austen.logratio.plot.barh()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the results, doing lots of matplotlib fiddling to make it look pretty."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "def gender_plot(plot_items, author):\n",
    "    plt.figure(figsize=(10,10))\n",
    "\n",
    "    plot_items['colour'] = plot_items.logratio.apply(lambda l: 'r' if l > 0 else 'b')\n",
    "\n",
    "    plt.plot(plot_items[plot_items.logratio > 0].logratio, \n",
    "             plot_items[plot_items.logratio > 0].index_pos, \n",
    "             marker='o', linestyle='', markersize=15.0, color='r')\n",
    "\n",
    "    plt.plot(plot_items[plot_items.logratio < 0].logratio, \n",
    "             plot_items[plot_items.logratio < 0].index_pos, \n",
    "             marker='o', linestyle='', markersize=15.0, color='b')\n",
    "\n",
    "    for _, r in plot_items.iterrows():\n",
    "        plt.plot([0, r.logratio], [r.index_pos, r.index_pos], color=r.colour, linestyle='-', linewidth=3)\n",
    "\n",
    "\n",
    "    words = plot_items.sort_values('index_pos').index\n",
    "    plt.yticks(np.arange(len(plot_items)), words)\n",
    "    plt.ylim([plot_items.index_pos.min() - 1, plot_items.index_pos.max() + 1])\n",
    "    \n",
    "    xts = list(range(int(min(plot_items.logratio)) - 1, int(max(plot_items.logratio)) + 2))\n",
    "    plt.xticks(xts, ['×{}'.format(2**i) for i in xts])\n",
    "#     plt.xticks([-2, -1, 0, 1, 2, 3, 4, 5, 6], ['×¼', '×½', 'same', '×2', '×4', '×8', '×16', '×32', '×64'])\n",
    "\n",
    "    plt.tick_params(axis='y', which='major', labelsize=16)\n",
    "    plt.tick_params(axis='x', which='major', labelsize=12)\n",
    "\n",
    "    plt.xlabel(\"Relative appearance after 'she' compared to 'he'\", fontsize=16)\n",
    "\n",
    "    plt.suptitle(\"Words paired with 'he' and 'she' in {}'s novels\".format(author), fontsize=20)\n",
    "    plt.title((\"Women {}, {}, and {} \".format(words[-1], words[-2], words[-3]) + \n",
    "              \"while men {}, {}, and {}\".format(words[0], words[1], words[2])) , fontsize=16)\n",
    "\n",
    "    plt.grid()\n",
    "\n",
    "    for spine in plt.gca().spines.values():\n",
    "        spine.set_visible(False)    \n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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AcXgb+nwmjX1T2A3AZ4Gj0vk9B0xtZbtua13uBJwHHIS/nTkEeAB4AthoHWs/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\nLTru1N124+xZs+Kz5UHQ8dTNRRgj1a1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6EF3Kp3pdI2kY8CNgB9yofhL/yMv/rkcZUaFB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\nV8UVCjNj1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WaY2asyjn3AlHqSFhMNpl8L7ACWmdlQdQHhnFB3UBdc8BlaWxdxeFerx4aA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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xad79e70c>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gender_plot(plot_items_austen, 'Jane Austen')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This shows a clear difference. Men take direct, physical action while women are more passive and have more emotions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# George Eliot<a name=\"eliot\"></a>\n",
    "Do the same with the books of George Eliot, another 19th century female novelist, reusing the functions defined above.\n",
    "\n",
    "* [Back to top](#top)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "eliot_books = {title: open(eliot_books_filenames[title],  \n",
    "                            encoding='utf-8', errors='replace').read().lower()\n",
    "                for title in eliot_books_filenames}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "612175"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eliot_books_all_tokens = [token \n",
    "                          for book in eliot_books \n",
    "                          for token in tokens(eliot_books[book])]\n",
    "len(eliot_books_all_tokens)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(he, had)       1424\n",
       "(he, was)       1038\n",
       "(she, had)       761\n",
       "(she, was)       705\n",
       "(he, would)      367\n",
       "(he, said)       357\n",
       "(he, could)      283\n",
       "(she, said)      239\n",
       "(he, is)         226\n",
       "(she, would)     209\n",
       "dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gendered_bigrams_eliot = gendered_bigrams(eliot_books_all_tokens)\n",
    "gendered_bigrams_eliot.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "run_control": {
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>he</th>\n",
       "      <th>she</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>added</th>\n",
       "      <td>65</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>always</th>\n",
       "      <td>31</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>and</th>\n",
       "      <td>20</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>answered</th>\n",
       "      <td>22</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>asked</th>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          he  she\n",
       "added     65   44\n",
       "always    31   12\n",
       "and       20   17\n",
       "answered  22   15\n",
       "asked     12    8"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "useful_gender_counts_eliot = gender_counts(gendered_bigrams_eliot, lower_limit=10) \n",
    "useful_gender_counts_eliot.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Testing that we get the same results as with Silge's original post."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>he</th>\n",
       "      <th>she</th>\n",
       "      <th>logratio</th>\n",
       "      <th>abslogratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>herself</th>\n",
       "      <td>0.000136</td>\n",
       "      <td>0.005437</td>\n",
       "      <td>5.326106</td>\n",
       "      <td>5.326106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>himself</th>\n",
       "      <td>0.003931</td>\n",
       "      <td>0.000227</td>\n",
       "      <td>-4.116838</td>\n",
       "      <td>4.116838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>read</th>\n",
       "      <td>0.000542</td>\n",
       "      <td>0.002719</td>\n",
       "      <td>2.326106</td>\n",
       "      <td>2.326106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ran</th>\n",
       "      <td>0.000542</td>\n",
       "      <td>0.002266</td>\n",
       "      <td>2.063071</td>\n",
       "      <td>2.063071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>left</th>\n",
       "      <td>0.002982</td>\n",
       "      <td>0.000906</td>\n",
       "      <td>-1.718288</td>\n",
       "      <td>1.718288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>means</th>\n",
       "      <td>0.001491</td>\n",
       "      <td>0.000453</td>\n",
       "      <td>-1.718288</td>\n",
       "      <td>1.718288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>shall</th>\n",
       "      <td>0.001491</td>\n",
       "      <td>0.000453</td>\n",
       "      <td>-1.718288</td>\n",
       "      <td>1.718288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>needed</th>\n",
       "      <td>0.000949</td>\n",
       "      <td>0.002719</td>\n",
       "      <td>1.518751</td>\n",
       "      <td>1.518751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>told</th>\n",
       "      <td>0.003117</td>\n",
       "      <td>0.001133</td>\n",
       "      <td>-1.460491</td>\n",
       "      <td>1.460491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>married</th>\n",
       "      <td>0.000678</td>\n",
       "      <td>0.001812</td>\n",
       "      <td>1.419215</td>\n",
       "      <td>1.419215</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               he       she  logratio  abslogratio\n",
       "herself  0.000136  0.005437  5.326106     5.326106\n",
       "himself  0.003931  0.000227 -4.116838     4.116838\n",
       "read     0.000542  0.002719  2.326106     2.326106\n",
       "ran      0.000542  0.002266  2.063071     2.063071\n",
       "left     0.002982  0.000906 -1.718288     1.718288\n",
       "means    0.001491  0.000453 -1.718288     1.718288\n",
       "shall    0.001491  0.000453 -1.718288     1.718288\n",
       "needed   0.000949  0.002719  1.518751     1.518751\n",
       "told     0.003117  0.001133 -1.460491     1.460491\n",
       "married  0.000678  0.001812  1.419215     1.419215"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gender_ratio_eliot = find_ratios(useful_gender_counts_eliot, smoothing_add=1, smoothing_scale=1)\n",
    "gender_ratio_eliot.sort_values('abslogratio', ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>he</th>\n",
       "      <th>she</th>\n",
       "      <th>logratio</th>\n",
       "      <th>abslogratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>herself</th>\n",
       "      <td>0.000014</td>\n",
       "      <td>0.005202</td>\n",
       "      <td>8.589442</td>\n",
       "      <td>8.589442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>himself</th>\n",
       "      <td>0.003795</td>\n",
       "      <td>0.000023</td>\n",
       "      <td>-7.396733</td>\n",
       "      <td>7.396733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>read</th>\n",
       "      <td>0.000419</td>\n",
       "      <td>0.002500</td>\n",
       "      <td>2.577913</td>\n",
       "      <td>2.577913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>shall</th>\n",
       "      <td>0.001364</td>\n",
       "      <td>0.000248</td>\n",
       "      <td>-2.461087</td>\n",
       "      <td>2.461087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>means</th>\n",
       "      <td>0.001364</td>\n",
       "      <td>0.000248</td>\n",
       "      <td>-2.461087</td>\n",
       "      <td>2.461087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ran</th>\n",
       "      <td>0.000419</td>\n",
       "      <td>0.002049</td>\n",
       "      <td>2.291292</td>\n",
       "      <td>2.291292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>left</th>\n",
       "      <td>0.002850</td>\n",
       "      <td>0.000698</td>\n",
       "      <td>-2.029210</td>\n",
       "      <td>2.029210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>told</th>\n",
       "      <td>0.002985</td>\n",
       "      <td>0.000923</td>\n",
       "      <td>-1.692657</td>\n",
       "      <td>1.692657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dreaded</th>\n",
       "      <td>0.001499</td>\n",
       "      <td>0.000473</td>\n",
       "      <td>-1.664405</td>\n",
       "      <td>1.664405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>needed</th>\n",
       "      <td>0.000824</td>\n",
       "      <td>0.002500</td>\n",
       "      <td>1.601372</td>\n",
       "      <td>1.601372</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               he       she  logratio  abslogratio\n",
       "herself  0.000014  0.005202  8.589442     8.589442\n",
       "himself  0.003795  0.000023 -7.396733     7.396733\n",
       "read     0.000419  0.002500  2.577913     2.577913\n",
       "shall    0.001364  0.000248 -2.461087     2.461087\n",
       "means    0.001364  0.000248 -2.461087     2.461087\n",
       "ran      0.000419  0.002049  2.291292     2.291292\n",
       "left     0.002850  0.000698 -2.029210     2.029210\n",
       "told     0.002985  0.000923 -1.692657     1.692657\n",
       "dreaded  0.001499  0.000473 -1.664405     1.664405\n",
       "needed   0.000824  0.002500  1.601372     1.601372"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gender_ratio_eliot = find_ratios(useful_gender_counts_eliot)\n",
    "gender_ratio_eliot.sort_values('abslogratio', ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is where the stopwords come in useful. Let's exclude the very common phrases 'he himself' and 'her herself', which means \"he himself dreaded\" is converted to \"he dreaded\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>he</th>\n",
       "      <th>she</th>\n",
       "      <th>logratio</th>\n",
       "      <th>abslogratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>read</th>\n",
       "      <td>0.000418</td>\n",
       "      <td>0.002502</td>\n",
       "      <td>2.580082</td>\n",
       "      <td>2.580082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>shall</th>\n",
       "      <td>0.001363</td>\n",
       "      <td>0.000248</td>\n",
       "      <td>-2.458917</td>\n",
       "      <td>2.458917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>means</th>\n",
       "      <td>0.001363</td>\n",
       "      <td>0.000248</td>\n",
       "      <td>-2.458917</td>\n",
       "      <td>2.458917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ran</th>\n",
       "      <td>0.000418</td>\n",
       "      <td>0.002051</td>\n",
       "      <td>2.293461</td>\n",
       "      <td>2.293461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>left</th>\n",
       "      <td>0.002847</td>\n",
       "      <td>0.000699</td>\n",
       "      <td>-2.027040</td>\n",
       "      <td>2.027040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dreaded</th>\n",
       "      <td>0.001633</td>\n",
       "      <td>0.000473</td>\n",
       "      <td>-1.786683</td>\n",
       "      <td>1.786683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>told</th>\n",
       "      <td>0.002982</td>\n",
       "      <td>0.000924</td>\n",
       "      <td>-1.690488</td>\n",
       "      <td>1.690488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>called</th>\n",
       "      <td>0.002173</td>\n",
       "      <td>0.000699</td>\n",
       "      <td>-1.636858</td>\n",
       "      <td>1.636858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>needed</th>\n",
       "      <td>0.000823</td>\n",
       "      <td>0.002502</td>\n",
       "      <td>1.603541</td>\n",
       "      <td>1.603541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>knows</th>\n",
       "      <td>0.002712</td>\n",
       "      <td>0.000924</td>\n",
       "      <td>-1.553637</td>\n",
       "      <td>1.553637</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               he       she  logratio  abslogratio\n",
       "read     0.000418  0.002502  2.580082     2.580082\n",
       "shall    0.001363  0.000248 -2.458917     2.458917\n",
       "means    0.001363  0.000248 -2.458917     2.458917\n",
       "ran      0.000418  0.002051  2.293461     2.293461\n",
       "left     0.002847  0.000699 -2.027040     2.027040\n",
       "dreaded  0.001633  0.000473 -1.786683     1.786683\n",
       "told     0.002982  0.000924 -1.690488     1.690488\n",
       "called   0.002173  0.000699 -1.636858     1.636858\n",
       "needed   0.000823  0.002502  1.603541     1.603541\n",
       "knows    0.002712  0.000924 -1.553637     1.553637"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eliot_books_all_tokens = [token \n",
    "                          for book in eliot_books \n",
    "                          for token in tokens(eliot_books[book], stopwords=['himself', 'herself'])]\n",
    "gendered_bigrams_eliot = gendered_bigrams(eliot_books_all_tokens)\n",
    "useful_gender_counts_eliot = gender_counts(gendered_bigrams_eliot, lower_limit=10) \n",
    "gender_ratio_eliot = find_ratios(useful_gender_counts_eliot)\n",
    "gender_ratio_eliot.sort_values('abslogratio', ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "# eliot_books_all_tokens = [token \n",
    "#                           for book in eliot_books \n",
    "#                           for token in tokens(eliot_books[book], \n",
    "#                                               stopwords=['himself', 'herself',\n",
    "#                                                         'shall', 'should', 'may', 'might',\n",
    "#                                                          'will', 'would', 'can', 'could'])]\n",
    "# gendered_bigrams_eliot = gendered_bigrams(eliot_books_all_tokens)\n",
    "# useful_gender_counts_eliot = gender_counts(gendered_bigrams_eliot, lower_limit=10) \n",
    "# gender_ratio_eliot = find_ratios(useful_gender_counts_eliot)\n",
    "# gender_ratio_eliot.sort_values('abslogratio', ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "run_control": {
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_items_eliot = extract_plot_items(gender_ratio_eliot, window=15)\n",
    "len(plot_items_eliot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "run_control": {
     "read_only": false
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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px2bJnVF+SedHMARagE+m4p1FMAp2KLNOXh33t07EeRfhRvhQO8o9P+7vlIjT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GLe2trO+vWn099fPWre/v4rWL/+dDZubI+gZscqpJjnC4959Dzm0fOY55eca7RKeomk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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xaa9c59ac>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gender_plot(plot_items_eliot, 'George Eliot')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Again, women are associated passive verbs while men are associated with action and speaking."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Charlotte Brontë's book, _Jane Eyre_<a name=\"bronte\"></a>\n",
    "This was called out in the original paper fitting the pattern less well, with women taking more active roles and men having more emotions. Does this pan out?\n",
    "\n",
    "* [Back to top](#top)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": true,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "bronte_books = {title: open(bronte_books_filenames[title],  \n",
    "                            encoding='utf-8', errors='replace').read().lower()\n",
    "                for title in bronte_books_filenames}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "189448"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bronte_books_all_tokens = [token \n",
    "                           for book in bronte_books \n",
    "                           for token in tokens(bronte_books[book])]\n",
    "len(bronte_books_all_tokens)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(he, was)      155\n",
       "(she, was)     154\n",
       "(he, had)      132\n",
       "(she, had)     122\n",
       "(he, said)      96\n",
       "(he, is)        88\n",
       "(he, would)     83\n",
       "(she, is)       79\n",
       "(she, said)     57\n",
       "(he, has)       44\n",
       "dtype: int64"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gendered_bigrams_bronte = gendered_bigrams(bronte_books_all_tokens)\n",
    "gendered_bigrams_bronte[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "run_control": {
     "read_only": false
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>he</th>\n",
       "      <th>she</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>added</th>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>again</th>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>and</th>\n",
       "      <td>21</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>answered</th>\n",
       "      <td>14</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>asked</th>\n",
       "      <td>31</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          he  she\n",
       "added     10    6\n",
       "again      7    9\n",
       "and       21   11\n",
       "answered  14    6\n",
       "asked     31   12"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "useful_gender_counts_bronte = gender_counts(gendered_bigrams_bronte, lower_limit=10) \n",
    "useful_gender_counts_bronte.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "run_control": {
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>he</th>\n",
       "      <th>she</th>\n",
       "      <th>logratio</th>\n",
       "      <th>abslogratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>shall</th>\n",
       "      <td>0.002629</td>\n",
       "      <td>0.012429</td>\n",
       "      <td>2.241019</td>\n",
       "      <td>2.241019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>s</th>\n",
       "      <td>0.004382</td>\n",
       "      <td>0.018079</td>\n",
       "      <td>2.044621</td>\n",
       "      <td>2.044621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>paused</th>\n",
       "      <td>0.011394</td>\n",
       "      <td>0.004520</td>\n",
       "      <td>-1.333890</td>\n",
       "      <td>1.333890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>made</th>\n",
       "      <td>0.007888</td>\n",
       "      <td>0.019209</td>\n",
       "      <td>1.284087</td>\n",
       "      <td>1.284087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stood</th>\n",
       "      <td>0.013146</td>\n",
       "      <td>0.005650</td>\n",
       "      <td>-1.218413</td>\n",
       "      <td>1.218413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>asked</th>\n",
       "      <td>0.028046</td>\n",
       "      <td>0.014689</td>\n",
       "      <td>-0.933011</td>\n",
       "      <td>0.933011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>were</th>\n",
       "      <td>0.006135</td>\n",
       "      <td>0.011299</td>\n",
       "      <td>0.881123</td>\n",
       "      <td>0.881123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>can</th>\n",
       "      <td>0.007011</td>\n",
       "      <td>0.012429</td>\n",
       "      <td>0.825981</td>\n",
       "      <td>0.825981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>spoke</th>\n",
       "      <td>0.007888</td>\n",
       "      <td>0.004520</td>\n",
       "      <td>-0.803376</td>\n",
       "      <td>0.803376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>took</th>\n",
       "      <td>0.017528</td>\n",
       "      <td>0.010169</td>\n",
       "      <td>-0.785454</td>\n",
       "      <td>0.785454</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              he       she  logratio  abslogratio\n",
       "shall   0.002629  0.012429  2.241019     2.241019\n",
       "s       0.004382  0.018079  2.044621     2.044621\n",
       "paused  0.011394  0.004520 -1.333890     1.333890\n",
       "made    0.007888  0.019209  1.284087     1.284087\n",
       "stood   0.013146  0.005650 -1.218413     1.218413\n",
       "asked   0.028046  0.014689 -0.933011     0.933011\n",
       "were    0.006135  0.011299  0.881123     0.881123\n",
       "can     0.007011  0.012429  0.825981     0.825981\n",
       "spoke   0.007888  0.004520 -0.803376     0.803376\n",
       "took    0.017528  0.010169 -0.785454     0.785454"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gender_ratio_bronte = find_ratios(useful_gender_counts_bronte, smoothing_add=1, smoothing_scale=1)\n",
    "gender_ratio_bronte.sort_values('abslogratio', ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "run_control": {
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>he</th>\n",
       "      <th>she</th>\n",
       "      <th>logratio</th>\n",
       "      <th>abslogratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>shall</th>\n",
       "      <td>0.001826</td>\n",
       "      <td>0.011298</td>\n",
       "      <td>2.629181</td>\n",
       "      <td>2.629181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>s</th>\n",
       "      <td>0.003565</td>\n",
       "      <td>0.016890</td>\n",
       "      <td>2.244140</td>\n",
       "      <td>2.244140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>paused</th>\n",
       "      <td>0.010522</td>\n",
       "      <td>0.003468</td>\n",
       "      <td>-1.601380</td>\n",
       "      <td>1.601380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stood</th>\n",
       "      <td>0.012261</td>\n",
       "      <td>0.004586</td>\n",
       "      <td>-1.418712</td>\n",
       "      <td>1.418712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>made</th>\n",
       "      <td>0.007043</td>\n",
       "      <td>0.018009</td>\n",
       "      <td>1.354354</td>\n",
       "      <td>1.354354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>spoke</th>\n",
       "      <td>0.007043</td>\n",
       "      <td>0.003468</td>\n",
       "      <td>-1.022367</td>\n",
       "      <td>1.022367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>asked</th>\n",
       "      <td>0.027043</td>\n",
       "      <td>0.013535</td>\n",
       "      <td>-0.998620</td>\n",
       "      <td>0.998620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>were</th>\n",
       "      <td>0.005304</td>\n",
       "      <td>0.010179</td>\n",
       "      <td>0.940344</td>\n",
       "      <td>0.940344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>saw</th>\n",
       "      <td>0.008783</td>\n",
       "      <td>0.004586</td>\n",
       "      <td>-0.937372</td>\n",
       "      <td>0.937372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>took</th>\n",
       "      <td>0.016609</td>\n",
       "      <td>0.009060</td>\n",
       "      <td>-0.874292</td>\n",
       "      <td>0.874292</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              he       she  logratio  abslogratio\n",
       "shall   0.001826  0.011298  2.629181     2.629181\n",
       "s       0.003565  0.016890  2.244140     2.244140\n",
       "paused  0.010522  0.003468 -1.601380     1.601380\n",
       "stood   0.012261  0.004586 -1.418712     1.418712\n",
       "made    0.007043  0.018009  1.354354     1.354354\n",
       "spoke   0.007043  0.003468 -1.022367     1.022367\n",
       "asked   0.027043  0.013535 -0.998620     0.998620\n",
       "were    0.005304  0.010179  0.940344     0.940344\n",
       "saw     0.008783  0.004586 -0.937372     0.937372\n",
       "took    0.016609  0.009060 -0.874292     0.874292"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gender_ratio_bronte = find_ratios(useful_gender_counts_bronte)\n",
    "gender_ratio_bronte.sort_values('abslogratio', ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "run_control": {
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_items_bronte = extract_plot_items(gender_ratio_bronte, window=15)\n",
    "len(plot_items_bronte)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "run_control": {
     "read_only": false
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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o/+kNqMe/xTS+ABwOnAKMB/ZoQFwXAefF/B0dy/Id4JspfxuAp4A7geOAk6P5\nCWCrhL+fxHZzJTAI+CHwn2g3qoz2WazTH0V5JkS7n8f2cyowBHgstrGtE+HPBn4Q3QcA5wNvAZen\n0rkRWBdlGxRlfSbKeHrC32djO/kzcHz83Ae8AexaRz52iHF9JWG3GFgN/C5hdznwYsI8Oub1EeB7\nMf+/iHZnJPwNiPEfmWqv8yrtRyXkH5Co7//H5n11Qmx7ZwDHEF5qVwE9Ky03yuxj9bSV5YTxZBCh\nLb8DXJvy+1vgqzFfQ4CbY/1/PGNs3CMVdjRQW+HYMTzK9tvofirwOGGVqUul7bApxgWgK3BOjHsE\n8On46RrD3hzLfXSUZVQ0J9trZ0J/f4XwfBgc87ABOCvhb/+Yr7tj3Rfz/yyJMTDH41Sd4y+bxvZn\ngZnAsbF9vRjDdUrEVU65F+M7M5p7xXjuA7pX2u7y9Gl1AfzThJUZGu4G4PvRfDzhobg1sG90K3ai\nqcBKQNG8FfAyMCcV52Ex3HkJu6eB14uDW7T7VvQ3KRX+IWBuwnwxYU/qXil/1wCvFgcRNj2ARqX8\n3QYsraccfg38ox4/8+MAl1Q2P0h4gP+wjnCdYpnUAv+TsG9KBXRVsrybsH0oyn8N8HDKragYJgfP\nU2I+D43m7lG236TCjsyqq4z0i3X6o1R5vhLrYo+E/fEx7SPqqYuLgBUJu/1iHV6Q8jueLRXQJ4G7\nUv66Aq8BP68nL0uAKfF3jxj3OOD5hJ9FwM0J8+i0DNH+EWBWwlyOAlpWPyohe1EB/W3K/qGYbr+E\n3Sej369WWm4N7WOptpJuaxcRFMR9SoTbKraLpcAvUvGVo4DWOXYAXYA3M8puz5jXb1faDivov3WO\nC4l6PTpl//Fof3HK/kdRlk9E83lk9DmCQvYym54VN8U2tl3Cz24x/w1SQFPptfY4VV85F8f2R1P2\n/aP91yss940KKHAAYTLntlT5ltXu8vbxJfh2hJktB55n01L7EcD9ZrbezJ4kDBpJt/sstmLgo8BO\nhDe2ZJz3Ed7aB6SSW2Sb71FbGr/vSvlbCiSXCAYD9wPPSOpU/MRwOwLJZRIjzKgmeZTwxl8XDwJ9\nJP0qLhW9v4S/J83sqY2Jmb1GKKON8UvaRtJFCsubNYSH3z3R+aP1yNFQHgQukPRtSZ9oTEQK2yJ+\nL+l5guzvAWeRLfvfzKw2YX6U8DAolscnCbMk01Lh/lCBSAbM2mgI6f0HeMLMnk34WxrT3th24vLg\nJIVl63ecXJq8AAAgAElEQVRjXi4DukvaKXo7JIbLklGJuPYB9gZuTrXDdQTFsc7lb8Jy51Hx91GE\nl7lfAB+W9FFJXYGDCUpYmnSb/hf1t+k0dfWjHdi8H5ViVsq8FFhjZotSdhDroQHlVm8fqwMjux47\nEWb3iDINikusrxOUvvcIL9wN6Z/1jR39CLPP6fy/QCirYv4PpYx22ADZGjIuHEkoy5tS9r+LshTH\n9iOAF8zsngx/H2RTmzoUuMPM1hU9mNnzhBm7BtHGxqlyy/mWpMHMFhKev/2i1QDKK3cS/hcQ+vAJ\nyfKl/HaXK1wBbX/cTVg2gE37P4vcCxwpaVfCbGnyGqKe8XuL65oIb789U3YrU+Z367BP7tnaKcr1\nXurzJ0Jn3SEV/o2U+R1g2wwZN2JmNxCWoz5NeMi+IemW9P6cjLiL8SflvYKwbHIDYXnvU8BJhAFk\nuy1CNw2fB/4CXAD8M+5FurieMFsgqQswhzAgjyS0i77AtWSXYVZZw6Z87hK/X0n5S5vrI6uNlGpP\n2wFIEmFWYAgwhqD09QV+2kAZiwrrFDZvh+8SlpjT7T3NfGBPhStvqoAFFg4CLouyHUlQlLKuTsoq\n50rbUl39CLbsR1lklfmbSQszey/+LMpXX7nV13+hsvyWqsddART2Cd8OvE2YQTqE0C4eqSCNjZQx\nduxE6Ptz2TL/n2BTuynum29sX0nS0HGh1Nj+csq9Z4afLH+7kJ2PBuWtDY5T5ZZzqTLYNf7uEb/r\nK/cixxJmOq8xsw0pt/raXTn9vc1R50ZYJ5csAL4o6VDgIMJ0f5F7CINr8c0sqYAWO/VmB44Sdv9o\nIvlWEDrpt8meCVjWFImY2W+B30ranrCv6OeEN+B+dQbcktOAqWb2s6KFpG5NIWMpzOx1wpaGb0na\nl7B8eKmkV81sUgVR9SPMXB2enNWStE0DRXuJUGc7A/9O2O/cwPgqYW/CjOKXzez3RUttebCsONjv\nTNg/SMKcZEX8/l/Cwy/Nuxl2Se4mLKMNZNNeVggK50DCqsELZvbfeuJpKC3Sj0qkCw0vt0op1dZe\niN+nEB7EJycf2pJ6sLmCXZxNSh9g3OLBXc/YUcz/6YT9d2lWxe9y22HZlDkuWEbQ5Nj+dMK+ONav\nSPhLX9WX5e8lsvPR0Ly1qXGqgvG3VBk8HH/XV+5pRfpiQnubJenYOKNapNx2lyt8BrT9cTeh8/0w\nmpPLafcSBpjPE/aPPZhwW0Z4oH0hGZmk/oR9JllLiWmyBr80s4DewHNmtjjjs6aMOMrGzN4ys2mE\nmaGGLGd3JizrJTmTFrruxMyeNLMfEx6mlcrfOX5vlD8+mD/XQHEeAdYQ2k+SLzYwvkrIyss2hEMj\nSe4n1E2WjBvrzMyKB3g+XqId/qsuYczsLcI+0C8QDmUUZzrnEV7wBlJen2koje1HDWq/jS23ChHZ\n9VhLqGeA90fzpkDS0Wy5xP9MjO8TCX+dCA/8TEqMHQsJD/t9S+T/yeivrHbYUEqMC+8Q8pjeNlB8\nJnwhZf+VKEvxJpQFwG6S0i/pXyZsmygqc4uAIcntCfEk9mENzE6bHafqGX+HJg2SDiPshS0qjvWV\neyFl/16U+S6CEpr8U5ly212u8BnQdoaZLZP0KuEQxz/MrCbh/DDhUNLxhAMNtYlwxX+ImCjpRsI+\nld0Ie+yWEQ7Z1Ec5e5t+Qehk90r6RYy7C+FheoSZ1XtFS71CSJMInXURYeD8KOGU7OwGRDcLOEPS\nvwh7FU+m8lnUolwDCErJ1+JSX5afDxBmlm4i7O15DziRsLF+dsJfgXCgoq5Lp4uD1m8kXUI4KPIj\nwmGRD1Qqv5m9FevsIkmrCQPlpwgnZptbIf83QYn4qcK1OeuB7xI27ydlfELSzcCYqGA8SFAyjs2I\n81zgVknbEpSM1wkzGP2BZ8zsqnpkmk9YpnvFzIoP5wJhVm0HoL7wdVFfX2psP2roPkRofLlVwhBJ\nYwlt7RDCdpipiZnlWcB3gKmSriP09R8T9uIleZBwWnhcbBfvEE6Lb7bEW9/YYWarJF0AXB33Hd9J\nuIlhV8KLx3wz+0Ml7bAJx4UnCP3iTEkrYx6Xmtljkn4PXBJf2hYS6urHhENyj8Xw18ey/LOkYhl+\nhfAy9c3EeYHLCCew/yZpXCzD0WxaWk7KXSBH41S542+km6SZwCTCEvnlhH54Y5Sj3HJPyl4r6Qsx\n/TslDTGze8ptdxUVVFug1Okk/+T3Q3go1ALjMtxmR7cflwj7JYKiupYwAFwP7Jzy8xThIZC0K57c\nTZ/AvI7wUErabQ/8H+GBsI4wcC0gcZKPTadW06d8RwPr68n/VwkzUS/HfPyXcN1M8tT+fMK+vXTY\np4inm6N5B8LBrBXxcwNhKTh9ovo64L8J854ZfoZEu8/UIfv7CMu5jxL2tb1JmE05LeXvAVJXQZWI\nr4pwunkN4fTyeaRO/kZ/tcClKbusPGxF2IP5YoxzLkHpqSV12jNDllJ1ukVdJNI+M2F3AGFWYTXh\nCpRLCLPRm51uJuwF+w1BMXobmEF4acg6gX4IYb/XCsKqwFOxvg8po2yrY5w3peyXEBSBrBPX60md\nUM9oO8W+lD4FPzcVrt5+VELusvtqHW2j3nLLqtesPlZPWzkcuDXW4+vAr4BtU37PjWWwhtBXjib0\n/3R5FWeq3ybM4n6XLU/B1zt2JOp+LqF/riYoHpOB3pW2Q5p2XPgG4UX53WQbIkw2jSEsBb8Tvy8l\ncZo8+tuZcEPKqzH/S4AvZshzNGFcWRvT+wZhz+ZTKX+5GqfKKWc2nVofHtvGq7EN/AXYMxVfveVO\n9lgnghK6ChhQSbvL06d4rYLjOM2MpMuB48zsgEbG05kwAH3RzG6pz7/j5A1JZxAUmn0tcYq+PdJU\n40Jbo72OU/FA2tOEu1GvbW158ozvAXWcluMINp3abgz9CdfbtJtB3XE6ME01LrQ1fJxy6sT3gDpO\nC2FmRzRRPHMIlxw7jpNzmmpcaGu083HKl46bAF+CdxzHcRzHcVoUX4JvQQqFghHenPyTw4/XX74/\nXn/5/Xjd5fvj9Zffj6QqmglXQFuQ66+/vrVFcBqB11++8frLL153+cbrL9dUNVfEroA6juM4juM4\nLYoroC1Ir169WlsEpxF4/eUbr7/84nWXb7z+nCxcAW1BqqqqWlsEpxF4/eUbr7/84nWXb7z+ck2h\nuSJ2BdRxHMdxHMfZAjMrNFfcroA6juM4juM4LYrfA9qyeGE7juM4jpMX1FwR+wyo4ziO4ziO06K4\nAtqCFAqF1hbBaQRef/nG6y+/eN3lG68/Jwv/L3jHcRzHcZxmxswozJrF3EmT6LRqFQC13box8Oyz\nqaquRmq21e42SYfaAyppADAfGGRm85ogvj2Bp4GvmdkN0e56YICZfSQjSMcpbMdxHMdxAJgxeTKL\nJkygaulSqmtqNi4/1wKzO3em0Ls3/UaM4KRhw1pTzCx8D2gT0txKYPE/VB3HcRzH6eBMHDWK2pEj\nGbt4MUMSyidAJ2BITQ1jFy9m/QUXMHHUqNYSs8XpiApoq81x+z6YfOP1l2+8/vKL112+6cj1N2Py\nZHa8+mqGrlxZr99TV65kx6uvZsaUKS0gWevT7hRQSftKmiHpFUlrJT0j6Y+Sink1oIukX0t6LX5u\nlPSBVDznSlooaYWklZIWSRrS8jlyHMdxHCdvmBmLJkwoS/ksMnTlShaOH09H2B7Z7vaASnoSWAFc\nEb93BYYAZwKHEfaAPg38FbgN+CgwDvijmX09Ec844Angv4RZ8uOBc4Fjzeyu6CdrD+h1hD2ge2WI\n174K23Ecx3GcTObfeSdrhw5lSE1NReFu79KFLtOnU1Vd3UySVUSzrRq3q1PwknYA9ga+Z2Z/TTj9\nIboXzQvM7Dvx9xxJvYFhwEYF1MwuSMQrYB5BWT0HuKu58uA4juM4Tv6ZO2kSl1aofAJUr1nD6IkT\n24oC2my0KwXUzFZIegq4QtKHgIKZ/SfD6x0p86PAtpJ2MrNXASQdDFwK9AU+yKa3gKUNla9QKFBV\nVdXQ4E4r4/WXYyQKQFUri+E0jAJed3mmQMesv8saGK4T0Gn16qYUpU3SrhTQyCDgEuByYEdJTwPj\nzGxiws8bqTDvxO/tACTtBswBHgPOA54F1hPaU++GCjZ9+nSuv/56evXqBUD37t3p06fPRqWmuFHb\nzW3TvGTJkjYlj5srM4fa2/QgLLjZzW52cxs1L1+5crNJj5YaL4u/ly9fDsDUqVOrzGyTYxPS7vaA\nJpF0AEGBPAs4FlhHxj2gks4ArgU+YmbPSvoGMBHYzcxeSvgrAHsU93f6HlDHyQkd7IJnx3HySy0w\n+oQTuOzWW1tbFPA9oA3DzB6RdD5BAf0E8I8yg74/fq8vWkjaj3CI6bkmFdJxnOanHb9oO47TNmno\nIaRZXbowaPjwZpKq7bBVawvQlEj6pKR5ks6WNFDSZ4BrgPcIh4jKZQ7hJeRGScfEGdLZwDONkS85\nxe3kD6+/fOP1l1+87vJNR62/qupqCr0r37W3oHdvBgwe3AwStS3alQIKvExQEr8HzARuBj4EfNbM\nHo5+6p0KMbPHgS8Be8R4fgBcCNyT5b1MO8dxHMdxOgiS6DdiBNN79Cg7zLSePek/YkSH+F/4dr0H\ntA3ihe04juM4HYiJo0aV9W9I03r2ZMW55zJ8zJgWkqwsmk0TdgW0ZfHCdhzHcZwOxowpU1g4fjxV\ny5ZRvWYNnaJ9LWHP54Level3zjmcNGxYa4qZhSug7YFCoWB+j2R+SV6J4eQPr7/84nWXb7z+AmbG\ngtmzmTNx4sZ7Pmu7dmXQ8OEMGDy4rS67+yl4x3Ecx3GcvCKJqurqdv8PR+XiM6Atixe24ziO4zh5\nwWdAHcdxHMdxkpgZhVmzmDtpEp1WrQKgtls3Bp59NlXV1W11Wduh/V3D1KbpqHehtRe8/vKN119+\n8brLN81VfzMmT+bCvn1ZO3QoY2bO5NJ588Jn5kzWDh3KhX37MmPKlGZJ22k8roA6juM4jpMrJo4a\nRe3IkYxdvJghNTWbKTOdgCE1NYxdvJj1F1zAxFGjWktMpw58D2jL4oXtOI7jOI1gxuTJ1I4cWe+9\nmkWm9+hBp3Hj2uIVR3mg2fYw+AxoEyBpX0kzJL0iaa2kZyT9UZKXr+M4juM0EWbGogkTylY+AYau\nXMnC8ePxCbe2hStITcMdwC7A2cBnCH/b+Q6p8vV9TPnG6y/feP3lF6+7fNOU9VeYNYuqpUsrDle1\nbBkLZs9uMjmcxuMKaCORtAOwN3CZmd1qZveY2R/M7HQzW9/a8jmO4zhOe2HupEkMrqmpOFz1mjXM\nmTixGSRyGorvAW0CJP0HWAdcBRTM7D8lvHphO05r4FexOE6HZ/TAgVw6Z05ri5E3/B7QNs4g4BLg\ncmBHSU8D48xss9etq666iiVLltCrVy8AunfvTp8+fTb+RVlxmcLNbnZzM5gJVMVvN7vZzR3LvDyx\nb7TVx6M2ai7+Xr58OQBTp06tMrNNjk2Iz4A2MZIOAM4DzgKONbONm04K/l/wuaZQKOD1l1MkCmx6\nGDn5ooDXXZ4p0Pr1VwuMPuEELrv11laWJHf4Kfi8YGaPAOdH4ydaUxbHcSJmMH9++PZP/j5ed/n+\nNGH9zb/jDu7o3LniIWBWly4MGj68GQYXp6H4DGgjkfRJ4JfAH4H/EO7A/TpwMnComT2c8O6F7TiO\n4zgNxMy4sG9fxi5eXFG4kQcfzJUPPuh/zVk5PgPahnkZeAb4HjATuBn4EPDZlPLpOI7jOE4jkES/\nESOY3qNH2WGm9exJ/xEjXPlsY7gC2kjM7DUz+7qZ9Tazrma2o5kdZWZbHLVLbvJ18ofXX77x+ssv\nXnf5pqnr76Rhw3j9vPPKUkKn9ezJinPP5cQzz2xSGZzG4wqo4ziO4zi5YviYMXQaN44LDjqI27t0\noTbhVgvc3qULIw8+mK3HjmX4mDGtJaZTB74HtGXxwnYcx3GcJsLMWDB7NnMmTqTT6tUA1HbtyqDh\nwxkweLAvuzeeZitAV0BbFi9sx3Ecx3Hygh9Cag/4PqZ84/WXb7z+8ovXXb7x+nOy8H9CchzHcZwK\nMDMKs2Yxd9IkOq1aBUBtt24MPPtsqqqrfdnXccrAl+DLQNIAYD5QZWZ3NyIqL2zHcZwcM2PyZBZN\nmEDV0qVU19RsXEasBWZ37kyhd2/6jRjBScOGtaaYjtNU+B7Q1iQqoPOAo1wBdRzH6ZhMHDWKHa++\nmqGJ/xTPYlqPHqw47zw/fe20B3wPaHvA98HkG6+/fOP1l1/aQt3NmDy5LOUT4NSVK9nx6quZMWVK\nC0jW9mkL9ee0PXKlgEq6RNIGSb0l3SVpjaTlkr4e3b8uaZmkVZLmSdorEfY0SXMlvRrdF0s6PSON\nHSXdLOktSSslXQ90J+MtQNLJkhZFOVZK+pOk3ZuxCBzHcZwWxsxYNGFCWcpnkaErV7Jw/Hh8ldFx\nssnVEryk0cBo4F/ANcC/gRHAScDPgUOBK4H3Ab8CnjWzfjHsRcDbwBPAeuBI4H+Bb5nZNYk07gE+\nGd3+A5wGfAbYlcQSvKThwHhgCnAL0A24FNgWOMDM1mRkIT+F7TiO4wAw/847WTt0KENqaioKd3uX\nLnSZPp2q6upmksxxmp1mW4LP4yl4A640s5sAJD0EfA44A+hVVPwkfRi4StLuZvacmV1ejEDhiOIC\n4MPAOQRlFknHAIcBp5nZtOj9b5LuICigxfBdgCuAKWb2jYT9AwQFdxhBAXYcx3FyztxJk7i0QuUT\noHrNGkZPnOgKqONkkKsl+ASzij/M7E3gVeDvqVnHpfF7dwBJ+0j6vaTngffi5yzgo4kwhxJmR/+c\nSu8PKXM/woznzZI6FT/ACzHdI7OE9n0w+cbrL8dIFCTwTy4/rV13l82cSacGNLtOsPHfeToyPnY6\nWeRxBhQgvRHn3RJ2AraLM5ZzgNXASOCp6D4C+HoizC7ASjOrTcX1Ssq8U4x7boZsBryRJfT06dO5\n/vrr6dWrFwDdu3enT58+VFVVAZs6qZvbpnnJkiVtSh43V2YOtQdV8bvgZje3gLlIa7d/N7u5HHPx\n9/LlywGYOnVqlZltcmxC8rgHdBSwjZltSNg/DdxjZqcn7IpXJx1DmOmdDRxuZosSfqYCXzGzTtF8\nMXAx8P6kEhoPK11H3AMqaTBwJ3A68HiGqKvM7MkM+/wUtuO0J6TWlsDpgNQCo084gctuvbW1RXGc\nhtJsg2deZ0ArpTOhENcXLST1IOwdTbKIUCanAH9K2H8x5W8hsArY18x+1+TSOo7TtOToRdtpezT0\nENKsLl0YNHx4M0nlOPlmq9YWoJkpau4LCSfgfyNpiKTPE1ZIXkt6NrM5wL3AJEnnSvqMpCnAx1P+\nVgEXAP8raYKkz0kaIOlLkiZJ+kKWMMkpbid/eP3lG6+//NLadVdVXU2hd++Kwy3o3ZsBgwc3g0T5\norXrz2mb5FEBzZrKsDrsMbPXgRMJe8KnAT8FfgvclBHmJOAO4HLC4aOtgHO3iDhc3fQ5YD/gBuB2\nwhVRndi03cxxHMfJOZLoN2IE03v0KDvMtJ496T9iBP6/8I6TTa72gLYDvLAdx3FyStl/xdmzJyvO\nPdf/itNpDzTbG5QroC2LF7bjOE6OmTFlCgvHj6dq2TKq16zZeD1TLWHP54Level3zjmcNGxYa4rp\nOE2FK6DtgUKhYMUrD5z8USgU8PrLL15/+aWt1Z2ZsWD2bOZMnLjxns/arl0ZNHw4AwYP9mX3FG2t\n/pyK8FPwjuM4jtMWkERVdbX/w5HjNAKfAW1ZvLAdx3Ecx8kLPgPqOI7jtB/MjMKsWcydNIlOq1YB\nUNutGwPPPpuq6mpfxnacdk4er2HKLX4XWr7x+ss3Xn9thxmTJ3Nh376sHTqUMTNncum8eeEzcyZr\nhw7lwr59mTFlykb/Xnf5xuvPycIVUMdxHKfFmDhqFLUjRzJ28WKG1NRs9hDqBAypqWHs4sWsv+AC\nJo4a1VpiOo7TzPge0AqR9D4ze7eBwb2wHcfpsMyYPJnakSPrvUezyPQePeg0bpxfaeQ4rUez7YXJ\n/QyopIMkbZDUP2H3rWg3JmG3T7Q7Npp7SbpJ0quS1kl6WNKJqbgviWE+LmmWpFXAHxPuJ0taJGmN\npJWS/iRp9xbItuM4Tq4wMxZNmFC28gkwdOVKFo4fj0+UOE77I/cKKPAw8CZwdMLuKKAmZTcQeA+4\nOyqJDwCfBL4DHA88BNwi6bhEmOKodyvhv+OPB34BIGk4MB34F3AK8E3gE0BBUpcsQX0fTL7x+ss3\nXn+tS2HWLKqWLq04XNWyZfxy3LhmkMhpKbzvOVnkXgG18Gp8N0HpROHo5ABgAvApSZ2j1yrgITNb\nA1xCUC6PNLPfm9nfzOwsYC6Q/u80A35pZleYWcHM7o4K5hXAFDP7hpnNMrNpwLHAboCvFzmO4ySY\nO2kSg2tqKg5XvWYND/3lL80gkeM4rUl7uYZpHnCFpPcRZiG3B8YCw4EjgNkEBXVy9D8YuANYJan4\nT2oC7gKulNTVzFYn4r81lV4/oBtwcyI8wAvAUuBI4FdpIf2fIPKN11+OkahqbRk6OJc1MFwnYK/t\ntmtKUZwWxsdOJ4v2ooDOB7YF+gMHAf80s9ck3QscJek5YCeCokr8fTpwRkZcBuwAJBXQl1J+diIo\nrHNLhH8jS8irrrqKJUuW0KtXLwC6d+9Onz59NnbO4jKFm93s5mYwE6iK327Ombm124+b3dwBzMXf\ny5cvB2Dq1KlVZrbJsQlpN6fgJb0KTAIOBP5tZhdIugD4PDAVGAf0MLN1kl4iLNtfQfYJr0fN7D1J\no4FRwDZmtiGR1mDgToIS+3hG+FVm9mTasuD/BZ9rCoUCXn85RaLAJmXGyQ+1wNcOO4wb7723tUVx\nGoiPnbnG/wmpDArAMUBv4DfRbh7wM+Bt4AEzWxftZwGHAo+b2TsNSGshsArY18x+1xihHcdpAcyg\nUAB/CLYa8++8k7VDhzKkwn2gs7p04eDPfa6ZpHIcp7VoTzOg5xAUz/VATzNbLWkrYAXwAWCMmV0a\n/e4O3A88D1wNLAd6EPaPfiQeSKLUDGh0+2YMO4UwG/oWsCvhANR8M/tDhpjto7Adx3EqxMy4sG9f\nxi5eXFG4kQcfzJUPPuh/zek4rYPfA1oG8wkK3oPFA0RRaVwQ7ecXPZrZc0BfYAnwU8Lho/GEw0Pz\nNo82W2k0s2uAzwH7ATcAtwOjCXvmlzRVphzHcdoDkug3YgTTe/QoO8y0nj3pP2KEK5+O0w5pNzOg\necD3gOYb38eUb7z+2gYTR41ix6uvrvdC+mk9e7Li3HMZPmaM113O8frLNT4D6jiO4+Sf4WPG0Gnc\nOC446CBu79KF2oRbLXB7ly6MPPhgth47luFjxrSWmI7jNDM+A9qyeGE7juMQ9oQumD2bORMn0ml1\nuPWutmtXBg0fzoDBg33Z3XHaBs3WEV0BbVm8sB3HcRzHyQu+BN8eSF706uQPr7984/WXX7zu8o3X\nn5NFe7oH1HEcx2lDmBmFWbOYO2kSnVatAqC2WzcGnn02VdXVvszuOB0YX4JvWbywHcfpEMyYPJlF\nEyZQtXQp1TU1G5fbaoHZnTtT6N2bfiNGcNKwYa0ppuM4deN7QNsJXtiO47R7yr5qqUcPVpx3np92\nd5y2i+8BbQ/4Pph84/WXb7z+WoYZkyeXpXwCnLpyJTtefTUzpkyp05/XXb7x+nOy6JAKqKT/kTRD\n0uuSaiQtlXRhdDtG0u2SXpS0RtKjkr4f/9YzGcfTkm6UdJqkxyWtlvSgpMNaJ1eO4ziti5mxaMKE\nspTPIkNXrmTh+PH4apzjdCw63BK8pE8T/pbzSWAc8AKwL3CAmX1L0tlAN+BxYA3hLztHAb8xs4sS\n8TxNmJp+BbgCeAe4DOgF9DKztzOS71iF7ThOh2L+nXeyduhQhtTUVBTu9i5d6DJ9OlXV1c0kmeM4\nDcT3gDYVku4G9gT2M7N3yvDfCbgQON/MdkjYPw18APhIUdmUdDDwIPAlM/tDRnQdq7Adx+lQ/PjE\nE7l05kw6VRiuFhh9wglcduutzSGW4zgNx/eANgWS3g/0B35XSvmU9CFJkyQtl/Qu8B5hZrO7pJ1S\n3helZjofjd97ZMXt+2DyjddfjpEoSOCfZv1c1gDlE6ATbPw3pCy87+Ubrz8ni452D2gPgtL9Qpaj\nwqV0twEfAkYDy4C1wEnARcB2qSBvJA1m9m681y7tD4Dp06dz/fXX06tXLwC6d+9Onz59qKqqAjZ1\nUje3TfOSJUvalDxurswcag+q4nfBzW3KvHzlSgqFQptpL252c0c0F38vX74cgKlTp1aZ2SbHJqRD\nLcHHGdBVwJVm9qMM932AJ4Avm9nvE/aXAj8mLLc/G+2eBu4xs9NTcWwALjGzMRkidJzCdpy2hF94\n3qbxJXjHabP4EnxTYGZrgXuBr0jaNsNL5/i9vmghaRvgyy0gnuM4zYWZf1rgM/+OO7ijc+f66yPF\nrC5dGDR8eDNUvOM4bZUOpYBGfgDsAPxd0lckVUkaJumXwL+BZ4CfSjpF0gnAXcCGpkg4OcXt5A+v\nv3zj9df8VFVXU+jdu+JwC3r3ZsDgwSXdve7yjdefk0WHU0DN7B/AYcCzwK+A24HzgefN7D3gBOBl\nYCrwa2AB4ZqlLaIie0m9lL3jOE67RhL9Roxgeo8eZYeZ1rMn/UeM8P+Fd5wORofaA9oG8MJ2HKfd\nU/ZfcfbsyYpzz/W/4nSctovfA9pO8MJ2HKdDMGPKFBaOH0/VsmVUr1mz8XqmWsKezwW9e9PvnHM4\nadiw1hTTcZy6cQW0PVAoFKx45YGTP5JXxDj5w+uv5TEzFsyezZyJEzfe81nbtSuDhg9nwODBZS+7\ne93lG6+/XNNsCmhHuwfUcRzHaSEkUVVd7X+x6TjOFvgMaMvihe04juM4Tl7wGVDHcRyn9TEzCrNm\nMRT8DBMAACAASURBVHfSJDqtWgVAbbduDDz7bKqqq/00u+M4ZZHLa5gknSGpVlLmf66XCHO9pOea\nU66YTkHSvCw3vwst33j95Ruvv8YzY/JkLuzbl7VDhzJm5kwunTcvfGbOZO3QoVzYty8zpkxp8nS9\n7vKN15+TRS4VUOCvQD/gpQrCtNT9nL7M7jhOu2PiqFHUjhzJ2MWLGVJTs9nDoxMwpKaGsYsXs/6C\nC5g4alRriek4Tk7oMHtAJV0HDDSzsmdNG5jOfMDM7OgM545R2I7jtCtmTJ5M7ciR9d7rWWR6jx50\nGjfOr1hynPzTfv8LXtJBkjZI6p+w+1a0G5Ow2yfaHRuX4Dckl+AlfUnSYkmrJL0l6RFJ38hIr4+k\nuyWtkfSEpLMz/PSSdJOkVyWtk/SwpBMz/H1B0r+jn0ez/DiO4+QZM2PRhAllK58AQ1euZOH48XSU\nCQ7HcSqn1RVQ4GHgTSA5Y3gUUJOyGwi8B9wdzRtHNkmHAzcC8wl/pXkKcA3QPZXW9sBN0e/ngAeA\nCZIGJOLaLdp/EvgOcDzwEHCLpOMS/gbFuJYBJwHjgF8CHy2VUd8Hk2+8/vKN11/DKMyaRdXSpRWH\nq1q2jAWzZzeNDF53ucbrz8mi1U/Bm5lJupugdF6mcIRyADAB+LakzmZWA1QBD5nZmoxTlocAK83s\n/ITdnIzkugLnmNndAJLuAaqBLxL+8x3gUoJye6SZvRnt/hZnW8cQ9p8W/f3bzDbOekpaBiwCKh+t\nHcdx2iBzJ03i0pqaisNVr1nD6IkT/Q5Qx3EyaXUFNDIPuELS+4BPEGYqxwLDgSOA2QQFtdTxygeB\nHpJuBP4A3Gtmb2X4qykqnwBm9q6kJ4DkvtDBwB3AKknFf48TcBdwpaSuhNnZvsDPkpGb2f2SlpfK\npP8TRL7x+ssxElWtLUNOuayB4TrBxn8/aize9/KN15+TRVtRQOcD2wL9gYOAf5rZa5LuBY6K1yft\nBMzNCmxmd0s6FfgW8GdAkhYA3zezRxNeszYxvQNslzDvBJwOnJGVFLAD0BnYBnglw0+WHQBXXXUV\nS5YsoVevXgB0796dPn36bOycxWUKN7vZzc1gJlAVv93c/ObliX2jrV7/bnazm+s1F38vX74cgKlT\np1aZ2SbHJqTNnIKX9CowCTiQsLR9gaQLgM8DUwl7LHuY2TpJZwDXAh8xs2dT8XQmjIFjge5mtlu0\nzzwFnz61Luklwj7TK8g+/fUoUAusBX5mZpek4nsKWJ51Cr7g/wWfawqFAl5/OUWiwCblyGl+aoHR\nJ5zAZbfe2ui4vO/lG6+/XNN+T8EnKADHAIcTluSJ3wcSDvk8YGbr6ovEzGrM7A6CMruLpB0qlGMW\ncADwuJktzvi8Z2YbCMv+Q5MBJR0C9KowPcdxmhszmD8/fPunos/8O+7gjs6dKy7yWV26MGj48Gao\nTMdx2gNtaQb0HOA3wHqgp5mtlrQVsAL4ADDGzC6NfjebAZV0KbAzYSn/RWB3wiGht8ysbwxT7gzo\n7sD9wPPA1cByoAdhb+pHzOys6G8gYW/q7QRldyfgEsLS/DK/B9RxnPaAmXFh376MXby4onAjDz6Y\nKx980P+a03HyTYeYAZ1PUNAeNLPVAHGmcUG0n19H2PuBPYGfEw4L/Sz6Py7lr5QCuNHezJ4jHDBa\nAvw0xjceOJJNM7OY2Vzgy8B+wC3A+YRrm5bVkY7jOE6ukES/ESOY3qNH2WGm9exJ/xEjXPl0HKck\nbWYGtCPge0Dzje9jyjdef41j4qhR7Hj11fVeSD+tZ09WnHsuw8eMabK0ve7yjddfrukQM6CO4zhO\nG2X4mDF0GjeOCw46iNu7dKE24VYL3N6lCyMPPpitx45tUuXTcZz2ic+Atixe2I7j5BozY8Hs2cyZ\nOHHjPZ+1XbsyaPhwBgwe7MvujtO+aLYO7Qpoy+KF7TiO4zhOXvAl+PZA8qJXJ394/eUbr7/84nWX\nb7z+nCzayj8hOY7jODnAzCjMmsXcSZPotGoVALXdujHw7LOpqq72JXjHccqi3S3BSzoB2MvMfpGw\nG0C4lmmQmc0rGbj5aV+F7ThOh2LG5MksmjCBqqVLqa6p2biEVgvM7tyZQu/e9BsxgpOGDWtNMR3H\naTp8D2i5ZF04HxXQecAxroD+f/buPj7Oqs7//+tDRCBpNYksK7suBGU1+tU1S+tNu2IHWsxQYUs1\n4LLqglRpSNHV1ZafK6a0Vhdbvlq/283NmkIjq6ANpGFpSZY2nYDbqqwxyCqtCgS8A7mJWpKCkn5+\nf1zXtNNh2mbaTGaumffz8ZjH5FzXOdecXJ/HpKfnnOscEZHsTXgZpqoqnr76aj0JL1IcNAd0EuR9\nXEjzYKJN8Ys2xe/odXd0TKjxCXDxyAgnr1tH9/r1k/b5il20KX6SSVE1QMPez8uAPzezfeHr4ZQs\nFWb2r2b2ZPi62cxelnaNMjP7tJk9aGbPmdkvzewGMzshJc/p4bWvNLMVZvYrMxsxszvM7M+n6NcV\nEck5d2dna+uEGp9JDSMj7GhpodhG2ERk8hTVELyZnQH8K8FWmhcS9Ho+D1QSzAF9BLgT+E/gdcAa\n4Jvu/qGUa9wKvBu4HtgJvB5YBWx194vDPKeH1xoGdgA3E+wF/yXggUPsAw8agheRiNl+113sbWhg\n/thYVuU2V1RQ0dVFLB7PUc1EZArkbPS4qJ6Cd/dHzOxJ4A/ufl/yeDgHFGDA3f8x/HmrmdUCi4AP\nhfnOBi4BPujuXw/z9ZvZCHCzmf2Vu/8w5SMfcfcPpHzOKcBqM3uluz+ek19SRGQKbWtvZ0WWjU+A\n+Ogoy9va1AAVkYyKagh+ArakpR8ATggbjgD1BD2mt4VD8WVmVgbcTfC/gHemlb8rw/UATiMDzYOJ\nNsUvwsxImIFeWb9W9fRQdhS3vAz275R0rPTdizbFTzIpqh7QCXgmLf18+H5i+H4KcAKQ6b/7Drwi\ny+sdpKuriw0bNlBTUwNAZWUldXV1xGIx4MCXVOnCTA8NDRVUfZTOLh1ED2Lhe0LpnKeHU+aN5jv+\nSiut9JHTyZ+Hh4cB6OzsjLn7gZOTqKjmgMJhl2F60TqgZnYZcCNwhrs/Zmb/AnwMeAeZ5z38yt0f\nT5kD+mF3vzHtc/qBc9z9ngzli+tmi0SFFkefcuPA8gULWLVpU76rIiJHT3NAs/A8cFKG4xNp/PUC\ny4BKd98+qbUSkfwpsv9oT6WjfQipt6KCeY2NOaqViETdcfmuQA78GKg2s0Yzm2lmbwyPH7EV7+4D\nwK1Al5lda2bvMrN5ZvYRM7vdzM6cwOcf8nNSu7glehS/aFP8jk4sHidRW5t1uYHaWubU109KHRS7\naFP8JJNibIB2EDQiPw98F7gjPD6hLhB3fz9wHfBeYBOwEWgCfgI8kZr1UJfIusYiIgXKzJjV1ERX\nVdWEy2ysrmZ2U5P2hReRQyq6OaAFTjdbRCJpwltxVlfz9JIl2opTpDhoL/gioZstIpHVvX49O1pa\niO3eTXx0dP/yTOMEcz4HamuZddVVLFy0KJ/VFJHJowZoMUgkEp5c8kCiJ5FIoPhFl+I3Odydgb4+\ntra17V/nc3zaNOY1NjKnvj4nw+6KXbQpfpGmp+BFRCT/zIxYPK4djkTkmKgHdGrpZouIiEhUqAdU\nRESmjruT6O1lW3s7ZXv2ADA+fTpzFy8mFo/rCXcROSbFuAxTTpjZcjMbP5ZraC20aFP8ok3xm7ju\njg6umTmTvQ0NrOzpYUV/f/Dq6WFvQwPXzJxJ9/r1U1YfxS7aFD/JRA3QifsqMCvflRARyaW25mbG\nly1j9eAg88fGDvpHogyYPzbG6sFBXli6lLbm5nxVU0QiTnNAp5ZutogUrO6ODsaXLTviWp9JXVVV\nlK1Zo2WXRIpXzubaRK4H1MxeY2ZfM7OHzWzMzB4ysxYzq8yQ9+Nm9oiZ7TWz75jZrDB9Y0qek82s\nzcx2m9momT1mZl83sz9Lu9Z1ZrYv7dg+M1tpZh8N6/N7M0uY2RtydwdERCafu7OztXXCjU+AhpER\ndrS0oI4MEclW5BqgwJ8BvwQ+AdQDK4Bzgc2pmczsw8CXgP8C/hbYAHwDeHna9aqB54F/BuLAp4Az\ngW+b2UtT8jmZezA/AMwHPgZcDpwGbDKzF91bzYOJNsUv2hS/w0v09hLbtSvrcrHduxno68tBjQ5Q\n7KJN8ZNMIvcUvLvfC9ybTJvZDuAh4B4ze7O732/B45nNwGZ3XxxmvdvMngBuS7veT4B/TLneccAO\n4DHgfKDnCFX6I3CBu4+H5Q34FvBW4DtH/YuKiEyhbe3trBgby7pcfHSU5W1tWhdURLISuQaomR0P\nLAU+CJwOnBiecuB1wP3Aq8LXZ9OK9wAvZLjmVcBi4DVARdr1juTuZOMz9ADBnInTSGuAaieIaFP8\nIsyMWL7rUOBWHWW5Mti/I1Ku6LsXbYqfZBK5BihwPbCEYOh9J7CHoLHZzYHG6Knh+29SC7r7PjN7\nKvWYmX0U+ApwA8Fw/QjB1ITvplzvcJ5JSz8fvr+o7Nq1axkaGqKmpgaAyspK6urq9n85k8MUSiut\ndA7SBGLhu9KTnM53fJVWWuljTid/Hh4eBqCzszPm7gdOTqLIPQVvZr/g4KF1zCwG9AOXu/vXzOwv\ngEeBD7l7Z0q+44DngP9w9yvCY98Gxtz9XSn5aoCHgevcfWV4bDnQ7O5lKfn2AavcvTnl2OnAI8m6\npNY9ob3gIy2RSKD4RZQZCQ40lmTyjAPLFyxg1aZNOfsMffeiTfGLND0Fn6KcFw+jX8HBDwj9Inxd\nnJZvIS/u9S0nmMd5uOuJSJS5w/btwbteGV/bt2xhS3l51re2t6KCeY2NOQiaiBSzKDZAe4HLzOwq\nMzvPzFpJWyDeg27dFcB8M/uqmb0rnOf5f4HfAvvSrldvZp82s7lm9nngfbmouP4HGG2KX7QpfocX\ni8dJ1NZmXW6gtpY59fU5qNEBil20KX6SSRQboB8F7iCYM38rwUNDf5eeyd3XAx8H5gGbgA8B7w9P\n/y4l60qgPcx7O/BGIDkcn94LmimdqadUvaciEilmxqymJrqqqiZcZmN1NbObmrQvvIhkLXJzQI+F\nmc0Evgd8wN2/MdWfrzmg0aZ5TNGm+E1MW3MzJ69bd8QF6TdWV/P0kiU0rlyZ8zopdtGm+EVazv53\nGcWn4CckfJBoCcGaob8H3gB8mmDN0NvzVjERkQLWuHIl3aefztKWFmK7dxMfHSX55OU4wZzPgdpa\nZl11FY3aglNEjlLR9oCa2Z8S7H7010AVwfJKdwOfdvdf5KlaxXmzRaTouDsDfX1sbWvbv87n+LRp\nzGtsZE59vYbdRUpDzr7oRdsALVC62SIiIhIVWoapGKQu9CrRo/hFm+IXXYpdtCl+kknRzgEVEZGD\nuTuJ3l62tbdTtmcPAOPTpzN38WJi8biG1UVkyuRsCD7TzkH5YGZzgO1AzN3vyfFnXQ7cCNS4+2MZ\nsmgIXkTyorujg52trcR27SI+NrZ/+Gsc6CsvJ1Fby6ymJhbqwSIROSB6c0DN7M+AV7n793LyAROv\nxzSCJ+B/7O7P5vizLiNogJ6hBqiIFIoJL61UVcXTV189JUsriUgkRG8OqLv/Kt+Nz7Aez7r7947U\n+DSzl+a6LpoHE22KX7SVavy6Ozom1PgEuHhkhJPXraN7/fopqNnElWrsioXiJ5nkrAFqZteZ2b6U\n9D4zW2lmHzWzh83s92aWMLM3pJU7zsxWmdmvzGzUzPrN7A1h+eaUfBvM7JEMn5sws/6U9Jyw7DvT\n8txrZheY2aCZ7QWuCs+VhdtyPmhmz5nZL83sBjM7Ie1zzjCzzWEdnzCztcBBeURE8snd2dnaOqHG\nZ1LDyAg7WlrQCikikku5fAgp0zaVHwB2Ax8DXgrcAGwys1p3TzZWVxAsGH8DwbqdMwm23jyWbTAz\nlX0t8BXgc8DDwDPhua8D7wauB3YCryfY9vN04GIAMzse2ErQ4LwKeBJYDLwnw2fvp50gok3xi7ZS\njF+it5fYrl1Zl4vt3s1AXx+xeDwHtcpeKcaumCh+kslUPwX/R+ACdx8HsOCRy28BbwW+Y2aVBHuy\nt7n7NWGZrWFP6vWTXJdXAPPc/YHkATM7G7gE+KC7fz083G9mI8DNZvZX7v5D4HKgBni7u98Xlu0F\nHgD+fJLrKSJyVLa1t7NibCzrcvHRUZa3tRVMA1REis9UrwN6d7LxGXqAYILraWH6TUA5sDGt3K05\nqMtwauMzVA88D9wWDsWXmVkZQU+sAclh/LcDP082PgE8GK/61uE+UPNgok3xizAzEmZQYq9VPT0c\nzTIkZbB/96NCoO9etCl+kslU94A+k5Z+Pnw/MXw/NXx/Ii1fenoy/DrDsVMIhtUzdRk4Qa8pBPXM\nVKfD1rOrq4sNGzZQU1MDQGVlJXV1dfuHJ5JfUqULMz00NFRQ9VE6u3QQPYiF7wmlD5seHhkhkUgU\nTPyUVlrp3KeTPw8PDwPQ2dkZc/cDJyfRlK0DGg6jr3L31AeJTgceAS5396+FQ+ADwLmpv7CZnQYM\nA9e5+8rwWCtwobu/Ku1zfwg85e7nhuk5QD9wTnIdUDPbDpS5+zvTyv4LwfzUd5B56YFfufvjZrYe\nmOvuNZl+Z7QMk0hh0QLrWRkHli9YwKpNm/JdFRHJr+gtw3SUfgiMEszDTHVphryPAn9qZsleSczs\nNcDrjuHzewl6YyvdfTDD6/Ew307gL8zsrSmfbRnqLSKFwL0kX9u3bGFLeXnWt6u3ooJ5jY05CISI\nSKCgGqDu/jvgy8CVZrbazOaZ2aeBj/Di3sPkPNGvm9m7zOz9wCaCJ9LTTagF7+4DBPNNu8zs2vC6\n88zsI2Z2u5mdGWbtJOi5vd3MLjOz88PPnn6466d2cUv0KH7RVorxi8XjJGprsy43UFvLnPr6HNTo\n6JRi7IqJ4ieZ5LoB6mk/T2SJpOuALxAs2dQDzAMuIK0R6e4PAe8F/gzoBj4FfAL4SYZrTnS5Jtz9\n/WEd3kvQqNwINIXXfSLM88ewXkPAvwEbCJZy+lyma4qI5IOZMaupia6qqgmX2VhdzeymJu0LLyI5\nlbM5oJMtnEO6fw5oREXjZotIUZnwVpzV1Ty9ZIm24hSRpJKZAyoiIpOsceVKytasYelZZ7G5ooLU\ntfDGgc0VFSybMYOXrF6txqeITIko9YCOE/SARnaYO5FIeHLJA4meRMqSNBI9il+wNedAXx9b29r2\nr/M5Pm0a8xobmVNfX7DD7opdtCl+kZazPwpTvQ7oUUsu5yQ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jypX5qqaISE6V9FPweaCb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\nHcThHWD8cRxGAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xad852f4c>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gender_plot(plot_items_bronte, 'Charlotte Brontë')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Yes, many of the verbs after 'she' are not related to emotions, while men are more passive, standing and pausing.\n",
    "\n",
    "'s' is an interesting token. It appears in direct speech, as \"she's …\" and, rarely, \"he's …\", so is an abbreviation of \"is\", which isn't reported as having gendered usage. Quite why women are referred to in this way in speech is something for another investigation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Now with Oscar Wilde's book, _The Picture of Dorian Grey_<a name=\"wilde\"></a>\n",
    "\n",
    "Not in Silge's article, but in the original paper, _The Picture of Dorian Grey_ was called out as another gender-swap, with women being active and men being emotional.\n",
    "\n",
    "* [Back to top](#top)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "wilde_books = {title: open(wilde_books_filenames[title],  \n",
    "                            encoding='utf-8', errors='replace').read().lower()\n",
    "                for title in wilde_books_filenames}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "80416"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wilde_books_all_tokens = [token \n",
    "                           for book in wilde_books \n",
    "                           for token in tokens(wilde_books[book])]\n",
    "len(wilde_books_all_tokens)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "run_control": {
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(he, had)         215\n",
       "(he, was)         161\n",
       "(he, is)           61\n",
       "(he, would)        61\n",
       "(he, said)         46\n",
       "(he, felt)         44\n",
       "(she, was)         41\n",
       "(she, is)          40\n",
       "(she, had)         39\n",
       "(he, answered)     33\n",
       "dtype: int64"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gendered_bigrams_wilde = gendered_bigrams(wilde_books_all_tokens)\n",
    "gendered_bigrams_wilde[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "run_control": {
     "read_only": false
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>he</th>\n",
       "      <th>she</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>answered</th>\n",
       "      <td>33</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>asked</th>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>could</th>\n",
       "      <td>31</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cried</th>\n",
       "      <td>23</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>did</th>\n",
       "      <td>17</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          he  she\n",
       "answered  33   10\n",
       "asked     16    4\n",
       "could     31    5\n",
       "cried     23   15\n",
       "did       17    6"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "useful_gender_counts_wilde = gender_counts(gendered_bigrams_wilde, lower_limit=10) \n",
    "useful_gender_counts_wilde.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "run_control": {
     "read_only": false
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>he</th>\n",
       "      <th>she</th>\n",
       "      <th>logratio</th>\n",
       "      <th>abslogratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>laughed</th>\n",
       "      <td>0.004331</td>\n",
       "      <td>0.027245</td>\n",
       "      <td>2.653113</td>\n",
       "      <td>2.653113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>will</th>\n",
       "      <td>0.008557</td>\n",
       "      <td>0.046431</td>\n",
       "      <td>2.439931</td>\n",
       "      <td>2.439931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>turned</th>\n",
       "      <td>0.022290</td>\n",
       "      <td>0.004221</td>\n",
       "      <td>-2.400750</td>\n",
       "      <td>2.400750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>saw</th>\n",
       "      <td>0.017008</td>\n",
       "      <td>0.004221</td>\n",
       "      <td>-2.010567</td>\n",
       "      <td>2.010567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>thought</th>\n",
       "      <td>0.014895</td>\n",
       "      <td>0.004221</td>\n",
       "      <td>-1.819202</td>\n",
       "      <td>1.819202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>remembered</th>\n",
       "      <td>0.014895</td>\n",
       "      <td>0.004221</td>\n",
       "      <td>-1.819202</td>\n",
       "      <td>1.819202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>got</th>\n",
       "      <td>0.013839</td>\n",
       "      <td>0.004221</td>\n",
       "      <td>-1.713074</td>\n",
       "      <td>1.713074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>went</th>\n",
       "      <td>0.022290</td>\n",
       "      <td>0.008058</td>\n",
       "      <td>-1.467864</td>\n",
       "      <td>1.467864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>is</th>\n",
       "      <td>0.064547</td>\n",
       "      <td>0.153876</td>\n",
       "      <td>1.253348</td>\n",
       "      <td>1.253348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cried</th>\n",
       "      <td>0.024403</td>\n",
       "      <td>0.057943</td>\n",
       "      <td>1.247574</td>\n",
       "      <td>1.247574</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  he       she  logratio  abslogratio\n",
       "laughed     0.004331  0.027245  2.653113     2.653113\n",
       "will        0.008557  0.046431  2.439931     2.439931\n",
       "turned      0.022290  0.004221 -2.400750     2.400750\n",
       "saw         0.017008  0.004221 -2.010567     2.010567\n",
       "thought     0.014895  0.004221 -1.819202     1.819202\n",
       "remembered  0.014895  0.004221 -1.819202     1.819202\n",
       "got         0.013839  0.004221 -1.713074     1.713074\n",
       "went        0.022290  0.008058 -1.467864     1.467864\n",
       "is          0.064547  0.153876  1.253348     1.253348\n",
       "cried       0.024403  0.057943  1.247574     1.247574"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gender_ratio_wilde = find_ratios(useful_gender_counts_wilde)\n",
    "gender_ratio_wilde.sort_values('abslogratio', ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "run_control": {
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_items_wilde = extract_plot_items(gender_ratio_wilde, window=15)\n",
    "len(plot_items_wilde)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "run_control": {
     "read_only": false
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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E4WFza+LYdxW2vsnXp9bcveKNAvH5F6CqAsdbWkZW/jvGv0MoPCJq1H/4l4SkXwPnEFx+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2+b2T4zGzCz65IrzOwYM/uMmT0Qrv+tmd1lZq9L34mZLTazH5nZATN70MyuiPZliEiSu9PUtCPj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\nHMdx6g43Yh3HcRzHcZy64/8B5RzzmDmI+MIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xaa3adeac>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gender_plot(plot_items_wilde, 'Oscar Wilde')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Yes, the men have the typically emotion-related verbs such as 'remember' and 'think', while women lauch and cry (which could be \"cry out\" so much as sob)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Charles Dickens's books<a name=\"dickens\"></a>\n",
    "A typically male 19th century author, with many books with male protagonists.\n",
    "\n",
    "* [Back to top](#top)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [],
   "source": [
    "dickens_books = {title: open(dickens_books_filenames[title],  \n",
    "                            encoding='utf-8', errors='replace').read().lower()\n",
    "                for title in dickens_books_filenames}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "756590"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dickens_books_all_tokens = [token \n",
    "                           for book in dickens_books \n",
    "                           for token in tokens(dickens_books[book])]\n",
    "len(dickens_books_all_tokens)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "run_control": {
     "read_only": false
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(he, had)       937\n",
       "(he, was)       895\n",
       "(she, was)      619\n",
       "(she, had)      490\n",
       "(he, said)      376\n",
       "(he, would)     296\n",
       "(he, is)        291\n",
       "(she, said)     188\n",
       "(she, would)    182\n",
       "(he, could)     165\n",
       "dtype: int64"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gendered_bigrams_dickens = gendered_bigrams(dickens_books_all_tokens)\n",
    "gendered_bigrams_dickens[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "run_control": {
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>he</th>\n",
       "      <th>she</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>added</th>\n",
       "      <td>33</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ain</th>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>always</th>\n",
       "      <td>25</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>and</th>\n",
       "      <td>45</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>answered</th>\n",
       "      <td>37</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          he  she\n",
       "added     33   12\n",
       "ain        4    8\n",
       "always    25   23\n",
       "and       45   29\n",
       "answered  37   44"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "useful_gender_counts_dickens = gender_counts(gendered_bigrams_dickens, lower_limit=10) \n",
    "useful_gender_counts_dickens.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "run_control": {
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>he</th>\n",
       "      <th>she</th>\n",
       "      <th>logratio</th>\n",
       "      <th>abslogratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>explained</th>\n",
       "      <td>0.001803</td>\n",
       "      <td>0.000276</td>\n",
       "      <td>-2.709410</td>\n",
       "      <td>2.709410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ate</th>\n",
       "      <td>0.001505</td>\n",
       "      <td>0.000276</td>\n",
       "      <td>-2.448758</td>\n",
       "      <td>2.448758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>certainly</th>\n",
       "      <td>0.001505</td>\n",
       "      <td>0.000276</td>\n",
       "      <td>-2.448758</td>\n",
       "      <td>2.448758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cried</th>\n",
       "      <td>0.001207</td>\n",
       "      <td>0.005538</td>\n",
       "      <td>2.198074</td>\n",
       "      <td>2.198074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rejoined</th>\n",
       "      <td>0.002250</td>\n",
       "      <td>0.000526</td>\n",
       "      <td>-2.096066</td>\n",
       "      <td>2.096066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ran</th>\n",
       "      <td>0.000909</td>\n",
       "      <td>0.003283</td>\n",
       "      <td>1.852707</td>\n",
       "      <td>1.852707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>resumed</th>\n",
       "      <td>0.002697</td>\n",
       "      <td>0.000777</td>\n",
       "      <td>-1.795628</td>\n",
       "      <td>1.795628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>carried</th>\n",
       "      <td>0.001803</td>\n",
       "      <td>0.000526</td>\n",
       "      <td>-1.776524</td>\n",
       "      <td>1.776524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>be</th>\n",
       "      <td>0.000760</td>\n",
       "      <td>0.002531</td>\n",
       "      <td>1.735808</td>\n",
       "      <td>1.735808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ain</th>\n",
       "      <td>0.000611</td>\n",
       "      <td>0.002030</td>\n",
       "      <td>1.732320</td>\n",
       "      <td>1.732320</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 he       she  logratio  abslogratio\n",
       "explained  0.001803  0.000276 -2.709410     2.709410\n",
       "ate        0.001505  0.000276 -2.448758     2.448758\n",
       "certainly  0.001505  0.000276 -2.448758     2.448758\n",
       "cried      0.001207  0.005538  2.198074     2.198074\n",
       "rejoined   0.002250  0.000526 -2.096066     2.096066\n",
       "ran        0.000909  0.003283  1.852707     1.852707\n",
       "resumed    0.002697  0.000777 -1.795628     1.795628\n",
       "carried    0.001803  0.000526 -1.776524     1.776524\n",
       "be         0.000760  0.002531  1.735808     1.735808\n",
       "ain        0.000611  0.002030  1.732320     1.732320"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gender_ratio_dickens = find_ratios(useful_gender_counts_dickens)\n",
    "gender_ratio_dickens.sort_values('abslogratio', ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "run_control": {
     "read_only": false
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_items_dickens = extract_plot_items(gender_ratio_dickens, window=15)\n",
    "len(plot_items_dickens)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "run_control": {
     "read_only": false
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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KlzVGFN3PitTnDrFfLVfo39OAPYvRZSPX1zPW0YuxDT0p6eepNH0l3RflXy1p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wDdwO3AqcDbzGzAzP5CMBXTRqCBoIW0A3gH0Jdwnu8ALcCVwHbgvqi+ATc1SaK+fi7l5Z1p56mo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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xad7d190c>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gender_plot(plot_items_dickens, 'Charles Dickens')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Women cry and run, while men explain and are certain. Very stereotypical genered behaviour."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}