--- /dev/null
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Gender roles and pronouns in Austen's texts\n",
+ "\n",
+ "Following examples from post at [GENDER ROLES WITH TEXT MINING AND N-GRAMS](https://juliasilge.com/blog/gender-pronouns/).\n",
+ "\n",
+ "Books from [Project Gutenberg](http://onlinebooks.library.upenn.edu/webbin/gutbook/author?name=Austen%2C%20Jane%2C%201775-1817)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "import re\n",
+ "import collections\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\n",
+ "\n",
+ "First, define the books and the files they're in."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "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'}"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Split a book into words, dropping punctuation and excessive whitespace."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "token_split_re = re.compile(r'\\W')\n",
+ "\n",
+ "def tokens(text):\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]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "austen_books = {title: open(austen_books_filenames[title], encoding='latin1').read().lower()\n",
+ " for title in austen_books_filenames}"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "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": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "tokens(austen_books['Emma'])[:30]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "733839"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "austen_books_all_tokens = [token for book in austen_books 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": 8,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "def bigrams(tokens):\n",
+ " return [(tokens[i-1], tokens[i]) for i in range(1, len(tokens))]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "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'),\n",
+ " ('existence', 'and'),\n",
+ " ('and', 'had'),\n",
+ " ('had', 'lived'),\n",
+ " ('lived', 'nearly'),\n",
+ " ('nearly', 'twenty'),\n",
+ " ('twenty', 'one'),\n",
+ " ('one', 'years'),\n",
+ " ('years', 'in'),\n",
+ " ('in', 'the'),\n",
+ " ('the', 'world'),\n",
+ " ('world', 'with'),\n",
+ " ('with', 'very'),\n",
+ " ('very', 'little'),\n",
+ " ('little', 'to'),\n",
+ " ('to', 'distress'),\n",
+ " ('distress', 'or'),\n",
+ " ('or', 'vex'),\n",
+ " ('vex', 'her'),\n",
+ " ('her', 'she'),\n",
+ " ('she', 'was'),\n",
+ " ('was', 'the'),\n",
+ " ('the', 'youngest'),\n",
+ " ('youngest', 'of'),\n",
+ " ('of', 'the'),\n",
+ " ('the', 'two'),\n",
+ " ('two', 'daughters'),\n",
+ " ('daughters', 'of'),\n",
+ " ('of', 'a'),\n",
+ " ('a', 'most'),\n",
+ " ('most', 'affectionate'),\n",
+ " ('affectionate', 'indulgent'),\n",
+ " ('indulgent', 'father'),\n",
+ " ('father', 'and'),\n",
+ " ('and', 'had'),\n",
+ " ('had', 'in'),\n",
+ " ('in', 'consequence'),\n",
+ " ('consequence', 'of'),\n",
+ " ('of', 'her'),\n",
+ " ('her', 'sisterâ'),\n",
+ " ('sisterâ', 's'),\n",
+ " ('s', 'marriage'),\n",
+ " ('marriage', 'been'),\n",
+ " ('been', 'mistress'),\n",
+ " ('mistress', 'of'),\n",
+ " ('of', 'his'),\n",
+ " ('his', 'house'),\n",
+ " ('house', 'from'),\n",
+ " ('from', 'a'),\n",
+ " ('a', 'very'),\n",
+ " ('very', 'early'),\n",
+ " ('early', 'period'),\n",
+ " ('period', 'her'),\n",
+ " ('her', 'mother'),\n",
+ " ('mother', 'had'),\n",
+ " ('had', 'died'),\n",
+ " ('died', 'too'),\n",
+ " ('too', 'long'),\n",
+ " ('long', 'ago'),\n",
+ " ('ago', 'for'),\n",
+ " ('for', 'her'),\n",
+ " ('her', 'to'),\n",
+ " ('to', 'have'),\n",
+ " ('have', 'more'),\n",
+ " ('more', 'than'),\n",
+ " ('than', 'an'),\n",
+ " ('an', 'indistinct'),\n",
+ " ('indistinct', 'remembrance'),\n",
+ " ('remembrance', 'of'),\n",
+ " ('of', 'her'),\n",
+ " ('her', 'caresses'),\n",
+ " ('caresses', 'and'),\n",
+ " ('and', 'her'),\n",
+ " ('her', 'place'),\n",
+ " ('place', 'had'),\n",
+ " ('had', 'been'),\n",
+ " ('been', 'supplied'),\n",
+ " ('supplied', 'by'),\n",
+ " ('by', 'an'),\n",
+ " ('an', 'excellent'),\n",
+ " ('excellent', 'woman'),\n",
+ " ('woman', 'as'),\n",
+ " ('as', 'governess'),\n",
+ " ('governess', 'who'),\n",
+ " ('who', 'had'),\n",
+ " ('had', 'fallen'),\n",
+ " ('fallen', 'little'),\n",
+ " ('little', 'short'),\n",
+ " ('short', 'of'),\n",
+ " ('of', 'a'),\n",
+ " ('a', 'mother'),\n",
+ " ('mother', 'in'),\n",
+ " ('in', 'affection'),\n",
+ " ('affection', 'sixteen'),\n",
+ " ('sixteen', 'years'),\n",
+ " ('years', 'had'),\n",
+ " ('had', 'miss'),\n",
+ " ('miss', 'taylor'),\n",
+ " ('taylor', 'been'),\n",
+ " ('been', 'in'),\n",
+ " ('in', 'mr'),\n",
+ " ('mr', 'woodhouseâ'),\n",
+ " ('woodhouseâ', 's'),\n",
+ " ('s', 'family'),\n",
+ " ('family', 'less'),\n",
+ " ('less', 'as'),\n",
+ " ('as', 'a'),\n",
+ " ('a', 'governess'),\n",
+ " ('governess', 'than'),\n",
+ " ('than', 'a'),\n",
+ " ('a', 'friend'),\n",
+ " ('friend', 'very'),\n",
+ " ('very', 'fond'),\n",
+ " ('fond', 'of'),\n",
+ " ('of', 'both'),\n",
+ " ('both', 'daughters'),\n",
+ " ('daughters', 'but'),\n",
+ " ('but', 'particularly'),\n",
+ " ('particularly', 'of'),\n",
+ " ('of', 'emma'),\n",
+ " ('emma', 'between'),\n",
+ " ('between', 'them'),\n",
+ " ('them', 'it'),\n",
+ " ('it', 'was'),\n",
+ " ('was', 'more'),\n",
+ " ('more', 'the'),\n",
+ " ('the', 'intimacy'),\n",
+ " ('intimacy', 'of'),\n",
+ " ('of', 'sisters'),\n",
+ " ('sisters', 'even'),\n",
+ " ('even', 'before'),\n",
+ " ('before', 'miss'),\n",
+ " ('miss', 'taylor'),\n",
+ " ('taylor', 'had'),\n",
+ " ('had', 'ceased'),\n",
+ " ('ceased', 'to'),\n",
+ " ('to', 'hold'),\n",
+ " ('hold', 'the'),\n",
+ " ('the', 'nominal'),\n",
+ " ('nominal', 'office'),\n",
+ " ('office', 'of'),\n",
+ " ('of', 'governess'),\n",
+ " ('governess', 'the'),\n",
+ " ('the', 'mildness'),\n",
+ " ('mildness', 'of'),\n",
+ " ('of', 'her'),\n",
+ " ('her', 'temper'),\n",
+ " ('temper', 'had'),\n",
+ " ('had', 'hardly'),\n",
+ " ('hardly', 'allowed'),\n",
+ " ('allowed', 'her'),\n",
+ " ('her', 'to'),\n",
+ " ('to', 'impose'),\n",
+ " ('impose', 'any'),\n",
+ " ('any', 'restraint'),\n",
+ " ('restraint', 'and'),\n",
+ " ('and', 'the'),\n",
+ " ('the', 'shadow'),\n",
+ " ('shadow', 'of'),\n",
+ " ('of', 'authority'),\n",
+ " ('authority', 'being'),\n",
+ " ('being', 'now'),\n",
+ " ('now', 'long'),\n",
+ " ('long', 'passed'),\n",
+ " ('passed', 'away'),\n",
+ " ('away', 'they'),\n",
+ " ('they', 'had'),\n",
+ " ('had', 'been'),\n",
+ " ('been', 'living'),\n",
+ " ('living', 'together'),\n",
+ " ('together', 'as'),\n",
+ " ('as', 'friend'),\n",
+ " ('friend', 'and'),\n",
+ " ('and', 'friend'),\n",
+ " ('friend', 'very'),\n",
+ " ('very', 'mutually'),\n",
+ " ('mutually', 'attached'),\n",
+ " ('attached', 'and'),\n",
+ " ('and', 'emma'),\n",
+ " ('emma', 'doing'),\n",
+ " ('doing', 'just'),\n",
+ " ('just', 'what'),\n",
+ " ('what', 'she'),\n",
+ " ('she', 'liked'),\n",
+ " ('liked', 'highly'),\n",
+ " ('highly', 'esteeming'),\n",
+ " ('esteeming', 'miss'),\n",
+ " ('miss', 'taylorâ'),\n",
+ " ('taylorâ', 's'),\n",
+ " ('s', 'judgment'),\n",
+ " ('judgment', 'but'),\n",
+ " ('but', 'directed'),\n",
+ " ('directed', 'chiefly'),\n",
+ " ('chiefly', 'by'),\n",
+ " ('by', 'her'),\n",
+ " ('her', 'own'),\n",
+ " ('own', 'the'),\n",
+ " ('the', 'real'),\n",
+ " ('real', 'evils'),\n",
+ " ('evils', 'indeed'),\n",
+ " ('indeed', 'of'),\n",
+ " ('of', 'emmaâ'),\n",
+ " ('emmaâ', 's'),\n",
+ " ('s', 'situation'),\n",
+ " ('situation', 'were'),\n",
+ " ('were', 'the'),\n",
+ " ('the', 'power'),\n",
+ " ('power', 'of'),\n",
+ " ('of', 'having'),\n",
+ " ('having', 'rather'),\n",
+ " ('rather', 'too'),\n",
+ " ('too', 'much'),\n",
+ " ('much', 'her'),\n",
+ " ('her', 'own'),\n",
+ " ('own', 'way'),\n",
+ " ('way', 'and'),\n",
+ " ('and', 'a'),\n",
+ " ('a', 'disposition'),\n",
+ " ('disposition', 'to'),\n",
+ " ('to', 'think'),\n",
+ " ('think', 'a'),\n",
+ " ('a', 'little'),\n",
+ " ('little', 'too'),\n",
+ " ('too', 'well'),\n",
+ " ('well', 'of'),\n",
+ " ('of', 'herself'),\n",
+ " ('herself', 'these'),\n",
+ " ('these', 'were'),\n",
+ " ('were', 'the'),\n",
+ " ('the', 'disadvantages'),\n",
+ " ('disadvantages', 'which'),\n",
+ " ('which', 'threatened'),\n",
+ " ('threatened', 'alloy'),\n",
+ " ('alloy', 'to'),\n",
+ " ('to', 'her'),\n",
+ " ('her', 'many'),\n",
+ " ('many', 'enjoyments'),\n",
+ " ('enjoyments', 'the'),\n",
+ " ('the', 'danger'),\n",
+ " ('danger', 'however'),\n",
+ " ('however', 'was'),\n",
+ " ('was', 'at'),\n",
+ " ('at', 'present'),\n",
+ " ('present', 'so'),\n",
+ " ('so', 'unperceived'),\n",
+ " ('unperceived', 'that'),\n",
+ " ('that', 'they'),\n",
+ " ('they', 'did'),\n",
+ " ('did', 'not'),\n",
+ " ('not', 'by'),\n",
+ " ('by', 'any'),\n",
+ " ('any', 'means'),\n",
+ " ('means', 'rank'),\n",
+ " ('rank', 'as'),\n",
+ " ('as', 'misfortunes'),\n",
+ " ('misfortunes', 'with'),\n",
+ " ('with', 'her'),\n",
+ " ('her', 'sorrow'),\n",
+ " ('sorrow', 'came'),\n",
+ " ('came', 'a'),\n",
+ " ('a', 'gentle'),\n",
+ " ('gentle', 'sorrow'),\n",
+ " ('sorrow', 'but'),\n",
+ " ('but', 'not'),\n",
+ " ('not', 'at'),\n",
+ " ('at', 'all'),\n",
+ " ('all', 'in'),\n",
+ " ('in', 'the'),\n",
+ " ('the', 'shape'),\n",
+ " ('shape', 'of'),\n",
+ " ('of', 'any'),\n",
+ " ('any', 'disagreeable'),\n",
+ " ('disagreeable', 'consciousness'),\n",
+ " ('consciousness', 'miss'),\n",
+ " ('miss', 'taylor'),\n",
+ " ('taylor', 'married'),\n",
+ " ('married', 'it'),\n",
+ " ('it', 'was'),\n",
+ " ('was', 'miss'),\n",
+ " ('miss', 'taylorâ'),\n",
+ " ('taylorâ', 's'),\n",
+ " ('s', 'loss'),\n",
+ " ('loss', 'which'),\n",
+ " ('which', 'first'),\n",
+ " ('first', 'brought'),\n",
+ " ('brought', 'grief'),\n",
+ " ('grief', 'it'),\n",
+ " ('it', 'was'),\n",
+ " ('was', 'on'),\n",
+ " ('on', 'the'),\n",
+ " ('the', 'wedding'),\n",
+ " ('wedding', 'day'),\n",
+ " ('day', 'of'),\n",
+ " ('of', 'this'),\n",
+ " ('this', 'beloved'),\n",
+ " ('beloved', 'friend'),\n",
+ " ('friend', 'that'),\n",
+ " ('that', 'emma'),\n",
+ " ('emma', 'first'),\n",
+ " ('first', 'sat'),\n",
+ " ('sat', 'in'),\n",
+ " ('in', 'mournful'),\n",
+ " ('mournful', 'thought'),\n",
+ " ('thought', 'of'),\n",
+ " ('of', 'any'),\n",
+ " ('any', 'continuance'),\n",
+ " ('continuance', 'the'),\n",
+ " ('the', 'wedding'),\n",
+ " ('wedding', 'over'),\n",
+ " ('over', 'and'),\n",
+ " ('and', 'the'),\n",
+ " ('the', 'bride'),\n",
+ " ('bride', 'people'),\n",
+ " ('people', 'gone'),\n",
+ " ('gone', 'her'),\n",
+ " ('her', 'father'),\n",
+ " ('father', 'and'),\n",
+ " ('and', 'herself'),\n",
+ " ('herself', 'were'),\n",
+ " ('were', 'left'),\n",
+ " ('left', 'to'),\n",
+ " ('to', 'dine'),\n",
+ " ('dine', 'together'),\n",
+ " ('together', 'with'),\n",
+ " ('with', 'no'),\n",
+ " ('no', 'prospect'),\n",
+ " ('prospect', 'of'),\n",
+ " ('of', 'a'),\n",
+ " ('a', 'third'),\n",
+ " ('third', 'to'),\n",
+ " ('to', 'cheer'),\n",
+ " ('cheer', 'a'),\n",
+ " ('a', 'long'),\n",
+ " ('long', 'evening'),\n",
+ " ('evening', 'her'),\n",
+ " ('her', 'father'),\n",
+ " ('father', 'composed'),\n",
+ " ('composed', 'himself'),\n",
+ " ('himself', 'to'),\n",
+ " ('to', 'sleep'),\n",
+ " ('sleep', 'after'),\n",
+ " ('after', 'dinner'),\n",
+ " ('dinner', 'as'),\n",
+ " ('as', 'usual'),\n",
+ " ('usual', 'and'),\n",
+ " ('and', 'she'),\n",
+ " ('she', 'had'),\n",
+ " ('had', 'then'),\n",
+ " ('then', 'only'),\n",
+ " ('only', 'to'),\n",
+ " ('to', 'sit'),\n",
+ " ('sit', 'and'),\n",
+ " ('and', 'think'),\n",
+ " ('think', 'of'),\n",
+ " ('of', 'what'),\n",
+ " ('what', 'she'),\n",
+ " ('she', 'had'),\n",
+ " ('had', 'lost'),\n",
+ " ('lost', 'the'),\n",
+ " ('the', 'event'),\n",
+ " ('event', 'had'),\n",
+ " ('had', 'every'),\n",
+ " ('every', 'promise'),\n",
+ " ('promise', 'of'),\n",
+ " ('of', 'happiness'),\n",
+ " ('happiness', 'for'),\n",
+ " ('for', 'her'),\n",
+ " ('her', 'friend'),\n",
+ " ('friend', 'mr'),\n",
+ " ('mr', 'weston'),\n",
+ " ('weston', 'was'),\n",
+ " ('was', 'a'),\n",
+ " ('a', 'man'),\n",
+ " ('man', 'of'),\n",
+ " ('of', 'unexceptionable'),\n",
+ " ('unexceptionable', 'character'),\n",
+ " ('character', 'easy'),\n",
+ " ('easy', 'fortune'),\n",
+ " ('fortune', 'suitable'),\n",
+ " ('suitable', 'age'),\n",
+ " ('age', 'and'),\n",
+ " ('and', 'pleasant'),\n",
+ " ('pleasant', 'manners'),\n",
+ " ('manners', 'and'),\n",
+ " ('and', 'there'),\n",
+ " ('there', 'was'),\n",
+ " ('was', 'some'),\n",
+ " ('some', 'satisfaction'),\n",
+ " ('satisfaction', 'in'),\n",
+ " ('in', 'considering'),\n",
+ " ('considering', 'with'),\n",
+ " ('with', 'what'),\n",
+ " ('what', 'self'),\n",
+ " ('self', 'denying'),\n",
+ " ('denying', 'generous'),\n",
+ " ('generous', 'friendship'),\n",
+ " ('friendship', 'she'),\n",
+ " ('she', 'had'),\n",
+ " ('had', 'always'),\n",
+ " ('always', 'wished'),\n",
+ " ('wished', 'and'),\n",
+ " ('and', 'promoted'),\n",
+ " ('promoted', 'the'),\n",
+ " ('the', 'match'),\n",
+ " ('match', 'but'),\n",
+ " ('but', 'it'),\n",
+ " ('it', 'was'),\n",
+ " ('was', 'a'),\n",
+ " ('a', 'black'),\n",
+ " ('black', 'morningâ'),\n",
+ " ('morningâ', 's'),\n",
+ " ('s', 'work'),\n",
+ " ('work', 'for'),\n",
+ " ('for', 'her'),\n",
+ " ('her', 'the'),\n",
+ " ('the', 'want'),\n",
+ " ('want', 'of'),\n",
+ " ('of', 'miss'),\n",
+ " ('miss', 'taylor'),\n",
+ " ('taylor', 'would'),\n",
+ " ('would', 'be'),\n",
+ " ('be', 'felt'),\n",
+ " ('felt', 'every'),\n",
+ " ('every', 'hour'),\n",
+ " ('hour', 'of'),\n",
+ " ('of', 'every'),\n",
+ " ('every', 'day'),\n",
+ " ('day', 'she'),\n",
+ " ('she', 'recalled'),\n",
+ " ('recalled', 'her'),\n",
+ " ('her', 'past'),\n",
+ " ('past', 'kindness'),\n",
+ " ('kindness', 'the'),\n",
+ " ('the', 'kindness'),\n",
+ " ('kindness', 'the'),\n",
+ " ('the', 'affection'),\n",
+ " ('affection', 'of'),\n",
+ " ('of', 'sixteen'),\n",
+ " ('sixteen', 'years'),\n",
+ " ('years', 'how'),\n",
+ " ('how', 'she'),\n",
+ " ('she', 'had'),\n",
+ " ('had', 'taught'),\n",
+ " ('taught', 'and'),\n",
+ " ('and', 'how'),\n",
+ " ('how', 'she'),\n",
+ " ('she', 'had'),\n",
+ " ('had', 'played'),\n",
+ " ('played', 'with'),\n",
+ " ('with', 'her'),\n",
+ " ('her', 'from'),\n",
+ " ('from', 'five'),\n",
+ " ('five', 'years'),\n",
+ " ('years', 'old'),\n",
+ " ('old', 'how'),\n",
+ " ('how', 'she'),\n",
+ " ('she', 'had'),\n",
+ " ('had', 'devoted'),\n",
+ " ('devoted', 'all'),\n",
+ " ('all', 'her'),\n",
+ " ('her', 'powers'),\n",
+ " ('powers', 'to'),\n",
+ " ('to', 'attach'),\n",
+ " ('attach', 'and'),\n",
+ " ('and', 'amuse'),\n",
+ " ('amuse', 'her'),\n",
+ " ('her', 'in'),\n",
+ " ('in', 'health'),\n",
+ " ('health', 'and'),\n",
+ " ('and', 'how'),\n",
+ " ('how', 'nursed'),\n",
+ " ('nursed', 'her'),\n",
+ " ('her', 'through'),\n",
+ " ('through', 'the'),\n",
+ " ('the', 'various'),\n",
+ " ('various', 'illnesses'),\n",
+ " ('illnesses', 'of'),\n",
+ " ('of', 'childhood'),\n",
+ " ('childhood', 'a'),\n",
+ " ('a', 'large'),\n",
+ " ('large', 'debt'),\n",
+ " ('debt', 'of'),\n",
+ " ('of', 'gratitude'),\n",
+ " ('gratitude', 'was'),\n",
+ " ('was', 'owing'),\n",
+ " ('owing', 'here'),\n",
+ " ('here', 'but'),\n",
+ " ('but', 'the'),\n",
+ " ('the', 'intercourse'),\n",
+ " ('intercourse', 'of'),\n",
+ " ('of', 'the'),\n",
+ " ('the', 'last'),\n",
+ " ('last', 'seven'),\n",
+ " ('seven', 'years'),\n",
+ " ('years', 'the'),\n",
+ " ('the', 'equal'),\n",
+ " ('equal', 'footing'),\n",
+ " ('footing', 'and'),\n",
+ " ('and', 'perfect'),\n",
+ " ('perfect', 'unreserve'),\n",
+ " ('unreserve', 'which'),\n",
+ " ('which', 'had'),\n",
+ " ('had', 'soon'),\n",
+ " ('soon', 'followed'),\n",
+ " ('followed', 'isabellaâ'),\n",
+ " ('isabellaâ', 's'),\n",
+ " ('s', 'marriage'),\n",
+ " ('marriage', 'on'),\n",
+ " ('on', 'their'),\n",
+ " ('their', 'being'),\n",
+ " ('being', 'left'),\n",
+ " ('left', 'to'),\n",
+ " ('to', 'each'),\n",
+ " ('each', 'other'),\n",
+ " ('other', 'was'),\n",
+ " ('was', 'yet'),\n",
+ " ('yet', 'a'),\n",
+ " ('a', 'dearer'),\n",
+ " ('dearer', 'tenderer'),\n",
+ " ('tenderer', 'recollection'),\n",
+ " ('recollection', 'she'),\n",
+ " ('she', 'had'),\n",
+ " ('had', 'been'),\n",
+ " ('been', 'a'),\n",
+ " ('a', 'friend'),\n",
+ " ('friend', 'and'),\n",
+ " ('and', 'companion'),\n",
+ " ('companion', 'such'),\n",
+ " ('such', 'as'),\n",
+ " ('as', 'few'),\n",
+ " ('few', 'possessed'),\n",
+ " ('possessed', 'intelligent'),\n",
+ " ('intelligent', 'well'),\n",
+ " ('well', 'informed'),\n",
+ " ('informed', 'useful'),\n",
+ " ('useful', 'gentle'),\n",
+ " ('gentle', 'knowing'),\n",
+ " ('knowing', 'all'),\n",
+ " ('all', 'the'),\n",
+ " ('the', 'ways'),\n",
+ " ('ways', 'of'),\n",
+ " ('of', 'the'),\n",
+ " ('the', 'family'),\n",
+ " ('family', 'interested'),\n",
+ " ('interested', 'in'),\n",
+ " ('in', 'all'),\n",
+ " ('all', 'its'),\n",
+ " ('its', 'concerns'),\n",
+ " ('concerns', 'and'),\n",
+ " ('and', 'peculiarly'),\n",
+ " ('peculiarly', 'interested'),\n",
+ " ('interested', 'in'),\n",
+ " ('in', 'herself'),\n",
+ " ('herself', 'in'),\n",
+ " ('in', 'every'),\n",
+ " ('every', 'pleasure'),\n",
+ " ('pleasure', 'every'),\n",
+ " ('every', 'scheme'),\n",
+ " ('scheme', 'of'),\n",
+ " ('of', 'hers'),\n",
+ " ('hers', 'one'),\n",
+ " ('one', 'to'),\n",
+ " ('to', 'whom'),\n",
+ " ('whom', 'she'),\n",
+ " ('she', 'could'),\n",
+ " ('could', 'speak'),\n",
+ " ('speak', 'every'),\n",
+ " ('every', 'thought'),\n",
+ " ('thought', 'as'),\n",
+ " ('as', 'it'),\n",
+ " ('it', 'arose'),\n",
+ " ('arose', 'and'),\n",
+ " ('and', 'who'),\n",
+ " ('who', 'had'),\n",
+ " ('had', 'such'),\n",
+ " ('such', 'an'),\n",
+ " ('an', 'affection'),\n",
+ " ('affection', 'for'),\n",
+ " ('for', 'her'),\n",
+ " ('her', 'as'),\n",
+ " ('as', 'could'),\n",
+ " ('could', 'never'),\n",
+ " ('never', 'find'),\n",
+ " ('find', 'fault'),\n",
+ " ('fault', 'how'),\n",
+ " ('how', 'was'),\n",
+ " ('was', 'she'),\n",
+ " ('she', 'to'),\n",
+ " ('to', 'bear'),\n",
+ " ('bear', 'the'),\n",
+ " ('the', 'change'),\n",
+ " ('change', 'it'),\n",
+ " ('it', 'was'),\n",
+ " ('was', 'true'),\n",
+ " ('true', 'that'),\n",
+ " ('that', 'her'),\n",
+ " ('her', 'friend'),\n",
+ " ('friend', 'was'),\n",
+ " ('was', 'going'),\n",
+ " ('going', 'only'),\n",
+ " ('only', 'half'),\n",
+ " ('half', 'a'),\n",
+ " ('a', 'mile'),\n",
+ " ('mile', 'from'),\n",
+ " ('from', 'them'),\n",
+ " ('them', 'but'),\n",
+ " ('but', 'emma'),\n",
+ " ('emma', 'was'),\n",
+ " ('was', 'aware'),\n",
+ " ('aware', 'that'),\n",
+ " ('that', 'great'),\n",
+ " ('great', 'must'),\n",
+ " ('must', 'be'),\n",
+ " ('be', 'the'),\n",
+ " ('the', 'difference'),\n",
+ " ('difference', 'between'),\n",
+ " ('between', 'a'),\n",
+ " ('a', 'mrs'),\n",
+ " ('mrs', 'weston'),\n",
+ " ('weston', 'only'),\n",
+ " ('only', 'half'),\n",
+ " ('half', 'a'),\n",
+ " ('a', 'mile'),\n",
+ " ('mile', 'from'),\n",
+ " ('from', 'them'),\n",
+ " ('them', 'and'),\n",
+ " ('and', 'a'),\n",
+ " ('a', 'miss'),\n",
+ " ('miss', 'taylor'),\n",
+ " ('taylor', 'in'),\n",
+ " ('in', 'the'),\n",
+ " ('the', 'house'),\n",
+ " ('house', 'and'),\n",
+ " ('and', 'with'),\n",
+ " ('with', 'all'),\n",
+ " ('all', 'her'),\n",
+ " ('her', 'advantages'),\n",
+ " ('advantages', 'natural'),\n",
+ " ('natural', 'and'),\n",
+ " ('and', 'domestic'),\n",
+ " ('domestic', 'she'),\n",
+ " ('she', 'was'),\n",
+ " ('was', 'now'),\n",
+ " ('now', 'in'),\n",
+ " ('in', 'great'),\n",
+ " ('great', 'danger'),\n",
+ " ('danger', 'of'),\n",
+ " ('of', 'suffering'),\n",
+ " ('suffering', 'from'),\n",
+ " ('from', 'intellectual'),\n",
+ " ('intellectual', 'solitude'),\n",
+ " ('solitude', 'she'),\n",
+ " ('she', 'dearly'),\n",
+ " ('dearly', 'loved'),\n",
+ " ('loved', 'her'),\n",
+ " ('her', 'father'),\n",
+ " ('father', 'but'),\n",
+ " ('but', 'he'),\n",
+ " ('he', 'was'),\n",
+ " ('was', 'no'),\n",
+ " ('no', 'companion'),\n",
+ " ('companion', 'for'),\n",
+ " ('for', 'her'),\n",
+ " ('her', 'he'),\n",
+ " ('he', 'could'),\n",
+ " ('could', 'not'),\n",
+ " ('not', 'meet'),\n",
+ " ('meet', 'her'),\n",
+ " ('her', 'in'),\n",
+ " ('in', 'conversation'),\n",
+ " ('conversation', 'rational'),\n",
+ " ('rational', 'or'),\n",
+ " ('or', 'playful'),\n",
+ " ('playful', 'the'),\n",
+ " ('the', 'evil'),\n",
+ " ('evil', 'of'),\n",
+ " ('of', 'the'),\n",
+ " ('the', 'actual'),\n",
+ " ('actual', 'disparity'),\n",
+ " ('disparity', 'in'),\n",
+ " ('in', 'their'),\n",
+ " ('their', 'ages'),\n",
+ " ('ages', 'and'),\n",
+ " ('and', 'mr'),\n",
+ " ('mr', 'woodhouse'),\n",
+ " ('woodhouse', 'had'),\n",
+ " ('had', 'not'),\n",
+ " ('not', 'married'),\n",
+ " ('married', 'early'),\n",
+ " ('early', 'was'),\n",
+ " ('was', 'much'),\n",
+ " ('much', 'increased'),\n",
+ " ('increased', 'by'),\n",
+ " ('by', 'his'),\n",
+ " ('his', 'constitution'),\n",
+ " ('constitution', 'and'),\n",
+ " ('and', 'habits'),\n",
+ " ('habits', 'for'),\n",
+ " ('for', 'having'),\n",
+ " ('having', 'been'),\n",
+ " ('been', 'a'),\n",
+ " ('a', 'valetudinarian'),\n",
+ " ('valetudinarian', 'all'),\n",
+ " ('all', 'his'),\n",
+ " ('his', 'life'),\n",
+ " ('life', 'without'),\n",
+ " ('without', 'activity'),\n",
+ " ('activity', 'of'),\n",
+ " ('of', 'mind'),\n",
+ " ('mind', 'or'),\n",
+ " ('or', 'body'),\n",
+ " ('body', 'he'),\n",
+ " ('he', 'was'),\n",
+ " ('was', 'a'),\n",
+ " ('a', 'much'),\n",
+ " ('much', 'older'),\n",
+ " ('older', 'man'),\n",
+ " ('man', 'in'),\n",
+ " ('in', 'ways'),\n",
+ " ('ways', 'than'),\n",
+ " ('than', 'in'),\n",
+ " ('in', 'years'),\n",
+ " ('years', 'and'),\n",
+ " ('and', 'though'),\n",
+ " ('though', 'everywhere'),\n",
+ " ('everywhere', 'beloved'),\n",
+ " ('beloved', 'for'),\n",
+ " ('for', 'the'),\n",
+ " ('the', 'friendliness'),\n",
+ " ('friendliness', 'of'),\n",
+ " ('of', 'his'),\n",
+ " ('his', 'heart'),\n",
+ " ('heart', 'and'),\n",
+ " ('and', 'his'),\n",
+ " ('his', 'amiable'),\n",
+ " ('amiable', 'temper'),\n",
+ " ('temper', 'his'),\n",
+ " ('his', 'talents'),\n",
+ " ('talents', 'could'),\n",
+ " ('could', 'not'),\n",
+ " ('not', 'have'),\n",
+ " ('have', 'recommended'),\n",
+ " ('recommended', 'him'),\n",
+ " ('him', 'at'),\n",
+ " ('at', 'any'),\n",
+ " ('any', 'time'),\n",
+ " ('time', 'her'),\n",
+ " ('her', 'sister'),\n",
+ " ('sister', 'though'),\n",
+ " ('though', 'comparatively'),\n",
+ " ('comparatively', 'but'),\n",
+ " ('but', 'little'),\n",
+ " ('little', 'removed'),\n",
+ " ('removed', 'by'),\n",
+ " ('by', 'matrimony'),\n",
+ " ('matrimony', 'being'),\n",
+ " ('being', 'settled'),\n",
+ " ('settled', 'in'),\n",
+ " ('in', 'london'),\n",
+ " ('london', 'only'),\n",
+ " ('only', 'sixteen'),\n",
+ " ('sixteen', 'miles'),\n",
+ " ('miles', 'off'),\n",
+ " ('off', 'was'),\n",
+ " ('was', 'much'),\n",
+ " ('much', 'beyond'),\n",
+ " ('beyond', 'her'),\n",
+ " ('her', 'daily'),\n",
+ " ('daily', 'reach'),\n",
+ " ('reach', 'and'),\n",
+ " ('and', 'many'),\n",
+ " ('many', 'a'),\n",
+ " ('a', 'long'),\n",
+ " ('long', 'october'),\n",
+ " ('october', 'and'),\n",
+ " ('and', 'november'),\n",
+ " ('november', 'evening'),\n",
+ " ('evening', 'must'),\n",
+ " ('must', 'be'),\n",
+ " ('be', 'struggled'),\n",
+ " ('struggled', 'through'),\n",
+ " ('through', 'at'),\n",
+ " ('at', 'hartfield'),\n",
+ " ('hartfield', 'before'),\n",
+ " ('before', 'christmas'),\n",
+ " ('christmas', 'brought'),\n",
+ " ('brought', 'the'),\n",
+ " ('the', 'next'),\n",
+ " ('next', 'visit'),\n",
+ " ('visit', 'from'),\n",
+ " ('from', 'isabella'),\n",
+ " ('isabella', 'and'),\n",
+ " ('and', 'her'),\n",
+ " ('her', 'husband'),\n",
+ " ('husband', 'and'),\n",
+ " ('and', 'their'),\n",
+ " ('their', 'little'),\n",
+ " ('little', 'children'),\n",
+ " ('children', 'to'),\n",
+ " ('to', 'fill'),\n",
+ " ('fill', 'the'),\n",
+ " ('the', 'house'),\n",
+ " ('house', 'and'),\n",
+ " ('and', 'give'),\n",
+ " ('give', 'her'),\n",
+ " ('her', 'pleasant'),\n",
+ " ('pleasant', 'society'),\n",
+ " ('society', 'again'),\n",
+ " ('again', 'highbury'),\n",
+ " ('highbury', 'the'),\n",
+ " ('the', 'large'),\n",
+ " ('large', 'and'),\n",
+ " ('and', 'populous'),\n",
+ " ('populous', 'village'),\n",
+ " ('village', 'almost'),\n",
+ " ('almost', 'amounting'),\n",
+ " ('amounting', 'to'),\n",
+ " ('to', 'a'),\n",
+ " ('a', 'town'),\n",
+ " ('town', 'to'),\n",
+ " ('to', 'which'),\n",
+ " ('which', 'hartfield'),\n",
+ " ('hartfield', 'in'),\n",
+ " ('in', 'spite'),\n",
+ " ('spite', 'of'),\n",
+ " ('of', 'its'),\n",
+ " ('its', 'separate'),\n",
+ " ('separate', 'lawn'),\n",
+ " ('lawn', 'and'),\n",
+ " ('and', 'shrubberies'),\n",
+ " ('shrubberies', 'and'),\n",
+ " ('and', 'name'),\n",
+ " ('name', 'did'),\n",
+ " ('did', 'really'),\n",
+ " ('really', 'belong'),\n",
+ " ('belong', 'afforded'),\n",
+ " ('afforded', 'her'),\n",
+ " ('her', 'no'),\n",
+ " ('no', 'equals'),\n",
+ " ('equals', 'the'),\n",
+ " ('the', 'woodhouses'),\n",
+ " ('woodhouses', 'were'),\n",
+ " ('were', 'first'),\n",
+ " ('first', 'in'),\n",
+ " ('in', 'consequence'),\n",
+ " ('consequence', 'there'),\n",
+ " ('there', 'all'),\n",
+ " ('all', 'looked'),\n",
+ " ('looked', 'up'),\n",
+ " ('up', 'to'),\n",
+ " ('to', 'them'),\n",
+ " ('them', 'she'),\n",
+ " ('she', 'had'),\n",
+ " ('had', 'many'),\n",
+ " ('many', 'acquaintance'),\n",
+ " ('acquaintance', 'in'),\n",
+ " ('in', 'the'),\n",
+ " ('the', 'place'),\n",
+ " ('place', 'for'),\n",
+ " ('for', 'her'),\n",
+ " ('her', 'father'),\n",
+ " ('father', 'was'),\n",
+ " ('was', 'universally'),\n",
+ " ('universally', 'civil'),\n",
+ " ('civil', 'but'),\n",
+ " ('but', 'not'),\n",
+ " ('not', 'one'),\n",
+ " ('one', 'among'),\n",
+ " ('among', 'them'),\n",
+ " ('them', 'who'),\n",
+ " ('who', 'could'),\n",
+ " ('could', 'be'),\n",
+ " ('be', 'accepted'),\n",
+ " ('accepted', 'in'),\n",
+ " ('in', 'lieu'),\n",
+ " ('lieu', 'of'),\n",
+ " ('of', 'miss'),\n",
+ " ('miss', 'taylor'),\n",
+ " ('taylor', 'for'),\n",
+ " ('for', 'even'),\n",
+ " ('even', 'half'),\n",
+ " ('half', 'a'),\n",
+ " ('a', 'day'),\n",
+ " ('day', 'it'),\n",
+ " ('it', 'was'),\n",
+ " ('was', 'a'),\n",
+ " ('a', 'melancholy'),\n",
+ " ('melancholy', 'change'),\n",
+ " ('change', 'and'),\n",
+ " ('and', 'emma'),\n",
+ " ('emma', 'could'),\n",
+ " ('could', 'not'),\n",
+ " ('not', 'but'),\n",
+ " ('but', 'sigh'),\n",
+ " ('sigh', 'over'),\n",
+ " ('over', 'it'),\n",
+ " ('it', 'and'),\n",
+ " ('and', 'wish'),\n",
+ " ('wish', 'for'),\n",
+ " ('for', 'impossible'),\n",
+ " ('impossible', 'things'),\n",
+ " ('things', 'till'),\n",
+ " ('till', 'her'),\n",
+ " ('her', 'father'),\n",
+ " ('father', 'awoke'),\n",
+ " ('awoke', 'and'),\n",
+ " ('and', 'made'),\n",
+ " ('made', 'it'),\n",
+ " ('it', 'necessary'),\n",
+ " ('necessary', 'to'),\n",
+ " ('to', 'be'),\n",
+ " ('be', 'cheerful'),\n",
+ " ('cheerful', 'his'),\n",
+ " ('his', 'spirits'),\n",
+ " ('spirits', 'required'),\n",
+ " ('required', 'support'),\n",
+ " ('support', 'he'),\n",
+ " ('he', 'was'),\n",
+ " ('was', 'a'),\n",
+ " ('a', 'nervous'),\n",
+ " ('nervous', 'man'),\n",
+ " ('man', 'easily'),\n",
+ " ('easily', 'depressed'),\n",
+ " ('depressed', 'fond'),\n",
+ " ('fond', 'of'),\n",
+ " ('of', 'every'),\n",
+ " ('every', 'body'),\n",
+ " ('body', 'that'),\n",
+ " ('that', 'he'),\n",
+ " ('he', 'was'),\n",
+ " ('was', 'used'),\n",
+ " ('used', 'to'),\n",
+ " ('to', 'and'),\n",
+ " ('and', 'hating'),\n",
+ " ('hating', 'to'),\n",
+ " ('to', 'part'),\n",
+ " ('part', 'with'),\n",
+ " ('with', 'them'),\n",
+ " ('them', 'hating'),\n",
+ " ('hating', 'change'),\n",
+ " ('change', 'of'),\n",
+ " ('of', 'every'),\n",
+ " ('every', 'kind'),\n",
+ " ('kind', 'matrimony'),\n",
+ " ('matrimony', 'as'),\n",
+ " ('as', 'the'),\n",
+ " ('the', 'origin'),\n",
+ " ('origin', 'of'),\n",
+ " ('of', 'change'),\n",
+ " ('change', 'was'),\n",
+ " ('was', 'always'),\n",
+ " ('always', 'disagreeable'),\n",
+ " ('disagreeable', 'and'),\n",
+ " ('and', 'he'),\n",
+ " ('he', 'was'),\n",
+ " ('was', 'by'),\n",
+ " ('by', 'no'),\n",
+ " ('no', 'means'),\n",
+ " ('means', 'yet'),\n",
+ " ('yet', 'reconciled'),\n",
+ " ('reconciled', 'to'),\n",
+ " ('to', 'his'),\n",
+ " ('his', 'own'),\n",
+ " ('own', 'daughterâ'),\n",
+ " ('daughterâ', 's'),\n",
+ " ('s', 'marrying'),\n",
+ " ('marrying', 'nor'),\n",
+ " ('nor', 'could'),\n",
+ " ('could', 'ever'),\n",
+ " ('ever', 'speak'),\n",
+ " ('speak', 'of'),\n",
+ " ...]"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "bigrams(tokens(austen_books['Emma']))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Gendered bigrams are those with 'he' or 'she' in the first position."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "gendered_bigrams = {book: \n",
+ " collections.Counter(bigram \n",
+ " for bigram in bigrams(tokens(austen_books[book]))\n",
+ " if bigram[0] == 'he' or bigram[0] == 'she') \n",
+ " for book in austen_books}"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Counter({('he', 'a'): 1,\n",
+ " ('he', 'acknowledged'): 1,\n",
+ " ('he', 'added'): 4,\n",
+ " ('he', 'admired'): 1,\n",
+ " ('he', 'advances'): 1,\n",
+ " ('he', 'affronted'): 1,\n",
+ " ('he', 'after'): 1,\n",
+ " ('he', 'agreeable'): 1,\n",
+ " ('he', 'agreed'): 1,\n",
+ " ('he', 'almost'): 1,\n",
+ " ('he', 'always'): 4,\n",
+ " ('he', 'and'): 8,\n",
+ " ('he', 'answered'): 4,\n",
+ " ('he', 'anticipated'): 1,\n",
+ " ('he', 'appear'): 1,\n",
+ " ('he', 'appeared'): 4,\n",
+ " ('he', 'appears'): 1,\n",
+ " ('he', 'argued'): 1,\n",
+ " ('he', 'as'): 1,\n",
+ " ('he', 'asked'): 6,\n",
+ " ('he', 'asks'): 1,\n",
+ " ('he', 'at'): 2,\n",
+ " ('he', 'attended'): 1,\n",
+ " ('he', 'avoid'): 1,\n",
+ " ('he', 'be'): 3,\n",
+ " ('he', 'bear'): 1,\n",
+ " ('he', 'became'): 3,\n",
+ " ('he', 'been'): 4,\n",
+ " ('he', 'began'): 13,\n",
+ " ('he', 'begged'): 1,\n",
+ " ('he', 'begun'): 1,\n",
+ " ('he', 'believed'): 4,\n",
+ " ('he', 'believes'): 1,\n",
+ " ('he', 'bends'): 2,\n",
+ " ('he', 'bowed'): 1,\n",
+ " ('he', 'brought'): 1,\n",
+ " ('he', 'but'): 1,\n",
+ " ('he', 'by'): 1,\n",
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+ " ('she', 'struggled'): 1,\n",
+ " ('she', 'submitted'): 1,\n",
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+ " ('she', 'suspected'): 2,\n",
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+ " ('she', 'talked'): 5,\n",
+ " ('she', 'tells'): 1,\n",
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+ " ('she', 'then'): 8,\n",
+ " ('she', 'therefore'): 1,\n",
+ " ('she', 'thinks'): 5,\n",
+ " ('she', 'thought'): 36,\n",
+ " ('she', 'tires'): 1,\n",
+ " ('she', 'to'): 5,\n",
+ " ('she', 'told'): 2,\n",
+ " ('she', 'too'): 2,\n",
+ " ('she', 'took'): 3,\n",
+ " ('she', 'touched'): 1,\n",
+ " ('she', 'trembling'): 1,\n",
+ " ('she', 'tried'): 5,\n",
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+ " ('she', 'turned'): 4,\n",
+ " ('she', 'understood'): 2,\n",
+ " ('she', 'unwell'): 1,\n",
+ " ('she', 'used'): 2,\n",
+ " ('she', 'valued'): 1,\n",
+ " ('she', 'varied'): 1,\n",
+ " ('she', 'very'): 2,\n",
+ " ('she', 'viewed'): 1,\n",
+ " ('she', 'visited'): 1,\n",
+ " ('she', 'voluntarily'): 1,\n",
+ " ('she', 'waives'): 1,\n",
+ " ('she', 'walked'): 7,\n",
+ " ('she', 'wanted'): 17,\n",
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+ " ('she', 'was'): 331,\n",
+ " ('she', 'watched'): 1,\n",
+ " ('she', 'went'): 9,\n",
+ " ('she', 'were'): 16,\n",
+ " ('she', 'who'): 1,\n",
+ " ('she', 'will'): 43,\n",
+ " ('she', 'wished'): 10,\n",
+ " ('she', 'with'): 3,\n",
+ " ('she', 'would'): 96,\n",
+ " ('she', 'writes'): 2,\n",
+ " ('she', 'wrote'): 4,\n",
+ " ('she', 'yet'): 2,\n",
+ " ('she', 'your'): 1,\n",
+ " ('she', 'â'): 47})"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "gendered_bigrams['Emma']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "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'), 96),\n",
+ " (('he', 'could'), 96),\n",
+ " (('he', 'would'), 85)]"
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "gendered_bigrams['Emma'].most_common(10)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "def gendered_bigrams(tokens):\n",
+ " return collections.Counter(bigram\n",
+ " for bigram in bigrams(tokens)\n",
+ " if bigram[0] == 'he' or bigram[0] == 'she')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[(('she', 'had'), 1478),\n",
+ " (('she', 'was'), 1391),\n",
+ " (('he', 'had'), 1030),\n",
+ " (('he', 'was'), 895),\n",
+ " (('she', 'could'), 825),\n",
+ " (('he', 'is'), 401),\n",
+ " (('she', 'would'), 387),\n",
+ " (('she', 'is'), 334),\n",
+ " (('he', 'could'), 311),\n",
+ " (('he', 'would'), 268)]"
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "gendered_bigrams_austen = gendered_bigrams(austen_books_all_tokens)\n",
+ "\n",
+ "gendered_bigrams_austen.most_common(10)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Count the number of occurrences of each gendered bigram, separated by gender."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "def gender_counts(bigrams, lower_limit=0):\n",
+ " gcounts = pd.DataFrame(\n",
+ " {gender: {bigram[1]: bigrams[bigram] \n",
+ " for bigram in bigrams \n",
+ " if bigram[0] == gender}\n",
+ " for gender in ['she', 'he']})\n",
+ " gcounts.fillna(value=0, inplace=True)\n",
+ " return gcounts[gcounts.sum(axis=1) > lower_limit]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
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+ " <td>1.0</td>\n",
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+ " </tr>\n",
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+ "</table>\n",
+ "<p>1101 rows × 2 columns</p>\n",
+ "</div>"
+ ],
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+ "\n",
+ "[1101 rows x 2 columns]"
+ ]
+ },
+ "execution_count": 16,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "gender_counts_austen = gender_counts(gendered_bigrams_austen) \n",
+ "gender_counts_austen"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
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+ "text/plain": [
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+ "\n",
+ "[163 rows x 2 columns]"
+ ]
+ },
+ "execution_count": 17,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "useful_gender_counts_austen = gender_counts(gendered_bigrams_austen, lower_limit=10) \n",
+ "useful_gender_counts_austen"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "326"
+ ]
+ },
+ "execution_count": 18,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "useful_gender_counts_austen.size"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "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."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {
+ "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": "code",
+ "execution_count": 20,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
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+ " <tr>\n",
+ " <th>loves</th>\n",
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+ " <td>0.058966</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " he she logratio abslogratio\n",
+ "i 0.003128 0.003153 0.011423 0.011423\n",
+ "have 0.003128 0.003153 0.011423 0.011423\n",
+ "always 0.001877 0.001914 0.028497 0.028497\n",
+ "acknowledged 0.001095 0.001126 0.040570 0.040570\n",
+ "it 0.001095 0.001126 0.040570 0.040570\n",
+ "remained 0.001095 0.001126 0.040570 0.040570\n",
+ "had 0.161245 0.166535 0.046574 0.046574\n",
+ "would 0.042071 0.043689 0.054447 0.054447\n",
+ "paused 0.001408 0.001464 0.056511 0.056511\n",
+ "loves 0.000938 0.000901 -0.058966 0.058966"
+ ]
+ },
+ "execution_count": 20,
+ "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": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
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+ " <td>0.002039</td>\n",
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+ " <td>0.087586</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " he she logratio abslogratio\n",
+ "a 0.000794 0.000797 0.005307 0.005307\n",
+ "loves 0.000794 0.000797 0.005307 0.005307\n",
+ "too 0.000794 0.000797 0.005307 0.005307\n",
+ "have 0.002972 0.003041 0.032705 0.032705\n",
+ "i 0.002972 0.003041 0.032705 0.032705\n",
+ "had 0.160312 0.165844 0.048944 0.048944\n",
+ "would 0.041724 0.043433 0.057920 0.057920\n",
+ "always 0.001727 0.001806 0.064486 0.064486\n",
+ "must 0.022115 0.020881 -0.082844 0.082844\n",
+ "wrote 0.002039 0.001919 -0.087586 0.087586"
+ ]
+ },
+ "execution_count": 21,
+ "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": 22,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
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+ " <td>2.275219</td>\n",
+ " <td>2.275219</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>longed</th>\n",
+ " <td>0.000327</td>\n",
+ " <td>0.001470</td>\n",
+ " <td>2.169090</td>\n",
+ " <td>2.169090</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>stopped</th>\n",
+ " <td>0.002039</td>\n",
+ " <td>0.000460</td>\n",
+ " <td>-2.147886</td>\n",
+ " <td>2.147886</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>takes</th>\n",
+ " <td>0.001416</td>\n",
+ " <td>0.000348</td>\n",
+ " <td>-2.025614</td>\n",
+ " <td>2.025614</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>felt</th>\n",
+ " <td>0.005774</td>\n",
+ " <td>0.021329</td>\n",
+ " <td>1.885252</td>\n",
+ " <td>1.885252</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>heard</th>\n",
+ " <td>0.001572</td>\n",
+ " <td>0.005509</td>\n",
+ " <td>1.809353</td>\n",
+ " <td>1.809353</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>replied</th>\n",
+ " <td>0.009664</td>\n",
+ " <td>0.002816</td>\n",
+ " <td>-1.778921</td>\n",
+ " <td>1.778921</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>married</th>\n",
+ " <td>0.001572</td>\n",
+ " <td>0.000460</td>\n",
+ " <td>-1.772675</td>\n",
+ " <td>1.772675</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " he she logratio abslogratio\n",
+ "remembered 0.000016 0.001806 6.858902 6.858902\n",
+ "read 0.000327 0.002592 2.987416 2.987416\n",
+ "resolved 0.000327 0.001582 2.275219 2.275219\n",
+ "longed 0.000327 0.001470 2.169090 2.169090\n",
+ "stopped 0.002039 0.000460 -2.147886 2.147886\n",
+ "takes 0.001416 0.000348 -2.025614 2.025614\n",
+ "felt 0.005774 0.021329 1.885252 1.885252\n",
+ "heard 0.001572 0.005509 1.809353 1.809353\n",
+ "replied 0.009664 0.002816 -1.778921 1.778921\n",
+ "married 0.001572 0.000460 -1.772675 1.772675"
+ ]
+ },
+ "execution_count": 22,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "gender_ratio_austen.sort_values('abslogratio', ascending=False).head(10)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "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 make.\n",
+ "\n",
+ "If we want to exclude words from plotting, pass in a list of stopwords."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {
+ "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": 24,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "30"
+ ]
+ },
+ "execution_count": 24,
+ "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": 25,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<matplotlib.axes._subplots.AxesSubplot at 0x7f9023f92cf8>"
+ ]
+ },
+ "execution_count": 25,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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81wKi3EYIIdRGvTuogkeACyStbfu5HKywBmm9aDVJg22Pk9STNMU3CviBpHts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\nygJqmnE6HwAAAABJRU5ErkJggg==\n",
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x7f904cc182e8>"
+ ]
+ },
+ "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": 26,
+ "metadata": {},
+ "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",
+ " \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": 27,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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GjMA3YJZ426O/szqapWgyBjO7GzggKXufwPcknAJcJ2memTVWIHKp7L80s9MrCF9TzGws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CU8s8Sf7UCzZ8O++0J9/eDK19fD5Mkwa9bwtsvMzMxGD09yzBMRf6LIEhERcUSRc48BJ2SvfCoo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\nYjZfDwAAAABJRU5ErkJggg==\n",
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x7f9023056b38>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "gender_plot(plot_items_austen, 'Jane Austen')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# George Eliot\n",
+ "Do the same with George Eliot's books, reusing the functions defined above."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {
+ "collapsed": true
+ },
+ "outputs": [],
+ "source": [
+ "eliot_books = {title: open(eliot_books_filenames[title], encoding='latin1').read().lower()\n",
+ " for title in eliot_books_filenames}"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "612202"
+ ]
+ },
+ "execution_count": 29,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "eliot_books_all_tokens = [token for book in eliot_books for token in tokens(eliot_books[book])]\n",
+ "len(eliot_books_all_tokens)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "metadata": {},
+ "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)]"
+ ]
+ },
+ "execution_count": 30,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "gendered_bigrams_eliot = gendered_bigrams(eliot_books_all_tokens)\n",
+ "gendered_bigrams_eliot.most_common(10)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "metadata": {
+ "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>65.0</td>\n",
+ " <td>44.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>always</th>\n",
+ " <td>31.0</td>\n",
+ " <td>12.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>and</th>\n",
+ " <td>20.0</td>\n",
+ " <td>17.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>answered</th>\n",
+ " <td>22.0</td>\n",
+ " <td>15.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>asked</th>\n",
+ " <td>12.0</td>\n",
+ " <td>8.0</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " he she\n",
+ "added 65.0 44.0\n",
+ "always 31.0 12.0\n",
+ "and 20.0 17.0\n",
+ "answered 22.0 15.0\n",
+ "asked 12.0 8.0"
+ ]
+ },
+ "execution_count": 31,
+ "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": "code",
+ "execution_count": 32,
+ "metadata": {},
+ "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": 32,
+ "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": 33,
+ "metadata": {},
+ "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": 33,
+ "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": "code",
+ "execution_count": 34,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "30"
+ ]
+ },
+ "execution_count": 34,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "plot_items_eliot = extract_plot_items(gender_ratio_eliot, window=15, stopwords=['herself', 'himself'])\n",
+ "len(plot_items_eliot)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 35,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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p8E2Ii/BytY5HUH0bqjS8XX63/pzTGtLCfaSavi7br4hz3UeAX0raiajN/V1KqbjdlKab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\nJh7ucjtXayG3gS2QNFVSPS8o6HPeNV6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p8C+S9ii63luBX0TErQCSjgE6ge4s7V+AacCN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\niIhIoagBKyIiIiKF8hdNKxXJi1oVSAAAAABJRU5ErkJggg==\n",
+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x7f9020311c88>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "gender_plot(plot_items_eliot, 'George Eliot')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Jane Eyre"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 36,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "bronte_books = {title: open(bronte_books_filenames[title], encoding='latin1').read().lower()\n",
+ " for title in bronte_books_filenames}"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 37,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "189449"
+ ]
+ },
+ "execution_count": 37,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "bronte_books_all_tokens = [token for book in bronte_books for token in tokens(bronte_books[book])]\n",
+ "len(bronte_books_all_tokens)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 38,
+ "metadata": {},
+ "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)]"
+ ]
+ },
+ "execution_count": 38,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "gendered_bigrams_bronte = gendered_bigrams(bronte_books_all_tokens)\n",
+ "gendered_bigrams_bronte.most_common(10)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 39,
+ "metadata": {
+ "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>10.0</td>\n",
+ " <td>6.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>again</th>\n",
+ " <td>7.0</td>\n",
+ " <td>9.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>and</th>\n",
+ " <td>21.0</td>\n",
+ " <td>11.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>answered</th>\n",
+ " <td>14.0</td>\n",
+ " <td>6.0</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>asked</th>\n",
+ " <td>31.0</td>\n",
+ " <td>12.0</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "text/plain": [
+ " he she\n",
+ "added 10.0 6.0\n",
+ "again 7.0 9.0\n",
+ "and 21.0 11.0\n",
+ "answered 14.0 6.0\n",
+ "asked 31.0 12.0"
+ ]
+ },
+ "execution_count": 39,
+ "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": 40,
+ "metadata": {},
+ "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": 40,
+ "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": 41,
+ "metadata": {},
+ "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": 41,
+ "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": 42,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "30"
+ ]
+ },
+ "execution_count": 42,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "plot_items_bronte = extract_plot_items(gender_ratio_bronte, window=15, stopwords=['s'])\n",
+ "len(plot_items_bronte)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 43,
+ "metadata": {
+ "scrolled": false
+ },
+ "outputs": [
+ {
+ "data": {
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A56S0M4G7gGOA9wH3Ao8BQzLjfwo4BZiclv8rwErgh7n5/B4Pjr4JHJjWw6P5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Fkwc2ZaWWnFCti4EcaMSaPuJ092H1EzMxtaHIyaDaKIFGS2tGwdZDY1bR1kSmm6Jk/ZZGZmQ52D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\nguMNkk6R1KrULaQ1e+96/L0q6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+ "text/plain": [
+ "<matplotlib.figure.Figure at 0x7f90204d66d8>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "gender_plot(plot_items_bronte, 'Charlotte Brontë')"
+ ]
+ },
+ {
+ "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
+}
projects / gender-roles-text-analysis.git / commitdiff