NLTK's Frequency Distribution Object.ipynb

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 "worksheets": [
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   "cells": [
    {
     "metadata": {},
     "cell_type": "markdown",
     "source": "#Introduction\n\n(Be sure to start this notebook with the command \"ipython notebook --pylab inline\".)\n\nSection 1.1 of the NLTK book describes some pre-loaded books and pre-defined functions that come with them.  Section 1.2 reviews fundamental concepts abßout python lists and strings -- if you need to brush up on these concepts, then study this subsection carefully.  Be sure you know the difference between a *set* and a *list* and that you can work easily with python slices.\n\nThe part that I am most interested in having you focus on is Section 1.3, which introduces NLTK's frequency distribution data structure.  You need to have the books loaded and accessible from section 1.1 for this part to work.\n\n##NLTK's Frequency Distribution Object\n\nThis data structure makes it easy to tally up frequencies across words and other items, and incorporate them into list comprehensions (and later we'll see the conditional frequency distribution as well).\n\nThe code below counts up all of the words in *Monty Python and the Holy Grail* (text6 in the nltk.book collection) and the final line shows the top 50 most frequent."
    },
    {
     "metadata": {},
     "cell_type": "code",
     "input": "import nltk\nfrom nltk.book import *        # loads in pre-defined texts\nmp_freqdist = FreqDist(text6)  # compute the frequency distribution\nmp_freqdist.items()[:50]       # show the top 50 (word, frequency) pairs",
     "prompt_number": 1,
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "*** Introductory Examples for the NLTK Book ***\nLoading text1, ..., text9 and sent1, ..., sent9\nType the name of the text or sentence to view it.\nType: 'texts()' or 'sents()' to list the materials.\ntext1:"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " Moby Dick by Herman Melville 1851\ntext2:"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " Sense and Sensibility by Jane Austen 1811\ntext3:"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " The Book of Genesis\ntext4:"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " Inaugural Address Corpus\ntext5:"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " Chat Corpus\ntext6: Monty Python and the Holy Grail\ntext7:"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " Wall Street Journal\ntext8: Personals Corpus\ntext9:"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " The Man Who Was Thursday by G . K . Chesterton 1908\n"
      },
      {
       "output_type": "pyout",
       "prompt_number": 1,
       "metadata": {},
       "text": "[(':', 1197),\n ('.', 816),\n ('!', 801),\n (',', 731),\n (\"'\", 421),\n ('[', 319),\n (']', 312),\n ('the', 299),\n ('I', 255),\n ('ARTHUR', 225),\n ('?', 207),\n ('you', 204),\n ('a', 188),\n ('of', 158),\n ('--', 148),\n ('to', 144),\n ('s', 141),\n ('and', 135),\n ('#', 127),\n ('...', 118),\n ('Oh', 110),\n ('it', 107),\n ('is', 106),\n ('-', 88),\n ('in', 86),\n ('that', 84),\n ('t', 77),\n ('1', 76),\n ('LAUNCELOT', 76),\n ('No', 76),\n ('your', 75),\n ('not', 70),\n ('GALAHAD', 69),\n ('KNIGHT', 68),\n ('What', 65),\n ('FATHER', 63),\n ('we', 62),\n ('BEDEVERE', 61),\n ('You', 61),\n ('We', 60),\n ('this', 59),\n ('no', 55),\n ('HEAD', 54),\n ('Well', 54),\n ('GUARD', 53),\n ('have', 53),\n ('Sir', 52),\n ('are', 52),\n ('A', 50),\n ('And', 50)]"
      }
     ],
     "language": "python",
     "trusted": true,
     "collapsed": false
    },
    {
     "metadata": {},
     "cell_type": "markdown",
     "source": "**Task 1** Wow, those are some weird results.  It might make some sense to look at the actual text itself.  In the line below, write a line of code that pulls out the first 500 words of the text and shows them to you (hint: the text object is simply a list of strings)."
    },
    {
     "metadata": {},
     "cell_type": "code",
     "input": "first500 = [word for word in text6[:500]]\nfirst500",
     "prompt_number": 2,
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 2,
       "metadata": {},
       "text": "['SCENE',\n '1',\n ':',\n '[',\n 'wind',\n ']',\n '[',\n 'clop',\n 'clop',\n 'clop',\n ']',\n 'KING',\n 'ARTHUR',\n ':',\n 'Whoa',\n 'there',\n '!',\n '[',\n 'clop',\n 'clop',\n 'clop',\n ']',\n 'SOLDIER',\n '#',\n '1',\n ':',\n 'Halt',\n '!',\n 'Who',\n 'goes',\n 'there',\n '?',\n 'ARTHUR',\n ':',\n 'It',\n 'is',\n 'I',\n ',',\n 'Arthur',\n ',',\n 'son',\n 'of',\n 'Uther',\n 'Pendragon',\n ',',\n 'from',\n 'the',\n 'castle',\n 'of',\n 'Camelot',\n '.',\n 'King',\n 'of',\n 'the',\n 'Britons',\n ',',\n 'defeator',\n 'of',\n 'the',\n 'Saxons',\n ',',\n 'sovereign',\n 'of',\n 'all',\n 'England',\n '!',\n 'SOLDIER',\n '#',\n '1',\n ':',\n 'Pull',\n 'the',\n 'other',\n 'one',\n '!',\n 'ARTHUR',\n ':',\n 'I',\n 'am',\n ',',\n '...',\n 'and',\n 'this',\n 'is',\n 'my',\n 'trusty',\n 'servant',\n 'Patsy',\n '.',\n 'We',\n 'have',\n 'ridden',\n 'the',\n 'length',\n 'and',\n 'breadth',\n 'of',\n 'the',\n 'land',\n 'in',\n 'search',\n 'of',\n 'knights',\n 'who',\n 'will',\n 'join',\n 'me',\n 'in',\n 'my',\n 'court',\n 'at',\n 'Camelot',\n '.',\n 'I',\n 'must',\n 'speak',\n 'with',\n 'your',\n 'lord',\n 'and',\n 'master',\n '.',\n 'SOLDIER',\n '#',\n '1',\n ':',\n 'What',\n '?',\n 'Ridden',\n 'on',\n 'a',\n 'horse',\n '?',\n 'ARTHUR',\n ':',\n 'Yes',\n '!',\n 'SOLDIER',\n '#',\n '1',\n ':',\n 'You',\n \"'\",\n 're',\n 'using',\n 'coconuts',\n '!',\n 'ARTHUR',\n ':',\n 'What',\n '?',\n 'SOLDIER',\n '#',\n '1',\n ':',\n 'You',\n \"'\",\n 've',\n 'got',\n 'two',\n 'empty',\n 'halves',\n 'of',\n 'coconut',\n 'and',\n 'you',\n \"'\",\n 're',\n 'bangin',\n \"'\",\n \"'\",\n 'em',\n 'together',\n '.',\n 'ARTHUR',\n ':',\n 'So',\n '?',\n 'We',\n 'have',\n 'ridden',\n 'since',\n 'the',\n 'snows',\n 'of',\n 'winter',\n 'covered',\n 'this',\n 'land',\n ',',\n 'through',\n 'the',\n 'kingdom',\n 'of',\n 'Mercea',\n ',',\n 'through',\n '--',\n 'SOLDIER',\n '#',\n '1',\n ':',\n 'Where',\n \"'\",\n 'd',\n 'you',\n 'get',\n 'the',\n 'coconuts',\n '?',\n 'ARTHUR',\n ':',\n 'We',\n 'found',\n 'them',\n '.',\n 'SOLDIER',\n '#',\n '1',\n ':',\n 'Found',\n 'them',\n '?',\n 'In',\n 'Mercea',\n '?',\n 'The',\n 'coconut',\n \"'\",\n 's',\n 'tropical',\n '!',\n 'ARTHUR',\n ':',\n 'What',\n 'do',\n 'you',\n 'mean',\n '?',\n 'SOLDIER',\n '#',\n '1',\n ':',\n 'Well',\n ',',\n 'this',\n 'is',\n 'a',\n 'temperate',\n 'zone',\n '.',\n 'ARTHUR',\n ':',\n 'The',\n 'swallow',\n 'may',\n 'fly',\n 'south',\n 'with',\n 'the',\n 'sun',\n 'or',\n 'the',\n 'house',\n 'martin',\n 'or',\n 'the',\n 'plover',\n 'may',\n 'seek',\n 'warmer',\n 'climes',\n 'in',\n 'winter',\n ',',\n 'yet',\n 'these',\n 'are',\n 'not',\n 'strangers',\n 'to',\n 'our',\n 'land',\n '?',\n 'SOLDIER',\n '#',\n '1',\n ':',\n 'Are',\n 'you',\n 'suggesting',\n 'coconuts',\n 'migrate',\n '?',\n 'ARTHUR',\n ':',\n 'Not',\n 'at',\n 'all',\n '.',\n 'They',\n 'could',\n 'be',\n 'carried',\n '.',\n 'SOLDIER',\n '#',\n '1',\n ':',\n 'What',\n '?',\n 'A',\n 'swallow',\n 'carrying',\n 'a',\n 'coconut',\n '?',\n 'ARTHUR',\n ':',\n 'It',\n 'could',\n 'grip',\n 'it',\n 'by',\n 'the',\n 'husk',\n '!',\n 'SOLDIER',\n '#',\n '1',\n ':',\n 'It',\n \"'\",\n 's',\n 'not',\n 'a',\n 'question',\n 'of',\n 'where',\n 'he',\n 'grips',\n 'it',\n '!',\n 'It',\n \"'\",\n 's',\n 'a',\n 'simple',\n 'question',\n 'of',\n 'weight',\n 'ratios',\n '!',\n 'A',\n 'five',\n 'ounce',\n 'bird',\n 'could',\n 'not',\n 'carry',\n 'a',\n 'one',\n 'pound',\n 'coconut',\n '.',\n 'ARTHUR',\n ':',\n 'Well',\n ',',\n 'it',\n 'doesn',\n \"'\",\n 't',\n 'matter',\n '.',\n 'Will',\n 'you',\n 'go',\n 'and',\n 'tell',\n 'your',\n 'master',\n 'that',\n 'Arthur',\n 'from',\n 'the',\n 'Court',\n 'of',\n 'Camelot',\n 'is',\n 'here',\n '.',\n 'SOLDIER',\n '#',\n '1',\n ':',\n 'Listen',\n '.',\n 'In',\n 'order',\n 'to',\n 'maintain',\n 'air',\n '-',\n 'speed',\n 'velocity',\n ',',\n 'a',\n 'swallow',\n 'needs',\n 'to',\n 'beat',\n 'its',\n 'wings',\n 'forty',\n '-',\n 'three',\n 'times',\n 'every',\n 'second',\n ',',\n 'right',\n '?',\n 'ARTHUR',\n ':',\n 'Please',\n '!',\n 'SOLDIER',\n '#',\n '1',\n ':',\n 'Am',\n 'I',\n 'right',\n '?',\n 'ARTHUR',\n ':',\n 'I',\n \"'\",\n 'm',\n 'not',\n 'interested',\n '!',\n 'SOLDIER',\n '#',\n '2',\n ':',\n 'It',\n 'could',\n 'be',\n 'carried',\n 'by',\n 'an',\n 'African',\n 'swallow',\n '!',\n 'SOLDIER',\n '#',\n '1',\n ':',\n 'Oh',\n ',',\n 'yeah',\n ',',\n 'an',\n 'African',\n 'swallow',\n 'maybe',\n ',',\n 'but',\n 'not',\n 'a',\n 'European',\n 'swallow',\n '.',\n 'That',\n \"'\",\n 's',\n 'my',\n 'point',\n '.',\n 'SOLDIER',\n '#',\n '2',\n ':',\n 'Oh',\n ',',\n 'yeah',\n ',',\n 'I',\n 'agree',\n 'with',\n 'that',\n '.',\n 'ARTHUR',\n ':',\n 'Will',\n 'you',\n 'ask',\n 'your']"
      }
     ],
     "language": "python",
     "trusted": true,
     "collapsed": false
    },
    {
     "metadata": {},
     "cell_type": "markdown",
     "source": "**Task 2** Now that you've looked at the text, what are two reasons for these strange results?\n* Answer 1: The code does not differentiate punctuation or numbers from words.\n* Answer 2: For contractions, the code sees the part of the word before the apostrophe as one word, the apostrophe as the second word, and the letters after the apostrophe as the third word. So for one contraction, that's three different words counted.\n"
    },
    {
     "metadata": {},
     "cell_type": "markdown",
     "source": "**Task 3** Address one of the problems by modifying the text of Monty Python and rerunning the frequency distribution calculation.  In the box below write your code to modify the text:"
    },
    {
     "metadata": {},
     "cell_type": "code",
     "input": "first500 = [word for word in text6[:500] if word not in('!','.',',',':','?','#','[',']',\"'\",'--','...','-')]\nmp_freqdist = FreqDist(first500)",
     "prompt_number": 3,
     "outputs": [],
     "language": "python",
     "trusted": true,
     "collapsed": false
    },
    {
     "metadata": {},
     "cell_type": "markdown",
     "source": "**Task 4** In the box below, show the output after applying this version of the text to a FreqDist."
    },
    {
     "metadata": {},
     "cell_type": "code",
     "input": "mp_freqdist.items()[:50]",
     "prompt_number": 4,
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 4,
       "metadata": {},
       "text": "[('SOLDIER', 16),\n ('1', 15),\n ('ARTHUR', 15),\n ('the', 14),\n ('of', 13),\n ('a', 8),\n ('I', 6),\n ('clop', 6),\n ('swallow', 6),\n ('you', 6),\n ('It', 5),\n ('and', 5),\n ('not', 5),\n ('What', 4),\n ('coconut', 4),\n ('could', 4),\n ('is', 4),\n ('s', 4),\n ('Camelot', 3),\n ('We', 3),\n ('coconuts', 3),\n ('in', 3),\n ('it', 3),\n ('land', 3),\n ('my', 3),\n ('this', 3),\n ('to', 3),\n ('with', 3),\n ('your', 3),\n ('2', 2),\n ('A', 2),\n ('African', 2),\n ('Arthur', 2),\n ('In', 2),\n ('Mercea', 2),\n ('Oh', 2),\n ('The', 2),\n ('Well', 2),\n ('Will', 2),\n ('You', 2),\n ('all', 2),\n ('an', 2),\n ('at', 2),\n ('be', 2),\n ('by', 2),\n ('carried', 2),\n ('from', 2),\n ('have', 2),\n ('master', 2),\n ('may', 2)]"
      }
     ],
     "language": "python",
     "trusted": true,
     "collapsed": false
    },
    {
     "metadata": {},
     "cell_type": "markdown",
     "source": "**Task 5** How if at all has the output changed?\n* Answer: The list now does not contain any punctuation marks. It's more in line with just displaying words, though it doesn't solve the issue of the letters coming after the apostrophes."
    },
    {
     "metadata": {},
     "cell_type": "markdown",
     "source": "**Task 6** Following the example from the book, show a cumulative frequency plot for the words in Monty Python as newly computed, in the box below."
    },
    {
     "metadata": {},
     "cell_type": "code",
     "input": "mp_freqdist.plot(50, cumulative=True)",
     "prompt_number": 5,
     "outputs": [
      {
       "output_type": "display_data",
       "metadata": {},
       "png": 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GoF+/fgwaNIiWlpYcf1hLi8IJmUyGefOWcv31m9C+PYwenSGT8Y4vXLiQLl26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\nZqjh3Ap1IAaikZTrktoML5j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       "text": "<matplotlib.figure.Figure at 0x10c36cfd0>"
      }
     ],
     "language": "python",
     "trusted": true,
     "collapsed": false
    },
    {
     "metadata": {},
     "cell_type": "markdown",
     "source": "**Task 7** In the box below, write a list comprehension that users the FreqDist you computed above to find all words in *Monty Python* that are longer than 5 characters long and occur at least 5 times (hint: the text shows how to do a variation of this).  \nShow the output sorted in alphabetical order."
    },
    {
     "metadata": {},
     "cell_type": "code",
     "input": "long_words = sorted([word.lower() for word in mp_freqdist if len(word) > 5 and mp_freqdist[word] >= 5])\nlong_words",
     "prompt_number": 6,
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 6,
       "metadata": {},
       "text": "['arthur', 'soldier', 'swallow']"
      }
     ],
     "language": "python",
     "trusted": true,
     "collapsed": false
    }
   ],
   "metadata": {}
  }
 ],
 "metadata": {
  "name": "",
  "signature": "sha256:2222887c602c2874cddb0b69033523e0ed933604ab673258ed94fa56e3e075cf"
 },
 "nbformat": 3
}