CreatingPages.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to\n",
      "[nltk_data]     C:\\Users\\laure\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n"
     ]
    }
   ],
   "source": [
    "import pandas as py\n",
    "import string\n",
    "import collections\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import collections\n",
    "import re\n",
    "import nltk\n",
    "nltk.download('stopwords')\n",
    "from nltk.corpus import stopwords\n",
    "%matplotlib inline\n",
    "import json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "cains_jawbone= open(\"CainsJawbone[Original].txt\", \"r\", encoding='utf-8').read().splitlines()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "filenames=[]\n",
    "for i in range(1,101):\n",
    "    filenames.append('Text/Page'+str(i)+\".txt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "i=0\n",
    "for file in filenames:\n",
    "    with open(file, \"w\", encoding='utf-8') as out:\n",
    "        out.write(cains_jawbone[i])\n",
    "        i+=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "i\n",
      "sit\n",
      "down\n",
      "alone\n",
      "at\n",
      "the\n",
      "appointed\n",
      "table\n",
      "and\n",
      "take\n",
      "up\n",
      "my\n",
      "pen\n",
      "to\n",
      "give\n",
      "all\n",
      "whom\n",
      "it\n",
      "may\n",
      "concern\n",
      "an\n",
      "exact\n",
      "account\n",
      "of\n",
      "what\n",
      "may\n",
      "happen\n",
      "call\n",
      "me\n",
      "nervous\n",
      "call\n",
      "me\n",
      "fey\n",
      "if\n",
      "you\n",
      "will\n",
      "\n",
      "at\n",
      "least\n",
      "this\n",
      "little\n",
      "pen\n",
      "this\n",
      "mottled\n",
      "black\n",
      "and\n",
      "silver\n",
      "aquarius\n",
      "with\n",
      "its\n",
      "nib\n",
      "specially\n",
      "tempered\n",
      "to\n",
      "my\n",
      "order\n",
      "in\n",
      "amsterdam\n",
      "is\n",
      "greedy\n",
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      "not\n",
      "had\n",
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      "work\n",
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      "the\n",
      "dead\n",
      "old\n",
      "man\n",
      "as\n",
      "i\n",
      "watch\n",
      "the\n",
      "sea\n",
      "casy\n",
      "ferris\n",
      "passes\n",
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      "downdropped\n",
      "eyes\n",
      "of\n",
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      "day\n",
      "her\n",
      "father\n",
      "reminds\n",
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      "a\n",
      "valetudinarian\n",
      "walrus\n",
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      "flotsam\n",
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      "the\n",
      "birds\n",
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      "making\n",
      "whaupee\n",
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      "i\n",
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      "thousand\n",
      "souls\n",
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      "\n",
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      "i\n",
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      "\n",
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      "three\n",
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      "buoyant\n",
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      "neck\n",
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      "a\n",
      "bellflower\n",
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      "would\n",
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      "\n",
      "less\n",
      "than\n",
      "twenty\n",
      "thousand\n",
      "yards\n",
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      "averageseventeen\n",
      "thousand\n",
      "six\n",
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      "a\n",
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      "glorious\n",
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      "galore\n",
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      "i\n",
      "felt\n",
      "excellent\n",
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      "the\n",
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      "half\n",
      "of\n",
      "a\n",
      "publishing\n",
      "firm\n",
      "\n",
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      "had\n",
      "always\n",
      "been\n",
      "very\n",
      "good\n",
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      "austin\n",
      "freeman\n",
      "oppenheim\n",
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      "mary\n",
      "roberts\n",
      "rinehart\n",
      "o\n",
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      "mother\n",
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      "a\n",
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      "a\n",
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      "line\n",
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      "are\n",
      "a\n",
      "rainproof\n",
      "coat\n",
      "good\n",
      "shoes\n",
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      "takes\n",
      "his\n",
      "ease\n",
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      "chair\n",
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      "i\n",
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      "maestro\n",
      "jimson’s\n",
      "title\n",
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      "that\n",
      "this\n",
      "piled\n",
      "abomination\n",
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      "the\n",
      "queen\n",
      "can\n",
      "do\n",
      "no\n",
      "wrong\n",
      "the\n",
      "rain\n",
      "that\n",
      "came\n",
      "heavily\n",
      "is\n",
      "drying\n",
      "off\n",
      "lightly\n",
      "there\n",
      "jauntily\n",
      "tripping\n",
      "from\n",
      "the\n",
      "edge\n",
      "of\n",
      "one\n",
      "puddle\n",
      "to\n",
      "another\n",
      "is\n",
      "crisp\n",
      "sir\n",
      "roland\n",
      "mowthalorn\n",
      "shuddering\n",
      "old\n",
      "thing\n",
      "intent\n",
      "to\n",
      "buy\n",
      "the\n",
      "day’s\n",
      "buttonhole\n",
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      "ginfaced\n",
      "annie\n",
      "behind\n",
      "the\n",
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      "i\n",
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      "roland’s\n",
      "father\n",
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      "weedon\n",
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      "henry\n",
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      "i\n",
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      "cerebos\n",
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      "cerebos\n",
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      "beyond\n",
      "endurance\n",
      "168\n",
      "i\n",
      "considered\n",
      "that\n",
      "venerable\n",
      "whose\n",
      "winter\n",
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      "him\n",
      "my\n",
      "ode\n",
      "on\n",
      "the\n",
      "intimations\n",
      "of\n",
      "immorality\n",
      "in\n",
      "early\n",
      "childhood\n",
      "well\n",
      "well\n",
      "how\n",
      "vividly\n",
      "whenever\n",
      "i\n",
      "adventure\n",
      "on\n",
      "stew\n",
      "now\n",
      "i\n",
      "remember\n",
      "the\n",
      "stew\n",
      "we\n",
      "had\n",
      "that\n",
      "night\n",
      "how\n",
      "it\n",
      "all\n",
      "comes\n",
      "back\n",
      "the\n",
      "whole\n",
      "circumstances\n",
      "of\n",
      "this\n",
      "meeting\n",
      "are\n",
      "so\n",
      "mysterious\n",
      "it\n",
      "gars\n",
      "me\n",
      "grue\n",
      "if\n",
      "i\n",
      "may\n",
      "be\n",
      "permitted\n",
      "the\n",
      "expression\n",
      "178\n",
      "to\n",
      "have\n",
      "slept\n",
      "and\n",
      "to\n",
      "wake\n",
      "right\n",
      "up\n",
      "surrounded\n",
      "by\n",
      "an\n",
      "atmosphere\n",
      "in\n",
      "which\n",
      "bunny\n",
      "and\n",
      "perry\n",
      "went\n",
      "at\n",
      "it\n",
      "hammer\n",
      "and\n",
      "tongs\n",
      "seemed\n",
      "almost\n",
      "sacrilege\n",
      "that\n",
      "was\n",
      "the\n",
      "day\n",
      "when\n",
      "i\n",
      "was\n",
      "going\n",
      "to\n",
      "do\n",
      "a\n",
      "thing\n",
      "i\n",
      "had\n",
      "never\n",
      "done\n",
      "before\n",
      "i\n",
      "looked\n",
      "at\n",
      "henry\n",
      "and\n",
      "felt\n",
      "a\n",
      "little\n",
      "sick\n",
      "i\n",
      "took\n",
      "two\n",
      "pills\n",
      "i\n",
      "had\n",
      "too\n",
      "soonperhaps\n",
      "i\n",
      "did\n",
      "not\n",
      "want\n",
      "to\n",
      "go\n",
      "even\n",
      "so\n",
      "quickly\n",
      "as\n",
      "my\n",
      "ordered\n",
      "slownessexchanged\n",
      "a\n",
      "tennis\n",
      "venue\n",
      "for\n",
      "a\n",
      "rowing\n",
      "one\n",
      "i\n",
      "was\n",
      "not\n",
      "in\n",
      "dorset\n",
      "\n",
      "but\n",
      "i\n",
      "murmured\n",
      "to\n",
      "myself\n",
      "that\n",
      "ellen\n",
      "brine\n",
      "of\n",
      "allenburn\n",
      "would\n",
      "never\n",
      "mwore\n",
      "return\n",
      "the\n",
      "connection\n",
      "was\n",
      "obvious\n",
      "what\n",
      "a\n",
      "day\n",
      "i\n",
      "thought\n",
      "for\n",
      "the\n",
      "despatch\n",
      "of\n",
      "paris\n",
      "and\n",
      "leonidas\n",
      "cambridge\n",
      "or\n",
      "thermopylae\n",
      "but\n",
      "paris\n",
      "i\n",
      "had\n",
      "never\n",
      "at\n",
      "school\n",
      "looked\n",
      "upon\n",
      "him\n",
      "as\n",
      "in\n",
      "any\n",
      "sort\n",
      "a\n",
      "healer\n",
      "i\n",
      "had\n",
      "infact\n",
      "never\n",
      "heard\n",
      "of\n",
      "john\n",
      "ayrton\n",
      "then\n",
      "oeuvre\n",
      "ton\n",
      "ame\n",
      "et\n",
      "ton\n",
      "oreille\n",
      "au\n",
      "son\n",
      "de\n",
      "ma\n",
      "mandoline\n",
      "\n",
      "pour\n",
      "toi\n",
      "j’ai\n",
      "fait\n",
      "pour\n",
      "toi\n",
      "cette\n",
      "chanson\n",
      "cruelle\n",
      "et\n",
      "caline\n",
      "but\n",
      "i\n",
      "wasn’t\n",
      "thinking\n",
      "of\n",
      "john\n",
      "ayrton\n",
      "174\n",
      "he\n",
      "stood\n",
      "and\n",
      "looked\n",
      "down\n",
      "at\n",
      "me\n",
      "\n",
      "but\n",
      "i\n",
      "was\n",
      "not\n",
      "to\n",
      "be\n",
      "hurried\n",
      "the\n",
      "money\n",
      "changed\n",
      "hands\n",
      "slowly\n",
      "\n",
      "for\n",
      "i\n",
      "wished\n",
      "to\n",
      "be\n",
      "able\n",
      "to\n",
      "describe\n",
      "him\n",
      "he\n",
      "seems\n",
      "to\n",
      "have\n",
      "had\n",
      "a\n",
      "great\n",
      "confidence\n",
      "in\n",
      "the\n",
      "opinion\n",
      "of\n",
      "his\n",
      "physicians\n",
      "i\n",
      "am\n",
      "glad\n",
      "however\n",
      "that\n",
      "he\n",
      "made\n",
      "up\n",
      "his\n",
      "mind\n",
      "at\n",
      "the\n",
      "last\n",
      "to\n",
      "some\n",
      "definite\n",
      "course\n",
      "of\n",
      "action\n",
      "and\n",
      "acted\n",
      "under\n",
      "proper\n",
      "medical\n",
      "advice\n",
      "but\n",
      "there\n",
      "were\n",
      "doctors\n",
      "and\n",
      "doctors\n",
      "i\n",
      "would\n",
      "have\n",
      "to\n",
      "think\n",
      "seriously\n",
      "of\n",
      "that\n",
      "and\n",
      "then\n",
      "he\n",
      "went\n",
      "he\n",
      "went\n",
      "simple\n",
      "faith\n",
      "or\n",
      "norman\n",
      "bluff\n",
      "but\n",
      "that\n",
      "douglas\n",
      "was\n",
      "perhaps\n",
      "less\n",
      "tender\n",
      "and\n",
      "more\n",
      "true\n",
      "my\n",
      "heart\n",
      "dilated\n",
      "as\n",
      "soon\n",
      "as\n",
      "the\n",
      "sedulous\n",
      "ape\n",
      "had\n",
      "got\n",
      "out\n",
      "from\n",
      "me\n",
      "gone\n",
      "in\n",
      "a\n",
      "relative\n",
      "sense\n",
      "alas\n",
      "not\n",
      "positively\n",
      "finally\n",
      "gone\n",
      "that\n",
      "was\n",
      "a\n",
      "consummation\n",
      "devoutly\n",
      "to\n",
      "be\n",
      "wished\n",
      "but\n",
      "yet\n",
      "to\n",
      "be\n",
      "compassed\n",
      "whom\n",
      "should\n",
      "i\n",
      "trust\n",
      "with\n",
      "that\n",
      "i\n",
      "thought\n",
      "of\n",
      "may\n",
      "may\n",
      "be\n",
      "may\n",
      "be\n",
      "not\n",
      "sunset\n",
      "was\n",
      "already\n",
      "reddishpurple\n",
      "above\n",
      "the\n",
      "quarry\n",
      "hills\n",
      "like\n",
      "a\n",
      "bruise\n",
      "on\n",
      "the\n",
      "breast\n",
      "of\n",
      "the\n",
      "evening\n",
      "173\n",
      "and\n",
      "she\n",
      "wore\n",
      "a\n",
      "mauve\n",
      "loveknot\n",
      "on\n",
      "her\n",
      "breast\n",
      "and\n",
      "the\n",
      "ends\n",
      "were\n",
      "unequal\n",
      "he\n",
      "said\n",
      "he’d\n",
      "put\n",
      "that\n",
      "right\n",
      "but\n",
      "he\n",
      "couldnt\n",
      "find\n",
      "the\n",
      "silly\n",
      "old\n",
      "jossers\n",
      "as\n",
      "jasmine\n",
      "might\n",
      "so\n",
      "easily\n",
      "have\n",
      "called\n",
      "them\n",
      "i\n",
      "was\n",
      "old\n",
      "enough\n",
      "to\n",
      "remember\n",
      "her\n",
      "\n",
      "she\n",
      "wasn’t\n",
      "the\n",
      "one\n",
      "i’d\n",
      "killed\n",
      "he\n",
      "had\n",
      "read\n",
      "out\n",
      "about\n",
      "some\n",
      "most\n",
      "excellent\n",
      "potent\n",
      "brilliant\n",
      "eyes\n",
      "swiftdarting\n",
      "as\n",
      "the\n",
      "stars\n",
      "steadfast\n",
      "as\n",
      "the\n",
      "sun\n",
      "\n",
      "grey\n",
      "we\n",
      "said\n",
      "of\n",
      "the\n",
      "azuregrey\n",
      "colour\n",
      "\n",
      "large\n",
      "enough\n",
      "not\n",
      "of\n",
      "glaring\n",
      "size\n",
      "\n",
      "the\n",
      "habitual\n",
      "expression\n",
      "of\n",
      "them\n",
      "vigilance\n",
      "and\n",
      "penetrating\n",
      "sense\n",
      "rapidity\n",
      "resting\n",
      "on\n",
      "depth\n",
      "when\n",
      "she\n",
      "asked\n",
      "him\n",
      "why\n",
      "he\n",
      "had\n",
      "chosen\n",
      "those\n",
      "and\n",
      "whose\n",
      "they\n",
      "were\n",
      "he\n",
      "answered\n",
      "\n",
      "father\n",
      "fred’s\n",
      "and\n",
      "because\n",
      "it\n",
      "was\n",
      "closing\n",
      "day\n",
      "in\n",
      "potsdam\n",
      "i\n",
      "had\n",
      "had\n",
      "a\n",
      "dry\n",
      "shampoo\n",
      "that\n",
      "morning\n",
      "a\n",
      "thing\n",
      "i\n",
      "adored\n",
      "especially\n",
      "on\n",
      "the\n",
      "old\n",
      "chest\n",
      "rather\n",
      "a\n",
      "waste\n",
      "of\n",
      "time\n",
      "though\n",
      "as\n",
      "it\n",
      "turned\n",
      "out\n",
      "of\n",
      "course\n",
      "i\n",
      "ought\n",
      "to\n",
      "have\n",
      "been\n",
      "more\n",
      "careful\n",
      "of\n",
      "such\n",
      "a\n",
      "trifle\n",
      "suddenly\n",
      "i\n",
      "felt\n",
      "that\n",
      "i\n",
      "had\n",
      "put\n",
      "my\n",
      "foot\n",
      "in\n",
      "it\n",
      "still\n",
      "i\n",
      "had\n",
      "three\n",
      "more\n",
      "left\n",
      "181\n",
      "i\n",
      "dimly\n",
      "guess\n",
      "why\n",
      "the\n",
      "old\n",
      "dead\n",
      "so\n",
      "wanted\n",
      "this\n",
      "i\n",
      "had\n",
      "worked\n",
      "for\n",
      "him\n",
      "henry\n",
      "had\n",
      "worked\n",
      "for\n",
      "him\n",
      "if\n",
      "i\n",
      "could\n",
      "get\n",
      "up\n",
      "as\n",
      "believe\n",
      "me\n",
      "i\n",
      "cannot\n",
      "i\n",
      "would\n",
      "have\n",
      "a\n",
      "thing\n",
      "to\n",
      "say\n",
      "to\n",
      "her\n",
      "she\n",
      "lolls\n",
      "over\n",
      "at\n",
      "me\n",
      "gloating\n",
      "her\n",
      "mouth\n",
      "bloodtinted\n",
      "on\n",
      "the\n",
      "puma\n",
      "freckle\n",
      "of\n",
      "her\n",
      "beauty\n",
      "why\n",
      "should\n",
      "i\n",
      "think\n",
      "of\n",
      "henry\n",
      "at\n",
      "this\n",
      "particular\n",
      "juncture\n",
      "i\n",
      "have\n",
      "it\n",
      "scotland\n",
      "yard\n",
      "of\n",
      "course\n",
      "and\n",
      "little\n",
      "‘twill\n",
      "matter\n",
      "to\n",
      "one\n",
      "a\n",
      "sorry\n",
      "thing\n",
      "to\n",
      "be\n",
      "last\n",
      "noticed\n",
      "\n",
      "the\n",
      "buttonhole\n",
      "has\n",
      "escaped\n",
      "from\n",
      "the\n",
      "buttonholer\n",
      "he\n",
      "the\n",
      "reckless\n",
      "old\n",
      "cock\n",
      "slips\n",
      "down\n",
      "past\n",
      "woolworth’s\n",
      "and\n",
      "she\n",
      "continues\n",
      "fullsail\n",
      "toward\n",
      "the\n",
      "kursal\n",
      "as\n",
      "flushoh\n",
      "you\n",
      "wicked\n",
      "womanas\n",
      "may\n",
      "the\n",
      "girl\n",
      "is\n",
      "smiling\n",
      "at\n",
      "me\n",
      "that’s\n",
      "not\n",
      "so\n",
      "good\n",
      "here\n",
      "i\n",
      "shake\n",
      "off\n",
      "the\n",
      "bur\n",
      "o’\n",
      "the\n",
      "world\n",
      "man’s\n",
      "congregation\n",
      "shun\n",
      "o\n",
      "beastly\n",
      "woman\n",
      "you\n",
      "know\n",
      "not\n",
      "how\n",
      "ill’s\n",
      "all\n",
      "here\n",
      "about\n",
      "my\n",
      "heart\n",
      "\n",
      "but\n",
      "i\n",
      "know\n",
      "henry\n",
      "i\n",
      "feel\n",
      "it\n",
      "is\n",
      "for\n",
      "the\n",
      "first\n",
      "and\n",
      "last\n",
      "time\n",
      "getting\n",
      "out\n",
      "of\n",
      "hand\n",
      "goodbye\n",
      "henry\n",
      "he\n",
      "drops\n",
      "awa\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "178\n"
     ]
    }
   ],
   "source": [
    "allWords = collections.Counter()\n",
    "skips = stopwords.words('english')\n",
    "skips.append(\"\")\n",
    "page_num=0\n",
    "NoStopWords = collections.Counter()\n",
    "total_info = {}\n",
    "for complete_line in cains_jawbone:\n",
    "    word_list=[]\n",
    "    page_num+=1\n",
    "    line=complete_line\n",
    "    line = line.lower()\n",
    "    line = line.translate(line.maketrans(\"\", \"\", string.punctuation))\n",
    "    words = line.split(\" \")\n",
    "    total_word_count =0\n",
    "    for word in words:\n",
    "            print(word)\n",
    "            total_word_count+=1\n",
    "            allWords[word]+=1\n",
    "            if word not in skips:\n",
    "                NoStopWords[word]+=1\n",
    "    print(total_word_count)  \n",
    "                  \n",
    "    \n",
    "    info ={\n",
    "        '_key':str(page_num),\n",
    "        'content':complete_line,\n",
    "        'key_words': [],\n",
    "        'gender' : \"?\",\n",
    "        'narrator':\"?\",\n",
    "        \"Possible Connections\": [],\n",
    "        \"Known Impossible Connections\":[],\n",
    "        \"People Named\":[],\n",
    "        \"Locations Mentioned\":[],\n",
    "        \"Weather\":[],\n",
    "        \"Other Notes\":[]\n",
    "    }\n",
    "    total_info[page_num] = info\n",
    "#DataFrame: \n",
    "all_words_df = pd.DataFrame.from_dict(allWords, orient='index').reset_index().rename(columns={'index':'common_word', 0:'frequency'}).sort_values('frequency',ascending=False)  \n",
    "no_stop_words_df = pd.DataFrame.from_dict(NoStopWords, orient='index').reset_index().rename(columns={'index':'common_word', 0:'frequency'}).sort_values('frequency',ascending=False)                    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_lines_with_phrase(specific_string):\n",
    "    specific_string=specific_string.lower()\n",
    "    new_list = []\n",
    "    idx=0\n",
    "    page_found = []\n",
    "    page_num =1\n",
    "    for page in cains_jawbone:\n",
    "        page_normalize = page.lower()\n",
    "        if specific_string in page_normalize:\n",
    "            new_list.insert(idx, page)\n",
    "            page_found.insert(idx, page_num)\n",
    "            idx+=1\n",
    "        page_num+=1\n",
    "        \n",
    "    if len(new_list)==0:\n",
    "       print(\"no\")\n",
    "    else:\n",
    "\n",
    "        # displaying the lines \n",
    "        # containing given string\n",
    "        lineLen = len(new_list)\n",
    "        print(\"\\n**** Lines containing \\\"\" +specific_string+ \"\\\" ****\\n\")\n",
    "        for i in range(lineLen):\n",
    "            print(\"Page \", page_found[i])\n",
    "            print(end=new_list[i])\n",
    "            print('\\n\\n\\n')\n",
    "        print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "total_info[3]['People Named'].append(\"Clement\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "initialdata= pd.DataFrame.from_dict(total_info, orient='index')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "**** Lines containing \"sonia\" ****\n",
      "\n",
      "Page  38\n",
      "I was feeling better already, and was glad that a memory, true though dim, had led me to the place. Video meliora proboque ; but I could not, for all my covert glances, see the modelling of the fossettes of the elbows of the woman sitting so near me. Were they, I wondered, like Sonia Gordon’s, triangular dimples with shadow in them? Poor Sonia Gordon. I pondered on that tragic fortnight at Southend : the pier with its electrical railway, and my cousin’s rash act, and Sonia’s lapse. Her temperament was against her. Still you couldn’t have an omelette without breaking eggs. And mine was excellent. “You would get off with a whole skin, would you?” I cried softly, as I stabbed once. And even as I did so, I thought of skinny old Marat in his slipper bath, the nightcap about his forehead, the dim light of the candle, the shadow at the door, the stealthy tread of Charlotte Brontë with the undulled blade. There was something wrong. \n",
      "\n",
      "\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "find_lines_with_phrase(\"Sonia\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "for line in cains_jawbone:\n",
    "    json.dumps(line)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "dnary = {page+1:text for page, text in enumerate(cains_jawbone)}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
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       "    index                                                  0\n",
       "0       1  ﻿I sit down alone at the appointed table and t...\n",
       "1       2  I plunged for the last time. The few remaining...\n",
       "2       3  At my meeting with Clement yesterday, he had b...\n",
       "3       4  And I really think I would have preferred the ...\n",
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       "..    ...                                                ...\n",
       "95     96  Now I think I will try a cup of what they inso...\n",
       "96     97  To have slept and to wake right up surrounded ...\n",
       "97     98  He stood and looked down at me ; but I was not...\n",
       "98     99  And she wore a mauve love-knot on her breast, ...\n",
       "99    100  I dimly guess why the old dead so wanted this....\n",
       "\n",
       "[100 rows x 2 columns]"
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     "execution_count": 12,
     "metadata": {},
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    "pd.DataFrame.from_dict(dnary, orient='index').reset_index()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "initialdata.to_json('InitialData.json', orient='records')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "     common_word  frequency\n",
       "165        would        110\n",
       "145          one         67\n",
       "34           old         62\n",
       "124        could         62\n",
       "17        little         56\n",
       "...          ...        ...\n",
       "266       church          2\n",
       "3737         toi          2\n",
       "3441         son          2\n",
       "930     situated          2\n",
       "940     fumbling          2\n",
       "\n",
       "[1118 rows x 2 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
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    "no_stop_words_df.query(\"frequency!= 1\")"
   ]
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  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_n_words(num):\n",
    "    topN = NoStopWords.most_common(num)\n",
    "    common_word = []\n",
    "    frequency = []\n",
    "    df= pd.DataFrame(topN, columns=['common_word','frequency'])\n",
    "    df.plot(kind='bar', x='common_word')"
   ]
  }
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