Comparison_nltk.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NLTK Downloader\n",
      "---------------------------------------------------------------------------\n",
      "    d) Download   l) List    u) Update   c) Config   h) Help   q) Quit\n",
      "---------------------------------------------------------------------------\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Downloader>  d\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Download which package (l=list; x=cancel)?\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "  Identifier>  l\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Packages:\n",
      "  [ ] abc................. Australian Broadcasting Commission 2006\n",
      "  [ ] alpino.............. Alpino Dutch Treebank\n",
      "  [ ] averaged_perceptron_tagger Averaged Perceptron Tagger\n",
      "  [ ] averaged_perceptron_tagger_ru Averaged Perceptron Tagger (Russian)\n",
      "  [ ] basque_grammars..... Grammars for Basque\n",
      "  [ ] biocreative_ppi..... BioCreAtIvE (Critical Assessment of Information\n",
      "                           Extraction Systems in Biology)\n",
      "  [ ] bllip_wsj_no_aux.... BLLIP Parser: WSJ Model\n",
      "  [ ] book_grammars....... Grammars from NLTK Book\n",
      "  [ ] brown............... Brown Corpus\n",
      "  [ ] brown_tei........... Brown Corpus (TEI XML Version)\n",
      "  [ ] cess_cat............ CESS-CAT Treebank\n",
      "  [ ] cess_esp............ CESS-ESP Treebank\n",
      "  [ ] chat80.............. Chat-80 Data Files\n",
      "  [ ] city_database....... City Database\n",
      "  [ ] cmudict............. The Carnegie Mellon Pronouncing Dictionary (0.6)\n",
      "  [ ] comparative_sentences Comparative Sentence Dataset\n",
      "  [ ] comtrans............ ComTrans Corpus Sample\n",
      "  [ ] conll2000........... CONLL 2000 Chunking Corpus\n",
      "  [ ] conll2002........... CONLL 2002 Named Entity Recognition Corpus\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Hit Enter to continue:  stopwords\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  [ ] conll2007........... Dependency Treebanks from CoNLL 2007 (Catalan\n",
      "                           and Basque Subset)\n",
      "  [ ] crubadan............ Crubadan Corpus\n",
      "  [ ] dependency_treebank. Dependency Parsed Treebank\n",
      "  [ ] dolch............... Dolch Word List\n",
      "  [ ] europarl_raw........ Sample European Parliament Proceedings Parallel\n",
      "                           Corpus\n",
      "  [ ] floresta............ Portuguese Treebank\n",
      "  [ ] framenet_v15........ FrameNet 1.5\n",
      "  [ ] framenet_v17........ FrameNet 1.7\n",
      "  [ ] gazetteers.......... Gazeteer Lists\n",
      "  [ ] genesis............. Genesis Corpus\n",
      "  [ ] gutenberg........... Project Gutenberg Selections\n",
      "  [ ] ieer................ NIST IE-ER DATA SAMPLE\n",
      "  [ ] inaugural........... C-Span Inaugural Address Corpus\n",
      "  [ ] indian.............. Indian Language POS-Tagged Corpus\n",
      "  [ ] jeita............... JEITA Public Morphologically Tagged Corpus (in\n",
      "                           ChaSen format)\n",
      "  [ ] kimmo............... PC-KIMMO Data Files\n",
      "  [ ] knbc................ KNB Corpus (Annotated blog corpus)\n",
      "  [ ] large_grammars...... Large context-free and feature-based grammars\n",
      "                           for parser comparison\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Hit Enter to continue:  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  [ ] lin_thesaurus....... Lin's Dependency Thesaurus\n",
      "  [ ] mac_morpho.......... MAC-MORPHO: Brazilian Portuguese news text with\n",
      "                           part-of-speech tags\n",
      "  [ ] machado............. Machado de Assis -- Obra Completa\n",
      "  [ ] masc_tagged......... MASC Tagged Corpus\n",
      "  [ ] maxent_ne_chunker... ACE Named Entity Chunker (Maximum entropy)\n",
      "  [ ] maxent_treebank_pos_tagger Treebank Part of Speech Tagger (Maximum entropy)\n",
      "  [ ] moses_sample........ Moses Sample Models\n",
      "  [ ] movie_reviews....... Sentiment Polarity Dataset Version 2.0\n",
      "  [ ] mte_teip5........... MULTEXT-East 1984 annotated corpus 4.0\n",
      "  [ ] mwa_ppdb............ The monolingual word aligner (Sultan et al.\n",
      "                           2015) subset of the Paraphrase Database.\n",
      "  [ ] names............... Names Corpus, Version 1.3 (1994-03-29)\n",
      "  [ ] nombank.1.0......... NomBank Corpus 1.0\n",
      "  [ ] nonbreaking_prefixes Non-Breaking Prefixes (Moses Decoder)\n",
      "  [ ] nps_chat............ NPS Chat\n",
      "  [ ] omw................. Open Multilingual Wordnet\n",
      "  [ ] opinion_lexicon..... Opinion Lexicon\n",
      "  [ ] panlex_swadesh...... PanLex Swadesh Corpora\n",
      "  [ ] paradigms........... Paradigm Corpus\n",
      "  [ ] pe08................ Cross-Framework and Cross-Domain Parser\n",
      "                           Evaluation Shared Task\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Hit Enter to continue:  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  [ ] perluniprops........ perluniprops: Index of Unicode Version 7.0.0\n",
      "                           character properties in Perl\n",
      "  [ ] pil................. The Patient Information Leaflet (PIL) Corpus\n",
      "  [ ] pl196x.............. Polish language of the XX century sixties\n",
      "  [ ] porter_test......... Porter Stemmer Test Files\n",
      "  [ ] ppattach............ Prepositional Phrase Attachment Corpus\n",
      "  [ ] problem_reports..... Problem Report Corpus\n",
      "  [ ] product_reviews_1... Product Reviews (5 Products)\n",
      "  [ ] product_reviews_2... Product Reviews (9 Products)\n",
      "  [ ] propbank............ Proposition Bank Corpus 1.0\n",
      "  [ ] pros_cons........... Pros and Cons\n",
      "  [ ] ptb................. Penn Treebank\n",
      "  [ ] punkt............... Punkt Tokenizer Models\n",
      "  [ ] qc.................. Experimental Data for Question Classification\n",
      "  [ ] reuters............. The Reuters-21578 benchmark corpus, ApteMod\n",
      "                           version\n",
      "  [ ] rslp................ RSLP Stemmer (Removedor de Sufixos da Lingua\n",
      "                           Portuguesa)\n",
      "  [ ] rte................. PASCAL RTE Challenges 1, 2, and 3\n",
      "  [ ] sample_grammars..... Sample Grammars\n",
      "  [ ] semcor.............. SemCor 3.0\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Hit Enter to continue:  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  [ ] senseval............ SENSEVAL 2 Corpus: Sense Tagged Text\n",
      "  [ ] sentence_polarity... Sentence Polarity Dataset v1.0\n",
      "  [ ] sentiwordnet........ SentiWordNet\n",
      "  [ ] shakespeare......... Shakespeare XML Corpus Sample\n",
      "  [ ] sinica_treebank..... Sinica Treebank Corpus Sample\n",
      "  [ ] smultron............ SMULTRON Corpus Sample\n",
      "  [ ] snowball_data....... Snowball Data\n",
      "  [ ] spanish_grammars.... Grammars for Spanish\n",
      "  [ ] state_union......... C-Span State of the Union Address Corpus\n",
      "  [ ] stopwords........... Stopwords Corpus\n",
      "  [ ] subjectivity........ Subjectivity Dataset v1.0\n",
      "  [ ] swadesh............. Swadesh Wordlists\n",
      "  [ ] switchboard......... Switchboard Corpus Sample\n",
      "  [ ] tagsets............. Help on Tagsets\n",
      "  [ ] timit............... TIMIT Corpus Sample\n",
      "  [ ] toolbox............. Toolbox Sample Files\n",
      "  [ ] treebank............ Penn Treebank Sample\n",
      "  [ ] twitter_samples..... Twitter Samples\n",
      "  [ ] udhr2............... Universal Declaration of Human Rights Corpus\n",
      "                           (Unicode Version)\n",
      "  [ ] udhr................ Universal Declaration of Human Rights Corpus\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Hit Enter to continue:  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  [ ] unicode_samples..... Unicode Samples\n",
      "  [ ] universal_tagset.... Mappings to the Universal Part-of-Speech Tagset\n",
      "  [ ] universal_treebanks_v20 Universal Treebanks Version 2.0\n",
      "  [ ] vader_lexicon....... VADER Sentiment Lexicon\n",
      "  [ ] verbnet3............ VerbNet Lexicon, Version 3.3\n",
      "  [ ] verbnet............. VerbNet Lexicon, Version 2.1\n",
      "  [ ] webtext............. Web Text Corpus\n",
      "  [ ] wmt15_eval.......... Evaluation data from WMT15\n",
      "  [ ] word2vec_sample..... Word2Vec Sample\n",
      "  [ ] wordnet............. WordNet\n",
      "  [ ] wordnet_ic.......... WordNet-InfoContent\n",
      "  [ ] words............... Word Lists\n",
      "  [ ] ycoe................ York-Toronto-Helsinki Parsed Corpus of Old\n",
      "                           English Prose\n",
      "\n",
      "Collections:\n",
      "  [ ] all-corpora......... All the corpora\n",
      "  [ ] all-nltk............ All packages available on nltk_data gh-pages\n",
      "                           branch\n",
      "  [ ] all................. All packages\n",
      "  [ ] book................ Everything used in the NLTK Book\n",
      "  [ ] popular............. Popular packages\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Hit Enter to continue:  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  [ ] tests............... Packages for running tests\n",
      "  [ ] third-party......... Third-party data packages\n",
      "\n",
      "([*] marks installed packages)\n",
      "\n",
      "Download which package (l=list; x=cancel)?\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "  Identifier>  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "---------------------------------------------------------------------------\n",
      "    d) Download   l) List    u) Update   c) Config   h) Help   q) Quit\n",
      "---------------------------------------------------------------------------\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Downloader>  d\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Download which package (l=list; x=cancel)?\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "  Identifier>  stopwords\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "    Downloading package stopwords to /Users/bbbb/nltk_data...\n",
      "      Unzipping corpora/stopwords.zip.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "---------------------------------------------------------------------------\n",
      "    d) Download   l) List    u) Update   c) Config   h) Help   q) Quit\n",
      "---------------------------------------------------------------------------\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      "Downloader>  q\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import nltk\n",
    "nltk.download_shell()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "BCS = pd.read_csv('better_call_saul.csv')\n",
    "BCS['Name']= 'better_call_saul'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>plot</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>better_call_saul</td>\n",
       "      <td>In a monochrome flash-forward sequence, set af...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>better_call_saul</td>\n",
       "      <td>Tuco is preparing salsa in his kitchen when hi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>better_call_saul</td>\n",
       "      <td>Some time in the past, Chuck goes to a jail to...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>better_call_saul</td>\n",
       "      <td>In a flashback, Jimmy (under the alias \"Saul G...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>better_call_saul</td>\n",
       "      <td>Two cops arrive at Chucks house after his neig...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Name                                               plot\n",
       "0  better_call_saul  In a monochrome flash-forward sequence, set af...\n",
       "1  better_call_saul  Tuco is preparing salsa in his kitchen when hi...\n",
       "2  better_call_saul  Some time in the past, Chuck goes to a jail to...\n",
       "3  better_call_saul  In a flashback, Jimmy (under the alias \"Saul G...\n",
       "4  better_call_saul  Two cops arrive at Chucks house after his neig..."
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bcs_plot = BCS[['Name','plot']]\n",
    "bcs_plot.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "BB = pd.read_csv('breaking_bad.csv')\n",
    "BB['Name']= 'breaking_bad'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>plot</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>breaking_bad</td>\n",
       "      <td>In the desert outside of Albuquerque, New Mexi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>breaking_bad</td>\n",
       "      <td>Walt finishes having rough sex with Skyler and...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>breaking_bad</td>\n",
       "      <td>As Walt and Jesse are scrubbing up the dissolv...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>breaking_bad</td>\n",
       "      <td>Hank puts his DEA team on notice that \"new pla...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>breaking_bad</td>\n",
       "      <td>Donning a jacket and tie, Jesse interviews for...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Name                                               plot\n",
       "0  breaking_bad  In the desert outside of Albuquerque, New Mexi...\n",
       "1  breaking_bad  Walt finishes having rough sex with Skyler and...\n",
       "2  breaking_bad  As Walt and Jesse are scrubbing up the dissolv...\n",
       "3  breaking_bad  Hank puts his DEA team on notice that \"new pla...\n",
       "4  breaking_bad  Donning a jacket and tie, Jesse interviews for..."
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bb_plot = BB[['Name','plot']]\n",
    "bb_plot.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>plot</th>\n",
       "      <th>length</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>better_call_saul</td>\n",
       "      <td>In a monochrome flash-forward sequence, set af...</td>\n",
       "      <td>4634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>better_call_saul</td>\n",
       "      <td>Tuco is preparing salsa in his kitchen when hi...</td>\n",
       "      <td>2386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>better_call_saul</td>\n",
       "      <td>Some time in the past, Chuck goes to a jail to...</td>\n",
       "      <td>2827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>better_call_saul</td>\n",
       "      <td>In a flashback, Jimmy (under the alias \"Saul G...</td>\n",
       "      <td>2642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>better_call_saul</td>\n",
       "      <td>Two cops arrive at Chucks house after his neig...</td>\n",
       "      <td>2162</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Name                                               plot  length\n",
       "0  better_call_saul  In a monochrome flash-forward sequence, set af...    4634\n",
       "1  better_call_saul  Tuco is preparing salsa in his kitchen when hi...    2386\n",
       "2  better_call_saul  Some time in the past, Chuck goes to a jail to...    2827\n",
       "3  better_call_saul  In a flashback, Jimmy (under the alias \"Saul G...    2642\n",
       "4  better_call_saul  Two cops arrive at Chucks house after his neig...    2162"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bcs_plot['length']= bcs_plot['plot'].apply(len)\n",
    "bcs_plot.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set_style(\"whitegrid\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Frequency'>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bcs_plot['length'].plot.hist(bins=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count      50.00000\n",
       "mean     1997.34000\n",
       "std       985.58106\n",
       "min       492.00000\n",
       "25%      1313.75000\n",
       "50%      1854.50000\n",
       "75%      2371.00000\n",
       "max      4634.00000\n",
       "Name: length, dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bcs_plot['length'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>plot</th>\n",
       "      <th>length</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>breaking_bad</td>\n",
       "      <td>In the desert outside of Albuquerque, New Mexi...</td>\n",
       "      <td>2942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>breaking_bad</td>\n",
       "      <td>Walt finishes having rough sex with Skyler and...</td>\n",
       "      <td>2289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>breaking_bad</td>\n",
       "      <td>As Walt and Jesse are scrubbing up the dissolv...</td>\n",
       "      <td>2178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>breaking_bad</td>\n",
       "      <td>Hank puts his DEA team on notice that \"new pla...</td>\n",
       "      <td>1759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>breaking_bad</td>\n",
       "      <td>Donning a jacket and tie, Jesse interviews for...</td>\n",
       "      <td>2542</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Name                                               plot  length\n",
       "0  breaking_bad  In the desert outside of Albuquerque, New Mexi...    2942\n",
       "1  breaking_bad  Walt finishes having rough sex with Skyler and...    2289\n",
       "2  breaking_bad  As Walt and Jesse are scrubbing up the dissolv...    2178\n",
       "3  breaking_bad  Hank puts his DEA team on notice that \"new pla...    1759\n",
       "4  breaking_bad  Donning a jacket and tie, Jesse interviews for...    2542"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bb_plot['length']= bb_plot['plot'].apply(len)\n",
    "bb_plot.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Frequency'>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bb_plot['length'].plot.hist(bins=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count      57.000000\n",
       "mean     1222.315789\n",
       "std       788.062229\n",
       "min       188.000000\n",
       "25%       629.000000\n",
       "50%       940.000000\n",
       "75%      1936.000000\n",
       "max      2942.000000\n",
       "Name: length, dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bb_plot['length'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "import string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.corpus import stopwords"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-block alert-info\">\n",
    "<b>TOKENIZATION: </b>  \n",
    "Extract clean words from raw plot summary\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "def clean_plot(mess):\n",
    "    \"\"\"\n",
    "    1. Remove punctuation\n",
    "    2. Remove stopwords\n",
    "    3. Return clean plot\n",
    "    \"\"\"\n",
    "    nopunc = [char for char in mess if char not in string.punctuation]\n",
    "    \n",
    "    nopunc = ''.join(nopunc)\n",
    "    \n",
    "    return [word for word in nopunc.split() if word.lower() not in stopwords.words('english')]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    [desert, outside, Albuquerque, New, Mexico, ma...\n",
       "1    [Walt, finishes, rough, sex, Skyler, gets, was...\n",
       "2    [Walt, Jesse, scrubbing, dissolved, remains, E...\n",
       "3    [Hank, puts, DEA, team, notice, new, players, ...\n",
       "4    [Donning, jacket, tie, Jesse, interviews, job,...\n",
       "Name: plot, dtype: object"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bb_plot['plot'].apply(clean_plot).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "BOW_bcs = CountVectorizer(analyzer = clean_plot).fit(bcs_plot['plot'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3569\n"
     ]
    }
   ],
   "source": [
    "print(len(BOW_bcs.vocabulary_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2856\n"
     ]
    }
   ],
   "source": [
    "BOW_bb = CountVectorizer(analyzer = clean_plot).fit(bb_plot['plot'])\n",
    "print(len(BOW_bb.vocabulary_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.probability import FreqDist\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "bcs_words = bcs_plot['plot'].apply(clean_plot)\n",
    "bb_words = bb_plot['plot'].apply(clean_plot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Samples', ylabel='Counts'>"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bcs_list = []\n",
    "for l in list(bcs_words):\n",
    "    bcs_list += l\n",
    "fdist = FreqDist(bcs_list)\n",
    "plt.figure(figsize=(16,8))\n",
    "plt.xlabel('Samples', fontsize=18)\n",
    "fdist.plot(40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fdist['flashback']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fdist['flashforward']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}