CATS-Main.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os # accessing directory structure\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "import re\n",
    "\n",
    "import pickle \n",
    "#import mglearn\n",
    "import time\n",
    "\n",
    "\n",
    "from nltk.tokenize import TweetTokenizer # doesn't split at apostrophes\n",
    "import nltk\n",
    "from nltk import Text\n",
    "from nltk.tokenize import regexp_tokenize\n",
    "from nltk.tokenize import word_tokenize  \n",
    "from nltk.tokenize import sent_tokenize \n",
    "from nltk.corpus import stopwords\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "from nltk.stem import PorterStemmer\n",
    "\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.linear_model import LogisticRegression \n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.multiclass import OneVsRestClassifier\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.pipeline import make_pipeline\n",
    "\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.svm import LinearSVC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['folksonomication_source-master', 'mpst_full_data.csv', '.ipynb_checkpoints', 'CATS-Main.ipynb', 'partition.json', 'MPSTGenreCorrected.csv', 'GenreCorrected.csv', 'wiki_movie_plots_deduped.csv', 'Untitled.ipynb']\n"
     ]
    }
   ],
   "source": [
    "print(os.listdir('../NLPStuff'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 14828 rows and 6 columns\n",
      "There are 34886 rows and 8 columns\n"
     ]
    }
   ],
   "source": [
    "nRowsRead = 1000 # specify 'None' if want to read whole file\n",
    "# mpst_full_data.csv has 14828 rows in reality, but we are only loading/previewing the first 1000 rows\n",
    "df1 = pd.read_csv('../NLPStuff/mpst_full_data.csv', delimiter=',')\n",
    "df1.dataframeName = 'mpst_full_data.csv'\n",
    "movies = pd.read_csv('../NLPStuff/wiki_movie_plots_deduped.csv', delimiter=',')\n",
    "movies.dataframeName = 'wiki_movie_plots_deduped.csv'\n",
    "nRow1, nCol1 = df1.shape\n",
    "nRow2, nCol2 = movies.shape\n",
    "print(f'There are {nRow1} rows and {nCol1} columns')\n",
    "print(f'There are {nRow2} rows and {nCol2} columns')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>imdb_id</th>\n",
       "      <th>title</th>\n",
       "      <th>plot_synopsis</th>\n",
       "      <th>tags</th>\n",
       "      <th>split</th>\n",
       "      <th>synopsis_source</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>tt0057603</td>\n",
       "      <td>I tre volti della paura</td>\n",
       "      <td>Note: this synopsis is for the orginal Italian...</td>\n",
       "      <td>cult, horror, gothic, murder, atmospheric</td>\n",
       "      <td>train</td>\n",
       "      <td>imdb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tt1733125</td>\n",
       "      <td>Dungeons &amp; Dragons: The Book of Vile Darkness</td>\n",
       "      <td>Two thousand years ago, Nhagruul the Foul, a s...</td>\n",
       "      <td>violence</td>\n",
       "      <td>train</td>\n",
       "      <td>imdb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>tt0033045</td>\n",
       "      <td>The Shop Around the Corner</td>\n",
       "      <td>Matuschek's, a gift store in Budapest, is the ...</td>\n",
       "      <td>romantic</td>\n",
       "      <td>test</td>\n",
       "      <td>imdb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>tt0113862</td>\n",
       "      <td>Mr. Holland's Opus</td>\n",
       "      <td>Glenn Holland, not a morning person by anyone'...</td>\n",
       "      <td>inspiring, romantic, stupid, feel-good</td>\n",
       "      <td>train</td>\n",
       "      <td>imdb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tt0086250</td>\n",
       "      <td>Scarface</td>\n",
       "      <td>In May 1980, a Cuban man named Tony Montana (A...</td>\n",
       "      <td>cruelty, murder, dramatic, cult, violence, atm...</td>\n",
       "      <td>val</td>\n",
       "      <td>imdb</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     imdb_id                                          title  \\\n",
       "0  tt0057603                        I tre volti della paura   \n",
       "1  tt1733125  Dungeons & Dragons: The Book of Vile Darkness   \n",
       "2  tt0033045                     The Shop Around the Corner   \n",
       "3  tt0113862                             Mr. Holland's Opus   \n",
       "4  tt0086250                                       Scarface   \n",
       "\n",
       "                                       plot_synopsis  \\\n",
       "0  Note: this synopsis is for the orginal Italian...   \n",
       "1  Two thousand years ago, Nhagruul the Foul, a s...   \n",
       "2  Matuschek's, a gift store in Budapest, is the ...   \n",
       "3  Glenn Holland, not a morning person by anyone'...   \n",
       "4  In May 1980, a Cuban man named Tony Montana (A...   \n",
       "\n",
       "                                                tags  split synopsis_source  \n",
       "0          cult, horror, gothic, murder, atmospheric  train            imdb  \n",
       "1                                           violence  train            imdb  \n",
       "2                                           romantic   test            imdb  \n",
       "3             inspiring, romantic, stupid, feel-good  train            imdb  \n",
       "4  cruelty, murder, dramatic, cult, violence, atm...    val            imdb  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the first dataset, the only relevant categories for our purposes are df1['title'], df1['plot_synopsis'] and df1['tags']."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>Release Year</th>\n",
       "      <th>Title</th>\n",
       "      <th>Origin/Ethnicity</th>\n",
       "      <th>Director</th>\n",
       "      <th>Cast</th>\n",
       "      <th>Genre</th>\n",
       "      <th>Wiki Page</th>\n",
       "      <th>Plot</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1901</td>\n",
       "      <td>Kansas Saloon Smashers</td>\n",
       "      <td>American</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unknown</td>\n",
       "      <td>https://en.wikipedia.org/wiki/Kansas_Saloon_Sm...</td>\n",
       "      <td>A bartender is working at a saloon, serving dr...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1901</td>\n",
       "      <td>Love by the Light of the Moon</td>\n",
       "      <td>American</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unknown</td>\n",
       "      <td>https://en.wikipedia.org/wiki/Love_by_the_Ligh...</td>\n",
       "      <td>The moon, painted with a smiling face hangs ov...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1901</td>\n",
       "      <td>The Martyred Presidents</td>\n",
       "      <td>American</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unknown</td>\n",
       "      <td>https://en.wikipedia.org/wiki/The_Martyred_Pre...</td>\n",
       "      <td>The film, just over a minute long, is composed...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1901</td>\n",
       "      <td>Terrible Teddy, the Grizzly King</td>\n",
       "      <td>American</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unknown</td>\n",
       "      <td>https://en.wikipedia.org/wiki/Terrible_Teddy,_...</td>\n",
       "      <td>Lasting just 61 seconds and consisting of two ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1902</td>\n",
       "      <td>Jack and the Beanstalk</td>\n",
       "      <td>American</td>\n",
       "      <td>George S. Fleming, Edwin S. Porter</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unknown</td>\n",
       "      <td>https://en.wikipedia.org/wiki/Jack_and_the_Bea...</td>\n",
       "      <td>The earliest known adaptation of the classic f...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Release Year                             Title Origin/Ethnicity  \\\n",
       "0          1901            Kansas Saloon Smashers         American   \n",
       "1          1901     Love by the Light of the Moon         American   \n",
       "2          1901           The Martyred Presidents         American   \n",
       "3          1901  Terrible Teddy, the Grizzly King         American   \n",
       "4          1902            Jack and the Beanstalk         American   \n",
       "\n",
       "                             Director Cast    Genre  \\\n",
       "0                             Unknown  NaN  unknown   \n",
       "1                             Unknown  NaN  unknown   \n",
       "2                             Unknown  NaN  unknown   \n",
       "3                             Unknown  NaN  unknown   \n",
       "4  George S. Fleming, Edwin S. Porter  NaN  unknown   \n",
       "\n",
       "                                           Wiki Page  \\\n",
       "0  https://en.wikipedia.org/wiki/Kansas_Saloon_Sm...   \n",
       "1  https://en.wikipedia.org/wiki/Love_by_the_Ligh...   \n",
       "2  https://en.wikipedia.org/wiki/The_Martyred_Pre...   \n",
       "3  https://en.wikipedia.org/wiki/Terrible_Teddy,_...   \n",
       "4  https://en.wikipedia.org/wiki/Jack_and_the_Bea...   \n",
       "\n",
       "                                                Plot  \n",
       "0  A bartender is working at a saloon, serving dr...  \n",
       "1  The moon, painted with a smiling face hangs ov...  \n",
       "2  The film, just over a minute long, is composed...  \n",
       "3  Lasting just 61 seconds and consisting of two ...  \n",
       "4  The earliest known adaptation of the classic f...  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the second dataset, the only relevant categories for our purposes are df1['title'] and df1['plot_synopsis']. Need to generate tags ourselves."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2265"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# creation of the column count for aggregation\n",
    "movies['Count']=1\n",
    "movies[['Genre','Count']].groupby(['Genre'], as_index=False).count().shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5604"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# creation of the column count for aggregation\n",
    "df1['Count']=1\n",
    "df1[['tags','Count']].groupby(['tags'], as_index=False).count().shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# harmonization\n",
    "movies['GenreCorrected'] =movies['Genre'] \n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.strip()\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace(' - ', '|')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace(' / ', '|')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('/', '|')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace(' & ', '|')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace(', ', '|')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('; ', '|')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('bio-pic', 'biography')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('biopic', 'biography')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('biographical', 'biography')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('biodrama', 'biography')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('bio-drama', 'biography')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('biographic', 'biography')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace(' \\(film genre\\)', '')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('animated','animation')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('anime','animation')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('children\\'s','children')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('comedey','comedy')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('\\[not in citation given\\]','')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace(' set 4,000 years ago in the canadian arctic','')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('historical','history')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('romantic','romance')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('3-d','animation')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('3d','animation')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('viacom 18 motion pictures','')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('sci-fi','science_fiction')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('ttriller','thriller')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('.','')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('based on radio serial','')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace(' on the early years of hitler','')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('sci fi','science_fiction')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('science fiction','science_fiction')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace(' (30min)','')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('16 mm film','short')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('\\[140\\]','drama')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('\\[144\\]','')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace(' for ','')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('adventures','adventure')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('kung fu','martial_arts')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('kung-fu','martial_arts')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('martial arts','martial_arts')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('world war ii','war')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('world war i','war')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('biography about montreal canadiens star|maurice richard','biography')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('bholenath movies|cinekorn entertainment','')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace(' \\(volleyball\\)','')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('spy film','spy')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('anthology film','anthology')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('biography fim','biography')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('avant-garde','avant_garde')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('biker film','biker')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('buddy cop','buddy')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('buddy film','buddy')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('comedy 2-reeler','comedy')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('films','')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('film','')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('biography of pioneering american photographer eadweard muybridge','biography')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('british-german co-production','')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('bruceploitation','martial_arts')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('comedy-drama adaptation of the mordecai richler novel','comedy-drama')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('movies by the mob\\|knkspl','')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('movies','')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('movie','')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('coming of age','coming_of_age')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('coming-of-age','coming_of_age')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('drama about child soldiers','drama')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('(( based).+)','')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('(( co-produced).+)','')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('(( adapted).+)','')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('(( about).+)','')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('musical b','musical')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('animationchildren','animation|children')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace(' period','period')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('drama loosely','drama')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace(' \\(aquatics|swimming\\)','')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace(' \\(aquatics|swimming\\)','')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace(\"yogesh dattatraya gosavi's directorial debut \\[9\\]\",'')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace(\"war-time\",\"war\")\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace(\"wartime\",\"war\")\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace(\"ww1\",\"war\")\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('unknown','')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace(\"wwii\",\"war\")\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('psychological','psycho')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('rom-coms','romance')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('true crime','crime')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('\\|007','')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('slice of life','slice_of_life')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('computer animation','animation')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('gun fu','martial_arts')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('j-horror','horror')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace(' \\(shogi|chess\\)','')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('afghan war drama','war drama')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('\\|6 separate stories','')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace(' \\(30min\\)','')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace(' (road bicycle racing)','')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace(' v-cinema','')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('tv miniseries','tv_miniseries')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('\\|docudrama','\\|documentary|drama')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace(' in animation','|animation')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('((adaptation).+)','')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('((adaptated).+)','')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('((adapted).+)','')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('(( on ).+)','')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('american football','sports')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('dev\\|nusrat jahan','sports')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('television miniseries','tv_miniseries')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace(' \\(artistic\\)','')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace(' \\|direct-to-dvd','')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('history dram','history drama')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('martial art','martial_arts')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('psycho thriller,','psycho thriller')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('\\|1 girl\\|3 suitors','')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace(' \\(road bicycle racing\\)','')\n",
    "filterE = movies['GenreCorrected']==\"ero\"\n",
    "movies.loc[filterE,'GenreCorrected']=\"adult\"\n",
    "filterE = movies['GenreCorrected']==\"music\"\n",
    "movies.loc[filterE,'GenreCorrected']=\"musical\"\n",
    "filterE = movies['GenreCorrected']==\"-\"\n",
    "movies.loc[filterE,'GenreCorrected']=''\n",
    "filterE = movies['GenreCorrected']==\"comedy–drama\"\n",
    "movies.loc[filterE,'GenreCorrected'] = \"comedy|drama\"\n",
    "filterE = movies['GenreCorrected']==\"comedy–horror\"\n",
    "movies.loc[filterE,'GenreCorrected'] = \"comedy|horror\"\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace(' ','|')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace(',','|')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('-','')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('actionadventure','action|adventure')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('actioncomedy','action|comedy')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('actiondrama','action|drama')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('actionlove','action|love')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('actionmasala','action|masala')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('actionchildren','action|children')\n",
    "\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('fantasychildren\\|','fantasy|children')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('fantasycomedy','fantasy|comedy')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('fantasyperiod','fantasy|period')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('cbctv_miniseries','tv_miniseries')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('dramacomedy','drama|comedy')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('dramacomedysocial','drama|comedy|social')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('dramathriller','drama|thriller')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('comedydrama','comedy|drama')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('dramathriller','drama|thriller')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('comedyhorror','comedy|horror')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('sciencefiction','science_fiction')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('adventurecomedy','adventure|comedy')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('animationdrama','animation|drama')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('\\|\\|','|')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('muslim','religious')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('thriler','thriller')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('crimethriller','crime|thriller')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('fantay','fantasy')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('actionthriller','action|thriller')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('comedysocial','comedy|social')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('martialarts','martial_arts')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('\\|\\(children\\|poker\\|karuta\\)','')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('epichistory','epic|history')\n",
    "\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('erotica','adult')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('erotic','adult')\n",
    "\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('((\\|produced\\|).+)','')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('chanbara','chambara')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('comedythriller','comedy|thriller')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('biblical','religious')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('biblical','religious')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('colour\\|yellow\\|productions\\|eros\\|international','')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('\\|directtodvd','')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('liveaction','live|action')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('melodrama','drama')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('superheroes','superheroe')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('gangsterthriller','gangster|thriller')\n",
    "\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('heistcomedy','comedy')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('heist','action')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('historic','history')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('historydisaster','history|disaster')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('warcomedy','war|comedy')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('westerncomedy','western|comedy')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('ancientcostume','costume')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('computeranimation','animation')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('dramatic','drama')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('familya','family')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('familya','family')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('dramedy','drama|comedy')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('dramaa','drama')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('famil\\|','family')\n",
    "\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('superheroe','superhero')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('biogtaphy','biography')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('devotionalbiography','devotional|biography')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('docufiction','documentary|fiction')\n",
    "\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('familydrama','family|drama')\n",
    "\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('espionage','spy')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('supeheroes','superhero')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('romancefiction','romance|fiction')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('horrorthriller','horror|thriller')\n",
    "\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('suspensethriller','suspense|thriller')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('musicaliography','musical|biography')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('triller','thriller')\n",
    "\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('\\|\\(fiction\\)','|fiction')\n",
    "\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('romanceaction','romance|action')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('romancecomedy','romance|comedy')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('romancehorror','romance|horror')\n",
    "\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('romcom','romance|comedy')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('rom\\|com','romance|comedy')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('satirical','satire')\n",
    "\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('science_fictionchildren','science_fiction|children')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('homosexual','adult')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('sexual','adult')\n",
    "\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('mockumentary','documentary')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('periodic','period')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('romanctic','romantic')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('politics','political')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('samurai','martial_arts')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('tv_miniseries','series')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('serial','series')\n",
    "\n",
    "filterE = movies['GenreCorrected']==\"musical–comedy\"\n",
    "movies.loc[filterE,'GenreCorrected'] = \"musical|comedy\"\n",
    "\n",
    "filterE = movies['GenreCorrected']==\"roman|porno\"\n",
    "movies.loc[filterE,'GenreCorrected'] = \"adult\"\n",
    "\n",
    "\n",
    "filterE = movies['GenreCorrected']==\"action—masala\"\n",
    "movies.loc[filterE,'GenreCorrected'] = \"action|masala\"\n",
    "\n",
    "\n",
    "filterE = movies['GenreCorrected']==\"horror–thriller\"\n",
    "movies.loc[filterE,'GenreCorrected'] = \"horror|thriller\"\n",
    "\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('family','children')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('martial_arts','action')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('horror','thriller')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('war','action')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('adventure','action')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('science_fiction','action')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('western','action')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('western','action')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('noir','black')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('spy','action')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('superhero','action')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('social','')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('suspense','action')\n",
    "\n",
    "\n",
    "filterE = movies['GenreCorrected']==\"drama|romance|adult|children\"\n",
    "movies.loc[filterE,'GenreCorrected'] = \"drama|romance|adult\"\n",
    "\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('\\|–\\|','|')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.strip(to_strip='\\|')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.replace('actionner','action')\n",
    "movies['GenreCorrected']=movies['GenreCorrected'].str.strip()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# harmonization\n",
    "df1['GenreCorrected'] =df1['tags'] \n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.strip()\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace(' - ', '|')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace(' / ', '|')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('/', '|')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace(' & ', '|')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace(', ', '|')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('; ', '|')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('bio-pic', 'biography')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('biopic', 'biography')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('biographical', 'biography')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('biodrama', 'biography')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('bio-drama', 'biography')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('biographic', 'biography')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace(' \\(film genre\\)', '')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('animated','animation')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('anime','animation')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('children\\'s','children')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('comedey','comedy')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('\\[not in citation given\\]','')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace(' set 4,000 years ago in the canadian arctic','')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('historical','history')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('romantic','romance')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('3-d','animation')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('3d','animation')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('viacom 18 motion pictures','')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('sci-fi','science_fiction')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('ttriller','thriller')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('.','')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('based on radio serial','')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace(' on the early years of hitler','')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('sci fi','science_fiction')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('science fiction','science_fiction')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace(' (30min)','')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('16 mm film','short')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('\\[140\\]','drama')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('\\[144\\]','')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace(' for ','')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('adventures','adventure')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('kung fu','martial_arts')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('kung-fu','martial_arts')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('martial arts','martial_arts')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('world war ii','war')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('world war i','war')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('biography about montreal canadiens star|maurice richard','biography')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('bholenath df1|cinekorn entertainment','')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace(' \\(volleyball\\)','')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('spy film','spy')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('anthology film','anthology')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('biography fim','biography')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('avant-garde','avant_garde')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('biker film','biker')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('buddy cop','buddy')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('buddy film','buddy')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('comedy 2-reeler','comedy')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('films','')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('film','')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('biography of pioneering american photographer eadweard muybridge','biography')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('british-german co-production','')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('bruceploitation','martial_arts')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('comedy-drama adaptation of the mordecai richler novel','comedy-drama')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('df1 by the mob\\|knkspl','')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('df1','')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('movie','')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('coming of age','coming_of_age')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('coming-of-age','coming_of_age')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('drama about child soldiers','drama')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('(( based).+)','')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('(( co-produced).+)','')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('(( adapted).+)','')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('(( about).+)','')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('musical b','musical')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('animationchildren','animation|children')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace(' period','period')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('drama loosely','drama')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace(' \\(aquatics|swimming\\)','')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace(' \\(aquatics|swimming\\)','')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace(\"yogesh dattatraya gosavi's directorial debut \\[9\\]\",'')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace(\"war-time\",\"war\")\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace(\"wartime\",\"war\")\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace(\"ww1\",\"war\")\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('unknown','')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace(\"wwii\",\"war\")\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('psychological','psycho')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('rom-coms','romance')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('true crime','crime')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('\\|007','')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('slice of life','slice_of_life')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('computer animation','animation')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('gun fu','martial_arts')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('j-horror','horror')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace(' \\(shogi|chess\\)','')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('afghan war drama','war drama')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('\\|6 separate stories','')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace(' \\(30min\\)','')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace(' (road bicycle racing)','')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace(' v-cinema','')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('tv miniseries','tv_miniseries')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('\\|docudrama','\\|documentary|drama')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace(' in animation','|animation')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('((adaptation).+)','')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('((adaptated).+)','')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('((adapted).+)','')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('(( on ).+)','')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('american football','sports')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('dev\\|nusrat jahan','sports')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('television miniseries','tv_miniseries')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace(' \\(artistic\\)','')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace(' \\|direct-to-dvd','')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('history dram','history drama')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('martial art','martial_arts')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('psycho thriller,','psycho thriller')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('\\|1 girl\\|3 suitors','')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace(' \\(road bicycle racing\\)','')\n",
    "filterE = df1['GenreCorrected']==\"ero\"\n",
    "df1.loc[filterE,'GenreCorrected']=\"adult\"\n",
    "filterE = df1['GenreCorrected']==\"music\"\n",
    "df1.loc[filterE,'GenreCorrected']=\"musical\"\n",
    "filterE = df1['GenreCorrected']==\"-\"\n",
    "df1.loc[filterE,'GenreCorrected']=''\n",
    "filterE = df1['GenreCorrected']==\"comedy–drama\"\n",
    "df1.loc[filterE,'GenreCorrected'] = \"comedy|drama\"\n",
    "filterE = df1['GenreCorrected']==\"comedy–horror\"\n",
    "df1.loc[filterE,'GenreCorrected'] = \"comedy|horror\"\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace(' ','|')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace(',','|')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('-','')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('actionadventure','action|adventure')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('actioncomedy','action|comedy')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('actiondrama','action|drama')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('actionlove','action|love')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('actionmasala','action|masala')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('actionchildren','action|children')\n",
    "\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('fantasychildren\\|','fantasy|children')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('fantasycomedy','fantasy|comedy')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('fantasyperiod','fantasy|period')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('cbctv_miniseries','tv_miniseries')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('dramacomedy','drama|comedy')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('dramacomedysocial','drama|comedy|social')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('dramathriller','drama|thriller')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('comedydrama','comedy|drama')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('dramathriller','drama|thriller')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('comedyhorror','comedy|horror')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('sciencefiction','science_fiction')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('adventurecomedy','adventure|comedy')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('animationdrama','animation|drama')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('\\|\\|','|')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('muslim','religious')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('thriler','thriller')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('crimethriller','crime|thriller')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('fantay','fantasy')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('actionthriller','action|thriller')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('comedysocial','comedy|social')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('martialarts','martial_arts')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('\\|\\(children\\|poker\\|karuta\\)','')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('epichistory','epic|history')\n",
    "\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('erotica','adult')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('erotic','adult')\n",
    "\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('((\\|produced\\|).+)','')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('chanbara','chambara')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('comedythriller','comedy|thriller')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('biblical','religious')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('biblical','religious')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('colour\\|yellow\\|productions\\|eros\\|international','')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('\\|directtodvd','')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('liveaction','live|action')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('melodrama','drama')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('superheroes','superheroe')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('gangsterthriller','gangster|thriller')\n",
    "\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('heistcomedy','comedy')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('heist','action')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('historic','history')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('historydisaster','history|disaster')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('warcomedy','war|comedy')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('westerncomedy','western|comedy')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('ancientcostume','costume')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('computeranimation','animation')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('dramatic','drama')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('familya','family')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('familya','family')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('dramedy','drama|comedy')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('dramaa','drama')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('famil\\|','family')\n",
    "\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('superheroe','superhero')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('biogtaphy','biography')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('devotionalbiography','devotional|biography')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('docufiction','documentary|fiction')\n",
    "\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('familydrama','family|drama')\n",
    "\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('espionage','spy')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('supeheroes','superhero')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('romancefiction','romance|fiction')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('horrorthriller','horror|thriller')\n",
    "\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('suspensethriller','suspense|thriller')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('musicaliography','musical|biography')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('triller','thriller')\n",
    "\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('\\|\\(fiction\\)','|fiction')\n",
    "\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('romanceaction','romance|action')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('romancecomedy','romance|comedy')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('romancehorror','romance|horror')\n",
    "\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('romcom','romance|comedy')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('rom\\|com','romance|comedy')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('satirical','satire')\n",
    "\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('science_fictionchildren','science_fiction|children')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('homosexual','adult')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('sexual','adult')\n",
    "\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('mockumentary','documentary')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('periodic','period')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('romanctic','romantic')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('politics','political')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('samurai','martial_arts')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('tv_miniseries','series')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('serial','series')\n",
    "\n",
    "filterE = df1['GenreCorrected']==\"musical–comedy\"\n",
    "df1.loc[filterE,'GenreCorrected'] = \"musical|comedy\"\n",
    "\n",
    "filterE = df1['GenreCorrected']==\"roman|porno\"\n",
    "df1.loc[filterE,'GenreCorrected'] = \"adult\"\n",
    "\n",
    "\n",
    "filterE = df1['GenreCorrected']==\"action—masala\"\n",
    "df1.loc[filterE,'GenreCorrected'] = \"action|masala\"\n",
    "\n",
    "\n",
    "filterE = df1['GenreCorrected']==\"horror–thriller\"\n",
    "df1.loc[filterE,'GenreCorrected'] = \"horror|thriller\"\n",
    "\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('family','children')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('martial_arts','action')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('horror','thriller')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('war','action')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('adventure','action')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('science_fiction','action')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('western','action')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('western','action')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('noir','black')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('spy','action')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('superhero','action')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('social','')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('suspense','action')\n",
    "\n",
    "\n",
    "filterE = df1['GenreCorrected']==\"drama|romance|adult|children\"\n",
    "df1.loc[filterE,'GenreCorrected'] = \"drama|romance|adult\"\n",
    "\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('\\|–\\|','|')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.strip(to_strip='\\|')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.replace('actionner','action')\n",
    "df1['GenreCorrected']=df1['GenreCorrected'].str.strip()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>Release Year</th>\n",
       "      <th>Title</th>\n",
       "      <th>Origin/Ethnicity</th>\n",
       "      <th>Director</th>\n",
       "      <th>Cast</th>\n",
       "      <th>Genre</th>\n",
       "      <th>Wiki Page</th>\n",
       "      <th>Plot</th>\n",
       "      <th>Count</th>\n",
       "      <th>GenreCorrected</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1901</td>\n",
       "      <td>Kansas Saloon Smashers</td>\n",
       "      <td>American</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unknown</td>\n",
       "      <td>https://en.wikipedia.org/wiki/Kansas_Saloon_Sm...</td>\n",
       "      <td>A bartender is working at a saloon, serving dr...</td>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1901</td>\n",
       "      <td>Love by the Light of the Moon</td>\n",
       "      <td>American</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unknown</td>\n",
       "      <td>https://en.wikipedia.org/wiki/Love_by_the_Ligh...</td>\n",
       "      <td>The moon, painted with a smiling face hangs ov...</td>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1901</td>\n",
       "      <td>The Martyred Presidents</td>\n",
       "      <td>American</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unknown</td>\n",
       "      <td>https://en.wikipedia.org/wiki/The_Martyred_Pre...</td>\n",
       "      <td>The film, just over a minute long, is composed...</td>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1901</td>\n",
       "      <td>Terrible Teddy, the Grizzly King</td>\n",
       "      <td>American</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unknown</td>\n",
       "      <td>https://en.wikipedia.org/wiki/Terrible_Teddy,_...</td>\n",
       "      <td>Lasting just 61 seconds and consisting of two ...</td>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1902</td>\n",
       "      <td>Jack and the Beanstalk</td>\n",
       "      <td>American</td>\n",
       "      <td>George S. Fleming, Edwin S. Porter</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unknown</td>\n",
       "      <td>https://en.wikipedia.org/wiki/Jack_and_the_Bea...</td>\n",
       "      <td>The earliest known adaptation of the classic f...</td>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Release Year                             Title Origin/Ethnicity  \\\n",
       "0          1901            Kansas Saloon Smashers         American   \n",
       "1          1901     Love by the Light of the Moon         American   \n",
       "2          1901           The Martyred Presidents         American   \n",
       "3          1901  Terrible Teddy, the Grizzly King         American   \n",
       "4          1902            Jack and the Beanstalk         American   \n",
       "\n",
       "                             Director Cast    Genre  \\\n",
       "0                             Unknown  NaN  unknown   \n",
       "1                             Unknown  NaN  unknown   \n",
       "2                             Unknown  NaN  unknown   \n",
       "3                             Unknown  NaN  unknown   \n",
       "4  George S. Fleming, Edwin S. Porter  NaN  unknown   \n",
       "\n",
       "                                           Wiki Page  \\\n",
       "0  https://en.wikipedia.org/wiki/Kansas_Saloon_Sm...   \n",
       "1  https://en.wikipedia.org/wiki/Love_by_the_Ligh...   \n",
       "2  https://en.wikipedia.org/wiki/The_Martyred_Pre...   \n",
       "3  https://en.wikipedia.org/wiki/Terrible_Teddy,_...   \n",
       "4  https://en.wikipedia.org/wiki/Jack_and_the_Bea...   \n",
       "\n",
       "                                                Plot  Count GenreCorrected  \n",
       "0  A bartender is working at a saloon, serving dr...      1                 \n",
       "1  The moon, painted with a smiling face hangs ov...      1                 \n",
       "2  The film, just over a minute long, is composed...      1                 \n",
       "3  Lasting just 61 seconds and consisting of two ...      1                 \n",
       "4  The earliest known adaptation of the classic f...      1                 "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\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>imdb_id</th>\n",
       "      <th>title</th>\n",
       "      <th>plot_synopsis</th>\n",
       "      <th>tags</th>\n",
       "      <th>split</th>\n",
       "      <th>synopsis_source</th>\n",
       "      <th>Count</th>\n",
       "      <th>GenreCorrected</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>tt0057603</td>\n",
       "      <td>I tre volti della paura</td>\n",
       "      <td>Note: this synopsis is for the orginal Italian...</td>\n",
       "      <td>cult, horror, gothic, murder, atmospheric</td>\n",
       "      <td>train</td>\n",
       "      <td>imdb</td>\n",
       "      <td>1</td>\n",
       "      <td>cult|thriller|gothic|murder|atmospheric</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tt1733125</td>\n",
       "      <td>Dungeons &amp; Dragons: The Book of Vile Darkness</td>\n",
       "      <td>Two thousand years ago, Nhagruul the Foul, a s...</td>\n",
       "      <td>violence</td>\n",
       "      <td>train</td>\n",
       "      <td>imdb</td>\n",
       "      <td>1</td>\n",
       "      <td>violence</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>tt0033045</td>\n",
       "      <td>The Shop Around the Corner</td>\n",
       "      <td>Matuschek's, a gift store in Budapest, is the ...</td>\n",
       "      <td>romantic</td>\n",
       "      <td>test</td>\n",
       "      <td>imdb</td>\n",
       "      <td>1</td>\n",
       "      <td>romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>tt0113862</td>\n",
       "      <td>Mr. Holland's Opus</td>\n",
       "      <td>Glenn Holland, not a morning person by anyone'...</td>\n",
       "      <td>inspiring, romantic, stupid, feel-good</td>\n",
       "      <td>train</td>\n",
       "      <td>imdb</td>\n",
       "      <td>1</td>\n",
       "      <td>inspiring|romance|stupid|feelgood</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tt0086250</td>\n",
       "      <td>Scarface</td>\n",
       "      <td>In May 1980, a Cuban man named Tony Montana (A...</td>\n",
       "      <td>cruelty, murder, dramatic, cult, violence, atm...</td>\n",
       "      <td>val</td>\n",
       "      <td>imdb</td>\n",
       "      <td>1</td>\n",
       "      <td>cruelty|murder|drama|cult|violence|atmospheric...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     imdb_id                                          title  \\\n",
       "0  tt0057603                        I tre volti della paura   \n",
       "1  tt1733125  Dungeons & Dragons: The Book of Vile Darkness   \n",
       "2  tt0033045                     The Shop Around the Corner   \n",
       "3  tt0113862                             Mr. Holland's Opus   \n",
       "4  tt0086250                                       Scarface   \n",
       "\n",
       "                                       plot_synopsis  \\\n",
       "0  Note: this synopsis is for the orginal Italian...   \n",
       "1  Two thousand years ago, Nhagruul the Foul, a s...   \n",
       "2  Matuschek's, a gift store in Budapest, is the ...   \n",
       "3  Glenn Holland, not a morning person by anyone'...   \n",
       "4  In May 1980, a Cuban man named Tony Montana (A...   \n",
       "\n",
       "                                                tags  split synopsis_source  \\\n",
       "0          cult, horror, gothic, murder, atmospheric  train            imdb   \n",
       "1                                           violence  train            imdb   \n",
       "2                                           romantic   test            imdb   \n",
       "3             inspiring, romantic, stupid, feel-good  train            imdb   \n",
       "4  cruelty, murder, dramatic, cult, violence, atm...    val            imdb   \n",
       "\n",
       "   Count                                     GenreCorrected  \n",
       "0      1            cult|thriller|gothic|murder|atmospheric  \n",
       "1      1                                           violence  \n",
       "2      1                                            romance  \n",
       "3      1                  inspiring|romance|stupid|feelgood  \n",
       "4      1  cruelty|murder|drama|cult|violence|atmospheric...  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "moviesGenre = movies[['GenreCorrected','Count']].groupby(['GenreCorrected']).count()\n",
    "moviesGenre.to_csv('GenreCorrected.csv',sep=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1Genre = df1[['GenreCorrected','Count']].groupby(['GenreCorrected']).count()\n",
    "df1Genre.to_csv('MPSTGenreCorrected.csv',sep=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1199"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies[['GenreCorrected','Count']].groupby(['GenreCorrected'], as_index=False).count().shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5586"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1[['GenreCorrected','Count']].groupby(['GenreCorrected'], as_index=False).count().shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>GenreCorrected</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td></td>\n",
       "      <td>6206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>516</th>\n",
       "      <td>drama</td>\n",
       "      <td>6107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>359</th>\n",
       "      <td>comedy</td>\n",
       "      <td>4411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>action</td>\n",
       "      <td>3790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1111</th>\n",
       "      <td>thriller</td>\n",
       "      <td>2163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>964</th>\n",
       "      <td>romance</td>\n",
       "      <td>954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>animation</td>\n",
       "      <td>601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>973</th>\n",
       "      <td>romance|comedy</td>\n",
       "      <td>577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>447</th>\n",
       "      <td>crime</td>\n",
       "      <td>573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>387</th>\n",
       "      <td>comedy|drama</td>\n",
       "      <td>560</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      GenreCorrected  Count\n",
       "0                      6206\n",
       "516            drama   6107\n",
       "359           comedy   4411\n",
       "2             action   3790\n",
       "1111        thriller   2163\n",
       "964          romance    954\n",
       "203        animation    601\n",
       "973   romance|comedy    577\n",
       "447            crime    573\n",
       "387     comedy|drama    560"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies[['GenreCorrected','Count']].groupby(['GenreCorrected'],as_index=False).count().sort_values(['Count'], ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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",
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       "    }\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>GenreCorrected</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3303</th>\n",
       "      <td>murder</td>\n",
       "      <td>1004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4668</th>\n",
       "      <td>romance</td>\n",
       "      <td>731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5217</th>\n",
       "      <td>violence</td>\n",
       "      <td>584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4108</th>\n",
       "      <td>psychedelic</td>\n",
       "      <td>437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2706</th>\n",
       "      <td>flashback</td>\n",
       "      <td>332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5427</th>\n",
       "      <td>violence|murder</td>\n",
       "      <td>262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4389</th>\n",
       "      <td>revenge</td>\n",
       "      <td>232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1775</th>\n",
       "      <td>cult</td>\n",
       "      <td>226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>781</th>\n",
       "      <td>comedy</td>\n",
       "      <td>157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4574</th>\n",
       "      <td>revenge|murder</td>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       GenreCorrected  Count\n",
       "3303           murder   1004\n",
       "4668          romance    731\n",
       "5217         violence    584\n",
       "4108      psychedelic    437\n",
       "2706        flashback    332\n",
       "5427  violence|murder    262\n",
       "4389          revenge    232\n",
       "1775             cult    226\n",
       "781            comedy    157\n",
       "4574   revenge|murder    150"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1[['GenreCorrected','Count']].groupby(['GenreCorrected'],as_index=False).count().sort_values(['Count'], ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "movies['GenreSplit']=movies['GenreCorrected'].str.split('|')\n",
    "movies['GenreSplit']= movies['GenreSplit'].apply(np.sort).apply(np.unique)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1['GenreSplit']=df1['GenreCorrected'].str.split('|')\n",
    "df1['GenreSplit']= df1['GenreSplit'].apply(np.sort).apply(np.unique)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['', '', '', ..., 'comedy', 'comedy', 'romance'], dtype='<U32')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "genres_array = np.array([])\n",
    "\n",
    "for i in range(0,movies.shape[0]-1):\n",
    "    genres_array = np.concatenate((genres_array, movies['GenreSplit'][i] ))\n",
    "    \n",
    "genres_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['atmospheric', 'cult', 'gothic', ..., 'action', 'anti', 'murder'],\n",
       "      dtype='<U32')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1genres_array = np.array([])\n",
    "\n",
    "for i in range(0,df1.shape[0]-1):\n",
    "    df1genres_array = np.concatenate((df1genres_array, df1['GenreSplit'][i] ))\n",
    "    \n",
    "df1genres_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "genres = pd.DataFrame({'Genre':genres_array})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1genres = pd.DataFrame({'tags':df1genres_array})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>Genre</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>drama</td>\n",
       "      <td>9487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>comedy</td>\n",
       "      <td>7320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td></td>\n",
       "      <td>6222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>action</td>\n",
       "      <td>5952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426</th>\n",
       "      <td>thriller</td>\n",
       "      <td>3291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>337</th>\n",
       "      <td>romance</td>\n",
       "      <td>2639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>crime</td>\n",
       "      <td>1607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244</th>\n",
       "      <td>musical</td>\n",
       "      <td>951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>animation</td>\n",
       "      <td>914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>children</td>\n",
       "      <td>684</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Genre  Count\n",
       "116      drama   9487\n",
       "85      comedy   7320\n",
       "0                6222\n",
       "4       action   5952\n",
       "426   thriller   3291\n",
       "337    romance   2639\n",
       "94       crime   1607\n",
       "244    musical    951\n",
       "16   animation    914\n",
       "73    children    684"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# histogram for the genres is most welcome\n",
    "genres['Count']=1\n",
    "genres[['Genre','Count']].groupby(['Genre'], as_index=False).sum().sort_values(['Count'], ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\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>tags</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>murder</td>\n",
       "      <td>5782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>violence</td>\n",
       "      <td>4426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>flashback</td>\n",
       "      <td>2937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>romance</td>\n",
       "      <td>2906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>cult</td>\n",
       "      <td>2647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>revenge</td>\n",
       "      <td>2468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>comedy</td>\n",
       "      <td>1930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>psychedelic</td>\n",
       "      <td>1897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>action</td>\n",
       "      <td>1087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>actionful</td>\n",
       "      <td>1086</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           tags  Count\n",
       "44       murder   5782\n",
       "71     violence   4426\n",
       "31    flashback   2937\n",
       "59      romance   2906\n",
       "21         cult   2647\n",
       "58      revenge   2468\n",
       "18       comedy   1930\n",
       "53  psychedelic   1897\n",
       "1        action   1087\n",
       "2     actionful   1086"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# histogram for the genres is most welcome\n",
    "df1genres['Count']=1\n",
    "df1genres[['tags','Count']].groupby(['tags'], as_index=False).sum().sort_values(['Count'], ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "genres=genres[['Genre','Count']].groupby(['Genre'], as_index=False).sum().sort_values(['Count'], ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1genres=df1genres[['tags','Count']].groupby(['tags'], as_index=False).sum().sort_values(['Count'], ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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>Genre</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>drama</td>\n",
       "      <td>9487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>comedy</td>\n",
       "      <td>7320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>action</td>\n",
       "      <td>5952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426</th>\n",
       "      <td>thriller</td>\n",
       "      <td>3291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>337</th>\n",
       "      <td>romance</td>\n",
       "      <td>2639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>crime</td>\n",
       "      <td>1607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244</th>\n",
       "      <td>musical</td>\n",
       "      <td>951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>animation</td>\n",
       "      <td>914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>children</td>\n",
       "      <td>684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>fantasy</td>\n",
       "      <td>542</td>\n",
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       "      <th>245</th>\n",
       "      <td>mystery</td>\n",
       "      <td>481</td>\n",
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       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>biography</td>\n",
       "      <td>463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>black</td>\n",
       "      <td>412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>history</td>\n",
       "      <td>256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>368</th>\n",
       "      <td>short</td>\n",
       "      <td>241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>documentary</td>\n",
       "      <td>131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>391</th>\n",
       "      <td>sports</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>357</th>\n",
       "      <td>series</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>adult</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>288</th>\n",
       "      <td>political</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>costume</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>429</th>\n",
       "      <td>tokusatsu</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>411</th>\n",
       "      <td>supernatural</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>223</th>\n",
       "      <td>masala</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>307</th>\n",
       "      <td>psycho</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>epic</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>348</th>\n",
       "      <td>satire</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>421</th>\n",
       "      <td>teen</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>disaster</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>281</th>\n",
       "      <td>period</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>390</th>\n",
       "      <td>sport</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>360</th>\n",
       "      <td>sexploitation</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>362</th>\n",
       "      <td>shakespearean</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>avant_garde</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>293</th>\n",
       "      <td>pow</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>prison</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>christian</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>experimental</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238</th>\n",
       "      <td>monster</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>382</th>\n",
       "      <td>sociofantasy</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>227</th>\n",
       "      <td>mecha</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>buddy</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>277</th>\n",
       "      <td>patriotic</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>268</th>\n",
       "      <td>operetta</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>453</th>\n",
       "      <td>yakuza</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>idol</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>459</th>\n",
       "      <td>zombie</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>chambara</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>292</th>\n",
       "      <td>postapocalyptic</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>cult</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>charmme</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>313</th>\n",
       "      <td>raj</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>d'cruz</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>sword</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>231</th>\n",
       "      <td>military</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>285</th>\n",
       "      <td>pink</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>boxing</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>322</th>\n",
       "      <td>ravi</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>ileana</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>422</th>\n",
       "      <td>teja</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               Genre  Count\n",
       "116            drama   9487\n",
       "85            comedy   7320\n",
       "4             action   5952\n",
       "426         thriller   3291\n",
       "337          romance   2639\n",
       "94             crime   1607\n",
       "244          musical    951\n",
       "16         animation    914\n",
       "73          children    684\n",
       "131          fantasy    542\n",
       "245          mystery    481\n",
       "46         biography    463\n",
       "47             black    412\n",
       "149          history    256\n",
       "368            short    241\n",
       "114      documentary    131\n",
       "391           sports    121\n",
       "357           series     86\n",
       "6              adult     71\n",
       "288        political     60\n",
       "90           costume     49\n",
       "429        tokusatsu     43\n",
       "411     supernatural     41\n",
       "223           masala     41\n",
       "307           psycho     39\n",
       "126             epic     38\n",
       "348           satire     37\n",
       "421             teen     31\n",
       "112         disaster     31\n",
       "281           period     28\n",
       "..               ...    ...\n",
       "390            sport      8\n",
       "360    sexploitation      8\n",
       "362    shakespearean      7\n",
       "30       avant_garde      7\n",
       "293              pow      7\n",
       "300           prison      6\n",
       "78         christian      6\n",
       "128     experimental      6\n",
       "238          monster      6\n",
       "382     sociofantasy      6\n",
       "227            mecha      6\n",
       "57             buddy      6\n",
       "277        patriotic      5\n",
       "268         operetta      5\n",
       "453           yakuza      5\n",
       "158             idol      5\n",
       "459           zombie      5\n",
       "66          chambara      5\n",
       "292  postapocalyptic      4\n",
       "96              cult      4\n",
       "70           charmme      4\n",
       "313              raj      4\n",
       "100           d'cruz      4\n",
       "417            sword      4\n",
       "231         military      4\n",
       "285             pink      4\n",
       "51            boxing      4\n",
       "322             ravi      4\n",
       "159           ileana      4\n",
       "422             teja      3\n",
       "\n",
       "[100 rows x 2 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "genres = genres[genres['Genre']!='']\n",
    "genres.head(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>tags</th>\n",
       "      <th>Count</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>44</th>\n",
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       "      <th>71</th>\n",
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       "      <td>4426</td>\n",
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       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>flashback</td>\n",
       "      <td>2937</td>\n",
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       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>romance</td>\n",
       "      <td>2906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>cult</td>\n",
       "      <td>2647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>revenge</td>\n",
       "      <td>2468</td>\n",
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       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>comedy</td>\n",
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       "      <th>53</th>\n",
       "      <td>psychedelic</td>\n",
       "      <td>1897</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>actionful</td>\n",
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       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>versus</td>\n",
       "      <td>875</td>\n",
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       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>good</td>\n",
       "      <td>875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>evil</td>\n",
       "      <td>875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>drama</td>\n",
       "      <td>846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>humor</td>\n",
       "      <td>822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>satire</td>\n",
       "      <td>816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>entertaining</td>\n",
       "      <td>749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>black</td>\n",
       "      <td>745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>neo</td>\n",
       "      <td>745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>sadist</td>\n",
       "      <td>652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>insanity</td>\n",
       "      <td>636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>tragedy</td>\n",
       "      <td>597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>fantasy</td>\n",
       "      <td>564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>paranormal</td>\n",
       "      <td>546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>boring</td>\n",
       "      <td>525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>mystery</td>\n",
       "      <td>519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>history</td>\n",
       "      <td>500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>thriller</td>\n",
       "      <td>486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>cruelty</td>\n",
       "      <td>442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>gothic</td>\n",
       "      <td>441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>depressing</td>\n",
       "      <td>205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>reality</td>\n",
       "      <td>205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>twist</td>\n",
       "      <td>205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>plot</td>\n",
       "      <td>205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>cute</td>\n",
       "      <td>197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>stupid</td>\n",
       "      <td>190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>fiction</td>\n",
       "      <td>180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>intrigue</td>\n",
       "      <td>168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>pornographic</td>\n",
       "      <td>163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>home</td>\n",
       "      <td>153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>haunting</td>\n",
       "      <td>151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>allegory</td>\n",
       "      <td>139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>adult</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>thoughtprovoking</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>anti</td>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>inspiring</td>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>comic</td>\n",
       "      <td>114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>brainwashing</td>\n",
       "      <td>107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>queer</td>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>clever</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>claustrophobic</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>whimsical</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>feelgood</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>blaxploitation</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>grindhouse</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>suicidal</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>magical</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>autobiography</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>christian</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>non</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>73 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                tags  Count\n",
       "44            murder   5782\n",
       "71          violence   4426\n",
       "31         flashback   2937\n",
       "59           romance   2906\n",
       "21              cult   2647\n",
       "58           revenge   2468\n",
       "18            comedy   1930\n",
       "53       psychedelic   1897\n",
       "1             action   1087\n",
       "2          actionful   1086\n",
       "70            versus    875\n",
       "33              good    875\n",
       "27              evil    875\n",
       "25             drama    846\n",
       "39             humor    822\n",
       "61            satire    816\n",
       "26      entertaining    749\n",
       "10             black    745\n",
       "46               neo    745\n",
       "60            sadist    652\n",
       "40          insanity    636\n",
       "68           tragedy    597\n",
       "28           fantasy    564\n",
       "48        paranormal    546\n",
       "13            boring    525\n",
       "45           mystery    519\n",
       "37           history    500\n",
       "67          thriller    486\n",
       "20           cruelty    442\n",
       "34            gothic    441\n",
       "..               ...    ...\n",
       "24        depressing    205\n",
       "57           reality    205\n",
       "69             twist    205\n",
       "50              plot    205\n",
       "22              cute    197\n",
       "64            stupid    190\n",
       "30           fiction    180\n",
       "42          intrigue    168\n",
       "51      pornographic    163\n",
       "38              home    153\n",
       "36          haunting    151\n",
       "4           allegory    139\n",
       "3              adult    128\n",
       "66  thoughtprovoking    120\n",
       "6               anti    118\n",
       "41         inspiring    118\n",
       "19             comic    114\n",
       "14      brainwashing    107\n",
       "55             queer     98\n",
       "17            clever     87\n",
       "16    claustrophobic     84\n",
       "72         whimsical     79\n",
       "29          feelgood     76\n",
       "11    blaxploitation     74\n",
       "35        grindhouse     66\n",
       "65          suicidal     54\n",
       "43           magical     54\n",
       "8      autobiography     44\n",
       "15         christian     41\n",
       "47               non     37\n",
       "\n",
       "[73 rows x 2 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1genres = df1genres[df1genres['tags']!='']\n",
    "df1genres.head(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "TotalCountGenres=sum(genres['Count'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1TotalCountGenres=sum(df1genres['Count'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "genres['Frequency'] = genres['Count']/TotalCountGenres"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1genres['Frequency'] = df1genres['Count']/df1TotalCountGenres"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "genres['CumulativeFrequency'] = genres['Frequency'].cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1genres['CumulativeFrequency'] = df1genres['Frequency'].cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['drama', 'comedy', 'action', 'thriller', 'romance', 'crime',\n",
       "       'musical', 'animation', 'children', 'fantasy', 'mystery',\n",
       "       'biography', 'black', 'history', 'short', 'documentary', 'sports',\n",
       "       'series', 'adult', 'political', 'costume', 'tokusatsu',\n",
       "       'supernatural', 'masala', 'psycho', 'epic', 'satire', 'teen',\n",
       "       'disaster', 'period', 'mythology', 'dark', 'parody', 'science',\n",
       "       'religious', 'literary', 'slice_of_life', 'slasher', 'subject',\n",
       "       'youth', 'propaganda', 'dance', 'devotional', 'feature',\n",
       "       'folklore', 'docudrama', 'anthology', 'kaiju', 'wuxia', 'national',\n",
       "       'board', 'sex', 'slapstick', 'music', 'swashbuckler',\n",
       "       'exploitation', 'love', 'gangster', 'detective', 'screwball',\n",
       "       'fiction', 'neoblack', 'coming_of_age', 'biker', 'road', 'girl',\n",
       "       'live', 'african', 'magical', 'jidaigeki', 'sport',\n",
       "       'sexploitation', 'shakespearean', 'avant_garde', 'pow', 'prison',\n",
       "       'christian', 'experimental', 'monster', 'sociofantasy', 'mecha',\n",
       "       'buddy', 'patriotic', 'operetta', 'yakuza', 'idol', 'zombie',\n",
       "       'chambara', 'postapocalyptic', 'cult', 'charmme', 'raj', \"d'cruz\",\n",
       "       'sword', 'military', 'pink', 'boxing', 'ravi', 'ileana', 'teja',\n",
       "       'campus', 'kalyan', 'reddy', 'raja', 'spoof', 'concert', 'ram',\n",
       "       'srikanth', 'footage', 'found', 'tragedy', 'nature', 'opera',\n",
       "       'reincarnation', 'hit', 'nfb', 'educational', 'patriotism', 'babu',\n",
       "       'triad', 'silent', 'yash', 'medical', 'art', 'school', 'legend',\n",
       "       'imdb', 'bros', 'independent', 'australian', 'steampunk', 'allari',\n",
       "       'story', 'by', 'stopmotion', 'lgbtthemed', 'mob', 'naresh', 's',\n",
       "       'legal', 'jr', 'sociopolitical', 'mythological', 'murder',\n",
       "       'mukherjee', 'space', 'splatter', 'romantic', 'blaxploitation',\n",
       "       'siddharth', 'and', 'revenge', 'shriya', 'remake', 'krishnan',\n",
       "       'cartoon', 'sumanth', 'ntr', 'charlie', 'venu', 'piece', 'trisha',\n",
       "       'race', 'triangle', 'travel', 'pawan', 'police', 'eiga', 'bharath',\n",
       "       'the', 'vampire', 'rajendra', 'fairy', 'portmanteau', 'rao',\n",
       "       'fighting', 'prasad', 'prakash', 'super', 'ali', 'surreal', 'usa',\n",
       "       'chan', 'futuristic', 'tale', 'samskruti', 'scarlett', 'saali',\n",
       "       'productions', 'sada', 'saloni', 'pseudodocumentary', 'sameera',\n",
       "       'priyadarshini', 'balakrishna', 'saran', 'priyanka', 'psy',\n",
       "       'sandhya', 'rock', 'roy', 'rajasekhar', 'red', 'reality', 'reema',\n",
       "       'reese', 'ramanaidu', 'raman', 'bajwa', 'rajiv', 'rahman',\n",
       "       'balaji', 'revolutionary', 'rights', 'rishi', 'raghava', 'august',\n",
       "       'radhika', 'aviation', 'punk', \"auli'i\", 'sivaji', 'semistaged',\n",
       "       'aids', 'tolani', 'agarwal', 'timetravelling', 'time', 'ahlawat',\n",
       "       'thai', 'television', 'tapas', 'sen', 'tabu', 'swashbuckling',\n",
       "       'ajay', 'survival', 'surf', 'sunil', 'summer', 'tragerdy',\n",
       "       'tragic', 'travelogue', 'trilogy', 'zindagi', 'yuen', '25', 'yeh',\n",
       "       'acclaimed', 'adaptation', 'witherspoon', 'with', 'viswendar',\n",
       "       'vimala', 'vigilante', 'urban', 'united', 'uday', 'true',\n",
       "       'sukachenko', 'suhasini', 'stylish', 'sinje', 'shraddha', 'arti',\n",
       "       'shetty', 'shenoy', 'shashank', 'sharwanand', 'sharma', 'artists',\n",
       "       'sf', 'arya', 'asin', 'series\\\\', 'aswani', 'sergey', 'sentiment',\n",
       "       'sindhu', 'siva', 'studios', 'priquitianism', 'studio', 'student',\n",
       "       'stop', 'stoner', 'stoker', 'srinivasa', 'amperiani', 'south',\n",
       "       'sood', 'sonu', 'anushka', 'sneha', 'apocalyptic', 'arjun',\n",
       "       'slahser', 'balu', 'bhaskara', 'prehistory', 'prabhu', 'indira',\n",
       "       'immigrant', 'imax', 'hygiene', 'humour', 'human', 'huangmei',\n",
       "       'house', 'holocaust', 'hockey', 'chakraborty', 'hills', 'harem',\n",
       "       'gothic', 'gore', 'gopi', 'chakravarthy', 'inspired',\n",
       "       'interactive', 'international', 'joshi', 'kaur', 'kanakala',\n",
       "       'kamalini', 'kamalinee', 'kalyani', 'k9', 'jyothika', 'johnson',\n",
       "       'inuit', 'johansson', 'can', 'jeet', 'jayasudha', 'james',\n",
       "       'jagapati', 'investigative', 'genelia', 'from', 'chanchal', 'd',\n",
       "       'debashree', 'day', 'dasgupta', 'das', 'cold', 'colonial',\n",
       "       \"d'souza\", 'cyberpunk', 'deva', 'cultural', 'critically',\n",
       "       'comedic', 'cravalho', 'cowboy', 'counterculture', 'compilation',\n",
       "       'clay', 'classic', 'faria', 'emergency', 'charmy', 'chawla',\n",
       "       'event', 'chillies', 'environmental', 'entertainment',\n",
       "       'entertainer', 'dystopia', 'dharma', 'dwayne', 'duology',\n",
       "       'chiranjeevi', 'doublerole', 'chopra', 'chowdhury', 'cinema',\n",
       "       'keiji', \"ken'ichi\", 'khokhlova', 'biju', 'oleg', 'occult',\n",
       "       'nusrat', 'novel', 'bhoomika', 'nedumudi', 'nayantara',\n",
       "       'narayanan', 'bhavana', 'narayan', 'nandamuri', 'nagarjuna',\n",
       "       'nadeemshravan', 'nabila', 'bond', 'br', 'olga', 'outlaw',\n",
       "       'motion', 'perodic', 'prabhas', 'barua', 'poonam', 'pooja', 'bbc',\n",
       "       'play', 'physiological', 'bhagavaan', 'p', 'paul', 'passion',\n",
       "       'partha', 'conspiracy', 'paramount', 'panorama', 'padmapriya',\n",
       "       'mukesh', 'brahmanandam', 'khushboo', 'krishna', 'lgbtrelated',\n",
       "       'lgbt', 'lee', 'lawrence', 'lakshmana', 'laikei', 'krishnashankar',\n",
       "       'krapiventsev', 'camp', 'koyuki', 'kothari', 'kota', 'korean',\n",
       "       'kondavalasa', 'kiran', 'kids', 'liberation', 'business', 'mohit',\n",
       "       'mazhar', 'mohanlal', 'modern', 'mocatsu', 'mimi', '16\\xa0mm',\n",
       "       'meera', 'bram', 'maysuyama', 'made', 'masuma', 'bramhanandam',\n",
       "       'mammootty', 'malyana'], dtype=object)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(genres[genres['CumulativeFrequency']<=.9999]['Genre'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "genres[genres['CumulativeFrequency']<=.9999][['Genre','Count']].plot(x='Genre', y='Count', kind='bar', legend=False, grid=True, figsize=(8, 5))\n",
    "plt.title(\"Number of movies per genre\")\n",
    "plt.ylabel('# of Occurrences', fontsize=12)\n",
    "plt.xlabel('Movie genres', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['murder', 'violence', 'flashback', 'romance', 'cult', 'revenge',\n",
       "       'comedy', 'psychedelic', 'action', 'actionful', 'versus', 'good',\n",
       "       'evil', 'drama', 'humor', 'satire', 'entertaining', 'black', 'neo',\n",
       "       'sadist', 'insanity', 'tragedy', 'fantasy', 'paranormal', 'boring',\n",
       "       'mystery', 'history', 'thriller', 'cruelty', 'gothic', 'dark',\n",
       "       'atmospheric', 'storytelling', 'psycho', 'alternate', 'absurd',\n",
       "       'realism', 'prank', 'sentimental', 'philosophical', 'avant',\n",
       "       'garde', 'bleak', 'depressing', 'reality', 'twist', 'plot', 'cute',\n",
       "       'stupid', 'fiction', 'intrigue', 'pornographic', 'home',\n",
       "       'haunting', 'allegory', 'adult', 'thoughtprovoking', 'anti',\n",
       "       'inspiring', 'comic', 'brainwashing', 'queer', 'clever',\n",
       "       'claustrophobic', 'whimsical', 'feelgood', 'blaxploitation',\n",
       "       'grindhouse', 'suicidal', 'magical', 'autobiography', 'christian'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(df1genres[df1genres['CumulativeFrequency']<=.9999]['tags'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df1genres[df1genres['CumulativeFrequency']<=.9999][['tags','Count']].plot(x='tags', y='Count', kind='bar', legend=False, grid=True, figsize=(8, 5))\n",
    "plt.title(\"Number of movies per tag\")\n",
    "plt.ylabel('# of Occurrences', fontsize=12)\n",
    "plt.xlabel('Movie genres', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "mainGenres=np.array(genres[genres['CumulativeFrequency']<=.9999]['Genre'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1mainGenres=np.array(df1genres[df1genres['CumulativeFrequency']<=.9999]['tags'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['adult', 'romance', 'drama', 'and'], dtype='<U7')"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr1=np.array(['adult', 'romance', 'drama','and'])\n",
    "arr1[np.in1d(arr1,mainGenres)] # genres not in the mainGenres array will be deleted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1                         []\n",
       "2                         []\n",
       "3                         []\n",
       "4                         []\n",
       "5                         []\n",
       "6                   [action]\n",
       "7                   [comedy]\n",
       "8                         []\n",
       "9                         []\n",
       "10                   [short]\n",
       "11    [action, crime, short]\n",
       "Name: GenreSplit, dtype: object"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies['GenreSplit'][1:12].apply(lambda x: x[np.in1d(x,mainGenres)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1                                            [violence]\n",
       "2                                             [romance]\n",
       "3                [feelgood, inspiring, romance, stupid]\n",
       "4     [action, atmospheric, cruelty, cult, drama, mu...\n",
       "5                           [flashback, queer, romance]\n",
       "6     [cruelty, cult, gothic, revenge, sadist, viole...\n",
       "7       [action, actionful, flashback, murder, mystery]\n",
       "8                                            [violence]\n",
       "9     [black, flashback, murder, neo, revenge, viole...\n",
       "10                                        [cult, prank]\n",
       "11                [cult, murder, psychedelic, violence]\n",
       "Name: GenreSplit, dtype: object"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1['GenreSplit'][1:12].apply(lambda x: x[np.in1d(x,df1mainGenres)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "movies['GenreSplitMain'] = movies['GenreSplit'].apply(lambda x: x[np.in1d(x,mainGenres)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1['GenreSplitMain'] = df1['GenreSplit'].apply(lambda x: x[np.in1d(x,df1mainGenres)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>GenreSplitMain</th>\n",
       "      <th>GenreSplit</th>\n",
       "      <th>Genre</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>[drama]</td>\n",
       "      <td>[drama]</td>\n",
       "      <td>drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>[drama]</td>\n",
       "      <td>[drama]</td>\n",
       "      <td>drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>[comedy]</td>\n",
       "      <td>[comedy]</td>\n",
       "      <td>comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>[drama]</td>\n",
       "      <td>[drama]</td>\n",
       "      <td>drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>[comedy]</td>\n",
       "      <td>[comedy]</td>\n",
       "      <td>comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>[comedy, drama]</td>\n",
       "      <td>[comedy, drama]</td>\n",
       "      <td>comedy drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>[mystery]</td>\n",
       "      <td>[mystery]</td>\n",
       "      <td>mystery</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>207</th>\n",
       "      <td>[drama]</td>\n",
       "      <td>[drama]</td>\n",
       "      <td>drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>208</th>\n",
       "      <td>[drama]</td>\n",
       "      <td>[drama]</td>\n",
       "      <td>drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209</th>\n",
       "      <td>[comedy, drama]</td>\n",
       "      <td>[comedy, drama]</td>\n",
       "      <td>comedy drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210</th>\n",
       "      <td>[comedy]</td>\n",
       "      <td>[comedy]</td>\n",
       "      <td>comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211</th>\n",
       "      <td>[fantasy]</td>\n",
       "      <td>[fantasy]</td>\n",
       "      <td>fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>212</th>\n",
       "      <td>[drama]</td>\n",
       "      <td>[drama]</td>\n",
       "      <td>drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>213</th>\n",
       "      <td>[action, drama]</td>\n",
       "      <td>[action, drama]</td>\n",
       "      <td>war drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>214</th>\n",
       "      <td>[action, drama]</td>\n",
       "      <td>[action, drama]</td>\n",
       "      <td>war drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>215</th>\n",
       "      <td>[action]</td>\n",
       "      <td>[action]</td>\n",
       "      <td>adventure</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216</th>\n",
       "      <td>[comedy, romance]</td>\n",
       "      <td>[comedy, romance]</td>\n",
       "      <td>romantic comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217</th>\n",
       "      <td>[propaganda]</td>\n",
       "      <td>[propaganda]</td>\n",
       "      <td>propaganda</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>218</th>\n",
       "      <td>[action, propaganda]</td>\n",
       "      <td>[action, propaganda]</td>\n",
       "      <td>ww1 propaganda</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>219</th>\n",
       "      <td>[action]</td>\n",
       "      <td>[action]</td>\n",
       "      <td>western</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           GenreSplitMain            GenreSplit            Genre\n",
       "200               [drama]               [drama]            drama\n",
       "201               [drama]               [drama]            drama\n",
       "202              [comedy]              [comedy]           comedy\n",
       "203               [drama]               [drama]            drama\n",
       "204              [comedy]              [comedy]           comedy\n",
       "205       [comedy, drama]       [comedy, drama]     comedy drama\n",
       "206             [mystery]             [mystery]          mystery\n",
       "207               [drama]               [drama]            drama\n",
       "208               [drama]               [drama]            drama\n",
       "209       [comedy, drama]       [comedy, drama]     comedy drama\n",
       "210              [comedy]              [comedy]           comedy\n",
       "211             [fantasy]             [fantasy]          fantasy\n",
       "212               [drama]               [drama]            drama\n",
       "213       [action, drama]       [action, drama]        war drama\n",
       "214       [action, drama]       [action, drama]        war drama\n",
       "215              [action]              [action]        adventure\n",
       "216     [comedy, romance]     [comedy, romance]  romantic comedy\n",
       "217          [propaganda]          [propaganda]       propaganda\n",
       "218  [action, propaganda]  [action, propaganda]   ww1 propaganda\n",
       "219              [action]              [action]          western"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies[['GenreSplitMain','GenreSplit','Genre']][200:220]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "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>GenreSplitMain</th>\n",
       "      <th>GenreSplit</th>\n",
       "      <th>tags</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>[allegory, cult, fantasy, flashback, psychedel...</td>\n",
       "      <td>[allegory, cult, fantasy, flashback, psychedel...</td>\n",
       "      <td>fantasy, allegory, cult, flashback, psychedeli...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>[boring, cult, drama, fantasy, flashback, insa...</td>\n",
       "      <td>[boring, cult, drama, fantasy, flashback, insa...</td>\n",
       "      <td>fantasy, psychological, murder, boring, cult, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>[black, flashback, murder, neo]</td>\n",
       "      <td>[black, flashback, murder, neo]</td>\n",
       "      <td>neo noir, murder, flashback</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>[cruelty, cult, violence]</td>\n",
       "      <td>[cruelty, cult, violence]</td>\n",
       "      <td>cult, cruelty, violence</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>[psychedelic]</td>\n",
       "      <td>[psychedelic]</td>\n",
       "      <td>psychedelic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>[cult]</td>\n",
       "      <td>[cult]</td>\n",
       "      <td>cult</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>[evil, good, paranormal, versus]</td>\n",
       "      <td>[evil, good, paranormal, versus]</td>\n",
       "      <td>good versus evil, paranormal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>207</th>\n",
       "      <td>[murder]</td>\n",
       "      <td>[murder]</td>\n",
       "      <td>murder</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>208</th>\n",
       "      <td>[cute, entertaining, psychedelic, romance]</td>\n",
       "      <td>[cute, entertaining, psychedelic, romance]</td>\n",
       "      <td>psychedelic, cute, entertaining, romantic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>209</th>\n",
       "      <td>[actionful, black, flashback, murder, neo, sad...</td>\n",
       "      <td>[actionful, black, flashback, murder, neo, sad...</td>\n",
       "      <td>neo noir, murder, violence, flashback, suspens...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>210</th>\n",
       "      <td>[psychedelic]</td>\n",
       "      <td>[psychedelic]</td>\n",
       "      <td>psychedelic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211</th>\n",
       "      <td>[action, actionful, alternate, black, bleak, c...</td>\n",
       "      <td>[action, actionful, alternate, black, bleak, c...</td>\n",
       "      <td>mystery, gothic, murder, neo noir, bleak, dram...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>212</th>\n",
       "      <td>[cult, insanity, violence]</td>\n",
       "      <td>[cult, insanity, violence]</td>\n",
       "      <td>insanity, cult, violence</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>213</th>\n",
       "      <td>[action, alternate, anti, atmospheric, comic, ...</td>\n",
       "      <td>[action, alternate, anti, atmospheric, comic, ...</td>\n",
       "      <td>murder, stupid, paranormal, anti war, cult, vi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>214</th>\n",
       "      <td>[atmospheric, bleak, insanity]</td>\n",
       "      <td>[atmospheric, bleak, insanity]</td>\n",
       "      <td>bleak, insanity, atmospheric</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>215</th>\n",
       "      <td>[cult]</td>\n",
       "      <td>[cult]</td>\n",
       "      <td>cult</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216</th>\n",
       "      <td>[entertaining]</td>\n",
       "      <td>[entertaining]</td>\n",
       "      <td>entertaining</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217</th>\n",
       "      <td>[blaxploitation, cult, flashback, history, mur...</td>\n",
       "      <td>[blaxploitation, cult, flashback, history, mur...</td>\n",
       "      <td>murder, cult, violence, flashback, historical,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>218</th>\n",
       "      <td>[actionful, black, cult, flashback, murder, ne...</td>\n",
       "      <td>[actionful, black, cult, flashback, murder, ne...</td>\n",
       "      <td>neo noir, murder, thought-provoking, cult, rev...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>219</th>\n",
       "      <td>[boring, cult, murder]</td>\n",
       "      <td>[boring, cult, murder]</td>\n",
       "      <td>cult, boring, murder</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        GenreSplitMain  \\\n",
       "200  [allegory, cult, fantasy, flashback, psychedel...   \n",
       "201  [boring, cult, drama, fantasy, flashback, insa...   \n",
       "202                    [black, flashback, murder, neo]   \n",
       "203                          [cruelty, cult, violence]   \n",
       "204                                      [psychedelic]   \n",
       "205                                             [cult]   \n",
       "206                   [evil, good, paranormal, versus]   \n",
       "207                                           [murder]   \n",
       "208         [cute, entertaining, psychedelic, romance]   \n",
       "209  [actionful, black, flashback, murder, neo, sad...   \n",
       "210                                      [psychedelic]   \n",
       "211  [action, actionful, alternate, black, bleak, c...   \n",
       "212                         [cult, insanity, violence]   \n",
       "213  [action, alternate, anti, atmospheric, comic, ...   \n",
       "214                     [atmospheric, bleak, insanity]   \n",
       "215                                             [cult]   \n",
       "216                                     [entertaining]   \n",
       "217  [blaxploitation, cult, flashback, history, mur...   \n",
       "218  [actionful, black, cult, flashback, murder, ne...   \n",
       "219                             [boring, cult, murder]   \n",
       "\n",
       "                                            GenreSplit  \\\n",
       "200  [allegory, cult, fantasy, flashback, psychedel...   \n",
       "201  [boring, cult, drama, fantasy, flashback, insa...   \n",
       "202                    [black, flashback, murder, neo]   \n",
       "203                          [cruelty, cult, violence]   \n",
       "204                                      [psychedelic]   \n",
       "205                                             [cult]   \n",
       "206                   [evil, good, paranormal, versus]   \n",
       "207                                           [murder]   \n",
       "208         [cute, entertaining, psychedelic, romance]   \n",
       "209  [actionful, black, flashback, murder, neo, sad...   \n",
       "210                                      [psychedelic]   \n",
       "211  [action, actionful, alternate, black, bleak, c...   \n",
       "212                         [cult, insanity, violence]   \n",
       "213  [action, alternate, anti, atmospheric, comic, ...   \n",
       "214                     [atmospheric, bleak, insanity]   \n",
       "215                                             [cult]   \n",
       "216                                     [entertaining]   \n",
       "217  [blaxploitation, cult, flashback, history, mur...   \n",
       "218  [actionful, black, cult, flashback, murder, ne...   \n",
       "219                             [boring, cult, murder]   \n",
       "\n",
       "                                                  tags  \n",
       "200  fantasy, allegory, cult, flashback, psychedeli...  \n",
       "201  fantasy, psychological, murder, boring, cult, ...  \n",
       "202                        neo noir, murder, flashback  \n",
       "203                            cult, cruelty, violence  \n",
       "204                                        psychedelic  \n",
       "205                                               cult  \n",
       "206                       good versus evil, paranormal  \n",
       "207                                             murder  \n",
       "208          psychedelic, cute, entertaining, romantic  \n",
       "209  neo noir, murder, violence, flashback, suspens...  \n",
       "210                                        psychedelic  \n",
       "211  mystery, gothic, murder, neo noir, bleak, dram...  \n",
       "212                           insanity, cult, violence  \n",
       "213  murder, stupid, paranormal, anti war, cult, vi...  \n",
       "214                       bleak, insanity, atmospheric  \n",
       "215                                               cult  \n",
       "216                                       entertaining  \n",
       "217  murder, cult, violence, flashback, historical,...  \n",
       "218  neo noir, murder, thought-provoking, cult, rev...  \n",
       "219                               cult, boring, murder  "
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1[['GenreSplitMain','GenreSplit','tags']][200:220]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# function for cleaning the plots of the movies\n",
    "def clean_text(text):\n",
    "    text = text.lower()\n",
    "    text = re.sub(r\"what's\", \"what is \", text)\n",
    "    text = re.sub(r\"\\'s\", \" \", text)\n",
    "    text = re.sub(r\"\\'ve\", \" have \", text)\n",
    "    text = re.sub(r\"can't\", \"can not \", text)\n",
    "    text = re.sub(r\"n't\", \" not \", text)\n",
    "    text = re.sub(r\"i'm\", \"i am \", text)\n",
    "    text = re.sub(r\"\\'re\", \" are \", text)\n",
    "    text = re.sub(r\"\\'d\", \" would \", text)\n",
    "    text = re.sub(r\"\\'ll\", \" will \", text)\n",
    "    text = re.sub(r\"\\'scuse\", \" excuse \", text)\n",
    "    #text = re.sub('\\W', ' ', text)\n",
    "    #text = re.sub('\\s+', ' ', text)\n",
    "    text = text.strip(' ')\n",
    "    return text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['the rarebit fiend gorges on welsh rarebit at a restaurant. when he leaves, he begins to get dizzy as he starts to hallucinate. he desperately tries to hang onto a lamppost as the world spins all around him. a man helps him get home. he falls into bed and begins having more hallucinatory dreams. during a dream sequence, the furniture begins moving around the room. imps emerge from a floating welsh rarebit container and begin poking his head as he sleeps. his bed then begins dancing and spinning wildly around the room before flying out the window with the fiend in it. the bed floats across the city as the fiend floats up and off the bed. he hangs off the back and eventually gets caught on a weathervane atop a steeple. his bedclothes tear and he falls from the sky, crashing through his bedroom ceiling. the fiend awakens from the dream after falling out of his bed.',\n",
       " 'the film features a train traveling through the rockies and a hold up created by two thugs placing logs on the line. they systematically rob the wealthy occupants at gunpoint and then make their getaway along the tracks and later by a hi-jacked horse and cart.']"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(movies['Plot'][10:12].apply(clean_text))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['the action takes place in the years 1958-1959 in sweden. troubled 12 year-old ingemar (anton glanzelius) gets into all sorts of trouble, which bothers his mother (anki lidén); ingemar does not know that his mother is in fact terminally ill. when he and his older brother become too much for her, they are split up and sent to live with relatives. ingemar ends up with his maternal uncle gunnar (tomas von brömssen) and his wife ulla (kicki rundgren) in a small rural town in småland. gunnar and ingemar bond over povel ramel  recording of \"far, jag kan inte få upp min kokosnöt\".\\nin the town he encounters a variety of characters. saga (melinda kinnaman), an assertive tomboy his own age, likes him, and shows it by beating him in a boxing match. among the more eccentric residents is fransson (magnus rask), a man who continually fixes the roof of his house, and mr. arvidsson (didrik gustavsson), an old man living downstairs who gets ingemar to read to him from a lingerie catalog.\\nlater, ingemar is reunited with his family, but his mother soon takes a turn for the worse and is hospitalized. he and his brother go to stay with their uncle sandberg (leif ericson) in the city, but his wife thinks the boy is mentally disturbed. after his mother passes away, he is sent back to småland.\\nmr. arvidsson has died in the interim; gunnar and ulla now share the house with a large greek family. gunnar welcomes him and consoles him as best he can, but the house is so crowded, he has ingemar live with mrs. arvidsson in another house. meanwhile, ingemar becomes the object of contention between saga and another girl. when they start fighting over him, he grabs onto saga  leg and starts barking like a dog. she becomes upset by his strange behavior and gets him into the boxing ring. during the bout, out of spite, she tells him that his beloved dog (which he had thought was in a kennel) was actually euthanized. this, along with his mother  death, is too much for him and he locks himself inside gunnar  one-room \"summer house\" in the backyard. while secluded here, ingemar reflects on the death of his mother, the loss of his dog and a changing world. ingemar uses the experiences of others and of his own personal loss to reconcile a life which is sometimes tough.\\nthroughout the film, ingemar tells himself over and over that it could have been worse, reciting several examples, such as a man who took a shortcut onto the field during a track meet and was killed by a javelin and the story of the dog laika several times, the first creature sent into orbit by the russians (without any way to get her back down).\\nthe film ends with the radio broadcast of a famous heavyweight championship boxing match, between swede ingemar johansson and american floyd patterson. when johansson wins, the whole town erupts with joy, but the now-reconciled ingemar and saga are fast asleep together on a couch.',\n",
       " 'at the somafree institute, dr. hal raglan humiliates his patient mike by saying: \"you are  just a weak person. you must have got that from your mother. it probably would have been better for you if you had been born a girl!\" on the dimly lit stage, raglan demands a demonstration of anger, and mike reveals angry red blotches covering his torso. the audience gasps. raglan announces that this is psychoplasmcis; the physical manifestation of mental rage by the appearance of the welts on one  body.meanwhile, frank carveth collects his nine-year-old daughter candy from a \\'private guest room\\'. candy wears a red coat with fur trim and hood. bathing her, frank finds bruises and scratches on her back. he drives to the somafree institute to confront raglan and demands to see his wife nola, whom has been committed there. raglan refuses. frank accuses nola of abusing their daughter and says he will stop candy  next visit. raglan threatens legal action if frank withholds a vital part of nola  treatment. frank then goes to his lawyer, al resnikoff, who tells him that nola has a stronger legal position despite the fact that she is committed to a mental hospital. frank says that he will do what he has to. he takes candy to her maternal grandmother, juliana, who seems highly strung out.back at the institute, raglan goes into nola  room (play-acting as candy) and he asks her why she hurt her daughter candice. raglan/candice says: \"mummies do not  hurt their own children\". nola sobs that they do. she tells raglan that her own mother was \"fucked up and bad\". raglan encourages her anger by telling her: \"go all the way through it, right to the end.\"that evening at juliana  house, she investigates a noise in the kitchen. food, juice, glasses and dishes are thrown all over the floor. she is bludgeoned by what appears to be a small child wearing a red hooded raincoat with fur trim. as candy watches from behind a door, small claw-like hands leaves bloodstains on the banisters. the next morning at his workplace, frank is informed of juliana  murder. police psychologist dr. birkin tells frank so encourage candy to remember what happened, a breakdown is possible if she does not  remember. \"these things tend to express themselves in one way or another\" says dr. birkin.at the institute, raglan speaks to nola as her father. she quickly gets very angry saying: \"you should not  have walked way when she hit me.\" red welts appear on nola  forehead as she speaks.at the local airport, frank and candy meet barton kelly, nola  estranged father, who has come for his ex-wife  funeral. on resnikoff  advice, frank visits jan hartog, an ex-somafree patient taking legal action against dr. raglan. uncovering a row of tumors on his neck, hartog says bitterly: \"raglan encourages my body to revolt against me. and it did. i have a small revolution on my hands and i am  not putting it down very successfully.\"back at the institute, a drunk barton kelly arrives and is furious when raglan will not allow him to see nola. meanwhile, frank arrives at candy  school where her teacher, ruth mayer, sits with her. candy invites ruth to dinner at her house. that evening, ruth arrives and has dinner with frank and candy. a little later, barton calls frank from juliana  house, saying he needs frank  help to see nola. frank leaves ruth babysitting candy, who picks up a book to read which is titled \\'the shape of rage\\'. raglan  book on psychoplasmics.at juliana  house, barton is alone when the same small figure wearing the hooded red raincoat emerges from under his bed and batters him to a very gory death with a pair of paperweights. frank arrives a few minutes later and finds the body. the strange-looking \"child\" jumps on him like some aggressive animal, clawing at him until it suddenly falls off his back to the floor and seemingly dies. frank picks up the phone to call the police. back at frank  house, ruth mayer answers a phone call from nola calling from the institute, who then goes in a berserk rage when ruth answers the phone and suspects that she is having an affair with her husband.at the police station, frank gives his statement and then meets the pathologist who performed the autopsy on the mysterious \\'child creature\\' that attacked him where the doctor tells him that the creature died simply because: \"it ran out of gas\". or more likely \"its batteries expired\". the pathologist points out that the creature has strange eyes, no sexual organs, and no navel. \"this creature has never really been born... at least not the way human beings are born\" says the pathologist.raglan (as ruth mayer) speaks to nola, raglan/ruth says that frank will divorce nola and marry her. the jealous nola screams to leave frank alone. when frank gets home from the police station, ruth leaves in a hurry. he finds candy cowering in a corner of her bedroom after a nightmare. he tells her that the \\'thing\\' is dead.the next morning, raglan reads about barton kelly  murder. taking a gun from his desk, he instructs his assistant, chris, to get all the patients out of the institute. meanwhile, frank visits the hospital where mike is now a fellow patient of hartog  and is told that nola is in fact the only patient at somafree. mike becomes angry about being dumped by his \"daddy\", his face becomes one giant red sore.frank takes candy to school. in ruth mayer  art class, two \\'children creatures\\' in pastel coats with hoods pick up wooden hammers and beat ruth mercilessly to a gory death. alerted by a boy  cries for help, frank enters the classroom to find ruth dead, and candy gone.at the institute, raglan wakes nola. she relates a dream that candy was coming back to her, and says she does not  feel threatened by ruth mayer any more. at the same time, candy is being led along a snowy highway by the two creatures.that evening, mike turns up on frank  doorstep raving about: \"the disturbed kids in the work shed. the ones your wife  taking care of.\" frank drives to the somafree institute where outside raglan pulls a gun on him, saying they will  kill him if he tries to take candy away from them. in a long monologue, raglan tells frank that the child creatures that have been doing all the killing are nola  children. \"nola is not their surrogate mother, she  their real mother.\" they are the brood; the parthenogenesis \\'children\\' of nola  rage. over the past year-and-a-half nola  psychoplasmics have manifested into these children which carry out her bidding whenever she  in an angry mood. raglan tells frank that candy is locked up in the attic where the brood are being kept, and that if he wants candy back, frank must go to nola to convince her that he wants candy back to live with him. if he can do that, the brood will be neutral so raglan can go into the attic and take candy, if not... the brood will attack as long as nola is in a rage.while raglan waits outside the attic door where the brood are being kept, frank goes to nola  room. he finds nola rocking back and forth, wearing a white robe bathed in light. nola wakes from her trance-like state and seems happy that he has come to see her. frank says that he wants to be back with her. (note: throughout the entire movie, nola is only shown from the chest up which is apparently to hide something). at this point, nola throws back her white robe, revealing an umbilical cord and external sac on her abdomen. the years of the welts that have appeared on her chest have merged together to create this womb outside her body as an incubator for the brood that she produces. frank recoils in horror and disgust.in the attic, raglan quietly enters where all the sleeping brood lie in their bunk beds. he finds candy in one and picks her up to carry her out. just then, the brood stirs, sensing nola  mounting anger.in nola  room, she tears open the sac, removing a bloody \\'infant\\' which she licks clean, as an animal might after having given birth. frank  true feelings towards all this become apparent to nola and her rage erupts.in the attic, the brood finally become active, leaping on raglan, who manages to shoot a few of them, but he gets overwhelmed and beaten to death by the brood. in nola  room, she angrily tells frank that she would see candy die then let him take her away. upstairs, the brood then turn their attention to the petrified candy who runs and locks herself in the closet room in the attic, but they break through the door to grab at her. frank attacks nola and strangles her to death with his bare hands to save candy when he hears the noises coming from the attic. when nola stops breathing, all is suddenly silent.frank goes upstairs to the attic to find all the brood dead outside the room where candy locked herself in, realizing that the brood died without their mother  connection. raglan is also dead nearby having been beaten to death and lying next to a few of the brood he managed to kill before being overwhelmed. candy is quivering in a corner of the room and frank enters picks her up and tells her that he is taking her home. as frank drives home with candy, she seems to have withdrawn into a state of shock and cannot speak. frank does not  notice two raised lumps on her left arm... signs of her inner rage. the welts suggests that eventually like mother, like daughter...']"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(df1['plot_synopsis'][10:12].apply(clean_text))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['The Rarebit Fiend gorges on Welsh rarebit at a restaurant. When he leaves, he begins to get dizzy as he starts to hallucinate. He desperately tries to hang onto a lamppost as the world spins all around him. A man helps him get home. He falls into bed and begins having more hallucinatory dreams. During a dream sequence, the furniture begins moving around the room. Imps emerge from a floating Welsh rarebit container and begin poking his head as he sleeps. His bed then begins dancing and spinning wildly around the room before flying out the window with the Fiend in it. The bed floats across the city as the Fiend floats up and off the bed. He hangs off the back and eventually gets caught on a weathervane atop a steeple. His bedclothes tear and he falls from the sky, crashing through his bedroom ceiling. The Fiend awakens from the dream after falling out of his bed.',\n",
       " 'The film features a train traveling through the Rockies and a hold up created by two thugs placing logs on the line. They systematically rob the wealthy occupants at gunpoint and then make their getaway along the tracks and later by a hi-jacked horse and cart.']"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(movies['Plot'][10:12])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "movies['PlotClean'] = movies['Plot'].apply(clean_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1['PlotClean'] = df1['plot_synopsis'].apply(clean_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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>Plot</th>\n",
       "      <th>PlotClean</th>\n",
       "      <th>GenreSplitMain</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>The film opens with two bandits breaking into ...</td>\n",
       "      <td>the film opens with two bandits breaking into ...</td>\n",
       "      <td>[action]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>The film is about a family who move to the sub...</td>\n",
       "      <td>the film is about a family who move to the sub...</td>\n",
       "      <td>[comedy]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>The opening scene shows the interior of the ro...</td>\n",
       "      <td>the opening scene shows the interior of the ro...</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Scenes are introduced using lines of the poem....</td>\n",
       "      <td>scenes are introduced using lines of the poem....</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>The Rarebit Fiend gorges on Welsh rarebit at a...</td>\n",
       "      <td>the rarebit fiend gorges on welsh rarebit at a...</td>\n",
       "      <td>[short]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>The film features a train traveling through th...</td>\n",
       "      <td>the film features a train traveling through th...</td>\n",
       "      <td>[action, crime, short]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 Plot  \\\n",
       "6   The film opens with two bandits breaking into ...   \n",
       "7   The film is about a family who move to the sub...   \n",
       "8   The opening scene shows the interior of the ro...   \n",
       "9   Scenes are introduced using lines of the poem....   \n",
       "10  The Rarebit Fiend gorges on Welsh rarebit at a...   \n",
       "11  The film features a train traveling through th...   \n",
       "\n",
       "                                            PlotClean          GenreSplitMain  \n",
       "6   the film opens with two bandits breaking into ...                [action]  \n",
       "7   the film is about a family who move to the sub...                [comedy]  \n",
       "8   the opening scene shows the interior of the ro...                      []  \n",
       "9   scenes are introduced using lines of the poem....                      []  \n",
       "10  the rarebit fiend gorges on welsh rarebit at a...                 [short]  \n",
       "11  the film features a train traveling through th...  [action, crime, short]  "
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies[['Plot','PlotClean','GenreSplitMain']][6:12]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(movies['GenreSplitMain'][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0\n",
       "1    0\n",
       "2    0\n",
       "3    0\n",
       "4    0\n",
       "Name: GenreSplitMain, dtype: int64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies['GenreSplitMain'][0:5].apply(len)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     5\n",
       "1     1\n",
       "2     1\n",
       "3     4\n",
       "4    10\n",
       "Name: GenreSplitMain, dtype: int64"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1['GenreSplitMain'][0:5].apply(len)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "movies['MainGenresCount'] = movies['GenreSplitMain'].apply(len)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1['MainGenresCount'] = df1['GenreSplitMain'].apply(len)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "movies['MainGenresCount'].hist()\n",
    "\n",
    "plt.title(\"Number of movies by number of genres\")\n",
    "plt.ylabel('# of movies', fontsize=12)\n",
    "plt.xlabel('# of genres', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "df1['MainGenresCount'].hist()\n",
    "\n",
    "plt.title(\"Number of movies by number of tags\")\n",
    "plt.ylabel('# of movies', fontsize=12)\n",
    "plt.xlabel('# of genres', fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize a CountVectorizer object: count_vectorizer\n",
    "count_vec = CountVectorizer(stop_words=\"english\", analyzer='word', \n",
    "                            ngram_range=(1, 1), max_df=1.0, min_df=1, max_features=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>action</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>comedy</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td></td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td></td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>short</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>action</td>\n",
       "      <td>crime</td>\n",
       "      <td>short</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>short</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>biography</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>comedy</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            0      1      2\n",
       "6      action   None   None\n",
       "7      comedy   None   None\n",
       "8               None   None\n",
       "9               None   None\n",
       "10      short   None   None\n",
       "11     action  crime  short\n",
       "12      short   None   None\n",
       "13  biography   None   None\n",
       "14     comedy   None   None"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.GenreSplitMain[6:15].apply(lambda x: '-'.join(x)).str.split(pat='-',n=5,expand=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>cruelty</td>\n",
       "      <td>cult</td>\n",
       "      <td>gothic</td>\n",
       "      <td>revenge</td>\n",
       "      <td>sadist</td>\n",
       "      <td>violence</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>action</td>\n",
       "      <td>actionful</td>\n",
       "      <td>flashback</td>\n",
       "      <td>murder</td>\n",
       "      <td>mystery</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>violence</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>black</td>\n",
       "      <td>flashback</td>\n",
       "      <td>murder</td>\n",
       "      <td>neo</td>\n",
       "      <td>revenge</td>\n",
       "      <td>violence</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>cult</td>\n",
       "      <td>prank</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>cult</td>\n",
       "      <td>murder</td>\n",
       "      <td>psychedelic</td>\n",
       "      <td>violence</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>drama</td>\n",
       "      <td>murder</td>\n",
       "      <td>tragedy</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>murder</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>autobiography</td>\n",
       "      <td>flashback</td>\n",
       "      <td>home</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                0          1            2         3        4         5\n",
       "6         cruelty       cult       gothic   revenge   sadist  violence\n",
       "7          action  actionful    flashback    murder  mystery      None\n",
       "8        violence       None         None      None     None      None\n",
       "9           black  flashback       murder       neo  revenge  violence\n",
       "10           cult      prank         None      None     None      None\n",
       "11           cult     murder  psychedelic  violence     None      None\n",
       "12          drama     murder      tragedy      None     None      None\n",
       "13         murder       None         None      None     None      None\n",
       "14  autobiography  flashback         home      None     None      None"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.GenreSplitMain[6:15].apply(lambda x: '-'.join(x)).str.split(pat='-',n=5,expand=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "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>action</th>\n",
       "      <th>biography</th>\n",
       "      <th>comedy</th>\n",
       "      <th>crime</th>\n",
       "      <th>short</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    action  biography  comedy  crime  short\n",
       "6        1          0       0      0      0\n",
       "7        0          0       1      0      0\n",
       "8        0          0       0      0      0\n",
       "9        0          0       0      0      0\n",
       "10       0          0       0      0      1\n",
       "11       1          0       0      1      1\n",
       "12       0          0       0      0      1\n",
       "13       0          1       0      0      0\n",
       "14       0          0       1      0      0"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.GenreSplitMain[6:15].apply(lambda x: '-'.join(x)).str.get_dummies(sep='-')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>action</th>\n",
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       "      <th>black</th>\n",
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      ],
      "text/plain": [
       "    action  actionful  autobiography  black  cruelty  cult  drama  flashback  \\\n",
       "6        0          0              0      0        1     1      0          0   \n",
       "7        1          1              0      0        0     0      0          1   \n",
       "8        0          0              0      0        0     0      0          0   \n",
       "9        0          0              0      1        0     0      0          1   \n",
       "10       0          0              0      0        0     1      0          0   \n",
       "11       0          0              0      0        0     1      0          0   \n",
       "12       0          0              0      0        0     0      1          0   \n",
       "13       0          0              0      0        0     0      0          0   \n",
       "14       0          0              1      0        0     0      0          1   \n",
       "\n",
       "    gothic  home  murder  mystery  neo  prank  psychedelic  revenge  sadist  \\\n",
       "6        1     0       0        0    0      0            0        1       1   \n",
       "7        0     0       1        1    0      0            0        0       0   \n",
       "8        0     0       0        0    0      0            0        0       0   \n",
       "9        0     0       1        0    1      0            0        1       0   \n",
       "10       0     0       0        0    0      1            0        0       0   \n",
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       "12       0     0       1        0    0      0            0        0       0   \n",
       "13       0     0       1        0    0      0            0        0       0   \n",
       "14       0     1       0        0    0      0            0        0       0   \n",
       "\n",
       "    tragedy  violence  \n",
       "6         0         1  \n",
       "7         0         0  \n",
       "8         0         1  \n",
       "9         0         1  \n",
       "10        0         0  \n",
       "11        0         1  \n",
       "12        1         0  \n",
       "13        0         0  \n",
       "14        0         0  "
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.GenreSplitMain[6:15].apply(lambda x: '-'.join(x)).str.get_dummies(sep='-')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6                   [action]\n",
       "7                   [comedy]\n",
       "8                         []\n",
       "9                         []\n",
       "10                   [short]\n",
       "11    [action, crime, short]\n",
       "12                   [short]\n",
       "13               [biography]\n",
       "14                  [comedy]\n",
       "Name: GenreSplitMain, dtype: object"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies.GenreSplitMain[6:15]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6     [cruelty, cult, gothic, revenge, sadist, viole...\n",
       "7       [action, actionful, flashback, murder, mystery]\n",
       "8                                            [violence]\n",
       "9     [black, flashback, murder, neo, revenge, viole...\n",
       "10                                        [cult, prank]\n",
       "11                [cult, murder, psychedelic, violence]\n",
       "12                             [drama, murder, tragedy]\n",
       "13                                             [murder]\n",
       "14                     [autobiography, flashback, home]\n",
       "Name: GenreSplitMain, dtype: object"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.GenreSplitMain[6:15]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "# the dummy classes\n",
    "movies = pd.concat([movies, movies.GenreSplitMain.apply(lambda x: '-'.join(x)).str.get_dummies(sep='-')], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "# the dummy classes\n",
    "df1 = pd.concat([df1, df1.GenreSplitMain.apply(lambda x: '-'.join(x)).str.get_dummies(sep='-')], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "# the train and the test data set will be build when there is at least one genre for a movie\n",
    "MoviesTrain, MoviesTest = train_test_split(movies[movies.GenreCorrected!=''], random_state=42, test_size=0.30, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "# the train and the test data set will be build when there is at least one genre for a movie\n",
    "df1MoviesTrain, df1MoviesTest = train_test_split(df1[df1.GenreCorrected!=''], random_state=42, test_size=0.30, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "# definition the algorithm for feature extraction\n",
    "tfidf = TfidfVectorizer(stop_words ='english', smooth_idf=False, sublinear_tf=False, norm=None, analyzer='word')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "# building the features\n",
    "x_train = tfidf.fit_transform(MoviesTrain.PlotClean) \n",
    "x_test  = tfidf.transform(MoviesTest.PlotClean)\n",
    "### for test data, the feature extraction will be done through the function transform()\n",
    "### to make sure there is no features dimensionality mismatch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train2 = tfidf.fit_transform(df1MoviesTrain.PlotClean) \n",
    "x_test2 = tfidf.transform(df1MoviesTest.PlotClean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array(['1930s', '1937', 'abilities', 'able', 'accept', 'accepts',\n",
       "        'acquaintance', 'actively', 'adversely', 'affects', 'aggressive',\n",
       "        'agrees', 'aide', 'allow', 'amidst', 'apartment', 'arena', 'army',\n",
       "        'arouses', 'arrives', 'artist', 'artists', 'arts', 'attempts',\n",
       "        'away', 'bag', 'bandit', 'bandits', 'barely', 'barrage', 'battle',\n",
       "        'beaten', 'beating', 'beats', 'blows', 'bruce', 'brutal',\n",
       "        'challenge', 'challenges', 'chance', 'chaos', 'cheered', 'child',\n",
       "        'chinese', 'ching', 'chow', 'chuen', 'chun', 'claimed', 'closed',\n",
       "        'coal', 'colleagues', 'collect', 'colonel', 'come', 'compete',\n",
       "        'competitions', 'consents', 'conserve', 'contain', 'cotton',\n",
       "        'crowd', 'days', 'declines', 'decrepit', 'defeat', 'defeats',\n",
       "        'defends', 'defense', 'demands', 'desperate', 'despite', 'direct',\n",
       "        'disciples', 'discussing', 'displays', 'does', 'door', 'earlier',\n",
       "        'earn', 'effective', 'embarrassing', 'energy', 'enhanced',\n",
       "        'enraged', 'epilogue', 'equally', 'escaped', 'establishes',\n",
       "        'eventually', 'exhibited', 'extort', 'family', 'feels', 'fellow',\n",
       "        'fight', 'fighters', 'fighting', 'figures', 'finally', 'finds',\n",
       "        'flee', 'food', 'forced', 'forshan', 'foshan', 'friend',\n",
       "        'friendly', 'friends', 'gang', 'general', 'grows', 'gun', 'harass',\n",
       "        'harassing', 'having', 'headquarters', 'held', 'help', 'highly',\n",
       "        'hong', 'house', 'hub', 'humiliation', 'igniting', 'impatient',\n",
       "        'impeccable', 'incapacitates', 'including', 'independent',\n",
       "        'inflicting', 'insist', 'instead', 'instruct', 'invasion',\n",
       "        'invisible', 'ip', 'japanese', 'jin', 'karate', 'karateka',\n",
       "        'killed', 'kills', 'kong', 'later', 'learn', 'led', 'lee', 'lets',\n",
       "        'li', 'life', 'lin', 'liu', 'local', 'lose', 'love', 'maintains',\n",
       "        'man', 'martial', 'master', 'masters', 'match', 'matched',\n",
       "        'matches', 'meeting', 'mercilessly', 'midway', 'military', 'miura',\n",
       "        'money', 'moves', 'need', 'northern', 'occupied', 'offers',\n",
       "        'officer', 'overwhelm', 'owns', 'participate', 'personally',\n",
       "        'police', 'practiced', 'pride', 'professional', 'profile',\n",
       "        'protection', 'public', 'rage', 'recruit', 'refuses', 'refusing',\n",
       "        'regional', 'relentless', 'reputation', 'resentful', 'respected',\n",
       "        'restraint', 'return', 'returning', 'reveals', 'rice', 'robbers',\n",
       "        'room', 'rude', 'runs', 'sadistic', 'sato', 'school', 'schools',\n",
       "        'scuffle', 'sees', 'self', 'sends', 'severe', 'shanzhao', 'shoots',\n",
       "        'shoulder', 'showing', 'skill', 'skilled', 'soldiers', 'son',\n",
       "        'southern', 'spared', 'spending', 'spends', 'square', 'students',\n",
       "        'stylists', 'support', 'survived', 'taken', 'takes', 'taught',\n",
       "        'teach', 'techniques', 'tells', 'time', 'trainees', 'training',\n",
       "        'trains', 'translator', 'trying', 'unassuming', 'upholding',\n",
       "        'used', 'using', 'various', 'visits', 'wants', 'warns', 'watch',\n",
       "        'watches', 'wealth', 'wealthy', 'wife', 'win', 'wing', 'winning',\n",
       "        'work', 'workers', 'working', 'zhao'], dtype='<U34')]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tfidf.inverse_transform(x_train[0].toarray())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array(['abandons', 'accident', 'adding', 'address', 'ago', 'ana', 'area',\n",
       "        'arrived', 'asks', 'backpack', 'beer', 'bom', 'boy', 'boys',\n",
       "        'brazil', 'brother', 'brothers', 'bus', 'buses', 'calls',\n",
       "        'carried', 'case', 'catch', 'central', 'come', 'containing',\n",
       "        'corrupt', 'couple', 'current', 'customers', 'deciding',\n",
       "        'departure', 'destitution', 'died', 'dinner', 'disappeared',\n",
       "        'does', 'dora', 'drawer', 'drink', 'drinking', 'driver', 'drunken',\n",
       "        'embittered', 'encourages', 'ends', 'evangelical', 'explains',\n",
       "        'expressing', 'family', 'father', 'feel', 'follows', 'forgetting',\n",
       "        'friend', 'friendly', 'geraldo', 'gets', 'gone', 'grows', 'guilty',\n",
       "        'half', 'hangs', 'homeless', 'hopes', 'house', 'illiterate',\n",
       "        'impatient', 'initially', 'insists', 'introduced', 'introducing',\n",
       "        'invites', 'irene', 'isaías', 'janeiro', 'jesus', 'josué', 'just',\n",
       "        'killed', 'kind', 'know', 'late', 'later', 'learns', 'leave',\n",
       "        'leaves', 'leaving', 'left', 'letter', 'letters', 'live', 'lives',\n",
       "        'locate', 'longer', 'looking', 'looks', 'lost', 'lottery', 'lover',\n",
       "        'mail', 'mailed', 'make', 'married', 'massive', 'meet', 'met',\n",
       "        'moisés', 'money', 'months', 'morning', 'mother', 'neighbor',\n",
       "        'new', 'norte', 'northeast', 'old', 'order', 'outside', 'pauses',\n",
       "        'penniless', 'photos', 'picked', 'pilgrimage', 'pilgrims', 'point',\n",
       "        'poor', 'posts', 'pregnant', 'prevent', 'promises', 'putting',\n",
       "        'read', 'refrigerator', 'reluctant', 'remember', 'residents',\n",
       "        'responsible', 'retired', 'return', 'returned', 'reunite',\n",
       "        'reunited', 'ride', 'rio', 'roof', 'saves', 'say', 'saying',\n",
       "        'says', 'schoolteacher', 'sell', 'sends', 'settled', 'settlements',\n",
       "        'sleep', 'sneaks', 'sofa', 'son', 'sons', 'soon', 'station',\n",
       "        'steals', 'stop', 'stranger', 'suggesting', 'suspicious', 'taken',\n",
       "        'takes', 'tearing', 'television', 'tells', 'think', 'time', 'told',\n",
       "        'trades', 'traffics', 'train', 'tries', 'trip', 'truck', 'wait',\n",
       "        'wakes', 'watch', 'wish', 'won', 'working', 'works', 'write',\n",
       "        'writes', 'writing', 'wrote', 'year', 'years'], dtype='<U63')]"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tfidf.inverse_transform(x_train2[0].toarray())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "# building the classes\n",
    "y_train = MoviesTrain[MoviesTrain.columns[14:]]\n",
    "y_test = MoviesTest[MoviesTest.columns[14:]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "# building the classes\n",
    "y_train2 = df1MoviesTrain[df1MoviesTrain.columns[14:]]\n",
    "y_test2 = df1MoviesTest[df1MoviesTest.columns[14:]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "multinomialNB=OneVsRestClassifier(MultinomialNB(fit_prior=True, class_prior=None))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Genre</th>\n",
       "      <th>accuracy_multinomialNB</th>\n",
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   "source": [
    "accuracy_multinomialNB=pd.DataFrame(columns=['Genre', 'accuracy_multinomialNB'])\n",
    "accuracy_multinomialNB.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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      "/home/albert/anaconda3/lib/python3.7/site-packages/sklearn/multiclass.py:76: UserWarning: Label not 0 is present in all training examples.\n",
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      "/home/albert/anaconda3/lib/python3.7/site-packages/sklearn/multiclass.py:76: UserWarning: Label not 0 is present in all training examples.\n",
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      "/home/albert/anaconda3/lib/python3.7/site-packages/sklearn/multiclass.py:76: UserWarning: Label not 0 is present in all training examples.\n",
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     ]
    },
    {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Genre</th>\n",
       "      <th>accuracy_multinomialNB</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>drama</td>\n",
       "      <td>0.673756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>comedy</td>\n",
       "      <td>0.733147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>action</td>\n",
       "      <td>0.826592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>thriller</td>\n",
       "      <td>0.884589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>romance</td>\n",
       "      <td>0.843096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>crime</td>\n",
       "      <td>0.911088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>musical</td>\n",
       "      <td>0.937587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>animation</td>\n",
       "      <td>0.973849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>children</td>\n",
       "      <td>0.957578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>fantasy</td>\n",
       "      <td>0.974314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>mystery</td>\n",
       "      <td>0.974314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>biography</td>\n",
       "      <td>0.967573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>black</td>\n",
       "      <td>0.971292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>history</td>\n",
       "      <td>0.98245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>short</td>\n",
       "      <td>0.988726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>documentary</td>\n",
       "      <td>0.992562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>sports</td>\n",
       "      <td>0.991399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>series</td>\n",
       "      <td>0.996048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>adult</td>\n",
       "      <td>0.995932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>political</td>\n",
       "      <td>0.995932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>costume</td>\n",
       "      <td>0.997327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>tokusatsu</td>\n",
       "      <td>0.998024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>supernatural</td>\n",
       "      <td>0.996397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>masala</td>\n",
       "      <td>0.995816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>psycho</td>\n",
       "      <td>0.997675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>epic</td>\n",
       "      <td>0.997675</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>satire</td>\n",
       "      <td>0.998257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>teen</td>\n",
       "      <td>0.997908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>disaster</td>\n",
       "      <td>0.997792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>period</td>\n",
       "      <td>0.997559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>425</th>\n",
       "      <td>lawrence</td>\n",
       "      <td>0.999768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426</th>\n",
       "      <td>lakshmana</td>\n",
       "      <td>0.999768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>427</th>\n",
       "      <td>laikei</td>\n",
       "      <td>0.999768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>428</th>\n",
       "      <td>krishnashankar</td>\n",
       "      <td>0.999884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>429</th>\n",
       "      <td>krapiventsev</td>\n",
       "      <td>0.999884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>430</th>\n",
       "      <td>camp</td>\n",
       "      <td>0.999651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>431</th>\n",
       "      <td>koyuki</td>\n",
       "      <td>0.999768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>432</th>\n",
       "      <td>kothari</td>\n",
       "      <td>0.999768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>433</th>\n",
       "      <td>kota</td>\n",
       "      <td>0.999768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>434</th>\n",
       "      <td>korean</td>\n",
       "      <td>0.999768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>435</th>\n",
       "      <td>kondavalasa</td>\n",
       "      <td>0.999768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>436</th>\n",
       "      <td>kiran</td>\n",
       "      <td>0.999768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>437</th>\n",
       "      <td>kids</td>\n",
       "      <td>0.999768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>438</th>\n",
       "      <td>liberation</td>\n",
       "      <td>0.999884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>439</th>\n",
       "      <td>business</td>\n",
       "      <td>0.999768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>440</th>\n",
       "      <td>mohit</td>\n",
       "      <td>0.999768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>441</th>\n",
       "      <td>mazhar</td>\n",
       "      <td>0.999768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>442</th>\n",
       "      <td>mohanlal</td>\n",
       "      <td>0.999768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>443</th>\n",
       "      <td>modern</td>\n",
       "      <td>0.999768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>444</th>\n",
       "      <td>mocatsu</td>\n",
       "      <td>0.999768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>445</th>\n",
       "      <td>mimi</td>\n",
       "      <td>0.999768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>446</th>\n",
       "      <td>16 mm</td>\n",
       "      <td>0.999651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>447</th>\n",
       "      <td>meera</td>\n",
       "      <td>0.999768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>448</th>\n",
       "      <td>bram</td>\n",
       "      <td>0.999768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>449</th>\n",
       "      <td>maysuyama</td>\n",
       "      <td>0.999768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>450</th>\n",
       "      <td>made</td>\n",
       "      <td>0.999884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>451</th>\n",
       "      <td>masuma</td>\n",
       "      <td>0.999768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>452</th>\n",
       "      <td>bramhanandam</td>\n",
       "      <td>0.999768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>453</th>\n",
       "      <td>mammootty</td>\n",
       "      <td>0.999768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>454</th>\n",
       "      <td>malyana</td>\n",
       "      <td>0.999884</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>455 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Genre accuracy_multinomialNB\n",
       "0             drama               0.673756\n",
       "1            comedy               0.733147\n",
       "2            action               0.826592\n",
       "3          thriller               0.884589\n",
       "4           romance               0.843096\n",
       "5             crime               0.911088\n",
       "6           musical               0.937587\n",
       "7         animation               0.973849\n",
       "8          children               0.957578\n",
       "9           fantasy               0.974314\n",
       "10          mystery               0.974314\n",
       "11        biography               0.967573\n",
       "12            black               0.971292\n",
       "13          history                0.98245\n",
       "14            short               0.988726\n",
       "15      documentary               0.992562\n",
       "16           sports               0.991399\n",
       "17           series               0.996048\n",
       "18            adult               0.995932\n",
       "19        political               0.995932\n",
       "20          costume               0.997327\n",
       "21        tokusatsu               0.998024\n",
       "22     supernatural               0.996397\n",
       "23           masala               0.995816\n",
       "24           psycho               0.997675\n",
       "25             epic               0.997675\n",
       "26           satire               0.998257\n",
       "27             teen               0.997908\n",
       "28         disaster               0.997792\n",
       "29           period               0.997559\n",
       "..              ...                    ...\n",
       "425        lawrence               0.999768\n",
       "426       lakshmana               0.999768\n",
       "427          laikei               0.999768\n",
       "428  krishnashankar               0.999884\n",
       "429    krapiventsev               0.999884\n",
       "430            camp               0.999651\n",
       "431          koyuki               0.999768\n",
       "432         kothari               0.999768\n",
       "433            kota               0.999768\n",
       "434          korean               0.999768\n",
       "435     kondavalasa               0.999768\n",
       "436           kiran               0.999768\n",
       "437            kids               0.999768\n",
       "438      liberation               0.999884\n",
       "439        business               0.999768\n",
       "440           mohit               0.999768\n",
       "441          mazhar               0.999768\n",
       "442        mohanlal               0.999768\n",
       "443          modern               0.999768\n",
       "444         mocatsu               0.999768\n",
       "445            mimi               0.999768\n",
       "446           16 mm               0.999651\n",
       "447           meera               0.999768\n",
       "448            bram               0.999768\n",
       "449       maysuyama               0.999768\n",
       "450            made               0.999884\n",
       "451          masuma               0.999768\n",
       "452    bramhanandam               0.999768\n",
       "453       mammootty               0.999768\n",
       "454         malyana               0.999884\n",
       "\n",
       "[455 rows x 2 columns]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "i = 0\n",
    "for genre in mainGenres:\n",
    "    multinomialNB.fit(x_train, y_train[genre])\n",
    "    prediction = multinomialNB.predict(x_test)\n",
    "    accuracy_multinomialNB.loc[i,'Genre'] = genre\n",
    "    accuracy_multinomialNB.loc[i,'accuracy_multinomialNB'] = accuracy_score(y_test[genre], prediction)\n",
    "    i=i+1\n",
    "    \n",
    "\n",
    "    \n",
    "accuracy_multinomialNB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "predict() missing 1 required positional argument: 'X'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-69-b124f0481aac>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmultinomialNB\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m: predict() missing 1 required positional argument: 'X'"
     ]
    }
   ],
   "source": [
    "multinomialNB.predict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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