student.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Final Project Submission\n",
    "\n",
    "Please fill out:\n",
    "* Student name: Kaliku Sharon Fatuma\n",
    "* Student pace:  full time\n",
    "* Scheduled project review date/time: 15/09/2023\n",
    "* Instructor name: Nikita Njoroge\n",
    "* Blog post URL: \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Microsoft  wants to establish their own movie studio just like many big companies and compete within the digital content market.\n",
    "\n",
    "To assist Microsoft in solving this problem, I will consider:\n",
    "\n",
    "How many movies are being made of each genre?\n",
    "What genres are the most successful, both in terms of audience rating and gross earnings?\n",
    "Of the top grossing films, which genres were most successful (had the highest gross earnings)?\n",
    "Answering these questions will allow Microsoft to better decide what kind of movies to create in order to match its competitors."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data Understanding\n",
    "The data for this analysis is from:\n",
    "\n",
    "1. Box Office Mojo (movie title, gross earnings)\n",
    "2. The Movie Database (movie title, audience rating)\n",
    "3. IMDb (movie title, release year, genres, and audience rating)\n",
    "\n",
    "The target variables for this analysis are:\n",
    "1. Release year\n",
    "2. Gross earnings\n",
    "3. Genres \n",
    "4. Audience ratings\n",
    "\n",
    "These variables are all important and necessary to address Microsoft's business problem. Audience ratings, gross earnings, and release year are numeric variables and genres are categorical.\n",
    "\n",
    "The data used in this analysis has been compiled from three different platforms and includes all movies these platforms monitor. Since we are providing Microsoft with recommendations for success, we can safely assume these platforms accurately account for the movies of interest.\n",
    "\n",
    "As we consider this data however, we should note that the data includes movies from 2010 to 2018. This is noteworthy because the data does not include the most recent three years of movie-making; since movie-watching trends can shift dramatically year to year, the conclusions and recommendations presented here may change based on more recent movies."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sqlite3 \n",
    "import seaborn as sns \n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "%matplotlib inline "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Loading the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>studio</th>\n",
       "      <th>domestic_gross</th>\n",
       "      <th>foreign_gross</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Toy Story 3</td>\n",
       "      <td>BV</td>\n",
       "      <td>415000000.0</td>\n",
       "      <td>652000000</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alice in Wonderland (2010)</td>\n",
       "      <td>BV</td>\n",
       "      <td>334200000.0</td>\n",
       "      <td>691300000</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Harry Potter and the Deathly Hallows Part 1</td>\n",
       "      <td>WB</td>\n",
       "      <td>296000000.0</td>\n",
       "      <td>664300000</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Inception</td>\n",
       "      <td>WB</td>\n",
       "      <td>292600000.0</td>\n",
       "      <td>535700000</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Shrek Forever After</td>\n",
       "      <td>P/DW</td>\n",
       "      <td>238700000.0</td>\n",
       "      <td>513900000</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3382</th>\n",
       "      <td>The Quake</td>\n",
       "      <td>Magn.</td>\n",
       "      <td>6200.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3383</th>\n",
       "      <td>Edward II (2018 re-release)</td>\n",
       "      <td>FM</td>\n",
       "      <td>4800.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3384</th>\n",
       "      <td>El Pacto</td>\n",
       "      <td>Sony</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3385</th>\n",
       "      <td>The Swan</td>\n",
       "      <td>Synergetic</td>\n",
       "      <td>2400.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3386</th>\n",
       "      <td>An Actor Prepares</td>\n",
       "      <td>Grav.</td>\n",
       "      <td>1700.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3387 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            title      studio  domestic_gross  \\\n",
       "0                                     Toy Story 3          BV     415000000.0   \n",
       "1                      Alice in Wonderland (2010)          BV     334200000.0   \n",
       "2     Harry Potter and the Deathly Hallows Part 1          WB     296000000.0   \n",
       "3                                       Inception          WB     292600000.0   \n",
       "4                             Shrek Forever After        P/DW     238700000.0   \n",
       "...                                           ...         ...             ...   \n",
       "3382                                    The Quake       Magn.          6200.0   \n",
       "3383                  Edward II (2018 re-release)          FM          4800.0   \n",
       "3384                                     El Pacto        Sony          2500.0   \n",
       "3385                                     The Swan  Synergetic          2400.0   \n",
       "3386                            An Actor Prepares       Grav.          1700.0   \n",
       "\n",
       "     foreign_gross  year  \n",
       "0        652000000  2010  \n",
       "1        691300000  2010  \n",
       "2        664300000  2010  \n",
       "3        535700000  2010  \n",
       "4        513900000  2010  \n",
       "...            ...   ...  \n",
       "3382           NaN  2018  \n",
       "3383           NaN  2018  \n",
       "3384           NaN  2018  \n",
       "3385           NaN  2018  \n",
       "3386           NaN  2018  \n",
       "\n",
       "[3387 rows x 5 columns]"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BOM_movies = pd.read_csv(\"./zippedData/bom.movie_gross.csv\")\n",
    "BOM_movies"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Previewing the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>studio</th>\n",
       "      <th>domestic_gross</th>\n",
       "      <th>foreign_gross</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Toy Story 3</td>\n",
       "      <td>BV</td>\n",
       "      <td>415000000.0</td>\n",
       "      <td>652000000</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alice in Wonderland (2010)</td>\n",
       "      <td>BV</td>\n",
       "      <td>334200000.0</td>\n",
       "      <td>691300000</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Harry Potter and the Deathly Hallows Part 1</td>\n",
       "      <td>WB</td>\n",
       "      <td>296000000.0</td>\n",
       "      <td>664300000</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Inception</td>\n",
       "      <td>WB</td>\n",
       "      <td>292600000.0</td>\n",
       "      <td>535700000</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Shrek Forever After</td>\n",
       "      <td>P/DW</td>\n",
       "      <td>238700000.0</td>\n",
       "      <td>513900000</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         title studio  domestic_gross  \\\n",
       "0                                  Toy Story 3     BV     415000000.0   \n",
       "1                   Alice in Wonderland (2010)     BV     334200000.0   \n",
       "2  Harry Potter and the Deathly Hallows Part 1     WB     296000000.0   \n",
       "3                                    Inception     WB     292600000.0   \n",
       "4                          Shrek Forever After   P/DW     238700000.0   \n",
       "\n",
       "  foreign_gross  year  \n",
       "0     652000000  2010  \n",
       "1     691300000  2010  \n",
       "2     664300000  2010  \n",
       "3     535700000  2010  \n",
       "4     513900000  2010  "
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking top of the dataset\n",
    "BOM_movies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>studio</th>\n",
       "      <th>domestic_gross</th>\n",
       "      <th>foreign_gross</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3382</th>\n",
       "      <td>The Quake</td>\n",
       "      <td>Magn.</td>\n",
       "      <td>6200.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3383</th>\n",
       "      <td>Edward II (2018 re-release)</td>\n",
       "      <td>FM</td>\n",
       "      <td>4800.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3384</th>\n",
       "      <td>El Pacto</td>\n",
       "      <td>Sony</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3385</th>\n",
       "      <td>The Swan</td>\n",
       "      <td>Synergetic</td>\n",
       "      <td>2400.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3386</th>\n",
       "      <td>An Actor Prepares</td>\n",
       "      <td>Grav.</td>\n",
       "      <td>1700.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            title      studio  domestic_gross foreign_gross  \\\n",
       "3382                    The Quake       Magn.          6200.0           NaN   \n",
       "3383  Edward II (2018 re-release)          FM          4800.0           NaN   \n",
       "3384                     El Pacto        Sony          2500.0           NaN   \n",
       "3385                     The Swan  Synergetic          2400.0           NaN   \n",
       "3386            An Actor Prepares       Grav.          1700.0           NaN   \n",
       "\n",
       "      year  \n",
       "3382  2018  \n",
       "3383  2018  \n",
       "3384  2018  \n",
       "3385  2018  \n",
       "3386  2018  "
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking the bottom of the dataset\n",
    "BOM_movies.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3387, 5)"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BOM_movies.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3387 entries, 0 to 3386\n",
      "Data columns (total 5 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   title           3387 non-null   object \n",
      " 1   studio          3382 non-null   object \n",
      " 2   domestic_gross  3359 non-null   float64\n",
      " 3   foreign_gross   2037 non-null   object \n",
      " 4   year            3387 non-null   int64  \n",
      "dtypes: float64(1), int64(1), object(3)\n",
      "memory usage: 132.4+ KB\n"
     ]
    }
   ],
   "source": [
    "BOM_movies.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "title             3387\n",
       "studio            3382\n",
       "domestic_gross    3359\n",
       "foreign_gross     2037\n",
       "year              3387\n",
       "dtype: int64"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking number of entries in each column\n",
    "BOM_movies.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>domestic_gross</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3.359000e+03</td>\n",
       "      <td>3387.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.874585e+07</td>\n",
       "      <td>2013.958075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.698250e+07</td>\n",
       "      <td>2.478141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000e+02</td>\n",
       "      <td>2010.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.200000e+05</td>\n",
       "      <td>2012.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.400000e+06</td>\n",
       "      <td>2014.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.790000e+07</td>\n",
       "      <td>2016.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.367000e+08</td>\n",
       "      <td>2018.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       domestic_gross         year\n",
       "count    3.359000e+03  3387.000000\n",
       "mean     2.874585e+07  2013.958075\n",
       "std      6.698250e+07     2.478141\n",
       "min      1.000000e+02  2010.000000\n",
       "25%      1.200000e+05  2012.000000\n",
       "50%      1.400000e+06  2014.000000\n",
       "75%      2.790000e+07  2016.000000\n",
       "max      9.367000e+08  2018.000000"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#summary of descriptive statistics \n",
    "BOM_movies.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['title', 'studio', 'domestic_gross', 'foreign_gross', 'year'], dtype='object')"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BOM_movies.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Toy Story 3', 'Alice in Wonderland (2010)',\n",
       "       'Harry Potter and the Deathly Hallows Part 1', ..., 'El Pacto',\n",
       "       'The Swan', 'An Actor Prepares'], dtype=object)"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BOM_movies.title.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['BV', 'WB', 'P/DW', 'Sum.', 'Par.', 'Uni.', 'Fox', 'Wein.', 'Sony',\n",
       "       'FoxS', 'SGem', 'WB (NL)', 'LGF', 'MBox', 'CL', 'W/Dim.', 'CBS',\n",
       "       'Focus', 'MGM', 'Over.', 'Mira.', 'IFC', 'CJ', 'NM', 'SPC', 'ParV',\n",
       "       'Gold.', 'JS', 'RAtt.', 'Magn.', 'Free', '3D', 'UTV', 'Rela.',\n",
       "       'Zeit.', 'Anch.', 'PDA', 'Lorb.', 'App.', 'Drft.', 'Osci.', 'IW',\n",
       "       'Rog.', nan, 'Eros', 'Relbig.', 'Viv.', 'Hann.', 'Strand', 'NGE',\n",
       "       'Scre.', 'Kino', 'Abr.', 'CZ', 'ATO', 'First', 'GK', 'FInd.',\n",
       "       'NFC', 'TFC', 'Pala.', 'Imag.', 'NAV', 'Arth.', 'CLS', 'Mont.',\n",
       "       'Olive', 'CGld', 'FOAK', 'IVP', 'Yash', 'ICir', 'FM', 'Vita.',\n",
       "       'WOW', 'Truly', 'Indic.', 'FD', 'Vari.', 'TriS', 'ORF', 'IM',\n",
       "       'Elev.', 'Cohen', 'NeoC', 'Jan.', 'MNE', 'Trib.', 'Rocket',\n",
       "       'OMNI/FSR', 'KKM', 'Argo.', 'SMod', 'Libre', 'FRun', 'WHE', 'P4',\n",
       "       'KC', 'SD', 'AM', 'MPFT', 'Icar.', 'AGF', 'A23', 'Da.', 'NYer',\n",
       "       'Rialto', 'DF', 'KL', 'ALP', 'LG/S', 'WGUSA', 'MPI', 'RTWC', 'FIP',\n",
       "       'RF', 'ArcEnt', 'PalUni', 'EpicPics', 'EOne', 'LD', 'AF', 'TFA',\n",
       "       'Myr.', 'BM&DH', 'SEG', 'PalT', 'Outs', 'OutF', 'BSM', 'WAMCR',\n",
       "       'PM&E', 'A24', 'Cdgm.', 'Distrib.', 'Imax', 'PH', 'HTR', 'ELS',\n",
       "       'PI', 'E1', 'TVC', 'FEF', 'EXCL', 'MSF', 'P/108', 'FCW', 'XL',\n",
       "       'Shout!', 'SV', 'CE', 'VPD', 'KE', 'Saban', 'CF&SR', 'Triu', 'DR',\n",
       "       'Crnth', 'Ampl.', 'CP', 'Proud', 'BGP', 'Abk.', 'DLA', 'B360',\n",
       "       'BWP', 'SEA', 'RME', 'KS', 'VE', 'LGP', 'EC', 'FUN', 'STX', 'AR',\n",
       "       'BG', 'PFR', 'BST', 'BH Tilt', 'BSC', 'U/P', 'UHE', 'CLF', 'FR',\n",
       "       'AaF', 'Orch.', 'Alc', 'PBS', 'SHO', 'Grav.', 'Gathr', 'Asp.',\n",
       "       'ADC', 'Rel.', 'SM', 'AZ', 'UEP', 'ITL', 'TA', 'MR', 'BBC',\n",
       "       'CFilms', 'Part.', 'FOR', 'TAFC', 'JBG', 'PNT', 'CineGalaxy',\n",
       "       'Fathom', 'Zee', 'Men.', 'YFG', 'Gaatri', 'Mon', 'Ghop',\n",
       "       'Cleopatra', 'Dreamwest', 'SDS', 'Linn', 'Electric', 'Jampa', 'HC',\n",
       "       'GrtIndia', 'Neon', 'ENTMP', 'Good Deed', 'ParC', 'Aviron',\n",
       "       'Annapurna', 'Amazon', 'Affirm', 'MOM', 'Orion', 'CFI', 'UTMW',\n",
       "       'Crimson', 'CAVU', 'EF', 'Arrow', 'Hiber', 'Studio 8',\n",
       "       'Global Road', 'Trafalgar', 'Greenwich', 'Spanglish', 'Blue Fox',\n",
       "       'RLJ', 'Swen', 'PackYourBag', 'Gaum.', 'Grindstone',\n",
       "       'Conglomerate', 'MUBI', 'Darin Southa', 'Super', 'CARUSEL', 'PDF',\n",
       "       'Synergetic'], dtype=object)"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BOM_movies.studio.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Bluebeard                           2\n",
       "The Angry Birds Movie               1\n",
       "Iron Man 2                          1\n",
       "No Manches Frida                    1\n",
       "Next Time I'll Aim for the Heart    1\n",
       "                                   ..\n",
       "Love Crime                          1\n",
       "Zindagi Na Milegi Dobara            1\n",
       "Genius                              1\n",
       "Namaste England                     1\n",
       "The Time That Remains               1\n",
       "Name: title, Length: 3386, dtype: int64"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BOM_movies.title.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count          3387\n",
       "unique         3386\n",
       "top       Bluebeard\n",
       "freq              2\n",
       "Name: title, dtype: object"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BOM_movies.title.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. TheMovie dataset\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loading and reading the movie db\n",
    "TheMovie = pd.read_csv('./zippedData/tmdb.movies.csv', index_col=0)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Previewing the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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_ids</th>\n",
       "      <th>id</th>\n",
       "      <th>original_language</th>\n",
       "      <th>original_title</th>\n",
       "      <th>popularity</th>\n",
       "      <th>release_date</th>\n",
       "      <th>title</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>vote_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[12, 14, 10751]</td>\n",
       "      <td>12444</td>\n",
       "      <td>en</td>\n",
       "      <td>Harry Potter and the Deathly Hallows: Part 1</td>\n",
       "      <td>33.533</td>\n",
       "      <td>2010-11-19</td>\n",
       "      <td>Harry Potter and the Deathly Hallows: Part 1</td>\n",
       "      <td>7.7</td>\n",
       "      <td>10788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[14, 12, 16, 10751]</td>\n",
       "      <td>10191</td>\n",
       "      <td>en</td>\n",
       "      <td>How to Train Your Dragon</td>\n",
       "      <td>28.734</td>\n",
       "      <td>2010-03-26</td>\n",
       "      <td>How to Train Your Dragon</td>\n",
       "      <td>7.7</td>\n",
       "      <td>7610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[12, 28, 878]</td>\n",
       "      <td>10138</td>\n",
       "      <td>en</td>\n",
       "      <td>Iron Man 2</td>\n",
       "      <td>28.515</td>\n",
       "      <td>2010-05-07</td>\n",
       "      <td>Iron Man 2</td>\n",
       "      <td>6.8</td>\n",
       "      <td>12368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[16, 35, 10751]</td>\n",
       "      <td>862</td>\n",
       "      <td>en</td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>28.005</td>\n",
       "      <td>1995-11-22</td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>7.9</td>\n",
       "      <td>10174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[28, 878, 12]</td>\n",
       "      <td>27205</td>\n",
       "      <td>en</td>\n",
       "      <td>Inception</td>\n",
       "      <td>27.920</td>\n",
       "      <td>2010-07-16</td>\n",
       "      <td>Inception</td>\n",
       "      <td>8.3</td>\n",
       "      <td>22186</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             genre_ids     id original_language  \\\n",
       "0      [12, 14, 10751]  12444                en   \n",
       "1  [14, 12, 16, 10751]  10191                en   \n",
       "2        [12, 28, 878]  10138                en   \n",
       "3      [16, 35, 10751]    862                en   \n",
       "4        [28, 878, 12]  27205                en   \n",
       "\n",
       "                                 original_title  popularity release_date  \\\n",
       "0  Harry Potter and the Deathly Hallows: Part 1      33.533   2010-11-19   \n",
       "1                      How to Train Your Dragon      28.734   2010-03-26   \n",
       "2                                    Iron Man 2      28.515   2010-05-07   \n",
       "3                                     Toy Story      28.005   1995-11-22   \n",
       "4                                     Inception      27.920   2010-07-16   \n",
       "\n",
       "                                          title  vote_average  vote_count  \n",
       "0  Harry Potter and the Deathly Hallows: Part 1           7.7       10788  \n",
       "1                      How to Train Your Dragon           7.7        7610  \n",
       "2                                    Iron Man 2           6.8       12368  \n",
       "3                                     Toy Story           7.9       10174  \n",
       "4                                     Inception           8.3       22186  "
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking top of the dataset\n",
    "TheMovie.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>genre_ids</th>\n",
       "      <th>id</th>\n",
       "      <th>original_language</th>\n",
       "      <th>original_title</th>\n",
       "      <th>popularity</th>\n",
       "      <th>release_date</th>\n",
       "      <th>title</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>vote_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>26512</th>\n",
       "      <td>[27, 18]</td>\n",
       "      <td>488143</td>\n",
       "      <td>en</td>\n",
       "      <td>Laboratory Conditions</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2018-10-13</td>\n",
       "      <td>Laboratory Conditions</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26513</th>\n",
       "      <td>[18, 53]</td>\n",
       "      <td>485975</td>\n",
       "      <td>en</td>\n",
       "      <td>_EXHIBIT_84xxx_</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2018-05-01</td>\n",
       "      <td>_EXHIBIT_84xxx_</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26514</th>\n",
       "      <td>[14, 28, 12]</td>\n",
       "      <td>381231</td>\n",
       "      <td>en</td>\n",
       "      <td>The Last One</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2018-10-01</td>\n",
       "      <td>The Last One</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26515</th>\n",
       "      <td>[10751, 12, 28]</td>\n",
       "      <td>366854</td>\n",
       "      <td>en</td>\n",
       "      <td>Trailer Made</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2018-06-22</td>\n",
       "      <td>Trailer Made</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26516</th>\n",
       "      <td>[53, 27]</td>\n",
       "      <td>309885</td>\n",
       "      <td>en</td>\n",
       "      <td>The Church</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2018-10-05</td>\n",
       "      <td>The Church</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             genre_ids      id original_language         original_title  \\\n",
       "26512         [27, 18]  488143                en  Laboratory Conditions   \n",
       "26513         [18, 53]  485975                en        _EXHIBIT_84xxx_   \n",
       "26514     [14, 28, 12]  381231                en           The Last One   \n",
       "26515  [10751, 12, 28]  366854                en           Trailer Made   \n",
       "26516         [53, 27]  309885                en             The Church   \n",
       "\n",
       "       popularity release_date                  title  vote_average  \\\n",
       "26512         0.6   2018-10-13  Laboratory Conditions           0.0   \n",
       "26513         0.6   2018-05-01        _EXHIBIT_84xxx_           0.0   \n",
       "26514         0.6   2018-10-01           The Last One           0.0   \n",
       "26515         0.6   2018-06-22           Trailer Made           0.0   \n",
       "26516         0.6   2018-10-05             The Church           0.0   \n",
       "\n",
       "       vote_count  \n",
       "26512           1  \n",
       "26513           1  \n",
       "26514           1  \n",
       "26515           1  \n",
       "26516           1  "
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the bottom of the dataset\n",
    "TheMovie.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(26517, 9)"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking the dimensions of the data\n",
    "TheMovie.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 26517 entries, 0 to 26516\n",
      "Data columns (total 9 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   genre_ids          26517 non-null  object \n",
      " 1   id                 26517 non-null  int64  \n",
      " 2   original_language  26517 non-null  object \n",
      " 3   original_title     26517 non-null  object \n",
      " 4   popularity         26517 non-null  float64\n",
      " 5   release_date       26517 non-null  object \n",
      " 6   title              26517 non-null  object \n",
      " 7   vote_average       26517 non-null  float64\n",
      " 8   vote_count         26517 non-null  int64  \n",
      "dtypes: float64(2), int64(2), object(5)\n",
      "memory usage: 2.0+ MB\n"
     ]
    }
   ],
   "source": [
    "TheMovie.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "genre_ids            26517\n",
       "id                   26517\n",
       "original_language    26517\n",
       "original_title       26517\n",
       "popularity           26517\n",
       "release_date         26517\n",
       "title                26517\n",
       "vote_average         26517\n",
       "vote_count           26517\n",
       "dtype: int64"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking number of entries in each column\n",
    "TheMovie.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "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",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>popularity</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>vote_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>26517.000000</td>\n",
       "      <td>26517.000000</td>\n",
       "      <td>26517.000000</td>\n",
       "      <td>26517.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>295050.153260</td>\n",
       "      <td>3.130912</td>\n",
       "      <td>5.991281</td>\n",
       "      <td>194.224837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>153661.615648</td>\n",
       "      <td>4.355229</td>\n",
       "      <td>1.852946</td>\n",
       "      <td>960.961095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>27.000000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>157851.000000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>309581.000000</td>\n",
       "      <td>1.374000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>419542.000000</td>\n",
       "      <td>3.694000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>28.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>608444.000000</td>\n",
       "      <td>80.773000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>22186.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  id    popularity  vote_average    vote_count\n",
       "count   26517.000000  26517.000000  26517.000000  26517.000000\n",
       "mean   295050.153260      3.130912      5.991281    194.224837\n",
       "std    153661.615648      4.355229      1.852946    960.961095\n",
       "min        27.000000      0.600000      0.000000      1.000000\n",
       "25%    157851.000000      0.600000      5.000000      2.000000\n",
       "50%    309581.000000      1.374000      6.000000      5.000000\n",
       "75%    419542.000000      3.694000      7.000000     28.000000\n",
       "max    608444.000000     80.773000     10.000000  22186.000000"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#summary of descriptive statistics \n",
    "TheMovie.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['genre_ids', 'id', 'original_language', 'original_title', 'popularity',\n",
       "       'release_date', 'title', 'vote_average', 'vote_count'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TheMovie.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Harry Potter and the Deathly Hallows: Part 1',\n",
       "       'How to Train Your Dragon', 'Iron Man 2', ..., 'The Last One',\n",
       "       'Trailer Made', 'The Church'], dtype=object)"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TheMovie.title.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.0     1940\n",
       "7.0     1560\n",
       "5.0     1486\n",
       "10.0    1252\n",
       "8.0     1231\n",
       "        ... \n",
       "9.4        6\n",
       "1.2        3\n",
       "1.4        3\n",
       "9.1        2\n",
       "9.7        2\n",
       "Name: vote_average, Length: 91, dtype: int64"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TheMovie.vote_average.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     26517\n",
       "unique    24688\n",
       "top        Home\n",
       "freq          7\n",
       "Name: title, dtype: object"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TheMovie.title.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['genre_ids', 'id', 'original_language', 'original_title', 'popularity',\n",
       "       'release_date', 'title', 'vote_average', 'vote_count'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TheMovie.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [],
   "source": [
    "#TheMovie = TheMovie.drop(['original_language', 'original_title', 'vote_count'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>original_language</th>\n",
       "      <th>original_title</th>\n",
       "      <th>popularity</th>\n",
       "      <th>release_date</th>\n",
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       "      <th>vote_average</th>\n",
       "      <th>vote_count</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[12, 14, 10751]</td>\n",
       "      <td>12444</td>\n",
       "      <td>en</td>\n",
       "      <td>Harry Potter and the Deathly Hallows: Part 1</td>\n",
       "      <td>33.533</td>\n",
       "      <td>2010-11-19</td>\n",
       "      <td>Harry Potter and the Deathly Hallows: Part 1</td>\n",
       "      <td>7.7</td>\n",
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       "      <td>[14, 12, 16, 10751]</td>\n",
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       "      <td>How to Train Your Dragon</td>\n",
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       "      <td>How to Train Your Dragon</td>\n",
       "      <td>7.7</td>\n",
       "      <td>7610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[12, 28, 878]</td>\n",
       "      <td>10138</td>\n",
       "      <td>en</td>\n",
       "      <td>Iron Man 2</td>\n",
       "      <td>28.515</td>\n",
       "      <td>2010-05-07</td>\n",
       "      <td>Iron Man 2</td>\n",
       "      <td>6.8</td>\n",
       "      <td>12368</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             genre_ids     id original_language  \\\n",
       "0      [12, 14, 10751]  12444                en   \n",
       "1  [14, 12, 16, 10751]  10191                en   \n",
       "2        [12, 28, 878]  10138                en   \n",
       "\n",
       "                                 original_title  popularity release_date  \\\n",
       "0  Harry Potter and the Deathly Hallows: Part 1      33.533   2010-11-19   \n",
       "1                      How to Train Your Dragon      28.734   2010-03-26   \n",
       "2                                    Iron Man 2      28.515   2010-05-07   \n",
       "\n",
       "                                          title  vote_average  vote_count  \n",
       "0  Harry Potter and the Deathly Hallows: Part 1           7.7       10788  \n",
       "1                      How to Train Your Dragon           7.7        7610  \n",
       "2                                    Iron Man 2           6.8       12368  "
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TheMovie.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "genre_ids             object\n",
       "id                     int64\n",
       "original_language     object\n",
       "original_title        object\n",
       "popularity           float64\n",
       "release_date          object\n",
       "title                 object\n",
       "vote_average         float64\n",
       "vote_count             int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TheMovie.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "TheMovie['release_date'] = pd.to_datetime(TheMovie['release_date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "genre_ids                    object\n",
       "id                            int64\n",
       "original_language            object\n",
       "original_title               object\n",
       "popularity                  float64\n",
       "release_date         datetime64[ns]\n",
       "title                        object\n",
       "vote_average                float64\n",
       "vote_count                    int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TheMovie.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "TheMovie['release_date'] = pd.to_datetime(TheMovie['release_date'], format='%y/%m/%d')\n",
    "TheMovie['Year'] = TheMovie['release_date'].dt.strftime('%Y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>12368</td>\n",
       "      <td>2010</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[16, 35, 10751]</td>\n",
       "      <td>862</td>\n",
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       "      <td>Toy Story</td>\n",
       "      <td>28.005</td>\n",
       "      <td>1995-11-22</td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>7.9</td>\n",
       "      <td>10174</td>\n",
       "      <td>1995</td>\n",
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       "      <td>Inception</td>\n",
       "      <td>27.920</td>\n",
       "      <td>2010-07-16</td>\n",
       "      <td>Inception</td>\n",
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       "      <td>2010</td>\n",
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       "      <td>0.600</td>\n",
       "      <td>2018-10-13</td>\n",
       "      <td>Laboratory Conditions</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26513</th>\n",
       "      <td>[18, 53]</td>\n",
       "      <td>485975</td>\n",
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       "      <td>_EXHIBIT_84xxx_</td>\n",
       "      <td>0.600</td>\n",
       "      <td>2018-05-01</td>\n",
       "      <td>_EXHIBIT_84xxx_</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>2018</td>\n",
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       "    <tr>\n",
       "      <th>26514</th>\n",
       "      <td>[14, 28, 12]</td>\n",
       "      <td>381231</td>\n",
       "      <td>en</td>\n",
       "      <td>The Last One</td>\n",
       "      <td>0.600</td>\n",
       "      <td>2018-10-01</td>\n",
       "      <td>The Last One</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>2018-06-22</td>\n",
       "      <td>Trailer Made</td>\n",
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       "      <td>2018-10-05</td>\n",
       "      <td>The Church</td>\n",
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       "      <td>2018</td>\n",
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       "<p>26517 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 genre_ids      id original_language  \\\n",
       "0          [12, 14, 10751]   12444                en   \n",
       "1      [14, 12, 16, 10751]   10191                en   \n",
       "2            [12, 28, 878]   10138                en   \n",
       "3          [16, 35, 10751]     862                en   \n",
       "4            [28, 878, 12]   27205                en   \n",
       "...                    ...     ...               ...   \n",
       "26512             [27, 18]  488143                en   \n",
       "26513             [18, 53]  485975                en   \n",
       "26514         [14, 28, 12]  381231                en   \n",
       "26515      [10751, 12, 28]  366854                en   \n",
       "26516             [53, 27]  309885                en   \n",
       "\n",
       "                                     original_title  popularity release_date  \\\n",
       "0      Harry Potter and the Deathly Hallows: Part 1      33.533   2010-11-19   \n",
       "1                          How to Train Your Dragon      28.734   2010-03-26   \n",
       "2                                        Iron Man 2      28.515   2010-05-07   \n",
       "3                                         Toy Story      28.005   1995-11-22   \n",
       "4                                         Inception      27.920   2010-07-16   \n",
       "...                                             ...         ...          ...   \n",
       "26512                         Laboratory Conditions       0.600   2018-10-13   \n",
       "26513                               _EXHIBIT_84xxx_       0.600   2018-05-01   \n",
       "26514                                  The Last One       0.600   2018-10-01   \n",
       "26515                                  Trailer Made       0.600   2018-06-22   \n",
       "26516                                    The Church       0.600   2018-10-05   \n",
       "\n",
       "                                              title  vote_average  vote_count  \\\n",
       "0      Harry Potter and the Deathly Hallows: Part 1           7.7       10788   \n",
       "1                          How to Train Your Dragon           7.7        7610   \n",
       "2                                        Iron Man 2           6.8       12368   \n",
       "3                                         Toy Story           7.9       10174   \n",
       "4                                         Inception           8.3       22186   \n",
       "...                                             ...           ...         ...   \n",
       "26512                         Laboratory Conditions           0.0           1   \n",
       "26513                               _EXHIBIT_84xxx_           0.0           1   \n",
       "26514                                  The Last One           0.0           1   \n",
       "26515                                  Trailer Made           0.0           1   \n",
       "26516                                    The Church           0.0           1   \n",
       "\n",
       "       Year  \n",
       "0      2010  \n",
       "1      2010  \n",
       "2      2010  \n",
       "3      1995  \n",
       "4      2010  \n",
       "...     ...  \n",
       "26512  2018  \n",
       "26513  2018  \n",
       "26514  2018  \n",
       "26515  2018  \n",
       "26516  2018  \n",
       "\n",
       "[26517 rows x 10 columns]"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TheMovie"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['genre_ids', 'id', 'original_language', 'original_title', 'popularity',\n",
       "       'release_date', 'title', 'vote_average', 'vote_count', 'Year'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TheMovie.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. IMDb Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " \n",
    "Contains two tables \n",
    "1. movie ratings - movie id, avarage ratings and number of votes \n",
    "2. movie basics - movie id, primary and original titles, start year, runtime minutes and genres\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Loading and previewing the IMDB dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sqlite3.Connection at 0x1a945339b70>"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# reading data from im.db database\n",
    "conn = sqlite3.connect('./zippedData/im.db')\n",
    "conn\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "IMDB_ratings = pd.read_sql(\"\"\" SELECT * FROM movie_ratings;\"\"\", conn)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Previewing the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>movie_id</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>tt10356526</td>\n",
       "      <td>8.3</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tt10384606</td>\n",
       "      <td>8.9</td>\n",
       "      <td>559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>tt1042974</td>\n",
       "      <td>6.4</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>tt1043726</td>\n",
       "      <td>4.2</td>\n",
       "      <td>50352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tt1060240</td>\n",
       "      <td>6.5</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     movie_id  averagerating  numvotes\n",
       "0  tt10356526            8.3        31\n",
       "1  tt10384606            8.9       559\n",
       "2   tt1042974            6.4        20\n",
       "3   tt1043726            4.2     50352\n",
       "4   tt1060240            6.5        21"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking top of the dataset\n",
    "IMDB_ratings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "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>movie_id</th>\n",
       "      <th>averagerating</th>\n",
       "      <th>numvotes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>73851</th>\n",
       "      <td>tt9805820</td>\n",
       "      <td>8.1</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73852</th>\n",
       "      <td>tt9844256</td>\n",
       "      <td>7.5</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73853</th>\n",
       "      <td>tt9851050</td>\n",
       "      <td>4.7</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73854</th>\n",
       "      <td>tt9886934</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73855</th>\n",
       "      <td>tt9894098</td>\n",
       "      <td>6.3</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        movie_id  averagerating  numvotes\n",
       "73851  tt9805820            8.1        25\n",
       "73852  tt9844256            7.5        24\n",
       "73853  tt9851050            4.7        14\n",
       "73854  tt9886934            7.0         5\n",
       "73855  tt9894098            6.3       128"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking the bottom of the dataset\n",
    "IMDB_ratings.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(73856, 3)"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IMDB_ratings.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 73856 entries, 0 to 73855\n",
      "Data columns (total 3 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   movie_id       73856 non-null  object \n",
      " 1   averagerating  73856 non-null  float64\n",
      " 2   numvotes       73856 non-null  int64  \n",
      "dtypes: float64(1), int64(1), object(1)\n",
      "memory usage: 1.7+ MB\n"
     ]
    }
   ],
   "source": [
    "IMDB_ratings.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "movie_id         73856\n",
       "averagerating    73856\n",
       "numvotes         73856\n",
       "dtype: int64"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking number of entries in each column\n",
    "IMDB_ratings.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "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>averagerating</th>\n",
       "      <th>numvotes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>73856.000000</td>\n",
       "      <td>7.385600e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>6.332729</td>\n",
       "      <td>3.523662e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.474978</td>\n",
       "      <td>3.029402e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.500000</td>\n",
       "      <td>1.400000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6.500000</td>\n",
       "      <td>4.900000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.400000</td>\n",
       "      <td>2.820000e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>1.841066e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       averagerating      numvotes\n",
       "count   73856.000000  7.385600e+04\n",
       "mean        6.332729  3.523662e+03\n",
       "std         1.474978  3.029402e+04\n",
       "min         1.000000  5.000000e+00\n",
       "25%         5.500000  1.400000e+01\n",
       "50%         6.500000  4.900000e+01\n",
       "75%         7.400000  2.820000e+02\n",
       "max        10.000000  1.841066e+06"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#summary of descriptive statistics \n",
    "IMDB_ratings.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['movie_id', 'averagerating', 'numvotes'], dtype='object')"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IMDB_ratings.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(73856, 3)"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IMDB_ratings.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    73856.000000\n",
       "mean         6.332729\n",
       "std          1.474978\n",
       "min          1.000000\n",
       "25%          5.500000\n",
       "50%          6.500000\n",
       "75%          7.400000\n",
       "max         10.000000\n",
       "Name: averagerating, dtype: float64"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IMDB_ratings.averagerating.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Loading the other dataset from IMDB\n",
    "Movie_basics "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [],
   "source": [
    "IMdb_basics = pd.read_sql(\"\"\" SELECT * FROM movie_basics;\"\"\", conn)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Previewing the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "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",
       "\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>movie_id</th>\n",
       "      <th>primary_title</th>\n",
       "      <th>original_title</th>\n",
       "      <th>start_year</th>\n",
       "      <th>runtime_minutes</th>\n",
       "      <th>genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>tt0063540</td>\n",
       "      <td>Sunghursh</td>\n",
       "      <td>Sunghursh</td>\n",
       "      <td>2013</td>\n",
       "      <td>175.0</td>\n",
       "      <td>Action,Crime,Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tt0066787</td>\n",
       "      <td>One Day Before the Rainy Season</td>\n",
       "      <td>Ashad Ka Ek Din</td>\n",
       "      <td>2019</td>\n",
       "      <td>114.0</td>\n",
       "      <td>Biography,Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>tt0069049</td>\n",
       "      <td>The Other Side of the Wind</td>\n",
       "      <td>The Other Side of the Wind</td>\n",
       "      <td>2018</td>\n",
       "      <td>122.0</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>tt0069204</td>\n",
       "      <td>Sabse Bada Sukh</td>\n",
       "      <td>Sabse Bada Sukh</td>\n",
       "      <td>2018</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Comedy,Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tt0100275</td>\n",
       "      <td>The Wandering Soap Opera</td>\n",
       "      <td>La Telenovela Errante</td>\n",
       "      <td>2017</td>\n",
       "      <td>80.0</td>\n",
       "      <td>Comedy,Drama,Fantasy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    movie_id                    primary_title              original_title  \\\n",
       "0  tt0063540                        Sunghursh                   Sunghursh   \n",
       "1  tt0066787  One Day Before the Rainy Season             Ashad Ka Ek Din   \n",
       "2  tt0069049       The Other Side of the Wind  The Other Side of the Wind   \n",
       "3  tt0069204                  Sabse Bada Sukh             Sabse Bada Sukh   \n",
       "4  tt0100275         The Wandering Soap Opera       La Telenovela Errante   \n",
       "\n",
       "   start_year  runtime_minutes                genres  \n",
       "0        2013            175.0    Action,Crime,Drama  \n",
       "1        2019            114.0       Biography,Drama  \n",
       "2        2018            122.0                 Drama  \n",
       "3        2018              NaN          Comedy,Drama  \n",
       "4        2017             80.0  Comedy,Drama,Fantasy  "
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Viewing the top values\n",
    "IMdb_basics.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movie_id</th>\n",
       "      <th>primary_title</th>\n",
       "      <th>original_title</th>\n",
       "      <th>start_year</th>\n",
       "      <th>runtime_minutes</th>\n",
       "      <th>genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>146139</th>\n",
       "      <td>tt9916538</td>\n",
       "      <td>Kuambil Lagi Hatiku</td>\n",
       "      <td>Kuambil Lagi Hatiku</td>\n",
       "      <td>2019</td>\n",
       "      <td>123.0</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146140</th>\n",
       "      <td>tt9916622</td>\n",
       "      <td>Rodolpho Teóphilo - O Legado de um Pioneiro</td>\n",
       "      <td>Rodolpho Teóphilo - O Legado de um Pioneiro</td>\n",
       "      <td>2015</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Documentary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146141</th>\n",
       "      <td>tt9916706</td>\n",
       "      <td>Dankyavar Danka</td>\n",
       "      <td>Dankyavar Danka</td>\n",
       "      <td>2013</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146142</th>\n",
       "      <td>tt9916730</td>\n",
       "      <td>6 Gunn</td>\n",
       "      <td>6 Gunn</td>\n",
       "      <td>2017</td>\n",
       "      <td>116.0</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146143</th>\n",
       "      <td>tt9916754</td>\n",
       "      <td>Chico Albuquerque - Revelações</td>\n",
       "      <td>Chico Albuquerque - Revelações</td>\n",
       "      <td>2013</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Documentary</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         movie_id                                primary_title  \\\n",
       "146139  tt9916538                          Kuambil Lagi Hatiku   \n",
       "146140  tt9916622  Rodolpho Teóphilo - O Legado de um Pioneiro   \n",
       "146141  tt9916706                              Dankyavar Danka   \n",
       "146142  tt9916730                                       6 Gunn   \n",
       "146143  tt9916754               Chico Albuquerque - Revelações   \n",
       "\n",
       "                                     original_title  start_year  \\\n",
       "146139                          Kuambil Lagi Hatiku        2019   \n",
       "146140  Rodolpho Teóphilo - O Legado de um Pioneiro        2015   \n",
       "146141                              Dankyavar Danka        2013   \n",
       "146142                                       6 Gunn        2017   \n",
       "146143               Chico Albuquerque - Revelações        2013   \n",
       "\n",
       "        runtime_minutes       genres  \n",
       "146139            123.0        Drama  \n",
       "146140              NaN  Documentary  \n",
       "146141              NaN       Comedy  \n",
       "146142            116.0         None  \n",
       "146143              NaN  Documentary  "
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Viewing the tail\n",
    "IMdb_basics.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(146144, 6)"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IMdb_basics.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 146144 entries, 0 to 146143\n",
      "Data columns (total 6 columns):\n",
      " #   Column           Non-Null Count   Dtype  \n",
      "---  ------           --------------   -----  \n",
      " 0   movie_id         146144 non-null  object \n",
      " 1   primary_title    146144 non-null  object \n",
      " 2   original_title   146123 non-null  object \n",
      " 3   start_year       146144 non-null  int64  \n",
      " 4   runtime_minutes  114405 non-null  float64\n",
      " 5   genres           140736 non-null  object \n",
      "dtypes: float64(1), int64(1), object(4)\n",
      "memory usage: 6.7+ MB\n"
     ]
    }
   ],
   "source": [
    "IMdb_basics.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "movie_id           146144\n",
       "primary_title      146144\n",
       "original_title     146123\n",
       "start_year         146144\n",
       "runtime_minutes    114405\n",
       "genres             140736\n",
       "dtype: int64"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking number of entries in each column\n",
    "IMdb_basics.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>start_year</th>\n",
       "      <th>runtime_minutes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>146144.000000</td>\n",
       "      <td>114405.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2014.621798</td>\n",
       "      <td>86.187247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.733583</td>\n",
       "      <td>166.360590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2010.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2012.000000</td>\n",
       "      <td>70.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2015.000000</td>\n",
       "      <td>87.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2017.000000</td>\n",
       "      <td>99.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2115.000000</td>\n",
       "      <td>51420.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          start_year  runtime_minutes\n",
       "count  146144.000000    114405.000000\n",
       "mean     2014.621798        86.187247\n",
       "std         2.733583       166.360590\n",
       "min      2010.000000         1.000000\n",
       "25%      2012.000000        70.000000\n",
       "50%      2015.000000        87.000000\n",
       "75%      2017.000000        99.000000\n",
       "max      2115.000000     51420.000000"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#summary of descriptive statistics \n",
    "IMdb_basics.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['movie_id', 'primary_title', 'original_title', 'start_year',\n",
       "       'runtime_minutes', 'genres'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IMdb_basics.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Sunghursh', 'One Day Before the Rainy Season',\n",
       "       'The Other Side of the Wind', ..., 'Dankyavar Danka', '6 Gunn',\n",
       "       'Chico Albuquerque - Revelações'], dtype=object)"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IMdb_basics.primary_title.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Documentary                  32185\n",
      "Drama                        21486\n",
      "Comedy                        9177\n",
      "Horror                        4372\n",
      "Comedy,Drama                  3519\n",
      "                             ...  \n",
      "Biography,Horror                 1\n",
      "Action,Crime,Music               1\n",
      "Action,Adventure,Musical         1\n",
      "Family,Fantasy,History           1\n",
      "Biography,Fantasy,History        1\n",
      "Name: genres, Length: 1085, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "IMdb_basics.genres.unique()\n",
    "genres_count_summary = IMdb_basics['genres'].value_counts()\n",
    "print(genres_count_summary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['tt0063540', 'tt0066787', 'tt0069049', ..., 'tt9916706',\n",
       "       'tt9916730', 'tt9916754'], dtype=object)"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IMdb_basics.movie_id.unique()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cleaning the datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>studio</th>\n",
       "      <th>domestic_gross</th>\n",
       "      <th>foreign_gross</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Toy Story 3</td>\n",
       "      <td>BV</td>\n",
       "      <td>415000000.0</td>\n",
       "      <td>652000000</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alice in Wonderland (2010)</td>\n",
       "      <td>BV</td>\n",
       "      <td>334200000.0</td>\n",
       "      <td>691300000</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        title studio  domestic_gross foreign_gross  year\n",
       "0                 Toy Story 3     BV     415000000.0     652000000  2010\n",
       "1  Alice in Wonderland (2010)     BV     334200000.0     691300000  2010"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BOM_movies.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "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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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>genre_ids</th>\n",
       "      <th>id</th>\n",
       "      <th>original_language</th>\n",
       "      <th>original_title</th>\n",
       "      <th>popularity</th>\n",
       "      <th>release_date</th>\n",
       "      <th>title</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>vote_count</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[12, 14, 10751]</td>\n",
       "      <td>12444</td>\n",
       "      <td>en</td>\n",
       "      <td>Harry Potter and the Deathly Hallows: Part 1</td>\n",
       "      <td>33.533</td>\n",
       "      <td>2010-11-19</td>\n",
       "      <td>Harry Potter and the Deathly Hallows: Part 1</td>\n",
       "      <td>7.7</td>\n",
       "      <td>10788</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[14, 12, 16, 10751]</td>\n",
       "      <td>10191</td>\n",
       "      <td>en</td>\n",
       "      <td>How to Train Your Dragon</td>\n",
       "      <td>28.734</td>\n",
       "      <td>2010-03-26</td>\n",
       "      <td>How to Train Your Dragon</td>\n",
       "      <td>7.7</td>\n",
       "      <td>7610</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             genre_ids     id original_language  \\\n",
       "0      [12, 14, 10751]  12444                en   \n",
       "1  [14, 12, 16, 10751]  10191                en   \n",
       "\n",
       "                                 original_title  popularity release_date  \\\n",
       "0  Harry Potter and the Deathly Hallows: Part 1      33.533   2010-11-19   \n",
       "1                      How to Train Your Dragon      28.734   2010-03-26   \n",
       "\n",
       "                                          title  vote_average  vote_count  \\\n",
       "0  Harry Potter and the Deathly Hallows: Part 1           7.7       10788   \n",
       "1                      How to Train Your Dragon           7.7        7610   \n",
       "\n",
       "   Year  \n",
       "0  2010  \n",
       "1  2010  "
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TheMovie.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "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>movie_id</th>\n",
       "      <th>primary_title</th>\n",
       "      <th>original_title</th>\n",
       "      <th>start_year</th>\n",
       "      <th>runtime_minutes</th>\n",
       "      <th>genres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>tt0063540</td>\n",
       "      <td>Sunghursh</td>\n",
       "      <td>Sunghursh</td>\n",
       "      <td>2013</td>\n",
       "      <td>175.0</td>\n",
       "      <td>Action,Crime,Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tt0066787</td>\n",
       "      <td>One Day Before the Rainy Season</td>\n",
       "      <td>Ashad Ka Ek Din</td>\n",
       "      <td>2019</td>\n",
       "      <td>114.0</td>\n",
       "      <td>Biography,Drama</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    movie_id                    primary_title   original_title  start_year  \\\n",
       "0  tt0063540                        Sunghursh        Sunghursh        2013   \n",
       "1  tt0066787  One Day Before the Rainy Season  Ashad Ka Ek Din        2019   \n",
       "\n",
       "   runtime_minutes              genres  \n",
       "0            175.0  Action,Crime,Drama  \n",
       "1            114.0     Biography,Drama  "
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IMdb_basics.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "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",
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       "        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>movie_id</th>\n",
       "      <th>averagerating</th>\n",
       "      <th>numvotes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>tt10356526</td>\n",
       "      <td>8.3</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tt10384606</td>\n",
       "      <td>8.9</td>\n",
       "      <td>559</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     movie_id  averagerating  numvotes\n",
       "0  tt10356526            8.3        31\n",
       "1  tt10384606            8.9       559"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IMDB_ratings.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Joining IMDb tables to one \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "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",
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       "        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>movie_id</th>\n",
       "      <th>primary_title</th>\n",
       "      <th>original_title</th>\n",
       "      <th>start_year</th>\n",
       "      <th>runtime_minutes</th>\n",
       "      <th>genres</th>\n",
       "      <th>averagerating</th>\n",
       "      <th>numvotes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>tt0063540</td>\n",
       "      <td>Sunghursh</td>\n",
       "      <td>Sunghursh</td>\n",
       "      <td>2013</td>\n",
       "      <td>175.0</td>\n",
       "      <td>Action,Crime,Drama</td>\n",
       "      <td>7.0</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tt0066787</td>\n",
       "      <td>One Day Before the Rainy Season</td>\n",
       "      <td>Ashad Ka Ek Din</td>\n",
       "      <td>2019</td>\n",
       "      <td>114.0</td>\n",
       "      <td>Biography,Drama</td>\n",
       "      <td>7.2</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>tt0069049</td>\n",
       "      <td>The Other Side of the Wind</td>\n",
       "      <td>The Other Side of the Wind</td>\n",
       "      <td>2018</td>\n",
       "      <td>122.0</td>\n",
       "      <td>Drama</td>\n",
       "      <td>6.9</td>\n",
       "      <td>4517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>tt0069204</td>\n",
       "      <td>Sabse Bada Sukh</td>\n",
       "      <td>Sabse Bada Sukh</td>\n",
       "      <td>2018</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Comedy,Drama</td>\n",
       "      <td>6.1</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tt0100275</td>\n",
       "      <td>The Wandering Soap Opera</td>\n",
       "      <td>La Telenovela Errante</td>\n",
       "      <td>2017</td>\n",
       "      <td>80.0</td>\n",
       "      <td>Comedy,Drama,Fantasy</td>\n",
       "      <td>6.5</td>\n",
       "      <td>119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146115</th>\n",
       "      <td>tt9913084</td>\n",
       "      <td>Diabolik sono io</td>\n",
       "      <td>Diabolik sono io</td>\n",
       "      <td>2019</td>\n",
       "      <td>75.0</td>\n",
       "      <td>Documentary</td>\n",
       "      <td>6.2</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146122</th>\n",
       "      <td>tt9914286</td>\n",
       "      <td>Sokagin Çocuklari</td>\n",
       "      <td>Sokagin Çocuklari</td>\n",
       "      <td>2019</td>\n",
       "      <td>98.0</td>\n",
       "      <td>Drama,Family</td>\n",
       "      <td>8.7</td>\n",
       "      <td>136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146125</th>\n",
       "      <td>tt9914642</td>\n",
       "      <td>Albatross</td>\n",
       "      <td>Albatross</td>\n",
       "      <td>2017</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Documentary</td>\n",
       "      <td>8.5</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146129</th>\n",
       "      <td>tt9914942</td>\n",
       "      <td>La vida sense la Sara Amat</td>\n",
       "      <td>La vida sense la Sara Amat</td>\n",
       "      <td>2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>6.6</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146134</th>\n",
       "      <td>tt9916160</td>\n",
       "      <td>Drømmeland</td>\n",
       "      <td>Drømmeland</td>\n",
       "      <td>2019</td>\n",
       "      <td>72.0</td>\n",
       "      <td>Documentary</td>\n",
       "      <td>6.5</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>73856 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         movie_id                    primary_title  \\\n",
       "0       tt0063540                        Sunghursh   \n",
       "1       tt0066787  One Day Before the Rainy Season   \n",
       "2       tt0069049       The Other Side of the Wind   \n",
       "3       tt0069204                  Sabse Bada Sukh   \n",
       "4       tt0100275         The Wandering Soap Opera   \n",
       "...           ...                              ...   \n",
       "146115  tt9913084                 Diabolik sono io   \n",
       "146122  tt9914286                Sokagin Çocuklari   \n",
       "146125  tt9914642                        Albatross   \n",
       "146129  tt9914942       La vida sense la Sara Amat   \n",
       "146134  tt9916160                       Drømmeland   \n",
       "\n",
       "                    original_title  start_year  runtime_minutes  \\\n",
       "0                        Sunghursh        2013            175.0   \n",
       "1                  Ashad Ka Ek Din        2019            114.0   \n",
       "2       The Other Side of the Wind        2018            122.0   \n",
       "3                  Sabse Bada Sukh        2018              NaN   \n",
       "4            La Telenovela Errante        2017             80.0   \n",
       "...                            ...         ...              ...   \n",
       "146115            Diabolik sono io        2019             75.0   \n",
       "146122           Sokagin Çocuklari        2019             98.0   \n",
       "146125                   Albatross        2017              NaN   \n",
       "146129  La vida sense la Sara Amat        2019              NaN   \n",
       "146134                  Drømmeland        2019             72.0   \n",
       "\n",
       "                      genres  averagerating  numvotes  \n",
       "0         Action,Crime,Drama            7.0        77  \n",
       "1            Biography,Drama            7.2        43  \n",
       "2                      Drama            6.9      4517  \n",
       "3               Comedy,Drama            6.1        13  \n",
       "4       Comedy,Drama,Fantasy            6.5       119  \n",
       "...                      ...            ...       ...  \n",
       "146115           Documentary            6.2         6  \n",
       "146122          Drama,Family            8.7       136  \n",
       "146125           Documentary            8.5         8  \n",
       "146129                  None            6.6         5  \n",
       "146134           Documentary            6.5        11  \n",
       "\n",
       "[73856 rows x 8 columns]"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Joining tables using title as the key\n",
    "IMDB = IMdb_basics.join(IMDB_ratings.set_index('movie_id'), on = 'movie_id', how  = 'inner')\n",
    "IMDB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "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>movie_id</th>\n",
       "      <th>primary_title</th>\n",
       "      <th>start_year</th>\n",
       "      <th>genres</th>\n",
       "      <th>averagerating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>tt0063540</td>\n",
       "      <td>Sunghursh</td>\n",
       "      <td>2013</td>\n",
       "      <td>Action,Crime,Drama</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tt0066787</td>\n",
       "      <td>One Day Before the Rainy Season</td>\n",
       "      <td>2019</td>\n",
       "      <td>Biography,Drama</td>\n",
       "      <td>7.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>tt0069049</td>\n",
       "      <td>The Other Side of the Wind</td>\n",
       "      <td>2018</td>\n",
       "      <td>Drama</td>\n",
       "      <td>6.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>tt0069204</td>\n",
       "      <td>Sabse Bada Sukh</td>\n",
       "      <td>2018</td>\n",
       "      <td>Comedy,Drama</td>\n",
       "      <td>6.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tt0100275</td>\n",
       "      <td>The Wandering Soap Opera</td>\n",
       "      <td>2017</td>\n",
       "      <td>Comedy,Drama,Fantasy</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146115</th>\n",
       "      <td>tt9913084</td>\n",
       "      <td>Diabolik sono io</td>\n",
       "      <td>2019</td>\n",
       "      <td>Documentary</td>\n",
       "      <td>6.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146122</th>\n",
       "      <td>tt9914286</td>\n",
       "      <td>Sokagin Çocuklari</td>\n",
       "      <td>2019</td>\n",
       "      <td>Drama,Family</td>\n",
       "      <td>8.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146125</th>\n",
       "      <td>tt9914642</td>\n",
       "      <td>Albatross</td>\n",
       "      <td>2017</td>\n",
       "      <td>Documentary</td>\n",
       "      <td>8.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146129</th>\n",
       "      <td>tt9914942</td>\n",
       "      <td>La vida sense la Sara Amat</td>\n",
       "      <td>2019</td>\n",
       "      <td>None</td>\n",
       "      <td>6.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146134</th>\n",
       "      <td>tt9916160</td>\n",
       "      <td>Drømmeland</td>\n",
       "      <td>2019</td>\n",
       "      <td>Documentary</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>73856 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         movie_id                    primary_title  start_year  \\\n",
       "0       tt0063540                        Sunghursh        2013   \n",
       "1       tt0066787  One Day Before the Rainy Season        2019   \n",
       "2       tt0069049       The Other Side of the Wind        2018   \n",
       "3       tt0069204                  Sabse Bada Sukh        2018   \n",
       "4       tt0100275         The Wandering Soap Opera        2017   \n",
       "...           ...                              ...         ...   \n",
       "146115  tt9913084                 Diabolik sono io        2019   \n",
       "146122  tt9914286                Sokagin Çocuklari        2019   \n",
       "146125  tt9914642                        Albatross        2017   \n",
       "146129  tt9914942       La vida sense la Sara Amat        2019   \n",
       "146134  tt9916160                       Drømmeland        2019   \n",
       "\n",
       "                      genres  averagerating  \n",
       "0         Action,Crime,Drama            7.0  \n",
       "1            Biography,Drama            7.2  \n",
       "2                      Drama            6.9  \n",
       "3               Comedy,Drama            6.1  \n",
       "4       Comedy,Drama,Fantasy            6.5  \n",
       "...                      ...            ...  \n",
       "146115           Documentary            6.2  \n",
       "146122          Drama,Family            8.7  \n",
       "146125           Documentary            8.5  \n",
       "146129                  None            6.6  \n",
       "146134           Documentary            6.5  \n",
       "\n",
       "[73856 rows x 5 columns]"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop columns 'original_title', 'runtime_minutes', and 'numvotes' from the DataFrame\n",
    "\n",
    "IMDB.drop(['original_title', 'runtime_minutes', 'numvotes'], axis=1, inplace=True)\n",
    "IMDB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Rename 'start_year' to year and 'primary_title' to 'title'\n",
    "IMDB.rename(columns={'start_year': 'year', 'primary_title': 'title'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['movie_id', 'title', 'year', 'genres', 'averagerating'], dtype='object')"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IMDB.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "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>movie_id</th>\n",
       "      <th>title</th>\n",
       "      <th>year</th>\n",
       "      <th>genres</th>\n",
       "      <th>averagerating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>tt0063540</td>\n",
       "      <td>Sunghursh</td>\n",
       "      <td>2013</td>\n",
       "      <td>Action,Crime,Drama</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tt0066787</td>\n",
       "      <td>One Day Before the Rainy Season</td>\n",
       "      <td>2019</td>\n",
       "      <td>Biography,Drama</td>\n",
       "      <td>7.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>tt0069049</td>\n",
       "      <td>The Other Side of the Wind</td>\n",
       "      <td>2018</td>\n",
       "      <td>Drama</td>\n",
       "      <td>6.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    movie_id                            title  year              genres  \\\n",
       "0  tt0063540                        Sunghursh  2013  Action,Crime,Drama   \n",
       "1  tt0066787  One Day Before the Rainy Season  2019     Biography,Drama   \n",
       "2  tt0069049       The Other Side of the Wind  2018               Drama   \n",
       "\n",
       "   averagerating  \n",
       "0            7.0  \n",
       "1            7.2  \n",
       "2            6.9  "
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IMDB.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Checking for nulls \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1.Box Office Mojo dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "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",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>studio</th>\n",
       "      <th>domestic_gross</th>\n",
       "      <th>foreign_gross</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Toy Story 3</td>\n",
       "      <td>BV</td>\n",
       "      <td>415000000.0</td>\n",
       "      <td>652000000</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alice in Wonderland (2010)</td>\n",
       "      <td>BV</td>\n",
       "      <td>334200000.0</td>\n",
       "      <td>691300000</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Harry Potter and the Deathly Hallows Part 1</td>\n",
       "      <td>WB</td>\n",
       "      <td>296000000.0</td>\n",
       "      <td>664300000</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         title studio  domestic_gross  \\\n",
       "0                                  Toy Story 3     BV     415000000.0   \n",
       "1                   Alice in Wonderland (2010)     BV     334200000.0   \n",
       "2  Harry Potter and the Deathly Hallows Part 1     WB     296000000.0   \n",
       "\n",
       "  foreign_gross  year  \n",
       "0     652000000  2010  \n",
       "1     691300000  2010  \n",
       "2     664300000  2010  "
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BOM_movies.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [],
   "source": [
    "BOM_movies = BOM_movies.drop(['studio'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>domestic_gross</th>\n",
       "      <th>foreign_gross</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Toy Story 3</td>\n",
       "      <td>415000000.0</td>\n",
       "      <td>652000000</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alice in Wonderland (2010)</td>\n",
       "      <td>334200000.0</td>\n",
       "      <td>691300000</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Harry Potter and the Deathly Hallows Part 1</td>\n",
       "      <td>296000000.0</td>\n",
       "      <td>664300000</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Inception</td>\n",
       "      <td>292600000.0</td>\n",
       "      <td>535700000</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Shrek Forever After</td>\n",
       "      <td>238700000.0</td>\n",
       "      <td>513900000</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         title  domestic_gross foreign_gross  \\\n",
       "0                                  Toy Story 3     415000000.0     652000000   \n",
       "1                   Alice in Wonderland (2010)     334200000.0     691300000   \n",
       "2  Harry Potter and the Deathly Hallows Part 1     296000000.0     664300000   \n",
       "3                                    Inception     292600000.0     535700000   \n",
       "4                          Shrek Forever After     238700000.0     513900000   \n",
       "\n",
       "   year  \n",
       "0  2010  \n",
       "1  2010  \n",
       "2  2010  \n",
       "3  2010  \n",
       "4  2010  "
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BOM_movies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BOM_movies.isnull().values.any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "title                0\n",
       "domestic_gross      28\n",
       "foreign_gross     1350\n",
       "year                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BOM_movies.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "title                0\n",
       "domestic_gross      28\n",
       "foreign_gross     1350\n",
       "year                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#calculating the percentage of null values\n",
    "\n",
    "# Step 1: Calculate the total number of null values\n",
    "null_count = BOM_movies.isnull().sum()\n",
    "null_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3387"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Step 2: Calculate the total number of values (non-null)\n",
    "total_count = BOM_movies.shape[0]\n",
    "total_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "title              0.000000\n",
      "domestic_gross     0.826690\n",
      "foreign_gross     39.858282\n",
      "year               0.000000\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Step 3:Calculate the percentage of nulls for each column\n",
    "null_percentage = (null_count / total_count) * 100\n",
    "\n",
    "print(null_percentage)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above shows that there is a small percantage of null values, so we can do away with them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2009, 4)"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BOM_movies= BOM_movies.dropna()\n",
    "BOM_movies.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "title             0\n",
       "domestic_gross    0\n",
       "foreign_gross     0\n",
       "year              0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BOM_movies.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>title</th>\n",
       "      <th>domestic_gross</th>\n",
       "      <th>foreign_gross</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Toy Story 3</td>\n",
       "      <td>415000000.0</td>\n",
       "      <td>652000000</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alice in Wonderland (2010)</td>\n",
       "      <td>334200000.0</td>\n",
       "      <td>691300000</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Harry Potter and the Deathly Hallows Part 1</td>\n",
       "      <td>296000000.0</td>\n",
       "      <td>664300000</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         title  domestic_gross foreign_gross  \\\n",
       "0                                  Toy Story 3     415000000.0     652000000   \n",
       "1                   Alice in Wonderland (2010)     334200000.0     691300000   \n",
       "2  Harry Potter and the Deathly Hallows Part 1     296000000.0     664300000   \n",
       "\n",
       "   year  \n",
       "0  2010  \n",
       "1  2010  \n",
       "2  2010  "
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BOM_movies.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Checking for duplicates "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BOM_movies.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Checking for inconsistencies in the data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "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>domestic_gross</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2.009000e+03</td>\n",
       "      <td>2009.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.697311e+07</td>\n",
       "      <td>2013.503235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.159966e+07</td>\n",
       "      <td>2.598481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.000000e+02</td>\n",
       "      <td>2010.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6.650000e+05</td>\n",
       "      <td>2011.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.650000e+07</td>\n",
       "      <td>2013.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5.600000e+07</td>\n",
       "      <td>2016.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.367000e+08</td>\n",
       "      <td>2018.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       domestic_gross         year\n",
       "count    2.009000e+03  2009.000000\n",
       "mean     4.697311e+07  2013.503235\n",
       "std      8.159966e+07     2.598481\n",
       "min      4.000000e+02  2010.000000\n",
       "25%      6.650000e+05  2011.000000\n",
       "50%      1.650000e+07  2013.000000\n",
       "75%      5.600000e+07  2016.000000\n",
       "max      9.367000e+08  2018.000000"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BOM_movies.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Checking for anomalies "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "The Runaways                  1\n",
       "Creation                      1\n",
       "Love Crime                    1\n",
       "Kill List                     1\n",
       "The Beguiled (2017)           1\n",
       "                             ..\n",
       "Kisses                        1\n",
       "Far from the Madding Crowd    1\n",
       "Roman J. Israel, Esq.         1\n",
       "Son of God                    1\n",
       "The Time That Remains         1\n",
       "Name: title, Length: 2009, dtype: int64"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BOM_movies.title.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2010    309\n",
       "2011    291\n",
       "2012    243\n",
       "2014    234\n",
       "2013    200\n",
       "2016    192\n",
       "2015    190\n",
       "2017    177\n",
       "2018    173\n",
       "Name: year, dtype: int64"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BOM_movies.year.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1500000.0     14\n",
       "1000000.0     13\n",
       "1300000.0     12\n",
       "1100000.0     11\n",
       "1600000.0     10\n",
       "              ..\n",
       "64300000.0     1\n",
       "41100000.0     1\n",
       "46400000.0     1\n",
       "677000.0       1\n",
       "45900000.0     1\n",
       "Name: domestic_gross, Length: 1297, dtype: int64"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BOM_movies.domestic_gross.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['title', 'domestic_gross', 'foreign_gross', 'year'], dtype='object')"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BOM_movies.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['movie_id', 'title', 'year', 'genres', 'averagerating'], dtype='object')"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IMDB.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['genre_ids', 'id', 'original_language', 'original_title', 'popularity',\n",
       "       'release_date', 'title', 'vote_average', 'vote_count', 'Year'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TheMovie.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cleaning the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IMDB.isnull().values.any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "movie_id           0\n",
       "title              0\n",
       "year               0\n",
       "genres           804\n",
       "averagerating      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IMDB.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "movie_id           0\n",
       "title              0\n",
       "year               0\n",
       "genres           804\n",
       "averagerating      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#calculating the percentage of null values\n",
    "\n",
    "# Step 1: Calculate the total number of null values\n",
    "null_count = IMDB.isnull().sum()\n",
    "null_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "73856"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Step 2: Calculate the total number of values (non-null)\n",
    "total_count = IMDB.shape[0]\n",
    "total_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "movie_id         0.000000\n",
      "title            0.000000\n",
      "year             0.000000\n",
      "genres           1.088605\n",
      "averagerating    0.000000\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Step 3:Calculate the percentage of nulls for each column\n",
    "null_percentage = (null_count / total_count) * 100\n",
    "\n",
    "print(null_percentage)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above shows that there is a small percantage of null values, so we can do away with them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(73052, 5)"
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IMDB= IMDB.dropna()\n",
    "IMDB.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "movie_id         0\n",
       "title            0\n",
       "year             0\n",
       "genres           0\n",
       "averagerating    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 219,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IMDB.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "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>movie_id</th>\n",
       "      <th>title</th>\n",
       "      <th>year</th>\n",
       "      <th>genres</th>\n",
       "      <th>averagerating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>tt0063540</td>\n",
       "      <td>Sunghursh</td>\n",
       "      <td>2013</td>\n",
       "      <td>Action,Crime,Drama</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tt0066787</td>\n",
       "      <td>One Day Before the Rainy Season</td>\n",
       "      <td>2019</td>\n",
       "      <td>Biography,Drama</td>\n",
       "      <td>7.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>tt0069049</td>\n",
       "      <td>The Other Side of the Wind</td>\n",
       "      <td>2018</td>\n",
       "      <td>Drama</td>\n",
       "      <td>6.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>tt0069204</td>\n",
       "      <td>Sabse Bada Sukh</td>\n",
       "      <td>2018</td>\n",
       "      <td>Comedy,Drama</td>\n",
       "      <td>6.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tt0100275</td>\n",
       "      <td>The Wandering Soap Opera</td>\n",
       "      <td>2017</td>\n",
       "      <td>Comedy,Drama,Fantasy</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146114</th>\n",
       "      <td>tt9913056</td>\n",
       "      <td>Swarm Season</td>\n",
       "      <td>2019</td>\n",
       "      <td>Documentary</td>\n",
       "      <td>6.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146115</th>\n",
       "      <td>tt9913084</td>\n",
       "      <td>Diabolik sono io</td>\n",
       "      <td>2019</td>\n",
       "      <td>Documentary</td>\n",
       "      <td>6.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146122</th>\n",
       "      <td>tt9914286</td>\n",
       "      <td>Sokagin Çocuklari</td>\n",
       "      <td>2019</td>\n",
       "      <td>Drama,Family</td>\n",
       "      <td>8.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146125</th>\n",
       "      <td>tt9914642</td>\n",
       "      <td>Albatross</td>\n",
       "      <td>2017</td>\n",
       "      <td>Documentary</td>\n",
       "      <td>8.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146134</th>\n",
       "      <td>tt9916160</td>\n",
       "      <td>Drømmeland</td>\n",
       "      <td>2019</td>\n",
       "      <td>Documentary</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>73052 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         movie_id                            title  year  \\\n",
       "0       tt0063540                        Sunghursh  2013   \n",
       "1       tt0066787  One Day Before the Rainy Season  2019   \n",
       "2       tt0069049       The Other Side of the Wind  2018   \n",
       "3       tt0069204                  Sabse Bada Sukh  2018   \n",
       "4       tt0100275         The Wandering Soap Opera  2017   \n",
       "...           ...                              ...   ...   \n",
       "146114  tt9913056                     Swarm Season  2019   \n",
       "146115  tt9913084                 Diabolik sono io  2019   \n",
       "146122  tt9914286                Sokagin Çocuklari  2019   \n",
       "146125  tt9914642                        Albatross  2017   \n",
       "146134  tt9916160                       Drømmeland  2019   \n",
       "\n",
       "                      genres  averagerating  \n",
       "0         Action,Crime,Drama            7.0  \n",
       "1            Biography,Drama            7.2  \n",
       "2                      Drama            6.9  \n",
       "3               Comedy,Drama            6.1  \n",
       "4       Comedy,Drama,Fantasy            6.5  \n",
       "...                      ...            ...  \n",
       "146114           Documentary            6.2  \n",
       "146115           Documentary            6.2  \n",
       "146122          Drama,Family            8.7  \n",
       "146125           Documentary            8.5  \n",
       "146134           Documentary            6.5  \n",
       "\n",
       "[73052 rows x 5 columns]"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IMDB"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Checking for duplicates\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IMDB.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Checking for data inconsistency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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>year</th>\n",
       "      <th>averagerating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>73052.000000</td>\n",
       "      <td>73052.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2014.281430</td>\n",
       "      <td>6.330930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.615288</td>\n",
       "      <td>1.474699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2010.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2012.000000</td>\n",
       "      <td>5.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2014.000000</td>\n",
       "      <td>6.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2017.000000</td>\n",
       "      <td>7.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2019.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               year  averagerating\n",
       "count  73052.000000   73052.000000\n",
       "mean    2014.281430       6.330930\n",
       "std        2.615288       1.474699\n",
       "min     2010.000000       1.000000\n",
       "25%     2012.000000       5.500000\n",
       "50%     2014.000000       6.500000\n",
       "75%     2017.000000       7.400000\n",
       "max     2019.000000      10.000000"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IMDB.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Checking for Anomalies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.0     2229\n",
       "6.6     2221\n",
       "7.2     2217\n",
       "6.8     2216\n",
       "6.5     2205\n",
       "        ... \n",
       "9.6       18\n",
       "10.0      16\n",
       "9.8       15\n",
       "9.7       12\n",
       "9.9        5\n",
       "Name: averagerating, Length: 91, dtype: int64"
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IMDB.averagerating.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2017    8638\n",
       "2016    8613\n",
       "2015    8405\n",
       "2014    8269\n",
       "2013    7905\n",
       "2012    7602\n",
       "2018    7476\n",
       "2011    7274\n",
       "2010    6701\n",
       "2019    2169\n",
       "Name: year, dtype: int64"
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IMDB.year.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 73052 entries, 0 to 146134\n",
      "Data columns (total 5 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   movie_id       73052 non-null  object \n",
      " 1   title          73052 non-null  object \n",
      " 2   year           73052 non-null  int64  \n",
      " 3   genres         73052 non-null  object \n",
      " 4   averagerating  73052 non-null  float64\n",
      "dtypes: float64(1), int64(1), object(3)\n",
      "memory usage: 3.3+ MB\n"
     ]
    }
   ],
   "source": [
    "# Checking datatypes\n",
    "IMDB.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\USER\\anaconda3\\envs\\learn-env\\lib\\site-packages\\pandas\\core\\indexing.py:1596: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self.obj[key] = _infer_fill_value(value)\n",
      "c:\\Users\\USER\\anaconda3\\envs\\learn-env\\lib\\site-packages\\pandas\\core\\indexing.py:1745: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  isetter(ilocs[0], value)\n"
     ]
    }
   ],
   "source": [
    "# Convert 'year' to datetime\n",
    "IMDB.loc[:, 'start_year'] = pd.to_datetime(IMDB['year'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Making the dataframe smaller to have only relevant data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movie_id</th>\n",
       "      <th>title</th>\n",
       "      <th>year</th>\n",
       "      <th>genres</th>\n",
       "      <th>averagerating</th>\n",
       "      <th>start_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>tt0063540</td>\n",
       "      <td>Sunghursh</td>\n",
       "      <td>2013</td>\n",
       "      <td>Action,Crime,Drama</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1970-01-01 00:00:00.000002013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tt0066787</td>\n",
       "      <td>One Day Before the Rainy Season</td>\n",
       "      <td>2019</td>\n",
       "      <td>Biography,Drama</td>\n",
       "      <td>7.2</td>\n",
       "      <td>1970-01-01 00:00:00.000002019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>tt0069049</td>\n",
       "      <td>The Other Side of the Wind</td>\n",
       "      <td>2018</td>\n",
       "      <td>Drama</td>\n",
       "      <td>6.9</td>\n",
       "      <td>1970-01-01 00:00:00.000002018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>tt0069204</td>\n",
       "      <td>Sabse Bada Sukh</td>\n",
       "      <td>2018</td>\n",
       "      <td>Comedy,Drama</td>\n",
       "      <td>6.1</td>\n",
       "      <td>1970-01-01 00:00:00.000002018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tt0100275</td>\n",
       "      <td>The Wandering Soap Opera</td>\n",
       "      <td>2017</td>\n",
       "      <td>Comedy,Drama,Fantasy</td>\n",
       "      <td>6.5</td>\n",
       "      <td>1970-01-01 00:00:00.000002017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146114</th>\n",
       "      <td>tt9913056</td>\n",
       "      <td>Swarm Season</td>\n",
       "      <td>2019</td>\n",
       "      <td>Documentary</td>\n",
       "      <td>6.2</td>\n",
       "      <td>1970-01-01 00:00:00.000002019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146115</th>\n",
       "      <td>tt9913084</td>\n",
       "      <td>Diabolik sono io</td>\n",
       "      <td>2019</td>\n",
       "      <td>Documentary</td>\n",
       "      <td>6.2</td>\n",
       "      <td>1970-01-01 00:00:00.000002019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146122</th>\n",
       "      <td>tt9914286</td>\n",
       "      <td>Sokagin Çocuklari</td>\n",
       "      <td>2019</td>\n",
       "      <td>Drama,Family</td>\n",
       "      <td>8.7</td>\n",
       "      <td>1970-01-01 00:00:00.000002019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146125</th>\n",
       "      <td>tt9914642</td>\n",
       "      <td>Albatross</td>\n",
       "      <td>2017</td>\n",
       "      <td>Documentary</td>\n",
       "      <td>8.5</td>\n",
       "      <td>1970-01-01 00:00:00.000002017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146134</th>\n",
       "      <td>tt9916160</td>\n",
       "      <td>Drømmeland</td>\n",
       "      <td>2019</td>\n",
       "      <td>Documentary</td>\n",
       "      <td>6.5</td>\n",
       "      <td>1970-01-01 00:00:00.000002019</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>73052 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         movie_id                            title  year  \\\n",
       "0       tt0063540                        Sunghursh  2013   \n",
       "1       tt0066787  One Day Before the Rainy Season  2019   \n",
       "2       tt0069049       The Other Side of the Wind  2018   \n",
       "3       tt0069204                  Sabse Bada Sukh  2018   \n",
       "4       tt0100275         The Wandering Soap Opera  2017   \n",
       "...           ...                              ...   ...   \n",
       "146114  tt9913056                     Swarm Season  2019   \n",
       "146115  tt9913084                 Diabolik sono io  2019   \n",
       "146122  tt9914286                Sokagin Çocuklari  2019   \n",
       "146125  tt9914642                        Albatross  2017   \n",
       "146134  tt9916160                       Drømmeland  2019   \n",
       "\n",
       "                      genres  averagerating                    start_year  \n",
       "0         Action,Crime,Drama            7.0 1970-01-01 00:00:00.000002013  \n",
       "1            Biography,Drama            7.2 1970-01-01 00:00:00.000002019  \n",
       "2                      Drama            6.9 1970-01-01 00:00:00.000002018  \n",
       "3               Comedy,Drama            6.1 1970-01-01 00:00:00.000002018  \n",
       "4       Comedy,Drama,Fantasy            6.5 1970-01-01 00:00:00.000002017  \n",
       "...                      ...            ...                           ...  \n",
       "146114           Documentary            6.2 1970-01-01 00:00:00.000002019  \n",
       "146115           Documentary            6.2 1970-01-01 00:00:00.000002019  \n",
       "146122          Drama,Family            8.7 1970-01-01 00:00:00.000002019  \n",
       "146125           Documentary            8.5 1970-01-01 00:00:00.000002017  \n",
       "146134           Documentary            6.5 1970-01-01 00:00:00.000002019  \n",
       "\n",
       "[73052 rows x 6 columns]"
      ]
     },
     "execution_count": 227,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IMDB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [],
   "source": [
    "IMDB =IMDB.drop(['start_year'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <td>Action,Crime,Drama</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tt0066787</td>\n",
       "      <td>One Day Before the Rainy Season</td>\n",
       "      <td>2019</td>\n",
       "      <td>Biography,Drama</td>\n",
       "      <td>7.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>tt0069049</td>\n",
       "      <td>The Other Side of the Wind</td>\n",
       "      <td>2018</td>\n",
       "      <td>Drama</td>\n",
       "      <td>6.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>tt0069204</td>\n",
       "      <td>Sabse Bada Sukh</td>\n",
       "      <td>2018</td>\n",
       "      <td>Comedy,Drama</td>\n",
       "      <td>6.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tt0100275</td>\n",
       "      <td>The Wandering Soap Opera</td>\n",
       "      <td>2017</td>\n",
       "      <td>Comedy,Drama,Fantasy</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146114</th>\n",
       "      <td>tt9913056</td>\n",
       "      <td>Swarm Season</td>\n",
       "      <td>2019</td>\n",
       "      <td>Documentary</td>\n",
       "      <td>6.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146115</th>\n",
       "      <td>tt9913084</td>\n",
       "      <td>Diabolik sono io</td>\n",
       "      <td>2019</td>\n",
       "      <td>Documentary</td>\n",
       "      <td>6.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146122</th>\n",
       "      <td>tt9914286</td>\n",
       "      <td>Sokagin Çocuklari</td>\n",
       "      <td>2019</td>\n",
       "      <td>Drama,Family</td>\n",
       "      <td>8.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146125</th>\n",
       "      <td>tt9914642</td>\n",
       "      <td>Albatross</td>\n",
       "      <td>2017</td>\n",
       "      <td>Documentary</td>\n",
       "      <td>8.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146134</th>\n",
       "      <td>tt9916160</td>\n",
       "      <td>Drømmeland</td>\n",
       "      <td>2019</td>\n",
       "      <td>Documentary</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>73052 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         movie_id                            title  year  \\\n",
       "0       tt0063540                        Sunghursh  2013   \n",
       "1       tt0066787  One Day Before the Rainy Season  2019   \n",
       "2       tt0069049       The Other Side of the Wind  2018   \n",
       "3       tt0069204                  Sabse Bada Sukh  2018   \n",
       "4       tt0100275         The Wandering Soap Opera  2017   \n",
       "...           ...                              ...   ...   \n",
       "146114  tt9913056                     Swarm Season  2019   \n",
       "146115  tt9913084                 Diabolik sono io  2019   \n",
       "146122  tt9914286                Sokagin Çocuklari  2019   \n",
       "146125  tt9914642                        Albatross  2017   \n",
       "146134  tt9916160                       Drømmeland  2019   \n",
       "\n",
       "                      genres  averagerating  \n",
       "0         Action,Crime,Drama            7.0  \n",
       "1            Biography,Drama            7.2  \n",
       "2                      Drama            6.9  \n",
       "3               Comedy,Drama            6.1  \n",
       "4       Comedy,Drama,Fantasy            6.5  \n",
       "...                      ...            ...  \n",
       "146114           Documentary            6.2  \n",
       "146115           Documentary            6.2  \n",
       "146122          Drama,Family            8.7  \n",
       "146125           Documentary            8.5  \n",
       "146134           Documentary            6.5  \n",
       "\n",
       "[73052 rows x 5 columns]"
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IMDB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>652000000</td>\n",
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       "      <td>2010</td>\n",
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       "      <td>664300000</td>\n",
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       "      <td>2010</td>\n",
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       "      <td>I Still See You</td>\n",
       "      <td>1400.0</td>\n",
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       "    <tr>\n",
       "      <th>3286</th>\n",
       "      <td>The Catcher Was a Spy</td>\n",
       "      <td>725000.0</td>\n",
       "      <td>229000</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3309</th>\n",
       "      <td>Time Freak</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>256000</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3342</th>\n",
       "      <td>Reign of Judges: Title of Liberty - Concept Short</td>\n",
       "      <td>93200.0</td>\n",
       "      <td>5200</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3353</th>\n",
       "      <td>Antonio Lopez 1970: Sex Fashion &amp; Disco</td>\n",
       "      <td>43200.0</td>\n",
       "      <td>30000</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2009 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  title  domestic_gross  \\\n",
       "0                                           Toy Story 3     415000000.0   \n",
       "1                            Alice in Wonderland (2010)     334200000.0   \n",
       "2           Harry Potter and the Deathly Hallows Part 1     296000000.0   \n",
       "3                                             Inception     292600000.0   \n",
       "4                                   Shrek Forever After     238700000.0   \n",
       "...                                                 ...             ...   \n",
       "3275                                    I Still See You          1400.0   \n",
       "3286                              The Catcher Was a Spy        725000.0   \n",
       "3309                                         Time Freak         10000.0   \n",
       "3342  Reign of Judges: Title of Liberty - Concept Short         93200.0   \n",
       "3353            Antonio Lopez 1970: Sex Fashion & Disco         43200.0   \n",
       "\n",
       "     foreign_gross  year  \n",
       "0        652000000  2010  \n",
       "1        691300000  2010  \n",
       "2        664300000  2010  \n",
       "3        535700000  2010  \n",
       "4        513900000  2010  \n",
       "...            ...   ...  \n",
       "3275       1500000  2018  \n",
       "3286        229000  2018  \n",
       "3309        256000  2018  \n",
       "3342          5200  2018  \n",
       "3353         30000  2018  \n",
       "\n",
       "[2009 rows x 4 columns]"
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BOM_movies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2009 entries, 0 to 3353\n",
      "Data columns (total 4 columns):\n",
      " #   Column          Non-Null Count  Dtype  \n",
      "---  ------          --------------  -----  \n",
      " 0   title           2009 non-null   object \n",
      " 1   domestic_gross  2009 non-null   float64\n",
      " 2   foreign_gross   2009 non-null   object \n",
      " 3   year            2009 non-null   int64  \n",
      "dtypes: float64(1), int64(1), object(2)\n",
      "memory usage: 78.5+ KB\n"
     ]
    }
   ],
   "source": [
    "BOM_movies.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                  title  domestic_gross  \\\n",
      "0                                           Toy Story 3     415000000.0   \n",
      "1                            Alice in Wonderland (2010)     334200000.0   \n",
      "2           Harry Potter and the Deathly Hallows Part 1     296000000.0   \n",
      "3                                             Inception     292600000.0   \n",
      "4                                   Shrek Forever After     238700000.0   \n",
      "...                                                 ...             ...   \n",
      "3275                                    I Still See You          1400.0   \n",
      "3286                              The Catcher Was a Spy        725000.0   \n",
      "3309                                         Time Freak         10000.0   \n",
      "3342  Reign of Judges: Title of Liberty - Concept Short         93200.0   \n",
      "3353            Antonio Lopez 1970: Sex Fashion & Disco         43200.0   \n",
      "\n",
      "      foreign_gross  year  \n",
      "0          652.0000  2010  \n",
      "1          691.3000  2010  \n",
      "2          664.3000  2010  \n",
      "3          535.7000  2010  \n",
      "4          513.9000  2010  \n",
      "...             ...   ...  \n",
      "3275         1.5000  2018  \n",
      "3286         0.2290  2018  \n",
      "3309         0.2560  2018  \n",
      "3342         0.0052  2018  \n",
      "3353         0.0300  2018  \n",
      "\n",
      "[2009 rows x 4 columns]\n"
     ]
    }
   ],
   "source": [
    "# Remove commas and convert the column to numeric\n",
    "BOM_movies['foreign_gross'] = BOM_movies['foreign_gross'].str.replace(',', '').astype(float)\n",
    "# Divide the column by 1,000,000\n",
    "BOM_movies['foreign_gross'] = pd.to_numeric(BOM_movies['foreign_gross']) / 1000000\n",
    "# Display the DataFrame with the specified column divided by 1,000,000\n",
    "print(BOM_movies)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['title', 'domestic_gross', 'foreign_gross', 'year'], dtype='object')"
      ]
     },
     "execution_count": 233,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BOM_movies.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['genre_ids', 'id', 'original_language', 'original_title', 'popularity',\n",
       "       'release_date', 'title', 'vote_average', 'vote_count', 'Year'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 234,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TheMovie.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['movie_id', 'title', 'year', 'genres', 'averagerating'], dtype='object')"
      ]
     },
     "execution_count": 235,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IMDB.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Explatory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Start: 2010\n",
      "End: 2018\n",
      "Period: 8\n"
     ]
    }
   ],
   "source": [
    "# Display the start, end, and period of the movies\n",
    "start_date = BOM_movies.year.min()\n",
    "end_date = BOM_movies.year.max()\n",
    "period = end_date - start_date\n",
    "\n",
    "print(\"Start:\", start_date)\n",
    "print(\"End:\", end_date)\n",
    "print(\"Period:\", period)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>genre_ids</th>\n",
       "      <th>id</th>\n",
       "      <th>original_language</th>\n",
       "      <th>original_title</th>\n",
       "      <th>popularity</th>\n",
       "      <th>release_date</th>\n",
       "      <th>title</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>vote_count</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[12, 14, 10751]</td>\n",
       "      <td>12444</td>\n",
       "      <td>en</td>\n",
       "      <td>Harry Potter and the Deathly Hallows: Part 1</td>\n",
       "      <td>33.533</td>\n",
       "      <td>2010-11-19</td>\n",
       "      <td>Harry Potter and the Deathly Hallows: Part 1</td>\n",
       "      <td>7.7</td>\n",
       "      <td>10788</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[14, 12, 16, 10751]</td>\n",
       "      <td>10191</td>\n",
       "      <td>en</td>\n",
       "      <td>How to Train Your Dragon</td>\n",
       "      <td>28.734</td>\n",
       "      <td>2010-03-26</td>\n",
       "      <td>How to Train Your Dragon</td>\n",
       "      <td>7.7</td>\n",
       "      <td>7610</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[12, 28, 878]</td>\n",
       "      <td>10138</td>\n",
       "      <td>en</td>\n",
       "      <td>Iron Man 2</td>\n",
       "      <td>28.515</td>\n",
       "      <td>2010-05-07</td>\n",
       "      <td>Iron Man 2</td>\n",
       "      <td>6.8</td>\n",
       "      <td>12368</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[16, 35, 10751]</td>\n",
       "      <td>862</td>\n",
       "      <td>en</td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>28.005</td>\n",
       "      <td>1995-11-22</td>\n",
       "      <td>Toy Story</td>\n",
       "      <td>7.9</td>\n",
       "      <td>10174</td>\n",
       "      <td>1995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[28, 878, 12]</td>\n",
       "      <td>27205</td>\n",
       "      <td>en</td>\n",
       "      <td>Inception</td>\n",
       "      <td>27.920</td>\n",
       "      <td>2010-07-16</td>\n",
       "      <td>Inception</td>\n",
       "      <td>8.3</td>\n",
       "      <td>22186</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26512</th>\n",
       "      <td>[27, 18]</td>\n",
       "      <td>488143</td>\n",
       "      <td>en</td>\n",
       "      <td>Laboratory Conditions</td>\n",
       "      <td>0.600</td>\n",
       "      <td>2018-10-13</td>\n",
       "      <td>Laboratory Conditions</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26513</th>\n",
       "      <td>[18, 53]</td>\n",
       "      <td>485975</td>\n",
       "      <td>en</td>\n",
       "      <td>_EXHIBIT_84xxx_</td>\n",
       "      <td>0.600</td>\n",
       "      <td>2018-05-01</td>\n",
       "      <td>_EXHIBIT_84xxx_</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26514</th>\n",
       "      <td>[14, 28, 12]</td>\n",
       "      <td>381231</td>\n",
       "      <td>en</td>\n",
       "      <td>The Last One</td>\n",
       "      <td>0.600</td>\n",
       "      <td>2018-10-01</td>\n",
       "      <td>The Last One</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26515</th>\n",
       "      <td>[10751, 12, 28]</td>\n",
       "      <td>366854</td>\n",
       "      <td>en</td>\n",
       "      <td>Trailer Made</td>\n",
       "      <td>0.600</td>\n",
       "      <td>2018-06-22</td>\n",
       "      <td>Trailer Made</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26516</th>\n",
       "      <td>[53, 27]</td>\n",
       "      <td>309885</td>\n",
       "      <td>en</td>\n",
       "      <td>The Church</td>\n",
       "      <td>0.600</td>\n",
       "      <td>2018-10-05</td>\n",
       "      <td>The Church</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>26517 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 genre_ids      id original_language  \\\n",
       "0          [12, 14, 10751]   12444                en   \n",
       "1      [14, 12, 16, 10751]   10191                en   \n",
       "2            [12, 28, 878]   10138                en   \n",
       "3          [16, 35, 10751]     862                en   \n",
       "4            [28, 878, 12]   27205                en   \n",
       "...                    ...     ...               ...   \n",
       "26512             [27, 18]  488143                en   \n",
       "26513             [18, 53]  485975                en   \n",
       "26514         [14, 28, 12]  381231                en   \n",
       "26515      [10751, 12, 28]  366854                en   \n",
       "26516             [53, 27]  309885                en   \n",
       "\n",
       "                                     original_title  popularity release_date  \\\n",
       "0      Harry Potter and the Deathly Hallows: Part 1      33.533   2010-11-19   \n",
       "1                          How to Train Your Dragon      28.734   2010-03-26   \n",
       "2                                        Iron Man 2      28.515   2010-05-07   \n",
       "3                                         Toy Story      28.005   1995-11-22   \n",
       "4                                         Inception      27.920   2010-07-16   \n",
       "...                                             ...         ...          ...   \n",
       "26512                         Laboratory Conditions       0.600   2018-10-13   \n",
       "26513                               _EXHIBIT_84xxx_       0.600   2018-05-01   \n",
       "26514                                  The Last One       0.600   2018-10-01   \n",
       "26515                                  Trailer Made       0.600   2018-06-22   \n",
       "26516                                    The Church       0.600   2018-10-05   \n",
       "\n",
       "                                              title  vote_average  vote_count  \\\n",
       "0      Harry Potter and the Deathly Hallows: Part 1           7.7       10788   \n",
       "1                          How to Train Your Dragon           7.7        7610   \n",
       "2                                        Iron Man 2           6.8       12368   \n",
       "3                                         Toy Story           7.9       10174   \n",
       "4                                         Inception           8.3       22186   \n",
       "...                                             ...           ...         ...   \n",
       "26512                         Laboratory Conditions           0.0           1   \n",
       "26513                               _EXHIBIT_84xxx_           0.0           1   \n",
       "26514                                  The Last One           0.0           1   \n",
       "26515                                  Trailer Made           0.0           1   \n",
       "26516                                    The Church           0.0           1   \n",
       "\n",
       "       Year  \n",
       "0      2010  \n",
       "1      2010  \n",
       "2      2010  \n",
       "3      1995  \n",
       "4      2010  \n",
       "...     ...  \n",
       "26512  2018  \n",
       "26513  2018  \n",
       "26514  2018  \n",
       "26515  2018  \n",
       "26516  2018  \n",
       "\n",
       "[26517 rows x 10 columns]"
      ]
     },
     "execution_count": 237,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop NaN values \n",
    "TheMovie.dropna(inplace=True)\n",
    "TheMovie"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\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>movie_id</th>\n",
       "      <th>title</th>\n",
       "      <th>year</th>\n",
       "      <th>genres</th>\n",
       "      <th>averagerating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>tt0063540</td>\n",
       "      <td>Sunghursh</td>\n",
       "      <td>2013</td>\n",
       "      <td>Action,Crime,Drama</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tt0066787</td>\n",
       "      <td>One Day Before the Rainy Season</td>\n",
       "      <td>2019</td>\n",
       "      <td>Biography,Drama</td>\n",
       "      <td>7.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>tt0069049</td>\n",
       "      <td>The Other Side of the Wind</td>\n",
       "      <td>2018</td>\n",
       "      <td>Drama</td>\n",
       "      <td>6.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>tt0069204</td>\n",
       "      <td>Sabse Bada Sukh</td>\n",
       "      <td>2018</td>\n",
       "      <td>Comedy,Drama</td>\n",
       "      <td>6.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tt0100275</td>\n",
       "      <td>The Wandering Soap Opera</td>\n",
       "      <td>2017</td>\n",
       "      <td>Comedy,Drama,Fantasy</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146114</th>\n",
       "      <td>tt9913056</td>\n",
       "      <td>Swarm Season</td>\n",
       "      <td>2019</td>\n",
       "      <td>Documentary</td>\n",
       "      <td>6.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146115</th>\n",
       "      <td>tt9913084</td>\n",
       "      <td>Diabolik sono io</td>\n",
       "      <td>2019</td>\n",
       "      <td>Documentary</td>\n",
       "      <td>6.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146122</th>\n",
       "      <td>tt9914286</td>\n",
       "      <td>Sokagin Çocuklari</td>\n",
       "      <td>2019</td>\n",
       "      <td>Drama,Family</td>\n",
       "      <td>8.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146125</th>\n",
       "      <td>tt9914642</td>\n",
       "      <td>Albatross</td>\n",
       "      <td>2017</td>\n",
       "      <td>Documentary</td>\n",
       "      <td>8.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146134</th>\n",
       "      <td>tt9916160</td>\n",
       "      <td>Drømmeland</td>\n",
       "      <td>2019</td>\n",
       "      <td>Documentary</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>73052 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         movie_id                            title  year  \\\n",
       "0       tt0063540                        Sunghursh  2013   \n",
       "1       tt0066787  One Day Before the Rainy Season  2019   \n",
       "2       tt0069049       The Other Side of the Wind  2018   \n",
       "3       tt0069204                  Sabse Bada Sukh  2018   \n",
       "4       tt0100275         The Wandering Soap Opera  2017   \n",
       "...           ...                              ...   ...   \n",
       "146114  tt9913056                     Swarm Season  2019   \n",
       "146115  tt9913084                 Diabolik sono io  2019   \n",
       "146122  tt9914286                Sokagin Çocuklari  2019   \n",
       "146125  tt9914642                        Albatross  2017   \n",
       "146134  tt9916160                       Drømmeland  2019   \n",
       "\n",
       "                      genres  averagerating  \n",
       "0         Action,Crime,Drama            7.0  \n",
       "1            Biography,Drama            7.2  \n",
       "2                      Drama            6.9  \n",
       "3               Comedy,Drama            6.1  \n",
       "4       Comedy,Drama,Fantasy            6.5  \n",
       "...                      ...            ...  \n",
       "146114           Documentary            6.2  \n",
       "146115           Documentary            6.2  \n",
       "146122          Drama,Family            8.7  \n",
       "146125           Documentary            8.5  \n",
       "146134           Documentary            6.5  \n",
       "\n",
       "[73052 rows x 5 columns]"
      ]
     },
     "execution_count": 238,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop Nan values \n",
    "IMDB.dropna(inplace=True)\n",
    "IMDB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(73052, 5)"
      ]
     },
     "execution_count": 239,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To check if the NaN values were dropped \n",
    "IMDB.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.330930022449761"
      ]
     },
     "execution_count": 240,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Average rating \n",
    "ratings_average = IMDB['averagerating'].mean()\n",
    "ratings_average"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['movie_id', 'title', 'year', 'genres', 'averagerating'], dtype='object')"
      ]
     },
     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IMDB.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>averagerating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>73052.000000</td>\n",
       "      <td>73052.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2014.281430</td>\n",
       "      <td>6.330930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.615288</td>\n",
       "      <td>1.474699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2010.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2012.000000</td>\n",
       "      <td>5.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2014.000000</td>\n",
       "      <td>6.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2017.000000</td>\n",
       "      <td>7.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2019.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               year  averagerating\n",
       "count  73052.000000   73052.000000\n",
       "mean    2014.281430       6.330930\n",
       "std        2.615288       1.474699\n",
       "min     2010.000000       1.000000\n",
       "25%     2012.000000       5.500000\n",
       "50%     2014.000000       6.500000\n",
       "75%     2017.000000       7.400000\n",
       "max     2019.000000      10.000000"
      ]
     },
     "execution_count": 242,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IMDB.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>domestic_gross</th>\n",
       "      <th>foreign_gross</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Toy Story 3</td>\n",
       "      <td>415000000.0</td>\n",
       "      <td>652.0</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alice in Wonderland (2010)</td>\n",
       "      <td>334200000.0</td>\n",
       "      <td>691.3</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Harry Potter and the Deathly Hallows Part 1</td>\n",
       "      <td>296000000.0</td>\n",
       "      <td>664.3</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Inception</td>\n",
       "      <td>292600000.0</td>\n",
       "      <td>535.7</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Shrek Forever After</td>\n",
       "      <td>238700000.0</td>\n",
       "      <td>513.9</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         title  domestic_gross  foreign_gross  \\\n",
       "0                                  Toy Story 3     415000000.0          652.0   \n",
       "1                   Alice in Wonderland (2010)     334200000.0          691.3   \n",
       "2  Harry Potter and the Deathly Hallows Part 1     296000000.0          664.3   \n",
       "3                                    Inception     292600000.0          535.7   \n",
       "4                          Shrek Forever After     238700000.0          513.9   \n",
       "\n",
       "   year  \n",
       "0  2010  \n",
       "1  2010  \n",
       "2  2010  \n",
       "3  2010  \n",
       "4  2010  "
      ]
     },
     "execution_count": 243,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BOM_movies.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "46973114.3777999"
      ]
     },
     "execution_count": 244,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Domestic avarage gross\n",
    "domestic_gross_average = BOM_movies['domestic_gross'].mean()\n",
    "domestic_gross_average"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>movie_id</th>\n",
       "      <th>title</th>\n",
       "      <th>year</th>\n",
       "      <th>genres</th>\n",
       "      <th>averagerating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>tt0063540</td>\n",
       "      <td>Sunghursh</td>\n",
       "      <td>2013</td>\n",
       "      <td>Action,Crime,Drama</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tt0066787</td>\n",
       "      <td>One Day Before the Rainy Season</td>\n",
       "      <td>2019</td>\n",
       "      <td>Biography,Drama</td>\n",
       "      <td>7.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    movie_id                            title  year              genres  \\\n",
       "0  tt0063540                        Sunghursh  2013  Action,Crime,Drama   \n",
       "1  tt0066787  One Day Before the Rainy Season  2019     Biography,Drama   \n",
       "\n",
       "   averagerating  \n",
       "0            7.0  \n",
       "1            7.2  "
      ]
     },
     "execution_count": 245,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "IMDB.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['genre_ids', 'id', 'original_language', 'original_title', 'popularity',\n",
       "       'release_date', 'title', 'vote_average', 'vote_count', 'Year'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 246,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TheMovie.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>genre_ids</th>\n",
       "      <th>id</th>\n",
       "      <th>original_language</th>\n",
       "      <th>original_title</th>\n",
       "      <th>popularity</th>\n",
       "      <th>release_date</th>\n",
       "      <th>title</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>vote_count</th>\n",
       "      <th>Year</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[12, 14, 10751]</td>\n",
       "      <td>12444</td>\n",
       "      <td>en</td>\n",
       "      <td>Harry Potter and the Deathly Hallows: Part 1</td>\n",
       "      <td>33.533</td>\n",
       "      <td>2010-11-19</td>\n",
       "      <td>Harry Potter and the Deathly Hallows: Part 1</td>\n",
       "      <td>7.7</td>\n",
       "      <td>10788</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[14, 12, 16, 10751]</td>\n",
       "      <td>10191</td>\n",
       "      <td>en</td>\n",
       "      <td>How to Train Your Dragon</td>\n",
       "      <td>28.734</td>\n",
       "      <td>2010-03-26</td>\n",
       "      <td>How to Train Your Dragon</td>\n",
       "      <td>7.7</td>\n",
       "      <td>7610</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             genre_ids     id original_language  \\\n",
       "0      [12, 14, 10751]  12444                en   \n",
       "1  [14, 12, 16, 10751]  10191                en   \n",
       "\n",
       "                                 original_title  popularity release_date  \\\n",
       "0  Harry Potter and the Deathly Hallows: Part 1      33.533   2010-11-19   \n",
       "1                      How to Train Your Dragon      28.734   2010-03-26   \n",
       "\n",
       "                                          title  vote_average  vote_count  \\\n",
       "0  Harry Potter and the Deathly Hallows: Part 1           7.7       10788   \n",
       "1                      How to Train Your Dragon           7.7        7610   \n",
       "\n",
       "   Year  \n",
       "0  2010  \n",
       "1  2010  "
      ]
     },
     "execution_count": 247,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TheMovie.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>domestic_gross</th>\n",
       "      <th>foreign_gross</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Toy Story 3</td>\n",
       "      <td>415000000.0</td>\n",
       "      <td>652.0</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alice in Wonderland (2010)</td>\n",
       "      <td>334200000.0</td>\n",
       "      <td>691.3</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        title  domestic_gross  foreign_gross  year\n",
       "0                 Toy Story 3     415000000.0          652.0  2010\n",
       "1  Alice in Wonderland (2010)     334200000.0          691.3  2010"
      ]
     },
     "execution_count": 248,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BOM_movies.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Merging all the tables to one \n",
    "Box Office mojo\n",
    "The Movie\n",
    "IMDb "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {},
   "outputs": [],
   "source": [
    "merged_df = BOM_movies.merge(TheMovie, on='title', how='inner').merge(IMDB, on='title', how='inner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>averagerating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Toy Story 3</td>\n",
       "      <td>415000000.0</td>\n",
       "      <td>652.0</td>\n",
       "      <td>2010</td>\n",
       "      <td>[16, 10751, 35]</td>\n",
       "      <td>10193</td>\n",
       "      <td>en</td>\n",
       "      <td>Toy Story 3</td>\n",
       "      <td>24.445</td>\n",
       "      <td>2010-06-17</td>\n",
       "      <td>7.7</td>\n",
       "      <td>8340</td>\n",
       "      <td>2010</td>\n",
       "      <td>tt0435761</td>\n",
       "      <td>2010</td>\n",
       "      <td>Adventure,Animation,Comedy</td>\n",
       "      <td>8.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Inception</td>\n",
       "      <td>292600000.0</td>\n",
       "      <td>535.7</td>\n",
       "      <td>2010</td>\n",
       "      <td>[28, 878, 12]</td>\n",
       "      <td>27205</td>\n",
       "      <td>en</td>\n",
       "      <td>Inception</td>\n",
       "      <td>27.920</td>\n",
       "      <td>2010-07-16</td>\n",
       "      <td>8.3</td>\n",
       "      <td>22186</td>\n",
       "      <td>2010</td>\n",
       "      <td>tt1375666</td>\n",
       "      <td>2010</td>\n",
       "      <td>Action,Adventure,Sci-Fi</td>\n",
       "      <td>8.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Shrek Forever After</td>\n",
       "      <td>238700000.0</td>\n",
       "      <td>513.9</td>\n",
       "      <td>2010</td>\n",
       "      <td>[35, 12, 14, 16, 10751]</td>\n",
       "      <td>10192</td>\n",
       "      <td>en</td>\n",
       "      <td>Shrek Forever After</td>\n",
       "      <td>15.041</td>\n",
       "      <td>2010-05-16</td>\n",
       "      <td>6.1</td>\n",
       "      <td>3843</td>\n",
       "      <td>2010</td>\n",
       "      <td>tt0892791</td>\n",
       "      <td>2010</td>\n",
       "      <td>Adventure,Animation,Comedy</td>\n",
       "      <td>6.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>The Twilight Saga: Eclipse</td>\n",
       "      <td>300500000.0</td>\n",
       "      <td>398.0</td>\n",
       "      <td>2010</td>\n",
       "      <td>[12, 14, 18, 10749]</td>\n",
       "      <td>24021</td>\n",
       "      <td>en</td>\n",
       "      <td>The Twilight Saga: Eclipse</td>\n",
       "      <td>20.340</td>\n",
       "      <td>2010-06-23</td>\n",
       "      <td>6.0</td>\n",
       "      <td>4909</td>\n",
       "      <td>2010</td>\n",
       "      <td>tt1325004</td>\n",
       "      <td>2010</td>\n",
       "      <td>Adventure,Drama,Fantasy</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Iron Man 2</td>\n",
       "      <td>312400000.0</td>\n",
       "      <td>311.5</td>\n",
       "      <td>2010</td>\n",
       "      <td>[12, 28, 878]</td>\n",
       "      <td>10138</td>\n",
       "      <td>en</td>\n",
       "      <td>Iron Man 2</td>\n",
       "      <td>28.515</td>\n",
       "      <td>2010-05-07</td>\n",
       "      <td>6.8</td>\n",
       "      <td>12368</td>\n",
       "      <td>2010</td>\n",
       "      <td>tt1228705</td>\n",
       "      <td>2010</td>\n",
       "      <td>Action,Adventure,Sci-Fi</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        title  domestic_gross  foreign_gross  year_x  \\\n",
       "0                 Toy Story 3     415000000.0          652.0    2010   \n",
       "1                   Inception     292600000.0          535.7    2010   \n",
       "2         Shrek Forever After     238700000.0          513.9    2010   \n",
       "3  The Twilight Saga: Eclipse     300500000.0          398.0    2010   \n",
       "4                  Iron Man 2     312400000.0          311.5    2010   \n",
       "\n",
       "                 genre_ids     id original_language  \\\n",
       "0          [16, 10751, 35]  10193                en   \n",
       "1            [28, 878, 12]  27205                en   \n",
       "2  [35, 12, 14, 16, 10751]  10192                en   \n",
       "3      [12, 14, 18, 10749]  24021                en   \n",
       "4            [12, 28, 878]  10138                en   \n",
       "\n",
       "               original_title  popularity release_date  vote_average  \\\n",
       "0                 Toy Story 3      24.445   2010-06-17           7.7   \n",
       "1                   Inception      27.920   2010-07-16           8.3   \n",
       "2         Shrek Forever After      15.041   2010-05-16           6.1   \n",
       "3  The Twilight Saga: Eclipse      20.340   2010-06-23           6.0   \n",
       "4                  Iron Man 2      28.515   2010-05-07           6.8   \n",
       "\n",
       "   vote_count  Year   movie_id  year_y                      genres  \\\n",
       "0        8340  2010  tt0435761    2010  Adventure,Animation,Comedy   \n",
       "1       22186  2010  tt1375666    2010     Action,Adventure,Sci-Fi   \n",
       "2        3843  2010  tt0892791    2010  Adventure,Animation,Comedy   \n",
       "3        4909  2010  tt1325004    2010     Adventure,Drama,Fantasy   \n",
       "4       12368  2010  tt1228705    2010     Action,Adventure,Sci-Fi   \n",
       "\n",
       "   averagerating  \n",
       "0            8.3  \n",
       "1            8.8  \n",
       "2            6.3  \n",
       "3            5.0  \n",
       "4            7.0  "
      ]
     },
     "execution_count": 250,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['title', 'domestic_gross', 'foreign_gross', 'year_x', 'genre_ids', 'id',\n",
       "       'original_language', 'original_title', 'popularity', 'release_date',\n",
       "       'vote_average', 'vote_count', 'Year', 'movie_id', 'year_y', 'genres',\n",
       "       'averagerating'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 251,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "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>title</th>\n",
       "      <th>foreign_gross</th>\n",
       "      <th>id</th>\n",
       "      <th>popularity</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>Year</th>\n",
       "      <th>movie_id</th>\n",
       "      <th>genres</th>\n",
       "      <th>averagerating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Toy Story 3</td>\n",
       "      <td>652.000</td>\n",
       "      <td>10193</td>\n",
       "      <td>24.445</td>\n",
       "      <td>7.7</td>\n",
       "      <td>2010</td>\n",
       "      <td>tt0435761</td>\n",
       "      <td>Adventure,Animation,Comedy</td>\n",
       "      <td>8.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Inception</td>\n",
       "      <td>535.700</td>\n",
       "      <td>27205</td>\n",
       "      <td>27.920</td>\n",
       "      <td>8.3</td>\n",
       "      <td>2010</td>\n",
       "      <td>tt1375666</td>\n",
       "      <td>Action,Adventure,Sci-Fi</td>\n",
       "      <td>8.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Shrek Forever After</td>\n",
       "      <td>513.900</td>\n",
       "      <td>10192</td>\n",
       "      <td>15.041</td>\n",
       "      <td>6.1</td>\n",
       "      <td>2010</td>\n",
       "      <td>tt0892791</td>\n",
       "      <td>Adventure,Animation,Comedy</td>\n",
       "      <td>6.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>The Twilight Saga: Eclipse</td>\n",
       "      <td>398.000</td>\n",
       "      <td>24021</td>\n",
       "      <td>20.340</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2010</td>\n",
       "      <td>tt1325004</td>\n",
       "      <td>Adventure,Drama,Fantasy</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Iron Man 2</td>\n",
       "      <td>311.500</td>\n",
       "      <td>10138</td>\n",
       "      <td>28.515</td>\n",
       "      <td>6.8</td>\n",
       "      <td>2010</td>\n",
       "      <td>tt1228705</td>\n",
       "      <td>Action,Adventure,Sci-Fi</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2040</th>\n",
       "      <td>Bilal: A New Breed of Hero</td>\n",
       "      <td>1.700</td>\n",
       "      <td>332718</td>\n",
       "      <td>2.707</td>\n",
       "      <td>6.8</td>\n",
       "      <td>2018</td>\n",
       "      <td>tt3576728</td>\n",
       "      <td>Action,Adventure,Animation</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2041</th>\n",
       "      <td>I Still See You</td>\n",
       "      <td>1.500</td>\n",
       "      <td>448095</td>\n",
       "      <td>12.867</td>\n",
       "      <td>6.8</td>\n",
       "      <td>2018</td>\n",
       "      <td>tt2160105</td>\n",
       "      <td>Fantasy,Thriller</td>\n",
       "      <td>5.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2042</th>\n",
       "      <td>The Catcher Was a Spy</td>\n",
       "      <td>0.229</td>\n",
       "      <td>467952</td>\n",
       "      <td>9.727</td>\n",
       "      <td>6.2</td>\n",
       "      <td>2018</td>\n",
       "      <td>tt4602066</td>\n",
       "      <td>Biography,Drama,War</td>\n",
       "      <td>6.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2043</th>\n",
       "      <td>Time Freak</td>\n",
       "      <td>0.256</td>\n",
       "      <td>92968</td>\n",
       "      <td>1.604</td>\n",
       "      <td>6.9</td>\n",
       "      <td>2011</td>\n",
       "      <td>tt6769280</td>\n",
       "      <td>Comedy,Drama,Romance</td>\n",
       "      <td>5.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2044</th>\n",
       "      <td>Time Freak</td>\n",
       "      <td>0.256</td>\n",
       "      <td>451925</td>\n",
       "      <td>9.751</td>\n",
       "      <td>6.3</td>\n",
       "      <td>2018</td>\n",
       "      <td>tt6769280</td>\n",
       "      <td>Comedy,Drama,Romance</td>\n",
       "      <td>5.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2045 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           title  foreign_gross      id  popularity  \\\n",
       "0                    Toy Story 3        652.000   10193      24.445   \n",
       "1                      Inception        535.700   27205      27.920   \n",
       "2            Shrek Forever After        513.900   10192      15.041   \n",
       "3     The Twilight Saga: Eclipse        398.000   24021      20.340   \n",
       "4                     Iron Man 2        311.500   10138      28.515   \n",
       "...                          ...            ...     ...         ...   \n",
       "2040  Bilal: A New Breed of Hero          1.700  332718       2.707   \n",
       "2041             I Still See You          1.500  448095      12.867   \n",
       "2042       The Catcher Was a Spy          0.229  467952       9.727   \n",
       "2043                  Time Freak          0.256   92968       1.604   \n",
       "2044                  Time Freak          0.256  451925       9.751   \n",
       "\n",
       "      vote_average  Year   movie_id                      genres  averagerating  \n",
       "0              7.7  2010  tt0435761  Adventure,Animation,Comedy            8.3  \n",
       "1              8.3  2010  tt1375666     Action,Adventure,Sci-Fi            8.8  \n",
       "2              6.1  2010  tt0892791  Adventure,Animation,Comedy            6.3  \n",
       "3              6.0  2010  tt1325004     Adventure,Drama,Fantasy            5.0  \n",
       "4              6.8  2010  tt1228705     Action,Adventure,Sci-Fi            7.0  \n",
       "...            ...   ...        ...                         ...            ...  \n",
       "2040           6.8  2018  tt3576728  Action,Adventure,Animation            8.0  \n",
       "2041           6.8  2018  tt2160105            Fantasy,Thriller            5.7  \n",
       "2042           6.2  2018  tt4602066         Biography,Drama,War            6.2  \n",
       "2043           6.9  2011  tt6769280        Comedy,Drama,Romance            5.7  \n",
       "2044           6.3  2018  tt6769280        Comedy,Drama,Romance            5.7  \n",
       "\n",
       "[2045 rows x 9 columns]"
      ]
     },
     "execution_count": 252,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop multiple columns\n",
    "merged_df = merged_df.drop(columns=['domestic_gross', 'year_x', 'genre_ids', 'original_language', 'original_title','release_date', 'year_y', 'vote_count'])\n",
    "merged_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "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>foreign_gross</th>\n",
       "      <th>id</th>\n",
       "      <th>popularity</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>averagerating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2045.000000</td>\n",
       "      <td>2045.000000</td>\n",
       "      <td>2045.000000</td>\n",
       "      <td>2045.000000</td>\n",
       "      <td>2045.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>83.614927</td>\n",
       "      <td>201494.369682</td>\n",
       "      <td>11.712381</td>\n",
       "      <td>6.390856</td>\n",
       "      <td>6.439413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>142.689276</td>\n",
       "      <td>141355.561866</td>\n",
       "      <td>7.954126</td>\n",
       "      <td>0.938677</td>\n",
       "      <td>1.031724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000600</td>\n",
       "      <td>1771.000000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.600000</td>\n",
       "      <td>68722.000000</td>\n",
       "      <td>7.272000</td>\n",
       "      <td>5.900000</td>\n",
       "      <td>5.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>24.300000</td>\n",
       "      <td>181283.000000</td>\n",
       "      <td>10.305000</td>\n",
       "      <td>6.400000</td>\n",
       "      <td>6.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>88.000000</td>\n",
       "      <td>328387.000000</td>\n",
       "      <td>14.797000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>7.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>946.400000</td>\n",
       "      <td>522921.000000</td>\n",
       "      <td>80.773000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>9.200000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       foreign_gross             id   popularity  vote_average  averagerating\n",
       "count    2045.000000    2045.000000  2045.000000   2045.000000    2045.000000\n",
       "mean       83.614927  201494.369682    11.712381      6.390856       6.439413\n",
       "std       142.689276  141355.561866     7.954126      0.938677       1.031724\n",
       "min         0.000600    1771.000000     0.600000      1.000000       1.600000\n",
       "25%         5.600000   68722.000000     7.272000      5.900000       5.900000\n",
       "50%        24.300000  181283.000000    10.305000      6.400000       6.500000\n",
       "75%        88.000000  328387.000000    14.797000      7.000000       7.200000\n",
       "max       946.400000  522921.000000    80.773000     10.000000       9.200000"
      ]
     },
     "execution_count": 253,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2045 entries, 0 to 2044\n",
      "Data columns (total 9 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   title          2045 non-null   object \n",
      " 1   foreign_gross  2045 non-null   float64\n",
      " 2   id             2045 non-null   int64  \n",
      " 3   popularity     2045 non-null   float64\n",
      " 4   vote_average   2045 non-null   float64\n",
      " 5   Year           2045 non-null   object \n",
      " 6   movie_id       2045 non-null   object \n",
      " 7   genres         2045 non-null   object \n",
      " 8   averagerating  2045 non-null   float64\n",
      "dtypes: float64(4), int64(1), object(4)\n",
      "memory usage: 159.8+ KB\n"
     ]
    }
   ],
   "source": [
    "merged_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert the gross columns and rating column into integer\n",
    "merged_df['foreign_gross'] = merged_df['foreign_gross'].astype(int)\n",
    "merged_df['averagerating'] = merged_df['averagerating'].astype(int)\n",
    "merged_df['Year'] = merged_df['Year'].astype(int)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 2045 entries, 0 to 2044\n",
      "Data columns (total 9 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   title          2045 non-null   object \n",
      " 1   foreign_gross  2045 non-null   int32  \n",
      " 2   id             2045 non-null   int64  \n",
      " 3   popularity     2045 non-null   float64\n",
      " 4   vote_average   2045 non-null   float64\n",
      " 5   Year           2045 non-null   int32  \n",
      " 6   movie_id       2045 non-null   object \n",
      " 7   genres         2045 non-null   object \n",
      " 8   averagerating  2045 non-null   int32  \n",
      "dtypes: float64(2), int32(3), int64(1), object(3)\n",
      "memory usage: 135.8+ KB\n"
     ]
    }
   ],
   "source": [
    "merged_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filter out rows with non-numeric 'foreign_gross' values\n",
    "merged_df = merged_df[pd.to_numeric(merged_df['foreign_gross'], errors='coerce').notna()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'filtered_df' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32mc:\\Users\\USER\\Documents\\Project1\\dsc-phase-1-project-v2-4\\student.ipynb Cell 159\u001b[0m line \u001b[0;36m2\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/USER/Documents/Project1/dsc-phase-1-project-v2-4/student.ipynb#Y314sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m \u001b[39m# Splitting the genre column to have a genre for each movie in the table \u001b[39;00m\n\u001b[1;32m----> <a href='vscode-notebook-cell:/c%3A/Users/USER/Documents/Project1/dsc-phase-1-project-v2-4/student.ipynb#Y314sZmlsZQ%3D%3D?line=1'>2</a>\u001b[0m filtered_df \u001b[39m=\u001b[39m filtered_df\u001b[39m.\u001b[39massign(genres \u001b[39m=\u001b[39m filtered_df[\u001b[39m'\u001b[39m\u001b[39mgenres\u001b[39m\u001b[39m'\u001b[39m]\u001b[39m.\u001b[39mstr\u001b[39m.\u001b[39msplit(\u001b[39m'\u001b[39m\u001b[39m,\u001b[39m\u001b[39m'\u001b[39m))\u001b[39m.\u001b[39mexplode(\u001b[39m'\u001b[39m\u001b[39mgenres\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/USER/Documents/Project1/dsc-phase-1-project-v2-4/student.ipynb#Y314sZmlsZQ%3D%3D?line=2'>3</a>\u001b[0m filtered_df\u001b[39m.\u001b[39mhead(\u001b[39m4\u001b[39m)\n",
      "\u001b[1;31mNameError\u001b[0m: name 'filtered_df' is not defined"
     ]
    }
   ],
   "source": [
    "# Splitting the genre column to have a genre for each movie in the table \n",
    "filtered_df = filtered_df.assign(genres = filtered_df['genres'].str.split(',')).explode('genres')\n",
    "filtered_df.head(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "filtered_df = pd.DataFrame(filtered_df)\n",
    "filtered_df =filtered_df[(filtered_df['averagerating'] >= 5) &\n",
    "                        (filtered_df['foreign_gross'] >= 248) &\n",
    "                        (filtered_df['Year'] > 2017)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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>title</th>\n",
       "      <th>foreign_gross</th>\n",
       "      <th>id</th>\n",
       "      <th>popularity</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>Year</th>\n",
       "      <th>movie_id</th>\n",
       "      <th>genres</th>\n",
       "      <th>averagerating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1862</th>\n",
       "      <td>Black Panther</td>\n",
       "      <td>646</td>\n",
       "      <td>284054</td>\n",
       "      <td>44.140</td>\n",
       "      <td>7.4</td>\n",
       "      <td>2018</td>\n",
       "      <td>tt1825683</td>\n",
       "      <td>Action</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1862</th>\n",
       "      <td>Black Panther</td>\n",
       "      <td>646</td>\n",
       "      <td>284054</td>\n",
       "      <td>44.140</td>\n",
       "      <td>7.4</td>\n",
       "      <td>2018</td>\n",
       "      <td>tt1825683</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1862</th>\n",
       "      <td>Black Panther</td>\n",
       "      <td>646</td>\n",
       "      <td>284054</td>\n",
       "      <td>44.140</td>\n",
       "      <td>7.4</td>\n",
       "      <td>2018</td>\n",
       "      <td>tt1825683</td>\n",
       "      <td>Sci-Fi</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1863</th>\n",
       "      <td>Jurassic World: Fallen Kingdom</td>\n",
       "      <td>891</td>\n",
       "      <td>351286</td>\n",
       "      <td>34.958</td>\n",
       "      <td>6.5</td>\n",
       "      <td>2018</td>\n",
       "      <td>tt4881806</td>\n",
       "      <td>Action</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1863</th>\n",
       "      <td>Jurassic World: Fallen Kingdom</td>\n",
       "      <td>891</td>\n",
       "      <td>351286</td>\n",
       "      <td>34.958</td>\n",
       "      <td>6.5</td>\n",
       "      <td>2018</td>\n",
       "      <td>tt4881806</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               title  foreign_gross      id  popularity  \\\n",
       "1862                   Black Panther            646  284054      44.140   \n",
       "1862                   Black Panther            646  284054      44.140   \n",
       "1862                   Black Panther            646  284054      44.140   \n",
       "1863  Jurassic World: Fallen Kingdom            891  351286      34.958   \n",
       "1863  Jurassic World: Fallen Kingdom            891  351286      34.958   \n",
       "\n",
       "      vote_average  Year   movie_id     genres  averagerating  \n",
       "1862           7.4  2018  tt1825683     Action              7  \n",
       "1862           7.4  2018  tt1825683  Adventure              7  \n",
       "1862           7.4  2018  tt1825683     Sci-Fi              7  \n",
       "1863           6.5  2018  tt4881806     Action              6  \n",
       "1863           6.5  2018  tt4881806  Adventure              6  "
      ]
     },
     "execution_count": 1640,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtered_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Univariate Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Description table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count          55\n",
       "unique         18\n",
       "top       The Nun\n",
       "freq            4\n",
       "Name: title, dtype: object"
      ]
     },
     "execution_count": 1641,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking the categorical data\n",
    "filtered_df.title.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count            55\n",
       "unique           15\n",
       "top       Adventure\n",
       "freq             13\n",
       "Name: genres, dtype: object"
      ]
     },
     "execution_count": 1642,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtered_df.genres.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Measure of central tendency\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The average of ratings is: 6.381818181818182\n"
     ]
    }
   ],
   "source": [
    "# Finding mean\n",
    "print(\"The average of ratings is:\",filtered_df.averagerating.mean())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The average of Gross is: 491.0\n"
     ]
    }
   ],
   "source": [
    "# Finding mean\n",
    "print(\"The average of Gross is:\",filtered_df.foreign_gross.mean())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The median of gross is: 445.0\n"
     ]
    }
   ],
   "source": [
    "#Finding median\n",
    "print(\"The median of gross is:\",filtered_df.foreign_gross.median())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The median of ratings is: 6.0\n"
     ]
    }
   ],
   "source": [
    "#Finding median\n",
    "print(\"The median of ratings is:\",filtered_df.averagerating.median())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The mode of average rating is: 0    7\n",
      "dtype: int32\n",
      "The mode of gross income is: 0    361\n",
      "dtype: int32\n"
     ]
    }
   ],
   "source": [
    "#Finding mode\n",
    "print(\"The mode of average rating is:\",filtered_df.averagerating.mode())\n",
    "print(\"The mode of gross income is:\",filtered_df.foreign_gross.mode())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Measures of dispersion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "891 248\n",
      "643\n"
     ]
    }
   ],
   "source": [
    "# Range for the foreign gross\n",
    "Gross_max  = filtered_df['foreign_gross'].max()\n",
    "Gross_min = filtered_df['foreign_gross'].min()\n",
    "\n",
    "Gross_range = Gross_max-Gross_min\n",
    "\n",
    "print(Gross_max,Gross_min)\n",
    "print(Gross_range)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8 5\n",
      "3\n"
     ]
    }
   ],
   "source": [
    "# Range for the average rating\n",
    "rating_max  = filtered_df['averagerating'].max()\n",
    "rating_min = filtered_df['averagerating'].min()\n",
    "\n",
    "rating_range = rating_max-rating_min\n",
    "\n",
    "print(rating_max,rating_min)\n",
    "print(rating_range)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>foreign_gross</th>\n",
       "      <th>id</th>\n",
       "      <th>popularity</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>Year</th>\n",
       "      <th>averagerating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>55.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>55.0</td>\n",
       "      <td>55.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>491.000000</td>\n",
       "      <td>380070.200000</td>\n",
       "      <td>33.494436</td>\n",
       "      <td>6.903636</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>6.381818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>181.119666</td>\n",
       "      <td>63824.939502</td>\n",
       "      <td>11.049064</td>\n",
       "      <td>0.645200</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.827556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>248.000000</td>\n",
       "      <td>260513.000000</td>\n",
       "      <td>6.637000</td>\n",
       "      <td>5.600000</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>361.000000</td>\n",
       "      <td>338952.000000</td>\n",
       "      <td>29.690000</td>\n",
       "      <td>6.500000</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>445.000000</td>\n",
       "      <td>383498.000000</td>\n",
       "      <td>34.958000</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>634.000000</td>\n",
       "      <td>424783.000000</td>\n",
       "      <td>43.078000</td>\n",
       "      <td>7.400000</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>497984.000000</td>\n",
       "      <td>48.508000</td>\n",
       "      <td>8.100000</td>\n",
       "      <td>2018.0</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       foreign_gross             id  popularity  vote_average    Year  \\\n",
       "count      55.000000      55.000000   55.000000     55.000000    55.0   \n",
       "mean      491.000000  380070.200000   33.494436      6.903636  2018.0   \n",
       "std       181.119666   63824.939502   11.049064      0.645200     0.0   \n",
       "min       248.000000  260513.000000    6.637000      5.600000  2018.0   \n",
       "25%       361.000000  338952.000000   29.690000      6.500000  2018.0   \n",
       "50%       445.000000  383498.000000   34.958000      6.900000  2018.0   \n",
       "75%       634.000000  424783.000000   43.078000      7.400000  2018.0   \n",
       "max       891.000000  497984.000000   48.508000      8.100000  2018.0   \n",
       "\n",
       "       averagerating  \n",
       "count      55.000000  \n",
       "mean        6.381818  \n",
       "std         0.827556  \n",
       "min         5.000000  \n",
       "25%         6.000000  \n",
       "50%         6.000000  \n",
       "75%         7.000000  \n",
       "max         8.000000  "
      ]
     },
     "execution_count": 1650,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtered_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>Jurassic World: Fallen Kingdom</td>\n",
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       "      <th>1863</th>\n",
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       "      <td>Incredibles 2</td>\n",
       "      <td>Adventure</td>\n",
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       "      <th>1864</th>\n",
       "      <td>Incredibles 2</td>\n",
       "      <td>Animation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1865</th>\n",
       "      <td>Aquaman</td>\n",
       "      <td>Action</td>\n",
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       "    <tr>\n",
       "      <th>1865</th>\n",
       "      <td>Aquaman</td>\n",
       "      <td>Adventure</td>\n",
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       "      <th>1865</th>\n",
       "      <td>Aquaman</td>\n",
       "      <td>Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1866</th>\n",
       "      <td>Bohemian Rhapsody</td>\n",
       "      <td>Biography</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1866</th>\n",
       "      <td>Bohemian Rhapsody</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1866</th>\n",
       "      <td>Bohemian Rhapsody</td>\n",
       "      <td>Music</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1867</th>\n",
       "      <td>Mission: Impossible - Fallout</td>\n",
       "      <td>Action</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1867</th>\n",
       "      <td>Mission: Impossible - Fallout</td>\n",
       "      <td>Adventure</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1867</th>\n",
       "      <td>Mission: Impossible - Fallout</td>\n",
       "      <td>Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1868</th>\n",
       "      <td>Deadpool 2</td>\n",
       "      <td>Action</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1868</th>\n",
       "      <td>Deadpool 2</td>\n",
       "      <td>Adventure</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1868</th>\n",
       "      <td>Deadpool 2</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1869</th>\n",
       "      <td>Fantastic Beasts: The Crimes of Grindelwald</td>\n",
       "      <td>Adventure</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1869</th>\n",
       "      <td>Fantastic Beasts: The Crimes of Grindelwald</td>\n",
       "      <td>Family</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1869</th>\n",
       "      <td>Fantastic Beasts: The Crimes of Grindelwald</td>\n",
       "      <td>Fantasy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1870</th>\n",
       "      <td>Ant-Man and the Wasp</td>\n",
       "      <td>Action</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1870</th>\n",
       "      <td>Ant-Man and the Wasp</td>\n",
       "      <td>Adventure</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1870</th>\n",
       "      <td>Ant-Man and the Wasp</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1871</th>\n",
       "      <td>Ready Player One</td>\n",
       "      <td>Action</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1871</th>\n",
       "      <td>Ready Player One</td>\n",
       "      <td>Adventure</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1871</th>\n",
       "      <td>Ready Player One</td>\n",
       "      <td>Sci-Fi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1872</th>\n",
       "      <td>Operation Red Sea</td>\n",
       "      <td>Action</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1872</th>\n",
       "      <td>Operation Red Sea</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1872</th>\n",
       "      <td>Operation Red Sea</td>\n",
       "      <td>Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1873</th>\n",
       "      <td>The Meg</td>\n",
       "      <td>Action</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1873</th>\n",
       "      <td>The Meg</td>\n",
       "      <td>Horror</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1873</th>\n",
       "      <td>The Meg</td>\n",
       "      <td>Sci-Fi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1874</th>\n",
       "      <td>Ralph Breaks the Internet</td>\n",
       "      <td>Adventure</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1874</th>\n",
       "      <td>Ralph Breaks the Internet</td>\n",
       "      <td>Animation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1874</th>\n",
       "      <td>Ralph Breaks the Internet</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1875</th>\n",
       "      <td>Hotel Transylvania 3: Summer Vacation</td>\n",
       "      <td>Adventure</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1875</th>\n",
       "      <td>Hotel Transylvania 3: Summer Vacation</td>\n",
       "      <td>Animation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1875</th>\n",
       "      <td>Hotel Transylvania 3: Summer Vacation</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1876</th>\n",
       "      <td>Bumblebee</td>\n",
       "      <td>Action</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1876</th>\n",
       "      <td>Bumblebee</td>\n",
       "      <td>Adventure</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1876</th>\n",
       "      <td>Bumblebee</td>\n",
       "      <td>Sci-Fi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1877</th>\n",
       "      <td>Mamma Mia! Here We Go Again</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1877</th>\n",
       "      <td>Mamma Mia! Here We Go Again</td>\n",
       "      <td>Musical</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1877</th>\n",
       "      <td>Mamma Mia! Here We Go Again</td>\n",
       "      <td>Romance</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1882</th>\n",
       "      <td>The Nun</td>\n",
       "      <td>Drama</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1883</th>\n",
       "      <td>The Nun</td>\n",
       "      <td>Horror</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1883</th>\n",
       "      <td>The Nun</td>\n",
       "      <td>Mystery</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1883</th>\n",
       "      <td>The Nun</td>\n",
       "      <td>Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1884</th>\n",
       "      <td>Monster Hunt 2</td>\n",
       "      <td>Adventure</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1884</th>\n",
       "      <td>Monster Hunt 2</td>\n",
       "      <td>Comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1884</th>\n",
       "      <td>Monster Hunt 2</td>\n",
       "      <td>Sci-Fi</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            title     genres\n",
       "1862                                Black Panther     Action\n",
       "1862                                Black Panther  Adventure\n",
       "1862                                Black Panther     Sci-Fi\n",
       "1863               Jurassic World: Fallen Kingdom     Action\n",
       "1863               Jurassic World: Fallen Kingdom  Adventure\n",
       "1863               Jurassic World: Fallen Kingdom     Sci-Fi\n",
       "1864                                Incredibles 2     Action\n",
       "1864                                Incredibles 2  Adventure\n",
       "1864                                Incredibles 2  Animation\n",
       "1865                                      Aquaman     Action\n",
       "1865                                      Aquaman  Adventure\n",
       "1865                                      Aquaman    Fantasy\n",
       "1866                            Bohemian Rhapsody  Biography\n",
       "1866                            Bohemian Rhapsody      Drama\n",
       "1866                            Bohemian Rhapsody      Music\n",
       "1867                Mission: Impossible - Fallout     Action\n",
       "1867                Mission: Impossible - Fallout  Adventure\n",
       "1867                Mission: Impossible - Fallout   Thriller\n",
       "1868                                   Deadpool 2     Action\n",
       "1868                                   Deadpool 2  Adventure\n",
       "1868                                   Deadpool 2     Comedy\n",
       "1869  Fantastic Beasts: The Crimes of Grindelwald  Adventure\n",
       "1869  Fantastic Beasts: The Crimes of Grindelwald     Family\n",
       "1869  Fantastic Beasts: The Crimes of Grindelwald    Fantasy\n",
       "1870                         Ant-Man and the Wasp     Action\n",
       "1870                         Ant-Man and the Wasp  Adventure\n",
       "1870                         Ant-Man and the Wasp     Comedy\n",
       "1871                             Ready Player One     Action\n",
       "1871                             Ready Player One  Adventure\n",
       "1871                             Ready Player One     Sci-Fi\n",
       "1872                            Operation Red Sea     Action\n",
       "1872                            Operation Red Sea      Drama\n",
       "1872                            Operation Red Sea   Thriller\n",
       "1873                                      The Meg     Action\n",
       "1873                                      The Meg     Horror\n",
       "1873                                      The Meg     Sci-Fi\n",
       "1874                    Ralph Breaks the Internet  Adventure\n",
       "1874                    Ralph Breaks the Internet  Animation\n",
       "1874                    Ralph Breaks the Internet     Comedy\n",
       "1875        Hotel Transylvania 3: Summer Vacation  Adventure\n",
       "1875        Hotel Transylvania 3: Summer Vacation  Animation\n",
       "1875        Hotel Transylvania 3: Summer Vacation     Comedy\n",
       "1876                                    Bumblebee     Action\n",
       "1876                                    Bumblebee  Adventure\n",
       "1876                                    Bumblebee     Sci-Fi\n",
       "1877                  Mamma Mia! Here We Go Again     Comedy\n",
       "1877                  Mamma Mia! Here We Go Again    Musical\n",
       "1877                  Mamma Mia! Here We Go Again    Romance\n",
       "1882                                      The Nun      Drama\n",
       "1883                                      The Nun     Horror\n",
       "1883                                      The Nun    Mystery\n",
       "1883                                      The Nun   Thriller\n",
       "1884                               Monster Hunt 2  Adventure\n",
       "1884                               Monster Hunt 2     Comedy\n",
       "1884                               Monster Hunt 2     Sci-Fi"
      ]
     },
     "execution_count": 1651,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a new table with only top  titles and genres \n",
    "# Select specific columns to create a new DataFrame\n",
    "genres_df = filtered_df[['title', 'genres']]\n",
    "genres_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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>Genre</th>\n",
       "      <th>Number of Movies</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Adventure</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Action</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Sci-Fi</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Comedy</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Thriller</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Animation</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Drama</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Fantasy</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Horror</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Biography</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Music</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Family</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Romance</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Musical</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Mystery</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Genre  Number of Movies\n",
       "0   Adventure                13\n",
       "1      Action                11\n",
       "2      Sci-Fi                 6\n",
       "3      Comedy                 6\n",
       "4    Thriller                 3\n",
       "5   Animation                 3\n",
       "6       Drama                 3\n",
       "7     Fantasy                 2\n",
       "8      Horror                 2\n",
       "9   Biography                 1\n",
       "10      Music                 1\n",
       "11     Family                 1\n",
       "12    Romance                 1\n",
       "13    Musical                 1\n",
       "14    Mystery                 1"
      ]
     },
     "execution_count": 1652,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Group the data by 'genres' and count the number of movies per genre\n",
    "genre_counts = filtered_df['genres'].value_counts().reset_index()\n",
    "genre_counts.columns = ['Genre', 'Number of Movies']\n",
    "\n",
    "# Sort the table by the number of movies in descending order\n",
    "sorted_genre_table = genre_counts.sort_values(by='Number of Movies', ascending=False)\n",
    "genre_counts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting histograms to visualize patterns in the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.DataFrame(genre_counts)\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.bar(df['Genre'], df['Number of Movies'], color='blue')\n",
    "plt.xlabel('Genres')\n",
    "plt.ylabel('Title Counts')\n",
    "plt.title('Title Counts by Genre')\n",
    "plt.xticks(rotation=45, ha='right')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Adventure movies have the most number of titles in the market"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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>title</th>\n",
       "      <th>foreign_gross</th>\n",
       "      <th>id</th>\n",
       "      <th>popularity</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>Year</th>\n",
       "      <th>movie_id</th>\n",
       "      <th>genres</th>\n",
       "      <th>averagerating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1862</th>\n",
       "      <td>Black Panther</td>\n",
       "      <td>646</td>\n",
       "      <td>284054</td>\n",
       "      <td>44.140</td>\n",
       "      <td>7.4</td>\n",
       "      <td>2018</td>\n",
       "      <td>tt1825683</td>\n",
       "      <td>Action</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1862</th>\n",
       "      <td>Black Panther</td>\n",
       "      <td>646</td>\n",
       "      <td>284054</td>\n",
       "      <td>44.140</td>\n",
       "      <td>7.4</td>\n",
       "      <td>2018</td>\n",
       "      <td>tt1825683</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1862</th>\n",
       "      <td>Black Panther</td>\n",
       "      <td>646</td>\n",
       "      <td>284054</td>\n",
       "      <td>44.140</td>\n",
       "      <td>7.4</td>\n",
       "      <td>2018</td>\n",
       "      <td>tt1825683</td>\n",
       "      <td>Sci-Fi</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1863</th>\n",
       "      <td>Jurassic World: Fallen Kingdom</td>\n",
       "      <td>891</td>\n",
       "      <td>351286</td>\n",
       "      <td>34.958</td>\n",
       "      <td>6.5</td>\n",
       "      <td>2018</td>\n",
       "      <td>tt4881806</td>\n",
       "      <td>Action</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1863</th>\n",
       "      <td>Jurassic World: Fallen Kingdom</td>\n",
       "      <td>891</td>\n",
       "      <td>351286</td>\n",
       "      <td>34.958</td>\n",
       "      <td>6.5</td>\n",
       "      <td>2018</td>\n",
       "      <td>tt4881806</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               title  foreign_gross      id  popularity  \\\n",
       "1862                   Black Panther            646  284054      44.140   \n",
       "1862                   Black Panther            646  284054      44.140   \n",
       "1862                   Black Panther            646  284054      44.140   \n",
       "1863  Jurassic World: Fallen Kingdom            891  351286      34.958   \n",
       "1863  Jurassic World: Fallen Kingdom            891  351286      34.958   \n",
       "\n",
       "      vote_average  Year   movie_id     genres  averagerating  \n",
       "1862           7.4  2018  tt1825683     Action              7  \n",
       "1862           7.4  2018  tt1825683  Adventure              7  \n",
       "1862           7.4  2018  tt1825683     Sci-Fi              7  \n",
       "1863           6.5  2018  tt4881806     Action              6  \n",
       "1863           6.5  2018  tt4881806  Adventure              6  "
      ]
     },
     "execution_count": 1654,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtered_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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>Genre</th>\n",
       "      <th>averagerating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Adventure</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Action</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Sci-Fi</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Comedy</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Thriller</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Animation</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Drama</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Fantasy</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Horror</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Biography</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Music</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Family</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Romance</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Musical</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Mystery</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Genre  averagerating\n",
       "0   Adventure             13\n",
       "1      Action             11\n",
       "2      Sci-Fi              6\n",
       "3      Comedy              6\n",
       "4    Thriller              3\n",
       "5   Animation              3\n",
       "6       Drama              3\n",
       "7     Fantasy              2\n",
       "8      Horror              2\n",
       "9   Biography              1\n",
       "10      Music              1\n",
       "11     Family              1\n",
       "12    Romance              1\n",
       "13    Musical              1\n",
       "14    Mystery              1"
      ]
     },
     "execution_count": 1655,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Group the data by 'genres' and count averagerating  per genre\n",
    "rating_counts = filtered_df['genres'].value_counts().reset_index()\n",
    "rating_counts.columns = ['Genre', 'averagerating']\n",
    "\n",
    "rating_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.DataFrame(rating_counts)\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.bar(df['Genre'], df['averagerating'], color='blue')\n",
    "plt.xlabel('Genres')\n",
    "plt.ylabel('Rating')\n",
    "plt.title('Genres Counts by rating')\n",
    "plt.xticks(rotation=45, ha='right')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Adventure movies have the highest ratings in the movie industry"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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>title</th>\n",
       "      <th>foreign_gross</th>\n",
       "      <th>id</th>\n",
       "      <th>popularity</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>Year</th>\n",
       "      <th>movie_id</th>\n",
       "      <th>genres</th>\n",
       "      <th>averagerating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1862</th>\n",
       "      <td>Black Panther</td>\n",
       "      <td>646</td>\n",
       "      <td>284054</td>\n",
       "      <td>44.140</td>\n",
       "      <td>7.4</td>\n",
       "      <td>2018</td>\n",
       "      <td>tt1825683</td>\n",
       "      <td>Action</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1862</th>\n",
       "      <td>Black Panther</td>\n",
       "      <td>646</td>\n",
       "      <td>284054</td>\n",
       "      <td>44.140</td>\n",
       "      <td>7.4</td>\n",
       "      <td>2018</td>\n",
       "      <td>tt1825683</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1862</th>\n",
       "      <td>Black Panther</td>\n",
       "      <td>646</td>\n",
       "      <td>284054</td>\n",
       "      <td>44.140</td>\n",
       "      <td>7.4</td>\n",
       "      <td>2018</td>\n",
       "      <td>tt1825683</td>\n",
       "      <td>Sci-Fi</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1863</th>\n",
       "      <td>Jurassic World: Fallen Kingdom</td>\n",
       "      <td>891</td>\n",
       "      <td>351286</td>\n",
       "      <td>34.958</td>\n",
       "      <td>6.5</td>\n",
       "      <td>2018</td>\n",
       "      <td>tt4881806</td>\n",
       "      <td>Action</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1863</th>\n",
       "      <td>Jurassic World: Fallen Kingdom</td>\n",
       "      <td>891</td>\n",
       "      <td>351286</td>\n",
       "      <td>34.958</td>\n",
       "      <td>6.5</td>\n",
       "      <td>2018</td>\n",
       "      <td>tt4881806</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               title  foreign_gross      id  popularity  \\\n",
       "1862                   Black Panther            646  284054      44.140   \n",
       "1862                   Black Panther            646  284054      44.140   \n",
       "1862                   Black Panther            646  284054      44.140   \n",
       "1863  Jurassic World: Fallen Kingdom            891  351286      34.958   \n",
       "1863  Jurassic World: Fallen Kingdom            891  351286      34.958   \n",
       "\n",
       "      vote_average  Year   movie_id     genres  averagerating  \n",
       "1862           7.4  2018  tt1825683     Action              7  \n",
       "1862           7.4  2018  tt1825683  Adventure              7  \n",
       "1862           7.4  2018  tt1825683     Sci-Fi              7  \n",
       "1863           6.5  2018  tt4881806     Action              6  \n",
       "1863           6.5  2018  tt4881806  Adventure              6  "
      ]
     },
     "execution_count": 1657,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtered_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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>title</th>\n",
       "      <th>foreign_gross</th>\n",
       "      <th>id</th>\n",
       "      <th>popularity</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>Year</th>\n",
       "      <th>movie_id</th>\n",
       "      <th>genres</th>\n",
       "      <th>averagerating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1863</th>\n",
       "      <td>Jurassic World: Fallen Kingdom</td>\n",
       "      <td>891</td>\n",
       "      <td>351286</td>\n",
       "      <td>34.958</td>\n",
       "      <td>6.5</td>\n",
       "      <td>2018</td>\n",
       "      <td>tt4881806</td>\n",
       "      <td>Action</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1863</th>\n",
       "      <td>Jurassic World: Fallen Kingdom</td>\n",
       "      <td>891</td>\n",
       "      <td>351286</td>\n",
       "      <td>34.958</td>\n",
       "      <td>6.5</td>\n",
       "      <td>2018</td>\n",
       "      <td>tt4881806</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1863</th>\n",
       "      <td>Jurassic World: Fallen Kingdom</td>\n",
       "      <td>891</td>\n",
       "      <td>351286</td>\n",
       "      <td>34.958</td>\n",
       "      <td>6.5</td>\n",
       "      <td>2018</td>\n",
       "      <td>tt4881806</td>\n",
       "      <td>Sci-Fi</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1865</th>\n",
       "      <td>Aquaman</td>\n",
       "      <td>812</td>\n",
       "      <td>297802</td>\n",
       "      <td>38.102</td>\n",
       "      <td>6.8</td>\n",
       "      <td>2018</td>\n",
       "      <td>tt1477834</td>\n",
       "      <td>Action</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               title  foreign_gross      id  popularity  \\\n",
       "1863  Jurassic World: Fallen Kingdom            891  351286      34.958   \n",
       "1863  Jurassic World: Fallen Kingdom            891  351286      34.958   \n",
       "1863  Jurassic World: Fallen Kingdom            891  351286      34.958   \n",
       "1865                         Aquaman            812  297802      38.102   \n",
       "\n",
       "      vote_average  Year   movie_id     genres  averagerating  \n",
       "1863           6.5  2018  tt4881806     Action              6  \n",
       "1863           6.5  2018  tt4881806  Adventure              6  \n",
       "1863           6.5  2018  tt4881806     Sci-Fi              6  \n",
       "1865           6.8  2018  tt1477834     Action              7  "
      ]
     },
     "execution_count": 1658,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sort the DataFrame by 'foreign_gross' in descending order\n",
    "sorted_df = filtered_df.sort_values(by='foreign_gross', ascending=False)\n",
    "sorted_df.head(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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",
       "        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>title</th>\n",
       "      <th>foreign_gross</th>\n",
       "      <th>id</th>\n",
       "      <th>popularity</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>Year</th>\n",
       "      <th>movie_id</th>\n",
       "      <th>genres</th>\n",
       "      <th>averagerating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1863</th>\n",
       "      <td>Jurassic World: Fallen Kingdom</td>\n",
       "      <td>891</td>\n",
       "      <td>351286</td>\n",
       "      <td>34.958</td>\n",
       "      <td>6.5</td>\n",
       "      <td>2018</td>\n",
       "      <td>tt4881806</td>\n",
       "      <td>Action</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1863</th>\n",
       "      <td>Jurassic World: Fallen Kingdom</td>\n",
       "      <td>891</td>\n",
       "      <td>351286</td>\n",
       "      <td>34.958</td>\n",
       "      <td>6.5</td>\n",
       "      <td>2018</td>\n",
       "      <td>tt4881806</td>\n",
       "      <td>Sci-Fi</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1863</th>\n",
       "      <td>Jurassic World: Fallen Kingdom</td>\n",
       "      <td>891</td>\n",
       "      <td>351286</td>\n",
       "      <td>34.958</td>\n",
       "      <td>6.5</td>\n",
       "      <td>2018</td>\n",
       "      <td>tt4881806</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1865</th>\n",
       "      <td>Aquaman</td>\n",
       "      <td>812</td>\n",
       "      <td>297802</td>\n",
       "      <td>38.102</td>\n",
       "      <td>6.8</td>\n",
       "      <td>2018</td>\n",
       "      <td>tt1477834</td>\n",
       "      <td>Action</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1865</th>\n",
       "      <td>Aquaman</td>\n",
       "      <td>812</td>\n",
       "      <td>297802</td>\n",
       "      <td>38.102</td>\n",
       "      <td>6.8</td>\n",
       "      <td>2018</td>\n",
       "      <td>tt1477834</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               title  foreign_gross      id  popularity  \\\n",
       "1863  Jurassic World: Fallen Kingdom            891  351286      34.958   \n",
       "1863  Jurassic World: Fallen Kingdom            891  351286      34.958   \n",
       "1863  Jurassic World: Fallen Kingdom            891  351286      34.958   \n",
       "1865                         Aquaman            812  297802      38.102   \n",
       "1865                         Aquaman            812  297802      38.102   \n",
       "\n",
       "      vote_average  Year   movie_id     genres  averagerating  \n",
       "1863           6.5  2018  tt4881806     Action              6  \n",
       "1863           6.5  2018  tt4881806     Sci-Fi              6  \n",
       "1863           6.5  2018  tt4881806  Adventure              6  \n",
       "1865           6.8  2018  tt1477834     Action              7  \n",
       "1865           6.8  2018  tt1477834  Adventure              7  "
      ]
     },
     "execution_count": 1659,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sort the DataFrame by 'gross' in descending order\n",
    "sorted_df = sorted_df.sort_values(by='foreign_gross', ascending=False)\n",
    "sorted_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.DataFrame(sorted_df)\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.bar(df['genres'], df['foreign_gross'], color='blue')\n",
    "plt.xlabel('Genres')\n",
    "plt.ylabel('Gross earning')\n",
    "plt.title('Genres Counts by Gross earnings')\n",
    "plt.xticks(rotation=45, ha='right')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Action movies, adventure movies and sci-fi have the highest gross earnings in the movie market"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bivariate Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Shows relationship between 2 variables genres vs average ratings\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Scatter plot for Gross earnings vs. Ratings\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.scatterplot(data=filtered_df, x='averagerating', y='foreign_gross')\n",
    "plt.xlabel('Rating')\n",
    "plt.ylabel('Gross Earnings')\n",
    "plt.title('Scatter Plot: Ratings vs Gross earnings')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is no linear relation between Quantity and price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Box plot for Gross by Genres\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.boxplot(data=filtered_df, x='genres', y='foreign_gross')\n",
    "plt.xlabel('Genres')\n",
    "plt.ylabel('Gross Earnings')\n",
    "plt.title('Box Plot: Gross Earnings by Genres')\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is high distribution of genres with an earning of 300million dollars to 650 million dollarsas compared to the rest of the genres. Also noted there were no outliers in the genres."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 55 entries, 1862 to 1884\n",
      "Data columns (total 9 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   title          55 non-null     object \n",
      " 1   foreign_gross  55 non-null     int32  \n",
      " 2   id             55 non-null     int64  \n",
      " 3   popularity     55 non-null     float64\n",
      " 4   vote_average   55 non-null     float64\n",
      " 5   Year           55 non-null     int32  \n",
      " 6   movie_id       55 non-null     object \n",
      " 7   genres         55 non-null     object \n",
      " 8   averagerating  55 non-null     int32  \n",
      "dtypes: float64(2), int32(3), int64(1), object(3)\n",
      "memory usage: 4.9+ KB\n"
     ]
    }
   ],
   "source": [
    "filtered_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Genres gross earnings line graph\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.lineplot(data=filtered_df, x='genres', y='foreign_gross', color='blue')\n",
    "plt.xlabel('Genres')\n",
    "plt.ylabel('Total Gross')\n",
    "plt.title('Genres Gross Earnings')\n",
    "plt.xticks(rotation=90)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 55 entries, 1862 to 1884\n",
      "Data columns (total 9 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   title          55 non-null     object \n",
      " 1   foreign_gross  55 non-null     int32  \n",
      " 2   id             55 non-null     int64  \n",
      " 3   popularity     55 non-null     float64\n",
      " 4   vote_average   55 non-null     float64\n",
      " 5   Year           55 non-null     int32  \n",
      " 6   movie_id       55 non-null     object \n",
      " 7   genres         55 non-null     object \n",
      " 8   averagerating  55 non-null     int32  \n",
      "dtypes: float64(2), int32(3), int64(1), object(3)\n",
      "memory usage: 4.9+ KB\n"
     ]
    }
   ],
   "source": [
    "filtered_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# 2. Correlation matrix\n",
    "# Select the numerical variables for which you want to calculate the correlation matrix\n",
    "numerical_values =filtered_df[['averagerating', 'foreign_gross']]\n",
    "\n",
    "#Calculate the correlation matrix using the corr() method\n",
    "correlation_matrix = numerical_values.corr()\n",
    "\n",
    "#Visualize the correlation matrix using a heatmap\n",
    "plt.figure(figsize=(10, 8))\n",
    "sns.heatmap(correlation_matrix, annot=True)\n",
    "plt.title('Correlation Matrix')\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is positive correlation between the avarage rating and foreign gross, meaning as the rating increases the foreign gross also increases."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  Multivariate Analysis\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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>title</th>\n",
       "      <th>foreign_gross</th>\n",
       "      <th>id</th>\n",
       "      <th>popularity</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>Year</th>\n",
       "      <th>movie_id</th>\n",
       "      <th>genres</th>\n",
       "      <th>averagerating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1862</th>\n",
       "      <td>Black Panther</td>\n",
       "      <td>646</td>\n",
       "      <td>284054</td>\n",
       "      <td>44.140</td>\n",
       "      <td>7.4</td>\n",
       "      <td>2018</td>\n",
       "      <td>tt1825683</td>\n",
       "      <td>Action</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1862</th>\n",
       "      <td>Black Panther</td>\n",
       "      <td>646</td>\n",
       "      <td>284054</td>\n",
       "      <td>44.140</td>\n",
       "      <td>7.4</td>\n",
       "      <td>2018</td>\n",
       "      <td>tt1825683</td>\n",
       "      <td>Adventure</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1862</th>\n",
       "      <td>Black Panther</td>\n",
       "      <td>646</td>\n",
       "      <td>284054</td>\n",
       "      <td>44.140</td>\n",
       "      <td>7.4</td>\n",
       "      <td>2018</td>\n",
       "      <td>tt1825683</td>\n",
       "      <td>Sci-Fi</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1863</th>\n",
       "      <td>Jurassic World: Fallen Kingdom</td>\n",
       "      <td>891</td>\n",
       "      <td>351286</td>\n",
       "      <td>34.958</td>\n",
       "      <td>6.5</td>\n",
       "      <td>2018</td>\n",
       "      <td>tt4881806</td>\n",
       "      <td>Action</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               title  foreign_gross      id  popularity  \\\n",
       "1862                   Black Panther            646  284054      44.140   \n",
       "1862                   Black Panther            646  284054      44.140   \n",
       "1862                   Black Panther            646  284054      44.140   \n",
       "1863  Jurassic World: Fallen Kingdom            891  351286      34.958   \n",
       "\n",
       "      vote_average  Year   movie_id     genres  averagerating  \n",
       "1862           7.4  2018  tt1825683     Action              7  \n",
       "1862           7.4  2018  tt1825683  Adventure              7  \n",
       "1862           7.4  2018  tt1825683     Sci-Fi              7  \n",
       "1863           6.5  2018  tt4881806     Action              6  "
      ]
     },
     "execution_count": 1667,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtered_df.head(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       foreign_gross  averagerating  vote_average    Year  popularity\n",
      "count      55.000000      55.000000     55.000000    55.0   55.000000\n",
      "mean      491.000000       6.381818      6.903636  2018.0   33.494436\n",
      "std       181.119666       0.827556      0.645200     0.0   11.049064\n",
      "min       248.000000       5.000000      5.600000  2018.0    6.637000\n",
      "25%       361.000000       6.000000      6.500000  2018.0   29.690000\n",
      "50%       445.000000       6.000000      6.900000  2018.0   34.958000\n",
      "75%       634.000000       7.000000      7.400000  2018.0   43.078000\n",
      "max       891.000000       8.000000      8.100000  2018.0   48.508000\n"
     ]
    }
   ],
   "source": [
    "# Descriptive Statistics\n",
    "# Compute basic statistics for numerical variables\n",
    "numerical_stats = filtered_df[['foreign_gross', 'averagerating', 'vote_average', 'Year','popularity']].describe()\n",
    "print(numerical_stats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Conclusion "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The stated business problem presented by Microsoft is getting in the movie production market by having their own movie stuido to be bale to copete effectively in the movie market and their need to know what kind of movies will be the most succeful in the market.\n",
    "This analysis tried to solve the problem stated by Microsoft Cooporation by seeking to know what movies have been the  most succeful in the movie market based on the audience ratins, the total gross earnings for the last 1 year i.e. between 2017 and 2018. usimg three datasets from the movie industry giants of IMDb, The Movie Database and the Box Office Mojo, the data is trustworthy.\n",
    "\n",
    "This analysis of movies from 2017-2018 shows the following:\n",
    "\n",
    "1. There was a total of 13 adventure movies made.\n",
    "2. Adventure, Action, Sci-Fi and comedy  had the highest average rating.\n",
    "3. Sci-Fi, Adventure, Fantasy, and Action all averaged more than 812 million dollars per movie.\n",
    "4. Action and Adventure had the highest total gross earnings, 891 million and 812 million, respectively."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "RECOMENDATIONS "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the analysis and conclusions drawn from the data used in my analysis, here are some recommendations for Microsoft Corporation as they enter the movie production market:\n",
    "\n",
    "Microsoft should Consider Investing in Adventure Movies as the data suggests that adventure movies, on average, received high ratings and generated substantial earnings. Microsoft may want to consider producing adventure films as part of their movie lineup.\n",
    "\n",
    "They should Focus on High-Quality Content, genres like Adventure, Action, Sci-Fi, and Comedy received high average ratings. Microsoft should prioritize creating high-quality content within these genres to attract and retain audiences.\n",
    "\n",
    "Invest in Sci-Fi and Fantasy Genres as both had  high average ratings and significant earnings per movie. Microsoft may find it lucrative to explore these genres in their film production endeavors.\n",
    "\n",
    "Action and Adventure genres dominated in terms of total gross earnings. Consider allocating resources to produce movies in these genres, as they have a proven track record of success at the box office.\n",
    "\n",
    "The analysis is based on data from 2017-2018. Microsoft should continuously monitor market trends and audience preferences to stay up-to-date with changing tastes and adjust their production strategies accordingly.\n",
    "\n",
    "While the number of adventure movies was relatively low, their quality and earnings were high. Microsoft should prioritize producing fewer high-quality movies rather than flooding the market with a large quantity of films.\n",
    "\n",
    "Engage with the audience and gather feedback to understand their preferences and expectations. Building a strong relationship with the audience can lead to more successful films.\n",
    "\n",
    "Consider collaborating with experienced directors, producers, and writers in the industry to leverage their expertise and enhance the quality of movie production.\n",
    "\n",
    "Develop effective marketing and distribution strategies to ensure that the movies reach their target audience and maximize box office earnings.\n",
    "\n",
    "While the analysis points to specific genres, it's essential to maintain a diverse portfolio of movie genres to cater to a broad audience. Don't limit to one genre alone.\n",
    "\n",
    "It's important to note that the movie industry is highly competitive and subject to evolving trends. Microsoft should conduct thorough market research, stay flexible, and adapt to changing market dynamics to increase their chances of success in the movie production business.\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
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