First Project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# Project: Investigate IMDb movie dataset\n",
    "\n",
    "## Table of Contents\n",
    "<ul>\n",
    "<li><a href=\"#intro\">Introduction</a></li>\n",
    "<li><a href=\"#wrangling\">Data Wrangling</a></li>\n",
    "<li><a href=\"#eda\">Exploratory Data Analysis</a></li>\n",
    "<li><a href=\"#conclusions\">Conclusions</a></li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='intro'></a>\n",
    "## Introduction\n",
    "\n",
    "### We will Investigate and make conclusions about some movies using IMDb dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing the libraries that I may need in the analysis\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='wrangling'></a>\n",
    "## Data Wrangling\n",
    "\n",
    "### General Properties"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('tmdb-movies.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>imdb_id</th>\n",
       "      <th>popularity</th>\n",
       "      <th>budget</th>\n",
       "      <th>revenue</th>\n",
       "      <th>original_title</th>\n",
       "      <th>cast</th>\n",
       "      <th>homepage</th>\n",
       "      <th>director</th>\n",
       "      <th>tagline</th>\n",
       "      <th>...</th>\n",
       "      <th>overview</th>\n",
       "      <th>runtime</th>\n",
       "      <th>genres</th>\n",
       "      <th>production_companies</th>\n",
       "      <th>release_date</th>\n",
       "      <th>vote_count</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>release_year</th>\n",
       "      <th>budget_adj</th>\n",
       "      <th>revenue_adj</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>135397</td>\n",
       "      <td>tt0369610</td>\n",
       "      <td>32.985763</td>\n",
       "      <td>150000000</td>\n",
       "      <td>1513528810</td>\n",
       "      <td>Jurassic World</td>\n",
       "      <td>Chris Pratt|Bryce Dallas Howard|Irrfan Khan|Vi...</td>\n",
       "      <td>http://www.jurassicworld.com/</td>\n",
       "      <td>Colin Trevorrow</td>\n",
       "      <td>The park is open.</td>\n",
       "      <td>...</td>\n",
       "      <td>Twenty-two years after the events of Jurassic ...</td>\n",
       "      <td>124</td>\n",
       "      <td>Action|Adventure|Science Fiction|Thriller</td>\n",
       "      <td>Universal Studios|Amblin Entertainment|Legenda...</td>\n",
       "      <td>6/9/15</td>\n",
       "      <td>5562</td>\n",
       "      <td>6.5</td>\n",
       "      <td>2015</td>\n",
       "      <td>1.379999e+08</td>\n",
       "      <td>1.392446e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>76341</td>\n",
       "      <td>tt1392190</td>\n",
       "      <td>28.419936</td>\n",
       "      <td>150000000</td>\n",
       "      <td>378436354</td>\n",
       "      <td>Mad Max: Fury Road</td>\n",
       "      <td>Tom Hardy|Charlize Theron|Hugh Keays-Byrne|Nic...</td>\n",
       "      <td>http://www.madmaxmovie.com/</td>\n",
       "      <td>George Miller</td>\n",
       "      <td>What a Lovely Day.</td>\n",
       "      <td>...</td>\n",
       "      <td>An apocalyptic story set in the furthest reach...</td>\n",
       "      <td>120</td>\n",
       "      <td>Action|Adventure|Science Fiction|Thriller</td>\n",
       "      <td>Village Roadshow Pictures|Kennedy Miller Produ...</td>\n",
       "      <td>5/13/15</td>\n",
       "      <td>6185</td>\n",
       "      <td>7.1</td>\n",
       "      <td>2015</td>\n",
       "      <td>1.379999e+08</td>\n",
       "      <td>3.481613e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>262500</td>\n",
       "      <td>tt2908446</td>\n",
       "      <td>13.112507</td>\n",
       "      <td>110000000</td>\n",
       "      <td>295238201</td>\n",
       "      <td>Insurgent</td>\n",
       "      <td>Shailene Woodley|Theo James|Kate Winslet|Ansel...</td>\n",
       "      <td>http://www.thedivergentseries.movie/#insurgent</td>\n",
       "      <td>Robert Schwentke</td>\n",
       "      <td>One Choice Can Destroy You</td>\n",
       "      <td>...</td>\n",
       "      <td>Beatrice Prior must confront her inner demons ...</td>\n",
       "      <td>119</td>\n",
       "      <td>Adventure|Science Fiction|Thriller</td>\n",
       "      <td>Summit Entertainment|Mandeville Films|Red Wago...</td>\n",
       "      <td>3/18/15</td>\n",
       "      <td>2480</td>\n",
       "      <td>6.3</td>\n",
       "      <td>2015</td>\n",
       "      <td>1.012000e+08</td>\n",
       "      <td>2.716190e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>140607</td>\n",
       "      <td>tt2488496</td>\n",
       "      <td>11.173104</td>\n",
       "      <td>200000000</td>\n",
       "      <td>2068178225</td>\n",
       "      <td>Star Wars: The Force Awakens</td>\n",
       "      <td>Harrison Ford|Mark Hamill|Carrie Fisher|Adam D...</td>\n",
       "      <td>http://www.starwars.com/films/star-wars-episod...</td>\n",
       "      <td>J.J. Abrams</td>\n",
       "      <td>Every generation has a story.</td>\n",
       "      <td>...</td>\n",
       "      <td>Thirty years after defeating the Galactic Empi...</td>\n",
       "      <td>136</td>\n",
       "      <td>Action|Adventure|Science Fiction|Fantasy</td>\n",
       "      <td>Lucasfilm|Truenorth Productions|Bad Robot</td>\n",
       "      <td>12/15/15</td>\n",
       "      <td>5292</td>\n",
       "      <td>7.5</td>\n",
       "      <td>2015</td>\n",
       "      <td>1.839999e+08</td>\n",
       "      <td>1.902723e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>168259</td>\n",
       "      <td>tt2820852</td>\n",
       "      <td>9.335014</td>\n",
       "      <td>190000000</td>\n",
       "      <td>1506249360</td>\n",
       "      <td>Furious 7</td>\n",
       "      <td>Vin Diesel|Paul Walker|Jason Statham|Michelle ...</td>\n",
       "      <td>http://www.furious7.com/</td>\n",
       "      <td>James Wan</td>\n",
       "      <td>Vengeance Hits Home</td>\n",
       "      <td>...</td>\n",
       "      <td>Deckard Shaw seeks revenge against Dominic Tor...</td>\n",
       "      <td>137</td>\n",
       "      <td>Action|Crime|Thriller</td>\n",
       "      <td>Universal Pictures|Original Film|Media Rights ...</td>\n",
       "      <td>4/1/15</td>\n",
       "      <td>2947</td>\n",
       "      <td>7.3</td>\n",
       "      <td>2015</td>\n",
       "      <td>1.747999e+08</td>\n",
       "      <td>1.385749e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
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       "       id    imdb_id  popularity     budget     revenue  \\\n",
       "0  135397  tt0369610   32.985763  150000000  1513528810   \n",
       "1   76341  tt1392190   28.419936  150000000   378436354   \n",
       "2  262500  tt2908446   13.112507  110000000   295238201   \n",
       "3  140607  tt2488496   11.173104  200000000  2068178225   \n",
       "4  168259  tt2820852    9.335014  190000000  1506249360   \n",
       "\n",
       "                 original_title  \\\n",
       "0                Jurassic World   \n",
       "1            Mad Max: Fury Road   \n",
       "2                     Insurgent   \n",
       "3  Star Wars: The Force Awakens   \n",
       "4                     Furious 7   \n",
       "\n",
       "                                                cast  \\\n",
       "0  Chris Pratt|Bryce Dallas Howard|Irrfan Khan|Vi...   \n",
       "1  Tom Hardy|Charlize Theron|Hugh Keays-Byrne|Nic...   \n",
       "2  Shailene Woodley|Theo James|Kate Winslet|Ansel...   \n",
       "3  Harrison Ford|Mark Hamill|Carrie Fisher|Adam D...   \n",
       "4  Vin Diesel|Paul Walker|Jason Statham|Michelle ...   \n",
       "\n",
       "                                            homepage          director  \\\n",
       "0                      http://www.jurassicworld.com/   Colin Trevorrow   \n",
       "1                        http://www.madmaxmovie.com/     George Miller   \n",
       "2     http://www.thedivergentseries.movie/#insurgent  Robert Schwentke   \n",
       "3  http://www.starwars.com/films/star-wars-episod...       J.J. Abrams   \n",
       "4                           http://www.furious7.com/         James Wan   \n",
       "\n",
       "                         tagline  ...  \\\n",
       "0              The park is open.  ...   \n",
       "1             What a Lovely Day.  ...   \n",
       "2     One Choice Can Destroy You  ...   \n",
       "3  Every generation has a story.  ...   \n",
       "4            Vengeance Hits Home  ...   \n",
       "\n",
       "                                            overview runtime  \\\n",
       "0  Twenty-two years after the events of Jurassic ...     124   \n",
       "1  An apocalyptic story set in the furthest reach...     120   \n",
       "2  Beatrice Prior must confront her inner demons ...     119   \n",
       "3  Thirty years after defeating the Galactic Empi...     136   \n",
       "4  Deckard Shaw seeks revenge against Dominic Tor...     137   \n",
       "\n",
       "                                      genres  \\\n",
       "0  Action|Adventure|Science Fiction|Thriller   \n",
       "1  Action|Adventure|Science Fiction|Thriller   \n",
       "2         Adventure|Science Fiction|Thriller   \n",
       "3   Action|Adventure|Science Fiction|Fantasy   \n",
       "4                      Action|Crime|Thriller   \n",
       "\n",
       "                                production_companies release_date vote_count  \\\n",
       "0  Universal Studios|Amblin Entertainment|Legenda...       6/9/15       5562   \n",
       "1  Village Roadshow Pictures|Kennedy Miller Produ...      5/13/15       6185   \n",
       "2  Summit Entertainment|Mandeville Films|Red Wago...      3/18/15       2480   \n",
       "3          Lucasfilm|Truenorth Productions|Bad Robot     12/15/15       5292   \n",
       "4  Universal Pictures|Original Film|Media Rights ...       4/1/15       2947   \n",
       "\n",
       "   vote_average  release_year    budget_adj   revenue_adj  \n",
       "0           6.5          2015  1.379999e+08  1.392446e+09  \n",
       "1           7.1          2015  1.379999e+08  3.481613e+08  \n",
       "2           6.3          2015  1.012000e+08  2.716190e+08  \n",
       "3           7.5          2015  1.839999e+08  1.902723e+09  \n",
       "4           7.3          2015  1.747999e+08  1.385749e+09  \n",
       "\n",
       "[5 rows x 21 columns]"
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   "source": [
    "df.head()"
   ]
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   "cell_type": "code",
   "execution_count": 4,
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     "name": "stdout",
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10866 entries, 0 to 10865\n",
      "Data columns (total 21 columns):\n",
      " #   Column                Non-Null Count  Dtype  \n",
      "---  ------                --------------  -----  \n",
      " 0   id                    10866 non-null  int64  \n",
      " 1   imdb_id               10856 non-null  object \n",
      " 2   popularity            10866 non-null  float64\n",
      " 3   budget                10866 non-null  int64  \n",
      " 4   revenue               10866 non-null  int64  \n",
      " 5   original_title        10866 non-null  object \n",
      " 6   cast                  10790 non-null  object \n",
      " 7   homepage              2936 non-null   object \n",
      " 8   director              10822 non-null  object \n",
      " 9   tagline               8042 non-null   object \n",
      " 10  keywords              9373 non-null   object \n",
      " 11  overview              10862 non-null  object \n",
      " 12  runtime               10866 non-null  int64  \n",
      " 13  genres                10843 non-null  object \n",
      " 14  production_companies  9836 non-null   object \n",
      " 15  release_date          10866 non-null  object \n",
      " 16  vote_count            10866 non-null  int64  \n",
      " 17  vote_average          10866 non-null  float64\n",
      " 18  release_year          10866 non-null  int64  \n",
      " 19  budget_adj            10866 non-null  float64\n",
      " 20  revenue_adj           10866 non-null  float64\n",
      "dtypes: float64(4), int64(6), object(11)\n",
      "memory usage: 1.7+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>popularity</th>\n",
       "      <th>budget</th>\n",
       "      <th>revenue</th>\n",
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       "      <th>count</th>\n",
       "      <td>10866.000000</td>\n",
       "      <td>10866.000000</td>\n",
       "      <td>1.086600e+04</td>\n",
       "      <td>1.086600e+04</td>\n",
       "      <td>10866.000000</td>\n",
       "      <td>10866.000000</td>\n",
       "      <td>10866.000000</td>\n",
       "      <td>10866.000000</td>\n",
       "      <td>1.086600e+04</td>\n",
       "      <td>1.086600e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>66064.177434</td>\n",
       "      <td>0.646441</td>\n",
       "      <td>1.462570e+07</td>\n",
       "      <td>3.982332e+07</td>\n",
       "      <td>102.070863</td>\n",
       "      <td>217.389748</td>\n",
       "      <td>5.974922</td>\n",
       "      <td>2001.322658</td>\n",
       "      <td>1.755104e+07</td>\n",
       "      <td>5.136436e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>92130.136561</td>\n",
       "      <td>1.000185</td>\n",
       "      <td>3.091321e+07</td>\n",
       "      <td>1.170035e+08</td>\n",
       "      <td>31.381405</td>\n",
       "      <td>575.619058</td>\n",
       "      <td>0.935142</td>\n",
       "      <td>12.812941</td>\n",
       "      <td>3.430616e+07</td>\n",
       "      <td>1.446325e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000065</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>1960.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>10596.250000</td>\n",
       "      <td>0.207583</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>5.400000</td>\n",
       "      <td>1995.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>20669.000000</td>\n",
       "      <td>0.383856</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2006.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>75610.000000</td>\n",
       "      <td>0.713817</td>\n",
       "      <td>1.500000e+07</td>\n",
       "      <td>2.400000e+07</td>\n",
       "      <td>111.000000</td>\n",
       "      <td>145.750000</td>\n",
       "      <td>6.600000</td>\n",
       "      <td>2011.000000</td>\n",
       "      <td>2.085325e+07</td>\n",
       "      <td>3.369710e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>417859.000000</td>\n",
       "      <td>32.985763</td>\n",
       "      <td>4.250000e+08</td>\n",
       "      <td>2.781506e+09</td>\n",
       "      <td>900.000000</td>\n",
       "      <td>9767.000000</td>\n",
       "      <td>9.200000</td>\n",
       "      <td>2015.000000</td>\n",
       "      <td>4.250000e+08</td>\n",
       "      <td>2.827124e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  id    popularity        budget       revenue       runtime  \\\n",
       "count   10866.000000  10866.000000  1.086600e+04  1.086600e+04  10866.000000   \n",
       "mean    66064.177434      0.646441  1.462570e+07  3.982332e+07    102.070863   \n",
       "std     92130.136561      1.000185  3.091321e+07  1.170035e+08     31.381405   \n",
       "min         5.000000      0.000065  0.000000e+00  0.000000e+00      0.000000   \n",
       "25%     10596.250000      0.207583  0.000000e+00  0.000000e+00     90.000000   \n",
       "50%     20669.000000      0.383856  0.000000e+00  0.000000e+00     99.000000   \n",
       "75%     75610.000000      0.713817  1.500000e+07  2.400000e+07    111.000000   \n",
       "max    417859.000000     32.985763  4.250000e+08  2.781506e+09    900.000000   \n",
       "\n",
       "         vote_count  vote_average  release_year    budget_adj   revenue_adj  \n",
       "count  10866.000000  10866.000000  10866.000000  1.086600e+04  1.086600e+04  \n",
       "mean     217.389748      5.974922   2001.322658  1.755104e+07  5.136436e+07  \n",
       "std      575.619058      0.935142     12.812941  3.430616e+07  1.446325e+08  \n",
       "min       10.000000      1.500000   1960.000000  0.000000e+00  0.000000e+00  \n",
       "25%       17.000000      5.400000   1995.000000  0.000000e+00  0.000000e+00  \n",
       "50%       38.000000      6.000000   2006.000000  0.000000e+00  0.000000e+00  \n",
       "75%      145.750000      6.600000   2011.000000  2.085325e+07  3.369710e+07  \n",
       "max     9767.000000      9.200000   2015.000000  4.250000e+08  2.827124e+09  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## Data Cleaning \n",
    "- Replacing the zero values in budget and revenue with the mean(Needed to be added)\n",
    "- Drop the homepage column due to large number of missing values \n",
    "- Missing values in production_company and dircetor columns\n",
    "- Separate cast name, production company, and genres with , insted of |\n",
    "- drop imdb ID and ID columns as they won't be needed\n",
    "- turn the release date to date_time as it would be easier to handle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Replacing Zero values with the mean of each column\n",
    "column_list=['revenue', 'budget']\n",
    "for c in column_list:\n",
    "    df[c].replace({0: df[c].mean()}, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### As the revenue and budget are important in visuals we can not leave erroneous or missing data\n",
    "- Replacing it with mean may fix the issues or make it more adabtive to the visuals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>popularity</th>\n",
       "      <th>budget</th>\n",
       "      <th>revenue</th>\n",
       "      <th>runtime</th>\n",
       "      <th>vote_count</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>release_year</th>\n",
       "      <th>budget_adj</th>\n",
       "      <th>revenue_adj</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10866.000000</td>\n",
       "      <td>10866.000000</td>\n",
       "      <td>1.086600e+04</td>\n",
       "      <td>1.086600e+04</td>\n",
       "      <td>10866.000000</td>\n",
       "      <td>10866.000000</td>\n",
       "      <td>10866.000000</td>\n",
       "      <td>10866.000000</td>\n",
       "      <td>1.086600e+04</td>\n",
       "      <td>1.086600e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>66064.177434</td>\n",
       "      <td>0.646441</td>\n",
       "      <td>2.229255e+07</td>\n",
       "      <td>6.187164e+07</td>\n",
       "      <td>102.070863</td>\n",
       "      <td>217.389748</td>\n",
       "      <td>5.974922</td>\n",
       "      <td>2001.322658</td>\n",
       "      <td>1.755104e+07</td>\n",
       "      <td>5.136436e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>92130.136561</td>\n",
       "      <td>1.000185</td>\n",
       "      <td>2.801245e+07</td>\n",
       "      <td>1.110204e+08</td>\n",
       "      <td>31.381405</td>\n",
       "      <td>575.619058</td>\n",
       "      <td>0.935142</td>\n",
       "      <td>12.812941</td>\n",
       "      <td>3.430616e+07</td>\n",
       "      <td>1.446325e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000065</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>1960.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>10596.250000</td>\n",
       "      <td>0.207583</td>\n",
       "      <td>1.462570e+07</td>\n",
       "      <td>3.982332e+07</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>5.400000</td>\n",
       "      <td>1995.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>20669.000000</td>\n",
       "      <td>0.383856</td>\n",
       "      <td>1.462570e+07</td>\n",
       "      <td>3.982332e+07</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2006.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>75610.000000</td>\n",
       "      <td>0.713817</td>\n",
       "      <td>1.500000e+07</td>\n",
       "      <td>3.982332e+07</td>\n",
       "      <td>111.000000</td>\n",
       "      <td>145.750000</td>\n",
       "      <td>6.600000</td>\n",
       "      <td>2011.000000</td>\n",
       "      <td>2.085325e+07</td>\n",
       "      <td>3.369710e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>417859.000000</td>\n",
       "      <td>32.985763</td>\n",
       "      <td>4.250000e+08</td>\n",
       "      <td>2.781506e+09</td>\n",
       "      <td>900.000000</td>\n",
       "      <td>9767.000000</td>\n",
       "      <td>9.200000</td>\n",
       "      <td>2015.000000</td>\n",
       "      <td>4.250000e+08</td>\n",
       "      <td>2.827124e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  id    popularity        budget       revenue       runtime  \\\n",
       "count   10866.000000  10866.000000  1.086600e+04  1.086600e+04  10866.000000   \n",
       "mean    66064.177434      0.646441  2.229255e+07  6.187164e+07    102.070863   \n",
       "std     92130.136561      1.000185  2.801245e+07  1.110204e+08     31.381405   \n",
       "min         5.000000      0.000065  1.000000e+00  2.000000e+00      0.000000   \n",
       "25%     10596.250000      0.207583  1.462570e+07  3.982332e+07     90.000000   \n",
       "50%     20669.000000      0.383856  1.462570e+07  3.982332e+07     99.000000   \n",
       "75%     75610.000000      0.713817  1.500000e+07  3.982332e+07    111.000000   \n",
       "max    417859.000000     32.985763  4.250000e+08  2.781506e+09    900.000000   \n",
       "\n",
       "         vote_count  vote_average  release_year    budget_adj   revenue_adj  \n",
       "count  10866.000000  10866.000000  10866.000000  1.086600e+04  1.086600e+04  \n",
       "mean     217.389748      5.974922   2001.322658  1.755104e+07  5.136436e+07  \n",
       "std      575.619058      0.935142     12.812941  3.430616e+07  1.446325e+08  \n",
       "min       10.000000      1.500000   1960.000000  0.000000e+00  0.000000e+00  \n",
       "25%       17.000000      5.400000   1995.000000  0.000000e+00  0.000000e+00  \n",
       "50%       38.000000      6.000000   2006.000000  0.000000e+00  0.000000e+00  \n",
       "75%      145.750000      6.600000   2011.000000  2.085325e+07  3.369710e+07  \n",
       "max     9767.000000      9.200000   2015.000000  4.250000e+08  2.827124e+09  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Droping three columns for the large number of missing values\n",
    "df.drop(['id','homepage','imdb_id'], axis=1 ,inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### As the id, imdb_id won't be needed in the visuals I decided to delete them while the home page had a large number of missing values "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10866 entries, 0 to 10865\n",
      "Data columns (total 18 columns):\n",
      " #   Column                Non-Null Count  Dtype  \n",
      "---  ------                --------------  -----  \n",
      " 0   popularity            10866 non-null  float64\n",
      " 1   budget                10866 non-null  float64\n",
      " 2   revenue               10866 non-null  float64\n",
      " 3   original_title        10866 non-null  object \n",
      " 4   cast                  10790 non-null  object \n",
      " 5   director              10822 non-null  object \n",
      " 6   tagline               8042 non-null   object \n",
      " 7   keywords              9373 non-null   object \n",
      " 8   overview              10862 non-null  object \n",
      " 9   runtime               10866 non-null  int64  \n",
      " 10  genres                10843 non-null  object \n",
      " 11  production_companies  9836 non-null   object \n",
      " 12  release_date          10866 non-null  object \n",
      " 13  vote_count            10866 non-null  int64  \n",
      " 14  vote_average          10866 non-null  float64\n",
      " 15  release_year          10866 non-null  int64  \n",
      " 16  budget_adj            10866 non-null  float64\n",
      " 17  revenue_adj           10866 non-null  float64\n",
      "dtypes: float64(6), int64(3), object(9)\n",
      "memory usage: 1.5+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#swaping the popularity and title columns to make the dataframe looks nicer\n",
    "i = list(df.columns)\n",
    "a, b = i.index('original_title'), i.index('popularity')\n",
    "i[b], i[a] = i[a], i[b]\n",
    "df = df[i]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### I decided to that to make it look nicer and when you open the data frame looking for a movie you will find the name first and it'd be easier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Raplacing | with , to make it nicer and easier to work with(Needed to be edited)\n",
    "def replace_string(column_name):\n",
    "    df[column_name]=df[column_name].str.replace('|',',')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "replace_string('cast')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "replace_string('keywords')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "replace_string('genres')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "replace_string('production_companies')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Replacing the | with , will help to deal with strings if any further analysis needed using these columns and also it make the rows look nicer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        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>original_title</th>\n",
       "      <th>budget</th>\n",
       "      <th>revenue</th>\n",
       "      <th>popularity</th>\n",
       "      <th>cast</th>\n",
       "      <th>director</th>\n",
       "      <th>tagline</th>\n",
       "      <th>keywords</th>\n",
       "      <th>overview</th>\n",
       "      <th>runtime</th>\n",
       "      <th>genres</th>\n",
       "      <th>production_companies</th>\n",
       "      <th>release_date</th>\n",
       "      <th>vote_count</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>release_year</th>\n",
       "      <th>budget_adj</th>\n",
       "      <th>revenue_adj</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jurassic World</td>\n",
       "      <td>150000000.0</td>\n",
       "      <td>1.513529e+09</td>\n",
       "      <td>32.985763</td>\n",
       "      <td>Chris Pratt,Bryce Dallas Howard,Irrfan Khan,Vi...</td>\n",
       "      <td>Colin Trevorrow</td>\n",
       "      <td>The park is open.</td>\n",
       "      <td>monster,dna,tyrannosaurus rex,velociraptor,island</td>\n",
       "      <td>Twenty-two years after the events of Jurassic ...</td>\n",
       "      <td>124</td>\n",
       "      <td>Action,Adventure,Science Fiction,Thriller</td>\n",
       "      <td>Universal Studios,Amblin Entertainment,Legenda...</td>\n",
       "      <td>6/9/15</td>\n",
       "      <td>5562</td>\n",
       "      <td>6.5</td>\n",
       "      <td>2015</td>\n",
       "      <td>1.379999e+08</td>\n",
       "      <td>1.392446e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   original_title       budget       revenue  popularity  \\\n",
       "0  Jurassic World  150000000.0  1.513529e+09   32.985763   \n",
       "\n",
       "                                                cast         director  \\\n",
       "0  Chris Pratt,Bryce Dallas Howard,Irrfan Khan,Vi...  Colin Trevorrow   \n",
       "\n",
       "             tagline                                           keywords  \\\n",
       "0  The park is open.  monster,dna,tyrannosaurus rex,velociraptor,island   \n",
       "\n",
       "                                            overview  runtime  \\\n",
       "0  Twenty-two years after the events of Jurassic ...      124   \n",
       "\n",
       "                                      genres  \\\n",
       "0  Action,Adventure,Science Fiction,Thriller   \n",
       "\n",
       "                                production_companies release_date  vote_count  \\\n",
       "0  Universal Studios,Amblin Entertainment,Legenda...       6/9/15        5562   \n",
       "\n",
       "   vote_average  release_year    budget_adj   revenue_adj  \n",
       "0           6.5          2015  1.379999e+08  1.392446e+09  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Turning the release date column to date time\n",
    "df['release_date'] = pd.to_datetime(df['release_date'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### without doubt the date time library offers a lot of functionalities and it will help me create a column I needed in the visuals part"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10866 entries, 0 to 10865\n",
      "Data columns (total 18 columns):\n",
      " #   Column                Non-Null Count  Dtype         \n",
      "---  ------                --------------  -----         \n",
      " 0   original_title        10866 non-null  object        \n",
      " 1   budget                10866 non-null  float64       \n",
      " 2   revenue               10866 non-null  float64       \n",
      " 3   popularity            10866 non-null  float64       \n",
      " 4   cast                  10790 non-null  object        \n",
      " 5   director              10822 non-null  object        \n",
      " 6   tagline               8042 non-null   object        \n",
      " 7   keywords              9373 non-null   object        \n",
      " 8   overview              10862 non-null  object        \n",
      " 9   runtime               10866 non-null  int64         \n",
      " 10  genres                10843 non-null  object        \n",
      " 11  production_companies  9836 non-null   object        \n",
      " 12  release_date          10866 non-null  datetime64[ns]\n",
      " 13  vote_count            10866 non-null  int64         \n",
      " 14  vote_average          10866 non-null  float64       \n",
      " 15  release_year          10866 non-null  int64         \n",
      " 16  budget_adj            10866 non-null  float64       \n",
      " 17  revenue_adj           10866 non-null  float64       \n",
      "dtypes: datetime64[ns](1), float64(6), int64(3), object(8)\n",
      "memory usage: 1.5+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Repalcing missing values in production company and director columns\n",
    "df['production_companies'] = df['production_companies'].replace(np.nan, 'Unkown')\n",
    "df['director'] = df['director'].replace(np.nan, 'Unkown')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### This part to replace the missing values in the director and production company column \n",
    "### and will help in better understanding when you search for the director or company,etc..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10866 entries, 0 to 10865\n",
      "Data columns (total 18 columns):\n",
      " #   Column                Non-Null Count  Dtype         \n",
      "---  ------                --------------  -----         \n",
      " 0   original_title        10866 non-null  object        \n",
      " 1   budget                10866 non-null  float64       \n",
      " 2   revenue               10866 non-null  float64       \n",
      " 3   popularity            10866 non-null  float64       \n",
      " 4   cast                  10790 non-null  object        \n",
      " 5   director              10866 non-null  object        \n",
      " 6   tagline               8042 non-null   object        \n",
      " 7   keywords              9373 non-null   object        \n",
      " 8   overview              10862 non-null  object        \n",
      " 9   runtime               10866 non-null  int64         \n",
      " 10  genres                10843 non-null  object        \n",
      " 11  production_companies  10866 non-null  object        \n",
      " 12  release_date          10866 non-null  datetime64[ns]\n",
      " 13  vote_count            10866 non-null  int64         \n",
      " 14  vote_average          10866 non-null  float64       \n",
      " 15  release_year          10866 non-null  int64         \n",
      " 16  budget_adj            10866 non-null  float64       \n",
      " 17  revenue_adj           10866 non-null  float64       \n",
      "dtypes: datetime64[ns](1), float64(6), int64(3), object(8)\n",
      "memory usage: 1.5+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='eda'></a>\n",
    "## Exploratory Data Analysis\n",
    "\n",
    "\n",
    "### Research Question 1, which month has the most average revenues?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Month']= pd.DatetimeIndex(df['release_date']).month"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### As I said before date time library will help me in the visuals in this part I created a months column to see the average revenues with months"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "(((df.groupby('Month').revenue.mean())/(1e7))*10).plot(kind='bar')\n",
    "plt.xlabel('Months')\n",
    "plt.ylabel('Average Revenue in millions')\n",
    "plt.title('Releation between average revenue of each month');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- we can see fom this garph that the most revenues are in June (First in Summer)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Research Question 2, What happens with the number of movies through the years?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df.release_year, bins=10)\n",
    "plt.xlabel('Years')\n",
    "plt.ylabel(\"Count\")\n",
    "plt.title('Movies count through the years');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- We can see that the movies increase with years "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Research Question 3, Which movie has the maximum revenue ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2781505847.0"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.revenue.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\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>original_title</th>\n",
       "      <th>budget</th>\n",
       "      <th>revenue</th>\n",
       "      <th>popularity</th>\n",
       "      <th>cast</th>\n",
       "      <th>director</th>\n",
       "      <th>tagline</th>\n",
       "      <th>keywords</th>\n",
       "      <th>overview</th>\n",
       "      <th>runtime</th>\n",
       "      <th>genres</th>\n",
       "      <th>production_companies</th>\n",
       "      <th>release_date</th>\n",
       "      <th>vote_count</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>release_year</th>\n",
       "      <th>budget_adj</th>\n",
       "      <th>revenue_adj</th>\n",
       "      <th>Month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1386</th>\n",
       "      <td>Avatar</td>\n",
       "      <td>237000000.0</td>\n",
       "      <td>2.781506e+09</td>\n",
       "      <td>9.432768</td>\n",
       "      <td>Sam Worthington,Zoe Saldana,Sigourney Weaver,S...</td>\n",
       "      <td>James Cameron</td>\n",
       "      <td>Enter the World of Pandora.</td>\n",
       "      <td>culture clash,future,space war,space colony,so...</td>\n",
       "      <td>In the 22nd century, a paraplegic Marine is di...</td>\n",
       "      <td>162</td>\n",
       "      <td>Action,Adventure,Fantasy,Science Fiction</td>\n",
       "      <td>Ingenious Film Partners,Twentieth Century Fox ...</td>\n",
       "      <td>2009-12-10</td>\n",
       "      <td>8458</td>\n",
       "      <td>7.1</td>\n",
       "      <td>2009</td>\n",
       "      <td>2.408869e+08</td>\n",
       "      <td>2.827124e+09</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     original_title       budget       revenue  popularity  \\\n",
       "1386         Avatar  237000000.0  2.781506e+09    9.432768   \n",
       "\n",
       "                                                   cast       director  \\\n",
       "1386  Sam Worthington,Zoe Saldana,Sigourney Weaver,S...  James Cameron   \n",
       "\n",
       "                          tagline  \\\n",
       "1386  Enter the World of Pandora.   \n",
       "\n",
       "                                               keywords  \\\n",
       "1386  culture clash,future,space war,space colony,so...   \n",
       "\n",
       "                                               overview  runtime  \\\n",
       "1386  In the 22nd century, a paraplegic Marine is di...      162   \n",
       "\n",
       "                                        genres  \\\n",
       "1386  Action,Adventure,Fantasy,Science Fiction   \n",
       "\n",
       "                                   production_companies release_date  \\\n",
       "1386  Ingenious Film Partners,Twentieth Century Fox ...   2009-12-10   \n",
       "\n",
       "      vote_count  vote_average  release_year    budget_adj   revenue_adj  \\\n",
       "1386        8458           7.1          2009  2.408869e+08  2.827124e+09   \n",
       "\n",
       "      Month  \n",
       "1386     12  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('revenue==2781505847')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The movie Avatar has the highest revenue"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Research Question 4, Which movie has the maximum vote ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9.2"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.vote_average.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>original_title</th>\n",
       "      <th>budget</th>\n",
       "      <th>revenue</th>\n",
       "      <th>popularity</th>\n",
       "      <th>cast</th>\n",
       "      <th>director</th>\n",
       "      <th>tagline</th>\n",
       "      <th>keywords</th>\n",
       "      <th>overview</th>\n",
       "      <th>runtime</th>\n",
       "      <th>genres</th>\n",
       "      <th>production_companies</th>\n",
       "      <th>release_date</th>\n",
       "      <th>vote_count</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>release_year</th>\n",
       "      <th>budget_adj</th>\n",
       "      <th>revenue_adj</th>\n",
       "      <th>Month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3894</th>\n",
       "      <td>The Story of Film: An Odyssey</td>\n",
       "      <td>1.462570e+07</td>\n",
       "      <td>3.982332e+07</td>\n",
       "      <td>0.006925</td>\n",
       "      <td>Mark Cousins,Jean-Michel Frodon,Cari Beauchamp...</td>\n",
       "      <td>Mark Cousins</td>\n",
       "      <td>NaN</td>\n",
       "      <td>cinema,nouvelle vague,hindi cinema,cinema novo...</td>\n",
       "      <td>The Story of Film: An Odyssey, written and dir...</td>\n",
       "      <td>900</td>\n",
       "      <td>Documentary</td>\n",
       "      <td>Unkown</td>\n",
       "      <td>2011-09-03</td>\n",
       "      <td>14</td>\n",
       "      <td>9.2</td>\n",
       "      <td>2011</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     original_title        budget       revenue  popularity  \\\n",
       "3894  The Story of Film: An Odyssey  1.462570e+07  3.982332e+07    0.006925   \n",
       "\n",
       "                                                   cast      director tagline  \\\n",
       "3894  Mark Cousins,Jean-Michel Frodon,Cari Beauchamp...  Mark Cousins     NaN   \n",
       "\n",
       "                                               keywords  \\\n",
       "3894  cinema,nouvelle vague,hindi cinema,cinema novo...   \n",
       "\n",
       "                                               overview  runtime       genres  \\\n",
       "3894  The Story of Film: An Odyssey, written and dir...      900  Documentary   \n",
       "\n",
       "     production_companies release_date  vote_count  vote_average  \\\n",
       "3894               Unkown   2011-09-03          14           9.2   \n",
       "\n",
       "      release_year  budget_adj  revenue_adj  Month  \n",
       "3894          2011         0.0          0.0      9  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('vote_average==9.2')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='conclusions'></a>\n",
    "## Conclusions\n",
    "### Cleaning\n",
    "- We removed some columns for their large number of missing data\n",
    "- Replaced zero values with mean in two columns\n",
    "- Replaced '|' charachter with ',' in four columns\n",
    "- Turned one column to a type easier to handle\n",
    "### Visuals\n",
    "- The first visual helped me to discover that summer movies have higher revenues\n",
    "- the second one helped me see that movies production increase with years\n",
    "#### Though I got some conlcusions but they are based only on the data at hand and after cleaning the data of ways only by my choice if you are looking for more solid proofs try different aspects and research"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Save your final CLEAN datasets as new files!\n",
    "df.to_csv('clean_tmdb.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Saving the data in a clean sheet in case used later"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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