Investigate_a_Dataset.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Project: Investigate a Dataset (tmdb-movies.csv)\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",
    "> **The dataset**: I choose tdb-movies.csv dataset it's about 10865 movies. It's contain 10866 rows and 21 columns.\n",
    "<br> The dataset including the original_title of the movie name , budget, revenue , popularity, director and average_vote.\n",
    "\n",
    "### Questions:\n",
    "\n",
    ">1_What is the years tha have the most release numbers of movies? <br>\n",
    "2_What is change for run time over years? <br>\n",
    "3_What is the relation between budget and revenue and profit? <br>\n",
    "4_Which movies with the highest profit? <br>\n",
    "5_what is the most genres release ?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import packages\n",
    "import pandas as pd\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='wrangling'></a>\n",
    "## Data Wrangling\n",
    "\n",
    ">load data <br>\n",
    "know informations about data like data types , its columns , shape and null values\n",
    "### General Properties"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the data\n",
    "df=pd.read_csv('tmdb-movies.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <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",
       "</div>"
      ],
      "text/plain": [
       "       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]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#first few rows of the dataset\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "      <th>10861</th>\n",
       "      <td>21</td>\n",
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       "      <td>0.080598</td>\n",
       "      <td>0</td>\n",
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       "      <td>The Endless Summer</td>\n",
       "      <td>Michael Hynson|Robert August|Lord 'Tally Ho' B...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Bruce Brown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>The Endless Summer, by Bruce Brown, is one of ...</td>\n",
       "      <td>95</td>\n",
       "      <td>Documentary</td>\n",
       "      <td>Bruce Brown Films</td>\n",
       "      <td>6/15/66</td>\n",
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       "      <td>0.065543</td>\n",
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       "      <td>Grand Prix</td>\n",
       "      <td>James Garner|Eva Marie Saint|Yves Montand|Tosh...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>John Frankenheimer</td>\n",
       "      <td>Cinerama sweeps YOU into a drama of speed and ...</td>\n",
       "      <td>...</td>\n",
       "      <td>Grand Prix driver Pete Aron is fired by his te...</td>\n",
       "      <td>176</td>\n",
       "      <td>Action|Adventure|Drama</td>\n",
       "      <td>Cherokee Productions|Joel Productions|Douglas ...</td>\n",
       "      <td>12/21/66</td>\n",
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       "      <td>5.7</td>\n",
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       "      <td>0.065141</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Beregis Avtomobilya</td>\n",
       "      <td>Innokentiy Smoktunovskiy|Oleg Efremov|Georgi Z...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Eldar Ryazanov</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>An insurance agent who moonlights as a carthie...</td>\n",
       "      <td>94</td>\n",
       "      <td>Mystery|Comedy</td>\n",
       "      <td>Mosfilm</td>\n",
       "      <td>1/1/66</td>\n",
       "      <td>11</td>\n",
       "      <td>6.5</td>\n",
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       "      <th>10864</th>\n",
       "      <td>21449</td>\n",
       "      <td>tt0061177</td>\n",
       "      <td>0.064317</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>What's Up, Tiger Lily?</td>\n",
       "      <td>Tatsuya Mihashi|Akiko Wakabayashi|Mie Hama|Joh...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Woody Allen</td>\n",
       "      <td>WOODY ALLEN STRIKES BACK!</td>\n",
       "      <td>...</td>\n",
       "      <td>In comic Woody Allen's film debut, he took the...</td>\n",
       "      <td>80</td>\n",
       "      <td>Action|Comedy</td>\n",
       "      <td>Benedict Pictures Corp.</td>\n",
       "      <td>11/2/66</td>\n",
       "      <td>22</td>\n",
       "      <td>5.4</td>\n",
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       "      <td>22293</td>\n",
       "      <td>tt0060666</td>\n",
       "      <td>0.035919</td>\n",
       "      <td>19000</td>\n",
       "      <td>0</td>\n",
       "      <td>Manos: The Hands of Fate</td>\n",
       "      <td>Harold P. Warren|Tom Neyman|John Reynolds|Dian...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Harold P. Warren</td>\n",
       "      <td>It's Shocking! It's Beyond Your Imagination!</td>\n",
       "      <td>...</td>\n",
       "      <td>A family gets lost on the road and stumbles up...</td>\n",
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       "      <td>Horror</td>\n",
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       "      <td>0.0</td>\n",
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      "text/plain": [
       "          id    imdb_id  popularity  budget  revenue  \\\n",
       "10861     21  tt0060371    0.080598       0        0   \n",
       "10862  20379  tt0060472    0.065543       0        0   \n",
       "10863  39768  tt0060161    0.065141       0        0   \n",
       "10864  21449  tt0061177    0.064317       0        0   \n",
       "10865  22293  tt0060666    0.035919   19000        0   \n",
       "\n",
       "                 original_title  \\\n",
       "10861        The Endless Summer   \n",
       "10862                Grand Prix   \n",
       "10863       Beregis Avtomobilya   \n",
       "10864    What's Up, Tiger Lily?   \n",
       "10865  Manos: The Hands of Fate   \n",
       "\n",
       "                                                    cast homepage  \\\n",
       "10861  Michael Hynson|Robert August|Lord 'Tally Ho' B...      NaN   \n",
       "10862  James Garner|Eva Marie Saint|Yves Montand|Tosh...      NaN   \n",
       "10863  Innokentiy Smoktunovskiy|Oleg Efremov|Georgi Z...      NaN   \n",
       "10864  Tatsuya Mihashi|Akiko Wakabayashi|Mie Hama|Joh...      NaN   \n",
       "10865  Harold P. Warren|Tom Neyman|John Reynolds|Dian...      NaN   \n",
       "\n",
       "                 director                                            tagline  \\\n",
       "10861         Bruce Brown                                                NaN   \n",
       "10862  John Frankenheimer  Cinerama sweeps YOU into a drama of speed and ...   \n",
       "10863      Eldar Ryazanov                                                NaN   \n",
       "10864         Woody Allen                          WOODY ALLEN STRIKES BACK!   \n",
       "10865    Harold P. Warren       It's Shocking! It's Beyond Your Imagination!   \n",
       "\n",
       "          ...                                                overview runtime  \\\n",
       "10861     ...       The Endless Summer, by Bruce Brown, is one of ...      95   \n",
       "10862     ...       Grand Prix driver Pete Aron is fired by his te...     176   \n",
       "10863     ...       An insurance agent who moonlights as a carthie...      94   \n",
       "10864     ...       In comic Woody Allen's film debut, he took the...      80   \n",
       "10865     ...       A family gets lost on the road and stumbles up...      74   \n",
       "\n",
       "                       genres  \\\n",
       "10861             Documentary   \n",
       "10862  Action|Adventure|Drama   \n",
       "10863          Mystery|Comedy   \n",
       "10864           Action|Comedy   \n",
       "10865                  Horror   \n",
       "\n",
       "                                    production_companies release_date  \\\n",
       "10861                                  Bruce Brown Films      6/15/66   \n",
       "10862  Cherokee Productions|Joel Productions|Douglas ...     12/21/66   \n",
       "10863                                            Mosfilm       1/1/66   \n",
       "10864                            Benedict Pictures Corp.      11/2/66   \n",
       "10865                                          Norm-Iris     11/15/66   \n",
       "\n",
       "      vote_count  vote_average  release_year     budget_adj  revenue_adj  \n",
       "10861         11           7.4          1966       0.000000          0.0  \n",
       "10862         20           5.7          1966       0.000000          0.0  \n",
       "10863         11           6.5          1966       0.000000          0.0  \n",
       "10864         22           5.4          1966       0.000000          0.0  \n",
       "10865         15           1.5          1966  127642.279154          0.0  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#last rows of the data\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10866, 21)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#dimensions of the dataset\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10866 entries, 0 to 10865\n",
      "Data columns (total 21 columns):\n",
      "id                      10866 non-null int64\n",
      "imdb_id                 10856 non-null object\n",
      "popularity              10866 non-null float64\n",
      "budget                  10866 non-null int64\n",
      "revenue                 10866 non-null int64\n",
      "original_title          10866 non-null object\n",
      "cast                    10790 non-null object\n",
      "homepage                2936 non-null object\n",
      "director                10822 non-null object\n",
      "tagline                 8042 non-null object\n",
      "keywords                9373 non-null object\n",
      "overview                10862 non-null object\n",
      "runtime                 10866 non-null int64\n",
      "genres                  10843 non-null object\n",
      "production_companies    9836 non-null object\n",
      "release_date            10866 non-null object\n",
      "vote_count              10866 non-null int64\n",
      "vote_average            10866 non-null float64\n",
      "release_year            10866 non-null int64\n",
      "budget_adj              10866 non-null float64\n",
      "revenue_adj             10866 non-null float64\n",
      "dtypes: float64(4), int64(6), object(11)\n",
      "memory usage: 1.7+ MB\n"
     ]
    }
   ],
   "source": [
    "#information about data frame\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>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>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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#statistics description of the dataset\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                        int64\n",
       "imdb_id                  object\n",
       "popularity              float64\n",
       "budget                    int64\n",
       "revenue                   int64\n",
       "original_title           object\n",
       "cast                     object\n",
       "homepage                 object\n",
       "director                 object\n",
       "tagline                  object\n",
       "keywords                 object\n",
       "overview                 object\n",
       "runtime                   int64\n",
       "genres                   object\n",
       "production_companies     object\n",
       "release_date             object\n",
       "vote_count                int64\n",
       "vote_average            float64\n",
       "release_year              int64\n",
       "budget_adj              float64\n",
       "revenue_adj             float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#data types of each columns\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', 'imdb_id', 'popularity', 'budget', 'revenue', 'original_title',\n",
       "       'cast', 'homepage', 'director', 'tagline', 'keywords', 'overview',\n",
       "       'runtime', 'genres', 'production_companies', 'release_date',\n",
       "       'vote_count', 'vote_average', 'release_year', 'budget_adj',\n",
       "       'revenue_adj'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#columns names\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Cleaning\n",
    "> 1-remove duplicates <br>\n",
    "2-remove unuseful columns <br>\n",
    "3-drop rows with null values <br>\n",
    "4-replace zeros values in the revenue and budget with the mean of each one"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#duplcated rows in the data set\n",
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#remove duplicates\n",
    "df.drop_duplicates(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#to make sure that no duplicated rows\n",
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>budget</th>\n",
       "      <th>revenue</th>\n",
       "      <th>original_title</th>\n",
       "      <th>director</th>\n",
       "      <th>runtime</th>\n",
       "      <th>genres</th>\n",
       "      <th>release_date</th>\n",
       "      <th>vote_count</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>release_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>135397</td>\n",
       "      <td>32.985763</td>\n",
       "      <td>150000000</td>\n",
       "      <td>1513528810</td>\n",
       "      <td>Jurassic World</td>\n",
       "      <td>Colin Trevorrow</td>\n",
       "      <td>124</td>\n",
       "      <td>Action|Adventure|Science Fiction|Thriller</td>\n",
       "      <td>6/9/15</td>\n",
       "      <td>5562</td>\n",
       "      <td>6.5</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>76341</td>\n",
       "      <td>28.419936</td>\n",
       "      <td>150000000</td>\n",
       "      <td>378436354</td>\n",
       "      <td>Mad Max: Fury Road</td>\n",
       "      <td>George Miller</td>\n",
       "      <td>120</td>\n",
       "      <td>Action|Adventure|Science Fiction|Thriller</td>\n",
       "      <td>5/13/15</td>\n",
       "      <td>6185</td>\n",
       "      <td>7.1</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>262500</td>\n",
       "      <td>13.112507</td>\n",
       "      <td>110000000</td>\n",
       "      <td>295238201</td>\n",
       "      <td>Insurgent</td>\n",
       "      <td>Robert Schwentke</td>\n",
       "      <td>119</td>\n",
       "      <td>Adventure|Science Fiction|Thriller</td>\n",
       "      <td>3/18/15</td>\n",
       "      <td>2480</td>\n",
       "      <td>6.3</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>140607</td>\n",
       "      <td>11.173104</td>\n",
       "      <td>200000000</td>\n",
       "      <td>2068178225</td>\n",
       "      <td>Star Wars: The Force Awakens</td>\n",
       "      <td>J.J. Abrams</td>\n",
       "      <td>136</td>\n",
       "      <td>Action|Adventure|Science Fiction|Fantasy</td>\n",
       "      <td>12/15/15</td>\n",
       "      <td>5292</td>\n",
       "      <td>7.5</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>168259</td>\n",
       "      <td>9.335014</td>\n",
       "      <td>190000000</td>\n",
       "      <td>1506249360</td>\n",
       "      <td>Furious 7</td>\n",
       "      <td>James Wan</td>\n",
       "      <td>137</td>\n",
       "      <td>Action|Crime|Thriller</td>\n",
       "      <td>4/1/15</td>\n",
       "      <td>2947</td>\n",
       "      <td>7.3</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       id  popularity     budget     revenue                original_title  \\\n",
       "0  135397   32.985763  150000000  1513528810                Jurassic World   \n",
       "1   76341   28.419936  150000000   378436354            Mad Max: Fury Road   \n",
       "2  262500   13.112507  110000000   295238201                     Insurgent   \n",
       "3  140607   11.173104  200000000  2068178225  Star Wars: The Force Awakens   \n",
       "4  168259    9.335014  190000000  1506249360                     Furious 7   \n",
       "\n",
       "           director  runtime                                     genres  \\\n",
       "0   Colin Trevorrow      124  Action|Adventure|Science Fiction|Thriller   \n",
       "1     George Miller      120  Action|Adventure|Science Fiction|Thriller   \n",
       "2  Robert Schwentke      119         Adventure|Science Fiction|Thriller   \n",
       "3       J.J. Abrams      136   Action|Adventure|Science Fiction|Fantasy   \n",
       "4         James Wan      137                      Action|Crime|Thriller   \n",
       "\n",
       "  release_date  vote_count  vote_average  release_year  \n",
       "0       6/9/15        5562           6.5          2015  \n",
       "1      5/13/15        6185           7.1          2015  \n",
       "2      3/18/15        2480           6.3          2015  \n",
       "3     12/15/15        5292           7.5          2015  \n",
       "4       4/1/15        2947           7.3          2015  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(['homepage','cast','tagline','overview','budget_adj','revenue_adj','imdb_id','keywords','production_companies'],axis=1,inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10865, 12)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                 0\n",
       "popularity         0\n",
       "budget             0\n",
       "revenue            0\n",
       "original_title     0\n",
       "director          44\n",
       "runtime            0\n",
       "genres            23\n",
       "release_date       0\n",
       "vote_count         0\n",
       "vote_average       0\n",
       "release_year       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#number of null values in each columns\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#drop rows with null values\n",
    "df.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                0\n",
       "popularity        0\n",
       "budget            0\n",
       "revenue           0\n",
       "original_title    0\n",
       "director          0\n",
       "runtime           0\n",
       "genres            0\n",
       "release_date      0\n",
       "vote_count        0\n",
       "vote_average      0\n",
       "release_year      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#make sure that there's no null values\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#replace zero values of budget with budget mean\n",
    "budget_mean=df.budget.mean()\n",
    "df['budget']=df['budget'].replace(0,budget_mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#replace zero values of revenue with revenue mean\n",
    "revenue_mean=df.revenue.mean()\n",
    "df['revenue']=df['revenue'].replace(0,revenue_mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10800.000000</td>\n",
       "      <td>10800.000000</td>\n",
       "      <td>1.080000e+04</td>\n",
       "      <td>1.080000e+04</td>\n",
       "      <td>10800.000000</td>\n",
       "      <td>10800.000000</td>\n",
       "      <td>10800.000000</td>\n",
       "      <td>10800.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>65576.183426</td>\n",
       "      <td>0.649455</td>\n",
       "      <td>2.238817e+07</td>\n",
       "      <td>6.214616e+07</td>\n",
       "      <td>102.189722</td>\n",
       "      <td>218.600741</td>\n",
       "      <td>5.970880</td>\n",
       "      <td>2001.289537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>91756.079939</td>\n",
       "      <td>1.002441</td>\n",
       "      <td>2.807649e+07</td>\n",
       "      <td>1.113169e+08</td>\n",
       "      <td>30.783873</td>\n",
       "      <td>577.165055</td>\n",
       "      <td>0.933006</td>\n",
       "      <td>12.819545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000188</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>10570.500000</td>\n",
       "      <td>0.209157</td>\n",
       "      <td>1.471114e+07</td>\n",
       "      <td>4.006558e+07</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>5.400000</td>\n",
       "      <td>1995.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>20460.000000</td>\n",
       "      <td>0.385471</td>\n",
       "      <td>1.471114e+07</td>\n",
       "      <td>4.006558e+07</td>\n",
       "      <td>99.000000</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2006.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>74725.250000</td>\n",
       "      <td>0.717508</td>\n",
       "      <td>1.564374e+07</td>\n",
       "      <td>4.006558e+07</td>\n",
       "      <td>111.250000</td>\n",
       "      <td>147.000000</td>\n",
       "      <td>6.600000</td>\n",
       "      <td>2011.000000</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  id    popularity        budget       revenue       runtime  \\\n",
       "count   10800.000000  10800.000000  1.080000e+04  1.080000e+04  10800.000000   \n",
       "mean    65576.183426      0.649455  2.238817e+07  6.214616e+07    102.189722   \n",
       "std     91756.079939      1.002441  2.807649e+07  1.113169e+08     30.783873   \n",
       "min         5.000000      0.000188  1.000000e+00  2.000000e+00      0.000000   \n",
       "25%     10570.500000      0.209157  1.471114e+07  4.006558e+07     90.000000   \n",
       "50%     20460.000000      0.385471  1.471114e+07  4.006558e+07     99.000000   \n",
       "75%     74725.250000      0.717508  1.564374e+07  4.006558e+07    111.250000   \n",
       "max    417859.000000     32.985763  4.250000e+08  2.781506e+09    900.000000   \n",
       "\n",
       "         vote_count  vote_average  release_year  \n",
       "count  10800.000000  10800.000000  10800.000000  \n",
       "mean     218.600741      5.970880   2001.289537  \n",
       "std      577.165055      0.933006     12.819545  \n",
       "min       10.000000      1.500000   1960.000000  \n",
       "25%       17.000000      5.400000   1995.000000  \n",
       "50%       39.000000      6.000000   2006.000000  \n",
       "75%      147.000000      6.600000   2011.000000  \n",
       "max     9767.000000      9.200000   2015.000000  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#make sure no zeros in budget and revenue\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='eda'></a>\n",
    "## Exploratory Data Analysis\n",
    "\n",
    "\n",
    "### Research Question 1  (what is the years tha have the most release numbers of movies?)\n",
    "\n",
    "> **what i did here**:I'm trying to get release number of each year by count number of movies for each one the i describe it in this bar chart to be clear more than alot of numbers and columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd3004d52b0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#bar chart show release number of movies over years\n",
    "df['release_year'].value_counts().plot(kind='bar' , figsize=(15,15), title='release number over years') .set( xlabel=\"years\" , ylabel=\"release numbers\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">\n",
    "> **what i did here**: get the histograms of the dataset to know how it distributed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd3003aa668>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#histograms\n",
    "df.hist(figsize=(15,15));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Research Question 2  (what is change for run time over years?)\n",
    "> **what i did here**: I assume ther is a change in the run time over year so i grouped each of them by geting the mean of run time for each year and describe this relation by this graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd300061cf8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#group of data according to release years and runtime\n",
    "df.groupby('release_year')['runtime'].mean().plot(figsize=(15,15), title='Runtime over years') .set( xlabel=\"years\" , ylabel=\"Run time\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Research Question 3  (what is the relation between budget and revenue and profit?)\n",
    "> **what i did here**: the assuumption was if there a relationship between the revenue and budget of revenue and profit ?<br> by using scatter plot it's shows me that there's positive corelation between the revenue and budget.then i get the profit by get the subtraction of budget from revenue for each movie and add it as new column in the data. After that i get the scatter plot that show me the relation between thw profit and revenue is strond positive corelation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd30006c3c8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#use scatter plot to show the correlation between the budget and revenue\n",
    "df.plot(x='budget' , y='revenue' , kind ='scatter' , title='relationship between budget and revenue');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "#calculate profit for each movie as a new column in the df\n",
    "df['profit'] = df['revenue'] - df['budget']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', 'popularity', 'budget', 'revenue', 'original_title', 'director',\n",
       "       'runtime', 'genres', 'release_date', 'vote_count', 'vote_average',\n",
       "       'release_year', 'profit'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#to make sure the column is added\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f99fff42ef0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#use scatter plot to show the correlation between the profit and revenue\n",
    "df.plot(x='profit' , y='revenue' , kind ='scatter' , title='relationship between profit and revenue');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Research Question 4  (what is the movies with highest profit ?)\n",
    "> **what i did here**: I want to know the highest profit movie so i get the index of the max profit and shows the title in this index <br>\n",
    "To know the Top profit movies i sort by the profit descending and get the top movie names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Avatar'"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#get the index of max profit movie\n",
    "high=df['profit'].idxmax()\n",
    "df['original_title'][high]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>profit</th>\n",
       "      <th>original_title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1386</th>\n",
       "      <td>2.544506e+09</td>\n",
       "      <td>Avatar</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.868178e+09</td>\n",
       "      <td>Star Wars: The Force Awakens</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5231</th>\n",
       "      <td>1.645034e+09</td>\n",
       "      <td>Titanic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.363529e+09</td>\n",
       "      <td>Jurassic World</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.316249e+09</td>\n",
       "      <td>Furious 7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4361</th>\n",
       "      <td>1.299558e+09</td>\n",
       "      <td>The Avengers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3374</th>\n",
       "      <td>1.202818e+09</td>\n",
       "      <td>Harry Potter and the Deathly Hallows: Part 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1.125036e+09</td>\n",
       "      <td>Avengers: Age of Ultron</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5422</th>\n",
       "      <td>1.124219e+09</td>\n",
       "      <td>Frozen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8094</th>\n",
       "      <td>1.084280e+09</td>\n",
       "      <td>The Net</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.082731e+09</td>\n",
       "      <td>Minions</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4949</th>\n",
       "      <td>1.024889e+09</td>\n",
       "      <td>The Lord of the Rings: The Return of the King</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5425</th>\n",
       "      <td>1.015440e+09</td>\n",
       "      <td>Iron Man 3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3522</th>\n",
       "      <td>9.287470e+08</td>\n",
       "      <td>Transformers: Dark of the Moon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4365</th>\n",
       "      <td>9.085610e+08</td>\n",
       "      <td>Skyfall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5434</th>\n",
       "      <td>8.947619e+08</td>\n",
       "      <td>Despicable Me 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6555</th>\n",
       "      <td>8.656598e+08</td>\n",
       "      <td>Pirates of the Caribbean: Dead Man's Chest</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1930</th>\n",
       "      <td>8.631719e+08</td>\n",
       "      <td>Toy Story 3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10223</th>\n",
       "      <td>8.571000e+08</td>\n",
       "      <td>Jurassic Park</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2634</th>\n",
       "      <td>8.514756e+08</td>\n",
       "      <td>Harry Potter and the Philosopher's Stone</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3911</th>\n",
       "      <td>8.472874e+08</td>\n",
       "      <td>The Lord of the Rings: The Two Towers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4363</th>\n",
       "      <td>8.310413e+08</td>\n",
       "      <td>The Dark Knight Rises</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1921</th>\n",
       "      <td>8.254671e+08</td>\n",
       "      <td>Alice in Wonderland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2875</th>\n",
       "      <td>8.169218e+08</td>\n",
       "      <td>The Dark Knight</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2412</th>\n",
       "      <td>8.093176e+08</td>\n",
       "      <td>Star Wars: Episode I - The Phantom Menace</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1396</th>\n",
       "      <td>7.966868e+08</td>\n",
       "      <td>Ice Age: Dawn of the Dinosaurs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7388</th>\n",
       "      <td>7.882127e+08</td>\n",
       "      <td>Harry Potter and the Order of the Phoenix</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8889</th>\n",
       "      <td>7.824106e+08</td>\n",
       "      <td>E.T. the Extra-Terrestrial</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4375</th>\n",
       "      <td>7.822448e+08</td>\n",
       "      <td>Ice Age: Continental Drift</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2633</th>\n",
       "      <td>7.783684e+08</td>\n",
       "      <td>The Lord of the Rings: The Fellowship of the Ring</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6557</th>\n",
       "      <td>4.490460e+08</td>\n",
       "      <td>Casino Royale</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9983</th>\n",
       "      <td>4.490000e+08</td>\n",
       "      <td>Pretty Woman</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2637</th>\n",
       "      <td>4.478163e+08</td>\n",
       "      <td>Monsters, Inc.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>4.473514e+08</td>\n",
       "      <td>Cinderella</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2877</th>\n",
       "      <td>4.451742e+08</td>\n",
       "      <td>Iron Man</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5432</th>\n",
       "      <td>4.378455e+08</td>\n",
       "      <td>Man of Steel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6981</th>\n",
       "      <td>4.366429e+08</td>\n",
       "      <td>Meet the Fockers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7392</th>\n",
       "      <td>4.353490e+08</td>\n",
       "      <td>I Am Legend</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1394</th>\n",
       "      <td>4.340287e+08</td>\n",
       "      <td>Sherlock Holmes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10594</th>\n",
       "      <td>4.333061e+08</td>\n",
       "      <td>The Exorcist</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9180</th>\n",
       "      <td>4.261718e+08</td>\n",
       "      <td>Indiana Jones and the Last Crusade</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3460</th>\n",
       "      <td>4.249875e+08</td>\n",
       "      <td>Puss in Boots</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2639</th>\n",
       "      <td>4.244092e+08</td>\n",
       "      <td>Shrek</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1397</th>\n",
       "      <td>4.242706e+08</td>\n",
       "      <td>The Hangover</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1920</th>\n",
       "      <td>4.239333e+08</td>\n",
       "      <td>Iron Man 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>644</th>\n",
       "      <td>4.233601e+08</td>\n",
       "      <td>Lucy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8666</th>\n",
       "      <td>4.213881e+08</td>\n",
       "      <td>Mission: Impossible II</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9317</th>\n",
       "      <td>4.200000e+08</td>\n",
       "      <td>Terminator 2: Judgment Day</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6982</th>\n",
       "      <td>4.192724e+08</td>\n",
       "      <td>The Day After Tomorrow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10225</th>\n",
       "      <td>4.162862e+08</td>\n",
       "      <td>Mrs. Doubtfire</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8970</th>\n",
       "      <td>4.137996e+08</td>\n",
       "      <td>Armageddon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8974</th>\n",
       "      <td>4.118409e+08</td>\n",
       "      <td>Saving Private Ryan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>666</th>\n",
       "      <td>4.091607e+08</td>\n",
       "      <td>The Lego Movie</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4956</th>\n",
       "      <td>4.045728e+08</td>\n",
       "      <td>Bruce Almighty</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8486</th>\n",
       "      <td>4.024715e+08</td>\n",
       "      <td>Twister</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9985</th>\n",
       "      <td>4.022088e+08</td>\n",
       "      <td>Dances with Wolves</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2410</th>\n",
       "      <td>4.005174e+08</td>\n",
       "      <td>The Matrix</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1966</th>\n",
       "      <td>3.992115e+08</td>\n",
       "      <td>The King's Speech</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4381</th>\n",
       "      <td>3.990268e+08</td>\n",
       "      <td>Men in Black 3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3.979505e+08</td>\n",
       "      <td>The Revenant</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>149 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             profit                                     original_title\n",
       "1386   2.544506e+09                                             Avatar\n",
       "3      1.868178e+09                       Star Wars: The Force Awakens\n",
       "5231   1.645034e+09                                            Titanic\n",
       "0      1.363529e+09                                     Jurassic World\n",
       "4      1.316249e+09                                          Furious 7\n",
       "4361   1.299558e+09                                       The Avengers\n",
       "3374   1.202818e+09       Harry Potter and the Deathly Hallows: Part 2\n",
       "14     1.125036e+09                            Avengers: Age of Ultron\n",
       "5422   1.124219e+09                                             Frozen\n",
       "8094   1.084280e+09                                            The Net\n",
       "8      1.082731e+09                                            Minions\n",
       "4949   1.024889e+09      The Lord of the Rings: The Return of the King\n",
       "5425   1.015440e+09                                         Iron Man 3\n",
       "3522   9.287470e+08                     Transformers: Dark of the Moon\n",
       "4365   9.085610e+08                                            Skyfall\n",
       "5434   8.947619e+08                                    Despicable Me 2\n",
       "6555   8.656598e+08         Pirates of the Caribbean: Dead Man's Chest\n",
       "1930   8.631719e+08                                        Toy Story 3\n",
       "10223  8.571000e+08                                      Jurassic Park\n",
       "2634   8.514756e+08           Harry Potter and the Philosopher's Stone\n",
       "3911   8.472874e+08              The Lord of the Rings: The Two Towers\n",
       "4363   8.310413e+08                              The Dark Knight Rises\n",
       "1921   8.254671e+08                                Alice in Wonderland\n",
       "2875   8.169218e+08                                    The Dark Knight\n",
       "2412   8.093176e+08          Star Wars: Episode I - The Phantom Menace\n",
       "1396   7.966868e+08                     Ice Age: Dawn of the Dinosaurs\n",
       "7388   7.882127e+08          Harry Potter and the Order of the Phoenix\n",
       "8889   7.824106e+08                         E.T. the Extra-Terrestrial\n",
       "4375   7.822448e+08                         Ice Age: Continental Drift\n",
       "2633   7.783684e+08  The Lord of the Rings: The Fellowship of the Ring\n",
       "...             ...                                                ...\n",
       "6557   4.490460e+08                                      Casino Royale\n",
       "9983   4.490000e+08                                       Pretty Woman\n",
       "2637   4.478163e+08                                     Monsters, Inc.\n",
       "18     4.473514e+08                                         Cinderella\n",
       "2877   4.451742e+08                                           Iron Man\n",
       "5432   4.378455e+08                                       Man of Steel\n",
       "6981   4.366429e+08                                   Meet the Fockers\n",
       "7392   4.353490e+08                                        I Am Legend\n",
       "1394   4.340287e+08                                    Sherlock Holmes\n",
       "10594  4.333061e+08                                       The Exorcist\n",
       "9180   4.261718e+08                 Indiana Jones and the Last Crusade\n",
       "3460   4.249875e+08                                      Puss in Boots\n",
       "2639   4.244092e+08                                              Shrek\n",
       "1397   4.242706e+08                                       The Hangover\n",
       "1920   4.239333e+08                                         Iron Man 2\n",
       "644    4.233601e+08                                               Lucy\n",
       "8666   4.213881e+08                             Mission: Impossible II\n",
       "9317   4.200000e+08                         Terminator 2: Judgment Day\n",
       "6982   4.192724e+08                             The Day After Tomorrow\n",
       "10225  4.162862e+08                                     Mrs. Doubtfire\n",
       "8970   4.137996e+08                                         Armageddon\n",
       "8974   4.118409e+08                                Saving Private Ryan\n",
       "666    4.091607e+08                                     The Lego Movie\n",
       "4956   4.045728e+08                                     Bruce Almighty\n",
       "8486   4.024715e+08                                            Twister\n",
       "9985   4.022088e+08                                 Dances with Wolves\n",
       "2410   4.005174e+08                                         The Matrix\n",
       "1966   3.992115e+08                                  The King's Speech\n",
       "4381   3.990268e+08                                     Men in Black 3\n",
       "5      3.979505e+08                                       The Revenant\n",
       "\n",
       "[149 rows x 2 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "high_profit=df.sort_values(by=['profit'],ascending=False)\n",
    "high_profit.loc[:5 ,['profit','original_title']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Research Question 5  (what is the most genres release ?)\n",
    "> **what i did here**: i tried to split the genres by ('|') by using assign function to assign new column with geners after splitting then explode these column to make each gener in new row. then i count every genre movies and show it as a pie chart <br>\n",
    "#### **Note**:it dosen't work in the project workspace here but it work correctly in my local jupyter notebook.I think it because of pandas version  <br> "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "#split the genres column in '|' and explode it \n",
    "df_split=df.assign(genre=df['genres'].str.split('|')).explode('genre')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pie chart that diveded geners according to number of movies release\n",
    "df_split['genre'].value_counts().plot(kind='pie', figsize=(15,15));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='conclusions'></a>\n",
    "## Conclusions\n",
    "\n",
    "> \n",
    "- The most year release mvies is 2014 followed by 2014 then 2016\n",
    "- The run time decreased over years from about 110 min t0 97 min\n",
    "- revenue and budget have a weak positive correlation\n",
    "- revenue and profit have a strong positive correlation\n",
    "- The more revenue the more profit\n",
    "- Avatar, Star Wars: The Force Awakens and Titanic movies have the highest profits\n",
    "- Drama is the most released genre, following by comedy, and Thriller..\n",
    "\n",
    ">**Limitations** \n",
    "- cheked about duplictes and it was 1 row then i droped it\n",
    "- There was a lot of zero values in the budget and revenue columns so i replace it with mean of each of them to avoid losing alot of data.\n",
    "- I couldn't find any outliers in the data\n",
    "- there was columns with alot of null values and it wouldn't help me in my analysis so i drop these columns like(homepage , cast , tagline , overview , budget_adj , revenue_adj , imdb_id , keywords , production_companies )\n",
    "- then i droped the rows with null values.\n",
    "- by counting the number of released movies each year its show that the most high number of movies released in 2014 then 2015.\n",
    "- I assume that there's change of run time over years then i found it decreased over years.\n",
    "- by assuming relation between the revenue and budget and profit , i found that the more revenue, the more profit and there is strong positive correlation between both of them.\n",
    "- i split geners in ('|') to know the top movie genres released ana i found drama , comedy and thriller.\n",
    "\n",
    "\n",
    "## Submitting your Project \n",
    "\n",
    "> Before you submit your project, you need to create a .html or .pdf version of this notebook in the workspace here. To do that, run the code cell below. If it worked correctly, you should get a return code of 0, and you should see the generated .html file in the workspace directory (click on the orange Jupyter icon in the upper left).\n",
    "\n",
    "> Alternatively, you can download this report as .html via the **File** > **Download as** submenu, and then manually upload it into the workspace directory by clicking on the orange Jupyter icon in the upper left, then using the Upload button.\n",
    "\n",
    "> Once you've done this, you can submit your project by clicking on the \"Submit Project\" button in the lower right here. This will create and submit a zip file with this .ipynb doc and the .html or .pdf version you created. Congratulations!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from subprocess import call\n",
    "call(['python', '-m', 'nbconvert', 'Investigate_a_Dataset.ipynb'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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    "name": "ipython",
    "version": 3
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