investigate-a-dataset-TMDb-Copy1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# Project: Investigate a Dataset (TMDb Movie Data )\n",
    "\n",
    "Investigate a Dataset is the second project in Data Analyst Nanodegree, it aims to  use the Python libraries NumPy, Pandas, and Matplotlib. To analyze the dataset and then communicate the findings.\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",
    "**Dataset**: \n",
    " <br>\n",
    "I will investigate the dataset of The Movie Database (TMDb), which contains around 10,000 movies gathered from TMDb, including features such as revenue, rating, cast, and other features. Also, the dataset contains some missing  values            \n",
    " **Rows** = 10866  \n",
    " **Columns** = 21\n",
    " \n",
    " **Questions**: \n",
    " \n",
    "- 1- Which movie has the highest and lowest profit?\n",
    "-    1-A Top 5 movies which earned highest profit.\n",
    "\n",
    "- 2- Which Movie has the highest and lowest rate?\n",
    "-    2-A Top 5 lowest rated movies.\n",
    "\n",
    "- 3- Which the most popular runtime length of a movie?\n",
    "\n",
    "- 4- The average runtime of movies over years?\n",
    "\n",
    "- 5- Is the rating of movies increase or decrease or the same over years ?\n",
    "\n",
    "- 6- Which movie genre has the highest release?\n",
    "\n",
    "\n",
    "Find the original problem and the dataset here: https://www.kaggle.com/tmdb/tmdb-movie-metadata\n",
    "\n",
    "Helps me get more knowledge about the dataset: https://www.themoviedb.org/?language=ar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import some libraries needed for analyzing\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='wrangling'></a>\n",
    "## Data Wrangling\n",
    "\n",
    "> In this section, I will load in the data, check for cleanliness, and then trim and clean the dataset.\n",
    "\n",
    "\n",
    "### General Properties"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10866, 21)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the data and print. Perform operations to inspect data\n",
    "# Load the dataset\n",
    "df_tmdb = pd.read_csv('tmdb_movies.csv', sep = ',')\n",
    "\n",
    "# Print the shape of the dataset\n",
    "df_tmdb.shape\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " - The dataset contains 10866 Records (Rows) and 21 Features (Columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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",
      " #   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": [
    "# Print the summary of the dataset\n",
    "df_tmdb.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- There are missing value in cast, homepage, director, tagline, keywords, overview, genres, and production_companies comparing to the entries\n",
    "- The data type of release_date is 'object' should be 'datetime' data type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       id    imdb_id  popularity     budget     revenue      original_title  \\\n",
       "0  135397  tt0369610   32.985763  150000000  1513528810      Jurassic World   \n",
       "1   76341  tt1392190   28.419936  150000000   378436354  Mad Max: Fury Road   \n",
       "2  262500  tt2908446   13.112507  110000000   295238201           Insurgent   \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",
       "\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",
       "\n",
       "                      tagline  ...  \\\n",
       "0           The park is open.  ...   \n",
       "1          What a Lovely Day.  ...   \n",
       "2  One Choice Can Destroy You  ...   \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",
       "\n",
       "                                      genres  \\\n",
       "0  Action|Adventure|Science Fiction|Thriller   \n",
       "1  Action|Adventure|Science Fiction|Thriller   \n",
       "2         Adventure|Science Fiction|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",
       "\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",
       "\n",
       "[3 rows x 21 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print the first three rows\n",
    "df_tmdb.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>10863</th>\n",
       "      <td>39768</td>\n",
       "      <td>tt0060161</td>\n",
       "      <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",
       "      <td>1966</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <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",
       "      <td>1966</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10865</th>\n",
       "      <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",
       "      <td>74</td>\n",
       "      <td>Horror</td>\n",
       "      <td>Norm-Iris</td>\n",
       "      <td>11/15/66</td>\n",
       "      <td>15</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1966</td>\n",
       "      <td>127642.279154</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          id    imdb_id  popularity  budget  revenue  \\\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",
       "10863       Beregis Avtomobilya   \n",
       "10864    What's Up, Tiger Lily?   \n",
       "10865  Manos: The Hands of Fate   \n",
       "\n",
       "                                                    cast homepage  \\\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",
       "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",
       "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     production_companies release_date vote_count  \\\n",
       "10863  Mystery|Comedy                  Mosfilm       1/1/66         11   \n",
       "10864   Action|Comedy  Benedict Pictures Corp.      11/2/66         22   \n",
       "10865          Horror                Norm-Iris     11/15/66         15   \n",
       "\n",
       "       vote_average  release_year     budget_adj  revenue_adj  \n",
       "10863           6.5          1966       0.000000          0.0  \n",
       "10864           5.4          1966       0.000000          0.0  \n",
       "10865           1.5          1966  127642.279154          0.0  \n",
       "\n",
       "[3 rows x 21 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print the last three rows\n",
    "df_tmdb.tail(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Some budget and revenue of movies have a value of '0'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13434"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check for null values in the dataset\n",
    "sum(df_tmdb.isnull().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- There are 13434 null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check and sum the duplicate records in the dataset\n",
    "sum(df_tmdb.duplicated())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- There is one duplicate record in the dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **Observation of the Dataset** \n",
    "- Contains 10866 Records and 21 Features \n",
    "- 13434 missing value\n",
    "- The data type of release_date is 'object' should be 'datetime' data type\n",
    "- one duplicate\n",
    "- Some budget and revenue of movies have a value of '0'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### Data Cleaning ( Delete Unused Features and Entries From The Dataset )\n",
    "\n",
    "> **Data need to delete or change**\n",
    "- 1- Delete unuesd features \n",
    "- 2- Delete duplicated rows \n",
    "- 3- Change the data type of release date to 'datetime' format\n",
    "- 4- Delete inappropriate value and movies with zero value of budget, revenue, and runtime"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1- Delete unuesd features "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10866, 15)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Here I will drop unnecessary features that will not be helpful in the process of analysing.\n",
    "# Using drop function and print the new shape of dataset\n",
    "\n",
    "df_tmdb.drop(['budget_adj','revenue_adj','overview','imdb_id','homepage','tagline'], axis = 1, inplace = True)\n",
    "df_tmdb.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The columns are 15 after dropping unnecessary features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2- Delete duplicated rows  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10865, 15)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Here I will delete duplicate row\n",
    "# Using drop_duplicates function\n",
    "\n",
    "df_tmdb.drop_duplicates(inplace = True)\n",
    "df_tmdb.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- After dropping duplicated row, the entries are 10865"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3- Change the data type of release date  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                               int64\n",
       "popularity                     float64\n",
       "budget                           int64\n",
       "revenue                          int64\n",
       "original_title                  object\n",
       "cast                            object\n",
       "director                        object\n",
       "keywords                        object\n",
       "runtime                          int64\n",
       "genres                          object\n",
       "production_companies            object\n",
       "release_date            datetime64[ns]\n",
       "vote_count                       int64\n",
       "vote_average                   float64\n",
       "release_year                     int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Here I will change the data type of relese date\n",
    "# Using to_datetime function and check using dtypes function\n",
    "df_tmdb['release_date'] = pd.to_datetime(df_tmdb['release_date'])\n",
    "df_tmdb.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- As shown data type of release_date is in the proper format"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2- Delete inappropriate value and movies with zero value of budget, revenue, and runtime\n",
    "\n",
    "I will need to delete the value with zero because is not going to be helpful during the exploratation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rows which contain '0' values in budget column: 5696\n",
      "Rows which contain '0' values in revenue column: 6016\n",
      "Rows which contain '0' values in runtime column: 31\n"
     ]
    }
   ],
   "source": [
    "# Here I will check the zero value\n",
    "print(\"Rows which contain '0' values in budget column:\",df_tmdb[(df_tmdb['budget']==0)].shape[0])\n",
    "print(\"Rows which contain '0' values in revenue column:\",df_tmdb[(df_tmdb['revenue']==0)].shape[0])\n",
    "print(\"Rows which contain '0' values in runtime column:\",df_tmdb[(df_tmdb['runtime']==0)].shape[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- As shown the number of rows contain zero budget(5696), zero revenue (6016), and zero runtime (31)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make a list to replace the zeros \n",
    "budg_reve_runt_list = ['budget', 'revenue','runtime']\n",
    "\n",
    "# Replace the value of zero \n",
    "df_tmdb[budg_reve_runt_list] = df_tmdb[budg_reve_runt_list].replace(0, np.NaN)\n",
    "\n",
    "# Delete any row which contains NaN values \n",
    "# Using dropna funtion\n",
    "df_tmdb.dropna(subset = budg_reve_runt_list, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3854, 15)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tmdb.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The dataset contains 3854 rows and 15 columns after cleaning.\n",
    ">As now the rows, columns, and format of the dataset are proper, let's explore the data for the questions asked."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='eda'></a>\n",
    "## Exploratory Data Analysis\n",
    "\n",
    "> Here I will explore the questions asked here.\n",
    "\n",
    "### Research Question 1 (Which movie has the highest and lowest profit?)\n",
    "\n",
    "Here I will need to create a new column name 'profit' to calculate profit for each of the movies.  Also, I will create a function to find max and min. Which will be helpful along with the exploration.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Movie which has Highest profit :  Avatar\n",
      "Movie which has Lowest profit  :  The Warrior's Way\n"
     ]
    },
    {
     "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>1386</th>\n",
       "      <th>2244</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <td>19995</td>\n",
       "      <td>46528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>popularity</th>\n",
       "      <td>9.43277</td>\n",
       "      <td>0.25054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>budget</th>\n",
       "      <td>2.37e+08</td>\n",
       "      <td>4.25e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>revenue</th>\n",
       "      <td>2.78151e+09</td>\n",
       "      <td>1.10876e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>original_title</th>\n",
       "      <td>Avatar</td>\n",
       "      <td>The Warrior's Way</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cast</th>\n",
       "      <td>Sam Worthington|Zoe Saldana|Sigourney Weaver|S...</td>\n",
       "      <td>Kate Bosworth|Jang Dong-gun|Geoffrey Rush|Dann...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>director</th>\n",
       "      <td>James Cameron</td>\n",
       "      <td>Sngmoo Lee</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>keywords</th>\n",
       "      <td>culture clash|future|space war|space colony|so...</td>\n",
       "      <td>assassin|small town|revenge|deception|super speed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>runtime</th>\n",
       "      <td>162</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>genres</th>\n",
       "      <td>Action|Adventure|Fantasy|Science Fiction</td>\n",
       "      <td>Adventure|Fantasy|Action|Western|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>production_companies</th>\n",
       "      <td>Ingenious Film Partners|Twentieth Century Fox ...</td>\n",
       "      <td>Boram Entertainment Inc.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>release_date</th>\n",
       "      <td>2009-12-10 00:00:00</td>\n",
       "      <td>2010-12-02 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vote_count</th>\n",
       "      <td>8458</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vote_average</th>\n",
       "      <td>7.1</td>\n",
       "      <td>6.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>release_year</th>\n",
       "      <td>2009</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>profit</th>\n",
       "      <td>2.54451e+09</td>\n",
       "      <td>-4.13912e+08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                   1386  \\\n",
       "id                                                                19995   \n",
       "popularity                                                      9.43277   \n",
       "budget                                                         2.37e+08   \n",
       "revenue                                                     2.78151e+09   \n",
       "original_title                                                   Avatar   \n",
       "cast                  Sam Worthington|Zoe Saldana|Sigourney Weaver|S...   \n",
       "director                                                  James Cameron   \n",
       "keywords              culture clash|future|space war|space colony|so...   \n",
       "runtime                                                             162   \n",
       "genres                         Action|Adventure|Fantasy|Science Fiction   \n",
       "production_companies  Ingenious Film Partners|Twentieth Century Fox ...   \n",
       "release_date                                        2009-12-10 00:00:00   \n",
       "vote_count                                                         8458   \n",
       "vote_average                                                        7.1   \n",
       "release_year                                                       2009   \n",
       "profit                                                      2.54451e+09   \n",
       "\n",
       "                                                                   2244  \n",
       "id                                                                46528  \n",
       "popularity                                                      0.25054  \n",
       "budget                                                         4.25e+08  \n",
       "revenue                                                     1.10876e+07  \n",
       "original_title                                        The Warrior's Way  \n",
       "cast                  Kate Bosworth|Jang Dong-gun|Geoffrey Rush|Dann...  \n",
       "director                                                     Sngmoo Lee  \n",
       "keywords              assassin|small town|revenge|deception|super speed  \n",
       "runtime                                                             100  \n",
       "genres                        Adventure|Fantasy|Action|Western|Thriller  \n",
       "production_companies                           Boram Entertainment Inc.  \n",
       "release_date                                        2010-12-02 00:00:00  \n",
       "vote_count                                                           74  \n",
       "vote_average                                                        6.4  \n",
       "release_year                                                       2010  \n",
       "profit                                                     -4.13912e+08  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Created a new column in the dataframe name 'profit'\n",
    "df_tmdb['profit'] = df_tmdb['revenue'] - df_tmdb['budget']\n",
    "\n",
    "#I will use function 'idmin' to find the index of lowest profit movie\n",
    "# And function 'idmax' to find the index of Highest profit movie\n",
    "\n",
    "def find_max_min(x):\n",
    "    #Use the function 'idmin' to find the index of lowest profit movie.\n",
    "    low_index = df_tmdb[x].idxmin()\n",
    "    \n",
    "    #Use the function 'idmax' to find the index of Highest profit movie.\n",
    "    high_index = df_tmdb[x].idxmax()\n",
    "    #Indicating the index \n",
    "    high = pd.DataFrame(df_tmdb.loc[high_index,:])\n",
    "    low = pd.DataFrame(df_tmdb.loc[low_index,:])\n",
    "    \n",
    "    #Print the movie with high and low profit\n",
    "    print(\"Movie which has Highest \"+ x + \" : \",df_tmdb['original_title'][high_index])\n",
    "    print(\"Movie which has Lowest \"+ x + \"  : \",df_tmdb['original_title'][low_index])\n",
    "    return pd.concat([high,low],axis = 1)\n",
    "\n",
    "#calling the find_max_min function\n",
    "find_max_min('profit')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- As shown in the first column the highest profit made by **Avatar** movie directed by James Cameron.\n",
    "- And the second column the lowest profit made by **The Warrior's Way** movie directed by Sngmoo Lee."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**1-A  Top 5 movies which earn highest profit.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# I will create a plot which contains top 5 movies earn highest profit.\n",
    "# And sort the 'profit' column in decending order and store it in the new dataframe\n",
    "\n",
    "sort_profit = pd.DataFrame(df_tmdb['profit'].sort_values(ascending = False))\n",
    "sort_profit['original_title'] = df_tmdb['original_title']\n",
    "profit_data = list(map(str,(sort_profit['original_title'])))\n",
    "x = list(profit_data[:5])\n",
    "y = list(sort_profit['profit'][:5])\n",
    "\n",
    "# Here create a plot usinf pointplot for top 5 profitable movies.\n",
    "plo_five = sns.pointplot(x=y,y=x)\n",
    "\n",
    "\n",
    "#Name the title and labels of the plot and figure size.\n",
    "sns.set(rc={'figure.figsize':(8,8)})\n",
    "plo_five.set_title(\"Top 5 Profitable Movies\",fontsize = 13)\n",
    "plo_five.set_xlabel(\"Profit\",fontsize = 12)\n",
    "plo_five.set_ylabel(\"Movies\",fontsize = 12)\n",
    "sns.set_style(\"darkgrid\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Based on the plot the top is **Avatar** followed by **Star Wars: the force Awakens** and **Titanic**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Research Question 2  (Movie which has the highest and lowest rate?)\n",
    "\n",
    "I will use the function I made above 'find_max_min' to find the highest and lowest rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Movie which has Highest vote_average :  The Shawshank Redemption\n",
      "Movie which has Lowest vote_average  :  Foodfight!\n"
     ]
    },
    {
     "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>4178</th>\n",
       "      <th>4859</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <td>278</td>\n",
       "      <td>116977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>popularity</th>\n",
       "      <td>7.19204</td>\n",
       "      <td>0.111351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>budget</th>\n",
       "      <td>2.5e+07</td>\n",
       "      <td>6.5e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>revenue</th>\n",
       "      <td>2.83415e+07</td>\n",
       "      <td>73706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>original_title</th>\n",
       "      <td>The Shawshank Redemption</td>\n",
       "      <td>Foodfight!</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cast</th>\n",
       "      <td>Tim Robbins|Morgan Freeman|Bob Gunton|William ...</td>\n",
       "      <td>Charlie Sheen|Wayne Brady|Hilary Duff|Eva Long...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>director</th>\n",
       "      <td>Frank Darabont</td>\n",
       "      <td>Lawrence Kasanoff</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>keywords</th>\n",
       "      <td>prison|corruption|police brutality|prison cell...</td>\n",
       "      <td>supermarket|fight|product placement|computer a...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>runtime</th>\n",
       "      <td>142</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>genres</th>\n",
       "      <td>Drama|Crime</td>\n",
       "      <td>Animation|Action|Comedy|Family</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>production_companies</th>\n",
       "      <td>Castle Rock Entertainment</td>\n",
       "      <td>Threshold Entertainment|Lions Gate Family Ente...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>release_date</th>\n",
       "      <td>1994-09-10 00:00:00</td>\n",
       "      <td>2012-06-15 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vote_count</th>\n",
       "      <td>5754</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vote_average</th>\n",
       "      <td>8.4</td>\n",
       "      <td>2.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>release_year</th>\n",
       "      <td>1994</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>profit</th>\n",
       "      <td>3.34147e+06</td>\n",
       "      <td>-6.49263e+07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                   4178  \\\n",
       "id                                                                  278   \n",
       "popularity                                                      7.19204   \n",
       "budget                                                          2.5e+07   \n",
       "revenue                                                     2.83415e+07   \n",
       "original_title                                 The Shawshank Redemption   \n",
       "cast                  Tim Robbins|Morgan Freeman|Bob Gunton|William ...   \n",
       "director                                                 Frank Darabont   \n",
       "keywords              prison|corruption|police brutality|prison cell...   \n",
       "runtime                                                             142   \n",
       "genres                                                      Drama|Crime   \n",
       "production_companies                          Castle Rock Entertainment   \n",
       "release_date                                        1994-09-10 00:00:00   \n",
       "vote_count                                                         5754   \n",
       "vote_average                                                        8.4   \n",
       "release_year                                                       1994   \n",
       "profit                                                      3.34147e+06   \n",
       "\n",
       "                                                                   4859  \n",
       "id                                                               116977  \n",
       "popularity                                                     0.111351  \n",
       "budget                                                          6.5e+07  \n",
       "revenue                                                           73706  \n",
       "original_title                                               Foodfight!  \n",
       "cast                  Charlie Sheen|Wayne Brady|Hilary Duff|Eva Long...  \n",
       "director                                              Lawrence Kasanoff  \n",
       "keywords              supermarket|fight|product placement|computer a...  \n",
       "runtime                                                              87  \n",
       "genres                                   Animation|Action|Comedy|Family  \n",
       "production_companies  Threshold Entertainment|Lions Gate Family Ente...  \n",
       "release_date                                        2012-06-15 00:00:00  \n",
       "vote_count                                                           12  \n",
       "vote_average                                                        2.2  \n",
       "release_year                                                       2012  \n",
       "profit                                                     -6.49263e+07  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Using 'find_max_min' function passsing 'vote_average'\n",
    "find_max_min('vote_average')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The first column is the movie with the highest rate **The Shawshank Redemption** which has the max rating of 84% and directed by Frank Darabont\n",
    "\n",
    "- The second column is the movie with the lowest rate **Foodfight!** which has the min rating of 22% and directed by Lawrence Kasanoff"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**2-A Top 5 lowest rated movies**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#I will sort the 'vote_average' column in ascending order store it in the a new dataframe.\n",
    "sort_vote = pd.DataFrame(df_tmdb['vote_average'].sort_values(ascending = True))\n",
    "sort_vote['original_title'] = df_tmdb['original_title']\n",
    "sort_vote_data = list(map(str,(sort_vote['original_title'])))\n",
    "\n",
    "#Getting the top 5 lowest rate movies data \n",
    "x = list(sort_vote_data[:5])\n",
    "y = list(sort_vote['vote_average'][:5])\n",
    "\n",
    "# Make the point plot and name the title and labels.\n",
    "pol_vote = sns.pointplot(x=y,y=x)\n",
    "sns.set(rc={'figure.figsize':(10,5)})\n",
    "pol_vote.set_title(\"Top 5 Lowest Rate Movies\",fontsize = 15)\n",
    "pol_vote.set_xlabel(\"Vote Average\",fontsize = 13)\n",
    "# the stylesheet\n",
    "sns.set_style(\"darkgrid\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- As shown the **Foodfight!** is lowest rate followed by **FearDotCom** and **Disaster Movie**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Research Question 3  (Which the most popular runtime length of a movie?)\n",
    "\n",
    "I will need to group the popularity of a movie according to its runtime, to find the most popular length."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 936x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#using the groupby function to group the popularity based on its runtime and find the mean\n",
    "runt_popu = df_tmdb.groupby('runtime')['popularity'].mean()\n",
    "#create the plot\n",
    "runt_popu.plot(figsize = (13,5),xticks=np.arange(0,1000,100))\n",
    "\n",
    "#Name the title, x-label, y-label, and the figure size \n",
    "plt.title(\"Runtime vs Average Popularity\",fontsize = 14)\n",
    "plt.xlabel('Runtime',fontsize = 13)\n",
    "plt.ylabel('Average Popularity',fontsize = 13)\n",
    "sns.set(rc={'figure.figsize':(8,8)})\n",
    "sns.set_style(\"whitegrid\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Based on the plot the range of the movie runtime is 100-200,  is the most popular than others."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Research Question 4 (The average runtime of movies over years?)\n",
    "\n",
    "I will need to the group the data according to their release_year and find the mean."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Here I will calculate the mean and group by release year\n",
    "runt_year = df_tmdb.groupby('release_year')['runtime'].mean()\n",
    "\n",
    "#create the plot\n",
    "runt_year.plot(xticks = np.arange(1960,2016,5))\n",
    "\n",
    "#Name the title and the labels of the plot and figure size\n",
    "sns.set(rc={'figure.figsize':(10,8)})\n",
    "plt.title(\"Average Runtime vs Year\",fontsize = 14)\n",
    "plt.xlabel('Year',fontsize = 13)\n",
    "plt.ylabel('Runtime',fontsize = 13)\n",
    "sns.set_style(\"darkgrid\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Based on the plot the movie duration is decreasing over the years. And the average runtime duration of the movies is around 110 Minutes now. I assume nobody wants to watch a long movie nowadays."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Research Question 5  (Is the rating of movies increase or decrease or the same over years ?)\n",
    "\n",
    "I will need to work with release_year column and the the average movie rating in vote_average column. Also, I will need the group by release_year and calculate the mean of vote_average for each year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Average Rating')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Here I will  calculate the mean of vote_average then groupby the result by release_year \n",
    "\n",
    "rating_year_avg = df_tmdb.groupby('release_year')['vote_average'].mean()\n",
    "\n",
    "# Plot release_year and vote_average relationship \n",
    "rating_year_avg.plot(kind='line',color='blue', figsize = (10,5))\n",
    "\n",
    "# Name the title and the labels of the plot\n",
    "plt.title(\"Rating vs Years Average\",fontsize=14)\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Average Rating')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " - The ratings decrease over years. \n",
    " - According to the plot, the old movies had better ratings than the new ones. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Research Question 6 (Which movie genre has the highest release?)\n",
    "\n",
    "I will create a function that take genres and split it into catogry to find the hishest release"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 936x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# make a function that will split the genres, return the number of each genre\n",
    "def genre_split_and_count(x):\n",
    "    #concatenate all the rows of the genrs\n",
    "    dt_plot = df_tmdb[x].str.cat(sep = '|')\n",
    "    dt = pd.Series(dt_plot.split('|'))\n",
    "\n",
    "    no_genre = dt.value_counts(ascending=False)\n",
    "    return no_genre\n",
    "\n",
    "#Here I call the function to count the movies of each genre\n",
    "total_gen_mov = genre_split_and_count('genres')\n",
    "\n",
    "#plot a 'barh' plot using plot function for 'genre vs number of movies'\n",
    "total_gen_mov.plot(kind= 'barh',figsize = (13,6),fontsize=12,colormap='tab20c')\n",
    "\n",
    "# Name the title and the labels of the plot\n",
    "plt.title(\"Genre With Highest Release\",fontsize=14)\n",
    "plt.xlabel('Number of Movies',fontsize=12)\n",
    "plt.ylabel(\"Genres\",fontsize= 12)\n",
    "sns.set_style(\"whitegrid\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Based on to the plot **Drama** genre has the highest release of movies around(1755) followed by **Comedy** around(1300) and **Thriller** around(1150)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='conclusions'></a>\n",
    "## Conclusions\n",
    "\n",
    " **Conclusions :** \n",
    "\n",
    "- **Avatar**, **Star Wars**, and **Titanic** are the most profitable movies\n",
    "- **Foodfight!**, **FearDotCom**, and **Disaster Movie** have the worst rate\n",
    "- The movies rating decrease over years and the old movies have a high rate\n",
    "- The average runtime of a movie is in the range of 100-200, and is the most popular than others\n",
    "- The movie duration decreases over years\n",
    "- **Drama** genre has highest number of release followed by **Comedy** and **Thirller**\n",
    "\n",
    "**Limitations :**\n",
    " \n",
    " \n",
    "- Missing values in the dataset and duplicated rows \n",
    "- The zero value in budget, revenue, and runtime, which had to remove many rows.\n",
    "- Revenue and budget does not have currency unit.\n",
    "- Inappropriate data type."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}