investigate-tmdb-dataset.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Project: Investigate The Movie Database (TMDb) Dataset\n",
    "\n",
    "## Table of Contents\n",
    "<ul>\n",
    "<li><a href=\"#intro\">Introduction</a></li>\n",
    "<li><a href=\"#wrangling\">Data Wrangling</a></li>\n",
    "<li><a href=\"#eda\">Exploratory Data Analysis</a></li>\n",
    "<li><a href=\"#conclusions\">Conclusions</a></li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='intro'></a>\n",
    "## Introduction\n",
    "\n",
    "> The Movie Database (TMDb) dataset, originally from Kaggle was cleaned and provided by Udacity. The dataset contains information about 10,000 movies from the year 1960 to 2015 collected from The Movie Database (TMDb), including user ratings and revenue.\n",
    "The questions that I will explore over the course of the report:\n",
    "- **Q1: What are the properties and attributes of the movies from year 1960 to 2015?**\n",
    " - Movie that makes the most and least profit?\n",
    " - Movie with the highest and lowest budget?\n",
    " - Movie with highest and lowest revenue?\n",
    " - Movie with the longest and shortest runtime?\n",
    " - The average movie runtime?\n",
    " - The month with the most movies released?\n",
    " - The year with the most movies released?\n",
    " - The year that makes the most profit?\n",
    " - Who starred in the most movies?\n",
    " - Who directed the most movies?\n",
    " - The most popular production companies?\n",
    " - The most popular genre?\n",
    " - The most common keywords?\n",
    "<br/><br/>\n",
    "- **Q2: What kind of properties are associated with movies that have high profit?**\n",
    " - High profit movies average runtime?\n",
    " - High profit movies average budget?\n",
    " - High profit movies average revenue?\n",
    " - In which month the movies makes the most profit?\n",
    " - In which year the movies makes the most profit?\n",
    " - High profit movie casts?\n",
    " - High profit movie directors?\n",
    " - High profit production companies?\n",
    " - High profit movie genre?\n",
    " - High profit movie keywords?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import statements for all of the packages that I plan to use. \n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\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 of the report, I will load in the data, check for cleanliness, and then trim and clean the dataset for analysis. \n",
    "\n",
    "### General Properties\n",
    "Below are the questions to answer using pandas to explore ```tmdb-movies.csv``` and have a holistic understanding of the data set:\n",
    "\n",
    "- Number of samples & columns in the dataset\n",
    "- Duplicate rows in the dataset\n",
    "- Datatypes of the columns\n",
    "- Features with missing values\n",
    "- Number of unique values for the dataset\n",
    "- Number of rows with missing values in the dataset\n",
    "- Descriptive statistics for the dataset\n",
    "- Number of zero values in ```runtime```, ```budget_adj``` and ```revenue_adj```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "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": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the data and print out a few lines. Perform operations to inspect data\n",
    "#   types and look for instances of missing or possibly errant data.\n",
    "movie_df = pd.read_csv('tmdb-movies.csv')\n",
    "movie_df.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">**Observations:** \n",
    ">- Columns like ```cast```, ```keywords```, ```genres``` and ```production_companies```, contain multiple values separated by pipe (|) characters. \n",
    "- The final two columns ending with “_adj” show the budget and revenue of the associated movie in terms of 2010 dollars, accounting for inflation over time. I will drop the original value and retain the two columns so that I can better compare dollar values from one year to another.\n",
    "- Noticed that the ```vote_count``` is different for all the movies, so I will not be able to calculate the popularity of the movies based on the value."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Number of samples & columns in the dataset\n",
    "Based on the result shown below, there are a total of 10,866 number of movies and 21 columns in the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10866, 21)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To check the size of the dataset\n",
    "movie_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Duplicate rows in the dataset\n",
    "There is only 1 duplicated row which I will drop it in the next section on data cleaning."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To get the total number of duplicated rows in the dataset \n",
    "movie_df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "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>2089</th>\n",
       "      <td>42194</td>\n",
       "      <td>tt0411951</td>\n",
       "      <td>0.59643</td>\n",
       "      <td>30000000</td>\n",
       "      <td>967000</td>\n",
       "      <td>TEKKEN</td>\n",
       "      <td>Jon Foo|Kelly Overton|Cary-Hiroyuki Tagawa|Ian...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Dwight H. Little</td>\n",
       "      <td>Survival is no game</td>\n",
       "      <td>...</td>\n",
       "      <td>In the year of 2039, after World Wars destroy ...</td>\n",
       "      <td>92</td>\n",
       "      <td>Crime|Drama|Action|Thriller|Science Fiction</td>\n",
       "      <td>Namco|Light Song Films</td>\n",
       "      <td>3/20/10</td>\n",
       "      <td>110</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2010</td>\n",
       "      <td>30000000.0</td>\n",
       "      <td>967000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2090</th>\n",
       "      <td>42194</td>\n",
       "      <td>tt0411951</td>\n",
       "      <td>0.59643</td>\n",
       "      <td>30000000</td>\n",
       "      <td>967000</td>\n",
       "      <td>TEKKEN</td>\n",
       "      <td>Jon Foo|Kelly Overton|Cary-Hiroyuki Tagawa|Ian...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Dwight H. Little</td>\n",
       "      <td>Survival is no game</td>\n",
       "      <td>...</td>\n",
       "      <td>In the year of 2039, after World Wars destroy ...</td>\n",
       "      <td>92</td>\n",
       "      <td>Crime|Drama|Action|Thriller|Science Fiction</td>\n",
       "      <td>Namco|Light Song Films</td>\n",
       "      <td>3/20/10</td>\n",
       "      <td>110</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2010</td>\n",
       "      <td>30000000.0</td>\n",
       "      <td>967000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         id    imdb_id  popularity    budget  revenue original_title  \\\n",
       "2089  42194  tt0411951     0.59643  30000000   967000         TEKKEN   \n",
       "2090  42194  tt0411951     0.59643  30000000   967000         TEKKEN   \n",
       "\n",
       "                                                   cast homepage  \\\n",
       "2089  Jon Foo|Kelly Overton|Cary-Hiroyuki Tagawa|Ian...      NaN   \n",
       "2090  Jon Foo|Kelly Overton|Cary-Hiroyuki Tagawa|Ian...      NaN   \n",
       "\n",
       "              director              tagline     ...       \\\n",
       "2089  Dwight H. Little  Survival is no game     ...        \n",
       "2090  Dwight H. Little  Survival is no game     ...        \n",
       "\n",
       "                                               overview runtime  \\\n",
       "2089  In the year of 2039, after World Wars destroy ...      92   \n",
       "2090  In the year of 2039, after World Wars destroy ...      92   \n",
       "\n",
       "                                           genres    production_companies  \\\n",
       "2089  Crime|Drama|Action|Thriller|Science Fiction  Namco|Light Song Films   \n",
       "2090  Crime|Drama|Action|Thriller|Science Fiction  Namco|Light Song Films   \n",
       "\n",
       "     release_date vote_count  vote_average  release_year  budget_adj  \\\n",
       "2089      3/20/10        110           5.0          2010  30000000.0   \n",
       "2090      3/20/10        110           5.0          2010  30000000.0   \n",
       "\n",
       "      revenue_adj  \n",
       "2089     967000.0  \n",
       "2090     967000.0  \n",
       "\n",
       "[2 rows x 21 columns]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To check and make sure that the duplicated row is exactly the same\n",
    "movie_df[movie_df.duplicated(keep=False)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Datatype of column in the dataset\n",
    "```info``` is use to find the basic information like datatype of column in the dataset. Noticed that column such as ```release_date``` is not using the date datatype, ```budget_adj``` and ```revenue_adj``` are float datatype which I will work on it in the next section."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "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": [
    "movie_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Features with missing values in the dataset\n",
    "There are a number of null values in the dataset for these columns: ```imdb_id```, ```cast```, ```homepage```, ```director```, ```tagline```, ```keywords```, ```overview```, ```genres``` and ```production_companies```. Huge number of null values for ```homepage```, ```tagline```, ```keywords``` and ```production_companies``` but since some of them are not necessary for answering my above questions, I will drop them during the data cleaning section."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                         0\n",
       "imdb_id                   10\n",
       "popularity                 0\n",
       "budget                     0\n",
       "revenue                    0\n",
       "original_title             0\n",
       "cast                      76\n",
       "homepage                7930\n",
       "director                  44\n",
       "tagline                 2824\n",
       "keywords                1493\n",
       "overview                   4\n",
       "runtime                    0\n",
       "genres                    23\n",
       "production_companies    1030\n",
       "release_date               0\n",
       "vote_count                 0\n",
       "vote_average               0\n",
       "release_year               0\n",
       "budget_adj                 0\n",
       "revenue_adj                0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Number of unique values for the dataset\n",
    "To see the total number of unique values for each column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                      10865\n",
       "imdb_id                 10855\n",
       "popularity              10814\n",
       "budget                    557\n",
       "revenue                  4702\n",
       "original_title          10571\n",
       "cast                    10719\n",
       "homepage                 2896\n",
       "director                 5067\n",
       "tagline                  7997\n",
       "keywords                 8804\n",
       "overview                10847\n",
       "runtime                   247\n",
       "genres                   2039\n",
       "production_companies     7445\n",
       "release_date             5909\n",
       "vote_count               1289\n",
       "vote_average               72\n",
       "release_year               56\n",
       "budget_adj               2614\n",
       "revenue_adj              4840\n",
       "dtype: int64"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_df.nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Number of rows with missing values in the dataset\n",
    "To check the total number of rows with at least one column with missing values. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8874"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_df.isnull().any(axis=1).sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Descriptive statistics for the dataset\n",
    "Based on the table shown below, 50% of the value in ```budget``` and ```revenue``` are of zero value. The minimum value for runtime is 0 which means that there could be a number of values of zero value in ```runtime```."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Number of zero values in ```runtime```, ```budget_adj``` and ```revenue_adj``` \n",
    "In order to decide whether to drop the zero values in the dataset, I will need to get the total number of zero in each column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "31\n"
     ]
    }
   ],
   "source": [
    "# Return the number of zero in runtime column\n",
    "zero_runtime = (movie_df['runtime'] == 0).sum()\n",
    "print(zero_runtime)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5696\n"
     ]
    }
   ],
   "source": [
    "# Return the number of zero in budget_adj column\n",
    "zero_budget = (movie_df['budget_adj'] == 0).sum()\n",
    "print(zero_budget)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6016\n"
     ]
    }
   ],
   "source": [
    "# Return the number of zero in revenue_adj column\n",
    "zero_revenue = (movie_df['revenue_adj'] == 0).sum()\n",
    "print(zero_revenue)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">**Observations:** \n",
    ">- There are 31 zero values rows in ```runtime```, 5,698 zero values rows in ```budget_adj``` and 6,016 zero value rows in ```revenue_adj```. It is a huge amount of missing data for ```budget_adj``` and ```revenue_adj```. In order not to drop more than 50% of the data that will affect my statistics and visualization result, I decided to retain these rows and replace them with mean values. \n",
    "- As for the zero values in ```runtime```, in order to have the full dataset for exploration, I will also retain these rows and replace them with mean values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Cleaning\n",
    "After the above discussion on the structure of the dataset and the problems that need to be cleaned, the following are the cleaning steps to be performed:\n",
    "\n",
    "1. Drop Extraneous Columns\n",
    "2. Drop the Duplicates\n",
    "3. Add new column ```profit```\n",
    "4. Mark zero values with NaN\n",
    "5. Replace zero values with mean\n",
    "6. Convert ``` release_date``` column to Date datatype"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Drop Extraneous Columns\n",
    "- Drop columns that aren't relevant to our questions. Columns to Drop: ```id```, ```imdb_id```, ```popularity```, ```homepage```, ```overview```, ```tagline```\n",
    "- ```budget``` and ```revenue``` will also be drop as I will be using the final two columns ending with “_adj” which show the budget and revenue of the associated movie in terms of 2010 dollars, accounting for inflation over time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>original_title</th>\n",
       "      <th>cast</th>\n",
       "      <th>director</th>\n",
       "      <th>keywords</th>\n",
       "      <th>runtime</th>\n",
       "      <th>genres</th>\n",
       "      <th>production_companies</th>\n",
       "      <th>release_date</th>\n",
       "      <th>release_year</th>\n",
       "      <th>budget_adj</th>\n",
       "      <th>revenue_adj</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jurassic World</td>\n",
       "      <td>Chris Pratt|Bryce Dallas Howard|Irrfan Khan|Vi...</td>\n",
       "      <td>Colin Trevorrow</td>\n",
       "      <td>monster|dna|tyrannosaurus rex|velociraptor|island</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>2015</td>\n",
       "      <td>1.379999e+08</td>\n",
       "      <td>1.392446e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   original_title                                               cast  \\\n",
       "0  Jurassic World  Chris Pratt|Bryce Dallas Howard|Irrfan Khan|Vi...   \n",
       "\n",
       "          director                                           keywords  \\\n",
       "0  Colin Trevorrow  monster|dna|tyrannosaurus rex|velociraptor|island   \n",
       "\n",
       "   runtime                                     genres  \\\n",
       "0      124  Action|Adventure|Science Fiction|Thriller   \n",
       "\n",
       "                                production_companies release_date  \\\n",
       "0  Universal Studios|Amblin Entertainment|Legenda...       6/9/15   \n",
       "\n",
       "   release_year    budget_adj   revenue_adj  \n",
       "0          2015  1.379999e+08  1.392446e+09  "
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# drop columns from the dataset\n",
    "movie_df.drop(['id', 'imdb_id', 'popularity', 'budget', 'revenue', 'homepage', 'overview', 'vote_count', \n",
    "               'vote_average', 'tagline'], axis=1, inplace=True)\n",
    "\n",
    "# confirm changes\n",
    "movie_df.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Drop the Duplicates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# drop duplicates in the datasets\n",
    "movie_df.drop_duplicates(inplace=True)\n",
    "\n",
    "# print number of duplicates again to confirm dedupe - should be 0\n",
    "movie_df.duplicated().sum() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Add new column ```Profit```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Insert a new column profit calculated using revenue minus budget\n",
    "movie_df['profit'] = movie_df['revenue_adj'] - movie_df['budget_adj'] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>original_title</th>\n",
       "      <th>cast</th>\n",
       "      <th>director</th>\n",
       "      <th>keywords</th>\n",
       "      <th>runtime</th>\n",
       "      <th>genres</th>\n",
       "      <th>production_companies</th>\n",
       "      <th>release_date</th>\n",
       "      <th>release_year</th>\n",
       "      <th>budget_adj</th>\n",
       "      <th>revenue_adj</th>\n",
       "      <th>profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jurassic World</td>\n",
       "      <td>Chris Pratt|Bryce Dallas Howard|Irrfan Khan|Vi...</td>\n",
       "      <td>Colin Trevorrow</td>\n",
       "      <td>monster|dna|tyrannosaurus rex|velociraptor|island</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>2015</td>\n",
       "      <td>1.379999e+08</td>\n",
       "      <td>1.392446e+09</td>\n",
       "      <td>1.254446e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   original_title                                               cast  \\\n",
       "0  Jurassic World  Chris Pratt|Bryce Dallas Howard|Irrfan Khan|Vi...   \n",
       "\n",
       "          director                                           keywords  \\\n",
       "0  Colin Trevorrow  monster|dna|tyrannosaurus rex|velociraptor|island   \n",
       "\n",
       "   runtime                                     genres  \\\n",
       "0      124  Action|Adventure|Science Fiction|Thriller   \n",
       "\n",
       "                                production_companies release_date  \\\n",
       "0  Universal Studios|Amblin Entertainment|Legenda...       6/9/15   \n",
       "\n",
       "   release_year    budget_adj   revenue_adj        profit  \n",
       "0          2015  1.379999e+08  1.392446e+09  1.254446e+09  "
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To confirm the changes\n",
    "movie_df.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Mark zero values with NaN\n",
    "\n",
    "Based on the previous Descriptive statistics for the dataset, 50% of the value in ```budget``` and ```revenue``` are of zero value. The minimum value for runtime is 0 which means that there could be a number of values of zero value in ```runtime```.\n",
    "\n",
    "Considering the impact of dropping all the rows with zero might impact my analysis, I will first mark the zero values as NaN so that I can replace them with mean values in the next section."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a list with zero values \n",
    "zero_list=['runtime', 'budget_adj', 'revenue_adj']\n",
    "\n",
    "# Replace all the value from '0' to NAN in the list\n",
    "movie_df[zero_list] = movie_df[zero_list].replace(0, np.NAN)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "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>runtime</th>\n",
       "      <th>release_year</th>\n",
       "      <th>budget_adj</th>\n",
       "      <th>revenue_adj</th>\n",
       "      <th>profit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10834.000000</td>\n",
       "      <td>10865.000000</td>\n",
       "      <td>5.169000e+03</td>\n",
       "      <td>4.849000e+03</td>\n",
       "      <td>1.086500e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>102.363855</td>\n",
       "      <td>2001.321859</td>\n",
       "      <td>3.688907e+07</td>\n",
       "      <td>1.151009e+08</td>\n",
       "      <td>3.381911e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>30.948225</td>\n",
       "      <td>12.813260</td>\n",
       "      <td>4.196096e+07</td>\n",
       "      <td>1.988557e+08</td>\n",
       "      <td>1.252194e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>1960.000000</td>\n",
       "      <td>9.210911e-01</td>\n",
       "      <td>2.370705e+00</td>\n",
       "      <td>-4.139124e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>90.000000</td>\n",
       "      <td>1995.000000</td>\n",
       "      <td>8.102293e+06</td>\n",
       "      <td>1.046585e+07</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>99.000000</td>\n",
       "      <td>2006.000000</td>\n",
       "      <td>2.271505e+07</td>\n",
       "      <td>4.395666e+07</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>112.000000</td>\n",
       "      <td>2011.000000</td>\n",
       "      <td>5.008384e+07</td>\n",
       "      <td>1.316482e+08</td>\n",
       "      <td>1.293091e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>900.000000</td>\n",
       "      <td>2015.000000</td>\n",
       "      <td>4.250000e+08</td>\n",
       "      <td>2.827124e+09</td>\n",
       "      <td>2.750137e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            runtime  release_year    budget_adj   revenue_adj        profit\n",
       "count  10834.000000  10865.000000  5.169000e+03  4.849000e+03  1.086500e+04\n",
       "mean     102.363855   2001.321859  3.688907e+07  1.151009e+08  3.381911e+07\n",
       "std       30.948225     12.813260  4.196096e+07  1.988557e+08  1.252194e+08\n",
       "min        2.000000   1960.000000  9.210911e-01  2.370705e+00 -4.139124e+08\n",
       "25%       90.000000   1995.000000  8.102293e+06  1.046585e+07  0.000000e+00\n",
       "50%       99.000000   2006.000000  2.271505e+07  4.395666e+07  0.000000e+00\n",
       "75%      112.000000   2011.000000  5.008384e+07  1.316482e+08  1.293091e+07\n",
       "max      900.000000   2015.000000  4.250000e+08  2.827124e+09  2.750137e+09"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Confirm the changes\n",
    "movie_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Replace zero values with mean\n",
    "\n",
    "The choosen way to handle the missing values is imputing them with the mean."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "# use means to fill in missing values\n",
    "movie_df['runtime'].fillna((movie_df['runtime'].mean()), inplace=True)\n",
    "movie_df['budget_adj'].fillna((movie_df['budget_adj'].mean()), inplace=True)\n",
    "movie_df['revenue_adj'].fillna((movie_df['revenue_adj'].mean()), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Emily\\Anaconda3\\lib\\site-packages\\pandas\\core\\generic.py:4355: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self._update_inplace(new_data)\n"
     ]
    }
   ],
   "source": [
    "# Create a list with missing values \n",
    "missing_values=['runtime', 'budget_adj', 'revenue_adj']\n",
    "\n",
    "# Replace all the value from '0' to NAN in the list\n",
    "movie_df[missing_values].fillna((movie_df[missing_values].mean()), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 10865 entries, 0 to 10865\n",
      "Data columns (total 12 columns):\n",
      "original_title          10865 non-null object\n",
      "cast                    10789 non-null object\n",
      "director                10821 non-null object\n",
      "keywords                9372 non-null object\n",
      "runtime                 10865 non-null float64\n",
      "genres                  10842 non-null object\n",
      "production_companies    9835 non-null object\n",
      "release_date            10865 non-null object\n",
      "release_year            10865 non-null int64\n",
      "budget_adj              10865 non-null float64\n",
      "revenue_adj             10865 non-null float64\n",
      "profit                  10865 non-null float64\n",
      "dtypes: float64(4), int64(1), object(7)\n",
      "memory usage: 1.1+ MB\n"
     ]
    }
   ],
   "source": [
    "# Confirm the changes\n",
    "movie_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Convert ``` release_date``` column to Date datatype\n",
    "To convert ```release_date``` column to date format (year-month-day)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "original_title                  object\n",
       "cast                            object\n",
       "director                        object\n",
       "keywords                        object\n",
       "runtime                        float64\n",
       "genres                          object\n",
       "production_companies            object\n",
       "release_date            datetime64[ns]\n",
       "release_year                     int64\n",
       "budget_adj                     float64\n",
       "revenue_adj                    float64\n",
       "profit                         float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# convert release_date to datetime format\n",
    "movie_df['release_date'] = pd.to_datetime(movie_df['release_date'])\n",
    "\n",
    "# confirm changes\n",
    "movie_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save progress for the next section\n",
    "movie_df.to_csv('tmdb-movies-cleaned.csv', index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='eda'></a>\n",
    "## Exploratory Data Analysis\n",
    "\n",
    "Now that I've trimmed and cleaned the data, I can compute statistics and create visualizations with the goal of addressing the research questions that I have posed in the Introduction section. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Describe the correlation between profit vs runtime / budget_adj / revenue_adj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Correlation between profit and runtime\n",
    "movie_df.plot(x='profit', y='runtime', kind='scatter');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Based on the above image, there is a strong correlation between ```profit``` and ```runtime```."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Correlation between profit and budget_adj\n",
    "movie_df.plot(x='profit', y='budget_adj', kind='scatter');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Based on the above image, there is a strong correlation between ```profit``` and ```budget_adj```."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Correlation between profit and revenue_adj\n",
    "movie_df.plot(x='profit', y='revenue_adj', kind='scatter');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Based on the above image, there is a strong positive correlation between ```profit``` and ```revenue_adj```."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Q1: What are the properties and attributes of the movies from year 1960 to 2015?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Movie that makes the most and least profit?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "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>1329</th>\n",
       "      <th>2244</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>original_title</th>\n",
       "      <td>Star Wars</td>\n",
       "      <td>The Warrior's Way</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cast</th>\n",
       "      <td>Mark Hamill|Harrison Ford|Carrie Fisher|Peter ...</td>\n",
       "      <td>Kate Bosworth|Jang Dong-gun|Geoffrey Rush|Dann...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>director</th>\n",
       "      <td>George Lucas</td>\n",
       "      <td>Sngmoo Lee</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>keywords</th>\n",
       "      <td>android|galaxy|hermit|death star|lightsaber</td>\n",
       "      <td>assassin|small town|revenge|deception|super speed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>runtime</th>\n",
       "      <td>121</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>genres</th>\n",
       "      <td>Adventure|Action|Science Fiction</td>\n",
       "      <td>Adventure|Fantasy|Action|Western|Thriller</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>production_companies</th>\n",
       "      <td>Lucasfilm|Twentieth Century Fox Film Corporation</td>\n",
       "      <td>Boram Entertainment Inc.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>release_date</th>\n",
       "      <td>1977-03-20 00:00:00</td>\n",
       "      <td>2010-12-02 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>release_year</th>\n",
       "      <td>1977</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>budget_adj</th>\n",
       "      <td>3.95756e+07</td>\n",
       "      <td>4.25e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>revenue_adj</th>\n",
       "      <td>2.78971e+09</td>\n",
       "      <td>1.10876e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>profit</th>\n",
       "      <td>2.75014e+09</td>\n",
       "      <td>-4.13912e+08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                   1329  \\\n",
       "original_title                                                Star Wars   \n",
       "cast                  Mark Hamill|Harrison Ford|Carrie Fisher|Peter ...   \n",
       "director                                                   George Lucas   \n",
       "keywords                    android|galaxy|hermit|death star|lightsaber   \n",
       "runtime                                                             121   \n",
       "genres                                 Adventure|Action|Science Fiction   \n",
       "production_companies   Lucasfilm|Twentieth Century Fox Film Corporation   \n",
       "release_date                                        1977-03-20 00:00:00   \n",
       "release_year                                                       1977   \n",
       "budget_adj                                                  3.95756e+07   \n",
       "revenue_adj                                                 2.78971e+09   \n",
       "profit                                                      2.75014e+09   \n",
       "\n",
       "                                                                   2244  \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",
       "release_year                                                       2010  \n",
       "budget_adj                                                     4.25e+08  \n",
       "revenue_adj                                                 1.10876e+07  \n",
       "profit                                                     -4.13912e+08  "
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create function to calculate highest and lowest value\n",
    "def calculate_highest_lowest(column):\n",
    "    # get highest value\n",
    "    highest = movie_df[column].idxmax()\n",
    "    highest_details = pd.DataFrame(movie_df.loc[highest])\n",
    "    \n",
    "    # get lowest value\n",
    "    lowest = movie_df[column].idxmin()\n",
    "    lowest_details = pd.DataFrame(movie_df.loc[lowest])  \n",
    "    \n",
    "    # Combine both data into one table\n",
    "    info = pd.concat([highest_details, lowest_details], axis=1)\n",
    "    \n",
    "    return info\n",
    "\n",
    "# get movie that make the highest and lowest profit\n",
    "calculate_highest_lowest('profit')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Based on the table shown above, the movie 'Star Wars' has the highest profit, making more than 2.7 Billion dollars and the movie 'The Warrior's Way' make a lost by more than 400 Million dollars . Looking at the number of 'Stars Wars' series till today, I am not surprise 'Stars Wars' has the highest profit compare to all the movies. On the other hand, based on the statistics 'The Warrior's Way' has the lowest profit could be due to their high budget of more than 400 Million dollars."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Movie with the highest and lowest budget?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "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>2244</th>\n",
       "      <th>1151</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>original_title</th>\n",
       "      <td>The Warrior's Way</td>\n",
       "      <td>Fear Clinic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cast</th>\n",
       "      <td>Kate Bosworth|Jang Dong-gun|Geoffrey Rush|Dann...</td>\n",
       "      <td>Thomas Dekker|Robert Englund|Cleopatra Coleman...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>director</th>\n",
       "      <td>Sngmoo Lee</td>\n",
       "      <td>Robert Hall</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>keywords</th>\n",
       "      <td>assassin|small town|revenge|deception|super speed</td>\n",
       "      <td>phobia|doctor|fear</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>runtime</th>\n",
       "      <td>100</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>genres</th>\n",
       "      <td>Adventure|Fantasy|Action|Western|Thriller</td>\n",
       "      <td>Horror</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>production_companies</th>\n",
       "      <td>Boram Entertainment Inc.</td>\n",
       "      <td>Dry County Films|Anchor Bay Entertainment|Movi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>release_date</th>\n",
       "      <td>2010-12-02 00:00:00</td>\n",
       "      <td>2014-10-31 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>release_year</th>\n",
       "      <td>2010</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>budget_adj</th>\n",
       "      <td>4.25e+08</td>\n",
       "      <td>0.921091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>revenue_adj</th>\n",
       "      <td>1.10876e+07</td>\n",
       "      <td>1.15101e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>profit</th>\n",
       "      <td>-4.13912e+08</td>\n",
       "      <td>-0.921091</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                   2244  \\\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",
       "release_year                                                       2010   \n",
       "budget_adj                                                     4.25e+08   \n",
       "revenue_adj                                                 1.10876e+07   \n",
       "profit                                                     -4.13912e+08   \n",
       "\n",
       "                                                                   1151  \n",
       "original_title                                              Fear Clinic  \n",
       "cast                  Thomas Dekker|Robert Englund|Cleopatra Coleman...  \n",
       "director                                                    Robert Hall  \n",
       "keywords                                             phobia|doctor|fear  \n",
       "runtime                                                              95  \n",
       "genres                                                           Horror  \n",
       "production_companies  Dry County Films|Anchor Bay Entertainment|Movi...  \n",
       "release_date                                        2014-10-31 00:00:00  \n",
       "release_year                                                       2014  \n",
       "budget_adj                                                     0.921091  \n",
       "revenue_adj                                                 1.15101e+08  \n",
       "profit                                                        -0.921091  "
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get movie with the highest and lowest budget\n",
    "calculate_highest_lowest('budget_adj')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> As expected the movie 'The Warrior's Way' has the highest budget of more than 400 Million dollars. The movie 'Lost & Found 'has the lowest budget of only 1 dollar which doesn't seems to be right. I did a search on the web and found that the movie 'Lost & Found' has a budget of 30 million dollars which is different from the one shown in the dataset so there could be some error with the values. Therefore, this movie may not be the one with the lowest budget."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Movie with highest and lowest revenue?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "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>1386</th>\n",
       "      <th>5067</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>original_title</th>\n",
       "      <td>Avatar</td>\n",
       "      <td>Shattered Glass</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cast</th>\n",
       "      <td>Sam Worthington|Zoe Saldana|Sigourney Weaver|S...</td>\n",
       "      <td>Hayden Christensen|Peter Sarsgaard|ChloÃ« Sevi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>director</th>\n",
       "      <td>James Cameron</td>\n",
       "      <td>Billy Ray</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>keywords</th>\n",
       "      <td>culture clash|future|space war|space colony|so...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>runtime</th>\n",
       "      <td>162</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>genres</th>\n",
       "      <td>Action|Adventure|Fantasy|Science Fiction</td>\n",
       "      <td>Drama|History</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>production_companies</th>\n",
       "      <td>Ingenious Film Partners|Twentieth Century Fox ...</td>\n",
       "      <td>Lions Gate Films|Cruise/Wagner Productions|Bau...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>release_date</th>\n",
       "      <td>2009-12-10 00:00:00</td>\n",
       "      <td>2003-11-14 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>release_year</th>\n",
       "      <td>2009</td>\n",
       "      <td>2003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>budget_adj</th>\n",
       "      <td>2.40887e+08</td>\n",
       "      <td>7.11212e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>revenue_adj</th>\n",
       "      <td>2.82712e+09</td>\n",
       "      <td>2.37071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>profit</th>\n",
       "      <td>2.58624e+09</td>\n",
       "      <td>-7.11211e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                   1386  \\\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",
       "release_year                                                       2009   \n",
       "budget_adj                                                  2.40887e+08   \n",
       "revenue_adj                                                 2.82712e+09   \n",
       "profit                                                      2.58624e+09   \n",
       "\n",
       "                                                                   5067  \n",
       "original_title                                          Shattered Glass  \n",
       "cast                  Hayden Christensen|Peter Sarsgaard|ChloÃ« Sevi...  \n",
       "director                                                      Billy Ray  \n",
       "keywords                                                            NaN  \n",
       "runtime                                                              94  \n",
       "genres                                                    Drama|History  \n",
       "production_companies  Lions Gate Films|Cruise/Wagner Productions|Bau...  \n",
       "release_date                                        2003-11-14 00:00:00  \n",
       "release_year                                                       2003  \n",
       "budget_adj                                                  7.11212e+06  \n",
       "revenue_adj                                                     2.37071  \n",
       "profit                                                     -7.11211e+06  "
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get movie that make the highest and lowest revenue\n",
    "calculate_highest_lowest('revenue_adj')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Based on the table shown above, the movie 'Avatar' has the highest revenue, making more than 2.8 Billion dollars and the movie 'Shattered Glass' making only 2 dollars which doesn't seems to be right. I did a search on the web and found that the movie 'Shattered Glass' has a box office result of 2.9 Million which is different from the on shown in the datase. This could be due to typo in the dataset so this movie may not be the one with the lowest revenue."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Movie with the longest and shortest runtime?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "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>3894</th>\n",
       "      <th>4883</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>original_title</th>\n",
       "      <td>The Story of Film: An Odyssey</td>\n",
       "      <td>Fresh Guacamole</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cast</th>\n",
       "      <td>Mark Cousins|Jean-Michel Frodon|Cari Beauchamp...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>director</th>\n",
       "      <td>Mark Cousins</td>\n",
       "      <td>PES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>keywords</th>\n",
       "      <td>cinema|nouvelle vague|hindi cinema|cinema novo...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>runtime</th>\n",
       "      <td>900</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>genres</th>\n",
       "      <td>Documentary</td>\n",
       "      <td>Animation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>production_companies</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>release_date</th>\n",
       "      <td>2011-09-03 00:00:00</td>\n",
       "      <td>2012-03-02 00:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>release_year</th>\n",
       "      <td>2011</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>budget_adj</th>\n",
       "      <td>3.68891e+07</td>\n",
       "      <td>3.68891e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>revenue_adj</th>\n",
       "      <td>1.15101e+08</td>\n",
       "      <td>1.15101e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>profit</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                   3894  \\\n",
       "original_title                            The Story of Film: An Odyssey   \n",
       "cast                  Mark Cousins|Jean-Michel Frodon|Cari Beauchamp...   \n",
       "director                                                   Mark Cousins   \n",
       "keywords              cinema|nouvelle vague|hindi cinema|cinema novo...   \n",
       "runtime                                                             900   \n",
       "genres                                                      Documentary   \n",
       "production_companies                                                NaN   \n",
       "release_date                                        2011-09-03 00:00:00   \n",
       "release_year                                                       2011   \n",
       "budget_adj                                                  3.68891e+07   \n",
       "revenue_adj                                                 1.15101e+08   \n",
       "profit                                                                0   \n",
       "\n",
       "                                     4883  \n",
       "original_title            Fresh Guacamole  \n",
       "cast                                  NaN  \n",
       "director                              PES  \n",
       "keywords                              NaN  \n",
       "runtime                                 2  \n",
       "genres                          Animation  \n",
       "production_companies                  NaN  \n",
       "release_date          2012-03-02 00:00:00  \n",
       "release_year                         2012  \n",
       "budget_adj                    3.68891e+07  \n",
       "revenue_adj                   1.15101e+08  \n",
       "profit                                  0  "
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get movie that has the longest and shortest runtime\n",
    "calculate_highest_lowest('runtime')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">- 900 minutes is the longest movie titled 'The Story of Film: An Odyssey', which is equivalent to 15 hours! 'The Story of Film: An Odyssey' is a documentary film about the history of film, presented on television in 15 one-hour chapters.\n",
    ">- 2 minute is the shortest movie goes to 'Fresh Guacamole' with a running time of only 2 minutes. 'Fresh Guacamole' is a 2012 American animated short film written and directed by PES."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The average movie runtime?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "102.36385453202878"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Return the average runtime of the movies\n",
    "movie_df['runtime'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Based on the above, the average runtime of the movies is 102 minutes. Let's use visualization to see the distribution of the runtime."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1050x630 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create box plot to see the distribution of the runtime \n",
    "plt.figure(figsize=(10,6), dpi = 105)\n",
    "\n",
    "# Use seaborn to generate the boxplot\n",
    "sns.boxplot(movie_df['runtime'], linewidth = 2)\n",
    "# Diplay the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> We can see from the above that most of the movies have an average of 100 minutes. A number of outliers of movie with more than 700 minutes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create histogram to see the distribution of the runtime \n",
    "plt.figure(figsize=(10,5), dpi = 100)\n",
    "\n",
    "# x-axis \n",
    "plt.xlabel('Movie Runtime', fontsize = 15)\n",
    "# y-axis \n",
    "plt.ylabel('No. of Movies', fontsize=15)\n",
    "# Name of the graph\n",
    "plt.title('Movie Runtime Distribution', fontsize=15)\n",
    "\n",
    "# Plot the Histogram\n",
    "plt.hist(movie_df['runtime'], rwidth = 0.9, bins =35)\n",
    "# Diplay the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Again, I can see that more than 6,000 of the movies are of 100+ minutes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    10865.000000\n",
       "mean       102.363855\n",
       "std         30.904039\n",
       "min          2.000000\n",
       "25%         90.000000\n",
       "50%         99.000000\n",
       "75%        111.000000\n",
       "max        900.000000\n",
       "Name: runtime, dtype: float64"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the descriptive statistics for the runtime column\n",
    "movie_df['runtime'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">Based on the analysis above, I can conclude that:\n",
    "- 25% of movies have a runtime of 90 minutes.\n",
    "- 50% of movies have a runtime of 99 minutes.\n",
    "- 75% of movies have a runtime of 111 minutes.\n",
    "- Average of the movies have a runtime of 102 minutes.\n",
    "- The longest movie is 900 minutes and the movie is 'The Story of Film: An Odyssey'.\n",
    "- The shortest movie is 2 minute and the movie is 'Fresh Guacamole'."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The month with the most movies released?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a new column month by extracting the month from the release date\n",
    "movie_df['month'] = movie_df['release_date'].apply(lambda x: x.month)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "month\n",
       "1      919\n",
       "2      691\n",
       "3      822\n",
       "4      797\n",
       "5      809\n",
       "6      827\n",
       "7      799\n",
       "8      918\n",
       "9     1331\n",
       "10    1153\n",
       "11     814\n",
       "12     985\n",
       "Name: original_title, dtype: int64"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Group by month and count of the movies each month.\n",
    "movie_per_month = movie_df.groupby('month')['original_title'].count()\n",
    "movie_per_month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To get the month with the highest movie release\n",
    "movie_per_month.idxmax()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> The month with the highest movie release is September."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a bar chart\n",
    "sns.set_style('darkgrid')\n",
    "month_name = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']\n",
    "plt.figure(figsize=(15,8))\n",
    "plt.bar([1,2,3,4,5,6,7,8,9,10,11,12], movie_per_month, tick_label = month_name)\n",
    "plt.title('Month Released by month')\n",
    "plt.ylabel('No. of movies')\n",
    "plt.xlabel('Month');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">From the above analysis, we can see that Sep (> 1,200 movies) is the most popular month followed by Oct (> 1,000 movies) and Dec (> more than 800 movies) for movies release."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The year with the most movies released?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "release_year\n",
       "1960    32\n",
       "1961    31\n",
       "1962    32\n",
       "1963    34\n",
       "1964    42\n",
       "Name: original_title, dtype: int64"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Group by year and count of the movies each year.\n",
    "movie_per_year = movie_df.groupby('release_year')['original_title'].count()\n",
    "movie_per_year.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2014"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To get the year with the highest movie release\n",
    "movie_per_year.idxmax()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">The year with the highest movie release is in year 2014."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1560x780 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Figure size(width, height)\n",
    "plt.figure(figsize=(12,6), dpi = 130)\n",
    "\n",
    "# x-axis\n",
    "plt.xlabel('Year of Movie Released', fontsize = 12)\n",
    "# y-axis\n",
    "plt.ylabel('No. of Movies', fontsize = 12)\n",
    "# Title\n",
    "plt.title('Movie Released by Year')\n",
    "\n",
    "# Plot Line chart\n",
    "plt.plot(movie_per_year)\n",
    "\n",
    "# Display plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">Interestingly based on the above diagram, we can see that the trend of movie released each year increase year by year. I noticed that there is two significant drop, one is during late 2000s to 2010 and the other is drop around year 2015."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "release_year\n",
       "2006    408\n",
       "2007    438\n",
       "2008    496\n",
       "2009    533\n",
       "2010    489\n",
       "2011    540\n",
       "2012    588\n",
       "2013    659\n",
       "2014    700\n",
       "2015    629\n",
       "Name: original_title, dtype: int64"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Group by year and count of the movies each year.\n",
    "movie_per_year = movie_df.groupby('release_year')['original_title'].count()\n",
    "movie_per_year.tail(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">By looking at the last 10 rows from the movies by year, we can see that the significant drop of movies are in:\n",
    "- **Year 2009**: a drop of 44 movies from year 2009 (533 movies) to year 2010 (489 movies).\n",
    "- **Year 2015**: a drop of 71 movies from year 2014 (700 movies) to year 2015 (629 movies)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The year that makes the most profit?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1560x780 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Groupby release year and profit\n",
    "profit_by_year = movie_df.groupby('release_year')['profit'].sum()\n",
    "\n",
    "# Figure size(width, height)\n",
    "plt.figure(figsize=(12,6), dpi = 130)\n",
    "\n",
    "# x-axis\n",
    "plt.xlabel('Movies Released Year', fontsize = 12)\n",
    "# y-axis\n",
    "plt.ylabel('Total Profit', fontsize = 12)\n",
    "# Title\n",
    "plt.title('Total Profit by Movies Released Year')\n",
    "\n",
    "# Plot line Chart\n",
    "plt.plot(profit_by_year)\n",
    "\n",
    "# Display the line Chart\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2015"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Which year made the highest profit\n",
    "profit_by_year.idxmax()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">As shown above, the movies released in the year 2015 has the highest profit of more than 17 Billion dollars."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Who starred in the most movies?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "def extract_data(column_name):\n",
    "\n",
    "    data = movie_df[column_name].str.cat(sep = '|')\n",
    "    \n",
    "    # Create pandas series and store the values separately\n",
    "    data = pd.Series(data.split('|'))\n",
    "    \n",
    "    # Display value count in descending order\n",
    "    count = data.value_counts(ascending = False)\n",
    "    \n",
    "    return count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Robert De Niro       72\n",
       "Samuel L. Jackson    71\n",
       "Bruce Willis         62\n",
       "Nicolas Cage         61\n",
       "Michael Caine        53\n",
       "Robin Williams       51\n",
       "John Cusack          50\n",
       "John Goodman         49\n",
       "Morgan Freeman       49\n",
       "Liam Neeson          48\n",
       "dtype: int64"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display top 10 casts\n",
    "cast = extract_data('cast')\n",
    "cast.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">Looks like the top two most hardworking casts for the 55 years between 1960 to 2015 are Robert De Niro and Samuel L. Jackson. They are involved in more than 70 movies."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Who directed the most movies?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Woody Allen          46\n",
       "Clint Eastwood       34\n",
       "Martin Scorsese      31\n",
       "Steven Spielberg     30\n",
       "Steven Soderbergh    23\n",
       "Ridley Scott         23\n",
       "Ron Howard           22\n",
       "Joel Schumacher      21\n",
       "Brian De Palma       20\n",
       "Tim Burton           20\n",
       "dtype: int64"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display top 10 directors\n",
    "director = extract_data('director')\n",
    "director.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">The top director goes to Woody Allen who directed 46 movies for the 55 years between 1960 to 2015."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The most popular production companies?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Universal Pictures                        522\n",
       "Warner Bros.                              509\n",
       "Paramount Pictures                        431\n",
       "Twentieth Century Fox Film Corporation    282\n",
       "Columbia Pictures                         272\n",
       "New Line Cinema                           219\n",
       "Metro-Goldwyn-Mayer (MGM)                 218\n",
       "Walt Disney Pictures                      214\n",
       "Touchstone Pictures                       178\n",
       "Columbia Pictures Corporation             160\n",
       "dtype: int64"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display top 10 production companies\n",
    "production_companies = extract_data('production_companies')\n",
    "production_companies.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">The top production is Universal Pictures (522 movies), Warner Bros (509 movies) and Paramount Picture (431 movies). Amazing that the top three production companies produces more than 400 movies for the 55 years between 1960 to 2015."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The most popular genre?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Drama              4760\n",
       "Comedy             3793\n",
       "Thriller           2907\n",
       "Action             2384\n",
       "Romance            1712\n",
       "Horror             1637\n",
       "Adventure          1471\n",
       "Crime              1354\n",
       "Family             1231\n",
       "Science Fiction    1229\n",
       "dtype: int64"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display top 10 genres\n",
    "genres = extract_data('genres')\n",
    "genres.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">The top two genres are Drama (4,760 movies) and Comedy (3,793 movies) that has more than 3000 movies."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The most common keywords?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "woman director      413\n",
       "independent film    396\n",
       "based on novel      278\n",
       "sex                 272\n",
       "sport               216\n",
       "murder              204\n",
       "biography           169\n",
       "musical             169\n",
       "new york            163\n",
       "suspense            159\n",
       "dtype: int64"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display top 10 keywords\n",
    "keywords = extract_data('keywords')\n",
    "keywords.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">The top keywords are 'woman director' and 'independent film' with more than 300 movies. Surprisingly female director (413 movies) and independent film (396 movies) makes the most movies for the 55 years between 1960 to 2015."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Research Question 2 - What kind of properties are associated with movies that have high profit?\n",
    "\n",
    "In order to analyze on the properties that are associated with movies that have high profit, I will have to filter the dataset to movies that made profit of more than 100 Million dollars."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>original_title</th>\n",
       "      <th>cast</th>\n",
       "      <th>director</th>\n",
       "      <th>keywords</th>\n",
       "      <th>runtime</th>\n",
       "      <th>genres</th>\n",
       "      <th>production_companies</th>\n",
       "      <th>release_date</th>\n",
       "      <th>release_year</th>\n",
       "      <th>budget_adj</th>\n",
       "      <th>revenue_adj</th>\n",
       "      <th>profit</th>\n",
       "      <th>month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jurassic World</td>\n",
       "      <td>Chris Pratt|Bryce Dallas Howard|Irrfan Khan|Vi...</td>\n",
       "      <td>Colin Trevorrow</td>\n",
       "      <td>monster|dna|tyrannosaurus rex|velociraptor|island</td>\n",
       "      <td>124.0</td>\n",
       "      <td>Action|Adventure|Science Fiction|Thriller</td>\n",
       "      <td>Universal Studios|Amblin Entertainment|Legenda...</td>\n",
       "      <td>2015-06-09</td>\n",
       "      <td>2015</td>\n",
       "      <td>1.379999e+08</td>\n",
       "      <td>1.392446e+09</td>\n",
       "      <td>1.254446e+09</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mad Max: Fury Road</td>\n",
       "      <td>Tom Hardy|Charlize Theron|Hugh Keays-Byrne|Nic...</td>\n",
       "      <td>George Miller</td>\n",
       "      <td>future|chase|post-apocalyptic|dystopia|australia</td>\n",
       "      <td>120.0</td>\n",
       "      <td>Action|Adventure|Science Fiction|Thriller</td>\n",
       "      <td>Village Roadshow Pictures|Kennedy Miller Produ...</td>\n",
       "      <td>2015-05-13</td>\n",
       "      <td>2015</td>\n",
       "      <td>1.379999e+08</td>\n",
       "      <td>3.481613e+08</td>\n",
       "      <td>2.101614e+08</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Insurgent</td>\n",
       "      <td>Shailene Woodley|Theo James|Kate Winslet|Ansel...</td>\n",
       "      <td>Robert Schwentke</td>\n",
       "      <td>based on novel|revolution|dystopia|sequel|dyst...</td>\n",
       "      <td>119.0</td>\n",
       "      <td>Adventure|Science Fiction|Thriller</td>\n",
       "      <td>Summit Entertainment|Mandeville Films|Red Wago...</td>\n",
       "      <td>2015-03-18</td>\n",
       "      <td>2015</td>\n",
       "      <td>1.012000e+08</td>\n",
       "      <td>2.716190e+08</td>\n",
       "      <td>1.704191e+08</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       original_title                                               cast  \\\n",
       "0      Jurassic World  Chris Pratt|Bryce Dallas Howard|Irrfan Khan|Vi...   \n",
       "1  Mad Max: Fury Road  Tom Hardy|Charlize Theron|Hugh Keays-Byrne|Nic...   \n",
       "2           Insurgent  Shailene Woodley|Theo James|Kate Winslet|Ansel...   \n",
       "\n",
       "           director                                           keywords  \\\n",
       "0   Colin Trevorrow  monster|dna|tyrannosaurus rex|velociraptor|island   \n",
       "1     George Miller   future|chase|post-apocalyptic|dystopia|australia   \n",
       "2  Robert Schwentke  based on novel|revolution|dystopia|sequel|dyst...   \n",
       "\n",
       "   runtime                                     genres  \\\n",
       "0    124.0  Action|Adventure|Science Fiction|Thriller   \n",
       "1    120.0  Action|Adventure|Science Fiction|Thriller   \n",
       "2    119.0         Adventure|Science Fiction|Thriller   \n",
       "\n",
       "                                production_companies release_date  \\\n",
       "0  Universal Studios|Amblin Entertainment|Legenda...   2015-06-09   \n",
       "1  Village Roadshow Pictures|Kennedy Miller Produ...   2015-05-13   \n",
       "2  Summit Entertainment|Mandeville Films|Red Wago...   2015-03-18   \n",
       "\n",
       "   release_year    budget_adj   revenue_adj        profit  month  \n",
       "0          2015  1.379999e+08  1.392446e+09  1.254446e+09      6  \n",
       "1          2015  1.379999e+08  3.481613e+08  2.101614e+08      5  \n",
       "2          2015  1.012000e+08  2.716190e+08  1.704191e+08      3  "
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create new dataframe by filter to movies that made profit of more than 100Million dollars \n",
    "high_revenue_movie = movie_df[movie_df['profit'] >= 100000000]\n",
    "\n",
    "high_revenue_movie.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "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>runtime</th>\n",
       "      <th>release_year</th>\n",
       "      <th>budget_adj</th>\n",
       "      <th>revenue_adj</th>\n",
       "      <th>profit</th>\n",
       "      <th>month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1089.000000</td>\n",
       "      <td>1089.000000</td>\n",
       "      <td>1.089000e+03</td>\n",
       "      <td>1.089000e+03</td>\n",
       "      <td>1.089000e+03</td>\n",
       "      <td>1089.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>115.955923</td>\n",
       "      <td>1998.367309</td>\n",
       "      <td>7.353762e+07</td>\n",
       "      <td>3.715738e+08</td>\n",
       "      <td>3.005429e+08</td>\n",
       "      <td>7.463728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>28.492322</td>\n",
       "      <td>13.089591</td>\n",
       "      <td>5.536918e+07</td>\n",
       "      <td>2.909409e+08</td>\n",
       "      <td>2.682571e+08</td>\n",
       "      <td>3.256613</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>44.000000</td>\n",
       "      <td>1960.000000</td>\n",
       "      <td>2.248029e+02</td>\n",
       "      <td>1.000516e+08</td>\n",
       "      <td>1.000516e+08</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>99.000000</td>\n",
       "      <td>1991.000000</td>\n",
       "      <td>3.389464e+07</td>\n",
       "      <td>1.903475e+08</td>\n",
       "      <td>1.381282e+08</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>113.000000</td>\n",
       "      <td>2001.000000</td>\n",
       "      <td>5.793440e+07</td>\n",
       "      <td>2.746954e+08</td>\n",
       "      <td>2.073424e+08</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>127.000000</td>\n",
       "      <td>2009.000000</td>\n",
       "      <td>1.047242e+08</td>\n",
       "      <td>4.460595e+08</td>\n",
       "      <td>3.616719e+08</td>\n",
       "      <td>11.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>705.000000</td>\n",
       "      <td>2015.000000</td>\n",
       "      <td>3.683713e+08</td>\n",
       "      <td>2.827124e+09</td>\n",
       "      <td>2.750137e+09</td>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           runtime  release_year    budget_adj   revenue_adj        profit  \\\n",
       "count  1089.000000   1089.000000  1.089000e+03  1.089000e+03  1.089000e+03   \n",
       "mean    115.955923   1998.367309  7.353762e+07  3.715738e+08  3.005429e+08   \n",
       "std      28.492322     13.089591  5.536918e+07  2.909409e+08  2.682571e+08   \n",
       "min      44.000000   1960.000000  2.248029e+02  1.000516e+08  1.000516e+08   \n",
       "25%      99.000000   1991.000000  3.389464e+07  1.903475e+08  1.381282e+08   \n",
       "50%     113.000000   2001.000000  5.793440e+07  2.746954e+08  2.073424e+08   \n",
       "75%     127.000000   2009.000000  1.047242e+08  4.460595e+08  3.616719e+08   \n",
       "max     705.000000   2015.000000  3.683713e+08  2.827124e+09  2.750137e+09   \n",
       "\n",
       "             month  \n",
       "count  1089.000000  \n",
       "mean      7.463728  \n",
       "std       3.256613  \n",
       "min       1.000000  \n",
       "25%       5.000000  \n",
       "50%       7.000000  \n",
       "75%      11.000000  \n",
       "max      12.000000  "
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "high_revenue_movie.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> From 10,865 movies, we are now down with only 1,089 movies which have profit of at least 100 Million dollars. The highest earning movie is 2.75 Billion dollars and from our previous analysis we know that the movie is Star Wars."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### High revenue movie average runtime?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "115.95592286501378"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Return the average runtime of the movies\n",
    "high_revenue_movie['runtime'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">Based on the above, the average runtime for high revenue movies is 115 minutes. Let's use visualization to see the distribution of the runtime."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create histogram to see the distribution of the runtime \n",
    "plt.figure(figsize=(10,5), dpi = 100)\n",
    "\n",
    "# x-axis \n",
    "plt.xlabel('Movie Runtime', fontsize = 15)\n",
    "# y-axis \n",
    "plt.ylabel('No. of Movies', fontsize=15)\n",
    "# Name of the graph\n",
    "plt.title('Movie Runtime Distribution', fontsize=15)\n",
    "\n",
    "# Plot the histogram\n",
    "plt.hist(high_revenue_movie['runtime'], rwidth = 0.9, bins =35)\n",
    "# Displays the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">We can see from the above that most of the movies have a runtime of 80 - 140 minutes and the highest is around 100 - 120 minutes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### High profit movies average budget?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "73537621.57359695"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Return the average runtime of the movies\n",
    "high_revenue_movie['budget_adj'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">Based on the above, the average budget for high revenue movies is around 74 Million dollars ($73,537,622)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### High profit movies average revenue?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "371573840.3492238"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Return the average runtime of the movies\n",
    "high_revenue_movie['revenue_adj'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">Based on the above, the average revenue for high revenue movies is around 371 Million dollars. ($371,573,840)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### In which month the movies makes the most profit?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "month\n",
       "1      27\n",
       "2      52\n",
       "3      72\n",
       "4      52\n",
       "5     118\n",
       "6     159\n",
       "7     112\n",
       "8      62\n",
       "9      64\n",
       "10     84\n",
       "11    111\n",
       "12    176\n",
       "Name: original_title, dtype: int64"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Group by year and count of the movies each month.\n",
    "high_revenue_movie_per_month = high_revenue_movie.groupby('month')['original_title'].count()\n",
    "high_revenue_movie_per_month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To get the month with the highest movie release\n",
    "high_revenue_movie_per_month.idxmax()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> The month with the highest movie release is December."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a bar chart\n",
    "sns.set_style('darkgrid')\n",
    "month_name = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']\n",
    "plt.figure(figsize=(15,8))\n",
    "plt.bar([1,2,3,4,5,6,7,8,9,10,11,12], high_revenue_movie_per_month, tick_label = month_name)\n",
    "plt.title('Month Released by month')\n",
    "plt.ylabel('No. of movies')\n",
    "plt.xlabel('Month');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">From the above analysis, we can see that Dec (> 175 movies) is the most popular month follow by June (> 150 movies) and May (> 100 movies) for movies release that makes the most profit."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### In which year the movies makes the most profit?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "release_year\n",
       "1960    3\n",
       "1961    3\n",
       "1962    3\n",
       "1963    2\n",
       "1964    3\n",
       "Name: original_title, dtype: int64"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Group by year and count of the movies each year.\n",
    "high_revenue_movie_per_year = high_revenue_movie.groupby('release_year')['original_title'].count()\n",
    "high_revenue_movie_per_year.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2014"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To get the year with the most movie release\n",
    "high_revenue_movie_per_year.idxmax()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">Based on the above, we can see that the movies makes the most profit in year 2014."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### High profit movie casts?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "def extract_high_revenue_data(column_name):\n",
    "\n",
    "    data = high_revenue_movie[column_name].str.cat(sep = '|')\n",
    "    \n",
    "    # Create pandas series and store the values separately\n",
    "    data = pd.Series(data.split('|'))\n",
    "    \n",
    "    # Display value count in descending order\n",
    "    count = data.value_counts(ascending = False)\n",
    "    \n",
    "    return count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Tom Cruise               27\n",
       "Tom Hanks                22\n",
       "Brad Pitt                18\n",
       "Sylvester Stallone       18\n",
       "Eddie Murphy             17\n",
       "Harrison Ford            17\n",
       "Cameron Diaz             17\n",
       "Adam Sandler             17\n",
       "Bruce Willis             16\n",
       "Robert De Niro           16\n",
       "Jim Carrey               16\n",
       "Will Smith               15\n",
       "Arnold Schwarzenegger    15\n",
       "Robin Williams           15\n",
       "Mel Gibson               14\n",
       "Sean Connery             14\n",
       "Julia Roberts            14\n",
       "Clint Eastwood           14\n",
       "Dustin Hoffman           14\n",
       "Samuel L. Jackson        14\n",
       "dtype: int64"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get top 20 casts\n",
    "cast = extract_high_revenue_data('cast')\n",
    "cast.head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> The top two male actors with more than 20 movies are Tom Cruise (27 movies) and Tom Hanks (22 movies). As for female actress are Cameron Diaz (17 movies) and Julia Roberts (14 movies). Seems like there are more high revenue movies actors than actress as out of the 20 cast from the above list there are only 2 female."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### High profit movie directors?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Steven Spielberg    22\n",
       "Robert Zemeckis     12\n",
       "Ron Howard          11\n",
       "Clint Eastwood       9\n",
       "Tim Burton           8\n",
       "Ridley Scott         8\n",
       "Michael Bay          8\n",
       "Gore Verbinski       7\n",
       "Roland Emmerich      7\n",
       "Ivan Reitman         7\n",
       "dtype: int64"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get top 10 directors\n",
    "director = extract_high_revenue_data('director')\n",
    "director.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Steven Spielberg (22 movies) is the top director that makes the most movies for the 55 years between 1960 to 2015 with high profit. Surprisingly Woody Allen is not in the top 20 list when he had directed 46 movies."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### High profit production companies?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Universal Pictures                        135\n",
       "Warner Bros.                              133\n",
       "Paramount Pictures                        127\n",
       "Twentieth Century Fox Film Corporation    108\n",
       "Columbia Pictures                          65\n",
       "Walt Disney Pictures                       62\n",
       "New Line Cinema                            45\n",
       "Columbia Pictures Corporation              44\n",
       "Touchstone Pictures                        42\n",
       "Metro-Goldwyn-Mayer (MGM)                  38\n",
       "dtype: int64"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get top 10 production companies\n",
    "production_companies = extract_high_revenue_data('production_companies')\n",
    "production_companies.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">The top production is Universal Pictures (522 movies), Warner Bros (509 movies) and Paramount Picture (431 movies). Amazing that the top three production companies produces more than 400 movies for the 55 years between 1960 to 2015 and they are also the ones that top the production companies with most movies list."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### High profit movie genre?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Comedy             402\n",
       "Drama              395\n",
       "Action             394\n",
       "Adventure          337\n",
       "Thriller           310\n",
       "Family             194\n",
       "Romance            178\n",
       "Science Fiction    178\n",
       "Crime              166\n",
       "Fantasy            165\n",
       "dtype: int64"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get top 10 genres\n",
    "director = extract_high_revenue_data('genres')\n",
    "director.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Surprisingly, there is a swap from this high profit movies listing with the most movie listing. The genre Comedy (402 movies) have move up to be the top instead of Drama (395 movies)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### High profit movie keywords?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "based on novel          56\n",
       "dystopia                33\n",
       "duringcreditsstinger    31\n",
       "london                  30\n",
       "saving the world        27\n",
       "dtype: int64"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "keywords = extract_high_revenue_data('keywords')\n",
    "keywords.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">Interestingly, we have a totally different result from the previous common keywords. The top movie keywords with high profit goes to 'based on novel' followed by 'dystopia', 'duringcreditsstinger' and 'london' which I did a search on what they meant.\n",
    "<br/><br/>\n",
    "dystopia: an imagined place or state in which everything is unpleasant or bad, typically a totalitarian or environmentally degraded one.\n",
    "<br/><br/>\n",
    "duringcreditsstinger: A post-credits scene is a short clip that appears after all or some of the closing credits have rolled and sometimes after a production logo of a film, TV series or video game have run. It is usually included for humour or to set up a possible sequel."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='conclusions'></a>\n",
    "## Conclusions\n",
    "\n",
    ">My goal of this data analysis is to answer the 2 main questions  - (1)  properties and attributes of the movies from year 1960 to 2015? (2) kind of properties that are associated with movies with a high profit of at least 100 Million dollars? After the above analysis, I can conclude the following:\n",
    "<br/><br/>\n",
    ">** Properties and attributes of the movies from year 1960 to 2015: **\n",
    "- Average runtime of the movie 102 minutes\n",
    "- Month with the most movies released: September\n",
    "- Year with the most movies released: 2014\n",
    "- Year that makes the most profit: 2015\n",
    "- Cast starred in the most movies: Robert De Niro, Samuel L. Jackson, Bruce Willis\n",
    "- Director with the most movies: Woody Allen, Clint Eastwood, Martin Scorsese\n",
    "- Popular production companies: Universal Pictures, Warner Bros., Paramount Pictures\n",
    "- Most popular genre: Drama, Comedy, Thriller\n",
    "- Most common keywords: woman director, independent film, based on novel\n",
    "\n",
    "\n",
    ">** For a movie to have a profit of at least 100 Million Dollars: **\n",
    "- Average Budget must be around 74 Million Dollars\n",
    "- Average runtime of the movie must be 115 minutes\n",
    "- Month to release the movie: December or June\n",
    "- Actors to cast: Tom Cruise, Tom Hanks, Brad Pitt, Sylvester Stallone,\n",
    "- Actress to cast:  Cameron Diaz, Julia Roberts\n",
    "- Any one of these should be the director:  Steven Spielberg, Robert Zemeckis, Ron Howard\n",
    "- Produce by any of these production companies: Universal Pictures, Warner Bros., Paramount Pictures\n",
    "- Genre must any of these: Comedy, Drama, Action\n",
    "- Movie plot should be based on novel, related to dystopia and london with post-credits scene\n",
    "<br/><br/>\n",
    ">By meeting the above criteria, the movie will have a higher probability to be a hit and earn an average revenue of around 371 million dollar and profit of at least 100 million dollars.\n",
    "<br/><br/>\n",
    "But do note that the above analysis was done on the movies from 1960 to 2015 and with a profit of at least 100 million dollar. Also considering that there is a huge number of missing data and noticed that some of the error values for the movies (e.g. $1 for some of the movies)."
   ]
  }
 ],
 "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": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}